Ecology

Liang, J. et al. Positive biodiversity-productivity relationship predominant in global forests. Science 354, aaf8957 (2016).Article 
PubMed 

Google Scholar 
Huang, Y. et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83 (2018).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Luo, S. et al. Community‐wide trait means and variations affect biomass in a biodiversity experiment with tree seedlings. Oikos 129, 799–810 (2020).Article 

Google Scholar 
Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).Article 

Google Scholar 
Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, 1–10 (2020).Article 

Google Scholar 
Freschet, G. T. et al. Root traits as drivers of plant and ecosystem functioning: current understanding, pitfalls and future research needs. N. Phytol. 232, 1123–1158 (2021).Article 

Google Scholar 
Zhong, Y. et al. Arbuscular mycorrhizal trees influence the latitudinal beta-diversity gradient of tree communities in forests worldwide. Nat. Commun. 12, 1–12 (2021).Article 
ADS 

Google Scholar 
Carteron, A., Vellend, M. & Laliberté, E. Mycorrhizal dominance reduces local tree species diversity across US forests. Nat. Ecol. Evol. 6, 370–374 (2022).Article 
PubMed 

Google Scholar 
Phillips, R. P., Brzostek, E. & Midgley, M. G. The mycorrhizal‐associated nutrient economy: a new framework for predicting carbon–nutrient couplings in temperate forests. N. Phytol. 199, 41–51 (2013).Article 
CAS 

Google Scholar 
Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543–545 (2014).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Craig, M. E. et al. Tree mycorrhizal type predicts within‐site variability in the storage and distribution of soil organic matter. Glob. Chang. Biol. 24, 3317–3330 (2018).Article 
ADS 
PubMed 

Google Scholar 
van der Heijden, M. G. A. et al. Mycorrhizal fungal diversity determines plant biodiversity, ecosystem variability and productivity. Nature 396, 69–72 (1998).Article 
ADS 

Google Scholar 
Klironomos, J. N., McCune, J., Hart, M. & Neville, J. The influence of arbuscular mycorrhizae on the relationship between plant diversity and productivity. Ecol. Lett. 3, 137–141 (2000).Article 

Google Scholar 
Wagg, C., Jansa, J., Stadler, M., Schmid, B. & Van Der Heijden, M. G. A. Mycorrhizal fungal identity and diversity relaxes plant-plant competition. Ecology 92, 1303–1313 (2011).Article 
PubMed 

Google Scholar 
Luo, S., Schmid, B., De Deyn, G. B. & Yu, S. Soil microbes promote complementarity effects among co‐existing trees through soil nitrogen partitioning. Funct. Ecol. 32, 1879–1889 (2018).Article 

Google Scholar 
Ferlian, O. et al. Mycorrhiza in tree diversity–ecosystem function relationships: conceptual framework and experimental implementation. Ecosphere 9, e02226 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Tedersoo, L. & Bahram, M. Mycorrhizal types differ in ecophysiology and alter plant nutrition and soil processes. Biol. Rev. 94, 1857–1880 (2019).Article 
PubMed 

Google Scholar 
Rineau, F. et al. The ectomycorrhizal fungus Paxillus involutus converts organic matter in plant litter using a trimmed brown-rot mechanism involving Fenton chemistry. Environ. Microbiol. 14, 1477–1487 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lindahl, B. D. & Tunlid, A. Ectomycorrhizal fungi – potential organic matter decomposers, yet not saprotrophs. N. Phytol. 205, 1443–1447 (2015).Article 
CAS 

Google Scholar 
Hodge, A. Arbuscular mycorrhizal fungi influence decomposition of, but not plant nutrient capture from, glycine patches in soil. N. Phytol. 151, 725–734 (2001).Article 
CAS 

Google Scholar 
Read, D. J. & Perez-Moreno, J. Mycorrhizas and nutrient cycling in ecosystems – A journey towards relevance? N. Phytol. 157, 475–492 (2003).Article 
CAS 

Google Scholar 
Toju, H., Kishida, O., Katayama, N. & Takagi, K. Networks depicting the fine-scale co-occurrences of fungi in soil horizons. PLoS ONE 11, 1–18 (2016).Article 

Google Scholar 
Taylor, D. L. et al. A first comprehensive census of fungi in soil reveals both hyperdiversity and fine-scale niche partitioning. Ecol. Monogr. 84, 3–20 (2014).Article 

Google Scholar 
Chen, W. et al. Root morphology and mycorrhizal symbioses together shape nutrient foraging strategies of temperate trees. Proc. Natl Acad. Sci. USA 113, 8741–8746 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu, X. et al. Partitioning of soil phosphorus among arbuscular and ectomycorrhizal trees in tropical and subtropical forests. Ecol. Lett. 21, 713–723 (2018).Article 
PubMed 

Google Scholar 
Averill, C., Bhatnagar, J. M., Dietze, M. C., Pearse, W. D. & Kivlin, S. N. Global imprint of mycorrhizal fungi on whole-plant nutrient economics. Proc. Natl Acad. Sci. USA 116, 23163–23168 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dietrich, P. et al. Tree diversity effects on productivity depend on mycorrhizae and life strategies in a temperate forest experiment. Ecology 104, e3896 https://doi.org/10.1002/ecy.3896 (2022).Averill, C., Dietze, M. C. & Bhatnagar, J. M. Continental-scale nitrogen pollution is shifting forest mycorrhizal associations and soil carbon stocks. Glob. Chang. Biol. 24, 4544–4553 (2018).Article 
ADS 
PubMed 

Google Scholar 
Jo, I., Fei, S., Oswalt, C. M., Domke, G. M. & Phillips, R. P. Shifts in dominant tree mycorrhizal associations in response to anthropogenic impacts. Sci. Adv. 5, eaav6358, (2019).Fei, S. et al. Impacts of climate on the biodiversity-productivity relationship in natural forests. Nat. Commun. 9, 5436 (2018).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bongers, F. J. et al. Functional diversity effects on productivity increase with age in a forest biodiversity experiment. Nat. Ecol. Evol. 5, 1594–1603 (2021).Article 
PubMed 

Google Scholar 
Schoener, T. W. Resource partitioning in ecological communities. Science 185, 27–39 (1974).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Tilman, D., Lehman, C. L. & Thomson, K. T. Plant diversity and ecosystem productivity: theoretical considerations. Proc. Natl Acad. Sci. USA 94, 1857–1861 (1997).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schwilk, D. W. & Ackerly, D. D. Limiting similarity and functional diversity along environmental gradients. Ecol. Lett. 8, 272–281 (2005).Article 

Google Scholar 
Wagg, C., Jansa, J., Schmid, B. & van der Heijden, M. G. A. Belowground biodiversity effects of plant symbionts support aboveground productivity. Ecol. Lett. 14, 1001–1009 (2011).Article 
PubMed 

Google Scholar 
Agerer, R. Exploration types of ectomycorrhizae: a proposal to classify ectomycorrhizal mycelial systems according to their patterns of differentiation and putative ecological importance. Mycorrhiza 11, 107–114 (2001).Article 

Google Scholar 
Cheng, L. et al. Mycorrhizal fungi and roots are complementary in foraging within nutrient patches. Ecology 97, 2815–2823 (2016).Article 
PubMed 

Google Scholar 
Wambsganss, J. et al. Tree species mixing causes a shift in fine-root soil exploitation strategies across European forests. Funct. Ecol. 35, 1886–1902 (2021).Article 
CAS 

Google Scholar 
Gerz, M., Guillermo Bueno, C., Ozinga, W. A., Zobel, M. & Moora, M. Niche differentiation and expansion of plant species are associated with mycorrhizal symbiosis. J. Ecol. 106, 254–264 (2018).Article 
CAS 

Google Scholar 
Niklaus, P. A., Baruffol, M., He, J. S., Ma, K. & Schmid, B. Can niche plasticity promote biodiversity–productivity relationships through increased complementarity? Ecology 98, 1104–1116 (2017).Article 
PubMed 

Google Scholar 
Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180 (2019).Article 
PubMed 

Google Scholar 
Jacobs, L. M., Sulman, B. N., Brzostek, E. R., Feighery, J. J. & Phillips, R. P. Interactions among decaying leaf litter, root litter and soil organic matter vary with mycorrhizal type. J. Ecol. 106, 502–513 (2018).Article 
CAS 

Google Scholar 
Midgley, M. G., Brzostek, E. & Phillips, R. P. Decay rates of leaf litters from arbuscular mycorrhizal trees are more sensitive to soil effects than litters from ectomycorrhizal trees. J. Ecol. 103, 1454–1463 (2015).Article 

Google Scholar 
Kumar, A., Phillips, R. P., Scheibe, A., Klink, S. & Pausch, J. Organic matter priming by invasive plants depends on dominant mycorrhizal association. Soil Biol. Biochem. 140, 107645 (2020).Article 
CAS 

Google Scholar 
Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, eaba1223 (2020).Article 
CAS 
PubMed 

Google Scholar 
Kitajima, K. & Poorter, L. Functional basis for resource niche partitioning by tropical trees. Trop. For. community Ecol. 1936, 160–181 (2008).MacArthur, R. H. Patterns of species diverstiy. Biol. Rev. 40, 510–533 (1965).Article 

Google Scholar 
Pellissier, V., Barnagaud, J. Y., Kissling, W. D., Şekercioğlu, Ç. & Svenning, J. C. Niche packing and expansion account for species richness–productivity relationships in global bird assemblages. Glob. Ecol. Biogeogr. 27, 604–615 (2018).Article 

Google Scholar 
Huang, Y. et al. Effects of enemy exclusion on biodiversity–productivity relationships in a subtropical forest experiment. J. Ecol. 110, 2167–2178. https://doi.org/10.1111/1365-2745.13940 (2022).Tilman, D. Community invasibility, recruitment limitation, and grassland biodiversity. Ecology 78, 81–92 (1997).Article 

Google Scholar 
Feng, Y. et al. Multispecies forest plantations outyield monocultures across a broad range of conditions. Science 376, 865–868 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Harper, J. L. Population biology of plants. (1977).Ewel, J. J. Designing agricultural ecosystems for the humid tropics. Annu. Rev. Ecol. Syst. 17, 245–271 (1986).Article 

Google Scholar 
Grossiord, C. Having the right neighbors: how tree species diversity modulates drought impacts on forests. N. Phytol. 228, 42–49 (2020).Article 

Google Scholar 
Allen, M. F. Mycorrhizal fungi: highways for water and nutrients in arid soils. Vadose Zo. J. 6, 291–297 (2007).Article 

Google Scholar 
Brzostek, E. R. et al. Chronic water stress reduces tree growth and the carbon sink of deciduous hardwood forests. Glob. Chang. Biol. 20, 2531–2539 (2014).Article 
ADS 
PubMed 

Google Scholar 
Liese, R., Lübbe, T., Albers, N. W. & Meier, I. C. The mycorrhizal type governs root exudation and nitrogen uptake of temperate tree species. Tree Physiol. 38, 83–95 (2018).Article 
CAS 
PubMed 

Google Scholar 
Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Linton, M. J., Sperry, J. S. & Williams, D. G. Limits to water transport in Juniperus osteosperma and Pinus edulis: Implications for drought tolerance and regulation of transpiration. Funct. Ecol. 12, 906–911 (1998).Article 

Google Scholar 
Johnson, D. M. et al. Co-occurring woody species have diverse hydraulic strategies and mortality rates during an extreme drought. Plant. Cell Environ. 41, 576–588 (2018).Article 
CAS 
PubMed 

Google Scholar 
Lin, G. et al. Mycorrhizal associations of tree species influence soil nitrogen dynamics via effects on soil acid–base chemistry. Glob. Ecol. Biogeogr. 31, 168–182 (2022).Article 

Google Scholar 
Read, D. J. Mycorrhizas in ecosystems. Experientia 47, 376–391 (1991).Article 

Google Scholar 
Hobbie, S. E. Plant species effects on nutrient cycling: revisiting litter feedbacks. Trends Ecol. Evol. 30, 357–363 (2015).Article 
PubMed 

Google Scholar 
De Schrijver, A. et al. Tree species traits cause divergence in soil acidification during four decades of postagricultural forest development. Glob. Chang. Biol. 18, 1127–1140 (2012).Article 
ADS 

Google Scholar 
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Braghiere, R. K. et al. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. 14, 1–23 (2022).Article 

Google Scholar 
Eisenhauer, N. et al. Biotic interactions as mediators of context-dependent biodiversity-ecosystem functioning relationships. Res. Ideas Outcomes 8, e85873 (2022).Article 

Google Scholar 
Fisher, J. B. et al. Tree-mycorrhizal associations detected remotely from canopy spectral properties. Glob. Chang. Biol. 22, 2596–2607 (2016).Article 
ADS 
PubMed 

Google Scholar 
Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 1–10 (2019).Article 
CAS 

Google Scholar 
Burrill, E. A. et al. The forest inventory and analysis database. USDA . Serv. 2, 1026 (2015).
Google Scholar 
Chao, A., Chiu, C.-H. & Jost, L. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through hill numbers. Annu. Rev. Ecol. Evol. Syst. 45, 297–324 (2014).Article 

Google Scholar 
Cleland, D. T. et al. Ecological subregions: Sections and subsections for the conterminous United States. Gen. Tech. Rep. WO-76D (2007).Soudzilovskaia, N. A. et al. FungalRoot: global online database of plant mycorrhizal associations. N. Phytol. 227, 955–966 (2020).Article 

Google Scholar 
Gallion, J. et al. Indiana DNR State Forest Properties Report of Continuous Forest Inventory (CFI) Summary of years 2015–2019. 1–25 (2020).Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).Article 

Google Scholar 
Craven, D. et al. A cross-scale assessment of productivity–diversity relationships. Glob. Ecol. Biogeogr. 29, 1940–1955 (2020).Article 

