Seasonality in abundance and detection bias of birds in a tropical dry forest in north-eastern South America

Abstract

Abstract:Seasonal fluctuations in bird abundance are expected in semi-arid environments, but estimates may be biased if detectability is not considered. In a tropical dry forest in north-eastern Brazil, we evaluated whether bird abundance is highly seasonal, and associated with time-specific variability in detectability. We mark-recaptured birds with mist nets over three field visits (3487 records from 75 species), and used closed-capture models to estimate detectability and abundance in birds divided into three groups (all, residents, insectivores). In the two dry periods, the best models resulted in capture estimates at least three times larger than recapture, and both estimates were twice that of when rains occurred on the day preceding sampling. Abundance varied between dry and wet periods from 4.0 (from 115 ± 34 to 479 ± 144) to 13 times (183 ± 8 to 2463 ± 351). Estimates were 1.5–3.2 times greater in the dry period when behavioural responses of birds were excluded from capture-recapture models. Meanwhile, in the wet period the relative abundance was between 33–76% smaller than best-fit models estimated. This study found variation in avian abundance greater than that observed in other Neotropical dry forests, and indicates that biases may be common when not including detectability.

Comparison of hoop-net trapping and visual surveys to monitor abundance of the Rio Grande cooter (Pseudemys gorzugi)

“…are often complicated by sampling bias and variance. Indeed, the ability to capture individuals in a population can be related to an individual’s size, sex, and behavior, bait preferences, and the habitat characteristics of the sampling area, among other factors ( Mali et al, 2014a ; Chauvenet et al, 2017 ; Keiter et al, 2017 ; de Oliveira e Silva et al, 2017 ). This requires managers to develop sampling designs that allow them to distinguish true spatial and temporal variation in abundances from variation in sampling efficiency (i.e., detection/capture probability) so that monitoring data may appropriately inform management decisions ( MacKenzie & Kendall, 2002 ).…”