Environmental and geographical constraints on common swift and barn swallow spring arrival patterns throughout the Iberian Peninsula

• Published in: Journal of Biogeography
• Main Authors: Gordo, Oscar, Sanz, Juan José, Lobo, Jorge M.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2007
• Subjects: Ecology; Ecology, Evolution, Behavior and Systematics; Apus apus
• Online Access: http://dx.doi.org/10.1111/j.1365-2699.2006.01679.x; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1365-2699.2006.01679.x; https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2699.2006.01679.x; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/j.1365-2699.2006.01679.x

Abstract Aim Still poorly understood, the main migratory pathways for most trans‐Saharan species pass through the Iberian Peninsula, which acts as a gateway to the European–African migratory system. Arrival patterns in this region for the common swift ( Apus apus ) and barn swallow ( Hirundo rustica ), of similar morphology and flight capabilities, were described, and the environmental and geographical factors best explaining them were examined, in a search for common ecological constraints on these two migratory species. Location Latitude ranged from 36.02 to 43.68°N, longitude from 9.05°W to 3.17°E, and altitude from 0 to 1595 m a.s.l. for 482 common swift and 812 barn swallow Spanish localities spread widely over the Iberian breeding grounds of the two species. Methods Our data set, covering the years 1960–1990, consisted of 3206 first‐arrival dates for common swifts and 6036 for barn swallows. Forty topographical, climatic, river basin, geographical and spatial variables were used as explanatory variables in general regression models (GRMs). GRMs included polynomial terms up to cubic functions in all variables when they were significant. A backward stepwise selection procedure was applied in all models until only significant terms remained. GRMs were applied in two steps. First, we searched for the best model in each one of the five types of variables (topographical, climatic, river basin, geographical and spatial). To cope with the unavoidable correlation between explanatory variables, the relative importance of each type of variable was assessed by hierarchical variance partitioning. Secondly, we searched for that model able to explain the maximum amount of the observed variability in arrival date. To obtain this model all significant explanatory variables were subjected jointly to a GRM. Spatial variables were then added to this model to take any remaining spatial structure in the data into account. Moran's I autocorrelation coefficient was used to check for spatial autocorrelation. Results Both species ...