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Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah.

Zimmermann NE, Edwards TC, Moisen GG, Frescino TS, Blackard JA - J Appl Ecol (2007)

Bottom Line: The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species.Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species.Major improvements resulted for deciduous, early successional, satellite and rare species.The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change.

View Article: PubMed Central - PubMed

ABSTRACT
Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits.We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics.More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species.Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species.Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change.

No MeSH data available.


Model accuracies of all tree species as a function of observed frequencies. (a) AUC of stepwise optimized (open boxes) and additionally cross-validated (closed boxes) models. (b) AUC of cross-validated models calibrated from both predictor sets (closed boxes), from topo-climatic (grey triangles), and from remote sensing-based (open triangles) predictors.
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fig03: Model accuracies of all tree species as a function of observed frequencies. (a) AUC of stepwise optimized (open boxes) and additionally cross-validated (closed boxes) models. (b) AUC of cross-validated models calibrated from both predictor sets (closed boxes), from topo-climatic (grey triangles), and from remote sensing-based (open triangles) predictors.

Mentions: The cross-validated accuracy assessments confirmed this trend as well (Fig. 3). When comparing the cross-validated and stepwise optimized model accuracies using AUC, it becomes obvious that the number of observations had an influence on model performance. A minimum of 200 observations was needed to generate comparably stable models (Fig. 3a). Fewer observations resulted in a considerable loss in accuracy when tested by cross-validation. Further, the number of observations had an effect on model accuracy irrespective of predictor set used for the model calibration (Fig. 3b). Models calibrated from remote sensing data showed low cross-validated model accuracies when the number of observed presences was low.


Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah.

Zimmermann NE, Edwards TC, Moisen GG, Frescino TS, Blackard JA - J Appl Ecol (2007)

Model accuracies of all tree species as a function of observed frequencies. (a) AUC of stepwise optimized (open boxes) and additionally cross-validated (closed boxes) models. (b) AUC of cross-validated models calibrated from both predictor sets (closed boxes), from topo-climatic (grey triangles), and from remote sensing-based (open triangles) predictors.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2368764&req=5

fig03: Model accuracies of all tree species as a function of observed frequencies. (a) AUC of stepwise optimized (open boxes) and additionally cross-validated (closed boxes) models. (b) AUC of cross-validated models calibrated from both predictor sets (closed boxes), from topo-climatic (grey triangles), and from remote sensing-based (open triangles) predictors.
Mentions: The cross-validated accuracy assessments confirmed this trend as well (Fig. 3). When comparing the cross-validated and stepwise optimized model accuracies using AUC, it becomes obvious that the number of observations had an influence on model performance. A minimum of 200 observations was needed to generate comparably stable models (Fig. 3a). Fewer observations resulted in a considerable loss in accuracy when tested by cross-validation. Further, the number of observations had an effect on model accuracy irrespective of predictor set used for the model calibration (Fig. 3b). Models calibrated from remote sensing data showed low cross-validated model accuracies when the number of observed presences was low.

Bottom Line: The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species.Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species.Major improvements resulted for deciduous, early successional, satellite and rare species.The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change.

View Article: PubMed Central - PubMed

ABSTRACT
Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits.We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics.More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species.Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species.Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change.

No MeSH data available.