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Animal-borne sensors as a biologically informed lens on a changing climate

Nature Climate Change volume 13, pages 1042–1054 (2023)Cite this article

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Abstract

As climate change transforms the biosphere, more comprehensive and biologically relevant measurements of changing conditions are needed. Traditional climate measurements are often constrained by geographically static, coarse, sparse and biased sampling, and only indirect links to ecological responses. Here we discuss how animal-borne sensors can deliver spatially fine-grain, biologically fine-tuned, relevant sampling of climatic conditions in support of ecological and climatic forecasting. Millions of fine-scale meteorological observations from over a thousand species have already been collected by animal-borne sensors. We highlight the opportunities that these growing data have for the intersection of biodiversity and climate science, particularly in terrestrial environments. Tagged animals worldwide could close critical data gaps, provide insights about changing ecosystems and broadly function as active environmental sentinels.

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Fig. 1: Widespread use of ABSs for collection of essential climatic variables at the global scale by land, air and sea.
Fig. 2: Example of ABSs-based measurement of climate change relevant conditions.
Fig. 3: Differences and complementarity in the spatio-temporal sampling and data delivered by remote sensing, local weather stations and ABSs across Kruger National Park, South Africa.
Fig. 4: ABS-supported characterizations of the thermal environment on Santa Cruz Island, Galapagos, Ecuador.
Fig. 5: ABS-supported study of biological responses to the press and pulse of climate change.
Fig. 6: Animals as diverse biological buoys.

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Data availability

Animal tracking data used in this manuscript are available under a Creative Commons license CC0. Stork data35,112,139 are available at https://www.datarepository.movebank.org/handle/10255/move.1253 and https://www.datarepository.movebank.org/handle/10255/move.602. African elephant data52,140 are available at https://www.datarepository.movebank.org/handle/10255/move.980. Caribou data113 are available at https://www.datarepository.movebank.org/handle/10255/move.956. Wandering albatross data111 are available at https://www.datarepository.movebank.org/handle/10255/move.331. Galapagos tortoise data are available at https://datarepository.movebank.org/handle/10255/move.834. Elephant seal Argos tracking and dive data were sourced from IMOS. IMOS is a national collaborative research infrastructure, supported by the Australian government. It is operated by a consortium of institutions as an unincorporated joint venture, with the University of Tasmania as the lead agent. Fluxtower data141 were obtained as part of the Fluxnet2015 ZA-Kru Skukuza dataset (https://fluxnet.org/doi/FLUXNET2015/ZA-Kru).

Code availability

Code necessary to display case studies of animal-borne instruments is available on GitHub (https://github.com/diego-ellis-soto/ABS_climate_change).

References

  1. Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).

    Article  Google Scholar 

  2. Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Change 9, 323–329 (2019).

    Article  Google Scholar 

  3. Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).

    Article  CAS  Google Scholar 

  4. Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).

    Article  Google Scholar 

  5. Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).

    Article  Google Scholar 

  6. Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).

    Article  Google Scholar 

  7. Zellweger, F., Coomes, D., Frenne, P., De, Lenoir, J. & Rocchini, D. Advances in microclimate ecology arising from remote sensing. Trends Ecol. Evol. 34, 327–341 (2019).

    Article  Google Scholar 

  8. Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).

    Article  CAS  Google Scholar 

  9. De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).

    Article  Google Scholar 

  10. Lembrechts, J. J. et al. SoilTemp: a global database of near-surface temperature. Glob. Change Biol. 26, 6616–6629 (2020).

    Article  Google Scholar 

  11. Lembrechts, J. J., Nijs, I. & Lenoir, J. Incorporating microclimate into species distribution models. Ecography 42, 1267–1279 (2019).

    Article  Google Scholar 

  12. The Global Observing System for Climate: Implementation Needs (World Meteorological Organization, 2016).

  13. Roemmich, D. et al. On the future of Argo: a global, full-depth, multi-disciplinary array. Front. Mar. Sci. 6, 439 (2019).

