Philippa Kaur

Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28. https://doi.org/10.1086/343878 (2003).MathSciNet 
Article 
PubMed 

Google Scholar 
Peterson, A. T., Soberón, J. & Sánchez-Cordero, V. Conservatism of ecological niches in evolutionary time. Science 285, 1265–1267 (1999).CAS 
Article 

Google Scholar 
Wiens, J. J. & Graham, C. H. Niche conservatism: Integrating evolution, ecology, and conservation biology. Annu. Rev. Ecol. Evol. Syst. 36, 519–539 (2005).Article 

Google Scholar 
Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192. https://doi.org/10.1016/j.tree.2011.01.009 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Gentile, G., Bonelli, S. & Riva, F. Evaluating intraspecific variation in insect trait analysis. Ecol. Entomol. 46, 11–18 (2021).Article 

Google Scholar 
Ortego, J., Calabuig, G., Cordero, P. J. & Aparicio, J. M. Egg production and individual genetic diversity in lesser kestrels. Mol. Ecol. 16, 2383–2392 (2007).CAS 
Article 

Google Scholar 
Peacor, S. D., Schiesari, L. & Werner, E. E. Mechanisms of nonlethal predator effect on cohort size variation: Ecological and evolutionary implications. Ecology 88, 1536–1547 (2007).Article 

Google Scholar 
Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H.-H. & Warren, D. Niche estimation above and below the species level. Trends Ecol. Evol. 34, 260–273 (2019).Article 

Google Scholar 
Carlson, B. S., Rotics, S., Nathan, R., Wikelski, M. & Jetz, W. Individual environmental niches in mobile organisms. Nat. Commun. 12, 4572. https://doi.org/10.1038/s41467-021-24826-x (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hutchinson, G. E. Population studies: Animal ecology and demography. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).Article 

Google Scholar 
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).ADS 
CAS 
Article 

Google Scholar 
Wauchope, H. S. et al. Protected areas have a mixed impact on waterbirds, but management helps. Nature 605, 103 (2022).CAS 
Article 

Google Scholar 
Lowry, H., Lill, A. & Wong, B. B. Behavioural responses of wildlife to urban environments. Biol. Rev. 88, 537–549 (2013).Article 

Google Scholar 
Hällfors, M. H. et al. Combining range and phenology shifts offers a winning strategy for boreal Lepidoptera. Ecol. Lett. 24, 1619–1632 (2021).Article 

Google Scholar 
Joppa, L. N. & Pfaff, A. Global protected area impacts. Proc. R. Soc. B Biol. Sci. 278, 1633–1638. https://doi.org/10.1098/rspb.2010.1713 (2011).Article 

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

Google Scholar 
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl. Acad. Sci. 116, 23209–23215. https://doi.org/10.1073/pnas.1908221116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Blonder, B. Hypervolume concepts in niche- and trait-based ecology. Ecography 41, 1441–1455 (2018).Article 

Google Scholar 
Mammola, S. & Cardoso, P. Functional diversity metrics using kernel density n-dimensional hypervolumes. Methods Ecol. Evol. 11, 986–995. https://doi.org/10.1111/2041-210X.13424 (2020).Article 

Google Scholar 
Mammola, S. Assessing similarity of n-dimensional hypervolumes: Which metric to use? J. Biogeogr. 46, 2012 (2019).Article 

Google Scholar 
Carvalho, J. C. & Cardoso, P. Decomposing the causes for niche differentiation between species using hypervolumes. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2020.00243 (2020).Article 

Google Scholar 
Pavlek, M. & Mammola, S. Niche-based processes explaining the distributions of closely related subterranean spiders. J. Biogeogr. 48, 118–133. https://doi.org/10.1111/jbi.13987 (2021).Article 

Google Scholar 
Wang, X. et al. Exploring ecological specialization in pipefish using genomic, morphometric and ecological evidence. Divers. Distrib. 27, 1393–1406. https://doi.org/10.1111/ddi.13286 (2021).Article 

Google Scholar 
Jaturapruek, R., Fontaneto, D., Mammola, S. & Maiphae, S. Potential niche displacement in species of aquatic bdelloid rotifers between temperate and tropical areas. Hydrobiologia. https://doi.org/10.1007/s10750-021-04681-z (2021).Article 

Google Scholar 
Hu, Z. M. et al. Intraspecific genetic variation matters when predicting seagrass distribution under climate change. Mol. Ecol. 30, 3840–3855. https://doi.org/10.1111/mec.15996 (2021).Article 
PubMed 

Google Scholar 
Ho, D. E., Imai, K., King, G. & Stuart, E. A. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit. Anal. 15, 199–236 (2007).Article 

Google Scholar 
Terraube, J., Van Doninck, J., Helle, P. & Cabeza, M. Assessing the effectiveness of a national protected area network for carnivore conservation. Nat. Commun. 11, 2957. https://doi.org/10.1038/s41467-020-16792-7 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chichorro, F., Juslén, A. & Cardoso, P. A review of the relation between species traits and extinction risk. Biol. Conserv. 237, 220–229 (2019).Article 

Google Scholar 
Pearman, P. B., Guisan, A., Broennimann, O. & Randin, C. F. Niche dynamics in space and time. Trends Ecol. Evol. 23, 149–158 (2008).Article 

Google Scholar 
Santangeli, A., Högmander, J. & Laaksonen, T. Returning white-tailed eagles breed as successfully in landscapes under intensive forestry regimes as in protected areas. Anim. Conserv. 16, 500–508. https://doi.org/10.1111/acv.12017 (2013).Article 

Google Scholar 
Broennimann, O. et al. Evidence of climatic niche shift during biological invasion. Ecol. Lett. 10, 701–709 (2007).CAS 
Article 

Google Scholar 
Fitzpatrick, M. C., Weltzin, J. F., Sanders, N. J. & Dunn, R. R. The biogeography of prediction error: Why does the introduced range of the fire ant over-predict its native range? Glob. Ecol. Biogeogr. 16, 24–33 (2007).Article 

Google Scholar 
Dietz, H. & Edwards, P. J. Recognition that causal processes change during plant invasion helps explain conflicts in evidence. Ecology 87, 1359–1367 (2006).Article 

Google Scholar 
Holt, R. D., Keitt, T. H., Lewis, M. A., Maurer, B. A. & Taper, M. L. Theoretical models of species’ borders: Single species approaches. Oikos 108, 18–27 (2005).Article 

Google Scholar 
Zhang, Z., Mammola, S., McLay, C. L., Capinha, C. & Yokota, M. To invade or not to invade? Exploring the niche-based processes underlying the failure of a biological invasion using the invasive Chinese mitten crab. Sci. Total Environ. 728, 138815. https://doi.org/10.1016/j.scitotenv.2020.138815 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Liu, C., Wolter, C., Xian, W. & Jeschke, J. M. Most invasive species largely conserve their climatic niche. Proc. Natl. Acad. Sci. 117, 23643–23651 (2020).CAS 
Article 

Google Scholar 
Sarasola, J. H., Grande, J. M. & Negro, J. J. Birds of Prey: Biology and Conservation in the XXI Century 63–94 (Springer, 2018).Book 

Google Scholar 
Reif, J., Hořák, D., Krištín, A., Kopsová, L. & Devictor, V. Linking habitat specialization with species’ traits in European birds. Oikos 125, 405–413. https://doi.org/10.1111/oik.02276 (2016).Article 

Google Scholar 
Thornton, D., Branch, L. & Sunquist, M. Passive sampling effects and landscape location alter associations between species traits and response to fragmentation. Ecol. Appl. 21, 817–829. https://doi.org/10.1890/10-0549.1 (2011).Article 
PubMed 

Google Scholar 
Hatfield, J. H., Orme, C. D. L., Tobias, J. A. & Banks-Leite, C. Trait-based indicators of bird species sensitivity to habitat loss are effective within but not across data sets. Ecol. Appl. 28, 28–34. https://doi.org/10.1002/eap.1646 (2018).Article 
PubMed 

Google Scholar 
Kuuluvainen, T. Forest management and biodiversity conservation based on natural ecosystem dynamics in Northern Europe: The complexity challenge. Ambio 38, 309–315 (2009).Article 

Google Scholar 
Niemi, J. & Ahlstedt, J. Finnish Agriculture and Rural Industries 2011 (MTT Economic Research, Agrifood Research Finland, 2011).
Google Scholar 
Lehikoinen, P. et al. Increasing protected area coverage mitigates climate-driven community changes. Biol. Cons. 253, 108892. https://doi.org/10.1016/j.biocon.2020.108892 (2021).Article 

Google Scholar 
Virkkala, R. & Lehikoinen, A. Patterns of climate-induced density shifts of species: Poleward shifts faster in northern boreal birds than in southern birds. Glob. Change Biol. 20, 2995–3003. https://doi.org/10.1111/gcb.12573 (2014).ADS 
Article 

Google Scholar 
Lehikoinen, A. & Virkkala, R. North by north-west: Climate change and directions of density shifts in birds. Glob. Change Biol. 22, 1121–1129. https://doi.org/10.1111/gcb.13150 (2016).ADS 
Article 

Google Scholar 
Santangeli, A., Rajasärkkä, A. & Lehikoinen, A. Effects of high latitude protected areas on bird communities under rapid climate change. Glob. Change Biol. 23, 2241–2249. https://doi.org/10.1111/gcb.13518 (2017).ADS 
Article 

Google Scholar 
Lehikoinen, P., Santangeli, A., Jaatinen, K., Rajasärkkä, A. & Lehikoinen, A. Protected areas act as a buffer against detrimental effects of climate change—Evidence from large-scale, long-term abundance data. Glob. Change Biol. 25, 304–313. https://doi.org/10.1111/gcb.14461 (2019).ADS 
Article 

Google Scholar 
Santangeli, A. & Lehikoinen, A. Are winter and breeding bird communities able to track rapid climate change? Lessons from the high North. Divers. Distrib. 23, 308–316. https://doi.org/10.1111/ddi.12529 (2017).Article 

Google Scholar 
Lindén, H., Helle, E., Helle, P. & Wikman, M. Wildlife triangle scheme in Finland: Methods and aims for monitoring wildlife populations. Finnish Game Res. 49, 4–11 (1996).
Google Scholar 
Blonder, B. Do hypervolumes have holes? Am. Nat. 187, E93–E105. https://doi.org/10.1086/685444 (2016).Article 
PubMed 

Google Scholar 
Fuller, C., Ondei, S., Brook, B. W. & Buettel, J. C. First, do no harm: A systematic review of deforestation spillovers from protected areas. Glob. Ecol. Conserv. 18, e00591. https://doi.org/10.1016/j.gecco.2019.e00591 (2019).Article 

Google Scholar 
Hyvärinen, E., Juslén, A., Kemppainen, E., Uddström, A. & Liukko, U.-M. Suomen lajien uhanalaisuus–Punainen kirja 2019 (2019).Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027. https://doi.org/10.1890/13-1917.1 (2014).Article 

Google Scholar 
Morelli, F., Benedetti, Y., Møller, A. P. & Fuller, R. A. Measuring avian specialization. Ecol. Evol. 9, 8378–8386 (2019).Article 

Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444 (2012).ADS 
CAS 
Article 

Google Scholar 
Cimatti, M. et al. Large carnivore expansion in Europe is associated with human population density and land cover changes. Divers. Distrib. 27, 602–617. https://doi.org/10.1111/ddi.13219 (2021).Article 

Google Scholar 
Laaksonen, T. & Lehikoinen, A. Population trends in boreal birds: Continuing declines in agricultural, northern, and long-distance migrant species. Biol. Conserv. 168, 99–107. https://doi.org/10.1016/j.biocon.2013.09.007 (2013).Article 

Google Scholar 
Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n-dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609. https://doi.org/10.1111/geb.12146 (2014).Article 

Google Scholar 
Cardoso, P. M., Rigal, F. & Carvalho, J. BAT-Biodiversity Assessment Tools (2014).Zuur, A. F. & Ieno, E. N. A protocol for conducting and presenting results of regression-type analyses. Methods Ecol. Evol. 7, 636–645 (2016).Article 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x (2010).Article 

Google Scholar 
Sokal, R. R., Rohlf, F. J. & Rohlf, J. F. Biometry (Macmillan, 1995).MATH 

Google Scholar 
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x (2013).Article 

Google Scholar 
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. performance: An R package for assessment, comparison and testing of statistical models. J. Open Source Softw. https://doi.org/10.21105/joss.03139 (2021).Article 

Google Scholar 
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R 1–552 (Springer, 2009).Book 

Google Scholar 
R Core Development Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021). https://www.R-project.org/. More

Biere, J. M. & Uetz, G. W. Web orientation in the spider Micrathena gracilis (Araneae: Araneidae). Ecology 62(2), 336–344 (1981).Article 

Google Scholar 
Korb, J. & Linsenmair, K. E. The architecture of termite mounds: a result of a trade-off between thermoregulation and gas exchange? Behav. Ecol. 10(3), 312–316 (1999).Article 

Google Scholar 
Hansell, M. H. Bird nests and construction behaviour (Cambridge University Press, 2000).Book 

Google Scholar 
Kawase, H., Okata, Y. & Ito, K. Role of huge geometric circular structures in the reproduction of a Marine Pufferfish. Sci. Rep. 3, 1–5 (2013).Article 

Google Scholar 
Dawkins, R. The extended phenotype 295 (Oxford University Press, 1982).
Google Scholar 
Odling-Smee, F. J., Laland, K. N. & Feldman, M. W. Niche construction. Am. Nat. 147(4), 641–648 (1996).Article 

Google Scholar 
Odling-Smee, F. J., Laland, K. N. & Feldman, M. W. Niche construction: the Neglected process in evolution (Princeton University Press, 2003).
Google Scholar 
Short, L. L. Burdens of the picid hole-excavating habit. Wilson Bull. 91(1), 16–28 (1979).
Google Scholar 
Wiebe, K. L., Koenig, W. D. & Martin, K. Costs and benefits of nest reuse versus excavation in cavity-nesting birds. Ann. Zool. Fenn. 44(3), 209–217 (2007).
Google Scholar 
Landler, L. et al. Global trends in woodpecker cavity orientation: latitudinal and continental effects suggest regional climate influence. Acta Ornithol. 49(2), 257–266 (2014).Article 

Google Scholar 
Ojeda, V. et al. Latitude does not influence cavity entrance orientation of South American avian excavators. Auk 138(1), ukaa064 (2021).Article 

Google Scholar 
Wiebe, K. L. Microclimate of tree cavity nests: is it important for reproductive success in Northern Flickers? Auk 118(2), 412–421 (2001).Article 

Google Scholar 
Schaaf, A. A. Effects of sun exposure and vegetation cover on Woodpecker nest orientation in subtropical forests of South America. J. Ethol. 38, 117–120 (2019).Article 

Google Scholar 
Hooge, P. N., Stanback, M. T. & Koenig, W. D. Nest-site selection in the acorn woodpecker. Auk 116(1), 45–54 (1999).Article 

Google Scholar 
Schaaf, A. A. & de la Pena, M. R. Bird nest orientation and local temperature: an analysis over three decades. Ecology 20, e03042 (2020).
Google Scholar 
Charter, M. et al. Does nest box location and orientation affect occupation rate and breeding success of barn owls Tyto alba in a semi-arid environment? Acta Ornithol. 45(1), 115–119 (2010).Article 

Google Scholar 
Butler, M. W., Whitman, B. A. & Dufty, A. M. Nest box temperature and hatching success of American kestrels varies with nest box orientation. Wilson J. Ornithol. 121(4), 778–782 (2009).Article 

Google Scholar 
Goodenough, A. E. et al. Nestbox orientation: a species-specific influence on occupation and breeding success in woodland passerines. Bird Study 55(2), 222–232 (2008).Article 

