Assigning stranded bottlenose dolphins to source stocks using stable isotope ratios following the Deepwater Horizon oil spill

Abstract

The potential for stranded dolphins to serve as a tool for monitoring free-ranging populations would be enhanced if their stocks of origin were known. We used stable isotopes of carbon, nitrogen, and sulfur from skin to assign stranded bottlenose dolphins Tursiops truncatus to different habitats, as a proxy for stocks (demographically independent populations), following the Deepwater Horizon oil spill. Model results from biopsy samples collected from dolphins from known habitats (n = 205) resulted in an 80.5% probability of correct assignment. These results were applied to data from stranded dolphins (n = 217), resulting in predicted assignment probabilities of 0.473, 0.172, and 0.355 to Estuarine, Barrier Island (BI), and Coastal stocks, respectively. Differences were found west and east of the Mississippi River, with more Coastal dolphins stranding in western Louisiana and more Estuarine dolphins stranding in Mississippi. Within the Estuarine East Stock, 2 groups were identified, one predominantly associated with Mississippi and Alabama estuaries and another with western Florida. δ 15 N values were higher in stranded samples for both Estuarine and BI stocks, potentially indicating nutritional stress. High probabilities of correct assignment of the biopsy samples indicate predictable variation in stable isotopes and fidelity to habitat. The power of δ 34 S to discriminate habitats relative to salinity was essential. Stable isotopes may provide guidance regarding where additional testing is warranted to confirm demographic independence and aid in determining the source habitat of stranded dolphins, thus increasing the value of biological data collected from stranded individuals.

Where were they from? Modelling the source stock of dolphins stranded after the Deepwater Horizon oil spill using genetic and stable isotope data

“…This requires that the putative source stock datasets contain animals that were correctly assigned to stock, that the data source is informative about stock origin and that the analysis methods are unbiased. These issues are discussed by Rosel et al (2017) for genetic assignment, and Hohn et al (2017) for SIR. One concern, in particular, is that SIR data tell us where animals have been foraging over the past few weeks to months (Browning et al 2014, Giménez et al 2016, not necessarily from where they originated (Hohn et al 2017).…”

“…These issues are discussed by Rosel et al (2017) for genetic assignment, and Hohn et al (2017) for SIR. One concern, in particular, is that SIR data tell us where animals have been foraging over the past few weeks to months (Browning et al 2014, Giménez et al 2016, not necessarily from where they originated (Hohn et al 2017). For example, in 10 cases, animals that were assigned via genetics to the BSE population with high probability had stable isotope signatures more indicative of a coastal stock animal (see Table S1 in the Supplement).…”

“…Habitat preferences and prey selection variations for common bottlenose dolphins in estuarine and coastal environments in the study area lead to different SIR signatures between these groups (Barros et al 2010); hence, analysis of SIRs from carcasses can be used to identify recent habitat preferences for each dolphin, which may be used to infer stock (for more detail see Hohn et al 2017); the data and analyses used here are described fully in Hohn et al (2017). In summary, a training dataset of carbon, nitrogen and sulphur SIRs (δ 13 C, δ 15 N and δ 34 S respectively) was created using biopsy samples from dolphins sampled within de fined stock areas in Louisiana and Mississippi (including estuarine, barrier island and coastal) and assumed to represent those stocks.…”

“…A total of 205 remote biopsy and live capturerelease samples from BB, MS, and adjacent coastal waters were used to build the binary partition, which was then applied to the 217 stranded animals for which SIR data had been collected (see Hohn et al 2017 for details). Three of the 4 candidate variables (Region, δ 13 C and δ 34 S) were selected, with the first split being on δ 34 S (high values indicating coastal) and 4 subsequent partitions (6 terminal nodes, i.e.…”

Health Assessments of Common Bottlenose Dolphins (Tursiops truncatus): Past, Present, and Potential Conservation Applications

“…Utilizing a multidisciplinary approach to combine clinical veterinary knowledge with epidemiological analyses and population modeling enables long-term forecasting of population trajectories (36). In UMEs, modeling to estimate mortality based on the number of stranded carcasses can provide insight into the immediate losses to the population (71,(123)(124)(125). However, integration of available health information and veterinary interpretation of sublethal, chronic conditions, which are likely to influence long-term survival and reproductive potential, can provide a more accurate interpretation of the likely long-term impacts on the population (36).…”