Ecosystem-level Prediction
The ultimate purpose of ESPM is to make ecosystem predictions at both local and regional extents (Figure 1F). A key assumption of this objective is that disparate local ecosystems can be resolved, and that the arrangement and spatial properties of those individual ecosystems determine regional landscape structure and composition. Under these interrelated premises, the diversity and spatial organization of ecosystems, comprising a particular landscape, can be predicted by determining spatially structured differences among constituent ecosystem types. To achieve this ESPM objective, we propose analytical techniques to identify statistical commonalities among groups of biotic and abiotic variables co-responding in predicted geographic space (Table 1). These techniques provide a basis to identify ecosystem types, which we define as ecosystems that recur predictably across landscapes. Technical conventions and methods employed for aspatial classification of ecological units (e.g., community, habitat, landscape types) are suitable for this purpose. A number of these methods, and other less common techniques, have been applied to variables modelled in space (see Table 1).
An issue we considered was how to define statistically defensible thresholds of similarity or uniqueness among ecosystems. Ecosystems are spatially continuous, so the issue centers on quantifying patterns of relative discontinuity, arising from discordant patterns of abundance and or occurrence among ecosystem features jointly modelled in space. Outputs of spatial models are often continuous (e.g., probability of occurrence or co-occurrence) and many modelling applications require the identification of suitable thresholds for identifying ecological meaningful levels of relative continuity or discontinuity across space (Zurell et al 2020). For example, users may want to know if the probability of occurrence, for a particular species, is high enough to justify conservation actions (Bryn et al 2021). Thresholds have also been applied for determining whether co-occurrence patterns among groups of species reveal higher order levels of spatially structured biodiversity (e.g., communities - Ferrier and Guisan 2006) or ecological geography (e.g., bioregions - Hill et al 2020). For ESPM, and other spatially structured models of ecological diversity and organization, the challenge can be framed as discretizing multi-dimensional units from ecological gradients that are inherently continuous (Halvorsen et al 2020). This challenge is common to all pattern resolution (Levin 1992) but arguably amplified in ecosystem ecology. We do not propose a single analytical solution to this challenge, for it is embodies aspects of theory, philosophy, and pragmatism.