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    Metagenomic mapping of cyanobacteria and potential cyanotoxin producing taxa in large rivers of the United States

    Hallegraeff, G. M. Ocean climate change, phytoplankton community responses, and harmful algal blooms: A formidable predictive challenge 1. J. Phycol. 46, 220–235 (2010).Article 
    CAS 

    Google Scholar 
    Itakura, S. & Imai, I. Economic impacts of harmful algal blooms on fisheries and aquaculture in western Japan—An overview of interannual variability and interspecies comparison. PICES Sci. Rep. 47, 17 (2014).
    Google Scholar 
    Haigh, N. & Esenkulova, S. Economic losses to the British Columbia salmon aquaculture industry due to harmful algal blooms, 2009–2012. PICES Sci. Rep. 47, 2 (2014).
    Google Scholar 
    Sharma, N. K. et al. (eds) Cyanobacteria: An Economic Perspective 245–256 (Wiley, 2014).
    Google Scholar 
    O’Neil, J. M., Davis, T. W., Burford, M. A. & Gobler, C. J. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 14, 313–334. https://doi.org/10.1016/j.hal.2011.10.027 (2012).Article 
    CAS 

    Google Scholar 
    Paerl, H. W. & Huisman, J. Climate change: A catalyst for global expansion of harmful cyanobacterial blooms. Environ. Microbiol. Rep. 1, 27–37. https://doi.org/10.1111/j.1758-2229.2008.00004.x (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hallegraeff, G. M. et al. Perceived global increase in algal blooms is attributable to intensified monitoring and emerging bloom impacts. Commun. Earth Environ. 2, 117. https://doi.org/10.1038/s43247-021-00178-8 (2021).Article 
    ADS 

    Google Scholar 
    Hennon, G. M. M. & Dyhrman, S. T. Progress and promise of omics for predicting the impacts of climate change on harmful algal blooms. Harmful Algae 91, 101587. https://doi.org/10.1016/j.hal.2019.03.005 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kudela, R., Berdalet, E. & Urban, E. Harmful Algal Blooms: A Scientific Summary for Policy Makers (2015).Lezcano, M., Velázquez, D., Quesada, A. & El-Shehawy, R. Diversity and temporal shifts of the bacterial community associated with a toxic cyanobacterial bloom: An interplay between microcystin producers and degraders. Water Res. 125, 52–61. https://doi.org/10.1016/j.watres.2017.08.025 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Scherer, P. I. et al. Temporal dynamics of the microbial community composition with a focus on toxic cyanobacteria and toxin presence during harmful algal blooms in two South German Lakes. Front. Microbiol. 8, 02387. https://doi.org/10.3389/fmicb.2017.02387 (2017).Article 

    Google Scholar 
    Woodhouse, J. N. et al. Microbial communities reflect temporal changes in cyanobacterial composition in a shallow ephemeral freshwater lake. ISME J. 10, 1337–1351. https://doi.org/10.1038/ismej.2015.218 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Beaver, J. R. et al. Land use patterns, ecoregion, and microcystin relationships in U.S. lakes and reservoirs: A preliminary evaluation. Harmful Algae 36, 57–62. https://doi.org/10.1016/j.hal.2014.03.005 (2014).Article 
    CAS 

    Google Scholar 
    Loftin, K. A. et al. Cyanotoxins in inland lakes of the United States: Occurrence and potential recreational health risks in the EPA National Lakes Assessment 2007. Harmful Algae 56, 77–90. https://doi.org/10.1016/j.hal.2016.04.001 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Casero, M. C., Velázquez, D., Medina-Cobo, M., Quesada, A. & Cirés, S. Unmasking the identity of toxigenic cyanobacteria driving a multi-toxin bloom by high-throughput sequencing of cyanotoxins genes and 16S rRNA metabarcoding. Sci. Total Environ. 665, 367–378. https://doi.org/10.1016/j.scitotenv.2019.02.083 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Chaffin, J. D., Sigler, V. & Bridgeman, T. B. Connecting the blooms: Tracking and establishing the origin of the record-breaking Lake Erie Microcystis bloom of 2011 using DGGE. Aquat. Microb. Ecol. 73, 29–39 (2014).Article 

    Google Scholar 
    Stanley, E. H. & Jones, J. B. (eds) Stream Ecosystems in a Changing Environment 321–348 (Elsevier, 2016).Book 

    Google Scholar 
    Giblin, S. M. & Gerrish, G. A. Environmental factors controlling phytoplankton dynamics in a large floodplain river with emphasis on cyanobacteria. River Res. Appl. 36, 1137–1150. https://doi.org/10.1002/rra.3658 (2020).Article 

    Google Scholar 
    Graham, J. L., Ziegler, A. C., Loving, B. L. & Loftin, K. A. Fate and Transport of Cyanobacteria and Associated Toxins and Taste-and-Odor Compounds from Upstream Reservoir Releases in the Kansas River, Kansas, September and October 2011 65 (US Geological Survey, 2012).
    Google Scholar 
    Knowlton, M. F. & Jones, J. R. Seston, light, nutrients and chlorophyll in the lower Missouri River, 1994–1998. J. Freshw. Ecol. 15, 283–297. https://doi.org/10.1080/02705060.2000.9663747 (2000).Article 

    Google Scholar 
    Otten, T. G., Crosswell, J. R., Mackey, S. & Dreher, T. W. Application of molecular tools for microbial source tracking and public health risk assessment of a Microcystis bloom traversing 300 km of the Klamath River. Harmful Algae 46, 71–81 (2015).Article 

    Google Scholar 
    Preece, E. P., Hardy, F. J., Moore, B. C. & Bryan, M. A review of microcystin detections in Estuarine and Marine waters: Environmental implications and human health risk. Harmful Algae 61, 31–45. https://doi.org/10.1016/j.hal.2016.11.006 (2017).Article 
    CAS 

    Google Scholar 
    Reinl, K. L., Sterner, R. W., Lafrancois, B. M. & Brovold, S. Fluvial seeding of cyanobacterial blooms in oligotrophic Lake Superior. Harmful Algae 100, 101941. https://doi.org/10.1016/j.hal.2020.101941 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bridgeman, T. B. et al. From River to Lake: Phosphorus partitioning and algal community compositional changes in Western Lake Erie. J. Great Lakes Res. 38, 90–97 (2012).Article 
    CAS 

    Google Scholar 
    Brown, B. L. et al. Metagenomic analysis of planktonic microbial consortia from a non-tidal urban-impacted segment of James River. Stand Genomic Sci. 10, 65. https://doi.org/10.1186/s40793-015-0062-5 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hamner, S. et al. Metagenomic profiling of microbial pathogens in the Little Bighorn River, Montana. Int. J. Environ. Res. Public Health 16, 071097. https://doi.org/10.3390/ijerph16071097 (2019).Article 
    CAS 

    Google Scholar 
    Staley, C. et al. Application of Illumina next-generation sequencing to characterize the bacterial community of the Upper Mississippi River. J. Appl. Microbiol. 115, 1147–1158. https://doi.org/10.1111/jam.12323 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Winter, C., Hein, T., Kavka, G., Mach, R. L. & Farnleitner, A. H. Longitudinal changes in the bacterial community composition of the Danube River: A whole-river approach. Appl. Environ. Microbiol. 73, 421–431. https://doi.org/10.1128/aem.01849-06 (2007).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Jackson, C. R., Millar, J. J., Payne, J. T., Ochs, C. A. & Wommack, K. E. Free-living and particle-associated bacterioplankton in large rivers of the Mississippi River basin demonstrate biogeographic patterns. Appl. Environ. Microbiol. 80, 7186–7195. https://doi.org/10.1128/AEM.01844-14 (2014).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Payne, J. T., Jackson, C. R., Millar, J. J. & Ochs, C. A. Timescales of variation in diversity and production of bacterioplankton assemblages in the Lower Mississippi River. PLoS ONE 15, e0230945. https://doi.org/10.1371/journal.pone.0230945 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Payne, J. T., Millar, J. J., Jackson, C. R. & Ochs, C. A. Patterns of variation in diversity of the Mississippi river microbiome over 1,300 kilometers. PLoS ONE 12, e0174890. https://doi.org/10.1371/journal.pone.0174890 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Read, D. S. et al. Catchment-scale biogeography of riverine bacterioplankton. ISME J. 9, 516–526. https://doi.org/10.1038/ismej.2014.166 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Reddington, K. et al. Metagenomic analysis of planktonic riverine microbial consortia using nanopore sequencing reveals insight into river microbe taxonomy and function. GigaScience 9, 53. https://doi.org/10.1093/gigascience/giaa053 (2020).Article 
    CAS 

    Google Scholar 
    Staley, C. et al. Core functional traits of bacterial communities in the Upper Mississippi River show limited variation in response to land cover. Front. Microbiol. 5, 414 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Staley, C. et al. Species sorting and seasonal dynamics primarily shape bacterial communities in the Upper Mississippi River. Sci. Total Environ. 505, 435–445. https://doi.org/10.1016/j.scitotenv.2014.10.012 (2015).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Van Rossum, T. et al. Year-long metagenomic study of river microbiomes across land use and water quality. Front. Microbiol. 6, 1405 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Kim, K. H. et al. Application of metagenome analysis to characterize the molecular diversity and saxitoxin-producing potentials of a cyanobacterial community: A case study in the North Han River, Korea. Appl. Biol. Chem. 61, 153–161. https://doi.org/10.1007/s13765-017-0342-4 (2018).Article 
    CAS 

    Google Scholar 
    Graham, J. L. et al. Cyanotoxin occurrence in large rivers of the United States. Inland Waters 10, 109–117. https://doi.org/10.1080/20442041.2019.1700749 (2020).Article 
    CAS 

    Google Scholar 
    Zuellig, R. E., Graham, J. L., Stelzer, E. A., Loftin, K. A. & Rosen, B. H. Cyanobacteria, Cyanotoxin Synthetase Gene, and Cyanotoxin Occurrence Among Selected Large River Sites of the Conterminous United States, 2017–18 22 (US Geological Survey, 2021).
    Google Scholar 
    Kramer, B. J. et al. Nitrogen limitation, toxin synthesis potential, and toxicity of cyanobacterial populations in Lake Okeechobee and the St. Lucie River Estuary, Florida, during the 2016 state of emergency event. PLoS ONE 13, e0196278 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bouma-Gregson, K. et al. Impacts of microbial assemblage and environmental conditions on the distribution of anatoxin-a producing cyanobacteria within a river network. ISME J. 13, 1618–1634. https://doi.org/10.1038/s41396-019-0374-3 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tillett, D. et al. Structural organization of microcystin biosynthesis in Microcystis aeruginosa PCC7806: An integrated peptide–polyketide synthetase system. Chem. Biol. 7, 753–764 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dittmann, E., Fewer, D. P. & Neilan, B. A. Cyanobacterial toxins: Biosynthetic routes and evolutionary roots. FEMS Microbiol. Rev. 37, 23–43. https://doi.org/10.1111/j.1574-6976.2012.12000.x (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Jungblut, A. D. & Neilan, B. A. Molecular identification and evolution of the cyclic peptide hepatotoxins, microcystin and nodularin, synthetase genes in three orders of cyanobacteria. Arch. Microbiol. 185, 107–114. https://doi.org/10.1007/s00203-005-0073-5 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Meriluoto, J. et al. (eds) Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis 501–525 (Wiley, 2017).Book 

    Google Scholar 
    Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643. https://doi.org/10.1038/ismej.2017.119 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Graham, J. L., Dubrovsky, N. M., Loftin, K. A., Rosen, B. H. & Stelzer, E. A. Cyanotoxin, Chlorophyll-a, and Cyanobacterial Toxin Genetic Data for Samples Collected at Twelve Large River Sites Throughout the United States, June Through October 2019 (U.S. Geological Survey, 2022).
    Google Scholar 
    Dodds, W. K. & Smith, V. H. Nitrogen, phosphorus, and eutrophication in streams. Inland Waters 6, 155–164. https://doi.org/10.5268/IW-6.2.909 (2016).Article 
    CAS 

    Google Scholar 
    Debroas, D. et al. Overview of freshwater microbial eukaryotes diversity: A first analysis of publicly available metabarcoding data. FEMS Microbiol. Ecol. 93, 23. https://doi.org/10.1093/femsec/fix023 (2017).Article 
    CAS 

    Google Scholar 
    Henson, M. W. et al. Nutrient dynamics and stream order influence microbial community patterns along a 2914 kilometer transect of the Mississippi River. Limnol. Oceanogr. 63, 1837–1855. https://doi.org/10.1002/lno.10811 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Ghai, R. et al. Metagenomics of the water column in the pristine upper course of the Amazon river. PLoS ONE 6, e23785. https://doi.org/10.1371/journal.pone.0023785 (2011).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Liao, J. et al. Cyanobacteria in lakes on Yungui Plateau, China are assembled via niche processes driven by water physicochemical property, lake morphology and watershed land-use. Sci. Rep. 6, 36357. https://doi.org/10.1038/srep36357 (2016).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Monchamp, M.-E. et al. Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication. Nat. Ecol. Evol. 2, 317–324. https://doi.org/10.1038/s41559-017-0407-0 (2018).Article 
    PubMed 

    Google Scholar 
    Pessi, I. S., Maalouf, P. D. C., LaughinghouseBaurain, H. D. D. & Wilmotte, A. On the use of high-throughput sequencing for the study of cyanobacterial diversity in Antarctic aquatic mats. J. Phycol. 52, 356–368. https://doi.org/10.1111/jpy.12399 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Tanvir, R. U., Hu, Z., Zhang, Y. & Lu, J. Cyanobacterial community succession and associated cyanotoxin production in hypereutrophic and eutrophic freshwaters. Environ. Pollut. 290, 118056. https://doi.org/10.1016/j.envpol.2021.118056 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chételat, J., Pick, F. R. & Hamilton, P. B. Potamoplankton size structure and taxonomic composition: Influence of river size and nutrient concentrations. Limnol. Oceanogr. 51, 681–689 (2006).Article 
    ADS 

    Google Scholar 
    Heiskary, S. & Markus, H. Establishing relationships among nutrient concentrations, phytoplankton abundance, and biochemical oxygen demand in Minnesota, USA, rivers. Lake Reserv. Manag. 17, 251–262 (2001).Article 
    CAS 

    Google Scholar 
    Smith, V. H. Eutrophication of freshwater and coastal marine ecosystems a global problem. Environ. Sci. Pollut. Res. 10, 126–139 (2003).Article 
    CAS 

    Google Scholar 
    Verspagen, J. M. et al. Rising CO2 levels will intensify phytoplankton blooms in eutrophic and hypertrophic lakes. PLoS ONE 9, e104325 (2014).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zepernick, B. N. et al. Elevated pH conditions associated with Microcystis spp. blooms decrease viability of the cultured diatom Fragilaria crotonensis and natural diatoms in Lake Erie. Front. Microbiol. 12, 598736. https://doi.org/10.3389/fmicb.2021.598736 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Urban, L. et al. Freshwater monitoring by nanopore sequencing. Elife 10, 61504. https://doi.org/10.7554/eLife.61504 (2021).Article 

    Google Scholar 
    Lee, C. J. & Henderson, R. J. Tracking Water-Quality in U.S. Streams and Rivers: U.S. Geological Survey National Water Quality Network. https://nrtwq.usgs.gov/nwqn (2020).Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data (2010).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257. https://doi.org/10.1186/s13059-019-1891-0 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lu, J. B. F., Thielen, P. & Salzberg, S. L. Bracken: Estimating species abundance in metagenomics data. PeerJ Comput. Sci. 3, 104. https://doi.org/10.7717/peerj-cs.104 (2017).Article 

    Google Scholar 
    Bagley, M. et al. High-throughput environmental DNA analysis informs a biological assessment of an urban stream. Ecol. Ind. 104, 378–389. https://doi.org/10.1016/j.ecolind.2019.04.088 (2019).Article 
    CAS 

    Google Scholar 
    Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963. https://doi.org/10.1093/bioinformatics/btr507 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nübel, U., Garcia-Pichel, F. & Muyzer, G. PCR primers to amplify 16S rRNA genes from cyanobacteria. Appl. Environ. Microbiol. 63, 3327–3332. https://doi.org/10.1128/aem.63.8.3327-3332.1997 (1997).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Neilan, B. A. et al. rRNA sequences and evolutionary relationships among toxic and nontoxic cyanobacteria of the genus Microcystis. Int. J. Syst. Bacteriol. 47, 693–697. https://doi.org/10.1099/00207713-47-3-693 (1997).Article 
    CAS 
    PubMed 

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

    Google Scholar 
    Oksanen, J. et al. Vegan: Community Ecology Package. R Package Version 2.5-2 (2018).Wickham, H. ggplot2-Elegant Graphics for Data Analysis (Springer, 2016).MATH 

    Google Scholar 
    U.S. Geological Survey. National Water Information System Database. https://doi.org/10.5066/F7P55KJN (2022). More

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    Optimization of green and environmentally-benign synthesis of isoamyl acetate in the presence of ball-milled seashells by response surface methodology

    McElroy, C. R., Constantinou, A., Jones, L. C., Summerton, L. & Clark, J. H. Towards a holistic approach to metrics for the 21st century pharmaceutical industry. Green Chem. 17, 3111–3121. https://doi.org/10.1039/C5GC00340G (2015).Article 
    CAS 

    Google Scholar 
    Zimmerman, J. B., Anastas, P. T., Erythropel, H. C. & Leitner, W. Designing for a green chemistry future. Science 367, 397–400. https://doi.org/10.1126/science.aay3060 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Sheldon, R. A. Metrics of green chemistry and sustainability: Past, present, and future. ACS Sustain. Chem. Eng. 6, 32–48. https://doi.org/10.1021/acssuschemeng.7b03505 (2018).Article 
    CAS 

    Google Scholar 
    Anastas, P. T. & Williamson, T. C. in Green Chemistry, Vol. 626 ACS Symposium Series Ch. 1, 1–17 (American Chemical Society, 1996). https://doi.org/10.1021/bk-1996-0626.ch001.Clark, H. J. Green chemistry: Challenges and opportunities. Green Chem. 1, 1–8. https://doi.org/10.1039/A807961G (1999).Article 
    CAS 

    Google Scholar 
    Dekamin, M. G. & Eslami, M. Highly efficient organocatalytic synthesis of diverse and densely functionalized 2-amino-3-cyano-4 H-pyrans under mechanochemical ball milling. Green Chem. 16, 4914–4921 (2014).Article 
    CAS 

    Google Scholar 
    Eze, A. A. et al. Wet ball milling of niobium by using ethanol, determination of the crystallite size and microstructures. Sci. Rep. 11, 1–8 (2021).Article 

    Google Scholar 
    Gorrasi, G. & Sorrentino, A. Mechanical milling as a technology to produce structural and functional bio-nanocomposites. Green Chem. 17, 2610–2625 (2015).Article 
    CAS 

    Google Scholar 
    Li, L. H., Glushenkov, A. M., Hait, S. K., Hodgson, P. & Chen, Y. High-efficient production of boron nitride nanosheets via an optimized ball milling process for lubrication in oil. Sci. Rep. 4, 1–6 (2014).
    Google Scholar 
    Mac Naughton, G. E., Rolfe, S. A. & Siraj-Blatchford, I. E. Doing Early Childhood Research: International Perspectives on Theory and Practice (Open University Press, 2001).Evangelisti, L. et al. The borderline between reactivity and pre-reactivity of binary mixtures of gaseous carboxylic acids and alcohols. Angew. Chem. 129, 3930–3933 (2017).Article 
    ADS 

    Google Scholar 
    Gaspa, S., Porcheddu, A. & De Luca, L. Metal-free oxidative cross esterification of alcohols via acyl chloride formation. Adv. Synth. Catal. 358, 154–158 (2016).Article 
    CAS 

    Google Scholar 
    Fiorio, J. L., Braga, A. H., Guedes, C. L. S. B. & Rossi, L. M. Reusable heterogeneous tungstophosphoric acid-derived catalyst for green esterification of carboxylic acids. ACS Sustain. Chem. Eng. 7, 15874–15883 (2019).Article 
    CAS 

    Google Scholar 
    Karimi, B., Mirzaei, H. M. & Mobaraki, A. Periodic mesoporous organosilica functionalized sulfonic acids as highly efficient and recyclable catalysts in biodiesel production. Catal. Sci. Technol. 2, 828–834 (2012).Article 
    CAS 

    Google Scholar 
    Tran, T. T. V. et al. Selective production of green solvent (isoamyl acetate) from fusel oil using a sulfonic acid-functionalized KIT-6 catalyst. Mol. Catal. 484, 110724 (2020).Article 
    CAS 

    Google Scholar 
    Afshar, S. et al. Optimization of catalytic activity of sulfated titania for efficient synthesis of isoamyl acetate by response surface methodology. Mon. Chem. Chem. Mon. 146, 1949–1957 (2015).Article 
    CAS 

    Google Scholar 
    Chng, L. L., Yang, J. & Ying, J. Y. Efficient synthesis of amides and esters from alcohols under aerobic ambient conditions catalyzed by a Au/mesoporous Al2O3 nanocatalyst. Chemsuschem 8, 1916–1925 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Lozano, P., Bernal, J. M. & Navarro, A. A clean enzymatic process for producing flavour esters by direct esterification in switchable ionic liquid/solid phases. Green Chem. 14, 3026–3033 (2012).Article 
    CAS 

    Google Scholar 
    Su, L., Hong, R., Guo, X., Wu, J. & Xia, Y. Short-chain aliphatic ester synthesis using Thermobifida fusca cutinase. Food Chem. 206, 131–136 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Güvenç, A., Kapucu, N., Kapucu, H., Aydoğan, Ö. & Mehmetoğlu, Ü. Enzymatic esterification of isoamyl alcohol obtained from fusel oil: Optimization by response surface methodolgy. Enzyme Microb. Technol. 40, 778–785 (2007).Article 

    Google Scholar 
    Torres, S., Baigorí, M. D., Swathy, S., Pandey, A. & Castro, G. R. Enzymatic synthesis of banana flavour (isoamyl acetate) by Bacillus licheniformis S-86 esterase. Food Res. Int. 42, 454–460 (2009).Article 
    CAS 

    Google Scholar 
    Ando, H., Kurata, A. & Kishimoto, N. Antimicrobial properties and mechanism of volatile isoamyl acetate, a main flavour component of Japanese sake (Ginjo-shu). J. Appl. Microbiol. 118, 873–880 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ghamgui, H., Karra-Chaâbouni, M., Bezzine, S., Miled, N. & Gargouri, Y. Production of isoamyl acetate with immobilized Staphylococcus simulans lipase in a solvent-free system. Enzyme Microb. Technol. 38, 788–794 (2006).Article 
    CAS 

    Google Scholar 
    Romero, M., Calvo, L., Alba, C., Daneshfar, A. & Ghaziaskar, H. Enzymatic synthesis of isoamyl acetate with immobilized Candida antarctica lipase in n-hexane. Enzyme Microb. Technol. 37, 42–48 (2005).Article 
    CAS 

    Google Scholar 
    Borges, M. E. & Díaz, L. Recent developments on heterogeneous catalysts for biodiesel production by oil esterification and transesterification reactions: A review. Renew. Sustain. Energy Rev. 16, 2839–2849 (2012).Article 
    CAS 

    Google Scholar 
    Li, K.-T., Wang, C.-K., Wang, I. & Wang, C.-M. Esterification of lactic acid over TiO2–ZrO2 catalysts. Appl. Catal. A 392, 180–183 (2011).Article 
    CAS 

    Google Scholar 
    Clark, J. H. & Rhodes, C. N. In Clean Synthesis Using Porous Inorganic Solid Catalysts and Supported Reagents, Vol. 4, (Royal Society of Chemistry, London, 2000). https://doi.org/10.1039/9781847550569Dekamin, M. G. et al. Sodium alginate: An efficient biopolymeric catalyst for green synthesis of 2-amino-4H-pyran derivatives. Int. J. Biol. Macromol. 87, 172–179 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Melfi, D. T., dos Santos, K. C., Ramos, L. P. & Corazza, M. L. Supercritical CO2 as solvent for fatty acids esterification with ethanol catalyzed by Amberlyst-15. J. Supercrit. Fluids 158, 104736 (2020).Article 
    CAS 

    Google Scholar 
    Azudin, N. Y., Mashitah, M. & Abd Shukor, S. R. Optimization of isoamyl acetate production in a solvent-free system. J. Food Qual. 36, 441–446 (2013).Article 
    CAS 

    Google Scholar 
    Ćorović, M. et al. Immobilization of Candida antarctica lipase B onto Purolite® MN102 and its application in solvent-free and organic media esterification. Bioprocess Biosyst. Eng. 40, 23–34 (2017).Article 
    PubMed 

    Google Scholar 
    Liu, C. & Luo, G. Synthesis of isoamyl acetate catalyzed by ferric tri-dodecylsulfonate. Riyong Huaxue Gongye 34, 403–405 (2004).
    Google Scholar 
    Narwal, S. K., Saun, N. K., Dogra, P. & Gupta, R. Green synthesis of isoamyl acetate via silica immobilized novel thermophilic lipase from Bacillus aerius. Russ. J. Bioorg. Chem. 42, 69–73 (2016).Article 
    CAS 

    Google Scholar 
    Pizzio, L., Vázquez, P., Cáceres, C. & Blanco, M. Tungstophosphoric and molybdophosphoric acids supported on zirconia as esterification catalysts. Catal. Lett. 77, 233–239 (2001).Article 
    CAS 

    Google Scholar 
    Saha, B., Alqahtani, A. & Teo, H. T. R. Production of iso-Amyl Acetate: Heterogeneous Kinetics and Techno-feasibility Evaluation for Catalytic Distillation. Int. J. Chem. React. Eng. 3(1), https://doi.org/10.2202/1542-6580.1231 (2005).Osorio-Viana, W., Ibarra-Taquez, H. N., Dobrosz-Gomez, I. & Gómez-García, M. Á. Hybrid membrane and conventional processes comparison for isoamyl acetate production. Chem. Eng. Process. 76, 70–82 (2014).Article 
    CAS 

    Google Scholar 
    Fang, M. et al. Synthesis of isoamyl acetate using polyoxometalate-based sulfonated ionic liquid as catalyst. Indian J. Chem. Sect. A 53A, 1485–1492 (2014).Yang, Z., Zhou, C., Zhang, W., Li, H. & Chen, M. β-MnO2 nanorods: A new and efficient catalyst for isoamyl acetate synthesis. Colloids Surf., A 356, 134–139 (2010).Article 
    CAS 

    Google Scholar 
    Yang, Z. et al. Kinetic study and process simulation of transesterification of methyl acetate and isoamyl alcohol catalyzed by ionic liquid. Ind. Eng. Chem. Res. 54, 1204–1215 (2015).Article 
    CAS 

    Google Scholar 
    Dohendou, M., Pakzad, K., Nezafat, Z., Nasrollahzadeh, M. & Dekamin, M. G. Progresses in chitin, chitosan, starch, cellulose, pectin, alginate, gelatin and gum based (nano)catalysts for the Heck coupling reactions: A review. Int. J. Biol. Macromol. 192, 771–819. https://doi.org/10.1016/j.ijbiomac.2021.09.162 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Valiey, E., Dekamin, M. G. & Alirezvani, Z. Melamine-modified chitosan materials: An efficient and recyclable bifunctional organocatalyst for green synthesis of densely functionalized bioactive dihydropyrano[2,3-c]pyrazole and benzylpyrazolyl coumarin derivatives. Int. J. Biol. Macromol. 129, 407–421. https://doi.org/10.1016/j.ijbiomac.2019.01.027 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dekamin, M. G., Kazemi, E., Karimi, Z., Mohammadalipoor, M. & Naimi-Jamal, M. R. Chitosan: An efficient biomacromolecule support for synergic catalyzing of Hantzsch esters by CuSO4. Int. J. Biol. Macromol. 93, 767–774. https://doi.org/10.1016/j.ijbiomac.2016.09.012 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Valiey, E., Dekamin, M. G. & Bondarian, S. Sulfamic acid grafted to cross-linked chitosan by dendritic units: A bio-based, highly efficient and heterogeneous organocatalyst for green synthesis of 2,3-dihydroquinazoline derivatives. RSC Adv. 13, 320–334. https://doi.org/10.1039/D2RA07319F (2023).Article 
    ADS 
    CAS 

    Google Scholar 
    Dekamin, M. G., Azimoshan, M. & Ramezani, L. Chitosan: A highly efficient renewable and recoverable bio-polymer catalyst for the expeditious synthesis of α-amino nitriles and imines under mild conditions. Green Chem. 15, 811–820. https://doi.org/10.1039/C3GC36901C (2013).Article 
    CAS 

    Google Scholar 
    Alirezvani, Z., Dekamin, M. G. & Valiey, E. Cu (II) and magnetite nanoparticles decorated melamine-functionalized chitosan: A synergistic multifunctional catalyst for sustainable cascade oxidation of benzyl alcohols/Knoevenagel condensation. Sci. Rep. 9, 17758 (2019).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rostami, N., Dekamin, M., Valiey, E. & Fanimoghadam, H. Chitosan-EDTA-Cellulose network as a green, recyclable and multifunctional biopolymeric organocatalyst for the one-pot synthesis of 2-amino-4H-pyran derivatives. Sci. Rep. 12, 8642–8642 (2022).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Frindy, S., el Kadib, A., Lahcini, M., Primo, A. & García, H. Copper nanoparticles stabilized in a porous chitosan aerogel as a heterogeneous catalyst for C−S cross-coupling. ChemCatChem 7, 3307–3315 (2015).Article 
    CAS 

