Filters: Types: Citation (X) > partyWithName: Gretchen G Moisen (X)
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Summary 1. 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. 2. We developed two spatial predictor...
Categories: Publication;
Types: Citation,
Journal Citation;
Tags: Journal of Applied Ecology,
K-fold cross-validation,
Landsat TM,
core-satellite species hypothesis,
partial regression,
We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater...
Categories: Publication;
Types: Citation,
Journal Citation;
Tags: Accuracy assessment,
Classification trees,
Ecological Modelling,
Lichens,
Model accuracy,
In the U.S. National Biological Service's gap analysis, potential distributions of terrestrial vertebrate species are based on the synthesis of wildlife habitat relation data and then modeled using a vegetation cover map derived from Landsat Thematic Mapper imagery. Using long-term species lists from eight National Parks in Utah, we evaluated the adequacy of the wildlife habitat relations data generated by gap analysis in predicting species distributions at landscape scales. Omission and commission error rates were estimated for major taxonomic groups and for each national park. Depending on the taxonomic group, omission error ranged from 0 to 25%, whereas commission error ranged from 4 to 33%. Error rates were...
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