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.