Abstract
Animal movement data have transformed our understanding of ecological systems and shaped conservation practice, but have limited influence on tracking progress towards international biodiversity goals. Existing biodiversity indicators adopted in frameworks such as the Kunming–Montréal Global Biodiversity Framework — the primary multilateral conservation agreement that aims to halt and reverse biodiversity loss by 2030 — are typically not responsive enough to detect biodiversity change in time to guide action, nor sufficiently biologically informative to explain or predict changes. In this Perspective, we provide seven reasons why movement data can help to tackle these limitations by adding biological realism, mechanistic understanding, and early-warning capacity to current and future indicators. Movement data already inform conservation efforts, from local management to global treaties for migratory species, and are increasingly helpful to uncover sources of environmental change, while enhancing monitoring capability and policy relevance. We recommend that the scientific community ground existing connectivity metrics in empirical movement data, develop new indicators that flag rapid change, and invest in modelling and attribution studies that use movement data to identify drivers of biodiversity loss and recovery.
This is a preview of subscription content, access via your institution
Access options
Access through your institution
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to the full article PDF.
USD 39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Past, present, and future of the Living Planet Index
Biodiversity science and policy need more model intercomparisons
Conservation priority corridors enhance the effectiveness of protected area networks in China
References
Kays, R., Crofoot, M. C., Jetz, W. & Wikelski, M. Terrestrial animal tracking as an eye on life and planet. Science 348, aaa2478 (2015).
Hussey, N. E. et al. Aquatic animal telemetry: a panoramic window into the underwater world. Science 348, 1255642 (2015).
Brodie, J. F. et al. A well-connected earth: the science and conservation of organismal movement. Science 388, eadn2225 (2025).
Google Scholar
Katzner, T. E. & Arlettaz, R. Evaluating contributions of recent tracking-based animal movement ecology to conservation management. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00519 (2020).
Google Scholar
Wilmers, C. C. et al. The golden age of bio-logging: how animal-borne sensors are advancing the frontiers of ecology. Ecology 96, 1741–1753 (2015).
Gomez, S. et al. Understanding and predicting animal movements and distributions in the Anthropocene. J. Anim. Ecol. 94, 1146–1164 (2025).
Hays, G. C. et al. Translating marine animal tracking data into conservation policy and management. Trends Ecol. Evol. 34, 459–473 (2019).
Seegar, W. S. et al. Fifteen years of satellite tracking development and application to wildlife research and conservation. Johns Hopkins APL Tech. Dig. 17, 305–315 (1996).
Harrison, A.-L. et al. The collective application of shorebird tracking data to conservation. Conserv. Biol. https://doi.org/10.1111/cobi.70194 (2026).
Google Scholar
Verzuh, T. et al. Aligning tools and terminology to integrate movement ecology with conservation science. Conserv. Biol. 40, e70209 (2026).
Hindell, M. A. et al. Tracking of marine predators to protect southern ocean ecosystems. Nature 580, 87–92 (2020).
Google Scholar
Trathan, P. N. et al. in Advances in Marine Biology, vol. 69 (eds Johnson, M. L. & Sandell, J.) 15–78 (Academic Press, 2014).
Fraser, K. C. et al. Tracking the conservation promise of movement ecology. Front. Ecol. Evol. 6, 150 (2018).
Phillips, R. A. et al. The conservation status and priorities for albatrosses and large petrels. Biol. Conserv. 201, 169–183 (2016).
CBD. 15/4. Kunming–Montreal Global Biodiversity Framework (UN, 2022).
Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).
Hawkins, F. et al. Bottom-up global biodiversity metrics needed for businesses to assess and manage their impact. Conserv. Biol. 38, e14183 (2024).
Taskforce on Nature-related Financial Disclosures. Guidance on the Identification and Assessment of Nature-Related Issues: The LEAP Approach 277 (TFND, 2023).
Stevenson, S. L. et al. Matching biodiversity indicators to policy needs. Conserv. Biol. 35, 522–532 (2021).
