Ecology

Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (IPBES, 2019).Tingley, M. W., Monahan, W. B., Beissinger, S. R. & Moritz, C. Proc. Natl Acad. Sci. USA 106(Suppl 2), 19637–19643 (2009).CAS 
Article 

Google Scholar 
Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Proc. Natl Acad. Sci. USA 109, 8606–8611 (2012).CAS 
Article 

Google Scholar 
Senior, R. A., Hill, J. K. & Edwards, D. P. Nat. Clim. Chang. 9, 623–626 (2019).Article 

Google Scholar 
Viana, D. S. & Chase, J. M. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01814-y (2022).Article 

Google Scholar 
Sauer, J. R. et al. Condor 119, 576–593 (2017).Article 

Google Scholar 
Nowak, L., Schleuning, M., Bender, I. M. A., Kissling, W. D. & Fritz, S. A. Divers. Distrib. https://doi.org/10.1111/ddi.13518 (2022).Article 

Google Scholar 
Allen, C. D. et al. For. Ecol. Manage. 259, 660–684 (2010).Article 

Google Scholar 
Janis, C. M., Damuth, J. & Theodor, J. M. Proc. Natl Acad. Sci. USA 97, 7899–7904 (2000).CAS 
Article 

Google Scholar 
Stuart-Smith, R. D., Mellin, C., Bates, A. E. & Edgar, G. J. Nat. Ecol. Evol. 5, 656–662 (2021).Article 

Google Scholar 
Watanabe, Y. Y. Ecol. Lett. 19, 907–914 (2016).Article 

Google Scholar 
Bladon, A. J. et al. J. Anim. Ecol. 89, 2440–2450 (2020).Article 

Google Scholar 
Claramunt, S., Hong, M. & Bravo, A. Biotropica https://doi.org/10.1111/btp.13109 (2022).Article 

Google Scholar 
Zurell, D., Gallien, L., Graham, C. H. & Zimmermann, N. E. J. Biogeogr. 45, 1459–1468 (2018).Article 

Google Scholar 
Bowler, D. E., Heldbjerg, H., Fox, A. D., O’Hara, R. B. & Böhning-Gaese, K. J. Anim. Ecol. 87, 1034–1045 (2018).Article 

Google Scholar 
Warren, D. L., Cardillo, M., Rosauer, D. F. & Bolnick, D. I. Trends Ecol. Evol. 29, 572–580 (2014).Article 

Google Scholar 
Gómez, C., Tenorio, E. A., Montoya, P. & Cadena, C. D. Proc. R. Soc. Lond. B. Biol. Sci. 283, 20152458 (2016).
Google Scholar 
Amano, T., Lamming, J. D. L. & Sutherland, W. J. Bioscience 66, 393–400 (2016).Article 

Google Scholar 
Rosenberg, K. V. et al. Science 366, 120–124 (2019).CAS 
Article 

Google Scholar 
Howard, C. et al. Divers. Distrib. 26, 1442–1455 (2020).Article 

Google Scholar  More

Banks, N. C., Paini, D. R., Bayliss, K. L. & Hodda, M. The role of global trade and transport network topology in the human-mediated dispersal of alien species. Ecol. Lett. 18, 188–199 (2015).
Google Scholar 
Epanchin-Niell, R. et al. Controlling invasive species in complex social landscapes. Front. Ecol. Environ. 8, 210–216 (2009).
Google Scholar 
Charles, H. & Dukes, J. S. in Biological Invasions (ed. Nentwig, W.) 217–237 (Springer, 2007). https://doi.org/10.1007/978-3-540-36920-2_13Gallardo, B., Clavero, M., Sánchez, M. & Vilà, M. Global ecological impacts of invasive species in aquatic ecosystems. Glob. Change Biol. 22, 151–163 (2016).
Google Scholar 
Diagne, C. et al. High and rising economic costs of biological invasions worldwide. Nature 592, 571–576 (2021).CAS 

Google Scholar 
Sardain, A., Sardain, E. & Leung, B. Global forecasts of shipping traffic and biological invasions to 2050. Nat. Sustain. 2, 274–282 (2019).
Google Scholar 
Epanchin-Niell, R. S. & Hastings, A. Controlling established invaders: integrating economics and spread dynamics to determine optimal management. Ecol. Lett. 13, 528–541 (2010).
Google Scholar 
Chades, I. et al. General rules for managing and surveying networks of pests, diseases, and endangered species. Proc. Natl. Acad. Sci. USA 108, 8323–8328 (2011).CAS 

Google Scholar 
Epanchin-Niell, R. S. & Wilen, J. E. Optimal spatial control of biological invasions. J. Environ. Econ. Manag. 63, 260–270 (2012).
Google Scholar 
Epanchin-Niell, R. S. & Wilen, J. E. Individual and cooperative management of invasive species in human-mediated landscapes. Am. J. Agric. Econ. 97, 180–198 (2015).
Google Scholar 
Aadland, D., Sims, C. & Finnoff, D. Spatial dynamics of optimal management in bioeconomic systems. Comput. Econ. 45, 545–577 (2015).
Google Scholar 
Baker, C. M. Target the source: optimal spatiotemporal resource allocation for invasive species control. Conserv. Lett. 10, 41–48 (2017).
Google Scholar 
Bushaj, S., Büyüktahtakın, İ. E., Yemshanov, D. & Haight, R. G. Optimizing surveillance and management of emerald ash borer in urban environments. Nat. Res. Model. 34, e12267 (2021).
Google Scholar 
Fischer, S. M., Beck, M., Herborg, L.-M. & Lewis, M. A. Managing aquatic invasions: optimal locations and operating times for watercraft inspection stations. J. Environ. Manag. 283, 111923 (2021).
Google Scholar 
Büyüktahtakın, İ. E. & Haight, R. G. A review of operations research models in invasive species management: state of the art, challenges, and future directions. Ann. Oper. Res. 271, 357–403 (2018).
Google Scholar 
Epanchin-Niell, R. S. Economics of invasive species policy and management. Biol. Invasions 19, 3333–3354 (2017).
Google Scholar 
Bodin, Ö. et al. Improving network approaches to the study of complex social–ecological interdependencies. Nat. Sustain. 2, 551–559 (2019).CAS 

Google Scholar 
Nowzari, C., Precaido, V. M. & Pappas, G. J. Analysis and control of epidemics: a survey of spreading processes on complex networks. IEEE Control Syst. 36, 26–46 (2016).
Google Scholar 
Newman, M. E. J. Spread of epidemic disease on networks. Phys. Rev. E 66, 016128 (2002).CAS 

