More stories

  • in

    Primates facing climate crisis in a tropical forest hotspot will lose climatic suitable geographical range

    Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
    ADS 
    CAS 

    Google Scholar 
    Sandel, B. et al. The influence of late Quaternary climate-change velocity on species endemism. Science 334(6056), 660–664 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science https://doi.org/10.1126/science.aaf7671 (2016).Article 

    Google Scholar 
    Davis, M. B. & Shaw, R. G. Range shifts and adaptive responses to quaternary climate change. Science 292(5517), 673–679 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Lane, J. E., Kruuk, L. E. B., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science https://doi.org/10.1126/science.aai9214 (2017).Article 

    Google Scholar 
    Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C. & Mace, G. M. Beyond predictions: Biodiversity conservation in a changing climate. Science https://doi.org/10.1126/science.1200303 (2011).Article 

    Google Scholar 
    Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Change 7, 205–208 (2017).Article 
    ADS 

    Google Scholar 
    Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. PNAS 109(22), 8606–8611 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308(5730), 1912–1915 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Bradshaw, W. E., Zani, P. A. & Holzapfel, C. M. Adaptation to temperate climates. Evolution 58(8), 1748–1762 (2004).
    Google Scholar 
    Thomas, C. D. et al. Ecological and evolutionary processes at expanding range margins. Nature 411(6837), 577–581 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Urban, M. C. Accelerating extinction risk from climate change. Science 348(6234), 571–573 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Waller, N. L., Gynther, I. C., Freeman, A. B., Lavery, T. H. & Leung, L. K. P. The bramble cay melomys Melomys rubicola (Rodentia:Muridae): A first mammalian extinction caused by human-induced climate change?. Wildl. Res. 44(1), 9–21 (2017).Article 

    Google Scholar 
    Murray, K. A., Rosauer, D., McCallum, H. & Skerratt, L. F. Integrating species traits with extrinsic threats: Closing the gap between predicting and preventing species declines. Proc. R. Soc. B: Biol. Sci. 278(1711), 1515–1523 (2011).Article 

    Google Scholar 
    Stevens, G. C. The latitudinal gradient in geographical range: How so many species coexist in the Tropics. Am. Nat. 133(2), 240–256 (1989).Article 

    Google Scholar 
    Hickling, R., Roy, D. B., Hill, J. K., Fox, R. & Thomas, C. D. The distributions of a wide range of taxonomic groups are expanding polewards. Glob. Change Biol. 12(3), 450–455 (2006).Article 
    ADS 

    Google Scholar 
    Virkkala, R., Heikkinen, R. K., Leikola, N. & Luoto, M. Projected large-scale range reductions of northern-boreal land bird species due to climate change. Biol. Conserv. 141(5), 1343–1353 (2008).Article 

    Google Scholar 
    Sales, L. P. et al. Niche conservatism and the invasive potential of the wild boar. J. Anim. Ecol. 86(5), 1214–1223 (2017).Article 

    Google Scholar 
    Gouveia, S. F. et al. Climate and land use changes will degrade the configuration of the landscape for titi monkeys in eastern Brazil. Glob. Change Biol. 22(6), 2003–2012 (2016).Article 
    ADS 

    Google Scholar 
    Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful?. Glob. Ecol. Biogeogr. 12(5), 361–371 (2003).Article 

    Google Scholar 
    Engler, R. et al. Predicting future distributions of mountain plants under climate change: does dispersal capacity matter?. Ecography 32(1), 34–45 (2009).Article 

    Google Scholar 
    Ozinga, W. A. et al. Predictability of plant species composition from environmental conditions is constrained by dispersal limitation. Oikos 108(3), 555–561 (2005).Article 

    Google Scholar 
    Takahashi, K. & Kamitani, T. Effect of dispersal capacity on forest plant migration at a landscape scale. J. Ecol. 92(5), 778–785 (2004).Article 

    Google Scholar 
    Koo, K. A. & Park, S. U. The effect of interplays among climate change, land-use change, and dispersal capacity on plant redistribution. Ecol. Indic. 142, 109192 (2022).Article 

    Google Scholar 
    Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333(6045), 1024–1026 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3(10), 919–925 (2013).Article 
    ADS 

    Google Scholar 
    Vanderwal, J. et al. Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nat. Clim. Change 3, 239–243 (2013).Article 
    ADS 

    Google Scholar 
    Lira, A. F. de A., Badillo-Montaño, R., Lira-Noriega, A. & de Albuquerque, C. M. R. Potential distribution patterns of scorpions in north-eastern Brazil under scenarios of future climate change. Austral Ecol. 45(2), 215–228 (2020).Castro, M. B. et al. Will the emblematic southern conifer Araucaria angustifolia survive to climate change in Brazil?. Biodivers. Conserv. 29(2), 591–607 (2020).Article 

    Google Scholar 
    Wilson, O. J., Walters, R. J., Mayle, F. E., Lingner, D. V. & Vibrans, A. C. Cold spot microrefugia hold the key to survival for Brazil’s Critically Endangered Araucaria tree. Glob. Change Biol. 25(12), 4339–4351 (2019).Article 
    ADS 

    Google Scholar 
    Esser, L. F. et al. Future uncertainties for the distribution and conservation of Paubrasilia echinata under climate change. Acta Bot. Bras. 33(4), 770–776 (2019).Article 

    Google Scholar 
    Cabanne, G. S. et al. Effects of Pleistocene climate changes on species ranges and evolutionary processes in the Neotropical Atlantic Forest. Biol. J. Linn. Soc. 119(4), 856–872 (2016).Article 

    Google Scholar 
    Iturralde-Pólit, P., Dangles, O., Burneo, S. F. & Meynard, C. N. The effects of climate change on a mega-diverse country: predicted shifts in mammalian species richness and turnover in continental Ecuador. Biotropica 49(6), 821–831 (2017).Article 

    Google Scholar 
    Vu, T. T. et al. An assessment of the impact of climate change on the distribution of the grey-shanked douc Pygathrix cinerea using an ecological niche model. Primates 61(2), 267–275 (2020).Article 

