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LinkBehaviour_D7069881_5830_E561_41D2_E344534D99C2.source = https://www.sidestone.com/books/between-variability-and-singularity LinkBehaviour_0CF84F53_58F0_BBDC_41D1_65267B7FC615.source = https://www.zotero.org/groups/6615691/hescor/library LinkBehaviour_A0000F78_16BB_7C00_41AC_538A338D7BD5.source = https://www.zotero.org/groups/6615691/hescor/library ## Hotspot ### Text QuadHotspotPanoramaOverlayTextImage_E6EC95CF_B4B5_CC96_41E5_82E07A34B0C2.text = \ Consideration of further biases \ \ \ \ \ \ QuadHotspotPanoramaOverlayTextImage_E168107C_B4BC_447B_41E6_617B557637D2.text = \ Investigation of structured populations \ \ HotspotPanoramaOverlayTextImage_933284EE_3BCE_E712_41B2_9EAD2F8B5D49.text = 1 HotspotPanoramaOverlayTextImage_825522A0_3BFD_230E_41BF_E3A69A813C24.text = 1 HotspotPanoramaOverlayTextImage_917033B5_3BFB_6176_41A1_54B45FE14898.text = 2 HotspotPanoramaOverlayTextImage_917765C5_3BCD_2116_41C2_C3F5EF298FA8.text = 2 HotspotPanoramaOverlayTextImage_95E6D7E8_3BFB_211E_419A_B255ED4EFD45.text = 3 HotspotPanoramaOverlayTextImage_929DBCEF_3BC5_6711_41B4_034895566BAA.text = 3 HotspotPanoramaOverlayTextImage_97D4ACE4_3BCD_2716_41C4_24919A365470.text = 4 HotspotPanoramaOverlayTextImage_882E9E8C_3BC5_2316_41C5_68B58D4BEDFE.text = 4 HotspotPanoramaOverlayTextImage_91039499_3BC5_273E_41CC_D8FD2EAE4B38.text = 5 HotspotPanoramaOverlayTextImage_8E082602_3BC5_6312_418D_94CE090C8A79.text = 5 HotspotPanoramaOverlayTextImage_503CD5D2_FCF6_B0B7_41D4_68F47755A2C7.text = ANIMAL COUNCIL QuadHotspotPanoramaOverlayTextImage_ECD636EE_B4B4_4C97_41D8_167847031175.text = Application of Biases HEP in human disperal models \ \ QuadHotspotPanoramaOverlayTextImage_E9B30459_B4B4_43BD_41BD_3B521ACBC3C7.text = Application to other case studies (other populations, time periods and regions) \ \ HotspotPanoramaOverlayTextImage_60193A5C_17DB_4402_4163_29574D2C39EC.text = Author & Illustrator:- \ Dr. Frederik von Reumont, Institute of Geography Education, UoC. QuadHotspotPanoramaOverlayTextImage_22367F19_37B3_D794_41B8_75DE1696326E.text = BIASES IN ARCHAEOLOGY \ \ QuadHotspotPanoramaOverlayTextImage_6C443907_0DE3_5ACF_4193_2EFD29466A5D.text = BUILDING THE NETWORK \ \ The mathematics behind turning spatial data into probabilistic mobility networks. QuadHotspotPanoramaOverlayTextImage_DE366869_EDE6_54E9_41D8_AAE581692A7F.text = Basics QuadHotspotPanoramaOverlayTextImage_DE4ACE1B_EDE6_6C29_41E4_76E26EC19DD8.text = Basics HotspotPanoramaOverlayTextImage_BBD33A4D_AE2F_5B69_41D5_07FE8399A28F.text = Basics QuadHotspotPanoramaOverlayTextImage_E1C53D06_AAEA_EB8C_41D6_F914258CACF9.text = Basics HotspotPanoramaOverlayTextImage_C0A9A292_A96B_3842_41B6_614BB63226BA.text = Basics QuadHotspotPanoramaOverlayTextImage_3D8D7E23_15BF_5C4C_4193_FCCBCE9FC548.text = Be a vegetation modeller HotspotPanoramaOverlayTextImage_7BACCF59_43F0_4883_41C8_2BEB6CA84468.text = Beginning in the seventeenth century, the spread of horses across the North American Great Plains fundamentally transformed the lifeways of Indigenous peoples across the region. For those who adopted them, horses opened up a new world of mobility and created new possibilities for bison hunting, trade, warfare, and social organization. This shift was so powerful that it reshaped the human geography of the continent, drawing peoples such as the Comanche and Cheyenne onto the open prairies from neighboring regions, where some established themselves as formidable equestrian powers. Yet for both newcomers and long-standing grassland inhabitants such as the Blackfoot, adopting an equestrian way of life meant far more than simply using horses as “tools” in the service of human interests. Native communities developed extensive knowledge of horses and their behavior, and in effect learned to understand the grasslands through the needs and abilities of the equine partners upon whom they depended. HotspotPanoramaOverlayTextImage_00E66E45_15E5_3D4F_41A7_1CDE18307E78.text = CLEAR COMMUNICATION QuadHotspotPanoramaOverlayTextImage_23407492_37B2_AA97_41BC_5E2308D698FA.text = COMICS FlatHotspotPanoramaOverlayTextImage_5A961817_43B0_48F5_4190_F86A2BDE8660.text = CREDITS QuadHotspotPanoramaOverlayTextImage_E7CF65B2_B4AD_CC89_41D1_1A7DFB95D292.text = Considering uncertainties by using probabilistic formulation \ \ \ \ \ \ \ \ \ \ QuadHotspotPanoramaOverlayTextImage_23CED441_37B3_A9F2_41C1_FA2116E53275.text = DATA CENTER QuadHotspotPanoramaOverlayTextImage_E0DDCD0D_C84F_E330_41B5_5AA4ED21168B.text = Dorset Landscape QuadHotspotPanoramaOverlayTextImage_E2F92E15_B4BB_DF8A_41DD_60C8F1181F83.text = Downscaling of climate model \ \ \ \ QuadHotspotPanoramaOverlayTextImage_E6911647_B4B5_CF96_41BE_740146C70B95.text = Downscaling of climate model \ \ \ \ QuadHotspotPanoramaOverlayTextImage_24A4128E_37B5_AE8D_418D_B9F0FF383DCD.text = ENVIRONMENTS & HUMAN MIGRATION QuadHotspotPanoramaOverlayTextImage_23A4A549_37B5_ABF2_4192_7CFF4766F569.text = EVOLVING TECHNOLOGY QuadHotspotPanoramaOverlayTextImage_E2A8CD44_B4B7_BD8B_41A8_478152726C60.text = Estimation of regional HEPs for different populations \ \ HotspotPanoramaOverlayTextImage_5B7E7B46_FCF2_D19F_41D7_1FDE9D78B26A.text = FAMILY DRAMA QuadHotspotPanoramaOverlayTextImage_531E5862_0DE3_5B41_4195_4E6FD0C34CE6.text = FROM DATA TO NETWORKS \ \ We turn site and raw-material distributions into probabilistic mobility networks. \ \ QuadHotspotPanoramaOverlayTextImage_89491E23_9FBF_2DA3_41E3_8FD3BFB634CE.text = Final Quiz HotspotPanoramaOverlayTextImage_421DC182_176B_4407_4196_6D86E032B9DE.text = Further Reading... QuadHotspotPanoramaOverlayTextImage_E394061F_B4AB_CFB6_41E1_AE5F21F4AAEE.text = Generalise HEP considering past human innovation and social development \ \ \ \ \ \ \ \ HotspotPanoramaOverlayTextImage_8D11CD90_DC3C_CF34_41D1_4BF6439EC09C.text = H5P test \ QuadHotspotPanoramaOverlayTextImage_50C6C801_4370_C919_41AB_80FFC98A80D3.text = HEP Biases Paper \ (in Preparation) \ QuadHotspotPanoramaOverlayTextImage_24DDF0FC_37BE_EA93_41C3_468F67B3E6B8.text = HUMAN DYNAMICS IN THE \ EARTH SYSTEM QuadHotspotPanoramaOverlayTextImage_222AC4FE_37B6_AA8E_41B7_76592CE7AABA.text = HUMAN MOBILITY NETWORKS HotspotPanoramaOverlayTextImage_00E0B45B_15DB_CD6D_415A_62C3F1C4A6D4.text = INTER- \ DISCIPLINARY RESEARCH QuadHotspotPanoramaOverlayTextImage_230BCEBF_37B2_D68E_41C0_9C6D8016B3F6.text = MACHINE LEARNING IN \ PREHISTORIC STUDIES HotspotPanoramaOverlayTextImage_6A53E2F7_FCF7_F07D_41C9_D03B46F4818B.text = MARTIAL ARTS HotspotPanoramaOverlayTextImage_336D1833_1EB8_78D6_41B8_30FC6F7AA512.text = MOVE BACK HotspotPanoramaOverlayTextImage_33437B12_1EB8_78D6_41BE_38046B896D0B.text = MOVE BACK HotspotPanoramaOverlayTextImage_32A1EB52_1EB8_5F56_4196_B9705E135F30.text = MOVE BACK HotspotPanoramaOverlayTextImage_329086CA_1EB8_69B6_41BD_982392BB8651.text = MOVE ON HotspotPanoramaOverlayTextImage_77433936_17AB_C40E_41B5_4F217F009EF5.text = MOVE ON HotspotPanoramaOverlayTextImage_33B79DC5_1E88_5BBD_41A8_8163BBED369B.text = MOVE ON HotspotPanoramaOverlayTextImage_3281B469_1EB8_6972_41B1_7D54C994255D.text = MOVE ON HotspotPanoramaOverlayTextImage_BFC02C4A_3ACF_E712_41C6_72A86CBE9EEC.text = MOVE ON HotspotPanoramaOverlayTextImage_33C2D40F_1EB8_28CD_41BD_16A0F162E682.text = MOVE ON HotspotPanoramaOverlayTextImage_33C635DF_1E88_2B4E_41BA_8DD04A0C5BFD.text = MOVE ON HotspotPanoramaOverlayTextImage_BEF39E36_3AFD_6372_41B9_DCC05322B7D1.text = MOVE ON HotspotPanoramaOverlayTextImage_ACE60DB2_B274_0015_41DB_12FCF7010C69.text = More Reading... QuadHotspotPanoramaOverlayTextImage_7C7A3DC3_0D21_7546_41A6_0F8F4747E1A1.text = NETWORK TERMS \ \ Key components, measures and definitions. QuadHotspotPanoramaOverlayTextImage_EF51ABFB_B4BC_447E_41D6_C6A910306205.text = Optimization of vegetation data \ \ \ \ HotspotPanoramaOverlayTextImage_0A0A8C0D_58F0_7D4B_41C7_C78AD88A4642.text = Our Publications... QuadHotspotPanoramaOverlayTextImage_CC8E6D86_D270_A1F0_41C3_8D8DB66C8475.text = PALVEG - Interactive HotspotPanoramaOverlayTextImage_00D3A979_15A4_C723_41A1_4EA188AD0949.text = PLANNING DATA QuadHotspotPanoramaOverlayTextImage_91EC7C0D_D527_7546_41CF_47B067D72D8F.text = RESEARCH HotspotPanoramaOverlayTextImage_01686ED7_15AD_3D11_41A5_6754F0769332.text = RESEARCH CYCLE HotspotPanoramaOverlayTextImage_001A08AE_15E7_45C2_41B4_55F2FC62E5A1.text = RHYTHMS OF RESEARCH QuadHotspotPanoramaOverlayTextImage_EDBF1064_B4B4_C38A_417A_927085C55BE1.text = Resolution of chronological data from archaeological sites HotspotPanoramaOverlayTextImage_0156FBCE_15DF_5B50_41B0_062425CA8C58.text = SHARING ASSUMPTIONS HotspotPanoramaOverlayTextImage_DBE63909_C724_A5C4_41E7_E7B24641F942.text = START HotspotPanoramaOverlayTextImage_DB23C252_C73D_E441_41E2_E17BC2581D86.text = START HotspotPanoramaOverlayTextImage_DCFBBD02_C73D_9DC6_41D2_D86D1DB328B9.text = START HotspotPanoramaOverlayTextImage_DB3E796C_C73C_A442_41E1_0DBE8B80576F.text = START HotspotPanoramaOverlayTextImage_DB0EADDC_C73C_9C42_41D3_96968BB8E360.text = START HotspotPanoramaOverlayTextImage_DCFAA816_C73D_63CE_41DE_C1961F9FCC11.text = START HotspotPanoramaOverlayTextImage_77433935_17AB_C402_419C_ACD4794DA95F.text = START QuadHotspotPanoramaOverlayTextImage_220BBD39_37BD_BB95_41C7_EBEE1D9F8F39.text = STATE CRISES QuadHotspotPanoramaOverlayTextImage_97668411_B194_0004_41D0_50C9B4E77935.text = State Crisis Theory QuadHotspotPanoramaOverlayTextImage_5220E116_0DE1_2ACE_417A_AB33A72C733F.text = THE RESEARCH DESK \ \ Further Reading: \ Explore the motivation and approaches behind our research on prehistoric mobility networks. All literature are available in the HESCOR library. QuadHotspotPanoramaOverlayTextImage_93EB4C1D_D76B_BC34_41EA_504773242639.text = Texts QuadHotspotPanoramaOverlayTextImage_EC176BA2_D758_A40C_41AC_DAA3D01AED7F.text = Texts QuadHotspotPanoramaOverlayTextImage_95881445_D778_6C14_41AD_4BD514957867.text = Texts QuadHotspotPanoramaOverlayTextImage_9557505C_B194_0003_41E2_6A1D6156F14E.text = The Crises of the Roman Republic QuadHotspotPanoramaOverlayTextImage_ECEBC827_C841_E165_41D6_90B7559D8B0D.text = Thule Landscape QuadHotspotPanoramaOverlayTextImage_23C443FB_37BD_AE94_4193_39D3834A1749.text = VEGETATION RECONSTRUCTIONS HotspotPanoramaOverlayTextImage_FD6CB39B_A929_3F28_41D8_24601B3F45C0.text = WELCOME TO THE DESERT HotspotPanoramaOverlayTextImage_F119985D_A917_29D9_41E0_1A95DAC9F9FD.text = WELCOME TO THE FOREST HotspotPanoramaOverlayTextImage_FD6A70F9_A92B_7AEA_41DE_3D5107B44E09.text = WELCOME TO THE GRASSLANDS HotspotPanoramaOverlayTextImage_7BA41B92_4391_CF9F_41D0_91B907A2CC3E.text = Welcome to the Arctic icescapes of the past, a demanding environment that people endured and thrived in despite the extreme cold, shifting sea ice, and long seasonal changes. Life there depended on careful observation of weather and wildlife, on flexible planning, and on technologies and techniques suited to hunting and traveling across ice and coastline. Today we know of two iconic Arctic cultures that learned to read the Arctic’s rhythms: the Dorset, who inhabited the Canadian Arctic and Greenland from between 800 and 500 BCE to 1500 CE; and the Thule, who began to expand into that broader region from the western Arctic at around between 1200 and 1000 CE, eventually becoming the direct ancestors of the Inuit. Both Dorset and Thule peoples developed deep local knowledge and specialized toolsets to support a marine-based way of life, especially the hunting and processing of sea mammals. And in doing so, both Dorset and Thule peoples relied on key animal presences – polar bears in the case of the Dorset and dogs in the case of the Thule – to help them live and act in, as well as to think about, Arctic environments. HotspotPanoramaOverlayTextImage_7BA01BC8_4390_4F88_41CE_EE41683B8510.text = Welcome to the Arctic icescapes of the past, a demanding environment that people endured and thrived in despite the extreme cold, shifting sea ice, and long seasonal changes. Life there depended on careful observation of weather and wildlife, on flexible planning, and on technologies and techniques suited to hunting and traveling across ice and coastline. Today we know of two iconic Arctic cultures that learned to read the Arctic’s rhythms: the Dorset, who inhabited the Canadian Arctic and Greenland from between 800 and 500 BCE to 1500 CE; and the Thule, who began to expand into that broader region from the western Arctic at around between 1200 and 1000 CE, eventually becoming the direct ancestors of the Inuit. Both Dorset and Thule peoples developed deep local knowledge and specialized toolsets to support a marine-based way of life, especially the hunting and processing of sea mammals. And in doing so, both Dorset and Thule peoples relied on key animal presences – polar bears in the case of the Dorset and dogs in the case of the Thule – to help them live and act in, as well as to think about, Arctic environments. HotspotPanoramaOverlayTextImage_74115E87_43F0_4982_41AF_02F4C19E8A2F.text = Welcome to the vibrant wetland landscapes of post-glacial northern and eastern Europe, environments that emerged as the massive northern ice sheets retreated at the end of the Last Ice Age. Life in these river valleys and floodplains depended on navigating newly forming (semi-)aquatic ecosystems, where human survival relied on reading seasonal water flows, plant growth, and wildlife populations. Many Mesolithic and Neolithic foraging communities, who inhabited northern Europe between roughly 10,000 and 4,000 years ago, developed a way of life attuned to beaver-engineered environments, learning to orient their foraging, fishing, and mobility around beaver ponds and wetlands, harvesting the abundant fish, waterbirds, and edible aquatic plants promoted by beaver landscapes. ### Tooltip HotspotPanoramaOverlayArea_3A0B6960_147B_37C2_41B4_E9B9F3625B1F.toolTip = 3D Drill Core HotspotPanoramaOverlayArea_E01DE04F_F8AF_88F3_41EA_B7844598A87B.toolTip = A Radical Ecosystems View of Lithic Macro-Evolution (ECOLITHIC) HotspotPanoramaOverlayArea_D644D61B_C308_20B3_41E1_D4066F11657D.toolTip = A holarchy of lithic technological \ macroevolution HotspotPanoramaOverlayArea_D5B14A20_C8C3_E090_41C9_83450BE7698C.toolTip = Accessing Beaver Capabilities HotspotPanoramaOverlayArea_81581FF6_C84F_5EB0_41E3_0806BBC5F04F.toolTip = Action HotspotPanoramaOverlayArea_32574E11_1A72_48E2_41A1_97C4BED31B5D.toolTip = Adaptive Routing HotspotPanoramaOverlayArea_04784FBC_1FC1_425E_41B8_7BD7561BA6D8.toolTip = Agent-Based Simulation HotspotPanoramaOverlayArea_D3AA5817_CDC4_FFBA_41E2_CCD9F4F6A8FD.toolTip = Algorithm 4 HotspotPanoramaOverlayArea_FA5F5044_EC67_F35A_419C_8499F0BABE34.toolTip = Artefact Classification HotspotPanoramaOverlayArea_B247FC91_AD0D_C40F_41D4_11FAA14F8C9B.toolTip = Audio Overview HotspotPanoramaOverlayArea_E856483D_C8C1_217F_41DD_D96CC9A3A8F5.toolTip = Back HotspotPanoramaOverlayArea_D6173BF7_C8DF_675A_41A2_676992308688.toolTip = Back HotspotPanoramaOverlayArea_D654EF2F_C8C1_E0C6_41DA_BBDABE09198F.toolTip = Back HotspotPanoramaOverlayArea_EE2932BD_C843_6153_41C4_49027590E61F.toolTip = Back HotspotPanoramaOverlayArea_C1A64206_A969_1843_41D1_18C48DFEF6E8.toolTip = Back to Basics HotspotPanoramaOverlayArea_BBEC8A4D_AE2F_5B69_41DF_C2ED66CFF6CA.toolTip = Back to Basics HotspotPanoramaOverlayArea_C0A84297_A96B_3842_41E1_21D106E75CB2.toolTip = Back to Basics HotspotPanoramaOverlayArea_DE2F0868_EDE6_5417_41CA_7BCC972E15A3.toolTip = Back to Basics HotspotPanoramaOverlayArea_DE4ADE1B_EDE6_6C29_41E9_B9762A943DA2.toolTip = Back to Basics HotspotPanoramaOverlayArea_DE437E1A_EDE6_6C2B_4186_2979843FA010.toolTip = Back to Basics HotspotPanoramaOverlayArea_DE365869_EDE6_54E9_41E6_D9E5EDBF4FC9.toolTip = Back to Basics HotspotPanoramaOverlayArea_E1C52D06_AAEA_EB8C_41A8_41C5A6D248FB.toolTip = Back to Basics HotspotPanoramaOverlayArea_BBEB1A4C_AE2F_5B6F_41D5_6C846D9F3E85.toolTip = Back to Basics HotspotPanoramaOverlayArea_E1D5ED05_AAEA_EB8C_41C7_DA27AE0405B2.toolTip = Back to Basics HotspotPanoramaOverlayArea_77348933_17AB_C406_4180_E17D7AA02A90.toolTip = Back to Start HotspotPanoramaOverlayArea_143FED9A_39C5_213B_4188_ACC2F4757F10.toolTip = Back to the start HotspotPanoramaOverlayArea_140ABA16_39C5_E30A_4151_4D1CB2F3C28A.toolTip = Back to the start HotspotPanoramaOverlayArea_1407AEB8_39C5_6307_41B4_4082C48838DC.toolTip = Back to the start HotspotPanoramaOverlayArea_B79300DB_ADFE_DC3D_41DA_5B4EC20D9F21.toolTip = Back to the start HotspotPanoramaOverlayArea_1408C49C_39C5_273F_41C5_F129FDD07559.toolTip = Back to the start HotspotPanoramaOverlayArea_14033EBB_39C5_2379_41B5_9C9A306E3E90.toolTip = Back to the start HotspotPanoramaOverlayArea_29B0680D_16BC_C41F_4194_93B35C5CDAF1.toolTip = Basics overview HotspotPanoramaOverlayArea_29A36F05_16BC_FC0F_41B0_CA5BF7B84921.toolTip = Basics overview HotspotPanoramaOverlayArea_2A921113_16BD_C40B_41B5_A240DA125532.toolTip = Basics overview HotspotPanoramaOverlayArea_774C0939_17AB_C402_41A6_95215EC807A7.toolTip = Basics overview HotspotPanoramaOverlayArea_E7A466F1_CF5D_7026_41D3_DD1B1153957C.toolTip = Be a vegetation modeller! 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State Crisis Theory. Cliodynamics. 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HotspotPanoramaOverlayArea_D64152FF_C8C1_2167_41CC_C9B080D490EC.toolTip = Unique Landscape Architects HotspotPanoramaOverlayArea_FD5C5E18_CECC_93E6_41C7_4D44E8A23235.toolTip = Vegetation Reconstruction Problems HotspotPanoramaOverlayArea_8A593891_FB7E_50B2_4176_997169B9DFC1.toolTip = View the Crises of the Roman Republic HotspotPanoramaOverlayArea_8361775E_FC92_51AF_41D3_417002DE24E0.toolTip = View the State Crisis Theory HotspotPanoramaOverlayArea_34AA9276_2695_C69E_41B5_01AB57A50183.toolTip = Viktoria Fries HotspotPanoramaOverlayArea_96B5722F_D778_A415_41E7_F260FE31C00F.toolTip = Visit the Arctic HotspotPanoramaOverlayArea_9782128C_D77F_E41B_41A0_B305731BF6D1.toolTip = Visit the Grassland HotspotPanoramaOverlayArea_9758330C_D779_E41B_41B7_89E461ED19C4.toolTip = Visit the Wetland HotspotPanoramaOverlayArea_D0B428BE_C841_205F_41D7_46B0BC9F2B41.toolTip = Water HotspotPanoramaOverlayArea_32740E9B_1A6E_49E7_419B_A6B70CE2B27C.toolTip = Wavelet Compression HotspotPanoramaOverlayArea_EF7395E4_C842_E2F2_41CE_21CF1D3409DF.toolTip = Whales & whale harvesting HotspotPanoramaOverlayArea_9725B906_C85F_2351_41DE_2D548D7E1A8C.toolTip = What did we find? HotspotPanoramaOverlayArea_4643A98E_177F_C41F_4195_AB72E5455767.toolTip = biases overview HotspotPanoramaOverlayArea_4628DED3_176B_5C05_41AD_8E33F6B805D3.toolTip = biases overview ## Media ### Audio audiores_3B0E1BAC_1FC2_C26A_41AB_D52D9EE79AFF.url = media/audio_00FCE95B_1FC1_CE2D_41AD_937C954F1A69_en.mp3 audiores_0A73A771_4390_5892_41C7_4491CDB87551.url = media/audio_01303D1A_4390_4897_41BE_2D66C7259170_en.mp3 audiores_3FD388B8_1FC3_4E61_41A7_4F6836DBBDBB.url = media/audio_04E0C136_1FC1_7E6E_41B3_357D4A96D455_en.mp3 audiores_3FD2C68F_1FC3_4220_41A2_DA7953769247.url = media/audio_053A2BBC_1FC1_C25F_41AB_E4A037C6ACFB_en.mp3 audiores_3FD483C2_1FC3_4221_4192_CFD079585996.url = media/audio_053ECE95_1FC1_4228_4190_EB6926C13F13_en.mp3 audiores_3FD9615B_1FC3_5E27_419B_00B3330E840F.url = media/audio_057D2070_1FC2_BEE8_41B5_51E0AA43A795_en.mp3 audiores_2401C52F_531B_0C15_41CD_DE14209EAC4F.url = media/audio_264994E8_5319_0C1B_41C8_472853486BAD_en.mp3 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audiores_E4320EB7_D2E0_DDE9_41DA_6B5E777533E6.url = media/audio_FD3637F8_D2EF_4B67_41C0_5A9859FF5620_en.mp3 audiores_F8FBC390_B46C_C4B2_41D1_5F483D6A3FF8.url = media/audio_FEFD9BDE_B454_44AF_41C8_A2F298733BBD_en.mp3 ### Audio Subtitles ### Description album_A8D837FD_D670_6474_41DC_77AA0D902790_0.description = Approximate distributions of shortgrass, mixed-grass (or “mid-grass”), and tallgrass prairie. Each ecological zone offered horses different kinds and seasonal patterns of forage. The boundaries shown are generalized, because prairie vegetation formed a shifting mosaic rather than sharply divided regions. \ Wikimedia Commons. album_5BA844C7_D6B0_A4FC_41E0_A968F6434563_1.description = Bear effigies and other bear-evoking carvings. 1, 4-6, 11-12:swimming, jumping or “flying” polar bears with X-ray patterns and axial line; 2-3:“naturalistic,” standing polar bears; 7-8, 16: abstracted and greatly reduced polar bears (focalization: head and extremities); 9-10: polar bear paws; 13, 15: polar bear heads; 14:polar bear jaw/tooth piece. Photograph from Hardenberg, 2013, modified and rearranged. album_C4AB55A7_FBB5_D09D_41E5_7073610CD304_1.description = Box 1 album_EF6F0D71_F89F_98AF_41EA_6FFF0AC3D233_0.description = Box 1:- \ Key Concepts: technological macroevolution album_E0A14C3C_FB7D_D7F3_41E9_D41D005C4BAB_0.description = Box 1:- Key Terma: reciprocal causation. album_E43873DC_F8BA_8F95_41CA_FA3505CA9BB3_0.description = Box 1:- Key Terms: scale. album_E4EE198F_FB96_D0AD_41DF_97B28375E74A_0.description = Box 1:- Key Terms: technological world (s). album_C4AB55A7_FBB5_D09D_41E5_7073610CD304_2.description = Box 2 album_EF6F0D71_F89F_98AF_41EA_6FFF0AC3D233_1.description = Box 2:- \ Key Concepts: the Middle Palaeolithic album_E0A14C3C_FB7D_D7F3_41E9_D41D005C4BAB_1.description = Box 2:- Key Terma: technology as pharmakon. album_E4EE198F_FB96_D0AD_41DF_97B28375E74A_1.description = Box 2:- Key Terms: planetary technosphere. album_E43873DC_F8BA_8F95_41CA_FA3505CA9BB3_1.description = Box 2:- Key Terms: stratigraphy. album_C4AB55A7_FBB5_D09D_41E5_7073610CD304_3.description = Box 3 album_E0A14C3C_FB7D_D7F3_41E9_D41D005C4BAB_2.description = Box 3 album_EF6F0D71_F89F_98AF_41EA_6FFF0AC3D233_2.description = Box 3:- \ Key Concepts: the Upper Palaeolithic album_E4EE198F_FB96_D0AD_41DF_97B28375E74A_2.description = Box 3:- Key Terms: technological agency. photo_E2D614B7_F8B6_8993_41E3_C911689E3320.description = Box 3:- Key Terms: technostratigraphy. photo_E2D614B7_F8B6_8993_41E3_C911689E3320.description = Box 3:- Key Terms: technostratigraphy. album_EF6F0D71_F89F_98AF_41EA_6FFF0AC3D233_3.description = Box 4:- \ Key Concepts: technological ecosystems album_E0A14C3C_FB7D_D7F3_41E9_D41D005C4BAB_3.description = Box 4:- Key Terma: technological holarchy. album_E43873DC_F8BA_8F95_41CA_FA3505CA9BB3_2.description = Box 4:- Key Terms: materiality. album_E4EE198F_FB96_D0AD_41DF_97B28375E74A_3.description = Box 4:- Key Terms: planetarity of technology. album_E0A14C3C_FB7D_D7F3_41E9_D41D005C4BAB_4.description = Box 5:- Key Terma: technological levels of organization. album_E43873DC_F8BA_8F95_41CA_FA3505CA9BB3_3.description = Box 5:- Key Terms: technique. album_E43873DC_F8BA_8F95_41CA_FA3505CA9BB3_4.description = Box 6:- Key Terms: technological paradigm. album_E43873DC_F8BA_8F95_41CA_FA3505CA9BB3_5.description = Box 7:- Key Terms: technological rationality. album_E43873DC_F8BA_8F95_41CA_FA3505CA9BB3_6.description = Box 8:- Key Terms: technological normativity. album_C6FE789D_D75F_E435_41DA_14D343DC3362_0.description = Dated to the late glacial period and reconstructed coastline during the Allerød period (ca. 13,300 to 12,800 years ago). Dotted lines represent the present-day coastline, and complete lines Allerød coastlines. Different whale species are represented by icon (bowhead, humpback, and beluga). Archaeological sites dating to the Allerød period are shown as black dots. The red dot denotes the Hollendskær site complex in northernmost Jutland, which has produced stone tool evidence for early forager incursions into the European north. Polar bear remains dating to the same period are indicated by white polar bear outlines. Image from Hussain et al. 2025. album_5BA844C7_D6B0_A4FC_41E0_A968F6434563_2.description = Dorset longhouses (upper right of the diagram), large communal structures, perhaps evoke a bear’s body: their elongated central axis may have represented a spine, while their ends recalled a head and other parts of the axial skeleton. The placement of polar bear bones from corresponding body regions at the ends of some longhouses supports this interpretation. Together, these objects and structures may have preserved and expressed knowledge gained by observing bears. Figure created by Shumon T. Hussain. album_57D1D11B_D691_DDB9_41E2_09B6ABA9BBF9_0.description = During winter months when heavy snow blanketed prairie pastures, groves of Plains cottonwood trees (left) along river valleys served as vital lifelines for horses. Native people harvested the trees’ inner bark (right) as a nutrient- and moisture-rich food source, when no grass was available. album_B72AE58B_D5F0_A4B8_41E1_AF94E389B3A2_0.description = Feats of horsemanship. U.S. artist George Catlin (1796-1872) traveled extensively across the North American West during the 1830s to document Native American lifeways. And like many Euro-American observers, Catlin was awed by the equestrian skills of Plains riders. In this painting from his widely read Letters and Notes on the Manners, Customs, and Conditions of the North American Indians (1841), he captured a defense maneuver where Comanche riders drop along the side of a galloping horse, using the animal’s body as a shield. \ \ photo_ECCDBE56_FB7D_D3BF_41E1_B2C2EF931A55.description = Fig 1. Bernard Stiegler (1998): Technics and Time, 1. Stanford University Press. photo_ECCDBE56_FB7D_D3BF_41E1_B2C2EF931A55.description = Fig 1. Bernard Stiegler (1998): Technics and Time, 1. Stanford University Press. album_F6D4ADEA_30EE_905C_41AE_B8F79FDB05B0_0.description = Fig 1. Stuart Kauffman (2016): Humanity in a Creative Universe. Academic Press. album_D22D8FB2_FBB2_70F7_41CC_A08D915542C0_0.description = Fig 1. Stuart Kauffman (2016): Humanity in a Creative Universe. Academic Press. photo_DA20F53B_FB92_51F5_41C9_D77E150C1B53.description = Fig 1. U.S Gulf coast from the Earth's orbit at night. \ Copywrite NASA photo_DA20F53B_FB92_51F5_41C9_D77E150C1B53.description = Fig 1. U.S Gulf coast from the Earth's orbit at night. \ Copywrite NASA photo_E30BDA95_FB76_70BD_41E5_F86B343F14B0.description = Fig 2. Arthur Koestler (1967): The Ghost in the Machine. Penguin. photo_E30BDA95_FB76_70BD_41E5_F86B343F14B0.description = Fig 2. Arthur Koestler (1967): The Ghost in the Machine. Penguin. album_D22D8FB2_FBB2_70F7_41CC_A08D915542C0_1.description = Fig 2. Coral reefs as integrated assemblies of co-adapted, cooperating organisms, held together by a plethora of symbiotic relationships. © Wikipedia album_E4EE198F_FB96_D0AD_41DF_97B28375E74A_4.description = Fig 2. Simplified representation of the Earth’s major spheres. From: German National Academy of Sciences Leopoldina (2022): Report on Tomorrow’s Science. Earth System Science – Discovery, Diagnosis, and Solutions in Times of Global Change. Halle (Saale). https://doi.org/10.26164/leopoldina_03_00591 album_E4EE198F_FB96_D0AD_41DF_97B28375E74A_5.description = Fig 3. Brian Arthur (2009): The Nature of Technology. Free Press. album_F4C645D5_30E9_B077_41BC_9F6A598A8943_0.description = Fig 3. Brian Arthur (2009): The Nature of Technology. Free Press. photo_EDD6DBD1_FB76_70B5_41D5_C782E744C231.description = Fig 3. John J. Shea (2016): Stone Tools in Human Evolution. Cambridge University Press. photo_EDD6DBD1_FB76_70B5_41D5_C782E744C231.description = Fig 3. John J. Shea (2016): Stone Tools in Human Evolution. Cambridge University Press. album_E4EE198F_FB96_D0AD_41DF_97B28375E74A_6.description = Fig 4. Increase of artificial objects in the Earth’s orbit between 1960 and 2020. From: Aaron C. Boley & Michael Byers (2021): Satellite mega-constellations create risks in Low Earth Orbit, the atmosphere and on Earth. Scientific Reports 11:10642. https://doi.org/10.1038/s41598-021-89909-7 album_C528E0F3_D767_E40D_41E0_5910AD5CCF17_0.description = Human skills (spatial awareness, planning, and long-range scanning) paired with canine strengths (especially scent-reading and directionalhearing) to produce an integrated system of environmental perception and knowledge. album_C46EA9B7_D75B_A474_41E9_F7A71ED6E2E2_0.description = Inuit sledge (qamutiik) being pulled across the sea ice, illustratinga mobility system that possibly traces back to the Thule period. This photograph of CopperInuit (Inuinnait) in Coronation Gulf (Nunavut) demonstrates the flexibility of this historicalhuman-canine travel assemblage, where heavier loads were pulled collaboratively by dogsand humans. Photograph taken by Vilhjalmur Stefansson, early 1900s. Canadian Museum History, https://www.historymuseum.ca/collections/archive/3311876. \ album_53DB32D1_D690_BC9F_41E6_74FAB9107317_0.description = Mesolithic and Neolithic human foragers adapted beaver jaws and incisors as woodworking chisels or scrapers or to chip and shape stone tools. Image of mandible tools taken from Lozovskaya et al. 2017. album_53DB32D1_D690_BC9F_41E6_74FAB9107317_1.description = Mesolithic and Neolithic human foragers adapted beaver jaws and incisors as woodworking chisels or scrapers or to chip and shape stone tools. Image of mandible tools taken from Lozovskaya et al. 2017. album_C890368E_D768_6C17_41B3_D8417D8965E1_0.description = Multispecies dynamics in beaver-shaped wetlands. Beavers are central nodes in human lifeways and these relationships are reflected in beaver-sourced material culture (beaver mandible tools and perforated beaver teeth). Diagram taken from Hussain and Brusgaard 2024. album_51238D02_D670_6588_4173_A9D7C86D6510_0.description = Native American communities and their horses depended on rivers, tributaries, springs, ponds, and transient rain pools. Many smaller streams were sporadic, and water availability varied widely by location and season. \ Wikimedia Commons. album_C4AB55A7_FBB5_D09D_41E5_7073610CD304_0.description = Naval Panel album_5E074156_D690_BD85_41E2_7FC909960151_0.description = Northern pike (Esox lucius) was an important food source for Mesolithic forager communities across Europe and northern Eurasia as post-glacial waters warmed. album_B72AE58B_D5F0_A4B8_41E1_AF94E389B3A2_1.description = Picketing techniques. As recorded by Ewers, managing herds on the open plains required familiarity with each horse’s temperament. Distinct picketing techniques illustrate the various hands-on skills that Native people developed to manage their animals. \ Figure from Ewers 1955. album_5BA844C7_D6B0_A4FC_41E0_A968F6434563_0.description = Polar bears featured prominently in Dorset carvings, tools, and possibly architectural outlines, suggesting that these animals served as important nonhuman models for understanding and living in dynamic, ever-shifting Arctic sea-ice environments. album_A8D837FD_D670_6474_41DC_77AA0D902790_1.description = Rainfall, soil, and climate all affect plant life throughout the Great Plains. Tallgrasses like Big Bluestem (left) flourish in the humid east, while hardy, low-growing species like Blue Grama (right) – known for its comb-shaped seed heads – thrive in the drier west. Tall and short grass species coexist in the wide mixed-grass grasslands that lie between them. \ \ album_5BA844C7_D6B0_A4FC_41E0_A968F6434563_3.description = Simplified reconstruction of a seal-hunting sequence. The bear detects and approaches the opening, waits motionless for a seal to surface, strikes, and pulls the prey onto the ice. 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Marcel Schemmel | Alumnus & Project Researcher
Department of Prehistoric Archeology
Marcel Schemmel is a distinguished Alumnus of the University of Cologne, currently contributing his expertise to the Department of Prehistoric Archaeology. As a core member of the HESCOR project, Marcel focuses on the complex interactions between human environmental behavior and societal change.
His work bridges cultural history and environmental science, utilizing advanced archaeological methodologies to decode prehistoric settlement patterns. With a deep commitment to interdisciplinary collaboration, Marcel plays a vital role in synthesizing data that informs our understanding of human resilience. He remains an active figure in the university’s academic community, dedicated to preserving and interpreting our shared cultural heritage.
mschemm3@uni-koeln.de
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HESCOR publication:
Hussain, Shumon T., and Dominik Ohrem. 2026. “Learning Environments with Other Animals.” Humanimalia 16 (2): 251–95. https://doi.org/10.52537/humanimalia.23616
References and reading suggestions:
Bethke, Brandi. 2020. “Revisiting the Horse in Blackfoot Culture: Understanding the Development of Nomadic Pastoralism on the North American Plains.” International Journal of Historical Archaeology 24 (1): 44–61.
https://doi.org/10.1007/s10761-019-00502-1
Ewers, John C. 1955. The Horse in Blackfoot Indian Culture: With Comparative Material from Other Western Tribes. Government Printing Office.
Hämäläinen, Pekka. 2021. “The Kinetic Empires of Native American Nomads.” In The Oxford World History of Empire, edited by Peter Fibiger Bang, C. A. Bayly, and Walter Scheidel, vol. 2. Oxford University Press.
https://doi.org/10.1093/oso/9780197532768.003.0038
Sherow, James E. 1992. “Workings of the Geodialectic: High Plains Indians and Their Horses in the Region of the Arkansas River Valley, 1800-1870.” Environmental History Review 16 (2): 61–84.
https://doi.org/10.2307/3984929
Taylor, William Timothy Treal, Pablo Librado, Mila Hunska Tašunke Icu, et al. 2023. “Early Dispersal of Domestic Horses into the Great Plains and Northern Rockies.” Science 379 (6639): 1316–23.
https://doi.org/10.1126/science.adc9691
West, Elliott. 2016. “A Horse-Man’s View of a Grassland Revolution.” In Bison and People on the North American Great Plains: A Deep Environmental History, edited by Geoff Cunfer. Texas A&M University Press.
htmlText_ECF42593_D768_6C0C_41D0_F38EB6C2F85A.html =
HESCOR publication:
Hussain, Shumon T., and Dominik Ohrem. 2026. “Learning Environments with Other Animals.” Humanimalia 16 (2): 251–95. https://doi.org/10.52537/humanimalia.23616
References and reading suggestions:
Coles, Bryony. 2006. Beavers in Britain’s Past. Oxbow Books.
Hussain, Shumon T., and Nathalie Ø. Brusgaard. 2024. “Human-Beaver Cohabitation in the Early and Mid-Holocene of Northern Europe: Re-Visiting Mesolithic Material Culture and Ecology Through a Multispecies Lens.” The Holocene 34 (1): 25–55.
https://doi.org/10.1177/09596836231200444
Liarsou, Alexandra. 2013. “Interactions Between the Beaver (Castor Fiber L.) and Human Societies: A Long-Term Archaeological and Historical Approach.” Archaeological Review from Cambridge 28 (2): 171–85.
https://shs.hal.science/halshs-00961332
Lorimer, Jamie. 2025. “Worlding and Weirding with Beaver: A More-Than-Human Political Ecology of Ecosystem Engineering.” Transactions of the Institute of British Geographers 50 (2): e12698.
https://doi.org/10.1111/tran.12698
Lozovskaya, Olga et al. 2017. Beaver Mandible Tools During the Late Mesolithic and the Early Neolithic at Zamostje 2 (the Upper Volga Region, Russia). In From Hunter-Gatherers to Farmers. Human Adaptations at the End of the Pleistocene and the First Part of the Holocene, edited by Adina Boroneant and Monica Mărgărit. Editura Cetatea de Scaun.
Zhilin, Mikhail. 2020. “Beaver Mandible Tools in the Mesolithic of the Forest Zone of Eastern Europe and Urals.” Journal of Archaeological Science: Reports 30: 102199.
https://doi.org/10.1016/j.jasrep.2020.102199
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HESCOR publications:
Hussain, Shumon T., and Dominik Ohrem. 2026. “Learning Environments with Other Animals.” Humanimalia 16 (2): 251–95.
https://doi.org/10.52537/humanimalia.23616
Hussain, Shumon T. et al. 2025. “Was the Late Glacial Human Occupation of Northernmost Europe Facilitated by Whales? New Data and Perspectives on Lithic Technology and the Paleoecology of the Vendsyssel Area, Northern Jutland, Denmark.” Journal of Island and Coastal Archaeology 20 (2): 317–45.
https://doi.org/10.1080/15564894.2023.2277727
Hussain, Shumon T. 2025. “The Heath and the Sea: A Speculative Palaeohistory of Multispecies Life in Northern Jutland, ca 13,300–12,800 Years Ago.” In A Place for the Heathlands(?) Human-Heath Relations in Deep-Time and Contemporary Perspectives, edited by Mette Løvschal and Mark Haughton. Aarhus University Press.
https://heathland.place/the-heath-and-the-sea
References and reading suggestions:
Ameen, Carly, Tatiana R. Feuerborn, Sarah K. Brown, et al. 2019. “Specialized Sledge Dogs Accompanied Inuit Dispersal Across the North American Arctic.” Proceedings of the Royal Society B: Biological Sciences 286 (1916): 20191929.
https://doi.org/10.1098/rspb.2019.1929
Betts, Matthew W., Mari Hardenberg, and Ian Stirling. 2015. “How Animals Create Human History: Relational Ecology and the Dorset–Polar Bear Connection.” American Antiquity 80 (1): 89–112.
https://doi.org/10.7183/0002-7316.79.4.89
Crockford, Susan J. 2022
“Polar Bear Fossil and Archaeological Records from the Pleistocene and Holocene in Relation to Sea Ice Extent and Open Water Polynyas.” Open Quaternary 8 (1).
https://doi.org/10.5334/oq.107
Demuth, Bathsheba. 2021. “Labors of Love: People, Dogs, and Affect in North American Arctic Borderlands, 1700–1900.” Journal of American History 108 (2): 270–95.
https://doi.org/https://doi.org/10.1093/jahist/jaab122
Engelhard, Michael. 2016. Ice Bear: The Cultural History of an Arctic Icon. University of Washington Press.
Hill, Erica. 2018. “The Archaeology of Human-Dog Relations in Northwest Alaska.” In Dogs in the North: Stories of Cooperation and Co-Domestication, edited by Robert J. Losey, Robert P. Wishart, and Jan Peter Loovers. Routledge.
Laidre, Kristin L., Ian Stirling, James A. Estes, Anatoly Kochnev, and Jason Roberts. 2018. “Historical and Potential Future Importance of Large Whales as Food for Polar Bears.” Frontiers in Ecology and the Environment 16 (9): 515–24.
https://doi.org/10.1002/fee.1963
Miller, Katharina M., Georgina Berg, Michael Lickers, Nickia McIvor, and Dominique A. Henri. 2025. “Coexistence Between People and Polar Bears Supports Indigenous Knowledge Mobilization in Wildlife Management and Research.” Communications Earth & Environment 6 (1): 74.
https://doi.org/10.1038/s43247-025-02017-6
Vitale, Emma, Tatiana R. Feuerborn, and Matthew Walls. 2025. “Human-Dog Symbiosis and Ecological Dynamics in the Arctic.” Evolutionary Anthropology: Issues, News, and Reviews 34 (3): e70009.
https://doi.org/10.1002/evan.70009
Whitridge, Peter. 2013. “The Imbrication of Human and Animal Paths: An Arctic Case Study.” In Relational Archaeologies: Humans, Animals, Things, edited by Christopher M. Watts. Routledge.
Whitridge, Peter. 2017. “The Government of Dogs: Archaeological (Zo)Ontologies.” In Relational Engagements of the Indigenous Americas: Alterity, Ontology and Shifting Paradigms, edited by Melissa R. Baltus and Sarah E. Baires. Lexington Books.
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*WE NEED TO CLARIFY WITH ISABELL WHAT SHE WILL WANT FOR THE CATALOGUE*
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A key question pursued by HESCOR is how we can make (better) sense of human dynamics in the Earth system, which includes changing forms of human-environment relations. In this room, we present a basic framework for describing how humans encounter, and come to act and dwell in, changing or unfamiliar environments. To do so, we foreground the key elements of knowledge, learning, action, and perception (KLAP), as well as the relations between them, as they are configured in specific historical contexts. KLAP encourages us to consider how environments – and specific features or inhabitants – come into view and become relevant and meaningful to different groups of humans in different ways under different biological and cultural conditions, and what forms of environmental action these different ways of seeing and knowing might afford.
