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Vegetation maps based on remote sensing are informative predictors of habitat selection of grassland birds across a wetness gradient

Abstract : tVegetation is a major environmental factor influencing habitat selection in bird species. High resolutionmapping of vegetation cover is essential to model the distribution of populations and improve the man-agement of breeding habitats. However, the task is challenging for grassland birds because microhabitatvariations relevant at the territory scale cannot be measured continuously over large areas to delineateareas of higher suitability. Remote sensing may help to circumvent this problem. We addressed thisissue by using SPOT 5 imagery and phytosociological data. We mapped grassland vegetation in a flood-plain using two methods. We (i) mapped the continuous Ellenberg index of moisture and (ii) identified5 vegetation classes distributed across the wetness gradient. These two methods produced consistentoutput maps, but they also provided complementary results. Ellenberg index is a valuable proxy for soilmoisture while the class approach provided more information about vegetation structure, and possiblytrophic resources. In spite of the apparent uniformity of meadows, our data show that birds do not settlerandomly along the moisture and vegetation gradients. Overall birds tend to avoid the driest vegetationclasses, i.e. the highest grounds. Thus, vegetation maps based on remote sensing could be valuable toolsto study habitat selection and niche partition in grassland bird communities. It is also a valuable tool forconservation and habitat management.
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Submitted on : Wednesday, June 10, 2015 - 2:46:10 PM
Last modification on : Tuesday, October 6, 2020 - 11:06:03 AM
Long-term archiving on: : Friday, September 11, 2015 - 12:10:29 PM

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Aurélien G. Besnard, Aurélie Davranche, Sébastien Maugenest, Jan-Bernard Bouzillé, Alain Vian, et al.. Vegetation maps based on remote sensing are informative predictors of habitat selection of grassland birds across a wetness gradient. Ecological Indicators, Elsevier, 2015, 58, pp.47-54. ⟨10.1016/j.ecolind.2015.05.033⟩. ⟨hal-01161554⟩

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