Article
Effects of sample size and network depth on a deep learning approach to species distribution modeling
Ecological Informatics
(2020)
Abstract
Deep learning algorithms have improved predictive model performance in a variety of disciplines because of their ability to approximate complex functions. However, the amount of data and depth of the neural network needed to improve model performance is not well understood and may depend on many factors associated with the specific field of research. In ecology, ecologists rely on accurate species distribution modelsto inform conservation and management efforts. Here, we present the first study to systematically examine the effects of sample size and network depth on the performance of species distribution models built with artificial neural networks. We found that one or several deeper network architectures (>1 hidden layer) consistently led to slightly higher model performance than a shallow neural network on validation data when trained with a large sample size (10,000 sites). However, comparing deep network model performance with random forest model performance showed that random forest generally performed as well or slightly better. There was no clear or consistent benefit of using deep neural networks with smaller sample sizes (100 and 1000 sites). Our results suggest that, given sufficiently big data, increasing the number of hidden layers in a neural network can potentially improve species distribution model performance. As datasets become larger and high performance computing resources become more available, a deep learning approach to species distribution modeling is likely to be used more frequently.
Disciplines
Publication Date
2020
DOI
https://doi.org/10.1016/j.ecoinf.2020.101137
Citation Information
Charles P Hawkins. "Effects of sample size and network depth on a deep learning approach to species distribution modeling" Ecological Informatics Vol. 60 (2020) p. 101137 Available at: http://works.bepress.com/charles_hawkins/260/