Ecology

Zhu, Y. G. et al. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat. Microbiol. 2, 16270, https://doi.org/10.1038/nmicrobiol.2016.270 (2017).CAS 
Article 
PubMed 

Google Scholar 
Hutchins, D. A. & Fu, F. Microorganisms and ocean global change. Nat. Microbiol. 2, 17058, https://doi.org/10.1038/nmicrobiol.2017.58 (2017).CAS 
Article 
PubMed 

Google Scholar 
Wang, J. & Jia, H. Metagenome-wide association studies: fine-mining the microbiome. Nat. Rev. Microbiol. 14, 508–522, https://doi.org/10.1038/nrmicro.2016.83 (2016).CAS 
Article 
PubMed 

Google Scholar 
Kan, J., Suzuki, M. T., Wang, K., Evans, S. E. & Chen, F. High temporal but low spatial heterogeneity of bacterioplankton in the Chesapeake bay. Appl. Environ. Microbiol. 73, 6776–6789, https://doi.org/10.1128/Aem.00541-07 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bouvier, T. C. & del Giorgio, P. A. Compositional changes in free-living bacterial communities along a salinity gradient in two temperate estuaries. Limnol. Oceanogr. 47, 453–470, https://doi.org/10.4319/lo.2002.47.2.0453 (2002).ADS 
CAS 
Article 

Google Scholar 
Campbell, B. J. & Kirchman, D. L. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 7, 210–220, https://doi.org/10.1038/ismej.2012.93 (2013).CAS 
Article 
PubMed 

Google Scholar 
Fortunato, C. S., Herfort, L., Zuber, P., Baptista, A. M. & Crump, B. C. Spatial variability overwhelms seasonal patterns in bacterioplankton communities across a river to ocean gradient. ISME J. 6, 554–563, https://doi.org/10.1038/ismej.2011.135 (2012).CAS 
Article 
PubMed 

Google Scholar 
Ghosh, A. & Bhadury, P. Exploring biogeographic patterns of bacterioplankton communities across global estuaries. MicrobiologyOpen 8, https://doi.org/10.1002/mbo3.741 (2019).Zhang, C. J., Chen, Y. L., Pan, J., Wang, Y. M. & Li, M. Spatial and seasonal variation of methanogenic community in a river-bay system in South China. Appl. Microbiol. Biotechnol. 104, 4593–4603, https://doi.org/10.1007/s00253-020-10613-z (2020).CAS 
Article 
PubMed 

Google Scholar 
Yu, T. et al. Characteristics of Microbial Communities and Their Correlation With Environmental Substrates and Sediment Type in the Gas-Bearing Formation of Hangzhou Bay, China. Front. Microbiol. 10, https://doi.org/10.3389/fmicb.2019.02421 (2019).Zhou, L. et al. Stochastic determination of the spatial variation of potentially pathogenic bacteria communities in a large subtropical river. Environ. Pollut. 264, 114683, https://doi.org/10.1016/j.envpol.2020.114683 (2020).CAS 
Article 
PubMed 

Google Scholar 
Zhou, L. et al. Environmental filtering dominates bacterioplankton community assembly in a highly urbanized estuarine ecosystem. Environ. Res. 196, 110934, https://doi.org/10.1016/j.envres.2021.110934 (2021).CAS 
Article 
PubMed 

Google Scholar 
Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676, https://doi.org/10.1093/bioinformatics/btv033 (2015).CAS 
Article 
PubMed 

Google Scholar 
Mikheenko, A., Saveliev, V. & Gurevich, A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics 32, 1088–1090, https://doi.org/10.1093/bioinformatics/btv697 (2016).CAS 
Article 
PubMed 

Google Scholar 
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, https://doi.org/10.1186/s40168-018-0541-1 (2018).Prjibelski, A., Antipov, D., Meleshko, D., Lapidus, A. & Korobeynikov, A. Using SPAdes de novo assembler. Curr. Protoc. Bioinf. 70, e102, https://doi.org/10.1002/cpbi.102 (2020).CAS 
Article 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication[J]. ISME J. 11, 2864–2868, https://doi.org/10.1038/ismej.2017.126 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Uritskiy, G. et al. Halophilic microbial community compositional shift after a rare rainfall in the Atacama Desert. ISME J. 13, 2737–2749, https://doi.org/10.1038/s41396-019-0468-y (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419, https://doi.org/10.1038/nmeth.4197 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil, P. A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927, https://doi.org/10.1093/bioinformatics/btz848 (2020).CAS 
Article 

Google Scholar 
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086, https://doi.org/10.1038/s41587-020-0501-8 (2020).CAS 
Article 
PubMed 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2–approximately maximum-likelihood trees for large alignments. PloS one 5, e9490, https://doi.org/10.1371/journal.pone.0009490 (2010).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274, https://doi.org/10.1093/molbev/msu300 (2015).CAS 
Article 
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, https://doi.org/10.1093/nar/gkab301 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
NCBI Sequence Read Archive https://identifiers.org/ncbi/insdc.sra:SRP320016 (2021).Zhou, L., Huang, S., Gong, J., Xu, P. & Huang, X. 500 metagenome-assembled microbial genomes from 30 subtropical estuaries in South China. Figshare https://doi.org/10.6084/m9.figshare.14717061.v4 (2021).Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055, https://doi.org/10.1101/gr.186072.114 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).CAS 
Article 
PubMed 

Google Scholar 
Muhlfeld, C. C. et al. Invasive hybridization in a threatened species is accelerated by climate change. Nat. Clim. Change 4, 620–624 (2014).Article 

Google Scholar 
Taylor, S. A. et al. Climate-mediated movement of an avian hybrid zone. Curr. Biol. 24, 671–676 (2014).CAS 
Article 
PubMed 

Google Scholar 
Cahill, J. A. et al. Genomic evidence of widespread admixture from polar bears into brown bears during the last ice age. Mol. Biol. Evol. 35, 1120–1129 (2018).CAS 
Article 
PubMed 

Google Scholar 
Mao, Y., Economo, E. P. & Satoh, N. The roles of introgression and climate change in the rise to dominance of Acropora corals. Curr. Biol. 28, 3373–3382.e5 (2018).CAS 
Article 
PubMed 

Google Scholar 
Vianna, J. A. et al. Genome-wide analyses reveal drivers of penguin diversification. Proc. Natl Acad. Sci. USA 117, 22303–22310 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Racimo, F., Sankararaman, S., Nielsen, R. & Huerta-Sánchez, E. Evidence for archaic adaptive introgression in humans. Nat. Rev. Genet. 16, 359–371 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McKelvey, K. S. et al. Patterns of hybridization among cutthroat trout and rainbow trout in northern Rocky Mountain streams. Ecol. Evol. 6, 688–706 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, B. Y., Huber, C. D. & Lohmueller, K. E. Deleterious variation shapes the genomic landscape of introgression. PLoS Genet. 14, e1007741 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, D.-D. et al. Pervasive introgression facilitated domestication and adaptation in the Bos species complex. Nat. Ecol. Evol. 2, 1139–1145 (2018).Article 
PubMed 

Google Scholar 
Wang, M.-S. et al. Ancient hybridization with an unknown population facilitated high-altitude adaptation of canids. Mol. Biol. Evol. 37, 2616–2629 (2020).CAS 
Article 
PubMed 

Google Scholar 
Meier, J. I. et al. Ancient hybridization fuels rapid cichlid fish adaptive radiations. Nat. Commun. 8, 14363 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Haig, S. M., Mullins, T. D., Forsman, E. D., Trail, P. W. & Wennerberg, L. I. V. Genetic identification of spotted owls, barred owls, and their hybrids: legal implications of hybrid identity. Conserv. Biol. 18, 1347–1357 (2004).Article 

Google Scholar 
vonHoldt, B. M. et al. Whole-genome sequence analysis shows that two endemic species of North American wolf are admixtures of the coyote and gray wolf. Sci. Adv. 2, e1501714 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Liu, S. et al. Population genomics reveal recent speciation and rapid evolutionary adaptation in polar bears. Cell 157, 785–794 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar, V. et al. The evolutionary history of bears is characterized by gene flow across species. Sci. Rep. 7, 46487 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Preuß, A., Gansloßer, U., Purschke, G. & Magiera, U. Bear-hybrids: behaviour and phenotype. Zool. Gart. 78, 204–220 (2009).Article 

Google Scholar 
Cahill, J. A. et al. Genomic evidence for island population conversion resolves conflicting theories of polar bear evolution. PLoS Genet. 9, e1003345 (2013).CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
Pongracz, J. D., Paetkau, D., Branigan, M. & Richardson, E. Recent hybridization between a polar bear and grizzly bears in the Canadian Arctic. Arctic 70, 151–160 (2017).Article 

Google Scholar 
Pugach, I., Matveyev, R., Wollstein, A., Kayser, M. & Stoneking, M. Dating the age of admixture via wavelet transform analysis of genome-wide data. Genome Biol. 12, R19 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Farquharson, L. et al. Alaskan marine transgressions record out-of-phase Arctic Ocean glaciation during the last interglacial. Geology 46, 783–786 (2018).Article 

Google Scholar 
Kapp, J. D., Green, R. E. & Shapiro, B. A fast and efficient single-stranded genomic library preparation method optimized for ancient DNA. J. Hered. 112, 241–249 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl Acad. Sci. USA 104, 14616–14621 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fu, Q. et al. Genome sequence of a 45,000-year-old modern human from western Siberia. Nature 514, 445–449 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Schiffels, S. & Durbin, R. Inferring human population size and separation history from multiple genome sequences. Nat. Genet. 46, 919–925 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pease, J. B. & Hahn, M. W. Detection and polarization of introgression in a five-taxon phylogeny. Syst. Biol. 64, 651–662 (2015).CAS 
Article 
PubMed 

Google Scholar 
Barlow, A. et al. Middle Pleistocene genome calibrates a revised evolutionary history of extinct cave bears. Curr. Biol. 31, 1771–1779.e7 (2021).CAS 
Article 
PubMed 

Google Scholar 
Barlow, A. et al. Partial genomic survival of cave bears in living brown bears. Nat. Ecol. Evol. 2, 1563–1570 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, K., Mathieson, I., O’Connell, J. & Schiffels, S. Tracking human population structure through time from whole genome sequences. PLoS Genet. 16, e1008552 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Polyak, L. et al. History of sea ice in the Arctic. Quat. Sci. Rev. 29, 1757–1778 (2010).Article 

Google Scholar 
Dutton, A. et al. Sea-level rise due to polar ice-sheet mass loss during past warm periods. Science 349, aaa4019 (2015).CAS 
Article 
PubMed 

Google Scholar 
Salonen, J. S. et al. Abrupt high-latitude climate events and decoupled seasonal trends during the Eemian. Nat. Commun. 9, 2851 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guarino, M.-V. et al. Sea-ice-free Arctic during the Last Interglacial supports fast future loss. Nat. Clim. Change 10, 928–932 (2020).Article 

Google Scholar 
Rode, K. D., Robbins, C. T., Nelson, L. & Amstrup, S. C. Can polar bears use terrestrial foods to offset lost ice-based hunting opportunities? Front. Ecol. Environ. 13, 138–145 (2015).Article 

Google Scholar 
Laidre, K. L., Stirling, I., Estes, J. A., Kochnev, A. & Roberts, J. Historical and potential future importance of large whales as food for polar bears. Front. Ecol. Environ. 16, 515–524 (2018).Article 

Google Scholar 
Miller, S., Wilder, J. & Wilson, R. R. Polar bear–grizzly bear interactions during the autumn open-water period in Alaska. J. Mammal. 96, 1317–1325 (2015).Article 

Google Scholar 
Steyaert, S. M. J. G., Endrestøl, A., Hackländer, K., Swenson, J. E. & Zedrosser, A. The mating system of the brown bear Ursus arctos. Mamm. Rev. 42, 12–34 (2012).Article 

Google Scholar 
Stirling, I., Spencer, C. & Andriashek, D. Behavior and activity budgets of wild breeding polar bears (Ursus maritimus). Mar. Mamm. Sci. 32, 13–37 (2016).Article 

Google Scholar 
Méheust, M., Stein, R., Fahl, K. & Gersonde, R. Sea-ice variability in the subarctic North Pacific and adjacent Bering Sea during the past 25 ka: new insights from IP25 and Uk′37 proxy records. Arktos 4, 1–19 (2018).Article 

