Dataset Information

Incorporating seascape connectivity in conservation prioritisation.

ABSTRACT: In conservation prioritisation, it is often implicit that representation targets for individual habitat types act as surrogates for the species that inhabit them. Yet for many commercially and ecologically important coral reef fish species, connectivity among different habitats in a seascape may be more important than any single habitat alone. Approaches to conservation prioritisation that consider seascape connectivity are thus warranted. I demonstrate an approach that can be implemented within a relatively data-poor context, using widely available conservation planning software. Based on clearly stated assumptions regarding species' habitat usage and movement ability, this approach can be adapted to different focal species and contexts, or refined as further data become available. I first derive a seascape connectivity metric based on area-weighted proximity between juvenile and adult habitat patches, and then apply this during spatial prioritisation using the decision-support software Marxan. Using a case study from Micronesia, I present two applications: first, to inform prioritisation for a network of marine protected areas to achieve regional objectives for habitat representation; and second, to identify nursery habitat patches that are most likely to supply juveniles to adult populations on reefs within existing protected areas. Incorporating seascape connectivity in conservation prioritisation highlights areas where small marine protected areas placed on coral reefs might benefit from proximity to other habitats in the seascape, and thus be more effective. Within the context of community tenure over resources, identification of critical nursery habitats to improve the effectiveness of existing marine protected areas indicates where collaboration across community boundaries might be required. Outputs from these analyses are likely to be most useful in regions where management is highly decentralised, imposing spatial constraints on the size of individual protected areas.

SUBMITTER: Weeks R

PROVIDER: S-EPMC5533427 | biostudies-other | 2017

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Spatial conservation prioritisation (SCP) is a set of computational tools designed to support the efficient spatial allocation of priority areas for conservation actions, but it is subject to many sources of uncertainty which should be accounted for during the prioritisation process. We quantified the sensitivity of an SCP application (using software Zonation) to possible sources of uncertainty in data-poor situations, including the use of different surrogate options; correction for sampling bias; how to integrate connectivity; the choice of species distribution modelling (SDM) algorithm; how cells are removed from the landscape; and two methods of assigning weights to species (red-list status or prediction uncertainty). Further, we evaluated the effectiveness of the Egyptian protected areas for conservation, and spatially allocated the top priority sites for further on-the-ground evaluation as potential areas for protected areas expansion. RESULTS:Focal taxon (butterflies, reptiles, and mammals), sampling bias, connectivity and the choice of SDM algorithm were the most sensitive parameters; collectively these reflect data quality issues. In contrast, cell removal rule and species weights contributed much less to overall variability. Using currently available species data, we found the current effectiveness of Egypt's protected areas for conserving fauna was low. CONCLUSIONS:For SCP to be useful, there is a lower limit on data quality, requiring data-poor countries to improve sampling strategies and data quality to obtain unbiased data for as many taxa as possible. Since our sensitivity analysis may not generalise, conservation planners should use sensitivity analyses more routinely, particularly relying on more than one combination of SDM algorithm and surrogate group, consider correction for sampling bias, and compare the spatial patterns of predicted priority sites using a variety of settings. The sensitivity of SCP to connectivity parameters means that the responses of each species to habitat loss are important knowledge gaps.

Dataset Information

Incorporating seascape connectivity in conservation prioritisation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets