Philippa Kaur

Ethics statementAnimals’ care was in accordance with institutional guidelines. Ethical permit was issued by Malmö-Lund djurförsöksetiska nämnd 5.8.18-00848/2018.Field workWe carried out the field work in Sweden during four breeding seasons (2018–2021). Adult male willow warblers were captured in their breeding territories using mist nets and playback of a song. From each bird, we collected the innermost primary feather from the right wing. From the birds that returned with a logger we also collected ~20 μl of blood from the brachial wing vein. The blood was stored in SET buffer (0.015 M NaCl, 0.05 M Tris, 0.001 M of EDTA, pH 8.0) at room temperature until deposited for permanent storage at −20 °C. We deployed Migrate Technology Ltd geolocators (Intigeo-W30Z11-DIP 12 × 5 × 4 mm, 0.32 g) and used a nylon string to mount them on birds with the “leg-loop” harness method as outlined in our previous work24. The mass of the logger relative to that of the bird was on average 3.3% (range 2.7–3.8%).The tagged birds were ringed with a numbered aluminum ring, and two, colored plastic rings for later identification in the field. In total, we tagged 466 males (349 in 2018 and 117 in 2020) at breeding territories. During the first tagging season (2018), birds were trapped at 17 locations (average 22 birds per site; range 7–30) distributed across Sweden (Fig. S1). Three of the sites were in southern Sweden to document migration routes of allopatric trochilus and three sites were located above the Arctic circle to record migratory routes of allopatric acredula, whereas the remaining (239) loggers were spread over 11 sites located in the migratory divide. Given the observed densities and distribution of hybrids after analyzing returning birds in 2019, we deployed 117 more loggers at one single site (63.439°N, 14.831°E) in 2020. We successfully retrieved tracks from 57 birds tagged in 2019 and 16 from birds tagged in 2021. In search for birds with loggers, we checked circa 3000 willow warbler males and covered an area of at least 0.5 km radius around each site the year after tagging.Geolocator data treatmentThe R package GeoLight (version 2.0)25 was used to extract and analyze locations from raw geolocator data. All twilight events were obtained with light threshold of 3 lux. The most extreme outliers were trimmed with “loessFilter” function and a K value of 3. We used GeoLight’s function “getElevation” for estimating the sun elevation angle for the breeding period: these sets of locations were used to infer the positions for autumn departure direction. In addition, we carried out a “Hill-Ekström” calibration for the longest stationary winter site during the period before the spring equinox. Winter calibration produced location sets that better reflected the winter coordinates of the main winter site in sub-Saharan Africa26. We reduced some of the inherent geolocation “noise” by applying cantered 5-day rolling means to the coordinates. The equinox periods were visually identified by inspecting standard deviations in latitude. Latitudes from equinox periods were omitted (on average autumn equinox obscured data for 45 days (range 25–68). For the main winter site, we used the longest period at which bird stayed stationary and from which in all cases begun the spring migration (mean = 118, SD = 23 days). Timing of autumn departure was estimated by manual inspection of longitudes and latitudes plotted in time series. To estimate at which longitude the birds crossed the Mediterranean, we extracted the longitude when birds crossed latitude 35 N° (Mediterranean crossing longitude). For 29 birds, it was possible to directly extract the longitude at crossing latitude 35 N°. For the rest of the cases, the birds had not reached latitude 35 N° before the latitude was obscured by the equinox, we calculated the mean longitude of 10 days from the onset of fall equinox as a measure of the Mediterranean crossing. This measurement correlated highly with the winter longitude (r = 0.78, p = 2.8 × 10−16). To control for the birds relative breeding site longitude, we extracted the departure direction (1°–360°) relative from the tagging site to the location where the birds crossed the Mediterranean (departure direction). The departure data was of circular type (measured in 360°), however the variance did not span more than 180° degrees (range 151°–224°). Therefore, we proceeded with analyses using linear statistics. Geographic distances and departure direction were calculated using R package “geosphere” (version 1.5-10). Complete set of positions of each individual bird with equinoxes excluded is presented in Supplementary Data 1.Laboratory work and molecular data extractionWe extracted DNA from blood samples following the ammonium acetate protocol16. Genotyping for divergent regions on chromosome 1 (InvP-Ch1) and chromosome 5 (InvP-Ch5) was done using a qPCR SNP assay16, which is based on one informative SNP per region (SNP 65 for chromosome 1 and SNP 285 for chromosome 5). Probes and primers were produced by Thermo Fisher Scientific and were designed using the online Custom TaqMan® Assay Design tool (Table S4). We used Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) and the universal Fast-two-steps protocol: 95 °C, 15 min—40*(95 °C, 10 s–60 °C, 30 s, plate read. Both regions contain inversion polymorphisms that restrict recombination between subspecies-specific haplotypes and contain nearly all the SNPs separating the two subspecies13. For each region, we scored genotypes as either “Tro” (homozygous for trochilus haplotypes), “Acr” (homozygous for acredula haplotypes) or “Het” (heterozygous). The method that we used to assess the presence of MARB-a is based on a qPCR assay that quantifies the copy number of a novel TE (previously known as AFLP-WW212) that has expanded in acredula. The quantification of repeats by this method has been shown to be highly repeatable (R2 = 0.88) when comparing estimates obtained from DNA in blood and feathers15. We used the forward (5′-CCTTGCATACTTCTATTTCTCCC-3′) and reverse (5′-CATAGGACAGACATTGTTGAGG-3′) primers developed by Caballero-López et al.15 to amplify the TE motif. For reference of a single copy region we used the primers SFRS3F and SFRS3R27. We diluted DNA to 1 ng/μl−1 and used a Bio-Rad CFX96™ Real-time PCR system (Bio-Rad Laboratories, CA, USA) with SYBR-green-based detection. Total reaction volume was 25 μl of which 4 μl of DNA, 12.5 μl of SuperMix, 0.1 μl ROX, 1 μl of primer (forward and reverse), and 6.4 μl of double distilled H2O. We ran quantifications of the single copy gene and the TE variant found on MARB-a on separate plates with the following settings: 50 °C for 2 min as initial incubation, 95 °C for 2 min X 43 (94 °C for 30 s [55.3 °C SFRS3 and 55.5 °C for TE, 30 s] and 72 °C for 45 s). Each sample was run in duplicate and together with a two-fold serial standard dilution (2.5–7.8 × 10−2 ng). Allopatric trochilus have 0–6 copies whereas allopatric acredula have 8–45 copies15; a bimodal distribution was also confirmed in this new data set (Fig. S2). Accordingly, for the present analyses, we split the data in two groups: birds with ≤6 TE copies and birds with >7, translating into absence or presence of MARB-a, with the former assumed to be homozygous for the absence of MARB-a and the latter heterozygous or homozygous for the presence of MARB-a. Data from two investigated willow warbler families suggest a Mendelian inheritance pattern and provide support for our interpretation of how TE copy numbers reflect the three genotypes (Table S5). Moreover, the TE copy numbers within the hybrid swarm have a distribution similar to a combination of allopatric trochilus and acredula, further supporting that the copies are inherited as intact blocks (haplotypes). However, a precise distinction between heterozygotes and homozygotes on MARB-a is still not possible15.Statistical analysisWe used linear models with departure direction, winter longitude, migration distance and departure timing as response variables and the three genetic markers: MARB-a (a factor with two levels), InvP-Ch1 (a factor with three levels) and InvP-Ch5 (a factor with three levels) as explanatory variables. Models were constructed with R base package “stats”. We reported Type II ANOVA for models with more than one explanatory variable and no interactions and type III ANOVA results for models with interaction term by using R package “Car” (version 3.0-12)28. We initially constructed mixed effect models with timing of departure and tagging year as random factors however, this delivered singular fits due to insufficient sample sizes across categories. Normality of residuals was checked with a Shapiro–Wilk test. For carrying out circular statistics on autumn migration direction we used the R package “circular” (version 0.4-93). Watson’s U2 pairwise comparisons of different groups delivered the same results as linear models (Table S2 and Fig. S5). Circular means were identical to conventional linear means in our data set, which we take as another evidence that linear models are appropriate for the analysis of our data (Table S3 and Fig. S5). Maps in Figs. 1 and 2b and S1, S3 and S4 were created with R package “ggplot2” (version 3.3.6) using continent contours from Natural Earth, naturalearthdata.com/. Heat gradient over the maps in Fig. 1a–d were created with R package “gstat” (version 2.0-8) and the inverse distance weighting power of 3.0. Circular plots were created with ORIANA (version 4.02). All analyses were carried out with R version 4.1.1 (R Core Team 2021).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

Newmark, W. D. A land-bridge island perspective on mammalian extinctions in western North American parks. Nature 325, 430–432 (1987).Article 
ADS 
CAS 

Google Scholar 
Newmark, W. D. Isolation of African protected areas. Front. Ecol. Environ. 6, 321–328 (2008).Article 

Google Scholar 
Radeloff, V. C. et al. Housing growth in and near United States protected areas limits their conservation value. Proc. Natl. Acad. Sci. U. S. A. 107, 940–945 (2010).Article 
ADS 
CAS 

Google Scholar 
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).Article 
CAS 

Google Scholar 
Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. https://doi.org/10.1126/sciadv.aay0814 (2020).Article 

Google Scholar 
Wasser, S. K. et al. Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science 349, 84–87 (2015).Article 
ADS 
CAS 

Google Scholar 
Davis, C. R. & Hansen, A. J. Trajectories in land use change around U,S. national parks and challenges and opportunities for management. Ecol. Appl. 21, 3299–3316 (2011).Article 

Google Scholar 
Newmark, W. D. Extinction of mammal populations in western North American national parks. Conserv. Biol. 9, 512–526 (1995).Article 

Google Scholar 
Newmark, W. D. Insularization of Tanzanian parks and the local extinction of large mammals. Conserv. Biol. 10, 1549–1556 (1996).Article 

Google Scholar 
Brashares, J. S., Arcese, P. & Sam, M. K. Human demography and reserve size predict wildlife extinction in West Africa. Proc. R. Soc. B Biol. Sci. 268, 2473–2478 (2001).Article 
CAS 

Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
ADS 
CAS 

Google Scholar 
Turner, M. G. & Dale, V. H. Comparing large, infrequent disturbances: What have we learned?. Ecosystems 1, 493–496 (1998).Article 

Google Scholar 
Berger, J. The last mile: How to sustain long-distance migration in mammals. Conserv. Biol. 18, 320–331 (2004).Article 

