2010
DOI: 10.1603/en08179
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Deriving the Species Richness Distribution of Geotrupinae (Coleoptera: Scarabaeoidea) in Mexico From the Overlap of Individual Model Predictions

Abstract: Predictions from individual distribution models for Mexican Geotrupinae species were overlaid to obtain a total species richness map for this group. A database (GEOMEX) that compiles available information from the literature and from several entomological collections was used. A Maximum Entropy method (MaxEnt) was applied to estimate the distribution of each species, taking into account 19 climatic variables as predictors. For each species, suitability values ranging from 0 to 100 were calculated for each grid… Show more

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Cited by 24 publications
(18 citation statements)
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“… Geotrupes pecki : Howden (1974: 572, Plate II), Trotta-Moreu et al (2008: 43, 47, 50, 51), Trotta-Moreu and Lobo (2010: 46).…”
Section: Taxonomyunclassified
“… Geotrupes pecki : Howden (1974: 572, Plate II), Trotta-Moreu et al (2008: 43, 47, 50, 51), Trotta-Moreu and Lobo (2010: 46).…”
Section: Taxonomyunclassified
“…Modelling at community level can be performed following different strategies [15], such as direct versus species-assembly approaches [16], each involving different modelling options [17]. The direct strategy of aggregating biological survey data to produce community-level entities that are then modelled (i.e., assemble first, predict later in [15]) has been much used and evaluated, but the alternative strategy of assembling individual species models (i.e., predict first, assemble later in [15]) has been evaluated far less and only more recently [18][21], even though many of the assessments of the global threat to biodiversity that have been published were based on such an approach [22][24]. This option involves making individual models for all the species included in the analysis separately and then combine them to generate a community level analysis.…”
Section: Introductionmentioning
confidence: 99%
“…One such landscape based approach, species distribution modeling (SDM), has been used extensively to understand both single and multi-species (i.e. richness/diversity) distributions in both natural and invaded landscapes [25][27]. The multitude of SDM methodologies all have the ability (with varying accuracy) to both define and predict the theorized realized niche of an organism (based on biotic and abiotic variables), and project that habitat onto specific climate change scenarios [26], [28], [29].…”
Section: Introductionmentioning
confidence: 99%
“…The multitude of SDM methodologies all have the ability (with varying accuracy) to both define and predict the theorized realized niche of an organism (based on biotic and abiotic variables), and project that habitat onto specific climate change scenarios [26], [28], [29]. By combining these niche estimates for multiple species, conservationists and ecologists can predict and project hotspots of native and non-native species richness and diversity [27], [28], [30], [31]. These types of landscape-based predictive analyses offer a powerful tool to predict actual and potential habitat degradation in relation to non-native species invasion.…”
Section: Introductionmentioning
confidence: 99%