Ecology

Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).Article 
CAS 
PubMed 

Google Scholar 
Rockström, J. et al. A safe operating space for humanity. Nature 461, 472–475 (2009).Article 
PubMed 

Google Scholar 
Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).Article 
CAS 
PubMed 

Google Scholar 
Schulze, K. et al. An assessment of threats to terrestrial protected areas. Conserv. Lett. 11, e12435 (2018).Article 

Google Scholar 
Bingham, H. C. et al. (eds). Protected Planet Report 2020 (UNEP-WCMC & IUCN, 2021); https://livereport.protectedplanet.net/Buchanan, G. M., Butchart, S. H., Chandler, G. & Gregory, R. D. Assessment of national-level progress towards elements of the Aichi Biodiversity Targets. Ecol. Indic. 116, 106497 (2020).Article 

Google Scholar 
Xu, H. et al. Ensuring effective implementation of the post-2020 global biodiversity targets. Nat. Ecol. Evol. 5, 411–418 (2021).Article 
PubMed 

Google Scholar 
Report of the Open-ended Working Group on the Post-2020 Global Biodiversity Framework on Its Third Meeting (CBD Secretariat, 2022); https://www.cbd.int/conferences/post2020/wg2020-03/documentsRodrigues, A. S. & Cazalis, V. The multifaceted challenge of evaluating protected area effectiveness. Nat. Commun. 11, 5147 (2020).Article 
PubMed Central 
CAS 
PubMed 

Google Scholar 
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23215 (2019).Article 
PubMed Central 
CAS 
PubMed 

Google Scholar 
Starnes, T. et al. The extent and effectiveness of protected areas in the UK. Glob. Ecol. Conserv. 30, e01745 (2021).Article 

Google Scholar 
Kremen, C. et al. Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science 320, 222–226 (2008).Article 
CAS 
PubMed 

Google Scholar 
Cazalis, V. et al. Mismatch between bird species sensitivity and the protection of intact habitats across the Americas. Ecol. Lett. 24, 2394–2405 (2021).Article 
PubMed 

Google Scholar 
Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).Article 
PubMed Central 
PubMed 

Google Scholar 
Gamero, A. et al. Tracking progress toward EU biodiversity strategy targets: EU policy effects in preserving its common farmland birds. Conserv. Lett. 10, 395–402 (2017).Article 

Google Scholar 
Pellissier, V. et al. Effects of Natura 2000 on nontarget bird and butterfly species based on citizen science data. Conserv. Biol. 34, 666–676 (2020).Article 
CAS 
PubMed 

Google Scholar 
Princé, K., Rouveyrol, P., Pellissier, V., Touroult, J. & Jiguet, F. Long-term effectiveness of Natura 2000 network to protect biodiversity: a hint of optimism for common birds. Biol. Conserv. 253, 108871 (2021).Article 

Google Scholar 
Cunningham, C. A., Thomas, C. D., Morecroft, M. D., Crick, H. Q. P. & Beale, C. M. The effectiveness of the protected area network of Great Britain. Biol. Conserv. 257, 109146 (2021).Article 

Google Scholar 
Duckworth, G. D. & Altwegg, R. Effectiveness of protected areas for bird conservation depends on guild. Divers. Distrib. 24, 1083–1091 (2018).Article 
PubMed Central 
PubMed 

Google Scholar 
Rada, S. et al. Protected areas do not mitigate biodiversity declines: a case study on butterflies. Divers. Distrib. 25, 217–224 (2019).Article 

Google Scholar 
Terraube, J., Van Doninck, J., Helle, P., & Cabeza, M. Assessing the effectiveness of a national protected area network for carnivore conservation. Nat. Commun. 11, 2957 (2020).Article 
PubMed Central 
CAS 
PubMed 

Google Scholar 
Lenoir, J. et al. Species better track the shifting isotherms in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 
PubMed 

Google Scholar 
van Teeffelen, A., Meller, L., van Minnen, J., Vermaat, J. & Cabeza, M. How climate proof is the European Union’s biodiversity policy? Regional Environ. Change 15, 997–1010 (2015).Article 

Google Scholar 
Thomas, C. D. & Gillingham, P. K. The performance of protected areas for biodiversity under climate change. Biol. J. Linn. Soc. Lond. 115, 718–730 (2015).Article 

Google Scholar 
Gillingham, P. K. et al. The effectiveness of protected areas in the conservation of species with changing geographical ranges. Biol. J. Linn. Soc. Lond. 115, 707–717 (2015).Article 

Google Scholar 
Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).Article 

Google Scholar 
Stokstad, E. Species? Climate? Cost? Ambitious goal means trade-offs. Science 371, 555 (2021).Article 
CAS 
PubMed 

Google Scholar 
Brlík, V. et al. Long-term and large-scale multispecies dataset tracking population changes of common European breeding birds. Sci. Data 8, 21 (2021).Article 
PubMed Central 
PubMed 

Google Scholar 
Stanbury, A. et al. The status of bird populations: the fifth Birds of Conservation Concern in the United Kingdom, Channel Islands and Isle of Man and second IUCN Red List assessment of extinction risk for Great Britain. Br. Birds 114, 723–747 (2021).
Google Scholar 
Dudley, N. (ed). Guidelines for Applying Protected Area Management Categories (IUCN, 2008).Deguignet, M. et al. Measuring the extent of overlaps in protected area designations. PLoS ONE 12, e0188681 (2017).Article 
PubMed Central 
PubMed 

Google Scholar 
JNCC. Common Standards Monitoring: Introduction to the Guidance Manual (JNCC Resource Hub, 2004).Hayhow, D. B. et al. State of Nature 2019 (RSPB, 2019).Schleicher, J. et al. Statistical matching for conservation science. Conserv. Biol. 34, 538–549 (2019).Article 
PubMed Central 
PubMed 

Google Scholar 
Waldron, A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (Campaign for Nature, 2020); https://helda.helsinki.fi/handle/10138/326470Franks, S. E., Roodbergen, M., Teunissen, W., Carrington Cotton, A. & Pearce‐Higgins, J. W. Evaluating the effectiveness of conservation measures for European grassland‐breeding waders. Ecol. Evol. 8, 10555–10568 (2018).Article 
PubMed Central 
PubMed 

Google Scholar 
Pearce-Higgins, J. W. et al. Site-based adaptation reduces the negative effects of weather upon a southern range margin Welsh black grouse Tetrao tetrix population that is vulnerable to climate change. Clim. Change 153, 253–265 (2019).Article 

Google Scholar 
Jellesmark, S. et al. A counterfactual approach to measure the impact of wet grassland conservation on U.K. breeding bird populations. Conserv. Biol. 35, 1575–1585 (2021).Article 
PubMed 

Google Scholar 
Morrison, C. A. et al. Covariation in population trends and demography reveals targets for conservation action. Proc. Biol. Sci. 288, 20202955 (2021).PubMed Central 
PubMed 

Google Scholar 
Donald, P. F. et al. International conservation policy delivers benefits for birds in Europe. Science 317, 810–813 (2007).Article 
CAS 
PubMed 

Google Scholar 
Martay, B. et al. Monitoring landscape-scale environmental changes with citizen scientists: Twenty years of land use change in Great Britain. J. Nat. Conserv. 44, 33–42 (2018).Article 

Google Scholar 
Sullivan, M. J. P., Newson, S. E. & Pearce‐Higgins, J. W. Changing densities of generalist species underlie apparent homogenization of UK bird communities. Ibis 158, 645–655 (2016).Article 

Google Scholar 
Wauchope, H. S. et al. Evaluating impact using time-series data. Trends Ecol. Evol. 36, 196–205 (2021).Article 
PubMed 

Google Scholar 
Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Change 2, 121–124 (2012).Article 

Google Scholar 
Lehikoinen, P., Santangeli, A., Jaatinen, K., Rajasärkkä, A. & Lehikoinen, A. Protected areas act as a buffer against detrimental effects of climate change—evidence from large‐scale, long‐term abundance data. Glob. Change Biol. 25, 304–313 (2019).Article 

