Ecology

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

Google Scholar 
2.Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).Article 

Google Scholar 
3.Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Both, C., van Asch, M., Bijlsma, R. G., van den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: constraints or adaptations? J. Anim. Ecol. 78, 73–83 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
5.Visser, M. E., van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. B 265, 1867–1870 (1998).Article 

Google Scholar 
6.Stenseth, N. C. & Mysterud, A. Climate, changing phenology, and other life history traits: nonlinearity and match–mismatch to the environment. Proc. Natl Acad. Sci. USA 99, 13379–13381 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Blackford, C., Germain, R. M. & Gilbert, B. Species differences in phenology shape coexistence. Am. Nat. 195, E168–E180 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Møller, A. P., Rubolini, D. & Lehikoinen, E. Populations of migratory bird species that did not show a phenological response to climate change are declining. Proc. Natl Acad. Sci. USA 105, 16195–16200 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Rudolf, V. H. W. The role of seasonal timing and phenological shifts for species coexistence. Ecol. Lett. 22, 1324–1338 (2019).PubMed 
PubMed Central 

Google Scholar 
10.Mynott, J. Birds in the Ancient World: Winged Words (Oxford Univ. Press, 2018).11.Hurlbert, A. H. & Liang, Z. Spatiotemporal variation in avian migration phenology: citizen science reveals effects of climate change. PLoS ONE 7, e31662 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Mayor, S. J. et al. Increasing phenological asynchrony between spring green-up and arrival of migratory birds. Sci. Rep. 7, 1902 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Horton, K. G. et al. Phenology of nocturnal avian migration has shifted at the continental scale. Nat. Clim. Change 10, 63–68 (2020).Article 

Google Scholar 
14.Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Sullivan, B. L. et al. The eBird enterprise: an integrated approach to development and application of citizen science. Biol. Conserv. 169, 31–40 (2014).Article 

Google Scholar 
16.Friedl, M., Gray, J. & Sulla-Menashe, D. MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid v.6 (NASA EOSDIS Land Processes DAAC, accessed 26 March 2020); https://doi.org/10.5067/MODIS/MCD12Q2.00617.Richardson, A. D., Hufkens, K., Milliman, T. & Frolking, S. Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing. Sci. Rep. 8, 5679 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Cole, E. F., Long, P. R., Zelazowski, P., Szulkin, M. & Sheldon, B. C. Predicting bird phenology from space: satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment. Ecol. Evol. 5, 5057–5074 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Pettorelli, N. et al. The normalized difference vegetation index (NDVI): unforeseen successes in animal ecology. Clim. Res. 46, 15–27 (2011).Article 

Google Scholar 
20.Winger, B. M., Auteri, G. G., Pegan, T. M. & Weeks, B. C. A long winter for the Red Queen: rethinking the evolution of seasonal migration. Biol. Rev. 94, 737–752 (2019).21.Åkesson, S. et al. Timing avian long-distance migration: from internal clock mechanisms to global flights. Philos. Trans. R. Soc. B 372, 20160252 (2017).Article 

Google Scholar 
22.Helm, B. et al. Two sides of a coin: ecological and chronobiological perspectives of timing in the wild. Philos. Trans. R. Soc. B 372, 20160246 (2017).Article 

Google Scholar 
23.Haest, B., Hüppop, O. & Bairlein, F. The influence of weather on avian spring migration phenology: what, where and when? Glob. Change Biol. 24, 5769–5788 (2018).Article 

Google Scholar 
24.Studds, C. E. & Marra, P. P. Rainfall-induced changes in food availability modify the spring departure programme of a migratory bird. Proc. R. Soc. B 278, 3437–3443 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Thorup, K. et al. Resource tracking within and across continents in long-distance bird migrants. Sci. Adv. 3, e1601360 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
26.Post, E., Steinman, B. A. & Mann, M. E. Acceleration of phenological advance and warming with latitude over the past century. Sci. Rep. 8, 3927 (2018).27.Van der Graaf, A., Stahl, J., Klimkowska, A., Bakker, J. P. & Drent, R. H. Surfing on a green wave—how plant growth drives spring migration in the Barnacle Goose Branta leucopsis. Ardea Wagening. 94, 567 (2006).
Google Scholar 
28.Schmaljohann, H. & Both, C. The limits of modifying migration speed to adjust to climate change. Nat. Clim. Change 7, 573–576 (2017).Article 

Google Scholar 
29.Marra, P. P., Francis, C. M., Mulvihill, R. S. & Moore, F. R. The influence of climate on the timing and rate of spring bird migration. Oecologia 142, 307–315 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Zurell, D., Gallien, L., Graham, C. H. & Zimmermann, N. E. Do long-distance migratory birds track their niche through seasons? J. Biogeogr. 45, 1459–1468 (2018).Article 

Google Scholar 
31.Horton, K. G. et al. Holding steady: little change in intensity or timing of bird migration over the Gulf of Mexico. Glob. Change Biol. 25, 1106–1118 (2019).Article 

Google Scholar 
32.Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–803 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Curley, S. R., Manne, L. L. & Veit, R. R. Differential winter and breeding range shifts: implications for avian migration distances. Divers. Distrib. 26, 415–425 (2020).Article 

Google Scholar 
34.Root, T. Energy constraints on avian distributions and abundances. Ecology 69, 330–339 (1988).Article 

Google Scholar 
35.La Sorte, F. A., Fink, D., Hochachka, W. M., DeLong, J. P. & Kelling, S. Population-level scaling of avian migration speed with body size and migration distance for powered fliers. Ecology 94, 1839–1847 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Somveille, M., Manica, A. & Rodrigues, A. S. L. Where the wild birds go: explaining the differences in migratory destinations across terrestrial bird species. Ecography 42, 225–236 (2019).Article 

Google Scholar 
37.Knudsen, E. et al. Challenging claims in the study of migratory birds and climate change. Biol. Rev. 86, 928–946 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Allstadt, A. J. et al. Spring plant phenology and false springs in the conterminous US during the 21st century. Environ. Res. Lett. 10, 104008 (2015).Article 

Google Scholar 
39.Franks, S. E. et al. The sensitivity of breeding songbirds to changes in seasonal timing is linked to population change but cannot be directly attributed to the effects of trophic asynchrony on productivity. Glob. Change Biol. 24, 957–971 (2018).Article 

Google Scholar 
40.Both, C., Bouwhuis, S., Lessells, C. M. & Visser, M. E. Climate change and population declines in a long-distance migratory bird. Nature 441, 81–83 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Kharouba, H. M. & Wolkovich, E. M. Disconnects between ecological theory and data in phenological mismatch research. Nat. Clim. Change 10, 406–415 (2020).Article 

Google Scholar 
42.Samplonius, J. M. et al. Strengthening the evidence base for temperature-mediated phenological asynchrony and its impacts. Nat. Ecol. Evol. 5, 155–164 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Abdala‐Roberts, L. et al. Tri‐trophic interactions: bridging species, communities and ecosystems. Ecol. Lett. 22, 2151–2167 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Burgess, M. D. et al. Tritrophic phenological match–mismatch in space and time. Nat. Ecol. Evol. 2, 970–975 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Helm, B., Van Doren, B. M., Hoffmann, D. & Hoffmann, U. Evolutionary response to climate change in migratory pied flycatchers. Curr. Biol. 29, 3714–3719 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Newson, S. E. et al. Long-term changes in the migration phenology of UK breeding birds detected by large-scale citizen science recording schemes. Ibis 158, 481–495 (2016).Article 

Google Scholar 
47.Townsend, A. K. et al. The interacting effects of food, spring temperature, and global climate cycles on population dynamics of a migratory songbird. Glob. Change Biol. 22, 544–555 (2016).Article 

Google Scholar 
48.Grüebler, M. U. & Naef-Daenzer, B. Fitness consequences of pre- and post-fledging timing decisions in a double-brooded passerine. Ecology 89, 2736–2745 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Lany, N. K. et al. Breeding timed to maximumimize reproductive success for a migratory songbird: the importance of phenological asynchrony. Oikos 125, 656–666 (2016).Article 

Google Scholar 
50.Samplonius, J. M. & Both, C. Climate change may affect fatal competition between two bird species. Curr. Biol. 29, 327–331 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Potvin, D. A., Välimäki, K. & Lehikoinen, A. Differences in shifts of wintering and breeding ranges lead to changing migration distances in European birds. J. Avian Biol. 47, 619–628 (2016).Article 

Google Scholar 
52.Bassett, F. & Cubie, D. Wintering hummingbirds in Alabama and Florida: species diversity, sex and age ratios, and site fidelity. J. Field Ornithol. 80, 154–162 (2009).Article 

Google Scholar 
53.Socolar, J. B., Epanchin, P. N., Beissinger, S. R. & Tingley, M. W. Phenological shifts conserve thermal niches in North American birds and reshape expectations for climate-driven range shifts. Proc. Natl Acad. Sci. USA 114, 12976–12981 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Chmura, H. E. et al. The mechanisms of phenology: the patterns and processes of phenological shifts. Ecol. Monogr. 89, e01337 (2019).Article 

Google Scholar 
55.Kharouba, H. M. et al. Global shifts in the phenological synchrony of species interactions over recent decades. Proc. Natl Acad. Sci. USA 115, 5211–5216 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Cressie, N., Calder, C. A., Clark, J. S., Hoef, J. M. V. & Wikle, C. K. Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecol. Appl. 19, 553–570 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Miller-Rushing, A. J., Inouye, D. W. & Primack, R. B. How well do first flowering dates measure plant responses to climate change? The effects of population size and sampling frequency. J. Ecol. 96, 1289–1296 (2008).Article 

Google Scholar 
58.Miller-Rushing, A. J., Lloyd-Evans, T. L., Primack, R. B. & Satzinger, P. Bird migration times, climate change, and changing population sizes. Glob. Change Biol. 14, 1959–1972 (2008).Article 

Google Scholar 
59.Barnes, R. dggridR: Discrete global grids for R. R package version 0.1.12 https://github.com/r-barnes/dggri (2017).60.Data Zone (BirdLife International, 2019); http://datazone.birdlife.org/species/requestdis61.Sulla-Menashe, D. et al. Hierarchical mapping of Northern Eurasian land cover using MODIS data. Remote Sens. Environ. 115, 392–403 (2011).Article 

Google Scholar 
62.Friedl, M. & Sulla-Menashe, D. MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid v.6 (NASA EOSDIS Land Processes DAAC, accessed 26 February 2020); https://doi.org/10.5067/MODIS/MCD12Q2.00663.Wood, S. N. & Augustin, N. H. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol. Model. 157, 157–177 (2002).Article 

Google Scholar 
64.Fink, D. et al. Spatiotemporal exploratory models for broad-scale survey data. Ecol. Appl. 20, 2131–2147 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
65.Wood, S. N. Thin plate regression splines. J. R. Stat. Soc. B 65, 95–114 (2003).Article 

Google Scholar 
66.Lindén, A., Meller, K. & Knape, J. An empirical comparison of models for the phenology of bird migration. J. Avian Biol. 48, 255–265 (2017).Article 

Google Scholar 
67.Goodrich, B., Gabry, J., Ali, I. & Brilleman, S. rstanarm: Bayesian applied regression modeling via Stan. R package version 2.21.1 https://mc-stan.org/rstanarm (2020).68.Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. https://www.jstatsoft.org/v076/i01 (2017).69.Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434 (1998).
Google Scholar 
70.Besag, J. & Kooperberg, C. On conditional and intrinsic autoregression. Biometrika 82, 733 (1995).
Google Scholar 
71.Banerjee, S., Carlin, B. P. & Gelfand, A. E. Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC, 2004).72.Morris, M. et al. Bayesian hierarchical spatial models: implementing the Besag York Mollié model in stan. Spat. Spatiotemporal Epidemiol. 31, 100301 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Stan Development Team. RStan: the R interface to Stan. R package version 2.17.3 http://mc-stan.org (2018).74.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).75.Monnahan, C. C., Thorson, J. T. & Branch, T. A. Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo. Methods Ecol. Evol. 8, 339–348 (2017).Article 

Google Scholar 
76.Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Stat. Soc. A 182, 389–402 (2019).Article 

Google Scholar 
77.Gelman, A., Carlin, J. B, Stern, H. S. & Rubin, D. B. Bayesian Data Analysis (Chapman & Hall/CRC, 2014).78.Finley, A. O. Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence: spatially-varying coefficients models. Methods Ecol. Evol. 2, 143–154 (2011).Article 

Google Scholar 
79.Stan Modeling Language Users Guide and Reference Manual v. 2.18.0 (Stan Development Team, 2018).80.Menzel, A., von Vopelius, J., Estrella, N., Schleip, C. & Dose, V. Farmers’ annual activities are not tracking the speed of climate change. Clim. Res. 32, 201–207 (2006).
Google Scholar  More

1.Pugh, P. Gelatinous zooplankton: the forgotten fauna. Sci. Prog. 14, 67–78 (1989).
Google Scholar 
2.Robison, B. H. Deep pelagic biology. J. Exp. Mar. Biol. Ecol. 300, 253–272. https://doi.org/10.1016/j.jembe.2004.01.012 (2004).Article 

Google Scholar 
3.Condon, R. H. et al. Questioning the rise of gelatinous zooplankton in the world’s oceans. Bioscience 62, 160–169. https://doi.org/10.1525/bio.2012.62.2.9 (2012).Article 

Google Scholar 
4.Haddock, S. H. D. A golden age of gelata: past and future research on planktonic ctenophores and cnidarians. Hydrobiologia 530, 549–556. https://doi.org/10.1007/s10750-004-2653-9 (2004).Article 

Google Scholar 
5.Lebrato, M. et al. Sinking of gelatinous zooplankton biomass increases deep carbon transfer efficiency globally. Glob. Biogeochem. Cycles 33, 1764–1783. https://doi.org/10.1029/2019GB006265 (2019).ADS 
CAS 
Article 

Google Scholar 
6.Luo, J. Y. et al. Gelatinous zooplankton-mediated carbon flows in the global oceans: a data-driven modeling study. Glob. Biogeochem. Cycles https://doi.org/10.1029/2020GB006704 (2020).Article 

Google Scholar 
7.Lucas, C. H. et al. Gelatinous zooplankton biomass in the global oceans: geographic variation and environmental drivers. Glob. Ecol. Biogeogr. 23, 701–714. https://doi.org/10.1111/geb.12169 (2014).Article 

Google Scholar 
8.Robison, B. H. Conservation of deep pelagic biodiversity. Conserv. Biol. 23, 847–858 (2009).Article 

Google Scholar 
9.Décima, M., Stukel, M. R., López-López, L. & Landry, M. R. The unique ecological role of pyrosomes in the Eastern Tropical Pacific. Limnol. Oceanogr. 64, 728–743. https://doi.org/10.1002/lno.11071 (2019).ADS 
Article 

Google Scholar 
10.Henschke, N. et al. Large vertical migrations of Pyrosoma atlanticum play an important role in active carbon transport. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2018jg004918 (2019).Article 

Google Scholar 
11.Sutherland, K. R., Sorensen, H. L., Blondheim, O. N., Brodeur, R. D. & Galloway, A. W. E. Range expansion of tropical pyrosomes in the northeast Pacific Ocean. Ecology 99, 2397–2399. https://doi.org/10.1002/ecy.2429 (2018).Article 
PubMed 

Google Scholar 
12.Perissinotto, R., Mayzaud, P., Nichols, P. D. & Labat, J. P. Grazing by Pyrosoma atlanticum (Tunicata, Thaliacea) in the south Indian Ocean. Mar. Ecol. Prog. Ser. 330, 1–11 (2007).ADS 
CAS 
Article 

Google Scholar 
13.van Soest, R. W. M. A monograph of the order Pyrosomatida (Tunicata, Thaliacea). J. Plankton Res. 3, 603–631. https://doi.org/10.1093/plankt/3.4.603 (1981).Article 

