Estimating Cetacean Bycatch From Non-representative Samples (I): A Simulation Study With Regularized Multilevel Regression and Post-stratification

Abstract

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.

Assessment of the bycatch level for the Black Sea harbour porpoise in the light of new data on population abundance

“…The relatively small sample size (48 trips, 63 hauls) might have affected precision of the obtained result. However, unlike many situations elsewhere when bycatch sampling effort is considered low due to the rarity of bycatch events (Authier et al, 2021), bycatch frequency in the Black Sea is high (57.1% observed occurrence rate, 95% probability of observing bycatch is achieved at 0.2% observation effort: Curtis and Carretta, 2020), and here the high variance of cetacean bycatches is observed, which is due to outliersextremely high bycatch events. Moreover, the highest bycatch incidents could be missed and thus increasing the observation effort would only increase the variance.…”

“…Widening sub-regional coverage, i.e. further research in the eastern Black Sea is important for enhancing the accuracy of scaling or further stratified modelling (Authier et al, 2021), since most of the data for both studies come from the western Black Sea. However, the estimates from the western subregion are particularly important due to the summer concentration of the major part of the Black Sea harbour porpoise population in this area (Paiu et al, 2021a).…”

Development of a new control rule for managing anthropogenic removals of protected, endangered or threatened species in marine ecosystems

“…While the ability to set nill limits may be desirable from a conservation point of view, it may also entrench polarization between stakeholders vying for the attention of policy makers (see for example the discussion in Authier, Rouby & Macleod, 2021 ). Conservationists should, however, be aware that the RLA rule (and PBR to a lesser extent) also allows limits to increase over time as populations recover.…”

The ATTAC guiding principles to openly and collaboratively share wildlife ecotoxicology data

“…For instance, to overcome the absence of clear information on sex and life stage, it is possible to categorise data into classes including one or multiple specific classes for unknowns (e.g., males of unknown life stage or juveniles of unknown sex, [ 59 , 83 ]), although this can render subsequent analyses fragmentary and less information rich. More advanced modeling techniques, e.g., hierarchical modeling, can also provide a solution when data are scattered [5] .…”