Philippa Kaur

1.
Buzby, J. C., Wells, H. F. & Hyman, J. The Estimated Amount, Value, and Calories of Postharvest Food Losses at the Retail and Consumer Levels in the United States. (EIB-121, U.S. Department of Agriculture, Economic Research Service, Washington, 2014).
2.
Huis In’t Veld, J. H. J. Microbial and biochemical spoilage of foods: an overview. Int. J. Food Microbiol.33, 1–18 (1996).
Google Scholar 

3.
Havelaar, A. H. et al. World Health Organization global estimates and regional comparisons of the burden of foodborne disease in 2010. PLoS Med.12, e1001923 (2015).
PubMed  PubMed Central  Google Scholar 

4.
EFSA (European Food Safety Authority) and ECDC (European Centre for Disease Prevention and Control), The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2015. EFSA J. 14(12): 4634, 231, (2016).

5.
Gill, C. O. Meat spoilage and evaluation of the potential storage life of fresh meat. J. Food Prot.46, 444–452 (1983).
CAS  PubMed  Google Scholar 

6.
Giaouris, E. et al. Attachment and biofilm formation by foodborne bacteria in meat processing environments: causes, implications, role of bacterial interactions and control by alternative novel methods. Meat Sci.97, 289–309 (2014).
Google Scholar 

7.
Choi, Y. M. et al. Changes in microbial contamination levels of porcine carcasses and fresh pork in slaughterhouses, processing lines, retail outlets, and local markets by commercial distribution. Res. Vet. Sci.94, 413–418 (2013).
CAS  PubMed  Google Scholar 

8.
Sheridan, J. J. Sources of contamination during slaughter and measures of control. J. Food Saf.18, 321–339 (1998).
Google Scholar 

9.
International Organization for Standardization. Microbiology of the Food Chain—Carcass Sampling for Microbiological Analysis. (2015). ISO 17604:2015, Retrieved from https://www.iso.org/standard/62769.html

10.
Nocker, A., Burr, M. & Camper, A. K. Genotypic microbial community profiling: a critical technical review. Microb. Ecol.54, 276–289 (2007).
CAS  PubMed  Google Scholar 

11.
Hultman, J., Rahkila, R., Ali, J., Rousu, J. & Björkroth, K. J. Meat processing plant microbiome and contamination patterns of cold-tolerant bacteria causing food safety and spoilage risks in the manufacture of vacuum-packaged cooked sausages. Appl. Environ. Microbiol.81, 7088–7097 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

12.
Chaillou, S. et al. Origin and ecological selection of core and food-specific bacterial communities associated with meat and seafood spoilage. ISME J.9, 1105–1118 (2015).
PubMed  Google Scholar 

13.
Yang, H. et al. Uncovering the composition of microbial community structure and metagenomics among three gut locations in pigs with distinct fatness. Sci. Rep.6, 27427 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

14.
Bokulich, N. A., Bergsveinson, J., Ziola, B. & Mills, D. A. Mapping microbial ecosystems and spoilage-gene flow in breweries highlights patterns of contamination and resistance. Elife4, e04634 (2015).
PubMed Central  Google Scholar 

15.
Mann, E. et al. Psychrophile spoilers dominate the bacterial microbiome in musculature samples of slaughter pigs. Meat Sci.117, 36–40 (2016).
CAS  PubMed  Google Scholar 

16.
Bokulich, N. A., Lewis, Z. T., Boundy-Mills, K. & Mills, D. A. A new perspective on microbial landscapes within food production. Curr. Opin. Biotechnol.37, 182–189 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

17.
Bridier, A. et al. Impact of cleaning and disinfection procedures on microbial ecology and Salmonella antimicrobial resistance in a pig slaughterhouse. Sci. Rep.9, 12947 (2019).
PubMed  PubMed Central  Google Scholar 

18.
Kang, S., Ravensdale, J., Coorey, R., Dykes, G. A. & Barlow, R. A comparison of 16S rRNA profiles through slaughter in Australian export beef abattoirs. Front. Microbiol.10, 2747 (2019).

19.
Stellato, G. et al. Overlap of spoilage microbiota between meat and meat processing environment in small-scale 2 vs. large-scale retail distribution. Appl. Environ. Microbiol.82, 4045–4054 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

20.
Campos Calero, G. et al. Deciphering resistome and virulome diversity in a porcine slaughterhouse and pork products through its production chain. Front. Microbiol.9, 2099 (2018).

21.
Johnson, J. S. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun.10, 5029 (2019).
PubMed  PubMed Central  Google Scholar 

22.
Spescha, C., Stephan, R. & Zweifel, C. Microbiological contamination of pig carcasses at different stages of slaughter in two European Union—approved abattoirs. J. Food Prot.69, 2568–2575 (2006).
CAS  PubMed  Google Scholar 

23.
Warriner, K., Aldsworth, T. G., Kaur, S. & Dodd, C. E. R. Cross-contamination of carcasses and equipment during pork processing. J. Appl. Microbiol.93, 169–177 (2002).
CAS  PubMed  Google Scholar 

24.
Wheatley, P., Giotis, E. S. & McKevitt, A. I. Effects of slaughtering operations on carcass contamination in an Irish pork production plant. Ir. Vet. J.67, 1 (2014).
PubMed  PubMed Central  Google Scholar 

25.
Gill, C. O. in Woodhead Publishing Series in Food Science, Technology and Nutrition (ed. Sofos, J. N. et al.) 630–672 (Woodhead Publishing, Sawston, 2005). https://doi.org/10.1533/9781845691028.2.630

26.
de Filippis, F., La Storia, A., Villani, F. & Ercolini, D. Exploring the sources of bacterial spoilers in beefsteaks by culture-independent high-throughput sequencing. PLoS ONE8, e70222 (2013).

27.
de Smidt, O. The use of PCR-DGGE to determine bacterial fingerprints for poultry and red meat abattoir effluent. Lett. Appl. Microbiol.62, 1–8 (2016).
PubMed  Google Scholar 

28.
Andrew, D. & Board, R. Microbiology of Meat and Poultry. (Blackie Academic & Professional, Glasgow, 1998).

29.
Khan, I. U. et al. Anoxybacillus sediminis sp. nov., a novel moderately thermophilic bacterium isolated from a hot spring. Antonie Van. Leeuwenhoek111, 2275–2282 (2018).
PubMed  Google Scholar 

30.
Pikuta, E. et al. Anoxybacillus pushchinensis gen. nov., sp. nov., a novel anaerobic, alkaliphilic, moderately thermophilic bacterium from manure, and description of Anoxybacillus flavitherms comb. nov. Int. J. Syst. Evol. Microbiol.50, 2109–2117 (2000).
CAS  PubMed  Google Scholar 

31.
Burgess, S. A., Lindsay, D. & Flint, S. H. Thermophilic bacilli and their importance in dairy processing. Int. J. Food Microbiol.144, 215–225 (2010).
CAS  PubMed  Google Scholar 

32.
Burgess, S. A., Brooks, J. D., Rakonjac, J., Walker, K. M. & Flint, S. H. The formation of spores in biofilms of Anoxybacillus flavithermus. J. Appl. Microbiol.107, 1012–1018 (2009).
CAS  PubMed  Google Scholar 

33.
Goh, K. M. et al. Recent discoveries and applications of Anoxybacillus. Appl. Microbiol. Biotechnol.97, 1475–1488 (2013).
CAS  PubMed  Google Scholar 

34.
Knights, D. et al. Bayesian community-wide culture-independent microbial source tracking. Nat. Methods8, 761–763 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Henry, R. et al. Into the deep: evaluation of sourcetracker for assessment of faecal contamination of coastal waters. Water Res.93, 242–253 (2016).
CAS  PubMed  Google Scholar 

36.
Liu, G. et al. Assessing the origin of bacteria in tap water and distribution system in an unchlorinated drinking water system by SourceTracker using microbial community fingerprints. Water Res.138, 86–96 (2018).
CAS  PubMed  Google Scholar 

37.
Bik, H. M. et al. Microbial community patterns associated with automated teller machine keypads in New York City. mSphere1, e00226–16 (2016).
PubMed  PubMed Central  Google Scholar 

38.
Hewitt, K. M. et al. Bacterial diversity in two neonatal intensive care units (NICUs). PLoS ONE8, e54703 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

39.
Li, L.-G., Yin, X. & Zhang, T. Tracking antibiotic resistance gene pollution from different sources using machine-learning classification. Microbiome6, 93 (2018).
PubMed  PubMed Central  Google Scholar 

40.
Bolton, D. J. et al. Washing and chilling as critical control points in pork slaughter hazard analysis and critical control point (HACCP) systems. J. Appl. Microbiol.92, 893–902 (2002).

41.
Yu, S. L. et al. Effect of dehairing operations on microbiological quality of swine carcasses. J. Food Prot.62, 1478–1481 (1999).
CAS  PubMed  Google Scholar 

42.
Jagadeesan, B. et al. The use of next generation sequencing for improving food safety: translation into practice. Food Microbiol.79, 96–115 (2019).
CAS  PubMed  Google Scholar 

43.
Bergholz, T. M., Moreno Switt, A. I. & Wiedmann, M. Omics approaches in food safety: fulfilling the promise? Trends Microbiol.22, 275–281 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

44.
Leonard, S. R., Mammel, M. K., Lacher, D. W. & Elkins, C. A. Application of metagenomic sequencing to food safety: detection of shiga toxin-producing Escherichia coli on fresh bagged spinach. Appl. Environ. Microbiol.81, 8183–8191 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

45.
Moura, A. et al. Real-time whole-genome sequencing for surveillance of listeria monocytogenes, France. Emerg. Infect. Dis.23, 1462–1470 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Wang, S. et al. Food safety trends: from globalization of whole genome sequencing to application of new tools to prevent foodborne diseases. Trends Food Sci. Technol.57, 188–198 (2016).
CAS  Google Scholar 

47.
Nastasijevic, I. et al. Tracking of listeria monocytogenes in meat establishment using whole genome sequencing as a food safety management tool: a proof of concept. Int. J. Food Microbiol.257, 157–164 (2017).
PubMed  Google Scholar 

48.
Weimer, B. C. et al. Defining the food microbiome for authentication, safety, and process management. IBM J. Res. Dev.60, 1:1–1:13 (2016).
Google Scholar 

49.
Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics28, 2520–2522 (2012).
PubMed  Google Scholar 

50.
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol.37, 852–857 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

51.
Martijn, J. et al. Confident phylogenetic identification of uncultured prokaryotes through long read amplicon sequencing of the 16S-ITS-23S rRNA operon. Environ. Microbiol.21, 2485–2498 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

52.
Pearce, R. A. & Bolton, D. J. Excision vs sponge swabbing—a comparison of methods for the microbiological sampling of beef, pork and lamb carcasses. J. Appl. Microbiol.98, 896–900 (2005).
CAS  PubMed  Google Scholar 

53.
Zwirzitz, B. et al. Culture-independent evaluation of bacterial contamination patterns on pig carcasses at a commercial slaughter facility. J. Food Prot.82, 1677–1682 (2019).
CAS  PubMed  Google Scholar 

54.
Muyzer, G., De Waal, E. C. & Uitterlinden, A. G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol.59, 695–700 (1993).
CAS  PubMed  PubMed Central  Google Scholar 

55.
Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. K. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res.43, D593–D598 (2015).
CAS  PubMed  Google Scholar 

56.
Větrovský, T. & Baldrian, P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE8, 1–10 (2013).
Google Scholar 

57.
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res.41, 1–11 (2013).
Google Scholar 

58.
Pacific Biosciences SMRT® Tools Reference Guide. (2018).

59.
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods13, 581–583 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

60.
Callahan, B. J. et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res.47, e103–e103 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

61.
Alishum, A. et al. DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea. https://doi.org/10.5281/zenodo.2541239 (2019).

62.
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol.36, 996–1004 (2018).
CAS  PubMed  Google Scholar 

63.
Davis, N. M., Proctor, D., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. bioRxiv221499, (2017).

64.
McMurdie, P. J. & Holmes, S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One8, e61217 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

65.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer, New York, 2016).