Google Scholar 
Gaüzère, P., Jiguet, F. & Devictor, V. Can protected areas mitigate the impacts of climate change on bird’s species and communities? Diversity Distrib. 22, 625–637 (2016).Article 

Google Scholar 
Neate‐Clegg, M. H. C., Jones, S. E. I., Burdekin, O., Jocque, M. & Şekercioğlu, Ç. H. Elevational changes in the avian community of a Mesoamerican cloud forest park. Biotropica 50, 805–815 (2018).Article 

Google Scholar 
Oliver, T. H. et al. Large extents of intensive land use limit community reorganization during climate warming. Glob. Change Biol. 23, 2272–2283 (2017).Article 

Google Scholar 
Hiley, J. R., Bradbury, R. B., Holling, M. & Thomas, C. D. Protected areas act as establishment centres for species colonizing the UK. Proc. Biol. Sci. 280, 20122310 (2013).PubMed Central 
PubMed 

Google Scholar 
Thomas, C. D. et al. Protected areas facilitate species’ range expansions. Proc. Natl Acad. Sci. USA 109, 14063–14068 (2012).Article 
PubMed Central 
CAS 
PubMed 

Google Scholar 
Grace, M. K. et al. Testing a global standard for quantifying species recovery and assessing conservation impact. Conserv. Biol. 35, 1833–1849 (2021).Article 
PubMed 

Google Scholar 
Gibbons, D. W., Reid, J. B. & Chapman, R. A. The New Atlas of Breeding Birds in Britain & Ireland 1988–1991 (T. & A. D. Poyser, 1993).Balmer, D. E. et al. Bird Atlas 2007–11: the Breeding and Wintering Birds of Britain and Ireland (BTO, 2013).Gillings, S. et al. Breeding and wintering bird distributions in Britain and Ireland from citizen science bird atlases. Glob. Ecol. Biogeogr. 28, 866–874 (2019).Article 

Google Scholar 
Freeman, S. N., Noble, D. G., Newson, S. E. & Baillie, S. R. Modelling population changes using data from different surveys: the Common Birds Census and the Breeding Bird Survey. Bird Study 54, 61–72 (2007).Article 

Google Scholar 
Robinson, R. A., Julliard, R. & Saracco, J. F. Constant effort: studying avian population processes using standardised ringing. Ring. Migr. 24, 199–204 (2009).Article 

Google Scholar 
Cave, V. M., Freeman, S. N., Brooks, S. P., King, R. & Balmer, D. E. in Modeling Demographic Processes in Marked Populations, 949–963 (Springer, 2009).Rowland, C. S. et al. Land Cover Map 2015 (1km Percentage Aggregate Class, GB) (eds Thomson, D. L. et al) (Environmental Information Data Centre, 2017); https://doi.org/10.5285/7115bc48-3ab0-475d-84ae-fd3126c20984Rowland, C. S. et al. Land Cover Map 2015 (1km Percentage Aggregate Class, N. Ireland) (Environmental Information Data Centre, 2017); https://doi.org/10.5285/362feaea-0ccf-4a45-b11f-980c6b89a858ASTER Global Digital Elevation Model V003 (dataset). NASA EOSDIS Land Processes DAAC (NASA/METI/AIST/Japan Space Systems and U.S./Japan ASTER Science Team, 2019); https://doi.org/10.5067/ASTER/ASTGTM.003Schiavina, M., Freire, S. & MacManus, K. GHS-SMOD R2019A – GHS Settlement Layers, Updated and Refined REGIO Model 2014 in Application to GHS-BUILT R2018A and GHS-POP R2019A, Multitemporal (1975-1990-2000-2015) (European Commission Joint Research Centre, 2019); https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218Robinson, R. A. BirdFacts: Profiles of Birds Occurring in Britain & Ireland (BTO, 2005).Gibbons, D. W. et al. Bird species of conservation concern in the United Kingdom, Channel Islands and Isle of Man: revising the Red Data List. RSPB Conserv. Rev. 10, 7–18 (1996).
Google Scholar 
Stone, B. H. et al. Population estimates of birds in Britain and in the United Kingdom. Br. Birds 90, 1–22 (1997).
Google Scholar 
Woodward, I. et al. Population estimates of birds in Great Britain and the United Kingdom. Br. Birds 113, 69–104 (2020).
Google Scholar 
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).Article 
PubMed Central 
PubMed 

Google Scholar 
Bull, J. W., Strange, N., Smith, R. J. & Gordon, A. Reconciling multiple counterfactuals when evaluating biodiversity conservation impact in social‐ecological systems. Conserv. Biol. 35, 510–521 (2020).Article 
PubMed 

Google Scholar 
Jellesmark, S. et al. Assessing the global impact of targeted conservation actions on species abundance. Preprint at bioRxiv https://doi.org/10.1101/2022.01.14.476374 (2022).Wauchope, H. S. et al. Protected areas have a mixed impact on waterbirds but management helps. Nature 605, 103–107 (2022).Article 
CAS 
PubMed 

Google Scholar 
Ho, D. E., Imai, K., King, G. & Stuart, E. A. MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42, 1–28 (2011).Article 

Google Scholar 
Wood, S. N. Generalized Additive Models: an Introduction with R 2nd edn (Chapman and Hall/CRC, 2017).Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package v.0.4.4 (2021); https://CRAN.R-project.org/package=DHARMaJetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).Article 
CAS 
PubMed 

Google Scholar 
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).Article 
CAS 
PubMed 

Google Scholar 
Johnston, A. et al. Species traits explain variation in detectability of UK birds. Bird Study 61, 340–350 (2014).Article 

Google Scholar 
Hill, M. O. Diversity and evenness: a unifying notation and its consequences. Ecology 54, 427–432 (1973).Article 

Google Scholar 
Ho, D. E., Imai, K., King, G. & Stuart, E. A. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Anal. 15, 199–236 (2007).Article 

Google Scholar 
Julliard, R., Clavel, J., Devictor, V., Jiguet, F. & Couvet, D. Spatial segregation of specialists and generalists in bird communities. Ecol. Lett. 9, 1237–1244 (2006).Article 
PubMed 

Google Scholar 
Devictor, V., Julliard, R., Couvet, D. & Jiguet, F. Birds are tracking climate warming, but not fast enough. Proc. Biol. Sci. 275, 2743–2748 (2008).PubMed Central 
PubMed 

Google Scholar  More

Global statistics on declining biodiversity can give the impression that every population of every species is in a downward spiral. In fact, many populations are stable or growing, while a small number of species faces truly existential challenges. These photos capture some specific crises. They are images of threats unfolding, of desperate attempts at species defence and of the beautiful living world that is at stake.
The 15th United Nations Biodiversity Conference, COP15, opens in Montreal, Canada, on 7 December. At the meeting, delegates will attempt to agree on goals for stabilizing species’ declines by 2030 and reverse them by mid-century. The current draft framework agreement promises nothing less than a “transformation in society’s relationship with biodiversity”.
Help for the kelp. Tasmania’s forests of giant kelp (Macrocystis pyrifera) are dying as climate change shifts ocean currents, bringing warm water to the east coast of the temperate Australian island. The kelp forests host an entire ecosystem, including abalone and crayfish — both economically important species and part of local food culture. Now, researchers at the Institute for Marine and Antarctic Studies in Hobart are breeding kelp plants that can tolerate warmer conditions, and replanting them along the coast — a trial for what they hope will become a landscape-scale restoration. More

Machado, D. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Price, M. Erroneous predictions of auxotrophies by CarveMe. https://doi.org/10.1038/s41559-022-01936-3 (2022).Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Price, M. N., Deutschbauer, A. M. & Arkin, A. P. GapMind: automated annotation of amino acid biosynthesis. mSystems 5, e00291-20 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Mee, M. T., Collins, J. J., Church, G. M. & Wang, H. H. Syntrophic exchange in synthetic microbial communities. Proc. Natl. Acad. Sci. USA 111, E2149–E2156 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ponomarova, O. et al. Yeast creates a niche for symbiotic lactic acid bacteria through nitrogen overflow. Cell Syst. 5, 345–357.e6 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zengler, K. & Zaramela, L. S. The social network of microorganisms—how auxotrophies shape complex communities. Nat. Rev. Microbiol. 16, 383–390 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Giri, S. et al. Metabolic dissimilarity determines the establishment of cross-feeding interactions in bacteria. Curr. Biol. 31, 5547–5557.e6 (2021).Article 
CAS 
PubMed 

