Optimizing the ecological connectivity of landscapes

Abstract

In this paper we consider the problem of optimizing the ecological connectivity of a landscape under a budget constraint by improving habitat areas and ecological corridors between them. We consider a formulation of this problem in terms of graphs in which vertices represent the habitat areas and arcs represent a probability of connection between two areas that depend on the quality of the respective corridor. We propose a new generalized flow model that improves existing models for this problem and an efficient preprocessing algorithm that reduces the size of the graphs on which generalized flows is computed.Reported numerical experiments highlight the benefice of these two contributions on computation times and show that larger problems can be solved using them. Our experiments also show that several variants of greedy algorithms perform relatively well on practical instances while they return arbitrary bad solutions in the worst case.

Connectivity conservation planning through deep reinforcement learning

“…As mentioned in Hamonic et al (2023), the brute-force approach proposed in Rubio et al (2015) would naturally eventually find global optimums but was shown to be impractical for landscapes with more than 20 habitat patches, and the mixed integer approach proposed in Xue et al (2017) did not scale to landscapes with more than a few hundred pixels on a grid dataset. In Hamonic et al (2023), the PC was targeted by modelling the problem with discrete optimisation directly on graph representations. They propose an ingenious problem simplification, which allows the approximation of good solutions to even large problems.…”

“…As mentioned in Hamonic et al. (2023), the brute‐force approach proposed in Rubio et al. (2015) would naturally eventually find global optimums but was shown to be impractical for landscapes with more than 20 habitat patches, and the mixed integer approach proposed in Xue et al.…”

“…In Hamonic et al. (2023), the PC was targeted by modelling the problem with discrete optimisation directly on graph representations. They propose an ingenious problem simplification, which allows the approximation of good solutions to even large problems.…”

“…Xue et al (2017) proposed a classical mixed integer program formulation to optimise PCI on a general graph; they chose a set of edges to protect in order to best maintain connectivity. In Hamonic et al (2023) the problem of optimising the PCI of a landscape was considered under a restricted budget. This was modelled as a discrete optimisation problem directly on graph representations.…”

“…In Hamonic et al. (2023) the problem of optimising the PCI of a landscape was considered under a restricted budget. This was modelled as a discrete optimisation problem directly on graph representations.…”