Philippa Kaur

Cave-adapted populations of the Mexican tetra, Astyanax mexicanus, have dysregulated blood glucose homeostasis and are insulin-resistant compared to river-adapted (‘surface’) populations. We found that multiple cave populations, including those inhabiting the Tinaja and Pachón caves, carry a mutation in the insulin receptor that leads to decreased insulin binding in vitro and contributes to hyperglycaemia. As part of the analysis that led to this conclusion, we measured fasting blood glucose levels in F2 fish derived from a cross between a surface fish homozygous for the ancestral insulin receptor allele and a cavefish homozygous for the derived allele, allowing us to correlate inheritance of the mutation with inheritance of glucose dysregulation. In this Article, we inadvertently indicated that the cavefish grandparent used in this cross was descended from the Tinaja population. However, subsequent analysis has definitively indicated that this individual actually belongs to the Pachón population. However, as both the Tinaja and Pachón populations carry the same P211L mutation in the insulin receptor, the logic of the experiment, the genotype–phenotype correlation we observed, and the conclusions of the study remain unchanged. Everything in the manuscript is still accurate, other than the name of the cave in the second and third paragraphs on page 649 of the PDF version of the original Article and in Fig. 3b and its legend. This error has not been corrected online. More

1.
IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2014).
2.
Kirschke, S. et al. Three decades of global methane sources and sinks. Nat. Geosci. 6, 813–823 (2013).
ADS  CAS  Article  Google Scholar 

3.
Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K. & Zhuang, Q. L. Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales. Glob. Change Biol. 19, 1325–1346 (2013).
ADS  Article  Google Scholar 

4.
Harriss, R. C., Gorham, E., Sebacher, D. I., Bartlett, K. B. & Flebbe, P. A. Methane flux from northern peatlands. Nature 315, 652–654 (1985).
ADS  CAS  Article  Google Scholar 

5.
Yvon-Durocher, G. et al. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature 507, 488–491 (2014).
ADS  CAS  Article  Google Scholar 

6.
Segers, R. Methane production and methane consumption: a review of processes underlying wetland methane fluxes. Biogeochemistry 41, 23–51 (1998).
CAS  Article  Google Scholar 

7.
Inglett, K. S., Inglett, P. W., Reddy, K. R. & Osborne, T. Z. Temperature sensitivity of greenhouse gas production in wetland soils of different vegetation. Biogeochemistry 108, 77–90 (2012).
CAS  Article  Google Scholar 

8.
Dean, J. F. et al. Methane feedbacks to the global climate system in a warmer world. Rev. Geophys. 56, 207–250 (2018).
ADS  Article  Google Scholar 

9.
Xu, X. et al. Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems. Biogeosciences 13, 3735–3755 (2016).
ADS  CAS  Article  Google Scholar 

10.
McCalley, C. K. et al. Methane dynamics regulated by microbial community response to permafrost thaw. Nature 514, 478–481 (2014).
ADS  CAS  Article  Google Scholar 

11.
Wuebbles, D. J. & Hayhoe, K. Atmospheric methane and global change. Earth-Sci. Rev. 57, 177–210 (2002).
ADS  CAS  Article  Google Scholar 

12.
Smith, T. P. et al. Community-level respiration of prokaryotic microbes may rise with global warming. Nat. Commun. 10, 5124–5124 (2019).
ADS  Article  CAS  Google Scholar 

13.
Melillo, J. M. et al. Soil warming and carbon-cycle feedbacks to the climate system. Science 298, 2173–2176 (2002).
ADS  CAS  Article  Google Scholar 

14.
Carey, J. C. et al. Temperature response of soil respiration largely unaltered with experimental warming. Proc. Natl Acad. Sci. USA 113, 13797–13802 (2016).
ADS  CAS  Article  Google Scholar 

15.
Crowther, T. W. et al. Quantifying global soil carbon losses in response to warming. Nature 540, 104–108 (2016).
ADS  CAS  Article  Google Scholar 

16.
Bradford, M. A. et al. Cross-biome patterns in soil microbial respiration predictable from evolutionary theory on thermal adaptation. Nat. Ecol. Evol. 3, 223–231 (2019).
Article  Google Scholar 

17.
Dacal, M., Bradford, M. A., Plaza, C., Maestre, F. T. & Garcia-Palacios, P. Soil microbial respiration adapts to ambient temperature in global drylands. Nat. Ecol. Evol. 3, 232–238 (2019).
Article  Google Scholar 

18.
Bradford, M. A. et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol. Lett. 11, 1316–1327 (2008).
Article  Google Scholar 

19.
Bradford, M. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. 4, 00333 (2013).
Article  Google Scholar 

20.
Karhu, K. et al. Temperature sensitivity of soil respiration rates enhanced by microbial community response. Nature 513, 81–84 (2014).
ADS  CAS  Article  Google Scholar 

21.
Wei, H. et al. Thermal acclimation of organic matter decomposition in an artificial forest soil is related to shifts in microbial community structure. Soil Biol. Biochem. 71, 1–12 (2014).
CAS  Article  Google Scholar 

22.
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).
ADS  CAS  Article  Google Scholar 

23.
Hochachka, P. W. & Somero, G. N. Biochemical Adaptation Mechanism and Process in Physiological Evolution (Oxford Univ. Press, New York, 2002).

24.
Angilletta Jr, M. J. & Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis. (Oxford Univ. Press, New York, 2009).

25.
Ren, J. et al. Shifts in soil bacterial and archaeal communities during freeze-thaw cycles in a seasonal frozen marsh, Northeast China. Sci. Total Environ. 625, 782–791 (2018).
ADS  CAS  Article  Google Scholar 

26.
Cui, M. et al. Warmer temperature accelerates methane emissions from the Zoige wetland on the Tibetan Plateau without changing methanogenic community composition. Sci. Rep. 5, 11616 (2015).
ADS  CAS  Article  Google Scholar 

27.
Hartley, I. P., Hopkins, D. W., Garnett, M. H., Sommerkorn, M. & Wookey, P. A. Soil microbial respiration in arctic soil does not acclimate to temperature. Ecol. Lett. 11, 1092–1100 (2008).
Article  Google Scholar 

28.
Crowther, T. W. & Bradford, M. A. Thermal acclimation in widespread heterotrophic soil microbes. Ecol. Lett. 16, 469–477 (2013).
Article  Google Scholar 

29.
Bradford, M. A., Watts, B. W. & Davies, C. A. Thermal adaptation of heterotrophic soil respiration in laboratory microcosms. Glob. Change Biol. 16, 1576–1588 (2010).
ADS  Article  Google Scholar 

30.
Luo, Y. Q., Wan, S. Q., Hui, D. F. & Wallace, L. L. Acclimatization of soil respiration to warming in a tall grass prairie. Nature 413, 622–625 (2001).
ADS  CAS  Article  Google Scholar 

31.
Oertel, C., Matschullat, J., Zurba, K., Zimmermann, F. & Erasmi, S. Greenhouse gas emissions from soils—a review. Geochemistry 76, 327–352 (2016).
CAS  Article  Google Scholar 

32.
Wagner, R., Zona, D., Oechel, W. & Lipson, D. Microbial community structure and soil pH correspond to methane production in Arctic Alaska soils. Environ. Microbiol. 19, 3398–3410 (2017).
CAS  Article  Google Scholar 

33.
Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Change 6, 751–758 (2016).
ADS  Article  CAS  Google Scholar 

34.
Atkin, O. K. & Tjoelker, M. G. Thermal acclimation and the dynamic response of plant respiration to temperature. Trends Plant Sci. 8, 343–351 (2003).
CAS  Article  Google Scholar 

35.
Lou, J., Yang, L., Wang, H. Z., Wu, L. S. & Xu, J. M. Assessing soil bacterial community and dynamics by integrated high-throughput absolute abundance quantification. Peerj 6, e4514 (2018).
Article  CAS  Google Scholar 

36.
Zhang, Z. J. et al. Soil bacterial quantification approaches coupling with relative abundances reflecting the changes of taxa. Sci. Rep. 7, 4837 (2017).
ADS  Article  CAS  Google Scholar 

37.
Tilman, D. Biodiversity: population versus ecosystem stability. Ecology 77, 350–363 (1996).
Article  Google Scholar 

38.
Tilman, D., Reich, P. B. & Knops, J. M. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).
ADS  CAS  Article  Google Scholar 

39.
Treseder, K. K. et al. Integrating microbial ecology into ecosystem models: challenges and priorities. Biogeochemistry 109, 7–18 (2012).
CAS  Article  Google Scholar 

40.
Singh, B. Soil Carbon Storage: Modulators, Mechanisms and Modeling. (Academic Press, 2018).

41.
Auffret, M. D. et al. The role of microbial community composition in controlling soil respiration responses to temperature. Plos ONE 11, e0165448 (2016).
Article  CAS  Google Scholar 

42.
Allison, S. D. & Martiny, J. B. H. Resistance, resilience, and redundancy in microbial communities. Proc. Natl Acad. Sci. USA 105, 11512–11519 (2008).
ADS  CAS  Article  Google Scholar 

43.
Shade, A. et al. Fundamentals of microbial community resistance and resilience. Front. Microbiol. 3, 417 (2012).
Article  Google Scholar 

44.
Chen, H., Zou, J., Cui, J., Nie, M. & Fang, C. Wetland drying increases the temperature sensitivity of soil respiration. Soil Biol. Biochem. 120, 24–27 (2018).
CAS  Article  Google Scholar 

45.
Blodau, C. Carbon cycling in peatlands – a review of processes and controls. Environ. Revi 10, 111–134 (2002).
CAS  Article  Google Scholar 

46.
Sander, R. Compilation of Henry’s law constants (version 4.0) for water as solvent. Atmos. Chem. Phys. 15, 4399–4981 (2015).
ADS  CAS  Article  Google Scholar 

47.
Conrad, R. Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal. Org. Geochem. 36, 739–752 (2005).
CAS  Article  Google Scholar 

48.
Brigham, B. A., Montero, A. D., O’Mullan, G. D. & Bird, J. A. Acetate additions stimulate CO2 and CH4 production from urban wetland soils. Soil Sci. Soc. Am. J. 82, 1147–1159 (2018).
ADS  CAS  Article  Google Scholar 

49.
Wang, Z., Delaune, R., Patrick, W. & Masscheleyn, P. Soil redox and pH effects on methane production in a flooded rice soil. Soil Sci. Soc. Am. J. 57, 382–385 (1993).
ADS  CAS  Article  Google Scholar 

50.
Hazel, J. R. & Prosser, C. L. Molecular mechanisms of temperature compensation in poikilotherms. Physiol. Rev. 54, 620–677 (1974).
CAS  Article  Google Scholar 

51.
Walker, T. W. et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat. Clim. Chan 8, 885–889 (2018).
ADS  CAS  Article  Google Scholar 

52.
Morris, R. et al. Methyl coenzyme M reductase (mcrA) gene abundance correlates with activity measurements of methanogenic H2/CO2-enriched anaerobic biomass. Microb. Biotechnol. 7, 77–84 (2014).
CAS  Article  Google Scholar 

53.
Steinberg, L. M. & Regan, J. M. mcrA-targeted real-time quantitative PCR method to examine methanogen communities. Appl. Environ. Micro. 75, 4435–4442 (2009).
CAS  Article  Google Scholar 

54.
Zeleke, J. et al. Methyl coenzyme M reductase A (mcrA) gene-based investigation of methanogens in the mudflat sediments of Yangtze River Estuary, China. Microb. Ecol. 66, 257–267 (2013).
CAS  Article  Google Scholar 

55.
Nocker, A. & Camper, A. K. Novel approaches toward preferential detection of viable cells using nucleic acid amplification techniques. FEMS Microbiol. Lett. 291, 137–142 (2009).
CAS  Article  Google Scholar 

56.
Salazar-Villegas, A., Blagodatskaya, E. & Dukes, J. S. Changes in the size of the active microbial pool explain short-term soil respiratory responses to temperature and moisture. Front. Microbiol. 7, 524 (2016).
Article  Google Scholar 

57.
Strickland, M. S. & Rousk, J. Considering fungal: bacterial dominance in soils–methods, controls, and ecosystem implications. Soil Biol. Biochem 42, 1385–1395 (2010).
CAS  Article  Google Scholar 

58.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. J. P. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
Article  Google Scholar 

59.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
CAS  Article  Google Scholar 

60.
Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. J. T. Ij UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).
Article  Google Scholar 

61.
Reich, P. B. et al. Boreal and temperate trees show strong acclimation of respiration to warming. Nature 531, 633–636 (2016).
ADS  CAS  Article  Google Scholar 

62.
Loveys, B. R. et al. Thermal acclimation of leaf and root respiration: an investigation comparing inherently fast- and slow-growing plant species. Glob. Change Biol. 9, 895–910 (2003).
ADS  Article  Google Scholar 

63.
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).
ADS  CAS  Article  Google Scholar 

64.
Fierer, N., Colman, B. P., Schimel, J. P. & Jackson, R. B. Predicting the temperature dependence of microbial respiration in soil: a continental-scale analysis. Glob. Biogeochem. Cy 20, GB3026 (2006).
ADS  Article  CAS  Google Scholar 

65.
Fang, C. & Moncrieff, J. B. The dependence of soil CO2 efflux on temperature. Soil Biol. Biochem. 33, 155–165 (2001).
CAS  Article  Google Scholar  More

1.
Newton, I. Population Limitation in Birds (Academic Press, London, 1998).
Google Scholar 
2.
Rockwood, L. L. Introduction to Population Ecology (Blackwell Publishing, Hoboken, 2015).
Google Scholar 

3.
Bell, G. Selection the Mechanism of Evolution (Oxford University Press, Oxford, 2008).
Google Scholar 

4.
Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).
Article  CAS  Google Scholar 

5.
Tilman, D. G. et al. Forecasting agriculturally driven global environmental change. Science 292, 281–284 (2001).
ADS  Article  CAS  Google Scholar 

6.
Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).
ADS  Article  CAS  Google Scholar 

7.
Carvalho, F. P. Agriculture, pesticides, food security and food safety. Environ. Sci. Policy 9, 685–692 (2006).
Article  Google Scholar 

8.
Grande, J. M., Orozcovalor, P. M., Liébana, M. S. & Sarasola, J. H. Birds of prey in agricultural landscapes: The role of agriculture expansion and intensification. In Birds of Prey Ecology and Conservation in the XXI Century (eds Sarasola, J. H. et al.) 197–228 (Springer, Berlin, 2018).
Google Scholar 

9.
Sergio, F., Newton, I. & Marchesi, L. Conservation: Top predators and biodiversity. Nature 436, 192 (2005).
ADS  Article  CAS  Google Scholar 

10.
Butet, A. et al. Responses of common buzzard (Buteo buteo) and Eurasian kestrel (Falco tinnunculus) to land use changes in agricultural landscapes of Western France. Agric. Ecosyst. Environ. 138, 152–159 (2010).
Article  Google Scholar 

11.
Amar, A. & Redpath, S. M. Habitat use by Hen Harriers Circus cyaneus on Orkney: Implications of land-use change for this declining population. Ibis. 147, 37–47 (2005).
Article  Google Scholar 