Google Scholar 
Kempe, D., Kleinberg, J. & Tardos, E. Maximizing the spread of influence through a social network. In Proc. 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 137–146 (ACM Press, 2003).Pastor-Satorras, R. & Vespignani, A. Immunization of complex networks. Phys. Rev. E 65, 036104 (2002).
Google Scholar 
Pastor-Satorras, R., Castellano, C., Van Mieghem, P. & Vespignani, A. Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925–979 (2015).
Google Scholar 
Holme, P., Kim, B. J., Yoon, C. N. & Han, S. K. Attack vulnerability of complex networks. Phys. Rev. E 65, 056109 (2002).
Google Scholar 
Muirhead, J. R. & Macisaac, H. J. Development of inland lakes as hubs in an invasion network. J. Appl. Ecol. 42, 80–90 (2005).
Google Scholar 
de la Fuente, B., Saura, S. & Beck, P. S. Predicting the spread of an invasive tree pest: the pine wood nematode in southern europe. J. Appl. Ecol. 55, 2374–2385 (2018).
Google Scholar 
Minor, E. S. & Urban, D. L. A graph-theory framework for evaluating landscape connectivity and conservation planning. Conserv. Biol. 22, 297–307 (2008).
Google Scholar 
Morel-Journel, T., Assa, C. R., Mailleret, L. & Vercken, E. Its all about connections: hubs and invasion in habitat networks. Ecol. Lett. 22, 313–321 (2019).
Google Scholar 
Perry, G. L. W., Moloney, K. A. & Etherington, T. R. Using network connectivity to prioritise sites for the control of invasive species. J. Appl. Ecol. 54, 1238–1250 (2017).
Google Scholar 
Kvistad, J. T., Chadderton, W. L. & Bossenbroek, J. M. Network centrality as a potential method for prioritizing ports for aquatic invasive species surveillance and response in the Laurentian Great Lakes. Manag. Biol. Invasions 10, 403 (2019).
Google Scholar 
Haight, R. G., Kinsley, A. C., Kao, S.-Y., Yemshanov, D. & Phelps, N. B. Optimizing the location of watercraft inspection stations to slow the spread of aquatic invasive species. Biol. Invasions 23, 3907–3919 (2021).
Google Scholar 
McEachran, M. C. et al. Stable isotopes indicate that zebra mussels (Dreissena polymorpha) increase dependence of lake food webs on littoral energy sources. Freshw, Biol. 64, 183–196 (2019).CAS 

Google Scholar 
Karatayev, A. Y., Burlakova, L. E. & Padilla, D. K. in Invasive Aquatic Species of Europe. Distribution, Impacts and Management (eds Leppäkoski, E. et al.) 433–446 (Springer, 2002).Prescott, T. H., Claudi, R. & Prescott, K. L. Impact of Dreissenid mussels on the infrastructure of dams and hydroelectric power plants. In Quagga and Zebra Mussels (eds Nalepa, T. F. & Schloesser, D. W.) 243–258 (CRC Press, 2013).Invasive Species of Aquatic Plants and Wild Animals in Minnesota: Annual Report for 2020 (Minnesota Department of Natural Resources, 2020).Kanankege, K. S., Alkhamis, M. A., Phelps, N. B. & Perez, A. M. A probability co-kriging model to account for reporting bias and recognize areas at high risk for zebra mussels and eurasian watermilfoil invasions in Minnesota. Front. Vet. Sci. 4, 231 (2018).
Google Scholar 
Mallez, S. & McCartney, M. Dispersal mechanisms for zebra mussels: population genetics supports clustered invasions over spread from hub lakes in Minnesota. Biol. Invasions 20, 2461–2484 (2018).
Google Scholar 
Kao, S.-Y. Z. et al. Network connectivity of Minnesota waterbodies and implications for aquatic invasive species prevention. Biol. Invasions 23, 3231–3242 (2021).
Google Scholar 
Kleinberg, J. M. Authoritative sources in a hyperlinked environment. In Proc. 9th Annual ACM-SIAM Symposium on Discrete Algorithms 668–677 (1998).McDonald-Madden, E. et al. Using food-web theory to conserve ecosystems. Nat. Commun. 7, 10245 (2016).CAS 

Google Scholar 
Bossenbroek, J. M., Kraft, C. E. & Nekola, J. C. Prediction of long-distance dispersal using gravity models: zebra mussel invasion of inland lakes. Ecol. Appl. 11, 1778–1788 (2001).
Google Scholar 
Leung, B., Bossenbroek, J. M. & Lodge, D. M. Boats, pathways, and aquatic biological invasions: estimating dispersal potential with gravity models. Biol. Invasions 8, 241–254 (2006).
Google Scholar 
Beger, M. et al. Integrating regional conservation priorities for multiple objectives into national policy. Nat. Commun. 6, 8208 (2015).Runting, R. K. et al. Larger gains from improved management over sparing–sharing for tropical forests. Nat. Sustain. 2, 53–61 (2019).
Google Scholar 
Kinsley, A. C. et al. AIS Explorer: prioritization for watercraft inspections. A decision-support tool for aquatic invasive species management. J. Environ. Manage. 314, 115037 (2022).
Google Scholar 
Vander Zanden, M. J. & Olden, J. D. A management framework for preventing the secondary spread of aquatic invasive species. Can. J. Fish. Aquat. Sci. 65, 1512–1522 (2008).
Google Scholar 
Kanankege, K. S. et al. Lessons learned from the stakeholder engagement in research: application of spatial analytical tools in one health problems. Front. Vet. Sci. 7, 254 (2020).
Google Scholar 
Kroetz, K. & Sanchirico, J. The bioeconomics of spatial-dynamic systems in natural resource management. Annu. Rev. Resour. Econ. 7, 189–207 (2015).
Google Scholar 
Cade, B. S. & Noon, B. R. A gentle introduction to quantile regression for ecologists. Front. Ecol. Environ. 1, 412–420 (2003).
Google Scholar 
Koenker, R. in Asymptotic Statistics (eds Mandl, P. & Hušková, M.) 349–359 (Springer, 1994).Ashander, J. Analysis code and data for ‘Guiding large-scale management of invasive species using network metrics’. figshare https://doi.org/10.6084/m9.figshare.14402447 (2021). More

