Modelling habitat‐suitability using museum collections: an example with three sympatric <i>Apodemus</i> species from the Alps

Reutter, Brigitte A.; Helfer, Véronique; Hirzel, Alexandre H.; Vogel, Peter

doi:10.1046/j.1365-2699.2003.00855.x

Cited by 86 publications

(54 citation statements)

References 17 publications

(19 reference statements)

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“…While this is less likely to be a problem with large numbers of records, as can often be available for terrestrial species from sources such as museum collections (e.g. Robertson et al, 2001;Reutter et al, 2003), this may be an issue when a small number of records is used to generate the model.…”

Section: Introductionmentioning

confidence: 99%

A comparison of approaches for modelling the occurrence of marine animals

et al. 2008

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Approaches for modelling the distribution of animals in relation to their environment can be divided into two basic types, those which use records of absence as well as records of presence and those which use only presence records. For terrestrial species, presence-absence approaches have been found to produce models with greater predictive ability than presence-only approaches. This study compared the predictive ability of both approaches for a marine animal, the harbour porpoise (Phoceoena phocoena). Using data on the occurrence of harbour porpoises in the Sea of Hebrides, Scotland, the predictive abilities of one presence-absence approach (generalised linear modelling-GLM) and three presence-only approaches (Principal component analysis-PCA, ecological niche factor analysis-ENFA and genetic algorithm for rule-set prediction-GARP) were compared. When the predictive ability of the models was assessed using receiver operating characteristic (ROC) plots, the presence-absence approach (GLM) was found to have the greatest predictive ability. However, all approaches were found to produce models that predicted occurrence significantly better than a random model and the GLM model did not perform significantly better than ENFA and GARP. The PCA had a significantly lower predictive ability than GLM but not the other approaches. In addition, all models predicted a similar spatial distribution. Therefore, while models constructed using presence-absence approaches are likely to provide the best understanding of species distribution within a surveyed area, presence-only models can perform almost as well. However, careful consideration of the potential limitations and biases in the data, especially with regards to representativeness, is needed if the results of presence-only models are to be used for conservation and/or management purposes.

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Section: Introductionmentioning

confidence: 99%

A comparison of approaches for modelling the occurrence of marine animals

et al. 2008

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“…It is comparatively much easier to collect occurrence data than abundance/density data over broad, heterogeneous areas. Occurrence data could even come from museums, atlases, or online databases (Reutter et al, 2003;Soberón and Peterson, 2004;Gaubert et al, 2006). This approach can be thought of as a hierarchical process: the HS model provides a first assessment, which is then refined by collecting more-focused data.…”

Section: Discussionmentioning

confidence: 99%

Using Relative Capacity to Measure Habitat Suitability

Hirzel¹

2008

Israel Journal of Ecology & Evolution

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Habitat suitability (HS) modeling relates a species' potential presence/absence to a set of environmental variables. because of stochastic or demographic population fluctuations, relating abundance to environment is difficult and generally requires time-series of population-density data. Here, I propose an approach to compute relative capacity of a set of habitat classes. I defined relative capacity of a habitat class as being proportional to its carrying capacity, and replaced time-series of density measures with spatial replicates. A hypothetical environment was first divided into several habitat classes and population densities were measured in a sample of these habitats. Three methods of computing relative capacity were compared: (1) maximum density per habitat class, (2) coefficients of a Generalized Linear Model (GLM) of the habitat densities, and (3) slopes of isodars computed for all pairs of habitat classes. Accuracy of these methods was evaluated using spatially-explicit demographic simulations. I investigated biological assumptions (species' sensitivity, reactivity and selectivity, regional stochasticity) and sampling design (sample size, distance between pairs of habitat classes, number of habitat classes, uncertainty over abundance). GLM and maximum-density methods provided accurate results when at least five site-pairs were available. Isodars outperformed other methods in landscapes with many habitat classes, but are best suited to large or noise-free data sets. Optimal performance was reached with more than 50 site-pairs and five habitat classes.

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“…This may be done either in geographical space by comparing the predicted distributions of species (Anderson et al, 2002;Rice et al, 2003), or in the environmental space by measuring niche similarities (e.g. Reutter et al, 2003;Chefaoui et al, 2005). These correlative approaches can only show spatial relationships among species.…”

Section: Platysepalum Hirsutum Triclisia Patensmentioning

confidence: 99%

Comparing Suitable Habitat Models to Predict Rare and Endemic Plant Species Distributions: What are the Limits of the Niche of Cola lorougnonis (Malvaceae) in Cote d'Ivoire?

Vroh

Yao²,

Kpangui³

et al. 2016

ENRR

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Cola lorougnonis is an endemic, rare, and treated species. It was recently recorded in Dékpa forest near Agbaou (a village of Divo region, Côte d'Ivoire). In the same forest, 20 other rare and endemic plant species were recorded. Accurate modeling of geographical distributions of these species is crucial to various applications in ecology and biodiversity conservation. The present study analyzed suitable habitat models for the 21 species. The main objective was to test geographical predictions for the focused species: Cola lorougnonis. We used Maxent modelling method for predicting potential suitable habitats combining environmental variables and species records. We evaluated Maxent predictions using the area under the receiver-operating characteristic curve (AUC). For each species, the map of distribution was engineered using DIVA-GIS. We compared the suitable habitat areas among species. Principal Canonical Analysis allowed the ordination of species according to environmental variables. AUC values allowed to get 11 species with excellent distribution models, 8 species with good distribution models, and 2 species with predictive models considered as acceptable. Cola lorougnonis (AUC = 0.99) and Drypetes singroboensis (AUC = 0.96) have the same focused area: moist semi-deciduous forest in Côte d'Ivoire. They are more sensitive to changes in rainfall of both warmest and coldest seasons. The State of Côte d'Ivoire has to undertake monitoring, assessment and reporting of conservation status facilitation for all habitats where these species could be found within the territory. Additional studies focusing on the investigation areas and niche models onto future conditions of climate could be considered.

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Modelling habitat‐suitability using museum collections: an example with three sympatric Apodemus species from the Alps

Cited by 86 publications

References 17 publications

A comparison of approaches for modelling the occurrence of marine animals

A comparison of approaches for modelling the occurrence of marine animals

Using Relative Capacity to Measure Habitat Suitability

Comparing Suitable Habitat Models to Predict Rare and Endemic Plant Species Distributions: What are the Limits of the Niche of Cola lorougnonis (Malvaceae) in Cote d'Ivoire?

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