The Experts below are selected from a list of 754215 Experts worldwide ranked by ideXlab platform
Wilfried Thuiller - One of the best experts on this subject based on the ideXlab platform.
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Modelling plant Species Distribution in alpine grasslands using airborne imaging spectroscopy
Biology Letters, 2014Co-Authors: Julien Pottier, Wilfried Thuiller, Antoine Guisan, Zbynek Malenovsky, Achilleas Psomas, Lucie Homolova, Michael E. Schaepman, Philippe Choler, Niklaus E ZimmermannAbstract:Remote sensing using airborne imaging spectroscopy(AIS)is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant Species Distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant Distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS datafor predicting alpine plant Species Distribution. Contrary to expectations, differences between Species Distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of Species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.
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Selecting pseudo-absences for Species Distribution models: How, where and how many?
Methods in Ecology and Evolution, 2012Co-Authors: Morgane Barbet-massin, Cécile Hélène Albert, Frederic Jiguet, Wilfried ThuillerAbstract:Summary 1. Species Distribution models are increasingly used to address questions in conservation biology, ecology and evolution. The most effective Species Distribution models require data on both Species presence and the available environmental conditions (known as background or pseudo-absence data) in the area. However, there is still no consensus on how and where to sample these pseudo-absences and how many. 2. In this study, we conducted a comprehensive comparative analysis based on simple simulated Species Distributions to propose guidelines on how, where and how many pseudo-absences should be generated to build reliable Species Distribution models. Depending on the quantity and quality of the initial presence data (unbiased vs. climatically or spatially biased), we assessed the relative effect of the method for selecting pseudo-absences (random vs. environmentally or spatially stratified) and their number on the predictive accuracy of seven common modelling techniques (regression, classification and machine-learning techniques). 3. When using regression techniques, the method used to select pseudo-absences had the greatest impact on the model’s predictive accuracy. Randomly selected pseudo-absences yielded the most reliable Distribution models. Models fitted with a large number of pseudo-absences but equally weighted to the presences (i.e. the weighted sum of presence equals the weighted sum of pseudo-absence) produced the most accurate predicted Distributions. For classification and machine-learning techniques, the number of pseudo-absences had the greatest impact on model accuracy, and averaging several runs with fewer pseudo-absences than for regression techniques yielded the most predictive models. 4. Overall, we recommend the use of a large number (e.g. 10 000) of pseudo-absences with equal weighting for presences and absences when using regression techniques (e.g. generalised linear model and generalised additive model); averaging several runs (e.g. 10) with fewer pseudo-absences (e.g. 100) with equal weighting for presences and absences with multiple adaptive regression splines and discriminant analyses; and using the same number of pseudo-absences as available presences (averaging several runs if few pseudo-absences) for classification techniques such as boosted regression trees, classification trees and random forest. In addition, we recommend the random selection of pseudo-absences when using regression techniques and the random selection of geographically and environmentally stratified pseudo-absences when using classification and machine-learning techniques.
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uncertainty in ensemble forecasting of Species Distribution
Global Change Biology, 2010Co-Authors: Wilfried Thuiller, Laetitia Buisson, Nicolas Casajus, Sovan Lek, Gael GrenouilletAbstract:Species Distribution modelling has been widely applied in order to assess the potential impacts of climate change on biodiversity. Many methodological decisions, taken during the modelling process and forecasts, may, however, lead to a large variability in the assessment of future impacts. Using measures of Species range change and turnover, the potential impacts of climate change on French stream fish Species and assemblages were evaluated. Our main focus was to quantify the uncertainty in the projections of these impacts arising from four sources of uncertainty: initial datasets (Data), statistical methods [Species Distribution models (SDM)], general circulation models (GCM), and gas emission scenarios (GES). Several modalities of the aforementioned uncertainty sources were combined in an ensemble forecasting framework resulting in 8400 different projections. The variance explained by each source was then extracted from this whole ensemble of projections. Overall, SDM contributed to the largest variation in projections, followed by GCM, whose contribution increased over time equalling almost the proportion of variance explained by SDM in 2080. Data and GES had little influence on the variability in projections. Future projections of range change were more consistent for Species with a large geographical extent (i.e., Distribution along latitudinal or stream gradients) or with restricted environmental requirements (i.e., small thermal or elevation ranges). Variability in projections of turnover was spatially structured at the scale of France, indicating that certain particular geographical areas should be considered with care when projecting the potential impacts of climate change. The results of this study, therefore, emphasized that particular attention should be paid to the use of predictions ensembles resulting from the application of several statistical methods and climate models. Moreover, forecasted impacts of climate change should always be provided with an assessment of their uncertainty, so that management and conservation decisions can be taken in the full knowledge of their reliability.