Google Scholar 
Bolger, D. T., Newmark, W. D., Morrison, T. A. & Doak, D. F. The need for integrative approaches to understand and conserve migratory ungulates. Ecol. Lett. 11, 63–77 (2008).
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 
Tucker, M. A. et al. Moving in the anthropocene: Global reductions in terrestrial mammalian movements. Science 469, 466–469 (2018).Article 
ADS 

Google Scholar 
Soulé, M. E. & Terborgh, J. Conserving nature at regional and continental scales-a scientific program for North America. Bioscience 49, 809–817 (1999).Article 

Google Scholar 
Hilty, J. et al. Guidelines for conserving connectivity through ecological networks and corridors. Best Pract. Prot. Area Guidel. Ser. 30, 122 (2020).
Google Scholar 
Haddad, N. & Tewksbury, J. Impacts of corridors on populations and communities. in Connectivity Conservation (eds. Crooks, K. R. & Sanjayan, M.) 390–415 (Cambridge University Press, 2010).
Google Scholar 
Ramiadantsoa, T., Ovaskainen, O., Rybicki, J. & Hanski, I. Large-scale habitat corridors for biodiversity conservation: A forest corridor in Madagascar. PLoS One 10, 1–18 (2015).Article 
CAS 

Google Scholar 
Newmark, W. D., Jenkins, C. N., Pimm, S. L., McNeally, P. B. & Halley, J. M. Targeted habitat restoration can reduce extinction rates in fragmented forests. Proc. Natl. Acad. Sci. USA. 114, 9635–9640 (2017).Article 
ADS 
CAS 

Google Scholar 
Diamond, J. M. Biogeographic kinetics: Estimation of relaxation times for avifaunas of southwest Pacific islands. Proc. Natl. Acad. Sci. 69, 3199–3203 (1972).Article 
ADS 
CAS 

Google Scholar 
Terborgh, J. Preservation of natural diversity: The problem of extinction prone species. Bioscience 24, 715–722 (1974).Article 

Google Scholar 
Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt revisited. Nature 371, 65–66 (1994).Article 
ADS 

Google Scholar 
Halley, J. M., Monokrousos, N., Mazaris, A. D., Newmark, W. D. & Vokou, D. Dynamics of extinction debt across five taxonomic groups. Nat. Commun. 7, 1–6 (2016).Article 

Google Scholar 
Wearn, O. R., Reuman, D. C. & Ewers, R. M. Extinction debt and windows of conservation opportunity in the Brazilian amazon. Science 337, 228–232 (2012).Article 
ADS 
CAS 

Google Scholar 
Hanski, I. Extinction debt and species credit in boreal forests: Modelling the consequences of different approaches to conservation. Ann. Zool. Fennici 37, 271–280 (2000).
Google Scholar 
LaBarbera, M. Analyzing body size as a factor in ecology and evolution. Annu. Rev. Ecol. Syst. 20, 97–117 (1989).Article 

Google Scholar 
Oakleaf, J. K. et al. Habitat selection by recolonizing wolves in the northern Rocky mountains of the United States. J. Wildl. Manage. 70, 554–563 (2006).Article 

Google Scholar 
Cushman, S. A., McKelvey, K. S. & Schwartz, M. K. Use of empirically derived source-destination models to map regional conservation corridors. Conserv. Biol. 23, 368–376 (2009).Article 

Google Scholar 
Schwartz, M. K. et al. Wolverine gene flow across a narrow climatic niche. Ecology 90, 3222–3232 (2014).Article 

Google Scholar 
McKelvey, K. S. et al. Climate change predicted to shift wolverine distributions, connectivity, and dispersal corridors. Ecol. Appl. 21, 2882–2897 (2011).Article 

Google Scholar 
Carroll, C., Mcrae, B. H. & Brookes, A. Use of linkage mapping and centrality analysis across habitat gradients to conserve connectivity of gray wolf populations in western North America. Conserv. Biol. 26, 78–87 (2012).Article 

Google Scholar 
Parks, S. A., McKelvey, K. S. & Schwartz, M. K. Effects of weighting schemes on the identification of wildlife corridors generated with least-cost methods. Conserv. Biol. 27, 145–154 (2013).Article 

Google Scholar 
Peck, C. P. et al. Potential paths for male-mediated gene flow to and from an isolated grizzly bear population. Ecosphere 8, e01969 (2017).Article 

Google Scholar 
Wild Migrations: Atlas of Wyoming’s Ungulates. (Oregon State University, 2018).Singleton, P. H., Gaines, W. L. & Lehmkuhl, J. F. Landscape permeability for large carnivores in Washington: A geographic information system weighted-distance and least-cost corridor assessment. (2002).Long, R. A. et al. The Cascades carnivore connectivity project: A landscape genetic assessment of connectivity in Washington’s north Cascades ecosystem. Final report for the Seattle City Light Wildlife Research Program (2013).Diamond, J. M. The island dilemma: Lessons of modern biogeographic studies for the design of natural reserves. Biol. Conserv. 7, 129–146 (1975).Article 

Google Scholar 
Wilson, E. O. & Willis, E. O. Applied biogeography. In Ecological structure of ecological communities (eds. Cody, M. L, & Diamond, J. M.) 522–534 (Harvard University Press, 1975)
Google Scholar 
Halley, J. M. & Iwasa, Y. Neutral theory as a predictor of avifaunal extinctions after habitat loss. Proc. Natl. Acad. Sci. USA 108, 2316–2321 (2011).Article 
ADS 
CAS 

Google Scholar 
Cushman, S. A., Lewis, J. S. & Landguth, E. L. Evaluating the intersection of a regional wildlife connectivity network with highways. Mov. Ecol. 1, 1–11 (2013).Article 

Google Scholar 
Singleton, P. H. & Lehmkuhl, J. F. I-90 Snoqualmie pass wildlife habitat linkage assessment. Final Report. USDA, Pacific Northwest Research Station. (2000).Craighead, L., Craighead, A., Oeschslia, L. & Kociolek, A. Bozeman pass post-fencing wildlife monitoring. Final Report. FHWA/MT-10-006/8173 (2011).Andis, A. Z., Huijser, M. P. & Broberg, L. Performance of arch-style road crossing structures from relative movement rates of large mammals. Front. Ecol. Evol. 5, 1–13 (2017).Article 

Google Scholar 
Millward, L. Small mammal microhabitat use and species composition at a wildlife crossing structure compared with nearby forest (Central Washington University, 2018).
Google Scholar 
Bischof, R., Steyaert, S. M. J. G. & Kindberg, J. Caught in the mesh: Roads and their network-scale impediment to animal movement. Ecography 40, 1369–1380 (2017).Article 

Google Scholar 
Balkenhol, N. & Waits, L. P. Molecular road ecology: Exploring the potential of genetics for investigating transportation impacts on wildlife. Mol. Ecol. 18, 4151–4164 (2009).Article 

Google Scholar 
Clevenger, A. P. & Wierzchowski, J. Maintaining and restoring connectivity in landscapes fragmented by roads. In Connectivity Conservation, (eds. Crooks, K. R. & Sanjayan, M.) 502–535 (Cambridge University Press, 2010.)
Google Scholar 
Sawaya, M. A., Kalinowski, S. T. & Clevenger, A. P. Genetic connectivity for two bear species at wildlife crossing structures in Banff National Park. Proc. R. Soc. B Biol. Sci. 281, 20131705 (2014).Article 

Google Scholar 
Sawaya, M. A., Clevenger, A. P. & Schwartz, M. K. Demographic fragmentation of a protected wolverine population bisected by a major transportation corridor. Biol. Conserv. 236, 616–625 (2019).Article 

Google Scholar 
Kamal, S., Grodzińska-Jurczak, M. & Brown, G. Conservation on private land: A review of global strategies with a proposed classification system. J. Environ. Plan. Manag. 58, 576–597 (2015).Article 

Google Scholar 
Wasserman, T. N., Cushman, S. A., Littell, J. S., Shirk, A. J. & Landguth, E. L. Population connectivity and genetic diversity of American marten (Martes americana) in the United States northern Rocky Mountains in a climate change context. Conserv. Genet. 14, 529–541 (2013).Article 

Google Scholar 
Wasserman, T. N., Cushman, S. A., Shirk, A. S., Landguth, E. L. & Littell, J. S. Simulating the effects of climate change on population connectivity of American marten (Martes americana) in the northern Rocky Mountains, USA. Landsc. Ecol. 27, 211–225 (2012).Article 

Google Scholar 
Cushman, S. A., Landguth, E. L. & Flather, C. H. Evaluating the sufficiency of protected lands for maintaining wildlife population connectivity in the U.S. northern Rocky Mountains. Divers. Distrib. 18, 873–884 (2012).Article 

Google Scholar 
Beier, P., Spencer, W., Baldwin, R. F. & Mcrae, B. H. Toward best practices for developing regional connectivity maps. Conserv. Biol. 25, 879–892 (2011).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (2020). More

Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, ChinaQian Zhao, Yao Zhang & Shilong PiaoSchool of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, ChinaZaichun Zhu & Hui ZengKey Laboratory of Earth Surface System and Human—Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen, ChinaZaichun Zhu & Hui ZengDepartment of Earth and Environment, Boston University, Boston, MA, USARanga B. MyneniCSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Catalonia, SpainJosep PeñuelasCREAF, Barcelona, Catalonia, SpainJosep PeñuelasState Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaShilong Piao More

Magnaval, J.-F., Glickman, L. T., Dorchies, P. & Morassin, B. Highlights of human toxocariasis. Korean J. Parasitol. 39, 1 (2001).Article 

Google Scholar 
Parise, M. E., Hotez, P. J. & Slutsker, L. Neglected parasitic infections in the United States: Needs and opportunities. Am. J. Trop. Med. Hyg. 90, 783–785 (2014).Article 

Google Scholar 
Holland, C. V. Knowledge gaps in the epidemiology of Toxocara: The enigma remains. Parasitology 144, 81–94 (2017).Article 

Google Scholar 
Richards, D. T. & Lewis, J. W. Fecundity and egg output by Toxocara canis in the red fox Vulpes vulpes. J. Helminthol. 75, 157–164 (2001).
Google Scholar 
Glickman, L. T. & Schantz, P. M. Epidemiology and pathogenesis of zoonotic toxocariasis. Epidemiol. Rev. 3, 230–250 (1981).Article 

Google Scholar 
Keegan, J. D. & Holland, C. V. A comparison of Toxocara canis embryonation under controlled conditions in soil and hair. J. Helminthol. 87, 78–84 (2013).Article 