Google Scholar 
Paquette, A. & Messier, C. The effect of biodiversity on tree productivity: from temperate to boreal forests. Glob. Ecol. Biogeogr. 20, 170–180 (2011).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2020).Dowle, M. & Srinivasan, A. data.table: Extension of ‘data.frame‘. R package version 1.14.2 (2021).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.4.0 (2020).Dunnington, D. ggspatial: Spatial Data Framework for ggplot2. R package version 1.1.5 (2021).Robert, J. Hijmans. raster: Geographic Data Analysis and Modeling. R package version 3.5-2 (2021).Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 1.0.8 (2022).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

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

Google Scholar 
Luo, S. et al. High productivity in forests with mixed mycorrhizal strategies. Figshare https://doi.org/10.6084/m9.figshare.22060238. (2023). More

Proctor, L. Priorities for the next 10 years of human microbiome research. Nature 569(7758), 623–625 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Bahl, M. I., Bergström, A. & Licht, T. R. Freezing fecal samples prior to DNA extraction affects the Firmicutes to Bacteroidetes ratio determined by downstream quantitative PCR analysis. FEMS Microbiol. Lett. 329, 193–197 (2012).Article 
CAS 
PubMed 

Google Scholar 
Wu, X. et al. Metagenomic insights into nitrogen and phosphorus cycling at the soil aggregate scale driven by organic material amendments. Sci. Total Environ. 785, 147329 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Singh, B. K., Millard, P., Whiteley, A. S. & Murrell, J. C. Unravelling rhizosphere-microbial interactions: Opportunities and limitations. Trends Microbiol. 12, 386–393 (2004).Article 
CAS 
PubMed 

Google Scholar 
Methé, B. A. et al. A framework for human microbiome research. Nature 486, 215–221 (2012).Article 
ADS 
PubMed Central 

Google Scholar 
Pascoe, E. L., Hauffe, H. C., Marchesi, J. R. & Perkins, S. E. Network analysis of gut microbiota literature: An overview of the research landscape in non-human animal studies. ISME J. 11, 2644–2651 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Gilbert, J. A., Jansson, J. K. & Knight, R. Earth microbiome project and global systems biology. mSystems 3, e00217-17 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18(11), 607–621 (2020).Article 
CAS 
PubMed 

Google Scholar 
Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569(7758), 655–662 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chen, T. et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature 580(7805), 653–657 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Holman, D. B. & Gzyl, K. E. A meta-analysis of the bovine gastrointestinal tract microbiota. FEMS Microbiol. Ecol. 95, 72 (2019).Article 

Google Scholar 
Chen, L. et al. Plant growth–promoting bacteria improve maize growth through reshaping the rhizobacterial community in low-nitrogen and low-phosphorus soil. Biol. Fertil. Soils 57, 1075–1088. https://doi.org/10.1007/S00374-021-01598-6 (2021).Article 
CAS 

Google Scholar 
Sommer, F. et al. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep. 14, 1655–1661 (2016).Article 
CAS 
PubMed 

Google Scholar 
Hauffe, H. C. & Barelli, C. Conserve the germs: The gut microbiota and adaptive potential. Conserv. Genet. 20(1), 19–27 (2019).Article 

Google Scholar 
Pollock, J., Glendinning, L., Wisedchanwet, T. & Watson, M. The madness of microbiome: Attempting to find consensus ‘best practice’ for 16S microbiome studies. Appl. Environ. Microbiol. 84(7), e02627-17 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551(7681), 457–463 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Costea, P. I. et al. Towards standards for human fecal sample processing in metagenomic studies. Nat. Biotechnol. 35, 1069–1076 (2017).Article 
CAS 
PubMed 

Google Scholar 
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Tourlousse, D. M. et al. Synthetic spike-in standards for high-throughput 16S rRNA gene Amplicon sequencing. Nucleic Acids Res. 45, e23–e23 (2017).PubMed 

Google Scholar 
Thissen, J. B. et al. Axiom Microbiome Array, the next generation microarray for high-throughput pathogen and microbiome analysis. PLoS ONE 14, e0212045 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ducarmon, Q. R., Hornung, B. V. H., Geelen, A. R., Kuijper, E. J. & Zwittink, R. D. Toward standards in clinical microbiota studies: Comparison of three DNA extraction methods and two bioinformatic pipelines. mSystems 5, e00547-19 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Ray, T. et al. The microbiome of common bedding materials before and after use on commercial dairy farms. Anim. Microbiome 4(1), 1–21 (2022).Article 
MathSciNet 
CAS 

Google Scholar 
Akhremchuk, K. V. et al. Gut microbiome of healthy people and patients with hematological malignancies in Belarus. Microbiol. Indep. Res. J. (MIR J.) 9, 18–30 (2022).Article 

Google Scholar 
Smets, W. et al. A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing. Soil Biol. Biochem. 96, 145–151 (2016).Article 
CAS 

Google Scholar 
Palmer, J. M., Jusino, M. A., Banik, M. T. & Lindner, D. L. Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6, e4925 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Alteio, L. V. et al. A critical perspective on interpreting amplicon sequencing data in soil ecological research. Soil Biol. Biochem. 160, 108357 (2021).Article 
CAS 

Google Scholar 
Stämmler, F. et al. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4, 1–13 (2016).Article 

Google Scholar 
Risely, A., Wilhelm, K., Clutton-Brock, T., Manser, M. B. & Sommer, S. Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats. Nat. Commun. 12(1), 1–12 (2021).Article 

Google Scholar 
Risely, A., et al. Gut microbiota repeatability is contingent on temporal scale and age in wild meerkats. ecoevorxiv (2022). https://doi.org/10.32942/OSF.IO/DSQFRSzóstak, N. et al. The standardisation of the approach to metagenomic human gut analysis: From sample collection to microbiome profiling. Sci. Rep. 12(1), 1–21 (2022).Article 

Google Scholar 
Tourlousse, D. M. et al. Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res. 45, e23 (2017).PubMed 

Google Scholar 
Sheu, S. Y., Arun, A. B., Jiang, S. R., Young, C. C. & Chen, W. M. Allobacillus halotolerans gen. nov., sp. Nov. isolated from shrimp paste. Int. J. Syst. Evol. Microbiol. 61, 1023–1027 (2011).Article 
CAS 
PubMed 

Google Scholar 
Surendra, V., Bhawana, P., Suresh, K., Srinivas, T. N. R. & Anil Kumar, P. Imtechella halotolerans gen. nov., sp. nov., a member of the family Flavobacteriaceae isolated from estuarine water. Int. J. Syst. Evol. Microbiol. 62, 2624–2630 (2012).Article 
CAS 
PubMed 

Google Scholar 
Praeg, N. et al. The role of land management and elevation in shaping soil microbial communities: Insights from the Central European Alps. Soil Biol. Biochem. 150, 107951 (2020).Article 
CAS 

Google Scholar 
Albonico, F. et al. Raw milk and fecal microbiota of commercial Alpine dairy cows varies with herd, fat content and diet. PLoS ONE 15, e0237262 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Watson, S. E. et al. Global change-driven use of onshore habitat impacts polar bear faecal microbiota. ISME J. https://doi.org/10.1038/s41396-019-0480-2 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Huebner, K. L. et al. Effects of a Saccharomyces cerevisiae fermentation product on liver abscesses, fecal microbiome, and resistome in feedlot cattle raised without antibiotics. Sci. Rep. 9(1), 1–11 (2019).Article 

Google Scholar 
Fan, P. et al. Host genetic effects upon the early gut microbiota in a bovine model with graduated spectrum of genetic variation. ISME J. 14(1), 302–317 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Mtshali, K., Khumalo, Z. T. H., Kwenda, S., Arshad, I. & Thekisoe, O. M. M. Exploration and comparison of bacterial communities present in bovine faeces, milk and blood using 16S rRNA metagenomic sequencing. PLoS ONE 17, e0273799 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Johnson, J. S. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 10(1), 5029 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Pei, A. Y. et al. Diversity of 16S rRNA genes within individual prokaryotic genomes. Appl. Environ. Microbiol. 76, 3886 (2010).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stoler, N. & Nekrutenko, A. Sequencing error profiles of Illumina sequencing instruments. NAR Genomics Bioinforma. 3, lqab019 (2021).Article 

Google Scholar 
Schirmer, M. et al. Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res. 43, e37–e37 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
McLaren, M. R., Willis, A. D. & Callahan, B. J. Consistent and correctable bias in metagenomic sequencing experiments. Elife 8, e46923 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Gonzalez, J. M., Portillo, M. C., Belda-Ferre, P. & Mira, A. Amplification by PCR artificially reduces the proportion of the rare biosphere in microbial communities. PLoS ONE 7, e29973 (2012).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gilbert, J. A., Jansson, J. K. & Knight, R. The earth microbiome project: Successes and aspirations. BMC Biol. 12, 1–4 (2014).Article 

Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, 1–8 (2011).Article 

Google Scholar 
McDonald, D. et al. American gut: An open platform for citizen science microbiome research. mSystems 3, e00031-18 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Illumina. IMPORTANT NOTICE This document provides information for an application for 16S Metagenomic Sequencing Library Preparation Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System.Teng, F. et al. Impact of DNA extraction method and targeted 16S-rRNA hypervariable region on oral microbiota profiling. Sci. Rep. 8(1), 1–12 (2018).Article 
ADS 

Google Scholar 
Willis, C., Desai, D. & Laroche, J. Influence of 16S rRNA variable region on perceived diversity of marine microbial communities of the Northern North Atlantic. FEMS Microbiol. Lett. 366, fnz152 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chen, Z. et al. Impact of preservation method and 16S rRNA hypervariable region on gut microbiota profiling. mSystems 4, e00271-18 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Sanada, T. J. et al. Gut microbiota modification suppresses the development of pulmonary arterial hypertension in an SU5416/hypoxia rat model. Pulm. Circ. 10(3), 1–3. https://doi.org/10.1177/2045894020929147 (2020).Article 
MathSciNet 
CAS 

Google Scholar 
Praeg, N., Schwinghammer, L. & Illmer, P. Larix decidua and additional light affect the methane balance of forest soil and the abundance of methanogenic and methanotrophic microorganisms. FEMS Microbiol. Lett. 366, 259 (2019).Article 

Google Scholar 
Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551(7681), 507–511 (2017).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sanders, H. L. Marine benthic diversity: A comparative study. Am. Nat. 102, 243–282. https://doi.org/10.1086/282541 (2015).Article 

Google Scholar 
Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B 44, 139–160 (1982).MathSciNet 
MATH 

Google Scholar 
Stanaway, I. B. et al. Human oral buccal microbiomes are associated with farmworker status and azinphos-methyl agricultural pesticide exposure. Appl. Environ. Microbiol. 83, e02149-16 (2017).Article 
PubMed 

Google Scholar 
Grice, E. A. et al. A diversity profile of the human skin microbiota. Genome Res. 18, 1043–1050 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Payne, M. A. et al. Horizontal and vertical transfer of oral microbial dysbiosis and periodontal disease. J. Dent. Res. 98, 1503–1510 (2019).Article 
CAS 
PubMed 

Google Scholar 
Karasov, T. L. et al. The relationship between microbial population size and disease in the Arabidopsis thaliana phyllosphere. bioRxiv https://doi.org/10.1101/828814 (2020).Article 

Google Scholar 
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6(8), 1621–1624 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

Google Scholar 
Albanese, D., Fontana, P., De Filippo, C., Cavalieri, D. & Donati, C. MICCA: A complete and accurate software for taxonomic profiling of metagenomic data. Sci. Rep. 5(1), 1–7 (2015).Article 

Google Scholar 
Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv https://doi.org/10.1101/081257 (2016).Article 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. (2019).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
De Mendiburu, F. Agricolae: statistical procedures for agricultural research. R package version, 1(1). https://scholar.google.com/scholar?hl=it&as_sdt=0%2C5&q=Agricolae%3A+Statistical+Procedures+for+Agricultural+Research&btnG (2014).Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5(7), 621–628 (2008).Article 
CAS 
PubMed 

Google Scholar 
Metsalu, T. & Vilo, J. ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 43, W566–W570 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gloor, G. B. & Reid, G. Compositional analysis: A valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. https://doi.org/10.1139/cjm-2015-082162,692-703 (2016).Article 
PubMed 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens M. H. H., Szöcs, E. & Wagner, H. vegan: Community Ecology Package. R package version 2.5-7. 2020 (2022).Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York. More

Cheung, W. W. L. et al. Shrinking of fishes exacerbates impacts of global ocean changes on marine ecosystems. Nat. Clim. Change 3, 1–5 (2012).
Google Scholar 
Pilotto, F. et al. Meta-analysis of multidecadal biodiversity trends in Europe. Nat. Commun. 11, 3486 (2010).Article 
ADS 

Google Scholar 
Ong, J. J. L. et al. Contrasting environmental drivers of adult and Juvenile growth in a marine fish: Implications for the effects of climate change. Sci. Rep. 5, 10859 (2015).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Rijnsdorp, A. D., Peck, M. A., Engelhard, G. H., Moellmann, C. & Pinnegar, J. K. Resolving the effect of climate change on fish populations. ICES J. Mar. Sci. 66(7), 1570–1583 (2009).Article 

Google Scholar 
Pankhurst, N. W. & Munday, P. L. Effects of climate change on fish reproduction and early life history stages. Mar. Freshw. Res. 62(9), 1015 (2011).Article 
CAS 

Google Scholar 
Ainsworth, C. H. et al. Potential impacts of climate change on Northeast Pacific marine foodwebs and fisheries. ICES J. Mar. Sci. 68, 1217–1229 (2011).Article 

