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    The European Green Deal misses Europe’s subterranean biodiversity hotspots

    European Commission. Communication From The Commission To The European Parliament, The European Council, The Council, The European Economic And Social Committee And The Committee Of The Regions: The European Green Deal (European Commission, 2019).European Commission. Communication From The Commission To The European Parliament, The Council, The European Economic And Social Committee And The Committee Of The Regions: EU Biodiversity Strategy for 2030 (European Commission, 2020).Fan, P. et al. Proc. Natl Acad. Sci. USA 119, e2108038119 (2022).CAS 
    Article 

    Google Scholar 
    Schwarz, U. Hydropower Projects on the Balkan Rivers – Update. RiverWatch & EuroNatur; https://balkanrivers.net/sites/default/files/Hydropower%20dams%20in%20the%20Balkan230915_FINAL_EdUS.pdf (2015).Knez, S., Štrbac, S. & Podbregar, I. Energy Sustain. Soc. 12, 1 (2022).Article 

    Google Scholar 
    Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. & Kent, J. Nature 403, 853–858 (2000).CAS 
    Article 

    Google Scholar 
    Zagmajster, M. et al. Glob. Ecol. Biogeogr. 23, 1135–1145 (2014).Article 

    Google Scholar 
    Borko, Š., Trontelj, P., Seehausen, O., Moškrič, A. & Fišer, C. Nat. Commun. 12, 3688 (2021).CAS 
    Article 

    Google Scholar 
    Bregović, P., Fišer, C. & Zagmajster, M. Ecol. Evol. 9, 11606–11618 (2019).Article 

    Google Scholar 
    Bilandžija, H., Morton, B., Podnar, M. & Cetković, H. Front. Zool. 10, 5 (2013).Article 

    Google Scholar 
    Griebler, C. & Avramov, M. Freshw. Sci. 34, 355–367 (2015).Article 

    Google Scholar 
    Mammola, S. et al. Bioscience 69, 641–650 (2019).Article 

    Google Scholar 
    Jaćimović, N. et al. Vodoprivreda 47, 29–40 (2015).
    Google Scholar 
    Borko, Š., Altermatt, F., Zagmajster, M. & Fišer, C. Divers. Distrib. https://doi.org/10.1111/ddi.13500 (2022).European Commission. Evaluation of the EU Biodiversity Strategy to 2020 (European Commission, 2020); https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/1832-Evaluation-of-the-EU-Biodiversity-Strategy-to-2020_en More

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    The gut microbiota affects the social network of honeybees

    Wilson, E. O. Sociobiology: The New Synthesis (Harvard Univ. Press, 1975).Diamond, J. M. & Ordunio, D. Guns, Germs, and Steel (Books on Tape, 1999).Couzin, I. D. et al. Self-organization and collective behavior in vertebrates. Adv. Study Behav. 32, 1–75 (2003).
    Google Scholar 
    Keller, L. Adaptation and the genetics of social behaviour. Philos. Trans. R. Soc. Lond. B 364, 3209–3216 (2009).
    Google Scholar 
    Kay, T., Keller, L. & Lehmann, L. The evolution of altruism and the serial rediscovery of the role of relatedness. Proc. Natl Acad. Sci. USA 117, 28894–28898 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13, 701–712 (2012).CAS 
    PubMed 

    Google Scholar 
    Johnson, K. V. A. & Foster, K. R. Why does the microbiome affect behaviour? Nat. Rev. Microbiol. 16, 647–655 (2018).CAS 
    PubMed 

    Google Scholar 
    Sherwin, E., Bordenstein, S. R., Quinn, J. L., Dinan, T. G. & Cryan, J. F. Microbiota and the social brain. Science 366, eaar2016 (2019).CAS 
    PubMed 

    Google Scholar 
    Desbonnet, L., Clarke, G., Shanahan, F., Dinan, T. G. & Cryan, J. F. Microbiota is essential for social development in the mouse. Mol. Psychiatry 19, 146–148 (2014).CAS 
    PubMed 

    Google Scholar 
    Sharon, G. et al. Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice. Cell 177, 1600–1618 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, M. et al. A quasi-paired cohort strategy reveals the impaired detoxifying function of microbes in the gut of autistic children. Sci. Adv. 6, eaba3760 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wu, W.-L. et al. Microbiota regulate social behaviour via stress response neurons in the brain. Nature 595, 409–414 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vuong, H. E., Yano, J. M., Fung, T. C. & Hsiao, E. Y. The microbiome and host behavior. Annu. Rev. Neurosci. 40, 21–49 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Douglas, A. E. Simple animal models for microbiome research. Nat. Rev. Microbiol. 17, 764–775 (2019).CAS 
    PubMed 

    Google Scholar 
    Schretter, C. E. Links between the gut microbiota, metabolism, and host behavior. Gut Microbes 11, 245–248 (2020).PubMed 

    Google Scholar 
    Liberti, J. & Engel, P. The gut microbiota–brain axis of insects. Curr. Opin. Insect Sci. 39, 6–13 (2020).PubMed 

    Google Scholar 
    O’Donnell, M. P., Fox, B. W., Chao, P.-H., Schroeder, F. C. & Sengupta, P. A neurotransmitter produced by gut bacteria modulates host sensory behaviour. Nature 583, 415–420 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Wilson, E. O. The Insect Societies (Harvard Univ. Press, 1971).Hölldobler, B. & Wilson, E. O. The Ants (Harvard Univ. Press, 1990).Teseo, S. et al. The scent of symbiosis: gut bacteria may affect social interactions in leaf-cutting ants. Anim. Behav. 150, 239–254 (2019).
    Google Scholar 
    Vernier, C. L. et al. The gut microbiome defines social group membership in honey bee colonies. Sci. Adv. 6, eabd3431 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, L. et al. Gut microbiome drives individual memory variation in bumblebees. Nat. Commun. 12, 6588 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Choi, S. H. et al. Individual variations lead to universal and cross-species patterns of social behavior. Proc. Natl Acad. Sci. USA 117, 31754–31759 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Geffre, A. C. et al. Honey bee virus causes context-dependent changes in host social behavior. Proc. Natl Acad. Sci. USA 117, 10406–10413 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bonilla-Rosso, G. & Engel, P. Functional roles and metabolic niches in the honey bee gut microbiota. Curr. Opin. Microbiol. 43, 69–76 (2018).CAS 
    PubMed 

    Google Scholar 
    Raymann, K. & Moran, N. A. The role of the gut microbiome in health and disease of adult honey bee workers. Curr. Opin. Insect Sci. 26, 97–104 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Zheng, H., Powell, J. E., Steele, M. I., Dietrich, C. & Moran, N. A. Honeybee gut microbiota promotes host weight gain via bacterial metabolism and hormonal signaling. Proc. Natl Acad. Sci. USA 114, 4775–4780 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kešnerová, L. et al. Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol. 15, e2003467 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Kešnerová, L. et al. Gut microbiota structure differs between honeybees in winter and summer. ISME J. 14, 801–814 (2020).PubMed 

    Google Scholar 
    Mersch, D. P., Crespi, A. & Keller, L. Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science 340, 1090–1093 (2013).CAS 
    PubMed 

    Google Scholar 
    Stroeymeyt, N. et al. Social network plasticity decreases disease transmission in a eusocial insect. Science 362, 941–945 (2018).CAS 
    PubMed 

    Google Scholar 
    Kao, A. B. & Couzin, I. D. Modular structure within groups causes information loss but can improve decision accuracy. Philos. Trans. R. Soc. Lond. B 374, 20180378 (2019).
    Google Scholar 
    de Groot, A. P. Protein and amino acid requirements of the honeybee (Apis mellifica L.). Physiol. Comp. Oecol. 3, 197–285 (1953).
    Google Scholar 
    Billard, J.-M. d-Amino acids in brain neurotransmission and synaptic plasticity. Amino Acids 43, 1851–1860 (2012).CAS 
    PubMed 

    Google Scholar 
    Marcaggi, P. & Attwell, D. Role of glial amino acid transporters in synaptic transmission and brain energetics. Glia 47, 217–225 (2004).PubMed 

    Google Scholar 
    Gage, S. L., Calle, S., Jacobson, N., Carroll, M. & DeGrandi-Hoffman, G. Pollen alters amino acid levels in the honey bee brain and this relationship changes with age and parasitic stress. Front. Neurosci. 14, 231 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Kawase, T. et al. Gut microbiota of mice putatively modifies amino acid metabolism in the host brain. Br. J. Nutr. 117, 775–783 (2017).CAS 
    PubMed 

    Google Scholar 
    Socha, E., Koba, M. & Koslinski, P. Amino acid profiling as a method of discovering biomarkers for diagnosis of neurodegenerative diseases. Amino Acids 51, 367–371 (2019).CAS 
    PubMed 

    Google Scholar 
    Tarlungeanu, D. C. et al. Impaired amino acid transport at the blood brain barrier is a cause of autism spectrum disorder. Cell 167, 1481–1494 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maynard, T. M. & Manzini, M. C. Balancing act: maintaining amino acid levels in the autistic brain. Neuron 93, 476–479 (2017).CAS 
    PubMed 

    Google Scholar 
    Kurochkin, I. et al. Metabolome signature of autism in the human prefrontal cortex. Commun. Biol. 2, 234 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    van der Velpen, V. et al. Systemic and central nervous system metabolic alterations in Alzheimer’s disease. Alzheimer’s Res. Ther. 11, 93 (2019).
    Google Scholar 
    Aldana, B. I. et al. Glutamate–glutamine homeostasis is perturbed in neurons and astrocytes derived from patient iPSC models of frontotemporal dementia. Mol. Brain 13, 125 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Galizia, C. G., Eisenhardt, D. & Giurfa M. (eds) Honeybee Neurobiology and Behavior: A Tribute to Randolf Menzel (Springer Science & Business Media, 2011).Menzel, R. The honeybee as a model for understanding the basis of cognition. Nat. Rev. Neurosci. 13, 758–768 (2012).CAS 
    PubMed 

    Google Scholar 
    Ellegaard, K. M. & Engel, P. Genomic diversity landscape of the honey bee gut microbiota. Nat. Commun. 10, 446 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bruno, F., Angilica, A., Cosco, F., Luchi, M. L. & Muzzupappa, M. Mixed prototyping environment with different video tracking techniques. In IMProVe 2011 International Conference on Innovative Methods in Product Design (eds Concheri, G. et al.) 105–113 (Libreria Internazionale Cortina Padova, 2011).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Anderson, K. E., Rodrigues, P. A. P., Mott, B. M., Maes, P. & Corby-Harris, V. Ecological succession in the honey bee gut: shift in Lactobacillus strain dominance during early adult development. Microb. Ecol. 71, 1008–1019 (2016).CAS 
    PubMed 

    Google Scholar 
    Almasri, H., Liberti, J., Brunet, J. L., Engel, P. & Belzunces, L. P. Mild chronic exposure to pesticides alters physiological markers of honey bee health without perturbing the core gut microbiota. Sci. Rep. 12, 4281 (2022).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29, e45 (2001).Gallup, J. M. in PCR Troubleshooting and Optimization: The Essential Guide (eds Kennedy, S. & Oswald, N.) 23–65 (Caister Academic Press, 2011).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
    Google Scholar 
    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    PubMed 
    PubMed Central 

    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).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 1–14 (2018).
    Google Scholar 
    Patassini, S. et al. Identification of elevated urea as a severe, ubiquitous metabolic defect in the brain of patients with Huntington’s disease. Biochem. Biophys. Res. Commun. 468, 161–166 (2015).CAS 
    PubMed 

    Google Scholar 
    Gonzalez-Riano, C., Garcia, A. & Barbas, C. Metabolomics studies in brain tissue: a review. J. Pharm. Biomed. Anal. 130, 141–168 (2016).CAS 
    PubMed 

    Google Scholar 
    Belle, J. E. L., Harris, N. G., Williams, S. R. & Bhakoo, K. K. A comparison of cell and tissue extraction techniques using high-resolution 1H-NMR spectroscopy. NMR Biomed. 15, 37–44 (2002).PubMed 

