Advancing restoration ecology: A new approach to predict time to recovery

Abstract

Abstract1. 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: (a) the general form of the relationship between compositional change and time must be known; (b) a sufficiently strong successional gradient must be present and adequately represented in a species compositional dataset; and (c) 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 9 years after disturbance were used to develop models, the subsequent 9 years for validation.3. Rates of compositional recovery in the example dataset 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. 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.

Assessing restoration success by predicting time to recovery—But by which metric?

“…Our results show that successional rates in the four studied spoil heaps decline over time, a common characteristic of successions (Chang et al, ; Foster & Tilman, ; del Moral, Saura, & Emenegger, ; Myster & Pickett, ). Furthermore, we obtain a prominent successional gradient along the closely similar first axes of the GNMDS and DCA ordinations which justifies their use for predicting time to recovery (Rydgren, Halvorsen, et al, ) and ensures that the basic assumption of the ordination‐based approach for predicting time to recovery (ORBA), that a proxy for the successional gradient is available, is satisfied (Rydgren, Halvorsen, et al, ).…”

“…Inclusion of more time points gives more realistic time‐to‐recovery predictions which explains part of the difference between the linear predictions reported here and the results of Rydgren et al (). However, the fact that taking declining successional rates with time into account by applying asymptotic models of successional distance, obtaining predictions for time to full compositional recovery from 115 to 212 years, indicates that the major reason for the difference is that successional rates decrease over time (Foster & Tilman, ; Myster & Pickett, ; Rydgren, Halvorsen, et al, ; Rydgren, Halvorsen, Töpper, & Njøs, ). Although it is not possible from our data to conclude which of the models, linear or asymptotic, that gives the most accurate predictions, two strong arguments point in favour of the asymptotic models.…”

“…We used ORBA to predict time to species compositional recovery (Rydgren, Halvorsen, et al, ). This entailed first extracting the gradient structure of a species compositional dataset by parallel use of detrended correspondence analysis (DCA; Hill & Gauch, ) and global non‐metric multidimensional scaling (GNMDS; Minchin, ) as implemented in the vegan package version 2.3.3 (Oksanen et al, ), see Appendix for specification details.…”

“…Until recently, restoration ecology has lacked proper methods to predict time to recovery for informed evaluation of restoration success. The novel ordination regression based approach (ORBA; Rydgren, Halvorsen, et al, ) facilitates prediction of time to recovery through modelling either linear or asymptotic trajectories of compositional change over time. In particular, the asymptotic model appears promising since it accounts for declining successional rates, a common characteristics of successions (Anderson, ; Rydgren, Økland, & Hestmark, ).…”

Learning from scientific literature: Can indicators for measuring success be standardized in “on the ground” restoration?

“…The complexity of reference states is striking in our study, as the papers included use comparisons to a nondegraded site, a degraded nonrestored site, to the site itself before the restoration, or no comparison at all (in almost one third of the projects). Model predictions can be an additional approach to develop goals, for example, in large projects where controls/references are unavailable (Zedler & Callaway ; Rydgren et al ). However, this alternative was not used in any of the papers in our study.…”