Interpretation of Models of Fundamental Ecological Niches and Species’ Distributional Areas

Ecological niche modeling?that is, estimation of the dimensions of fundamental ecological niches of species?to predict their geographic distributions is increasingly being employed in systematics, ecology, conservation, public health, etc. This technique is often (of necessity) based on data comprising records of presences only. In recent years, many modeling approaches have been devised to estimate these interrelated expressions of a species’ ecology, distributional biology, and evolutionary history?nevertheless, in many cases, a formal basis in ecological and evolutionary theory has been lacking. In this paper, we outline such a formal basis for the suite of techniques that can be termed ‘ecological niche modeling,’ analyze example situations that can be modeled using these techniques, and clarify the interpretation of results

Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome

A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities. The geographic predictions resulting from ecological niche modelling have been widely used for modelling the future dispersion of vectors based on the occurrence records and the potential prevalence of the disease. The establishment of risk maps for disease systems with complex cycles such as cutaneous leishmaniasis (CL) can be very challenging due to the many inference networks between large sets of host and vector species, with considerable heterogeneity in disease patterns in space and time. One novelty in the present study is the use of human CL cases to predict the risk of leishmaniasis occurrence in response to anthropogenic, climatic and environmental factors at two different scales, in the Neotropical moist forest biome (Amazonian basin and surrounding forest ecosystems) and in the surrounding region of French Guiana. With a consistent data set never used before and a conceptual and methodological framework for interpreting data cases, we obtained risk maps with high statistical support. The predominantly identified human CL risk areas are those where the human impact on the environment is significant, associated with less contributory climatic and ecological factors. For both models this study highlights the importance of considering the anthropogenic drivers for disease risk assessment in human, although CL is mainly linked to the sylvatic and peri-urban cycle in Meso and South America. © 2019 Chavy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Interpretation of Models of Fundamental Ecological Niches and Species’ Distributional Areas

This is the published version.Ecological niche modeling?that is, estimation of the dimensions of fundamental ecological niches of species?to predict their geographic distributions is increasingly being employed in systematics, ecology, conservation, public health, etc. This technique is often (of necessity) based on data comprising records of presences only. In recent years, many modeling approaches have been devised to estimate these interrelated expressions of a species’ ecology, distributional biology, and evolutionary history?nevertheless, in many cases, a formal basis in ecological and evolutionary theory has been lacking. In this paper, we outline such a formal basis for the suite of techniques that can be termed ‘ecological niche modeling,’ analyze example situations that can be modeled using these techniques, and clarify the interpretation of results

Interpretation of models of fundamental ecological niches and species' distributional areas

Estimation of the dimensions of fundamental ecological niches of species to predict their geographic distributions is increasingly being attempted in systematics, ecology, conservation, public health, etc. This technique is often (of necessity) based on data comprising records of presences only. In recent years, modeling approaches have been devised to estimate these interrelated expressions of a species’ ecology, distributional biology, and evolutionary history—nevertheless, a formal basis in ecological and evolutionary theory has largely been lacking. In this paper, we outline such a formal basis to clarify the use of techniques applied to the challenge of estimating ‘ecological niches;’ we analyze example situations that can be modeled using these techniques, and clarify interpretation of results

Methodological advancements for improving performance and generating ensemble ecological niche models

This study employs spatial filtering of occurrence data with the aim of reducing overfitting to sampling bias in ecological niche models (ENMs). Sampling bias in geographic space leads to localities that may also be biased in environmental space. If so, the model can overfit to those biases. As a preliminary test addressing this issue, we used Maxent, bioclimatic variables, and occurrence localities of a broadly distributed Malagasy tenrec, Microgale cowani (Family Tenrecidae: Subfamily Oryzorictinae). We modeled the abiotically suitable area of this species using three distinct datasets: unfiltered, spatially filtered, and rarefied unfiltered localities. To quantify overfitting and model performance, we calculated evaluation AUC, the difference between calibration and evaluation AUC (= AUCdiff), and omission rates. Models made with the filtered dataset showed lower overfitting and better performance than the other two suites of models, having lower omission rates and AUCdiff, and a higher AUCevaluation. Additionally, the rarefied unfiltered dataset performed better than the unfiltered one for three evaluation metrics, likely because the larger datasets reinforced the biases. These results indicate that spatial filtering of occurrence localities may allow biogeographers to produce better models

Predicting population responses to environmental change from individual-level mechanisms: towards a standardized mechanistic approach

