Overview
We define ecosystem spatial pattern models (ESPM) as statistical models
formulated to predict spatially structured dimensions of intra- and
inter-ecosystem variation. We adapt elements of
community-level spatial models
(see review in D’Amen et al 2017) to configure our analytical strategy
for ESPM. Community-level models are multi-species extensions of species
distribution models (Norberg et al 2019, Zurell et al 2020), formulated
to simultaneously model the presence or abundance of species comprising
communities, or to model patterns of species compositional dissimilarity
among sites, at various scales of resolution (Nieto-Lugilde et al 2018).
These models are also commonly applied to predict community-level
properties (e.g., species richness and composition – Norberg et al
2019) and or assemblages (e.g., community type – Pinto-Ledezma and
Cavender-Bares 2021) across geographic gradients. Ecosystem spatial
pattern models differ from community-level distribution models in three
main respects: modeled response variables, prerequisite test and
predictor data, and the specific assembly determinants underpinning
model prediction. We briefly summarize these features below in the
workflow applied in our case study.
To build our strategy for ESPM, we focus on one of the three main
strategies to community-level modelling (see Ferrier and Guisan 2006).
This approach, referred to as assemble and predict together ,
models species-environmental relationships concurrently with spatial
predictions of community-level properties and or entities (Ferrier and
Guisan 2006, D’Amen et al 2017). Our analytical approach most closely
mirrors joint-species distribution modelling, a strategy to predict the
responses of multiple species occurring throughout a study space (see
review in Warton et al 2015). We extend this to model ecosystem spatial
patterns.
Under our approach to ESPM, spatially concordant patterns of
biotic-abiotic co-occurrence and or abundance are the basis for
identifying ecosystems at local and landscape extents. To illustrate
implementation of an ESPM, we outline a typical modelling workflow
(Figure 1) and provide an overview of key procedures. Key components
include aspects of ecosystem survey (Figure 1A); ESPM training and
predictor data compilation and refinement (Figure 1B), and finalization
(Figure 1C); model building (Figure 1D); spatial prediction of
individual ecosystem constituents and their properties (Figure 1E); and
lastly, prediction of ecosystem-level patterns and properties at local
(i.e., within individual ecosystems ) and landscape (i.e., among
ecosystems or ecosystem types)(Figure 1F) extents. Below we summarize
these ESPM components as they relate to our case study. We also provide
a detailed overview of the more prominent challenges (see Table 1 for
complete list) encountered during model implementation, offering
practical solutions to address these obstacles. Our overall intent is
not to report on ESPM outcomes, as they relate to our case study, but to
illustrate key aspects of model set-up, operationalization, and problem
resolution.