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Marine mammal bycatch poses a particular challenge in developing countries, where data to document bycatch and its effects are often lacking. Using the Bycatch Risk Assessment (ByRA) toolkit, based on InVEST open-source models, we chose 4 field sites in Southeast Asia with varying amounts of data on marine mammals and fishing occurrence: Trat province in the eastern Gulf of Thailand, the Sibu-Tinggi Islands and Kuching Bay, Malaysia, and Kien Giang Biosphere Reserve in southwestern Vietnam. These field sites have similar species of coastal marine mammals, small-scale and commercial fisheries, and support for research from universities and/or management. In Thailand and Kuching, results showed changing patterns of fishing and Irrawaddy dolphin Orcaella brevirostris habitat use across seasons, showing how bycatch risk could change throughout the year. Risk maps for dugongs Dugong dugon in peninsular Malaysia highlighted patterns of bycatch risk concentrated around a mainland fishing pier, and revealed high risk in a northern subregion. In Vietnam, first maps of bycatch risk for the Irrawaddy dolphin showed the highest risk driven by intensive use of gillnets and trawling gear. ByRA pinpointed areas of spatial and seasonal bycatch exposure, and estimated the consequence of bycatch on local species, providing managers with critical information on where to focus bycatch mitigation and meet new global standards for US Marine Mammal Protection Act and other international regulation (e.g. Official Journal of the European Union 2019; Regulation 2019/1241) compliance. The toolbox, a transferable open-source tool, can be used to guide fisheries management, marine mammal conservation, spatial planning, and further research. KEY WORDS: Incidental bycatch · Marine mammals · Spatial risk assessment · Open-source GIS toolkit · Small-scale fisheries · Southeast Asia

Funding information: Marine Scotland. Grant Number: MMSS/001/11 1. Identifying and understanding ecological drivers that influence wildlife populations is challenging but critical for conservation. This typically requires integrating long-term data on both the population and potential drivers within statistical models that are suitable for analysing these complex relationships. State-space models offer one method for integrating such data. Once implemented within a Bayesian framework, these analyses can control for multifactorial influences on populations, allowing one to extract otherwise undetectable correlations between the environment and the underlying, inferred demography. 2. In the Moray Firth, Scotland, harbour seals have been counted annually for 30 years (1988?2018). A Bayesian state-space model was used to explore whether patterns in vital rates were correlated to changes in prey abundance, inter-specific competition (grey seal abundance), environmental variables [the North Atlantic Oscillation (NAO) and sea-surface temperature], or level of biotoxins (saxitoxin and domoic acid) in the Moray Firth waters. 3. The credible interval of the posterior distributions of three of these covariate coefficients (sandeel proxy, NAO and grey seal abundance) suggested that there was a relationship between those covariates and vital rates. Both the sandeel proxy and NAO showed a positive correlation with fecundity, whereas grey seal abundance had a negative impact on pup survival. 4. This work demonstrates how an integrated state-space modelling approach can bring together diverse data sets and point to important interactions with prey, and with other predators in the system. This suggests that the wider-scale management of UK harbour seal populations with their contrasting temporal trends needs to account for variation in the marine ecosystem at appropriate spatial scales, in line with current policy concerning spatial planning in the marine environment. Postprint Postprint

Fisheries bycatch has been identified as the greatest threat to marine mammals worldwide. Characterizing the impacts of bycatch on marine mammals is challenging because it is difficult to both observe and quantify, particularly in small-scale fisheries where data on fishing effort and marine mammal abundance and distribution are often limited. The lack of risk frameworks that can integrate and visualize existing data have hindered the ability to describe and quantify bycatch risk. Here, we describe the design of a new geographic information systems tool built specifically for the analysis of bycatch in small-scale fisheries, called Bycatch Risk Assessment (ByRA). Using marine mammals in Malaysia and Vietnam as a test case, we applied ByRA to assess the risks posed to Irrawaddy dolphins (Orcaella brevirostris) and dugongs (Dugong dugon) by five small-scale fishing gear types (hook and line, nets, longlines, pots and traps, and trawls). ByRA leverages existing data on animal distributions, fisheries effort, and estimates of interaction rates by combining expert knowledge and spatial analyses of existing data to visualize and characterize bycatch risk. By identifying areas of bycatch concern while accounting for uncertainty using graphics, maps and summary tables, we demonstrate the importance of integrating available geospatial data in an accessible format that taps into local knowledge and can be corroborated by and communicated to stakeholders of data-limited fisheries. Our methodological approach aims to meet a critical need of fisheries managers: to identify emergent interaction patterns between fishing gears and marine mammals and support the development of management actions that can lead to sustainable fisheries and mitigate bycatch risk for species of conservation concern.

In their Policy Forum “U.S. seafood import restriction presents opportunity and risk” (16 December, p. [1372][1]), R. Williams et al. describe some possible effects of the U.S. National Oceanic and Atmospheric Administration (NOAA) rule requiring that seafood imported into the United States must

