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Tipping Points in Ecological Networks

Final Report Summary - TIPPEN (Tipping Points in Ecological Networks)

Ecosystems occasionally respond abruptly to small changes in external conditions. Such abrupt responses can represent critical transitions that occur at tipping points where the ecosystem shifts to an alternative regime. Despite the profound consequences of tipping events, our ability to predict them is still limited. To meet this challenge, generic early-warning signals for critical transitions have recently been proposed. These signals are generic, because they may in principle work irrespectively of the specific mechanism responsible for the tipping, making their potential field of application very broad. However, they have so-far been mostly analyzed in simple models that neglect the high spatial and structural complexity that characterizes most ecosystems. The proposed project aims to help bridging this gap between complex reality and simple models by 1) analyzing how critical transitions arise in structurally complex ecological networks, and 2) investigating how this kind of critical transitions might be detected by generic early-warning signals.

In particular, the three specific objectives of TipPEN were:

1) Understand how structure affects the occurrence of alternative states and critical transitions in ecological networks.
If an ecological community (be it species composition of a foodweb or a mutualistic network) can be found in different configurations for the same range of conditions, this has important implications for an additional dimension of the fragility of the system, that of resilience. So far, there is only little theoretical work on the occurrence of alternative states in multispecies ecological communities, like foodwebs. Therefore, in this objective we investigated how differences in architecture influence the occurrence of alternative states and critical transitions in ecological networks.

2) Detect critical transitions in ecological networks using generic early-warning signals.
It is possible that network architecture can affect the performance of early-warning indicators in anticipating tipping points. In this objective, we focused on mutualistic communities to study the performance of early-warning signals in detecting tipping points. Moreover, under this objective our aim was to understand if early-warnings can also hint at the most vulnerable tipping elements in an ecological network.

3) Apply early-warning signals in conservation management of ecological networks.
Early-warning signals may improve conservation management given that they provide reliable and timely information for avoiding a critical transition. In this objective, we tested the practical application of early-warnings under eco-evolutionary conditions. Our intention was to develop both dynamical and structural indicators that can inform the loss of resilience in structurally evolving communities.

We showed how gradual environmental change could lead to abrupt extinctions and complete community collapse in mutualistic networks. We identified the increasing proximity to this collapse by critical slowing down indicators derived from time series abundances at species and community level. We found that it is possible to detect tipping points even in such structurally complex ecological networks.

We studied how structural species traits and changes in patterns of species dynamics can be related to their resilience and risk of extinction in an ecological network. We found that indicators measured at species level before the onset of community collapse were strongly correlated to the timing of extinction of each species. This correlation offers a novel way of mapping species risk to extinction in a given community.

We explored the detection of tipping points in the presence of evolutionary dynamics. We studied how an evolving community of cooperators ran the risk of being invaded by cheaters and shifting to the collapse of cooperation. For such evolutionary induced transitions, we identified the increasing fragility of cooperation based on structural indicators on top of conventional indicators of resilience.

Our results are of more than academic interest, as the complex webs of interactions in nature are under increasing pressure and there is an urgent need to understand how we may estimate the risk of systemic collapses. We demonstrate that early-warnings may help us design appropriate monitoring schemes that could ultimately help us navigate away from critical transitions in ecological networks. Overall, this project has contributed to novel ways of informing nature conservancy practices in ecological networks.