Google Scholar 
Morris, J. J., Lenski, R. E. & Zinser, E. R. The black queen hypothesis: evolution of dependencies through adaptive gene loss. mBio 3, e00036-12 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Campbell, K. et al. Self-establishing communities enable cooperative metabolite exchange in a eukaryote. eLife 4, e09943 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
D’Souza, G. & Kost, C. Experimental evolution of metabolic dependency in bacteria. PLOS Genet. 12, e1006364 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Ziesack, M. et al. Engineered interspecies amino acid cross-feeding increases population evenness in a synthetic bacterial consortium. mSystems 4, e00352-19 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Ryback, B., Bortfeld-Miller, M. & Vorholt, J. A. Metabolic adaptation to vitamin auxotrophy by leaf-associated bacteria. ISME J. https://doi.org/10.1038/s41396-022-01303-x (2022). More

Ah-King, M. Flexible mate choice in Encyclopedia of Animal Behavior, 2nd edn Vol. 4 (ed Jae Chun Choe) 421–431 (Academic Press, 2019).Harestad, A. S. & Bunnell, F. L. Home range and body weight—A reevaluation. Ecology 60, 389–402 (1979).Article 

Google Scholar 
O’Neill, R. V., Milne, B. T., Turner, M. G. & Gardner, R. H. Resource utilization scales and landscape pattern. Landsc. Ecol. 2, 63–69 (1988).Article 

Google Scholar 
Tricas, T. C. Determinants of feeding territory size in the corallivorous butterflyfish, Chaetodon multicinctus. Anim. Behav. 37, 830–841. https://doi.org/10.1016/0003-3472(89)90067-5 (1989).Article 

Google Scholar 
Tremblay, I., Thomas, D., Blondel, J., Perret, P. & Lambrechts, M. M. The effect of habitat quality on foraging patterns, provisioning rate and nestling growth in Corsican Blue Tits Parus caeruleus. Ibis 147, 17–24. https://doi.org/10.1111/j.1474-919x.2004.00312.x (2005).Article 

Google Scholar 
Watts, D. P. The influence of male mating tactics on habitat use in Mountain Gorillas (Gorilla gorilla beringei). Primates 35, 35–47. https://doi.org/10.1007/BF02381484 (1994).Article 

Google Scholar 
Lescroël, A. et al. Working less to gain more: when breeding quality relates to foraging efficiency. Ecology 91, 2044–2055. https://doi.org/10.1890/09-0766.1 (2010).Article 
PubMed 

Google Scholar 
Tufto, J., Anderson, R. & Linnell, J. Habitat use and ecological correlates of home range size in a small cervid: the roe deer. J. Anim. Ecol. 65, 715–724. https://doi.org/10.2307/5670 (1996).Article 

Google Scholar 
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. https://doi.org/10.1111/1365-2656.12105 (2013).Article 
PubMed 

Google Scholar 
Anderson, D. P. et al. Scale-dependent summer resource selection by reintroduced elk in Wisconsin, USA. J. Wildl. Manag. 69, 298–310. https://doi.org/10.2193/0022-541X(2005)069%3c0298:SSRSBR%3e2.0.CO;2 (2005).Article 

Google Scholar 
Olsson, P. M. O. et al. Movement and activity patterns of translocated elk (Cervus elaphus nelsoni) on an active coal mine in Kentucky. Wildl. Biol. Pract. 3, 1–8. https://doi.org/10.2461/wbp.2007.3.1 (2007).Article 

Google Scholar 
Porter, W. P., Sabo, J. L., Tracy, C. R., Reichman, O. J. & Ramankutty, N. Physiology on a landscape scale: plant–animal interactions. Integr. Comp. Biol. 42, 431–453. https://doi.org/10.1093/icb/42.3.431 (2002).Article 
PubMed 

Google Scholar 
Berg, J. E. et al. Mothers’ movements: shifts in calving area selection by partially migratory elk. J. Wildl. Manag. 85, 1476–1489. https://doi.org/10.1002/jwmg.22099 (2021).Article 

Google Scholar 
Lehman, C. P. et al. Elk resource selection at parturition sites, Black Hills, South Dakota. J. Wildl. Manag. 80, 465–478. https://doi.org/10.1002/jwmg.1017 (2016).Article 

Google Scholar 
Johnson, B. K., Kern, J. W., Wisdom, M. J., Findholt, S. L. & Kie, J. G. Resource selection and spatial separation of mule deer and elk during spring. J. Wildl. Manag. 64, 685–697. https://doi.org/10.2307/3802738 (2000).Article 

Google Scholar 
Grace, J. & Easterbee, N. The natural shelter for red deer (Cervus elaphus) in a Scottish glen. J. Appl. Ecol. 16, 37–48. https://doi.org/10.2307/2402726 (1979).Article 

Google Scholar 
Demarchi, M. W. & Bunnell, F. L. Estimating forest canopy effects on summer thermal cover for Cervidae (deer family). Can. J. For. Res. 23, 2419–2426. https://doi.org/10.1139/x93-299 (1993).Article 

Google Scholar 
Parker, K. L. & Gillingham, M. P. Estimates of critical thermal environments for mule deer. J. Range. Manag. 43, 73–81 (1990).Article 

Google Scholar 
Proffitt, K. M. et al. Changes in elk resource selection and distributions associated with a late-season elk hunt. J. Wildl. Manag. 74, 210–218. https://doi.org/10.2193/2008-593 (2010).Article 

Google Scholar 
Webb, S. L., Dzialak, M. R., Harju, S. M., Hayden-Wing, L. D. & Winstead, J. B. Influence of land development on home range use dynamics of female elk. Wildl. Res. 38, 163–167. https://doi.org/10.1071/WR10101 (2011).Article 

Google Scholar 
Rumble, M. A., Benkobi, L. & Gamo, R. S. Elk responses to humans in a densely roaded area. Intermt. J. Sci. 11, 10–24 (2005).
Google Scholar 
McCorquodale, S. M. Sex-specific movements and habitat use by elk in the Cascade Range of Washington. J. Wildl. Manag. 67, 729–741. https://doi.org/10.1890/15-1607.1 (2003).Article 

Google Scholar 
Saïd, S. & Servanty, S. The influence of landscape structure on female roe deer home-range size. Landsc. Ecol. 20, 1003–1012. https://doi.org/10.1007/s10980-005-7518-8 (2005).Article 

Google Scholar 
Seddon, P. J., Armstrong, D. P. & Maloney, R. F. Developing the science of reintroduction biology. Conserv. Biol. 21, 303–312. https://doi.org/10.1111/j.1523-1739.2006.00627.x (2007).Article 
PubMed 

Google Scholar 
Hale, S. L. & Koprowski, J. L. Ecosystem-level effects of keystone species reintroduction: a literature review. Restor. Ecol. 26, 439–445. https://doi.org/10.1111/rec.12684 (2018).Article 

Google Scholar 
Cheyne, S. M. Wildlife reintroduction: considerations of habitat quality at the release site. BMC Ecol. 6, 5. https://doi.org/10.1186/1472-6785-6-5 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
Hegel, T. M., Gates, C. C. & Eslinger, D. The geography of conflict between elk and agricultural values in the Cypress Hills, Canada. J. Eniron. Manag. 90, 222–235. https://doi.org/10.1016/j.jenvman.2007.09.005 (2009).Article 

Google Scholar 
Walter, W. D. et al. Management of damage by elk (Cervus elaphus) in North America: a review. Wildl. Res. 37, 630–646. https://doi.org/10.1071/WR10021 (2010).Article 

Google Scholar 
Jung, T. S. Extralimital movements of reintroduced bison (Bison bison): implications for potential range expansion and human–wildlife conflict. Eur. J. Wildl. Res. 63, 35. https://doi.org/10.1007/s10344-017-1094-5 (2017).Article 