12.
Vergara, P. et al. Low frequency of anti-acetylcholinesterase pesticide poisoning in lesser and Eurasian kestrels of Spanish grassland and farmland populations. Biol. Conserv. 141, 499–505 (2008).
Article  Google Scholar 

13.
Arroyo, B. E., García, J. T. & Bretagnolle, V. Conservation of the Montagu’s harrier (Circus pygargus) in agricultural areas. Anim. Conserv. 5, 283–290 (2002).
Article  Google Scholar 

14.
Goldstein, M. I. et al. Monocrotophos induced mass mortality of Swainson’s Hawks in Argentina, 1995–96. Crop Prot. 8(3), 201–214 (1999).
CAS  Google Scholar 

15.
Costantini, D., Dell’Omo, G., La Fata, I. & Casagrande, S. Reproductive performance of Eurasian Kestrel Falco tinnunculus in an agricultural landscape with a mosaic of land uses. Ibis. 156, 768–776 (2014).
Article  Google Scholar 

16.
Touihri, M., Séguy, M., Imbeau, L., Mazerolle, M. J. & Bird, D. M. Effects of agricultural lands on habitat selection and breeding success of American kestrels in a boreal context. Agric. Ecosyst. Environ. 272, 146–154 (2019).
Article  Google Scholar 

17.
Cardador, L., Carrete, M. & Mañosa, S. Can intensive agricultural landscapes favour some raptor species? The Marsh harrier in north-eastern Spain. Anim. Conserv. 14, 382–390 (2011).
Article  Google Scholar 

18.
Murgatroyd, M., Avery, G., Underhill, L. G. & Amar, A. Adaptability of a specialist predator: The effects of land use on diet diversification and breeding performance of Verreaux’s eagles. J. Avian Biol. 47, 834–845 (2016).
Article  Google Scholar 

19.
Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, 1211–1219 (2007).
Article  CAS  Google Scholar 

20.
Crick, H. Q. P. The impact of climate change on birds. Ibis. 146, 48–56 (2004).
Article  Google Scholar 

21.
Catry, I., Franco, A. M. A. & Sutherland, W. J. Landscape and weather determinants of prey availability: Implications for the Lesser Kestrel Falco naumanni. Ibis. 154, 111–123 (2012).
Article  Google Scholar 

22.
Garcia-Heras, M.-S., Arroyo, B. E., Mougeot, F., Amar, A. & Simmons, R. E. Does timing of breeding matter less where the grass is greener? Seasonal declines in breeding performance differ between regions in an endangered endemic raptor. Nat. Conserv. 15, 23–45 (2016).
Article  Google Scholar 

23.
García, J. T. & Arroyo, B. E. Effect of abiotic factors on reproduction in the centre and periphery of breeding ranges: A comparative analysis in sympatric harriers. Ecography 24, 393–402 (2001).
Article  Google Scholar 

24.
Senapathi, D., Nicoll, M. A. C., Teplitsky, C., Jones, C. G. & Norris, K. Climate change and the risks associated with delayed breeding in a tropical wild bird population. Proc. R. Soc. B Biol. Sci. 278, 3184–3190 (2011).
Article  Google Scholar 

25.
Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–803 (2008).
ADS  Article  CAS  Google Scholar 

26.
Dunn, P. Breeding dates and reproductive performance. Adv. Ecol. Res. 35, 69–87 (2004).
Article  Google Scholar 

27.
Newton, I. Population Ecology of Raptors (T & A D Poyser, Berkhamsted, 1979).
Google Scholar 

28.
Rodríguez, C. & Bustamante, J. The effect of weather on lesser kestrel breeding success: Can climate change explain historical population declines?. J. Anim. Ecol. 72, 793–810 (2003).
Article  Google Scholar 

29.
Steenhof, K., Kochert, M. N. & Mcdonald, T. L. Interactive effects of prey and weather on Golden Eagle reproduction. J. Anim. Ecol. 66, 350 (1997).
Article  Google Scholar 

30.
Keane, J. J., Morrison, M. L. & Fry, D. M. Prey and weather factors associated with temporal variation in Northern Goshawk reproduction in the Sierra Nevada. California. Stud. Avian Biol. 31, 85–99 (2006).
Google Scholar 

31.
Redpath, S. M. et al. Temperature and hen harrier productivity: From local mechanisms to geographical patterns. Ecography 25, 533–540 (2002).
Article  Google Scholar 

32.
Zak, M. R., Cabido, M., Cáceres, D. & Díaz, S. What drives accelerated land cover change in central Argentina? Synergistic consequences of climatic, socioeconomic, and technological factors. Environ. Manag. 42, 181–189 (2008).
ADS  Article  Google Scholar 

33.
Graesser, J., Aide, T. M., Grau, H. R. & Ramankutty, N. Cropland/pastureland dynamics and the slowdown of deforestation in Latin America. Environ. Res. Lett. 10, 0–10 (2015).
Article  Google Scholar 

34.
Filloy, J. & Bellocq, M. Respuesta de las aves rapaces al uso de la tierra: un enfoque regional. Hornero 22, 131–140 (2007).
Google Scholar 

35.
Pedrana, J., Isacch, J. P. & Bó, M. S. Habitat relationships of diurnal raptors at local and landscape scales in southern temperate grasslands of Argentina. Emu 108, 301–310 (2008).
Article  Google Scholar 

36.
Filloy, J. & Bellocq, M. I. Patterns of bird abundance along the agricultural gradient of the Pampean region. Agric. Ecosyst. Environ. 120, 291–298 (2007).
Article  Google Scholar 

37.
Ferguson-Lees, J. & Christie, D. A. Raptors of The World (Houghton Miffli Harcourt, Boston, 2001).
Google Scholar 

38.
McClure, C. J. W., Schulwitz, S. E., Van, R., Pauli, B. P. & Heath, J. A. Commentary: Research recommendations for understanding the decline of American Kestrels (Falco sparverius) across much of North America. J. Raptor Res. 51, 455–464 (2017).
Article  Google Scholar 

39.
Smallwood, J. A. et al. Why are American Kestrel (Falco sparverius) populations declining in North America? Evidence from nest-box programs. J. Raptor Res. 43, 274–282 (2009).
Article  Google Scholar 

40.
De la Peña, M. R. & Rumboll, M. Birds of Southern South America and Antarctica (Harper Collins Publishers, New York, 1998).
Google Scholar 

41.
Carrete, M., Tella, J. L., Blanco, G. & Bertellotti, M. Effects of habitat degradation on the abundance, richness and diversity of raptors across Neotropical biomes. Biol. Conserv. 142, 2002–2011 (2009).
Article  Google Scholar 

42.
Schrag, A. M., Zaccagnini, M. E., Calamari, N. & Canavelli, S. Climate and land-use influences on avifauna in central Argentina: Broad-scale patterns and implications of agricultural conversion for biodiversity. Agric. Ecosyst. Environ. 132, 135–142 (2009).
Article  Google Scholar 

43.
Goijman, A. P., Conroy, M. J., Bernardos, J. N. & Zaccagnini, M. E. Multi-season regional analysis of multi-species occupancy: Implications for bird conservation in agricultural lands in east-central Argentina. PLoS ONE 10, e0130874 (2015).
Article  CAS  PubMed Central  Google Scholar 

44.
Baldi, G. & Paruelo, J. M. Land use and land cover dynamics in South American temperate grasslands. Ecol. Soc. 13, 1–32 (2008).
Article  Google Scholar 

45.
Liébana, M. S., Sarasola, J. H. & Bó, M. S. Parental care and behavior of breeding American Kestrels (Falco sparverius) in central Argentina. J. Raptor Res. 43, 338–344 (2009).
Article  Google Scholar 

46.
De Lucca, E. R. & Saggesse, M. D. Nidificación del Halconcito Colorado (Falco sparverius) en la Patagonia. Hornero 13, 302–305 (1993).
Google Scholar 

47.
Smallwood, J. A. & Bird, D. M. American Kestrel (Falco sparverius). In The Birds of North America 602 (2002).

48.
Liébana, M. S., Sarasola, J. H. & Santillán, M. Á. Nest-Box occupancy by neotropical raptors in a native forest of central Argentina. J. Raptor Res. 47, 208–213 (2013).
Article  Google Scholar 

49.
Lopez, F. G. Oferta de cavidades para vertebrados en relación a parámetros de sustrato de bosques en distinto grado de estado sucesional en el caldenal pampeano (Universidad Nacional de La Pampa, Santa Rosa, 2014).
Google Scholar 

50.
De Lucca, E. R. Nidificación del halconcito colorado (Falco sparverius) en nidos de cotorra (Myiopsitta monachus). Hornero 13, 238–240 (1992).
Google Scholar 

51.
Orozco Valor, P. M. & Grande, J. M. Exceptionally large clutches in two raptors breeding in nest boxes. J. Raptor Res. 50, 232–236 (2016).
Article  Google Scholar 

52.
Korpimäki, E. Breeding performance of Tengmalm’s Owl Aegolius funereus: Effects of supplementary feeding in a peak vole year. Ibis. 131, 51–56 (1989).
Article  Google Scholar 

53.
Meijer, T., Daan, S. & Michal, H. Family planning in the kestrel (Falco Tinnunculus): The proximate control of covariation of laying date and clutch size. Behaviour 114, 117–136 (1990).
Article  Google Scholar 

54.
Smallwood, J. A. Sexual segregation by habitat in American Kestrels wintering in Southcentral Florida: Vegetative structure and responses to differential prey availability. Condor 89, 842 (1987).
Article  Google Scholar 

55.
Visser, M. E., Holleman, L. J. M. & Caro, S. P. Temperature has a causal effect on avian timing of reproduction. Proc. R. Soc. B Biol. Sci. 276, 2323–2331 (2009).
Article  Google Scholar 

56.
Lorda, H. et al. Descripción de zonas y subzonas agroecológicas RIAP. Area de influencia de la EEA Anguil. (2008).

57.
Smith, S. H., Steenhof, K., McClure, C. J. W. & Heath, J. A. Earlier nesting by generalist predatory bird is associated with human responses to climate change. J. Anim. Ecol. 86, 98–107 (2017).
Article  Google Scholar 

58.
Verhulst, S. & Nilsson, J. A. The timing of birds’ breeding seasons: A review of experiments that manipulated timing of breeding. Philos. Trans. R. Soc. B Biol. Sci. 363, 399–410 (2008).
Article  Google Scholar 

59.
Robinson, R. A., Baillie, S. R. & Crick, H. Q. P. Weather-dependent survival: Implications of climate change for passerine population processes. Ibis. 149, 357–364 (2007).
Article  Google Scholar 

60.
Fraschina, J., León, V. A. & Busch, M. Long-term variations in rodent abundance in a rural landscape of the Pampas, Argentina. Ecol. Res. 27, 191–202 (2012).
Article  Google Scholar 

61.
Sumasgutner, P. et al. Landscape homogenization due to agricultural intensification disrupts the relationship between reproductive success and main prey abundance in an avian predator. Front. Zool. 16, 31 (2019).

62.
Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: Is habitat heterogeneity the key?. Trends Ecol. Evol. 18, 182–188 (2003).
Article  Google Scholar 

63.
Amar, A., Redpath, S. & Thirgood, S. Evidence for food limitation in the declining hen harrier population on the Orkney Islands, Scotland. Biol. Conserv. 111, 377–384 (2003).
Article  Google Scholar 

64.
Cardador, L., Planas, E., Varea, A. & Mañosa, S. Feeding behaviour and diet composition of Marsh Harriers Circus aeruginosus in agricultural landscapes. Bird Study 59, 228–235 (2012).
Article  Google Scholar 

65.
Rodríguez, C., Tapia, L., Ribeiro, E. & Bustamante, J. Crop vegetation structure is more important than crop type in determining where Lesser Kestrels forage. Bird Conserv. Int. 24, 438–452 (2014).

66.
Ursúa, E., Serrano, D. & Tella, J. L. Does land irrigation actually reduce foraging habitat for breeding lesser kestrels? The role of crop types. Biol. Conserv. 122, 643–648 (2005).
Article  Google Scholar 

67.
Traba, J. & Morales, M. B. The decline of farmland birds in Spain is strongly associated to the loss of fallowland. Sci. Rep. 9, 1–6 (2019).
Article  CAS  Google Scholar 

68.
Aizen, M. A., Garibaldi, L. A. & Dondo, M. Expansión de la soja y diversidad de la agricultura argentina. Ecol. Austral 19, 45–54 (2009).
Google Scholar 

69.
Datos agroindustriales. Datos Agroindustriales. https://datos.agroindustria.gob.ar/ (2017).

70.
Codesido, M., González-Fischer, C. & Bilenca, D. N. Distributional changes of landbird species in agroecosystems of Central Argentina. Condor 113, 266–273 (2011).
Article  Google Scholar 

71.
Dawson, R. D. & Bortolotti, G. R. Experimental evidence for food limitation and sex-specific strategies of American kestrels (Falco sparverius) provisioning offspring. Behav. Ecol. Sociobiol. 52, 43–52 (2002).
Article  Google Scholar 

72.
Murgatroyd, M., Underhill, L. G., Rodrigues, L. & Amar, A. The influence of agricultural transformation on the breeding performance of a top predator: Verreaux’s Eagles in contrasting land use areas. Condor 118, 238–252 (2016).
Article  Google Scholar 

73.
Dawson, R. D. & Bortolotti, G. R. Reproductive success of American Kestrels: The role of prey abundance and weather. Condor 102, 814–822 (2000).
Article  Google Scholar 

74.
Salaberria, C., Celis, P., López-Rull, I. & Gil, D. Effects of temperature and nest heat exposure on nestling growth, dehydration and survival in a Mediterranean hole-nesting passerine. Ibis. 156, 265–275 (2014).
Article  Google Scholar 

75.
Catry, I., Franco, A. M. A. & Sutherland, W. J. Adapting conservation efforts to face climate change: Modifying nest-site provisioning for lesser kestrels. Biol. Conserv. 144, 1111–1119 (2011).
Article  Google Scholar 

76.
Greño, J. L., Belda, E. J. & Barba, E. Influence of temperatures during the nestling period on post-fledging survival of great tit Parus major in a Mediterranean habitat. J. Avian Biol. 39(1), 41–49 (2008).
Article  Google Scholar 

77.
Luck, G. W. Variability in provisioning rates to nestlings in the cooperatively breeding Rufous Treecreeper, Climacteris rufa. Emu 101, 221–224 (2001).
Article  Google Scholar 

78.
Mantyka-Pringle, C. S. et al. Climate change modifies risk of global biodiversity loss due to land-cover change. Biol. Conserv. 187, 103–111 (2015).
Article  Google Scholar 

79.
Goldstein, M. I. et al. Monocrotophos-induced mass mortality of Swainson’s hawks in Argentina, 1995–96. Ecotoxicology 8, 201–214 (1999).
Article  CAS  Google Scholar 

80.
Agroindustria. Estimaciones agrícolas. Miniesterio de Agroindustria https://datosestimaciones.magyp.gob.ar/reportes.php?reporte=Estimaciones (2018).

