Ecology

1.
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 
2.
Macgregor, C. J., Williams, J. H., Bell, J. R. & Thomas, C. D. Moth biomass increases and decreases over 50 years in Britain. Nat. Ecol. Evol. 3, 1645–1649 (2019).
PubMed  Article  Google Scholar 

3.
Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).
Article  Google Scholar 

4.
Thomas, C. D., Jones, T. H. & Hartley, S. E. “Insectageddon”: A call for more robust data and rigorous analyses. Glob. Chang. Biol. 25, 1891–1892 (2019).
ADS  PubMed  Article  Google Scholar 

5.
van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science (80-) 368, 417–420 (2020).
ADS  Article  CAS  Google Scholar 

6.
Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).
PubMed  Article  Google Scholar 

7.
Pérez-Méndez, N. et al. The economic cost of losing native pollinator species for orchard production. J. Appl. Ecol. 57, 599–608 (2020).
Article  Google Scholar 

8.
Porto, R. G. et al. Pollination ecosystem services: A comprehensive review of economic values, research funding and policy actions. Food Secur. 12, 1425–1442 (2020).
Article  Google Scholar 

9.
Winfree, R., Aguilar, R., Vázquez, D. P., LeBuhn, G. & Aizen, M. A. A meta-analysis of bees’ responses to anthropogenic disturbance. Ecology 90, 2068–2076 (2009).
PubMed  PubMed Central  Article  Google Scholar 

10.
Godfray, H. C. J. et al. A restatement of the natural science evidence base concerning neonicotinoid insecticides and insect pollinators. Proc. R. Soc. B Biol. Sci. 281, 20140558 (2014).
Article  Google Scholar 

11.
Fortel, L. et al. Decreasing abundance, increasing diversity and changing structure of the Wild Bee Community (Hymenoptera: Anthophila) along an urbanization gradient. PLoS ONE 9, e104679 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

12.
Geslin, B. et al. The proportion of impervious surfaces at the landscape scale structures wild bee assemblages in a densely populated region. Ecol. Evol. 6, 6599–6615 (2016).
PubMed  PubMed Central  Article  Google Scholar 

13.
Geslin, B., Le Féon, V., Kuhlmann, M., Vaissière, B. E. & Dajoz, I. The bee fauna of large parks in downtown Paris, France. Ann. la Société Entomol. Fr. 51, 487–493 (2015).
Article  Google Scholar 

14.
Baldock, K. C. R. et al. A systems approach reveals urban pollinator hotspots and conservation opportunities. Nat. Ecol. Evol. 3, 363–373 (2019).
PubMed  PubMed Central  Article  Google Scholar 

15.
Lerman, S. B., Contosta, A. R., Milam, J. & Bang, C. To mow or to mow less: Lawn mowing frequency affects bee abundance and diversity in suburban yards. Biol. Conserv. 221, 160–174 (2018).
Article  Google Scholar 

16.
Kerr, J. T. et al. Climate change impacts on bumblebees converge across continents. Science 349, 177–180 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).
ADS  CAS  PubMed  Article  Google Scholar 

18.
McFrederick, Q. S. & LeBuhn, G. Are urban parks refuges for bumble bees Bombus spp. (Hymenoptera: Apidae)?. Biol. Conserv. 129, 372–382 (2006).
Article  Google Scholar 

19.
Hall, D. M. et al. The city as a refuge for insect pollinators. Conserv. Biol. 31, 24–29 (2017).
PubMed  Article  Google Scholar 

20.
Ropars, L., Dajoz, I. & Geslin, B. La ville un désert pour les abeilles sauvages? J. Bot. Soc. Bot. Fr. 79, 29–35 (2017).
Google Scholar 

21.
Falk, S. et al. Evaluating the ability of citizen scientists to identify bumblebee (Bombus) species. PLoS ONE 14, e0218614 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Bloom, E. H. & Crowder, D. W. Promoting data collection in pollinator citizen science projects. Citiz. Sci. Theory Pract. 5(1), 3 https://doi.org/10.5334/cstp.217 (2020).

23.
Levé, M., Baudry, E. & Bessa-Gomes, C. Domestic gardens as favorable pollinator habitats in impervious landscapes. Sci. Total Environ. 647, 420–430 (2019).
ADS  PubMed  Article  CAS  Google Scholar 

24.
Mason, L. & Arathi, H. S. Assessing the efficacy of citizen scientists monitoring native bees in urban areas. Glob. Ecol. Conserv. 17, e00561 (2019).
Article  Google Scholar 

25.
Sheffield, C. S. et al. Contribution of DNA barcoding to the study of the bees (Hymenoptera: Apoidea) of Canada: Progress to date. Can. Entomol. 149, 736–754 (2017).
Article  Google Scholar 

26.
Sheffield, C. S., Hebert, P. D. N., Kevan, P. G. & Packer, L. DNA barcoding a regional bee (Hymenoptera: Apoidea) fauna and its potential for ecological studies. Mol. Ecol. Resour. 9, 196–207 (2009).
CAS  PubMed  Article  Google Scholar 

27.
Schmidt, S., Schmid-Egger, C., Morinière, J., Haszprunar, G. & Hebert, P. D. N. DNA barcoding largely supports 250 years of classical taxonomy: Identifications for Central European bees (Hymenoptera, Apoidea partim ). Mol. Ecol. Resour. 15, 985–1000 (2015).
CAS  PubMed  Article  Google Scholar 

28.
Packer, L. & Ruz, L. DNA barcoding the bees (Hymenoptera: Apoidea) of Chile: Species discovery in a reasonably well known bee fauna with the description of a new species of Lonchopria (Colletidae). Genome 60, 414–430 (2017).
CAS  PubMed  Article  Google Scholar 

29.
Tang, M. et al. High-throughput monitoring of wild bee diversity and abundance via mitogenomics. Methods Ecol. Evol. 6, 1034–1043 (2015).
PubMed  PubMed Central  Article  Google Scholar 

30.
Sonet, G. et al. Using next-generation sequencing to improve DNA barcoding: Lessons from a small-scale study of wild bee species (Hymenoptera, Halictidae). Apidologie 49, 671–685 (2018).
CAS  Article  Google Scholar 

31.
Creedy, T. J. et al. A validated workflow for rapid taxonomic assignment and monitoring of a national fauna of bees (Apiformes) using high throughput DNA barcoding. Mol. Ecol. Resour. 20, 40–53 (2020).
CAS  PubMed  Article  Google Scholar 

32.
Gueuning, M. et al. Evaluating next-generation sequencing (NGS) methods for routine monitoring of wild bees: Metabarcoding, mitogenomics or NGS barcoding. Mol. Ecol. Resour. 19, 847–862 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Lanner, J., Curto, M., Pachinger, B., Neumüller, U. & Meimberg, H. Illumina midi-barcodes: Quality proof and applications. Mitochondrial DNA Part A 30, 490–499 (2019).
CAS  Article  Google Scholar 

34.
González-Vaquero, R. A., Roig-Alsina, A. & Packer, L. DNA barcoding as a useful tool in the systematic study of wild bees of the tribe Augochlorini (Hymenoptera: Halictidae). Genome 59, 889–898 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

35.
Gibbs, J. DNA barcoding a nightmare taxon: Assessing barcode index numbers and barcode gaps for sweat bees. Genome 61, 21–31 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

36.
Dorey, J. P., Schwarz, M. P. & Stevens, M. I. Review of the bee genus Homalictus Cockerell (Hymenoptera: Halictidae) from Fiji with description of nine new species. Zootaxa 4674, 1–46 (2019).
Article  Google Scholar 

37.
Williams, P. H. et al. Unveiling cryptic species of the bumblebee subgenus Bombus s. str. worldwide with COI barcodes (Hymenoptera: Apidae). Syst. Biodivers. 10, 21–56 (2012).
Article  Google Scholar 

38.
Magnacca, K. N. & Brown, M. J. F. DNA barcoding a regional fauna: Irish solitary bees. Mol. Ecol. Resour. 12, 990–998 (2012).
CAS  PubMed  Article  Google Scholar 

39.
de Waard, J. R. et al. A reference library for Canadian invertebrates with 1.5 million barcodes, voucher specimens, and DNA samples. Sci. Data 6, 308 (2019).
Article  CAS  Google Scholar 

40.
Hua, F. et al. Opportunities for biodiversity gains under the world’s largest reforestation programme. Nat. Commun. 7, 12717 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

41.
Gueuning, M., Frey, J. E. & Praz, C. Ultraconserved yet informative for species delimitation: UCEs resolve long-standing systematic enigma in Central European bees. Mol. Ecol. Mec. https://doi.org/10.1111/mec.15629 (2020).
Article  Google Scholar 

42.
Phillips, J. D., French, S. H., Hanner, R. H. & Gillis, D. J. HACSim: An R package to estimate intraspecific sample sizes for genetic diversity assessment using haplotype accumulation curves. PeerJ Comput. Sci. 6, e243 (2020).
Article  Google Scholar 

43.
Phillips, J. D., Gwiazdowski, R. A., Ashlock, D. & Hanner, R. An exploration of sufficient sampling effort to describe intraspecific DNA barcode haplotype diversity: Examples from the ray-finned fishes (Chordata: Actinopterygii). DNA Barcodes 3(1), 66–73 (2015).

44.
Phillips, J. D., Gillis, D. J. & Hanner, R. H. Incomplete estimates of genetic diversity within species: Implications for DNA barcoding. Ecol. Evol. 9, 2996–3010 (2019).
PubMed  PubMed Central  Article  Google Scholar 

45.
Muséum national d’Histoire naturelle (ed). 2003-2020. Inventaire National du Patrimoine Naturel. https://inpn.mnhn.fr.

46.
Zayed, A., Constantin, ŞA. & Packer, L. Successful biological invasion despite a severe genetic load. PLoS ONE 2, e868 (2007).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

47.
Lecocq, T. et al. The alien’s identity: Consequences of taxonomic status for the international bumblebee trade regulations. Biol. Conserv. 195, 169–176 (2016).
Article  Google Scholar 

48.
Danforth, B. N. Phylogeny of the bee genus Lasioglossum (Hymenoptera: Halictidae) based on mitochondrial COI sequence data. Syst. Entomol. 24, 377–393 (1999).
Article  Google Scholar 

49.
Hebert, P. D. N. et al. A Sequel to Sanger: Amplicon sequencing that scales. BMC Genom. 19, 219 (2018).
Article  CAS  Google Scholar 

50.
Ratnasingham, S. & Hebert, P. D. N. A DNA-based registry for all animal species: The Barcode Index Number (BIN) system. PLoS ONE 8, e66213 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

51.
Carolan, J. C. et al. Colour patterns do not diagnose species: Quantitative evaluation of a DNA barcoded cryptic bumblebee complex. PLoS ONE 7, e29251 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Praz, C., Müller, A. & Genoud, D. Hidden diversity in European bees: Andrena amieti sp. n., a new Alpine bee species related to Andrena bicolor (Fabricius, 1775) (Hymenoptera, Apoidea, Andrenidae). Alp. Entomol. 3, 11–38 (2019).
Article  Google Scholar 

53.
Pauly, A. Abeilles de Belgique et des régions limitrophes (Insecta: Hymenoptera: Apoidea) Famille Halictidae. (Institut royal des sciences naturelles de Belgique, 2019).

54.
Gonçalves, R. B. & Oliveira, P. S. Preliminary results of bowl trapping bees (Hymenoptera, Apoidea) in a southern Brazil forest fragment. J. Insect Biodivers. 1, 1–9 (2013).
Article  Google Scholar 

55.
Buri, P., Humbert, J.-Y. & Arlettaz, R. Promoting pollinating insects in intensive agricultural matrices: Field-scale experimental manipulation of hay-meadow mowing regimes and its effects on bees. PLoS One 9(1), e85635 (2014).

56.
Rhoades, P. et al. Sampling technique affects detection of habitat factors influencing wild bee communities. J. Insect Conserv. 21, 703–714 (2017).
Article  Google Scholar 

57.
Lettow, M. C. et al. Bee community responses to a gradient of oak savanna restoration practices. Restor. Ecol. 26, 882–890 (2018).
Article  Google Scholar 

58.
Onuferko, T. M., Skandalis, D. A., Cordero, R. L. & Richards, M. H. Rapid initial recovery and long-term persistence of a bee community in a former landfill. Insect Conserv. Divers. 11, 88–99 (2018).
Article  Google Scholar 

59.
Geroff, R. K., Gibbs, J. & McCravy, K. W. Assessing bee (Hymenoptera: Apoidea) diversity of an Illinois restored tallgrass prairie: Methodology and conservation considerations. J. Insect Conserv. 18, 951–964 (2014).
Article  Google Scholar 

60.
Griffin, S. R., Bruninga-Socolar, B., Kerr, M. A., Gibbs, J. & Winfree, R. Wild bee community change over a 26-year chronosequence of restored tallgrass prairie. Restor. Ecol. 25, 650–660 (2017).
Article  Google Scholar 

61.
Ropars, L., Dajoz, I. & Geslin, B. La diversité des abeilles parisiennes. Osmia 7, 14–19 (2018).
Article  Google Scholar 

62.
Portman, Z. M., Bruninga-Socolar, B. & Cariveau, D. P. The state of bee monitoring in the United States: A call to refocus away from bowl traps and towards more effective methods. Ann. Entomol. Soc. Am. 113, 337–342 (2020).
Article  Google Scholar 

63.
Magnacca, K. N. & Brown, M. J. Mitochondrial heteroplasmy and DNA barcoding in Hawaiian Hylaeus (Nesoprosopis) bees (Hymenoptera: Colletidae). BMC Evol. Biol. 10, 174 (2010).

64.
Ballare, K. M. et al. Utilizing field collected insects for next generation sequencing: Effects of sampling, storage, and DNA extraction methods. Ecol. Evol. 9, 13690–13705 (2019).
PubMed  PubMed Central  Article  Google Scholar 

65.
Hill, G. E. Mitonuclear coevolution as the genesis of speciation and the mitochondrial DNA barcode gap. Ecol. Evol. 6, 5831–5842 (2016).
PubMed  PubMed Central  Article  Google Scholar 

66.
Ascher, J. S. & Pickering, J.  Life bee species guide and world checklist (Hymenoptera Apoidea Anthophila). http://www.discoverlife.org/mp/20q?guide=Apoidea_species (2020).

