Philippa Kaur

Investigation method
From March 2015 to December 2018, we surveyed 36 important tributaries of the TGR (Fig. 3) and conducted an investigation of macroinvertebrates. For the sake of convenience, we labeled tributaries from the reservoir dam to its tail area sequentially as R01–R36, i.e., R01 (Xiangxi River), R02 (Qinggan River), R03 (Shennong River), R04 (Baolong River), R05 (Guandu River), R06 (Daning River), R07 (Daxi-F River), R08 (Caotang River), R09 (Meixi River), R10 (Changtan River), R11 (Modao River), R12 (Tangxi River), R13 (Xiao River), R14 (Zhuxi River), R15 (Rangdu River), R16 (Ruxi River), R17 (Huangjin River), R18 (Dongxi River), R19 (Chixi River), R20 (Long River), R21 (Bixi River), R22 (Quxi River), R23 (Zhenxi River), R24 (Wu River), R25 (Lixiang River), R26 (Longxi River), R27 (Taohua River), R28 (Yulin River), R29 (Wubu River), R30 (Changtang River), R31 (Chaoyang River), R32 (Jialing River), R33 (Huaxi River), R34 (Yipin River), R35 (Daxi-J River), and R36 (Qi River).
Figure 3

Schematic diagram of important tributaries of TGR and sampling points (plotted by ArcGIS 10.5, https://www.32r.com/soft/16101.html).

Full size image

This study was approved by the Environmental Protection Bureau of the Three Georges Reservoir.
A total of 175 sampling points were set up in all tributaries. Four parallel samples were taken from each sampling point. At least one sample was taken from each microhabitat (mainly including four microhabitats, i.e., shoal, deep pool, pebble and aquatic habitat). Parallel samples from the same sampling point were mixed together. The quantitative and qualitative sample collection methods were combined in this study. The quantitative collection was performed first, and then the qualitative collection for the same sampling point. The qualitative samples were collected by D-net. Quantitative samples of wadable sampling points were collected using a Surber net with an area of 0.3 m × 0.3 m. Quantitative samples of non-wadable sampling points were collected using a D-net with a bottom side length of 0.3 m. The collected samples were put into sample bottles (bags) and fixed with 5% formaldehyde solution. Then the samples were identified and classified under the laboratory conditions.
Selection of reference sites and impaired sites
A reference site refers to a sampling point with no or little anthropogenic disturbance, while a impaired site refers to a sampling point subject to obvious anthropogenic disturbance29. A total of 15 reference sites and 160 impaired sites were selected from 175 sampling points based on anthropogenic disturbance, vegetation coverage, population distribution, and the distribution of industry and agriculture in the vicinity of the sampling site6,8 (Table 7, Supplementary Fig. 1).
Table 7 Assessment criteria for reference sites and impaired sites and the assessment outcomes.
Full size table

Creation and selection of the assessment metric index system
With reference to the river health assessment indexes in China13,15,18,24, North America6,24 and Europe5, and based on the ecological characteristics such as species composition and abundance, sensitivity, tolerance and functional feeding groups, we constructed 26 candidate metrics (Table 8) for B-IBI. These candidate metrics have significant or noticeable response to human activities, and normally, can be applied to relatively large geographic areas; therefore, they can be used to indicate the ecological quality of rivers6,23,24. Among these metrics, 17 were associated with species composition and abundance, which included the total number of taxon, the number of EPT taxa, the number of crustacean and mollusca taxa, the number of ephemerida taxa, the number of pteroptera taxa, the number of trichoptera taxa, the number of diptera taxa, the number of chironomidea taxa, the percentage of EPT, the percentage of crustacean and mollusca, the percentage of ephemerida, the percentage of pteroptera, the percentage of trichoptera, the percentage of dipteral, the percentage of chironomidea, the percentage of oligochaeta and the Shannon-Weiner diversity index. Species composition and abundance-related indexes reflect the diversity of macrobenthic communities. An increase in species diversity is associated with the improvement of community health, which indicates that the niche space and food sources are sufficient to support the survival and reproduction of multiple species. The candidate metrics related to sensitivity and tolerance in this study were the number of sensitive taxa, the number of tolerant taxa, the percentage of dominant species and the percentage of the top three dominant species. Different zoobenthos show different degrees of sensitivity and tolerance to the influencing factors in the river habitat, for which these characteristics can be used to assess the health status of the river. In addition, the taxa and percentage of functional feeding groups are closely associated with their living environment, and the parameters that were used to represent functional feeding in this study were the percentages of shredders, herbivores, filterers, scrapers and predators. Some of the representative images of the identified taxa were shown in Supplementary Fig. 1E,F.
Table 8 Candidate parameters for B-IBI and their response to anthropogenic disturbance.
Full size table

The selection of core metrics for B-IBI mainly includes three steps: analysis of distribution range of candidate metrics, analysis of discriminant ability of candidate metrics and analysis of correlation between candidate metrics23.
Analysis of distribution range of candidate metrics
According to the numerical value of each biological metric in the reference site, an initial analysis was conducted to exclude the following two types of metrics: metrics with excessive nought values, which did not meet the requirement for a universal applicability; metrics with a scatter value distribution, and a standard deviation greater than or equal to the mean, indicating that the standard deviation of this value was relatively big and unstable, thereby unsuitable to be used as biological metrics6.
Analysis of discriminant ability of candidate metrics
After analyzing the distribution range of candidate metrics, those unsuitable for biological evaluation were eliminated. The distribution of the remaining eligible metrics for the reference site and the impaired site was analyzed using the box-plot, to mainly compare the distribution range of the 25th quantile to the 75th quantile of the reference site and the impaired site and the overlap of “box” InterQuartile Range (IQR), and judge which biological metrics could best distinguish between the reference site and impaired site. An IQ value ≥ 2 indicates a small overlapping part between the reference site and the impacted site, which means a significant difference in the related parameter between the reference site and the impacted site, suggesting a noticeable response to human activity6,24. The IQ scoring criteria were as follows6,24: 3 point, no overlapping between the two box bodies; 2 points, the box bodies have a small part of overlapping, but the median of neither body falls within the limits of its counterpart; 1 point, most parts of the box bodies overlap, and the median of at least one box body lies within the limits of its counterpart; 0 points, one box body falls within the limits of the other, or the medians of each body are within the other’s limits.
Correlation analysis of candidate metrics
Pearson correlation analysis was further performed of the metrics that met the preliminary conditions. If the correlation coefficient (left| {text{r}} right|) between two metrics is greater than 0.75, and they are intrinsically linked. Then most of the information reflected is overlapping. Therefore, it is OK to select one of them. If no intrinsic connection is found between two metrics, then both metrics can be selected even if the correlation coefficient is greater than 0.758.
After screening through the above three steps, core metrics of the B-IBI are finally determined.
Construction of B-IBI
The core biological metrics screened out by the above method were used as the metrics for final biological assessment. The metrics used for biological assessment were standardized using the ratio scoring method, to unify the evaluation metric23.
(1)
For a metric that decreased with increasing interference, the metric was normalized by dividing the value of this metric at each sample point with the 95% quantile of all sample points:

$${text{V}}_{{text{i}}}^{prime } = {text{V}}_{{text{i}}} /{text{V}}_{{{95}% }} ;$$

(2)
For a metric that increased with increasing interference, the metric was normalized by using the 5% quantile of this metric at all sample points as the reference object:

$${text{V}}_{{text{i}}}^{prime } = left( {{text{V}}_{{{text{MAX}}}} – {text{V}}_{{text{i}}} } right)/left( {{text{V}}_{{{text{MAX}}}} – {text{V}}_{{{5}% }} } right),$$

where Vi′ is the normalized value of the metric at the ith sampling point; Vi the actual value of the metric at the ith sampling point; V95% the 95% quantile of the metric; V5% is the 5% quantile of the metric; VMAX is the maximum value of this metric in all sampling points. The health thresholds of 5% quantile and 95% quantile can eliminate extreme abnormal values and retain most of biological information.

B-IBI assessment criteria
The 95% quantile of B-IBI distribution of all the sections/tributaries used for the health threshold can eliminate extreme abnormal values and retain most biological information. The distribution range lower than this value is divided into four portions, and the quartile close to the 95% quantile indicates a small disturbance. The biological integrity grade and the corresponding range of IBI6 are determined according to the 95% quantile and the quartile value, and the section/river health was classified into five grades, namely, excellent, good, fair, poor and very poor. More

1.
Salam N, Jiao J-Y, Zhang X-T, Li W-J. Update on the classification of higher ranks in the phylum Actinobacteria. Int J Syst Evol Microbiol. 2020;70:1331–55.
CAS  Article  Google Scholar 
2.
Ningsih F, et al. Gandjariella thermophila gen. nov., sp. nov., a new member of the family Pseudonocardiaceae, isolated from forest soil in a geothermal area. Int J Syst Evol Microbiol. 2019;69:3080–6.
CAS  Article  Google Scholar 

3.
Nouioui I, et al. Genome-based taxonomic classification of the phylum Actinobacteria. Front Microbiol. 2018;9:2007.
Article  Google Scholar 

4.
Parte AC. LPSN—list of prokaryotic names with standing in nomenclature (bacterio.net), 20 years on. Int J Syst Evol Microbiol. 2018;68:1825–9.
Article  Google Scholar 

5.
Alanjary M, Steinke K, Ziemert N. AutoMLST: an automated web server for generating multi-locus species trees highlighting natural product potential. Nucleic Acids Res. 2019;47:W276–82.
CAS  Article  Google Scholar 

6.
Teo WFA, Srisuk N, Duangmal K. Amycolatopsis acidicola sp. nov., isolated from peat swamp forest soil. Int J Syst Evol Microbiol. 2020;70:1547–54.
CAS  Article  Google Scholar 

7.
Niu M-M, et al. Amycolatopsis nivea sp. nov., isolated from a Yellow River sample. Int J Syst Evol Microbiol. 2020;70:3084–90.
CAS  Article  Google Scholar 

8.
Narsing Rao MP, et al. Amycolatopsis alkalitolerans sp. nov., isolated from Gastrodia elata Blume. J Antibiot. 2020;73:35–39.
CAS  Article  Google Scholar 

9.
Mingma R, Inahashi Y, Matsumoto A, Takahashi Y, Duangmal K. Amycolatopsis pithecelloba sp. nov., a novel actinomycete isolated from roots of Pithecellobium dulce in Thailand. J Antibiot. 2020;73:230–5.
CAS  Article  Google Scholar 

10.
Wang H-F, et al. Amycolatopsis anabasis sp. nov., a novel endophytic actinobacterium isolated from roots of Anabasis elatior. Int J Syst Evol Microbiol. 2020;70:3391–8.
CAS  Article  Google Scholar 

11.
Sangal V, et al. Revisiting the taxonomic status of the biomedically and industrially important genus Amycolatopsis, using a phylogenomic approach. Front Microbiol. 2018;9:2281.
Article  Google Scholar 

12.
Adamek M, et al. Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species. BMC Genomics. 2018;19:426.
Article  Google Scholar 

13.
Waksman SA. The Actinomycetes: their nature, occurrence, activities, and importance. Waltham, Massachusetts: Chronica Botanica Company; 1950.

14.
Donadio S, Cavaletti L, Monciardini P. Genus I Actinospica Cavaletti, Monciardini, Schumann, Rohde, Bamonte, Busti, Sosio and Donadio 2006, 1751VP. In: Goodfellow M, et al., editors. Bergey’s Manual of Systematic Bacteriology. 2nd. New York: Springer; 2012. p. 232–4.

15.
Shirling EB, Gottlieb D. Methods for characterization of Streptomyces species. Int J Syst Bacteriol. 1966;16:313–40.
Article  Google Scholar 

16.
Tan GYA, Ward AC, Goodfellow M. Exploration of Amycolatopsis diversity in soil using genus-specific primers and novel selective media. Syst Appl Microbiol. 2006;29:557–69.
CAS  Article  Google Scholar 

17.
Williams ST, Davies FL, Mayfield CI, Khan MR. Studies on the ecology of actinomycetes in soil—II: The pH requirements of streptomycetes from two acid soils. Soil Biol Biochem. 1971;3:187–95.
CAS  Article  Google Scholar 

18.
Flowers TH, Williams ST. Nutritional requirements of acidophilic streptomycetes. Soil Biol Biochem. 1977;9:225–6.
CAS  Article  Google Scholar 

19.
Becker B, Lechevalier MP, Lechevalier HA. Chemical composition of cell-wall preparations from strains of various form-genera of aerobic actinomycetes. Appl Microbiol. 1965;13:236–43.
CAS  Article  Google Scholar 

20.
Hasegawa T, Takizawa M, Tanida S. A rapid analysis for chemical grouping of aerobic actinomycetes. J Gen Appl Microbiol. 1983;29:319–22.
CAS  Article  Google Scholar 

21.
Staneck JL, Roberts GD. Simplified approach to identification of aerobic actinomycetes by thin-layer chromatography. Appl Microbiol. 1974;28:226–31.
CAS  Article  Google Scholar 

22.
Tomiyasu I. Mycolic acid composition and thermally adaptative changes in Nocardia asteroides. J Bacteriol. 1982;151:828–37.
CAS  Article  Google Scholar 

23.
Minnikin DE, Patel PV, Alshamaony L, Goodfellow M. Polar lipid composition in the classification of nocardia and related bacteria. Int J Syst Evol Microbiol. 1977;27:104–17.
CAS  Google Scholar 

24.
Kieser T, Bibb MJ, Buttner MJ, Chater KF, Hopwood DA. Practical Streptomyces Genetics. Norwich: John Innes Foundation; 2000.

