Inter-individual consistency in habitat selection patterns and spatial range constraints of female little bustards during the non-breeding season

Abstract

BackgroundIdentifying the factors that affect ranging behavior of animals is a central issue to ecology and an essential tool for designing effective conservation policies. This knowledge provides the information needed to predict the consequences of land-use change on species habitat use, especially in areas subject to major habitat transformations, such as agricultural landscapes. We evaluate inter-individual variation relative to environmental predictors and spatial constraints in limiting ranging behavior of female little bustards (Tetrax tetrax) in the non-breeding season. Our analyses were based on 11 females tracked with GPS during 5 years in northeastern Spain. We conducted deviance partitioning analyses based on different sets of generalized linear mixed models constructed with environmental variables and spatial filters obtained by eigenvector mapping, while controlling for temporal and inter-individual variation.ResultsThe occurrence probability of female little bustards in response to environmental variables and spatial filters within the non-breeding range exhibited inter-individual consistency. Pure spatial factors and joint spatial-habitat factors explained most of the variance in the models. Spatial predictors representing aggregation patterns at ~ 18 km and 3–5 km respectively had a high importance in female occurrence. However, pure habitat effects were also identified. Terrain slope, alfalfa, corn stubble and irrigated cereal stubble availability were the variables that most contributed to environmental models. Overall, models revealed a non-linear negative effect of slope and positive effects of intermediate values of alfalfa and corn stubble availability. High levels of cereal stubble in irrigated land and roads had also a positive effect on occurrence at the population level.ConclusionsOur results provide evidence that female little bustard ranging behavior was spatially constrained beyond environmental variables during the non-breeding season. This pattern may result from different not mutually exclusive processes, such as cost–benefit balances of animal movement, configurational heterogeneity of environment or from high site fidelity and conspecific attraction. Measures aimed at keeping alfalfa availability and habitat heterogeneity in open landscapes and flat terrains, in safe places close to breeding grounds, could contribute to protect little bustard populations during the non-breeding season.Electronic supplementary materialThe online version of this article (10.1186/s12898-018-0205-9) contains supplementary material, which is available to authorized users.

Metabarcoding insights into the diet and trophic diversity of six declining farmland birds

“…With all this in mind, the little bustard is possibly the most vulnerable species to changes in trophic resources, such as the loss of weed and arthropod biodiversity and abundance resulting from agriculture intensification 15 . During winter, this species has been seen using legume fields, stubbles and fallow lands 45 – 47 . Alfalfa fields provide an important plant trophic resource that the species uses throughout the year 30 , 45 , 46 and also provide a high abundance of arthropods 46 .…”

“…During winter, this species has been seen using legume fields, stubbles and fallow lands 45 – 47 . Alfalfa fields provide an important plant trophic resource that the species uses throughout the year 30 , 45 , 46 and also provide a high abundance of arthropods 46 . Stubbles, although they maintain a lower abundance of arthropods, are positively selected because their vegetation structure facilitates the location and capture of prey 46 .…”

Influence of individual biological traits on GPS fix-loss errors in wild bird tracking

“…Thirdly, we performed a deviance partitioning analysis 49 to evaluate the effect on FLR of the single and joint contributions of each of the three groups of variables comparing by basic algebra the percentage of the explained conditional R 2 of each of the best generalized linear mixed models (GLMMs) 50 built including the aforementioned biological, technical and environmental factors as fixed factors (where applicable) and the individual as a random factor. Thus, we built seven separate GLMMs to evaluate: (1) the single contribution of the biological factors, (2) the single contribution of the technical factor; (3) the single contribution of the environmental factor; (4) the joint contribution of the biological and technical factors; (5) the joint contribution of the biological and environmental factors; (6) the joint contribution of the technical and environmental factors; and (7) the joint contribution of the biological, technical and environmental factors (see more details about how to perform a deviance partitioning analysis in 51 ).…”