    Google Scholar 
    Sales, L. P., Ribeiro, B. R., Pires, M. M., Chapman, C. A. & Loyola, R. Recalculating route: dispersal constraints will drive the redistribution of Amazon primates in the Anthropocene. Ecography 42(10), 1789–1801 (2019).Article 

    Google Scholar 
    Hill, S. E. & Winder, I. C. Predicting the impacts of climate change on Papio baboon biogeography: Are widespread, generalist primates ‘safe’?. J. Biogeogr. 46(7), 1380–1405 (2019).
    Google Scholar 
    Gillings, S., Balmer, D. E. & Fuller, R. J. Directionality of recent bird distribution shifts and climate change in Great Britain. Glob. Change Biol. 21(6), 2155–2168 (2015).Article 
    ADS 

    Google Scholar 
    Fernández, D. et al. The current status of the world’s primates: Mapping threats to understand priorities for primate conservation. Int. J. Primatol. 43, 15–39 (2022).Article 

    Google Scholar 
    Stewart, B. M., Turner, S. E. & Matthews, H. D. Climate change impacts on potential future ranges of non-human primate species. Clim. Change 162, 2301–2318 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Estrada, A. et al. Primates in peril: The significance of Brazil, Madagascar, Indonesia and the Democratic Republic of the Congo for global primate conservation. PeerJ 6, e4869; https://doi.org/10.7717/peerj.4869 (2018).Estrada, A. et al. Impending extinction crisis of the world’s primates: Why primates matter. Sci. Adv. https://doi.org/10.1126/sciadv.1600946 (2017).Article 

    Google Scholar 
    Graham, T. L., Matthews, H. D. & Turner, S. E. A global-scale evaluation of primate exposure and vulnerability to climate change. Int. J. Primatol. 37(2), 158–174 (2016).Article 

    Google Scholar 
    Meyer, A. L. S., Pie, M. R. & Passos, F. C. Assessing the exposure of lion tamarins (Leontopithecus spp.) to future climate change. Am. J. Primatol. 76(6), 551–562 (2014).Article 

    Google Scholar 
    Braz, A. G., Lorini, M. L. & Vale, M. M. Climate change is likely to affect the distribution but not parapatry of the Brazilian marmoset monkeys (Callithrix spp.). Divers. Distrib. 25(4), 536–550 (2019).Article 

    Google Scholar 
    Lima, A. A. de, Ribeiro, M. C., Grelle, C. E. de V. & Pinto, M. P. Impacts of climate changes on spatio-temporal diversity patterns of Atlantic Forest primates. Perspect. Ecol. Conserv. 17(2), 50–56 (2019).Colombo, A. F. & Joly, C. A. Brazilian Atlantic Forest lato sensu: the most ancient Brazilian forest, and a biodiversity hotspot, is highly threatened by climate change. Braz. J. Biol. 70(3), 697–708 (2010).Article 
    CAS 

    Google Scholar 
    Zwiener, V. P., Lira-Noriega, A., Grady, C. J., Padial, A. A. & Vitule, J. R. Climate change as a driver of biotic homogenization of woody plants in the Atlantic Forest. Glob. Ecol. Biogeogr. 27(3), 298–309 (2018).Article 

    Google Scholar 
    Lemes, P., Melo, A. S. & Loyola, R. D. Climate change threatens protected areas of the Atlantic Forest. Biodivers. Conserv. 23(2), 357–368 (2014).Article 

    Google Scholar 
    Rezende, G. C., Sobral-Souza, T. & Culot, L. Integrating climate and landscape models to prioritize areas and conservation strategies for an endangered arboreal primate. Am. J. Primatol. 82(12), e23202. https://doi.org/10.1002/ajp.23202 (2020).Article 

    Google Scholar 
    Silva, L. B. et al. How future climate change and deforestation can drastically affect the species of monkeys endemic to the eastern Amazon, and priorities for conservation. Biodivers. Conserv. 31, 971–988 (2022).Article 

    Google Scholar 
    Sales, L., Ribeiro, B. R., Chapman, C. A. & Loyola, R. Multiple dimensions of climate change on the distribution of Amazon primates. Perspect. Ecol. Conserv. 18(2), 83–90 (2020).
    Google Scholar 
    Moraes, B., Razgour, O., Souza-Alves, J., Boubli, J. & Bezerra, B. Habitat suitability for primate conservation in north-east Brazil. Oryx 54(6), 803–813 (2020).Article 

    Google Scholar 
    Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Hanson, J. O., Rhodes, J. R., Riginos, C. & Fuller, R. A. Environmental and geographic variables are effective surrogates for genetic variation in conservation planning. PNAS 114(48), 12755–12760 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Scheele, B. C., Foster, C. N., Banks, S. C. & Lindenmayer, D. B. Niche contractions in declining species: Mechanisms and consequences. Trends Ecol. Evol. 32(5), 346–355 (2017).Article 

    Google Scholar 
    Travis, J. M. J. et al. Dispersal and species’ responses to climate change. Oikos 122, 1532–1540 (2013).Article 

    Google Scholar 
    Lenoir, J. & Svenning, J.-C. Climate-related range shifts – a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).Article 

    Google Scholar 
    Raghunathan, N., François, L., Huynen, M. C., Oliveira, L. C. & Hambuckers, A. Modelling the distribution of key tree species used by lion tamarins in the Brazilian Atlantic forest under a scenario of future climate change. Reg. Environ. Change 15, 683–693 (2015).Article 

    Google Scholar 
    Lawler, J. J., Ruesch, A. S., Olden, J. D. & McRae, B. H. Projected climate-driven faunal movement routes. Ecol. Lett. 16(8), 1014–1022 (2013).Article 
    CAS 

    Google Scholar 
    Årevall, J., Early, R., Estrada, A., Wennergren, U. & Eklöf, A. C. Conditions for successful range shifts under climate change: The role of species dispersal and landscape configuration. Divers. Distrib. 24, 1598–1611 (2018).Article 

    Google Scholar 
    Carroll, C., Lawler, J. J., Roberts, D. R. & Hamann, A. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10(10), e0142024. https://doi.org/10.1371/journal.pone.0140486 (2015).Article 
    CAS 