    Google Scholar 
    Wanichthanarak, K., Jeamsripong, S., Pornputtapong, N. & Khoomrung, S. Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data. Comput. Struct. Biotechnol. J. 17, 611–618 (2019).PubMed 
    PubMed Central 

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

    Google Scholar 
    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS 
    PubMed 

    Google Scholar 
    Wallberg, A. et al. A hybrid de novo genome assembly of the honeybee, Apis mellifera, with chromosome-length scaffolds. BMC Genomics 20, 275 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).CAS 
    PubMed 

    Google Scholar 
    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).CAS 
    PubMed 

    Google Scholar 
    Reijnders, M. J. & Waterhouse, R. M. Summary visualisations of gene ontology terms with GO-Figure! Front. Bioinform. 1, 638255 (2021).
    Google Scholar  More

  • in

    Tropical tree species differ in damage and mortality from lightning

    Dale, V. H. et al. Climate change and forest disturbances. BioScience 51, 723 (2001).Article 

    Google Scholar 
    McDowell, N. et al. Drivers and mechanisms of tree mortality in moist tropical forests. New Phytol. 219, 851–869 (2018).Article 

    Google Scholar 
    Esquivel-Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25, 39–56 (2019).Article 

    Google Scholar 
    McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).CAS 
    Article 

    Google Scholar 
    Gora, E. M. & Esquivel-Muelbert, A. Implications of size-dependent tree mortality for tropical forest carbon dynamics. Nat. Plants 7, 384–391 (2021).CAS 
    Article 

    Google Scholar 
    Yanoviak, S. P. et al. Lightning is a major cause of large tree mortality in a lowland neotropical forest. New Phytol. 225, 1936–1944 (2020).Article 

    Google Scholar 
    Gora, E. M. et al. A mechanistic and empirically supported lightning risk model for forest trees. J. Ecol. 108, 1956–1966 (2020).Article 

    Google Scholar 
    Gora, E. M., Burchfield, J. C., Muller‐Landau, H. C., Bitzer, P. M. & Yanoviak, S. P. Pantropical geography of lightning‐caused disturbance and its implications for tropical forests. Glob. Change Biol. 26, 5017–5026 (2020).Article 

    Google Scholar 
    Harel, M. & Price, C. Thunderstorm trends over Africa. J. Clim. 33, 2741–2755 (2020).Article 

    Google Scholar 
    Maxwell, H. Observations on trees, as conductors of lightning. Mem. Am. Acad. Arts Sci. 2, 143 (1793).
    Google Scholar 
    Covert, R. N. Why an oak is often struck by lightning: a method of protecting trees against lightning. Mon. Weather Rev. 52, 492–493 (1924).Article 

    Google Scholar 
    Taylor, A. R. Lightning damage to forest trees in Montana. Weatherwise 17, 61–65 (1964).Article 

    Google Scholar 
    Furtado, C. X. Lightning injuries to trees. J. Malays. Branch R. Asiat. Soc. 13, 157–162 (1935).
    Google Scholar 
    Magnusson, W. E., Lima, A. P. & De Lima, O. Group lightning mortality of trees in a neotropical forest. J. Trop. Ecol. 12, 899–903 (1996).Article 

    Google Scholar 
    Yanoviak, S. P., Gora, E. M., Burchfield, J. M., Bitzer, P. M. & Detto, M. Quantification and identification of lightning damage in tropical forests. Ecol. Evol. 7, 5111–5122 (2017).Article 

    Google Scholar 
    Makela, J., Karvinen, E., Porjo, N., Makela, A. & Tuomi, T. Attachment of natural lightning flashes to trees: preliminary statistical characteristics. J. Light. Res. 1, 9–21 (2009).Article 

    Google Scholar 
    Yanoviak, S. P. in Treetops at Risk (eds Lowman, M. et al.) 147–153 (Springer, 2013).Gora, E. M., Bitzer, P. M., Burchfield, J. C., Schnitzer, S. A. & Yanoviak, S. P. Effects of lightning on trees: a predictive model based on in situ electrical resistivity. Ecol. Evol. 7, 8523–8534 (2017).Article 

    Google Scholar 
    Orville, R. E. Photograph of a close lightning flash. Science 162, 666–667 (1968).CAS 
    Article 

    Google Scholar 
    Gora, E. M. & Yanoviak, S. P. Electrical properties of temperate forest trees: a review and quantitative comparison with vines. Can. J. For. Res. 45, 236–245 (2015).Article 

    Google Scholar 
    Hietz, P., Rosner, S., Hietz-Seifert, U. & Wright, S. J. Wood traits related to size and life history of trees in a Panamanian rainforest. New Phytol. 213, 170–180 (2017).CAS 
    Article 

    Google Scholar 
    Clarke, P. J. et al. Resprouting as a key functional trait: how buds, protection and resources drive persistence after fire. New Phytol. 197, 19–35 (2013).CAS 
    Article 

    Google Scholar 
    Kozlowski, T. T. & Pallardy, S. G. Physiology of Woody Plants (Academic Press, 1997).Bruijning, M. et al. Surviving in a cosexual world: a cost–benefit analysis of dioecy in tropical trees. Am. Nat. 189, 297–314 (2017).Article 

    Google Scholar 
    Visser, M. D. et al. Strict mast fruiting for a tropical dipterocarp tree: a demographic cost–benefit analysis of delayed reproduction and seed predation. J. Ecol. 99, 1033–1044 (2011).Article 

    Google Scholar 
    Charles, A. E. Coconut lightning strike. Papua New Guin. Agric. J. 12, 192–195 (1960).
    Google Scholar 
    Sharples, A. Lightning storms and their significance in relation to diseases of Cocos nucifera and Hevea brasilensis. Ann. Appl. Biol. 20, 1–22 (1933).Article 

    Google Scholar 
    Wright, S. J. et al. Functional traits and the growth–mortality trade‐off in tropical trees. Ecology 91, 3664–3674 (2010).Article 

    Google Scholar 
    Camac, J. S. et al. Partitioning mortality into growth-dependent and growth-independent hazards across 203 tropical tree species. Proc. Natl Acad. Sci. USA 115, 12459–12464 (2018).CAS 
    Article 

    Google Scholar 
    Poorter, L. Leaf traits show different relationships with shade tolerance in moist versus dry tropical forests. New Phytol. 181, 890–900 (2009).Article 

    Google Scholar 
    Gora, E. M., Bitzer, P. M., Burchfield, J. C., Gutiérrez, C. & Yanoviak, S. P. The contributions of lightning to biomass turnover, gap formation, and plant mortality in a tropical forest. Ecology 102, e03541 (2021).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Brooks, M. E. et al. glmmTMB: Generalized linear mixed models using template model builder. R package version 1.1.3 (2019).Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002).
    Google Scholar 
    Condit, R. et al. Complete data from the Barro Colorado 50-ha plot: 423617 trees, 35 years, 2019 version. Dryad https://doi.org/10.15146/5xcp-0d46 (2019).Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).Article 

    Google Scholar 
    Zanne, A. E. et al. Data from: Towards a worldwide wood economics spectrum. Dryad https://doi.org/10.5061/dryad.234 (2009).Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Change Biol. 26, 70 (2020).Article 

    Google Scholar 
    Gora, E. M. et al. Data from: A mechanistic and empirically-supported lightning risk model for forest trees. Dryad https://doi.org/10.5061/dryad.c59zw3r48 (2020). More

  • in

    Spatial point patterns generation on remote sensing data using convolutional neural networks with further statistical analysis

    Appel, M., Lahn, F., Buytaert, W. & Pebesma, E. Open and scalable analytics of large earth observation datasets: From scenes to multidimensional arrays using SCIDB and GDAL. ISPRS J. Photogramm. Remote Sens. 138, 47–56 (2018).ADS 
    Article 

    Google Scholar 
    Audebert, N., Saux, B. L. & Lefvre, S. Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks. ISPRS J. Photogramm. Remote Sens. 140, 20–32 (2018).ADS 
    Article 

    Google Scholar 
    Ball J. E., Anderson D. T., & Chan C. S. Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community. J. Appl. Remote Sens. https://doi.org/10.1117/1.JRS.11.042609 (2017).Proceedings of the Royal Society B: Biological Sciences. Vol. 282. 20141657 (2015).Velázquez, E., Paine, C. T., May, F. & Wiegand, T. Linking trait similarity to interspecific spatial associations in a moist tropical forest. J. Veg. Sci. 26, 1068–1079 (2015).Article 

    Google Scholar 
    Ben-Said, M. Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review. Ecol. Process. 10, 1–23 (2021).Article 

    Google Scholar 
    Watt, A. S. Pattern and process in the plant community. J. Ecol. 35, 1–22 (1947).Article 

    Google Scholar 
    Pielou, E.C. Mathematical Ecology; Number 574.50151 P613 1977. (Wiley, 1977).Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).Article 

    Google Scholar 
    Brown, C., Law, R., Illian, J. B. & Burslem, D. F. Linking ecological processes with spatial and non-spatial patterns in plant communities. J. Ecol. 99, 1402–1414 (2011).Article 

    Google Scholar 
    Detto, M. & Muller-Landau, H. C. Fitting ecological process models to spatial patterns using scalewise variances and moment equations. Am. Nat. 181, E68–E82 (2013).Article 

    Google Scholar 
    May, F., Huth, A., & Wiegand, T. Moving beyond abundance distributions: neutral theory and spatial patterns in a tropical forest. Proceedings. Biological sciences 282(1802), 20141657. https://doi.org/10.1098/rspb.2014.1657 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kerr, J. T. & Ostrovsky, M. From space to species: Ecological applications for remote sensing. Trends Ecol. Evol. 18, 299–305 (2003).Article 

    Google Scholar 
    Gillespie, T. W., Foody, G. M., Rocchini, D., Giorgi, A. P. & Saatchi, S. Measuring and modelling biodiversity from space. Prog. Phys. Geogr. 32, 203–221 (2008).Article 

    Google Scholar 
    He, J., Zhang, L., Wang, Q. & Li, Z. Using diffusion geometric coordinates for hyperspectral imagery representation. IEEE Geosci. Remote Sens. Lett. 6(4), 767–771 (2009).ADS 
    Article 

    Google Scholar 
    Lechner, A.M., Foody, G.M., & Boyd, D.S. Applications in remote sensing to forest ecology and management. One Earth 2.5, 405–412 (2020).Arévalo, P., Olofsson, P. & Woodcock, C. E. Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting. Remote Sens. Environ. 238, 111051 (2020).ADS 
    Article 

    Google Scholar 
    Gillespie, T.W. et al. Measuring and modelling biodiversity from space. Prog. Phys. Geogr. 32.2, 203–221 (2008).Lausch, A., Erasmi, S., King, D. J., Magdon, P. & Heurich, M. Understanding forest health with remote sensing-part II—A review of approaches and data models. Remote Sens. 9(2), 129 (2017).ADS 
    Article 

    Google Scholar 
    Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., et al. Monitoring vegetation systems in the Great Plains with ERTS. in NASA Special Publication. Vol. 351. 309 (1974).Chen, J. M. & Black, T. Defining leaf area index for non-flat leaves. Plant Cell Environ. 15, 421–429 (1992).Article 

    Google Scholar 
    Zha, Y., Gao, J. & Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24, 583–594 (2003).Article 

    Google Scholar 
    Zhao, S. et al. Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient. Int. J. Appl. Earth Obs. Geoinf. 12, 351–358 (2010).ADS 

    Google Scholar 
    Gao, B. C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266 (1996).ADS 
    Article 

    Google Scholar 
    Wan, Z. & Dozier, J. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans. Geosci. Remote Sens. 34, 892–905 (1996).ADS 
    Article 

    Google Scholar 
    Xu, H., Wang, Y., Guan, H., Shi, T. & Hu, X. Detecting ecological changes with a remote sensing based ecological index (RSEI) produced time series and change vector analysis. Remote Sensing 11, 2345 (2019).ADS 
    Article 

    Google Scholar 
    List of Top 10 Sources of Free Remote Sensing Data (2017).USGS Earth Explorer: Download Free Landsat Imagery (2021).Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote. Sens. 65, 2–16 (2010).ADS 
    Article 

