Philippa Kaur

Srivastava, R. K., Mequanint, F., Chakraborty, A., Panda, R. K. & Halder, D. Augmentation of maize yield by strategic adaptation to cope with climate change for a future period in Eastern India. J. Clean. Prod. 339, 130599 (2022).
Google Scholar 
Pooniya, V. et al. Six years of conservation agriculture and nutrient management in maize–mustard rotation: Impact on soil properties, system productivity and profitability. Field Crops Res. 260, 108002 (2021).
Google Scholar 
Tsimba, R., Edmeades, G. O., Millner, J. P. & Kemp, P. D. The effect of planting date on maize grain yields and yield components. Field Crops Res. 150, 135–144 (2013).
Google Scholar 
Maresma, A., Ballesta, A., Santiveri, F. & Lloveras, J. Sowing date affects maize development and yield in irrigated mediterranean environments. Agriculture 9(3), 67 (2019).
Google Scholar 
Srivastava, R. K., Panda, R. K., Chakraborty, A. & Halder, D. Enhancing grain yield, biomass and nitrogen use efficiency of maize by varying sowing dates and nitrogen rate under rainfed and irrigated conditions. Field Crops Res. 221, 339–349 (2018).
Google Scholar 
Van Roekel, R. J. & Coulter, J. A. Agronomic responses of corn hybrids to row width and plant density. Agronomy J. 104(3), 612–620 (2012).
Google Scholar 
Santiveri, F., Royo, C. & Romagosa, I. Growth and yield responses of spring and winter triticale cultivated under Mediterranean conditions. Eur. J. Agron. 20(3), 281–292 (2004).
Google Scholar 
Masoni, A., Ercoli, L., Mariotti, M. & Arduini, I. Post-anthesis accumulation and remobilization of dry matter, nitrogen and phosphorus in durum wheat as affected by soil type. Eur. J. Agron. 26(3), 179–186 (2007).CAS 

Google Scholar 
Yang, W., Peng, S., Dionisio-Sese, M. L., Laza, R. C. & Visperas, R. M. Grain filling duration, a crucial determinant of genotypic variation of grain yield in field-grown tropical irrigated rice. Field Crops Res. 105, 221–227 (2008).
Google Scholar 
Wei, H. et al. Comparisons of grain yield and nutrient accumulation and translocation in high-yielding japonica/indica hybrids, indica hybrids, and japonica conventional varieties. Field Crops Res. 204, 101–109 (2017).
Google Scholar 
Wu, H. et al. Effects of post-anthesis nitrogen uptake and translocation on photosynthetic production and rice yield. Sci. Rep. 8(1), 1–11 (2018).ADS 

Google Scholar 
Laza, M. R., Peng, S., Akita, S. & Saka, H. Contribution of biomass partitioning and translocation to grain yield under sub-optimum growing conditions in irrigated rice. Plant Prod. Sci. 6(1), 28–35 (2003).
Google Scholar 
Gao, H. et al. Intercropping modulates the accumulation and translocation of dry matter and nitrogen in maize and peanut. Field Crops Res. 284, 108561 (2022).
Google Scholar 
Yang, Y. et al. Solar radiation effects on dry matter accumulations and transfer in maize. Front. Plant Sci. 12, 1927 (2021).
Google Scholar 
Jamshidi, A. & Javanmard, H. R. Evaluation of barley (Hordeum vulgare L.) genotypes for salinity tolerance under field conditions using the stress indices. Ain Shams Eng. J. 9(4), 2093–2099 (2018).
Google Scholar 
Tyagi, B. S. et al. Identification of wheat cultivars for low nitrogen tolerance using multivariable screening approaches. Agronomy 10(3), 417 (2020).CAS 

Google Scholar 
Fischer, R. A. & Maurer, R. Drought resistance in spring wheat cultivars. I. Grain yield responses. Aust. J. Agric. Res. 29(5), 897–912 (1978).
Google Scholar 
Fernandez, G. C. Effective selection criteria for assessing plant stress tolerance. In Proceeding of the International Symposium on Adaptation of Vegetables and other Food Crops in Temperature and Water Stress, Aug. 13–16, Shanhua, Taiwan. 257–270 (1992).Bouslama, M. & Schapaugh, W. T. Jr. Stress tolerance in soybeans. I. Evaluation of three screening techniques for heat and drought tolerance 1. Crop sci. 24(5), 933–937 (1984).
Google Scholar 
Ciampitti, I. A. & Vyn, T. J. Grain nitrogen source changes over time in maize: A review. Crop Sci. 53(2), 366–377 (2013).CAS 

Google Scholar 
Chen, Y. et al. Characterization of the plant traits contributed to high grain yield and high grain nitrogen concentration in maize. Field Crops Res. 159, 1–9 (2014).
Google Scholar 
Mi, G. et al. Nitrogen uptake and remobilization in maize hybrids differing in leaf senescence. J. plant nutr. 26(1), 237–247 (2003).CAS 

Google Scholar 
Tollenaar, M. & Lee, E. A. Dissection of physiological processes underlying grain yield in maize by examining genetic improvement and heterosis. Maydica 51(2), 399 (2006).
Google Scholar 
Samonte, S. O. P. et al. Nitrogen utilization efficiency: Relationships with grain yield, grain protein, and yield-related traits in rice. Agronomy J. 98(1), 168–176 (2006).CAS 

Google Scholar 
Qiao, J., Yang, L., Yan, T., Xue, F. & Zhao, D. Nitrogen fertilizer reduction in rice production for two consecutive years in the Taihu Lake area. Agric. Ecosyst. Environ. 146(1), 103–112 (2012).CAS 

Google Scholar 
Marschner, P. Marschner’s Mineral Nutrition of Higher Plants 3rd edn. (Academic Press, 2012).
Google Scholar 
Ning, P., Li, S., Yu, P., Zhang, Y. & Li, C. Post-silking accumulation and partitioning of dry matter, nitrogen, phosphorus and potassium in maize varieties differing in leaf longevity. Field Crops Res. 144, 19–27 (2013).
Google Scholar 
Hawkesford, M. et al. Functions of macronutrients. In Marschners Mineral Nutrition of Higher Plants 3rd edn (ed. Marschner, P.) 178–189 (Academic Press, 2012).
Google Scholar 
Palta, J. A. et al. Large root systems: Are they useful in adapting wheat to dry environments?. Funct. Plant Biol. 38(5), 347–354 (2011).
Google Scholar 
Pooniya, V., Palta, J. A., Chen, Y., Delhaize, E. & Siddique, K. H. Impact of the TaMATE1B gene on above and below-ground growth of durum wheat grown on an acid and Al3+-toxic soil. Plant Soil 447(1), 73–84 (2020).CAS 

Google Scholar 
Bonelli, L. E., Monzon, J. P., Cerrudo, A., Rizzalli, R. H. & Andrade, F. H. Maize grain yield components and source-sink relationship as affected by the delay in sowing date. Field Crops Res. 198, 215–225 (2016).
Google Scholar 
Sorensen, I., Stone, P. & Rogers, B. Effect of sowing time on yield of a short and a long season maize hybrid. Proc. Agron. Soc. NZ 30, 63–66 (2000).
Google Scholar 
Tsimba, R., Edmeades, G. O., Millner, J. P. & Kemp, P. D. The effect of planting date on maize: Phenology, thermal time durations and growth rates in a cool temperate climate. Field Crops Res. 150, 145–155 (2013).
Google Scholar 
Zhou, B. et al. Maize kernel weight responses to sowing date-associated variation in weather conditions. Crop J. 5(1), 43–51 (2017).
Google Scholar 
Cirilo, A. G. & Andrade, F. H. Sowing date and maize productivity: I. Crop growth and dry matter partitioning. Crop Sci. 34(4), 1039–1043 (1994).
Google Scholar 
Shi, Y. et al. Tillage practices affect dry matter accumulation and grain yield in winter wheat in the North China Plain. Soil Till. Res. 160, 73–81 (2016).
Google Scholar 
He, P., Zhou, W. & Jin, J. Carbon and nitrogen metabolism related to grain formation in two different senescent types of maize. J. Plant Nutrit. 27(2), 295–311 (2004).CAS 

Google Scholar 
Pommel, B. et al. Carbon and nitrogen allocation and grain filling in three maize hybrids differing in leaf senescence. Eur. J. Agron. 24(3), 203–211 (2006).CAS 

Google Scholar 
Clarke, J. M., Campbell, C. A., Cutforth, H. W., DePauw, R. M. & Winkleman, G. E. Nitrogen and phosphorus uptake, translocation, and utilization efficiency of wheat in relation to environment and cultivar yield and protein levels. Can. J. Plant Sci. 70(4), 965–977 (1990).CAS 

Google Scholar 
Mardeh, A. S. S., Ahmadi, A., Poustini, K. & Mohammadi, V. Evaluation of drought resistance indices under various environmental conditions. Field Crops Res. 98(2–3), 222–229 (2006).
Google Scholar 
Naderi, A., Majidi-Harvan, E., Hashemi-Dezfoli, A., Rezaei, A. & Normohamadi, G. Analysis of efficiency of drought tolerance indices in crop plants and introduction of a new criteria. Seed Plant 15(4), 390–402 (1999).
Google Scholar 
Zeng, W. et al. Comparative proteomics analysis of the seedling root response of drought-sensitive and drought-tolerant maize varieties to drought stress. Int. J. Mol. Sci. 20(11), 2793 (2019).CAS 

Google Scholar 
Hajibabaei, M. & Azizi, F. Evaluation of drought tolerance indices in some new hybrids of corn. Electron. J. Crop Prod. 3, 139–155 (2011).
Google Scholar 
Zhao, J. et al. Yield and water use of drought-tolerant maize hybrids in a semiarid environment. Field Crops Res. 216, 1–9 (2018).
Google Scholar 
Fageria, N. K. Nitrogen harvest index and its association with crop yields. J. Plant Nutri. 37(6), 795–810 (2014).CAS 

Google Scholar 
Raghuram, N., Sachdev, M. S. & Abrol, Y. P. Towards an integrative understanding of reactive nitrogen. In Agricultural Nitrogen Use & Its Environmental Implications (eds Abrol, Y. P. et al.) 1–6 (I.K. International Publishing House Pvt. Ltd., 2007).
Google Scholar 
Baligar, V. C., Fageria, N. K. & He, Z. L. Nutrient use efficiency in plants. Commun. Soil Sci. Plant Anal. 32(7–8), 921–950 (2001).CAS 