Google Scholar 
14.Drits, A. V., Arashkevich, E. G. & Semenova, T. N. Pyrosoma atlanticum (Tunicata, Thaliacea): grazing impact on phytoplankton standing stock and role in organic carbon flux. J. Plankton Res. 14, 799–809. https://doi.org/10.1093/plankt/14.6.799 (1992).Article 

Google Scholar 
15.Lebrato, M. & Jones, D. O. B. Mass deposition event of Pyrosoma atlanticum carcasses off Ivory Coast (West Africa). Limnol. Oceanogr. 54, 1197–1209. https://doi.org/10.4319/lo.2009.54.4.1197 (2009).ADS 
CAS 
Article 

Google Scholar 
16.Lebrato, M. et al. Sinking jelly-carbon unveils potential environmental variability along a continental margin. PLoS ONE 8, e82070. https://doi.org/10.1371/journal.pone.0082070 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Archer, S. K. et al. Pyrosome consumption by benthic organisms during blooms in the northeast Pacific and Gulf of Mexico. Ecology 99, 981–984. https://doi.org/10.1002/ecy.2097 (2018).Article 
PubMed 

Google Scholar 
18.Harbison, G. R. in The Biology of Pelagic Tunicates (ed Q. Bone) Ch. 12, 186–214 (Oxford University Press, 1998).19.James, G. D. & Stahl, J. C. Diet of southern Buller’s albatross (Diomedea bulleri bulleri) and the importance of fishery discards during chick rearing. NZ J. Mar. Freshwat. Res. 34, 435–454. https://doi.org/10.1080/00288330.2000.9516946 (2000).Article 

Google Scholar 
20.Hedd, A. & Gales, R. The diet of shy albatrosses (Thalassarche cauta) at Albatross Island, Tasmania. J. Zool. 253, 69–90. https://doi.org/10.1017/S0952836901000073 (2001).Article 

Google Scholar 
21.Brodeur, R. et al. An unusual gelatinous plankton event in the NE Pacific: the great pyrosome bloom of 2017. PICES Press 26, 22–27 (2018).
Google Scholar 
22.Childerhouse, S., Dix, B. & Gales, N. Diet of New Zealand sea lions at the Auckland Islands. Wildl. Res. 28, 291–298. https://doi.org/10.1071/WR00063 (2001).Article 

Google Scholar 
23.Lindsay, D., Hunt, J. & Hayashi, K.-I. Associations in the midwater zone: The penaeid shrimp Funchalia sagamiensis FUJINO 1975 and pelagic tunicates (Order: Pyrosomatida). Marine Freshwater Behav. Phys. 34, 157–170. https://doi.org/10.1080/10236240109379069 (2001).Article 

Google Scholar 
24.Andersen, V. in The Biology of Pleagic Tunicates (ed Q. Bone) Ch. 7, 125–137 (Oxford University Press, 1998).25.Madin, L. P. Production, composition and sedimentation of salp fecal pellets in oceanic waters. Mar. Biol. 67, 39–45. https://doi.org/10.1007/BF00397092 (1982).Article 

Google Scholar 
26.Thomsen, P. F. & Willerslev, E. Environmental DNA: an emerging tool in conservation for monitoring past and present biodiversity. Biol. Cons. 183, 4–18. https://doi.org/10.1016/j.biocon.2014.11.019 (2015).Article 

Google Scholar 
27.Andruszkiewicz, E. A. et al. Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding. PLoS ONE 12, e0176343. https://doi.org/10.1371/journal.pone.0176343 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Doty, M. S. & Oguri, M. The Island mass effect. ICES J. Mar. Sci. 22, 33–37. https://doi.org/10.1093/icesjms/22.1.33 (1956).Article 

Google Scholar 
29.Gove, J. M. et al. Near-island biological hotspots in barren ocean basins. Nat. Commun. 7, 10581. https://doi.org/10.1038/ncomms10581 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
30.Faye, S., Lazar, A., Sow, B. & Gaye, A. A model study of the seasonality of sea surface temperature and circulation in the Atlantic North-eastern tropical upwelling system. Front. Phys. https://doi.org/10.3389/fphy.2015.00076 (2015).Article 

Google Scholar 
31.Schütte, F., Brandt, P. & Karstensen, J. Occurrence and characteristics of mesoscale eddies in the tropical northeastern Atlantic Ocean. Ocean Sci. 12, 663–685. https://doi.org/10.5194/os-12-663-2016 (2016).ADS 
Article 

Google Scholar 
32.Gilly, W. F., Beman, J. M., Litvin, S. Y. & Robison, B. H. Oceanographic and biological effects of shoaling of the oxygen minimum zone. Ann. Rev. Mar. Sci. 5, 393–420. https://doi.org/10.1146/annurev-marine-120710-100849 (2013).Article 
PubMed 

Google Scholar 
33.Schütte, F. et al. Characterization of “dead-zone” eddies in the eastern tropical North Atlantic. Biogeosciences 13, 5865–5881. https://doi.org/10.5194/bg-13-5865-2016 (2016).ADS 
Article 

Google Scholar 
34.GEOMAR Helmholtz-Zentrum für Ozeanforschung. CVOO Cape Verde Ocean Observatory, http://cvoo.geomar.de/ (n.d.).35.NASA Goddard Space Flight Center, O. E. L., Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Chlorophyll Data. https://doi.org/10.5067/AQUA/MODIS/L3B/CHL/2018 (2019).36.Hoving, H. J. et al. The Pelagic in situ observation system (PELAGIOS) to reveal biodiversity, behavior, and ecology of elusive oceanic fauna. Ocean Sci. 15, 1327–1340. https://doi.org/10.5194/os-15-1327-2019 (2019).ADS 
CAS 
Article 

Google Scholar 
37.Schlining, B. & Stout, N. MBARI’s Video Annotation and reference system. Vol. 2006 (2006).38.O’Loughlin, J. H. et al. Implications of Pyrosoma atlanticum range expansion on phytoplankton standing stocks in the Northern California Current. Prog. Oceanogr. 188, 102424. https://doi.org/10.1016/j.pocean.2020.102424 (2020).Article 

Google Scholar 
39.Al-Mutairi, H. & Landry, M. R. Active export of carbon and nitrogen at Station ALOHA by diel migrant zooplankton. Deep Sea Res. Part II Top. Stud. Ocean. 48, 2083–2103. https://doi.org/10.1016/S0967-0645(00)00174-0 (2001).ADS 
CAS 
Article 

Google Scholar 
40.Mayzaud, P., Boutoute, M., Gasparini, S., Mousseau, L. & Lefevre, D. Respiration in marine zooplankton—the other side of the coin: CO2 production. Limnol. Oceanogr. 50, 291–298. https://doi.org/10.4319/lo.2005.50.1.0291 (2005).ADS 
CAS 
Article 

Google Scholar 
41.GEOMAR Helmholtz-Zentrum für Ozeanforschung, Hissmann, K. & Schauer, J. Manned submersible JAGO. J. Large-Scale Res. Facil. 3, 1–12, https://doi.org/10.17815/jlsrf-3-157 (2017).42.Lavaniegos, B. E. & Ohman, M. D. Long-term changes in pelagic tunicates of the California current. Deep Sea Res. Part II Top. Stud. Ocen. 50, 2473–2498. https://doi.org/10.1016/S0967-0645(03)00132-2 (2003).ADS 
CAS 
Article 

Google Scholar 
43.GEBCO Compilation Group. GEBCO 2019 Grid. https://doi.org/10.5285/836f016a-33be-6ddc-e053-6c86abc0788e (2019).44.Schram, J. B., Sorensen, H. L., Brodeur, R. D., Galloway, A. W. E. & Sutherland, K. R. Abundance, distribution, and feeding ecology of Pyrosoma atlanticum in the Northern California current. Mar. Ecol. Prog. Ser. 651, 97–110 (2020).ADS 
CAS 
Article 

Google Scholar 
45.Goy, J. Vertical migration of zooplankton. Résultats des Campagnes à la mer, GNEXO 13, 71–73 (1977).
Google Scholar 
46.Andersen, V. & Sardou, J. Pyrosoma atlanticum (Tunicata, Thaliacea): diel migration and vertical distribution as a function of colony size. J. Plankton Res. 16, 337–349. https://doi.org/10.1093/plankt/16.4.337 (1994).Article 

Google Scholar 
47.Andersen, V., Sardou, J. & Nival, P. The diel migrations and vertical distributions of zooplankton and micronekton in the Northwestern Mediterranean Sea. 2. Siphonophores, hydromedusae and pyrosomids. J. Plankton Res. 14, 1155–1169. https://doi.org/10.1093/plankt/14.8.1155 (1992).Article 

Google Scholar 
48.Roe, H. S. J. et al. Great Meteor East: a biological characterisation (Wormley, 1987).
Google Scholar 
49.Williamson, C. E., Fischer, J. M., Bollens, S. M., Overholt, E. P. & Breckenridge, J. K. Toward a more comprehensive theory of zooplankton diel vertical migration: Integrating ultraviolet radiation and water transparency into the biotic paradigm. Limnol. Oceanogr. 56, 1603–1623. https://doi.org/10.4319/lo.2011.56.5.1603 (2011).ADS 
Article 

Google Scholar 
50.Bianchi, D., Galbraith, E. D., Carozza, D. A., Mislan, K. A. S. & Stock, C. A. Intensification of open-ocean oxygen depletion by vertically migrating animals. Nat. Geosci. 6, 545–548. https://doi.org/10.1038/ngeo1837 (2013).ADS 
CAS 
Article 

Google Scholar 
51.Purcell, J. et al. in Coastal Hypoxia: Consequences for Living Resources and Ecosystems Vol. 58 77–100 (2001).52.Neitzel, P. The impact of the oxygen minimum zone on the vertical distribution and abundance of gelatinous macrozooplankton in the Eastern Tropical Atlantic, Christian-Albrechts-Universität Kiel, (2017).53.Hoving, H. J. T. et al. In situ observations show vertical community structure of pelagic fauna in the eastern tropical North Atlantic off Cape Verde. Sci. Rep. 10, 21798. https://doi.org/10.1038/s41598-020-78255-9 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Thuesen, E. V. et al. Intragel oxygen promotes hypoxia tolerance of scyphomedusae. J. Exp. Biol. 208, 2475. https://doi.org/10.1242/jeb.01655 (2005).Article 
PubMed 

Google Scholar 
55.Keeling, R. F., Körtzinger, A. & Gruber, N. Ocean deoxygenation in a warming world. Ann. Rev. Mar. Sci. 2, 199–229. https://doi.org/10.1146/annurev.marine.010908.163855 (2009).Article 

Google Scholar 
56.Wiebe, P. H., Madin, L. P., Haury, L. R., Harbison, G. R. & Philbin, L. M. Diel vertical migration by Salpa aspera and its potential for large-scale particulate organic matter transport to the deep-sea. Mar. Biol. 53, 249–255. https://doi.org/10.1007/BF00952433 (1979).Article 

Google Scholar 
57.Ariza, A., Garijo, J. C., Landeira, J. M., Bordes, F. & Hernández-León, S. Migrant biomass and respiratory carbon flux by zooplankton and micronekton in the subtropical northeast Atlantic Ocean (Canary Islands). Prog. Oceanogr. 134, 330–342. https://doi.org/10.1016/j.pocean.2015.03.003 (2015).ADS 
Article 

Google Scholar 
58.Hernández-León, S. et al. Zooplankton and micronekton active flux across the tropical and subtropical Atlantic Ocean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00535 (2019).Article 

Google Scholar 
59.Kiko, R. et al. Zooplankton-mediated fluxes in the eastern tropical North Atlantic. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00358 (2020).Article 

Google Scholar 
60.Cascão, I., Domokos, R. K., Lammers, M. O., Santos, R. S. & Silva, M. N. A. Seamount effects on the diel vertical migration and spatial structure of micronekton. Prog. Ocean. 175, 1–13. https://doi.org/10.1016/j.pocean.2019.03.008 (2019).Article 

Google Scholar 
61.Fock, H., Matthiessen, B., Zidowitz, H. & Westernhagen, H. Diel and habitat-dependent resource utilisation of deep-sea fishes at the Great Meteor seamount (subtropical NE Atlantic): niche overlap and support for the sound-scattering layer-interception hypothesis. Mar. Ecol. Progr. Ser. 244, 219–233. https://doi.org/10.3354/meps244219 (2002).ADS 
Article 

Google Scholar 
62.Laval, P. Hyperiid amphipods as crustacean parasitoids associated with gelatinous zooplankton. Oceanogr. Mar. Biol. Annu. Rev. 18, 11–56 (1980).
Google Scholar 
63.Madin, L. P. & Harbison, G. R. The associations of Amphipoda Hyperiidea with gelatinous zooplankton—I Associations with Salpidae. Deep-Sea Res. 24, 449–463. https://doi.org/10.1016/0146-6291(77)90483-0 (1977).ADS 
Article 

Google Scholar 
64.Gasca, R., Hoover, R. & Haddock, S. H. D. New symbiotic associations of hyperiid amphipods (Peracarida) with gelatinous zooplankton in deep waters off California. J. Mar. Biol. Assoc. UK 95, 503–511. https://doi.org/10.1017/S0025315414001416 (2015).Article 

Google Scholar 
65.Harbison, G. R., Biggs, D. C. & Madin, L. P. The associations of Amphipoda Hyperiidea with gelatinous zooplankton—II. Associations with Cnidaria, Ctenophora and Radiolaria. Deep Sea Res. 24, 465–488. https://doi.org/10.1016/0146-6291(77)90484-2 (1977).ADS 
Article 

Google Scholar 
66.Harbison, G. R., Madin, L. P. & Swanberg, N. R. On the natural history and distribution of oceanic ctenophores. Deep-Sea Res. 25, 233–256 (1978).ADS 
Article 

Google Scholar 
67.Laval, P. The barrel of the pelagic amphipod Phronima sedentaria (Forsk.) (Crustacea: hyperiidea). J. Exp. Mar. Biol. Ecol. 33, 187–211. https://doi.org/10.1016/0022-0981(78)90008-4 (1978).Article 

Google Scholar 
68.Desmarest, A.-G. in Dictionnaire des Sciences Naturelles, 28. (ed F.G. Levrault) 138–425 (Paris and Strasbourg, 1823).69.Laval, P. Observations on biology of Phronima curvipes Voss (Amphipoda Hyperidae) and description of adult male. Cah. Biol. Mar. 9, 347–362 (1968).
Google Scholar 
70.Janssen, J. & Harbison, G. R. Fish in Salps: the Association of Squaretails (Tetragonurus Spp) with Pelagic Tunicates. J. Mar. Biol. Assoc. UK. 61, 917–927. https://doi.org/10.1017/S0025315400023055 (1981).Article 

Google Scholar 
71.Choy, C. A., Haddock, S. H. D. & Robison, B. H. Deep pelagic food web structure as revealed by in situ feeding observations. Proc. R. Soc. B Biol. Sci. 284, 20172116. https://doi.org/10.1098/rspb.2017.2116 (2017).Article 

Google Scholar 
72.Robison, B. H., Sherlock, R. E., Reisenbichler, K. R. & McGill, P. R. Running the gauntlet: assessing the threats to vertical migrators. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00064 (2020).Article 

Google Scholar 
73.Hoving, H. J., Neitzel, P. & Robison, B. In situ observations lead to the discovery of the large ctenophore Kiyohimea usagi (Lobata: Eurhamphaeidae) in the eastern tropical Atlantic. Zootaxa 4526, 232–238. https://doi.org/10.11646/zootaxa.4526.2.8 (2018).Article 
PubMed 

