Methodological advancements for improving performance and generating ensemble ecological niche models

<p>This study employs spatial filtering of occurrence data with the aim of reducing overfitting to sampling bias in ecological niche models (ENMs). Sampling bias in geographic space leads to localities that may also be biased in environmental space. If so, the model can overfit to those biases. As a preliminary test addressing this issue, we used Maxent, bioclimatic variables, and occurrence localities of a broadly distributed Malagasy tenrec, Microgale cowani (Family Tenrecidae: Subfamily Oryzorictinae). We modeled the abiotically suitable area of this species using three distinct datasets: unfiltered, spatially filtered, and rarefied unfiltered localities. To quantify overfitting and model performance, we calculated evaluation AUC, the difference between calibration and evaluation AUC (= AUCdiff), and omission rates. Models made with the filtered dataset showed lower overfitting and better performance than the other two suites of models, having lower omission rates and AUCdiff, and a higher AUCevaluation. Additionally, the rarefied unfiltered dataset performed better than the unfiltered one for three evaluation metrics, likely because the larger datasets reinforced the biases. These results indicate that spatial filtering of occurrence localities may allow biogeographers to produce better models.</p>

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Biology

Year dc:date.available

2014

Author dc:creator

• Boria, Robert

Contributors dc:contributor

• Robert P. Anderson
• Amy C. Berkov
• Ana Carnaval

• Modeling
• Niche
• Ecology
• Biology

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/537

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-1537