Reduced Lepeophtheirus salmonis larval abundance in a sea loch on the west coast of Scotland between 2002 and 2006.

A survey for planktonic sea louse larvae was carried out in Loch Shieldaig, Scotland, between 2002 and 2006, and spanned 2 successive production cycles (Cycles 1 and 2) at a local Atlantic salmon Salmo salar L. farm. The vast majority of the caligid copepodids recovered were Lepeophtheirus salmonis; however, the methodology was unable to determine the species of the caligid nauplii. Greatest densities of nauplii were found at the sampling station adjacent to the salmon farm, and larval densities were low during the fallow period of both cycles. Peaks in nauplius densities occurred around the same time in the 2 cycles, but the peaks were significantly lower during Cycle 2 than Cycle 1. Lepeophtheirus salmonis copepodid densities varied temporally, but not spatially. During most of Cycle 2, copepodid densities were significantly lower than those recovered during Cycle 1. Numbers of gravid L. salmonis at the local salmon farm correlated significantly with densities of louse nauplii and L. salmonis copepodids in the water at time lags of 0 and 1 wk, and 1 and 2 wk, respectively. This survey demonstrated a reduction in densities of L. salmonis larvae in the plankton (an indication of L. salmonis infectious pressure) between the 2 cycles and indicated that the farm was an important source of L. salmonis larvae. The application of anti-louse treatments using emamectin benzoate reduced the numbers of gravid L. salmonis at the farm, and this was the main factor influencing the apparent reduction in L. salmonis infectious pressure in the loch between cycles.

A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change.

The thermal suitability of riverine habitats for cold water adapted species may be reduced under climate change. Riparian tree planting is a practical climate change mitigation measure, but it is often unclear where to focus effort for maximum benefit. Recent developments in data collection, monitoring and statistical methods have facilitated the development of increasingly sophisticated river temperature models capable of predicting spatial variability at large scales appropriate to management. In parallel, improvements in temporal river temperature models have increased the accuracy of temperature predictions at individual sites. This study developed a novel large scale spatio-temporal model of maximum daily river temperature (Twmax) for Scotland that predicts variability in both river temperature and climate sensitivity. Twmax was modelled as a linear function of maximum daily air temperature (Tamax), with the slope and intercept allowed to vary as a smooth function of day of the year (DoY) and further modified by landscape covariates including elevation, channel orientation and riparian woodland. Spatial correlation in Twmax was modelled at two scales; (1) river network (2) regional. Temporal correlation was addressed through an autoregressive (AR1) error structure for observations within sites. Additional site level variability was modelled with random effects. The resulting model was used to map (1) spatial variability in predicted Twmax under current (but extreme) climate conditions (2) the sensitivity of rivers to climate variability and (3) the effects of riparian tree planting. These visualisations provide innovative tools for informing fisheries and land-use management under current and future climate.

An Applied Framework for Incorporating Multiple Sources of Uncertainty in Fisheries Stock Assessments.

Estimating fish stock status is very challenging given the many sources and high levels of uncertainty surrounding the biological processes (e.g. natural variability in the demographic rates), model selection (e.g. choosing growth or stock assessment models) and parameter estimation. Incorporating multiple sources of uncertainty in a stock assessment allows advice to better account for the risks associated with proposed management options, promoting decisions that are more robust to such uncertainty. However, a typical assessment only reports the model fit and variance of estimated parameters, thereby underreporting the overall uncertainty. Additionally, although multiple candidate models may be considered, only one is selected as the 'best' result, effectively rejecting the plausible assumptions behind the other models. We present an applied framework to integrate multiple sources of uncertainty in the stock assessment process. The first step is the generation and conditioning of a suite of stock assessment models that contain different assumptions about the stock and the fishery. The second step is the estimation of parameters, including fitting of the stock assessment models. The final step integrates across all of the results to reconcile the multi-model outcome. The framework is flexible enough to be tailored to particular stocks and fisheries and can draw on information from multiple sources to implement a broad variety of assumptions, making it applicable to stocks with varying levels of data availability The Iberian hake stock in International Council for the Exploration of the Sea (ICES) Divisions VIIIc and IXa is used to demonstrate the framework, starting from length-based stock and indices data. Process and model uncertainty are considered through the growth, natural mortality, fishing mortality, survey catchability and stock-recruitment relationship. Estimation uncertainty is included as part of the fitting process. Simple model averaging is used to integrate across the results and produce a single assessment that considers the multiple sources of uncertainty.