67.
LaBerge, W. E. A revision of the bees of the genus Andrena of the western hemisphere. Part XI. Minor subgenera and subgeneric key. Trans. Am. Entomol. Soc. 111, 441–567 (1985).
Google Scholar 

68.
Warncke, K. Die Untergattungen der westpalaarktischen Bienengattung Andrena F. Memorias e Estud Muséu Zool. da Univ. Coimbra 307, 1–110 (1968).
Google Scholar 

69.
Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Apidae 6: Andrena, Melitturga, Panurginus, Panurgus. Fauna Helv. 26, 1–317 (2010).

70.
Michener, C. The bees of the world. (Johns Hopkins University Press, Baltimore, 2000).
Google Scholar 

71.
Michener, C. D. The Social Behavior of the Bees: A Comparative Study (Harvard University Press, Cambridge, 1974).
Google Scholar 

72.
Pauly, A., Noël, G., Sonet, G., Notton, D. G. & Boevé, J.-L. Integrative taxonomy resuscitates two species in the Lasioglossum villosulum complex (Kirby, 1802) (Hymenoptera: Apoidea: Halictidae). Eur. J. Taxon. 541 (2019).

73.
Eberle, J., Ahrens, D., Mayer, C., Niehuis, O. & Misof, B. A plea for standardized nuclear markers in metazoan DNA taxonomy. Trends Ecol. Evol. 35, 336–345 (2020).
PubMed  Article  Google Scholar 

74.
Roulston, T. H., Smith, S. A. & Brewster, A. L. A comparison of pan trap and intensive net sampling techniques for documenting a bee (Hymenoptera: Apiformes) Fauna. J. Kansas Entomol. Soc. 80, 179–181 (2007).
Article  Google Scholar 

75.
Westphal, C. et al. Measuring bee diversity in different European habitats and biogeographical regions. Ecol. Monogr. 78, 653–671 (2008).
Article  Google Scholar 

76.
Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Apidae 5: Ammobates, Ammobatoides, Anthophora, Biastes, Ceratina, Dasypoda, Epeoloides, Epeolus, Eucera, Macropis, Melecta, Melitta, Nomada, Pasites, Tetralonia, Thyreus, Xylocopa. Fauna Helv. 20, 1–356 (2007).

77.
Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Apidae 2: Colletes, Dufourea, Hylaeus, Nomia, Nomioides, Rhophitoides, Rophites, Sphecodes, Systropha. Fauna Helv. 4, 1–239 (1999).

78.
Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Apidae 3: Halictus, Lasioglossum. Fauna Helv. 6, 1–208 (2001).

79.
Amiet, F., Herrmann, M., Müller, A. & Neumeyer, R. Apidae 4: Anthidium, Chelostoma, Coelioxys, Dioxys, Heriades, Lithurgus, Megachile, Osmia, Stelis. Fauna Helv. 9, 1–273 (2004).

80.
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).
CAS  PubMed  Google Scholar 

81.
Ratnasingham, S. & Hebert, P. D. N. BOLD: The barcode of life data system. Mol. Ecol. Notes 7, 355–364 (2007).

82.
Katoh, K. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).
ADS  CAS  PubMed  Article  Google Scholar 

84.
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

85.
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: Recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

86.
Wickham, H. ggplot2 (Springer, New York, 2009). https://doi.org/10.1007/978-0-387-98141-3.
Google Scholar 

87.
Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).
CAS  PubMed  Article  Google Scholar 

88.
Nei, M. Molecular evolutionary genetics (Columbia University Press, New York, 1987). More

1.
Vitousek, P. M., D’Antonio, C. M., Loope, L. L., Rejmánek, M. & Westbrooks, R. Introduced species: a significant component of human-caused global change. N. Z. J. Ecol. 21, 1–16 (1997).
Google Scholar 
2.
Courchamp, F. et al. Invasion biology: specific problems and possible solutions. Trends Ecol. Evol. 32, 13–22 (2017).
PubMed  Article  PubMed Central  Google Scholar 

3.
Sanders, N. J., Gotelli, N. J., Heller, N. E. & Gordon, D. M. Community disassembly by an invasive species. PNAS 100, 2474–2477 (2003).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Christian, C. E. Consequences of a biological invasion reveal the importance of mutualism for plant communities. Nature 413, 635–639 (2001).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Suarez, A. V., McGlynn, T. P. & Tsutsui, N. D. Biogeographic and taxonomic patterns of introduced ants. In Ant ecology (eds Lach, L. et al.) 233–244 (Oxford University Press, Oxford, 2010).
Google Scholar 

6.
Moloney, S. D. & Vanderwoude, C. Potential ecological impacts of red imported fire ants in eastern Australia. J. Agric. Urban Entomol. 20, 131–142 (2003).
Google Scholar 

7.
Rajesh, T. P., Ballullaya, U. P., Unni, A. P., Parvathy, S. & Sinu, P. A. Interactive effects of urbanization and year on invasive and native ant diversity of sacred groves of South India. Urban Ecosyst. 23, 1335–1348 (2020).
Article  Google Scholar 

8.
Hoffmann, B. D., Luque, G. M., Bellard, C., Holmes, N. D. & Donlan, C. J. Improving invasive ant eradication as a conservation tool: a review. Biol. Conserv. 198, 37–49 (2016).
Article  Google Scholar 

9.
Traveset, A. & Richardson, D. M. Biological invasions as disruptors of plant reproductive mutualisms. Trends Ecol. Evol. 21, 208–216 (2006).
PubMed  Article  PubMed Central  Google Scholar 

10.
Carney, S. E., Byerley, M. B. & Holway, D. A. Invasive Argentine ants (Linepithema humile) do not replace native ants as seed dispersers of Dendromecon rigida (Papaveraceae) in California, USA. Oecologia 135, 576–582 (2003).
ADS  PubMed  Article  PubMed Central  Google Scholar 

11.
Styrsky, J. D. & Eubanks, M. D. Ecological consequences of interactions between ants and honeydew-producing insects. Proc. R. Soc. B Biol. Sci. 274, 151–164 (2007).
Article  Google Scholar 

12.
Lach, L. Invasive ants: unwanted partners in ant-plant interactions?. Ann. Mo. Bot. Garden 90, 91–108 (2003).
Article  Google Scholar 

13.
Willmer, P. G. et al. Floral volatiles controlling ant behaviour. Funct. Ecol. 23, 888–900 (2009).
Article  Google Scholar 

14.
Vilamil, N., Boege, K. & Stone, G. N. Testing the distraction hypothesis: do extrafloral nectaries reduce ant-pollinator conflict?. J. Ecol. 107, 1377–1391 (2019).
Article  Google Scholar 

15.
Raine, N., Willmer, P. & Stone, G. Spatial structuring and floral avoidance behavior prevent ant-pollinator conflict in a Mexican antacacia. Ecology 83, 3086–3096 (2002).
Google Scholar 

16.
Weber, M. G., Porturas, L. D. & Keeler, K. H. World list of plants with extrafloral nectaries. www.extrafloralnectaries.org (2015)

17.
Dutton, E. M. & Frederickson, M. E. Why ant pollination is rare: new evidence and implications of the antibiotic hypothesis. Arthropod-Plant Interact. 6, 561–569 (2012).
Article  Google Scholar 

18.
Gonzálvez, F. G., Santamaría, L., Corlett, R. T. & Rodríguez-Gironés, M. A. Flowers attract weaver ants that deter less effective pollinators. J. Ecol. 101, 78–85 (2013).
Article  Google Scholar 

19.
Cembrowski, A. R., Tan, M. G., Thomson, J. D. & Frederickson, M. E. Ants and ant scent reduce bumblebee pollination of artificial flowers. Am. Nat. 183, 133–139 (2014).
PubMed  Article  PubMed Central  Google Scholar 

20.
Sinu, P. A. et al. Invasive ant (Anoplolepis gracilipes) disrupts pollination in pumpkin. Biol. Invasions 19, 2599–2607 (2017).
Article  Google Scholar 

21.
Hanna, C. et al. Floral visitation by the Argentine ant reduces bee visitation and plant seed set. Ecology 96, 222–230 (2015).
PubMed  Article  PubMed Central  Google Scholar 

22.
LeVan, K. E., Hung, K.-L.-J., McCann, K. R., Ludka, J. T. & Holway, D. A. Floral visitation by the Argentine ant reduces pollinator visitation and seed set in the coast barrel cactus, Ferocactus viridescens. Oecologia 174, 163–171 (2014).
ADS  PubMed  Article  PubMed Central  Google Scholar 

23.
Fuster, F., Kaiser-Bunbury, C. N. & Traveset, A. Pollination effectiveness of specialist and opportunistic nectar feeders influenced by invasive alien ants in the Seychelles. Am. J. Bot. 107, 957–969 (2020).
PubMed  Article  PubMed Central  Google Scholar 

24.
Bissessur, P., Baider, C. & Florens, F. B. V. Infestation by pollination-disrupting alien ants varies temporally and spatially and is worsened by alien plant invasion. Biol. Invasions 22, 2573–2585 (2020).
Article  Google Scholar 

25.
Del-Claro, K., Rodriguez-Morales, D., Calixto, E. S., Martins, A. S. & Torezan-Silingardi, H. M. Ant pollination of Paepalanthus lundii (Eriocaulaceae) in Brazilian savanna. Ann. Bot. 123, 1159–1165 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

26.
Kuriakose, G., Sinu, P. A. & Shivanna, K. R. Ant pollination of Syzygium occidentale, an endemic tree species of tropical rain forests of the Western Ghats, India. Arthropod-Plant Interact. 12, 647–655 (2018).
Article  Google Scholar 

27.
Galen, C. & Cuba, J. Down the tube: pollinators, predators, and the evolution of flower shape in the alpine skypilot Polemonium viscosum. Evolution 55, 1963–1971 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Tsuji, K., Hasyim Harlion, A. & Nakamura, K. Asian weaver ants, Oecophylla smaragdina, and their repelling of pollinators. Ecol. Res. 19, 669–673 (2004).
Article  Google Scholar 

29.
Ness, J. H. A mutualism’s indirect costs: the most aggressive plant bodyguards also deter pollinators. Oikos 113, 506–514 (2006).
Article  Google Scholar 

30.
Lach, L. Argentine ants displace floral arthropods in a biodiversity hotspot. Divers. Distrib. 14, 281–290 (2008).
Article  Google Scholar 

31.
Hansen, D. M. & Müller, C. B. Invasive ants disrupt gecko pollination and seed dispersal of the endangered plant Roussea simplex in Mauritius. Biotropica 41, 202–208 (2009).
Article  Google Scholar 

32.
Lach, L. Interference and exploitation competition of three nectar-thieving invasive ant species. Insectes Soc. 52, 257–262 (2005).
Article  Google Scholar 

33.
Blancafort, X. & Gómez, C. Consequences of the Argentine ant, Linepithema humile (Mayr), invasion on pollination of Euphorbia characias (L.) (Euphorbiaceae). Acta Oecol. 28, 49–55 (2005).
ADS  Article  Google Scholar 

34.
Holway, D. A. Competitive mechanisms underlying the displacement of native ants by the invasive Argentine ant. Ecology 80, 238–251 (1999).
Article  Google Scholar 

35.
Holway, D. A., Lach, L., Suarez, A. V., Tsutsui, N. D. & Case, T. J. The causes and consequences of ant invasions. Annu. Rev. Ecol. Syst. 33, 181–233 (2002).
Article  Google Scholar 

36.
Silverman, J. & Buczkowski, G. 13 Behaviours mediating ant invasions. in Biological Invasions and Animal Behaviour 221 (2016).

37.
Sinu, P. A. et al. Effect of flower sex ratio on fruit set in pumpkin (Cucurbita maxima). Sci. Hortic. 246, 1005–1008 (2019).
Article  Google Scholar 

38.
Sinu, P. A., Pooja, A. R. & Aneha, K. Overhead sprinkler irrigation affects pollinators and pollination in pumpkin (Cucurbita maxima). Sci. Hortic. 258, 108803 (2019).
Article  Google Scholar 

39.
Bharti, H., Guénard, B., Bharti, M. & Economo, E. P. An updated checklist of the ants of India with their specific distributions in Indian states (Hymenoptera, Formicidae). ZooKeys 551, 1–83 (2016).
Article  Google Scholar 

40.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2018).
Google Scholar 

41.
Anusree, T. et al. Flower sex expression in cucurbit crops of Kerala: implications for pollination and fruitset. Curr. Sci. 109, 2299–2302 (2015).
Article  Google Scholar 

42.
das Vidal, M. G., de Jong, D., Wien, H. C. & Morse, R. A. Produção de néctar e pólen em abóbora (Cucurbita pepo L). Braz. J. Bot. 29, 267–273 (2006).
Article  Google Scholar 

43.
Junker, R., Chung, A. Y. & Blüthgen, N. Interaction between flowers, ants and pollinators: additional evidence for floral repellence against ants. Ecol. Res. 22, 665–670 (2007).
Article  Google Scholar 

44.
Ibarra-Isassi, J. & Sendoya, S. F. Ants as floral visitors of Blutaparon portulacoides (A. St-Hil.) Mears (Amaranthaceae): an ant pollination system in the Atlantic Rainforest. Arthropod-Plant Interact. 10, 221–227 (2016).
Article  Google Scholar 

45.
Beattie, A. J., Turnbull, C., Knox, R. B. & Williams, E. G. Ant inhibition of pollen function: a possible reason why ant pollination is rare. Am. J. Bot. 71, 421–426 (1984).
Article  Google Scholar 

46.
Hickman, J. C. Pollination by ants: a low-energy system. Science 184, 1290–1292 (1974).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Galen, C. The effects of nectar thieving ants on seedset in floral scent morphs of Polemonium viscosum. Oikos 41, 245–249 (1983).
Article  Google Scholar 

