Integrating spatial and ecological information into comprehensive biodiversity monitoring on agricultural land.

Biodiversity loss on agricultural land is a major concern. Comprehensive monitoring is needed to quantify the ongoing changes and assess the effectiveness of agri-environmental measures. However, current approaches to monitoring biodiversity on agricultural land are limited in their ability to capture the complex pattern of species and habitats. Using a real-world example of plant and habitat monitoring on Swiss agricultural land, we show how meaningful and efficient sampling can be achieved at the relevant scales. The multi-stage sampling design of this approach uses unequal probability sampling in combination with intermediate small-scale habitat sampling to ensure broad representation of regions, landscape types, and plant species. To achieve broad coverage of temporary agri-environmental measures, the baseline survey on permanent plots is complemented by dynamic sampling of these specific areas. Sampling efficiency and practicality are ensured at all stages of sampling through modern sampling techniques, such as unequal probability sampling with fixed sample size, self-weighting, spatial spreading, balancing on additional information, and stratified balancing. In this way, the samples are well distributed across ecological and geographic space. Despite the high complexity of the sampling design, simple estimators are provided. The effects of stratified balancing and clustering of samples are demonstrated in Monte Carlo simulations using modelled habitat data. A power analysis based on actual survey data is also presented. Overall, the study could serve as a useful example for improving future biodiversity monitoring networks on agricultural land at multiple scales.

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.

Tree species distribution in temperate forests is more influenced by soil than by climate.

Knowledge of the ecological requirements determining tree species distributions is a precondition for sustainable forest management. At present, the abiotic requirements and the relative importance of the different abiotic factors are still unclear for many temperate tree species. We therefore investigated the relative importance of climatic and edaphic factors for the abundance of 12 temperate tree species along environmental gradients. Our investigations are based on data from 1,075 forest stands across Switzerland including the cold-induced tree line of all studied species and the drought-induced range boundaries of several species. Four climatic and four edaphic predictors represented the important growth factors temperature, water supply, nutrient availability, and soil aeration. The climatic predictors were derived from the meteorological network of MeteoSwiss, and the edaphic predictors were available from soil profiles. Species cover abundances were recorded in field surveys. The explanatory power of the predictors was assessed by variation partitioning analyses with generalized linear models. For six of the 12 species, edaphic predictors were more important than climatic predictors in shaping species distribution. Over all species, abundances depended mainly on nutrient availability, followed by temperature, water supply, and soil aeration. The often co-occurring species responded similar to these growth factors. Drought turned out to be a determinant of the lower range boundary for some species. We conclude that over all 12 studied tree species, soil properties were more important than climate variables in shaping tree species distribution. The inclusion of appropriate soil variables in species distribution models allowed to better explain species' ecological niches. Moreover, our study revealed that the ecological requirements of tree species assessed in local field studies and in experiments are valid at larger scales across Switzerland.