Departure, routing and landing decisions of long-distance migratory songbirds in relation to weather

Abstract

Migrating birds flexibly adjust their individual migratory decisions, i.e. departing, routing and landing, based on intrinsic (e.g. energy stores) and extrinsic (e.g. landscape features and weather) factors modulating the endogenous stimuli. So far, these decisions have mostly been studied separately. Notably, we lack information on which factors landing decisions during active flight are based on. Therefore, we simultaneously recorded all three decisions in free-flying long-distance migratory songbirds in a coastal stopover area via regional-scale radio-telemetry and related them to the prevailing weather. Birds departed under favourable weather conditions resulting in specific nights with increased departure probability. Once departed, birds could either fly offshore or take a route along the coast, which was predicted by wind support. Radio-tracking revealed that departed individuals more likely interrupted their migratory endurance flight under overcast or headwind conditions. Studying departure, routing and landing decisions in concert, we highlight the importance of weather as a common driver across all migratory decisions. By radio-tracking individuals between stopovers, we provide evidence that avoidance of adverse weather conditions is an important function of stopover. Understanding how birds adjust migratory decisions and how they affect the timing of migration and survival is key to link migration performance to individual fitness.

Keywords: bird migration; departure decision; landing decision; routing; stopover; weather.

Figure 1.

Maps of the study area illustrating individually detected flights per species (shown are begin and end locations of each flight connected by straight lines). Circles indicate receiver locations of the radio-telemetry network within the map section operational during the study (for temporal changes, see www.motus.org). The locations of tag deployment are indicated by triangles. Dashed black lines indicate threshold latitude and longitude for flight categorization as offshore (to the west) or onshore (to the east). Location of the island of Helgoland is indicated.

Figure 2.

Time-dependent influence of wind on the departure decision from stopover during autumn, predicted by the proportional hazards model. Predictions are given for the 25% (solid lines) and 75% quantiles (dashed lines) of observed minimum stopover duration of 6 and 12 days together with 95% credible intervals (shaded areas). For time-dependent results in more detail, see electronic supplementary material, S6. Predictions were made for rainless conditions with the remaining numeric model covariates set to their means. Estimates are given in table 2. Raw data of observed parameter values per daily departure decisions of each bird from tag deployment until departure are indicated by dots and boxplots.

Figure 3.

Time-dependent influence of weather parameters on the departure decision from stopover during autumn, predicted by the proportional hazards model. Predictions are given for the 25% (solid lines or dots) and 75% quantiles (dashed lines or triangles) of observed minimum stopover duration of 6 and 12 days together with 95% credible intervals (shaded areas, bars). For time-dependent results in more detail, see electronic supplementary material, S6. Predictions for pressure change and cloud cover were made for rainless conditions, and all remaining numeric model covariates were set to their means. Estimates are given in table 2. Raw data of observed parameter values per daily departure decisions of each bird from tag deployment until departure are indicated by dots and boxplots or given as the number of observed cases.

Figure 4.

Influence of eastward ($u$) wind component on routing decision, predicted by the final binary logistic regression. Predictions (line) and 95% credible intervals (shaded area) are given together with the observed raw data (dots, boxplots). Predictions were made with the remaining numeric model covariates set to their means.

Figure 5.

Influence of change in northward ($v$) wind and cloud cover on landing decision of individually tracked night-migratory songbirds during migratory endurance flight, predicted by the final binary logistic regression. Positive values of changing northward wind can be due to either freshening-up northward wind or a drop in southward wind. Predictions (lines) and 95% credible intervals (shaded areas) are given together with the observed raw data (dots, boxplots). Predictions were made with the remaining numeric model covariates set to their means.