Timmermans, J. & Daniel Kissling, W. Advancing terrestrial biodiversity monitoring with satellite remote sensing in the context of the Kunming–Montreal global biodiversity framework. Ecol. Indic. 154, 110773 (2023).
Google Scholar
Jones, J. P. G. et al. The why, what, and how of global biodiversity indicators beyond the 2010 target. Conserv. Biol. 25, 450–457 (2011).
Kissling, W. D. et al. Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol. Rev. 93, 600–625 (2018).
Scholes, R. J. & Biggs, R. A biodiversity intactness index. Nature 434, 45–49 (2005).
Google Scholar
Butchart, S. H. M. et al. Using Red List indices to measure progress towards the 2010 target and beyond. Philos. Trans. R. Soc. B 360, 255–268 (2005).
Google Scholar
Noss, R. F. Indicators for monitoring biodiversity: a hierarchical approach. Conserv. Biol. 4, 355–364 (1990).
Gregory, R. D. et al. Developing indicators for European birds. Philos. Trans. R. Soc. B 360, 269–288 (2005).
de Heer, M., Kapos, V. & ten Brink, B. J. E. Biodiversity trends in Europe: development and testing of a species trend indicator for evaluating progress towards the 2010 target. Philos. Trans. R. Soc. B 360, 297–308 (2005).
Loh, J. et al. The living planet index: using species population time series to track trends in biodiversity. Philos. Trans. R. Soc. B 360, 289–295 (2005).
Affinito, F., Williams, J. M., Campbell, J. E., Londono, M. C. & Gonzalez, A. Progress in developing and operationalizing the monitoring framework of the global biodiversity framework. Nat. Ecol. Evol. 8, 2163–2171 (2024).
CBD. 15/5. Monitoring framework for the Kunming–Montreal Global Biodiversity Framework (UN, 2022).
Gonzalez, A. et al. From data to decisions: toward a biodiversity monitoring standards framework. Proc. Natl Acad. Sci. USA 123, e2519347123 (2026).
Google Scholar
Hoban, S. et al. Genetic diversity targets and indicators in the CBD post-2020 global biodiversity framework must be improved. Biol. Conserv. 248, 108654 (2020).
Ledger, S. E. H. et al. Past, present, and future of the living planet index. npj Biodivers. 2, 12 (2023).
De Palma, A. et al. Annual changes in the biodiversity intactness index in tropical and subtropical forest biomes, 2001–2012. Sci. Rep. 11, 20249 (2021).
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).
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).
Dossman, B. C., Studds, C. E., LaDeau, S. L., Sillett, T. S. & Marra, P. P. The role of tropical rainfall in driving range dynamics for a long-distance migratory bird. Proc. Natl Acad. Sci. USA 120, e2301055120 (2023).
Google Scholar
Contina, A. et al. Dynamic environments generate geographic fluctuations in population structure of an inland shorebird. Ecosphere 16, e70312 (2025).
Yanco, S. W. et al. Migratory birds modulate niche tradeoffs in rhythm with seasons and life history. Proc. Natl Acad. Sci. USA 121, e2316827121 (2024).
Google Scholar
Cerini, F., Childs, D. Z. & Clements, C. F. A predictive timeline of wildlife population collapse. Nat. Ecol. Evol. 7, 320–331 (2023).
Matley, J. K. et al. Global trends in aquatic animal tracking with acoustic telemetry. Trends Ecol. Evol. 37, 79–94 (2022).
Shaw, A. K. et al. Perceived and observed biases within scientific communities: a case study in movement ecology. Proc. R. Soc. B 292, 20250679 (2025).
Scarpignato, A. L. et al. Shortfalls in tracking data available to inform North American migratory bird conservation. Biol. Conserv. 286, 110224 (2023).
Kays, R., McShea, W. J. & Wikelski, M. Born-digital biodiversity data: millions and billions. Divers. Distrib. 26, 644–648 (2020).
