Abstract 1. Understanding the mechanisms by which environmental change has impacted natural processes typically requires good time series for the environmental change. Unfortunately, other than for climate, detailed time series of historical environments are scarce. In many instances, researchers can only collate disparate and sometimes fragmented information from the literature or from historical or pre-historical sources. 2. Here we apply modern statistical methods to reconstruct a recent historical time series of environmental change from sparse data collected from heterogeneous sources. Specifically, we deal with record irregularity and the varying levels of uncertainty associated with each datum using state-space models in a hierarchical Bayesian framework. 3. As an example, we reconstruct a time series of a simple landscape feature (hedgerow length) over a large spatial scale (Britain) over a long time period (50 years), by combining both stock estimates and rate of change estimates, gathered from different historical sources. 4. We illustrate the utility of the method by relating the population trends of a hedgerow nesting passerine bird, the yellowhammer Emberiza citrinella to the reconstructed trends in hedgerow length. Population density was closely related to hedgerow availability, suggesting a potential key role for nesting habitat loss in the yellowhammer decline. 5. The modelling framework we used is flexible and general. The method can be adapted to reconstruct time series of any environmental variables from a variety of sparse and heterogeneous historical sources.