Associate Editor, Lars Brudvig looks at the recently published Review, Advancing restoration ecology: A new approach to predict time to recovery by Rydgren et al.
Restoring degraded ecosystems is a global priority, hailed for its potential to recover biodiversity and promote ecosystem functioning and services. Yet successful restoration doesn’t happen overnight. It may take years, decades, or longer for restoration projects to meet their goals and so tools are needed that can accurately forecast this time to recovery. A new paper by Knut Rydgren and colleagues develops and validates such an approach for forecasting the recovery of community composition, during restoration.
They begin by developing a statistical technique to predict how long after disturbance it will take for plant community composition to recover to that of an undisturbed reference composition. The authors’ approach is based on ordination but with a twist. After ordinating community composition, they then regress ordination scores against time since disturbance. Then, based on the slope and shape of this relationship, they predict how long into the future full community recovery will occur.
Next, they validate this statistical technique using an 18-year time series of plant community composition, following experimental disturbance in old-growth boreal forest from Norway. Using data collected for nine years after disturbance, they parameterize a model to forecast recovery, relative to undisturbed community composition. They then test the predictions of this model using data from the next nine years of recovery. Their model has accurately forecasted rates and levels of recovery, but only when they accounted for the non-linear rate of recovery: communities did recover toward reference conditions, but the rate of recovery slowed over time.
This paper represents an important advance in restoration ecology. There are pressing needs to develop predictive capacity in restoration and, while community composition is a common focus during ecological restoration, it is also a notoriously idiosyncratic property of ecosystems. Looking ahead, it will be exciting to see restoration and other community ecologists employ Rydgren and colleagues’ approach in new settings and systems, during efforts to predict (and then evaluate predictions of) community recovery. Doing so will further validate this statistical technique, more broadly explore the timescales over which communities recover during restoration, evaluate how competing techniques might minimize recovery times, and explore systems for which complete recovery does not occur.
Read the full open access Review, Advancing restoration ecology: A new approach to predict time to recovery in Journal of Applied Ecology.