Variation among individuals within species is a biological precondition for co‐existence. Traditional geochemical analysis based on bulk averages facilitates rapid data gathering but necessarily means the loss of large amounts of potentially crucial information into variability within a given sample. As the sensitivity of geochemical analysis improves, it is now feasible to build sufficiently powerful datasets to investigate paleoclimatic variation at the level of individual specimens. Here, we investigate geochemical and morphological variation among the sensu stricto, sensu lato and sensu lato extreme subspecies of the workhorse extant planktic foraminifera Globigerinoides ruber. Our experimental design distinguishes between subspecies and intraspecific variability as well as the repeatability of laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS). We show that geochemical variability in Mg/Ca ratios is driven by differences in subspecies depth habitat and that ontogenetic trends in Mg/Ca ratios are evident in the final whorl, with the final chamber consistently showing depleted Mg/Ca. These ontogenetic trends are not driven by individual chamber or test size. The Mg/Ca value variance among individuals is ∼100 times higher than the variance among repeated laser spot analyses of single chambers, directing laboratory protocols towards the need to sample ecologically and environmentally homogeneous samples. Our results emphasize that we can use LA‐ICP‐MS to quantify how individual variability aggregates to bulk results, and highlights that, with sufficient sample sizes, it is possible to reveal how intraspecific variability alters geochemical inference.

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.

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 algorithmicallyidentifying 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. | INTRODUCTIONLaser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is a powerful analytical tool to quantify the elemental composition of a wide variety of natural and anthropogenic materials.A laser beam is focussed to the surface of a target and then pulsed to ablate the sample. Particles from the ablated sample are subsequently transported into an inductively coupled plasma ionisation source then to a mass spectrometer for detection based on the mass-to-charge ratio, which can be converted into a time-resolved isotopic or

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