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Evaluation of consensus methods in predictive Species Distribution modelling
Diversity and Distributions, 2009Co-Authors: M. Marmion, M. Parviainen, M. Luoto, R. K. Heikkinen, Wilfried ThuillerAbstract:Spatial modelling techniques are increasingly used in Species Distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant Species. North-eastern Finland, Europe. The spatial Distributions of the plant Species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot. The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods. Consensus methods based on average function algorithms may increase significantly the accuracy of Species Distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.
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evaluation of consensus methods in predictive Species Distribution modelling
Diversity and Distributions, 2009Co-Authors: M. Marmion, M. Parviainen, M. Luoto, R. K. Heikkinen, Wilfried ThuillerAbstract:Aim Spatial modelling techniques are increasingly used in Species Distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant Species. Location North-eastern Finland, Europe. Methods The spatial Distributions of the plant Species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot. Results The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods. Main conclusions Consensus methods based on average function algorithms may increase significantly the accuracy of Species Distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.
Jane Elith - One of the best experts on this subject based on the ideXlab platform.
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Species Distribution Modeling
Ecology, 2019Co-Authors: Jane ElithAbstract:Models of Species Distributions aim to describe and often to predict the spatial Distribution of individual Species, using as a basis the Species’ relationship with its environment. At a broad level this can be done in two main ways. The first is to model the processes that underpin where the Species occur: demographic or physiological processes that fundamentally define the Species Distribution. The second and much more common approach is to fit a numerical model that defines the relationships between observations of the Species occurrence and any covariates considered relevant. This article focuses on the second, aiming to introduce the reader to key texts and ideas in this large and popular field of modeling whose applications span ecology, biogeography, evolutionary biology, conservation, biosecurity, health, and computation. It focuses on the models and the mapped predictions often derived from them. Referred to as Species Distribution models (SDMs) here, these (or their variants) are also referred to as ecological niche models, habitat models, or bioclimatic envelope models. Several textbooks have now been published on SDMs, giving good insights into background, theory, applications, data, and models. Thousands of manuscripts are published including those developing new methods, those that apply SDMs to ecological theory and understanding, and those that apply the maps in conservation, planning, and management applications. This bibliography leads the reader through the literature, first covering the background and standard approaches to fitting, evaluating, and reporting SDMs. Then, aiming to extend beyond the information presented thoroughly in existing textbooks, it describes related models that are still correlative and applicable for modeling individual Species but that provide important extensions. These allow modelers to deal with the common complexities in data (structured datasets, imperfect detection, spatio-temporal issues) and to broaden the models to include biological processes or issues of interest such as biotic interactions, movement, traits and phylogenetic data.
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Species Distribution Modeling
Encyclopedia of Biodiversity, 2013Co-Authors: Jane Elith, Janet FranklinAbstract:Species Distribution modeling (SDM) links ecological theory of Species–environment relationships with statistical learning methods and geospatial data to understand and predict the Distributions of Species and their habitats. Also called ecological niche modeling , SDM is widely used for biodiversity assessment and to predict the impacts of environmental change on biodiversity in terrestrial and aquatic habitats. It can also provide insight and understanding about ecological relationships.
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sensitivity of predictive Species Distribution models to change in grain size
Diversity and Distributions, 2007Co-Authors: Antoine Guisan, Jane Elith, Catherine H. Graham, Falk HuettmannAbstract:Predictive Species Distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 Species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or Species considered. Results show that a 10 times change in grain size does not severely affect predictions from Species Distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and Species types. The strongest effect is on regions and Species types, with tree Species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.
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sensitivity of conservation planning to different approaches to using predicted Species Distribution data
Biological Conservation, 2005Co-Authors: Kerrie A. Wilson, Michael I Westphal, Hugh P. Possingham, Jane ElithAbstract:The main role of conservation planning is to design reserve networks to protect biodiversity in situ. Research within the field of conservation planning has focused on the development of theories and tools to design reserve networks that protect biodiversity in an efficient and representative manner. Whilst much progress has been made in this regard, there has been limited assessment of the sensitivity of conservation planning outcomes to uncertainty associated with the datasets used for conservation planning. Predicted Species Distribution data are commonly used for conservation planning because the alternatives (e.g. survey data) are incomplete or biased spatially. However, there may be considerable uncertainty associated with the use of predicted Species Distribution data, particularly given the variety of approaches available to generate a dataset from such predictions for use in conservation planning. These approaches range from using the probabilistic data directly to using a threshold identified a priori or a posteriori to convert the probabilistic data to presence/absence data. We assess the sensitivity of conservation planning outcomes to different uses of predicted Species Distribution data. The resulting reserve networks differed, and had different expected Species representation. The choice of approach will depend on how much risk a conservation planner is willing to tolerate and how much efficiency can be sacrificed.