25.
Bankevich A, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77.
CAS  Article  Google Scholar 

26.
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.
CAS  Article  Google Scholar 

27.
Tatusova T, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44:6614–24.
CAS  Article  Google Scholar 

28.
Cosentino S, Voldby Larsen M, Møller Aarestrup F, Lund O. PathogenFinder—distinguishing friend from foe using bacterial whole genome sequence data. PLoS ONE. 2013;8:e77302.
CAS  Article  Google Scholar 

29.
Blin K, et al. AntiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 2019;47:W81–7.
CAS  Article  Google Scholar 

30.
Chun J, et al. Proposed minimal standards for the use of genome data for the taxonomy of prokaryotes. Int J Syst Evol Microbiol. 2018;68:461–6.
CAS  Article  Google Scholar 

31.
Yoon S-H, et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol. 2017;67:1613–7.
CAS  Article  Google Scholar 

32.
Tarlachkov SV, Starodumova IP. TaxonDC: calculating the similarity value of the 16S rRNA gene sequences of prokaryotes or ITS regions of fungi. J Bioinf Genom. 2017;3:1–4.
Google Scholar 

33.
Richter M, Rosselló-Móra R, Oliver Glöckner F, Peplies J. JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison. Bioinformatics. 2016;32:929–31.
CAS  Article  Google Scholar 

34.
Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinform. 2013;14:1–14.
Article  Google Scholar 

35.
Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2017;33:1870–4.
Article  Google Scholar 

36.
Meier-Kolthoff JP, Göker M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy. Nat Commun. 2019;10:2182.
Article  Google Scholar 

37.
Lechevalier MP, De Bievre C, Lechevalier H. Chemotaxonomy of aerobic actinomycetes: phospholipid composition. Biochem Syst Ecol. 1977;5:249–60.
CAS  Article  Google Scholar 

38.
Lechevalier MP, Lechevalier H. Chemical composition as a criterion in the classification of aerobic actinomycetes. Int J Syst Evol Microbiol. 1970;20:435–43.
CAS  Google Scholar 

39.
Seyedsayamdost MR, Traxler MF, Zheng S-L, Kolter R, Clardy J. Structure and biosynthesis of amychelin, an unusual mixed-ligand siderophore from Amycolatopsis sp. AA4. J Am Chem Soc. 2011;133:11434–7.
CAS  Article  Google Scholar 

40.
Kodani S, Komaki H, Suzuki M, Hemmi H, Ohnishi-Kameyama M. Isolation and structure determination of new siderophore albachelin from Amycolatopsis alba. BioMetals. 2015;28:381–9.
CAS  Article  Google Scholar  More

1.
Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 4, 1–23 (1973).
Article  Google Scholar 
2.
Dai, L., Korolev, K. S. & Gore, J. Relation between stability and resilience determines the performance of early warning signals under different environmental drivers. Proc. Natl. Acad. Sci. 112, 10056–10061 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Dakos, V., Carpenter, S. R., van Nes, E. H. & Scheffer, M. Resilience indicators: Prospects and limitations for early warnings of regime shifts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130263–20130263 (2014).
Article  Google Scholar 

4.
Colchero, F. et al. The diversity of population responses to environmental change. Ecol. Lett. https://doi.org/10.1111/ele.13195 (2018).
Article  PubMed  PubMed Central  Google Scholar 

5.
Coulson, T. et al. Data from: Modeling adaptive and nonadaptive responses of populations to environmental change. Am. Nat. https://doi.org/10.5061/dryad.4c117 (2017).
Article  PubMed  PubMed Central  Google Scholar 

6.
Donohue, I. et al. Navigating the complexity of ecological stability. Ecol. Lett. 19, 1172–1185 (2016).
PubMed  Article  PubMed Central  Google Scholar 

7.
Fernández-Chacón, A. et al. When to stay, when to disperse and where to go: Survival and dispersal patterns in a spatially structured seabird population. Ecography 36, 1117–1126 (2013).
Article  Google Scholar 

8.
Sterk, M., van de Leemput, I. A. & Peeters, E. T. How to conceptualize and operationalize resilience in socio-ecological systems?. Curr. Opin. Environ. Sustain. 28, 108–113 (2017).
Article  Google Scholar 

9.
Brand, F. S. & Jax, K. Focusing the meaning(s) of resilience: Resilience as a descriptive concept and a boundary object. Ecol. Soc. 12, 23 (2007).
Article  Google Scholar 

10.
Barrett, L., Henzi, S. P. & Lusseau, D. Taking sociality seriously: The structure of multi-dimensional social networks as a source of information for individuals. Philos. Trans. R. Soc. B Biol. Sci. 367, 2108–2118 (2012).
Article  Google Scholar 

11.
Centola, D. How Behavior Spreads: The Science of Complex Contagions. (2018).

12.
Firth, J. A. Considering complexity: Animal social networks and behavioural contagions. Trends Ecol. Evol. 35, 100–104 (2020).
PubMed  Article  PubMed Central  Google Scholar 

13.
Kerth, G., Perony, N. & Schweitzer, F. Bats are able to maintain long-term social relationships despite the high fission–fusion dynamics of their groups. Proc. R. Soc. B Biol. Sci. 278, 2761–2767 (2011).
Article  Google Scholar 

14.
Rosenthal, S. B., Twomey, C. R., Hartnett, A. T., Wu, H. S. & Couzin, I. D. Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion. Proc. Natl. Acad. Sci. 112, 4690–4695 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Snijders, L., Blumstein, D. T., Stanley, C. R. & Franks, D. W. Animal social network theory can help wildlife conservation. Trends Ecol. Evol. 32, 567–577 (2017).
PubMed  Article  PubMed Central  Google Scholar 

16.
Webber, Q. M. R. & Vander Wal, E. An evolutionary framework outlining the integration of individual social and spatial ecology. J. Anim. Ecol. 87, 113–127 (2018).
PubMed  Article  PubMed Central  Google Scholar 

17.
Sueur, C. & Mery, F. Social Interaction in Animals: Linking Experimental Approach and Social Network Analysis (Frontiers Media SA, Lausanne, 2017).
Google Scholar 

18.
LaBarge, L. R., Allan, A. T. L., Berman, C. M., Margulis, S. W. & Hill, R. A. Reactive and pre-emptive spatial cohesion in a social primate. Anim. Behav. 163, 115–126 (2020).
Article  Google Scholar 

19.
Firth, J. A. et al. Wild birds respond to flockmate loss by increasing their social network associations to others. Proc. R. Soc. B Biol. Sci. 284, 20170299 (2017).
Article  Google Scholar 

20.
Farine, D. R. Structural trade-offs can predict rewiring in shrinking social networks. J. Anim. Ecol. 1365–2656, 13140. https://doi.org/10.1111/1365-2656.13140 (2019).
Article  Google Scholar 

21.
Maldonado-Chaparro, A. A., Alarcón-Nieto, G., Klarevas-Irby, J. A. & Farine, D. R. Experimental disturbances reveal group-level costs of social instability. Proc. R. Soc. B Biol. Sci. 285, 20181577 (2018).
Article  Google Scholar 

22.
Puga-Gonzalez, I., Sosa, S. & Sueur, C. Social style and resilience of macaques’ networks, a theoretical investigation. Primates 60, 233–246 (2019).
PubMed  Article  PubMed Central  Google Scholar 

23.
Williams, R. & Lusseau, D. A killer whale social network is vulnerable to targeted removals. Biol. Lett. 2, 497–500 (2006).
PubMed  PubMed Central  Article  Google Scholar 

24.
Oro, D. Perturbation, Social Feedbacks, and Population Dynamics in Social Animals (Oxford Univerity Press, Oxford, 2020).
Google Scholar 

25.
Firth, J. A. & Sheldon, B. C. Experimental manipulation of avian social structure reveals segregation is carried over across contexts. Proc. R. Soc. B Biol. Sci. 282, 20142350–20142350 (2015).
Article  Google Scholar 

26.
Genton, C. et al. How Ebola impacts social dynamics in gorillas: A multistate modelling approach. J. Anim. Ecol. 84, 166–176 (2015).
PubMed  Article  PubMed Central  Google Scholar 

27.
Leu, S. T., Farine, D. R., Wey, T. W., Sih, A. & Bull, C. M. Environment modulates population social structure: Experimental evidence from replicated social networks of wild lizards. Anim. Behav. 111, 23–31 (2016).
Article  Google Scholar 

28.
Silk, J., Cheney, D. & Seyfarth, R. A practical guide to the study of social relationships: A practical guide to the study of social relationships. Evol. Anthropol. Issues News Rev. 22, 213–225 (2013).
Article  Google Scholar 

29.
Brown, C. R. The ecology and evolution of colony-size variation. Behav. Ecol. Sociobiol. 70, 1613–1632 (2016).
Article  Google Scholar 

30.
Rolland, C., Danchin, E. & de Fraipont, M. The evolution of coloniality in birds in relation to food, habitat, predation, and life-history traits: A comparative analysis. Am. Nat. 151, 514–529 (1998).
CAS  PubMed  Article  Google Scholar 

31.
Shizuka, D. et al. Across-year social stability shapes network structure in wintering migrant sparrows. Ecol. Lett. 17, 998–1007 (2014).
PubMed  Article  PubMed Central  Google Scholar 

32.
Brandl, H. B., Griffith, S. C., Farine, D. R. & Schuett, W. Wild zebra finches that nest synchronously have long-term stable social ties. J. Anim. Ecol. 1365–2656, 13082. https://doi.org/10.1111/1365-2656.13082 (2019).
Article  Google Scholar 

33.
Moreno, J. L. Who Shall Survive?: A New Approach to the Problem of Human Interrelations (Nervous and Mental Disease Publishing Co, New York, 1934). .

34.
Scott, J. Social network analysis. Sociology 22, 109–127 (1988).
Article  Google Scholar 

35.
Croft, D. P., James, R. & Krause, J. Exploring Animal Social Networks (Princeton University Press, Princeton, 2008).
Google Scholar 

36.
Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163 (2015).
PubMed  PubMed Central  Article  Google Scholar 

37.
Ward, A. & Webster, M. Sociality: The Behaviour of Group-Living Animals (Springer, New York, 2016).
Google Scholar 

38.
Whitehead, H. Analyzing Animal Societies Quantitative Methods for Vertebrate Social Analysis. (2014).

39.
James, R., Croft, D. P. & Krause, J. Potential banana skins in animal social network analysis. Behav. Ecol. Sociobiol. 63, 989–997 (2009).
Article  Google Scholar 

40.
Hasenjager, M. J. & Dugatkin, L. A. Chapter three—social network analysis in behavioral ecology. In Advances in the Study of Behavior (ed. Naguib, M.) 47, 39–114 (Academic Press, New York, 2015).
Google Scholar 

41.
Payo-Payo, A. et al. Predator arrival elicits differential dispersal, change in age structure and reproductive performance in a prey population. Sci. Rep. 8, 1971 (2018).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Martínez-Abraín, A., Oro, D., Forero, M. G. & Conesa, D. Modeling temporal and spatial colony-site dynamics in a long-lived seabird. Popul. Ecol. 45, 133–139 (2003).
Article  Google Scholar 

43.
Genovart, M., Oro, D. & Tenan, S. Immature survival, fertility, and density dependence drive global population dynamics in a long-lived species. Ecology 99, 2823–2832 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

44.
Almaraz, P. & Oro, D. Size-mediated non-trophic interactions and stochastic predation drive assembly and dynamics in a seabird community. Ecology 92, 1948–1958 (2011).
PubMed  Article  PubMed Central  Google Scholar 

45.
Shizuka, D. & Johnson, A. E. How demographic processes shape animal social networks. Behav. Ecol. https://doi.org/10.1093/beheco/arz083 (2019).
Article  Google Scholar 

46.
Francesiaz, C. et al. Familiarity drives social philopatry in an obligate colonial breeder with weak interannual breeding-site fidelity. Anim. Behav. 124, 125–133 (2017).
Article  Google Scholar 

47.
Cantor, M. & Farine, D. R. Simple foraging rules in competitive environments can generate socially structured populations. Ecol. Evol. 8, 4978–4991 (2018).
PubMed  PubMed Central  Article  Google Scholar 

48.
Cantor, M. et al. Animal social networks: Revealing the causes and implications of social structure in ecology and evolution. https://osf.io/m62gb (2019). https://doi.org/10.32942/osf.io/m62gb.

49.
Anderson, D. J. & Hodum, P. J. Predator behavior favors clumped nesting in an oceanic seabird. Ecology 74, 2462–2464 (1993).
Article  Google Scholar 

50.
Oro, D. Colonial seabird nesting in dense and small sub-colonies: An advantage against aerial predation. Condor 98, 848–850 (1996).
Article  Google Scholar 

51.
Gil, M. A., Hein, A. M., Spiegel, O., Baskett, M. L. & Sih, A. Social information links individual behavior to population and community dynamics. Trends Ecol. Evol. 33, 535–548 (2018).
PubMed  Article  PubMed Central  Google Scholar 

52.
Lewanzik, D., Sundaramurthy, A. K. & Goerlitz, H. R. Insectivorous bats integrate social information about species identity, conspecific activity and prey abundance to estimate cost–benefit ratio of interactions. J. Anim. Ecol. 88, 1462–1473 (2019).
PubMed  PubMed Central  Article  Google Scholar 

53.
Doligez, B. Public information and breeding habitat selection in a wild bird population. Science 297, 1168–1170 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Payo-Payo, A. et al. Colonisation in social species: The importance of breeding experience for dispersal in overcoming information barriers. Sci. Rep. 7, 20 (2017).
ADS  Article  CAS  Google Scholar 

55.
Arganda, S., Pérez-Escudero, A. & de Polavieja, G. G. A common rule for decision making in animal collectives across species. Proc. Natl. Acad. Sci. 109, 20508–20513 (2012).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Pérez-Escudero, A. & de Polavieja, G. G. Adversity magnifies the importance of social information in decision-making. J. R. Soc. Interface 14, 20170748 (2017).
PubMed  PubMed Central  Article  Google Scholar 

57.
Maldonado-Chaparro, A. A., Blumstein, D. T., Armitage, K. B. & Childs, D. Z. Transient LTRE analysis reveals the demographic and trait-mediated processes that buffer population growth. Ecol. Lett. 21, 1693–1703 (2018).
PubMed  PubMed Central  Article  Google Scholar 

58.
Pruitt, J. N. et al. Social tipping points in animal societies. Proc. R. Soc. B 285, 20181282 (2018).
PubMed  Article  PubMed Central  Google Scholar 

59.
Dall, S. R. X., Houston, A. I. & McNamara, J. M. The behavioural ecology of personality: Consistent individual differences from an adaptive perspective. Ecol. Lett. 7, 734–739 (2004).
Article  Google Scholar 

60.
Doering, G. N., Scharf, I., Moeller, H. V. & Pruitt, J. N. Social tipping points in animal societies in response to heat stress. Nat. Ecol. Evol. 2, 1298–1305 (2018).
PubMed  Article  PubMed Central  Google Scholar 

61.
Wolf, M., van Doorn, G. S., Leimar, O. & Weissing, F. J. Life-history trade-offs favour the evolution of animal personalities. Nature 447, 581–584 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).
PubMed  Article  PubMed Central  Google Scholar 

63.
Cote, J., Clobert, J., Brodin, T., Fogarty, S. & Sih, A. Personality-dependent dispersal: Characterization, ontogeny and consequences for spatially structured populations. Philos. Trans. R. Soc. B Biol. Sci. 365, 4065–4076 (2010).
CAS  Article  Google Scholar 

64.
Fogarty, S., Cote, J. & Sih, A. Social personality polymorphism and the spread of invasive species: A model. Am. Nat. 177, 273–287 (2011).
PubMed  Article  PubMed Central  Google Scholar 

65.
O’Shea-Wheller, T. A., Masuda, N., Sendova-Franks, A. B. & Franks, N. R. Variability in individual assessment behaviour and its implications for collective decision-making. Proc. R. Soc. B Biol. Sci. 284, 20162237 (2017).
Article  Google Scholar 

66.
Nimmo, D. G., Mac Nally, R., Cunningham, S. C., Haslem, A. & Bennett, A. F. Vive la résistance: Reviving resistance for 21st century conservation. Trends Ecol. Evol. 30, 516–523 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
IUCN. Larus audouinii: BirdLife International: The IUCN Red List of Threatened Species 2018: e.T22694313A132541241. (2018). https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22694313A132541241.en.

