Ecology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

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

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

DEFRA. Strategy for Achieving Officially Bovine Tuberculosis Free Status for England: The ‘edge area’ strategy. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/300447/pb14088-bovine-tb-strategy-140328.pdf (2014).Campbell, E. L. et al. Interspecific visitation of cattle and badgers to fomites: A transmission risk for bovine tuberculosis?. Ecol. Evol. 9(15), 8479–8489 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Roberts, T., O’Connor, C., Nuñez-Garcia, J., De La Rua-Domenech, R. & Smith, N. H. Unusual cluster of Mycobacterium bovis infection in cats. Vet. Rec. 174(13), 326–326 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Phipps, E. et al. Bovine tuberculosis in working foxhounds: Lessons learned from a complex public health investigation. Epidemiol. Infect. 147, 1–6 (2019).Article 

Google Scholar 
Delahay, R. J., De Leeuw, A. N. S., Barlow, A. M., Clifton-Hadley, R. S. & Cheeseman, C. L. The status of Mycobacterium bovis infection in UK wild mammals: A review. Vet. J. 164(2), 90–105 (2002).Article 
CAS 
PubMed 

Google Scholar 
Fitzgerald, S. D. & Kaneene, J. B. Wildlife reservoirs of bovine tuberculosis worldwide: Hosts, pathology, surveillance, and control. Vet. Pathol. 50(3), 488–499 (2013).Article 
CAS 
PubMed 

Google Scholar 
Skuce, R. A., Allen, A. R. & McDowell, S. W. J. Herd-level risk factors for bovine tuberculosis: A literature review. Vet Med Int 2012, 621210 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Ayele, W. Y., Neill, S. D., Zinsstag, J., Weiss, M. G. & Pavlik, I. Bovine tuberculosis: An old disease but a new threat to Africa. Int. J. Tuberc. Lung Dis. 8(8), 924–937 (2004).CAS 
PubMed 

Google Scholar 
Gallagher, J. & Clifton-Hadley, R. S. Tuberculosis in badgers; a review of the disease and its significance for other animals. Res. Vet. 69(3), 203–217 (2000).Article 
CAS 

Google Scholar 
Allen, A. et al. Genome epidemiology of Mycobacterium bovis infection in contemporaneous, sympatric badger and cattle populations in Northern Ireland. Access Microbiol. 1(1A), 385 (2019).Article 

Google Scholar 
APHA. Bovine Tuberculosis in England in 2020—Epidemiological analysis of the 2020 data and historical trends. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1027591/tb-epidemiological-report-2020.pdf (2021).DAERA. Tuberculosis disease statistics in Northern Ireland 2022. https://www.daera-ni.gov.uk/publications/tuberculosis-disease-statistics-northern-ireland-2022 (2022).Woodroffe, R. et al. Effects of culling on badger Meles meles spatial organization: Implications for the control of bovine tuberculosis. J. Appl. Ecol. 43(1), 1–10 (2006).Article 

Google Scholar 
Byrne, A. W., Paddy Sleeman, D., O’Keeffe, J. & Davenport, J. The ecology of the European badger (Meles meles) in Ireland: A review. Biol. Environ. 112, 105–132 (2012).Article 

Google Scholar 
McDonald, J., Robertson, A. & Silk, M. Wildlife disease ecology from the individual to the population: Insights from a long-term study of a naturally infected European badger population. J. Anim. Ecol. 87(1), 101–112 (2017).Article 
PubMed 

Google Scholar 
Macdonald, D. W., Newman, C. & Buesching, C. D. Badgers in the rural landscape—conservation paragon or farmland pariah? Lessons from the Wytham Badger Project. Wildlife conservation on farmland 2, 65–95 (2015).
Google Scholar 
Judge, J., Wilson, G. J., Macarthur, R., McDonald, R. A. & Delahay, R. J. Abundance of badgers (Meles meles) in England and Wales. Sci. Rep. 7(1), 1–8 (2017).Article 
CAS 

