Philippa Kaur

Chromosomal-scale genome assembly and full spidroin gene set of T. clavata
To explore dragline silk production in T. clavata, we sought to assemble a high-quality genome of this species. Thus, we first performed a cytogenetic analysis of T. clavata captured from the wild in Dali City, Yunnan Province, China, and found a chromosomal complement of 2n = 26 in females and 2n = 24 in males, comprising eleven pairs of autosomal elements and unpaired sex chromosomes (X1X1X2X2 in females and X1X2 in males) (Fig. 1a). Then, DNA from adult T. clavata was used to generate long-read (Oxford Nanopore Technologies (ONT)), short-read (Illumina), and Hi-C data (Supplementary Data 1). A total of 349.95 Gb of Nanopore reads, 199.55 Gb of Illumina reads, and ~438.41 Gb of Hi-C raw data were generated. Our sequential assembly approach (Supplementary Fig. 1c) resulted in a 2.63 Gb genome with a scaffold N50 of 202.09 Mb and a Benchmarking Universal Single-Copy Ortholog (BUSCO) genome completeness score of 93.70% (Table 1; Supplementary Data 3). Finally, the genome was assembled into 13 pseudochromosomes. Sex-specific Pool-Seq analysis of spiders indicated that Chr12 and Chr13 were sex chromosomes (Fig. 1b; Supplementary Fig. 2). Based on the MAKER2 pipeline34 (Supplementary Fig. 1e), we annotated 37,607 protein-encoding gene models and predicted repetitive elements with a collective length of 1.42 Gb, accounting for 53.94% of the genome.Table 1 Characteristics of the T. clavata genome assemblyFull size tableTo identify T. clavata spidroin genes, we searched the annotated gene models for sequences similar to 443 published spidroins (Supplementary Data 6) and performed a phylogenetic analysis of the putative spidroin sequences for classification (Supplementary Fig. 12a). Based on the knowledge that a typical spidroin gene consists of a long repeat domain sandwiched between the nonrepetitive N/C-terminal domains16, 128 nonrepetitive hits were primarily identified. These candidates were further validated and reconstructed using full-length transcript isoform sequencing (Iso-seq) and transcriptome sequencing (RNA-seq) data. We thus identified 28 spidroin genes, among which 26 were full-length (Supplementary Fig. 11a), including 9 MaSps, 5 minor ampullate spidroins (MiSps), 2 flagelliform spidroins (FlSps), 1 tubuliform spidroin (TuSp), 2 aggregate spidroins (AgSp), 1 aciniform spidroin (AcSp), 1 pyriform spidroin (PySp), and 5 other spidroins. This full set of spidroin genes was located across nine of the 13 T. clavata chromosomes. Interestingly, we found that the MaSp1a–c & MaSp2e, MaSp2a–d, and MiSp-a–e genes were distributed in three independent groups on chromosomes 4, 7, and 6, respectively (Fig. 1c). Notably, using the genomic data of another orb-weaving spider species, Trichonephila antipodiana35, we identified homologous group distributions of spidroin genes on T. antipodiana chromosomes (Fig. 1d), which indicated the reliability of the grouping results of our study. When we compared the spidroin gene catalog of T. clavata and those of five other orb-web spider species with genomic data28,29,36,37, we found that T. clavata and Trichonephila clavipes possessed the largest number of spidroin genes (28 genes in both species; Fig. 1e).To further explore the expression of spidroin genes in different glands, all morphologically distinct glands (major and minor ampullate- (Ma and Mi), flagelliform- (Fl), tubuliform- (Tu), and aggregate (Ag) glands) were cleanly and separately dissected from adult female T. clavata spiders except for the aciniform and pyriform glands, which could not be cleanly separated because of their proximal anatomical locations and were therefore treated as a combined sample (aciniform & pyriform gland (Ac & Py)). After RNA sequencing of these silk glands, we performed expression clustering analysis of transcriptomic data and found that the Ma and Mi glands showed the closest relationship in terms of both morphological structure (Fig. 1g) and gene expression (Fig. 1f, h). We noted that the expression profiles of spidroin genes were largely consistent with their putative roles in the corresponding morphologically distinct silk glands; for example, MaSp expression was found in the Ma gland (Fig. 1h). However, some spidroin transcripts, such as MiSps and TuSp, were expressed in several silk glands (Fig. 1h). Unclassified spidroin genes, such as Sp-GP-rich, did not appear to show gland-specific expression (Fig. 1h).In summary, the chromosomal-scale genome of T. clavata allowed us to obtain detailed structural and location information for all spidroin genes of this species. We also found a relatively diverse set of spidroin genes and a grouped distribution of MaSps and MiSps in T. clavata.Dragline silk origin and the functional character of the Ma gland segmentsTo further evaluate the detailed molecular characteristics of the Ma gland-mediated secretion of dragline silk, we performed integrated analyses of the transcriptomes of the three T. clavata Ma gland segments and the proteome and metabolome of T. clavata dragline silk (Fig. 2a). Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) analysis of dragline silk mainly showed a thick band above 240 kDa, suggesting a relatively small variety of total proteins (Fig. 2b). Subsequent liquid chromatography–mass spectrometry (LC–MS) analysis identified 28 proteins, including ten spidroins (nine MaSps and one MiSp) and 18 nonspidroin proteins (one glucose dehydrogenase (GDH), one mucin-19, one venom protein, and 15 SpiCEs of dragline silk (SpiCE-DS)) (Fig. 2b; Supplementary Data 10). Among these proteins, we found that the core protein components of dragline silk in order of intensity-based absolute quantification (iBAQ) percentages were MaSp1c (37.7%), MaSp1b (12.2%), SpiCE-DS1 (11.9%, also referred to as SpiCE-NMa1 in a previous study28), MaSp1a (10.4%), and MaSp-like (7.2%), accounting for approximately 80% of the total protein abundance in dragline silk (Fig. 2b). These results revealed potential protein components that might be highly correlated with the excellent strength and toughness of dragline silk.Fig. 2: Dragline silk origin and the functional character of the Ma gland segments.a Schematic illustration of Ma gland segmentation. b Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) (left) and LC–MS (right) analyses of dragline silk protein. iBAQ, intensity-based absolute quantification. Similar results were obtained in three independent experiments and summarized in Source data. c Classification of the identified metabolites in dragline silk. d LC–MS analyses of the metabolites. e LC–MS analyses of the golden extract from T. clavata dragline silk. The golden pigment was extracted with 80% methanol. The extracted ion chromatograms (EICs) showed a peak at m/z 206 [M + H]+ for xanthurenic acid. f Pearson correlation of different Ma gland segments (Tail, Sac, and Duct). g Expression clustering of the Tail, Sac, and Duct. The transcriptomic data were clustered according to the hierarchical clustering (HC) method. h Combinational analysis of the transcriptome and proteome showing the expression profile of the dragline silk genes in the Tail, Sac, and Duct. i Concise biosynthetic pathway of xanthurenic acid (tryptophan metabolism) in the T. clavata Ma gland. Gene expression levels mapped to tryptophan metabolism are shown in three segments of the Ma gland. Enzymes involved in the pathway are indicated in red, and the genes encoding the enzymes are shown beside them. j Gene Ontology (GO) enrichment analysis of Ma gland segment-specific genes indicating the biological functions of the Tail, Sac, and Duct. The top 12 significantly enriched GO terms are shown for each segment of the Ma gland. A P-value  2) were identified in the 2 kb regions upstream and downstream of genes, and 10,501,151 (Tail), 11,356,55 (Sac), and 9,778,368 (Duct) significant ATAC peaks (RPKM  > 2) were identified at the whole-genome level. The Tail (mean RPKM: 1.78) and Sac (mean RPKM: 2.04) plots showed genes with more accessible chromatin than the Duct (mean RPKM: 1.59) plots (Fig. 3a). We then analyzed the genome-wide DNA methylation level in the Tail, Sac, and Duct. We found the highest levels of DNA methylation in the CG context (beta value: 0.12 in Tail, 0.13 in Sac, and 0.10 in Duct) and only a small amount in the CHH (beta value: 0.04 in Tail, 0.05 in Sac, and 0.03 in Duct) and CHG (beta value: 0.04 in Tail, 0.05 in Sac, and 0.04 in Duct) contexts (Fig. 3b). Overall, there was no significant difference in methylation levels among the Tail, Sac, and Duct. Taken together, our results suggest a potential regulatory role of CA rather than DNA methylation in the transcription of dragline silk genes.Fig. 3: Comprehensive epigenetic features and ceRNA network of the tri-sectional Ma gland.a Metagene plot of ATAC-seq signals and heatmap of the ATAC-seq read densities in the Tail, Sac, and Duct. The chromatin accessibility was indicated by the mean RPKM value (upper) and the blue region (bottom). b Metagene plot of DNA methylation levels in CG/CHG/CHH contexts in the Tail, Sac, and Duct. (c, d) Screenshots of the methylation and ATAC-seq tracks of the MaSp1b (c) and MaSp2b (d) genes within the Tail, Sac, and Duct. The potential TF motifs (E-value More