  14. Miloslavich, P. et al. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Glob. Change Biol. 24, 2416–2433 (2018).

    Article  Google Scholar 

  15. IPCC Climate Change 2021: The Physical Science Basis (Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  16. Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol. 14, e1002415 (2016).

    Article  Google Scholar 

  17. Anderson, C. B. Biodiversity monitoring, earth observations and the ecology of scale. Ecol. Lett. 21, 1572–1585 (2018).

    Article  Google Scholar 

  18. Karger, D. N., Wilson, A. M., Mahony, C., Zimmermann, N. E. & Jetz, W. Global daily 1 km land surface precipitation based on cloud cover-informed downscaling. Sci. Data 8, 307 (2021).

    Article  Google Scholar 

  19. Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).

    Article  Google Scholar 

  20. Kays, R., McShea, W. J. & Wikelski, M. Born digital biodiversity data: millions and billions. Divers. Distrib. 26, 644–648 (2019).

    Article  Google Scholar 

  21. Kays, R. et al. The Movebank system for studying global animal movement and demography. Methods Ecol. Evol. 13, 419–431 (2021).

  22. Harcourt, R. et al. Animal-borne telemetry: an integral component of the ocean observing toolkit. Front. Mar. Sci. 39, 326 (2019).

  23. McMahon, C. R. et al. Animal Borne Ocean Sensors – AniBOS – an essential component of the Global Ocean Observing System. Front. Mar. Sci. 8, 751840 (2021).

  24. Jetz, W. et al. Biological Earth observation with animal sensors. Trends Ecol. Evol. 37, 293–298 (2022).

    Article  Google Scholar 

  25. Bojinski, S. et al. The concept of essential climate variables in support of climate research, applications, and policy. Bull. Am. Meteorol. Soc. 95, 1431–1443 (2014).

    Article  Google Scholar 

  26. McIntyre, T. Trends in tagging of marine mammals: a review of marine mammal biologging studies. Afr. J. Mar. Sci. 36, 409–422 (2014).

    Article  Google Scholar 

  27. Boehlert, G. W. et al. Autonomous pinniped environmental samplers: using instrumented animals as oceanographic data collectors. J. Atmos. Ocean. Technol. 18, 1882–1893 (2001).

    Article  Google Scholar 

  28. Mallett, H. K. W. et al. Variation in the distribution and properties of circumpolar deep water in the Eastern Amundsen Sea, on seasonal timescales, using seal-borne tags. Geophys. Res. Lett. 45, 4982–4990 (2018).

    Article  Google Scholar 

  29. Treasure, A. et al. Marine mammals exploring the oceans pole to pole: a review of the MEOP Consortium. Oceanography 30, 132–138 (2017).

    Article  Google Scholar 

  30. Charrassin, J.-B. et al. Southern Ocean frontal structure and sea-ice formation rates revealed by elephant seals. Proc. Natl Acad. Sci. USA 105, 11634–11639 (2008).

    Article  CAS  Google Scholar 

  31. Roquet, F. et al. Estimates of the Southern Ocean general circulation improved by animal-borne instruments. Geophys. Res. Lett. 40, 6176–6180 (2013).

    Article  Google Scholar 

  32. March, D., Boehme, L., Tintoré, J., Vélez-Belchi, P. J. & Godley, B. J. Towards the integration of animal-borne instruments into global ocean observing systems. Glob. Change Biol. 26, 586–596 (2020).

    Article  Google Scholar 

  33. Ardyna, M. et al. Hydrothermal vents trigger massive phytoplankton blooms in the Southern Ocean. Nat. Commun. 10, 2451 (2019).

    Article  Google Scholar 

  34. Carlson, B. S., Rotics, S., Nathan, R., Wikelski, M. & Jetz, W. Individual environmental niches in mobile organisms. Nat. Commun. 12, 4572 (2021).