    Google Scholar 
    Davies, T. J., Purvis, A. & Gittleman, J. L. Quaternary climate change and the geographic ranges of mammals. Am. Nat. 174(3), 297–307 (2009).Article 

    Google Scholar 
    Gaston, K.J. The structure and dynamics of geographic ranges (Oxford University Press, 2003).Meyer, A. L. S. & Pie, M. R. Climate change estimates surpass rates of climatic niche evolution in primates. Int. J. Primatol. 43, 40–56 (2021).Article 

    Google Scholar 
    Zeigler, S. L., Fagan, W. F., DeFries, R. & Raboy, B. E. Identifying important forest patches for the long-term persistence of the endangered golden-headed lion tamarin (Leontopithecus chrysomelas). Trop. Conserv. Sci. 3(1), 63–77 (2010).Article 

    Google Scholar 
    Dosen, J., Fortin, M. J. & Raboy, B. E. Restoration strategies to improve connectivity for golden-headed lion tamarins (Leontopithecus chrysomelas) in the Bahian Atlantic Forest. Brazil. Int. J. Primatol. 38(5), 962–983 (2017).Article 

    Google Scholar 
    Piffer, P. R., Rosa, M. R., Tambosi, L. R., Metzger, J. P. & Uriarte, M. Turnover rates of regenerated forests challenge restoration efforts in the Brazilian Atlantic Forest. Environ. Res. Lett. 17(4), 045009. https://doi.org/10.1088/1748-9326/ac5ae1 (2022).Article 
    ADS 

    Google Scholar 
    Estrada, A., Raboy, B. E. & Oliveira, L. C. Agroecosystems and primate conservation in the tropics: A review. Am. J. Primatol. 74, 696–711 (2012).Article 

    Google Scholar 
    Galea, B., Humle, T. Identifying and mitigating the impacts on primates of transportation and service corridors. Conserv. Biol. 36, e13836; https://doi.org/10.1111/cobi.13836 (2022).Gouveia, S. F. et al. Functional planning units for the management of an endangered Brazilian titi monkey. Am. J. Primatol. 79(5), e22637; https://doi.org/10.1002/ajp.22637 (2017).Rezende, G. et al. Leontopithecus chrysopygus. The IUCN Red List of Threatened Species, e.T11505A17935400; https://doi.org/10.2305/IUCN.UK.2020-2.RLTS.T11505A17935400.en (2020).Culot, L. et al. ATLANTIC-PRIMATES: A dataset of communities and occurrences of primates in the Atlantic Forests of South America. Ecology 100(1), e02525; https://doi.org/10.1002/ecy.2525 (2018).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 
    Quinn, G. P. & Keough, M. J. Experimental design and data analysis for biologists (Cambridge University Press, 2002).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1(1), 3–14 (2010).Article 

    Google Scholar 
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17(1), 43–57 (2011).Article 

    Google Scholar 
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190(3–4), 231–259 (2006).Article 

    Google Scholar 
    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29(2), 129–151 (2006).Article 

    Google Scholar 
    Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for ecological niche models. Methods Ecol. Evol. 5(11), 1198–1205 (2014).Article 

    Google Scholar 
    Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 34(1), 102–117 (2007).Article 

    Google Scholar 
    Shcheglovitova, M. & Anderson, R. P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecol. Modell. 269, 9–17 (2013).Article 

    Google Scholar 
    Wenger, S. J. & Olden, J. D. Assessing transferability of ecological models: An underappreciated aspect of statistical validation. Methods Ecol. Evol. 3(2), 260–267 (2012).Article 

    Google Scholar 
    Hidasi-Neto, J. et al. Climate change will drive mammal species loss and biotic homogenization in the Cerrado Biodiversity Hotspot. Perspect. Ecol. Conserv. 17(2), 57–63 (2019).
    Google Scholar 
    Bowman, J., Jaeger, J. A. G. & Fahrig, L. Dispersal distance of mammals is proportional to home range size. Ecology 83(7), 2049–2055 (2002).Article 

    Google Scholar 
    Galán-Acedo, C., Arroyo-Rodríguez, V., Andresen, E. & Arasa-Gisbert, R. Ecological traits of the world’s primates. Sci. Data 6, 55. https://doi.org/10.1038/s41597-019-0059-9 (2019).Article 

    Google Scholar 
    Pacifici, M. et al. Generation length for mammals. Nat. Conserv. 5, 89–94 (2013).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna Austria (2017).QGIS Development Team. QGIS Geographic Information System (2016). More

  • in

    Unaltered fungal community after fire prevention treatments over widespread Mediterranean rockroses (Halimium lasianthum)

    Cairney, J. W. G. & Bastias, B. A. Influences of fire on forest soil fungal communities. Can. J. For. Res. 37, 207–215 (2007).Article 

    Google Scholar 
    Fernández, C., Vega, J. A. & Fonturbel, T. The effects of fuel reduction treatments on runoff, infiltration and erosion in two shrubland areas in the north of Spain. J. Environ. Manage. 105, 96–102 (2012).Article 

    Google Scholar 
    Reazin, C., Morris, S., Smith, J. E., Cowan, A. D. & Jumpponen, A. Fires of differing intensities rapidly select distinct soil fungal communities in a Northwest US ponderosa pine forest ecosystem. For. Ecol. Manage. 377, 118–127 (2016).Article 

    Google Scholar 
    Durán-Manual, F. et al. Prescribed burning in Pinus cubensis-dominated tropical natural forests: A myco-friendly fire-prevention tool. For. Syst. 31, e012 (2022).
    Google Scholar 
    Busse, M. D., Hubbert, K. R., Fiddler, G. O., Shestak, C. J. & Powers, R. F. Lethal soil temperatures during burning of masticated forest residues. Int. J. Wildl. Fire 14, 267–276 (2005).Article 

    Google Scholar 
    Frazão, D. F. et al. Cistus ladanifer (Cistaceae): A natural resource in Mediterranean-type ecosystems. Planta 247, 289–300 (2018).Article 

