The population of short-beaked common dolphins Delphinus delphis of the Bay of Biscay (northeast Atlantic) has been subjected to potentially dangerous levels of bycatch since the 1990s. As the phenomenon intensifies, it represents a potent threat to the population. Here, we investigated the relationship between bycatch mortality and oceanographic processes. We assumed that oceanographic processes spatiotemporally structure the availability and aggregation of prey, creating areas prone to attract both common dolphins and fish targeted by fisheries. We used 2 datasets from 2012 to 2019: oceanographic data resulting from a circulation model and mortality data inferred from strandings. The latter allows location of mortality areas and quantification of the intensity of mortality events at sea. We fitted a series of spatiotemporal hierarchical Bayesian models using integrated nested Laplace approximations (INLA). Results provided first insights on how bycatch of common dolphins in the Bay of Biscay might be related to key seasonal and dynamic oceanographic features. We showed that from a statistical predictive point of view, the monthly trend of 2019 bycatch mortality could be predicted with few oceanographic covariates. This study highlights how gaining knowledge about environmental influences on interactions between short-beaked common dolphins and fisheries could have great conservation and management value. Identified relationships with oceanographic covariates were complex, as expected given the dynamic aspects of oceanographic processes, dolphins and fisheries distributions. Further research focusing on smaller time scales is needed to elucidate proximal drivers of common dolphin bycatch in the Bay of Biscay.
Bycatch, the non-intentional capture or killing of non-target species in commercial or recreational fisheries, is a world wide threat to protected, endangered or threatened species (PETS) of marine megafauna. Obtaining accurate bycatch estimates of PETS is challenging: the only data available may come from non-dedicated schemes, and may not be representative of the whole fisheries effort. We investigated, with simulated data, a model-based approach for estimating PETS bycatch from non-representative samples. We leveraged recent development in the statistical analysis of surveys, namely regularized multilevel regression with post-stratification, to infer total bycatch under realistic scenarios of data sampling such as under-sampling or over-sampling when PETS bycatch risk is high. Post-stratification is a survey technique to re-align the sample with the population and addresses the problem of non-representative samples. Post-stratification requires to sub-divide a population of interest into potentially hundreds of cells corresponding to the cross-classification of important attributes. Multilevel regression accommodate this data structure, and the statistical technique of regularization can be used to predict for each of these hundreds of cells. We illustrated these statistical ideas by modeling bycatch risk for each week within a year with as few as a handful of observed PETS bycatch events. The model-based approach led to improvements, under mild assumptions, both in terms of accuracy and precision of estimates and was more robust to non-representative samples compared to more design-based methods currently in use. In our simulations, there was no detrimental effects of using the model-based even when sampling was representative. Estimating PETS bycatch ideally requires dedicated observer schemes and adequate coverage of fisheries effort. We showed how a model-based approach combining sparse data typical of PETS bycatch and recent methodological developments can help when both dedicated observer schemes and adequate coverage are challenging to implement.
Many long‐lived vertebrate species are under threat in the Anthropocene, but their conservation is hampered by a lack of demographic information to assess population long‐term viability. When longitudinal studies (e.g., Capture‐Mark‐Recapture design) are not feasible, the only available data may be cross‐sectional, for example, stranding for marine mammals. Survival analysis deals with age at death (i.e., time to event) data and allows to estimate survivorship and hazard rates assuming that the cross‐sectional sample is representative. Accommodating a bathtub‐shaped hazard, as expected in wild populations, was historically difficult and required specific models. We identified a simple linear regression model with individual frailty that can fit a bathtub‐shaped hazard, take into account covariates, allow goodness‐of‐fit assessments and give accurate estimates of survivorship in realistic settings. We first conducted a Monte Carlo study and simulated age at death data to assess the accuracy of estimates with respect to sample size. Secondly, we applied this framework on a handful of case studies from published studies on marine mammals, a group with many threatened and data‐deficient species. We found that our framework is flexible and accurate to estimate survivorship with a sample size of 300. This approach is promising for obtaining important demographic information on data‐poor species.
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