Google Scholar 
74.Arai, M. N. Predation on pelagic coelenterates: a review. J. Mar. Biol. Assoc. UK. 85, 523–536. https://doi.org/10.1017/S0025315405011458 (2005).Article 

Google Scholar  More

1.IEA. Renewables information 2019 overview. Clim. Change 2013 Phys. Sci. Basis 53, 1–30 (2019).
Google Scholar 
2.IPCC. Summary for Policymakers. In: Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. [V. Masson-Delmotte, P. Zhai, H. O. Pörtner, D. Roberts, J. Skea, P. R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, T. Waterfield (eds.)]. (2018).3.IPCC. Foreword, Preface, Dedication and In Memoriam. Clim. Chang. 2014 Mitig. Clim. Chang. Contrib. Work. Gr. III to Fifth Assess. Rep. Intergov. Panel Clim. Chang. 1454 (2014).4.Shakoor, A. et al. Biogeochemical transformation of greenhouse gas emissions from terrestrial to atmospheric environment and potential feedback to climate forcing. Environ. Sci. Pollut. Res. 27, 38513–38536 (2020).CAS 
Article 

Google Scholar 
5.Mani, M., Bandyopadhyay, S., Chonabayashi, S., Markandya, A. & Mosier, T. South Asia’s Hotspots: The Impact of Temperature and Precipitation Changes on Living Standards. South Asia’s Hotspots: The Impact of Temperature and Precipitation Changes on Living Standards (2018). https://doi.org/10.1596/978-1-4648-1155-5.6.Sarkar, M. S. K., Sadeka, S., Sikdar, M. M. H. & Badiuzzaman. Energy consumption and CO2 emission in Bangladesh: Trends and policy implications. Asia Pac. J. Energy Environ. 5, 41–48 (2018).Article 

Google Scholar 
7.Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
8.Dixon, R. K. et al. Carbon pools and flux of global forest ecosystems. Science 263, 185–190 (1994).ADS 
CAS 
PubMed 
Article 

Google Scholar 
9.Adame, M. F. et al. Carbon stocks of tropical coastal wetlands within the Karstic landscape of the Mexican Caribbean. PLoS ONE 8, e56569 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Bonan, G. B. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
11.Henry, M. et al. Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder farming systems of western Kenya. Agric. Ecosyst. Environ. 129, 238–252 (2009).CAS 
Article 

Google Scholar 
12.Kumar, B. M. Species richness and aboveground carbon stocks in the homegardens of central Kerala, India. Agric. Ecosyst. Environ. 140, 430–440 (2011).Article 

Google Scholar 
13.Mattsson, E., Ostwald, M., Nissanka, S. P. & Pushpakumara, D. K. N. G. Quantification of carbon stock and tree diversity of homegardens in a dry zone area of Moneragala district, Sri Lanka. Agrofor. Syst. 89, 435–445 (2015).Article 

Google Scholar 
14.Nair, P. K. R., Kumar, B. M. & Nair, V. D. Agroforestry as a strategy for carbon sequestration. J. Plant Nutr. Soil Sci. 172, 10–23 (2009).CAS 
Article 

Google Scholar 
15.Murthy, I. K. Carbon sequestration potential of agroforestry systems in India. J. Earth Sci. Clim. Change 4, 1–7 (2013).Article 
CAS 

Google Scholar 
16.Delgado, J. A. et al. Conservation practices to mitigate and adapt to climate change. J. Soil Water Conserv. 66, 118–129 (2011).Article 

Google Scholar 
17.Kabir, M. E. & Webb, E. L. Can homegardens conserve biodiversity in Bangladesh?. Biotropica 40, 95–103 (2008).
Google Scholar 
18.FD [Forest Department]. Bangladesh Forestry Master Plan 2017–2036, 2036 (2017).19.Mather, A. Global forest resources assessment 2000 main report. Land Use Policy 20, 195 (2003).Article 

Google Scholar 
20.FD [Forest Department]. District wise forest area of Bangladesh 2016. Preprint at http://www.bforest.gov.bd/ (2020).21.Mukul, S. A. et al. A new estimate of carbon for Bangladesh forest ecosystems with their spatial distribution and REDD+ implications. Int. J. Res. Land-use Sustain. 1, 33–41 (2014).
Google Scholar 
22.Nath, T. K., Aziz, N. & Inoue, M. Contribution of homestead forests to rural economy and climate change mitigation: A study from the ecologically critical area of Cox’s Bazar—Teknaf Peninsula, Bangladesh. Small-scale For. 14, 1–18 (2015).Article 

Google Scholar 
23.Jaman, M. S. et al. Quantification of carbon stock and tree diversity of homegardens in Rangpur District, Bangladesh. Int. J. Agric. For. 6, 169–180 (2016).
Google Scholar 
24.Wang, S. & Huang, Y. Determinants of soil organic carbon sequestration and its contribution to ecosystem carbon sinks of planted forests. Glob. Change Biol. 26, 3163–3173 (2020).ADS 
Article 

Google Scholar 
25.Khan, M. N. I. et al. Allometric relationships of stand level carbon stocks to basal area, tree height and wood density of nine tree species in Bangladesh. Glob. Ecol. Conserv. 22, e01025 (2020).Article 

Google Scholar 
26.Shen, Y. et al. Tree aboveground carbon storage correlates with environmental gradients and functional diversity in a tropical forest. Sci. Rep. 6, 25304 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Petrokofsky, G. et al. Comparison of methods for measuring and assessing carbon stocks and carbon stock changes in terrestrial carbon pools. How do the accuracy and precision of current methods compare? A systematic review protocol. Environ. Evid. 1, 1–21 (2012).Article 

Google Scholar 
28.Dondini, M., Hastings, A., Saiz, G., Jones, M. B. & Smith, P. The potential of Miscanthus to sequester carbon in soils: Comparing field measurements in Carlow, Ireland to model predictions. GCB Bioenergy 1, 413–425 (2009).CAS 
Article 

Google Scholar 
29.Ullah, M. R. & Al-Amin, M. Above- and below-ground carbon stock estimation in a natural forest of Bangladesh. J. For. Sci. 58, 372–379 (2012).Article 

Google Scholar 
30.Nouvellon, Y. et al. Age-related changes in litter inputs explain annual trends in soil CO2 effluxes over a full Eucalyptus rotation after afforestation of a tropical savannah. Biogeochemistry 111, 515–533 (2012).CAS 
Article 

Google Scholar 
31.Krishna, M. P. & Mohan, M. Litter decomposition in forest ecosystems: A review. Energy Ecol. Environ. 2, 236–249 (2017).Article 

Google Scholar 
32.Patra, P. K. et al. The carbon budget of South Asia. Biogeosciences 10, 513–527 (2013).ADS 
Article 

Google Scholar 
33.Ostertag, R., Marín-Spiotta, E., Silver, W. L. & Schulten, J. Litterfall and decomposition in relation to soil carbon pools along a secondary forest chronosequence in Puerto Rico. Ecosystems 11, 701–714 (2008).CAS 
Article 

Google Scholar 
34.Nair, P. K. R. & Garrity, D. Afroforestry—The Future of Global Land Use, Advances in Agroforestry (Springer, 2012) https://doi.org/10.1007/978-94-007-4676-3_1.Book 

Google Scholar 
35.Abrar, M. M. et al. Variations in the profile distribution and protection mechanisms of organic carbon under long-term fertilization in a Chinese Mollisol. Sci. Total Environ. 723, 138181 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
36.Baul, T. K., Datta, D. & Alam, A. A comparative study on household level energy consumption and related emissions from renewable (biomass) and non-renewable energy sources in Bangladesh. Energy Policy https://doi.org/10.1016/j.enpol.2017.12.037 (2018).Article 

Google Scholar 
37.Mackey, B. et al. Understanding the importance of primary tropical forest protection as a mitigation strategy. Mitig. Adapt. Strateg. Glob. Change 25, 763–787 (2020).Article 

Google Scholar 
38.Zaman, M. A., Osman, K. T. & Sirajul Haque, S. M. Comparative study of some soil properties in forested and deforested areas in Cox’s Bazar and Rangamati Districts, Bangladesh. J. For. Res. 21, 319–322 (2010).CAS 
Article 

Google Scholar 
39.Akhtaruzzaman, M., Osman, K. T. & Sirajul Haque, S. M. Soil properties in two forest sites in Cox’s Bazar, Bangladesh. J. For. Environ. Sci. 31, 280–287 (2015).
Google Scholar 
40.Islam, M., Deb, G. P. & Rahman, M. Forest fragmentation reduced carbon storage in a moist tropical forest in Bangladesh: Implications for policy development. Land Use Policy 65, 15–25 (2017).Article 

Google Scholar 
41.Nair, P. K. R., Nair, V. D., Kumar, B. M. & Haile, S. G. Soil carbon sequestration in tropical agroforestry systems: A feasibility appraisal. Environ. Sci. Policy 12, 1099–1111 (2009).CAS 
Article 

Google Scholar 
42.Aryal, D. R., Gómez-González, R. R., Hernández-Nuriasmú, R. & Morales-Ruiz, D. E. Carbon stocks and tree diversity in scattered tree silvopastoral systems in Chiapas, Mexico. Agrofor. Syst. 93, 213–227 (2019).Article 

Google Scholar 
43.Islam, M., Dey, A. & Rahman, M. Effect of tree diversity on soil organic carbon content in the homegarden agroforestry system of North-Eastern Bangladesh. Small-scale For. 14, 91–101 (2015).Article 

Google Scholar 
44.Saha, S. K., Nair, P. K. R., Nair, V. D. & Kumar, B. M. Soil carbon stock in relation to plant diversity of homegardens in Kerala, India. Agrofor. Syst. 76, 53–65 (2009).Article 

Google Scholar 
45.Kothandaraman, S., Dar, J. A., Sundarapandian, S., Dayanandan, S. & Khan, M. L. Ecosystem-level carbon storage and its links to diversity, structural and environmental drivers in tropical forests of Western Ghats, India. Sci. Rep. 10, 1–15 (2020).Article 
CAS 

Google Scholar 
46.Youkhana, A. & Idol, T. Tree pruning mulch increases soil C and N in a shaded coffee agroecosystem in Hawaii. Soil Biol. Biochem. 41, 2527–2534 (2009).CAS 
Article 

Google Scholar 
47.Flessa, H. et al. Storage and stability of organic matter and fossil carbon in a Luvisol and Phaeozem with continuous maize cropping: A synthesis. J. Plant Nutr. Soil Sci. 171, 36–51 (2008).CAS 
Article 

Google Scholar 
48.Semere, M. Biomass and soil carbon stocks assessment of agroforestry systems and adjacent cultivated land, in Cheha Wereda, Gurage Zone, Ethiopia. Int. J. Environ. Sci. Nat. Resour. 20, 119–125 (2019).
Google Scholar 
49.Ramachandran Nair, P. K., Nair, V. D., Mohan Kumar, B. & Showalter, J. M. Carbon sequestration in agroforestry systems. Adv. Agron. 108, 237–307 (2010).Article 
CAS 

Google Scholar 
50.Mustafa, A. et al. Soil aggregation and soil aggregate stability regulate organic carbon and nitrogen storage in a red soil of southern China. J. Environ. Manag. 270, 110894 (2020).CAS 
Article 

Google Scholar 
51.Sayer, E. J. et al. Tropical forest soil carbon stocks do not increase despite 15 years of doubled litter inputs. Sci. Rep. 9, 1–9 (2019).ADS 
Article 
CAS 

Google Scholar 
52.Rahman, M., Biswas, J., Maniruzzaman, M., Choudhury, A. & Ahmed, F. Effect of tillage practices and rice straw management on soil environment and carbon dioxide emission. Agriculture 15, 127–142 (2017).
Google Scholar 
53.Day, M., Baldauf, C., Rutishauser, E. & Sunderland, T. C. H. Relationships between tree species diversity and above-ground biomass in Central African rainforests: Implications for REDD. Environ. Conserv. 41, 64–72 (2014).Article 

Google Scholar 
54.Poorter, L. et al. Diversity enhances carbon storage in tropical forests. Glob. Ecol. Biogeogr. 24, 1314–1328 (2015).Article 

Google Scholar 
55.Rahman, M. M., Kabir, M. E., Jahir Uddin Akon, A. S. M. & Ando, K. High carbon stocks in roadside plantations under participatory management in Bangladesh. Glob. Ecol. Conserv. 3, 412–423 (2015).Article 

Google Scholar 
56.Kamruzzaman, M., Ahmed, S., Paul, S., Rahman, M. M. & Osawa, A. Stand structure and carbon storage in the oligohaline zone of the Sundarbans mangrove forest, Bangladesh. For. Sci. Technol. 14, 23–28 (2018).
Google Scholar 
57.Asok, S. & Sobha, V. Analysis of variation of soil bulk densities with respect to different vegetation classes, in a tropical rain forest—A study in Shendurney Wildlife Sanctuary, S. Kerala, India. Glob. J. Environ. Res. 8, 17–20 (2014).
Google Scholar 
58.Périé, C. & Ouimet, R. Organic carbon, organic matter and bulk density relationships in boreal forest soils. Can. J. Soil Sci. 88, 315–325 (2008).Article 

Google Scholar 
59.Biswas, A., Alamgir, M., Haque, S. M. S. & Osman, K. T. Study on soils under shifting cultivation and other land use categories in Chittagong Hill Tracts, Bangladesh. J. For. Res. 23, 261–265 (2012).CAS 
Article 

Google Scholar 
60.Leff, J. W. et al. Experimental litterfall manipulation drives large and rapid changes in soil carbon cycling in a wet tropical forest. Glob. Change Biol. 18, 2969–2979 (2012).ADS 
Article 

Google Scholar 
61.Wang, Q., He, T., Wang, S. & Liu, L. Carbon input manipulation affects soil respiration and microbial community composition in a subtropical coniferous forest. Agric. For. Meteorol. 178–179, 152–160 (2013).ADS 
Article 

Google Scholar 
62.Ali Shah, S. A. et al. Long-term fertilization affects functional soil organic carbon protection mechanisms in a profile of Chinese loess plateau soil. Chemosphere 267, 128897 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
63.Miah, D., Uddin, M. F., Bhuiyan, M. K., Koike, M. & Shin, M. Y. Carbon sequestration by the indigenous tree species in the reforestation program in Bangladesh-aphanamixis polystachya Wall. and Parker. Forest Sci. Technol. 5, 62–65 (2009).Article 

Google Scholar 
64.Kibria, M. G. & Saha, N. Analysis of existing agroforestry practices in Madhupur Sal forest: An assessment based on ecological and economic perspectives. J. For. Res. 22, 533–542 (2011).
CAS 
Article 

Google Scholar 
65.Mikrewongel Tadesse, A. B. Estimation of carbon stored in agroforestry practices in Gununo Watershed, Wolayitta Zone, Ethiopia. J. Ecosyst. Ecogr. 05, 1–5 (2015).Article 

Google Scholar 
66.Abrar, M. M. et al. Carbon, nitrogen, and phosphorus stoichiometry mediate sensitivity of carbon stabilization mechanisms along with surface layers of a Mollisol after long-term fertilization in Northeast China. J. Soils Sediments 21, 705–723 (2021).CAS 
Article 

Google Scholar 
67.Ahmed, N. & Glaser, M. Coastal aquaculture, mangrove deforestation and blue carbon emissions: Is REDD+ a solution?. Mar. Policy 66, 58–66 (2016).Article 

Google Scholar 
68.BBS [Bangladesh Bureau of Statistics]. Statistical yearbook of Bangladesh 2018. Statistics Division, Ministry of Planning, Government of the People’s Republic of Bangladesh (2019).69.BMD [Bangladesh Metereological Department]. Cox’s Bazar region, Chittagong, Bangladesh (2020).70.Osman, K. S., Jashimuddin, M., Haque, S. M. S. & Miah, S. Effect of shifting cultivation on soil physical and chemical properties in Bandarban hill district, Bangladesh. J. For. Res. 24, 791–795 (2013).CAS 
Article 

