Incorporating bioclimatic and biogeographic data in the construction of species distribution models in order to prioritize searches for new populations of threatened flora

Abstract

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Potential impacts of climate change on habitat suitability of Fagus sylvatica L. forests in spain

“…The results of this research reveal that areas with a Temperate macrobioclimate may be reduced in future, mostly in the southernmost territories of the central and eastern areas, due to decreases in their ombrothermic indices. distribution models gave good results in some preceding research works (Alfaro et al 2015).…”

“…Biogeography was also a very important variable in our models ( Table 1). The incorporation of this variable in species distribution models gave good results in some preceding research works (Alfaro et al 2015).The incorporation of this variable in species determination (Allouche et al 2006;Jiménez-Valverde and Lobo 2007). Maps of habitat suitability were imported into ArcGis 10.2.…”

A combined approach of methods for determining the regional assessment of the disjunct plant Primula pedemontana in Spain

“…Data for the 23 specimen-containing points was integrated with different variables using a grid size of 100 × 100 m 2 : topography (altitude, slope, aspect and degree-day) calculated from a Digital Model of Elevations, and lithology (JCYL 1997). Following Alfaro-Saiz et al (2015), bioclimatic maps (ombrotype and thermotype) for the present and for 2025 (del Río 2005) were included in the modelling. The percentage of potential lost area in the future was calculated comparing the models (Settele et al 2008).…”

Predicting the Potential Current and Future Distribution of the Endangered Endemic Vascular Plant Primula boveana Decne. ex Duby in Egypt

“…SDMs applications include the study of ecological niche patterns, recognition of suitable localities for conservation and translocation concerns, prediction of future distribution as a result of climate and land-use changes, and to assess fundamental ecological and evolutionary issues [4,13,14]. For unknown populations, the predictive maps of SDMs can help to guide field surveys, search for new populations of poorly known species and better identify spatial distribution areas for monitoring or reintroductions [4,[15][16][17][18][19][20]. Conceptualization (e.g., model objective, taxon, location, predictors, scale), data (presence data), model fitting (variables selection, model setting and selection, threshold selection), assessment (performance statistics), and predictions (outputs) are the main steps for building SDMs [21].…”