skip to main content

Explore Research Products in the NSF-PAR

Title: Exploring the impact of trait number and type on functional diversity metrics in real-world ecosystems
The use of trait-based approaches to understand ecological communities has increased in the past two decades because of their promise to preserve more information about community structure than taxonomic methods and their potential to connect community responses to subsequent effects of ecosystem functioning. Though trait-based approaches are a powerful tool for describing ecological communities, many important properties of commonly-used trait metrics remain unexamined. Previous work in studies that simulate communities and trait distributions show consistent sensitivity of functional richness and evenness measures to the number of traits used to calculate them, but these relationships have yet to be studied in actual plant communities with a realistic distribution of trait values, ecologically meaningful covariation of traits, and a realistic number of traits available for analysis. Therefore, we propose to test how the number of traits used and the correlation between traits used in the calculation of functional diversity indices impacts the magnitude of eight functional diversity metrics in real plant communities. We will use trait data from three grassland plant communities in the US to assess the generality of our findings across ecosystems and experiments. We will determine how eight functional diversity metrics (functional richness, functional evenness, functional divergence, functional dispersion, kernel density estimation (KDE) richness, KDE evenness, KDE dispersion, Rao’s Q) differ based on the number of traits used in the metric calculation and on the correlation of traits when holding the number of traits constant. Without a firm understanding of how a scientist’s choices impact these metric, it will be difficult to compare results among studies with different metric parametrization and thus, limit robust conclusions about functional composition of communities across systems.  more » « less
Award ID(s):
1831944 1655499 1753859
NSF-PAR ID:
10379635
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Editor(s):
Prieto Aguilar, Iván
Date Published:
Journal Name:
PLOS ONE
Volume:
17
Issue:
8
ISSN:
1932-6203
Page Range / eLocation ID:
e0272791
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ( , Frontiers in Forests and Global Change)
    Choosing appropriate forest restoration interventions is challenging. Natural regeneration can rapidly facilitate forest recovery in many situations. However, barriers such as dispersal limitation and competition with non-native species can require assisted restoration approaches to facilitate plant community recovery. We used a study that has directly compared the outcomes of tropical wet forest restoration interventions across 11 replicate sites in southern Costa Rica. Within this framework, we examined the functional recovery trajectories of recruiting tree sapling communities across a gradient of restoration interventions including low (natural regeneration), intermediate (applied nucleation), and high (plantation) initial resource-investment, which we compared to remnant reference forest. We collated leaf and stem functional traits for tree species that comprised the bulk of recruiting saplings, then determined how community-weighted trait means and functional diversity metrics changed over a decade across treatments. Results show that assisted restoration approaches (applied nucleation, plantation) sped the development of more functionally diverse tree communities, more than tripling the functional richness (FRic) of recruiting communities when compared to natural regeneration. However, functional dispersion (i.e., the trait range of dominant species) was equivalent across interventions, and between 28 and 44% lower than remnant forest, indicating that increases in FRic under assisted restoration were driven by species recruiting in low abundances (<10 individuals across treatments). Recruits in assisted restoration treatments also had 10–15% tougher, less-palatable leaves, and leaves were even tougher in reference forest, which could be driven by increasing herbivory pressure along the gradient of interventions. Results show that tracking simple metrics such as species richness can mask a more mechanistic understanding of ecosystem recovery that is elucidated by taking a functional trait-driven approach toward evaluating outcomes. For example, our work identified a paucity of dense-wooded species recruiting across restoration interventions, wood density was 11–13% lower in restoration treatments than reference forests, underscoring such species as prime targets for enrichment planting. Overall, findings suggest that assisted restoration can catalyze the functional recovery of naturally recruiting tree communities in landscapes that are slow to recover naturally and highlight the importance of evaluating how different components of functional diversity shift over time to fully understand restoration outcomes. 
    more » « less
  2. ; ; ; ( , Ecology and Evolution)
    Abstract

    Most existing functional diversity indices focus on a single facet of functional diversity. Although these indices are useful for quantifying specific aspects of functional diversity, they often present some conceptual or practical limitations in estimating functional diversity. Here, we present a new functional extension and evenness (FEE) index that encompasses two important aspects of functional diversity. This new index is based on the straightforward notion that a community has high diversity when its species are distant from each other in trait space. The index quantifies functional diversity by evaluating the overall extension of species traits and the interspecific differences of a species assemblage in trait space. The concept of minimum spanning tree (MST) of points was adopted to obtain the essential distribution properties for a species assembly in trait space. We combined the total length of MST branches (extension) and the variation of branch lengths (evenness) into a raw FEE0metric and then translated FEE0to a species richness‐independent FEE index using a null model approach. We assessed the properties of FEE and used multiple approaches to evaluate its performance. The results show that the FEE index performs well in quantifying functional diversity and presents the following desired properties: (a) It allows a fair comparison of functional diversity across different species richness levels; (b) it preserves the essence of single‐facet indices while overcoming some of their limitations; (c) it standardizes comparisons among communities by taking into consideration the trait space of the shared species pool; and (d) it has the potential to distinguish among different community assembly processes. With these attributes, we suggest that the FEE index is a promising metric to inform biodiversity conservation policy and management, especially in applications at large spatial and/or temporal scales.

