Dataset for: The hierarchy of predictability in ecological restoration: are vegetation structure and functional diversity more predictable than community composition?

Laughlin, Daniel C. and Strahan, Robert T. and Moore, Margaret M. and Fule, Peter Z. and Huffman, David W. and Covington, William Wallace (2017) Dataset for: The hierarchy of predictability in ecological restoration: are vegetation structure and functional diversity more predictable than community composition? [Dataset] (In Press)

Spreadsheet (Dataset for Laughlin et al. 2017, "The hierarchy of predictability in ecological restoration: are vegetation structure and functional diversity more predictable than community composition?" Journal Applied Ecology) Laughlin_etal_2017_JApplEcol_DATASET.xls - Published Version Download (431kB)

Abstract

1. Predicting restoration outcomes requires an understanding of the natural variability of ecosystem properties. A hierarchy of predictability has been proposed that ranks measures of restoration success from most-to-least predictable in the following order: vegetation structure > taxonomic diversity > functional diversity > taxonomic composition. This hierarchy has not been tested empirically, and the location within the hierarchy of trait-based measures, such as community-level trait means and variances, is not well understood. 2. Our objective was to test the hierarchy of predictability in one of the longest running ecological restoration experiments in the western USA. We used linear mixed effects models to analyze changes in herbaceous biomass, species richness, two functional diversity indices, community-weighted mean traits, and taxonomic composition among experimental restoration treatments from 1992 to 2014 in a ponderosa pine-bunchgrass ecosystem. Restoration treatments included combinations of light or heavy tree thinning and no fire or repeated prescribed fire every four years to release the herbaceous understorey from overstorey competition. 3. Herbaceous biomass and species richness were the two most predictable and least variable measures of success, whereas taxonomic composition exhibited the highest variability among plots through time. Trait-based measures of functional diversity tended to be more predictable and less variable than community-weighted mean trait values in this experiment. Both community-weighted mean trait values and functional diversity were less variable among plots than taxonomic composition. 4. Synthesis and applications. Ecosystem properties that are intrinsically more variable over space and time will often be the least predictable restoration outcomes. Restoration practitioners can expect vegetation structure, species richness, and functional diversity to be more predictable and less variable than taxonomic composition, which can exhibit dynamic responses to restoration treatments over time. Monitoring dominant native and invasive species will always be important, but given the functional redundancy that can occur within communities, strict targets based on composition may rarely be met. Trait-based metrics that integrate taxonomic composition into their calculation are less variable and potentially more meaningful for evaluating ecosystem responses. The hierarchy of predictability should be tested in a range of ecosystems to determine its generality.

Item Type:	Dataset
Related URLs:	Publisher
Keywords:	functional diversity, community-weighted mean trait, restoration ecology, trait-based restoration, functional composition, reference conditions, ponderosa pine, montane forest, natural range of variability
Subjects:	Q Science > QK Botany S Agriculture > SD Forestry
MeSH Subjects:	H Disciplines and Occupations > H01 Natural Science Disciplines
NAU Depositing Author Academic Status:	Faculty/Staff
Department/Unit:	Research Centers > Ecological Restoration Institute College of Engineering, Forestry, and Natural Science > School of Forestry
Date Deposited:	01 May 2017 21:09
URI:	http://openknowledge.nau.edu/id/eprint/2987

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