Effects of sample size on the performance of species distribution models


Abstract:

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to pbkp_redict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated pbkp_redictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model pbkp_redictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between pbkp_redictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best pbkp_redictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm pbkp_redicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of pbkp_redictions based on small sample size and restrict their use to exploratory modelling. © 2008 The Authors.

Año de publicación:

2008

Keywords:

  • OM-GARP
  • species distribution model
  • Model comparison
  • ecological niche model
  • maxent
  • sample size

Fuente:

scopusscopus

Tipo de documento:

Article

Estado:

Acceso restringido

Áreas de conocimiento:

  • Ecología
  • Ecología

Áreas temáticas:

  • Ecología
  • Temas específicos de historia natural de los animales
  • Agricultura y tecnologías afines