Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1111/2041-210x.14300 |
Licence  |
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| Title (Primary) | Connectivity conservation planning through deep reinforcement learning |
| Author | Equihua, J.; Beckmann, M.
; Seppelt, R. |
| Source Titel | Methods in Ecology and Evolution |
| Year | 2024 |
| Department | CLE |
| Volume | 15 |
| Issue | 4 |
| Page From | 779 |
| Page To | 790 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.5281/zenodo.10618900 |
| Keywords | connectivity conservation planning; deep reinforcement learning; ecological restoration; machine learning; spatial optimisation; systematic conservation planning |
| Abstract | 1. The United Nations has declared 2021–2030 the decade on ecosystem
restoration with the aim of preventing, stopping and reversing the
degradation of the ecosystems of the world, often caused by the
fragmentation of natural landscapes. Human activities separate and
surround habitats, making them too small to sustain viable animal
populations or too far apart to enable foraging and gene flow. Despite
the need for strategies to solve fragmentation, it remains unclear how
to efficiently reconnect nature. In this paper, we illustrate the
potential of deep reinforcement learning (DRL) to tackle the spatial
optimisation aspect of connectivity conservation planning. 2. The propensity of spatial optimisation problems to explode in complexity depending on the number of input variables and their states is and will continue to be one of its most serious obstacles. DRL is an emerging class of methods focused on training deep neural networks to solve decision-making tasks and has been used to learn good heuristics for complex optimisation problems. While the potential of DRL to optimise conservation decisions seems huge, only few examples of its application exist. 3. We applied DRL to two real-world raster datasets in a connectivity planning setting, targeting graph-based connectivity indices for optimisation. We show that DRL converges to the known optimums in a small example where the objective is the overall improvement of the Integral Index of Connectivity and the only constraint is the budget. We also show that DRL approximates high-quality solutions on a large example with additional cost and spatial configuration constraints where the more complex Probability of Connectivity Index is targeted. To the best of our knowledge, there is no software that can target this index for optimisation on raster data of this size. 4. DRL can be used to approximate good solutions in complex spatial optimisation problems even when the conservation feature is non-linear like graph-based indices. Furthermore, our methodology decouples the optimisation process and the index calculation, so it can potentially target any other conservation feature implemented in current or future software. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=28756 |
| Equihua, J., Beckmann, M., Seppelt, R. (2024): Connectivity conservation planning through deep reinforcement learning Methods Ecol. Evol. 15 (4), 779 - 790 10.1111/2041-210x.14300 |
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