4.7 Article

Can functional leaf traits be used for monitoring wetland restoration? A comparison between commonly used monitoring metrics and functional leaf traits

Journal

ECOLOGICAL INDICATORS
Volume 140, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.ecolind.2022.109032

Keywords

Functional traits; Wetland restoration; Monitoring metrics

Funding

  1. Midwest-Great Lakes Chapter of the Society for Ecological Restoration
  2. Illinois Native Plant Society
  3. University of Illinois, College of ACES, Office of Research
  4. National Institute of Food and Agriculture, U.S. Department of Agriculture, under Hatch project [1018621]

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This study aimed to explore the relationship between abiotic factors influencing wetland community composition and functional leaf traits as well as standard vegetation monitoring metrics. The results suggest that specific leaf area (SLA) and leaf dry matter content (LDMC) may not be suitable as monitoring tools in Midwestern floodplain wetlands, and existing monitoring tools may already be sufficient in reflecting abiotic conditions.
Monitoring is essential to restoration, but the standard metrics used to monitor wetland restoration do not explicitly account for function in plant communities. Functional traits may be a useful addition to the wetland monitoring toolkit, because they can represent aspects of ecosystem functioning that standard metrics may not. Our objective was to determine how abiotic factors that influence wetland community composition and structure relate to both functional leaf traits and standard vegetation monitoring metrics to determine if functional leaf traits could add a functional component to wetland monitoring. We surveyed 66 100-m(2) plots in 22 floodplain wetlands in Illinois that were restored between 1997 and 2010. We used plant species data to calculate the mean coefficient of conservatism (mean C) and richness, and collected leaves to determine community weighted means of specific leaf area (SLA) and leaf dry matter content (LDMC) at each plot. Hydrologic data were used to calculate variables related to frequency, depth, and duration of inundation, and soil samples were collected to determine soil pH, organic matter content, and nitrogen and phosphorous content at each plot. We used struc-tural equation models to understand how predictor variables (hydrological variables, soil variables, canopy cover, time since restoration, and latitude) influenced each other, and ultimately how they influenced response variables (mean C, richness, percent non-native species cover, SLA, and LDMC). LDMC and SLA were poorly explained by predictor variables and had relatively few significant relationships within models. Predictor variables best explained variance in mean C, followed by percent non-native cover, richness, then LDMC and SLA. Mean C was positively influenced by canopy cover and negatively influenced by soil fertility, whereas richness was negatively influenced by latitude. There was a strong latitudinal gradient of species richness from north to south, such that southern plots were significantly more diverse and had less cover by non-native species than northern plots. Our research suggests that SLA and LDMC may not be well suited for use as wetland restoration monitoring tools in Midwestern floodplain wetlands, and that monitoring tools already in place may sufficiently reflect abiotic conditions.

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