Data from: Advancing restoration ecology: a new approach to predict time to recovery

1. Species composition is a vital attribute of any ecosystem. Accordingly,
ecological restoration often has the original, or ‘natural’, species
composition as its target. However, we still lack adequate methods for
predicting the expected time to compositional recovery in restoration
studies. 2. We describe and explore a new, ordination regression-based
approach (ORBA) for predicting time to recovery that allows both linear
and asymptotic (logarithmic) relationships of compositional change with
time. The approach uses distances between restored plots and reference
plots along the successional gradient, represented by a vector in
ordination space, to predict time to recovery. Thus, the approach rests on
three requirements: (1) the general form of the relationship between
compositional change and time must be known; (2) a sufficiently strong
successional gradient must be present and adequately represented in a
species compositional data set; and (3) a restoration target must be
specified. We tested the approach using data from a boreal old-growth
forest that was followed for 18 years after experimental disturbance. Data
from the first nine years after disturbance were used to develop models,
the subsequent nine years for validation. 3. Rates of compositional
recovery in the example data set followed the general pattern of decrease
with time since disturbance. Accordingly, linear models were too
optimistic about the time to recovery whereas the asymptotic models
provided more precise predictions. 4. Synthesis and applications. Our
results demonstrate that the new approach opens for reliable prediction of
recovery rates and time to recovery using species compositional data.
Moreover, it allows us to assess whether recovery proceeds in the desired
direction and to quantitatively compare restoration speed, and hence
effectiveness, between alternative management options.