Google Scholar 
Brigham-Grette, J. & Hopkins, D. M. Emergent marine record and paleoclimate of the last interglaciation along the northwest Alaskan coast. Quat. Res. 43, 159–173 (1995).Article 

Google Scholar 
Boessenkool, S. et al. Combining bleach and mild predigestion improves ancient DNA recovery from bones. Mol. Ecol. Resour. 17, 742–751 (2017).CAS 
Article 
PubMed 

Google Scholar 
Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl Acad. Sci. USA 110, 15758–15763 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010, pdb.prot5448 (2010).Article 
PubMed 

Google Scholar 
Kircher, M., Sawyer, S. & Meyer, M. Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. 40, e3 (2012).CAS 
Article 
PubMed 

Google Scholar 
Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).Article 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Jónsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. F. & Orlando, L. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Prüfer, K. snpAD: an ancient DNA genotype caller. Bioinformatics 34, 4165–4171 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Green, R. E. et al. A complete Neandertal mitochondrial genome sequence determined by high-throughput sequencing. Cell 134, 416–426 (2008).CAS 
Article 
PubMed 
PubMed Central 

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

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA–MEM. Preprint at https://doi.org/10.48550/arXiv.1303.3997 (2013).McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar, S., Stecher, G., Peterson, D. & Tamura, K. MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis. Bioinformatics 28, 2685–2686 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Vihtakari, M. PlotSvalbard: User Manual. Github https://mikkovihtakari.github.io/PlotSvalbard/articles/PlotSvalbard.html (2020).Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).Article 

Google Scholar 
Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinformatics 69, e96 (2020).Article 
PubMed 

Google Scholar 
Yu, G., Lam, T. T., Zhu, H. & Guan, Y. Two methods for mapping and visualizing associated data on phylogeny using ggtree. Mol. Biol. Evol. 35, 3041–3043 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, L.-G. et al. Treeio: an R package for phylogenetic tree input and output with richly annotated and associated data. Mol. Biol. Evol. 37, 599–603 (2020).CAS 
Article 
PubMed 

Google Scholar 
Lindqvist, C. et al. Complete mitochondrial genome of a Pleistocene jawbone unveils the origin of polar bear. Proc. Natl Acad. Sci. USA 107, 5053–5057 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kelleher, J., Etheridge, A. M. & McVean, G. Efficient coalescent simulation and genealogical analysis for large sample sizes. PLoS Comput. Biol. 12, e1004842 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Palkopoulou, E. et al. Complete genomes reveal signatures of demographic and genetic declines in the woolly mammoth. Curr. Biol. 25, 1395–1400 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Patterson, N. et al. Ancient admixture in human history. Genetics 192, 1065–1093 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Vershinina, A. O. et al. Ancient horse genomes reveal the timing and extent of dispersals across the Bering Land Bridge. Mol. Ecol. 30, 6144–6161 (2021).Article 
PubMed 

Google Scholar 
Chen, L., Wolf, A. B., Fu, W., Li, L. & Akey, J. M. Identifying and interpreting apparent Neanderthal ancestry in African individuals. Cell 180, 677–687.e16 (2020).CAS 
Article 
PubMed 

Google Scholar 
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lisiecki, L. E. & Raymo, M. E. A Pliocene-Pleistocene stack of 57 globally distributed benthic δ18O records. Paleoceanography 20, PA1003 (2005).
Google Scholar  More

Gattuso, J.-P. et al. Ocean solutions to address climate change and its effects on marine ecosystems. Front. Mar. Sci. 5, 337 (2018).Article 

Google Scholar 
Pauly, D. The gill-oxygen limitation theory (GOLT) and its critics. Sci. Adv. 7, 6050 (2021).Article 
ADS 
CAS 

Google Scholar 
Miller, D. D., Ota, Y., Sumaila, U. R., Cisneros-Montemayor, A. M. & Cheung, W. W. L. Adaptation strategies to climate change in marine systems. Glob. Change Biol. 24, e1–e14 (2018).Article 
ADS 

Google Scholar 
Chan, F. T. et al. Climate change opens new frontiers for marine species in the Arctic: Current trends and future invasion risks. Glob. Change Biol. 25, 25–38 (2019).Article 
ADS 

Google Scholar 
Cheung, W. W. L. et al. Structural uncertainty in projecting global fisheries catches under climate change. Ecol. Model. 325, 57–66 (2016).CAS 
Article 

Google Scholar 
Pita, I., Mouillot, D., Moullec, F. & Shin, Y. Contrasted patterns in climate change risk for Mediterranean fisheries. Glob. Change Biol. 27, 5920–5933 (2021).Article 

Google Scholar 
Tacon, A. G. J. & Metian, M. Fishing for aquaculture: Non-food use of small pelagic forage fish—a global perspective. Rev. Fish. Sci. 17, 305–317 (2009).Article 

Google Scholar 
Coll, M., Pennino, M. G., Steenbeek, J., Sole, J. & Bellido, J. M. Predicting marine species distributions: Complementarity of food-web and Bayesian hierarchical modelling approaches. Ecol. Model. 405, 86–101 (2019).Article 

Google Scholar 
Schickele, A. et al. Improving predictions of invasive fish ranges combining functional and ecological traits with environmental suitability under climate change scenarios. Glob. Change Biol. 27, 6086–6102 (2021).Article 

Google Scholar 
Lejeusne, C., Chevaldonné, P., Pergent-Martini, C., Boudouresque, C. F. & Pérez, T. Climate change effects on a miniature ocean: The highly diverse, highly impacted Mediterranean Sea. Trends Ecol. Evol. 25, 250–260 (2010).PubMed 
Article 

Google Scholar 
Cramer, W. et al. Climate change and interconnected risks to sustainable development in the Mediterranean. Nat. Clim. Change 8, 972–980 (2018).Article 
ADS 

Google Scholar 
FAO. The State of Mediterranean and Black Sea Fisheries 2020—At a glance. 20 (2020).McGinty, N., Barton, A. D., Finkel, Z. V., Johns, D. G. & Irwin, A. J. Niche conservation in copepods between ocean basins. Ecography https://doi.org/10.1111/ecog.05690 (2021).Article 

Google Scholar 
Dormann, C. F. et al. Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Glob. Ecol. Biogeogr. 27, 1004–1016 (2018).Article 

Google Scholar 
Hannemann, H., Willis, K. J. & Macias-Fauria, M. The devil is in the detail: unstable response functions in species distribution models challenge bulk ensemble modelling: Unstable response functions in SDMs. Glob. Ecol. Biogeogr. 25, 26–35 (2016).Article 

Google Scholar 
Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Chang. 9, 237–243 (2019).Article 
ADS 

Google Scholar 
Lasram, B. R. et al. An open-source framework to model present and future marine species distributions at local scale. Ecol. Inform. 59, 101130 (2020).Article 

Google Scholar 
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, 4858 (2019).Article 
ADS 

Google Scholar 
Schickele, A. et al. European small pelagic fish distribution under global change scenarios. Fish Fish 22, 212–225 (2021).Article 

Google Scholar 
Duarte, R., Azevedo, M., Landa, J. & Pereda, P. Reproduction of angler®sh (Lophius budegassa Spinola and Lophius piscatorius Linnaeus) from the Atlantic Iberian coast. Fish. Res. 13, 2 (2001).
Google Scholar 
Nunes, P., Svensson, L. & Markandya, A. Handbook on the Economics and Management of Sustainable Oceans (Edward Elgar Publishing, 2017).Book 

Google Scholar 
Schickele, A. et al. Modelling European small pelagic fish distribution: Methodological insights. Ecol. Model. 416, 108902 (2020).Article 

Google Scholar 
Cheung, W. W. L., Jones, M. C., Reygondeau, G. & Frölicher, T. L. Opportunities for climate-risk reduction through effective fisheries management. Glob. Change Biol. 24, 5149–5163 (2018).Article 
ADS 

Google Scholar 
Bossier, S. et al. The Baltic Sea Atlantis: An integrated end-to-end modelling framework evaluating ecosystem-wide effects of human-induced pressures. PLoS ONE 13, e0199168 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Valle, C., Bayle-Sempere, J. T., Dempster, T., Sanchez-Jerez, P. & Giménez-Casalduero, F. Temporal variability of wild fish assemblages associated with a sea-cage fish farm in the south-western Mediterranean Sea. Estuar. Coast. Shelf Sci. 72, 299–307 (2007).Article 
ADS 

Google Scholar 
Madurell, T., Cartes, J. E. & Labropoulou, M. Changes in the structure of fish assemblages in a bathyal site of the Ionian Sea (eastern Mediterranean). Fish. Res. 66, 245–260 (2004).Article 

Google Scholar 
Volkoff, H. & Rønnestad, I. Effects of temperature on feeding and digestive processes in fish. Temperature 7, 307–320 (2020).Article 

Google Scholar 
Rutterford, L. A. et al. Future fish distributions constrained by depth in warming seas. Nat. Clim. Change 5, 569–573 (2015).Article 
ADS 

Google Scholar 
Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).CAS 
Article 
ADS 

Google Scholar 
Conti, L. & Scardi, M. Fisheries yield and primary productivity in large marine ecosystems. Mar. Ecol. Prog. Ser. 410, 233–244 (2010).Article 
ADS 

Google Scholar 
Chérif, M. et al. Food and feeding habits of the red mullet, Mullus barbatus (Actinopterygii: Perciformes: Mullidae), off the northern Tunisian coast (central Mediterranean). Acta Icth et Piscat 41, 109–116 (2011).Article 

Google Scholar 
Mellon-Duval, C. et al. Trophic ecology of the European hake in the Gulf of Lions, northwestern Mediterranean Sea. Sci. Mar. 81, 7 (2017).Article 

Google Scholar 
Steingrund, P. & Gaard, E. Relationship between phytoplankton production and cod production on the Faroe Shelf. ICES J. Mar. Sci. 62, 163–176 (2005).Article 

Google Scholar 
Friedland, K. D. et al. Pathways between primary production and fisheries yields of large marine ecosystems. PLoS ONE 7, e28945 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Frederiksen, M., Edwards, M., Richardson, A. J., Halliday, N. C. & Wanless, S. From plankton to top predators: Bottom-up control of a marine food web across four trophic levels. J. Anim. Ecol. 75, 1259–1268 (2006).PubMed 
Article 

Google Scholar 
Vasilakopoulos, P., Raitsos, D. E., Tzanatos, E. & Maravelias, C. D. Resilience and regime shifts in a marine biodiversity hotspot. Sci. Rep. 7, 13647 (2017).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
Issifu, I., Alava, J. J., Lam, V. W. Y. & Sumaila, U. R. Impact of ocean warming, overfishing and mercury on European fisheries: A risk assessment and policy solution framework. Front. Mar. Sci. 8, 770805 (2022).Article 

Google Scholar 
Lima, A. R. A. et al. Forecasting shifts in habitat suitability across the distribution range of a temperate small pelagic fish under different scenarios of climate change. Sci. Total Environ. 804, 150167 (2022).CAS 
PubMed 
Article 
ADS 

Google Scholar 
Sumaila, U. R. et al. Benefits of the Paris Agreement to ocean life, economies, and people. Sci. Adv. 5, 3855 (2019).Article 
ADS 

Google Scholar 
Holsman, K. K. et al. Ecosystem-based fisheries management forestalls climate-driven collapse. Nat. Commun. 11, 4579 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
Sumaila, U. R. & Tai, T. C. End overfishing and increase the resilience of the ocean to climate change. Front. Mar. Sci. 7, 523 (2020).Article 

Google Scholar 
Lindegren, M. & Brander, K. Adapting fisheries and their management to climate change: A review of concepts, tools, frameworks, and current progress toward implementation. Rev. Fish. Sci. Aquacult. 26, 400–415 (2018).Article 

Google Scholar 
Demirel, N., Zengin, M. & Ulman, A. First large-scale eastern mediterranean and black sea stock assessment reveals a dramatic decline. Front. Mar. Sci. 7, 103 (2020).Article 

Google Scholar 
Weiss, C. V. C. et al. Climate change effects on marine renewable energy resources and environmental conditions for offshore aquaculture in Europe. ICES J. Mar. Sci. 77, 3168–3182 (2020).Article 

