Philippa Kaur

Glasgow Leaders’ Declaration on Forests and Land Use (UNFCCC, 2021); https://go.nature.com/3FmrE2iIPCC: Summary for Policymakers. In Special Report on Climate Change and Land (eds Shukla, P. R. et al.) (WMO, 2019); https://go.nature.com/3itqkRWTomppo, E. et al. National Forest Inventories: Pathways for Common Reporting (Springer, 2010).Jeanjean, H. & Achard, F. Int. J. Remote Sens. 18, 2455–2461 (1997).Article 

Google Scholar 
Ceccherini, G. et al. Nature 583, 72–77 (2020).CAS 
Article 

Google Scholar 
Palahí, M. et al. Nature 592, E15–E17 (2021).Article 

Google Scholar 
Breidenbach, J. et al. Ann. For. Sci. 79, 2 (2022).Article 

Google Scholar 
ForestPlots.net Forest. et al. Biol. Conserv. 260, 108849 (2021).Article 

Google Scholar 
A Fresh Perspective: Global Forest Resources Assessment 2020 (FAO, 2020); https://go.nature.com/3uhpfBZCurtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Science 361, 1108–1111 (2018).CAS 
Article 

Google Scholar 
Chazdon, R. L. et al. Ambio 45, 538–550 (2016).Article 

Google Scholar 
Sasaki, N. & Putz, F. E. Conserv. Lett. 2, 226–232 (2009).Article 

Google Scholar 
Wulder, M. A. & Coops, N. C. Nature 513, 30–31 (2014).CAS 
Article 

Google Scholar 
Reiche, J. et al. Nat. Clim. Change 6, 120–122 (2016).Article 

Google Scholar 
Gorelick, N. et al. Remote Sens. Environ. 202, 18–27 (2017).Article 

Google Scholar 
Valbuena, R. et al. Trends Ecol. Evol. 35, 656–667 (2020).CAS 
Article 

Google Scholar 
Porter-Bolland, L. et al. For. Ecol. Manage. 268, 6–17 (2012).Article 

Google Scholar 
Boissière, M. et al. PLoS ONE 12, e0176897 (2017).Article 

Google Scholar 
Armenteras, D. Nat. Ecol. Evol. 5, 1193–1194 (2021).Article 

Google Scholar 
Forest Information System for Europe (FISE) (EEA, 2022); https://go.nature.com/3D1CcUw More

Biamonte, E., Sandoval, L., Chacón, E. & Barrantes, G. Effect of urbanization on the avifauna in a tropical metropolitan area. Landsc. Ecol. 26, 183–194 (2011).Article 

Google Scholar 
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).Article 

Google Scholar 
Montgomery, M. R. The urban transformation of the developing world. Science 319, 761–764 (2008).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Nuissl, H. & Siedentop, S. Urbanisation and Land Use Change. In Sustainable Land Management in a European Context: A Co-Design Approach (eds Weith, T. et al.) 75–99 (Springer International Publishing, 2021). https://doi.org/10.1007/978-3-030-50841-8_5.Chapter 

Google Scholar 
Scolozzi, R. & Geneletti, D. A multi-scale qualitative approach to assess the impact of urbanization on natural habitats and their connectivity. Environ. Impact Assess. Rev. 36, 9–22 (2012).Article 

Google Scholar 
Pauchard, A., Aguayo, M., Peña, E. & Urrutia, R. Multiple effects of urbanization on the biodiversity of developing countries: The case of a fast-growing metropolitan area (Concepción, Chile). Biol. Conserv. 127, 272–281 (2006).Article 

Google Scholar 
Xu, X., Xie, Y., Qi, K., Luo, Z. & Wang, X. Detecting the response of bird communities and biodiversity to habitat loss and fragmentation due to urbanization. Sci. Total Environ. 624, 1561–1576 (2018).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Bélisle, M. & St. Clair, C. C. Cumulative effects of barriers on the movements of forest birds. Conserv. Ecol. 5, 9; http://www.consecol.org/vol5/iss2/art9 (2001).Blair, R. B. Land use and avian species diversity along an urban gradient. Ecol. Appl. 6, 506–519 (1996).Article 

Google Scholar 
Tremblay, M. A. & St Clair, C. C. Permeability of a heterogeneous urban landscape to the movements of forest songbirds. J. Appl. Ecol. 48, 679–688 (2011).Article 

Google Scholar 
Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, 8327 (2017).Article 
CAS 

Google Scholar 
Isaksson, C. Impact of Urbanization on Birds. In Bird Species: How They Arise, Modify and Vanish (ed. Tietze, D. T.) 235–257 (Springer International Publishing, Berlin, 2018). https://doi.org/10.1007/978-3-319-91689-7_13.Chapter 

Google Scholar 
Miles, L. S., Rivkin, L. R., Johnson, M. T. J., Munshi-South, J. & Verrelli, B. C. Gene flow and genetic drift in urban environments. Mol. Ecol. 28, 4138–4151 (2019).PubMed 
Article 

Google Scholar 
Delaney, K. S., Riley, S. P. D. & Fisher, R. N. A rapid, strong, and convergent genetic response to urban Habitat fragmentation in four divergent and widespread vertebrates. PLoS ONE 5, e12767 (2010).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Unfried, T. M., Hauser, L. & Marzluff, J. M. Effects of urbanization on Song Sparrow (Melospiza melodia) population connectivity. Conserv. Genet. 14, 41–53 (2013).Article 

Google Scholar 
Brewer, V. N., Lane, S. J., Sewall, K. B. & Mabry, K. E. Effects of low-density urbanization on genetic structure in the Song Sparrow. PLoS ONE 15, e0234008 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Björklund, M., Ruiz, I. & Senar, J. C. Genetic differentiation in the urban habitat: the great tits (Parus major) of the parks of Barcelona city. Biol. J. Linn. Soc. 99, 9–19 (2010).Article 

Google Scholar 
Perrier, C. et al. Great tits and the city: Distribution of genomic diversity and gene–environment associations along an urbanization gradient. Evol. Appl. 11, 593–613 (2018).PubMed 
Article 

Google Scholar 
Tan, D. J. X. et al. Novel genome and genome-wide SNPs reveal early fragmentation effects in an edge-tolerant songbird population across an urbanized tropical metropolis. Sci. Rep. 8, 12804 (2018).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
McRae, B. H., Dickson, B. G., Keitt, T. H. & Shah, V. B. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology 89, 2712–2724 (2008).PubMed 
Article 

Google Scholar 
Howell, S. N. G. & Webb, S. A Guide to the Birds of Mexico and Northern Central America (Oxford University Press, 1995).
Google Scholar 
Sandoval, L. & Mennill, D. J. Breeding biology of White-eared Ground-sparrow (Melozone leucotis), with a description of a new nest type. Ornitol. Neotropical 23, 225–234 (2012).
Google Scholar 
Stiles, F. G. & Skutch, A. F. A Guide to the Birds of Costa Rica (Cornell University Press, 1989).
Google Scholar 
Carlson, T. N. & Sanchez-Azofeifa, G. A. Satellite remote sensing of land use changes in and around San José Costa Rica. Remote Sens. Environ. 70, 247–256 (1999).Article 
ADS 

Google Scholar 
Sánchez, J. E., Criado, J., Sánchez, C. & Sandoval, L. Costa Rica. In Important Bird Areas of Americas: priority sites for biodiversity conservation (eds Davendish, C. et al.) 149–156 (Birdlife International, 2009).
Google Scholar 
Sandoval, L. et al. The forgotten habitats in conservation: early successional vegetation. Rev. Biol. Trop. 67, 36–52 (2019).Article 

Google Scholar 
Juárez, R., Chacón-Madrigal, E. & Sandoval, L. Urbanization has opposite effects on the territory size of two passerine birds. Avian Res. 11, 11 (2020).Article 

Google Scholar 
Skutch, A. F. Life history of the Southern House Wren. Condor 55, 121–149 (1953).Article 

Google Scholar 
Johnson, L. S. House Wren (Troglodytes aedon), Version 10. In Birds of the World (ed. Poole, A. F.) (Cornell Lab of Ornithology, 2020).
Google Scholar 
Markowski, M. et al. Genetic structure of urban and non-urban populations differs between two common parid species. Sci. Rep. 11, 10428. https://doi.org/10.1038/s41598-021-89847-4 (2021).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Mueller, J. C. et al. Evolution of genomic variation in the burrowing owl in response to recent colonization of urban areas. Proc. R. Soc. B Biol. Sci. 285, 20180206 (2018).Article 

Google Scholar 
Vangestel, C. et al. Genetic diversity and population structure in contemporary house sparrow populations along an urbanization gradient. Heredity 109, 163–172 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Joyce, A. T. Land Use Change in Costa Rica: 1966–2006, as Influenced by Social, Economic, Political, and Environmental Factors (Litografía e imprenta LIL, 2016).
Google Scholar 
Fuchs, E. J. & Hamrick, J. L. Mating system and pollen flow between remnant populations of the endangered tropical tree, Guaiacum sanctum (Zygophyllaceae). Conserv. Genet. 12, 175–185 (2011).Article 

Google Scholar 
Stevens, K., Harrisson, K. A., Hogan, F. E., Cooke, R. & Clarke, R. H. Reduced gene flow in a vulnerable species reflects two centuries of habitat loss and fragmentation. Ecosphere 9, e02114 (2018).Article 

Google Scholar 
Quesada-Román, A., Villalobos-Portilla, E. & Campos-Durán, D. Hydrometeorological disasters in urban areas of Costa Rica Central America. Environ. Hazards 20, 264–278 (2021).Article 

Google Scholar 
Muñoz, P., García-Rodríguez, A. & Sandoval, L. Urbanization, habitat extension and spatial pattern, threaten a Costa Rican endemic bird. Rev. Biol. Trop. 69, 170–180 (2021).
Google Scholar 
Sandoval, L., Bitton, P. P., Doucet, S. M. & Mennill, D. J. Analysis of plumage, morphology, and voice reveals species-level differences between two subspecies of Prevost’s Ground-sparrow Melozone biarcuata (Prévost and Des Murs) (Aves: Emberizidae). Zootaxa 3895, 103–116 (2014).PubMed 
Article 

Google Scholar 
Arguedas, N. & Parker, P. G. Seasonal migration and genetic population structure in House Wrens. Condor 102, 517–528 (2000).Article 

Google Scholar 
Pujol, R. & Pérez, E. Crecimiento urbano en la región metropolitana de San José, Costa Rica. Una exploración espacial y temporal de los determinantes del cambio de uso del suelo, 1986–2010. Lincoln Institute of Land Policy https://www.lincolninst.edu/sites/default/files/pubfiles/2242_1578_Pujol_WP13RP1SP.pdf (2012).Sandoval, L., Dabelsteen, T. & Mennill, D. J. Transmission characteristics of solo songs and duets in a neotropical thicket habitat specialist bird. Bioacoustics 24, 289–306 (2015).Article 

Google Scholar 
Sandoval, L., Méndez, C. & Mennill, D. J. Vocal behaviour of White-eared Ground-sparrows (Melozone leucotis) during the breeding season: repertoires, diel variation, behavioural contexts, and individual distinctiveness. J. Ornithol. 157, 1–12 (2016).Article 

Google Scholar 
Carro, M. E., Llambías, P. E., Mahler, B. & Fernández, G. J. Contrasting patterns of natal dispersal of a south temperate House Wren population at local and regional scales. J. Ornithol. 162, 895–907 (2021).Article 