Google Scholar 
Morrongiello, J. R., Horn, P. L., Ó Maolagáin, C. & Sutton, P. J. H. Synergistic effects of harvest and climate drive synchronous somatic growth within key New Zealand fisheries. Glob. Change Biol. 27(7), 1470–1484 (2021).Article 
ADS 
CAS 

Google Scholar 
Ottersen, G., Hjermann, D. O. & Stensenth, N. C. Changes in spawning stocks structure strengthen the link between climate and recruitment in a heavily fished cod (Gadus morhua) stock. Fish. Oceanogr. 15(3), 230–243 (2006).Article 

Google Scholar 
Cheung, W. W. L. & Oyinlola, M. A. Vulnerability of flatfish and their fisheries to climate change. J. Sea Res. 140, 1–10 (2018).Article 
ADS 

Google Scholar 
Fedewa, E. J., Miller, J. A. & Hurst, T. P. Pre-settlement process of northern rock sole (Lepidopsetta polyxystra) in relation to interannual variability in the Gulf of Alaska. J. Sea Res. 111, 25–36 (2016).Article 
ADS 

Google Scholar 
Cabral, H. N. et al. Relative importance of estuarine flatfish nurseries along the Portuguese coast. J. Sea Res. 57, 209–217 (2007).Article 
ADS 

Google Scholar 
Martinho, F., van der Veer, H. W., Cabral, H. N. & Pardal, M. A. Juvenile nursery colonization patterns for the European flounder (Platichthys flesus): A latitudinal approach. J. Sea Res. 84, 61–69 (2013).Article 
ADS 

Google Scholar 
Primo, A. L. et al. Contrasting links between growth and survival in the early life stages of two flatfish species. Estuar. Coast. Shelf Sci. 254, 107314 (2021).Article 

Google Scholar 
Vaz, A., Scarcella, G., Pardal, M. A. & Martinho, F. Water temperature gradients drive early life-history patterns of the common sole (Solea solea L.) in the Northeast Atlantic and Mediterranean. Aquat. Ecol. 53(5) (2019).Geffen, A., van der Veer, H. W. & Nash, R. The cost of metamorphosis in flatfishes. J. Sea Res. 58(1), 35–45 (2007).Article 
ADS 

Google Scholar 
Cowen, R. K., Lwiza, K. M. M., Sponaugle, S., Paris, C. B. & Olson, D. B. Connectivity in marine populations: Open or closed?. Science 287, 857–859 (2000).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Gillanders, B. M., Black, B. A., Meekan, M. G. & Morrison, M. A. Climatic effects on the growth of a temperate reef fish from the Southern Hemisphere: a biochronological approach. Mar. Biol. 159, 1327–1333 (2012).Article 

Google Scholar 
Treml, E. A., Ford, J. R., Black, K. P. & Swearer, S. E. Identifying the key biophysical drivers, connectivity outcomes, and metapopulation consequences of larval dispersal in the sea. Mov. Ecol. 3(1), 345 (2015).Article 

Google Scholar 
Gibson, R. N. Behaviour and the distribution of flatfishes. J. Sea Res. 37(1997), 241–256 (1997).Article 
ADS 

Google Scholar 
Mellado-Cano, J., Barriopedro, D., García-Herrera, R., Trigo, R. M. & Hernández, A. Examining the North Atlantic Oscillation, East Atlantic Pattern, and jet variability since 1685. J. Clim. 32, 6285–6298 (2019).Article 
ADS 

Google Scholar 
Tanner, S. E. et al. Marine regime shifts impact synchrony of deep-sea fish growth in the northeast Atlantic. Oikos 129(12), 1781–1794 (2020).Article 

Google Scholar 
Trigo, R. M., Osborn, T. J. & Corte-Real, J. M. The North Atlantic Oscillation influence on Europe: Climate impacts and associated physical mechanisms. Clim. Res. 20, 9–17 (2002).Article 

Google Scholar 
Leis, J. M. et al. Does fish larval dispersal differ between high and low latitudes?. Proc. R. Soc. B Biol. Sci. 280(1759), 20130327 (2013).Article 

Google Scholar 
Raventos, N., Torrado, H., Arthur, R., Alcoverro, T. & Macpherson, E. Temperature reduces fish dispersal as larvae grow faster to their settlement size. J. Anim. Ecol. 90(6), 1419–1432 (2021).Article 
PubMed 

Google Scholar 
Santos, A. M. P. et al. Physical-biological interactions in the life history of small Pelagic Fish in the Western Iberia upwelling ecosystem. Prog. Oceanogr. 74(2), 192–209 (2007).Article 
ADS 

Google Scholar 
Le Pape, O. & Bonhommeau, S. The food limitation hypothesis for juvenile marine fish. Fish Fish. 16(3), 373–398 (2015).Article 

Google Scholar 
Fox, C. et al. Birth-date selection in early life stage of plaice Pleuronectes platessa in the eastern Irish Sea (British Isles). Mar. Ecol. Prog. Ser. 345, 255–269 (2007).Article 
ADS 

Google Scholar 
Joh, M. & Wada, A. Inter-annual and spatial difference in hatch date and settlement date distribution and planktonic larval duration in yellow striped flounder Pseudopleuronectes Herzensteini. J. Sea Res. 137, 26–34 (2018).Article 
ADS 

Google Scholar 
Pinto, M. et al. Influence of oceanic and climate conditions on the early life history of European seabass Dicentrarchus labrax. Mar. Environ. Res. 169, 105362 (2021).Article 
CAS 
PubMed 

Google Scholar 
Morais, P., Dias, E., Babaluk, J. & Antunes, C. The migration patterns of the European flounder Platichthys flesus (Linnaeus, 1758) (Pleuronectidae, Pisces) at the southern limit of its distribution range: Ecological implications and fishery management. J. Sea Res. 65, 235–246 (2011).Article 
ADS 

Google Scholar 
Lacroix, G., Maes, G. E., Bolle, L. J. & Volckaert, F. Modelling dispersal dynamics of the early life stages of a marine flatfish (Solea Solea L.). J. Sea Res. 84(C), 13–25 (2013).Article 
ADS 

Google Scholar 
Tanner, S. E., Teles-Machado, A., Martinho, F., Peliz, A. & Cabral, H. N. Modelling larval dispersal Dynamics of common sole (Solea solea) along the western Iberian coast. Prog. Oceanogr. 156, 78–90 (2017).Article 
ADS 

Google Scholar 
Amorim, E., Ramos, S., Elliott, M. & Bordalo, A. A. Immigration and early life stages recruitment of the European flounder (Platichthys flesus) to an estuarine nursery: The influence of environmental factors. J. Sea Res. 107(Part 1), 56–66 (2016).Article 
ADS 

Google Scholar 
Vasconcelos, R. P., Reis-Santos, P., Costa, M. J. & Cabral, H. N. Connectivity between estuaries and marine environment: Integrating metrics to assess estuarine nursery function. Ecol. Indic. 11(5), 1123–1133 (2011).Article 

Google Scholar 
Orio, A. et al. Spatial contraction of demersal fish populations in a large marine ecosystem. J. Biogeogr. 46(3), 633–645 (2019).Article 

Google Scholar 
Peliz, A., Rosa, T. L., Santos, A. M. P. & Pissarra, J. L. Fronts, jets, and counter-flows in the Western Iberian upwelling system. J. Mar. Syst. 35, 61–77 (2002).Article 

Google Scholar 
Teles-Machado, A., Peliz, A., McWilliams, J. C., Dubert, J. & Le Cann, B. Circulation on the Northwestern Iberian Margin: Swoddies. Prog. Oceanogr 140, 116–133 (2016).Article 
ADS 

Google Scholar 
Primo, A. L. et al. Colonization and nursery habitat use patterns of larval and juvenile flatfish species in a small temperate estuary. J. Sea. Res. 76(C), 126–134 (2013).Article 
ADS 

Google Scholar 
Vasconcelos, R. P. et al. Evidence of estuarine nursery origin of five coastal fish species along the Portuguese coast through otolith elemental fingerprints. Estuar. Coast. Shelf Sci. 79, 317–327 (2008).Article 
ADS 

Google Scholar 
du Sert, N. P. et al. The ARRIVAGE guidelines 2.0: updated guidelines for reporting animal research. J. Physiol. Lond. 598(18), 3793–3801 (2020).Article 

Google Scholar 
Trigo, R. M. et al. The impact of north atlantic wind and cyclone trends on European precipitation and significant wave height in the Atlantic. Ann. N. Y. Acad. Sci. 1146(1), 212–234 (2008).Article 
ADS 
PubMed 

Google Scholar 
Murase, H., Nagashima, H., Yonezaki, S., Matsukura, R. & Kitakado, T. Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of Pelagic Fish and Krill: a Case Study in Senday Bay, Japan. ICES J. Mar. Sci. 66(6), 1417–1424 (2009).Article 

Google Scholar 
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 73(1), 3–36 (2011).Article 
MathSciNet 
MATH 

Google Scholar 
Tanner, S. E. et al. Regional climate, primary productivity and fish biomass drive growth cariation and population resilience in a small pelagic fish. Ecol. Indic. 103, 530–541 (2019).Article 

Google Scholar 
Almeida, J. R., Gravato, C. & Guilermino, L. Effects of temperature in juvenile Seabass (Dicentrarchus labrax L.) biomarker responses and behaviour: implications for environmental monitoring. Estuaries Coasts 38, 45–55 (2015).Article 
CAS 

Google Scholar 
Sims, D. W., Wearmouth, V. J., Genner, M. J., Southward, A. J. & Hawkins, S. J. Low-temperature-driven early spawning migration of a temperate marine fish. J. Anim. Ecol. 73(2), 333–341 (2004).Article 

Google Scholar 
Faria, A. M., Muha, T., Morote, R. & Chicharro, M. A. Influence of starvation on the critical swimming behaviour of the Senegalensis sole (Solea senegalensis) and its relationship with RNA/DNA ratios during ontogeny. Sci. Mar. 75(1), 87–94 (2011).Article 
CAS 

Google Scholar 
Downie, A. T., Illing, B., Faria, A. M. & Rummer, J. L. Swimming performance of marine fish larvae: review of a universal trait under ecological and environmental pressure. Rev. Fish Biol. Fish. 30, 93–108 (2020).Article 

Google Scholar 
Durant, J. M. et al. Contrasting effects of rising temperatures on trophic interactions in marine ecosystems. Na. Sci. Rep. 9(1), 15213 (2019).Article 
ADS 

Google Scholar 
Stenseth, N. C. et al. Ecological effects of climate fluctuations. Science 297(5585), 1292–1296 (2002).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Harrington, A. M., Clark, K. F. & Hamlin, H. J. Expected ocean warming conditions significantly alter the transcriptone of developing postlarval American lobsters (Homarus americanus): Implications for energetic trade-offs. Comp. Biochem. Physiol. D Genom. Proteom. 36, 100716 (2020).CAS 

Google Scholar 
Pörtner, H. O. & Farrell, A. P. Ecology. Physiol. Clim. Change. Sci. 322(5902), 690–692 (2008).
Google Scholar 
Drinkwater, K. F. et al. On the processes linking climate to ecosystem changes. J. Mar. Syst. 79, 374–388 (2010).Article 

Google Scholar 
Alix, M., Kjesbu, O. S. & Anderson, K. C. From Gametogenesis to spawning: How climate-driven warming affects teleost reproductive biology. J. Fish Biol. 97(3), 607–632 (2020).Article 
PubMed 

Google Scholar 
Conover, D. O. & Present, T. M. C. Countergradient variation in growth rate: compensation for length of the growing season among Atlantic silversides from different latitudes. Oceanologia 83, 316–324 (1990).ADS 

Google Scholar 
van de Wolfshaar, K. E., Barbut, L. & Lacroix, G. From spawning to first-year recruitment: the fate of Juvenile Sole Growth and survival under future climate conditions in the North Sea. ICES J. Mar. Sci. (2021).Cabral, H. et al. Contrasting impacts of climate change on connectivity and larval recruitment to estuarine nursery areas. Prog. Oceanogr. 196, 102608 (2011).Article 

Google Scholar 
Iglesias, I., Lorenzo, M. N. & Taboada, J. J. Seasonal predictability of the East Atlantic Pattern from sea surface temperatures. PLoS ONE 9(1), 86439–86448 (2014).Article 
ADS 

Google Scholar 
Rodríguez-Puebla, C., Encinas, A. H., García-Casado, L. A. & Nieto, S. Trends in warm days and cold nights over the Iberian Peninsula: relationships to large-scale variables. Clim. Change 100(3), 667–684 (2010).Article 
ADS 

Google Scholar 
Hurrell, J. W. & Van Loon, H. Decadal variations in climate associated with the North Atlantic oscillation. Clim. Change 36, 301–326 (1997).Article 

Google Scholar 
Henderson, P. A. & Seaby, R. M. The role of climate in determining the temporal variation in abundance, recruitment and growth of sole Solea solea in the Bristol Channel. JMBA 85, 197–204 (2005).
Google Scholar 
Rodwell, M. J., Rowell, D. P. & Folland, C. K. Oceanic forcing of the wintertime North Atlantic Oscillation and European Climate. Letters to Nature 398, 320–323 (1999).Article 
ADS 
CAS 

Google Scholar 
Hurrell, J. W. Decadal trends in the North Atlantic oscillation: Regional temperatures and precipitation. Sci. 269, 676–679 (1995).Article 
ADS 
CAS 

Google Scholar 
Avalos, M. R. et al. Comparing the foraging strategies of a seabird predator when recovering from drastic climatic event. Mar. Biol. 164, 48 (2017).Article 

Google Scholar 
Wang, C., Liu, H. & Lee, S. K. The record-breaking cold temperatures during the winter of 2009/2010 in the Northern Hemisphere. Atmos. Sci. Lett. 11(3), 161–168 (2010).Article 
ADS 
CAS 