Lebrija-Trejos, E., Hernández, A. & Wright, S. J. Nature https://doi.org/10.1038/s41586-023-05717-1 (2023).Article 

Google Scholar 
Chesson, P. J. Ecol. 106, 1773–1794 (2018).Article 

Google Scholar 
Barabás, G., D’Andrea, R. & Stump, S. M. Ecol. Monogr. 88, 277–303 (2018).Article 

Google Scholar 
Broekman, M. J. E. et al. Ecol. Lett. 22, 1957–1975 (2019).Article 
PubMed 

Google Scholar 
Freckleton, R. P. & Lewis, O. T. Proc. R. Soc. B 273, 2909–2916 (2006).Article 
PubMed 

Google Scholar 
Bagchi, R. et al. Nature 506, 85–88 (2014).Article 
PubMed 

Google Scholar 
Chen, L. et al. Science 366, 124–128 (2019).Article 
PubMed 

Google Scholar 
Milici, V. R., Dalui, D., Mickley, J. G. & Bagchi, R. J. Ecol. 108, 1800–1809 (2020).Article 

Google Scholar 
Song, X. & Corlett, R. T. Oikos 2022, e08509 (2022).Article 

Google Scholar 
Engelbrecht, B. M. J. et al. Nature 447, 80–82 (2007).Article 
PubMed 

Google Scholar 
Krishnadas, M. & Stump, S. M. J. Ecol. 109, 2137–2151 (2021).Article 

Google Scholar 
Van Dyke, M. N., Levine, J. M. & Kraft, N. J. B. Nature 611, 507–511 (2022).Article 
PubMed 

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Fine, P. V. Ecological and evolutionary drivers of geographic variation in species diversity. Annu. Rev. Ecol. Evol. Syst. 46, 369–392 (2015).Article 

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

Google Scholar 
Mittelbach, G. G. et al. Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography. Ecol. Lett. 10, 315–331 (2007).Article 
PubMed 

Google Scholar 
Willig, M. R., Kaufman, D. M. & Stevens, R. D. Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Annu. Rev. Ecol. Evol. Syst. 34, 273–309 (2003).Article 