Google Scholar 
Overgaauw, P. A. M. & Nederland, V. Aspects of toxocara epidemiology: Toxocarosis in dogs and cats. Crit. Rev. Microbiol. 23, 233–251 (1997).Article 

Google Scholar 
Strube, C., Heuer, L. & Janecek, E. Toxocara spp. infections in paratenic hosts. Vet. Parasitol. 193, 375–89 (2013).Parsons, J. C. Ascarid infections of cats and dogs. Vet. Clin. N. Am. Small Anim. Pract. 17, 1307–1339 (1987).Article 

Google Scholar 
Brunaská, M., Dubinský, P. & Reiterová, K. Toxocara canis: Ultrastructural aspects of larval moulting in the maturing eggs. Int. J. Parasitol. 25, 683–690 (1995).Article 

Google Scholar 
Dubinsky, P., Havasiova-Reiterova, K., Petko, B., Hovorka, I. & Tomasovicova, O. Role of small mammals in the epidemiology of toxocariasis. Parasitology 110(Pt 2), 187–193 (1995).Ma, G. et al. Human toxocariasis. Lancet Infect. Dis. 18, e14–e24 (2018).Article 

Google Scholar 
Despommier, D. Toxocariasis: clinical aspects, epidemiology, medical ecology, and molecular aspects. Clin. Microbiol. Rev. 16, 265–272 (2003).Article 

Google Scholar 
Hotez, P. J. & Wilkins, P. P. Toxocariasis: America’s Most Common Neglected Infection of Poverty and a Helminthiasis of Global Importance?. PLoS Negl. Trop. Dis. 3, e400 (2009).Article 

Google Scholar 
Fan, C.-K., Holland, C. V., Loxton, K. & Barghouth, U. Cerebral toxocariasis: Silent progression to neurodegenerative disorders?. Clin. Microbiol. Rev. 28, 663–686 (2015).Article 

Google Scholar 
Nathwani, D., Laing, R. B. S. & Currie, P. F. Covert toxocariasis—A cause of recurrent abdominal pain in childhood. Br. J. Clin. Pract. (1992).Rostami, A. et al. Seroprevalence estimates for toxocariasis in people worldwide: A systematic review and meta-analysis. PLoS Negl. Trop. Dis. 13, e0007809 (2019).Article 

Google Scholar 
Borecka, A. & Kłapeć, T. Epidemiology of human toxocariasis in Poland—A review of cases 1978–2009. Ann. Agric. Environ. Med. 22, 28–31 (2015).Article 

Google Scholar 
Krasnov, B. R., Mouillot, D., Shenbrot, G. I., Khokhlova, I. S. & Poulin, R. Geographical variation in host specificity of fleas (Siphonaptera) parasitic on small mammals: The influence of phylogeny and local environmental conditions. Ecography 27, 787–797 (2004).Article 

Google Scholar 
Andreassen, H. P. et al. Population cycles and outbreaks of small rodents: Ten essential questions we still need to solve. Oecologia 195, 601–622 (2021).Article 
ADS 

Google Scholar 
Ylönen, H. Vole cycles and antipredatory behaviour. Trends Ecol. Evol. https://doi.org/10.1016/0169-5347(94)90125-2 (1994).Article 

Google Scholar 
Hanski, I., Hansson, L. & Henttonen, H. Specialist predators, generalist predators, and the microtine rodent cycle. J. Anim. Ecol. https://doi.org/10.2307/5465 (1991).Article 

Google Scholar 
Martinez-Bakker, M. & Helm, B. The influence of biological rhythms on host–parasite interactions. Trends Ecol. Evol. 30, 314–326 (2015).Article 

Google Scholar 
Bajer, A., Pawelczyk, A., Behnke, J. M., Gilbert, F. S. & Sinski, E. Factors affecting the component community structure of haemoparasites in bank voles (Clethrionomys glareolus) from the Mazury Lake District region of Poland. Parasitology 122(Pt 1), 43–54 (2001).Article 

Google Scholar 
Behnke, J. M., Lewis, J. W., Zain, S. N. & Gilbert, F. S. Helminth infections in Apodemus sylvaticus in southern England: interactive effects of host age, sex and year on the prevalence and abundance of infections. J. Helminthol. 73, 31–44 (1999).Article 

Google Scholar 
Ferrari, N., Cattadori, I. M., Nespereira, J., Rizzoli, A. & Hudson, P. J. The role of host sex in parasite dynamics: Field experiments on the yellow-necked mouse Apodemus flavicollis. Ecol. Lett. 7, 88–94 (2003).Article 

Google Scholar 
Grzybek, M. et al. Long-term spatiotemporal stability and dynamic changes in helminth infracommunities of bank voles (Myodes glareolus) in NE Poland. Parasitology 142, 1722–1743 (2015).Article 

Google Scholar 
Reiterová, K. et al. Small rodents—permanent reservoirs of toxocarosis in different habitats of Slovakia. Helminthologia 50, (2013).Habig, B., Doellman, M. M., Woods, K., Olansen, J. & Archie, E. A. Social status and parasitism in male and female vertebrates: A meta-analysis. Sci. Rep. 8, 3629 (2018).Article 
ADS 

Google Scholar 
Izhar, R. & Ben-Ami, F. Host age modulates parasite infectivity, virulence and reproduction. J. Anim. Ecol. 84, 1018–1028 (2015).Article 

Google Scholar 
Migalska, M. et al. Long term patterns of association between MHC and helminth burdens in the bank vole support Red Queen dynamics. Mol. Ecol. 31, 3400–3415 (2022).Article 

Google Scholar 
Grzybek, M. et al. Zoonotic viruses in three species of voles from Poland. Animals 10, 1820 (2020).Article 

Google Scholar 
Bajer, A. et al. Rodents as intermediate hosts of cestode parasites of mammalian carnivores and birds of prey in Poland, with the first data on the life-cycle of Mesocestoides melesi. Parasit. Vectors 13, 95 (2020).Article 

Google Scholar 
Rabalski, L. et al. Zoonotic spillover of SARS-CoV-2: Mink-adapted virus in humans. bioRxiv (2021). https://doi.org/10.1101/2021.03.05.433713.Rabalski, L. et al. Severe acute respiratory syndrome coronavirus 2 in Farmed Mink (Neovison vison) Poland. Emerg. Infect. Dis. 27, 2333–2339 (2021).Article 

Google Scholar 
Grzybek, M. et al. Seroprevalence of Trichinella spp. infection in bank voles (Myodes glareolus)—A long term study. Int. J. Parasitol. Parasites Wildl. 9, 144–148 (2019).Binder, F. et al. Heterogeneous Puumala orthohantavirus situation in endemic regions in Germany in summer 2019. Transbound Emerg. Dis. 67, 502–509 (2020).Article 

Google Scholar 
Tołkacz, K. et al. Prevalence, genetic identity and vertical transmission of Babesia microti in three naturally infected species of vole, Microtus spp. (Cricetidae). Parasit. Vectors 10, 1–12 (2017).Tołkacz, K. et al. Bartonella infections in three species of Microtus: Prevalence and genetic diversity, vertical transmission and the effect of concurrent Babesia microti infection on its success. Parasit. Vectors 11, 491 (2018).Article 

Google Scholar 
Behnke, J. M. et al. Variation in the helminth community structure in bank voles (Clethrionomys glareolus) from three comparable localities in the Mazury Lake District region of Poland. Parasitology 123, 401–414 (2001).Article 

Google Scholar 
Behnke, J. M. et al. Temporal and between-site variation in helminth communities of bank voles (Myodes glareolus) from N.E. Poland. 2. The infracommunity level. Parasitology 135, 999–1018 (2008).Behnke, J. M. et al. Temporal and between-site variation in helminth communities of bank voles (Myodes glareolus) from N.E. Poland. 1. Regional fauna and component community levels. Parasitology 135, 985–997 (2008).Tołkacz, K. et al. Prevalence, genetic identity and vertical transmission of Babesia microti in three naturally infected species of vole, Microtus spp. (Cricetidae). Parasit. Vectors 10, 66 (2017).Morris, P. A review of mammalian age determination methods. Mamm. Rev. 2, 69–104 (1972).Antolová, D. et al. Small mammals: Paratenic hosts for species of Toxocara in eastern Slovakia. J. Helminthol. 87, 52–58 (2013).Article 

Google Scholar 
Reiterová, K. et al. Small rodents—permanent reservoirs of toxocarosis in different habitats of Slovakia. Helminthologia 50, 20–26 (2013).Article 

Google Scholar 
Reperant, L. A., Hegglin, D., Tanner, I., Fischer, C. & Deplazes, P. Rodents as shared indicators for zoonotic parasites of carnivores in urban environments. Parasitology 136, 329–337 (2009).Article 

Google Scholar 
Savigny, D. H. In vitro maintenance of Toxocara canis larvae and a simple method for the production of Toxocara ES antigen for use in serodiagnostic tests for visceral larva migrans. J. Parasitol. 61, 781–782 (1975).Article 

Google Scholar 
Cuéllar, C., Fenoy, S. & Guillén, J. L. Cross-reactions of sera from Toxascaris leonina and Ascaris suum infected mice with Toxocara canis, Toxascaris leonina and Ascaris suum antigens. Int. J. Parasitol. 25, 731–739 (1995).Article 

Google Scholar 
Naguleswaran, A., Hemphill, A., Rajapakse, R. P. V. J. & Sager, H. Elaboration of a crude antigen ELISA for serodiagnosis of caprine neosporosis: Validation of the test by detection of Neospora caninum-specific antibodies in goats from Sri Lanka. Vet. Parasitol. 126, 257–262 (2004).Article 

Google Scholar 
Sokal, R. R. & Rohlf, F. J. Statistical Tables (Freeman, 1995).MATH 

Google Scholar 
Behnke, J. M. et al. Variation in the helminth community structure in bank voles (Clethrionomys glareolus) from three comparable localities in the mazury lake istrict region of Poland. Parasitology 123, 401–414 (2001).Article 

Google Scholar 
Grzybek, M., Bajer, A., Behnke-Borowczyk, J., Al-Sarraf, M. & Behnke, J. M. Female host sex-biased parasitism with the rodent stomach nematode Mastophorus muris in wild bank voles (Myodes glareolus). Parasitol. Res. 114, 523–533 (2014).Article 

Google Scholar 
Grzybek, M. et al. Seroprevalence of TBEV in bank voles from Poland-a long-term approach. Emerg. Microbes Infect. 7, 145 (2018).Article 

