Ecology

Thornton, I. Island Colonization: The Origin and Development of Island Communities (Cambridge University Press, 2007).Book 

Google Scholar 
Weigelt, P., Jetz, W. & Kreft, H. Bioclimatic and physical characterization of the world’s islands. Proc. Natl Acad. Sci. 110, 15307–15312 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Vitousek, P., Adsersen, H. & Loope, L. Introduction. In Islands: Biological Diversity and Ecosystem Function (eds Vitousek, P. et al.) 1–6 (Berlin, 1995).Chapter 

Google Scholar 
Whittaker, R. J. & Fernández-Palacios, J. M. Island Biogeography: Ecology, Evolution, and Conservation (Oxford University Press, 2007).
Google Scholar 
Lomolino, M., Brown, J. & Sax, D. Island biogeography theory. In The Theory of Island Biogeography Revisited (eds Losos, J. & Ricklefs, R.) 13–51 (Princeton University Press, 2010).
Google Scholar 
Colom, P., Carreras, D. & Stefanescu, C. Long-term monitoring of Menorcan butterfly populations reveals widespread insular biogeographical patterns and negative trends. Biodivers. Conserv. 28, 1837–1851 (2019).Article 

Google Scholar 
Preston, F. W. The canonical distribution of commonness and rarity, part II. Ecology 43, 410–432 (1962).Article 

Google Scholar 
Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge University Press, 1995).Book 

Google Scholar 
Drakare, S., Lennon, J. J. & Hillebrand, H. The imprint of the geographical, evolutionary and ecological context on species–area relationships. Ecol. Lett. 9(2), 215–227 (2006).Article 
PubMed 

Google Scholar 
Field, R. et al. Spatial species-richness gradients across scales: A meta-analysis. J. Biogeogr. 36, 132–147 (2009).Article 

Google Scholar 
Brehm, G., Süssenbach, D. & Fiedler, K. Unique elevational diversity patterns of geometrid moths in an Andean montane rainforest. Ecography 26, 456–466 (2003).Article 

Google Scholar 
Brehm, G., Colwell, R. K. & Kluge, J. The role of environment and mid-domain effect on moth species richness along a tropical elevational gradient. Glob. Ecol. Biogeogr. 16, 205–219 (2007).Article 

Google Scholar 
Beck, J. & Kitching, I. J. Drivers of moth species richness on tropical altitudinal gradients: A cross-regional comparison. Glob. Ecol. Biogeogr. 18, 361–371 (2009).Article 

Google Scholar 
Ashton, L. A. et al. Altitudinal patterns of moth diversity in tropical and subtropical A ustralian rainforests. Aust. Ecol. 41, 197–208 (2016).Article 

Google Scholar 
Maunsell, S. C. et al. Elevational turnover in the composition of leaf miners and their interactions with host plants in Australian subtropical rainforest. Aust. Ecol. 41, 238–247 (2016).Article 

Google Scholar 
McCain, C. M. Global analysis of reptile elevational diversity. Glob. Ecol. Biogeogr. 19, 541–553 (2010).
Google Scholar 
Yu, X. D., Lü, L., Luo, T. H. & Zhou, H. Z. Elevational gradient in species richness pattern of epigaeic beetles and underlying mechanisms at east slope of Balang Mountain in Southwestern China. PLoS ONE 8, e69177 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Beck, J. et al. Elevational species richness gradients in a hyperdiverse insect taxon: A global meta-study on geometrid moths. Glob. Ecol. Biogeogr. 26, 412–424 (2017).Article 

Google Scholar 
Szewczyk, T. & McCain, C. M. A systematic review of global drivers of ant elevational diversity. PLoS ONE 11, e0155404 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Rahbek, C. The elevational gradient of species richness: A uniform pattern?. Ecography 18, 200–205 (1995).Article 

Google Scholar 
Vitousek, P. M. Oceanic islands as model systems for ecological studies. J. Biogeogr. 29, 573–582 (2002).Article 

Google Scholar 
Kidane, Y. O., Steinbauer, M. J. & Beierkuhnlein, C. Dead end for endemic plant species? A biodiversity hotspot under pressure. Global Ecol. Conserv. 19, e00670 (2019).Article 

Google Scholar 
Meyer, W. M. III. et al. Ground-dwelling arthropod communities of a sky island mountain range in Southeastern Arizona, USA: Obtaining a baseline for assessing the effects of climate change. PLoS ONE 10, e0135210 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Kong, W. S. Biogeography of Korean plants 335 (Geobook, 2007) (in Korean).
Google Scholar 
Kitching, R. L. et al. Moth assemblages as indicators of environmental quality in remnants of upland Australian rain forest. J. Appl. Ecol. 37, 284–297 (2000).Article 

Google Scholar 
Froidevaux, J. S., Broyles, M. & Jones, G. Moth responses to sympathetic hedgerow management in temperate farmland. Agric. Ecosyst. Environ. 270, 55–64 (2019).Article 
PubMed 

Google Scholar 
Fox, R. The decline of moths in Great Britain: A review of possible causes. Insect Conserv. Div. 6, 5–19 (2013).Article 

Google Scholar 
Keret, N. M., Mutanen, M. J., Orell, M. I., Itämies, J. H. & Välimäki, P. M. Climate change-driven elevational changes among boreal nocturnal moths. Oecologia 192, 1085–1098 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Wenzel, M., Schmitt, T., Weitzel, M. & Seitz, A. The severe decline of butterflies on western German calcareous grasslands during the last 30 years: A conservation problem. Biol. Conserv. 128, 542–552 (2006).Article 

Google Scholar 
Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406 (2014).Article 
PubMed 

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

Google Scholar 
Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).Article 

Google Scholar 
Zenker, M. M. et al. Diversity and composition of Arctiinae moth assemblages along elevational and spatial dimensions in Brazilian Atlantic Forest. J. Insect Conserv. 19, 129–140 (2015).Article 

Google Scholar 
Brehm, G. & Fiedler, K. Faunal composition of geometrid moths changes with altitude in an Andean montane rain forest. J. Biogeogr. 30, 431–440 (2003).Article 

Google Scholar 
McCain, C. M. & Grytnes, J. A. Elevational gradients in species richness. In Encyclopedia of Life Sciences (Wiley, Chichester, 2010).
Google Scholar 
Heinrich, B. The Hot-Blooded Insects: Strategies and Mechanisms of Thermoregulation 601 (Harvard University Press, 1993).Book 

Google Scholar 
Heinrich, B. Thermoregulation in Endothermic Insects: Body temperature is closely attuned to activity and energy supplies. Science 185, 747–756 (1974).Article 
PubMed 

Google Scholar 
May, M. L. Insect thermoregulation. Annu. Rev. Entomol. 24, 313–349 (1979).Article 

Google Scholar 
Heidrich, L. et al. Noctuid and geometrid moth assemblages show divergent elevational gradients in body size and color lightness. Ecography 44, 1169–1179 (2021).Article 
MathSciNet 

Google Scholar 
Holloway, J. D. Macrolepidoptera diversity in the Indo-Australian tropics, geographic, biotopic and taxonomic variations. Biol. J. Linn. Soc. 30, 325–341 (1987).Article 

Google Scholar 
Axmacher, J. C. et al. Diversity of geometrid moths (Lepidoptera: Geometridae) along an Afrotropical elevational rainforest transect. Divers. Distrib. 10, 293–302 (2004).Article 

Google Scholar 
Heinrich, B. & Mommsen, T. P. Flight of winter moths near 0°C. Science 228, 177–179 (1985).Article 
PubMed 

Google Scholar 
Rydell, J. & Lancaster, W. C. Flight and thermoregulation in moths were shaped by predation from bats. Oikos 88, 13–18 (2000).Article 

Google Scholar 
Skou, P. The geometroid moths of North Europe. Entomonograph, Vol. 6. Brill, Leiden. (1986).Zahiri, R. et al. Molecular phylogenetics of Erebidae (Lepidoptera, noctuoidea). Syst. Entomol. 37, 102–124 (2012).Article 

Google Scholar 
Fiedler, K., Brehm, G., Hilt, N., Sussenbach, D. & Hauser, C. L. Variation of diversity patterns across moth families along a tropical altitudinal gradient. Ecol. Stud. 198, 167–179 (2008).Article 

