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Estimating changes in the biomass of a fish stock is crucial for successful management. However, fishery assessment may be affected by the quality of the inputs used in stock assessment models. Survey biomass indices derived from fishery-independent and catch per unit effort (CPUE) biomass indices derived from fishery-dependent data are key inputs for model calibration. These indices have biases that could compromise the accuracy of the stock assessment models results. Therefore, there are plenty proposed methods to standardize survey or CPUE biomass data. From simpler models like generalized linear models (GLMs) to more complex models that take into account spatio-temporal correlation, like geostatistical models, and sampling dependence, like marked point processes. But many of them do not consider the underlying spatio-temporal or sampling dependence of these data. Hence, the goal of the study is to present a spatio-temporal simulation and Bayesian modeling framework to assess the impact of applying models that do not consider spatio-temporal and sampling dependence. Results indicate that geostatistical models and marked point processes achieve the lowest measures of error. Hence, to capture the underlying spatio-temporal process of the survey and CPUE biomass indices and data sampling preferentiality, it is essential to apply models that consider the spatio-temporal and sampling dependence. [ABSTRACT FROM AUTHOR]

The proportion of mature fish at length is one of the most important population attributes when evaluating reproductive potential for fish stock assessment purposes. Bias in maturity ogive parameters can lead to fishery management decisions based on misspecified biological reference points. These parameters can vary spatially and temporally, and this variability should be understood and included in the assessment models. However, integrating this variability becomes challenging when specific spatial-dependent ogives cannot be used in the stock assessment model. Hence, this study proposes a novel use of a multivariate response Bayesian regression model, employing an integrated nested Laplace approximation to estimate a single global maturity ogive using data from various spatial areas. This model explicitly accounts for differences in the sampling process and combines information from different areas to estimate shared maturity ogive parameters using joint-likelihood procedures. The model is applied to the European hake stock in ICES (International Council for the Exploration of the Sea) Divisions 27.8.c and 27.9.a, serving as a practical guide. In this model, we have considered different predictors to handle the relationship between the probability of being mature and the length and year covariates. Our results suggest that the logistic formulation correctly captures the relationship between the probability of being mature and length. For year variability, including a year factor covariate or year random effect in the predictor model produces similar values of goodness of fit measures. [ABSTRACT FROM AUTHOR]

Summary: The Neotropics have vast river catchments with untapped hydroelectric potential, but there are multiple expected negative impacts of dams, including those on local food security and livelihoods. Yet, monitoring of dam effects on subsistence is rare, particularly during initial implementation. Our study assessed changes in human fish consumption near the Belo Monte Dam in the Brazilian Amazon during the period 2012–2021, which covers construction, operation and a severe El Niño-induced drought. Over time, fish became less common and were consumed in smaller amounts, even though fewer people shared meals. The largest changes occurred between 2013 and 2016 (post-construction but prior to full operation), resulting in a downward trend in fish consumption, particularly during the drought season. Adding more fish species to the diet did not increase consumption per person. These changes in fish consumption suggest that they stem from environmental impacts of the project (e.g., reduced river level), despite secondary effects from climatic events. These findings underscore the urgent need for comprehensive assessments of the social and ecological impacts of large infrastructure projects in the Amazon, along with sustainable and equitable management strategies to ensure food security and meet the needs of local communities. [ABSTRACT FROM AUTHOR]

The two-sample problem refers to the comparison of two probability distributions via two independent samples. With high-dimensional data, such comparison is performed along a large number p of possibly correlated variables or outcomes. In genomics, for instance, the variables may represent gene expression levels for p locations, recorded for two (usually small) groups of individuals. In this paper we introduce TwoSampleTest.HD, a new R package to test for the equal distribution of the p outcomes. Specifically, TwoSampleTest.HD implements the tests recently proposed by (Cousido-Rocha, Uña-Álvarez, and Hart 2019) for the low sample size, large dimensional setting. These tests take the possible dependence among the p variables into account, and work for sample sizes as small as two. The tests are based on the distance between the empirical characteristic functions of the two samples, when averaged along the p locations. Different options to estimate the variance of the test statistic under dependence are allowed. The package TwoSampleTest.HD provides the user with individual permutation p-values too, so feature discovery is possible when the null hypothesis of equal distribution is rejected. We illustrate the usage of the package through the analysis of simulated and real data, where results provided by alternative approaches are considered for comparison purposes. In particular, benefits of the implemented tests relative to ordinary multiple comparison procedures are highlighted. Practical recommendations are given. [ABSTRACT FROM AUTHOR]

Discrete uniform and homogeneous p‐values often arise in applications with multiple testing. For example, this occurs in genome wide association studies whenever a non‐parametric one‐sample (or two‐sample) test is applied throughout the gene loci. In this paper, we consider multiple comparison procedures for such scenarios based on several existing estimators for the proportion of true null hypotheses, π0, which take the discreteness of the p‐values into account. The theoretical guarantees of the several approaches with respect to the estimation of π0 and the false discovery rate control are reviewed. The performance of the discrete procedures is investigated through intensive Monte Carlo simulations considering both independent and dependent p‐values. The methods are applied to three real data sets for illustration purposes too. Since the particular estimator of π0 used to compute the q‐values may influence its performance, relative advantages and disadvantages of the reviewed procedures are discussed. Practical recommendations are given. [ABSTRACT FROM AUTHOR]

Fishing is one of the most widespread and important human activities in coastal ecosystems and it plays a fundamental role in employment and the economy of coastal communities. However, in the period 2020–2021, the global outbreak of COVID-19 negatively affected fishing economic activity. Against this background, Andalusia (South of Spain) is an important region in which the resilience of different fishing exploitation systems can be studied, but within the same social and economic framework. Therefore, the main study aim was to investigate the resilience of fishing activity to the COVID-19 pandemic in two Andalusian fishing grounds (i.e. Atlantic and Mediterranean). We analysed daily landings and the first-sale prices of fresh fish of the most caught species in both fishing grounds, while taking into account the different seasonal behaviour of the fisheries. Generalised Linear Models were used to compare the data, which were obtained during periods in which the COVID-19 severity levels differed. These levels were implemented according to political measures. The final objective was to understand how the degree of industrialisation in the fleets can hinder or help maintain the economic activity of fisheries during major crises. [ABSTRACT FROM AUTHOR]