Börger, L. Editorial: stuck in motion? Reconnecting questions and tools in movement ecology. J. Anim. Ecol. 85, 5–10 (2016).
Kays, R. & Wikelski, M. The internet of animals: what it is, what it could be. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2023.04.007 (2023).
Google Scholar
Davidson, S. C. et al. Establishing bio-logging data collections as dynamic archives of animal life on Earth. Nat. Ecol. Evol. 9, 204–213 (2025).
Bridge, E. S. et al. Technology on the move: recent and forthcoming innovations for tracking migratory birds. BioScience 61, 689–698 (2011).
Payne, A. R., Hale, C. M., Davidson, S. C., Kendall-Bar, J. M. & Beltran, R. S. Towards a minimum reporting standard to promote animal welfare and data quality in biologging research. Anim. Biotelemetry 14, 1 (2026).
Putman, R. J. Ethical considerations and animal welfare in ecological field studies. Biodivers. Conserv. 4, 903–915 (1995).
Iverson, S. J. et al. The Ocean Tracking Network: advancing frontiers in aquatic science and management. Can. J. Fish. Aquat. Sci. 76, 1041–1051 (2019).
Carneiro, A. P. B. et al. The birdlife seabird tracking database: 20 years of collaboration for marine conservation. Biol. Conserv. 299, 110813 (2024).
Rutz, C. et al. COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife. Nat. Ecol. Evol. 4, 1156–1159 (2020).
Piipponen-Doyle, S., Bolam, F. C. & Mair, L. Disparity between ecological and political timeframes for species conservation targets. Biodivers. Conserv. 30, 1899–1912 (2021).
Hébert, K. et al. Five recommendations to fill the blank space in indicators at local and short-term scales. Biol. Conserv. 302, 111007 (2025).
Spake, R. et al. Precision ecology for targeted conservation action. Nat. Ecol. Evol. 9, 1102–1111 (2025).
Cazalis, V. et al. Accelerating and standardising IUCN Red List assessments with sRedList. Biol. Conserv. 298, 110761 (2024).
Wall, J., Wittemyer, G., Klinkenberg, B. & Douglas-Hamilton, I. Novel opportunities for wildlife conservation and research with real-time monitoring. Ecol. Appl. 24, 593–601 (2014).
Williams, H. J. et al. Optimizing the use of biologgers for movement ecology research. J. Anim. Ecol. 89, 186–206 (2020).
Beumer, L. T. et al. MoveTraits — a database for integrating animal behaviour into trait-based ecology. Ecol. Lett. 29, e70297 (2026).
Potts, J. R. & Börger, L. How to scale up from animal movement decisions to spatiotemporal patterns: an approach via step selection. J. Anim. Ecol. 92, 16–29 (2023).
Saura, S., Bastin, L., Battistella, L., Mandrici, A. & Dubois, G. Protected areas in the world’s ecoregions: how well connected are they? Ecol. Indic. 76, 144–158 (2017).
Metaxas, A., Harrison, A.-L. & Dunn, D. From oceans apart to the global ocean: Including marine connectivity in global conservation targets. npj Ocean Sustain. 3, 40 (2024).
UNEP-WCMC. Factsheet — indicators for the post 2020 Global Biodiversity Framework. Protected area representativeness and connectedness (PARC) indices. UNEP-WCMC https://www.gbf-indicators.org/metadata/other/A-5-C (2024).
Brennan, A. et al. Functional connectivity of the world’s protected areas. Science 376, 1101–1104 (2022).
Google Scholar
Hofmann, D. D., Behr, D. M., McNutt, J. W., Ozgul, A. & Cozzi, G. Bound within boundaries: do protected areas cover movement corridors of their most mobile, protected species? J. Appl. Ecol. 58, 1133–1144 (2021).
Hofmann, D. D., Cozzi, G., McNutt, J. W., Ozgul, A. & Behr, D. M. A three-step approach for assessing landscape connectivity via simulated dispersal: African wild dog case study. Landsc. Ecol. 38, 981–998 (2023).