68.
Martínez-Abraín, A., Jiménez, J. & Oro, D. Pax Romana: ‘refuge abandonment’ and spread of fearless behavior in a reconciling world. Anim. Conserv. 22, 3–13 (2019).
Article  Google Scholar 

69.
Genovart, M., Jover, L., Ruiz, X. & Oro, D. Offspring sex ratios in subcolonies of Audouin’s gull, Larus audouinii, with differential breeding performance. Can. J. Zool. 81, 905–910 (2003).
Article  Google Scholar 

70.
Oro, D. Audouin’s gull account. In The Birds of Western Palearctic (ed. Ogilvie, M. A.) 47–61 (Oxford University Press, Oxford, 1998).
Google Scholar 

71.
Genovart, M., Pradel, R. & Oro, D. Exploiting uncertain ecological fieldwork data with multi-event capture-recapture modelling: An example with bird sex assignment. J. Anim. Ecol. 81, 970–977 (2012).
PubMed  Article  Google Scholar 

72.
Oro, D., Tavecchia, G. & Genovart, M. Comparing demographic parameters for philopatric and immigrant individuals in a long-lived bird adapted to unstable habitats. Oecologia 165, 935–945 (2010).
ADS  PubMed  Article  Google Scholar 

73.
Hoff, P. D. Additive and multiplicative effects network models. arXiv:180708038 Stat (2018).

74.
Minhas, S., Hoff, P. D. & Ward, M. D. Inferential approaches for network analyses: AMEN for latent factor models. arXiv:161100460 Stat (2016).

75.
Warner, R. M., Kenny, D. A. & Stoto, M. A new round robin analysis of variance for social interaction data. J. Pers. Soc. Psychol. 37, 1742–1757 (1979).
Article  Google Scholar 

76.
Gimenez, O. et al. Inferring animal social networks with imperfect detection. Ecol. Model. 401, 69–74 (2019).
Article  Google Scholar 

77.
Hoppitt, W. J. E. & Farine, D. R. Association indices for quantifying social relationships: How to deal with missing observations of individuals or groups. Anim. Behav. 136, 227–238 (2018).
Article  Google Scholar 

78.
Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).
Article  Google Scholar 

79.
Warnes,GR, Bolker, G, Gorjanc, G & Grothendieck, G. gdata: Various R programming tools for data manipulation. R package version (2014).

80.
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal 20, 20 (2006).
Google Scholar 

81.
Farine, D. R. A guide to null models for animal social network analysis. Methods Ecol. Evol. 8, 1309–1320 (2017).
PubMed  PubMed Central  Article  Google Scholar 

82.
Ginsberg, J. R. & Young, T. P. Measuring association between individuals or groups in behavioural studies. Anim. Behav. 44, 377–379 (1992).
Article  Google Scholar 

83.
Cairns, S. J. & Schwager, S. J. A comparison of association indices. Anim. Behav. 35, 1454–1469 (1987).
Article  Google Scholar  More

Barton N (1998) The effect of hitchhiking on neutral genealogies. Genet Res 72:123–133
CAS  Article  Google Scholar 

Barton N (2000) Genetic hitchhiking. Phil Trans R Soc Lond B 355:1553–1562
CAS  Article  Google Scholar 

Barton NH, Etheridge AM, Véber A (2010) A new model for evolution in a spatial continuum. Electron J Probab 15:162–216
Article  Google Scholar 

Beaumont M, Cornuet JM, Marin JM, Robert C (2009) Adaptive approximate Bayesian computation. Biometrika 96:983–990
Article  Google Scholar 

Beaumont M, Zhang W, Balding D (2002) Approximate Bayesian computation in population genetics. Genetics 162:2025–2035
Google Scholar 

Bedford T, Cobey S, Pascual M (2011) Strength and tempo of selection revealed in viral gene geneaologies. BMC Evol Biol 11:220
Article  Google Scholar 

Beichman A, Huerta-Sanchez E, Lohmuller K (2018) Using genomic data to infer historic population dynamics of nonmodel organisms. Annu Rev Ecol, Evol Syst 49:433–456
Article  Google Scholar 

Blancas, AB, Duchamps, JJ, Lambert, A, Siri-Jégousse, A (2018) Trees within trees: simple nested coalescents. Electron J Probab. 23:1–27

Blancas, AB, Gufler, S, Kliem, S, Tran, V, Wakolbinger, A (2020) Evolving genealogies for branching populations under selection and competition. In preparation

Bentley G, Goldberg T, ska G (1993) The fertility of agricultural and non-agricultural traditional societies. Popul Stud 47:269–281
Article  Google Scholar 

Billiard S, Ferrière R, Méléard S, Tran V (2015) Stochastic dynamics of adaptive trait and neutral marker driven by eco-evolutionary feedbacks. J Math Biol 71:1211–1242
Article  Google Scholar 

Blum M (2010) Approximate Bayesian Computation: a non-parametric perspective. J Am Stat Assoc 105:1178–1187
CAS  Article  Google Scholar 

Blum M, François O (2005) On statistical tests of phylogenetic tree imbalance: the Sackin and other indices revisited. Math Biosci 195:141–153
Article  Google Scholar 

Blum M, François O (2010) Non-linear regression models for Approximate Bayesian Computation. Stat Comput 20:63–73
Article  Google Scholar 

Blum M, Tran V (2010) HIV with contact-tracing: a case study in Approximate Bayesian Computation. Biostatistics 11:644–660
Article  Google Scholar 

Chaix R, Quintana-Murci L, Hegay T, Hammer M, Mobasher Z, Austerlitz F et al. (2007) From social to genetic structures in Central Asia. Curr Biol 17:43–48
CAS  Article  Google Scholar 

Champagnat N (2006) A microscopic interpretation for adaptative dynamics trait substitution sequence models. Stoch Processes Appl 116:1127–1160
Article  Google Scholar 

Champagnat N, Ferrière R, Méléard S (2006) Unifying evolutionary dynamics: from individual stochastic processes to macroscopic models via timescale separation. Theor Popul Biol 69:297–321
Article  Google Scholar 

Champagnat N, Jabin PE, Raoul G (2010) Convergence to equilibrium in competitive Lotka-Volterra and chemostat systems. Comptes-Rendus Mathématiques de laAcadémie des Sciences de Paris 348:1267–1272
Google Scholar 

Champagnat N, Méléard S (2007) Invasion and adaptive evolution for individual-based spatially structured populations. J Math Biol 55:147–188
Article  Google Scholar 

Champagnat N, Méléard S (2011) Polymorphic evolution sequence and evolutionary branching. Probab Theory Relat Fields 151:45–94
Article  Google Scholar 

Charlesworth B, Charlesworth D, Barton NH (2003) The effects of genetic and geographic structure on neutral variation. Annu Rev Ecol Evol Syst 34:99–125
Article  Google Scholar 

Csillery, K, Francois, O, Blum, MGB (2012) abc: an r package for Approximate Bayesian Computation (ABC). Methods in ecology and evolution.

Dawson D, Hochberg K (1982) Wandering random measures in the Fleming-Viot model. Ann Probab 10:554–580
Article  Google Scholar 

Dawson, DA (1993) Mesure-valued Markov processes. In: Springer (ed.), Ecole d’Eté de probabilités de Saint-Flour XXI. New York, Lectures Notes in Math, 1541, p. 1–260

Dieckmann U, Doebeli M (1999) On the origin of species by sympatric speciation. Nature 400:354–357
CAS  Article  Google Scholar 

Donnelly P, Kurtz T (1996) A countable representation of the Fleming-Viot measure-valued diffusion. Ann Probab 24:698–742
Article  Google Scholar 

Donnelly P, Kurtz T (1999) Particle representations for measure-valued population models. Ann Probab 27:166–205
Article  Google Scholar 

Duchamps, JJ (2020) Trees within trees ii: nested fragmentations. Ann. Inst. H. Poincaré Probab. Statist. 56:1203–1229

Durrett R, Schweinsberg J (2004) Approximating selective sweeps. Theoret Popul Biol 66:129–138
Article  Google Scholar 

Durrett R, Schweinsberg J (2005) Random partitions approximating the coalescence of lineages during a selective sweep. Ann Appl Probab 15:1591–1651
Article  Google Scholar 

Etheridge, A (2000) An introduction to superprocesses, University Lecture Series, vol. 20, American Mathematical Society, Providence.

Etheridge A, Pfaffelhuber P, Wakolbinger A (2006) An approximate sampling formula under genetic hitchhiking. Ann Appl Probab 16:685–729
Article  Google Scholar 

Ethier S, Kurtz T (1986) Markov processus, characterization and convergence. John Wiley, Sons, New York
Google Scholar 

Felsenstein J (1975) A pain in the torus: some difficulties with the model of isolation by distance. Am Nat 109:359–368
Article  Google Scholar 

Fournier N, Méléard S (2004) A microscopic probabilistic description of a locally regulated population and macroscopic approximations. Ann Appl Probab 14:1880–1919
Article  Google Scholar 

Frost S, Pybus O, Gog J, Viboud C, Bonhoeffer S, Bedford T (2015) Eight challenges in phylodynamic inference. Epidemics 10:88–92
Article  Google Scholar 

Fu YX, Li WH (1993) Statistical tests of neutrality of mutations. Genetics 133:693–709
CAS  Google Scholar 

Gallieni L (2017) Intransitive competition and its effects on community functional diversity. Oikos 126:615–623
Article  Google Scholar 

Goldstein D, Chikhi L (2002) Human migrations and population structure: what we know and why it matters. Annu Rev Genom Hum Genet 3:129–152
CAS  Article  Google Scholar 

Grelaud A, Robert C, Marin J, Rodolphe F, Taly J (2009) ABC likelihood-free methods for model choice in Gibbs random fields. Bayesian Anal 4:317–336
Article  Google Scholar 

Haller BC, Messer PW (2019) SLiM 3: forward genetic simulations beyond the Wright-Fisher model. Mol Biol Evol 36(3):632–637
CAS  Article  Google Scholar 

Heyer E, Brandenburg JT, Leonardi M, Toupance B, Balaresque P, Hegay T et al. (2015) Patrilineal populations show more male transmission of reproductive success than cognatic populations in Central Asia, which reduces their genetic diversity. Am J Phys Anthropol 157:537–543
Article  Google Scholar 

Jansen S, Kurt N (2014) On the notion(s) of duality for Markov processes. Probab Surveys 11:59–120
Article  Google Scholar 

Janson S, Kersting G (2011) On the total external length of the kingman coalescent. Electron J Probab 16:2203–2218
Article  Google Scholar 

Johri, P, Riall, K, Jensen, JD (2020) The impact of purifying and background selection on the inference of population history: problems and prospects. bioRxiv. https://doi.org/10.1101/2020.04.28.066365

Legendre S, Clobert J (1995) ULM, a software for conservation and evolutionary biologists. J Appl Stat 22:817–834
Article  Google Scholar 

Marin JM, Pudlo P, Robert C, Ryder R (2012) Approximate Bayesian computation methods. Stat Comput 22:1167–1180
Article  Google Scholar 

Metz J, Geritz S, Meszéna G, Jacobs F, Heerwaarden JV (1996) Adaptative dynamics, a geometrical study of the consequences of nearly faithful reproduction. In: Van Strien SJ, Verduyn Lunel SM (eds) Stochastic and spatial structures of dynamical systems. pp 183–231

Müller N, Rasmussen DA, Stadler T (2017) The structured coalescent and its approximations. Mol Biol Evol 34:2970–2981
Article  Google Scholar 

Neher R, Bedford T (2015) nextflu: real-time tracking of seasonal influenza virus evolution in humans. Bioinformatics 31:3546–8
CAS  Article  Google Scholar 

Pitman J (1999) Coalescents with multiple collisions. Ann Probab 27:1870–1902
Article  Google Scholar 

Prangle, D, Fearnhead, P, Cox, M, Biggs, P, French, N (2013) Semi-automatic selection of summary statistics for abc model choice. Stat Appl Genet Mol Biol pp. 1–16

Pudlo P, Marin J, Estoup A, Cornuet J, Gautier M, Robert C (2016) Reliable ABC model choice via random forests. Bioinformatics 32:859–866
CAS  Article  Google Scholar 

Rasmussen D, Stadler T (2019) Coupling adaptive molecular evolution to phylodynamics using fitness-dependent birth-death models. eLife 8:e45562
Article  Google Scholar 

Ross CT, Mulder MB, Winterhalder B, Uehara R, Headland J, Headland T (2016) Evidence for quantity-quality trade-offs, sex-specific parental investment, and variance compensation in colonized agta foragers undergoing demographic transition. Evol Hum Behav 37:350–365
Article  Google Scholar 

Roughgarden J (1979) Theory of population genetics and evolutionary ecology: an introduction. Macmillan, New York
Google Scholar 

Sagitov S (1999) The general coalescent with asynchronous mergers of ancestral lines. J Appl Probab 36:1116–1125
Article  Google Scholar 

Sellen D, Mace R (1997) Fertility and mode of subsistence: a phylogenetic analysis. Curr Anthropol 38:878–889
Article  Google Scholar 

Spor A, Nidelet T, Simon J, Bourgais A, de Vienne D, Sicard D (2009) Niche-driven evolution of metabolic and life-history strategies in natural and domesticated populations of Saccharomyces cerevisiae. BMC Evol Biol 9:296
Article  Google Scholar 

Stephan W (2016) Signatures of positive selection: from selective sweeps at individual loci to subtle allele frequency changes in polygenic adaptation. Mol Ecol 25:79–88
CAS  Article  Google Scholar 

Stoehr J, Pudlo P, Cucala L (2015) Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields. Stat Comput 25:129–141
Article  Google Scholar 

Strelkowa N, Lässing M (2012) Clonal interference in the evolution of influenza. Genetics 192:671–682
CAS  Article  Google Scholar 

Verdu P, Austerlitz F, Estoup A, Vitalis R, Georges M, Théry S et al. (2009) Origins and genetic diversity of pygmy hunter-gatherers from western central africa. Curr Biol 19:312–318
CAS  Article  Google Scholar 

Zeeman M (1993) Hopf bifurcations in competitive three-dimensional Lotka-Volterra systems. Dynam Stab Syst 8:189–217
Google Scholar  More

1.
Froese, R. & Pauley, D. (2020) FishBase. World Wide Web electronic publication. https://www.fishbase.org, version (02/2012).
2.
Connaughton, M. A. & Taylor, M. H. Seasonal and daily cycles in sound production associated with spawning in the weakfish, Cynoscion regalis. Environ. Biol. Fish 42, 233–240 (1995).
Article  Google Scholar 

3.
Ueng, J. P., Huang, B. Q. & Mok, H. K. Sexual differences in spawning sounds of the Japanese croaker Argyrosomus japonicus (Sciaenidae). Zool. Stud. 46, 103–110 (2007).
Google Scholar 