    Google Scholar 
    Keeley, J. E., Bond, W. J., Bradstock, R. A., Pausas, J. G. & Rundel, P. W. Fire in mediterranean ecosystems. Fire Medit. Ecosyst. https://doi.org/10.1017/cbo9781139033091 (2011).Article 

    Google Scholar 
    Louro, R., Peixe, A. & Santos-silva, C. New insights on Cistus salviifolius L. micropropagation. J. Bot. Sci. 6, 10–14 (2017).CAS 

    Google Scholar 
    Valbuena, L., Tarrega, R. & Luis, E. Influence of heat on seed germination of Cistus laurifolius and Cistus ladanifer. J. Wildl. Fire 2, 15–20 (1992).Article 

    Google Scholar 
    Martín-Pinto, P., Vaquerizo, H., Peñalver, F., Olaizola, J. & Oria-De-Rueda, J. A. Early effects of a wildfire on the diversity and production of fungal communities in Mediterranean vegetation types dominated by Cistus ladanifer and Pinus pinaster in Spain. For. Ecol. Manage. 225, 296–305 (2006).Article 

    Google Scholar 
    Comandini, O., Contu, M. & Rinaldi, A. C. An overview of Cistus ectomycorrhizal fungi. Mycorrhiza 16, 381–395 (2006).Article 
    CAS 

    Google Scholar 
    Zuzunegui, M. et al. Growth response of Halimium halimifolium at four sites with different soil water availability regimes in two contrasted hydrological cycles. Plant Soil 247, 271–281 (2002).Article 

    Google Scholar 
    Civeyrel, L. et al. Molecular systematics, character evolution, and pollen morphology of Cistus and Halimium (Cistaceae). Plant Syst. Evol. 295, 23–54 (2011).Article 

    Google Scholar 
    Leonardi, M., Furtado, A. N. M., Comandini, O., Geml, J. & Rinaldi, A. C. Halimium as an ectomycorrhizal symbiont: New records and an appreciation of known fungal diversity. Mycol. Prog. 19, 1495–1509 (2020).Article 

    Google Scholar 
    Oria-De-Rueda, J. A., Martín-Pinto, P. & Olaizola, J. Bolete productivity of cistaceous scrublands in northwestern Spain. Econ. Bot. 62, 323–330 (2008).Article 

    Google Scholar 
    Fernández, C., Vega, J. A. & Fonturbel, T. Does shrub recovery differ after prescribed burning, clearing and mastication in a Spanish heathland?. Plant Ecol. 216, 429–437 (2015).Article 

    Google Scholar 
    Ponte, E. D., Costafreda-Aumedes, S. & Vega-Garcia, C. Lessons learned from arson wildfire incidence in reforestations and natural stands in Spain. Forests 10, 1–18 (2019).Article 

    Google Scholar 
    Franco-Manchón, I., Salo, K., Oria-de-Rueda, J. A., Bonet, J. A. & Martín-Pinto, P. Are wildfires a threat to fungi in European Pinus forests? A case study of boreal and Mediterranean forests. Forests 10, 309 (2019).Article 

    Google Scholar 
    Mediavilla, O., Oria-de-Rueda, J. A. & Martin-Pinto, P. Changes in sporocarp production and vegetation following wildfire in a Mediterranean Forest Ecosystem dominated by Pinus nigra in Northern Spain. For. Ecol. Manage. 331, 85–92 (2014).Article 

    Google Scholar 
    Tomao, A., Antonio Bonet, J., Castaño, C. & De-Miguel, S. How does forest management affect fungal diversity and community composition? Current knowledge and future perspectives for the conservation of forest fungi. For. Ecol. Manage. 457, 117678 (2020).
    Article 

    Google Scholar 
    Espinosa, J., Rodríguez de Rivera, O., Madrigal, J., Guijarro, M. & Hernando, C. Predicting potential cambium damage and fire resistance in Pinus nigra Arn. ssp. salzmannii. For. Ecol. Manage. 474, 118372 (2020).Article 

    Google Scholar 
    Potts, J. B. & Stephens, S. L. Invasive and native plant responses to shrubland fuel reduction: Comparing prescribed fire, mastication and treatment season. Biol. Conserv. 142, 1657–1664 (2009).Article 

    Google Scholar 
    Agee, J. K. & Skinner, C. N. Basic principles of forest fuel reduction treatments. For. Ecol. Manage. 211, 83–96 (2005).Article 

    Google Scholar 
    Fernández, C., Vega, J. A. & Fonturbel, T. Fuel reduction at a Spanish heathland by prescribed fire and mechanical shredding: Effects on seedling emergence. J. Environ. Manage. 129, 621–627 (2013).Article 

    Google Scholar 
    Huggett, R. J., Abt, K. L. & Shepperd, W. Efficacy of mechanical fuel treatments for reducing wildfire hazard. For. Policy Econ. 10, 408–414 (2008).Article 

    Google Scholar 
    Fernández, C. & Vega, J. A. Shrub recovery after fuel reduction treatments and a subsequent fire in a Spanish heathland. Plant Ecol. 215, 1233–1243 (2014).Article 

    Google Scholar 
    Fernández, C., Vega, J. A. & Fonturbel, T. Does fire severity influence shrub resprouting after spring prescribed burning?. Acta Oecologica 48, 30–36 (2013).Article 
    ADS 

    Google Scholar 
    Ellsworth, J. W., Harrington, R. A. & Fownes, J. H. Seedling emergence, growth, and allocation of Oriental bittersweet: Effects of seed input, seed bank, and forest floor litter. For. Ecol. Manage. 190, 255–264 (2004).Article 

    Google Scholar 
    Castaño, C. et al. Resistance of the soil fungal communities to medium-intensity fire prevention treatments in a Mediterranean scrubland. For. Ecol. Manage. 472, 118217 (2020).Article 

    Google Scholar 
    Anderson, I. C., Bastias, B. A., Genney, D. R., Parkin, P. I. & Cairney, J. W. G. Basidiomycete fungal communities in Australian sclerophyll forest soil are altered by repeated prescribed burning. Mycol. Res. 111, 482–486 (2007).Article 
    CAS 