Hopefully, this approach represents a process that leads to open and constructive debate, to progress towards a better scientific understanding and to improvements in our ability to manage wildlife. Lonergan has examined our modeling in detail, and his critique raises a series of specific points and also some general questions about how data and expert opinion should be integrated into statistical and modeling frameworks with the aim of advising on conservation methods. we agree with several of Lonergan’s dictums, such as the need for open dialogue between the field biologists collecting data and the modelers subsequently using these data. Indeed, several of his specific points could have been resolved through such dialogue. For example, he queries the lack of data on harbor seal pups after mid-July, but this situation arises because pups cannot be reliably distinguished from juveniles after this time (thompson and Rothery 1987). similarly, detailed evaluation of previous studies from the moray Firth (e.g. thompson et al. 2007) would also call into question Lonergan’s assertion that shooting is the sole driver of historical declines in this population. Lonergan highlights his concerns about the performance and sensitivity of our model to specific assumptions and priors. these are important questions. we hope that in our publication, prior justification was explicit (pp. 155–158), prior sensitivity methodology was described in detail (supplement 3) and the implications of our modeling on our predictions were discussed in full (our points 1–5, pp. 158–159; points 1, 2, p 160). work continues, and our aim is to improve and simplify our modeling—for example, by considering seasonal variation in haulout probability. we are also exploring a question about the mis-match between one datum (2009 population estimate) and model predictions. the sensitivity of our results to these two issues was In his critique of our recent paper (matthiopoulos et al. 2014) on the population dynamics of harbor seals, Lonergan (2014a) argues that although our state–space modeling approach represents an interesting academic exercise, it nevertheless risks misleading those attempting to understand and manage observed declines of this species in UK waters. we argue that approaches like ours need not be misleading as long as information is provided on the assumptions and simplifications used. when such information is available, all assumptions (such as the shape and parameters of prior distributions in a Bayesian analysis) can then be subjected to scrutiny [such as that performed by Lonergan (2014a)], inevitably leading to future improvements.

This work was funded through the Natural Environment Research Council and SMRU Ltd. To estimate the density or abundance of a cetacean species using acoustic detection data, it is necessary to correctly identify the species that are detected. Developing an automated species classifier with 100% correct classification rate for any species is likely to stay out of reach. It is therefore necessary to consider the effect of misidentified detections on the number of observed data and consequently on abundance or density estimation, and develop methods to cope with these misidentifications. If misclassification rates are known, it is possible to estimate the true numbers of detected calls without bias. However, misclassification and uncertainties in the level of misclassification increase the variance of the estimates. If the true numbers of calls from different species are similar, then a small amount of misclassification between species and a small amount of uncertainty around the classification probabilities does not have an overly detrimental effect on the overall variance. However, if there is a difference in the encounter rate between species calls and/or a large amount of uncertainty in misclassification rates, then the variance of the estimates becomes very large and this dramatically increases the variance of the final abundance estimate. Publisher PDF

Methods for the fully automatic detection and species classification of odontocete whistles are described. The detector applies a number of noise cancellation techniques to a spectrogram of sound data and then searches for connected regions of data which rise above a pre-determined threshold. When tested on a dataset of recordings which had been carefully annotated by a human operator, the detector was able to detect (recall) 79.6% of human identified sounds that had a signal-to-noise ratio above 10 dB, with 88% of the detections being valid. A significant problem with automatic detectors is that they tend to partially detect whistles or break whistles into several parts. A classifier has been developed specifically to work with fragmented whistle detections. By accumulating statistics over many whistle fragments, correct classification rates of over 94% have been achieved for four species. The success rate is, however, heavily dependent on the number of species included in the classifier mix, with the mean correct classification rate dropping to 58.5% when 12 species were included.

Declines in large vertebrate populations are widespread but difficult to detect from monitoring data and hard to understand due to a multiplicity of plausible biological explanations. In parts of Scotland, harbour seals (Phoca vitulina) have been in decline for 10 years. To evaluate the contributions of different proximate causes (survival, fecundity, observation artefacts) to this decline, we collated behavioural, demographic and population data from one intensively studied population in part of the Moray Firth (north-east Scotland). To these, we fit a state-space model comprising age-structured dynamics and a detailed account of observation errors. After accounting for culling (estimated by our model as 14 % of total mortality), the main driver of the historical population decline was a decreasing trend in survival of young individuals combined with (previously unrecognised) low levels of pupping success. In more recent years, the model provides evidence for considerable increases in breeding success and consistently high levels of adult survival. However, breeding success remains the most volatile demographic component of the population. Forecasts from the model indicate a slow population recovery, providing cautious support for recent management measures. Such investigations of the proximate causes of population change (survival, fecundity and observation errors) provide valuable short-term support for the management of population declines, helping to focus future data collection on those ultimate causal mechanisms that are not excluded by the demographic evidence. The contribution of specific ultimate drivers (e.g. shooting mortality or competitors) can also be quantified by including them as covariates to survival or fecundity.

Beaked whales are extremely difficult to sight at sea and this hampers attempts to study them, and makes real time mitigation difficult. Passive acoustic monitoring could improve detection efficiency. Blainville's beaked whales, (Mesoplodon densirostris) are known to produce most of their vocalizations at depth. They are routinely detected on bottom mounted hydrophones arrays but the extent to which they can be detected using near‐surface hydrophones is not known. Continuous recordings were made at a sampling rate of 192 kHz from towed hydrophone arrays during line transect surveys in the Bahamas in conjunction with teams monitoring bottom‐mounted hydrophones at the AUTEC Tongue of the Ocean navy range. A beaked whale click detector and classifier was developed within Rainbow Click and PAMGUARD and this was both run in real time and used to analyze recordings to pick out beaked whale click trains. Detected click trains correlated well with detection of beaked whales on bottom‐mounted hydrophones. Three species of beaked whale were encountered visually and detected acoustically: Mesoplodon densirostris, Ziphius cavirostris and Mesoplodon europaeus. Target motion analysis of bearings to sequences of clicks suggests a maximum detection range of approximately three kilometers and preliminary results indicate that clicks can be identified to species.