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：随着能源消耗的持续增长，扩展能源传输与分配网络已成为必然需求。输电线布设对鸟类种群构成威胁。因此，深入理解输电线导致的鸟类死亡现象及其背后的影响因素，对于降低输电线的影响至关重要。为解决这一问题，现有专业调查与公民科学数据可供使用。前者由专家通过昂贵的标准化采样协议在小范围空间内观测获得，后者则由公民科学家以机会性方式收集。我们构建了完整的贝叶斯空间模型，该模型能够：1）融合专业调查与公民科学数据；2）明确考虑影响专业调查数据的偏好性采样，以及影响公民科学数据质量的因素。所提出的模型属于潜高斯模型家族，因为两种数据类型均被解释为稀疏化的空间点模式，并建模为对数高斯Cox过程。这些模型的规范假设存在一个共同的潜空间过程，该过程是两种数据观测的基础。所提出的模型在模拟数据及挪威特伦德拉格地区输电线导致鸟类死亡的真实数据上均得到应用。模拟研究明确表明，当融合两种数据类型并适当校正收集过程中的偏差因素时，参数估计的准确性显著提高。对输电线导致鸟类死亡的研究显示，输电线网络密度与输电线对鸟类种群构成的风险之间存在明确关联。模型的选择对于该案例研究的结论具有重要影响，因为不同模型对输电线死亡风险与暴露鸟类数量之间关联的估计有所不同。