48.
Witte, V., Attygalle, A. B. & Meinwald, J. Complex chemical communication in the crazy ant Paratrechina longicornis Latreille (Hymenoptera: Formicidae). Chemoecology 17, 57–62 (2007).
Article  Google Scholar 

49.
Wetterer, J. Worldwide spread of the ghost ant, Tapinoma melanocephalum (Hymenoptera: Formicidae). Myrmecol. News 12, 23–33 (2009).
Google Scholar 

50.
Todd, B. D. et al. Habitat alteration increases invasive fire ant abundance to the detriment of amphibians and reptiles. Biol. Invasions 10, 539–546 (2008).
Article  Google Scholar  More

1.
Boyer, J. S. Plant productivity and environment. Science 218, 443–448 (1982).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Abiko, T. et al. Enhanced formation of aerenchyma and induction of a barrier to radial oxygen loss in adventitious roots of Zea nicaraguensis contribute to its waterlogging tolerance as compared with maize (Zea mays ssp mays). Plant Cell Environ. 35, 1618–1630 (2012).
CAS  PubMed  Article  Google Scholar 

3.
Jackson, M. B. Ethylene and responses of plants to soil waterlogging and submergence. Annu. Rev. Plant Physiol. Plant Mol. Biol. 36, 145–174 (1985).
CAS  Article  Google Scholar 

4.
Colmer, T. D. & Voesenek, L. A. C. J. Flooding tolerance: Suites of plant traits in variable environments. Funct. Plant Biol. 36, 665–681 (2009).
CAS  PubMed  Article  Google Scholar 

5.
Bailey-Serres, J. & Voesenek, L. A. C. J. Flooding stress: Acclimations and genetic diversity. Annu. Rev. Plant Biol. 59, 313–339 (2008).
CAS  PubMed  Article  Google Scholar 

6.
Colmer, T. D. & Greenway, H. Ion transport in seminal and adventitious roots of cereals during O2 deficiency. J. Exp. Bot. 62, 39–57 (2011).
CAS  PubMed  Article  Google Scholar 

7.
Huang, S., Greenway, H. & Colmer, T. D. Responses of coleoptiles of intact rice seedlings to anoxia: K+ net uptake from the external solution and translocation from the caryopses. Ann. Bot. 91, 271–278 (2003).
CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Vartapetian, B. B. et al. Functional electron microscopy in studies of plant response and adaptation to anaerobic stress. Ann. Bot. 91, 155–172 (2003).
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Visser, E. J. W., Voesenek, L. A. C. J., Vartapetian, B. B. & Jackson, M. B. Flooding and plant growth. Ann. Bot. 91, 107–109 (2003).
CAS  PubMed Central  Article  PubMed  Google Scholar 

10.
Voesenek, L. A. & Bailey-Serres, J. Flood adaptive traits and processes: An overview. New Phytol. 206, 57–73 (2015).
CAS  PubMed  Article  Google Scholar 

11.
Evans, D. E. Aerenchyma formation. New Phytol. 161, 35–49 (2004).
Article  Google Scholar 

12.
Armstrong, W. Aeration in higher plants. In Advances in Botanical Research (ed. Woolhouse, H. W.) (Academic Press, Burlington, 1980).
Google Scholar 

13.
Colmer, T. D. Aerenchyma and an inducible barrier to radial oxygen loss facilitate root aeration in upland, paddy and deep-water rice (Oryza sativa L.). Ann. Bot. 91, 301–309 (2003).
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Jackson, M. B. & Armstrong, W. Formation of aerenchyma and the processes of plant ventilation in relation to soil flooding and submergence. Plant Biology 1, 274–287 (1999).
CAS  Article  Google Scholar 

15.
Seago, J. L. et al. A re-examination of the root cortex in wetland flowering plants with respect to aerenchyma. Ann. Bot. 96, 565–579 (2005).
PubMed  Article  Google Scholar 

16.
Drew, M. C., He, C. J. & Morgan, P. W. Programmed cell death and aerenchyma formation in roots. Trends Plant Sci. 5, 123–127 (2000).
CAS  PubMed  Article  Google Scholar 

17.
Yamauchi, T., Rajhi, I. & Nakazono, M. Lysigenous aerenchyma formation in maize root is confined to cortical cells by regulation of genes related to generation and scavenging of reactive oxygen species. Plant Signal. Behav. 6, 759–761 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Takahashi, H., Yamauchi, T., Colmer, T. D. & Nakazono, M. Aerenchyma formation in plants. in Low-Oxygen Stress in Plants: Oxygen Sensing and Adaptive Responses to Hypoxia 247–265. (Springer, Wien, 2014).

19.
Stevens, K. J., Peterson, R. L. & Reader, R. J. The aerenchymatous phellem of Lythrum salicaria (L.): A pathway for gas transport and its role in flood tolerance. Ann. Bot. 89, 621–625 (2002).
PubMed  PubMed Central  Article  Google Scholar 

20.
Shimamura, S., Mochizuki, T., Nada, Y. & Fukuyama, M. Formation and function of secondary aerenchyma in hypocotyl, roots and nodules of soybean (Glycine max) under flooded conditions. Plant Soil 251, 351–359 (2003).
CAS  Article  Google Scholar 

21.
Shimamura, S., Yamamoto, R., Nakamura, T., Shimada, S. & Komatsu, S. Stem hypertrophic lenticels and secondary aerenchyma enable oxygen transport to roots of soybean in flooded soil. Ann. Bot. 106, 277–284 (2010).
PubMed  PubMed Central  Article  Google Scholar 

22.
De Simone, O. et al. Impact of root morphology on metabolism and oxygen distribution in roots and rhizosphere from two Central Amazon floodplain tree species. Funct. Plant Biol. 29, 1025–1035 (2002).
PubMed  Article  Google Scholar 

23.
Colmer, T. D. & Pedersen, O. Oxygen dynamics in submerged rice (Oryza sativa). New Phytol. 178, 326–334 (2008).
CAS  PubMed  Article  Google Scholar 

24.
Haase, K., De Simone, O., Junk, W. J. & Schmidt, W. Internal oxygen transport in cuttings from flood-adapted várzea tree species. Tree Physiol. 23, 1069–1076 (2003).
PubMed  Article  Google Scholar 

25.
Sou, H. D., Masumori, M., Kurokochi, H. & Tange, T. Histological observation of primary and secondary aerenchyma formation in adventitious roots of Syzygium kunstleri (King) Bahadur and R. C. Gaur grown in hypoxic medium. Forests 10, 137 (2019).
Article  Google Scholar 

26.
Rubinigg, M., Stulen, I., Elzenga, J. T. M. & Colmer, T. D. Spatial patterns of radial oxygen loss and nitrate net flux along adventitious roots of rice raised in aerated or stagnant solution. Funct. Plant Biol. 29, 1475–1481 (2002).
CAS  PubMed  Article  Google Scholar 

27.
Kotula, L., Ranathunge, K., Schreiber, L. & Steudle, E. Functional and chemical comparison of apoplastic barriers to radial oxygen loss in roots of rice (Oryza sativa L.) grown in aerated or deoxygenated solution. J. Exp. Bot. 60, 2155–2167 (2009).
CAS  PubMed  Article  Google Scholar 

28.
Shiono, K. et al. Contrasting dynamics of radial O2-loss barrier induction and aerenchyma formation in rice roots of two lengths. Ann. Bot. 107, 89–99 (2011).
CAS  PubMed  Article  Google Scholar 

29.
Watanabe, K., Nishiuchi, S., Kulichikhin, K. & Nakazono, M. Does suberin accumulation in plant roots contribute to waterlogging tolerance?. Front. Plant Sci. 4, 178 (2013).
PubMed  PubMed Central  Article  Google Scholar 

30.
Khan, N. et al. Root iron plaque on wetland plants as dynamic pool of nutrients and contaminants. In Advances in Agronomy Vol. 138 (ed. Sparks, D. L.) 1–96 (Academic Press, Cambridge, 2016).
Google Scholar 

31.
Uteau, D. et al. Oxygen and redox potential gradients in the rhizosphere of alfalfa grown on a loamy soil. J. Plant Nutr. Soil Sci. 178, 278–287 (2015).
CAS  Article  Google Scholar 

32.
Tian, C., Wang, C., Tian, Y., Wu, X. & Xiao, B. Root radial oxygen loss and the effects on rhizosphere microarea of two submerged plants. Polish J. Environ. Studies 24, 1795–1802 (2015).
Article  Google Scholar 

33.
Shimamura, S., Mochizuki, T., Nada, Y. & Fukuyama, M. Secondary aerenchyma formation and its relation to nitrogen fixation in root nodules of soybean plants (Glycine max) grown under flooded conditions. Plant Product. Sci. 5, 294–300 (2002).
CAS  Article  Google Scholar 

34.
Shiba, H. & Daimon, H. Histological observation of secondary aerenchyma formed immediately after flooding in Sesbania cannabina and S. rostrata. Plant Soil 255, 209–215 (2003).
CAS  Article  Google Scholar 

35.
Somavilla, N. S. & Graciano-Ribeiro, D. Ontogeny and characterization of aerenchymatous tissues of Melastomataceae in the flooded and well-drained soils of a Neotropical savanna. Flora 207, 212–222 (2012).
Article  Google Scholar 

36.
Thomas, A. L., Guerreiro, S. M. C. & Sodek, L. Aerenchyma formation and recovery from hypoxia of the flooded root system of nodulated soybean. Ann. Bot. 96, 1191–1198 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Wiengweera, A., Greenway, H. & Thomson, C. J. The use of agar nutrient solution to simulate lack of convection in waterlogged soils. Ann. Bot. 80, 115–123 (1997).
Article  Google Scholar 

38.
Dacey, J. W. Internal winds in water lilies: An adaptation for life in anaerobic sediments. Science 210, 1017–1019 (1980).
ADS  CAS  PubMed  Article  Google Scholar 

39.
Drew, M. C., Saglio, P. H. & Pradet, A. J. P. Larger adenylate energy charge and ATP/ADP ratios in aerenchymatous roots of Zea mays in anaerobic media as a consequence of improved internal oxygen transport. Planta 165, 51–58 (1985).
CAS  PubMed  Article  Google Scholar 

40.
Drew, M. C. Oxygen deficiency and root metabolism: Injury and acclimation under hypoxia and anoxia. Annu. Rev. Plant Physiol. Plant Mol. Biol. 48, 223–250 (1997).
CAS  PubMed  Article  Google Scholar 

41.
Shimamura, S., Yoshida, S. & Mochizuki, T. Cortical aerenchyma formation in hypocotyl and adventitious roots of Luffa cylindrica subjected to soil flooding. Ann. Bot. 100, 1431–1439 (2007).
PubMed  PubMed Central  Article  Google Scholar 

42.
Armstrong, W., Cousins, D., Armstrong, J., Turner, D. W. & Beckett, P. M. Oxygen distribution in wetland plant roots and permeability barriers to gas-exchange with the rhizosphere: A microelectrode and modelling study with Phragmites australis. Ann. Bot. 86, 687–703 (2000).
Article  Google Scholar 

43.
Herzog, M. & Pedersen, O. Partial versus complete submergence: Snorkelling aids root aeration in Rumex palustris but not in R. acetosa. Plant Cell Environ. 37, 2381–2390 (2014).
CAS  PubMed  Google Scholar 

44.
Tanaka, K., Masumori, M., Yamanoshita, T. & Tange, T. Morphological and anatomical changes of Melaleuca cajuputi under submergence. Trees 25, 695–704 (2011).
Article  Google Scholar 

45.
Armstrong, W. Polarographic oxygen electrodes and their use in plant aeration studies. Proc. R. Soc. Edinburgh Sect. B. Biol. Sci. 102, 511–527 (1994).
Article  Google Scholar 

46.
Hitchman, M. L. Measurement of Dissolved Oxygen (Wiley, New York, 1978).
Google Scholar 

47.
Ober, E. S. & Sharp, R. E. A microsensor for direct measurement of O2 partial pressure within plant tissues. J. Exp. Bot. 47, 447–454 (1996).
CAS  Article  Google Scholar  More

1.
Dirzo, R. et al. Defaunation in the anthropocene. Science 345, 401–406 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Boivin, N. L. et al. Ecological consequences of human niche construction: examining long-term anthropogenic shaping of global species distributions. Proc. Natl. Acad. Sci. 113, 6388–6396 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Hagen, M. et al. Biodiversity, species interactions and ecological networks in a fragmented world. In Advances in Ecological Research Vol. 46 (eds Jacob, U. & Woodward, G.) 89–210 (Academic Press, Cambridge, 2012).
Google Scholar 

5.
Gallego-Zamorano, J. et al. Combined effects of land use and hunting on distributions of tropical mammals. Conserv. Biol. 34, 1271–1280 (2020).
PubMed  PubMed Central  Article  Google Scholar 

6.
Ellis, E. C. & Ramankutty, N. Putting people in the map: anthropogenic biomes of the world. Front. Ecol. Environ. 6, 439–447 (2008).
Article  Google Scholar 

7.
Estrada, A., Raboy, B. E. & Oliveira, L. C. Agroecosystems and primate conservation in the tropics: a review. Am. J. Primatol. 74, 696–711 (2012).
PubMed  Article  Google Scholar 

8.
Bhagwat, S. A., Willis, K. J., Birks, H. J. B. & Whittaker, R. J. Agroforestry: a refuge for tropical biodiversity?. Trends Ecol. Evol. 23, 261–267 (2008).
PubMed  Article  Google Scholar 

9.
Galán-Acedo, C. et al. The conservation value of human-modified landscapes for the world’s primates. Nat. Commun. 10, 1–8 (2019).
Article  CAS  Google Scholar 

10.
Arroyo-Rodríguez, V. et al. Designing optimal human-modified landscapes for forest biodiversity conservation. Ecol. Lett. 23, 1404–1420 (2020).
PubMed  Article  Google Scholar 

11.
Kshettry, A., Vaidyanathan, S., Sukumar, R. & Athreya, V. Looking beyond protected areas: identifying conservation compatible landscapes in agro-forest mosaics in north-eastern India. Glob. Ecol. Conserv. 22, e00905 (2020).
Article  Google Scholar 

12.
Osborn, F. V. & Hill, C. M. Techiques to reduce crop loss: human and technical dimensions in Africa. In People and Wildlife, Conflict or Co-existence? 72–85 (Cambridge University Press, Cambridge, 2005).

