Are plot data effective for landscape prediction? A simulation study of tree species response to climate warming under varying environmental heterogeneity
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Extrapolating plot data to broader spatial scales depends largely on environmental heterogeneity.
We studied this subject in the Changbai Mountains Natural Reserve in northeastern China by examining three scenarios of environmental heterogeneity. Scenarios 1, 2 and 3 represented local, class and zonal scale, and corresponded to the highest, intermediate and lowest level of environmental heterogeneity, respectively. Plot-level observation was represented by species establishment probability derived from an ecosystem process model that used plot observational data (e.g., weather, soil, vegetation, etc.) as input. Response variables at broader spatial scales, which were derived from a landscape model, included species total area and spatial pattern (measured by mean patch size) in the short, medium and long term. We examined whether these response variables differ statistically among the three scenarios.
Our results indicate that for species whose total area changes occur mainly within the same elevation zone in which the experimental plots reside, individual plots can capture the changes for the entire elevation zone. By contrast, for species that span many elevation zones under warming climate, plot-level observations are not reliable in predicting broader spatial scale change. Our results also suggest that species spatial patterns do not always coincide with those found for total area.
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- Are plot data effective for landscape prediction? A simulation study of tree species response to climate warming under varying environmental heterogeneity
Annals of Forest Science
Volume 68, Issue 5 , pp 899-909
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- Climate warming
- Environmental heterogeneity
- Changbai Mountains
- Landscape prediction
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