ABSTRACT
Ferulic acid esterase (FAE+)-producing lactobacilli are being studied as silage inoculants due to their potential of increasing forage fiber digestibility. In this work, three FAE+ Lactobacillus (L.) johnsonii strains were isolated from caprine feces and characterized according to their potential probiotic characteristics and as silage inoculants. Limosilactobacillus fermentum CRL1446, a human probiotic isolated from goat cheese, was also included in the experiments as a potential silage inoculant. FAE activity quantification, probiotic characterization, and growth in maize aqueous extract indicated that L. johnsonii ETC187 might have a better inoculant and probiotic aptitude. Nevertheless, results in whole-corn mini silos indicated that, although acid detergent fiber (ADF) was significantly reduced by this strain (3% compared with the uninoculated (UN) group), L. johnsonii ETC150 and CRL1446 not only induced similar ADF reduction but also reduced dry matter (DM) loss (by 7.3% and 6.5%, respectively) compared with the UN group. Additionally, CRL1446 increased in vitro DM degradability by 10%. All treatments reduced gas losses when compared with the UN group. The potential probiotic features of these strains, as well as their beneficial impact on corn fermentation shown in this study, encourage further studies as enhancers in animal production.
ABSTRACT
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.