81.
SA & DS. Primer inventario nacional de bosques nativos. Informe regional Monte. Secr. Ambient. y Desarro. Sustentable 54 (2007).

82.
Cabrera, Á. L. Regiones fitogeográficas Argentinas. (Enciclopedia Argentina de Agricultura y Jardinería. Segunda Edición. Tomo II fascículo I. Ed. Acme., 1976).

83.
Pérez, S. et al. Abrupt changes in rainfall in the Eastern area of La Pampa Province, Argentina. Theor. Appl. Climatol. 103, 159–165 (2011).
ADS  Article  Google Scholar 

84.
Casagrande, G. A., Vergara, G. T. & Bellini, Y. Cartas agroclímáticas actuales de temperaturas, heladas y lluvia de la provincia de La Pampa (Argentina). Rev. Fac. Agron. – UNLPam 17, 15–22 (2006).
Google Scholar 

85.
Johnsgard, P. A. Hawks, Eagles, & Falcons of North America: Biology and Natural History (Smithsonian Institution Press, Washington, 1990).
Google Scholar 

86.
Miller, K. E. & Smallwood, J. A. Natal dispersal and philopatry of Southeastern American Kestrels in Florida. Wilson Bull. 109, 226–232 (1997).
Google Scholar 

87.
Steenhof, K. & Heath, J. A. Local recruitment and natal dispersal distances of American kestrels. Condor 115, 584–592 (2013).
Article  Google Scholar 

88.
Bird, D. M. & Palmer, R. S. American Kestrel. In Handbook of North American Birds (ed. Palmer, R. S.) 253–290 (Yale Univ. Press, New Haven, 1988).
Google Scholar 

89.
Torrado Porto, R. Diversidad y complejidad de los modelos de toma de decisiones y organización productiva en el sector agropecuario del Noreste Pampeano. Aportes para la mejora de la extensión y el desarrollo rural (Universidad Nacional de La Plata, 2019). https://doi.org/10.1037/0033-2909.I26.1.78.

90.
ESRI. ArcGis Software. (2015).

91.
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).
Article  Google Scholar 

92.
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 1–18 (2020).
Article  Google Scholar 

93.
Klucsarits, J. R. & Rusbuldt, J. A photographic timeline of Hawk Mountain Sanctuary’s American Kestrel Nestlings (Asst. Ctr., U.SZip Publishing, Columbus, 2007).
Google Scholar 

94.
Steenhof, K. & Newton, I. Assessing Nesting Success and Productivity. Raptor Res. Manag. Tech. 181–192 (2007).

95.
R Core Team. R: A Language and Environment for Statistical Computing. (2019).

96.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

97.
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Article  Google Scholar 

98.
Bates, D., Kliegl, R., Vasishth, S. & Baayen, H. Parsimonious mixed models. arXiv preprint, arXiv:1506.04967 (2015).

99.
Naimi, B., Hamm, N., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling. Ecography 37, 191–203 (2014).
Article  Google Scholar 

100.
Hosmer, D. W., Lemeshow, S. & Sturdivant, R. X. Applied logistic regression (Wiley, New York, 2013).
Google Scholar  More

1.
Lawton, J. H., May, R. M. & Raup, D. M. Extinction Rates Vol. 11 (Oxford University Press, Oxford, 1995).
Google Scholar 
2.
Loh, J. & Wackernagel, M. Living Planet Report 2004. Report No. 288085265X (WWF, Gland, 2004).
Google Scholar 

3.
Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived?. Nature 471, 51–57 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

4.
Vitousek, P. M., Mooney, H. A., Lubchenco, J. & Melillo, J. M. Human domination of Earth’s ecosystems. Science 277, 494–499 (1997).
CAS  Article  Google Scholar 

5.
Amici, V. et al. Anthropogenic drivers of plant diversity: perspective on land use change in a dynamic cultural landscape. Biodivers. Conserv. 24, 3185–3199 (2015).
Article  Google Scholar 

6.
Mack, R. N. et al. Biotic invasions: causes, epidemiology, global consequences, and control. Ecol. Appl. 10, 689–710 (2000).
Article  Google Scholar 

7.
Leroy, C. J. & Marks, J. C. Litter quality, stream characteristics and litter diversity influence decomposition rates and macroinvertebrates. Freshw. Biol. 51, 605–617 (2006).
Article  Google Scholar 

8.
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108. https://doi.org/10.1038/nature11118 (2012).
ADS  CAS  Article  PubMed  Google Scholar 

9.
Suurkuukka, H. et al. Woodland key habitats and stream biodiversity: Does small-scale terrestrial conservation enhance the protection of stream biota?. Biol. Conserv. 170, 10–19 (2014).
Article  Google Scholar 

10.
Vannote, R. L., Minshall, G. W., Cummins, K. W., Sedell, J. R. & Cushing, C. E. The river continuum concept. Can. J. Fish. Aquat. Sci. 37, 130–137 (1980).
Article  Google Scholar 

11.
Wallace, J., Eggert, S., Meyer, J. & Webster, J. Multiple trophic levels of a forest stream linked to terrestrial litter inputs. Science 277, 102–104. https://doi.org/10.1126/science.277.5322.102 (1997).
CAS  Article  Google Scholar 

12.
Marks, J. C. Revisiting the fates of dead leaves that fall into streams. Annu. Rev. Ecol. Evol. Syst. https://doi.org/10.1146/annurev-ecolsys-110218-024755 (2019).
Article  Google Scholar 

13.
Kominoski, J. S. et al. Forecasting functional implications of global changes in riparian plant communities. Front. Ecol. Environ. 11, 423–432. https://doi.org/10.1890/120056 (2013).
Article  Google Scholar 

14.
Swan, C. M. & Palmer, M. A. Leaf diversity alters litter breakdown in a piedmont stream. J. N. Am. Benthol. Soc. 23, 15–28 (2004).
Article  Google Scholar 

15.
López-Rojo, N. et al. Plant diversity loss affects stream ecosystem multifunctionality. Ecology 100, e02847 (2019).
Article  PubMed  Google Scholar 

16.
Stout, B. M. III., Benfield, E. & Webster, J. Effects of a forest disturbance on shredder production in southern Appalachian headwater streams. Freshw. Biol. 29, 59–69 (1993).
Article  Google Scholar 

17.
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72. https://doi.org/10.1038/35083573 (2001).
ADS  CAS  Article  PubMed  Google Scholar 

18.
Gessner, M. O. et al. Diversity meets decomposition. Trends Ecol. Evol. 25, 372–380. https://doi.org/10.1016/j.tree.2010.01.010 (2010).
Article  PubMed  Google Scholar 

19.
Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: consolidation and progress in functional biodiversity research. Ecol. Lett. 12, 1405–1419. https://doi.org/10.1111/j.1461-0248.2009.01388.x (2009).
Article  PubMed  Google Scholar 

20.
Krause, S. et al. Trait-based approaches for understanding microbial biodiversity and ecosystem functioning. Front. Microbiol. 5, 251 (2014).
Article  PubMed  PubMed Central  Google Scholar 

21.
Petchey, O. L. & Gaston, K. J. Functional diversity: back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).
Article  PubMed  Google Scholar 

22.
Burns, J. H. & Strauss, S. Y. More closely related species are more ecologically similar in an experimental test. Proc. Natl. Acad. Sci. 108, 5302–5307 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

23.
Cavender-Bares, J., Kozak, K. H., Fine, P. V. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715 (2009).
Article  PubMed  Google Scholar 

24.
Mouquet, N. et al. Ecophylogenetics: advances and perspectives. Biol. Rev. Camb. Philos. Soc. 87, 769–785 (2012).
Article  PubMed  Google Scholar 

25.
López-Rojo, N. et al. Shifts in key leaf litter traits can predict effects of plant diversity loss on decomposition in streams. Ecosystems (2020) (in press).

26.
Cadotte, M. W., Cardinale, B. J. & Oakley, T. H. Evolutionary history and the effect of biodiversity on plant productivity. Proc. Natl. Acad. Sci. 105, 17012–17017 (2008).
ADS  CAS  Article  PubMed  Google Scholar 

27.
Boyero, L. et al. Biotic and abiotic variables influencing plant litter breakdown in streams: a global study. Proc. R. Soc. B Biol. Sci. 283, 20152664. https://doi.org/10.1098/rspb.2015.2664 (2016).
CAS  Article  Google Scholar 

28.
Fernandes, I., Duarte, S., Cássio, F. & Pascoal, C. Plant litter diversity affects invertebrate shredder activity and the quality of fine particulate organic matter in streams. Mar. Freshw. Res. 66, 449–458 (2015).
CAS  Article  Google Scholar 

29.
Handa, I. T. et al. Consequences of biodiversity loss for litter decomposition across biomes. Nature 509, 218–221. https://doi.org/10.1038/nature13247 (2014).
ADS  CAS  Article  PubMed  Google Scholar 

30.
López-Rojo, N. et al. Leaf traits drive plant diversity effects on litter decomposition and FPOM production in streams. PLoS ONE 13, e0198243 (2018).
Article  PubMed  PubMed Central  Google Scholar 

31.
Tonin, A. M. et al. Stream nitrogen concentration, but not plant N-fixing capacity, modulates litter diversity effects on decomposition. Funct. Ecol. https://doi.org/10.1111/1365-2435.12837 (2017).
Article  Google Scholar 

32.
Vos, V. C. A., van Ruijven, J., Berg, M. P., Peeters, E. T. H. M. & Berendse, F. Macro-detritivore identity drives leaf litter diversity effects. Oikos 120, 1092–1098. https://doi.org/10.1111/j.1600-0706.2010.18650.x (2011).
Article  Google Scholar 

33.
Gessner, M. O. & Chauvet, E. Ergosterol-to-biomass conversion factors for aquatic hyphomycetes. Appl. Environ. Microbiol. 59, 502–507 (1993).
CAS  Article  PubMed  PubMed Central  Google Scholar 

34.
Tonin, A. M. et al. Stream nitrogen concentration, but not plant N-fixing capacity, modulates litter diversity effects on decomposition. Funct. Ecol. 31, 1471–1481 (2017).
Article  Google Scholar 

35.
Graça, M. A. S. et al. Food quality, feeding preferences, survival and growth of shredders from temperate and tropical streams. Freshw. Biol. 46, 947–957. https://doi.org/10.1046/j.1365-2427.2001.00729.x (2001).
Article  Google Scholar 

36.
McArthur, J. V., Aho, J. M., Rader, R. B. & Mills, G. L. Interspecific leaf interactions during decomposition in aquatic and floodplain ecosystems. J. N. Am. Benthol. Soc. 13, 57–67 (1994).
Article  Google Scholar 

37.
Gessner, M. O., Chauvet, E. & Dobson, M. A perspective on leaf litter breakdown in streams. Oikos 85, 377–384. https://doi.org/10.2307/1939639 (1999).
Article  Google Scholar 

38.
Hättenschwiler, S. & Gasser, P. Soil animals alter plant litter diversity effects on decomposition. Proc. Natl. Acad. Sci. 102, 1519–1524 (2005).
ADS  Article  PubMed  Google Scholar 

39.
Laitung, B. & Chauvet, E. Vegetation diversity increases species richness of leaf-decaying fungal communities in woodland streams. Arch. Hydrobiol. 164, 217–235 (2005).
Article  Google Scholar 

40.
Rajashekhar, M. & Kaveriappa, K. Diversity of aquatic hyphomycetes in the aquatic ecosystems of the Western Ghats of India. Hydrobiologia 501, 167–177 (2003).
Article  Google Scholar 

41.
Friberg, N. & Jacobsen, D. J. Variation in growth of the detritivore-shredder Sericostoma personatum (Trichoptera). Freshw. Biol. 42, 625–635 (1999).
Article  Google Scholar 

42.
France, R. Leaves as “crackers”, biofilm as “peanut butter”: exploratory use of stable isotopes as evidence for microbial pathways in detrital food webs. Oceanol. Hydrobiol. Stud. https://doi.org/10.2478/s13545-011-0047-y (2011).
Article  Google Scholar 

43.
Frainer, A. et al. Stoichiometric imbalances between detritus and detritivores are related to shifts in ecosystem functioning. Oikos 125, 861–871. https://doi.org/10.1111/oik.02687 (2016).
CAS  Article  Google Scholar 

44.
Boyero, L. et al. Biotic and abiotic variables influencing plant litter breakdown in streams: a global study. Proc. R. Soc. B Biol. Sci. 283, 20152664 (2016).
Article  Google Scholar 

45.
Friberg, N. & Jacobsen, D. Feeding plasticity of two detritivore-shredders. Freshw. Biol. 32, 133–142 (1994).
Article  Google Scholar 

46.
Lecerf, A. & Richardson, J. S. Biodiversity-ecosystem function research: insights gained from streams. River Res. Appl. 26, 45–54. https://doi.org/10.1002/rra.1286 (2010).
Article  Google Scholar 

47.
Lecerf, A., Risnoveanu, G., Popescu, C., Gessner, M. O. & Chauvet, E. Decomposition of diverse litter mixtures in streams. Ecology 88, 219–227 (2007).
Article  PubMed  Google Scholar 

48.
Taylor, B. R., Mallaley, C. & Cairns, J. F. Limited evidence that mixing leaf litter accelerates decomposition or increases diversity of decomposers in streams of eastern Canada. Hydrobiologia 592, 405–422. https://doi.org/10.1007/s10750-007-0778-3 (2007).
Article  Google Scholar 

49.
Vos, V. C., van Ruijven, J., Berg, M. P., Peeters, E. T. & Berendse, F. Leaf litter quality drives litter mixing effects through complementary resource use among detritivores. Oecologia 173, 269–280 (2013).
ADS  Article  PubMed  Google Scholar 

50.
Boyero, L., Cardinale, B. J., Bastian, M. & Pearson, R. G. Biotic vs. abiotic control of decomposition: a comparison of the effects of simulated extinctions and changes in temperature. PLoS ONE 9, e87426. https://doi.org/10.1371/journal.pone.0087426 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

51.
McKie, B. G., Schindler, M., Gessner, M. O. & Malmqvist, B. Placing biodiversity and ecosystem functioning in context: environmental perturbations and the effects of species richness in a stream field experiment. Oecologia 160, 757–770. https://doi.org/10.1007/s00442-009-1336-7 (2009).
ADS  Article  PubMed  Google Scholar 

52.
Tonin, A. M. et al. Interactions between large and small detritivores influence how biodiversity impacts litter decomposition. J. Anim. Ecol. 87, 1465–1474. https://doi.org/10.1111/1365-2656.12876 (2018).
Article  PubMed  Google Scholar 

53.
Boyero, L. & Pearson, R. G. Intraspecific interference in a tropical stream shredder guild. Mar. Freshw. Res. 57, 201–206 (2006).
Article  Google Scholar 

54.
Reiss, J., Bailey, R. A., Perkins, D. M., Pluchinotta, A. & Woodward, G. Testing effects of consumer richness, evenness and body size on ecosystem functioning. J. Anim. Ecol. 80, 1145–1154. https://doi.org/10.1111/j.1365-2656.2011.01857.x (2011).
Article  PubMed  Google Scholar 

55.
McKie, B. G. et al. Ecosystem functioning in stream assemblages from different regions: contrasting responses to variation in detritivore richness, evenness and density. J. Anim. Ecol. 77, 495–504. https://doi.org/10.1111/j.1365-2656.2008.01357.x (2008).
CAS  Article  PubMed  Google Scholar 

56.
LeRoy, C. J. et al. Plant phylogenetic history explains in-stream decomposition at a global scale. J. Ecol. https://doi.org/10.1111/1365-2745.13262 (2019).
Article  Google Scholar 

57.
Correa-Araneda, F., Basaguren, A., Abdala-Díaz, R. T., Tonin, A. M. & Boyero, L. Resource-allocation tradeoffs in caddisflies facing multiple stressors. Ecol. Evol/ 7, 5103–5110 (2017).
Article  Google Scholar 

58.
López-Rojo, N. et al. Leaf traits drive plant diversity effects on litter decomposition and FPOM production in streams. PLoS ONE 13, e0198243 (2018).
Article  PubMed  PubMed Central  Google Scholar 

59.
Rao, C. R. Diversity and dissimilarity coefficients – a unified approach. Theor. Popul. Biol. 21, 24–43 (1982).
MathSciNet  Article  Google Scholar 

60.
Roscher, C. et al. Using plant functional traits to explain diversity-productivity relationships. PLoS ONE 7, e36760. https://doi.org/10.1371/journal.pone.0036760 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

61.
APHA. in Standard Methods for the Examination of Water and Wastewater 20th edn (ed M. A. H. Franson) 148–149 (American Public Health Association, 1998).