4.
Parmentier, E., Tock, J., Falguière, J. C. & Beauchaud, M. Sound production in Sciaenops ocellatus: preliminary study for the development of acoustic cues in aquaculture. Aquaculture 432, 204–211 (2014).
Article  Google Scholar 

5.
Parsons, M. J. G., McCauley, R. D. & Mackie, M. C. Characterisation of mulloway Argyrosomus japonicus advertisement sounds. Acoustics Aust. 41, 196–201 (2013).
Google Scholar 

6.
Bolgan, M. et al. Calling activity and calls’ temporal features inform about fish reproductive condition and spawning in three cultured Sciaenidae species. Aquaculture 524, 1–14 (2020).
Article  CAS  Google Scholar 

7.
Tower, R. W. The production of sound in the drumfishes, the sea-robin and the toadfish. Ann. N. Y. Acad. Sci. 18, 149–180 (1908).
ADS  Article  Google Scholar 

8.
Connaughton, M. A., Taylor, M. H. & Fine, M. L. Effects of fish size and temperature on weakfish disturbance calls: implications for the mechanism of sound generation. J. Exp. Biol. 203, 1503–1512 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

9.
Ladich, F. & Fine, M. L. Sound-generating mechanisms in fishes: a unique diversity in vertebrates. In Communication in Fishes (eds Ladich, F. et al.) 3–43 (Science Publishers, Enfield, 2006).
Google Scholar 

10.
Chao, L. N. A basis for classifying Western Atlantic Sciaenidae (Teleostei: Perciformes). NOAA Technical Report, Circular 415 (National Oceanic and Atmospheric Administration, Washington, 1978).
Google Scholar 

11.
Ono, R. D. & Poss, S. G. Structure and innervation of the swimbladder musculature in the weakfish, Cynoscionregalis. Can. J. Zool. 60, 1955–1967 (1982).
Article  Google Scholar 

12.
Hill, G. L., Fine, M. L. & Musick, J. A. Ontogeny of the sexually dimorphic sonic muscle in three sciaenid species. Copeia 1987, 708–713 (1987).
Article  Google Scholar 

13.
Sasaki, K. Phylogeny of the family Sciaenidae, with notes on its zoogeography (Teleostei, Perciformes). Mem. Fac. Fish. Hokkaido Univ. 36, 1–137 (1989).
Google Scholar 

14.
Mok, H. K. et al. An intermediate in the evolution of superfast sonic muscles. Front. Zool. 8, 1–8 (2011).
Article  Google Scholar 

15.
Lin, Y. C., Mok, H. K. & Huang, B. Q. Sound characteristics of big-snout croaker, Johnius macrorhynus (Sciaenidae). J. Acoust. Soc. Am er. 121, 586–593 (2007).
ADS  Article  Google Scholar 

16.
Griffiths, M. H. & Hecht, T. Age and growth of South African dusky kob Argyrosomus japonicus (Sciaenidae) based on otoliths. S. Afr. J. Mar. Sci. 16, 119–128 (1995).
Article  Google Scholar 

17.
Lo P. C. Sound characteristics of the large yellow croaker, Larimichthys crocea and phylogeny of the Western Pacific sciaenid genera inferred by molecular evidence. Master’s Thesis. National Sun Yat-sen University, Taiwan (2011).

18.
Tellechea, J. S., Martinzez, C., Fine, M. L. & Norbis, W. Sound production in the whitemouth croaker and relationship between fish size and call characteristics. Environ. Biol. Fish. 89, 163–172 (2010).
Article  Google Scholar 

19.
Tellechea, J. S., Norbis, W., Olsson, D. & Fine, M. L. Calls of the black drum (Pogonius chromis: Sciaenidae): Geographical differences in sound production between Northern and Southern Hemisphere populations. J. Exp. Zool. 313A, 1–8 (2010).
Article  CAS  Google Scholar 

20.
Pereira, B. P. et al. Sound production in the Meagre, Argyrosomus regius (Asso, 1801): intraspecific variability associated with size, sex and context. PeerJ 8, e8559 (2020).
PubMed  PubMed Central  Article  Google Scholar 

21.
Wongratana, T. Boesemania microlepis (Bleeker), a common but misidentified riverine drumfish (Pisces: Sciaenidae) from Thailand and Mekong River. In Proceedings of the 23rd Kasetsart University Conference Fisheries Section, Kasetsart University, Bangkok (Thailand), Vol. 23, 3–20 (1985).

22.
Baird, I. G., Phylavanh, B., Vongsenesouk, B. & Xaiyamanivoni, K. The ecology and conservation of the smallscale croaker Boesemania microlepis (Bleeker 1858–59) in the mainstream Mekong River, southern Laos. Nat. Hist. Bull. Siam Soc. 49, 161–176 (2001).
Google Scholar 

23.
Feldberg, E., Porto, J. I. R., Santos, E. B. P. & Vlantim, F. C. S. Cytogenetic studies of two freshwater sciaenids of the genus Plagioscion (Perciformes, Sciaenidae) from the central Amazon. Gen. Mol. Biol. 22, 351–356 (2020).
Article  Google Scholar 

24.
Boeger, W. A. & Kritsky, D. Parasites, fossils and geologic history: Historical biogeography of the South America freshwater croakers, Plagioscion spp. (Teleostei, Sciaenidae). Zool. Scr. 32, 3–11 (2002).
Article  Google Scholar 

25.
Chao, N. L. A synopsis on zoogeography of Sciaenidae. In Indo Pacific Fish Biology. Proceedings of the Second International Conference on Indo-Pacific Fishes (eds Uyeano, T. et al.) (Ichthyological Society of Japan, Tokyo, 1986).
Google Scholar 

26.
Sasaki, K. Comparative anatomy and phylogenetic relationships of the family Sciaenidae (Teleostei, Perciformes) (MS Hokkaido University, Sapporo, 1985).
Google Scholar 

27.
Montie, E. W., Kehrer, C., Yost, J. & Brenkert, K. Long-term monitoring of captive red drum Sciaenops ocellatus reveals that calling incidence and structure correlate with egg deposition. J. Fish Biol. 88, 1776–1795 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Locascio, J. V. & Mann, D. A. Diel periodicity of fish sound production in Charlotte Harbor, Florida. Trans. Am. Fish. Soc. 137, 606–615 (2008).
Article  Google Scholar 

29.
Monczak, A., Berry, A., Kehrer, C. & Montie, E. W. Long-term acoustic monitoring of fish calling provides baseline estimates of reproductive timelines in the May River estuary, southeastern USA. Mar. Ecol. Prog. Ser. 581, 1–19 (2017).
ADS  Article  Google Scholar 

30.
Lagardere, J. P. & Mariani, A. Spawning sounds in meagre Argyrosomus regius recorded in the Gironde estuary, France. J. Fish Biol. 69, 1697–1708 (2006).
Article  Google Scholar 

31.
Skoglund, C. R. Functional analysis of swimbladder muscles engaged in sound productivity of the toadfish. J. Biophys. Biochem. Cytol. 10(Suppl), 187–200 (1961).
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Fine, M. L., Malloy, K. L., King, C. B., Mitchell, S. L. & Cameron, T. M. Movement and sound generation by the toadfish swimbladder. J. Comp. Physiol. 187A, 371–379 (2001).
Article  Google Scholar 

33.
Fine, M. L. & Lenhardt, M. L. Shallow-water propagation of the toadfish mating call. Comp. Biochem. Physiol. 76A, 225–231 (1983).
Article  Google Scholar 

34.
Thorson, R. F. & Fine, M. L. Crepuscular changes in emission rate and parameters of the boatwhistle advertisement call of the gulf toadfish, Opsanus beta. Environ. Biol. Fish. 63, 321–331 (2002).
Article  Google Scholar 

35.
Urick, R. J. Principles of Underwater Sound (McGraw-Hill, New York, 1975).
Google Scholar 

36.
Lugli, M. & Fine, M. L. Acoustic communication in two freshwater gobies: ambient noise and short-range propagation in shallow streams. J. Acoust. Soc. Am. 114, 512–521 (2003).
ADS  CAS  PubMed  Article  Google Scholar 

37.
Mann, D. A. Propagation of fish sounds. In Communication in Fishes Vol. 1 (eds Ladich, F. et al.) 107–120 (Science Publishers, Enfield, 2006).
Google Scholar 

38.
Ghahramani, Z. N., Mohajer, Y. J. & Fine, M. L. Developmental variation in sound production in water and air in the blue catfish Ictalurus furcatus. J. Exp. Biol. 217, 4244–4251 (2014).
PubMed  Article  PubMed Central  Google Scholar 

39.
Akamatsu, T., Okumura, T., Novarini, N. & Yan, H. Y. Empirical refinements applicable to the recording of fish sounds in small tanks. J. Acoust. Soc. Am. 112, 3073–3082 (2002).
ADS  PubMed  Article  PubMed Central  Google Scholar 

40.
Smith, M. E., Weller, K. K., Kynard, B., Sato, Y. & Godinho, A. L. Mating calls of three prochilodontid fish species from Brazil. Environ. Biol. Fish 101, 327–339 (2018).
Article  Google Scholar 

41.
Minnaert, F. On musical air bubbles and the sounds of running water. Philos. Mag. 16, 235–248 (1933).
Article  Google Scholar 

42.
Weston, D. E. Sound propagation in the presence of bladder fish. In Underwater Acoustics Vol. 2 (ed. Albers, V. M.) 55–88 (Plenum Press, New York, 1967).
Google Scholar 

43.
Batzler, W. E. & Pickwell, G. V. Resonant acoustic scattering from gas-bladder fishes. In Proceedings of an International Symposium on Biological Sound Scattering in the Ocean (ed. Farquhar, G. B.) 168–179 (U.S. Government Printing Office, Washington, 1970).
Google Scholar 

44.
McCartney, B. S. & Stubbs, A. R. Measurement of the target strength of fish in dorsal aspect, including swimbladder resonance. In Proceedings of an International Symposium on Biological Sound Scattering in the Ocean (ed. Farquhar, G. B.) 180–211 (U.S. Government Printing Office, Washington, 1970).
Google Scholar 

45.
Fine, M. L. Seasonal and geographic variation of the mating call of the oyster toadfish Opsanus tau. Oecologia 36, 45–57 (1978).
ADS  PubMed  Article  PubMed Central  Google Scholar 

46.
Fine, M. L., King, T. L., Ali, H., Sidker, N. & Cameron, T. M. Wall structure and material properties cause viscous damping of swimbladder sounds in the oyster toadfish Opsanus tau. Proc. R. Soc. Lond. B 283, 1–9 (2016).
Google Scholar 

47.
Parmentier, E., Lagardère, J. P., Braquegnier, J. B., Vandewalle, P. & Fine, M. L. Sound production mechanism in carapid fish: first example with a slow sonic muscle. J. Exp. Biol. 209, 2952–2960 (2006).
PubMed  Article  PubMed Central  Google Scholar 

48.
Parmentier, E., Fine, M. L. & Mok, H. K. Sound production by a recoiling system in the Pempheridae and Terapontidae. J. Morphol. 277, 717–724 (2016).
PubMed  Article  PubMed Central  Google Scholar 

49.
Parmentier, E. & Fine, M. L. Fish sound production: insights. In Vertebrate Sound Production and Acoustic Communication (eds Suthers, R. A. & Fitch, T.) 19–49 (Springer, New York, 2016).
Google Scholar 

50.
Ramcharitar, J. U., Deng, X., Ketten, D. & Popper, A. N. Form and function in the unique inner ear of a teleost: the silver perch (Bairdiella chrysoura). J. Comp. Neurol. 475, 531–539 (2004).
PubMed  Article  PubMed Central  Google Scholar 

51.
Robertson, G. N., McGee, C. A. S., Dunbarton, T. C., Croll, R. P. & Smith, F. M. Development of the swimbladder and Its innervation in the zebrafish, Danio rerio. J. Morphol. 268, 967–985 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Birindelli, J. L. O., Sousa, L. M. & Sabaj Perez, M. H. Morphology of the gas bladder in thorny catfishes (Siluriformes: Doradidae). Proc. Acad. Nat. Sci. Philadelphia 158, 261–296 (2009).
Article  Google Scholar 

53.
Borie, A. et al. Disturbance calls of five migratory Characiformes species and advertisement choruses in Amazon spawning sites. J. Fish Biol. 95, 820–832 (2019).
PubMed  PubMed Central  Google Scholar 

54.
King, T. L. The Relationship Between Collagen Fibers and Material Properties of Swim Bladders in Sonic Teleosts. MS Virginia Commonwealth University (2005).

55.
Connaughton, M. A., Fine, M. L. & Taylor, M. H. Weakfish sonic muscle: influence of size, temperature and season. J. Exp. Biol. 205, 2183–2188 (2002).
CAS  PubMed  PubMed Central  Google Scholar 

56.
Locascio, J. V. & Mann, D. A. Localization and source level estimates of black drum (Pogonias chromis) calls. J. Acoust. Soc. Am. 130, 1868–1879 (2011).
ADS  PubMed  Article  PubMed Central  Google Scholar 

57.
Bradbury, J. W. & Vehrencamp, S. L. Principles of Animal Communication (Sinauer, Massachusetts, 1998).
Google Scholar 

58.
Ramcharitar, J. U., Higgs, D. M. & Popper, A. N. Audition in sciaenid fishes with different swim bladder-inner ear configurations. J. Acoust. Soc. Am. 119, 439–443 (2006).
ADS  PubMed  Article  PubMed Central  Google Scholar 

59.
Salas, A. K., Wilson, P. S. & Fuiman, L. A. Ontogenetic change in predicted acoustic pressure sensitivity in larval red drum (Sciaenops ocellatus). J. Exp. Biol. 222, 1–12 (2019).
Article  Google Scholar 

60.
Rice, A. N., Soldevilla, M. S. & Quinlan, J. A. Nocturnal patterns in fish chorusing off the coasts of Georgia and eastern Florida. Bull. Mar. Sci. 93, 455–474 (2017).
Article  Google Scholar 

61.
Sprague, M. W. The single sonic muscle twitch model for the sound-production mechanism in the weakfish, Cynoscion regalis. J. Acoust. Soc. Am. 108, 2430–2437 (2000).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Picculin, M. et al. Diagnostics of nocturnal calls of Sciaenaumbra (L., fam. Sciaenidae) in a nearshore Mediterranean marine reserve. Bioacoustics 12, 292–294 (2012).
Google Scholar 

63.
Tellechea, J. S. & Norbis, W. Sexual dimorphism in sound production and call characteristics in the striped weakfish Cynoscion guatucupa. Zool. Stud. 51, 946–955 (2012).
Google Scholar 

64.
Rountree, R. A. & Juanes, F. Potential of passive acoustic recording for monitoring invasive species: freshwater drum invasion of the Hudson River via the New York canal system. Biol. Invasions 19, 2075–2088 (2017).
Article  Google Scholar 