    Google Scholar 
    Hernández-Rodríguez, M. et al. Soil fungal community composition in a Mediterranean shrubland is primarily shaped by history of major disturbance, less so by current fire fuel reduction treatments. Unpublished (2015).Oria de Rueda, J. A., Martín-Pinto, P. & Olaizola, J. Boletus edulis PRODUCTION IN XEROPHILIC AND PIROPHITIC SCHRUBS OF Cistus ladanifer AND Halimium lasianthum IN WESTERN SPAIN. in IV International Workshop on Edible Mycorrhizal Mushrooms (2005).Hart, B. T. N., Smith, J. E., Luoma, D. L. & Hatten, J. A. Recovery of ectomycorrhizal fungus communities fifteen years after fuels reduction treatments in ponderosa pine forests of the Blue Mountains. Oregon. For. Ecol. Manage. 422, 11–22 (2018).Article 

    Google Scholar 
    Hernández-Rodríguez, M., Oria-de-Rueda, J. A., Pando, V. & Martín-Pinto, P. Impact of fuel reduction treatments on fungal sporocarp production and diversity associated with Cistus ladanifer L. ecosystems. For. Ecol. Manage. 353, 10–20 (2015).Article 

    Google Scholar 
    Fernandes, P. M. Scientific support to prescribed underburning in southern Europe: What do we know?. Sci. Total Environ. 630, 340–348 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Day, N. J. et al. Wildfire severity reduces richness and alters composition of soil fungal communities in boreal forests of western Canada. Glob. Chang. Biol. 25, 2310–2324 (2019).Article 
    ADS 

    Google Scholar 
    Salo, K., Domisch, T. & Kouki, J. Forest wildfire and 12 years of post-disturbance succession of saprotrophic macrofungi (Basidiomycota, Ascomycota). For. Ecol. Manage. 451, 117454 (2019).Article 

    Google Scholar 
    Zakaria, A. J. & Boddy, L. Mycelial foraging by Resinicium bicolor: Interactive effects of resource quantity, quality and soil composition. FEMS Microbiol. Ecol. 40, 135–142 (2002).Article 
    CAS 

    Google Scholar 
    Hul, S. et al. Fungal community shifts in structure and function across a boreal forest fire chronosequence. Appl. Environ. Microbiol. 81, 7869–7880 (2015).Article 
    ADS 

    Google Scholar 
    Vázquez-Veloso, A. et al. Prescribed burning in spring or autumn did not affect the soil fungal community in Mediterranean Pinus nigra natural forests. For. Ecol. Manage. 512, 120161 (2022).Article 

    Google Scholar 
    Lindahl, B. D. et al. Spatial separation of litter decomposition and mycorrhizal nitrogen uptake in a boreal forest. New Phytol. 173, 611–620 (2007).Article 
    CAS 

    Google Scholar 
    Salomón, R., Rodríguez-Calcerrada, J., González-Doncel, I., Gil, L. & Valbuena-Carabaña, M. On the general failure of coppice conversion into high forest in Quercus pyrenaica stands: A genetic and physiological approach. Folia Geobot. 52, 101–112 (2017).Article 

    Google Scholar 
    Williams, R. J., Hallgren, S. W. & Wilson, G. W. T. Frequency of prescribed burning in an upland oak forest determines soil and litter properties and alters the soil microbial community. For. Ecol. Manage. 265, 241–247 (2012).Article 

    Google Scholar 
    Semenova-Nelsen, T. A., Platt, W. J., Patterson, T. R., Huffman, J. & Sikes, B. A. Frequent fire reorganizes fungal communities and slows decomposition across a heterogeneous pine savanna landscape. New Phytol. 224, 916–927 (2019).Article 

    Google Scholar 
    Oliver, A. K., Callaham, M. A. & Jumpponen, A. Soil fungal communities respond compositionally to recurring frequent prescribed burning in a managed southeastern US forest ecosystem. For. Ecol. Manage. 345, 1–9 (2015).Article 

    Google Scholar 
    Sanz-Benito, I., Mediavilla, O., Casas, A., Oria-de-Rueda, J. A. & Martín-Pinto, P. Effects of fuel reduction treatments on the sporocarp production and richness of a Quercus/Cistus mixed system. For. Ecol. Manage. 503, 119798 (2022).Article 

    Google Scholar 
    Santos-Silva, C., Gonçalves, A. & Louro, R. Canopy cover influence on macrofungal richness and sporocarp production in montado ecosystems. Agrofor. Syst. 82, 149–159 (2011).Article 

    Google Scholar 
    Lin, W. R. et al. The impacts of thinning on the fruiting of saprophytic fungi in Cryptomeria japonica plantations in central Taiwan. For. Ecol. Manage. 336, 183–193 (2015).Article 

    Google Scholar 
    Aragón, G., López, R. & Martínez, I. Effects of Mediterranean dehesa management on epiphytic lichens. Sci. Total Environ. 409, 116–122 (2010).Article 
    ADS 

    Google Scholar 
    Hämäläinen, A., Kouki, J. & Lohmus, P. The value of retained Scots pines and their dead wood legacies for lichen diversity in clear-cut forests: The effects of retention level and prescribed burning. For. Ecol. Manage. 324, 89–100 (2014).Article 

    Google Scholar 
    Schimmel, J. & Granstrom, A. Fire severity and vegetation response in the boreal Swedish. Ecol. Soc. Am. 77, 1436–1450 (1996).
    Google Scholar 
    Hinojosa, M. B., Albert-Belda, E., Gómez-Muñoz, B. & Moreno, J. M. High fire frequency reduces soil fertility underneath woody plant canopies of Mediterranean ecosystems. Sci. Total Environ. 752, 141877 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Clemmensen, K. E. et al. Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. New Phytol. 205, 1525–1536 (2015).Article 
    CAS 

    Google Scholar 
    Tedersoo, L. et al. Disentangling global soil fungal diversity. Science 346, 1052–1053 (2014).Article 

