BUSCADOR RECOLECTA

Zeale sequences Sequences filtered by DAMe using 2/3 filtering strategy and a minimum copy number of 2. zeale_sequences.zip Epp sequences Sequences filtered by DAMe using 2/3 filtering strategy and a minimum copy number of 2. epp_sequences.zip, The interaction between agricultural production and wildlife can shape, and even condition, the functioning of both systems. In this study we i) explored the degree to which a widespread European bat, namely the common bent-wing bat Miniopterus schreibersii, consumes crop-damaging insects at a continental scale, and ii) tested whether its dietary niche is shaped by the extension and type of agricultural fields. We employed a dual-primer DNA metabarcoding approach to characterise arthropod 16S and COI DNA sequences within bat faecal pellets collected across 16 Southern European localities, to first characterise the bat species’ dietary niche, secondly measure the incidence of agricultural pests across their ranges, and thirdly assess whether geographical dietary variation responds to climatic, landscape diversity, agriculture type and vegetation productivity factors. We detected 12 arthropod orders, among which lepidopterans were predominant. We identified >200 species, 44 of which are known to cause agricultural damage. Pest species were detected at all but one sampling site and in 94% of the analysed samples. Furthermore, the dietary diversity of M. schreibersii exhibited a negative linear relation with the area of intensive agricultural fields, thus suggesting crops restrict the dietary niche of bats to prey taxa associated with agricultural production within their foraging range. Overall our results imply that M. schreibersii might be a valuable asset for biological pest suppression in a variety of agricultural productions, and highlight the dynamic interplay between wildlife and agricultural systems., Peer reviewed

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Morocco

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Gold Coast - Ghana

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Madagascar

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Tunisia

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: German South West Africa (Namibia)

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Réunion

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Nigeria

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Mauritius

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: German West Africa (Togo)