Google Scholar 
Viñuela, J. & Sunyer, C. Nest orientation and hatching success of black kites milvus migrans in Spain. Ibis 134(4), 340–345 (1992).Article 

Google Scholar 
Larson, E. R. et al. How does nest box temperature affect nestling growth rate and breeding success in a parrot?. Emu 115(3), 247–255 (2015).Article 

Google Scholar 
Austin, G. T. Nesting success of the cactus wren in relation to nest orientation. Condor 76(2), 216–217 (1974).Article 

Google Scholar 
Verbeek, N. A. Nesting success and orientation of water pipit Anthus spinoletta nests. Ornis Scand. 25, 37–39 (1981).Article 

Google Scholar 
Conner, R. N. & Rudolph, D. C. Excavation dynamics and use patterns of red-cockaded woodpecker cavities: relationships with cooperative breeding. Red cockaded Woodpecker: recovery, ecology, and management. Center for Applied Studies in Forestry, College of Forestry, Stephen F. Austin State University, Nacogdoches, TX, 1995: 343–352.Harding, S. R. & Walters, J. R. Dynamics of cavity excavation by red-cockaded woodpeckers. In Red-Cockaded Woodpecker: Road to Recovery (eds Costa, R. & Daniels, S.) 412–422 (Hancock House, 2004).
Google Scholar 
Harding, S. R. & Walters, J. R. Processes regulating the population dynamics of red-cockaded woodpecker cavities. J. Wildl. Manage. 66(4), 1083–1095 (2002).Article 

Google Scholar 
Dennis, J. V. The yellow-shafted flicker (Colaptes Auratus) on Nantucket Island, Massachusetts. Bird Banding 40(4), 290–308 (1969).Article 

Google Scholar 
Baker, W. W. Progress report on life history studies of the red-cockaded woodpecker at Tall Timbers Research Station. Ecology and Management of the Redcockaded Woodpecker 44–59 (US Bureau of Sport Fisheries and Wildlife and Tall Timbers Research Station, 1971).
Google Scholar 
Dennis, J. V. Species using red-cockaded woodpecker holes in Northeastern South Carolina. Bird-Banding 42(2), 79–87 (1971).Article 

Google Scholar 
Conner, R. N. et al. Red-cockaded woodpecker nest-cavity selection: relationships with cavity age and resin production. Auk 115(2), 447–454 (1998).Article 

Google Scholar 
Conner, R. N. Orientation of entrances to woodpecker nest cavities. Auk 92(2), 371–374 (1975).Article 

Google Scholar 
Copeyon, C. K., Walters, J. R. & Carter, J. III. Induction of red-cockaded woodpecker group formation by artificial cavity construction. J. Wildl. Manage. 55(4), 549–556 (1991).Article 

Google Scholar 
Locke, B. A. & Conner, R. N. A statistical analysis of the orientation of entrances to redcockaded woodpecker cavities. In Red-Cockaded Woodpecker Symposium II (Florida Game and Fresh Water Fish Commission, 1983).
Google Scholar 
Lay, D. W., Red-cockaded woodpecker study. Texas Parks and Wildlife Department. Project W-80-R-16. 1973. p. 33.Jones, H. K. & Ott, F. T. Some characteristics of red-cockaded woodpecker cavity trees in Georgia. Oriole 38, 33–39 (1973).
Google Scholar 
Hopkins, M. L. & Lynn, T. E. Jr. Some characteristics of red-cockaded woodpecker cavity trees and management implications in South Carolina. Ecology and Management of The Red-Cockaded Woodpecker 140–169 (US Bureau of Sport Fishing and Wildlife and Tall Timbers Research Station, 1971).
Google Scholar 
Wood, D. A. Foraging and colony habitat characteristics of the red-cockaded woodpecker in Oklahoma. In Red-Cockaded Woodpecker Symposium II 51–58 (Florida Game and Fresh Water Fish Commission, 1983).
Google Scholar 
Kalisz, P. J. & Boettcher, S. E. Active and abandoned red-cockaded woodpecker habitat in Kentucky. J. Wildl. Manage. 25, 146–154 (1991).Article 

Google Scholar 
Walters, J. R., Doerr, P. D. & J. H. Carter, III. The cooperative breeding system of the red cockaded woodpecker. Ethology 78, 275–305 (1988).Article 

Google Scholar 
Batschelet, E. Circular statistics in biology (Academic Press, 1981).MATH 

Google Scholar 
Agostinelli, C. & U. Lund, R package “circular”: circular statistics. R package version 0.4-7. https://r-forge.r-project.org/projects/circular (2013).Hijmans, R. J. & Etten, J. V. Raster: Geographic analysis and modeling with raster data. R package version 2.0-12 (2012).R Development Core Team R. A language and environment for statistical computing (R Foundation for Statistical Computing, 2012).
Google Scholar 
Goslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22(7), 1–19 (2007).Article 

Google Scholar 
Cox, N. J. Speaking Stata: In praise of trigonometric predictors. Stand. Genomic Sci. 6(4), 561–579 (2006).
Google Scholar 
Smith, J. A. et al. How effective is the Safe Harbor program for the conservation of Red-cockaded Woodpeckers? Condor Ornithol. Appl. 120(1), 223–233 (2018).
Google Scholar 
Zuur, A. et al. Mixed effects models and extensions in ecology with R (Springer, 2009).MATH 
Book 

Google Scholar 
Bates, D., et al., lme4: Linear mixed-effects models using Eigen and S4. 2014: http://CRAN.R-project.org/package=lme4.Conner, R. N., Rudolph, D. C. & Walters, J. R. The red-cockaded woodpecker: surviving in a fire-maintained ecosystem (University of Texas Press, 2001).Book 

Google Scholar 
Rudolph, D. C., Kyle, H. & Conner, R. N. Red-cockaded woodpeckers vs rat snakes: the effectiveness of the resin barrier. Wilson Bull. 102(1), 14–22 (1990).
Google Scholar 
Conner, R. N. The effect of tree hardness on woodpecker nest entrance orientation. Auk 94(2), 369–370 (1977).Article 

Google Scholar 
Jackson, J. A. & Jackson, B. J. Ecological relationships between fungi and woodpecker cavity sites. Condor 106(1), 37–49 (2004).Article 

Google Scholar 
Jusino, M. A. et al. Experimental evidence of a symbiosis between red-cockaded woodpeckers and fungi. Proc. R. Soc. B Biol. Sci. 2016(283), 20160106 (1827).
Google Scholar 
Losin, N. et al. Relationship between aspen heartwood rot and the location of cavity excavation by a primary cavity-nester, the Red-naped Sapsucker. Condor 108(3), 706–710 (2006).Article 

Google Scholar 
Williamson, L., Garcia, V. & Walters, J. R. Life history trait differences in isolated populations of the endangered Red-cockaded Woodpecker. Ornis Hungar. 24(1), 55–68 (2016).Article 

Google Scholar 
DeMay, S. M. & Walters, J. R. Variable effects of a changing climate on lay dates and productivity across the range of the Red-cockaded Woodpecker. Condor 20, 20 (2019).
Google Scholar 
Garcia, V. Lifetime fitness and changing life history traits in red-cockaded woodpeckers (Virginia Tech, 2014).
Google Scholar 
Delmore, K. E. & Irwin, D. E. Hybrid songbirds employ intermediate routes in a migratory divide. Ecol. Lett. 17(10), 1211–1218 (2014).PubMed 
Article 

Google Scholar 
Helbig, A. J. Inheritance of migratory direction in a bird species: a cross-breeding experiment with SE-and SW-migrating blackcaps (Sylvia atricapilla). Behav. Ecol. Sociobiol. 28(1), 9–12 (1991).Article 