    Google Scholar 
    Li, M., Zang, S., Zhang, B., Li, S. & Wu, C. A review of remote sensing image classification techniques: The role of spatio-contextual information. Eur. J. Remote Sens. 47, 389–411 (2014).Article 

    Google Scholar 
    Gómez-Chova, L., Tuia, D., Moser, G. & Camps-Valls, G. Multimodal classification of remote sensing images: A review and future directions. Proc. IEEE 103, 1560–1584 (2015).Article 

    Google Scholar 
    Alajlan, N., Bazi, Y., Melgani, F. & Yager, R. R. Fusion of supervised and unsupervised learning for improved classification of hyperspectral images. Inf. Sci. 217, 39–55 (2012).Article 

    Google Scholar 
    Csillik, O. Fast segmentation and classification of very high resolution remote sensing data using SLIC superpixels. Remote Sens. 9, 243 (2017).ADS 
    Article 

    Google Scholar 
    Thanh Noi, P. & Kappas, M. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors 18, 18 (2018).ADS 
    Article 

    Google Scholar 
    Jiang, S., Zhao, H., Wu, W., & Tan, Q. A novel framework for remote sensing image scene classification. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2018, 42 (2018).Baddeley, A. Spatial Point Process Modelling and Its Applications. Vol. 20. (Publicacions de la Universitat Jaume I, 2004).Vasudevan, K., Eckel, S., Fleischer, F., Schmidt, V. & Cook, F. Statistical analysis of spatial point patterns on deep seismic reflection data: A preliminary test. Geophys. J. Int. 171, 823–840 (2007).ADS 
    Article 

    Google Scholar 
    Cheng, Y. & Luo, J. Statistical analysis of metastable pitting events on carbon steel. Br. Corros. J. 35, 125–130 (2000).CAS 
    Article 

    Google Scholar 
    Velázquez, E., Martínez, I., Getzin, S., Moloney, K. A. & Wiegand, T. An evaluation of the state of spatial point pattern analysis in ecology. Ecography 39, 1042–1055 (2016).Article 

    Google Scholar 
    Clark, P. J. & Evans, F. C. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35, 445–453 (1954).Article 

    Google Scholar 
    Stoyan, D., & Penttinen, A. Recent applications of point process methods in forestry statistics. Stat. Sci. 2000, 61–78 (2000).Illian, J., Penttinen, A., Stoyan, H., & Stoyan, D. Statistical Analysis and Modelling of Spatial Point Patterns. Vol. 70. (Wiley, 2008).Brodrick, P. G., Davies, A. B. & Asner, G. P. Uncovering ecological patterns with convolutional neural networks. Trends Ecol. Evol. 34, 734–745 (2019).Article 

    Google Scholar 
    Liu, S., Luo, H., Tu, Y., He, Z., & Li, J. Wide contextual residual network with active learning for remote sensing image classification. in IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium. 7145–7148 (IEEE, 2018).Lee, H. & Kwon, H. Going deeper with contextual CNN for hyperspectral image classification. IEEE Trans. Image Process. 26, 4843–4855 (2017).ADS 
    MathSciNet 
    Article 

    Google Scholar 
    Cheng, G., Xie, X., Han, J., Guo, L. & Xia, G. S. Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 3735–3756 (2020).ADS 
    Article 

    Google Scholar 
    Lewy, D., & Mandziuk, J. An overview of mixing augmentation methods and augmentation strategies. arXiv preprint arXiv:2107.09887 (2021).Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q.V. Autoaugment: Learning augmentation policies from data. arXiv preprint arXiv:1805.09501 (2018).Naveed, H. Survey: Image mixing and deleting for data augmentation. arXiv preprint arXiv:2106.07085 (2021).Freeman, I., Roese-Koerner, L. & Kummert, A. Effnet: An efficient structure for convolutional neural networks. 25th IEEE international conference on image processing (ICIP). IEEE 2018, 6–10 (2018).
    Google Scholar 
    LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989).Article 

    Google Scholar 
    Raeisi, M., Bonneu, F. & Gabriel, E. A spatio-temporal multi-scale model for Geyer saturation point process: Application to forest fire occurrences. Spatial Stat. 41, 100492 (2021).MathSciNet 
    Article 

    Google Scholar 
    Baddeley, A. Analysing spatial point patterns in R. in Workshop Notes Version. Vol. 3 (2008). More

  • in

    Spatial and temporal variation in New Hampshire bat diets

    Whitaker, J. O., McCracken, G. F. & Siemers, B. M. Food habits analysis of insectivorous bats. in Ecological and Behavioral Methods for the Study of Bats. 567–592. (2011).Clare, E. L., Barber, B. R., Sweeney, B. W., Hebert, P. D. N. & Fenton, M. B. Eating local: Influences of habitat on the diet of little brown bats (Myotis lucifugus). Mol. Ecol. 20(8), 1772–1780. https://doi.org/10.1111/j.1365-294X.2011.05040.x (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Clare, E. L. et al. The diet of Myotis lucifugus across Canada: Assessing foraging quality and diet variability. Mol. Ecol. 23(15), 3618–3632. https://doi.org/10.1111/mec.12542 (2014).Article 
    PubMed 

    Google Scholar 
    Wray, A. K. et al. Predator preferences shape the diets of arthropodivorous bats more than quantitative local prey abundance. Mol. Ecol. https://doi.org/10.1111/mec.15769 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Agosta, S. J., Morton, D. & Kuhn, K. M. Feeding ecology of the bat Eptesicus fuscus: ‘Preferred’ prey abundance as one factor influencing prey selection and diet breadth. J. Zool. 260(2), 169–177. https://doi.org/10.1017/S0952836903003601 (2003).Article 

    Google Scholar 
    Clare, E. L., Symondson, W. O. C. & Fenton, M. B. An inordinate fondness for beetles? Variation in seasonal dietary preferences of night-roosting big brown bats (Eptesicus fuscus). Mol. Ecol. 23(15), 3633–3647. https://doi.org/10.1111/mec.12519 (2014).Article 
    PubMed 

    Google Scholar 
    O’Rourke, D. R. et al. Lord of the Diptera (and moths and a spider): Molecular diet analyses and foraging ecology of Indiana bats in Illinois. Front. Ecol. Evol. 9, 12 (2021).ADS 

    Google Scholar 
    Hope, P. R. et al. Second generation sequencing and morphological faecal analysis reveal unexpected foraging behaviour by Myotis nattereri (Chiroptera, Vespertilionidae) in winter. Front. Zool. 11(1), 39. https://doi.org/10.1186/1742-9994-11-39 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vesterinen, E. J., Puisto, A. I. E., Blomberg, A. S. & Lilley, T. M. Table for five, please: Dietary partitioning in boreal bats. Ecol. Evol. 8, 10914–10937 (2018).Article 

    Google Scholar 
    Vesterinen, E. J. et al. What you need is what you eat? Prey selection by the bat Myotis daubentonii. Mol. Ecol. 25, 1581–1594 (2016).CAS 
    Article 

    Google Scholar 
    Barclay, R. M. R. Population structure of temperate zone insectivorous bats in relation to foraging behaviour and energy demand. J. Anim. Ecol. 60(1), 165. https://doi.org/10.2307/5452 (1991).Article 

    Google Scholar 
    Fraser, E. E. & Fenton, M. B. Age and food hardness affect food handling by insectivorous bats. Can. J. Zool. 85, 985–993 (2007).Article 

    Google Scholar 
    von Frenckell, B. & Barclay, R. M. R. Bat activity over calm and turbulent water. Can. J. Zool. 65, 219–222 (1987).Article 

    Google Scholar 
    Kaupas, L. A. & Barclay, R. M. R. Temperature-dependent consumption of spiders by little brown bats (Myotis lucifugus), but not northern long-eared bats (M. septentrionalis), in northern Canada. Can. J. Zool. 96(3), 261 (2018).Article 

    Google Scholar 
    Alberdi, A., Aizpurua, O., Gilbert, M. T. P. & Bohmann, K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol. Evol. 9, 134–147 (2018).Article 

    Google Scholar 
    Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291 (2018).Article 

    Google Scholar 
    Kunz, T. H. & Whitaker, J. O. An evaluation of fecal analysis for determining food habits of insectivorous bats. Can. J. Zool. 61, 1317–1321 (1983).Article 

    Google Scholar 
    Hamilton, I. M. & Barclay, R. M. R. Diets of juvenile, yearling, and adult big brown bats (Eptesicus fuscus) in Southeastern Alberta. J. Mammal. 79(3), 764. https://doi.org/10.2307/1383087 (1998).Article 

    Google Scholar 
    Moosman, P. R., Thomas, H. H. & Veilleux, J. P. Food habits of eastern small-footed bats (Myotis leibii) in New Hampshire. Am. Midl. Nat. 158(2), 354–360 (2007).Article 

    Google Scholar 
    Ober, H. K. & Hayes, J. P. Prey selection by bats in forests of Western Oregon. J. Mammal. 89(5), 1191–1200. https://doi.org/10.1644/08-MAMM-A-025.1 (2008).Article 

    Google Scholar 
    Long, B. L., Kurta, A. & Clemans, D. L. Analysis of DNA from feces to identify prey of big brown bats (Eptesicus fuscus) caught in apple orchards. Am. Midl. Nat. 170(2), 287–297 (2013).Article 

    Google Scholar 
    Gordon, R. et al. Molecular diet analysis finds an insectivorous desert bat community dominated by resource sharing despite diverse echolocation and foraging strategies. Ecol. Evol. 9, 3117–3129 (2019).Article 

    Google Scholar 
    Alberdi, A. et al. Promises and pitfalls of using high-throughput sequencing for diet analysis. Mol. Ecol. Resour. 19, 327–348 (2019).Article 

    Google Scholar 
    Clare, E. L. Molecular detection of trophic interactions: Emerging trends, distinct advantages, significant considerations and conservation applications. Evol. Appl. 7, 1144–1157 (2014).Article 

    Google Scholar 
    Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323(5911), 227–227. https://doi.org/10.1126/science.1163874 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Frick, W. F. et al. Disease alters macroecological patterns of North American bats: Disease alters macroecology of bats. Glob. Ecol. Biogeogr. 24(7), 741–749. https://doi.org/10.1111/geb.12290 (2015).Article 

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

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

    Google Scholar 
    Anthony, E. L. P. & Kunz, T. H. Feeding strategies of the little brown bat, Myotis lucifugus, Southern New Hampshire. Ecology 58(4), 775–786. https://doi.org/10.2307/1936213 (1977).Article 

    Google Scholar 
    Pompanon, F. et al. Who is eating what: diet assessment using next generation sequencing. Mol. Ecol. 21, 1931–1950 (2012).CAS 
    Article 

    Google Scholar 
    Jusino, M. A. et al. An improved method for utilizing high-throughput amplicon sequencing to determine the diets of insectivorous animals. Mol. Ecol. Resour. 19, 176–190 (2019).CAS 
    Article 

    Google Scholar 
    O’Rourke, D. R., Bokulich, N. A., Jusino, M. A., MacManes, M. D., & Foster, J. T. A total crapshoot? Evaluating bioinformatic decisions in animal diet metabarcoding analyses. Ecol. Evolut. https://doi.org/10.1002/ece3.6594 (2020).Langwig, K. E. et al. Resistance in persisting bat populations after white-nose syndrome invasion. Philos. Trans. R. Soc. B Biol. Sci. 372, 2160044 (2017).Article 

    Google Scholar 
    Maslo, B., Valent, M., Gumbs, J. F. & Frick, W. F. Conservation implications of ameliorating survival of little brown bats with white-nose syndrome. Ecol. Appl. 25, 1832–1840 (2015).Article 

