2009
DOI: 10.1111/j.1365-2699.2008.02062.x
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Predicting global habitat suitability for stony corals on seamounts

Abstract: Aim Globally, species distribution patterns in the deep sea are poorly resolved, with spatial coverage being sparse for most taxa and true absence data missing. Increasing human impacts on deep-sea ecosystems mean that reaching a better understanding of such patterns is becoming more urgent. Cold-water stony corals (Order Scleractinia) form structurally complex habitats (dense thickets or reefs) that can support a diversity of other associated fauna. Despite their widely accepted ecological importance, records… Show more

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Cited by 254 publications
(242 citation statements)
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References 95 publications
(138 reference statements)
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“…water temperature, salinity, depth, nutrient concentrations, seabed types, etc), which are typically easier to record and map across vast expanses (i.e. regional, global scale) in contrast to species and habitat data [42][43][44] . Despite inherent limitations and associated uncertainties, predictive modelling is a cost-effective alternative to field surveys as it can help identifying and mapping where sensitive marine ecosystems may occur.…”
mentioning
confidence: 99%
“…water temperature, salinity, depth, nutrient concentrations, seabed types, etc), which are typically easier to record and map across vast expanses (i.e. regional, global scale) in contrast to species and habitat data [42][43][44] . Despite inherent limitations and associated uncertainties, predictive modelling is a cost-effective alternative to field surveys as it can help identifying and mapping where sensitive marine ecosystems may occur.…”
mentioning
confidence: 99%
“…Seamounts have become a target for fisheries worldwide, and researchers created the first integrated public database of global seamount biological data (Seamounts Online 2012). They compiled available data, new surveys, and the latest modeling methods in the first global seamount classification identifying regions most vulnerable to fishing and climate change (Tittensor et al 2009;Clark et al 2011). Their data showed that seamount communities are vulnerable to fishing and that these communities, particularly those with hard corals, have high sensitivity and low resilience to bottom trawling.…”
Section: Poorly Explored Habitatsmentioning
confidence: 99%
“…The AUC method was first developed for the evaluation of presence-absence models. Afterwards, this test has been adapted to evaluate the models based on presence-only data by replacing absences with pseudo-absences in the background locations (the grid cells without species presence) (Wiley et al 2003;Philips et al 2006;Tittensor et al 2009). The application of the adapted AUC method on presenceonly data models has been debated (Lobo et al 2008;Hernandez et al 2006) but with the lack of other valuable alternatives, AUC remains the most used procedure (Merow et al 2013).…”
Section: Modeling Of the American Jackknife Clam Distributionmentioning
confidence: 99%
“…The AUC is a threshold independent measure that allows an assessment of the model performance by given a value ranged between 0 and 1 (0.5 for a random model and 1 for a perfect one). We used a cross-validation procedure, as recommended by (Merow et al 2013) and performed by (Tittensor et al 2009) by selecting 70 % of data to run the model NPPEN and 30 % to evaluate its performance. The AUC method was first developed for the evaluation of presence-absence models.…”
Section: Modeling Of the American Jackknife Clam Distributionmentioning
confidence: 99%