Google Scholar 
Feore, S. & Montgomery, W. I. Habitat effects on the spatial ecology of the European badger (Meles meles). J. Zool. 247(4), 537–549 (1999).Article 

Google Scholar 
Reid, N., Etherington, T. R., Wilson, G. J., Montgomery, W. I. & McDonald, R. A. Monitoring and population estimation of the European badger Meles meles in Northern Ireland. Wildlife Biol. 18(1), 46–57 (2012).Article 

Google Scholar 
DAERA. Farm animal populations: Cattle populations in Northern Ireland from 1981 to 2019. https://www.daera-ni.gov.uk/publications/farm-animal-population-data (2019).DEFRA. Livestock numbers in the UK (data to December 2019). https://www.gov.uk/government/statistical-data-sets/structure-of-the-livestock-industry-in-england-at-december.39 (2020).DEFRA. Setting the minimum and maximum numbers in badger cull areas in 2021—Advice to Natural England. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1015421/tb-min-max-numbers-2021.pdf (2021).Griffin, J. M. et al. The impact of badger removal on the control of tuberculosis in cattle herds in Ireland. Prev. Vet. Med. 67(4), 237–266 (2005).Article 
CAS 
PubMed 

Google Scholar 
Ham, C., Donnelly, C. A., Astley, K. L., Jackson, S. Y. B. & Woodroffe, R. Effect of culling on individual badger Meles meles behaviour: Potential implications for bovine tuberculosis transmission. J. Appl. Ecol. 56(11), 2390–2399 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Olea-Popelka, F. J. et al. Targeted badger removal and the subsequent risk of bovine tuberculosis in cattle herds in county Laois, Ireland. Prev. Vet. Med. 88(3), 178–184 (2009).Article 
CAS 
PubMed 

Google Scholar 
Donnelly, C. A. et al. Positive and negative effects of widespread badger culling on tuberculosis in cattle. Nature 439(7078), 843–846 (2006).Article 
CAS 
PubMed 

Google Scholar 
Byrne, A. W., White, P. W., McGrath, G., O’Keeffe, J. & Martin, S. W. Risk of tuberculosis cattle herd breakdowns in Ireland: Effects of badger culling effort, density and historic large-scale interventions. Vet. Res. 45(1), 1–10 (2014).Article 

Google Scholar 
Wright, D. M. et al. Herd-level bovine tuberculosis risk factors: Assessing the role of low-level badger population disturbance. Sci. Rep. 5, 1–11 (2015).Article 

Google Scholar 
Jenkins, H. E., Woodroffe, R. & Donnelly, C. A. The duration of the effects of repeated widespread badger culling on cattle tuberculosis following the cessation of culling. PLoS ONE 5(2), e9090 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Tuyttens, F. A. M. et al. Spatial perturbation caused by a badger (Meles meles) culling operation: Implications for the function of territoriality and the control of bovine tuberculosis (Mycobacterium bovis). J. Anim. Ecol. 69(5), 815–828 (2000).Article 
CAS 
PubMed 

Google Scholar 
Carter, S. P. et al. Culling-induced social perturbation in Eurasian badgers Meles meles and the management of TB in cattle: An analysis of a critical problem in applied ecology. Proc. R. Soc. B. 274(1626), 2769–2777 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Donnelly, C. A. et al. Impact of localized badger culling on tuberculosis incidence in British cattle. Nature 426(6968), 834–837 (2003).Article 
CAS 
PubMed 

Google Scholar 
Vicente, J., Delahay, R. J., Walker, N. J. & Cheeseman, C. L. Social organization and movement influence the incidence of bovine tuberculosis in an undisturbed high-density badger Meles meles population. J Anim Ecol. 76(2), 348–360 (2007).Article 
CAS 
PubMed 

