Remotely-Sensed Predictor Data
Predictors included gridded environmental data derived from remote
sensing. These rasters encompassed topographic, edaphic, hydrological,
climatic, and vegetation gradients. We emphasized predictors shaping the
distribution of each ecosystem property represented in our independent
pool of response data; selections were informed from published models
built to predict spatial variations in biodiversity andgeodiversity (Hjort and Luoto 2012, Mod et al. 2016, Simensen
et al. 2020). Where possible, we also sought predictors with a direct
influence on the distribution of ecosystem properties, instead of
employing proxies (Soley-Guardia et al. 2024).
Following these conditions, we included 22 rasters in our final
predictor dataset (see S1). We conducted all raster processing using the
terra package (v1.7.29) (Hijmans 2023) in R (v4.3.2) (R Core Team 2023).
In addition, we cropped and masked rasters to the extent of Nova Scotia;
rescaled them to 10 m using bilinear interpolation, where necessary; and
projected them to NAD1983 CSRS v6 UTM Zone 20N. To minimize false
accuracies imparted by down scaling (Sillero and Barbosa 2021), we only
selected predictor variables with a native spatial grain of ≤10 m
(details in S1) to match the finest grain in nested provincial ecosystem
surveys. Leaf area index was an exception; data were downscaled from 20
m (see S1).
We extracted predictor values at plot locations using the simple method (Hijmans 2023). Afterward, we used correlation (r = 0.7) (Guisan
et al. 2017) and variance inflation factor (VIF = 3) analyses (Zuur et
al. 2009), to identify correlated environmental predictors, employing
the corrplot (v0.92) (Wei and Simko 2021) and usdm (v2.1-6) (Naimi et
al. 2014) R packages respectively. To choose between highly correlated
predictor pairs, we ran individual models with each predictor, from
those pairs, and selected the predictor returning better model fit (see
S1). These procedures reduced the number of predictors from 22 to 18.
Coupled with geography (i.e., the Euclidian distance between each plot
pair), those 18 environmental variables were employed as baseline
predictors for modelling (see S1).