    Article  CAS  Google Scholar 

  35. Weinzierl, R. et al. Wind estimation based on thermal soaring of birds. Ecol. Evol. 6, 8706–8718 (2016).

    Article  Google Scholar 

  36. Nagy, M., Couzin, I. D., Fiedler, W., Wikelski, M. & Flack, A. Synchronization, coordination and collective sensing during thermalling flight of freely migrating white storks. Philos. Trans. R. Soc. Lond. B 373, 20170011 (2018).

    Article  Google Scholar 

  37. Davy, R. The climatology of the atmospheric boundary layer in contemporary global climate models. J. Clim. 31, 9151–9173 (2018).

    Article  Google Scholar 

  38. Scholander, P. F. Experimental Investigations on the Respiratory Function in Diving Mammals and Birds (I kommisjon hos Jacob Dybwad, 1940).

  39. Tsontos, V. et al. The oceanographic in situ data interoperability project (OIIP) - a year in review. In Oceans 2017—Anchorage (IEEE, 2017).

  40. Doi, T., Storto, A., Fukuoka, T. & Suganuma, H. Impacts of temperature measurements from sea turtles on seasonal prediction around the Arafura Sea. Front. Mar. Sci. 6, 719 (2019).

    Article  Google Scholar 

  41. Keates, T. R. et al. Chlorophyll fluorescence as measured in situ by animal-borne instruments in the northeastern Pacific Ocean. J. Mar. Syst. 203, 103265 (2020).

    Article  Google Scholar 

  42. Coffey, D. M. & Holland, K. N. First autonomous recording of in situ dissolved oxygen from free-ranging fish. Anim. Biotelem. 3, 47 (2015).

    Article  Google Scholar 

  43. Treep, J. et al. Using high-resolution GPS tracking data of bird flight for meteorological observations. Bull. Am. Meteorol. Soc. 97, 951–961 (2016).

    Article  Google Scholar 

  44. Safi, K. et al. Flying with the wind: scale dependency of speed and direction measurements in modelling wind support in avian flight. Mov. Ecol. 1, 1–13 (2013).

    Article  Google Scholar 

  45. Yonehara, Y. et al. Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction. Proc. Natl Acad. Sci. USA 113, 9039–9044 (2016).

    Article  CAS  Google Scholar 

  46. Goto, Y., Yoda, K. & Sato, K. Asymmetry hidden in birds’ tracks reveals wind, heading, and orientation ability over the ocean. Sci. Adv. 3, e1700097 (2017).

    Article  Google Scholar 

  47. Bohrer, G. et al. Estimating updraft velocity components over large spatial scales: contrasting migration strategies of golden eagles and turkey vultures. Ecol. Lett. 15, 96–103 (2012).

    Article  Google Scholar 

  48. Miyazawa, Y. et al. Temperature profiling measurements by sea turtles improve ocean state estimation in the Kuroshio-Oyashio Confluence region. Ocean Dyn. 69, 267–282 (2019).

    Article  Google Scholar 

  49. Miyazawa, Y. et al. Assimilation of the seabird and ship drift data in the north-eastern sea of Japan into an operational ocean nowcast/forecast system. Sci. Rep. 5, 17672 (2015).

    Article  CAS  Google Scholar 

  50. Thomas, R. M. et al. Avian sensor packages for meteorological measurements. Bull. Am. Meteorol. Soc. 99, 499–511 (2018).

    Article  Google Scholar 

  51. Austen, K. Environmental science: pollution patrol. Nature 517, 136–138 (2015).

    Article  CAS  Google Scholar 

  52. Thaker, M., Gupte, P. R., Prins, H. H. T., Slotow, R. & Vanak, A. T. Fine-scale tracking of ambient temperature and movement reveals shuttling behavior of elephants to water. Front. Ecol. Evol. 7, 4 (2019).