Authors:
  • Dr. Dominik Ohrem, HESCOR, English Department II, MESH, UoC.
  • Dr. Shumon T. Hussain, HESCOR, Department of Prehistoric Archaeology, MESH, UoC.
Contributors:
  • Prof. Dr. Kate Rigby, HESCOR PI, Multidisciplinary Environmental Studies in the Humanities (MESH), UoC.
  • Prof. Dr. Roman Bartosch, HESCOR PI, English Department II, MESH, UoC.
  • Dr. Caroline Schwegler, HESCOR PI, Department of German Linguistics and Literature 1, MESH, UoC.
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Archaeological evidence is always incomplete. If no site is known from a place, this may mean that people were absent – but the evidence may also have been destroyed or never found.
This room shows how we implemented biases in accessibility and uneven research infrastructure into the Human Existence Potential model, and how accounting for these biases changes its results.
Authors:-
  • Dr. Annika Vogel, HESCOR, Institute of Geophysics and Meteorology, UoC.
  • Dr. Isabell Schmidt, HESCOR, Department of Prehistoric Archaeology, UoC.
  • Anna-Katharina Köpke, HESCOR, Institute of Geophysics and Meteorology, UoC; Earth and Climate System Science, University of Hohenheim.
Contributors:-
• Philipp Schlüter, HESCOR, Institute of Geophysics and Meteorology, UoC.
• Dr. Oliver Kern, HESCOR, Institute of Geophysics and Meteorology, UoC.
• Dr. Götz Ossendorf, HESCOR, Department of Prehistoric Archaeology, UoC.
• Dr. Oliver Vogels, HESCOR, Department of Prehistoric Archaeology, UoC.
• Tin Andrašec, HESCOR, Department of Prehistoric Archaeology, UoC.
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Data are everywhere.
Data form the basis for a lot of the knowledge and opinions we have. Data provide us information about tomorrow’s weather or the potential traffic on our drive home. We produce data through our shopping, travel, algorithm, etc. habits online. Data are used by researchers to better understand the world: past, present, and future.
Despite us using data every single day, most of us don’t have a great understanding of where it comes from or how researchers at the university might use it. Through this Virtual Exhibition room you can explore interactive material to better understand what data are and the important themes related to how researchers at the university might use them.
Authors:-
  • Dr. Elena Robakiewicz, HESCOR, Institute of Geography Education, UoC
  • Philipp Schlüter, HESCOR, Institute of Geophysics and Meteorology, UoC
  • Vittorio Iannuzzi, HESCOR, Institute of Geography Education, UoC
  • Dr. Verena Foerster, HESCOR PI, Institute of Geography Education, UoC
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How people moved and interacted across past landscapes is a central question in archaeology, and the HESCOR project brings together archaeology and mathematics to explore it through raw material networks. Stone tools made from rocks transported over long distances preserve traces of contact, exchange, and communication between prehistoric groups, helping us reconstruct mobility beyond the visible remains of settlements. We translate observed raw-material spheres into connectivity patterns, using mathematical proximity-based rules to define who is likely to have been in contact with whom across the landscape. By translating archaeological evidence into network models, this research opens a way to ask not only where people may have moved, but also how landscape, environment, and social connections shaped those movements.
Authors:
  • Max Brockmann, HESCOR, Division of Mathematics, UoC.
  • Dr. Johanna Hilpert, HESCOR, Department of Prehistoric Archaeology, UoC.
Contributors:
• Rebecca Gnau, HESCOR, Department of Prehistoric Archaeology, UoC.
• Prof. Dr. Angela Kunoth, HESCOR PI, Division of Mathematics, UoC.
• Prof. Dr. Andreas Maier, HESCOR PI, Department of Prehistoric Archaeology, UoC.
• Lena Perlberg, HESCOR, Division of Mathematics, UoC.
• Prof. Dr. Silviane Scharl, HESCOR PI, Department of Prehistoric Archaeology, UoC.
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Prehistoric societies left no written records, yet they adapted to dynamic environments, developed complex technologies, and migrated across continents. Modern archaeology is undergoing a major shift as machine learning enables researchers to analyze vast, complex datasets that were once unmanageable. By combining paleoclimate models, predictive habitat modeling, automated artifact classification, culture clustering, and agent-based simulations, machine learning helps reconstruct past environments and human mobility. Ultimately, these advanced computational methods move the discipline beyond isolated artifact studies, providing a systems-level view of how ancient populations interacted with changing climates and evolved culturally across deep time.
Author:
  • Dr. Boqiang Huang, HESCOR, Department of Mathematics and Computer Science, UoC.
Contributors:
  • Tin Andrašec, HESCOR, Department of Prehistoric Archaeology, UoC.
  • Dr. Oliver Kern, HESCOR, Institute of Geophysics and Meteorology, UoC.
  • Dr. Götz Ossendorf, HESCOR, Department of Prehistoric Archaeology, UoC.
  • Dr. Isabell Schmidt, HESCOR, Department of Prehistoric Archaeology, UoC.
  • Dr. Christian Wegener, HESCOR, Institute of Geophysics and Meteorology, UoC.
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Step into the history of Earth’s landscapes and discover how scientists reconstruct and simulate the vegetation of the past. Follow the journey from fossil pollen preserved in lake sediments to interactive maps of past European vegetation. Watch landscapes change over hundreds of thousands of years and adjust temperature and rainfall yourself to see how changes impact the distribution of forests, grasslands and deserts globally. Along the way, explore how changes in Earth’s orbit have shaped climate and vegetation through time.
Author:
• Dr. Oliver Kern, HESCOR, Institute of Geophysics and Meteorology, UoC.
• Philipp Schlüter, HESCOR, Institute of Geophysics and Meteorology, UoC.
• Dr. Anne Dallmeyer, HESCOR, Institute of Geophysics and Meteorology, UoC.
Contributor:
• Bethan Callow, student at ‘Modelling the Human-Earth Connections' HESCOR Lecture Series, UoC.
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Technology surrounds us all and it fundamentally defines our daily lives, whether we rejoice it or not. We live in altogether technological worlds. Enter this room to explore how HESCOR researchers and their collaborators have tried to analyze and make sense of the many ways in which technology has shaped the evolution of the Earth system. The room guides you through cutting-edge research on the theoretical foundations of how we think about technology and its long-term evolution, showcases some of the researchers currently working on these questions through short video interviews, and illustrates the new vision of technological evolution we work on through engaging examples from our increasingly digital world and the historical development of naval technologies.
The room aims to convince you that understanding the relationship between technological evolution and Earth-system change poses unique challenges that only interdisciplinary collaborations can adequately address. Another objective is to show that technological questions allow us to bridge observations and issues from today with perennial problems in archaeology and other disciplines concerned with the human past and deep past. As you will learn as you venture into the room, the rich archaeological record of the deep past offers unique opportunities to study technological change over extremely long periods of time and on extended spatial scales. It allows us to gain insights on the basic dynamics of large-scale technological change that are otherwise difficult to come by.
Author:
  • Dr. Shumon T. Hussain, HESCOR, Department of Prehistoric Archaeology, MESH, UoC
Contributors:
  • Jenny Wasem, MESH, UoC.
  • Dr. Anaïs L. Vignoles, Department of Prehistoric Archaeology, UoC.
  • Prof. Dr. Mathieu Charbonneau, Africa Institute of Research in Economics and Social Sciences, Mohammed VI Polytechnic University, Morocco.
  • Dr. Tim E. Feiten, Department of Philosophy, Pennsylvania State University, USA.
  • Prof. Dr. Andrej Spiridonov, Department of Geology and Mineralogy, Vilnius University, Lithuania.
  • Prof. Dr. Ilya Tëmkin, Department of Biology, Northern Virginia Community College, Department of Biology, George Mason University, USA.
  • Dr. Sergi Valverde, Evolution of Networks Lab, Institute of Evolutionary Biology (UPF-CSIC), Spain.
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This virtual room explores how climatic and environmental conditions shaped the routes and locations of human migration and settlement, focusing on the first farming communities of Central Europe - the Linearbandkeramik (LBK) culture between about 5400 and 4950 BCE. Drawing on the HESCOR project’s Human Existence Potential (HEP) model, researchers combine archaeological settlements with climate reconstructions, soil maps, and palaeoenvironmental data to identify areas with the most potential for early agriculture and to trace how people moved across the Northern European Plains.
Authors:
  • Dr. Johanna Hilpert, HESCOR, Department of Prehistoric Archaeology, UoC.
  • Dr. Christian Wegener, HESCOR, Institute of Geophysics and Meteorology, UoC.
Contributors:
  • Dr. Anna-Leena Fischer, Department of Prehistoric Archaeology, UoC.
  • Dr. Oliver Kern, HESCOR, Institute of Geophysics and Meteorology, UoC.
  • Philipp Schlüter, HESCOR, Institute of Geophysics and Meteorology, UoC.
  • Prof. Dr. Silviane Scharl, HESCOR PI, Department of Prehistoric Archaeology, UoC.
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Why do states fall into crisis? And when crises occur, what influences their trajectory?
These questions have been asked almost as long as political states have existed. Plato, Aristotle, Han Feizi, and Sima Qian all saw connections between overpopulation, food shortage, and rebellion, and Ibn Khaldun and Machiavelli worried about moral decline, inequality, and violence in their own societies. Now at the start of the twenty-first century, the State Crisis Theory framework provides the fullest picture yet of the various factors that influence the genesis and evolution of state crises.
Author:
  • Dr. Tilman Hartley, HESCOR, Multidisciplinary Environmental Studies in the Humanities (MESH), UoC.
Contributor:
  • Dr. Elena Robakiewicz,HESCOR, Institute of Geography Education, Faculty of Mathematics and Natural Sciences, UoC.
  • Lana Allan, HESCOR, Department of Prehistoric Archaeology, UoC.
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How technologies work together in larger ecosystems
We often think of technology as a collection of standalone gadgets, as an assortment of individual parts – a smartphone, a laptop, or a software application. These parts do indeed matter for understanding technological evolution but in order to develop a better understanding of structural transitions and to address big-picture questions, we need to zoom out and look at the larger context in which these parts occur, especially how they participate in the operation of other parts with which they co-populate technological worlds. In the real world, technology operates in scalable, higher-level structures, so called technological ecosystems. Rather than evolving in isolation, individual tools and technical systems assemble into interconnected webs that partition tasks, support one another, and achieve higher-level functionalities that no single piece of hardware or software could accomplish on its own. This differentiation of tasks, functions, services, and user interactions is what sits at the core of technological ecosystems. They are true macrophenomena – larger technological structures that can only come into view when we abstract from the details of our everyday observation and focus our attention on how technological worlds work in unity.
Three key mechanisms of technological ecosystem evolution
Zooming out in this way and focusing attention on the macro-dynamics of technological worlds means to no longer regard the mentioned technological standalone gadgets, including diverse pieces of software and hardware, as the only or even the most relevant units of evolution. Looking at technological ecosystems and their internal organizational relationships means to position the ecosystems themselves as key contexts of such evolution. We can then meaningfully speak of technological ecosystem evolution, or ecosystem-level evolution, rather than the evolution of artefacts, tools, lineages, production systems, etc.
Adopting an ecosystem-level perspective on evolution brings its own challenges and difficulties, but it also offers a host of advantages and opportunities. For example, it enables the careful study of relationships and interactions that help to assemble, coordinate, and eventually integrate larger technological structures. We may then answer the question: how do these parts come together to sustain and integrated, functional whole?
Three mechanisms or principles appear to play a key role in this:
1. Structural deepening. As technological systems mature, are refined, and transposed to new contexts, they are also subjected to processes of internal tinkering. One example of this is when an important technology encounters operational limits or unforeseen problems (possibly because new components and demands alter, and thereby begin to undermine, its original functionality). New sub-systems or specialized components are then often added to compensate for those problems, refine performances, or help existing components do their job more effectively. The result can be the reorganization and re-combination of existing parts, the partitioning of these parts into new parts with split functions, or the adaptation and alteration of existing parts responding to the novel task and challenges.
2. Division of labour. To ensure the integration and functionality of a technological ecosystem, different technologies tend to take on distinct, more-or-less specialized roles, occupying a spectrum from highly specialized components to flexible and generalized components. In addition, some of these technological components may be focal for the system as a whole and thus serve as central system anchors, while secondary or auxiliary components perform supporting tasks to enhance system operations. Task division and specialization is thereby always relational – what a given component (artefacts, tools, and applications) does depends on what other components in the system are doing. The tendency of large technological structures to “structurally deepen” over time (see 1.) in this manner strongly reinforces the tendency of technological ecosystems to develop functionally segmented and often modularized structures in the course of their evolution.
3. Niche construction and the “adjacent possible”. Once a component has been added to the system, it will actively carve out “opportunity niches” for new technologies to emerge (including modified variants of itself). This is another way of saying that what is “useful” for a larger technological ecosystem to operate economically and effectively depends on what is already in place and works well. Innovations, modifications, and re-combinations are in this way made possible by the structure of the components already present, hence illustrating how technologies actively construct the adaptive environments for each other (niche construction) and thereby structure the space of more-or-less possible and impossible evolutionary developments (in the sense of the “space of the adjacent possible” as originally theorized by complexity scientist Stewart Kauffman, see Fig.1). All of this shows that history matters, and that ecosystem-level evolution cannot be understood in the abstract; it requires to pay attention to the details, components, organizational relationships, and particular structural constraints of specific technological worlds.
Real world examples of technological ecosystem evolution
The evolution of personal computing (PC) technologies after the Second World War and the development of digital ecosystems since the 1990s exemplify the benefits and opportunities of documenting and examining ecosystem-level dynamics:
  • Personal computing (PC): early personal computing relied on basic hardware –towers, monitors, keyboards, mice, etc. – which had to be carefully adapted to each other to operate well together. Alternative solutions which in theory should have been “superior” were not always incorporated because of the ripple effects (transaction costs, etc.) across the entire system (e.g., the QWERTY keyboard whose abandonment would have required encultured users to re-habituate their body routines to a different layout and motor scheme). Over time, the PC setup was considerably expanded – “structurally deepened” – yielding a far more extensive and differentiated technological ecosystem incorporating things such as printers, scanners, audio devices, internet connectivity, and cross-adapted operating systems for diverse desktop and mobile environments. Today, PC technology is fully integrated into larger internet-based infrastructures, including mobile phones, tablets, and increasingly into fully “digital homes” as well as digital public areas and workspaces.
  • Modern digital ecosystems (the example of Apple): digital ecosystems link hardware (e.g., iPhones, MacBooks, iPads) with cloud infrastructure (e.g., iCloud) and application hubs (e.g., App Store). These components are carefully configured to be compatible with each other, and one of their main functions is to support each other – the result being an ecosystem-wide cooperation of components, which divide tasks and achieve outcomes impossible to reach for one component alone. Software apps for example depend on specific operating system requirements, while hardware capabilities derive their value precisely from what they can do for users in specific app ecosystems, and not in others. Taken together, digital ecosystem components thereby serve as a “medium of cooperation” for each other, enabling other functions and services. Importantly, they render other components “more capable” in ecosystem-specific ways. What is “needed” – from a user perspective – is always context-dependent to this effect: technological needs cannot be meaningfully discussed outside of the ecosystem that promotes them. This is the reasons why classical functional accounts of technological evolution cannot be successful if they do not calibrate their assumptions against the specificities of real-world technological structures and the distinct possibilities, needs, and demands they come with.
The big picture: holotechnologies
At the macro level, technology must thus be discussed as an interconnected whole, a complex network of components that variously work together to achieve a shared goal. Understanding long-term technological macroevolution therefore requires distinguishing between what individual technologies do and how they evolve (the components of larger technological ecosystems) and what technologies in their ecosystem do together, how they interact, co-adjust, self-organize, and ultimately co-evolve. We propose to call these higher-level units of technological evolution holotechnologies. From the Greek holos (ὅλος) for “whole”, “entire”, or “complete”, holotechnologies describe the total structure of interacting technologies (or autonomous technological components) within a larger technological ecosystem. We can then qualify macroevolutionary change in technological ecosystems as holotechnological change – a type of change that primarily concerns dynamic, structural re-configurations and transitions of how individual technologies relate to and enable each other (or not). We can think about holotechnologies as the counterparts to biological “holobionts” – assemblages of distinct organisms, such as coral reefs (Fig. 2), that thrive through symbiotic partnerships.
References
Hussain, S.T. (2024). “Transformation analysis and systems-theory: from micro-scale lithic technical systems to macro-scale lithic ecosystems.” In: T. Uthmeier and A. Maier (eds.), Studying Technologies of Non-Analogous Environments and Glacial Ecosystems. Papers in Honor of Jürgen Richter, pp. 531-547 Bonn: Habelt.
Hussain, S.T. (submitted). “Holotechnologies: Trading Theory for A New Macroarchaeology of Large-Scale Technological Structures.” Ethnographisch-Archäologische Zeitschrift (EAZ).
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What is a naval ecosystem?
A naval ecosystem (or a “holotechnology” of maritime warfare; see panel on PC and digital technologies) is a large-scale, self-organizing technological structure, in which warships, aircraft, sensors, weapons, communications networks, and terrestrial and orbital infrastructure co-adapt and operate together as a single unified system. Rather than viewing naval power as a simple aggregate of individual water, air, and/or hybrid assets and to foreground their discrete capabilities or waiting for a single “breakthrough” development in weapon, engine, or sensory technology, the historical analysis of naval warfare illustrates that naval military capability emerges from how these diverse components are positioned in relation to each other, partitioning tasks, sharing support services, and assembling into larger operational networks that ultimately integrate diverse domains of military and non-military technology. To understand how naval technology evolves requires understanding how such naval ecosystems operate, how they initially assemble, and what matters most for their operational prowess in the ever-changing arenas of global conflict and politics.
In broadly analogous ways as biological ecosystems or digital ecosystems, naval operations rely on a coordinated division of technological labour, while the structural deepening and refinement of its components promotes functional integration (Box 1) across the ecosystem – individual assets (ships, airplanes, etc.) and individual technologies take on specialized roles allowing fleets and other assemblies of autonomous naval assets to achieve capabilities far beyond what any single platform or technology could accomplish on its own.
Functional integration as a key to understanding the history of naval warfare
The history of naval warfare from the late 19th century up to the contemporary period provides a useful illustration of ecosystem-level technological evolution. At the core of this process sits the observable transformation and re-allocation of functional roles within naval operations, against the background of historically changing military needs. These transformations and re-allocations reflect broader changes and innovations in supporting and enabling technologies such as sensory systems, defensive capabilities, and the speed and range of airborne operations, but they also tell a unique story of finding ever better-adapted ways of jointly utilizing the available assets for a shared goal.
Three broader stages of naval history can be identified that, even though simplified, showcase such ecosystem-level transitions, each associated with major re-organizations of naval assets as well as their interactions, cooperation, and synergies:
  • High seas combat (late 19th century): naval power centred on divisions of heavy battleships engaging in direct, confrontational gunnery duels on the open ocean. Selection pressure was on the fire power and range of these vessels, which acted as the central capital ships and served as flagships of larger fleets.
  • Structural deepening & destroyer integration (interwar period): as fast torpedo boats emerged as a novel threat, navies responded with a new class of specialized vessels – destroyers – to serve as defensive screens and torpedo assault arms, physically separating fleet defence from gunnery, supporting and thereby empowering the functionality of battleships. In simplified terms, this broadly amounts to a first differentiation of ecosystem tasks into primary, secondary, and tertiary, and it fosters internal and external cross-adaptation between technologies.
  • Shift to carrier task groups (WWII and post-WWII era): after the Battle of Midway had demonstrated that securing airborne superiority fundamentally changes the dynamics, rules, and winning conditions of naval warfare, aircraft carriers rapidly displaced the traditional battleship as the primary capital ship anchoring the fleet. Aircraft carriers moved from secondary support roles within broader naval strategies to their centre pieces, while the former focal assets – battleships – and their supports – cruisers and destroyers – were allocated to secondary and tertiary roles, such as providing heavy anti-aircraft screening. Aircraft themselves also split into specialized sub-roles (strike/bomb, air fight, cover, anti-submarine defence, electronic countermeasures, etc.). Over time, carrier task groups were not only formalized as integrated operational units of naval warfare and the projection of geopolitical power, they were also increasingly integrated into terrestrial and orbital infrastructures, supported for example by satellites, large reconnaissance aircraft operating form ground bases far away, and autonomous marine vessels performing varied context-dependent tasks. Part of this general development is also the growing modularization of both primary and secondary assets, to allow for greater flexibility in synergistic functioning – again illustrating that the evolution of naval technology follows the lead of the operating whole (the naval ecosystem), not its parts.
Interim summary: Ocean Area Tactical Integration
From the 1970s to the present day, the dominant naval ecosystems of the planet have expanded across land, sea, air, and space to form what has been called a “Total Force Battle Network”. Different carrier task groups are linked via satellite navigation (GPS/NNSS), orbital weather and surveillance sensors, automated data networks (such as NTDS), underwater acoustic arrays (SOSUS/SURTASS), and airborne early-warning planes (AWACS). In this way, the entire ocean arena has become a single, integrated tactical grid. Individual technological ecosystems anchored by aircraft carriers are further integrated into larger ecosystems comprising many such smaller ecosystems and other more-or-less specialized technologies (task forces, etc.), helping to secure and improve the functionality of the whole. Naval warfare and the technological ecosystems developed to support it therefore provide a powerful real-world illustration that larger technological structures are the proper contexts of longer term technological macroevolution, and that functional integration, alongside other mechanisms and principles of higher-level organization, can be a strong selective force driving technological evolution.
The big picture: integration follows complementarity and synergy
Understanding naval history through the lens of technological ecosystems reveals that naval effectiveness and dominance is rarely achieved by merely adding bigger guns or faster ships. Additive evolution does rarely do the trick, and additive explanations are typically weak or incomplete because of this. Instead, it is necessary to pay more attention to the organizational principles that help internally structuring naval ecosystems in such a way that their joint operation is promoted, without sacrificing operational flexibility. This has most often been achieved by designing and organizing complementary technologies into a cohesive, adaptable whole – where every asset, platform, sensor, and offered service enables, supports, and ideally enhances the rest. Seen from this perspective, complementarity (Box 2) and synergy (Box 3) are the component-level flipsides of functional integration on the ecosystem level: they define actionable pathways to beneficially integrating individual technologies.
References
Lautenschläger, K., & D. Olive (1983). ‘Technology and the evolution of naval warfare.’ International Security 8.2: 3-51.
Hussain, S.T. (2024). “Transformation analysis and systems-theory: from micro-scale lithic technical systems to macro-scale lithic ecosystems.” In: T. Uthmeier and A. Maier (eds.), Studying Technologies of Non-Analogous Environments and Glacial Ecosystems. Papers in Honor of Jürgen Richter, pp. 531-547 Bonn: Habelt.
Hussain, S.T. (submitted). “Holotechnologies: Trading Theory for A New Macroarchaeology of Large-Scale Technological Structures.” Ethnographisch-Archäologische Zeitschrift (EAZ).
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Affordances
American psychologist James J. Gibson (1904-1979), together with his wife and fellow psychologist Eleanor J. Gibson (1910-2002), was a pioneer in the field of ecological psychology. Gibson was interested in how the world offers possibilities for action to different kinds of animals, including humans, and how such possibilities are perceived. He coined the term “affordances” to describe what a particular environment makes possible in relation to a particular animal, a fundamental relationship that, he argued, existing vocabulary failed to adequately capture. For Gibson, perception is not so much about constructing an “internal picture” of the world but about detecting salient information that enables and guides action within it. Affordances can exist without being noticed, and learning involves becoming more attuned to the information that reveals them. Importantly, many animals also actively reshape their environments – for example, by building nests, digging burrows, or making tools – and thus create or modify affordances both for themselves and for other creatures.
The affordances of the environment are what it offers the animal, what it provides or furnishes, either for good or ill. The verb to afford is found in the dictionary, but the noun affordance is not. I have made it up. I mean by it something that refers to both the environment and the animal in a way that no existing term does. It implies the complementarity of the animal and the environment. The antecedents of the term and the history of the concept will be treated later; for the present, let us consider examples of an affordance. If a terrestrial surface is nearly horizontal (instead of slanted), nearly flat (instead of convex or concave), and sufficiently extended (relative to the size of the animal) and if its substance is rigid (relative to the weight of the animal), then the surface affords support. It is a surface of support, and we call it a substratum, ground, or floor. It is stand-on-able, permitting an upright posture for quadrupeds and bipeds. It is therefore walk-on-able and run-over-able. It is not sink-into-able like a surface of water or a swamp, that is, not for heavy terrestrial animals.
Note that the four properties listed – horizontal, flat, extended, and rigid – would be physical properties of a surface if they were measured with the scales and standard units used in physics. As an affordance of support for a species of animal, however, they have to be measured relative to the animal. They are unique for that animal. They are not just abstract physical properties.
The different substances of the environment have different affordances for nutrition and for manufacture. The different objects of the environment have different affordances for manipulation. The other animals afford, above all, a rich and complex set of interactions, sexual, predatory, nurturing, fighting, playing, cooperating, and communicating. What other persons afford, comprises the whole realm of social significance for human beings.
An important fact about the affordances of the environment is that they are in a sense objective, real, and physical, unlike values and meanings, which are often supposed to be subjective, phenomenal, and mental. But, actually, an affordance is neither an objective property nor a subjective property; or it is both if you like. An affordance cuts across the dichotomy of subjective-objective and helps us to understand its inadequacy. It is equally a fact of the environment and a fact of behavior. It is both physical and psychical, yet neither. An affordance points both ways, to the environment and to the observer.
James J. Gibson, The Ecological Approach to Visual Perception (Houghton Mifflin, 1979), pp. 119-121. Book cover from the Psychology Press Classic Edition (2015).
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Human and Nonhuman Points of View
British anthropologist Tim Ingold explores how people come to know their environments through living and acting within them, challenging the idea that meaning is something that human minds uniquely impose on an otherwise meaningless world of “nature.” His essay “Hunting and gathering as ways of perceiving the environment” draws on accounts of Indigenous communities, for whom meaning emerges through practical and attentive engagement with the more-than-human world. Central to his argument is a critique of a common distinction in Western thought between the “organism” – understood as a “mere” biological entity – and the “person” – understood as a being capable of intentions, meaning, and social relationships and as a status typically reserved for humans. The Indigenous cosmologies he engages with do not operate on this distinction but instead treat both humans and other animals as “organism-persons.” This involves an acknowledgement of other animals as beings who encounter a meaningful world in relation to their particular abilities, purposes, and experiences – not because they are, or think, exactly like humans, but because they, too, perceive, act, and learn.
Now the ontological equivalence of humans and animals, as organism-persons and as fellow participants in a life process, carries a corollary of capital importance. It is that both can have points of view. In other words, for both the world exists as a meaningful place, constituted in relation to the purposes and capabilities of action of the being in question. Western ontology, as we have seen, denies this, asserting that meaning does not lie in the relational contexts of the perceiver’s involvement in the world, but is rather laid over the world by the mind. Humans alone, it is said, are capable of representing an external reality in this way, organising the data of experience according to their diverse cultural schemata. So when the Cree claim, as indeed they do, that the same events surrounding a hunt afford two possible interpretations, from the points of view, respectively, of the human hunter and of the animal hunted, the Western observer is inclined to regard the former as literal and the latter as figurative, ‘as if ’ the animal were human and so could participate with ‘real’ humans in a common world of meanings.
A creature can have a point of view because its action in the world is, at the same time, a process of attending to it. Different creatures have different points of view because, given their capabilities of action and perception, they attend to the world in different ways. Cree hunters, for example, notice things about the environment that geese do not, yet […] geese also notice things that humans do not. What is certain, however, is that humans figure in the perceptual world of geese just as geese figure in that of humans. It is clearly of vital importance to geese that they should be as attentive to the human presence as to the presence of any other potential predator. On the basis of past experience, they learn to pick up the relevant warning signs, and continually adjust their behaviour accordingly. And human hunters, for their part, attend to the presence of geese in the knowledge that geese are attending to them.
Tim Ingold, The Perception of the Environment: Essays on Livelihood, Dwelling and Skill (Routledge, 2000), p. 51. Book cover from the 2022 edition.
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Knowing-How and Knowing-That
Can someone know how to do something – such as riding a bike – without being able to explain exactly what it is they’re doing? British philosopher Gilbert Ryle (1900-1976) addressed this problem by establishing a distinction between “knowing-how” and “knowing-that.” If the knowing-how of riding a bike shows in the performance of riding it (well), a related example of knowing-that would be being able to state a claim or proposition about riding – for example, knowing that to turn left you steer the handlebars to the left (and coordinate your balance to match). The work of Michael Polanyi (1891-1976) helps weave this together by arguing that much of what we actually rely on in knowing is tacit rather than explicit. We can “know more than we can tell” (as he put it), because our competence is carried in skills and meanings that we can use without fully articulating them. Such distinctions matter for KLAP because they support treating Action and Learning as practical, perception-guided competence that isn’t reducible to Knowledge in its explicit propositional form.
[T]here are many classes of performances in which intelligence is displayed, but the rules or criteria of which are unformulated. The wit, when challenged to cite the maxims, or canons, by which he constructs and appreciates jokes, is unable to answer. He knows how to make good jokes and how to detect bad ones, but he cannot tell us or himself any recipes for them. So the practice of humour is not a client of its theory. The canons of aesthetic taste, of tactful manners and of inventive technique similarly remain unpropounded without impediment to the intelligent exercise of those gifts.
Consider first a boy learning to play chess. Clearly before he has yet heard of the rules of the game he might by accident make a move with his knight which the rules permit. The fact that he makes a permitted move does not entail that he knows the rule which permits it. Nor need the spectator be able to discover in the way the boy makes this move any visible feature which shows whether the move is a random one, or one made in knowledge of the rules. However, the boy now begins to learn the game properly, and this generally involves his receiving explicit instruction in the rules. He probably gets them by heart and is then ready to cite them on demand. During his first few games he probably has to go over the rules aloud or in his head, and to ask now and then how they should be applied to this or that particular situation. But very soon he comes to observe the rules without thinking of them. He makes the permitted moves and avoids the forbidden ones; he notices and protests when his opponent breaks the rules. But he no longer cites to himself or to the room the formulae in which the bans and permissions are declared. It has become second nature to him to do what is allowed and to avoid what is forbidden. At this stage he might even have lost his former ability to cite the rules. If asked to instruct another beginner, he might have forgotten how to state the rules and he would show the beginner how to play only by himself making the correct moves and cancelling the beginner’s false moves.
