Introduction
The ecosystem (bolded terms defined in S1, Supplementary
Information) is a major focus in several prominent conservation
strategies established to curb impacts to biodiversity. For example, the
Kunming-Montreal Global Biodiversity Framework (GBF) (CBD 2022a)
identifies the maintenance or restoration of ecosystems as a long-term
goal. The GBF, and national strategies issued by GBF signatories (e.g.,
ECCC 2024), includes targets for realizing this goal, and those targets
encompass specific ecosystem properties including condition
(e.g., resilience), type (e.g., terrestrial), functions (e.g., carbon
sequestration), and geographic dimensions (e.g., area). To achieve these
targets, conservation actions must be informed by relevant spatial
information (GBF 2022a, ECCC 2024). This information is needed to ensure
decision-makers and practitioners can quantify, and make spatial
predictions about, key ecosystem patterns , such as
where different ecosystems occur and how their properties vary across
space. Spatial models are essential for addressing these questions and
ensuring targets are based on statistical evidence (Nicholson et al.
2019). Many spatial models will be built at the national or sub-national
scale, as regional strategies are the main mechanism for implementing
GBF resolutions (Perino et al. 2022, GBF 2022b). Yet spatial models
established for predicting ecosystem patterns at regional extents are
scarce (Geary et al. 2020, Naas et al. 2024), constraining plans to
achieve GBF objectives.
Ecosystems are inherently complex (Riva et al. 2023). To help understand
and reduce this complexity, ecologists formulate models to predict
ecosystem patterns (Holling and Allen 2002). Spatial ecosystem patterns
arise from differing combinations of biotic (e.g., taxa or traits) and
abiotic (e.g., soil type) constituents , and fromaggregate properties (e.g., productivity, sequestered carbon)
emerging from system development (Holling 1992, Artime and De Domenico
2022) (Figure 1). These patterns are shaped by the varied environmental
circumstances within which ecosystems occur (Holling 1992). The rarity
of spatial ecosystem models built for predicting these patterns (Geary
et al. 2020) may stem from the very complexities that motivate
modelling, as they present challenges for model design (Evans et al.
2013). More specific challenges include which ecosystem patterns andspatial scales should be selected for modelling, and why (Levin
1998, 2011, Gallagher et al. 2021). We present a general strategy for
addressing these challenges with an alternative approach to ecosystem
spatial pattern modelling. The patterns we model emerge from spatially
structured biotic and abiotic ecosystem properties, predicted as a
function of their shared relationships to environmental gradients. Our
simultaneous modelling of both biotic and abiotic responses, including
ecosystem structural, compositional, and functional variables,
differs from the approaches taken in most spatial ecosystem models (see
Geary et al. 2020).
Spatial ecosystem models are typically built to predict patterns of one
or more ecosystem properties and those properties are often biotic
(e.g., biomass, dispersal, decomposition) (Geary et al. 2020). In such
models, abiotic variables are usually employed as predictors (van der
Plas 2019, Hjort et al. 2022). For example, Huxley et al. (2023) show
how topographic conditions shape linkages between biotic traits and
primary productivity. Abiotic properties have less commonly been
incorporated as response variables in spatial ecosystem models
(Halvorsen et al. 2020). Here, abiotic properties are frequently limited
to those chemical constituents (e.g., nutrients, carbon), with a direct
link to biotic processes. Representative models (e.g., Lapierre et al.
2018, Soranno et al. 2019) predict how biotic (productivity) and abiotic
(phosphorus, nitrogen) ecosystem properties vary with environmental
drivers. Recent formulations (e.g., van der Plas et al. 2020, Gottschall
et al. 2022) also predict biotic and abiotic properties, broadening
their scope to include