Unravelling the processes between phenotypic plasticity and population dynamics in migratory birds.

Populations can rapidly respond to environmental change via adaptive phenotypic plasticity, which can also modify interactions between individuals and their environment, affecting population dynamics. Bird migration is a highly plastic resource-tracking tactic in seasonal environments. However, the link between the population dynamics of migratory birds and migration tactic plasticity is not well-understood. The quality of staging habitats affects individuals' migration timing and energy budgets in the course of migration and can consequently affect individuals' breeding and overwintering performance, and impact population dynamics. Given staging habitats being lost in many parts of the world, our goal is to investigate responses of individual migration tactics and population dynamics in the face of loss of staging habitat and to identify the key processes connecting them. We started by constructing and analysing a general full-annual-cycle individual-based model with a stylized migratory population to generate hypotheses on how changes in the size of staging habitat might drive changes in individual stopover duration and population dynamics. Next, through the interrogation of survey data, we tested these hypotheses by analysing population trends and stopover duration of migratory waterbirds experiencing the loss of staging habitat. Our modelling exercise led to us posing the following hypotheses: the loss of staging habitat generates plasticity in migration tactics, with individuals remaining on the staging habitat for longer to obtain food due to a reduction in per capita food availability. The subsequent increasing population density on the staging habitat has knock-on effects on population dynamics in the breeding and overwintering stage. Our empirical results were consistent with the modelling predictions. Our results demonstrate how environmental change that impacts one energetically costly life-history stage in migratory birds can have population dynamic impacts across the entire annual cycle via phenotypic plasticity.

Non-parallel changes in songbird migration timing are not explained by changes in stopover duration.

Shifts in the timing of animal migration are widespread and well-documented; however, the mechanism underlying these changes is largely unknown. In this study, we test the hypothesis that systematic changes in stopover duration-the time that individuals spend resting and refueling at a site-are driving shifts in songbird migration timing. Specifically, we predicted that increases in stopover duration at our study site could generate increases in passage duration-the number of days that a study site is occupied by a particular species-by changing the temporal breadth of observations and vise versa. We analyzed an uninterrupted 46-year bird banding dataset from Massachusetts, USA using quantile regression, which allowed us to detect changes in early-and late-arriving birds, as well as changes in passage duration. We found that median spring migration had advanced by 1.04 days per decade; that these advances had strengthened over the last 13 years; and that early-and late-arriving birds were advancing in parallel, leading to negligible changes in the duration of spring passage at our site (+0.07 days per decade). In contrast, changes in fall migration were less consistent. Across species, we found that median fall migration had delayed by 0.80 days per decade, and that changes were stronger in late-arriving birds, leading to an average increase in passage duration of 0.45 days per decade. Trends in stopover duration, however, were weak and negative and, as a result, could not explain any changes in passage duration. We discuss, and provide some evidence, that changes in population age-structure, cryptic geographic variation, or shifts in resource availability are consistent with increases in fall passage duration. Moreover, we demonstrate the importance of evaluating changes across the entire phenological distribution, rather than just the mean, and stress this as an important consideration for future studies.

Accounting for heterogeneity when estimating stopover duration, timing and population size of red knots along the Luannan Coast of Bohai Bay, China.

To successfully perform their long-distance migrations, migratory birds require sites along their migratory routes to rest and refuel. Monitoring the use of so-called stopover and staging sites provides insights into (a) the timing of migration and (b) the importance of a site for migratory bird populations. A recently developed Bayesian superpopulation model that integrates mark-recapture data and ring density data enabled the estimation of stopover timing, duration, and population size. Yet, this model did not account for heterogeneity in encounter (p) and staying (ϕ) probabilities.Here we extended the integrated superpopulation model by implementing finite mixtures to account for heterogeneity in p and ϕ. We used simulations and real data (from 2009-2016) on red knots Calidris canutus, mostly of the subspecies piersmai, staging in Bohai Bay, China, during spring migration to (a) show the importance of accounting for heterogeneity in encounter and staying probabilities to get unbiased estimates of stopover timing, duration, and numbers of migratory birds at staging sites and (b) get accurate stopover parameter estimates for a migratory bird species at a key staging site that is threatened by habitat destruction.Our simulations confirmed that heterogeneity in p affected stopover parameter estimates more than heterogeneity in ϕ, especially when most birds had low p. Bias was particularly severe when most birds had both low ϕ and p. Bias was largest for population size, intermediate for stopover duration and negligible for stopover timing.A total of 50,000-100,000 red knots were estimated to annually stop for 5-9 days in Bohai Bay between 10 and 30 May. This shows the key importance of this staging site for this declining species. There were no clear changes in stopover parameters over time, although stopover population size was substantially lower in 2016 than in preceding years.Our study shows the importance of accounting for heterogeneity in both encounter and staying probabilities for accurately estimating stopover duration and population size and provides an appropriate modeling framework.