It should be noticed that the boy is not said to know how to play, if all that he can do is to recite the rules accurately. He must be able to make the required moves. But he is said to know how to play if, although he cannot cite the rules, he normally does make the permitted moves, avoid the forbidden moves and protest if his opponent makes forbidden moves. His knowledge how is exercised primarily in the moves that he makes, or concedes, and in the moves that he avoids or vetoes. So long as he can observe the rules, we do not care if he cannot also formulate them. It is not what he does in his head or with his tongue, but what he does on the board that shows whether or not he knows the rules in the executive way of being able to apply them.
Gilbert Ryle, The Concept of Mind (Barnes & Noble, 1949), pp. 30, 40-41. Book cover from the University of Chicago Press edition (2000).
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Structure and Agency
The longstanding “structure-agency debate” centers on the ways in which social conditions shape human action and how people, in turn, are able to reproduce or transform those conditions. American historian William H. Sewell Jr. understands structures as combinations of “cultural schemas” – shared understandings and procedures – and resources that enable and constrain action. Agency involves drawing on these schemas and resources, including applying them creatively in new situations. Agency is therefore socially formed and unequally distributed but also capable of transforming the structures that make it possible. For KLAP, this raises the question of how knowledge, learning, action, and perception are organized through unequal social conditions, and how people might collectively change those conditions.
I would argue that a capacity for agency – for desiring, for forming intentions, and for acting creatively – is inherent in all humans. But I would also argue that humans are born with only a highly generalized capacity for agency, analogous to their capacity to use language. Just as linguistic capacity takes the form of becoming a competent speaker of some particular language – French, or Arabic, or Swahili, or Urdu – agency is formed by a specific range of cultural schemas and resources available in a person’s particular social milieu. The specific forms that agency will take consequently vary enormously and are culturally and historically determined. But a capacity for agency is as much a given for humans as the capacity for respiration.
It is equally important, however, to insist that the agency exercised by different persons is far from uniform, that the agency differs enormously in both kind and extent. What kind of desires people can have, what intentions they can form, and what sort of creative transpositions they can carry out vary dramatically from one social world to another depending on the nature of the particular structures that inform those social worlds. Without a notion of heaven and hell a person cannot strive for admission into paradise; only in a modern capitalist economy can one attempt to make a killing on the futures market; if they are denied access to the public sphere, women’s ambitions will be focused on private life. Agency also differs in extent, both between and within societies. Occupancy of different social positions – as defined, for example, by gender, wealth, social prestige, class, ethnicity, occupation, generation, sexual preference, or education – gives people knowledge of different schemas and access to different kinds and amounts of resources and hence different possibilities for transformative action.
Agency entails an ability to coordinate one’s actions with others and against others, to form collective projects, to persuade, to coerce, and to monitor the simultaneous effect of one’s own and others’ activities. Moreover, the extent of the agency exercised by individual persons depends profoundly on their positions in collective organizations. [...] Agency, then, characterizes all persons. But the agency exercised by persons is collective in both its sources and its mode of exercise. Personal agency is, therefore, laden with collectively produced differences of power and implicated in collective struggles and resistances.
William H. Sewell, “A Theory of Structure: Duality, Agency, and Transformation,” in Logics of History: Social Theory and Social Transformation, by William H. Sewell (University of Chicago Press, 2005). Sewell’s essay was first published in 1992 in the American Journal of Sociology.
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Helen Hoffmann
Research Assistant
Hi, I'm Helen and I just finished my Master’s in Prehistoric Archaeology! From April 2024 to June 2026, I worked as a student research assistant in the HESCOR project.
I was hired to support project coordinator Dr. Isabell Schmidt and worked closely with both her and Dr. Elena Robakiewicz. One of the things I appreciated most right from the start was being asked whether I wanted to work in areas I was already familiar with, or whether I wanted to branch out into something new. I chose the latter – and I'm glad I did!
Being part of a large interdisciplinary research project felt like a unique opportunity to gain hands-on experience in science communication and outreach, two areas I care deeply about. As a researcher, I feel a responsibility to make science accessible – especially when it's publicly funded. HESCOR turned out to be the perfect setting to learn more about that!
My job in the research project spanned a range of tasks: For example, I contributed to the design of the project website, developed the concept for the introductory video, and was responsible for new posts on the website. I also developed the social media strategy and curated the project's Bluesky account. Some of my favourite moments, though, came from assisting in social events such as the KinderUni as well as various workshops and vision forums, where I was involved in different tasks ranging from designing flyers to helping with catering.
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Lana Allan
Research Assistant
My name is Lana and I am studying my Masters in Digital & Computational Archaeology at the University of Cologne. My relationship with HESCOR began in October 2025 when I started an internship for Dr. Isabell Schmidt and Prof. Andreas Maier. My interest is agent-based modelling, and as part of this internship I designed an agent-based model to explore the exchange of personal ornaments in the Aurignacian. At the time I was still quite inexperienced with agent-based modelling, so for me this was an excellent opportunity to develop myself and my interests.
Following that internship, I was hired as a student assistant for HESCOR. It was, and still is, quite exciting! I received a very warm welcome from everyone at HESCOR. I am always encouraged to be involved with events, talks, and projects.
I particularly enjoy the diversity of the HESCOR project. Not only do I get to meet researchers from all over the world, but there are so many different disciplines represented within the project. The interdisciplinary nature of HESCOR is, in my opinion, its greatest strength. And it is incredibly rewarding to experience that firsthand. For instance, I helped with the HESCOR Vision Forum in March 2026. This included checking the attendance register, preparing for lunch and coffee breaks, and running the microphone during presentations. The content of the Vision Forum was very engaging, and it was quite fascinating to see the experts of their fields to discuss the challenges currently facing HESCOR.
My work at HESCOR is highly varied, and may range from researching the economics of Medieval Germany, to preparing geospatial data in QGIS. The variety and the challenge make my work very rewarding, and it’s refreshing to know, even with some of the more minor tasks that I’m involved with, I’m still making a positive contribution to scientific research.
Over these last few months my interest in academia has only grown, and I am very grateful for the opportunities available to me.
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Lena Perlberg
Research Assistant
Wavelet-based image compression offers a way to reduce large topographic datasets while preserving the
terrain information needed for archaeological least-cost path analysis. By combining multiresolution
approximation with graph-based routing, the study shows how more realistic paths between archaeological
sites can be computed while substantially reducing computational effort
Link to publication:
Link to post on HESCOR Website:
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Viktoria Fries
Research Assistant
Department of Prehistoric Archeology
Phone +49 221 470-2306 (secretary)
My name is Viki and I’m studying archaeology. I’ve been working as a student research assistant for HESCOR for about two years now. What particularly appealed to me about HESCOR was not only its multidisciplinary approach, but also the opportunity to get involved in various projects and actively participate in them.
It is always fascinating to see, primarily through participation in workshops and smaller conferences covering a wide range of topics, just how different individual disciplines can be – and yet how it is still possible to bring them together. For me, it is precisely these perspectives that make interdisciplinary work so appealing. HESCOR therefore offers students a valuable opportunity to discover new approaches and gain inspiration for their own academic interests.
As part of my work, I deal, amongst other things, with databases of ornaments from the Aurignacian and Gravettian in Europe (approx. 45,000–28,000 years ago). These objects have been discovered at numerous sites and are made from bone, antler, teeth, as well as snails and seashells. This not only gives me the opportunity to gain a better understanding of these archaeological cultural groups, but also to explore interesting questions regarding the distribution of animal species in the late Pleistocene and the associated procurement of raw materials. For me, as a student of archaeozoology, this work presents a unique opportunity to engage intensively with animal species from
a very early period.
Working with the databases and analysing the archaeological finds thus not only provide insights into the material remains of the Aurignacian and Gravettian periods, but also help to answer questions about the use of animal resources and the relationship between humans and animals in the late Pleistocene.
Link to post on HESCOR website:
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Archaeological data are never complete - which means they are inherently biased. For example, if an area has no known archaeological sites, this could mean that no one lived there in the past… or that sites were destroyed over time… or simply that no one has looked for them yet.
The researchers here want to understand how research biases affect the results of the Human Existence Potential model — and how the results can be improved by accounting for bias in the modelling process.
In this room, you are taken behind the scenes: How did they approach the problem? What did they discover along the way? And, perhaps most importantly, what can be learned from it to make future modelling setups even better?
Follow the trail across the playboard, explore the monitors and panels at each step, and see how the pieces come together.
And if you are curious to dig a little deeper, feel invited to explore the exhibition`s publications and related research rooms.
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Archaeological data are never complete — which means they are inherently biased. For example, if an area has no known archaeological sites, this could mean that no one lived there in the past… or that sites were destroyed over time… or simply that no one has looked for them yet.
The researchers here want to understand how research biases affect the results of the Human Existence Potential model — and how the results can be improved by accounting for bias in the modelling process.
In this room, you are taken behind the scenes: How did they approach the problem? What did they discover along the way? And, perhaps most importantly, what can be learned from it to make future modelling setups even better?
Follow the trail across the playboard, explore the monitors and panels at each step, and see how the pieces come together.
And if you are curious to dig a little deeper, feel invited to explore the exhibition`s publications and related research rooms.
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Are you familiar with the Human Existence Potential (HEP) model? It uses data on past vegetation and archaeological sites to quantify where people most likely lived in the past. More information is given on the panels.
Use the interactive table to explore the content!
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Are you familiar with the Human Existence Potential (HEP) model? It uses data on past vegetation and archaeological sites to quantify where people most likely lived in the past. More information is given on the panels.
Use the interactive table to explore the content!
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Are you familiar with the Human Existence Potential (HEP) model? It uses data on past vegetation and archaeological sites to quantify where people most likely lived in the past. More information is given on the panels.
Use the interactive table to explore the content!
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Are you familiar with the Human Existence Potential (HEP) model? It uses data on past vegetation and archaeological sites to quantify where people most likely lived in the past. More information is given on the panels.
Use the interactive table to explore the content!
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Dogs extended Thule perception beyond human limitations, filling in gaps that human vision alone could not reliably resolve across shifting ice, snow cover, and distance. With acute noses tuned to trace cues in cold air and windblown scent trails, dog teams could detect faint, brief, and invisible signs of living presence, including around places where seals surfaced to breathe. In practice, this meant that seal hunting could be organized not only around broad human expectations and experience – where seals might be – but around where dogs indicated they were, guiding attention toward active breathing holes. Dog teams could help the group notice approaching disturbances (such as changing wind conditions or storms) sooner than sight alone might suggest, and their fine-grained attention to sound and directionality contributed another channel of information, helping the group interpret small changes in the Arctic landscape’s “noise” and movement. The animals thus functioned as a moving sensory network, converting scattered environmental variation into actionable leads. People could align human planning and visual assessment with such canine indications, making the ice less of a static surface to be searched and more of a dynamic, sensory map.
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How is technology organized (and organizes itself) from the very small to the very large? What are the units of technological evolution? How do they interact? And how do these units evolve over shorter and longer periods of time?
These are some of the foundational questions we have to address when trying to better understand how technological evolution and Earth system dynamics meet. None of these questions are trivial or intuitive to answer, however. And there are many unwarranted assumptions, stereotypes, and outright falsehoods that cloud our view. One of them is the idea – still very popular across academic and public spheres – that technological worlds, even if also containing very large structures, are built, composed, and regulated by their much smaller components, be it units of information, individual artefacts, or smaller technological systems. Technological worlds, in this view, emerge from a one-directional movement of composition and integration, from the small to the large. The associated theories of causation (how things come into being and hang together) tend to reduce the question of higher-level technological organization to the mere issue of “bottom-up” determination.
Adopting a dedicated planetary lens considerably complicates this perspective. Large-scale technological structures such as the so-called Anthropocene technosphere or the internet (a global system of interconnected computers, devices, and applications using shared information and communication protocols) have independent existence. They are not just created through the interaction of their smaller components and then have consequences on the same level or “further up” (e.g., for even larger structures). Instead, they also influence, and in the process often change, the nature of the very components from which they are built, regulate the interaction of these components with other smaller components, and generally constrain what is possible on lower levels of organization. Importantly, this includes human cognition, decision-making, and situated action. The superstructure that is the technosphere for example promotes new rules, normalities, rationalities, and specific corridors of (preferred) action and thought for those who try inhabit it and make a life in it. In other words, what researchers call downward or “top-down” causation cannot be ignored: higher-level structures have the capacity to shape, regulate, and variously administer the behaviour and organization of lower-level structures. It is this dynamic interaction between bottom-up constitution and top-down regulation that we need to better understand. This dynamic can be captured with the concept of reciprocal causality (Box 1). Reciprocal causality is central to understanding how technological and Earth system processes come together, and what it may mean to speak of Earth system-technology coupling(s).
Reciprocal causality also explains the distinct human pedigree of living in technology-saturated environments – what the eminent French philosopher of technology Bernard Stiegler has called the “pharmakological condition” (Fig. 1). The “pharmakological condition” highlights the human dilemma of only being able to utilize, and thus benefit from, the diverse possibilities and resources provided by a given technological world from within this world. This means that whatever a technological world enables and allows us to do (and which would thus not be possible outside of it), it also always profoundly narrows, and in some cases even closes down entirely, other possibilities of action and thought (Box 2). Technology, in Stiegler’s portrayal, is hence “salvation” and “curse” at the same time – a polarity also reproduced in and through contemporary imaginaries of technological futures either cast as progressivist hope (transhumanism, etc.) or as apocalyptic doom (critical theory, posthumanism, environmental humanities, etc.).
Taking questions of scale and organization seriously squarely brings into view technology as a complex, multi-level phenomenon with a hierarchical organization (Box 3). A hierarchy defines focal levels of organization within technological worlds and specifies what kind of entities populate these different levels. It helps to determine what is “lower” and what is “higher” in relation to which organizational patterns and processes. Based on what has already been said above, we can further specify this technological hierarchy. The kind of hierarchical organization we are interested in is a holarchy (Box 4). A technological holarchy is a representation of a multi-level hierarchy of organization in which individual units of technological organization (holons) are parts and wholes at the same time. Following Arthur Koestler, who originally coined the concept (Fig. 2), a holarchy balances autonomy and dependence across levels of organization: each identifiable unit on a focal level of observation and theorization is composed by a set of relevant lower-level parts (and to this effect can count as a whole), while itself constituting, together with other units on the same focal level, even higher-level units (and to this effect can count as a part). Each unit (holon) within the technological holarchy is therefore coevally characterized by its relative autonomy (as a whole) and relative dependency (as a part), and this is only understandable if we adopt an explicit holarchical perspective, in which these relations are interpreted as the outcome of technology-specific interactions between levels of organization (Box 5).
The development of such a holarchical perspective on technological organization and change is still in its infancy. No generally agreed upon understanding of the relevant technological units, levels, and processes of constitution, (self-)organization, and constraint is currently available in the scientific literature. In the HESCOR project, we have thus attempted to work on, and advance, some of the relevant questions, aiming to develop a first theoretically robust, operationalizable, and empirically applicable framework for the study of long-term technological change in the European deep past, from between ca. 130,000 years ago to about 15,000 years ago – the period archaeologists call the Late Pleistocene (the latter part of the last “Ice Age” in colloquial terms). The idea was to first understand some of the earliest technological worlds – dominated by the production of useable stone artefacts by Neanderthals and early Homo sapiens communities – as model systems for the more complex and densely organized technological worlds of the contemporary period. The deep-time archaeological record uniquely allowed us to “dry test” some of the implications of such a framework for our apprehension of macro-evolutionary change, as Late Pleistocene stone technologies can be traced, and hence holarchically analysed, over many tens of millennia – timescales unavailable for the study of modern technologies. The Late Pleistocene is also the period in which early humans (Neanderthals and other hominins are included in this category) have been argued to develop heightened dependencies on technology. John J. Shea, for example, has shown that there is good reason to associate this period with the rise of what he calls “obligatory tool-use” – the fact that living in technological worlds is no longer a mere added benefit but becomes inseparable from being human. Shea’s obligatory tool-use thus describes an evolutionary situation in which the “pharmakological condition” of technology (see above) assumes a driving seat. The Late Pleistocene, in other words, is an instructive laboratory for the development of hierarchical frameworks needed to better understand (and anticipate) the kind of technological change that affects and will affect the Earth system today and in the near future, including its human, ecological, and technological consequences.
The poster associated with this panel, presented by the ECOLITHIC team at the 16th annual meeting of the European Society for the Study of Human Evolution in Burgos, Spain (16-19 September 2026), outlines some initial results of our efforts to develop such a framework. It presents a first formal representation of a technological holarchy capable of theorizing and analysing macro-evolutionary change in Late Pleistocene stone tool technologies in novel ways. A detailed scientific presentation and discussion of this framework and its far-reaching implications for macroarchaeology and broader interdisciplinary research on long-term technological change will be provided by a companion paper to be published with the HESCOR special issue in the Journal of Quaternary Science in late 2026 or early 2027.
References
Arthur, W.B. (2010) The Nature of Technology: What It Is and How It Evolves. Penguin Adult.
Hussain, S.T. (2024). “Transformation analysis and systems-theory: from micro-scale lithic technical systems to macro-scale lithic ecosystems.” In: T. Uthmeier and A. Maier (eds.), Studying Technologies of Non-Analogous Environments and Glacial Ecosystems. Papers in Honor of Jürgen Richter, pp. 531-547 Bonn: Habelt.
Hussain, S.T. (submitted). “Holotechnologies: Trading Theory for A New Macroarchaeology of Large-Scale Technological Structures.” Ethnographisch-Archäologische Zeitschrift (EAZ).
Hussain, S.T., A.L. Vignoles, M. Charbonneau, T.E. Feiten, A. Spiridonov, I. Tëmkin, and S. Valverde (submitted). “Technospheric Dynamics in the Deep Human Past: Multi-Level Causation in the Macroevolution of Late Pleistocene Lithic Technology.” Journal of Quaternary Science (HESCOR special issue).
Zalasiewicz, J. et al. (2017) “Scale and diversity of the physical technosphere: A geological perspective,” The Anthropocene Review, 4(1), pp. 9–22. https://doi.org/10.1177/2053019616677743.
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Humans can be surprisingly flexible and adaptive to all different kinds of environments. However, stable and predictable conditions can ensure the survival for longer period. Finding such a suitable place is difficult, as many factors need to be considered and can sway the decision to stay or move to another place. These factors include:
  • the climate conditions, their stability and seasonality
  • the shape of the landscape, and availability of natural resources for tools and shelter
  • freshwater availability in form of rivers, lakes, and ground water
  • availability of food sources through gathering, hunting, and fishing
  • soil fertility and space for domestic animals for farming communities
  • connection to a social network for the exchange of goods, knowledge, social exchange, and help
  • additional fuzzy factors in form of traditions, common diseases and other threads, new opportunities
Most of these factors can be explained in a descriptive way, some can be measured with numbers. But all of them are difficult to find for hunter-gatherer or early farmer communities several thousand to tens of thousands of years ago. To look that far into the past, we combine the archaeological record based on excavations and age estimations of the finds with climate reconstructions based on natural climate records and computer models.
This is what the Human Existence Potential (HEP) model does: it combines the knowledge where and when humans lived with the climate conditions present at these places and times. The result is a model that can predict how good another place and time could be in terms of survival based on the climate conditions in this new setting. The model assumes that the archaeological record reflects the preferences and adaptation of the humans of their environment, and therefore depends heavily on the available data.
The HEP model is very flexible in its application. Different kinds of “presence” and “absence” estimations can be used for the archaeological record. The climate conditions usually consist of average temperature and rainfall (precipitation) values and seasonal variations of these two variables. But the model can be extended with additional data, such as the soil fertility rate for early farms, if it is available and fits the society that is investigated.
The model uses a method called “logistic regression”, a mathematical approach that tries to fit a function to the given data using several placeholder variables, called coefficients. This approach is nowadays categorized under “machine-learning” algorithms, and therefore often connected with artificial intelligence. However, the Large-Language Models (LLMs) that are commonly called AI today are a different kind of methods, albeit using similar principles. The HEP model works with numerical data, not text tokens as in LLMs. And the only thing the model “learns” is how the presence or absence of humans is connected to the climate values given during the training. This connection is stored in the coefficients of the function, and can be used to generate HEP values for other inputs of climate data. Compared to current LLMs that use billions of coefficients, the HEP model uses only tens to hundreds of coefficients, depending on the number of input variables. It is therefore highly specialized in its application. Other applications of machine-learning in the HESCOR project are shown in the room “Machine learning in prehistoric studies”.
Both sources of information, the archaeological record and the climatic reconstruction, are not free of errors and biases. The archaeological record is limited in space and time, as we can only acknowledge what we find. Empty areas and time periods in the record could be correct or just be a result of lacking research. The impact of these biases on the HEP model are further discussed in the room “Biases in Archaeology”. The climate reconstruction is based on several climate proxies derived from natural climate records, such as sediment cores of lakes and ice cores of glaciers. Computer models then combine these records with the physical understanding of our planet to calculate the past climate conditions. In each of these steps, errors and biases can influence the end result. We therefore often combine several different types of climate records and computer models to estimate an average or an optimal solution and look at how strong the variations between the different sources are.
An interactive and simplified version of the HEP model can be found on the computer on the desk in this room. It does not use the more involved logistic regression method, but a simpler direct calculation using Gaussian functions to convert the input variables into a HEP score. For the ease of use, three categories were created for temperature and precipitation: low, medium and high. The average values for these categories are listed in the following table. The temperature uses a standard deviation of 6 °C, the precipitation uses a value of 300 mm.
The HEP model is a variation of so called “species distribution models” in literature (Tallavara et al. 2015, Guillera-Arroita et al. 2015). It follows the same principles, but includes further assumptions and additions to allow the application to humans. These alterations are application specific and can be expanded as needed (Shao et al., 2021). The model is publicly available on GitHub:
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Is the absence of archaeological sites real - or the result of BIASES?
Many biases are known – here we highlight two examples.
For detailed information, see our publication HEPbiases paper.
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Now we include the bias information in the HEP model and see how this affects the results…
Take a look at the interactive map on the table and explore how considering each bias changes the modelled HEP maps.
Select "Difference to default” to display only the increase (red) or decrease (blue) in HEP-values caused by the selected biases compared to the default setting.
At first, the effects of each bias may appear quite subtle. However, you can see significant changes in some regions. These changes become even more pronounced when you account for both biases.
This demonstrates why it is important to consider biases when modelling the Human Existence Potential – even seemingly small biases can have a significant impact on the results.
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Read the paper: Hartley. 2024. State Crisis Theory. Cliodynamics.
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The ECOLITHIC project is the successor of HESCOR’s efforts to integrate theory and data across archaeology, the sciences, and the broader humanities to review and refine our understanding of long-term technological change. Focusing attention on the opportunities, difficulties, and challenges of rethinking technology in an Earth system framework, especially how technology responds to, influences, and interacts with other patterns and processes on the planetary scale, ECOLITHIC uses the Pleistocene deep past (ca. 1.8 million until 11,800 years ago) as a testing ground for the development of new concepts, perspectives, and methodologies to better understand the millennial-scale macroevolution (Box 1) of some of the earliest human technologies – the production and use of stone artefacts.
ECOLITHIC is the acronym for “A Radical Ecosystems View of Lithic Macro-Evolution” (2025-2028) and has received funding from the VolkswagenFoundation through its blue-sky funding line Pioneering Research – Exploring the Unknown Unknown. ECOLITHIC’s disciplinary core lies in deep-time archaeology and the study of early stone tool technologies from the later part of the last Ice Age in Europe – the so-called Middle and Upper Palaeolithic periods (Box 2 and 3). It musters a collaborative research network bridging archaeological research on in these periods with the philosophy of technology, the environmental humanities, Anthropocene studies, geoanthropology, Earth system modelling, macroecology, macroevolution and palaeontology, as well as complexity and network science. ECOLITHIC combines theory-building and methodological translation across these disciplinary specializations with innovative data collection and computational analysis of long-term trends in Palaeolithic stone artefact technologies. It responds to the need to leverage deep-time data to improve understanding of general patterns of technological macroevolution and to extract lessons for today – a time in which human lives are fundamentally entangled with technology.
Building on previous work in HESCOR, ECOLITHIC tackles a blatant unknown unknown of deep-time research on technological evolution: identifying long macro-level structures of technology, building a scale-sensitive framework of technological organization, and escaping the dominating bind that technology is either a product of human demography and culture, and thus bound to its particularities and idiosyncrasies, or shaped by environmental externalities such as resource landscapes and climates. The project argues that technology is not just derivative, but also exhibits its own scaling and self-organizational properties which until now remain understudied. In contrast to past research which has overwhelmingly focused on artefact-level properties, the history of individual technologies or isolated lineages of such ways of working and using stones, or key qualities such as complexity and assemblage-level diversity, ECOLITHIC proposes that on higher-levels of organization stone artefact technologies form specific arrangements of jointly operating totalities – ecosystems of technical systems (Box 4) – whose evolutionary dynamics and regularities may strongly differ from those of lower-level arrangements of properties and artefacts. This ecosystem perspective shifts the attention away from individual artefacts and the mere inventorization of technical systems to the dedicated analysis of how systems interact: what roles do different technological systems play in their ecosystem? Do they support or complement each other? Do they fill distinct or overlapping technological niches? What is the division of technological labour and how do technological generalization and specialization are balanced? Asking these questions opens a window, we propose, to the study of patterns and processes genuine to macro-levels of technological organization, and these levels are central for advancing our understanding and knowledge on a basic but crucial issue: how does technology evolve on larger spatial scales and over extended periods of time?
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The central role of sledge dogs in Thule lifeways and Arctic adaptations also changed the ways in which these people were able to make use of whale bodies. Thule developed a rich whaling culture which not only sustained large seasonal settlements close to hunting spots but also provided plenty of nourishment for their dogs and yielded a range of useful resources, including building materials for Thule houses. In contrast to the Dorset, who potentially oriented their dwelling structures onto polar bears, the Thule constructed large whale houses. They also used sophisticated kayak technology and developed toggling harpoon technologies which allowed them to hunt and retrieve seals on the open water. The lack of such technologies by the earlier Dorset people is less evidence for their technological “inferiority,” as early research has often concluded, but arguably demonstrates the different ecological emphases and different animal presences that anchored the respective forms of environmental knowledge and perception of Dorset and Thule peoples.
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This model is a simplified version of the HEP. It uses modern climate information and broad categories of “low”, “medium” and “high” for temperature and precipitation preferences. You can change the preferences using the button on the left and see the change of HEP on the map of Europe on the right. Red colors indicate high scores and good conditions for the chosen preferences, blue colors indicate low scores and bad conditions.
Try it out and see for yourself.
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To learn more about our research on environmental potential, settlement preferences and population dynamics in the Early Neolithic, see:
J. Hilpert,C. Wegener, O. A. Kern, A.-L. Fischer, Silviane Scharl (2026): Settlement Preferences and Population Dynamics in Early Farming Societies of Central Europe (5400 - 4950 BCE). Journal of Quaternary Science:
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Understanding the relationship between large-scale processes of human technological practice and the operation of the Earth system calls for adopting an unfamiliar planetary perspective, zooming out both in space and time. Two concepts are central to this endeavour: scale (Box 1) and stratigraphy (Box 2). These can guide the challenging exercise of better documenting, analyzing, and understanding how technological evolution intersects with Earth system change, and what the short-term and long-term consequences are.
Mobilising these two concepts and effectively combining them for the study of the evolution of technology-Earth system interaction from early human history into the contemporary period results in new attention to the Earth’s technostratigraphy (Box 3).
The Earth’s technostratigraphy is extremely challenging to describe. How we describe it depends on how we look at technology and what processes and aspects of technological evolution we foreground. There are many different complementary representations of the Earth’s more than 3 million-year-spanning technostratigraphy and the relationship between these different representations would merit its own critical investigation.
