Contrasting patterns of population connectivity between regions in a commercially important mollusc Haliotis rubra: integrating population genetics, genomics and marine LiDAR data.

Estimating contemporary genetic structure and population connectivity in marine species is challenging, often compromised by genetic markers that lack adequate sensitivity, and unstructured sampling regimes. We show how these limitations can be overcome via the integration of modern genotyping methods and sampling designs guided by LiDAR and SONAR data sets. Here we explore patterns of gene flow and local genetic structure in a commercially harvested abalone species (Haliotis rubra) from southeastern Australia, where the viability of fishing stocks is believed to be dictated by recruitment from local sources. Using a panel of microsatellite and genomewide SNP markers, we compare allele frequencies across a replicated hierarchical sampling area guided by bathymetric LiDAR imagery. Results indicate high levels of gene flow and no significant genetic structure within or between benthic reef habitats across 1400 km of coastline. These findings differ to those reported for other regions of the fishery indicating that larval supply is likely to be spatially variable, with implications for management and long-term recovery from stock depletion. The study highlights the utility of suitably designed genetic markers and spatially informed sampling strategies for gaining insights into recruitment patterns in benthic marine species, assisting in conservation planning and sustainable management of fisheries.

Survey estimates of fishable biomass following a mass mortality in an Australian molluscan fishery.

Mass mortality events are relatively uncommon in commercially fished populations, but when they occur, they reduce production and degrade ecosystems. Observing and documenting mass mortalities is simpler than quantifying the impact on stocks, monitoring or predicting recovery, and re-establishing commercial fishing. Direct survey measures of abundance, distribution and harvestable biomass provide the most tenable approach to informing decisions about future harvests in cases where stock collapses have occurred because conventional methods have been disrupted and are less applicable. Abalone viral ganglioneuritis (AVG) has resulted in high levels of mortality across all length classes of blacklip abalone, Haliotis rubra Leach, off western Victoria, Australia, since May 2006. Commercial catches in this previously valuable fishery were reduced substantially. This paper describes the integration of research surveys with commercial fishermen's knowledge to estimate the biomass of abalone on AVG-impacted reefs. Experienced commercial abalone divers provided credible information on the precise locations of historical fishing grounds within which fishery-independent surveys were undertaken. Abalone density estimates remained low relative to pre-AVG levels, and total biomass estimates were similar to historical annual catch levels, indicating that the abalone populations have yet to adequately recover. Survey biomass estimates were incorporated into harvest decision tables and used with prior accumulated knowledge of the populations to determine a conservative harvest strategy for the fishery.

Parameterization of European perch Perca fluviatilis length-at-age data using stochastic Gompertz growth models.

Three stochastic versions of the Gompertz growth model were used to parameterize total length (L(T) )-at-age data for perch Perca fluviatilis, an important target species for commercial and recreational fishers and a food species for predatory fishes and aquatic birds. Each model addresses growth heterogeneity by incorporating random parameters from a specific positive distribution: Weibull, gamma or log-normal. The modelling outputs for each version of the model provide L(T) distributions for selected ages and percentiles of L(T) at age for both males and females. The results highlight the importance of using a stochastic approach and the logistic-like growth pattern for analysing growth data for P. fluviatilis in Curonian Lagoon (Lithuania). Outputs from this modelling can be extended to a stochastic analysis of fish cohort dynamics, incorporating all length-based biological relationships, and the selectivity-related interactions between fish cohorts and fishing gear.