66.
Lindstrom, J. C. Tsnemicrobiota: T-distributed stochastic neighbor embedding for microbiota data. (2017). Github Repository, https://github.com/opisthokonta/tsnemicrobiota

67.
Cardoso, P., Rigal, F. & Carvalho, J. C. BAT—biodiversity Assessment Tools, an R package for the measurement and estimation of alpha and beta taxon, phylogenetic and functional diversity. Methods Ecol. Evol.6, 232–236 (2015).
Google Scholar  More

1.
Wikramanayake, E. D. et al. An ecology-based method for defining priorities for large mammal conservation: the tiger as case study. Conserv. Biol. 12, 865–868 (1998).
Google Scholar 
2.
Walston, J. et al. Bringing the tiger back from the brink—the six percent solution. PLoS Biol. 8(9), e1000485 (2010).
PubMed  PubMed Central  Google Scholar 

3.
Smith, J. L. D. The role of dispersal in structuring the Chitwan tiger population. Behaviour 124(3–4), 165–195 (1993).
Google Scholar 

4.
Dinerstein, E. et al. The fate of wild tigers. Bioscience 57, 508–514 (2007).
Google Scholar 

5.
Sanderson, E. et al. Setting priorities for the conservation and recovery of wild tigers: 2005–2015. The technical assessment. In Tigers of the World. A Review of Tigers of the World: The Biology, Biopolitics, Management, and Conservation of an Endangered Species 2nd edn (eds Ronald, L. T. & Ulysses, S. S.) 143–161 (Elsevier, New York, 2006).
Google Scholar 

6.
Jhala, Y. V., Qureshi, Q. & Gopal, R. Can the abundance of tigers be assessed from their signs?. J. Appl. Ecol. 48, 14–24 (2011).
Google Scholar 

7.
IPCC. Global Warming of 15 °C. 26 (Intergovernmental Panel on Climate Change, Switzerland, 2018).
Google Scholar 

8.
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15(4), 365–377 (2012).
PubMed  PubMed Central  Google Scholar 

9.
Gaston, K. J. The structure and dynamics of geographic ranges (Oxford University Press, London, 2003).
Google Scholar 

10.
Cahill, A. E. et al. How does climate change cause extinction?. Proc. R. Soc. B. 280, 20121890. https://doi.org/10.1098/rspb.2012.1890 (2012).
Article  PubMed  Google Scholar 

11.
Gienapp, P., Teplitsky, C., Alho, J., Mills, J. & Merilä, J. Climate change and evolution: disentangling environmental and genetic responses. Mol. Ecol. 17(1), 167–178 (2008).
CAS  PubMed  Google Scholar 

12.
Moritz, C. et al. Impact of a century of climate change on small-mammal communities in Yosemite National Park, USA. Science 322(5899), 261–264 (2008).
ADS  CAS  PubMed  Google Scholar 

13.
Myers, P., Lundrigan, B. L., Hoffman, S. M., Haraminac, A. P. & Seto, S. H. Climate induced changes in the small mammal communities of the northern Great Lakes region. Glob. Change Biol. 15(6), 1434–1454 (2009).
ADS  Google Scholar 

14.
Burns, C. E., Johnston, K. M. & Schmitz, O. J. Global climate change and mammalian species diversity in US national parks. PNAS 100(20), 11474–11477 (2003).
ADS  CAS  PubMed  Google Scholar 

15.
Bradter, U., Kunin, W. E., Altringham, J. D., Thom, T. J. & Benton, T. G. Identifying appropriate spatial scales of predictors in species distribution models with the random forest algorithm. Methods Ecol. Evol. 4, 167–174 (2013).
Google Scholar 

16.
Wiens, J. A. Spatial scaling in ecology. Funct. Ecol. 3, 385–397 (1989).
Google Scholar 

17.
Cunningham, M. A. & Johnson, D. H. Proximate and landscape factors influence grassland bird distributions. Ecol. Appl. 16, 1062–1075 (2006).
PubMed  Google Scholar 

18.
Thogmartin, W. E. & Knutson, M. G. Scaling local species-habitat relations to the larger landscape with a hierarchical spatial count model. Landsc. Ecol. 22, 61–75 (2007).
Google Scholar 

19.
Wasserman, T. N., Cushman, S. A., Wallin, D. O. & Hayden, J. Multi Scale Habitat Relationships of Martes americana in Northern Idaho, USA (US Department of Agriculture and Forest Service Rocky Mountain Research Station, Fort Collins, 2012).
Google Scholar 

20.
Mateo Sanchez, M. C., Cushman, S. A. & Saura, S. Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain). Int. J. Geogr. Inf. Sci. 28(8), 1531–1546 (2013).
Google Scholar 

21.
Vergara, M., Cushman, S. A., Urra, F. & Ruiz-Gonzalez, A. Shaken but not stirred: multiscale habitat suitability modeling of sympatric marten species (Martes martes and Martes foina) in the northern Iberian Peninsula. Landsc. Ecol. 31(6), 1241–1260 (2016).
Google Scholar 

22.
Elith, J. Quantitative methods for modeling species habitat: comparative performance and an application to Australian plants. In Quantitative Methods for Conservation Biology (eds Ferson, S. & Burgman, M.) 39–58 (Springer, New York, 2002).
Google Scholar 

23.
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar 

24.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 

25.
Phillips, S. J. & Dudik, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).
Google Scholar 

26.
Breiman, L. Random forests. Mach. Learn. 45(1), 5–32 (2001).
MATH  Google Scholar 

27.
Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. Classification and Regression Trees (Chapman and Hall/CRC Press, Boca Raton, 1984).
Google Scholar 

28.
Cushman, S. A. & Wasserman, T. N. Landscape applications of machine learning: comparing random forests and logistic regression in multi-scale optimized predictive modeling of American marten occurrence in northern Idaho, USA. In Machine Learning for Ecology and Sustainable Natural Resource Management (eds Humpshires, G., Magness, D. et al.) 185–203 (Springer, New York, 2018).
Google Scholar 

29.
Cushman, S. A., Gutzwiller, K., Evans, J. S. & McGarigal, K. The gradient paradigm: a conceptual and analytical framework for landscape ecology. In Spatial Complexity, Informatics, and Wildlife Conservation (eds Cushman, S. A. & Huettman, F.) 83–108 (Springer, Tokyo, 2010).
Google Scholar 

30.
Evans, J. S., Murphy, M. A., Holden, Z. A. & Cushman, S. A. Modeling species distribution and change using random forest. In Predictive Species and Habitat Modeling in Landscape Ecology: Concepts and Applications (eds Drew, C. A. et al.) 139–159 (Springer, New York, 2011).
Google Scholar 

31.
Drew, C. A., Wiersma, Y. F. & Huettmann, F. Predictive Species and Habitat Modeling in Landscape Ecology: Concepts and Applications (Springer, New York, 2010).
Google Scholar 

32.
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M. & Rigol-Sanchez, J. P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. 67, 93–104 (2012).
Google Scholar 

33.
Schneider, A. Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sens. Environ. 124, 689–704 (2012).
ADS  Google Scholar 

34.
Cushman, S. A., Macdonald, E. A., Landguth, E. L., Halhi, Y. & Macdonald, D. W. Multiple-scale prediction of forest-loss risk across Borneo. Landsc. Ecol. 32, 1581–1598 (2017).
Google Scholar 

35.
Buermann, W. et al. Predicting species distributions across the Amazonian and Andean regions using remote sensing data. J. Biogeogr. 35, 1160–1176 (2008).
Google Scholar 

36.
Lentz, D. L., Bye, R. & Sánchez-Cordero, V. Ecological niche modeling and distribution of wild sunflower (Helianthus annuus L.) in Mexico. Int. J. Plant Sci. 169(4), 541–549 (2008).
Google Scholar 

37.
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62, 2868–3288 (2008).
PubMed  Google Scholar 

38.
Peterson, A. T., Soberon, J. & Sanchez-Cordero, V. Conservatism of ecological niches in evolutionary time. Science 285, 1265–1267 (1999).
CAS  PubMed  Google Scholar 

39.
Graham, C. H., Ron, S. R., Santos, J. C., Schneider, C. J. & Moritz, C. Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution 58, 1781–1793 (2004).
PubMed  Google Scholar 

40.
Pearman, P. B., Guisan, A., Broennimann, O. & Randin, C. F. Niche dynamics in space and time. Trends Ecol. Evol. 23, 149–158 (2008).
PubMed  Google Scholar 

41.
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33, 607–611 (2010).
Google Scholar 

42.
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).
Google Scholar 

43.
Cola, Di. et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).
Google Scholar 

44.
Brown, J. L. & Carnaval, A. C. A tale of two niches: methods, concepts and evolution. Front. Biogeogr. 11, 44158. https://doi.org/10.21425/F5FBG44158 (2019).
Article  Google Scholar 

45.
Qiao, H., Escobar, L. E. & Peterson, A. T. Accessible areas in ecological niche comparisons of invasive species: recognized but still overlooked. Sci. Rep. 7, 1213 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

46.
Wan, J. Z., Wang, C. J., Tan, J. F. & Yu, F. H. Climatic niche divergence and habitat suitability of eight alien invasive weeds in China under climate change. Ecol. Evol. 7(5), 1541–1552 (2017).
PubMed  PubMed Central  Google Scholar 

47.
Khosravi, R., Hemani, M. R. & Cushman, S. A. Multi-scale niche modeling of three sympatric felids of conservation importance in central Iran. Landsc. Ecol. 34, 2451–2467 (2019).
Google Scholar 

48.
Hayward, M. W., Jędrzejewski, W. & Jêdrzejewska, B. Prey preferences of the tiger. J. Zool. (London) 286, 221–231 (2012).
Google Scholar 

49.
Wilson, D. E. M. & Russell, A. Handbook of the Mammals of the World Vol. 2 (Lynx Edicions, Barcelona, 2009).
Google Scholar 

50.
Myers, N. Conservation of Africa’s cats: problems and opportunities. In Cats of the World (eds Miller, S. D. & Everett, D. D.) 437–457 (National Wildlife Federation, Washington, DC, 1986).
Google Scholar 

51.
Hamilton, P.H. The movements of leopards in Tsavo National Park, Kenya, as determined by radio-tracking. M.Sc. Thesis (University of Nairobi, Kenya 1976).

52.
Odden, M., Wegge, P. & Fredriksen, T. Do tigers displace leopards? If so, why?. Ecol. Res. 25, 875–881 (2010).
Google Scholar 

53.
Bagchi, S., Goyal, S. P. & Sankar, K. Prey abundance and prey selection by tigers (Panthera tigris) in a semi-arid, dry deciduous forests in western India. J. Zool. (London) 260(3), 285–290 (2003).
Google Scholar 

54.
Johnsingh, A. J. T. Large mammalian predators in Bandipur. J. Bombay Nat. Hist. Soc. 80, 1–57 (1983).
Google Scholar 

55.
Khan, J. A., Chellam, R., Rodgers, W. A. & Johnsingh, A. J. T. Ungulate densities and biomass in the tropical dry deciduous forests of Gir, Gujarat, India. J. Trop. Ecol. 12(01), 149–162 (1996).
Google Scholar 

56.
Wilson, D. E. & Russell, A. Handbook of the Mammals of the World. Carnivores Vol. 1 (Lynx Edicions, Barcelona, 2009).
Google Scholar 

57.
Singh, H. S. Status of the leopard Panthera pardus in India. Cat News 42, 15–17 (2005).
Google Scholar 

58.
Athreya, V. Is relocation a viable management option for unwanted animals? The case of the leopard in India. Conserv. Soc. 4, 419–423. https://www.conservationandsociety.org/text.asp?2006/4/3/419/49275 (2006).

59.
Karanth, K. U. & Stith, B. M. Prey depletion as a critical determinant of tiger population viability. In Riding the Tiger: Tiger Conservation in Human Dominated Landscapes (eds Seidensticker, J., Christie, S. et al.) 100–113 (Cambridge University Press, Cambridge, 1999).
Google Scholar 

60.
Rowe, K. C. et al. Spatially heterogeneous impact of climate change on small mammals of montane California. Proc. R. Soc. 282, 20141857. https://doi.org/10.1098/rspb.2014.1857 (2015).
Article  Google Scholar 

61.
Pandey, R. & Papeş, M. Changes in future potential distributions of apex predator and mesopredator mammals in North America. Reg. Environ. Change 18, 1223–1233 (2018).
Google Scholar 

62.
Tian, Y., Wu, J., Wang, T. & Ge, J. Climate change and landscape fragmentation jeopardize the population viability of Siberian tiger (Panthera tigris altaica). Landsc. Ecol. 29, 621–637 (2014).
CAS  Google Scholar 

63.
Ashrafzadeh, M. R., Naghipour, A. A., Haidarian, M. & Igor, K. Modeling the response of an endangered flagship predator to climate in Iran. Mamm. Res. 64(1), 39–51 (2019).
Google Scholar 

64.
Karanth, K. U., Nichols, J. D., Kumar, N. S., Link, W. A. & Hines, J. E. Tigers and their prey: predicting carnivore densities from prey abundance. PNAS 101(14), 4854–4858 (2004).
ADS  CAS  PubMed  Google Scholar 

65.
Seidensticker, J. On the ecological separation between tigers and leopards. Biotropica 8(4), 225–234 (1976).
Google Scholar 

66.
McDougal, C. Leopard and tiger interactions at Royal Chitwan National Park, Nepal. J. Bombay Nat. Hist. Soc. 85, 609–610 (1988).
Google Scholar 

67.
Seidensticker, J., Sunquist, M. E. & McDougal, C. Leopards living at the edge of the Royal Chitwan National Park, Nepal. In Conservation in Developing Countries: Problems and Prospects (eds Daniel, J. C. & Serrao, J. S.) 415–423 (Bombay Natural History Society and Oxford University Press, Bombay, 1990).
Google Scholar 

68.
Schoener, T. W. Resource partitioning in ecological communities. Science 185, 27–39 (1974).
ADS  CAS  PubMed  Google Scholar 

69.
Schoener, T. W. Field experiments on interspecific competition. Am. Nat. 122, 240–285 (1983).
Google Scholar 

70.
Fedriani, J. M. et al. Niche relations among three sympatric Mediterranean carnivores. Oecologia 121, 138–148 (1999).
ADS  PubMed  Google Scholar 

71.
Loveridge, A. J. & Macdonald, D. W. Niche separation in sympatric jackals (Canis mesomelas and Canis adustus). J. Zool. 259, 143–153 (2003).
Google Scholar 

72.
Vieira, E. M. & Port, D. Niche overlap and resource partitioning between two sympatric fox species in southern Brazil. J. Zool. 272, 57–63 (2007).
Google Scholar 

73.
Champion, H. G. & Seth, S. K. A Revised Survey of the Forest Types of India (Government of India Press, New Delhi, 1968).
Google Scholar 

74.
Johnsingh, A. J. T. Prey selection in three sympatric carnivores in Bandipur. Mammalia 56, 517–526 (1992).
Google Scholar 

75.
Karanth, K. U. & Sunquist, M. E. Prey selection by tiger, leopard and dhole in tropical forests. J. Appl. Ecol. 64, 439–450 (1995).
Google Scholar 

76.
Andheria, A., Karanth, K. U. & Kumar, N. Diet and prey profiles of three sympatric large carnivores in Bandipur Tiger Reserve, India. J. Zool. 273, 169–175 (2007).
Google Scholar 

77.
Brown, J. L. SDMtoolbox: a python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5(7), 694–700 (2014).
Google Scholar 

78.
Titeux, N. Modelling species distribution when habitat occupancy depart from suitability. Application to birds in a landscape context. Ph.D. thesis (Universite´ Catholique de Louvain, Louvain-la-Neuve, 2006).