Google Scholar 
Buchholtz, E. K., Stronza, A., Songhurst, A., McCulloch, G. & Fitzgerald, L. A. Using landscape connectivity to predict human-wildlife conflict. Biol. Conserv. 248, 108677. https://doi.org/10.1016/j.biocon.2020.108677 (2020).Article 

Google Scholar 
Hodgson, J. A., Moilanen, A., Wintle, B. A. & Thomas, C. D. Habitat area, quality and connectivity: striking the balance for efficient conservation. J. Appl. Ecol. 48, 148–152. https://doi.org/10.1111/j.1365-2664.2010.01919.x (2011).Article 

Google Scholar 
Murie, O. The Elk of North America (Stackpole Co., 1951).
Google Scholar 
VDWR. Virginia elk management plan 2019–2028 (ed Virginia Department of Wildlife Resources) (Virginia Department of Wildlife Resources, 2019).Lituma, C. M. et al. Terrestrial wildlife in the post-mined Appalachian landscape: status and opportunities in Appalachia’s Coal-Mined Landscapes (eds Carl E. Zipper & Jeff Skousen) 135–166 (Springer, 2021).Lupardus, J. L., Muller, L. I. & Kindall, J. L. Seasonal forage availability and diet for reintroduced elk in the Cumberland Mountains, Tennessee. Southeast. Nat. 10, 53–74. https://doi.org/10.1656/058.010.0105 (2011).Article 

Google Scholar 
Schneider, J. et al. Food habits of reintroduced elk in southeastern Kentucky. Southeast. Nat. 5, 535–546. https://doi.org/10.1656/1528-7092(2006)5[535:Fhorei]2.0.Co;2 (2006).Article 

Google Scholar 
Smith, T. N., Keller, B. J., Chitwood, M. C., Hansen, L. P. & Millspaugh, J. J. Diet composition and selection of recently reintroduced elk in Missouri. Am. Midl. Nat. 180, 143–159. https://doi.org/10.1674/0003-0031-180.1.143 (2018).Article 

Google Scholar 
Franklin, J. A., Zipper, C. E., Burger, J. A., Skousen, J. G. & Jacobs, D. F. Influence of herbaceous ground cover on forest restoration of eastern US coal surface mines. New. For. 43, 905–924. https://doi.org/10.1007/s11056-012-9342-8 (2012).Article 

Google Scholar 
Popp, J. N., Toman, T., Mallory, F. F. & Hamr, J. A century of elk restoration in eastern North America. Restor. Ecol. 22, 723–730. https://doi.org/10.1111/rec.12150 (2014).Article 

Google Scholar 
Cook, J. G., Irwin, L. L., Bryant, L. D., Riggs, R. A. & Thomas, J. W. Relations of forest cover and condition of elk: a test of the thermal cover hypothesis in the summer and winter. Wildl. Monogr. 141, 3–61 (1998).
Google Scholar 
Parker, K. L. & Robbins, C. T. Thermoregulation in mule deer and elk. Can. J. Zool. 62, 1409–1422. https://doi.org/10.1139/z84-202 (1984).Article 

Google Scholar 
Mao, J. S. et al. Habitat selection by elk before and after wolf reintroduction in Yellowstone National Park. J. Wildl. Manag. 69, 1691–1707. https://doi.org/10.2193/0022-541X (2005).Article 

Google Scholar 
Wolff, J. O. & Van Horn, T. Vigilance and foraging patterns of American elk during the rut in habitats with and without predators. Can. J. Zool. 81, 266–271. https://doi.org/10.1139/z03-011 (2003).Article 

Google Scholar 
Beck, J. L. & Peek, J. M. Diet composition, forage selection, and potential for forage competition among elk, deer, and livestock on aspen–sagebrush summer range. Rangel. Ecol. Manag. 58, 135–147. https://doi.org/10.2111/03-13.1 (2005).Article 

Google Scholar 
Ford, W. M., Johnson, A. S. & Hale, P. E. Nutritional quality of deer browse in southern Appalachian clearcuts and mature forests. For. Ecol. Manag. 67, 149–157. https://doi.org/10.1016/0378-1127(94)90013-2 (1994).Article 

Google Scholar 
Sikes, R. S., Gannon, W. L. & The American Care and Use Committee of the American Society of Mammalogists. Guidelines of the American Society of Mammalogists for the use of wild mammals in research. J. Mammal. 92, 235–253. https://doi.org/10.1644/10-mamm-f-355.1 (2011).Percie du Sert, N. et al. The ARRIVE guidelines 2.0: updated guidelines for reporting animal research. PLoS Biol. 18, e3000410. https://doi.org/10.1371/journal.pbio.3000410 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Powell, J. W. Physiographic Regions of the United States. (American Book Company, 1895).Braun, E. L. Forests of the Cumberland Mountains. Ecol. Monogr. 12, 413–447. https://doi.org/10.2307/1943039 (1942).Article 

Google Scholar 
Clark, J. B. The Vascular Flora of Breaks Interstate Park, Pike County, Kentucky, and Dickenson County, Virginia Master of Science thesis, Eastern Kentucky University (2012).Pericak, A. A. et al. Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine. PLoS ONE 13, e0197758. https://doi.org/10.1371/journal.pone.0197758 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Boettner, F. et al. An assessment of the natural assets in the Appalachian Region: forest resources (ed Appalachian Regional Commission Report) 97 (Washington, DC, 2014).NOAA. Summary of monthly normals Grundy, VA 1991 – 2020 data (National Oceanic and Atmospheric Administration (2022).U.S. Geological Survey (USGS) Gap Analysis Project (GAP). GAP/LANDFIRE national terrestrial ecosystems 2011: U.S. Geological Survey data release (2016).Clark, M. The Nature Conservancy Eastern Division & North Atlantic Landscape Conservation Cooperative. Terrestrial habitat, Northeast data (2017).ESRI. ArcGIS desktop version 10.8.1 (Environmental Systems Research Institute, 2020).Ford, W. M. et al. Influence of elevation and forest type on community assemblage and species distribution of shrews in the central and southern Appalachians in Advances in the Biology of the Shrews II Vol. 1(eds. J.F. Merritt, S. Churchfield, R. Hutterer and B.A. Sheftel) 303–315(Special Publication of the International Society of Shrew Biologists, 2006).Kniowski, A. B. & Ford, W. M. Predicting intensity of white-tailed deer herbivory in the Central Appalachian Mountains. J. For. Res. 29, 841–850. https://doi.org/10.1007/s11676-017-0476-6 (2018).Article 

Google Scholar 
Fleming, C. H. & Calabrese, J. M. ctmm: continuous-time movement modeling. R package version 0.6.0 (2021).R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).Fleming, C. H. et al. Estimating where and how animals travel: an optimal framework for path reconstruction from autocorrelated tracking data. Ecology 97, 576–582. https://doi.org/10.1890/15-1607.1 (2016).Article 
PubMed 

Google Scholar 
Hijmans, R. J. raster: geographic data analysis and modeling. R package version 3.4-5 (2020).Becker, R. A., Chambers, J. M. & Wilks, A. R. The New S Language (Wadsworth and Brooks/Cole, 1988).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. B. H. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Inference: A Practical Use of the Information-Theoretic Approach (Springer, 1998).Turner, M. G., Wu, Y., Romme, W. H. & Wallace, L. L. A landscape simulation model of winter foraging by large ungulates. Ecol. Modell. 69, 163–184. https://doi.org/10.1016/0304-3800(93)90026-O (1993).Article 

Google Scholar 
Taper, M. L. & Gogan, P. J. P. The northern Yellowstone elk: density dependence and climatic conditions. J. Wildl. Manag. 66, 106–122. https://doi.org/10.2307/3802877 (2002).Article 

Google Scholar 
Green, R. A. & Bear, G. D. Seasonal cycles and daily activity patterns of Rocky Mountain elk. J. Wildl. Manag. 54, 272–279. https://doi.org/10.2307/3809041 (1990).Article 