The representation chosen for the purposes of this virtual exhibition places emphasis on the materiality (Box 4) of the technostratigraphic record as well as the techniques (Box 5) and larger technical infrastructures implicated in its production and maintenance.
Based on this focus, the Earth’s technostratigraphy can be summarized as the nested succession of five broader stages or “ages”. These are not mutually exclusive, and they do not neatly replace each other but instead persist and coexist across transitions. The major transitions in the technostratigraphic record they help to draw attention to can be described by reference to the idea of technological paradigms (Box 6). Each “age” is not characterized by the exclusive usage of one particular type-material or a set of associated techniques, but rather marks the forceful emergence a new assemblage of materials and techniques giving rise to a distinct technological paradigm characterizing the “age” in question. This also means that the hierarchy of technological practices is constantly re-negotiated and the technostratigraphic record is in part the physical manifestation of this development: the technologies at the centre of a given technological paradigm share, and divide among themselves, the status of “primary technologies”, delegating other technologies to a status as “secondary”, “tertiary”, etc. Another implication is that technological paradigms establish their own technological rationality (Box 7), since the various human users of the associated technological worlds interact with, perceive, and think about the world to a large extent through the lens of the “primary” technologies. What can count as a problem and how these problems ought to be solved, for example, is often strongly shaped by the active technological paradigm, and to this effect technology-mediated. Each technological paradigm in this way also inaugurates and reinforces a specific technological normativity (Box 8) – it brings into play, and helps to develop, its own set of values. Technostratigraphic change can therefore not be understood as a “neutral” form of disinterested evolution, but is politically charged – and technostratigraphic evolution thus a political concern.
A simplified representation of how Earth system change and technological evolution collide over very long timescales could then look as follows:
  • Quartz Age (the Silicon and Digital Strata): starts in the final part of the 20th century and will last into the foreseeable future. Marked by an emerging topmost stratigraphic layer characterized by the enrichment of ultra-pure silicon wafers, refined quartz, rare earth elements, and synthetic crystals used in microchips, fiber optics, solar panels, and digital storage. It is often associated with the consolidation and maturation of the information age and the global infrastructure of information technology, shifting energy flows from physical to digital spaces. It however also coincides with a rush for rare minerals – a “second eve” of industrial mining – which can be refined to fuel the growing needs of the age, and is thus inevitably bound to foster new conflicts and to rearrange geopolitical cartographies.
  • Plastic Age (the Synthetic Horizon): starts at about 1950 and lasts until today. Defined by synthetic polymers (polyethylene, polypropylene, polystyrene) or “plastics”, which do not easily biodegrade; instead, these materials fragment into microplastics and novel technofossils like nylon fibers and synthetic pellets, which parasitize the Earth’s distributive systems such as oceans, rivers, and the air and in this manner have contaminated nearly all planetary systems. Plastic is found in the Arctic, forms large garbage patches in the world’s oceans, is now found in terrestrial and marine sediments, and in some areas has even fused with natural rock to form entirely new lithologies known as “plastiglomerates”.
  • Iron Age (the Late Metallurgic Horizon, including Industrial and Mechanical Strata): starts at about 3000 years ago (1000 BC) and lasts until at least the mid-20th century, encompassing the Industrial Revolution in Europe. Scholars therefore sometimes speak of the “long iron age” to signal that it concerns not only the “Iron Age” sensu stricto in archaeological terms, but also comprises the Middle Ages, as well as the colonial and imperial periods, and early modernity. It has seen innovations such as firearms and proper machines such as the steam engine and the car. It is in the Iron Age that most scholars are confident to localize the earliest consequential transformative influence of human agency on Earth system dynamics. The later part of the Iron Age coincides with the age of fossil fuels.
  • Bronze Age (the Early Metallurgic Horizon): starts ca. 5000 years ago (3000 BC) and lasts until about 3200 years ago (1200 BC). Marked by the sudden appearance of copper and tin alloys, alongside smelting byproducts such as slag, technological infrastructure (including mining projects), and new worldviews and complex forms of social organization based on these practices.
  • Stone Age (the re-furnished and re-furbished lithosphere): starts at ca. 4-3 million years ago with the production of the first stone tools by hominins who biologically differ from Homo sapiens. Includes the Palaeolithic, Mesolithic/Epipalaeolithic and Neolithic periods which have seen major developments in human lifeways and an expanding ecological fingerprint brought by innovations such as widespread fire use, horticultural, pastoral, and agricultural land-use and the emergence and expansion of early sedentary life including plant cultivation and animal husbandry.
The ECOLITHIC project – a derivative of HESCOR’s attempt to better understand technostratigraphic evolution – focuses on the lower rung of this deep-time technostratigraphy, examining in detail the Palaeolithic segments of the Stone Age and what they can teach us about the macrodynamics of long-term technological evolution, including possible lessons for today.
References
Franklin, U. (1999) The Real World of Technology. Toronto: House of Anansi Press.
Pollard, A.M. and Gosden, C. (2023) An archaeological perspective on the history of technology. Cambridge: Cambridge University Press.
Renn, J. (2020) The evolution of knowledge: rethinking science for the Anthropocene. Princeton: Princeton University Press.
Sigaut, F. (2012) Comment homo devint faber: comment l’outil fit l’homme. Paris: CNRS Éditions.
Schlaudt, O. (2022) Das Technozän: eine Einführung in die evolutionäre Technikphilosophie. Frankfurt am Main: Vittorio Klostermann.
Hussain, S.T., Ohrem, O., Bartosch, R., Schwegler, C., Stutz, A.J., Simon, Z.B., and Rigby, K. (submitted). ‘Earth System Agency and Telecausality: Bringing the Earth Sciences and the Environmental Humanities into Conversation.’ Journal of Quaternary Science.
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Welcome!
Look around, our research case study is placed in Southern Africa so you will see some impressive landscapes during our tour.
We chose this region because we know that inherent biases likely shape the archaeological record.
But before we go to the biases, we have to check the basics:
For any modelling procedure, we do need data and a model. The data will go into the calculation procedure of the model.
We recommend starting with "The Basics" scene when you close this window...
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Welcome!
Look around, our research case study is placed in Southern Africa so you will see some impressive landscapes during our tour.
We chose this region because we know that inherent biases likely shape the archaeological record.
But before we go to the biases, we have to check the basics:
For any modelling procedure, we do need data and a model. The data will go into the calculation procedure of the model.
We recommend starting with "The Basics" scene when you close this window...
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What happens when the Earth system collides with technology, when the boundaries between the two increasingly dissolve? What happens when technology becomes an omnipresent and all-encompassing phenomenon of planetary proportions? What happens when technological processes become deeply engrained in Earth system processes and technological change therefore increasingly shapes the future development of the planet?
The concept of the technosphere – developed in recent years in disciplines such as geology, Earth system science, and the young field of Anthropocene studies – is an attempt to respond to these questions. The questions are also not merely abstract, and thus seemingly primarily of academic interest. They have emerged from real-world observations that show that the technological world we have built in the 20th and early 21st centuries is now profoundly implicated in how the Earth as a total system operates and evolves. For some, technology has even become the privileged and primary way in which humanity has come to interact with, and thereby substantially shape, the planet as a whole.
The technosphere encapsulates the idea that the technological world with its characteristic structures, processes, and objects has come to substantially overlap both in its spatial extension and its functionality with the planet we call Earth. A classic representation of this idea is found in night sky photographs of the contemporary Earth (Fig. 1), showing how global energy and transportation infrastructure now spans the entire planet, with hardly any region remaining unaffected by the associated technologies.
The technosphere is thus a particular historical variant of the technological world. In fact, we can say that the technosphere is when technological worlds (Box 1) evolve into an integrated whole whose boundaries formally coincide with the boundaries of the planet. This also gives us an evolutionary prediction: prior to the emergence of the technosphere, the technological worlds humanity has created and curated were much less integrated on the planetary level. This in turn suggests that proper talk of an integrated technological world on planetary scales is impossible prior to the emergence of the Anthropocene technosphere. Before it, we are confronted with a plurality of disparate, heterogeneous, and only weakly coupled technological worlds. Likely, these technological worlds were thus organized and integrated not in relation to planetary phenomena and processes, but in relation to more localized phenomena and processes, including but not limited to geography, history, and culture (and in the case of very early hominins perhaps also biological categories).
This reconstruction suggests that understanding the evolution of the technosphere and its status in the deeper history of our species requires a hierarchical, multi-level perspective on technological organization. We need to understand how technological worlds are composed and themselves integrated, and on what scales of observation this kind of integration can happens, and what its consequences are. Box 2 outlines a first provisional definition of the technosphere that this understanding enables.
The sphere-like quality of the higher-level technological world that is the technosphere is important. As a distinct sphere of the Earth, technological services and functions become central to Earth system integration. On an even higher level, technology in this way becomes integrated into the Earth system as a whole. This means that on the planetary scale, technology shapes some of the basic conditions of operation, organization, and evolution for the other planetary spheres – the biosphere, hydrosphere, geosphere or lithosphere, and atmosphere (Fig. 2). This is not a mere conceptual proposal. It its well-documented, for example, how the technological means deployed in human land-use systems or the technological structures implicated in human urban systems have profoundly altered global climates and how the space taken by technologized human lifeways in many Earth regions competes for space with natural ecosystems and often feeds on these ecosystems to prosper – a paramount example being the destruction of the Amazon. Technology in this way interferes with and transforms processes in the biosphere and atmosphere. It is also well-documented how, for example, humanity’s use of technology to regulate, alter, and co-opt hydrological systems has dramatically transformed these systems and their interaction with adjacent systems, showcasing how technology is now involved in processes linking the hydrosphere, biosphere, and geosphere. This “spherization” of technology neatly captures what the idea of the technosphere is all about, and it showcases a fundamental property of technological systems which past scholarship has often neglected: the scaling properties of technology and its tendency to integrate the systems it supports across multiple scales.
This raises an important paradox. The “spherization” of technology (the becoming of technology as a distinct sphere of the Earth) creates a rift between technology and its human makers, users, and maintainers. It therefore does not neatly map onto other influential proposals that humans in the Anthropocene inhabit a so-called “anthroposphere” as distinct from the other spheres (see our two HESCOR blog entries for a discussion of some of the important issues; here and here). We of course cannot address or in any satisfactory way resolve the problem of this “anthroposphere” proposal here, but the key point we want to make does not hinge on doing so either. This important point is all about integration and disintegration, and how the two dynamically condition each other. This brings us back to the paradox. Crucially, a hierarchical, multi-level perspective on technological evolution that leaves space for the higher-level integration of technological worlds on the planetary scale – the technosphere – helps explaining how technology can become an autonomous agency in the Earth system (Box 3). This is because higher-level integration creates novel wholes that exhibit relative organizational and operative independence. This means that if the evolution of technological systems has at some point favoured the emergence of higher-level structures with enough operational and organizational “distance” to human agency, it would have promoted the externalization of human and technological agency. The implication is that human-technology couplings must be expected to be stronger on lower levels of organization, while on the planetary level the kind of higher-level integration observed in proper technospheric structures should result in the structural de-coupling of human and technological organization, operation, and agency. Another way of saying this is: it is only through the completion of higher-level integration that independent technological agency can truly evolve. This is because the evolutionary precondition for this to happen is the differentiation of human and technological systems, tipping the balance between autonomy and dependency in favour of the former. This is why contemporary research is growing increasingly sceptical about our capacity to fully anticipate and appropriately govern long-term technological evolution, and why some experts have begun to talk about a related “governance gap” – higher-level technological integration leads to higher-level technological individuation and thus makes controlling technology increasingly difficult. The idea of the technosphere can thus also help to account for the problematic circumstance that the elevated status of human agency in the Anthropocene has not necessarily led (and arguably will never lead) to better control of intended or preferred outcomes. And this is to a large extent because human action on the planet is profoundly mediated by technology, or more precisely by the evolved planetarity of technology (Box 4).
A final point concerns the organizational flipside of this planetary technological individual we call the technosphere. The higher-level de-coupling of human and technological systems explained above is accompanied by the internal extension and differentiation of technological systems to sustain themselves and accommodate the growing range of tasks and functions they are drawn upon to fulfil or respond to. Part of this dynamic is what economist Brian Arthur (Fig. 3) has called the tendency of technological systems to “structurally deepen”. And again, the balance between autonomy and dependence is at stake, but this time on the human side of the equation. As humans become increasingly dependent on technology and seek solutions to their everyday problems in technological worlds (even if many of these problems are created by the very same worlds in the first place), they tend to proliferate the respective technological worlds, adding more and more components, diversifying functions, etc. – a process that from a planetary perspective calls into question who is in the “driver’s seat”, and indeed has led some to suggest that technology increasingly “recruits” or at least “enlists” human agency rather than the other way around. The technosphere hence entails a serious critique of the old idea – rooted in human exceptionalism – that the power-relations between humans and their technologies will always favour the former.
The tendency for “structural deepening” and the expansive propensity of the technosphere is well-reflected in a full range of data collected in the last few decades, pointing to a self-reinforcing dynamic difficult to disrupt without major political changes. The total weight of human-created mass – much of which falls under the purview of technology – is for example estimated to have exceeded the total amount of dry biomass available on the entire planet already by 2020. Buildings, streets, and other critical human technological infrastructure today weight more than 275 times as much as all living nonhuman animals combined. Other more recent research has estimated the total weight of the technosphere (the technomass) to be around 30 trillion tonnes, while the total planetary biomass is only in the magnitude of about 550 billion tonnes. The largest contributors to this progressively dominant planetary technomass are artificial surfaces and ambient buildings. And disproportional growth trajectories within the technosphere are not limited to its terrestrial compartments. Recent years have also seen the rapid proliferation of orbital technomass (Fig. 4) – some speak of the expansion of the technosphere into the Earth’s orbit. More than 25,000 on-orbit objects populate this region, the majority of which are debris. Critical scholars such as Rosi Braidotti anticipate that this may only be the beginning, that technology is bound to free itself from its terrestrial shackles to eventually leave the Earth altogether. As things currently stand, Silicon Valley technofascists, self-declared tech-barons of our time, and techno-feudal kings in the couleur of Elon Musk will all be instrumental in assisting technology in its great cosmological expansion into space, and perhaps even onto other extraterrestrial bodies.
References
Arthur, W.B. (2010) The Nature of Technology: What It Is and How It Evolves. Penguin Adult.
Haff, P. (2014) “Humans and technology in the Anthropocene: Six rules,” The Anthropocene Review, 1(2), pp. 126–136. https://doi.org/10.1177/2053019614530575.
Hussain, S.T. (2024). “Transformation analysis and systems-theory: from micro-scale lithic technical systems to macro-scale lithic ecosystems.” In: T. Uthmeier and A. Maier (eds.), Studying Technologies of Non-Analogous Environments and Glacial Ecosystems. Papers in Honor of Jürgen Richter, pp. 531-547 Bonn: Habelt.
Hussain, S.T. (submitted). “Holotechnologies: Trading Theory for A New Macroarchaeology of Large-Scale Technological Structures.” Ethnographisch-Archäologische Zeitschrift (EAZ).
Hussain, S.T., A.L. Vignoles, M. Charbonneau, T.E. Feiten, A. Spiridonov, I. Tëmkin, and S. Valverde (submitted). “Technospheric Dynamics in the Deep Human Past: Multi-Level Causation in the Macroevolution of Late Pleistocene Lithic Technology.” Journal of Quaternary Science (HESCOR special issue).
Renn, J. (2020) The evolution of knowledge: rethinking science for the Anthropocene. Princeton: Princeton University Press.
Schlaudt, O. (2022) Das Technozän: eine Einführung in die evolutionäre Technikphilosophie. Frankfurt am Main: Vittorio Klostermann.
Zalasiewicz, J. et al. (2017) “Scale and diversity of the physical technosphere: A geological perspective,” The Anthropocene Review, 4(1), pp. 9–22. https://doi.org/10.1177/2053019616677743.
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Where can this lead us to? We have many visions for future research in this direction.
Explore our vision board for a glimpse of where we are heading – or look into our open-access publications for more insights and details.
Thank you for being so interested in our research!
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  • Cologne Panorama (Home 360): Maximilian Schönherr, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons Link here
Exhibition produced by: Domingos Studios
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A vegetation model is a computer-based tool that predicts where different types of plants can grow under given climate conditions. Some models are simple and use only a few basic rules, while others are more complex and include processes such as photosynthesis or plant competition.
Despite these differences, most vegetation models share the same goal: they calculate the vegetation distribution that is in equilibrium with a given climate.
Important drivers of vegetation change are temperature and precipitation, which can cause vegetation to shift toward a new equilibrium.
Act as a vegetation modeler and examine how Earth’s vegetation in the PaleoVeg model responds to changes in temperature and precipitation!
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Beaver-shaped landscapes could make fish not only more abundant but also easier to get at. By backing water up behind dams, beavers slow the flow and create deeper, calmer pockets where fish gather. The lodge-and-pond system beavers maintain thus offered people a more usable “working water” for fishing, as human technology – like fish traps – could be set in ways that took advantage of the pond’s altered currents and predictable fish movement. These slower waters, some of which quickly turn murky, are ideal hunting grounds for large freshwater predators such as the pike. These fish are in turn attractive to human foragers as they are rich nutrient sources and can be relatively predictably found where beavers are active. The beaver’s re-shaping of river systems also cuts of some fish from the main river channels and thus provides a blueprint of how to trap fish through channel building and “fencing”. Because beaver dams and channels can shift over time, people likely had to keep maintaining and adjusting these traps in response to an evolving beaver-made landscape, closely monitoring beaver activities.
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During the Dorset period, human foragers seem to have adapted a key part of their hunting techniques directly from polar bears, the original master hunters of the sea ice. By observing the bear’s “still-standing” lurking technique – crouching motionless at seal breathing holes for long periods – Dorset hunters emulated these postures and translated them into specialized tool-use and conversed about them in their sophisticated figurative art made from ivory. Carved bear figures from this era emphasize the exact head and limb postures and body motions used by the bears in seal-hunting, reflecting how humans learned to inhabit the frozen ice by watching and emulating their bear neighbors.
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Euro-American observers often imagined the Great Plains as a treeless expanse of grass, but trees were an important part of prairie ecology. Cottonwoods (Populus species) formed ribbons of woodland along rivers and streams, where dependable moisture allowed them to flourish. These groves supplied people with fuel, lodge poles, shade, and shelter from wind, and after horses became central to Plains life, they acquired another vital role as emergency winter feed. When snow, ice, and extreme cold made grasses inaccessible, Native people fed their horses cottonwood bark.
For horses, the rough outer bark was not the main attraction. Horses consumed the more palatable inner bark and cambium – the softer living tissue beneath it – as well as small twigs. People could cut down young trees or branches and helped horses to access this inner layer (for example, by loosening the bark with axes). Some horses also learned to gnaw accessible trunks themselves. Cottonwood was never an ideal replacement for grass, and reliance on it signaled difficult conditions, but it supplied digestible material that could help horses survive periods when normal forage was unavailable.
With the rise of equestrianism, then, a river valley was no longer valued only for water, wood, game, and shelter. Cottonwood groves became part of an equine-centered map of the grasslands. Horse pastoralists had to know where cottonwood groves occurred, which trees provided usable feed, how long local supplies might last, and when cutting too heavily would exhaust an important resource. The location of cottonwood groves heavily influenced where communities built their winter homes. For example, Blackfoot elders remembered choosing winter camp sites along Montana’s Marias River specifically because its valleys offered plenty of cottonwood trees alongside the winter grasses their horses preferred.
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In contrast to Dorset seal hunters, whose strategies relied on polar-bear presence, bear-guided learning, and capture mechanisms on the sea ice or along its edges, Thule seal hunting made proficient use of an alternative form of nonhuman expertise. At the core of this shift were sledge dogs, which helped Thule re-map Arctic terrain and distances into something significantly more navigable, allowing them to quickly traverse the ice and follow routes that remained efficient under changing conditions. The presence and movement of dog teams contributed to routing and avoidance: dogs could help track routes, interpret patterns of activity in the environment, and reduce risks from other animals and competitors (like polar bears) by enabling more responsive movement choices. Their practical behavior during travel – how confidently they moved and how they reacted to particular ground conditions – could function as a cue for assessing whether routes were traversable and for managing risk while moving across ice. Mobility thus wasn’t just about faster travel but also changed what could be learned and acted on in a given season, tightening the link between observation, decision-making, and response.
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In shifting Arctic environments, human foragers relied on other animals as living indicators to locate vital resources. Large stranded whales provided abundant food, but they were difficult to spot across vast frozen coasts. Alleviating this problem were polar bears and aggregating flocks of seabirds who, attracted by these novel food bonanzas, thus acted as environmental beacons altering Arctic people. By watching where bears fed on or excavated frozen carcasses or where flocks of birds circled in the sky, Arctic people followed nonhuman guides to locate food sources, and such knowledge still features in some of today’s Arctic Indigenous knowledge systems.
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KLAP offers a conceptual lens for how humans relate to the Earth system through Knowledge, Learning, Action, and Perception capacities. It is a heuristic framework, which means that it is not intended as a one-to-one model of reality but as a usable way to think about complex human-environment processes and as a guide for asking better questions and comparing situations. At its core, KLAP is action-oriented, because it is concerned with how people come to act in specific environments and how their actions come to shape these environments – and, by extension, the Earth system – in specific ways. KLAP describes a set of linked elements: Knowledge informs what people take to be true or possible; Learning produces or modifies Knowledge and develops new ways of seeing; Action expresses these understandings in the world and/or follows acquired norms and habits; and Perception shapes what becomes noticeable, meaningful, and worth responding to. The arrows between the K, L, A, and P elements represent this mutually reinforcing cycle: what people know influences how they act and what they notice; their actions and perceptions affect what they learn; and learning, in turn, reshapes their knowledge and ways of seeing. These feedbacks form historically specific “KLAP configurations” that guide how humans live in – and actively transform – their social and ecological environments.
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Mesolithic and Neolithic huma foraging groups not only learned to inhabit beaver-shaped landscapes but also to use beaver remains as practical tools to work materials they encountered in those landscapes. In the archaeological record, such items show clear signs of regular use and maintenance (resharpening, repair), suggesting that they were more than occasional substitutes for better tools. Beaver incisors – enamel-coated and notably hard – were durable cutting edges for tasks such as slicing plant fibres, processing wood, or working through tough hides. Just as importantly, people re-purposed the softer but resilient structure around the teeth. Finds show that beaver mandibles were often split and the tooth-bearing parts separated, with the larger bone portions functioning as natural handles. In this way, people essentially acquired the capacities of beavers, and integrated key body parts with which the animals did much of their own “wetland work” into human material culture. Dwelling in beaver-shaped landscapes was thus both made possible by, and further enabled through, the harnessing of beaver bodies and capabilities.
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Read the paper: Hartley. 2024. State Crisis Theory. Cliodynamics. https://doi.org/10.21237%2FC7clio15163324
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The horses themselves were an important object of learning for aspiring equestrians. They were larger, stronger, and more unpredictable than the dogs that Native people had long been familiar with and employed as helpers when moving camp and transporting possessions. Horses could carry riders, bear packs, and fundamentally transform bison-hunting – but only when people learned how to understand and work with them. This required a new, specialized body of “horse knowledge” that was practical and embodied and went well beyond the practice of riding. As horse pastoralists, Native people learned how to catch, halter, hobble, picket, and corral horses; how to handle breeding animals and mares with foals; and how to prevent herds from straying or being stolen. Making saddles, bridles, ropes, blankets, and transport gear required additional materials and craft skills. Horses also had to be trained differently for hunting, warfare, racing, packing, or pulling a travois. A reliable “buffalo horse,” for example, needed speed and endurance but also the composure to approach a bison closely and respond quickly to its rider. As the ethnologist John C. Ewers (1909-1997) noted in his study of the Blackfoot people of the northern Great Plains, “every Blackfoot born and raised in the Horse Culture Period was required to learn motor and manual habits, owing to the presence of the horse in his cultural environment, of which Indians of the Pedestrian Culture Period were entirely ignorant.”
Much of this expertise grew from sustained observation and daily contact. Among the Blackfoot, knowledgeable people could reportedly recognize individual horses on sight without brands and judge their health, temperament, training, and value. They distinguished horses by their abilities and roles, including war and bison-hunting mounts, winter hunting horses, ordinary saddle horses, pack animals, racehorses, breeding stallions, brood mares, and mares that led grazing herds – each animal possessed particular capacities and required particular forms of care.
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To live with large horse herds, Native peoples had to re-learn the Plains not only as a mosaic of pastures but also as a network of water sources. Horses required dependable access to water – often daily, especially during hot weather, hard travel, or when mares were lactating – and a sizable herd quickly exhausted a small spring or shallow pool. Existing knowledge of waterways and seasonal conditions remained essential, but it now had to be recalibrated around equine bodies. Horse pastoralists learned which streams flowed throughout the year, which springs survived drought, where steep or unstable banks prevented animals from drinking safely, and which waters were stagnant, saline, or otherwise unsuitable. They also had to judge whether a source could support only a traveling party or an entire camp and herd.
Horses themselves contributed to this knowledge. People attended to their eagerness or reluctance to drink at a particular source and their movements toward familiar watering places. Animals might follow low ground, game trails, or the smell and sound of water, making springs and pools more noticeable to human companions. Yet horses were not infallible guides, and their preferences had to be interpreted alongside human knowledge of weather, terrain, water quality, and seasonal change. Water thus helped organize routes and rhythms of movement. Camps often clustered near river valleys, where water coincided with forage, fuel, and shelter. At the same time, remaining too long could deplete nearby grass, foul shallow water, and force herds to range farther from camp. Knowledgeable pastoralists therefore evaluated grass and water together.
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To speak of “beaver-shaped landscapes” means acknowledging beavers as significant ecological (and hydrological) agents who do not just live in wetlands but actively make and remake them. By building dams and lodges, they slow water, raise water levels, and create ponds and channels, modifications that fundamentally reorganize the landscape, including where different plants can grow and what kinds of animals visit. In this shifting mosaic of water conditions – never one uniform habitat – beaver activity can increase nutrient build-up in lake and pond environments, fostering aquatic life and, in turn, expanding the kinds and quantities of food available. At the same time, beavers shape plant growth and the creation of woodland-riparian edge habitats, where many animals forage. As beavers shaped water, plants, and edges, they also shaped the landscape of opportunities that met human foragers who came to live alongside and within these beaver-made wetland systems. These systems in part functioned as animal-mediated and animal-oriented learning environments in which humans observed and responded to what beavers made and changed, and what became possible as a consequence.
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To thrive with horses on the grasslands, Native peoples had to effectively “re-learn” the prairie landscape from an equine perspective. This did not mean replacing existing knowledge of plants, soils, seasons, and bison ecology, but adapting and extending it to account for the needs of their grazing herds. Plains horse pastoralists learned to recognize the distribution, accessibility, quality, and seasonal cycles of forage – from the bluestem grasses of the tallgrass and mixed-grass prairies to the grama and buffalograss of the drier shortgrass plains.
Because horses are selective grazers, their bodies and behavior supplied environmental information. By observing where horses chose – or refused – to feed, whether they gained or lost weight on particular pastures, and how quickly grazed areas recovered, knowledgeable herders could assess differences in forage quality that were not always readily visible to inexperienced people. Horses thus became a kind of living measure of the land: their movements, physical condition, and preferences helped people distinguish the prairie as a complex mosaic of grazing environments rather than an undifferentiated sea of grass. Equine behavior also shaped decisions about mobility. Once the most desirable forage near a camp had been consumed, horses tended to range farther in search of better grass. Animals wandering increasingly far from camp therefore indicated to Native people that the local pasture was becoming depleted and that it might be time to move.
Keeping large herds of horses healthy through freezing northern winters was one of the biggest challenges on the Great Plains. To survive the cold months, Native communities learned to adapt their lifestyle by splitting into smaller groups and moving into sheltered river valleys and low-lying hollows. These low-lying areas created natural sanctuaries, with the surrounding hills blocking biting prairie winds and the warmer micro-climates inside the valleys keeping patches of grass clear of heavy snow cover so horses could continue grazing.
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Welcome to this room.
You are probably here because you want to learn more about how and why technology evolves and how this can help us better understand both ourselves and the Earth system as a whole.
I can tell you that you are absolutely right here.
In this room, you will learn why the researchers currently working on these questions believe that we need to develop a new theoretical framework to understand large-scale technological change, why they think that we need to scale up how we think about technology, and why we need to find new ways of describing and analyzing technological worlds on such larger scales.
You will also have the opportunity to learn a bit more about the major technological transitions that have shaped the Earth system over the past 3 to 4 million years and why perhaps especially research on the deep past can help us better understand how technology evolves over such tremendous temporal scales.
You will, finally, also meet some of the researchers themselves and you will encounter already familiar things such as PC technologies, digital technologies, and military technologies deployed by the world’s dominant navies. We hope that you will learn to look at these things with novel eyes before you leave our room.
The room contains seven separate panels numbered from 1 to 7 and we recommend that you explore them in numerical order.
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Welcome
to HESCOR's Virtual Exhibition!


