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

Escobar, Luis E.; Qiao, Huijie; Cabello, Javier; Peterson, A. Townsend

doi:10.1002/ece3.4014

Cited by 61 publications

(54 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…More specifically, to obtain the most accurate representation of Leptospira circulation in the landscape, it would be necessary to assess the presence of Leptospira serovars in wildlife and the environment to provide and integrative estimation of the geographic and environmental risk (Albert, Goarant, & Mathieu, ). The second limitation that we found was related to the method used for the estimation of the ENM performance, since a recent paper (Velasco & González‐Salazar, ) highlighted the need to reduce the use of AIC for geographic predictions in ecological niche modelling studies due to the lack of accuracy of this approach on evaluating model performance, which is currently one of the most used methods to estimate ENM complexity (Cobos, Peterson, Barve, & Osorio‐Olvera, ; Escobar, Qiao, Cabello, & Peterson, ; Freeman, Sunnarborg, & Peterson, ; Raghavan, Peterson, Cobos, Ganta, & Foley, ).…”

Section: Discussionmentioning

confidence: 99%

Spatial distribution and spread potential of sixteenLeptospiraserovars in a subtropical region of Brazil

Jara

Escobar

Rodriges³

et al. 2019

Transbound Emerg Dis

Self Cite

View full text Add to dashboard Cite

Leptospirosis is a bacterial disease that represents a major problem in animal and public health due to its high prevalence and widespread distribution. This zoonotic disease is most prevalent in tropical environments where conditions favour pathogen survival. The ecological preferences of Leptospira serovars are poorly understood, limiting our knowledge of where and when outbreaks can occur, which may result in misinformed prevention and control plans. While the disease can occur consistently in time and space in tropical regions, research on the ecology of leptospirosis remains limited in subtropical regions. This research gap regarding Leptospira ecology brings public and veterinary health problems, impacting local economies. To fill this gap of knowledge, we suggest to assess geographic and ecological features among Leptospira serovars in a subtropical area of Brazil where leptospirosis is endemic to (a) highlight environmental conditions that facilitate or limit Leptospira spread and survival and (b) reconstruct its geographic distribution. An ecological niche modelling framework was used to characterize and compare Leptospira serovars in both geographic and environmental space. Our results show that despite the geographic overlap exhibited by the different serovars assessed, we found ecological divergence among their occupied ecological niches. Ecological divergences were expressed as ranges of potential distributions and environmental conditions found suitably by serovar, Sejroe being the most asymmetric (<0.15). Most important predictors for the potential distribution of most serovars were soil pH (31.7%) and landscape temperature (24.2%). Identification of environmental preferences will allow epidemiologists to better infer the presence of a serovar based on the environmental characteristics of regions rather than inferences based solely on historical epidemiological records. Including geographic and ecological ranges of serovars also may help to forecast transmission potential of Leptospira in public health and the food animal practice.

show abstract

Section: Discussionmentioning

confidence: 99%

Spatial distribution and spread potential of sixteenLeptospiraserovars in a subtropical region of Brazil

Jara

Escobar

Rodriges³

et al. 2019

Transbound Emerg Dis

Self Cite

View full text Add to dashboard Cite

show abstract

“…Assessing model performance is fundamental in ENM; researchers have focused on optimizing ENM algorithms, but not necessarily distinguishing between interpolation and extrapolation (but see Escobar et al 2018). For instance, Elith et al (2006) evaluated 16 algorithms applied to species from five regions; because calibration and evaluation localities were from the same area, that study investigated interpolative abilities.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of transferability of ecological niche models

