The Experts below are selected from a list of 82026 Experts worldwide ranked by ideXlab platform
Sandra Lavorel - One of the best experts on this subject based on the ideXlab platform.
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generalized models vs Classification Tree analysis predicting spatial distributions of plant species at different scales
Journal of Vegetation Science, 2003Co-Authors: Wilfried Thuiller, Miguel B Araujo, Sandra LavorelAbstract:Abstract Statistical models of the realized niche of species are increasingly used, but systematic comparisons of alternative methods are still limited. In particular, only few studies have explored the effect of scale in model outputs. In this paper, we investigate the predictive ability of three statistical methods (generalized linear models, generalized additive models and Classification Tree analysis) using species distribution data at three scales: fine (Catalonia), intermediate (Portugal) and coarse (Europe). Four Mediterranean Tree species were modelled for comparison. Variables selected by models were relatively consistent across scales and the predictive accuracy of models varied only slightly. However, there were slight differences in the performance of methods. Classification Tree analysis had a lower accuracy than the generalized methods, especially at finer scales. The performance of generalized linear models also increased with scale. At the fine scale GLM with linear terms showed better acc...
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generalized models vs Classification Tree analysis predicting spatial distributions of plant species at different scales
Journal of Vegetation Science, 2003Co-Authors: Wilfried Thuiller, Miguel B Araujo, Sandra LavorelAbstract:Statistical models of the realized niche of species are increasingly used, but systematic comparisons of alterna- tive methods are still limited. In particular, only few studies have explored the effect of scale in model outputs. In this paper, we investigate the predictive ability of three statistical methods (generalized linear models, generalized additive mod- els and Classification Tree analysis) using species distribution data at three scales: fine (Catalonia), intermediate (Portugal) and coarse (Europe). Four Mediterranean Tree species were modelled for comparison. Variables selected by models were relatively consistent across scales and the predictive accuracy of models varied only slightly. However, there were slight differences in the performance of methods. Classification Tree analysis had a lower accuracy than the generalized methods, especially at finer scales. The performance of generalized linear models also increased with scale. At the fine scale GLM with linear terms showed better accuracy than GLM with quadratic and polynomial terms. This is probably because distributions at finer scales represent a linear sub-sample of entire realized niches of species. In contrast to GLM, the performance of GAM was constant across scales being more data-oriented. The predictive accuracy of GAM was always at least equal to other techniques, suggesting that this modelling approach is more robust to variations of scale because it can deal with any response shape.
Wilfried Thuiller - One of the best experts on this subject based on the ideXlab platform.
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generalized models vs Classification Tree analysis predicting spatial distributions of plant species at different scales
Journal of Vegetation Science, 2003Co-Authors: Wilfried Thuiller, Miguel B Araujo, Sandra LavorelAbstract:Abstract Statistical models of the realized niche of species are increasingly used, but systematic comparisons of alternative methods are still limited. In particular, only few studies have explored the effect of scale in model outputs. In this paper, we investigate the predictive ability of three statistical methods (generalized linear models, generalized additive models and Classification Tree analysis) using species distribution data at three scales: fine (Catalonia), intermediate (Portugal) and coarse (Europe). Four Mediterranean Tree species were modelled for comparison. Variables selected by models were relatively consistent across scales and the predictive accuracy of models varied only slightly. However, there were slight differences in the performance of methods. Classification Tree analysis had a lower accuracy than the generalized methods, especially at finer scales. The performance of generalized linear models also increased with scale. At the fine scale GLM with linear terms showed better acc...
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generalized models vs Classification Tree analysis predicting spatial distributions of plant species at different scales
Journal of Vegetation Science, 2003Co-Authors: Wilfried Thuiller, Miguel B Araujo, Sandra LavorelAbstract:Statistical models of the realized niche of species are increasingly used, but systematic comparisons of alterna- tive methods are still limited. In particular, only few studies have explored the effect of scale in model outputs. In this paper, we investigate the predictive ability of three statistical methods (generalized linear models, generalized additive mod- els and Classification Tree analysis) using species distribution data at three scales: fine (Catalonia), intermediate (Portugal) and coarse (Europe). Four Mediterranean Tree species were modelled for comparison. Variables selected by models were relatively consistent across scales and the predictive accuracy of models varied only slightly. However, there were slight differences in the performance of methods. Classification Tree analysis had a lower accuracy than the generalized methods, especially at finer scales. The performance of generalized linear models also increased with scale. At the fine scale GLM with linear terms showed better accuracy than GLM with quadratic and polynomial terms. This is probably because distributions at finer scales represent a linear sub-sample of entire realized niches of species. In contrast to GLM, the performance of GAM was constant across scales being more data-oriented. The predictive accuracy of GAM was always at least equal to other techniques, suggesting that this modelling approach is more robust to variations of scale because it can deal with any response shape.
