Exploring the effects of habitat management on grassland biodiversity: A case study from northern Serbia

Milić, Dubravka and Rat, Milica and Bokić, Bojana and Mudri-Stojnić, Sonja and Milošević, Nemanja and Sukur, Nataša and Jakovetić, Dušan and Radak, Boris and Tot, Tamara and Vujanović, Dušanka and Anačkov, Goran and Radišić, Dimitrije and Chiang, Tzen-Yuh (2024) Exploring the effects of habitat management on grassland biodiversity: A case study from northern Serbia. PLOS ONE, 19 (3). e0301391. ISSN 1932-6203

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Abstract

Grasslands represent a biodiversity hotspot in the European agricultural landscape, their restoration is necessary and offers a great opportunity to mitigate or halt harmful processes. These measures require a comprehensive knowledge of historical landscape changes, but also adequate management strategies. The required data was gathered from the sand grasslands of northern Serbia, as this habitat is of high conservation priority. This area also has a long history of different habitat management approaches (grazing and mowing versus unmanaged), which has been documented over of the last two decades. This dataset enabled us to quantify the effects of different measures across multiple taxa (plants, insect pollinators, and birds). We linked the gathered data on plants, pollinators, and birds with habitat management measures. Our results show that, at the taxon level, the adopted management strategies were beneficial for species richness, abundance, and composition, as the highest diversity of plant, insect pollinator, and bird species was found in managed areas. Thus, an innovative modelling approach was adopted in this work to identify and explain the effects of management practices on changes in habitat communities. The findings yielded can be used in the decision making as well as development of new management programmes. We thus posit that, when restoring and establishing particular communities, priority needs to be given to species with a broad ecological response. We recommend using the decision tree as a suitable machine learning model for this purpose.

Item Type: Article
Subjects: GO for STM > Multidisciplinary
Depositing User: Unnamed user with email support@goforstm.com
Date Deposited: 29 Mar 2024 03:55
Last Modified: 29 Mar 2024 03:55
URI: http://archive.article4submit.com/id/eprint/2745

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