Predicting the exposure of diving grey seals to shipping noise.

There is high spatial overlap between grey seals and shipping traffic, and the functional hearing range of grey seals indicates sensitivity to underwater noise emitted by ships. However, there is still very little data regarding the exposure of grey seals to shipping noise, constraining effective policy decisions. Particularly, there are few predictions that consider the at-sea movement of seals. Consequently, this study aimed to predict the exposure of adult grey seals and pups to shipping noise along a three-dimensional movement track, and assess the influence of shipping characteristics on sound exposure levels. Using ship location data, a ship source model, and the acoustic propagation model, RAMSurf, this study estimated weighted 24-h sound exposure levels (10-1000 Hz) (SELw). Median predicted 24-h SELw was 128 and 142 dB re 1 µPa2s for the pups and adults, respectively. The predicted exposure of seals to shipping noise did not exceed best evidence thresholds for temporary threshold shift. Exposure was mediated by the number of ships, ship source level, the distance between seals and ships, and the at-sea behaviour of the seals. The results can inform regulatory planning related to anthropogenic pressures on seal populations.

Risk factors for, and prediction of, exertional heat illness in Thoroughbred racehorses at British racecourses.

The development of exertional heat illness (EHI) is a health, welfare and performance concern for racehorses. However, there has been limited multivariable assessment of the possible risk factors for EHI in racehorses, despite such information being vital for regulators to effectively manage the condition. Consequently, this study aimed to identify the risk factors associated with the occurrence of EHI in Thoroughbred racehorses and assess the ability of the risk factor model to predict the occurrence of EHI in racehorses to assist in early identification. Runners at British racecourses recorded in the British Horseracing Authority database between 1st July 2010 and 30th April 2018 were used to model the probability that a horse would present with EHI as a function of a suite of environmental, horse level and race level factors. EHI was reported in 0.1% of runners. Race distance, wet bulb globe temperature, preceding 5-day temperature average, occurrence of a previous EHI incident, going, year and race off time were identified as risk factors for EHI. The model performed better than chance in classifying incidents with a mean area under the receiver operating characteristic curve score of 0.884 (SD = 0.02) but had a large number of false positives. The results provide vital evidence for industry on the need to provide appropriate cool down facilities, identify horses that have repeated EHI incidents for early intervention, and collect new data streams such as on course wet bulb globe temperature measurements. The results are especially relevant as the sport is operating in a changing climate and must mitigate against more extreme and longer spells of hot weather.

Racehorse welfare across a training season.

Racehorse welfare is gaining increasing public attention, however scientific evidence in this area is lacking. In order to develop a better understanding of racehorse welfare, it must be measured and monitored. This is the first study to assess racehorse welfare using scientific objective methods across a training season. The aim of this study was threefold, firstly to investigate welfare measures which could be used in the first welfare assessment protocol for racehorses. Secondly, to understand the effect that a racing and training season had on individual racehorses and thirdly to identify risk factors for both good and poor welfare. Thirteen racehorse training yards were visited at the beginning and the peak of the racing season in England. Behavioral observations along with individual environmental and animal-based welfare measures were carried out on 353 horses in 13 training yards selected for variability. In our sample the horses were generally in good physical health: 94% of horses recorded as an ideal body condition score, no horses had signs of hoof neglect and 77.7% had no nasal discharge. The overall prevalence of external Mouth Corner Lesions was 12.9% and was significantly higher for Flat racing than Jump racing horses. The majority of horses (67.5%) showed positive horse human interactions. When stabled 54.1% horses had physical social contact and nasal discharge was not associated with increased physical contact. The training season significantly affected Human Reactivity Tests, Horse Grimace Scale scores and time spent resting and feeding. A total of 14.5% of horses displayed stereotypic behavior on at least two occasions. Horses with windows in their stables spent more time surveying their surroundings. Overall, in this population of racehorses, horses spent around a third of their daytime feeding (33.7%) followed by time spent standing resting (22.6%). The welfare assessment protocol used in this study is suitable for use in industry to collect welfare data on racehorses.

An adaptive grid to improve the efficiency and accuracy of modelling underwater noise from shipping.

Underwater noise pollution from shipping is a significant ecological concern. Acoustic propagation models are essential to predict noise levels and inform management activities to safeguard ecosystems. However, these models can be computationally expensive to execute. To increase computational efficiency, ships are spatially partitioned using grids but the cell size is often arbitrary. This work presents an adaptive grid where cell size varies with distance from the receiver to increase computational efficiency and accuracy. For a case study in the Celtic Sea, the adaptive grid represented a 2 to 5 fold increase in computational efficiency in August and December respectively, compared to a high resolution 1 km grid. A 5 km grid increased computational efficiency 5 fold again. However, over the first 25 km, the 5 km grid produced errors up to 13.8 dB compared to the 1 km grid, whereas, the adaptive grid generated errors of less than 0.5 dB.