The constant increase in energy consumption has created the necessity of extending the energy transmission and distribution network. Placement of powerlines represent a risk for bird population. Hence, better understanding of deaths induced by powerlines, and the factors behind them are of paramount importance to reduce the impact of powerlines. To address this concern, professional surveys and citizen science data are available. While the former data type is observed in small portions of the space by experts through expensive standardized sampling protocols, the latter is opportunistically collected by citizen scientists. We set up full Bayesian spatial models that 1) fusion both professional surveys and citizen science data and 2) explicitly account for preferential sampling that affects professional surveys data and for factors that affect the quality of citizen science data. The proposed models are part of the family of latent Gaussian models as both data types are interpreted as thinned spatial point patterns and modeled as log-Gaussian Cox processes. The specification of these models assume the existence of a common latent spatial process underlying the observation of both data types. The proposed models are used both on simulated data and on real-data of powerline-induced death of birds in the Trondelag in Norway. The simulation studies clearly show increased accuracy in parameter estimates when both data types are fusioned and factors that bias their collection processes are properly accounted for. The study of powerline-induced deaths shows a clear association between the density of the powerline network and the risk that powerlines represent for bird populations. The choice of model is relevant for the conclusions from this case study as different models estimated the association between risk of powerline-induced deaths and the amount of exposed birds differently.
翻译:能源消耗的不断增加使得有必要扩大能源传输和分配网络。电线的布局对鸟类构成风险。因此,更好地了解电线造成的死亡以及电线背后的各种因素对于减少电线的影响至关重要。为了解决这一关切,可以进行专业调查和公民科学数据。专家通过昂贵的标准化采样程序在空间的一小部分观察到了前一种数据类型,而后者则由公民科学家随机收集。我们建立了完整的巴伊西亚空间模型,1)专业调查和公民科学数据相结合,2)明确说明优选抽样,影响专业调查数据以及影响公民科学数据质量的因素。拟议的模型是隐性高斯模型的一部分,因为这两种数据类型都被解释为薄度的空间点模式,并被建模为对古斯考克斯过程的模型。这些模型的规格假定存在一种共同的潜伏空间进程,作为两种数据类型观测的基础。拟议模型既用于模拟数据,又用于对Trondeus调查数据数据和影响公民科学数据质量的因素进行精度的精度抽样抽样抽样抽样抽样抽样抽样抽样抽样调查。在挪威的模型和指数中清楚地显示,这种指数的精确度的指数分析显示,这种指数的指数是不同的死亡率的指数的精确度。</s>