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

Wallace, A. R. Tropical Nature and Other Essays (Macmillan, 1878).
Google Scholar 
von Humboldt, A. Ansichten der Natur: mit wissenschaftlichen Erläuterungen (Cotta, 1808).
Google Scholar 
Brown, J. H. J. Biogeogr. 41, 8–22 (2014).Article 
PubMed 

Google Scholar 
Fenton, I. S., Aze, T., Farnsworth, A., Valdes, P. & Saupe, E. E. Nature https://doi.org/10.1038/s41586-023-05712-6 (2023).Article 

Google Scholar 
Woodhouse, A., Swain, A., Fagan, W. F., Fraass, A. J. & Lowery, C. M. Nature https://doi.org/10.1038/s41586-023-05694-5 (2023).Article 

Google Scholar 
Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Biol. Rev. 92, 199–215 (2017).Article 
PubMed 

Google Scholar 
Yasuhara, M. et al. Proc. Natl Acad. Sci. USA 117, 12891–12896 (2020).Article 
PubMed 

Google Scholar 
Song, H. et al. Proc. Natl Acad. Sci. USA 117, 17578–17583 (2020).Article 
PubMed 

Google Scholar 
Penn, J. L., Deutsch, C., Payne, J. L. & Sperling, E. A. Science 362, eaat1327 (2018).Article 
PubMed 

Google Scholar 
Janzen, D. H. Am. Nat. 101, 233–249 (1967).Article 

Google Scholar 
Hahn, L. C., Armour, K. C., Zelinka, M. D., Bitz, C. M. & Donohoe, A. Front. Earth Sci. 9, 710036 (2021).Article 

Google Scholar 
Penn, J. L. & Deutsch, C. Science 376, 524–526 (2022).Article 
PubMed 

Google Scholar  More

Jonkers, L., Hillebrand, H. & Kucera, M. Global change drives modern plankton communities away from the pre-industrial state. Nature 570, 372–375 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Barton, A. D., Irwin, A. J., Finkel, Z. V. & Stock, C. A. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl Acad. Sci. USA 113, 2964–2969 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Beaugrand, G., Reid, P. C., Ibanez, F., Lindley, J. A. & Edwards, M. Reorganization of North Atlantic marine copepod biodiversity and climate. Science 296, 1692–1694 (2002).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Cheung, W. W., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368 (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Herbert-Read, J. E. et al. A global horizon scan of issues impacting marine and coastal biodiversity conservation. Nat. Ecol. Evol. 6, 1262–1270 (2022).Article 
PubMed 

Google Scholar 
Yasuhara, M. & Deutsch, C. A. Paleobiology provides glimpses of future ocean. Science 375, 25–26 (2022).Article 
ADS 
CAS 
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 
Strack, A., Jonkers, L., Rillo, M. C., Hillebrand, H. & Kucera, M. Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age. Nat. Ecol. Evol. 6, 1871–1880 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Mokany, K. & Ferrier, S. Predicting impacts of climate change on biodiversity: a role for semi‐mechanistic community‐level modelling. Divers. Distrib. 17, 374–380 (2011).Article 

Google Scholar 
Pörtner, H.-O. et al. eds IPCC: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge Univ. Press, 2022).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 
Schumm, M. et al. Common latitudinal gradients in functional richness and functional evenness across marine and terrestrial systems. Proc. R. Soc. B 286, 20190745 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rutherford, S., D’Hondt, S. & Prell, W. Environmental controls on the geographic distribution of zooplankton diversity. Nature 400, 749–753 (1999).Article 
ADS 
CAS 

Google Scholar 
Worm, B., Lotze, H. K. & Myers, R. A. Predator diversity hotspots in the blue ocean. Proc. Natl Acad. Sci. USA 100, 9884–9888 (2003).Article 
ADS 
CAS 
PubMed 
PubMed Central 