Google Scholar 
Foulkes, M. J. et al. Identifying traits to improve the nitrogen economy of wheat: Recent advances and future prospects. Field Crops Res. 114(3), 329–342 (2009).
Google Scholar 
Gaju, O. et al. Identification of traits to improve the nitrogen-use efficiency of wheat genotypes. Field Crops Res. 123(2), 139–152 (2011).
Google Scholar 
Ehdaie, B. A. H. M. A. N., Mohammadi, S. A. & Nouraein, M. QTLs for root traits at mid-tillering and for root and shoot traits at maturity in a RIL population of spring bread wheat grown under well-watered conditions. Euphytica 211(1), 17–38 (2016).
Google Scholar 
Piper, C. S. Soil and Plant Analysis (Adelaide University, 1950).
Google Scholar 
Subbiah, B. V. & Asija, G. L. A rapid method for the estimation of nitrogen in soil. Curr. Sci. 26, 259–260 (1956).
Google Scholar 
Olsen, S. R., Cole, C. V., Watanabe, F. S. & Dean, L. A. Estimation of Available Phosphorus in Soil by Extraction with Sodium Carbonate (USDA, 1954).
Google Scholar 
Hanway, J. J. & Heidel, H. Soil Analysis Methods as used in Iowa State College Soil Testing Laboratory, Bulletin 57 (Iowa State College of Agriculture, 1952).
Google Scholar 
Walkley, A. L. & Black, I. A. An examination of the Degtjareff method for determination of soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38 (1934).ADS 
CAS 

Google Scholar 
Ntanos, D. A. & Koutroubas, S. D. Dry matter and N accumulation and translocation for Indica and Japonica rice under Mediterranean conditions. Field Crops Res. 74, 93–101 (2002).
Google Scholar 
Prasad, R., Shivay, Y. S., Kumar, D., & Sharma, S. N. Learning by doing exercises in soil fertility (A practical manual for soil fertility). Division of Agronomy, Indian Agricultural Research Institute, India, (2006).Jiang, L. et al. Characterizing physiological N-use efficiency as influenced by nitrogen management in three rice cultivars. Field Crops Res. 88, 239–250 (2004).
Google Scholar 
Dai, X. et al. Managing the seeding rate to improve nitrogen-use efficiency of winter wheat. Field Crops Res. 154, 100–109 (2013).
Google Scholar 
Liu, W. et al. Root growth, water and nitrogen use efficiencies in winter wheat under different irrigation and nitrogen regimes in North China Plain. Front. Plant Sci. 9, 1798 (2018).
Google Scholar 
Gomez, K. A. & Gomez, A. A. Statistical Procedures for Agricultural Research 2nd edn, 180–209 (Wiley, 1984).
Google Scholar  More

Ackerly, D. D. Community assembly, niche conservatism, and adaptive evolution in changing environments. Int. J. Plant Sci. 164, S165–S184 (2003).Article 

Google Scholar 
Kellermann, V., Van Heerwaarden, B., Sgrò, C. M. & Hoffmann, A. A. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science 325, 1244–1246 (2009).Article 
CAS 

Google Scholar 
Hansen, M. M., Olivieri, I., Waller, D. M. & Nielsen, E. E. Monitoring adaptive genetic responses to environmental change. Mol. Ecol. 21, 1311–1329 (2012).Article 

Google Scholar 
Aitken, S. N. & Whitlock, M. C. Assisted gene flow to facilitate local adaptation to climate change. Annu. Rev. Ecol. Evol. Syst. 44, 367–388 (2013).Article 

Google Scholar 
Becker, M. et al. Hybridization may facilitate in situ survival of endemic species through periods of climate change. Nat. Clim. Change 3, 1039–1043 (2013).Article 

Google Scholar 
Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. The problems with hybrids: setting conservation guidelines. Trends Ecol. Evol. 16, 613–622 (2001).Article 

Google Scholar 
Todesco, M. et al. Hybridization and extinction. Evol. Appl. 9, 892–908 (2016).Article 
CAS 

Google Scholar 
Rhymer, J. M. & Simberloff, D. Extinction by hybridization and introgression. Annu. Rev. Ecol. Syst. 27, 83–109 (1996).Article 

Google Scholar 
Taylor, S. A. & Larson, E. L. Insights from genomes into the evolutionary importance and prevalence of hybridization in nature. Nat. Ecol. Evol. 3, 170–177 (2019).Article 

Google Scholar 
vonHoldt, B. M., Brzeski, K. E., Wilcove, D. S. & Rutledge, L. Y. Redefining the role of admixture and genomics in species conservation. Conserv. Lett. 11, e12371 (2018).Article 

Google Scholar 
Hamilton, J. A. & Miller, J. M. Adaptive introgression as a resource for management and genetic conservation in a changing climate. Conserv. Biol. 30, 33–41 (2016).Article 

Google Scholar 
Ralls, K., Sunnucks, P., Lacy, R. C. & Frankham, R. Genetic rescue: a critique of the evidence supports maximizing genetic diversity rather than minimizing the introduction of putatively harmful genetic variation. Biol. Conserv. 251, 108784 (2020).Article 

Google Scholar 
Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M. & Keller, S. R. Genomic prediction of (mal) adaptation across current and future climatic landscapes. Annu. Rev. Ecol. Evol. Syst. 51, 245–269 (2020).Article 

Google Scholar 
Rellstab, C., Dauphin, B. & Exposito‐Alonso, M. Prospects and limitations of genomic offset in conservation management. Evol. Appl. 14, 1202–1212 (2021).Article 

Google Scholar 
Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).Article 
CAS 

Google Scholar 
Rellstab, C. et al. Signatures of local adaptation in candidate genes of oaks (Quercus spp.) with respect to present and future climatic conditions. Mol. Ecol. 25, 5907–5924 (2016).Article 

Google Scholar 
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).Article 

Google Scholar 
Exposito-Alonso, M. et al. Genomic basis and evolutionary potential for extreme drought adaptation in Arabidopsis thaliana. Nat. Ecol. Evol. 2, 352–358 (2018).Article 

Google Scholar 
Kindt, R. AlleleShift: an R package to predict and visualize population-level changes in allele frequencies in response to climate change. PeerJ 9, e11534 (2021).Article 

Google Scholar 
Gain, C. & François, O. LEA 3: factor models in population genetics and ecological genomics with R. Mol. Ecol. Resour. 21, 2738–2748 (2020).Article 

Google Scholar 
Aguirre-Liguori, J. A., Ramírez-Barahona, S. & Gaut, B. S. The evolutionary genomics of species’ responses to climate change. Nat. Ecol. Evol. 5, 1350–1360 (2021).Article 

Google Scholar 
Taylor, S. A., Larson, E. L. & Harrison, R. G. Hybrid zones: windows on climate change. Trends Ecol. Evol. 30, 398–406 (2015).Article 

Google Scholar 
Hoffmann, A. A. & Sgro, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).Article 
CAS 

Google Scholar 
McGuigan, K., Franklin, C. E., Moritz, C. & Blows, M. W. Adaptation of rainbow fish to lake and stream habitats. Evolution 57, 104–118 (2003).
Google Scholar 
Smith, S., Bernatchez, L. & Beheregaray, L. RNA-seq analysis reveals extensive transcriptional plasticity to temperature stress in a freshwater fish species. BMC Genomics 14, 375 (2013).Article 
CAS 

Google Scholar 
Smith, S. et al. Latitudinal variation in climate‐associated genes imperils range edge populations. Mol. Ecol. 29, 4337–4349 (2020).Article 
CAS 

Google Scholar 
Sandoval-Castillo, J. et al. Adaptation of plasticity to projected maximum temperatures and across climatically defined bioregions. Proc. Natl Acad. Sci. USA 117, 17112–17121 (2020).Article 
CAS 

Google Scholar 
Brauer, C., Unmack, P. J., Smith, S., Bernatchez, L. & Beheregaray, L. B. On the roles of landscape heterogeneity and environmental variation in determining population genomic structure in a dendritic system. Mol. Ecol. 27, 3484–3497 (2018).Article 
CAS 

Google Scholar 
Attard, C. R. et al. Fish out of water: genomic insights into persistence of rainbowfish populations in the desert. Evolution 76, 171–183 (2022).Article 

Google Scholar 
Gates, K. et al. Environmental selection, rather than neutral processes, best explain patterns of diversity in a tropical rainforest fish. Preprint at bioRxiv https://doi.org/10.1101/2022.1105.1113.491913 (2022).Article 

Google Scholar 
McCairns, R. J. S., Smith, S., Sasaki, M., Bernatchez, L. & Beheregaray, L. B. The adaptive potential of subtropical rainbowfish in the face of climate change: heritability and heritable plasticity for the expression of candidate genes. Evol. Appl. 9, 531–545 (2016).Article 
CAS 

Google Scholar 
McGuigan, K., Zhu, D., Allen, G. & Moritz, C. Phylogenetic relationships and historical biogeography of melanotaeniid fishes in Australia and New Guinea. Mar. Freshwat. Res. 51, 713–723 (2000).Article 

Google Scholar 
Unmack, P. J. et al. Malanda Gold: the tale of a unique rainbowfish from the Atherton Tablelands, now on the verge of extinction. Fish. Sahul. 30, 1039–1054 (2016).
Google Scholar 
Moritz, C. Strategies to protect biological diversity and the evolutionary processes that sustain it. Syst. Biol. 51, 238–254 (2002).Article 

Google Scholar 
Pope, L., Estoup, A. & Moritz, C. Phylogeography and population structure of an ecotonal marsupial, Bettongia tropica, determined using mtDNA and microsatellites. Mol. Ecol. 9, 2041–2053 (2000).Article 
CAS 

Google Scholar 
Hugall, A., Moritz, C., Moussalli, A. & Stanisic, J. Reconciling paleodistribution models and comparative phylogeography in the Wet Tropics rainforest land snail Gnarosophia bellendenkerensis (Brazier 1875). Proc. Natl Acad. Sci. USA 99, 6112–6117 (2002).Article 
CAS 

Google Scholar 
Moritz, C. et al. Identification and dynamics of a cryptic suture zone in tropical rainforest. Proc. R. Soc. B. 276, 1235–1244 (2009).Article 
CAS 

Google Scholar 
Phillips, B. L., Baird, S. J. & Moritz, C. When vicars meet: a narrow contact zone between morphologically cryptic phylogeographic lineages of the rainforest skink, Carlia rubrigularis. Evolution 58, 1536–1548 (2004).
Google Scholar 
Krosch, M. N., Baker, A. M., Mckie, B. G., Mather, P. B. & Cranston, P. S. Deeply divergent mitochondrial lineages reveal patterns of local endemism in chironomids of the Australian Wet Tropics. Austral Ecol. 34, 317–328 (2009).Article 

Google Scholar 
Williams, S. E., Bolitho, E. E. & Fox, S. Climate change in Australian tropical rainforests: an impending environmental catastrophe. Proc. R. Soc. B. 270, 1887–1892 (2003).Article 

Google Scholar 
Whitehead, P. et al. Temporal development of the Atherton Basalt Province, north Queensland. Aust. J. Earth Sci. 54, 691–709 (2007).Article 
CAS 