et al. 2018

Self Cite

View full text Add to dashboard Cite

Ecological niche modeling (ENM) is used widely to study species’ geographic distributions. ENM applications frequently involve transferring models calibrated with environmental data from one region to other regions or times that may include novel environmental conditions. When novel conditions are present, transferability implies extrapolation, whereas, in absence of such conditions, transferability is an interpolation step only. We evaluated transferability of models produced using 11 ENM algorithms from the perspective of interpolation and extrapolation in a virtual species framework. We defined fundamental niches and potential distributions of 16 virtual species distributed across Eurasia. To simulate real situations of incomplete understanding of species’ distribution or existing fundamental niche (environmental conditions suitable for the species contained in the study area; N* F ), we divided Eurasia into six regions and used 1–5 regions for model calibration and the rest for model evaluation. The models produced with the 11 ENM algorithms were evaluated in environmental space, to complement the traditional geographic evaluation of models. None of the algorithms accurately estimated the existing fundamental niche (N* F ) given one region in calibration, and model evaluation scores decreased as the novelty of the environments in the evaluation regions increased. Thus, we recommend quantifying environmental similarity between calibration and transfer regions prior to model transfer, providing an avenue for assessing uncertainty of model transferability. Different algorithms had different sensitivity to completeness of knowledge of N* F , with implications for algorithm selection. If the goal is to reconstruct fundamental niches, users should choose algorithms with limited extrapolation when N* F is well known, or choose algorithms with increased extrapolation when N* F is poorly known. Our assessment can inform applications of ecological niche modeling transference to anticipate species invasions into novel areas, disease emergence in new regions, and forecasts of species distributions under future climate conditions.

show abstract

“…The most important change that MIAmaxent implements to cause this shift is to select models by subset selection instead of lasso regularization. Any distribution modeling approach-including how the model is produced and how it is evaluated-must be adapted to the purpose of the study and the characteristics of the data (Halvorsen, 2012;Merow et al, 2014), as no single approach is most suitable for all studies (Escobar, Qiao, Cabello, & Peterson, 2018;Mazzoni, 2016;Qiao, Soberón, & Peterson, 2015). MIAmaxent expands a distribution modeler's statistical toolbox, and for studies aiming to do something other than predict with minimal error the geographic distribution of the modeled target in the same spatial and temporal context as the data, MIAmaxent may frequently be more suitable than the Maxent software.…”

Section: Con Clus Ionmentioning

confidence: 99%

The MIAmaxent R package: Variable transformation and model selection for species distribution models

Vollering

Halvorsen

Mazzoni

2019

Ecology and Evolution

View full text Add to dashboard Cite

The widely used “Maxent” software for modeling species distributions from presence‐only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high‐predictive performance but low‐ecological interpretability, and implications of Maxent's statistical approach to variable transformation, model fitting, and model selection remain underappreciated. In particular, Maxent's approach to model selection through lasso regularization has been shown to give less parsimonious distribution models—that is, models which are more complex but not necessarily predictively better—than subset selection. In this paper, we introduce the MIAmaxent R package, which provides a statistical approach to modeling species distributions similar to Maxent's, but with subset selection instead of lasso regularization. The simpler models typically produced by subset selection are ecologically more interpretable, and making distribution models more grounded in ecological theory is a fundamental motivation for using MIAmaxent. To that end, the package executes variable transformation based on expected occurrence–environment relationships and contains tools for exploring data and interrogating models in light of knowledge of the modeled system. Additionally, MIAmaxent implements two different kinds of model fitting: maximum entropy fitting for presence‐only data and logistic regression (GLM) for presence–absence data. Unlike Maxent, MIAmaxent decouples variable transformation, model fitting, and model selection, which facilitates methodological comparisons and gives the modeler greater flexibility when choosing a statistical approach to a given distribution modeling problem.

show abstract

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

Cited by 61 publications

References 57 publications

Spatial distribution and spread potential of sixteenLeptospiraserovars in a subtropical region of Brazil

Spatial distribution and spread potential of sixteenLeptospiraserovars in a subtropical region of Brazil

An evaluation of transferability of ecological niche models

The MIAmaxent R package: Variable transformation and model selection for species distribution models

Contact Info

Product

Resources

About