Thomas C. Edwards

Bio: Thomas C. Edwards is an academic researcher from Utah State University. The author has contributed to research in topics: Species distribution & Population. The author has an hindex of 23, co-authored 40 publications receiving 7732 citations. Previous affiliations of Thomas C. Edwards include United States Geological Survey.

Random Forests for Classification in Ecology

Abstract: Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.

Gap analysis: a geographic approach to protection of biological diversity

Abstract: The conventional approach to maintaining biological diversity generally has been to proceed species by species and threat by threat. We suggest that piecemeal approaches are not adequate by themselves to address the accelerating extinction crisis and, furthermore, they contribute to an unpredictable ecological and economic environment. Here, we describe a methodology called Gap Analysis, which identifies the gaps in representation of biological diversity (biodiversity) in areas managed exclusively or primarily for the longterm maintenance of populations of native species and natural ecosystems (hereinafter referred to as biodiversity management areas). Once identified, gaps are filled through new reserve acquisitions or designations, or through changes in management practices. The goal is to ensure that all ecosystems and areas rich in species diversity are represented adequately in biodiversity management areas. We believe this proactive strategy will eliminate the need to list many species as threatened or endangered in the future. Gap Analysis uses vegetation types and vertebrate and butterfly species (and/or other taxa, such as vascular plants, if adequate distributional data are available) as indicators of biodiversity. Maps of existing vegetation are prepared from satellite imagery (LANDSAT) and other sources and entered into a geographic information system (GIS). Because entire states or regions are mapped, the smallest area identified on vegetation maps is 100 ha. Vegetation maps are verified through field checks and examination of aerial photographs. Predicted species distributions are based on existing range maps and other distributional data, combined with information on the habitat affinities of each species. Distribution maps for individual species are overlaid in the GIS to produce maps of species richness, which can be created for any group of species of biological or political interest. An additional GIS layer of land ownership and management status allows identification of gaps in the representation of vegetation types and centers of species richness in biodiversity management areas through a comparison of the vegetation and species richness maps with ownership and management status maps. Underrepresented plant communities (e.g., present on only 1 or 2 biodiversity management areas or with a small total acreage primarily managed for biodiversity) also can be identified in this manner. Realization of the full potential of Gap Analysis requires regionalization of state data bases and use of the data in resource management and planning. Gap Analysis is a powerful and efficient first step toward setting land management priorities. It provides focus, direction, and accountability for conservation efforts. Areas identified as important through Gap Analysis can then be examined more closely for their biological qualities and management needs. As a coarse-filter approach to conservation evaluation, Gap Analysis is not a panacea. Limitations related to minimum mapping unit size (where small habitat patches are missed), failure to distinguish among most seral stages, failure to indicate gradual ecotones, and other factors must be recognized so that Gap Analysis can be supplemented by more intensive inventories. WILDL. MONOGR. 123, 1-41 GAP ANALYSIS: A GEOGRAPHIC APPROACH TO PROTECTION OF BIOLOGICAL DIVERSITY WILDLIFE MONOGRAPHS 6

Climatic extremes improve predictions of spatial patterns of tree species

Abstract: Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

Predicting species distribution: offering more than simple habitat models.

Abstract: In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

A working guide to boosted regression trees

Abstract: Summary 1 Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions 2 This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance) The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion 3 Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods 4 The unique features of BRT raise a number of practical issues in model fitting We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel ( Anguilla australis Richardson), a native freshwater fish of New Zealand We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data We provide code and a tutorial to enable the wider use of BRT by ecologists

Rebuilding community ecology from functional traits.

Abstract: There is considerable debate about whether community ecology will ever produce general principles. We suggest here that this can be achieved but that community ecology has lost its way by focusing on pairwise species interactions independent of the environment. We assert that community ecology should return to an emphasis on four themes that are tied together by a two-step process: how the fundamental niche is governed by functional traits within the context of abiotic environmental gradients; and how the interaction between traits and fundamental niches maps onto the realized niche in the context of a biotic interaction milieu. We suggest this approach can create a more quantitative and predictive science that can more readily address issues of global change.