Google Scholar 
Garrigues, R. & Dean, R. The Birds of Costa Rica: A Field Guide (Cornell University Press, 2014).
Google Scholar 
Sandoval, L., Epperly, K. L., Klicka, J. & Mennill, D. J. The biogeographic and evolutionary history of an endemic clade of Middle American sparrows: Melozone and Aimophila (Aves: Passerellidae). Mol. Phylogenet. Evol. 110, 50–59 (2017).PubMed 
Article 

Google Scholar 
MacGregor-Fors, I. & Escobar-Ibáñez, J. F. Birds from Urban Latin America, Where Economic Inequality and Urbanization Meet Biodiversity. In Avian Ecology in Latin American Cityscapes (eds MacGregor-Fors, I. & Escobar-Ibáñez, J. F.) 1–10 (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-63475-3_1.Chapter 

Google Scholar 
MacGregor-Fors, I. & García-Arroyo, M. Who Is Who in the City? Bird Species Richness and Composition in Urban Latin America. In Avian Ecology in Latin American Cityscapes (eds MacGregor-Fors, I. & Escobar-Ibáñez, J. F.) 33–55 (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-63475-3_3.Chapter 

Google Scholar 
Lande, R. & Barrowclough, G. F. Effective population size, genetic variation, and their use in population management. In Viable Populations for Conservation (ed. Soulé, M. E.) 87–124 (Cambridge University Press, 1987). https://doi.org/10.1017/CBO9780511623400.007.Chapter 

Google Scholar 
Newman, D. & Pilson, D. Increased probability of extinction due to decreased genetic effective population size: Experimental populations of Clarkia Pulchella. Evolution 51, 354–362 (1997).PubMed 
Article 

Google Scholar 
Longmire, J. L., Maltbie, M. & Baker, R. J. Use of ‘Lysis Buffer’ in DNA isolation and its implication for museum collections (Museum of Texas Tech University, 1997).Book 

Google Scholar 
Bulgin, N. L., Gibbs, H. L., Vickery, P. & Baker, A. J. Ancestral polymorphisms in genetic markers obscure detection of evolutionarily distinct populations in the endangered Florida grasshopper sparrow (Ammodramus savannarum floridanus). Mol. Ecol. 12, 831–844 (2003).CAS 
PubMed 
Article 

Google Scholar 
Hanotte, O. et al. Isolation and characterization of microsatellite loci in a passerine bird: the reed bunting Emberiza schoeniclus. Mol. Ecol. 3, 529–530 (1994).CAS 
PubMed 
Article 

Google Scholar 
Jeffery, K. J., Keller, L. F., Arcese, P. & Bruford, M. W. The development of microsatellite loci in the song sparrow, Melospiza melodia (Aves) and genotyping errors associated with good quality DNA. Mol. Ecol. Notes 1, 11–13 (2001).CAS 
Article 

Google Scholar 
Petren, K. Microsatellite primers from Geospiza fortis and cross-species amplification in Darwin’s finches. Mol. Ecol. 7, 1782–1784 (1998).CAS 
PubMed 
Article 

Google Scholar 
Brar, R. K. et al. Eleven microsatellite loci isolated from the banded wren (Thryothorus pleurostictus). Mol. Ecol. Notes 7, 69–71 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cabe, P. R. & Marshall, K. E. Microsatellite loci from the house wren (Troglodytes aedon). Mol. Ecol. Notes 1, 155–156 (2001).CAS 
Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ (2021).RStudio Team. RStudio: integrated development environment for R. RStudio http://www.rstudio.com/ (2021).Goudet, J. hierfstat, a package for r to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-Statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).CAS 
PubMed 

Google Scholar 
Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
Article 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 

Google Scholar 
Shah, V. B. & McRae, B. Circuitscape: A Tool for Landscape Ecology. in Proceedings of the 7th Python in Science Conference (eds. Varoquaux, G., Vaught, T. & Millman, J.) 62–65 (2008).Ortiz-Malavasi, E. Atlas digital de costa rica está a disposición del público. Invest. TEC 23, 1659–3383 (2015).
Google Scholar 
McRae, B., Shirk, A. & Platt, J. Gnarly landscape utilities: Resistance and habitat calculator user guide. The Nature Conservancy https://circuitscape.org/gnarly-landscape-utilities/ (2013).Kass, J. M. et al. Wallace: A flexible platform for reproducible modeling of species niches and distributions built for community expansion. Methods Ecol. Evol. 9, 1151–1156 (2018).Article 

Google Scholar  More

Rochman CM. Microplastics research—from sink to source. Science. 2018;360:28–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Galloway TS, Cole M, Lewis C. Interactions of microplastic debris throughout the marine ecosystem. Nat Ecol Evol. 2017;1:116.PubMed 
Article 
PubMed Central 

Google Scholar 
Hale RC, Seeley ME, La Guardia MJ, Mai L, Zeng EY. A global perspective on microplastics. J Geophys Res Oceans. 2020;125:1–40.Article 

Google Scholar 
Harrison JP, Hoellein TJ, Sapp M, Tagg AS, Ju-Nam Y, Ojeda JJ. Microplastic-associated biofilms: a comparison of freshwater and marine environments. In: Freshwater microplastics. Cham: Springer; 2018. p. 181–201.Dunne WM Jr. Bacterial adhesion: seen any good biofilms lately? Clin Microbiol Rev. 2002;15:155–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dang H, Lovell CR. Microbial surface colonization and biofilm development in marine environments. Microbiol Mol Biol Rev. 2016;80:91–138.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
McCormick AR, Hoellein TJ, London MG, Hittie J, Scott JW, Kelly JJ. Microplastic in surface waters of urban rivers: concentration, sources, and associated bacterial assemblages. Ecosphere. 2016;7:e01556.Article 

Google Scholar 
Kesy K, Oberbeckmann S, Kreikemeyer B, Labrenz M. Spatial environmental heterogeneity determines young biofilm assemblages on microplastics in Baltic Sea mesocosms. Front Microbiol. 2019;10:1665.PubMed 
PubMed Central 
Article 

Google Scholar 
Oberbeckmann S, Loeder MG, Gerdts G, Osborn AM. Spatial and seasonal variation in diversity and structure of microbial biofilms on marine plastics in Northern European waters. FEMS Microbiol Ecol. 2014;90:478–92.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Masó M, Garcés E, Pagès F, Camp J. Drifting plastic debris as a potential vector for dispersing Harmful Algal Bloom (HAB) species. Sci Mar. 2003;67:107–11.Article 

Google Scholar 
Kirstein IV, Kirmizi S, Wichels A, Garin-Fernandez A, Erler R, Loder M, et al. Dangerous hitchhikers? Evidence for potentially pathogenic Vibrio spp. on microplastic particles. Mar Environ Res. 2016;120:1–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Zettler ER, Mincer TJ, Amaral-Zettler LA. Life in the “plastisphere”: microbial communities on plastic marine debris. Environ Sci Technol. 2013;47:7137–46.CAS 
Article 

Google Scholar 
Oberbeckmann S, Kreikemeyer B, Labrenz M. Environmental factors support the formation of specific bacterial assemblages on microplastics. Front Microbiol. 2018;8:2709.PubMed 
PubMed Central 
Article 

Google Scholar 
Dussud C, Meistertzheim AL, Conan P, Pujo-Pay M, George M, Fabre P, et al. Evidence of niche partitioning among bacteria living on plastics, organic particles and surrounding seawaters. Environ Pollut. 2018;236:807–16.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Frère L, Maignien L, Chalopin M, Huvet A, Rinnert E, Morrison H, et al. Microplastic bacterial communities in the Bay of Brest: Influence of polymer type and size. Environ Pollut. 2018;242:614–25.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
Amaral-Zettler LA, Zettler ER, Slikas B, Boyd GD, Melvin DW, Morrall CE, et al. The biogeography of the Plastisphere: implications for policy. Front Ecol Environ. 2015;13:541–6.Article 

Google Scholar 
Amaral-Zettler LA, Ballerini T, Zettler ER, Asbun AA, Adame A, Casotti R, et al. Diversity and predicted inter- and intra-domain interactions in the Mediterranean Plastisphere. Environ Pollut. 2021;286.Li W, Zhang Y, Wu N, Zhao Z, Xu W, Ma Y, et al. Colonization characteristics of bacterial communities on plastic debris influenced by environmental factors and polymer types in the Haihe Estuary of Bohai Bay, China. Environ Sci Technol. 2019;53:10763–73.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Oberbeckmann S, Labrenz M. Marine microbial assemblages on microplastics: diversity, adaptation, and role in degradation. Ann Rev Mar Sci. 2020;12:209–32.PubMed 
Article 
PubMed Central 

Google Scholar 
Yang Y, Liu W, Zhang Z, Grossart HP, Gadd GM. Microplastics provide new microbial niches in aquatic environments. Appl Microbiol Biotechnol. 2020;104:6501–11.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lebreton LCM, van der Zwet J, Damsteeg JW, Slat B, Andrady A, Reisser J. River plastic emissions to the world’s oceans. Nat Commun. 2017;8:15611.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Song J, Jongmans-Hochschulz E, Mauder N, Imirzalioglu C, Wichels A, Gerdts G. The Travelling Particles: Investigating microplastics as possible transport vectors for multidrug resistant E. coli in the Weser estuary (Germany). Sci Total Environ. 2020;720:137603.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1–e.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.PubMed 
PubMed Central 
Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Janssen S, McDonald D, Gonzalez A, Navas-Molina JA, Jiang L, Xu ZZ, et al. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems. 2018;3:e00021-18.PubMed 
PubMed Central 
Article 

Google Scholar 
Team RC. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2013.Beule L, Karlovsky P. Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ. 2020;8:e9593.PubMed 
PubMed Central 
Article 

Google Scholar 
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’hara R, et al. Package ‘vegan’. Community ecology package, version. 2013;2:1–295.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Dinno A. dunn. test: Dunn’s test of multiple comparisons using rank sums. R package version. Vienna, Austria: R Foundation for Statistical Computing. 2017;1:1.Foster ZS, Sharpton TJ, Grunwald NJ. Metacoder: an R package for visualization and manipulation of community taxonomic diversity data. PLoS Comput Biol. 2017;13:e1005404.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Clarke K, Gorley R. Getting started with PRIMER v7. PRIMER-E, 20. Plymouth: Plymouth Marine Laboratory; 2015.Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H. UpSet: visualization of intersecting sets. IEEE Trans Vis Comput Graph. 2014;20:1983–92.PubMed 
PubMed Central 
Article 

Google Scholar 
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Anderson MJ. Permutational multivariate analysis of variance (PERMANOVA). Wiley Statsref: Statistics Reference Online; 2014. p. 1–15.Baselga A, Orme CDL. betapart: an R package for the study of beta diversity. Methods Ecol Evol. 2012;3:808–12.Article 

Google Scholar 
Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2016.Stegen JC, Lin X, Fredrickson JK, Konopka AE. Estimating and mapping ecological processes influencing microbial community assembly. Front Microbiol. 2015;6:370.PubMed 
PubMed Central 
Article 

Google Scholar 
Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79.PubMed 
PubMed Central 
Article 

Google Scholar 
Richter-Heitmann T, Hofner B, Krah FS, Sikorski J, Wust PK, Bunk B, et al. Stochastic dispersal rather than deterministic selection explains the spatio-temporal distribution of soil bacteria in a temperate grassland. Front Microbiol. 2020;11:1391.PubMed 
PubMed Central 
Article 

Google Scholar 
Chase JM, Kraft NJ, Smith KG, Vellend M, Inouye BD. Using null models to disentangle variation in community dissimilarity from variation in α‐diversity. Ecosphere. 2011;2:1–11.Article 