Google Scholar 
Moy, K. G., Unmack, P. J., Lintermans, M., Duncan, R. P. & Brown, C. Barriers to hybridisation and their conservation implications for a highly threatened Australian fish species. Ethology 125, 142–152 (2019).Article 

Google Scholar 
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).Article 
CAS 

Google Scholar 
Buerkle, C. A. Maximum‐likelihood estimation of a hybrid index based on molecular markers. Mol. Ecol. Notes 5, 684–687 (2005).Article 
CAS 

Google Scholar 
Anderson, E. & Thompson, E. A model-based method for identifying species hybrids using multilocus genetic data. Genetics 160, 1217–1229 (2002).Article 
CAS 

Google Scholar 
Dorion, S. & Landry, J. Activation of the mitogen-activated protein kinase pathways by heat shock. Cell Stress Chaperones 7, 200 (2002).Article 
CAS 

Google Scholar 
Blumstein, M. et al. Protocol for projecting allele frequency change under future climate change at adaptive-associated loci. STAR Protoc. 1, 100061 (2020).Article 

Google Scholar 
Gougherty, A. V., Keller, S. R. & Fitzpatrick, M. C. Maladaptation, migration and extirpation fuel climate change risk in a forest tree species. Nat. Clim. Change 11, 166–171 (2021).Article 

Google Scholar 
Blumstein, M. et al. A new perspective on ecological prediction reveals limits to climate adaptation in a temperate tree species. Curr. Biol. 30, 1447–1453. e1444 (2020).Article 
CAS 

Google Scholar 
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl Acad. Sci. USA 116, 10418–10423 (2019).Article 
CAS 

Google Scholar 
Goicoechea, P. G. et al. Adaptive introgression promotes fast adaptation in oaks marginal populations. Preprint available at bioRxiv https://doi.org/10.1101/731919 (2019).Lavergne, S. & Molofsky, J. Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proc. Natl Acad. Sci. USA 104, 3883–3888 (2007).Article 
CAS 

Google Scholar 
De Carvalho, D. et al. Admixture facilitates adaptation from standing variation in the European aspen (Populus tremula L.), a widespread forest tree. Mol. Ecol. 19, 1638–1650 (2010).Article 

Google Scholar 
De-Kayne, R. et al. Genomic architecture of adaptive radiation and hybridization in Alpine whitefish. Nat. Commun. 13, 4479 (2022).Article 
CAS 

Google Scholar 
Baskett, M. L. & Gomulkiewicz, R. Introgressive hybridization as a mechanism for species rescue. Theor. Ecol. 4, 223–239 (2011).Article 

Google Scholar 
Meier, J. I. et al. The coincidence of ecological opportunity with hybridization explains rapid adaptive radiation in Lake Mweru cichlid fishes. Nat. Commun. 10, 1–11 (2019).Article 
CAS 

Google Scholar 
Svardal, H. et al. Ancestral hybridization facilitated species diversification in the Lake Malawi cichlid fish adaptive radiation. Mol. Biol. Evol. 37, 1100–1113 (2020).Article 
CAS 

Google Scholar 
Racimo, F., Sankararaman, S., Nielsen, R. & Huerta-Sánchez, E. Evidence for archaic adaptive introgression in humans. Nat. Rev. Genet. 16, 359–371 (2015).Article 
CAS 

Google Scholar 
Jeong, C. et al. Admixture facilitates genetic adaptations to high altitude in Tibet. Nat. Commun. 5, 1–7 (2014).Article 

Google Scholar 
Nolte, A. W., Freyhof, J., Stemshorn, K. C. & Tautz, D. An invasive lineage of sculpins, Cottus sp. (Pisces, Teleostei) in the Rhine with new habitat adaptations has originated from hybridization between old phylogeographic groups. Proc. R. Soc. B. 272, 2379–2387 (2005).Article 

Google Scholar 
Fitzpatrick, M. C., Chhatre, V. E., Soolanayakanahally, R. Y. & Keller, S. R. Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests. Mol. Ecol. Resour. 21, 2749–2765 (2021).Article 
CAS 

Google Scholar 
Schneider, C., Cunningham, M. & Moritz, C. Comparative phylogeography and the history of endemic vertebrates in the Wet Tropics rainforests of Australia. Mol. Ecol. 7, 487–498 (1998).Article 

Google Scholar 
Hewitt, G. M. Quaternary phylogeography: the roots of hybrid zones. Genetica 139, 617–638 (2011).Article 

Google Scholar 
Pfennig, K. S., Kelly, A. L. & Pierce, A. A. Hybridization as a facilitator of species range expansion. Proc. R. Soc. B. 283, 20161329 (2016).Article 

Google Scholar 
Soulé, M. E. What is conservation biology? A new synthetic discipline addresses the dynamics and problems of perturbed species, communities, and ecosystems. Bioscience 35, 727–734 (1985).
Google Scholar 
Biermann, C. & Havlick, D. Genetics and the question of purity in cutthroat trout restoration. Restor. Ecol. 29, e13516 (2021).Article 

Google Scholar 
Fredrickson, R. J. & Hedrick, P. W. Dynamics of hybridization and introgression in red wolves and coyotes. Conserv. Biol. 20, 1272–1283 (2006).Article 

Google Scholar 
Hirashiki, C., Kareiva, P. & Marvier, M. Concern over hybridization risks should not preclude conservation interventions. Conserv. Sci. Pract. 3, e424 (2021).
Google Scholar 
Unmack, P. J., Allen, G. R. & Johnson, J. B. Phylogeny and biogeography of rainbowfishes (Melanotaeniidae) from Australia and New Guinea. Mol. Phylogenet. Evol. 67, 15–27 (2013).Article 

Google Scholar 
Allen, G. Rainbowfishes in Nature and the Aquarium (Tetra Publications, 1995).Seehausen, O. Hybridization and adaptive radiation. Trends Ecol. Evol. 19, 198–207 (2004).Article 

Google Scholar 
Pusey, B., Kennard, M. J. & Arthington, A. H. Freshwater Fishes of North-eastern Australia (CSIRO Publishing, 2004).Zhu, D., Degnan, S. & Moritz, C. Evolutionary distinctiveness and status of the endangered Lake Eacham rainbowfish (Melanotaenia eachamensis). Conserv. Biol. 12, 80–93 (1998).Article 

Google Scholar 
McGuigan, K., Chenoweth, S. F. & Blows, M. W. Phenotypic divergence along lines of genetic variance. Am. Nat. 165, 32–43 (2005).Article 

Google Scholar 
Sunnucks, P. & Hales, D. F. Numerous transposed sequences of mitochondrial cytochrome oxidase I-II in aphids of the genus Sitobion (Hemiptera: Aphididae). Mol. Biol. Evol. 13, 510–524 (1996).Article 
CAS 

Google Scholar 
Peterson, B., Weber, J., Kay, E., Fisher, H. & Hoekstra, H. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).Article 
CAS 

Google Scholar 
Catchen, J. M., Amores, A., Hohenlohe, P., Cresko, W. & Postlethwait, J. H. Stacks: building and genotyping loci de novo from short-read sequences. G3: Genes Genomes Genet. 1, 171–182 (2011).Article 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357 (2012).Article 
CAS 

Google Scholar 
DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).Article 
CAS 

Google Scholar 
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).Article 

Google Scholar 
Goudet, J. Hierfstat, a package for R to compute and test hierarchical F‐statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Bailey, R. ribailey/gghybrid: gghybrid R package for Bayesian hybrid index and genomic cline estimation. v2.0.0 https://doi.org/10.5281/zenodo.3676498 (2020).Wringe, B. hybriddetective: automates the process of detecting hybrids from genetic data. R package version 0.1.0.9000 https://github.com/bwringe/hybriddetective (2016).Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).Article 
CAS 

Google Scholar 
Malinsky, M., Matschiner, M. & Svardal, H. Dsuite‐Fast D‐statistics and related admixture evidence from VCF files. Mol. Ecol. Resour. 21, 584–595 (2021).Article 

Google Scholar 
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).Article 
CAS 

Google Scholar 
Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).Article 
CAS 

Google Scholar 
Durand, E. Y., Patterson, N., Reich, D. & Slatkin, M. Testing for ancient admixture between closely related populations. Mol. Biol. Evol. 28, 2239–2252 (2011).Article 
CAS 

Google Scholar 
Malinsky, M. et al. Genomic islands of speciation separate cichlid ecomorphs in an East African crater lake. Science 350, 1493–1498 (2015).Article 
CAS 

Google Scholar 
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).Article 
CAS 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).Article 

Google Scholar 
Karger, D. N. et al. CHELSA climatologies at high resolution for the Earth’s land surface areas (v.1.0). https://doi.org/10.1594/WDCC/CHELSA_v1 (2016).Ackerley, D. & Dommenget, D. Atmosphere-only GCM (ACCESS1.0) simulations with prescribed land surface temperatures. Geosci. Model Dev. 9, 2077–2098 (2016).Article 

Google Scholar 
Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim: high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 1–9 (2018).Article 

Google Scholar 
Fordham, D. A. et al. PaleoView: a tool for generating continuous climate projections spanning the last 21,000 years at regional and global scales. Ecography 40, 1348–1358 (2017).Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD–a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).Article 

Google Scholar 
Lemus-Canovas, M., Lopez-Bustins, J. A., Martin-Vide, J. & Royé, D. synoptReg: an R package for computing a synoptic climate classification and a spatial regionalization of environmental data. Environ. Model. Softw. 118, 114–119 (2019).Article 

Google Scholar 
Hao, T., Elith, J., Guillera‐Arroita, G. & Lahoz‐Monfort, J. J. A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD. Divers. Distrib. 25, 839–852 (2019).Article 

Google Scholar 
Galpern, P., Peres‐Neto, P. R., Polfus, J. & Manseau, M. MEMGENE: spatial pattern detection in genetic distance data. Methods Ecol. Evol. 5, 1116–1120 (2014).Article 

Google Scholar 
Peres‐Neto, P. R. & Galpern, P. memgene: spatial pattern detection in genetic distance data using Moran’s eigenvector maps. R package version 1.0.1 https://cran.r-project.org/web/packages/memgene/ (2019).Oksanen, J. et al. vegan: community ecology package. R package version 2.3–0 https://cran.r-project.org/web/packages/vegan/ (2015).Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L. & Lasky, J. R. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol. Ecol. 25, 104–120 (2015).Article 

Google Scholar 
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).Article 
CAS 

Google Scholar 
Szklarczyk, D. et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).Article 
CAS 

Google Scholar 
Brauer, C. J. et al. Data for ‘Natural hybridisation reduces vulnerability to climate change’. figshare https://doi.org/10.6084/m9.figshare.21692918 (2022).Brauer, C. J. et al. Code for ‘Natural hybridisation reduces vulnerability to climate change’. GitHub https://github.com/pygmyperch/NER (2022). More

Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. PNAS 114, E6089–E6096 (2017).ADS 
CAS 

