Summary 1.In many European agricultural landscapes, species richness is declining considerably. Studies performed at a very large spatial scale are helpful in understanding the reasons for this decline and as a basis for guiding policy. In a unique, large-scale study of 25 agricultural landscapes in seven European countries, we investigated relationships between species richness in several taxa, and the links between biodiversity and landscape structure and management. 2. We estimated the total species richness of vascular plants, birds and five arthropod groups in each 16-km 2 landscape, and recorded various measures of both landscape structure and intensity of agricultural land use. We studied correlations between taxonomic groups and the effects of landscape and land-use parameters on the number of species in different taxonomic groups. Our statistical approach also accounted for regional variation in species richness unrelated to landscape or land-use factors. 3. The results reveal strong geographical trends in species richness in all taxonomic groups. No single species group emerged as a good predictor of all other species groups. Species richness of all groups increased with the area of semi-natural habitats in the landscape. Species richness of birds and vascular plants was negatively associated with fertilizer use. 4. Synthesis and applications. We conclude that indicator taxa are unlikely to provide an effective means of predicting biodiversity at a large spatial scale, especially where there is large biogeographical variation in species richness. However, a small list of landscape and land-use parameters can be used in agricultural landscapes to infer large-scale patterns of species richness. Our results suggest that to halt the loss of biodiversity in these landscapes, it is important to preserve and, if possible, increase the area of semi-natural habitat.

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Observed patterns of species richness at landscape scale (gamma diversity) cannot always be attributed to a specific set of explanatory variables, but rather different alternative explanatory statistical models of similar quality may exist. Therefore predictions of the effects of environmental change (such as in climate or land cover) on biodiversity may differ considerably, depending on the chosen set of explanatory variables. Here we use multimodel prediction to evaluate effects of climate, land-use intensity and landscape structure on species richness in each of seven groups of organisms (plants, birds, spiders, wild bees, ground beetles, true bugs and hoverflies) in temperate Europe. We contrast this approach with traditional best-model predictions, which we show, using cross-validation, to have inferior prediction accuracy. Multimodel inference changed the importance of some environmental variables in comparison with the best model, and accordingly gave deviating predictions for environmental change effects. Overall, prediction uncertainty for the multimodel approach was only slightly higher than that of the best model, and absolute changes in predicted species richness were also comparable. Richness predictions varied generally more for the impact of climate change than for land-use change at the coarse scale of our study. Overall, our study indicates that the uncertainty introduced to environmental change predictions through uncertainty in model selection both qualitatively and quantitatively affects species richness projections.

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Aims:The link between spectral diversity and in-situ plant biodiversity is one promising approach to using remote sensing for biodiversity assessment. Nevertheless, there is little evidence as to whether this link is maintained at fine scales, as well as to how it is influenced by vegetation's vertical complexity. Here we test, at the community level in grasslands, the link between diversity of the spectral signal (S Div ) and taxonomic diversity (T Div ), and the influence of vertical complexity. Methods:We used 196 1.5 m × 1.5 m experimental communities with different biodiversity levels. To measure vertical complexity, we quantified height diversity (H Div ) of the most abundant species in the community. T Div was calculated using the Shannon index based on species cover. Canopy spectral information was gathered using an unmanned aerial vehicle (UAV) mounted with a multi-spectral sensor providing spectral information via six 10-nm bands covering the visible and near-infrared region at a spatial resolution of 3 cm. We measured S Div in a core area of 1 m ×1 m within the communities as mean Euclidean distance of all pixels in a feature space spanned between the two first components of a PCA calculated for the complete raster stack. We modelled S Div through mixed-effect linear models, using T Div , H Div , and their interaction as fixed-effect predictors.Results: Contrary to our expectations, T Div was negatively linked to S Div . The diversity in plant height was positively related to S Div . More importantly, diversity in plant height and T Div had a significant negative interaction, meaning the more complex the vegetation was in terms of height, the more the S Div -T Div relationship became negative. Conclusions:Our results suggest that in order to exploit the S Div -T Div link for monitoring purposes, it needs to be contextualized. Moreover, the results highlight that communities' functional characteristics (i.e. plant height) mediate such a link, calling for new insights into the relation between S Div and functional diversity.

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In the light of unprecedented change in global biodiversity, realtime and accurate ecosystem and biodiversity assessments are becoming increasingly essential. Nevertheless, estimation of biodiversity using ecological field data can be difficult for several reasons. For instance, for very large areas, it is challenging to collect data that provide reliable information. Some of these restrictions in Earth observation can be avoided through the use of remote sensing approaches.Various studies have estimated biodiversity on the basis of the Spectral Variation Hypothesis (SVH). According to this hypothesis, spectral heterogeneity over the different pixel units of a spatial grid reflects a higher niche heterogeneity, allowing more organisms to coexist. Recently, the spectral species concept has been derived, following the consideration that spectral heterogeneity at a landscape scale corresponds to a combination of subspaces sharing a similar spectral signature. With the use of high resolution remote sensing data, on a local scale, these subspaces can be identified as separate spectral entities, the so called "spectral species". Our approach extends this concept over wide spatial extents and to a higher level of biological organization. We applied this method to MODIS imagery data across Europe.