Google Scholar 
Miao L, Wang P, Hou J, Yao Y, Liu Z, Liu S, et al. Distinct community structure and microbial functions of biofilms colonizing microplastics. Sci Total Environ. 2019;650:2395–402.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Cai L, Wu D, Xia J, Shi H, Kim H. Influence of physicochemical surface properties on the adhesion of bacteria onto four types of plastics. Sci Total Environ. 2019;671:1101–7.CAS 
Article 

Google Scholar 
Wang L, Luo Z, Zhen Z, Yan Y, Yan C, Ma X, et al. Bacterial community colonization on tire microplastics in typical urban water environments and associated impacting factors. Environ Pollut. 2020;265:114922.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Vukanti R, Crissman M, Leff LG, Leff AA. Bacterial communities of tyre monofill sites: growth on tyre shreds and leachate. J Appl Microbiol. 2009;106:1957–66.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Wagner S, Huffer T, Klockner P, Wehrhahn M, Hofmann T, Reemtsma T. Tire wear particles in the aquatic environment – a review on generation, analysis, occurrence, fate and effects. Water Res. 2018;139:83–100.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Degaffe FS, Turner A. Leaching of zinc from tire wear particles under simulated estuarine conditions. Chemosphere. 2011;85:738–43.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Halsband C, Sørensen L, Booth AM, Herzke D. Car tire crumb rubber: does leaching produce a toxic chemical cocktail in coastal marine systems? Front Environ Sci. 2020;8:1–15.Article 

Google Scholar 
Thavamani P, Malik S, Beer M, Megharaj M, Naidu R. Microbial activity and diversity in long-term mixed contaminated soils with respect to polyaromatic hydrocarbons and heavy metals. J Environ Manage. 2012;99:10–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Toshchakov SV, Korzhenkov AA, Chernikova TN, Ferrer M, Golyshina OV, Yakimov MM, et al. The genome analysis of Oleiphilus messinensis ME102 (DSM 13489(T)) reveals backgrounds of its obligate alkane-devouring marine lifestyle. Mar. Genomics. 2017;36:41–7.Article 

Google Scholar 
Love CR, Arrington EC, Gosselin KM, Reddy CM, Van Mooy BAS, Nelson RK, et al. Microbial production and consumption of hydrocarbons in the global ocean. Nat Microbiol. 2021;6:489–98.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Ribicic D, McFarlin KM, Netzer R, Brakstad OG, Winkler A, Throne-Holst M, et al. Oil type and temperature dependent biodegradation dynamics – combining chemical and microbial community data through multivariate analysis. BMC Microbiol. 2018;18:83.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ribicic D, Netzer R, Hazen TC, Techtmann SM, Drablos F, Brakstad OG. Microbial community and metagenome dynamics during biodegradation of dispersed oil reveals potential key-players in cold Norwegian seawater. Mar Pollut Bull. 2018;129:370–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Rezaei Somee M, Dastgheib SMM, Shavandi M, Ghanbari Maman L, Kavousi K, Amoozegar MA, et al. Distinct microbial community along the chronic oil pollution continuum of the Persian Gulf converge with oil spill accidents. Sci Rep. 2021;11:11316.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ren X, Tang J, Wang L, Sun H. Combined effects of microplastics and biochar on the removal of polycyclic aromatic hydrocarbons and phthalate esters and its potential microbial ecological mechanism. Front Microbiol. 2021;12:647766.PubMed 
PubMed Central 
Article 

Google Scholar 
Dussud C, Hudec C, George M, Fabre P, Higgs P, Bruzaud S, et al. Colonization of non-biodegradable and biodegradable plastics by marine microorganisms. Front Microbiol. 2018;9:1571.PubMed 
PubMed Central 
Article 

Google Scholar 
Vaksmaa A, Knittel K, Abdala Asbun A, Goudriaan M, Ellrott A, Witte HJ, et al. Microbial communities on plastic polymers in the Mediterranean Sea. Front Microbiol. 2021;12:673553.PubMed 
PubMed Central 
Article 

Google Scholar 
Pinto M, Langer TM, Huffer T, Hofmann T, Herndl GJ. The composition of bacterial communities associated with plastic biofilms differs between different polymers and stages of biofilm succession. PLoS ONE. 2019;14:e0217165.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Erni-Cassola G, Wright RJ, Gibson MI, Christie-Oleza JA. Early colonization of weathered polyethylene by distinct bacteria in Marine Coastal Seawater. Microb Ecol. 2020;79:517–26.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Berry D, Gutierrez T. Evaluating the detection of hydrocarbon-degrading bacteria in 16S rRNA gene sequencing surveys. Front Microbiol. 2017;8:896.PubMed 
PubMed Central 
Article 

Google Scholar 
Jiang P, Zhao S, Zhu L, Li D. Microplastic-associated bacterial assemblages in the intertidal zone of the Yangtze Estuary. Sci Total Environ. 2018;624:48–54.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Dang H, Li T, Chen M, Huang G. Cross-ocean distribution of Rhodobacterales bacteria as primary surface colonizers in temperate coastal marine waters. Appl Environ Microbiol. 2008;74:52–60.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J. 2017;11:853–62.PubMed 
PubMed Central 
Article 

Google Scholar 
Tobias-Hunefeldt S. Community assembly drivers shift from bottom-up to top-down in a maturing in situ marine biofilm model. University of Otago; 2020.Stegen JC, Lin X, Konopka AE, Fredrickson JK. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 2012;6:1653–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lozupone CA, Knight R. Global patterns in bacterial diversity. Proc Natl Acad Sci USA. 2007;104:11436–40.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vellend M. Conceptual synthesis in community ecology. Q Rev Biol. 2010;85:183–206.PubMed 
Article 
PubMed Central 

Google Scholar 
Rocca JD, Simonin M, Bernhardt ES, Washburne AD, Wright JP. Rare microbial taxa emerge when communities collide: freshwater and marine microbiome responses to experimental mixing. Ecology. 2020;101:e02956.PubMed 
Article 
PubMed Central 

Google Scholar 
Crump BC, Hopkinson CS, Sogin ML, Hobbie JE. Microbial biogeography along an estuarine salinity gradient: combined influences of bacterial growth and residence time. Appl Environ Microbiol. 2004;70:1494–505.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stewart PS. Diffusion in biofilms. J Bacteriol. 2003;185:1485–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Palomo A, Dechesne A, Smets BF. Genomic profiling of Nitrospira species reveals ecological success of comammox Nitrospira. 2019. https://www.biorxiv.org/content/10.1101/612226v1.Kielak AM, van Veen JA, Kowalchuk GA. Comparative analysis of acidobacterial genomic fragments from terrestrial and aquatic metagenomic libraries, with emphasis on acidobacteria subdivision 6. Appl Environ Microbiol. 2010;76:6769–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McCormick A, Hoellein TJ, Mason SA, Schluep J, Kelly JJ. Microplastic is an abundant and distinct microbial habitat in an urban river. Environ Sci Technol. 2014;48:11863–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Teixeira L, Merquior V. The family moraxellaceae. The prokaryotes: Gammaproteobacteria. Berlin: Springer. 2014. p. 443–76.Stalder T, Press MO, Sullivan S, Liachko I, Top EM. Linking the resistome and plasmidome to the microbiome. ISME J. 2019;13:2437–46.PubMed 
PubMed Central 
Article 

Google Scholar 
Lu SY, Zhang YL, Geng SN, Li TY, Ye ZM, Zhang DS, et al. High diversity of extended-spectrum beta-lactamase-producing bacteria in an urban river sediment habitat. Appl Environ Microbiol. 2010;76:5972–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Lawrence, H. In City Trees: A Historical Geography from the Renaissance through to the Nineteenth Century (Charlottesville and London: University of Virginia Press, 2006, Lewis Mumford. The City in History: Its Origins, Its Transformations and Its Prospects (San Diego: Harvest Book Harcourt, 1961).Frawley, J. Campaigning for street trees, Sydney botanic gardens, 1890s–1920s. Environ. Hist. 15(3), 303–322. https://doi.org/10.3197/096734009X12474738199953 (2009).Article 

Google Scholar 
Seburanga, J. L., Kaplin, B. A., Zhang, Q.-X. & Gatesire, T. Amenity trees and green space structure in urban settlements of Kigali, Rwanda. Urban. For. Urban Green. 13(84–9313), 84–93. https://doi.org/10.1016/j.ufug.2013.08.001 (2014).Article 

Google Scholar 
Wilson, E. H. Northern trees in southern lands. J. Arnold Arbor. 4(2), 61–90 (1923).Article 

Google Scholar 
Gwedla, N. & Shackleton, C. M. Population size and development history determine street tree distribution and composition within and between Eastern Cape towns, South Africa. Urban. For. Urban. Gree. 25, 11–18. https://doi.org/10.1016/j.ufug.2017.04.014 (2017).Article 

Google Scholar 
Jacobs, A. B., Macdonald, E. & Rofé, Y. In The Boulevard Book: History, Evolution, Design of Multiway Boulevards (MIT Press, Cambridge, MA 2002), Robinson, W. The Parks and Gardens of Paris Considered in Relation to the Wants of Other Cities and of Private and Public Gardens (McMillan and Co., London , 1878).Akbari, A. H., Pomerantz, M. & Taha, H. Cool surfaces and shade trees to reduce energy use and improve air quality in urban. Sol. Energy. 70(3), 295–310 (2001).ADS 
Article 

Google Scholar 
Roy, S., Byrne, J. & Pickering, C. A systematic quantitative review of urban tree benefits, costs, and assessment methods across cities in different climatic zones. Urban For. Urban Green. 11, 351–363. https://doi.org/10.1016/j.ufug.2012.06.006 (2012).Article 

Google Scholar 
Schäffler, A. & Swilling, M. Valuing green infrastructure in an urban environment under pressure—The Johannesburg case. Ecol. Econ. 86, 246–257. https://doi.org/10.1016/j.ecolecon.2012.05.008 (2013).Article 

Google Scholar 
Santamour, F. S. Trees for urban planting: Diversity, uniformity and common sense. In Proceedings of the 7th Conference of the Metropolitan Tree Improvement Alliance (METRIA), vol. 7, 57–65 (1990).Shams, Z. I. Changes in diversity and composition of flora along a corridor of different land uses in Karachi over 20 years: caUses and implications. Urban. For. Urban Green. 17, 71–79. https://doi.org/10.1016/j.ufug.2016.03.002 (2016).Article 

Google Scholar 
Kambites, C. & Owen, S. Renewed prospects for green infrastructure planning in the UK. Plan. Prac. Res. 21(94), 483–496. https://doi.org/10.1080/02697450601173413 (2006).Article 

Google Scholar 
Cho, M. A., Malahlelac, O. & Ramoeloa, A. Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications: Dukuduku forest patch as case study. Int. J. Appl. Earth. Obs. 38, 349–357. https://doi.org/10.1016/j.jag.2015.01.015 (2015).Article 

Google Scholar 
Niculescu, S., Lardeux, C., Grigoras, I., Hanganu, J. & David, L. Synergy between LiDAR, RADARSAT-2, and spot-5 images for the detection and mapping of wetland vegetation in the Danube Delta. IEEE J Sel. Top. Appl. Earth. Obs. Remote Sens. 9, 3651–3666 (2016).ADS 
Article 

Google Scholar 
Lefebvre, A., Picand, P.-A. & Sannier, C. Mapping tree cover in European cities: Comparison of classification algorithms for an operational production framework. In 2015 Joint Urban Remote Sensing Event (JURSE), IEEE, 1–4 (2015) https://doi.org/10.1109/JURSE.2015.7120511.Wyndham, C. H., Strydom, N. B., Van Rensburg, A. J. & Rogers, G. G. Effects on maximal oxygen intake of acute changes in altitude in a deep mine. J. Appl. Physiol. 29(5), 552–555 (1970).CAS 
Article 