    Google Scholar 
    Frick, W. F. et al. An emerging disease causes regional population collapse of a common North American bat species. Science 329(5992), 679–682. https://doi.org/10.1126/science.1188594 (2010).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Turner, G. G., Reeder, D. M. & Coleman, J. T. H. A five-year assessment of mortality and geographic spread of white-nose syndrome in North American bats and a look to the future. Bat Res. News 52, 13–27 (2011).
    Google Scholar 
    Coleman, J. et al. A National Plan for Assisting States, Federal Agencies, and Tribes in Managing White-Nose Syndrome in Bats. https://s3.us-west-2.amazonaws.com/prod-is-cms-assets/wns/prod/b0634260-77d3-11e8-b37b-4f3513704a5e-white-nose_syndrome_national_plan_may_2011.pdf (2011).Szymanski, J. A., Runge, M. C., Parkin, M. J. & Armstrong, M. White-Nose Syndrome Management: Report on Structured Decision Making Initiative. Vol. 51. http://pubs.er.usgs.gov/publication/70003465 (2009).Kunz, T. H., Braun de Torrez, E., Bauer, D., Lobova, T. & Fleming, T. H. Ecosystem services provided by bats. Ann. N. Y. Acad. Sci. 1223, 1–38 (2011).ADS 
    Article 

    Google Scholar 
    Boyles, J. G., Cryan, P. M., McCracken, G. F. & Kunz, T. H. Economic importance of bats in agriculture. Science 332(6025), 41–42. https://doi.org/10.1126/science.1201366 (2011).ADS 
    Article 
    PubMed 

    Google Scholar 
    Agosta, S. J. & Morton, D. Diet of the big brown bat, Eptesicus fuscus, from Pennsylvania and Western Maryland. Northeast. Nat. 10(1), 89–104 (2003).Article 

    Google Scholar 
    Brown, V. A., Braun de Torrez, E. & McCracken, G. F. Crop pests eaten by bats in organic pecan orchards. Crop Prot. 67, 66–71 (2015).Article 

    Google Scholar 
    Williams-Guillén, K., Perfecto, I. & Vandermeer, J. Bats limit insects in a Neotropical agroforestry system. Science 320(5872), 70–70. https://doi.org/10.1126/science.1152944 (2008).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Held, D. W. & Ray, C. H. Asiatic garden beetle Maladera castanea (Coleoptera: Scarabaeidae) grubs found in damaged turf in Alabama. Fla. Entomol. 92(4), 670–672 (2009).Article 

    Google Scholar 
    Forschler, B. T. & Gardner, W. A. A review of the scientific literature on the biology and distribution of the genus Phyllophaga (Coleoptera: Scarabaeidae) in the Southeastern United States. J. Entomol. Sci. 25(4), 628–651. https://doi.org/10.18474/0749-8004-25.4.628 (1990).Article 

    Google Scholar 
    United States Forest Service. White Grubs in Forest Tree Nurseries and Plantations. Vol. 4. https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/fsbdev2_043588.pdf (1961).Chandler, D. University of New Hampshire—Entomology Collection. UNH Insect and Arachnid Collections. https://duncan.unh.edu/ento/home.php (2020).United States Forest Service. The Early Warning System for Forest Health Threads in the United States. https://www.fs.fed.us/foresthealth/publications/EWS_final_draft.pdf (2004).Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K., & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79(17), 5112–5120. https://doi.org/10.1128/AEM.01043-13 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).Article 

    Google Scholar 
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
    Article 

    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    Article 

    Google Scholar 
    Ratnasingham, S. & Hebert, P. D. N. bold: The barcode of life data system. http://www.barcodinglife.org. Mol. Ecol. Notes 7, 355–364 (2007).Robeson, M. S. et al. RESCRIPt: Reproducible sequence taxonomy reference database management for the masses. bioRxiv. https://doi.org/10.1101/2020.10.05.326504 (2020).Article 

    Google Scholar 
    Chamberlain, S. BOLD: Interface to BOLD Systems API. (2017).Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30(4), 772–780. https://doi.org/10.1093/molbev/mst010 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).Article 

    Google Scholar 
    Beule, L. & Karlovsky, P. Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ 8, e9593 (2020).Article 

    Google Scholar 
    Oksanen, J. et al. vegan: Community Ecology Package. (2018).McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cáceres, M. D. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology 90, 3566–3574 (2009).Article 

    Google Scholar 
    McKinney, W. Data structures for statistical computing in Python. Proc. Python Sci. Conf. https://doi.org/10.25080/Majora-92bf1922-00a (2010).Article 

    Google Scholar 
    McDonald, D. et al. The Biological Observation Matrix (BIOM) format or: How I learned to stop worrying and love the ome-ome. GigaScience 1, 7 (2012).Article 

    Google Scholar 
    Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
    Article 

    Google Scholar 
    Battaglia, T. btools: A Suite of R Function for All Types of Microbial Diversity Analyses. (2020).Wilke, C. O. cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. (2017).Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).Article 

    Google Scholar 
    Ogle, D. H. & Wheeler, P. FSA: Fisheries Stock Analysis. (2018).Bisanz, J. E. qiime2R: Importing QIIME2 Artifacts and Associated Data into R Sessions. (2018).Kahle, D. & Wickham, H. ggmap: Spatial visualization with ggplot2. R J. 5, 144–161 (2013).Article 

    Google Scholar 
    Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. (2018).Slowikowski, K. ggrepel: Automatically Position Non-Overlapping Text Labels with ‘ggplot2’. (2018).Hesselbarth, M. H. K., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: an open-source R tool to calculate landscape metrics. Ecography 42, 1648–1657 (2019).Article 

    Google Scholar 
    Grolemund, G., & Wickham, H. Dates and times made easy with lubridate. J. Stat. Softw. 40(3). https://www.jstatsoft.org/index.php/jss/article/view/v040i03/v40i03.pdf (2011).Makiyama, K. magicfor: Magic Functions to Obtain Results from for Loops. (2016).Bates, D. & Maechler, M. Matrix: Sparse and Dense Matrix Classes and Methods. (2018).Graves, S., Piepho, H.-P. & Selzer, L. multcompView: Visualizations of Paired Comparisons. (2019).Martinez Arbizu, P. pairwiseAdonis: Pairwise Multilevel Comparison using Adonis. (2017).Hijmans, R. J. raster: Geographic Data Analysis and Modeling. (2020).Wickham, H. Reshaping data with the reshape Package. J. Stat. Softw. 21(1), 1–20. https://doi.org/10.18637/jss.v021.i12 (2007).MathSciNet 
    Article 

    Google Scholar 
    Wickham, H. scales: Scale Functions for Visualization. (2018).Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439 (2018).Article 

    Google Scholar 
    Wickham, H. et al. svglite: An ‘SVG’ Graphics Device. (2020).Wickham, H. tidyverse: Easily Install and Load the ‘Tidyverse’. (2017).Strochak, S., Ueyama, K. & Williams, A. urbnmapr: State and County Shapefiles in sf and Tibble Format. (2020).Bittinger, K. usedist: Distance Matrix Utilities. (2020). More

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    Abiotic and biotic factors controlling the dynamics of soil respiration in a coastal dune ecosystem in western Japan