Google Scholar 
Cascarano, M. C. et al. Mediterranean aquaculture in a changing climate: temperature effects on pathogens and diseases of three farmed fish species. Pathogens 10, 1205 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Kleitou, P. et al. Fishery reforms for the management of non-indigenous species. J. Environ. Manag. 280, 111690 (2021).Article 

Google Scholar 
Hamida, B.-B. & O, Ben Hadj Hamida N, Chaouch H, Missaoui H,. Allometry, condition factor and growth of the swimming blue crab Portunus segnis in the Gulf of Gabes, Southeastern Tunisia (Central Mediterranean). Medit. Mar. Sci. 20, 566 (2019).Article 

Google Scholar 
Wisz, M. S. et al. Reply to ‘Sources of uncertainties in cod distribution models’. Nat. Clim. Change 5, 790–791 (2015).Article 
ADS 

Google Scholar 
Kramer-Schadt, S. et al. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers. Distrib. 19, 1366–1379 (2013).Article 

Google Scholar 
Buisson, L., Thuiller, W., Casajus, N., Lek, S. & Grenouillet, G. Uncertainty in ensemble forecasting of species distribution. Glob. Change Biol. 16, 1145–1157 (2010).Article 
ADS 

Google Scholar 
Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43, 549–558 (2020).Article 

Google Scholar 
Thuiller, W., Damie, G., Robin, E., Frank, F.Biomod2: Ensemble Platform for Species Distribution Modeling (2016).Stolar, J. & Nielsen, S. E. Accounting for spatially biased sampling effort in presence-only species distribution modelling. Divers. Distrib. 21, 595–608 (2015).Article 

Google Scholar 
Stockwell, D. The GARP modelling system: Problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13, 143–158 (1999).Article 

Google Scholar 
Cornwell, W. K., Schwilk, D. W. & Ackerly, D. D. A trait-based test for habitat filtering: Convex hull volume. Ecology 87(6), 1465–1471 (2003).Article 

Google Scholar 
Hengl, T., Sierdsema, H., Radović, A. & Dilo, A. Spatial prediction of species’ distributions from occurrence-only records: Combining point pattern analysis ENFA and regression-kriging. Ecol. Modell. 220, 3499–3511 (2009).Article 

Google Scholar 
Faillettaz, R., Beaugrand, G., Goberville, E. & Kirby, R. R. Atlantic Multidecadal Oscillations drive the basin-scale distribution of Atlantic bluefin tuna. Sci. Adv. 5, eaar6993 (2019).Lavoie, D., Lambert, N. & Gilbert, D. Projections of future trends in biogeochemical conditions in the northwest Atlantic using CMIP5 earth system models. Atmos. Ocean 57, 18–40 (2019).CAS 
Article 

Google Scholar 
Taylor, K. E. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. 106, 7183–7192 (2001).Article 
ADS 

Google Scholar 
Cristofari, R. et al. Climate-driven range shifts of the king penguin in a fragmented ecosystem. Nat. Clim. Change 8, 245–251 (2018).Article 
ADS 

Google Scholar 
Zeller, D. et al. Still catching attention: Sea Around Us reconstructed global catch data, their spatial expression and public accessibility. Mar. Policy 70, 145–152 (2016).Article 

Google Scholar 
GBIF.org (27 May 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.2crvdpGBIF.org (7 June 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.y8ujd7GBIF.org (7 June 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.hs8py7GBIF.org (7 June 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.kqwq3aGBIF.org (14 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.raka7jGBIF.org (14 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.fwbk43GBIF.org (30 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.845mcwGBIF.org (30 July 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.wdavbrGBIF.org (11 September 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.ucuavw More

Panti-May, J. A. et al. A two-year ecological study of Norway rats (Rattus norvegicus) in a Brazilian Urban Slum. PLoS ONE 11(3), 1–12. https://doi.org/10.1371/journal.pone.0152511 (2016).CAS 
Article 

Google Scholar 
Himsworth, C. G. et al. A mixed methods approach to exploring the relationship between Norway rat (Rattus norvegicus) abundance and features of the urban environment in an inner-city neighborhood of Vancouver, Canada. PLoS ONE 9(5), 97776. https://doi.org/10.1371/journal.pone.0097776 (2014).ADS 
CAS 
Article 

Google Scholar 
Lambert, M. S., Quy, R. J., Smith, R. H. & Cowan, D. P. The effect of habitat management on home-range size and survival of rural Norway rat populations. J. Appl. Ecol. 45(6), 1753–1761. https://doi.org/10.1111/j.1365-2664.2008.01543.x (2008).Article 

Google Scholar 
Meerburg, B. G., Singleton, G. R. & Kijlstra, A. Rodent-borne diseases and their risks for public health (Vol. 7828). https://doi.org/10.1080/10408410902989837 (2009)Buckle, A. & Smith, R. Rodent Pests and Their Control 2nd edn. (CABI Press, Wallingford, 2015).Book 

Google Scholar 
Byers, K. A., Lee, M. J., Patrick, D. M. & Himsworth, C. G. Rats about town: A systematic review of rat movement in urban ecosystems. Front. Ecol. Evol. 7, 1–12. https://doi.org/10.3389/fevo.2019.00013 (2019).Article 

Google Scholar 
Carvalho-Pereira, T. et al. The helminth community of a population of Rattus norvegicus from an urban Brazilian slum and the threat of zoonotic diseases. Parasitology 145(6), 797–806. https://doi.org/10.1017/S0031182017001755 (2018).Article 
PubMed 

Google Scholar 
Costa, F. et al. Patterns in Leptospira shedding in Norway rats (Rattus norvegicus) from Brazilian slum communities at high risk of disease transmission. PLoS Negl. Trop. Dis. 9(6), 1–14. https://doi.org/10.1371/journal.pntd.0003819 (2015).CAS 
Article 

Google Scholar 
Parsons, M. H. et al. Rats and the COVID-19 pandemic: Early data on the global emergence of rats in response to social distancing. MedRxiv https://doi.org/10.1101/2020.07.05.20146779 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Awoniyi, A. M. et al. Effect of chemical and sanitary intervention on rat sightings in urban communities of New Providence, the Bahamas. SN Appl. Sci. 3, 495. https://doi.org/10.1007/s42452-021-04459-x (2021).CAS 
Article 

Google Scholar 
Costa, F. et al. Influence of household rat infestation on leptospira transmission in the urban slum environment. PLoS Negl. Trop. Dis. 8(12), 3338. https://doi.org/10.1371/journal.pntd.0003338 (2014).Article 

Google Scholar 
Khalil, H. et al. Poverty, sanitation, and Leptospira transmission pathways in residents from four Brazilian slums. PLoS Negl. Trop. Dis. 15(3), 1–15. https://doi.org/10.1371/journal.pntd.0009256 (2021).Article 

Google Scholar 
Zeppelini, C. G. et al. Demographic drivers of Norway rat populations from urban slums in Brazil. Urban Ecosyst. https://doi.org/10.1007/s11252-020-01075-2 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
United Nations -UN. World Urbanization Prospects: The 2018 Revision. https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html. Accessed 24 Dec 2020 (2018)United Nations UN-SDG. Sustainable Development Goals: Make cities and human settlements inclusive, safe, resilient and sustainable. https://unstats.un.org/sdgs/report/2019/goal-11/#:~:text=The%20absolute%20number%20of%20people,Southern%20Asia%20(227%20million). Accessed 24 Dec 2020 (2018)Russell, J. C., Towns, D. R. & Clout, M. N. Review of rat invasion biology: Implications for island biosecurity. Sci. Conserv. 286, 1–53 (2008).
Google Scholar 
Minter, A. et al. Optimal control of rat-borne leptospirosis in an urban environment. Front. Ecol. Evol. 7, 1–10. https://doi.org/10.3389/fevo.2019.00209 (2019).ADS 
Article 

Google Scholar 
Mathur, R. P. Effectiveness of various rodent control measures in cereal crops and plantations in India. In: Leirs H. and Schockaert E. ed. Proceedings of the International Workshop on Rodent Biology and Integrated Pest Management in Africa, 21-25 October 1996, Morogoro, Tanzania. Belg. J. Zool. 127(supplement 1), 137–144 (1997).
Google Scholar 
Pascal, M., Siorat, F., Lorvelec, O., Yésou, P. & Simberloff, D. A pleasing consequence of Norway rat eradication : Two shrew species recover. Divers. Distrib. 11, 193–198. https://doi.org/10.1111/j.1366-9516.2005.00137.x (2005).Article 

Google Scholar 
Singleton, G. R., Hinds, L. & Leirs, H. Ecologically-based management of rodent pests. Australian Centre for International Agricultural Research, (ACIAR Monograph 59), 494. (1999)Sullivan, L. M. Roof rat control around homes and other structures. Cooper. Extens. Bull. AZ 1280, 1–6 (2002).
Google Scholar 
Childs, J. E. Size-dependent predation on rats (Rattus norvegicus) by house cats (Felis catus) in an urban setting. J. Mammol. 67(1), 196–199 (1986).Article 

Google Scholar 
Davis, D. E. The characteristics of rat populations. Quart. Rev. Biol. 28, 373–401. https://doi.org/10.1086/399860 (1953).CAS 
Article 
PubMed 

Google Scholar 
Glass, G. E. et al. Trophic garnishes: Cat-Rat interactions in an urban environment. PLoS ONE 4(6), e5794. https://doi.org/10.1371/journal.pone.0005794 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lenton, G. M. Biological control of rats by owls in oil palm and other plantations. Biotrop Spec. Publ. 12, 87–94 (1980).
Google Scholar 
Smith, R. H. & Meyer, A. N. Rodent controlmethods: Non-chemical and non-lethal chemical, with special reference to food stores. In Rodent Pests and Their Control 2nd edn (eds Buckle, A. & Smith, R.) 81–101 (CABI International, 2015) (ISBN-13: 978-1-84593-817-8).
Google Scholar 
Oyedele, D. T., Sah, S. A. M., Kairuddin, L. & Ibrahim, W. M. M. W. Range measurement and a habitat suitability map for the Norway rat in a highly developed urban environment. Trop. Life Sci. Res. 26(2), 27–44 (2015).PubMed 
PubMed Central 

Google Scholar 
Hansen, N., Hughes, N. K., Bryom, A. E. & Banks, P. B. Population recovery of alien black rats Rattus rattus: A test of reinvasion theory. Austral Ecol. 45, 291–304. https://doi.org/10.1111/aec.12855 (2020).Article 

Google Scholar 
Awoniyi, A. M. et al. Using Rhodamine B to assess the movement of small mammals in an urban slum. Methods Ecol. Evol. 12(11), 2234–2242. https://doi.org/10.1111/2041-210X.13693 (2021).Article 

Google Scholar 
Glass, G. E., Klein, S. L., Norris, D. E. & Gardner, L. C. Multiple paternity in urban Norway rats: Extended ranging for mates. Vector-Borne Zoonotic Dis. 16(5), 342–248. https://doi.org/10.1089/vbz.2015.1816 (2016).Article 
PubMed 

Google Scholar 
Buckle, A. P. & Eason, C. T. Rodent control methods: Chemical. In Rodent Pests and Their Control 2nd edn (eds Buckle, A. & Smith, R.) 81–101 (CABI International, Wallingford, 2015) (ISBN-13: 978-1-84593-817-8).Chapter 

Google Scholar 
de Masi, E., Pedro, J. V. & Maria, T. P. Evaluation on the effectiveness of actions for controlling infestation by rodents in Campo Limpo region, São Paulo Municipality, Brazil Access details: Access Details: [subscription number 913003116]. Int. J. Environ. Health Res. 19(4), 291–304. https://doi.org/10.1080/09603120802592723 (2009).Article 
PubMed 

Google Scholar 
Lambropoulos, A. S. et al. Rodent control in urban areas—An interdisciplinary approach. J. Environ. Health 61, 12–17 (1999).
Google Scholar 
Reis, R. B. et al. Impact of environment and social gradient on Leptospira infection in urban slums. PLoS Negl. Trop. Dis. 2(4), 11–18. https://doi.org/10.1371/journal.pntd.0000228 (2008).MathSciNet 
Article 

Google Scholar 
Instituto Brasileiro de Geografia e Estatistica (IBGE). Accessed 15 November 2019 (2010)CDC. Integrated pest management: conducting urban rodent surveys. Centers for Disease Control and Prevention-Atlanta: US Department of Health and Human Services (2006)Hacker, K. P. et al. A comparative assessment of track plates to quantify fine scale variations in the relative abundance of Norway rats in urban slums. Urban Ecosyst. 19(2), 561–575. https://doi.org/10.1007/s11252-015-0519-8 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Eyre, M. T. et al. A multivariate geostatistical framework for combining multiple indices of abundance for disease vectors and reservoirs: A case study of rattiness in a low-income urban Brazilian community: A multivariate geostatistical framework for combining multiple ind. J. R. Soc. Interface 17(170), 1–21. https://doi.org/10.1098/rsif.2020.0398 (2020).Article 

Google Scholar 
Bursac, Z., Gauss, C. H., Williams, D. K. & Hosmer, D. W. Purposeful selection of variables in logistic regression. Source Code Biol. Med. 8, 1–8. https://doi.org/10.1186/1751-0473-3-17 (2008).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: A practical information-theoretic approach (Springer, 2002).MATH 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org (2019).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020)Kassambara A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.4.0. https://CRAN.R-project.org/package=ggpubr (2020)Richardson, J. L. et al. Using fine-scale spatial genetics of Norway rats to improve control efforts and reduce leptospirosis risk in urban slum environments. Evol. Appl. 10(4), 323–337. https://doi.org/10.1111/eva.12449 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Santos, N. D. J., Sousa, E., Reis, M. G., Ko, A. I. & Costa, F. Rat infestation associated with environmental deficiencies in an urban slum community with high risk of leptospirosis. Cad. Saúde Pública 33(2), 1–13. https://doi.org/10.1590/0102-311X00132115 (2017).CAS 
Article 

Google Scholar 
Murray, M. H. & Sanchez, C. A. Urban rat exposure to anticoagulant rodenticides and zoonotic infection risk. Biol. Lett. 17, 20210311. https://doi.org/10.1098/rsbl.2021.0311 (2021).CAS 
Article 
PubMed 

Google Scholar 
Parsons, M. H., Banks, P. B., Deutsch, M. A., Corrigan, R. F. & Munshi-South, J. Trends in urban rat ecology: A framework to define the prevailing knowledge gaps and incentives for academia, pest management professionals (PMPs) and public health agencies to participate. J. Urban Ecol. 3(1), 1–8. https://doi.org/10.1093/jue/jux005 (2017).Article 

Google Scholar 
Costa, F. et al. Household rat infestation in urban slum populations: Development and validation of a predictive score for leptospirosis Household rat infestation in urban slum populations: Development and validation of a predictive score for leptospirosis. PLoS Negl. Trop. Dis. 15(3), 9154. https://doi.org/10.1371/journal.pntd.0009154 (2021).Article 

Google Scholar 
Mwanjabe, P. S. & Leirs, H. An early warning system for IPM-based rodent control in smallholder farming systems in Tanzania. In: Leirs, H., & Schockaert, E., ed., Proceedings of the International Workshop on Rodent Biology and Integrated Pest Management in Africa, 21-25 October 1996, Morogoro, Tanzania. Belg. J. Zool. 127(supplement 1), 4–58 (1997).
Google Scholar 
Richards, C. G. J. R. & Buckle, A. P. Towards integrated rodent pest management at the village level. In Control of Mammal Pests (eds Richards, C. G. J. R. & Ku, T. Y.) 293–312 (Taylor and Francis, 1987).
Google Scholar 
Masi, E. Socioeconomic and environmental risk factors for urban rodent infestation in Sao Paulo, Brazil. J. Pest Sci. 83(3), 231–241. https://doi.org/10.1007/s10340-010-0290-9 (2010).Article 

Google Scholar 
Brooks, J. E. Methods of sewer rat control. In Proceedings of the 1st Vertebrate Pest Conference. https://digitalcommons.unl.edu/vpcone/17. Accessed 20 August 2021 (1962) More

Geospatial dataSampling locations were established, flagged, and recorded in June 2016, using a Trimble Geo7X global navigation satellite system (GNSS) receiver using the Trimble® VRS Now real-time kinematic (RTK) correction. Location accuracies were verified to within ±2 cm. Points were imported into a geodatabase using Esri ArcMap (Advanced license, Version 10.5) and projected using the Universal Transverse Mercator (UTM), Zone 17 North projection, with the 1983 North American datum (NAD83). Field investigators navigated to the flagged locations by visually locating them in the field or by using recreational grade GNSS receivers with the locations stored as waypoints.Plant tissue sampling and preparationMiscanthus x giganteus grows in clumps of bamboo-like canes. A single cane was cut at soil level from each of the five sample collection points in each circular plot, individually labelled, and brought to the lab for processing (Fig. 2). Each stem was measured from the cut at the base to the last leaf node, and the length was recorded. Green, fully expanded leaves were cut from each stem and leaves and stems from each plant were placed in separate paper bags and dried at 60 °C. The dry leaf and stem tissues were ground to pass a 1 mm screen (Wiley Mill Model 4, Thomas Scientific, Swedesboro, New Jersey, USA). Subsamples of the ground material were analyzed for total carbon (C) and nitrogen (N), acid-digested for the analysis of total macro- and micronutrients, and water-extracted for spectroscopic analysis and the characterization of the water extractable organic matter (WEOM) (Fig. 2).Fig. 2Images of field samples, and diagram of plant tissue processing. Center panel – flow chart outlining the procedures for plant tissue processing, the kinds of analyses performed, and the type of data generated. Upper left inset panel – ground level picture of Miscanthus x giganteus circular plots. Upper right inset panel – some plant samples on the day of collection.Full size imageTotal carbon and nitrogenDried and ground leaf and stem material (~4–6 mg) was analyzed for total C and N content by combustion (Vario EL III, Elementar Americas Inc., Mt. Laurel, New Jersey, USA). The instrument was calibrated using an aspartic acid standard (36.08% C ± 0.52% and 10.53% N ± 0.18%). Validation by inclusion of two aspartic acid samples as checks in each autosampler carousel (80 wells) resulted in a net positive bias of 1.44 and 1.68% for C and N, respectively. The mean C and N concentrations and standard deviations for the sample set are presented in Table 1.Table 1 Giant miscanthus composition including leaf (L) and stem (S) dry weight, length, and carbon (C) and nitrogen (N) concentrations (n = 165). Values are reported as means ± standard deviations.Full size tableMacro- and micronutrientsPlant tissue samples were analyzed for a suite of macro- and micronutrients including aluminum (Al), arsenic (As), boron (B), calcium (Ca), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), potassium (K), magnesium (Mg), manganese (Mn), molybdenum (Mo), sodium (Na), nickel (Ni), phosphorus (P), lead (Pb), sulfur (S), selenium (Se), silicon (Si), titanium (Ti), vanadium (V), and zinc (Zn) using Inductively Coupled Plasma with Optical Emission Spectroscopy (ICP-OES). Samples (0.5 g) were digested using 10 mL of trace metal grade nitric acid (HNO3) in a microwave digestion system (Mars 6, CEM, Matthews, North Carolina, USA). During the digestion procedure (CEM Mars 6 Plant Material Method), the oven temperature was increased from room temperature to 200 °C in 15 minutes and held at 200 °C for 10 minutes. The pressure limit of the digestion vessels was set to 800 psi although it was not monitored during individual runs. Sample digestates were transferred quantitatively to centrifuge tubes, diluted to 50 mL with 2% HNO3 (prepared with lab grade deionized water), and centrifuged at 2500 rpm for 10 min (Sorvall ST8 centrifuge, Thermo Fisher Scientific, San Jose, California, USA). The digestates were decanted into clean centrifuge tubes and analyzed using an iCAP 7400 ICP-OES Duo equipped with a Charge Injection Device detector (Thermo Fisher Scientific, San Jose, California, USA). An aliquot of digested sample was aspirated from the centrifuge tube using a CETAC ASX-520 autosampler (Teledyne CETAC Technologies, Omaha, Nebraska, USA) and passed through a concentric tube nebulizer. The resulting aerosol was then swept through the plasma using argon as the carrier gas with a flow rate of 0.5 L/min and a nebulizer gas flow rate of 0.7 L/min. Macro- and micronutrients were quantified by monitoring the emission wavelengths (Em λ) reported in Table 2.Table 2 Macro- and micronutrients measured, and emission wavelengths (Em λ) used to quantify them in the miscanthus leaves (L) and stems (S), the total number and percentage detected (n = 150 for leaves and 162 for stems), the mean detected concentration ± standard deviation, and the mean method detection limit (MDL) ± standard deviation.Full size tableCharacterization of the water extractable organic matter (WEOM)The WEOM of the giant miscanthus leaves and stems was isolated by extracting the plant material with deionized water at room temperature6. The water extractions were performed by mixing ~0.2 g of dry, ground leaves and stems with 100 mL of deionized water in 125 mL pre-washed brown Nalgene bottles. All brown Nalgene bottles used for these extractions were pre-washed by soaking them for 24 hours in a 10% hydrochloric acid solution followed by 24 hours in a 10% sodium hydroxide solution, and a thorough rinse with deionized water. The bottles containing the extraction solution were shaken on an orbital shaker at 180 rpm for 24 hours. The extract was vacuum filtered using 0.45 µm glass fibre filters (GF/F, Whatman) into pre-washed 60 mL brown Nalgene bottles. The filtered water extracts containing the WEOM were stored in the dark in a refrigerator (4 °C) until analysis by UV-Visible and fluorescence spectroscopy. Samples were visually inspected just prior to analysis to ensure no colloids or precipitates had formed during storage. Samples that had become visually cloudy were re-filtered.On the day of analysis, the water extracts were removed from the refrigerator and allowed to warm up to room temperature. Chemical characteristics of the WEOM were assessed through the analysis of optical properties on an Aqualog spectrofluorometer (Horiba Scientific, New Jersey, USA) equipped with a 150 W continuous output Xenon arc lamp. Excitation-emission matrix (EEM) scans were acquired in a 1 cm quartz cuvette with excitation wavelengths (Ex λ) scanned using a double-grating monochrometer from 240 to 621 nm at 3 nm intervals. Emission wavelengths (Em λ) were scanned from 246 to 693 nm at 2 nm intervals and emission spectra were collected using a Charge Coupled Device (CCD) detector. All fluorescence spectra were acquired in sample over reference ratio mode to account for potential fluctuations and wavelength dependency of the excitation lamp output. Samples were corrected for the inner filter effect7 and each sample EEM underwent spectral subtraction with a deionized water blank to remove the effects due to Raman scattering. Rayleigh masking was applied to remove the signal intensities for both the first and second order Rayleigh lines. Instrument bias related to wavelength-dependent efficiencies of the specific instrument’s optical components (gratings, mirrors, etc.) was automatically corrected by the Aqualog software after each spectral acquisition. The fluorescence intensities were normalized to the area under the water Raman peak collected on each day of analysis and are expressed in Raman-normalized intensity units (RU). All sample EEM processing was performed with the Aqualog software (version 4.0.0.86).The optical data obtained from the EEM scans were used to calculate several indices representative of WEOM chemical composition (Table 3) including the absorbance at 254 nm (Abs254), the ratio of the absorbance at 254 to 365 nm (Abs254:365), the ratio of the absorbance at 280 to 465 nm (Abs280:465), the spectral slope ratio (SR), the fluorescence index (FI), the humification index (HIX), the biological index (BIX), and the freshness index (β:α). The SR was calculated as the ratio of two spectral slope regions of the absorbance spectra (275–295 and 350–400 nm)8. The FI was calculated as the ratio of the emission intensities at Em λ 470 and 520 nm, at an Ex λ of 370 nm9. The HIX was calculated by dividing the emission intensity in the 435–480 nm region by the sum of emission intensities in the 300–345 and 435–480 nm regions, at an Ex λ of 255 nm10. The BIX was calculated as the ratio of emission intensities at 380 and 430 nm, at an Ex λ of 310 nm11. The freshness index β:α was calculated as the emission intensity at 380 nm divided by the maximum emission intensity between 420 and 432 nm, at an Ex λ of 310 nm12. To further characterize the giant miscanthus WEOM, the fluorescence intensity at specific excitation-emission pairs was also identified. The fluorescence peaks identified here have previously been reported for surface water samples and water extracts13 and include peak A (Ex λ 260, Em λ 450), peak C (Ex λ 340, Em λ 440), peak M (Ex λ 300, Em λ 390), peak B (Ex λ 275, Em λ 310), and peak T (Ex λ 275, Em λ 340). A brief description of these optical indices is provided in Table 3.Table 3 Description of the optical indices calculated from the excitation-emission matrix (EEM) fluorescence scans and used to analyze the WEOM composition of giant miscanthus leaves and stems.Full size table More