Bentley, L. K. et al. Marine megavertebrate migrations connect the global ocean. Nat. Commun. 16, 4089 (2025).
Google Scholar
Cooper, N. W. & Marra, P. P. Hidden long-distance movements by a migratory bird. Curr. Biol. 30, 4056–4062 (2020).
Google Scholar
McKinnon, E. A. & Love, O. P. Ten years tracking the migrations of small landbirds: lessons learned in the golden age of bio-logging. Auk 135, 834–856 (2018).
Morten, J. M. et al. Global marine flyways identified for long-distance migrating seabirds from tracking data. Glob. Ecol. Biogeogr. 34, e70004 (2025).
Joly, K. et al. Longest terrestrial migrations and movements around the world. Sci. Rep. 9, 15333 (2019).
Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).
Cordes, L. S., Bishop, C. M., Börger, L., Nabe-Nielsen, J. & Harris, S. M. Optimal movement decisions in complex landscapes. Trends Ecol. Evol. 40, 960–969 (2025).
Rogers, W., Yanco, S. & Jetz, W. Choices to landscapes: mechanisms of animal movement scale to landscape patterns of space use. Ecol. Lett. 28, e70279 (2025).
Klappstein, N. J., Thomas, L. & Michelot, T. Flexible hidden Markov models for behaviour-dependent habitat selection. Mov. Ecol. 11, 30 (2023).
Google Scholar
Pohle, J., Signer, J., Eccard, J. A., Dammhahn, M. & Schlägel, U. E. How to account for behavioral states in step-selection analysis: a model comparison. PeerJ 12, e16509 (2024).
Jetz, W. et al. Essential biodiversity variables for mapping and monitoring species populations. Nat. Ecol. Evol. 3, 539–551 (2019).
Miloslavich, P. et al. Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Glob. Chang. Biol. 24, 2416–2433 (2018).
Barkley, A. N. et al. Complex transboundary movements of marine megafauna in the Western Indian Ocean. Anim. Conserv. 22, 420–431 (2019).
Harrison, A.-L. et al. The political biogeography of migratory marine predators. Nat. Ecol. Evol. 2, 1571–1578 (2018).
Beal, M. et al. Global political responsibility for the conservation of albatrosses and large petrels. Sci. Adv. 7, eabd7225 (2021).
Cooke, S. J. et al. Animal migration in the anthropocene: threats and mitigation options. Biol. Rev. 99, 1242–1260 (2024).
Dunn, D. C. et al. The importance of migratory connectivity for global ocean policy. Proc. R. Soc. B 286, 20191472 (2019).
Kauffman, M. J. et al. Mapping out a future for ungulate migrations. Science 372, 566–569 (2021).
Google Scholar
United Nations Environment Programme/Convention on Migratory Species. Resolution 12.11 (Rev.COP14) (UNEP/CMS, 2024).
United Nations Environment Programme/Agreement on the Conservation of African-Eurasian Migratory Waterbirds. AEWA Strategic Plan 2019–2027: Agreement on the Conservation of African-Eurasian Migratory Waterbirds (AEWA) (UNAP/AEWA, 2018).
Beal, M. et al. track2KBA: an R package for identifying important sites for biodiversity from tracking data. Methods Ecol. Evol. 12, 2372–2378 (2021).
Butchart, S. H. M. et al. Extent, characteristics and policy applications of key biodiversity areas. Biol. Rev. Camb. Philos. Soc. https://doi.org/10.1002/brv.70144 (2026).
Google Scholar
Davies, T. E. et al. Multispecies tracking reveals a major seabird hotspot in the north atlantic. Conserv. Lett. 14, e12824 (2021).
Marra, P. P. & Scarpignato, A. L. The Atlas of North America’s Migratory Birds: Tracking Movement Across Seasons and Continents (Princeton Univ. Press, 2026).
