Dead useful: methods for quantifying baseline variability in stranding rates to improve the ecological value of the strandings record as a monitoring tool

• Published in: Journal of the Marine Biological Association of the United Kingdom
• Main Authors: ten Doeschate, MTI, Brownlow, AC, Davison, NJ, Thompson, PM
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2017
• Subjects: Cetaceans; Generalized Additive Mixed Model; Harbour porpoise; Monitoring; Scotland; Stranding process; Strandings; Unusual Mortality Event
• Online Access: https://pure.sruc.ac.uk/en/publications/254145df-e5b5-457e-add2-5151c4c32e3a; https://doi.org/10.1017/S0025315417000698

The ecological value of the stranding record is often challenged due to the complexity in quantifying the biases associated with multiple components of the stranding process. There are biological, physical and social aspects that complicate the interpretation of stranding data particularly at a population level. We show how examination of baseline variability in the historical stranding record can provide useful insights into temporal trends and facilitate the detection of unusual variability in stranding rates. Seasonal variability was examined using harbour porpoise strandings between 1992 and 2014 on the east coast of Scotland. Generalized Additive Mixed modelling revealed a strong seasonal pattern, with numbers increasing from February towards a peak in April. Profiling seasonality this way facilitates detection of unusual variations in stranding frequencies and permits for any change in the incidence of strandings to be quantified by evaluation of the normalized model residuals. Consequently, this model can be used to identify unusual mortality events, and quantify the degree to which they deviate from baseline. With this study we demonstrate that a described baseline in strandings allows the detection of abnormalities at an early stage and can be used as a regional framework of reference for monitoring. This methodology provides means to quantify and partition the variability associated with strandings data and is a useful first step towards improving the stranding record as a management resource.