Google Scholar  More

Renewal processes represent development under variable conditionsThe consequence of a drastic environmental change can be demonstrated by introducing a shift in development time during the process. For demonstration, we consider a scenario where a group of individuals enter into a favourable environment reducing development time from (40pm 5) time units to (20pm 5).We show, in Fig. 1, that our dynamic pseudo-stage-structured MPM yields a gradual stage completion with an average development time of approximately (30pm 5) steps (solid dark lines) when conditions shift at ({tau }=20) (each step corresponds to 1 time unit). The target Erlang-distributed development trajectories without the shift are shown as dashed gray lines. The snapshots of the population structure, represented by the development indicator q, taken at each time step, show that half of the development is complete at the time of the switch and the switch accelerates the accumulation of q (Fig. S1).Figure 1Response to change in development time. The number of developing individuals is simulated by using the cumulative development process and compared to (a) the age-dependent development process, (b) an ODE representation, (c) an LCT representation, and (d) a DDE representation. Solid dark lines show the cumulative development and thick blue lines show the alternative models. Dashed gray lines mark the two target trajectories before and after the shift in development time (marked with red crosses).Full size imageIn age-dependent development, a sharp transition, instead of a gradual one, is observed at the (20^{th}) step (Fig. 1a). The switch results in the majority of individuals reaching target development age immediately at the time of switch. Previous work, reported in Erguler et al.59 and Erguler et al.55, aimed at modelling population dynamics under variable conditions, based on this dynamic age-dependent framework. Our results suggest that cumulative development might improve the fit to the data, prediction accuracy, and applicable geospatial range of these models.We see in Fig. 1b that the canonical ODE framework represents an exponentially distributed development time and a shift in rate at (t=20). The LCT extension to the framework helps to incorporate time dependence and represent the long and short development time distributions (Fig. 1c). The resulting model accommodates change in the rate parameter (gamma ) (Eq. 8), e.g doubling of (gamma ) changes development time from (40pm 5) to (20pm 2.5). However, to accommodate the required shift, the model needs to be transformed from a 66-dimensional system to an 18-dimensional one, which is beyond the scope of this work. We argue that in cases where development time distribution is fixed a priori (excluded from model calibration), the LCT framework provides a significant advantage over canonical ODEs. Although the framework has been used in the field of infectious disease epidemiology64,65, it has recently been applied to the modelling of vector population dynamics30.The DDE framework also yields a gradual development trajectory with an intermediate duration (Fig. 1d). However, the distribution tends towards the longer development trajectory compared to the one achieved with cumulative development. The canonical DDE framework assumes a homogenous cohort, where all individuals react in the same way to variations in development rate. The assumption gives rise to sharp stage transitions within a single generation if all individuals are introduced at the same time. As a potential workaround, it has been proposed to generate a plausible population history, through variable entry times, until the required (or observed) developmental variation builds up31,32. Variation in development rates then acts upon the population and results in the modification of the existing age-structure. It is worthwhile to mention that a recent extension to the DDE framework to accommodate trait variation in population dynamics34 might also accommodate changing development rates within a single stage; however, it has not yet been employed at this scale.Cumulative development is in agreement with the widely known degree-day (DD) framework, where development time is predicted by the heat accumulating in organisms46. Although the rate of accumulation in response to environmental conditions varies considerably, the DD framework implies that the combination of two different rates yields an average development time (also seen with cumulative development in Fig. 1). Experimental evaluation of this will be the topic of future research.It is worth mentioning that our dynamically structured renewal process-based MPM follows the assumption of random population heterogeneity9,11; namely, at the individual level, the future behaviour of an organism is not affected by its historical behaviour. However, trait variation within a population is prevalent in many species, and is known to impact population dynamics and species interactions34,66,67. Future development of our framework will consider improving upon this limitation.Environmental variation transformed into development timesSeveral non-linear relationships have been proposed to represent the temperature dependence of insect development68. A common feature is the presence of low and high temperature thresholds beyond which development is prohibitively slow. Often, there exists an optimum between the thresholds where the process is most efficient. A typical relationship between temperature and development rate, reported in Briere et al.50, is seen in Fig. 2a. Mean development time, given by the reciprocal of rate in Fig. 2b, exhibits the two thresholds and the optimum.Figure 2Development under environmental variation. In (a), development rate (Eq. 9) is shown with (alpha =1.5times 10^{-5}), (T_L=0^oC), and (T_H=50^oC). In (b), mean development time is shown together with the probability densities of three temperature regimes ((rho _L), (rho _M), and (rho _H)). In (c), the number of individuals completing development at each step are shown with respect to the three temperature regimes. Solid lines indicate the median, shaded areas indicate the (90%) range of 1000 simulations, and thick lines indicate simulations with the expected values of each regime.Full size imageTo investigate how temperature variation is transformed into cumulative development time, we assumed three variation regimes at relatively low, medium, and high temperatures ((rho _L), (rho _M), and (rho _H), respectively). Densities of the corresponding Gaussian probability distributions are plotted in Fig. 2b. Accordingly, each variation is transformed by a slightly different region of the rate function (Eq. 9). Eventually, the three development time distributions emerge as shown in Fig. 2c.We found that the output of (rho _H) is skewed towards longer durations compared to what we would otherwise obtain if we simulated the process under constant conditions with the mean of (rho _H). The impact of variation in the middle range, (rho _M), is similar to that of (rho _H), but less pronounced. Conversely, the output of (rho _L) is skewed towards shorter durations. Our results suggest that, when development is already highly efficient, variation in temperature causes frequent encounters of longer (but not shorter) development durations, eventually extending the overall duration of the process. In the low efficiency range, development takes long to complete, but frequent encounters of relatively short durations—especially as the process approaches its optimum duration—triggers completion earlier than in the case of no variation.Overall, our model predictions are in agreement with the rate summation effect, which states that the different outcomes obtained under constant and varying temperatures is due to the non-linear relationship between temperature and development rate (the Kaufmann effect)16. Furthermore, acceleration of development in insects subjected to varying high temperatures, its retardation at varying low temperatures, and low variability of development time in the linear range of the rate curve have been widely discussed69. Several groups have reported evidence in support of this effect, which is also in agreement with our results. For instance, Vangansbeke et al. (2015) reported for three insect species, Phytoseiulus persimilis, Neoseiulus californicus, and Tetranychus urticae, that varying temperatures with a lower mean yields faster development compared to the yield at mean constant temperatures70. However, observations of this phenomenon might result in different responses for different species at similar temperatures due to the difference in rate curves. Identification of the optimum temperature range may facilitate comparison. For instance, Carrington et al. (2013) assumed (26^oC) as optimum based on the high dengue incidence in Thailand, and showed that large variations around (26^oC) increases development time for the dengue vector, Aedes aegypti71. Wu et al. (2015) demonstrated that development is faster at around (26^oC) compared to (23^oC) for the fly, Megaselia scalaris, and found that varying temperatures at around (23^oC) accelerates the process47. Finally, in a modelling study employing DDs, Chen et al. (2013) reported that larger diurnal temperature ranges relate to additional DD accumulation and faster development in grape berry moth, Paralobesia viteana72. Under the realistic non-optimum field conditions, where these simulations had been performed, a decrease in development time is expected in response to varying temperatures according to our results.We note that the variation in development times is due to temperature since we ignore intrinsic stochasticity to demonstrate the impact of (rho ) in isolation. The deterministic setup removes the upper limit in the number of distinct pseudo-stage indicators: a different q emerges from each k, and a different k emerges from each (rho ). Since the number of pseudo-stages quickly exhausts the computational resources, we set the precision of q to the nearest 100(^{th}) decimal point, effectively capping the number of pseudo-stages at 100 (see Accuracy of the pseudo-stage approximation). As shown in Fig. S2, the approximation has a negligible impact on accuracy.Environmental dependency extracted from life tables under constant conditionsHaving discussed the importance of environmental variability in development, in this section, we employ a well-established experimental method to unravel the relationship between temperature and development time in a common mosquito species. In contrast to invasive vectors, which effectively render new territories suitable for disease transmission, Culex species pose an imminent threat with their wide distribution and ornitophilic (Cx. pipiens biotype pipiens), mamophilic (Cx. pipiens biotype molestus), and intermixed (their hybrids) blood feeding behaviour. Here, we investigate the temperature dependencies of mortality and development of Cx. quinquefasciatus, the southern house mosquito, which is an important disease vector, widely distributed across the tropics and sub-tropics73,74.To infer the dependencies, we used a generic temperature-driven insect development model, described in Methods, and the life history observations performed at five constant temperatures (15, 20, 23, 27, and (30,^{circ })C) under laboratory conditions60,61. As a result of the inverse modelling procedure, detailed in Methods, we found that the generic model yields an overall match between the simulations and observations. In Fig. 3a, we present a comparison of observed and simulated maximum production and the stage-emergence times for pupae and adults. Here, we define the stage-emergence time as the time taken from the beginning of an experiment to the time when half of the maximum production of a stage (pupa or adult) is observed. In addition, in Fig. S3, we present the comparison of time trajectories separately for each temperature.Figure 3Inverse modelling of Cx. quinquefasciatus environmental dependency. The comparison of observed and simulated maximum pupa (P) and adult (A) production and the corresponding stage-emergence times is given in (a). Observations are represented with dots and simulations with box plots. The environmental dependency of larva and pupa development time (b) and mortality (c), derived by the posterior mode sample (Theta _q), is shown in (b,c). Solid lines represent the median and shaded areas represent the (90%) range.Full size imageWe found that the generic model faithfully replicates the observed development times of larvae and pupae. On the other hand, stage mortalities are predicted well at three temperatures, but are overestimated at 20 or (27,^{circ })C. The impact of temperature on mortality might be more complex than it is captured by the quartic equation (Eq. 11). Optimum survival seen at (27,^{circ })C suggests that the relationship might be non-symmetrical or multimodal. In addition, the observed variability in mortality suggests that the mismatch could also be due to experimental error or the intrinsic stochasticity of the biological processes.We extracted the functional forms of temperature dependence from the posterior samples, shown in Fig. 3b, c, and found that the data inform the model as expected within the temperature range of the experiments ((15{-}30,^{circ })C). Stage durations are well informed, and reflect the low variability seen in the data (the standard deviation is less than 1.5 days at all temperatures for both stages). Accordingly, pupae develop in less than 4 days, which is much shorter than the larva development time (between 10 and 20 days above (20,^{circ })C). The model predicts that the minimum temperature at which development occurs (from the larva stage) is (10.5,^{circ })C, which is close to (10.9,^{circ })C, reported in Grech et al.75.The observed variability in pupa and adult production suggests that survival is a highly stochastic process regardless of the controlled laboratory conditions. A deterministic model, such as the one used in this context, represents the mean of such processes but does not capture their variability. The simulated variability is a result of the uncertainty in parameter estimates. Model parameters contribute unequally to the output as a result of the model structure and the functional forms of temperature dependence, and the data inform certain parameters better than others76,77. For instance, daily mortality, shown in Fig. 3c, is more constrained for larva than pupa, which is likely due to the short duration of the pupa stage—changes in daily mortality have larger consequences as development time increases.We note that a well-informed model yields predictions in the form of verifiable hypotheses; however, these are not necessarily accurate predictions. Model accuracy is assessed when such hypotheses are experimentally tested as part of the cyclic process of model development78. Here, we demonstrated that our modelling framework can be used to derive biologically meaningful inferences and to help improve the understanding of the temperature dependence of Cx. quinquefasciatus.Greater information content of semi-field experimentsThe number of experiments required to test a range of conditions, including different combinations of multiple drivers, may quickly exhaust available resources. Moreover, variable conditions may have a previously unaccounted impact on development and mortality. In this section, we demonstrate that observations performed under variable conditions are valuable sources of information for our modelling framework, which is capable of representing the dynamics under such conditions.Cx. pipiens, the northern house mosquito, is a competent disease vector, widely distributed across the temperate countries in North America, Europe, Asia, and North and East Africa74,79. Unlike Cx. quinquefasciatus, Cx. pipiens biotype pipiens is known to enter a reproductive diapause phase, where adult females arrest oogenesis during harsh winter conditions80,81. When larvae are exposed to short photoperiods and low temperatures during development, they emerge as adults destined to diapause. Although Cx. pipiens biotype molestus has lost the ability to diapause, its immature stages have been reported to retain metabolic sensitivity to photoperiod82,83.To reveal the environmental dependence of the molestus biotype, we exposed its eggs to variable temperatures in semi-field conditions until adult emergence (or loss of cohort). The numbers of viable larvae, pupae, and adults observed in different experimental batches are given in Fig. S4. We employed the extended model with both temperature and photoperiod dependence (see Methods), and calibrated the model against seven of the semi-field experiments, performed in March, May, June, July, August, and September (Fig. S4(a), (b), (d), (f), (g), (i) and (j)).As a result, we found that the model replicates the patterns of abundance emerging in the observations, e.g. stage timing and maximum adult production, reasonably well in most of the experiments, regardless of the times during which they were performed (Figs. S5 and S6). Quantitative evaluation of the agreement reveals that the observed and simulated adult emergence times are less than a week apart (Table 1).Table 1 Comparison of observed and simulated adult emergence time and the total number of adults produced. Simulation output is given in terms of the median and (90%) range.Full size tableOn the other hand, we found that egg and larva mortalities, and also, pupa and adult production are highly variable in the observations (see Fig. S4(c), (f), and (g)). Spikes of larva mortality are seen in Spring and Autumn (especially in May, September, and October). Despite this variability, the difference between the predicted and observed adult production was around 11 or less, except in the case of the experiment E7, which unexpectedly yielded only one pupa and no adults.We obtain relatively large mismatches when predicting larva abundances, specifically where egg mortality is not predicted well (E5, E7, E8, E10, E11, E12). We hypothesise that the stress associated with rearing lab-grown specimens under variable conditions might elevate egg mortality, induce premature hatching, or affect the survival of the larvae produced. Since egg development starts inside gravid females, i.e. under the optimum conditions of the laboratory, the observable part of development subjected to variable conditions remains mainly the hatching behaviour. Consequently, we observed rapid and synchronous completion of the egg stage in all experiments (see Figs. S5 and S6). Being exposed to a narrow range of temperatures, relatively less information can be obtained on the environmental dependency of the egg stage. As a potential improvement, we recommend that future adaptations of the semi-field experiments consider using field-captured adult female mosquitoes as the source of eggs.In addition to egg mortality, we observed spikes of larva mortality in May (E3), July (E8), and in Autumn (E14, E15, and E16). A likely cause of such transient high mortality is brief temperature shifts towards the extremes. However, the rarity of such events prevents the inverse modelling procedure from adequately capturing their impacts on life processes. As a potential improvement, we recommend that the experiments are performed in overlapping time frames, increasing the likelihood of observing the impact of an extreme event at different times during development. We note that the early decline in larva abundance seen in Autumn could be a result of insufficient food supply due to the increased nutritional requirements. According to the proposed metabolic response to short photoperiods, larvae would require additional food to accumulate fat reserves in preparation for diapause, the state where adult females endure several months without feeding. This implies that development takes longer than it would at long photoperiods when subjected to similar temperature regimes.Using the extended model and the semi-field data, we identified the environmental dependencies shown in Fig. 4. The data informed about the temperature dependency of each life stage as well as the photoperiod dependency of larvae. As expected, the overall variability in the inferred dependencies is higher for Cx. pipiens compared to Cx. quinquefasciatus (Fig. 3). We found that the larva and pupa development times closely match the observations reported by Spanoudis et al.62 at long photoperiods (see Fig. S7). However, the development times reported in Kiarie-Makara et al.84 at short photoperiods and moderate temperatures do not suggest a significant impact of daylight, which could be due to the particular strain of Cx. pipiens used in these experiments. As expected, the temperature dependency of egg development was not well informed by the data in the current configuration of the model and the functional forms of environmental dependence.Figure 4Environmental dependency of Cx. pipiens development and mortality inferred from semi-field life table experiments. Solid lines represent the median and shaded areas represent the (90%) range.Full size imageWe found that the photoperiod dependency is significantly non-linear with an average threshold of 13.7 hours of daylight (Fig. 4c). Photoperiod-driven extension in development time (about 1.7 times more at 13:11 h L:D than at 15:9 h L:D) contributes to improving the accuracy of predictions at the end of the high season (Fig. S8). The critical photoperiod (CPP) agrees well with the ones identified for Cx. pipiens biotype pipiens85,86. For instance, Sanburg and Larsen reported that there is an exponential relationship between follicle sizes in adult females (signifying commitment to diapause) and the photoperiods they were exposed to during immature stages85. We inferred a similar (but reverse) gradient between photoperiod and the extension of larva development time from 15 to 12 hours of daylight (Fig. 4c).Risk assessment with annual development profilesWe extrapolated the development dynamics of Cx. pipiens over the calendar year by setting up a hypothetical experiment at the beginning of each week. We simulated the subsequent development dynamics and obtained the annual development profile as shown in Fig. 5. Accordingly, the immature stages begin development from late February and the first adults emerge in May (adults emerging late in May start developing in the experiments set up late in March). The profile is consistent with the regular Cx. pipiens high season in the region.Figure 5Annual development profile of Cx. pipiens in Petrovaradin, Serbia, in 2017. The outcome of each hypothetical semi-field experiment is plotted vertically along the y-axis at the date when the experiment is initiated. The maximum number of adults produced is given in blue, and the time it takes (from the date indicated on the x-axis) to produce half of the maximum is given in green. Solid lines represent the median and shaded areas represent the 90% range of model predictions. Outcomes of the semi-field experiments (dots) are plotted together with the model predictions. The time points marked with circles indicate the experiments used to calibrate the model. Estimated time of first adult emergence is given in the inset.Full size imageAs seen in Fig. 5, predicted adult emergence times agree well with the observations throughout the high season. However, there is a greater variation in the maximum number of adults than the times of emergence (extending to almost (40%) of the possible outcomes in early August). A greater variability (almost (80%) in August) is seen in the corresponding observations, which we transformed into the percentage of eggs emerging as adults (where available) to facilitate comparison. According to the model, variation in adult production is associated with the variation in both development times and mortality during immature stages. We recall that the uncertainty in the informed environmental dependencies is high around relatively less frequently encountered values—especially the lower and higher temperature extremes (Fig. 4). Specifically, egg development times cannot be identified precisely, but immediate hatching of the larvae is predicted between 20 and 25 °C. Consequently, we found that frequent exposure to temperatures outside the well-informed range have a significant impact on the variation in adult production (Fig. S9).We adopt the time of first adult emergence as a proxy of the first generation of adults in the season. According to our model, early adult emergence is a result of shorter development times and higher success rates, which indicates that the temperature conditions allow for an early first generation of adults. An early first generation greatly contributes to an early peak of adult abundance, which may increase the risk of vector-borne disease transmission in humans. For instance, an early peak of abundance may cause an early start of West Nile virus circulation and amplification in Culex pipiens and their avian hosts, which increases the likelihood of virus spillover to humans51,87. Anecdotal evidence shows that the anomalously hot April and May that occurred in 2018 in Serbia shifted the peak of Cx. pipiens abundance forward by more than one month (Petrić et al., unpublished). Similarly, 2018 was the year with the largest number of autochthonous West Nile virus infections throughout Europe (more than the total of the previous seven years together)88,89.In summary, our results showed that the semi-field experiments, when used in combination with our dynamic pseudo-stage-structured MPM, help to develop predictive models and inform over a wide range of environmental conditions. We developed a predictive model of Cx. pipiens biotype molestus development and gained insights into the specifics of temperature and photoperiod dependencies by reducing the need of extensive laboratory data. We used life history observations from 7 experiments performed under semi-field conditions and employed a generic model structure, largely uninformed on the specific environmental dependencies of the species. The cumulative development framework we introduced applies broadly to poikilotherms subjected to highly variable environmental conditions. Although the generic model structure helps to develop exploratory models and identify potential environmental dependencies, accuracy can be improved by customising the models for the known dependencies of particular species. With a straightforward extension of the development model to cover the complete life cycle (with egg laying and density dependence), it is possible to incorporate field observations of eggs or adult mosquitoes, and develop an environment-driven population dynamics model. More

Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Canadell, J. G., et al “[Global Carbon and other Biogeochemical Cycles and Feedbacks”] in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, in Press, 2021).Li, W. et al. Revisiting Global Vegetation Controls Using Multi-Layer Soil Moisture. Geophys. Res. Lett. 48, e2021GL092856 (2021).ADS 

Google Scholar 
Stocker, B. D. et al. Quantifying soil moisture impacts on light use efficiency across biomes. N. Phytol. 218, 1430–1449 (2018).Article 

Google Scholar 
Greve, P. et al. Global assessment of trends in wetting and drying over land. Nat. Geosci. 7, 716–721 (2014).ADS 
CAS 
Article 

Google Scholar 
Jiao, W. et al. Observed increasing water constraint on vegetation growth over the last three decades. Nat. Commun. 12, 3777 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
De Kauwe, M. G., Medlyn, B. E. & Tissue, D. T. To what extent can rising [CO2] ameliorate plant drought stress? N. Phytol. 231, 2118–2124 (2021).Article 
CAS 

Google Scholar 
Gampe, D. et al. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nat. Clim. Change 11, 772–779 (2021).ADS 
Article 

Google Scholar 
Konings, A. G., Williams, A. P. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–288 (2017).ADS 
CAS 
Article 

Google Scholar 
Carminati, A. & Javaux, M. Soil rather than xylem vulnerability controls stomatal response to drought. Trends Plant Sci. 25, 868–880 (2020).CAS 
PubMed 
Article 

Google Scholar 
Anderegg, W. R. L., Trugman, A. T., Bowling, D. R., Salvucci, G. & Tuttle, S. E. Plant functional traits and climate influence drought intensification and land–atmosphere feedbacks. Proc. Natl Acad. Sci. USA 116, 14071–14076 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Humphrey, V. et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature 592, 65–69 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Myneni, R. B., Hall, F. G., Sellers, P. J. & Marshak, A. L. The interpretation of spectral vegetation indexes. IEEE Trans. Geosc. Rem. Sens. 33, 481–486 (1995).ADS 
Article 

Google Scholar 
Forzieri, G., Alkama, R., Miralles, D. G. & Cescatti, A. Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science 356, 1180–1184 (2017).CAS 
PubMed 
Article 

Google Scholar 
Frankenberg, C. et al. Comment on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”. Science 373, eaabg2947 (2021).Article 
CAS 

Google Scholar 
Wang, S. et al. Response to Comments on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”. Science 373 (2021).Forzieri, G. et al. Increased control of vegetation on global terrestrial energy fluxes. Nat. Clim. Change 10, 356–362 (2020).ADS 
Article 

Google Scholar 
Seneviratne, S. I. et al. Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Sci. Rev. 99, 125–161 (2010).ADS 
CAS 
Article 

Google Scholar 
Miguez-Macho, G. & Fan, Y. Spatiotemporal origin of soil water taken up by vegetation. Nature 598, 624–628 (2021).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Balsamo, G. et al. Satellite and in situ observations for advancing global Earth surface modelling: A Review. Remote Sens. 10, 2038 (2018).ADS 
Article 

Google Scholar 
Muñoz-Sabater, J. et al. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).ADS 
Article 

Google Scholar 
Molnar, C. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (2021). [online: https://christophm.github.io/interpretable-ml-book/].Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7, eabc7447 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ohta, T. et al. Effects of waterlogging on water and carbon dioxide fluxes and environmental variables in a Siberian larch forest, 1998–2011. Agric. Meteorol. 188, 64–75 (2014).Article 

Google Scholar 
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fort, F. et al. Root traits are related to plant water‐use among rangeland Mediterranean species. Funct. Ecol. 31, 1700–1709 (2017).Article 

Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. evolution 3, 772–779 (2019).Article 

Google Scholar 
Rogers, A. et al. A roadmap for improving the representation of photosynthesis in Earth system models. N. Phytol. 213, 22–42 (2016).Article 