Google Scholar 
71.SRDI. Soil resource development institute. Annu. Report. Soil Resour. Dev. Institute, Dhaka, Bangladesh (2018).72.Upazila Parishad Office. Bandarban Sadar Upazila, Bandarban District, Chittagong Hill Trcats, Bangladesh (2019).73.Blake, G. R. Bulk density. In Methods of Soil Analysis. Part 1 (eds Black, C. A. et al.) 894–895 (American Society of Agronomy Inc., 1965).
Google Scholar 
74.Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).ADS 
Article 

Google Scholar 
75.Macdicken, K. G. A guide to monitoring carbon storage in forestry and agroforestry projects (2015).76.Sattar, M. A., Bhattacharje, D. K. & Kabir, M. F. Physical and Mechanical Properties and Uses of Timbers of Bangladesh (Bangladesh Forest Research Institute, 1999).
Google Scholar 
77.Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).Article 

Google Scholar 
78.Data Set. Definitions (2020). https://doi.org/10.32388/5b0dft.79.Hairiah, K. Measuring carbon stocks: Across land use systems: a manual. Published in close cooperation with Brawijaya University and ICALRRD (Indonesian Center for Agricultural Land Resources Research and Development) (2011).80.Frangi, J. L., Lugo, A. E., Forest, F., Frangi, J. L. & Service, F. Ecosystem dynamics of a subtropical floodplain forest published by: Ecological Society of America. Ecosyst. Dyn. Subtrop. 55, 351–369 (2016).
Google Scholar 
81.Issa, S., Dahy, B., Ksiksi, T. & Saleous, N. Development of a new allometric equation correlated WTH RS variables for the assessment of date palm biomass. Proc. 39th Asian Conf. Remote Sens. Remote Sens. Enabling Prosper. ACRS 2018 2, 730–739 (2018).82.Brown, S. Estimating biomass and biomass change of tropical forests: A Primer. FAO For. Pap. 134, 13–33 (1997).
Google Scholar 
83.Margalef, R. Information theory in ecology. Gen. Syst. 3, 36–71 (1958).
Google Scholar 
84.Michael, P. Ecological Methods for Field and Laboratory Investigation (Tata Mc Graw Hill, 1990).
Google Scholar 
85.Shukla, R. S. & Chandel, P. S. Plant Ecology and Soil Science 9th edn. (S. Chand and Company, 2000).
Google Scholar 
86.Ball, D. F. Loss-on-ignition as an estimate. J. Soil Sci. 15, 84–92 (1964).CAS 
Article 

Google Scholar 
87.Pearson, T., Walker, S. & Brown, S. Sourcebook for Land Use, Land-Use Change and Forestry Projects 29 (Winrock International and the BioCarbon Fund of the World Bank, 2005).
Google Scholar 
88.Pearson, T. R. H., Brown, S. L. & Birdsey, R. A. Measurement guidelines for the sequestration of forest carbon. Gen. Tech. Rep. NRS-18. Delaware United States Dep. Agric. For. Serv. 18, 42 (2007).89.Coleman, D. C. Soil carbon balance in a successional grassland. Oikos 24, 195–199. https://doi.org/10.2307/3543875 (1973).CAS 
Article 

Google Scholar  More

1.Abbott, R. J., Barton, N. H. & Good, J. M. Genomics of hybridization and its evolutionary consequences. Mol. Ecol. 25, 2325–2332 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Harrison, R. G. & Larson, E. L. Heterogeneous genome divergence, differential introgression, and the origin and structure of hybrid zones. Mol. Ecol. 25, 2454–2466 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Gompert, Z., Mandeville, E. G. & Buerkle, C. A. Analysis of population genomic data from hybrid zones. Annu. Rev. Ecol. Evol. Syst. 48, 207–229 (2017).Article 

Google Scholar 
4.Jiggins, C. D. & Mallet, J. Bimodal hybrid zones and speciation. Trends Ecol. Evol. 15, 250–255 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Doebeli, M. & Dieckmann, U. Speciation along environmental gradients. Nature 421, 259–264 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).PubMed 
Article 
PubMed Central 

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

Google Scholar 
8.Newman, C. E. & Rissler, L. J. Phylogeographic analyses of the southern leopard frog: The impact of geography and climate on the distribution of genetic lineages vs. subspecies. Mol. Ecol. 20, 5295–5312 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Smith, K. L. et al. Spatio-temporal changes in the structure of an Australian frog hybrid zone: A 40-year perspective. Evolution 67, 3442–3454 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Visser, M., Leeuw, M. D., Zuiderwijk, A. & Arntzen, J. W. Stabilization of a salamander moving hybrid zone. Ecol. Evol. 7, 689–696 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Carneiro, M. et al. Steep clines within a highly permeable genome across a hybrid zone between two subspecies of the European rabbit. Mol. Ecol. 22, 2511–2525 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Gompert, Z., Parchman, T. L. & Buerkle, C. A. Genomics of isolation in hybrids. Philos. Trans. R. Soc. B 367, 439–450 (2012).Article 

Google Scholar 
13.Zieliński, P. et al. Differential introgression across newt hybrid zones–evidence from replicated transects. Mol. Ecol. 28, 4811–4824 (2019).PubMed 
Article 
CAS 
PubMed Central 

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

Google Scholar 
15.Naciri, Y. & Linder, H. P. The genetics of evolutionary radiations. Biol. Rev. 95, 1055–1072 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Kearns, A. M. et al. Genomic evidence of speciation reversal in ravens. Nat. Commun. 9, 1–13 (2018).CAS 
Article 

Google Scholar 
17.Butlin, R. Speciation by reinforcement. Trends Ecol. Evol. 2, 8–13 (1987).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Arntzen, J. W., de Vries, W., Canestrelli, D. & Martínez-Solano, I. Hybrid zone formation and contrasting outcomes of secondary contact over transects in common toads. Mol. Ecol. 26, 5663–5675 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Zamudio, K. R., Bell, R. C. & Mason, N. A. Phenotypes in phylogeography: Species’ traits, environmental variation, and vertebrate diversification. Proc. Natl. Acad. Sci. 113, 8041–8048 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Devitt, T. J., Baird, S. J. & Moritz, C. Asymmetric reproductive isolation between terminal forms of the salamander ring species Ensatina eschscholtzii revealed by fine-scale genetic analysis of a hybrid zone. BMC Evol. Biol. 11, 245 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
21.Melo, M. C., Salazar, C., Jiggins, C. D. & Linares, M. Assortative mating preferences among hybrids offers a route to hybrid speciation. Evolution 63, 1660–1665 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Cornetti, L. et al. Reproductive isolation between oviparous and viviparous lineages of the Eurasian common lizard Zootoca vivipara in a contact zone. Biol. J. Linn. Soc. 114, 566–573 (2015).Article 

Google Scholar 
23.Rafati, N. et al. A genomic map of clinal variation across the European rabbit hybrid zone. Mol. Ecol. 27, 1457–1478 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Shipilina, D., Serbyn, M., Ivanitskii, V., Marova, I. & Backström, N. Patterns of genetic, phenotypic, and acoustic variation across a chiffchaff (Phylloscopus collybita abietinus/tristis) hybrid zone. Ecol. Evol. 7(7), 2169–2180 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Grabenstein, K. C. & Taylor, S. A. Breaking barriers: Causes, consequences, and experimental utility of human-mediated hybridization. Trends Ecol. Evol. 33(3), 198–212 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Coates, D. J., Byrne, M. & Moritz, C. Genetic diversity and conservation units: Dealing with the species-population continuum in the age of genomics. Front. Ecol. Evol. 6, 165 (2018).Article 

Google Scholar 
27.Velo-Antón, G., Santos, X., Sanmartín-Villar, I., Cordero-Rivera, A. & Buckley, D. Intraspecific variation in clutch size and maternal investment in pueriparous and larviparous Salamandra salamandra females. Evol. Ecol. 29(1), 185–204 (2015).Article 

Google Scholar 
28.Beukema, W., Nicieza, A. G., Lourenço, A. & Velo-Antón, G. Colour polymorphism in Salamandra salamandra (Amphibia: Urodela), revealed by a lack of genetic and environmental differentiation between distinct phenotypes. J. Zool. Syst. Evol. Res. 54(2), 127–136 (2016).Article 

Google Scholar 
29.Alarcón-Ríos, L., Nicieza, A. G., Kaliontzopoulou, A., Buckley, D. & Velo-Antón, G. Evolutionary history and not heterochronic modifications associated with viviparity drive head shape differentiation in a reproductive polymorphic species, Salamandra salamandra. Evol. Biol. 47(1), 43–55 (2020).Article 

Google Scholar 
30.Burgon, J. D. et al. Phylogenomic inference of species and subspecies diversity in the Palearctic salamander genus Salamandra. Mol. Phylogenet. Evol. 157, 107063 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
31.García-París, M., Alcobendas, M., Buckley, D. & Wake, D. Dispersal of viviparity across contact zones in Iberian populations of Fire salamanders (Salamandra) inferred from discordance of genetic and morphological traits. Evolution 57(1), 129–143 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Velo-Antón, G., García-París, M., Galán, P. & CorderoRivera, A. The evolution of viviparity in holocene islands: ecological adaptation versus phylogenetic descent along the transition from aquatic to terrestrial environments. J. Zool. Syst. Evol. Res. 45(4), 345–352 (2007).Article 

Google Scholar 
33.Velo-Antón, G., Zamudio, K. R. & Cordero-Rivera, A. Genetic drift and rapid evolution of viviparity in insular fire salamanders (Salamandra salamandra). Heredity 108(4), 410–418 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Uotila, E., Díaz, A. C., Azkue, I. S. & Rubio Pilarte, X. Variation in the reproductive strategies of Salamandra salamandra (Linnaeus, 1758) populations in the province of Gipuzkoa (Basque Country). Munibe Cienc. Nat. Nat. Zientziak 61, 91–101 (2013).
Google Scholar 
35.Galán, P. Viviparismo y distribución de Salamandra salamandra bernardezi en el norte de Galicia. Bol. Asoc. Herpetol. Esp. 18, 44–49 (2007).
Google Scholar 
36.Alcobendas, M., Dopazo, H. & Alberch, P. Geographic variation in allozymes of populations of Salamandra salamandra (Amphibia: Urodela) exhibiting distinct reproductive modes. J. Evol. Biol. 9(1), 83–102 (1996).Article 

Google Scholar 
37.Alarcón-Ríos, L., Nicieza, A. G., Lourenço, A. & Velo-Antón, G. The evolution of pueriparity maintains multiple paternity in a polymorphic viviparous salamander. Sci. Rep. 10, 14744 (2020).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Lourenço, A., Gonçalves, J., Carvalho, F., Wang, I. J. & Velo-Antón, G. Comparative landscape genetics reveals the evolution of viviparity reduces genetic connectivity in fire salamanders. Mol. Ecol. 28(20), 4573–4591 (2019).PubMed 
Article 
CAS 

Google Scholar 
39.Velo-Antón, G., & Buckley, D. Salamandra común—Salamandra salamandra. in Enciclopedia Virtual de los Vertebrados Españoles (L.M. Carrascal, A Salvador, Eds.) (Museo Nacional de Ciencias Naturales, 2015). Retrieved from http://www.vertebradosibericos.org/anfibios/salsal.html40.Cordero, A., Velo-Antón, G. & Galán, P. Ecology of amphibians in small coastal Holocene islands: Local adaptations and the effect of exotic tree plantations. Munibe 25, 94–103 (2007).
Google Scholar 
41.Antunes, B. et al. Combining phylogeography and landscape genetics to infer the evolutionary history of a short-range Mediterranean relict, Salamandra salamandra longirostris. Conserv. Genet. 19(6), 1411–1424 (2018).CAS 
Article 

Google Scholar 
42.Lourenço, A., Álvarez, D., Wang, I. J. & Velo-Antón, G. Trapped within the city: Integrating demography, time since isolation and population-specific traits to assess the genetic effects of urbanization. Mol. Ecol. 26(6), 1498–1514 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
43.Landguth, E. L., Cushman, S. A., Murphy, M. A. & Luikart, G. Relationships between migration rates and landscape resistance assessed using individual-based simulations. Mol. Ecol. Resour. 10(5), 854–862 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Zhang, P., Papenfuss, T. J., Wake, M. H., Qu, L. & Wake, D. B. Phylogeny and biogeography of the family Salamandridae (Amphibia: Caudata) inferred from complete mitochondrial genomes. Mol. Phylogenet. Evol. 49(2), 586–597 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Hendrix, R., Hauswaldt, S., Veith, M. & Steinfartz, S. Strong correlation between cross-amplification success and genetic distance across all members of ‘True Salamanders’ (Amphibia: Salamandridae) revealed by Salamandra salamandra-specific microsatellite loci. Mol. Ecol. Resour. 10(6), 1038–1047 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Steinfartz, S., Kuesters, D. & Tautz, D. Isolation and characterization of polymorphic tetranucleotide microsatellite loci in the Fire salamander Salamandra salamandra (Amphibia: Caudata). Mol. Ecol. Notes 4(4), 626–628 (2004).CAS 
Article 

Google Scholar 
47.Álvarez, D., Lourenço, A., Oro, D. & Velo-Antón, G. Assessment of census (N) and effective population size (N e) reveals consistency of N e single-sample estimators and a high N e/N ratio in an urban and isolated population of fire salamanders. Conserv. Genet. Resour. 7(3), 705–712 (2015).Article 

Google Scholar 
48.Antunes, B., Velo-Antón, G., Buckley, D., Pereira, R. & Martínez-Solano, I. Physical and ecological isolation contribute to maintain genetic differentiation between fire salamander subspecies. Heredity. https://doi.org/10.1038/s41437-021-00405-0 (2021). 49.Lourenço, A., Sequeira, F., Buckley, D. & Velo-Antón, G. Role of colonization history and species-specific traits on contemporary genetic variation of two salamander species in a Holocene island-mainland system. J. Biogeogr. 45(5), 1054–1066 (2018).Article 

Google Scholar 
50.Lourenço, A., Antunes, B., Wang, I. J. & Velo-Antón, G. Fine-scale genetic structure in a salamander with two reproductive modes: Does reproductive mode affect dispersal?. Evol. Ecol. 32(6), 699–732 (2018).Article 

Google Scholar 
51.Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 17. Mol. Biol. Evol. 29(8), 1969–1973 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 9(8), 772 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Ehl, S., Vences, M. & Veith, M. Reconstructing evolution at the community level: A case study on Mediterranean amphibians. Mol. Phylogenet. Evol. 134, 211–225 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Miller, M. A., Pfeiffer, W., & Schwartz, T. Creating the CIPRES Science Gateway for inference of large phylogenetic trees. in 2010 gateway computing environments workshop (GCE pp. 1–8) (2010).55.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155(2), 945–959 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Jakobsson, M. & Rosenberg, N. A. CLUMPP: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23(14), 1801–1806 (2007).CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
58.Anderson, E. C. & Thompson, E. A. A model-based method for identifying species hybrids using multilocus genetic data. Genetics 160(3), 1217–1229 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Anderson, E. C. Bayesian inference of species hybrids using multilocus dominant genetic markers. Philos. Trans. R. Soc. B 363(1505), 2841–2850 (2008).Article 

