Coastal runoff effects on an exploited Hawaiian fishery


Meeting Abstract

P1.171  Saturday, Jan. 4 15:30  Coastal runoff effects on an exploited Hawaiian fishery COCKETT, PM*; HOGAN, JD; GURSKI, LM; PENNOYER, K; BIRD, CE; Texas A&M University – Corpus Christi; Texas A&M University – Corpus Christi; Texas A&M University – Corpus Christi; Texas A&M University – Corpus Christi; Texas A&M University – Corpus Christi pcockett@islander.tamucc.edu

Coastal pollution from fresh water run-off has been implicated in altering the population structure of invertebrates and reducing genetic diversity and adaptive capacity. Overharvested fisheries can be particularly vulnerable to anthropogenic impacts on coastal runoff due to reductions in genetic diversity and resiliency in response to stressors such as sewage, pesticides, fertilizers, industrial chemicals and other contaminants. Hawaiian broadcast-spawning limpets, Cellana exarata, are subject to varying levels of harvesting pressure on different islands, ranging from a reduction in fishery output on Maui and Kaua’i to extermination on O’ahu, the most populous of the Hawaiian Islands. Previous analyses of neutral genetic variation in mtDNA and nDNA indicate that gene flow is restricted among islands, indicating that decimated O’ahu populations could experience reductions in adaptively advantageous genetic variation. Here, we use genome-wide surveys of genetic variation on Kaua’i, Maui, and O’ahu to test for adaptive differences among limpets on open-ocean coastlines, coastlines near unpolluted stream outflows, and coastlines near polluted stream outflows (2 replicates per island, 48 individuals per sample). Our data confirm previous conclusions of neutral gene flow restrictions among main Hawaiian Islands, and we identified some reduction in genetic diversity on O’ahu. We also detected significant genetic partitioning among locations within islands, although there is only weak support for an effect of stream outflow on limpet genetic composition. We conclude the population genomic analysis shows great promise for detecting spatial patterns on scales of km or less.

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