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Agricultural buffer zone thresholds to safeguard functional bee diversity: Insights from a community modeling approach - PubMed

  • ️Sat Jan 01 2022

. 2022 Mar 18;12(3):e8748.

doi: 10.1002/ece3.8748. eCollection 2022 Mar.

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Agricultural buffer zone thresholds to safeguard functional bee diversity: Insights from a community modeling approach

Jette Reeg et al. Ecol Evol. 2022.

Abstract

Wild bee species are important pollinators in agricultural landscapes. However, population decline was reported over the last decades and is still ongoing. While agricultural intensification is a major driver of the rapid loss of pollinating species, transition zones between arable fields and forest or grassland patches, i.e., agricultural buffer zones, are frequently mentioned as suitable mitigation measures to support wild bee populations and other pollinator species. Despite the reported general positive effect, it remains unclear which amount of buffer zones is needed to ensure a sustainable and permanent impact for enhancing bee diversity and abundance. To address this question at a pollinator community level, we implemented a process-based, spatially explicit simulation model of functional bee diversity dynamics in an agricultural landscape. More specifically, we introduced a variable amount of agricultural buffer zones (ABZs) at the transition of arable to grassland, or arable to forest patches to analyze the impact on bee functional diversity and functional richness. We focused our study on solitary bees in a typical agricultural area in the Northeast of Germany. Our results showed positive effects with at least 25% of virtually implemented agricultural buffer zones. However, higher amounts of ABZs of at least 75% should be considered to ensure a sufficient increase in Shannon diversity and decrease in quasi-extinction risks. These high amounts of ABZs represent effective conservation measures to safeguard the stability of pollination services provided by solitary bee species. As the model structure can be easily adapted to other mobile species in agricultural landscapes, our community approach offers the chance to compare the effectiveness of conservation measures also for other pollinator communities in future.

Keywords: agricultural landscape; buffer zones; community model; functional traits; solitary bees; spatially explicit.

© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

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Conflict of interest statement

The authors declare no conflict of interests.

Figures

FIGURE 1
FIGURE 1

Overview of the different processes within the model. A detailed description can be found in the ODD protocol (Appendix S1). FT, functional bee type

FIGURE 2
FIGURE 2

Functional bee type community after 50 years against the amount of virtually implemented agricultural buffer zones (ABZs) represented by (a) the mean number of functional types (with a population size larger than 0), (b) the mean Shannon diversity index, (c) quasi‐extinction risk of a functional type within the landscape, and (d) community weighted mean values of different traits. Quasi‐extinction risk is defined as the mean probability of a functional type to fall below a threshold of 10,000 individuals in the landscape at least once within the last 10 years of the simulation (40–50). Lines and bars show the mean of the 4 different landscape cluster (colors), and the gray dashed line and gray bars the mean for all simulated landscapes. Error bars show the standard deviation. ABZs are defined as cells in the arable land use class that are located at the border to forest or grassland patches. Note that each landscape has a different number of potential ABZs (see Appendix S4). Overall, twelve 3 × 3 km2 landscapes were simulated and grouped into 4 landscape clusters (3 landscapes per cluster) with similar landscape parameters: Cluster 1: low heterogeneity, high amount of arable land, and medium amount of natural land; Cluster 2: high heterogeneity, low amount of arable land, and high amount of natural land; Cluster 3: medium heterogeneity, high‐low amount of arable land, and low amount of natural land; Cluster 4: medium heterogeneity, high amount of arable land, and medium amount of natural land. Simulations were repeated 10 times

FIGURE 3
FIGURE 3

Feeding intensity within each grid cell (20 × 20 m2) of one exemplary landscape (LID: 1c) for different amounts of virtually implemented agricultural buffer zones (ABZs) (see Appendix S3 for all other landscape rasters). Feeding intensity was calculated as the sum of the resource uptake of all foraging functional bee type populations within the specific grid cell, exactly as in the growth function of the model (see Section 2). The layers show the last year of one Monte‐Carlo repetition. ABZs can be easily detected as grid cells with highest feeding intensity; but also near the ABZs, the arable and the nonarable patch resource uptakes are increasing with the amount of virtually implemented ABZs

FIGURE 4
FIGURE 4

Parameter sensitivity. The sensitivity is represented by the relative change in the number of functional types compared with the mean number of functional types in simulations with original values as shown in Figure 2a. Each box represents one of the tested parameters; the lines show the mean relative change in number of functional types. Colors represent the percentual change in the specific parameter (−25%, −10%, 10%, and 25%). Gray ribbons show the minimal and maximal variation occurring in the original simulations. Local sensitivity analysis was conducted on one representative landscape (LID: 1c, see Appendix S3). ABZs, agricultural buffer zones

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