Google Scholar 
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).ADS 
CAS 

Google Scholar 
Ceballos, G., Ehrlich, P. R. & Raven, P. H. Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. PNAS 117, 13596–13602 (2020).ADS 
CAS 

Google Scholar 
Butchart, S. H. et al. Global biodiversity: Indicators of recent declines. Science 328, 1164–1168 (2010).ADS 
CAS 

Google Scholar 
Excoffier, L., Foll, M. & Petit, R. J. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).
Google Scholar 
Arenas, M., Ray, N., Currat, M. & Excoffier, L. Consequences of range contractions and range shifts on molecular diversity. Mol. Biol. Evol. 29, 207–218 (2012).CAS 

Google Scholar 
Banks, S. C. et al. How does ecological disturbance influence genetic diversity?. Trends Ecol. Evol. 28, 670–679 (2013).
Google Scholar 
Branco, C., Ray, N., Currat, M. & Arenas, M. Influence of Paleolithic range contraction, admixture and long-distance dispersal on genetic gradients of modern humans in Asia. Mol. Ecol. 29, 2150–2159 (2020).
Google Scholar 
Lomolino, M. V. & Channell, R. Splendid isolation: Patterns of geographic range collapse in endangered mammals. J. Mammal. 76(2), 335–347 (1995).
Google Scholar 
Lomolino, M. V. & Channell, R. Range collapse, re-introductions, and biogeographic guidelines for conservation. Conserv. Biol. 12, 481–484 (1998).
Google Scholar 
Channell, R. & Lomolino, M. V. Dynamic biogeography and conservation of endangered species. Nature 403, 84–86 (2000).ADS 
CAS 

Google Scholar 
Channell, R. & Lomolino, M. V. Trajectories to extinction: Spatial dynamics of the contraction of geographical ranges. J. Biogeogr. 27, 169–179 (2000).
Google Scholar 
Laliberte, A. S. & Ripple, W. J. Range contractions of North American carnivores and ungulates. Bioscience 54, 123–138 (2004).
Google Scholar 
Donald, P. F. & Greenwood, J. J. Spatial patterns of range contraction in British breeding birds. Ibis 143, 593–601 (2001).
Google Scholar 
Boakes, E. H., Isaac, N. J., Fuller, R. A., Mace, G. M. & McGowan, P. J. Examining the relationship between local extinction risk and position in range. Conserv. Biol. 32, 229–239 (2018).
Google Scholar 
Spielman, D., Brook, B. W. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. PNAS 101(42), 15261–15264 (2004).ADS 
CAS 

Google Scholar 
Hoelzel, A. R. et al. Elephant seal genetic variation and the use of simulation models to investigate historical population bottlenecks. J. Hered. 84, 443–449 (1993).CAS 

Google Scholar 
Amos, W. & Balmford, A. When does conservation genetics matter?. Heredity 87, 257–265 (2001).CAS 

Google Scholar 
Reed, D. H. & Frankham, R. Correlation between fitness and genetic diversity. Conserv. Biol. 17, 230–237 (2003).
Google Scholar 
Carvalho, C. D. S. et al. Habitat loss does not always entail negative genetic consequences. Front. Genet. 10, 1101 (2019).CAS 

Google Scholar 
Wheeler, B. A., Prosen, E., Mathis, A. & Wilkinson, R. F. Population declines of a long-lived salamander: A 20+-year study of hellbenders, Cryptobranchus alleganiensis. Biol. Cons. 109, 151–156 (2003).
Google Scholar 
Walkup, D. K., Leavitt, D. J. & Fitzgerald, L. A. Effects of habitat fragmentation on population structure of dune-dwelling lizards. Ecosphere 8, e01729 (2017).
Google Scholar 
Mikle, N., Graves, T. A., Kovach, R., Kendall, K. C. & Macleod, A. C. Demographic mechanisms underpinning genetic assimilation of remnant groups of a large carnivore. Proc. R. Soc. B Biol. Sci. 283, 20161467 (2016).
Google Scholar 
DeWoody, J. A., Harder, A. M., Mathur, S. & Willoughby, J. R. The long-standing significance of genetic diversity in conservation. Mol. Ecol. 30(17), 4147–4154 (2021).
Google Scholar 
Kardos, M., Armstrong, E. E., Fitzpatrick, S. W. & Funk, W. C. The crucial role of genome-wide genetic variation in conservation. PNAS 118(48), e210462118 (2021).
Google Scholar 
García-Dorado, A. & Caballero, A. Neutral genetic diversity as a useful tool for conservation biology. Conserv. Genet. 22, 541–545 (2021).
Google Scholar 
Charlesworth, B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 10, 195–205 (2009).CAS 

Google Scholar 
Eyre-Walker, A. & Keightley, P. D. The distribution of fitness effects of new mutations. Nat. Rev. Genet. 8, 610–618 (2007).CAS 

Google Scholar 
Haller, B. C., Galloway, J., Kelleher, J., Messer, P. W. & Ralph, P. L. Tree-sequence recording in SLiM opens new horizons forward-time simulation of whole genomes. Mol. Ecol. Resour. 19, 552–566 (2018).
Google Scholar 
Kelleher, J., Thornton, K. R., Ashander, J. & Ralph, P. L. Efficient pedigree recording for fast population genetics simulation. PLoS Comput. Biol. 14, e1006581 (2018).ADS 

Google Scholar 
Haller, B. C. & Messer, P. W. SLiM 3: Forward genetic simulations beyond the Wright–Fisher model. Mol. Biol. Evol. 36, 632–637 (2019).CAS 

Google Scholar 
Rodríguez, J. P. Range contraction in declining North American bird populations. Ecol. Appl. 12, 238–248 (2002).
Google Scholar 
Fisher, D. O. Trajectories from extinction: where are missing mammals rediscovered?. Glob. Ecol. Biogeogr. 20, 415–425 (2011).
Google Scholar 
Lino, A., Fonseca, C., Rojas, D., Fischer, E. & Pereira, M. J. R. A meta-analysis of the effects of habitat loss and fragmentation on genetic diversity in mammals. Mamm. Biol. 94, 69–76 (2019).
Google Scholar 
Vandergast, A. G., Bohonak, A. J., Weissman, D. B. & Fisher, R. N. Understanding the genetic effects of recent habitat fragmentation in the context of evolutionary history: Phylogeography and landscape genetics of a southern California endemic Jerusalem cricket (Orthoptera: Stenopelmatidae: Stenopelmatus). Mol. Ecol. 16, 977–992 (2007).CAS 

Google Scholar 
Young, A., Boyle, T. & Brown, T. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 11, 413–418 (1996).CAS 

Google Scholar 
Wilkins, J. F. & Wakeley, J. The coalescent in a continuous, finite, linear population. Genetics 161, 873–888 (2002).
Google Scholar 
Ringbauer, H., Coop, G. & Barton, N. H. Inferring recent demography from isolation by distance of long shared sequence blocks. Genetics 205, 1335–1351 (2017).
Google Scholar 
Bradburd, G. S. & Ralph, P. L. Spatial population genetics: It’s about time. Annu. Rev. Ecol. Evol. Syst. 50, 427–429 (2019).
Google Scholar 
Barton, N. H., Etheridge, A. M., Kelleher, J. & Véber, A. Inference in two dimensions: Allele frequencies versus lengths of shared sequence blocks. Theor. Popul. Biol. 87, 105–119 (2013).CAS 
MATH 

Google Scholar 
Aguillon, S. M. et al. Deconstructing isolation-by-distance: The genomic consequences of limited dispersal. PLoS Genet. 13, e1006911 (2017).
Google Scholar 
Blanco-Pastor, J. L., Fernández-Mazuecos, M. & Vargas, P. Past and future demographic dynamics of alpine species: Limited genetic consequences despite dramatic range contraction in a plant from the Spanish Sierra Nevada. Mol. Ecol. 22, 4177–4195 (2013).CAS 

Google Scholar 
Chen, N. et al. Allele frequency dynamics in a pedigreed natural population. PNAS 116, 2158–2164 (2019).ADS 
CAS 

Google Scholar 
Exposito-Alonso, M., Booker, T. A., Czech, L., Fukami, T., Gillespie, L., Hateley, S. et al. Quantifying the scale of genetic diversity extinction in the Anthropocene. bioRxiv (2021).Keller, I. & Largiadèr, C. R. Recent habitat fragmentation caused by major roads leads to reduction of gene flow and loss of genetic variability in ground beetles. Proc. R. Soc. B Biol. Sci. 270, 417–423 (2003).CAS 

Google Scholar 
Chan, L. M. et al. Phylogeographic structure of the dunes sagebrush lizard, an endemic habitat specialist. PLoS ONE 15, 0238194 (2020).
Google Scholar 
Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).
Google Scholar 
Cayuela, H. et al. Demographic and genetic approaches to study dispersal in wild animal populations: A methodological review. Mol. Ecol. 27, 3976–4010 (2018).
Google Scholar 
Battey, C. J., Ralph, P. L. & Kern, A. D. Space is the place: Effects of continuous spatial structure on analysis of population genetic data. Genetics 215, 193–214 (2020).CAS 

Google Scholar 
Stubbs, D. & Swingland, I. R. The ecology of a Mediterranean tortoise (Testudo hermanni): A declining population. Can. J. Zool. 63, 169–180 (1985).
Google Scholar 
Channell, R. The conservation value of peripheral populations: The supporting science. in Proceedings of the Species at Risk 2004 Pathways to Recovery Conference. 1–17. (Species at Risk 2004 Pathways to Recovery Conference Organizing Committee, 2004).Brown, J. H. On the relationship between abundance and distribution of species. Am. Nat. 124(2), 255–279 (1984).
Google Scholar 
Brown, J. H. Macroecology (University of Chicago Press, 1995).
Google Scholar 
Brown, J. H., Stevens, G. C. & Kaufman, D. M. The geographic range: Size, shape, boundaries, and internal structure. Annu. Rev. Ecol. Syst. 27(1), 597–623 (1996).
Google Scholar 
Sagarin, R. D. & Gaines, S. D. The ‘abundant centre’distribution: To what extent is it a biogeographical rule?. Ecol. Lett. 5, 137–147 (2002).
Google Scholar 
Eckert, C. G., Samis, K. E. & Lougheed, S. C. Genetic variation across species’ geographical ranges: The central-marginal hypothesis and beyond. Mol. Ecol. 17, 1170–1188 (2008).CAS 

Google Scholar 
Yackulic, C. B., Sanderson, E. W. & Uriarte, M. Anthropogenic and environmental drivers of modern range loss in large mammals. PNAS 108, 4024–4029 (2011).ADS 
CAS 

