Perspective
We frame ESPM as an outgrowth in the advancement of spatial ecosystem ecology. As this latter branch of ecology continues to evolve, efforts to model within- and cross-ecosystem patterns and processes are increasingly being tested in complex real-world study landscapes. This is where the potential to bridge theoretical and applied modelling traditions is greatest. While most spatial ecosystem models, at this frontier, are formulated to predict responses to ecosystem heterogeneity, we focus on the origins and organization of that heterogeneity. Our framework is intended to help encourage new strategic directions in the type of integrative modelling needed to strengthen conservation assessments of ecosystems, at those region-specific extents relevant to environmental decision making (Perino et al 2022). While forecasting ecosystem change in future scenarios is essential for decision making, quantifying current ecosystem patterns is comparably important (Watson et al 2020). Outputs of our framework can also help catalyze efforts needed to improve basic understanding of structural, compositional, and functional relationships across scales and among extant ecosystems.
Our approach is not intended as an endpoint. Instead, we offer an alternative analytical perspective to quantify emergent aspects of ecosystems, and how they assemble across space. Patterns of heterogeneity reflect past and present interactions among biotic and abiotic elements. The unique combination of ecosystem elements and processes, expressed at a specific time and place, is like a dynamic blueprint. We do not model dynamic aspects of that blueprint, or assembly processes themselves. Our focus is on the spatially structured determinants and outcomes established by those processes. We do not view this apparent divergence from the functional or mechanistic (often referred to as process-based) emphasis typical in ecosystem and meta-ecosystem ecology, as a disciplinary incongruency. Pattern and process span all levels of organization in nature, including ecosystems. Numerous spatial models have been formulated to predict and help understand biodiversity distributional patterns (Araújo et al 2019), but spatial models of ecosystem pattern are scarce (Geary et al 2020) and frequently absent from broad-scoped thematic reviews. Even in extensive reviews (e.g., Zurell et al 2021) of spatial biodiversity models, only process-based spatial ecosystem models are sufficiently common for consideration. These trends persist in general ecology almost twenty years after Loreau et al (2003) highlighted the schism between models of biotic (e.g., population, community) elements, and those built for ecosystem prediction. Our approach is intended to help provide a common basis for linking predictions across scales and levels of ecological organization, including constituent facets of biotic and abiotic complexity.
Recognition of the importance of abiotic pattern and process for understanding ecosystems, and other components of biodiversity, has largely originated within the geoscience community (Richter and Billings 2015). Here, biogeochemical elements are often emphasized, while other physical and more strictly abiotic variables and processes are rarely addressed as completely (Johnson and Martin 2016, Dor-Haim et al 2019). This disparity in purview has affected perceptions of what an ecosystem is, how it can be modelled, and which abiotic variables should be considered in empirical ecosystem research (Richter and Billings 2015, Johnson and Martin 2016). We suggest abiotic complexity is not singularly important because biota rely on it – it is fundamental to ecosystem organization. Moreover, many abiotic variables and properties originate, or are shaped by, from biotic processes (e.g., humus, water chemistry, soil structure). Recognition of abiotic components of ecosystem constitution and dynamics, and their influence on ecosystem properties has begun to rise within the ecological research community (van der Plas 2019). In addition, recent perspectives and methodological guides (e.g., Zarnetske et al 2019, Alahuhta et al 2020, Hjort et al 2022) hold considerable promise for increasing broader awareness of abiotic complexity, and for catalyzing new approaches for understanding joint biotic-abiotic patterns and processes shaping ecosystems.
In what follows, we outline recommendations for moving forward. While our case study includes the kind of detailed workflow deemed essential to advance predictive ecology (McIntire et al 2022), much effort remains to explore the strengths, and to identify and address the limitations, of ESPM as an analytical template for predicting ecosystem organization. We summarize three overarching issues required to refine our framework; model data and testing, linking model and theory refinement, and applying model outputs to support environmental decision making.
Data required for spatial modelling of species and communities are generally well documented (Bryn et al 2021). The extraordinary popularity of these models has also prompted development of new training and predictor data, and investigations of the suitability these data have for biodiversity modelling are on-going (e.g., Guillera-Arroita et al 2015, Brodie et al 2020). However, comparable overviews of basic data necessary for ESPM, and similar spatial ecosystem models, are rare and standardized guidelines for data selection do not exist. Prominent advances in spatial ecosystem modelling largely center on the application of remote sensing data for predictions of individual ecosystem components or properties, such as structure (e.g., topography, vegetation physiognomy - D’Urban Jackson et al 2020) or function (e.g., productivity, disturbance – Anderson 2018). For the most part, these latter spatial models do not incorporate ground-derived field measures of biotic and abiotic ecosystem compositional and structural complexity, although recent efforts promote better integration across varied data sources (e.g., Pasetto et al 2018,). Instead, such field data are typically applied for independent spatial predictions of population, community, or geophysical components of nature. Data prerequisite for ESPM include both the detailed type of in situ ecosystem data collected as part of ecosystem inventories, or long-term monitoring, coupled with specific predictors, many of them remotely derived, necessary for modelling ecosystem gradients. Environmental predictors employed for spatial modelling of other components of biodiversity and geodiversity can be adapted for this latter purpose (Simensen et al 2020).
