Sampling effort determination in bird surveys: do current norms meet best-practice recommendations?

Abstract

A critical design component of studies measuring diversity is sampling effort. Allocation of sampling effort dictates how many sites can be sampled within a particular time-frame or budget, as well as sample duration, frequency and intensity, thereby determining the resolution and reliability of emergent inferences. Conventional survey techniques use fixed-effort methods that assume invariant detectabilities among sites and species. Several approaches have been developed in the past decade that account for variable detectability by using alternative sampling methods or by adjusting standard counts before analysis, but it is unclear how widely adopted these techniques have been or how current bird surveying norms compare with best-practice recommendations. I conducted a systematic search of the primary literature to ascertain how sampling effort is determined, how much effort is devoted to sampling each site and how variation in detectability is dealt with. Of 225 empirical studies of bird diversity published between 2004 and 2016, five used results-based stopping rules (each derived independently), 54 used proportional sampling, and 159 (71%) used implicit effort-based stopping rules (fixed effort). Effort varied widely, but 61% of studies used samples of 10min or less and 62% of studies expended total effort per datum of 2h or less, with 78% providing no justification for sampling efforts used and just 15% explicitly accounting for estimated detectability. Given known variation in detectability, relying on short-duration fixed-effort approaches without validation or post hoc correction means that most bird diversity studies necessarily under-sample some sites and/or species. Having identified current bird surveying norms and highlighted their shortcomings, I provide five practical solutions to improve sampling effort determination, urging contributors and consumers of empirical ecological literature to consider survey data in terms of sample completeness.