79.
Mateo, R. G., Croat, T. B., Felicisimo, A. M. & Munoz, J. Profile or group discriminative techniques? Generating reliable pseudo-absences and target-group absences from natural history collections. Divers. Distrib. 16, 84–94 (2010).
Google Scholar 

80.
Graham, C. H. & Hijmans, R. J. A comparison of methods for mapping species richness. Glob. Ecol. Biogeogr. 15, 578–587 (2006).
Google Scholar 

81.
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 3, 18–22 https://www.R-project.org (2002)

82.
R core Team. R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna https://www.R-project.org/ (2019).

83.
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36(1), 27–46 (2013).
Google Scholar 

84.
Heikkinen, R. K. et al. Methods and uncertainties in bioclimatic envelope modelling under climate change. Prog. Phys. Geogr. 30(6), 751–777 (2006).
Google Scholar 

85.
Farr, T. G. et al. The Shuttle Radar Topography Mission. Rev. Geophys. 45, RG2004. https://doi.org/10.1029/2005RG000183 (2007).
ADS  Article  Google Scholar 

86.
Rabus, B., Eineder, M., Roth, A. & Bamler, R. The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar. ISPRS J. Photogramm. 57(4), 241–262 (2003).
Google Scholar 

87.
Beaumont, L. J., Hughes, L. & Pitman, A. J. Why is the choice of future climate scenarios for species distribution modelling important?. Ecol. Lett. 11(11), 1135–1146 (2008).
PubMed  Google Scholar 

88.
Perkins, S., Pitman, A., Holbrook, N. & McAveney, J. Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature and precipitation over Australia using probability density functions. J. Clim. 20, 4356–4376 (2007).
ADS  Google Scholar 

89.
Watanabe, M. et al. Improved Climate Simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23(23), 6312–6335 (2010).
ADS  Google Scholar 

90.
Calvente, M. E. et al. Can gypsophytes distinguish different types of gypsum habitats?. Acta. Bot. Gallica. 156(1), 63–78 (2009).
Google Scholar 

91.
van Vurren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5. https://doi.org/10.1007/s10584-011-0148-z (2011).
ADS  Article  Google Scholar 

92.
Wayne, G.P. The beginner’s guide to representative concentration pathways. Skeptical Science 25 https://skepticalscience.com/rcp.php (2013)

93.
Rogelj, J., Meinshausen, M. & Knutti, R. Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nat. Clim. Change 2, 248–253 (2012).
ADS  Google Scholar 

94.
Steffan-Dewenter, I., Munzenberg, U., Burger, C., Thies, C. & Tscharntke, T. Scale-dependent effects of landscape context on three pollinator guilds. Ecology 83, 1421–1432 (2002).
Google Scholar 

95.
Holland, J. D., Bert, D. G. & Fahrig, L. Determining the spatial scale of species’ response to habitat. Bioscience 54, 227–233 (2004).
Google Scholar 

96.
McGarigal, K., Wan, H. Y., Zeller, K. A., Timm, B. C. & Cushman, S. A. Multi-scale habitat modeling: a review and outlook. Landsc. Ecol. 31, 1161–1175 (2016).
Google Scholar 

97.
Sandri, M. & Zuccolotto, P. Variable selection using random forests. In Data Analysis, Classification and the Forward Search (eds Zani, S. & Cerioli, A.) 263–270 (Springer, Berlin, 2005).
Google Scholar 

98.
Diaz-Uriarte, R. & Alvarez de Andres, S. Gene selection and classification of microarray data using randomForest. BMC Bioinform. 7, 3 (2006).
Google Scholar 

99.
Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. Variable selection using random forests. Pattern Recognit. Lett. 31, 2225–2236 (2010).
Google Scholar 

100.
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T. & Zeileis, A. Conditional variable importance for randomforests. BMC Bioinform. 9, 307 (2008).
Google Scholar 

101.
Evans, J. S. & Cushman, S. A. Gradient modeling of conifer species using random forests. Landsc. Ecol. 24(5), 673–683 (2009).
Google Scholar 

102.
Pontius, R. G. Jr. & Milones, M. Death to Kappa: Birth of quality disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 32, 4407–4429 (2011).
ADS  Google Scholar 

103.
Pontius, R. G. Jr. & Si, K. The total operating characteristic to measure diagnostic ability for multiple thresholds. Int. J. Geogr. Inf. Sci. 28, 570–583 (2014).
Google Scholar 

104.
Hof, C., Rahbek, C. & Araújo, M. B. Phylogenetic signals in the climatic niches of the world’s amphibians. Ecography 33, 242–250 (2010).
Google Scholar 

105.
Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).
Google Scholar 

106.
Saupe, E. E. et al. Reconstructing ecological niche evolution when niches are incompletely characterized. Syst. Biol. 67, 428–438 (2017).
Google Scholar  More

1.
Vinson, S. B. Host selection by insect parasitoids. Annual Review of Entomology 21, 109–133 (1976).
Google Scholar 
2.
Tormos, J. et al. Superparasitism in laboratory rearing of Spalangia cameroni (Hymenoptera: Pteromalidae), a parasitoid of medfly (Diptera: Tephritidae). Bulletin of Entomological Research 102, 51–61 (2012).
CAS  PubMed  Google Scholar 

3.
Reynolds, K. T. & Hardy, I. C. W. Superparasitism: a non-adaptive strategy? Trends in Ecology & Evolution 19, 347–348 (2004).
Google Scholar 

4.
Parker, G. A. & Courtney, S. P. Models of clutch size in insect oviposition. Theoretical Population Biology 26, 27–48 (1984).
MATH  Google Scholar 

5.
Potting, R. P. J., Snellen, H. M. & Vet, L. E. M. Fitness consequences of superparasitism and mechanism of host discrimination in the stemborer parasitoid Cotesia flavipes. Entomologia Experimentalis et Applicata 82, 341–348 (1997).
Google Scholar 

6.
Van Alphen, J. J. & Visser, M. E. Superparasitism as an adaptive strategy for insect parasitoids. Annual Review of Entomology 35, 59–79 (1990).
PubMed  Google Scholar 

7.
Hubbard, S. F., Harvey, I. F. & Fletcher, J. P. Avoidance of superparasitism: a matter of learning? Animal Behaviour 57, 1193–1197 (1999).
CAS  PubMed  Google Scholar 

8.
Khafagi, W. E. & Hegazi, E. M. Does superparasitism improve host suitability for parasitoid development? A case study in the Microplitis rufiventris – Spodoptera littoralis system. Biocontrol 53, 427–438 (2008).
Google Scholar 

9.
Böckmann, E. A., Tormos, J., Beitia, F. & Fischer, K. Offspring production and self-superparasitism in the solitary ectoparasitoid Spalangia cameroni (Hymenoptera: Pteromalidae) in relation to host abundance. Bulletin of Entomological Research 102, 131–137 (2012).
PubMed  Google Scholar 

10.
Luna, M. G., Desneux, N. & Schneider, M. I. Encapsulation and self-superparasitism of Pseudapanteles dignus (Muesebeck) (Hymenoptera: Braconidae), a parasitoid of Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Plos One 11 (2016).

11.
Varaldi, J., Fouillet, P., Bouletreau, M. & Fleury, F. Superparasitism acceptance and patch-leaving mechanisms in parasitoids: a comparison between two sympatric wasps. Animal Behaviour 69, 1227–1234 (2005).
Google Scholar 

12.
Van Lenteren, J. C. The development of host discrimination and the prevention of superparasitism in the parasite Pseudeucoila bochei Weld (Hym.: Cynipidae). Netherlands Journal of Zoology 26, 1–83 (1975).
Google Scholar 

13.
Agboka, K. et al. Self-, intra-, and interspecific host discrimination in Telenomus busseolae Gahan and Tisis Polaszek (Hymenoptera: Scelionidae), sympatric egg parasitoids of the African cereal stem borer Sesamia calamistis Hampson (Lepidoptera: Noctuidae). Journal of Insect Behavior 15, 1–12 (2002).
Google Scholar 

14.
Gauthier, N. & Bénédet, F. Y., T., Monge, J. P. & Huignard, J. Marking behavior and discrimination of concealed hosts by the ectoparasitoid, Dinarmus basalis Rond. (Hym. Pteromalidae). Journal of Insect Behavior 15, 589–606 (2002).
Google Scholar 

15.
Jaloux, B., Errard, C., Mondy, N., Vannier, F. & Monge, J. P. Sources of chemical signals which enhance multiparasitism preference by a cleptoparasitoid. Journal of Chemical Ecology 31, 1325–1337 (2005).
CAS  PubMed  Google Scholar 

16.
Stelinski, L. L., Oakleaf, R. & Rodriguez, C. Oviposition-deterring pheromone deposited on blueberry fruit by the parasitic wasp, Diachasma alloeum. Behaviour 144, 429–445 (2007).
Google Scholar 

17.
Wu, Z. X. & Nordlund, D. A. Superparasitism of Lygus hesperus Knight eggs by Anaphes iole Girault in the Laboratory. Biological Control 23, 121–126 (2002).
Google Scholar 

18.
Ganesalingam, V. K. Mechanism of discrimination between parasitized and unparasitized hosts by Venturia canescens (Hymenoptera: Ichneumonidae). Entomologia Experimentalis et Applicata 17, 36–44 (1974).
Google Scholar 

19.
Micha, S. G., Stammel, J. & Höller, C. 6-methyl-5-heptene-2-one, a putative sex and spacing pheromone of the aphid hyperparasitoid, Alloxysta victrix (Hymenoptera: Alloxystidae). European Journal of Entomology 90, 439–442 (1993).
CAS  Google Scholar 

20.
Sheehan, W., Wäckers, F. L. & Lewis, W. J. Discrimination of previously searched, host-free sites by Microplitis croceipes (Hymenoptera: Braconidae). Journal of Insect Behavior 6, 323–331 (1993).
Google Scholar 

21.
Syvertsen, T. C., Jackson, L. L., Blomquist, G. J. & Vinson, S. B. Alkadienes mediating courtship in the parasitoid Cardiochiles nigriceps (Hymenoptera: Braconidae). Journal of Chemical Ecology 21, 1971–1989 (1995).
CAS  PubMed  Google Scholar 

22.
Van Baaren, J. & Boivin, G. Learning affects host discrimination behavior in a parasitoid wasp. Behavioral Ecology & Sociobiology 42, 9–16 (1998).
Google Scholar 

23.
Ruschioni, S., van Loon, J. J. A., Smid, H. M. & van Lenteren, J. C. Insects can count: Sensory basis of host discrimination in parasitoid wasps revealed. Plos One 10 (2015).

24.
Nufio, C. R. & Papaj, D. R. Host marking behavior in phytophagous insects and parasitoids. Entomologia Experimentalis et Applicata 99, 273–293 (2001).
Google Scholar 

25.
Velasco, L. R. I. The life history of Apanteles plutellae Kurdj. (Braconidae), a parasitoids of the diamondback moth. Philippines Entomology 5, 385–399 (1982).
Google Scholar 

26.
Talekar, N. S. & Yang, J. C. Characteristic of parasitism of diamondback moth by two larval parasites. Entomophaga 36, 95–104 (1991).
Google Scholar 

27.
Shi, Z. H., Liu, S. S. & Li, Y. X. Cotesia plutellae parasitizing Plutella xylostella: Host-age dependent parasitism and its effect on host development and food consumption. Biocontrol 47, 499–511 (2002).
Google Scholar 

28.
De Boer, J. G., Ode, P. J., Vet, L. E. M., Whitfield, J. & Heimpel, G. E. Complementary sex determination in the parasitoid wasp Cotesia vestalis (C. plutellae). Journal of Evolutionary Biology 20, 340–348 (2010).
Google Scholar 

29.
Li, Y., Liu, Y. & Liu, S. Effect of superparasitism on bionomics of Cotesia plutellae. Chinese Journal of Biological Control 17, 151–154 (2001).
ADS  Google Scholar 

30.
Yu, R.-X., Shi, M., Huang, F. & Chen, X.-X. Immature development of Cotesia vestalis (Hymenoptera: Braconidae), an endoparasitoid of Plutella xylostella (Lepidoptera: Plutellidae). Annals of the Entomological Society of America 101, 189–196 (2008).
Google Scholar 

31.
Mitsunaga, T., Shimoda, T. & Yano, E. Influence of food supply on longevity and parasitization ability of a larval endoparasitoid, Cotesia plutellae (Hymenoptera: Braconidae). Appl. Entomol. Zoolog 39, 691–697 (2004).
Google Scholar 

32.
Roux, O., Van Baaren, J., Gers, C., Arvanitakis, L. & Legal, L. Antennal structure and oviposition behavior of the Plutella xylostella specialist parasitoid: Cotesia plutellae. Microscopy Research & Technique 68, 36–44 (2005).
Google Scholar 

33.
Potting, R. P. J., Poppy, G. M. & Schuler, T. H. The role of volatiles from cruciferous plants and pre‐flight experience in the foraging behaviour of the specialist parasitoid Cotesia plutellae. Entomologia Experimentalis et Applicata 93, 87–95 (1999).
CAS  Google Scholar 

34.
Chen, W. B. et al. Parasitised caterpillars suffer reduced predation: potential implications for intra-guild predation. Scientific Reports 7 (2017).

35.
Liu, T. S. et al. Isolation and characterization of microsatellite loci for Cotesia plutellae (Hymenoptera: Braconidae). Insects 8 (2017).

36.
Peakall, R. & Smouse, P. E. Genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6, 288–295 (2006).
Google Scholar 

37.
Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

38.
Jervis, M. A., Ellers, J. & Harvey, J. A. Resource acquisition, allocation, and utilization in parasitoid reproductive strategies. Annual Review of Entomology 53, 361–385 (2008).
CAS  PubMed  Google Scholar 

39.
Couchoux, C. & van Nouhuys, S. Effects of intraspecific competition and host-parasitoid developmental timing on foraging behaviour of a parasitoid wasp. Journal of Insect Behavior 27, 283–301 (2014).
PubMed  Google Scholar 

40.
McKay, T. & Broce, A. B. Discrimination of self-parasitized hosts by the pupal parasitoid Muscidifurax zaraptor (Hymenoptera: Pteromalidae). Annals of the Entomological Society of America 97, 592–599 (2004).
Google Scholar 

41.
Yamada, Y. Y. & Sugaura, K. Evidence for adaptive self-superparasitism in the dryinid parasitoid Haplogonatopus atratus when conspecifics are present. Oikos 103, 175–181 (2003).
Google Scholar 

42.
Gariepy, T. D., Kuhlmann, U., Gillott, C. & Erlandson, M. Parasitoids, predators and PCR: the use of diagnostic molecular markers in biological control of Arthropods. Journal of Applied Entomology 131, 225–240 (2007).
CAS  Google Scholar 

43.
Santolamazza-Carbone, S. & Rivera, A. C. Superparasitism and sex ratio adjustment in a wasp parasitoid: results at variance with local mate competition? Oecologia 136, 365–373 (2003).
ADS  PubMed  Google Scholar 

44.
Zhang, L., Bai, S., Yu, H. & Li, X. Intraspecific competition in Cotesia vestalis, a solitary endoparasitoid of Plutella xylostella larvae. Chinese Journal of Biological Control 30, 128–133 (2014).
Google Scholar 

45.
Lebreton, S., Christidès, J. P., Bagnères, A. G., Chevrier, C. & Darrouzet, E. Modifications of the chemical profile of hosts after parasitism allow parasitoid females to assess the time elapsed since the first attack. Journal of Chemical Ecology 36, 513–521 (2010).
CAS  PubMed  Google Scholar 

46.
Strand, M. R. & Godfray, H. C. J. Superparasitism and ovicide in parasitic Hymenoptera: theory and a case study of the ectoparasitoid Bracon hebetor. Behavioral Ecology & Sociobiology 24, 421–432 (1989).
Google Scholar 

47.
Tena, A., Kapranas, A., Garcia-Marí, F. & Luck, R. F. Host discrimination, superparasitism and infanticide by a gregarious endoparasitoid. Animal Behaviour 76, 789–799 (2008).
Google Scholar 

48.
Yamada, Y. Y. & Ikawa, K. Superparasitism strategy in a semisolitary parasitoid with imperfect self/non-self recognition, Echthrodelphax fairchildii. Entomologia Experimentalis et Applicata 114, 143–152 (2005).
Google Scholar 

49.
Field, S., Keller, M. & Calbert, G. The pay-off from superparasitism in the egg parasitoid Trissolcus basalis, in relation to patch defence. Ecological Entomology 22, 142–149 (1997).
Google Scholar 

50.
Díazfleischer, F., Galvez, C. & Montoya, P. Oviposition, superparasitism, and egg load in the solitary parasitoid Diachasmimorpha longicaudata (Hymenoptera: Braconidae): response to host availability. Annals of the Entomological Society of America 108, 235–241 (2015).
Google Scholar 

51.
Gauthier, N., Monge, J. P. & Huignard, J. Superparasitism and host discrimination in the solitary ectoparasitoid Dinarmus basalis. Entomologia Experimentalis et Applicata 79, 91–99 (1996).
Google Scholar 

52.
Weber, C. A., Smilanick, J. M., Ehler, L. E. & Zalom, F. G. Ovipositional behavior and host discrimination in three scelionid egg parasitoids of stink bugs. Biological Control 6, 245–252 (1996).
Google Scholar 

53.
Darrouzet, E., Bignon, L. & Chevrier, C. Impact of mating status on egg-laying and superparasitism behaviour in a parasitoid wasp. Entomologia Experimentalis et Applicata 123, 279–285 (2007).
Google Scholar 

54.
Lebreton, S., Labarussias, M., Chevrier, C. & Darrouzet, E. Discrimination of the age of conspecific eggs by an ovipositing ectoparasitic wasp. Entomologia Experimentalis et Applicata 130, 28–34 (2009).
Google Scholar 

55.
Hoffmeister, T. S. & Roitberg, B. D. Evolutionary ecology of oviposition marking pheromones. In: Hilker, M., Meiners, T., (eds.), Chemoecology of insect eggs and egg deposition. (Germany: Blackwell Publishing, 2008).

56.
Ito, E. & Yamada, Y. Y. Self-/conspecific discrimination and superparasitism strategy in the ovicidal parasitoid Echthrodelphax fairchildii (Hymenoptera: Dryinidae). Insect Science 21, 741–749 (2014).
PubMed  Google Scholar 

57.
Liang, Q. F., Jia, Y. J. & Liu, T. X. Self- and conspecific discrimination between unparasitized and parasitized green peach aphid (Hemiptera: Aphididae), by Aphelinus asychis (Hymenoptera: Aphelinidae). Journal of Economic Entomology 110, 430–437 (2017).
PubMed  Google Scholar 

58.
Yazdani, M., Glatz, R. & Keller, M. Host discrimination by the solitary endoparasitoid Dolichogenidea tasmanica (Hymenopotera: Braconidae). Biocontrol Sci. Technol. 25, 155–162 (2015).
Google Scholar 

59.
Wang, X. G. & Keller, M. A. A comparison of the host-searching efficiency of two larval parasitoids of Plutella xylostella. Ecological Entomology 27, 105–114 (2002).
CAS  Google Scholar  More

1.
Hamilton, W. D. The genetical evolution of social behavior I. J. Theor. Biol. 7, 1–6 (1964).
CAS  PubMed  Google Scholar 
2.
Trivers, R. L. The evolution of reciprocal altruism. Q. Rev. Biol. 46, 35–57 (1971).
Google Scholar 

3.
Axelrod, R. & Hamilton, W. D. The evolution of cooperation. Science 211, 1390–1396 (1981).
ADS  MathSciNet  CAS  PubMed  MATH  Google Scholar 

4.
Maynard Smith, J. Game theory and the evolution of cooperation. In Evolution from Molecules to Men (ed. Bendall, D. S.) 445–456 (Cambridge University Press, Cambridge, 1983).
Google Scholar 

5.
Clutton-Brock, T. Cooperation between non-kin in animal societies. Nature 462, 51–57 (2009).
ADS  CAS  PubMed  Google Scholar 

6.
Kokko, H., Johnstone, R. A. & Clutton-Brock, T. H. The evolution of cooperative breeding through group augmentation. Proc. R. Soc. B 268, 187–196 (2001).
CAS  PubMed  Google Scholar 

7.
Kingma, S. A., Santema, P., Taborsky, M. & Komdeur, J. Group augmentation and the evolution of cooperation. Trends Ecol. Evol. 29, 476–484 (2014).
PubMed  Google Scholar 

8.
Hamilton, W. D. Geometry for the selfish herd. J. Theor. Biol. 31, 295–311 (1971).
CAS  PubMed  Google Scholar 

9.
Reluga, T. C. & Viscido, S. Simulated evolution of selfish herd behavior. J. Theor. Biol. 234, 213–225 (2005).
MathSciNet  PubMed  Google Scholar 

10.
Nowak, M. A., Tarnita, C. E. & Wilson, E. O. The evolution of eusociality. Nature 446, 1057–1062 (2010).
ADS  Google Scholar 

11.
Wilson, E. O. The Social Conquest of Earth (Norton, New York, 2012).
Google Scholar 

12.
Dawkins, R. The Descent of Edward Wilson. Prospect. May 24 (2012).

13.
Whitehead, H. & Rendell, L. The Cultural Lives of Whales and Dolphins (The University of Chicago Press Ltd., Chicago, 2015).
Google Scholar 

14.
Aplin, L. M. Culture and cultural evolution in birds: a review of the evidence. Anim. Behav. 147, 179–187. https://doi.org/10.1016/j.anbehav.2018.05.001 (2019).
Article  Google Scholar 

15.
Allen, J. A. Community through culture: from insects to whales. BioEssays 41, 1900060. https://doi.org/10.1002/bies.201900060 (2019).
Article  Google Scholar 

16.
Kleinenberg, S. E., Yablokov, A. V., Bel’kovich, B. M. & Tarasevich, M. N. Beluga (Delphinapterus leucas) Investigation of the Species (Academy of Sciences of the USSR, Moscow, 1964).
Google Scholar 

17.
Karlsen, J. D., Bisther, A., Lydersen, C., Haug, T. & Kovacs, K. M. Summer vocalizations of adult male white whales (Delphinapterus leucas) in Svalbard, Norway. Polar Biol. 25, 808–817 (2002).
Google Scholar 

18.
Chmelnitsky, E. G. & Ferguson, S. H. Beluga whale, Delphinapterus leucas, vocalizations from the Churchill River, Manitoba, Canada. J. Acoust. Soc. Am. 131, 4821–4835 (2012).
ADS  PubMed  Google Scholar 

19.
Bel’kovitch, V. M. & Sh’ekotov, M. N. The Belukha Whale: Natural Behavior and Bioacoustics (Woods Hole Oceanographic Institution, Woods Hole, 1993).
Google Scholar 

20.
Panova, E. M., Belikov, R. A., Agafonov, A. V. & Bel’kovich, V. M. The relationship between the behavioral activity and the underwater vocalization of the beluga whale (Delphinapterus leucas). Oceanology 52, 79–87 (2012).
ADS  Google Scholar 

21.
Smith, T. G., Hammill, M. O. & Martin, A. R. Herd composition and behaviour of white whales (Delphinapterus leucas) in two Canadian arctic estuaries. Meddelelsser Grønland. Biosci. 39, 175–184 (1994).
Google Scholar 

22.
Loseto, L. L., Richard, P., Stern, G. A., Orr, J. & Ferguson, S. H. Segregation of Beaufort Sea beluga whales during the open-water season. Can. J. Zool. 84, 1743–1751 (2006).
Google Scholar 

23.
Krasnova, V. V., Chernetsky, A. D., Kirillova, O. I. & Bel’kovich, V. M. The dynamics of the abundance, age, and sex structure of the Solovetsky reproductive gathering of the beluga whale Delphinapterus leucas (Onega Bay, White Sea). Russ. J. Mar. Biol. 38, 218–225 (2012).
Google Scholar 

24.
Palsbøll, P. J., Heidi-Jøgensen, M. P. & Bérubé, M. Analysis of mitochondrial control region nucleotide sequences from Baffin Bay belugas (Delphinapterus leucas): detecting pods or subpopulations?. NAMMCO Sci. Pub. 4, 39–50 (2002).
Google Scholar 

25.
Bigg, M. A., Olesiuk, P. F., Ellis, G. M., Ford, J. K. B. & Balcomb, K. C. III. Social organization and genealogy of resident killer whales (Orcinus orca) in the coastal waters of British Columbia and Washington State. Rep. Int. Whal. Commun. SI 12, 383–405 (1990).
Google Scholar 

26.
Amos, B., Schlötterer, C. & Tautz, D. Social structure of pilot whales revealed by analytical DNA profiling. Science 260, 670–672 (1993).
ADS  CAS  PubMed  Google Scholar 

27.
Richard, K. R., Dillon, M. C., Whitehead, H. & Wright, J. M. Patterns of kinship in groups of free-living sperm whales (Physeter macrocephalus) revealed by multiple molecular generic analyses. Proc. Natl. Acad. Sci. USA 93, 8792–8795 (1996).
ADS  CAS  PubMed  Google Scholar 

28.
Barrett-Lennard, L. G. Population Structure and Mating Patterns of Killer Whales (Orcinus orca) as Revealed by DNA Analysis. PhD thesis (2000).