Google Scholar 
Longino, J. T. & Colwell, R. K. Density compensation, species composition, and richness of ants on a neotropical elevational gradient. Ecosphere 2, 1–20 (2011).Article 

Google Scholar 
Beck, J. & Chey, V. K. Explaining the elevational diversity pattern of geometrid moths from Borneo: A test of five hypotheses. J. Biogeogr. 35, 1452–1464 (2008).Article 

Google Scholar 
Nogués-Bravo, D., Araújo, M. B., Romdal, T. & Rahbek, C. Scale effects and human impact on the elevational species richness gradients. Nature 453, 216–219 (2008).Article 
PubMed 

Google Scholar 
Kwon, T. S. Ants foraging on grasses in South Korea: High diversity in Jeju Island and negative correlation with aphids. J. Asia-Pac. Biodivers. 10, 465–471 (2017).Article 

Google Scholar 
Han, E. K. et al. A disjunctive marginal edge of evergreen broad-leaved oak (Quercus gilva) in East Asia: The high genetic distinctiveness and unusual diversity of Jeju island populations and insight into a massive, independent postglacial colonization. Genes 11, 1114 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Chi, Y., Shi, H., Wang, Y., Guo, Z. & Wang, E. Evaluation on island ecological vulnerability and its spatial heterogeneity. Mar. Pollut. Bull. 125, 216–241 (2017).Article 
PubMed 

Google Scholar 
Vehviläinen, H., Koricheva, J. & Ruohomäki, K. Tree species diversity influences herbivore abundance and damage: Meta-analysis of long-term forest experiments. Oecologia 152, 287–298 (2007).Article 
PubMed 

Google Scholar 
Root, R. B. Organization of plant–arthropod association in simple and diverse habitats: The fauna of collards (I. Brassica oleracea). Ecol. Monogr. 43, 95–124 (1973).Article 

Google Scholar 
Otway, S. J., Hector, A. & Lawton, J. H. Resource dilution effects on specialist insect herbivores in a grassland biodiversity experiment. J. Anim. Ecol. 74, 234–240 (2005).Article 

Google Scholar 
Hawkins, B. A. et al. Energy, water, and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117 (2003).Article 

Google Scholar 
Qian, H. Environment–richness relationships for mammals, birds, reptiles, and amphibians at global and regional scales. Ecol. Res. 25, 629–637 (2010).Article 

Google Scholar 
Major, J. A climatic index to vascular plant activity. Ecology 44, 485–498 (1963).Article 

Google Scholar 
Latham, R. E. & Ricklefs, R. E. Global patterns of tree species richness in moist forests: Energy-diversity theory does not account for variation in species richness. Oikos 67, 325–333 (1993).Article 

Google Scholar 
Francis, A. P. & Currie, D. J. A globally consistent richness-climate relationship for angiosperms. Am. Nat. 161, 523–536 (2003).Article 
PubMed 

Google Scholar 
Storch, D. et al. Energy, range dynamics and global species richness patterns: Reconciling mid-domain effects and environmental determinants of avian diversity. Ecol. Lett. 9, 1308–1320 (2006).Article 
PubMed 

Google Scholar 
Intachat, J., Holloway, J. D. & Staines, H. Effects of weather and phenology on the abundance and diversity of geometroid moths in a natural Malaysian tropical rain forest. J. Trop. Ecol. 17, 411–429 (2001).Article 

Google Scholar 
Choi, S. W. Effects of weather factors on the abundance and diversity of moths in a temperate deciduous mixed forest of Korea. Zool. Sci. 25, 53–58 (2008).Article 

Google Scholar 
Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. 104, 5925–5930 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Lennon, J. J., Koleff, P., Greenwood, J. J. D. & Gaston, K. J. The geographical structure of British bird distributions: Diversity, spatial turnover and scale. J. Anim. Ecol. 70, 966–979 (2001).Article 

Google Scholar 
Choi, S. W. A high mountain moth assemblage quickly recovers after fire. Ann. Entomol. Soc. Am. 111, 304–311 (2018).
Google Scholar 
van Swaay, C., Warren, M. & Loïs, G. Biotope use and trends of European butterflies. J. Insect Conserv. 10, 189–209 (2006).Article 

Google Scholar 
De Frenne, P. et al. Microclimate moderates plant responses to macroclimate warming. Proc. Natl Acad. Sci. 110, 18561–18565 (2013).Article 
PubMed 
PubMed Central 

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

Google Scholar 
Conrad, K. F., Warren, M. S., Fox, R., Parsons, M. S. & Woiwod, I. P. Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis. Biol. Conserv. 132, 279–291 (2006).Article 

Google Scholar 
White, E. R. Minimum time required to detect population trends: The need for long-term monitoring programs. Bioscience 69, 40–46 (2019).Article 

Google Scholar 
Forister, M. L., Pelton, E. M. & Black, S. H. Declines in insect abundance and diversity: We know enough to act now. Conserv. Sci. Pract. 1, e80 (2019).
Google Scholar 
Didham, R. K. et al. Interpreting insect declines: Seven challenges and a way forward. Insect Conserv. Div. 13, 103–114 (2020).Article 

Google Scholar 
Kim, J. W., Boo, K. O., Choi, J. T. & Byun, Y. H. Climate Change of 100 Years on the Korean Peninsula (National Institute of Meteorological Science, 2018).
Google Scholar 
Kim, S. S., Beljaev, E. A. & Oh, S. H. Illustrated Catalogue of Geometridae in Korea (Lepidoptera: Geometrinae, Ennominae) (Korea Research Institute of Bioscience and Biotechnology & Center for Insect Systematics, 2001).
Google Scholar 
Kononenko, V.S., Ahn, S.B. & Ronkay, L. Illustrated catalogue of Noctuidae in Korea (Lepidoptera). Insects of Korea 3. KRIBB & CIS, Junghaengsa (1998).Shin, Y.H. Coloured illustrations of the moths of Korea. Academybook (2001).Kim, S.S., Choi, S.W., Sohn, J.C., Kim, T. & Lee, B.W. The Geometrid moths of Korea (Lepidoptera: Geometridae). Junghaengsa (2016).Kim, C. G. & Kim, N. W. Altitudinal pattern of evapotranspiration and water need for upland crops in Jeju Island. J. Korea Water Resour. Assoc. 48, 915–923 (2015).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Magurran, A. E. Ecological Diversity and its Measurement (Princeton University Press, 1988).Book 

Google Scholar 
Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

Google Scholar 
R Development Core Team. R 4.0.3. R: A language and environment for statistical computing. R Foundation for statistical computing Vienna. Austria. URL http://www.R-project.org. (2020).Pohlert, T. Non-parametric trend tests and change-point detection. R-package version 0.0.1. (2020).Hipel, K. W. & McLeod, A. I. Time Series Modelling of Water Resources and Environmental Systems (Elsevier, 1994).
Google Scholar 
Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T. J. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62, 361–371 (2006).Article 
MathSciNet 
PubMed 
MATH 

Google Scholar 
Colwell, R. K. Estiamtes, Version 91: Statistical Estimation of Species Richness and Shared Species from Samples (University of Connecticut, 2013).
Google Scholar 
Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Global Ecol. Biogeogr. 19, 134–143 (2010).Article 

Google Scholar 
Baselga, A. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Glob. Ecol. Biogeogr. 21, 1223–1232 (2012).Article 

Google Scholar  More

Time using the rock as refugeTemperature had an effect in the refuge usage of both species when analysed together (lme.zig: F3,192 = 7.97, p = 0.0001; Fig. 1A). However, species behaved differently (lme.zig: F1,192 = 14.79, p = 0.0004; Fig. 1A). As hypothesised, there was an interaction between temperature and species (lme.zig: F3,192 = 11.90, p  0.14, Fig. 1B).Size had an effect in the time exploring the rock (lme: F1,192 = 6.91, p = 0.012, Fig. 3) when species were analysed together, but there was no interaction with temperatures (lme: F3,192 = 0.42, p = 0.74, Fig. 3). We found that the interaction between species and size was close to be significant (lme: F1,192 = 3.62, p = 0.064, Fig. 3), implying that possibly smaller fish spent more time exploring the rock than bigger fish. However, when analysed separately, we did not find an effect of size in the exploring behaviour neither for twoline skiffias (lme: F1,96 = 2.99, p = 0.099, Fig. 3) nor for guppies (lme: F1,96 = 0.33, p = 0.569, Fig. 3).Figure 3Proportion of the total time observed (600 s) fish of different sizes spent exploring the rock. Lines represent the areas where the density of data is higher.Full size imageTime spent swimmingTemperature had an effect in the time spent swimming for both species when analysed together (lme: F3,192 = 23.48, p  More

