Algorithm Selection
Previous efforts to model ecosystems in space have largely emphasized
biotic, abiotic, or functional response variables (Box 1, Table
S1) to predict ecosystem-level variation (Table S2). Our case study
goals differ as we emphasize biotic and abiotic variables
equally. Furthermore, we aim to “assemble and predict together” (sensu
Ferrier and Guisan 2006) (see case study overview). Given these
conditions, we sought candidate ESPM algorithms which could model biotic
and abiotic ecosystem constituents simultaneously and predict individual
and shared responses of each variable. In addition, we fit ESPM with in
situ (i.e., recorded in the field) training data, thereby better
capturing the range of biotic and abiotic complexity characterizing
ecosystems. By applying this integrative strategy for ESPM, ecosystems
are treated as an emergent function (sensu Nieto-Lugilde et al
2018) of biotic-abiotic co-occurrence and patterns of local concordance.
Our initial challenge was defining criteria to select an algorithm from
the numerous examples (Table 2) applied for spatial biodiversity
modelling. Following our evaluation, we screened algorithms according to
their flexibility (e.g., regarding data inputs), implementation (e.g.,
ease of application), analytical properties, and performance (Table 3).
Overall, we emphasized algorithms with the capacity to accommodate pools
of presence/absence, or abundance, records of both biotic and abiotic
variables as training data; employ species traits; and to allow for
interactions among predictors. We also prioritized algorithms which
could predict individual and joint responses in space. To assist with
our selection, we drew on recent review articles (Nieto-Lugilde et al
2018, Norberg et al 2019) and individual model algorithm assessments
(e.g., Warton et al 2015, Wilkinson et al 2021) to identify spatial
algorithms with relatively high predictive power. Candidate algorithms
that met our requirements include joint species distribution modelling
(implemented with Hierarchical Modelling of Species Communities (HMSC))
(Ovaskainen et al 2017), generalized dissimilarity modelling (Mokany et
al 2022) and probabilistic bioregion modelling (Hill et al 2020). Of
these three algorithms, we selected HMSC for our case study as recent
reviews demonstrate its overall flexibility and moderate to high
predictive performance for spatial biodiversity modelling (Warton et al
2015, Zhang et al 2018, Norberg et al 2019). Furthermore, availability
of a comprehensive methodological guide with tutorials in R (Ovaskainen
and Abrego 2020), training modules, and communication (Ovaskainen,
Tikhonov pers comm) facilitated implementation for our purpose.