    Google Scholar 
    Pettignano, A. et al. Alginic acid aerogel: A heterogeneous Brønsted acid promoter for the direct Mannich reaction. New J. Chem. 39, 4222–4226 (2015).Article 
    CAS 

    Google Scholar 
    Schnepp, Z. Biopolymers as a flexible resource for nanochemistry. Angew. Chem. Int. Ed. 52, 1096–1108 (2013).Article 
    CAS 

    Google Scholar 
    Khrunyk, Y., Lach, S., Petrenko, I. & Ehrlich, H. Progress in modern marine biomaterials research. Mar. Drugs 18, 589 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lee, I. Molecular self-assembly: Smart design of surface and interface via secondary molecular interactions. Langmuir 29, 2476–2489. https://doi.org/10.1021/la304123b (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Shaheed, N., Javanshir, S., Esmkhani, M., Dekamin, M. G. & Naimi-Jamal, M. R. Synthesis of nanocellulose aerogels and Cu-BTC/nanocellulose aerogel composites for adsorption of organic dyes and heavy metal ions. Sci. Rep. 11, 18553 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Abdullah, M. A. et al. Processing Aspects and biomedical and environmental applications of sustainable nanocomposites containing nanofillers. In Sustainable Polymer Composites and Nanocomposites, (eds Inamuddin et al.) 727–757 (Springer, Cham, 2019). https://doi.org/10.1007/978-3-030-05399-4_25Dekamin, M. G. et al. Alginic acid: A highly efficient renewable and heterogeneous biopolymeric catalyst for one-pot synthesis of the Hantzsch 1,4-dihydropyridines. RSC Adv. 4, 56658–56664. https://doi.org/10.1039/C4RA11801D (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Ilkhanizadeh, S., Khalafy, J. & Dekamin, M. G. Sodium alginate: A biopolymeric catalyst for the synthesis of novel and known polysubstituted pyrano[3,2-c]chromenes. Int. J. Biol. Macromol. 140, 605–613. https://doi.org/10.1016/j.ijbiomac.2019.08.154 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dekamin, M. G. et al. Alginic acid: A mild and renewable bifunctional heterogeneous biopolymeric organocatalyst for efficient and facile synthesis of polyhydroquinolines. Int. J. Biol. Macromol. 108, 1273–1280. https://doi.org/10.1016/j.ijbiomac.2017.11.050 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Rostami, N., Dekamin, M. G. & Valiey, E. Chitosan-EDTA-cellulose bio-based network: A recyclable multifunctional organocatalyst for green and expeditious synthesis of Hantzsch esters. Carbohydr. Polym. Technol. Appl. 5, 100279. https://doi.org/10.1016/j.carpta.2022.100279 (2023).Article 
    CAS 

    Google Scholar 
    Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S. & Escaleira, L. A. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76, 965–977. https://doi.org/10.1016/j.talanta.2008.05.019 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hill, W. J. & Hunter, W. G. A review of response surface methodology: A literature survey. Technometrics 8, 571–590. https://doi.org/10.1080/00401706.1966.10490404 (1966).Article 
    MathSciNet 

    Google Scholar 
    Hamidi, F. et al. Acid red 18 removal from aqueous solution by nanocrystalline granular ferric hydroxide (GFH); optimization by response surface methodology & genetic-algorithm. Sci. Rep. 12, 1–15 (2022).Article 

    Google Scholar 
    Han, X.-X. et al. Syntheses of novel halogen-free Brønsted–Lewis acidic ionic liquid catalysts and their applications for synthesis of methyl caprylate. Green Chem. 17, 499–508 (2015).Article 
    CAS 

    Google Scholar 
    Rehman, K. et al. Operational parameters optimization for remediation of crude oil-polluted water in floating treatment wetlands using response surface methodology. Sci. Rep. 12, 1–11 (2022).Article 

    Google Scholar 
    Kamari, S., Ghorbani, F. & Sanati, A. M. Adsorptive removal of lead from aqueous solutions by amine–functionalized magMCM-41 as a low–cost nanocomposite prepared from rice husk: Modeling and optimization by response surface methodology. Sustain. Chem. Pharm. 13, 100153. https://doi.org/10.1016/j.scp.2019.100153 (2019).Article 

    Google Scholar 
    Sanati, A. M., Kamari, S. & Ghorbani, F. Application of response surface methodology for optimization of cadmium adsorption from aqueous solutions by Fe3O4@SiO2@APTMS core–shell magnetic nanohybrid. Surf. Interfaces 17, 100374. https://doi.org/10.1016/j.surfin.2019.100374 (2019).Article 
    CAS 

    Google Scholar 
    Guner, S. G. & Dericioglu, A. Nacre-mimetic epoxy matrix composites reinforced by two-dimensional glass reinforcements. RSC Adv. 6, 33184–33196 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Shao, Y., Zhao, H.-P. & Feng, X.-Q. Optimal characteristic nanosizes of mineral bridges in mollusk nacre. RSC Adv. 4, 32451–32456 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Jaji, A. Z. et al. Synthesis, characterization, and cytocompatibility of potential cockle shell aragonite nanocrystals for osteoporosis therapy and hormonal delivery. Nanotechnol. Sci. Appl. 10, 23 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Çam, M. & Aaby, K. Optimization of extraction of apple pomace phenolics with water by response surface methodology. J. Agric. Food Chem. 58, 9103–9111 (2010).Article 
    PubMed 

    Google Scholar 
    Iwuchukwu, I. J. et al. Optimization of photosynthetic hydrogen yield from platinized photosystem I complexes using response surface methodology. Int. J. Hydrog. Energy 36, 11684–11692 (2011).Article 
    CAS 

    Google Scholar 
    Hu, C. et al. Characterization and photocatalytic activity of noble-metal-supported surface TiO2/SiO2. Appl. Catal. A 253, 389–396 (2003).Article 
    CAS 

    Google Scholar 
    Noda, L. K., de Almeida, R. M., Probst, L. F. D. & Gonçalves, N. S. Characterization of sulfated TiO2 prepared by the sol–gel method and its catalytic activity in the n-hexane isomerization reaction. J. Mol. Catal. A Chem. 225, 39–46 (2005).Article 
    CAS 

    Google Scholar 
    Jalali-Heravi, M., Parastar, H. & Ebrahimi-Najafabadi, H. Characterization of volatile components of Iranian saffron using factorial-based response surface modeling of ultrasonic extraction combined with gas chromatography–mass spectrometry analysis. J. Chromatogr. A 1216, 6088–6097 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sendzikiene, E., Sinkuniene, D., Kazanceva, I. & Kazancev, K. Optimization of low quality rapeseed oil transesterification with butanol by applying the response surface methodology. Renew. Energy 87, 266–272 (2016).Article 
    CAS 

    Google Scholar 
    Das, R., Sarkar, S. & Bhattacharjee, C. Photocatalytic degradation of chlorhexidine—a chemical assessment and prediction of optimal condition by response surface methodology. J. Water Process Eng. 2, 79–86 (2014).Article 

    Google Scholar 
    Nandiwale, K. Y., Galande, N. D. & Bokade, V. V. Process optimization by response surface methodology for transesterification of renewable ethyl acetate to butyl acetate biofuel additive over borated USY zeolite. RSC Adv. 5, 17109–17116 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Soltani, R. D. C. & Safari, M. Periodate-assisted pulsed sonocatalysis of real textile wastewater in the presence of MgO nanoparticles: Response surface methodological optimization. Ultrason. Sonochem. 32, 181–190 (2016).Article 

    Google Scholar 
    Tan, K. T., Lee, K. T. & Mohamed, A. R. A glycerol-free process to produce biodiesel by supercritical methyl acetate technology: An optimization study via response surface methodology. Biores. Technol. 101, 965–969 (2010).Article 
    CAS 

    Google Scholar 
    Nagaraju, N., Peeran, M. & Prasad, D. Synthesis of isoamyl acetate usin NaX and NaY zeolites as catalysts. React. Kinet. Catal. Lett. 61, 155–160 (1997).Article 
    CAS 

    Google Scholar 
    Pizzio, L. R. & Blanco, M. N. Isoamyl acetate production catalyzed by H3PW12O40 on their partially substituted Cs or K salts. Appl. Catal. A 255, 265–277 (2003).Article 
    CAS 

    Google Scholar 
    Dekamin, M. G., Karimi, Z. & Farahmand, M. Tetraethylammonium 2-(N-hydroxycarbamoyl)benzoate: A powerful bifunctional metal-free catalyst for efficient and rapid cyanosilylation of carbonyl compounds under mild conditions. Catal. Sci. Technol. 2, 1375–1381. https://doi.org/10.1039/C2CY20037F (2012).Article 
    CAS 

    Google Scholar 
    Dekamin, M. G., Sagheb-Asl, S. & Reza Naimi-Jamal, M. An expeditious synthesis of cyanohydrin trimethylsilyl ethers using tetraethylammonium 2-(carbamoyl)benzoate as a bifunctional organocatalyst. Tetrahedron Lett. 50, 4063–4066. https://doi.org/10.1016/j.tetlet.2009.04.090 (2009).Article 
    CAS 

    Google Scholar 
    Alirezvani, Z., Dekamin, M. G. & Valiey, E. New hydrogen-bond-enriched 1,3,5-tris(2-hydroxyethyl) isocyanurate covalently functionalized MCM-41: An efficient and recoverable hybrid catalyst for convenient synthesis of acridinedione derivatives. ACS Omega 4, 20618–20633. https://doi.org/10.1021/acsomega.9b02755 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Info-gap theory to determine cost-effective eradication of invasive species

    Peterson, A. T. & Vieglais, D. A. Predicting species invasions using ecological niche modeling: New approaches from bioinformatics attack a pressing problem. Bioscience 51, 363–371 (2001).Article 

    Google Scholar 
    Atkinson, I. A. E. Introduced mammals and models for restoration. Biol. Conserv. 99, 81–96 (2001).Article 

    Google Scholar 
    Parkes, J. P. & Panetta, F. D. Eradication of invasive species: progress and emerging issues in the 21st century. In Invasive Species Management: A Handbook of Principles and Techniques (eds Clout, M. N. & Williams, P. A.) (Oxford University Press, 2009).
    Google Scholar 
    Baker, C. M., Hodgson, J. C., Tartaglia, E. & Clarke, R. H. Modelling tropical fire ant (Solenopsis geminata) dynamics and detection to inform an eradication project. Biol. Invasions 19, 2959–2970 (2017).Article 

    Google Scholar 
    Simberloff, D. How much information on population biology is needed to manage introduced species?. Conserv. Biol. 17, 83–92 (2003).Article 

    Google Scholar 
    Hulme, P. E. Trade, transport and trouble: Managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46, 10–18 (2009).Article 

    Google Scholar 
    Meyerson, L. A. & Mooney, H. A. Invasive alien species in an era of globalization. Front. Ecol. Environ. 5, 199–208 (2007).Article 

    Google Scholar 
    Sanchirico, J. N., Albers, H. J., Fischer, C. & Coleman, C. Spatial Management of invasive species: Pathways and policy options. Environ. Resour. Econ. 45, 517–535 (2010).Article 

    Google Scholar 
    Caplat, P., Hui, C., Maxwell, B. D. & Peltzer, D. A. Cross-scale management strategies for optimal control of trees invading from source plantations. Biol. Invasions 16, 677–690 (2014).Article 

    Google Scholar 
    Long, Y., Van der Merwe, J., Thomas, M. L., McKirdy, S. & Kompas, T. Biosecurity for valuable environmental island assets: Spatial post-border surveillance for early detection. Ecol. Econ. forthcoming (2022).Kroetz, K. & Sanchirico, J. N. The bioeconomics of spatial-dynamic systems in natural resource management. Annu. Rev. Resour. Econ. 7, 189–207 (2015).Article 

    Google Scholar 
    Liu, Y., Wang, P., Thomas, M. L., Zheng, D. & McKirdy, S. J. Cost-effective surveillance of invasive species using info-gap theory. Sci. Rep. 11, 22828 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Homans, F. & Horie, T. Optimal detection strategies for an established invasive pest. Ecol. Econ. 70, 1129–1138 (2011).Article 

    Google Scholar 
    Mehta, S. V., Haight, R. G., Homans, F. R., Polasky, S. & Venette, R. C. Optimal detection and control strategies for invasive species management. Ecol. Econ. 61, 237–245 (2007).Article 

    Google Scholar 
    Moffitt, L. J., Stranlund, J. K. & Osteen, C. D. Robust detection protocols for uncertain introductions of invasive species. J. Environ. Manage. 89, 293–299 (2008).Article 
    PubMed 

    Google Scholar 
    Yokomizo, H., Possingham, H. P., Hulme, P. E., Grice, A. C. & Buckley, Y. M. Cost-benefit analysis for intentional plant introductions under uncertainty. Biol. Invasions 14, 839–849 (2011).Article 

    Google Scholar 
    Ben-Haim, Y. Info-gap Decision Theory: Decisions Under Severe Uncertainty 2nd edn. (Academic Press, 2006).
    Google Scholar 
    Knight, F. H. Risk, Uncertainty, and Profit (Houghton Mifflin Company, 1921).
    Google Scholar 
    Regan, H. M. et al. Robust decision-making under severe uncertainty for conservation management. Ecol. Appl. 15, 1471–1477 (2005).Article 

    Google Scholar 
    Ben-Haim, Y. Uncertainty, probability and information-gaps. Reliab. Eng. Syst. Saf. 85, 249–266 (2004).Article 

    Google Scholar 
    Ben-Haim, Y. & Demertzis, M. Decision making in times of Knightian uncertainty: An info-gap perspective. Economics 10, 1 (2016).Article 

    Google Scholar 
    Lever, C. Naturalized Reptiles and Amphibians of the World (Oxford University Press, 2003).
    Google Scholar 
    Wilson, J. R. U., Dormontt, E. E., Prentis, P. J., Lowe, A. J. & Richardson, D. M. Something in the way you move: Dispersal pathways affect invasion success. Trends Ecol. Evol. 24, 136–144 (2009).Article 
    PubMed 

    Google Scholar 
    Torres-Carvajal, O. On the origin of South American populations of the common house gecko (Gekkonidae: Hemidactylus frenatus). NeoBiota 27, 69–79 (2015).Article 

    Google Scholar 
    Hoskin, C. J. The invasion and potential impact of the Asian House Gecko (Hemidactylus frenatus) in Australia. Austral Ecol. 36, 240–251 (2011).Article 

    Google Scholar 
    Barnett, L. K. Understanding Range Expansion of Asian House Geckos (Hemidactylus frenatus) in Natural Environments (James Cook University, 2017).
    Google Scholar 
    Norval, G. & Mao, J.-J. An instance of a house gecko (Hemidactylus frenatus Schlegel, 1836) utilizing an electrical timer for thermoregulation. IRCF Reptil. Amphib. 22, 76–78 (2015).Article 

    Google Scholar 
    Greenslade, P., Burbidge, A. A. & Lynch, A. J. J. Keeping Australias islands free of introduced rodents Barrow Island. Pac. Conserv. Biol. 19, 284–294 (2013).Article 

    Google Scholar 
    Perella, C. D. & Behm, J. E. Understanding the spread and impact of exotic geckos in the greater Caribbean region. Biodivers. Conserv. 29, 1109–1134 (2020).Article 

    Google Scholar 
    Davis, M. A. Invasion biology. In Encyclopedia of Biological Invasions (eds Simberloff, D. & RejmÁNek, M.) 364–369 (University of California Press, 2011).
    Google Scholar 
    García-Díaz, P., Ross, J. V., Vall-llosera, M. & Cassey, P. Low detectability of alien reptiles can lead to biosecurity management failure: A case study from Christmas Island (Australia). NeoBiota. 45, 75–92 (2019).Article 

    Google Scholar 
    Koopman, B. O. Search and Screening. Operations Evaluation Group (OEG) Report. (1946).Grasinger, M., O’Malley, D., Vesselinov, V. & Karra, S. Decision analysis for robust CO2 injection: Application of Bayesian-Information-Gap Decision Theory. Int. J. Greenh. Gas Control 49, 73–80 (2016).Article 
    CAS 

    Google Scholar 
    MathWorks. MATLAB R2018b. (MathWorks, 2018).Commonwealth Government of Australia. Approval—Gorgon Gas Development (EPBC Reference: 2008/4178). (2009).Kalaris, T. et al. The role of surveillance methods and technologies in plant biosecurity. In The Handbook of Plant Biosecurity: Principles and Practices for the Identification, Containment and Control of Organisms that Threaten Agriculture and the Environment Globally (eds Gordh, G. & McKirdy, S.) 309–337 (Springer, 2014).Chapter 

    Google Scholar 
    Sharma, S., Mckirdy, S. & Macbeth, F. The biosecurity continuum and trade: Tools for post-border biosecurity. In The Handbook of Plant Biosecurity: Principles and Practices for the Identification, Containment and Control of Organisms that Threaten Agriculture and the Environment Globally (eds Gordh, G. & McKirdy, S.) 189–206 (Springer, 2014).Chapter 

    Google Scholar 
    Epanchin-Niell, R. S. Economics of invasive species policy and management. Biol. Invasions 19, 3333–3354 (2017).Article 

    Google Scholar 
    Gregg, H. et al. Invasive rodent eradication on islands. Conserv. Biol. 21, 1258–1268 (2007).Article 

    Google Scholar 
    Parkes, J. Feasibility plan to eradicate Common mynas (Acridotheres tristis) from Mangaia Island, Cook Islands. Landcare Research Contract Report LC0506/184. (2006).Barun, A. & Simberloff, D. Carnivores. In Encyclopedia of Biological Invasions (eds Simberloff, D. & RejmÁNek, M.) 95–100 (University of California Press, 2011).
    Google Scholar 
    Pluess, T. et al. When are eradication campaigns successful? A test of common assumptions. Biol. Invasions 14, 1365–1378 (2012).Article 

    Google Scholar 
    Epanchin-Niell, R. S., Haight, R. G., Berec, L., Kean, J. M. & Liebhold, A. M. Optimal surveillance and eradication of invasive species in heterogeneous landscapes. Ecol. Lett. 15, 803–812 (2012).Article 
    PubMed 

    Google Scholar 
    Rout, T. M., Thompson, C. J. & McCarthy, M. A. Robust decisions for declaring eradication of invasive species. J. Appl. Ecol. 46, 782–786 (2009).Article 

    Google Scholar 
    Hauser, C. E. & McCarthy, M. A. Streamlining “search and destroy”: Cost-effective surveillance for invasive species management. Ecol. Lett. 12, 683–692 (2009).Article 
    PubMed 

    Google Scholar 
    Epanchin-Niell, R. S. & Hastings, A. Controlling established invaders: Integrating economics and spread dynamics to determine optimal management. Ecol. Lett. 13, 528–541 (2010).Article 
    PubMed 

    Google Scholar 
    Moore, J. L. et al. Protecting islands from pest invasion: Optimal allocation of biosecurity resources between quarantine and surveillance. Biol. Conserv. 143, 1068–1078 (2010).Article 

    Google Scholar 
    Rout, T. M., Moore, J. L., Possingham, H. P. & McCarthy, M. A. Allocating biosecurity resources between preventing, detecting, and eradicating island invasions. Ecol. Econ. 71, 54–62 (2011).Article 

    Google Scholar  More

  • in

    Genetic monitoring on the world’s first MSC eco-labeled common octopus (O. vulgaris) fishery in western Asturias, Spain

    FAO. El estado mundial de la pesca y la acuicultura 2020 (FAO, 2020).
    Google Scholar 
    Jackson, J. B. C. Historical overfishing and the recent collapse of coastal ecosystems. Science 293, 629–637 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Scheffer, M., Carpenter, S. & de Young, B. Cascading effects of overfishing marine systems. Trends Ecol. Evol. 20, 579–581 (2005).Article 
    PubMed 

    Google Scholar 
    Coll, M., Libralato, S., Tudela, S., Palomera, I. & Pranovi, F. Ecosystem overfishing in the ocean. PLoS ONE 3, e3881 (2008).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Peterson, M. S. & Lowe, M. R. Implications of cumulative impacts to estuarine and marine habitat quality for fish and invertebrate resources. Rev. Fish. Sci. 17, 505–523 (2009).Article 

    Google Scholar 
    Claudet, J. & Fraschetti, S. Human-driven impacts on marine habitats: A regional meta-analysis in the Mediterranean Sea. Biol. Cons. 143, 2195–2206 (2010).Article 

    Google Scholar 
    Smith, V. H., Tilman, G. D. & Nekola, J. C. Eutrophication: Impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environ. Pollut. 100, 179–196 (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Derraik, J. G. B. The pollution of the marine environment by plastic debris: A review. Mar. Pollut. Bull. 44, 842–852 (2002).Article 
    CAS 
    PubMed 

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

    Google Scholar 
    Molnar, J. L., Gamboa, R. L., Revenga, C. & Spalding, M. D. Assessing the global threat of invasive species to marine biodiversity. Front. Ecol. Environ. 6, 485–492 (2008).Article 

    Google Scholar 
    Wojnarowska, M., Sołtysik, M. & Prusak, A. Impact of eco-labelling on the implementation of sustainable production and consumption. Environ. Impact Assess. Rev. 86, 106505 (2021).Article 

    Google Scholar 
    Yan, H. F. et al. Overfishing and habitat loss drive range contraction of iconic marine fishes to near extinction. Sci. Adv. 7, 6026 (2021).Article 
    ADS 

    Google Scholar 
    Bastardie, F. et al. Spatial planning for fisheries in the Northern Adriatic: Working toward viable and sustainable fishing. Ecosphere 8, e01696 (2017).Article 

    Google Scholar 
    Arkema, K. K. et al. Integrating fisheries management into sustainable development planning. Ecol. Soc. 24, 0201 (2019).Article 

    Google Scholar 
    Aguión, A. et al. Establishing a governance threshold in small-scale fisheries to achieve sustainability. Ambio. https://doi.org/10.1007/s13280-021-01606-x (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gudmundsson, E. & Wessells, C. R. Ecolabeling seafood for sustainable production: Implications for fisheries management. Mar. Resour. Econ. 15, 97–113 (2000).Article 

    Google Scholar 
    FAO. Guidelines for the Ecolabelling of Fish and Fishery Products from Marine Capture Fisheries. Revision 1 (FAO, 2009).
    Google Scholar 
    Hilborn, R. & Ovando, D. Reflections on the success of traditional fisheries management. ICES J. Mar. Sci. 71, 1040–1046 (2014).Article 

    Google Scholar 
    Casey, J., Jardim, E. & Martinsohn, J. T. H. The role of genetics in fisheries management under the E.U. common fisheries policy. J. Fish Biol. 89, 2755–2767 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    MSC. MSC Fisheries Standard v2.01. https://www.msc.org/docs/default-source/default-document-library/for-business/program-documents/fisheries-program-documents/msc-fisheries-standard-v2-01.pdf?sfvrsn=8ecb3272_9 (2018).Costello, C. et al. Status and solutions for the world’s unassessed fisheries. Science 338, 517–520 (2012).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hilborn, R. et al. Effective fisheries management instrumental in improving fish stock status. PNAS 117, 2218–2224 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Worm, B. & Branch, T. A. The future of fish. Trends Ecol. Evol. 27, 594–599 (2012).Article 
    PubMed 

    Google Scholar 
    Palomares, M. L. D. et al. Fishery biomass trends of exploited fish populations in marine ecoregions, climatic zones and ocean basins. Estuar. Coast. Shelf Sci. 243, 106896 (2020).Article 

    Google Scholar 
    Ihssen, P. E. et al. Stock identification: Materials and methods. Can. J. Fish. Aquat. Sci. 38, 1838–1855 (1981).Article 

    Google Scholar 
    Carvalho, G. R. & Hauser, L. Molecular genetics and the stock concept in fisheries. In Molecular Genetics in Fisheries (eds Carvalho, G. R. & Pitcher, T. J.) 55–79 (Springer, 1995).Chapter 

    Google Scholar 
    Worm, B. et al. Rebuilding global fisheries. Science 325, 578–585 (2009).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gough, C. L. A., Dewar, K. M., Godley, B. J., Zafindranosy, E. & Broderick, A. C. Evidence of overfishing in small-scale fisheries in Madagascar. Front. Mar. Sci. 7, 317 (2020).Article 

    Google Scholar 
    Widjaja, S. et al. Illegal, Unreported and Unregulated Fishing and Associated Drivers 60 (2020).Walters, C. & Martell, S. J. D. Stock assessment needs for sustainable fisheries management. Bull. Mar. Sci. 70, 629–638 (2002).
    Google Scholar 
    Moreira, A. A., Tomás, A. R. G. & Hilsdorf, A. W. S. Evidence for genetic differentiation of Octopus vulgaris (Mollusca, Cephalopoda) fishery populations from the southern coast of Brazil as revealed by microsatellites. J. Exp. Mar. Biol. Ecol. 407, 34–40 (2011).Article 

    Google Scholar 
    Allendorf, F. W., Ryman, N. & Utter, F. M. Genetics and fishery management. In Population Genetics and Fishery Management 1–19 (1987).Oosthuizen, A., Jiwaji, M. & Shaw, P. Genetic analysis of the Octopus vulgaris population on the coast of South Africa. S. Afr. J. Sci. 100, 603–607 (2004).CAS 

    Google Scholar 
    Botsford, L. W., Castilla, J. C. & Peterson, C. H. The management of fisheries and marine ecosystems. Science 277, 509–515 (1997).Article 
    CAS 

    Google Scholar 
    Hilborn, R., Orensanz, J. M. & Parma, A. M. Institutions, incentives and the future of fisheries. Philos. Trans. R. Soc. B Biol. Sci. 360, 47. https://doi.org/10.1098/rstb.2004.1569 (2005).Article 

    Google Scholar 
    Ovenden, J. R., Berry, O., Welch, D. J., Buckworth, R. C. & Dichmont, C. M. Ocean’s eleven: A critical evaluation of the role of population, evolutionary and molecular genetics in the management of wild fisheries. Fish Fish. 16, 125–159 (2015).Article 

    Google Scholar 
    Aguirre-Sarabia, I. et al. Evidence of stock connectivity, hybridization, and misidentification in white anglerfish supports the need of a genetics-informed fisheries management framework. Evol. Appl. 14, 2221 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grover, A. & Sharma, P. C. Development and use of molecular markers: Past and present. Crit. Rev. Biotechnol. 36, 290 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Valenzuela-Quiñonez, F. How fisheries management can benefit from genomics? Brief. Funct. Genom. 15, 352–357 (2016).Article 

    Google Scholar 
    Khoufi, W., Jabeur, C. & Bakhrouf, A. Stock assessment of the common octopus (Octopus vulgaris) in Monastir; the Mid-eastern Coast of Tunisia. Int. J. Mar. Sci. 2, 1 (2012).
    Google Scholar 
    Pita, C. et al. Fisheries for common octopus in Europe: Socioeconomic importance and management. Fish. Res. 235, 105820 (2021).Article 

    Google Scholar 
    Melis, R. et al. Genetic population structure and phylogeny of the common octopus Octopus vulgaris Cuvier, 1797 in the western Mediterranean Sea through nuclear and mitochondrial markers. Hydrobiologia 807, 277–296 (2018).Article 
    CAS 

    Google Scholar 
    De Luca, D., Catanese, G., Procaccini, G. & Fiorito, G. Octopus vulgaris (Cuvier, 1797) in the Mediterranean Sea: Genetic diversity and population structure. PLoS ONE 11, e0149496 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fernández-Rueda, P. & García-Flórez, L. Octopus vulgaris (Mollusca: Cephalopoda) fishery management assessment in Asturias (north-west Spain). Fish. Res. 83, 351–354 (2007).Article 

    Google Scholar 
    Gobierno del Principado de Asturias. BOPA núm. 233 de 03-XII-2021, Vol. 233 (2021).Roa-Ureta, R. H. et al. Estimation of the spawning stock and recruitment relationship of Octopus vulgaris in Asturias (Bay of Biscay) with generalized depletion models: Implications for the applicability of MSY. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsab113 (2021).Article 

    Google Scholar 
    González, A. F., Macho, G., de Novoa, J. & García, M. Western Asturias Octopus Traps Fishery of Artisanal Cofradías 181 (2015).Sánchez, J. L. F., Fernández Polanco, J. M. & Llorente García, I. Evidence of price premium for MSC-certified products at fishers’ level: The case of the artisanal fleet of common octopus from Asturias (Spain). Mar. Policy 119, 104098 (2020).Article 

    Google Scholar 
    Murphy, J. M., Balguerías, E., Key, L. N. & Boyle, P. R. Microsatellite DNA markers discriminate between two Octopus vulgaris (Cephalopoda: Octopoda) fisheries along the northwest African coast. Bull. Mar. Sci. 71, 545–553 (2002).
    Google Scholar 
    Cabranes, C., Fernandez-Rueda, P. & Martínez, J. L. Genetic structure of Octopus vulgaris around the Iberian Peninsula and Canary Islands as indicated by microsatellite DNA variation. ICES J. Mar. Sci. 65, 12–16 (2008).Article 