29.
Konrad, C. M., Gero, S., Frasier, T. & Whitehead, H. Kinship influences sperm whale social organization within, but generally not among, social units. R. Soc. Open Sci. 5, 180914. https://doi.org/10.1098/rsos.180914 (2018).
ADS  Article  PubMed  PubMed Central  Google Scholar 

30.
Connor, R. C., Wells, R., Mann, J. & Read, A. The bottlenose dolphin: social relationships in a fission–fusion society. In Cetecean Societies: Field Studies of Dolphins and Whales, 91–126 (ed. Mann, J.) (The Univerisity of Chicago Press Ltd., Chicago, 2000).
Google Scholar 

31.
Wiszniewski, J., Lusseau, D. & Möller, L. M. Female bisexual kinship ties maintain social cohesion in a dolphin network. Anim. Behav. 80, 895–904. https://doi.org/10.1016/j.anbehav.2010.08.013 (2010).
Article  Google Scholar 

32.
Parsons, K. M. et al. Kinship as a basis for alliance formation between male bottlenose dolphins, Tursiops truncatus, in the Bahamas. Anim. Behav. 66, 185–194. https://doi.org/10.1006/anbe.2003.2186 (2003).
Article  Google Scholar 

33.
Frère, C. H. et al. Home range overlap, matrilineal and biparental kinship drive female associations in bottlenose dolphins. Anim. Behav. 80, 481–486. https://doi.org/10.1016/j.anbehav.2010.06.007 (2010).
Article  Google Scholar 

34.
Louis, M. et al. Evaluating the influences of ecology, sex and kinship on the social structure of resident coastal bottlenose dolphins. Mar. Biol. 165, 80. https://doi.org/10.1007/s00227-018-3341-z (2018).
Article  Google Scholar 

35.
O’Corry-Crowe, G. M., Suydam, R. S., Rosenberg, A., Frost, K. J. & Dizon, A. E. Phylogeography, population structure and dispersal patterns of the beluga whale Delphinapterus leucas in the western Nearctic revealed by mitochondrial DNA. Mol. Ecol. 6, 955–970 (1997).
Google Scholar 

36.
de March, B. G. E. & Postma, L. D. Molecular genetic stock discrimination of belugas (Delphinapterus leucas) hunted in eastern Hudson Bay, northern Quebec, Hudson Strait, and Sanikiluaq (Belcher Islands), Canada, and comparisons to adjacent populations. Arctic 56, 111–124 (2003).
Google Scholar 

37.
Turgeon, J., Duchesne, P., Colbeck, G. J., Postma, L. D. & Hammill, M. O. Spatiotemporal segregation among summer stocks of beluga (Delphinapterus leucas) despite nuclear gene flow: implication for the endangered belugas in eastern Hudson Bay. Conserv. Gen. 13, 419–433 (2012).
Google Scholar 

38.
Meschersky, I. G. et al. A genetic analysis of the beluga whale Delphinapterus leucas (Cetacea: Monodontidae) from summer aggregations in the Russian Far East. Russ. J. Mar. Biol. 39, 125–135 (2013).
CAS  Google Scholar 

39.
Colbeck, G. J. et al. Groups of related belugas (Delphinapterus leucas) travel together during their seasonal migrations in and around Hudson Bay. Proc. R. Soc. B. 280, 2012–2552. https://doi.org/10.1098/rspb.2012.2552 (2013).
Article  Google Scholar 

40.
O’Corry-Crowe, G. et al. Migratory culture, population structure and stock identity in North Pacific beluga whales (Delphinapterus leucas). PLoS ONE 13(3), e0194201. https://doi.org/10.1371/journal.pone.0194201 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

41.
Krasnova, V. V., Chernetsky, A. D., Zheludkova, A. I. & Bel’kovich, V. M. Parental behavior of the beluga whale (Delphinapterus leucas) in natural environment. Biol. Bull. 41, 349–356 (2014).
Google Scholar 

42.
Vergara, V. & Mikus, M. Contact call diversity in natural beluga entrapments in an Arctic estuary: preliminary evidence of vocal signatures in wild belugas. Mar. Mamm. Sci. 35, 434–465 (2019).
Google Scholar 

43.
Suydam, R. S., Frost, K. J., Lowry, L. F., O’Corry-Crowe, G. M. & Pikok, D. Satellite tracking of eastern Chukchi Sea beluga whales in the Arctic Ocean. Arctic 54, 237–243 (2001).
Google Scholar 

44.
Lydersen, C., Martin, A. R., Kovacs, K. M. & Gjertz, I. Summer and autumn movements of white whales Delphinapterus leucas in Svalbard, Norway. Mar. Ecol. Prog. Ser. 219, 265–274 (2001).
ADS  Google Scholar 

45.
Michaud, R. Distribution estivale du béluga du St.-Laurent: synthèse 1986 à 1992. Can. Tech. Rep. Fish. Aquat. Sci. 1906, 28 (1993).
Google Scholar 

46.
Suydam, R. S. Age, Growth, Reproduction, and Movements of Beluga Whales (Delphinapterus leucas) from the Eastern Chukchi Sea. PhD thesis (2009).

47.
Lefebvre, S. L., Michaud, R., Lesage, V. & Berteaux, D. Identifying high residency areas of the threatened St. Lawrence beluga whale from fine-scale movements of individuals and coarse-scale movements of herds. Mar. Ecol. Prog. Ser. 450, 243–257 (2012).
ADS  Google Scholar 

48.
Sjare, B. L. & Smith, T. G. The relationship between behavioral activity and underwater vocalizations of the white whale, Delphinapterus leucas. Can. J. Zool. 64, 2824–2831 (1986).
Google Scholar 

49.
Alekseeva, Y. I., Panova, E. M. & Belkovich, V. M. Behavioral and acoustical characteristics of the reproductive gathering of beluga whales (Delphinapterus leucas) in the vicinity of Myagostrov, Golyi Sosnovets, and Roganka Islands (Onega Bay, the White Sea). Biol. Bull. 40, 307–317 (2013).
Google Scholar 

50.
Smith, T. G., St. Aubin, D. J. & Hammill, M. O. Rubbing behavior of belugas, Delphinapterus leucas, in a high Arctic estuary. Can. J. Zool. 70, 2405–2409 (1992).
Google Scholar 

51.
Howe, M. et al. Beluga, Delphinapterus leucas, ethogram: a tool for Cook Inlet beluga conservation. Mar. Fish. Rev. 77, 32–40 (2015).
Google Scholar 

52.
Anderson, P. A., Poe, R. B., Thompson, L. A., Weber, N. & Romano, T. A. Behavioral responses of beluga whales (Delphinapterus leucas) to environmental variation in an Arctic estuary. Behav. Process. 145, 48–59 (2017).
Google Scholar 

53.
Ford, J. K. B., Ellis, G. M. & Balcomb, K. C. III. Killer Whales (University of British Columbia Press, Vancouver, 200).
Google Scholar 

54.
Heimlich-Boran, J. R. Social Organization of the Short-Finned Pilot Whale, Globicephala macrorhynchus, with Special Reference to the Social Ecology of Delphinids. PhD thesis (1993).

55.
Rendell, L., Cantor, M., Gero, S., Whitehead, H. & Mann, J. Causes and consequences of female centrality in cetacean societies. Phil. Trans. R. Soc. B 374, 20180066. https://doi.org/10.1098/rstb.2018.0066 (2019).
Article  PubMed  Google Scholar 

56.
Connor, R. C. et al. Male alliance behaviour and mating access varies with habitat in a dolphin social network. Sci. Rep. 7, 46354. https://doi.org/10.1038/srep46354 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

57.
Packer, C., Gilbert, D. A., Pusey, A. E. & O’Brien, S. J. A molecular genetic analysis of kinship and cooperation in African lions. Nature 351, 562–565 (1991).
ADS  CAS  Google Scholar 

58.
Grinnell, J., Packer, C. & Pusey, A. E. Cooperation in male lions: kinship, reciprocity or mutualism?. Anim. Behav. 49, 95–105 (1995).
Google Scholar 

59.
Gilby, I. C. et al. Fitness benefits of coalitionary aggression in male chimpanzees. Behav. Ecol. Sociobiol. 67, 373–381 (2013).
PubMed  Google Scholar 

60.
Croft, D. P., Brent, L. J. N., Franks, D. W. & Cant, M. A. The evolution of prolonged life after reproduction. TREE 30(7), 407–416. https://doi.org/10.1016/j.tree.2015.04.011 (2015).
Article  PubMed  Google Scholar 

61.
Brent, L. J. N. et al. Ecological knowledge, leadership, and the evolution of menopause in killer whales. Curr. Biol. 25, 746–750 (2015).
CAS  PubMed  Google Scholar 

62.
Ellis, S. et al. Analyses of ovarian activity reveal repeated evolution of postreproductive lifespans in toothed Whales. Sci. Rep. 8, 12833. https://doi.org/10.1038/s41598-018-31047-8 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

63.
Nattrass, A. et al. Postreproductive killer whale grandmothers improve the survival of their grandoffspring. PNAS https://doi.org/10.1073/pnas.1903844116 (2019).
Article  PubMed  Google Scholar 

64.
McComb, K., Moss, C., Durant, S. M., Baker, L. & Sayialel, S. Matriarchs as repositories of social knowledge in African elephants. Science 292, 491–494. https://doi.org/10.1126/science.1057895 (2001).
ADS  CAS  Article  PubMed  Google Scholar 

65.
McComb, K. et al. Leadership in elephants: the adaptive value of age. Proc. R. Soc. B 278, 3270–3276. https://doi.org/10.1098/rspb.2011.0168 (2011).
Article  PubMed  Google Scholar 

66.
Boran, J. & Heimlich, S. Pilot whales: delphinid matriarchies in deep seas. In Ethology and Behavioral Ecology of Odontocetes (ed. Würsig, B.) 281–304 (Springer, Berlin, 2019). https://doi.org/10.1007/978-3-030-16663-2_13.
Google Scholar 

67.
Whitehead, H. Cultural selection and genetic diversity in matrilineal whales. Science 282, 1708–1711 (1998).
ADS  CAS  PubMed  Google Scholar 

68.
Whitehead, H. Gene–culture coevolution in whales and dolphins. PNAS 114, 7814–7821. https://doi.org/10.1073/pnas.162073611 (2017).
CAS  Article  PubMed  Google Scholar 

69.
Johnstone, R. A. & Cant, M. A. The evolution of menopause in cetaceans and humans: the role of demography. Proc. R. Soc. B 277, 3765–3771 (2010).
PubMed  Google Scholar 

70.
Foote, A. D. Mortality rate acceleration and post-reproductive lifespan in matrilineal whale species. Biol. Let. 4, 189–191. https://doi.org/10.1098/rsbl.2008.0006 (2008).
Article  Google Scholar 

71.
Westdal, K. H., Davies, J., MacPherson, A., Orr, J. & Ferguson, S. H. Behavioural changes in belugas (Delphinpaterus leucas) during a killer whale (Orcinus orca) attack in southwest Hudson Bay. Can. Field Nat. 130(4), 315–319 (2016).
Google Scholar 

72.
Smith, T. G. & Sjare, B. Predation of belugas and narwhals by polar bears in nearshore areas of the Canadian High Arctic. Arctic 43, 99–102 (1990).
Google Scholar 

73.
Schino, G. & Aureli, F. Reciprocity in group-living animals: partner control versus partner choice. Biol. Rev. 92, 665–672. https://doi.org/10.1111/brv.12248 (2017).
Article  PubMed  Google Scholar 

74.
Gowans, S., Whitehead, H. & Hooker, S. K. Social organization in northern bottlenose whales, Hyperoodon ampullatus: not driven by deep-water foraging?. Anim. Behav. 62, 369–377 (2001).
Google Scholar 

75.
Feduitin, I. D., Filatova, O. A., Mamev, E. G., Burdin, A. M. & Hoyt, E. Occurrence and social structure of Baird’s beaked whales, Berardius bairdii, in the Commander islands, Russia. Mar. Mamm. Sci. 31, 853–865 (2015).
Google Scholar 

76.
Hill, K. R. et al. Co-residence patterns in hunter-gatherer societies show unique human social structure. Science 331, 1286–1289 (2011).
ADS  CAS  PubMed  Google Scholar 

77.
Conkova, N., Fokkema, T. & Dykstra, P. A. Non-kin ties as a source of support in Europe: understanding the role of cultural context. Eur. Soc. 20, 131–156. https://doi.org/10.1080/14616696.2017.1405058 (2017).
Article  Google Scholar 

78.
Wade, P. R., Reeves, R. R. & Mesnick, S. L. Social and behavioural factors in cetacean responses to overexploitation: are odontocetes less “resilient” than mysticetes?. J. Mar. Biol. https://doi.org/10.1155/2012/567276 (2012).
Article  Google Scholar 

79.
Brakes, P. et al. Animal cultures matter for conservation. Science 363, 1032–1034 (2019).
ADS  CAS  PubMed  Google Scholar 

80.
O’Corry-Crowe, G., Lucey, B., Castellote, M. & Stafford, K. Abundance, Habitat Use and Behavior of Beluga Whales in Yakutat Bay, May 2008; as Revealed by Passive Acoustic Monitoring, Visual Observations and Photo-ID. HBOI-Florida Atlantic University Rep. (2009).

81.
Richard, P. R., Martin, A. R. & Orr, J. R. Summer and autumn movements of belugas of the eastern Beaufort Sea stock. Arctic 54, 223–236 (2001).
Google Scholar 

82.
Litovka, D. I. et al. Research of belugas Delphinapterus leucas in Anadyr Gulf (Chukotka) using satellite telemetry. Mar. Mamm. Holarctic 10, 70–80 (2002).
Google Scholar 

83.
O’Corry-Crowe, G. M., Dizon, A. E., Suydam, R. S. & Lowry, L. F. Molecular genetic studies of population structure and movement patterns in a migratory species: the beluga whale (Delphinapterus leucas) in the western Nearctic. In Molecular and Cell Biology of Marine Mammals (ed. Pfeiffer, C. J.) 53–64 (Krieger Publishing Co., Malabar, 2002).
Google Scholar 

84.
O’Corry-Crowe, G., Lucey, W., Archer, F. I. & Mahoney, B. The genetic ecology and population origins of the beluga whales of Yakutat Bay. Mar. Fish. Rev. 71, 47–48 (2015).
Google Scholar 

85.
O’Corry-Crowe, G. M. L. et al. Population genetic structure and evolutionary history of North Atlantic beluga whales (Delphinapterus leucas) from West Greenland, Svalbard and the White Sea. Polar Biol. 33, 1179–1194 (2010).
Google Scholar 

86.
Fain, S. R. & LeMay, J. P. Gender identification of humans and mammalian wildlife species from PCR amplified sex linked genes. Proc. Am. Acad. Forensic Sci. 1, 34 (1995).
Google Scholar 

87.
Citta, J. J. et al. Assessing the abundance of Bristol Bay belugas with genetic mark-recapture methods. Mar. Mamm. Sci. 34, 666–686. https://doi.org/10.1111/mms.12472 (2018).
Article  Google Scholar 

88.
Wang, J. conancestry: a program for simulating, estimating and analyzing relatedness and inbreeding coefficients. Mol. Ecol. Resour. 11, 141–145 (2011).
PubMed  Google Scholar 

89.
Kalinowski, S. T., Wagner, A. P. & Taper, M. L. ml-relate: a computer program for maximum likelihood estimation of relatedness and relationships. Mol. Ecol. Notes 6, 576–579 (2006).
CAS  Google Scholar 

90.
Wang, J. Triadic IBD coefficients and applications to estimating pairwise relatedness. Genet. Res. 89, 135–153 (2007).
CAS  PubMed  Google Scholar 

91.
Queller, D. C. & Goodnight, K. F. Estimating relatedness using molecular markers. Evolution 43, 258–275 (1989).
PubMed  Google Scholar 

92.
Kraemer, P. & Gerlach, G. Demrelate: calculating interindividual relatedness for kinship analysis based on codominant diploid genetic markers in R. Mol. Ecol. Resour. 17, 1371–1377 (2017).
CAS  PubMed  Google Scholar 

93.
Kraemer, P. & Gerlach G. Package ‘Demrelate’ Version 0.9-3: Functions to Calculate Relatedness on Diploid Genetic Data. https://cran.r-project.org/web/packages%20s%20Demrelate/index.html (2017).