To facilitate overall improvement of our framework, we suggest adoption of standardized data compilation and processing protocols, comparable to those recently established for spatial biodiversity models (Zurell et al 2020). Test and predictor data should be made available directly or via data extraction pipelines provided in study documentation. These provisions will not only help ensure model repeatability and interoperability but facilitate efforts to transfer models to new locations or times. These measures will be particularly helpful where data are sparse or absent, or where environmental circumstances are significantly dissimilar. Furthermore, records of data, metadata, and data processing routines are necessary to determine the influence spatial context has on predictive outcomes.
To make models useful, they must be reliable, flexible, and informative. Many models are only applicable in specific circumstances, compromising generality or predictive inference for accuracy. Predictive models are usually improved by testing with different data, parameters, performance metrics, and algorithms. And ultimately, the only way to improve predictive capacity, and to refine modelling approaches, is to predict frequently and to implement model assessment protocols (Dietze 2017, McIntire et al 2022). We suggest ESPM models need to be tested in different biomes, at different landscape extents, and with different subsets of total regional ecosystem variation. As realistic ecosystem models tend to be more complex (Evans 2012, Mouquet et al 2015), often creating computational challenges, reduced datasets from smaller, less complex, regions are recommended for initial model testing. Extending predictions to unsurveyed areas, as part of testing routines, may be informed by new methods intended to identify the area of applicability for predictive spatial models (Meyer and Pebesma 2021). Lastly, running modelling trials with simulated data can help simplify and expediate testing scenarios before applying them to real data. Simulated data can also be employed to help evaluate model performance, as part of assessment trials (Gallagher et al 2021).
Theories established to explain ecosystem organization have been closely tied with efforts to quantify pattern within and among ecosystems (Levin 1992). And while predictions typically accompany theory development, they also serve to corroborate or refine theory (Houlahan et al 2017). However, ecological theories are often incomplete and fragmented (Mouquet et al 2015) and models may employ elements of multiple theories with different origins. Our predictive framework draws from several interrelated theories all coupling aspects of both static and dynamic pattern resolution. Process-based approaches for predicting ecosystem dynamics are essential, but they can be greatly strengthened by efforts to resolve contemporary ecosystem organizational complexities, many of which remain vague and inadequately understood. Moreover, process-based (mechanistic) and static pattern-based (phenomenological or correlative) models lie along a continuum, sharing many of the same theoretical underpinnings and, in some cases, overlapping research objectives (Mouquet et al 2015). We suggest any model along this continuum can serve as a starting point to abstract complexity, further understanding, and test theory. Such models are different representations of the same ecological entity, and we posit greater effort is required to reconcile and facilitate knowledge exchange among various ecosystem modelling strategies. To help advance this synthesis, we further suggest ESPM outputs could be applied as inputs in process-based (e.g., simulated) spatial ecosystem models. This approach could help determine how spatial ecosystem patterns respond under dynamic scenarios.
We close with a note on matching models to purpose. Part of our motivation with this Forum has been to promote a novel predictive approach for guiding ecosystem-based environmental decision making. Regulatory authorities and conservation practitioners need reliable, timely, and accessible evidence to inform and evaluate their actions. Many decisions are made on short time horizons, tied to fluctuating budgetary and political circumstances. To underpin these decisions, tactical ecosystem predictions are required to determine which individual ecosystems occur where and why; how ecosystems relate to one another (i.e., spatially, functionally, biotically, and abiotically) within their respective ranges; what environmental drivers are important across varied spatial and temporal contexts; which ecosystems support priority constituents (e.g., rare species, functional traits, carbon) or properties (e.g., productivity); and which ecosystems are declining and or otherwise vulnerable. These aspects of contemporary ecosystem geography reflect past assembly pathways, including the consequences of natural disturbances and human land use, and provide a basis to improve strategic predictions of ecosystem change. Tools which facilitate synthesis of ecosystem organization and dynamics are critical for successful natural resource management because decision makers must be mindful of both existing and projected circumstances when settling trade-offs among conflicting land uses and societal pressures.