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Abstract Assessing trends in the relative abundance of populations is a key yet complex issue for management and conservation. This is a major aim of many large‐scale censusing schemes such as the International Waterbird Count (IWC). However, owing to the lack of sampling strategy and standardization, such schemes likely suffer from biases due to spatial heterogeneity in sampling effort. Despite huge improvements of the statistical tools that allow tackling these statistical issues (e.g., GLMM, Bayesian inference), many conservationists still prefer to rely on stand‐alone turn‐key statistical tools, often violating the prerequisites put forward by the developers of these tools. Here, we propose a straightforward and flexible approach to tackle the typical statistical issues one can encounter when analyzing count data of monitoring schemes such as the IWC. We rely on IWC counts of the declining common pochard populations of the Northwest European flyway as a case study (period 2002–2012). To standardize the size of sampling units and mitigate spatial autocorrelation, we grouped sampling sites using a 75 × 75 km grid cells overlaid over the flyway of interest. Then, we used a hierarchical modeling approach, assessing population trends with random effects at two spatial scales (grid cells, and sites within grid cells) in order to derive spatialized values and to compute the average population trend at the whole flyway scale. Our approach allowed to tackle many statistical issues inherent to this type of analysis but often neglected, including spatial autocorrelation. Concerning the case study, our main findings are that: (1) the northwestern population of common pochards experienced a steep decline (4.9% per year over the 2002–2012 period); (2) the decline was more pronounced at high than low latitude (11.6% and 0.5% per year at 60° and 46° of latitude, respectively); and, (3) the decline was independent of the initial number of individuals in a given site (random across sites). Beyond the case study of the common pochard, our study provides a conceptual statistical framework for estimating and assessing potential drivers of population trends at various spatial scales.