    Site descriptionThe study site (about 1 ha) is within a coastal dune ecosystem (35° 32′ 26.0″ N, 134° 12′ 27.5″ E) located at the Arid Land Research Center of Tottori University, Tottori, Japan. The mean annual temperature is 15.2 °C, and the mean total precipitation is 1931 mm, based on records collected from 1991 to 2020 at the Tottori observation station of the Japan Meteorological Agency. Dominant plant species around the measurement plot were Vitex rotundifolia and Artemisia capillaris. Carex kobomugi and Ischaemum anthephoroides were also scattered around the coastal side of the study site, and planted Pinus thunbergii trees cover the inland side.Experimental designIn May 2020, we established four measurement plots at the study site (Fig. 9). Plot 1 was a gap area surrounded by V. rotundifolia seedlings. Plot 2 consisted of clusters of V. rotundifolia seedlings and was adjacent to plot 1. Within plots 1 and 2, C. kobomugi and I. anthephoroides were also scattered. Plot 3 was in a mixed area of V. rotundifolia and A. capillaris; this plot was in the center of the study site. Plot 4 was located in front of P. thunbergii trees and was in the most inland area of the study site. On 10 June 2020, we set an environmental measurement system at the center of the study site adjacent to plot 3, and we then obtained continuous data for soil temperature and soil moisture. In each plot (main plot), we set 10 plastic (polypropylene) collars (n = 10) before the start of the Rs measurement. We measured Rs every 2 weeks from 15 June to 2 December 2020 in the main plots. Vitex rotundifolia and C. kobomugi invaded a part of plot 1 in late June and early July, after the first Rs measurement on 15 June. Therefore, we set new measurement points for plot 1 in early July (Fig. 9), and flux calculations for plot 1 were conducted after removing data from the invaded area measured on June 15.Figure 9Diagram and photos of measurement plots in the focal coastal dune ecosystem. Vitex rotundifolia and C. kobomugi invaded a part of plot 1 in late June to early July, after the first Rs measurement on 15 June. Therefore, we set new measurement points for plot 1 in early July.Full size imageEnvironmental measurement systemThe environmental measurement system was composed of a data logger (CR1000, Campbell Scientific Inc., Logan, UT, USA), battery (SC dry battery, Kind Techno Structure Co. Ltd, Saitama, Japan), solar panel (RNG-50D-SS, RENOGY International Inc., Ontario, CA, USA), charge controller (Solar Amp mini, CSA-MN05-8, DENRYO, Tokyo, Japan), thermocouples (E type), and soil moisture sensors (CS616, Campbell Scientific Inc.). The data logger, battery, and charge controller were kept in a plastic box to avoid exposure to rainfall and sand. Each end of the thermocouple was inserted into a copper tube (4-mm inner diameter, 5-cm length) and affixed with glue. To measure the reference soil temperature at different depths, copper tubes enclosing E-type thermocouples were buried horizontally in the sand at depths of 5, 10, 30, and 50 cm (n = 1 for each depth) at the center of plot 3 as reference soil temperature (the data was recorded every 30 min). In addition, we set stand-alone soil temperature sensors (Thermochron SL type, KN Laboratories, Inc. Osaka, Japan) at the center of plots 1 and 4 at depths of 5, 10, and 30 cm (n = 1 for each plot, each depth), and they recorded soil temperature data every 30 min. Reference soil temperature at the depth of 5, 10, and 30 cm was used for gap-filling for soil temperature measured by stand-alone sensors at each depth and plot. Soil moisture sensors were buried horizontally in the sand at a depth of 30 cm in the center of plots 1, 3, and 4 (n = 1 for each plot) and recorded data every 30 min. Raw values of soil moisture sensors were converted to volumetric soil moisture (%) using a calibration line from 0 to 15% measured in the laboratory using dune sand and three sensors (CS616) referring to the procedure of Bongiovanni et al.53. Data for precipitation at the local meteorological observatory in Tottori was downloaded from the home page of the Japan Meteorological Agency (https://www.data.jma.go.jp/gmd/risk/obsdl/index.php).
    R
    s measurement in the main plotsPolypropylene collars (30-cm inner diameter, 5-cm depth, n = 10) were set in each measurement plot in late May 2020. The first Rs measurement was conducted on 15 June 2020. However, V. rotundifolia and C. kobomugi then invaded about half of the gap area of plot 1, so on 1 July we set 5 new polypropylene collars for plot 1 to replace the 5 invaded measurement points (Fig. 9). The second Rs measurement was conducted on 2 July, and all polypropylene collars then remained in the same position until the end of the measurement period.Rs was measured using an automated closed dynamic chamber system54 composed of two cylindrical aluminum chambers (30 cm diameter, 30 cm height) equipped with thermistor temperature sensors (44006, Omega Engineering, Stanford, CA, USA) for measuring air temperature inside the chamber during Rs measurement. Those chambers were connected to a control box equipped with a pump, data logger (CR1000, Campbell Scientific Inc.), CO2 analyzer (Gascard NG infrared gas sensor, Edinburgh Sensors, Lancashire, UK), and thermometer (MHP, Omega Engineering). The composition of the control box is basically the same as used in previous studies54,55. The measurement period for each point was 3 min, and the CO2 concentration and air temperature inside the chamber were recorded every 5 s. During the measurement, another chamber was set on the next polypropylene collar with the lid opened, and the next measurement was started at that moment of finishing the previous measurement by automatically closing the chamber lid on the next polypropylene collar in the same plot. Soil temperature at a depth of 0–5 cm was recorded simultaneously by inserting the rod of the thermometer vertically into the soil surface near the polypropylene collar (about 1–2 m from the collar).Rs was calculated by using the following equation:$$R_{{text{s}}} = frac{{PV}}{{RS(T_{{{text{air}}}} + 273.15)}}frac{{partial C}}{{partial t}},$$
    (1)
    where P is the air pressure (Pa), V is the effective chamber volume (m3), R is the ideal gas constant (8.314 Pa m3 K−1 mol−1), S is the soil surface area (m2), Tair is the air temperature inside the chamber (°C). ∂C/∂t is the rate of change of the CO2 mole fraction (μmol mol−1 s−1), which was calculated using least-squares regression of the CO2 changes inside the chamber12. For the flux calculation, we removed data for the first 35 s (dead band) of each measurement as an outlier.Trench treatment and soil CO2 efflux (F
    c) measurement in subplotsIn November 2020, we conducted root-cut treatment (trench treatment) in subplots using polyvinyl chloride (PVC) tubes to estimate the contribution of Ra to Rs in the soil layer above 50 cm in each plot (Ra_50/Rs). Small PVC collars (10.7 cm inner diameter, 5 cm depth, n = 10 for each plot), with the upper ends about 1–2 cm above the soil surface, were set in subplots adjacent to the main plots on 23 October 2020. Rs was measured in subplots using two cylindrical mini PVC chambers (11.8 cm inner diameter at the bottom, 30 cm height, equipped with the same thermistors as cylindrical aluminum chambers for air temperature measurement) connected to the same control box as used for Rs measurement in the main plots. The measurement period was 3 min, and the measurement procedure and the flux calculation were the same as the main plot. Rs was first measured in subplots on 3 November to examine the spatial variation of Rs before trench treatment. Using the data, we selected subplots to conduct trench treatment and control plots for comparison, while aiming to achieve a minimal difference in the average Rs between control and pre-trenched plots. On 4 November, we inserted PVC tubes (10.7 cm inner diameter, 50 cm length) into about half (n = 3–5) of the subplots (the same position as PVC collars were set on 23 October) by using a hammer and aluminum lid until the upper end of each PVC tube was 1–2 cm above the soil surface to exclude roots to a depth of about 50 cm. On 19 November, after 15 days of trench treatment, respiration was measured in the same subplots.The Ra_50/Rs was calculated as follows:$$R_{{{text{a}}_{5}0}} /R_{{text{s}}} = (F_{{{text{c}}_{text{control}}}} -F_{{{text{c}}_{text{trenched}}}}) /F_{{{text{c}}_{text{control}}}} ,$$
    (2)
    where Fc_trenched and Fc_control (= Rs) are the Fc values in trenched and control plots on 19 November, respectively.In late December 2020, all the belowground plant biomass (BPB) in subplots (control and trenched plots) to a depth of 50 cm was collected for biomass analysis, about 2 months after trench treatment. In the laboratory, all the collected plant materials were washed and oven-dried for 72 h at 70 °C, and then the dry weight of the BPB samples was measured.Biomass measurementWe conducted BPB analysis from 18 May to 8 June 2021 in each plot (n = 1). At that time, 100 cm × 100 cm sampling plots near the CO2 measurement plots (100 cm × 100 cm for plots 2–4 and 50 cm × 50 cm in plot 1 because of the narrow gap area) were dug to a depth of 100–220 cm, according to the root distribution in each plot, and all plant materials were collected by passing the soil through 5- to 7-mm sieves. Once we reached a depth where no roots were visible, no more digging was conducted. In plots 2 and 3, stolons of V. rotundifolia were difficult to distinguish from roots if underground. Therefore, we defined plant material as BPB if it was underground. In the laboratory, all of the collected plant materials were washed and air-dried at room temperature for 0–6 days depending on the biomass. After that, samples were oven-dried for 15–25 h at 70–80 °C, and the dry weight of those samples was then measured.Soil organic carbon and nitrogenOn 21 October 2020, soil pits were dug to a depth of 50 cm near each plot (n = 3), and soil core samples were collected. Cylindrical stainless core samplers (5 cm diameter, 5 cm height, 100 cc) were horizontally inserted into the soil pit at depths of 0–5, 5–10, 10–20, and 20–30 cm. In the laboratory, soil core samples were weighed and oven-dried at 105 °C for 48 h, and the dry weight was measured. Oven-dried soil samples were sieved with a 2-mm-pore stainless wire mesh screen, and visible fungal mycelia in soil samples from plot 4 were removed as well as possible. Sieved samples were ground with an agate mortar. Samples (fine powder) were oven-dried for 24 h at 105 °C and weighed before SOC and nitrogen analysis. About 1.5 g of powdered samples were used for the analysis. Organic carbon content (combustion at 400 °C) and total nitrogen in samples were analyzed using a Soli TOC cube (Elementar Analysensysteme GmbH, Langenselbold, Germany) by the combustion method.Microbial abundanceOn 21 October 2020, soil samples for microbial analysis were collected at the same time as soil core sampling for SOC and nitrogen analysis. Soil samples were collected at depths of 0–10, 10–20, and 20–30 cm using a stainless spatula and placed individually in a polyethylene bag. The bags were kept in a cooler box with ice in the field and then placed in a freezer (− 30 °C) in the laboratory soon after sampling.DNA was extracted from 0.5 g of the fresh soils using NucleoSpin Soil (Takara Bio, Inc., Shiga, Japan) according to the manufacturer’s instructions (SL1 buffer), and the extracts were stored at − 20 °C until further analysis. Bacterial and archaeal 16S rRNA and fungal internal transcribed spacer (ITS) gene were targeted to investigate the microbial abundance. Bacterial and archaeal 16S rRNA (V4 region) and fungal ITS were determined using the universal primer sets 515F/806R and ITS1F_KYO2/ITS2_KYO2, respectively56,57.For qPCR, samples were prepared with 10 μL of the KAPA SYBR Fast qPCR kit (Kapa Biosystems, Wilmington, MA, USA), 0.8 μL of forward primer, 0.8 μL of reverse primer, and 3 μL of 1–50 × diluted soil DNA. Nuclease-free water was added to make up to a final volume of 20 μL. Cycling conditions of 16S rRNA were 95 °C for 30 s, followed by 40 cycles at 95 °C for 30 s, 58 °C for 30 s, and 72 °C for 1 min. Cycling conditions of ITS were 95 °C for 30 s, followed by 40 cycles at 95 °C for 30 s, 55 °C for 1 min, and 72 °C for 1 min. A melting curve analysis was performed in a final cycle of 95 °C for 15 s, 60 °C for 1 min, and 95 °C for 15 s. High amplification efficiencies of 99% for bacterial and archaeal 16S rRNA genes and 101% for the fungal ITS were obtained based on the standard curves.Data analysisTo examine the environmental response (soil temperature and soil moisture) of Rs, nonlinear and quadratic regression models were applied. We conducted F-tests by comparing the regression model to a constant model whose value is the mean of the observations (significance set at p  More

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    Fine-scale movement of northern Gulf of Mexico red snapper and gray triggerfish estimated with three-dimensional acoustic telemetry

    Fodrie, F. J. et al. Measuring individuality in habitat use across complex landscapes: Approaches, constraints, and implications for assessing resource specialization. Oecologia 178, 75–87 (2015).ADS 
    PubMed 
    Article 

    Google Scholar 
    Bacheler, N. M., Michelot, T., Cheshire, R. T. & Shertzer, K. W. Fine-scale movement patterns and behavioral states of gray triggerfish Balistes capriscus determined from acoustic telemetry and hidden Markov models. Fish. Res. 215, 76–89 (2019).Article 

    Google Scholar 
    Furey, N. B., Dance, M. A. & Rooker, J. R. Fine-scale movements and habitat use of juvenile southern flounder Paralichthys lethostigma in an estuarine seascape. J. Fish Biol. 82, 1469–1483 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Froehlich, C. Y. M., Garcia, A. & Kline, R. J. Daily movement patterns of red snapper (Lutjanus campechanus) on a large artificial reef. Fish. Res. 209, 49–57 (2019).Article 

    Google Scholar 
    Williams-Grove, L. J. & Szedlmayer, S. T. Acoustic positioning and movement patterns of red snapper, Lutjanus campechanus, around artificial reefs in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 553, 233–251 (2016).ADS 
    Article 

    Google Scholar 
    Secor, D. H., Zhang, F., O’Brien, M. H. P. & Li, M. Ocean destratification and fish evacuation caused by a Mid-Atlantic tropical storm. ICES J. Mar. Sci. 76, 573–584 (2019).Article 

    Google Scholar 
    Bacheler, N. M., Shertzer, K. W., Cheshire, R. T. & MacMahan, J. H. Tropical storms influence the movement behavior of a demersal oceanic fish species. Sci. Rep. 9, 1–13 (2019).CAS 
    Article 

    Google Scholar 
    Lowerre-Barbieri, S. K., Walters, S., Bickford, J., Cooper, W. & Muller, R. Site fidelity and reproductive timing at a spotted seatrout spawning aggregation site: Individual versus population scale behavior. Mar. Ecol. Prog. Ser. 481, 181–197 (2013).ADS 
    Article 

    Google Scholar 
    Espinoza, M., Farrugia, T. J., Webber, D. M., Smith, F. & Lowe, C. G. Testing a new acoustic telemetry technique to quantify long-term, fine-scale movements of aquatic animals. Fish. Res. 108, 364–371 (2011).Article 

    Google Scholar 
    Roy, R. et al. Testing the VEMCO positioning system: Spatial distribution of the probability of location and the positioning error in a reservoir. Anim. Biotelemetry 2, 1 (2014).CAS 
    Article 

    Google Scholar 
    Guzzo, M. M. et al. Field testing a novel high residence positioning system for monitoring the fine-scale movements of aquatic organisms. Methods Ecol. Evol. 9, 1478–1488 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Smedbol, S., Smith, F., Webber, D., Vallée, R. & King, T. Using underwater coded acoustic telemetry for fine scale positioning of aquatic animals. In 20th Symposium of the International Society on Biotelemetry Proceedings, 9–11 (2014).Dean, M. J., Hoffman, W. S., Zemeckis, D. R. & Armstrong, M. P. Fine-scale diel and gender-based patterns in behaviour of Atlantic cod (Gadus morhua) on a spawning ground in the western Gulf of Maine. ICES J. Mar. Sci. 71, 1474–1489 (2014).Article 

    Google Scholar 
    Tarnecki, J. H. & Patterson, W. F. A mini ROV-based method for recovering marine instruments at depth. PLoS One 15, 1–9 (2020).
    Google Scholar 
    Ellis, R. D. et al. Acoustic telemetry array evolution: From species- and project-specific designs to large-scale, multispecies, cooperative networks. Fish. Res. 209, 186–195 (2019).Article 

    Google Scholar 
    Friess, C. et al. Regional-scale variability in the movement ecology of marine fishes revealed by an integrative acoustic tracking network. Mar. Ecol. Prog. Ser. 663, 157–177 (2021).ADS 
    Article 

    Google Scholar 
    Walters, C. J. & Juanes, F. Recruitment limitation as a consequence of natural selection for use of restricted feeding habitats and predation risk taking by juvenile fishes. Can. J. Fish. Aquat. Sci. 50, 2058–2070 (1993).Article 

    Google Scholar 
    Ahrens, R. N. M., Walters, C. J. & Christensen, V. Foraging arena theory. Fish Fish. 13, 41–59 (2012).Article 

    Google Scholar 
    Schwartzkopf, B. D., Langland, T. A. & Cowan, J. H. Habitat selection important for red snapper feeding ecology in the northwestern Gulf of Mexico. Mar. Coast. Fish. 9, 373–387 (2017).Article 

    Google Scholar 
    Wells, R. J. D., Cowan, J. H. Jr. & Fry, B. Feeding ecology of red snapper Lutjanus campechanus in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 361, 213–225 (2008).ADS 
    Article 

    Google Scholar 
    Goldman, S. F., Glasgow, D. M. & Falk, M. M. Feeding habits of 2 reef-associated fishes, red porgy (Pagrus pagrus) and gray triggerfish (Balistes capriscus), off the Southeastern United States. Fish. Bull. 114, 317–329 (2016).Article 

