A risk assessment and prioritisation approach to the selection of indicator species for the assessment of multi-species, multi-gear, multi-sector fishery resources

Assessing the stock status of mixed and/or multi-species fishery resources is challenging. This is especially true in highly diverse systems, where landed catches are small, but comprise many species. In these circumstances, whole-of-ecosystem management requires consideration of the impact of harvesting on a plethora of species. However, this is logistically infeasible and cost prohibitive. To overcome this issue, selected ‘indicator’ species are used to assess the risk to sustainability of all ‘like’ species susceptible to capture within a fishery resource. Indicator species are determined via information on their (1) inherent vulnerability, i.e. biological attributes; (2) risk to sustainability, i.e. stock status; and (3) management importance, i.e. commercial prominence, social and/or cultural amenity value of the resource. These attributes are used to determine an overall score for each species which is used to identify ‘indicator’ species. The risk status (i.e. current risk) of the indicator species then determines the risk-level for the biological sustainability of the entire fishery resource and thus the level of priority for management, monitoring, assessment and compliance. A range of fishery management regimes are amenable to the indicator species approach, including both effort limited fisheries (e.g. individually transferable effort systems) and output controlled fisheries (e.g. species-specific catch quotas). The indicator species approach has been used and refined for fisheries resources in Western Australia over two decades. This process is now widely understood and accepted by stakeholders, as it focuses fishery dependent- and/or independent-monitoring, biological sampling, stock assessment and compliance priorities, thereby optimising the use of available jurisdictional resources

Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci

To identify genetic factors contributing to type 2 diabetes (T2D), we performed large-scale meta-analyses by using a custom similar to 50,000 SNP genotyping array (the ITMAT-Broad-CARe array) with similar to 2000 candidate genes in 39 multiethnic population-based studies, case-control studies, and clinical trials totaling 17,418 cases and 70,298 controls. First, meta-analysis of 25 studies comprising 14,073 cases and 57,489 controls of European descent confirmed eight established T2D loci at genome-wide significance. In silico follow-up analysis of putative association signals found in independent genome-wide association studies (including 8,130 cases and 38,987 controls) performed by the DIAGRAM consortium identified a T2D locus at genome-wide significance (GATAD2A/CILP2/PBX4; p = 5.7 x 10(-9)) and two loci exceeding study-wide significance (SREBF1, and TH/INS; p < 2.4 x 10(-6)). Second, meta-analyses of 1,986 cases and 7,695 controls from eight African-American studies identified study-wide-significant (p = 2.4 x 10(-7)) variants in HMGA2 and replicated variants in TCF7L2 (p = 5.1 x 10(-15)). Third, conditional analysis revealed multiple known and novel independent signals within five T2D-associated genes in samples of European ancestry and within HMGA2 in African-American samples. Fourth, a multiethnic meta-analysis of all 39 studies identified T2D-associated variants in BCL2 (p = 2.1 x 10(-8)). Finally, a composite genetic score of SNPs from new and established T2D signals was significantly associated with increased risk of diabetes in African-American, Hispanic, and Asian populations. In summary, large-scale meta-analysis involving a dense gene-centric approach has uncovered additional loci and variants that contribute to T2D risk and suggests substantial overlap of T2D association signals across multiple ethnic groups