DAN MAJKA

Modeling bird distributions

Purdue University, 2003-2005

For my MS thesis, I modeled the distributions of 41 species of birds in a local area of the Tilaran Mountains surrounding Monteverde, Costa Rica. I used 5 different statistical modeling techniques to relate species occurrence to topographic variables. These models were interfaced with GIS to map predicted probabilities of occurrence. With my research, I focused on two topics:

  • comparing distribution modeling techniques
  • inference into bird-topography relationships

Surprisingly, I found found that simpler techniques such as logistic regression best predicted species occurrences, while complicated techniques such as artificial neural networks tended to overfit their training dataset.

This approach revealed that the distance from the continental divide which separates the Caribbean and Pacific mountain slopes in Costa Rica was the most important predictor variable in accounting for species distributions. This may be due to its high correlation with microclimatic precipitation, suggesting that precipitation may directly or indirectly place the largest constraint on many avian species distributions in the study.

map of Costa Rica study site

Challenges With this study I created over 25,000 total models. I would not have been able to complete this modeling study without writing scripts. I used ArcInfo AMLs to sum up 50 GARP models at-a-time and tweak output from generalized additive models, DOS batch scripts to run Neural Network models, SAS scripts to run batch AIC logistic regression models, and batch scripts to create generalized additive models in statistics program R. I also mashed up a fair amount of Avenue scripts in ArcView 3.3 to handle data extraction back-and-forth between GIS and statistics software.


maps of different statistical modeling techniques for Rufous-capped Warbler graph of median AUC