65.
Tellechea, J. S., Fine, M. L. & Norbis, W. Passive acoustic monitoring, development of disturbance calls and differentiation of disturbance and advertisement calls in the Argentine croaker Umbrina canosai (Sciaenidae). J. Fish Biol. 90, 1631–1643 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Tang, S. K. On Helmholtz resonators with tapered necks. J. Acoust. Soc. Am. 279, 1085–1096 (2005).
Google Scholar 

67.
Pillaia, M. A. & Da, E. Improved acoustic energy harvester using tapered neck Helmholtz resonator and piezoelectric cantilever undergoing concurrent bending and twisting. Procedia Eng. 144, 674–681 (2016).
Article  Google Scholar 

68.
Yoshida, T. et al. (eds) Fishes of Northern Gulf of Thailand (National Museum of Nature and Science, Tuskuba, 2013).
Google Scholar  More

1.
Anderegg, W. R., Kane, J. M. & Anderegg, L. D. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Chang. 3, 30–36 (2013).
ADS  Article  Google Scholar 
2.
Huang, J. et al. Tree defence and bark beetles in a drying world: Carbon partitioning, functioning and modelling. New Phytol. 225, 26–36 (2020).
PubMed  Article  PubMed Central  Google Scholar 

3.
Kautz, M., Meddens, A. J., Hall, R. J. & Arneth, A. Biotic disturbances in Northern Hemisphere forests—A synthesis of recent data, uncertainties and implications for forest monitoring and modelling. Glob. Ecol. Biogeogr. 26, 533–552 (2017).
Article  Google Scholar 

4.
Netherer, S. et al. Do water-limiting conditions predispose N orway spruce to bark beetle attack?. New Phytol. 205, 1128–1141 (2015).
PubMed  Article  PubMed Central  Google Scholar 

5.
Seybold, S. J., Huber, D. P., Lee, J. C., Graves, A. D. & Bohlmann, J. Pine monoterpenes and pine bark beetles: A marriage of convenience for defense and chemical communication. Phytochem. Rev. 5, 143–178 (2006).
CAS  Article  Google Scholar 

6.
Raffa, K. F. & Smalley, E. B. Interaction of pre-attack and induced monoterpene concentrations in host conifer defense against bark beetle-fungal complexes. Oecologia 102, 285–295 (1995).
ADS  PubMed  Article  PubMed Central  Google Scholar 

7.
Reid, M. L. & Purcell, J. Condition-dependent tolerance of monoterpenes in an insect herbivore. Arthropod-Plant Interact. 5, 331–337 (2011).
Article  Google Scholar 

8.
Erbilgin, N., Krokene, P., Christiansen, E., Zeneli, G. & Gershenzon, J. Exogenous application of methyl jasmonate elicits defenses in Norway spruce (Picea abies) and reduces host colonization by the bark beetle Ips typographus. Oecologia 148, 426–436 (2006).
ADS  PubMed  Article  PubMed Central  Google Scholar 

9.
Hayes, J. L. & Strom, B. L. 4-Allylanisole as an inhibitor of bark beetle (Coleoptera: Scolytidae) aggregation. J. Econ. Entomol. 87, 1586–1594 (1994).
CAS  Article  Google Scholar 

10.
Franceschi, V. R., Krokene, P., Christiansen, E. & Krekling, T. Anatomical and chemical defenses of conifer bark against bark beetles and other pests. New Phytol. 167, 353–376 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Zhao, T., Borg-Karlson, A.-K., Erbilgin, N. & Krokene, P. Host resistance elicited by methyl jasmonate reduces emission of aggregation pheromones by the spruce bark beetle, Ips typographus. Oecologia 167, 691–699 (2011).
ADS  PubMed  Article  PubMed Central  Google Scholar 

12.
12Schmidt, A. et al. In Chemical Ecology and Phytochemistry in Forest Ecosystems (ed Romeo, J. T.) 1–28 (Elsevier, Amsterdam, 2005).

13.
Keeling, C. I. & Bohlmann, J. Genes, enzymes and chemicals of terpenoid diversity in the constitutive and induced defence of conifers against insects and pathogens. New Phytol. 170, 657–675 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Despres, L., David, J.-P. & Gallet, C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 22, 298–307 (2007).
PubMed  Article  PubMed Central  Google Scholar 

15.
Raffa, K., Andersson, M. N. & Schlyter, F. In Advances in Insect Physiology, Vol. 50 (ed Blomquist Claus Tittiger, G.J.) 1–74 (Elsevier, Amsterdam, 2016).

16.
Adams, A. S. et al. Mountain pine beetles colonizing historical and naive host trees are associated with a bacterial community highly enriched in genes contributing to terpene metabolism. Appl. Environ. Microbiol. 79, 3468–3475 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
Six, D. L. Ecological and evolutionary determinants of bark beetle—fungus symbioses. Insects 3, 339–366 (2012).
PubMed  PubMed Central  Article  Google Scholar 

18.
Raffa, K. F. Terpenes tell different tales at different scales: Glimpses into the chemical ecology of conifer-bark beetle-microbial interactions. J. Chem. Ecol. 40, 1–20 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

19.
Douglas, A. E. Multiorganismal insects: Diversity and function of resident microorganisms. Annu. Rev. Entomol. 60, 17–34 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Douglas, A. E. The microbial dimension in insect nutritional ecology. Funct. Ecol. 23, 38–47 (2009).
Article  Google Scholar 

21.
Ceja-Navarro, J. A. et al. Gut microbiota mediate caffeine detoxification in the primary insect pest of coffee. Nat. Commun. 6, 7618 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Welte, C. U. et al. Plasmids from the gut microbiome of cabbage root fly larvae encode SaxA that catalyses the conversion of the plant toxin 2-phenylethyl isothiocyanate. Environ. Microbiol. 18, 1379–1390 (2016).
CAS  PubMed  Article  Google Scholar 

23.
Hammer, T. J. & Bowers, M. D. Gut microbes may facilitate insect herbivory of chemically defended plants. Oecologia 179, 1–14 (2015).
ADS  PubMed  Article  PubMed Central  Google Scholar 

24.
Bakkali, F., Averbeck, S., Averbeck, D. & Idaomar, M. Biological effects of essential oils—A review. Food Chem. Toxicol. 46, 446–475 (2008).
CAS  Article  Google Scholar 

25.
Mithöfer, A. & Boland, W. Plant defense against herbivores: Chemical aspects. Annu. Rev. Plant Biol. 63, 431–450 (2012).
PubMed  Article  CAS  PubMed Central  Google Scholar 

26.
Douglas, A. Nutritional interactions in insect-microbial symbioses: Aphids and their symbiotic bacteria Buchnera. Annu. Rev. Entomol. 43, 17–37 (1998).
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Ayres, M. P., Wilkens, R. T., Ruel, J. J., Lombardero, M. J. & Vallery, E. Nitrogen budgets of phloem-feeding bark beetles with and without symbiotic fungi. Ecology 81, 2198–2210 (2000).
Article  Google Scholar 

28.
Adams, A., Currie, C., Cardoza, Y., Klepzig, K. & Raffa, K. Effects of symbiotic bacteria and tree chemistry on the growth and reproduction of bark beetle fungal symbionts. Can. J. For. Res. 39, 1133–1147 (2009).
CAS  Article  Google Scholar 

29.
Cardoza, Y. J., Moser, J. C., Klepzig, K. D. & Raffa, K. F. Multipartite symbioses among fungi, mites, nematodes, and the spruce beetle, Dendroctonus rufipennis. Environ. Entomol. 37, 956–963 (2008).
PubMed  Article  PubMed Central  Google Scholar 

30.
Therrien, J. et al. Bacteria influence mountain pine beetle brood development through interactions with symbiotic and antagonistic fungi: Implications for climate-driven host range expansion. Oecologia 179, 467–485 (2015).
ADS  PubMed  Article  PubMed Central  Google Scholar 

31.
Morales-Jiménez, J., Zúñiga, G., Ramírez-Saad, H. C. & Hernández-Rodríguez, C. Gut-associated bacteria throughout the life cycle of the bark beetle Dendroctonus rhizophagus Thomas and Bright (Curculionidae: Scolytinae) and their cellulolytic activities. Microb. Ecol. 64, 268–278 (2012).
PubMed  Article  PubMed Central  Google Scholar 

32.
Delalibera, I. Jr., Handelsman, J. & Raffa, K. F. Contrasts in cellulolytic activities of gut microorganisms between the wood borer, Saperda vestita (Coleoptera: Cerambycidae), and the bark beetles, Ips pini and Dendroctonus frontalis (Coleoptera: Curculionidae). Environ. Entomol. 34, 541–547 (2005).
Article  Google Scholar 

33.
Hu, X., Yu, J., Wang, C. & Chen, H. Cellulolytic bacteria associated with the gut of Dendroctonus armandi larvae (Coleoptera: Curculionidae: Scolytinae). Forests 5, 455–465 (2014).
Article  Google Scholar 

34.
Menéndez, E. et al. Pseudomonas coleopterorum sp. nov., a cellulase-producing bacterium isolated from the bark beetle Hylesinus fraxini. Int. J. Syst. Evol. Microbiol. 65, 2852–2858 (2015).
PubMed  Article  CAS  PubMed Central  Google Scholar 

35.
Boone, C. K. et al. Bacteria associated with a tree-killing insect reduce concentrations of plant defense compounds. J. Chem. Ecol. 39, 1003–1006 (2013).
CAS  PubMed  Article  Google Scholar 

36.
Xu, L. T., Lu, M. & Sun, J. H. Invasive bark beetle-associated microbes degrade a host defensive monoterpene. Insect Sci. 23, 183–190 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Berasategui, A. et al. Gut microbiota of the pine weevil degrades conifer diterpenes and increases insect fitness. Mol. Ecol. 26, 4099–4110 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Engl, T. & Kaltenpoth, M. Influence of microbial symbionts on insect pheromones. Nat. Prod. Rep. 35, 386–397 (2018).
CAS  PubMed  Article  Google Scholar 

39.
Howe, M., Keefover-Ring, K. & Raffa, K. F. Pine engravers carry bacterial communities whose members reduce concentrations of host monoterpenes with variable degrees of redundancy, specificity, and capability. Environ. Entomol. 47, 638–645 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Xu, L., Lou, Q., Cheng, C., Lu, M. & Sun, J. Gut-associated bacteria of Dendroctonus valens and their involvement in verbenone production. Microb. Ecol. 70, 1012–1023 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

41.
Skrodenytė-Arbačiauskienė, V., Radžiutė, S., Stunžėnas, V. & Būda, V. Erwiniatypographi sp. nov., isolated from bark beetle (Ips typographus) gut. Int. J. Syst. Evol. Microbiol. 62, 942–948 (2012).
PubMed  Article  CAS  PubMed Central  Google Scholar 

42.
Smith, D. J., Park, J., Tiedje, J. M. & Mohn, W. W. A large gene cluster in Burkholderia xenovorans encoding abietane diterpenoid catabolism. J. Bacteriol. 189, 6195–6204 (2007).
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Martin, V. J. & Mohn, W. W. Genetic investigation of the catabolic pathway for degradation of abietane diterpenoids by Pseudomonas abietaniphila BKME-9. J. Bacteriol. 182, 3784–3793 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Muratoğlu, H., Sezen, K. & Demirbağ, Z. Determination and pathogenicity of the bacterial flora associated with the spruce bark beetle, Ips typographus (L.) (Coleoptera: Curculionidae: Scolytinae). Turk. J. Biol. 35, 9–20 (2011).
Google Scholar 

45.
Skrodenytė-Arbačiauskienė, V., Būda, V., Radžiutė, S. & Stunžėnas, V. Myrcene-resistant bacteria isolated from the gut of phytophagous insect Ips typographus. Ekologija 4, 1–6 (2006).
Google Scholar 

46.
Sevim, A., Gökçe, C., Erbaş, Z. & Özkan, F. Bacteria from Ips sexdentatus (Coleoptera: Curculionidae) and their biocontrol potential. J. Basic Microbiol. 52, 695–704 (2012).
PubMed  Article  PubMed Central  Google Scholar 

47.
Vasanthakumar, A. et al. Composition of the bacterial community in the gut of the pine engraver, Ips pini (Say) (Coloptera) colonizing red pine. Symbiosos 43, 97–104 (2007).
Google Scholar 

48.
48Grégoire, J.-C. & Evans, H. In Bark and Wood Boring Insects in Living Trees in Europe, a Synthesis (eds Lieutier, F., Day, K.R., Battisti, A., Grégoire, J.-C., Evans, H.F.) 19–37 (Springer, Berlin, 2007).

49.
Kolk, A., Starzyk, J., Kinelski, S. & Dzwonkowski, R. Atlas of Forest Insect Pests. (MULTICO Publishing House Ltd., 1996).

50.
Davydenko, K., Vasaitis, R. & Menkis, A. Fungi associated with Ips acuminatus (Coleoptera: Curculionidae) in Ukraine with a special emphasis on pathogenicity of ophiostomatoid species. Eur. J. Entomol. 114, 77–85 (2017).
Article  Google Scholar 

51.
Fettig, C. J. & Hilszczański, J. In Bark Beetles: Biology and Ecology of Native and Invasive Species (eds Vega, F.E, Hofstetter, R.W.) 555–584 (Springer, Berlin, 2015).

52.
Knížek, M., Liška, J. & Modlinger, R. Výskyt lesních škodlivých činitelů v roce 2015 a jejich očekávaný stav v roce 2016. Strnady, VÚLHM, Zpravodaj ochrany lesa (2016).