Google Scholar 
60.Shurtliff, Q. R., Murphy, P. J. & Matocq, M. D. Ecological segregation in a small mammal hybrid zone: Habitat-specific mating opportunities and selection against hybrids restrict gene flow on a fine spatial scale. Evolution 68(3), 729–742 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
61.Shirk, A. J., Landguth, E. L. & Cushman, S. A. A comparison of individual-based genetic distance metrics for landscape genetics. Mol. Ecol. Resour. 17(6), 1308–1317 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Wang, J. COANCESTRY: A program for simulating, estimating and analysing relatedness and inbreeding coefficients. Mol. Ecol. Resour. 11(1), 141–145 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
63.Queller, D. C. & Goodnight, K. F. Estimating relatedness using genetic markers. Evolution 43, 258–275 (1989).PubMed 
Article 
PubMed Central 

Google Scholar 
64.Wang, J. Triadic IBD coefficients and applications to estimating pairwise relatedness. Genet. Res. 89, 135–153 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.Estrada-Peña, A., Estrada-Sánchez, A. & de la Fuente, J. A global set of Fourier-transformed remotely sensed covariates for the description of abiotic niche in epidemiological studies of tick vector species. Parasit. Vectors 7(1), 302 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
66.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
67.Nosil, P., Egan, S. P. & Funk, D. J. Heterogeneous genomic differentiation between walking-stick ecotypes: “Isolation by adaptation” and multiple roles for divergent selection. Evolution 62, 316–336 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
68.Graves, T. A., Beier, P. & Royle, J. A. Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal. Mol. Ecol. 22(15), 3888–3903 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
69.Peterman, W. E., Connette, G. M., Semlitsch, R. D. & Eggert, L. S. Ecological resistance surfaces predict fine-scale genetic differentiation in a terrestrial woodland salamander. Mol. Ecol. 23(10), 2402–2413 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Tarroso, P., Carvalho, S. B. & Velo-Antón, G. Phylin 2.0: Extending the phylogeographical interpolation method to include uncertainty and user-defined distance metrics. Mol. Ecol. Resour. 19(4), 1081–1094 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
71.Peterman, W. E. ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol. Evol. 9(6), 1638–1647 (2018).Article 

Google Scholar 
72.Hutchison, D. W. & Templeton, A. R. Correlation of pairwise genetic and geographic distance measures: Inferring the relative influences of gene flow and drift on the distribution of genetic variability. Evolution 53(6), 1898–1914 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
73.Horreo, J. L. et al. Genetic introgression among differentiated clades is lower among clades exhibiting different parity modes. Heredity 123(2), 264–272 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
74.Sota, T. & Tanabe, T. Multiple speciation events in an arthropod with divergent evolution in sexual morphology. Proc. R. Soc. B 277(1682), 689–696 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
75.Merrill, R. M., Van Schooten, B., Scott, J. A. & Jiggins, C. D. Pervasive genetic associations between traits causing reproductive isolation in Heliconius butterflies. Proc. R. Soc. B 278(1705), 511–518 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
76.Singhal, S. & Moritz, C. Reproductive isolation between phylogeographic lineages scales with divergence. Proc. R. Soc. B 280(1772), 20132246 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
77.Donaire, D. & Rivera, X. L. salamandra común Salamandra salamandra (Linnaeus, 1758) en el subcantábrico: Origen, dispersión, subspecies y zonas de introgresión. Bull. Soc. Catal. Herpetol. 23, 7–38 (2016).
Google Scholar 
78.Recuero, E. & García-París, M. Evolutionary history of Lissotriton helveticus: multilocus assessment of ancestral vs. recent colonization of the Iberian Peninsula. Mol. Phylogenet. Evol. 60(1), 170–182 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
79.Dufresnes, C. et al. Are glacial refugia hotspots of speciation and cyto-nuclear discordances? Answers from the genomic phylogeography of Spanish common frogs. Mol. Ecol. 29, 986–1000 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Toews, D. P. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21(16), 3907–3930 (2012).CAS 
Article 

Google Scholar 
81.Bisconti, R., Porretta, D., Arduino, P., Nascetti, G. & Canestrelli, D. Hybridization and extensive mitochondrial introgression among fire salamanders in peninsular Italy. Sci. Rep. 8(1), 1–10 (2018).CAS 
Article 

Google Scholar 
82.Dinis, M. et al. Allopatric diversification and evolutionary melting pot in a North African Palearctic relict: The biogeographic history of Salamandra algira. Mol. Phylogenet. Evol. 130, 81–91 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Buckley, D., Alcobendas, M., García-París, M. & Wake, M. H. Heterochrony, cannibalism, and the evolution of viviparity in Salamandra salamandra. Evol. Dev. 9(1), 105–115 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Helfer, V., Broquet, T. & Fumagalli, L. Sex-specific estimates of dispersal show female philopatry and male dispersal in a promiscuous amphibian, the alpine salamander (Salamandra atra). Mol. Ecol. 21(19), 4706–4720 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Vörös, J. et al. Increased genetic structuring of isolated Salamandra salamandra populations (Caudata: Salamandridae) at the margins of the Carpathian Mountains. J. Zool. Syst. Evol. Res. 55(2), 138–149 (2017).Article 

Google Scholar 
86.Dudaniec, R. Y., Spear, S. F., Richardson, J. S. & Storfer, A. Current and historical drivers of landscape genetic structure differ in core and peripheral salamander populations. PLoS ONE 7(5), e36769 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Richardson, J. L. Divergent landscape effects on population connectivity in two co-occurring amphibian species. Mol. Ecol. 21(18), 4437–4451 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
88.Mulder, K. P., Cortes-Rodriguez, N., Campbell Grant, E. H., Brand, A. & Fleischer, R. C. North-facing slopes and elevation shape asymmetric genetic structure in the range-restricted salamander Plethodon shenandoah. Ecol. Evol. 9(9), 5094–5105 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
89.Velo-Antón, G., Parra, J. L., Parra-Olea, G. & Zamudio, K. R. Tracking climate change in a dispersal-limited species: Reduced spatial and genetic connectivity in a montane salamander. Mol. Ecol. 22(12), 3261–3278 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Sánchez-Montes, G., Wang, J., Ariño, A. H. & Martínez-Solano, Í. Mountains as barriers to gene flow in amphibians: Quantifying the differential effect of a major mountain ridge on the genetic structure of four sympatric species with different life history traits. J. Biogeogr. 45(2), 318–331 (2018).Article 

Google Scholar 
91.Figueiredo-Vázquez, C., Lourenço, A. & Velo-Antón, G. Riverine barriers to gene flow in a salamander with both aquatic and terrestrial reproduction. Evol Ecol https://doi.org/10.1007/s10682-021-10114-z (2021). 92.Czypionka, T., Goedbloed, D. J., Steinfartz, S. & Nolte, A. W. Plasticity and evolutionary divergence in gene expression associated with alternative habitat use in larvae of the European Fire Salamander. Mol. Ecol. 27(12), 2698–2713 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
93.Arntzen, J. W. & van Belkom, J. ‘Mainland-island’population structure of a terrestrial salamander in a forest-bocage landscape with little evidence for in situ ecological speciation. Sci. Rep. 10(1), 1–15 (2020).Article 
CAS 

Google Scholar 
94.Burgon, J. D. et al. Functional colour genes and signals of selection in colour-polymorphic salamanders. Mol. Ecol. 29(7), 1284–1299 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
95.Velo-Antón, G. & Cordero-Rivera, A. Ethological and phenotypic divergence in insular fire salamanders: Diurnal activity mediated by predation?. Acta Ethol. 20(3), 243–253 (2017).Article 

Google Scholar 
96.González, T. E. D., & Penas, Á. The high mountain area of Northwestern Spain: The Cantabrian Range, the Galician-Leonese Mountains and the Bierzo Trench. In The vegetation of the Iberian Peninsula (pp. 251–321). (Springer, 2017). More

1.Sadd, B. M. & Schmid-Hempel, P. Principles of ecological immunology. Evol. Appl. 2, 113–121. https://doi.org/10.1111/j.1752-4571.2008.00057.x (2009).Article 
PubMed 

Google Scholar 
2.Kew, C. et al. Evolutionarily conserved regulation of immunity by the splicing factor RNP-6/PUF60. eLife 9, e57591, https://doi.org/10.7554/eLife.57591 (2020).3.Jurk, D. et al. Chronic inflammation induces telomere dysfunction and accelerates ageing in mice. Nat. Commun. 2, 4172–4172. https://doi.org/10.1038/ncomms5172 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
4.Lee, K. A. Linking immune defenses and life history at the levels of the individual and the species. Integr. Comp. Biol. 46, 1000–1015. https://doi.org/10.1093/icb/icl049 (2006).CAS 
Article 
PubMed 

Google Scholar 
5.Demas, G. E. & Nelson, R. J. Ecoimmunology. (Oxford University Press, 2012).6.Brock, P. M., Murdock, C. C. & Martin, L. B. The history of ecoimmunology and its integration with disease ecology. Integr. Comp. Biol. 54, 353–362. https://doi.org/10.1093/icb/icu046 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Gurven, M., Kaplan, H., Winking, J., Finch, C. & Crimmins, E. M. Aging and inflammation in two epidemiological worlds. J. Gerontol. A Biol. Sci. Med. Sci. 63, 196–199, https://doi.org/10.1093/gerona/63.2.196 (2008).8.Blackwell, A. D. et al. Immune function in Amazonian horticulturalists. Ann. Hum. Biol. 43, 382–396. https://doi.org/10.1080/03014460.2016.1189963 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
9.Blackwell, A. D., Martin, M., Kaplan, H. & Gurven, M. Antagonism between two intestinal parasites in humans: the importance of co-infection for infection risk and recovery dynamics. Proc. Biol. Sci. 280, 20131671–20131671. https://doi.org/10.1098/rspb.2013.1671 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Vasunilashorn, S. et al. Blood lipids, infection, and inflammatory markers in the Tsimane of Bolivia. Am. J. Hum. Biol. 22, 731–740. https://doi.org/10.1002/ajhb.21074 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Kraft, T. S. et al. Multi-system physiological dysregulation and ageing in a subsistence population. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 375, 20190610. https://doi.org/10.1098/rstb.2019.0610 (2020).Article 
PubMed 

Google Scholar 
12.Dansereau, G. et al. Conservation of physiological dysregulation signatures of aging across primates. Aging Cell 18, e12925–e12925. https://doi.org/10.1111/acel.12925 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Birkett, L. P. & Newton-Fisher, N. E. How abnormal is the behaviour of captive, zoo-living chimpanzees?. PLoS ONE 6, e20101. https://doi.org/10.1371/journal.pone.0020101 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Lewton, K. L. The effects of captive versus wild rearing environments on long bone articular surfaces in common chimpanzees (Pan troglodytes). PeerJ 5, e3668–e3668. https://doi.org/10.7717/peerj.3668 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Atsalis, S. & Videan, E. Reproductive aging in captive and wild common chimpanzees: Factors influencing the rate of follicular depletion. Am. J. Primatol. 71, 271–282. https://doi.org/10.1002/ajp.20650 (2009).Article 
PubMed 

Google Scholar 
16.Michaud, M. et al. Proinflammatory cytokines, aging, and age-related diseases. J. Am. Med. Dir. Assoc. 14, 877–882. https://doi.org/10.1016/j.jamda.2013.05.009 (2013).Article 
PubMed 

Google Scholar 
17.Ian, D. G. The effect of aging on susceptibility to infection. Rev. Infect. Dis. 2, 801–810. https://doi.org/10.1093/clinids/2.5.801 (1980).Article 

Google Scholar 
18.Monti, D., Ostan, R., Borelli, V., Castellani, G. & Franceschi, C. Inflammaging and human longevity in the omics era. Mech. Ageing Dev. 165, 129–138. https://doi.org/10.1016/j.mad.2016.12.008 (2017).Article 
PubMed 

Google Scholar 
19.Walker, E. M. et al. Inflammaging phenotype in rhesus macaques is associated with a decline in epithelial barrier-protective functions and increased pro-inflammatory function in CD161-expressing cells. Geroscience 41, 739–757. https://doi.org/10.1007/s11357-019-00099-7 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
20.Baylis, D., Bartlett, D. B., Patel, H. P. & Roberts, H. C. Understanding how we age: insights into inflammaging. Longev. Healthspan 2, 8–8. https://doi.org/10.1186/2046-2395-2-8 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
21.Peters, A., Delhey, K., Nakagawa, S., Aulsebrook, A. & Verhulst, S. Immunosenescence in wild animals: Meta-analysis and outlook. Ecol. Lett. 22, 1709–1722. https://doi.org/10.1111/ele.13343 (2019).Article 
PubMed 

Google Scholar 
22.Cheynel, L. et al. Immunosenescence patterns differ between populations but not between sexes in a long-lived mammal. Sci. Rep. 7, 13700–13700. https://doi.org/10.1038/s41598-017-13686-5 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Nussey, D. H., Watt, K., Pilkington, J. G., Zamoyska, R. & McNeilly, T. N. Age-related variation in immunity in a wild mammal population. Aging Cell 11, 178–180. https://doi.org/10.1111/j.1474-9726.2011.00771.x (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Dibakou, S. E. et al. Ecological, parasitological and individual determinants of plasma neopterin levels in a natural mandrill population. Int. J. Parasitol. Parasites Wildl. 11, 198–206. https://doi.org/10.1016/j.ijppaw.2020.02.009 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
25.Bateman, A. J. Intra-sexual selection in Drosophila. Heredity 2, 349–368. https://doi.org/10.1038/hdy.1948.21 (1948).CAS 
Article 
PubMed 

Google Scholar 
26.Klein, S. L. & Flanagan, K. L. Sex differences in immune responses. Nat. Rev. Immunol. 16, 626. https://doi.org/10.1038/nri.2016.90 (2016).CAS 
Article 
PubMed 

Google Scholar 
27.Lemaître, J.-F. et al. Sex differences in adult lifespan and aging rates of mortality across wild mammals. Proc. Natl. Acad. Sci. U.S.A. 117, 8546–8553. https://doi.org/10.1073/pnas.1911999117 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Moore, S. L. & Wilson, K. Parasites as a viability cost of sexual selection in natural populations of mammals. Science 297, 2015–2018. https://doi.org/10.1126/science.1074196 (2002).ADS 
CAS 
Article 
PubMed 

Google Scholar 
29.Giefing-Kröll, C., Berger, P., Lepperdinger, G. & Grubeck-Loebenstein, B. How sex and age affect immune responses, susceptibility to infections, and response to vaccination. Aging Cell 14, 309–321. https://doi.org/10.1111/acel.12326 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
30.Faas, M. et al. The immune response during the luteal phase of the ovarian cycle: A Th2-type response?. Fertil. Steril. 74, 1008–1013. https://doi.org/10.1016/S0015-0282(00)01553-3 (2000).CAS 
Article 
PubMed 

Google Scholar 
31.Murphy, S. P. et al. Interferon gamma in successful pregnancies. Biol. Reprod. 80, 848–859. https://doi.org/10.1095/biolreprod.108.073353 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Morison, L. et al. Bacterial vaginosis in relation to menstrual cycle, menstrual protection method, and sexual intercourse in rural Gambian women. Sex Transm. Infect 81, 242–247. https://doi.org/10.1136/sti.2004.011684 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Wira, C. R. & Fahey, J. V. A new strategy to understand how HIV infects women: Identification of a window of vulnerability during the menstrual cycle. AIDS 22, 1909–1917. https://doi.org/10.1097/QAD.0b013e3283060ea4 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Raghupathy, R. Th1-type immunity is incompatible with successful pregnancy. Immunol. Today 18, 478–482. https://doi.org/10.1016/s0167-5699(97)01127-4 (1997).CAS 
Article 
PubMed 

Google Scholar 
35.Sappenfield, E., Jamieson, D. J. & Kourtis, A. P. Pregnancy and susceptibility to infectious diseases. Infect Dis. Obstet. Gynecol. 752852–752852, 2013. https://doi.org/10.1155/2013/752852 (2013).Article 

