Connectivity conservation planning through deep reinforcement learning
Abstract 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 t...
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| Format: | Article |
| Language: | English |
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Wiley
2024-04-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.14300 |
| _version_ | 1797226561451065344 |
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| author | Julián Equihua Michael Beckmann Ralf Seppelt |
| author_facet | Julián Equihua Michael Beckmann Ralf Seppelt |
| author_sort | Julián Equihua |
| collection | DOAJ |
| description | Abstract 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. 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. 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. 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. |
| first_indexed | 2024-04-24T14:26:52Z |
| format | Article |
| id | doaj.art-81f523cff5e34bf79a34e881ba153ee0 |
| institution | Directory Open Access Journal |
| issn | 2041-210X |
| language | English |
| last_indexed | 2024-04-24T14:26:52Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj.art-81f523cff5e34bf79a34e881ba153ee02024-04-03T04:38:59ZengWileyMethods in Ecology and Evolution2041-210X2024-04-0115477979010.1111/2041-210X.14300Connectivity conservation planning through deep reinforcement learningJulián Equihua0Michael Beckmann1Ralf Seppelt2Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ) Leipzig GermanyDepartment of Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ) Leipzig GermanyDepartment of Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ) Leipzig GermanyAbstract 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. 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. 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. 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.https://doi.org/10.1111/2041-210X.14300connectivity conservation planningdeep reinforcement learningecological restorationmachine learningspatial optimisationsystematic conservation planning |
| spellingShingle | Julián Equihua Michael Beckmann Ralf Seppelt Connectivity conservation planning through deep reinforcement learning Methods in Ecology and Evolution connectivity conservation planning deep reinforcement learning ecological restoration machine learning spatial optimisation systematic conservation planning |
| title | Connectivity conservation planning through deep reinforcement learning |
| title_full | Connectivity conservation planning through deep reinforcement learning |
| title_fullStr | Connectivity conservation planning through deep reinforcement learning |
| title_full_unstemmed | Connectivity conservation planning through deep reinforcement learning |
| title_short | Connectivity conservation planning through deep reinforcement learning |
| title_sort | connectivity conservation planning through deep reinforcement learning |
| topic | connectivity conservation planning deep reinforcement learning ecological restoration machine learning spatial optimisation systematic conservation planning |
| url | https://doi.org/10.1111/2041-210X.14300 |
| work_keys_str_mv | AT julianequihua connectivityconservationplanningthroughdeepreinforcementlearning AT michaelbeckmann connectivityconservationplanningthroughdeepreinforcementlearning AT ralfseppelt connectivityconservationplanningthroughdeepreinforcementlearning |