Traits-based global change predictions
Of the 822 studies we reviewed, a small portion (23%) focused on
applying traits in the context of global change, and even fewer (3%)
applied traits to generate predictions about patterns of diversity,
abundance, or distribution beyond the data used for the initial
analysis. Global change drivers, in order of decreasing frequency within
the studies we evaluated, included habitat degradation (8%), climate
change (6%), biological invasion (5%), multiple/non-specified drivers
(3%), and exploitation (1%). Predictive studies emerged primarily
within the last 10 years (82%); half of all predictive studies were
published in the last five years (Figure 2F). More than 30% of
predictive studies focused on applying plant morphological traits to
predict the outcomes of abiotic environmental filtering in terrestrial
ecosystems. Crucially, studies that generated trait-based predictions of
global change (the main focus of this review) represent fewer than 3%
(22) of all studies. Of these, more than half (12) focus on ecological
prediction in a climate change context, three on biological invasions,
and a single study each on the consequences of habitat degradation and
exploitation. Five studies used traits to predict the outcomes of
multiple global change drivers (two marine [Jacob et al. 2011, Eklof
et al. 2015]; three terrestrial [Cardillo et al. 2004; Dury et al.
2018; Knott et al. 2019]).
The narrowest subset of journal articles (and subsequently, traits)
corresponded to studies that were both predictive and investigated
global change impacts to ecosystems (nstudies = 22).
Accordingly, the assemblage of traits used by papers that assessed
drivers of global change and performed predictive ecological
modelling were highly nested within the broader suite of traits used
across descriptive and non-global change studies, and thus differed
statistically with traits used more broadly (Figures 6E/F, Figures
S1E/F, Figure S2E/F). Habitat associations and life history were the
most important suites of traits in studies assessing climate change
impacts, as well as undertaking predictive analyses, while morphological
traits were most important to studies that investigated habitat
degradation and biological invasions (Figure S3&S4, Tables S4-8). While
size is also the most common single trait type in predictive studies, in
general physiological traits related to resource acquisition and
requirements, such as thermal tolerance, and life history traits are
more often applied within predictive studies compared with descriptive
trait-based work (Figures S5&S6, Tables S8-11).
More closely examining predictive global change studies identified in
this review highlights a range of methodological approaches that require
data inputs at varied spatio-temporal scales, and therefore resulting in
predictions at a range of resolutions (Figure 7). Each method has
strengths and weaknesses; trait-based experiments offer opportunities to
generate and test fine-scale predictions about response to global change
drivers, yet insights gained through experimentation are most relevant
under the set of conditions under which the study takes place (e.g.
Eckloff et al. 2015; eelgrass communities under climate change and
grazer loss; Figure 7). The results of experiments and environmental
correlations can be synthesized via meta-analyses to generalize effect
sizes for trait types that recur across taxa and ecosystem type (e.g.
Cattano et al. 2018’s synthesis of acidification effects on marine
fishes; Figure 7). Process models offer an opportunity to examine the
effect of more complex interactions on ecological phenomena under global
change; however, insights gained from this approach may not be at a
resolution needed for conservation and management decision-making (e.g.
Jacob et al. 2011’s trait-based polar sea food web model; Figure 7).
Traits-based distribution models—the most common approach to global
change predictions identified by our review—have generally been
applied to forecasting range and abundances under future abiotic
(climate) conditions (e.g. Fordham et al. 2012’s and Whitney et al.
2017’s distribution models of trees and freshwater fish, respectively;
Figure 7). Spatial projections generated from distribution models can be
intuitively applied to place-based biodiversity conservation and natural
resource management, but generally omit biotic interactions and
feedbacks that further refine species’ ranges and abundances across the
landscape (Figure 1). Spatially explicit process models offer a means to
generate range and abundance projections that account for multiple
environmental filtering processes simultaneously (e.g. Edmunds et al.
2014’s spatial process model of coral survival and growth under
competition and climate warming scenarios; Figure 7). However, this
approach has not yet been widely applied to global change prediction,
perhaps due to the complexity and scale of the required trait data
inputs.