Google Scholar 
Arora, V. K. & Boer, G. J. A parameterization of leaf phenology for the terrestrial ecosystem component of climate models. Glob. Change Biol. 11, 39–59 (2004).ADS 
Article 

Google Scholar 
Trugman, A. T., Medvigy, D., Mankin, J. S. & Anderegg, W. R. L. Soil moisture stress as a major driver of carbon cycle uncertainty. Geophys. Res. Lett. 45, 6495–6503 (2018).ADS 
Article 

Google Scholar 
Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).ADS 
Article 

Google Scholar 
Medlyn, B. E., De Kauwe, M. G. & Duursma, R. A. New developments in the effort to model ecosystems under water stress. N. Phytol. 212, 5–7 (2016).Article 

Google Scholar 
Ito, A. & Oikawa, T. A simulation model of the carbon cycle in land ecosystems (Sim-CYCLE): a description based on dry-matter production theory and plot-scale validation. Ecol. Model. 151, 143–176 (2002).CAS 
Article 

Google Scholar 
Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Mu, M. et al. Exploring how groundwater buffers the influence of heatwaves on vegetation function during multi-year droughts. Earth Syst. Dyn. 12, 919–938 (2021).ADS 
Article 

Google Scholar 
O, S. & Orth, R. Global soil moisture data derived through machine learning trained with in-situ measurements. Sci. Data 8, 170 (2021).Article 

Google Scholar 
Dorigo, W. et al. ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sens. Environ. 203, 185–215 (2017).ADS 
Article 

Google Scholar 
Wahr, J., Swenson, S., Zlotnicki, V. & Velicogna, I. Time-variable gravity from GRACE: first results. Geophys. Res. Lett. 31, L11501 (2004).ADS 
Article 

Google Scholar 
Tucker, C. J. et al. An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498 (2005).Article 

Google Scholar 
Jiang, C. et al. Inconsistencies of interannual variability and trends in long‐term satellite leaf area index products. Glob. Change Biol. 23, 4133–4146 (2017).ADS 
Article 

Google Scholar 
Liu, Y. et al. Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes. Remote Sens. Environ. 206, 174–188 (2018).ADS 
Article 

Google Scholar 
Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).ADS 
Article 

Google Scholar 
Pedelty, J. et al. Generating a long-term land data record from the AVHRR and MODIS instruments. 2007 IEEE international Geoscience and remote sensing Symposium, 1021-1025 (2017).Xiao, Z. et al. Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Trans. Geosci. Remote Sens. 54, 5301–5318 (2016).ADS 
Article 

Google Scholar 
Liu, Y., Liu, R. & Chen, J. M. Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data. J. Geophys. Res. 117, G04003 (2012).ADS 

Google Scholar 
Verger, A., Baret, F. & Weiss, M. (2020). Algorithm Theoretical Basis Document – GEOV2/AVHRR: Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Fraction of green Vegetation Cover (FCOVER) from LTDR AVHRR. (Available at https://www.theia-land.fr/wp-content/uploads/2022/03/THEIA-SP-44-0207-CREAF_I2.50-1.pdf).Liu, Y., De Jeu, R. A., McCabe, M. F., Evans, J. P. & Van Dijk, A. I. Global long‐term passive microwave satellite‐based retrievals of vegetation optical depth. Geophys. Res. Lett. 38 (2011).Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Update high-resolution grids of monthly climatic observations-the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
Kobayashi, S. et al. The JRA-55 Reanalysis: General Specifications and Basic Characteristics. J. Met. Soc. Jpn. 93, 5–48 (2015).Article 

Google Scholar 
Li, X. & Xiao, J. Global climatic controls on interannual variability of ecosystem productivity: Similarities and differences inferred from solar-induced chlorophyll fluorescence and enhanced vegetation index. Agric. For. Meteorol. 288–289, 108018 (2020).ADS 
Article 

Google Scholar 
Walther, S. et al. Satellite observations of the contrasting response of trees and grasses to variations in water availability. Geophys. Res. Lett. 46, 1429–1440 (2020).ADS 
Article 

Google Scholar 
Li, M., Wu, P. & Ma, Z. A comprehensive evaluation of soil moisture and soil temperature from third-generation atmospheric and land reanalysis data sets. Int. J. Climatol. 40, 5744–5766 (2020).Article 

Google Scholar 
Liu, L., Zhang, R. & Zuo, Z. Intercomparison of spring soil moisture among multiple reanalysis data sets over eastern China. J. Geophys. Res.: Atmospheres 119, 54–64 (2014).ADS 
Article 

Google Scholar 
Albergel, C., De Rosnay, P., Balsamo, G., Isaksen, L. & Muñoz-Sabater, J. Soil moisture analyses at ECMWF: Evaluation using global ground-based in situ observations. J. Hydrometeorol. 13, 1442–1460 (2012).ADS 
Article 

Google Scholar 
Albergel, C. et al. Skill and global trend analysis of soil moisture from reanalyses and microwave remote sensing. J. Hydrometeorol. 14, 1259–1277 (2013).ADS 
Article 

Google Scholar 
Jing, W., Song, J. & Zhao, X. Validation of ECMWF multi-layer reanalysis soil moisture based on the OzNet hydrology network. Water 10, 1123 (2018).Article 

Google Scholar 
Albergel, C. et al. ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better? Hydrol. Earth Syst. Sci. 22, 3515–3532 (2018).ADS 
Article 

Google Scholar 
Gelaro, R. et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).ADS 
PubMed 
Article 

Google Scholar 
Martens, B. et al. GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Dev. 10, 1903–1925 (2017).ADS 
Article 

Google Scholar 
Le Quéré, C. et al. Global Carbon Budget 2018. Earth Syst. Sci. Data. 10, 2141–2194 (2018).ADS 
Article 

Google Scholar 
Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).ADS 
Article 

Google Scholar 
Song, X. P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Siebert, S. et al. A global data set of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci. 19, 1521–1545 (2015).ADS 
Article 

Google Scholar 
Budyko, M. I. & Miller, D. H. Climate and life. New York (Academic press, 1974).Cleveland, W. S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74, 829–836 (1979).MathSciNet 
MATH 
Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
Article 

Google Scholar 
Kraft, B., Jung, M., Körner, M., Koirala, S. & Reichstein, M. Towards hybrid modeling of the global hydrological cycle. Hydrol. Earth Syst. Sci. 26, 1579–1614 (2022).ADS 
Article 

Google Scholar 
Lundberg, S. M. & Lee, S. I. A unified approach to interpreting model predictions. (2017).Besnard, S. et al. Global sensitivities of forest carbon changes to environmental conditions. Glob. Change Biol. 27, 6467–6483 (2021).Article 

Google Scholar 
Hirsch, R. M., Slack, J. R. & Smith, R. A. Techniques of trend analysis for monthly water quality data. Water Resour. Res. 18, 107–121 (1982).ADS 
Article 

Google Scholar  More

Ottenburghs, J. et al. A history of hybrids? Genomic patterns of introgression in the True Geese. BMC Evol. Biol. 17, 14 (2017).Article 

Google Scholar 
Baiz, M. D., Tucker, P. K. & Cortés-Ortiz, L. Multiple forms of selection shape reproductive isolation in a primate hybrid zone. Mol. Ecol. 28, 1056–1069 (2019).CAS 
PubMed 
Article 

Google Scholar 
Slager, D. L. et al. Cryptic and extensive hybridization between ancient lineages of American crows. Mol. Ecol. 29, 956–969 (2020).CAS 
PubMed 
Article 

Google Scholar 
Grant, P. R. & Grant, B. R. Introgressive hybridization and natural selection in Darwin’s finches. Biol. J. Linnean Soc. 117, 812–822 (2016).Article 

Google Scholar 
Pauquet, G., Salzburger, W. & Egger, B. The puzzling phylogeography of the haplochromine cichlid fish Astatotilapia burtoni. Ecol. Evol. 8, 5637–5648 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Schluter, D. Ecological character displacement in adaptive radiation. Am. Nat. 156, S4–S16 (2000).Article 

Google Scholar 
Song, Y. et al. Adaptive introgression of anticoagulant rodent poison resistance by hybridization between old world mice. Curr. Biol. 21, 1296–1301 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Anderson, R. P., Peterson, A. T. & Gómez-Laverde, M. Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 98, 3–16 (2002).Article 

Google Scholar 
Gramlich, S., Wagner, N. D. & Horandl, E. RAD-seq reveals genetic structure of the F-2-generation of natural willow hybrids (Salix L.) and a great potential for interspecific introgression. BMC Plant Biol. 18, 12 (2018).Article 

Google Scholar 
Mavárez, J. et al. Speciation by hybridization in Heliconius butterflies. Nature 441, 868–871 (2006).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Cahill, J. A. et al. Genomic evidence of geographically widespread effect of gene flow from polar bears into brown bears. Mol. Ecol. 24, 1205–1217 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Djogbénou, L. et al. Evidence of introgression of the ace-1(R) mutation and of the ace-1 duplication in West African Anopheles gambiae s. s. PLoS ONE 3, e2172 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Enciso-Romero, J. et al. Evolution of novel mimicry rings facilitated by adaptive introgression in tropical butterflies. Mol. Ecol. 26, 5160–5172 (2017).PubMed 
Article 

Google Scholar 
Dasmahapatra, K. K. et al. Butterfly genome reveals promiscuous exchange of mimicry adaptations among species. Nature 487, 94–98 (2012).ADS 
CAS 
PubMed Central 
Article 

Google Scholar 
Latch, E. K., Harveson, L. A., King, J. S., Hobson, M. D. & Rhodes, J. R. Assessing hybridization in wildlife populations using molecular markers: a case study in wild turkeys. J. Wildl. Manag. 70, 485–492 (2006).Article 

Google Scholar 
Oliveira, R., Godinho, R., Randi, E. & Alves, P. C. Hybridization versus conservation: are domestic cats threatening the genetic integrity of wildcats (Felis silvestris silvestris) in Iberian Peninsula?. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 363, 2953–2961 (2008).Article 

Google Scholar 
Nichols, P. et al. Secondary contact seeds phenotypic novelty in cichlid fishes. Proc. R. Soc. B Biol. Sci. 282, 8 (2015).
Google Scholar 
Yang, W. Z. et al. Genomic evidence for asymmetric introgression by sexual selection in the common wall lizard. Mol. Ecol. 27, 4213–4224 (2018).CAS 
PubMed 
Article 

Google Scholar 
Boratyński, Z. et al. Introgression of mitochondrial DNA among Myodes voles: consequences for energetics?. BMC Evol. Biol. 11, 355 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mondal, M. et al. Genomic analysis of Andamanese provides insights into ancient human migration into Asia and adaptation. Nat. Genet. 48, 1066–1070 (2016).CAS 
PubMed 
Article 

Google Scholar 
Melo-Ferreira, J., Seixas, F. A., Cheng, E., Mills, L. S. & Alves, P. C. The hidden history of the snowshoe hare, Lepus americanus: extensive mitochondrial DNA introgression inferred from multilocus genetic variation. Mol. Ecol. 23, 4617–4630 (2014).CAS 
PubMed 
Article 

Google Scholar 
Mims, M. C., Hulsey, C. D., Fitzpatrick, B. M. & Streelman, J. T. Geography disentangles introgression from ancestral polymorphism in Lake Malawi cichlids. Mol. Ecol. 19, 940–951 (2010).PubMed 
Article 

Google Scholar 
Salazar, C. et al. Genetic evidence for hybrid trait speciation in heliconius butterflies. PLoS Genet. 6, e1000930 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Naisbit, R. E., Jiggins, C. D. & Mallet, J. Mimicry: developmental genes that contribute to speciation. Evol. Dev. 5, 269–280 (2003).CAS 
PubMed 
Article 

Google Scholar 
Zhang, W., Dasmahapatra, K. K., Mallet, J., Moreira, G. R. P. & Kronforst, M. R. Genome-wide introgression among distantly related Heliconius butterfly species. Genome Biol. 17, 15 (2016).Article 
CAS 

Google Scholar 
Zhang, W., Kunte, K. & Kronforst, M. R. Genome-wide characterization of adaptation and speciation in tiger swallowtail butterflies using De Novo transcriptome assemblies. Genome Biol. Evol. 5, 1233–1245 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jones, M. R. et al. Adaptive introgression underlies polymorphic seasonal camouflage in snowshoe hares. Science (New York, NY). 360, 1355–1358 (2018).ADS 
CAS 
Article 

Google Scholar 
Melville, J. Competition and character displacement in two species of scincid lizards. Ecol. Lett. 5, 386–393 (2002).Article 

Google Scholar 
Pfennig, D. W. & Pfennig, K. S. Character displacement and the origins of diversity. Am. Nat. 176, S26–S44 (2010).PubMed 
PubMed Central 
MATH 
Article 

Google Scholar 
Kooyers, N. J., James, B. & Blackman, B. K. Competition drives trait evolution and character displacement between Mimulus species along an environmental gradient. Evol. Int. J. Org. Evol. 71, 1205–1221 (2017).CAS 
Article 

Google Scholar 
Adams, D. C. & Rohlf, F. J. Ecological character displacement in Plethodon: Biomechanical differences found from a geometric morphometric study. Proc. Natl. Acad. Sci. 97, 4106–4111 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Grant, P. R. & Grant, B. R. Evolution of character displacement in Darwin’s finches. Science (New York, NY) 313, 224–226 (2006).ADS 
CAS 
Article 

Google Scholar 
Pfennig, D. W. & Murphy, P. J. Character displacement in polyphenic tadpoles. Evol. Int. J. Org. Evol. 54, 1738–1749 (2000).CAS 
Article 

Google Scholar 
Jones, G. Acoustic signals and speciation: the roles of natural and sexual selection in the evolution of cryptic species. Adv. Study Behav. 26, 317–354 (1997).Article 

Google Scholar 
Marsteller, S., Adams, D. C., Collyer, M. L. & Condon, M. Six cryptic species on a single species of host plant: morphometric evidence for possible reproductive character displacement. Ecol. Entomol. 34, 66–73 (2009).Article 

Google Scholar 
Tene Fossog, B. et al. Habitat segregation and ecological character displacement in cryptic African malaria mosquitoes. Evol. Appl. 8, 326–345 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Ibáñez, C., García-Mudarra, J. L., Ruedi, M., Stadelmann, B. & Juste, J. The Iberian contribution to cryptic diversity in European bats. Acta Chiropterol. 8, 277–297 (2006).Article 

Google Scholar 
Juste, J. et al. Mitochondrial phylogeography of the long-eared bats (Plecotus) in the Mediterranean Palaearctic and Atlantic Islands. Mol. Phylogenet. Evol. 31, 1114–1126 (2004).CAS 
PubMed 
Article 

Google Scholar 
Schreber J. Die Säugthiere in Abbildungen nach der Natur, mit Beschreibungen. Erlangen – Expedition des Schreber’schen säugthier- und des Esper’schen Schmetterlingswerkes. Ernst Mayr Library of the MCZ, 1774–1855 (Harvard, 1774).Temminck, C. J. Monographies de Mammologie, ou description de quelques genres de Mammifères, dont les espèces ont été observes dans les différens Musées de l’Europe, Vol. 2, No. 302, 26–70 (G. Dufour et Ed. D’Ocagne, 1840).Centeno-Cuadros, A. et al. Comparative phylogeography and asymmetric hybridization between cryptic bat species. J. Zool. Syst. Evol. Res. 57, 1004–1018 (2019).Article 

Google Scholar 
Santos, H. et al. Shaping of bat cryptic distribution in Iberia. Biol. J. Linnean Soc. 112, 150–162 (2014).Article 

Google Scholar 
Novella-Fernandez, R. et al. Broad-scale patterns of geographic avoidance between species emerge in the absence of fine-scale mechanisms of coexistence. Divers. Distrib. 27, 1606–1618 (2021).Article 

