Disentangling direct and indirect drivers of farmland biodiversity at landscape scale

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.