  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.