By exploring this exhibition, you will learn more about our research efforts as well as more about the planet and humans that occupy it!
HTMLText_B4F16444_A34F_0CFF_41E0_A913BFF16DA2.html =
Welcome
to HESCOR's Virtual Exhibition!


By exploring this exhibition, you will learn more about our research efforts as well as more about the planet and humans that occupy it!
HTMLText_6071162D_4390_B8E4_418B_D0EAD18B4C11.html =
The distinction between explicit and implicit knowledge has been used to challenge overly intellectualist views of knowledge, highlighting that much reliable understanding is expressed not just in statements but in skilled practice and performance. In the literature, “implicit” and “tacit” are often used interchangeably, with no fully consistent, universally adopted distinction, even though some authors treat them as related but separable categories.
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Action is never simply “what someone does” but unfolds within historically specific spaces of possibilities that define what feels feasible and appropriate, and which constraints or permissions guide outcomes in a given setting.
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One way of conceptualizing the relationship between action and learning, following Chris Argyris and Donald Schön (Organizational Learning: A Theory of Action Perspective, 1978). Single-loop learning adjusts strategies and actions but leaves underlying goals and assumptions intact, while double-loop learning revisits and potentially changes those goals and assumptions.
HTMLText_64D56517_43F1_D8B2_41CA_DD39EE7C16B5.html =
Perception is active sense-making: what becomes noticeable and meaningful is always shaped by context and learning.
HTMLText_67439F1B_4390_48B7_41C3_53B21894A118_mobile.html =
Action is never simply “what someone does” but unfolds within historically specific spaces of possibilities that define what feels feasible and appropriate, and which constraints or permissions guide outcomes in a given setting.
HTMLText_667A3A48_4397_C895_41B5_3A1A7F3C4D20_mobile.html =
One way of conceptualizing the relationship between action and learning, following Chris Argyris and Donald Schön (Organizational Learning: A Theory of Action Perspective, 1978). Single-loop learning adjusts strategies and actions but leaves underlying goals and assumptions intact, while double-loop learning revisits and potentially changes those goals and assumptions.
HTMLText_64D56517_43F1_D8B2_41CA_DD39EE7C16B5_mobile.html =
Perception is active sense-making: what becomes noticeable and meaningful is always shaped by context and learning.
HTMLText_6071162D_4390_B8E4_418B_D0EAD18B4C11_mobile.html =
The distinction between explicit and implicit knowledge has been used to challenge overly intellectualist views of knowledge, highlighting that much reliable understanding is expressed not just in statements but in skilled practice and performance. In the literature, “implicit” and “tacit” are often used interchangeably, with no fully consistent, universally adopted distinction, even though some authors treat them as related but separable categories.
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A particularly important social dimension of learning is that understanding is often formed within communities of practice. These are groups of people who share a domain of activity and learn by participating in it over time, thus tying together learning processes and specific forms of action. In such communities, learning is not only about individual cognitive change but also also about learning what counts as competence and evidence and about of standards of assessment. Learners often develop their abilities through what has been called “legitimate peripheral participation” – starting with smaller, supported tasks, observing experienced members, and gradually taking on fuller responsibilities as they build trust and credibility. This approach highlights how learning is a culturally patterned social activity in which different communities shape attention and define what is relevant – or, indeed, what is “real” in the first place – and socialize newcomers into specific ways of interpreting situations and solving problems. Learning tends to support the needs and reproduction of particular forms of life, and it is woven into existing rules, expectations, and hierarchies in a given community. In different historical contexts, some models of learning are encouraged and can solidify into institutions, while others are discouraged or seen as a threat to the accepted ways of seeing and doing.



