Figure 1. Range of ecosystem properties available for modelling forest ecosystem patterns in our study. Data encompass constituent and aggregate ecosystem properties occurring across biotic (labels 1 and 2 – green font) and abiotic (labels 3, 4, and 5 – brown font) domains. Properties marked with an asterisk were calculated from field data records. Tree and shrub graphics (Natural Resources Canada 2015).
physical and chemical abiotic variables (e.g., exposed ground, rain throughfall, and soil temperature, bulk density, and nitrogen). This inclusive approach helps underscore reciprocal relationships between biotic and abiotic properties. It also strengthens recognition of the joint contributions biotic and abiotic properties make to ecosystem patterns. We contend these two premises are key to improving predictions of ecosystem spatial patterns and we have incorporated them into our integrative modelling approach. What is more, our approach is applied at a regional extent which is the extent where many conservation decisions are made (Nicholson et al. 2019).
Our two primary study objectives are to (1) model independent and shared, biotic and abiotic, field-collected response variables to geographic and remotely-sensed environmental predictors; and (2) employ continuous spatial turnover in modelled responses to resolve ecosystem patterns at landscape extents. Model outcomes reveal emergent relationships among ecosystem properties, across biotic, abiotic, and ecosystem levels of ecological organization. The approach also provides a basis for partitioning the relative contributions of biotic, abiotic, and geographic predictors to ecosystem spatial patterns.
To quantify variation in ecosystem patterns across space, we predict biotic-abiotic dissimilarities between pairs of ecosystem survey sites using generalized dissimilarity modelling. Generalized dissimilarity modelling (GDM) is an extension of matrix regression developed by Ferrier et al. (2002) for spatial biodiversity modelling. Ferrier and Guisan (2006) highlight GDM as an analytical tool to ‘assemble and predict together’ , one of three general strategies they proposed for spatial prediction of community patterns. With this strategy, community constituents (i.e., species, traits) are modelling simultaneously to predict biotic turnover in space or time (Ferrier et al. 2007). Here, we extend this strategy to model ecosystem patterns, arising from simultaneous prediction and mapping of both biotic and abiotic properties. We show how predicted shifts in dissimilarity among combinations of these properties can be used to map spatial ecosystem patterns across landscapes.