Google Scholar 
Pontarp, M. et al. The latitudinal diversity gradient: novel understanding through mechanistic eco-evolutionary models. Trends Ecol. Evol. 34, 211–223 (2019).Article 
PubMed 

Google Scholar 
Crame, J. A. Taxonomic diversity gradients through geological time. Divers Distrib. 7, 175–189 (2011).
Google Scholar 
Mannion, P. D., Upchurch, P., Benson, R. B. J. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).Article 
PubMed 

Google Scholar 
Powell, M. G. Latitudinal diversity gradients for brachiopod genera during late Palaeozoic time: links between climate, biogeography and evolutionary rates. Glob. Ecol. Biogeogr. 16, 519–528 (2007).Article 

Google Scholar 
Powell, M. G., Beresford, V. P. & Colaianne, B. A. The latitudinal position of peak marine diversity in living and fossil biotas. J. Biogeogr. 39, 1687–1694 (2012).Article 

Google Scholar 
Hillebrand, H. Strength, slope and variability of marine latitudinal gradients. Mar. Ecol. Prog. Ser. 273, 251–267 (2004).Article 
ADS 

Google Scholar 
Beaugrand, G., Rombouts, I. & Kirby, R. R. Towards an understanding of the pattern of biodiversity in the oceans. Glob. Ecol. Biogeogr. 22, 440–449 (2013).Article 

Google Scholar 
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Pianka, E. R. Latitudinal gradients in species diversity: a review of concepts. Am. Nat. 100, 33–46 (1966).Article 

Google Scholar 
Saupe, E. E. et al. Spatio-temporal climate change contributes to latitudinal diversity gradients. Nat. Ecol. Evol. 3, 1419–1429 (2019).Article 
PubMed 

Google Scholar 
Stehli, F. G., Douglas, R. G. & Newell, N. D. Generation and maintenance of gradients in taxonomic diversity. Science 164, 947–949 (1969).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Rutherford, S., D’Hondt, S. & Prell, W. Environmental controls on the geographic distribution of zooplankton diversity. Nature 4000, 749–752 (1999).Article 
ADS 

Google Scholar 
Klopfer, P. H. Environmental determinants of faunal diversity. Am. Nat. 93, 337–342 (1959).Article 

Google Scholar 
Haffer, J. & Prance, G. T. Climatic forcing of evolution in Amazonia during the Cenozoic: on the refuge theory of biotic differentiation. Amazoniana 16, 579–607 (2001).
Google Scholar 
Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species’ geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. USA 97, 9115–9120 (2000).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dobzhansky, T. Evolution in the tropics. Am. Sci. 38, 209–221 (1950).
Google Scholar 
Williams, C. B. Patterns in the Balance of Nature (Academic Press, 1964).Paine, R. T. Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).Article 

Google Scholar 
Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).Article 

Google Scholar 
Currie, D. J. Energy and large-scale patterns of animal and plant species richness. Am. Nat. 137, 27–49 (1991).Article 

Google Scholar 
Connell, J. H. & Orias, E. The ecological regulation of species diversity. Am. Nat. 98, 399–414 (1964).Article 

Google Scholar 
Rosenzweig, M. L. Species Diversity in Space and Time (Cambridge Univ. Press, 1995).Fenton, I. S. et al. The impact of Cenozoic cooling on assemblage diversity in planktonic foraminifera. Phil. Trans. R. Soc. B 371, 20150224 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Yasuhara, M. et al. Past and future decline of tropical pelagic biodiversity. Proc. Natl Acad. Sci. USA 117, 12891–12896 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yasuhara, M., Hunt, G., Dowsett, H. J., Robinson, M. M. & Stoll, D. K. Latitudinal species diversity gradient of marine zooplankton for the last three million years. Ecol. Lett. 15, 1174–1179 (2012).Article 
PubMed 

Google Scholar 
Jablonski, D., Roy, K. & Valentine, J. W. Out of the tropics: evolutionary dynamics of the latitudinal diversity gradient. Science 314, 102–106 (2006).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Combining marine macroecology and palaeoecology in understanding biodiversity: microfossils as a model. Biol. Rev. 92, 199–215 (2017).Article 
PubMed 

Google Scholar 
Fenton, I. S. et al. Triton, a new species-level database of Cenozoic planktonic foraminiferal occurrences. Sci. Data 8, 160 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Yasuhara, M. & Deutsch, C. A. Paleobiology provides glimpses of future ocean. Science 375, 25–26 (2022).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Yasuhara, M. et al. Time machine biology cross-timescale integration of ecology, evolution, and oceanography. Oceanography 33, 16–28 (2020).Article 

Google Scholar 
Westerhold, T. et al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science 369, 1383–1387 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Al-Sabouni, N., Kucera, M. & Schmidt, D. N. Vertical niche separation control of diversity and size disparity in planktonic foraminifera. Mar. Micropaleontol. 63, 75–90 (2007).Article 
ADS 

Google Scholar 
Lowery, C. M., Bown, P. R., Fraass, A. J. & Hull, P. M. Ecological response of plankton to environmental change: thresholds for extinction. Annu. Rev. Earth Planet. Sci. 48, 403–429 (2020).Article 
ADS 
CAS 

Google Scholar 
Lear, C. H., Elderfield, H. & Wilson, P. A. Cenozoic deep-sea temperatures and global ice volumes from Mg/Ca in benthic foraminiferal calcite. Science 287, 269–272 (2000).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Weiner, A., Aurahs, R., Kurasawa, A., Kitazato, H. & Kučera, M. Vertical niche partitioning between cryptic sibling species of a cosmopolitan marine planktonic protist. Mol. Ecol. 21, 4063–4073 (2012).Article 
PubMed 