53.
Villari, C. et al. Nutritional and pathogenic fungi associated with the pine engraver beetle trigger comparable defenses in Scots pine. Tree Physiol. 32, 867–879 (2012).
PubMed  Article  PubMed Central  Google Scholar 

54.
Wermelinger, B., Rigling, A., Schneider Mathis, D. & Dobbertin, M. Assessing the role of bark-and wood-boring insects in the decline of Scots pine (Pinus sylvestris) in the Swiss Rhone valley. Ecol. Entomol. 33, 239–249 (2008).
Article  Google Scholar 

55.
Pineau, X., Bourguignon, M., Jactel, H., Lieutier, F. & Sallé, A. Pyrrhic victory for bark beetles: Successful standing tree colonization triggers strong intraspecific competition for offspring of Ips sexdentatus. For. Ecol. Manag. 399, 188–196 (2017).
Article  Google Scholar 

56.
Engel, P. & Moran, N. A. The gut microbiota of insects–diversity in structure and function. FEMS Microbiol. Rev. 37, 699–735 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
Hernández-García, J. A., Briones-Roblero, C. I., Rivera-Orduña, F. N. & Zúñiga, G. Revealing the gut bacteriome of Dendroctonus bark beetles (Curculionidae: Scolytinae): Diversity, core members and co-evolutionary patterns. Sci. Rep. 7, 1–12 (2017).
Article  CAS  Google Scholar 

58.
Morrison, M. & Miron, J. Adhesion to cellulose by Ruminococcus albus: A combination of cellulosomes and Pil-proteins?. FEMS Microbiol. Lett. 185, 109–115 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

59.
Fabryová, A. et al. On the bright side of a forest pest-the metabolic potential of bark beetles’ bacterial associates. Sci. Total Environ. 619, 9–17 (2018).
ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

60.
Briones-Roblero, C. I. et al. Structure and dynamics of the gut bacterial microbiota of the bark beetle, Dendroctonus rhizophagus (Curculionidae: Scolytinae) across their life stages. PLoS ONE 12, e0175470 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

61.
Sudachkova, N., Milyutina, I., Romanova, L. & Semenova, G. The annual dynamics of reserve compounds and hydrolitic enzymes activity in the tissues of Pinus sylvestris L. and Larix sibirica Ledeb.: The metabolism of reserve compounds in the tissues of Siberian conifers. Eurasian J. For. Res. 7, 1–10 (2004).
Google Scholar 

62.
Horne, I., Haritos, V. S. & Oakeshott, J. G. Comparative and functional genomics of lipases in holometabolous insects. Insect Biochem. Mol. Biol. 39, 547–567 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Arrese, E. L. & Soulages, J. L. Insect fat body: Energy, metabolism, and regulation. Annu. Rev. Entomol. 55, 207–225 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
García-Fraile, P. Roles of bacteria in the bark beetle holobiont–how do they shape this forest pest?. Ann. Appl. Biol. 172, 111–125 (2018).
Article  Google Scholar 

65.
Morales-Jiménez, J. et al. Nitrogen-fixing and uricolytic bacteria associated with the gut of Dendroctonus rhizophagus and Dendroctonus valens (Curculionidae: Scolytinae). Microb. Ecol. 66, 200–210 (2013).
PubMed  Article  CAS  PubMed Central  Google Scholar 

66.
Morales-Jiménez, J., Zúñiga, G., Villa-Tanaca, L. & Hernández-Rodríguez, C. Bacterial community and nitrogen fixation in the red turpentine beetle, Dendroctonus valens LeConte (Coleoptera: Curculionidae: Scolytinae). Microb. Ecol. 58, 879–891 (2009).
PubMed  Article  CAS  PubMed Central  Google Scholar 

67.
Menna, P. M. & Hungria, M. Phylogeny of nodulation and nitrogen-fixation genes in Bradyrhizobium: Supporting evidence for the theory of monophyletic origin, and spread and maintenance by both horizontal and vertical transfer. Int. J. Syst. Evol. Microbiol. 61, 3052–3067 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Chen, W.-M. et al. Legume symbiotic nitrogen fixation byβ-proteobacteria is widespread in nature. J. Bacteriol. 185, 7266–7272 (2003).
CAS  PubMed  PubMed Central  Article  Google Scholar 

69.
Gurevitch, J., Scheiner, S. M. & Fox, G. A. The Ecology of Plants (Sinauer Associates, Sunderland, 2002).
Google Scholar 

70.
Gibson, C. M. & Hunter, M. S. Extraordinarily widespread and fantastically complex: Comparative biology of endosymbiotic bacterial and fungal mutualists of insects. Ecol. Lett. 13, 223–234 (2010).
PubMed  Article  PubMed Central  Google Scholar 

71.
Six, D. L. & Bentz, B. J. Fungi associated with the North American spruce beetle, Dendroctonus rufipennis. Can. J. For. Res. 33, 1815–1820 (2003).
Article  Google Scholar 

72.
Naik, P. R. & Sakthivel, N. Functional characterization of a novel hydrocarbonoclastic Pseudomonas sp. strain PUP6 with plant-growth-promoting traits and antifungal potential. Res. Microbiol. 157, 538–546 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
Park, G.-K., Lim, J.-H., Kim, S.-D. & Shim, S.-H. Elucidation of antifungal metabolites produced by Pseudomonas aurantiaca IB5-10 with broad-spectrum antifungal activity. J. Microbiol. Biotechnol. 22, 326–330 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

74.
Elsden, S. R., Hilton, M. G. & Waller, J. M. The end products of the metabolism of aromatic amino acids by Clostridia. Arch. Microbiol. 107, 283–288 (1976).
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Byers, J. & Birgersson, G. Pheromone production in a bark beetle independent of myrcene precursor in host pine species. Naturwissenschaften 77, 385–387 (1990).
ADS  CAS  Article  Google Scholar 

76.
Blomquist, G. J. et al. Pheromone production in bark beetles. Insect Biochem. Mol. Biol. 40, 699–712 (2010).
CAS  PubMed  Article  PubMed Central  Google Scholar 

77.
Cao, Q. et al. Effect of oxygen on verbenone conversion from cis-verbenol by gut facultative anaerobes of Dendroctonus valens. Front. Microbiol. 9, 464 (2018).
PubMed  PubMed Central  Article  Google Scholar 

78.
Wang, Y. & Zhang, Y. Investigation of gut-associated bacteria in Tenebrio molitor (Coleoptera: Tenebrionidae) larvae using culture-dependent and DGGE methods. Ann. Entomol. Soc. Am. 108, 941–949 (2015).
CAS  Article  Google Scholar 

79.
Durand, A.-A. et al. Surveying the endomicrobiome and ectomicrobiome of bark beetles: The case of Dendroctonus simplex. Sci. Rep. 5, 17190 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

80.
Scott, J. J. et al. Bacterial protection of beetle-fungus mutualism. Science 322, 63–63 (2008).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

81.
Dale, C. & Maudlin, I. Sodalis gen. nov. and Sodalis glossinidius sp. nov., a microaerophilic secondary endosymbiont of the tsetse fly Glossina morsitans morsitans. Int. J. Syst. Evol. Microbiol. 49, 267–275 (1999).
CAS  Article  Google Scholar 

82.
Santos-Garcia, D., Silva, F. J., Morin, S., Dettner, K. & Kuechler, S. M. The all-rounder Sodalis: A new bacteriome-associated endosymbiont of the lygaeoid bug Henestaris halophilus (Heteroptera: Henestarinae) and a critical examination of its evolution. Genome Biol. Evol. 9, 2893–2910 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

83.
Lawson, E. T., Mousseau, T. A., Klaper, R., Hunter, M. D. & Werren, J. H. Rickettsia associated with male-killing in a buprestid beetle. Heredity 86, 497–505 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

84.
Hurst, G. & Jiggins, F. M. Male-killing bacteria in insects: Mechanisms, incidence, and implications. Emerg. Infect. Dis. 6, 329 (2000).
CAS  PubMed  PubMed Central  Article  Google Scholar 

85.
Stackebrandt, E. & Schumann, P. In The Prokaryotes: Actinobacteria (eds Rosenberg, E. et al.) 163–184 (Springer, Berlin, 2014).

86.
Pfeffer, A. Fauna ČSR. Svazek 6: Kůrovci-Scolytoidea. Řád: Brouci-Coleoptera. (Nakladatelství Československé akadmie věd, 1955).

87.
Pfeffer, A. Zentral-und westpaläarktische Borken-und Kernkäfer:(Coloptera: Scolytidae, Platypodidae). (Pro Entomologia, 1995).

88.
Nunberg, M. Klucze do rozpoznawania owadów Polski [Keys for the identification of Polish Insects]. Część XIX. Chrząszcze–Coleoptera, Korniki–Scolytidae, Wyrynniki–Platypodidae, PWN, Warszawa-Wroclaw. Zeszyt, 99–100 (1981).

89.
Chakraborty, A. et al. Core mycobiome and their ecological relevance in the gut of five ips bark beetles (Coleoptera: Curculionidae: Scolytinae). Front. Microbiol. 11, 2134 (2020).
Google Scholar 

90.
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, e1–e1 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

91.
Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
PubMed  PubMed Central  Article  CAS  Google Scholar 

92.
Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

93.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

94.
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

95.
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

96.
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
CAS  PubMed  PubMed Central  Article  Google Scholar 

97.
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).
PubMed  PubMed Central  Article  CAS  Google Scholar 

98.
Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
CAS  PubMed  PubMed Central  Article  Google Scholar 

99.
Chao, A., Lee, S.-M. & Chen, T.-C. A generalized Good’s nonparametric coverage estimator. Chin. J. Math. 16, 189–199 (1988).
MathSciNet  MATH  Google Scholar 

100.
Magurran, A. E. Ecological Diversity and its Measurement (Princeton University Press, Princeton, 1988).
Google Scholar 

101.
Team, R. C. R: A Language and Environment for Statistical Computing (Version 2.15. 3) [Computer software] (R Foundation for Statistical Computing, Vienna, 2013).

102.
Oksanen, J. et al. Vegan: community ecology package. R package version 1.17–4. https://CRAN.R-project.org/package=vegan (2010).

103.
Lozupone, C. A., Hamady, M., Kelley, S. T. & Knight, R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. 73, 1576–1585 (2007).
CAS  PubMed  PubMed Central  Article  Google Scholar 

104.
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).
Article  Google Scholar 

105.
Cai, L. Multi-response permutation procedure as an alternative to the analysis of variance: An SPSS implementation. Behav. Res. Methods 38, 51–59 (2006).
PubMed  Article  Google Scholar 

106.
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
Google Scholar 

107.
Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).
CAS  PubMed  PubMed Central  Google Scholar 

108.
D’Argenio, V., Casaburi, G., Precone, V. & Salvatore, F. Comparative metagenomic analysis of human gut microbiome composition using two different bioinformatic pipelines. Biomed. Res. Int. 325340, 1–10 (2014).
Article  CAS  Google Scholar 

109.
Paulson, J. N., Pop, M. & Bravo, H. C. Metastats: An improved statistical method for analysis of metagenomic data. Genome Biol. 12, P17 (2011).
PubMed Central  Article  PubMed  Google Scholar 

110.
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
PubMed  PubMed Central  Article  Google Scholar 

111.
Douglas, G. M., Beiko, R. G. & Langille, M. G. In Microbiome Analysis: Methods and Protocols. (eds Beiko, R. G., Hsiao, W. & Parkinson, J.) 169–177 (Springer, Berlin, 2018).

112.
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar  More

Adjeroud M, Guerecheau A, Vidal-Dupiol J, Flot JF, Arnaud-Haond S, Bonhomme F (2014) Genetic diversity, clonality and connectivity in the scleractinian coral Pocillopora damicornis: a multi-scale analysis in an insular, fragmented reef system. Mar Biol 161(3):531–541
Google Scholar 

Arrigoni R, Berumen ML, Mariappan KG, Beck PSA, Hulver AM, Montano S et al. (2020) Towards a rigorous species delimitation framework for scleractinian corals based on RAD sequencing: the case study of Leptastrea from the Indo-Pacific. Coral Reefs 39:1001–1025

Baird AH (2001) The ecology of coral larvae: settlement patterns, habitat selection and the length of the larval phase. Doctoral dissertation, James Cook University, Townsville
Google Scholar 

Collins C, Hermes JC, Reason CJC (2014) Mesoscale activity in the Comoros Basin from satellite altimetry and a high-resolution ocean circulation model. J Geophys Res Oceans 119:4745–4760
Google Scholar 

Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144(4):2001–2014
CAS  Google Scholar 

Costanza R, de Groot R, Sutton P, van der Ploeg S, Anderson SJ, Kubiszewski I et al. (2014) Changes in the global value of ecosystem services. Glob Environ Chang 26:152–158
Google Scholar 

Cowen R, Sponaugle S (2009) Larval dispersal and marine population connectivity. Ann Rev Mar Sci 1:443–466
Google Scholar 

Crochelet E, Roberts J, Lagabrielle E, Obura DO, Petit M, Chabanet P (2016) A model-based assessment of reef larvae dispersal in the Western Indian Ocean reveals regional connectivity patterns—potential implications for conservation policies. Reg Stud Mar Sci 7:159–167
Google Scholar 

Dana JD (1846) United States Exploring Expedition. Vol. VII. Zoophytes. C. Sherman, Philadelphia

Dellicour S, Flot JF (2015) Delimiting species-poor data sets using single molecular markers: a study of barcode gaps, haplowebs and GMYC. Syst Biol 64(6):900–908
CAS  Google Scholar 

Dellicour S, Flot JF (2018) The hitchhiker’s guide to single‐locus species delimitation. Mol Ecol Resour 18(6):1234–1246
Google Scholar 

DiBattista JD, Berumen ML, Gaither MR, Rocha LA, Eble JA, Choat JH et al. (2013) After continents divide: comparative phylogeography of reef fishes from the Red Sea and Indian Ocean. J Biogeogr 40(6):1170–1181
Google Scholar 

DiBattista JD, Roberts MB, Bouwmeester J, Bowen BW, Coker DJ, Lozano-Cortes DF et al. (2016) A review of contemporary patterns of endemism for shallow water reef fauna in the Red Sea. J Biogeogr 43(3):423–439
Google Scholar 

Doyle JJ (1995) The irrelevance of allele tree topologies for species delimitation, and a non-topological alternative. Syst Bot 20(4):574

Earl D, Vonholdt B (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4(2):359–361
Google Scholar 

Erickson KL, Pentico A, Quattrini AMMc, Fadden CS (2020) New approaches to species delimitation and population structure of anthozoans: two case studies of octocorals using ultraconserved elements and exons Mol Ecol Resour https://doi.org/10.1111/1755-0998.13241

Eriksson H, Wickel J, Jamon A (2012) Coral bleaching and associated mortality in Mayotte, Western Indian Ocean. West Indian Ocean J Mar Sci 11:113–118
Google Scholar 

Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14(8):2611–2620
CAS  Google Scholar 

Fisher R, O’Leary RA, Low-Choy S, Mengersen K, Knowlton N, Brainard RE et al. (2015) Species richness on coral reefs and the pursuit of convergent global estimates. Curr Biol 25(4):500–505
CAS  Google Scholar 

Flot JF (2007) CHAMPURU 1.0: a computer software for unraveling mixtures of two DNA sequences of unequal lengths. Mol Ecol Notes 7(6):974–977
CAS  Google Scholar 

Flot JF (2010) SeqPHASE: a web tool for interconverting phase input/output files and fasta sequence alignments. Mol Ecol Resour 10(1):162–166

Flot JF (2015) Species delimitation’s coming of age. Syst Biol 64(6):897–899
Google Scholar 

Flot JF, Couloux A, Tillier S (2010) Haplowebs as a graphical tool for delimiting species: a revival of Doyle’s “field for recombination” approach and its application to the coral genus Pocillopora in Clipperton. BMC Evol Biol 10:372
Google Scholar 

Flot JF, Licuanan WY, Nakano Y, Payri C, Cruaud C, Tillier S (2008) Mitochondrial sequences of Seriatopora corals show little agreement with morphology and reveal the duplication of a tRNA gene near the control region. Coral Reefs 27:789–794
Google Scholar 

