1. Pollination is a key ecosystem service for global agriculture but evidence of pollinator population declines is growing. Reliable spatial modelling of pollinator abundance is essential if we are to identify areas at risk of pollination service deficit and effectively target resources to support pollinator populations. Many models exist which predict pollinator abundance but few have been calibrated against observational data from multiple habitats to ensure their predictions are accurate.
2. We selected the most advanced process‐based pollinator abundance model available and calibrated it for bumblebees and solitary bees using survey data collected at 239 sites across Great Britain. We compared three versions of the model: one parameterised using estimates based on expert opinion, one where the parameters are calibrated using a purely data‐driven approach and one where we allow the expert opinion estimates to inform the calibration process.
3. All three model versions showed significant agreement with the survey data, demonstrating this model's potential to reliably map pollinator abundance. However, there were significant differences between the nesting/floral attractiveness scores obtained by the two calibration methods and from the original expert opinion scores.
4. Our results highlight a key universal challenge of calibrating spatially explicit, process‐based ecological models. Notably, the desire to reliably represent complex ecological processes in finely mapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that are often noisy, biased, asynchronous and sometimes inaccurate. Purely data‐driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model‐data agreement over initial expert opinion estimates. We therefore advocate a combined approach where data‐driven calibration and expert opinion are integrated into an iterative Delphi‐like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliable model predictions for ecological systems with expert knowledge gaps and patchy ecological data.

中文翻译：

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可靠地预测授粉媒介的数量：校准基于过程的生态模型的挑战

1. 授粉是全球农业的关键生态系统服务，但传粉媒介数量下降的证据正在增加。如果我们要确定有授粉服务短缺风险的地区，并有效地利用资源来支持授粉媒介种群，那么可靠的授粉媒介丰度空间建模至关重要。存在许多可预测传粉媒介丰度的模型，但很少针对来自多个生境的观测数据进行校准以确保其预测准确。
2. 我们选择了最先进的基于过程的传粉媒介丰度模型，并使用在英国239个地点收集的调查数据对大黄蜂和单蜂进行了校准。我们比较了该模型的三种版本：一种使用基于专家意见的估计值进行参数设置，一种使用纯数据驱动的方法对参数进行校准，另一种允许专家意见估计值用于校准过程。
3. 所有三个模型版本均与调查数据显示出显着一致性，表明该模型具有可靠地绘制授粉媒介丰度的潜力。但是，通过两种校准方法获得的嵌套/植物吸引力得分与原始专家意见得分之间存在显着差异。
4. 我们的结果凸显了校准空间明确的，基于过程的生态模型面临的主要普遍挑战。值得注意的是，在精细映射的景观中可靠地表示复杂的生态过程的愿望必然会产生大量参数，而要用通常是嘈杂，有偏见，异步甚至有时不准确的生态和地理数据进行校准具有挑战性。因此，尽管看起来比最初的专家意见估计要改善模型数据一致性，但纯粹由数据驱动的校准仍可能导致参数值不切实际。因此，我们提倡一种将数据驱动的校准和专家意见整合到迭代式Delphi流程中的组合方法，该过程同时将模型校准和可信度评估相结合。