Utilizing the great blue heron (Ardea herodias) in ecological risk assessments of bioaccumulative contaminants

Abstract

Selection of an appropriate species is a key element of effective ecological risk assessments (ERA), especially when site-specific field studies are to be employed. Great blue herons (GBH) possess several ideal characteristics of a receptor species for the assessment of bioaccumulative compounds in the environment, such as ease of study, high potential for exposure, widespread distribution, and territorial foraging behavior. Methodologies for assessing exposure and population health are described herein. As outlined, the collection of GBH eggs, GBH nestling blood, and adult GBH blood allows for the determination of contaminant concentrations in various GBH tissues, a top-down assessment, which can be done in conjunction with predicted dietary exposure, a bottom-up assessment, to support a multiple lines of evidence approach. Additionally, population parameters, such as productivity and survival, can also be measured to elucidate if the contaminant exposure may be causing population level effects. Over the course of two years, three GBH rookeries were monitored for productivity and nestling exposure. Nests were monitored from blinds and individually accessed at multiple time points to obtain measures of nestling health, band nestlings, and collect eggs and nestling plasma. Multiple nests could frequently be accessed by climbing one tree, resulting in minimal effort to obtain the necessary sample size. Additionally, 51 adult GBH, captured in their foraging areas, were banded, and provided a blood sample. With these samples, a statistical difference in tissue based exposure was identified between the reference and target area. Statistically significant differences were also identified between the upper and lower reaches of the target area, thereby identifying a range of doses geographically which could be correlated to specific measurement endpoints. The ability to identify a dose response greatly increases the ability of the dataset to determine causation, a key goal of such studies. Overall, the use of the described methods allowed for the collection of a statistically sufficient and ecologically relevant dataset with reasonable effort and minimal impact on GBH.