Quantifying the total number of individuals (abundance) of species is the basis for spatial ecology and biodiversity conservation. Abundance data are mostly collected through professional surveys as part of monitoring programs, often at a national level. These surveys rarely follow exactly the same sampling protocol in different countries, which represents a challenge for producing biogeographical abundance maps based on the transboundary information available covering more than one country. Moreover, not all species are properly covered by a single monitoring scheme, and countries typically collect abundance data for target species through different monitoring schemes. We present a new methodology to model total abundance by merging count data information from surveys with different sampling protocols. The proposed methods are used for data from national breeding bird monitoring programs in Norway and Sweden. Each census collects abundance data following two different sampling protocols in each country, i.e., these protocols provide data from four different sampling processes. The modeling framework assumes a common Gaussian Random Field shared by both the observed and true abundance with either a linear or a relaxed linear association between them. The models account for particularities of each sampling protocol by including terms that affect each observation process, i.e., accounting for differences in observation units and detectability. Bayesian inference is performed using the Integrated Nested Laplace Approximation (INLA) and the Stochastic Partial Differential Equation (SPDE) approach for spatial modeling. We also present the results of a simulation study based on the empirical census data from mid-Scandinavia to assess the performance of the models under model misspecification. Finally, maps of the expected abundance of birds in our study region in mid-Scandinavia are presented with uncertainty estimates. We found that the framework allows for consistent integration of data from surveys with different sampling protocols. Further, the simulation study showed that models with a relaxed linear specification are less sensitive to misspecification, compared to the model that assumes linear association between counts. Relaxed linear specifications of total bird abundance in mid-Scandinavia improved both goodness of fit and the predictive performance of the models.

量化物种个体总数（丰度）是空间生态学和生物多样性保护的基础。丰度数据主要通过专业调查收集，作为监测计划的一部分，通常是在国家层面。这些调查在不同国家很少遵循完全相同的采样协议，这对根据涵盖多个国家的可用跨界信息制作生物地理丰度图提出了挑战。此外，并非所有物种都被单一监测计划适当覆盖，各国通常通过不同的监测计划收集目标物种的丰度数据。我们提出了一种新方法，通过将来自调查的计数数据信息与不同的采样协议合并来模拟总丰度。建议的方法用于挪威和瑞典国家繁殖鸟类监测计划的数据。每次普查按照每个国家的两种不同抽样协议收集丰度数据，即这些协议提供来自四种不同抽样过程的数据。建模框架假设观察到的丰度和真实丰度共享一个共同的高斯随机场，它们之间存在线性或松弛的线性关联。这些模型通过包括影响每个观察过程的术语来解释每个采样协议的特殊性，即考虑观察单位和可检测性的差异。贝叶斯推理是使用集成嵌套拉普拉斯近似 (INLA) 和随机偏微分方程 (SPDE) 方法进行空间建模的。我们还展示了基于斯堪的纳维亚中部的经验普查数据的模拟研究结果，以评估模型在模型错误规格下的性能。最后，我们在斯堪的纳维亚半岛中部研究区域的预期鸟类丰度地图显示了不确定性估计。我们发现该框架允许将来自调查的数据与不同的抽样协议进行一致的整合。此外，模拟研究表明，与假设计数之间存在线性关联的模型相比，具有宽松线性规范的模型对错误指定的敏感性较低。斯堪的纳维亚中部鸟类总丰度的宽松线性规范提高了模型的拟合优度和预测性能。我们在斯堪的纳维亚半岛中部的研究区域中预期的鸟类数量地图带有不确定性估计值。我们发现该框架允许将来自调查的数据与不同的抽样协议进行一致的整合。此外，模拟研究表明，与假设计数之间存在线性关联的模型相比，具有宽松线性规范的模型对错误指定的敏感性较低。斯堪的纳维亚中部鸟类总丰度的宽松线性规范提高了模型的拟合优度和预测性能。我们在斯堪的纳维亚半岛中部的研究区域中预期的鸟类数量地图带有不确定性估计值。我们发现该框架允许将来自调查的数据与不同的抽样协议进行一致的整合。此外，模拟研究表明，与假设计数之间存在线性关联的模型相比，具有宽松线性规范的模型对错误指定的敏感性较低。斯堪的纳维亚中部鸟类总丰度的宽松线性规范提高了模型的拟合优度和预测性能。模拟研究表明，与假设计数之间存在线性关联的模型相比，具有宽松线性规范的模型对错误指定的敏感性较低。斯堪的纳维亚中部鸟类总丰度的宽松线性规范提高了模型的拟合优度和预测性能。模拟研究表明，与假设计数之间存在线性关联的模型相比，具有宽松线性规范的模型对错误指定的敏感性较低。斯堪的纳维亚中部鸟类总丰度的宽松线性规范提高了模型的拟合优度和预测性能。