94.
Blouin, M., Parsons, M., Lacaille, V. & Lotz, S. Use of microsatellite loci to classify individuals by relatedness. Mol. Ecol. 5, 393–401 (1996).
CAS  PubMed  Google Scholar 

95.
Kivelä, M., Arnaud-Haond, S. & Saramäki, J. EDENetworks Version 218: Ecological and Evolutionary Networks (Oxford University Press, Oxford, 2014).
Google Scholar  More

1.
Kumar, V. Biological Timekeeping. (Springer, India, 2017). .
2.
Tessmar-Raible, K., Raible, F. & Arboleda, E. Another place, another timer: marine species and the rhythms of life. Bioessays 33, 165–172 (2011).
PubMed  PubMed Central  Google Scholar 

3.
de la Iglesia, H. O. & Johnson, C. H. Biological clocks: riding the tides. Curr. Biol. 23, R921–R923 (2013).
PubMed  PubMed Central  Google Scholar 

4.
Oosthuizen, M. K., Cooper, H. M. & Bennett, N. C. Circadian rhythms of locomotor activity in solitary and social species of African mole-rats (Family: Bathyergidae). J. Biol. Rhythms 18, 481–490 (2003).
PubMed  Google Scholar 

5.
van Oort, B. E. H. et al. Circadian organization in reindeer. Nature 438, 1095–1096 (2005).
ADS  PubMed  Google Scholar 

6.
Childress, J. J. & Thuesen, E. V. in Deep-Sea Food Chains and the Global Carbon Cycle (eds. Rowe, G. T. & Pariente, V.) 217–236 (Springer, Netherlands, 1992).

7.
Turekian, K. K. et al. Slow growth rate of a deep-sea clam determined by 228Ra chronology. Proc. Natl Acad. Sci. USA 72, 2829–2832 (1975).
ADS  CAS  PubMed  Google Scholar 

8.
Levin, L. A. et al. Hydrothermal vents and methane seeps: rethinking the sphere of influence. Front. Marine Sci. https://doi.org/10.3389/fmars.2016.00072 (2016).

9.
Du Preez, C. & Fisher, C. R. Long-term stability of back-arc basin hydrothermal vents. Front. Marine Sci. https://doi.org/10.3389/fmars.2018.00054 (2018).

10.
Cuvelier, D. et al. Community dynamics over 14 years at the Eiffel Tower hydrothermal edifice on the Mid-Atlantic Ridge. Limnol. Oceanogr. 56, 1624–1640 (2011).
ADS  Google Scholar 

11.
Garrett, C. Internal tides and ocean mixing. Science 301, 1858–1859 (2003).
CAS  PubMed  Google Scholar 

12.
Childress, J. J. & Fisher, C. R. The biology of hydrothermal vent animals: Physiology, biochemistry, and autotrophic symbioses. Oceanogr. Mar. Biol. Ann. Rev. 30, 337–441 (1992).

13.
Barreyre, T. et al. Temporal variability and tidal modulation of hydrothermal exit-fluid temperatures at the Lucky Strike deep-sea vent field, Mid-Atlantic Ridge: MAR vent-field temperature monitoring. J. Geophys. Res.: Solid Earth 119, 2543–2566 (2014).
ADS  Google Scholar 

14.
Lelièvre, Y. et al. Astronomical and atmospheric impacts on deep-sea hydrothermal vent invertebrates. Proc. R. Society B: Biol. Sci. 284, 20162123 (2017).

15.
Cuvelier, D., Legendre, P., Laes, A., Sarradin, P.-M. & Sarrazin, J. Rhythms and community dynamics of a hydrothermal tubeworm assemblage at main endeavour field – a multidisciplinary deep-sea observatory approach. PLoS ONE 9, e96924 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

16.
Cuvelier, D., Legendre, P., Laës-Huon, A., Sarradin, P.-M. & Sarrazin, J. Biological and environmental rhythms in (dark) deep-sea hydrothermal ecosystems. Biogeosciences 14, 2955–2977 (2017).
ADS  Google Scholar 

17.
Nedoncelle, K. et al. Bathymodiolus growth dynamics in relation to environmental fluctuations in vent habitats. Deep Sea Res. Part I: Oceanographic Res. Pap. 106, 183–193 (2015).
ADS  Google Scholar 

18.
Chiesa, J. J., Aguzzi, J., García, J. A., Sardà, F. & de la Iglesia, H. O. Light intensity determines temporal niche switching of behavioral activity in deep-water Nephrops norvegicus (Crustacea: Decapoda). J. Biol. Rhythms 25, 277–287 (2010).
PubMed  Google Scholar 

19.
Sbragaglia, V. et al. Identification, characterization, and diel pattern of expression of canonical clock genes in Nephrops norvegicus (Crustacea: Decapoda) eyestalk. PLOS ONE 10, e0141893 (2015).
PubMed  PubMed Central  Google Scholar 

20.
Modica, L., Cartes, J. E. & Carrassón, M. Food consumption of five deep-sea fishes in the Balearic Basin (western Mediterranean Sea): are there daily feeding rhythms in fishes living below 1000 m?: feeding rhythm and rations in deep-sea fishes. J. Fish. Biol. 85, 800–820 (2014).
CAS  PubMed  Google Scholar 

21.
Wagner, H.-J., Kemp, K., Mattheus, U. & Priede, I. G. Rhythms at the bottom of the deep sea: cyclic current flow changes and melatonin patterns in two species of demersal fish. Deep Sea Res. Part I: Oceanographic Res. Pap. 54, 1944–1956 (2007).
ADS  Google Scholar 

22.
Hui, M., Song, C., Liu, Y., Li, C. & Cui, Z. Exploring the molecular basis of adaptive evolution in hydrothermal vent crab Austinograea alayseae by transcriptome analysis. PLoS ONE 12, e0178417 (2017).
PubMed  PubMed Central  Google Scholar 

23.
Mercier, A. & Hamel, J.-F. in Annual, Lunar, and Tidal Clocks: Patterns and Mechanisms of Nature’s Enigmatic Rhythms (eds. Numata, H. & Helm, B.) 99–120 (Springer, Japan, 2014).

24.
Husson, B., Sarradin, P.-M., Zeppilli, D. & Sarrazin, J. Picturing thermal niches and biomass of hydrothermal vent species. Deep Sea Res. Part II: Topical Stud. Oceanogr. 137, 6–25 (2017).
ADS  Google Scholar 

25.
Duperron, S. in The Vent and Seep Biota: Aspects from Microbes to Ecosystems (ed. Kiel, S.) 137–167 (Springer, Netherlands, 2010).

26.
Appeltans, W. et al. The magnitude of global marine species diversity. Curr. Biol. 22, 2189–2202 (2012).
CAS  PubMed  Google Scholar 

27.
Gosling, E. M. Marine Bivalve Molluscs. (Wiley Blackwell, 2015).

28.
Takeuchi, T. et al. Draft genome of the Pearl Oyster Pinctada fucata: a platform for understanding bivalve biology. DNA Res. 19, 117–130 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

29.
Simakov, O. et al. Insights into bilaterian evolution from three spiralian genomes. Nature 493, 526–531 (2012).
ADS  PubMed  PubMed Central  Google Scholar 

30.
Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
PubMed  Google Scholar 

31.
Ponnudurai, R. et al. Genome sequence of the sulfur-oxidizing Bathymodiolus thermophilus gill endosymbiont. Standards Genomic Sci. 12, 50 (2017).
Google Scholar 

32.
Takishita, K. et al. Genomic evidence that methanotrophic endosymbionts likely provide deep-sea bathymodiolus mussels with a sterol intermediate in cholesterol biosynthesis. Genome Biol. Evolution 9, 1148–1160 (2017).
Google Scholar 

33.
Payton, L. et al. Remodeling of the cycling transcriptome of the oyster Crassostrea gigas by the harmful algae Alexandrium minutum. Sci. Rep. 7, 3480 (2017).
Google Scholar 

34.
Sorek, M. et al. Setting the pace: host rhythmic behaviour and gene expression patterns in the facultatively symbiotic cnidarian Aiptasia are determined largely by Symbiodinium. Microbiome 6, 83 (2018).
PubMed  PubMed Central  Google Scholar 

35.
Thaben, P. F. & Westermark, P. O. Detecting rhythms in time series with RAIN. J. Biol. Rhythms 29, 391–400 (2014).
PubMed  PubMed Central  Google Scholar 

36.
Ren, Y., Hong, C. I., Lim, S. & Song, S. Finding clocks in genes: a bayesian approach to estimate periodicity. BioMed. Res. Int. 2016, 1–14 (2016).
Google Scholar 

37.
Radford-Knoery, J. et al. Distribution of dissolved sulfide, methane, and manganese near the seafloor at the Lucky Strike (37°17′N) and Menez Gwen (37°50′N) hydrothermal vent sites on the mid-Atlantic Ridge. Deep Sea Res. Part I: Oceanographic Res. Pap. 45, 367–386 (1998).
ADS  CAS  Google Scholar 

38.
Waeles, M. et al. On the early fate of hydrothermal iron at deep-sea vents: a reassessment after in situ filtration: Fe Sulfide Precipitation Is Very Limited. Geophys. Res. Lett. 44, 4233–4240 (2017).
ADS  Google Scholar 

39.
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).
Google Scholar 

40.
Sung, Windsor & Morgan, J. J. Kinetics and product of ferrous iron oxygenation in aqueous systems. Environ. Sci. Technol. 14, 561–568 (1980).
ADS  CAS  Google Scholar 

41.
Tapley, D. W., Buettner, G. R. & Shick, J. M. Free radicals and chemiluminescence as products of the spontaneous oxidation of sulfide in seawater, and their biological implications. Biol. Bull. 196, 52–56 (1999).
CAS  PubMed  PubMed Central  Google Scholar 

42.
Hayden, M. S. & Ghosh, S. Signaling to NF-kappaB. Genes Dev. 18, 2195–2224 (2004).
CAS  PubMed  Google Scholar 

43.
Courtial, L. et al. The c-Jun N-terminal kinase prevents oxidative stress induced by UV and thermal stresses in corals and human cells. Sci. Rep. 7, 45713 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

44.
Praveen, K. & Saxena, N. Crosstalk between Fas and JNK determines lymphocyte apoptosis after ionizing radiation. Radiat. Res. 179, 725–736 (2013).
ADS  CAS  PubMed  Google Scholar 

45.
Distel, D. L. et al. Do mussels take wooden steps to deep-sea vents? Nature 403, 725–726 (2000).
ADS  CAS  PubMed  Google Scholar 

46.
Mohawk, J. A., Green, C. B. & Takahashi, J. S. Central and peripheral circadian clocks in mammals. Annu. Rev. Neurosci. 35, 445–462 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

47.
Connor, K. M. & Gracey, A. Y. Circadian cycles are the dominant transcriptional rhythm in the intertidal mussel Mytilus californianus. Proc. Natl Acad. Sci. USA 108, 16110–16115 (2011).

48.
Gracey, A. Y. et al. Rhythms of gene expression in a fluctuating intertidal environment. Curr. Biol. 18, 1501–1507 (2008).
CAS  PubMed  Google Scholar 

49.
White, S. N., Chave, A. D., Reynolds, G. T. & Van Dover, C. L. Ambient light emission from hydrothermal vents on the Mid-Atlantic Ridge. Geophys. Res. Lett. 29, 34-1–34-4 (2002).
Google Scholar 

50.
Devey, C., Fisher, C. & Scott, S. Responsible science at hydrothermal vents. Oceanography 20, 162–171 (2007).
Google Scholar 

51.
Hughes, M. E. et al. Guidelines for genome-scale analysis of biological rhythms. J. Biol. Rhythms 32, 380–393 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

52.
Yoo, S.-H. et al. PERIOD2::LUCIFERASE real-time reporting of circadian dynamics reveals persistent circadian oscillations in mouse peripheral tissues. Proc. Natl Acad. Sci. USA 101, 5339–5346 (2004).
ADS  CAS  PubMed  Google Scholar 

53.
Zhang, R., Lahens, N. F., Ballance, H. I., Hughes, M. E. & Hogenesch, J. B. A circadian gene expression atlas in mammals: Implications for biology and medicine. Proc. Natl Acad. Sci. USA 111, 16219–16224 (2014).
ADS  CAS  PubMed  Google Scholar 

54.
Li, J., Grant, G. R., Hogenesch, J. B. & Hughes, M. E. Considerations for RNA-seq analysis of circadian rhythms. Methods Enzymol. 551, 349–367 (2015).

