Modeling Intra-Annual Growth of Freshwater Mussels

North American rivers and streams are home to the richest diversity of freshwater mussels in the world. Furthermore, because of strong ecological links with numerous taxa, ranging from invertebrates to fish to mammals, these long-lived animals (some >100 years) are excellent indicators of freshwater ecosystem health. Unfortunately, nearly half of the ~300 species in the United States are threatened or endangered. It is surprising that, despite their ecological importance and endangered status, relatively little is known about their growth. Here we show how a technique originally applied to marine mollusks can be used to reconstruct intra-annual growth rates of freshwater mollusks.

The concept is straightforward: the 1^st derivative of a function relating cumulative linear growth to time represents intra-annual growth rates—the growth function. In practice, this technique involves converting observed oxygen-isotope values from shell carbonate (δ¹⁸O_carb) to temperatures, which requires knowledge of the oxygen-isotopic composition of water. Then calculated temperatures are converted to dates using observed temperature records. Next, dates are plotted versus sample distance (measured from sampled shells), and fit with a monotonic cubic spline. Finally, the 1^st derivative of this function is evaluated. This procedure is then repeated (~50000x), by taking advantage of the uncertainly associated with δ¹⁸O_carb measurement (e.g., ±0.08‰). This iterative procedure produces an average growth function, which can, in turn, provide valuable ecological information, such as the timing of highest intra-annual growth rates, growth-limiting temperatures, and optimal-growth temperatures.

Because these data are often established using growth increments, which are equivocal in many freshwater mollusks, this modeling procedure may provide researchers and resource managers an important new tool for assessing and/or predicting accretional histories. Furthermore, because this method is based on readily available environmental records and requires relatively few specimens, it is ideal for studying threatened or endangered species.