Google Scholar 
Schneider, E. & Kennett, J. P. Segregation and speciation in the Neogene planktonic foraminiferal clade Globoconella. Paleobiology 25, 383–395 (1999).Article 

Google Scholar 
Raja, N. B. & Kiessling, W. Out of the extratropics: the evolution of the latitudinal diversity gradient of Cenozoic marine plankton. Proc. Biol. Sci. 288, 20210545 (2021).PubMed 
PubMed Central 

Google Scholar 
Allen, A. P. & Gillooly, J. F. Assessing latitudinal gradients in speciation rates and biodiversity at the global scale. Ecol. Lett. 9, 947–954 (2006).Article 
PubMed 

Google Scholar 
Irigoien, X., Huisman, J. & Harris, R. P. Global biodiversity patterns of marine phytoplankton and zooplankton. Nature 429, 863–886 (2004).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Schiebel, R. & Hemleben, C. Planktic Foraminifers in the Modern Ocean (Springer-Verlag, 2017).Ruddimann, W. F. Recent planktonic foraminifera: dominance and diversity in North Atlantic surface sediments. Science 164, 1164–1167 (1969).Article 
ADS 

Google Scholar 
Bé, A. W. H. & Tolderlund, D. S. in Micropaleontology of Marine Bottom Sediments (eds Funnell, B. M. & Riedel, W. K.) 105–149 (Cambridge Univ. Press, 1971).Sibert, E., Norris, R., Cuevas, J. & Graves, L. Eighty-five million years of Pacific Ocean gyre ecosystem structure: long-term stability marked by punctuated change. Proc. Biol. Sci. 283, 20160189 (2016).PubMed 
PubMed Central 

Google Scholar 
Chaudhary, C., Richardson, A. J., Schoeman, D. S. & Costello, M. J. Global warming is causing a more pronounced dip in marine species richness around the equator. Proc. Natl Acad. Sci. USA 118, e2015094118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Worm, B. & Tittensor, D. P. A Theory of Global Biodiversity (Princeton Univ. Press, 2018).Boscolo-Galazzo, F. et al. Temperature controls carbon cycling and biological evolution in the ocean twilight zone. Science 371, 1148–1152 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Boscolo-Galazzo, F. et al. Late Neogene evolution of modern deep-dwelling plankton. Biogeosciences 19, 743–762 (2022).Article 
ADS 

Google Scholar 
Aze, T. et al. A phylogeny of Cenozoic macroperforate planktonic foraminifera from fossil data. Biol. Rev. 86, 900–927 (2011).Article 
PubMed 

Google Scholar 
Matthews, K. J. et al. Global plate boundary evolution and kinematics since the late Paleozoic. Glob. Planet. Change 146, 226–250 (2016).Article 
ADS 

Google Scholar 
Gyldenfeldt, A.-B. V., Carstens, J. & Meincke, J. Estimation of the catchment area of a sediment trap by means of current meters and foraminiferal tests. Deep Sea Res. Part II 47, 1701–1717 (2000).Article 
ADS 

Google Scholar 
Qiu, Z., Doglioli, A. M. & Carlotti, F. Using a Lagrangian model to estimate source regions of particles in sediment traps. Sci. China Earth Sci. 57, 2447–2456 (2014).Article 
ADS 

Google Scholar 
Siegel, D. A. & Deuser, W. G. Trajectories of sinking particles in the Sargasso Sea: modeling of statistical funnels above deep-ocean sediment traps. Deep Sea Res. Part I 44, 1519–1541 (1997).Article 

Google Scholar 
Waniek, J., Koeve, W. & Prien, R. D. Trajectories of sinking particles and the catchment areas above sediment traps in the Northeast Atlantic. J. Mar. Res. 58, 983–1006 (2000).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing http://www.R-project.org (R Foundation for Statistical Computing, 2019).Alroy, J. The fossil record of North American mammals: evidence for a Paleocene evolutionary radiation. Syst. Biol. 48, 107–118 (1999).Article 
CAS 
PubMed 

Google Scholar 
Marcot, J. D. The fossil record and macroevolutionary history of North American ungulate mammals: standardizing variation in intensity and geography of sampling. Paleobiology 40, 238–255 (2014).Article 

Google Scholar 
Gaston, K. J., Williams, P. H., Eggleton, P. & Humphries, C. J. Large scale patterns of biodiversity: spatial variation in family richness. Proc. R. Soc. Lond. B 260, 149–154 (1995).Article 
ADS 

Google Scholar 
Valdes, P. J. et al. The BRIDGE HadCM3 family of climate models: HadCM3@Bristol v1.0. Geosci. Model Dev. 10, 3715–3743 (2017).Article 
ADS 
CAS 

Google Scholar 
Cox, P. M. et al. The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Clim. Dyn. 15, 183–203 (1999).Article 

Google Scholar 
Sagoo, N., Valdes, P., Flecker, R. & Gregoire, L. J. The Early Eocene equable climate problem: can perturbations of climate model parameters identify possible solutions? Phil. Trans. R. Soc. A 371, 20130123 (2013).Article 
ADS 
PubMed 

Google Scholar 
Kiehl, J. T. & Shields, C. A. Sensitivity of the Palaeocene–Eocene thermal maximum climate to cloud properties. Phil. Trans. R. Soc. A 371, 20130093 (2013).Article 
ADS 
PubMed 

