Five (or so) challenges for species distribution modelling

Araújo, Miguel B.; Guisan, Antoine

doi:10.1111/j.1365-2699.2006.01584.x

Cited by 1,511 publications

(1,320 citation statements)

References 112 publications

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“…Ecologic niches can be estimated by integrating information on spatial occurrences of the species with relevant raster data layers summarizing aspects of the environment (Araú jo and Guisan, 2006). Once developed, niche models can be used to identify suitable areas for populations of the species, effectively creating potential distribution maps (Austin et al, 1990;Peterson, 2003).…”

Section: Ecologic Niche Modelingmentioning

confidence: 99%

Ecology and Geography of Human Monkeypox Case Occurrences Across Africa

Ellis¹,

Carroll²,

Lash³

et al. 2012

Journal of Wildlife Diseases

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ABSTRACT:As ecologic niche modeling (ENM) evolves as a tool in spatial epidemiology and public health, selection of the most appropriate and informative environmental data sets becomes increasingly important. Here, we build on a previous ENM analysis of the potential distribution of human monkeypox in Africa by refining georeferencing criteria and using more-diverse environmental data to identify environmental parameters contributing to monkeypox distributional ecology. Significant environmental variables include annual precipitation, several temperature-related variables, primary productivity, evapotranspiration, soil moisture, and pH. The potential distribution identified with this set of variables was broader than that identified in previous analyses but does not include areas recently found to hold monkeypox in southern Sudan. Our results emphasize the importance of selecting the most appropriate and informative environmental data sets for ENM analyses in pathogen transmission mapping.

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Section: Ecologic Niche Modelingmentioning

confidence: 99%

Ecology and Geography of Human Monkeypox Case Occurrences Across Africa

Ellis¹,

Carroll²,

Lash³

et al. 2012

Journal of Wildlife Diseases

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“…The gathering of the environmental data with the highest predictive potential, supported by prior knowledge about the prevailing factors in the studied species distribution, is foreseen as one of the major steps to be taken in species distribution modelling (Araújo and Guisan, 2006). In this sense it is crucial to make an appropriate review of existing knowledge concerning the ecological factors considered influential in the distribution of a species as this allows a better and safer choice of the independent variables eligible for integrating the predictive statistic models.…”

Section: Environmental Factorsmentioning

confidence: 99%

“…atroparvus, it was necessary to evaluate their predictive performances. Since it is only possible to evaluate the appropriateness of the results for the intended purpose after this procedure has been carried out, it is considered a fundamental stage in species distribution modelling (Araújo and Guisan, 2006). In the context of the present work, the results evaluation also allowed us to differentiate the performances obtained by each of the modelling methods.…”

Section: Calibration Calculationsmentioning

confidence: 99%

Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal

Capinha

Gomes²,

Reis

et al. 2009

Geospat Health

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Abstract. Malaria was a major health problem in the first half of the 20th Century in mainland Portugal. Nowadays, although the disease is no longer endemic, there is still the risk of future endemic infections due to the continuous occurrence of imported cases and the possibility of transmission in the country by Anopheles atroparvus Van Thiel, 1927. Since vector abundance constitute one of the foremost factors in malaria transmission, we have created several habitat suitability models to describe this vector species' current distribution. Three different correlative models; namely (i) a multilayer perceptron artificial neural network (MLP-ANN); (ii) binary logistic regression (BLR); and (iii) Mahalanobis distance were used to combine the species records with a set of five environmental predictors. Kappa coefficient values from k-fold cross-validation records showed that binary logistic regression produced the best predictions, while the other two models also produced acceptable results. Therefore, in order to reduce uncertainty, the three suitability models were combined. The resulting model identified high suitability for An. atroparvus in the majority of the country with exception of the northern and central coastal areas. Malaria distribution during the last endemic period in the country was also compared with the combined suitability model, and a high degree of spatial agreement was obtained (kappa = 0.62). It was concluded that habitat suitability for malaria vectors can constitute valuable information on the assessment of several spatial attributes of the disease. In addition, the results suggest that the spatial distribution of An. atroparvus in the country remains very similar to the one known about seven decades ago.

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“…The area under the curve (AUC) measures the ability of the model to classify correctly a species as present or absent. A rough guide for classifying the model accuracy is: 0·50 -0·60 = insufficient; 0·60 -0·70 = poor; 0·70 -0·80 = average; 0·80 -0·90 = good; 0·90 -1 = excellent (Araujo & Guisan 2006) …”

Section: Model Selection and Evaluationmentioning

confidence: 99%

Predicting the potential distributions of Senecio vulgaris L. in China

Cheng

2015

Preprint

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Predicting potential distribution for alien plants by species distribution model (SDM, or Ecological Niche Model) using occurrence data and habitat environmental variables plays an important role in management of the invasive risk by an alien plant. Common groundsels (Senecio vulgaris, Asteracea), native in Eurasia and North Africa, has been a cosmopolitan weed in temperature and also listed as one of invasive plants in China. We predict the potential distribution of this species in the world and in China particularly in Maxent (maximum entropy) models by using global occurrence records of S. vulgaris and the associated climate variables. The occurrence data were collected from the online databases, Global Biodiversity Information Facility database (GBIF), Chinese Virtual Herbarium database (CVH), and also from field work in China. The climate variables were download from WorldClim (http://www.worldclim.org). The occurrence records showed that S. vulgaris is present in 16 provinces or regions in north -eastern, south -western, central and north China, and almost not present in southeastern, north -western China. The mapping of S. vulgaris potential distribution is diagonally across China, including the north -eastern, south -western China, and the cool area between the two regions. Analysis of the contribution and importance of climatic factors in the prediction model indicated that S. vulgaris adapts to the climate in humid and cool area in China (annual mean temperature ranges 2.4 -17.5 ℃, and annual precipitation ranges 550 -1500 mm). It is suggested that special attention should be paid to the plain in NE China and Shandong Peninsula, Yungui Plateau, the cool mountain area around Sichuan basin, in western Hubei, southern Shaanxi, Shanxi and around Beijing in order to manage the invasion risk by S. vulgaris. The better performance of the model built by using occurrence data in China than that by using the global data in relation the predict outcome in China imply that it is might be better to use regional data than the global data when predict potential distribution for an alien plant with long invasive history in study area.

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Five (or so) challenges for species distribution modelling

Cited by 1,511 publications

References 112 publications

Ecology and Geography of Human Monkeypox Case Occurrences Across Africa

Ecology and Geography of Human Monkeypox Case Occurrences Across Africa

Present habitat suitability for Anopheles atroparvus (Diptera, Culicidae) and its coincidence with former malaria areas in mainland Portugal

Predicting the potential distributions of Senecio vulgaris L. in China

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