Reliably predicting pollinator abundance: Challenges of calibrating process‐based ecological models. Issue 12 (30th September 2020) - Gardner, Emma; Breeze, Tom D.; Clough, Yann; Smith, Henrik G.; Baldock, Katherine C. R.; Campbell, Alistair; Garratt, Michael P. D.; Gillespie, Mark A. K.; Kunin, William E.; McKerchar, Megan; Memmott, Jane; Potts, Simon G.; et al.
Abstract: 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. 摘要: 授粉是全球农业的一项关键性生态服务，但证据显示授粉者数量正逐步衰退。如果我们能甄别授粉服务不足的风险区域，并有效分配资源以支援授粉者数量，为授粉昆虫丰度提供可靠的空间模型则为必要措施。多种现有模型可以预测授粉者丰度，但仅少数可与多个栖息地的观测数据校准以确保预测准确。 在可获得的模型中，我们选择了最先进的、基于流程的授粉者丰度模型，使用从英国境内239地收集的熊蜂与独居蜂的调查数据为其校准。我们对比了模型的三种版本：第一种为使用专家估算的参数化版本，第二种版本则纯粹使用数据驱动的方法为参数较准，第三种版本中我们允许专家估算为校准程序提供信息。 三种模型版本都与调查数据呈高度一致，证明此模型具有为授粉者丰度作可靠区域测绘的潜力。尽管如此，由两种校准方式取得的筑巢 / 花朵吸引度评分仍与初始的专家评分存在巨大差异。 我们得到的结果着重显示了空间显式校准和基于流程的生态性模型所普遍存在的关键性挑战。显而易见的，对在精确区域测绘中，能可靠表现复杂生态性流程的需求必然会产生大量参数，这与常为嘈杂、偏颇、不同期或有时不正确的生态性及地理性数据作校准是具有挑战性的。也正因此，尽管以初始专家估算为基础来改进模型数据一致度，纯粹的数据驱动校准仍能导致不切实际的参数值。由此，我们提倡采取将数据驱动校准和专家意见整合为迭代性的、具德尔菲式流程的这样一种结合性方式，将模型校准与可信评估同期合并。这样的方式可能在专家知识空缺和不完整的生态数据前提下，为在生态系统中取得的切合实际的参数估算和可靠模型预测提供最好机会。