Google Scholar 
Neubaum, M. A., Douglas, M. R., Douglas, M. E. & O’Shea, T. J. Molecular ecology of the big brown bat (Eptesicus fuscus): genetic and natural history variation in a hybrid zone. J. Mammal. 88, 1230–1238 (2007).Article 

Google Scholar 
Worthington-Wilmer, J. & Barratt, E. A non-lethal method of tissue sampling for genetic studies of chiropterans. Bat Res. News 37(1), 1–4 (1996).
Google Scholar 
Illumination, I.C.o. A colour appearance model for colour management systems: CIECAM02. Technical Report No CIE 159, 2004 (2004).Maroco, J. Análise estatística com utilização do SPSS. 3ª edição. Edições Silabo (2010).Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4(43), 1686. https://doi.org/10.21105/joss.01686 (2019).ADS 
Article 

Google Scholar 
Karatzoglou, A., Smola, A., Hornik, K. & Zeileis, A. Kernlab: an S4 package for kernel methods in R. J. Stat. Softw. 11(9), 1–20 (2004).Article 

Google Scholar 
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. & Leisch, F. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. https://cran.r-project.org/web/packages/e1071/index.html (2012).Redgwell, R. D., Szewczak, J. M., Jones, G. & Parsons, S. Classification of echolocation calls from 14 species of bat by support vector machines and ensembles of neural networks. Algorithms 2, 907–924 (2009).Article 

Google Scholar 
Ochoa-López, S. et al. Ontogenetic changes in the targets of natural selection in three plant defenses. New Phytol. 226, 1480–1491 (2020).PubMed 
Article 
CAS 

Google Scholar 
Grant, P. R. & Grant, B. R. Phenotypic and genetics effects of hybridization in Darwin’s finches. Evol. Int. J. Org. Evol. 48, 297–316 (1994).Article 

Google Scholar 
Abzhanov, A., Protas, M., Grant, B. R., Grant, P. R. & Tabin, C. J. Bmp4 and morphological variation of beaks in Darwin’s finches. Science (New York, NY). 305, 1462–1465 (2004).ADS 
CAS 
Article 

Google Scholar 
von Holdt, B. M., Kays, R., Pollinger, J. P. & Wayne, R. K. Admixture mapping identifies introgressed genomic regions in North American canids. Mol. Ecol. 25, 2443–2453 (2016).Article 

Google Scholar 
Santana, S. E., Strait, S. & Dumont, E. R. The better to eat you with: functional correlates of tooth structure in bats. Funct. Ecol. 25, 839–847 (2011).Article 

Google Scholar 
Kalcounis, M. C. & Brigham, R. M. Intraspecific variation in wing loading affects habitat use by little brown bats (Myotis lucifugus). Can. J. Zool. 73, 89–95 (1995).Article 

Google Scholar 
Muijres, F. T., Johansson, L. C., Winter, Y. & Anders, H. Comparative aerodynamic performance of flapping flight in two bat species using time-resolved wake visualization. J. R. Soc. Interface 8, 1418–1428 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Bradley, B. J. & Mundy, N. I. The primate palette: the evolution of primate coloration. Evol. Anthropolo. Issues News Rev. 17, 97–111 (2008).Article 

Google Scholar 
Müller, B. & Peichl, L. Retinal cone photoreceptors in microchiropteran bats. Investig. Ophthalmol. Vis. Sci. 46, 2259–2259 (2005).
Google Scholar 
Winter, Y., López, J. & von Helversen, O. Ultraviolet vision in a bat. Nature 425, 612–614 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Caro, T. The adaptive significance of coloration in mammals. Bioscience 55, 125–136 (2005).Article 

Google Scholar 
Chaverri, G., Ancillotto, L. & Russo, D. Social communication in bats. Biol. Rev. 93, 1938–1954 (2018).PubMed 
Article 

Google Scholar 
Dietz, C., Von Helversen, O. & Nill, D. Bats of Britain, Europe and Northwest Africa 320–333 (A&C Black Publishers Ltd, 2009).
Google Scholar 
Martinoli, A., Mazzamuto, M.V. & Spada, M. Serotine Eptesicus serotinus (Schreber, 1774). In Handbook of the Mammals of Europe, 1–17 (2020).Dinger, G. Winternachweise von Breitflügelfledermaus (Eptesicus serotinus) in Kirchen. Nyctalus (N.F.) 7, 614–616 (1991).
Google Scholar 
Kowalski, K. & Rzebik-Kowalska, B. Mammals of algeria (1991).Novella-Fernandez, R. et al. Trophic resource partitioning drives fine-scale coexistence in cryptic bat species. Ecol. Evol. 10(24), 14122–14136 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Galván, I., Vargas-Mena, J. C. & Rodríguez-Herrera, B. Tent-roosting may have driven the evolution of yellow skin coloration in Stenodermatinae bats. J. Zool. Syst. Evol. Res. 58, 519–527 (2020).Article 

Google Scholar 
Wang, Z. L., Zhang, D. Y. & Wang, G. Does spatial structure facilitate coexistence of identical competitors. Ecol. Model. 181, 17–23 (2005).Article 

Google Scholar 
Anderson, T. M. et al. Molecular and evolutionary history of melanism in North American gray wolves. Science (New York, NY). 323, 1339–1343 (2009).ADS 
CAS 
PubMed Central 
Article 

Google Scholar 
Mingo-Casas, P. et al. First cases of European bat lyssavirus type 1 in Iberian serotine bats: implications for the molecular epidemiology of bat rabies in Europe. PLoS Negl. Trop. Dis. 12(4), e0006290 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Vázquez-Moron, S., Juste, J., Ibáñez, C., Berciano, J. M. & Echevarria, J. E. Phylogeny of European bat Lyssavirus 1 in Eptesicus isabellinus bats, Spain. Emerg. Infect. Dis. 17, 520–523 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Burgarella, C. et al. Detection of hybrids in nature: application to oaks (Quercus suber and Q. ilex). Heredity 102, 442–452 (2009).CAS 
PubMed 
Article 

Google Scholar 
Abrams, P. A. Character displacement and niche shift analyzed using consumer-resource models of competition. Theor. Popul. Biol. 29, 107–160 (1986).MathSciNet 
CAS 
PubMed 
MATH 
Article 

Google Scholar  More

Gattuso JP, Frankignoulle M, Wollast R. Carbon and carbonate metabolism in coastal aquatic ecosystems. Annu Rev Ecol Syst. 1998;29:405–34.Article 

Google Scholar 
Arnosti C, Wietz M, Brinkhoff T, Hehemann JH, Probandt D, Zeugner L, et al. The biogeochemistry of marine polysaccharides: sources, inventories, and bacterial drivers of the carbohydrate cycle. Ann Rev Mar Sci. 2021;13:81–108.CAS 
PubMed 
Article 

Google Scholar 
Moran MA, Kujawinski EB, Stubbins A, Fatland R, Aluwihare LI, Buchan A, et al. Deciphering ocean carbon in a changing world. Proc Natl Acad Sci USA. 2016;113:3143–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Poretsky RS, Sun S, Mou X, Moran MA. Transporter genes expressed by coastal bacterioplankton in response to dissolved organic carbon. Environ Microbiol. 2010;12:616–27.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Azam F. Microbial control of oceanic carbon flux: The plot thickens. Science.1998;280:694–96.CAS 
Article 

Google Scholar 
Buchan A, LeCleir GR, Gulvik CA, González JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.CAS 
PubMed 
Article 

Google Scholar 
Bunse C, Bertos-Fortis M, Sassenhagen I, Sildever S, Sjoqvist C, Godhe A, et al. Spatio-temporal interdependence of bacteria and phytoplankton during a baltic sea spring bloom. Front Microbiol. 2016;7:517.PubMed 
PubMed Central 
Article 

Google Scholar 
Cram JA, Chow CE, Sachdeva R, Needham DM, Parada AE, Steele JA, et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 2015;9:563–80.PubMed 
Article 

Google Scholar 
Seymour JR, Amin SA, Raina JB, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. Nat Microbiol. 2017;2:17065.CAS 
PubMed 
Article 

Google Scholar 
Taylor JD, Cottingham SD, Billinge J, Cunliffe M. Seasonal microbial community dynamics correlate with phytoplankton-derived polysaccharides in surface coastal waters. ISME J. 2014;8:245–8.CAS 
PubMed 
Article 

Google Scholar 
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science.2012;336:608–11.CAS 
PubMed 
Article 

Google Scholar 
Hernando-Morales V, Varela MM, Needham DM, Cram J, Fuhrman JA, Teira E. Vertical and seasonal patterns control bacterioplankton communities at two horizontally coherent coastal upwelling sites off galicia (NW Spain). Microb Ecol. 2018;76:866–84.Needham DM, Fuhrman JA. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat Microbiol. 2016;1:16005.Tada Y, Taniguchi A, Nagao I, Miki T, Uematsu M, Tsuda A, et al. Differing growth responses of major phylogenetic groups of marine bacteria to natural phytoplankton blooms in the western North Pacific Ocean. Appl Environ Microbiol. 2011;77:4055–65.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williams TJ, Wilkins D, Long E, Evans F, DeMaere MZ, Raftery MJ, et al. The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics. Environ Microbiol. 2013;15:1302–17.CAS 
PubMed 
Article 

Google Scholar 
Ottesen EA, Young CR, Eppley JM, Ryan JP, Chavez FP, Scholin CA, et al. Pattern and synchrony of gene expression among sympatric marine microbial populations. Proc Natl Acad Sci USA. 2013;110:E488–97.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ottesen EA, Young CR, Gifford SM, Eppley JM, Marin R 3rd, Schuster SC, et al. Ocean microbes. Multispecies diel transcriptional oscillations in open ocean heterotrophic bacterial assemblages. Science. 2014;345:207–12.CAS 
PubMed 
Article 

Google Scholar 
Beier S, Rivers AR, Moran MA, Obernosterer I. The transcriptional response of prokaryotes to phytoplankton-derived dissolved organic matter in seawater. Environ Microbiol. 2015;17:3466–80.CAS 
PubMed 
Article 

Google Scholar 
Sarmento H, Gasol JM. Use of phytoplankton-derived dissolved organic carbon by different types of bacterioplankton. Environ Microbiol. 2012;14:2348–60.CAS 
PubMed 
Article 

Google Scholar 
Shi Y, McCarren J, DeLong EF. Transcriptional responses of surface water marine microbial assemblages to deep-sea water amendment. Environ Microbiol. 2012;14:191–206.CAS 
PubMed 
Article 

Google Scholar 
Vorobev A, Sharma S, Yu M, Lee J, Washington BJ, Whitman WB, et al. Identifying labile DOM components in a coastal ocean through depleted bacterial transcripts and chemical signals. Environ Microbiol. 2018;20:3012–30.CAS 
PubMed 
Article 

Google Scholar 
Moran MA, Belas R, Schell MA, González JM, Sun F, Sun S, et al. Ecological genomics of marine Roseobacters. Appl Environ Microbiol. 2007;73:4559–69.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nowinski B, Moran MA. Niche dimensions of a marine bacterium are identified using invasion studies in coastal seawater. Nat Microbiol. 2021;6:524–32.CAS 
PubMed 
Article 

Google Scholar 
Rinta-Kanto JM, Sun S, Sharma S, Kiene RP, Moran MA. Bacterial community transcription patterns during a marine phytoplankton bloom. Environ Microbiol. 2012;14:228–39.CAS 
PubMed 
Article 

Google Scholar 
Sharma AK, Becker JW, Ottesen EA, Bryant JA, Duhamel S, Karl DM, et al. Distinct dissolved organic matter sources induce rapid transcriptional responses in coexisting populations of Prochlorococcus, Pelagibacter and the OM60 clade. Environ Microbiol. 2013;16:2815–30.PubMed 
Article 
CAS 

Google Scholar 
Kieft B, Li Z, Bryson S, Hettich RL, Pan C, Mayali X, et al. Phytoplankton exudates and lysates support distinct microbial consortia with specialized metabolic and ecophysiological traits. Proc Natl Acad Sci USA. 2021;118:e2101178118.McCarren J, Becker JW, Repeta DJ, Shi Y, Young CR, Malmstrom RR, et al. Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea. Proc Natl Acad Sci USA. 2010;107:16420–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sosa OA, Gifford SM, Repeta DJ, DeLong EF. High molecular weight dissolved organic matter enrichment selects for methylotrophs in dilution to extinction cultures. ISME J. 2015;9:2725–39.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pontiller B, Martínez-García S, Lundin D, Pinhassi J. Labile dissolved organic matter compound characteristics select for divergence in marine bacterial activity and transcription. Front Microbiol. 2020;11:588778.PubMed 
PubMed Central 
Article 

Google Scholar 
Bryson S, Li Z, Chavez F, Weber PK, Pett-Ridge J, Hettich RL, et al. Phylogenetically conserved resource partitioning in the coastal microbial loop. ISME J. 2017;11:2781–92.PubMed 
PubMed Central 
Article 

Google Scholar 
Joglar V, Prieto A, Barber-Lluch E, Hernández-Ruiz M, Fernández E, Teira E. Spatial and temporal variability in the response of phytoplankton and prokaryotes to B-vitamin amendments in an upwelling system. Biogeosciences.2020;17:2807–23.Article 

Google Scholar 
Martínez-García S, Fernández E, Álvarez-Salgado XA, González J, Lønborg C, Marañón E, et al. Differential responses of phytoplankton and heterotrophic bacteria to organic and inorganic nutrient additions in coastal waters off the NW Iberian Peninsula. Mar Ecol Prog Ser. 2010;416:17–33.Article 
CAS 

Google Scholar 
Welschmeyer NA. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnol Oceanogr. 1994;39:1985–92.CAS 
Article 

Google Scholar 
Parsons TR, Maita Y, Lalli CM. Fluorometric determination of chlorophylls. A manual of chemical & biological methods for seawater analysis: Oxford, UK: Pergamon Press; 1984. p. 107–09.Hansen H, Grasshoff K. Automated chemical analysis. Methods of seawater analysis. 2nd ed: Verlag Chemie, Weinheim; 1983. p. 347–95.Álvarez-Salgado XA, Miller AEJ. Simultaneous determination of dissolved organic carbon and total dissolved nitrogen in seawater by high temperature catalytic oxidation: conditions for precise shipboard measurements. Mar Chem. 1998;62:325–33.Article 

Google Scholar 
Calvo-Díaz A, Morán XAG. Seasonal dynamics of picoplankton in shelf waters of the southern Bay of Biscay. Aquat Micro Ecol. 2006;42:159–74.Article 

Google Scholar 
Smith DC, Farooq A. A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine. Mar Micro Food Webs. 1992;6:107–14.
Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
Article 

Google Scholar 
Logares R, Sunagawa S, Salazar G, Cornejo-Castillo FM, Ferrera I, Sarmento H, et al. Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environ Microbiol. 2014;16:2659–71.CAS 
PubMed 
Article 

Google Scholar 
Straub D, Blackwell N, Langarica-Fuentes A, Peltzer A, Nahnsen S, Kleindienst S. Interpretations of environmental microbial community studies are biased by the selected 16S rRNA (Gene) amplicon sequencing pipeline. Front Microbiol. 2020;11:1–18.Article 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 
Article 

Google Scholar 
Poretsky RS, Gifford S, Rinta-Kanto J, Vila-Costa M, Moran MA. Analyzing gene expression from marine microbial communities using environmental transcriptomics. J Vis Exp. 2009;24:1–6.
Google Scholar 
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. 2011;17:10–12.Article 

Google Scholar 
Joshi NA, Fass JN. Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files. 2011. https://github.com/najoshi/sickle.Del Fabbro C, Scalabrin S, Morgante M, Giorgi FM. An extensive evaluation of read trimming effects on Illumina NGS data analysis. PLoS One. 2013;8:1–13.
Google Scholar 
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics.2015;31:1674–6.CAS 
PubMed 
Article 