Google Scholar 
Rodrigo, F. S. Exploring combined influences of Seasonal East Atlantic (EA) and North Atlantic Oscillation (NAO) on the temperature-precipitation relationship in the Iberian Peninsula. Geosciences 11(5), 211 (2021).Article 
ADS 

Google Scholar 
Alvarez, I., Gommez-Gesteira, M., Decastro, M. & Dias, J. M. Spatiotemporal evolution of upwelling regime along the western coast of the Iberian Peninsula. J. Geophys. Res. Oceans 113(C7), C07020 (2008).Article 
ADS 

Google Scholar 
Demarcq, H. Trends in primary production, Sea surface temperature and wind in upwelling systems (1998–2007). Prog. Oceanogr. 83(1), 376–385 (2009).Article 
ADS 

Google Scholar 
Thorrold, S. R., Latkoczy, C., Swart, P. K. & Jones, C. M. Natal homing in a marine fish metapopulation. Science 291, 297–299 (2001).Article 
ADS 
CAS 
PubMed 

Google Scholar  More

Randelhoff, A. et al. Pan-Arctic ocean primary production constrained by turbulent nitrate fluxes. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00150 (2020).Article 

Google Scholar 
Wegner, C. et al. Variability in transport of terrigenous material on the shelves and the deep Arctic Ocean during the Holocene. Polar Res. https://doi.org/10.3402/polar.v%v.24964 (2015).Article 

Google Scholar 
Arrigo, K. R. & van Dijken, G. L. Continued increases in Arctic Ocean primary production. Prog. Oceanogr. 136, 60–70. https://doi.org/10.1016/j.pocean.2015.05.002 (2015).Article 
ADS 

Google Scholar 
Lewis, K. M., van Dijken, G. L. & Arrigo, K. R. Changes in phytoplankton concentration now drive increased Arctic Ocean primary production. Science 369, 198–202. https://doi.org/10.1126/science.aay8380 (2020).Article 
CAS 
PubMed 

Google Scholar 
Mueter, F. J. et al. Possible future scenarios in the gateways to the Arctic for Subarctic and Arctic marine systems: II. Prey resources, food webs, fish, and fisheries. ICES J. Mar. Sci. 78, 3017–3045. https://doi.org/10.1093/icesjms/fsab122 (2021).Article 

Google Scholar 
Alabia, I. D. et al. Multiple facets of marine biodiversity in the Pacific Arctic under future climate. Sci. Total Environ. 744, 140913. https://doi.org/10.1016/j.scitotenv.2020.140913 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
CAFF. Arctic Biodiversity Assessment. Status and trends in Arctic biodiversity. (Conservation of Arctic Flora and Fauna, Akureyri, Iceland, 2013).Stafford, K. M., Farley, E. V., Ferguson, M., Kuletz, K. J. & Levine, R. Northward range expansion of subarctic upper trophic level animals into the Pacific Arctic Region. Oceanography. 35, 158–166. https://doi.org/10.5670/oceanog.2022.101 (2022).Csapó, H. K., Grabowski, M. & Węsławski, J. M. Coming home—Boreal ecosystem claims Atlantic sector of the Arctic. Sci. Total Environ. 771, 144817. https://doi.org/10.1016/j.scitotenv.2020.144817 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Frainer, A. et al. Climate-driven changes in functional biogeography of Arctic marine fish communities. Proc. Natl. Acad. Sci. 114, 12202–12207. https://doi.org/10.1073/pnas.1706080114 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gordó-Vilaseca, C., Stephenson, F., Coll, M., Lavin, C. & Costello, M. J. Three decades of increasing fish biodiversity across the northeast Atlantic and the Arctic Ocean. Proc. Natl. Acad. Sci. 120, e2120869120. https://doi.org/10.1073/pnas.2120869120 (2023).Article 
CAS 
PubMed 

Google Scholar 
Kalenitchenko, D., Joli, N., Potvin, M., Tremblay, J. -É. & Lovejoy, C. Biodiversity and species change in the arctic ocean: A view through the lens of nares strait. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00479 (2019).Article 

Google Scholar 
Michel, C. et al. Arctic Ocean outflow shelves in the changing Arctic: A review and perspectives. Prog. Oceanogr. 139, 66–88. https://doi.org/10.1016/j.pocean.2015.08.007 (2015).Article 
ADS 

Google Scholar 
Ribeiro, S. et al. Vulnerability of the North Water ecosystem to climate change. Nat. Commun. 12, 4475. https://doi.org/10.1038/s41467-021-24742-0 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Poisot, T., Stouffer, D. B. & Gravel, D. Beyond species: Why ecological interaction networks vary through space and time. Oikos 124, 243–251. https://doi.org/10.1111/oik.01719 (2015).Article 

Google Scholar 
Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evolut. 4, 376–383. https://doi.org/10.1038/s41559-020-1099-4 (2020).Article 

Google Scholar 
Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063. https://doi.org/10.1111/ele.13525 (2020).Article 
PubMed 

Google Scholar 
Michael, E. L. Marine ecology and the coefficient of association: A plea in behalf of quantitative biology. J. Ecol. 8, 54–59. https://doi.org/10.2307/2255213 (1920).Article 

Google Scholar 
Gotelli, N. J., Graves, G. R. & Rahbek, C. Macroecological signals of species interactions in the Danish avifauna. Proc. Natl. Acad. Sci. 107, 5030–5035. https://doi.org/10.1073/pnas.0914089107 (2010).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Gotelli, N. J. & McCabe, D. J. Species co-occurrence: A meta-analysis of J. M. Diamond’s assembly rules model. Ecology 83, 2091–2096. https://doi.org/10.1890/0012-9658(2002)083[2091:SCOAMA]2.0.CO;2 (2002).Article 

Google Scholar 
Ulrich, W. Species co-occurrences and neutral models: Reassessing J. M. Diamond’s Assembly Rules. Oikos 107, 603–609 (2004).Article 

Google Scholar 
Kraan, C., Thrush, S. F. & Dormann, C. F. Co-occurrence patterns and the large-scale spatial structure of benthic communities in seagrass meadows and bare sand. BMC Ecol. 20, 37. https://doi.org/10.1186/s12898-020-00308-4 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Tulloch, A. I. T., Chadès, I. & Lindenmayer, D. B. Species co-occurrence analysis predicts management outcomes for multiple threats. Nat. Ecol. Evolut. 2, 465–474. https://doi.org/10.1038/s41559-017-0457-3 (2018).Article 

Google Scholar 
Drinkwater, K. F. et al. Possible future scenarios for two major Arctic Gateways connecting Subarctic and Arctic marine systems: I. Climate and physical–chemical oceanography. ICES J. Mar. Sci. 78, 3046–3065. https://doi.org/10.1093/icesjms/fsab182 (2021).Article 

Google Scholar 
Pilfold, N. W., McCall, A., Derocher, A. E., Lunn, N. J. & Richardson, E. Migratory response of polar bears to sea ice loss: To swim or not to swim. Ecography 40, 189–199. https://doi.org/10.1111/ecog.02109 (2017).Article 

Google Scholar 
Chambault, P. et al. The impact of rising sea temperatures on an Arctic top predator, the narwhal. Sci. Rep. 10, 18678. https://doi.org/10.1038/s41598-020-75658-6 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Perovich, D. et al. Arctic Report Card 2020: Sea Ice. https://doi.org/10.25923/n170-9h57 (2020).Post, E. et al. Ecological dynamics across the arctic associated with recent climate change. Science 325, 1355–1358. https://doi.org/10.1126/science.1173113 (2009).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Post, E. et al. Ecological consequences of sea-ice decline. Science 341, 519–524. https://doi.org/10.1126/science.1235225 (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Bienhold, C. et al. Effects of sea ice retreat and ocean warming on the Laptev Sea continental slope ecosystem (1993 vs 2012). Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.1004959 (2022).Article 

Google Scholar 
Olafsdottir, A. H. et al. Geographical expansion of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic Seas from 2007 to 2016 was primarily driven by stock size and constrained by low temperatures. Deep Sea Res. Part II 159, 152–168. https://doi.org/10.1016/j.dsr2.2018.05.023 (2019).Article 

Google Scholar 
MacKenzie, B. R., Payne, M. R., Boje, J., Høyer, J. L. & Siegstad, H. A cascade of warming impacts brings bluefin tuna to Greenland waters. Glob. Change Biol. 20, 2484–2491. https://doi.org/10.1111/gcb.12597 (2014).Article 
ADS 

Google Scholar 
Alabia, I. D. et al. Distribution shifts of marine taxa in the Pacific Arctic under contemporary climate changes. Divers. Distrib. 24, 1583–1597. https://doi.org/10.1111/ddi.12788 (2018).Article 

Google Scholar 
Stewart, D. B. & Barber, D. G. in A Little Less Arctic: Top Predators in the World’s Largest Northern Inland Sea, Hudson Bay (eds Steven H. Ferguson, Lisa L. Loseto, & Mark L. Mallory) 1–38 (Springer Netherlands, 2010).Ferland, J., Gosselin, M. & Starr, M. Environmental control of summer primary production in the Hudson Bay system: The role of stratification. J. Mar. Syst. 88, 385–400. https://doi.org/10.1016/j.jmarsys.2011.03.015 (2011).Article 

Google Scholar 
Peacock, E., Derocher, A. E., Lunn, N. J. & Obbard, M. E. in A Little Less Arctic: Top Predators in the World’s Largest Northern Inland Sea, Hudson Bay (eds Steven H. Ferguson, Lisa L. Loseto, & Mark L. Mallory) 93–116 (Springer Netherlands, 2010).Chambellant, M. in A Little Less Arctic: Top Predators in the World’s Largest Northern Inland Sea, Hudson Bay (eds Steven H. Ferguson, Lisa L. Loseto, & Mark L. Mallory) 137–158 (Springer Netherlands, 2010).Mallory, M. L., Gaston, A. J., Gilchrist, H. G., Robertson, G. J. & Braune, B. M. in A Little Less Arctic: Top Predators in the World’s Largest Northern Inland Sea, Hudson Bay (eds Steven H. Ferguson, Lisa L. Loseto, & Mark L. Mallory) 179–195 (Springer Netherlands, 2010).Lone, K., Hamilton, C. D., Aars, J., Lydersen, C. & Kovacs, K. M. Summer habitat selection by ringed seals (Pusa hispida) in the drifting sea ice of the northern Barents Sea. Polar Res. https://doi.org/10.33265/polar.v38.3483 (2019).Article 

Google Scholar 
Jackson, R. et al. Holocene polynya dynamics and their interaction with oceanic heat transport in northernmost Baffin Bay. Sci. Rep. 11, 10095. https://doi.org/10.1038/s41598-021-88517-9 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stafford, K. M. et al. Beluga whales in the western Beaufort Sea: Current state of knowledge on timing, distribution, habitat use and environmental drivers. Deep Sea Res. Part II 152, 182–194. https://doi.org/10.1016/j.dsr2.2016.11.017 (2018).Article 

Google Scholar 
Kuletz, K. J. et al. Seasonal spatial patterns in seabird and marine mammal distribution in the eastern Chukchi and western Beaufort seas: Identifying biologically important pelagic areas. Prog. Oceanogr. 136, 175–200. https://doi.org/10.1016/j.pocean.2015.05.012 (2015).Article 
ADS 

Google Scholar 
Polyakov, I. V. et al. Borealization of the Arctic Ocean in response to anomalous advection from sub-arctic seas. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00491 (2020).Article 

Google Scholar 
Fossheim, M. et al. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Change 5, 673–677. https://doi.org/10.1038/nclimate2647 (2015).Article 
ADS 

Google Scholar 
Ardyna, M. et al. Recent Arctic Ocean sea ice loss triggers novel fall phytoplankton blooms. Geophys. Res. Lett. 41, 6207–6212. https://doi.org/10.1002/2014GL061047 (2014).Article 
ADS 

Google Scholar 
Randelhoff, A. & Sundfjord, A. Short commentary on marine productivity at Arctic shelf breaks: Upwelling, advection and vertical mixing. Ocean Sci. 14, 293–300. https://doi.org/10.5194/os-14-293-2018 (2018).Article 
ADS 

Google Scholar 
Bluhm, B. A. et al. The Pan-Arctic continental slope: sharp gradients of physical processes affect pelagic and benthic ecosystems. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.544386 (2020).Article 

Google Scholar 
Daase, M., Berge, J., Søreide, J. E. & Falk-Petersen, S. in Arctic Ecology (ed David N. Thomas) Ch. 9, 219–259 (Wiley, 2021).McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185. https://doi.org/10.1016/j.tree.2006.02.002 (2006).Article 
PubMed 

Google Scholar 
Young, K. A. Asymmetric competition, habitat selection, and niche overlap in Juvenile Salmonids. Ecology 85, 134–149 (2004).Article 

Google Scholar 
Aguilera, M. A., Valdivia, N., Broitman, B. R., Jenkins, S. R. & Navarrete, S. A. Novel co-occurrence of functionally redundant consumers induced by range expansion alters community structure. Ecology 101, e03150. https://doi.org/10.1002/ecy.3150 (2020).Article 
PubMed 

Google Scholar 
Usinowicz, J. & Levine, J. M. Species persistence under climate change: A geographical scale coexistence problem. Ecol. Lett. 21, 1589–1603. https://doi.org/10.1111/ele.13108 (2018).Article 
PubMed 

Google Scholar 
Durant, J. M. et al. Contrasting effects of rising temperatures on trophic interactions in marine ecosystems. Sci. Rep. 9, 15213. https://doi.org/10.1038/s41598-019-51607-w (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
García-Baquero, G. & Crujeiras, R. M. Can environmental constraints determine random patterns of plant species co-occurrence?. Ecol. Evol. 5, 1088–1099. https://doi.org/10.1002/ece3.1349 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Bar-Massada, A. Complex relationships between species niches and environmental heterogeneity affect species co-occurrence patterns in modelled and real communities. Proc. R. Soc. B Biol. Sci. 282, 20150927. https://doi.org/10.1098/rspb.2015.0927 (2015).Article 