Google Scholar 
Craighead, J. J., Craighead, F. C. J., Ruff, R. L. & O’Gara, B. W. Home ranges and activity patterns of nonmigratory elk of the Madison Drainage herd as determined by biotelemetry. Wildl. Monogr. 33, 3–50 (1973).
Google Scholar 
Gittleman, J. L. & Thompson, S. D. Energy allocation in mammalian reproduction. Am. Zool. 28, 863–875. https://doi.org/10.1093/icb/28.3.863 (1988).Article 

Google Scholar 
Beier, P. & McCullough, D. R. Factors influencing white-tailed deer activity patterns and habitat use. Wildl. Monogr. 109, 3–51 (1990).
Google Scholar 
Ciuti, S., Davini, S., Luccarini, S. & Apollonio, M. Variation in home range size of female fallow deer inhabiting a sub-Mediterranean habitat. Rev. Ecol. 58, 381–395 (2003).
Google Scholar 
Vore, J. M. & Schmidt, E. M. Movements of female elk during calving season in northwest Montana. Wildl. Soc. Bull. 29, 720–725 (2001).
Google Scholar 
Wickstrom, M. L., Robbins, C. T., Hanley, T. A., Spalinger, D. E. & Parish, S. M. Food intake and foraging energetics of elk and mule deer. J. Wildl. Manag. 48, 1285–1301. https://doi.org/10.2307/3801789 (1984).Article 

Google Scholar 
Van Soest, P. J. Allometry and ecology of feeding behavior and digestive capacity in herbivores: a review. Zoo. Biol. 15, 455–479 (1996). https://doi.org/10.1002/(SICI)1098-2361(1996)15:53.0.CO;2-AEsmaeili, S. et al. Body size and digestive system shape resource selection by ungulates: a cross-taxa test of the forage maturation hypothesis. Ecol. Lett. 24, 2178–2191. https://doi.org/10.1111/ele.13848 (2021).Article 
PubMed 

Google Scholar 
Demment, M. W. & Van Soest, P. J. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125, 641–672 (1985).Article 

Google Scholar 
Anderson, D. P. et al. Factors influencing female home range sizes in elk (Cervus elaphus) in North American landscapes. Landsc. Ecol. 20, 257–271. https://doi.org/10.1007/s10980-005-0062-8 (2005).Article 

Google Scholar 
Maigret, T. A., Cox, J. J. & Yang, J. Persistent geophysical effects of mining threaten ridgetop biota of Appalachian forests. Front. Ecol. Environ. 17, 85–91. https://doi.org/10.1002/fee.1992 (2019).Article 

Google Scholar 
Beier, P. Sex differences in quality of white-tailed deer diets. J. Mammal. 68, 323–329. https://doi.org/10.2307/1381471 (1987).Article 

Google Scholar 
Parker, K. L., Barboza, P. S. & Gillingham, M. P. Nutrition integrates environmental responses of ungulates. Funct. Ecol. 23, 57–69. https://doi.org/10.1111/j.1365-2435.2008.01528.x (2009).Article 

Google Scholar 
Wichrowski, M. W., Maehr, D. S., Larkin, J. L., Cox, J. J. & Olsson, M. P. O. Activity and movements of reintroduced elk in southeastern Kentucky. Southeast. Nat. 4, 365–374. https://doi.org/10.1656/1528-7092(2005)004[0365:Aamore]2.0.Co;2 (2005).Article 

Google Scholar 
Relyea, R. A., Lawrence, R. K. & Demarais, S. Home range of desert mule deer: testing the body-size and habitat-productivity hypotheses. J. Wildl. Manag. 64, 146–153. https://doi.org/10.2307/3802984 (2000).Article 

Google Scholar  More

Whether land acts as a carbon sink or source depends largely on two opposite fluxes: carbon uptake through photosynthesis and carbon release through turnover. Turnover occurs through multiple processes, including but not limited to, leaf senescence, tree mortality, and respiration by plants, microbes, and animals. Each of these processes is sensitive to climate, and ecologists and climatologists have been working to figure out how temperature regulates biological activities and to what extent the carbon cycle responds to global warming. Previous theoretical and experimental studies have yielded conflicting relationships between temperature and carbon turnover, with large variations across ecosystems, climate and time-scale1,2,3,4. Writing in Nature Geoscience, Fan et al.5 find that hydrometeorological factors have an important influence on how the turnover time of land carbon responds to changes in temperature. More

