In the past, creating protected areas for nature was an ad hoc practice that didn’t approach protecting biodiversity in a strategic way. The result was an uneven representation of biodiversity in protected areas and inefficient use of conservation resources for management of protected areas. In reaction to these practices, the discipline of systematic conservation planning came into being and it has fostered the growth of data-driven and algorithmic approaches to planning protected areas for nature to maximise efficiency. This study has applied such an approach to the conservation priorities of the U.K. government’s planned Nature Recovery Network – which it claims will protect biodiversity while enhancing the services that nature offers to people in England. Our study is seeking to identify which plots of land in England may support some of the government’s stated goals for the NRN: optimization of the provision of pollination and natural flood mitigation, the protection of carbon storage in nature throughout England as well as the protection of habitats for biodiversity features such as plants and insects. We also look at constraints to potential protected areas such as the economic losses of taking cropland out of use to protect conservation features or the government’s ambition that the NRN add to its network of protected areas in England and not subsume them – meaning existing protected areas can serve as a constraint in our study. To examine the feasibility of the government’s goals, this study gathers data sets on conservation features and constraints and processes them to administer the minimum set problem to them using Marine Reserve Design using Spatially Explicit Annealing (Marxan) software to iteratively create solutions that would fulfill the goal of protecting 10% of selected conservation features. This study also made use of a feature of the Marxan software that would prioritise connectivity between connected areas – as the government had also stipulated connectivity between habitats would be a further priority of the NRN. With the priorities of conservation features, constraints and connectivity as inputs, our study enters different combinations of data layers representing these priorities into the software for 16 different combinations of inputs to serve as varied scenarios of priorities. The study is interested to see which land parcels – or planning units – would be selected for conservation most often by the software as it iteratively creates varying solutions in order to fulfill the aims of the NRN based on different combinations of data inputs. We hypothesised that the configuration of frequently selected planning units would change as we entered different combinations of data layer inputs for the software to formulate solutions from. We found this was the case – though it was nearly imperceptible in some scenarios. We also found that inputting constraints with conservation features into the software would often produce clearer and more varied patterns of frequently selected planning units than entering conservation features alone. Our study demonstrates the feasibility of inexpensive, data-driven conservation planning support methods - even over large geographic areas – because it resulted in patterns of frequently selected planning units in certain scenarios.
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Raffety, Jerret Scott
Rights Holders: Raffety, Jerret Scott Supervisors: Long, Peter
Department of Biological and Medical Sciences
MSc Conservation Ecology
2023
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