Climate and land cover dataOur study of tropical dry season dynamics required climatic variables with high temporal resolution (i.e., daily) and full coverage of tropic regions. To reduce uncertainties associated with the choice of precipitation (P) and evapotranspiration (Ep or E) datasets, we used an ensemble of eight precipitation products, three reanalysis-based products for Ep, and one satellite-based land E product. These precipitation datasets were derived four gauge-based or satellite observation (CHIRPS58, GPCC59, CPC-U60 and PERSIANN-CDR61), three reanalyses (ERA-562, MERRA-263, and PGF64) and a multi-source weighted ensemble product (MSWEP v2.865). The potential evapotranspiration (Ep) was calculated using the FAO Penman–Monteith equation66 (Eqs. (1, 2)), which requires meteorological inputs of wind speed, net radiation, air temperature, specific humidity, and surface pressure. We derived these meteorological variables from the three reanalysis products (ERA-5, MERRA-2, and GLDAS-2.067). Since PGF reanalysis lacked upward short- and long-wave radiation output and thus net radiation, we used available meteorological outputs from GLDAS-2.0 instead, which was forced entirely with the PGF input data.$${Ep}=frac{0.408cdot triangle cdot left({R}_{n}-Gright)+gamma cdot frac{900}{T+273}cdot {u}_{2}cdot left({e}_{s}-{e}_{a}right)}{triangle +{{{{{rm{gamma }}}}}}cdot left(1+0.34cdot {u}_{2}right)}$$
(1)
$${VPD}={e}_{s}-{e}_{a}=0.6108cdot {e}^{frac{17.27cdot T}{T+237.3}}cdot left(1-frac{{RH}}{100}right)$$
(2)
Where Ep is the potential evapotranspiration (mm day−1). Rn is net radiation at the surface (MJ m−2 day−1), T is mean daily air temperature at 2 m height (°C), ({u}_{2}) is wind speed at 2 m height (m s−1), ((,{e}_{s}-{e}_{a})) is the vapor pressure deficit of the air (kPa), ({RH}) is the relative air humidity near surface (%), ∆ is the slope of the saturation vapor pressure-temperature relationship (kPa °C−1), γ is the psychrometric constant (kPa °C−1), G is the soil heat flux (MJ m−2 day−1, is often ignored for daily time steps G ≈ 0).We derived the daily evapotranspiration data from the Global Land Evaporation Amsterdam Model (GLEAM v3.3a68), which is a set of algorithms dedicated to developing terrestrial evaporation and root-zone soil moisture data. GLEAM fully assimilated the satellite-based soil moisture estimates from ESA CCI, microwave L-band vegetation optical depth (VOD), reanalysis-based temperature and radiation, and multi-source precipitation forcings. The direct assimilation of observed soil moisture allowed us to detect true soil moisture dynamic and its impacts on evapotranspiration. Besides, the incorporation of VOD, which is closely linked to vegetation water content69,70, allowed us to detect the effect of water stress, heat stress, and vegetation phenological constraints on evaporation. Other observation-driven ET products from remote-sensing physical estimation and flux-tower are not included due to their low temporal resolution (i.e., monthly)71 or short duration72,73. ET outputs of reanalysis products are not considered in our analysis, because the assimilation systems lack explicit representation of inter-annual variability of vegetation activities and thus may not fully capture hydrological response to vegetation changes62,63,67.We used land cover maps for the year 2001 from the Moderate-Resolution Imaging Spectroradiometer (MODIS, MCD12C1 C574) based on the IGBP classification scheme to exclude water-dominated and sparely-vegetated pixels (like Sahara, Arabian Peninsula). All climate and land cover datasets mentioned above were remapped to a common 0.25° × 0.25° grid and unified to daily resolution. The main characteristics of the datasets mentioned above are summarized in Supplementary Table 1.Outputs of CMIP6 simulationsTo understand how modeled dry season changes compare with observed changes, we analyzed outputs from the “historical” (1983-2014) runs of 34 coupled models participating in the 6th Coupled Model Inter-comparison Project75 (CMIP6, Supplementary Table 3). We used these models because they offered daily outputs of all climatic variables needed for our analysis, including precipitation, latent heat (convert to E), and multiple meteorological variables for Ep (air temperature, surface specific humidity, wind speed, and net radiation). All outputs were remapped to a common 1.0° × 1.0° grid and unified to daily resolution.Defining dry season length and timingFor each grid cell and each dry season definition (P  More

The management of introduced species, whether kudzu or zebra mussels, is costly and complex. Now, a paper reports a workable, effective solution that harnesses network analyses of ecological phenomena.Invasive species can pose severe economic and environmental problems, costing more than US$1 trillion worldwide since 1970 (ref. 1). Yet managing this human-driven issue is difficult in itself. The regions involved can be vast — entire continents or countries, for instance — while budgets are typically limited. As well, the sites potentially affected and management options can be numerous. Real systems (for example, all the lakes in the United States) can have thousands of locations that could potentially be infested. By contrast, considering just 40 locations means dealing theoretically with over 1 trillion unique combinations (240) where management could be applied (for instance, to reduce the number of invasive species leaving infested areas or entering uninfested ones). Given these constraints, a key problem is how and where to deploy control measures such as invasive-species removal. While sophisticated optimization approaches exist2, which use mathematical rules to exclude most suboptimal combinations and quickly zoom in to which locations should be managed to minimize new invasions, these algorithms are generally unfeasible for very large systems. Now, writing in Nature Sustainability, Ashander et al.3 demonstrate that simpler network metrics revealing linkages between patches can provide solutions that are often comparable to the more complex optimization algorithms. More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Gause, G. F. Experimental analysis of Vito Volterra’s mathematical theory of the struggle for existence. Science 79, 16–17 (1934).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Amarasekare, P. Competitive coexistence in spatially structured environments: A synthesis. Ecol. Lett. 6, 1109–1122 (2003).Article 

Google Scholar 
HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43, 227–248 (2012).Article 

Google Scholar 
Wisz, M. S. et al. The role of biotic interactions in shaping distributions and realised assemblages of species: Implications for species distribution modelling. Biol. Rev. 88, 15–30 (2013).PubMed 
Article 

Google Scholar 
Frey, S., Fisher, J. T., Burton, A. C. & Volpe, J. P. Investigating animal activity patterns and temporal niche partitioning using camera-trap data: Challenges and opportunities. Remote Sens. Ecol. Conserv. 3, 123–132 (2017).Article 

Google Scholar 
Davis, C. L. et al. Ecological correlates of the spatial co-occurrence of sympatric mammalian carnivores worldwide. Ecol. Lett. 21, 1401–1412 (2018).PubMed 
Article 

Google Scholar 
Durant, S. M. Competition refuges and coexistence: An example from Serengeti carnivores. J. Anim. Ecol. 67, 370–386 (1998).Article 

Google Scholar 
Fedriani, J. M., Fuller, T. K., Sauvajot, R. M. & York, E. C. Competition and intraguild predation among three sympatric carnivores. Oecologia 125, 258–270 (2000).ADS 
PubMed 
Article 

Google Scholar 
Kamler, J. F., Ballard, W. B., Gilliland, R. L. & Mote, K. Spatial relationships between swift foxes and coyotes in northwestern Texas. Can. J. Zool. 81, 168–172 (2003).Article 

Google Scholar 
Vanak, A. T. et al. Moving to stay in place: Behavioral mechanisms for coexistence of African large carnivores. Ecology 94, 2619–2631 (2013).PubMed 
Article 

Google Scholar 
Donadio, E. & Buskirk, S. W. Diet, morphology, and interspecific killing in carnivora. Am. Nat. 167, 524–536 (2006).PubMed 
Article 

Google Scholar 
Tsunoda, H. et al. Food niche segregation between sympatric golden jackals and red foxes in central Bulgaria. J. Zool. 303, 64–71 (2017).Article 

Google Scholar 
Palomares, F. & Caro, T. M. Interspecific killing among mammalian carnivores. Am. Nat. 153, 492–508 (1999).CAS 
PubMed 
Article 

Google Scholar 
Linnell, J. D. C. & Strand, O. Interference interactions, co-existence and conservation of mammalian carnivores. Divers. Distrib. 6, 169–176 (2000).Article 

Google Scholar 
Kamler, J. F., Stenkewitz, U., Klare, U., Jacobsen, N. F. & MacDonald, D. W. Resource partitioning among cape foxes, bat-eared foxes, and black-backed jackals in South Africa. J. Wildl. Manag. 76, 1241–1253 (2012).Article 

Google Scholar 
Di Bitetti, M. S., Di Blanco, Y. E., Pereira, J. A., Paviolo, A. & Pírez, I. J. Time Partitioning favors the coexistence of sympatric crab-eating foxes (Cerdocyon thous) and Pampas Foxes (Lycalopex gymnocercus). J. Mammal. 90, 479–490 (2009).Article 

Google Scholar 
Lesmeister, D. B., Nielsen, C. K., Schauber, E. M. & Hellgren, E. C. Spatial and temporal structure of a mesocarnivore guild in Midwestern North America. Wildl. Monogr. 191, 1–61 (2015).Article 