Google Scholar 
Fitzgerald L.A., Walkup, D. Chyn, K. Buchholtz, E. Angeli, N. & Parker M. The future for reptiles: Advances and challenges in the Anthropocene. in Encyclopedia of the Anthropocene. (eds. Dellasala, D.A., & Goldstein, M.I.). 163–174 (Elsevier, 2018).Segelbacher, G., Höglund, J. & Storch, I. From connectivity to isolation: Genetic consequences of population fragmentation in capercaillie across Europe. Mol. Ecol. 12, 1773–1780 (2003).CAS 

Google Scholar 
Cegelski, C. C., Waits, L. P. & Anderson, N. J. Assessing population structure and gene flow in Montana wolverines (Gulo gulo) using assignment-based approaches. Mol. Ecol. 12, 2907–2918 (2003).CAS 

Google Scholar 
Proctor, M. F., McLellan, B. N., Strobeck, C. & Barclay, R. M. Genetic analysis reveals demographic fragmentation of grizzly bears yielding vulnerably small populations. Proc. R. Soc. B Biol. Sci. 272, 2409–2416 (2005).
Google Scholar 
Leavitt, D. J. & Fitzgerald, L. A. Disassembly of a dune–dwelling lizard community due to landscape fragmentation. Ecosphere 4, 97 (2013).
Google Scholar 
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
Google Scholar 
Rogan, J.E., & Lacher Jr., T.E. Impacts of habitat loss and fragmentation on terrestrial biodiversity. in Reference Modules in Earth Systems and Environmental Sciences. 1–18 (Elsevier, 2018).Hurtado, L. A., Santamaria, C. A. & Fitzgerald, L. A. Conservation genetics of the critically endangered St. Croix ground lizard (Ameiva polops Cope 1863). Conserv. Genet. 13, 665–679 (2012).
Google Scholar 
Lawton, J. H. Range, population abundance and conservation. Trends Ecol. Evol. 8, 409–413 (1993).CAS 

Google Scholar 
Purvis, A., Gittleman, J. L., Cowlishaw, G. & Mace, G. M. Predicting extinction risk in declining species. Proc. R. Soc. B Biol. Sci. 267, 1947–1952 (2000).CAS 

Google Scholar 
Cardillo, M. et al. The predictability of extinction: Biological and external correlates of decline in mammals. Proc. R. Soc. B Biol. Sci. 275, 1441–1448 (2008).
Google Scholar 
Templeton, A. R. Coadaptation and outbreeding depression. in Conservation Biology: The Science of Scarcity and Diversity. (ed. Soulé, M.E.). 105–116 (Sinauer, 1986). Lomolino, M. V. & Smith, G. A. Dynamic biogeography of prairie dog (Cynomys ludovicianus) towns near the edge of their range. J. Mammal. 82, 937–945 (2001).
Google Scholar 
Wright, S. Isolation by distance. Genetics 28, 114 (1943).CAS 

Google Scholar 
Maruyama, T. Rate of decrease of genetic variability in a two-dimensional continuous population of finite size. Genetics 4(1), 639–651 (1972).
Google Scholar 
Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 645, 330–338 (1922).
Google Scholar 
Kelleher, J. & EtheridgeMcVean, A. M. G. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS Comput. Biol. 12, e1004842 (2016).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2019).Greenstein, B. J. & Pandolfi, J. M. Escaping the heat: Range shifts of reef coral taxa in coastal Western Australia. Glob. Change Biol. 14, 513–528 (2008).ADS 

Google Scholar 
Wilcove, D. S. & Terborgh, J. W. Patterns of population decline in birds. Am. Birds 38, 10–13 (1984).
Google Scholar 
Gabelli, F. M. et al. Range contraction in the Pampas meadowlark Sturnella defilippii in the southern Pampas grasslands of Argentina. Oryx 38, 164–170 (2004).
Google Scholar 
Pomara, L. Y., LeDee, O. E., Martin, K. J. & Zuckerberg, B. Demographic consequences of climate change and land cover help explain a history of extirpations and range contraction in a declining snake species. Glob. Change Biol. 20, 2087–2099 (2014).ADS 

Google Scholar 
Towns, D. R. & Daugherty, C. H. Patterns of range contractions and extinctions in the New Zealand herpetofauna following human colonisation. N. Z. J. Zool. 21, 325–339 (1994).
Google Scholar 
Rudolph, D. C., Burgdorf, S. J., Schaefer, R. R., Conner, R. N. & Maxey, R. W. Status of Pituophis ruthveni (Louisiana pine snake). Southeast. Nat. 5(3), 463–472 (2006).
Google Scholar 
Russell, R. W., Lipps, G. J. Jr., Hecnar, S. J. & Haffner, G. D. Persistent organic pollutants in Blanchard’s cricket frogs (Acris crepitans blanchardi) from Ohio. Ohio J. Sci. 102, 119–122 (2002).CAS 

Google Scholar 
Fellers, G. M. & Drost, C. A. Disappearance of the Cascades frog Rana cascadae at the southern end of its range, California, USA. Biol. Cons. 65, 177–181 (1993).
Google Scholar 
Franco, A. M. et al. Impacts of climate warming and habitat loss on extinctions at species’ low-latitude range boundaries. Glob. Change Biol. 12, 1545–1553 (2006).ADS 

Google Scholar 
Stewart, J. A., Wright, D. H. & Heckman, K. A. Apparent climate-mediated loss and fragmentation of core habitat of the American pika in the Northern Sierra Nevada, California, USA. PLoS ONE 12, e0181834 (2017).
Google Scholar 
Rodríguez, A. & Delibes, M. Internal structure and patterns of contraction in the geographic range of the Iberian lynx. Ecography 25, 314–328 (2002).
Google Scholar 
Kattan, G. et al. Range fragmentation in the spectacled bear Tremarctos ornatus in the northern Andes. Oryx 38(2), 155–163 (2004).
Google Scholar 
Jones, S. J., Lima, F. P. & Wethey, D. S. Rising environmental temperatures and biogeography: poleward range contraction of the blue mussel, Mytilus edulis L., in the western Atlantic. J. Biogeogr. 37, 2243–2259 (2010).
Google Scholar 
Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. P. R. Soc. B Biol. Sci. 280, 20122829 (2013).
Google Scholar  More

Millard, J. et al. Global effects of land-use intensity on local pollinator biodiversity. Nat. Commun. 12, 2902 (2021).Article 
CAS 

Google Scholar 
Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).Article 

Google Scholar 
Valiente-Banuet, A. et al. Beyond species loss: the extinction of ecological interactions in a changing world. Funct. Ecol. 29, 299–307 (2015).Article 

Google Scholar 
Rybicki, J., Abrego, N. & Ovaskainen, O. Habitat fragmentation and species diversity in competitive communities. Ecol. Lett. 23, 506–517 (2020).Article 

Google Scholar 
Chase, J. M., Blowes, S. A., Knight, T. M., Gerstner, K. & May, F. Ecosystem decay exacerbates biodiversity loss with habitat loss. Nature 584, 238–243 (2020).Article 
CAS 

Google Scholar 
Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. 81, 117–142 (2006).Article 

Google Scholar 
Didham, R. K. Ecological consequences of habitat fragmentation. In Encyclopedia of Life Sciences (ed Jansson, R.), 61, 1–39 (Wiley, UK2010).Aizen, M. A., Sabatino, M. & Tylianakis, J. M. Specialization and rarity predict nonrandom loss of interactions from mutualist networks. Science 335, 1486–1489 (2012).Article 
CAS 

Google Scholar 
Spiesman, B. J. & Inouye, B. D. Habitat loss alters the architecture of plant-pollinator interaction networks. Ecology 94, 2688–2696 (2013).Article 

Google Scholar 
Aizen, M. A. et al. The phylogenetic structure of plant-pollinator networks increases with habitat size and isolation. Ecol. Lett. 19, 29–36 (2016).Article 

Google Scholar 
Emer, C. et al. Seed-dispersal interactions in fragmented landscapes-a metanetwork approach. Ecol. Lett. 21, 484–493 (2018).Article 

Google Scholar 
Fortuna, M. A. & Bascompte, J. Habitat loss and the structure of plant-animal mutualistic networks. Ecol. Lett. 9, 278–283 (2006).Article 

Google Scholar 
Grass, I., Jauker, B., Steffan-Dewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant-pollinator and host-parasitoid networks. Nat. Ecol. Evol. 2, 1408–1417 (2018).Article 

Google Scholar 
Glenn R. Matlack & John A. Litvaitis. Forest edges. In Maintaining Biodiversity in Forest Ecosystems (ed Hunter, M.) 6, 210–233 (Cambridge Univ. Press, 1999).Hadley, A. S. & Betts, M. G. The effects of landscape fragmentation on pollination dynamics: absence of evidence not evidence of absence. Biol. Rev. 87, 526–544 (2012).Article 

Google Scholar 
Ibanez, I., Katz, D. S. W., Peltier, D., Wolf, S. M. & Barrie, B. T. C. Assessing the integrated effects of landscape fragmentation on plants and plant communities: the challenge of multiprocess-multiresponse dynamics. J. Ecol. 102, 882–895 (2014).Article 

Google Scholar 
Morreale, L. L., Thompson, J. R., Tang, X., Reinmann, A. B. & Hutyra, L. R. Elevated growth and biomass along temperate forest edges. Nat. Commun. 12, 7181 (2021).Article 
CAS 

Google Scholar 
Martinez-Ramos, M., Alvarez-Buylla, E. & Sarukhan, J. Tree demography and gap dynamics in a tropical rain forest. Ecology 70, 555–558 (1989).Article 

Google Scholar 
Yamamoto, S. I. Forest gap dynamics and tree regeneration. J. For. Res. 5, 223–229 (2000).Article 

Google Scholar 
Schnitzer, S. A. & Carson, W. P. Treefall gaps and the maintenance of species diversity in a tropical forest. Ecology 82, 913–919 (2001).Article 

Google Scholar 
Kricher, J. A Shifting Mosaic: Rain Forest Development and Dynamics. In Tropical Ecology 6, 188–226 (Princeton Univ. Press, 2011).Gayer, C. et al. Flowering fields, organic farming and edge habitats promote diversity of plants and arthropods on arable land. J. Appl. Ecol. 58, 1155–1166 (2021).Article 

Google Scholar 
Bailey, S. et al. Distance from forest edge affects bee pollinators in oilseed rape fields. Ecol. Evol. 4, 370–380 (2014).Article 

Google Scholar 
Thebault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).Article 
CAS 

Google Scholar 
Hagen, M. et al. Biodiversity, species interactions and ecological networks in a fragmented world. Adv. Ecol. Res. 46, 89–210 (2012).Article 

Google Scholar 
Traveset, A., Castro-Urgal, R., Rotllan-Puig, X. & Lazaro, A. Effects of habitat loss on the plant-flower visitor network structure of a dune community. Oikos 127, 45–55 (2018).Article 

Google Scholar 
Rezende, E. L., Lavabre, J. E., Guimaraes, P. R., Jordano, P. & Bascompte, J. Non-random coextinctions in phylogenetically structured mutualistic networks. Nature 448, 925–928 (2007).Article 
CAS 

