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Designing an efficient sampling strategy is of crucial importance for habitat suitability modelling.
We use habitat suitability modelling to map the potential spatial distribution of low-intensity farmland, with emphasis on grassland systems.
Due to the better coverage of entire feeding ranges, the limited influence of historic factors and the lack of bias from sampling design, marine animal tracking may provide better data than at-sea surveys for habitat suitability modelling.
To inform future conservation measures for this poorly known species, we used ecological niche factor analysis, habitat suitability modelling and distance sampling to determine landscape-scale habitat requirements and estimate the population size.
Several studies have been conducted that utilise a range of different presence-only habitat suitability modelling techniques on deep-sea species [4], [5], [6], [7], [15], [16].
Participating states could not reach consensus on the bottom trawling moratorium, but there is growing international support for habitat suitability modelling in the deep sea.
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This study uses a mechanistic suitability modeling approach since it deals with a plant whose environmental requirements are well documented.
This project uses a spatial suitability modeling approach that incorporates fuzzy logic and utilizes both physical and economic variables.
Habitat suitability modeling relates a species' occurrence to a set of environmental variables to model its ecological niche and predict its potential distribution (Soberón 2007; Hirzel and Le Lay 2008).
Suitability modeling under current and future climate conditions suggests that there is a strong spatiotemporal overlap between suitable areas of cultivated rice (whether rainfed or irrigated), and its four CWRs.
Somewhat inconsistent with the refugia hypotheses was the low habitat suitability modeled in the southern Appalachian and Texas coastal plain regions of our paleodistribution model.
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