62.
Newell, S., Arsuffi, T. & Fallon, R. Fundamental procedures for determining ergosterol content of decaying plant material by liquid chromatography. Appl. Environ. Microbiol. 54, 1876–1879 (1988).
CAS  Article  PubMed  PubMed Central  Google Scholar 

63.
Suberkropp, K. & Weyers, H. Application of fungal and bacterial production methodologies to decomposing leaves in streams. Appl. Environ. Microbiol. 62, 1610–1615 (1996).
CAS  Article  PubMed  PubMed Central  Google Scholar 

64.
Ieno, E. N. & Zuur, A. F. A Beginner’s Guide to Data Exploration and Visualisation with R (Highland Statistics Limited, Newburgh, 2015).
Google Scholar 

65.
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experients. Nature 412, 72–76. https://doi.org/10.1111/j.1365-2427.2008.02092.x (2001).
ADS  CAS  Article  PubMed  Google Scholar 

66.
Davison, A. C. & Hinkley, D. V. Bootstrap Methods and Their Application (Cambridge University Press, Cambridge, 1997).
Google Scholar 

67.
boot: Bootstrap R (S-Plus) Functions. R Package Version 1.3–18 (Vienna: R Foundation for Statistical Computing, 2016).

68.
R: A language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

1.
White, R., Murray, S. & Rohweder, M. Pilot Analysis of Global Ecosystems: Grassland Ecosystems (World Resources Institute, Washington, 2000).
Google Scholar 
2.
Overbeck, G. E. et al. Brazil’s neglected biome: The south Brazilian Campos. Perspect. Plant Ecol. Evol. Syst. 9, 101–116 (2007).
Article  Google Scholar 

3.
Wilson, J. B., Peet, R. K., Dengler, J. & Pärtel, M. Plant species richness: The world records. J. Veg. Sci. 23, 796–802 (2012).
Article  Google Scholar 

4.
Dengler, J., Janišová, M. & Wellstein, C. Biodiversity of palaearctic grasslands: A synthesis. Agric. Ecosyst. Environ. 182, 1–14 (2014).
Article  Google Scholar 

5.
Gibson, D. J. Grasses and Grassland Ecology (Oxford University Press, Oxford, 2009).
Google Scholar 

6.
Būhning-Gaese, K. Determinants of avian species richness at different spatial scales. J. Biogeogr. 24, 49–60 (1997).
Article  Google Scholar 

7.
Andrade, B. O. et al. Vascular plant species richness and distribution in the Río de la Plata grasslands. Bot. J. Linn. Soc. 188, 250–256 (2018).
Google Scholar 

8.
Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: Is habitat heterogeneity the key?. Trends Ecol. Evol. 18, 182–188 (2003).
Article  Google Scholar 

9.
Reynolds, C. & Symes, C. T. Grassland bird response to vegetation structural heterogeneity and clearing of invasive bramble. Afr. Zool. 48, 228–239 (2013).
Article  Google Scholar 

10.
Hovick, T. J., Elmore, R. D., Fuhlendorf, S. D., Engle, D. M. & Hamilton, R. G. Spatial heterogeneity increases diversity and stability in grassland bird communities. Ecol. Appl. 25, 662–672 (2015).
Article  PubMed  Google Scholar 

11.
Bond, W. J. & Van Wilgen, B. W. Fire and Plants (Springer, New York, 2012).
Google Scholar 

12.
Laterra, P., Vignolio, O. R., Linares, M. P., Giaquinta, A. & Maceira, N. Cumulative effects of fire on a tussock pampa grassland. J. Veg. Sci. 14, 43–54 (2003).
Article  Google Scholar 

13.
Pillar, V. D. P. & Quadros, F. Grassland-forest boundaries in Southern Brazil. In Conference on Recent Shifts in Vegetation Boundaries of Deciduous Forests, Especially Due to General Global Warming 301–316 (Birkhäuser Basel, 1999). https://doi.org/10.1007/978-3-0348-8722-9_17

14.
Overbeck, G. E., Müller, S. C., Pillar, V. D. & Pfadenhauer, J. Fine-scale post-fire dynamics in southern Brazilian subtropical grassland. J. Veg. Sci. 16, 655–664 (2005).
Article  Google Scholar 

15.
Loydi, A., Funk, F. A. & García, A. Vegetation recovery after fire in mountain grasslands of Argentina. J. Mt. Sci. 17, 373–383 (2020).
Article  Google Scholar 

16.
Altesor, A., Oesterheld, M., Leoni, E., Lezama, F. & Rodríguez, C. Effect of grazing on community structure and productivity of a Uruguayan grassland. Plant Ecol. 179, 83–91 (2005).
Article  Google Scholar 

17.
López‐Mársico, L., Lezama, F. & Altesor, A. Heterogeneity decreases as time since fire increases in a South American grassland. Appl. Veg. Sci. avsc.12521 (2020). doi:https://doi.org/10.1111/avsc.12521

18.
Pickett, S. T. A., Kolasa, J., Armesto, J. J. & Collins, S. L. The Ecological Concept of Disturbance and Its Expression at Various Hierarchical Levels. Oikos 54, 129 (1989).
Article  Google Scholar 

19.
Grime, J. P. Plant strategies and vegetation processes (Plant Strateg. Veg, Process, 1979).
Google Scholar 

20.
Senft, R. L. et al. Large herbivore foraging and ecological hierarchies. Bioscience 37, 789–799 (1987).
Article  Google Scholar 

21.
Coughenour, M. B. Spatial components of plant-herbivore interactions in pastoral, ranching, and native ungulate ecosystems. Rangel. Ecol. Manag./J. Range Manag. Arch. 44(6), 530–542 (1991)

22.
Bond, W. J. & Keeley, J. E. Fire as a global ‘herbivore’: The ecology and evolution of flammable ecosystems. Trends Ecol. Evol. 20, 387–394 (2005).
Article  PubMed  Google Scholar 

23.
Bond, W. J. & Parr, C. L. Beyond the forest edge: Ecology, diversity and conservation of the grassy biomes. Biol. Conserv. 143, 2395–2404 (2010).
Article  Google Scholar 

24.
Gibson, D. J. & Hulbert, L. C. Effects of fire, topography and year-to-year climatic variation on species composition in tallgrass prairie. Vegetatio 72, 175–185 (1987).
Google Scholar 

25.
Vincent, C. A queima dos campos. Rev. Ind. Anim 3, 286–299 (1935).
Google Scholar 

26.
Overbeck, G. E. & Pfadenhauer, J. Adaptive strategies in burned subtropical grassland in southern Brazil. Flora Morphol. Distrib. Funct. Ecol. Plants 202, 27–49 (2007).
Article  Google Scholar 

27.
Quadros, F. L. F. & de Pillar, V. P. Dinâmica vegetacional em pastagem natural submetida a tratamentos de queima e pastejo. Ciência Rural. St. Maria 31, 863–868 (2001).
Article  Google Scholar 

28.
Rodríguez, C., Leoni, E., Lezama, F. & Altesor, A. Temporal trends in species composition and plant traits in natural grasslands of Uruguay. J. Veg. Sci. 14, 433–440 (2003).
Article  Google Scholar 

29.
Swengel, A. B. A literature review of insect responses to fire, compared to other conservation managements of open habitat. Biodivers. Conserv. 10, 1141–1169 (2001).
Article  Google Scholar 

30.
Fuhlendorf, S. D. et al. Should heterogeneity be the basis for conservation? Grassland bird response to fire and grazing. Ecol. Appl. 16, 1706–1716 (2006).
Article  PubMed  Google Scholar 

31.
Joern, A. & Laws, A. N. Ecological mechanisms underlying arthropod species diversity in grasslands. Annu. Rev. Entomol. 58, 19–36 (2013).
CAS  Article  PubMed  Google Scholar 

32.
Podgaiski, L. R. et al. Spider trait assembly patterns and resilience under fire-induced vegetation change in south Brazilian grasslands. PLoS ONE 8, e60207 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

33.
Pausas, J. G. Generalized fire response strategies in plants and animals. Oikos 128, 147–153 (2019).
Article  Google Scholar 

34.
Williams, P. R., Congdon, R. A., Grice, A. C. & Clarke, P. J. Germinable soil seed banks in a tropical savanna: Seasonal dynamics and effects of fire. Aust. Ecol. 30, 79–90 (2005).
Article  Google Scholar 

35.
Ooi, M. K. J. Delayed emergence and post-fire recruitment success: Effects of seasonal germination, fire season and dormancy type. Aust. J. Bot. 58, 248–256 (2010).
Article  Google Scholar 

36.
Canadell, J. & Zedler, P. H. Underground structures of woody plants in mediterranean ecosystems of Australia, California, and Chile. In 177–210 (Springer, New York, NY, 1995). https://doi.org/10.1007/978-1-4612-2490-7_8

37.
Fidelis, A., Appezzato-da-Glória, B., Pillar, V. D. & Pfadenhauer, J. Does disturbance affect bud bank size and belowground structures diversity in Brazilian subtropical grasslands?. Flora Morphol. Distrib. Funct. Ecol. Plants 209, 110–116 (2014).
Article  Google Scholar 

38.
Fidelis, A. T. et al. Fire intensity and severity in Brazilian campos grasslands. Interciencia Rev. Cienc. y Tecnol. Am. 35, 739–745 (2010).
Google Scholar 

39.
Oliveira, J. M. & Pillar, V. D. Vegetation dynamics on mosaics of Campos and Araucaria forest between 1974 and 1999 in Southern Brazil. Commun. Ecol. 5, 197–202 (2004).
Article  Google Scholar 

40.
Lezama, F. et al. Variation of grazing-induced vegetation changes across a large-scale productivity gradient. J. Veg. Sci. 25, 8–21 (2014).
Article  Google Scholar 

41.
Ferreira, P. M. A. et al. Long-term ecological research in southern Brazil grasslands: Effects of grazing exclusion and deferred grazing on plant and arthropod communities. PLoS ONE 15, e0227706 (2020).
Article  PubMed  PubMed Central  Google Scholar 

42.
Van Auken, O. W. Shrub invasions of north american semiarid grasslands. Annu. Rev. Ecol. Syst. 31, 197–215 (2000).
Article  Google Scholar 

43.
Guido, A., Salengue, E. & Dresseno, A. Effect of shrub encroachment on vegetation communities in Brazilian forest-grassland mosaics. Perspect. Ecol. Conserv. 15, 52–55 (2017).
Google Scholar 

44.
Sühs, R. B., Giehl, E. L. H. & Peroni, N. Preventing traditional management can cause grassland loss within 30 years in southern Brazil. Sci. Rep. 10, 1–9 (2020).
Article  CAS  Google Scholar 

45.
Moretti, M. & Legg, C. Combining plant and animal traits to assess community functional responses to disturbance. Ecography (Cop.) 32, 299–309 (2009).
Article  Google Scholar 

46.
Gerisch, M., Agostinelli, V., Henle, K. & Dziock, F. More species, but all do the same: Contrasting effects of flood disturbance on ground beetle functional and species diversity. Oikos 121, 508–515 (2012).
Article  Google Scholar 

47.
McIntyre, S., Lavorel, S. & Tremont, R. M. Plant life-history attributes: Their Relationship to disturbance response in herbaceous vegetation. J. Ecol. 83, 31 (1995).
Article  Google Scholar 

48.
Casas, G., Darski, B., Ferreira, P. M. A., Kindel, A. & Müller, S. C. Habitat structure influences the diversity, richness and composition of bird assemblages in successional atlantic rain forests. Trop. Conserv. Sci. 9, 503–524 (2016).
Article  Google Scholar 

49.
Mackey, R. L. & Currie, D. J. The diversity–disturbance relationship: Is it generally strong and peaked?. Ecology 82, 3479–3492 (2001).
Google Scholar 

50.
Connell, J. H. Diversity in tropical rain forests and coral reefs. Science (80-. ) 199, 1302–1310 (1978).
ADS  CAS  Article  Google Scholar 

51.
Hutchinson, G. E. The paradox of the plankton. Am. Nat. 95, 137–145 (1961).
Article  Google Scholar 

52.
Grime, J. P. Competitive exclusion in herbaceous vegetation. Nature 242, 344–347 (1973).
ADS  Article  Google Scholar 

53.
Fox, J. W. The intermediate disturbance hypothesis should be abandoned. Trends Ecol. Evol. 28, 86–92 (2013).
Article  PubMed  Google Scholar 

54.
Milchunas, D. G., Sala, O. E. & Lauenroth, W. K. A Generalized model of the effects of grazing by large herbivores on grassland community structure. Am. Nat. 132, 87–106 (1988).
Article  Google Scholar 

55.
Cingolani, A. M., Noy-Meir, I. & Díaz, S. Grazing effects on rangeland diversity: A synthesis of contemporary models. Ecol. Appl. 15, 757–773 (2005).
Article  Google Scholar 

56.
Harrison, S., Inouye, B. D. & Safford, H. D. Ecological heterogeneity in the effects of grazing and fire on grassland diversity. Conserv. Biol. 17, 837–845 (2003).
Article  Google Scholar 

57.
Spasojevic, M. J. et al. Fire and grazing in a mesic tallgrass prairie: Impacts on plant species and functional traits. Ecology 91, 1651–1659 (2010).
Article  PubMed  Google Scholar 

58.
Noy-Meir, I. Interactive effects of fire and grazing on structure and diversity of Mediterranean grasslands. J. Veg. Sci. 6, 701–710 (1995).
Article  Google Scholar 

59.
Peterson, D. W. & Reich, P. B. Fire frequency and tree canopy structure influence plant species diversity in a forest-grassland ecotone. Plant Ecol. 194, 5–16 (2008).
Article  Google Scholar 