Rasmussen, R. S. & Morrissey, M. T. Application of DNA-based methods to identify fish and seafood substitution on the commercial market. Compr. Rev. Food Sci. Food Saf. 8, 118–154 (2009).CAS 
Article 

Google Scholar 
Chiu, M.-C., Huang, C.-G., Wu, W.-J. & Shiao, S.-F. A new horsehair worm, Chordodes formosanus sp. N. (Nematomorpha, Gordiida) from Hierodula mantids of Taiwan and Japan with redescription of a closely related species, Chordodes japonensis. ZooKeys 160, 1–22 (2011).Article 

Google Scholar 
Robins, J. H. et al. Phylogenetic species identification in Rattus highlights rapid radiation and morphological similarity of new Guinean species. PLoS One 9, e98002. https://doi.org/10.1371/journal.pone.0098002 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sutherland, W. J., Roy, D. B. & Amano, T. An agenda for the future of biological recording for ecological monitoring and citizen science. Biol. J. Linn. Soc. 115, 779–784 (2015).Article 

Google Scholar 
Ho, J. K. I., Puniamoorthy, J., Srivathsan, A. & Meier, R. MinION sequencing of seafood in Singapore reveals creatively labelled flatfishes, confused roe, pig DNA in squid balls, and phantom crustaceans. Food Control 112, 107144. https://doi.org/10.1016/j.foodcont.2020.107144 (2020).CAS 
Article 

Google Scholar 
Elson, J. & Lightowlers, R. Mitochondrial DNA clonality in the dock: Can surveillance swing the case?. Trends Genet. 22, 603–607 (2006).CAS 
PubMed 
Article 

Google Scholar 
Bernt, M., Braband, A., Schierwater, B. & Stadler, P. F. Genetic aspects of mitochondrial genome evolution. Mol. Phylogenet. Evol. 69, 328–338 (2013).CAS 
PubMed 
Article 

Google Scholar 
Blaxter, M. L. The promise of a DNA taxonomy. Philos. Trans. R. Soc. Lond. B Biol. Sci. 359, 669–679 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Waugh, J. DNA barcoding in animal species: progress, potential and pitfalls. BioEssays 29, 188–197 (2007).CAS 
PubMed 
Article 

Google Scholar 
Grandjean, F. et al. Rapid recovery of nuclear and mitochondrial genes by genome skimming from Northern Hemisphere freshwater crayfish. Zool. Scr. 46, 718–728 (2017).Article 

Google Scholar 
Trevisan, B., Alcantara, D. M. C., Machado, D. J., Marques, F. P. L. & Lahr, D. J. G. Genome skimming is a low-cost and robust strategy to assemble complete mitochondrial genomes from ethanol preserved specimens in biodiversity studies. PeerJ 7, e7543. https://doi.org/10.7717/peerj.7543 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Franco-Sierra, N. D. & Díaz-Nieto, J. F. Rapid mitochondrial genome sequencing based on Oxford Nanopore Sequencing and a proxy for vertebrate species identification. Ecol. Evol. 10, 3544–3560 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Baeza, J. A. Yes, we can use it: a formal test on the accuracy of low-pass nanopore long-read sequencing for mitophylogenomics and barcoding research using the Caribbean spiny lobster Panulirus argus. BMC Genomics 21, 882. https://doi.org/10.1186/s12864-020-07292-5 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Phillips, A. R., Robertson, A. L., Batzli, J., Harris, M. & Miller, S. Aligning goals, assessments, and activities: An approach to teaching PCR and gel electrophoresis. CBE Life Sci. Educ. 7, 96–106 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Dhorne-Pollet, S., Barrey, E. & Pollet, N. A new method for long-read sequencing of animal mitochondrial genomes: application to the identification of equine mitochondrial DNA variants. BMC Genomics 21, 785. https://doi.org/10.1186/s12864-020-07183-9 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jain, M., Olsen, H. E., Paten, B. & Akeson, M. The Oxford Nanopore MinION: Delivery of nanopore sequencing to the genomics community. Genome Biol. 17, 239. https://doi.org/10.1186/s13059-016-1103-0 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Krehenwinkel, H. et al. Nanopore sequencing of long ribosomal DNA amplicons enables portable and simple biodiversity assessments with high phylogenetic resolution across broad taxonomic scale. GigaScience 8, giz006. https://doi.org/10.1093/gigascience/giz006 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Srivathsan, A. et al. ONTbarcoder and MinION barcodes aid biodiversity discovery and identification by everyone, for everyone. BMC Biol. 19, 217. https://doi.org/10.1186/s12915-021-01141-x (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Prost, S. et al. Education in the genomics era: Generating high-quality genome assemblies in university courses. GigaScience 9, giaa058. https://doi.org/10.1093/gigascience/giaa058 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Salazar, A. N. et al. An educational guide for nanopore sequencing in the classroom. PLoS Comput. Biol. 16, e1007314. https://doi.org/10.1371/journal.pcbi.1007314 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Watsa, M., Erkenswick, G. A., Pomerantz, A. & Prost, S. Portable sequencing as a teaching tool in conservation and biodiversity research. PLoS Biol. 18, e3000667. https://doi.org/10.1371/journal.pbio.3000667 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Egeter, B. et al. Speeding up the detection of invasive bivalve species using environmental DNA: A Nanopore and Illumina sequencing comparison. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13610 (2022).Article 
PubMed 

Google Scholar 
Oxford Nanopore. Flongle. https://nanoporetech.com/products/flongle. Last accessed 05 May 2022 (2022).Oxford Nanopore. MinION. https://nanoporetech.com/products/minion. Last accessed 05 May 2022 (2022).Baeza, J. A. & García-De León, F. J. Are we there yet? Benchmarking low-coverage nanopore long-read sequencing for the assembling of mitochondrial genomes using the vulnerable silky shark Carcharhinus falciformis. BMC Genomics 23, 320. https://doi.org/10.1186/s12864-022-08482-z (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Ghiselli, F. et al. Molluscan mitochondrial genomes break the rules. Philos. Trans. R. Soc. B Biol. Sci. 376, 20200159. https://doi.org/10.1098/rstb.2020.0159 (2021).Article 

Google Scholar 
Zhang, Z.-Q. Animal biodiversity: An introduction to higher-level classification and taxonomic richness. Zootaxa 3148, 7–12 (2011).Article 

Google Scholar 
Bouchet, P., Bary, S., Héros, V. & Marani, G. How many species of molluscs are there in the world’s oceans, and who is going to describe them? In Tropical Deep-Sea Benthos 29 (eds Héros, V. et al.) 9–24 (Muséum national d’histoire naturelle, 2016).
Google Scholar 
Reese, D. S. Palaikastro shells and bronze age purple-dye production in the Mediterranean Basin. Annu. Br. Sch. Athens 82, 201–206 (1987).Article 

Google Scholar 
Lardans, V. & Dissous, C. Snail control strategies for reduction of schistosomiasis transmission. Parasitol. Today 14, 413–417 (1998).CAS 
PubMed 
Article 

Google Scholar 
Baker, G. M. (ed.) Molluscs as Crop Pests. (CABI, 2002). https://doi.org/10.1079/9780851993201.0000Mannino, M. A. & Thomas, K. D. Depletion of a resource? The impact of prehistoric human foraging on intertidal mollusc communities and its significance for human settlement, mobility and dispersal. World Archaeol. 33, 452–474 (2002).Article 

Google Scholar 
Carter, R. The history and prehistory of pearling in the Persian Gulf. J. Econ. Soc. Hist. Orient 48, 139–209 (2005).Article 

Google Scholar 
Vilariño, M. L. et al. Assessment of human enteric viruses in cultured and wild bivalve molluscs. Int. Microbiol. Off. J. Span. Soc. Microbiol. 12, 145–151 (2009).
Google Scholar 
Tedde, T. et al. Toxoplasma gondii and other zoonotic protozoans in Mediterranean mussel (Mytilus galloprovincialis) and blue mussel (Mytilus edulis): A food safety concern?. J. Food Prot. 82, 535–542 (2019).CAS 
PubMed 
Article 

Google Scholar 
Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 44, D67–D72 (2016).CAS 
PubMed 
Article 

Google Scholar 
Grande, C., Templado, J. & Zardoya, R. Evolution of gastropod mitochondrial genome arrangements. BMC Evol. Biol. 8, 61. https://doi.org/10.1186/1471-2148-8-61 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Formenti, G. et al. Complete vertebrate mitogenomes reveal widespread repeats and gene duplications. Genome Biol. 22, 120. https://doi.org/10.1186/s13059-021-02336-9 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).CAS 
PubMed 
Article 

Google Scholar 
Meng, G., Li, Y., Yang, C. & Liu, S. MitoZ: A toolkit for animal mitochondrial genome assembly, annotation and visualization. Nucleic Acids Res. 47, e63. https://doi.org/10.1093/nar/gkz173 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bernt, M. et al. MITOS: Improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).PubMed 
Article 

Google Scholar 
Chaisson, M. J. P., Wilson, R. K. & Eichler, E. E. Genetic variation and the de novo assembly of human genomes. Nat. Rev. Genet. 16, 627–640 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Alexander, J. & Valdés, A. The ring doesn’t mean a thing: Molecular data suggest a new taxonomy for two pacific species of sea hares (Mollusca: Opisthobranchia, Aplysiidae). Pac. Sci. 67, 283–294 (2013).Article 

Google Scholar 
WoRMS Editorial Board. World Register of Marine Species. https://www.marinespecies.org at VLIZ. Accessed 10 Jan 2022 (2022).Barco, A. et al. A molecular phylogenetic framework for the Muricidae, a diverse family of carnivorous gastropods. Mol. Phylogenet. Evol. 56, 1025–1039 (2010).CAS 
PubMed 
Article 

Google Scholar 
Houart, R. Description of eight new species and one new genus of Muricidae (Gastropoda) from the Indo-West Pacific. Novapex 18, 81–103 (2017).
Google Scholar 
Shao, K.-T. & Chung, K.-F. The National Checklist of Taiwan (Catalogue of Life in Taiwan, TaiCoL). GBIF. https://www.gbif.org/dataset/1ec61203-14fa-4fbd-8ee5-a4a80257b45a (2021).Gaitán-Espitia, J. D., González-Wevar, C. A., Poulin, E. & Cardenas, L. Antarctic and sub-Antarctic Nacella limpets reveal novel evolutionary characteristics of mitochondrial genomes in Patellogastropoda. Mol. Phylogenet. Evol. 131, 1–7 (2019).PubMed 
Article 
CAS 

Google Scholar 
Feng, J. et al. Comparative analysis of the complete mitochondrial genomes in two limpets from Lottiidae (Gastropoda: Patellogastropoda): rare irregular gene rearrangement within Gastropoda. Sci. Rep. 10, 19277. https://doi.org/10.1038/s41598-020-76410-w (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Xu, T., Qi, L., Kong, L. & Li, Q. Mitogenomics reveals phylogenetic relationships of Patellogastropoda (Mollusca, Gastropoda) and dynamic gene rearrangements. Zool. Scr. 51, 147–160 (2022).Article 

Google Scholar 
Ranjard, L. et al. Complete mitochondrial genome of the green-lipped mussel, Perna canaliculus (Mollusca: Mytiloidea), from long nanopore sequencing reads. Mitoch. DNA Part B 3, 175–176 (2018).Article 

Google Scholar 
Sun, J. et al. The Scaly-foot Snail genome and implications for the origins of biomineralised armour. Nat. Commun. 11, 1657. https://doi.org/10.1038/s41467-020-15522-3 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dixit, B., Vanhoozer, S., Anti, N. A., O’Connor, M. S. & Boominathan, A. Rapid enrichment of mitochondria from mammalian cell cultures using digitonin. MethodsX 8, 101197. https://doi.org/10.1016/j.mex.2020.101197 (2021).Article 
PubMed 

