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Rationale: organisms that grow a hard carbonate shell or skeleton, such as foraminifera, corals or molluscs, incorporate trace elements into their shell during growth that reflect the environmental change and biological activity they experienced during life. These geochemical signals locked within the carbonate are archives used in proxy reconstructions to study past environments and climates, to decipher taxonomy of cryptic species and to resolve evolutionary responses to climatic changes.Methods: here, we use laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) as a time-resolved acquisition to quantify the elemental composition of carbonate shells and skeletons. We present the LABLASTER (Laser Ablation BLASt Through Endpoint in R) package, which imports a single time-resolved LA-ICP-MS analysis, then detects when the laser has ablated through the carbonate as a function of change in signal over time and outputs key summary statistics. We provide two examples within the package: a fossil planktic foraminifer and a tropical coral skeleton.Results: we present the first R package that automates the selection of desired data during data reduction workflows. This is achieved by automating the detection of when the laser has ablated through a sample using a smoothed time series, followed by removal of off-target data points. The functions are flexible and adjust dynamically to maximise the duration of the desired geochemical target signal, making this package applicable to a wide range of heterogenous bioarchives. Visualisation tools for manual validation are also included.Conclusions: LABLASTER increases transparency and repeatability by algorithmically identifying when the laser has either ablated fully through a sample or across a mineral boundary and is thus no longer documenting a geochemical signal associated with the desired sample. LABLASTER's focus on better data targeting means more accurate extraction of biological and geochemical signals.

Rational: Organisms that grow a hard carbonate shell or skeleton, such as foraminifera, corals or molluscs, incorporate trace elements into their shell during growth that absorbs the environmental change and biological activity they experienced. These geochemical signals locked within the carbonate are archives used in proxy reconstructions to study past environments and climates, to decipher taxonomy of cryptic species and to resolve evolutionary responses to climatic changes. Methods: Here we use a laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) as a time resolved acquisition to quantify the elemental composition of carbonate shells. We present the LABLASTER (Laser Ablation BLASt Through Endpoint in R) package, which imports a single time resolved LA-ICP-MS analysis, then detects when the laser has ablated through the carbonate as a function of change in signal over time, and outputs key summary statistics. We provide two worked examples within the package: a planktic foraminifera and a tropical coral. Results: We present the first R package that improves signal: noise ratios in data reduction workflows by automating the detection of when the laser has ablated through a sample using a smoothed time-series and subsequent removal of off-target data points. The functions are flexible and adjust dynamically to enhance the signal: noise ratio of the desired geochemical target. Visualisation tools for manual validation are also included. Conclusions: LABLASTER increases transparency and repeatability by algorithmically identifying when the laser has either ablated fully through a sample or across a mineral boundary and is thus no longer documenting a geochemical signal associated with the desired sample. LABLASTER’s focus on better data targeting means more accurate extraction of biological and geochemical signals.

Foraminifera are one of the few taxa that preserve their entire ontogeny in their fossilised remains. Revealing this ontogeny through micro-computed tomography (CT) of fossil planktonic foraminifera has greatly improved our understanding of their life history and allows accurate quantification of total shell volume, growth rates and developmental constraints throughout an individual's life. Studies using CT scans currently mainly focus on chamber size, but the wealth of three-dimensional data generated by CT scans has the potential to reconstruct complete growth trajectories. Here we present an open-source R package to analyse growth in three-dimensional space. Using only the centroid xyz coordinates of every chamber, the functions determine the growth sequence and check that chambers are in the correct order. Once the order of growth has been verified, the functions calculate distances and angles between subsequent chambers, determine the total number of whorls and the number of chambers in the final whorl at the time each chamber was built, and, for the first time, quantify trochospirality. The applications of this package will enable repeatable analysis of large data sets and quantification of key taxonomic traits and ultimately provide new insights into the effects of ontogeny on evolution.

The sheer volume of 3D data restricts understanding of genetic speciation when analyzing specimens of planktonic foraminifera and so we develop an end-to-end computer vision system to solve and extend this. The observed fossils are planktonic foraminifera, which are single-celled organisms that live in vast numbers in the world’s oceans. Each foram retains a complete record of its size and shape at each stage along its journey through life. In this study, a variety of individual foraminifera are analyzed to study the differences among them and compared with manually labelled ground truth. This is an approach which (i) automatically reconstructs individual chambers for each specimen from image sequences, (ii) uses a shape signature to describe different types of species. The automated analysis by computer vision gives insight that was hitherto unavailable in biological analysis: analyzing shape implies understanding spatial arrangement and this is new to the biological analysis of these specimens. By processing datasets of 3D samples containing 9GB of points, we show that speciation can indeed now be analyzed and that automated analysis from morphological features leads to new insight into the origins of life.