55.
O’Neill, J. S. & Reddy, A. B. Circadian clocks in human red blood cells. Nature 469, 498–503 (2011).
ADS  PubMed  PubMed Central  Google Scholar 

56.
Rey, G. et al. Metabolic oscillations on the circadian time scale in Drosophila cells lacking clock genes. Mol. Syst. Biol. 14, e8376 (2018).
PubMed  PubMed Central  Google Scholar 

57.
Zhang, L. et al. Dissociation of Circadian and Circatidal Timekeeping in the Marine Crustacean Eurydice pulchra. Curr. Biol. 23, 1863–1873 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

58.
Zantke, J. et al. Circadian and Circalunar Clock Interactions in a Marine Annelid. Cell Rep. 5, 99–113 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

59.
Mat, A. M., Perrigault, M., Massabuau, J.-C. & Tran, D. Role and expression of cry1 in the adductor muscle of the oyster Crassostrea gigas during daily and tidal valve activity rhythms. Chronobiol. Int. 33, 949–963 (2016).
CAS  PubMed  Google Scholar 

60.
O’Neill, J. S. et al. Metabolic molecular markers of the tidal clock in the marine crustacean Eurydice pulchra. Curr. Biol. 25, R326–R327 (2015).
PubMed  PubMed Central  Google Scholar 

61.
Schnytzer, Y. et al. Tidal and diel orchestration of behaviour and gene expression in an intertidal mollusc. Sci. Rep. 8, 4917 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

62.
Saurel, C., Gascoigne, J. C., Palmer, M. R. & Kaiser, M. J. In situ mussel feeding behavior in relation to multiple environmental factors: regulation through food concentration and tidal conditions. Limnol. Oceanogr. 52, 1919–1929 (2007).
ADS  Google Scholar 

63.
Comeau, L. A., Babarro, J. M. F., Longa, A. & Padin, X. A. Valve-gaping behavior of raft-cultivated mussels in the Ría de Arousa, Spain. Aquac. Rep. 9, 68–73 (2018).
Google Scholar 

64.
Connor, K. M. & Gracey, A. Y. High-resolution analysis of metabolic cycles in the intertidal mussel Mytilus californianus. Am. J. Physiol.-Regulatory, Integr. Comp. Physiol. 302, R103–R111 (2012).
CAS  Google Scholar 

65.
Ameyaw-Akumfi, C. & Naylor, E. Temporal patterns of shell-gape in Mytilus edulis. Mar. Biol. 95, 237–242 (1987).
Google Scholar 

66.
Chapman, E. C., O’Dell, A. R., Meligi, N. M., Parsons, D. R. & Rotchell, J. M. Seasonal expression patterns of clock-associated genes in the blue mussel Mytilus edulis. Chronobiol. Int. 34, 1300–1314 (2017).
CAS  PubMed  Google Scholar 

67.
Mat, A. M., Massabuau, J.-C., Ciret, P. & Tran, D. Looking for the clock mechanism responsible for circatidal behavior in the oyster Crassostrea gigas. Mar. Biol. 161, 89–99 (2014).
Google Scholar 

68.
Tran, D., Perrigault, M., Ciret, P. & Payton, L. Bivalve mollusc circadian clock genes can run at tidal frequency. Proc. R. Soc. B 287, 20192440 (2020).
PubMed  Google Scholar 

69.
Martin, W., Baross, J., Kelley, D. & Russell, M. J. Hydrothermal vents and the origin of life. Nat. Rev. Microbiol. 6, 805–814 (2008).
CAS  PubMed  Google Scholar 

70.
Dodd, M. S. et al. Evidence for early life in Earth’s oldest hydrothermal vent precipitates. Nature 543, 60–64 (2017).
ADS  CAS  PubMed  Google Scholar 

71.
Sarrazin, J. et al. TEMPO: a new ecological module for studying deep-sea community dynamics at hydrothermal vents. in OCEANS 2007 – Europe 1–4 (IEEE, 2007).

72.
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

73.
Dutilleul, P. Multi-frequential periodogram analysis and the detection of periodic components in time series. Commun. Stat. – Theory Methods 30, 1063–1098 (2001).
MathSciNet  MATH  Google Scholar 

74.
Johnson, C. H., Elliott, J., Foster, R., Honma, K.-I. & Kronauer, R. in Chronobiology: Biological Timekeeping (eds. Dunlap, J. C., Loros, J. J. & DeCoursey, P. J.) 406 (Sinauer Associates, Inc. Publishers, 2004).

75.
Pegau, W. S., Gray, D. & Zaneveld, J. R. V. Absorption and attenuation of visible and near-infrared light in water: dependence on temperature and salinity. Appl. Opt. 36, 6035–6046 (1997).
ADS  CAS  PubMed  Google Scholar 

76.
Francis, W. R. et al. A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly. BMC Genomics 14, 167 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

77.
Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. https://doi.org/10.1186/s13059-016-0881-8 (2016).

78.
Cabau, C. et al. Compacting and correcting Trinity and Oases RNA-Seq de novo assemblies. PeerJ 5, e2988 (2017).
PubMed  PubMed Central  Google Scholar 

79.
Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).
CAS  PubMed  Google Scholar 

80.
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
PubMed  PubMed Central  Google Scholar 

81.
R. Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015).

82.
Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).
CAS  PubMed  Google Scholar 

83.
Alexa, A. & Rahnenfuhrer, J. topGO: Enrichment Analysis for Gene Ontology. R package version 2.36.0. (2019).

84.
Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).
CAS  PubMed  Google Scholar 

85.
Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol. Biol. Evolution 30, 772–780 (2013).
CAS  Google Scholar 

86.
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evolution 32, 268–274 (2015).
CAS  Google Scholar 

87.
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).
CAS  PubMed  Google Scholar 

88.
Oliveri, P. et al. The Cryptochrome/Photolyase Family in aquatic organisms. Mar. Genomics 14, 23–37 (2014).
PubMed  Google Scholar  More

Sampling of organisms and bioassay experiments
Shrimp post-larvae (PL5) of Litopenaeus vannamei were collected from the aquaculture farm Parque acuícola Cruz de Piedra, Guaymas, Sonora, Mexico (27° 51′ 05.9″ N 110° 31′ 57.0″ W). Afterward, post-larval shrimp were transported in aerated tanks with the same pond water as the farm to the Departamento de Investigaciones Científicas y Tecnológicas de la Universidad de Sonora (DICTUS), where a lab-scale system was previously tested and used. The bioassay was conducted for 80 days with healthy shrimp, each weighing 0.5 ± 0.1 g, and post-larvae were randomly distributed in the lab-scale system. The system consisted of nine 80-L culture units linked to a recirculation aquaculture system (RAS), and sterile seawater was used to fill the units to an operating volume of 60 L. Influent seawater was filtrated and flowed through a UV lamp for sterilization, and the seawater was equally dispensed to all culture units, as depicted in the supplementary material (Fig. S2). The culture units were maintained under similar indoor conditions with artificial aeration (2000F heat bonded silica; pore size, 140 µm), and the salinity was maintained at around 35‰ with the addition of sterile freshwater (MilliQ grade, Millipore) to avoid the incorporation of outside bacteria and to compensate evaporation. Finally, the effluent generated by the system flowed through a biofilter containing nitrifying bacteria to control toxic nitrogen compounds in the recirculation system (Fig. S2). The unconsumed feed, feces, moults, and dead organisms (if any) were removed daily.
The salinity, dissolved oxygen (DO), pH, and temperature were measured twice per day (07:00 and 18:00 h) using a YSI multiprobe system 556 (YSI Incorporated).
The bioassay started with PL5 on day 0. At this point, 40 organisms were randomly introduced into each culture unit, and the experiment lasted 80 days. Throughout the experiment, shrimp were fed twice a day at a rate of 4% wet biomass day–1 using feeding trays with the same formulated feed consisting of commercial grow-out pelletized feed with 25% crude protein, 5% lipids, and 4% fiber.
Water quality and productive response
The water quality was monitored daily throughout the bioassay, and samples were collected weekly from each culture unit using sterile falcon tubes by filtering the water through 0.45 μm membranes (Millipore). Nitrite (NO2–N), nitrate (NO3–N), ammonia (NH3–NH4), and phosphate (P–PO4) concentrations were measured using commercial Hanna reagent kits HI 93707-01, HI 93728-01, HI 93700-01, and HI 93717-01, respectively (Hanna Instruments, Romania).
Biometry analyses were performed at the four different developmental stages, denominated as I, II, III, and IV, corresponding to 0–20, 20–40, 40–60, and 60–80 culture days, respectively, and the productive response was calculated15.
Collection of intestine and water samples and DNA and RNA extraction
To discard the transitory microbiota, the shrimp were fasted for 6 h before sampling. Once the intestines were empty, these were dissected on the corresponding dates using a sterile dissection kit, placed in sterile cryogenic tubes, and stored at – 80 °C until nucleic acid extraction. Each sample date belongs to an experimental unit with three culture tanks. The intestine samples from culture tanks were pooled and considered as a replicate, giving three replicates per sampling time. At the same sampling points, 1 L samples of water (W) from each culture unit corresponding to an experimental replicate were collected and pooled for filtration through 0.22 µm sterile filters of mixed cellulose ester membrane (Whatman, Sigma, St Louis, USA) and placed in sterile, 50 mL falcon tubes for storage at – 80 °C until nucleic acid extraction.
Total DNA and RNA were extracted and purified from the membrane filters previously used to filter seawater samples and from intestines that were also previously sampled, both of them with the FastDNA Spin Kit for Soil15, and the FastRNA Pro Blue Kit (MP-Bio, Santa Ana, CA, USA) in combination with mechanical lysis using the FastPrep Systems (MP-BIO, Santa Ana, CA, USA). The obtained RNA samples were digested according to the TURBO DNA protocol (Ambion, Life Technologies Corporation, Carlsbad, CA, USA) and EDTA to stop the DNase activity and to ensure that any DNA residuals were presented. Finally, samples were purified according to the RNA Cleanup protocol from the RNeasy Mini Kit (Qiagen, Hamburg, Germany). The quality and concentration of nucleic acids were tested as previously described Maza-Márquez et al.39.
qPCR and RT-qPCR assays
Real-time polymerase chain reaction (qPCR) assays have been widely implemented for estimating total cell count based on DNA gene markers, regardless of their level of metabolic activity40,41, while reverse transcription qPCR (RT-qPCR) is a useful method for analyzing the expression of specific genes. RT-qPCR is also used because of its high sensitivity, accuracy, specificity, and rapidity in analyzing the time-specific expression of particular genes, allowing for the detection of low-abundance transcripts42. The absolute abundance of total and metabolically active populations of bacteria and Vibrio in both target samples were measured by qPCR and RT-qPCR, respectively, using a StepOne Real-Time PCR system (Applied Biosystems, USA). For RT-qPCR, the synthesis of cDNA was performed by reverse transcription of RNA with the aid of SuperScript III Reverse Transcriptase (Invitrogen, Life Technologies Corporation, Carlsbad, CA, EEUU), following the manufacturer’s specifications, in a final volume of 20 µL and using 150–200 ng of total RNA as a template (specific primers described in Table S4) (Sigma Aldrich; St. Louis, MO, USA) and dNTPs (Invitrogen; Carlsbad, USA). In addition, the cDNA quality and concentration were measured using a NanoDrop ND-1000 spectrophotometer (Thermo Scientific Waltham, MA USA). The number of copies (nbc) of 16S rRNA genes (16S rDNA) and 16S rRNA (16S rRNA) were evaluated in each sample using either extracted DNA or cDNA, respectively, as templates with a set of primers previously described (Table S4) based on the increasing fluorescence intensity of the SYBR Green dye during amplification. For the amplification and detection of specific fragments, the iTaq Universal SYBR Green Supermix (Biorad, USA) was used in a final volume of 15 µL for each reaction. All quantitative amplifications were performed in triplicate. The qPCR reaction mixtures contained 1.8 µL of cDNA or DNA, 250 ng of T4 gene 32 (QBiogene, Illkirch, France), 1.2 µL of each primer (10 mM), supplied by Sigma Aldrich (St. Louis, MO, USA), and 1 × SYBR Green Supermix. The amplification and detection conditions are described in Table S5.
To provide absolute quantification of the target microorganisms, standard curves were constructed with the aid of a standard plasmid that contained the inserts of the targeted genes. Amplicons of the 16S rDNA were generated from culture strains of Pseudomonas putida NCB957 (quantification of bacteria) and Vibrio parahaemolyticus ATCC17802 (quantification of Vibrio). The PCR products were cloned with the aid of the pCR2.1-TOPO plasmid vector using the TOPO TA cloning system (Invitrogen, Life Technologies Corporation, Carlsbad, CA, USA), following the manufacturer’s protocols. The calibration curves for absolute quantification in the DNA samples (16S rDNA) were generated using serial ten-fold dilutions of linearized plasmid standards, and for absolute quantification in RNA samples (16S rRNA), non-linearized plasmid standards were used as templates for in vitro transcription of the target genes into RNA39,40,43. The copy number per ng was calculated as previously described43. All calibration curves had a correlation coefficient (r2) of  > 0.99 in all assays, and the efficiency of PCR amplification was always between 90 and 110%. Finally, the number of copies of the targeted genes was expressed per gram of tissue sampled, while for water samples these were expressed as the number of copies per mL.
Statistical analyses
All statistical analyses were performed in R Studio (version 3.6.0)44 using the following R packages: maggrittr45, ade446, factoextra47, vegan48, and gplots49. Analyses of variance (ANOVA) and multiple-range tests (Student’s-test) were used with a significance level of 95% (p  More

1.
Saunders, S. C., Mislivets, M. R., Chen, J. & Cleland, D. T. Effects of roads on landscape structure within nested ecological units of the Northern Great Lakes Region, USA. Biol. Conserv. 103, 209–225 (2002).
Google Scholar 
2.
Potvin, F., Breton, L. & Courtois, R. Response of beaver, moose, and snowshoe hare to clear-cutting in a Quebec boreal forest: A reassessment 10 years after cut. Can. J. For. Res. 35, 151–160 (2005).
Google Scholar 

3.
Sahlén, E., Støen, O. & Swenson, J. E. Brown bear den site concealment in relation to human activity in Sweden. Ursus 22, 152–158 (2011).
Google Scholar 

4.
James, A. & Stuart-Smith, A. Distribution of caribou and wolves in relation to linear corridors. J. Wildl. Manage. 64, 154–159 (2000).
Google Scholar 

5.
Vitousek, P. M., Mooney, H. A., Lubchenco, J. & Melillo, J. M. Human domination of earth’s ecosystems. Science 277, 494–499 (1997).
CAS  Google Scholar 