Google Scholar 
Di Bitetti, M. S., De Angelo, C. D., Di Blanco, Y. E. & Paviolo, A. Niche partitioning and species coexistence in a Neotropical felid assemblage. Acta Oecologica 36, 403–412 (2010).ADS 
Article 

Google Scholar 
Monterroso, P., Alves, P. C. & Ferreras, P. Plasticity in circadian activity patterns of mesocarnivores in southwestern Europe: Implications for species coexistence. Behav. Ecol. Sociobiol. 68, 1403–1417 (2014).Article 

Google Scholar 
Tsunoda, H., Ito, K., Peeva, S., Raichev, E. & Kaneko, Y. Spatial and temporal separation between the golden jackal and three sympatric carnivores in a human-modified landscape in central Bulgaria. Zool. Ecol. 28, 172–179 (2018).Article 

Google Scholar 
Tsunoda, H. et al. Spatio-temporal partitioning facilitates mesocarnivore sympatry in the Stara Planina Mountains, Bulgaria. Zoology 141, 125801 (2020).PubMed 
Article 

Google Scholar 
Ramesh, T., Kalle, R., Sankar, K. & Qureshi, Q. Spatio-temporal partitioning among large carnivores in relation to major prey species in Western Ghats. J. Zool. 287, 269–275 (2012).Article 

Google Scholar 
Gómez-Ortiz, Y., Monroy-Vilchis, O. & Castro-Arellano, I. Temporal coexistence in a carnivore assemblage from central Mexico: Temporal-domain dependence. Mammal Res. 64, 333–342 (2019).Article 

Google Scholar 
Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Stat. 14, 322–337 (2009).MathSciNet 
MATH 
Article 

Google Scholar 
Meredith, M. & Ridout, M. Overlap: Estimates of coefficient of overlapping for animal activity patterns. https://cran.r-project.org/web/packages/overlaphttps://cran.r-project.org/web/packages/overlap/index.html (2018).Marinho, P. H., Fonseca, C. R., Sarmento, P., Fonseca, C. & Venticinque, E. M. Temporal niche overlap among mesocarnivores in a Caatinga dry forest. Eur. J. Wildl. Res. 66, 1–13 (2020).Article 

Google Scholar 
Vilella, M., Ferrandiz-Rovira, M. & Sayol, F. Coexistence of predators in time: Effects of season and prey availability on species activity within a Mediterranean carnivore guild. Ecol. Evol. 10, 11408–11422 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhao, G. et al. Spatio-temporal coexistence of sympatric mesocarnivores with a single apex carnivore in a fine-scale landscape. Glob. Ecol. Conserv. 21, e00897 (2020).Article 

Google Scholar 
Farmer, M. J., Allen, M. L., Olson, E. R., Van Stappen, J. & Van Deelen, T. R. Agonistic interactions and island biogeography as drivers of carnivore spatial and temporal activity at multiple scales. Can. J. Zool. 99, 309–317 (2021).Article 

Google Scholar 
Watabe, R. & Saito, M. U. Diel activity patterns of three sympatric medium-sized carnivores during winter and spring in a heavy snowfall area in northeastern Japan. Mammal Study 46, 69–75 (2021).Article 

Google Scholar 
Lashley, M. A. et al. Estimating wildlife activity curves: comparison of methods and sample size. Sci. Rep. 8, 4173 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Niedballa, J., Wilting, A., Sollmann, R., Hofer, H. & Courtiol, A. Assessing analytical methods for detecting spatiotemporal interactions between species from camera trapping data. Remote Sens. Ecol. Conserv. 5, 272–285 (2019).Article 

Google Scholar 
Karanth, K. U. et al. Spatio-temporal interactions facilitate large carnivore sympatry across a resource gradient. Proc. R. Soc. B Biol. Sci. 284, 20161860 (2017).Article 

Google Scholar 
Cusack, J. J. et al. Revealing kleptoparasitic and predatory tendencies in an African mammal community using camera traps: A comparison of spatiotemporal approaches. Oikos 126, 812–822 (2017).Article 

Google Scholar 
Balme, G. et al. Big cats at large: density, structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality. Popul. Ecol. 61, 256–267 (2019).Article 

Google Scholar 
Li, Z. et al. Coexistence of two sympatric flagship carnivores in the human-dominated forest landscapes of Northeast Asia. Landsc. Ecol. 34, 291–305 (2019).Article 

Google Scholar 
Lahkar, D., Ahmed, M. F., Begum, R. H., Das, S. K. & Harihar, A. Inferring patterns of sympatry among large carnivores in Manas National Park: A prey-rich habitat influenced by anthropogenic disturbances. Anim. Conserv. 24, 589–601 (2021).Article 

Google Scholar 
Paúl, M. J., Layna, J. F., Monterroso, P. & Álvares, F. Resource partitioning of sympatric African Wolves (Canis lupaster) and side-striped jackals (Canis adustus) in an arid environment from West Africa. Diversity 12, 477 (2020).Article 

Google Scholar 
Prat-Guitart, M., Onorato, D. P., Hines, J. E. & Oli, M. K. Spatiotemporal pattern of interactions between an apex predator and sympatric species. J. Mammal. 101, 1279–1288 (2020).Article 

Google Scholar 
Stone, L. & Roberts, A. The checkerboard score and species distributions. Oecologia 85, 74–79 (1990).ADS 
PubMed 
Article 

Google Scholar 
Griffith, D. M., Veech, J. A. & Marsh, C. J. Cooccur: Probabilistic species co-occurrence analysis in r. J. Stat. Softw. 69, 1–17 (2016).Article 

Google Scholar 
Noor, A., Mir, Z. R., Veeraswami, G. G. & Habib, B. Activity patterns and spatial co-occurrence of sympatric mammals in the moist temperate forest of the Kashmir Himalaya, India. Folia Zool. 66, 231–241 (2017).Article 

Google Scholar 
de Satgé, J., Teichman, K. & Cristescu, B. Competition and coexistence in a small carnivore guild. Oecologia 184, 873–884 (2017).ADS 
PubMed 
Article 

Google Scholar 
Kass, J. M., Tingley, M. W., Tetsuya, T. & Koike, F. Co-occurrence of invasive and native carnivorans affects occupancy patterns across environmental gradients. Biol. Invasions 22, 2251–2266 (2020).Article 

Google Scholar 
Louppe, V., Herrel, A., Pisanu, B., Grouard, S. & Veron, G. Assessing occupancy and activity of two invasive carnivores in two Caribbean islands: implications for insular ecosystems. J. Zool. 313, 182–194 (2020).Article 

Google Scholar 
Proulx, G. et al. World distribution and status of the genus Martes in 20. In Martens and Fishers (Martes) in Human-Altered Environments (eds Harrison, D. J. et al.) 21–76 (Springer, Berlin, 2005). https://doi.org/10.1007/b99487.Chapter 

Google Scholar 
Ohdachi, S. D., Ishibashi, Y., Iwasa, M., Fukuki, D. & Saitoh, T. The Wild Mammals of Japan 2nd edn. (Shokadoh Book Seller, Kyoto, 2015).
Google Scholar 
Kauhala, K. & Saeki, M. Nyctereutes procyonoides. The IUCN Red List of Threatened Species. https://www.iucnredlist.org/species/14925/85658776 (2016).Yamamoto, Y. Comparative analyses on food habits of Japanese marten, red fox, badger and raccoon dog in the Mt. Nyugasa, Nagano Prefecture, Japan. Nat. Environ. Sci. Res. 7, 45–52 (1994) (in Japanese with English summary).
Google Scholar 
Hisano, M. et al. A comparison of visual and genetic techniques for identifying Japanese marten scats enabling diet examination in relation to seasonal food availability in a sub-alpine area of Japan. Zool. Sci. 34, 137–146 (2017).Article 