It is well known that parrots are excellent vocal learners; here we quantified that ability across a wide variety of species, using human mimicry as a proxy for vocal learning of natural repertoires. Results confirm that parrot vocal mimicry varies substantially both within and among species22. Parrot age, social interactions, and sex do not appear to be universal drivers of vocal learning ability within the order Psittaciformes, but all of these factors may have effects within individual species.Vocal learning variation by speciesWithin species, mimicry sound repertoires are extremely variable bird to bird; for example, our data indicate that a grey parrot may mimic anywhere from 0 to 600 different human words. Many other species showed smaller repertoires but similar variability. It is not entirely clear whether this range of variation would be present in natural sounds within wild parrot populations, but research has demonstrated intraspecific repertoire size variation in multiple species of parrots30,31.The vast majority of parrots presented a pattern in which their repertoire size was largest for words, intermediate for phrases (composed of the reported words), and smallest for non-linguistic sounds (Fig. 2). In the wild, parrots mimic the most socially relevant vocalizations, and presumably do so in captivity as well15. Thus, the spoken word and phrase interactions with their human “flock” likely reflect the most socially relevant cues. The interesting exceptions to this pattern were Fischer’s lovebirds, cockatiels, and Senegal parrots who all used more sounds than phrases. Cockatiels are well-known in the pet world to be excellent whistlers, and thus it was satisfying to see that our data support that informal information. We suspect that deviations from the typical patterns may represent acoustic learning preferences, templates, or limitations32.Although individual variation was substantial, we nevertheless saw strong evidence that overall vocal learning abilities differed by species. Pacific parrotlets and sun parakeets showed very limited human mimicry, while grey parrots, Amazona parrots, cockatoos, and macaws were generally very accomplished mimics. The patterns that we documented appeas to reflect natural vocal repertoire variation across species. The documented calls of wild parrots generally range from 5 to 15 calls25,33,34,35,36. Several species, however, present additional complexity: yellow-naped parrots (Amazona auropalliata), palm cockatoos (Probosciger aterrimus), and grey parrots all have natural repertoires of more than 25 discrete elements, with additional elements given in duets13,27,37 Members of these three groups, grey parrots, Amazona parrots and cockatoos also had relatively large repertoires in our study. In several of these species (particularly grey parrots) our measure of mimicked “words” (60) was higher than estimates of natural call “elements” (39) in the literature27. This discrepancy suggests that parrots are capable of learning vocalizations with more than 25 elements and, simultaneously, might reflect a sampling bias wherein survey-takers are more likely to report on individuals with high mimicry ability.Parrot species varied in their tendency to improvise new combinations of elements, although most species did rearrange words to some degree. Research shows that parrot vocalization length and structure carry signal content, so there may be selective pressures favoring this ability24,33. If so, then our data suggest that those pressures are strongest in some cockatoos and weakest in sun parakeets and green-cheeked parakeets. In general, species with larger repertoires also showed more vocal flexibility (Fig. 2, Appendix 6). Additionally, wild birds typically use particular vocalizations in set contexts, so the ability to do so is likely to be adaptive24. Previous studies of captive parrots have demonstrated contextual use of mimicked words, both in tutored lab settings and in home-raised birds28,38. In our sample, contextual use of learned sounds was supported across 89% of individuals and most species. Survey-taker responses on this topic are necessarily subjective, so we emphasize that this rate of contextual use should be interpreted as a general estimate. Nevertheless, the data indicated that parrots frequently associated mimicked human sounds with appropriate human contexts. This finding is particularly revealing because the relevant human contexts are, by their nature, outside the range of typical wild parrot experiences. Contextual vocalization use must, therefore, rely on extremely flexible vocal learning mechanisms.Vocal learning variation by ageOn average, birds aged with high confidence were younger than those aged with low or medium confidence. This pattern might indicate that people tend to overestimate the age of captive birds of uncertain age. This pattern might also reflect the facts that older birds are more likely to be wild-caught and that younger birds are more likely to have good hatch-date documentation. In either case, there are few ramifications of inaccurate age estimates relating to vocal behavior because our data gave no evidence that adult vocal mimicry repertoires varied with age. Our analyses of grey parrots confirmed that repertoires expanded through the juvenile phase, but did not show reliable expansion among adults. Studies of wild birds indicate that parrots can learn vocalizations throughout life; such open-ended learning is limited to a subset of vocal learning species, and can generate different outcomes as animals age15. In some species, animals can add new vocal features over the course of a lifetime, leading to repertoire expansion39,40. In other species, animals may replace parts of their repertoire with newly-learned vocalizations, leading to stable vocal production repertoire sizes across age groups39,41. Our data suggest that parrots fit the second pattern; although they are open-ended vocal learners, their adult repertoires change more by element replacement, than by expansion. This does not necessarily imply that vocalizations are “forgotten” through time, but merely that some sounds are no longer used as conditions change42. Many parrot vocalizations function in social coordination with flock-mates22. The fission–fusion nature of parrot flocks creates changing social conditions for each individual over its lifetime43. A vocal replacement model for repertoire learning would allow individuals to adjust their vocal signatures to match new social situations and stop producing vocalizations that are no longer socially relevant11,44.Vocal learning variation by sexOur analyses of the full data set confirmed the generally held understanding that males and females in most species of parrots have similar vocal learning abilities15. We did, however see sex differences in some species that merit future study. First, we found a substantial overrepresentation of males in our sample. This could be interpreted several ways; (1) there are legitimately more males in the parrot pet trade, (2) pet owners are giving us accurate data but are more likely to give us data on males or (3) some bias exists in which pet owners assume their talking parrots are males, rather than females. Possibilities 1 and 2 seem unlikely because after we eliminated all parrots sexed with low confidence, we were left with a nearly 1:1 ratio of males:females in the subset of parrots that were sexed with high confidence. That trend suggests that the male bias in our data comes (at least in part) from a human tendency to label their pet parrots as male when the sex is not clear. Among songbirds, there is a strong tendency to assume that singing birds are male, and a similar bias may hold true for parrots45. It is unclear whether parrots in this study were mislabeled as male because they vocalize or, more simply, because that is the default human tendency for any animal.Although we conclude that some of the male bias in our data is human error, we also saw patterns that suggest real sex differences in vocal learning some species. For example, Pacific parrotlets are a dimorphic species, and all of our sampled birds were sexed by plumage46. Thus, we expect sexing in this species to be fairly accurate. Our data set included 10 males and no females, a bias unlikely to result purely from sampling error. We saw a similar trend in cockatiels for which there was a large overabundance of males in the data set, even among the 17 birds sexed with high confidence. Humans may be more likely to report on parrots that are good mimics. Therefore, the results likely reflect a real-world tendency for male cockatiels to mimic more human sounds than females. Figure 3 suggests that the same might be true for galahs, sulphur-crested cockatoos, rose-ringed parakeets, Senegal parrots, and budgerigars. Existing research supports the idea that sex differences in vocal behavior are important in several of these species. Among galahs, male and female calls evoke different responses47, and patterns of call adjustment vary by sex among budgerigars20. We also note that several of these species (Pacific parrotlets, rose-ringed parakeets, budgerigars, and cockatiels; Appendix 2b) show sex-based differences in both plumage and vocal learning, raising questions about whether those traits co-evolve.In addition to sex-based differences in the tendency to mimic humans, several well-sampled species showed evidence of sex-based differences in repertoire sizes. Particularly interesting are the blue-and-yellow macaws, in which repertoire size was significantly male-biased. We had more females (15) than males (9) in the data set, but males used on average 3–4 times as many mimicry sounds, phrases and words as females did. Galahs and budgerigars showed a similar male-bias in repertoire sizes, matching the trend of males being overrepresented in our data set for those two species. Prior research on galahs and budgerigars has found that males can be more vocal and more flexible with their vocalizations; perhaps these abilities translate to learning more call types20,47. A similar, but weaker, male mimicry increase occurred in rose-ringed parakeets. In only one species, yellow-headed parrots, did females show a significantly larger mimicry repertoire than males in any category (Appendix 5). Interestingly, the tendency to mimic humans (measured as sampling in the data set) and repertoire sizes did not always show the same patterns. Among sulphur-crested cockatoos, cockatiels, and Senegal parrots, males were more likely to show human mimicry, but their repertoires were not larger than the repertoires of females. This suggests that in some species, females may be less likely to mimic vocalizations, but when they do so they have just as large a vocabulary as males.The reported sex differences in parrot vocal mimicry repertoires are intriguing, but also are tentative conclusions. In many species, including our best sampled species, grey parrots, we saw no evidence of sex-differences in repertoire size. The sex-biases that we did document lose statistical significance after controlling for the many comparisons that we conducted. Nevertheless, we expect that some of our data represent true biological differences, especially because studies of wild birds have shown similar trends47,48. Thus, we offer our data as a starting point for additional research. Taken together, the analyses by sex provide interesting points of comparison to other vocal learning animals. Our combined analyses suggest that sex differences in vocal learning are vastly smaller and less common among parrots than they are among oscine passerines and hummingbirds45,49,50. Sex-based patterns of vocal learning in parrots appear more similar to those of vocal learning mammals than to those of other vocal learning birds51. Overall, parrots and songbirds present excellent comparative study systems for all aspects of sex differences in song learning, from the mechanistic to the functional17,51.Vocal learning variation by social contextMany parrot vocalizations function in social organization for individuals within flocks, and the ability to learn from conspecifics is essential to parrot familial and social integration12,15,52. Although our study specifically examined vocal learning of human sounds, we thought it possible that the presence of other parrots would increase mimicry rates if parrots learned human vocalizations from their parrot companions. Anecdotal stories of parrots teaching words to other parrots abound53, and studies of grey parrot cognition show that vocal modeling by multiple tutors can lead to better learning of human words54. Most existing results, however, are based on human tutoring, with controlled studies of parrot-parrot word transmission lacking. Here we tested whether social interactions with other parrots correlated with more vocal learning of human sounds. Our data gave no evidence that parrot-parrot social interactions drive human vocal mimicry. This was true across the full sample (controlling for species identity), and for our best sampled species, grey parrots. Although companion parrots are known to learn from conspecifics, that learning does not appear to shape repertoire sizes53. Open questions remain about whether signal complexity, repertoire size, or aspects of vocal learning covary with social complexity at a larger scale among parrots55. Follow up studies should address these questions using phylogenetically-controlled methods56. More

