Fine-scale larval fish distributions and predator-prey dynamics in a coastal river-dominated ecosystem

Abstract

River plumes discharging into continental shelf waters have the potential to influence the distributions, predator-prey relationships, and thus survival of nearshore marine fish larvae, but few studies have been able to characterize the plume environment at sufficiently fine scales to resolve the underlying mechanisms. We used a high-resolution plankton imaging system and a sparse convolutional neural network to automate image classification of larval fishes, their planktonic prey (calanoid copepods), and gelatinous planktonic predators (ctenophores, hydromedusae, and siphonophores) over broad spatial scales (km) and multiple pulses of estuarine water exiting Mobile Bay (Alabama, USA) into the northern Gulf of Mexico from 9-11 April 2016. Fine-scale (1 m) plankton distributions were examined to analyze predator-prey relationships across 3 distinct plume regimes that varied by degree of wind-forcing and mixing rates. In calm wind conditions, the water column was highly stratified, and fish larvae and zooplankton were observed aggregating in a region of river plume-derived hydrodynamic convergence. As winds strengthened, the water column was subjected to downwelling and highly turbulent conditions, and there was decreasing spatial overlap between larval fishes and their zooplankton prey and predators. Our results indicate that high-discharge plume regimes characterized by strong wind-forcing and turbulence can rapidly shift the physical and trophic environments from favorable to unfavorable for fish larvae. Multiple pathways for both nearshore retention and advective dispersal of fish larvae were also identified. Documenting this variability is a first step toward understanding how high discharge events and physical forcing can affect fisheries production in river-dominated coastal ecosystems worldwide.

Combining multi-marker metabarcoding and digital holography to describe eukaryotic plankton across the Newfoundland Shelf

“…Future studies investigating plankton size structure from the HoloSea or similar holographic microscopes should implement a correction factor to account for the scattering of coherent light that biases size estimates non-linearly depending on object distance from the point source 64 . The promise for digital in-line holography is shared by digital imaging technologies more broadly, and is typically prohibitive for conventional light microscopy, which is to yield data-rich, high-frequency observations which to date have captured planktonic fish-larvae survivorship under river plume discharge regimes 65 , the evolution of particulate-mediated carbon export in rapidly changing marginal zones 66 , spatial patterns in plankton community compositions through time 67 , at regional scales 4 , 68 and specific taxonomic lineages at global scales 69 . Novel biotic interactions have recently been observed using in-situ imaging, including pseudopodial feeding in acantharians from the East China Sea likely missed previously due to destructive sampling by conventional plankton nets 4 and the frequent parasitization of the cosmopolitan copepod Oithona at the Scripps Pier in the Pacific Ocean 70 .…”

Using self organizing maps to analyze larval fish assemblage vertical dynamics through environmental-ontogenetic gradients

“…However, the full power of SOM would be further revealed in even more complex data sets, for example, using fine-scale image-based vertical sampling techniques. In this respect, artificial intelligence that uses complex neural network approaches (e.g., Deep Learning) is irrupting in the analyses of larval fish ecology (e.g., Axler et al, 2020;Catalán et al, 2020).…”

In situ imaging across ecosystems to resolve the fine-scale oceanographic drivers of a globally significant planktonic grazer

“…A convolutional neural network (CNN) classified the images using different training sets for each ecosystem. This method has produced accurate distributions for many types of zooplankton, including gelatinous organisms and larval fishes (Faillettaz et al 2016; Schmid et al 2020; Axler et al 2020; Swieca et al 2020). The doliolids were classified into three life stages (nurse, phorozooid, and gonozooid) in all ecosystems except the Straits of Florida, which classified all doliolids as one category.…”

“…A convolutional neural network (CNN) classified the images using different training sets for each ecosystem. This method has produced accurate distributions for many types of zooplankton, including gelatinous organisms and larval fishes (Faillettaz et al 2016;Schmid et al 2020;Axler et al . CC-BY-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.…”