StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling

TL;DR

StatEcoNet introduces a neural network framework that incorporates ecological statistical models to improve species distribution predictions, effectively handling structured noise in observational data.

Contribution

This paper presents a novel framework combining graphical generative models with neural networks for species distribution modeling, addressing observation noise biases.

Findings

Outperforms traditional models on simulated data

Effective in real bird species datasets

Handles structured observation noise better

Abstract

This paper focuses on a core task in computational sustainability and statistical ecology: species distribution modeling (SDM). In SDM, the occurrence pattern of a species on a landscape is predicted by environmental features based on observations at a set of locations. At first, SDM may appear to be a binary classification problem, and one might be inclined to employ classic tools (e.g., logistic regression, support vector machines, neural networks) to tackle it. However, wildlife surveys introduce structured noise (especially under-counting) in the species observations. If unaccounted for, these observation errors systematically bias SDMs. To address the unique challenges of SDM, this paper proposes a framework called StatEcoNet. Specifically, this work employs a graphical generative model in statistical ecology to serve as the skeleton of the proposed computational framework and carefully integrates neural networks under the framework. The advantages of StatEcoNet over related approaches are demonstrated on simulated datasets as well as bird species data. Since SDMs are critical tools for ecological science and natural resource management, StatEcoNet may offer boosted computational and analytical powers to a wide range of applications that have significant social impacts, e.g., the study and conservation of threatened species.