Rapid seafloor mapping of the northern Galapagos Islands, Darwin and Wolf

Darwin and Wolf are the most remote of the Galapagos islands and are famous for their remarkable pelagic and benthic marine species abundance and diversity. However, little is known about their surrounding bathymetry. Rapid surveys were carried out in 2008 and 2009 to collect geo-referenced depth soundings down to 100 m around both islands, as a step towards a better understanding of their habitat and species distribution. Five spatial interpolation methods were tested on the data, to find the most accurate. The Triangular Irregular Network (TIN) was the best interpolator for these data sets with the fewest interpolation errors, and was then used to create contour and three dimensional maps of the seafloor topography of both islands. Darwin has a bigger insular platform with gentle submarine slopes whereas Wolf has very steep slopes with a smaller platform

Genetic identification of three CITES-listed sharks using a paper-based Lab-on-a-Chip (LOC)

Threatened shark species are caught in large numbers by artisanal and commercial fisheries and traded globally. Monitoring both which shark species are caught and sold in fisheries, and the export of CITES-restricted products, are essential in reducing illegal fishing. Current methods for species identification rely on visual examination by experts or DNA barcoding techniques requiring specialist laboratory facilities and trained personnel. The need for specialist equipment and/or input from experts means many markets are currently not monitored. We have developed a paper-based Lab-on-a-Chip (LOC) to facilitate identification of three threatened and CITES-listed sharks, bigeye thresher (Alopias superciliosus), pelagic thresher (A. pelagicus) and shortfin mako shark (Isurus oxyrinchus) at market source. DNA was successfully extracted from shark meat and fin samples and combined with DNA amplification and visualisation using Loop Mediated Isothermal Amplification (LAMP) on the LOC. This resulted in the successful identification of the target species of sharks in under an hour, with a working positive and negative control. The LOC provided a simple “yes” or “no” result via a colour change from pink to yellow when one of the target species was present. The LOC serves as proof-of-concept (PoC) for field-based species identification as it does not require specialist facilities. It can be used by non-scientifically trained personnel, especially in areas where there are suspected high frequencies of mislabelling or for the identification of dried shark fins in seizures

Fig 3 -

Entry distance to the IEEZ and the GMR per (A) flag state, (B) gear type, (C) season, and (D) ecoregion. The segmented red line represents the boundary between the IEEZ and the GMR (0 to 296 km corresponds to IEEZ; 296 to 500 km corresponds to the edge of the GMR towards its interior). Data from ‘Unknown flags’ were not used in these plots.</p

Percent of overlap of core effort areas (50% kernels) and spatial extent of fishing activities (95% kernels) by fleets.

Percent of overlap of core effort areas (50% kernels) and spatial extent of fishing activities (95% kernels) by fleets.</p

Characteristics of Galápagos Islands Ecoregions based on Spalding, Fox [38].

Oceanographic characteristics taken from: Dirección del Parque Nacional Galápagos [40], Liu, Xie [37] and Sullivan and Bustamante [41]. Area estimated in km2 using R Core Team [42].</p

Fig 2 -

Fishing effort distribution in the IEEZ and the GMR per (A) flag state, (B) gear type, (C) season, and (D) ecoregion. Data from ‘Unknown flags’ were only used in flag state plot. The y-axis was standardized to 15 hours for better visual representation of the data. The outliers for ECU, PAN, UNK, POLE, SETL, TUNA, Cold, Warm, Eastern and Western are over 25 hours.</p

Spatial distribution of fishing effort by fishing gear.

Fishing gears displayed here are those used by both fleets and that recorded enough information during the study period. Data from ‘Unknown flags’ was not used in these plots.</p

Percent of overlap of core effort areas (50% kernels) and spatial extent of fishing activities (95% kernels) by gear types.

Percent of overlap of core effort areas (50% kernels) and spatial extent of fishing activities (95% kernels) by gear types.</p

Summary statistics for fishing effort per vessel flag as seen from AIS data.

Abbreviations: N, number of vessels; Cells, number of grid cells (0.01 decimal degrees) associated with fishing activity; Sum, the quantity of fishing hours per category; Mean, the arithmetic mean; sd, the standard deviation. Only the flag state category contains information on ‘Unknown flags’ (UNK).</p