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.