Google Scholar 
Riordan, P., Delahay, R. J., Cheeseman, C., Johnson, P. J. & Macdonald, D. W. Culling-induced changes in badger (Meles meles) behaviour, social organisation and the epidemiology of bovine tuberculosis. PLoS ONE 6(12), e28904 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kowalczyk, R., Jȩdrzejewska, B. & Zalewski, A. Annual and circadian activity patterns of badgers (Meles meles) in Białowieża Primeval Forest (eastern Poland) compared with other palaearctic populations. J. Biogeogr. 30(3), 463–472 (2003).Article 

Google Scholar 
Smith, G. C., Delahay, R. J., McDonald, R. A. & Budgey, R. Model of selective and non-selective management of badgers (Meles meles) to control bovine tuberculosis in badgers and cattle. PLoS ONE 11(11), e0167206 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Garnett, B. T., Delahay, R. J. & Roper, T. J. Ranging behaviour of European badgers (Meles meles) in relation to bovine tuberculosis (Mycobacterium bovis) infection. Appl. Anim. Behav. Sci. 94(3–4), 331–340 (2005).Article 

Google Scholar 
Weber, N. et al. Badger social networks correlate with tuberculosis infection. Curr. 23(20), 915–916 (2013).Article 

Google Scholar 
Ellwood, S. A. et al. An active-radio-frequency-identification system capable of identifying co-locations and social-structure: Validation with a wild free-ranging animal. Methods Ecol. Evol. 8(12), 1822–1831 (2017).Article 

Google Scholar 
Noonan, M. et al. A new Magneto-Inductive tracking technique to uncover subterranean activity: what do animals do underground?. Methods Ecol. Evol. 6(5), 510–520 (2015).Article 

Google Scholar 
Schütz, K. et al. Behavioral and physiological responses of trap-induced stress in European badgers. J. Wildl. Manag. 70(3), 884–891 (2006).Article 

Google Scholar 
Clinchy, M. et al. Fear of the human “super predator” far exceeds the fear of large carnivores in a model mesocarnivore. Behav. Ecol. 27(6), 1826–1832 (2016).
Google Scholar 
Bidder, O. R. et al. Step by step: Reconstruction of terrestrial animal movement paths by dead-reckoning. Mov. Ecol. https://doi.org/10.1186/s40462-015-0055-4 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Gunner, R. M. et al. Dead-reckoning animal movements in R: a reappraisal using Gundog. Tracks. Anim. Biotelem. 9(1), 1–37 (2021).
Google Scholar 
McClune, D. W., Marks, N. J., Delahay, R. J., Montgomery, W. I. & Scantlebury, D. M. Behaviour-time budget and functional habitat use of a free-ranging European badger (Meles meles). Anim. Biotelem. 3(7), 1–7 (2015).
Google Scholar 
McClune, D. et al. Tri-axial accelerometers quantify behaviour in the Eurasian badger (Meles meles): towards an automated interpretation of field data. Anim. Biotelem. 2(1), 1–6 (2014).Article 

Google Scholar 
Gaughran, A. et al. Dispersal patterns in a medium-density Irish badger population: Implications for understanding the dynamics of tuberculosis transmission. Ecol. Evol. 9(23), 13142–13152 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Kelly, D. J. et al. Extra Territorial Excursions by European badgers are not limited by age, sex or season. Sci. Rep. 10(1), 1–2 (2020).Article 

Google Scholar 
Macdonald, D. W., Newman, C., Buesching, C. D. & Johnson, P. J. Male-biased movement in a high-density population of the Eurasian badger (Meles meles). J. Mammal. 89(5), 1077–1086 (2008).Article 

Google Scholar 
Courcier, E. A. et al. Evaluating the application of the dual path platform VetTB test for badgers (Meles meles) in the test and vaccinate or remove (TVR) wildlife research intervention project in Northern Ireland. Res. Vet. Sci. 130, 170–178 (2020).Article 
CAS 
PubMed 

Google Scholar 
Menzies, F. D. et al. Test and vaccinate or remove: Methodology and preliminary results from a badger intervention research project. Vet. Rec. 189, e248 (2021).Article 
PubMed 

