A method to evaluate body length of live aquatic vertebrates using digital images

Traditional methods to measure body lengths of aquatic vertebrates rely on anesthetics, and extended handling times. These procedures can increase stress, potentially affecting the animal’s welfare after its release. We developed a simple procedure using digital images to estimate body lengths of coastal cutthroat trout (Oncorhynchus clarkii clarkii) and larval coastal giant salamander (Dicamptodon tenebrosus). Images were post-processed using ImageJ2. We measured more than 1,800 individuals of these two species from 200 pool habitats along 9.6 river kilometers. The percent error (mean ± SE) of our approach compared to the use of a traditional graded measuring board was relatively small for all metrics of the two species. Total length of trout was -2.2% ± 1.0. Snout-vent length and total length of larval salamanders was 3.5% ± 3.3 and -0.6% ± 1.7, respectively. We cross-validated our results by two independent observers that followed our protocol to measure the same animals and found no significant differences (p > 0.7) in body size distributions for all metrics of the two species. Our procedure provides reliable information of body size reducing stress and handling time in the field. The method is transferable across taxa and the inclusion of multiple animals per image increases sampling efficiency with stored images that can be reviewed multiple times. This practical tool can improve data collection of animal size over large sampling efforts and broad spatiotemporal contexts.

传统水生脊椎动物体长测量方法依赖麻醉操作与较长的处理时长，此类操作会增加受试动物的应激反应，可能影响其放归后的生存福利。我们开发了一种基于数码图像的简易流程，用于估算沿海切喉鳟（Oncorhynchus clarkii clarkii）以及沿海巨蝾螈幼体（Dicamptodon tenebrosus）的体长。所有图像均通过ImageJ2进行后处理。我们沿9.6公里的河道，在20处潭水环境中采集了这两个物种共1800余尾/只个体的样本。相较于传统的分级式测板法，本方法的百分比误差（均值±标准误）在两个物种的各项体长指标中均处于较低水平：鳟鱼的全长误差为-2.2% ± 1.0；蝾螈幼体的肛吻长与全长误差分别为3.5% ± 3.3与-0.6% ± 1.7。我们通过两名遵循本实验流程的独立观察者对相同个体进行复测以交叉验证，结果显示两个物种的各项体长指标对应的体型分布均无显著差异（p > 0.7）。本方法可在野外场景中有效降低动物应激与处理时长，同时提供可靠的体型数据。该方法可跨类群推广，且单张图像可容纳多只受试动物，结合可反复审阅的存储图像，可大幅提升采样效率。这款实用工具可在大规模采样与宽时空尺度的研究中，优化动物体型数据的采集工作。