MacPherson, E. Ontogenetic shifts in habitat use and aggregation in juvenile sparid fishes. J. Exp. Mar. Bio. Ecol. 220, 127–150 (1998).Article 

Google Scholar 
Freitas, C., Olsen, E. M., Knutsen, H., Albretsen, J. & Moland, E. Temperature-associated habitat selection in a cold-water marine fish. J. Anim. Ecol. 85, 628–637 (2016).Article 
PubMed 

Google Scholar 
Michelot, C. et al. Seasonal variation in coastal marine habitat use by the European shag: Insights from fine scale habitat selection modeling and diet. Deep. Res. Part II Top. Stud. Oceanogr. 141, 224–236 (2017).Article 

Google Scholar 
Davoren, G. K., Montevecchi, W. A. & Anderson, J. T. Distributional patterns of a marine bird and its prey: Habitat selection based on prey and conspecific behaviour. Mar. Ecol. Prog. Ser. 256, 229–242 (2003).Article 

Google Scholar 
Chiarello, A. G. et al. A translocation experiment for the conservation of maned sloths, Bradypus torquatus (Xenarthra, Bradypodidae). Biol. Conserv. 118, 421–430 (2004).Article 

Google Scholar 
Fukuda, Y. et al. Environmental resistance and habitat quality influence dispersal of the saltwater crocodile. Mol. Ecol. 31, 1076–1092 (2022).Article 
PubMed 

Google Scholar 
O’Leary, S. J., Dunton, K. J., King, T. L., Frisk, M. G. & Chapman, D. D. Genetic diversity and effective size of Atlantic sturgeon, Acipenser oxyrhinchus oxyrhinchus river spawning populations estimated from the microsatellite genotypes of marine-captured juveniles. Conserv. Genet. 15, 1173–1181 (2014).Article 

Google Scholar 
Brüniche-Olsen, A. et al. Genetic data reveal mixed-stock aggregations of gray whales in the North Pacific Ocean. Biol. Lett. 14, 1–4 (2018).Article 

Google Scholar 
Carroll, E. L. et al. Genetic diversity and connectivity of southern right whales (Eubalaena australis) found in the Brazil and Chile-Peru wintering grounds and the South Georgia (Islas Georgias Del Sur) feeding ground. J. Hered. 111, 263–276 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Bowen, A. B. W. et al. Origin of hawksbill turtles in a Caribbean feeding area as indicated by genetic markers. Ecol. Appl. 6, 566–572 (1996).Article 

Google Scholar 
Paxton, K. L., Yau, M., Moore, F. R. & Irwin, D. E. Differential migratory timing of western populations of Wilson’s Warbler (Cardellina pusilla) revealed by mitochondrial DNA and stable isotopes. Auk 130, 689–698 (2013).Article 

Google Scholar 
Anderson, E. C., Waples, R. S. & Kalinowski, S. T. An improved method for predicting the accuracy of genetic stock identification. Can. J. Fish. Aquat. Sci. 65, 1475–1486 (2008).Article 

Google Scholar 
Debevec, E. M. SPAM (version 3.2): Statistics program for analyzing mixtures. J. Hered. 91, 509–511 (2000).Article 
PubMed 

Google Scholar 
Bolker, B. M., Okuyama, T., Bjorndal, K. A. & Bolten, A. B. Incorporating multiple mixed stocks in mixed stock analysis: ‘Many-to-many’ analyses. Mol. Ecol. 16, 685–695 (2007).Article 
PubMed 

Google Scholar 
Neaves, P. I., Wallace, C. G., Candy, J. R. & Beacham, T. D. CBayes: Computer Program for Mixed Stock Analysis of Allelic Data. Free Program Distributed by the Authors Over the Internet. at (2005).Pella, J. & Masuda, M. Bayesian methods for analysis of stock mixtures from genetic characters. Fish. Bull. 99, 151–167 (2001).
Google Scholar 
Bolker, B., Okuyama, T., Bjorndal, K. A. & Bolten, A. B. Sea turtle stock estimation using genetic markers: Accounting for sampling error of rare genotypes. Ecol. Appl. 13, 763–775 (2003).Article 

Google Scholar 
Okuyama, T. & Bolker, B. M. Combining genetic and ecological data to estimate sea turtle origins. Ecol. Appl. 15, 315–325 (2005).Article 

Google Scholar 
Nishizawa, H. et al. Composition of green turtle feeding aggregations along the Japanese archipelago: Implications for changes in composition with current flow. Mar. Biol. 160, 2671–2685 (2013).Article 

Google Scholar 
Naro-Maciel, E. et al. Predicting connectivity of green turtles at Palmyra Atoll, central Pacific: A focus on mtDNA and dispersal modelling. J. R. Soc. Interface 11, 20130888 (2014).Proietti, M. C. et al. Green turtle Chelonia mydas mixed stocks in the western South Atlantic, as revealed by mtDNA haplotypes and drifter trajectories. Mar. Ecol. Prog. Ser. 447, 195–209 (2012).Article 

Google Scholar 
van der Zee, J. P. et al. Population recovery changes population composition at a major southern Caribbean juvenile developmental habitat for the green turtle, Chelonia mydas. Sci. Rep. 9, 1–11 (2019).
Google Scholar 
Shamblin, B. M. et al. Mexican origins for the Texas green turtle foraging aggregation: A cautionary tale of incomplete baselines and poor marker resolution. J. Exp. Mar. Bio. Ecol. 488, 111–120 (2017).Article 

Google Scholar 
Seminoff, J. A. et al. Status Review of the Green Turtle (Chelonia mydas) Under the Endangered Species Act. (NOAA Technical Memorandum, NOAA-NMFS-SWFSC, 2015).Chaloupka, M. et al. Encouraging outlook for recovery of a once severely exploited marine megaherbivore. Glob. Ecol. Biogeogr. 17, 297–304 (2008).Article 

Google Scholar 
Bjorndal, K. A. & Bolten, A. B. Annual variation in source contributions to a mixed stock: Implications for quantifying connectivity. Mol. Ecol. 17, 2185–2193 (2008).Article 
PubMed 

Google Scholar 
Roland, J., Keyghobadi, N. & Fownes, S. Alpine Parnassius butterfly dispersal: Effects of landscape and population size. Ecology 81, 1642–1653 (2000).Article 

Google Scholar 
Vanschoenwinkel, B., De Vries, C., Seaman, M. & Brendonck, L. The role of metacommunity processes in shaping invertebrate rock pool communities along a dispersal gradient. Oikos 116, 1255–1266 (2007).Article 

Google Scholar 
Shamblin, B. M. et al. Mitogenomic sequences better resolve stock structure of southern Greater Caribbean green turtle rookeries. Mol. Ecol. 21, 2330–2340 (2012).Article 
PubMed 

Google Scholar 
Witherington, B., Hirama, S. & Hardy, R. Young sea turtles of the pelagic Sargassum-dominated drift community: Habitat use, population density, and threats. Mar. Ecol. Prog. Ser. 463, 1–22 (2012).Article 

Google Scholar 
Putman, N. F. & Mansfield, K. L. Direct evidence of swimming demonstrates active dispersal in the sea turtle ‘lost years’. Curr. Biol. 25, 1221–1227 (2015).Article 
PubMed 

Google Scholar 
Mansfield, K. L., Wyneken, J. & Luo, J. First Atlantic satellite tracks of ‘lost years’ green turtles support the importance of the Sargasso Sea as a sea turtle nursery. Proc. R. Soc. B Biol. Sci. 288, 20210057 (2021).Putman, N. F. et al. Predicted distributions and abundances of the sea turtle ‘lost years’ in the western North Atlantic Ocean. Ecography (Cop.) 43, 506–517 (2020).Article 