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 
Fenton, I. S., Pearson, P. N., Dunkley Jones, T. & Purvis, A. Environmental predictors of diversity in recent planktonic foraminifera as recorded in marine sediments. PLoS ONE 11, e0165522 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Chaudhary, C., Saeedi, H. & Costello, M. J. Bimodality of latitudinal gradients in marine species richness. Trends Ecol. Evol. 31, 670–676 (2016).Article 
PubMed 

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 
Rillo, M. C., Miller, C. G., Kučera, M. & Ezard, T. H. G. Intraspecific size variation in planktonic foraminifera cannot be consistently predicted by the environment. Ecol. Evol. 10, 11579–11590 (2020).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 
Thomas, E. Descent into the icehouse. Geology 36, 191–192 (2008).Article 
ADS 

Google Scholar 
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 
Crame, J. A. Early Cenozoic evolution of the latitudinal diversity gradient. Earth Sci. Rev. 202, 103090 (2020).Article 

Google Scholar 
Yasuhara, M. et al. Time machine biology. Oceanography 33, 16–28 (2020).Article 

Google Scholar 
Alegret, L., Arreguín-Rodríguez, G. J., Trasviña-Moreno, C. A. & Thomas, E. Turnover and stability in the deep sea: benthic foraminifera as tracers of Paleogene global change. Global Planet. Change 196, 103372 (2021).Article 

Google Scholar 
Gaskell, D. E. et al. The latitudinal temperature gradient and its climate dependence as inferred from foraminiferal δ18O over the past 95 million years. Proc. Natl Acad. Sci. USA 119, e2111332119 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mannion, P. D., Upchurch, P., Benson, R. B. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).Article 
PubMed 

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

Google Scholar 
Herbert, T. D. et al. Late Miocene global cooling and the rise of modern ecosystems. Nat. Geosci. 9, 843–847 (2016).Article 
ADS 
CAS 

Google Scholar 
Steinthorsdottir, M. et al. The Miocene: the future of the past. Paleoceanogr. Paleoclimatology 36, e2020PA004037 (2021).Article 

Google Scholar 
Brown, R. M., Chalk, T. B., Crocker, A. J., Wilson, P. A. & Foster, G. L. Late Miocene cooling coupled to carbon dioxide with Pleistocene-like climate sensitivity. Nat. Geosci. 15, 664–670 (2022).Article 
ADS 
CAS 

Google Scholar 
Guillermic, M., Misra, S., Eagle, R. & Tripati, A. Atmospheric CO2 estimates for the Miocene to Pleistocene based on foraminiferal δ11B at Ocean Drilling Program Sites 806 and 807 in the Western Equatorial Pacific. Clim. Past 18, 183–207 (2022).Article 

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., 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 
Ezard, T. H. G., Aze, T., Pearson, P. N. & Purvis, A. Interplay between changing climate and species’ ecology drives macroevolutionary dynamics. Science 332, 349–351 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Peters, S. E., Kelly, D. C. & Fraass, A. J. Oceanographic controls on the diversity and extinction of planktonic foraminifera. Nature 493, 398–401 (2013).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Woodhouse, A. et al. Adaptive ecological niche migration does not negate extinction susceptibility. Sci. Rep. 11, 15411 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

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 
Bindoff, N. L. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) (IPCC, Cambridge Univ. Press, 2019).Aze, T. et al. A phylogeny of Cenozoic macroperforate planktonic foraminifera from fossil data. Biol. Rev. 86, 900–927 (2011).Article 
PubMed 

Google Scholar 
Delmas, E. et al. Analysing ecological networks of species interactions. Biol. Rev. 94, 16–36 (2019).Article 
PubMed 

Google Scholar 
Rojas, A., Calatayud, J., Kowalewski, M., Neuman, M. & Rosvall, M. A multiscale view of the Phanerozoic fossil record reveals the three major biotic transitions. Commun. Biol. 4, 309 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Swain, A., Devereux, M. & Fagan, W. F. Deciphering trophic interactions in a mid-Cambrian assemblage. iScience 24, 102271 (2021).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Shaw, J. O. et al. Disentangling ecological and taphonomic signals in ancient food webs. Paleobiology 47, 385–401 (2021).Article 

