Identifying fish diversity hot-spots in data-poor situations.

One of the more challenging tasks in Marine Spatial Planning (MSP) is identifying critical areas for management and conservation of fish stocks. However, this objective is difficult to achieve in data-poor situations with different sources of uncertainty. In the present study we propose a combination of hierarchical Bayesian spatial models and remotely sensed estimates of environmental variables to be used as flexible and reliable statistical tools to identify and map fish species richness and abundance hot-spots. Results show higher species aggregates in areas with higher sea floor rugosity and habitat complexity, and identify clear richness hot-spots. Our findings identify sensitive habitats through essential and easy-to-use interpretation tools, such as predictive maps, which can contribute to improving management and operability of the studied data-poor situations.

The use of Local Ecological Knowledge as a complementary approach to understand the temporal and spatial patterns of fishery resources distribution.

BACKGROUND: Acquiring fast and accurate information on ecological patterns of fishery resources is a basic first step for their management. However, some countries may lack the technical and/or the financial means to undergo traditional scientific samplings to get such information; therefore affordable and reliable alternatives need to be sought. METHODS: We compared two different approaches to identify occurrence patterns and catch for three main fish species caught with bottom-set gillnets used by artisanal fishers from northeast Brazil: (1) scientific on-board record data of small-scale fleet (n = 72 trips), and (2) interviews with small-scale fishers on Local Ecological Knowledge (LEK) (n = 32 interviews). We correlated (Pearson correlations) the months cited by fishers (LEK) as belonging to the rainy or to the dry season with observed periods of higher and lower precipitation (SK). The presence of the three main fish species at different depths was compared between LEK and SK by Spearman correlations. Spearman correlations were also used to compare the depths of greatest abundance (with the highest Capture per Unit Effort - CPUE) of these species; the CPUEs were descendly ordered. RESULTS: Both methods provided similar and complementary bathymetric patterns of species occurrence and catch. The largest catches occured in deeper areas, which also happened to be less intensively fished. The preference for fishing in shallower and less productive areas was mostly due to environmental factors, such as weaker currents and less drifting algae at such depths. CONCLUSION: Both on-board and interview methods were accurate and brought complementary information, even though fishers provided faster data when compared to scientific on-board observations. When time and funding are not limited, integrative approaches such as the one presented here are likely the best option to obtain information, otherwise fishers' LEK could be a better choice for when a compromise between speed, reliability and cost needs to be reached.