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Ecological niche modeling re-examined: A case study with the Darwin's fox.

ABSTRACT: Many previous studies have attempted to assess ecological niche modeling performance using receiver operating characteristic (ROC) approaches, even though diverse problems with this metric have been pointed out in the literature. We explored different evaluation metrics based on independent testing data using the Darwin's Fox (Lycalopex fulvipes) as a detailed case in point. Six ecological niche models (ENMs; generalized linear models, boosted regression trees, Maxent, GARP, multivariable kernel density estimation, and NicheA) were explored and tested using six evaluation metrics (partial ROC, Akaike information criterion, omission rate, cumulative binomial probability), including two novel metrics to quantify model extrapolation versus interpolation (E-space index I) and extent of extrapolation versus Jaccard similarity (E-space index II). Different ENMs showed diverse and mixed performance, depending on the evaluation metric used. Because ENMs performed differently according to the evaluation metric employed, model selection should be based on the data available, assumptions necessary, and the particular research question. The typical ROC AUC evaluation approach should be discontinued when only presence data are available, and evaluations in environmental dimensions should be adopted as part of the toolkit of ENM researchers. Our results suggest that selecting Maxent ENM based solely on previous reports of its performance is a questionable practice. Instead, model comparisons, including diverse algorithms and parameterizations, should be the sine qua non for every study using ecological niche modeling. ENM evaluations should be developed using metrics that assess desired model characteristics instead of single measurement of fit between model and data. The metrics proposed herein that assess model performance in environmental space (i.e., E-space indices I and II) may complement current methods for ENM evaluation.

SUBMITTER: Escobar LE

PROVIDER: S-EPMC5980497 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Ecological niche modeling re-examined: A case study with the Darwin's fox.

Escobar Luis E LE Qiao Huijie H Cabello Javier J Peterson A Townsend AT

Ecology and evolution 20180416 10

Many previous studies have attempted to assess ecological niche modeling performance using receiver operating characteristic (ROC) approaches, even though diverse problems with this metric have been pointed out in the literature. We explored different evaluation metrics based on independent testing data using the Darwin's Fox (<i>Lycalopex fulvipes</i>) as a detailed case in point. Six ecological niche models (ENMs; generalized linear models, boosted regression trees, Maxent, GARP, multivariable ...[more]

PMID: 29876055

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Project description:BACKGROUND:Emerging and understudied pathogens often lack information that most commonly used analytical tools require, such as negative controls or baseline data; thus, new analytical strategies are needed to analyze transmission patterns and drivers of disease emergence. Zoonotic infections with Vaccinia virus (VACV) were first reported in Brazil in 1999, VACV is an emerging zoonotic Orthopoxvirus, which primarily infects dairy cattle and farmers in close contact with infected cows. Prospective studies of emerging pathogens could provide critical data that would inform public health planning and response to outbreaks. By using the location of 87-recorded outbreaks and publicly available bioclimatic data, we demonstrate one such approach. Using an ecological niche model (ENM) algorithm, we identify the environmental conditions under which VACV outbreaks have occurred, and determine additional locations in two affected countries that may be susceptible to transmission. Further, we show how suitability for the virus responds to different levels of various environmental factors and highlight the most important factors in determining its transmission. METHODS:A literature review was performed and the geospatial coordinates of 87 molecularly confirmed VACV outbreaks in Brazil were identified. An ENM was generated using MaxENT software by combining principal component analysis results of 19 bioclim spatial layers, and 25 randomly selected subsets of the original list of 87 outbreaks. RESULTS:The final ENM predicted all areas where Brazilian outbreaks occurred, one out of five of the Colombian outbreak regions and identified new regions within Brazil that are suitable for transmission based on bioclimatic factors. Further, the most important factors in determining transmission suitability are precipitation of the wettest quarter, annual precipitation, mean temperature of the coldest quarter and mean diurnal range. CONCLUSION:The analyses here provide a means by which to study patterns of an emerging infectious disease and identify regions that are potentially suitable for its transmission, in spite of the paucity of high-quality critical data. Policy and methods for the control of infectious diseases often use a reactionary model, addressing diseases only after significant impact on human health has ensued. The methodology used in the present work allows the identification of areas where disease is likely to appear, which could be used for directed intervention.

