Interactive Marine Spatial Planning: Siting Tidal Energy Arrays around the Mull of Kintyre

The rapid development of the offshore renewable energy sector has led to an increased requirement for Marine Spatial Planning (MSP) and, increasingly, this is carried out in the context of the ‘ecosystem approach’ (EA) to management. We demonstrate a novel method to facilitate implementation of the EA. Using a real-time interactive mapping device (touch-table) and stakeholder workshops we gathered data and facilitated negotiation of spatial trade-offs at a potential site for tidal renewable energy off the Mull of Kintyre (Scotland). Conflicts between the interests of tidal energy developers and commercial and recreational users of the area were identified, and use preferences and concerns of stakeholders were highlighted. Social, cultural and spatial issues associated with conversion of common pool to private resource were also revealed. The method identified important gaps in existing spatial data and helped to fill these through interactive user inputs. The workshops developed a degree of consensus between conflicting users on the best areas for potential development suggesting that this approach should be adopted during MSP

Proxy Measures of Fitness Suggest Coastal Fish Farms Can Act as Population Sources and Not Ecological Traps for Wild Gadoid Fish

Background: Ecological traps form when artificial structures are added to natural habitats and induce mismatches between habitat preferences and fitness consequences. Their existence in terrestrial systems has been documented, yet little evidence suggests they occur in marine environments. Coastal fish farms are widespread artificial structures in coastal ecosystems and are highly attractive to wild fish. Methodology/Principal Findings: To investigate if coastal salmon farms act as ecological traps for wild Atlantic cod (Gadus morhua) and saithe (Pollachius virens), we compared proxy measures of fitness between farm-associated fish and control fish caught distant from farms in nine locations throughout coastal Norway, the largest coastal fish farming industry in the world. Farms modified wild fish diets in both quality and quantity, thereby providing farm-associated wild fish with a strong trophic subsidy. This translated to greater somatic (saithe: 1.06–1.12 times; cod: 1.06–1.11 times) and liver condition indices (saithe: 1.4–1.8 times; cod: 2.0–2.8 times) than control fish caught distant from farms. Parasite loads of farm-associated wild fish were modified from control fish, with increased external and decreased internal parasites, however the strong effect of the trophic subsidy overrode any effects of altered loads upon condition. Conclusions and Significance: Proxy measures of fitness provided no evidence that salmon farms function as ecological traps for wild fish. We suggest fish farms may act as population sources for wild fish, provided they are protected from fishing while resident at farms to allow their increased condition to manifest as greater reproductive output.Funding was provided by the Norwegian Research Council Havet og kysten program to the CoastACE project (no: 173384)

Removing the bad apples: A simple bioinformatic method to improve loci-recovery in de novo RADseq data for non-model organisms

1. The restriction site-associated DNA (RADseq) family of protocols involves digesting DNA and sequencing the region flanking the cut site, thus providing a cost and time-efficient way for obtaining thousands of genomic markers. However, when working with non-model taxa with few genomic resources, optimization of RADseq wet-lab and bioinformatic tools may be challenging, often resulting in allele dropout—that is when a given RADseq locus is not sequenced in one or more individuals resulting in missing data. Additionally, as datasets include divergent taxa, rates of dropout will increase since restriction sites may be lost due to mutation. Mitigating the impacts of allele dropout is crucial, as missing data may lead to incorrect inferences in population genetics and phylogenetics. 2. Here, we demonstrate a simple pipeline for the optimization of RADseq datasets which involves partitioning datasets into subgroups, namely by reducing and analysing the dataset at a population or species level. By running the software Stacks at a subgroup level, we were able to reliably identify and remove individuals with high levels of missing data (bad apples) likely stemming from artefacts in library preparation, DNA quality or sequencing artefacts. 3. Removal of the bad apples generally led to an increase in loci and decrease in missing data in the final datasets. 4. The biological interpretability of the data, as measured by the number of retrieved loci and missing data, was considerably increased

Removing the bad apples: A simple bioinformatic method to improve loci-recovery in de novo RADseq data for non-model organisms

1. The restriction site-associated DNA (RADseq) family of protocols involves digesting DNA and sequencing the region flanking the cut site, thus providing a cost and time-efficient way for obtaining thousands of genomic markers. However, when working with non-model taxa with few genomic resources, optimization of RADseq wet-lab and bioinformatic tools may be challenging, often resulting in allele dropout—that is when a given RADseq locus is not sequenced in one or more individuals resulting in missing data. Additionally, as datasets include divergent taxa, rates of dropout will increase since restriction sites may be lost due to mutation. Mitigating the impacts of allele dropout is crucial, as missing data may lead to incorrect inferences in population genetics and phylogenetics. 2. Here, we demonstrate a simple pipeline for the optimization of RADseq datasets which involves partitioning datasets into subgroups, namely by reducing and analysing the dataset at a population or species level. By running the software Stacks at a subgroup level, we were able to reliably identify and remove individuals with high levels of missing data (bad apples) likely stemming from artefacts in library preparation, DNA quality or sequencing artefacts. 3. Removal of the bad apples generally led to an increase in loci and decrease in missing data in the final datasets. 4. The biological interpretability of the data, as measured by the number of retrieved loci and missing data, was considerably increased

Direct Determination of Chitosan–Mucin Interactions Using a Single-Molecule Strategy: Comparison to Alginate–Mucin Interactions

Aqueous chitosan possesses attractive interaction capacities with various molecular groups that can be involved in hydrogen bonds and electrostatic and hydrophobic interactions. In the present paper, we report on the direct determination of chitosan–mucin molecular pair interactions at various solvent conditions as compared to alginate–mucin interactions. Two chitosans of high molecular weight with different degrees of acetylation—thus possessing different solubility profiles in aqueous solution as a function of pH and two alginates with different fractions of α-guluronic acid were employed. The interaction properties were determined through a direct unbinding assay at the single-molecular pair level using an atomic force microscope. When probed against immobilized mucin, both chitosans and alginates revealed unbinding profiles characteristic of localized interactions along the polymers. The interaction capacities and estimated parameters of the energy landscapes of the pairwise chitosan–mucin and alginate–mucin interactions are discussed in view of possible contributions from various fundamental forces. Signatures arising both from an electrostatic mechanism and hydrophobic interaction are identified in the chitosan–mucin interaction properties. The molecular nature of the observed chitosan–mucin and alginate–mucin interactions indicates that force spectroscopy provides fundamental insights that can be useful in understanding the surface binding properties of other potentially mucoadhesive polymers