Google Scholar 
Pullan, R. L., Smith, J. L., Jasrasaria, R. & Brooker, S. J. Global numbers of infection and disease burden of soil transmitted helminth infections in 2010. Parasit Vectors 7, 37 (2014).Article 

Google Scholar 
GBD 2017 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1859–1922 (2018).Reperant, L. A., Hegglin, D., Tanner, I., Fisher, C. & Deplazes, P. Rodents as shared indicators for zoonotic parasites of carnivores in urban environments. Parasitology 136, 329–337 (2009).Article 

Google Scholar 
Antolová, D., Reiterová, K., Miterpáková, M., Stanko, M. & Dubinský, P. Circulation of Toxocara spp. in suburban and rural ecosystems in the Slovak Republic. Vet. Parasitol 126, 317–324 (2004).Reiterová, K. et al. Small rodents—permanent reservoirs of toxocarosis in different habitats of Slovakia. Helminthologia (Poland) 50, 20–26 (2013).Article 

Google Scholar 
Dvorožňáková, E., Kołodziej-Sobocińska, M., Hurníková, Z., Víchová, B. & Zub, K. Prevalence of zoonotic pathogens in wild rodents living in the Białowieża Primeval Forest, Poland. Ann. Parasitol. 62 (2016).Dubinský, P., Havasiova-Reiterova, K., Peťko, B., Hovorka, I. & Tomašovičová, O. Role of small mammals in the epidemiology of toxocariasis. Parasitology 110, 187–193 (1995).Article 

Google Scholar 
Antolová, D., Reiterová, K., Miterpáková, M., Stanko, M. & Dubinský, P. Circulation of Toxocara spp. in suburban and rural ecosystems in the Slovak Republic. Vet. Parasitol. 126, 317–24 (2004).Hildebrand, J., Zalesny, G., Okulewicz, A. & Baszkiewicz, K. Preliminary studies on the zoonotic importance of rodents as a reservoir of toxocariasis from recreation grounds in Wroclaw (Poland). Helminthologia 46, 80–84 (2009).Article 

Google Scholar 
Dvorožňáková, E., Kołodziej-Sobocińska, M., Hurníková, Z., Víchová, B. & Zub, K. Prevalence of zoonotic pathogens in wild rodents living in the Białowieża Primeval Forest Poland. Ann. Parasitol. 62, 183 (2016).
Google Scholar 
Azam, D., Ukpai, O. M., Said, A., Abd-Allah, G. A. & Morgan, E. R. Temperature and the development and survival of infective Toxocara canis larvae. Parasitol. Res. 110, 649–656 (2012).Article 

Google Scholar 
Kloch, A., Bednarska, M. & Bajer, A. Intestinal macro- and microparasites of wolves (Canis lupus L.) from north-eastern Poland recovered by coprological study. Ann. Agric. Environ. Med. 12, 237–45 (2005).Mierzejewska, E. J. et al. The efficiency of live-capture traps for the study of Red Fox (Vulpes vulpes) cubs: A three-year study in Poland. Animals 10, 374 (2020).Article 

Google Scholar 
Karamon, J. et al. Intestinal helminths of raccoon dogs (Nyctereutes procyonoides) and red foxes (Vulpes vulpes) from the Augustów Primeval Forest (north-eastern Poland). J. Vet. Res. (Poland) 60, 273–277 (2016).Article 

Google Scholar 
Cisek, A., Ramisz, A., Balicka-Ramisz, A., Pilarczyk, B. & Laurans, L. The prevalence of Toxocara canis (Werner, 1782) in dogs and red foxes in north-west Poland. Wiad Parazytol 50, 641–646 (2004).
Google Scholar 
Jarošová, J., Antolová, D., Lukáč, B. & Maďari, A. A Survey of Intestinal Helminths of dogs in Slovakia with an emphasis on zoonotic species. Animals 11, 3000 (2021).Article 

Google Scholar 
Kidawa, D. & Kowalczyk, R. The effects of sex, age, season and habitat on diet of the red fox Vulpes vulpes in northeastern Poland. Acta Theriol. (Warsz) 56, 209–218 (2011).Article 

Google Scholar 
Grzybek, M. et al. Seroprevalence of Trichinella spp. infection in bank voles (Myodes glareolus)—A long term study. Int. J. Parasitol. Parasites Wildl. (2019). https://doi.org/10.1016/j.ijppaw.2019.03.005.Grzybek, M. et al. Seroprevalence of Toxoplasma gondii among Sylvatic Rodents in Poland. Animals 11, 1048 (2021).Article 

Google Scholar 
Maciag, L., Morgan, E. R. & Holland, C. Toxocara: Time to let cati ‘out of the bag’. Trends Parasitol. 38, 280–289 (2022).Article 

Google Scholar 
Foreman-Worsley, R., Finka, L. R., Ward, S. J. & Farnworth, M. J. Indoors or outdoors? an international exploration of owner demographics and decision making associated with lifestyle of pet cats. Animals 11, 253 (2021).Article 

Google Scholar 
Liberg, O. Food habits and prey impact by feral and house-based domestic cats in a rural area in southern Sweden. J. Mammal. 65, 424–432 (1984).Article 

Google Scholar 
Krücken, J. et al. Small rodents as paratenic or intermediate hosts of carnivore parasites in Berlin Germany. PLoS ONE 12, e0172829 (2017).Article 

Google Scholar 
Dunsmore, J. D., Thompson, R. C. A. & Bates, I. A. The accumulation of Toxocara canis larvae in the brains of mice. Int. J. Parasitol. 13, 517–521 (1983).Article 

Google Scholar 
Nijsse, R., Mughini-Gras, L., Wagenaar, J. A., Franssen, F. & Ploeger, H. W. Environmental contamination with Toxocara eggs: a quantitative approach to estimate the relative contributions of dogs, cats and foxes, and to assess the efficacy of advised interventions in dogs. Parasit. Vectors 8, 397 (2015).Article 

Google Scholar 
Dunsmore, J. D., Thompson, R. C. A. & Bates, I. A. Prevalence and survival of Toxocara canis eggs in the urban environment of Perth Australia. Vet. Parasitol. 16, 303–311 (1984).Article 

Google Scholar 
Duscher, G. G., Leschnik, M., Fuehrer, H.-P. & Joachim, A. Wildlife reservoirs for vector-borne canine, feline and zoonotic infections in Austria. Int. J. Parasitol. Parasites Wildl. 4, 88–96 (2015).Article 

Google Scholar 
Ghai, R. R. et al. A generalizable one health framework for the control of zoonotic diseases. Sci. Rep. 12, 8588 (2022).Article 
ADS 

Google Scholar 
Grzybek, M. et al. Zoonotic virus seroprevalence among bank voles, Poland, 2002–2010. Emerg. Infect. Dis. 25, 1607–1609 (2019).Article 

Google Scholar  More

van der Ent, R. J. & Tuinenburg, O. A. The residence time of water in the atmosphere revisited. Hydrol. Earth Syst. Sci. 21, 779–790 (2017).Article 
ADS 

Google Scholar 
Risi, C., Noone, D., Frankenberg, C. & Worden, J. Role of continental recycling in intraseasonal variations of continental moisture as deduced from model simulations and water vapor isotopic measurements. Water Resour. Res. 49, 4136–4156 (2013).Article 
ADS 
CAS 

Google Scholar 
Gupta, S. K. & Deshpande, R. D. Water for India in 2050: First-order assessment of available options. Curr. Sci. 86, 9 (2004).
Google Scholar 
Kuttippurath, J. et al. Observed rainfall changes in the past century (1901–2019) over the wettest place on Earth. Environ. Res. Lett. 16, 024018 (2021).Article 
ADS 

Google Scholar 
Rao, M. P. et al. Seven centuries of reconstructed Brahmaputra River discharge demonstrate underestimated high discharge and flood hazard frequency. Nat. Commun. 11, 6017 (2020).Article 
ADS 
CAS 

Google Scholar 
Bassi, N., Kumar, M. D., Sharma, A. & Pardha-Saradhi, P. Status of wetlands in India: A review of extent, ecosystem benefits, threats and management strategies. J. Hydrol. Regional Stud. 2, 1–19 (2014).Article 

Google Scholar 
Dikshit, K. R. & Dikshit, J. K. Natural vegetation: Forests and grasslands of North-East India. in North-East India: Land, People and Economy (eds. Dikshit, K. R. & Dikshit, J. K.) 213–255 (Springer, 2014). https://doi.org/10.1007/978-94-007-7055-3_9.Chakraborty, S. et al. Linkage between precipitation isotopes and biosphere-atmosphere interaction observed in northeast India. Npj Clim. Atmos. Sci. 5, 1–11 (2022).Article 

Google Scholar 
Pathak, A., Ghosh, S. & Kumar, P. Precipitation recycling in the Indian subcontinent during summer monsoon. J. Hydrometeorol. 15, 2050–2066 (2014).Article 
ADS 

Google Scholar 
Mahanta, R., Sarma, D. & Choudhury, A. Heavy rainfall occurrences in northeast India. Int. J. Climatol. 33, 1456–1469 (2013).Article 

Google Scholar 
Murata, F. et al. dominant synoptic disturbance in the extreme rainfall at cherrapunji, northeast India, based on 104 years of rainfall data (1902–2005). J. Clim. 30, 8237–8251 (2017).Article 
ADS 

Google Scholar 
Roy, S. C. & Chatterji, G. Origin of nor’westers. Nature 124, 481–481 (1929).Article 
ADS 

Google Scholar 
Dhar, O. N. & Nandargi, S. A study of floods in the Brahmaputra basin in India. Int. J. Climatol. 20, 771–781 (2000).Article 

Google Scholar 
Reager, J. T., Thomas, B. F. & Famiglietti, J. S. River basin flood potential inferred using GRACE gravity observations at several months lead time. Nat. Geosci. 7, 588–592 (2014).Article 
ADS 
CAS 

Google Scholar 
Gat, J. R. Isotope Hydrology: A Study of the Water Cycle (World Scientific, 2010).Book 

Google Scholar 
Salati, E., DallOlio, A., Matsui, E. & Gat, J. R. Recycling of water in the Amazon basin: An isotopic study. Water Resourc. Res. 15, 1250–1258 (1979).Article 
ADS 
CAS 

Google Scholar 
Victoria, R. L., Martinelli, L. A., Mortatti, J. & Richey, J. Mechanisms of water recycling in the Amazon basin: Isotopic insights. Ambio 20, 384–387 (1991).
Google Scholar 
Wright, J. S. et al. Rainforest-initiated wet season onset over the southern Amazon. PNAS 114, 8481–8486 (2017).Article 
ADS 
CAS 

