Modelos de distribución de especies: Una revisión sintética

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

En los últimos años se ha generalizado una nueva herramienta que permite analizar objetivamente los patrones espaciales de presencia de organismos: los modelos de distribución de especies. Estos modelos se basan en procedimientos estadísticos y cartográficos que partiendo de datos reales de presencia permiten inferir zonas potencialmente idóneas en función de sus características ambientales. Los datos de colecciones de historia natural pueden ser utilizados para este fin adquiriendo así una nueva utilidad. Los modelos han evolucionado desde su aplicación a especies aisladas hasta análisis de cientos o miles de taxones para combinarlos en el análisis de la biodiversidad y riqueza específica. En este trabajo se hace una revisión sobre la variedad de métodos utilizables, sus potencialidades e inconvenientes y los factores limitantes que influyen en la interpretación de lo que los modelos de distribución significan.

Translated abstract

In the last years a new tool has become widely used in ecological studies: species distribution models. These models analyze the spatial patterns of presence of organisms objectively, by means of statistical and cartographic procedures based on real data. They infer the presence of potentially suitable areas according to their environmental characteristics. Data stored in natural history collections can be used for this purpose, which gives new opportunities to use to these types of data. The models have evolved from the analysis of single species to the study of hundreds or thousands of taxa which are combined for the assessment of biodiversity and species richness. In this paper we review the variety of methods used, their potential and weaknesses, and the limiting factors that influence the interpretation of species distribution models.

Related collections

Most cited references 469

Record: found
Abstract: found
Article: not found

A working guide to boosted regression trees.

J Elith, J R Leathwick, T. Hastie (2008)

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.