Google Scholar 
Cox, M. D. A Primitive Equation, 3-Dimensional Model of the Ocean. GFDL Ocean Group Technical Report No. 1 (GFDL Princeton Univ., 1984).Collins, M., Tett, S. F. B. & Cooper, C. The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments. Clim. Dyn. 17, 61–81 (2001).Article 

Google Scholar 
Farnsworth, A. et al. Climate sensitivity on geological timescales controlled by nonlinear feedbacks and ocean circulation. Geophys. Res. Lett. 46, 9880–9889 (2019).Article 
ADS 

Google Scholar 
Valdes, P. J., Scotese, C. R. & Lunt, D. J. Deep ocean temperatures through time. Clim. Past 17, 1483–1506 (2021).Article 

Google Scholar 
Farnsworth, A. et al. Past East Asian monsoon evolution controlled by paleogeography, not CO2. Sci. Adv. 5, eaax1697 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones, L. A., Mannion, P. D., Farnsworth, A., Bragg, F. & Lunt, D. J. Climatic and tectonic drivers shaped the tropical distribution of coral reefs. Nat. Commun. 13, 3120 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Scotese, C. R. & Wright, N. PALEOMAP paleodigital elevation models (PaleoDEMS) for the Phanerozoic. Zenodo https://doi.org/10.5281/zenodo.5460860 (2018).Foster, G. L., Royer, D. L. & Lunt, D. J. Future climate forcing potentially without precedent in the last 420 million years. Nat. Commun. 8, 14845 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gough, D. O. Solar interior structure and luminosity variations. Sol. Phys. 74, 21–34 (1981).Article 
ADS 
CAS 

Google Scholar 
Farnsworth, A. et al. Paleoclimate model-derived thermal lapse rates: towards increasing precision in paleoaltimetry studies. Earth Planet. Sci. Lett. 564, 116903 (2021).Article 
CAS 

Google Scholar 
Bahcall, J. N., Pinsonneault, M. H. & Basu, S. Solar models: current epoch and time dependences, neutrinos, and helioseismological properties. Astrophys. J. 555, 990–1012 (2001).Article 
ADS 
CAS 

Google Scholar 
Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1108 (2009).Article 
ADS 

Google Scholar 
Kraus, E. B. & Turner, J. S. A one-dimensional model of the seasonal thermocline II. The general theory and its consequences. Tellus 19, 98–105 (1967).ADS 

Google Scholar 
Foreman, S. J. The Ocean Model Report. Unified Model Documentaiton Paper Number 40 (The Met Office, 2005).HH: Statistical Analysis and Data Display: Heiberger and Holland. R package version 3.1-47 (2022).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Bivand, R., Millo, G. & Piras, G. A review of software for spatial econometrics in R. Mathematics 9, 1276 (2021).Article 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
Cooper, N. & Purvis, A. Body size evolution in mammals: complexity in tempo and mode. Am. Nat. 175, 727–738 (2010).Article 
PubMed 

Google Scholar 
geosphere: Spherical Trigonometry. R package version 1.5-14 (2021).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).Wade, B. S., Pearson, P. N., Berggren, W. A. & Pälike, H. Review and revision of Cenozoic tropical planktonic foraminiferal biostratigraphy and calibration to the geomagnetic polarity and astronomical time scale. Earth Sci. Rev. 104, 111–142 (2011).Article 
ADS 

Google Scholar  More

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

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

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

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

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

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

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

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

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

Estes, J. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).Article 
CAS 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the World’s Largest Carnivores. Science 343, 151–162 (2014).Article 
CAS 

Google Scholar 
Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).Article 
CAS 

Google Scholar 
De La Torre, J. A., González-Maya, J. F., Zarza, H., Ceballos, G. & Medellín, R. A. The jaguar’s spots are darker than they appear: assessing the global conservation status of the jaguar (Panthera onca). Oryx 52, 300–315 (2018).Article 

Google Scholar 
Lindsey, P. A. et al. The performance of African protected areas for lions and their prey. Biol. Conserv. 209, 137–149 (2017).Article 

Google Scholar 
Carbone, C., Cowlishaw, G., Isaac, N. J. B. & Rowcliffe, J. M. How far do animals go? Determinants of day range in mammals. Am. Nat. 165, 290–297 (2005).Article 

Google Scholar 
Sanderson, E. W. et al. Planning to save a species: the jaguar as a model. Conserv. Biol. 16, 58–72 (2002).Article 

Google Scholar 
Rabinowitz, A. & Zeller, K. A. A range-wide model of landscape connectivity and conservation for the jaguar, Panthera onca. Biol. Conserv. 143, 939–945 (2010).Article 

Google Scholar 
Woodroffe, R. Predators and people: using human densities to interpret declines of large carnivores. Anim. Conserv. 3, 165–173 (2000).Article 

Google Scholar 
Crooks, K. R. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. Biol. 16, 488–502 (2002).Article 

Google Scholar 
Ferreira, A. S., Peres, C. A., Bogoni, J. A. & Cassano, C. G. Use of agroecosystem matrix habitats by mammalian carnivores (Carnivora): a global-scale analysis. Mammal Rev. https://doi.org/10.1111/mam12137 (2018).Thompson, J. J. et al. Range-wide factors shaping space use and movements by the Neotropic’s flagship predator: the jaguar. Curr. Biol. https://doi.org/10.1016/jcub202106029 (2021).Sunquist, M. & Sunquist, F. Wild Cats of the World. University of Chicago Press (2002).Leader-Williams, N. & Dublin, H. T. in Priorities for The Conservation Of Mammalian Diversity: Has The Panda Had Its Day? (eds. Entwistle, A., Dunstone, N.) 53−81 (Cambridge University Press, 2000).Thornton, D. et al. Assessing the umbrella value of a range-wide conservation network for jaguars (Panthera onca). Ecol. Appl. 26, 1112–1124 (2015).Article 

