Evaluating stakeholder‐derived strategies to reduce the risk of ships striking whales

Automated identification system for ships data as a proxy for marine vessel related stressors

An increasing number of marine conservation initiatives rely on data from Automatic Identification System (AIS) to inform marine vessel traffic associated impact assessments and mitigation policy. However, a considerable proportion of vessel traffic is not captured by AIS in many regions of the world. Here we introduce two complementary techniques for collecting traffic data in the Canadian Salish Sea that rely on optical imagery. Vessel data pulled from imagery captured using a shore-based autonomous camera system ("Photobot") were used for temporal analyses, and data from imagery collected by the National Aerial Surveillance Program (NASP) were used for spatial analyses. The photobot imagery captured vessel passages through Boundary Pass every minute (Jan-Dec 2017), and NASP data collection occurred opportunistically across most of the Canadian Salish Sea (2017-2018). Based on photobot imagery data, we found that up to 72 % of total vessel passages through Boundary Pass were not broadcasting AIS, and in some vessel categories this proportion was much higher (i.e., 96 %). We fit negative binomial General Linearized Models to our photobot data and found a strong seasonal variation in non-AIS, and a weekend/weekday component that also varied by season (interaction term p < 0.0001). Non-AIS traffic was much higher during the summer (Apr-Sep) and during the weekend (Sat-Sun), reflecting patterns in recreational vessel traffic not obligated to broadcast AIS. Negative binomial General Additive Models based on the NASP data revealed strong spatial associations with distance from shore (up to 10 km) and non-AIS vessel traffic for both summer and winter seasons. There were also associations between non-AIS vessels and marina and anchorage densities, particularly during the winter, which again reflect seasonal recreational vessel traffic patterns. Overall, our GAMs explained 20-37 % of all vessel traffic during the summer and winter, and highlighted subregions where vessel traffic is under represented by AIS.

Assessing variability in marine traffic exposure between baleen whale species off the Galician Coast, Spain

Increases in marine traffic represent a growing issue for marine wildlife, posing threats through the impacts of ship strikes and noise pollution. Baleen whales are especially vulnerable to these impacts, yet regional and species-specific information on exposure to such threats is lacking. This study uses AIS and observational data to provide the first assessment of baleen whale exposure to vessel traffic on the NW coast of Spain. Overlap with vessel traffic was detected for all areas where whales were sighted, indicating that these species may be at risk of vessel exposure and its associated impacts. Level of exposure to vessel traffic experienced by whales was species-specific, with risk of exposure appearing highest for minke whales. Vessel exposure also displayed intra- and inter-annual variability and a significant influence of feeding behaviour highlighting the need for dynamic management tools to minimise interactions between baleen whales and marine traffic off the Galician Coast.

Estimating uncertainty in density surface models

Providing uncertainty estimates for predictions derived from species distribution models is essential for management but there is little guidance on potential sources of uncertainty in predictions and how best to combine these. Here we show where uncertainty can arise in density surface models (a multi-stage spatial modelling approach for distance sampling data), focussing on cetacean density modelling. We propose an extensible, modular, hybrid analytical-simulation approach to encapsulate these sources. We provide example analyses of fin whales Balaenoptera physalus in the California Current Ecosystem.

Occupational stress and health risk of employees working in the garments sector of Bangladesh: An empirical study

The present study was conducted with a view to examining the impact of occupational stress on employees' health risk. A total number of 350 garment employees (114 supervisors and 236 workers) were selected from 25 readymade garment factories of Dhaka, Narayanganj, and Gazipur industrial areas of Bangladesh on a random sampling basis. Occupational stress was estimated using an ERIs modified questionnaire; when self-reported health problems, work related information and socio-demographic information were obtained using face-to-face interviews using a pre-formed questionnaire. The survey was conducted for 2 years from January 2020 to December 2021 in Dhaka, Narayanganj and Gazipur districts where most of the garment industries in Bangladesh are located. All data were processed by using Statistical Package for Social Sciences (SPSS) and Decision Analyst Stats, Version 2.0. For analyzing data, suitable statistical tools such as two-way ANOVA, z-test, chi-square test, Pearson's product-moment correlation, stepwise multiple regressions, and descriptive statistics were used. The results of the present study reveal that the occupational stress had a significant positive influence on health risk. The findings also reveal that both the male and female employees perceived garment job highly stressful and risky for their health causes many dies and sickness, but it was higher among the female employees than their counterparts. Study suggests that due to major illness and diseases garments' employees are lacks of sound health that have to consider remedying for reducing occupational stress and health risk.

Estimating effectiveness of speed reduction measures for decreasing whale-strike mortality in a high-risk region

Recent estimates of blue (Balaenoptera musculus) and humpback (Megaptera novaeangliae) whale ship-strike deaths on the US west coast are above the Potential Biological Removal limit determined by the National Marine Fisheries Service. Beginning in 2015, the National Oceanographic and Atmospheric Administration requested voluntary Vessel Speed Reductions (VSR) in the designated shipping routes off San Francisco, California, USA, in order to decrease whale mortality from ship strikes. We applied a ship strike model based on whale density and Automatic Identification System (AIS) vessel data. We bootstrapped speeds from vessels that transited when no VSR was in place to assess the effect of the VSR on strike mortality rates. Finally, we calculated the expected mortality for hypothetical compliance scenarios by programmatically imposing speed caps. Average predicted mortality for the region was 2.7 blue whales and 7.0 humpback whales in a 4 month period. Compared to years prior to the VSR (2012-2014), vessel speeds during the VSR were slower. This lowered blue whale deaths within the shipping lanes by 11-13% and humpback whale deaths by 9-10% in 2016-2017. If 95% of mariners adhered to recommended 10 knot (kn) limits in the shipping lanes alone, we predicted twice as many blue whale and 3 times as many humpback whale deaths would be avoided relative to current adherence. Adding a 10 kn speed limit (with 95% cooperation) at the ends of each of the lanes would result in about 5- and 4-fold reductions in blue whale and humpback whale mortality, respectively, relative to current practices. Our approach can evaluate ship strikes and mitigation measures for whale populations around the globe.

Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees

Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km2) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991-2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest.