Contrasting phytoplankton-zooplankton distributions observed through autonomous platforms, in-situ optical sensors and discrete sampling

Abstract

Plankton distributions are remarkably ‘patchy’ in the ocean. In this study, we investigated the contrasting phytoplankton-zooplankton distributions in relation to wind mixing events in waters around a biodiversity-rich island (Runde) located off the western coast of Norway. We used adaptive sampling from AUV and shipboard profiles of in-situ phytoplankton photo-physiology and particle identification (copepods, fecal pellets and the dinoflagellate Tripos spp.) and quantification using optical and imaging sensors. Additionally, traditional seawater and net sampling were collected for nutrient and in-vitro chlorophyll a concentrations and phytoplankton and meso-zooplankton abundances. Persistent strong wind conditions (~5 days) disrupted the stratification in offshore regions, while stratification and a subsurface chlorophyll maximum (SCM) were observed above the base of the mixed layer depth (MLD ~30 m) in inshore waters. Contrasting phytoplankton and zooplankton abundances were observed between inshore (with the presence of a SCM) and offshore waters (without the presence of a SCM). At the SCM, phytoplankton abundances (Tripos spp., the diatom Proboscia alata and other flagellates) were half (average of 200 cell L-1) of those observed offshore. On the contrary, meso-zooplankton counts were ~6× higher (732 ind m-3 for Calanus spp.) inshore (where a SCM was observed) compared to offshore areas. In parallel, fecal pellets and ammonium concentrations were high (>1000 ind m-3 for the upper 20 m) at the SCM, suggesting that the shallow mixed layer might have increased encounter rates and promoted strong grazing pressure. Low nutrient concentrations (< 1μM for nitrate) were found below the MLD (60 m) in offshore waters, suggesting that mixing and nutrient availability likely boosted phytoplankton abundances. The size of the absorption cross-section (σPII’) and yield of photosystem II photochemistry under ambient light (φPII’) changed according to depth, while the depth-related electron flow (JPII) was similar between regions, suggesting a high degree of community plasticity to changes in the light regime. Our results emphasize the importance of using multiple instrumentation, in addition to traditional seawater and net sampling for a holistic understanding of plankton distributions.

Multi-vehicle adaptive 3D mapping for targeted ocean sampling

“…Sample depths were selected based on layers of relatively high, medium and low chlorophyll a concentrations estimated by the model generated in the operator hub from data from the AUVs. Seawater was filtered (0.5 L) onto Whatman GF/F glass fiber filters [ 5 ]. In the laboratory, chlorophyll a was extracted in 100% methanol after 20 hours at −20°C in darkness.…”

“…CF measured in live phytoplankton cells using in-situ sensors can be used as a biomass proxy, but the values must be verified from in-vitro analyses of chlorophyll a from water samples. Therefore, the combination of adaptive robotic sampling and physical sampling of phytoplankton is needed [ 3 , 5 ]. However, by not sharing the data collected by the AUVs in real time, the ocean sampling is still done deterministically.…”

Unmanned Vehicle and Hyperspectral Imager for a More Rapid Microplastics Sampling and Analysis

“…Winds speed, water level, currents, and chlorophyll are all important KEVs that should be measured alongside MPs concentration. Fossum et al 2018 [50] investigated coastal ocean processes by coupling autonomous sampling and ocean models at the coast of mid-Norway and, then, further linked KEVs to phytoplankton distribution [51], [52]. Incorporating MPs concentration into these research would be a goal to achieve in MPs research.…”

The Role of Rapid Changes in Weather on Phytoplankton Spring Bloom Dynamics From Mid‐Norway Using Multiple Observational Platforms

“…Modeling approaches can offer complementary information regarding areas not covered by satellites and USVs and can also provide the vertical structure of the water column. An “observational pyramid” for ocean systems, which combines the integration of in situ autonomous platforms, fixed buoys, satellite imagery and modeling approaches with discrete net and water sampling have the capacity to unveil the dynamics of a phytoplankton spring bloom in coastal, productive hot‐spots (Fragoso et al., 2022; Williamson et al., 2023).…”