Google Scholar 
Hegnauer, R. Chemotaxonomie der Pflanzen, vol. 3, 268–281 (Birkhäuser Verlag, Basel, 1964).Mabberley, D. J. The Plant-Book, 2nd edn. 87, 368–369 (Cambridge University Press, Cambridge, 1997).Gachet, M. S. & Schühly, W. Jacaranda—An ethnopharmacological and phytochemical review. J. Ethnopharmacol. 121, 14–27. https://doi.org/10.1016/j.jep.2008.10.015 (2009).CAS 
Article 
PubMed 

Google Scholar 
Gilman, E. F. & Watson, D. G. Jacaranda mimosifolia. Fact Sheet ST-317, Environmental Horticulture Department, Florida Cooperative Extension Service, University of Florida, Gainesville, http://www.ci.milpitas.ca.gov/_pdfs/council/2016/021616/item_04.pdf Accessed 6 June 2020 (1993).Dineva, S. B. Comparative studies of the leaf morphology and structure of white ash Fraxinus americana L. and London plane tree Platanus acerifolia Willd growing in polluted area. Dendrobiology 52, 3–8 (2004).
Google Scholar 
Liu, G., Li, Z. & Bao, M. Colchicine-induced chromosome doubling in Platanus acerifolia and its effect on plant morphology. Euphytica 157, 145–154. https://doi.org/10.1007/s10681-007-9406-6 (2007).Article 

Google Scholar 
Henry, A. & Flood, M. G. The history of the London plane, Platanus acerifolia, with notes on the Genus Platanus. Proc. R. Irish Acad Sect. B Biol. Geol. Chem. Sci. 35, 9–28 (1919).
Google Scholar 
Chavez, P. S. Image-based atmospheric corrections revisited and improved. Photogram. Eng. Rem. S. 62, 1025–1036 (1996).
Google Scholar 
Riano, D., Chuvieco, E., Salas, J. & Aguado, I. Assessment of different topographic corrections in Landsat-T. M. data for mapping vegetation types. IEEE Trans. Geosci. Remote Sens. 41, 1056–1061. https://doi.org/10.1109/TGRS.2003.811693 (2003).ADS 
Article 

Google Scholar 
Rouse J. W., Haas, R. H., Schell, J. A. & Deering, D. W. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, Greenbelt, USA: NASASP-351; 1974. Monitoring vegetation system in the great plains with ERTS, 3010–3017 (1974).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2021).Du, Y. et al. New hyperspectral discrimination measure for spectral characterization. Opt. Eng. 43(8), 1777–1786 (2004).ADS 
Article 

Google Scholar 
Bhattacharyya, A. On a measure of divergence between two statistical populations defined by their probability distributions’. Bull. Calcutta Math. Soc. 35, 99–109 (1943).MathSciNet 
MATH 

Google Scholar 
Bruzzone, L., Roli, F. & Serpico, S. B. An extension to multiclass cases of the Jefferys-Matusita distance. IEEE Trans. Pattern. Anal. Mach. Intell. 33, 1318–1321 (1995).
Google Scholar 
Kaufman, Y. & Remer, L. Detection of forests using mid-IR reflectance: An application for aerosol studies. IEEE Trans. Geosci. Remote Sens. 32(3), 672–683 (1994).ADS 
Article 

Google Scholar 
Padma, S. & Sanjeevi, S. Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis. Int. J. Appl. Earth. Obs. 32, 138–151. https://doi.org/10.1016/j.jag.2014.04.001 (2014).Article 

Google Scholar 
Kavzoglu, T. & Mather, P. M.. The use of feature selection techniques in the context of artificial neural networks. In Proceedings of the 26th Annual Conference of the Remote Sensing Society (CD-ROM), 12–14 September (Leicester, UK, 2000).Gunal, S. & Edizkan, R. Subspace based feature selection for pattern recognition. Info. Sci. 178, 3716–3726. https://doi.org/10.1016/j.ins.2008.06.001 (2008).Article 

Google Scholar 
Tolpekin, V. A. & Stein, A. Quantification of the effects of land-cover-class spectral separability on the accuracy of markov-random-field-based superresolution mapping. IEEE Trans. Geosci. Remote Sens. 47(9), 3283–3297. https://doi.org/10.1109/TGRS.2009.2019126 (2009).ADS 
Article 

Google Scholar 
Paterson, M., Lucas, R. M. & Chisholm, L. Differentiation of selected Australian woodland species using CASI data. In Proceedings IEEE International Geoscience and Remote Sensing Symposium, 643–645 (University of New South Wales, Australia, 2001).Richards, J. A. & Jai, X. Remote Sensing Digital Analysis: An Introduction, 4th edition (Springer, Berlin, 1999).Veraverbeke, S., Harris, S. & Hook, S. Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data. Remote Sens. Environ. 115, 2702–2709. https://doi.org/10.1016/j.rse.2011.06.010 (2011).ADS 
Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

Google Scholar 
Georganos, S. et al. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling. Geocarto Int. https://doi.org/10.1080/10106049.2019.1595177 (2019).Article 

Google Scholar 
Mellor, A., Haywood, A., Stone, C. & Jones, S. The performance of random forests in an operational setting for large area sclerophyll forest classification. Remote Sens. 5, 2838–2856. https://doi.org/10.3390/rs5062838 (2013).ADS 
Article 

Google Scholar 
Congalton, R. G. Accuracy assessment and validation of remotely sensed and other spatial information. Int. J. Wildland. Fire. 10, 321–328 (2001).Article 

Google Scholar 
Thomas, I. L., Ching, N. P., Benning, V. M. & D’aguanno, J. A. Review Article A review of multi-channel indices of class separability. Int. J. Remote Sens. 8(3), 331–350. https://doi.org/10.1080/01431168708948645 (1987).Article 

Google Scholar 
Mausel, P. W., Kramber, W. J. & Lee, J. K. Optimum band selection for supervised classification of multispectral data. Photogramm. Eng. Remote. Sens. 56(1), 55–60 (1990).
Google Scholar 
Singh, A. Some clarifications about the pairwise divergence measure in remote sensing. Int. J. Remote Sens. 5(3), 623–627. https://doi.org/10.1080/01431168408948845 (1984).Article 

Google Scholar 
Kumar, P. et al. A statistical significance of differences in classification accuracy of crop types using different classification algorithms. Geocarto Int. 32(2), 206–224. https://doi.org/10.1080/10106049.2015.1132483 (2017).Article 

Google Scholar 
McPherson, E. G., Simpson, J. R., Peper, P. J., Xiao, Q. & Wu, C. Los Angeles 1-Million Tree Canopy Cover Assessment. General Technical Report PSW-GTR-207. U.S. Department of Agriculture Forest Service Pacific Southwest Research Station. Albany, CA, 1–64 (2008).Rahimizadeh, N., Kafaky, S. B., Sahebi, M. R. & Mataji, A. Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods. Environ. Monit. Assess. 192, 43. https://doi.org/10.1007/s10661-019-8015-x (2020).Article 

Google Scholar 
McRoberts, R. E. Satellite image-based maps: Scientific inference or pretty pictures?. Remote. Sens. Environ. 115, 715–724. https://doi.org/10.1016/j.rse.2010.10.013 (2011).ADS 
Article 

Google Scholar 
McRoberts, R. E. Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data. Remote. Sens. Environ. 114, 1017–1025. https://doi.org/10.1016/j.rse.2009.12.013 (2010).ADS 
Article 

Google Scholar 
Kokubu, Y., Hara, S. & Tani, A. Mapping seasonal tree canopy cover and leaf area using worldview-2/3 satellite imagery: A megacity-scale case study in Tokyo urban area. Remote. Sens. 12(9), 1505. https://doi.org/10.3390/rs12091505 (2020).Article 

Google Scholar 
Johannesburg City Parks and Zoo. 2018. The city that’s a rain forest. http://www.jhbcityparks.com/index.php/street-trees-contents-29. Accessed 14 June 2020.Tesfamichael, S. G., Newete, S. W., Adam, E. & Dubula, B. Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants. GIsci. Remote Sens. 55(3), 417–436. https://doi.org/10.1080/15481603.2017.1396658 (2018).Article 

Google Scholar 
McPherson, E. G., Simpsona, J. R., Xiao, Q. & Wu, C. Million trees Los Angeles canopy cover and benefit assessment. Landsc. Urban. Plan. 99, 40–50 (2011).Article 

Google Scholar 
Baines, O., Wilkes, P. & Disney, M. Quantifying urban forest structure with open-access remote sensing data sets. Urban For. Urban Green. 50, 126653. https://doi.org/10.1016/j.ufug.2020.126653 (2020).Article 

Google Scholar 
Nowak, D. J. et al. Measuring and analyzing urban tree cover. Landsc. Urban Plan. 36, 49–57 (1996).Article 

Google Scholar 
Estoque, R. C. et al. Remotely sensed tree canopy cover-based indicators for monitoring global sustainability and environmental initiatives. Environ. Res. Lett. 16, 044047. https://doi.org/10.1088/1748-9326/abe5d9 (2021).ADS 
CAS 
Article 

Google Scholar 
Paap, T., de Beer, W., Migliorini, D., Nel, W. J. & Wingfield, M. J. The polyphagous shot hole borer (PSHB) and its fungal symbiont Fusarium euwallaceae: A new invasion in South Africa Trudy. Aust. Plant. Pathol. 47, 231–237. https://doi.org/10.1007/s13313-018-0545-0 (2018).Article 

Google Scholar  More

Cox, P. A., Banack, S. A. & Murch, S. J. Biomagnification of cyanobacterial neurotoxins and neurodegenerative disease among the Chamorro people of Guam. Proc. Natl. Acad. Sci. U.S.A. 100, 13380–13383 (2003).ADS 
CAS 
Article 

Google Scholar 
Brand, L. E., Pablo, J., Compton, A., Hammerschlag, N. & Mash, D. C. Cyanobacterial blooms and the occurrence of the neurotoxin, beta-N-methylamino-L-alanine (BMAA), in south Florida aquatic food webs. Harmful Algae 9, 620–635 (2010).CAS 
Article 

Google Scholar 
Metcalf, J. S., Banack, S. A., Richer, R. & Cox, P. A. Neurotoxic amino acids and their isomers in desert environments. J. Arid Environ. 112, 140–144 (2015).ADS 
Article 

Google Scholar 
Violi, J. P., Mitrovic, S. M., Colville, A., Main, B. J. & Rodgers, K. J. Prevalence of (beta)-methylamino-L-alanine (BMAA) and its isomers in freshwater cyanobacteria isolated from eastern Australia. Ecotoxicol. Environ. Saf. 172, 72–81 (2019).CAS 
Article 

Google Scholar 
Jonasson, S. et al. Transfer of a cyanobacterial neurotoxin within a temperate aquatic ecosystem suggests pathways for human exposure. Proc. Natl. Acad. Sci. 107, 9252–9257 (2010).ADS 
CAS 
Article 

Google Scholar 
Metcalf, J. et al. Toxin analysis of freshwater cyanobacterial and marine harmful algal blooms on the west coast of Florida and implications for estuarine environments. Neurotox. Res. 39, 27–35 (2021).CAS 
Article 

Google Scholar 
Cox, P. A. et al. Cyanobacteria and BMAA exposure from desert dust: a possible link to sporadic ALS among Gulf War veterans. Amyotroph. Lateral Scler. 10, 109–117 (2009).CAS 
Article 