Google Scholar 
O’Hagan, M. J. H. et al. Effect of selective removal of badgers (Meles meles) on ranging behaviour during a “test and Vaccinate or Remove” intervention in Northern Ireland. Epidemiol. Infect. 149(1), e125 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Roper, T. J. The structure and function of badger setts. J. Zool. 227(4), 691–698 (1992).Article 

Google Scholar 
DAERA. The Test and Vaccinate or Remove (TVR) Wildlife Intervention Research Project. Year 1 Report—2014. https://www.daera-ni.gov.uk/sites/default/files/publications/dard/tvr-year-1-report.pdf (2014).Brown, E., Cooney, R. & Rogers, F. Veterinary guidance on the practical use of the BadgerBCG tuberculosis vaccine. In Pract. 35(3), 143–146 (2013).Article 

Google Scholar 
Magowan, E. A. et al. Dead-reckoning elucidates fine-scale habitat use by European badgers Meles meles. Anim. Biotelem. 10(1), 1–11 (2022).Article 

Google Scholar 
McGill, K. et al. Seroconversion against antigen MPB83 in badgers (Meles meles) vaccinated with multiple doses of BCG strain Sofia. Res. Vet. Sci. 149, 119–124. https://doi.org/10.1016/j.rvsc.2022.06.011 (2022).Article 
CAS 
PubMed 

Google Scholar 
Gaughran, A. et al. Super-ranging. A new ranging strategy in European badgers. PLoS ONE 13(2), e0191818 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Williams, H. J. et al. Identification of animal movement patterns using tri-axial magnetometry. Mov. Ecol. 5(1), 6 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Brendel C, Helder R, Chevallier D, Zaytoon J, Georges JY, and Handrich Y. Testing a global positioning system on free ranging badgers Meles meles. Mammal Notes, The Mammal Society, Southampton. https://www.mammal.org.uk/wp-content/uploads/2016/04/Note–Brendel-MN-2012-1.pdf (2012).Börger, L. et al. Effects of sampling regime on the mean and variance of home range size estimates. J. Anim. Ecol. 75(6), 1393–1405 (2006).Article 
PubMed 

Google Scholar 
Calenge, C. The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals. Ecol. Modell. 197(3–4), 516–519 (2006).Article 

Google Scholar 
Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: An r package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7(9), 1124–1132 (2016).Article 

Google Scholar 
QGIS.org. QGIS Geographic Information System. QGIS Association. https://qgis.org/en/site/ (2021).Fleming, C. H. et al. Rigorous home range estimation with movement data: A new autocorrelated kernel density estimator. Ecology 96(5), 1182–1188 (2015).Article 
CAS 
PubMed 

Google Scholar 
Fleming, C. H. et al. Estimating where and how animals travel: An optimal framework for path reconstruction from autocorrelated tracking data. Ecology 97(3), 576–582 (2016).CAS 
PubMed 

Google Scholar 
Fleming, C. H. et al. Correcting for missing and irregular data in home-range estimation. Ecol. Appl. 28(4), 1003–1010 (2018).Article 
CAS 
PubMed 

Google Scholar 
Gula, R. & Theuerkauf, J. The need for standardization in wildlife science: Home range estimators as an example. Eur. J. Wildl. Res. 59, 713–718 (2013).Article 

Google Scholar 
Schuler, K. L., Schroeder, G. M., Jenks, J. A. & Kie, J. G. Ad hoc smoothing parameter performance in kernel estimates of GPS-derived home ranges. Wildl. Biol. 20(5), 259–266 (2014).Article 

Google Scholar 
Huck, M., Davison, J. & Roper, T. J. Comparison of two sampling protocols and four home-range estimators using radio-tracking data from urban badgers Meles meles. Wildl. Biol. 14(4), 467–477 (2008).Article 