Google Scholar 
Wanner, N., Larsen, P. A., McLain, A. & Faulk, C. The mitochondrial genome and Epigenome of the Golden lion Tamarin from fecal DNA using Nanopore adaptive sequencing. BMC Genomics 22, 726. https://doi.org/10.1186/s12864-021-08046-7 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Malukiewicz, J. et al. Genomic skimming and nanopore sequencing uncover cryptic hybridization in one of world’s most threatened primates. Sci. Rep. 11, 17279. https://doi.org/10.1038/s41598-021-96404-6 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kipp, E. J. et al. Nanopore adaptive sampling for mitogenome sequencing and bloodmeal identification in hematophagous insects. bioRxiv. https://doi.org/10.1101/2021.11.11.468279 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Sereika, M. et al. Oxford Nanopore R10.4 long-read sequencing enables near-perfect bacterial genomes from pure cultures and metagenomes without short-read or reference polishing. bioRxiv. https://doi.org/10.1101/2021.10.27.466057 (2021).Article 

Google Scholar 
Oxford Nanopore. Nanopore Community. https://nanoporetech.com/community. Last accessed 05 May 2022 (2022).Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaser, R., Sović, I., Nagarajan, N. & Šikić, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. 27, 737–746 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oxford Nanopore. medaka. https://github.com/nanoporetech/medaka. Last accessed 05 May 2022 (2022).Walker, B. J. et al. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One 9, e112963. https://doi.org/10.1371/journal.pone.0112963 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Faust, G. G. & Hall, I. M. SAMBLASTER: Fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pedersen, B. S. & Quinlan, A. R. Mosdepth: Quick coverage calculation for genomes and exomes. Bioinformatics 34, 867–868 (2018).CAS 
PubMed 
Article 

Google Scholar 
Tsai, I. J. Genome skimming exercise (last updated 2022.04.14). https://introtogenomics.readthedocs.io/en/latest/emcgs.html (2022).Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vaidya, G., Lohman, D. J. & Meier, R. SequenceMatrix: Concatenation software for the fast assembly of multi-gene datasets with character set and codon information. Cladistics 27, 171–180 (2011).PubMed 
Article 

Google Scholar 
Edler, D., Klein, J., Antonelli, A. & Silvestro, D. raxmlGUI 2.0: A graphical interface and toolkit for phylogenetic analyses using RAxML. Methods Ecol. Evol. 12, 373–377 (2021).Article 

Google Scholar 
Rabiee, M., Sayyari, E. & Mirarab, S. Multi-allele species reconstruction using ASTRAL. Mol. Phylogenet. Evol. 130, 286–296 (2019).PubMed 
Article 

Google Scholar 
Rambaut, A. FigTree, version 1.4.4. http://tree.bio.ed.ac.uk/software/figtree/ (2018).Hackl, T. & Ankenbrand, M. J. gggenomes: A Grammar of Graphics for Comparative Genomics. https://github.com/thackl/gggenomes (2022).Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 

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 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Pourret, O. & Hursthouse, A. It’s time to replace the term “heavy metals” with “potentially toxic elements” when reporting environmental research. IJERPH 16, 4446 (2019).CAS 
PubMed Central 

Google Scholar 
Wuana, R. A. & Okieimen, F. E. Heavy metals in contaminated soils: A review of sources, chemistry, risks and best available strategies for remediation. ISRN Ecol. 2011, 1–20 (2011).
Google Scholar 
Mahar, A. et al. Challenges and opportunities in the phytoremediation of heavy metals contaminated soils: A review. Ecotoxicol. Environ. Saf. 126, 111–121 (2016).CAS 
PubMed 

Google Scholar 
Vangronsveld, J. et al. Phytoremediation of contaminated soils and groundwater: Lessons from the field. Environ. Sci. Pollut. Res. 16, 765–794 (2009).CAS 

Google Scholar 
Desjardins, D., Nissim, W. G., Pitre, F. E., Naud, A. & Labrecque, M. Distribution patterns of spontaneous vegetation and pollution at a former decantation basin in southern Québec, Canada. Ecol. Eng. 64, 385–390 (2014).
Google Scholar 
Marchiol, L. et al. Gentle remediation at the former “Pertusola Sud” zinc smelter: Evaluation of native species for phytoremediation purposes. Ecol. Eng. 53, 343–353 (2013).
Google Scholar 
van Oort, F. et al. Les pollutions métalliques d’un site industriel et des sols environnants : distributions hétérogènes des métaux et relations avec l’usage des sols. In: Contaminations métalliques des agrosystèmes et écosystèmes péri-urbains 15–44 (Editions Quae, 2009).Hodge, A. Plastic plants and patchy soils. J. Exp. Bot. 57, 401–411 (2006).CAS 
PubMed 

Google Scholar 
Huber-Sannwald, E. & Jackson, R. B. Heterogeneous soil-resource distribution and plant responses—from individual-plant growth to ecosystem functioning. In Progress in Botany Vol. 62 (eds Esser, K. et al.) 451–476 (Springer, 2001).
Google Scholar 
Loecke, T. D. & Philip Robertson, G. Soil resource heterogeneity in the form of aggregated litter alters maize productivity. Plant Soil 325, 231–241 (2009).CAS 

Google Scholar 
Reynolds, H. L., Hungate, B. A., Iii, F. S. C. & D’Antonio, C. M. Soil Heterogeneity and Plant Competition in an Annual Grassland. 16 (2021).Maestre, F. T., Cortina, J., Bautista, S., Bellot, J. & Vallejo, R. Small-scale environmental heterogeneity and spatiotemporal dynamics of seedling establishment in a semiarid degraded ecosystem. Ecosystems 6, 630–643 (2003).
Google Scholar 
Shutcha, M. N. et al. Three years of phytostabilisation experiment of bare acidic soil extremely contaminated by copper smelting using plant biodiversity of metal-rich soils in tropical Africa (Katanga, DR Congo). Ecol. Eng. 82, 81–90 (2015).
Google Scholar 
Testiati, E. et al. Trace metal and metalloid contamination levels in soils and in two native plant species of a former industrial site: Evaluation of the phytostabilization potential. J. Hazard. Mater. 248–249, 131–141 (2013).PubMed 

Google Scholar 
Cabrera, F., Clemente, L., Díaz Barrientos, E., López, R. & Murillo, J. M. Heavy metal pollution of soils affected by the Guadiamar toxic fiood. Sci. Total Environ. 242, 117–129 (1999).CAS 
PubMed 

Google Scholar 
Imperato, M. et al. Spatial distribution of heavy metals in urban soils of Naples city (Italy). Environ. Pollut. 124, 247–256 (2003).CAS 
PubMed 

Google Scholar 
Gallagher, F. J., Pechmann, I., Bogden, J. D., Grabosky, J. & Weis, P. Soil metal concentrations and vegetative assemblage structure in an urban brownfield. Environ. Pollut. 153, 351–361 (2008).CAS 
PubMed 

Google Scholar 
Gallagher, F. J., Pechmann, I., Holzapfel, C. & Grabosky, J. Altered vegetative assemblage trajectories within an urban brownfield. Environ. Pollut. 159, 1159–1166 (2011).CAS 
PubMed 

Google Scholar 
Heckenroth, A. et al. Selection of native plants with phytoremediation potential for highly contaminated Mediterranean soil restoration: Tools for a non-destructive and integrative approach. J. Environ. Manag. 183, 850–863 (2016).CAS 

Google Scholar 
Dickinson, N. M., Turner, A. P. & Lepp, N. W. How do trees and other long-lived plants survive in polluted environments?. Funct. Ecol. 5, 5 (1991).
Google Scholar 
Partida-Martínez, L. P. & Heil, M. The microbe-free plant: Fact or artifact?. Front. Plant Sci. 2, 100 (2011).PubMed 
PubMed Central 

Google Scholar 
Giller, K. E., Witter, E. & Mcgrath, S. P. Toxicity of heavy metals to microorganisms and microbial processes in agricultural soils: A review. Soil Biol. Biochem. 30, 1389–1414 (1998).CAS 

Google Scholar 
Kabata-Pendias, A. & Pendias, H. Trace Elements in Soils and Plants (CRC Press, 2001).
Google Scholar 
Tyler, G. Heavy metal pollution and mineralisation of nitrogen in forest soils. Nature 255, 701–702 (1975).CAS 

Google Scholar 
Seshadri, B., Bolan, N. S. & Naidu, R. Rhizosphere-induced heavy metal(loid) transformation in relation to bioavailability and remediation. J. Soil Sci. Plant Nutr. https://doi.org/10.4067/S0718-95162015005000043 (2015).Article 

Google Scholar 
Kidd, P. et al. Trace element behaviour at the root–soil interface: Implications in phytoremediation. Environ. Exp. Bot. 67, 243–259 (2009).CAS 

Google Scholar 
Rivera-Becerril, F. Cadmium accumulation and buffering of cadmium-induced stress by arbuscular mycorrhiza in three Pisum sativum L. genotypes. J. Exp. Bot. 53, 1177–1185 (2002).CAS 
PubMed 

Google Scholar 
Krupa, P. & Kozdrój, J. Ectomycorrhizal fungi and associated bacteria provide protection against heavy metals in inoculated pine (Pinus sylvestris L.) seedlings. Water Air Soil Pollut. 182, 83–90 (2007).CAS 

Google Scholar 
Janoušková, M., Pavlíková, D. & Vosátka, M. Potential contribution of arbuscular mycorrhiza to cadmium immobilisation in soil. Chemosphere 65, 1959–1965 (2006).PubMed 

Google Scholar 
Leyval, C., Turnau, K. & Haselwandter, K. Effect of heavy metal pollution on mycorrhizal colonization and function: Physiological, ecological and applied aspects. Mycorrhiza 7, 139–153 (1997).CAS 

Google Scholar 
Zhang, Y., Zhang, Y., Liu, M., Shi, X. & Zhao, Z. Dark septate endophyte (DSE) fungi isolated from metal polluted soils: Their taxonomic position, tolerance, and accumulation of heavy metals in vitro. J. Microbiol. 46, 624–632 (2008).PubMed 

Google Scholar 
Krumins, J. A., Goodey, N. M. & Gallagher, F. Plant–soil interactions in metal contaminated soils. Soil Biol. Biochem. 80, 224–231 (2015).CAS 

Google Scholar 
Glick, B. R. Phytoremediation: Synergistic use of plants and bacteria to clean up the environment. Biotechnol. Adv. 21, 383–393 (2003).CAS 
PubMed 

Google Scholar 
Heckenroth, A. et al. What are the potential environmental solutions for diffuse pollution ? In Pollution of Marseille’s Industrial Calanques—The Impact of the Past on the Present 291–328 (REF2C, 2016).Li, M. S. Ecological restoration of mineland with particular reference to the metalliferous mine wasteland in China: A review of research and practice. Sci. Total Environ. 357, 38–53 (2006).CAS 
PubMed 

Google Scholar 
Mendez, M. O. & Maier, R. M. Phytoremediation of mine tailings in temperate and arid environments. Rev. Environ. Sci. Biotechnol. 7, 47–59 (2008).CAS 

Google Scholar 
Yaalon, D. H. Soils in the Mediterranean region: What makes them different?. CATENA 28, 157–169 (1997).CAS 

Google Scholar 
Li, S. et al. A comprehensive survey on the horizontal and vertical distribution of heavy metals and microorganisms in soils of a Pb/Zn smelter. J. Hazard. Mater. 400, 123255 (2020).CAS 
PubMed 

Google Scholar 
Pérez-de-Mora, A. et al. Microbial community structure and function in a soil contaminated by heavy metals: Effects of plant growth and different amendments. Soil Biol. Biochem. 38, 327–341 (2006).
Google Scholar 
Keller, C. et al. Root development and heavy metal phytoextraction efficiency: Comparison of different plant species in the field. Plant Soil. 249, 67–81 (2003).CAS 

Google Scholar 
Lambrechts, T. et al. Comparative analysis of Cd and Zn impacts on root distribution and morphology of Lolium perenne and Trifolium repens: Implications for phytostabilization. Plant Soil 376, 229–244 (2014).CAS 

Google Scholar 
Pauwels, M., Frérot, H., Bonnin, I. & Saumitou-Laprade, P. A broad-scale analysis of population differentiation for Zn tolerance in an emerging model species for tolerance study: Arabidopsis halleri (Brassicaceae). J. Evol. Biol. 19, 1838–1850 (2006).CAS 
PubMed 