    Google Scholar 
    Villegas-Ríos, D., Réale, D., Freitas, C., Moland, E. & Olsen, E. M. Personalities influence spatial responses to environmental fluctuations in wild fish. J. Anim. Ecol. 87, 1309–1319 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rooker, J. R. et al. Seascape connectivity and the influence of predation risk on the movement of fishes inhabiting a back-reef ecosystem. Ecosphere 9, e02200 (2018).Article 

    Google Scholar 
    Forman, R. T. T. & Godron, M. Patches and structural components for a landscape ecology. Bioscience 31, 733–740 (1981).Article 

    Google Scholar 
    Dahl, K. A. & Patterson, W. F. Movement, home range, and depredation of invasive lionfish revealed by fine-scale acoustic telemetry in the northern Gulf of Mexico. Mar. Biol. 167, 1–22 (2020).Article 
    CAS 

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

    Google Scholar 
    Moulton, D. L. et al. Habitat partitioning and seasonal movement of red drum and spotted seatrout. Estuaries Coasts 40, 905–916 (2017).Article 

    Google Scholar 
    Hammerschlag, N., Luo, J., Irschick, D. J. & Ault, J. S. A Comparison of spatial and movement patterns between sympatric predators: bull sharks (Carcharhinus leucas) and Atlantic tarpon (Megalops atlanticus). PLoS ONE 7, e45958 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Novak, A. J. et al. Scale of biotelemetry data influences ecological interpretations of space and habitat use in yellowtail snapper. Mar. Coast. Fish. 12, 364–377 (2020).Article 

    Google Scholar 
    Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

    Google Scholar 
    Werner, E. E. & Gilliam, J. F. The ontogenetic niche and species interactions in size-structured populations. Annu. Rev. Ecol. Syst. 15, 393–425 (1984).Article 

    Google Scholar 
    Reale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B Biol. Sci. 365, 4051–4063 (2010).Article 

    Google Scholar 
    Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 
    Article 

    Google Scholar 
    Huntingford, F. A. The relationship between anti-predator behavior and aggression among conspecifics in the three-spined stickleback, Gasterosteus aculeatus. Anim. Behav. 24, 245–260 (1976).Article 

    Google Scholar 
    Wilson, D. S., Clark, A. B., Coleman, K. & Dearstyne, T. Shyness and boldness in humans and other animals. Trends Ecol. Evol. 9, 442–446 (1994).Article 

    Google Scholar 
    Harrison, P. M. et al. Personality-dependent spatial ecology occurs independently from dispersal in wild burbot (Lota lota). Behav. Ecol. 26, 483–492 (2015).Article 

    Google Scholar 
    Gosling, S. D. From mice to men: What can we learn about personality from animal research?. Psychol. Bull. 127, 45–86 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hussey, N. E. et al. Aquatic animal telemetry: A panoramic window into the underwater world. Science 348, 1255642–1255642 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    Lowerre-Barbieri, S. K., Kays, R., Thorson, J. T. & Wikelski, M. The ocean’s movescape: Fisheries management in the bio-logging decade (2018–2028). ICES J. Mar. Sci. 76, 477–488 (2019).Article 

    Google Scholar 
    National Marine Fisheries Service. Fisheries Economics of the United State 2016. NOAA Tech. Memo. NMFS-F/SPO-187a. https://www.fisheries.noaa.gov/resource/document/fisheries-economics-united-states-report-2016 (2018). Accessed 08 January 2018.Patterson, W. F. III, Tarnecki, J., Addis, D. T. & Barbieri, L. R. Reef fish community structure at natural versus artificial reefs in the northern Gulf of Mexico. In Proc. 66th Gulf Caribb. Fish. Inst. 4–8 (2014).Streich, M. K. et al. Effects of a new artificial reef complex on red snapper and the associated fish community: An evaluation using a before–after control–impact approach. Mar. Coast. Fish. 9, 404–418 (2017).Article 

    Google Scholar 
    Dance, M. A., Patterson, W. F. III. & Addis, D. T. Fish community and trophic structure at artificial reef sites in the northeastern Gulf of Mexico. Bull. Mar. Sci. 87, 301–324 (2011).Article 

    Google Scholar 
    Cowan, J. H. Red snapper in the Gulf of Mexico and the U.S. South Atlantic: data, doubt, and debate. Fisheries 36, 319–331 (2011).Article 

    Google Scholar 
    Addis, D. T., Patterson, W. F. III. & Dance, M. A. The potential for unreported artificial reefs to serve as refuges from fishing mortality for reef fishes. N. Am. J. Fish. Manag. 36, 131–139 (2016).Article 

    Google Scholar 
    McCawley, J. R., Cowan, J. H. Jr. & Shipp, R. L. Feeding periodicity and prey habitat preference of red snapper, Lutjanus campechanus (Poey, 1860), on Alabama artificial reefs. Gulf Mex. Sci. 24, 14–27 (2006).
    Google Scholar 
    Glenn, H. D., Cowan, J. H. Jr. & Powers, J. E. A comparison of red snapper reproductive potential in the northwestern Gulf of Mexico: Natural versus artificial habitats. Mar. Coast. Fish. 9, 139–148 (2017).Article 

    Google Scholar 
    Kulaw, D. H., Cowan, J. H. Jr. & Jackson, M. W. Temporal and spatial comparisons of the reproductive biology of northern Gulf of Mexico (USA) red snapper (Lutjanus campechanus) collected a decade apart. PLoS One 12, e0172360 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Vose, F. E. & Nelson, W. G. Gray triggerfish (Balistes capriscus Gmelin) feeding from artificial and natural substrate in shallow Atlantic waters of Florida. Bull. Mar. Sci. 55, 1316–1323 (1994).
    Google Scholar 
    Herbig, J. L. & Szedlmayer, S. T. Movement patterns of gray triggerfish, Balistes capriscus, around artificial reefs in the northern Gulf of Mexico. Fish. Manag. Ecol. 23, 418–427 (2016).Article 

    Google Scholar 
    Szedlmayer, S. T. & Schroepfer, R. L. Long-term residence of red snapper on artificial reefs in the northeastern Gulf of Mexico. Trans. Am. Fish. Soc. 134, 315–325 (2005).Article 

    Google Scholar 
    Watterson, J. C. III., Patterson, W. F. I. I. I., Shipp, R. L. & Cowan, J. H. Jr. Movement of red snapper, Lutjanus campechanus, in the north central Gulf of Mexico: Potential effects of hurricanes. Gulf Mex. Sci. 16, 92–104 (1998).
    Google Scholar 
    Ingram, G. W. Jr. & Patterson, W. F. I. I. I. Movement patterns of red snapper (Lutjanus campechanus), greater amberjack (Seriola dumerili), and gray triggerfish (Balistes capriscus) in the Gulf of Mexico and the utility of marine reserves as management tools. Proc. Gulf Caribb. Fish. Inst. 52, 686–699 (2001).
    Google Scholar 
    Strelcheck, A. J., Cowan, J. H. Jr. & Patterson, W. F. III. Site fidelity, movement, and growth of red snapper Lutjanus campechanus: implications for artificial reef management. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III, Cowan, J. H. Jr., Nieland, D. A. & Fitzhugh, G. R.), 147–162 (2007).Addis, D. T., Patterson, W. F. I. I. I., Dance, M. A. & Ingram, G. W. Jr. Implications of reef fish movement from unreported artificial reef sites in the northern Gulf of Mexico. Fish. Res. 147, 349–358 (2013).Article 

    Google Scholar 
    Topping, D. T. & Szedlmayer, S. T. Site fidelity, residence time and movements of red snapper Lutjanus campechanus estimated with long-term acoustic monitoring. Mar. Ecol. Prog. Ser. 437, 183–200 (2011).ADS 
    Article 

    Google Scholar 
    Everett, A. G., Szedlmayer, S. T. & Gallaway, B. J. Movement patterns of red snapper Lutjanus campechanus based on acoustic telemetry around oil and gas platforms in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 649, 155–173 (2020).Article 

    Google Scholar 
    Tarnecki, J. H. & Patterson, W. F. I. I. I. Changes in red snapper diet and trophic ecology following the Deepwater Horizon Oil Spill. Mar. Coast. Fish. 7, 135–147 (2015).Article 

    Google Scholar 
    McCawley, J. R. & Cowan, J. H. Jr. Seasonal and size specific diet and prey demand of Red Snapper on Alabama artificial reefs. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III., Cowan, J. H. Jr., Fitzhugh, G. R. & Nieland, D. L.), 77–104 (2007).Piraino, M. N. & Szedlmayer, S. T. Fine-scale movements and home ranges of red snapper around artificial reefs in the northern Gulf of Mexico. Trans. Am. Fish. Soc. 143, 988–998 (2014).Article 

    Google Scholar 
    Williams-Grove, L. J. & Szedlmayer, S. T. Depth preferences and three-dimensional movements of red snapper, Lutjanus campechanus, on an artificial reef in the northern Gulf of Mexico. Fish. Res. 190, 61–70 (2017).Article 

    Google Scholar 
    Topping, D. T. & Szedlmayer, S. T. Home range and movement patterns of red snapper (Lutjanus campechanus) on artificial reefs. Fish. Res. 112, 77–84 (2011).Article 

    Google Scholar 
    Baker, M. S. J. & Wilson, C. A. Use of bomb radiocarbon to validate otolith section ages of red snapper Lutjanus campechanus from the northern Gulf of Mexico. Limnol. Oceanogr. 46, 1819–1824 (2001).ADS 
    Article 

    Google Scholar 
    Allman, R. J., Fioramonti, C. L., Patterson, W. F. III. & Pacicco, A. E. Validation of annual growth-zone formation in gray triggerfish Balistes capriscus dorsal spines, fin rays, and vertebrae. Gulf Mex. Sci. 33, 68–76 (2016).
    Google Scholar 
    Frazer, T. K., Lindberg, W. J. & Stanton, G. R. Predation on sand dollars by gray triggerfish, Balistes capriscus, in the northeastern Gulf of Mexico. Bull. Mar. Sci. 48, 159–164 (1991).
    Google Scholar 
    Delorenzo, D. M., Bethea, D. M. & Carlson, J. K. An assessment of the diet and trophic level of Atlantic sharpnose shark Rhizoprionodon terraenovae. J. Fish Biol. 86, 385–391 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Aines, A. C., Carlson, J. K., Boustany, A., Mathers, A. & Kohler, N. E. Feeding habits of the tiger shark, Galeocerdo cuvier, in the northwest Atlantic Ocean and Gulf of Mexico. Environ. Biol. Fish. 101, 403–415 (2018).Article 

    Google Scholar 
    Castro, J. I. The Sharks of North America (Oxford University Press, 2011).
    Google Scholar 
    Springer, S. A collection of fishes from the stomachs of sharks taken off Salerno, Florida. Copeia 3, 174–175 (1946).Article 

    Google Scholar 
    Bohaboy, E. C., Guttridge, T. L., Hammerschlag, N., Van Zinnicq Bergmann, M. P. M. & Patterson, W. F. III. Application of three-dimensional acoustic telemetry to assess the effects of rapid recompression on reef fish discard mortality. ICES J. Mar. Sci. 77, 83–96 (2020).Article 

    Google Scholar 
    Drymon, J. M., Powers, S. P., Dindo, J., Dzwonkowski, B. & Henwood, T. Distributions of sharks across a continental shelf in the northern Gulf of Mexico. Mar. Coast. Fish. Dyn. Manag. Ecosyst. Sci. 2, 440–450 (2010).Article 

    Google Scholar 
    Ajemian, M. J. et al. Movement patterns and habitat use of tiger sharks (Galeocerdo cuvier) across ontogeny in the Gulf of Mexico. PLoS One 15, 1–24 (2020).
    Google Scholar 
    Ouzts, A. C. & Szedlmayer, S. T. Diel feeding patterns of Red Snapper on artificial reefs in the north-central Gulf of Mexico. Trans. Am. Fish. Soc. 132, 1186–1193 (2003).Article 