Google Scholar 
Scull, P., Palmer, M., Frey, F. & Kraly, E. A comparison of two home range modeling methods using Ugandan mountain gorilla data. Int. J. Geogr. Inf. Sci. 26(11), 2111–2121 (2012).Article 

Google Scholar 
Woodroffe, R. et al. Ranging behaviour of badgers Meles meles vaccinated with Bacillus Calmette Guerin. J. Appl. Ecol. 54(3), 718–725 (2017).Article 

Google Scholar 
Signer, J. & Fieberg, J. R. A fresh look at an old concept: Home-range estimation in a tidy world. PeerJ 9, e11031 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Woodroffe, R. et al. Badgers prefer cattle pasture but avoid cattle: implications for bovine tuberculosis control. Ecology 19(10), 1201–1208 (2016).
Google Scholar 
Hijmans RJ. Introduction to the geosphere package (version 1 .5–10). Cran (2019).Dewhirst, O. P. et al. Improving the accuracy of estimates of animal path and travel distance using GPS drift-corrected dead reckoning. Ecol. Evol. 6(17), 6210–6222 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
QGIS.org. Working with vector data. QGIS Desktop 3.16 User Guide. pp 304. https://docs.qgis.org/3.22/en/docs/user_manual/index.html (2022).Qasem, L. et al. Tri-axial acceleration as a proxy for animal energy expenditure; should we be summing values or calculating the vector?. PLoS ONE 7(2), e31187 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies; a reappraisal. J anim Ecol. 89(1), 161–172 (2020).Article 
PubMed 

Google Scholar 
RStudio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA http://www.rstudio.com/ (2021).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Barton K. Package “MuMin”. Cran (2018).Rogers, L. M., Cheeseman, C. L., Mallinson, P. J. & Clifton-Hadley, R. The demography of a high-density badger (Meles meles) population in the west of England. J. Zool. 242(4), 705–728 (1997).Article 

Google Scholar 
Macdonald, D. W. & Newman, C. Population dynamics of badgers (Meles meles) in Oxfordshire, UK: Numbers, density and cohort life histories, and a possible role of climate change in population growth. J. Zool. 256(1), 121–138 (2002).Article 

Google Scholar 
Kruuk, H., & MacDonald, D. Group territories of carnivores: empires and enclaves. In 25th Symposium of the British Ecological Society (1985).Roper, T. J., Shepherdson, D. J. & Davies, J. M. Scent marking with faeces and anal secretion in the European badger (Meles meles): seasonal and spatial characteristics of latrine use in relation to territoriality. Behaviour 97(1–2), 94–117 (1986).
Google Scholar 
Sleeman, D. P. et al. How many Eurasian badgers Meles meles L. are there in the republic of Ireland?. Eur. J. Wildl. Res. 55(4), 333–344 (2009).Article 

Google Scholar 
Carter, S. P. et al. BCG vaccination reduces risk of tuberculosis infection in vaccinated badgers and unvaccinated badger cubs. PLoS ONE 7(12), e49833 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Byrne, A., Parnell, A., O’Keeffe, J. & Madden, J. The challenge of estimating wildlife populations at scale: the case of the European badger (Meles meles) in Ireland. Eur. J. Wildl. Res. 67(5), 1–10 (2021).Article 

Google Scholar 
Minta, S. C. Sexual differences in spatio-temporal interaction among badgers. Oecologia 96(3), 402–409 (1993).Article 
PubMed 

Google Scholar 
Annavi, G. et al. Neighbouring-group composition and within-group relatedness drive extra-group paternity rate in the European badger (Meles meles). J. Evol. Biol. 27(10), 2191–2203 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
DEFRA. Monitoring regional changes in badger numbers. Research Project Final Report. http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&ProjectID=14237. Accessed 07 February 2023 (2009).Johnson, D. D., Jetz, W. & Macdonald, D. W. Environmental correlates of badger social spacing across Europe. J. Biogeogr. 29(3), 411–425 (2002).Article 