Google Scholar 
Olsoy, P. J. et al. Quantifying the effects of deforestation and fragmentation on a range-wide conservation plan for jaguars. Biol. Conserv. 203, 8–16 (2016).Article 

Google Scholar 
Morato, R. G., Beisiegel, B. M., Ramalho, E. E. & Boulhosa, R. L. P. Avaliação do risco de extinção da Onça-pintada Panthera onca (Linnaeus, 1758) no Brasil. Biodivers. Brasil. 3, 122–132 (2013).
Google Scholar 
Hunter, L. Carnivores of the World. Princeton Univ Press (2011).Morato, R. G. et al. Space use and movement of a neotropical top predator: The Endangered Jaguar. PLoS ONE 11, e0168176 (2016).Article 

Google Scholar 
Eriksson, C. E. et al. Extensive aquatic subsidies lead to territorial breakdown and high density of an apex predator. Ecology 103, e03543 (2022).Article 

Google Scholar 
Chapman, B. et al. in Animal Movement Across Scales 1st edn. (eds. Hansson, L-A, Akesson, S.) 11–30 (Oxford University Press, 2014).Quigley, H. et al. Panthera onca. (errata version published in 2018). The IUCN Red List of Threatened Species 2017:e.T15953A123791436. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T15953A50658693.en (2017).Paviolo, A. et al. A biodiversity hotspot losing its top predator: the challenge of jaguar conservation in the Atlantic Forest of South America. Sci. Rep. 6, 37147 (2016).Article 
CAS 

Google Scholar 
Tobler, M. W., Carillo-Perscastegui, S. E., Hartley, A. Z. & Powell, G. V. N. High jaguar densities and large population sizes in the core habitat of the southwestern Amazon. Biol. Conserv. 159, 375–381 (2013).Article 

Google Scholar 
Jędrzejewski, W. et al. Estimating large carnivore populations at global scale based on spatial predictions of density and distribution: application to the jaguar (Panthera onca). PLoS ONE 13, e0194719 (2018).Article 

Google Scholar 
Eva, H. D. et al. A proposal for defining the geographical boundaries of Amazonia; synthesis of the results from an expert consultation workshop organized by the European Commission in collaboration with the Amazon Cooperation Treaty Organization-JRC Ispra (No 21808-EN). https://core.ac.uk/download/pdf/38630683.pdf (2005).Nepstad, D. C., Stickler, C. M., Soares-Filho, B., Merry, F. & Nin, E. Interactions among Amazon land use, forests and climate: prospects for a near-term forest tipping point. Philos. Trans. R. Soc. B 363, 1737–1746 (2008).Article 

Google Scholar 
Marques, A. A. B., Schneider, M. & Peres, C. A. Human population and socioeconomic modulators of conservation performance in 788 Amazonian and Atlantic Forest reserves. PeerJ 4, pe2206 (2016).Article 

Google Scholar 
Jaguar 2030 Roadmap. Regional plan to save America’s largest cat and its ecosystems. https://www.internationaljaguarday.org/jaguar-conservation-roadmap (2018).Sanderson, E. W. et al. A systematic review of potential habitat suitability for the jaguar Panthera onca in central Arizona and New Mexico, USA. Oryx 2021, 1–12 (2021).
Google Scholar 
Simberloff, D. Flagships, umbrellas, and keystones: is single-species management passe’ in the landscape era. Biol. Conserv. 83, 247–57 (1998).Article 

Google Scholar 
Silvério, D. V. et al. Testing the Amazon savannization hypothesis: fire effects on invasion of a neotropical forest by native Cerrado and exotic pasture grasses. Philos. Trans. R. Soc. B 368, 20120427 (2013).Article 

Google Scholar 
Brazil’s National Institute for Space Research (INPE). Banco de dados de Queimadas INPE—Programa Queimadas. http://queimadasdgiinpebr/queimadas/bdqueimadas (2020b).Silva-Jr, C. H. L. et al. The Brazilian Amazon deforestation rate in 2020 is the greatest of the decade. Nat. Ecol. Evol. 5, 144–145 (2020).Article 

Google Scholar 
Walker, R. et al. Protecting the Amazon with protected areas. Proc. Natl Acad. Sci. USA 106, 10582–10586 (2009).Article 
CAS 

Google Scholar 
Gray, C. L. et al. Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nat. Commun. 7, 12306 (2016).Article 
CAS 

Google Scholar 
Begotti, R. A. & Peres, C. A. Rapidly escalating threats to the biodiversity and ethnocultural capital of Brazilian Indigenous Lands. Land Use Policy 96, 104694 (2020).Article 

Google Scholar 
Walker, W. S. et al. The role of forest conversion, degradation, and disturbance in the carbon dynamics of Amazon indigenous territories and protected areas. Proc. Natl Acad. Sci. USA 117, 3015–3025 (2020).Article 
CAS 

Google Scholar 
Moilanen, A., Arponen, A., Stokland, J. N. & Cabeza, M. Assessing replacement cost of conservation areas: How does habitat loss influence priorities? Biol. Conserv. 142, 575–585 (2009).Article 

Google Scholar 
Almeida-Rocha, J. A. & Peres, C. A. Nominally protected buffer zones around tropical protected areas are as highly degraded as the wider unprotected countryside. Biol. Conserv. 256, 109068 (2021).Article 

Google Scholar 
Terborgh, J. The role of felid predators in Neotropical Forests. Vida Silv. Neotrop. 2, 3–5 (1990).
Google Scholar 
Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).Article 
CAS 

