Limits...
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.


Linkages between species characteristics and model accuracy and fit. (a) Core-satellite types significantly differ in model accuracy. (b) Remote sensing-based predictors and successional types. (c) Remote sensing-based predictors increase model fit for broadleaf trees more than for conifers. (d) Topo-climatic predictors add more to model fit of deciduous than to evergreen trees. See Table 4 for significance tests. Box and whisker boundaries represent quartiles.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2368764&req=5

fig05: Linkages between species characteristics and model accuracy and fit. (a) Core-satellite types significantly differ in model accuracy. (b) Remote sensing-based predictors and successional types. (c) Remote sensing-based predictors increase model fit for broadleaf trees more than for conifers. (d) Topo-climatic predictors add more to model fit of deciduous than to evergreen trees. See Table 4 for significance tests. Box and whisker boundaries represent quartiles.

Mentions: Accuracy as measured by kappa differed significantly among core-satellite species types (P = 0·032; Table 4). Core species – the most abundant in the data set – had highest kappa values (Fig. 5a). Sample size also explained significantly variations in kappa tested in a regression (P < 0·001). AUC, on the other hand, could be explained only from the number of observations (P = 0·014), while core-satellite types did not show significant differences among AUC values of all modelled species. The overall adj.D2 of all models did not differ between any of the species characteristics analysed. However, the species characteristics did differ significantly in the percentages explained by the two predictor sets used. Remote sensing-based predictors improved the deviance explained significantly more for broadleaf trees than for conifers (P = 0·03, Fig. 5c). In contrast, topo-climatic predictors showed significant differences in improving the explained deviance among leaf longevity types (P = 0·01), with deciduous trees showing higher gains from this predictor set (Fig. 5d). Finally, we observed a considerable difference among successional types in the deviance explained by the remote sensing-based predictors, although not significant (P =0·07; Fig. 5b).


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)

Linkages between species characteristics and model accuracy and fit. (a) Core-satellite types significantly differ in model accuracy. (b) Remote sensing-based predictors and successional types. (c) Remote sensing-based predictors increase model fit for broadleaf trees more than for conifers. (d) Topo-climatic predictors add more to model fit of deciduous than to evergreen trees. See Table 4 for significance tests. Box and whisker boundaries represent quartiles.
© Copyright Policy
Related In: Results  -  Collection

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

fig05: Linkages between species characteristics and model accuracy and fit. (a) Core-satellite types significantly differ in model accuracy. (b) Remote sensing-based predictors and successional types. (c) Remote sensing-based predictors increase model fit for broadleaf trees more than for conifers. (d) Topo-climatic predictors add more to model fit of deciduous than to evergreen trees. See Table 4 for significance tests. Box and whisker boundaries represent quartiles.
Mentions: Accuracy as measured by kappa differed significantly among core-satellite species types (P = 0·032; Table 4). Core species – the most abundant in the data set – had highest kappa values (Fig. 5a). Sample size also explained significantly variations in kappa tested in a regression (P < 0·001). AUC, on the other hand, could be explained only from the number of observations (P = 0·014), while core-satellite types did not show significant differences among AUC values of all modelled species. The overall adj.D2 of all models did not differ between any of the species characteristics analysed. However, the species characteristics did differ significantly in the percentages explained by the two predictor sets used. Remote sensing-based predictors improved the deviance explained significantly more for broadleaf trees than for conifers (P = 0·03, Fig. 5c). In contrast, topo-climatic predictors showed significant differences in improving the explained deviance among leaf longevity types (P = 0·01), with deciduous trees showing higher gains from this predictor set (Fig. 5d). Finally, we observed a considerable difference among successional types in the deviance explained by the remote sensing-based predictors, although not significant (P =0·07; Fig. 5b).

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.