Species distribution modeling of North American mammals using data integration

Mentors:
Lead: Brian Reich (Statistics, NCSU)
Collaborators: L Krishna Pacifici (Forestry, NCSU), Roland Kays (Biodiversity Laboratory at the North Carolina Museum of Natural Sciences)

Intellectual merit and significance:
Spatial patterns of biodiversity are one of the longest-standing approaches to ecology and are the foundation of many applied conservation questions. Species Distribution Models (SDMs) are often used to discover ecological relationships, which can be used to predict the species distribution in areas without observations or future scenarios [1]. This rapidly developing field has continually improved through larger datasets describing the occurrence of biodiversity (e.g., Global Biodiversity Information Facility, GBIF), better environmental predictors (e.g., climate, remote sensing), and by developing statistical approaches to account for species interactions [1,2] and spatial autocorrelation [4]. The output of models not only provides basic ecological knowledge but is now a key part of assessing the conservation status of a species [5] and a primary tool for predicting the effects of climate change.

Objectives:
Development of SDMs for different species using available data types (survey data, museum collections, citizen science records) and generalized linear models (GLMs) to understand how environmental predictors influence changes in distributions of North American mammals across space and time.

Outcomes:
Maps of species distributions and estimated drivers of distribution changes for select mammals.

References:
1. Law K, Stuart A, Zygalakis K. Data Assimilation: A Mathematical Introduction. Cham. Switzerland:Springer; 2015.
2. Clark J, Gelfand A, Woodall C, Zhu K. More than the sum of the parts: Forest climate response from joint species distribution models. Ecol Appl. 2014;24:990-999.
3. Ovaskainen O, Roy D, Fox R, Anderson B. Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods Ecol Evol. 2016;7:428-36.
4. Dormann C, McPherson J, Araújo M, Bivand R, Bolliger J, Carl G, Davies R, Hirzel A, Jetz W, Kissling W, Kühn I, Ohlemüller R, Peres-Neto P, Reineking B, Schröder B, Schurr F, Wilson R. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography (Cop.). Ecography (Cop). 2007;30:609-28.
5. Brooks T, Pimm S, Akçakaya H, Buchanan G, Butchart S, Foden W, Hilton-Taylor C, Hoffmann M, Jenkins C, Joppa L, Li B, Menon V, Ocampo-Peñuela N, Rondinini C. Measuring terrestrial area of habitat (AOH) and its utility for the IUCN red list. Trends Ecol Evol. 2019;34:977-986.