To estimate the total number of non-broadcasting vessels, including those that were not detected by SAR, we: (1) obtained SAR detections of vessels from RADARSAT-2 and the corresponding vessel lengths as estimated from the SAR image; (2) processed a global feed of AIS data to identify every broadcasting vessel that should have appeared in the SAR images at the moment the images were taken; (3) developed a novel technique to determine which vessels in AIS matched to detections in SAR, which AIS vessels were not detected by SAR, and which SAR detections represented non-broadcasting vessels; (4) after matching SAR to AIS, we could then (a) model the relationship between a vessel’s actual length and the length as estimated by the SAR image (Fig. 3b) and (b) model the relationship between the likelihood that a vessel is detected and its length (Fig. 3a); and (5) finally, we combined these relationships to develop an estimate of the number and lengths of non-broadcasting vessels in the region.SAR imagery and vessel detectionsWorking with the satellite company Kongsberg Satellite Services (KSAT), we tasked the Canadian Space Agency’s satellite RADARSAT-2 to acquire SAR images from its ship detection mode (DVWF mode, GRD product), with a pixel size of about 40 m and a swath width over 400 km (19). These images were processed following standard procedures for GRD products (e.g. applying radiometric calibration and geometric corrections)29,30. Vessel locations were extracted from the images with the widely used ship detection algorithms, which discriminates objects at sea based on the backscatter difference (pixel values) between the sea clutter and the targets31. Vessel lengths were estimated by measuring distances directly on the images with the aid of a graphical user interface tool31.Identifying Vessels using AISIn each region, AIS data, obtained from satellite providers ORBCOMM and Spire, were processed using Global Fishing Watch’s data pipeline1. The identities and lengths of all AIS devices that operated near the SAR scenes in both space and time were first obtained using Global Fishing Watch’s database1. To be sure vessels were identified correctly, two analysts reviewed the tracks of every AIS device in each region.In both regions, it is common practice for fishers to put AIS beacons on their longlines, likely to aid in retrieving them, meaning that many AIS devices were longline gear and not vessels. Because gear outnumbered vessels by several-fold, it was critical to differentiate gear and fishing vessels. In the Indian Ocean, 521 unique AIS devices associated with gear were detected that were likely within the SAR scenes, and 390 unique AIS devices associated with gear in the Pacific that were likely within the SAR scenes. Transponders were determined to be associated with gear by inspecting the name broadcast in the AIS messages (gear frequently broadcasts one of several standard names and/or a voltage reading) and classification using the Global Fishing Watch vessel classification algorithm1. Most gear also had an MMSI number (unique identifier number for AIS) that started with 1, 8, or 9 or broadcast names that signified gear. We eliminated all gear from the analysis because (1) these gear buoys have reflectors that are only ~ 1 m in size, and they should not be visible in ~ 40 m resolution SAR images, and (2) we found that gear matched to SAR detections only when traveling faster than 2 knots (and thus was on the deck of a boat); of 159 instances of gear in scenes where the gear was traveling slower than two knots, zero matched to a radar detection (Fig. S9).Generating probability rasters for matching AIS to SARMost AIS positions did not correspond to the exact time when the SAR images were taken. Hence, to determine the likelihood that a vessel broadcasting AIS corresponded to a specific SAR detection, we first developed probability rasters of where a vessel was likely to be minutes before or after a GPS position was recorded (Figs. S1,S2). We mined one year of global AIS data, including roughly 10 billion GPS positions, and computed these rasters for six different vessel classes (trawlers, purse seines, tug, cargo or tanker, drifting longlines, and others) and considered six different speeds (1, 3, 5, 7, 9, and 12.5 knots) and 36 time intervals (− 448, − 320, − 224, − 160, − 112, − 80, − 56, − 40, − 28, − 20, − 14, − 10, − 7, − 5, − 3.5, − 2.5, − 1.5, − 0.5, 0.5, 1.5, 2.5, 3.5, 5, 7, 10, 14, 20, 28, 40, 56, 80, 112, 160, 224, 320, and 448 min).For example, we queried a year of AIS data to find every example of where a tugboat had two positions that were 10 min apart from one another when the vessel had been traveling at 10 knots at the first position. We then recorded each of these locations relative to the location the vessel would have been if it traveled in a straight line, with x coordinates being in the direction of travel and the y coordinates being perpendicular to the direction of travel. When collected for hundreds of thousands of examples across the AIS dataset, the result is a heatmap of where tug boats are located 10 min after a position when it was traveling at 10 knots. The raster is centered on a point that is the extrapolated position of the vessel based on its speed. For instance, the purse seine raster that corresponds to a vessel traveling between 6 and 8 knots between 96 and 128 min after the most recent position is centered at a point that is 13.1 km (7 knots × 112 min) straight ahead of the direction the vessel was traveling. Figure S1 shows samples of these rasters for different vessels.We built rasters of 1000 by 1000 pixels for each vessel class and time interval, with the area covered by the raster dependent on the time interval (longer time intervals imply longer traveled distances, covering more area). The scale of each pixel was given by:$${text{pixel}};{text{width = max(1, }}Delta {text{m) / 1000}}$$
(1)
where Δm is the time interval in minutes, and pixel width is measured in km. Thus, if the Δm is under one minute, the entire raster is one kilometer wide with each pixel one meter by one meter. If the time is 10 min, then each pixel is 10 m wide, and the entire raster is 10 km by 10 km.Since the pixel width varies between rasters, the units of the rasters are probability per km2, thus summing the area of each pixel times its value equals one. Six vessel classes with 36 time intervals for each and six speeds led to 1296 different rasters. This probability raster approach could be seen as a utilization distribution32—for each vessel class, speed and time interval—where the space is relative to the position of the individual.Combining probability rasters to produce a matching scoreFor a few vessels (~ 4%) there was only one AIS position available before or after the scene. This resulted from a long gap in the AIS data due to poor reception, a weak AIS device, or cases where the vessels disabled their AIS. For these vessels, we used the raster values for a single position. For the vast majority of vessels, however, there was a GPS position right before and after the scene, and thus two probability rasters. We used two methods to combine these probability rasters to obtain information about the most likely location:Multiply and renormalize the rastersTo multiply the rasters, we interpolated the raster values, using bilinear interpolation, to a constant grid at the highest resolution between the before and after rasters. Then, we multiplied the values at each point and renormalized the resulting raster (Fig. S2):$$p_{i} = frac{{p_{ai} cdot p_{bi} }}{{mathop sum nolimits_{k = 0}^{N} p_{ak} cdot p_{bk} cdot da}}$$
(2)
where pi is the probability in vessel density per km2 at location i, pai is the value of the raster before the image, pbi is the value of the raster after the image. The denominator is the sum of all multiplied values across the raster, scaled by the area of each cell, da.Weight and average the rasters For this method, we weighted the raster by the squared value of the probabilities of that scene. This has the effect of giving the concentrated raster a higher weight, thus weighting higher the raster that is closer in time to the image:$${w}_{a}=sum_{k=0}^{N} {p}_{ak}^{2}cdot da$$
(3)
and the weighted average at location i is:$${p}_{i}=frac{{p}_{ai}cdot {w}_{a}+{p}_{bi}cdot {w}_{b}}{{w}_{a}+{w}_{b}}$$
(4)
where wa is the weight for raster a, wb the weight for raster b (calculation analogous to wa’s in Eq. 3), pi is the probability in vessel density per km2 at location i.To determine whether we should multiply (Eq. 2) or average (Eq. 4) the probabilities, we compared the performance of these two metrics against a direct inspection of the detections. We found that at short intervals, multiplying the rasters and renormalizing often made probability values extremely small ( {d}_{d}cdot {p}_{d} + {p}_{f}$$
(5)
where ({p}_{v}) is the probability density of the vessel presence at the location of the SAR detection (the score listed above), ({p}_{d}) is the probability that the vessel is detected by SAR, ({d}_{d}) is the density of non-broadcasting vessels in the region, and ({p}_{f}) is the density of false detections in the scene. The greater ({p}_{d}), the more dark vessels there are in a scene, and the more likely it is that any given detection is a dark vessel instead of a vessel broadcasting AIS. The right-hand side of the equation ({d}_{d}cdot {p}_{d} + {p}_{f}) should roughly equal the number of detections per unit area that do not match to AIS in the region. In other words, the probability of the vessel with AIS being at that specific location and detected by SAR (left side of the equation) should be greater than the probability of a dark vessel or a false detection at that location (right side of the equation).The total number of unmatched vessels in each studied region normalized by total area covered gives a density of non-broadcasting vessels of 2.6–2.8 × 10–5 vessels km-2 (Indian Ocean) and 6.8–7.2 × 10–6 vessels km−2 (Pacific Ocean), similar to the thresholds estimated by analysts. For the most likely number of matched vessels, we use a threshold that is halfway between the higher and lower bound of the analyst (5 × 10–5 to 1 × 10–4), 2.5 × 10–5 which is also roughly equal to the theoretical estimate of the Indian Ocean.This threshold approach performed significantly better than a metric based on the distance between the SAR detection and the most likely location of the vessel, where the likely location is based on extrapolating speed and course of the position closest in time to the image (Fig. S4).Determining whether a vessel with AIS was within a sceneVessel positions from AIS are usually available before and/or after the SAR images, and sometimes it is unclear if a vessel should have been within the scene footprint at the time of the image.To estimate the probability that a vessel (with AIS) was within a scene, we used the multiplied probability raster, summing the values inside the scene boundaries. This provides an estimate of the likelihood that the vessel was within the scene footprint at the time of the image. We applied this to every vessel that had at least one AIS position within 12 h and 200 nautical miles of the scene footprint. The vast majority of vessels were either very likely inside or outside the scene footprints, with 516 vessels having a probability of  > 95% and only 16 having a probability between 5 and 95%. We filtered out all vessels that were definitely outside of the image footprint before matching.Estimating the likelihood of detecting a vessel with SARThe AIS data show that not all vessels broadcasting AIS were captured by the RADARSAT-2 images (Fig. 3a). Using the known lengths of detected vessels with AIS, we estimated the likelihood of detecting a vessel with SAR as a function of vessel length (Fig. 3a). For vessels shorter than 60 m, we approximated the detection rate as a linear function. Treating each vessel as an individual detection, we fitted the 50th percentile using quantile regression to approximate the detection rate. For vessels above 60 m, we assumed a constant detection rate as very few vessels above this length did now show up in the SAR images. Of the 46 unique vessels larger than 62 m, 42 were detected, implying a detection rate of ~ 91%. Given that it is highly likely that large vessels will be captured by medium-resolution SAR imagery, we manually reviewed these cases to confirm that they were (almost surely) inside the scene footprints at the time the images were taken.We should note that the probability of detecting a vessel in SAR also depends on the sea state, incidence angle, polarization, material of the vessel, and orientation of the vessel. We are unable, however, to measure these effects directly so we cannot explicitly model these effects.With sufficient scenes, these effects should be randomly distributed across our scenes, so they likely account for some of the variability in detectability and the inaccuracy in our length estimates from SAR.Estimating the number and length of non-broadcasting vesselsBecause SAR does not detect all vessels, and because the length as estimated by SAR can be incorrect, there are many possible distributions of actual non-broadcasting vessels that could have produced the distribution of unmatched SAR detections that we found in the scenes. To estimate the most likely such distribution, we built a model to combine the two key relationships—between vessel length and likelihood of detection, and between vessel length and the length as estimated by SAR. This model allowed us to estimate, based on the number and distribution of SAR vessels, the likely number and distribution of actual vessels present (Fig. 3c,d).We binned the likelihood of vessel detection as a function of length into 1 m intervals, yielding a vector (alpha) of length 400. We also binned into 1 m intervals the population of lengths of all detected vessels ((ell_{D})) as reported by AIS (i.e. number of vessels at each length bin), the population of expected SAR lengths ((ell_{E})), and the population of lengths of all vessels ((ell_{A}), the quantity we wish to estimate). Thus, (ell_{D}) can be expressed as the product of (alpha) and (ell_{A}):$$ell_{D} = {upalpha } odot ell_{{text{A}}}$$
(6)
where (odot) is the element-wise product. We then estimated a matrix (L_{{}}) that relates (ell_{D}) to (ell_{E}).$$ell_{E} = Lell_{D}$$
(7)
where each element (L_{ij}) represents the probability that a vessel with length in bin j would be estimated by SAR to be of length in bin i. We calculated these probabilities as lognormal probability density functions, with one distribution per column. To estimate the scale and shape parameters of these distributions, we first fitted a quantile regression using the (non-binned) lengths from AIS of detected vessels as the predictor for the lengths reported by SAR. Assuming that the predicted 1/3 and 2/3 quantiles (as shown in Fig. 3a) represent the quantiles of a lognormal distribution, allow us to calculate the shape and scale parameters. We chose a lognormal distribution because: 1) the variable of interest, length, was always greater than zero, 2) the population of lengths was skewed towards larger values, and 3) there is an explicit and relatively simple relationship between the lognormal quantiles and the shape and scale parameters that simplified the calculations.Combining Eqs. (6) and (7) provides a relation between (ell_{A}) and (ell_{E}):$$ell_{E} = {text{L}}left( {alpha odot ell_{A} } right)$$
(8)
To estimate ({mathcal{l}}_{A}) we minimized an objective function (O({mathcal{l}}_{E},{mathcal{l}}_{o})) between the vector of expected counts binned by length (({mathcal{l}}_{E})) and the vector of counts observed in SAR binned by length (({mathcal{l}}_{o})). For this objective function, we chose the sum of the Kolmogorov –Smirnov distance between length distributions and the squared difference of the total numbers of detections. The first term controls the shape of the resulting distribution while the second one controls the magnitude. Specifically:$$Oleft( {ell_{E} ,ell_{o} } right) = max left( {left| {C_{E} – C_{O} } right|} right) + left( {T_{E} – T_{O} } right)^{2}$$
(9)
where:$$T_{x} = mathop sum limits_{ } ell_{x}$$$$D_{x} = ell_{x} /T_{x}$$$$C_{x} = cumsumleft( {D_{x} } right)$$Assessing the uncertainty in the estimationTo test how accurately our approach predicts the correct number of vessels, we performed a bootstrap simulation. We computed the vector (alpha) and the matrix L from a random subset of vessels with AIS that had a high confidence ( > 95%) of appearing within the scenes. We then used our method on the SAR detections that matched the remaining vessels to predict the number of vessels they corresponded to ((ell_{text{A}})). By running 10,000 experiments we found a mean absolute percent error of + − 9% (Figs. S5 and S6). This provides a rough estimate of the uncertainty in our prediction due to the estimation process itself. We used the distribution of these samples to estimate the 90% confidence interval that we report with our estimates. We note that this uncertainty refers to the parametrization of the model and there may be other sources of error, such as the possibility that vessels without AIS have different radar properties (e.g. made out of materials with different reflectiveness), that we did not account for in our model.Catch and effort data in the overlapping area between WCPFC and IATTCWe downloaded gridded effort and catch data from the WCPFC and IATTC websites, and compared the reported number of hooks and catch from September to December of 2019 for the area between − 140 to − 150 longitude and − 5 to − 15 latitude, a bounding box that contains our study region in the Pacific and which is entirely within both the WCPFC and IATTC convention zones. We found that the reported number of hooks for Korea is three times higher for the IATTC as it is for the WCPFC (Fig. S7), and the numbers of hooks also disagree by more than 10% for most other flag states. Catch is also 2.5 times higher for IATTC than for WCPFC for Korea as well, with catch also differing by more than 10% for most other flag states. This finding suggests that the different RFMOs may not be accounting for the same vessels in the overlap region between the two RFMOs. More