Google Scholar 
Charlton, T. S., Marini, A. M., Markey, S. P., Norstog, K. & Duncan, M. W. Quantification of the neurotoxin 2-amino-3-(methylamino)-propanoic acid (BMAA) in Cycadales. Phytochemistry 31, 3429–3432 (1992).CAS 
Article 

Google Scholar 
Whiting, M. G. Toxicity of cycads. Econ. Bot. 17, 270–302 (1963).Article 

Google Scholar 
Cox, P. A., Davis, D. A., Mash, D. C., Metcalf, J. S. & Banack, S. A. Dietary exposure to an environmental toxin triggers neurofibrillary tangles and amyloid deposits in the brain. Proc. R. Soc. B: Biol. Sci. 283, 20152397 (2016).Article 

Google Scholar 
Scott, L. L. & Downing, T. G. A single neonatal exposure to BMAA in a rat model produces neuropathology consistent with neurodegenerative diseases. Toxins 10, 22 (2018).Article 

Google Scholar 
Roy, U. et al. Metabolic profiling of zebrafish (Danio rerio) embryos by NMR spectroscopy reveals multifaceted toxicity of (beta)-methylamino-L-alanine (BMAA). Sci. Rep. 7, 1–12 (2017).ADS 
Article 

Google Scholar 
Purdie, E. L., Metcalf, J. S., Kashmiri, S. & Codd, G. A. Toxicity of the cyanobacterial neurotoxin (beta)-N-methylamino-L-alanine to three aquatic animal species. Amyotroph. Lateral Scler. 10, 67–70 (2009).CAS 
Article 

Google Scholar 
Brenner, E. D. et al. Arabidopsis mutants resistant to s (+)-(beta)-methyl-(alpha), (beta)-diaminopropionic acid, a cycad-derived glutamate receptor agonist. Plant Physiol. 124, 1615–1624 (2000).CAS 
Article 

Google Scholar 
Schneider, D., Wink, M., Sporer, F. & Lounibos, P. Cycads: Their evolution, toxins, herbivores and insect pollinators. Naturwissenschaften 89, 281–294 (2002).ADS 
CAS 
Article 

Google Scholar 
Koi, S. & Daniels, J. Life history variations and seasonal polyphenism in Eumaeus atala (Lepidoptera: Lycaenidae). Florida Entomol. 100, 219–229 (2017).Article 

Google Scholar 
Koi, S. A butterfly picks its poison: Cycads (Cycadaceae), integrated pest management and Eumaeus atala Poey (Lepidoptera: Lycaenidae). Entomol. Ornithol. Herpetol. 6 (2017).Brenner, E. D., Stevenson, D. W. & Twigg, R. W. Cycads: Evolutionary innovations and the role of plant-derived neurotoxins. Trends Plant Sci. 8, 446–452 (2003).CAS 
Article 

Google Scholar 
Prado, A. The cycad herbivores. Bull. Soc. D’entomol. Quebec 18, 3–6 (2011).
Google Scholar 
Popova, A. & Koksharova, O. Neurotoxic non-proteinogenic amino acid (beta)-N-methylamino-L-alanine and its role in biological systems. Biochem. Mosc. 81, 794–805 (2016).CAS 
Article 

Google Scholar 
Salzman, S., Whitaker, M. R. L. & Pierce, N. E. Cycad-feeding insects share a core gut microbiome. Biol. J. Lin. Soc. 123, 728–738 (2018).Article 

Google Scholar 
Whitaker, M. R. & Salzman, S. Ecology and evolution of cycad-feeding Lepidoptera. Ecol. Lett. 23, 1862–1877 (2020).Article 

Google Scholar 
Zhou, X., Escala, W., Papapetropoulos, S., Bradley, W. G. & Zhai, R. G. BMAA neurotoxicity in Drosophila. Amyotroph. Lateral Scler. 10, 61–66 (2009).CAS 
Article 

Google Scholar 
Zhou, X., Escala, W., Papapetropoulos, S. & Zhai, R. G. (beta)-N-methylamino-L-alanine induces neurological deficits and shortened life span in Drosophila. Toxins 2, 2663–2679 (2010).CAS 
Article 

Google Scholar 
Mekdara, N. T. et al. A novel lenticular arena to quantify locomotor competence in walking fruit flies. J. Exp. Zool. A Ecol. Genet. Physiol. 317, 382–394 (2012).Article 

Google Scholar 
Goto, J. J., Koenig, J. H. & Ikeda, K. The physiological effect of ingested (beta)-N-methylamino-L-alanine on a glutamatergic synapse in an in vivo preparation. Comp. Biochem. Physiol. Part C: Toxicol. Pharmacol. 156, 171–177 (2012).CAS 

Google Scholar 
Okle, O., Rath, L., Galizia, C. G. & Dietrich, D. R. The cyanobacterial neurotoxin (beta)-N-methylamino-L-alanine (BMAA) induces neuronal and behavioral changes in honeybees. Toxicol. Appl. Pharmacol. 270, 9–15 (2013).CAS 
Article 

Google Scholar 
Spencer, P. S. et al. Guam amyotrophis lateral sclerosis-parkinsonism-dementia linked to a plant excitant neurotoxin. Science 237, 517–522 (1987).ADS 
CAS 
Article 

Google Scholar 
Bernays, E. A. & Chapman, R. F. Host-plant selection by phytophagous insects. In Host-Plant Selection by Phytophagous Insects. Contemporary Topics in Entomology, vol. 2, 201–213 (Springer, Boston, MA, 1994).Zandt, P. A. V. Plant defense, growth, and habitat: A comparative assessment of constitutive and induced resistance. Ecology 88, 1984–1993 (2007).Article 

Google Scholar 
Duncan, M. W. Role of the cycad neurotoxin BMAA in the amyotrophic lateral sclerosi-parkisonism dementia complex of the Western Pacific. Adv. Neurol. 56, 301–310 (1991).CAS 
PubMed 

Google Scholar 
Banack, S. A. & Cox, P. A. Distribution of the neurotoxic nonprotein amino acid BMAA in Cycas micronesica. Bot. J. Linn. Soc. 143, 165–168 (2003).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2021).Therneau, T. M. A Package for Survival Analysis in R. R package version 3.2-11 (2021).Kassambara, A., Kosinski, M. & Biecek, P. survminer: Drawing Survival Curves using ’ggplot2’. R package version 0.4.9 (2021).Pennington, Z. T. et al. eztrack: An open-source video analysis pipeline for the investigation of animal behavior. Sci. Rep. 9, 1–11 (2019).Article 

Google Scholar 
Pérez, F. & Granger, B. E. IPython: A system for interactive scientific computing. Comput. Sci. Eng. 9, 21–29 (2007).Article 

Google Scholar 
Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P. & Fierer, N. Caterpillars lack a resident gut microbiome. Proc. Natl. Acad. Sci. 114, 9641–9646 (2017).CAS 
Article 

Google Scholar 
Karlsson, O., Roman, E. & Brittebo, E. B. Long-term cognitive impairments in adult rats treated neonatally with (beta)-N-methylamino-L-alanine. Toxicol. Sci. 112, 185–195 (2009).CAS 
Article 

Google Scholar 
Whitaker, M. R. L., Salzman, S., Gratacos, X. & Tucker Lima, J. Localized overabundance of an otherwise rare butterfly threatens endangered cycads. Florida Entomol. 103, 519–522 (2021).Article 

Google Scholar 
Backmann, P. et al. Delayed chemical defense: Timely expulsion of herbivores can reduce competition with neighboring plants. Am. Nat. 193, 125–139 (2019).Article 

Google Scholar 
Yáñez-Espinosa, L. & Sosa-Sosa, F. Population structure of Dioon purpusii rose in Oaxaca, Mexico. Neotrop. Biol. Conserv. 2, 46–54 (2007).
Google Scholar 
Robbins, R. K. et al. A switch to feeding on cycads generates parallel accelerated evolution of toxin tolerance in two clades of Eumaeus caterpillars (Lepidoptera: Lycaenidae). Proc. Natl. Acad. Sci.118 (2021).Grunseich, J. M., Thompson, M. N., Aguirre, N. M. & Helms, A. M. The role of plant-associated microbes in mediating host-plant selection by insect herbivores. Plants 9, 6 (2020).CAS 
Article 

Google Scholar 
Zhang, Y. & Whalen, J. K. Production of the neurotoxin beta-N-methylamino-L-alanine may be triggered by agricultural nutrients: An emerging public health issue. Water Res. 170, 115335 (2020).CAS 
Article 

Google Scholar  More

Abbott RJ, Brennan AC (2014) Altitudinal gradients, plant hybrid zones and evolutionary novelty. Philos Trans R Soc B Biol Sci 369:6–9Article 

Google Scholar 
Avise JC (2000) Phylogeography: the history and formation of species. Harvard University Press, Cambridge, MABook 

Google Scholar 
Baldassarre DT, White TA, Karubian J, Webster MS (2014) Genomic and morphological analysis of a semipermeable avian hybrid zone suggests asymmetrical introgression of a sexual signal. Evolution 68:2644–2657PubMed 
Article 
PubMed Central 

Google Scholar 
Barr KR, Dharmarajan G, Rhodes OE, Lance R, Leberg PL (2007) Novel microsatellite loci for the study of the black-capped vireo (Vireo atricapillus). Mol Ecol Notes 7:1067–1069CAS 
Article 

Google Scholar 
Barton NH, Gale KS (1993) Hybrid zones and the evolutionary process. In: Harrison RG (ed.) Hybrid Zones and the Evolutionary Process. Oxford University Press, New York, NY
Google Scholar 
Barton NH, Hewitt GM (1989) Adaption, speciation and hybrid zones. Nature 341:497–503CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Billerman SM, Murphy MA, Carling MD (2016) Changing climate mediates sapsucker (Aves: Sphyrapicus) hybrid zone movement. Ecol Evol 6:7976–7990PubMed 
PubMed Central 
Article 

Google Scholar 
Bell RC, Irian CG (2019) Phenotypic and genetic divergence in reed frogs across a mosaic hybrid zone on São Tomé Island. Biol J Linn Soc 128:672–680Article 

Google Scholar 
Bensch S, Price T, Kohn J (1997) Isolation and characterization of microsatellite loci in a Phylloscopus warbler. Mol Ecol 6:91–92CAS 
PubMed 
Article 

Google Scholar 
Bradbury IR, Bowman S, Borza T, Snelgrove PVR, Hutchings JA, Berg PR et al. (2014) Long distance linkage disequilibrium and limited hybridization suggest cryptic speciation in Atlantic cod. PLoS ONE 9:e106330Article 
CAS 

Google Scholar 
Brelsford A, Irwin DE (2009) Incipient speciation despite little assortative mating: the yellow-rumped warbler hybrid zone. Evolution 63:3050–3060PubMed 
Article 

Google Scholar 
Burg TM, Croxall JP (2004) Global population structure and taxonomy of the wandering albatross species complex. Mol Ecol 13:2345–2355CAS 
PubMed 
Article 

Google Scholar 
Carling MD, Zuckerberg B (2011) Spatio-temporal changes in the genetic structure of the Passerina bunting hybrid zone. Mol Ecol 20:1166–1175PubMed 
Article 

Google Scholar 
Carling MD, Thomassen HA (2012) The role of environmental heterogeneity in maintaining reproductive isolation between hybridizing Passerina (Aves: Cardinalidae) buntings. Int J Ecol 2012:295463Article 