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Action sits at the center of KLAP as an action-oriented framework. It covers what people do, that is, any activity or behavior that leaves a trace in the world and helps shape or change it in some way. KLAP is action-oriented, but it is premised on the idea that doing cannot be separated from thinking and observing. Much human action is purposeful and goal-directed, yet what counts as meaningful, competent, or “effective” action depends on local practices and cannot be disentangled from the other KLAP elements. What we can see of human action – whether in texts, speech, tools and buildings, gestures, online activity, or patterns of movement across landscapes – is therefore always already shaped by knowledge, learning, and perception.


Action is always situated in more than one way: it unfolds in specific material conditions and often in institutional settings, follows established habits and routines, draws on shared schemes of seeing and thinking, responds to social norms and power relations, and is generally constrained by what is available and permitted. The same intention can produce different outcomes in different contexts because the perceived situation, relevant skills, and available means may differ significantly.



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But other knowledge is more implicit – expressed through practice and often difficult to fully articulate, even when people can perform it well. This distinction between explicit and implicit knowledge partially corresponds with the distinction between “knowing-that” and “knowing-how.” Whereas the former centers on propositional or declarative content – statements that can, in principle, be written down, taught, and checked – the latter is procedural and aims at competent performance, such as carrying out tasks effectively, making judgments in context, and adapting to changing conditions.