Google Scholar 
Staddon, P., Lindo, Z., Crittenden, P. D., Gilbert, F. & Gonzalez, A. Connectivity, non-random extinction and ecosystem function in experimental metacommunities. Ecol. Lett. 13, 543–552 (2010).Article 

Google Scholar 
Wardle, D. A., Bardgett, R. D., Callaway, R. M. & Van der Putten, W. H. Terrestrial ecosystem responses to species gains and losses. Science 332, 1273–1277 (2011).Article 
CAS 

Google Scholar 
Sargent, R. D. & Ackerly, D. D. Plant-pollinator interactions and the assembly of plant communities. Trends Ecol. Evol. 23, 123–130 (2008).Article 

Google Scholar 
Bastolla, U. et al. The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458, 1018–1020 (2009).Article 
CAS 

Google Scholar 
Rohr, R. P., Saavedra, S. & Bascompte, J. On the structural stability of mutualistic systems. Science 345, 1253497 (2014).Article 

Google Scholar 
Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

Google Scholar 
Pawar, S. Why are plant-pollinator networks nested? Science 345, 383–383 (2014).Article 
CAS 

Google Scholar 
Kaiser-Bunbury, C. N., Muff, S., Memmott, J., Muller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).Article 

Google Scholar 
Evans, D. M., Pocock, M. J. O. & Memmott, J. The robustness of a network of ecological networks to habitat loss. Ecol. Lett. 16, 844–852 (2013).Article 

Google Scholar 
Ponisio, L. C., Gaiarsa, M. P. & Kremen, C. Opportunistic attachment assembles plant-pollinator networks. Ecol. Lett. 20, 1261–1272 (2017).Article 

Google Scholar 
Wilson, M. C. et al. Habitat fragmentation and biodiversity conservation: key findings and future challenges. Landsc. Ecol. 31, 219–227 (2016).Article 

Google Scholar 
Zhong, L., Didham, R. K., Liu, J., Jin, Y. & Yu, M. Community re-assembly and divergence of woody plant traits in an island-mainland system after more than 50 years of regeneration. Divers. Distrib. 27, 1435–1448 (2021).Article 

Google Scholar 
Liu, J. et al. The asymmetric relationships of the distribution of conspecific saplings and adults in forest fragments. J. Plant Ecol. 13, 398–404 (2020).Article 
CAS 

Google Scholar 
Ewers, R. M., Bartlam, S. & Didham, R. K. Altered species interactions at forest edges: contrasting edge effects on bumble bees and their phoretic mite loads in temperate forest remnants. Insect Conserv. Divers. 6, 598–606 (2013).Article 

Google Scholar 
Wardhaugh, C. W. The spatial and temporal distributions of arthropods in forest canopies: uniting disparate patterns with hypotheses for specialisation. Biol. Rev. Camb. Philos. Soc. 89, 1021–1041 (2015).Article 

Google Scholar 
Lowman, M. Life in the treetops – an overview of forest canopy science and its future directions. Plants People Planet 3, 16–21 (2021).Article 

Google Scholar 
Nakamura, A. et al. Forests and their canopies: achievements and horizons in canopy science. Trends Ecol. Evol. 32, 438–451 (2017).Article 

Google Scholar 
Lennartsson, T. Extinction thresholds and disrupted plant-pollinator interactions in fragmented plant populations. Ecology 83, 3060–3072 (2002).
Google Scholar 
Aguilar, R., Ashworth, L., Galetto, L. & Aizen, M. A. Plant reproductive susceptibility to habitat fragmentation: review and synthesis through a meta-analysis. Ecol. Lett. 9, 968–980 (2006).Article 

Google Scholar 
Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: a conceptual framework for the effects of land-use change. Ecol. Lett. 10, 299–314 (2007).Article 

Google Scholar 
Goulson, D., Nicholls, E., Botias, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).Article 

Google Scholar 
Gathmann, A. & Tscharntke, T. Foraging ranges of solitary bees. J. Anim. Ecol. 71, 757–764 (2002).Article 

Google Scholar 
Winfree, R., Bartomeus, I. & Cariveau, D. P. Native pollinators in anthropogenic habitats. Annu. Rev. Entomol. 42, 1–22 (2011).
Google Scholar 
Torné-Noguera, A. et al. Determinants of spatial distribution in a bee community: nesting resources, flower resources, and body size. PLoS ONE 9, e97255 (2014).Article 

Google Scholar 
Roswell, M., Dushoff, J. & Winfree, R. A conceptual guide to measuring species diversity. Oikos 130, 321–338 (2021).Article 

Google Scholar 
Schoereder, J. H. et al. Should we use proportional sampling for species-area studies? J. Biogeogr. 31, 1219–1226 (2004).Article 

Google Scholar 
Jordano, P. Patterns of mutualistic interactions in pollination and seed dispersal: connectance, dependence asymmetries, and coevolution. Am. Nat. 129, 657–677 (1987).Article 

Google Scholar 
Devoto, M., Medan, D. & Montaldo, N. H. Patterns of interaction between plants and pollinators along an environmental gradient. Oikos 109, 461–472 (2005).Article 

Google Scholar 
Petanidou, T., Kallimanis, A. S., Tzanopoulos, J., Sgardelis, S. P. & Pantis, J. D. Long-term observation of a pollination network: fluctuation in species and interactions, relative invariance of network structure and implications for estimates of specialization. Ecol. Lett. 11, 564–575 (2008).Article 

Google Scholar 
Brodie, J. F. et al. Secondary extinctions of biodiversity. Trends Ecol. Evol. 29, 664–672 (2014).Article 

Google Scholar 
Vazquez, D. P. & Aizen, M. A. Asymmetric specialization: a pervasive feature of plant-pollinator interactions. Ecology 85, 1251–1257 (2004).Article 

Google Scholar 
Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. Lond. B 271, 2605–2611 (2004).
Google Scholar 
Malhi, Y., Gardner, T. A., Goldsmith, G. R., Silman, M. R. & Zelazowski, P. Tropical forests in the Anthropocene. Annu. Rev. Environ. Resour. 39, 125–159 (2014).Article 

Google Scholar 
Lewis, S. L., Edwards, D. P. & Galbraith, D. Increasing human dominance of tropical forests. Science 349, 827–832 (2015).Article 
CAS 

Google Scholar 
Fletcher, R. J. Jr et al. Is habitat fragmentation good for biodiversity? Biol. Conserv. 226, 9–15 (2018).Article 

Google Scholar 
Ren, P., Si, X. & Ding, P. Stable species and interactions in plant-pollinator networks deviate from core position in fragmented habitats. Ecography 2022, e06102 (2022).Article 

Google Scholar 
Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Anim. Ecol. 79, 811–817 (2010).
Google Scholar 
Bascompte, J., Jordano, P., Melian, C. J. & Olesen, J. M. The nested assembly of plant-animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).Article 
CAS 

Google Scholar 
Almeida-Neto, M., Guimaraes, P., Guimaraes, P. R. Jr, Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).Article 

Google Scholar 
Ulrich, W., Almeida-Neto, M. & Gotelli, N. J. A consumer’s guide to nestedness analysis. Oikos 118, 3–17 (2009).Article 

Google Scholar 
Dicks, L. V., Corbet, S. A. & Pywell, R. F. Compartmentalization in plant-insect flower visitor webs. J. Anim. Ecol. 71, 32–43 (2002).Article 

Google Scholar 
Beckett, S. J. Improved community detection in weighted bipartite networks. R. Soc. Open Sci. 3, 140536 (2016).Article 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020). https://CRAN.R-project.org/package=veganDormann, C. F. et al. bipartite: Visualising Bipartite Networks and Calculating Some (Ecological) Indices. R package version 2.16 (2021). https://CRAN.R-project.org/package=bipartitePocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977 (2012).Article 
CAS 

Google Scholar 
Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).Article 
CAS 

Google Scholar 
Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).Article 
CAS 

Google Scholar 
Shipley, B. Confirmatory path analysis in a generalized multilevel context. Ecology 90, 363–368 (2009).Article 

Google Scholar 
Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Grace, J. B., Scheiner, S. M. & Schoolmaster, D. R. Jr. Structural equation modeling: building and evaluating causal models. In Ecological Statistics: From Principles to Applications (eds Fox, G. A. et al.), 8, 168–199 (Oxford Univ. Press, 2015).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2014).
Google Scholar 
Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).Article 

Google Scholar 
Murphy, M. semEff: Automatic Calculation of Effects for Piecewise Structural Equation Models. R package version 0.6.0 (2021). https://CRAN.R-project.org/package=semEffDudgeon, P. A comparative investigation of confidence intervals for independent variables in linear regression. Multivar. Behav. Res. 51, 139–153 (2016).Article 

Google Scholar 
Gotelli, N. J. & Graves, G. R. Null Models in Ecology (Smithsonian Inst. Press, 1996).Jung, V., Violle, C., Mondy, C., Hoffmann, L. & Muller, S. Intraspecific variability and trait-based community assembly. J. Ecol. 98, 1134–1140 (2010).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

Hoffmann, A. A. & Sgrò, C. M. Nature 470, 479–485 (2011).Article 
CAS 

Google Scholar 
Taylor, S. A. & Larson, E. L. Nat. Ecol. Evol. 3, 170–177 (2019).Article 

Google Scholar 
Brauer, C. J. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01585-1 (2023).Article 

Google Scholar 
Grinnell, J. Auk 34, 427–433 (1917).Article 

Google Scholar 
Peterson, A. T. et al. Ecological Niches and Geographic Distributions (Princeton Univ. Press, 2011).Wiens, J. A., Stralberg, D., Jongsomjit, D., Howell, C. A. & Snyder, M. A. Proc. Natl Acad. Sci. USA 106, 19729–19736 (2009).Article 
CAS 

Google Scholar 
Aguirre-Liguori, J. A., Ramírez-Barahona, S. & Gaut, B. S. Nat. Ecol. Evol. 5, 1350–1360 (2021).Article 

Google Scholar 
Fitzpatrick, M. C. & Keller, S. R. Ecol. Lett. 18, 1–16 (2015).Article 

Google Scholar 
Bay, R. A. et al. Science 359, 83–86 (2018).Article 
CAS 

Google Scholar 
Capblancq, T., Fitzpatrick, M. C., Bay, R. A., Exposito-Alonso, M. & Keller, S. R. Annu. Rev. Ecol. Evol. Syst. 51, 245–269 (2020).Article 

Google Scholar 
Rellstab, C., Dauphin, B. & Exposito‐Alonso, M. Evol. Appl. 14, 1202–1212 (2021).Article 

Google Scholar 
Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. Trends Ecol. Evol. 16, 613–622 (2001).Article 

Google Scholar 
Rhymer, J. M. & Simberloff, D. Annu. Rev. Ecol. Syst. 27, 83–109 (1996).Article 