Google Scholar 
Carpenter AM, Graham BA, Spellman GM, Klicka J, Burg TM (2021) Genetic, bioacoustic and morphological analyses reveal cryptic speciation in the warbling vireo complex (Vireo gilvus: Vireonidae: Passeriformes). Zool J Linn Soc zlab036 https://doi.org/10.1093/zoolinnean/zlab036Cicero C, Johnson NK (1998) Molecular phylogeny and ecological diversification in a clade of New World songbirds (genus Vireo). Mol Ecol 7:1359–1370CAS 
PubMed 
Article 

Google Scholar 
Chenuil A, Cahill AE, Délémontey N, Du Salliant du Luc E, Fanton H (2019) Problems and questions posed by cryptic species. A framework to guide future studies. Assessing to conserving biodiversity. History, philosophy and theory of the life sciences, Vol. 24. Springer. Daubenmire, Cham
Google Scholar 
Cheviron ZA, Brumfield RT (2012) Genomic insights into adaptation to high-altitude environments. Heredity 108:354–361CAS 
PubMed 
Article 

Google Scholar 
Coyne JA, Orr HA (2004) Speciation. Sinauer and Associates, Sunderland, Massachusetts
Google Scholar 
Culumber ZW, Shepard DB, Colemans SW, Rosenthal GG, Tobler M (2012) Physiological adaptation along environmental gradients and replicated hybrid zone structure in swordtails (Teleostei: Xiphophorus). J Evol Biol 25:1800–1814CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Dubay SG, Witt CC (2014) Differential high-altitude adaptation and restricted gene flow across a mid-elevation hybrid zone in Andean tit-tyrant flycatchers. Mol Ecol 23:3551–3565PubMed 
Article 
PubMed Central 

Google Scholar 
Garroway CJ, Bowman J, Cascaden TJ, Holloway GL, Mahan CG, Malcolm JR et al. (2010) Climate change induced hybridization in flying squirrels. Glob Chang Biol 16:113–121Article 

Google Scholar 
Grabenstein KC, Taylor SA (2018) Breaking barriers: Causes, consequences, and experimental utility of human-mediated hybridization. Trends Ecol Evol 33:198–212PubMed 
Article 

Google Scholar 
Graham BA, Cicero C, Strickland D, Woods JG, Coneybeare H, Dohms KM et al. (2021) Cryptic genetic diversity and cytonuclear discordance characterize contact among Canada jay (Perisoreus canadensis) morphotypes in western North America. Biol J Linn Soc 132:725–740Article 

Google Scholar 
Hammer Ø, Harper DA, Ryan PD (2001) Paleontological statistics software package for education and data analysis. Palaeontol Electron 4:9Haselhorst MSH, Parchman TL, Buerkle CA (2019) Genetic evidence for species cohesion, substructure and hybrids in spruce. Mol Ecol 28:2029–2045PubMed 
Article 

Google Scholar 
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978Article 

Google Scholar 
Hawley DM (2005) Isolation and characterization of eight microsatellite loci from the house finch (Carpodactus mexicanus). Mol Ecol Notes 5:443–445CAS 
Article 

Google Scholar 
Hebert PDN, Stoeckle MY, Zemlak TS, Francis CM (2004) Identification of birds through DNA barcodes. PLoS Biol 2:e312PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hewitt GM (1988) Hybrid zones-natural laboratories for evolutionary studies. Trends Ecol Evol 3:158–167CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hewitt GM (2001) Speciation, hybrid zones and phylogeography—or seeing genes in space and time. Mol Ecol 10:537–549CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978Article 

Google Scholar 
Hindley JA, Graham BA, Pulgarin-R PC, Burg TM (2018) The influence of latitude, geographic distance, and habitat discontinuities on genetic variation in a high latitude montane species. Sci Rep. 8:11846CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hinojosa JC, Koubínová D, Szenteczki MA, Pitteloud C, Dincă V, Alvarez N et al. (2019) A mirage of cryptic species: Genomics uncover striking mitonuclear discordance in the butterfly Thymelicus sylvestris. Mol Ecol 28:3857–3868PubMed 
Article 
PubMed Central 

Google Scholar 
Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Res 9:1322–1332Article 

Google Scholar 
Irwin DE (2020) Assortative mating in hybrid zones is remarkably ineffective in promoting speciation. Evolution 195:E150–E167
Google Scholar 
Johnson NK (1995) Speciation in vireos. I. Macrogeographic patterns of allozymic variation in the Vireo solitarius complex in the contiguous United States. Condor 97:903–919Article 

Google Scholar 
Johnson NK, Cicero C (2004) New mitochondrial DNA data affirm the importance of Pleistocene speciation in North American birds. Evolution 58:1122–1130PubMed 
Article 
PubMed Central 

Google Scholar 
Larson EL, Tinghitella RM, Taylor SA (2019) Insect hybridization and climate change. Front Ecol Evol 7:348Article 

Google Scholar 
Legendre P, Fortin M-J (2010) Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Mol Ecol Resour 10:831–844PubMed 
Article 

Google Scholar 
Lovell SF, Lein MR, Rogers SM (2021) Cryptic speciation in the warbling vireo (Vireo gilvus). Ornithology 138:ukaa071Article 

Google Scholar 
MacDonald ZG, Dupuis JR, Davis CS, Acorn JH, Nielsen SE, Sperling FAH (2020) Gene flow and climate-associated genetic variation in a vagile habitat specialist. Mol Ecol 29:3889–3906PubMed 
Article 

Google Scholar 
Manthey JD, Klicka J, Spellman GM (2011) Cryptic diversity in a widespread North American songbird: phylogeography of the brown creeper (Certhia americana). Mol Phylogenet Evol 58:502–512PubMed 
Article 

Google Scholar 
Marchetti K, Price T, Richman A (1995) Correlates of wing morphology with foraging behaviour and migration distance in the genus Phylloscopus. J Av Biol 26:177–181Article 

Google Scholar 
Martin H, Touzet P, Dufay M, Gode C, Schmitt E, Lahiani E et al. (2017) Lineages of Silene nutans developed rapid, strong, asymmetric postzygotic reproductive isolation in allopatry. Evolution 71:1519–1531CAS 
PubMed 
Article 

Google Scholar 
Martinez JG, Soler JJ, Soler M, Moller AP, Burke T (1999) Comparative population structure and gene flow of a brood parasite, the great spotted cuckoo (Clamator glandarius) and its primary host, the magpie (Pica pica). Evolution 53:269–278CAS 
PubMed 
PubMed Central 

Google Scholar 
Mettler RD, Spellman GM (2009) A hybrid zone revisited: Molecular and morphological analysis of the maintenance, movement, and evolution of a Great Plains avian (Cardinalidae: Pheucticus) hybrid zone. Mol Ecol 18:3256–3267CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meirmans PG, Van Tienderen PH (2004) GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes 4:792–794Article 

Google Scholar 
Nowakowski JK, Szulc J, Remisiewicz M (2014) The further the flight, the longer the wing: relationship between wing length and migratory distance in Old World reed and bush warblers (Acrocephalidae and Locustellidae). Ornis Fennica 91:178–186
Google Scholar 
Pavolova A, Amos JN, Joseph L, Loynes K, Austin JJ, Keogh JS et al. (2013) Perched at the mito-nuclear crossroads: divergent mitochondrial lineages correlate with environment in the face of ongoing nuclear gene flow in an Australian bird. Evol 67:3412–3428Article 
CAS 

Google Scholar 
Piertney SB, Marquiss M, Summers R (1998) Characterization of tetranucleotide microsatellite markers in the Scottish crossbill (Loxia scotica). Mol Ecol 7:1261–1263CAS 
PubMed 
Article 

Google Scholar 
Porras-Hurtado L, Ruiz Y, Santos C, Phillips C, Carracedo A, Lareu MV (2013) An overview of STRUCTURE: Applications, parameter settings, and supporting software. Front Genet 4:98PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pritchard J, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reding DM, Castañeda-Rico S, Shirazi S, Hofman CA, Cancellare IA, Lance SL et al. (2021) Mitochondrial genomes of the United States distribution of gray fox (Urocyon cinereoargenteus) reveal a major phylogeographic break at the Great Plains suture zone. Front Ecol Evol. https://doi.org/10.3389/fevo.2021.666800.Richardson DS, Jury FL, Dawson DA, Salgueiro P, Komdeur J, Burke T (2003) Fifty Seychelles warbler (Acrocephalus sechellensis) microsatellite loci polymorphic in Sylviidae species and their cross-species amplification in other passerine birds. Mol Ecol 9:2225–2230Article 

Google Scholar 
Riordan EC, Gugger PF, Ortego J, Smith C, Gaddis K, Thompson P et al. (2016) Association of genetic and phenotypic variability with geography and climate in three southern California oaks. Am J Bot 103:73–85PubMed 
Article 

Google Scholar 
Rush AC, Cannings RJ, Irwin DE (2009) Analysis of multilocus DNA reveals hybridization in a contact zone between Empidonax flycatchers. J Avian Biol 40:614–624Article 

Google Scholar 
Sartor CC, Cushman SA, Wan HY, Kretschmer R, Pereira JA, Bou N et al. (2021) The role of the environment in the spatial dynamics of an extensive hybrid zone between two neotropical cats. J Evol Biol 34:614–627PubMed 
Article 

Google Scholar 
Schukman JM, Lira-Noriega A, Townsend Peterson A (2011) Multiscalar ecological characterization of Say’s and eastern phoebes and their zone of contact in the Great Plains. Condor 113:372–384Article 

Google Scholar 
Seehausen O, Takimoto G, Roy D, Jokela J (2008) Speciation reversal and biodiversity dynamics with hybridization in changing environments. Mol Ecol 17:30–44PubMed 
Article 
PubMed Central 

Google Scholar 
Semenchuk GP (1992) The Atlas of Breeding Birds of Alberta. Fed. of Alberta Naturalists, Edmonton, p 243
Google Scholar 
Peakall R, Smouse PE (2012) GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research–an update. Bioinformatics 28:2537–2539CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sorenson MD, Ast JC, Dimcheff DE, Yuri T, Mindell DP (1999) Primers for a PCR-based approach to mitochondrial genome sequencing in birds and other vertebrates. Mol Phylogent Evol 12:105–114CAS 
Article 

Google Scholar 
Spellman GM, Klicka J (2007) Phylogeography of the white-breasted nuthatch (Sitta carolinensis): diversification in North American pine and oak woodlands. Mol Ecol 16:1729–1740CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Stenzler LM, Fitzpatrick JW (2002) Isolation of microsatellite loci in the Florida scrub jay Aphelocoma coerulescens. Mol Ecol Notes 2:547–550CAS 
Article 

Google Scholar 
Swenson NG (2006) GIS-based niche models reveal unifying climatic mechanisms that maintain location of avian hybrid zones in a North America suture zone. J Evol Biol. 19:717–725CAS 
PubMed 
Article 

Google Scholar 
Swenson NG, Howard DJ (2005) Clustering of contact zones, hybrid zones, and phylogeographic breaks in North America. Am Nat 166:581–591PubMed 
Article 

Google Scholar 
Tarr CL, Fleischer RC (1998) Primers for polymorphic GT microsatellites isolated from the Mariana crow, Corvus kubaryi. Mol Ecol 7:253–255CAS 
PubMed 
Article 

Google Scholar 
Tarroso P, Pereira RJ, Martínez-Freiría F, Godinho R, Brito JC (2014) Hybridization at an ecotone: Ecological and genetic barriers between three Iberian vipers. Mol Ecol 23:1108–1123CAS 
PubMed 
Article 

Google Scholar 
Taylor SA, Larson EL, Harrison RG (2015) Hybrid zones: windows on climate change. Trends Ecol Evol 30:398–406PubMed 
PubMed Central 
Article 