Google Scholar 
Lindstrom, E. R., Brainerd, S. M., Helldin, J. O. & Overskaug, K. Pine marten-red fox interactions: A case of intraguild predation?. Ann. Zool. Fenn. 32, 123–130 (1995).
Google Scholar 
Waggershauser, C. N., Ruffino, L., Kortland, K. & Lambin, X. Lethal interactions among forest-grouse predators are numerous, motivated by hunger and carcasses, and their impacts determined by the demographic value of the victims. Ecol. Evol. 11, 7164–7186 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Watabe, R., Saito, M. U., Enari, H. S. & Enari, H. Mammalian fauna of the Kaminagawa Experimental Forest of Yamagata University detected by camera traps. Tohoku J. For. Sci. 25, 37–40 (2020) (in Japanese).
Google Scholar 
Hofmeester, T. R., Rowcliffe, J. M. & Jansen, P. A. A simple method for estimating the effective detection distance of camera traps. Remote Sens. Ecol. Conserv. 3, 81–89 (2017).Article 

Google Scholar 
Di Bitetti, M. S., Paviolo, A. & De Angelo, C. Camera trap photographic rates on roads vs. off roads: Location does matter. Mastozoología Neotrop. 21, 37–46 (2014).
Google Scholar 
Borcard, D. & Legendre, P. Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology 93, 1473–1481 (2012).PubMed 
Article 

Google Scholar 
Oksanen, J. et al. Vegan: community ecology package. https://cran.r-project.org/web/packages/veganhttps://cran.r-project.org/web/packages/vegan/index.html (2019).R Core Team. R: a language environment for statistical computing. r foundation for statistical computing, Vienna, Austria. https://www.r-project.org/https://www.r-project.org/ (2021).Linkie, M. & Ridout, M. S. Assessing tiger-prey interactions in Sumatran rainforests. J. Zool. 284, 224–229 (2011).Article 

Google Scholar 
Watabe, R. & Saito, M. U. Effects of vehicle-passing frequency on forest roads on the activity patterns of carnivores. Landsc. Ecol. Eng. 17, 225–231 (2021).Article 

Google Scholar 
Furukawa, G. genkiFurukawa/rSetDayNightAttr documentation. https://rdrr.io/github/genkiFurukawa/rSetDayNightAhttps://rdrr.io/github/genkiFurukawa/rSetDayNightAttr/ (2019).Mielke, P. W., Berry, K. J. & Johnson, E. S. Multi-response permutation procedures for a priori classifications. Commun. Stat. Theory Methods 5, 1409–1424 (1976).MATH 
Article 

Google Scholar 
Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).Article 

Google Scholar 
Monterroso, P., Alves, P. C. & Ferreras, P. Catch me if you can: Diel activity patterns of mammalian prey and predators. Ethology 119, 1044–1056 (2013).Article 

Google Scholar 
Hendrichsen, D. K. & Tyler, N. J. C. How the timing of weather events influences early development in a large mammal. Ecology 95, 1737–1745 (2014).CAS 
PubMed 
Article 

Google Scholar 
Herfindal, I. et al. Weather affects temporal niche partitioning between moose and livestock. Wildlife Biol. https://doi.org/10.2981/wlb.00275 (2017).Article 

Google Scholar 
Haswell, P. M., Jones, K. A., Kusak, J. & Hayward, M. W. Fear, foraging and olfaction: How mesopredators avoid costly interactions with apex predators. Oecologia 187, 573–583 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barrull, J. et al. Factors and mechanisms that explain coexistence in a Mediterranean carnivore assemblage: An integrated study based on camera trapping and diet. Mamm. Biol. 79, 123–131 (2014).Article 

Google Scholar 
Tattersall, E. R., Burgar, J. M., Fisher, J. T. & Burton, A. C. Boreal predator co-occurrences reveal shared use of seismic lines in a working landscape. Ecol. Evol. 10, 1678–1691 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Moll, R. J. et al. Humans and urban development mediate the sympatry of competing carnivores. Urban Ecosyst. 21, 765–778 (2018).Article 

Google Scholar 
McCreadie, J. W. & Bedwell, C. R. Patterns of co-occurrence of stream insects and an examination of a causal mechanism: Ecological checkerboard or habitat checkerboard?. Insect Conserv. Divers. 6, 105–113 (2013).Article 

Google Scholar  More

Losey, J. E. & Vaughan, M. The economic value of ecological services provided by insects. Bioscience 56, 311–323 (2006).Article 

Google Scholar 
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Powney, G. D. et al. Widespread losses of pollinating insects in Britain. Nat. Commun. 10, 1–6 (2019).ADS 
CAS 
Article 

Google Scholar 
Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Seto, K. C., Güneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. 109, 16083–16088 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).PubMed 
Article 
CAS 

Google Scholar 
Petersen, H. & Luxton, M. A comparative analysis of soil fauna populations and their role in decomposition processes. Oikos 39, 288–388 (1982).Article 

Google Scholar 
Frizzo, T. L., Souza, L. M., Sujii, E. R. & Togni, P. H. Ants provide biological control on tropical organic farms influenced by local and landscape factors. Biol. Control 151, 104378 (2020).CAS 
Article 

Google Scholar 
Elizalde, L. et al. The ecosystem services provided by social insects: Traits, management tools and knowledge gaps. Biol. Rev. 95, 1418–1441 (2020).PubMed 
Article 

Google Scholar 
Zhong, Z. et al. Soil engineering by ants facilitates plant compensation for large herbivore removal of aboveground biomass. Ecology 102, e03312 (2021).PubMed 
Article 

Google Scholar 
Ortiz, D. P., Elizalde, L. & Pirk, G. I. Role of ants as dispersers of native and exotic seeds in an understudied dryland. Ecol. Entomol. 46, 626–636 (2021).Article 

Google Scholar 
Li, X. et al. A facilitation between large herbivores and ants accelerates litter decomposition by modifying soil microenvironmental conditions. Funct. Ecol. 35, 1822–1832 (2021).Article 

Google Scholar 
Wendt, C. F. et al. Local environmental variables are key drivers of ant taxonomic and functional beta-diversity in a Mediterranean dryland. Sci. Rep. 11, 1–10 (2021).ADS 
Article 
CAS 

Google Scholar 
Lach, L. Invasive ant establishment, spread, and management with changing climate. Curr. Opin. Insect Sci. 47, 119–124 (2021).PubMed 
Article 

Google Scholar 
Buczkowski, G. & Richmond, D. S. The effect of urbanization on ant abundance and diversity: A temporal examination of factors affecting biodiversity. PLoS ONE 7, e41729 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Holway, D. A. & Suarez, A. V. Homogenization of ant communities in mediterranean California: The effects of urbanization and invasion. Biol. Conserv. 127, 319–326 (2006).Article 

Google Scholar 
Miguelena, J. G. & Baker, P. B. Effects of urbanization on the diversity, abundance, and composition of ant assemblages in an arid city. Environ. Entomol. 48, 836–846 (2019).PubMed 
Article 