Gonzalez, A., Chase, J. M. & O’Connor, M. I. A framework for the detection and attribution of biodiversity change. Philos. Trans. R. Soc. B 378, 20220182 (2023).
Ward, M. P. et al. Estimating apparent survival of songbirds crossing the gulf of mexico during autumn migration. Proc. R. Soc. B 285, 20181747 (2018).
Cooper, N. W., Yanco, S. W., Rushing, C. S., Sillett, T. S. & Marra, P. P. Non-breeding conditions induce carry-over effects on survival of migratory birds. Curr. Biol. 34, 5097–5103 (2024).
Google Scholar
Prugh, L. R. et al. Fear of large carnivores amplifies human-caused mortality for mesopredators. Science 380, 754–758 (2023).
Google Scholar
Buechley, E. R. et al. Differential survival throughout the full annual cycle of a migratory bird presents a life-history trade-off. J. Anim. Ecol. 90, 1228–1238 (2021).
Overton, C. et al. Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl. Mov. Ecol. 10, 23 (2022).
Picardi, S. et al. Analysis of movement recursions to detect reproductive events and estimate their fate in central place foragers. Mov. Ecol. 8, 24 (2020).
Rushing, C. S., Dudash, M. R., Studds, C. E. & Marra, P. P. Annual variation in long-distance dispersal driven by breeding and non-breeding season climatic conditions in a migratory bird. Ecography 38, 1006–1014 (2015).
Studds, C. E., Kyser, T. K. & Marra, P. P. Natal dispersal driven by environmental conditions interacting across the annual cycle of a migratory songbird. Proc. Natl Acad. Sci. USA 105, 2929–2933 (2008).
Google Scholar
Yanco, S. W. et al. Tracking individual animals can reveal the mechanisms of species loss. Trends Ecol. Evol. 40, 47–56 (2025).
Google Scholar
Marra, P. P., Hobson, K. A. & Holmes, R. T. Linking winter and summer events in a migratory bird by using stable-carbon isotopes. Science 282, 1884–1886 (1998).
Google Scholar
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).
Google Scholar
Briscoe, N. J. et al. Mechanistic forecasts of species responses to climate change: the promise of biophysical ecology. Glob. Chang. Biol. 29, 1451–1470 (2023).
Google Scholar
Talmon, I. et al. Using wild-animal tracking for detecting and managing disease outbreaks. Trends Ecol. Evol. 40, 760–771 (2025).
Grabow, M. et al. Sick without signs. Subclinical infections reduce local movements, alter habitat selection, and cause demographic shifts. Commun. Biol. 7, 1426 (2024).
Monk, C. T. et al. The battle between harvest and natural selection creates small and shy fish. Proc. Natl Acad. Sci. USA 118, e2009451118 (2021).
Google Scholar
Nathan, R. et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780 (2022).
Google Scholar
Jetz, W. et al. Biological earth observation with animal sensors. Trends Ecol. Evol. 37, 293–298 (2022).
Tuomainen, U. & Candolin, U. Behavioural responses to human-induced environmental change. Biol. Rev. 86, 640–657 (2011).
Risely, A., Klaassen, M. & Hoye, B. J. Migratory animals feel the cost of getting sick: a meta-analysis across species. J. Anim. Ecol. 87, 301–314 (2018).
Binning, S. A., Shaw, A. K. & Roche, D. G. Parasites and host performance: incorporating infection into our understanding of animal movement. Integr. Comp. Biol. 57, 267–280 (2017).
Kim, D., Michelot, T., Mertes, K., Stabach, J. A. & Fieberg, J. Detecting disease progression from animal movement using hidden Markov models. J. Appl. Ecol. 63, e70323 (2026).
Aikens, E. O., Merkle, J. A., Xu, W. & Sawyer, H. Pronghorn movements and mortality during extreme weather highlight the critical importance of connectivity. Curr. Biol. 35, 1927–1934.e2 (2025).