Google Scholar 
Overland, J. E., Wang, M., Walsh, J. E. & Stroeve, J. C. Future Arctic climate changes: Adaptation and mitigation time scales. Earth’s Future 2, 68–74. https://doi.org/10.1002/2013EF000162 (2014).Article 
ADS 

Google Scholar 
Hirawake, T. et al. Response and biodiversity of Arctic ecosystems to environmental change: Findings from the ArCS project. Polar Sci. https://doi.org/10.1016/j.polar.2020.100533 (2020).Article 

Google Scholar 
Solan, M., Archambault, P., Renaud, P. E. & März, C. The changing Arctic Ocean: Consequences for biological communities, biogeochemical processes and ecosystem functioning. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 378, 20200266. https://doi.org/10.1098/rsta.2020.0266 (2020).Article 
ADS 

Google Scholar 
Timmermans, M.-L. & Marshall, J. Understanding Arctic Ocean circulation: A review of ocean dynamics in a changing climate. J. Geophys. Res. Oceans. 125, e2018JC014378. https://doi.org/10.1029/2018JC014378 (2020).Article 
ADS 

Google Scholar 
Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496. https://doi.org/10.1175/2007JCLI1824.1 (2007).Article 
ADS 

Google Scholar 
Amante, C. & Eakins, B. W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA. https://doi.org/10.7289/V5C8276M (2009).Lehodey, P., Murtugudde, R. & Senina, I. Bridging the gap from ocean models to population dynamics of large marine predators: A model of mid-trophic functional groups. Prog. Oceanogr. 84, 69–84. https://doi.org/10.1016/j.pocean.2009.09.008 (2010).Article 
ADS 

Google Scholar 
Green, D. B. et al. Modelled mid-trophic pelagic prey fields improve understanding of marine predator foraging behaviour. Ecography 43, 1014–1026. https://doi.org/10.1111/ecog.04939 (2020).Article 

Google Scholar 
Pérez-Jorge, S. et al. Environmental drivers of large-scale movements of baleen whales in the mid-North Atlantic Ocean. Divers. Distrib. 26, 683–698. https://doi.org/10.1111/ddi.13038 (2020).Article 

Google Scholar 
Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545. https://doi.org/10.1111/ecog.01132 (2015).Article 

Google Scholar 
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x (2012).Article 

Google Scholar 
Thuiller, W., Georges D., Gueguen, M., Engler, R., & Breiner, F. biomod2: Ensemble Platform for species Distribution Modeling. R package version 3.5.1. http://CRAN.R-project.org/package=biomod2 (2021). Accessed on 15 January 2022.
Baselga, A. & Orme, C. D. L. betapart: An R package for the study of beta diversity. Methods Ecol. Evol. 3, 808–812. https://doi.org/10.1111/j.2041-210X.2012.00224.x (2012).Article 

Google Scholar 
Griffith, D. M., Veech, J. A. & Marsh, C. J. cooccur: Probabilistic species co-occurrence analysis in R. J. Stat. Softw. Code Snippets 69, 1–17. https://doi.org/10.18637/jss.v069.c02 (2016).Article 

Google Scholar 
Veech, J. A. A probabilistic model for analysing species co-occurrence. Glob. Ecol. Biogeogr. 22, 252–260. https://doi.org/10.1111/j.1466-8238.2012.00789.x (2013).Article 

Google Scholar 
Abdi, A. M. et al. First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems. Int. J. Appl. Earth Obs. Geoinf. 78, 249–260. https://doi.org/10.1016/j.jag.2019.01.018 (2019).Article 
ADS 

Google Scholar 
Ban, S. S., Alidina, H. M., Okey, T. A., Gregg, R. M. & Ban, N. C. Identifying potential marine climate change Refugia: A case study in Canada’s Pacific marine ecosystems. Glob. Ecol. Conserv. 8, 41–54. https://doi.org/10.1016/j.gecco.2016.07.004 (2016).Article 

Google Scholar 
Alabia, I. D. et al. Marine biodiversity Refugia in a climate-sensitive subarctic shelf. Glob. Change Biol. 27, 3299–3311. https://doi.org/10.1111/gcb.15632 (2021).Article 

Google Scholar 
Alabia, I. D., Saitoh, S.-I., Igarashi, H., Ishikawa, Y. & Imamura, Y. Spatial habitat shifts of oceanic cephalopod (Ommastrephes bartramii) in oscillating climate. Remote Sensing. https://doi.org/10.3390/rs12030521 (2020).Article 

Google Scholar  More

Verkerk, P., et al. Forest products in the global bioeconomy. The role of forest products in the global bioeconomy—Enabling substitution by wood-based products and contributing to the Sustainable Development Goals (2022). https://doi.org/10.4060/cb7274enChen, J., Ter-Mikaelian, M. T., Ng, P. Q. & Colombo, S. J. Ontario’s managed forests and harvested wood products contribute to greenhouse gas mitigation from 2020 to 2100. For. Chron. 43, 269–282 (2018).
Google Scholar 
Howard, C., Dymond, C. C., Griess, V. C., Tolkien-Spurr, D. & van Kooten, G. C. Wood product carbon substitution benefits: A critical review of assumptions. Carbon Balance Manag. 16, 1–11 (2021).Article 

Google Scholar 
Eriksson, L. O. et al. Climate change mitigation through increased wood use in the European construction sector-towards an integrated modelling framework. Eur. J. For. Res. 131, 131–144 (2012).Article 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science (80-.) 333, 988–993 (2011).Article 
ADS 
CAS 

Google Scholar 
Chuine, I. Why does phenology drive species distribution?. Philos. Trans. R. Soc. B Biol. Sci. 365, 3149–3160 (2010).Article 

Google Scholar 
Silvestro, R. et al. From phenology to forest management: Ecotypes selection can avoid early or late frosts, but not both. For. Ecol. Manag. 436, 21–26 (2019).Article 

Google Scholar 
Buttò, V., Rossi, S., Deslauriers, A. & Morin, H. Is size an issue of time? Relationship between the duration of xylem development and cell traits. Ann. Bot. 123, 1257–1265 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Cartenì, F. et al. The physiological mechanisms behind the earlywood-to-latewood transition: A process-based modeling approach. Front. Plant Sci. 9, 1053 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Buttò, V., Rozenberg, P., Deslauriers, A., Rossi, S. & Morin, H. Environmental and developmental factors driving xylem anatomy and micro-density in black spruce. New Phytol. 230, 957–971 (2021).Article 
PubMed 

Google Scholar 
Buttó, V. et al. Regionwide temporal gradients of carbon allocation allow for shoot growth and latewood formation in boreal black spruce. Glob. Ecol. Biogeogr. 30, 1657–1670 (2021).Article 

Google Scholar 
Rathgeber, C. B. K. et al. Anatomical, developmental and physiological bases of tree-ring formation in relation to environmental factors. In Stable Isotopes in Tree Rings Vol. 8 (eds Siegwolf, R. T. W. et al.) 61–99 (Springer, Cham, 2022).Chapter 

Google Scholar 
Dória, L. C., Sonsin-Oliveira, J., Rossi, S. & Marcati, C. R. Functional trade-offs in volume allocation to xylem cell types in 75 species from the Brazilian savanna Cerrado. Ann. Bot. 130, 445–456 (2022).Article 
PubMed 

Google Scholar 
Rossi, S., Cairo, E., Krause, C. & Deslauriers, A. Growth and basic wood properties of black spruce along an alti-latitudinal gradient in Quebec, Canada. Ann. For. Sci. 72, 77–87 (2015).Article 

Google Scholar 
Shi, J. L., Riedl, B., Deng, J., Cloutier, A. & Zhang, S. Y. Impact of log position in the tree on mechanical and physical properties of black spruce medium-density fibreboard panels. Can. J. For. Res. 37, 866–873 (2007).Article 

Google Scholar 
Rathgeber, C. B. K., Decoux, V. & Leban, J. M. Linking intra-tree-ring wood density variations and tracheid anatomical characteristics in Douglas fir (Pseudotsuga menziesii (Mirb.) Franco). Ann. For. Sci. 63, 699–706 (2006).Article 

Google Scholar 
Cuny, H. E., Rathgeber, C. B. K., Frank, D., Fonti, P. & Fournier, M. Kinetics of tracheid development explain conifer tree-ring structure. New Phytol. 203, 1231–1241 (2014).Article 
PubMed 

Google Scholar 
Wodzicki, T. J. & Zajaczkowski, S. Methodical problems in studies on seasonal production of cambial xylem derivatives. Acta Soc. Bot. Pol. 39, 519–520 (1970).
Google Scholar 
Silvestro, R. et al. Upscaling xylem phenology: Sample size matters. Ann. Bot. https://doi.org/10.1093/aob/mcac110 (2022).Article 
PubMed 

Google Scholar 
Rossi, S., Girard, M. J. & Morin, H. Lengthening of the duration of xylogenesis engenders disproportionate increases in xylem production. Glob. Chang. Biol. 20, 2261–2271 (2014).Article 
ADS 
PubMed 

Google Scholar 
Gonsamo, A., Chen, J. M. & Ooi, Y. W. Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems. Glob. Change Biol. 24, 2117–2128 (2018).Article 
ADS 

Google Scholar 
Dow, C. et al. Warm springs alter timing but not total growth of temperate deciduous trees. Nature 608, 552–557 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Oribe, Y., Funada, R. & Kubo, T. Relationships between cambial activity, cell differentiation and the localization of starch in storage tissues around the cambium in locally heated stems of Abies sachalinensis (Schmidt) Masters. Trees Struct. Funct. 17, 185–192 (2003).Article 

Google Scholar 
Schrader, J. et al. Polar auxin transport in the wood-forming tissues of hybrid aspen is under simultaneous control of developmental and environmental signals. Proc. Natl. Acad. Sci. USA 100, 10096–10101 (2003).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Deslauriers, A., Huang, J. G., Balducci, L., Beaulieu, M. & Rossi, S. The contribution of carbon and water in modulating wood formation in black spruce saplings. Plant Physiol. 170, 2072–2084 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Silvestro, R., Brasseur, S., Klisz, M., Mencuccini, M. & Rossi, S. Bioclimatic distance and performance of apical shoot extension: Disentangling the role of growth rate and duration in ecotypic differentiation. For. Ecol. Manag. 477, 118483 (2020).Article 

Google Scholar 
Perrin, M., Rossi, S. & Isabel, N. Synchronisms between bud and cambium phenology in black spruce: Early-flushing provenances exhibit early xylem formation. Tree Physiol. 37, 593–603 (2017).Article 
PubMed 

Google Scholar 
Begum, S., Nakaba, S., Yamagishi, Y., Oribe, Y. & Funada, R. Regulation of cambial activity in relation to environmental conditions: Understanding the role of temperature in wood formation of trees. Physiol. Plant. 147, 46–54 (2013).Article 
CAS 
PubMed 

Google Scholar 
Kagawa, A., Sugimoto, A. & Maximov, T. C. 13CO2 pulse-labelling of photoassimilates reveals carbon allocation within and between tree rings. Plant Cell Environ. 29, 1571–1584 (2006).Article 
CAS 
PubMed 

Google Scholar 
Hansen, J. & Beck, E. The fate and path of assimilation products in the stem of 8-year-old Scots pine (Pinus sylvestris L.) trees. Trees 4, 16–21 (1990).Article 

Google Scholar 
Fu, P. L., Grießinger, J., Gebrekirstos, A., Fan, Z. X. & Bräuning, A. Earlywood and latewood stable carbon and oxygen isotope variations in two pine species in Southwestern China during the recent decades. Front. Plant Sci. 7, 2050 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Anfodillo, T. et al. Widening of xylem conduits in a conifer tree depends on the longer time of cell expansion downwards along the stem. J. Exp. Bot. 63, 837–845 (2012).Article 
CAS 
PubMed 

Google Scholar 
Linares, J. C., Camarero, J. J. & Carreira, J. A. Plastic responses of Abies pinsapo xylogenesis to drought and competition. Tree Physiol. 29, 1525–1536 (2009).Article 
PubMed 

Google Scholar 
Rossi, S., Morin, H. & Deslauriers, A. Causes and correlations in cambium phenology: Towards an integrated framework of xylogenesis. J. Exp. Bot. 63, 2117–2126 (2012).Article 
CAS 
PubMed 

Google Scholar 
Li, X. et al. Age dependence of xylogenesis and its climatic sensitivity in Smith fir on the south-eastern Tibetan Plateau. Tree Physiol. 33, 48–56 (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Rathgeber, C. B. K., Rossi, S. & Bontemps, J. D. Cambial activity related to tree size in a mature silver-fir plantation. Ann. Bot. 108, 429–438 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Buttò, V. et al. Comparing the cell dynamics of tree-ring formation observed in microcores and as predicted by the Vaganov-Shashkin model. Front. Plant Sci. 11, 1268 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Koga, S. & Zhang, S. Y. Relationships between wood density and annual growth rate components in balsam fir (Abies balsamea). Wood Fiber Sci. 34, 146–157 (2002).CAS 

Google Scholar 
Messier, C. et al. Functional ecology of advance regeneration in relation to light in boreal forests. Can. J. For. Res. 29, 812–823 (1999).Article 

Google Scholar 
Pothier, D., Elie, J. G., Auger, I., Mailly, D. & Gaudreault, M. Spruce budworm-caused mortality to balsam fir and black spruce in pure and mixed conifer stands. For. Sci. 58, 24–33 (2012).Article 