60.
Collins, S. L. Fire frequency and community heterogeneity in tallgrass prairie vegetation. Ecology 73, 2001–2006 (1992).
Article  Google Scholar 

61.
Collins, S. L., Glenn, S. M. & Gibson, D. J. Experimental analysis of intermediate disturbance and initial floristic composition: Decoupling cause and effect. Ecology 76, 486–492 (1995).
Article  Google Scholar 

62.
Belsky, A. J. Effects of grazing, competition, disturbance and fire on species composition and diversity in grassland communities. J. Veg. Sci. 3, 187–200 (1992).
Article  Google Scholar 

63.
Malavasi, R., Battisti, C. & Carpaneto, G. M. Seasonal bird assemblages in a Mediterranean patchy wetland: Corroborating the intermediate disturbance hypothesis. Polish J. Ecol. 57, 171–179 (2009).
Google Scholar 

64.
Millenbah, K. F., Winterstein, S. R., Campa, H. I. I. I., Furrow, L. T. & Minnis, R. B. Effects of Conservation Reserve Program field age on avian relative abundance, diversity, and productivity. Wilson Bull. 108, 760–770 (1996).
Google Scholar 

65.
Shochat, E., Wolfe, D. H., Patten, M. A., Reinking, D. L. & Sherrod, S. K. Tallgrass prairie management and bird nest success along roadsides. Biol. Conserv. 121, 399–407 (2005).
Article  Google Scholar 

66.
Sick, H. Ornitologia Brasileira (Nova Fronteira, Rio de Janeiro, 1997).
Google Scholar 

67.
dos Reis, M. G., Fieker, C. Z. & Dias, M. M. The influence of fire on the assemblage structure of foraging birds in grasslands of the Serra da Canastra National Park, Brazil. An. Acad. Bras. Cienc. 88, 891–901 (2016).
Article  PubMed  Google Scholar 

68.
Weier, A., Radford, I. J., Woolley, L.-A. & Lawes, M. J. Fire regime effects on annual grass seeds as food for threatened grass-finch. Fire Ecol. 14, 8 (2018).
Article  Google Scholar 

69.
Fontana, C. S., Rovedder, C. E., Repenning, M. & Gonçalves, M. L. Estado atual do conhecimento e conservação da avifauna dos campos de cima da serra do sul do Brasil, Rio Grande do Sul e Santa Catarina. Rev. Bras. Ornitol. 16, 281–307 (2008).
Google Scholar 

70.
Repenning, M. & Fontana, C. S. Breeding biology of the Tropeiro seedeater (Sporophila beltoni). Auk Ornithol. Adv. 133, 484–496 (2016).
Google Scholar 

71.
Chiarani, E. & Fontana, C. S. Breeding biology of the Lesser grass-finch (Emberizoides ypiranganus) in southern Brazilian upland grasslands. Wilson J. Ornithol. 127, 441–456 (2015).
Article  Google Scholar 

72.
Reinking, D. L. Fire regimes and avian responses in the central tallgrass prairie. Stud. Avian Biol. 30, 116–126 (2005).
Google Scholar 

73.
Churchwell, R. T., Davis, C. A., Fuhlendorf, S. D. & Engle, D. M. Effects of patch-burn management on dickcissel nest success in a tallgrass prairie. J. Wildl. Manag. 72, 1596–1604 (2008).
Google Scholar 

74.
Rohrbaugh, R. W., Reinking, D. L., Wolfe, D. H., Sherrod, S. K. & Jenkins, M. A. Effects of prescribed burning and grazing on nesting and reproductive success of three grassland passerine species in tallgrass prairie. Stud. Avian Biol. 19, 165–170 (1999).
Google Scholar 

75.
Andrade, B. O., Bonilha, C. L., Ferreira, P. M. A., Boldrini, I. I. & Overbeck, G. E. Highland grasslands at the southern tip of the Atlantic forest biome: Management options and conservation challenges. Oecol. Aust. 20, 37–61 (2016).
Article  Google Scholar 

76.
de Pillar, V. P. & Vélez, E. Extinção dos Campos Sulinos em unidades de conservação: um fenômeno natural ou um problema ético?. Nat. a Conserv. 8, 84–86 (2010).
Article  Google Scholar 

77.
Overbeck, G. E. et al. Conservation in Brazil needs to include non-forest ecosystems. Divers. Distrib. 21, 1455–1460 (2015).
Article  Google Scholar 

78.
Azpiroz, A. B. et al. Ecology and conservation of grassland birds in southeastern South America: a review. J. F.Ornithol. 83(3), 217–246 (2012).
Article  Google Scholar 

79.
Andrade, B. O. et al. Classification of South Brazilian grasslands: Implications for conservation. Appl. Veg. Sci. 22, 168–184 (2019).
Article  Google Scholar 

80.
Boldrini, I. I. & Eggers, L. Vegetação campestre do sul do Brasil: dinâmica de espécies à exclusão do gado. Acta Bot. Brasilica 10, 37–50 (1996).
Article  Google Scholar 

81.
Bibby, C. J., Burgess, N. D. & Hill, D. A. Bird Census Techniques (Academic Press, Cambridge, 1992).
Google Scholar 

82.
Lavorel, S., McIntyre, S., Landsberg, J. & Forbes, T. D. A. Plant functional classifications: From general groups to specific groups based on response to disturbance. Trends Ecol. Evol. 12, 474–478 (1997).
CAS  Article  PubMed  Google Scholar 

83.
Lavorel, S., Rochette, C. & Lebreton, J.-D. Functional groups for response to disturbance in mediterranean old fields. Oikos 84, 480 (1999).
Article  Google Scholar 

84.
Díaz, S., Acosta, A. & Cabido, M. Community structure in montane grasslands of central Argentina in relation to land use. J. Veg. Sci. 5, 483–488 (1994).
Article  Google Scholar 

85.
Belton, H. Aves do Rio Grande do Sul: distribuição e biologia. Unisinos (1994).

86.
Fontana, C. S., Repenning, M., Rovedder, C. E. & Gonçalves, M. L. Biodiversidade dos campos de Cima da Serra. In (eds. Bond-Buckup, G., Buckup, L. & Dreier, C.) 118–135 (Libretos, 2010).

87.
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 027–046 (2010).
Article  Google Scholar 

88.
Hebbali, A. olsrr: Tools for Building OLS Regression Models. R package version 0.5.3. https://CRAN.R-project.org/package=olsrr (2020). 

89.
Akinwande, M. O., Dikko, H. G. & Samson, A. Variance inflation factor: As a condition for the inclusion of suppressor variable(s) in regression analysis. Open J. Stat. 05, 754–767 (2015).
Article  Google Scholar 

90.
Del Hoyo, J., Elliott, A., Sargatal, J., Christie, D. A. & de Juana, E. Handbook of the Birds of the World Alive (Lynx Edicions, Barcelona, 2018).
Google Scholar 

91.
Pillar, V. D., da Duarte, L. S., Sosinski, E. E. & Joner, F. Discriminating trait-convergence and trait-divergence assembly patterns in ecological community gradients. J. Veg. Sci. 20, 334–348 (2009).
Article  Google Scholar 

92.
Magurran, A. E. Measuring Biological Diversity (Wiley, New York, 2013).
Google Scholar 

93.
Podani, J. Extending Gower’s general coefficient of similarity to ordinal characters. Taxon 48, 331–340 (1999).
Article  Google Scholar 

94.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020).

95.
Zuur, A., Ieno, E., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects Models and Extensions in Ecology with R (Springer, New York, 2009).
Google Scholar 

96.
Pinheiro, J., Bates, D., DebRoy, & S., Sarkar, D. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-149. https://CRAN.Rproject.org/package=nlme (2020).

97.
Zelený, D. Which results of the standard test for community-weighted mean approach are too optimistic?. J. Veg. Sci. 29, 953–966 (2018).
Article  Google Scholar 

98.
Zeleny, D. Bias in Community-Weighted Mean Analysis Relating Species Attributes to Sample Attributes: Justification and Remedy. bioRxiv (Cold Spring Harbor Laboratory, 2016). https://doi.org/10.1101/046946

99.
Ter Braak, C. J. F. New robust weighted averaging- and model-based methods for assessing trait–environment relationships. Methods Ecol. Evol. 10, 1962–1971 (2019).
Article  Google Scholar 

100.
Hawkins, B. A. et al. Structural bias in aggregated species-level variables driven by repeated species co-occurrences: A pervasive problem in community and assemblage data. J. Biogeogr. 44, 1199–1211 (2017).
Article  Google Scholar 

101.
Mangiafico, S. Functions to support extension education program evaluation. (2020).

102.
Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, Amsterdam, 2012).
Google Scholar 

103.
Ter Braak, C. J. F. Canonical correspondence analysis: A new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).
Article  Google Scholar 

104.
IUCN 2020. The IUCN Red List of Threatened Species. 2020-2 https://www.iucnredlist.org. Accessed 10th Oct 2020

105.
Christensen, N. L. et al. Interpreting the Yellowstone fires of 1988. Bioscience 39, 678–685 (1989).
Article  Google Scholar 

106.
Ramos-Neto, M. B. & Pivello, V. R. Lightning fires in a Brazilian Savanna national park: Rethinking management strategies. Environ. Manag. 26, 675–684 (2000).
ADS  CAS  Article  Google Scholar 

107.
Dias, R. A. et al. Livestock disturbance in Brazilian grasslands influences avian species diversity via turnover. Biodivers. Conserv. 26, 2473–2490 (2017).
Article  Google Scholar 

108.
Mazzoni, L. & Perillo, A. Range extension of Anthus nattereri Sclater, 1878 (Aves: Motacillidae) in Minas Gerais, southeastern Brazil. Check List 7, 589 (2011).
Article  Google Scholar 

109.
Lombardi, V. T. et al. Registros notáveis de aves para o sul do estado de Minas Gerais, Brasil. Cotinga 34, 32–45 (2012).
Google Scholar 

110.
Petry, M. V. & Krüger, L. Frequent use of burned grasslands by the vulnerable Saffron-Cowled Blackbird Xanthopsar flavus: Implications for the conservation of the species. J. Ornithol. 151, 599–605 (2010).
Article  Google Scholar 

111.
Fraga, R. M., Casañas, H. & Pugnali, G. Natural history and conservation of the endangered saffron-cowled blackbird Xanthopsar flavus in Argentina. Bird Conserv. Int. 8, 255–267 (1998).
Article  Google Scholar 

112.
Silva, J. F., Raventos, J. & Caswell, H. Fire and fire exclusion effects on the growth and survival of two savanna grasses. Acta Ecol. 6, 783–800 (1990).
Google Scholar 

113.
Rovedder, C. E. & Fontana, C. S. Nest, eggs, and nest placement of the Brazilian endemic Black-bellied seedeater (Sporophila melanogaster). Wilson J. Ornithol. 124, 173–176 (2012).
Article  Google Scholar 

114.
Franz, I. & Fontana, C. S. Breeding biology of the Tawny-bellied seedeater (Sporophila hypoxantha) in southern Brazilian upland grasslands. Wilson J. Ornithol. 125, 280–292 (2013).
Article  Google Scholar 

115.
Dias, R. A., Bastazini, V. A. G. & Gianuca, A. T. Bird-habitat associations in coastal rangelands of southern Brazil. Iheringia. Série Zool. 104, 200–208 (2014).
Article  Google Scholar 

116.
Terborgh, J. W. Toward a trophic theory of species diversity. Proc. Natl. Acad. Sci. U. S. A. 112, 11415–11422 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

117.
Hunter, M. D. & Price, P. W. Playing chutes and ladders: heterogeneity and the relative roles of bottom-up and top-down forces in natural communities. Ecology 73, 724–732 (1992).
Google Scholar 

118.
QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org (2020). More

1.
Moen, R., Pastor, J., Cohen, Y. & Schwartz, C. C. Effects of moose movement and habitat use on GPS collar performance. J. Wildl. Manag. 60, 659–668 (1996).
Article  Google Scholar 
2.
Cain, J. W. III., Krausman, P. R., Jansen, B. D. & Morgart, J. R. Influence of topography and GPS fix interval on GPS collar performance. Wildl. Soc. Bull. 33, 926–934 (2005).
Article  Google Scholar 

3.
Graves, T. A. & Waller, J. S. Understanding the causes of missed global positioning system telemetry fixes. J. Wildl. Manag. 70, 844–851 (2006).
Article  Google Scholar 

4.
Moen, R., John, P. & Cohen, Y. Effects of animal activity on GPS telemetry location attempts. Alces 37, 207–216 (2001).
Google Scholar 

5.
D’Eon, R. G. Effects of a stationary GPS fix-rate bias on habitat-selection analyses. J. Wildl. Manag. 67, 858–863 (2003).
Article  Google Scholar 

6.
Dussault, C., Courtois, R., Ouellet, J. P. & Huot, J. Evaluation of GPS telemetry collar performance for habitat studies in the boreal forest. Wildl. Soc. Bull. 27, 965–972 (1999).
Google Scholar 

7.
Nielson, R. M., Manly, B. F. J., Mcdonald, L. L., Sawyer, H. & Mcdonald, T. L. Estimating habitat selection when GPS fix success is less than 100 %. Ecology 90, 2956–2962 (2009).
Article  PubMed  Google Scholar 

8.
Rempel, R. S., Rodgers, A. R. & Abraham, K. F. Performance of a GPS animal location system under boreal forest canopy. J. Wildl. Manag. 59, 543–551 (1995).
Article  Google Scholar 

9.
Bowman, J. L., Kochanny, C. O., Demarais, S. & Leopold, B. D. Evaluation of a GPS collar for white-tailed deer. Wildl. Soc. Bull. 28, 141–145 (2000).
Google Scholar 

10.
Jung, T. S. & Kuba, K. Performance of GPS collars on free-ranging bison (Bison bison) in north-western Canada. Wildl. Res. 42, 315–323 (2015).
Article  Google Scholar 

11.
Recio, M. R., Mathieu, R., Denys, P., Sirguey, P. & Seddon, P. J. Lightweight GPS-tags, one giant leap for wildlife tracking? An assessment approach. PLoS One 6, e28225 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

12.
Mattisson, J., Andrén, H., Persson, J. & Segerström, P. Effects of species behavior on global positioning system collar fix rates. J. Wildl. Manag. 74, 557–563 (2010).
Article  Google Scholar 

13.
Kaczensky, P., Ito, T. Y. & Walzer, C. Satellite telemetry of large mammals in Mongolia: What expectations should we have for collar function?. Wildl. Biol. Pract. 6, 108–126 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

14.
Harris, R. B. et al. Tracking Wildlife by Satellite: Current systems and Performance. Fish and Wildlife Technical Report https://pubs.er.usgs.gov/publication/70185512 (1990).

