Contrasting Seasonal Survivorship of Two Migratory Songbirds Wintering in Threatened Mangrove Forests

Long-distance migrants wintering in tropical regions face a number of critical conservation threats throughout their lives, but seasonal estimates of key demographic parameters such as winter survival are rare. Using mist-netting-based mark-recapture data collected in coastal Costa Rica over a six-year period, we examined variation in within- and between-winter survivorship of the Prothonotary Warbler (Protonotaria citrea; 753 young and 376 adults banded), a declining neotropical habitat specialist that depends on threatened mangrove forests during the nonbreeding season. We derived parallel seasonal survivorship estimates for the Northern Waterthrush (Seiurus noveboracensis; 564 young and 93 adults banded), a cohabitant mangrove specialist that has not shown the same population decline in North America, to assess whether contrasting survivorship might contribute to the observed differences in the species’ population trajectories. Although average annual survival probability was relatively similar between the two species for both young and adult birds, monthly estimates indicated that relative to Northern Waterthrush, Prothonotary Warblers exhibited: greater interannual variation in survivorship, especially within winters; greater variation in survivorship among the three study sites; lower average between-winter survivorship, particularly among females, and; a sharp decline in between-winter survivorship from 2003 to 2009 for both age groups and both sexes. Rather than identifying one seasonal vital rate as a causal factor of Prothonotary Warbler population declines, our species comparison suggests that the combination of variable within-winter survival with decreasing between-winter survival demands a multi-seasonal approach to the conservation of this and other tropical-wintering migrants.

A Field Evaluation of the Time-of-Detection Method to Estimate Population Size and Density for Aural Avian Point Counts

The time-of-detection method for aural avian point counts is a new method of estimating abundance, allowing for uncertain probability of detection. The method has been specifically designed to allow for variation in singing rates of birds. It involves dividing the time interval of the point count into several subintervals and recording the detection history of the subintervals when each bird sings. The method can be viewed as generating data equivalent to closed capture–recapture information. The method is different from the distance and multiple-observer methods in that it is not required that all the birds sing during the point count. As this method is new and there is some concern as to how well individual birds can be followed, we carried out a field test of the method using simulated known populations of singing birds, using a laptop computer to send signals to audio stations distributed around a point. The system mimics actual aural avian point counts, but also allows us to know the size and spatial distribution of the populations we are sampling. Fifty 8-min point counts (broken into four 2-min intervals) using eight species of birds were simulated. Singing rate of an individual bird of a species was simulated following a Markovian process (singing bouts followed by periods of silence), which we felt was more realistic than a truly random process. The main emphasis of our paper is to compare results from species singing at (high and low) homogenous rates per interval with those singing at (high and low) heterogeneous rates. Population size was estimated accurately for the species simulated, with a high homogeneous probability of singing. Populations of simulated species with lower but homogeneous singing probabilities were somewhat underestimated. Populations of species simulated with heterogeneous singing probabilities were substantially underestimated. Underestimation was caused by both the very low detection probabilities of all distant individuals and by individuals with low singing rates also having very low detection probabilities.