10.5061/DRYAD.K5ST3
Rinella, Matthew J.
Oregon State University
Espeland, Erin K.
United States Department of Agriculture
Moffatt, Bruce J.
Oregon State University
Data from: Studying long-term, large-scale grassland restoration outcomes
to improve seeding methods and reveal knowledge gaps
Dryad
dataset
2017
rangeland
Shrubs
crested wheatgrass
seed rate
Great Plains
reclamation
Agropyron cristatum
compositional data analysis
2017-06-14T00:00:00Z
2017-06-14T00:00:00Z
en
https://doi.org/10.1111/1365-2664.12722
340548 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Studies are increasingly investigating effects of large-scale management
activities on grassland restoration outcomes. These studies are providing
useful comparisons among currently used management strategies, but not the
novel strategies needed to rapidly improve restoration efforts. Here we
illustrate how managing restoration projects adaptively can allow
promising management innovations to be identified and tested. We studied
327 Great Plains fields seeded after coal mining. We modelled plant
responses to management strategies to identify the most effective
previously used strategies for constraining weeds and establishing desired
plants. Then, we used the model to predict responses to new strategies our
analysis identified as potentially more effective. Where established, the
weed crested wheatgrass (Agropyron cristatum L.) increased through time,
indicating a need to manage establishment of this grass. Seeding
particular grasses reduced annual weed cover, and because these grasses
appeared to become similarly abundant whether sown at low or high rates,
low rates could likely be safely used to reduce seeding costs. More
importantly, lower than average grass seed rates increased cover of
shrubs, the plants most difficult to restore to many grassland ecosystems.
After identifying grass seed rates as a driver, we formulated model
predictions for rates below the range managers typically use. These
predictions require testing but indicated atypically low grass seed rates
would further increase shrubs without hindering long-term grass stand
development. Synthesis and applications. Designing management around
empirically based predictions is a logical next step towards improving
ecological restoration efforts. Our predictions are that reducing grass
seed rates to atypically low levels will boost shrubs without compromising
grasses. Because these predictions derive from the fitted model, they
represent quantitative hypotheses based on current understanding of the
system. Generating data needed to test and update these hypotheses will
require monitoring responses to shifts in management, specifically shifts
to lower grass seed rates. A paucity of data for confronting hypotheses
has been a major sticking point hindering adaptive management of most
natural resources, but this need not be the case with degraded grasslands,
because ongoing restoration efforts around the globe are providing
continuous opportunities to monitor and manage processes regulating
grassland restoration outcomes.
predictor and response dataData on environmental and management predictors
and vegetation responses.full dataset formatted for others2.csv
Great Plains
U.S.A