Google Scholar
Streby, H. M. et al. Tornadic storm avoidance behavior in breeding songbirds. Curr. Biol. 25, 98–102 (2015).
Google Scholar
Bailey, H. & Secor, D. H. Coastal evacuations by fish during extreme weather events. Sci. Rep. 6, 30280 (2016).
Google Scholar
Kölzsch, A. et al. MoveApps: a serverless no-code analysis platform for animal tracking data. Mov. Ecol. 10, 30 (2022).
Wall, J. et al. EarthRanger: an open-source platform for ecosystem monitoring, research and management. Methods Ecol. Evol. 15, 1968–1979 (2024).
Wolfson, D. W., Andersen, D. E. & Fieberg, J. R. Using piecewise regression to identify biological phenomena in biotelemetry datasets. J. Anim. Ecol. 91, 1755–1769 (2022).
Hollaway, M. J. & Killick, R. Detection of spatiotemporal changepoints: a generalised additive model approach. Stat. Comput. 34, 162 (2024).
Wilkinson, C. E. Public interest in individual study animals can bolster wildlife conservation. Nat. Ecol. Evol. 7, 478–479 (2023).
McAfee, D., Doubleday, Z. A., Geiger, N. & Connell, S. D. Everyone loves a success story: optimism inspires conservation engagement. BioScience 69, 274–281 (2019).
Naugle, D. E., Allred, B. W., Jones, M. O., Twidwell, D. & Maestas, J. D. Coproducing science to inform working lands: the next frontier in nature conservation. BioScience 70, 90–96 (2020).
Beier, P., Hansen, L. J., Helbrecht, L. & Behar, D. A how-to guide for coproduction of actionable science. Conserv. Lett. 10, 288–296 (2017).
Costa-Pereira, R., Moll, R. J., Jesmer, B. R. & Jetz, W. Animal tracking moves community ecology: opportunities and challenges. J. Anim. Ecol. 91, 1334–1344 (2022).
Beltran, R. S. et al. Integrating animal tracking and trait data to facilitate global ecological discoveries. J. Exp. Biol. 228, JEB247981 (2025).
Buckland, S. T., Magurran, A. E., Green, R. E. & Fewster, R. M. Monitoring change in biodiversity through composite indices. Philos. Trans. R. Soc. B https://doi.org/10.1098/rstb.2004.1589 (2005).
Google Scholar
van Strien, A. J., Soldaat, L. L. & Gregory, R. D. Desirable mathematical properties of indicators for biodiversity change. Ecol. Indic. 14, 202–208 (2012).
Hébert, K. et al. Selecting indicators to track progress towards the global biodiversity framework: a case study of Quebec’s 2030 nature plan. FACETS 10, 1–13 (2025).
Morellet, N. et al. Seasonality, weather and climate affect home range size in roe deer across a wide latitudinal gradient within Europe. J. Anim. Ecol. 82, 1326–1339 (2013).
Singh, N. J., Börger, L., Dettki, H., Bunnefeld, N. & Ericsson, G. From migration to nomadism: movement variability in a northern ungulate across its latitudinal range. Ecol. Appl. 22, 2007–2020 (2012).
Wall, J. et al. Human footprint and protected areas shape elephant range across Africa. Curr. Biol. 31, 2437–2445 (2021).
Google Scholar
Hirt, M. R. et al. Environmental and anthropogenic constraints on animal space use drive extinction risk worldwide. Ecol. Lett. 24, 2576–2585 (2021).
Stabach, J. A. et al. Increasing anthropogenic disturbance restricts wildebeest movement across East African grazing systems. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2022.846171 (2022).
Google Scholar
Broekman, M. J. E. et al. Environmental drivers of global variation in home range size of terrestrial and marine mammals. J. Anim. Ecol. 93, 488–500 (2024).
Müller, M. F., Banks, S. C., Crewe, T. L. & Campbell, H. A. The rise of animal biotelemetry and genetics research data integration. Ecol. Evol. 13, e9885 (2023).