Google Scholar 
36.Wood, B. M., Watts, D. P., Mitani, J. C. & Langergraber, K. E. Favorable ecological circumstances promote life expectancy in chimpanzees similar to that of human hunter-gatherers. J. Hum. Evol. 105, 41–56. https://doi.org/10.1016/j.jhevol.2017.01.003 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Johnson, P. T. J. et al. Living fast and dying of infection: Host life history drives interspecific variation in infection and disease risk. Ecol. Lett. 15, 235–242. https://doi.org/10.1111/j.1461-0248.2011.01730.x (2012).Article 
PubMed 

Google Scholar 
38.Previtali, M. A. et al. Relationship between pace of life and immune responses in wild rodents. Oikos 121, 1483–1492. https://doi.org/10.1111/j.1600-0706.2012.020215.x (2012).Article 

Google Scholar 
39.Haigwood, N. & Walker, C. Chimpanzees in Biomedical and Behavioral Research: Assessing the Necessity (eds Bruce M. Altevogt, Diana E. Pankevich, Marilee K. Shelton-Davenport, & Jeffrey P. Kahn) 91–165 (National Academies Press (US), 2011).40.Muehlenbein, M. P. Parasitological analyses of the male chimpanzees (Pan troglodytes schweinfurthii) at Ngogo, Kibale National Park, Uganda. Am. J. Primatol. 65, 167–179. https://doi.org/10.1002/ajp.20106 (2005).Article 
PubMed 

Google Scholar 
41.Gillespie, T. R. et al. Demographic and ecological effects on patterns of parasitism in eastern chimpanzees (Pan troglodytes schweinfurthii) in Gombe National Park, Tanzania. Am. J. Phys. Anthropol. 143, 534–544. https://doi.org/10.1002/ajpa.21348 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
42.Muehlenbein, M. P. & Lewis, C. M. Primate Ecology and Conservation: A Handbook of Techniques (eds E. J. Sterling, N. Bynum, & M. E. Blair) 40–57 (Oxford University Press, 2013).43.Behringer, V., Stevens, J. M. G., Leendertz, F. H., Hohmann, G. & Deschner, T. Validation of a method for the assessment of urinary neopterin levels to monitor health status in non-human-primate species. Front. Physiol. 8, 51–51. https://doi.org/10.3389/fphys.2017.00051 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
44.Higham, J. P. et al. Evaluating noninvasive markers of nonhuman primate immune activation and inflammation. Am. J. Phys. Anthropol. 158, 673–684. https://doi.org/10.1002/ajpa.22821 (2015).Article 
PubMed 

Google Scholar 
45.Berdowska, A. & Zwirska-Korczala, K. Neopterin measurement in clinical diagnosis. J. Clin. Pharm. Ther. 26, 319–329. https://doi.org/10.1046/j.1365-2710.2001.00358.x (2001).CAS 
Article 
PubMed 

Google Scholar 
46.Murr, C., Widner, B., Wirleitner, B. & Fuchs, D. Neopterin as a marker for immune system activation. Curr. Drug Metab. 3, 175–187. https://doi.org/10.2174/1389200024605082 (2002).CAS 
Article 
PubMed 

Google Scholar 
47.Denz, H. et al. Value of urinary neopterin in the differential diagnosis of bacterial and viral infections. Klin. Wochenschr. 68, 218–222. https://doi.org/10.1007/bf01662720 (1990).CAS 
Article 
PubMed 

Google Scholar 
48.Wu, D. F., Behringer, V., Wittig, R. M., Leendertz, F. H. & Deschner, T. Urinary neopterin levels increase and predict survival during a respiratory outbreak in wild chimpanzees (Taï National Park, Côte d’Ivoire). Sci. Rep. 8, 13346–13346. https://doi.org/10.1038/s41598-018-31563-7 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Behringer, V. et al. Elevated neopterin levels in wild, healthy chimpanzees indicate constant investment in unspecific immune system. BMC Zool. 4, 2. https://doi.org/10.1186/s40850-019-0041-1 (2019).MathSciNet 
Article 

Google Scholar 
50.González, N. T. et al. Urinary markers of oxidative stress respond to infection and late-life in wild chimpanzees. PLoS ONE 15, e0238066. https://doi.org/10.1371/journal.pone.0238066 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
51.Negrey, J. D. et al. Demography, life history trade-offs, and the gastrointestinal virome of wild chimpanzees. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190613, https://doi.org/10.1098/rstb.2019.0613 (2020).52.Phillips, S. R. et al. Faecal parasites increase with age but not reproductive effort in wild female chimpanzees. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190614, https://doi.org/10.1098/rstb.2019.0614 (2020).53.Emery Thompson, M. et al. Risk factors for respiratory illness in a community of wild chimpanzees (Pan troglodytes schweinfurthii). R. Soc. Open Sci. 5, 180840. https://doi.org/10.1098/rsos.180840 (2018).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Dyke, B., Gage, T. B., Alford, P. L., Swenson, B. & Williams-Blangero, S. Model life table for captive chimpanzees. Am. J. Primatol. 37, 25–37. https://doi.org/10.1002/ajp.1350370104 (1995).Article 
PubMed 

Google Scholar 
55.Obanda, V., Omondi, G. P. & Chiyo, P. I. The influence of body mass index, age and sex on inflammatory disease risk in semi-captive Chimpanzees. PLoS ONE 9, e104602–e104602. https://doi.org/10.1371/journal.pone.0104602 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.De Nys, H. M. et al. Malaria parasite detection increases during pregnancy in wild chimpanzees. Malar. J. 13, 413. https://doi.org/10.1186/1475-2875-13-413 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
57.Deschner, T., Heistermann, M., Hodges, K. & Boesch, C. Timing and probability of ovulation in relation to sex skin swelling in wild West African chimpanzees, Pan troglodytes verus. Anim. Behav. 66, 551–560. https://doi.org/10.1006/anbe.2003.2210 (2003).Article 

Google Scholar 
58.Knott, C. D. Field collection and preservation of urine in orangutans and chimpanzees. Trop. Biodivers. 4, 95–102 (1997).
Google Scholar 
59.Fuchs, D. et al. Urinary neopterin concentrations vs total neopterins for clinical utility. Clin. Chem. 35, 2305–2307 (1989).CAS 
Article 

Google Scholar 
60.Anestis, S. F., Breakey, A. A., Beuerlein, M. M. & Bribiescas, R. G. Specific gravity as an alternative to creatinine for estimating urine concentration in captive and wild chimpanzee (Pan troglodytes) samples. Am. J. Primatol. 71, 130–135. https://doi.org/10.1002/ajp.20631 (2009).CAS 
Article 
PubMed 

Google Scholar 
61.Emery Thompson, M., Muller, M. N. & Wrangham, R. W. Technical note: Variation in muscle mass in wild chimpanzees: Application of a modified urinary creatinine method. Am. J. Phys. Anthropol. 149, 622–627, https://doi.org/10.1002/ajpa.22157 (2012).62.Miller, R. C. et al. Comparison of specific gravity and creatinine for normalizing urinary reproductive hormone concentrations. Clin. Chem. 50, 924–932. https://doi.org/10.1373/clinchem.2004.032292 (2004).CAS 
Article 
PubMed 

Google Scholar 
63.Negrey, J. D. et al. Simultaneous outbreaks of respiratory disease in wild chimpanzees caused by distinct viruses of human origin. Emerg. Microbes Infect. 8, 139–149. https://doi.org/10.1080/22221751.2018.1563456 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
64.R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).65.Auzéby, A., Bogdan, A., Krosi, Z. & Touitou, Y. Time-dependence of urinary neopterin, a marker of cellular immune activity. Clin. Chem. 34, 1866–1867. https://doi.org/10.1093/clinchem/34.9.1863 (1988).Article 
PubMed 

Google Scholar 
66.Löhrich, T., Behringer, V., Wittig, R. M., Deschner, T. & Leendertz, F. H. The use of neopterin as a noninvasive marker in monitoring diseases in wild chimpanzees. EcoHealth 15, 792–803. https://doi.org/10.1007/s10393-018-1357-y (2018).Article 
PubMed 

Google Scholar 
67.Wood, S. Generalized Additive Models: An Introduction With R. Vol. 66 (2006).68.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 1, https://doi.org/10.18637/jss.v082.i13 (2017).69.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
70.Stolwijk, A. M., Straatman, H. & Zielhuis, G. A. Studying seasonality by using sine and cosine functions in regression analysis. J. Epidemiol. Commun. Health 53, 235–238. https://doi.org/10.1136/jech.53.4.235 (1999).CAS 
Article 

Google Scholar 
71.Peacock, L. J. & Rogers, C. M. Gestation period and twinning in chimpanzees. Science 129, 959–959. https://doi.org/10.1126/science.129.3354.959 (1959).ADS 
CAS 
Article 
PubMed 

Google Scholar 
72.Caro, T. M. et al. Termination of reproduction in nonhuman and human female primates. Int. J. Primatol. 16, 205–220. https://doi.org/10.1007/BF02735478 (1995).Article 

Google Scholar 
73.Box, G. E. P. & Cox, D. R. An analysis of transformations. J. R. Stat. Soc. Ser. B. Stat. Methodol. 26, 211–252, https://doi.org/10.1111/j.2517-6161.1964.tb00553.x (1964).74.Luke, S. G. Evaluating significance in linear mixed-effects models in R. Behav. Res. Methods 49, 1494–1502. https://doi.org/10.3758/s13428-016-0809-y (2017).Article 
PubMed 

Google Scholar 
75.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x (2013).Article 

Google Scholar 
76.Shapiro, S. S. & Wilk, M. B. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611. https://doi.org/10.1093/biomet/52.3-4.591 (1965).MathSciNet 
Article 
MATH 

Google Scholar 
77.Wilk, M. B. & Gnanadesikan, R. Probability plotting methods for the analysis of data. Biometrika 55, 1–17. https://doi.org/10.1093/biomet/55.1.1 (1968).CAS 
Article 
PubMed 

Google Scholar 
78.Fox, J., Weisberg, S. & Fox, J. An R Companion to Applied Regression. 2nd edn (Sage, 2011).79.Reibnegger, G. et al. Approach to define “normal aging” in man. Immune function, serum lipids, lipoproteins and neopterin levels. Mech. Ageing Dev. 46, 67–82, https://doi.org/10.1016/0047-6374(88)90115-7 (1988).80.Müller, N., Heistermann, M., Strube, C., Schülke, O. & Ostner, J. Age, but not anthelmintic treatment, is associated with urinary neopterin levels in semi-free ranging Barbary macaques. Sci. Rep. 7, 41973–41973. https://doi.org/10.1038/srep41973 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
81.Flatt, T. & Partridge, L. Horizons in the evolution of aging. BMC Biol. 16, 93–93. https://doi.org/10.1186/s12915-018-0562-z (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
82.Surbeck, M. et al. Males with a mother living in their group have higher paternity success in bonobos but not chimpanzees. Curr. Biol. 29, R354–R355. https://doi.org/10.1016/j.cub.2019.03.040 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
83.Reibnegger, G. et al. Urinary neopterin reflects clinical activity in patients with rheumatoid arthritis. Arthritis Rheum. 29, 1063–1070. https://doi.org/10.1002/art.1780290902 (1986).CAS 
Article 
PubMed 

Google Scholar 
84.Eisenhut, M. Neopterin in diagnosis and monitoring of infectious diseases. J. Biomark. 196432–196432, 2013. https://doi.org/10.1155/2013/196432 (2013).Article 

Google Scholar 
85.Emery Thompson, M., Muller, M. N. & Wrangham, R. W. The energetics of lactation and the return to fecundity in wild chimpanzees. Behav. Ecol. 23, 1234–1241, https://doi.org/10.1093/beheco/ars107 (2012).86.Muller, M. N. in Behavioral Diversity in Chimpanzees and Bonobos (eds C. Boesch, G. Hohmann, & L. Marchant) 112–124 (Cambridge University Press, 2002).87.Pepper, J. W., Mitani, J. C. & Watts, D. P. General gregariousness and specific social preferences among wild chimpanzees. Int. J. Primatol. 20, 613–632. https://doi.org/10.1023/A:1020760616641 (1999).Article 

Google Scholar 
88.Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997. https://doi.org/10.1126/sciadv.1500997 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
89.Habig, B. et al. Multi-scale predictors of parasite risk in wild male savanna baboons (Papio cynocephalus). Behav. Ecol. Sociobiol. 73, 134. https://doi.org/10.1007/s00265-019-2748-y (2019).Article 

Google Scholar 
90.Foo, Y. Z., Nakagawa, S., Rhodes, G. & Simmons, L. W. The effects of sex hormones on immune function: A meta-analysis. Biol. Rev. 92, 551–571. https://doi.org/10.1111/brv.12243 (2017).Article 
PubMed 

Google Scholar 
91.Franceschi, C. et al. Inflammaging and anti-inflammaging: A systemic perspective on aging and longevity emerged from studies in humans. Mech. Ageing Dev. 128, 92–105. https://doi.org/10.1016/j.mad.2006.11.016 (2007).CAS 
Article 
PubMed 

Google Scholar 
92.Brod, S. A. Unregulated inflammation shortens human functional longevity. Inflamm. Res. 49, 561–570. https://doi.org/10.1007/s000110050632 (2000).CAS 
Article 
PubMed 

Google Scholar 
93.Gurven, M. & Kaplan, H. Longevity among hunter-gatherers: A cross-cultural examination. Popul. Dev. Rev. 33, 321–365 (2007).Article 

Google Scholar 
94.Bichler, A. et al. Measurement of urinary neopterin in normal pregnant and non-pregnant women and in women with benign and malignant genital tract neoplasms. Arch. Gynecol. 233, 121–130. https://doi.org/10.1007/BF02114788 (1983).CAS 
Article 
PubMed 

Google Scholar 
95.Deschner, T., Heistermann, M., Hodges, K. & Boesch, C. Female sexual swelling size, timing of ovulation, and male behavior in wild West African chimpanzees. Horm. Behav. 46, 204–215. https://doi.org/10.1016/j.yhbeh.2004.03.013 (2004).CAS 
Article 
PubMed 

Google Scholar 
96.Matsumoto-Oda, A. Mahale chimpanzees: Grouping patterns and cycling females. Am. J. Primatol. 47, 197–207. https://doi.org/10.1002/(sici)1098-2345(1999)47:3%3c197::aid-ajp2%3e3.0.co;2-3 (1999).CAS 
Article 
PubMed 

Google Scholar 
97.Relloso, M. et al. Estradiol impairs the Th17 immune response against Candida albicans. J. Leukoc. Biol. 91, 159–165. https://doi.org/10.1189/jlb.1110645 (2012).CAS 
Article 
PubMed 

Google Scholar 
98.Muller, M. N., Kahlenberg, S. M., Thompson, M. E. & Wrangham, R. W. Male coercion and the costs of promiscuous mating for female chimpanzees. Proc. Biol. Sci. 274, 1009–1014. https://doi.org/10.1098/rspb.2006.0206 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
99.Uyar, I. S. et al. Evaluation of systemic inflammatory response in cardiovascular surgery via interleukin-6, interleukin-8, and neopterin. Heart Surg. Forum 17, E13-17. https://doi.org/10.1532/hsf98.2013267 (2014).Article 
PubMed 

Google Scholar 
100.Jerin, A. et al. Neopterin – An early marker of surgical stress and hypoxic reperfusion damage during liver surgery. Clin. Chem. Lab. Med. 40, 663–666. https://doi.org/10.1515/CCLM.2002.113 (2002).CAS 
Article 
PubMed 

Google Scholar 
101.Baxter-Parker, G. et al. Knee replacement surgery significantly elevates the urinary inflammatory biomarkers neopterin and 7,8-dihydroneopterin. Clin. Biochem. 63, 39–45. https://doi.org/10.1016/j.clinbiochem.2018.11.002 (2019).CAS 
Article 
PubMed 