Google Scholar 
Putman, N. F. & Naro-Maciel, E. Finding the ‘lost years’ in green turtles: Insights from ocean circulation models and genetic analysis. Proc. R. Soc. B Biol. Sci. 280, 20131468 (2013).Naro-Maciel, E., Hart, K. M., Cruciata, R. & Putman, N. F. DNA and dispersal models highlight constrained connectivity in a migratory marine megavertebrate. Ecography (Cop.) 40, 586–597 (2017).Article 

Google Scholar 
Ehrhart, L. M., Redfoot, W. E. & Bagley, D. A. Marine turtles of the central region of the Indian River Lagoon system, Florida. Florida Sci. 70, 415–434 (2007).
Google Scholar 
Redfoot, W. & Ehrhart, L. Trends in size class distribution, recaptures, and abundance of juvenile green turtles (Chelonia mydas) utilizing a rock riprap lined embayment at Port Canaveral, Florida, USA, as developmental habitat. Chelonian Conserv. Biol. 12, 252–261 (2013).Article 

Google Scholar 
Ehrhart, L., Redfoot, W., Bagley, D. & Mansfield, K. Long-term trends in loggerhead (Caretta caretta) nesting and reproductive success at an important western Atlantic rookery. Chelonian Conserv. Biol. 13, 173–181 (2014).Article 

Google Scholar 
Bolten, A. B. Techniques for measuring sea turtles. in Research and Management Techniques for the Conservation of Sea Turtles. (eds. Eckert, K. L., Bjorndal, K. A., Abreu-Grobois, F. A. & Donnelly, M.). 1–5 (1999).Bagley, D. A. Characterizing Juvenile Green Turtles, (Chelonia mydas), from Three East Central Florida Developmental Habitats. (University of Central Florida, 2003).Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Faircloth, B. & Glenn, T. Preparation of an AMPure XP Substitute. AKA Serapure https://doi.org/10.6079/J9MW2F26 (2016).Article 

Google Scholar 
Abreu-Grobois, F. A. et al. New mtDNA Dloop primers which work for a variety of marine turtle species may increase the resolution of mixed stock analyses. in Proceedings of the 26th Annual Symposium on Sea Turtle Biology. 179 (International Sea Turtle Society, 2006).Kearse, M. et al. Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Leigh, J. W. & Bryant, D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

Google Scholar 
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).Article 
PubMed 

Google Scholar 
Wright, S. Evolution and the Genetics of Populations. Vol. 4. Variability Within and Among Natural Populations. (University of Chicago Press, 1978).Hays, G. C. Ocean currents and marine life. Curr. Biol. 27, R470–R473 (2017).Article 
PubMed 

Google Scholar 
Engstrom, T. N., Meylan, P. A. & Meylan, A. B. Origin of juvenile loggerhead turtles (Caretta caretta) in a tropical developmental habitat in Caribbean Panamá. Anim. Conserv. 5, 125–133 (2002).Article 

Google Scholar 
Florida Fish and Wildlife Conservation Commission-Fish and Wildlife Research Institute, F. W. C. F. W. R. I. Index Nesting Beach Survey (INBS). (2021).Cuevas Flores, E. A., Guzmán Hernández, V., Guerra Santos, J. J. & Rivas Hernández, G. A. El uso del Conocimiento de las Tortugas Marinas Como Herramienta para la Restauración de sus Poblaciones y Hábitats Asociados. (Universidad Autónoma del Carmen, 2019).Pineda, O. G. & Rocha, A. R. B. Las Tortugas Marinas en México: Logros y Perspectivas para su Conservación. (CONANP, 2016).Varela, R. G., Quílez, G. Z. & Harrison, E. Report on the 2014 Green Turtle Program at Tortuguero, Costa Rica. (2015).Azanza Ricardo, J. et al. Nesting ecology of Chelonia mydas (Testudines: Cheloniidae) on the Guanahacabibes Peninsula. Cuba. Rev. Biol. Trop. 61, 1935–1945 (2013).PubMed 

Google Scholar 
Nalovic, M. A. et al. Sea Turtles in the North Atlantic & Wider Caribbean Region. (2020).Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).Article 

Google Scholar 
Kruschke, J. K. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. https://doi.org/10.1016/B978-0-12-405888-0.09999-2 (Academic Press, 2015).Ruiz-Urquiola, A. et al. Population genetic structure of greater Caribbean green turtles (Chelonia mydas) based on mitochondrial DNA sequences, with an emphasis on rookeries from southwestern Cuba. Rev. Investig. Mar. 31, 33–52 (2010).
Google Scholar 
Long, C. A. et al. Incongruent long-term trends of a marine consumer and primary producers in a habitat affected by nutrient pollution. Ecosphere 12, e03553 (2021).Article 

Google Scholar 
Phillips, K. F., Stahelin, G. D., Chabot, R. M. & Mansfield, K. L. Long-term trends in marine turtle size at maturity at an important Atlantic rookery. Ecosphere 12, 7 (2021).Article 

Google Scholar 
Bjorndal, K. A., Bolten, A. B. & Chaloupka, M. Y. Evaluating trends in abundance of immature green turtles, Chelonia mydas, in the Greater Caribbean. Ecol. Appl. 15, 304–314 (2005).Article 

Google Scholar 
Naro-Maciel, E. et al. The interplay of homing and dispersal in green turtles: A focus on the southwestern atlantic. J. Hered. 103, 792–805 (2012).Article 
PubMed 

Google Scholar 
Monzón-Argüello, C. et al. Evidence from genetic and Lagrangian drifter data for transatlantic transport of small juvenile green turtles. J. Biogeogr. 37, 1752–1766 (2010).Article 

Google Scholar 
Luke, K., Horrocks, J. A., LeRoux, R. A. & Dutton, P. H. Origins of green turtle (Chelonia mydas) feeding aggregations around Barbados, West Indies. Mar. Biol. 144, 799–805 (2004).Article 

Google Scholar 
Bass, A. L., Epperly, S. P. & Braun-McNeill, J. Green turtle (Chelonia mydas) foraging and nesting aggregations in the Caribbean and Atlantic: Impact of currents and behavior on dispersal. J. Hered. 97, 346–354 (2006).Article 
PubMed 

Google Scholar 
Lahanas, P. N. et al. Genetic composition of a green turtle (Chelonia mydas) feeding ground population: Evidence for multiple origins. Mar. Biol. 130, 345–352 (1998).Article 

Google Scholar 
Foley, A. M. et al. Characteristics of a green turtle (Chelonia mydas) assemblage in northwestern Florida determined during a hypothermic stunning event. Gulf Mex. Sci. 25, 131–143 (2007).
Google Scholar 
Bass, A. L., Lagueux, C. J. & Bowen, B. W. Origin of green turtles, Chelonia mydas, at ‘Sleeping Rocks’ off the Northeast coast of Nicaragua. Copeia 1998, 1064 (1998).Article 

Google Scholar 
Bass, A. L. & Witzell, W. N. Demographic composition of immature green turtles (Chelonia mydas) from the East Central Florida Coast: Evidence from mtDNA markers. Herpetologica 56, 357–367 (2000).
Google Scholar 
Bjorndal, K. A., Parsons, J., Mustin, W. & Bolten, A. B. Threshold to maturity in a long-lived reptile: Interactions of age, size, and growth. Mar. Biol. 160, 607–616 (2013).Article 

Google Scholar 
Perrault, J. R. et al. Maternal health status correlates with nest success of leatherback sea turtles (Dermochelys coriacea) from Florida. PLoS ONE 7, e31841 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Montero, N. et al. Warmer and wetter conditions will reduce offspring production of hawksbill turtles in Brazil under climate change. PLoS ONE 13, 1–16 (2018).Article 

Google Scholar 
Shamblin, B. M. et al. Geographic patterns of genetic variation in a broadly distributed marine vertebrate: New insights into loggerhead turtle stock structure from expanded mitochondrial DNA sequences. PLoS ONE 9, 85956 (2014).Article 

Google Scholar 
Anderson, J. D., Shaver, D. J. & Karel, W. J. Genetic Diversity and Natal Origins of Green Turtles (Chelonia mydas) in the Western Gulf of Mexico. J. Herpetol. 47, 251–257 (2013).Article 