Flot JF, Tillier A, Samadi S, Tillier S (2006) Phase determination from direct sequencing of length-variable DNA regions. Mol Ecol Notes 6(3):627–630
CAS  Google Scholar 

Flot JF, Tillier S (2007) The mitochondrial genome of Pocillopora (Cnidaria: Scleractinia) contains two variable regions: the putative D-loop and a novel ORF of unknown function. Gene 401(1–2):80–87
CAS  Google Scholar 

Fontaneto D, Flot JF, Tang CQ (2015) Guidelines for DNA taxonomy, with a focus on the meiofauna. Mar Biodivers 45(3):433–451
Google Scholar 

Froukh T, Kochzius M (2008) Species boundaries and evolutionary lineages in the blue green damselfishes Chromis viridis and Chromis atripectoralis (Pomacentridae). J Fish Biol 72(2):451–457
Google Scholar 

Gamoyo M, Obura DO, Reason CJC (2019) Estimating connectivity through larval dispersal in the Western Indian Ocean. J Geophys Res. 124(8):2446–2459

Goudet J (1995) FSTAT (Version 1.2): a computer program to calculate F-statistics. J Hered 86(6):485–486
Google Scholar 

Hancke L, Roberts MJ, Ternon JF (2014) Surface drifter trajectories highlight flow pathways in the Mozambique Channel. Deep Sea Res II Top Stud Oceanogr 100:27–37
Google Scholar 

Hardy OJ, Charbonnel N, Freville H, Heuertz M (2003) Microsatellite allele sizes: a simple test to assess their significance on genetic differentiation. Genetics 163(4):1467–1482
CAS  Google Scholar 

Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2(4):618–620
Google Scholar 

Harrison PL (2011) Sexual reproduction of scleractinian corals. In: Dubinsky Z, Stambler N (eds) Coral reefs: an ecosystem in transition. Springer, Dordrecht. pp 59–85

Hoegh-Guldberg O, Poloczanska ES, Skirving W, Dove S (2017) Coral reef ecosystems under climate change and ocean acidification. Front Mar Sci 4:158
Google Scholar 

Hui M, Kraemer WE, Seidel C, Nuryanto A, Joshi A, Kochzius M (2016) Comparative genetic population structure of three endangered giant clams (Cardiidae: Tridacna species) throughout the Indo-West Pacific: implications for divergence, connectivity and conservation. J Molluscan Stud 82:403–414
Google Scholar 

Huyghe F, Kochzius M (2018) Sea surface currents and geographic isolation shape the genetic population structure of a coral reef fish in the Indian Ocean. PLoS ONE 13(3):e0193825
Google Scholar 

Iacchei M, Gaither MR, Bowen BW, Toonen RJ (2016) Testing dispersal limits in the sea: range-wide phylogeography of the pronghorn spiny lobster Panulirus penicillatus. J Biogeogr 43(5):1032–1044
Google Scholar 

Isomura N, Nishihira M (2001) Size variation of planulae and its effect on the lifetime of planulae in three pocilloporid corals. Coral Reefs 20:309–315
Google Scholar 

Jombart T (2008) Adegenet: a R package for the multivariate analysis of genetic markers. J Bioinform 24(11):1403–1405

Jost L (2008) GST and its relatives do not measure differentiation. Mol Ecol 17(18):4015–4026

Kochzius M, Blohm D (2005) Genetic population structure of the lionfish Pterois miles (Scorpaenidae, Pteroinae) in the Gulf of Aqaba and northern Red Sea. Gene 347(2):295–301

Lutjeharms J, Bornman T (2010) The importance of the greater Agulhas Current is increasingly being recognised. S Afr J Sci 106(3/4):1–4
Google Scholar 

Maier E, Tollrian R, Rinkevich B, Nurnberger B (2005) Isolation by distance in the scleractinian coral Seriatopora hystrix from the Red Sea. Mar Biol 147(5):1109–1120
Google Scholar 

Marshall P, Baird AH (2000) Bleaching of corals on the Great Barrier Reef: differential susceptibilities among taxa. Coral Reefs 19(2):155–163
Google Scholar 

McClanahan TR, Ateweberhan M, Graham N, Wilson S, Sebastian C, Guillaume M et al. (2007a) Western Indian Ocean coral communities: bleaching responses and susceptibility to extinction. Mar Ecol Prog Ser 337:1–13

McClanahan TR, Ateweberhan M, Darling ES, Graham NAJ, Muthiga NA (2014) Biogeography and change among regional coral communities across the Western Indian Ocean. PLoS ONE 9(4):e93385
Google Scholar 

McClanahan TR, Ateweberhan M, Muhando CA, Maina J, Mohammed SM (2007b) Effects of climate and seawater temperature variation on coral bleaching and mortality. Ecol Monogr 77(4):503–525
Google Scholar 

McClanahan TR, Maina JM, Muthiga NA (2011) Associations between climate stress and coral reef diversity in the western Indian Ocean. Glob Change Biol 17(6):2023–2032
Google Scholar 

McClanahan TR, Muthiga NA, Mangi S (2001) Coral and algal changes after the 1998 coral bleaching: interaction with reef management and herbivores on Kenyan reefs. Coral Reefs 19(4):380–391
Google Scholar 

McLeod E, Anthony KRN, Mumby PJ, Maynard J, Beeden R, Graham NAJ et al. (2019) The future of resilience-based management in coral reef ecosystems. J Environ Manag 233:291–301
Google Scholar 

Motta H, Pereira MAM, Gonçalves M, Ridgway T, Schleyer MH (2002) Mozambique coral reef management programme. MICOA/CORDIO/ORI/WWF, Maputo, p 31

Nakajima Y, Nishikawa A, Iguchi A, Nagata T, Uyeno D, Sakai K et al. (2017) Elucidating the multiple genetic lineages and population genetic structure of the brooding coral Seriatopora (Scleractinia: Pocilloporidae) in the Ryukyu Archipelago. Coral Reefs 36(2):415–426
Google Scholar 

Nakajima Y, Nishikawa A, Iguchi A, Sakai K (2010) Gene flow and genetic diversity of a broadcast-spawning coral in northern peripheral populations. PLoS ONE 5:e11149
Google Scholar 

Nehemia A, Ngendu Y, Kochzius M (2019) Genetic population structure of the mangrove snails Littoraria subvittata and L. pallescens in the Western Indian Ocean. J Exp Mar Biol Ecol 514-515:27–33
Google Scholar 

Obura DO (2012) The diversity and biogeography of Western Indian Ocean reef-building corals. PLoS ONE 7(9):e45013
CAS  Google Scholar 

Obura DO, Bandeira SO, Bodin N, Burgener V, Braulik G, Chassot E, et al. (2019) The Northern Mozambique Channel. In: Sheppard W (ed.) World seas: an environmental evaluation. Academic Press, Cambridge. pp 75–99

Obura DO, Bigot L, Benzoni F (2018) Coral responses to a repeat bleaching event in Mayotte in 2010. PeerJ 6:e5305
Google Scholar 

Obura DO, Gudka M, Rabi FA, Gian SB, Bijoux J, Freed S et al. (2017) Coral reef status report for the Western Indian Ocean. Global Coral Reef Monitoring Network (GCRMN)/International Coral Reef Initiative (ICRI). pp 1–144

Palumbi S (2003) Population genetics, demographic connectivity, and the design of marine reserves. Ecol Appl 13:146–158
Google Scholar 

Peakall R, Smouse P (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28(19):2537–2539
CAS  Google Scholar 

Peery MZ, Kirby R, Reid BN, Stoelting R, Doucet-Bëer E, Robinson S, Vásquez-Carillo C, Pauli JN, Palsbøll PJ (2012) Reliability of genetic bottleneck tests for detecting recent population declines. Mol Ecol 21:3403–3418
Google Scholar 

Pinzón JH, Sampayo E, Cox E, Chauka LJ, Chen CA, Voolstra CR et al. (2013) Blind to morphology: genetics identifies several widespread ecologically common species and few endemics among Indo-Pacific cauliflower corals (Pocillopora, Scleractinia). J Biogeogr 40(8):1595–1608
Google Scholar 

Prasetia R, Sinniger F, Hashizume K, Harii S (2017) Reproductive biology of the deep brooding coral Seriatopora hystrix: implications for shallow reef recovery. PLoS ONE 12(5):e0177034
Google Scholar 

Pritchard J, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155(2):945–959
CAS  Google Scholar 

Ramanantsoa JD, Penven P, Krug M, Gula J, Rouault M (2018) Uncovering a new current: The Southwest Madagascar Coastal Current. Geophys Res Lett 45(4):1930–1938
Google Scholar 

Ratsimbazafy HA, Kochzius M (2018) Restricted gene flow among Western Indian Ocean populations of the mangrove whelk Terebralia palustris (Linnaeus, 1767) (Caenogastropoda: Potamididae). J Molluscan Stud 84:163–169
Google Scholar 

Richards ZT, Berry O, van Oppen MJH (2016) Cryptic genetic divergence within threatened species of Acropora coral from the Indian and Pacific Oceans. Conserv Genet 17:577–591
Google Scholar 

Ridgway T, Riginos C, Davis J, Hoegh-Guldberg O (2008) Genetic connectivity patterns of Pocillopora verrucosa in southern African Marine Protected Areas. Mar Ecol Prog Ser 354:161–168
Google Scholar 

Roberts CM, Shepherd ARD, Ormond RFG (1992) Large-scale variation in assemblage structure of Red-Sea butterflyfishes and angelfishes. J Biogeogr 19(3):239–250
Google Scholar 

Rumisha C, Huyghe F, Rapanoel D, Mascaux N, Kochzius M (2017) Genetic diversity and connectivity in the East African giant mud crab Scylla serrata: Implications for fisheries management. PLoS ONE 12(10):e0186817
Google Scholar 

Schleyer MH, Celliers L (2000) A survey of the coral reefs at Ilha Caldeira in the Segundas Archipelago, Mozambique, and an assessment of the marine environment impacts of a proposed heavy minerals mine. South African Association for Marine Biological Research, vol 190, Durban, 1–18

Schleyer MH, Celliers L (2005) The coral reefs of Bazaruto Island, Mozambique, with recommendations for their management. West Indian Ocean J Mar Sci 4:227–236
Google Scholar 

Schmidt-Roach S, Miller KJ, Lundgren P, Andreakis N (2014) With eyes wide open: a revision of species within and closely related to the Pocillopora damicornis species complex (Scleractinia; Pocilloporidae) using morphology and genetics. Zool J Linn Soc 170(1):1–33
Google Scholar 

Schott F, McCreary J (2001) The monsoon circulation of the Indian Ocean. Prog Oceanogr 51(1):1–123
Google Scholar 

Shearer TL, Coffroth MA (2008) Barcoding corals: limited by interspecific divergence, not intraspecific variation. Mol Ecol Resour 8(2):247–255
CAS  Google Scholar 

Sheets EA, Warner PA, Palumbi SR (2018) Accurate population genetic measurements require cryptic species identification in corals. Coral Reefs 37(2):549–563
Google Scholar 

Siddall M, Rohling EJ, Almogi-Labin A, Hemleben C, Meischner D, Schmelzer I et al. (2003) Sea-level fluctuations during the last glacial cycle. Nature 423(6942):853–858
CAS  Google Scholar 

Sofianos SS, Johns WE (2003) An Oceanic General Circulation Model (OGCM) investigation of the Red Sea circulation: 2. Three-dimensional circulation in the Red Sea. J Geophys Res Oceans 108(C3):15
Google Scholar 

Souter P, Grahn M (2008) Spatial genetic patterns in lagoonal, reef-slope and island populations of the coral Platygyra daedalea in Kenya and Tanzania. Coral Reefs 27(2):433–439
Google Scholar 

Souter P, Henriksson O, Olsson N, Grahn M (2009) Patterns of genetic structuring in the coral Pocillopora damicornis on reefs in East Africa. BMC Ecol 9(19):13
Google Scholar 

Spalding MD, Fox HE, Halpern BS, McManus MA, Molnar J, Allen GR et al. (2007) Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. Bioscience 57(7):573–583
Google Scholar 

Spöri Y, Flot JF (2020) HaplowebMaker and CoMa: two web tools to delimit species using haplowebs and conspecificity matrices. Methods Ecol Evol. https://doi.org/10.1111/2041-210X.13454

Stephens M, Scheet P (2005) Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. Am J Hum Genet 76(3):449–462
CAS  Google Scholar 

Stephens M, Smith NJ, Donnelly P (2001) A new statistical method for haplotype reconstruction from population data. Am J Hum Genet 68(4):978–989
CAS  Google Scholar 

Underwood JN, Smith LD, van Oppen MJH, Gilmour JP (2007) Multiple scales of genetic connectivity in a brooding coral on isolated reefs following catastrophic bleaching. Mol Ecol 16(4):771–784
CAS  Google Scholar 

Underwood JN, Smith LD, van Oppen MJH, Gilmour JP (2009) Ecologically relevant dispersal of corals on isolated reefs: implications for managing resilience. Ecol Appl 19(1):18–29
Google Scholar 

van der Ven RM, Triest L, De Ryck DJR, Mwaura JM, Mohammed MS, Kochzius M (2016) Population genetic structure of the stony coral Acropora tenuis shows high but variable connectivity in East Africa. J Biogeogr 43(3):510–519
Google Scholar 

van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4(3):535–538
Google Scholar 

van Oppen MJH, Bongaerts P, Underwood JN, Peplow LM, Cooper TF (2011) The role of deep reefs in shallow reef recovery: an assessment of vertical connectivity in a brooding coral from west and east Australia. Mol Ecol 20(8):1647–1660
Google Scholar 

van Oppen MJH, Gates RD (2006) Conservation genetics and the resilience of reef-building corals. Mol Ecol 15:3863–3883
Google Scholar 

van Oppen MJH, Lutz A, De’ath G, Peplow L, Kininmonth S (2008) Genetic traces of recent long-distance dispersal in a predominantly self-recruiting coral. PLoS ONE 3(10):e3401
Google Scholar 

Veron JEN (2000) Corals of the world. Australian Institute of Marine science, Townsville

Vogler C, Benzie J, Lessios H, Barber PH, Wörheide G (2008) A threat to coral reefs multiplied? Four species of crown-of-thorns starfish. Biol Lett 4(6):696–699
Google Scholar 

Vollmer SV, Palumbi SR (2002) Hybridization and the evolution of reef coral diversity. Science 296:2023–2025
CAS  Google Scholar 

Waits LP, Luikart G, Taberlet P (2001) Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Mol Ecol 10(1):249–256
CAS  Google Scholar 

Warner PA, van Oppen MJH, Willis BL (2015) Unexpected cryptic species diversity in the widespread coral Seriatopora hystrix masks spatial-genetic patterns of connectivity. Mol Ecol 24(12):2993–3008
Google Scholar 

Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population-structure. Evolution 38(6):1358–1370

Wilkinson C (2002) Status of coral reefs of the world. Coral Reef Monitoring Network, Townsville
Google Scholar 

