CiteULike: neteler distribution_model

Novel methods improve prediction of species distributions from occurrence data

Jane Elith — 2006-04-13T15:45:22-00:00

Ecography, Vol. 29, No. 2. (April 2006), pp. 129-151.

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest)

Rick Lawrence — 2006-01-29T15:56:06-00:00

Remote Sensing of Environment, Vol. 100, No. 3. (15 February 2006), pp. 356-362.

Invasive nonindigenous plants are threatening the biological integrity of North American rangelands, as well as the economies that are supported by those ecosystems. Spatial information is critical to fulfilling invasive plant management strategies. Traditional invasive plant mapping has utilized ground-based hand or GPS mapping. The shortfalls of ground-based methods include the limited spatial extent covered and the associated time and cost. Mapping vegetation with remote sensing covers large spatial areas and maps can be updated at an interval determined by management needs. The objective of the study was to map leafy spurge (Euphorbia esula L.) and spotted knapweed (Centaurea maculosa Lam.) using 128-band hyperspectral (5-m and 3-m resolution) imagery and assess the accuracy of the resulting maps. Beiman Cutler classifications (BCC) were used to classify the imagery using the randomForest package in the R statistical program. BCC builds multiple classification trees by repeatedly taking random subsets of the observational data and using random subsets of the spectral bands to determine each split in the classification trees. The resulting classification trees vote on the correct classification. Overall accuracy was 84% for the spotted knapweed classification, with class accuracies ranging from 60% to 93%; overall accuracy was 86% for the leafy spurge classification, with class accuracies ranging from 66% to 93%. Our results indicate that (1) BCC can achieve substantial improvements in accuracy over single classification trees with these data and (2) it might be unnecessary to have separate accuracy assessment data when using BCC, as the algorithm provides a reliable internal estimate of accuracy.

Should data be partitioned spatially before building large-scale distribution models?

PE Osborne — 2006-01-24T16:21:15-00:00

Ecological Modelling, Vol. 157 (2002), pp. 249-259.

There is growing interest in building predictive models of species distributions over large geographic areas. As larger areas are modelled, however, it is highly likely that heterogeneity in the predictors variable increases and that areas are included where animals respond to habitats in different ways, for example, due to social status. These effects (spatial non-stationary) may weaken model performance. This paper explores whether data partitioning prior to analysis can improve the fit of models and provide ecological insight into distribution patterns. Data on three bird species were modelled for the whole of Spain at 1 km2 resolution using logistic regression analysis. Data were partitioned into geographic quarters, concentric rings around the centroid of the distribution, and into random samples for comparison. In all cases, data partitioning produced better models as assessed by Receiver Operating Characteristic curve (AUC) statistics than analysis of the global data set. Inclusion of latitude and longitude improved the global models only when added as smoothed splines but produced different probabilities to the partitioned data. Geographic partitioning is a very crude local modelling approach and we suggest that some form of geographically-weighted regression could offer the best solution to large-scale modelling but is computationally intensive on Geographical Information Systems (GIS) data. It is concluded that simple partitioning by geographic quarters may detect spatial non-stationary and alert the modeller to possible problems; that partitioning into more novel arrangements may be used to test ecological hypotheses; and that data should not be partitioned spatially to build and test models if non-stationary is suspected.