Study on selecting sensitive environmental variables in modelling species spatial distribution

Wang, Hongshuo; Liu, Desheng; Munroe, Darla K.; Cao, Kai; Biermann, Christine

doi:10.1080/19475683.2015.1114523

Cited by 8 publications

(7 citation statements)

References 53 publications

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“…It is a fact that bioclimatic variables are among the most frequently used variables in SDM based studies and rightly so as climate is a strong determinant of species’ distribution. However, an injudicious use of these variables without considering factors like species’ ecology, scale of study and optimal grain size is questionable 13 , 14 . Thus, we speculate that in many SDM based studies – especially at small spatial scale of study area - biophysical variables may be the more important ones and inclusion of bioclimatic variables in such cases may reduce the model accuracy.…”

Section: Introductionmentioning

confidence: 99%

Species distribution model transferability and model grain size – finer may not always be better

Manzoor

Griffiths

Lukáč

2018

Sci Rep

100

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Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.

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Section: Introductionmentioning

confidence: 99%

Species distribution model transferability and model grain size – finer may not always be better

Manzoor

Griffiths

Lukáč

2018

Sci Rep

100

View full text Add to dashboard Cite

show abstract

“…Temperature layers (BIO1‐BIO11) were determined in degrees Celsius (°C) and humidity layers (BIO12‐BIO17) in kg of water/kg of air (Vega et al 2017). We used principal components analysis (PCA) to generate a subset of independent variables and retained the components that explained ≥90% of the total variation in the climatic dataset (Wang et al 2016).…”

Section: Methodsmentioning

confidence: 99%

An assessment of the niche centroid hypothesis: Pteropus lylei (Chiroptera)

et al. 2020

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Recent ecological theories propose that species reach their highest abundance and genetic diversity in the center of their ecological niche and decline toward the edges. We assessed whether Lyle's flying fox, Pteropus lylei, abundance and genetic diversity were correlated with niche centroid distance using an ecological niche model as a proxy for fundamental niche (N F ). Alternatively, we assessed whether P. lylei abundance and genetic diversity were correlated with fine-scale environmental factors as a proxy of the species' realized niche (N R ). We examined relationships between abundance and environmental factors at coarse and fine scales as proxies of N F and N R , respectively. For coarse scale, ecological niche of P. lylei was modeled using all available occurrence records in Thailand, Cambodia, and Vietnam, coupled with climatic data. We conducted field measurements of P. lylei abundance and used genetic structure data across a large portion of the species' range. We measured Euclidean distances between abundance and genetic data and the niche centroid in environmental dimensions. Spearman's correlation was estimated between abundance and genetic diversity vs. distance to the niche centroid. Complementarily, for the fine-scale test, we measured multiple regression models between abundance and genetic diversity versus the normalized difference vegetation index (NDVI), local temperature, percent area of waterbodies, human density, and number of Buddhist temples. We failed to detect relationships between abundance and genetic diversity with the distance to the niche centroid in the coarse-scale model. When using the fine-scale, landscape-level data, we found negative correlation between genetic diversity and number of temples. The data available were unable to support niche centroid hypothesis for the current distribution and abundances of P. lylei. We note that our failure to find an association does not support nor reject the niche centroid hypothesis. Instead, our capacity to test the niche centroid hypothesis may be limited by our ability to use empirical data to accurately reconstruct N F from field observations only. Future research may require physiology-based experimental approaches to explore relationships between species abundances and the niche structure.

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“…We confirmed that the selected predictor variables were related to likely occupied areas rather than potentially suitable areas, thus avoiding the influence of accuracy on SDM predictions (Elith & Leathwick, ; Syfert et al., ). The modeling process was performed with two sets of predictors (a set of 5 and a set of 20 variables) following previous studies which concluded that some algorithms are more sensitive to collinearity, while others are very restrictive when using more predictor variables (Elith et al., ; Stockwell & Peterson, ; Wang, Liu, Munroe, Cao, & Biermann, ). For the set of 20 variables, we used the 19 climatic variables and altitude (m).…”

Section: Methodsmentioning

confidence: 99%

Species distribution models and empirical test: Comparing predictions with well‐understood geographical distribution of Bothrops alternatus in Argentina

Sarquis

Cristaldi

Arzamendia

et al. 2018

Ecology and Evolution

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Species distribution models (SDMs) estimate the geographical distribution of species although with several limitations due to sources of inaccuracy and biases. Empirical tests arose as the most important steps in scientific knowledge to assess the efficiency of model predictions, which are poorly rigorous in SDMs. A good approach to the empirical distribution (ED) of a species can be obtained from comprehensive empirical knowledge, that is, well‐understood distributions gathered from large amount of data generated with appropriate spatial and temporal sample coverage. The aims of this study were to (a) compare different SDMs predictions with an ED; and (b) evaluate if fuzzy global matching (FGM) could be used as an index to compare SDMs predictions and ED. Six algorithms with 5 and 20 variables were used to assess their accuracy in predicting the ED of the venomous snake Bothrops alternatus (Viperidae). Its entire distribution is known, thanks to thorough field surveys across Argentina, with 1,767 records. ED was compared with SDMs predictions using Map Comparison Kit. SDMs predictions showed important biases in all methods used, from 70% sub‐estimation to 40% over‐estimation of ED. BIOCLIM predicted ≈31% of B. alternatus ED. DOMAIN predicted 99% of ED, but over‐estimated 40% of the area. GLM with five variables calculated 75% of ED, while Genetic Algorithm for Rule‐set Prediction showed ≈60% of ED; the last two presenting overpredictions in areas with favorable climatic conditions but not inhabited by the species. MaxEnt and RF were the only methods to detect isolated populations in the southern distribution of B. alternatus. Although SDMs proved useful in making predictions about species distribution, predictions need validation with expert maps knowledge and ED. Moreover, FGM showed a good performance as an index with values similar to True Skill Statistic, so that it could be used to relate ED and SDMs predictions.

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Study on selecting sensitive environmental variables in modelling species spatial distribution

Cited by 8 publications

References 53 publications

Species distribution model transferability and model grain size – finer may not always be better

Species distribution model transferability and model grain size – finer may not always be better

An assessment of the niche centroid hypothesis: Pteropus lylei (Chiroptera)

Species distribution models and empirical test: Comparing predictions with well‐understood geographical distribution of Bothrops alternatus in Argentina

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