Reliably predicting pollinator abundance: Challenges of calibrating process-based ecological models
Reliably predicting pollinator abundance: Challenges of calibrating process-based ecological models
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DOI：
10.1111/2041-210x.13483
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发表时间：
2020-09-30
影响因子：
6.6
通讯作者：
Oliver, Tom H.
中科院分区：
文献类型：
--
作者：
Gardner, Emma;Breeze, Tom D.;Oliver, Tom H.
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越来越多。如果我们要确定存在授粉服务不足风险的地区，并有效地定位资源以支持传粉媒介种群，可靠的传粉媒介丰度空间模型是必不可少的。有许多模型可以预测传粉媒介的丰度，但很少有模型根据多个栖息地的观测数据进行校准，以确保其预测的准确性。我们选择了目前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独居蜜蜂进行了校准。我们比较了模型的三个版本：一个使用基于专家意见的估计来参数化，一个使用纯粹的数据驱动方法来校准参数，另一个我们允许专家意见估计来通知校准过程。所有三个模型版本都与调查数据显示出显著的一致性，证明了该模型可靠地绘制传粉媒介丰度的潜力。然而，两种校准方法获得的巢/花吸引力得分与原始专家意见得分之间存在显著差异。我们的研究结果强调了校准空间明确的、基于过程的生态模型的一个关键的普遍挑战。值得注意的是，在精细绘制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的参数，这些参数很难与生态和地理数据进行校准，这些数据通常是嘈杂的，有偏见的，异步的，有时是不准确的。因此，纯数据驱动的校准可能导致不切实际的参数值，尽管似乎比最初的专家意见估计提高了模型数据一致性。因此，我们提倡一种结合的方法，将数据驱动的校准和专家意见集成到迭代的德尔菲式过程中，该过程同时结合了模型校准和可信度评估。这可能为在专家知识空白和生态数据不完整的情况下获得现实的参数估计和可靠的模型预测提供了最好的机会。
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. 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. 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. 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.