    Article  Google Scholar 

  53. Hetem, R. S., Maloney, S. K., Fuller, A., Meyer, L. C. R. & Mitchell, D. Validation of a biotelemetric technique, using ambulatory miniature black globe thermometers, to quantify thermoregulatory behaviour in ungulates. J. Exp. Zool. Part A 307, 342–356 (2007).

    Article  Google Scholar 

  54. Davidson, S. C. et al. Continental-scale and decadal patterns in animal phenology discovered using the Arctic Animal Movement Archive. In AGU Fall Meeting Abstracts Vol. 2020, B061-B0005 (2020).

  55. Guide to Meteorological Instruments and Methods of Observation (World Meteorological Organization, 2008).

  56. Lembrechts, J. J. et al. Comparing temperature data sources for use in species distribution models: from in-situ logging to remote sensing. Glob. Ecol. Biogeogr. 28, 1578–1596 (2019).

    Article  Google Scholar 

  57. De Frenne, P. & Verheyen, K. Weather stations lack forest data. Science 351, 2–3 (2016).

    Article  Google Scholar 

  58. Hik, D. S. & Williamson, S. N. Need for mountain weather stations climbs. Science 366, 1083 (2019).

    Article  Google Scholar 

  59. Maclean, I. M. D. Predicting future climate at high spatial and temporal resolution. Glob. Change Biol. 26, 1003–1011 (2020).

    Article  Google Scholar 

  60. Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Change 5, 424–430 (2015).

    Article  Google Scholar 

  61. Davidson, S. C. et al. Ecological insights from three decades of animal movement tracking across a changing Arctic. Science 370, 712–715 (2020).

    Article  CAS  Google Scholar 

  62. Lembrechts, J. J., Lenoir, J., Scheffers, B. R. & De Frenne, P. Designing countrywide and regional microclimate networks. Glob. Ecol. Biogeogr. 30, 1168–1174 (2021).

    Article  Google Scholar 

  63. Lu, M. & Jetz, W. Scale-sensitivity in the measurement and interpretation of environmental niches. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2023.01.003 (2023).

    Article  Google Scholar 

  64. Maclean, I. & Early, R. Macroclimate data overestimate species range shifts in response to climate change. Nat. Clim. Change 13, 484–490 (2023).

    Article  Google Scholar 

  65. Hannah, L. et al. Fine-grain modeling of species’ response to climate change: holdouts, stepping-stones, and microrefugia. Trends Ecol. Evol. 29, 390–397 (2014).

    Article  Google Scholar 

  66. Diehl, R. H. The airspace is habitat. Trends Ecol. Evol. 28, 377–379 (2013).

    Article  Google Scholar 

  67. Dee, D. P. et al. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

    Article  Google Scholar 

  68. Zeng, Z. et al. A reversal in global terrestrial stilling and its implications for wind energy production. Nat. Clim. Change 9, 979–985 (2019).

    Article  Google Scholar 

  69. Scott, G. R. Elevated performance: the unique physiology of birds that fly at high altitudes. J. Exp. Biol. 214, 2455–2462 (2011).

    Article  CAS  Google Scholar 

  70. Hawkes, L. A. et al. The trans-Himalayan flights of bar-headed geese (Anser indicus). Proc. Natl Acad. Sci. USA 108, 9516–9519 (2011).

    Article  CAS  Google Scholar 

  71. Laybourne, R. C. & Laybourne, R. C. Collision between a vulture and an aircraft at an altitude of 37,000 feet. Wilson Bull. 86, 461–462 (1974).

    Google Scholar 

  72. Hewitt, H., Fox-Kemper, B., Pearson, B., Roberts, M. & Klocke, D. The small scales of the ocean may hold the key to surprises. Nat. Clim. Change 12, 496–499 (2022).

    Article  Google Scholar 

  73. Hazen, E. L. et al. Marine top predators as climate and ecosystem sentinels. Front. Ecol. Environ. 17, 565–574 (2019).