13.
McLennan, M. R. & Asiimwe, C. Cars kill chimpanzees: case report of a wild chimpanzee killed on a road at Bulindi, Uganda. Primates J. Primatol. 57, 377–388 (2016).
Article  Google Scholar 

14.
Chapman, C. A. et al. Do food availability, parasitism, and stress have synergistic effects on red colobus populations living in forest fragments?. Am. J. Phys. Anthropol. 131, 525–534 (2006).
PubMed  Article  Google Scholar 

15.
Goldberg, T. L., Gillespie, T. R., Rwego, I. B., Estoff, E. L. & Chapman, C. A. Forest fragmentation as cause of bacterial transmission among nonhuman primates, humans, and livestock, Uganda. Emerg. Infect. Dis. 14, 1375–1382 (2008).
PubMed  PubMed Central  Article  Google Scholar 

16.
McLennan, M. R., Hyeroba, D., Asiimwe, C., Reynolds, V. & Wallis, J. Chimpanzees in mantraps: lethal crop protection and conservation in Uganda. Oryx 46, 598–603 (2012).
Article  Google Scholar 

17.
Kalema-Zikusoka, G., Rubanga, S., Mutahunga, B. & Sadler, R. Prevention of Cryptosporidium and GIARDIA at the human/gorilla/livestock interface. Front. Public Health 6, (2018).

18.
Kenney, J., Allendorf, F. W., McDougal, C. & Smith, J. L. D. How much gene flow is needed to avoid inbreeding depression in wild tiger populations?. Proc. R. Soc. B Biol. Sci. 281, 20133337 (2014).
Article  Google Scholar 

19.
Willems, E. P. & Hill, R. A. Predator-specific landscapes of fear and resource distribution: effects on spatial range use. Ecology 90, 546–555 (2009).
PubMed  Article  PubMed Central  Google Scholar 

20.
Coleman, B. T. & Hill, R. A. Living in a landscape of fear: the impact of predation, resource availability and habitat structure on primate range use. Anim. Behav. 88, 165–173 (2014).
Article  Google Scholar 

21.
Palmer, M. S., Fieberg, J., Swanson, A., Kosmala, M. & Packer, C. A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle. Ecol. Lett. 20, 1364–1373 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

22.
Laundré, J. W., Hernandez, L. & Ripple, W. J. The landscape of fear: ecological implications of being afraid. Open Ecol. J. 3, (2010).

23.
Theuerkauf, J. & Rouys, S. Habitat selection by ungulates in relation to predation risk by wolves and humans in the Białowieża Forest, Poland. For. Ecol. Manag. 256, 1325–1332 (2008).
Article  Google Scholar 

24.
Ciuti, S. et al. Effects of humans on behaviour of wildlife exceed those of natural predators in a landscape of fear. PLoS ONE 7, e50611 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

25.
Nowak, K., Wimberger, K., Richards, S. A., Hill, R. A. & le Roux, A. Samango monkeys (Cercopithecus albogularis labiatus) manage risk in a highly seasonal, human-modified landscape in Amathole Mountains, South Africa. Int. J. Primatol. 38, 194–206 (2017).
PubMed  Article  Google Scholar 

26.
Suraci, J. P., Clinchy, M., Zanette, L. Y. & Wilmers, C. C. Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. Ecol. Lett. 22, 1578–1586 (2019).
PubMed  Article  Google Scholar 

27.
Carter, N. H., Shrestha, B. K., Karki, J. B., Pradhan, N. M. B. & Liu, J. Coexistence between wildlife and humans at fine spatial scales. Proc. Natl. Acad. Sci. 109, 15360–15365 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

28.
Carter, N. H., Jasny, M., Gurung, B. & Liu, J. Impacts of people and tigers on leopard spatiotemporal activity patterns in a global biodiversity hotspot. Glob. Ecol. Conserv. 3, 149–162 (2015).
Article  Google Scholar 

29.
Lamb, C. T. et al. The ecology of human–carnivore coexistence. Proc. Natl. Acad. Sci. 117, 17876–17883. https://doi.org/10.1073/pnas.1922097117 (2020).
CAS  Article  PubMed  Google Scholar 

30.
Bryson-Morrison, N., Tzanopoulos, J., Matsuzawa, T. & Humle, T. Activity and habitat use of chimpanzees (Pan troglodytes verus) in the anthropogenic landscape of Bossou, Guinea, West Africa. Int. J. Primatol. 38, 282–302 (2017).
PubMed  PubMed Central  Article  Google Scholar 

31.
de Almeida-Rocha, J. M., Peres, C. A. & Oliveira, L. C. Primate responses to anthropogenic habitat disturbance: a pantropical meta-analysis. Biol. Conserv. 215, 30–38 (2017).
Article  Google Scholar 

32.
Galán‐Acedo, C., Arroyo‐Rodríguez, V., Cudney‐Valenzuela, S. J. & Fahrig, L. A global assessment of primate responses to landscape structure. Biol. Rev. 94, 1605–1618 (2019).
PubMed  Article  PubMed Central  Google Scholar 

33.
Garriga, R. M. et al. Factors influencing wild chimpanzee (Pan troglodytes verus) relative abundance in an agriculture-swamp matrix outside protected areas. PLoS ONE 14, e0215545 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Hockings, K. J., Anderson, J. R. & Matsuzawa, T. Road crossing in chimpanzees: a risky business. Curr. Biol. 16, R668–R670 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Estrada, A. et al. Impending extinction crisis of the world’s primates: why primates matter. Sci. Adv. 3, e1600946 (2017).
ADS  PubMed  PubMed Central  Article  Google Scholar 

36.
IUCN SSC Primate Specialist Group. Regional action plan for the conservation of western chimpanzees (Pan troglodytes verus) 2020–2030. (2020).

37.
Kalan, A. K. et al. Environmental variability supports chimpanzee behavioural diversity. Nat. Commun. 11, 4451 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Hockings, K. J., Anderson, J. R. & Matsuzawa, T. Socioecological adaptations by chimpanzees, Pan troglodytes verus, inhabiting an anthropogenically impacted habitat. Anim. Behav. 83, 801–810 (2012).
Article  Google Scholar 

39.
McLennan, M. R. & Hockings, K. J. Wild chimpanzees show group differences in selection of agricultural crops. Sci. Rep. 4, 5956 (2014).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Kalan, A. K. et al. Novelty response of wild African apes to camera traps. Curr. Biol.  29, 1211–1217.e3 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

41.
Hockings, K. J. & McLennan, M. R. From forest to farm: systematic review of cultivar feeding by chimpanzees—management implications for wildlife in anthropogenic landscapes. PLoS ONE 7, e33391 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Hockings, K. J., Anderson, J. R. & Matsuzawa, T. Use of wild and cultivated foods by chimpanzees at Bossou, Republic of Guinea: feeding dynamics in a human-influenced environment. Am. J. Primatol. 71, 636–646 (2009).
PubMed  Article  PubMed Central  Google Scholar 

43.
McLennan, M. R. Diet and feeding ecology of chimpanzees (Pan troglodytes) in Bulindi, Uganda: foraging strategies at the forest–farm interface. Int. J. Primatol. 34, 585–614 (2013).
Article  Google Scholar 

44.
McLennan, M. R. & Ganzhorn, J. U. Nutritional characteristics of wild and cultivated foods for chimpanzees (Pan troglodytes) in agricultural landscapes. Int. J. Primatol. 38, 122–150 (2017).
Article  Google Scholar 

45.
Matthews, A. & Matthews, A. Survey of gorillas (Gorilla gorilla gorilla) and chimpanzees (Pan troglodytes troglodytes) in Southwestern Cameroon. Primates 45, 15–24 (2004).
PubMed  Article  PubMed Central  Google Scholar 

46.
Morgan, D. et al. African apes coexisting with logging: comparing chimpanzee (Pan troglodytes troglodytes) and gorilla (Gorilla gorilla gorilla) resource needs and responses to forestry activities. Biol. Conserv. 218, 277–286 (2018).
Article  Google Scholar 

47.
Krief, S. et al. Wild chimpanzees on the edge: nocturnal activities in croplands. PLoS ONE 9, e109925 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Riley, E. P. & Priston, N. E. C. Macaques in farms and folklore: exploring the human–nonhuman primate interface in Sulawesi, Indonesia. Am. J. Primatol. 72, 848–854 (2010).
PubMed  Article  PubMed Central  Google Scholar 

49.
Parathian, H. E., McLennan, M. R., Hill, C. M., Frazão-Moreira, A. & Hockings, K. J. Breaking through disciplinary barriers: human–wildlife interactions and multispecies ethnography. Int. J. Primatol. 39, 749–775. https://doi.org/10.1007/s10764-018-0027-9 (2018).
Article  PubMed  PubMed Central  Google Scholar 

50.
Fuentes, A. & Gamerl, S. Disproportionate participation by age/sex classes in aggressive interactions between long-tailed macaques (Macaca fascicularis) and human tourists at Padangtegal monkey forest, Bali, Indonesia. Am. J. Primatol. 66, 197–204 (2005).
PubMed  Article  Google Scholar 

51.
McLennan, M. R. & Hockings, K. J. The aggressive apes? Causes and contexts of great ape attacks on local persons. In Problematic Wildlife (ed. Angelici, F. M.) 373–394 (Springer, Cham, 2016). https://doi.org/10.1007/978-3-319-22246-2_18.

52.
Hill, C. M. & Webber, A. D. Perceptions of nonhuman primates in human–wildlife conflict scenarios. Am. J. Primatol. 72, 919–924 (2010).
PubMed  Article  Google Scholar 

53.
McLennan, M. R. & Hill, C. M. Troublesome neighbours: changing attitudes towards chimpanzees (Pan troglodytes) in a human-dominated landscape in Uganda. J. Nat. Conserv. 20, 219–227 (2012).
Article  Google Scholar 

54.
Mito, Y. & Sprague, D. S. The Japanese and Japanese monkeys: dissonant neighbors seeking accommodation in a shared habitat. In The Macaque Connection: Cooperation and Conflict Between Humans and Macaques (eds Radhakrishna, S. et al.) 33–51 (Springer, Berlin, 2013).
Google Scholar 

55.
Morzillo, A., de Beurs, K. & Martin-Mikle, C. A conceptual framework to evaluate human-wildlife interactions within coupled human and natural systems. Ecol. Soc. 19, (2014).

56.
Martin, J. et al. Coping with human disturbance: spatial and temporal tactics of the brown bear (Ursus arctos). Can. J. Zool. 88, 875–883 (2010).
Article  Google Scholar 

57.
Hockings, K. J. et al. Chimpanzees share forbidden fruit. PLoS ONE 2, e886 (2007).
ADS  PubMed  PubMed Central  Article  Google Scholar 

58.
Duvall, C. S. Human settlement ecology and chimpanzee habitat selection in Mali. Landsc. Ecol. 23, 699 (2008).
Article  Google Scholar 

59.
Hockings, K. J., Parathian, H., Bessa, J. & Frazão-Moreira, A. Extensive overlap in the selection of wild fruits by chimpanzees and humans: implications for the management of complex social-ecological systems. Front. Ecol. Evol. 8, (2020).

60.
Nowak, K., Hill, R. A., Wimberger, K. & le Roux, A. Risk-taking in samango monkeys in relation to humans at two sites in South Africa. In Ethnoprimatology: Primate Conservation in the 21st Century (ed. Waller, M. T.) 301–314 (Springer, Berlin, 2016).
Google Scholar 

61.
INE. Recenseamento Geral da População e Habitação: População por Região, Sector e Localidades por Sexo Censo 2009. 160 (2009).

62.
Heinicke, S. et al. Characteristics of positive deviants in western chimpanzee populations. Front. Ecol. Evol. 7, (2019).

63.
Bersacola, E. Zooming in on Human-Wildlife Coexistence: Primate Community Responses in a Shared Agroforest Landscape in Guinea-Bissau (Oxford Brookes University, Oxford, 2020).
Google Scholar 

64.
Bessa, J., Sousa, C. & Hockings, K. J. Feeding ecology of chimpanzees (Pan troglodytes verus) inhabiting a forest-mangrove-savanna-agricultural matrix at Caiquene-Cadique, Cantanhez National Park, Guinea-Bissau. Am. J. Primatol. 77, 651–665 (2015).
PubMed  Article  Google Scholar 

65.
Hockings, K. J. et al. Leprosy in wild chimpanzees. bioRxiv 2020.11.10.374371 (2020) https://doi.org/10.1101/2020.11.10.374371.