Google Scholar 
Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.Article 
CAS 

Google Scholar 
Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: a versatile open source tool for metagenomics. PeerJ.2016;4:e2584.PubMed 
PubMed Central 
Article 

Google Scholar 
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
PubMed 
Article 

Google Scholar 
Huson DH, Beier S, Flade I, Górska A, El-Hadidi M, Mitra S, et al. MEGAN community edition – interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput Biol. 2016;12:1–12.Article 
CAS 

Google Scholar 
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: A meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gloor GB, Wu JR, Pawlowsky-Glahn V, Egozcue JJ. It’s all relative: analyzing microbiome data as compositions. Ann Epidemiol. 2016;26:322–9.PubMed 
Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. 4.1.0 ed. Vienna, Austria: R Foundation for Statistical Computing; 2021.Cottrell MT, Kirchman DL. Transcriptional control in marine copiotrophic and oligotrophic bacteria with streamlined genomes. Appl Environ Microbiol. 2016;82:6010–18.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Giovannoni SJ. SAR11 bacteria: The most abundant plankton in the oceans. Ann Rev Mar Sci. 2017;9:231–55.PubMed 
Article 

Google Scholar 
Newton RJ, Griffin LE, Bowles KM, Meile C, Gifford S, Givens CE, et al. Genome characteristics of a generalist marine bacterial lineage. ISME J. 2010;4:784–98.CAS 
PubMed 
Article 

Google Scholar 
Allers E, Gomez-Consarnau L, Pinhassi J, Gasol JM, Simek K, Pernthaler J. Response of Alteromonadaceae and Rhodobacteriaceae to glucose and phosphorus manipulation in marine mesocosms. Environ Microbiol. 2007;9:2417–29.CAS 
PubMed 
Article 

Google Scholar 
Alonso C, Pernthaler J. Roseobacter and SAR11 dominate microbial glucose uptake in coastal North Sea waters. Environ Microbiol. 2006;8:2022–30.CAS 
PubMed 
Article 

Google Scholar 
Noell SE, Giovannoni SJ. SAR11 bacteria have a high affinity and multifunctional glycine betaine transporter. Environ Microbiol. 2019;21:2559–75.CAS 
PubMed 
Article 

Google Scholar 
Sowell SM, Wilhelm LJ, Norbeck AD, Lipton MS, Nicora CD, Barofsky DF, et al. Transport functions dominate the SAR11 metaproteome at low-nutrient extremes in the Sargasso Sea. ISME J. 2009;3:93–105.CAS 
PubMed 
Article 

Google Scholar 
Chenard C, Wijaya W, Vaulot D, Lopes Dos Santos A, Martin P, Kaur A, et al. Temporal and spatial dynamics of bacteria, archaea and protists in equatorial coastal waters. Sci Rep. 2019;9:16390.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pajares S, Varona-Cordero F, Hernández-Becerril DU. Spatial distribution patterns of bacterioplankton in the oxygen minimum zone of the tropical mexican pacific. Micro Ecol. 2020;80:519–36.CAS 
Article 

Google Scholar 
Signori CN, Pellizari VH, Enrich-Prast A, Sievert SM. Spatiotemporal dynamics of marine bacterial and archaeal communities in surface waters off the northern Antarctic Peninsula. Deep-Sea Res Pt Ii. 2018;149:150–60.Article 

Google Scholar 
Ling SK, Xia J, Liu Y, Chen GJ, Du ZJ. Agarilytica rhodophyticola gen. nov., sp. nov., isolated from Gracilaria blodgettii. Int J Syst Evol Microbiol. 2017;67:3778–83.CAS 
PubMed 
Article 

Google Scholar 
Cheng H, Zhang S, Huo YY, Jiang XW, Zhang XQ, Pan J, et al. Gilvimarinus polysaccharolyticus sp. nov., an agar-digesting bacterium isolated from seaweed, and emended description of the genus Gilvimarinus. Int J Syst Evol Microbiol. 2015;65:562–69.CAS 
PubMed 
Article 

Google Scholar 
Malmstrom RR, Kiene RP, Cottrell MT, Kirchman DL. Contribution of SAR11 bacteria to dissolved dimethylsulfoniopropionate and amino acid uptake in the North Atlantic ocean. Appl Environ Microbiol. 2004;70:4129–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kirchman DL. Growth rates of microbes in the oceans. Ann Rev Mar Sci. 2016;8:285–309.PubMed 
Article 

Google Scholar 
Durham BP, Dearth SP, Sharma S, Amin SA, Smith CB, Campagna SR, et al. Recognition cascade and metabolite transfer in a marine bacteria-phytoplankton model system. Environ Microbiol. 2017;19:3500–13.CAS 
PubMed 
Article 

Google Scholar 
Ferrer-González FX, Widner B, Holderman NR, Glushka J, Edison AS, Kujawinski EB, et al. Resource partitioning of phytoplankton metabolites that support bacterial heterotrophy. ISME J. 2021;15:762–73.PubMed 
Article 
CAS 

Google Scholar 
Landa M, Burns AS, Roth SJ, Moran MA. Bacterial transcriptome remodeling during sequential co-culture with a marine dinoflagellate and diatom. ISME J. 2017;11:2677–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pedler BE, Aluwihare LI, Azam F. Single bacterial strain capable of significant contribution to carbon cycling in the surface ocean. Proc Natl Acad Sci USA. 2014;111:7202–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Koch H, Dürwald A, Schweder T, Noriega-Ortega B, Vidal-Melgosa S, Hehemann JH, et al. Biphasic cellular adaptations and ecological implications of Alteromonas macleodii degrading a mixture of algal polysaccharides. ISME J. 2019;13:92–103.CAS 
PubMed 
Article 

Google Scholar 
López-Pérez M, Gonzaga A, Martin-Cuadrado AB, Onyshchenko O, Ghavidel A, Ghai R, et al. Genomes of surface isolates of Alteromonas macleodii: the life of a widespread marine opportunistic copiotroph. Sci Rep. 2012;2:696.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bergauer K, Fernandez-Guerra A, Garcia JAL, Sprenger RR, Stepanauskas R, Pachiadaki MG, et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc Natl Acad Sci USA. 2018;115:E400–8.CAS 
PubMed 
Article 

Google Scholar 
Hou S, López-Pérez M, Pfreundt U, Belkin N, Stüber K, Huettel B, et al. Benefit from decline: the primary transcriptome of Alteromonas macleodii str. Te101 during Trichodesmium demise. ISME J. 2018;12:981–96.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pinhassi J, Sala MM, Havskum H, Peters F, Guadayol O, Malits A, et al. Changes in bacterioplankton composition under different phytoplankton regimens. Appl Environ Microbiol. 2004;70:6753–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Riemann L, Steward GF, Azam F. Dynamics of bacterial community composition and activity during a mesocosm diatom bloom. Appl Environ Microbiol. 2000;66:578–87.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cottrell MT, Kirchman DL. Natural assemblages of marine Proteobacteria and members of the Cytophaga-Flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl Environ Microbiol. 2000;66:1692–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Krüger K, Chafee M, Ben Francis T, Glavina Del Rio T, Becher D, Schweder T, et al. In marine Bacteroidetes the bulk of glycan degradation during algae blooms is mediated by few clades using a restricted set of genes. ISME J. 2019;13:2800–16.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ben Hania W, Joseph M, Bunk B, Spröer C, Klenk HP, Fardeau ML, et al. Characterization of the first cultured representative of a Bacteroidetes clade specialized on the scavenging of cyanobacteria. Environ Microbiol. 2017;19:1134–48.PubMed 
Article 
CAS 

Google Scholar 
Fernández-Gómez B, Richter M, Schüler M, Pinhassi J, Acinas SG, González JM, et al. Ecology of marine Bacteroidetes: a comparative genomics approach. ISME J. 2013;7:1026–37.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016;10:2158–73.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xing P, Hahnke RL, Unfried F, Markert S, Huang S, Barbeyron T, et al. Niches of two polysaccharide-degrading Polaribacter isolates from the North Sea during a spring diatom bloom. ISME J. 2015;9:1410–22.CAS 
PubMed 
Article 

Google Scholar 
Sichert A, Corzett CH, Schechter MS, Unfried F, Markert S, Becher D, et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat Microbiol. 2020;5:1026–39.Ivanova AA, Naumoff DG, Miroshnikov KK, Liesack W, Dedysh SN. Comparative genomics of four Isosphaeraceae Planctomycetes: a common pool of plasmids and glycoside hydrolase genes shared by Paludisphaera borealis PX4(T), Isosphaera pallida IS1B(T), Singulisphaera acidiphila DSM 18658(T), and Strain SH-PL62. Front Microbiol. 2017;8:412.PubMed 
PubMed Central 
Article 

Google Scholar 
Vidal-Melgosa S, Sichert A, Francis TB, Bartosik D, Niggemann J, Wichels A, et al. Diatom fucan polysaccharide precipitates carbon during algal blooms. Nat Commun. 2021;12:1150.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Le Costaouëc T, Unamunzaga C, Mantecon L, Helbert W. New structural insights into the cell-wall polysaccharide of the diatom Phaeodactylum tricornutum. Algal Res. 2017;26:172–9.Article 

Google Scholar 
Francis TB, Bartosik D, Sura T, Sichert A, Hehemann JH, Markert S, et al. Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom. ISME J. 2021;15:2336–50.Bode A, Estévez MG, Varela M, Vilar JA. Annual trend patterns of phytoplankton species abundance belie homogeneous taxonomical group responses to climate in the NE Atlantic upwelling. Mar Environ Res. 2015;110:81–91.CAS 
PubMed 
Article 

Google Scholar 
Cermeño P, Marañón E, Pérez V, Serret P, Fernández E, Castro CG. Phytoplankton size structure and primary production in a highly dynamic coastal ecosystem (Ría de Vigo, NW-Spain): Seasonal and short-time scale variability. Estuar Coast Shelf Sci. 2006;67:251–66.Article 

Google Scholar 
Nogueira E, Pérez FF, Rı́os AF. Modelling thermohaline properties in an estuarine upwelling ecosystem (Rı́a de Vigo: NW Spain) using box-jenkins transfer function models. Estuar Coast Shelf Sci. 1997;44:685–702.Article 

Google Scholar 
Broullón E, López-Mozos M, Reguera B, Chouciño P, Doval MD, Fernández-Castro B, et al. Thin layers of phytoplankton and harmful algae events in a coastal upwelling system. Prog Oceanogr. 2020;189:102449.Fraga F. Upwelling off the Galician Coast, Northwest Spain. In: Richards FA, editor. Coastal Upwelling. Washington: American Geophysical Union; 1981. p. 176–82.Nogueira E, Figueiras FG. The microplankton succession in the Ría de Vigo revisited: species assemblages and the role of weather-induced, hydrodynamic variability. J Mar Syst. 2005;54:139–55.Article 

Google Scholar 
Pitcher GC, Walker DR, Mitchellinnes BA, Moloney CL. Short-term variability during an anchor station study in the southern Benguela Upwelling System – phytoplankton dynamics. Prog Oceanogr. 1991;28:39–64.Article 

Google Scholar 
Smayda TJ, Trainer VL. Dinoflagellate blooms in upwelling systems: Seeding, variability, and contrasts with diatom bloom behaviour. Prog Oceanogr. 2010;85:92–107.Article 

Google Scholar 
Wilkerson FP, Lassiter AM, Dugdale RC, Marchi A, Hogue VE. The phytoplankton bloom response to wind events and upwelled nutrients during the CoOP WEST study. Deep-Sea Res Pt Ii. 2006;53:3023–48.Article 

Google Scholar 
Reintjes G, Arnosti C, Fuchs B, Amann R. Selfish, sharing and scavenging bacteria in the Atlantic Ocean: a biogeographical study of bacterial substrate utilisation. ISME J. 2019;13:1119–32.CAS 
PubMed 
Article 

Google Scholar 
Mayali X, Weber PK, Pett-Ridge J. Taxon-specific C/N relative use efficiency for amino acids in an estuarine community. FEMS Microbiol Ecol. 2013;83:402–12.CAS 
PubMed 
Article 

Google Scholar  More

Dickie, M., Serrouya, R., McNay, R. S. & Boutin, S. Faster and farther: wolf movement on linear features and implications for hunting behaviour. J. Appl. Ecol. 54, 253–263 (2017).Article 

Google Scholar 
Owen-Smith, N., Fryxell, J. M. & Merrill, E. H. Foraging theory upscaled: The behavioural ecology of herbivore movement. Philos. Trans. R. Soc. B Biol. Sci. 365, 2267–2278. https://doi.org/10.1098/rstb.2010.0095 (2010).CAS 
Article 

Google Scholar 
Holling, C. S. The functional response of predators to prey density and its role in mimicry and population regulation. Mem. Entomol. Soc. Can. 97, 5–60 (1965).Article 

Google Scholar 
Holling, C. The components of predation as revealed by a study of small-mammal predation of the European pine sawfly (1959).Dickie, M., McNay, S. R., Sutherland, G. D., Cody, M. & Avgar, T. Corridors or risk? Movement along, and use of, linear features varies predictably among large mammal predator and prey species. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13130 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
DeCesare, N. J. Separating spatial search and efficiency rates as components of predation risk. Proc. Biol. Sci. 279, 4626–4633. https://doi.org/10.1098/rspb.2012.1698 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Muhly, T. B., Semeniuk, C., Massolo, A., Hickman, L. & Musiani, M. Human activity helps prey win the predator-prey space race. PLoS ONE 6, e17050. https://doi.org/10.1371/journal.pone.0017050 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fleming, P. A. & Bateman, P. W. Novel predation opportunities in anthropogenic landscapes. Anim. Behav. 138, 145–155. https://doi.org/10.1016/j.anbehav.2018.02.011 (2018).Article 

Google Scholar 
Whittington, J. et al. Caribou encounters with wolves increase near roads and trails: A time-to-event approach. J. Appl. Ecol. 48, 1535–1542. https://doi.org/10.1111/j.1365-2664.2011.02043.x (2011).Article 

Google Scholar 
Larivière, S. & Messier, F. Effect of density and nearest neighbours on simulated waterfowl nests: Can predators recognize high-density nesting patches?. Oikos 83, 12–20. https://doi.org/10.2307/3546541 (1998).Article 

Google Scholar 
Taitt, M. J. & Krebs, C. J. Predation, cover, and food manipulations during a spring decline of Microtus townsendii. J. Anim. Ecol. 52, 837–848. https://doi.org/10.2307/4458 (1983).Article 

Google Scholar 
Fisher, J. T. & Wilkinson, L. The response of mammals to forest fire and timber harvest in the North American boreal forest. Mammal. Rev. 35, 51–81 (2005).Article 

Google Scholar 
Fisher, J. T. & Burton, A. C. Wildlife winners and losers in an oil sands landscape. Front. Ecol. Environ. 16, 323–328. https://doi.org/10.1002/fee.1807 (2018).Article 

Google Scholar 
Francis, A. L., Procter, C., Kuzyk, G. & Fisher, J. T. Female Moose Prioritize Forage Over Mortality Risk in Harvested Landscapes. J. Wildl. Manag. (2021).Hebblewhite, M., Munro, R. H. & Merrill, E. H. Trophic consequences of postfire logging in a wolf–ungulate system. For. Ecol. Manag. 257, 1053–1062. https://doi.org/10.1016/j.foreco.2008.11.009 (2009).Article 

Google Scholar 
Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).Article 

Google Scholar 
Battin, J. When good animals love bad habitats: Ecological traps and the conservation of animal populations. Conserv. Biol. 18, 1482–1491 (2004).Article 

Google Scholar 
Nielsen, S. E., Stenhouse, G. B. & Boyce, M. S. A habitat-based framework for grizzly bear conservation in Alberta. Biol. Conserv. 130, 217–229 (2006).Article 

