Many approaches have been developed to help conservation practitioners identify where it would be the most effective and cost-efficient to conserve habitat in order to maintain (or enhance) landscape connectivity.
In most cases, habitat patches (or corridors) are ranked according to their relative contributions to landscape connectivity based on network methods. Relative contributions are usually assessed for each patch or corridor independently (e.g. with single-node removal experiments). This means that the cumulative effects that may occur with the simultaneous or sequential loss or gain of several habitat patches are often overlooked.
Hamonic, F., Couëtoux, B., Vaxès, Y. and Albert, C.H. 2023. Cumulative effects on habitat networks: How greedy should we be? Biological Conservation 282: 110066.
Accounting for these cumulative effects in connectivity conservation remains a challenge. Simple algorithms can provide suboptimal solutions in unpredictable ways. As they work sequentially, they are unable to challenge previous decisions on the basis of new decisions. For example, single-node approaches will overlook the potential benefit of connecting large patches through a series of stepping stones.
Our new research proposes an original pipeline to optimize conservation actions that maximizes landscape connectivity under a budget constraint. This new pipeline overcomes previous computational limitations and quantifies cumulative effects by comparing the optimal selection with selections obtained with simpler, frequently-used algorithms (e.g. single-node, greedy algorithms) for networks with up to hundreds of habitat patches. We applied the new optimization pipeline to four case studies across France and Canada that cover a wide range of possible applications.
We provide two key results:
- A comparison in terms of solution quality and computation time which could help practitioners to choose between exact and approximate algorithms; and
- An assessment of the extent to which synergies and redundancies between conservation or restoration options influence prioritization analyses.
We recommend favoring the optimal resolution whenever possible, in particular when the budget is low, when the network is weakly connected, or when conservation options have a significant impact on the least-cost paths of the network. However, when the network is large with a large number of options, the optimal algorithm may become impractical. In this case, we suggest redefining the project, for example, by decomposing it into geographical areas to reduce the size of the network and the number of options. If that is not possible, we suggest using a greedy or single-node algorithm, keeping in mind that the computed solutions will not take into account the cumulative effects.
The new pipeline can be applied to any connectivity conservation or restoration project looking to maximize the amount of reachable habitat for biodiversity. The flexibility of the algorithm allows for a wide range of applications (type of ecosystem and organism, type of network, restoration or conservation, based on patches or corridors, etc.) and could easily be adapted for multi-species cases.
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