Animal populations will mediate the response of global biodiversity to environmental changes. Population models are thus important tools for both understanding and predicting animal responses to uncertain future conditions. Most approaches, however, are correlative and ignore the individual-level mechanisms that give rise to population dynamics. Here, we assess several existing population modelling approaches, and find limitations to both ‘correlative’ and ‘mechanistic’ models. We advocate the need for a standardised mechanistic approach for linking individual mechanisms (physiology, behaviour and evolution) to population dynamics in spatially explicit landscapes. Such an approach is potentially more flexible and informative than current population models. Key to realising this goal, however, is overcoming current data limitations, the development and testing of eco-evolutionary theory to represent interactions between individual mechanisms, and standardised multidimensional environmental change scenarios which incorporate multiple stressors. Such progress is essential in supporting environmental decisions in uncertain future conditions

Advocating better habitat use and selection models in bird ecology

Studies on habitat use and habitat selection represent a basic aspect of bird ecology, due to its importance in natural history, distribution, response to environmental changes, management and conservation. Basically, a statistical model that identifies environmental variables linked to a species presence is searched for. In this sense, there is a wide array of analytical methods that identify important explanatory variables within a model, with higher explanatory and predictive power than classical regression approaches. However, some of these powerful models are not widespread in ornithological studies, partly because of their complex theory, and in some cases, difficulties on their implementation and interpretation. Here, I describe generalized linear models and other five statistical models for the analysis of bird habitat use and selection outperforming classical approaches: generalized additive models, mixed effects models, occupancy models, binomial N-mixture models and decision trees (classification and regression trees, bagging, random forests and boosting). Each of these models has its benefits and drawbacks, but major advantages include dealing with non-normal distributions (presence-absence and abundance data typically found in habitat use and selection studies), heterogeneous variances, non-linear and complex relationships among variables, lack of statistical independence and imperfect detection. To aid ornithologists in making use of the methods described, a readable description of each method is provided, as well as a flowchart along with some recommendations to help them decide the most appropriate analysis. The use of these models in ornithological studies is encouraged, given their huge potential as statistical tools in bird ecology.Fil: Palacio, Facundo Xavier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. División Zoología de Vertebrados. Sección Ornitología; Argentin

Commonly collected thermal performance data can inform species distributions in a data‑limited invader

Predicting potential distributions of species in new areas is challenging. Physiological data can improve interpretation of predicted distributions and can be used in directed distribution models. Nonnative species provide useful case studies. Panther chameleons (Furcifer pardalis) are native to Madagascar and have established populations in Florida, USA, but standard correlative distribution modeling predicts no suitable habitat for F. pardalis there. We evaluated commonly collected thermal traits– thermal performance, tolerance, and preference—of F. pardalis and the acclimatization potential of these traits during exposure to naturally-occurring environmental conditions in North Central Florida. Though we observed temperature-dependent thermal performance, chameleons maintained similar thermal limits, performance, and preferences across seasons, despite long-term exposure to cool temperatures. Using the physiological data collected, we developed distribution models that varied in restriction: time-dependent exposure near and below critical thermal minima, predicted activity windows, and predicted performance thresholds. Our application of commonly collected physiological data improved interpretations on potential distributions of F. pardalis, compared with correlative distribution modeling approaches that predicted no suitable area in Florida. These straightforward approaches can be applied to other species with existing physiological data or after brief experiments on a limited number of individuals, as demonstrated here

Integrating physiology into correlative models can alter projections of habitat suitability under climate change for a threatened amphibian

Rapid global change has increased interest in developing ways to identify suitable refu-gia for species of conservation concern. Correlative and mechanistic species distribu-tion models (SDMs) represent two approaches to generate spatially-explicit estimates of climate vulnerability. Correlative SDMs generate distributions using statistical associations between environmental variables and species presence data. In contrast, mechanistic SDMs use physiological traits and tolerances to identify areas that meet the conditions required for growth, survival and reproduction. Correlative approaches assume modeled environmental variables influence species distributions directly or indirectly; however, the mechanisms underlying these associations are rarely verified empirically. We compared habitat suitability predictions between a correlative-only SDM, a mechanistic SDM and a correlative framework that incorporated mechanis-tic layers (‘hybrid models’). Our comparison focused on green salamanders Aneides aeneus, a priority amphibian threatened by climate change throughout their disjunct range. We developed mechanistic SDMs using experiments to measure the thermal sensitivity of resistance to water loss (ri) and metabolism. Under current climate con-ditions, correlative-only, hybrid and mechanistic SDMs predicted similar overlap in habitat suitability; however, mechanistic SDMs predicted habitat suitability to extend into regions without green salamanders but known to harbor many lungless salaman-ders. Under future warming scenarios, habitat suitability depended on climate sce-nario and SDM type. Correlative and hybrid models predicted a 42% reduction or 260% increase in area considered to be suitable depending on the climate scenario. In mechanistic SDMs, energetically suitable habitat declined with both climate scenarios and was driven by the thermal sensitivity of ri. Our study indicates that correlative-only and hybrid approaches produce similar predictions of habitat suitability; however, discrepancies can arise for species that do not occupy their entire fundamental niche, which may hold consequences of conservation planning of threatened species