66.
Hockings, K. J. & Sousa, C. Differential utilization of cashew—a low-conflict crop—by sympatric humans and chimpanzees. Oryx 46, 375–381 (2012).
Article  Google Scholar 

67.
Calenge, C. The package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2006).
Article  Google Scholar 

68.
Schmid, F. & Schmidt, A. Nonparametric estimation of the coefficient of overlapping—theory and empirical application. Comput. Stat. Data Anal. 50, 1583–1596 (2006).
MathSciNet  MATH  Article  Google Scholar 

69.
Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Stat. 14, 322–337 (2009).
MathSciNet  MATH  Article  Google Scholar 

70.
Hijmans, R. J. Raster: geographic data analysis and modeling. (2020).

71.
Khorozyan, I., Stanton, D., Mohammed, M., Al-Rail, W. & Pittet, M. Patterns of co-existence between humans and mammals in Yemen: some species thrive while others are nearly extinct. Biodivers. Conserv. 23, 1995–2013 (2014).
Article  Google Scholar 

72.
Sousa, J., Barata, A. V., Sousa, C., Casanova, C. C. N. & Vicente, L. Chimpanzee oil-palm use in southern Cantanhez National Park, Guinea-Bissau. Am. J. Primatol. 73, 485–497 (2011).
PubMed  Article  PubMed Central  Google Scholar 

73.
Tutin, C. E. G. et al. Foraging profiles of sympatric lowland gorillas and chimpanzees in the Lope Reserve, Gabon [and discussion]. Philos. Trans. Biol. Sci. 334, 179–186 (1991).
ADS  CAS  Article  Google Scholar 

74.
Yamakoshi, G. Dietary responses to fruit scarcity of wild chimpanzees at Bossou, Guinea: possible implications for ecological importance of tool use. Am. J. Phys. Anthropol. 106, 283–295 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Wilson, M. L., Hauser, M. D. & Wrangham, R. W. Chimpanzees (Pan troglodytes) modify grouping and vocal behaviour in response to location-specific risk. Behaviour 144, 1621–1653 (2007).
Article  Google Scholar 

76.
Lindshield, S., Danielson, B. J., Rothman, J. M. & Pruetz, J. D. Feeding in fear? How adult male western chimpanzees (Pan troglodytes verus) adjust to predation and savanna habitat pressures. Am. J. Phys. Anthropol. 163, 480–496 (2017).
PubMed  Article  PubMed Central  Google Scholar 

77.
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).
Article  Google Scholar 

78.
Sousa, J., Vicente, L., Gippoliti, S., Casanova, C. & Sousa, C. Local knowledge and perceptions of chimpanzees in Cantanhez National Park, Guinea-Bissau. Am. J. Primatol. 76, 122–134 (2014).
PubMed  Article  PubMed Central  Google Scholar 

79.
Sharma, K. et al. Conservation and people: towards an ethical code of conduct for the use of camera traps in wildlife research. Ecol. Solut. Evid. 1, e12033 (2020).
Article  Google Scholar 

80.
Sun, C. et al. Tree phenology in a tropical montane forest in Rwanda. Biotropica 28, 668–681 (1996).
Article  Google Scholar 

81.
McLennan, M. R. Chimpanzee Ecology and Interactions with People in an Unprotected Human-Dominated Landscape at Bulindi, Western Uganda (Oxford Brookes University, Oxford, 2010).
Google Scholar 

82.
Jenks, K. E. et al. Using relative abundance indices from camera-trapping to test wildlife conservation hypotheses—an example from Khao Yai National Park, Thailand. Trop. Conserv. Sci. 4, 113–131 (2011).
ADS  Article  Google Scholar 

83.
O’Brien, T. G., Kinnaird, M. F. & Wibisono, H. T. Crouching tigers, hidden prey: sumatran tiger and prey populations in a tropical forest landscape. Anim. Conserv. Forum 6, 131–139 (2003).
Article  Google Scholar 

84.
Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Ser. B Stat. Methodol. 71, 319–392 (2009).
MathSciNet  MATH  Article  Google Scholar 

85.
Blangiardo, M., Cameletti, M., Baio, G. & Rue, H. Spatial and spatio-temporal models with R-INLA. Spat. Spatio-Temporal Epidemiol. 4, 33–49 (2013).
Article  Google Scholar 

86.
Cameletti, M., Lindgren, F., Simpson, D. & Rue, H. Spatio-temporal modeling of particulate matter concentration through the SPDE approach. AStA Adv. Stat. Anal. 97, 109–131 (2013).
MathSciNet  MATH  Article  Google Scholar 

87.
Lindgren, F., Rue, H. & Lindström, J. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. J. R Stat. Soc. Ser. B Stat. Methodol. 73, 423–498 (2011).
MathSciNet  MATH  Article  Google Scholar 

88.
Bakka, H. et al. Spatial modeling with R-INLA: a review. Wiley Interdiscip. Rev. Comput. Stat. 10, e1443 (2018).
MathSciNet  Article  Google Scholar 

89.
Noor, A. M. et al. The changing risk of Plasmodium falciparum malaria infection in Africa: 2000–10: a spatial and temporal analysis of transmission intensity. Lancet 383, 1739–1747 (2014).
PubMed  PubMed Central  Article  Google Scholar 

90.
Rue, H. et al. Bayesian computing with INLA: a review. Annu. Rev. Stat. Its Appl. 4, 395–421 (2017).
ADS  Article  Google Scholar 

91.
Cressie, N. & Wikle, C. K. Statistics for Spatio-Temporal Data (Wiley, Hoboken, 2015).
Google Scholar 

92.
Lindgren, F. & Rue, H. Bayesian spatial modelling with R-INLA. J. Stat. Softw. 63, 1–25 (2015).
Article  Google Scholar 

93.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Linde, A. V. D. Bayesian measures of model complexity and fit. J. R. Stat. Soc. Ser. B Stat. Methodol. 64, 583–639 (2002).
MathSciNet  MATH  Article  Google Scholar 

94.
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020). More

1.
Williams, B. K., Nichols, J. D. & Conroy, M. J. Analysis and Management of Animal Populations (Academic Press, New York, 2002).
Google Scholar 
2.
Lindenmayer, D. B. & Likens, G. E. Adaptive monitoring: a new paradigm for long-term research and monitoring. Trends Ecol. Evol. 24, 482–486 (2009).
Article  Google Scholar 

3.
MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).
Article  Google Scholar 

4.
Buckland, S. T., Rexstad, E. A., Marques, T. A. & Oedekoven, C. S. Distance Sampling: Methods and Applications (Springer, Berlin, 2015).
Google Scholar 

5.
Royle, J. A. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60, 108–115 (2004).
MathSciNet  Article  Google Scholar 

6.
Kéry, M. & Royle, J. A. Applied Hierarchical Modelling in Ecology (Academic Press, New York, 2016).
Google Scholar 

7.
Ariefiandy, A. et al. Evaluation of three field monitoring-density estimation protocols and their relevance to Komodo dragon conservation. Biodivers. Conserv. 23, 2473–2490 (2014).
Article  Google Scholar 

8.
Romano, A. et al. Conservation of salamanders in managed forests: methods and costs of monitoring abundance and habitat selection. For. Ecol. Manag. 400, 12–18 (2017).
Article  Google Scholar 

9.
Chandler, R. B., Royle, J. A. & King, D. I. Inference about density and temporary emigration in unmarked populations. Ecology 92, 1429–1435 (2011).
Article  Google Scholar 

10.
Dail, D. & Madsen, L. Models for estimating abundance from repeated counts of an open metapopulation. Biometrics 67, 577–587 (2011).
MathSciNet  CAS  Article  Google Scholar 

11.
Augustynczik, L. D. et al. Diversification of forest management regimes secures tree microhabitats and bird abundance under climate change. Sci. Total Environ. 650, 2717–2730 (2019).
ADS  CAS  Article  Google Scholar 

12.
Peterman, W. E. & Semlitsch, R. D. Fine-scale habitat associations of a terrestrial salamander: the role of environmental gradients and implications for population dynamics. PLoS ONE 8, e62184. https://doi.org/10.1371/journal.pone.0062184 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
Balestrieri, R. et al. A guild-based approach to assessing the influence of beech forest structure on bird communities. For. Ecol. Manage. 356, 216–223 (2015).
Article  Google Scholar 

14.
Barker, R. J., Schofield, M. R., Link, W. A. & Sauer, J. R. On the reliability of N-mixture models for count data. Biometrics 74, 369–377 (2017).
MathSciNet  Article  Google Scholar 

15.
Link, W. A., Schofield, M. R., Barker, R. J. & Sauer, J. R. On the robustness of N-mixture models. Ecology 99, 1547–1551 (2018).
Article  Google Scholar 

16.
Kéry, M. Identifiability in N-mixture models: a large-scale screening test with bird data. Ecology 99, 281–288 (2018).
Article  Google Scholar 

17.
Priol, P. M. Using dynamic N-mixture models to test cavity limitation on northern flying squirrel demographic parameters using experimental nest box supplementation. Ecol. Evol. 4, 2165–2177 (2014).
Article  Google Scholar 

18.
Basile, M. et al. Measuring bird abundance—a comparison of methodologies between capture/recapture and audio-visual surveys. Avocetta 40, 55–61 (2016).
Google Scholar 

19.
Ficetola, G. F. et al. N-mixture models reliably estimate the abundance of small vertebrates. Sci. Rep. 8, 10357. https://doi.org/10.1038/s41598-018-28432-8 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

20.
Costa, A., Oneto, F. & Salvidio, S. Time-for-space substitution in N-mixture modeling and population monitoring. J. Wildl. Manag. 83, 737–741 (2019).
Article  Google Scholar 

21.
Yamaura, Y. et al. Modelling community dynamics based on species-level abundance models from detection/nondetection data. J. Appl. Ecol. 48, 67–75 (2011).
Article  Google Scholar 

22.
Dennis, E. B., Morgan, B. J. & Ridout, M. S. Computational aspects of N-mixture models. Biometrics 71, 237–246 (2015).
MathSciNet  Article  Google Scholar 

23.
Duarte, A., Adams, M. J. & Peterson, J. T. Fitting N-mixture models to count data with unmodeled heterogeneity: bias, diagnostics, and alternative approaches. Ecol. Model. 374, 51–59 (2018).
Article  Google Scholar 

24.
Costa, A., Romano, A. & Salvidio, S. Reliability of multinomial N-mixture models for estimating abundance of small terrestrial vertebrates. Biodiv. Conserv. 29, 2951–2965 (2020).
Article  Google Scholar 

25.
Ficetola, G. F., Romano, A., Salvidio, S. & Sindaco, R. Optimizing monitoring schemes to detect trends in abundance over broad scales. Anim. Conserv. 21, 221–231 (2018).
Article  Google Scholar 

26.
Fiske, I. & Chandler, R. unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Soft. 43, 1–23 (2011).
Article  Google Scholar 

27.
Kéry, M., Royle, J.A., & Meredith, M. Package AHMbook version 0.1.4 (2016).

28.
MacKenzie, D. I. & Bailey, L. L. Assessing the fit of site-occupancy models. J. Agric. Biol. Environ. Stat. 9, 300–318 (2004).
Article  Google Scholar 

29.
Knape, J. et al. Sensitivity of binomial N-mixture models to overdispersion: the importance of assessing model fit. Met. Ecol. Evol. 9, 2102–2114 (2018).
Article  Google Scholar 

30.
Mazerolle, M. J. AICcmodavg: model selection and multimodel inference based on (Q)AIC(c). R package version 2.1-1 (2017).

31.
McIntyre, A. P. Empirical and simulation evaluations of an abundance estimator using unmarked individuals of cryptic forest-dwelling taxa. For. Ecol. Manage. 286, 129–136 (2012).
Article  Google Scholar 

32.
Veech, J. A., Ott, J. R. & Troy, J. R. Intrinsic heterogeneity in detection probability and its effect on N-mixture models. Met. Ecol. Evol. 7, 1019–1028 (2016).
Article  Google Scholar 

33.
Gervasi, V. A preliminary estimate of the apennine brown bear population size based on hair-snag sampling and multiple data source mark–recapture huggins models. Ursus 19, 105–121 (2008).
Article  Google Scholar 

34.
Welsh, H. N. & Conroy, M. J. A Case for using plethodontid salamanders for monitoring biodiversity and ecosystem integrity of North American forests. Conserv. Biol. 15, 558–569 (2001).
Article  Google Scholar 

35.
Warton, D. I., Stoklosa, J., Guillera-Arroita, G., MacKenzie, D. I. & Welsh, A. H. Graphical diagnostics for occupancy models with imperfect detection. Met. Ecol. Evol. 8, 408–419 (2017).
Article  Google Scholar 

36.
Lunghi, E. Environmental suitability models predict population density, performance and body condition for microendemic salamanders. Sci. Rep. 8, 7527. https://doi.org/10.1038/s41598-018-25704-1 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

37.
Kopler, I. & Malkinson, D. Differential response of mammals to agricultural fences: the effects of species vagility and body size. Basic Appl. Ecol. 33, 79–88 (2018).
Article  Google Scholar  More

1.
Hoffmann, B. D. & Broadhurst, L. M. The economic cost of managing invasive species in Australia. NeoBiota 31, 1–18 (2016).
Article  Google Scholar 
2.
Dueñas, M. A. et al. The role played by invasive species in interactions with endangered and threatened species in the United States: a systematic review. Biodivers. Conserv. 27, 3171–3183 (2018).
Article  Google Scholar 

3.
Dudgeon, D. et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol. Rev. Camb. Philos. Soc. 81, 163–182 (2006).
PubMed  Article  PubMed Central  Google Scholar 

4.
Ricciardi, A. & MacIsaac, H. J. Impacts of biological invasions on freshwater ecosystems. Fifty Years Invas. Ecol. Legacy Charles Elton https://doi.org/10.1002/9781444329988.ch16 (2010).
Article  Google Scholar 

5.
Moorhouse, T. P. & Macdonald, D. W. Are invasives worse in freshwater than terrestrial ecosystems?. Wiley Interdiscip. Rev. Water 2, 1–8 (2015).
Article  Google Scholar 

6.
Rosewarne, P. J. et al. Feeding behaviour, predatory functional responses and trophic interactions of the invasive Chinese mitten crab (Eriocheir sinensis) and signal crayfish (Pacifastacus leniusculus). Freshw. Biol. 61, 426–443 (2016).
Article  Google Scholar 

7.
Dick, J. T. A. et al. Advancing impact prediction and hypothesis testing in invasion ecology using a comparative functional response approach. Biol. Invasions 16, 735–753 (2014).
Article  Google Scholar 

8.
Dick, J. T. A. et al. Invader Relative Impact Potential: a new metric to understand and predict the ecological impacts of existing, emerging and future invasive alien species. J. Appl. Ecol. 54, 1259–1267 (2017).
Article  Google Scholar 

9.
Cuthbert, R. N., Dickey, J. W. E., Coughlan, N. E., Joyce, P. W. S. & Dick, J. T. A. The Functional Response Ratio (FRR): advancing comparative metrics for predicting the ecological impacts of invasive alien species. Biol. Invasions 21, 2543–2547 (2019).
Article  Google Scholar 

10.
Devin, S., Piscart, C., Beisel, J. N. & Moreteau, J. C. Life History Traits of the Invader Dikerogammarus villosus (Crustacea: Amphipoda) in the Moselle River. France. Int. Rev. Hydrobiol. 89, 21–34 (2004).
ADS  Article  Google Scholar 

11.
Nentwig, W., Bacher, S., Kumschick, S., Pyšek, P. & Vilà, M. More than “100 worst” alien species in Europe. Biol. Invasions 20, 1611–1621 (2018).
Article  Google Scholar 

12.
Gallardo, B. & Aldridge, D. C. Is Great Britain heading for a Ponto-Caspian invasional meltdown?. J. Appl. Ecol. 52, 41–49 (2015).
Article  Google Scholar 

13.
Kramer, A. M. et al. Suitability of Laurentian Great Lakes for invasive species based on global species distribution models and local habitat. Ecosphere 8, e01883 (2017).
Article  Google Scholar 

14.
Van Riel, M. C. et al. Trophic relationships in the Rhine food web during invasion and after establishment of the Ponto-Caspian invader Dikerogammarus villosus. Hydrobiologia 565, 39–58 (2006).
Article  Google Scholar 

15.
MacNeil, C., Boets, P., Lock, K. & Goethals, P. L. M. Potential effects of the invasive ‘killer shrimp’ (Dikerogammarus villosus) on macroinvertebrate assemblages and biomonitoring indices. Freshw. Biol. 58, 171–182 (2013).
Article  Google Scholar 

16.
Dodd, J. A. et al. Predicting the ecological impacts of a new freshwater invader: Functional responses and prey selectivity of the ‘killer shrimp’, Dikerogammarus villosus, compared to the native Gammarus pulex. Freshw. Biol. 59, 337–352 (2014).
Article  Google Scholar 

17.
Bruijs, M. C. M., Kelleher, B., Van Der Velde, G. & De Vaate, A. B. Oxygen consumption, temperature and salinity tolerance of the invasive amphipod Dikerogammarus villosus: Indicators of further dispersal via ballast water transport. Arch. fur Hydrobiol. 152, 633–646 (2001).
Article  Google Scholar 

18.
Pöckl, M. Strategies of a successful new invader in European fresh waters: Fecundity and reproductive potential of the Ponto-Caspian amphipod Dikerogammarus villosus in the Austrian Danube, compared with the indigenous Gammarus fossarum and G. roeseli. Freshw. Biol. 52, 50–63 (2007).