    Article  Google Scholar 

  74. Wikelski, M. & Tertitski, G. Living sentinels for climate change effects. Science 352, 775–776 (2016).

    Article  CAS  Google Scholar 

  75. Braun, C. D., Gaube, P., Sinclair-Taylor, T. H., Skomal, G. B. & Thorrold, S. R. Mesoscale eddies release pelagic sharks from thermal constraints to foraging in the ocean twilight zone. Proc. Natl Acad. Sci. USA 116, 17187–17192 (2019).

    Article  CAS  Google Scholar 

  76. Cazau, D., Pradalier, C., Bonnel, J. & Guinet, C. Do southern elephant seals behave like weather buoys? Oceanography 30, 140–149 (2017).

    Article  Google Scholar 

  77. Campbell, E. C. et al. Antarctic offshore polynyas linked to Southern Hemisphere climate anomalies. Nature 570, 319–325 (2019).

    Article  CAS  Google Scholar 

  78. Williams, G. D. et al. The suppression of Antarctic bottom water formation by melting ice shelves in Prydz Bay. Nat. Commun. 7, 12577 (2016).

    Article  CAS  Google Scholar 

  79. Remelgado, R. From ecology to remote sensing: using animals to map land cover. Remote Sens. Ecol. Conserv. 6, 93–104 (2020).

    Article  Google Scholar 

  80. Curk, T. et al. Arctic avian predators synchronise their spring migration with the northern progression of snowmelt. Sci. Rep. 10, 7220 (2020).

    Article  CAS  Google Scholar 

  81. Musselman, K. N., Addor, N., Vano, J. A. & Molotch, N. P. Winter melt trends portend widespread declines in snow water resources. Nat. Clim. Change 11, 418–424 (2021).

    Article  Google Scholar 

  82. Boelman, N. T. et al. Integrating snow science and wildlife ecology in Arctic-boreal North America. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aaeec1 (2019).

    Article  Google Scholar 

  83. Riddell, E. A. et al. Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science 371, 633–636 (2021).

    Article  CAS  Google Scholar 

  84. Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).

    Article  CAS  Google Scholar 

  85. Fischer, E. M., Sippel, S. & Knutti, R. Increasing probability of record-shattering climate extremes. Nat. Clim. Change 11, 689–695 (2021).

    Article  Google Scholar 

  86. Zhang, L. et al. Global assessment of primate vulnerability to extreme climatic events. Nat. Clim. Change 9, 554–561 (2019).

    Article  Google Scholar 

  87. Clusella-Trullas, S., Garcia, R. A., Terblanche, J. S. & Hoffmann, A. A. How useful are thermal vulnerability indices? Trends Ecol. Evol. 36, 1000–1010 (2021).

    Article  Google Scholar 

  88. Cohen, J. M., Fink, D. & Zuckerberg, B. Avian responses to extreme weather across functional traits and temporal scales. Glob. Change Biol. 26, 4240–4250 (2020).

    Article  Google Scholar 

  89. Nourani, E. et al. Seabird morphology determines operational wind speeds, tolerable maxima, and responses to extremes. Curr. Biol. 33, 1179–1184 (2023).

    Article  CAS  Google Scholar 

  90. Semenzato, P. et al. Behavioural heat-stress compensation in a cold-adapted ungulate: forage-mediated responses to warming Alpine summers. Ecol. Lett. 24, 1556–1568 (2021).

    Article  Google Scholar 

  91. Beever, E. A. et al. Behavioral flexibility as a mechanism for coping with climate change. Front. Ecol. Environ. 15, 299–308 (2017).

    Article  Google Scholar 

  92. Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. Proc. Natl Acad. Sci. USA 116, 21609–21615 (2019).

    Article  CAS  Google Scholar 

  93. Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).

    Article  Google Scholar 

  94. Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).