What counts as knowledge, who is recognized as a (credible) “knower,” and how epistemic authority is assessed differs across times and places. Cultural traditions, educational systems and standards, and other factors shape which claims are trusted and which are dismissed. Such decisions are always influenced by values, worldviews, and power relations, including who gets to speak and whose evidence is accepted. Knowledge is a social phenomenon and process, and individual knowledge is both enabled and constrained by the social arrangements in which it exists and develops.
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Knowledge is whatever people accept as reliable understanding of how the world works. It includes practical skills, factual claims, interpretations, and more abstract ideas that help us act, explain, or predict. It is not only a matter of content (what is known), but also a matter of process (how it is justified and maintained). People come to know through different sources and pathways, such as direct observation, personal experience, reasoning and deduction, or testimony from others. These pathways produce different kinds of reliability and specificity, and so knowledge may vary in strength, ranging from tentative hypotheses to well-established theories and from repeatable procedures to situational know-how.


Different forms of knowledge can be distinguished by both their format and their accessibility. Explicit knowledge can be communicated clearly as facts, concepts, definitions, and general principles.
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Learning is the process by which people and communities gain, change, and pass on knowledge, values, and ways of doing things. It includes how people come to see certain behaviors as “correct,” useful, or appropriate for living in particular settings. Learning can take different modalities: from explicit instruction and formal education, to guided apprenticeship and coaching, to observational learning and imitation, to trial-and-error experimentation, and to learning through participation in activities where understanding is produced “on the job.” It also involves different modes of engagement with information win that some learning may be strongly conceptual (for example, learning explanations and principles) while other learning has a strongly practical bent (for example, learning procedures, judgment, and timing through doing). In many cases, explicit teaching is paired with tacit socialization, where people acquire norms, interpretations, and skilled ways of coordinating with others without those elements being fully articulated.
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Perception is much more than the passive uptake of sensory signals. It is an active, selective process through which people interpret what they see, hear, feel, and otherwise encounter, turning raw input into meaningful distinctions. What we notice or ignore, what we judge as important or irrelevant, is guided by our actions, our past experiences, and our training. Perception is tied to habits we have learned, such as scientific ways of looking at things, religious or cultural framings, or everyday forms of “common sense.” Because perception is guided, it is closely shaped by knowledge and learning. People often perceive through established schemes of seeing – for example, on the basis of learned categories – so that seeing is already informed by prior experience and the social norms that organize everyday life.
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Through repeated actions, people create and maintain patterned, socially meaningful ways of acting that organize life within a given social and environmental field. Many actions become automatic or semi-automatic through repeated engagement, so that people can act without “re-deriving” everything from first principles each time. Even when actions appear spontaneous, they are typically supported by learned expectations – about timing, tools, language, and appropriate responses – but they can also be reflective and adaptive. People revise how they act when circumstances change, when problems emerge, or when new information is encountered, making action a site where stability and variation coexist.


Action both reflects and reproduces existing hierarchies. Through everyday practices, people sustain certain forms of order while making some changes possible and others difficult. Over time, patterned actions can crystallize into recognizable practices, routines, and institutions, so that what people do becomes part of how the world is structured. KLAP thus treats action not only as individual behavior, but as an ongoing production of social reality: people act, and those actions, in their traces and regularities, shape what future agents perceive as possible, normal, and intelligible.



HTMLText_EF8768BF_D590_6CD8_41DF_DDD07D99B896.html =
Tools and technologies further mediate perception by extending human sensory reach or by encouraging certain kinds of attention (for example, through measuring instruments, satellite imagery, or cameras). As a result, perception is no less historically and socially situated than the other KLAP elements. It depends on how environments, situations, and other beings present themselves, and on how people are prepared – through knowledge, learning, and prior action – to receive and interpret these signals or perceptual cues. In this sense, perception is also inseparable from practice. What people count as “what is there” and “what is relevant” is not simply discovered but stabilized through ongoing cycles of acting, observing, modifying, and teaching.







HTMLText_EF15C73F_D590_65D9_41D5_3466A856A7CA_mobile.html =
A particularly important social dimension of learning is that understanding is often formed within communities of practice. These are groups of people who share a domain of activity and learn by participating in it over time, thus tying together learning processes and specific forms of action. In such communities, learning is not only about individual cognitive change but also also about learning what counts as competence and evidence and about of standards of assessment. Learners often develop their abilities through what has been called “legitimate peripheral participation” – starting with smaller, supported tasks, observing experienced members, and gradually taking on fuller responsibilities as they build trust and credibility. This approach highlights how learning is a culturally patterned social activity in which different communities shape attention and define what is relevant – or, indeed, what is “real” in the first place – and socialize newcomers into specific ways of interpreting situations and solving problems. Learning tends to support the needs and reproduction of particular forms of life, and it is woven into existing rules, expectations, and hierarchies in a given community. In different historical contexts, some models of learning are encouraged and can solidify into institutions, while others are discouraged or seen as a threat to the accepted ways of seeing and doing.



HTMLText_E889FAE4_D590_6C6F_41B5_B1E00562CA9C_mobile.html =
Action sits at the center of KLAP as an action-oriented framework. It covers what people do, that is, any activity or behavior that leaves a trace in the world and helps shape or change it in some way. KLAP is action-oriented, but it is premised on the idea that doing cannot be separated from thinking and observing. Much human action is purposeful and goal-directed, yet what counts as meaningful, competent, or “effective” action depends on local practices and cannot be disentangled from the other KLAP elements. What we can see of human action – whether in texts, speech, tools and buildings, gestures, online activity, or patterns of movement across landscapes – is therefore always already shaped by knowledge, learning, and perception.


Action is always situated in more than one way: it unfolds in specific material conditions and often in institutional settings, follows established habits and routines, draws on shared schemes of seeing and thinking, responds to social norms and power relations, and is generally constrained by what is available and permitted. The same intention can produce different outcomes in different contexts because the perceived situation, relevant skills, and available means may differ significantly.



HTMLText_EB7E528E_D277_BCB8_4172_C5BC73587786_mobile.html =
But other knowledge is more implicit – expressed through practice and often difficult to fully articulate, even when people can perform it well. This distinction between explicit and implicit knowledge partially corresponds with the distinction between “knowing-that” and “knowing-how.” Whereas the former centers on propositional or declarative content – statements that can, in principle, be written down, taught, and checked – the latter is procedural and aims at competent performance, such as carrying out tasks effectively, making judgments in context, and adapting to changing conditions.


What counts as knowledge, who is recognized as a (credible) “knower,” and how epistemic authority is assessed differs across times and places. Cultural traditions, educational systems and standards, and other factors shape which claims are trusted and which are dismissed. Such decisions are always influenced by values, worldviews, and power relations, including who gets to speak and whose evidence is accepted. Knowledge is a social phenomenon and process, and individual knowledge is both enabled and constrained by the social arrangements in which it exists and develops.
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Knowledge is whatever people accept as reliable understanding of how the world works. It includes practical skills, factual claims, interpretations, and more abstract ideas that help us act, explain, or predict. It is not only a matter of content (what is known), but also a matter of process (how it is justified and maintained). People come to know through different sources and pathways, such as direct observation, personal experience, reasoning and deduction, or testimony from others. These pathways produce different kinds of reliability and specificity, and so knowledge may vary in strength, ranging from tentative hypotheses to well-established theories and from repeatable procedures to situational know-how.


