Optimizing taxonomic resolution and sampling effort to design cost‐effective ecological models for environmental assessment

Abstract

Summary Predictive models relating ecological assemblages to environmental conditions are widely used in environmental impact assessment and biomonitoring. Such models are often parameterized using comprehensive ecological sampling and taxonomic identification efforts. Limited resources mean that expensive sampling and analytical procedures should be planned to maximize information gain and minimize unnecessary expense. However, there has been little consideration of cost‐effectiveness in parameterizing predictive models using ecological assemblages and no explicit consideration of cost‐effectiveness in balancing investment in the crucial aspects of sample size and taxonomic resolution. Using lacustrine diatom (Bacillariophyceae) assemblages from four large‐scale (c. 77 000–1·3 million km2) data sets containing between 207 and 493 lakes, we address the following questions: (1) how does taxonomic resolution affect information content; (2) how does sample size affect information content for different taxonomic resolutions; and (3) what are the most cost‐effective strategies for constructing environmental assessment models using diatom assemblages across a range of budgets? We use weighted averaging regression models for pH, phosphorus, salinity and lake depth and realistic data collection costs to examine the relationship between cost and model information content (R2 and root mean squared error of prediction). For diatom‐based models, finer taxonomic resolutions almost always provide more cost‐effective information content than collecting more samples, with (morpho)species being the most appropriate taxonomic resolution for nearly all budget scenarios. Information content exhibits an asymptotic relationship with sample size and budget, with greatest information gain during initial sample size increases, and little gain beyond c. 100 samples. Smaller sample sizes can also achieve surprising predictive power in some cases, suggesting low‐cost regional models may be achievable. However, caution is necessary in such an approach, because spatial dependencies in predictions may be missed and analogues with predicted assemblages may be poor. Synthesis and applications. We demonstrate the utility of explicitly considering cost estimates to determine optimal sampling effort and taxonomic resolution for ecological assemblage models. For large, regional biomonitoring programmes, cost‐effective sampling could save millions of dollars. Our framework for determining optimal trade‐offs in ecological assemblage models is easily adaptable to other taxa and analytical techniques used in biomonitoring and environmental assessment.