19.
Rolla, M., Consuegra, S. & de Leaniz, C. G. Predator recognition and anti-predatory behaviour in a recent aquatic invader, the killer shrimp (Dikerogammarus villosus). Aquat. Invasions 15, 482–496 (2020).
Article  Google Scholar 

20.
Kobak, J., Rachalewski, M. & Bącela-Spychalska, K. Conquerors or exiles? Impact of interference competition among invasive Ponto-Caspian gammarideans on their dispersal rates. Biol. Invasions 18, 1953–1965 (2016).
Article  Google Scholar 

21.
Rewicz, T., Grabowski, M., MacNeil, C. & Bącela-Spychalska, K. The profile of a ‘perfect’ invader – the case of killer shrimp. Dikerogammarus villosus. Aquat. Invasions 9, 267–288 (2014).
Article  Google Scholar 

22.
Hellmann, C. et al. The trophic function of Dikerogammarus villosus (Sowinsky, 1894) in invaded rivers: a case study in the Elbe and Rhine. Aquat. Invasions 10, 385–397 (2015).
Article  Google Scholar 

23.
Platvoet, D., Van Der Velde, G., Dick, J. T. A. & Li, S. Flexible omnivory in Dikerogammarus villosus (Sowinsky, 1894) (Amphipoda) – Amphipod Pilot Species Project (AMPIS) Report 5. Crustaceana 82, 703–720 (2009).
Article  Google Scholar 

24.
Taylor, N. G. & Dunn, A. M. Size matters: predation of fish eggs and larvae by native and invasive amphipods. Biol. Invasions 19, 89–107 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Alford, R. A. Ecology: Bleak future for amphibians. Nature 480, 461–462 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Alroy, J. Current extinction rates of reptiles and amphibians. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1508681112 (2015).
Article  PubMed  PubMed Central  Google Scholar 

27.
González-del-Pliego, P. et al. Phylogenetic and Trait-Based Prediction of Extinction Risk for Data-Deficient Amphibians. Curr. Biol. 29, 1557–1563.e3 (2019)

28.
Fisher, M. C. & Garner, T. W. J. Chytrid fungi and global amphibian declines. Nat. Rev. Microbiol. 18, 332–343 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Hayes, T. B., Falso, P., Gallipeau, S. & Stice, M. The cause of global amphibian declines: a developmental endocrinologist’s perspective. J. Exp. Biol. 213, 921–933 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Bellard, C., Genovesi, P. & Jeschke, J. M. Global patterns in threats to vertebrates by biological invasions. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2015.2454 (2016).
Article  Google Scholar 

31.
IUCN. The IUCN Red List of Threatened Species. (2020).

32.
Nunes, A. L. et al. A global meta-analysis of the ecological impacts of alien species on native amphibians. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2018.2528 (2019).
Article  Google Scholar 

33.
Ilhéu, M., Bernardo, J. & Fernandes, S. Biological invaders in inland waters: Profiles, distribution, and threats. Biol. invaders Inl. waters profiles, Distrib. Threat. 2, 543–558 (2007).

34.
Kats, L. B. & Ferrer, R. P. Alien predators and amphibian declines: Review of two decades of science and the transition to conservation. Divers. Distrib. 9, 99–110 (2003).
Article  Google Scholar 

35.
Beebee, T. J. C. & Griffiths, R. A. The amphibian decline crisis: A watershed for conservation biology?. Biol. Conserv. 125, 271–285 (2005).
Article  Google Scholar 

36.
National Biodiversity Network. NBN Atlas. Nbn (2017).

37.
Uehlinger, U., Wantzen, K. M., Leuven, R. S. E. W. & Arndt, H. The Rhine River Basin. in Rivers of Europe 199–245 (2009). https://doi.org/10.1016/B978-0-12-369449-2.00006-0

38.
Koester, M., Bayer, B. & Gergs, R. Is Dikerogammarus villosus (Crustacea, Gammaridae) a ‘killer shrimp’ in the River Rhine system?. Hydrobiologia 768, 299–313 (2016).
Article  Google Scholar 

39.
Gergs, R. & Rothhaupt, K. O. Invasive species as driving factors for the structure of benthic communities in Lake Constance. Germany. Hydrobiologia 746, 245–254 (2014).
Article  CAS  Google Scholar 

40.
Haubrock, P. J. et al. Shared histories of co-evolution may affect trophic interactions in a freshwater community dominated by alien species. Frontiers in Ecology and Evolution 7, 355 (2019).
Article  Google Scholar 

41.
Marguillier, S. Stable isotope ratios and food web structure of aquatic ecosystems. (1998).

42.
Dick, J. T. A. & Platvoet, D. Invading predatory crustacean Dikerogammarus villosus eliminates both native and exotic species. Proc. R. Soc. B Biol. Sci. 267, 977–983 (2000).
CAS  Article  Google Scholar 

43.
Bollache, L., Dick, J. T., Farnsworth, K. D. & Montgomery, W. I. Comparison of the functional responses of invasive and native amphipods. Biol Lett 4, 166–169 (2008).
PubMed  Article  PubMed Central  Google Scholar 

44.
MacNeil, C. et al. The Ponto-Caspian ‘killer shrimp’, Dikerogammarus villosus (Sowinsky, 1894), invades the British Isles. Aquat. Invasions 5, 441–445 (2010).
Article  Google Scholar 

45.
Worischka, S. et al. Food consumption of the invasive amphipod Dikerogammarus villosus in field mesocosms and its effects on leaf decomposition and periphyton. Aquat. Invasions 13, 261–275 (2018).
Article  Google Scholar 

46.
Jourdan, J. et al. Pronounced species turnover, but no functional equivalence in leaf consumption of invasive amphipods in the river Rhine. Biol. Invasions 18, 763–774 (2016).
Article  Google Scholar 

47.
Fries, G. & Der Tesch, F. W. Einfluss der Massenvorkommens von Gammarus tigrinus Sexton auf Fische und niedere Tierwelt in der Weser. Arch. für Fischer Wiss. 16, 133–150 (1965).
Google Scholar 

48.
Hudgens, B. & Harbert, M. Amphipod Predation on Northern Red-Legged Frog (Rana Aurora) Embryos. Northwest. Nat. 100, 126 (2019).
Article  Google Scholar 

49.
Räsänen, K., Pahkala, M., Laurila, A. & Merilä, J. Does Jelly Envelope Protect the Common Frog Rana Temporaria Embryos From Uv-B Radiation?. Herpetologica 59, 293–300 (2003).
Article  Google Scholar 

50.
Ward, D. & Sexton, O. J. Anti-Predator Role of Salamander Egg Membranes. Copeia 1981, 724 (1981).
Article  Google Scholar 

51.
Henrikson, B.-I. Predation on amphibian eggs and tadpoles by common predators in acidified lakes. Ecography (Cop.) 13, 201–206 (1990).
Article  Google Scholar 

52.
Duellman, W. E. (William E. & Trueb, L. Biology of amphibians. (Johns Hopkins University Press, 1994).

53.
Latham, D., Jones, E. & Fasham, M. Amphibians. in Handbook of Biodiversity Methods: Survey, Evaluation and Monitoring (eds. Hill, D., Fasham, M., Tucker, G., Shewry, M. & Shaw, P.) (Cambridge University Press, 2005).

54.
Tinsley, R. C., Stott, L. C., Viney, M. E., Mable, B. K. & Tinsley, M. C. Extinction of an introduced warm-climate alien species, Xenopus laevis, by extreme weather events. Biol. Invasions 17, 3183–3195 (2015).
PubMed  PubMed Central  Article  Google Scholar 

55.
Rall, B. C. et al. Universal temperature and body-mass scaling of feeding rates. Philos. Trans. R. Soc. B Biol. Sci. 367, 2923–2934 (2012).

56.
Glazier, D. S. A unifying explanation for diverse metabolic scaling in animals and plants. Biol. Rev. 85, 111–138 (2010).
PubMed  Article  PubMed Central  Google Scholar 

57.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Article  Google Scholar 

58.
Mayer, G., Waloszek, D., Maier, G. & Maas, A. Mouthparts of the Ponto-Caspian Invader Dikerogammarus Villosus (Amphipoda: Pontogammaridae). J. Crustac. Biol. 28, 1–15 (2008).
Article  Google Scholar 

59.
Vucic-Pestic, O., Rall, B. C., Kalinkat, G. & Brose, U. Allometric functional response model: Body masses constrain interaction strengths. J. Anim. Ecol. 79, 249–256 (2010).
PubMed  Article  PubMed Central  Google Scholar 

60.
Maazouzi, C., Piscart, C., Legier, F. & Hervant, F. Ecophysiological responses to temperature of the ‘killer shrimp’ Dikerogammarus villosus: Is the invader really stronger than the native Gammarus pulex? Comp. Biochem. Physiol. – A Mol. Integr. Physiol. 159, 268–274 (2011).

61.
Álvarez, D. & Nicieza, A. G. Differential success of prey escaping predators: tadpole vulnerability or predator selection??. Copeia 2009, 453–457 (2009).
Article  Google Scholar 

62.
Ward, A. & Webster, M. Sociality. in Sociality: The Behaviour of Group-Living Animals 1–8 (Springer International Publishing, 2016).https://doi.org/10.1007/978-3-319-28585-6_1

63.
Price, P. W., Denno, R. F., Eubanks, M. D., Finke, D. L. & Kaplan, I. Insect Ecology: Behaviour, Populations and Communities. (Cambridge University Press, 2011).

64.
Juliano, S. A. Nonlinear Curve Fitting: Predation and Functional Response Curves. in Design and Analysis of Ecological Experiments (eds. Cheiner, S. M. & Gurven, J.) 178–196 (Chapman and Hall, 2001).

65.
Barrios-O’Neill, D. et al. Fortune favours the bold: A higher predator reduces the impact of a native but not an invasive intermediate predator. J. Anim. Ecol. 83, 693–701 (2014).

66.
Sentis, A. & Boukal, D. S. On the use of functional responses to quantify emergent multiple predator effects. Sci. Rep. 8, (2018).

67.
Médoc, V., Albert, H. & Spataro, T. Functional response comparisons among freshwater amphipods: ratio-dependence and higher predation for Gammarus pulex compared to the non-natives Dikerogammarus villosus and Echinogammarus berilloni. Biol. Invasions 17, 3625–3637 (2015).
Article  Google Scholar 

68.
Laverty, C., Nentwig, W., Dick, J. & Lucy, F. Alien aquatics in Europe: assessing the relative environmental and socio-economic impacts of invasive aquatic macroinvertebrates and other taxa. Manag. Biol. Invasions 6, 341–350 (2015).
Article  Google Scholar 

69.
Dickey, J. W. E. et al. On the RIP: using Relative Impact Potential to assess the ecological impacts of invasive alien species. NeoBiota 55, 27–60 (2020).
Article  Google Scholar 

70.
Gallardo, B., Errea, M. P. & Aldridge, D. C. Application of bioclimatic models coupled with network analysis for risk assessment of the killer shrimp, Dikerogammarus villosus. Great Britain. Biol. Invasions 14, 1265–1278 (2012).
Article  Google Scholar 

71.
Gallardo, B. & Aldridge, D. C. Priority setting for invasive species management by the water industry. Water Res. 178, 115771 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

72.
Gosner, K. L. A simplified table for staging anuran embryos larvae. Herpetodologists’ Leag. 16, 183–190 (1960).
Google Scholar 

73.
Currie, S. P., Combes, D., Scott, N. W., Simmers, J. & Sillar, K. T. A behaviorally related developmental switch in nitrergic modulation of locomotor rhythmogenesis in larval Xenopus tadpoles. J. Neurophysiol. 115, 1446–1457 (2016).
PubMed  PubMed Central  Article  Google Scholar 

74.
Müller, J. C., Schramm, S. & Seitz, A. Genetic and morphological differentiation of Dikerogammarus invaders and their invasion history in Central Europe. Freshw. Biol. 47, 2039–2048 (2002).
Article  Google Scholar 

75.
Blackman, R. C. et al. Detection of a new non-native freshwater species by DNA metabarcoding of environmental samples – first record of gammarus fossarum in the UK. Aquat. Invasions 12, 177–189 (2017).
Article  Google Scholar 

76.
van der Velde, G. et al. Environmental and morphological factors influencing predatory behaviour by invasive non-indigenous gammaridean species. Biol. Invasions 11, 2043–2054 (2009).
Article  Google Scholar 

77.
Dick, J. T. A. et al. Parasitism may enhance rather than reduce the predatory impact of an invader. Biol. Lett. 6, 636–638 (2010).
PubMed  PubMed Central  Article  Google Scholar 

78.
Iltis, C., Spataro, T., Wattier, R. & Médoc, V. Parasitism may alter functional response comparisons: a case study on the killer shrimp Dikerogammarus villosus and two non-invasive gammarids. Biol. Invasions 20, (2018).