    Google Scholar 
    Quinteiro, J., Rodríguez-Castro, J., Rey-Méndez, M. & González-Henríquez, N. Phylogeography of the insular populations of common octopus, Octopus vulgaris Cuvier, 1797, in the Atlantic Macaronesia. PLoS ONE 15, e0230294 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Greatorex, E. C. et al. Microsatellite markers for investigating population structure in Octopus vulgaris (Mollusca: Cephalopoda). Mol. Ecol. 9, 641–642 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    De Luca, D., Catanese, G., Fiorito, G. & Procaccini, G. A new set of pure microsatellite loci in the common octopus Octopus vulgaris Cuvier, 1797 for multiplex PCR assay and their cross-amplification in O. maya Voss & Solís Ramírez, 1966. Conserv. Genet. Resour. 7, 299–301 (2015).Article 

    Google Scholar 
    Zuo, Z., Zheng, X., Liu, C. & Li, Q. Development and characterization of 17 polymorphic microsatellite loci in Octopus vulgaris Cuvier, 1797. Conserv. Genet. Resour. 4, 367–369 (2012).Article 

    Google Scholar 
    Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358 (1984).CAS 
    PubMed 

    Google Scholar 
    Chapuis, M. P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Nei, M. & Takezaki, N. Estimation of Genetic Distances and Phylogenetic Trees from DNA Analysis 8 (1983).Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Waples, R. S. Separating the wheat from the chaff: Patterns of genetic differentiation in high gene flow species. J. Hered. 89, 438–450 (1998).Article 

    Google Scholar 
    Taboada, F. G. & Anadón, R. Patterns of change in sea surface temperature in the North Atlantic during the last three decades: Beyond mean trends. Clim. Change 115, 419–431 (2012).Article 
    ADS 

    Google Scholar 
    Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sinclair, M. & Valdimarsson, G. Responsible Fisheries in the Marine Ecosystem (CABI, 2003).Book 

    Google Scholar 
    Pinsky, M. L. & Palumbi, S. R. Meta-analysis reveals lower genetic diversity in overfished populations. Mol. Ecol. 23, 29–39 (2014).Article 
    PubMed 

    Google Scholar 
    Bradbury, I. R., Laurel, B., Snelgrove, P. V. R., Bentzen, P. & Campana, S. E. Global patterns in marine dispersal estimates: The influence of geography, taxonomic category and life history. Proc. R. Soc. B Biol. Sci. 275, 1803–1809 (2008).Article 

    Google Scholar 
    Waples, R. S. Testing for Hardy-Weinberg proportions: Have we lost the plot? J. Hered. 106, 1–19 (2015).Article 
    PubMed 

    Google Scholar 
    Casu, M. et al. Genetic structure of Octopus vulgaris (Mollusca, Cephalopoda) from the Mediterranean Sea as revealed by a microsatellite locus. Ital. J. Zool. 69, 295–300 (2002).Article 

    Google Scholar 
    Fadhlaoui-Zid, K. et al. Genetic structure of Octopus vulgaris (Cephalopoda, Octopodidae) in the central Mediterranean Sea inferred from the mitochondrial COIII gene. C.R. Biol. 335, 625–636 (2012).Article 
    PubMed 

    Google Scholar 
    Queiroga, H. et al. Oceanographic and behavioural processes affecting invertebrate larval dispersal and supply in the western Iberia upwelling ecosystem. Prog. Oceanogr. 74, 174–191 (2007).Article 
    ADS 

    Google Scholar 
    Mereu, M. et al. Mark–recapture investigation on Octopus vulgaris specimens in an area of the central western Mediterranean Sea. J. Mar. Biol. Assoc. U.K. 95, 131–138 (2015).Article 
    ADS 

    Google Scholar 
    Mereu, M. et al. Movement estimation of Octopus vulgaris Cuvier, 1797 from mark recapture experiment. J. Exp. Mar. Biol. Ecol. 470, 64–69 (2015).Article 

    Google Scholar 
    Roura, Á. et al. Life strategies of cephalopod paralarvae in a coastal upwelling system (NW Iberian Peninsula): Insights from zooplankton community and spatio-temporal analyses. Fish. Oceanogr. 25, 241–258 (2016).Article 

    Google Scholar 
    Moreno, A. et al. Essential habitats for pre-recruit Octopus vulgaris along the Portuguese coast. Fish. Res. 152, 74–85 (2014).Article 
    ADS 

    Google Scholar 
    Chédia, J., Widien, K. & Amina, B. Role of sea surface temperature and rainfall in determining the stock and fishery of the common octopus (Octopus vulgaris, Mollusca, Cephalopoda) in Tunisia. Mar. Ecol. 31, 431–438 (2010).Article 
    ADS 

    Google Scholar 
    Otero, J. et al. Bottom-up control of common octopus Octopus vulgaris in the Galician upwelling system, northeast Atlantic Ocean. Mar. Ecol. Prog. Ser. 362, 181–192 (2008).Article 
    ADS 

    Google Scholar 
    Hedgecock, D. & Pudovkin, A. I. A. I. Sweepstakes reproductive success in highly fecund marine fish and shellfish: A review and commentary. Bull. Mar. Sci. 87, 971–1002 (2011).Article 

    Google Scholar 
    Kalinowski, S. T. & Waples, R. S. Relationship of effective to census size in fluctuating populations. Conserv. Biol. 16, 129–136 (2002).Article 
    PubMed 

    Google Scholar 
    Sonderblohm, C. P., Pereira, J. & Erzini, K. Environmental and fishery-driven dynamics of the common octopus (Octopus vulgaris) based on time-series analyses from leeward Algarve, southern Portugal. ICES J. Mar. Sci. 71, 2231–2241 (2014).Article 

    Google Scholar 
    Sonderblohm, C. P. et al. Participatory assessment of management measures for Octopus vulgaris pot and trap fishery from southern Portugal. Mar. Policy 75, 133–142 (2017).Article 

    Google Scholar 
    Arkhipkin, A. I. et al. Stock assessment and management of cephalopods: Advances and challenges for short-lived fishery resources. ICES J. Mar. Sci. 78, 714–730 (2021).Article 

    Google Scholar 
    Franklin, I. R. Evolutionary change in small populations. In Conservation Biology: An Evolutionary-Ecological Perspective (eds Soulé, M. E. & Wilcox, B. A.) 395 (Sinauer Associates, 1980).
    Google Scholar 
    Slatkin, M. Rare alleles as indicators of gene flow. Evolution 39, 53–65 (1985).Article 
    PubMed 

    Google Scholar 
    Holleley, C. E. & Geerts, P. G. Multiplex manager 1.0: A cross-platform computer program that plans and optimizes multiplex PCR. Biotechniques 46, 511–517 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 

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

    Google Scholar 
    Paradis, E. Pegas: An R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419–420 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Goudet, J. HIERFSTAT, a package for R to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

    Google Scholar 
    Adamack, A. T. & Gruber, B. PopGenReport: Simplifying basic population genetic analyses in R. Methods Ecol. Evol. 5, 384–387 (2014).Article 

    Google Scholar 
    Goudet, J. FSTAT (Version 1.2): A computer program to calculate F-STATISTICS. J. Hered. 86, 485–486 (1995).Article 

    Google Scholar 
    Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223 (1989).Article 
    PubMed 

    Google Scholar 
    Piry, S., Luikart, G. & Cornuet, J. M. M. Bottleneck: A computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503 (1999).Article 

    Google Scholar 
    Luikart, G., Allendorf, F. W., Cornuet, J.-M.M. & Sherwin, W. B. Distortion of allele frequency distributions provides a test for recent population bottlenecks. J. Hered. https://doi.org/10.1093/jhered/89.3.238 (1998).Article 
    PubMed 

    Google Scholar 
    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Besnier, F. & Glover, K. A. ParallelStructure: A R package to distribute parallel runs of the population genetics program STRUCTURE on multi-core computers. PLoS ONE 8, e70651 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gilbert, K. J. et al. Recommendations for utilizing and reporting population genetic analyses: The reproducibility of genetic clustering using the program structure. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2012.05754.x (2012).Article 
    PubMed 

    Google Scholar 
    Earl, D. A. & VonHoldt, B. M. Structure harvester: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

    Google Scholar 
    Takezaki, N., Nei, M. & Tamura, K. POPTREEW: Web version of POPTREE for constructing population trees from allele frequency data and computing some other quantities. Mol. Biol. Evol. 31, 1622–1624 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Letunic, I. & Bork, P. Interactive tree of life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    Slatkin, M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47, 264–279 (1993).Article 
    PubMed 

    Google Scholar 
    Cavalli-Sforza, L. L. & Edwards, A. W. F. Phylogenetic analysis. Models and estimation procedures. Am. J. Hum. Genet. 19, 233–257 (1967).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Foll, M. & Gaggiotti, O. A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Waples, R. S. A generalized approach for estimating effective population size from temporal changes in allele frequency. Genetics 121, 379–391 (1989).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Katsanevakis, S. & Verriopoulos, G. Seasonal population dynamics of Octopus vulgaris in the eastern Mediterranean. ICES J. Mar. Sci. 63, 151–160 (2006).Article 

    Google Scholar 
    Jereb, P. et al. Cephalopod Biology and Fisheries in Europe: II Species Accounts 360 (ICES, 2015).
    Google Scholar  More

  • in

    An ankylosaur larynx provides insights for bird-like vocalization in non-avian dinosaurs

    Reilly, S. M. & Lauder, G. V. The evolution of tetrapod feeding behavior: kinematic homologies in prey transport. Evolution 44, 1542–1557 (1990).Article 

    Google Scholar 
    Iwasaki, S. Evolution of the structure and function of the vertebrate tongue. J. Anat. 201, 1–13 (2002).Article 

    Google Scholar 
    Fitch, W. T. & Suthers, R. A. In Vertebrate Sound Production and Acoustic Communication (eds Suthers, R. A., Fitch, W. T., Fay, R. R., & Popper, A. N.) 1–18 (Springer, 2016).Carroll, R. L. The Palaeozoic ancestry of salamanders, frogs and caecilians. Zool. J. Linn. Soc. 150, 1–140 (2007).Article 

    Google Scholar 
    Schwenk, K. in Feeding: Form, Function and Evolution in Tetrapod Vertebrates (ed. Schwenk, K.) 175–291 (Academic Press, 2000).Schwenk, K. & Rubega, M. In Physiological and ecological adaptations to feeding in vertebrates, (eds. Starck, M. & Wang, T.) 1–41 (Science Pub. Inc., 2005).Schumacher, G. H. In Biology of the Reptilia, 4 (ed Gans, C.) 101–200 (Academic Press, 1973).Reese, A. M. The laryngeal region of Alligator mississippiensis. Anat. Rec. 92, 273–277 (1945).Article 

    Google Scholar 
    Riede, T., Li, Z., Tokuda, I. & Farmer, C. G. Functional morphology of the Alligator mississippiensis larynx with implications for vocal production. J. Exp. Biol. 218, 991–998 (2015).Article 

    Google Scholar 
    McLelland, J. In Form and Function in Birds, 4 (eds King, A. S. & McLelland, J.) 69–103 (Academic Press, 1989).Homberger, D. G. In The Biology of the Avian Respiratory System (ed Maina, J. N.) 27–97 (Springer, 2017).Fitch, W. T. In Encyclopedia of Language & Linguistics (ed Brown, K.) 115–121 (Elsevier, 2006).Clarke, J. A. et al. Fossil evidence of the avian vocal organ from the Mesozoic. Nature 538, 502–505 (2016).Article 

    Google Scholar 
    Kingsley, E. P. et al. Identity and novelty in the avian syrinx. Proc. Natl Acad. Sci. USA 115, 10209–10217 (2018).Article 
    CAS 

    Google Scholar 
    Riede, T., Thomson, S. L., Titze, I. R. & Goller, F. The evolution of the syrinx: an acoustic theory. PLoS Biol. 17, e2006507 (2019).Nowicki, S. Vocal tract resonances in oscine bird sound production: evidence from birdsongs in a helium atmosphere. Nature 325, 53–55 (1987).Article 
    CAS 

    Google Scholar 
    Hill, R. V. et al. A complex hyobranchial apparatus in a Cretaceous dinosaur and the antiquity of avian paraglossalia. Zool. J. Linn. Soc. 175, 892–909 (2015).Article 

    Google Scholar 
    Li, Z. H., Zhou, Z. H. & Clarke, J. A. Convergent evolution of a mobile bony tongue in flighted dinosaurs and pterosaurs. PLoS One 13, e0198078 (2018).Article 

    Google Scholar 
    Bonaparte, J. F., Novas, F. E. & Coria, R. A. Carnotaurus sastrei Bonaparte, the horned, lightly built carnosaur from the Middle Cretaceous of Patagonia. Contrib. in Sci. Nat. Hist. Mus. L. A. 416, 1–42 (1990).Maryanska, T. Ankylosauridae (Dinosauria) from Mongolia. Palaeontol. Pol. 37, 85–151 (1977).
    Google Scholar 
    Mori, C. A comparative anatomical study on the laryngeal cartilages and laryngeal muscles of birds, and a developmental study on the larynx of the domestic fowl. Acta Med. 27, 2629–2678 (1957).
    Google Scholar 
    Siebenrock, F. Über den Kehlkopf und die Luftröhre der Schildkröten. Sitzungsberichte Der Kais. 108, 581–595 (1899).
    Google Scholar 
    Soley, J. T., Tivane, C. & Crole, M. R. Gross morphology and topographical relationships of the hyobranchial apparatus and laryngeal cartilages in the ostrich (Struthio camelus). Acta Zool. 96, 442–451 (2015).Article 

    Google Scholar 
    Olson, S. L. & Feduccia, A. Presbyornis and the origin of the Anseriformes (Aves: Charadriomorphae). Smithson. Contrib. Zool. 323, 1–24 (1980).Soley, J. T., Tivane, C. & Crole, M. R. A Gross morphology and topographical relationships of the hyobranchial apparatus and laryngeal cartilages in the ostrich (Struthio camelus). Acta Zool. 94, 442–451 (2015).Article 

    Google Scholar 
    Hogg, D. A. Ossification of the laryngeal, tracheal and syringeal cartilages in the domestic fowl. J. Anat. 134, 57–71 (1982).CAS 

    Google Scholar 
    Gaunt, A. S., Stein, R. C. & Gaunt, S. L. Pressure and air flow during distress calls of the starling, Sturnus vulgaris (Aves; Passeriformes). J. Exp. Zool. 183, 241–261 (1973).Article 

    Google Scholar 
    Sacchi, R., Galeotti, P., Fasola, M. & Gerzeli, G. Larynx morphology and sound production in three species of Testudinidae. J. Morphol. 261, 175–183 (2004).Article 

    Google Scholar 
    Titze, I. R. The physics of small-amplitude oscillation of the vocal folds. J. Acoust. Soc. Am. 83, 1536–1552 (1988).Article 
    CAS 

    Google Scholar 
    Russell, A. P., Hood, H. A. & Bauer, A. M. Laryngotracheal and cervical muscular anatomy in the genus Uroplatus (Gekkota: Gekkonidae) in relation to distress call emission. Afr. J. Herpetol. 63, 127–151 (2014).Article 