Google Scholar 
Bentz, B. et al. Salt Lake City 42 (University of Utah Press, 2005).
Google Scholar 
Carroll, A. L., Taylor, S. W., Régnière, J. & Safranyik, L. in Mountain pine beetle symposium: challenges and solutions. 223–232 (Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre).Lindenmayer, D. B. & Noss, R. F. Salvage logging, ecosystem processes, and biodiversity conservation. Conserv. Biol. 20, 949–958. https://doi.org/10.1111/j.1523-1739.2006.00497.x (2006).CAS 
Article 
PubMed 

Google Scholar 
Leverkus, A. B., Lindenmayer, D. B., Thorn, S. & Gustafsson, L. Salvage logging in the world’s forests: Interactions between natural disturbance and logging need recognition. Glob. Ecol. Biogeogr. 27, 1140–1154. https://doi.org/10.1111/geb.12772 (2018).Article 

Google Scholar 
Kuzyk, G. et al. Moose population dynamics during 20 years of declining harvest in British Columbia. Alces 54, 101–119 (2018).
Google Scholar 
Kuzyk, G. W. Provincial population and harvest estimates of moose in British Columbia. Alces J. Devot. Biol. Manag. Moose 52, 1–11 (2016).Procter, C. et al. Factors affecting moose population declines in British Columbia. 2020 Progress Report: February 2012-May 2020. B.C. Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Victoria, B.C., Wildlife Working Report No. WR-128. Pp. 89. https://www2.gov.bc.ca/gov/content/environment/plants-animals-ecosystems/wildlife/wildlife-conservation/moose/moose-conservation/moose-research. (2020).Wittmer, H. U., Sinclair, A. R. E. & McLellan, B. N. The role of predation in the decline and extirpation of woodland caribou. Oecologia 144, 257–267. https://doi.org/10.1007/s00442-005-0055-y (2005).ADS 
Article 
PubMed 

Google Scholar 
Latham, A. D. M., Latham, M. C., Boyce, M. S. & Boutin, S. Movement responses by wolves to industrial linear features and their effect on woodland caribou in northeastern Alberta. Ecol. Appl. 21, 2854–2865 (2011).Article 

Google Scholar 
James, A. R. C. & Stuart-Smith, A. K. Distribution of caribou and wolves in relation to linear corridors. J. Wildl. Manag. 64, 154–159. https://doi.org/10.2307/3802985 (2000).Article 

Google Scholar 
DeMars, C. A. & Boutin, S. Nowhere to hide: Effects of linear features on predator–prey dynamics in a large mammal system. J. Anim. Ecol. 87, 274–284. https://doi.org/10.1111/1365-2656.12760 (2018).Article 
PubMed 

Google Scholar 
McKenzie, H. W., Merrill, E. H., Spiteri, R. J. & Lewis, M. A. How linear features alter predator movement and the functional response. Interface Focus 2, 205–216. https://doi.org/10.1098/rsfs.2011.0086 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Houle, M., Fortin, D., Dussault, C., Courtois, R. & Ouellet, J.-P. Cumulative effects of forestry on habitat use by gray wolf (Canis lupus) in the boreal forest. Landsc. Ecol. 25, 419–433. https://doi.org/10.1007/s10980-009-9420-2 (2010).Article 

Google Scholar 
Kuzyk, G. W., Kneteman, J. & Schmiegelow, F. K. Winter habitat use by wolves, Canis lupus, in relation to forest harvesting in west-central Alberta. Can. Field Nat. 118, 368–375 (2004).Article 

Google Scholar 
Mumma, M. A. et al. Regional moose (Alces alces) responses to forestry cutblocks are driven by landscape-scale patterns of vegetation composition and regrowth. For. Ecol. Manag. 481, 118763 (2021).Article 

Google Scholar 
Scheideman, M. Use and selection at two spatial scales by female moose (Alces alces) across central British Columbia following a mountain pine beetle outbreak MSc thesis, University of Northern British Columbia (2018).Alfaro, R. I., van Akker, L. & Hawkes, B. Characteristics of forest legacies following two mountain pine beetle outbreaks in British Columbia Canada. Can. J. For. Res. 45, 1387–1396 (2015).Article 

Google Scholar 
Dhar, A., Parrott, L. & Hawkins, C. D. B. Aftermath of mountain pine beetle outbreak in British Columbia: Stand dynamics, management response and ecosystem resilience. Forests 7, 171 (2016).Article 

Google Scholar 
Shackelford, N., Standish, R. J., Ripple, W. & Starzomski, B. M. Threats to biodiversity from cumulative human impacts in one of North America’s last wildlife frontiers. Conserv. Biol. 32, 672–684 (2018).Article 

Google Scholar 
Corbett, L. J., Withey, P., Lantz, V. A. & Ochuodho, T. O. The economic impact of the mountain pine beetle infestation in British Columbia: Provincial estimates from a CGE analysis. For. Int. J. For. Res. 89, 100–105. https://doi.org/10.1093/forestry/cpv042 (2015).Latham, A. D. M. Wolf ecology and caribou-primary prey-wolf spatial relationships in low productivity peatland complexes in northeastern Alberta PhD thesis, University of Alberta, (2009).Person, D. K. & Russell, A. L. Reproduction and den site selection by wolves in a disturbed landscape. Northw. Sci. 83, 211–224. https://doi.org/10.3955/046.083.0305 (2009).Article 

Google Scholar 
Gillingham, M. Documentation for using Find Points Cluster Identification Program (Version 2) (University of Northern British Columbia, 2009).
Google Scholar 
Avgar, T., Potts, J. R., Lewis, M. A. & Boyce, M. S. Integrated step selection analysis: Bridging the gap between resource selection and animal movement. Methods Ecol. Evol. 7, 619–630. https://doi.org/10.1111/2041-210X.12528 (2016).Article 

Google Scholar 
Signer, J., Fieberg, J. & Avgar, T. Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecol. Evol. 9, 880–890 (2019).Article 

Google Scholar 
Thurfjell, H., Ciuti, S. & Boyce, M. S. Applications of step-selection functions in ecology and conservation. Mov. Ecol. 2, 4. https://doi.org/10.1186/2051-3933-2-4 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Benson, J. F. & Patterson, B. R. Spatial overlap, proximity, and habitat use of individual wolves within the same packs. Wildl. Soc. Bull. (2011-) 39, 31–40 (2015).Fieberg, J., Matthiopoulos, J., Hebblewhite, M., Boyce, M. S. & Frair, J. L. Correlation and studies of habitat selection: problem, red herring or opportunity?. Philos. Trans. R. Soc. B Biol. Sci. 365, 2233–2244 (2010).Article 

Google Scholar 
Ladle, A. et al. Grizzly bear response to spatio-temporal variability in human recreational activity. J. Appl. Ecol. 56, 375–386. https://doi.org/10.1111/1365-2664.13277 (2019).Article 

Google Scholar 
Kohl, M. T. et al. Diel predator activity drives a dynamic landscape of fear. Ecol. Monogr. 88, 638–652 (2018).Article 

Google Scholar 
Scrafford, M. A., Avgar, T., Heeres, R. & Boyce, M. S. Roads elicit negative movement and habitat-selection responses by wolverines (Gulo gulo luscus). Behav. Ecol. 29, 534–542. https://doi.org/10.1093/beheco/arx182 (2018).Article 

Google Scholar 
Prokopenko, C. M., Boyce, M. S. & Avgar, T. Characterizing wildlife behavioural responses to roads using integrated step selection analysis. J. Appl. Ecol. 54, 470–479. https://doi.org/10.1111/1365-2664.12768 (2017).Article 

Google Scholar 
Avgar, T., Lele, S. R., Keim, J. L. & Boyce, M. S. Relative selection strength: Quantifying effect size in habitat- and step-selection inference. Ecol. Evol. 7, 5322–5330. https://doi.org/10.1002/ece3.3122 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300. https://doi.org/10.1016/S0304-3800(02)00200-4 (2002).Article 

Google Scholar 
Visscher, D. R. & Merrill, E. H. Temporal dynamics of forage succession for elk at two scales: Implications of forest management. For. Ecol. Manag. 257, 96–106. https://doi.org/10.1016/j.foreco.2008.08.018 (2009).Article 

Google Scholar 
Stelfox, J. G., Lynch, G. M. & McGillis, J. R. Effects of clearcut logging on wild ungulates in the Central Albertan foothills. For. Chron. 52, 65–70. https://doi.org/10.5558/tfc52065-2 (1976).Article 

Google Scholar 
Gagné, C., Mainguy, J. & Fortin, D. The impact of forest harvesting on caribou–moose–wolf interactions decreases along a latitudinal gradient. Biol. Conserv. 197, 215–222. https://doi.org/10.1016/j.biocon.2016.03.015 (2016).Article 

Google Scholar 
Potvin, F., Breton, L. & Courtois, R. Response of beaver, moose, and snowshoe hare to clear-cutting in a Quebec boreal forest: a reassessment 10 years after cut. Can. J. For. Res. 35, 151–160 (2005).Article 

Google Scholar 
Rempel, R. S., Elkie, P. C., Rodgers, A. R. & Gluck, M. J. Timber-management and natural-disturbance effects on moose habitat: landscape evaluation. J. Wildl. Manag. 61, 517–524. https://doi.org/10.2307/3802610 (1997).Article 

Google Scholar 
Kunkel, K. E. & Pletscher, D. H. Habitat factors affecting vulnerability of moose to predation by wolves in southeastern British Columbia. Can. J. Zool. 78, 150–157. https://doi.org/10.1139/z99-181 (2000).Article 

Google Scholar 
Mech, L. D. & Boitani, L. Wolves: behavior, ecology, and conservation. (University of Chicago Press, 2007).Charnov, E. L. Optimal foraging, the marginal value theorem. (1976).Hebblewhite, M. & Merrill, E. H. Trade-offs between predation risk and forage differ between migrant strategies in a migratory ungulate. Ecology 90, 3445–3454. https://doi.org/10.1890/08-2090.1 (2009).Article 
PubMed 

Google Scholar 
Lendrum, P. E., Anderson Jr, C. R., Long, R. A., Kie, J. G. & Bowyer, R. T. Habitat selection by mule deer during migration: effects of landscape structure and natural-gas development. Ecosphere 3, art82. https://doi.org/10.1890/ES12-00165.1 (2012).Mumma, M. & Gillingham, M. Determining factors that affect survival of moose in Central British Columbia. Technical report to the Habitat Conservation Trust Foundation for Grant Agreement CAT19-0-522 (1 April 2017 through 31 March 2019). 56 (2019).Roffler, G. H., Gregovich, D. P. & Larson, K. R. Resource selection by coastal wolves reveals the seasonal importance of seral forest and suitable prey habitat. For. Ecol. Manag. 409, 190–201. https://doi.org/10.1016/j.foreco.2017.11.025 (2018).Article 

Google Scholar 
Lesmerises, F., Dussault, C. & St-Laurent, M.-H. Wolf habitat selection is shaped by human activities in a highly managed boreal forest. For. Ecol. Manag. 276, 125–131. https://doi.org/10.1016/j.foreco.2012.03.025 (2012).Article 

Google Scholar 
Muhly, T. B. et al. Functional response of wolves to human development across boreal North America. Ecol. Evol. 9, 10801–10815. https://doi.org/10.1002/ece3.5600 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Mladenoff, D. J., Sickley, T. A. & Wydeven, A. P. Predicting gray wolf landscape recolonization: logistic regression models vs. new field data. Ecol. Appl. 9, 37–44. https://doi.org/10.1890/1051-0761(1999)009[0037:PGWLRL]2.0.CO;2 (1999).Rogala, J. K. et al. Human activity differentially redistributes large mammals in the Canadian Rockies National Parks. Ecol. Soc. 16 (2011).Robertson, B. A. & Hutto, R. L. A framework for understanding ecological traps and an evaluation of existing evidence. Ecology 87, 1075–1085. https://doi.org/10.1890/0012-9658(2006)87[1075:AFFUET]2.0.CO;2 (2006).Article 
PubMed 

Google Scholar 
Finnegan, L. et al. Natural regeneration on seismic lines influences movement behaviour of wolves and grizzly bears. PLoS ONE 13, e0195480. https://doi.org/10.1371/journal.pone.0195480 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dickie, M., Serrouya, R., DeMars, C., Cranston, J. & Boutin, S. Evaluating functional recovery of habitat for threatened woodland caribou. Ecosphere 8, e01936. https://doi.org/10.1002/ecs2.1936 (2017).Article 

Google Scholar  More

Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mamm. Sci. 26, 509–572. https://doi.org/10.1111/j.1748-7692.2009.00354.x (2010).CAS 
Article 

Google Scholar 
Layman, C. A. et al. Applying stable isotopes to examine food-web structure: An overview of analytical tools. Biol. Rev. Camb. Philos. Soc. 87, 545–562. https://doi.org/10.1111/j.1469-185X.2011.00208.x (2011).Article 
PubMed 

Google Scholar 
Larsen, T. et al. Tracing carbon sources through aquatic and terrestrial food webs using amino acid stable isotope fingerprinting. PLoS ONE 8, e73441. https://doi.org/10.1371/journal.pone.0073441 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Post, D. M. Using stable isotopes to estimate trophic position: Models, methods and assumptions. Ecology 83, 703–718 (2002).Article 

Google Scholar 
Inger, R. & Bearhop, S. Applications of stable isotope analyses to avian ecology. Ibis 150, 447–461 (2008).Article 

Google Scholar 
McCutchan, J. H., Lewis, W. M., Kendall, C. & McGrath, C. C. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390 (2003).CAS 
Article 

Google Scholar 
Olive, P. J. W., Pinnegar, J. K., Polunin, N. V. C., Richards, G. & Welch, R. Isotope trophic-step fractionation: A dynamic equilibrium model. J. Anim. Ecol. 72, 608–617 (2003).Article 

Google Scholar 
McMahon, K. W., Polito, M. J., Abel, S., McCarthy, M. D. & Thorrold, S. R. Carbon and nitrogen isotope fractionation of amino acids in an avian marine predator, the gentoo penguin (Pygoscelis papua). Ecol. Evol. 5, 1278–1290. https://doi.org/10.1002/ece3.1437 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Webb, E. C. et al. Compound-specific amino acid isotopic proxies for distinguishing between terrestrial and aquatic resource consumption. Archaeol. Anthropol. Sci. 10, 1–18. https://doi.org/10.1007/s12520-015-0309-5 (2016).Article 

Google Scholar 
Whiteman, J. P., Kim, S. L., McMahon, K. W., Koch, P. L. & Newsome, S. D. Amino acid isotope discrimination factors for a carnivore: Physiological insights from leopard sharks and their diet. Oecologia 188, 977–989. https://doi.org/10.1007/s00442-018-4276-2 (2018).ADS 
Article 
PubMed 

Google Scholar 
Rogers, M., Bare, R., Gray, A., Scott-Moelder, T. & Heintz, R. Assessment of two feeds on survival, proximate composition, and amino acid carbon isotope discrimination in hatchery-reared Chinook salmon. Fisher. Res. https://doi.org/10.1016/j.fishres.2019.06.001 (2019).Article 

Google Scholar 
Wang, Y. V., Wan, A. H. L., Krogdahl, A., Johnson, M. & Larsen, T. (13)C values of glycolytic amino acids as indicators of carbohydrate utilization in carnivorous fish. PeerJ 7, e7701. https://doi.org/10.7717/peerj.7701 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
McMahon, K. W., Fogel, M. L., Elsdon, T. S. & Thorrold, S. R. Carbon isotope fractionation of amino acids in fish muscle reflects biosynthesis and isotopic routing from dietary protein. J. Anim. Ecol. 79, 1132–1141. https://doi.org/10.1111/j.1365-2656.2010.01722.x (2010).Article 
PubMed 

