Gorka Bidegain

Parasite transmission through suspension feeding.

Suspension-feeding bivalve molluscs are confronted with a wide range of materials in the benthic marine environment. These materials include various sized plankton and the organic material derived from it, macroalgae, detritus and a diversity of microbial parasites that have adapted life stages to survive in the water column. For bivalve parasites to infect hosts though, they must first survive and remain infectious in the water column to make initial contact with hosts, and once in contact, enter and overcome elaborate pathways for particle sorting and selection. Even past these defenses, bivalve parasites are challenged with efficient systems of mechanical and chemical digestion and highly evolved systems of innate immunity. Here we review how bivalve parasites evade these hurdles to complete their life cycles and establish within bivalve hosts. We broadly cover significant viral, bacterial, and protozoan parasites of marine bivalve molluscs, and illustrate the emergent properties of these host-parasite systems where parasite transmission occurs through suspension feeding.

Fishing diseased abalone to promote yield and conservation

Past theoretical models suggest fishing disease-impacted stocks can reduce parasite transmission, but this is a good management strategy only when the exploitation required to reduce transmission does not overfish the stock. We applied this concept to a red abalone fishery so impacted by an infectious disease (withering syndrome) that stock densities plummeted and managers closed the fishery. In addition to the non-selective fishing strategy considered by past disease-fishing models, we modelled targeting (culling) infected individuals, which is plausible in red abalone because modern diagnostic tools can determine infection without harming landed abalone and the diagnostic cost is minor relative to the catch value. The non-selective abalone fishing required to eradicate parasites exceeded thresholds for abalone sustainability, but targeting infected abalone allowed the fishery to generate yield and reduce parasite prevalence while maintaining stock densities at or above the densities attainable if the population was closed to fishing. The effect was strong enough that stock and yield increased even when the catch was one-third uninfected abalone. These results could apply to other fisheries as the diagnostic costs decline relative to catch value.

Discrete stochastic marine metapopulation disease model

Some marine microparasitic pathogens can survive several months outside the host in the water column to make contact with hosts or to be absorbed or filtered by hosts. Once inside, pathogens invade the host if they find suitable conditions for for reproduction within the host. This transmission from the environment occurs via pathogens released from infected animals and dead infected animals. Some recent modeling studies concentrated on the disease dynamic imposed by this complex interaction between population and water column at the host-pathogen level in single populations. However, only when a marine disease can be understood at the metapopulation scale effective approaches to management will become routinely achievable. In this paper we explore the disease dynamics at the metapopulation applying a stochastic version. The discrete-time disease model in this paper investigates both spatial and temporal dynamics of hosts and waterborne pathogens in a three patch system. This metapopulation with a patch providing infective particles and susceptible and infected individuals by dispersal tries to imitate the effect of current forces in the ocean on the passive dispersal of organisms. The model detects system behaviors that are not present in single population continuous-time and deterministic models

Distribution Patterns of the Gooseneck Barnacle (Pollicipes pollicipes [Gmelin, 1789]) in the Cantabria Region (N Spain): Exploring Different Population Assessment Methods

ABSTRACT The gooseneck barnacle Pollicipes pollicipes is a very valuable marine resource on the coasts of Spain and Portugal. To maintain the sustainable exploitation of this species, periodical large-scale population assessments are essential. Because of the heterogeneous distribution of these populations in aggregates, together with the difficulties associated with sampling (i.e., access to rocky reefs, wave exposure, high tides, etc.), there is a lack of studies in this regard. In light of these constraints, the coverage, biomass, and available stock of gooseneck barnacle were first estimated using a novel semiquantitative method along a 215-km long coast at 10 fishing zones and three tidal levels. This study contributed to the first assessment of the distribution variability of gooseneck barnacle in the Cantabria region (N Spain), as the first step toward a long-term monitoring goal. The proposed method is based on a general coverage (GC) estimation, by means of (1) quantitative coverage measurements on quadrats (50 cm × 50 cm) located along vertical transects covering the intertidal bandwidth and corrected by tidal level bandwidths, (2) semiquantitative coverage estimates in larger areas, including 5 m on either side of the quadrats along the transect. Biomass samples were collected at each sampling point by scraping the 50 cm × 50 cm quadrat and fresh weight of the samples was measured. This method arrives at the biomass estimates by means of a power regression model for the coverage-biomass relationship. The population distribution pattern along the coast was also explored separately, by commonly used (1) quantitative coverage estimates in quadrats with no bandwidth correction (sample coverage, SC) and (2) semiquantitative estimates, as in the proposed method (transect coverage, TC), both of which included biomass sampling. Biomass and standing stocks values obtained using GC were lower and consumed less sampling time than those obtained by TC, and particularly SC. The results suggest that the proposed method might be suitable for the assessment of P. pollicipes populations in large coastal areas, as it potentially avoids stock overestimation by detecting the spatial distribution heterogeneity and reduces the sampling time.