Snail Inventories for Short-Term Impacts: Methodological Differences and Their Underlying Causes

In research on terrestrial gastropod communities, a fundamental methodological question is whether to include empty shells in the investigations alongside living individuals. In this study, we examined potential biases introduced in the analyses by the inclusion of fresh empty shells (with intact periostracum). We started by reanalysing a dataset about the short-term effects of mowing and mulching on a grassland snail community. For the original analysis, only live specimen data were used, whereas here we used a so-called combined dataset including live individuals and fresh empty shells. While the live data revealed significant negative impacts of mowing on the snail community, these effects were undetectable with the combined data. Concurrently, we observed substantial interspecific variation in the overrepresentation value among species (the ratio of a species' combined abundance to its live abundance), which represents a distortion in the distribution of the total number of individuals across species. To explore the underlying causes of this phenomenon, we investigated the physical and chemical resistance of the shells of eleven species under laboratory conditions and simulated the effects of life history traits and shell-degradation rate on overrepresentation using a multistage population model. Contrary to expectations, our results indicated that this variation in overrepresentation was not explained by shell resistance but rather by shell size: significantly higher overrepresentation was observed in smaller species, even though their shells are weaker, so may decompose faster. This relationship may be driven by the faster life cycles and higher turnover rates of smaller species, a finding corroborated by our population model. We concluded that the inclusion of empty shells can significantly distort the perception of the instantaneous state of the community and mask short-term temporal changes. We recommend prioritising live data for detecting short-term impacts and explicitly stating and justifying the chosen data type in scientific publications.