Google Scholar 
Paixao, C., Krause, C., Morin, H. & Achim, A. Wood quality of black spruce and balsam fir trees defoliated by spruce budworm: A case study in the boreal forest of Quebec, Canada. For. Ecol. Manag. 437, 201–210 (2019).Article 

Google Scholar 
Pretzsch, H., Biber, P., Schütze, G., Kemmerer, J. & Uhl, E. Wood density reduced while wood volume growth accelerated in Central European forests since 1870. For. Ecol. Manag. 429, 589–616 (2018).Article 

Google Scholar 
Reyer, C. et al. Projections of regional changes in forest net primary productivity for different tree species in Europe driven by climate change and carbon dioxide. Ann. For. Sci. 71, 211–225 (2014).Article 

Google Scholar 
Fang, J. et al. Evidence for environmentally enhanced forest growth. Proc. Natl. Acad. Sci. USA 111, 9527–9532 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pretzsch, H., Biber, P., Schütze, G., Uhl, E. & Rötzer, T. Forest stand growth dynamics in Central Europe have accelerated since 1870. Nat. Commun. 5, 1–10 (2014).Article 

Google Scholar 
Gao, S. et al. An earlier start of the thermal growing season enhances tree growth in cold humid areas but not in dry areas. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01668-4 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Soil Classification Working Group. The Canadian System of Soil Classification. (1998).Rossi, S., Anfodillo, T. & Menardi, R. Trephor: A new tool for sampling microcores from tree stems. IAWA J. 27, 89–97 (2006).Article 

Google Scholar 
Deslauriers, A., Morin, H. & Begin, Y. Cellular phenology of annual ring formation of Abies balsamea in the Quebec boreal forest (Canada). Can. J. For. Res. 33, 190–200 (2003).Article 

Google Scholar 
Rossi, S., Deslauriers, A. & Anfodillo, T. Assessment of cambial activity and xylogenesis by microsampling tree species: An example at the Alpine timberline. IAWA J. 27, 383–394 (2006).Article 

Google Scholar 
Filion, L. & Cournoyer, L. Variation in wood structure of eastern larch defoliated by the larch sawfly in subarctic Quebec, Canada. Can. J. For. Res. 25, 1263–1268 (1995).Article 

Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. (2015). More

Bindoff, N. L. et al. Changing ocean, marine ecosystems, and dependent communities. in IPCC special report on the ocean and cryosphere in a changing climate (eds. Pörtner, H.-O. et al.) (2019).Johnson, G. C. & Lyman, J. M. Warming trends increasingly dominate global ocean. Nat. Clim. Chang. 10, 757–761 (2020).ADS 

Google Scholar 
Doney, S. C. et al. Climate change impacts on marine ecosystems. Ann. Rev. Mar. Sci. 4, 11–37 (2012).PubMed 

Google Scholar 
Pinsky, M. L. & Mantua, N. J. Emerging adaptation approaches for climate- ready fisheries management. Oceanography 27, 146–159 (2014).
Google Scholar 
Bailey, K. M. & Houde, E. D. Predation on eggs and larvae of marine fishes and the recruitment problem. Adv. Mar. Biol. 25, 1–83 (1989).
Google Scholar 
Houde, E. D. Comparative growth, mortality, and energetics of marine fish larvae: temperature and implied latitudinal effects. Fish. Bull. 87, 471–495 (1989).
Google Scholar 
Wang, H., Shen, S., Chen, Y.-S., Kiang, Y.-K. & Heino, M. Life histories determine divergent population trends for fishes under climate warming. Nat. Commun. 11, 1–9 (2020).
Google Scholar 
Llopiz, J. K. et al. Early life history and fisheries oceanography: New questions in a changing world. Oceanography 27, 26–41 (2014).
Google Scholar 
Lasker, R. Field criteria for survival of anchovy larvae: The relation between inshore chlorophyll maximum layers and successful first feeding. Fish. Bull. 73, 453–462 (1975).
Google Scholar 
Cury, P. & Roy, C. Optimal environmental window and pelagic fish recruitment success in upwelling areas. Can. J. Fish. Aquat. Sci. 46, 670–680 (1989).
Google Scholar 
Iles, T. D. & Sinclair, M. Atlantic herring: Stock discreteness and abundance. Science 215, 627–633 (1982).ADS 
CAS 
PubMed 

Google Scholar 
Houde, E. D. Fish early life dynamics and recruitment variability. Am. Fish. Soc. Symp. 2, 17–29 (1987).ADS 

Google Scholar 
Searcy, S. P. & Sponaugle, S. Selective mortality during the larval – juvenile transition in two coral reef fishes. Ecology 82, 2452–2470 (2001).
Google Scholar 
Shima, J. S. & Findlay, A. M. Pelagic larval growth rate impacts benthic settlement and survival of a temperate reef fish. Mar. Ecol. Prog. Ser. 235, 303–309 (2002).ADS 

Google Scholar 
Bakun, A. Global climate change and intensification of coastal ocean upwelling. Science 247(198), 201 (1990).ADS 

Google Scholar 
Snyder, M. A., Sloan, L., Diffenbaugh, N. & Bell, J. Future climate change and upwelling in the California Current. Geophys. Res. Lett. 30, 1823 (2003).ADS 

Google Scholar 
Bakun, A., Field, D. B., Redondo-Rodriguez, A. & Weeks, S. J. Greenhouse gas, upwelling-favorable winds, and the future of coastal ocean upwelling ecosystems. Glob. Chang. Biol. 16, 1213–1228 (2010).ADS 

Google Scholar 
Bakun, A. & Nelson, C. The seasonal cycle of wind-stress curl in subtropical eastern boundary current regions. J. Phys. Oceanogr. 21, 1815–1834 (1991).ADS 

Google Scholar 
Shanks, A. L. & Eckert, G. L. Population persistence of California Current fishes and benthic crustaceans: A marine drift paradox. Ecol. Monogr. 75, 505–524 (2005).
Google Scholar 
Cushing, D. H. Plankton production and year-class strength in fish populations: An update of the match/mismatch hypothesis. Adv. Mar. Biol. 26, 249–293 (1990).
Google Scholar 
Carr, M. H. Habitat selection and recruitment of an assemblage of temperate zone reef fishes. J. Exp. Mar. Bio. Ecol. 146, 113–137 (1991).
Google Scholar 
Asch, R. G. Climate change and decadal shifts in the phenology of larval fishes in the California Current ecosystem. Proc. Natl. Acad. Sci. U. S. A. 112, E4065–E4074 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Auth, T. D., Daly, E. A., Brodeur, R. D. & Fisher, J. L. Phenological and distributional shifts in ichthyoplankton associated with recent warming in the northeast Pacific Ocean. Glob. Chang. Biol. 24, 259–272 (2018).ADS 
PubMed 

Google Scholar 
Sydeman, W. J. et al. Climate change and wind intensification in coastal upwelling ecosystems. Science 345, 77–80 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Bond, N. A., Cronin, M. F., Freeland, H. & Mantua, N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 42, 3414–3420 (2015).ADS 

Google Scholar 
Cavole, A. et al. Biological impacts of the 2013–2015 warm water anomaly in the Northeast Pacific: Winner, losers, and the future. Oceanography 29, 273–285 (2016).
Google Scholar 
Lenarz, W. H. A history of California rockfish fisheries. In Proceeding of the International Rockfish Symposium. Anchorage, Alaska, Univ. of Alaska (1987).Brodeur, R. D., Buchanan, J. C. & Emmett, R. L. Pelagic and demersal fish predators on juvenile and adult forage fishes in the northern California Current: Spatial and temporal variations. CalCOFI Rep. 55, 96–116 (2014).
Google Scholar 
Mills, K. L., Laidig, T., Ralston, S. & Sydeman, W. J. Diets of top predators indicate pelagic juvenile rockfish (Sebastes spp.) abundance in the California Current System. Fish. Oceanogr. 16, 273–283 (2007).
Google Scholar 
Santora, J. A., Schroeder, I. D., Field, J. C., Wells, B. K. & Sydeman, W. J. Spatio-temporal dynamics of ocean conditions and forage taxa reveal regional structuring of seabird-prey relationships. Ecol. Appl. 24, 1730–1747 (2014).PubMed 

Google Scholar 
McClatchie, S. et al. Food limitation of sea lion pups and the decline of forage off central and southern California. R. Soc. Open Sci. 3, 150628 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Love, B. M. S., Yoklavich, M. & Thorsteinson, L. The Rockfishes of the Northeast Pacific (Univ of California Press, 2002).
Google Scholar 
Ralston, S. & Howard, D. F. On the development of year-class stength and cohort variability in two northern California rockfishes. Fish. Bull. 93, 710–720 (1995).
Google Scholar 
Wells, B. K. et al. Untangling the relationships among climate, prey, and top predators in an ocean ecosystem. Mar. Ecol. Prog. Ser. 364, 15–29 (2008).ADS 

Google Scholar 
Zabel, R. W., Levin, P. S., Tolimieri, N. & Mantua, N. J. Interactions between climate and population density in the episodic recruitment of bocaccio, Sebastes paucispinis, a Pacific rockfish. Fish. Oceanogr. 20, 294–304 (2011).
Google Scholar 
Peterson, W. T. et al. Applied fisheries oceanography: Ecosystem indicators of ocean conditions inform fisheries management in the California Current. Oceanography 27, 80–89 (2014).
Google Scholar 
Wheeler, S. G., Anderson, T. W., Bell, T. W., Morgan, S. G. & Hobbs, J. A. Regional productivity predicts individual growth and recruitment of rockfishes in a northern California upwelling system. Limnol. Oceanogr. 62, 754–767 (2016).ADS 

Google Scholar 
Ralston, S., Sakuma, K. M. & Field, J. C. Interannual variation in pelagic juvenile rockfish (Sebastes spp.) abundance – going with the flow. Fish. Oceanogr. 22, 288–308 (2013).
Google Scholar 
Schroeder, I. D. et al. Source water variability as a driver of rockfish recruitment in the california current ecosystem: Implications for climate change and fisheries management. Can. J. Fish. Aquat. Sci. 76, 950–960 (2019).CAS 

Google Scholar 
Ottmann, D., Grorud-Colvert, K., Huntington, B. & Sponaugle, S. Interannual and regional variability in settlement of groundfishes to protected and fished nearshore waters of Oregon, USA. Mar. Ecol. Prog. Ser. 598, 131–145 (2018).ADS 

Google Scholar 
Haggarty, D. R., Lotterhos, K. E. & Shurin, J. B. Young-of-the-year recruitment does not predict the abundance of older age classes in black rockfish in Barkley Sound, British Columbia. Canada. Mar. Ecol. Prog. Ser. 574, 113–126 (2017).ADS 

Google Scholar 
Checkley, D. M. & Barth, J. A. Patterns and processes in the California Current System. Prog. Oceanogr. 83, 49–64 (2009).ADS 

Google Scholar 
Jacox, M. G. et al. Forcing of multiyear extreme ocean temperatures that impacted California Current living marine resources in 2016. Bull. Am. Meteorol. Soc. 99, S27–S33 (2018).
Google Scholar 
Thompson, A. R. et al. Indicators of pelagic forage community shifts in the California Current Large Marine Ecosystem, 1998–2016. Ecol. Indic. 105, 215–228 (2019).
Google Scholar 
Du, X. & Peterson, W. T. Phytoplankton community structure in 2011–2013 compared to the extratropical warming event of 2014–2015. Geophys. Res. Lett. 45, 1534–1540 (2018).ADS 

Google Scholar 
Peterson, W. T. et al. The pelagic ecosystem in the Northern California Current off Oregon during the 2014–2016 warm anomalies within the context of the past 20 years. J. Geophys. Res. Ocean. 122, 7267–7290 (2017).ADS 

Google Scholar 
Brodeur, R. D., Auth, T. D. & Phillips, A. J. Major shifts in pelagic micronekton and macrozooplankton community structure in an upwelling ecosystem related to an unprecedented marine heatwave. Front. Mar. Sci. 6, 1–15 (2019).
Google Scholar 
Sutherland, K. R., Sorensen, H. L., Blondheim, O. N., Brodeur, R. D. & Galloway, A. W. E. Range expansion of tropical pyrosomes in the northeast Pacific Ocean. Ecology 99, 2397–2399 (2018).PubMed 

Google Scholar 
Brodeur, R. D., Hunsicker, M. E., Hann, A. & Miller, T. W. Effects of warming ocean conditions on feeding ecology of small pelagic fishes in a coastal upwelling ecosystem: A shift to gelatinous food sources. Mar. Ecol. Prog. Ser. 617–618, 149–163 (2019).ADS 

Google Scholar 
Bosley, K. L. et al. Feeding ecology of juvenile rockfishes off Oregon and Washington based on stomach content and stable isotope analyses. Mar. Biol. 161, 2381–2393 (2014).CAS 

Google Scholar 
Reilly, C. A., Echeverria, T. W. & Ralston, S. Interannual variation and overlap in the diets of pelagic juvenile rockfish (Genus: Sebastes) off central California. Fish. Bull. 90, 505–515 (1992).
Google Scholar 
Sumida, B. Y. & Moser, H. G. Food and feeding of bocaccio (Sebastes paucispinis) and comparison with Pacific hake (Merluccius productus) larvae in the California Current. Calif. Coop. Ocean. Fish. Investig. Reports 25, 112–118 (1984).
Google Scholar 
Auth, T. D., Brodeur, R. D., Soulen, H. L., Ciannelli, L. & Peterson, W. T. The response of fish larvae to decadal changes in environmental forcing factors off the Oregon coast. Fish. Oceanogr. 20, 314–328 (2011).
Google Scholar 
Frölicher, T. L. & Laufkötter, C. Emerging risks from marine heat waves. Nat. Commun. 9, 1–4 (2018).
Google Scholar 
Campana, S. E. Year-class strength and growth rate in young Atlantic cod Gadus morhua. Mar. Ecol. Prog. Ser. 135, 21–26 (1996).ADS 