Google Scholar 
Brando, P. M. et al. The gathering firestorm in southern Amazonia. Sci. Adv. 6, 1632 (2020).Convention on the Conservation of Migratory Species of Wild Animals (CMS). Proposal for the Inclusion of the Jaguar in Appendices I and II of the Convention. https://www.cms.int/en/document/proposal-inclusion-jaguar-appendices-i-and-ii-convention (2022).Ceddia, M. G., Bardsley, N. O., Gomez-y-Paloma, S. & Sedlacek, S. Governance, agricultural intensification, and land sparing in tropical South America. Proc. Natl Acad. Sci. USA 111, 7242–7247 (2014).Laurance, W. F. et al. Impacts of roads and hunting on central African rainforest mammals. Conserv. Biol. 20, 1251–1261 (2006).Article 

Google Scholar 
Brancalion, P. H. S. et al. Análise crítica da Lei de Proteção da Vegetação Nativa (2012), que substituiu o antigo Código Florestal: atualizações e ações em curso. Natureza Conservação 14, 1–16 (2016).Wilkie, D. S., Bennett, E. L., Peres, C. A. & Cunningham, A. A. The empty forest revisited. Ann. N. Y. Acad. Sci. 1223, 120–128 (2011).Article 

Google Scholar 
Bogoni, J. A., Peres, C. A. & Ferraz, K. M. P. M. B. Extent, intensity and drivers of mammal defaunation: a continental-scale analysis across the Neotropics. Sci. Rep. 10, 14750 (2020).Article 
CAS 

Google Scholar 
Ferrante, L. & Fearnside, P. M. Brazil’s new president and ‘ruralists’ threaten Amazonia’s environment, traditional peoples and the global climate. Environ. Conserv. 46, 261–263 (2019).Article 

Google Scholar 
Aragão, L. E. O. C. & Shimabukuro, Y. E. The incidence of fire in Amazonian forests with implications for REDD. Science 328, 1275–1278 (2010).Article 

Google Scholar 
Barlow, J. & Peres, C. A. Fire-mediated dieback and compositional cascade in an Amazonian forest. Philos. Trans. R. Soc. B 363, 1787 (2008).Article 

Google Scholar 
Michalski, F., Boulhosa, R. L. P., Faria, A. & Peres, C. A. Human–wildlife conflicts in a fragmented Amazonian forest landscape: determinants of large felid depredation on livestock. Anim. Conserv. https://doi.org/10.1111/j1469-1795200600025x (2006).Article 

Google Scholar 
Jorge, M. L. S. P., Galetti, M., Ribeiro, M. C. & Ferraz, K. M. P. M. B. Mammal defaunation as surrogate of trophic cascades in a biodiversity hotspot. Biol. Conserv. 163, 49–57 (2013).Article 

Google Scholar 
Menezes, J. F. S., Tortato, F. R., Roque, F. O., Oliveira-Santos, L. G. & Morato, R. G. Deforestation, fires, and lack of governance are displacing thousands of jaguars in Brazilian Amazon. Conserv. Sci. Pract. 3, e477 (2021).Morato, R. G. et al. Resource selection in an apex predator and variation in response to local landscape characteristics. Biol. Conserv. 228, 233–240 (2018).Article 

Google Scholar 
Romero-Muñoz, A. et al. Habitat loss and overhunting synergistically drive the extirpation of jaguars from the Gran Chaco. Divers. Distrib. 25, 176–190 (2018).Romero-Muñoz, A., Morato, R. G., Tortato, F. & Kuemmerle, T. Beyond fangs: beef and soybean trade drive jaguar extinction. Front. Ecol. Environ. 18, 67–68 (2020).Article 

Google Scholar 
Vilela, T. et al. A better Amazon road network for people and the environment. Proc. Natl Acad. Sci. USA 117, 7095–7102 (2020).Article 
CAS 

Google Scholar 
Benítez-López, A., Alkemade, R. & Verweij, P. A. The impacts of roads and other infrastructure on mammal and bird populations: a meta-analysis. Biol. Conserv. 143, 1307–1316 (2010).Article 

Google Scholar 
Carter, N., Killion, A., Easter, T., Brandt, J. & Ford, A. Road development in Asia: assessing the range-wide risks to tigers. Sci. Adv. 6, eaaz9619 (2020).Article 

Google Scholar 
Abra, F. D. et al. Pay or prevent? Human safety, costs to society and legal perspectives on animal-vehicle collisions in São Paulo state, Brazil. PLoS ONE 14, e0215152 (2019).Article 
CAS 

Google Scholar 
Joshi, A. R. et al. Tracking changes and preventing loss in critical tiger habitat. Sci. Adv. 2, e1501675 (2016).Article 

Google Scholar 
Peres, C. A. & Terborgh, J. Amazonian nature reserves: an analysis of the defensibility status of existing conservation units and design criteria for the future. Conserv. Biol. 9, 34–46 (1995).Article 

Google Scholar 
Sistema Nacional de Unidades de Conservação (SNUC). Lei 9985 de 18 de julho de 2000; Ministério do Meio Ambiente. (2000).Stocks, A. Too much for too few: problems of indigenous land rights in Latin America Annual. Rev. Anthropol. 34, 85–104 (2005).Article 

Google Scholar 
Mooers, A. Ø., Faith, D. P. & Maddison, W. P. Converting endangered species categories to probabilities of extinction for phylogenetic conservation prioritization. PLoS ONE 3, e3700 (2008).Article 

Google Scholar 
Staal, A. et al. Hysteresis of tropical forests in the 21st century. Nat. Commun. 11, 4978 (2020).Article 
CAS 