15.
Schwartz, C. C. & Arthur, S. M. Radiotracking large wilderness mammals: Integration of GPS and argostechnology. Ursus 11, 261–274 (1999).
Google Scholar 

16.
Tomkiewicz, S. M., Fuller, M. R., Kie, J. G. & Bates, K. K. Global positioning system and associated technologies in animal behaviour and ecological research. Philos. Trans. R. Soc. B Biol. Sci. 365, 2163–2176 (2010).
Article  Google Scholar 

17.
Rodgers, A. R. Recent telemetry technology. In Radio Tracking and Animal Populations (eds Marzluff, J. M. & Millspaugh, J. J.) 79–121 (Elsevier, New York, 2001). https://doi.org/10.1016/B978-012497781-5/50005-0.
Google Scholar 

18.
Thomas, B., Holland, J. D. & Minot, E. O. Wildlife tracking technology options and cost considerations. Wildl. Res. 38, 653–663 (2011).
Article  Google Scholar 

19.
Margalida, A., Pérez-García, J. M., Afonso, I. & Moreno-Opo, R. Spatial and temporal movements in Pyrenean bearded vultures (Gypaetus barbatus): Integrating movement ecology into conservation practice. Sci. Rep. 6, 35746 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

20.
García-Jiménez, R., Pérez-García, J. M. & Margalida, A. Drivers of daily movement patterns affecting an endangered vulture flight activity. BMC Ecol. 18, 39 (2018).
Article  PubMed  PubMed Central  Google Scholar 

21.
BirdLife International. (2017). Gypaetus barbatus (Amended Version of 2017 Assessment). The IUCN Red List of Threatened Species 2017: e.T22695174A118590506. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T22695174A118590506.en. Accessed: 12th Mar 2020.

22.
Houston D. C. Reintroduction programmes for vulture species. In Proceedings of the International Conference on Conservation and Management of Vulture populations 1, (eds Houston D. C. & Piper, S. E., 2006). Natural History Museum, University of Crete, Thessaloniki.

23.
Britten, M. W., Kennedy, P. L. & Ambrose, S. Performance and accuracy evaluation of small satellite transmitters. J. Wildl. Manag. 63, 1349–1358 (1999).
Article  Google Scholar 

24.
Soutullo, A., Cadahía, L., Urios, V., Ferrer, M. & Negro, J. J. Accuracy of lightweight satellite telemetry: A case study in the Iberian Peninsula. J. Wildl. Manag. 71, 1010–1015 (2007).
Article  Google Scholar 

25.
Silva, R., Afán, I., Gil, J. A. & Bustamante, J. Seasonal and circadian biases in bird tracking with solar GPS-tags. PLoS One 12, e0185344 (2017).
Article  CAS  PubMed  PubMed Central  Google Scholar 

26.
Byrne, M. E., Holland, A. E., Bryan, A. L. & Beasley, J. C. Environmental conditions and animal behavior influence performance of solar-powered GPS-GSM transmitters. Condor 119, 389–404 (2017).
Article  Google Scholar 

27.
Hofman, M. P. G. et al. Right on track? Performance of satellite telemetry in terrestrial wildlife research. PLoS One 14, 1–26 (2019).
Google Scholar 

28.
Aubrecht, C. et al. Vertical roughness mapping – ALS based classification of the vertical vegetation structure in forested areas. In Symposium A Quarterly Journal In Modern Foreign Literatures (eds. Wagner, W. & Székely, B.) XXXVIII, 35–40 (2010).

29.
Frair, J. L. et al. Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data. Philos. Trans. R. Soc. B Biol. Sci. 365, 2187–2200 (2010).
Article  Google Scholar 

30.
Péron, G. et al. The challenges of estimating the distribution of flight heights from telemetry or altimetry data. Anim. Biotelemetry 8, 1–13 (2020).
Article  Google Scholar 

31.
Cargnelutti, B. et al. Testing global positioning system performance for wildlife monitoring using mobile collars and known reference points. J. Wildl. Manag. 71, 1380–1387 (2007).
Article  Google Scholar 

32.
Edenius, L. Field test of a GPS location system for moose Alces alces under Scandinavian boreal conditions. Wildl. Biol. 3, 39–43 (1997).
Article  Google Scholar 

33.
Jurdak, R., Corke, P., Dharman, D. & Salagnac, G. Adaptive GPS duty cycling and radio ranging for energy-efficient localization. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems-SenSys ’10 57–70 (ACM Press, 2010). https://doi.org/10.1145/1869983.1869990.

34.
Gau, R. J. et al. Uncontrolled field performance of Televilt GPS-SimplexTM collars on grizzly bears in western and northern Canada. Wildl. Soc. Bull. 32, 693–701 (2004).
Article  Google Scholar 

35.
Girard, I. et al. Feasibility of GPS use to locate wild ungulates in high mountain environment. Pirineos 157, 7–14 (2002).
Article  Google Scholar 

36.
Krüger, S., Reid, T. & Amar, A. Differential range use between age classes of Southern African bearded vultures Gypaetus barbatus. PLoS One 9, e114920 (2014).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

37.
Augustine, B. C., Crowley, P. H. & Cox, J. J. A mechanistic model of GPS collar location data: Implications for analysis and bias mitigation. Ecol. Modell. 222, 3616–3625 (2011).
Article  Google Scholar 

38.
Douglas, D. C. et al. Moderating Argos location errors in animal tracking data. Methods Ecol. Evol. 3, 999–1007 (2012).
Article  Google Scholar 

39.
Cuadrat, J. M. et al. El clima de los Pirineos. Base de datos y primeros resultados. Tiempo Clima 45, 38–41 (2010).
Google Scholar 

40.
Margalida, A., Bertran, J. & Heredia, R. Diet and food preferences of the endangered Bearded Vulture Gypaetus barbatus: A basis for their conservation. Ibis (Lond. 1859) 151, 235–243 (2009).
Google Scholar 

41.
del Hoyo, J., Elliott, A., Sargatal, J., Christie, D. A. & Kirwan, G. Handbook of the Birds of the World,2 (Lynx Edicions, Barcelona, 1994).
Google Scholar 

42.
Antor, R. J. et al. First breeding age in captive and wild bearded vultures Gypaetus barbatus. Acta Ornithol. 42, 114–118 (2007).
Article  Google Scholar 

43.
Gil, J. A. et al. Home ranges and movements of non-breeding bearded vultures tracked by satellite telemetry in the Pyrenees. Ardeola 61, 379–387 (2014).
Article  Google Scholar 

44.
Sunyer, C. El periodo de emancipación en el Quebrantahuesos (Gypaetus barbatus): Consideraciones sobre su conservación. In El quebrantahuesos (Gypaetus barbatus) en los Pirineos. Características Ecológicas y Biología (eds Heredia, R. & Heredia, B.) 47–65 (ICONA, Turin, 1991).
Google Scholar 

45.
Margalida, A. et al. Uneven large-scale movement patterns in wild and reintroduced pre-adult Bearded Vultures: Conservation implications. PLoS One 8, e65857 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

46.
Ellergren, H. First gene on the avian W chromosome (CHD) provides a tag for universal sexing of non-ratite birds. Proc. R Soc. Lond. Ser. B Biol. Sci. 263, 1635–1641 (1996).
ADS  Article  Google Scholar 

47.
Cruz, S., Proaño, C. B., Anderson, D., Huyvaert, K. & Wikelski, M. Data from: The Environmental-Data Automated Track Annotation (Env-DATA) System: Linking animal tracks with environmental data. (2013). https://doi.org/10.5441/001/1.3hp3s250.

48.
Dodge, S. et al. The environmental-data automated track annotation (Env-DATA) system: Linking animal tracks with environmental data. Mov. Ecol. 1, 3 (2013).
Article  PubMed  PubMed Central  Google Scholar 

49.
Cuscó, F., Cardador, L., Bota, G., Morales, M. B. & Mañosa, S. Inter-individual consistency in habitat selection patterns and spatial range constraints of female little bustards during the non-breeding season. BMC Ecol. 18, 1–12 (2018).
Article  Google Scholar 

50.
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Article  Google Scholar 

51.
Anadón, J. D. et al. Factors determining the distribution of the spur-thighed tortoise Testudo graeca in south-east Spain: A hierarchical approach. Ecography (Cop.) 29, 339–346 (2006).
Article  Google Scholar 

52.
R Foundation for Statistical Computing. R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (2019).

53.
Bates, D., Maechler, M. & Dai, B. lme4: Linear mixed-effects models using S4 classes. 2009. R package version 0.999375-31. https://CRAN.R-project.org/package=lme4 (2009).

54.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach Vol 2 (Springer, Berlin, 2002).
Google Scholar 

55.
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, Berlin, 2009).
Google Scholar 

56.
Barton, K. Package ‘MuMIn’. R package version 1.43. 15. https://CRAN.R-project.org/package=MuMIn (2019).

57.
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage publications, Thousand Oaks, 2018).
Google Scholar 

58.
Mitchell, L. J., White, P. C. & Arnold, K. E. The trade-off between fix rate and tracking duration on estimates of home range size and habitat selection for small vertebrates. PLoS One 14, e0219357 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar  More

1.
O’Malley, M. P., Lee-Brooks, K. & Medd, H. B. The global economic impact of manta ray watching tourism. PLoS ONE 8(5), e65051. https://doi.org/10.1371/journal.pone.0065051 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 
2.
Balmford, A. et al. Walk on the wild side: estimating the global magnitude of visits to protected areas. PLoS Biol. 13, e1002074. https://doi.org/10.1371/journal.pbio.1002074 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

3.
Zimmerhackel, J. S. et al. How shark conservation in the Maldives affects demand for dive tourism. Tourism Manage. 69, 263–271 (2018).
Article  Google Scholar 

4.
Burgin, S. & Hardiman, N. Effects of non-consumptive wildlife-orientated tourism on marine species and prospects for their sustainable management. J. Environ. Manage. 151, 210–220 (2015).
Article  PubMed  Google Scholar 

5.
Bruce, B. D. & Bradford, R. W. The effects of shark cage-diving operations on the behaviour and movements of white sharks, Carcharodon carcharias, at the Neptune Islands South Australia. Mar. Biol. 160, 889–907 (2013).
Article  Google Scholar 

6.
Corcoran, M. J. et al. Supplemental feeding for ecotourism reverses diel activity and alters movement patterns and spatial distribution of the Southern stingrays Dasyatis americana. PLoS ONE 8(3), e59235. https://doi.org/10.1371/journal.pone.0059235 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

7.
Arlettaz, R., Christe, P. & Schaub, M. Food availability as a major driver in the evolution of life-history strategies of sibling species. Ecol. Evol. 7, 4163–4172. https://doi.org/10.1002/ece3.2909 (2017).
Article  PubMed  PubMed Central  Google Scholar 

8.
Huveneers, C. et al. The effects of cage-diving activities on the fine-scale swimming behavior and space use of white sharks. Mar. Biol. 160, 2863–2875 (2013).
Article  Google Scholar 

9.
Semeniuk, C. A. D., Bourgeon, S., Smith, S. L. & Rothley, K. D. Hematological differences between stingrays at tourist and non-visited sites suggest physiological costs of wildlife tourism. Biol. Cons. 142, 1818–1829. https://doi.org/10.1016/j.biocon.2009.03.022 (2009).
Article  Google Scholar 

10.
Maljkovic, A. & Côté, I. M. Effects of tourism-related provisioning on the trophic signatures and movement patterns of an apex predator, the Caribbean reef shark. Biol. Conserv. 144, 859–865. https://doi.org/10.1016/j.biocon.2010.11.019 (2011).
Article  Google Scholar 

11.
Brena, P. F., Mourier, J., Planes, S. & Clua, E. Shark and ray provisioning: functional insights into behavioral, ecological and physiological responses across multiple scales. Mar. Ecol. Prog. Ser. 538, 273–283 (2015).
ADS  CAS  Article  Google Scholar 

12.
Kelly, J. F. Stable isotopes of carbon and nitrogen in the study of avian and mammalian trophic ecology. Can. J. Zool. 78, 1–27. https://doi.org/10.1139/z99-165 (2000).
Article  Google Scholar 

13.
Jeanniard-du-Dot, T., Thomas, A. C., Cherel, Y., Trites, A. W. & Guinet, C. Combining hard-part and DNA analyses of scats with biologging and stable isotopes can reveal different diet compositions and feeding strategies within a fur seal population. Mar. Ecol. Prog. Ser. 584, 1–16 (2017).
ADS  CAS  Article  Google Scholar 

14.
Wetherbee, B.M., Cortés, E. Food consumption and feeding habits In Sharks and Their Relatives I (eds Musick, J.A., Heithaus, M., & Carrier, J.C.) 225–246 (CRC Press, 2004).

15.
Dehn, L.-A. et al. Feeding ecology of phocid seals and some walrus in the Alaskan and Canadian Arctic as determined by stomach contents and stable isotope analysis. Polar Biol. 30(2), 167–181 (2006).
Article  Google Scholar 

16.
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506 (1978).
ADS  CAS  Article  Google Scholar 

17.
DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of nitrogen isotopes in animals. Geochim Cosmochim Acta 45, 341–351 (1981).
ADS  CAS  Article  Google Scholar 

18.
Iverson, S. J., Field, C., Bowen, W. D. & Blanchard, W. Quantitative fatty acid signature analysis: a new method of estimating predator diets. Ecol. Monogr. 74, 211–235 (2004).
Article  Google Scholar 

19.
Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mammal Sci. 26, 509–572 (2010).
CAS  Google Scholar 

20.
Polito, M. J. et al. Integrating stomach content and stable isotope analyses to quantify the diets of Pygoscelid penguins. PLoS ONE 6, e26642. https://doi.org/10.1371/journal.pone.0026642 (2011).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

21.
Couturier, L. I. E. et al. Stable isotope and signature fatty acid analyses suggest reef manta rays feed on demersal zooplankton. PLoS ONE 8(10), e77152. https://doi.org/10.1371/journal.pone.0077152 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

22.
Käkelä, A. et al. Fatty acid signatures and stable isotopes as dietary indicators in North Sea seabirds. Mar. Ecol. Prog. Ser. 342, 291–301 (2007).
ADS  Article  Google Scholar 

23.
Carlisle, A. B. et al. Using stable isotope analysis to understand the migration and trophic ecology of northeastern Pacific white sharks (Carcharodon carcharias). PLoS ONE 7, e30492. https://doi.org/10.1371/journal.pone.0030492 (2012).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

24.
Watt, C. A. & Ferguson, S. H. Fatty acid and stable isotopes (δ13C and δ15N) reveal temporal changes in narwhal (Monodon monoceros) diet linked to migration patterns. Mar. Mammal Sci. 31, 21–44 (2015).
CAS  Article  Google Scholar 

25.
Minagawa, M. & Wada, E. Stepwise enrichment of 15N along food chains: further evidence and the relation between 15N and animal age. Geochim. Cosmochim. Acta 48, 1135–1140 (1984).
ADS  CAS  Article  Google Scholar 

26.
Vander Zanden, M.J. & Rasmussen, J.B. Variation in delta N-15 and delta C-13 trophic fractionation: implications for aquatic food web studies. Limnol. Oceanogr. 46, 2061-2066 (2001).