    Google Scholar 
    Russell, A. P., Rittenhouse, D. R. & Bauer, A. M. Laryngotracheal morphology of Afro‐Madagascan Geckos: a comparative survey. J. Morphol. 245, 241–268 (2000).Article 
    CAS 

    Google Scholar 
    Gans, C. & Maderson, P. F. Sound producing mechanisms in recent reptiles: review and comment. Am. Zool. 13, 1195–1203 (1973).Article 

    Google Scholar 
    Galeotti, P., Sacchi, R., Fasola, M. & Ballasina, D. Do mounting vocalisations in tortoises have a communication function? A comparative analysis. Herpetol. J. 15, 61–71 (2005).
    Google Scholar 
    Fletcher, N. H. Bird song—a quantitative acoustic model. J. Theor. Biol. 135, 455–481 (1988).Article 

    Google Scholar 
    Vergne, A. L., Pritz, M. B. & Mathevon, N. Acoustic communication in crocodilians: from behaviour to brain. Biol. Rev. 84, 391–411 (2009).Article 
    CAS 

    Google Scholar 
    Marler, P. R. & Slabbekoorn, H. Nature’s music: The science of birdsong (Academic Press, San Diego, USA, 2004).White, S. S. In Sisson and Grossman’s The Anatomy of the Domestic Animals. 2 (ed Getty, R.) 1891–1897 (Saunders, Philadelphia, USA 975).Kirchner, J. A. The vertebrate larynx: adaptations and aberrations. Laryngoscope 103, 1197–1201 (1993).Article 
    CAS 

    Google Scholar 
    Mackelprang, R. & Goller, F. Ventilation patterns of the songbird lung/air sac system during different behaviors. J. Exp. Biol. 216, 3611–3619 (2013).
    Google Scholar 
    Brocklehurst, R. J., Schachner, E. R. & Sellers, W. I. Vertebral morphometrics and lung structure in non-avian dinosaurs. R. Soc. Open Sci. 5, 180983 (2018).Article 

    Google Scholar 
    Cerda, I. A., Salgado, L. & Powell, J. E. Extreme postcranial pneumaticity in sauropod dinosaurs from South America. Paläontol. Z. 86, 441–449 (2012).Article 

    Google Scholar 
    Sereno, P. C. et al. Evidence for avian intrathoracic air sacs in a new predatory dinosaur from Argentina. PLoS One 3, e3303 (2008).Chiari, Y., Cahais, V., Galtier, N. & Delsuc, F. Phylogenomic analyses support the position of turtles as the sister group of birds and crocodiles (Archosauria). BMC Biol. 10, 65 (2012).Article 

    Google Scholar  More

  • in

    Effects of moisture and density-dependent interactions on tropical tree diversity

    Gentry, A. H. Changes in plant community diversity and floristic composition on environmental and geographical gradients. Ann. Missouri Bot. Gard. 75, 1–34 (1988).Article 

    Google Scholar 
    Givnish, T. J. On the causes of gradients in tropical tree diversity. J. Ecol. 87, 193–210 (1999).Article 

    Google Scholar 
    Janzen, D. H. Herbivores and the number of tree species in tropical forests. Am. Nat. 104, 501–528 (1970).Article 

    Google Scholar 
    Connell, J. H. in Dynamics of Populations (eds Den Boer, P. J. & Gradwell, G. R.) 298–312 (PUDOC, 1971).Esquivel-Muelbert, A. et al. Seasonal drought limits tree species across the Neotropics. Ecography 40, 618–629 (2017).Article 

    Google Scholar 
    Gillett, J. B. Pest pressure, an underestimated factor in evolution. Syst. Assoc. Publ. 4, 37–46 (1962).
    Google Scholar 
    Engelbrecht, B. M. J. et al. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447, 80–82 (2007).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Condit, R., Engelbrecht, B. M. J., Pino, D., Pérez, R. & Turner, B. L. Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proc. Natl Acad. Sci. USA 110, 5064–5068 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage. 259, 660–684 (2010).Article 

    Google Scholar 
    Harrison, S., Spasojevic, M. J. & Li, D. Climate and plant community diversity in space and time. Proc. Natl Acad. Sci. USA 117, 4464–4470 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Milici, V. R., Dalui, D., Mickley, J. G. & Bagchi, R. Responses of plant–pathogen interactions to precipitation: Implications for tropical tree richness in a changing world. J. Ecol. 108, 1800–1809 (2020).Article 

    Google Scholar 
    Mangan, S. A. et al. Negative plant-soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gripenberg, S. et al. Testing for enemy-mediated density-dependence in the mortality of seedlings: field experiments with five Neotropical tree species. Oikos 123, 185–193 (2014).Article 

    Google Scholar 
    Bagchi, R. et al. Pathogens and insect herbivores drive rainforest plant diversity and composition. Nature 506, 85–88 (2014).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Fricke, E. C., Tewksbury, J. J. & Rogers, H. S. Multiple natural enemies cause distance-dependent mortality at the seed-to-seedling transition. Ecol. Lett. 17, 593–598 (2014).Article 
    PubMed 

    Google Scholar 
    Augspurger, C. K. & Kelly, C. K. Pathogen mortality of tropical tree seedlings: experimental studies of the effects of dispersal distance, seedling density, and light conditions. Oecologia 61, 211–217 (1984).Article 
    ADS 
    PubMed 

    Google Scholar 
    Chen, L. et al. Differential soil fungus accumulation and density dependence of trees in a subtropical forest. Science 366, 124–128 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Eck, J. L., Stump, S. M., Delavaux, C. S., Mangan, S. A. & Comita, L. S. Evidence of within-species specialization by soil microbes and the implications for plant community diversity. Proc. Natl Acad. Sci. USA 116, 7371–7376 (2019).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kishimoto-Yamada, K. & Itioka, T. How much have we learned about seasonality in tropical insect abundance since Wolda (1988)? Entomol. Sci. 18, 407–419 (2015).Article 

    Google Scholar 
    Huberty, A. F. & Denno, R. F. Plant water stress and its consequences for herbivorous insects: a new synthesis. Ecology 85, 1383–1398 (2004).Article 

    Google Scholar 
    Janzen, D. H. & Hallwachs, W. To us insectometers, it is clear that insect decline in our Costa Rican tropics is real, so let’s be kind to the survivors. Proc. Natl Acad. Sci. USA 118, e2002546117 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rodríguez-Castañeda, G. The world and its shades of green: a meta-analysis on trophic cascades across temperature and precipitation gradients. Glob. Ecol. Biogeogr. 22, 118–130 (2013).Article 

    Google Scholar 
    Janzen, D. H. & Schoener, T. W. Differences in insect abundance and diversity between wetter and drier sites during a tropical dry season. Ecology 49, 96–110 (1968).Article 

    Google Scholar 
    Sturrock, R. N. et al. Climate change and forest diseases. Plant Pathol 60, 133–149 (2011).Article 

    Google Scholar 
    Desprez-Loustau, M.-L., Marçais, B., Nageleisen, L.-M., Piou, D. & Vannini, A. Interactive effects of drought and pathogens in forest trees. Ann. For. Sci. 63, 597–612 (2006).Article 

    Google Scholar 
    Swinfield, T., Lewis, O. T., Bagchi, R. & Freckleton, R. P. Consequences of changing rainfall for fungal pathogen-induced mortality in tropical tree seedlings. Ecol. Evol. 2, 1408–1413 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jactel, H. et al. Drought effects on damage by forest insects and pathogens: a meta-analysis. Glob. Chang. Biol. 18, 267–276 (2012).Article 
    ADS 

    Google Scholar 
    Maharjan, S. K. et al. Plant functional traits and the distribution of West African rain forest trees along the rainfall gradient. Biotropica 43, 552–561 (2011).Article 

    Google Scholar 
    Klironomos, J. N. Feedback with soil biota contributes to plant rarity and invasiveness in communities. Nature 417, 67–70 (2002).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Petermann, J. S., Fergus, A. J. F., Turnbull, L. A. & Schmid, B. Janzen–Connell effects are widespread and strong enough to maintain diversity in grasslands. Ecology 89, 2399–2406 (2008).Article 
    PubMed 

    Google Scholar 
    Chesson, P. Updates on mechanisms of maintenance of species diversity. J. Ecol. 106, 1773–1794 (2018).Article 

    Google Scholar 
    Barabás, G., Michalska-Smith, M. J. & Allesina, S. The effect of intra- and interspecific competition on coexistence in multispecies communities. Am. Nat. 188, E1–E12 (2016).Article 
    PubMed 

    Google Scholar 
    Lebrija-Trejos, E., Wright, S. J., Hernández, A. & Reich, P. B. Does relatedness matter? Phylogenetic density-dependent survival of seedlings in a tropical forest. Ecology 95, 940–951 (2014).Article 
    PubMed 

    Google Scholar 
    Lebrija-Trejos, E., Reich, P. B., Hernández, A. & Wright, S. J. Species with greater seed mass are more tolerant of conspecific neighbours: a key driver of early survival and future abundances in a tropical forest. Ecol. Lett. 19, 1071–1080 (2016).Article 
    PubMed 

    Google Scholar 
    Green, P. T., Harms, K. E. & Connell, J. H. Nonrandom, diversifying processes are disproportionately strong in the smallest size classes of a tropical forest. Proc. Natl Acad. Sci. USA 111, 18649–18654 (2014).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Comita, L. S. et al. Testing predictions of the Janzen–Connell hypothesis: a meta-analysis of experimental evidence for distance- and density-dependent seed and seedling survival. J. Ecol. 102, 845–856 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moles, A. T. & Westoby, M. What do seedlings die from and what are the implications for evolution of seed size? Oikos 106, 193–199 (2004).Article 

    Google Scholar 
    Paine, C. E. T., Harms, K. E., Schnitzer, S. A. & Carson, W. P. Weak competition among tropical tree seedlings: implications for species coexistence. Biotropica 40, 432–440 (2008).Article 

    Google Scholar 
    Weissflog, A., Markesteijn, L., Lewis, O. T., Comita, L. S. & Engelbrecht, B. M. J. Contrasting patterns of insect herbivory and predation pressure across a tropical rainfall gradient. Biotropica 50, 302–311 (2018).Article 

    Google Scholar 
    Brenes-Arguedas, T., Coley, P. D. & Kursar, T. A. Pests vs. drought as determinants of plant distribution along a tropical rainfall gradient. Ecology 90, 1751–1761 (2009).Article 
    PubMed 

    Google Scholar 
    Gaviria, J. & Engelbrecht, B. M. J. Effects of drought, pest pressure and light availability on seedling establishment and growth: their role for distribution of tree species across a tropical rainfall gradient. PLoS ONE 10, e0143955 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Spear, E. R., Coley, P. D. & Kursar, T. A. Do pathogens limit the distributions of tropical trees across a rainfall gradient? J. Ecol. 103, 165–174 (2015).Article 

    Google Scholar 
    Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Glob. Chang. Biol. 22, 2329–2352 (2016).Article 
    ADS 
    PubMed 

    Google Scholar 
    Riutta, T. et al. Experimental evidence for the interacting effects of forest edge, moisture and soil macrofauna on leaf litter decomposition. Soil Biol. Biochem. 49, 124–131 (2012).Article 
    CAS 

    Google Scholar 
    Lebrija-Trejos, E., Pérez-García, E. A., Meave, J. A., Poorter, L. & Bongers, F. Environmental changes during secondary succession in a tropical dry forest in Mexico. J. Trop. Ecol. 27, 477–489 (2011).Article 

    Google Scholar 
    Krishnadas, M. & Comita, L. S. Edge effects on seedling diversity are mediated by impacts of fungi and insects on seedling recruitment but not survival. Front. Glob. Chang. 2, 76 (2019).Article 

    Google Scholar 
    Garcia, R. A., Cabeza, M., Rahbek, C. & Araujo, M. B. Multiple dimensions of climate change and their implications for biodiversity. Science 344, 1247579 (2014).Article 
    PubMed 

    Google Scholar 
    Uriarte, M., Muscarella, R. & Zimmerman, J. K. Environmental heterogeneity and biotic interactions mediate climate impacts on tropical forest regeneration. Glob. Chang. Biol. 24, e692–e704 (2018).Article 
    ADS 
    PubMed 

    Google Scholar 
    Bachelot, B., Kobe, R. K. & Vriesendorp, C. Negative density-dependent mortality varies over time in a wet tropical forest, advantaging rare species, common species, or no species. Oecologia 179, 853–861 (2015).Article 
    ADS 
    PubMed 

    Google Scholar 
    Zhu, Y. et al. Density‐dependent survival varies with species life‐history strategy in a tropical forest. Ecol. Lett. 21, 506–515 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wright, S. J., Calderón, O., Hernandéz, A. & Muller-Landau, H. C. Annual and spatial variation in seedfall and seedling recruitment in a neotropical forest. Ecology 86, 848–860 (2005).Article 

    Google Scholar 
    Condit, R. Tropical Forest Census Plots https://doi.org/10.1007/978-3-662-03664-8 (Springer, 1998).Kupers, S. J., Wirth, C., Engelbrecht, B. M. J. & Rüger, N. Dry season soil water potential maps of a 50 hectare tropical forest plot on Barro Colorado Island, Panama. Sci. Data 6, 63 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Garwood, N. C. in The Ecology of a Tropical Forest: Seasonal Rhythms and Long-term Changes (eds Leigh, E. G., Rand, A. S. & Windsor, D. M.) 173–185 (Smithsonian Institution Press, 1982).Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).Article 

    Google Scholar 
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference https://doi.org/10.1007/b97636 (Springer, 2004).Muller-Landau, H. C. et al. Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol. Lett. 9, 575–588 (2006).Article 
    PubMed 

    Google Scholar 
    Detto, M., Visser, M. D., Wright, S. J. & Pacala, S. W. Bias in the detection of negative density dependence in plant communities. Ecol. Lett. 22, 1923–1939 (2019).Article 
    PubMed 

    Google Scholar 
    Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).Article 
    PubMed 

    Google Scholar 
    Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68, 255–278 (2013).Article 

    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Bates, D. et al. Package ‘lme4’ Reference Manual https://cran.r-project.org/web/packages/lme4/lme4.pdf (2021).Wilkinson, G. N. & Rogers, C. E. Symbolic description of factorial models for analysis of variance. Appl. Stat. 22, 392 (1973).Article 