Google Scholar 
McMahon, K. W., Thorrold, S. R., Houghton, L. A. & Berumen, M. L. Tracing carbon flow through coral reef food webs using a compound-specific stable isotope approach. Oecologia 180, 809–821. https://doi.org/10.1007/s00442-015-3475-3 (2016).ADS 
Article 
PubMed 

Google Scholar 
Wang, Y. V. et al. Know your fish: A novel compound-specific isotope approach for tracing wild and farmed salmon. Food Chem 256, 380–389. https://doi.org/10.1016/j.foodchem.2018.02.095 (2018).CAS 
Article 
PubMed 

Google Scholar 
Jim, S., Jones, V., Ambrose, S. H. & Evershed, R. P. Quantifying dietary macronutrient sources of carbon for bone collagen biosynthesis using natural abundance stable carbon isotope analysis. Br J. Nutr. 95, 1055–1062. https://doi.org/10.1079/bjn20051685 (2006).CAS 
Article 
PubMed 

Google Scholar 
Newsome, S. D., Fogel, M. L., Kelly, L. & del Rio, C. M. Contributions of direct incorporation from diet and microbial amino acids to protein synthesis in Nile tilapia. Funct. Ecol. 25, 1051–1062. https://doi.org/10.1111/j.1365-2435.2011.01866.x (2011).Article 

Google Scholar 
Griffiths, H. Applications of stable isotope technology in physiological ecology. Funct. Ecol. 5, 254–269 (1991).Article 

Google Scholar 
Lorrain, A. et al. Differential δ13C and δ15N signatures among scallop tissues: Implications for ecology and physiology. J. Exp. Mar. Biol. Ecol. 275, 47–61 (2002).CAS 
Article 

Google Scholar 
Li, P., Mai, K., Trushenski, J. & Wu, G. New developments in fish amino acid nutrition: Towards functional and environmentally oriented aquafeeds. Amino Acids 37, 43–53. https://doi.org/10.1007/s00726-008-0171-1 (2009).CAS 
Article 
PubMed 

Google Scholar 
Boecklen, W. J., Yarnes, C. T., Cook, B. A. & James, A. C. On the use of stable isotopes in trophic ecology. Annu. Rev. Ecol. Evol. Syst. 42, 411–440. https://doi.org/10.1146/annurev-ecolsys-102209-144726 (2011).Article 

Google Scholar 
Perga, M. E. & Gerdeaux, D. “Are fish what they eat” all year round?. Oecologia 144, 598–606. https://doi.org/10.1007/s00442-005-0069-5 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Sponheimer, M. et al. Turnover of stable carbon isotopes in the muscle, liver, and breath CO2 of alpacas (Lama pacos). Rapid Commun. Mass Spectrom. 20, 1395–1399. https://doi.org/10.1002/rcm.2454 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Logan, J. M. & Lutcavage, M. E. Stable isotope dynamics in elasmobranch fishes. Hydrobiologia 644, 231–244. https://doi.org/10.1007/s10750-010-0120-3 (2010).CAS 
Article 

Google Scholar 
Madigan, D. J. et al. Tissue turnover rates and isotopic trophic discrimination factors in the endothermic teleost, pacific bluefin tuna (Thunnus orientalis). PLoS ONE 7, e49220. https://doi.org/10.1371/journal.pone.0049220 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Skinner, M. M., Cross, B. K. & Moore, B. C. Estimating in situ isotopic turnover in Rainbow Trout (Oncorhynchus mykiss) muscle and liver tissue. J. Freshw. Ecol. 32, 209–217. https://doi.org/10.1080/02705060.2016.1259127 (2016).CAS 
Article 

Google Scholar 
Kaushik, S. J. & Seiliez, I. Protein and amino acid nutrition and metabolism in fish: Current knowledge and future needs. Aquac. Res. 41, 322–332. https://doi.org/10.1111/j.1365-2109.2009.02174.x (2010).CAS 
Article 

Google Scholar 
Hou, Y., Hu, S., Li, X., He, W. & Wu, G. Amino Acid Metabolism in the Liver: Nutritional and Physiological Significance. Vol. 1265 (2020).Gannes, L. Z., O’Brien, D. M. & Del Rio, C. M. Stable isotopes in animal ecology: Assumptions, caveats and a call for more laboratory experiments. Ecology 78, 1271–1276 (1997).Article 

Google Scholar 
Martinez del Rio, C. M., Wolf, N., Carleton, S. A. & Gannes, L. Z. Isotopic ecology ten years after a call for more laboratory experiments. Biol. Rev. Camb. Philos Soc. 84, 91–111. https://doi.org/10.1111/j.1469-185X.2008.00064.x (2009).Article 

Google Scholar 
Hendry, A. P., Peichel, C. L., Boughman, J. W., Matthews, B. & Nosil, P. Stickleback research: The now and the next. Evol. Ecol. Res. 15, 111–141 (2013).
Google Scholar 
Fang, B., Merila, J., Ribeiro, F., Alexandre, C. M. & Momigliano, P. Worldwide phylogeny of three-spined sticklebacks. Mol Phylogenet Evol 127, 613–625. https://doi.org/10.1016/j.ympev.2018.06.008 (2018).Article 
PubMed 

Google Scholar 
Kume, M. & Kitano, J. Genetic and stable isotope analyses of threespine stickleback from the Bering and Chukchi seas. Ichthyol. Res. 64, 478–480. https://doi.org/10.1007/s10228-017-0580-9 (2017).Article 

Google Scholar 
Reimchen, T. E., Ingram, T. & Hansen, S. C. Assessing niche differences of sex, armour and asymmetry phenotypes using stable isotope analyses in Haida Gwaii sticklebacks. Behaviour 145, 561–577 (2008).Article 

Google Scholar 
Pinnegar, J. Unusual stable isotope fractionation patterns observed for fish host–parasite trophic relationships. J. Fish Biol. 59, 494–503. https://doi.org/10.1006/jfbi.2001.1660 (2001).Article 

Google Scholar 
Power, M. & Klein, G. M. Fish host-cestode parasite stable isotope enrichment patterns in marine, estuarine and freshwater fishes from northern Canada. Isotopes Environ. Health Stud. 40, 257–266 (2004).CAS 
Article 

Google Scholar 
Li, X., Zheng, S. & Wu, G. Nutrition and metabolism of glutamate and glutamine in fish. Amino Acids 52, 671–691. https://doi.org/10.1007/s00726-020-02851-2 (2020).CAS 
Article 
PubMed 

Google Scholar 
Vander Zanden, M. J., Clayton, M. K., Moody, E. K., Solomon, C. T. & Weidel, B. C. Stable isotope turnover and half-life in animal tissues: A literature synthesis. PLoS ONE 10, e0116182. https://doi.org/10.1371/journal.pone.0116182 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Newsome, S. D., del Rio, C. M., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5, 429–436. https://doi.org/10.1890/060150.01 (2007).Article 

Google Scholar 
Voigt, C. C., Rex, K., Michener, R. H. & Speakman, J. R. Nutrient routing in omnivorous animals tracked by stable carbon isotopes in tissue and exhaled breath. Oecologia 157, 31–40. https://doi.org/10.1007/s00442-008-1057-3 (2008).ADS 
Article 
PubMed 

Google Scholar 
Tieszen, L. L., Boutton, T. W., Tesdahl, K. G. & Slade, N. A. Fractionation and turnover of stable carbon isotopes in animal tissues: Implications for δ13C analysis of diet. Oecologia 57, 21–37 (1983).Article 

Google Scholar 
Cerling, T. E. et al. Determining biological tissue turnover using stable isotopes: The reaction progress variable. Oecologia 151, 175–189. https://doi.org/10.1007/s00442-006-0571-4 (2007).ADS 
Article 
PubMed 

Google Scholar 
Martínez del Rio, C. & Carleton, S. A. How fast and how faithful: The dynamics of isotopic incorporation into animal tissues: Fig. 1. J. Mammal. 93, 353–359. https://doi.org/10.1644/11-mamm-s-165.1 (2012).Article 

Google Scholar 
McCullagh, J. S., Juchelka, D. & Hedges, R. E. Analysis of amino acid 13C abundance from human and faunal bone collagen using liquid chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 20, 2761–2768. https://doi.org/10.1002/rcm.2651 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Raghavan, M., McCullagh, J. S., Lynnerup, N. & Hedges, R. E. Amino acid δ13C analysis of hair proteins and bone collagen using liquid chromatography/isotope ratio mass spectrometry: Paleodietary implications from intra-individual comparisons. Rapid Commun. Mass Spectrom. 24, 541–548. https://doi.org/10.1002/rcm.4398 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Newsome, S. D., Wolf, N., Peters, J. & Fogel, M. L. Amino acid δ13C analysis shows flexibility in the routing of dietary protein and lipids to the tissue of an omnivore. Integr. Comp. Biol. 54, 890–902. https://doi.org/10.1093/icb/icu106 (2014).CAS 
Article 
PubMed 

Google Scholar 
Walton, M. J. & Cowey, C. B. Aspects of intermediary metabolism in salmonid fish. Comp. Biochem. Physiol. 73B, 59–79 (1982).CAS 

Google Scholar 
Fernandes, R., Nadeau, M.-J. & Grootes, P. M. Macronutrient-based model for dietary carbon routing in bone collagen and bioapatite. Archaeol. Anthropol. Sci. 4, 291–301. https://doi.org/10.1007/s12520-012-0102-7 (2012).Article 

Google Scholar 
Ohkouchi, N., Ogawa, N. O., Chikaraishi, Y., Tanaka, H. & Wada, E. Biochemical and physiological bases for the use of carbon and nitrogen isotopes in environmental and ecological studies. Prog. Earth Planet Sci. 2, 1–17. https://doi.org/10.1186/s40645-015-0032-y (2015).ADS 
Article 

Google Scholar 
Wu, G. & Morris, M. Arginine metabolism: Nitric oxide and beyond. Biochem. J. 336, 1–17 (1998).CAS 
Article 

Google Scholar 
Metges, C. C., Petzke, K. J. & Henning, U. Gas chromatography/combustion/isotope ratio mass spectrometric comparison of N-acetyl- and N-pivaloyl amino acid esters to measure 15N isotopic abundances in physiological samples : A pilot study on amino acid synthesis in the upper gastro-intestinal tract of minipigs. J. Mass Spectrom. 31, 367–376 (1996).ADS 
CAS 
Article 

Google Scholar 
Dunn, P. J., Honch, N. V. & Evershed, R. P. Comparison of liquid chromatography-isotope ratio mass spectrometry (LC/IRMS) and gas chromatography-combustion-isotope ratio mass spectrometry (GC/C/IRMS) for the determination of collagen amino acid δ13C values for palaeodietary and palaeoecological reconstruction. Rapid Commun. Mass Spectrom. 25, 2995–3011. https://doi.org/10.1002/rcm.5174 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ayayee, P. A., Jones, S. C. & Sabree, Z. L. Can (13)C stable isotope analysis uncover essential amino acid provisioning by termite-associated gut microbes?. PeerJ 3, e1218. https://doi.org/10.7717/peerj.1218 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ayayee, P. A., Larsen, T. & Sabree, Z. Symbiotic essential amino acids provisioning in the American cockroach, Periplaneta americana (Linnaeus) under various dietary conditions. PeerJ 4, e2046. https://doi.org/10.7717/peerj.2046 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Larsen, T. et al. The dominant detritus-feeding invertebrate in Arctic peat soils derives its essential amino acids from gut symbionts. J. Anim. Ecol. 85, 1275–1285. https://doi.org/10.1111/1365-2656.12563 (2016).Article 
PubMed 

Google Scholar 
Romero-Romero, S., Miller, E. C., Black, J. A., Popp, B. N. & Drazen, J. C. Abyssal deposit feeders are secondary consumers of detritus and rely on nutrition derived from microbial communities in their guts. Sci. Rep. 11, 12594. https://doi.org/10.1038/s41598-021-91927-4 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McCullagh, J. S. Mixed-mode chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 24, 483–494. https://doi.org/10.1002/rcm.4322 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Tsai, Y. et al. Histamine contents of fermented fish products in Taiwan and isolation of histamine-forming bacteria. Food Chem. 98, 64–70. https://doi.org/10.1016/j.foodchem.2005.04.036 (2006).CAS 
Article 

Google Scholar 
Landete, J. M., De Las Rivas, B., Marcobal, A. & Munoz, R. Updated molecular knowledge about histamine biosynthesis by bacteria. Crit. Rev. Food Sci. Nutr. 48, 697–714. https://doi.org/10.1080/10408390701639041 (2008).CAS 
Article 
PubMed 

Google Scholar 
Kanki, M., Yoda, T., Tsukamoto, T. & Baba, E. Histidine decarboxylases and their role in accumulation of histamine in tuna and dried saury. Appl. Environ. Microbiol. 73, 1467–1473. https://doi.org/10.1128/AEM.01907-06 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fernandez-Salguero, J. & Mackie, I. M. Histidine metabolism in mackerel (Scomber scombrus). Studies on histidine decarboxylase activity and histamine formation during storage of flesh and liver under sterile and non-sterile conditions. J. Fd Technol. 14, 131–139 (1979).CAS 
Article 

Google Scholar 
Sánchez-Muros, M.-J., Barroso, F. G. & Manzano-Agugliaro, F. Insect meal as renewable source of food for animal feeding: A review. J. Clean. Prod. 65, 16–27. https://doi.org/10.1016/j.jclepro.2013.11.068 (2014).CAS 
Article 

Google Scholar 
Khan, M. A. Histidine requirement of cultivable fish species: A review. Oceanogr Fish Open Access J. 8, 1–7. https://doi.org/10.19080/ofoaj.2018.08.555746 (2018).Article 

Google Scholar 
Hatch, K. A. in Comparative Physiology of Fasting, Starvation, and Food Limitation Ch. Chapter 20, 337–364 (2012).Bertinetto, C., Engel, J. & Jansen, J. ANOVA simultaneous component analysis: A tutorial review. Anal. Chim. Acta X 6, 100061. https://doi.org/10.1016/j.acax.2020.100061 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nogales-Mérida, S. et al. Insect meals in fish nutrition. Rev. Aquac. 11, 1080–1103. https://doi.org/10.1111/raq.12281 (2018).Article 

Google Scholar 
Thongprajukaew, K., Pettawee, S., Muangthong, S., Saekhow, S. & Phromkunthong, W. Freeze-dried forms of mosquito larvae for feeding of Siamese fighting fish (Betta splendens Regan, 1910). Aquac. Res. 50, 296–303. https://doi.org/10.1111/are.13897 (2018).CAS 
Article 

Google Scholar 
Jackson, G. P., An, Y., Konstantynova, K. I. & Rashaid, A. H. Biometrics from the carbon isotope ratio analysis of amino acids in human hair. Sci. Justice 55, 43–50. https://doi.org/10.1016/j.scijus.2014.07.002 (2015).Article 
PubMed 

Google Scholar 
Werner, R. A. & Brand, W. A. Referencing strategies and techniques in stable isotope ratio analysis. Rapid. Commun. Mass Spectrom. 15, 501–519. https://doi.org/10.1002/rcm.258 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Marks, R. G. H., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. How to couple LC-IRMS with HRMS─a proof-of-concept study. Anal. Chem. 94, 2981–2987 (2022).CAS 
Article 

Google Scholar 
Lynch, A. H., McCullagh, J. S. & Hedges, R. E. Liquid chromatography/isotope ratio mass spectrometry measurement of δ13C of amino acids in plant proteins. Rapid Commun. Mass Spectrom. 25, 2981–2988. https://doi.org/10.1002/rcm.5142 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Falco, F., Stincone, P., Cammarata, M. & Brandelli, A. Amino acids as the main energy source in fish tissues. Aquac. Fish Stud. 3, 1–11 (2020).
Google Scholar  More