Google Scholar 
Kruuk, H. Spatial organization and territorial behaviour of the European badger Meles meles. J Zool. 184(1), 1–19 (1978).Article 

Google Scholar 
Macdonald, D., Newman, C., Dean, J., Buesching, C. & Johnson, P. The distribution of Eurasian badger, Meles meles, setts in a high-density area: field observations contradict the sett dispersion hypothesis. Oikos 106(2), 295–307 (2004).Article 

Google Scholar 
Sleeman, D. P. & Mulcahy, M. F. Loss of territoriality in a local badger Meles meles population at Kilmurry, Co Cork, Irealnd. Irish Nat. J. 28(1), 11–19 (2005).
Google Scholar 
Byrne, A. W., O’Keeffe, J., Buesching, C. D. & Newman, C. Push and pull factors driving movement in a social mammal: Context dependent behavioral plasticity at the landscape scale. Curr. Zool. 65(5), 517–525 (2019).Article 
PubMed 

Google Scholar 
Cheeseman, C. L., Cresswell, W. J., Harris, S. & Mallinson, P. J. Comparison of dispersal and other movements in two Badger (Meles meles) populations. Mamm. Rev. 18(1), 51–59 (1988).Article 

Google Scholar 
Seebacher, F. & Krause, J. Epigenetics of social behaviour. TREE 34(9), 818–830 (2019).PubMed 

Google Scholar 
Allen, A. et al. European badger (Meles meles) responses to low-intensity, selective culling: Using mark–recapture and relatedness data to assess social perturbation. Ecol. Solut. Evid. 3(3), e12165 (2022).Article 

Google Scholar 
Loureiro, F., Rosalino, L. M., Macdonald, D. W. & Santos-Reis, M. Path tortuosity of Eurasian badgers (Meles meles) in a heterogeneous Mediterranean landscape. Ecol. Res. 22(5), 837–844 (2007).Article 

Google Scholar 
Sun, Q., Stevens, C., Newman, C., Buesching, C. & Macdonald, D. Cumulative experience, age-class, sex and season affect the behavioural responses of European badgers (Meles meles) to handling and sedation. Anim Welf. 24(4), 373–385 (2015).Article 

Google Scholar 
Conlan, A. et al. Potential benefits of cattle vaccination as a supplementary control for bovine tuberculosis. PLoS Comput. Biol. 11(2), e1004038 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Gormley, E. et al. Oral vaccination of free-living badgers (Meles meles) with Bacille Calmette Guérin (BCG) vaccine confers protection against tuberculosis. PLoS ONE 12(1), e0168851 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Benton, C. H. et al. Badger vaccination in England: Progress, operational effectiveness and participant motivations. People Nat. 2(3), 761–775 (2020).Article 

Google Scholar  More

Shaw, J., Weyrich, L. & Cooper, A. Using environmental (e)DNA sequencing for aquatic biodiversity surveys: A beginner’s guide. Mar. Freshw. Res. 68, 68 (2016).
Google Scholar 
Smith, K. J. et al. Stable isotope analysis of specimens of opportunity reveals ocean-scale site fidelity in an elusive whale species. Front. Conserv. Sci. 2, 1–11 (2021).Article 

Google Scholar 
Coll, M. et al. The biodiversity of the Mediterranean Sea: Estimates, patterns, and threats. PLoS One 5, (2010).Cavanagh, R. D. & Gibson, C. Overview of the conservation status of cartilaginous fishes (Chondrichthyans) in the Mediterranean Sea. https://doi.org/10.2305/iucn.ch.2007.mra.3.en (2007).Pace, D. S., Tizzi, R. & Mussi, B. Cetaceans value and conservation in the Mediterranean Sea. Journal Biodivers. Endanger. Species S1:
S1.004 (2015).Carlucci, R. et al. Modeling the spatial distribution of the striped dolphin (Stenella coeruleoalba) and common bottlenose dolphin (Tursiops truncatus) in the Gulf of Taranto (Northern Ionian Sea, Central-eastern Mediterranean Sea). Ecol. Indic. 69, 707–721 (2016).Article 

