StripesCounter: A new image software for increment measurement in paleoclimate archives

Most natural paleoclimate archives are accretionary material presenting periodic structures that bear environmental and chronological information. Growth patterns of shells of marine bivalves also known as sclerochronology reflect changes in both biological and environmental factors and can potentially provide unique daily-resolved records of past environmental variability. Traditional methods of analyzing growth bands are time consuming and often face challenges in terms of accuracy and efficiency. To address these limitations, we introduce StripesCounter, an open access Python software designed for semi-automated banding detection and measurement. To test its effectiveness and determine whether shells of marine bivalves (Tridacna gigas) respond to rapid climate change, we analyzed daily growth variations in a modern specimen from Papua New Guinea (Hu-04-MT7), which experienced three major ENSO events during the 20th century. By using a laser scanning confocal microscope (LSCM) and the StripesCounter software, we counted and measured daily increments of shell growth. The results demonstrate a high degree of reproducibility and consistency compare to traditional manual counting methods. We used several detrending methods to subtract biological trends. Results show that, shell growth is sensitive to seasonal climatic variability caused by ENSO in this region. This can be observed in the semi-annual variability of the growth rate through a disturbance that can be attributed to a disruption in the semi-annual cycle of sea surface productivity and temperature. Our findings not only validate the method’s reliability for high temporal resolution studies but also enable the detection of subtle growth variations linked to environmental changes. This automated growth increment analysis can be extended to other archives with cyclic structures, including tree rings, corals, and other biogenic or abiotic laminated materials. StripesCounter offers a powerful and accessible tool for generating long high-resolution, temporally explicit datasets, opening new perspectives for investigating rapid environmental changes across diverse ecosystems and geological timescales.