Google Scholar 
Swain, A., Maccracken, S., Fagan, W. & Labandeira, C. Understanding the ecology of host plant–insect herbivore interactions in the fossil record through bipartite networks. Paleobiology 48, 239–260 (2022).Article 

Google Scholar 
Poisot, T., Canard, E., Mouquet, N. & Hochberg, M. E. A comparative study of ecological specialization estimators. Methods Ecol. Evol. 3, 537–544 (2012).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 
Boscolo-Galazzo, F. and Crichton, K.A. 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 
Keller, G. in The Miocene Ocean: Paleoceanography and Biogeography Vol. 163, 177–196 (Geological Society of America, 1985).Holbourn, A. E. et al. Late Miocene climate cooling and intensification of southeast Asian winter monsoon. Nat. Commun. 9, 1584 (2018).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Willeit, M., Ganopolski, A., Calov, R., Robinson, A. & Maslin, M. The role of CO2 decline for the onset of Northern Hemisphere glaciation. Quat. Sci. Rev. 119, 22–34 (2015).Article 
ADS 

Google Scholar 
Hayashi, T. et al. Latest Pliocene Northern Hemisphere glaciation amplified by intensified Atlantic meridional overturning circulation. Commun. Earth Environ. 1, 25–10 (2020).Article 
ADS 

Google Scholar 
Lam, A. R., Crundwell, M. P., Leckie, R. M., Albanese, J. & Uzel, J. P. Diachroneity rules the mid-latitudes: a test case using late Neogene planktic foraminifera across the Western Pacific. Geosciences 12, 190 (2022).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 
Rillo, M. C. et al. On the mismatch in the strength of competition among fossil and modern species of planktonic Foraminifera. Global Ecol. Biogeogr. 28, 1866–1878 (2019).Article 

Google Scholar 
Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).Article 
ADS 

Google Scholar 
Monllor-Hurtado, A., Pennino, M. G. & Sanchez-Lizaso, J. L. Shift in tuna catches due to ocean warming. PLoS ONE 12, e0178196 (2017).Article 
PubMed 
PubMed Central 

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

Google Scholar 
Mora, C. et al. Biotic and human vulnerability to projected changes in ocean biogeochemistry over the 21st century. PLoS Biol. 11, e1001682 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Renaudie, J., Lazarus, D.B. & Diver, P. NSB (Neptune Sandbox Berlin): an expanded and improved database of marine planktonic microfossil data and deep-sea stratigraphy. Palaeontol. Electron. 23, p.a11 (2020).
Google Scholar 
Pearson, P. N. in Atlas of Oligocene Planktonic Foraminifera (eds Wade, B. S. et al) 415–428 (Cushman Foundation of Foraminiferal Research, 2018).Liow, L. H., Skaug, H. J., Ergon, T. & Schweder, T. Global occurrence trajectories of microfossils: environmental volatility and the rise and fall of individual species. Paleobiology 36, 224–252 (2010).Article 

Google Scholar 
Lazarus, D., Weinkauf, M. & Diver, P. Pacman profiling: a simple procedure to identify stratigraphic outliers in high-density deep-sea microfossil data. Paleobiology 38, 144–161 (2012).Article 

Google Scholar 
Woodhouse, A. et al. Paleoecology and evolutionary response of planktonic foraminifera to the Plio-Pleistocene intensification of Northern Hemisphere glaciations. Preprint at EGUsphere https://doi.org/10.5194/egusphere-2022-844 (2022).Woodhouse, A. et al. Paleoecology and evolutionary response of planktonic foraminifera to the mid-Pliocene Warm Period and Plio-Pleistocene bipolar ice sheet expansion. Biogeosciences 20, 121–139 (2023).Article 
ADS 

Google Scholar 
Dormann, C. F., Fründ, J., Blüthgen, N. & Gruber, B. Indices, graphs and null models: analyzing bipartite ecological networks. Op. Ecol. J. 2, 7–24 (2009).Article 

