Fig 1.
Considerations for broadening inference in plant, microbial, and plant-microbe interaction studies.
The center circle (“current perspective”) describes current perspectives on each methodological component. The larger circle (“broadened perspective”) provides additional considerations that would help broaden inference across climate change studies.
Table 1.
Evaluation of the primary methods used to study plant and microbial responses to climate change in the context of five common barriers to broad inference.
Common methods (rows) were qualitatively evaluated for the incorporation of each barrier (columns); “explicit” indicates that the method inherently incorporates or addresses the barrier, “possible” indicates that it is possible for the method to incorporate or address the barrier based on the experimental design, “no” indicates that, to our knowledge, the method cannot incorporate or address the barrier. References were chosen based on applicability, impact, and novelty.
Fig 2.
Framework for understanding organismal responses to climate change.
(A) Proposed theoretical framework whereby three vignettes (meta-community theory, range dynamics, and resurrection studies) are employable across the entire range of a species or communities. As time increases (x-axis), range shifts move leading, core, and relict populations across the landscape, exposing relict populations to extinction risks. (B) Dominant population-level processes and dynamics that vary in prevalence across the natural range of a species, sorted by empirical support. (C) Hypothesized community-level processes and dynamics should also vary across the natural range of a species. (B, C) Y-axis is the density of each process and dynamic relative to the rightmost natural range in (A).
Fig 3.
Influence of four meta-community effects on community assembly in experimentally warmed plots.
All panels are representative of a single experimental design whereby warmed plots are embedded in an un-warmed control matrix; individual panels represent different effects from four types of meta-community dispersal. Dark green background indicates the surrounding matrix, from which the control plots (light green boxes) originate; dark red plots indicate experimentally warmed plots, and light green boxes indicate control, un-warmed plots. Light green boxes (labeled “C”) indicate the control community; light red boxes (labeled “T”) indicate the treatment community. The size of each light-colored inset box indicates the relative proportion of each community (treatment or control), as determined by dispersal strength and ability. Strong dispersal is indicated by solid arrows; weak dispersal is indicated by dashed arrows. Gradient arrows indicate dispersal from the surrounding matrix environment. (A) Mass effects are present when dispersal from the surrounding matrix dominates community assembly; if propagule pressure is strong enough, dispersal could mask treatment effects. (B) Species sorting is present when environmental selection or differential niches prevent dispersal from influencing plots. (C) Patch dynamics are present when one community (here, the control) is a better colonist, and the other (here, treatment) is a better competitor, resulting in random dispersal. (D) Neutral processes are present when the communities are ecologically equivalent; random dispersal and chance outcomes neutralize any community dominance.
Fig 4.
Model selection depends on genetic and environmental precision.
Representation of model selection for the best environmental variables to explain trait variation determined by AIC model selection across species-level, provenance-level, or population-level analyses. Panel (A) shows a map of data collection sites. In panels (B-F), white bars represent models where the dryness index and evaporative (evapo.) index were selected as the best predictors while black bars represent models where temperature (temp.) and precipitation (precip.) were selected as the best predictors. Panel (B) represents all examined traits and panels (C-F) split all trait into those measured in the field (C), greenhouse (D), and into plant (E) or soil (F) traits. Base map shapefile downloaded from: https://gadm.org/download_country_v3.html. Base map license information: https://gadm.org/license.html.
Fig 5.
Conceptual framework for maximizing broad inference in climate change studies on plants, microbes and their interactions.
To optimize the robustness of a study, each of the following components should be considered, measured, or incorporated. (A) Especially in studies across multiple species or populations, accounting for evolutionary history or genetic hierarchy reduces the possibility of effect size inflation due to phylogenetic relatedness and informs the researcher of the effect of genetic variation on phenotypic responses. (B) Ideally, sampling should take place across the entire range of the species examined. At the very least, where in the species’ range sampling took place should be noted and accounted for in analyses. Here, we are assuming that Species A (flower) and Species B (microbe) have overlapping ranges (blue and red background) for graphical simplicity. Across the range, multiple environmental variables should be assessed for analyses (again, only one, mean annual temperature, is shown for simplicity). Sequencing and genotyping field samples can assess the strength and type of meta-community effects driving population and community patterns. (C) Examples of cross-validating experimental methods. Field observations can validate or inform experimental manipulations, as well as resurrection or paleoecological studies. Note that to our knowledge, resurrection and paleoecological studies are only possible for plants and not microorganisms at this time. Base map shapefile downloaded from: https://gadm.org/download_country_v3.html. Base map license information: https://gadm.org/license.html.