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Abstract In the past, the floristic diversity of arable fields has been described in terms of species diversity (SD) and their degree of coverage (C), but never in combination with the recording of the actually flowered species (FS) and their flowering intensity (FI) to striking differences in the cultivation methods on arable land. In relation to SD and C, however, FS and FI may provide important additional information on the functional biodiversity of fields. The aim was therefore to investigate the effects of (a) conventional, (b) organic, and (c) smallholder (never application of herbicides) on the floristic diversity. Using a region in Germany, we investigated SD, C, FS, and FI synchronously in (a), (b), and (c), by 356 vegetation surveys (5 × 5 m plots) conducted in spring and summer in 2019 in winter cereals. Statistical tests were used to analyze the differences between (a), (b), and (c). The medians were used to compare the floristic diversity of (a), (b), and (c) and finally relationships of FS and FI to SD were analyzed in relation to the cultivation methods. Significant differences in SD, C, FS, and FI were found between the (a), (b), and (c) in spring and summer characterized by sharp declines from (c) to (b) to (a). A drastic reduction in floristic diversity from (c) 100 to (b) 52 to (a) 3 was determined. Plants in flower (FS, FI) were very poorly in (a), moderately well to well in (b), and well to very well represented in (c). (C) to (a) was characterized by a sharp decline and from (a) to (b) by sharp increase in floristic diversity. With current acreage proportions of (a) in mind, this would affect, about one third of land area in Germany, associated with a drastic reduction in functional biodiversity for insects.

In the past, the floristic diversity of arable fields has been described in terms of species diversity (SD) and their degree of coverage (C), but never in combination with the recording of the actually flowered species (FS) and their flowering intensity (FI) to striking differences in the cultivation methods on arable land. In relation to SD and C, however, FS and FI may provide important additional information on the functional biodiversity of fields. The aim was therefore to investigate the effects of (a) conventional, (b) organic, and (c) smallholder (never application of herbicides) on the floristic diversity. Using a region in Germany, we investigated SD, C, FS, and FI synchronously in (a), (b), and (c), by 356 vegetation surveys (5 × 5 m plots) conducted in spring and summer in 2019 in winter cereals. Statistical tests were used to analyze the differences between (a), (b), and (c). The medians were used to compare the floristic diversity of (a), (b), and (c) and finally relationships of FS and FI to SD were analyzed in relation to the cultivation methods. Significant differences in SD, C, FS, and FI were found between the (a), (b), and (c) in spring and summer characterized by sharp declines from (c) to (b) to (a). A drastic reduction in floristic diversity from (c) 100 to (b) 52 to (a) 3 was determined. Plants in flower (FS, FI) were very poorly in (a), moderately well to well in (b), and well to very well represented in (c). (C) to (a) was characterized by a sharp decline and from (a) to (b) by sharp increase in floristic diversity. With current acreage proportions of (a) in mind, this would affect, about one third of land area in Germany, associated with a drastic reduction in functional biodiversity for insects., Competing Interests: None declared., (© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs' usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.