Google Scholar 
Swain, A. et al. Sampling bias and the robustness of ecological metrics for plant-damage-type association networks. Ecology https://doi.org/10.1002/ecy.3922 (2022).Julliard, R., Clavel, J., Devictor, V., Jiguet, F. & Couvet, D. Spatial segregation of specialists and generalists in bird communities. Ecol. Lett. 9, 1237–1244 (2006).Article 
PubMed 

Google Scholar 
Vaughan, I. P. et al. econullnetr: an R package using null models to analyse the structure of ecological networks and identify resource selection. Methods Ecol. Evol. 9, 728–733 (2018).Article 
MathSciNet 

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

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

Insect rearingWT B. dorsalis were collected from Haikou, Hainan province, China, in 2008. They were maintained at the Key Laboratory of Entomology and Pest Control Engineering in Chongqing at 27 ± 1 °C, 70 ± 5% relative humidity, with a 14-h photoperiod. Adult flies were reared on an artificial diet containing honey, sugar, yeast powder, and vitamin C. Newly hatched larvae were transferred to an artificial diet containing corn and wheat germ flour, yeast powder, agar, sugar, sorbic acid, linoleic acid, and filter paper.Behavioral assaysDouble trap lure assays were set up to compare the olfactory preferences of gravid and virgin females in a 20 × 20 × 20 cm transparent cage with evenly distributed holes (diameter = 1.5 mm) on the side walls. The traps were refitted from inverted 50-mL centrifuge tubes and were placed along the diagonal of the cage. The top of each trap was pierced with a 1-mL pipette tip, which was shortened to ensure flies could access the trap from the pipette. For the olfactory preference assay with mango, one trap was loaded with 60 mg mango flesh and the other trap with 20 μL MO in the cap of a 200-μL PCR tube. For the olfactory preference assay with 1-octen-3-ol (≥98%, sigma, USA), one trap was loaded with 20 μL 10% (v/v) 1-octen-3-ol diluted in MO, and the other with 20 μL MO. A cotton ball soaked in water was placed at the center of the cage to provide water for the flies. Groups of 30 female flies were introduced into the cage for each experiment, and each experiment was repeated to provide eight biological replicates. All experiments commenced at 10 am to ensure circadian consistency. The number of flies in each trap was counted every 2 h for 24 h. We compared the preferences of 3-day-old immature females, 15-day-old virgin females, and 15-day-old mated females. The olfactory preference index was calculated using the following formula41: (number of flies in mango/odorant trap – number of flies in control trap)/total number of flies.Oviposition behavior was monitored in a 10 × 10 × 10 cm transparent cage with evenly distributed holes on the side walls as above. A 9-cm Petri dish filled with 1% agar was served as an oviposition substrate, and the mango flesh, 10% (v/v) 1-octen-3-ol or MO were added at opposite edges of the dish. We tested the preference of flies for different substrates: (1) ~60 mg of mango flesh on one edge and 20 μL of MO on the other; (2) 20 μL of 1-octen-3-ol on one edge and 20 μL of MO on the other; (3) ~60 mg mango flesh on one edge and 20 μL of 1-octen-3-ol on the other; and (4) ~60 mg mango flesh plus 20 μL 1-octen-3-ol on one side and ~60 mg of mango flesh plus 20 μL MO on the other. The agar disc was covered in a pierced plastic wrap to mimic fruit skin, encouraging flies extend their ovipositor into the plastic wrap to lay eggs. The agar disc was placed at the center of the cage, and we introduced eight 15-day-old gravid females. Two Sony FDR-AX40 cameras recorded the behavior of the flies for 24 h, one fixed above the cage to record the tracks and the other placed in front of the cage to record the oviposition behavior. Based on the results from double traps luring assays, a 3 h duration (6–9 h) of the videos was selected to analyze the tracks and spent time of all flies in observed area (the surface of Petri dish). The videos were