Google Scholar 
Padilla, F. M. & Pugnaire, F. I. The role of nurse plants in the restoration of degraded environments. Front. Ecol. Environ. 4, 196–202 (2006).
Google Scholar 
Robles, A. B., Allegretti, L. I. & Passera, C. B. Coronilla juncea is both a nutritive fodder shrub and useful in the rehabilitation of abandoned Mediterranean marginal farmland. J. Arid Environ. 50, 381–392 (2002).
Google Scholar 
Grime, J. P. Plant Strategies and Vegetation Processes (Wiley, 1979).
Google Scholar 
Laffont-Schwob, I. et al. Diffuse and widespread present-day pollution. In Pollution of Marseille’s industrial Calanques—The Impact of the Past on the Future 204–249 (REF2C, 2016).Gelly, R. et al. Lead, zinc, and copper redistributions in soils along a deposition gradient from emissions of a Pb-Ag smelter decommissioned 100 years ago. Sci. Total Environ. 665, 502–512 (2019).CAS 
PubMed 

Google Scholar 
Tóth, G. et al. Soils of the European Union. JRC Scientific and Technical Reports 85 (2008).IUSS Working Group WRB. Base de référence mondiale pour les ressources en sols 2014, Mise à jour 2015. Système international de classification des sols pour nommer les sols et élaborer des légendes de cartes pédologiques. Rapport sur les ressources en sols du monde. Vol. 106 (2015).Dias, T. et al. Ammonium as a driving force of plant diversity and ecosystem functioning: Observations based on 5 years’ manipulation of n dose and form in a Mediterranean ecosystem. PLoS ONE 9, e92517 (2014).PubMed 
PubMed Central 

Google Scholar 
Remon, E. et al. Soil characteristics, heavy metal availability and vegetation recovery at a former metallurgical landfill: Implications in risk assessment and site restoration. Environ. Pollut. 137, 316–323 (2005).CAS 
PubMed 

Google Scholar 
Baumberger, T. et al. Plant community changes as ecological indicator of seabird colonies’ impacts on Mediterranean Islands. Ecol. Ind. 15, 76–84 (2012).
Google Scholar 
Navas, M.-L., Roumet, C., Bellmann, A., Laurent, G. & Garnier, E. Suites of plant traits in species from different stages of a Mediterranean secondary succession: Plant traits and succession. Plant Biol. 12, 183–196 (2010).CAS 
PubMed 

Google Scholar 
Guillamot, F., Calvert, V., Millot, M.-V. & Criquet, S. Does antimony affect microbial respiration in Mediterranean soils? A microcosm experiment. Pedobiologia 57, 119–121 (2014).
Google Scholar 
Wang, A., He, M., Ouyang, W., Lin, C. & Liu, X. Effects of antimony (III/V) on microbial activities and bacterial community structure in soil. Sci. Total Environ. 789, 148073 (2021).CAS 
PubMed 

Google Scholar 
Oleńska, E. et al. Trifolium repens-associated bacteria as a potential tool to facilitate phytostabilization of zinc and lead polluted waste heaps. Plants 9, 1002 (2020).PubMed Central 

Google Scholar 
Stambulska, U. Y., Bayliak, M. M. & Lushchak, V. I. Chromium(VI) toxicity in legume plants: Modulation effects of rhizobial symbiosis. BioMed Res. Int. 2018, 1–13 (2018).
Google Scholar 
Karthika, K. S., Rashmi, I. & Parvathi, M. S. Biological functions, uptake and transport of essential nutrients in relation to plant growth. In Plant Nutrients and Abiotic Stress Tolerance 1–49 (Springer Singapore, 2018). https://doi.org/10.1007/978-981-10-9044-8_1.Dary, M., Chamber-Pérez, M. A., Palomares, A. J. & Pajuelo, E. “In situ” phytostabilisation of heavy metal polluted soils using Lupinus luteus inoculated with metal resistant plant-growth promoting rhizobacteria. J. Hazard. Mater. 177, 323–330 (2010).CAS 
PubMed 

Google Scholar 
Reichman, S. M. The potential use of the legume–rhizobium symbiosis for the remediation of arsenic contaminated sites. Soil Biol. Biochem. 39, 2587–2593 (2007).CAS 

Google Scholar 
Parraga-Aguado, I., Querejeta, J.-I., González-Alcaraz, M.-N., Jiménez-Cárceles, F. J. & Conesa, H. M. Usefulness of pioneer vegetation for the phytomanagement of metal(loid)s enriched tailings: Grasses vs. shrubs vs. trees. J. Environ. Manag. 133, 51–58 (2014).CAS 

Google Scholar 
Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. Oikos 69, 373 (1994).
Google Scholar 
Carrasco, L., Azcón, R., Kohler, J., Roldán, A. & Caravaca, F. Comparative effects of native filamentous and arbuscular mycorrhizal fungi in the establishment of an autochthonous, leguminous shrub growing in a metal-contaminated soil. Sci. Total Environ. 409, 1205–1209 (2011).CAS 
PubMed 

Google Scholar 
Padilla, F. M., Ortega, R., Sánchez, J. & Pugnaire, F. I. Rethinking species selection for restoration of arid shrublands. Basic Appl. Ecol. 10, 640–647 (2009).
Google Scholar 
Ilunga wa Ilunga, E. et al. Plant functional traits as a promising tool for the ecological restoration of degraded tropical metal-rich habitats and revegetation of metal-rich bare soils: A case study in copper vegetation of Katanga, DRC. Ecol. Eng. 82, 214–221 (2015).
Google Scholar 
Salducci, M.-D. et al. How can a rare protected plant cope with the metal and metalloid soil pollution resulting from past industrial activities? Phytometabolites, antioxidant activities and root symbiosis involved in the metal tolerance of Astragalus tragacantha. Chemosphere 217, 887–896 (2019).CAS 
PubMed 

Google Scholar 
Kachout, S. S. et al. Accumulation of Cu, Pb, Ni and Zn in the halophyte plant Atriplex grown on polluted soil. J. Sci. Food Agric. 92, 336–342 (2012).CAS 
PubMed 

Google Scholar 
Schaeffer, A. et al. The impact of chemical pollution on the resilience of soils under multiple stresses: A conceptual framework for future research. Sci. Total Environ. 568, 1076–1085 (2016).CAS 
PubMed 

Google Scholar 
Tosini, L. et al. Gain in biodiversity but not in phytostabilization after 3 years of ecological restoration of contaminated Mediterranean soils. Ecol. Eng. 157, 105998 (2020).
Google Scholar 
Michelaki, C. et al. An integrated phenotypic trait-network in thermo-Mediterranean vegetation describing alternative, coexisting resource-use strategies. Sci. Total Environ. 672, 583–592 (2019).CAS 
PubMed 

Google Scholar 
Affholder, M.-C. et al. Transfer of metals and metalloids from soil to shoots in wild rosemary (Rosmarinus officinalis L.) growing on a former lead smelter site: Human exposure risk. Sci. Total Environ. 454–455, 219–229 (2013).PubMed 

Google Scholar 
Affholder, M.-C. et al. As, Pb, Sb, and Zn transfer from soil to root of wild rosemary: Do native symbionts matter?. Plant Soil 382, 219–236 (2014).CAS 

Google Scholar 
Ellili, A. et al. Decision-making criteria for plant-species selection for phytostabilization: Issues of biodiversity and functionality. J. Environ. Manag. 201, 215–226 (2017).CAS 

Google Scholar 
Laffont-Schwob, I. et al. Insights on metal-tolerance and symbionts of the rare species Astragalus tragacantha aiming at phytostabilization of polluted soils and plant conservation. ecmed 37, 57–62 (2011).
Google Scholar 
Rabier, J. et al. Heavy metal and arsenic resistance of the halophyte Atriplex halimus L. along a gradient of contamination in a French Mediterranean spray zone. Water Air Soil Pollut. 225, 1993 (2014).
Google Scholar 
Quevauviller, Ph. et al. Interlaboratory comparison of EDTA and DTPA procedures prior to certification of extractable trace elements in calcareous soil. Sci. Total Environ. 178, 127–132 (1996).CAS 

Google Scholar 
Anderson, J. P. E. & Domsch, K. H. A physiological method for the quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10, 215–221 (1978).CAS 

Google Scholar 
R Development Core Team.pdf.Dray, S., Dufour, A. B. & Chessel, D. The ade4 package—II: Two-table and K-table methods. R News 7, 6 (2007).
Google Scholar  More

Faisal, A. A., Selen, L. P. J. & Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tsetsos, K. et al. Economic irrationality is optimal during noisy decision making. Proc. Natl. Acad. Sci. 113, 3102–3107 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bushnell, P. J. Behavioral approaches to the assessment of attention in animals. Psychopharmacology 138, 231–259 (1998).CAS 
PubMed 
Article 

Google Scholar 
Katsuki, F. & Constantinidis, C. Bottom-up and top-down attention: Different processes and overlapping neural systems. Neuroscientist 20, 509–521 (2014).PubMed 
Article 

Google Scholar 
Moore, T. & Zirnsak, M. Neural mechanisms of selective visual attention. Annu. Rev. Psychol. 68, 47–72 (2017).CAS 
PubMed 
Article 

Google Scholar 
Ferguson, K. I. & Stiling, P. Non-additive effects of multiple natural enemies on aphid populations. Oecologia 108, 375–379 (1996).ADS 
PubMed 
Article 

Google Scholar 
Sih, A., Englund, G. & Wooster, D. Emergent impacts of multiple predators on prey. Trends Ecol. Evol. 13, 350–355 (1998).CAS 
PubMed 
Article 

Google Scholar 
Soluk, D. A. & Collins, N. C. Synergistic interactions between fish and stoneflies: Facilitation and interference among stream predators. Oikos. 52, 94–100 (1988).
Article 

Google Scholar 
Cooper, W. E., Pérez-Mellado, V. & Hawlena, D. Number, speeds, and approach paths of predators affect escape behavior by the Balearic lizard, Podarcis lilfordi. J. Herpetol. 41, 197–204 (2007).Article 

Google Scholar 
Relyea, R. A. How prey respond to combined predators: A review and an empirical test. Ecology 84, 1827–1839 (2003).Article 

Google Scholar 
Krupa, J. J. & Sih, A. Fishing spiders, green sunfish, and a stream-dwelling water strider: Male–female conflict and prey responses to single versus multiple predator environments. Oecologia 117, 258–265 (1998).ADS 
PubMed 
Article 

Google Scholar 
Nityananda, V. Attention-like processes in insects. Proc. R. Soc. B Biol. Sci. 283, 20161986 (2016).Article 

Google Scholar 
Amo, L., López, P. & Martín, J. in Annales Zoologici Fennici, 671–679 (JSTOR).Bagheri, Z. M., Donohue, C. G. & Hemmi, J. M. Evidence of predictive selective attention in fiddler crabs during escape in the natural environment. J. Exp. Biol. 223, 234963 (2020).Article 

Google Scholar 
Geist, C., Liao, J., Libby, S. & Blumstein, D. T. Does intruder group size and orientation affect flight initiation distance in birds?. Anim. Biodivers. Conserv. 28, 69–73 (2005).
Google Scholar 
McIntosh, A. R. & Peckarsky, B. L. Criteria determining behavioural responses to multiple predators by a stream mayfly. Oikos. 554–564 (1999).Hemmi, J. M. & Tomsic, D. The neuroethology of escape in crabs: From sensory ecology to neurons and back. Curr. Opin. Neurobiol. 22, 194–200 (2012).CAS 
PubMed 
Article 

Google Scholar 
Zeil, J. & Hemmi, J. M. The visual ecology of fiddler crabs. J. Comp. Physiol. A. 192, 1–25 (2006).ADS 
Article 

Google Scholar 
Nalbach, H.-O., Nalbach, G. & Forzin, L. Visual control of eye-stalk orientation in crabs: Vertical optokinetics, visual fixation of the horizon, and eye design. J. Comp. Physiol. A. 165, 577–587 (1989).Article 

Google Scholar 
Zeil, J. & Al-Mutairi, M. The variation of resolution and of ommatidial dimensions in the compound eyes of the fiddler crab Uca lactea annulipes (Ocypodidae, Brachyura, Decapoda). J. Exp. Biol. 199, 1569–1577 (1996).CAS 
PubMed 
Article 

Google Scholar 
Howard, J. & Snyder, A. W. Transduction as a limitation on compound eye function and design. Proc. R. Soc. Lond. Series B Biol. Sci. 217, 287–307 (1983).ADS 

Google Scholar 
Land, M. F. Visual acuity in insects. Annu. Rev. Entomol. 42, 147–177 (1997).CAS 
PubMed 
Article 