M. Marmion - One of the best experts on this subject based on the ideXlab platform.
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Evaluation of consensus methods in predictive Species Distribution modelling
Diversity and Distributions, 2009Co-Authors: M. Marmion, M. Parviainen, M. Luoto, R. K. Heikkinen, Wilfried ThuillerAbstract:Spatial modelling techniques are increasingly used in Species Distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant Species. North-eastern Finland, Europe. The spatial Distributions of the plant Species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot. The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods. Consensus methods based on average function algorithms may increase significantly the accuracy of Species Distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.
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evaluation of consensus methods in predictive Species Distribution modelling
Diversity and Distributions, 2009Co-Authors: M. Marmion, M. Parviainen, M. Luoto, R. K. Heikkinen, Wilfried ThuillerAbstract:Aim Spatial modelling techniques are increasingly used in Species Distribution modelling. However, the implemented techniques differ in their modelling performance, and some consensus methods are needed to reduce the uncertainty of predictions. In this study, we tested the predictive accuracies of five consensus methods, namely Weighted Average (WA), Mean(All), Median(All), Median(PCA), and Best, for 28 threatened plant Species. Location North-eastern Finland, Europe. Methods The spatial Distributions of the plant Species were forecasted using eight state-of-the-art single-modelling techniques providing an ensemble of predictions. The probability values of occurrence were then combined using five consensus algorithms. The predictive accuracies of the single-model and consensus methods were assessed by computing the area under the curve (AUC) of the receiver-operating characteristic plot. Results The mean AUC values varied between 0.697 (classification tree analysis) and 0.813 (random forest) for the single-models, and from 0.757 to 0.850 for the consensus methods. WA and Mean(All) consensus methods provided significantly more robust predictions than all the single-models and the other consensus methods. Main conclusions Consensus methods based on average function algorithms may increase significantly the accuracy of Species Distribution forecasts, and thus they show considerable promise for different conservation biological and biogeographical applications.
Falk Huettmann - One of the best experts on this subject based on the ideXlab platform.
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sensitivity of predictive Species Distribution models to change in grain size
Diversity and Distributions, 2007Co-Authors: Antoine Guisan, Jane Elith, Catherine H. Graham, Falk HuettmannAbstract:Predictive Species Distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 Species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or Species considered. Results show that a 10 times change in grain size does not severely affect predictions from Species Distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and Species types. The strongest effect is on regions and Species types, with tree Species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.
W. Douglas Robinson - One of the best experts on this subject based on the ideXlab platform.
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Comparing multi- and single-scale Species Distribution and abundance models built with the boosted regression tree algorithm
Landscape Ecology, 2020Co-Authors: Tyler A. Hallman, W. Douglas RobinsonAbstract:Context Species are influenced by factors operating at multiple scales, but multi-scale Species Distribution and abundance models are rarely used. Though multi-scale Species Distribution models outperform single-scale models, when compared through model selection, multi- and single-scale models built with computer learning algorithms have not been compared. Objectives We compared the performance of models using a simple and accessible, multi-scale, machine learning, Species Distribution and abundance modeling framework to pseudo-optimized and unoptimized single-scale models. Methods We characterized environmental variables at four spatial scales and used boosted regression trees to build multi-scale and single-scale Distribution and abundance models for 28 bird Species. For each Species and across Species, we compared the performance of multi-scale models to pseudo-optimized and lowest-performing unoptimized single-scale models. Results Multi-scale Distribution models consistently performed as well or better than pseudo-optimized single-scale models and significantly better than unoptimized single-scale models. Abundance model performance showed a similar, but less pronounced pattern. Mixed-effects models, that controlled for Species, provided strong evidence that multi-scale models performed better than unoptimized single-scale models. Although mean improvement in model performance across Species appeared minor, for individual Species, arbitrary selection of scale could result in discrepancies of up to fourteen percent for area of suitable habitat and population estimates. Conclusions Scale selection should be explicitly addressed in Distribution and abundance modeling. The multi-scale Species Distribution and abundance modeling framework presented here provides a concise and accessible alternative to standard pseudo-scale optimization while addressing the scale-dependent response of Species to their environment.