title: Is there an optimum scale for predicting bird species' distribution in agricultural landscapes?
abstract: Changes in forest cover in agricultural landscapes affect biodiversity. Its management needs some indications about scale to predict occurrence of populations and communities. In this study we considered a forest cover index to predict bird species and community patterns in agricultural landscapes in south-western France. We used generalized linear models for that purpose with prediction driven by wooded areas' spatial distribution at nine different radii. Using 1064 point counts, we modelled the distribution of 10 bird species whose habitat preferences are spread along a landscape opening gradient. We also modelled the distribution of species richness for farmland species and for forest species. We used satellite images to construct a 'wood/non-wood' map and calculated a forest index, considering the surface area of wooded areas at nine radii from 110m to 910m. The models' predictive quality was determined by the AUC (for predicted presences) and ρ (for predicted species richness) criteria. We found that the forest cover was a good predictor of the distribution of seven bird species in agricultural landscapes (mean AUC for the seven species = 0.74 for the radius 110m). Species richness of farmland and forest birds was satisfactorily predicted by the models (ρ = 0.55 and 0.49, respectively, for the radius 110m). The presence of the studied species and species richness metrics were better predicted at smaller scales (i.e. radii between 110 m and 310 m) within the range tested. These results have implications for bird population management in agricultural landscapes since better pinpointing the scale to predict species distributions will enhance targeting efforts to be made in terms of landscape management.
authors.affiliation: INRA, UMR1201 DYNAFOR, INRA-INPT/ENSAT, Chemin de Borde Rouge BP 52627, 31326 Castanet Tolosan Cedex, France; INRA, UR251 PESSAC, F-78026 Versailles Cedex, France. Electronic address: celine.pelosi@versailles.inra.fr.
authors.forename: Céline
authors.initials: C
authors.lastname: Pelosi
authors.affiliation: INRA, UMR1201 DYNAFOR, INRA-INPT/ENSAT, Chemin de Borde Rouge BP 52627, 31326 Castanet Tolosan Cedex, France; UMR CITERES 7324, ENSNP, Blois, France.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Bonthoux
authors.affiliation: INRA, UMR1201 DYNAFOR, INRA-INPT/ENSAT, Chemin de Borde Rouge BP 52627, 31326 Castanet Tolosan Cedex, France; IMBIV, CONICET-UNC, Córdoba, Argentina.
authors.forename: Fabiana
authors.initials: F
authors.lastname: Castellarini
authors.affiliation: INRA, UMR1201 DYNAFOR, INRA-INPT/ENSAT, Chemin de Borde Rouge BP 52627, 31326 Castanet Tolosan Cedex, France.
authors.forename: Michel
authors.initials: M
authors.lastname: Goulard
authors.affiliation: INRA, UMR1201 DYNAFOR, INRA-INPT/ENSAT, Chemin de Borde Rouge BP 52627, 31326 Castanet Tolosan Cedex, France.
authors.forename: Sylvie
authors.initials: S
authors.lastname: Ladet
authors.affiliation: INRA, UMR1201 DYNAFOR, INRA-INPT/ENSAT, Chemin de Borde Rouge BP 52627, 31326 Castanet Tolosan Cedex, France.
authors.forename: Gérard
authors.initials: G
authors.lastname: Balent
mesh_terms: D000383:Agriculture; D000818:Animals; D044822:Biodiversity; D001717:Birds / Q000145:classification*; D065928:Forests; D005602:France
keywords: Agricultural landscape; Birds; Conservation; Forest index; Predictive models; Scale