Predictive Modelling Strategy
We used generalized dissimilarity modelling (GDM) to predict shifts in
combinations of spatial ecosystem response variables. Given this novel
application of GDM, we take an exploratory modelling approach (sensu
Tredennick et al. 2021) to developing three classes of models with 1)
ecosystem, 2) biotic, and 3) abiotic responses. Each model is built with
the same combination of predictors and the same model settings, but
differing combinations of response variables. GDM formulations predict
dissimilarities between pairs of sites as a function of their ecological
distance in environmental and or geographic space. Since the modelled
response in GDM is a pair-wise distance metric, predictor variables are
also represented as pair-wise distances (i.e., difference in predictor
values between site pairs). Ferrier et al. (2007) further summarize the
statistical underpinnings of GDM, while Mokany et al. (2022) propose a
workflow for model fitting. We adapted this workflow (see Figure 2) and
employed the GDM package (Fitzpatrick et al. 2024) with default model
settings (see S1 for additional detail).
We began the modelling process by selecting ecologically meaningful
combinations of response variables informed by patterns summarized in
the Nova Scotia Forest Ecosystem Classification (Neily et al. 2023). We
built a suite of models to evaluate how varied combinations of these
response variables affected model performance (see S1). Performance was
assessed using deviance explained, a key GDM performance metric
representing the percentage of observed variation (dissimilarity)
explained by the model (Mokany et al. 2022). We retained models in all
three