Google Scholar 
Leite-Filho, A. T., Soares-Filho, B. S., Davis, J. L., Abrahão, G. M. & Börner, J. Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nat. Commun. 12, 2591 (2021).Article 
ADS 
CAS 

Google Scholar 
Spracklen, D. V. & Garcia-Carreras, L. The impact of Amazonian deforestation on Amazon basin rainfall. Geophys. Res. Lett. 42, 9546–9552 (2015).Article 
ADS 

Google Scholar 
Lele, N. & Joshi, P. K. Analyzing deforestation rates, spatial forest cover changes and identifying critical areas of forest cover changes in North-East India during 1972–1999. Environ. Monit. Assess. 156, 159 (2008).Article 

Google Scholar 
Sudhakar Reddy, C. et al. Quantification and monitoring of deforestation in India over eight decades (1930–2013). Biodivers. Conserv. 25, 93–116 (2016).Article 

Google Scholar 
Kathayat, G. et al. Interannual oxygen isotope variability in Indian summer monsoon precipitation reflects changes in moisture sources. Commun. Earth Environ. 2, 1–10 (2021).Article 

Google Scholar 
Kathayat, G. et al. Protracted Indian monsoon droughts of the past millennium and their societal impacts. Proc. Natl. Acad. Sci. 119, e2207487119 (2022).Article 
CAS 

Google Scholar 
Breitenbach, S. F. M. et al. Strong influence of water vapor source dynamics on stable isotopes in precipitation observed in Southern Meghalaya, NE India. Earth Planet. Sci. Lett. 292, 212–220 (2010).Article 
ADS 
CAS 

Google Scholar 
Pradhan, R., Singh, N. & Singh, R. P. Onset of summer monsoon in Northeast India is preceded by enhanced transpiration. Sci. Rep. 9, 18646 (2019).Article 
ADS 
CAS 

Google Scholar 
Das, S., Sarkar, A., Das, M. K., Rahman, Md. M. & Islam, Md. N. Composite characteristics of Nor’westers based on observations and simulations. Atmos. Res. 158–159, 158–178 (2015).Article 

Google Scholar 
Vinay Kumar, P. & Venkateswara Naidu, C. Is pre-monsoon rainfall activity over India increasing in the recent era of global warming?. Pure Appl. Geophys. 177, 4423–4442 (2020).Article 
ADS 

Google Scholar 
Narayanan, P., Sarkar, S., Basistha, A. & Sachdeva, K. Trend analysis and forecast of pre-monsoon rainfall over India. Weather 71, 94–99 (2016).Article 
ADS 

Google Scholar 
Stevens, B. Atmospheric moist convection. Annu. Rev. Earth Planet. Sci. 33, 605–643 (2005).Article 
ADS 
MathSciNet 
CAS 
MATH 

Google Scholar 
Bershaw, J. Controls on deuterium excess across Asia. Geosciences 8, 257 (2018).Article 
ADS 

Google Scholar 
Clark, I. D. & Fritz, P. Environmental Isotopes in Hydrogeology (CRC Press, 1997). https://doi.org/10.1201/9781482242911.Book 

Google Scholar 
Cui, J., Tian, L., Biggs, T. W. & Wen, R. Deuterium-excess determination of evaporation to inflow ratios of an alpine lake: Implications for water balance and modeling. Hydrol. Process. 31, 1034–1046 (2017).Article 
ADS 

Google Scholar 
Laskar, A. H. et al. Stable isotopic characterization of Nor’westers of southern Assam, NE India. J. Clim. Chang. 1, 75–87 (2015).Article 

Google Scholar 
Tanoue, M. et al. Seasonal variation in isotopic composition and the origin of precipitation over Bangladesh. Prog. Earth Planet Sci. 5, 77 (2018).Article 
ADS 

Google Scholar 
Oza, H., Ganguly, A., Padhya, V. & Deshpande, R. D. Hydrometeorological processes and evaporation from falling rain in Indian sub-continent: Insights from stable isotopes and meteorological parameters. J. Hydrol. 591, 125601 (2020).Article 
CAS 

Google Scholar 
Chen, F. et al. Relationship between sub-cloud secondary evaporation and stable isotopes in precipitation of Lanzhou and surrounding area. Quatern. Int. 380–381, 68–74 (2015).Article 

Google Scholar 
Sinha, N. et al. Isotopic investigation of the moisture transport processes over the Bay of Bengal. J. Hydrol. X 2, 100021 (2019).Article 
CAS 

Google Scholar 
Rawson, H. M., Begg, J. E. & Woodward, R. G. The effect of atmospheric humidity on photosynthesis, transpiration and water use efficiency of leaves of several plant species. Planta 134, 5–10 (1977).Article 
CAS 

Google Scholar 
Chakraborty, S., Belekar, A. R., Datye, A. & Sinha, N. Isotopic study of intraseasonal variations of plant transpiration: An alternative means to characterise the dry phases of monsoon. Sci. Rep. 8, 8647 (2018).Article 
ADS 
CAS 

Google Scholar 
Sinha, N., Chakraborty, S. & Mohan, P. M. Modern rain-isotope data from Indian island and the mainland on the daily scale for the summer monsoon season. Data Brief 23, 103793 (2019).Article 

Google Scholar 
Grujic, D. et al. Formation of a Rain Shadow: O and H stable isotope records in authigenic clays from the Siwalik group in eastern Bhutan. Geochem. Geophys. Geosyst. 19, 3430–3447 (2018).Article 
CAS 

Google Scholar 
Lambs, L., Balakrishna, K., Brunet, F. & Probst, J. L. Oxygen and hydrogen isotopic composition of major Indian rivers: A first global assessment. Hydrol. Process. 19, 3345–3355 (2005).Article 
ADS 
CAS 

Google Scholar 
Ek, M. B. & Holtslag, A. A. M. Influence of soil moisture on boundary layer cloud development. J. Hydrometeorol. 5, 86–99 (2004).Article 
ADS 

Google Scholar 
Findell, K. L. & Eltahir, E. A. B. Atmospheric controls on soil moisture-boundary layer interactions. Part I: Framework development. J. Hydrometeorol. 4, 552–569 (2003).Article 
ADS 

Google Scholar 
Syroka, J. & Toumi, R. On the withdrawal of the Indian summer monsoon. Q. J. R. Meteorol. Soc. 130, 989–1008 (2004).Article 
ADS 

Google Scholar 
Bhatta, L. D. et al. Ecosystem service changes and livelihood impacts in the maguri-motapung wetlands of Assam. India. Land 5, 15 (2016).Article 

Google Scholar 
Choudhury, B. A., Saha, S. K., Konwar, M., Sujith, K. & Deshamukhya, A. Rapid drying of northeast India in the last three decades: Climate change or natural variability?. J. Geophys. Res. Atmos. 124, 227–237 (2019).Article 
ADS 

Google Scholar 
Das, D. Changing climate and its impacts on Assam, Northeast India. Bandung J. Glob. South 2, 26 (2016).Article 

Google Scholar 
Deka, R. L., Mahanta, C., Pathak, H., Nath, K. K. & Das, S. Trends and fluctuations of rainfall regime in the Brahmaputra and Barak basins of Assam, India. Theor. Appl. Climatol. 114, 61–71 (2013).Article 
ADS 

Google Scholar 
Maurya, A. S., Shah, M., Deshpande, R. D. & Gupta, S. K. Protocol for δ18O and δD analyses of water sample using Delta V plus IRMS in CF Mode with Gas Bench II for IWIN National Programme at PRL, Ahmedabad. in 11th ISMAS Triennial Conference of Indian Society for Mass Spectrometry vol. 314, 314–317 (Indian Society for Mass Spectrometry Hyderabad, 2009).Deshpande, R. D. & Gupta, S. K. Oxygen and hydrogen isotopes in hydrological cycle: new data from IWIN national programme. Proc. Indian Natl. Sci. Acad. 78, 321–331 (2012).CAS 

Google Scholar 
Deshpande, R. D. & Gupta, S. K. National programme on isotope fingerprinting of waters of India (IWIN). Glimpses of Geosciences Research in India, the Indian Report to IUGS, Indian National Science Academy (eds Singhvi, AK, Bhattacharya, A. & Guha, S.), 10–16 (2008).Oza, H., Padhya, V., Ganguly, A. & Deshpande, R. D. Investigating hydrometeorology of the Western Himalayas: Insights from stable isotopes of water and meteorological parameters. Atmos. Res. 268, 105997 (2022).Article 
CAS 

Google Scholar 
Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteor. Soc. 96, 2059–2077 (2015).Article 
ADS 

Google Scholar 
Sodemann, H., Schwierz, C. & Wernli, H. Interannual variability of Greenland winter precipitation sources: Lagrangian moisture diagnostic and North Atlantic Oscillation influence. J. Geophys. Res. Atmos. 113, D3 (2008).
Google Scholar 
Oza, H. et al. Hydrometeorological processes in semi-arid western India: insights from long term isotope record of daily precipitation. Clim. Dyn. 54, 2745–2757 (2020).Article 

Google Scholar 
Su, L., Yuan, Z., Fung, J. C. H. & Lau, A. K. H. A comparison of HYSPLIT backward trajectories generated from two GDAS datasets. Sci. Total Environ. 506–507, 527–537 (2015).Article 
ADS 

Google Scholar 
Ahmed, M., Seraj, R. & Islam, S. M. S. The k-means algorithm: A comprehensive survey and performance evaluation. Electronics 9, 1295 (2020).Article 

Google Scholar  More

Ward, M. et al. Impact of 2019–2020 mega-fires on Australian fauna habitat. Nat. Ecol. Evol. 4(10), 1321–1326. https://doi.org/10.1038/s41559-020-1251-1 (2020).Article 

Google Scholar 
Legge, S. et al. Estimates of the impacts of the 2019–20 fires on populations of native animal species, Brisbane (2021).Yibo, H. et al. Genomic evidence for two phylogenetic species and long-term population bottlenecks in red pandas. Sci. Adv. 6(9), eaax5751. https://doi.org/10.1126/sciadv.aax5751 (2022).Article 

Google Scholar 
Grossen, C., Guillaume, F., Keller, L. F. & Croll, D. Purging of highly deleterious mutations through severe bottlenecks in Alpine ibex. Nat. Commun. 11(1), 1001. https://doi.org/10.1038/s41467-020-14803-1 (2020).Article 
ADS 

