Abstract
Effective decision-making in conservation often is constrained by data quality. Uncertainties associated with poor quality or sparse data can lead to the misuse of limited resources and potentially the failure of conservation practice. Data acquisition, which can help improve decision-making, is constrained by limited budgets and time. This is especially concerning for rare species, the most in need of conservation, but the most difficult to accurately represent in conservation plans. Here we test the suitability of three different sampling design strategies (two systematic vs random) designed to improve the quality of information available for conservation planning involving rare species. We modelled the spatial distribution of freshwater fish species in a data rich area in northern Australia using a large dataset (representing the best attainable data or true distribution) and simulate increasing subsets of data acquired through the three alternative sampling designs. We then evaluated omission and commission errors in conservation planning outcomes, efficiency and return on investment of data acquisition for conservation planning outcomes obtained from the different data availability × sampling design strategies. Even though we were able to find new species more effectively through systematic sampling designs, this did not 1) translate into reduced errors in conservation planning outcomes for rare species and 2) meet our goal of enhancing cost-effectiveness of conservation planning. Our results suggest that collecting more biodiversity data, irrespective of the sampling design used, does not necessarily reduce data uncertainty issues and could lead to the misuse of the limited resources and ultimately the failure of conservation practice.