79.
Welton, J. S. Life-history and production of the amphipod Gammarus pulex in a Dorset chalk stream. Freshw. Biol. 9, 263–275 (1979).
Article  Google Scholar 

80.
Oertli, B. Leaf litter processing and energy flow through macroinvertebrates in a woodland pond (Switzerland). Oecologia 96, 466–477 (1993).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Lods-Crozet, B. & Reymond, O. Bathymetric expansion of an invasive gammarid (Dikerogammarus villosus, Crustacea, Amphipoda) in Lake Léman. J. Limnol. 65, 141–144 (2006).
Article  Google Scholar 

82.
Harkness, J. B. The relationships between stressors, macroinvertebrate community structure and leaf processing in stream ecosystems. (University of Sheffield, 2008).

83.
Leberfinger, K. & Herrmann, J. Secondary production of invertebrate shredders in open-canopy, intermittent streams on the island of land, southeastern Sweden. J. North Am. Benthol. Soc. 29, 934–944 (2010).
Article  Google Scholar 

84.
Lods-Crozet, B. Long-term biomonitoring of invertebrate neozoans in Lake Geneva. Arch. des Sci. 67, 101–108 (2014).
Google Scholar 

85.
Johns, T., Smith, D. C., Homann, S. & England, J. A. Time-series analysis of a native and a non-native amphipod shrimp in two English rivers. BioInvasions Rec. 7, 101–110 (2018).
Article  Google Scholar 

86.
Clinton, K. E., Mathers, K. L., Constable, D., Gerrard, C. & Wood, P. J. Substrate preferences of coexisting invasive amphipods, Dikerogammarus villosus and Dikerogammarus haemobaphes, under field and laboratory conditions. Biol. Invasions 20, 2187–2196 (2018).
Article  Google Scholar 

87.
Haas, G., Brunke, M. & Streit, B. Fast Turnover in Dominance of Exotic Species in the Rhine River Determines Biodiversity and Ecosystem Function: An Affair Between Amphipods and Mussels. in Invasive Aquatic Species of Europe. Distribution, Impacts and Management 426–432 (2002). doi:https://doi.org/10.1007/978-94-015-9956-6_42

88.
Krisp, H. & Maier, G. Consumption of macroinvertebrates by invasive and native gammarids: A comparison. J. Limnol. 64, 55–59 (2005).
Article  Google Scholar 

89.
Mulattieri, P. Etude de l’impact des aménagements riverains sur les macroinvertébrés benthiques des rives genevoises du Léman. (Université de Genève, 2006).

90.
Platvoet, D., Dick, J. T. A., MacNeil, C., van Riel, M. C. & van der Velde, G. Invader-invader interactions in relation to environmental heterogeneity leads to zonation of two invasive amphipods, dikerogammarus villosus (sowinsky) and gammarus tigrinus sexton: Amphipod pilot species project (ampis) report 6. Biol. Invasions 11, 2085–2093 (2009).
Article  Google Scholar 

91.
Tricarico, E. et al. The killer shrimp, Dikerogammarus villosus (Sowinsky, 1894), is spreading in Italy. Aquat. Invasions 5, 211–214 (2010).
Article  Google Scholar 

92.
Muskó, I. B., Balogh, C., Tóth, Á. P., Varga, É. & Lakatos, G. Differential response of invasive malacostracan species to lake level fluctuations. Hydrobiologia 590, 65–74 (2007).
Article  Google Scholar 

93.
Hellmann, C., Schöll, F., Worischka, S., Becker, J. & Winkelmann, C. River-specific effects of the invasive amphipod Dikerogammarus villosus (Crustacea: Amphipoda) on benthic communities. Biol. Invasions 19, 381–398 (2017).
Article  Google Scholar 

94.
GBIF.org. Global Biodiversity Information Facility. Choice Reviews Online 41, 41–5289–41–5289 (2004).

95.
INaturalist.org. iNaturalist. (2020). Available at: https://www.inaturalist.org/. (Accessed: 16th October 2020)

96.
R Core Team. R: A Language and Environment for Statistical Computing. (2018).

97.
Pritchard, D. W., Paterson, R. A., Bovy, H. C. & Barrios-O’Neill, D. frair: an R package for fitting and comparing consumer functional responses. Methods Ecol. Evol. 8, 1528–1534 (2017).

98.
Rogers, D. Random Search and Insect Population Models. J. Anim. Ecol. 41, 369 (1972).
Article  Google Scholar 

99.
Bolker, B. & R Core Team. bbmle: Tools for General Maximum Likelihood Estimation. R package version 1.0.20. (2017).

100.
Laverty, C. et al. Assessing the ecological impacts of invasive species based on their functional responses and abundances. Biol. Invasions 19, 1653–1665 (2017).
Article  Google Scholar 

101.
Cuthbert, R. N., Dick, J. T. A., Callaghan, A. & Dickey, J. W. E. Biological control agent selection under environmental change using functional responses, abundances and fecundities; the Relative Control Potential (RCP) metric. Biol. Control 121, 50–57 (2018).
Article  Google Scholar 

102.
Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biometrical Journal 50, 346–363 (2008).
MathSciNet  PubMed  MATH  Article  PubMed Central  Google Scholar  More

1.
Lafferty, K. D. et al. Parasites in food webs: The ultimate missing links. Ecol. Lett. 11, 533–546. https://doi.org/10.1111/j.1461-0248.2008.01174.x (2008).
Article  PubMed  PubMed Central  Google Scholar 
2.
Welicky, R. L., Demopoulos, A. W. J. & Sikkel, P. C. Host-dependent differences in resource use associated with Anilocra spp. parasitism in two coral reef fishes, as revealed by stable carbon and nitrogen isotope analyses. Mar. Ecol. https://doi.org/10.1111/maec.12413 (2017).
Article  Google Scholar 

3.
Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518. https://doi.org/10.1038/nature06970 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

4.
Dunne, J. A. et al. Parasites affect food web structure primarily through increased diversity and complexity. PLoS Biol. 11, e1001579 (2013).
CAS  Article  Google Scholar 

5.
Poulin, R. Parasite species richness in New Zealand fishes: A grossly underestimated component of biodiversity?. Divers. Distrib. 10, 31–37 (2004).
Article  Google Scholar 

6.
Justine, J.-L. Parasite biodiversity in a coral reef fish: Twelve species of monogeneans on the gills of the grouper Epinephelus maculatus (Perciformes: Serranidae) off New Caledonia, with a description of eight new species of Pseudorhabdosynochus (Monogenea: Diplectanidae). Syst. Parasitol. 66, 81 (2007).
Article  Google Scholar 

7.
Lafferty, K. D. & Kuris, A. M. Trophic strategies, animal diversity and body size. Trends Ecol. Evol. 17, 507–513 (2002).
Article  Google Scholar 

8.
Minagawa, M. & Wada, E. Stepwise enrichment of 15N along food chains: Further evidence and the relation between δ15N and animal age. Geochim. Cosmochim. Acta 48, 1135–1140. https://doi.org/10.1016/0016-7037(84)90204-7 (1984).
ADS  CAS  Article  Google Scholar 

9.
Fry, B. Food web structure on Georges Bank from stable C, N, and S isotopic compositions. Limnol. Oceanogr. 33, 1182–1190. https://doi.org/10.4319/lo.1988.33.5.1182 (1988).
ADS  CAS  Article  Google Scholar 

10.
Post, D. M. Using stable isotopes to estimate trophic position: Models, methods, and assumptions. Ecology 83, 703–718. https://doi.org/10.1890/0012-9658(2002)083[0703:usitet]2.0.co;2 (2002).
Article  Google Scholar 

11.
McCutchan, J. H., Lewis, W. M. Jr., Kendall, C. & McGrath, C. C. Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390. https://doi.org/10.1034/j.1600-0706.2003.12098.x (2003).
CAS  Article  Google Scholar 

12.
Caut, S., Angulo, E. & Courchamp, F. Variation in discrimination factors (Δ15N and Δ13C): The effect of diet isotopic values and applications for diet reconstruction. J. Appl. Ecol. 46, 443–453. https://doi.org/10.1111/j.1365-2664.2009.01620.x (2009).
CAS  Article  Google Scholar 

13.
Pinnegar, J. K., Campbell, N. & Polunin, N. V. C. Unusual stable isotope fractionation patterns observed for fish host–parasite trophic relationships. J. Fish Biol. 59, 494–503. https://doi.org/10.1111/j.1095-8649.2001.tb02355.x (2001).
Article  Google Scholar 

14.
Thieltges, D. W., Goedknegt, M. A., O’Dwyer, K., Senior, A. M. & Kamiya, T. Parasites and stable isotopes: A comparative analysis of isotopic discrimination in parasitic trophic interactions. Oikos 128, 1329–1339. https://doi.org/10.1111/oik.06086 (2019).
Article  Google Scholar 

15.
Deudero, S., Pinnegar, J. K. & Polunin, N. V. C. Insights into fish host-parasite trophic relationships revealed by stable isotope analysis. Dis. Aquat. Org. 52, 77–86 (2002).
Article  Google Scholar 

16.
Power, M. & Klein, G. M. Fish host–cestode parasite stable isotope enrichment patterns in marine, estuarine and freshwater fishes from northern Canada. Isot. Environ. Health Stud. 40, 257–266 (2004).
CAS  Article  Google Scholar 

17.
Navarro, J. et al. Isotopic discrimination of stable isotopes of nitrogen (δ15N) and carbon (δ13C) in a host-specific holocephalan tapeworm. J. Helminthol. 88, 371–375 (2014).
CAS  Article  Google Scholar 

18.
Kamiya, E., Urabe, M. & Okuda, N. Does atypical 15N and 13C enrichment in parasites result from isotope ratio variation of host tissues they are infected?. Limnology https://doi.org/10.1007/s10201-019-00596-w (2019).
Article  Google Scholar 

19.
Kanaya, G. et al. Application of stable isotopic analyses for fish host–parasite systems: An evaluation tool for parasite-mediated material flow in aquatic ecosystems. Aquat. Ecol. 53, 217–232. https://doi.org/10.1007/s10452-019-09684-6 (2019).
CAS  Article  Google Scholar 

20.
Gilbert, B. M. et al. You are how you eat: Differences in trophic position of two parasite species infecting a single host according to stable isotopes. Parasitol. Res. 1–8. https://doi.org/10.1007/s00436-020-06619-1 (2020).

21.
Demopoulos, A. W. & Sikkel, P. C. Enhanced understanding of ectoparasite–host trophic linkages on coral reefs through stable isotope analysis. Int. J. Parasitol. Parasites Wildl. 4, 125–134 (2015).
Article  Google Scholar 

22.
Jenkins, W. G., Demopoulos, A. W., Nicholson, M. D. & Sikkel, P. C. Stable isotope dynamics of herbivorous reef fishes and their ectoparasites. Diversity 12, 429 (2020).
CAS  Article  Google Scholar 

23.
Jenkins, W. G., Demopoulos, A. W. J. & Sikkel, P. C. Host feeding ecology and trophic position significantly influence isotopic discrimination between a generalist ectoparasite and its hosts: Implications for parasite–host trophic studies. Food Webs 16, e00092. https://doi.org/10.1016/j.fooweb.2018.e00092 (2018).
Article  Google Scholar 

24.
International Helminth Genomes, C. Comparative genomics of the major parasitic worms. Nat. Genet. 51, 163–174. https://doi.org/10.1038/s41588-018-0262-1 (2019).
CAS  Article  Google Scholar 

25.
Tyagi, R., Rosa, B. A., Lewis, W. G. & Mitreva, M. Pan-phylum comparison of nematode metabolic potential. PLoS Negl. Trop. Dis. 9, e0003788. https://doi.org/10.1371/journal.pntd.0003788 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

26.
Yohannes, E., Grimm, C., Rothhaupt, K.-O. & Behrmann-Godel, J. The effect of parasite infection on stable isotope turnover rates of δ15N, δ13C and δ34S in multiple tissues of Eurasian perch Perca fluviatilis. PLoS ONE 12, e0169058. https://doi.org/10.1371/journal.pone.0169058 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
Behrmann-Godel, J. & Yohannes, E. Multiple isotope analyses of the pike tapeworm Triaenophorus nodulosus reveal peculiarities in consumer–diet discrimination patterns. J. Helminthol. 89, 238–243. https://doi.org/10.1017/S0022149X13000849 (2015).
CAS  Article  PubMed  Google Scholar 

28.
Nachev, M. et al. Understanding trophic interactions in host-parasite associations using stable isotopes of carbon and nitrogen. Parasites Vectors 10, 90. https://doi.org/10.1186/s13071-017-2030-y (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

29.
Goedknegt, M. A. et al. Trophic relationship between the invasive parasitic copepod Mytilicola orientalis and its native blue mussel (Mytilus edulis) host. Parasitology 145, 814–821. https://doi.org/10.1017/S0031182017001779 (2017).
CAS  Article  PubMed  Google Scholar 

30.
Persson, M. E., Larsson, P. & Stenroth, P. Fractionation of δ15N and δ13C for Atlantic salmon and its intestinal cestode Eubothrium crassum. J. Fish Biol. 71, 441–452. https://doi.org/10.1111/j.1095-8649.2007.01500.x (2007).
CAS  Article  Google Scholar 