Google Scholar 
van Aalst, M. K. The impacts of climate change on the risk of natural disasters. Disasters 30(1), 5–18. https://doi.org/10.1111/j.1467-9523.2006.00303.x (2006).Article 

Google Scholar 
Banholzer, S., Kossin, J. & Donner, S. The impact of climate change on natural disasters. In Reducing Disaster: Early Warning Systems For Climate Change (eds Singh, A. & Zommers, Z.) 21–49 (Springer Netherlands, 2014). https://doi.org/10.1007/978-94-017-8598-3_2.Chapter 

Google Scholar 
Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to Conservation Genetics 2nd edn. (Cambridge University Press, 2010).Book 

Google Scholar 
Bouzat, J. L. Conservation genetics of population bottlenecks: The role of chance, selection, and history. Conserv. Genet. 11(2), 463–478. https://doi.org/10.1007/s10592-010-0049-0 (2010).Article 

Google Scholar 
Leigh, D. M., Hendry, A. P., Vázquez-Domínguez, E. & Friesen, V. L. Estimated six per cent loss of genetic variation in wild populations since the industrial revolution. Evol. Appl. 12(8), 1505–1512. https://doi.org/10.1111/eva.12810 (2019).Article 

Google Scholar 
Willi, Y., Van Buskirk, J. & Hoffmann, A. A. Limits to the adaptive potential of small populations. Annu. Rev. Ecol. Evol. Syst. 37(1), 433–458 (2006).Article 

Google Scholar 
Tanaka, M. M., Wahl, L. M. & Wahl, L. M. Surviving environmental change: When increasing population size can increase extinction risk. Proc. R. Soc. B 289, 20220439. https://doi.org/10.1098/rspb.2022.0439 (2022).Article 

Google Scholar 
Gomulkiewicz, R. & Holt, R. D. When does evolution by natural selection prevent extinction?. Evolution 49(1), 201–207. https://doi.org/10.1111/j.1558-5646.1995.tb05971.x (1995).Article 

Google Scholar 
Bell, G. Evolutionary rescue. Annu. Rev. Ecol. Evol. Syst. 48(1), 605–627. https://doi.org/10.1146/annurev-ecolsys-110316-023011 (2017).Article 

Google Scholar 
Wood, J. L. A., Yates, M. C. & Fraser, D. J. Are heritability and selection related to population size in nature? Meta-analysis and conservation implications. Evol. Appl. 9(5), 640–657. https://doi.org/10.1111/eva.12375 (2016).Article 

Google Scholar 
Sgrò, C. M., Lowe, A. J. & Hoffmann, A. A. Building evolutionary resilience for conserving biodiversity under climate change. Evol. Appl. 4(2), 326–337 (2011).Article 

Google Scholar 
Hohenlohe, P. A., Funk, W. C. & Rajora, O. P. Population genomics for wildlife conservation and management. Mol. Ecol. 30(1), 62–82. https://doi.org/10.1111/mec.15720 (2021).Article 

Google Scholar 
Walters, A. D. & Schwartz, M. K. Population genomics for the management of wild vertebrate populations. In Population Genomics: Wildlife 419–436 (Springer, 2020).Chapter 

Google Scholar 
Willi, Y. et al. Conservation genetics as a management tool: The five best-supported paradigms to assist the management of threatened species. Proc. Natl. Acad. Sci. USA 119(1), 1–10. https://doi.org/10.1073/pnas.2105076119 (2022).Article 

Google Scholar 
Moore, J. F. et al. The potential and practice of arboreal camera trapping. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13666 (2021).Article 

Google Scholar 
Frankham, R. Challenges and opportunities of genetic approaches to biological conservation. Biol. Conserv. 143(9), 1919–1927. https://doi.org/10.1016/j.biocon.2010.05.011 (2010).Article 

Google Scholar 
Allendorf, F. W., Luikart, G. H. & Aitken, S. N. Conservation and the Genetics of Populations 2nd edn. (Wiley, 2012).
Google Scholar 
Franklin, I. Evolutionary change in small populations. In Conservation Biology—An Evolutionary-Ecological Perspective 135–149 (Sinauer Associates, 1980).
Google Scholar 
Soulé, M. E. Thresholds for survival: maintaining fitness and evolutionary potential. In Conservation Biology: An Evolutionary-Ecological Perspective 151–169 (Sinauer, 1980).
Google Scholar 
Hoban, S. et al. Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biol. Conserv. https://doi.org/10.1016/J.BIOCON.2020.108654 (2020).Article 

Google Scholar 
McGregor, D. C. et al. Genetic evidence supports three previously described species of greater glider, Petauroides volans, P. minor, and P. armillatus. Sci. Rep. 10(1), 1–11. https://doi.org/10.1038/s41598-020-76364-z (2020).Article 

Google Scholar 
Hogg, C. J. et al. Threatened species initiative: Empowering conservation action using genomic resources. Proc. Natl. Acad. Sci. USA 119(4), e2115643118. https://doi.org/10.1073/pnas.2115643118 (2022).Article 

Google Scholar 
Pierson, J. C. et al. Genetic factors in threatened species recovery plans on three continents. Front. Ecol. Environ. 14(8), 433–440. https://doi.org/10.1002/fee.1323 (2016).Article 

Google Scholar 
Harris, J. M. & Maloney, K. S. S. Petauroides volans (Diprotodontia: Pseudocheiridae). Mamm. Species 42(866), 207–219. https://doi.org/10.1644/866.1 (2010).Article 

Google Scholar 
Kavanagh, R. P. & Lambert, M. J. Food selection by the greater glider, Petauroides volans: Is foliar nitrogen a determinant of habitat quality?. Austral. Wildl. Res. 17(3), 285–299 (1990).Article 

Google Scholar 
Youngentob, K. N. et al. Foliage chemistry influences tree choice and landscape use of a gliding marsupial folivore. J. Chem. Ecol. 37(1), 71–84. https://doi.org/10.1007/s10886-010-9889-9 (2011).Article 

Google Scholar 
Jensen, L. M., Wallis, I. R. & Foley, W. J. The relative concentrations of nutrients and toxins dictate feeding by a vertebrate browser, the greater glider Petauroides volans. PLoS ONE 10(5), 1–12. https://doi.org/10.1371/journal.pone.0121584 (2015).Article 

Google Scholar 
Kehl, J. & Borsboom, A. Home range, den tree use and activity patterns in the greater glider, Petauroides volans. Possums Gliders 229–236 (1984).Goldingay, R. L. Characteristics of tree hollows used by Australian arboreal and scansorial mammals. Aust. J. Zool. 59(5), 277–294 (2012).Article 

Google Scholar 
Eyre, T. J. Regional habitat selection of large gliding possums at forest stand and landscape scales in southern Queensland, Australia: I. Greater glider (Petauroides volans). For. Ecol. Manag 235(1–3), 270–282. https://doi.org/10.1016/j.foreco.2006.08.338 (2006).Article 

Google Scholar 
Kavanagh, R. P. & Bamkin, K. L. Distribution of nocturnal forest birds and mammals in relation to the logging mosaic in south-eastern New South Wales, Australia. Biol. Conserv. 71(1), 41–53. https://doi.org/10.1016/0006-3207(94)00019-M (1995).Article 

Google Scholar 
Lindenmayer, D. B. et al. Fire severity and landscape context effects on arboreal marsupials. Biol. Conserv. 167, 137–148 (2013).Article 

Google Scholar 
May-Stubbles, J. C., Gracanin, A. & Mikac, K. M. Increasing fire severity negatively affects greater glider density. Wildl. Res. https://doi.org/10.1071/wr21091 (2022).Article 

Google Scholar 
Smith, P. & Smith, J. Decline of the greater glider (Petauroides volans) in the lower Blue Mountains, New South Wales. Aust. J. Zool. 66(2), 103–114. https://doi.org/10.1071/ZO18021 (2019).Article 

Google Scholar 
Kearney, M. R., Wintle, B. A. & Porter, W. P. Correlative and mechanistic models of species distribution provide congruent forecasts under climate change. Conserv. Lett. 3(3), 203–213. https://doi.org/10.1111/j.1755-263X.2010.00097.x (2010).Article 

Google Scholar 
Wagner, B. et al. Climate change drives habitat contraction of a nocturnal arboreal marsupial at its physiological limits. Ecosphere 11(10), e03262 (2020).Article 

Google Scholar 
McLean, C. M., Kavanagh, R. P., Penman, T. & Bradstock, R. The threatened status of the hollow dependent arboreal marsupial, the greater glider (Petauroides volans), can be explained by impacts from wildfire and selective logging. For. Ecol. Manag. 415, 19–25 (2018).Article 

Google Scholar 
Lindenmayer, D. B. et al. Conservation conundrums and the challenges of managing unexplained declines of multiple species. Biol. Conserv. 221, 279–292. https://doi.org/10.1016/j.biocon.2018.03.007 (2018).Article 

Google Scholar 
Lindenmayer, D. B. B. et al. How to make a common species rare: a case against conservation complacency. Biol. Conserv. 144(5), 1663–1672. https://doi.org/10.1016/j.biocon.2011.02.022 (2011).Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species (2022) https://www.iucnredlist.org (Accessed 17 Nov 2022).Rübsamen, K., Hume, I. D., Foley, W. J. & Rübsamen, U. Implications of the large surface area to body mass ratio on the heat balance of the greater glider (Petauroides volans: Marsupialia). J. Comp. Physiol. B. 154(1), 105–111. https://doi.org/10.1007/BF00683223 (1984).Article 

Google Scholar 
Wintle, B. A., Legge, S. & Woinarski, J. C. Z. After the megafires: What next for Australian wildlife?. Trends Ecol. Evol. 35(9), 753–757. https://doi.org/10.1016/j.tree.2020.06.009 (2020).Article 

Google Scholar 
Legge, S. et al. Estimates of the impacts of the 2019–2020 fires on populations of native animal species, Brisbane (2021).Hoffmann, A. A. & Sgró, C. M. Climate change and evolutionary adaptation. Nature 470(7335), 479–485. https://doi.org/10.1038/nature09670 (2011).Article 
ADS 

Google Scholar 
Hoffmann, A. A., Sgrò, C. M. & Kristensen, T. N. Revisiting adaptive potential, population size, and conservation. Trends Ecol. Evol. 32(7), 506–517. https://doi.org/10.1016/j.tree.2017.03.012 (2017).Article 

