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For seasonally migrating birds, aspects of migratory behavior, such as the use of temperate versus tropical wintering areas, may influence their ability to respond to environmental change. Here, we infer potential flexibility in songbird migration from variation in two alternative stopover behaviors. Hierarchical Bayesian mark-recapture modeling was used to quantify stopover decisions over 19 years for four temperate and four tropical migratory species at a stopover site in southern Canada. Short-distance temperate migrants exhibited higher variability in behavior and greater responses to local weather than longer-distance tropical migrants, as measured by transience (the proportion of birds stopping <24 h, i.e. seeking brief sanctuary or subsequently relocating) and departure (re-initiation of migration by birds that stopped over for >24 h). In contrast to many previous works on climate-migration associations, annual variation in stopover behavior did not show strong links to broad-scale climatic fluctuations for either temperate or tropical migrants, nor was there any indication of directional changes in stopover behavior over the past two decades. In addition to suggesting that migratory songbirds-particularly tropical-wintering species-may face increasing threats with future climatic variability, our study highlights the potential importance of flexibility in en-route behavior for resilience to environmental change.

Many species of birds and bats undertake seasonal migrations between breeding and over-wintering sites. En-route, migrants alternate periods of flight with time spent at stopover--the time and space where individuals rest and refuel for subsequent flights. We assessed the spatial scale of movements made by migrants during stopover by using an array of automated telemetry receivers with multiple antennae to track the daily location of individuals over a geographic area ~20 × 40 km. We tracked the movements of 322 individuals of seven migratory vertebrate species (5 passerines, 1 owl and 1 bat) during spring and fall migratory stopover on and adjacent to a large lake peninsula. Our results show that many individuals leaving their capture site relocate within the same landscape at some point during stopover, moving as much as 30 km distant from their site of initial capture. We show that many apparent nocturnal departures from stopover sites are not a resumption of migration in the strictest sense, but are instead relocations that represent continued stopover at a broader spatial scale.

1. Mark-recapture models are valuable for assessing diverse demographic and behavioural parameters, yet the precision of traditional estimates is often constrained by sparse empirical data. Bayesian inference explicitly recognizes estimation uncertainty, and hierarchical Bayes has proven particularly useful for dealing with sparseness by combining information across data sets. 2. We developed a general hierarchical Bayesian multi-state mark-recapture model, tested its performance on simulated data sets and applied it to real ecological data on stopovers by migratory birds. 3. Our hierarchical model performed well in terms of both precision and accuracy of parameters when tested with simulated data of varying quality (sample size, capture and survivorship probabilities). It also provided more precise and accurate parameter estimates than a non-hierarchical model when data were sparse. 4. A specific version of the model, designed for estimation of daily transience and departure of migratory birds at a mid-route stopover, was applied to 11 years of autumn migration data from Atlantic Canada. Hierarchical estimates of departure and transience were more precise than those derived from parallel non-hierarchical and frequentist methods, and indicated that inter-annual variability in parameters suggested by these other methods was largely due to sampling error. 5. Synthesis and applications. Estimates of demographic parameters, often derived from mark-recapture studies, provide the basis for evaluating the status of species at risk, for developing conservation and management strategies and for evaluating the results of current protocols. The hierarchical Bayesian multi-state mark-recapture model presented here permits partitioning of complex parameter variation across space or time, and the simultaneous analysis of multiple data sets results in a marked increase in the precision of estimates derived from sparse capture data. Its structural flexibility should make it a valuable tool for conservation ecologists and wildlife managers. [ABSTRACT FROM AUTHOR]