2021
DOI: 10.1016/j.jmarsys.2021.103643
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Predictive habitat suitability models to aid the conservation of elasmobranchs in Isla del Coco National Park (Costa Rica)

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Cited by 7 publications
(3 citation statements)
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“…Stations on the northern shores of the SoM, located in close proximity to human settlements, industrial areas, or heavily trafficked zones might exhibit lower species diversity due to heightened anthropogenic pressures on the marine environment. Differences in abundance between stations might be indicative of distinct ecological niches present at each location (González‐Andrés et al, 2021). Sharks and rays are sensitive to environmental factors such as water temperature, salinity, and depth.…”
Section: Discussionmentioning
confidence: 99%
“…Stations on the northern shores of the SoM, located in close proximity to human settlements, industrial areas, or heavily trafficked zones might exhibit lower species diversity due to heightened anthropogenic pressures on the marine environment. Differences in abundance between stations might be indicative of distinct ecological niches present at each location (González‐Andrés et al, 2021). Sharks and rays are sensitive to environmental factors such as water temperature, salinity, and depth.…”
Section: Discussionmentioning
confidence: 99%
“…Many studies have widely demonstrated the relationship between the primary productivity and fisheries catch (Pauly and Christensen, 1995;Watson et al, 2014), and primary productivity variability is significantly and positively correlated to the mean trophic level of catches (Conti and Scardi, 2010); therefore, the primary productivity is used in most SDMs (Gonzalez-Pestana et al, 2020;Chan et al, 2021). Other environmental variables like depth, dissolved oxygen, salinity, and offshore distance are often used in the study of marine shark distribution (Sequeira et al, 2014;Meyers et al, 2017;Chan et al, 2021;Pottie et al, 2021;Gonzaĺez-Andreś et al, 2021). In the present study, eight environmental variables including SST, salinity, depth, distance from land, current velocity, primary productivity, dissolved oxygen concentration, and pH were selected as the model predictors.…”
Section: Data Sources and Preparationmentioning
confidence: 98%
“…Species distribution models of large pelagic fishes have been extensively used to predict suitable areas 29 , identify hotspots 1 , 30 , and assess the potential impacts of climate change 31 and of anthropogenic events such as oil spills 30 , 32 or fishing mortality 13 , 19 . We implemented mixed-effects Bayesian spatial models with an integrated nested Laplace approximation (INLA) 33 , which has demonstrated to be a great alternative for modeling species distributions and environmental preferences 1 , 34 , giving results in terms of posterior probability distributions instead of fixed values, and incorporating several types of random effects of both spatial and temporal nature, with reduced computational times 35 , 36 .…”
Section: Introductionmentioning
confidence: 99%