Sympathy for the Devil: Detailing the Effects of Planning-Unit Size, Thematic Resolution of Reef Classes, and Socioeconomic Costs on Spatial Priorities for Marine Conservation

Abstract

Spatial data characteristics have the potential to influence various aspects of prioritising biodiversity areas for systematic conservation planning. There has been some exploration of the combined effects of size of planning units and level of classification of physical environments on the pattern and extent of priority areas. However, these data characteristics have yet to be explicitly investigated in terms of their interaction with different socioeconomic cost data during the spatial prioritisation process. We quantify the individual and interacting effects of three factors—planning-unit size, thematic resolution of reef classes, and spatial variability of socioeconomic costs—on spatial priorities for marine conservation, in typical marine planning exercises that use reef classification maps as a proxy for biodiversity. We assess these factors by creating 20 unique prioritisation scenarios involving combinations of different levels of each factor. Because output data from these scenarios are analogous to ecological data, we applied ecological statistics to determine spatial similarities between reserve designs. All three factors influenced prioritisations to different extents, with cost variability having the largest influence, followed by planning-unit size and thematic resolution of reef classes. The effect of thematic resolution on spatial design depended on the variability of cost data used. In terms of incidental representation of conservation objectives derived from finer-resolution data, scenarios prioritised with uniform cost outperformed those prioritised with variable cost. Following our analyses, we make recommendations to help maximise the spatial and cost efficiency and potential effectiveness of future marine conservation plans in similar planning scenarios. We recommend that planners: employ the smallest planning-unit size practical; invest in data at the highest possible resolution; and, when planning across regional extents with the intention of incidentally representing fine-resolution features, prioritise the whole region with uniform costs rather than using coarse-resolution data on variable costs.

Using individual‐based movement information to identify spatial conservation priorities for mobile species

“…We also standardized planning‐unit size throughout our study region to eliminate additional biases that could influence planning‐unit selection other than shark number and time of year (Cheok et al. ).…”

“…Planning unit size was selected based on the maximum width across the Wenlock and Ducie Rivers, the maximum width of the intertidal area within Port Musgrave and the width of the existing closure of the western shore of Port Musgrave. We also standardized planningunit size throughout our study region to eliminate additional biases that could influence planning-unit selection other than shark number and time of year (Cheok et al 2016).…”

Area requirements to safeguard Earth’s marine species

“…We used a uniform proxy for cost data (area of conservation zones), rather than socio-economic data (e.g. fishing effort), to avoid inaccuracies and errors in socio-economic data biasing selection of planning units ( 27 ).…”

Designing connected marine reserves in the face of global warming

“…While both models are robust and have been validated , testing the effectiveness of downscaling techniques was beyond our scope. Further modeling (Lett et al, 2010) and sensitivity analyses (Cheok et al, 2016, Richardson et al, 2006 to explore different methods to address these data limitations are needed. Additionally, our larval dispersal model could be improved using smaller units, more release sites, modeling major inter-annual variability (e.g., ENSO events), and adjusting larval output based on habitat suitability.…”