    Google Scholar 
    Adamo, I. et al. Sampling forest soils to describe fungal diversity and composition. Which is the optimal sampling size in Mediterranean pure and mixed pine oak forests?. Fungal Biol. https://doi.org/10.1016/j.funbio.2021.01.005 (2021).Article 

    Google Scholar 
    Tedersoo, L. et al. Regional-scale in-depth analysis of soil fungal diversity reveals strong pH and plant species effects in northern Europe. Front. Microbiol. 11, 1953 (2020).Article 

    Google Scholar 
    Peay, K., Garbelotto, M. & Bruns, T. Evidence of dispersal limitation in soil microorganisms: Isolation reduces species richness on mycorrhizal tree islands. Ecology 91, 3631–3640 (2010).Article 

    Google Scholar 
    Koivula, M. & Vanha-Majamaa, I. Experimental evidence on biodiversity impacts of variable retention forestry, prescribed burning, and deadwood manipulation in Fennoscandia. Ecol. Process. 9, 1–22 (2020).Article 

    Google Scholar 
    Fox, S. et al. Fire as a driver of fungal diversity—A synthesis of current knowledge. Mycologia 00, 1–27 (2022).
    Google Scholar 
    Raudabaugh, D. B. et al. Where are they hiding? Testing the body snatchers hypothesis in pyrophilous fungi. Fungal Ecol. 43, 100870 (2020).Article 

    Google Scholar 
    Izzo, A., Canright, M. & Bruns, T. D. The effects of heat treatments on ectomycorrhizal resistant propagules and their ability to colonize bioassay seedlings. Mycol. Res. 110, 196–202 (2006).Article 

    Google Scholar 
    Kipfer, T., Moser, B., Egli, S., Wohlgemuth, T. & Ghazoul, J. Ectomycorrhiza succession patterns in Pinus sylvestris forests after stand-replacing fire in the Central Alps. Oecologia 167, 219–228 (2011).Article 
    ADS 

    Google Scholar 
    Glassman, S. I., Levine, C. R., Dirocco, A. M., Battles, J. J. & Bruns, T. D. Ectomycorrhizal fungal spore bank recovery after a severe forest fire: Some like it hot. ISME J. 10, 1228–1239 (2016).Article 

    Google Scholar 
    Buscardo, E. et al. Impact of wildfire return interval on the ectomycorrhizal resistant propagules communities of a Mediterranean open forest. Fungal Biol. 114, 628–636 (2010).Article 

    Google Scholar 
    Pringle, A., Vellinga, E. & Peay, K. The shape of fungal ecology: Does spore morphology give clues to a species’ niche?. Fungal Ecol. 17, 213–216 (2015).Article 

    Google Scholar 
    Zhang, K., Cheng, X., Shu, X., Liu, Y. & Zhang, Q. Linking soil bacterial and fungal communities to vegetation succession following agricultural abandonment. Plant Soil 431, 19–36 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Xiang, X. et al. Arbuscular mycorrhizal fungal communities show low resistance and high resilience to wildfire disturbance. Plant Soil 397, 347–356 (2015).Article 
    CAS 

    Google Scholar 
    Dove, N. C., Klingeman, D. M., Carrell, A. A., Cregger, M. A. & Schadt, C. W. Fire alters plant microbiome assembly patterns: Integrating the plant and soil microbial response to disturbance. New Phytol. 230, 2433–2446 (2021).Article 
    CAS 

    Google Scholar 
    Fernandes, P. M. Fire-smart management of forest landscapes in the Mediterranean basin under global change. Landsc. Urban Plan. 110, 175–182 (2013).Article 

    Google Scholar 
    Fontúrbel, M. T., Fernández, C. & Vega, J. A. Prescribed burning versus mechanical treatments as shrubland management options in NW Spain: Mid-term soil microbial response. Appl. Soil Ecol. 107, 334–346 (2016).Article 

    Google Scholar 
    Geml, J. et al. Large-scale fungal diversity assessment in the Andean Yungas forests reveals strong community turnover among forest types along an altitudinal gradient. Mol. Ecol. 23, 2452–2472 (2014).Article 
    CAS 

    Google Scholar 
    Chu, H. et al. Effects of slope aspects on soil bacterial and arbuscular fungal communities in a boreal forest in China. Pedosphere 26, 226–234 (2016).Article 

    Google Scholar 
    Geml, J. Soil fungal communities reflect aspect-driven environmental structuring and vegetation types in a Pannonian forest landscape. Fungal Ecol. 39, 63–79 (2019).Article 

    Google Scholar 
    Castaño, C. et al. Soil microclimate changes affect soil fungal communities in a Mediterranean pine forest. New Phytol. 220, 1211–1221 (2018).Article 

    Google Scholar 
    Collado, E. et al. Mushroom productivity trends in relation to tree growth and climate across different European forest biomes. Sci. Total Environ. 689, 602–615 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Ihrmark, K., Bödeker, I. & Cruz-Martinez, K. New primers to amplify the fungal ITS2 region—Evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol. Ecol. 82, 666–677 (2012).Article 
    CAS 

    Google Scholar 
    White, T., Bruns, S., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) 315–322 (Academic Press, 1990).
    Google Scholar 
    Kent, M. Vegetation Description and Data Analysis: A Practical Approach (Wiley, 2011).