Google Scholar 
Land, M. F. & Nilsson, D.-E. Animal Eyes (OUP, 2012).Book 

Google Scholar 
Bagheri, Z. M. et al. A new method for mapping spatial resolution in compound eyes suggests two visual streaks in fiddler crabs. J. Exp. Biol. 223, 210195 (2020).Article 

Google Scholar 
Smolka, J. & Hemmi, J. M. Topography of vision and behaviour. J. Exp. Biol. 212, 3522–3532 (2009).PubMed 
Article 

Google Scholar 
Land, M. & Layne, J. The visual control of behaviour in fiddler crabs. J. Comp. Physiol. A. 177, 91–103 (1995).Article 

Google Scholar 
Layne, J., Land, M. & Zeil, J. Fiddler crabs use the visual horizon to distinguish predators from conspecifics: A review of the evidence. J. Mar. Biol. Assoc. UK. 77, 43–54 (1997).Article 

Google Scholar 
Hemmi, J. M. Predator avoidance in fiddler crabs: 1. Escape decisions in relation to the risk of predation. Animal Behav. 69, 603–614 (2005).Article 

Google Scholar 
Layne, J. E. Retinal location is the key to identifying predators in fiddler crabs (Uca pugilator). J. Exp. Biol. 201, 2253–2261 (1998).CAS 
PubMed 
Article 

Google Scholar 
Nalbach, H.-O. Frontiers in Crustacean Neurobiology 165–172 (Springer, 1990).Book 

Google Scholar 
Smolka, J., Zeil, J. & Hemmi, J. M. Natural visual cues eliciting predator avoidance in fiddler crabs. Proc. R. Soc. B Biol. Sci. 278, 3584–3592 (2011).Article 

Google Scholar 
Hemmi, J. M. Predator avoidance in fiddler crabs: 2. The visual cues. Animal Behav. 69, 615–625 (2005).Article 

Google Scholar 
Hemmi, J. M. & Pfeil, A. A multi-stage anti-predator response increases information on predation risk. J. Exp. Biol. 213, 1484–1489 (2010).PubMed 
Article 

Google Scholar 
Smolka, J., Raderschall, C. A. & Hemmi, J. M. Flicker is part of a multi-cue response criterion in fiddler crab predator avoidance. J. Exp. Biol. 216, 1219–1224 (2013).PubMed 

Google Scholar 
How, M. J., Pignatelli, V., Temple, S. E., Marshall, N. J. & Hemmi, J. M. High e-vector acuity in the polarisation vision system of the fiddler crab Uca vomeris. J. Exp. Biol. 215, 2128–2134 (2012).PubMed 
Article 

Google Scholar 
Paulk, A. C. et al. Selective attention in the honeybee optic lobes precedes behavioral choices. Proc. Natl. Acad. Sci. 111, 5006–5011 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tang, S. & Juusola, M. Intrinsic activity in the fly brain gates visual information during behavioral choices. Nat. Precedings. 1–1 (2010).Bagheri, Z. M., Cazzolato, B. S., Grainger, S., O’Carroll, D. C. & Wiederman, S. D. An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments. J. Neural Eng. 14, 046030 (2017).ADS 
PubMed 
Article 

Google Scholar 
Chancán, M., Hernandez-Nunez, L., Narendra, A., Barron, A. B. & Milford, M. A hybrid compact neural architecture for visual place recognition. IEEE Robot. Automat. Lett. 5, 993–1000 (2020).Article 

Google Scholar 
Colonnier, F., Ramirez-Martinez, S., Viollet, S. & Ruffier, F. A bio-inspired sighted robot chases like a hoverfly. Bioinspir. Biomim. 14, 036002 (2019).ADS 
PubMed 
Article 

Google Scholar 
Medan, V., Oliva, D. & Tomsic, D. Characterization of lobula giant neurons responsive to visual stimuli that elicit escape behaviors in the crab Chasmagnathus. J. Neurophysiol. 98, 2414–2428 (2007).PubMed 
Article 

Google Scholar 
Oliva, D. & Tomsic, D. Computation of object approach by a system of visual motion-sensitive neurons in the crab Neohelice. J. Neurophysiol. 112, 1477–1490 (2014).PubMed 
Article 

Google Scholar 
Oliva, D. & Tomsic, D. Object approach computation by a giant neuron and its relationship with the speed of escape in the crab Neohelice. J. Exp. Biol. 219, 3339–3352 (2016).PubMed 

Google Scholar 
Sztarker, J., Strausfeld, N. J. & Tomsic, D. Organization of optic lobes that support motion detection in a semiterrestrial crab. J. Comparat. Neurol. 493, 396–411 (2005).Article 

Google Scholar 
Medan, V., De Astrada, M. B., Scarano, F. & Tomsic, D. A network of visual motion-sensitive neurons for computing object position in an arthropod. J. Neurosci. 35, 6654–6666 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tomsic, D. & Sztarker, J. in Oxford Research Encyclopedia of Neuroscience (2019).Sztarker, J. & Tomsic, D. Neuronal correlates of the visually elicited escape response of the crab Chasmagnathus upon seasonal variations, stimuli changes and perceptual alterations. J. Comp. Physiol. A. 194, 587–596 (2008).Article 

Google Scholar 
Tomsic, D., de Astrada, M. B. & Sztarker, J. Identification of individual neurons reflecting short-and long-term visual memory in an arthropodo. J. Neurosci. 23, 8539–8546 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Layne, J. E., Barnes, W. J. P. & Duncan, L. M. J. Mechanisms of homing in the fiddler crab Uca rapax 1. Spatial and temporal characteristics of a system of small-scale navigation. J. Exp. Biol. 206, 4413–4423 (2003).PubMed 
Article 

Google Scholar 
Dahmen, H., Wahl, V. L., Pfeffer, S. E., Mallot, H. A. & Wittlinger, M. Naturalistic path integration of Cataglyphis desert ants on an air-cushioned lightweight spherical treadmill. J. Exp. Biol. 220, 634–644 (2017).PubMed 
Article 

Google Scholar 
Hemmi, J. M. & Merkle, T. High stimulus specificity characterizes anti-predator habituation under natural conditions. Proc. R. Soc. B Biol. Sci. 276, 4381–4388 (2009).Article 

Google Scholar 
Scarano, F. & Tomsic, D. Escape response of the crab Neohelice to computer generated looming and translational visual danger stimuli. J. Physiol.-Paris 108, 141–147 (2014).PubMed 
Article 

Google Scholar 
Ryan, T. P. & Morgan, J. P. Modern experimental design. J. Stat. Theory Practice 1, 501–506 (2007).MATH 
Article 

Google Scholar 
Hemmi, J. M. & Zeil, J. Burrow surveillance in fiddler crabs I. Description of behaviour. J. Exp. Biol. 206, 3935–3950 (2003).PubMed 
Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. (2014).emmeans: Estimated Marginal Means, aka Least-Squares Means. v. R package version 1.5.2-1. (2020).Cremers, J. Bpnreg: Bayesian projected normal regression models for circular data. R Package Version 1, 3 (2018).
Google Scholar 
Cremers, J. & Klugkist, I. One direction? A tutorial for circular data analysis using R with examples in cognitive psychology. Front. Psychol. 2040 (2018).Oliva, D., Medan, V. & Tomsic, D. Escape behavior and neuronal responses to looming stimuli in the crab Chasmagnathus granulatus (Decapoda: Grapsidae). J. Exp. Biol. 210, 865–880 (2007).PubMed 
Article 

Google Scholar 
Gabbiani, F., Krapp, H. G. & Laurent, G. Computation of object approach by a wide-field, motion-sensitive neuron. J. Neurosci. 19, 1122–1141 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Simultaneous Inference in General Parametric Models. v. R package version v1.4-10 (2019).Avargues-Weber, A., Deisig, N. & Giurfa, M. Visual cognition in social insects. Annu. Rev. Entomol. 56, 423–443 (2011).CAS 
PubMed 
Article 

Google Scholar 
Avarguès-Weber, A. & Giurfa, M. Conceptual learning by miniature brains. Proc. R. Soc. B Biol. Sci. 280, 20131907 (2013).Article 

Google Scholar 
De Bivort, B. L. & Van Swinderen, B. Evidence for selective attention in the insect brain. Curr. Opin. Insect Sci. 15, 9–15 (2016).PubMed 
Article 

Google Scholar 
Klapoetke, N. C. et al. Ultra-selective looming detection from radial motion opponency. Nature 551, 237–241 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Von Reyn, C. R. et al. A spike-timing mechanism for action selection. Nat. Neurosci. 17, 962–970 (2014).Article 
CAS 

Google Scholar 
Fotowat, H. & Gabbiani, F. Collision detection as a model for sensory-motor integration. Annu. Rev. Neurosci. 34, 1–19 (2011).CAS 
PubMed 
Article 

Google Scholar 
Strausfeld, N. J. & Olea-Rowe, B. Convergent evolution of optic lobe neuropil in Pancrustacea. Arthropod. Struct. Dev. 61, 101040 (2021).PubMed 
Article 

Google Scholar 
Tomsic, D. Visual motion processing subserving behavior in crabs. Curr. Opin. Neurobiol. 41, 113–121 (2016).CAS 
PubMed 
Article 

Google Scholar 
Giribet, G. & Edgecombe, G. D. The phylogeny and evolutionary history of arthropods. Curr. Biol. 29, R592–R602 (2019).CAS 
PubMed 
Article 

Google Scholar 
Christian, E. V. Sprung der Collembolen. Zoologische Jahrbucher. Abteilung fur Systematik, Okologie und Geographie der Tiere (1979).Brackenbury, J. Regulation of swimming in the Culex pipiens (Diptera, Culicidae) pupa: Kinematics and locomotory trajectories. J. Exp. Biol. 202, 2521–2529 (1999).CAS 
PubMed 
Article 

Google Scholar 
Domenici, P. & Blake, R. W. Escape trajectories in angelfish (Pterophyllum eimekei). J. Exp. Biol. 177, 253–272 (1993).Article 

Google Scholar 
Kimura, H. & Kawabata, Y. Effect of initial body orientation on escape probability of prey fish escaping from predators. Biol. Open. 7, bio023812 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Martín, J. & López, P. The escape response of juvenile Psammodromus algirus lizards. J. Comp. Psychol. 110, 187 (1996).Article 

Google Scholar 
Lancer, B. H., Evans, B. J. E., Fabian, J. M., O’Carroll, D. C. & Wiederman, S. D. A target-detecting visual neuron in the dragonfly locks on to selectively attended targets. J. Neurosci. 39, 8497–8509 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nityananda, V. & Pattrick, J. G. Bumblebee visual search for multiple learned target types. J. Exp. Biol. 216, 4154–4160 (2013).PubMed 

Google Scholar 
Pollack, G. S. Selective attention in an insect auditory neuron. J. Neurosci. 8, 2635–2639 (1988).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rossel, S. Binocular vision in insects: How mantids solve the correspondence problem. Proc. Natl. Acad. Sci. 93, 13229–13232 (1996).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wiederman, S. D. & O’Carroll, D. C. Selective attention in an insect visual neuron. Curr. Biol. 23, 156–161 (2013).CAS 
PubMed 
Article 

Google Scholar 
Jackson, R. R. & Cross, F. R. Spider cognition. Adv. Insect Physiol. 41, 115–174 (2011).Article 

Google Scholar 
Jackson, R. R. & Li, D. One-encounter search-image formation by araneophagic spiders. Anim. Cogn. 7, 247–254 (2004).PubMed 
Article 

Google Scholar 
Guest, B. B. & Gray, J. R. Responses of a looming-sensitive neuron to compound and paired object approaches. J. Neurophysiol. 95, 1428–1441 (2006).PubMed 
Article 

Google Scholar 
Eliassen, S., Jørgensen, C., Mangel, M. & Giske, J. Quantifying the adaptive value of learning in foraging behavior. Am. Nat. 174, 478–489 (2009).PubMed 
Article 

Google Scholar 
Eliassen, S., Andersen, B. S., Jørgensen, C. & Giske, J. From sensing to emergent adaptations: Modelling the proximate architecture for decision-making. Ecol. Model. 326, 90–100 (2016).Article 

Google Scholar 
Gigerenzer, G. Why heuristics work. Perspect. Psychol. Sci. 3, 20–29 (2008).PubMed 
Article 

Google Scholar  More