A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features

Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution

“…In contrast, others propose using more direct (also termed "proximal") variables whenever possible (Austin, 2007(Austin, , 2002. However, studies that separate direct versus indirect associations in species distribution modelling are rare (Aguilera et al, 2011;Chen and Pollino, 2012;Gieder et al, 2014;Hamilton et al, 2015). Our study shows that path models such as Gaussian Bayesian Networks can be used to retrieve meaningful direct and indirect associations between physiographic variables, environmental variables and species distribution.…”

Smartphone-Based Distributed Data Collection Enables Rapid Assessment of Shorebird Habitat Suitability

“…These included fields for nest Site ID, Geomorphic Setting, Substrate Type, Vegetation Type and Vegetation Density. The selections for the biogeomorphic characterization are based on standard classifications (e.g., [12]) and previous work [13, 14]. …”

Analysis of the socioecological structure and dynamics of the territory using a hybrid Bayesian network classifier

“…A classification problem in which no information about the class variable is available (called an unsupervised classification or clustering problem) can be solved by a BN classifier (Aguilera et al, 2013;Anderberg, 1973;Fernández et al, 2014;Gieder et al, 2014). This soft-clustering methodology implies the partition of the data into groups in such a way that the observations belonging to one group are similar to each other but differ from the observations in the other groups.…”