27.
Fry, B. Food web structure on Georges Bank from stable C, N, and S isotopic compositions. Limnol. Oceanogr. 33, 1182–1190 (1988).
ADS  CAS  Article  Google Scholar 

28.
Mackenzie, K. M. et al. Locations of marine animals revealed by carbon isotopes. Sci. Rep. 1, 1–6. https://doi.org/10.1038/srep00021 (2011).
CAS  Article  Google Scholar 

29.
DeNiro, M.J. & Epstein, S. You are what you eat (plus a few ‰): the carbon isotope cycle in food chains. Geol. Soc. Amer., Abstr. Programs 8, 834–835 (1976).

30.
Budge, S. M., Iverson, S. J. & Koopman, H. N. Studying trophic ecology in marine ecosystems using fatty acids: a primer on analysis and interpretation. Mar. Mammal Sci. 22(4), 759–801 (2006).
Article  Google Scholar 

31.
Ackman, R.G. Fish lipids in Advances in Fish Science And Technology (ed. Connell, J.J.) 86–103 (Fishing News Books Ltd., 1980).

32.
Tocher, D. R. Metabolism and functions of lipids and fatty acids in teleost fish. Rev. Fish. Sci. 11, 107–184. https://doi.org/10.1080/713610925 (2003).
CAS  Article  Google Scholar 

33.
McMeans, B. C., Arts, M. T. & Fisk, A. T. Similarity between predator and prey fatty acid profiles is tissue dependent in Greenland sharks (Somniosus microcephalus): implications for diet reconstruction. J. Exp. Mar. Biol. Ecol. 429, 55–63. https://doi.org/10.1016/j.jembe.2012.06.017 (2012).
CAS  Article  Google Scholar 

34.
Bigelow, H., & Schroeder, W. Fishes of the Western North Atlantic, Part 2. Sawfishes, Guitarfishes, Skates, Rays and Chimaeroids. 1–588 (Yale University Press, 1953).

35.
Aguiar, A., Valentin, J. & Rosa, R. S. Habitat use by Dasyatis americana in a south-western Atlantic oceanic island. J. Mar. Biol. Assoc. 89, 1147–1152 (2009).
Article  Google Scholar 

36.
Snelson, F. F. Jr. & Williams, S. E. Notes on the occurrence, distribution, and biology of elasmobranch fishes in the Indian River Lagoon system Florida. Estuaries 4, 110–120 (1981).
Article  Google Scholar 

37.
Gilliam, D. S. & Sullivan, K. M. Diet and feeding habits of the Southern stingray Dasyatis americana in the Central Bahamas. Bull. Mar. Sci. 52(3), 1007–1013 (1993).
Google Scholar 

38.
Bowman, R., Stillwell, C., Michaels, W. & Grosslein, M. Food of Northwest Atlantic fishes and two common species of squid. NOAA Technical Memorandum NMFS-NE 155, 1–137 Reprint at https://pdfs.semanticscholar.org/c013/400022949952cc0f261fa71c76195c173e04.pdf (2000).

39.
Vaudo, J. J. et al. Characterization and monitoring of one of the world’s most valuable ecotourism animals, the southern stingray at Stingray City Grand Cayman. Mar. Freshwater Res. 69, 144–154 (2018).
Article  Google Scholar 

40.
Nelson, M. Swim with the rays: a guide to Stingray City, Grand Cayman 37 (Blueline Press, Colorado, 1995).
Google Scholar 

41.
Shackley, M. ‘Stingray city’-managing the impact of underwater tourism in the Cayman Islands. J. Sustain. Tour. 6, 328–338 (1998).
Article  Google Scholar 

42.
Semeniuk, C. A. D., Speers-Roesch, B. & Rothley, K. D. Using fatty-acid profile analysis as an ecologic indicator in the management of tourist impacts on marine wildlife: a case of stingray-feeding in the Caribbean. Environ. Manag. 40, 665–677 (2007).
ADS  Article  Google Scholar 

43.
Semeniuk, C. A. D. & Rothley, K. D. Costs of group-living for a normally solitary forager: effects of provisioning tourism on southern stingrays Dasyatis americana. Mar. Ecol. Prog. Ser. 357, 271–282 (2008).
ADS  Article  Google Scholar 

44.
Abdi, H. The bonferonni and Šidák corrections for multiple comparisons in Encyclopedia of Measurements and Statistics (ed Salkind, N.L.) 1–9 (Sage Publishing, 2007).

45.
Dale, J. J., Wallsgrove, N. J., Popp, B. N. & Holland, K. N. Nursery habitat use and foraging ecology of the brown stingray Dasyatis lata determined from stomach contents, bulk and amino acid stable isotopes. Mar. Ecol. Prog. Ser. 433, 221–236 (2011).
ADS  Article  Google Scholar 

46.
Tilley, A., López-Angarita, J. & Turner, J. R. Diet reconstruction and resource partitioning of a Caribbean marine mesopredator using stable isotope Bayesian modeling. PLoS ONE 8(11), e79560. https://doi.org/10.1371/journal.pone.0079560 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

47.
Hobson, K. A. & Welch, H. E. Determination of trophic relationships within a high Arctic marine food web using δ13C and δ15N analysis. Mar. Ecol. Prog. Ser. 84, 9–18 (1992).
ADS  CAS  Article  Google Scholar 

48.
Galván, D. E., Jañez, J. & Irigoyen, A. J. Estimating tissue-specific discrimination factors and turnover rates of stable isotopes of nitrogen and carbon in the smallnose fanskate Sympterygia bonapartii (Rajidae). J. Fish. Biol. 89, 1258–1270. https://doi.org/10.1111/jfb.13024 (2016).
CAS  Article  PubMed  Google Scholar 

49.
Ohkouchi, N. et al. Advances in the application of amino acid nitrogen isotopic analysis in ecological and biogeochemical studies. Org. Geochem. 113, 150–174. https://doi.org/10.1016/j.orggeochem.2017.07.009 (2017).
CAS  Article  Google Scholar 

50.
Smith, K. & Herrnkind, W. Predation on early juvenile spiny lobsters Panulirus argus (Latreille): influence of size and shelter. J. Exp. Mar. Biol. Ecol. 157, 3–18 (1992).
Article  Google Scholar 

51.
Randall, J. Food Habits of Reef Fishes of the West Indies. University of Hawaii (1967).

52.
Newsome, D., Lewis, A. & Moncrieff, D. Impacts and risks associated with developing, but unsupervised stingray tourism at Hameline Bay Western Australia. Int. J. Tour. Res. 6, 305–323. https://doi.org/10.1002/jtr.491 (2004).
Article  Google Scholar 

53.
Hobson, K. A., Alisauskas, R. T. & Clark, R. G. Stable-nitrogen isotope enrichment in avian tissues due to fasting and nutritional stress: implications for isotopic analyses of diet. Condor 95, 388–394 (1993).
Article  Google Scholar 

54.
Oelbermann, K. & Sheu, S. Stable isotope enrichment (δ15N and δ13C) in a generalist predator (Pardosa lugubris, Araneae: Lycosidae): effects of prey quality. Oecologia 130, 337–344 (2002).
ADS  Article  PubMed  Google Scholar 

55.
Hertz, E., Trudel, M., Cox, M. K. & Mazumder, A. Effects of fasting and nutritional restriction on the isotopic ratios of nitrogen and carbon: a meta-analysis. Ecol. Evol. 5(21), 4829–4839. https://doi.org/10.1002/ece3.1738 (2015).
Article  PubMed  PubMed Central  Google Scholar 

56.
Doi, H. F., Akamatsu, F. & González, A. L. Starvation effects on nitrogen and carbon stable isotopes of animals: an insight from meta-analysis of fasting experiments. R. Soc. open sci. 4, 170633. https://doi.org/10.1098/rsos.170633 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

57.
Doucett, R. R., Booth, R. K., Power, G. & McKinley, R. S. Effects of the spawning migration on the nutritional status of anadromous Atlantic salmon (Salmo salar): insights from stable-isotope analysis. Can. J. Fish. Aquat. Sci. 56, 2172–2180 (1999).
Article  Google Scholar 

58.
Cherel, Y., Hobson, K. A., Bailleul, F. & Groscolas, R. Nutrition, physiology, and stable isotopes: new information from fasting and molting penguins. Ecology 86, 2881–2888 (2005).
Article  Google Scholar 

59.
Kempster, B. et al. Do stable isotopes reflect nutritional stress? Results from a laboratory experiment on song sparrows. Oecologia 151, 365–371 (2007).
ADS  Article  PubMed  Google Scholar 

60.
Logan, J. M. & Lutcavage, M. E. Stable isotope dynamics in elasmobranch fishes. Hydrobiologia 644, 231–244 (2010).
CAS  Article  Google Scholar 

61.
Wyatt, A. S. J. et al. Enhancing insights into foraging specialization in the world’s largest fish using a multi-tissue, multi-isotope approach. Ecol. Monogr. 89, e01339. https://doi.org/10.1002/ecm.1339 (2019).
Article  Google Scholar 

62.
Williams, C.T., Buck, C.L., Sears, J. & Kitaysky, A.S. 2007. Effects of nutritional restriction on nitrogen and carbon stable isotopes in growing seabirds. Oecologia 153, 11–18 (2007).

63.
McMahon, K. W., Thorrold, S. R., Elsdon, T. S. & McCarthy, M. D. Trophic discrimination of nitrogen stable isotopes in amino acids varies with diet quality in a marine fish. Limnol. Oceanogr. 60, 1076–1087 (2015).
ADS  CAS  Article  Google Scholar 

64.
Rajapakse, N., Mendis, E., Byun, H.-G. & Kim, S.-K. Purification and in vitro antioxidative effects of giant squid muscle peptides on free radical-mediated oxidative systems. J. Nutr. Biochem. 16, 562–569 (2005).
CAS  Article  PubMed  Google Scholar 

65.
Hussey, N. E. et al. Expanded trophic complexity among large sharks. Food Webs 4, 1–7 (2015).
Article  Google Scholar 

66.
Bosley, K. L., Witting, D. A., Chambers, R. C. & Wainright, S. C. Estimating turnover rates of carbon and nitrogen in recently metamorphosed winter flounder Pseudopleuronectes americanus with stable isotopes. Mar. Ecol. Prog. Ser. 236, 233–240 (2002).
ADS  Article  Google Scholar 

67.
Fry, B. & Arnold, C. Rapid 13C/12C turnover during growth of brown shrimp (Penaeus aztecus). Oecologia 54, 200–204 (1982).
ADS  Article  PubMed  Google Scholar 

68.
Boecklen, W. J., Yarnes, C. T., Cook, B. A. & James, A. C. On the use of stable isotopes in trophic ecology. Annu. Rev. Ecol. Evol. Syst. 42, 411–440 (2011).
Article  Google Scholar 

69.
Kim, S. L., del Rio, C. M., Casper, D. & Koch, P. L. Isotopic incorporation rates for shark tissues from a long-term captive feeding study. J. Exp. Biol. 215, 2495–2500 (2012).
Article  PubMed  Google Scholar 

70.
Thomas, S. M. & Crowther, T. W. Predicting rates of isotopic turnover across the animal kingdom: a synthesis of existing data. J. Anim. Ecol. 84, 861–870. https://doi.org/10.1111/1365-2656.12326 (2015).
Article  PubMed  Google Scholar 

71.
Hussey, N. E. et al. Stable isotopes and elasmobranchs: tissue types, methods, applications and assumptions. J. Fish Biol. 80(5), 1449–1484. https://doi.org/10.1111/j.1095-8649.2012.03251.x (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar 

72.
MacNeil, M. A., Drouillard, K. G. & Fisk, A. T. Variable uptake and elimination of stable nitrogen isotopes between tissues in fish. Can. J. Fish Aquat. Sci. 63, 345–353. https://doi.org/10.1139/f05-219 (2006).
CAS  Article  Google Scholar 

73.
Caut, S., Jowers, M., Michel, L., Lepoint, G. & Fisk, A. Diet- and tissue-specific incorporation of isotopes in the shark Scyliorhinus stellaris, a North Sea mesopredator. Mar. Ecol. Prog. Ser. 492, 185–198 (2013).
ADS  CAS  Article  Google Scholar 

74.
Miller, T. W., Brodeur, R. D. & Rau, G. H. Carbon stable isotopes reveal relative contribution of shelf-slope production to the northern California Current pelagic community. Limnol. Oceanogr. 53, 1493–1503 (2008).
ADS  CAS  Article  Google Scholar 

75.
Lytle, J. S. & Lytle, T. F. Fatty acid and cholesterol content of sharks and rays. J. Food Compos. Anal. 7, 110–118 (1994).
CAS  Article  Google Scholar 

76.
Jangaard, P. M. & Ackman, R. G. Lipids and component fatty acids of the Newfoundland squid, Illex illecebrosus (Le Sueur). J. Fish. Res. Board Can. 22(1), 131–137. https://doi.org/10.1139/f65-012 (1965).
CAS  Article  Google Scholar 

77.
Kirsch, P. E., Iverson, S. J., Bowen, W. D., Kerr, S. R. & Ackman, R. G. Dietary effects on the fatty acid signature of whole Atlantic cod (Gadus morhua). Can. J. Fish Aquat. Sci. 55, 1378–1386. https://doi.org/10.1139/f98-019 (1998).
CAS  Article  Google Scholar 

78.
Phillips, K. L., Jackson, G. D. & Nichols, P. D. Predation on myctophids by the squid Moroteuthis ingens around Macquarie and Heard Islands: stomach contents and fatty acid analyses. Mar. Ecol. Prog. Ser. 215, 179–189 (2001).
ADS  CAS  Article  Google Scholar 

79.
Premarathna, A. D. et al. Nutritional analysis of some selected fish and crab meats and fatty acid analysis of oil extracted from Portunus pelagicus. IJSRST 4, 197–201 (2015).
Google Scholar 

80.
Javaheri Baboli, J., Velayatzahed, M., Roomiani, L. & Khoramadadi, A. Effects of sex and tissue fatty acid composition in the meat of blue swimming crab (Portunus pelagicus) from the Persian Gulf, Iran. Iran J. Fish. Sci. 15, 818–826 (2016).