    Article  Google Scholar 

  95. Tøttrup, A. P. et al. Drought in Africa caused delayed arrival of European songbirds. Science 338, 1307 (2012).

    Article  Google Scholar 

  96. Cerini, F., Childs, D. Z. & Clements, C. F. A predictive timeline of wildlife population collapse. Nat. Ecol. Evol. 7, 320–331 (2023).

    Article  Google Scholar 

  97. Tye, S. P. et al. Climate warming amplifies the frequency of fish mass mortality events across north temperate lakes. Limnol. Oceanogr. Lett. 7, 510–519 (2022).

    Article  Google Scholar 

  98. Lv, L. et al. Winter mortality of a passerine bird increases following hotter summers and during winters with higher maximum temperatures. Sci. Adv. 9, eabm0197 (2023).

    Article  Google Scholar 

  99. Cohen, J. M., Sauer, E. L., Santiago, O., Spencer, S. & Rohr, J. R. Divergent impacts of warming weather on wildlife disease risk across climates. Science 370, eabb1702 (2020).

    Article  CAS  Google Scholar 

  100. Carlson, C. J. et al. Climate change increases cross-species viral transmission risk. Nature 607, 555–562 (2022).

    Article  CAS  Google Scholar 

  101. van Toor, M. L., Avril, A., Wu, G., Holan, S. H. & Waldenström, J. As the duck flies—estimating the dispersal of low-pathogenic avian influenza viruses by migrating mallards. Front. Ecol. Evol. 6, 208 (2018).

    Article  Google Scholar 

  102. Jax, E. et al. Health monitoring in birds using bio-loggers and whole blood transcriptomics. Sci. Rep. 11, 10815 (2021).

    Article  CAS  Google Scholar 

  103. Hertel, A. G., Niemelä, P. T., Dingemanse, N. J. & Mueller, T. A guide for studying among-individual behavioral variation from movement data in the wild. Mov. Ecol. 8, 30 (2020).

    Article  Google Scholar 

  104. Jetz, W. et al. Include biodiversity representation indicators in area-based conservation targets. Nat. Ecol. Evol. 6, 123–126 (2022).

    Article  Google Scholar 

  105. Stewart, J. R., Lister, A. M., Barnes, I. & Dalén, L. Refugia revisited: individualistic responses of species in space and time. Proc. R. Soc. B 277, 661–671 (2010).

    Article  Google Scholar 

  106. Lenoir, J., Hattab, T. & Pierre, G. Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography 40, 253–266 (2017).

    Article  Google Scholar 

  107. Strangas, M. L., Navas, C. A., Rodrigues, M. T. & Carnaval, A. C. Thermophysiology, microclimates, and species distributions of lizards in the mountains of the Brazilian Atlantic Forest. Ecography 42, 354–364 (2019).

    Article  Google Scholar 

  108. Williams, J. W., Ordonez, A. & Svenning, J.-C. A unifying framework for studying and managing climate-driven rates of ecological change. Nat. Ecol. Evol. 5, 17–26 (2021).

    Article  Google Scholar 

  109. Kölzsch, A. et al. MoveApps: a serverless no-code analysis platform for animal tracking data. Mov. Ecol. 10, 30 (2022).

    Article  Google Scholar 

  110. Huey, R. B., Hertz, P. E. & Sinervo, B. Behavioral drive versus behavioral inertia in evolution: a null model approach. Am. Nat. 161, 357–366 (2003).

    Article  Google Scholar 

  111. Cruz, S., Proaño, C. B., Anderson, D., Huyvaert, K. & Wikelski, M. Data from: the Environmental-Data Automated Track Annotation (Env-DATA) system: linking animal tracks with environmental data. Movebank Data Repository https://doi.org/10.5441/001/1.3hp3s250 (2013).

  112. Carlson B. S., Rotics S., Nathan R., Wikelski M. & Jetz W. Data from: individual environmental niches in mobile organisms. Movebank Data Repository https://doi.org/10.5441/001/1.rj21g1p1 (2021).