Google Scholar 
Boldrocchi, G. et al. Distribution, ecology, and status of the white shark, Carcharodon carcharias, in the Mediterranean Sea. Rev. Fish Biol. Fish. 27, 515–534 (2017).Article 

Google Scholar 
Karamanlidis, A. A. et al. The Mediterranean monk seal Monachus monachus: Status, biology, threats, and conservation priorities. Mammal Review 46, 92–105. https://doi.org/10.1111/mam.12053 (2016).Article 

Google Scholar 
Johnson, W. M. The role of the Mediterranean monk seal (Monachus monachus) in European history and culture, from the fall of Rome to the 20th century Monk Seals in Post-Classical History. (2004).Johnson, W. M. & Lavigne, D. M. The Mediterranean Monk Seal (Monachus monachus) in Ancient History and Literature Monk Seals in Antiquity. (1999).Israëls, l. D. Thirty Years of Mediterranean Monk Seal Protection – A Review. Netherlands Com- Mission Int. Nat. Prot. Inst. voor Taxon. Zoölogie/Zoölogische Museum, Univ. van Amsterdam, Amsterdam, Netherlands. Meded. No. 281–65. (1992).Stringer, C. B. et al. Neanderthal exploitation of marine mammals in Gibraltar. Proc. Natl. Acad. Sci. U. S. A. 105, 14319–14324 (2008).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
La Mesa, G., Lauriano, G., Mo, G., Paglialonga, A. & Tunesi, L. Assessment of the conservation status of marine species of the Habitats Directive (92/43/EEC) in Italy: results, drawbacks and perspectives of the fourth national report (2013–2018). Biodivers Conserv (2021).Adamantopoulou, S., Karamanlidis, A. A., Dendrinos, P. & Gimenez, O. Citizen science indicates significant range recovery and defines new conservation priorities for Earth’s most endangered pinniped in Greece. Anim. Conserv. https://doi.org/10.1111/acv.12806 (2022).Article 

Google Scholar 
Nicolaou, H., Dendrinos, P., Marcou, M., Michaelides, S. & Karamanlidis, A. A. Re-establishment of the Mediterranean monk seal Monachus monachus in Cyprus: Priorities for conservation. Oryx 55, 526–528 (2021).Article 

Google Scholar 
Tenan, S. et al. Evaluating mortality rates with a novel integrated framework for nonmonogamous species. Conserv. Biol. 30, 1307–1319 (2016).Article 
PubMed 

Google Scholar 
Vanpe, C. et al. Estimating abundance of a recovering transboundary brown bear population with capture- recapture models. Peer Community Journal, 2, e71. (2022).Lecaudey, L. A., Schletterer, M., Kuzovlev, V. V., Hahn, C. & Weiss, S. J. Fish diversity assessment in the headwaters of the Volga River using environmental DNA metabarcoding. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 1785–1800 (2019).Article 

Google Scholar 
Itakura, H. et al. Environmental DNA analysis reveals the spatial distribution, abundance, and biomass of Japanese eels at the river-basin scale. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 361–373 (2019).Article 

Google Scholar 
Closek, C. J. et al. Marine vertebrate biodiversity and distribution within the central California current using environmental DNA (eDNA) metabarcoding and ecosystem surveys. Front. Mar. Sci. Vol. 6. (2019).Boldrocchi, G. & Storai, T. Data-mining social media platforms highlights conservation action for the Mediterranean Critically Endangered blue shark Prionace glauca. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 3087–3099 (2021).Article 

Google Scholar 
Thiel, M. et al. Citizen scientists and marine research: Volunteer participants, their contributions, and projection for the future. Oceanogr. Mar. Biol. An Annu. Rev. 52, 257–314 (2014).
Google Scholar 
Araujo, G. et al. Citizen science sheds light on the cryptic ornate eagle ray Aetomylaeus vespertilio. Aquat. Conserv. Mar. Freshw. Ecosyst. 30, 2012–2018 (2020).Article 