    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).Article 
    CAS 

    Google Scholar 
    Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).Article 

    Google Scholar 
    Abarenkov, K. et al. Plutof-a web based workbench for ecological and taxonomic research, with an online implementation for fungal its sequences. Evol. Bioinforma. 2010, 189–196 (2010).
    Google Scholar 
    Põlme, S. et al. FungalTraits: A user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).Article 

    Google Scholar 
    Agerer, R. Fungal relationships and structural identity of their ectomycorrhizae. Mycol. Prog. 5, 67–107 (2006).Article 

    Google Scholar 
    Tedersoo, L. & Smith, M. E. Lineages of ectomycorrhizal fungi revisited: Foraging strategies and novel lineages revealed by sequences from belowground. Fungal Biol. Rev. 27, 83–99 (2013).Article 

    Google Scholar 
    Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. Nlme: Linear and Nonlinear Mixed Effects Models. R Package Version 3.1–128. http://CRAN.R-project.org/package=nlme (2016).Chao, A. & Chiu, C. Species richness: Estimation and comparison. Wiley StatsRef https://doi.org/10.1002/9781118445112.stat03432.pub2 (2016).Article 

    Google Scholar 
    Chiu, C. H., Wang, Y. T., Walther, B. A. & Chao, A. An improved nonparametric lower bound of species richness via a modified good-turing frequency formula. Biometrics 70, 671–682 (2014).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Oksanen, J. et al. Vegan: Community Ecology Package. R package version 2.4–2. https://CRAN.R-project.org/package=vegan. (2017).Oksanen, J., Blanchet, F., Kindt, R. & Al, E. vegan: Community Ecology Package. R package version 2.3–0. (2015). More

  • in

    Enhanced regional connectivity between western North American national parks will increase persistence of mammal species diversity

    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

  • in

    Migration direction in a songbird explained by two loci

    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

  • in

    Predator-mediated diversity of stream fish assemblages in a boreal river basin, China

    Chase, J. M. et al. The interaction between predation and competition: A review and synthesis. Ecol. Lett. 5, 302–315. https://doi.org/10.1046/j.1461-0248.2002.00315.x (2002).Article 

    Google Scholar 
    Droge, E., Creel, S., Becker, M. S. & M’Soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evol. 1, 1123–1128. https://doi.org/10.1038/s41559-017-0220-9 (2017).Article 

    Google Scholar 
    Allesina, S. & Levine Jonathan, M. A competitive network theory of species diversity. Proc. Natl. Acad. Sci. U.S.A. 108, 5638–5642. https://doi.org/10.1073/pnas.1014428108 (2011).Article 
    ADS 

    Google Scholar 
    Bairey, E., Kelsic, E. D. & Kishony, R. High-order species interactions shape ecosystem diversity. Nat. Commun. 7, 12285. https://doi.org/10.1038/ncomms12285 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Letten, A. D. & Stouffer, D. B. The mechanistic basis for higher-order interactions and non-additivity in competitive communities. Ecol. Lett. 22, 423–436. https://doi.org/10.1111/ele.13211 (2019).Article 

    Google Scholar 
    Lotka, A. J. Elements of physical biology. Sci. Prog. Twent. Century (1919–1933) 21, 341–343 (1926).
    Google Scholar 
    Volterra, V. Variazioni e Fluttuazioni del Numero d’Individui in Specie Animali Conviventi. (Società Anonima Tipografica “Leonardo da Vinci”, 1926).Schmitz, O. J. Top predator control of plant biodiversity and productivity in an old-field ecosystem. Ecol. Lett. 6, 156–163. https://doi.org/10.1046/j.1461-0248.2003.00412.x (2003).Article 

    Google Scholar 
    Fey, K., Banks, P. B., Oksanen, L. & Korpimäki, E. Does removal of an alien predator from small islands in the Baltic Sea induce a trophic cascade?. Ecography 32, 546–552. https://doi.org/10.1111/j.1600-0587.2008.05637.x (2009).Article 

    Google Scholar 
    Terborgh John, W. Toward a trophic theory of species diversity. Proc. Natl. Acad. Sci. U.S.A. 112, 11415–11422. https://doi.org/10.1073/pnas.1501070112 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Pringle, R. M. et al. Predator-induced collapse of niche structure and species coexistence. Nature 570, 58–64. https://doi.org/10.1038/s41586-019-1264-6 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Sandom, C. et al. Mammal predator and prey species richness are strongly linked at macroscales. Ecology 94, 1112–1122. https://doi.org/10.1890/12-1342.1 (2013).Article 

    Google Scholar 
    Louette, G. & De Meester, L. Predation and priority effects in experimental zooplankton communities. Oikos 116, 419–426. https://doi.org/10.1111/j.2006.0030-1299.15381.x (2007).Article 

    Google Scholar 
    Johnston, N. K., Pu, Z. & Jiang, L. Predator identity influences metacommunity assembly. J. Anim. Ecol. 85, 1161–1170. https://doi.org/10.1111/1365-2656.12551 (2016).Article 

    Google Scholar 
    Karakoc, C., Radchuk, V., Harms, H. & Chatzinotas, A. Interactions between predation and disturbances shape prey communities. Sci. Rep. 8, 2968. https://doi.org/10.1038/s41598-018-21219-x (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32) (Princeton University Press, 2011).Book 

    Google Scholar 
    MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 2001).Book 

    Google Scholar 
    Daniel, J., Gleason, J. E., Cottenie, K. & Rooney, R. C. Stochastic and deterministic processes drive wetland community assembly across a gradient of environmental filtering. Oikos 128, 1158–1169. https://doi.org/10.1111/oik.05987 (2019).Article 

    Google Scholar 
    Lehner, B. & Döll, P. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296, 1–22. https://doi.org/10.1016/j.jhydrol.2004.03.028 (2004).Article 
    ADS 

    Google Scholar 
    Chase, J. M., Biro, E. G., Ryberg, W. A. & Smith, K. G. Predators temper the relative importance of stochastic processes in the assembly of prey metacommunities. Ecol. Lett. 12, 1210–1218. https://doi.org/10.1111/j.1461-0248.2009.01362.x (2009).Article 

    Google Scholar 
    Werner, E. E. & Peacor, S. D. A review of trait-mediated indirect interactions in ecological communities. Ecology 84, 1083–1100. https://doi.org/10.1890/0012-9658(2003)084[1083:AROTII]2.0.CO;2 (2003).Article 

    Google Scholar 
    Pearson, D. E., Ortega, Y. K., Eren, Ö. & Hierro, J. L. Community assembly theory as a framework for biological invasions. Trends Ecol. Evol. 33, 313–325. https://doi.org/10.1016/j.tree.2018.03.002 (2018).Article 

    Google Scholar 
    Duchesne, É. et al. Variable strength of predator-mediated effects on species occurrence in an arctic terrestrial vertebrate community. Ecography 44, 1236–1248. https://doi.org/10.1111/ecog.05760 (2021).Article 