Google Scholar 
Nielsen, A. B., van den Bosch, M., Maruthaveeran, S. & van den Bosch, C. K. Species richness in urban parks and its drivers: A review of empirical evidence. Urban Ecosyst. 17, 305–327 (2014).Article 

Google Scholar 
Clarke, K. M., Fisher, B. L. & LeBuhn, G. The influence of urban park characteristics on ant (Hymenoptera, Formicidae) communities. Urban Ecosyst. 11, 317–334 (2008).Article 

Google Scholar 
Peng, M.-H., Hung, Y.-C., Liu, K.-L. & Neoh, K.-B. Landscape configuration and habitat complexity shape arthropod assemblage in urban parks. Sci. Rep. 10, 16043 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Santos, M. N., Delabie, J. H. & Queiroz, J. M. Biodiversity conservation in urban parks: A study of ground-dwelling ants (Hymenoptera: Formicidae) in Rio de Janeiro City. Urban Ecosyst. 22, 927–942 (2019).Article 

Google Scholar 
McKinney, M. L. Effects of urbanization on species richness: A review of plants and animals. Urban Ecosyst. 11, 161–176 (2008).Article 

Google Scholar 
Lahr, E. C., Dunn, R. R. & Frank, S. D. Getting ahead of the curve: Cities as surrogates for global change. Proc. R. Soc. B Biol. Sci. 285, 20180643 (2018).Article 
CAS 

Google Scholar 
Abdel-Dayem, M. S. et al. Ant diversity and composition patterns along the urbanization gradients in an arid city. J. Nat. Hist. 55, 2521–2547 (2021).Article 

Google Scholar 
Nooten, S. S., Lee, R. H. & Guénard, B. Evaluating the conservation value of sacred forests for ant taxonomic, functional and phylogenetic diversity in highly degraded landscapes. Biol. Conserv. 261, 109286 (2021).Article 

Google Scholar 
Bhagwat, S. A. & Rutte, C. Sacred groves: Potential for biodiversity management. Front. Ecol. Environ. 4, 519–524 (2006).Article 

Google Scholar 
Ballullaya, U. P. et al. Stakeholder motivation for the conservation of sacred groves in south India: An analysis of environmental perceptions of rural and urban neighbourhood communities. Land Use Policy 89, 104213 (2019).Article 

Google Scholar 
Lowman, M. D. & Sinu, P. A. Can the spiritual values of forests inspire effective conservation?. Bioscience 67, 688–690 (2017).Article 

Google Scholar 
Rajesh, T. P., Ballullaya, U. P., Unni, A. P., Parvathy, S. & Sinu, P. A. Interactive effects of urbanization and year on invasive and native ant diversity of sacred groves of South India. Urban Ecosyst. 23, 1335–1348 (2020).Article 

Google Scholar 
Rajesh, T. P., Unni, A. P., Ballullaya, U. P., Manoj, K. & Sinu, P. A. An insight into the quality of sacred groves–an island habitat–using leaf-litter ants as an indicator in a context of urbanization. J. Trop. Ecol. 37, 82–90 (2021).Article 

Google Scholar 
Holway, D. A., Lach, L., Suarez, A. V., Tsutsui, N. D. & Case, T. J. The causes and consequences of ant invasions. Annu. Rev. Ecol. Syst. 33, 181–233 (2002).Article 

Google Scholar 
Plowes, R. M., Dunn, J. G. & Gilbert, L. E. The urban fire ant paradox: Native fire ants persist in an urban refuge while invasive fire ants dominate natural habitats. Biol. Invasions 9, 825–836 (2007).Article 

Google Scholar 
Rajesh, T. P., Ballullaya, U. P., Surendran, P. & Sinu, P. A. Ants indicate urbanization pressure in sacred groves of southwest India: A pilot study. Curr. Sci. 113, 317–322 (2017).Article 

Google Scholar 
Wetterer, J. K. Worldwide distribution and potential spread of the long-legged ant, Anoplolepis gracilipes (Hymenoptera: Formicidae). Sociobiology 45, 77–97 (2005).
Google Scholar 
Bhagwat, S. A., Kushalappa, C. G., Williams, P. H. & Brown, N. D. A landscape approach to biodiversity conservation of sacred groves in the Western Ghats of India. Conserv. Biol. 19, 1853–1862 (2005).Article 

Google Scholar 
Chandrashekara, U. M. & Sankar, S. Ecology and management of sacred groves in Kerala, India. For. Ecol. Manag. 112, 165–177 (1998).Article 

Google Scholar 
Asha, G., Navya, K. K., Rajesh, T. P. & Sinu, P. A. Roller dung beetles of dung piles suggest habitats are alike, but that of guarding pitfall traps suggest habitats are different. J. Trop. Ecol. 37, 209–213 (2021).Article 

Google Scholar 
Manoj, K. et al. Diversity of Platygastridae in leaf litter and understory layers of tropical rainforests of the Western Ghats Biodiversity Hotspot, India. Environ. Entomol. 46, 685–692 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hariraveendra, M., Rajesh, T. P., Unni, A. P. & Sinu, P. A. Prey–predator interaction suggests sacred groves are not functionally different from neighbouring used lands. J. Trop. Ecol. 36, 220–224 (2020).Article 

Google Scholar 
Bingham, C. T. The fauna of British India, including Ceylon and Burma. Hymenoptera, Vol. II. Ants and Cuckoo-wasps. (1903).Bolton, B. Identification Guide to the Ant Genera of the World (Harvard University Press, 1994).
Google Scholar 
Bellow, J. G. & Nair, P. K. R. Comparing common methods for assessing understory light availability in shaded-perennial agroforestry systems. Agric. For. Meteorol. 114, 197–211 (2003).ADS 
Article 

Google Scholar 
Dobson, A. J. & Barnett, A. G. An Introduction to Generalized Linear Models (Chapman and Hall/CRC, 2018).MATH 

Google Scholar 
Fox, J. et al. Package ‘car’, Vol. 16, (R Foundation for Statistical Computing, 2012).Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (H ill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).Article 

Google Scholar 
Kim, T. N., Savannah, B., Bill, D. W., Douglas, A. L. & Claudio, G. Disturbance differentially affects alpha and beta diversity of ants in tallgrass prairies. Ecosphere 9, e02399 (2018).Article 

Google Scholar 
Hartig, F. & Hartig, M. F. Package ‘DHARMa’. R package (2017).Nash, J. C. On best practice optimization methods in R. J. Stat. Softw. 60, 1–14 (2014).Article 

Google Scholar 
Berman, M., Andersen, A. N. & Ibanez, T. Invasive ants as back-seat drivers of native ant diversity decline in New Caledonia. Biol. Invasions 15, 2311–2331 (2013).Article 

Google Scholar 
Melliger, R. L., Braschler, B., Rusterholz, H.-P. & Baur, B. Diverse effects of degree of urbanisation and forest size on species richness and functional diversity of plants, and ground surface-active ants and spiders. PLoS ONE 13, e0199245 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Guénard, B., Cardinal-De Casas, A. & Dunn, R. R. High diversity in an urban habitat: Are some animal assemblages resilient to long-term anthropogenic change?. Urban Ecosyst. 18, 449–463 (2015).Article 

