Ensemble Habitat Mapping of Invasive Plant Species

Abstract

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis.

Can I Trust My One-Class Classification?

“…It has been shown that the ensemble outperformed feature fusion approach, i.e., the classification with the stacked features, which can possibly attributed to the higher dimensionality. It is worth noting that classifier ensembles have also been applied successfully in the field of species distribution modeling [28,29]. Furthermore they are a focus of intense research in pattern recognition and machine learning [30,31].…”

The impact of modelling method selection on predicted extent and distribution of deep-sea benthic assemblages

“…Downie et al (2013) suggest the use of an 'ensemble model' approach to mapping, combining the outputs of several models. However, other authors have found that whilst ensemble models can be of use, they do not necessarily improve predictions (Marmion et al 2009;Grenouillet et al 2011;Stohlgren et al 2010). More research is needed in this area.…”

Habitat Modeling of Alien Plant Species at Varying Levels of Occupancy

“…While logistic and MaxEnt models may be compared individually to select the best overall model for particular datasets, methods that combine the two models have the potential to reduce the uncertainty associated with any one particular algorithm [23,24]. A number of approaches have been proposed for combining the outputs of individual models for ensemble predictions [23].…”

“…These approaches can be used individually or collectively in an ensemble approach. Ensemble SDMs combine the strengths of several models while limiting the weakness of any one model [23,24] and offer a broad perspective to model results.…”