Abstract
Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach.
Highlights
Long-term ecological datasets are disproportionately valuable for understanding ecology and informing policy (Hughes et al, 2017)
A land use review of the public land along these creeks led to the reclassification of much of the public land to “conservation reserve” in 2002, with the result that grazing by stock was removed from many creek frontages, and the boundaries of public land were fenced from adjacent private land, preventing other land use incursions into the public estate
Predictions for tree density closely conformed to the midpoints or implied categorical ranges of the subjective survey method
Summary
Long-term ecological datasets are disproportionately valuable for understanding ecology and informing policy (Hughes et al, 2017). In the absence of long-term or large-scale datasets, comparisons between different datasets are often required for analysis through time and space. The methods used to collect each dataset are the same, so that the data have the same errors and biases. It is essential to acknowledge and account for any changes in methods that may have occurred for any comparisons of data (e.g., Magurran et al, 2010). Researchers deal with this problem of dataset differences in various ways. Using category midpoints can produce highly inaccurate estimates when the categories are large, and neglects the uncertainty implicit in the ordinal score, and so a range of alternatives for use of these types of data have been proposed (e.g., Furman et al, 2018; McNellie et al, 2019)
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