Wörheide G, Epp LS, Macis L (2008) Deep genetic divergences among Indo-Pacific populations of the coral reef sponge Leucetta chagosensis (Leucettidae): founder effects, vicariance, or both? BMC Evol Biol 8:24 More

1.
Brooks, D. R. & Hoberg, E. P. How will global climate change affect parasite-host assemblages?. Trends Parasitol. 23, 571–574 (2007).
PubMed  Article  PubMed Central  Google Scholar 
2.
Brunner, F. S. & Eizaguirre, C. Can environmental change affect host/parasite-mediated speciation?. Zoology 119, 384–394 (2016).
PubMed  Article  PubMed Central  Google Scholar 

3.
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).
PubMed  PubMed Central  Article  Google Scholar 

4.
Lachish, S., Knowles, S. C. L., Alves, R., Wood, M. J. & Sheldon, B. C. Infection dynamics of endemic malaria in a wild bird population: parasite species-dependent drivers of spatial and temporal variation in transmission rates. J. Anim. Ecol. 80, 1207–1216 (2011).
PubMed  Article  PubMed Central  Google Scholar 

5.
Lachish, S., Knowles, S. C. L., Alves, R., Wood, M. J. & Sheldon, B. C. Fitness effects of endemic malaria infections in a wild bird population: the importance of ecological structure. J. Anim. Ecol. 80, 1196–1206 (2011).
PubMed  Article  PubMed Central  Google Scholar 

6.
Coltman, D. W., Pilkington, J. G., Smith, J. A. & Pemberton, J. M. Parasite-mediated selection against inbred Soay sheep in a free-living, island population. Evolution 53, 1259–1267 (1999).
Google Scholar 

7.
Harvell, C. D. et al. Climate warming and disease risks for terrestrial and marine biota. Science 296, 2158–2162 (2002).
ADS  CAS  Article  Google Scholar 

8.
Goedknegt, M. A., Welsh, J. E., Drent, J. & Thieltges, D. W. Climate change and parasite transmission: how temperature affects parasite infectivity via predation on infective stages. Ecosphere 6, 1–9 (2015).
Article  Google Scholar 

9.
Watson, M. J. What drives population-level effects of parasites? Meta-analysis meets life-history. Int. J. Parasitol. Parasites Wildl. 2, 190–196 (2013).
PubMed  PubMed Central  Article  Google Scholar 

10.
De Castro, F. & Bolker, B. Mechanisms of disease-induced extinction. Ecol. Lett. 8, 117–126 (2005).
Article  Google Scholar 

11.
Scheele, B. C. et al. Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity. Science 363, 1459–1463 (2019).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
McCallum, H. & Dobson, A. Detecting disease and parasite threats to endangered species and ecosystems. Trends Ecol. Evol. 10, 190–194 (1995).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Godwin, S. C., Dill, L. M., Reynolds, J. D. & Krkošek, M. Sea lice, sockeye salmon, and foraging competition: Lousy fish are lousy competitors. Can. J. Fish. Aquat. Sci. 72, 1113–1120 (2015).
Article  Google Scholar 

14.
Werner, E. E. & Anholt, B. R. Ecological consequences of the trade-off between growth and mortality rates mediated by foraging activity. Am. Nat. 142, 242–272 (1993).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Simpson, S. J., Sibly, K. P. L., Behmer, S. T. & Raubenheimer, D. Optimal foraging when regulating intake of multiple nutrients. Anim. Behav. 68, 1299–1311 (2004).
Article  Google Scholar 

16.
Povey, S., Cotter, S. C., Simpson, S. J., Lee, K. P. & Wilson, K. Can the protein costs of bacterial resistance be offset by altered feeding behaviour?. J. Anim. Ecol. 78, 437–446 (2008).
PubMed  Article  PubMed Central  Google Scholar 

17.
Brunner, F. S., Anaya-Rojas, J. M., Matthews, B. & Eizaguirre, C. Experimental evidence that parasites drive eco-evolutionary feedbacks. Proc. Natl. Acad. Sci. 114, 3678–3683 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

18.
Milinski, M. Parasites determine a predator’s optimal feeding strategy. Behav. Ecol. Sociobiol. 15, 35–37 (1984).
Article  Google Scholar 

19.
Herbst, L. H. Fibropapillomatosis of marine turtles. Annu. Rev. Fish Dis. 4, 389–425 (1994).
Article  Google Scholar 

20.
Aguirre, A. & Lutz, P. L. Marine turtles as sentinels of ecosystem health: is fibropapillomatosis an indicator?. EcoHealth 1, 275–283 (2004).
Google Scholar 

21.
Médoc, V., Piscart, C., Maazouzi, C., Simon, L. & Beisel, J. N. Parasite-induced changes in the diet of a freshwater amphipod: field and laboratory evidence. Parasitology 138, 537–546 (2011).
PubMed  Article  PubMed Central  Google Scholar 

22.
Britton, J. R. & Andreou, D. Parasitism as a driver of trophic niche specialisation. Trends Parasitol. 32, 437–445 (2016).
PubMed  Article  PubMed Central  Google Scholar 

23.
Rabinovich, J. E. et al. Ecological patterns of blood-feeding by kissing-bugs (Hemiptera: Reduviidae: Triatominae). Mem. Inst. Oswaldo Cruz 106, 479–494 (2011).
PubMed  Article  PubMed Central  Google Scholar 

24.
Post, D. M. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83, 703–718 (2002).
Article  Google Scholar 

25.
Lochmiller, R. L. & Deerenberg, C. Trade-offs in evolutionary immunology: just what is the cost of immunity? Oikos 18, 87–98 (2000).
Article  Google Scholar 

26.
Durso, A. M. & French, S. S. Stable isotope tracers reveal a trade-off between reproduction and immunity in a reptile with competing needs. Funct. Ecol. 32, 648–656 (2018).
Article  Google Scholar 

27.
Richner, H., Oppliger, A. & Christe, P. Effect of an ectoparasite on reproduction in great tits. J. Anim. Ecol. 62, 703–710 (1993).
Article  Google Scholar 

28.
Eizaguirre, C., Yeates, S. E., Lenz, T. L., Kalbe, M. & Milinski, M. MHC-based mate choice combines good genes and maintenance of MHC polymorphism. Mol. Ecol. 18, 3316–3329 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Schwanz, L. E. Persistent effects of maternal parasitic infection on offspring fitness: implications for adaptive reproductive strategies when parasitized. Funct. Ecol. 22, 691–698 (2008).
Article  Google Scholar 

30.
Kalbe, M. et al. Lifetime reproductive success is maximized with optimal major histocompatibility complex diversity. Proc. R. Soc. B Biol. Sci. 276, 925–934 (2009).
Article  Google Scholar 

31.
Duffield, K. R., Bowers, E. K., Sakaluk, S. K. & Sadd, B. M. A dynamic threshold model for terminal investment. Behav. Ecol. Sociobiol. 71, 185 (2017).
PubMed  PubMed Central  Article  Google Scholar 

32.
Hurd, H. Host fecundity reduction: a strategy for damage limitation?. Trends Parasitol. 17, 363–368 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Uller, T., Isaksson, C. & Olsson, M. Immune challenge reduces reproductive output and growth in a lizard. Funct. Ecol. 20, 873–879 (2006).
Article  Google Scholar 

34.
Velando, A., Drummond, H. & Torres, R. Senescent birds redouble reproductive effort when ill: confirmation of the terminal investment hypothesis. Proc. R. Soc. B Biol. Sci. 273, 1443–1448 (2006).
Article  Google Scholar 

35.
Kaufmann, J., Lenz, T. L., Milinski, M. & Eizaguirre, C. Experimental parasite infection reveals costs and benefits of paternal effects. Ecol. Lett. 17, 1409–1417 (2014).
PubMed  PubMed Central  Article  Google Scholar 

36.
Pigeault, R., Garnier, R., Rivero, A. & Gandon, S. Evolution of transgenerational immunity in invertebrates. Proc. R. Soc. B Biol. Sci. 283, 20161136 (2016).
Article  CAS  Google Scholar 

37.
Roth, O., Beemelmanns, A., Barribeau, S. M. & Sadd, B. M. Recent advances in vertebrate and invertebrate transgenerational immunity in the light of ecology and evolution. Heredity (Edinb). 121, 225–238 (2018).
PubMed  PubMed Central  Article  Google Scholar 

38.
Mcgowin, A. E. et al. Genetic barcoding of marine leeches (Ozobranchus spp.) from Florida sea turtles and their divergence in host specificity. Mol. Ecol. Resour. 11, 271–278 (2011).
CAS  PubMed  Article  Google Scholar 

39.
Davies, R. W. & Chapman, C. G. First record from North America of the Piscicolid Leech, Ozobranchus margoi, a parasite of Marine Turtles. J. Fish. Res. Board Canada 31, 104–106 (1974).
Article  Google Scholar 

40.
Bunkley-Williams, L. et al. New leeches and diseases for the hawksbill sea turtle and the West Indies. Comp. Parasitol. 75, 263–270 (2008).
Article  Google Scholar 

41.
Greenblatt, R. J. et al. Genomic variation of the fibropapilloma-associated marine turtle herpesvirus across seven geographic areas and three host species. J. Virol. 79, 1125–1132 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Jones, K., Ariel, E., Burgess, G. & Read, M. A review of fibropapillomatosis in green turtles (Chelonia mydas). Vet. J. 212, 48–57 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

43.
Marco, A., Abella, E., Martins, S., López, O. & Medina, M. Abundance and exploitation of loggerhead turtles nesting in Boa Vista island, Cape Verde: the only substantial rookery in the eastern Atlantic. Anim. Conserv. 15, 351–360 (2012).
Article  Google Scholar 

44.
Stiebens, V. A. et al. Living on the edge: how philopatry maintains adaptive potential. Proc. R. Soc. 280, 1–9 (2013).
Google Scholar 

45.
Baltazar-Soares, M. et al. Distribution of genetic diversity reveals colonization and philopatry of the loggerhead sea turtles across geographic scales. Sci. Rep. https://doi.org/10.1038/s41598-020-74141-6 (2020).
Article  Google Scholar 

46.
Light, J. E. & Siddall, M. E. Phylogeny of the Leech family glossiphoniidae based on mitochondrial gene sequences and morphological data. J. Parasitol. 85, 815–823 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene 71, 491–499 (1988).
CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Cameron, S. J. K. et al. Diversity of feeding strategies in loggerhead sea turtles from the Cape Verde archipelago. Mar. Biol. 166, 130 (2019).
Article  Google Scholar 

49.
Scott, R., Biastoch, A., Roder, C., Stiebens, V. A. & Eizaguirre, C. Nano-tags for neonates and ocean-mediated swimming behaviours linked to rapid dispersal of hatchling sea turtles. Proc. R. Soc. 281, 20141209 (2014).
Google Scholar 

50.
Maulany, R. I., Booth, D. T. & Baxter, G. S. The effect of incubation temperature on hatchling quality in the olive ridley turtle, Lepidochelys olivacea, from Alas Purwo National Park, East Java, Indonesia: Implications for hatchery management. Mar. Biol. 159, 2651–2661 (2012).
Article  Google Scholar 

51.
Hays, G. C. & Speakman, J. R. Clutch size for Mediterranean loggerhead turtles (Caretta caretta). J. Zool. 226, 321–327 (1992).
Article  Google Scholar 

52.
Rodenbusch, C. R., Marks, F. S., Canal, C. W. & Reck, J. Marine leech Ozobranchus margoi parasitizing loggerhead turtle (Caretta caretta) in Rio Grande do Sul Brazil. Rev. Bras. Parasitol. Vet. 21, 301–303 (2012).
PubMed  Article  PubMed Central  Google Scholar 

53.
Eder, E. et al. Foraging dichotomy in loggerhead sea turtles Caretta caretta off northwestern Africa. Mar. Ecol. Prog. Ser. 470, 113–122 (2012).
ADS  Article  Google Scholar 

54.
Decaestecker, E. et al. Host-parasite ‘Red Queen’ dynamics archived in pond sediment. Nature 450, 870–873 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

55.
Van Velan, L. A new evolutionary law. Evol. Theory 1, 1–30 (1973).
Google Scholar 

56.
Altizer, S. et al. Seasonality and the dynamics of infectious diseases. Ecol. Lett. 9, 467–484 (2006).
PubMed  Article  PubMed Central  Google Scholar 

57.
Greenblatt, R. J. et al. The Ozobranchus leech is a candidate mechanical vector for the fibropapilloma-associated turtle herpesvirus found latently infecting skin tumors on Hawaiian green turtles (Chelonia mydas). Virology 321, 101–110 (2004).
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Bertrand, M., Marcogliese, D. J. & Magnan, P. Trophic polymorphism in brook charr revealed by diet, parasites and morphometrics. J. Fish Biol. 72, 555–572 (2008).
Article  Google Scholar 

59.
Venesky, M. D., Parris, M. J. & Storfer, A. Impacts of Batrachochytrium dendrobatidis infection on tadpole foraging performance. EcoHealth 6, 565–575 (2009).
PubMed  Article  PubMed Central  Google Scholar 

60.
Naug, D. Infected honeybee foragers incur a higher loss in efficiency than in the rate of energetic gain. Biol. Lett. 10, 1–4 (2014).
Article  Google Scholar 

61.
Frick, M. G., Williams, K. L., Bolten, A. B., Bjorndal, K. A. & Martins, H. R. Foraging ecology of oceanic-stage loggerhead turtles Caretta caretta. Endanger. Species Res. 9, 91–97 (2009).
Article  Google Scholar 

62.
Hawkes, L. A. et al. Phenotypically linked dichotomy in sea turtle foraging requires multiple conservation approaches. Curr. Biol. 16, 990–995 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Zuk, M. & Stoehr, A. M. Immune defense and host life history. Am. Nat. 160, S9–S22 (2002).
PubMed  Article  PubMed Central  Google Scholar 

64.
Bonneaud, C. et al. Assessing the cost of mounting an immune response. Am. Nat. 161, 367–379 (2003).
PubMed  Article  PubMed Central  Google Scholar 

65.
Omeyer, L. C. M., Godley, B. J. & Broderick, A. C. Growth rates of adult sea turtles. Endanger. Species Res. 34, 357–371 (2017).
Article  Google Scholar 

66.
Agnew, P., Koella, J. C. & Michalakis, Y. Host life history responses to parasitism. Microbes Infect. 2, 891–896 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

67.
Sorci, G., Massot, M. & Clobert, J. Maternal parasite load increases sprint speed and philopatry in female offspring of the common lizard. Am. Nat. 144, 153–164 (1994).
Article  Google Scholar 

68.
Booth, D. T., Feeney, R. & Shibata, Y. Nest and maternal origin can influence morphology and locomotor performance of hatchling green turtles (Chelonia mydas) incubated in field nests. Mar. Biol. 160, 127–137 (2013).
Article  Google Scholar  More