Using crisp and fuzzy modelling to identify favourability hotspots useful to perform gap analysis

Estrada, Alba; Real, Raimundo; Vargas, J. Mario

doi:10.1007/s10531-008-9328-1

Cited by 56 publications

(61 citation statements)

References 35 publications

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“…This is in contrast with crisp models (Estrada et al, 2008), which try to describe the relationship in a binary format (i.e. The major advantage is that existing knowledge about the species-environment relationship can be encapsulated into the modelling process.…”

Section: Discussionmentioning

confidence: 99%

Finessing atlas data for species distribution models

Niamir

Skidmore

Toxopeus

et al. 2011

Diversity and Distributions

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Aim The spatial resolution of species atlases and therefore resulting model predictions are often too coarse for local applications. Collecting distribution data at a finer resolution for large numbers of species requires a comprehensive sampling effort, making it impractical and expensive. This study outlines the incorporation of existing knowledge into a conventional approach to predict the distribution of Bonelli's eagle (Aquila fasciata) at a resolution 100 times finer than available atlas data.Location Malaga province, Andalusia, southern Spain.Methods A Bayesian expert system was proposed to utilize the knowledge from distribution models to yield the probability of a species being recorded at a finer resolution (1 · 1 km) than the original atlas data (10 · 10 km). The recorded probability was then used as a weight vector to generate a sampling scheme from the species atlas to enhance the accuracy of the modelling procedure. The maximum entropy for species distribution modelling (MaxEnt) was used as the species distribution model. A comparison was made between the results of the MaxEnt using the enhanced and, the random sampling scheme, based on four groups of environmental variables: topographic, climatic, biological and anthropogenic. ResultsThe models with the sampling scheme enhanced by an expert system had a higher discriminative capacity than the baseline models. The downscaled (i.e. finer scale) species distribution maps using a hybrid MaxEnt/expert system approach were more specific to the nest locations and were more contrasted than those of the baseline model. Main conclusionsThe proposed method is a feasible substitute for comprehensive field work. The approach developed in this study is applicable for predicting the distribution of Bonelli's eagle at a local scale from a nationallevel occurrence data set; however, the usefulness of this approach may be limited to well-known species.

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Section: Discussionmentioning

confidence: 99%

Finessing atlas data for species distribution models

Niamir

Skidmore

Toxopeus

et al. 2011

Diversity and Distributions

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“…In biogeography, environmental favourability, chorotypes, biotic boundaries, and species richness can be identified as fuzzy sets (Estrada et al. , Olivero et al. , , Romero et al.…”

Section: Methodsmentioning

confidence: 99%

Mammalian biogeography and the Ebola virus in Africa

Olivero

Real

et al. 2016

Mammal Review

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Ebola virus is responsible for the fatal Ebola virus disease (EVD). Identifying the distribution area of the Ebola virus is crucial for understanding the risk factors conditioning the emergence of new EVD cases. Existing distribution models have underrepresented the potential contribution that reservoir species and vulnerable species make in sustaining the presence of the virus. In this paper, we map favourable areas for Ebola virus in Africa according to environmental and zoogeographical descriptors, independent of human‐to‐human transmissions. We combine two different biogeographical approaches: analysis of mammalian distribution types (chorotypes), and distribution modelling of the Ebola virus. We first obtain a model defining the distribution of environmentally favourable areas for the presence of Ebola virus. Based on a review of mammal taxa affected by or suspected of exposure to the Ebola virus, we model favourable areas again, this time according to mammalian chorotypes. We then build a combined model in which both the environment and mammalian distributions explain the favourable areas for Ebola virus in the wild. We demonstrate that mammalian biogeography contributes to explaining the distribution of Ebola virus in Africa, although vegetation may also underscore clear limits to the presence of the virus. Our model suggests that the Ebola virus may be even more widespread than previously suspected, given that additional favourable areas are found throughout the coastal areas of West and Central Africa, stretching from Cameroon to Guinea, and extend further East into the East African Lakes region. Our findings show that the most favourable area for the Ebola virus is significantly associated with the presence of the virus in non‐human mammals. Core areas are surrounded by regions of intermediate favourability, in which human infections of unknown source were found. The difference in association between humans and other mammals and the virus may offer further insights on how EVD can spread.

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“…This approach has been used before by Estrada et al [38] in which the accumulated favorability was used as part of a fuzzy set method for detecting diversity hot spots. Moreover, basing hot spots on favorability models have further allowed us to extrapolate observed patterns in this study to future human and climate scenarios.…”

Section: Discussionmentioning

confidence: 99%

“…High values of this index represent fuzzy favorability hot spots, and have been considered in the assessment of site networks for the protection of biodiversity [38]. The accumulated favorability is the result of adding up the favorability ( F i ) value for all i taxa in each j cell in the study area: …”

Section: Methodsmentioning

confidence: 99%

Integrating Sustainable Hunting in Biodiversity Protection in Central Africa: Hot Spots, Weak Spots, and Strong Spots

Olivero

Farfán

et al. 2014

PLoS ONE

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Wild animals are a primary source of protein (bushmeat) for people living in or near tropical forests. Ideally, the effect of bushmeat harvests should be monitored closely by making regular estimates of offtake rate and size of stock available for exploitation. However, in practice, this is possible in very few situations because it requires both of these aspects to be readily measurable, and even in the best case, entails very considerable time and effort. As alternative, in this study, we use high-resolution, environmental favorability models for terrestrial mammals (N = 165) in Central Africa to map areas of high species richness (hot spots) and hunting susceptibility. Favorability models distinguish localities with environmental conditions that favor the species' existence from those with detrimental characteristics for its presence. We develop an index for assessing Potential Hunting Sustainability (PHS) of each species based on their ecological characteristics (population density, habitat breadth, rarity and vulnerability), weighted according to restrictive and permissive assumptions of how species' characteristics are combined. Species are classified into five main hunting sustainability classes using fuzzy logic. Using the accumulated favorability values of all species, and their PHS values, we finally identify weak spots, defined as high diversity regions of especial hunting vulnerability for wildlife, as well as strong spots, defined as high diversity areas of high hunting sustainability potential. Our study uses relatively simple models that employ easily obtainable data of a species' ecological characteristics to assess the impacts of hunting in tropical regions. It provides information for management by charting the geography of where species are more or less likely to be at risk of extinction from hunting.

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Using crisp and fuzzy modelling to identify favourability hotspots useful to perform gap analysis

Cited by 56 publications

References 35 publications

Finessing atlas data for species distribution models

Finessing atlas data for species distribution models

Mammalian biogeography and the Ebola virus in Africa

Integrating Sustainable Hunting in Biodiversity Protection in Central Africa: Hot Spots, Weak Spots, and Strong Spots

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