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.