Planning for Persistence in Marine Reserves: A Question of Catastrophic Importance

Abstract

Abstract. Large-scale catastrophic events, although rare, lie generally beyond the control of local management and can prevent marine reserves from achieving biodiversity outcomes. We formulate a new conservation planning problem that aims to minimize the probability of missing conservation targets as a result of catastrophic events. To illustrate this approach we formulate and solve the problem of minimizing the impact of large-scale coral bleaching events on a reserve system for the Great Barrier Reef, Australia. We show that by considering the threat of catastrophic events as part of the reserve design problem it is possible to substantially improve the likely persistence of conservation features within reserve networks for a negligible increase in cost. In the case of the Great Barrier Reef, a 2% increase in overall reserve cost was enough to improve the long-run performance of our reserve network by .60%. Our results also demonstrate that simply aiming to protect the reefs at lowest risk of catastrophic bleaching does not necessarily lead to the best conservation outcomes, and enormous gains in overall persistence can be made by removing the requirement to represent all bioregions in the reserve network. We provide an explicit and well-defined method that allows the probability of catastrophic disturbances to be included in the site selection problem without creating additional conservation targets or imposing arbitrary presence/absence thresholds on existing data. This research has implications for reserve design in a changing climate.

Designing Climate-Resilient Marine Protected Area Networks by Combining Remotely Sensed Coral Reef Habitat with Coastal Multi-Use Maps

“…2015, 7, page-page closure"; (ii) "low compliance and recent closure"; (iii) "most destructive gear restricted"; or (iv) "no gear restricted" based on maps showing the location of protected areas, and expert knowledge on the age of protection, fishing pressure, and existing gear restrictions, (Figure 1). We used the Marxan with Probability [36] spatial prioritization tool to identify spatial priorities for a marine reserve network, while meeting a minimum representation level for all habitat types identified in the habitat maps (Table S4). Marxan with Probability uses a simulated annealing algorithm to identify a set of planning units that fulfill pre-determined quantitative targets for biodiversity features, while minimizing costs, and a probability-based cost from the likelihood of a disturbance emanating from a threat such as climate [36].…”

“…We used the Marxan with Probability [36] spatial prioritization tool to identify spatial priorities for a marine reserve network, while meeting a minimum representation level for all habitat types identified in the habitat maps (Table S4). Marxan with Probability uses a simulated annealing algorithm to identify a set of planning units that fulfill pre-determined quantitative targets for biodiversity features, while minimizing costs, and a probability-based cost from the likelihood of a disturbance emanating from a threat such as climate [36]. Our analyses allowed selection within all possible planning units regardless of the existing fisheries management status associated with the We used the Marxan with Probability [36] spatial prioritization tool to identify spatial priorities for a marine reserve network, while meeting a minimum representation level for all habitat types identified in the habitat maps (Table S4).…”

“…Marxan with Probability uses a simulated annealing algorithm to identify a set of planning units that fulfill pre-determined quantitative targets for biodiversity features, while minimizing costs, and a probability-based cost from the likelihood of a disturbance emanating from a threat such as climate [36]. Our analyses allowed selection within all possible planning units regardless of the existing fisheries management status associated with the We used the Marxan with Probability [36] spatial prioritization tool to identify spatial priorities for a marine reserve network, while meeting a minimum representation level for all habitat types identified in the habitat maps (Table S4). Marxan with Probability uses a simulated annealing algorithm to identify a set of planning units that fulfill pre-determined quantitative targets for biodiversity features, while minimizing costs, and a probability-based cost from the likelihood of a disturbance emanating from a threat such as climate [36].…”

“…Our analyses allowed selection within all possible planning units regardless of the existing fisheries management status associated with the We used the Marxan with Probability [36] spatial prioritization tool to identify spatial priorities for a marine reserve network, while meeting a minimum representation level for all habitat types identified in the habitat maps (Table S4). Marxan with Probability uses a simulated annealing algorithm to identify a set of planning units that fulfill pre-determined quantitative targets for biodiversity features, while minimizing costs, and a probability-based cost from the likelihood of a disturbance emanating from a threat such as climate [36]. Our analyses allowed selection within all possible planning units regardless of the existing fisheries management status associated with the planning units.…”

Addressing longitudinal connectivity in the systematic conservation planning of fresh waters

“…Hereby, if a target was 10, this target is fulfilled both by ten presences of p i =1 being reserved, as well as 20 presences of p i =0.5 being reserved (cf Game et al 2008). We set a general target of 10 planning units, which roughly equates to 70 km of habitat for each species.…”