analyzed using EthoVision XT v16 (Noldus Information Technology) to determine the total time of all flies spent on each side in seconds and the total distance of movement in centimeters, and the tracks were visualized in the form of heat maps17. The number of eggs laid by the eight flies in each experiment was counted under a CNOPTEC stereomicroscope, and each experimental group comprised 7–16 replicates.Annotation of B. dorsalis OR genesD. melanogaster amino acid sequences downloaded from the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/) were used as BLASTP queries against the B. dorsalis amino acid database with an identity cut-off of 30%. The candidate OR genes were compared with deep transcriptome data from B. dorsalis antennae42, maxillary palps and proboscis, and other tissues.Cloning of candidate B. dorsalis OR genesHigh-fidelity PrimerSTAR Max DNA polymerase (TaKaRa, Dalian, China) was used to amplify the full open reading frame of BdorOR genes by nested PCR using primers (Supplementary Table 2) designed according to B. dorsalis genome data. Each 25-μL reaction comprised 12.5 μL 2 × PrimerSTAR Max Premix (TaKaRa), 10.5 μL ultrapure water, 1 μL of each primer (10 μM), and 1 μL of the cDNA template. An initial denaturation step at 98 °C for 2 min was followed by 35 cycles of 10 s at 98 °C, 15 s at 55 °C and 90 s at 72 °C, and a final extension step of 10 min at 72 °C. Purified PCR products were transferred to the vector pGEM-T Easy (Promega, Madison, WI) for sequencing (BGI, Beijing, China).Transcriptional profilingTotal RNA was extracted from (i) male and female antennae, maxillary palps, head cuticle (without antenna, maxillary palps, and proboscis), proboscis, legs, wings and ovipositors, and (ii) from the heads of 15-day-old virgin and mated females using TRIzol reagent (Invitrogen, Carlsbad, CA). Genomic DNA was eliminated with RNase-free DNase I (Promega) and first-strand cDNA was synthesized from 1 µg total RNA using the PrimeScript RT reagent kit (TaKaRa). Standard curves were used to evaluate primer efficiency (Supplementary Table 3) with fivefold serial dilutions of cDNA. Quantitative real-time PCR (qRT-PCR) was carried out using a CFX Connect Real-Time System (Bio-Rad, Hercules, CA) in a total reaction volume of 10 µL containing 5 μL SYBR Supermix (Novoprotein, Shanghai, China), 3.9 μL nuclease-free water, 0.5 μL cDNA (~200 ng/μL) and 0.3 μL of the forward and reverse primers (10 μM). We used α-tubulin (GenBank: GU269902) and ribosomal protein S3 (GenBank: XM_011212815) as internal reference genes. Four biological replicates were prepared for each experiment. Relative expression levels were determined using the 2−∆∆Ct method43, and data were analyzed using SPSS v20.0 (IBM).Two-electrode voltage clamp electrophysiological recordingsVerified PCR products representing candidate B. dorsalis OR genes and BdorOrco were transferred to vector pT7Ts for expression in oocytes. The plasmids were linearized for the synthesis of cRNAs using the mMESSAGE mMACHINE T7 Kit (Invitrogen, Lithuania). The purified cRNA was diluted to 2 µg/µL and ~60 ng cRNA was injected into X. laevis oocytes. The oocytes were pre-treated with 1.5 mg/mL collagenase I (GIBCO, Carlsbad, CA) in washing buffer (96 mM NaCl, 5 mM MgCl2, 2 mM KCl, 5 mM HEPES, pH 7.6) for 30–40 min at room temperature before injection. After incubation for 2 days at 18 °C in Ringer’s solution (96 mM NaCl, 5 mM MgCl2, 2 mM KCl, 5 mM HEPES, 0.8 mM CaCl2), the oocytes were exposed to different concentrations of 1-octen-3-ol diluted in Ringer’s solution from a 1 M stock in DMSO. Odorant-induced whole-cell inward currents were recorded from injected oocytes using a two-electrode voltage clamp and an OC-725C amplifier (Warner Instruments, Hamden, CT) at a holding potential of –80 mV. The signal was processed using a low-pass filter at 50 Hz and digitized at 1 kHz. Oocytes injected with nuclease-free water served as a negative control. Data were acquired using a Digidata 1550 A device (Warner Instruments, Hamden, CT) and