Google Scholar  More

Area-based sub-targetWe found that 13.2% of Asian terrestrial landscapes were covered by PAs by the target date for Aichi 11 based on our in-country sources. However, it was 17.4% lower based on WDPA data (10.9%). The average increase in coverage across Asia during the 2010s was 0.4% ± SE 0.1% per year. PA coverage at the level of individual countries increased from a mean 11.1% in 2010 (SE = 1.4%) to 14.1% by 2020 (SE = 1.8%) based on our in-country sources, which was 16.5% higher than WDPA data (12.1 ± SE 1.6%). However, these overall figures concealed considerable country-level and sub-regional heterogeneity.A total of 8,673,433 km2 across 10 countries, equaling 19.6% of Asian terrestrial landscapes was managed as hunting concessions, governed by governments, communities or private sectors, but these areas have not been included in the countries’ report to the Protected Planet Initiative databases. Most of these areas are locally important in terms of biodiversity conservation and local socioeconomic outcomes which may qualify them as examples of “other effective area-based conservation measures” (OECMs). The increase in area-based conservation coverage represented by these areas, above the current Protected Planet Initiative statistic, ranged from 0.2% (Iran) to 41.4% (Russia). With that update incorporated, a total of 32.9% of Asian terrestrial landscapes are under protection, either as protected areas or hunting concessions (potentially as one type of OECMs).We found that 40% of Asian countries met a target of 17% coverage for PAs by 2020 based on our in-country sources, mainly in East and some South Asia, whereas West and Central Asian countries had generally not achieved this target (Figs. 1 and 2). We did not find any statistically significant association between the proportions of highly at-risk (CR/EN) mammalian species range outside PAs and the % PA extent in 2020 (β = −0.22 ± SE 0.15, t = −1.51, P = 0.14 in a Generalized Linear Model). The highest proportions of the highly at-risk (CR/EN) mammalian species range outside PAs were seen in West (βCR/EN_outsidePA = 1.77 ± SE 0.46, t = 3.86, P 10%, but Kuwait lost area. In East Asia, all countries showed at least some PA expansion (South Korea and Japan by >10%) whereas in Central Asia, almost no change was seen. It is also noteworthy that between 2010 and 2015, agricultural lands increased by 2.0% across the continent, averaging 0.51 ± SE 0.03% per year at country level, although 18 counties (45.0%) had agricultural land loss, mainly in West and Central Asia (12 out of 18 countries with agricultural land loss; Fig. 2).In our attempt to model the variation in achievement of area-based target (% PA extent), we found a single model with a ΔAICc weight of 1.0 (R2adj = 0.66; Table 1). There was no evidence to reject the null hypothesis that the model fits well (P = 0.99). This model included the predictors % agricultural extent in 2015, % PA extent in 2010, and sub-region (Table 1). Specifically, the coefficients suggested that countries with greater PA extent in 2010 and a smaller percentage of agricultural lands in 2015 were more likely to achieve higher percentage of PA extent by 2020 (βPAExtent2020 = 0.58 ± SE 0.10, t = 5.74, P  0.05).Table 2 Results of generalized linear models testing different hypotheses on the association between the percentage of ecoregions protected by the PA network in 2020 and ecological and geopolitical factors in Asian countries.Full size tableFor the coverage of highly at-risk (CR/EN) mammalian species, a single statistical model was also selected, with non-significant deviance goodness of fit (P = 0.83), which included only the % PA extent by 2020 and Region as predictors (R2adj = 0. 27). Although there was no evidence for association between the % PA extent by 2020 and the coverage of threatened species (βPAExtent2020 = −0.23 ± SE 0.15, t = −1.57, P = 0.13). However, the coverage of threatened species varied geographically, with high intercept differences for East Asia (βEastAsia = −0.23 ± SE 0.15, t = −1.57, P = 0.13), implying the largest median of range of highly at-risk (CR/EN) mammalian species outside the current network of PAs within each country.PA management effectiveness sub-targetFor the level of PAME assessment, we found that out of 22781 PAs within the 40 studied Asian countries, only 7.0% have been assessed based on PAME criteria (n = 1599), averaging 17.4% ± of PAs per country (SE = 2.5%). Israel, Japan, Lao, Bahrain, Oman and Qatar had no PA assessed based on the PAME criteria while over 1/3 of PAs in Indonesia, Cambodia, Bhutan, Jordan, Nepal, Turkey, Singapore and the UAE were PAME assessed. When modeling the level of PAME assessment, three best supported models were averaged (Table 3), with the averaged model including GDP2019, % PA extent 2020 and the Region as predictors. The averaged model coefficients would be non-significant under a hypothesis-testing approach (βGDP2019 = −0.18 ± SE 0.12, t = 1.47, P = 0.14 and βPAExtent2020 = −0.15 ± SE 0.11, t = 1.31, P = 0.19). Similarly, there was no evidence for the association between the ratio of PAs with PAME and Asian regions (P  > 0.05).Table 3 Results of generalized linear models testing different hypotheses on the association between the ratio of PAs with management effectiveness (PAME) in 2020 and ecological and geopolitical factors in Asian countries.Full size table More

Millien, V. et al. Ecotypic variation in the context of global climate change: Revisiting the rules. Ecol. Lett. 9, 853–869 (2006).Article 
PubMed 

Google Scholar 
Calder, W. A. Size, Function and Life History (Harvard University Press, 1984).
Google Scholar 
Bergmann, C. Über die verhältnisse der wärmeökonomie der thiere zu ihrer grösse. Göttinger Stud. 3, 595–708 (1847).
Google Scholar 
Mayr, E. Geographical character gradients and climatic adaptation. Evolution 10, 105–108 (1956).Article 

Google Scholar 
Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. PNAS 116, 21609–21615 (2019).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Foster, J. B. Evolution of mammals on islands. Nature 202, 234–235 (1964).Article 
ADS 

Google Scholar 
Lomolino, M. V. Body size evolution in insular vertebrates: Generality of the island rule. J. Biogeogr. 32, 1683–1699 (2005).Article 

Google Scholar 
Benítez-López, A. et al. The island rule explains consistent patterns of body size evolution in terrestrial vertebrates. Nat. Ecol. Evol. 5, 768–786 (2021).Article 
PubMed 

Google Scholar 
Meiri, S. & Dayan, T. On the validity of Bergmann’s rule. J. Biogeogr. 30, 331–351 (2003).Article 

Google Scholar 
Meiri, S., Cooper, N. & Purvis, A. The island rule: Made to be broken?. Proc. R. Soc. B. 275, 141–148 (2008).Article 
PubMed 

Google Scholar 
Millien, V. Relative effects of climate change, isolation and competition on body-size evolution in the Japanese field mouse, Apodemus argenteus. J. Biogeogr. 31, 1267–1276 (2004).Article 

Google Scholar 
Millien, V. & Damuth, J. Climate change and size evolution in an island rodent species: New perspectives on the island rule. Evolution 58, 1353–1360 (2004).Article 
PubMed 

Google Scholar 
Lomolino, M. V., Sax, D. F., Riddle, B. R. & Brown, J. H. The island rule and a research agenda for studying ecogeographical patterns. J. Biogeogr. 33, 1503–1510 (2006).Article 

Google Scholar 
Sargis, E. J., Millien, V., Woodman, N. & Olson, L. E. Rule reversal: Ecogeographical patterns of body size variation in the common treeshrew (Mammalia, Scandentia). Ecol. Evol. 8, 1634–1645 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Barnosky, A. D., Hadly, E. A. & Bell, C. J. Mammalian response to global warming on varied temporal scales. J. Mammal. 84, 354–368 (2003).Article 

Google Scholar 
Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).Article 
ADS 

Google Scholar 
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: A third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).Article 
PubMed 

Google Scholar 
Teplitsky, C., Mills, J. A., Alho, J. S., Yarrall, J. W. & Merilä, J. Bergmann’s rule and climate change revisited: Disentangling environmental and genetic responses in a wild bird population. PNAS 105, 13492–13496 (2008).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Teplitsky, C. & Millien, V. Climate warming and Bergmann’s rule through time: Is there any evidence?. Evol. Appl. 7, 156–168 (2014).Article 
PubMed 

Google Scholar 
James, F. C. Geographic size variation in birds and its relationship to climate. Ecology 51, 385–390 (1970).Article 

Google Scholar 
Wigginton, J. D. & Dobson, F. S. Environmental influences on geographic variation in body size of western bobcats. Can. J. Zool. 77, 802–813 (1999).Article 

