Ecosystem restoration – insights from theory

This blog post is provided by Klementyna Gawecka and tells the #StoryBehindThePaper for the paper “Habitat restoration in spatially explicit metacommunity models“, which was recently published in Journal of Animal Ecology.

Healthy ecosystems are vital for supporting the great diversity of life on Earth and providing us, humans, with benefits such as clean water, flood mitigation or recreation. But our activities, such as pollution, overexploitation or clearing land for agriculture are degrading ecosystems at an alarming rate. This degradation in turn accelerates the many crises, such as climate change and biodiversity loss, we now hear about every day in the news.

But there is hope. We still have a chance to restore the damaged or lost ecosystems and to replenish the life they harbour and the benefits they provide. With growing support from international organisations and governments, and improving socio-economic conditions in many parts of the world, the opportunities for restoration have never been greater. In fact, the United Nations has declared the current decade (2021-2030) as the Decade on Ecosystem Restoration

Habitat destruction is the major cause of biodiversity loss but restoration is a solution to this problem that has been gaining momentum in the recent decade (Credit: Wikimedia Commons).

The health and stability of ecosystems is maintained and regulated by the diversity of species present. This diversity in turn depends on the interactions between the species within the ecosystem. Therefore, to restore an ecosystem, we must also restore the groups of interacting species (communities), and the ecological processes that result from those interactions.

So where do we start? How do we design a successful restoration project? What strategies do we adopt so that communities recover? The aim of our recent study was to answer these questions using a theoretical approach. We built models which allowed us to disentangle and explain complex processes, and thus to provide rules of thumb for restoration projects.

In our simulations, we divided a landscape into small areas. We started by destroying this landscape a few areas at a time, and then reversed this process by restoring it, again, a few areas at a time. During this process, we allowed species to recolonise the newly restored areas. We explored two different restoration strategies: one where the areas were restored in clusters, and one where they were picked at random. In each case, we considered communities involving different interactions ranging from two-species competition, predation, and mutualism to more complex three- and four- species food webs. To compare the various scenarios, we came up with a new measure of the efficiency of restoration which corresponds to the rate of recovery of species abundance (see figure below).

Changes in species abundance as landscape is destroyed (dashed lines) and restored (solid lines). The figure on the left shows a restoration lag – a scenario where the species abundance during restoration is lower than during destruction. The figure on the right shows the opposite situation with abundance during restoration being higher than during destruction – a restoration boost. R represents the restoration efficiency.

First, we found that the more landscape we destroy, the less efficient and less successful restoration is. This means that, in highly degraded landscapes, we need to restore larger areas than we destroyed to recover the original species abundance. We refer to such scenario as a restoration lag. Moreover, there is a chance that the species will not recover at all. However, we also found that adopting the right restoration strategy can substantially improve this outcome. Restoring areas which are adjacent to undestroyed areas, where the species are still present, is more efficient than restoring areas at random. Essentially, in the former case, the landscape is restored in clusters, minimising fragmentation of the habitable landscape. This strategy can even eliminate the restoration lag, resulting in higher species abundances during restoration than in the original degraded landscape – a restoration boost.

Second, we show that species involved in different interaction types can respond differently to different restoration strategies. Let’s consider two species which compete with each other for the same resource. This could be, for example, tree species which compete for space and nutrients. In this case, restoring areas adjacent to those where one species is present, benefits the recovery of that species but is less favourable for the other species. This means that we may need to make a compromise between the recovery of the two species. As a second example, let’s consider a mutualistic interaction between two species such as a flowering plant and its pollinator. Here, restoring areas adjacent to those where one species is present, has a positive effect on the recovery of not only that species, but also its mutualistic partner. In other words, choosing the best restoration strategy is simpler than for the competitors, as both species benefit equally from restoring the landscape in clusters.

Ecosystem restoration is still a relatively new field. By considering simple communities, our study provides insights into effective design of restoration projects. However, the next step is to consider complex, real-world communities involving more species and types of interactions. Nonetheless, our study shows that the glass is still half full, and if we give it a chance through restoration, “life finds a way” as Dr Ian Malcolm from Jurassic Park used to say.

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