  113. Seip, D. R. & Price, E. Data from: science update for the South Peace Northern Caribou (Rangifer tarandus caribou pop. 15) in British Columbia. Movebank Data Repository https://doi.org/10.5441/001/1.p5bn656k (2019).

  114. Fauchald, P. & Tveraa, T. Using first-passage time in the analysis of area-restricted search and habitat selection. Ecology 84, 282–288 (2003).

    Article  Google Scholar 

  115. Bastille-Rousseau, G. et al. Flexible characterization of animal movement pattern using net squared displacement and a latent state model. Mov. Ecol. 4, 15 (2016).

    Article  Google Scholar 

  116. Siegelman, L. et al. Correction and accuracy of high- and low-resolution CTD data from animal-borne instruments. J. Atmos. Ocean. Technol. 36, 745–760 (2019).

    Article  Google Scholar 

  117. Frazer, E. K., Langhorne, P. J., Williams, M. J. M., Goetz, K. T. & Costa, D. P. A method for correcting seal-borne oceanographic data and application to the estimation of regional sea ice thickness. J. Mar. Syst. 187, 250–259 (2018).

    Article  Google Scholar 

  118. Snyder, S. & Franks, P. J. S. Quantifying the effects of sensor coatings on body temperature measurements. Anim. Biotelem. 4, 8 (2016).

    Article  Google Scholar 

  119. Shero, M. R. et al. Tracking wildlife energy dynamics with unoccupied aircraft systems and three-dimensional photogrammetry. Methods Ecol. Evol. 12, 2458–2472 (2021).

    Article  Google Scholar 

  120. Kay, W. P. et al. Minimizing the impact of biologging devices: using computational fluid dynamics for optimizing tag design and positioning. Methods Ecol. Evol. 10, 1222–1233 (2019).

    Article  Google Scholar 

  121. Kearney, M. R., Briscoe, N. J., Mathewson, P. D. & Porter, W. P. NicheMapR – an R package for biophysical modelling: the endotherm model. Ecography 44, 1595–1605 (2021).

    Article  Google Scholar 

  122. Ray, C., Beever, E. A. & Rodhouse, T. J. Distribution of a climate-sensitive species at an interior range margin. Ecosphere 7, e01379 (2016).

    Article  Google Scholar 

  123. Avgar, T., Potts, J. R., Lewis, M. A. & Boyce, M. S. Integrated step selection analysis: bridging the gap between resource selection and animal movement. Methods Ecol. Evol. 7, 619–630 (2016).

    Article  Google Scholar 

  124. Michelot, T. & Blackwell, P. G. State-switching continuous-time correlated random walks. Methods Ecol. Evol. 10, 637–649 (2019).

    Article  Google Scholar 

  125. Patterson, T. A., Thomas, L., Wilcox, C., Ovaskainen, O. & Matthiopoulos, J. State–space models of individual animal movement. Trends Ecol. Evol. 23, 87–94 (2008).

    Article  Google Scholar 

  126. Williams, H. J. et al. Optimising the use of biologgers for movement ecology research. J. Anim. Ecol. 89, 186–206 (2020).

  127. Michelot, T., Langrock, R. & Patterson, T. A. moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models. Methods Ecol. Evol. 7, 1308–1315 (2016).

    Article  Google Scholar 

  128. Tradowsky, J. S., Burrows, C. P., Healy, S. B. & Eyre, J. R. A new method to correct radiosonde temperature biases using radio occultation data. J. Appl. Meteorol. Climatol. 56, 1643–1661 (2017).

    Article  Google Scholar 

  129. Finazzi, F. et al. Statistical harmonization and uncertainty assessment in the comparison of satellite and radiosonde climate variables. Environmetrics 30, e2528 (2019).

    Article  Google Scholar 

  130. Dinsdale, D. & Salibian-Barrera, M. Modelling ocean temperatures from bio-probes under preferential sampling. Ann. Appl. Stat. 13, 713–745 (2019).

