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Parallel-laser photogrammetry is growing in popularity as a way to collect non-invasive body size data from wild mammals. Despite its many appeals, this method requires researchers to hand-measure (i) the pixel distance between the parallel laser spots (inter-laser distance) to produce a scale within the image, and (ii) the pixel distance between the study subject’s body landmarks (inter-landmark distance). This manual effort is time-consuming and introduces human error: a researcher measuring the same image twice will rarely return the same values both times (resulting in within-observer error), as is also the case when two researchers measure the same image (resulting in between-observer error). Here, we present two independent methods that automate the inter-laser distance measurement of parallel-laser photogrammetry images. One method uses machine learning and image processing techniques in Python, and the other uses image processing techniques in ImageJ. Both of these methods reduce labor and increase precision without sacrificing accuracy. We first introduce the workflow of the two methods. Then, using two parallel-laser datasets of wild mountain gorilla and wild savannah baboon images, we validate the precision of these two automated methods relative to manual measurements and to each other. We also estimate the reduction of variation in final body size estimates in centimeters when adopting these automated methods, as these methods have no human error. Finally, we highlight the strengths of each method, suggest best practices for adopting either of them, and propose future directions for the automation of parallel-laser photogrammetry data. 

Abstract ObjectivesSeveral theories have been proposed to explain the impact of ecological conditions on differences in life history variables within and between species. Here we compare female life history parameters of one western lowland gorilla population(Gorilla gorilla gorilla) and two mountain gorilla populations(Gorilla beringei beringei). Materials and MethodsWe compared the age of natal dispersal, age of first birth, interbirth interval, and birth rates using long‐term demographic datasets from Mbeli Bai (western gorillas), Bwindi Impenetrable National Park and the Virunga Massif (mountain gorillas). ResultsThe Mbeli western gorillas had the latest age at first birth, longest interbirth interval, and slowest surviving birth rate compared to the Virunga mountain gorillas. Bwindi mountain gorillas were intermediate in their life history patterns. DiscussionThese patterns are consistent with differences in feeding ecology across sites. However, it is not possible to determine the evolutionary mechanisms responsible for these differences, whether a consequence of genetic adaptation to fluctuating food supplies (“ecological risk aversion hypothesis”) or phenotypic plasticity in response to the abundance of food (“energy balance hypothesis”). Our results do not seem consistent with the extrinsic mortality risks at each site, but current conditions for mountain gorillas are unlikely to match their evolutionary history. Not all traits fell along the expected fast‐slow continuum, which illustrates that they can vary independently from each other (“modularity model”). Thus, the life history traits of each gorilla population may reflect a complex interplay of multiple ecological influences that are operating through both genetic adaptations and phenotypic plasticity.