Amstrup, S. C., McDonald, T. L. & Durner, G. M. Using satellite radiotelemetry data to delineate and manage wildlife populations. Wildl. Soc. Bull. 32, 661–679 (2004).
Lewin, P. J. et al. Climate change drives migratory range shift via individual plasticity in shearwaters. Proc. Natl Acad. Sci. USA 121, e2312438121 (2024).
Google Scholar
Patterson, A., Gilchrist, H. G., Gaston, A. & Elliott, K. H. Northwest range shifts and shorter wintering period of an Arctic seabird in response to four decades of changing ocean climate. Mar. Ecol. Prog. Ser. 679, 163–179 (2021).
Map of life. The species protection report 2025. E. O. Wilson Biodiversity Foundation https://eowilsonfoundation.org/which-half/national-report-cards/the-species-protection-report-2025/ (2025).
Bauer, S., Tielens, E. K. & Haest, B. Monitoring aerial insect biodiversity: a radar perspective. Philos. Trans. R. Soc. B https://doi.org/10.1098/rstb.2023.0113 (2024).
Google Scholar
Ramos, R. et al. Meta-population feeding grounds of Cory’s shearwater in the subtropical atlantic ocean: implications for the definition of marine protected areas based on tracking studies. Divers. Distrib. 19, 1284–1298 (2013).
Barham, K. E. et al. Cooling down is as important as warming up for a large-bodied tropical reptile. Proc. R. Soc. B 291, 20241804 (2024).
Ortega, A. C., Aikens, E. O., Merkle, J. A., Monteith, K. L. & Kauffman, M. J. Migrating mule deer compensate en route for phenological mismatches. Nat. Commun. 14, 2008 (2023).
Google Scholar
Martell, M. S., Henny, C. J., Nye, P. E. & Solensky, M. J. Fall migration routes, timing, and wintering sites of North American ospreys as determined by satellite telemetry. Ornithol. Appl. 103, 715–724 (2001).
Frankish, C. K., Phillips, R. A., Clay, T. A., Somveille, M. & Manica, A. Environmental drivers of movement in a threatened seabird: insights from a mechanistic model and implications for conservation. Divers. Distrib. 26, 1315–1329 (2020).
Pastor-Prieto, M. et al. Spatial ecology, phenological variability and moulting patterns of the endangered atlantic petrel pterodroma incerta. Endanger. Species Res. 40, 189–206 (2019).
Hertel, A. G. et al. Don’t poke the bear: using tracking data to quantify behavioural syndromes in elusive wildlife. Anim. Behav. 147, 91–104 (2019).
Russo, N. et al. Monitoring animal movement diversity as a component of biodiversity. Front. Ecol. Environ. 24, e70038 (2026).
Miller, P. I., Scales, K. L., Ingram, S. N., Southall, E. J. & Sims, D. W. Basking sharks and oceanographic fronts: quantifying associations in the north-east Atlantic. Funct. Ecol. 29, 1099–1109 (2015).
Craft, T. B. et al. Remote sensing and GPS tracking reveal temporal shifts in habitat use in nonbreeding black-tailed godwits. J. Appl. Ecol. 62, 119–131 (2025).
Overton, C. T. et al. Megafires and thick smoke portend big problems for migratory birds. Ecology 103, e03552 (2022).
Bevanda, M. et al. Adding structure to land cover — using fractional cover to study animal habitat use. Mov. Ecol. 2, 26 (2014).
Cox, S. L. et al. Seabird diving behaviour reveals the functional significance of shelf-sea fronts as foraging hotspots. R. Soc. Open Sci. https://doi.org/10.1098/rsos.160317 (2016).
Google Scholar
Mills, L. S. & Allendorf, F. W. The one-migrant-per-generation rule in conservation and management. Conserv. Biol. 10, 1509–1518 (1996).
