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Abstract Parallel laser photogrammetry (PLP), which consists of attaching two or three parallel laser beams at a known inter‐beam distance to a camera, can be used to collect morphological measurements of organisms noninvasively. The lasers project onto the photo being taken, and because the inter‐beam distance is known, they act as a scale for image analysis programs like ImageJ. Traditionally, this method has been used to measure larger morphological traits (e.g., limb length, crown‐rump length) to serve as proxies for overall body size, whereas applications to smaller anatomical features remain limited. To that end, we used PLP to measure the testes of 18 free‐living mantled howler monkeys (Alouatta palliata)at La Selva Biological Station, Costa Rica. We tested whether this method could reliably measure this relatively small and globular morphology, and whether it could detect differences among individuals. We tested reliability in three ways: within‐photo (coefficient of variation [CV] = 4.7%), between‐photo (CV = 5.5%), and interobserver (intraclass correlation = 0.92). We found an average volume of 36.2 cm3and a range of 16.4–54.4 cm3, indicating variation in testes size between individuals. Furthermore, these sizes are consistent with a previous study that collected measurements by hand, suggesting that PLP is a useful method for making noninvasive measurements of testes.
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Automated, high-throughput image calibration for parallel-laser photogrammetry
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.
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- Award ID(s):
- 1753651
- PAR ID:
- 10321226
- Date Published:
- Journal Name:
- Mammalian Biology
- ISSN:
- 1616-5047
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s42991-021-00174-7