Google Scholar 
Miranda, E. B. P. et al. Tropical deforestation induces thresholds of reproductive viability and habitat suitability in Earth’s largest eagles. Sci. Rep. 11, 1–17 (2021).Article 

Google Scholar 
Bowman, K. W. et al. Environmental degradation of indigenous protected areas of the Amazon as a slow onset event. Curr. Opin. Environ. Sustain. 50, 260–271 (2021).Article 

Google Scholar 
Wilson, K. A., Carwardine, J. & Possingham, H. P. Setting conservation priorities. Ann. N. Y. Acad. Sci. 1162, 237–264 (2009).Article 

Google Scholar 
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).Article 
CAS 

Google Scholar 
Sales, L. P., Galetti, M. & Pires, M. M. Climate and land‐use change will lead to a faunal “savannization” on tropical rainforests. Glob. Change Biol. 26, 7036–7044 (2020).Article 

Google Scholar 
da Silva, J. M. C., Dias, T. C. A. C., da Cunha, A. C. & Cunha, H. F. A. Funding deficits of protected areas in Brazil. Land Use Policy 100, 104926 (2021).Article 

Google Scholar 
Nobre, C. A. et al. Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proc. Natl Acad. Sci. USA 113, 10759–10768 (2016).Article 
CAS 

Google Scholar 
Kauano, E. E., Silva, J. M. C. & Michalski, F. Illegal use of natural resources in federal protected areas of the Brazilian Amazon. PeerJ 5, e3902 (2017).Article 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2011).Article 

Google Scholar 
Instituto Brasileiro de Geografia e Estatística (IBGE). Censo demográfico Rio de Janeiro. http://www.ibge.gov.br (2020).Instituto Brasileiro de Geografia e Estatística (IBGE). Censo demográfico Rio de Janeiro. http://www.ibge.gov.br (2010).Instituto Brasileiro de Geografia e Estatística (IBGE). BC250—Base Cartográfica Contínua do Brasil—1:250,000—2017 Diretoria de Geociências—DGC / Coordenação de Cartografia—CCAR. http://www.metadadosgeoibgegovbr/geonetwork_ibge/srv/por/metadatashow?uuid=5a47e9ea-e2cd-423b-8646-53f67ff4ed2d (2017).MapBiomas. Projeto MapBiomas Coleção 5 da Série Anual de Mapas de Cobertura e Uso de Solo do Brasil. https://mapbiomas.org/colecoes-mapbiomas-1 (2019).Brazil’s National Institute for Space Research (INPE). Monitoramento do Desmatamento da Floresta Amazônica Brasileira por Satélite. http://www.obtinpebr/OBT/assuntos/programas/amazonia/prodes (2020a).ESRI. ArcGIS Desktop: Release 10 Redlands. (Environmental Systems Research Institute, 2019).Ministério do Meio Ambiente (MMA). Cadastro Nacional de Unidades de Conservação (CNUC). https://antigo.mma.gov.br/areas-protegidas/cadastro-nacional-de-ucs/dados-georreferenciados.html (2019).Fundação Nacional dos Povos Indígenas (FUNAI). Modalidades de Terra Indígenas. http://www.funaigovbr/indexphp/indios-no-brasil/terras-indigenas (2019).Tobler, M. W. & Powell, G. V. N. Estimating jaguar densities with camera traps: Problems with current designs and recommendations for future studies. Biol. Conserv. 159, 109–118 (2013).Article 

Google Scholar 
de Oliveira, T. G. et al. Red list assessment of the jaguar in Brazilian Amazonia. CatNews 7, 8–13 (2012).
Google Scholar 
Ramalho, F. B. L. Jaguar (Panthera Onca) Population Dynamics, Feeding Ecology, Human Induced Mortality, and Conservation in the Várzea Floodplain Forests of Amazonia. PhD Thesis. (University of São Paulo, 2012).Duarte, H. O. B., Boron, V., Carvalho, W. D. & Toledo, J. J. Amazon islands as predator refugia: jaguar density and temporal activity in Maracá-Jipioca. J. Mammal. 103, 440–446 (2022).Article 

Google Scholar 
Zar, J. H. Biostatistical Analysis 4th edn., (Pretince-Hall, 1999).Medellín, R. A. et al. El jaguar en el nuevo milenio. Fondo de Cultura Económica (Universidad Nacional Autónoma de México, Wildlife Conservation Society, 2002).Quigley, H. et al. Observations and preliminary testing of Jaguar depredation reduction techniques in and between core Jaguar populations. Parks 21, 63–72 (2015).Article 

Google Scholar 
Bogoni, J. A., Ferraz, K. M. P. M. B. & Peres, C. A. Continental-scale local extinctions in mammal assemblages are synergistically induced by habitat loss and hunting pressure. Biol. Conserv. 272, 109635 (2022).Article 

Google Scholar 
Valsecchi, J., Monteiro, M. C., Alvarenga, G. C., Lemos, L. P. & Ramalho, E. E. Community-based monitoring of wild felid hunting in Central Amazonia. Animal Conser. https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1111/acv.12811 (2022).WWF. WWF Jaguar Strategy 2020–2030. https://wwflac.awsassets.panda.org/downloads/estrategia_jaguar_2020_2030_wwf.pdf (2020).Chape, S., Harrison, J., Spalding, M. D. & Lysenko, I. Measuring the extent and effectiveness of protected areas as an indicator for meeting global biodiversity targets. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 443–455 (2005).Article 
CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).Souza-Jr, C. M. et al. Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine. Remote Sens. 12, 2735 (2020).Article 

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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