skip to main content


Title: Using local and regional trait hypervolumes to study the effects of environmental factors on community assembly
Abstract

Determining how local and environmental conditions affect community assembly processes is critical to understanding and preserving ecosystem functions. A combination of plant traits is required to capture the broad spectrum of strategies that species employ to respond to varying environmental conditions. The trait hypervolume (i.e.,n‐dimensional trait space) accurately describes such multi‐trait characteristics. Here we use hypervolume mismatch metric, defined as the difference between the observed trait hypervolume and the trait hypervolume inferred from local and/or regional species pools, to investigate plant community assembly. Our method suggests plant traits should be categorized a priori to quantify trait hypervolumes associated with environmental variation (i.e., resource utilization strategies). Using the plant trait data from North American and South African grassland communities, this hypervolume mismatch metric can be applied to different categories of traits and scales, thus providing new insights into community assembly processes. For example, the trait hypervolumes calculated from physiological traits (e.g., mean stomatal length, stomatal pore index, and mean stomatal density) were highly correlated with regional environmental factors. By contrast, local species pool factors explained a greater proportion of variation in hypervolumes estimated from leaf stoichiometric traits (e.g., leaf nitrogen [N] content, leaf carbon [C] content, and leaf C/N ratio). Therefore, this hypervolume mismatch framework can accurately identify the separate impacts of regional versus local species pools on community assembly across environmental gradients.

 
more » « less
NSF-PAR ID:
10392426
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Ecosphere
Volume:
13
Issue:
10
ISSN:
2150-8925
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Dominant species can act as a biotic filter in structuring plant communities by constraining the establishment and survival of subordinate species. The effect of intraspecific trait variability of dominant species on the functional response of subordinate species, however, is not well understood.

    We quantified intraspecific variation in four functional traits of 26 subordinate species in an experimental grassland established with two population sources (i.e. cultivars and local ecotypes) of three dominant grasses (Sorghastrum nutans, Andropogon gerardiiandSchizachyrium scoparium) and three pools of subordinate species (each from one origin) within each of the dominant grass source treatments.

    Twenty of the 26 subordinate species exhibited intraspecific trait variability for one trait or more in response to dominant species population source, and variation among population sources of the dominant species was non‐random. Dominant grass population source affected intraspecific variability in functional traits of multiple subordinate species. Cultivar sources of the dominant grasses and some of the subordinate species that established with them had higher and generally more variable functional leaf area and leaf nitrogen content compared to local ecotypes of the dominant grasses and the subordinate species that established with them. Local ecotype sources of the dominant grasses increased leaf area based functional diversity of subordinate species.

    Synthesis.This study provides evidence that intraspecific trait variability in dominant species acts as a biotic filter to constrain niche availability and dimensionality affecting trait variation of subordinate species during community assembly.

     
    more » « less
  2. Abstract

    Plant species can show considerable morphological and functional variation along environmental gradients. This intraspecific trait variation (ITV) can have important consequences for community assembly, biotic interactions, ecosystem functions and responses to global change. However, directly measuring ITV across many species and wide geographic areas is often infeasible. Thus, a method to predict spatial variation in a species’ functional traits could be valuable.

    We measured specific leaf area (SLA), height and leaf area (LA) of grasses across California, covering 59 species at 230 sampling locations. We asked how these traits change along climate gradients within each species and used machine learning to predict local trait values for any species at any location based on phylogenetic position, local climate and that species’ mean traits. We then examined how much these local predictions alter patterns of assemblage‐level trait variation across the state.

    Most species exhibited higher SLA and grew taller at higher temperatures and produced larger leaves in drier conditions. The random forests predicted spatial variation in functional traits very accurately, with correlations up to 0.97. Because trait records were spatially biased towards warmer areas, and these areas tend to have higher SLA individuals within each species, species means of SLA were upwardly biased. As a result, using species means over‐estimates SLA in the cooler regions of the state. Our results also suggest that height may be substantially under‐predicted in the warmest areas.

    Synthesis. Using only species mean traits to characterize the functional composition of communities risks introducing substantial error into trait‐based estimates of ecosystem properties including decomposition rates or NPP. The high performance of random forests in predicting local trait values provides a way forward for estimating high‐resolution patterns of ITV without a massive data collection effort.

     
    more » « less
  3. Abstract

    Understanding the drivers of trait selection is critical for resolving community assembly processes. Here, we test the importance of environmental filtering and trait covariance for structuring the functional traits of understory herbaceous communities distributed along a natural environmental resource gradient that varied in soil moisture, temperature, and nitrogen availability, produced by different topographic positions in the southern Appalachian Mountains.

    To uncover potential differences in community‐level trait responses to the resource gradient, we quantified the averages and variances of both abundance‐weighted and unweighted values for six functional traits (vegetative height, leaf area, specific leaf area, leaf dry matter content, leaf nitrogen, and leaf δ13C) using 15 individuals of each of the 108 species of understory herbs found at two sites in the southern Appalachians of western North Carolina, USA.

    Environmental variables were better predictors of weighted than unweighted community‐level average trait values for all but height and leaf N, indicating strong environmental filtering of plant abundance. Community‐level variance patterns also showed increased convergence of abundance‐weighted traits as resource limitation became more severe.

    Functional trait covariance patterns based on weighted averages were uniform across the gradient, whereas coordination based on unweighted averages was inconsistent and varied with environmental context. In line with these results, structural equation modeling revealed that unweighted community‐average traits responded directly to local environmental variation, whereas weighted community‐average traits responded indirectly to local environmental variation through trait coordination.

    Our finding that trait coordination is more important for explaining the distribution of weighted than unweighted average trait values along the gradient indicates that environmental filtering acts on multiple traits simultaneously, with abundant species possessing more favorable combinations of traits for maximizing fitness in a given environment.

     
    more » « less
  4. Abstract

    Traits differentially adapt plant species to particular conditions generating compositional shifts along environmental gradients. As a result, community‐scale trait values show concomitant shifts, termed trait‒environment relationships. Trait‒environment relationships are often assessed by evaluating community‐weighted mean (CWM) traits observed along environmental gradients. Regression‐based approaches (CWMr) assume that local communities exhibit traits centred at a single optimum value and that traits do not covary meaningfully. Evidence suggests that the shape of trait‒abundance relationships can vary widely along environmental gradients—reflecting complex interactions—and traits are usually interrelated. We used a model that accounts for these factors to explore trait‒environment relationships in herbaceous forest plant communities in Wisconsin (USA).

    We built a generalized linear mixed model (GLMM) to analyse how abundances of 185 species distributed among 189 forested sites vary in response to four functional traits (vegetative height—VH, leaf size—LS, leaf mass per area—LMA and leaf carbon content), six environmental variables describing overstorey, soil and climate conditions, and their interactions. The GLMM allowed us to assess the nature and relative strength of the resulting 24 trait‒environment relationships. We also compared results between GLMM and CWMr to explore how conclusions differ between approaches.

    The GLMM identified five significant trait‒environment relationships that together explain ~40% of variation in species abundances across sites. Temperature appeared as a key environmental driver, with warmer and more seasonal sites favouring taller plants. Soil texture and temperature seasonality affected LS and LMA; seasonality effects on LS and LMA were nonlinear, declining at more seasonal sites. Although often assumed for CWMr, only some traits under certain conditions had centred optimum trait‒abundance relationships. CWMr more liberally identified (13) trait‒environment relationships as significant but failed to detect the temperature seasonality‒LMA relationship identified by the GLMM.

    Synthesis. Although GLMM represents a more methodologically complex approach than CWMr, it identified a reduced set of trait‒environment relationships still capable of accounting for the responses of forest understorey herbs to environmental gradients. It also identified separate effects of mean and seasonal temperature on LMA that appear important in these forests, generating useful insights and supporting broader application of GLMM approach to understand trait‒environment relationships.

     
    more » « less
  5. Anthropogenic nitrogen (N) addition might alter the evolutionary trajectories of plant populations, in part because it alters the abiotic and biotic environment by increasing aboveground primary productivity, light asymmetry, and herbivory intensity, and reducing plant species diversity. Such evolutionary impacts could be caused by N altering patterns of natural selection (i.e., trait-fitness relationships) and the opportunity for selection (i.e., variance in relative fitness). Because at the community level N addition favors species with light acquisition strategies (e.g., tall species), we predict that N would also increase selection favoring those same traits. We also hypothesize that N could alter the opportunity for selection via its effects on mean fitness and/or competitive asymmetries.To investigate these evolutionary consequences of N, we quantified the strength of selection and the opportunity for selection in replicated populations of the annual grass Setaria faberi Herrm. (giant foxtail) growing in a long-term N addition experiment. We also correlated our measures of selection and opportunity for selection with light asymmetry, diversity, and herbivory intensity to identify the proximate causes of any N effects on evolutionary processes. N addition increased aboveground productivity, light asymmetry, and reduced species diversity. Contrary to expectations, N addition did not strengthen selection for trait values associated with higher light acquisition such as greater height and specific leaf area (SLA); rather, it strengthened selection favoring lower SLA. Increased light asymmetry was associated with stronger selection for lower SLA and lower species diversity was associated with stronger selection for greater height and lower SLA, suggesting a role for these factors in driving N-mediated selection. The opportunity for selection was not influenced by N addition (despite increased mean fitness) but was negatively associated with species diversity. Our results indicate that anthropogenic N enrichment can affect evolutionary processes, but that evolutionary changes in plant traits within populations are unlikely to parallel the shifts in plant traits observed at the community level. Data was collected in 2020 from a field experiment in a long-term ecological research site (Kellogg Biological Station LTER site in Michigan, USA). The Data folder contains 3 separate datasets as CSV files, each with accompanying .txt metadata files: 1) a dataset of individual-level data (Waterton2022_NitrogenEvolution_Individual_Data.csv); 2) a dataset of annual net primary productivity (ANPP; Waterton2022_NitrogenEvolution_ANPP_Data.csv); 3) a dataset of light measurements (Waterton2022_NitrogenEvolution_Light_Data.csv). An R script for reproducing the analyses and figures is available at https://doi.org/10.5281/zenodo.7121361. R statistical software is required to run the R script. 
    more » « less