Ecosystem survey (A) |
Optimizing survey effort across
disparate environmental gradients, scales, and spatial contexts |
Implement stratified random survey design (e.g., Metzger et al
2013) |
|
Fixed spatial grains may bias some survey data |
Explore utility of
plotless survey methods (Cogbill et al 2018) |
|
Subjective survey site selection may introduce spatial bias |
Coarse-grain (sensu Newman et al 2019) data to common raster grid cells
(Miller et al 2004) |
Training Data Compilation (B) and
Finalization (C)
|
Measurement scales and units employed for biotic and abiotic field
survey variables differ markedly
|
Convert survey measurements to presence/absence records (König et al
2019)
|
|
Disproportionate ratios of biotic to abiotic variables |
Pool species
by common ecological traits (Kissling et al 2018) |
|
Adequacy of survey data |
Utilize rarefaction methods (Chao and Jost
2012) |
|
Effects of spatial survey grain and extent on training data structure |
Utilize rarefaction methods (Chao and Jost 2012); test for scale
dependencies using independent data sets with differing spatial grains
(Mod et al 2020) |
|
Training data dimensionality impacts model performance |
Scale pools
of test data across levels of ecological complexity; reduce or eliminate
disjunctions (sensu Miller et al 2004) in test data |
Predictor Data Compilation (B) and
Finalization (C)
|
Determining relevant conceptual, theoretical, and statistical criteria
for selecting predictor data
|
Draw on key theory from relevant disciples. Select predictors to resolve
joint biotic-abiotic patterns and processes shaping ecosystems at
various scales
|
|
Mismatched spatial resolution of predictors and response variables
(Bryn et al 2021) |
Coarse-grain training data to match predictor grain
(Newman et al 2019); assess effect of spatial scaling on model
performance (König et al 2021) |
|
Unequal numbers of biotic and abiotic predictors |
Employ pilot models
to test the effect varying ratios, and combinations, of biotic and
abiotic predictors has on ESPM performance (Brodie et al
2020) |
|
Implications of pooling disparate predictors shaping ecosystem pattern
across spatial grains |
Determine whether modelled responses are shaped
more by local and or regional drivers (e.g., Soranno et al 2019), and
whether responses vary among ecosystems and ecosystem
constituents |
|
Implications of grouping predictors of direct, indirect, and resource
gradients (Austin 2013) |
Clarify expected responses of individual and
aggregate ecosystem features (Table S1) to individual environmental
gradients (Austin 2013) |
|
Optimizing combinations of predictors |
Explore implications of
co-variate trade-off (Brodie et al 2020) |
Model Building and Assessment (D) |
Model algorithm selection |
Seek ESPM algorithms with adequate flexibility, functionality, and
predictive capacity. Candidate algorithms may be adapted from
community-level (e.g., Nieto-Lugilde et al 2018) or bioregion modelling
(e.g., Hill et al 2020) techniques. |
|
Determining model settings |
Settings (e.g., cross-validation, latent
variables, random effects, residual associations) depend on selected
algorithm and on results of trial model implementations; consult
published methodological guidelines (e.g., Ovaskainen and Abrego 2020,
Mokany et al 2022) |
|
Evaluating model fit and performance |
Metrics for evaluating model
fit and performance vary by algorithm and modelling intent; consult
appropriate guidelines (Araújo et al 2019, Zurell et al
2020) |
|
Translating model prediction to mapped spatial outputs |
Continuous
(e.g., ordination) or discrete (e.g., region of common profile) post-hoc
analytical outputs can be mapped in YUV colour space (e.g., Tikhonov et
al 2020) |
Intrinsic Predictions (E) |
Determine value of lower-order
intrinsic (e.g., biotic and abiotic ecosystem constituents and
properties) predictions for informing higher-order modelling objectives |
Improve understanding of commonalities, potential interactions, and
properties of individual variables characterizing ecosystems or sites;
disentangle the relative contributions of individual variables to
assembly mechanisms; relate model results to existing ecological
knowledge |
Extrinsic Predictions (F) |
Select method for resolving the
identities, features, and distribution of disparate ecosystems and
ecosystem types |
Adapt classification methods and indices (e.g.,
similarity, clustering, ordination) developed for community-level
(Ferrier and Guisan 2006) or bioregion modelling (Hill et al
2020) |
|
|
Explore utility of less common tools: concordance analysis (e.g.,
Taranu et al 2020), ecological uniqueness indices (e.g., Dansereau et al
2022), or embedding techniques such as T-SNE (e.g., Sonnewald et al
2020). |