    Google Scholar 
    White, D. B. & Palmer, S. M. Age, growth, and reproduction of the red snapper, Lutjanus campechanus, from the Atlantic waters of the Southeastern US. Bull. Mar. Sci. 75, 335–360 (2004).
    Google Scholar 
    Fitzhugh, G. R., Lyon, H. M. & Barnett, B. K. Reproductive parameters of gray triggerfish (Balistes capriscus) from the Gulf of Mexico: Sex ratio, maturity and spawning fraction. SEDAR43-WP-03. (2015). http://sedarweb.org/sedar-82-rd14-sedar43-wp-03reproductive-parameters-gray-triggerfish-balistes-capriscus-gulf-mexico. Accessed 12 April 2021.Kelly-Stormer, A. et al. Gray Triggerfish reproductive biology, age, and growth off the Atlantic coast of the Southeastern USA. Trans. Am. Fish. Soc. 146, 523–538 (2017).Article 

    Google Scholar 
    Porch, C. E., Fitzhugh, G. R., Lang, E. T., Lyon, H. M. & Linton, B. C. Estimating the dependence of spawning frequency on size and age in Gulf of Mexico red snapper. Mar. Coast. Fish. 7, 233–245 (2015).Article 

    Google Scholar 
    Lang, E. T. & Fitzhugh, G. R. Oogenesis and fecundity type of gray triggerfish in the Gulf of Mexico. Mar. Coast. Fish. Dyn. Manag. Ecosyst. Sci. 7, 338–348 (2015).Article 

    Google Scholar 
    Woods, M. K. et al. Size and age at maturity of female red snapper Lutjanus campechanus in the Northern Gulf of Mexico. Proc. Gulf Caribb. Fish. Inst. 54, 526–537 (2003).
    Google Scholar 
    Simmons, C. M. & Szedlmayer, S. T. Territoriality, reproductive behavior, and parental care in gray triggerfish, Balistes capriscus, from the Northern Gulf of Mexico. Bull. Mar. Sci. 88, 197–209 (2012).Article 

    Google Scholar 
    Mackichan, C. A. & Szedlmayer, S. T. Reproductive behavior of the gray triggerfish, Balistes capriscus, in the northeastern Gulf of Mexico. Proc. Gulf Caribb. Fish. Inst. 59, 213–218 (2007).
    Google Scholar 
    Diamond, S. L. et al. Movers and stayers: Individual variability in site fidelity and movements of red snapper off Texas. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III, Cowan, J. H. Jr., Nieland, D. A. & Fitzhugh, G. R.), 163–187 (2007).Spiegel, O., Leu, S. T., Bull, C. M. & Sih, A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol. Lett. 20, 3–18 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    Smith, F. Understanding HPE in the VEMCO Positioning System (VPS). (2013).US Department of Defense. Global Positioning System Standard Positioning Service Performance Standard. http://www.gps.gov/technical/ps/2008-SPS-performance-standard.pdf (2008). Accessed 08 July 2020.Heupel, M. R., Reiss, K. L., Yeiser, B. G. & Simpfendorfer, C. A. Effects of biofouling on performance of moored data logging acoustic receivers. Limnol. Oceanogr. Methods 6, 327–335 (2008).Article 

    Google Scholar 
    National Oceanic and Atmospheric Administration & National Weather Service. National Data Buoy Center: Station 42012—Orange Beach. http://www.ndbc.noaa.gov/station_page.php?station=42012 (2017). Accessed 07 November 2017.National Oceanic and Atmospheric Administration & National Weather Service. National Data Buoy Center: Station 42040- Luke Offshore Test Platform. https://www.ndbc.noaa.gov/station_page.php?station=42040 (2019). Accessed 07 January 2019.Lazaridis, E. R Package ‘lunar’: lunar phase & distance, seasons and other environmental factors. https://cran.r-project.org/web/packages/lunar/lunar.pdf (2015). Accessed 12 August 2019.Thieurmel, B. & Elmarhraoui, A. R Package ‘suncalc’: compute sun position, sunlight phases, moon position and lunar phase. https://cran.r-project.org/web/packages/suncalc/suncalc.pdf (2019). Accessed 22 June 2019.National Geophysical Data Center. U.S. Coastal Relief Model—Central Gulf of Mexico. https://doi.org/10.7289/V54Q7RW0 (2001).Cox, D. R. & Oakes, D. Analysis of Survival Data (Chapman and Hall, 1984).Benhamou, S. Dynamic approach to space and habitat use based on biased random bridges. PLoS One 6, e14592 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Horne, J. S., Garton, E. O., Krone, S. M. & Lewis, J. S. Analyzing animal movements using Brownian bridges. Ecology 88, 2354–2363 (2007).PubMed 
    Article 

    Google Scholar 
    Tracey, J. A. et al. Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation. PLoS One 9, e101205 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Tracey, J. A. et al. R Package ‘mkde’: 2D and 3D movement-based kernel density estimates (MKDEs). https://CRAN.R-project.org/package=mkde (2014). Accessed 17 June 2019.Worton, B. J. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70, 164–168 (1989).Article 

    Google Scholar 
    Wood, S. N. Package ‘mgcv’: Mixed GAM computation vehicle with automatic smoothness estimation. https://doi.org/10.1201/9781315370279 (2019). More

  • in

    A climate risk index for marine life

    Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).CAS 
    Article 

    Google Scholar 
    Brown, S. C., Wigley, T. M. L., Otto-Bliesner, B. L., Rahbek, C. & Fordham, D. A. Persistent Quaternary climate refugia are hospices for biodiversity in the Anthropocene. Nat. Clim. Change 10, 244–248 (2020).Article 

    Google Scholar 
    O’Hara, C. C., Frazier, M. & Halpern, B. S. At-risk marine biodiversity faces extensive, expanding, and intensifying human impacts. Science 372, 84–87 (2021).Article 
    CAS 

    Google Scholar 
    Halpern, B. S. et al. An index to assess the health and benefits of the global ocean. Nature 488, 615–620 (2012).CAS 
    Article 

    Google Scholar 
    Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).CAS 
    Article 

    Google Scholar 
    Costello, C. et al. The future of food from the sea. Nature 588, 95–100 (2020).CAS 
    Article 

    Google Scholar 
    Lotze, H. K., Bryndum-Buchholz, A. & Boyce, D. G. in The Impacts of Climate Change: Comprehensive Study of the Physical, Societal and Political Issues (ed. Letcher, T.) 205–231 (Elsevier, 2021); https://doi.org/10.1016/B978-0-12-822373-4.00017-3Boyce, D. G., Lotze, H. K., Tittensor, D. P., Carozza, D. A. & Worm, B. Future ocean biomass losses may widen socioeconomic equity gaps. Nat. Commun. 11, 2235 (2020).CAS 
    Article 

    Google Scholar 
    Tittensor, D. P. et al. Integrating climate adaptation and biodiversity conservation in the global ocean. Sci. Adv. 5, 2235 (2019).Article 

    Google Scholar 
    Wilson, K. L., Tittensor, D. P., Worm, B. & Lotze, H. K. Incorporating climate change adaptation into marine protected area planning. Glob. Change Biol. 26, 3251–3267 (2020).Article 

    Google Scholar 
    Barange, M. et al. (eds) Impacts of Climate Change on Fisheries and Aquaculture: Synthesis of Current Knowledge, Adaptation and Mitigation Options FAO Fisheries and Aquaculture Technical Paper No. 627 (FAO, 2018).Hare, J. A. et al. A vulnerability assessment of fish and invertebrates to climate change on the northeast U.S. continental shelf. PLoS ONE 11, 1–654 (2016).CAS 
    Article 

    Google Scholar 
    Boyce, D. G., Fuller, S., Karbowski, C., Schleit, K. & Worm, B. Leading or lagging: how well are climate change considerations being incorporated into Canadian fisheries management? Can. J. Fish. Aquat. Sci. 78, 1120–1129 (2021).Article 

    Google Scholar 
    IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Change 5, 215–225 (2015).Article 

    Google Scholar 
    de los Ríos, C., Watson, J. E. M. & Butt, N. Persistence of methodological, taxonomical, and geographical bias in assessments of species’ vulnerability to climate change: a review. Glob. Ecol. Conserv. 15, e00412 (2018).Article 

    Google Scholar 
    Foden, W. B. et al. Climate change vulnerability assessment of species. WIREs Clim. Change 10, e551 (2019).Article 

    Google Scholar 
    Comte, L. & Olden, J. D. Climatic vulnerability of the world’s freshwater and marine fishes. Nat. Clim. Change 7, 718–722 (2017).Article 

    Google Scholar 
    Albouy, C. et al. Global vulnerability of marine mammals to global warming. 1–12 (2020).Foden, W. B. et al. Identifying the world’s most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals. PLoS ONE 8, e65427 (2013).CAS 
    Article 

    Google Scholar 
    Kesner-Reyes, K. et al. AquaMaps: algorithm and data sources for aquatic organisms. In FishBase v.04/2012 (eds. Froese, R. & Pauly, D.) www.fishbase.org (2016).Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J. & Bates, A. E. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92 (2015).CAS 
    Article 

    Google Scholar 
    Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 
    Article 

    Google Scholar 
    Cheung, W. W. L., Watson, R., Morato, T., Pitcher, T. J. & Pauly, D. Intrinsic vulnerability in the global fish catch. Mar. Ecol. Prog. Ser. 333, 1–12 (2007).Article 

    Google Scholar 
    IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, in the press).IPCC Climate Change 2001: Impacts, Adaptation, and Vulnerability (eds McCarthy, J. J. et al.) (Cambridge Univ. Press, 2001).The IUCN Red List of Threatened Species v.2021-1 (IUCN, 2021); https://www.iucnredlist.orgTittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).CAS 
    Article 

    Google Scholar 
    Rogers, A. et al. Critical Habitats and Biodiversity: Inventory, Thresholds and Governance. Sci. Rep. 10, 548 (World Resources Institute, 2020).Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).CAS 
    Article 

    Google Scholar 
    Halpern, B. S. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat. Commun. 6, 7615 (2015).CAS 
    Article 

    Google Scholar 
    Pontavice, H., Gascuel, D., Reygondeau, G., Stock, C. & Cheung, W. W. L. Climate‐induced decrease in biomass flow in marine food webs may severely affect predators and ecosystem production. Glob. Change Biol. 27, 2608–2622 (2021).Article 
    CAS 

    Google Scholar 
    Estes, J. A., Heithaus, M., McCauley, D. J., Rasher, D. B. & Worm, B. Megafaunal impacts on structure and function of ocean ecosystems. Annu. Rev. Environ. Res. 41, 83–116 (2016).Article 

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

    Google Scholar 
    Moilanen, A., Kujala, H. & Mikkonen, N. A practical method for evaluating spatial biodiversity offset scenarios based on spatial conservation prioritization outputs. Methods Ecol. Evol. 11, 794–803 (2020).Article 

    Google Scholar 
    Ceballos, G. & Ehrlich, P. R. Global mammal distributions, biodiversity hotspots, and conservation. Proc. Natl Acad. Sci. USA 103, 19374–19379 (2006).CAS 
    Article 

    Google Scholar 
    Williams, P. H., Gaston, K. J. & Humphries, C. J. Mapping biodiversity value worldwide: combining higher-taxon richness from different groups. Proc. R. Soc. Lond. B 264, 141–148 (1997).Article 

    Google Scholar 
    Blanchard, J. L. et al. Linked sustainability challenges and trade-offs among fisheries, aquaculture and agriculture. Nat. Ecol. Evol. 1, 1240–1249 (2017).Article 

    Google Scholar 
    Robiou Du Pont, Y. et al. Equitable mitigation to achieve the Paris Agreement goals. Nat. Clim. Change 7, 38–43 (2017).Article 

    Google Scholar 
    Payne, N. L. et al. Fish heating tolerance scales similarly across individual physiology and populations. Commun. Biol. 4, 264 (2021).Article 

    Google Scholar 
    First Draft of the Post-2020 Global Biodiversity Framework (Convention on Biological Diversity, 2021).Keppel, G. et al. Refugia: identifying and understanding safe havens for biodiversity under climate change. Glob. Ecol. Biogeogr. 21, 393–404 (2012).Article 

