Carvalho Jessica, Chérubin Laurent M, O'Corry-Crowe Greg
Florida Atlantic University (Harbor Branch Campus), Fort Pierce, FL, United States of America.
PeerJ. 2025 Apr 4;13:e19204. doi: 10.7717/peerj.19204. eCollection 2025.
As climate change and anthropogenic activities continue to impact cetacean species, it becomes increasingly urgent to efficiently monitor cetacean populations. Continuing technological advances enable innovative research methodologies which broaden monitoring approaches. In our study, we utilized an autonomous wave glider equipped with acoustic and environmental sensors to assess delphinid species presence on the east Florida shelf and compared this approach with traditional marine mammal monitoring methods. Acoustic recordings were analyzed to detect delphinid presence along the glider track in conjunction with subsurface environmental variables such as temperature, salinity, current velocity, and chlorophyll-a concentration. Additionally, occurrences of soniferous fish and anthropogenic noise were also documented. These variables were incorporated into generalized additive models (GAMs) to identify predictors of delphinid presence. The top-performing GAM found that location, sound pressure level (SPL), temperature, and chlorophyll-a concentration explained 50.8% of the deviance in the dataset. The use of satellite environmental variables with the absence of acoustic variables found that location, derived current speed and heading, and chlorophyll-a explained 44.8% of deviance in the dataset. Our research reveals the explanatory power of acoustic variables, measurable with autonomous platforms such as wave gliders, in delphinid presence drivers and habitat characterization.
随着气候变化和人类活动持续影响鲸类物种，高效监测鲸类种群变得愈发紧迫。不断的技术进步催生了创新的研究方法，拓宽了监测途径。在我们的研究中，我们利用配备声学和环境传感器的自主波浪滑翔器来评估佛罗里达东部陆架上海豚科物种的存在情况，并将这种方法与传统的海洋哺乳动物监测方法进行比较。对声学记录进行分析，以检测沿滑翔器轨迹的海豚科物种的存在情况，并结合诸如温度、盐度、流速和叶绿素a浓度等次表层环境变量。此外，还记录了发声鱼类和人为噪声的出现情况。这些变量被纳入广义相加模型（GAMs），以识别海豚科物种存在的预测因子。表现最佳的广义相加模型发现，位置、声压级（SPL）、温度和叶绿素a浓度解释了数据集中50.8%的偏差。在没有声学变量的情况下使用卫星环境变量发现，位置、推算的当前速度和航向以及叶绿素a解释了数据集中44.8%的偏差。我们的研究揭示了声学变量在海豚科物种存在驱动因素和栖息地特征描述方面的解释力，这些变量可以通过波浪滑翔器等自主平台进行测量。