Google Scholar 
Brander, K. Effects of environmental variability on growth and recruitment in cod (Gadus morhua) using a comparative approach. Oceanol. Acta 23, 485–496 (2000).
Google Scholar 
Sponaugle, S., Grorud-Colvert, K. & Pinkard, D. Temperature-mediated variation in early life history traits and recruitment success of the coral reef fish Thalassoma bifasciatum in the Florida Keys. Mar. Ecol. Prog. Ser. 308, 1–15 (2006).ADS 

Google Scholar 
Grorud-Colvert, K. & Sponaugle, S. Variability in water temperature affects trait-mediated survival of a newly settled coral reef fish. Oecologia 165, 675–686 (2011).ADS 
PubMed 

Google Scholar 
Boehlert, G. W. & Yoklavich, M. M. Effects of temperature, ration, and fish size on the growth of juvenile black rockfish, Sebastes melanops. Environ. Biol. Fishes 8, 17–28 (1983).
Google Scholar 
Chin, B., Nakagawa, M. & Yamashita, Y. Effects of feeding and temperature on survival and growth of larval black rockfish Sebastes schlegeli in rearing conditions. Aquac. Sci. 55, 619–627 (2007).
Google Scholar 
Woodbury, D. & Ralston, S. Interannual variation in growth rates and back-calculated birthdate distributions of pelagic juvenile rockfishes (Sebastes spp.) off the central California coast. Fish. Bull. 89, 523–533 (1991).
Google Scholar 
Fennie, H., Sponaugle, S., Daly, E. & Brodeur, R. Prey tell: what quillback rockfish early life history traits reveal about their survival in encounters with juvenile coho salmon. Mar. Ecol. Prog. Ser. 650, 7–18 (2020).ADS 

Google Scholar 
Laidig, T. E., Chess, J. R. & Howard, D. F. Relationship between abundance of juvenile rockfishes (Sebastes spp.) and environmental variables documented off northern California and potential mechanisms for the covariation. Fish. Bull. 105, 39–48 (2007).
Google Scholar 
Robert, D., Castonguay, M. & Fortier, L. Early growth and recruitment in Atlantic mackerel Scomber scombrus: discriminating the effects of fast growth and selection for fast growth. Mar. Ecol. Prog. Ser. 337, 209–219 (2007).ADS 

Google Scholar 
Hare, J. A. & Cowen, R. K. Size, growth, development, and survival of the planktonic larvae of Pomatomus saltatrix (Pisces: Pomatomidae). Ecology 78, 2415–2431 (1997).
Google Scholar 
Takasuka, A., Aoki, I. & Mitani, I. Evidence of growth-selective predation on larval Japanese anchovy Engraulis japonicus in Sagami Bay. Mar. Ecol. Prog. Ser. 252, 223–238 (2003).ADS 

Google Scholar 
Anderson, J. T. A review of size dependent survival during pre-recruit stages of fishes in relation to recruitment. J. Northwest Atl. Fish. Sci. 8, 55–66 (1988).
Google Scholar 
Miller, T., Crowder, L. B., Rice, J. A. & Marschall, E. A. Larval size and recruitment mechanisms in fishes: toward a conceptual framework. Can. J. Fish. Aquat. Sci. 45, 1657–1670 (1988).
Google Scholar 
Chambers, R. C. & Leggett, W. C. Size and age at metamorphosis in marine fishes: analysis of laboratory-reared winter flounder (Pseudopieuronectes americanus) with a review of variation in other species. Can. J. Fish. Aquat. Sci. 44, 1936–1947 (1987).
Google Scholar 
Kashef, N., Sogard, S., Fisher, R. & Largier, J. Ontogeny of critical swimming speeds for larval and pelagic juvenile rockfishes (Sebastes spp., family Scorpaenidae). Mar. Ecol. Prog. Ser. 500, 231–243 (2014).ADS 

Google Scholar 
Paradis, A. R., Pepin, P. & Brown, J. A. Vulnerability of fish eggs and larvae to predation: review of the influence of the relative size of prey and predator. Can. J. Fish. Aquat. Sci. 53, 1226–1235 (1996).
Google Scholar 
Purcell, J. E. Predation on fish larvae and eggs by the hydromedusa Aequorea victoria at a herring spawning ground in British Columbia. Can. J. Fish. Aquat. Sci. 46, 1415–1427 (1989).
Google Scholar 
McLeod, I. M. & Clark, T. D. Limited capacity for faster digestion in larval coral reef fish at an elevated temperature. PLoS ONE 11, 1–13 (2016).
Google Scholar 
Takahashi, M., Checkley, D. M., Litz, M. N. C., Brodeur, R. D. & Peterson, W. T. Responses in growth rate of larval northern anchovy (Engraulis mordax) to anomalous upwelling in the northern California Current. Fish. Oceanogr. 21, 393–404 (2012).
Google Scholar 
Team, R. C. R: A language and environment for statistical computing. (2013).Brady, R. X., Alexander, M. A., Lovenduski, N. S. & Rykaczewski, R. R. Emergent anthropogenic trends in California Current upwelling. Geophys. Res. Lett. 44, 5044–5052 (2017).ADS 

Google Scholar 
Peterson, W. T. & Keister, J. E. Interannual variability in copepod community composition at a coastal station in the northern California Current: A multivariate approach. Deep Res. Part II Top. Stud. Oceanogr. 50, 2499–2517 (2003).ADS 

Google Scholar 
Ammann, A. J. SMURFs: Standard monitoring units for the recruitment of temperate reef fishes. J. Exp. Mar. Bio. Ecol. 299, 135–154 (2004).
Google Scholar 
Anderson, T. W. & Carr, M. H. BINCKE: A highly efficient net for collecting reef fishes. Environ. Biol. Fishes 51, 111–115 (1998).
Google Scholar 
Kilkenny, C., Browne, W., Cuthill, I. C., Emerson, M. & Altman, D. G. Animal research: Reporting in vivo experiments: The ARRIVE guidelines. Br. J. Pharmacol. 160, 1577–1579 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Laidig, T. E. & Adams, P. B. Methods used to identify pelagic juvenile rockfish (Genus Sebastes) occuring along the coast of central California. NOAA Technical Memorandum NMFS (1991).Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Chang. 6, 1042–1047 (2016).ADS 

Google Scholar 
Yoklavich, M. M. & Boehlert, G. W. Daily growth increments in otoliths of juvenile black rockfish, Sebastes melanops: An evaluation of autoradiography as a new method of validation. Fish. Bull. 85, 826–832 (1987).
Google Scholar 
Miller, J. A. & Shanks, A. L. Evidence for limited larval dispersal in black rockfish (Sebastes melanops): Implications for population structure and marine-reserve design. Can. J. Fish. Aquat. Sci. 61, 1723–1735 (2004).
Google Scholar 
Sponaugle, S. Daily otolith increments in the early stages of tropical fish. In Tropical Fish Otoliths: Information for Assessment, Management and Ecology (eds Green, B. et al.) 93–132 (Springer, 2009).
Google Scholar 
Laidig, T., Ralston, S. & Bence, J. R. Dynamics of growth in the early life history of shortbelly rockfish Sebastes jordani. Fish. Bull. 89, 611–621 (1991).
Google Scholar 
Thorrold, S. R. & Hare, J. A. Otolith applications in reef fish ecology. In Coral Reef Fishes: Dynamics and Diversity in a Complex Ecosystem (ed. Sale, P. F.) 243–264 (Academic Press, 2002).
Google Scholar 
Field, J. C., MacCall, A. D., Ralston, S., Love, M. S. & Miller, E. F. Bocaccionomics: The effectiveness of pre-recruit indices for assessment and management of bocaccio. Calif. Coop. Ocean. Fish. Investig. Reports 51, 77–90 (2010).
Google Scholar 
Carrascal, L. M., Galván, I. & Gordo, O. Partial least squares regression as an alternative to current regression methods used in ecology. Oikos 118, 681–690 (2009).
Google Scholar  More

Study area and camera trapping systemThe study area included the northern region of Tochigi Prefecture, Japan (Fig. 2). In Tochigi Prefecture, 54.4% of the land was covered by forest, 19.1% was covered by agricultural land in 2019 (Tochigi Prefecture 2021, https://www.pref.tochigi.lg.jp/a03/documents/keikakusho2267.pdf, accessed on Feb. 10, 2023). The northern region of Tochigi Prefecture has a relatively large area of forest. This area was the home range of the highest density of sika deer in Tochigi Prefecture in 2021. The camera trapping system consisted of 14 cameras (model no. 6210; Ltl-Acorn, Des Moines, IA, USA) that were placed in late April 2018 at 12 sites within the forest interior with two camera sets, namely ID 10–11, and ID 12–13 in neighboring areas (Fig. 2). The 12 sites spanned 84 km from west to east and 39 km from north to south (Fig. 2). The elevation of the sites ranged from 349 to 1033 m. The cameras were set horizontally at 50 cm above the ground and were operated until late November 2018. The cameras were checked every 1 or 2 months and the batteries and memory cards were replaced when necessary. Movements of the sika deer were reordered monthly from May to November. The month of April was excluded because the cameras were placed in late April. The virtual ecological model required the presence/absence of records for validation (described below), thus the number of deer captured in the photos was not considered. Finally, the visit and occupy of sika deer were recorded at 14 sites each month.Figure 2Study area, analytical units, and locations of the camera traps.Full size imageA grid size of approximately 1 km (termed “1-km mesh” hereafter) was used a as the study unit (Fig. 2). The 1-km mesh grid system is a standard Japanese unit used for several types of statistics (https://www.stat.go.jp/english/data/mesh/02.html, accessed on Feb. 10, 2023). To determine the appropriate number of 1-km mesh grids for the simulation study, a 10-km mesh grid, which is the high-order standard Japanese unit (i.e., one 10-km mesh includes 100 1-km meshes), was divided into the minimum number of areas to cover all 14 camera sites as the simulation target area to avoid arbitrary (Fig. 2). Finally, 4200 1-km mesh areas were included for the simulation (Fig. 2).Virtual ecological modelA simple cellular automaton (CA) model can predict the visit and occupy of a target species based on candidate habitats in consideration of the proximity to food resources32. The grid was set to the same size as the unit of the predicted ranges. The model yields a theoretical number of visits (described below) to each cell, which serves as an area preference of the target species. Each cell has two parameters: cell identification (ID) and movement path vector (Fig. 3a). The cell ID indicates the spatial location of the cell within the study area. The movement path involves four variables representing the four directional vectors into adjacent cells (i.e., top, left, bottom, and right) (Fig. 3b). Each variable is a probability value (i.e., 0 to 1) independent of the other three variables that indicates the probability of movement success to the adjacent cells. In this study, the probability value was based on the proximity to availability food resources.Figure 3Basic structure of the cellular automaton model. (a) Two values are associated with each cell: the cell ID “x,” a unique ID for each cell, and the movement probability “mx” indicating four directional vectors into adjacent cells. (b) Values m1, m2, m3, and m4 indicate the probability of movement along a path of the top, left, bottom, and right cells, respectively. If all movement probability values are 0, the virtual population in this cell cannot move to any other cell. If all movement probability values are 1, the virtual population in the cell can move to all adjacent cells.Full size imageA group of sika deer was used as the unit for analysis. The model simulates the capability of movement within the target area. Thus, if a virtual population visited a neighboring cell, the number of visits to the cell is increased without disappearance of the starting cell. The virtual population moves in accordance with the movement probability values.Movement probability between cellsThe term “movement probability” is defined as the probability of movement success into an adjacent cell to the top, left, bottom, or right (Fig. 3b) with four probability values:$$ {text{Movement probability x}} = {text{mx}};({text{m}}1,;{text{m}}2,;{text{m}}3,;{text{m}}4), $$
(1)
where m1, m2, m3, and m4 indicate the probability of movement success into the top, left, bottom, and right cells, respectively (Fig. 3b). Since these values are independent of one another, the maximum and minimum sums of m1, m2, m3, and m4 are theoretically 4 and 0, respectively. If all probability of movement success values are 0, the sika deer population in this cell cannot move to any other cell. Moreover, if all probability of movement success values are 1, the population in the cell can move to all adjacent cells.The amount of food resources of deer was acquired from remote sensing measurements35,36. Thus, two variables were used to represent food resource availability: the kernel normalized difference vegetation index (kNDVI)41 and landscape structure (Supplementary Fig. 1).The kNDVI uses remote sensing measurements to assess the components of green vegetation41. As compared to the ordinal NDVI, which is the most widely used index of the condition of vegetation on terrestrial surfaces, the kNDVI has greater resistance to saturation, bias, and complex phenological cycles, and exhibits enhanced robustness to noise and stability across spatial and temporal scales41. The kNDVI appropriately represents the condition of vegetation to reflect the food resource availability for sika deer. The kNDVI was analyzed from the atmospherically corrected surface reflectance observed with the Landsat 8 Operational Land Imager and Thermal Infrared Sensor instruments at approximately 16-day intervals with a spatial resolution of 30 m (data collected in 2018). The mean kNDVI was calculated monthly for each 1-km mesh within the study area. The probability values (m1, m2, m3, and m4) were defined as the proximity to available food resources in a destination cell divided by the maximum value of the target area as relative values throughout the study area. These values reflect the spatial positions of the available food resources in the study area. If the food resources are continuously available, then the sika deer population tend to visit and occupy linearly.The landscape structure is defined as a mixture of forests and grasslands because previous studies suggest that the forest edge has high availability of food resources for sika deer37,38,42,43. The dataset was generated from a current vegetation map that classified the dominant plant species provided by the Biodiversity Center of Japan (Ministry of the Environment, https://www.biodic.go.jp/index_e.html, accessed on Feb. 10, 2023). The types of vegetation of the forests and grasslands were retrieved from the literature, then the original vegetation classes were re-classified44 and overlayed on the 1-km mesh map. In this study, agricultural land types were classified as grassland. For a mesh with both forests and grasslands, the probability of movement was assigned a value of 1, while a mesh with either a forest or grassland was assigned a value of 0.5, because to treat these 2 components fairly. Every mesh of the study area included either a forest or grassland.Movement simulationFirst, simulations were conducted using two independent variables: kNDVI and landscape structure. Each simulation was initiated from one cell with the month, which is referred to as a “trial.” One step is defined as one day, thus the trial conducted in May consisted of 31 steps. A previous study reported that sika deer can travel about 50 km every 2 weeks34. Thus, one step (movement of 1 km) in one day was considered a reasonable distance. Each trial was repeated for all cells i.e., all cells was used as the starting cell of “trial”. The sum of all trials is termed a “run.” Thus, each “run” consisted of n trials, where n is the number of cells in the CA field. In this study, there were 4200 cells. At each step, each attempt to visit a neighboring cell (top, left, bottom, and right) was based on movement probabilities. For each successful movement, the presence/absence value assigned to the cell was increased from 0 to 1, i.e., change from absence to presence. The next step was then initiated from any newly visited cell and the previously visited cells. Cells with high values indicated the possibility of visitation by a virtual population from several other cells. The assigned value was used as a metric of the preference of the visited cell. In this study, 100 runs were conducted each month from May to November.Second, simulations were conducted using a combination of movement-related variables with two types of combination models: kNDVI AND landscape structure and kNDVI OR landscape structure. With both the logical AND and OR models, each step has two processes: probability approach with the kNDVI and landscape structure. With the AND model, if the virtual population passes the probability of the kNDVI to move to a neighboring cell, then the probability of movement to a neighboring cell is based on the landscape structure. In the logical AND model, we used kNDVI first because that could reflect a seasonal change in the availability of food resources. With the OR model, if the virtual population passes the probability of the kNDVI, or passes that of the landscape structure, the virtual population can move to any neighboring cell.Additionally, equivalence model simulation was conducted with all probability values (m1, m2, m3, and m4) set to 0.5.Validation of the simulation results using the camera trap dataThe results of the CA model simulation were validated by the presence/absence of the monthly records of sika deer collected with the cameras. The occurrence of a visit to a camera was determined using a generalized linear model with a binomial distribution (log link) and model selection based on Akaike’s information criterion (AIC). The explanatory variable was the theoretical number of simulated visits to a 1-km cell with a camera trap. If the AIC value of the model was  > 2 points lower than that of the null model45 (i.e., with no explanatory variable), the run was considered “correct”. The data from the kNDVI, landscape structure, AND/OR, and null/equivalence models were used. The number of “correct” runs of every 100 runs with each model was calculated. Therefore, all values could theoretically be 100.Then, the predictive ability of the model was evaluated using the results considered as “correct” with the AIC. The AIC values of all runs were compared, where one simulation set used four variables. If the four models (i.e., kNDVI, landscape, AND, and OR models) were all “correct” in one run, the AIC values were compared and the lowest AIC value of the model was recorded. Notably, differences among the AIC values were not considered because the effectiveness of the model was already evaluated in the first validation procedure. Calculations for all months were conducted. Therefore, the maximum value among the four models was 100, assuming that the run was “correct” with the lowest AIC.Finally, a map was generated of the theoretical number of visits by sika deer in each month based on the best performance among the four simulations. The map included the average number of theoretical visits over 100 runs. The results considered incorrect were not excluded because in real-world applications, simulated results are not evaluated.All statistical analyses were performed using R software (ver. 4.0.2; https://www.r-project.org/, accessed on Feb. 10, 2023). More

Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 1–8 (2015). 2015 61.Article 

Google Scholar 
Smart, S. M. et al. Biotic homogenization and changes in species diversity across human-modified ecosystems. Proc. R. Soc. B Biol. Sci. 273, 2659–2665 (2006).Article 

Google Scholar 
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).Article 
CAS 
PubMed 

Google Scholar 
Pigot, A. L., Jetz, W., Sheard, C. & Tobias, J. A. The macroecological dynamics of species coexistence in birds. Nat. Ecol. Evol. 2, 1112–1119 (2018). 2018 27.Article 
PubMed 

Google Scholar 
Reidsma, P., Tekelenburg, T., Van Den Berg, M. & Alkemade, R. Impacts of land-use change on biodiversity: An assessment of agricultural biodiversity in the European Union. Agric. Ecosyst. Environ. 114, 86–102 (2006).Article 

Google Scholar 
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 291–288 (2016).Article 
ADS 

Google Scholar 
Meier, E. S., Lüscher, G. & Knop, E. Disentangling direct and indirect drivers of farmland biodiversity at landscape scale. Ecol. Lett. 00, 1–13 (2022).
Google Scholar 
Martínez-Núñez, C. et al. Temporal and spatial heterogeneity of semi-natural habitat, but not crop diversity, is correlated with landscape pollinator richness. J. Appl. Ecol. 59, 1258–1267 (2022).Article 

Google Scholar 
Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188 (2003).Article 

Google Scholar 
Sparrow, A. D. A heterogeneity of heterogeneities. Trends Ecol. Evol. 14, 422–423 (1999).Article 
CAS 
PubMed 

Google Scholar 
Tscharntke, T., Grass, I., Wanger, T. C., Westphal, C. & Batáry, P. Spatiotemporal land-use diversification for biodiversity. Trends Ecol. Evol. 37, 734–735 (2022).Article 
PubMed 

Google Scholar 
Quintero, C., Morales, C. L. & Aizen, M. A. Effects of anthropogenic habitat disturbance on local pollinator diversity and species turnover across a precipitation gradient. Biodivers. Conserv. 19, 257–274 (2010).Article 

Google Scholar 
Allen, D. C. et al. Long-term effects of land-use change on bird communities depend on spatial scale and land-use type. Ecosphere 10, e02952 (2019).Article 

Google Scholar 
MacArthur, R. H. Patterns of species diversity. Biol. Rev. 40, 510–533 (1965).Article 

Google Scholar 
Kinlock, N. L. et al. Explaining global variation in the latitudinal diversity gradient: Meta-analysis confirms known patterns and uncovers new ones. Glob. Ecol. Biogeogr. 27, 125–141 (2018).Article 

Google Scholar 
Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).Article 
PubMed 

Google Scholar 
Jarzyna, M. A., Quintero, I. & Jetz, W. Global functional and phylogenetic structure of avian assemblages across elevation and latitude. Ecol. Lett. 24, 196–207 (2021).Article 
PubMed 

Google Scholar 
Guo, Q. et al. Global variation in elevational diversity patterns. Sci. Rep. 3, 1–7 (2013). 2013 31.Article 
CAS 

Google Scholar 
McCain, C. M. Elevational gradients in diversity of small mammals. Ecology 86, 366–372 (2005).Article 

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

Google Scholar 
Gillman, L. N. et al. Latitude, productivity and species richness. Glob. Ecol. Biogeogr. 24, 107–117 (2015).Article 

Google Scholar 
Cusens, J., Wright, S. D., McBride, P. D. & Gillman, L. N. What is the form of the productivity–animal-species-richness relationship? A critical review and meta-analysis. Ecology 93, 2241–2252 (2012).Article 
PubMed 

Google Scholar 
Currie, D. J. et al. Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecol. Lett. 7, 1121–1134 (2004).Article 

Google Scholar 
Gaston, K. J. Global patterns in biodiversity. Nature 405, 220–227 (2000).Article 
CAS 
PubMed 

Google Scholar 
Burrell, A. L., Evans, J. P. & De Kauwe, M. G. Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nat. Commun. 11, 1–11 (2020). 2020 111.Article 

Google Scholar 
Simkin, R. D., Seto, K. C., McDonald, R. I. & Jetz, W. Biodiversity impacts and conservation implications of urban land expansion projected to 2050. Proc. Natl Acad. Sci. U. S. A. 119, e2117297119 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hughes, E. C. et al. Global biogeographic patterns of avian morphological diversity. Ecol. Lett. 25, 598–610 (2022).Article 
PubMed 

Google Scholar 
Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).Article 

Google Scholar 
McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).Article 
PubMed 

Google Scholar 
Brun, P. et al. The productivity-biodiversity relationship varies across diversity dimensions. Nat. Commun. 10, 1–11 (2019).Article 

Google Scholar 
Santillán, V. et al. Different responses of taxonomic and functional bird diversity to forest fragmentation across an elevational gradient. Oecologia 189, 863–873 (2018).Article 
ADS 
PubMed 

Google Scholar 
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

Google Scholar 
Finke, D. L. & Snyder, W. E. Niche partitioning increases resource exploitation by diverse communities. Science 321, 1488–1490 (2008).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Stein, A., Gerstner, K. & Kreft, H. Environmental heterogeneity as a universal driver of species richness across taxa, biomes, and spatial scales. Ecol. Lett. 17, 866–880 (2014).Article 
PubMed 

Google Scholar 
Chisholm, R. A. et al. Species–area relationships and biodiversity loss in fragmented landscapes. Ecol. Lett. 21, 804–813 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Grinnell, J. The Niche-relationships of the California Thrasher. Auk 34, 427–433 (1917).Article 

Google Scholar 
Soberón, J. Grinnellian and Eltonian niches and geographic distributions of species. Ecol. Lett. 10, 1115–1123 (2007).Article 
PubMed 

Google Scholar 
Kraft, N. J. B. et al. Community assembly, coexistence, and the environmental filtering metaphor. Funct. Ecol. 29, 592–599 (2015).Article 

Google Scholar 
Tarifa, R. et al. Agricultural intensification erodes taxonomic and functional diversity in Mediterranean olive groves by filtering out rare species. J. Appl. Ecol. 58, 2266–2276 (2021).Article 

Google Scholar 
Noble, I. R. & Slatyer, R. O. The use of vital attributes to predict successional changes in plant communities subject to recurrent disturbances. Vegetatio 43, 5–21 (1980).Article 

Google Scholar 
Morelli, F. et al. Evidence of evolutionary homogenization of bird communities in urban environments across Europe. Glob. Ecol. Biogeogr. 25, 1284–1293 (2016).Article 

Google Scholar 
Veech, J. A. & Crist, T. O. Habitat and climate heterogeneity maintain beta-diversity of birds among landscapes within ecoregions. Glob. Ecol. Biogeogr. 16, 650–656 (2007).Article 

Google Scholar 
García-Navas, V. et al. Partitioning beta diversity to untangle mechanisms underlying the assembly of bird communities in Mediterranean olive groves. Divers. Distrib. 28, 112–127 (2022).Article 

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

Google Scholar 
Slatyer, R. A., Hirst, M. & Sexton, J. P. Niche breadth predicts geographical range size: a general ecological pattern. Ecol. Lett. 16, 1104–1114 (2013).Article 
PubMed 

Google Scholar 
Winkler, K., Fuchs, R., Rounsevell, M. & Herold, M. Global land use changes are four times greater than previously estimated. Nat. Commun. 12, 1–10 (2021).Article 

Google Scholar 
Reynolds, J. F. et al. Global desertification: building a science for dryland development. Science. 316, 847–851 (2007).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Meyfroidt, P. & Lambin, E. F. Global forest transition: prospects for an end to deforestation. 36, 343–371 https://doi.org/10.1146/annurev-environ-090710-143732 (2011).McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).Article 

Google Scholar 
Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. A. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).Article 
PubMed 

Google Scholar 
Tobias, J. A. et al. AVONET: morphological, ecological and geographical data for all birds. Ecol. Lett. 25, 581–597 (2022).Article 
PubMed 

Google Scholar 
Dray, S. & Dufour, A. B. The ade4 Package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

Google Scholar 
Gruson, H. & Grenié, M. Fundiversity: Easy computation of functional diversity Indices. https://doi.org/10.5281/ZENODO.7360757 (2022).Mammola, S., Carmona, C. P., Guillerme, T. & Cardoso, P. Concepts and applications in functional diversity. Funct. Ecol. 35, 1869–1885 (2021).Article 
CAS 

Google Scholar 
Kohli, B. A. & Jarzyna, M. A. Pitfalls of ignoring trait resolution when drawing conclusions about ecological processes. Glob. Ecol. Biogeogr. 30, 1139–1152 (2021).Article 

Google Scholar 
Buchhorn, M. et al. Copernicus global land cover layers—Collection 2. Remote Sens. 12, 1044 (2020). 2020, Vol. 12, Page 1044.Article 
ADS 

Google Scholar 
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 
ADS 

Google Scholar 
Mu, H. et al. A global record of annual terrestrial Human Footprint dataset from 2000 to 2018. Sci. Data 9, 1–9 (2022). 2022 91.Article 

Google Scholar 
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Article 
PubMed 

Google Scholar 
Stewart, P. S. et al. Global impacts of climate change on avian functional diversity. Ecol. Lett. 25, 673–685 (2022).Article 
PubMed 

Google Scholar 
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 73, 3–36 (2011).Article 
MathSciNet 
MATH 

Google Scholar 
Wickham, H. ggplot2. (Springer International Publishing, 2016). https://doi.org/10.1007/978-3-319-24277-4.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Breheny, P. & Burchett, W. Visualization of regression models using Visreg. R. J. 9, 56–71 (2017).Article 

Google Scholar 
Met Office. Cartopy: a cartographic python library with matplotlib support. (2013).Martinez-Nuñez, C., Martinez-Prentice, R. & García-Navas, V. Dataset: Environmental as well as bird taxonomic and functional richness data for ca. 18,000 grid cells in the world. Figshare https://doi.org/10.6084/m9.figshare.21747257.v1 (2023). More