Different forms of knowledge can be distinguished by both their format and their accessibility. Explicit knowledge can be communicated clearly as facts, concepts, definitions, and general principles.
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Learning is the process by which people and communities gain, change, and pass on knowledge, values, and ways of doing things. It includes how people come to see certain behaviors as “correct,” useful, or appropriate for living in particular settings. Learning can take different modalities: from explicit instruction and formal education, to guided apprenticeship and coaching, to observational learning and imitation, to trial-and-error experimentation, and to learning through participation in activities where understanding is produced “on the job.” It also involves different modes of engagement with information win that some learning may be strongly conceptual (for example, learning explanations and principles) while other learning has a strongly practical bent (for example, learning procedures, judgment, and timing through doing). In many cases, explicit teaching is paired with tacit socialization, where people acquire norms, interpretations, and skilled ways of coordinating with others without those elements being fully articulated.
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Perception is much more than the passive uptake of sensory signals. It is an active, selective process through which people interpret what they see, hear, feel, and otherwise encounter, turning raw input into meaningful distinctions. What we notice or ignore, what we judge as important or irrelevant, is guided by our actions, our past experiences, and our training. Perception is tied to habits we have learned, such as scientific ways of looking at things, religious or cultural framings, or everyday forms of “common sense.” Because perception is guided, it is closely shaped by knowledge and learning. People often perceive through established schemes of seeing – for example, on the basis of learned categories – so that seeing is already informed by prior experience and the social norms that organize everyday life.
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Through repeated actions, people create and maintain patterned, socially meaningful ways of acting that organize life within a given social and environmental field. Many actions become automatic or semi-automatic through repeated engagement, so that people can act without “re-deriving” everything from first principles each time. Even when actions appear spontaneous, they are typically supported by learned expectations – about timing, tools, language, and appropriate responses – but they can also be reflective and adaptive. People revise how they act when circumstances change, when problems emerge, or when new information is encountered, making action a site where stability and variation coexist.


Action both reflects and reproduces existing hierarchies. Through everyday practices, people sustain certain forms of order while making some changes possible and others difficult. Over time, patterned actions can crystallize into recognizable practices, routines, and institutions, so that what people do becomes part of how the world is structured. KLAP thus treats action not only as individual behavior, but as an ongoing production of social reality: people act, and those actions, in their traces and regularities, shape what future agents perceive as possible, normal, and intelligible.



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Tools and technologies further mediate perception by extending human sensory reach or by encouraging certain kinds of attention (for example, through measuring instruments, satellite imagery, or cameras). As a result, perception is no less historically and socially situated than the other KLAP elements. It depends on how environments, situations, and other beings present themselves, and on how people are prepared – through knowledge, learning, and prior action – to receive and interpret these signals or perceptual cues. In this sense, perception is also inseparable from practice. What people count as “what is there” and “what is relevant” is not simply discovered but stabilized through ongoing cycles of acting, observing, modifying, and teaching.







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Milankovitch (Orbital) Cycles and Their Role in Earth’s Climate


NASA Science Editorial Team



Our lives literally revolve around cycles: series of events that are repeated regularly in the same order. There are hundreds of different types of cycles in our world and in the universe. Some are natural, such as the change of the seasons, annual animal migrations or the circadian rhythms that govern our sleep patterns. Others are human-produced, like growing and harvesting crops, musical rhythms or economic cycles.


Cycles also play key roles in Earth’s short-term weather and long-term climate. A century ago, Serbian scientist Milutin Milankovitch hypothesized the long-term, collective effects of changes in Earth’s position relative to the Sun are a strong driver of Earth’s long-term climate, and are responsible for triggering the beginning and end of glaciation periods (Ice Ages).


Specifically, he examined how variations in three types of Earth orbital movements affect how much solar radiation (known as insolation) reaches the top of Earth’s atmosphere as well as where the insolation reaches. These cyclical orbital movements, which became known as the Milankovitch cycles, cause variations of up to 25 percent in the amount of incoming insolation at Earth’s mid-latitudes (the areas of our planet located between about 30 and 60 degrees north and south of the equator).


The Milankovitch cycles include:


The shape of Earth’s orbit, known as eccentricity;
The angle Earth’s axis is tilted with respect to Earth’s orbital plane, known as obliquity; and
The direction Earth’s axis of rotation is pointed, known as precession.
Let’s take a look at each (further reading on why Milankovitch cycles can't explain Earth's current warming here).



Eccentricity


Earth’s annual pilgrimage around the Sun isn’t perfectly circular, but it’s pretty close. Over time, the pull of gravity from our solar system’s two largest gas giant planets, Jupiter and Saturn, causes the shape of Earth’s orbit to vary from nearly circular to slightly elliptical. Eccentricity measures how much the shape of Earth’s orbit departs from a perfect circle. These variations affect the distance between Earth and the Sun.


Eccentricity is the reason why our seasons are slightly different lengths, with summers in the Northern Hemisphere currently about 4.5 days longer than winters, and springs about three days longer than autumns. As eccentricity decreases, the length of our seasons gradually evens out.


The difference in the distance between Earth’s closest approach to the Sun (known as perihelion), which occurs on or about January 3 each year, and its farthest departure from the Sun (known as aphelion) on or about July 4, is currently about 5.1 million kilometers (about 3.2 million miles), a variation of 3.4 percent. That means each January, about 6.8 percent more incoming solar radiation reaches Earth than it does each July.


When Earth’s orbit is at its most elliptic, about 23 percent more incoming solar radiation reaches Earth at our planet’s closest approach to the Sun each year than does at its farthest departure from the Sun. Currently, Earth’s eccentricity is very slowly decreasing and is approaching its least elliptic (most circular), in a cycle that spans about 100,000 years.


The total change in global annual insolation due to the eccentricity cycle is very small. Because variations in Earth’s eccentricity are fairly small, they’re a relatively minor factor in annual seasonal climate variations.



Obliquity


The angle Earth’s axis of rotation is tilted as it travels around the Sun is known as obliquity. Obliquity is why Earth has seasons. Over the last million years, it has varied between 22.1 and 24.5 degrees with respect to Earth’s orbital plane. The greater Earth’s axial tilt angle, the more extreme our seasons are, as each hemisphere receives more solar radiation during its summer, when the hemisphere is tilted toward the Sun, and less during winter, when it is tilted away. Larger tilt angles favor periods of deglaciation (the melting and retreat of glaciers and ice sheets). These effects aren’t uniform globally -- higher latitudes receive a larger change in total solar radiation than areas closer to the equator.


Earth’s axis is currently tilted 23.4 degrees, or about half way between its extremes, and this angle is very slowly decreasing in a cycle that spans about 41,000 years. It was last at its maximum tilt about 10,000 years ago and will reach its minimum tilt about 10,000 years from now. As obliquity decreases, it gradually helps make our seasons milder, resulting in increasingly warmer winters, and cooler summers that gradually, over time, allow snow and ice at high latitudes to build up into large ice sheets. As ice cover increases, it reflects more of the Sun’s energy back into space, promoting even further cooling.




Precession


As Earth rotates, it wobbles slightly upon its rotational axis, like a slightly off-center spinning toy top. This wobble is due to tidal forces caused by the gravitational influences of the Sun and Moon that cause Earth to bulge at the equator, affecting its rotation. The trend in the direction of this wobble relative to the fixed positions of stars is known as axial precession. The cycle of axial precession spans about 25,771.5 years.


Axial precession makes seasonal contrasts more extreme in one hemisphere and less extreme in the other. Currently perihelion occurs during winter in the Northern Hemisphere and in summer in the Southern Hemisphere. This makes Southern Hemisphere summers hotter and moderates Northern Hemisphere seasonal variations. But in about 13,000 years, axial precession will cause these conditions to flip, with the Northern Hemisphere seeing more extremes in solar radiation and the Southern Hemisphere experiencing more moderate seasonal variations.


Precession does affect seasonal timing relative to Earth's closest/farthest points around the Sun. However, the modern calendar system ties itself to the seasons, and so, for example, the Northern Hemisphere winter will never occur in July. Today Earth’s North Stars are Polaris and Polaris Australis, but a couple of thousand years ago, they were Kochab and Pherkad.


There’s also apsidal precession. Not only does Earth wobble on its rotational axis, but Earth’s entire orbital ellipse – that is, the oval-shaped path Earth follows in its orbit around the Sun — also wobbles irregularly, primarily due to its interactions with Jupiter and Saturn. The cycle of apsidal precession spans about 112,000 years. Apsidal precession changes the orientation of Earth’s orbit relative to the ecliptic plane.


The combined effects of axial and apsidal precession result in an overall precession cycle spanning about 23,000 years on average.


A Climate Time Machine


The small changes set in motion by Milankovitch cycles operate separately and together to influence Earth’s climate over very long timespans, leading to larger changes in our climate over tens of thousands to hundreds of thousands of years. Milankovitch combined the cycles to create a comprehensive mathematical model for calculating differences in solar radiation at various Earth latitudes along with corresponding surface temperatures. The model is sort of like a climate time machine: it can be run backward and forward to examine past and future climate conditions.


Milankovitch assumed changes in radiation at some latitudes and in some seasons are more important than others to the growth and retreat of ice sheets. In addition, it was his belief that obliquity was the most important of the three cycles for climate, because it affects the amount of insolation in Earth’s northern high-latitude regions during summer (the relative role of precession versus obliquity is still a matter of scientific study).


He calculated that Ice Ages occur approximately every 41,000 years. Subsequent research confirms that they did occur at 41,000-year intervals between one and three million years ago. But about 800,000 years ago, the cycle of Ice Ages lengthened to 100,000 years, matching Earth’s eccentricity cycle. While various theories have been proposed to explain this transition, scientists do not yet have a clear answer.


Milankovitch’s work was supported by other researchers of his time, and he authored numerous publications on his hypothesis. But it wasn’t until about 10 years after his death in 1958 that the global science community began to take serious notice of his theory. In 1976, a study in the journal Science by Hays et al. using deep-sea sediment cores found that Milankovitch cycles correspond with periods of major climate change over the past 450,000 years, with Ice Ages occurring when Earth was undergoing different stages of orbital variation.


Several other projects and studies have also upheld the validity of Milankovitch’s work, including research using data from ice cores in Greenland and Antarctica that has provided strong evidence of Milankovitch cycles going back many hundreds of thousands of years. In addition, his work has been embraced by the National Research Council of the U.S. National Academy of Sciences.


Scientific research to better understand the mechanisms that cause changes in Earth’s rotation and how specifically Milankovitch cycles combine to affect climate is ongoing. But the theory that they drive the timing of glacial-interglacial cycles is well accepted.


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Milankovitch (Orbital) Cycles and Their Role in Earth’s Climate


NASA Science Editorial Team



Our lives literally revolve around cycles: series of events that are repeated regularly in the same order. There are hundreds of different types of cycles in our world and in the universe. Some are natural, such as the change of the seasons, annual animal migrations or the circadian rhythms that govern our sleep patterns. Others are human-produced, like growing and harvesting crops, musical rhythms or economic cycles.


Cycles also play key roles in Earth’s short-term weather and long-term climate. A century ago, Serbian scientist Milutin Milankovitch hypothesized the long-term, collective effects of changes in Earth’s position relative to the Sun are a strong driver of Earth’s long-term climate, and are responsible for triggering the beginning and end of glaciation periods (Ice Ages).


Specifically, he examined how variations in three types of Earth orbital movements affect how much solar radiation (known as insolation) reaches the top of Earth’s atmosphere as well as where the insolation reaches. These cyclical orbital movements, which became known as the Milankovitch cycles, cause variations of up to 25 percent in the amount of incoming insolation at Earth’s mid-latitudes (the areas of our planet located between about 30 and 60 degrees north and south of the equator).


The Milankovitch cycles include:


The shape of Earth’s orbit, known as eccentricity;
The angle Earth’s axis is tilted with respect to Earth’s orbital plane, known as obliquity; and
The direction Earth’s axis of rotation is pointed, known as precession.
Let’s take a look at each (further reading on why Milankovitch cycles can't explain Earth's current warming here).



Eccentricity


Earth’s annual pilgrimage around the Sun isn’t perfectly circular, but it’s pretty close. Over time, the pull of gravity from our solar system’s two largest gas giant planets, Jupiter and Saturn, causes the shape of Earth’s orbit to vary from nearly circular to slightly elliptical. Eccentricity measures how much the shape of Earth’s orbit departs from a perfect circle. These variations affect the distance between Earth and the Sun.


Eccentricity is the reason why our seasons are slightly different lengths, with summers in the Northern Hemisphere currently about 4.5 days longer than winters, and springs about three days longer than autumns. As eccentricity decreases, the length of our seasons gradually evens out.


The difference in the distance between Earth’s closest approach to the Sun (known as perihelion), which occurs on or about January 3 each year, and its farthest departure from the Sun (known as aphelion) on or about July 4, is currently about 5.1 million kilometers (about 3.2 million miles), a variation of 3.4 percent. That means each January, about 6.8 percent more incoming solar radiation reaches Earth than it does each July.


When Earth’s orbit is at its most elliptic, about 23 percent more incoming solar radiation reaches Earth at our planet’s closest approach to the Sun each year than does at its farthest departure from the Sun. Currently, Earth’s eccentricity is very slowly decreasing and is approaching its least elliptic (most circular), in a cycle that spans about 100,000 years.


The total change in global annual insolation due to the eccentricity cycle is very small. Because variations in Earth’s eccentricity are fairly small, they’re a relatively minor factor in annual seasonal climate variations.



Obliquity


The angle Earth’s axis of rotation is tilted as it travels around the Sun is known as obliquity. Obliquity is why Earth has seasons. Over the last million years, it has varied between 22.1 and 24.5 degrees with respect to Earth’s orbital plane. The greater Earth’s axial tilt angle, the more extreme our seasons are, as each hemisphere receives more solar radiation during its summer, when the hemisphere is tilted toward the Sun, and less during winter, when it is tilted away. Larger tilt angles favor periods of deglaciation (the melting and retreat of glaciers and ice sheets). These effects aren’t uniform globally -- higher latitudes receive a larger change in total solar radiation than areas closer to the equator.


Earth’s axis is currently tilted 23.4 degrees, or about half way between its extremes, and this angle is very slowly decreasing in a cycle that spans about 41,000 years. It was last at its maximum tilt about 10,000 years ago and will reach its minimum tilt about 10,000 years from now. As obliquity decreases, it gradually helps make our seasons milder, resulting in increasingly warmer winters, and cooler summers that gradually, over time, allow snow and ice at high latitudes to build up into large ice sheets. As ice cover increases, it reflects more of the Sun’s energy back into space, promoting even further cooling.




Precession


As Earth rotates, it wobbles slightly upon its rotational axis, like a slightly off-center spinning toy top. This wobble is due to tidal forces caused by the gravitational influences of the Sun and Moon that cause Earth to bulge at the equator, affecting its rotation. The trend in the direction of this wobble relative to the fixed positions of stars is known as axial precession. The cycle of axial precession spans about 25,771.5 years.


Axial precession makes seasonal contrasts more extreme in one hemisphere and less extreme in the other. Currently perihelion occurs during winter in the Northern Hemisphere and in summer in the Southern Hemisphere. This makes Southern Hemisphere summers hotter and moderates Northern Hemisphere seasonal variations. But in about 13,000 years, axial precession will cause these conditions to flip, with the Northern Hemisphere seeing more extremes in solar radiation and the Southern Hemisphere experiencing more moderate seasonal variations.


Precession does affect seasonal timing relative to Earth's closest/farthest points around the Sun. However, the modern calendar system ties itself to the seasons, and so, for example, the Northern Hemisphere winter will never occur in July. Today Earth’s North Stars are Polaris and Polaris Australis, but a couple of thousand years ago, they were Kochab and Pherkad.


There’s also apsidal precession. Not only does Earth wobble on its rotational axis, but Earth’s entire orbital ellipse – that is, the oval-shaped path Earth follows in its orbit around the Sun — also wobbles irregularly, primarily due to its interactions with Jupiter and Saturn. The cycle of apsidal precession spans about 112,000 years. Apsidal precession changes the orientation of Earth’s orbit relative to the ecliptic plane.


The combined effects of axial and apsidal precession result in an overall precession cycle spanning about 23,000 years on average.


A Climate Time Machine


The small changes set in motion by Milankovitch cycles operate separately and together to influence Earth’s climate over very long timespans, leading to larger changes in our climate over tens of thousands to hundreds of thousands of years. Milankovitch combined the cycles to create a comprehensive mathematical model for calculating differences in solar radiation at various Earth latitudes along with corresponding surface temperatures. The model is sort of like a climate time machine: it can be run backward and forward to examine past and future climate conditions.


Milankovitch assumed changes in radiation at some latitudes and in some seasons are more important than others to the growth and retreat of ice sheets. In addition, it was his belief that obliquity was the most important of the three cycles for climate, because it affects the amount of insolation in Earth’s northern high-latitude regions during summer (the relative role of precession versus obliquity is still a matter of scientific study).


He calculated that Ice Ages occur approximately every 41,000 years. Subsequent research confirms that they did occur at 41,000-year intervals between one and three million years ago. But about 800,000 years ago, the cycle of Ice Ages lengthened to 100,000 years, matching Earth’s eccentricity cycle. While various theories have been proposed to explain this transition, scientists do not yet have a clear answer.


Milankovitch’s work was supported by other researchers of his time, and he authored numerous publications on his hypothesis. But it wasn’t until about 10 years after his death in 1958 that the global science community began to take serious notice of his theory. In 1976, a study in the journal Science by Hays et al. using deep-sea sediment cores found that Milankovitch cycles correspond with periods of major climate change over the past 450,000 years, with Ice Ages occurring when Earth was undergoing different stages of orbital variation.


Several other projects and studies have also upheld the validity of Milankovitch’s work, including research using data from ice cores in Greenland and Antarctica that has provided strong evidence of Milankovitch cycles going back many hundreds of thousands of years. In addition, his work has been embraced by the National Research Council of the U.S. National Academy of Sciences.


Scientific research to better understand the mechanisms that cause changes in Earth’s rotation and how specifically Milankovitch cycles combine to affect climate is ongoing. But the theory that they drive the timing of glacial-interglacial cycles is well accepted.


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As the ethnologist John C. Ewers (1909-1997) noted in his study of the Blackfoot people of the northern Great Plains, “every Blackfoot born and raised in the Horse Culture Period was required to learn motor and manual habits, owing to the presence of the horse in his cultural environment, of which Indians of the Pedestrian Culture Period were entirely ignorant.”


Much of this expertise grew from sustained observation and daily contact. Among the Blackfoot, knowledgeable people could reportedly recognize individual horses on sight without brands and judge their health, temperament, training, and value. They distinguished horses by their abilities and roles, including war and bison-hunting mounts, winter hunting horses, ordinary saddle horses, pack animals, racehorses, breeding stallions, brood mares, and mares that led grazing herds – each animal possessed particular capacities and required particular forms of care.



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The horses themselves were an important object of learning for aspiring equestrians. They were larger, stronger, and more unpredictable than the dogs that Native people had long been familiar with and employed as helpers when moving camp and transporting possessions. Horses could carry riders, bear packs, and fundamentally transform bison-hunting – but only when people learned how to understand and work with them. This required a new, specialized body of “horse knowledge” that was practical and embodied and went well beyond the practice of riding. As horse pastoralists, Native people learned how to catch, halter, hobble, picket, and corral horses; how to handle breeding animals and mares with foals; and how to prevent herds from straying or being stolen. Making saddles, bridles, ropes, blankets, and transport gear required additional materials and craft skills. Horses also had to be trained differently for hunting, warfare, racing, packing, or pulling a travois. A reliable “buffalo horse,” for example, needed speed and endurance but also the composure to approach a bison closely and respond quickly to its rider.
HTMLText_B2713991_D5F0_6CA8_41E1_ACA554A0107D_mobile.html =
As the ethnologist John C. Ewers (1909-1997) noted in his study of the Blackfoot people of the northern Great Plains, “every Blackfoot born and raised in the Horse Culture Period was required to learn motor and manual habits, owing to the presence of the horse in his cultural environment, of which Indians of the Pedestrian Culture Period were entirely ignorant.”


Much of this expertise grew from sustained observation and daily contact. Among the Blackfoot, knowledgeable people could reportedly recognize individual horses on sight without brands and judge their health, temperament, training, and value. They distinguished horses by their abilities and roles, including war and bison-hunting mounts, winter hunting horses, ordinary saddle horses, pack animals, racehorses, breeding stallions, brood mares, and mares that led grazing herds – each animal possessed particular capacities and required particular forms of care.



HTMLText_B272A991_D5F0_6CA8_41E4_EC30DFE01954_mobile.html =
The horses themselves were an important object of learning for aspiring equestrians. They were larger, stronger, and more unpredictable than the dogs that Native people had long been familiar with and employed as helpers when moving camp and transporting possessions. Horses could carry riders, bear packs, and fundamentally transform bison-hunting – but only when people learned how to understand and work with them. This required a new, specialized body of “horse knowledge” that was practical and embodied and went well beyond the practice of riding. As horse pastoralists, Native people learned how to catch, halter, hobble, picket, and corral horses; how to handle breeding animals and mares with foals; and how to prevent herds from straying or being stolen. Making saddles, bridles, ropes, blankets, and transport gear required additional materials and craft skills. Horses also had to be trained differently for hunting, warfare, racing, packing, or pulling a travois. A reliable “buffalo horse,” for example, needed speed and endurance but also the composure to approach a bison closely and respond quickly to its rider.
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Action
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Action
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Horses
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Horses
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Knowledge
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Knowledge
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Perception
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Perception
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Learning
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Learning
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