    Article  Google Scholar 

  131. Fraser, K. C. et al. Tracking the conservation promise of movement ecology. Front. Ecol. Evol. 6, 150 (2018).

    Article  Google Scholar 

  132. Soulsbury, C. D. et al. The welfare and ethics of research involving wild animals: a primer. Methods Ecol. Evol. 11, 1164–1181 (2020).

    Article  Google Scholar 

  133. Bauer, P., Thorpe, A. & Brunet, G. The quiet revolution of numerical weather prediction. Nature 525, 47–55 (2015).

    Article  CAS  Google Scholar 

  134. Lempidakis, E. et al. Estimating fine-scale changes in turbulence using the movements of a flapping flier. J. R. Soc. Interface 19, 20220577 (2022).

    Article  Google Scholar 

  135. Di Bernardino, A., Jennings, V. & Dell’Omo, G. Bird-borne samplers for monitoring CO2 and atmospheric physical parameters. Remote Sens. 14, 4876 (2022).

  136. Raymond, C., Matthews, T. & Horton, R. M. The emergence of heat and humidity too severe for human tolerance. Sci. Adv. 6, eaaw1838 (2020).

    Article  Google Scholar 

  137. Qian, Y. et al. Urbanization impact on regional climate and extreme weather: current understanding, uncertainties, and future research directions. Adv. Atmos. Sci. 39, 819–860 (2022).

    Article  Google Scholar 

  138. Venter, Z. S., Chakraborty, T. & Lee, X. Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms. Sci. Adv. 7, eabb9569 (2021).

    Article  Google Scholar 

  139. Flack, A., Fiedler, W. & Wikelski, M. Data from: wind estimation based on thermal soaring of birds. Movebank Data Repository https://doi.org/10.5441/001/1.bj96m274 (2016).

  140. Slotow, R., Thaker, M. & Vanak, A. T. Data from: fine-scale tracking of ambient temperature and movement reveals shuttling behavior of elephants to water. Movebank Data Repository https://doi.org/10.5441/001/1.403h24q5 (2019).

  141. Scholes, B. FLUXNET2015 ZA-Kru Skukuza. FLUXNET https://doi.org/10.18140/FLX/1440188 (28 January 2020).

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Acknowledgements

We thank S. Yanco, B. Jesmer, R. Y. Oliver, K. Winner, B. Carlson and J. Cohen for helpful feedback on this publication. We thank G. Amatulli for advice on obtaining MODIS LST for the Galapagos empirical test case. This work was supported by NASA FINESST (80NSSC22K1535) and a Yale Institute for Biospheric Studies Dissertation Grant to D.E.-S. This work was further supported by the French Polar Institute (programme 109: PI. H. Weimerskirch and 1201: PI. C. Gilbert and C. Guinet), the French National Observation System and the French National Centre for Space Studies. This research was supported by the Max Planck-Yale Center for Biodiversity Movement and Global Change.

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Authors and Affiliations

  1. Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA

    Diego Ellis-Soto & Walter Jetz

  2. Center for Biodiversity and Global Change, Yale University, New Haven, CT, USA

    Diego Ellis-Soto & Walter Jetz

  3. Department of Migration, Max Planck Institute of Animal Behavior, Konstanz, Germany

    Martin Wikelski

  4. Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany

    Martin Wikelski

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  1. Diego Ellis-Soto
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Correspondence to Diego Ellis-Soto.

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Supplementary Material 1.

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Supplementary Table 1

Case studies of ABSs shown in Fig. 1.

Supplementary Table 2

Databases with ABS data.

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Ellis-Soto, D., Wikelski, M. & Jetz, W. Animal-borne sensors as a biologically informed lens on a changing climate. Nat. Clim. Chang. 13, 1042–1054 (2023). https://doi.org/10.1038/s41558-023-01781-7

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