Acknowledgements
This Perspective is based upon work supported by the Smithsonian Institution’s Life on a Sustainable Planet Initiative and by the National Aeronautics and Space Administration under contract number 1718113 issued through the Jet Propulsion Laboratory, California Institute of Technology. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (no. 80NM0018D0004). This is contribution number 168 from the Smithsonian’s MarineGEO and Tennenbaum Marine Observatories Network. A.G. acknowledges the support of the Liber Ero Chair in Biodiversity Conservation. C.R. acknowledges funding from the Gordon and Betty Moore Foundation (grant no. GBMF9881) and the National Geographic Society (grant no. NGS-82515R-20). D.E.-S. acknowledges funding from the David H. Smith Conservation Research Fellowship and the Presidential Postdoctoral Fellowship Program from the University of California. J.F. acknowledges support from the Minnesota Agricultural Experimental Station. N.J.R. acknowledges funding from the Harvard University Dean’s Fund for Competitive Scientific Research. R.Y.O. acknowledges support from the Kuni Endowed Junior Faculty Fellowship and the UCSB Regents’ Junior Faculty Fellowship. S.C.D. acknowledges funding through NASA Ecological Forecasting Program grant 80NSSC21K1182.
Author information
Authors and Affiliations
Contributions
R.Y.O., K.H. and L.J.P. led the writing of the original draft with substantial contributions from L.F.H., L.B., F.C., N.W.C., S.C.D., A.G., A.-L.H., J.M.K.-B., K.L.M., J.M.F., T.M., W.R., T.S., J.A.S., M.A.T., W.X. and S.W.Y. All co-authors made substantial contributions to discussion of the content and reviewed and edited the manuscript before submission.
Corresponding authors
Ethics declarations
Competing interests
Many authors are members of Move BON, a scientific coordination initiative discussed in this paper. L.F.H. and S.C.D. serve as co-chairs, and some authors hold additional leadership roles. These roles are unpaid and supported in-kind by their institutions. L.F.H.’s institution previously received funding from NASA JPL to support the development of Move BON.
Peer review
Peer review information
Nature Reviews Biodiversity thanks Ron Efrat, Todd Katzner, Navinder Singh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Related links
Animal Movement Biodiversity Observation Network: https://geobon.org/move-bon
Atlas for the Americas Flyways: https://www.cms.int/atlas-americas-flyways
Atlas on Animal Migration: https://www.cms.int/en/topics/atlas-animal-migration
Bird Migration Explorer: https://explorer.audubon.org/
Eurasian African Bird Migration Atlas: https://migrationatlas.org/
Global Flyways: https://www.globalflywaynetwork.org/
Global Initiative on Ungulate Migration: https://www.cms.int/gium
Global Wader: https://www.globalwader.org
Group on Earth Observations Biodiversity Observation Network: https://geobon.org/
Kunming-Montréal Global Biodiversity Framework: https://www.cbd.int/gbf
Migratory Connectivity in the Ocean System: https://www.mico.eco
Motus: https://motus.org/
Movebank: https://www.movebank.org/
MoveTraits: https://www.movebank.org/cms/movebank-content/movetraits
Ocean Biodiversity Information System Spatial Ecological Analysis of Megavertebrate Populations: https://seamap.env.duke.edu/
P-22: https://friendsofgriffithpark.org/p-22/
Panthera: https://panthera.org/
Shorebird Science and Conservation Collective: https://nationalzoo.si.edu/migratory-birds/shorebird-collective
Tour de Turtles: https://tourdeturtles.org/
World Wildlife Fund: https://www.worldwildlife.org/
Wyoming Migration Initiative: https://migrationinitiative.org/
Supplementary information
Supplementary information (download PDF )
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
About this article
Cite this article
Oliver, R.Y., Hébert, K., Hughey, L.F. et al. A call to integrate animal movement into biodiversity indicators.
Nat. Rev. Biodivers. (2026). https://doi.org/10.1038/s44358-026-00173-x
Accepted:
Published:
Version of record:
DOI: https://doi.org/10.1038/s44358-026-00173-x
Source: Ecology - nature.com