Google Scholar 
Todesco, M. et al. Evol. Appl. 9, 892–908 (2016).Article 
CAS 

Google Scholar  More

Vegetation and land-cover dataTo monitor vegetation at the global scale, we use three datasets: (1) vegetation optical depth (VOD, 0.25°, Ku-Band, daily 1987–201723) (Fig. 1A), (2) AVHRR GIMMSv3g normalized difference vegetation index (NDVI, 1/12°, bi-weekly 1981–201524) (Fig. 1B), and (3) MODIS MOD13 NDVI at 0.05° (16-day, 2000–202125). We correct for spurious values in the NDVI data (e.g., cloud contamination) using the method of Chen et al.43. We resample the VOD data using bi-weekly medians to agree with the NDVI data time sampling.For all three vegetation datasets, we remove seasonality and long-term trends using seasonal trend decomposition by Loess4,44 based on the proposed optimal parameters listed in Cleveland et al.44 (code available on Zenodo45). That is, we use a period of 24 (bi-monthly, 1 year), 47 for the trend smoother (just under 2 years) and 25 for low-pass (just over 1 year). We only use the STL residual—the de-seasoned and de-trended NDVI and VOD time series—in our analysis.To contextualize our understanding of vegetation resilience, we use MODIS MCD12Q1 land cover46 (Fig. 1C) as well as a global average aridity index based on WorldCLIM data31 (Fig. 1D). We exclude from our analysis anthropogenic and non-vegetated landscapes (e.g., permanent snow and ice, desert, urban), as well as any land covers which have changed (e.g., forest to grassland) during the period 2001–2020.Precipitation data and variability metricsTo measure precipitation at the global scale, we rely upon ERA5 data (~30 km, monthly, 1981–2021)33. We process global-scale precipitation metrics using the Google Earth Engine47 platform. We further use the sum of soil moisture from the surface down to 28 cm of depth (first two layers of the ECMWF Integrated Forecasting System soil moisture estimates) to quantify soil moisture means and inter-annual variability33.It is well-documented that vegetation resilience is responsive to the MAP of certain regions1. However, the role of precipitation variability in controlling vegetation resilience has not been well-studied. Here we examine precipitation variability in terms of both intra- and inter-annual patterns. Intra-annual precipitation variability is determined in terms of the Walsh-Lawler Seasonality index32 (Fig. 1D), calculated using monthly data from ERA533.Partly due to the fact that precipitation is non-negative, simple inter-annual variability metrics such as the standard deviation of annual precipitation sums are biased by the absolute precipitation sums; higher precipitation regions have a higher possible range of variability. To limit the influence of MAP, we hence investigate the standard deviation of annual precipitation sums normalized by the MAP, over the period 1981–2021, based on ERA5 data33 (Fig. 1F). We motivate our normalization by MAP with the strong linear relationship between MAP and MAP standard deviation (Supplementary Fig. S2). We further confirm our discovered relationships (Fig. 5) using only those regions where MAP was between the 40 and 60th percentile of MAP for a given land cover (Supplementary Figs. S11,S12). This serves as an additional check that our normalization of MAP standard deviation by MAP does not bias the inferred relationship between vegetation resilience and precipitation variability. Similarly, we generate a normalized inter-annual soil moisture variability by normalizing year-on-year soil moisture standard deviation (Supplementary Fig. S8) by long-term mean soil moisture (Supplementary Fig. S5).Empirical resilience estimationResilience is defined as the ability of a system to recover from perturbations, and can be quantified empirically by the speed of recovery to the previous state16,17. To measure resilience on the global scale, we employ a recently introduced methodology4 which we will briefly summarize in the following.We first identify sharp transitions in the vegetation time series using an 18-point (9 month) moving window to define local slopes throughout the time series48. We then identify slopes above the 99th percentile, and define connected regions as individual perturbations. The highest peak (largest instantaneous slope) within each connected region is then labeled as an individual disturbance.The employed approach does not delineate every rapid transition in a time series due to our reliance on percentiles; our dataset will be inherently biased towards the largest transitions. Furthermore, the same transitions are not guaranteed to be captured for both NDVI and VOD data in each location, as the percentiles will naturally vary between the datasets. Finally, our method will in some cases produce false positives, especially in cases where a given time series does not have any significant rapid transitions. To limit the influence of false positives on our results, we discard any perturbations where the time series does not drop significantly, and where the period before and after a given transition does not pass a two-sample Kolmogorov–Smirnov test4.Finally, using our global set of time-series transitions, we can identify each local vegetation (NDVI or VOD) minima, and use the five following years of data to fit an exponential function to the residual time series, assuming that the recovery after a perturbation to a vegetation state x0 follows approximately the equation$$x(t),approx ,{x}_{0}{e}^{rt}$$
(1)
where x(t) denotes the vegetation state at time t after the perturbation. Negative r indicates that the vegetation system will return to the original stable state at rate ∣r∣. For positive r, the initial perturbation would be amplified, suggesting a non-resilient vegetation state. Our empirical recovery rates are defined as the fitted exponent r, obtained for each detected transition in the NDVI and VOD residual time series. We finally use the coefficient of determination R2 to remove instances where the fitted exponential poorly matches the underlying data4.For the empirical estimate of the restoring rate obtained from fitting an exponential to the recovery after an abrupt negative deviation of VOD or NDVI, abrupt changes in the mean state induced by changing sensors rather than an actual vegetation shift may impact the results. However, all datasets used here are tightly cross-calibrated to eliminate mean-shifts when new instruments are introduced23,24. It is therefore unlikely that changes in the instrumentation of the various datasets unduly influence our empirical estimates of λ.Dynamical system metrics of resilienceThe lag-one autocorrelation (AC1) has previously been proposed to measure the stability of real-world dynamical systems in general, and the resilience of vegetation systems in particular1,19,20,21,49. Based on the concept of critical slowing down, the AC1 has, together with the variance, also been suggested as an early-warning indicator for forthcoming critical transitions50,51. Mathematically, the suitability of the variance and AC1 as resilience measures and early-warning indicators can be motivated as follows4,52,53. First, linearize the system around a given stable state x*:$$dbar{x}=lambda bar{x}dt+sigma dW$$
(2)
for (bar{x}: !!=x-{x}^{*}), assuming a Wiener Process W with standard deviation σ. The dynamics are stable for λ  More

Habitat loss is one of main threats to biodiversity worldwide and in general is perceived as something to be avoided. However, the prevalence of negative effects of forest fragmentation is less clear. Fragmentation creates edges between once-pristine forest and the adjacent non-forest system or systems (for example, agricultural lands, cities or water reservoirs), but the effects of these edges on biodiversity are not always clear. By performing a robust study of the interaction between insect pollinators and flowering plants at forest edges and within the forest, Ren et al.1 add a new piece to this puzzle by showing that forest edges can have a positive buffering effect on interaction networks. More

Chala, B. & Hamde, F. Emerging and re-emerging vector-borne infectious diseases and the challenges for control: A review. Front. Public Health https://doi.org/10.3389/fpubh.2021.715759 (2021).Article 

Google Scholar 
Jongejan, F. & Uilenberg, G. The global importance of ticks. Parasitology 129, S3–S14 (2004).
Google Scholar 
Hornok, S., Kováts, D., Horváth, G., Kontschán, J. & Farkas, R. Checklist of the hard tick (Acari: Ixodidae) fauna of Hungary with emphasis on host-associations and the emergence of Rhipicephalus sanguineus. Exp. Appl. Acarol. 80, 311–328 (2020).
Google Scholar 
ECDC. Surveillance and disease data—Tick maps. https://www.ecdc.europa.eu/en/diseasevectors/surveillance-and-disease-data/tick-maps (2022). Accessed: 2022–09–02.Brites-Neto, J., Duarte, K. M. R. & Martins, T. F. Tick-borne infections in human and animal population worldwide. Vet. World 8, 301 (2015).
Google Scholar 
Hubálek, Z. Epidemiology of Lyme borreliosis. Lyme Borreliosis 37, 31–50 (2009).
Google Scholar 
Rizzoli, A. et al. Lyme borreliosis in Europe. Eurosurveillance 16, 19906 (2011).
Google Scholar 
Marques, A. R., Strle, F. & Wormser, G. P. Comparison of Lyme disease in the United States and Europe. Emerg. Infect. Dis. 27, 2017 (2021).
Google Scholar 
Jaenson, T. G., Jaenson, D. G., Eisen, L., Petersson, E. & Lindgren, E. Changes in the geographical distribution and abundance of the tick Ixodes ricinus during the past 30 years in Sweden. Parasit. Vectors 5, 1–15 (2012).
Google Scholar 
Medlock, J. M. et al. Driving forces for changes in geographical distribution of Ixodes ricinus ticks in Europe. Parasit. Vectors 6, 1–11 (2013).
Google Scholar 
Semenza, J. C. & Suk, J. E. Vector-borne diseases and climate change: a European perspective. FEMS Microbiol. Lett. https://doi.org/10.1093/femsle/fnx244 (2018).Article 

Google Scholar 
Sutherst, R. W. Global change and human vulnerability to vector-borne diseases. Clin. Microbiol. Rev. 17, 136–173 (2004).
Google Scholar 
Tabachnick, W. Challenges in predicting climate and environmental effects on vector-borne disease episystems in a changing world. J. Exp. Biol. 213, 946–954 (2010).CAS 

Google Scholar 
Sonenshine, D. E., Kocan, K. M. & de la Fuente, J. Tick control: Further thoughts on a research agenda. Trends Parasitol. 22, 550–551 (2006).
Google Scholar 
Willadsen, P. Tick control: Thoughts on a research agenda. Vet. Parasitol. 138, 161–168 (2006).
Google Scholar 
Goolsby, J. A. et al. Rationale for classical biological control of cattle fever ticks and proposed methods for field collection of natural enemies. Subtrop. Agric. Environ. 66, 7–15 (2016).
Google Scholar 
Singh, N. et al. Effect of immersion time on efficacy of entomopathogenic nematodes against engorged females of cattle fever tick, Rhipicephalus (= Boophilus) microplus. Southwest. Entomol. 43, 19–28 (2018).
Google Scholar 
Černý, J. et al. Management options for Ixodes ricinus-associated pathogens: A review of prevention strategies. Int. J. Environ. Res. Public Health 17, 1830 (2020).
Google Scholar 
Kapranas, A. et al. Encyrtid parasitoids of soft scale insects: Biology, behavior, and their use in biological control. Annu. Rev. Entomol. 60, 195–211 (2015).CAS 

Google Scholar 
Chirinos, D. T. & Kondo, T. Description and biological studies of a new species of Metaphycus Mercet, 1917 (Hymenoptera: Encyrtidae), a parasitoid of Capulinia linarosae Kondo & Gullan. Int. J. Insect Sci. 11, 1179543319857962 (2019).
Google Scholar 
Polaszek, A., Noyes, J. S., Russell, S. & Ramadan, M. M. Metaphycus macadamiae (Hymenoptera: Encyrtidae)–a biological control agent of macadamia felted coccid Acanthococcus ironsidei (Hemiptera: Eriococcidae) in Hawaii. PLoS ONE 15, e0230944 (2020).CAS 