Google Scholar 
Toews DPL, Mandic M, Richards JG, Irwin DE (2014) Migration, mitochondria and the yellow-rumped warbler. Evolution 68:241–255CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Toews DPL, Campagna L, Taylor SA, Balakrishnan CN, Baldassarre DT, Deane-Coe PE et al. (2016) Genomic approaches to understanding population divergence and speciation in birds. Auk 133:13–30Article 

Google Scholar 
Toews DPL, Irwin DE (2008) Cryptic speciation in a Holarctic passerine revealed by genetic and bioacoustic analyses. Mol Ecol 17:2691–2705CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
van Els P, Cicero C, Klicka J (2012) High latitudes and high genetic diversity: Phylogeography of a widespread boreal bird, the gray jay (Perisoreus canadensis). Mol Phylogenet Evol 63:456–465PubMed 
Article 
PubMed Central 

Google Scholar 
Voelker G, Rohwer S (1998) Contrasts in scheduling of molt and migration in eastern and western warbling vireos. Auk 155:142–155Article 

Google Scholar 
Walsh J, Billerman SM, Rohwer VG, Butcher BG, Lovette IJ (2020) Genomic and plumage variation across the controversial Baltimore and Bullock’s oriole hybrid zone. Auk 137:1–15Article 

Google Scholar 
Walsh J, Rowe RJ, Olsen BJ, Shriver WG, Kovach AI (2016) Genotype-environment associations support a mosaic hybrid zone between two tidal marsh birds. Ecol Evol 6:279–294PubMed 
Article 
PubMed Central 

Google Scholar 
Walsh P, Metzger D, Higuchi R (1991) Chelex 100 as a medium for simple extraction of DNA for PCR-based typing from forensic material. Biotechniques 10:506–513CAS 
PubMed 
PubMed Central 

Google Scholar 
Weir JT, Schluter D (2004) Ice sheets promote speciation in boreal birds. Proc R Soc B 271:1881–1887PubMed 
PubMed Central 
Article 

Google Scholar 
Williams JW (2003) Variations in tree cover in North America since the last glacial maximum. Glob Planet Change 35:1–23Article 

Google Scholar 
Williams DA, Berg EC, Hale AM, Hughes CR (2004) Characterization of microsatellites for parentage studies of white-throated magpie-jays (Calocitta formosa) and brown jays (Cyanocorax morio). Mol Ecol Notes 4:509–511CAS 
Article 

Google Scholar 
Zwartjes PW (2001) Genetic structuring among migratory populations of the black-whiskered vireo, with a comparison to the red-eyed vireo. Condor 103:439–448Article 

Google Scholar  More

Phylogenetic analysis with multiple markersThe final alignment of 5670 bp length contained 843 variable sites (14.7%). Missing data accounted for 53.5% of the alignment cells and the relative GC content was 39.5%. Our phylogeny suggests that the investigated Holarctic taxa of the niger clade sensu Ref.34 are divided into two major clades with strong statistical support (Fig. 1). The first major clade (L. niger group) consists exclusively of Palearctic species (L. niger, L. platythorax, L. japonicus, L. emarginatus, L. balearicus, L. grandis, L. cinereus, the L. alienus-complex, L. sakagamii, L. productus and L. hayashi), with the exception of an unnamed Nearctic subclade recovered as sister to the rest of the group. This unnamed subclade we describe as a new species below (L. ponderosae sp. nov.). Lasius ponderosae sp. nov. corresponds to what was previously known as the Nearctic form of “L. niger” sensu ref.17, but includes some western Nearctic populations formerly assigned to “L. alienus”17,52 as well. Monophyly of L. ponderosae sp. nov. was fully supported by Bayesian inference (pp = 1) and moderately supported by maximum likelihood (66% bootstrap support, Fig. 1). Lasius ponderosae sp. nov. is distantly related to L. niger; and L.niger is a close relative of L. japonicus and L. platythorax, as well as other Palearctic taxa. The second major clade (L. brunneus group) within the investigated Holarctic members of the L. niger clade contains both Nearctic and Palearctic species not closely related to the taxa of interest (Fig. 1).Figure 1Molecular phylogeny of 26 Holarctic ant taxa belonging to the subgenus Lasius sensu Wilson (1955) and two outgroup taxa (L. pallitarsis and L. mixtus). The phylogeny was calculated under the coalescent model and incorporates data from 9 genes (mtDNA: COI, COII, 16S, nuDNA: Defensin, H3, LR, Wg, Top1 & 28S). Names of species native to the Nearctic are shown in red and those of species native to the Palearctic in blue. Node labels show posterior probability (Bayesian inference) followed by bootstrap support (Maximum likelihood). The scale bar indicates the length of 0.01 substitutions/site.Full size imageDNA-barcodingThe native North American species L. ponderosae sp. nov. contains at least 15 COI-mitotypes (n = 28 sequenced specimens) belonging to four distinct deep lineages, with divergences of up to 5.9%. Haplotype diversity was 0.899 and nucleotide diversity was 0.012. None of the mitotypes of this species was found to be widespread or particularly abundant. In striking contrast, low genetic diversity was found in L. niger across its entire distribution (Fig. 2). No more than 7 different COI-mitotypes were detected in samples from distant localities representing most of the known range (n = 70 specimens from 12 countries), from Spain in the West to the Siberian Baikal-region in the East (Fig. 2). Their maximum pairwise divergence was only 0.6%, with a haplotype diversity of 0.682 and a nucleotide diversity below 0.001. One mitotype of L. niger is highly dominant within the native range, occurring from Western Europe to Central Siberia (mitotype h2 in Fig. 2).Figure 2Mitotype tree and distribution maps for 98 DNA-barcodes belonging to 7 mitotypes of the ant Lasius niger (blue, n = 70) and 15 mitotypes of L. ponderosae sp. nov. (red, n = 28). The red dashed line delimits the expected natural range of L. ponderosae sp. nov.53 Maps have been created using the free R-package “ggmap” v3.0.0 (https://github.com/dkahle/ggmap) in R v4.1.1. Map tiles by Stamen Design, under CC BY 3.0.Full size imageRecent Palearctic L. niger introduction to CanadaPalearctic Lasius niger was introduced to several localities in coastal Canada in recent times, where at least 11 populations were found in two metropolitan areas (Vancouver and Halifax areas, see Table S2 for details). Those populations consist of the most dominant Palearctic mitotype of L. niger (h2). However, in 3 localities in the Vancouver area, 3 specimens with a second mitotype were found (mitotype h4, Fig. 2, Table S2) in syntopy with those carrying the most common mitotype h2. This second Canadian COI-mitotype (h4) was not found among our samples from the Old World, although it only differs by a single nucleotide substitution from mitotypes found there. A review of BOLD data revealed that the Canadian barcoded specimens of L. niger were mostly collected in anthropogenic habitats such as schoolyards (Supplementary Table S2).Description of Lasius ponderosae sp. novLasius ponderosae Schär, Talavera, Rana, Espadaler, Cover, Shattuck and Vila. ZooBank LSID: urn:lsid:zoobank.org:act:22E2743A-2F1C-4870-B318-A1F2DF2B464C Etymology: ponderosae alludes to the ponderosa pine tree (Pinus ponderosa) that is at the centre of occurrence in the ponderosa pine—gambel oak communities in the western Rocky Mountains and northern Arizona.Type material: located at the Museum of Comparative Zoology, Cambridge, USA. Two paratype workers each will be deposited at the collections of University of California Davis (UCDC), the University of Utah (JTLC) and the Natural History Museum of Los Angeles County (LACM).Holotype: worker, Fig. 3a–c. Type locality: USA, Utah: Uintah Co., Uintah Mtns., 2408 m. 18.6 mi N. Jct. Rt. 40 on Rt. 191, 40.66378°N, − 109.47918°E, leg. 15.VII.2013, S. P. Cover; J. D. Rana, collection code SPC 8571. Measurements [mm]: HL: 0.899, HW: 0.823, SL: 0.821, EL: 0.239, EW: 0.189, ProW: 0.56, ML: 1.069, HTL: 0.863, CI: 92, SI: 100.Figure 3Frontal, lateral and dorsal view of the holotype worker (a–c), a paratype gyne (d–f) and a paratype male of Lasius ponderosae sp. nov. (g–i).Full size imageParatypes: 15 workers, two gynes (Fig. 3d–f), two males (Fig. 3g–i) from the same series as the holotype, morphometric data is given in the Appendix, Table S5 and Table S6. CO1 mitotype h17: Genbank Accession no. LT977508.Description of the worker caste: A member of a complex of cryptic species resembling L. niger. Intermediate in overall body size, antennal scape length and eye size and comparable to related species (Table 1). Terminal segment of maxillary palps and torulo-clypeal distance relative to head size shorter than in related Palearctic species (Table 1). Mandibles with 8 or rarely 7 or 9 regular denticles and lacking offset teeth at their basal angle. Penultimate and terminal basal mandibular teeth of subequal size, and the gap in between with subequal area than the basal tooth. Anterior margin of clypeus evenly rounded. Dorsofrontal profile of pronotum slightly angular (Fig. 4a). Propodeal dome short and flat, usually lower than mesonotum (Fig. 4a). Body with abundant and long pilosity, especially lateral propodeum, genae, hind margin and underside of head. Pilosity of tibiae and antennal scapes variable, ranging from almost no setae (“L. alienus”-like phenotype) to very hairy (“L. niger”-like phenotype). Microscopic pubescent hairs on forehead between frontal carinae long and fine. Clypeus typically with only few scattered pubescent hairs (Figs. 3, 4c). Coloration of body dark brown, occasionally yellowish- or reddish-brown or slightly bicolored with head and thorax lighter than abdomen. Femora and antennal scapes brown. Mandibles and distal parts of legs yellowish to dark brown. Specimens of all 3 castes are shown in Fig. 3a–i and morphometric data are summarized in Table 1 and raw measurements are available in Table S5 and S6.Table 1 Morphometric data of Lasius ponderosae sp. nov. and comparison to morphologically similar Palearctic species.Full size tableFigure 4Average thorax profile of Lasius ponderosae sp. nov. (a) and members of the Palearctic L. niger-complex (b). Figures were created by image averaging (L. ponderosae sp. nov n = 35; Palearctic L. niger-complex n = 30 specimens). Frontal view of head and detail of clypeus of the Holotype worker of L. ponderosae sp. nov. (c) and a non-type worker of L. niger (d).Full size imageDiagnosis: Lasius ponderosae sp. nov. workers key out to “L. niger” using Wilson’s 1955 key to the Nearctic Lasius species. However, some populations with reduced pilosity may also be identified as “L. alienus” using this key. Lasius alienus is a Eurasian species not known from North America33. The Nearctic “L. alienus” sensu Wilson (1955) includes both, L. americanus as well as populations of L. ponderosae sp. nov. with sparse setae counts on tibia and/or scapes. Lasius ponderosae sp. nov. can be distinguished from L. americanus by the presence of abundant, long setae surpassing the sides of the head in full face view (nGen  > 5 and nOcc  > 10 vs. nGen  0.8 across models and runs). The strongest predictors were: Annual Mean Temperature (mean variable importance = 0.32), Mean Temperature of Coldest Quarter (0.23), Temperature Annual Range (0.23) and Temperature Seasonality (0.24). The contribution of land cover was low (0.02). The model predicted high probabilities of occurrence of L. niger in the eastern United States and southeastern Canada, including the island of Newfoundland, and small areas of suitable habitat in southwestern Canada and the Aleutians (Fig. 6). The area with high predicted occurrence probability of L. niger in the New World includes the two sites where populations have actually established (which were not used in the modeling): Nova Scotia and Vancouver. Further areas with high occurrence probabilities are New England, Southern Ontario, the Great Lakes-region and the Northern Appalachians. Low occurrence probabilities were found for the central North American prairies as well as arctic, boreal, arid, subtropical and tropical regions (Fig. 6). Considering the highest occurrence probability range (0.8–1 on a 0–1 probability scale), the area of suitable habitats for L. niger is 4,547,537 km2 in Europe and 1,308,920 km2 in North America. For an intermediate to high occurrence probability range (0.5–1) we estimated 5,371,055 km2 in Europe and 3,054,283 km2 in North America, and for the widest probability range (0.2–1) we estimated 6,155,643 km2 of suitable areas in Europe and 6,889,745 km2 in North America (Fig. 6).Figure 6Projected occurrence probability from ecological niche modeling for the Palearctic ant Lasius niger which has been introduced to Canada, based on 19 climatic and one land use variable. The intensity of blue colour indicates the probability of occurrence on a 0–1 scale based on 180 presences (black circles) and 182 absences (white circles) in the native range in the Old World (a). The model was then projected to North America to estimate areas of suitable habitat for this introduced species (b). These maps have been created using the free R-package “ggplot2” v3.3.5 (https://ggplot2.tidyverse.org) in R v4.1.1.Full size image More