    Google Scholar 
    Ryberg, W. A., Smith, K. G. & Chase, J. M. Predators alter the scaling of diversity in prey metacommunities. Oikos 121, 1995–2000. https://doi.org/10.1111/j.1600-0706.2012.19620.x (2012).Article 

    Google Scholar 
    Carrete Vega, G. & Wiens, J. J. Why are there so few fish in the sea?. Proc. R. Soc. B 279, 2323–2329. https://doi.org/10.1098/rspb.2012.0075 (2012).Article 

    Google Scholar 
    Barrett, M. et al. Living planet report 2018: Aiming higher. (2018).Reid, A. J. et al. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biol. Rev. 94, 849–873. https://doi.org/10.1111/brv.12480 (2019).Article 

    Google Scholar 
    Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Glob. Ecol. Conserv. 10, 32–42. https://doi.org/10.1016/j.gecco.2017.01.008 (2017).Article 

    Google Scholar 
    Hammerschlag, N. et al. Ecosystem function and services of aquatic predators in the anthropocene. Trends Ecol. Evol. 34, 369–383. https://doi.org/10.1016/j.tree.2019.01.005 (2019).Article 

    Google Scholar 
    Wang, T. et al. Amur tigers and leopards returning to China: direct evidence and a landscape conservation plan. Landsc Ecol 31, 491–503. https://doi.org/10.1007/s10980-015-0278-1 (2016).Article 

    Google Scholar 
    Hong, S. et al. Stream health, topography, and land use influences on the distribution of the Eurasian otter Lutra lutra in the Nakdong River basin, South Korea. Ecol. Indic. 88, 241–249. https://doi.org/10.1016/j.ecolind.2018.01.004 (2018).Article 

    Google Scholar 
    Guter, A., Dolev, A., Saltz, D. & Kronfeld-Schor, N. Using videotaping to validate the use of spraints as an index of Eurasian otter (Lutra lutra) activity. Ecol. Indic. 8, 462–465. https://doi.org/10.1016/j.ecolind.2007.04.009 (2008).Article 

    Google Scholar 
    Sittenthaler, M., Bayerl, H., Unfer, G., Kuehn, R. & Parz-Gollner, R. Impact of fish stocking on Eurasian otter (Lutra lutra) densities: A case study on two salmonid streams. Mamm. Biol. 80, 106–113. https://doi.org/10.1016/j.mambio.2015.01.004 (2015).Article 

    Google Scholar 
    Zheng, B., Huang, H., Zhang, Y. & Dai, D. The Fishes of Tumen River (Jilin People’s Publishing House, 1980).
    Google Scholar 
    Fleishman, E., Murphy, D. D. & Brussard, P. F. A new method for selection of umbrella species for conservation planning. Ecol Appl 10, 569–579. https://doi.org/10.1890/1051-0761(2000)010[0569:ANMFSO]2.0.CO;2 (2000).Article 

    Google Scholar 
    Roberge, J.-M. & Angelstam, P. E. R. Usefulness of the umbrella species concept as a conservation tool. Conserv. Biol. 18, 76–85. https://doi.org/10.1111/j.1523-1739.2004.00450.x (2004).Article 

    Google Scholar 
    McGowan, J. et al. Conservation prioritization can resolve the flagship species conundrum. Nat. Commun. 11, 994. https://doi.org/10.1038/s41467-020-14554-z (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Katano, I., Doi, H., Eriksson, B. K. & Hillebrand, H. A cross-system meta-analysis reveals coupled predation effects on prey biomass and diversity. Oikos 124, 1427–1435. https://doi.org/10.1111/oik.02430 (2015).Article 

    Google Scholar 
    Leibold, M. A. A graphical model of keystone predators in food webs: Trophic regulation of abundance, incidence, and diversity patterns in communities. Am. Nat. 147, 784–812. https://doi.org/10.1086/285879 (1996).Article 

    Google Scholar 
    McPeek, M. A. The consequences of changing the top predator in a food web: A comparative experimental approach. Ecol. Monogr. 68, 1–23. https://doi.org/10.1890/0012-9615(1998)068[0001:TCOCTT]2.0.CO;2 (1998).Article 

    Google Scholar 
    Chase, J. M. & Leibold, M. A. Ecological Niches: Linking Classical and Contemporary Approaches (University of Chicago Press, 2003).Book 

    Google Scholar 
    Gravel, D., Canham, C. D., Beaudet, M. & Messier, C. Reconciling niche and neutrality: The continuum hypothesis. Ecol. Lett. 9, 399–409. https://doi.org/10.1111/j.1461-0248.2006.00884.x (2006).Article 

    Google Scholar 
    Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306. https://doi.org/10.1038/nature01767 (2003).Article 
    ADS 
    CAS 

    Google Scholar 
    Yin, X., Wang, J., Yin, H. & Ruan, Y. Does inducible defense mitigate physiological stress responses of prey to predation risk?. Hydrobiologia 843, 173–181. https://doi.org/10.1007/s10750-019-04046-7 (2019).Article 

    Google Scholar 
    Chalcraft, D. R. & Resetarits, W. J. Jr. Predator identity and ecological impacts: Functional redundancy or functional diversity?. Ecology 84, 2407–2418. https://doi.org/10.1890/02-0550 (2003).Article 

    Google Scholar 
    Petchey, O. L. & Gaston, K. J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 9, 741–758. https://doi.org/10.1111/j.1461-0248.2006.00924.x (2006).Article 

    Google Scholar 
    Burner, R. C. et al. Functional structure of European forest beetle communities is enhanced by rare species. Biol. Conserv. 267, 109491. https://doi.org/10.1016/j.biocon.2022.109491 (2022).Article 

    Google Scholar  More

  • in

    Publisher Correction: Seasonal peak photosynthesis is hindered by late canopy development in northern ecosystems

    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

  • in

    Rhizobial migration toward roots mediated by FadL-ExoFQP modulation of extracellular long-chain AHLs

    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

  • in

    Conservation genomics of an endangered arboreal mammal following the 2019–2020 Australian megafire

    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