Google Scholar 
Yom-Tov, Y. & Geffen, E. Geographic variation in body size: The effects of ambient temperature and precipitation. Oecologia 148, 213–218 (2006).Article 
PubMed 
ADS 

Google Scholar 
Wagner, J. A. Schreber’s saugthiere, supplementband, 2. Abtheilung 1841(37–44), 553 (1841).
Google Scholar 
Hawkins, M. T. Family Tupaiidae (treeshrews). In Handbook of the Mammals of the World, Volume 8 Insectivores, Sloths and Colugos (eds Wilson, D. E. & Mittermeier, R. A.) (Lynx Edicions, 2018).
Google Scholar 
Roberts, T. E., Lanier, H. C., Sargis, E. J. & Olson, L. E. Molecular phylogeny of treeshrews (Mammalia: Scandentia) and the timescale of diversification in Southeast Asia. Mol. Phylogenet. Evol. 60, 358–372 (2011).Article 
PubMed 

Google Scholar 
Zhang, L., Yang, F., Wang, Z. K. & Zhu, W. L. Role of thermal physiology and bioenergetics on adaptation in tree shrew (Tupaia belangeri): The experiment test. Sci. Rep. 7, 41352 (2017).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Zhu, W., Zhang, H. & Wang, Z. Seasonal changes in body mass and thermogenesis in tree shrews (Tupaia belangeri): The roles of photoperiod and cold. J. Therm. Biol. 37, 479–484 (2012).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Book 
MATH 

Google Scholar 
South, A. rnaturalearth: World Map Data from Natural Earth. R package version 0.1.0 (2017).Dunnington, D. ggspatial: Spatial Data Framework for ggplot2. R package version 1.1.4 (2020).R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2018).Helgen, K. M. Order Scandentia. In Mammal Species of the World: A Taxonomic and Geographic Reference 3rd edn (eds Wilson, D. E. & Reeder, D. M.) (Johns Hopkins University Press, 2005).
Google Scholar 
Collins, P. M. & Tsang, W. N. Growth and reproductive development in the male tree shrew (Tupaia belangeri) from birth to sexual maturity. Biol. Reprod. 37, 261–267 (1987).Article 
CAS 
PubMed 

Google Scholar 
Heaney, L. R. Island area and body size of insular mammals: Evidence from the tri-colored squirrel (Callosciurus prevosti) of Southeast Asia. Evolution 32, 29–44 (1978).PubMed 

Google Scholar 
Husson, L., Boucher, F. C., Sarr, A. C., Sepulchre, P. & Cahyarini, S. Y. Evidence of Sundaland’s subsidence requires revisiting its biogeography. J. Biogeogr. 47, 843–853 (2020).Article 

Google Scholar 
Juman, M. M., Woodman, N., Olson, L. E. & Sargis, E. J. Ecogeographic variation and taxonomic boundaries in Large Treeshrews (Scandentia, Tupaiidae: Tupaia tana Raffles, 1821) from Southeast Asia. J. Mammal. 102, 1054–1066 (2021).Article 

Google Scholar 
Hinckley, A. et al. Challenging ecogeographical rules: Phenotypic variation in the Mountain Treeshrew (Tupaia montana) along tropical elevational gradients. PLoS ONE 17, e0268213 (2022).Article 
CAS 
PubMed 

Google Scholar 
Lomolino, M. V., Sax, D. F., Palombo, M. R. & van der Geer, A. A. Of mice and mammoths: evaluations of causal explanations for body size evolution in insular mammals. J. Biogeogr. 39, 842–854 (2011).Article 

Google Scholar 
Teta, P., de la Sancha, N. U., D’Elía, G. & Patterson, B. D. Andean rain shadow effect drives phenotypic variation in a widely distributed Austral rodent. J. Biogeogr. 49, 1767–1778 (2022).Article 

Google Scholar 
Yom-Tov, Y. & Yom-Tov, S. Climatic change and body size in two species of Japanese rodents. Biol. J. Linn. Soc. 82, 263–267 (2004).Article 

Google Scholar 
Yom-Tov, Y. & Yom-Tov, J. Global warming, Bergmann’s rule and body size in the masked shrew Sorex cinereus in Alaska. J. Anim. Ecol. 74, 803–808 (2005).Article 

Google Scholar 
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. PNAS 105, 6668–6672 (2008).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Newbold, T., Oppenheimer, P., Etard, A. & Williams, J. J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 4, 1630–1638 (2020).Article 
PubMed 

Google Scholar 
Cronk, Q. C. B. Islands: stability, diversity, conservation. Biodivers. Conserv. 6, 477–493 (1997).Article 

Google Scholar 
Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. PNAS 106, 9322–9327 (2009).Article 
CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Yom-Tov, Y. & Geffen, E. Recent spatial and temporal changes in body size of terrestrial vertebrates: Probable causes and pitfalls. Biol. Rev. 86, 531–541 (2011).Article 
PubMed 

Google Scholar 
Theriot, M. K., Lanier, H. C. & Olson, L. E. Harnessing natural history collections to detect trends in body-size change as a response to warming: A critique and review of best practices. Methods Ecol. Evol. (2022).Rohwer, V. G., Rohwer, Y. & Dillman, C. B. Declining growth of natural history collections fails future generations. PLoS Biol. 20, e3001613 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Reese, A. T. & Olson, L. E. Morphological distinctiveness of Javan Tupaia hypochrysa (Scandentia, Tupaiidae). J. Mammal. 94, 938–947 (2013).Article 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Reese, A. T. & Olson, L. E. Island history affects faunal composition: The treeshrews (Mammalia: Scandentia: Tupaiidae) from the Mentawai and Batu Islands, Indonesia. Biol. J. Linn. Soc. 111, 290–304 (2014).Article 

Google Scholar 
Sargis, E. J., Campbell, K. K. & Olson, L. E. Taxonomic boundaries and craniometric variation in the treeshrews (Scandentia, Tupaiidae) from the Palawan faunal region. J. Mamm. Evol. 21, 111–123 (2014).Article 

Google Scholar 
Sargis, E. J., Woodman, N., Morningstar, N. C., Bell, T. N. & Olson, L. E. Skeletal variation and taxonomic boundaries among mainland and island populations of the common treeshrew (Mammalia: Scandentia: Tupaiidae). Biol. J. Linn. Soc. 120, 286–312 (2017).
Google Scholar 
Juman, M. M., Olson, L. E. & Sargis, E. J. Skeletal variation and taxonomic boundaries in the Pen-tailed Treeshrew (Scandentia, Ptilocercidae: Ptilocercus lowii Gray, 1848). J. Mamm. Evol. 28, 1193–1203 (2021).Article 

Google Scholar 
Juman, M. M., Woodman, N., Miller-Murthy, A., Olson, L. E. & Sargis, E. J. Taxonomic boundaries in Lesser Treeshrews (Scandentia, Tupaiidae: Tupaia minor Günther, 1876). J. Mammal. https://doi.org/10.1093/jmammal/gyac080 (2022).Article 

Google Scholar 
Woodman, N., Miller-Murthy, A., Olson, L. E. & Sargis, E. J. Coming of age: Morphometric variation in the hand skeletons of juvenile and adult Lesser Treeshrews (Scandentia: Tupaiidae: Tupaia minor Günther, 1876). J. Mammal. 101, 1151–1164 (2020).Article 

Google Scholar 
Chamberlain, S., Barve, V., Mcglinn, D., Oldoni, D., Desmet, P., Geffert, L. & Ram, K. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.7.2, https://CRAN.R-project.org/package=rgbif.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data. 7, 109 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Meiyappan, P. & Jain, A. K. Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years. Front. Earth Sci. 6, 122–139 (2012).Article 
ADS 

Google Scholar 
Ryan, W. B. F. et al. Global multi-resolution topography synthesis. Geochem. Geophys. 10, Q03014 (2009).
Google Scholar 
van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).Article 

Google Scholar 
Clavel, J., Merceron, G. & Escarguel, G. Missing data estimation in morphometrics: How much is too much? Syst. Biol. 63, 203–218 (2014).Article 
PubMed 

Google Scholar 
Nally, R. M. & Walsh, C. J. Hierarchical partitioning public-domain software. Biodivers. Conserv. 13, 659–660 (2004).Article 

Google Scholar 
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn. (Springer, 2013).Book 
MATH 

