Quantifying spatiotemporal variability in zooplankton dynamics in the Gulf of Mexico with a physical–biogeochemical model

Abstract

<jats:p>Abstract. Zooplankton play an important role in global biogeochemistry, and their secondary production supports valuable fisheries of the world's oceans. Currently, zooplankton standing stocks cannot be estimated using remote sensing techniques. Hence, coupled physical–biogeochemical models (PBMs) provide an important tool for studying zooplankton on regional and global scales. However, evaluating the accuracy of zooplankton biomass estimates from PBMs has been a major challenge due to sparse observations. In this study, we configure a PBM for the Gulf of Mexico (GoM) from 1993 to 2012 and validate the model against an extensive combination of biomass and rate measurements. Spatial variability in a multidecadal database of mesozooplankton biomass for the northern GoM is well resolved by the model with a statistically significant (p &lt; 0.01) correlation of 0.90. Mesozooplankton secondary production for the region averaged 66±8×109 kg C yr−1, equivalent to ∼10 % of net primary production (NPP), and ranged from 51 to 82×109 kg C yr−1, with higher secondary production inside cyclonic eddies and substantially reduced secondary production in anticyclonic eddies. Model results from the shelf regions suggest that herbivory is the dominant feeding mode for small mesozooplankton (&lt; 1 mm), whereas larger mesozooplankton are primarily carnivorous. In open-ocean oligotrophic waters, however, both mesozooplankton groups show proportionally greater reliance on heterotrophic protists as a food source. This highlights an important role of microbial and protistan food webs in sustaining mesozooplankton biomass in the GoM, which serves as the primary food source for early life stages of many commercially important fish species, including tuna. </jats:p>

Hydrodynamic and Biochemical Impacts on the Development of Hypoxia in the Louisiana–Texas Shelf Part I: Numerical Modeling and Hypoxia Mechanisms

“…NEMURO is known for its capability in distinguishing SZ, LZ, and PZ and provides a detailed analysis of the dynamics of different functional groups. It was widely used in studies of plankton biomass in regional scales (Fiechter and Moore 2009;Gomez et al, 2018;Shropshire et al, 2020). The embedded silicon cycle permits the inclusion of a diatom group (i.e., PL), which is deemed to be the dominant phytoplankton group in the nGoM.…”

“…In a recent effort, Shropshire et al (2020) adapted and modified NEMURO to the GoM with five structural changes as follows.…”

“…However, neither the modified (Shropshire et al, 2020) nor the original (Kishi et al, 2007) NEMURO model considered phosphorus and oxygen cycles. In this study, we introduced a phosphorus cycle into NEMURO, which includes three concentration-based state variables as phosphate (PO4), particulate organic phosphorus (POP), and dissolved organic phosphorus (DOP).…”

Quantifying biological carbon pump pathways with a data-constrained mechanistic model ensemble approach

“…Nevertheless, most of these approaches rely only on the assimilation of surface chlorophyll and/or other phytoplankton properties, thus leading to potentially high inaccuracies in parameterizing zooplankton dynamics (Löptien and Dietze 2015;Shropshire et al 2020). This is particularly important, because inaccurate parameterizations of mesozooplankton may lead to qualitatively and quantitatively inaccurate export dynamics (Anderson et al 2013;Cavan et al 2017).…”

Environmental conditions drive zooplankton community structure in the epipelagic oceanic water of the southern Gulf of Mexico: A molecular approach

“…Moreover, due to their small size, limited capacity for self-dispersal, and high sensitivity to environmental change, zooplankton are also useful bioindicators when evaluating the health of marine ecosystems (Buttay et al, 2015;Johnston et al, 2015;Parmar et al, 2016;Yang & Zhang, 2019). Environmental change and ecological processes shape zooplankton communities, both in terms of their spatial and temporal abundance (Färber Lorda et al, 2019;Shropshire et al, 2020) and taxonomic composition (Buttay et al, 2015;Cruz-Rosado et al, 2020), and may lead to the loss of zooplankton biodiversity (Gazonato Neto et al, 2014;Parmar et al, 2016), which can affect ecosystem services and result in economic consequences (Beaugrand et al, 2010;Bucklin et al, 2016;Everaert et al, 2018;Johnston et al, 2015).…”