Google Scholar 
Rossetto, M. et al. A conservation genomics workflow to guide practical management actions. Glob. Ecol. Conserv. 26, e01492. https://doi.org/10.1016/j.gecco.2021.e01492 (2021).Article 

Google Scholar 
Mcmahon, B. J., Teeling, E. C. & Höglund, J. How and why should we implement genomics into conservation?. Evol. Appl. 7(9), 999–1007. https://doi.org/10.1111/eva.12193 (2014).Article 

Google Scholar 
Hoffmann, A. et al. A framework for incorporating evolutionary genomics into biodiversity conservation and management. Clim. Change Responses https://doi.org/10.1186/s40665-014-0009-x (2015).Article 

Google Scholar 
Lindenmayer, D. B. et al. Integrating demographic and genetic studies of the greater glider Petauroides volans in fragmented forests: predicting movement patterns and rates for future testing. Pac. Conserv. Biol. 5(1), 2–8 (1999).Article 

Google Scholar 
Taylor, A. C., Kraaijeveld, K. & Lindenmayer, D. B. Microsatellites for the greater glider, Petauroides volans. Mol. Ecol. Notes 2(1), 57–59. https://doi.org/10.1046/j.1471-8286.2002.00148.x (2002).Article 

Google Scholar 
Taylor, A. C., Tyndale-Biscoe, H. & Lindenmayer, D. B. Unexpected persistence on habitat islands: Genetic signatures reveal dispersal of a eucalypt-dependent marsupial through a hostile pine matrix. Mol. Ecol. 16(13), 2655–2666. https://doi.org/10.1111/j.1365-294X.2007.03331.x (2007).Article 

Google Scholar 
NSW Scientific Committee. Greater glider population in the Mount Gibraltar Reserve area” endangered population listing. Final Determination to list an endangered ecological community under the Threatened Species Conservation Act 1995 (2015).NSW Scientific Committee. Greater glider, Petauroides volans, in the Eurobodalla local government area endangered population listing. Final Determination to list an endangered ecological community under the Threatened Species Conservation Act 1995. (2007).NSW Scientific Committee. Greater Glider population at Seven Mile Beach National Park Endangered population listing. Final Determination to list an endangered ecological community under the Threatened Species Conservation Act 1995 (2016).Woinarski, J. C. Z., Burbidge, A. A. & Harrison, P. L. The Action Plan for Australian Mammals 2012 (CSIRO Publishing, 2014).Book 

Google Scholar 
W. and the E. Department of Agriculture. Conservation advice for Petauroides volans (Greater Glider (southern)), Canberra (2021).Gracanin, A., Pearce, A., Hofman, M., Knipler, M. & Mikac, K. Greater glider (Petauroides volans) live capture methods. Austral. Mammal. 44(2), 280–286 (2021).Article 

Google Scholar 
Comport, S. S., Ward, S. J. & Foley, W. J. Home ranges, time budgets and food-tree use in a high-density tropical population of greater gliders, Petauroides volans minor (Pseudocheiridae: Marsupialia). Wildl. Res. 23(4), 401–419. https://doi.org/10.1071/WR9960401 (1996).Article 

Google Scholar 
Henry, S. R. Social organisation of the greater glider (Petauroides volans) in Victoria. In Possums and Gliders (eds Smith, A. P. & Hume, I. D.) 221–228 (1984).Kilian, A. et al. Diversity arrays technology: A generic genome profiling technology on open platforms. Methods Mol. Biol. 888, 67–89. https://doi.org/10.1007/978-1-61779-870-2_5 (2012).Article 

Google Scholar 
Gruber, B., Unmack, P. J., Berry, O. F. & Georges, A. dartr: An r package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Mol. Ecol. Resour. 18(3), 691–699. https://doi.org/10.1111/1755-0998.12745 (2018).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
Privé, F., Luu, K., Vilhjálmsson, B. J. & Blum, M. G. B. Performing highly efficient genome scans for local adaptation with R package pcadapt version 4. Mol. Biol. Evol. 37(7), 2153–2154. https://doi.org/10.1093/molbev/msaa053 (2020).Article 

Google Scholar 
Luu, K., Bazin, E. & Blum, M. G. B. pcadapt: An R package to perform genome scans for selection based on principal component analysis. Mol. Ecol. Resour. 17(1), 67–77. https://doi.org/10.1111/1755-0998.12592 (2017).Article 

Google Scholar 
Dabney, A., Storey, J. D. & Warnes, G. R. qvalue: Q-value estimation for false discovery rate control. R package version, vol. 1, no. 0 (2010).Oksanen, J. et al. Package “vegan”. Community ecology package, version, vol. 2, no. 9, 1–295 (2013).Pratt, E. A. L. et al. Seascape genomics of coastal bottlenose dolphins along strong gradients of temperature and salinity. Mol. Ecol. 31(8), 2223–2241 (2022).Article 

Google Scholar 
Forester, B. R., Lasky, J. R., Wagner, H. H. & Urban, D. L. Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations. Mol. Ecol. 27(9), 2215–2233 (2018).Article 

Google Scholar 
Zimmerman, S. J. et al. Environmental gradients of selection for an alpine-obligate bird, the white-tailed ptarmigan (Lagopus leucura). Heredity 126(1), 117–131 (2021).Article 

Google Scholar 
Lott, M. J. et al. Future‐proofing the koala: Synergising genomic and environmental data for effective species management. Mol. Ecol. (2022).Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).Article 

Google Scholar 
Goudet, J. HIERFSTAT, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5(1), 184–186. https://doi.org/10.1111/J.1471-8286.2004.00828.X (2005).Article 

Google Scholar 
Nei, M. Molecular Evolutionary Genetics (Columbia University Press, 1987).Book 

Google Scholar 
Meirmans, P. G. & Hedrick, P. W. Assessing population structure: FST and related measures. Mol. Ecol. Resour. 11(1), 5–18. https://doi.org/10.1111/J.1755-0998.2010.02927.X (2011).Article 

Google Scholar 
Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to Conservation Genetics (Cambridge University Press, 2002). https://doi.org/10.1016/j.foreco.2003.12.001.Book 

Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38(6), 1358. https://doi.org/10.2307/2408641 (1984).Article 

Google Scholar 
Pembleton, L. W., Cogan, N. O. I. & Forster, J. W. StAMPP: An R package for calculation of genetic differentiation and structure of mixed-ploidy level populations. Mol. Ecol. Resour. 13(5), 946–952. https://doi.org/10.1111/1755-0998.12129 (2013).Article 

Google Scholar 
Bonferroni, S. Teoria statistica delle classi e calcolo delle probabilita. cir.nii.ac.jp, vol. 8, 3–62 (1936).Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155(2), 945–959 (2000).Article 

Google Scholar 
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11(1), 1–15. https://doi.org/10.1186/1471-2156-11-94/FIGURES/9 (2010).Article 

Google Scholar 
Janes, J. K. et al. The K = 2 conundrum. Mol. Ecol. 26(14), 3594–3602. https://doi.org/10.1111/MEC.14187 (2017).Article 

Google Scholar 
Miller, J. M., Cullingham, C. I. & Peery, R. M. The influence of a priori grouping on inference of genetic clusters: Simulation study and literature review of the DAPC method. Heredity 125, 269–280. https://doi.org/10.1038/s41437-020-0348-2 (2020).Article 

Google Scholar 
Cullingham, C. I. et al. Confidently identifying the correct K value using the ΔK method: When does K = 2?. Mol. Ecol. 29(5), 862–869. https://doi.org/10.1111/mec.15374 (2020).Article 

Google Scholar 
Pritchard, J., Wen, X. & Falush, D. Documentation for STRUCTURE software: version 2.3|Request PDF (2003).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(2), 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).Article 

Google Scholar 
Stankiewicz, K. H., Vasquez Kuntz, K. L. & Baums, I. B. The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical data sets. Mol. Ecol. Resour. 22(3), 1135–1148. https://doi.org/10.1111/1755-0998.13522 (2022).Article 

Google Scholar 
Jombart, T. Adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24(11), 1403–1405. https://doi.org/10.1093/bioinformatics/btn129 (2008).Article 

Google Scholar 
Harmon, L. J. & Braude, S. Conservation of small populations: effective population sizes, inbreeding, and the 50/500 rule. In An Introduction to Methods and Models in Ecology, Evolution, and Conservation Biology 125–138 (Princeton University Press, 2010).Chapter 

Google Scholar 
Do, C. et al. NeEstimator v2: re-implementation of software for the estimation of contemporary effective population size (Ne ) from genetic data. Mol. Ecol. Resour. 14(1), 209–214. https://doi.org/10.1111/1755-0998.12157 (2014).Article 

Google Scholar 
Waples, R. S. & Do, C. LDNE: A program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8(4), 753–756. https://doi.org/10.1111/J.1755-0998.2007.02061.X (2008).Article 

Google Scholar 
Potvin, D. A. et al. Genetic erosion and escalating extinction risk in frogs with increasing wildfire frequency. J. Appl. Ecol. 54(3), 945–954. https://doi.org/10.1111/1365-2664.12809 (2017).Article 

Google Scholar 
Catullo, R. A. et al. Benchmarking taxonomic and genetic diversity after the fact: Lessons learned from the catastrophic 2019–2020 Australian bushfires. Front. Ecol. Evol. 9, 292. https://doi.org/10.3389/FEVO.2021.645820/BIBTEX (2021).Article 
ADS 

Google Scholar 
DPIE. Fire Extent and Severity Mapping (FESM) 2019/20 (2021) https://datasets.seed.nsw.gov.au/dataset/fire-extent-and-severity-mapping-fesm-2019-20 (Accessed 23 June 2021).Banks, S. C. et al. Fire severity and landscape context effects on arboreal marsupials. Biol. Conserv. 167, 137–148. https://doi.org/10.1016/j.biocon.2013.07.028 (2013).Article 

Google Scholar 
Andrew, D., Koffel, D., Harvey, G., Griffiths, K. & Fleming, M. Rediscovery of the greater glider Petauroides volans (Marsupialia: Petauroidea) in the Royal National Park, NSW. Austral. Zool. 37(1), 23–28. https://doi.org/10.7882/AZ.2013.008 (2014).Article 

Google Scholar 
Lindenmayer, D. et al. What 15 years of monitoring is telling us about mammals in Booderee National Park (2018).Chafer, C. J. et al. The post-fire measurement of fire severity and intensity in the Christmas 2001 Sydney wildfires. Int. J. Wildland Fire 13(2), 227–240. https://doi.org/10.1071/WF03041 (2004).Article 

