Using big ‘messy’ data to model species distributions for spatial conservation management

Spatial conservation management—via Protected Areas and other spatial measures—is key to reversing the rapid global decline in biodiversity. International spatial targets have been agreed to tackle the biodiversity crisis, yet spatial management is not being globally implemented at the requisite pace to achieve them. Species distribution modelling (SDM) is widely used to support spatial management, increasingly drawing on large, open‐source datasets. However, these datasets are often ‘messy’, being subject to bias and quality issues, which are especially pronounced for species that are difficult to access and sample. Carefully balanced trade‐offs between data volume and quality are therefore needed to enable timely and effective management. In this study, we examine these trade‐offs for a challenging case study species—the bivalve Arctica islandica —at the UK scale. Applying the random forest method, we build and evaluate six presence/absence models using data acquired by four different sampling gears (grabs and trawls) and their combinations. We explore the use of suboptimal, ‘messy’—but best available – data to build an SDM suitable for national‐scale conservation management applications. Model performance and predictor influence varied substantially, with geographical, operational and ecological biases evident across all gear types. No single‐gear model successfully captured the full range of A. islandica presences. Ultimately, the model combining all four gears was selected as the most suitable, with multiple indicators confirming its robustness for national management applications. Confidence thresholds were applied to the final predictions, supporting stringent or precautionary management in practical contexts. In our case study, we demonstrate that a gain in data volume (and spatial coverage) can ultimately outweigh a trade‐off in data quality. Practical implication. To address the rapid acceleration of biodiversity loss and avoid missing further global conservation targets, action must be taken now—using the best available data—even if those data are imperfect. When applied with caution and a clear understanding of bias and scale implications, our approach can be used across systems to maximise data volume and support a broad range of spatial management applications.