Google Scholar 
Slipinski, P., Zmihorski, M. & Czechowski, W. Species diversity and nestedness of ant assemblages in an urban environment. Eur. J. Entomol. 109, 197 (2012).Article 

Google Scholar 
Heterick, B. E., Lythe, M. & Smithyman, C. Urbanisation factors impacting on ant (Hymenoptera: Formicidae) biodiversity in the Perth metropolitan area, Western Australia: Two case studies. Urban Ecosyst. 16, 145–173 (2013).Article 

Google Scholar 
Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 1–13 (2020).Article 
CAS 

Google Scholar 
Goodman, M. & Warren, R. J. II. Non-native ant invader displaces native ants but facilitates non-predatory invertebrates. Biol. Invasions 21, 2713–2722 (2019).Article 

Google Scholar 
Philpott, S. M. & Armbrecht, I. Biodiversity in tropical agroforests and the ecological role of ants and ant diversity in predatory function. Ecol. Entomol. 31, 369–377 (2006).Article 

Google Scholar 
Philpott, S. M., Perfecto, I. & Vandermeer, J. Effects of management intensity and season on arboreal ant diversity and abundance in coffee agroecosystems. Biodivers. Conserv. 15, 139–155 (2006).Article 

Google Scholar 
García-Cárdenas, R., Montoya-Lerma, J. & Armbrecht, I. Ant diversity under three coverages in a Neotropical coffee landscape. Rev. Biol. Trop. 66, 1373–1389 (2018).Article 

Google Scholar 
Sinu, P. A. et al. Invasive ant (Anoplolepis gracilipes) disrupts pollination in pumpkin. Biol. Invasions 19, 2599–2607 (2017).Article 

Google Scholar 
Tsang, T. P., Dyer, E. E. & Bonebrake, T. C. Alien species richness is currently unbounded in all but the most urbanized bird communities. Ecography 42, 1426–1435 (2019).Article 

Google Scholar 
D’ettorre, P. Invasive eusocieties: commonalities between ants and humans. In Human Dispersal and Species Movement (eds Boivin, N. et al.) (Cambridge University Press, 2017).
Google Scholar 
Wetterer, J. K. Worldwide spread of the longhorn crazy ant, Paratrechina longicornis (Hymenoptera: Formicidae). Myrmecol. News 11, 137–149 (2008).
Google Scholar 
Lizon à l’Allemand, S. & Witte, V. sophisticated, modular communication contributes to ecological dominance in the invasive ant Anoplolepis gracilipes. Biol. invasions 12, 3551–3561 (2010).Article 

Google Scholar 
Silverman, J. & Buczkowski, G. Behaviours mediating ant invasions. In Biological Invasions and Animal Behaviour (eds Weis, J. S. & Sol, D.) (Cambridge University Press, 2016).
Google Scholar  More

Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. IPBES (2019): Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).CAS 
PubMed 

Google Scholar 
Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5 °C rather than 2 °C. Science 360, 791–795 (2018).CAS 
PubMed 

Google Scholar 
Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl Acad. Sci. USA 109, 8606–8611 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Travis, J. M. J. Climate change and habitat destruction: a deadly anthropogenic cocktail. Proc. Biol. Sci. 270, 467–473 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hill, J. K. et al. Impacts of landscape structure on butterfly range expansion. Ecol. Lett. 4, 313–321 (2001).
Google Scholar 
Guo, F., Lenoir, J. & Bonebrake, T. C. Land-use change interacts with climate to determine elevational species redistribution. Nat. Commun. 9, 1315 (2018).PubMed 
PubMed Central 

Google Scholar 
McLaughlin, J. F., Hellmann, J. J., Boggs, C. L. & Ehrlich, P. R. Climate change hastens population extinctions. Proc. Natl Acad. Sci. USA 99, 6070–6074 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, e157 (2007).PubMed 
PubMed Central 

Google Scholar 
Mantyka-Pringle, C. S. et al. Climate change modifies risk of global biodiversity loss due to land-cover change. Biol. Conserv. 187, 103–111 (2015).
Google Scholar 
Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Forister, M. L. et al. Compounded effects of climate change and habitat alteration shift patterns of butterfly diversity. Proc. Natl Acad. Sci. USA 107, 2088–2092 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Oliver, T. H. & Morecroft, M. D. Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. Wiley Interdiscip. Rev. Clim. Change 5, 317–335 (2014).
Google Scholar 
MacLean, S. A. & Beissinger, S. R. Species’ traits as predictors of range shifts under contemporary climate change: a review and meta-analysis. Glob. Change Biol. 23, 4094–4105 (2017).
Google Scholar 
Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Change 7, 205–208 (2017).
Google Scholar 
Root, T. Energy constraints on avian distributions and abundances. Ecology 69, 330–339 (1988).
Google Scholar 
Whitfield, M. C., Smit, B., McKechnie, A. E. & Wolf, B. O. Avian thermoregulation in the heat: scaling of heat tolerance and evaporative cooling capacity in three southern African arid-zone passerines. J. Exp. Biol. 218, 1705–1714 (2015).PubMed 

Google Scholar 
McKechnie, A. E. et al. Avian thermoregulation in the heat: evaporative cooling in five Australian passerines reveals within-order biogeographic variation in heat tolerance. J. Exp. Biol. 220, 2436–2444 (2017).PubMed 

Google Scholar 
Platts, P. J. et al. Habitat availability explains variation in climate-driven range shifts across multiple taxonomic groups. Sci. Rep. 9, 15039 (2019).PubMed 
PubMed Central 

Google Scholar 
Pearson, R. G. Climate change and the migration capacity of species. Trends Ecol. Evol. 21, 111–113 (2006).PubMed 

Google Scholar 
Partners in Flight. Avian Conservation Assessment Database Version 2021 (accessed 5 February 2021); http://pif.birdconservancy.org/ACADHill, M. J. & Guerschman, J. P. The MODIS global vegetation fractional cover product 2001–2018: characteristics of vegetation fractional cover in grasslands and savanna woodlands. Remote Sens. (Basel) 12, 406 (2020).
Google Scholar 
Wiebe, K. L. & Gerstmar, H. Influence of spring temperatures and individual traits on reproductive timing and success in a migratory woodpecker. Auk 127, 917–925 (2010).
Google Scholar 
Viana, D. S. & Chase, J. M. Ecological traits underlying interspecific variation in climate matching of birds. Glob. Ecol. Biogeogr. 31, 1021–1034 (2022).
Google Scholar 
Kellermann, V., Van Heerwaarden, B., Sgrò, C. M. & Hoffmann, A. A. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science 325, 1244–1246 (2009).CAS 
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).
Google Scholar 
Mason, L. R. et al. Population responses of bird populations to climate change on two continents vary with species’ ecological traits but not with direction of change in climate suitability. Clim. Change 157, 337–354 (2019).
Google Scholar 
Coyle, J. R., Hurlbert, A. H. & White, E. P. Opposing mechanisms drive richness patterns of core and transient bird species. Am. Nat. 181, E83–E90 (2013).PubMed 

Google Scholar 
Valiela, I. & Martinetto, P. Changes in bird abundance in eastern North America: urban sprawl and global footprint? BioScience 57, 360–370 (2007).
Google Scholar 
Smith, S. J., Edmonds, J., Hartin, C. A., Mundra, A. & Calvin, K. Near-term acceleration in the rate of temperature change. Nat. Clim. Change 5, 333–336 (2015).
Google Scholar 
Winkler, K., Fuchs, R., Rounsevell, M. & Herold, M. Global land use changes are four times greater than previously estimated. Nat. Commun. 12, 2501 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).CAS 
PubMed 