Oliver A. Chadwick - One of the best experts on this subject based on the ideXlab platform.
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the application of Classification Tree analysis to soil type prediction in a desert landscape
Ecological Modelling, 2005Co-Authors: Peter Scull, Janet Franklin, Oliver A. ChadwickAbstract:Classification Tree analysis is evaluated as a predictive soil mapping technique for developing a preliminary soil map for neighboring site from samples extracted from an existing soil map. The objective of the research is to help guide future soil mapping in a nearby area. In order to determine the best overall modeling approach several variations were explored: the dependent variable (soil map class) was grouped at several hierarchical levels (according to Soil Taxonomy), sensitivity analysis was performed on the predictor variables (environmental variables acting as surrogates for soil forming factors), and the study area was divided into meaningful sub-areas (mountains and basins). Soil great group was discovered the most parsimonious dependent variable based on model results (misClassification error rate of 30.0% based on a test data set). Geomorphology (as measured by several landform variables) best explains the distribution of soil types. The terrain analysis variables did not explain a large amount of variance within the models. Dividing the study area in two separate modeling units increased overall model accuracy. Our results suggest that soil taxonomic class can be predicted with reasonable accuracy from environmental variables. In addition, the technique can provide limited insight into the variables that are most responsible for driving soil development in a given area. This technique could be used in soil survey to extrapolate obvious soil landscape relationships from one site to another, allowing soil experts to concentrate their field mapping effort in unique areas.
Michelle Repp - One of the best experts on this subject based on the ideXlab platform.
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Identifying Three Types of Violent Offenders and Predicting Violent Recidivism While on Probation: A Classification Tree Analysis
Law and Human Behavior, 2004Co-Authors: Loretta J. Stalans, Paul R Yarnold, Magnus Seng, David E. Olson, Michelle ReppAbstract:This study employs Classification Tree analysis (CTA) to address whether 3 groups of violent offenders have similar or different risk factors for violent recidivism while on probation. A sample of 1,344 violent offenders on probation was classified as generalized aggressors ( N = 302), family only aggressors ( N = 321), or nonfamily only aggressors ( N = 717). The strongest predictor of violent recidivism while on probation was whether the offender was a generalized aggressor or not, with generalized aggressors more likely to be arrested for new violent crimes. Prior arrests for violent crimes predicted violent recidivism of generalized aggressors, but did not significantly predict violent recidivism of family only and nonfamily only aggressors. For generalized aggressors and family only batterers, treatment noncompliance was an important risk predictor of violent recidivism. CTA compared to logistic regression classified a higher percentage of cases into low-risk and high-risk groups, provided higher improvement in Classification accuracy of violent recidivists beyond chance performance, and provided a better balance of false positives and false negatives. The implications for the risk assessment and domestic violence literature are discussed.
Florian Jeltsch - One of the best experts on this subject based on the ideXlab platform.
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Sensitivity of plant functional types to climate change: Classification Tree analysis of a simulation model
Journal of Vegetation Science, 2010Co-Authors: Alexandra Esther, Jürgen Groeneveld, F. Benjamin Blank, Ben P Miller, N J Enright, George L. W. Perry, Byron B. Lamont, Florian JeltschAbstract:Question: The majority of studies investigating the impact of climate change on local plant communities ignores changes in regional processes, such as immigration from the regional seed pool. Here we explore: (i) the potential impact of climate change on composition of the regional seed pool, (ii) the influence of changes in climate and in the regional seed pool on local community structure, and (iii) the combinations of life history traits, i.e. plant funcfunctional types (PFTs), that are most affected by environmental changes. Location: Fire-prone, Mediterranean-type shrublands in southwestern Australia. Methods: Spatially explicit simulation experiments were conducted at the population level under different rainfall and fire regime scenarios to determine the effect of environmental change on the regional seed pool for 38 PFTs. The effects of environmental and seed immigration changes on local community dynamics were then derived from community-level experiments. Classification Tree analyses were used to investigate PFT-specific vulnerabilities to climate change. Results: The Classification Tree analyses revealed that responses of PFTs to climate change are determined by specific trait characteristics. PFT-specific seed production and community patterns responded in a complex manner to climate change. For example, an increase in annual rainfall caused an increase in numbers of dispersed seeds for some PFTs, but decreased PFT diversity in the community. Conversely, a simulated decrease in rainfall reduced the number of dispersed seeds and diversity of PFTs. Conclusions: PFT interactions and regional processes must be considered when assessing how local community structure will be affected by environmental change.