Google Scholar 
Silvertown, J. A new dawn for citizen science. Trends Ecol. Evol. 24, 467–471 (2009).Article 
PubMed 

Google Scholar 
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).Article 

Google Scholar 
Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, (2014).Strickler, K. M., Fremier, A. K. & Goldberg, C. S. Quantifying effects of UV-B, temperature, and pH on eDNA degradation in aquatic microcosms. Biol. Conserv. 183, 85–92 (2015).Article 

Google Scholar 
Eichmiller, J., Best, S. E. & Sorensen, P. W. Effects of temperature and trophic state on degradation of environmental DNA in lake water. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.5b05672 (2016).Article 
PubMed 

Google Scholar 
Mächler, E., Osathanunkul, M. & Altermatt, F. Shedding light on eDNA: neither natural levels of UV radiation nor the presence of a filter feeder affect eDNA-based detection of aquatic organisms. PLoS ONE 13, 1–15 (2018).Article 

Google Scholar 
Jo, T., Murakami, H., Yamamoto, S., Masuda, R. & Minamoto, T. Effect of water temperature and fish biomass on environmental DNA shedding, degradation, and size distribution. Ecol. Evol. 9, 1135–1146 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Mauvisseau, Q. et al. The multiple states of environmental DNA and what is known about their persistence in aquatic environments. Environ. Sci. Technol. 56, 5322–5333 (2022).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Valsecchi, E. et al. A species – specific qPCR assay provides novel insight into range expansion of the Mediterranean monk seal (Monachus monachus ) by means of eDNA analysis. Biodivers. Conserv. 31, 1175–1196 (2022).Article 

Google Scholar 
Collins, R. A. et al. Persistence of environmental DNA in marine systems. Commun. Biol. https://doi.org/10.1038/s42003-018-0192-6 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhao, B., P.M., B. & Timbros, K. The particle size distribution of environmental DNA varies with species and degradation. Sci. Total Environ. 797, 149175 (2021).Würtz, M. Mediterranean submarine canyons. in Ecology and Governance (ed. IUCN) 192 (2012).Valsecchi, E. et al. Ferries and environmental DNA: Underway sampling from commercial vessels provides new opportunities for systematic genetic surveys of marine biodiversity. Front. Mar. Sci. 8, 1–17 (2021).Article 

Google Scholar 
Bustin, S. A. et al. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 622, 611–622 (2009).Article 

Google Scholar 
Klymus, K. E. et al. Reporting the limits of detection and quantification for environmental DNA assays. Environ. DNA 1–12. https://doi.org/10.1002/edn3.29 (2019).Goldberg, G. et al. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol. Evol. 1299–1307. https://doi.org/10.1111/2041-210X.12595 (2016).Farrell, J. A. et al. Detection and population genomics of sea turtle species via noninvasive environmental DNA analysis of nesting beach sand tracks and oceanic water. Mol. Ecol. Resour. (2022).Shamblin, B. M. et al. Loggerhead turtle eggshells as a source of maternal nuclear genomic DNA for population genetic studies. Mol. Ecol. Resour. 11, 110–115 (2011).Article 
PubMed 

Google Scholar 
MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).Article 

Google Scholar 
White, G. C. & Burnham, K. P. Program MARK: survival estimation from populations of marked animals. Bird Study 37–41 (1999).Akaike, H. Information theory and an extension of the maximum likelihood principle in Breakthroughs in Statistics, Vol.I, Foundations and Basic Theory, (eds. Kotz, S. and Johnson, N.L.) 610–624 (Springer-Verlag, New York, 1992).Adamantopoulou, S. et al. Movements of Mediterranean Monk Seals (Monachus monachus) in the Eastern Mediterranean Sea. Aquat. Mamm. 37, 256–261 (2011).Article 

Google Scholar  More