analyzed using pCLAMP10.5 software (Axon Instruments Inc., Union City, CA).Calcium imaging assayVerified PCR products representing candidate B. dorsalis OR genes and BdorOrco were transferred to vector pcDNA3.1(+) along with an mCherry tag that confers red fluorescence to confirm transfection. High-quality plasmid DNA was prepared using the Qiagen plasmid MIDIprep kit (QIAgen, Düsseldorf, Germany). The B. dorsalis OR and BdorOrco plasmids were co-transfected into HEK 293 cell using TransIT-LT1 transfection reagent (Mirus Bio LLC, Japan) in 96-well plates. The fluorescent dye Fluo-4 AM (Invitrogen) was prepared as a 1 mM stock in DMSO and diluted to 2.5 μM in Hanks’ balanced salt solution (HBSS, Invitrogen, Lithuania) to serve as a calcium indicator. The cell culture medium was removed 24–30 h after transfection and cells were rinsed three times with HBSS before adding Fluo 4-AM and incubating the cells for 1 h in the dark. After three rinses in HBSS, 99 μL of fresh HBSS was added to each well before testing in the dark with 1 μL of diluted 1-octen-3-ol. Fluorescent images were acquired on a laser scanning confocal microscope (Zeiss, Germany). Fluo 4-AM was excited at 488 nm and mCherry at 555 nm. The relative change in fluorescence (ΔF/F0) was used to represent the change in Ca2+, where F0 is the baseline fluorescence and ΔF is the difference between the peak fluorescence induced by 1-octen-3-ol stimulation and the baseline. The healthy and successfully transfected cells (red when excited at 555 nm) were used for analysis. The final concentration of 10−4 M was initially used to screen corresponding ORs, and then to determine the response of screened ORs to stimulation with different concentrations of 1-octen-3-ol. Each concentration of 1-octen-3-ol was tested in triplicate. Concentration–response curves were prepared using GraphPad Prism v8.0 (GraphPad Software).Genome editingThe exon sequences of BdorOR7a-6 and BdorOR13a were predicted using the high-quality B. dorsalis genome assembly. Each gRNA sequence was 20 nucleotides in length plus NGG as the protospacer adjacent motif (PAM). The potential for off-target mutations was evaluated by using CasOT to screen the B. dorsalis genome sequence. Each gRNA was synthesized using the GeneArt Precision gRNA Synthesis Kit (Invitrogen) and purified using the GeneArt gRNA Clean-up Kit (Invitrogen). Embryos were microinjected as previously described20. Purified gRNA and Cas9 protein from the GeneArt Platinum Cas9 Nuclease Kit (Invitrogen) were mixed and diluted to final concentrations of 600 and 500 ng/µL, respectively. Fresh eggs (laid within 20 min) were collected and exposed to 1% sodium hypochlorite for 90 s to soften the chorion. The eggs were fixed on glass slides and injected with the mix of gRNA and Cas9 protein at the posterior pole using an IM-300 device (Narishige, Tokyo, Japan) and needles prepared using a Model P-97 micropipette puller (Sutter Instrument Co, Novato, CA). Eggs were injected with nuclease-free water as a negative control. Injection was completed within 2 h. The injected embryos were cultured in a 27 °C incubator and mortality was recorded during subsequent development.G0 mutants were screened as previously described20. G0 adult survivors were individually backcrossed to WT flies (single pair) to collect G1 offspring. Genomic DNA was extracted from G0 individuals after oviposition using the DNeasy Blood & Tissue Kit (Qiagen). The region surrounding each gRNA target was amplified by PCR using the extracted DNA as a template, the specific primers listed in Supplementary Table 2, and 2 × Taq PCR MasterMix (Biomed, Beijing, China). PCR products were analyzed by capillary electrophoresis using the QIAxcel DNA High Resolution Kit (Qiagen). PCR products differing from the WT alleles were purified and transferred to the vector pGEM-T Easy for sequencing. To confirm the mutation was inherited, genomic DNA was also extracted from one mesothoracic leg of G1 flies using InstaGene Matrix (Bio-Rad, Hercules, CA) and was