Data from: Linking functional diversity and ecosystem processes: a framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species
Kuebbing, Sara E., Yale University
Maynard, Daniel S., Yale University
Bradford, Mark A., Yale University
Published Jun 30, 2018 on Dryad.
Cite this dataset
Kuebbing, Sara E.; Maynard, Daniel S.; Bradford, Mark A. (2018). Data from: Linking functional diversity and ecosystem processes: a framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species [Dataset]. Dryad. https://doi.org/10.5061/dryad.cb2f5
1.Functional diversity (FD) metrics are widely used to assess invasion ecosystem impacts, but we have limited theory to predict how FD should respond to invasion. A key challenge to effectively using FD metrics is the complexity of conceptualizing alterations to multi-dimensional trait space, making it difficult to select a priori the most appropriate metric for specific ecological questions.
2.Here, we provide expectations on how invasion should change four commonly used FD metrics—functional richness (FRic), evenness (FEve), divergence (FDiv), and dispersion (FDis)—and then test these expectations in a lab decomposition experiment. We simulate invasion of a forest by understory plants by adding leaf litter from 18 natives and nonnatives to a representative canopy tree litter mixture to test changes in FD and decomposition.
3.All four metrics changed predictably with invasion. Species that were more functionally unique or when added at greater proportions had larger impacts on FD. Overall, FRic, FEve, and FDiv were poor choices for understanding impacts of nonnative species. FDis was the only metric that both changed predictably with addition of understory litter and correlated intuitively with changes in carbon mineralization. Furthermore, ranking species based upon how much they changed FDis of the litter mixture provided a fair assessment of which species had the largest impact on decomposition. As such, functional dispersion may be a key tool for predicting a priori which nonnatives will have the greatest impact on ecosystem processes.
4.Synthesis: We highlight the need to assess the suitability of each FD metric for the specific ecological question at hand. Our work reveals the pitfalls of considering multiple metrics or randomly choosing a single metric without suitability assessments. At the same time, it suggests a framework for metric assessment that should help lead to selection of a metric or metrics that provide robust a priori insights into how invasion by nonnative species can impact ecosystem processes.
Kuebbing et al. 2017, Journal of Ecology, Functional Diversity and Invasion Impacts
A full description of this data set can be found in Kuebbing, SE, DS Maynard, and MA Bradford (2017) Linking functional diversity and ecosystem processes: a framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species. Journal of Ecology. DATA SET 1: Brief Methods (for full methods see Kuebbing et al. 2017, Journal of Ecology). We used laboratory microcosm experiments to assess litter decomposition dynamics of litter mixtures. Each microcosm consisted of 1 g of 2 mm-milled leaf litter and 0.25 g of mineral soil in 50-mL centrifuge tubes. Each microcosm contained in equal proportion litter from each of the three dominant canopy trees (Carya cordiformis, Quercus alba, and Q. rubra), in addition to one of the 18 understory plants. The latter were added at three relative abundances (1%, 5%, and 25%). There were 216 microcosms (18 understory plants × 3 proportions [1%, 5%, and 25%] × 4 replicates) and 4 tree-canopy litter control replicates, bringing the total number of microcosms to 220. DATA SET 2: Brief Methods (for full methods see Kuebbing et al. 2017, Journal of Ecology). To assess green leaf traits, we collected 5 green leaves from a minimum of 5 individuals per species in summer 2014. For canopy trees, we “sling-shot sampled” canopy branches to collect full-sun leaves. For understory species, we randomly sampled leaves from individuals. We selected leaves that appeared free of herbivory as well as fungal or bacterial infection. We scanned fully-hydrated leaves on a flatbed scanner within 24 h of collection and then dried them at 60°C for 72 h before measuring mass. To estimate specific leaf area, we measured scanned leaf surface area with digital software (ImageJ v.1.51f) and divided area by dry mass. To assess the green leaf chemical traits, we ball milled the material (Spex CertiPrep 8000-D Mixer Mill, Metuchen, New Jersey, USA) and determined C and N concentrations using Micro-Dumas combustion analysis. To assess leaf litter traits, we collected litter in fall 2014 from senescing plants. We collected freshly-fallen leaves from the ground or shook senesced leaves free from branches. Litter was air-dried and leaves were selected that had no evidence of fungal colonization or herbivory. We milled all litter to pass a 2-mm mesh (Wiley® Cutting Mill, Model 2, Thomas Scientific, Swedesboro, New Jersey, USA), creating a consistent size class across species (Strickland et al. 2009; Keiser et al. 2011; 2013). We used the same method as for green leaves to determine litter total N and C. To assess litter P we used acid digestion and colorimetric analysis of milled litter. To measure lignin we used the ANKOM acid detergent lignin method (ANKOM Technology, Macedon NY; protocol at www.ankom.com). We report total C, N and P on a percent mass basis and acid detergent lignin (ADL) on a per gram dry mass basis.