Disentangling direct and indirect drivers of farmland biodiversity at landscape scale
Data files
Aug 15, 2022 version files 35.76 KB
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ELE_Meier_et_al_2022_data.csv
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README_file.txt
Abstract
To stop the ongoing decline of farmland biodiversity there are increasing claims for a paradigm shift in agriculture, namely from conserving and restoring farmland biodiversity at field scale (α-diversity) to doing it at landscape scale (γ-diversity). However, knowledge on factors driving farmland γ-diversity is currently limited. Here, we quantified farmland γ-diversity in 123 landscapes and analysed direct and indirect effects of abiotic and land-use factors shaping it using structural equation models. The direction and strength of effects of factors shaping γ-diversity were only partially consistent with what is known about factors shaping α-diversity, and indirect effects were often stronger than direct effects or even opposite. Thus, relationships between factors shaping α-diversity cannot simply be up-scaled to γ-diversity, and also indirect effects should no longer be neglected. Finally, we show that local mitigation measures benefit farmland γ-diversity at landscape scale and are therefore a useful tool for designing biodiversity-friendly landscapes.
Methods
We sampled the farmland in 123 study squares of 1 km2 each in Switzerland between 2015 and 2019.
The dataset contains for the farmland per square:
- species richness of plants (Plant_GAMM)
- species richness of butterflies (Butt_GAMM)
- species richness of birds (Bird_GAMM)
- multitrophic species richess (multitrophic_speciesrichness)
- percentage cover of plants with low dispersal ability (Plant_ShortDisp_abu)
- percentage cover of plants with high dispersal ability (Plant_LongDisp_abu)
- abundance of food-specialized butterflies (Butt_FoodSpec_abu)
- abundance of food non-specialized butterflies (Butt_FoodGen_abu)
- abundance of migration-limited birds (Bird_ShortMig_abu)
- abundance of migration non-limited birds (Bird_LongMig_abu)
- mean slope (slope)
- mean yearly precipitation days (pday)
- mean annual degree-days (ddeg)
- share of farmland (perc_farmland)
- share of land-use class “arable” (perc_arable)
- share of land-use class “wood” (perc_wood)
- mean land-use intensity index derived from habitat types (LUI_index)
- number of detailed land-cover types (LC_richness)
- interspersion (spatial intermixing) of the detailed land cover types (LC_interspersion)
- share of grass EFAs (EFA_perc_grass)
- share of arable EFAs (EFA_perc_arable)
- share of wood EFAs (EFA_perc_wood)
- number of different EFA types (EFA_types)
- mean EFA patch size (EFA_size)
- mean nearest distance between EFA patches (EFA distance)
Based on this data, additionally an R script is included to check for multicollinearity and to generate the SEMs in the article.
Usage notes
R