Google Scholar 
102.Higham, J. P., Stahl-Hennig, C. & Heistermann, M. Urinary suPAR: A non-invasive biomarker of infection and tissue inflammation for use in studies of large free-ranging mammals. R. Soc. Open Sci. 7, 191825–191825. https://doi.org/10.1098/rsos.191825 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
103.Boyunağa, H. et al. Urinary neopterin levels in the different stages of pregnancy. Gynecol. Obstet. Invest. 59, 171–174. https://doi.org/10.1159/000083748 (2005).CAS 
Article 
PubMed 

Google Scholar 
104.Oleszczuk, J., Wawrzycka, B. & Maj, J. G. Interleukin-6 and neopterin levels in serum of patients with preterm labour with and without infection. Eur. J. Obstet. Gynecol. Reprod. Biol. 74, 27–30. https://doi.org/10.1016/S0301-2115(97)00083-3 (1997).CAS 
Article 
PubMed 

Google Scholar 
105.Kaleli, I. et al. Serum levels of neopterin and interleukin-2 receptor in women with severe preeclampsia. J. Clin. Lab Anal. 19, 36–39. https://doi.org/10.1002/jcla.20053 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
106.Sencan, H., Keskin, N. & Khatib, G. The role of neopterin and anti-Mullerian hormone in unexplained recurrent pregnancy loss – A case-control study. J. Obstet. Gynaecol. 39, 996–999. https://doi.org/10.1080/01443615.2019.1586850 (2019).CAS 
Article 
PubMed 

Google Scholar 
107.Potts, K. B., Watts, D. P. & Wrangham, R. W. Comparative feeding ecology of two communities of chimpanzees (Pan troglodytes) in Kibale National Park, Uganda. Int. J. Primatol. 32, 669–690. https://doi.org/10.1007/s10764-011-9494-y (2011).Article 

Google Scholar 
108.Emery Thompson, M., Muller, M. N., Wrangham, R. W., Lwanga, J. S. & Potts, K. B. Urinary C-peptide tracks seasonal and individual variation in energy balance in wild chimpanzees. Horm. Behav. 55, 299–305, https://doi.org/10.1016/j.yhbeh.2008.11.005 (2009).109.Lochmiller, R. L. & Deerenberg, C. Trade-offs in evolutionary immunology: just what is the cost of immunity?. Oikos 88, 87–98. https://doi.org/10.1034/j.1600-0706.2000.880110.x (2000).Article 

Google Scholar  More

1.Xiao, X. M., Biradar, C. M., Czarnecki, C., Alabi, T. & Keller, M. A simple algorithm for large-scale mapping of evergreen forests in tropical America, Africa and Asia. Remote Sens. 1, 355–374 (2009).Article 

Google Scholar 
2.Pan, Y. D. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
Article 

Google Scholar 
3.Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).CAS 
Article 

Google Scholar 
4.Davidson, E. A. et al. The Amazon basin in transition. Nature 481, 321–328 (2012).CAS 
Article 

Google Scholar 
5.Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, E2602–E2610 (2013).CAS 
Article 

Google Scholar 
6.Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).CAS 
Article 

Google Scholar 
7.Fearnside, P. M. Brazilian politics threaten environmental policies. Science 353, 746–748 (2016).CAS 
Article 

Google Scholar 
8.Fearnside, P. M. Business as Usual: A Resurgence of Deforestation in the Brazilian Amazon (Yale School of Forestry & Environmental Studies, 2017).9.Berenguer, E. et al. A large-scale field assessment of carbon stocks in human-modified tropical forests. Glob. Change Biol. 20, 3713–3726 (2014).Article 

Google Scholar 
10.Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).CAS 
Article 

Google Scholar 
11.Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).CAS 
Article 

Google Scholar 
12.Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).CAS 
Article 

Google Scholar 
13.Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).CAS 
Article 

Google Scholar 
14.PRODES Legal Amazon Deforestation Monitoring System (INPE, 2018); http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes15.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
Article 

Google Scholar 
16.Tyukavina, A. et al. Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013. Sci. Adv. 3, e1601047 (2017).Article 

Google Scholar 
17.Qin, Y. et al. Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017. Nat. Sustain. 2, 764–772 (2019).Article 

Google Scholar 
18.Seymour, F. & Harris, N. L. Reducing tropical deforestation. Science 365, 756–757 (2019).CAS 
Article 

Google Scholar 
19.Richards, P., Arima, E., VanWey, L., Cohn, A. & Bhattarai, N. Are Brazil’s deforesters avoiding detection? Conserv. Lett. 10, 470–476 (2017).Article 

Google Scholar 
20.Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).CAS 
Article 

Google Scholar 
21.Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).Article 

Google Scholar 
22.Wigneron, J.-P. et al. Tropical forests did not recover from the strong 2015–2016 El Niño event. Sci. Adv. 6, eaay4603 (2020).CAS 
Article 

Google Scholar 
23.Wigneron, J.-P. et al. SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives. Remote Sens. Environ. 254, 112238 (2021).Article 

Google Scholar 
24.Qin, Y. W. et al. Annual dynamics of forest areas in South America during 2007–2010 at 50 m spatial resolution. Remote Sens. Environ. 201, 73–87 (2017).Article 

Google Scholar 
25.Ferrante, L. & Fearnside, P. M. Brazil’s new president and ‘ruralists’ threaten Amazonia’s environment, traditional peoples and the global climate. Environ. Conserv. 46, 261–263 (2019).Article 

Google Scholar 
26.Artaxo, P. Working together for Amazonia. Science 363, 323–323 (2019).CAS 
Article 

Google Scholar 
27.Aragão, L. E. O. C. et al. 21st century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9, 536 (2018).Article 
CAS 

Google Scholar 
28.Nunes, S., Oliveira, L., Siqueira, J., Morton, D. C. & Souza, C. M. Unmasking secondary vegetation dynamics in the Brazilian Amazon. Environ. Res. Lett. 15, 034057 (2020).Article 

Google Scholar 
29.Hilker, T. et al. Vegetation dynamics and rainfall sensitivity of the Amazon. Proc. Natl Acad. Sci. USA 111, 16041–16046 (2014).CAS 
Article 

Google Scholar 
30.Liu, J. et al. Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Nino. Science 358, eaam5690 (2017).Article 
CAS 

Google Scholar 
31.Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).CAS 
Article 

Google Scholar 
32.Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).CAS 
Article 

Google Scholar 
33.Yang, Y. et al. Post-drought decline of the Amazon carbon sink. Nat. Commun. 9, 3172 (2018).Article 
CAS 

Google Scholar 
34.Gatti, L. V. et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 506, 76–80 (2014).CAS 
Article 

Google Scholar 
35.Asner, G. P. et al. Selective logging in the Brazilian Amazon. Science 310, 480–482 (2005).CAS 
Article 

Google Scholar 
36.Silva, C. H. L.Jr et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaaz8360 (2020).Article 

Google Scholar 
37.Espírito-Santo, F. D. B. et al. Size and frequency of natural forest disturbances and the Amazon forest carbon balance. Nat. Commun. 5, 3434 (2014).Article 
CAS 

Google Scholar 
38.Lewis, S. L., Brando, P. M., Phillips, O. L., van der Heijden, G. M. F. & Nepstad, D. The 2010 Amazon drought. Science 331, 554–554 (2011).CAS 
Article 

Google Scholar 
39.Jiménez-Muñoz, J. C. et al. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci. Rep. 6, 33130 (2016).Article 
CAS 

Google Scholar 
40.Harris, N. L. et al. Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573–1576 (2012).CAS 
Article 

Google Scholar 
41.Aguiar, A. P. D. et al. Land use change emission scenarios: anticipating a forest transition process in the Brazilian Amazon. Glob. Change Biol. 22, 1821–1840 (2016).Article 

Google Scholar 
42.Aragão, L. E. O. C. et al. Environmental change and the carbon balance of Amazonian forests. Biol. Rev. 89, 913–931 (2014).Article 

Google Scholar 
43.Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).Article 

Google Scholar 
44.Silva, C. V. J. et al. Estimating the multi-decadal carbon deficit of burned Amazonian forests. Environ. Res. Lett. 15, 114023 (2020).CAS 
Article 

Google Scholar 
45.Silva, C. V. J. et al. Drought-induced Amazonian wildfires instigate a decadal-scale disruption of forest carbon dynamics. Phil. Trans. R. Soc. B 373, 20180043 (2018).Article 

Google Scholar 
46.Barlow, J., Peres, C. A., Lagan, B. O. & Haugaasen, T. Large tree mortality and the decline of forest biomass following Amazonian wildfires. Ecol. Lett. 6, 6–8 (2003).Article 

Google Scholar 
47.Fuchs, R. et al. Why the US–China trade war spells disaster for the Amazon. Nature 567, 451–454 (2019).CAS 
Article 

Google Scholar 
48.Hansen, M. C., Potapov, P. & Tyukavina, A. Comment on ‘Tropical forests are a net carbon source based on aboveground measurements of gain and loss’. Science 363, eaar3629 (2019).CAS 
Article 

Google Scholar 
49.Dubayah, R. et al. The Global Ecosystem Dynamics Investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 1, 100002 (2020).Article 

Google Scholar 
50.Doughty, R. et al. TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest. Proc. Natl Acad. Sci. USA 116, 22393–22398 (2019).CAS 
Article 

Google Scholar 
51.Moore, B. III et al. The potential of the Geostationary Carbon Cycle Observatory (GeoCarb) to provide multi-scale constraints on the carbon cycle in the Americas. Front. Environ. Sci. 6, 109 (2018).Article 

Google Scholar 
52.Landsat (NASA, USGS, 2019); https://landsat.gsfc.nasa.gov/news/media-resources53.Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).Article 

Google Scholar 
54.Fernandez-Moran, R. et al. SMOS-IC: an alternative SMOS soil moisture and vegetation optical depth product. Remote Sens. 9, 457 (2017).Article 

Google Scholar 
55.Rodriguez-Fernandez, N. J. et al. An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa. Biogeosciences 15, 4627–4645 (2018).CAS 
Article 

Google Scholar 
56.Konings, A. G. & Gentine, P. Global variations in ecosystem-scale isohydricity. Glob. Change Biol. 23, 891–905 (2017).Article 

Google Scholar 
57.Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).Article 

Google Scholar 
58.Moesinger, L. et al. The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth Syst. Sci. Data 12, 177–196 (2020).Article 

Google Scholar 
59.Tang, H. et al. Characterizing global forest canopy cover distribution using spaceborne lidar. Remote Sens. Environ. 231, 111262 (2019).Article 

Google Scholar 
60.Crisp, D. et al. The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products. Atmos. Meas. Tech. 10, 59–81 (2017).CAS 
Article 

Google Scholar 
61.Kiel, M. et al. How bias correction goes wrong: measurement of XCO2 affected by erroneous surface pressure estimates. Atmos. Meas. Tech. 12, 2241–2259 (2019).CAS 
Article 

Google Scholar 
62.Worden, J. R. et al. Evaluation and attribution of OCO-2 XCO2 uncertainties. Atmos. Meas. Tech. 10, 2759–2771 (2017).CAS 
Article 

Google Scholar 
63.Giglio, L. & Justice, C. MOD14A2 MODIS/Terra Thermal Anomalies/Fire 8-Day L3 Global 1 km SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015).64.Giglio, L., Justice, C., Boschetti, L. & Roy, D. MCD64A1 MODIS/Terra+Aqua Burned Area Monthly L3 Global 500 m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015).65.Huffman, G. et al. Integrated Multi-satellitE Retrievals for GPM (IMERG). Version 4.4 (NASA’s Precipitation Processing Center, 2014); ftp://arthurhou.pps.eosdis.nasa.gov/gpmdata/66.Running, S., Mu, Q. & Zhao, M. MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500 m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2017).67.Qin, Y., Xiao, X. & Wigneron, J.-P. Annual evergreen forest maps in the Brazilian Amazon during 2010–2019. Figshare https://doi.org/10.6084/m9.figshare.14115518.v1 (2021).68.Qin, Y., Xiao, X. & Wigneron, J.-P. Annual aboveground biomass maps in the Brazilian Amazon during 2010–2019. Figshare https://doi.org/10.6084/m9.figshare.14115566.v1 (2021).69.Qin, Y., Xiao, X. & Wigneron, J.-P. Code for evergreen forest and aboveground biomass analyses in the Brazilian Amazon. Figshare https://doi.org/10.6084/m9.figshare.14115680.v1 (2021). More

1.Hayward, M. W. et al. FORUM: Ecologists need robust survey designs, sampling and analytical methods. J. Appl. Ecol. 52, 286–290 (2015).Article 

Google Scholar 
2.Karanth, K. U. Estimating tiger Pantheratigris populations from camera-trap data using capture-recapture models. Biol. Conserv. 71, 333–338 (1995).Article 

Google Scholar 
3.O’Connell, A. F., Nichols, J. D. & Katranth, K. U. Camera Traps in Animal Ecology: Methods and Analyses (Springer, 2011).Book 

Google Scholar 
4.Molinari-Jobin, A. et al. Monitoring in the presence of species misidentification: The case of the Eurasian lynx in the Alps. Anim. Conserv. 15, 266–273 (2012).Article 

Google Scholar 
5.López-Bao, J. V. et al. Toward reliable population estimates of wolves by combining spatial capture-recapture models and non-invasive DNA monitoring. Sci. Rep. 8, 2177 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Foster, R. J. & Harmsen, B. J. A critique of density estimation from camera-trap data. J. Wildl. Manage. 76, 224–236 (2012).Article 

Google Scholar 
7.Rozylowicz, L., Popescu, V. D., Pǎtroescu, M. & Chişamera, G. The potential of large carnivores as conservation surrogates in the Romanian Carpathians. Biodivers. Conserv. 20, 561–579 (2011).Article 

Google Scholar 
8.Weingarth, K. et al. First estimation of Eurasian lynx (Lynx lynx) abundance and density using digital cameras and capture-recapture techniques in a German national park. Anim. Biodivers. Conserv. 35, 197–207 (2012).Article 

Google Scholar 
9.Pesenti, E. & Zimmermann, F. Density estimations of the Eurasian lynx (Lynx lynx) in the Swiss Alps. J. Mammal. 94, 73–81 (2013).Article 

Google Scholar 
10.Blanc, L., Marboutin, E., Gatti, S. & Gimenez, O. Abundance of rare and elusive species: Empirical investigation of closed versus spatially explicit capture-recapture models with lynx as a case study. J. Wildl. Manage. 77, 372–378 (2013).Article 

Google Scholar 
11.Kubala, J. et al. Robust monitoring of the Eurasian lynx Lynxlynx in the Slovak Carpathians reveals lower numbers than officially reported. Oryx 53, 548–556 (2019).Article 

Google Scholar 
12.Kaczensky, P. et al. Status, Management and Distribution of Large Carnivores: Bear, Lynx, Wolf & Wolverine—in Europe (European Commission, 2013).
Google Scholar 
13.Gimenez, O. et al. Spatial density estimates of Eurasian lynx (Lynx lynx) in the French Jura and Vosges Mountains. Ecol. Evol. 9, 11707–11715 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
14.Okarma, H. et al. Status of Carnivores in the Carpathian Ecoregion. Report of the Carpathian Ecoregion Initiative (2000).15.Stehlík, J. Znovuvysazení rysa ostrovida Lynx lynx L. v některých evropských zemích v letech 1970–1976. Poľovnícky zborník—Folia venatoria 9, 255–265 (1979).16.Červený, J. & Bufka, L. Lynx (Lynx lynx) in south-western Bohemia. Acta. Sci. Nat. Brno 30, 16–33 (1996).
Google Scholar 
17.Salvatori, V. et al. Hunting legislation in the Carpathian Mountains: Implications for the conservation and management of large carnivores. Wildlife Biol. 8, Pagination missing-please provide (2002).18.Smolko, P. et al. Lynx monitoring in the Muránska planina NP, Slovakia and its importance for the national and European management and conservation of the species. Technical report (2018).19.Kubala, J. et al. Monitoring rysa ostrovida (Lynx lynx) vo Veporských vrchoch a jeho význam pre národný a európsky manažment a ochranu druhu. Technická správa. (In Slovak) (2019).20.Kubala, J. et al. Monitoring rysa ostrovida (Lynx lynx) v Strážovských vrchoch a jeho význam pre národný a európsky manažment a ochranu druhu. Technická správa. (In Slovak) (2020).21.Duangchantrasiri, S. et al. Dynamics of a low-density tiger population in Southeast Asia in the context of improved law enforcement. Conserv. Biol. 30, 639–648 (2016).PubMed 
Article 

