Abstract
Key parameters of biological systems-e.g., productivity, population sizes, biomass-are best expressed as absolute values. Exotic markers (e.g., Lycopodium spores introduced into microfossil populations) have long been used to estimate population sizes from representative samples. However, the traditional approach-the 'linear method' herein-can be extremely time consuming and impractical for routine use. Here, we present a new variant of this technique: the 'field-of-view subsampling method' (FOVS method). This new method requires a few simple, easily obtainable statistical parameters, beyond the standard inputs for the traditional linear method. The FOVS method adds error from sample heterogeneity, but enables the collection of very large sample sizes with low additional effort. We compared the FOVS and linear methods with two case studies: 1, Monte Carlo simulations to validate the methods with idealised datasets; and 2, terrestrial organic microfossils from Permian-Triassic rock strata in southeastern Australia as 'real-world' empirical datasets. Three output parameters were measured: 1, absolute abundance; 2, precision (=error rate); and 3, data collection effort (typically, this translates to data collection time). The linear method showed superior efficiency only for assemblages with very low specimen densities and/or near-equivalent target-to-marker ratios, conditions we predict are rare under real-world conditions. In contrast, the FOVS method provided greater precision and/or reduced effort under almost all conditions, without sacrificing accuracy. Although originally developed for microfossils, the new method may apply to any spatial data collection where markers of known quantity can be introduced to a population. Given its demonstrable increased speed and precision, we recommend the FOVS method as the new standard for such absolute abundance estimates. Guidelines and a user-friendly digital interface for implementing both of these count methods are provided, in addition to simulation codes aimed to assist readers in designing their own experiments.