    Google Scholar 
    Afshartous, D. & Preston, R. A. Key results of interaction models with centering. J. Stat. Educ. https://doi.org/10.1080/10691898.2011.11889620 (2011).Cohen, J. Statistical Power Analysis for the Behavioral Sciences https://doi.org/10.1016/C2013-0-10517-X (Elsevier, 1977).Steiger, J. H. Tests for comparing elements of a correlation matrix. Psychol. Bull. 87, 245–251 (1980).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (2016).Pinheiro, J. et al. nlme: Linear and Nonlinear Mixed Effects Models https://CRAN.R-project.org/package=nlme (2020).Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007).Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-level/Mixed) Regression Models https://CRAN.R-project.org/package=DHARMa (2021).Lebrija-Trejos, E., Wright, S. J. & Hernández, A. Moisture, Density-dependent Interactions, and Tropical Tree Diversity https://figshare.com/s/a4d2dbb2a73b3eb09f9f (2022).Kupers, S. J., Wirth, C., Engelbrecht, B. M. J. & Rüger, N. Dry Season Soil Water Potential Maps of a 50 Hectare Tropical Forest Plot on Barro Colorado Island, Panama https://doi.org/10.6084/m9.figshare.7611005.v1 (2019).Paton, S. Barro Colorado Island, Lutz Catchment, Soil Moisture, Manual https://doi.org/10.25573/data.10042517.v1 (2019). More

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    The interplay between spatiotemporal overlap and morphology as determinants of microstructure suggests no ‘perfect fit’ in a bat-flower network

    Study siteThe study was conducted in the Brasília National Park (PNB), Federal District, Brazil (15º39′57″ S; 47º59′38″ W), a 42.355 ha Protected Area with a typical vegetation configuration found in the Cerrado of the central highlands of Brazil, i.e., a mosaic of gallery forest patches along rivers surrounded by a matrix of savannas and grasslands34. The climate in the region falls into the Aw category in the Köppen scale, categorizing a tropical wet savanna, with marked rainy (October to March) and dry (April to September) seasons.We carried out the study in eight fixed sampling sites scattered evenly throughout the PNB and separated by at least two kilometers from one another (Supplementary Fig. S1). The sites consisted of four cerrado sensu stricto sites (bushy savanna containing low stature trees); two gallery forest edges sites (ca. 5 m from forest edges, containing a transitional community), and two gallery forest interior sites. These three types reflect the overall availability of habitat types in the reserve (excluding grasslands) and are the most appropriate foraging areas to sample interactions as bat-visited plants are either bushes, trees, or epiphytes, but rarely herbs35.Bat and interaction samplingsWe sampled bat-plant interactions using pollen loads collected from bat individuals captured in the course of one phenological year, thus configuring an animal-centered sampling. We carried out monthly field campaigns to capture bats from October 2019 to February 2020, from August to September 2020, and from March to July 2021. In each month, we carried out eight sampling nights during periods of low moonlight intensity, each associated with one of the eight sites. Each night, we set 10 mist nets (2.6 × 12 m, polyester, denier 75/2, 36 mm mesh size, Avinet NET-PTX, Japan) at ground level randomly within the site, which were opened at sunset and closed after six hours. We accumulated a total sampling effort of 552 net-hours, 28,704 m2 of net area, or 172,224 m2h sensu Straube and Bianconi36.All captured bats were sampled for pollen, irrespective of family or feeding guild. We used glycerinated and stained gelatin cubes to collect pollen grains from the external body of bats (head, torso, wings, and uropatagium). Samples were stored individually, and care was taken not to cross-contaminate samples. Pollen types were identified by light microscopy, and palynomorphs were identified to the lowest-possible taxonomical level using an extensive personal reference pollen collection from plants from the PNB (details in next section). Palynomorphs were sometimes classified to the genus or family level or grouped in entities representing more than one species. Any palynomorph numbering five or fewer grains in one sample was considered contamination, alongside any anemophilous species irrespective of pollen number.Bats were identified using a specialized key37 and four ecomorphological variables were measured for each individual. (i) Forearm length and (ii) body mass were used to calculate the body condition index (BCI), a proxy of body robustness38, where higher BCI values indicate larger and heavier bats, which are less effective in interacting with flowers in general due to a lack of hovering behavior, the incapability of interacting with delicate flowers that cannot sustain them, a lower maneuverability and higher energetic requirements39. Moreover, we measured (iii) longest skull length (distance from the edge of the occipital region to the anterior edge of the lower lip) and (iv) rostrum length (distance from the anterior edge of the eye to the anterior edge of the lower lip) to calculate the rostrum-skull ratio (RSR), a proxy of morphological specialization to nectar consumption23. Higher RSR values indicate bats with proportionally longer rostra in relation to total skull length. Longer rostra in bats are associated with a weaker bite force and thus less effective in consuming harder food items such as fruits and insects, thus suggesting a higher adaptation to towards nectar40,41. Bats were then tagged with aluminum bands for individualization and released afterward. To evaluate the sampling completeness of the bat community and of the pollen types found on bats, we employed the Chao1 asymptotic species richness estimator and an individual-based sampling effort to estimate and plot rarefaction curves, calculating sampling completeness according to Chacoff et al.42.All methods were carried out in accordance with relevant guidelines and regulations. The permits to capture, handle and collect bats were granted by the Ethical Council for the Usage of Animals (CEUA) of the University of Brasília (permit 23106.119660/2019-07) and the Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) (permit: SISBIO 70268). Vouchers of each species, when the collection was possible, were deposited in the Mammal Collection of the University of Brasília.Assessment of the plant communityIn each of the eight sampling sites, we delimited a 1000 × 10 m transect, each of which was walked monthly for one phenological year (January and February 2020, August to December 2020, and March to July 2021) to build a floristic inventory of plants of interest and to estimate their monthly abundance of flowering individuals. Plant species of interest were any potential partner for bats, which included species already known to be pollinated by bats, presenting chiropterophilous traits sensu Faegri and Van Der Pijl43, or any plant that could be accessed by and reward bats, whose flowers passes all the three following criteria:(i) Nectar or pollen is presented as the primary reward to visitors. (ii) Corolla diameter of 1 cm or more. This criterion excludes small generalist and insect-pollinated flowers where the visitation by bats is mechanically unlikely. It applies to the corolla diameter in non-tubular flowers or the diameter of the tube opening. Exceptions were small and actinomorphic flowers aggregated in one larger pollination unit (pseudanthia) where the 1 cm threshold was applied to inflorescence diameter. (iii) Reward must be promptly available for bats. This criterion excludes species with selective morphological mechanisms, such as quill-shaped bee-pollinated flowers or flowers with long and narrow calcars.All flowering individuals of interest species found in the transects were registered. A variable number of flowers/inflorescences (n = 5–18) were collected per species for morphometric analysis. For each species, we calculated floral tube length (FTL), corresponding to the distance between the base of the corolla, calyx, or hypanthium (depending on the species) to its opening, and the corolla’s outermost diameter (COD), which corresponds to the diameter of the corolla opening (tubular flowers) or simply the corolla diameter (non-tubular flowers). For pseudanthia-forming species, inflorescence width was measured. Pseudanthia and non-tubular flowers received a dummy FTL value of 0.1 mm to represent low restriction and enable later calculations. Finally, we collected reference pollen samples from all species from anthers of open flowers, which were used to identify pollen types found on bats. For plant species found in pollen loads but not in the PNB, measures were taken from plants found either on the outskirts of the site (Inga spp.) or from dried material in an online database (Ceiba pentandra, in https://specieslink.net/) using the ImageJ software44. Vouchers were deposited in the Herbarium of the Botany Department, University of Brasília.Data analysisNetwork macrostructureWe built a weighted adjacency matrix i x j, where cells corresponded to the number of individuals of bat species i that interacted with plant species or morphotype j. All edges corresponding to legitimate interactions were included. With this matrix, we calculated three structural metrics to describe the network’s macrostructure. First, weighted modularity (Qw), calculated by the DIRTLPAwb + algorithm45. A modular network comprises subgroups of species in which interactions are stronger and more frequent than species out of these subgroups10, which may reveal functional groups in the network9. Qw varies from zero to one, the latter representing a perfectly modular network.Second, complementary specialization through the H2′ metric46. It quantifies how unique, on average, are the interactions made by species in the network, considering interaction weights and correcting for network size. It varies from zero to one, the latter corresponding to a specialized network where interactions perfectly complement each other because species do not share partners.Lastly, nestedness, using the weighted WNODA metric25. Nested networks are characterized by interaction asymmetries, where peripheral species are only a subset of the pool of species with which generalists interact47. The index was normalized to vary from zero to one, with one representing a perfectly nested network. Given that the network has a modular structure, we also tested for a compound topology, i.e., the existence of distinct network patterns within network modules, by calculating intra-module WNODA and between-module WNODA36. Internally nested modules appear in networks in which consumers specialize in groups of dissimilar or clustered resources and suggest the existence of distinct functional groups of consumers25,48. Metric significance (Qw, H2′, and WNODA) was assessed using a Monte Carlo procedure based on a null model. We used the vaznull model3, where random matrices are created by preserving the connectance of the observed matrix but allowing marginal totals to vary. One thousand matrices were generated and metrics were calculated for each of them. Metric significance (p) corresponded to the number of times the null model delivered a value equal to or higher than the observed metric, divided by the number of matrices. The significance threshold was considered p ≤ 0.05.Given a modular structure, we followed the framework of Phillips et al.49 that correlates network concepts (especially modularity) with the distribution of morphological variables of pollinators to unveil patterns of niche divergence in pollination networks. Given the most parsimonious module configuration suggested by the algorithm, we compared modules in terms of the distribution of morphological variables of the bat (RCR and BCI) and plant (FTL and COD) species that composed the module. Differences between modules means were tested with one-way ANOVAs.Drivers of network microstructureThe role of different ecological variables in determining pairwise interaction frequencies was assessed using a probability matrices approach3. This framework considers that an interaction matrix Y is a product of several probability matrices of the same size as Y, with each matrix representing the probability of species interacting based on an ecological mechanism. Thus, adapting it to our objectives, we have Eq. (1):$$mathrm{Y}=mathrm{f}(mathrm{A},mathrm{ M },mathrm{P},mathrm{ S})$$
    (1)
    where Y is the observed interaction matrix, and a function of interaction probability matrices based on species relative abundances (A), representing neutrality as species interact by chance; species morphological specialization (M), phenological overlap (P), and spatial overlap (S). We built models containing each of these matrices in the following ways:Relative abundance (A): matrix cells were the products of the relative abundances of bat and plant species. The relative abundances of bats were determined through capture frequencies (each species’ capture frequency divided by all captures, excluding recaptures) and the relative abundances of plants were determined by the number of flowering individuals recorded in transections (each species’ summed abundance in all transects and all months divided by the pooled abundance of all species in the network). Cell values were normalized to sum one.Morphological specialization (M): cells were the probability of species interacting based on their matching degree of morphological specialization. Morphologically specialized bats (i.e., longer rostra and smaller size) are more likely to interact with morphologically specialized flowers (i.e., longer tubes and narrower corollas), while unspecialized bats are more likely to interact with unspecialized, accessible flowers. For this purpose, we calculated a bat specialization index (BSI) as the ratio between RCR and BCI, where higher BSI values indicate overall lower body robustness and longer snout length. Likewise, the flower specialization index (FSI) was calculated for plants as the ratio between FTL and COD, where higher values indicate smaller, narrower, long-tubed flowers that require specialized morphology and behavior from bats for visitation. BSI and FTL were normalized to range between zero and one and were averaged between individuals of each species of bat or plant. Therefore, interaction probabilities were calculated as in Eq. (2):$${P}_{i,j}=1-|{BSI}_{i}-{FSI}_{j}|$$
    (2)
    where Pi,j is the interaction probability between bat species i and plant species j and |BSIi – FSIj| is the absolute difference between bat and plant specialization indexes. Similar index values (two morphologically specialized or unspecialized species interacting) lead to a low difference in specialization and thus to a high probability of interaction (Pi,j → 1), whereas the interaction between a morphologically specialized and a morphologically unspecialized species leads to a high absolute difference and thus lower probability of interaction (Pi,j → 0). Cell values of the resulting matrix were normalized to sum one.Phenological overlap (P): cells were the probability of species interacting based on temporal synchrony, calculated as the number of months that individuals of bat species i and flowering individuals of plant species j co-occurred in the research site, pooling all capture sites/transections. Cell values were normalized to sum one.Spatial overlap (S): cells were the probability of species interacting based on their co-occurrence over small-scale distances and vegetation types, calculated as the number of individuals from a bat species i captured in sampling sites where the plant species j was registered in the transection, considering all capture months. Cell values were normalized to sum one.Because more than one ecological mechanism may simultaneously drive interactions3,9, we built an additional set of seven models resultant from the element-wise multiplication of individual probability matrices:

    SP: The spatial and temporal distribution of species work simultaneously in driving a resource turnover in the community, driving interactions.

    AS: Abundance drives interactions between bats and plants, but within spatially clustered resources in the landscape caused by a turnover in species distributions.

    AP: Abundance drives interactions between bats and plants, but within temporally clustered resources caused by a seasonal distribution of resources.

    APS: Abundance drives interactions between bats and plants, but within resource clusters that emerge by a simultaneous temporal and spatial aggregation.

    MS: Similar to AS, but morphology drives interactions within spatial clusters.

    MP: Similar to MP, but morphology drives interactions within temporal clusters.

    MPS: Similar to APS, but morphology drives interactions within spatiotemporal clusters.