Google Scholar 
22.Karanth, K. U., Nichols, J. D., Kumar, N. S. & Hines, J. E. Assessing tiger population dynamics using photographic capture-recapture sampling. Ecology 87, 2925–2937 (2006).PubMed 
Article 

Google Scholar 
23.Bisht, S., Banerjee, S., Qureshi, Q. & Jhala, Y. Demography of a high-density tiger population and its implications for tiger recovery. J. Appl. Ecol. 56, 1725–1740 (2019).Article 

Google Scholar 
24.Zimmermann F. et al. Abundanz und Dichte des Luchses in den Nordwestalpen : Fang-Wiederfang-Schätzung mittels Fotofallen im K-VI im Winter 2015 / 16, Vol. 41 (2016).25.Pironon, S. et al. Geographic variation in genetic and demographic performance: new insights from an old biogeographical paradigm. Biol. Rev. 92, 1877–1909 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Sagarin, R. D. & Gaines, S. D. The ‘abundant centre’ distribution: to what extent is it a biogeographical rule?. Ecol. Lett. 5, 137–147 (2002).Article 

Google Scholar 
27.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 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.López-Bao, J. V. et al. Eurasian lynx fitness shows little variation across Scandinavian human-dominated landscapes. Sci. Rep. 9, 8903 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Krojerová-Prokešová, J. et al. Genetic constraints of population expansion of the Carpathian lynx at the western edge of its native distribution range in Central Europe. Heredity (Edinb). 122, (2019).30.Ján, K. & Štefan, D. Mammals of Slovakia distribution, bionomy and protection. (VEDA, 2012).31.Kubala, J. et al. The coat pattern in the Carpathian population of Eurasian lynx has changed: a sign of demographic bottleneck and limited connectivity. Eur. J. Wildl. Res. 66, 2 (2019).Article 

Google Scholar 
32.Kutal, M. et al. Occurrence of large carnivores—Lynx lynx, Canis lupus, and Ursus arctos—and of Felis silvestris in the Czech Republic and western Slovakia in 2012–2016 (Carnivora). Lynx, new Ser. 48, 93–107.33.Galvánek, J., Pietorová, E. & Matejová, M. Hodnotenie abiotických zložiek vybranej ekologicko-funkčnej jednotky. in Ochrana prírody Kysuckého regiónu a spolupráca na jeho trvalo udržateľnom rozvoji. (1996).34.Tolasz, R., Miková, T., Valeriánová, A. & Voženílek, V. Atlas podnebí Česka. (2007).35.Bochníček, O. Climate Atlas of Slovakia. (Slovak Hydrometeorological Institute, 2015).36.Czech Statistical Office. Statistical Yearbook of the Czech Republic 2017. Accessed 9 Nov 2020. https://www.czso.cz/csu/czso/statistical-yearbook-of-the-czech-republic (2017).37.Statistical Office of the Slovak Republic. Statistical Yearbook of the Slovak Republic 2017. Accessed 9 Nov 2020. https://slovak.statistics.sk:443/wps/portal?urile=wcm:path:/obsah-en-pub/publikacie/vsetkypublikacie/f3dc4a81-06ac-4fea-93b7-e0ff45a9fff6 (2017).38.Romportl, D., Zyka, V. & Kutal, M. Connectivity Conservation of Large Carnivores’ Habitats in the Carpathians. in 5th European Congress of Conservation Biology (2018). https://doi.org/10.17011/conference/eccb2018/107837.39.Weingarth, K. et al. Hide and seek: extended camera-trap session lengths and autumn provide best parameters for estimating lynx densities in mountainous areas. Biodivers. Conserv. 24, 2935–2952 (2015).Article 

Google Scholar 
40.Mohr, C. O. Table of equivalent populations of north american small mammals. Am. Midl. Nat. 37, 223–249 (1947).Article 

Google Scholar 
41.Karanth, K. U. & Nichols, J. D. Estimation of tiger densities in India using photographic captures and recaptures. Ecology 79, 2852–2862 (1998).Article 

Google Scholar 
42.Okarma, H., Sniezko, S. & Smietana, W. Home ranges of Eurasian lynx Lynxlynx in the Polish Carpathian Mountains. Wildlife Biol. 13, 481–487 (2007).Article 

Google Scholar 
43.ESRI. ArcGIS Desktop. (2019).44.Duľa, M., Drengubiak, P., Kutal, M., Trulík, V. & Hrdý, Ľ. Monitoring lynx in Kysuce PLA, Slovakia. (2015).45.Kutal, M., Váňa, M., Bojda, M., Kutalová, L. & Suchomel, J. Camera trapping of the Eurasian lynx in the Czech-Slovakian borderland. (2015).46.Duľa, M. et al. Recentný výskyt a reprodukcia rysa ostrovida (Lynx lynx) v CHKO Kysuce a NP Malá Fatra. in 75–78 (2017).47.Choo, Y. R. et al. Best practices for reporting individual identification using camera trap photographs. Glob. Ecol. Conserv. 24, e01294 (2020).Article 

Google Scholar 
48.Zimmermann, F., Breitenmoser-Würsten, C., Molinari-Jobin, A. & Breitenmoser, U. Optimizing the size of the area surveyed for monitoring a Eurasian lynx (Lynx lynx) population in the Swiss Alps by means of photographic capture-recapture. Integr. Zool. 8, 232–243 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Gopalaswamy, A. M. et al. Program SPACECAP: Software for estimating animal density using spatially explicit capture-recapture models. Methods Ecol. Evol. 3, 1067–1072 (2012).Article 

Google Scholar 
50.Gopalaswamy, A. et al. SPACECAP: An R package for estimating animal density using spatially explicit capture-recapture models. (2014).51.Team, R. C. R software. (2020).52.Stanley & Burnham_1999. A closure test for capture data.Env&EcolStats.pdf.53.Stanley, T. & Richards, J. CloseTest: A program for testing capture–recapture data for closure [Software Manual]. (2004).54.Copernicus Programme. CORINE Land Cover 2012. http://land.copernicus.e/an-europea/orine-land-cove/lc-2012.Google Scholar (2012).55.Gelman, A., Carlin, J., Stern, H. & DB, R. Bayesian data analysis.2nd edn. (2004).56.Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ, 2006).Book 

Google Scholar 
57.White, G. C. & Burnham, K. P. Program MARK: survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).Article 

Google Scholar 
58.Arnason, A. N. Parameter estimates from mark-recapture experiments on two populations subject to migration and death. Res. Popul. Ecol. (Kyoto) 13, 97–113 (1972).Article 

Google Scholar 
59.Arnason, A. N. The estimation of population size, migration rates and survival in a stratified population. Res. Popul. Ecol. (Kyoto) 15, 1–8 (1973).Article 

Google Scholar 
60.Chabanne, D. B. H., Pollock, K. H., Finn, H. & Bejder, L. Applying the multistate capture–recapture robust design to characterize metapopulation structure. Methods Ecol. Evol. 8, 1547–1557 (2017).Article 

Google Scholar 
61.Burnham, K. P. & Anderson, D. R. A practical information-theoretic approach. Model Sel. multimodel inference 2, (2002).62.Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346, 1517–1519 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63Royle, J., Chandler, R. B., Sollmann, R. & Gardner, B. Spatial Capture-Recapture (Academic Press, 2014).
Google Scholar 
64.Rovero, F. & Zimmermann, F. Introduction. in Camera Trapping for Wildlife Research 1–7. (2016).65.Avgan, B., Zimmermann, F., Güntert, M., Arikan, F. & Breitenmoser, U. The first density estimation of an isolated Eurasian lynx population in southwest Asia. Wildlife Biol. 20, 217–221 (2014).Article 

Google Scholar 
66.Harmsen, B. J., Foster, R. J. & Quigley, H. Spatially explicit capture recapture density estimates: Robustness, accuracy and precision in a long-term study of jaguars (Pantheraonca). PLoS ONE 15, e0227468 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67Sollmann, R., Gardner, B. & Belant, J. L. How does spatial study design influence density estimates from spatial capture-recapture models?. PLoS ONE 7, e34575 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Zimmermann, F. et al. Abondance et densité du lynx dans le Sud du Jura suisse : estimation par capture-recapture photographique dans le compartiment I , durant l ’ hiver 2014 / 15, Vol. 41 (2015).69.Breitenmoser-Würsten, C. et al. Spatial and Social stability of a Eurasian lynx Lynxlynx population: an assessment of 10 years of observation in the Jura Mountains. Wildlife Biol. 13, 365–380 (2007).Article 

Google Scholar 
70.Fabiano, E. C. et al. Trends in cheetah Acinonyxjubatus density in north-central Namibia. Popul. Ecol. 62, 233–243 (2020).Article 

Google Scholar 
71.Jedrzejewski, W. et al. Population dynamics (1869–1994), demography, and home ranges of the lynx in Bialowieza Primeval Forest (Poland and Belarus). Ecography (Cop.) 19, 122–138 (1996).Article 

Google Scholar 
72.Breitenmoser-Würsten, C., Vandel, J.-M., Zimmermann, F. & Breitenmoser, U. Demography of lynx Lynxlynx in the Jura Mountains. Wildlife Biol. 13, 381–392 (2007).Article 

Google Scholar 
73.Andren, H. et al. Survival rates and causes of mortality in Eurasian lynx (Lynx lynx) in multi-use landscapes. Biol. Conserv. 131, 23–32 (2006).Article 

Google Scholar 
74.Herrero, A. et al. Genetic analysis indicates spatial-dependent patterns of sex-biased dispersal in Eurasian lynx in Finland. PLoS ONE 16, e0246833 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
75.Port, M. et al. Rise and fall of a Eurasian lynx (Lynx lynx) stepping-stone population in central Germany. Mammal Res. 66, 45–55 (2021).Article 

Google Scholar 
76.Pereira, J. A. et al. Population density of Geoffroy’s cat in scrublands of central Argentina. J. Zool. 283, 37–44 (2011).Article 

Google Scholar 
77.Breitenmoser, U. et al. Conservation of the lynx Lynxlynx in the Swiss Jura Mountains. Wildlife Biol. 13, 340–355 (2007).Article 

Google Scholar 
78.Basille, M. et al. What shapes Eurasian lynx distribution in human dominated landscapes: selecting prey or avoiding people?. Ecography (Cop.) 32, 683–691 (2009).Article 

Google Scholar 
79.Filla, M. et al. Habitat selection by Eurasian lynx (Lynx lynx) is primarily driven by avoidance of human activity during day and prey availability during night. Ecol. Evol. 7, 6367–6381 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
80.Nowicki, P. Food habit and diet of the lynx (Lynx lynx) in Europe. J. Wildl. Res. 2, (1997).81.Statistical Office of the Slovak Republic. Spring stock and hunting of game. Accessed 9 Nov 2020. http://datacube.statistics.sk/#!/view/en/VBD_SLOVSTAT/pl2006rs/v_pl2006rs_00_00_00_en (2019).82.Czech Statistical Office. Number and hunting of selected game species 2010 – 2019. Accessed 9 Nov 2020. https://www.czso.cz/documents/10180/122461942/1000052006e.pdf/3cd18662-1691-45df-a398-040ecdeeef00?version=1.1 (2020).83.Kutal, M., Váňa, M., Suchomel, J., Chapron, G. & Lopez-Bao, J. Trans-boundary edge effects in the western carpathians: the influence of hunting on large carnivore occupancy. PLoS ONE 11, e0168292 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
84.Schmidt-Posthaus, H., Breitenmoser-Würsten, C., Posthaus, H., Bacciarini, L. & Breitenmoser, U. Causes of mortality in reintroduced Eurasian lynx in Switzerland. J. Wildl. Dis. 38, 84–92 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
85.Mattisson, J. et al. Lethal male-male interactions in Eurasian lynx. Mamm. Biol. 78, 304–308 (2013).Article 

Google Scholar 
86.Sindičić, M. et al. Mortality in the Eurasian lynx population in Croatia over the course of 40 years. Mamm. Biol. 81, 290–294 (2016).Article 

Google Scholar 
87.Heurich, M. et al. Illegal hunting as a major driver of the source-sink dynamics of a reintroduced lynx population in Central Europe. Biol. Conserv. 224, 355–365 (2018).Article 

Google Scholar 
88.Červený, J., Krojerová-Prokešová, J., Kušta, T. & Koubek, P. The change in the attitudes of Czech hunters towards Eurasian lynx: Is poaching restricting lynx population growth?. J. Nat. Conserv. 47, 28–37 (2019).Article 

Google Scholar 
89.Kalaš, M. Contribution on the collisions of the European Lynx (Lynx lynx) with car traffic. in Migration corridors in the Western Carpathians: Malá Fatra—Kysucké Beskydy—Moravskoslezské Beskydy—Javorníky (ed. Kutal, M.) (2013).90.Boitani, L. et al. Key actions for Large Carnivore populations in Europe. Report to DG Environment. Contract no. 07.0307/2013/654446/SER/B3 (European Commission, Bruxelles, 2015).91.Kratochvil, J. et al. History of the distribution of the lynx in Europe. Acta Sci. Nat. Brno 4, 1–50 (1968).
Google Scholar 
92.Zimmermann, F., BreitenmoserWursten, C. & Breitenmoser, U. Natal dispersal of Eurasian lynx (Lynx lynx) in Switzerland. J. Zool. 267, 381–395 (2005).Article 

Google Scholar 
93.Zimmermann, F., Breitenmoser-Würsten, C. & Breitenmoser, U. Importance of dispersal for the expansion of a Eurasian lynx Lynxlynx population in a fragmented landscape. Oryx 41, 358–368 (2007).Article 

Google Scholar 
94.Kowalczyk, R., Górny, M. & Schmidt, K. Edge effect and influence of economic growth on Eurasian lynx mortality in the Białowieża Primeval Forest, Poland. 3–8. https://doi.org/10.1007/s13364-014-0203-z (2015).95.Černecký, J. et al. Správa o stave biotopov a druhov európskeho významu za obdobie rokov 2013–2018 v Slovenskej republike. (2020). More

Comparing estimates of parasite’s EIP between the classic dissection approach and the non-destructive individual “spit” assayDestructive approach: mosquito dissection and microscopic observationA total of 121 mosquito females exposed to parasite isolate A and 114 to isolate B were dissected from 8 to 16 dpbm (between 8 and 20 females/day, median = 14) to assess microscopically the presence and number of oocysts in the midguts and of sporozoites in salivary glands. Salivary gland infections were also confirmed through qPCR. The infection rate was high with 117/121 (96.7%) and 114/114 (100%) of females exposed respectively to isolate A and B harboring parasite oocysts in their midguts (supplementary S44, Fig. S4a). The gametocytemia of isolate B (1208 gam/µl) was higher than that of isolate A (168 gam/µl), resulting in strong difference in the number of developing oocysts between the two isolates (B: 191.65 ± 21, A: 13.86 ± 2, supplementary S4, Fig. S4b, LRT X21 = 24.46, P  More