Returns from matching management resolution to ecological variation in a coral reef fishery

Abstract

When managing heterogeneous socioecological systems, decision-makers must choose a spatial resolution at which to define management policies. Complex spatial policies allow managers to better reflect underlying ecological and economic heterogeneity, but incur higher compliance and enforcement costs. To choose the most appropriate management resolution, we need to characterize the relationship between management resolution and performance. We parameterize a model of the commercial coral trout fishery in the Great Barrier Reef, Australia, which is currently managed by a single, spatially homogeneous management policy. We use this model to estimate how the spatial resolution of management policies affect the amount of revenue generated, and assess whether a more spatially complex policy can be justified. Our results suggest that economic variation is likely to be a more important source of heterogeneity than ecological differences, and that the majority of this variation can be captured by a relatively simple spatial management policy. Moreover, while an increase in policy resolution can improve performance, the location of policy changes also needs to align with ecological and socioeconomic variation. Interestingly, the highly complex process of larval dispersal, which plays a critical ecological role in coral reef ecosystem dynamics, may not demand equally complex management policies.

Estimating dispersal kernels using genetic parentage data

“…If decisions depend primarily on the mean distance travelled, then kernels—while imperfect—will still provide useful information. This conclusion mirrors bioeconomic analyses which suggest that kernels capture enough of the underlying dynamics to design efficient harvest strategies (Bode et al., ). On the other hand, our results also revealed that realistic dispersal patterns vary dramatically around the mean‐field estimates provided by kernels.…”

“…In reality, dispersal is a spatiotemporally complex process (Berkeley, Kendall, Mitarai, & Siegel, ; Bode, Bode, & Armsworth, ), shaped by the structure of the land or seascape through which the dispersing organisms move, and strong advective air flows (Nathan et al., ) or water currents (James et al., ). If averaged over a sufficiently long time span, dispersal may approximate a smooth kernel (Cowen & Sponaugle, ), but this does not mean that the ecological and conservation implications of dispersal can be understood using dispersal kernels (Berkeley et al., ; Bode, Bode, & Armsworth, ; Bode, Sanchirico, & Armsworth, ). We therefore assess whether kernels offer a reasonable fit to realistic patterns of larval dispersal.…”

Implications of Policy Adjustment Costs for Fisheries Management

“…[], Bode et al. []). More specifically, our work recognizes that policy makers cannot track ecological dynamics as swiftly as they might like, at least not without incurring significant policy adjustment costs, and examines the implications of this for the resource dynamics and the design of intervention strategies.…”

Size and spacing rules can balance conservation and fishery management objectives for marine protected areas

“…Reef habitats are patchily distributed, and adults of most reef fish species remain closely associated with the reefs on which they settle at the end of a pelagic larval stage (Jones et al., ; Planes, Jones, & Thorrold, ; Samoilys, ). Dispersal was assumed to occur only during the larval stage and upon settlement, larvae attempting to enter the recruiting cohort experience density dependent mortality following a Beverton–Holt relationship (Bode, Sanchirico, & Armsworth, ). Dispersal patterns were stored in seven annual connectivity matrices, generated by a detailed model of larval dispersal that is based on plausible assumptions about coral trout life history, including pelagic larval duration and larval swimming capabilities (Bode, Armsworth, Fox, & Bode, ; James et al., ).…”