     
    more » « less
  3. ; ; ; ; ; ; ; ( , Ecology and Evolution)
    Abstract

    An important focus of community ecology, including invasion biology, is to investigate functional trait diversity patterns to disentangle the effects of environmental and biotic interactions. However, a notable limitation is that studies usually rely on a small and easy‐to‐measure set of functional traits, which might not immediately reflect ongoing ecological responses to changing abiotic or biotic conditions, including those that occur at a molecular or physiological level. We explored the potential of using the diversity of expressed genes—functional genomic diversity (FGD)—to understand ecological dynamics of a recent and ongoing alpine invasion. We quantified FGD based on transcriptomic data measured for 26 plant species occurring along adjacent invaded and pristine streambeds. We used an RNA‐seq approach to summarize the overall number of expressed transcripts and their annotations to functional categories, and contrasted this with functional trait diversity (FTD) measured from a suite of characters that have been traditionally considered in plant ecology. We found greater FGD and FTD in the invaded community, independent of differences in species richness. However, the magnitude of functional dispersion was greater from the perspective of FGD than from FTD. Comparing FGD between congeneric alien–native species pairs, we did not find many significant differences in the proportion of genes whose annotations matched functional categories. Still, native species with a greater relative abundance in the invaded community compared with the pristine tended to express a greater fraction of genes at significant levels in the invaded community, suggesting that changes in FGD may relate to shifts in community composition. Comparisons of diversity patterns from the community to the species level offer complementary insights into processes and mechanisms driving invasion dynamics. FGD has the potential to illuminate cryptic changes in ecological diversity, and we foresee promising avenues for future extensions across taxonomic levels and macro‐ecosystems.

     
    more » « less
  4. ; ; ; ; ; ; ; ; ; ( , Ecology)
    Elizabeth Borer (Ed.)
    Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network’s (NEON’s) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high-resolution trait mapping. We tested the accuracy of readily available data products of NEON’s AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground-measured LAI (r = 0.32) and AOP Total Biomass and ground-measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380–2,500 nm), as opposed to derived products, could predict vegetation traits using partial least-squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validation of more than 0.25. For all vegetation traits, validation ranged from 0.08 to 0.29 and the range of the root mean square error of prediction (RMSEP) was 14–64%. Our results suggest that currently available AOP-derived data products should not be used without extensive ground-based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field-based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogeneous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time. But the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in-situ field measurements across a diversity of sites. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ( , Ecology)
    Abstract

    Understanding spatial and temporal variation in plant traits is needed to accurately predict how communities and ecosystems will respond to global change. The National Ecological Observatory Network’s (NEON’s) Airborne Observation Platform (AOP) provides hyperspectral images and associated data products at numerous field sites at 1 m spatial resolution, potentially allowing high‐resolution trait mapping. We tested the accuracy of readily available data products of NEON’s AOP, such as Leaf Area Index (LAI), Total Biomass, Ecosystem Structure (Canopy height model [CHM]), and Canopy Nitrogen, by comparing them to spatially extensive field measurements from a mesic tallgrass prairie. Correlations with AOP data products exhibited generally weak or no relationships with corresponding field measurements. The strongest relationships were between AOP LAI and ground‐measured LAI (r = 0.32) and AOP Total Biomass and ground‐measured biomass (r = 0.23). We also examined how well the full reflectance spectra (380–2,500 nm), as opposed to derived products, could predict vegetation traits using partial least‐squares regression (PLSR) models. Among all the eight traits examined, only Nitrogen had a validation of more than 0.25. For all vegetation traits, validation ranged from 0.08 to 0.29 and the range of the root mean square error of prediction (RMSEP) was 14–64%. Our results suggest that currently available AOP‐derived data products should not be used without extensive ground‐based validation. Relationships using the full reflectance spectra may be more promising, although careful consideration of field and AOP data mismatches in space and/or time, biases in field‐based measurements or AOP algorithms, and model uncertainty are needed. Finally, grassland sites may be especially challenging for airborne spectroscopy because of their high species diversity within a small area, mixed functional types of plant communities, and heterogeneous mosaics of disturbance and resource availability. Remote sensing observations are one of the most promising approaches to understanding ecological patterns across space and time. But the opportunity to engage a diverse community of NEON data users will depend on establishing rigorous links with in‐situ field measurements across a diversity of sites.

     
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email