6.
Wittmer, H. U., McLellan, B. N., Serrouya, R. & Apps, C. D. Changes in landscape composition influence the decline of a threatened woodland caribou population. J. Anim. Ecol. 76, 568–579 (2007).
PubMed  Google Scholar 

7.
Irwin, L. L., Rock, D. F. & Miller, G. P. Stand structures used by Northern spotted owls in managed forests. J. Raptor Res. 34, 175–186 (2000).
Google Scholar 

8.
Leblond, M., Dussault, C. & Ouellet, J. P. Avoidance of roads by large herbivores and its relation to disturbance intensity. J. Zool. 289, 32–40 (2013).
Google Scholar 

9.
Dickie, M., Serrouya, R., McNay, R. S. & Boutin, S. Faster and farther: Wolf movement on linear features and implications for hunting behaviour. J. Appl. Ecol. 54, 253–263 (2017).
Google Scholar 

10.
Finnegan, L. et al. Natural regeneration on seismic lines influences movement behaviour of wolves and grizzly bears. PLoS ONE https://doi.org/10.1371/journal.pone.0195480 (2018).
Article  PubMed  PubMed Central  Google Scholar 

11.
Whittington, J. et al. Caribou encounters with wolves increase near roads and trails: A time-to-event approach. J. Appl. Ecol. 48, 1535–1542 (2011).
Google Scholar 

12.
Sorensen, T. et al. Determining sustainable levels of cumulative effects for boreal caribou. J. Wildl. Manage. 72, 900–905 (2008).
Google Scholar 

13.
Dabros, A., Pyper, M. & Castilla, G. Seismic lines in the boreal and arctic ecoystems of North America: Environmental impacts, challenges and opportunities. Environ. Rev. 26, 214–229 (2018).
Google Scholar 

14.
Lee, P. & Boutin, S. Persistence and developmental transition of wide seismic lines in the western Boreal Plains of Canada. J. Environ. Manage. 78, 240–250 (2006).
PubMed  Google Scholar 

15.
Pigeon, K. E. et al. Toward the restoration of caribou habitat: Understanding factors associated with human motorized use of legacy seismic lines. Environ. Manage. 58, 821–832 (2016).
ADS  PubMed  Google Scholar 

16.
Schneider, R. R., Hauer, G., Adamowicz, W. L. V. & Boutin, S. Triage for conserving populations of threatened species: The case of woodland caribou in Alberta. Biol. Conserv. 143, 1603–1611 (2010).
Google Scholar 

17.
Environment Canada. Recovery strategy for the woodland Caribou (Rangifer tarandus caribou), boreal population, in Canada. in Species at Risk Act Recovery Strategy Series 138 (Environment Canada, 2012).

18.
Environment Canada. Recovery strategy for the woodland Caribou, southern mountain population (Rangifer tarandus caribou) in Canada. in Species at Risk Act Recovery Strategy Series. Environment 103 (Environment Canada, Ottawa, 2014).

19.
Dickie, M., Serrouya, R., DeMars, C., Cranston, J. & Boutin, S. Evaluating functional recovery of habitat for threatened woodland caribou. Ecosphere 8, e01936. https://doi.org/10.1002/ecs2.1936 (2017).
Article  Google Scholar 

20.
DeMars, C. A. & Boutin, S. Nowhere to hide: Effects of linear features on predator-prey dynamics in a large mammal system. J. Anim. Ecol. 87, 274–284 (2018).
PubMed  Google Scholar 

21.
Johnson, C. J., Ehlers, L. P. W. & Seip, D. R. Witnessing extinction—Cumulative impacts across landscapes and the future loss of an evolutionarily significant unit of woodland caribou in Canada. Biol. Conserv. 186, 176–186 (2015).
Google Scholar 

22.
Fisher, J. T. & Burton, A. C. Widlife winners and losers in an oil sands landscape. Front. Ecol. Environ. 16, 323–328 (2018).
Google Scholar 

23.
Ehlers, L. P. W., Johnson, C. J. & Seip, D. R. Evaluating the influence of anthropogenic landscape change on Wolf distribution: Implications for woodland caribou. Ecosphere 7, e01600. https://doi.org/10.1002/ecs2.1600 (2016).
Article  Google Scholar 

24.
Houle, M., Fortin, D., Dussault, C., Courtois, R. & Ouellet, J.-P. Cumulative effects of forestry on habitat use by gray wolf (Canis lupus) in the boreal forest. Landscape. Ecol. 25, 419–433 (2010).
Google Scholar 

25.
Mysterud, A. & Ims, R. A. Functional responses in habitat use: Availability influences relative use in trade-off situations. Ecology 79, 1435–1441 (1998).
Google Scholar 

26.
Lima, S. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–639 (1990).
Google Scholar 

27.
Hebblewhite, M., Merrill, E. H. & McDonald, T. L. Spatial decomposition of predation risk using resource selection functions: An example in a wolf-elk predator-prey system. Oikos 111, 101–111 (2005).
Google Scholar 

28.
Latham, A. D. M., Latham, M. C., Boyce, M. & Boutin, S. Movement responses by wolves to industrial linear features and their effect on woodland caribou in northeastern Alberta. Ecol. Appl. 21, 2854–2865 (2011).
Google Scholar 

29.
Visscher, D. R. & Merrill, E. H. Temporal dynamics of forage succession for elk at two scale: Implications of forest management. For. Ecol. Manage. 257, 96–106 (2009).
Google Scholar 

30.
McLoughlin, P., Dunford, J. & Boutin, S. Relating predation mortality to broad-scale habitat selection. J. Anim. Ecol. 74, 701–707 (2005).
Google Scholar 

31.
Ausband, D. E. et al. Surveying predicted rendezvous sites to monitor gray wolf populations. J. Wildlife. Manage. 71, 1043–1049 (2010).
Google Scholar 

32.
Corns, I. & Annas, R. M. Field Guide to Forest Ecosystems of West-Central Alberta 251 (Canadian Forest Service Northern Forestry Centre, Edmonton, 1986).
Google Scholar 

33.
van Rensen, C. K., Nielsen, S. E., White, B., Vinge, T. & Lieffers, V. J. Natural regeneration of forest vegetation on legacy seismic lines in boreal habitats in Alberta’s oil sands region. Biol. Conserv. 184, 127–135 (2015).
Google Scholar 

34.
Swanson, M. E. et al. The forgotten stage of forest succession: Early-successional ecoystems on forest sites. Front. Ecol. Environ. 9, 117–125 (2010).
Google Scholar 

35.
Melin, M., Matala, J., Mehtätalo, L., Pusenius, J. & Packalen, P. Ecological dimensions of airborne laser scanning—Analyzing the role of forest structure in moose habitat use within a year. Remote Sens. Environ. 173, 238–247 (2015).
ADS  Google Scholar 

36.
Roffler, G. H., Gregovich, D. P. & Larson, K. R. Resource selection by coastal wolves reveals the seasonal importance of seral forest and suitable prey habitat. For. Ecol. Manage. 409, 190–201 (2018).
Google Scholar 

37.
DeCesare, N. J. et al. Transcending scale dependence in identifying habitat with resource selection functions. Ecology 22, 1068–1083 (2012).
Google Scholar 

38.
Neufeld, L. M. Spatial Dynamics of Wolves and Woodland Caribou in an Industrial Forest Landscape in West-Central Alberta 155 (University of Alberta, Alberta, 2006).
Google Scholar 

39.
Webb, N., Hebblewhite, M. & Merrill, E. Statistical methods for identifying wolf kill sites using global positioning system locations. J. Wildl. Manage. 72, 1798–1804 (2008).
Google Scholar 

40.
Jedrzejewski, W., Schmidt, K., Theuerkauf, J., Jedrzejewska, B. & Okarma, H. Daily movements and territory use by radio-collared wolves (Canis lupus) in Bialowieza primeval forest in Poland. Can. J. Zool. 79, 1993–2004 (2001).
Google Scholar 

41.
Mech, L. D. & Boitani, L. Wolves 472 (University of Chicago Press, Chicago, Behaviour, Ecology and Conservation, 2003).
Google Scholar 

42.
Jenness, J. Topographic position index (tpi_jen.avx) extension for ArcView 3.x v. 1.3a https://www.jennessent.com/arcview/tpi.htm (2006). Accessed 15 June 2014.

43.
Gessler, P. E., Chadwick, O. A., Chamran, F., Althouse, L. & Holmes, K. Modeling soil–landscape and ecosystem properties using terrain attributes. Soil Sci. Soc. Am. J. 64, 2046 (2000).
ADS  CAS  Google Scholar 

44.
Franklin, S. E., Peddle, D. R. & Dechka, J. A. Evidential reasoning with Landsat TM, DEM and GIS data for landcover classification in support of grizzly bear habitat mapping. Int. J. Remote Sens. 23, 4633–4652 (2002).
ADS  Google Scholar 

45.
McDermid, G. J. et al. Remote sensing and forest inventory for wildlife habitat assessment. For. Ecol. Manage. 257, 2262–2269 (2009).
Google Scholar 

46.
Environmental Systems Research Institute [ESRI] ArcGIS Desktop: Release 10. Redlands, California, (2015).

47.
MacNearney, D. et al. Heading for the hills? Evaluating spatial distribution of woodland caribou in response to a growing anthropogenic disturbance footprint. Ecol. Evol. 6, 6484–6509 (2016).
PubMed  PubMed Central  Google Scholar 

48.
Nielsen, S. E., Cranston, J., Stenhouse, G. B. & Street, M. Identification of priority areas for grizzly bear conservation and recovery in Alberta, Canada. J. Conserv. Plan. 5, 38–60 (2009).
Google Scholar 

49.
White, B. et al. Using the cartographic depth-to-water index to locate small streams and associated wet areas across landscapes. Can. Water Resour. J. 37, 333–347 (2012).
Google Scholar 

50.
Canadell, J. et al. Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583–595 (1996).
ADS  CAS  PubMed  Google Scholar 

51.
Beyer, H. Geospatial Modelling Environment (version 0.7.2.1) https://www.spatialecology.com/gme (2012). Accessed 16 April 2016.

52.
Murtaugh, P. Simplicity and complexity in ecological data analysis. Ecology 88, 56–62 (2007).
PubMed  Google Scholar 

53.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inferences: A Practical Information-Theoretic Approach 2nd edn. (Springer, New Yirk, 2002).
Google Scholar 

54.
Takahata, C., Nielsen, S. E., Takii, A. & Izumiyama, S. Habitat selection of a large carnivore along human-wildlife boundaries in a highly modified landscape. PLoS ONE 9, e86181. https://doi.org/10.1371/journal.pone.0086181 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

55.
Fieberg, J., Matthiopoulos, J., Hebblewhite, M., Boyce, M. & Frair, J. Correlation and studies of habitat selection: Problem, red herring, or opportunity?. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 2233–2244 (2010).
PubMed  PubMed Central  Google Scholar 

56.
Muff, S., Signer, J. & Fieberg, J. Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation. J. Anim. Ecol. 89, 80–92 (2020).
PubMed  Google Scholar 

57.
Fieberg, J., Rieger, R. H., Zicus, M. C. & Schildcrout, J. S. Regression modelling of correlated data in ecology: Subject-specific and population averaged response patterns. J. Appl. Ecol. 46, 1018–1025 (2009).
Google Scholar 

58.
Glenn, E. M., Hansen, M. C. & Anthony, R. G. Spotted owl home-range and habitat use in young forests of western Oregon. J. Wildl. Manage. 68, 33–50 (2004).
Google Scholar 

59.
Sawyer, H., Nielson, R. M., Lindzey, F. & McDonald, L. L. Winter habitat selection of mule deer before and during development of a natural gas field. J. Wildl. Manage. 70, 396–403 (2006).
Google Scholar 

60.
Manly, B. F. J., McDonald, L. L., Thomas, D. L., McDonald, T. L. & Erickson, W. P. Resource Selection by Animals—Statistical Design and Analysis for Field Studies 2nd edn. (Kluwer Acadamic Publishers, Berlin, 2002).
Google Scholar 

61.
Hebblewhite, M., Percy, M. & Merrill, E. H. Are all global positioning system collars created equal? Correcting habitat-induced bias using three brands in the central Canadian Rockies. J. Wildl. Manage. 71, 2026–2033 (2007).
Google Scholar 

62.
Frair, J. L. et al. Removing GPS collar bias in habitat selection studies. J. Appl. Ecol. 41, 201–212 (2004).
Google Scholar 

63.
Lumley, T. Survey: Analysis of complex survey samples. R packages version 3.30 (2014).

64.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2015). Accessed 12 Dec 2016.

65.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Google Scholar 

66.
Matthiopoulos, J., Hebblewhite, M., Aarts, G. & Fieberg, J. Generalized functional responses for species distributions. Ecology 92, 583–589 (2011).
PubMed  Google Scholar 

67.
McKenzie, H. W., Merrill, E. H., Spiteri, R. J. & Lewis, M. A. How linear features alter predator movement and the functional response. Interface Focus. 2, 205–216 (2012).
PubMed  PubMed Central  Google Scholar 

68.
Droghini, A. & Boutin, S. Snow conditions influence grey wolf (Canis lupus) travel paths: The effect of human-created linear features. Can. J. Zool. 96, 39–47 (2017).
Google Scholar 

69.
García-Marmolejo, G., Chapa-Vargas, L., Weber, M. & Huber-Sannwald, E. Landscape composition influences abundance patterns and habitat use of three ungulate species in fragmented secondary deciduous tropical forests, Mexico. Glob. Ecol. Conserv. 3, 744–755 (2015).
Google Scholar 

70.
DeCesare, N. J. Separating spatial search and efficiency rates as components of predation risk. Proc. R. Soc. B 279, 4626–4633 (2012).
PubMed  Google Scholar  More