Google Scholar 
Vinson, S. G., Johnson, A. P. & Mikac, K. M. Current estimates and vegetation preferences of an endangered population of the vulnerable greater glider at Seven Mile Beach National Park. Austral. Ecol. 46(2), 303–314. https://doi.org/10.1111/aec.12979 (2020).Article 

Google Scholar 
Kavanagh, R. & Wheeler, R. Home-range of the greater glider Petauroides volans in tall montane forest of southeastern New South Wales, and changes following logging. In The Biology of Possums and Gliders (eds Goldingay, R. & Jackson, S.) 413–425 (Surrey Beatty & Sons, 2004).
Google Scholar 
Fleay, D. Gliders of the Gum Trees: The Most Beautiful and Enchanting Australian Marsupials (1947).Wright, S. Isolation by distance under diverse systems of mating. Genetics 31, 39–59 (1946).Article 

Google Scholar 
McGowan, B. & Wright, C. Braidwood’s enduring Chinese heritage. Historic Environ. 23(3), 34–39 (2011).
Google Scholar 
Pérez, I. et al. What is wrong with current translocations? A review and a decision-making proposal. Front. Ecol. Environ. 10(9), 494–501 (2012).Article 

Google Scholar 
Mace, G. M. et al. Quantification of extinction risk: IUCN’s system for classifying threatened species. Conserv. Biol. 22(6), 1424–1442. https://doi.org/10.1111/j.1523-1739.2008.01044.x (2008).Article 

Google Scholar 
Franklin, I. ‘Evolutionary change in small populations. In Conservation Biology—An Evolutionary-Ecological Perspective 135–149 (Sinauer Associates, 1980).
Google Scholar 
Frankham, R., Bradshaw, C. J. A. & Brook, B. W. Genetics in conservation management: Revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol. Conserv. 170, 56–63. https://doi.org/10.1016/J.BIOCON.2013.12.036 (2014).Article 

Google Scholar 
Seaborn, T. et al. Integrating genomics in population models to forecast translocation success. Restor. Ecol. 29(4), e13395. https://doi.org/10.1111/rec.13395 (2021).Article 

Google Scholar 
Christie, M. R. & Knowles, L. L. Habitat corridors facilitate genetic resilience irrespective of species dispersal abilities or population sizes. Evol. Appl. 8(5), 454–463 (2015).Article 

Google Scholar 
Office of Environment and Heritage. Woody extent and foliage projective cover (2016) http://data.auscover.org.au/xwiki/bin/view/Product+pages/nsw+5m+woody+extent+and+fpc (Accessed 29 Oct 2020).Ashman, K. R., Watchorn, D. J., Lindenmayer, D. B. & Taylor, M. F. J. Is Australia’s environmental legislation protecting threatened species? A case study of the national listing of the greater glider. Pac. Conserv. Biol. 1980, 277–289. https://doi.org/10.1071/PC20077 (2021).Article 

Google Scholar 
ESRI. ArcGIS 10.7.1. (Environmental Systems Research Institute, 2011). More

Identification of broad-host-range rhizoplane colonization genes by Tn-seqThis work was focused on SF2 harboring a typical multipartite genome of Sinorhizobium (chromosome, chromid, and symbiosis plasmid) [59]. To perform genome-wide survey of rhizoplane colonization genes of SF2 (Fig. 1), the input mutant library was inoculated on filter paper of plant culture dish, and output mutant libraries were collected from filter papers at 1 h post inoculation (F1h) and 7 days post inoculation (dpi; F7d), and from rhizoplane of cultivated soybean (CS7d), wild soybean (WS7d), rice (R7d), and maize (Z7d) at 7 dpi. To facilitate Tn-seq library construction, all output mutant libraries were subject to 32 h cultivation in the TY rich medium, with input libraries cultivated at the same condition as control (TY). Tn-seq revealed that transposon insertion density in three input and 21 output samples ranged from 57.03 to 86.99% (Table S3), which are above the threshold of 50% insertion density for a good Tn-seq dataset [49]. A reproducible rhizosphere effect was observed in three independent experiments (Fig. S1), i.e., rhizoplane samples (CS7d, WS7d, R7d, and Z7d) consistently formed distinct clusters compared to those of TY, F1h, and F7d. A considerable signature of three independent input libraries was also identified (Data S1, Data S2, and Fig. S1). These results highlight that stochastic variations among multiple independent input libraries should be considered before making conclusions on gene fitness, which has been largely overlooked in earlier studies based on just one input library [49].Based on gene fitness scores of rhizoplane samples (CS7d, WS7d, R7d and Z7d) compared to corresponding F1h datasets (Fig. S2A; Data S2), 93, 91, 127, and 206 genes were identified as rhizoplane colonization genes for test plants of cultivated soybean, wild soybean, maize, and rice, respectively, accounting for 1.4–3.1% of the SF2 genome (p values  More

One Health Institute, School of Veterinary Medicine, University of California, Davis, Davis, CA, 95616, USAPranav S. Pandit, Tracey Goldstein, Megan M. Doyle, Nicole R. Gardner, Brian Bird, Woutrina Smith, David Wolking, Kirsten Gilardi, Corina Monagin, Terra Kelly, Marcela M. Uhart, Lucy Keatts, Jonna A. K. Mazet & Christine K. JohnsonCenter for Infection and Immunity, Columbia University, New York, NY, 10032, USASimon J. AnthonyEcoHealth Alliance, 520 Eighth Avenue, New York, NY, 10018, USAKevin J. Olival, Jonathan H. Epstein, Catherine Machalaba, Melinda K. Rostal, Patrick Dawson, Emily Hagan, Ava Sullivan, Hongying Li, Aleksei A. Chmura, Alice Latinne, Ariful Islam, James Desmond, Tom Hughes, William B. Karesh & Peter DaszakLabyrinth Global Health, Inc., 546 15th Ave NE, St Petersburg, FL, 33704, USAChristian Lange, Tammie O’Rourke & Karen SaylorsWildlife Conservation Society, Health Program, Bronx, NY, USASarah Olson, A. Patricia Mendoza, Cátia Dejuste de Paula, Amanda Fine & Cátia Dejuste de PaulaWildlife Conservation Society (WCS), Peru Program, Lima, PeruA. Patricia Mendoza & Alberto PerezGlobal Health Program, Smithsonian’s National Zoological Park and Conservation Biology Institute, Washington, DC, USADawn Zimmerman, Marc Valitutto & Ohnmar AungMosaic/Global Viral Cameroon, Yaoundé, CameroonMatthew LeBreton, Moctar Mouiche & Suzan MurrayMetabiota Inc, Nanaimo, VC, CanadaDavid McIver & Soubanh SilithammavongInstitut Pasteur du Cambodge, 5 Monivong Blvd, PO Box 983, Phnom Penh, 12201, CambodiaVeasna DuongWuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, ChinaZhengli ShiKinshasa School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the CongoPrime MulembakaniMetabiota Inc., Kinshasa, Democratic Republic of the CongoCharles KumakambaEgypt National Research Centre, 12311, Dokki, Giza, EgyptMohamed AliAklilu Lemma Institute of Pathobiology, Addis Ababa University, Addis Ababa, EthiopiaNigatu KebedeMetabiota Cameroon Ltd, Yaoundé, Centre Region Avenue Mvog-Fouda Ada, Av 1.085, Carrefour Intendance, Yaoundé, BP 15939, CameroonUbald TamoufeMilitary Veterinarian (Rtd.), P.O. Box CT2585, Accra, GhanaSamuel Bel-NonoCentre de Recherche en Virologie (VRV) Projet Fievres Hemoraquiques en Guinée, BP 5680, Nongo/Contéya-Commune de Ratoma, GuineaAlpha CamaraPrimate Research Center, Bogor Agricultural University, Bogor, 16151, IndonesiaJoko PamungkasFaculty of Veterinary Medicine, Bogor Agricultural University, Darmaga Campus, Bogor, 16680, IndonesiaJoko PamungkasDepartment Environment and Health, Institut Pasteur de Côte d’Ivoire, PO BOX 490, Abidjan 01, Ivory CoastKalpy J. CoulibalyDepartment of Basic Medical Veterinary Sciences, College of Veterinary Medicine, Jordan University of Science and Technology, Ar-Ramtha, JordanEhab Abu-BashaMolecular Biology Laboratory, Institute of Primate Research, Nairobi, KenyaJoseph KamauDepartment of Biochemistry, University of Nairobi, Nairobi, KenyaJoseph KamauConservation Medicine, Sungai Buloh, Selangor, MalaysiaTom HughesWildlife Conservation Society (WCS), Mongolia Program, Ulaanbaatar, MongoliaEnkhtuvshin ShiilegdambaCenter for Molecular Dynamics Nepal (CMDN), Thapathali -11, Kathmandu, NepalDibesh KarmacharyaRegional Headquarters, Mountain Gorilla Veterinary Project, Musanze, RwandaJulius Nziza & Benard SsebideUniversité Cheikh Anta Diop, BP 5005, Dakar, SénégalDaouda NdiayeMetabiota, Inc. Sierra Leone, Freetown, Sierra LeoneAiah GbakimaDepartment of Veterinary Medicine and Public Health, College of Veterinary Medicine and Biomedical Sciences, Sokoine University of Agriculture, Morogoro, TanzaniaZikankuba sajaliThai Red Cross Emerging Infectious Diseases Clinical Center, King Chulalongkorn Memorial Hospital, Bangkok, ThailandSupaporn WacharapluesadeeWildlife Conservation Society (WCS), Bolivia Program, La Paz, BoliviaErika Alandia RoblesFacultad de Medicina Veterinaria y Zootecnia, Universidad Nacional Autónoma de México, México City, 04510, MexicoGerardo SuzánCentro de Biodiversidad y Genética, Universidad Mayor de San Simón, Cochabamba, BoliviaLuis F. AguirreLaboratório de Epidemiologia e Geoprocessamento (EpiGeo), Instituto de Medicina Veterinária (IMV) Universidade Federal do Pará (UFPA), BR-316 Km 31, Castanhal, Pará, 69746-360, BrazilMonica R. SolorioDepartment of Microbiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, IndiaTapan N. DholeWildlife Conservation Society (WCS), Vietnam Program, Hanoi, VietnamNguyen T. T. NgaMelbourne Veterinary School, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Werribee, VIC, 3030, AustraliaPeta L. HitchensNyati Health Consulting, 2175 Dodds Road, Nanaimo, BC, V9X0A4, CanadaDamien O. Joly More