Wild bee populations are threatened by current agricultural practices in many parts of the world, which may put pollination services and crop yields at risk. Loss of pollination services can potentially be predicted by models that link bee abundances with landscape‐scale land‐use, but there is little knowledge on the degree to which these statistical models are transferable across time and space. This study assesses the transferability of models for wild bee abundance in a mass‐flowering crop across space (from one region to another) and across time (from one year to another). The models used existing data on bumblebee and solitary bee abundance in winter oilseed rape fields, together with high‐resolution land‐use crop‐cover and semi‐natural habitats data, from studies conducted in five different regions located in four countries (Sweden, Germany, Netherlands and the UK), in three different years (2011, 2012, 2013). We developed a hierarchical model combining all studies and evaluated the transferability using cross‐validation. We found that both the landscape‐scale cover of mass‐flowering crops and permanent semi‐natural habitats, including grasslands and forests, are important drivers of wild bee abundance in all regions. However, while the negative effect of increasing mass‐flowering crops on the density of the pollinators is consistent between studies, the direction of the effect of semi‐natural habitat is variable between studies. The transferability of these statistical models is limited, especially across regions, but also across time. Our study demonstrates the limits of using statistical models in conjunction with widely available land‐use crop‐cover classes for extrapolating pollinator density across years and regions, likely in part because input variables such as cover of semi‐natural habitats poorly capture variability in pollinator resources between regions and years.

中文翻译：

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评估统计模型的预测性能，这些统计模型解释了大规模开花作物中的野生蜂丰度

世界许多地方目前的农业作法威胁着野生蜂种群，这可能使授粉服务和农作物产量面临风险。可以通过将蜜蜂数量与景观规模的土地利用联系起来的模型来预测授粉服务的损失，但是对于这些统计模型在时间和空间上可转移的程度知之甚少。这项研究评估了野生蜜蜂丰度模型在大花作物中跨空间（从一个区域到另一区域）和跨时间（从一年到另一年）的可移植性。这些模型使用了来自冬季油料油菜田中大黄蜂和单蜂丰度的现有数据，以及高分辨率的土地利用农作物覆盖和半自然生境数据，这些数据来自于四个国家（瑞典，德国）的五个不同地区进行的研究。 ，荷兰和英国），分别在三个不同的年份（2011年，2012年，2013年）。我们开发了一个结合所有研究的分层模型，并使用交叉验证评估了可移植性。我们发现，大规模开花作物的景观覆盖范围和包括草地和森林在内的永久性半自然栖息地，都是所有地区野生蜂丰富度的重要驱动力。然而，尽管在各研究之间增加大量开花作物对传粉媒介密度的负面影响是一致的，但半自然生境影响的方向在各研究之间是可变的。这些统计模型的可传递性受到限制，尤其是跨地区，也跨时间。