Sampling strategies for species with high breeding-site fidelity: A case study in burrow-nesting seabirds

• Published in: PLOS ONE
• Main Authors: Arneill, Gavin E., Perrins, Christopher M., Wood, Matt J., Murphy, David, Pisani, Luca, Jessopp, Mark J., Quinn, John L.
• Format: Text
• Language: English
• Published: Public Library of Science 2019
• Subjects: Research Article; Four Islands; North Atlantic
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6711508/; http://www.ncbi.nlm.nih.gov/pubmed/31454375; https://doi.org/10.1371/journal.pone.0221625

Sampling approaches used to census and monitor populations of flora and fauna are diverse, ranging from simple random sampling to complex hierarchal stratified designs. Usually the approach taken is determined by the spatial and temporal distribution of the study population, along with other characteristics of the focal species. Long-term monitoring programs used to assess seabird population trends are facilitated by their high site fidelity, but are often hampered by large and difficult to access colonies, with highly variable densities that require intensive survey. We aimed to determine the sampling effort required to (a) estimate population size with a high degree of confidence, and (b) detect different scenarios of population change in a regionally important species in the Atlantic, the Manx shearwater (Puffinus puffinus). Analyses were carried out using data collected from tape-playback surveys on four islands in the North Atlantic. To explore how sampling effort influenced confidence around abundance estimates, we used the heuristic approach of imagining the areas sampled represented the total population, and bootstrapped varying proportions of subsamples. This revealed that abundance estimates vary dramatically when less than half of all plots (n dependent on the size of the site) is randomly subsampled, leading to an unacceptable lack of confidence in population estimates. Confidence is substantially improved using a multi-stage stratified approach based on previous information on distribution in the colonies. In reality, this could lead to reducing the number of plots required by up to 80%. Furthermore, power analyses suggested that random selection of monitoring plots using a matched pairs approach generates little power to detect overall population changes of 10%, and density-dependent changes as large as 50%, because variation in density between plots is so high. Current monitoring programs have a high probability of failing to detect population-level changes due to inappropriate sampling efforts. ...