Google Scholar  More

Differences in environmental factorsEnvironmental factors showed value differences between forest types, while the significance of differences differed among variables, which were both found with corrected values and original measurements (Fig. 1).Figure 1The differences in environmental factors between naturally regenerating forests (NF) and planted forests (PF) in China. The environmental factors include three annual climatic factors (a–c), three seasonal temperature factors (d–f), three seasonal precipitation factors (g–i), three biotic factors (j–l), and two soil factors (m,n). Three annual climatic factors include mean annual air temperature (MAT, a), mean annual precipitation (MAP, b), and aridity index (AI, c) defined as the ratio of MAP to annual potential evapotranspiration. Three seasonal temperature factors include the temperature of the warmest month (Tw, d), the temperature of the coldest month (Tc, e), temperature annual range (TR, f). Three seasonal precipitation factors include precipitation of the wettest month (Pw, g), precipitation of the driest month (Pd, h), and precipitation seasonality (Ps, i) defined as the standard deviation of monthly precipitation during the measuring year. Three biological factors include the mean annual leaf area index (LAI, j), the maximum leaf area index (MLAI, k), and stand age (SA, l). Two soil factors include soil organic carbon content (SOC, m) and soil total nitrogen content (STN, n). The differences are tested for each variable with one-way analysis of variance (ANOVA), where * and ** indicate significant differences between forest types at significance levels of α = 0.05 and α = 0.01, respectively. The corrected values are mean values during 2003–2019 after correcting the original measurements with the interannual trend (See methods), which are listed in each panel, while original measurements are mean values during the measuring period of each ecosystem, which are not shown in each panel.Full size imageFor annual climatic factors, the significant difference between NF and PF only appeared in MAT (Fig. 1a). The mean MAT of NF was 10.50 ± 7.81 °C, which was significantly lower than that of PF (15.65 ± 6.23 °C) (p  0.05) (Fig. 2c). Even considering the significant effects of MAT on ER, ANCOVA results obtained by fixing MAT as a covariant also suggested that ER values did not significantly differ between forest types (F = 0.01, p  > 0.05). Fixing other variables as a covariant also drew a similar result.Therefore, NF showed a lower NEP resulting from the lower GPP than PF, while their differences were not statistically significant (Fig. 2).Differences in NEP latitudinal patternsCarbon fluxes showed divergent latitudinal patterns between NF and PF, while their latitudinal patterns varied among carbon fluxes, which were both found with corrected values and original measurements (Fig. 3).Figure 3The latitudinal patterns of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  0.05).The ER of NF showed a significant decreasing latitudinal pattern (Fig. 3e), while that of PF exhibited no significant latitudinal pattern (Fig. 3f). The increasing latitude caused the ER of NF to significantly decrease. Each unit increase in latitude led to a 28.71 gC m−2 year−1 decrease in ER, with an R2 of 0.31. However, the increasing latitude contributed little to the ER spatial variation of PF (p  > 0.05).In addition, the latitudinal patterns of carbon fluxes and their differences between forest types were also obtained with the original measurements (Fig. 3, grey points). The latitudinal patterns of random error adding carbon fluxes were comparable to those of our corrected carbon fluxes (Fig. 3), which confirmed that the latitudinal patterns of carbon fluxes and their differences between forest types would not be affected by the uncertainties in generating the corrected carbon fluxes.Therefore, among NFs, the similar decreasing latitudinal patterns of GPP and ER meant that NEP showed no significant latitudinal pattern, while the significant decreasing latitudinal pattern of GPP and no significant latitudinal pattern of ER caused NEP to show a decreasing latitudinal pattern among PFs.Differences in the environmental effects on NEP spatial variationsEnvironmental factors, including the annual climatic factors, seasonal temperature factors, seasonal precipitation factors, biological factors, and soil factors, exerted divergent effects on the spatial variations of NEP and its components, which also differed between forest types (Table 1). No factor was found to affect that the spatial variation of NEP among NFs, while most annual and seasonal climatic factors were found to affect that among PFs. The spatial variations of GPP and ER among NFs were both affected by most annual and seasonal climatic factors and LAI, while those among PFs were primarily shaped by most annual and seasonal climatic factors. Though LAI showed no significant effect on GPP and ER spatial variations among PFs, SA exerted a significant negative effect. In addition, the spatial variations of soil variables contributed little to the spatial variations of carbon fluxes. Therefore, among NFs, most annual and seasonal climatic factors and LAI were found to affect GPP and ER spatial variations, while no factor was found to significantly influent the NEP spatial variation. However, among PFs, most annual and seasonal climatic factors were found to affect the spatial variations of NEP and its components, while LAI showed no significant effect. Using the original measurements also generated the similar correlation coefficients (Supplementary Table S1).Table 1 Correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF).Full size tableGiven the high correlations among annual climatic factors and seasonal climatic factors (Supplementary Table S2), the partial correlation analysis was applied to determine which factors should be employed to reveal the mechanisms underlying the spatial variations of NEP. Partial correlation analysis showed that MAT and MAP exerted the most important roles in spatial variations of NEP and its components (Table 2). After controlling MAT (or MAP), other factors seldom showed significant correlation with carbon fluxes, especially fixing MAT (Table 2). In addition, MAT and MAP exerted similar effects on the spatial variations of NEP and its components (Table 1). Using the original measurements also generated the similar partial correlation coefficients (Supplementary Table S3). Therefore, we only presented the effects of MAT on carbon flux spatial variations and their differences between forest types in detail.Table 2 Partial correlation coefficients between carbon fluxes and environmental factors in naturally regenerating forests (NF) and planted forests (PF) with fixing mean annual air temperature (MAT) or mean annual precipitation (MAP).Full size tableThe increasing MAT increased carbon fluxes, while the increasing rates differed between forest types (Fig. 4). The increasing MAT contributed little to the NEP spatial variation of NF but raised the NEP of PF (Fig. 4a,b). Each unit increase in MAT caused the NEP of PF to increase at a rate of 27.77 gC m−2 year−1, with an R2 of 0.31 (Fig. 4b). The increasing MAT significantly raised GPP in NF and PF (Fig. 4c,d). For NF, each unit increase in MAT increased GPP at a rate of 43.76 gC m−2 year−1, with an R2 of 0.49 (Fig. 4c), while each unit increase in MAT increased the GPP of PF at a rate of 69.18 gC m−2 year−1, with an R2 of 0.57 (Fig. 4d). The GPP increasing rates did not significantly differ between NF and PF (F = 1.52, p  > 0.05). The increasing MAT also raised ER in both NF and PF (Fig. 4e,f), whose increasing rates were 38.97 gC m−2 year−1 (Fig. 4e) and 36.79 gC m−2 year−1 (Fig. 4f), respectively, while their differences were not statistically significant (F = 0.01, p  > 0.05). In addition, using the original measurements also generated the similar spatial variations and their differences between forest types (Fig. 4). Furthermore, the random error adding carbon fluxes responded similarly to those of our correcting carbon fluxes (Fig. 4), indicating that the effects of MAT on carbon fluxes would not be affected by the uncertainties in our correcting carbon fluxes. Therefore, the similar responses of GPP and ER to MAT made MAT contribute little to NEP spatial variations among NFs, while GPP and ER showed divergent response rates to MAT, which made NEP increase with MAT among PFs.Figure 4The effects of mean annual air temperature (MAT) on the spatial variations of carbon fluxes over Chinese naturally regenerating forests (NF) and planted forests (PF). The carbon fluxes include net ecosystem productivity (NEP, a,b), gross primary productivity (GPP, c,d), and ecosystem respiration (ER, e,f). Each panel is drawn with the corrected values (blue points) and original measurements (grey points), respectively. The blue and black lines represent the regression lines calculated from the corrected values and original measurements, respectively, with their regression statistics listed in blue and black letters. Only the regression slope (Sl) and R2 of each regression are listed. The grey lines represent the regressions between carbon fluxes added by random errors and latitude. Only significant (p  More

Alpha diversity differences among communitiesNematode gut microbiomes were assigned into their respective species categories of E. antarcticus and P. murrayi based on 18S host data that was consistent with morphology (see Methods “Microinvertebrate haplotypes”). In contrast, due to recovery of three undiscernible 18S tardigrade haplotypes, the gut microbiomes were assigned to Tardigrada. Mat bacterial communities were significantly (Tukey’s HSD, P  0.65, χ2(1)  0.38, χ2(3)  More

As humans warm the planet, biodiversity is plummeting. These two global crises are connected in multiple ways. But the details of the intricate feedback loops between biodiversity decline and climate change are astonishingly under-studied.It is well known that climate extremes such as droughts and heatwaves can have devastating impacts on ecosystems and, in turn, that degraded ecosystems have a reduced capacity to protect humanity against the social and physical impacts of such events. Yet only a few such relationships have been probed in detail. Even less well known is whether biodiversity-depleted ecosystems will also have a negative effect on climate, provoking or exacerbating weather extremes.For us, a group of researchers living and working mainly in Central Europe, the wake-up call was the sequence of heatwaves of 2018, 2019 and 2022. It felt unreal to watch a floodplain forest suffer drought stress in Leipzig, Germany. Across Germany, more than 380,000 hectares of trees have now been damaged (see go.nature.com/3etrrnp; in German), and the forestry sector is struggling with how to plan restoration activities over the coming decades1. What could have protected these ecosystems against such extremes? And how will the resultant damage further impact our climate?
Nature-based solutions can help cool the planet — if we act now
In June 2021, the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) published their first joint report2, acknowledging the need for more collaborative work between these two domains. And some good policy moves are afoot: the new EU Forest Strategy for 2030, released in July 2021, and other high-level policy initiatives by the European Commission, formally recognize the multifunctional value of forests, including their role in regulating atmospheric processes and climate. But much more remains to be done.To thoroughly quantify the risk that lies ahead, ecologists, climate scientists, remote-sensing experts, modellers and data scientists need to work together. The upcoming meeting of the United Nations Convention on Biological Diversity in Montreal, Canada, in December is a good opportunity to catalyse such collaboration.Buffers and responsesWhen lamenting the decline in biodiversity, most people think first about the tragedy of species driven to extinction. There are more subtle changes under way, too.For instance, a study across Germany showed that over the past century, most plant species have declined in cover, with only a few increasing in abundance3. Also affected is species functionality4 — genetic diversity, and the diversity of form and structure that can make communities more or less efficient at taking up nutrients, resisting heat or surviving pathogen attacks.When entire ecosystems are transformed, their functionality is often degraded. They are left with less capacity to absorb pollution, store carbon dioxide, soak up water, regulate temperature and support vital functions for other organisms, including humans5. Conversely, higher levels of functional biodiversity increase the odds of an ecosystem coping with unexpected events, including climate extremes. This is known as the insurance effect6.The effect is well documented in field experiments and modelling studies. And there is mounting evidence of it in ecosystem responses to natural events. A global synthesis of various drought conditions showed, for instance, that forests were more resilient when trees with a greater diversity of strategies for using and transporting water lived together7.

Dead trees near Iserlohn, Germany, in April 2020 (left) and after felling in June 2021 (right).Credit: Ina Fassbender/AFP via Getty

However, biodiversity cannot protect all ecosystems against all kinds of impacts. In a study this year across plots in the United States and Canada, for example, mortality was shown to be higher in diverse forest ecosystems8. The proposed explanation for this unexpected result was that greater biodiversity could also foster more competition for resources. When extreme events induce stress, resources can become scarce in areas with high biomass and competition can suddenly drive mortality, overwhelming the benefits of cohabitation. Whether or not higher biodiversity protects an ecosystem from an extreme is highly site-specific.Some plants respond to drought by reducing photosynthesis and transpiration immediately; others can maintain business as usual for much longer, stabilizing the response of the ecosystem as a whole. So the exact response of ecosystems to extremes depends on interactions between the type of event, plant strategies, vegetation composition and structure.Which plant strategies will prevail is hard to predict and highly dependent on the duration and severity of the climatic extreme, and on previous extremes9. Researchers cannot fully explain why some forests, tree species or individual plants survive in certain regions hit by extreme climate conditions, whereas entire stands disappear elsewhere10. One study of beech trees in Germany showed that survival chances had a genomic basis11, yet it is not clear whether the genetic variability present in forests will be sufficient to cope with future conditions.And it can take years for ecosystem impacts to play out. The effects of the two consecutive hot drought years, 2018 and 2019, were an eye-opener for many of us. In Leipzig, tree growth declined, pathogens proliferated and ash and maple trees died. The double blow, interrupted by a mild winter, on top of the long-term loss of soil moisture, led to trees dying at 4–20 times the usual rate throughout Germany, depending on the species (see go.nature.com/3etrrnp; in German). The devastation peaked in 2020.Ecosystem changes can also affect atmospheric conditions and climate. Notably, land-use change can alter the brightness (albedo) of the planet’s surface and its capacity for heat exchange. But there are more-complex mechanisms of influence.Vegetation can be a source or sink for atmospheric substances. A study published in 2020 showed that vegetation under stress is less capable of removing ozone than are unstressed plants, leading to higher levels of air pollution12. Pollen and other biogenic particles emitted from certain plants can induce the freezing of supercooled cloud droplets, allowing ice in clouds to form at much warmer temperatures13, with consequences for rainfall14. Changes to species composition and stress can alter the dynamics of these particle emissions. Plant stress also modifies the emission of biogenic volatile organic gases, which can form secondary particles. Wildfires — enhanced by drought and monocultures — affect clouds, weather and climate through the emission of greenhouse gases and smoke particles. Satellite data show that afforestation can boost the formation of low-level, cooling cloud cover15 by enhancing the supply of water to the atmosphere.Research prioritiesAn important question is whether there is a feedback loop: will more intense, and more frequent, extremes accelerate the degradation and homogenization of ecosystems, which then, in turn, promote further climate extremes? So far, we don’t know.One reason for this lack of knowledge is that research has so far been selective: most studies have focused on the impacts of droughts and heatwaves on ecosystems. Relatively little is known about the impacts of other kinds of extremes, such as a ‘false spring’ caused by an early-season bout of warm weather, a late spring frost, heavy rainfall events, ozone maxima, or exposure to high levels of solar radiation during dry, cloudless weather.Researchers have no overview, much less a global catalogue, of how each dimension of biodiversity interacts with the full breadth of climate extremes in different combinations and at multiple scales. In an ideal world, scientists would know, for example, how the variation in canopy density, vegetation age, and species diversity protects against storm damage; and whether and how the diversity of canopy structures controls atmospheric processes such as cloud formation in the wake of extremes. Researchers need to link spatiotemporal patterns of biodiversity with the responses of ecosystem processes to climate extremes.
Biodiversity needs every tool in the box: use OECMs
Creating such a catalogue is a huge challenge, particularly given the more frequent occurrence of extremes with little or no precedent16. Scientists will also need to account for the increasing likelihood of pile-ups of climate stressors. The ways in which ecosystems respond to compound events17 could be quite different. Researchers will have to study which facets of biodiversity (genetic, physiological, structural) are required to stabilize ecosystems and their functions against these onslaughts.There is at least one piece of good news: tools for data collection and analysis are improving fast, with huge advances over the past decade in satellite-based observations for both climate and biodiversity monitoring. The European Copernicus Earth-observation programme, for example — which includes the Sentinel 1 and 2 satellite fleet, and other recently launched missions that cover the most important wavelengths of the electromagnetic spectrum — offer metre-scale resolution observations of the biochemical status of plants and canopy structure. Atmospheric states are recorded in unprecedented detail, vertically and in time.Scientists must now make these data interoperable and integrate them with in situ observations. The latter is challenging. On the ground, a new generation of data are being collected by researchers and by citizen scientists18. For example, unique insights into plant responses to stress are coming from time-lapse photography of leaf orientation; accelerometer measures of movement patterns of stems have been shown to provide proxies for the drought stress of trees19.High-quality models are needed to turn these data into predictions. The development of functional ‘digital twins’ of the climate system is now in reach. These models replicate hydrometeorological processes at the metre scale, and are fast enough to allow for rapid scenario development and testing20. The analogous models for ecosystems are still in a more conceptual phase. Artificial-intelligence methods will be key here, to study links between climate extremes and biodiversity.Researchers can no longer afford to track global transformations of the Earth system in disciplinary silos. Instead, ecologists and climate scientists need to establish a joint agenda, so that humanity is properly forewarned: of the risks of removing biodiversity buffers against climate extremes, and of the risk of thereby amplifying these extremes. More