Google Scholar 
Currie, D. J. & Venne, S. Climate change is not a major driver of shifts in the geographical distributions of North American birds. Glob. Ecol. Biogeogr. 26, 333–346 (2017).
Google Scholar 
Socolar, J. B., Epanchin, P. N., Beissinger, S. R. & Tingley, M. W. Phenological shifts conserve thermal niches in North American birds and reshape expectations for climate-driven range shifts. Proc. Natl Acad. Sci. USA 114, 12976–12981 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Barnagaud, J.-Y. et al. Relating habitat and climatic niches in birds. PLoS ONE 7, e32819 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ponti, R., Arcones, A., Ferrer, X. & Vieites, D. R. Seasonal climatic niches diverge in migratory birds. Ibis 162, 318–330 (2020).
Google Scholar 
Zurell, D., Gallien, L., Graham, C. H. & Zimmermann, N. E. Do long-distance migratory birds track their niche through seasons? J. Biogeogr. 45, 1459–1468 (2018).
Google Scholar 
Stephens, P. A. et al. Consistent response of bird populations to climate change on two continents. Science 352, 84–87 (2016).CAS 
PubMed 

Google Scholar 
Ralston, J., DeLuca, W. V., Feldman, R. E. & King, D. I. Population trends influence species ability to track climate change. Glob. Change Biol. 23, 1390–1399 (2017).
Google Scholar 
Magurran, A. E. et al. Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. Trends Ecol. Evol. 25, 574–582 (2010).PubMed 

Google Scholar 
Jarzyna, M. A. & Jetz, W. A near half-century of temporal change in different facets of avian diversity. Glob. Change Biol. 23, 2999–3011 (2017).
Google Scholar 
van der Bolt, B., van Nes, E. H., Bathiany, S., Vollebregt, M. E. & Scheffer, M. Climate reddening increases the chance of critical transitions. Nat. Clim. Change 8, 478–484 (2018).
Google Scholar 
Bowler, D. E., Heldbjerg, H., Fox, A. D., O’Hara, R. B. & Böhning-Gaese, K. Disentangling the effects of multiple environmental drivers on population changes within communities. J. Anim. Ecol. 87, 1034–1045 (2018).PubMed 

Google Scholar 
Zurell, D., Graham, C. H., Gallien, L., Thuiller, W. & Zimmermann, N. E. Long-distance migratory birds threatened by multiple independent risks from global change. Nat. Clim. Change 8, 992–996 (2018).
Google Scholar 
Northrup, J. M., Rivers, J. W., Yang, Z. & Betts, M. G. Synergistic effects of climate and land-use change influence broad-scale avian population declines. Glob. Change Biol. 25, 1561–1575 (2019).
Google Scholar 
Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).PubMed 
PubMed Central 

Google Scholar 
Pardieck, K. L., Ziolkowski, D. J. Jr, Lutmerding, M., Aponte, V. & Hudson, M.-A. R. North American Breeding Bird Survey Dataset 1966–2018 Version 2018.0. (US Geological Survey, 2019); https://www.sciencebase.gov/catalog/item/5d65256ae4b09b198a26c1d7Harris, D. J., Taylor, S. D. & White, E. P. Forecasting biodiversity in breeding birds using best practices. PeerJ 6, e4278 (2018).PubMed 
PubMed Central 

Google Scholar 
Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: a Grammar of Data Manipulation. R package version 1.0.0 https://cran.r-project.org/web/packages/dplyr/index.html (2020).Wickham, H. & Henry, L. tidyr: Tidy Messy Data. R package version 1.1.0 https://cran.r-project.org/web/packages/tidyr/index.html (2020).Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.0-12 https://cran.r-project.org/web/packages/raster/index.html (2015).Bivand, R., Pebesma, E. J. & Gómez-Rubio, V. Applied Spatial Data Analysis with R (Springer, 2013).Hijmans, R. J. geosphere: Spherical Trigonometry. R package version 1.5–10 https://cran.r-project.org/web/packages/geosphere/index.html (2019).Hart, E. M. & Bell, K. prism. R package version 0.0.6 https://github.com/ropensci/prism (2015).Senyondo, H. et al. rdataretriever: R interface to the data retriever. J. Open Source Softw. 6, 2800 (2021).
Google Scholar 
Morris, B. D. & White, E. P. The EcoData retriever: improving access to existing ecological data. PLoS ONE 8, e65848 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Senyondo, H. et al. Retriever: data retrieval tool. J. Open Source Softw. 2, 451 (2017).
Google Scholar 
Hurlbert, A. H. & White, E. P. Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications. Ecol. Lett. 8, 319–327 (2005).
Google Scholar 
Harris, D. J. Generating realistic assemblages with a joint species distribution model. Methods Ecol. Evol. 6, 465–473 (2015).
Google Scholar 
Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat. Commun. 11, 2463 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Eyres, A., Böhning-Gaese, K. & Fritz, S. A. Quantification of climatic niches in birds: adding the temporal dimension. J. Avian Biol. 48, 1517–1531 (2017).
Google Scholar 
Martin, A. E. & Fahrig, L. Habitat specialist birds disperse farther and are more migratory than habitat generalist birds. Ecology 99, 2058–2066 (2018).PubMed 

Google Scholar 
Sauer, J. R. & Link, W. A. Analysis of the North American Breeding Bird Survey using hierarchical models. Auk 128, 87–98 (2011).
Google Scholar 
García Molinos, J., Schoeman, D. S., Brown, C. J. & Burrows, M. T. VoCC: an R package for calculating the velocity of climate change and related climatic metrics. Methods Ecol. Evol. 10, 2195–2202 (2019).
Google Scholar 
Krenek, S., Berendonk, T. U. & Petzoldt, T. Thermal performance curves of Paramecium caudatum: a model selection approach. Eur. J. Protistol. 47, 124–137 (2011).PubMed 

Google Scholar 
Bahn, V. & McGill, B. J. Can niche-based distribution models outperform spatial interpolation? Glob. Ecol. Biogeogr. 16, 733–742 (2007).
Google Scholar 
Dobson, L. L., La Sorte, F. A., Manne, L. L. & Hawkins, B. A. The diversity and abundance of North American bird assemblages fail to track changing productivity. Ecology 96, 1105–1114 (2015).PubMed 

Google Scholar 
Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).
Google Scholar 
Tikhonov, G. et al. Joint species distribution modelling with the R-package HMSC. Methods Ecol. Evol. 11, 442–447 (2020).PubMed 
PubMed Central 

Google Scholar 
Greenwell, B., Boehmke, B., Cunningham, J. & GBM Developers. gbm: Generalized boosted regression models. R package version 2.1.5 https://cran.r-project.org/web/packages/gbm/index.html (2019).Wood, S. N. Generalized Additive Models: an Introduction with R (CRC Press/Taylor & Francis Group, 2017).Jetz, 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).CAS 
PubMed 

Google Scholar 
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 

Google Scholar 
Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Google Scholar 
Stan Development Team. Stan Modeling Language Users Guide and Reference Manual (2020); https://mc-stan.org/users/documentation/ More