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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.


Allocation of species types based on the extended core-satellite species hypothesis (Hanski 1982; Collins et al. 1993). We used mean basal area per plot as importance measure and the frequency among all FIA plots used.
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fig02: Allocation of species types based on the extended core-satellite species hypothesis (Hanski 1982; Collins et al. 1993). We used mean basal area per plot as importance measure and the frequency among all FIA plots used.

Mentions: Last, we categorized the species according to the extended core-satellite hypothesis first proposed by Hanski (1982), and later extended by Collins, Glenn & Roberts (1993), who added urban and rural types. Core species are those with high frequency across the landscape and high abundance per plot, whereas satellite species are rare with low average abundance. Urban species are comparably infrequent, but show high abundance where they occur. Finally, rural species are low in abundance, but occur frequently. To assign species types, we analysed the species frequency of occurrence in the landscape and average abundance (cover) per plot using FIA data (Fig. 2). Because we analysed large gradients across many vegetation types with elevations spanning more than 3000 m of relief, we did not expect any of the tree species to occur everywhere. Thus, we classified core species at much lower frequencies, starting at around 10% of sites occupied.


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)

Allocation of species types based on the extended core-satellite species hypothesis (Hanski 1982; Collins et al. 1993). We used mean basal area per plot as importance measure and the frequency among all FIA plots used.
© Copyright Policy
Related In: Results  -  Collection

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

fig02: Allocation of species types based on the extended core-satellite species hypothesis (Hanski 1982; Collins et al. 1993). We used mean basal area per plot as importance measure and the frequency among all FIA plots used.
Mentions: Last, we categorized the species according to the extended core-satellite hypothesis first proposed by Hanski (1982), and later extended by Collins, Glenn & Roberts (1993), who added urban and rural types. Core species are those with high frequency across the landscape and high abundance per plot, whereas satellite species are rare with low average abundance. Urban species are comparably infrequent, but show high abundance where they occur. Finally, rural species are low in abundance, but occur frequently. To assign species types, we analysed the species frequency of occurrence in the landscape and average abundance (cover) per plot using FIA data (Fig. 2). Because we analysed large gradients across many vegetation types with elevations spanning more than 3000 m of relief, we did not expect any of the tree species to occur everywhere. Thus, we classified core species at much lower frequencies, starting at around 10% of sites occupied.

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.