Acoustic data are invaluable information sources for characterizing the distribution and abundance of mid-trophic-level organisms (micronekton). These organisms play a pivotal role in the ecosystem as prey of top predators and as predators of low-trophic-level organisms. Although shipboard-ADCP (acoustic Doppler current profiler) acoustic backscatter signal intensity cannot provide an absolute biomass estimate, it may be a useful proxy to investigate variability in the distribution and relative density of micronekton. This study used acoustic recordings data spread across 19 years (1999–2017) from 54 ADCP cruises in New Caledonia's subtropical EEZ (exclusive economic zone) to assess seasonal and interannual variabilities and spatial distribution of micronekton. The dataset was composed of two different ADCPs: 150 kHz for the first period, followed by 75 kHz for more recent years. We examined the 20–120 m averaged scattering layer. Using the few cruises with concurrent EK60 measurements, we proposed that the backscatter from the ADCPs and 70 kHz EK60 were sufficiently closely linked to allow the use of the backscatter signal from the ADCPs in a combined dataset over the full time series. We then designed a GAMM (generalized additive mixed model) model that takes into account the two ADCP devices as well as temporal variability. After accounting for the effect of the devices, we showed that the acoustic signal was mainly driven by diel vertical migration, season, year, and ENSO (El Niño-Southern Oscillation). In a second step, a consensus model between two statistical approaches (GAMM and SVM) (support vector machine) was constructed, linking the nighttime 20–120 m backscatter to the oceanographic and geographic environment. This model showed that sea surface temperature was the main factor driving backscatter variability in the EEZ, with intensified backscatter during the austral summer (December to May) in the northern part of the EEZ. We showed that acoustic density differed significantly, spatially and temporally from micronekton biomass predicted for the same period by the SEAPODYM-MTL (mid-trophic level) ecosystem model. The seasonal cycle given by ADCP data lagged behind the SEAPODYM-MTL seasonal cycle by around three months. Reasons to explain these differences and further needs in observation and modeling were explored in the discussion. In addition to providing new insights for micronekton dynamics in this EEZ (i.e., the science needed for ecosystem-based fisheries management), the data should help improve our ability to model this key trophic component.

声学数据是表征中营养水平生物（微神经元）分布和丰富度的宝贵信息来源。这些生物作为顶级捕食者和低营养水平生物的捕食者，在生态系统中起着举足轻重的作用。尽管舰载ADCP（声学多普勒电流剖面仪）的声学反向散射信号强度无法提供绝对的生物量估计值，但它可能是研究微神经元分布和相对密度变化的有用指标。这项研究使用了新喀里多尼亚亚热带专属经济区（专属经济区）的54次ADCP航行中分布在19年（1999-2017年）中的声音记录数据，以评估微结节的季节和年际变化以及空间分布。数据集由两个不同的ADCP组成：第一个周期为150 kHz，其次是75 kHz。我们检查了20-120 m的平均散射层。通过同时进行EK60测量的几次巡航，我们建议将ADCP和70 kHz EK60的反向散射充分紧密地联系在一起，以允许在整个时间序列的组合数据集中使用ADCP的反向散射信号。然后，我们设计了一个GAMM（广义加性混合模型）模型，该模型考虑了两个ADCP设备以及时间变异性。在考虑了设备的影响之后，我们表明声音信号主要由diel垂直迁移，季节，年份和ENSO（厄尔尼诺-南方涛动）驱动。第二步，构建了两种统计方法（GAMM和SVM）（支持向量机）之间的共识模型，将夜间20–120 m的反向散射与海洋和地理环境联系起来。该模型表明，海面温度是驱动专属经济区反向散射变化的主要因素，在专属经济区北部的夏季（12月至5月），反向散射加剧。我们表明，声密度与SEAPODYM-MTL（中营养水平）生态系统模型预测的同一时期的微核生物量显着不同。ADCP数据给出的季节周期比SEAPODYM-MTL季节周期落后三个月左右。讨论中探讨了解释这些差异的原因以及观察和建模的进一步需求。除了为该EEZ中的微神经元动力学提供新见解（即，