Low and annually variable migratory connectivity in a long‐distance migrant: Whinchats Saxicola rubetra may show a bet‐hedging strategy

The spatial scale of non‐breeding areas used by long‐distance migrant animals can vary from specific, relatively small non‐breeding areas for each independent breeding population (high connectivity) to a distribution over a large non‐breeding area with mixing of breeding populations (low connectivity). Measuring variation in the degree of connectivity and how it arises is crucial to predict how migratory animals can respond to global habitat and climate change because low connectivity is likely to be an adaptation to environmental uncertainty. Here, we assess whether use of non‐breeding areas in a long‐distance migrant may be stochastic by measuring the degree of connectivity, and whether it is annually variable. Twenty‐nine wintering Whinchats tagged with geolocators over 2 years within 40 km² in central Nigeria were found to be breeding over 2.55 million km² (26% of the land area of Europe), without an asymptote being approached in the relationship between area and sample size. Ranges differed in size between years by 1.51 million km² and only 15% of the total breeding range across both years overlapped (8% overlap between years when only first‐year birds were considered), well above the range size difference and below the proportion of overlap that would be predicted from two equivalent groups breeding at random locations within the observed range. Mean distance between breeding locations (i.e. migratory spread) differed significantly between years (604 ± 18 km in 2013 and 869 ± 33 km in 2014). The results showed very low and variable connectivity that was reasonably robust to the errors and assumptions inherent in the use of geolocators, but with the caveat of having only ranges of 2 years to compare, and the sensitivity of range to the breeding locations of a small number of individuals. However, if representative, the results suggest the scope for between‐year variation (cohort effects) to determine migrant distribution on a large scale. Furthermore, for species with similarly low connectivity, we would predict breeding population trends to reflect average conditions across large non‐breeding areas: thus, as large areas of Africa become subject to habitat loss, migrant populations throughout Europe will decline.