81.
Arai, T., Amalina, R. & Bachok, Z. Similarity in the feeding ecology of parrotfish (Scaridae) in coral reef habitats of the Malaysian South China Sea, as revealed by fatty acid signatures. Biochem. Syst. Ecol. 59, 85–90. https://doi.org/10.1016/j.bse.2015.01.011 (2015).
CAS  Article  Google Scholar 

82.
Ayas, D. & Ozogul, Y. The effects of seasonal changes on fat and fatty acid contents of mantis shrimp (Eurogosquilla massavensis). Adv. Food Sci. 34, 164–167 (2012).
CAS  Google Scholar 

83.
Balzano, M., Pacetti, D., Lucci, P., Fiorini, D. & Frega, N. G. Bioactive fatty acids in mantis shrimp, crab and caramote prawn: their content and distribution among the main lipid classes. J. Food Compos. Anal. 59, 88–94 (2017).
CAS  Article  Google Scholar 

84.
Lytle, J. S., Lytle, T. F. & Ogle, J. T. Polyunsaturated fatty acid profiles as a comparative tool in assessing maturation diets of Penaeus vannamei. Aquaculture 89, 287–299 (1990).
CAS  Article  Google Scholar 

85.
Pethybridge, H., Daley, R., Virtue, P. & Nicols, P. Lipid composition and partitioning of deepwater chondrichthyans: inferences of feeding ecology and distribution. Mar. Biol. 157, 1367–1384 (2010).
CAS  Article  Google Scholar 

86.
Pethybridge, P., Daley, R. K. & Nichols, P. D. Diet of demersal sharks and chimeras inferred by fatty acid profiles and stomach content analysis. J. Exp. Mar. Biol. Ecol. 409, 290–299. https://doi.org/10.1016/j.jembe.2011.09.009 (2011).
Article  Google Scholar 

87.
Beckmann, C. L., Mitchell, J. G., Stone, D. A. J. & Huveneers, C. A controlled feeding experiment investigating the effects of a dietary switch on muscle and liver fatty acid profiles in Port Jackson sharks Heterodontus portusjacksoni. J. Exp. Mar. Biol. Ecol. 448, 10–18. https://doi.org/10.1016/j.jembe.2013.06.009 (2013).
CAS  Article  Google Scholar 

88.
Beckmann, C. L., Mitchell, J. G., Stone, D. A. & Huveneers, C. Inter-tissue differences in fatty acid incorporation as a result of dietary oil manipulation in Port Jackson sharks (Heterodontus portusjacksoni). Lipids 49, 577–590 (2014).
CAS  Article  PubMed  Google Scholar 

89.
Gibson, R. A. Australian fish – an excellent source of both arachidonic acid and ω-3 polyunsaturated fatty acids. Lipids 18, 743–752 (1983).
CAS  Article  PubMed  Google Scholar 

90.
Dunstan, G. A., Sinclair, A. J., O’Dea, K. & Naughton, J. M. The lipid content and fatty acid composition of various marine species from southern Australian coastal waters. Comp. Biochem. Physiol. B 91, 165–169. https://doi.org/10.1016/0305-0491(88)90130-7 (1988).
Article  Google Scholar 

91.
Ballantyne, J.S. Jaws: the inside story. The metabolism of elasmobranch fishes. Comp. Biochem. Physiol. B 118, 703–742 (1997).

92.
Wood, C. M., Walsh, P. J., Kajimura, M., McClelland, G. B. & Chew, S. F. The influence of feeding and fasting on plasma metabolites in the dogfish shark (Squalus acanthias). Comp. Biochem. Physiol. A. 155, 435–444 (2010).
Article  CAS  Google Scholar 

93.
Meyer, L., Pethybridge, H., Nichols, P. D., Beckmann, C. & Huveneers, C. Abiotic and biotic drivers of fatty acid tracers in ecology: a global analysis of chondrichthyan profiles. Funct. Ecol. 33, 1–13. https://doi.org/10.1111/1365-2435.13328 (2019).
Article  Google Scholar 

94.
Preston, T. & Owens, N. J. P. Interfacing an automatic elemental analyser with an isotope ratio mass spectrometer: the potential for fully automated total nitrogen and nitrogen-15 analysis. Analyst 108, 971–977 (1983).
ADS  CAS  Article  Google Scholar 

95.
Kim, S. L. & Koch, P. L. Methods to collect, preserve, and prepare elasmobranch tissues for stable isotope analysis. Environ. Biol. Fish. 95, 53–63 (2012).
Article  Google Scholar 

96.
Folch, J., Lees, M. & Sloane-Stanly, G. H. A simple method for the isolation and purification of total lipids from animal tissues. J. Biol. Chem. 226, 497–509 (1957).
CAS  PubMed  Google Scholar  More

1.
Beyer, J. et al. Blue mussels (Mytilus edulis spp.) as sentinel organisms in coastal pollution monitoring: a review. Mar. Env. Res. 130, 338–365 (2017).
ADS  Article  CAS  Google Scholar 
2.
Metzger, M. J., Reinisch, C., Sherry, J. & Goff, S. P. Horizontal transmission of clonal cancer cells causes leukemia in soft-shell clams. Cell 161, 255–263 (2015).
Article  CAS  PubMed  PubMed Central  Google Scholar 

3.
Metzger, M. J. et al. Widespread transmission of independent cancer lineages within multiple bivalve species. Nature 534, 705–709 (2016).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

4.
Rozen, Y. & Belkin, S. Survival of enteric bacteria in seawater. FEMS Microbiol. Rev. 25, 513–529 (2001).
Article  CAS  PubMed  Google Scholar 

5.
Suttle, C. A. The significance of viruses to mortality in aquatic microbial communities. Microbiol. Ecol. 28, 237–243 (1994).
Article  CAS  Google Scholar 

6.
Allam, B. & Raftos, D. Immune responses to infectious diseases in bivalves. J. Invertebr. Pathol. 131, 121–136 (2015).
Article  CAS  PubMed  Google Scholar 

7.
Allam, B. & Espinosa, E. P. Mucosal immunity in mollusks. In Mucosal Health in Aquaculture (eds Beck, B. H. & Peatman, E.) 325–370 (Academic Press, Cambridge, 2015).
Google Scholar 

8.
Lau, Y. T., Sussman, L., Espinosa, E. P., Katalay, S. & Allam, B. Characterization of hemocytes from different body fluids of the eastern oyster Crassostrea virginica. Fish Shellfish Immunol. 71, 372–379 (2017).
Article  CAS  PubMed  Google Scholar 

9.
Lau, Y. T., Gambino, L., Santos, B., Espinosa, E. P. & Allam, B. Transepithelial migration of mucosal hemocytes in Crassostrea virginica and potential role in Perkinsus marinus pathogenesis. J. Invertebr. Pathol. 153, 122–129 (2018).
Article  PubMed  Google Scholar 

10.
Allam, B. & Espinosa, E. P. Bivalve immunity and response to infections: are we looking at the right place?. Fish Shellfish Immunol. 53, 4–12 (2016).
Article  CAS  PubMed  Google Scholar 

11.
Lau, Y. T., Gambino, L., Santos, B., Espinosa, E. P. & Allam, B. Regulation of oyster (Crassostrea virginica) hemocyte motility by the intracellular parasite Perkinsus marinus: a possible mechanism for host infection. Fish Shellfish Immunol. 78, 18–25 (2018).
Article  PubMed  Google Scholar 

12.
Bodkin, J. L. et al. Variation in abundance of Pacific blue mussel (Mytilus trossulus) in the Northern Gulf of Alaska, 2006–2015. Deep Sea Res. Part II(147), 87–97 (2018).
Article  Google Scholar 

13.
Bijlsma, R. & Loeschcke, V. Environmental stress, adaptation and evolution: an overview. J. Evol. Biol. 18, 744–749 (2005).
Article  CAS  PubMed  Google Scholar 

14.
Caza, F., Cledon, M. & St-Pierre, Y. Biomonitoring climate change and pollution in marine ecosystems: a review on Aulacomya ater. J. Mar. Biol. https://doi.org/10.1155/2016/183813 (2016).
Article  Google Scholar 

15.
Farcy, E., Voiseux, C., Lebel, J. M. & Fievet, B. Seasonal changes in mRNA encoding for cell stress markers in the oyster Crassostrea gigas exposed to radioactive discharges in their natural environment. Sci. Total Environ. 374, 328–341 (2007).
ADS  Article  CAS  PubMed  Google Scholar 

16.
Yao, C. L. & Somero, G. N. Thermal stress and cellular signaling processes in hemocytes of native (Mytilus californianus) and invasive (M. galloprovincialis) mussels: cell cycle regulation and DNA repair. Comp. Biochem. Physiol. Part A 165, 159–168 (2013).
Article  CAS  Google Scholar 

17.
Negri, A. et al. Transcriptional response of the mussel Mytilus galloprovincialis (Lam.) following exposure to heat stress and copper. PLoS ONE 8, e66802. https://doi.org/10.1371/journal.pone/0066802 (2013).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

18.
Heare, J. E., White, S. J., Vadopalas, B. & Roberts, S. B. Differential response to stress in Ostrea lurida as measured by gene expression. Peer J. 6, e4261. https://doi.org/10.7717/peerj.4261 (2018).
Article  CAS  PubMed  PubMed Central  Google Scholar 

19.
Caza, F. et al. Comparative analysis of hemocyte properties from Mytilus edulis desolationis and Aulacomya ater in the Kerguelen Islands. Mar. Environ. Res. 110, 174–182 (2015).
Article  CAS  PubMed  PubMed Central  Google Scholar 

20.
Comeau, L. A., Babarro, J. M., Longa, A. & Padin, X. A. Valve-gaping behavior of raft-cultivated mussels in the Ría de Arousa, Spain. Aquac. Rep. 9, 68–73 (2018).
Article  Google Scholar 

21.
Weston, S. A. & Parish, C. R. New fluorescent dyes for lymphocyte migration studies: analysis by flow cytometry and fluorescence microscopy. J. Immunol. Meth. 133, 87–97 (1990).
Article  CAS  Google Scholar 

22.
Daley, R. J. & Hobbie, J. E. Direct counts of aquatic bacteria by a modified epifluorescence technique 1. Limnol. Oceanogr. 20, 875–882 (1975).
ADS  Article  Google Scholar 

23.
Ferguson, R. L. & Rublee, P. Contribution of bacteria to standing crop of coastal plankton 1. Limnol. Oceanogr. 21, 141–145 (1976).
ADS  Article  Google Scholar 

24.
Aubry, A., Mougari, F., Reibel, F. & Cambau, E. Mycobacterium marinum. In Tuberculosis and Nontuberculous Mycobacterial Infections (ed. Schlossberg, D.) 735–752 (McGraw-Hill, New York, 2017).
Google Scholar 

25.
Kennedy, G. M., Morisaki, J. H. & Champion, P. A. Conserved mechanisms of Mycobacterium marinum pathogenesis within the environmental amoeba Acanthamoeba castellanii. Appl. Environ. Microbiol. 78, 20249–22052 (2012).
Article  CAS  Google Scholar 

26.
Barker, L. P., George, K. M., Falkow, S. & Small, P. L. Differential trafficking of live and dead Mycobacterium marinum organisms in macrophages. Infect. Immun. 65, 1497–1504 (1997).
Article  CAS  PubMed  PubMed Central  Google Scholar 

27.
Nguyen, L. & Pieters, J. The Trojan horse: survival tactics of pathogenic mycobacteria in macrophages. Trends Cell Biol. 15, 269–276 (2005).
Article  CAS  PubMed  Google Scholar 

28.
Jørgensen, C. B., Larsen, P. S. & Riisgård, H. U. Effects of temperature on the mussel pump. Mar. Ecol. Progr. Ser. 28, 89–97 (1990).
ADS  Article  Google Scholar 

29.
Podolsky, R. D. Temperature and water viscosity: physiological versus mechanical effects on suspension feeding. Science 265, 100–103 (1994).
ADS  Article  CAS  PubMed  Google Scholar 

30.
Riisgård, H. U. & Seerup, D. F. Filtration rates in the soft clam Mya arenaria: effects of temperature and body size. Sarsia 88, 416–428 (2003).
Article  Google Scholar 

31.
Dowd, W. W. & Somero, G. N. Behavior and survival of Mytilus congeners following episodes of elevated body temperature in air and seawater. J. Exp. Biol. 216, 502–514 (2013).
Article  PubMed  Google Scholar 

32.
Cellura, C., Toubiana, M., Parrinello, N. & Roch, P. HSP70 gene expression in Mytilus galloprovincialis hemocytes is triggered by moderate heat shock and Vibrio anguillarum, but not by V. splendidus or Micrococcus lysodeikticus. Dev. Comp. Immunol. 30, 984–997 (2006).
Article  CAS  PubMed  Google Scholar 

33.
Watermann, B. T. et al. Pathology and mass mortality of Pacific oysters, Crassostrea gigas (Thunberg), in 2005 at the East Frisian coast, Germany. J. Fish Dis. 31, 621–630 (2008).
Article  CAS  PubMed  Google Scholar 

34.
Polsenaere, P. et al. Potential environmental drivers of a regional blue mussel mass mortality event (winter of 2014, Breton Sound, France). J. Sea Res. 123, 39–50 (2017).
ADS  Article  Google Scholar 

35.
Vázquez-Luis, M. et al. SOS Pinna nobilis: a mass mortality event in western Mediterranean Sea. Front. Mar. Sci. 4, 220. https://doi.org/10.3389/fmars.2017.00220 (2017).
Article  Google Scholar 

36.
Lattos, A., Giantsis, I. A., Karagiannis, D. & Michaelidis, B. First detection of the invasive Haplosporidian and Mycobacteria parasites hosting the endangered bivalve Pinna nobilis in Thermaikos Gulf, North Greece. Mar. Environ. Res. https://doi.org/10.1016/j.marenvres.2020.104889 (2020).
Article  PubMed  Google Scholar 

37.
Rivetti, I., Fraschetti, S., Lionello, P., Zambianchi, E. & Boero, F. Global warming and mass mortalities of benthic invertebrates in the Mediterranean Sea. PLoS ONE 9, e115655. https://doi.org/10.1371/journal.pone.0115655 (2014).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

38.
Zhang, Y., Li, J., Yu, F., He, X. & Yu, Z. Allograft inflammatory factor-1 stimulates hemocyte immune activation by enhancing phagocytosis and expression of inflammatory cytokines in Crassostrea gigas. Fish Shellfish Immunol. 34, 1071–1077 (2013).
Article  CAS  PubMed  PubMed Central  Google Scholar 

39.
Cellura, C., Toubiana, M., Parrinello, N. & Roch, P. Specific expression of antimicrobial peptide and HSP70 genes in response to heat-shock and several bacterial challenges in mussels. Fish Shellfish Immunol. 22, 340–350 (2007).
Article  CAS  PubMed  PubMed Central  Google Scholar 

40.
Novoa, B. et al. Immune tolerance in Mytilus galloprovincialis haemocytes after repeated contact with Vibrio splendidus. Front. Immunol. 10, 1894. https://doi.org/10.3389/fimmu.2019.01894 (2019).
Article  CAS  PubMed  PubMed Central  Google Scholar 

41.
Palmer, C. V. Immunity and the coral crisis. Commun. Biol. 1, 1–7 (2018).
Article  Google Scholar 

42.
Yonemitsu, M. A. et al. A single clonal lineage of transmissible cancer identified in two marine mussel species in South America and Europe. Elife 8, e47788. https://doi.org/10.7554/eLife.47788 (2019).
Article  CAS  PubMed  PubMed Central  Google Scholar 

43.
Seuront, L., Nicastro, K. R., Zardi, G. I. & Goberville, E. Decreased thermal tolerance under recurrent heat stress conditions explains summer mass mortality of the blue mussel Mytilus edulis. Sci. Rep. 9, 1–4 (2019).
ADS  Article  Google Scholar 

44.
Carroll, P. et al. Sensitive detection of gene expression in mycobacteria under replicating and non-replicating conditions using optimized far-red reporters. PLoS ONE 5, e9823. https://doi.org/10.1371/journal.pone.0009823 (2010).
ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

45.
Li, Y. F. et al. Elevated seawater temperatures decrease microbial diversity in the gut of Mytilus coruscus. Front. Physiol. 9, 839. https://doi.org/10.3389/fphys.2018.00839 (2018).
Article  PubMed  PubMed Central  Google Scholar  More