analyzed as above. To avoid potential off-target mutations, heterozygous G1 mutants were backcrossed to WT flies more than 10 generations before self-crossing to generate homozygous mutant flies.Electroantennogram (EAG) recordingThe antennal responses of 15-day-old B. dorsalis adults to 1-octen-3-ol were determined by EAG recording (Syntech, the Netherlands) as previously reported20. Briefly, antennae were fixed to two electrodes using Spectra 360 electrode gel (Parker, Fairfield, NJ, USA). The signal response was amplified using an IDAC4 device and collected using EAG-2000 software (Syntech). Before each experiment, 1-octen-3-ol and other three volatiles (ethyl tiglate, ethyl acetate, ethyl butyrate) were diluted to 10%, 1% and 0.1% (v/v) with MO to serve as the electrophysiological stimulus, and MO was used as a negative control. A constant air flow (100 mL/min) was produced using a controller (Syntech) to stimulate the antenna. We then placed 10 µL of each dilution or MO onto a piece of filter paper (5 × 1 cm), and the negative control (MO) was applied before and after the diluted odorants to calibrate the response signal. The EAG responses at each concentration were recorded for 15–20 antennae, and each concentration was recorded twice. Each test lasted 1 s, and the interval between tests was 30 s. EAG response data from WT and mutant flies for the diluted odorants were analyzed using Student’s t test with SPSS v20.0.Molecular docking and site-directed mutagenesisThe three dimensional-structures of BdorOR7a-6 and BdorOR13a were modeled using AlphaFold 2.044. The quality and rationality of each protein structure was evaluated online using a PROCHECK Ramachandran plot in SAVES 6.0 (https://saves.mbi.ucla.edu/). AutoDock Vina 1.1.2 was used for docking analysis, and the receptor protein structure and ligand molecular structure were pre-treated using AutoDock 4.2.6. The docking parameters were set according to the protein structure and active sites, and the optimal docking model was selected based on affinity (kcal/mol). Docking models were imported into Pymol and Discovery Studio 2016 Client for analysis and image processing. Based on the molecular docking data, three residues (Asn86 in OR7a-6, Asp320, and Lys323 in OR13a) were replaced with alanine by site-directed mutagenesis45 using the primers listed in Supplementary Table 2. Calcium imaging assays and molecular docking of mutated proteins were then carried out as described above.Statistics reproducibilityAll of the olfactory preference assays, oviposition bioassays, expression profiles analysis, EAG recording assays were analyzed using Student’s t-test (*p  More

The pesticide hexachlorocyclohexane (HCH) is a toxic and persistent contaminant in the environment. Some bacteria and fungi can degrade HCH and its isomers under laboratory conditions. However, in heterogeneous environments, where many different factors are at play, the biodegradation capacity is challenged by the availability of nutrients to support degraders’ growth. As opposed to bacteria, fungi are more adapted to heterogeneous habitats, and in some cases mycelial fungi can facilitate the transport of organic substrates throughout the mycosphere, increasing their availability to promote bacterial contaminant biodegradation. However, how this occurs is not entirely understood. In this study, Khan et al. demonstrate that mycelial nutrients transferred from nutrient-rich to nutrient-deprived habitats promote co-metabolic degradation of HCH by bacteria. The authors incubated a non-HCH-degrading fungus (Fusarium equiseti K3) and a co-metabolically HCH-degrading bacterium (Sphingobium sp. S8) in a structured model ecosystem. Results from 13C isotope labelling and metaproteomics showed that fungal 13C was incorporated into bacterial proteins responsible for HCH degradation, thus illustrating the importance of synergistic fungal–bacterial interactions for contaminant biodegradation in nutrient-poor environments. 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