As part of my PhD thesis at Colorado State University in Fort Collins, I study the Texas brown tarantula (Aphonopelma hentzi) in the short-grass prairie. My colleagues and I work on the Southern Plains Land Trust, a piece of private conservation land about an hour south of Lamar, Colorado. These tarantulas’ habitats range from Louisiana to this southern part of Colorado. The prairie is a harsh environment — super dry, windy and sometimes very hot or cold. The tarantulas’ burrows become their lifeline; they stay in there for the long haul. Only the males, once mature, leave their burrows to wander aimlessly, looking for love.Tarantulas are ambush predators, meaning that they wait for food to walk by. We want to know if they build burrows in a consistent way, and how their burrows help them to survive the prairie’s harsh environment.We lure the tarantulas out of their burrow using a piece of grass, and then we collect them with a one-litre plastic cup. We pour quick-set plaster of Paris into the burrow. Once it’s dry, we dig out the cast. The first one, that I’m holding here, turned out to be 60 centimetres deep. This does destroy the burrow, but we dig the tarantula a new starter burrow nearby.The casts show us that some spiders are very clean and keep their burrows empty, whereas others are trashy, keeping previous moults or leftovers from eaten beetle. One of the burrows looked as if it had been borrowed from a much bigger animal. That is high-end lazy.About 90% of US prairies are gone because of agriculture and ranching. We strive to preserve the prairie and the creatures in it. Tarantulas serve as a force for keeping insect and even rodent populations under control in the prairie ecosystem. Tarantulas are big, but they won’t hurt you. Want fewer insects? Let spiders live in your house. They’re in your bathtub only because they are thirsty. More