Google Scholar 
Howard, L. Another chalcidoid parasite of a tick. Can. Entomol. 40, 239–241 (1908).
Google Scholar 
Hu, R., Hyland, K. & Oliver, J. A review on the use of Ixodiphagus wasps (Hymenoptera: Encyrtidae) as natural enemies for the control of ticks (Acari: Ixodidae). Syst. Appl. Acarol. 3, 19–28 (1998).
Google Scholar 
Collatz, J. et al. A hidden beneficial: Biology of the tick-wasp Ixodiphagus hookeri in Germany. J. Appl. Entomol. 135, 351–358 (2011).
Google Scholar 
Takasu, K. & Nakamura, S. Life history of the tick parasitoid Ixodiphagus hookeri (Hymenoptera: Encyrtidae) in Kenya. Biol. Control. 46, 114–121 (2008).
Google Scholar 
Collatz, J. et al. Being a parasitoid of parasites: host finding in the tick wasp Ixodiphagus hookeri by odours from mammals. Entomol. Experimentalis et Applicata 134, 131–137 (2010).
Google Scholar 
Krawczyk, A. I. et al. Tripartite interactions among Ixodiphagus hookeri, Ixodes ricinus and deer: Differential interference with transmission cycles of tick-borne pathogens. Pathogens 9, 339 (2020).
Google Scholar 
Plaire, D., Puaud, S., Marsolier-Kergoat, M.-C. & Elalouf, J.-M. Comparative analysis of the sensitivity of metagenomic sequencing and PCR to detect a biowarfare simulant (Bacillus atrophaeus) in soil samples. PLoS ONE 12, e0177112 (2017).
Google Scholar 
Wang, C.-X. et al. Comparison of broad-range polymerase chain reaction and metagenomic next-generation sequencing for the diagnosis of prosthetic joint infection. Int. J. Infect. Dis. 95, 8–12 (2020).CAS 

Google Scholar 
Tóth, A. G. et al. Ixodes ricinus tick bacteriome alterations based on a climatically representative survey in Hungary. bioRxiv (2022).Estrada-Peña, A., Mihalca, A. D. & Petney, T. N. Ticks of Europe and North Africa: A Guide to Species Identification (Springer, 2018).
Google Scholar 
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).CAS 

Google Scholar 
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 1–13 (2019).
Google Scholar 
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI Reference Sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 33, D501–D504 (2005).CAS 

Google Scholar 
NCBI Resource Coordinators. Database resources of the national center for biotechnology information. Nucleic Acids Res. 44, D7 (2016).
Google Scholar 
Katoh, K. & Standley, D. M. Mafft multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 

Google Scholar 
Tennekes, M. tmap: Thematic maps in R. J. Stat. Softw. 84, 1–39 (2018).
Google Scholar 
Bodenhofer, U., Bonatesta, E., Horejš-Kainrath, C. & Hochreiter, S. msa: An R package for multiple sequence alignment. Bioinformatics 31, 3997–3999 (2015).CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2022).Alfeev, N. & Klimas, Y. On the possibility of developing ichneumon flies, Hunterellus hookeri in climatic conditions of the USSR. Sovet. Vet. 15, 55 (1938).
Google Scholar 
Buczek, A., Buczek, W., Bartosik, K., Kulisz, J. & Stanko, M. Ixodiphagus hookeri wasps (Hymenoptera: Encyrtidae) in two sympatric tick species Ixodes ricinus and Haemaphysalis concinna (Ixodida: Ixodidae) in the Slovak Karst (Slovakia): Ecological and biological considerations. Sci. Rep. 11, 1–10 (2021).
Google Scholar 
Slovák, M. Finding of the endoparasitoid Ixodiphagus hookeri (Hymenoptera, Encyrtidae) in Haemaphysalis concinna ticks in Slovakia. Biol. Bratislava 58, 890–894 (2003).
Google Scholar 
Rehacek, J. & Kocianova, E. Attempt to infect Hunterellus hookeri Howard (Hymenoptera, Encyrtidae), an endoparasite of ticks, with Coxiella burnetti. Acta Virol. 36, 492–492 (1992).CAS 

Google Scholar 
Bohacsova, M., Mediannikov, O., Kazimirova, M., Raoult, D. & Sekeyova, Z. Arsenophonus nasoniae and Rickettsiae infection of Ixodes ricinus due to parasitic wasp Ixodiphagus hookeri. PLoS ONE 11, e0149950 (2016).
Google Scholar 
Boucek, Z. & Verny, V. A parasite of ticks, the chalcid Hunterellus hookeri in Czechoslovakia. Zool. Listy 3, 109–111 (1954).
Google Scholar 
Sormunen, J. J., Sippola, E., Kaunisto, K. M., Vesterinen, E. J. & Sääksjärvi, I. E. First evidence of Ixodiphagus hookeri (Hymenoptera: Encyrtidae) parasitization in Finnish castor bean ticks (Ixodes ricinus). Exp. Appl. Acarol. 79, 395–404 (2019).CAS 

Google Scholar 
Doby, J. & van Laere, G. Hunterellus hookeri howard, 1907, Hymenoptère Chalcididae parasite de la tique Ixodes ricinus dans l’ouest et le centre de la France. Bull. de la Société française de parasitologie 11, 265–270 (1993).
Google Scholar 
Plantard, O. et al. Detection of Wolbachia in the tick Ixodes ricinus is due to the presence of the hymenoptera endoparasitoid Ixodiphagus hookeri. PLoS ONE 7, e30692 (2012).ADS 
CAS 

Google Scholar 
Japoshvili, G. New records of Encyrtids (Hymenoptera: Chalcidoidea: Encyrtidae) from Georgia, with description of seven new species. J. Asia-Pacific Entomol. 20, 866–877 (2017).
Google Scholar 
Walter, G. Beitrag zur Biologie der Schlupfwespe Hunterellus hookeri Howard (Hymenoptera: Encyrtidae) in Norddeutschland. Beitr. Naturkunde Niedersachsens 33, 129–133 (1980).
Google Scholar 
Ramos, R. A. N. et al. Occurrence of Ixodiphagus hookeri (Hymenoptera: Encyrtidae) in Ixodes ricinus (Acari: Ixodidae) in Southern Italy. Ticks Tick-borne Dis. 6, 234–236 (2015).
Google Scholar 
Tijsse-Klasen, E., Braks, M., Scholte, E.-J. & Sprong, H. Parasites of vectors—Ixodiphagus hookeri and its Wolbachia symbionts in ticks in the Netherlands. Parasit. Vectors 4, 1–7 (2011).
Google Scholar 
Luu, L. et al. Bacterial pathogens and symbionts harboured by Ixodes ricinus ticks parasitising red squirrels in the United Kingdom. Pathogens 10, 458 (2021).CAS 

Google Scholar 
Pervomaisky, G. S. On the infestation of Ixodes persulcatus by Hunterellus hookeri How. (Hymenoptera). Zool. Zhurnal 22, 211–213 (1943).
Google Scholar 
Klyushkina, E. A parasite of the ixodid ticks, Hunterellus hookeri. How in the Crimea. Zool. Zh. 37, 1561–1563 (1958).
Google Scholar 
Gorman, M., Xu, R., Prakoso, D., Salvador, L. C. & Rajeev, S. Leptospira enrichment culture followed by ONT metagenomic sequencing allows better detection of Leptospira presence and diversity in water and soil samples. PLOS Neglected Trop. Dis. 16, e0010589 (2022).CAS 

Google Scholar 
Ranjan, R., Rani, A., Metwally, A., McGee, H. S. & Perkins, D. L. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem. Biophys. Res. Commun. 469, 967–977 (2016).CAS 

Google Scholar 
Laudadio, I. et al. Quantitative assessment of shotgun metagenomics and 16S rDNA amplicon sequencing in the study of human gut microbiome. OMICS 22, 248–254 (2018).CAS 

Google Scholar 
Munaf, H. et al. The first record of Hunterellus hookeri parasitizing Rhipicephalus sanguineus in Indonesia. Southeast Asian J. Trop. Medicine Public Heal. 7, 492 (1976).CAS 

Google Scholar 
Stafford, K. C. III., Denicola, A. J. & Kilpatrick, H. J. Reduced abundance of Ixodes scapularis (Acari: Ixodidae) and the tick parasitoid Ixodiphagus hookeri (Hymenoptera: Encyrtidae) with reduction of white-tailed deer. J. Med. Entomol. 40, 642–652 (2003).
Google Scholar 
Stafford, K. C. Jr., Denicola, A. J. & Magnarelli, L. A. Presence of Ixodiphagus hookeri (Hymenoptera: Encyrtidae) in two Connecticut populations of Ixodes scapularis (Acari: Ixodidae). J. Med. Entomol. 33, 183–188 (1996).
Google Scholar 
Gillespie, J., Johnston, J., Cannone, J. & Gutell, R. Characteristics of the nuclear (18S, 5.8 S, 28S and 5S) and mitochondrial (12S and 16S) rRNA genes of Apis mellifera (Insecta: Hymenoptera): Structure, organization, and retrotransposable elements. Insect Mol. Biol. 15, 657–686 (2006).CAS 

Google Scholar 
Zhao, Y., Zhang, W.-Y., Wang, R.-L. & Niu, D.-L. Divergent domains of 28S ribosomal RNA gene: DNA barcodes for molecular classification and identification of mites. Parasit. Vectors 13, 1–12 (2020).
Google Scholar 
Larrousse, F., King, A. G. & Wolbach, S. The overwintering in Massachusetts of Ixodiphagus caucurtei. Science 67, 351–353 (1928).ADS 
CAS 

Google Scholar 
Smith, C. N. et al. Studies of parasites of the American dog tick. J. Econ. Entomol. https://doi.org/10.1093/jee/36.4.569 (1943).Article 

Google Scholar 
Hu, R., Hyland, K. E. & Mather, T. N. Occurrence and distribution in Rhode Island of Hunterellus hookeri (Hymenoptera: Encyrtidae), a wasp parasitoid of Ixodes dammini. J. Med. Entomol. 30, 277–280 (1993).CAS 

Google Scholar 
Scatolini, D. & Penteado-Dias, A. A fauna de Braconidae (hymenoptera) como bioindicadora do grau de preservação de duas localidades do Estado do Paraná. Revista Brasileira de Ecol. 1, 84–87 (1997).
Google Scholar 
Anderson, A. et al. The potential of parasitoid Hymenoptera as bioindicators of arthropod diversity in agricultural grasslands. J. Appl. Ecol. 48, 382–390 (2011).
Google Scholar  More