Seymour, C. L. et al. Caught on camera: The impacts of urban domestic cats on wild prey in an African city and neighbouring protected areas. Glob. Ecol. Conserv. 23, e01198 (2020).Article 

Google Scholar 
Mori, E. et al. License to Kill? Domestic Cats Affect a Wide Range of Native Fauna in a Highly Biodiverse Mediterranean Country. Front. Ecol. Evol. 7, 477 (2019).Kays, R. et al. The small home ranges and large local ecological impacts of pet cats. Anim. Conserv. 23, 516–523 (2020).Loss, S. R., Will, T. & Marra, P. P. The impact of free-ranging domestic cats on wildlife of the United States. Nat. Commun. 4, 1396 (2013).ADS 
Article 

Google Scholar 
Van Heezik, Y., Smyth, A., Adams, A. & Gordon, J. Do domestic cats impose an unsustain386 able harvest on urban bird populations?. Biol. Conserv. 143, 121–130 (2010).Article 

Google Scholar 
Woods, M., McDonald, R. A. & Harris, S. Predation of wildlife by domestic cats Felis catus in Great Britain. Mammal Rev. 33, 174–188 (2003).Article 

Google Scholar 
Li, Y. et al. Estimates of wildlife killed by free-ranging cats in China. Biol. Conserv. 253, 108929 (2021).Article 

Google Scholar 
Barratt, D. G. Home range size, habitat utilisation and movement patterns of suburban and farm cats Felis catus. Ecography 20, 271–280 (1997).Article 

Google Scholar 
Moseby, K. E., Stott, J. & Crisp, H. Movement patterns of feral predators in an arid environment–implications for control through poison baiting. English. Wildl. Res. 36, 422–435 (2009).Article 

Google Scholar 
Hall, C. M. et al. Factors determining the home ranges of pet cats: A meta-analysis. Biol. Conserv. 203, 313–320 (2016).Article 

Google Scholar 
Castañeda, I. et al. Trophic patterns and home-range size of two generalist urban carnivores: A review. J. Zool. 307, 79–92 (2019).Article 

Google Scholar 
Hebblewhite, M. & Haydon, D. T. Distinguishing technology from biology: A critical review of the use of GPS telemetry data in ecology. Philos. Trans. R. Soc. B Biol. Sci. 365, 2303–2312 (2010).Article 

Google Scholar 
Allen, A. M. et al. Scaling up movements: From individual space use to population patterns. Ecosphere 7, e01524 (2016).
Google Scholar 
Trouwborst, A., McCormack, P. C. & Martínez Camacho, E. Domestic cats and their impacts on biodiversity: A blind spot in the application of nature conservation law. People Nat. 2, 235–250 (2020).Article 

Google Scholar 
Sims, V., Evans, K. L., Newson, S. E., Tratalos, J. A. & Gaston, K. J. Avian assemblage structure and domestic cat densities in urban environments. Divers. Distrib. 14, 387–399 (2008).Article 

Google Scholar 
Lepczyk, C. A., Mertig, A. G. & Liu, J. Landowners and cat predation across rural-to-urban landscapes. Biol. Conserv. 115, 191–201 (2004).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing (Vienna, Austria, 2021).Heggøy, O. & Shimmings, P. Huskattens predasjon på fugler i Norge. En vurdering basert på en litteraturgjennomgang tech. rep. 36 (2018).Morgan, S. et al. Urban cat (Felis catus) movement and predation activity associated with a wetland reserve in New Zealand. Wildl. Res. 36, 574–580 (2009).Calver, M., Grayson, J., Lilith, M. & Dickman, C. Applying the precautionary principle to the issue of impacts by pet cats on urban wildlife. Biol. Conserv. 144, 1895–1901 (2011).Article 

Google Scholar 
Crowley, S., Cecchetti, M. & Mcdonald, R. Diverse perspectives of cat owners indicate bar riers to and opportunities for managing cat predation of wildlife. Front. Ecol. Environ. 18, 544–549 (2020).Treves, A., Krofel, M., Ohrens, O. & van Eeden, L. M. Predator control needs a standard of unbiased randomized experiments with cross-over design. Front. Ecol. Evol. 7, 462 (2019).Ferreira, G. A., Machado, J. C., Nakano-Oliveira, E., Andriolo, A. & Genaro, G. The effect of castration on home range size and activity patterns of domestic cats living in a natural area in a protected area on a Brazilian island. Appl. Anim. Behav. Sci. 230, 105049 (2020).Bengsen, A. J. et al. Feral cat home-range size varies predictably with landscape productivity and population density. J. Zool. 298, 112–120 (2016).Article 

Google Scholar 
López-Jara, M. J. et al. Free-roaming domestic cats near conservation areas in Chile: Spatial movements, human care and risks for wildlife. Perspect. Ecol. Conserv. 19, 387–398 (2021).Gillies, C. & Clout, M. The prey of domestic cats (Felis catus) in two suburbs of Auckland City, New Zealand. J. Zool. 259, 309–315 (2003).Article 

Google Scholar 
Pirie, T. J., Thomas, R. L. & Fellowes, M. D. E. Pet cats (Felis catus) from urban boundaries use different habitats, have larger home ranges and kill more prey than cats from the suburbs. Landsc. Urban Plan. 220, 104338 (2022).Article 

Google Scholar 
Vucetich, J. A., Hebblewhite, M., Smith, D. W. & Peterson, R. O. Predicting prey population dynamics from kill rate, predation rate and predator-prey ratios in three wolf-ungulate systems. J. Anim. Ecol. 80, 1236–1245 (2011).Article 

Google Scholar 
Kennedy, M., Phillips, B. E. N. L., Legge, S., Murphy, S. A. & Faulkner, R. A. Do dingoes suppress the activity of feral cats in northern Australia?. Austral Ecol. 37, 134–139 (2012).Article 

Google Scholar 
Crooks, K. R. & Soule, M. E. Mesopredator release and avifaunal extinctions in a fragmented system. English. Nature 400, 563–566 (1999).ADS 
CAS 
Article 

Google Scholar 
Ferreira, J. P., Leita, O. I., Santos-Reis, M. & Revilla, E. Human-related factors regulate the spatial ecology of domestic cats in sensitive areas for conservation. PLOS ONE 6, e25970 (2011).ADS 
CAS 
Article 

Google Scholar 
Brook, L. A., Johnson, C. N. & Ritchie, E. G. Effects of predator control on behaviour of an apex predator and indirect consequences for mesopredator suppression. J. Appl. Ecol. 49, 1278–1286 (2012).Article 

Google Scholar 
Laundre, J. W., Hernandez, L. & Altendorf, K. B. Wolves, elk, and bison: Reestablishing the “landscape of fear’’ in Yellowstone National Park, USA. English. Can. J. Zool. 79, 1401–1409 (2001).Article 

Google Scholar 
Ritchie, E. G. & Johnson, C. N. Predator interactions, mesopredator release and biodiversity conservation. English. Ecol. Lett. 12, 9820–998 (2009).Article 

Google Scholar 
Milleret, C. et al. GPS collars have an apparent positive effect on the survival of a large carnivore. Biol. Lett. 17, 20210128 (2021).Cecchetti, M., Crowley, S. L., Goodwin, C. E. D. & McDonald, R. A. Provision of high meat content food and object play reduce predation of wild animals by domestic cats Felis catus. Curr. Biol. 31, 1107-1111.e5 (2021).CAS 
Article 

Google Scholar 
Linklater, W., Farnworth, M., van Heezik, Y., Stafford, K. & Macdonald, E. Prioritizing cat owner behaviors for a campaign to reduce wildlife depredation. Conserv. Sci. Pract. 1, 1:e29 (2019).Selinske, M. J. et al. Identifying and prioritizing human behaviors that benefit biodiversity. Conserv. Sci. Pract. 2, e249 (2020).
Google Scholar 
McDonald, J. L., Maclean, M., Evans, M. R. & Hodgson, D. J. Reconciling actual and perceived rates of predation by domestic cats. Ecol. Evol. 5, 2745–2753 (2015).Article 

Google Scholar 
Bischof, R. et al. Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring. Proc. Natl. Acad. Sci. 117, 30531–30538 (2020).CAS 
Article 

Google Scholar 
Bischof, R., Gjevestad, J. G. O., Ordiz, A., Eldegard, K. & Milleret, C. High frequency GPS bursts and path-level analysis reveal linear feature tracking by red foxes. Sci. Rep. 9, 8849 (2019).ADS 
Article 

Google Scholar 
Gupte, P. R. et al. A guide to pre-processing high-throughput animal tracking data. J. Anim. Ecol. 91, 287–307 (2022).Article 

Google Scholar 
Morris, G. & Conner, L. Assessment of accuracy, fix success rate, and use of estimated horizontal position error (EHPE) to filter inaccurate data collected by a common commercially available GPS logger. PLoS ONE 12, e0189020 (2017).Article 

Google Scholar 
Clapp, J. G., Holbrook, J. D. & Thompson, D. J. GPSeqClus: An R package for sequential clustering of animal location data for model building, model application and field site investigations. Methods Ecol. Evol. 12, 787–793 (2021).Article 

Google Scholar 
Nielson, M., R., Sawyer, H. & McDonald, T. L. BBMM: Brownian Bridge Movement Model R Package Version 3.0 (2013).Horne, J. S., Garton, E. O., Krone, S. M. & Lewis, J. S. Analyzing animal movements using Brownian bridges. Ecology 88, 2354–2363 (2007).Article 

Google Scholar 
Sawyer, H., Kauffman, M. J., Nielson, R. M. & Horne, J. S. Identifying and prioritizing ungulate migration routes for landscape-level conservation. Ecol. Appl. 19, 2016–2025 (2009).Article 

Google Scholar 
Fischer, J. W., Walter, W. D. & Avery, M. L. Brownian bridge movement models to characterize birds’ home ranges. Condor 115, 298–305 (2013).Article 

Google Scholar 
Seidler, R., Long, R., Berger, J., Bergen, S. & Beckmann, J. Identifying impediments to long-distance mammal migrations. Conserv. Biol. 29 (2014).Collins, G. Seasonal distribution and routes of pronghorn in the Northern Great Basin. West. N. Am. Nat. 76, 101–112 (2016).Article 

Google Scholar  More