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

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

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

    Article 
    CAS 

    Google Scholar
     

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

    Article 

    Google Scholar
     

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

    Article 

    Google Scholar
     

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

    Article 

    Google Scholar
     

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

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

  • 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
     

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

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

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

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

  • 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).

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

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

    Article 

    Google Scholar
     

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

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

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

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

    Article 
    CAS 

    Google Scholar
     

  • 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
     

  • 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
     

  • 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).

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

  • 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
     

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

    Article 

    Google Scholar
     

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

    Article 

    Google Scholar
     

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

    Article 

    Google Scholar
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

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

    Article 
    CAS 

    Google Scholar
     

  • 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
     

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

    Article 

    Google Scholar
     

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

    Article 
    CAS 

    Google Scholar
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

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

    Article 
    CAS 

    Google Scholar
     

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

    Article 

    Google Scholar
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

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

    Article 
    CAS 

    Google Scholar
     

  • 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
     

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

    Article 
    CAS 

    Google Scholar
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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).

  • 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).

  • 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).

  • 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
     

  • 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
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

  • 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
     

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

  • 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
     

  • 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
     

  • 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
     

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

    Article 

    Google Scholar
     

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

    Article 

    Google Scholar
     

  • 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
     

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

    Article 
    CAS 

    Google Scholar
     

  • 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
     

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

  • 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
     

  • 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
     

  • 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
     

  • 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).

  • 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).

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



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