    Finally, we created a benchmark null model in which all cells in the matrix had the same probability value. All the compound matrices and the null model were also normalized to sum one.To compare the fit of these probability models with the real data, we conducted a maximum likelihood analysis3,9. We calculated the likelihood of each of these models in predicting the observed interaction matrix, assuming a multinomial distribution for the probability of interaction between species12. To compare model fit, we calculated the Akaike Information Criterion (AIC) for each model and their variation in AIC (ΔAIC) in relation to the best-fitting model. The number of species used in the probability matrices was considered the number of model parameters to penalize model complexity. Intending to assess whether nectarivorous bats and non-nectarivorous bats assembly sub-networks with different assembly rules, we created two partial networks from the observed matrix. One contained nectarivores only (subfamilies Glossophaginae and Lonchophyllinae) and their interactions, and the other contained frugivore and insectivore bats and their interactions. We repeated the likelihood procedure for these two partial networks.To conduct the likelihood analysis, we excluded plant species from the network that could not have their interaction probabilities measured, such as species found in pollen samples but not registered in the park or pollen types that could not be identified to the species level. Therefore, the interaction network Y and probability matrices did not include these species (details in Supplementary Table S1).SoftwareAnalyses were performed in R 3.6.050. Network metrics and null models were generated with the bipartite package51, and the sampling completeness analysis was performed with the vegan package52. Gephi 0.9.253 was used to draw the graph. More

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    Seasonal variation in the lipid content of Fraser River Chinook Salmon (Oncorhynchus tshawytscha) and its implications for Southern Resident Killer Whale (Orcinus orca) prey quality

    Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215 (1994).Article 

    Google Scholar 
    Fisheries and Oceans Canada. National recovery strategy for northern and southern resident killer whales (Orcinus orca) in Canada [proposed]. vol. Species at (2018).National Marine Fisheries Service. Recovery Plan for Southern Resident Killer Whales (Orcinus orca). (2008).Barrett-Lennard, L. G. & Ellis, G. M. Population Structure and Genetic Variability in Northeastern Pacific Killer Whales: Towards an Assessment of Population Viability. DFO Can. Sci. Advis. Secr. Res. Deocument 2001/065 65 (2001).DFO. Evaluation of the scientific evidence to inform the probability of effectiveness of mitigation measures in reducing shipping-related noise levels received by southern resident killer whales. CSAS Science Advisory Report vol. 2017/041 (2017).Ross, P. S., Ellis, G. M., Ikonomou, M. G. & Addison, R. F. High PCB concentrations in free-ranging Pacific Killer Whales, Orcinus orca: Effects of age, sex and dietary preference. Mar. Pollut. Bull. 40, 504–515 (2000).Article 
    CAS 

    Google Scholar 
    Ward, E. J., Holmes, E. E. & Balcomb, K. C. Quantifying the effects of prey abundance on killer whale reproduction. J. Appl. Ecol. 46, 632–640 (2009).Article 

    Google Scholar 
    Ford, J. K. B., Ellis, G. M., Olesiuk, P. F. & Balcomb, K. C. Linking killer whale survival and prey abundance: Food limitation in the oceans’ apex predator ?. Biol. Lett. 6, 139–142 (2010).Article 
    PubMed 

    Google Scholar 
    Ford, J. K. B. et al. Dietary specialization in two sympatric populations of killer whales (Orcinus orca) in coastal British Columbia and adjacent waters. Can. J. Zool. 76, 1456–1471 (1998).Article 

    Google Scholar 
    Ford, J. K. B., Ellis, G. M. & Olesiuk, P. F. Linking Prey and Population Dynamics Did Food Limitation Cause Recent Declines of RKW in BC, vol. 3848 (2005).O’Neill, S. M., Ylitalo, G. M. & West, J. E. Energy content of Pacific salmon as prey of northern and southern resident killer whales. Endanger. Species Res. 25, 265–281 (2014).Article 

    Google Scholar 
    Ford, J. K. B. & Ellis, G. M. Selective foraging by fish-eating killer whales Orcinus orca in British Columbia. Mar. Ecol. Prog. Ser. 316, 185–199 (2006).Article 
    ADS 

    Google Scholar 
    Jeffrey, K. M., Côté, I. M., Irvine, J. R. & Reynolds, J. D. Changes in body size of Canadian Pacific salmon over six decades. Can. J. Fish. Aquat. Sci. 74, 191–201 (2017).Article 

    Google Scholar 
    Ohlberger, J., Schindler, D. E., Ward, E. J., Walsworth, T. E. & Essington, T. E. Resurgence of an apex marine predator and the decline in prey body size. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1910930116 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ohlberger, J., Ward, E. J., Schindler, D. E. & Lewis, B. Demographic changes in Chinook salmon across the Northeast Pacific Ocean. Fish Fish. 19, 533–546 (2018).Article 

    Google Scholar 
    Bigler, B. S., Welch, D. W. & Helle, J. H. A review of size trends among North Pacific salmon (Oncorhynchus spp.). Can. J. Fish. Aquat. Sci. 53, 455–465 (2011).Article 

    Google Scholar 
    Hanson, M. B. et al. Species and stock identification of prey consumed by endangered southern resident killer whales in their summer range. Endanger. Species Res. 11, 69–82 (2010).Article 
    ADS 

    Google Scholar 
    Losee, J. P., Kendall, N. W. & Dufault, A. Changing salmon: An analysis of body mass, abundance, survival, and productivity trends across 45 years in Puget Sound. Fish Fish. 20, 934–951 (2019).Article 

    Google Scholar 
    Riddell, B. et al. Assessment of Status and Factors for Decline of Southern BC Chinook Salmon: Independent Panel’s Report (2013).DFO. Integrated Biological Status of Southern British Columbia Chinook Salmon (Oncorhynchus tshawytscha) Under the Wild Salmon Policy. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2016/042, 15 (2016).
    Google Scholar 
    COSEWIC. COSEWIC assessment and status report on the Chinook Salmon Oncorhynchus tshawytscha, Designatable Units in Southern British Columbia, in Canada. (2019).Pacific Salmon Commission Joint Chinook Technical Committee. Annual Report of Catch and Escapement for 2021. Tcchinook (13)-01 (2021).Quinn, T. P. Behavior and ecology of Pacific Salmon and trout. Fish Fish. 7, 75–76 (2004).
    Google Scholar 
    Brett, J. R. Energetics. In Phsyiological Ecology of Pacific Salmon (eds Groot, C. et al.) 1–68 (UBC Press, 1995).
    Google Scholar 
    Chamberlain, M. W. & Parken, C. Utilizing the Albion test fishery as an in-season predictor of run size of the Fraser River spring and summer age 52 Chinook. DFO Can. Sci. Advis. Sec. Res. Doc. 2012, 42 (2012).
    Google Scholar 
    Schoener, T. W. Theory of feeding strategies. Annu. Rev. Ecol. Syst. 2, 369–404 (1971).Article 

    Google Scholar 
    Williams, R. et al. Competing conservation objectives for predators and prey: Estimating Killer Whale prey requirements for Chinook Salmon. PLoS ONE 6, e26738 (2011).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Courtney, K. R., Falke, J. A., Cox, M. K. & Nichols, J. Energetic status of Alaskan Chinook Salmon: Interpopulation comparisons and predictive modeling using bioelectrical impedance analysis. North Am. J. Fish. Manag. https://doi.org/10.1002/nafm.10398 (2019).Article 

    Google Scholar 
    Pothoven, S. A. et al. Reliability of bioelectrical impedance analysis for estimating whole-fish energy density and percent lipids. Trans. Am. Fish. Soc. 137, 1519–1529 (2008).Article 

    Google Scholar 
    Crossin, G. T. & Hinch, S. G. A Nonlethal, rapid method for assessing the somatic energy content of migrating adult pacific salmon. Trans. Am. Fish. Soc. 134, 184–191 (2005).Article 

    Google Scholar 
    Colt, J. & Shearer, K. D. Evaluation of the Use of the Torry Fish Fatmeter to Non-Lethally Estimate Lipid in Adult Salmon (2001).Hanson, K. C., Ostrand, K. G., Gannam, A. L. & Ostrand, S. L. Comparison and validation of nonlethal techniques for estimating condition in Juvenile Salmonids. Trans. Am. Fish. Soc. 139, 1733–1741 (2010).Article 

    Google Scholar 
    Naughton, G., Caudill, C. & Clabough, T. Migration Behavior and Spawning Success of Spring Chinook Salmon in Fall Creek and the North Fork Middle Fork Willamette River: Relationship Among Fate, Fish Condition, and Environmental Factors, 2011. (2012).Folch, J., Lees, M. & Sloane Stanley, G. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 226, 497–509 (1957).Article 
    CAS 
    PubMed 

    Google Scholar 
    Post, J. R. & Parkinson, E. A. Energy allocation strategy in young fish: Allometry and survival. Ecology 82, 1040–1051 (2001).Article 

    Google Scholar 
    Arrington, D. A., Davidson, B. K., Winemiller, K. O. & Layman, C. A. Influence of life history and seasonal hydrology on lipid storage in three neotropical fish species. J. Fish Biol. 68, 1347–1361 (2006).Article 
    CAS 

    Google Scholar 
    Holty, B. L. & Ciruna, K. A. Conservation units for Pacific Salmon under the Wild Salmon Policy. DFO Can. Sci. Advis. Sec. Res. Doc 2007/070, 350 (2007).
    Google Scholar 
    PSC. Catch and Escapement of Chinook Under Pacific Salmon Commission Jurisdiction, 2001 (PSC, 2002).
    Google Scholar 
    Waples, R. S., Teel, D. J., Myers, J. M. & Marshall, A. R. Life-history divergence in Chinook Salmon: Historic contingency and parallel evolution. Evolution 58, 386–403 (2004).PubMed 

    Google Scholar 
    Beacham, T. D. et al. Pacific rim population structure of chinook salmon as determined from microsatellite analysis. Trans. Am. Fish. Soc. 135, 1604–1621 (2006).Article 
    CAS 

    Google Scholar 
    Crossin, G. T. et al. Energetics and morphology of sockeye salmon: Effects of upriver migratory distance and elevation. J. Fish Biol. 65, 788–810 (2004).Article 

    Google Scholar 
    MacDonald, B. In-Season Forecasting of Fraser Chinook Salmon Using Genetic Stock Identification of Test Fishery Data By (2016).Parken, C. K., Candy, J. R., Irvine, J. R. & Beacham, T. D. Genetic and coded wire tag results combine to allow more-precise management of a complex Chinook salmon aggregate. North Am. J. Fish. Manag. 28, 328–340 (2008).Article 

    Google Scholar 
    Mann, R., Peery, C., Pinson, A. & Anderson, C. Energy use, migration times, and spawning success of adult spring–summer Chinook salmon returning to spawning areas in the South Fork Salmon River in Central Idaho: 2002–2007. Technical report 2009–4 http://www.cnr.uidaho.edu/uiferl/pdfreports/SFS_Tech_Report_2009-4_Final.pdf (2009).Hearsey, J. W. & Kinziger, A. P. Diversity in sympatric chinook salmon runs: Timing, relative fat content and maturation. Environ. Biol. Fishes 98, 413–423 (2015).Article 

    Google Scholar 
    Arimitsu, M. L. et al. Heatwave-induced synchrony within forage fish portfolio disrupts energy flow to top pelagic predators. Glob. Chang. Biol. 27, 1859–1878 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lloret-Lloret, E. et al. Small pelagic fish fitness relates to local environmental conditions and trophic variables. Prog. Oceanogr. 202, 102745 (2022).Article 

    Google Scholar 
    Mesa, M. G. & Magie, C. D. Evaluation of energy expenditure in adult spring Chinook salmon migrating upstream in the Columbia River Basin: An assessment based on sequential proximate analysis. River Res. Appl. 22, 1085–1095 (2006).Article 

    Google Scholar 
    Crossin, G. T., Hinch, S. G., Farrell, A. P., Higgs, D. A. & Healey, M. C. Somatic energy of sockeye salmon Oncorhynchus nerka at the onset of upriver migration: A comparison among ocean climate regimes. Fish. Oceanogr. 13, 345–349 (2004).Article 

    Google Scholar 
    Roni, P. & Quinn, T. P. Geographic variation in size and age of North American Chinook salmon. North Am. J. Fish. Manag. 15, 325–345 (1995).Article 

    Google Scholar 
    Hendry, A. P., Berg, O. K., Quinn, T. P. & Condition, T. P. Condition dependence and adaptation-by-time: Breeding date, life history, and energy allocation within a population of salmon. Oikos 85, 499–514 (1999).Article 

    Google Scholar 
    Hanson, M. B. et al. Endangered predators and endangered prey: Seasonal diet of Southern Resident killer whales. PLoS ONE 16, e0247031 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Weitkamp, L. A. Marine distributions of Chinook Salmon from the West Coast of North America determined by coded wire tag recoveries. Trans. Am. Fish. Soc. 139, 147–170 (2010).Article 

    Google Scholar 
    Shields, M. W., Lindell, J. & Woodruff, J. Declining spring usage of core habitat by endangered fish-eating killer whales reflects decreased availability of their primary prey. Pac. Conserv. Biol. 24, 189–193 (2018).Article 

    Google Scholar 
    Brown, G. S. et al. Pre-COSEWIC review of southern British Columbia Chinook Salmon (Oncorhynchus tshawytscha) conservation units Part I: Background. Can. Sci. Advis. Sec. Res. Doc. 2019/11, 67 (2019).
    Google Scholar 
    NOAA Fisheries West Coast & Washington Department of Fish and Wildlife. Southern Resident Killer Whale Priority Chinook Stocks Report. https://www.westcoast.fisheries.noaa.gov/publications/protected_species/marine_mammals/killer_whales/recovery/srkw_priority_chinook_stocks_conceptual_model_report___list_22june2018.pdf (2018).Chalifour, L. et al. Chinook salmon exhibit long-term rearing and early marine growth in the fraser river, british columbia, a large urban estuary. Can. J. Fish. Aquat. Sci. 78, 539–550 (2021).Article 
    CAS 

    Google Scholar 
    Lamperth, J. S., Quinn, T. P. & Zimmerman, M. S. Levels of stored energy but not marine foraging patterns differentiate seasonal ecotypes of wild and hatchery steelhead (Oncorhynchus mykiss) returning to the Kalama river, Washington. Can. J. Fish. Aquat. Sci. 74, 157–167 (2017).Article 
    CAS 

    Google Scholar 
    Von Biela, V. R. et al. Extreme reduction in nutritional value of a key forage fish during the pacific marine heatwave of 2014–2016. Mar. Ecol. Prog. Ser. 613, 171–182 (2019).Article 
    ADS 

    Google Scholar 
    Healey, M. C. Life history of Chinook Salmon (Oncorhynchus tshawytscha). In Pacific Salmon Life Histories (eds Groot, C. & Margolis, L.) 313–393 (University of British Columbia Press, 1991).
    Google Scholar 
    Freshwater, C. et al. An integrated model of seasonal changes in stock composition and abundance with an application to Chinook salmon. PeerJ 9, 1–27 (2021).Article 

    Google Scholar 
    Couture, F., Oldford, G., Christensen, V., Barrett-lennard, L. & Walters, C. Requirements and availability of prey for northeastern pacific southern resident killer whales. PLoS ONE 17, e0270523 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    DFO. Government of Canada Takes Action to Address Fraser River Chinook Decline (DFO, 2019).
    Google Scholar 
    Brown, R. F. & Musgrave, M. M. Preliminary Catalogue of Salmon Steams and Escapements of Misson-Harrison Sub District. Fisheries and Marine Service Data Report No. 133 (1979).Manzon, C. I. & Marshall, D. E. Catalogue of salmon streams and spawning escapements of Cariboo subdistrict. Can. Data Rep. Fish. Aquat. Sci. 211, 51 (1980).
    Google Scholar 
    Marshall, D. E. & Manzon, C. I. Catalogue of Salmon Streams and Spawning Escapements of the Prince George Subdistrict (Department of Fisheries and Oceans Fisheries and Marine Services Data Report N0o. 79, 1980).
    Google Scholar 
    Olmsted, W., Whelen, M. & Stewart, R. 1980 Investigations of fall-spawning chinook salmon (Oncorhynchus tshawytscha), Quesnel, blackwater (west road) and cottonwood river drainages, B.C. 34, 131–134 (1981).Brown, R. F., Musgrave, M. M. & Marshall, D. E. Catalogue of salmon streams and spawning escapements for Kamloops sub-district. Fish. Mar. Serv. Data Rep. 151, 226 (1979).
    Google Scholar 
    DFO. Information Document to Assist Development of a Fraser Chinook Management Plan 56 (DFO, 2006).
    Google Scholar 
    Kosakoski, G. T. & Hamilton, R. E. Water Requirements for the Fisheries Resource of the Nicola River, B.C. Can. Manuscr. Rep. Fish. Aquat. Sci. 140 (1982). More