    Google Scholar 
    Bryndum‐Buchholz, A., Tittensor, D. P. & Lotze, H. K. The status of climate change adaptation in fisheries management: policy, legislation and implementation. Fish Fish. https://doi.org/10.1111/faf.12586 (2021).Maureaud, A. et al. Are we ready to track climate‐driven shifts in marine species across international boundaries? A global survey of scientific bottom trawl data. Glob. Change Biol. 27, 220–236 (2021).Article 
    CAS 

    Google Scholar 
    Boyce, D. G. et al. Operationalizing climate risk for fisheries in a global warming hotspot. Preprint at: https://doi.org/10.1101/2022.07.19.500650 (2022).Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).CAS 
    Article 

    Google Scholar 
    Olden, J. D., Hogan, Z. S. & Vander Zanden, M. J. Small fish, big fish, red fish, blue fish: size-biased extinction risk of the world’s freshwater and marine fishes. Glob. Ecol. Biogeogr. 16, 694–701 (2007).Article 

    Google Scholar 
    Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).CAS 
    Article 

    Google Scholar 
    Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature https://doi.org/10.1038/s41586-019-1132-4 (2019).Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).Article 

    Google Scholar 
    Laidre, K. L. et al. Quantifying the sensitivity of Arctic marine mammals to climate-induced habitat change. Ecol. Appl. 18, S97–S125 (2008).Article 

    Google Scholar 
    Rosset, V. & Oertli, B. Freshwater biodiversity under climate warming pressure: identifying the winners and losers in temperate standing waterbodies. Biol. Conserv. 144, 2311–2319 (2011).Article 

    Google Scholar 
    Peters, R. L. The greenhouse effect and nature reserves. Biosciences 35, 707–717 (1985).Article 

    Google Scholar 
    Garcia, R. A. et al. Matching species traits to projected threats and opportunities from climate change. J. Biogeogr. 41, 724–735 (2014).Article 

    Google Scholar 
    IUCN Red List Categories and Criteria: Version 3.1 (IUCN, 2012).Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790 (2006).CAS 
    Article 

    Google Scholar 
    Worm, B., Lotze, H. K., Hillebrand, H. & Sommer, U. Consumer versus resource control of species diversity and ecosystem functioning. Nature 417, 848–851 (2002).CAS 
    Article 

    Google Scholar 
    Worm, B. & Duffy, J. E. Biodiversity, productivity, and stability in real food webs. Trends Ecol. Evol. 18, 628–632 (2003).Article 

    Google Scholar 
    Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952 (2008).CAS 
    Article 

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

    Google Scholar 
    Le Bris, A. et al. Climate vulnerability and resilience in the most valuable North American fishery. Proc. Natl Acad. Sci. USA 115, 1831–1836 (2018).Article 
    CAS 

    Google Scholar 
    Henson, S. A. et al. Rapid emergence of climate change in environmental drivers of marine ecosystems. Nat. Commun. 8, 14682 (2017).Article 

    Google Scholar 
    Bates, A. E. et al. Climate resilience in marine protected areas and the ‘Protection Paradox’. Biol. Conserv. 236, 305–314 (2019).Article 

    Google Scholar 
    Xu, C., Kohler, T. A., Lenton, T. M., Svenning, J.-C. & Scheffer, M. Future of the human climate niche. Proc. Natl Acad. Sci. USA 117, 11350–11355 (2020).CAS 
    Article 

    Google Scholar 
    Davies, T. E., Maxwell, S. M., Kaschner, K., Garilao, C. & Ban, N. C. Large marine protected areas represent biodiversity now and under climate change. Sci. Rep. 7, 9569 (2017).CAS 
    Article 

    Google Scholar 
    MacKenzie, B. R. et al. A cascade of warming impacts brings bluefin tuna to Greenland waters. Glob. Change Biol. 20, 2484–2491 (2014).Article 

    Google Scholar 
    Shackell, N. L., Ricard, D. & Stortini, C. Thermal habitat index of many Northwest Atlantic temperate species stays neutral under warming projected for 2030 but changes radically by 2060. PLoS ONE 9 (2014).Boyce, D. G., Frank, K. T., Worm, B. & Leggett, W. C. Spatial patterns and predictors of trophic control across marine ecosystems. Ecol. Lett. 18, 1001–1011 (2015).Article 

    Google Scholar 
    Boyce, D. G., Frank, K. T. & Leggett, W. C. From mice to elephants: overturning the ‘one size fits all’ paradigm in marine plankton food chains. Ecol. Lett. 18, 504–515 (2015).Article 

    Google Scholar 
    Frank, K. T., Petrie, B., Shackell, N. L. & Choi, J. S. Reconciling differences in trophic control in mid-latitude marine ecosystems. Ecol. Lett. 9, 1096–1105 (2006).Article 

    Google Scholar 
    Frank, K. T., Petrie, B. & Shackell, N. L. The ups and downs of trophic control in continental shelf ecosystems. Trends Ecol. Evol. 22, 236–242 (2007).Article 

    Google Scholar 
    Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1056 (2009).CAS 
    Article 

    Google Scholar 
    Burrows, M. T. et al. The pace of shifting climate in marine and terrestrial ecosystems. Science 334, 652–655 (2011).CAS 
    Article 

    Google Scholar 
    Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).CAS 
    Article 

    Google Scholar 
    Poloczanska, E. S. et al. Responses of marine organisms to climate change across oceans. Front. Mar. Sci. 3, 62 (2016).Article 

    Google Scholar 
    Boyce, D. G., Lewis, M. L. & Worm, B. Global phytoplankton decline over the past century. Nature 466, 591–596 (2010).CAS 
    Article 

    Google Scholar 
    Burek, K. A., Gulland, F. M. D. & O’Hara, T. M. Effects of climate change on Arctic marine mammal health. Ecol. Appl. 18, S126–S134 (2008).Article 

    Google Scholar 
    Staude, I. R., Navarro, L. M. & Pereira, H. M. Range size predicts the risk of local extinction from habitat loss. Glob. Ecol. Biogeogr. 29, 16–25 (2020).Article 

    Google Scholar 
    Moore, S. E. & Huntington, H. P. Arctic marine mammals and climate change: impacts and resilience. Ecol. Appl. 18, S157–S165 (2008).Article 

    Google Scholar 
    Kaschner, K., Watson, R., Trites, A. & Pauly, D. Mapping world-wide distributions of marine mammal species using a relative environmental suitability (RES) model. Mar. Ecol. Prog. Ser. 316, 285–310 (2006).Article 

    Google Scholar 
    Gonzalez-Suarez, M., Gomez, A. & Revilla, E. Which intrinsic traits predict vulnerability to extinction depends on the actual threatening processes. Ecosphere 4, 6 (2013).Article 

    Google Scholar 
    Rogan, J. E. & Lacher, T. E. in Reference Module in Earth Systems and Environmental Sciences (Elsevier, 2018); https://doi.org/10.1016/B978-0-12-409548-9.10913-3Warren, M. S. et al. Rapid responses of British butterflies to opposing forces of climate and habitat change. Nature 414, 65–69 (2001).CAS 
    Article 

    Google Scholar 
    Chessman, B. C. Identifying species at risk from climate change: traits predict the drought vulnerability of freshwater fishes. Biol. Conserv. 160, 40–49 (2013).Article 

    Google Scholar 
    Davidson, A. D. D. et al. Drivers and hotspots of extinction risk in marine mammals. Proc. Natl Acad. Sci. USA 109, 3395–3400 (2012).CAS 
    Article 

    Google Scholar 
    Cheung, W. W. L., Pauly, D. & Sarmiento, J. L. How to make progress in projecting climate change impacts. ICES J. Mar. Sci. 70, 1069–1074 (2013).Article 

    Google Scholar 
    Fenchel, T. Intrinsic rate of natural increase: the relationship with body size. Oecologia 14, 317–326 (1974).Article 

    Google Scholar 
    Healy, K. et al. Ecology and mode-of-life explain lifespan variation in birds and mammals. Proc. R. Soc. B 281, 20140298 (2014).Article 

    Google Scholar 
    Carilli, J., Donner, S. D. & Hartmann, A. C. Historical temperature variability affects coral response to heat stress. PLoS ONE 7, e34418 (2012).CAS 
    Article 

    Google Scholar 
    Guest, J. R. et al. Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS ONE 7, e33353 (2012).CAS 
    Article 

    Google Scholar 
    Donner, S. D. & Carilli, J. Resilience of Central Pacific reefs subject to frequent heat stress and human disturbance. Sci. Rep. 9, 3484 (2019).Article 
    CAS 

    Google Scholar 
    Rehm, E. M., Olivas, P., Stroud, J. & Feeley, K. J. Losing your edge: climate change and the conservation value of range‐edge populations. Ecol. Evol. 5, 4315–4326 (2015).Article 

    Google Scholar 
    Ready, J. et al. Predicting the distributions of marine organisms at the global scale. Ecol. Modell. 221, 467–478 (2010).Article 

    Google Scholar 
    Jones, M. C., Dye, S. R., Pinnegar, J. K., Warren, R. & Cheung, W. W. L. Modelling commercial fish distributions: prediction and assessment using different approaches. Ecol. Modell. 225, 133–145 (2012).Article 

    Google Scholar 
    Froese, R. & Pauly, D. FishBase v.02/2022 www.fishbase.org (2022).Palomares, M. L. D. & Pauly, D. SeaLifeBase v.11/2014 www.sealifebase.org (2022).van Buuren, S. Flexible Imputation of Missing Data (Chapman & Hall/CRC, 2012).Dahlke, F. T., Wohlrab, S., Butzin, M. & Portner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).CAS 
    Article 

    Google Scholar 
    Stortini, C. H., Shackell, N. L., Tyedmers, P. & Beazley, K. Assessing marine species vulnerability to projected warming on the Scotian Shelf, Canada. ICES J. Mar. Sci. 72, 1713–1743 (2015).Article 

    Google Scholar 
    Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496 (2007).Article 

    Google Scholar 
    Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev. 13, 3571–3605 (2020).CAS 
    Article 

    Google Scholar 
    Samhouri, J. F. et al. Sea sick? Setting targets to assess ocean health and ecosystem services. Ecosphere 3, art41 (2012).Article 

    Google Scholar 
    Rao, T. R. A curve for all reasons. Resonance 5, 85–90 (2000).Article 

    Google Scholar 
    Mora, C. et al. Biotic and human vulnerability to projected changes in ocean biogeochemistry over the 21st century. PLoS Biol. 11, 10 (2013).Article 
    CAS 

    Google Scholar 
    Lotze, H. K. et al. Ensemble projections of global ocean animal biomass with climate change. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1900194116 (2019).Eyring, V. et al. Taking climate model evaluation to the next level. Nat. Clim. Change 9, 102–110 (2019).Article 

    Google Scholar 
    Oppenheimer, M., Little, C. M. & Cooke, R. M. Expert judgement and uncertainty quantification for climate change. Nat. Clim. Change 6, 445–451 (2016).Article 

    Google Scholar 
    Budescu, D. V., Por, H. H. & Broomell, S. B. Effective communication of uncertainty in the IPCC reports. Climatic Change 113, 181–200 (2012).Article 

    Google Scholar 
    Swart, R., Bernstein, L., Ha-Duong, M. & Petersen, A. Agreeing to disagree: uncertainty management in assessing climate change, impacts and responses by the IPCC. Climatic Change 92, 1–29 (2009).Article 

    Google Scholar 
    NAFO Annual Fisheries Statistics Database (NAFO, 2021).Horton, T. et al. World Register of Marine Species (WoRMS) https://www.marinespecies.org (2020).Total Wealth per Capita, 1995 to 2014 (World Bank, 2022); https://ourworldindata.org/grapher/total-wealth-per-capitaDepth of the Food Deficit in Kilocalories per Person per Day, 1992 to 2016 (World Bank, 2022); https://ourworldindata.org/grapher/depth-of-the-food-deficitBoyce, D. G. et al. A climate risk index for marine life. Dryad https://doi.org/10.5061/dryad.7wm37pvwr (2022).R Core Team R: A Language and Environment for Statistical Computing Version 4.0.4 (R Foundation for Statistical Computing, 2021). More