Introduction
Predictive modelling is essential for understanding the natural world
and for making informed decisions about current and future states.
Dramatic biodiversity shifts, compounded with the poorly understood
ecological consequences of emerging climatic regimes, have served as a
clarion call for more predictive conservation science. These shifts have
also helped to reaffirm the broader role of prediction in basic and
applied ecology (Houlahan et al. 2017). However, while predictive
biodiversity models (e.g., species or community distribution models) are
generally well developed, and frequently employed to inform conservation
decision-making, these advancements have not been fully realized in
predictive spatial models of ecosystems (Fulton et al 2019, Geary et al
2020, Halvorsen et al 2020, Simensen et al 2020). Spatially explicit
models are required for predicting intra- (e.g., structure, composition,
function, spatial properties) and inter-ecosystem (e.g., identity,
spatial properties) patterns across landscapes. The scarcity of such
models hinders the scientific community’s ability to make objective
ecosystem predictions and near-term forecasts at sub-global scales.
Predictive spatial models have emerged as the principal tool to
understand biodiversity patterns, and to forecast the effects of
ecological change (Araújo et al 2019). While the scope of these models
has progressively broadened to address multiple components of
biodiversity, biotic predictions are by far the most common application
(Pollock et al 2020). In their review of spatially explicit biodiversity
models, Zurell et al (2022) report 80% of published models are focused
on species distributions. A smaller percentage of predictive spatial
models address higher order (e.g., communities) components of
biodiversity but these too are, almost exclusively, biotic entities
(Pollock et al 2020, Zurell et al 2022). For example, joint distribution
and generalized dissimilarity models of communities (e.g., community
distribution, diversity, or interactions) are common extensions of
predictive species distribution models (Zurell et al 2020, Wilkinson et
al 2021, Mokany et al 2022). In sharp contrast, comparable spatial
models of ecosystem pattern – and more specifically those models that
integrate biotic and abiotic variables and system-level properties
(Table S1) – are extremely rare (Dor-Haim et al 2019, Halvorsen et al
2020, Simensen et al 2020). This gap in modelling capacity is
remarkable. To fill this gap, new spatial modelling approaches are
needed for modelling ecosystem patterns across landscapes.
Development of suitable models is
especially timely for ecosystem assessment (e.g., Keith et al 2022) has
undergone a contemporary groundswell, as the scientific community (e.g.,
Watson et al 2020, Bullock et al 2022) propose ambitious targets for
ecosystem conservation and restoration.
Conservation practitioners and regulatory authorities rely on spatial
models to ensure decisions are informed by robust predictions and
related measures of statistical uncertainty. Most conservation decisions
are realized in geographic space and selecting the appropriate spatial
scale to maximize success is imperative (Di Minin et al 2022). Trend
analyses show the ecosystem concept is frequently employed to structure
spatially based conservation plans and to mitigate stressors including
climate change (Anderson et al 2021). The most prominent commitments to
ecosystem conservation have been established in global agreements (e.g.,
Convention on Biological Diversity 2021), while implementation falls to
national signatories and to sub-national governments and conservation
organizations (Perino et al 2022). In Canada, for example, standardized
spatial data of significant ‘key biodiversity areas’ including
threatened ecosystems, have been recommended to inform national
conservation planning (Shea et al 2018). However, achieving this
ecosystem objective presents a challenge in Canada that is shared
elsewhere. The challenge is that spatially explicit ecosystem prediction
is recognized as an essential tool to inform regional (i.e.,
continental, national, sub-national) commitments to global conservation
initiatives but very few fully integrated spatial models of ecosystem
pattern, at regional landscape scales, are available. Some members of
the scientific community have raised this as a problematic issue (Fulton
et al 2019, Geary et al 2020), but it is seldom acknowledged,
particularly in the applied literature (Halvorsen et al 2020).
In this Forum, we propose a novel framework for spatially explicit
modelling of ecosystem pattern. Our framework differs from other spatial
ecosystem models in that it accords biotic and abiotic components
equally, commensurate with their joint influence on ecosystem assembly
(sensu Keith et al 2022). Under this approach, the ecosystem is modelled
as a spatially explicit place demarcated by the strength of concordance
among constituent biotic and abiotic variables (Box 1). Different
ecosystems are in turn discriminated by relatively unique combinations
of biotic and abiotic variables that recur along spatial gradients
(Figure 1E, 1F). Under our approach, groups of local ecosystems scale up
to form the heterogenous landscapes within which they occur (Figure 1F).
The bounds of an ecosystem’s local extent can only be determined by
considering both the extent and identity of proximate ecosystems.
Consequently, the spatial organization of ecosystems, comprising a
particular landscape, can be predicted by identifying spatially
structured differences among constituent ecosystem types. Our framework
offers a quantitative strategy for modelling the range-wide occurrence,
identity, and characteristic properties of individual ecosystems.
Furthermore, it provides an approach for determining regional patterns
of ecosystem spatial organization, and related changes in biotic-abiotic
relationships, across heterogenous landscapes.
This Forum includes four primary components. First, we briefly review
existing ecosystem modelling approaches, with a focus on models
formulated to predict ecosystem or meta-ecosystem spatial patterns at
regional landscape extents – the spatial extent where most terrestrial
environmental decisions are made. Second, we demonstrate how a
limitation in predictive modeling capacity occurs because of disparate
scientific traditions and related conventions of modelling practice.
Third, we outline a novel conceptual framework for predictive ecosystem
spatial pattern modelling at regional landscape extents. Our framework
includes an analytical workflow illustrated with a case study. Lastly,
we conclude with a perspective where we propose future research
directions and opportunities.