Project description:Detailed understanding of a species' natural history and environmental needs across spatial scales is a primary requisite for effective conservation planning, particularly for species with complex life cycles in which different life stages occupy different niches and respond to the environment at different scales. However, niche models applied to conservation often neglect early life stages and are mostly performed at broad spatial scales. Using the endangered heath tiger beetle (Cicindela sylvatica) as a model species, we relate presence/absence and abundance data of locally dispersing adults and sedentary larvae to abiotic and biotic variables measured in a multiscale approach within the geographic extent relevant to active conservation management. At the scale of hundreds of meters, fine-grained abiotic conditions (i.e., vegetation structure) are fundamental determinants of the occurrence of both life stages, whereas the effect of biotic factors is mostly contained in the abiotic signature. The combination of dense heath vegetation and bare ground areas is thus the first requirement for the species' preservation, provided that accessibility to the suitable habitat is ensured. At a smaller scale (centimetres), the influence of abiotic factors on larval occurrence becomes negligible, suggesting the existence of important additional variables acting within larval proximity. Sustained significant correlations between neighbouring larvae in the models provide an indication of the potential impact of neighbourhood crowding on the larval niche within a few centimetres. Since the species spends the majority of its life cycle in the larval stage, it is essential to consider the hierarchical abiotic and biotic processes affecting the larvae when designing practical conservation guidelines for the species. This underlines the necessity for a more critical evaluation of the consequences of disregarding niche variation between life stages when estimating niches and addressing effective conservation measures for species with complex life cycles.

Project description:BackgroundVisceral leishmaniasis (VL) is re-emerging in Armenia since 1999 with 167 cases recorded until 2019. The objectives of this study were (i) to determine for the first time the genetic diversity and population structure of the causative agent of VL in Armenia; (ii) to compare these genotypes with those from most endemic regions worldwide; (iii) to monitor the diversity of vectors in Armenia; (iv) to predict the distribution of the vectors and VL in time and space by ecological niche modeling.Methodology/principal findingsHuman samples from different parts of Armenia previously identified by ITS-1-RFLP as L. infantum were studied by Multilocus Microsatellite Typing (MLMT). These data were combined with previously typed L. infantum strains from the main global endemic regions for population structure analysis. Within the 23 Armenian L. infantum strains 22 different genotypes were identified. The combined analysis revealed that all strains belong to the worldwide predominating MON1-population, however most closely related to a subpopulation from Southeastern Europe, Maghreb, Middle East and Central Asia. The three observed Armenian clusters grouped within this subpopulation with strains from Greece/Turkey, and from Central Asia, respectively. Ecological niche modeling based on VL cases and collected proven vectors (P. balcanicus, P. kandelakii) identified Yerevan and districts Lori, Tavush, Syunik, Armavir, Ararat bordering Georgia, Turkey, Iran and Azerbaijan as most suitable for the vectors and with the highest risk for VL transmission. Due to climate change the suitable habitat for VL transmission will expand in future all over Armenia.ConclusionsGenetic diversity and population structure of the causative agent of VL in Armenia were addressed for the first time. Further genotyping studies should be performed with samples from infected humans, animals and sand flies from all active foci including the neighboring countries to understand transmission cycles, re-emergence, spread, and epidemiology of VL in Armenia and the entire Transcaucasus enabling epidemiological monitoring.

Project description:BackgroundEcological niche modeling integrates known sites of occurrence of species or phenomena with data on environmental variation across landscapes to infer environmental spaces potentially inhabited (i.e., the ecological niche) to generate predictive maps of potential distributions in geographic space. Key inputs to this process include raster data layers characterizing spatial variation in environmental parameters, such as vegetation indices from remotely sensed satellite imagery. The extent to which ecological niche models reflect real-world distributions depends on a number of factors, but an obvious concern is the quality and content of the environmental data layers.MethodsWe assessed ecological niche model predictions of H5N1 avian flu presence quantitatively within and among four geographic regions, based on models incorporating two means of summarizing three vegetation indices derived from the MODIS satellite. We evaluated our models for predictive ability using partial ROC analysis and GLM ANOVA to compare performance among indices and regions.ResultsWe found correlations between vegetation indices to be high, such that they contain information that overlaps broadly. Neither the type of vegetation index used nor method of summary affected model performance significantly. However, the degree to which model predictions had to be transferred (i.e., projected onto landscapes and conditions not represented on the landscape of training) impacted predictive strength greatly (within-region model predictions far out-performed models projected among regions).ConclusionOur results provide the first quantitative tests of most appropriate uses of different remotely sensed data sets in ecological niche modeling applications. While our testing did not result in a decisive "best" index product or means of summarizing indices, it emphasizes the need for careful evaluation of products used in modeling (e.g. matching temporal dimensions and spatial resolution) for optimum performance, instead of simple reliance on large numbers of data layers.

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Ecological niche modeling re-examined: A case study with the Darwin's fox.

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Ecological niche modeling re-examined: A case study with the Darwin's fox.

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