31.
McMahon, K. W. & McCarthy, M. D. Embracing variability in amino acid δ15N fractionation: Mechanisms, implications, and applications for trophic ecology. Ecosphere 7, e01511. https://doi.org/10.1002/ecs2.1511 (2016).
Article  Google Scholar 

32.
Brouwers, J. F., Smeenk, I. M., van Golde, L. M. & Tielens, A. G. The incorporation, modification and turnover of fatty acids in adult Schistosoma mansoni. Mol. Biochem. Parasitol. 88, 175–185 (1997).
CAS  Article  Google Scholar 

33.
Briand, M. J., Bonnet, X., Goiran, C., Guillou, G. & Letourneur, Y. Major sources of organic matter in a complex coral reef lagoon: Identification from isotopic signatures (δ13C and δ15N). PLoS ONE 10, e0131555–e0131555. https://doi.org/10.1371/journal.pone.0131555 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Greenwood, N. D. W., Sweeting, C. J. & Polunin, N. V. C. Elucidating the trophodynamics of four coral reef fishes of the Solomon Islands using δ15N and δ13C. Coral Reefs 29, 785–792. https://doi.org/10.1007/s00338-010-0626-1 (2010).
ADS  Article  Google Scholar 

35.
Hussey, N. E. et al. Rescaling the trophic structure of marine food webs. Ecol. Lett. 17, 239–250 (2014).
Article  Google Scholar 

36.
McMahon, K. W., Thorrold, S. R., Elsdon, T. S. & McCarthy, M. D. Trophic discrimination of nitrogen stable isotopes in amino acids varies with diet quality in a marine fish. Limnol. Oceanogr. 60, 1076–1087. https://doi.org/10.1002/lno.10081 (2015).
ADS  CAS  Article  Google Scholar 

37.
Mill, A. C., Pinnegar, J. K. & Polunin, N. V. C. Explaining isotope trophic-step fractionation: Why herbivorous fish are different. Funct. Ecol. 21, 1137–1145. https://doi.org/10.1111/j.1365-2435.2007.01330.x (2007).
Article  Google Scholar 

38.
Kulbicki, M., Guillemot, N. & Amand, M. A general approach to length-weight relationships for New Caledonian lagoon fishes. Cybium 29, 235–252 (2005).
Google Scholar 

39.
Woodland, D. J. Revision of the fish family Siganidae with descriptions of two new species and comments on distribution and biology. Indo-Pacific Fishes, Vol. 19 (Bishop Museum, 1990).

40.
Moléana, T. Etude de la reproduction, de l’alimentation et de la composition en acides gras du picot rayé Siganus lineatus. Application à la domestication d’une nouvelle espèce tropicale pour la piscuculture marine (Nouvelle-Calédonie; Aqualagon SARL), PhD Thesis, Université de la Nouvelle-Calédonie (2016).

41.
Justine, J.-L., Briand, M. J. & Bray, R. A. A quick and simple method, usable in the field, for collecting parasites in suitable condition for both morphological and molecular studies. Parasitol. Res. 111, 341–351 (2012).
Article  Google Scholar 

42.
Pinnegar, J. K. & Polunin, N. V. C. Differential fractionation of δ13C and δ15N among fish tissues: Implications for the study of trophic interactions. Funct. Ecol. 13, 225–231. https://doi.org/10.1046/j.1365-2435.1999.00301.x (1999).
Article  Google Scholar 

43.
Abrantes, K. & Sheaves, M. Incorporation of terrestrial wetland material into aquatic food webs in a tropical estuarine wetland. Estuar. Coast. Shelf Sci. 80, 401–412 (2008).
ADS  Article  Google Scholar 

44.
Briand, M. J., Bonnet, X., Guillou, G. & Letourneur, Y. Complex food webs in highly diversified coral reefs: Insights from δ13C and δ15N stable isotopes. Food Webs 8, 12–22. https://doi.org/10.1016/j.fooweb.2016.07.002 (2016).
Article  Google Scholar  More

Results of risk assessment by the ITO3dE model
The results in the I dimension show that, overall, the distribution was high in the west and low in the northeast and the southeast in all three periods (Fig. 3, I, II, III; Table 2), and this tallies with the topography of Chongqing. The northwestern and central regions of Chongqing are mainly hilly and slightly mountainous, while the southeastern and northeastern regions represent the Dabashan Mountain system and the Daloushan Mountain system, respectively. Thus, farmland in Chongqing is mainly distributed in the western regions as well as in regions with extensive flat areas, such as Dianjiang and Liangping. Some regions in Dianjiang, Yongchuan, Dazu, Shapingba, Wansheng, and Jiangbei show relatively high risks, but the risk level is still medium. Hence, it can be concluded that the risk level in the I dimension during 2005–2015 is, overall, not high. Considering there are too many single-factor graphs, we omitted these graphs, but provide the following description: Among the three single factors, I1 has the highest value, and I1 and I2 both present a first increasing and then decreasing trend (the maximum values of I1 in 2005, 2010, and 2015 were 3.38, 4.08, and 2.78, respectively, and those of I2 in 2005, 2010, and 2015 were 2.71, 3.37, and 2.48, respectively). For the I1 results, the risk levels of the regions with higher levels in 2005, such as Yongchuan, Fuling, and Liangping, showed a certain decrease in 2015, but the risk levels of some regions such as Pengshui, Qianjiang, and Xiushan showed an increasing trend. The risk grade of I2 was relatively lower than that of I1, but overall, the spatiotemporal variation trend was consistent with that of I1, except for the increasing trend of the risk level of Qianjiang. Basically, the risk grade of I3 was zero; only the risk level of Bishan was in the medium risk status, while those of Hechuan and Fengdu were low.
Figure 3

Result distribution map of I, T, and O dimensions of Chongqing in 2005, 2010, 2015.

Full size image

Table 2 Statistical results of I, T, and O dimensions in 2005–2015.
Full size table

Spatially, the results in the T dimension presented, overall, an opposite distribution pattern when compared to the I dimension, that is, with low levels in the western regions and high levels in the northeastern and southeastern regions (Fig. 3, IV, V, VI; Table 2). The annual differences in the T dimension data are mainly determined by the variations in the factors I4 and I7, which showed relatively higher risk levels in all three periods. The values of I4 in the years 2005, 2010, and 2015 were 1.42–5.78, 0.84–6.12, and 0.14–6.93, respectively, while those of I7 were 0, 0–5.38, and 0–5.06, respectively. Because Chongqing is a typical mountainous city with purple soil33, high-risk and extremely high-risk regions, I5 and I6, are widely distributed across the city. In addition, due to the introduction of the factor I8, the water areas had a higher risk level, which is consistent with the actual situation of AGNPS.
The results in the O dimension showed a smaller interannual variation, with a low overall risk level (Fig. 3, VII, VIII, IX; Table 2). The O dimension levels were mainly affected by the spatial changes in the paddy field area. As mentioned above, during the 10 years, the area of paddy fields in Chongqing was nearly reduced by half, which led to the decrease in the spatial distribution of I12 and an increased risk in counties such as Kaizhou, Fengjie, Liangping, and Changshou. Spatially, Yongchuan, Shapingba, Bishan, Dianjiang, Changshou, and Kaizhou showed higher risk levels, and the risk levels of Kaizhou, Fengjie, Wanzhou, Liangping, and Changshou showed a significantly increasing trend. The high risk values of I9 were mainly distributed in Yongchuan, Shapingba, Jiangbei, Changshou, Dianjiang, and Liangping, with Shapingba showing the highest value of 3.75, while Chengkou, Wushan, Fengjie, Shizhu, and Xiushan had lower values. The high risk values of I10 were mainly distributed in the western regions and were below the medium risk levels. The risk values in 2010 were higher than those in 2005 or 2015, but did not surpass 3.0, and the high values were mainly distributed in the western regions as well as in Dianjiang, Wanzhou, and Liangping. The risk values of I11 were all below 3.0, and the highest value of 2.78 was found for Fengjie; higher values were mainly distributed in the northeastern and southeastern counties. The high risk values of I12 were mainly distributed in the northeastern and southeastern counties, which mostly have only small areas of paddy fields.
Figure 4 shows the data on AGNPS risks during 2005–2015 in Chongqing. The risk distribution trends in 2005, 2010, and 2015 were basically consistent and in the ranges of 0.40–2.28, 0.41–2.57, and 0.41–2.28, respectively. The maximum risk values were all below 3.0 for the three periods. Regions with medium levels were mostly distributed in the western regions of Chongqing (Dazu, Jiangjin, etc.) as well as in the counties Dianjiang, Liangping, Kaizhou, Wanzhou, and Zhongxian. Larger spatial differences were observed among different counties or different parts of a certain county; for example, the middle flatland part and the mountain systems at the two sides in Liangping or the northwestern and southeastern parts in Shizhu.
Figure 4

Spatiotemporal distribution graph of the evaluation results of agricultural NPSP risks in Chongqing during 2005–2015: (a) 2005; (b) 2010; (c) 2015.

Full size image

Spatiotemporal change results of risk by transition matrix analysis
By assigning no risk, low risk, and medium risk levels with 1, 2, and 3, respectively, in GIS, we can obtain the spatiotemporal transition matrix according to the formula of the transition matrix. Figure 5 shows the spatiotemporal transition situation of the AGNPS risk evaluation in Chongqing. Basically, high levels show no changes, and the proportions of ‘no-risk no-change’, ‘low-risk no-change’, and ‘medium-risk no-change’ situations were 10.86%, 33.42%, and 17.25%, respectively, accounting for 61.53% of the total area of Chongqing. Among these, the ‘no-risk no-change’ situation was mainly distributed in Rongchang, the east of Nanchuan, Shizhu, Pengshui, and Qianjiang; the ‘low-risk no-change’ situation was widely distributed in Wulong, the southeast of Fengdu, the south of Nanchuan, and the northeastern counties of Chongqing, while the ‘medium-risk no-change’ situation was mainly distributed in Shapingba, Yongchuan, Dianjiang, the north of Nanchuan, and Kaizhou.
Figure 5

Spatiotemporal transition situation of agricultural NPSP risks in Chongqing during 2005–2015.

Full size image

During 2005–2015, the proportions of risk increase, risk decline, and risk fluctuation were 13.45%, 17.66%, and 7.36%, respectively. Risk increases mainly occurred in central Jiangjin, central Fengdu, Pengshui, Qianjiang, the midwest of Yunyang, central Liangping, Wuxi, Wushan, and Chengkou, while risk declines were mainly observed for the main urban area of Chongqing, northern Tongliang, Dazu, Youyang, and Xiushan. Risk fluctuation was concentrated in Jiangjin, Bishan, Fuling, and Youyang.
Results of risk concentration degree by Kernel density analysis
Figure 6 shows the kernel density analysis results of the medium-risk regions. As seen in the figures, the peak values of the kernel density at these three periods were all around 1,110, suggesting that the maximum gathering degree of medium-risk pattern spots basically showed no changes. The spatial distribution of kernel density at these three periods showed a consistent trend, but the distribution differences at different periods were significant. In 2005, medium-risk regions were mainly concentrated in Shapingba, southern Dazu, central Yongchuan, eastern Beibei, Dianjiang, central Kaizhou, northwestern Shizhu, northern Nanchuan, central Wanzhou, southwestern Zhongxian, and southeastern Xiushan, while in 2010, such regions mainly occurred in Shapingba, eastern Jiangjin, southeastern Beibei, northern Nanchuan, northeastern Changshou, Dianjiang, northern Fuling, northern Fengdu, northeastern Shizhu, northeastern Liangping, central Kaizhou, Wanzhou, northeastern Pengshui, and eastern Xiushan. In 2015, medium-risk regions were mainly concentrated in Shapingba, Yongchuan, central Jiangjin, northwestern Nanchuan, northeastern Beibei, Dianjiang, Liangping, the junction of Fuling and Fengdu, central Kaizhou, northern Yunyang, eastern Pengshui, southeastern Qianjiang, and central Xiushan.
Figure 6

Kernel density graphs of medium-risk areas in Chongqing during 2005–2015: (a) 2005; (b) 2010; (c) 2015.

Full size image

To further explore the distribution of regions with the high-risk gathering zones (Table 3), we conducted a separate analysis on the regions with kernel density values higher than 1,000 (the kernel density values of these regions were divided into 10 grades with equal intervals, and the 10th grade had values from 1,000 to 1,110).
Table 3 Distribution of regions with high-risk gathering zones.
Full size table

Results of hot and cold spots by Getis-Ord Gi* analysis
Applying Getis-Ord Gi* analysis is helpful to clearly identify high-value hot spots (Hot Spot-99% Confidence) and low-value cold spots (Cold Spot-99% Confidence). Figure 7 shows the Getis-Ord Gi* analysis results; the overall variation trends of high-value hot spots and low-value cold spots were consistent in all periods, with significant distribution differences. The regions located in the high-value hot spot zones in all three periods were Yongchuan, Shapingba, Dianjiang, Liangping, northwestern Fengdu, and Zhongxian, while those located in the low-value cold spot zones were Chengkou, Wuxi, Wushan, Pengshui, and Rongchang. Throughout the 10 years, the high-value hot spot zones showed significant diffusion in Fengjie, Yunyang, Kaizhou, central Qianjiang, and northern Nanchuan, while the low-value cold spot zones showed significant diffusion in some parts of the midwestern counties such as central Fuling and southern Yubei. These high-value hot spots or low-value cold spots were mainly distributed in the above-mentioned regions and their surrounding areas and showed significant “gathering trends”. The spatiotemporal variation trend of the distribution of these high-value hot spots or low-value cold spots can reflect the variation tendencies of hot spots or cold spots in different regions. Over time, the high-value hot spot zones gradually migrated towards the northeastern counties of Chongqing, while the low-value cold spot zones in the midwestern counties presented an obvious diffusion trend. The low-value cold spot zones in the northeastern regions gradually decreased, while those in the southeastern regions tended to become more fragmented. These results indicate that the high-value hot spot zones gradually dominated the northeastern regions, while the low-value cold spot zones gradually dominated the midwestern regions.
Figure 7

Getis-Ord Gi analysis results in Chongqing during 2005–2015.

Full size image More