Experiments and Agent Based Models of Zooplankton Movement within Complex Flow Environments

Abstract

The movement of plankton is often dictated by local flow patterns, particularly during storms and in environments with strong flows. Reefs, macrophyte beds, and other immersed structures can provide shelter against washout and drastically alter the distributions of plankton as these structures redirect and slow the flows through them. Advection–diffusion and agent-based models are often used to describe the movement of plankton within marine and fresh water environments and across multiple scales. Experimental validation of such models of plankton movement within complex flow environments is challenging because of the difference in both time and spatial scales. Organisms on the scale of 1 mm or less swim by beating their appendages on the order of 1 Hz and are advected meters to kilometers over days, weeks, and months. One approach to study this challenging multiscale problem is to insert actively moving agents within a background flow field. Open source tools to implement this sort of approach are, however, limited. In this paper, we combine experiments and computational fluid dynamics with a newly developed agent-based modeling platform to quantify plankton movement at the scale of tens of centimeters. We use Artemia spp., or brine shrimp, as a model organism given their availability and ease of culturing. The distribution of brine shrimp over time was recorded in a flow tank with simplified physical models of macrophytes. These simplified macrophyte models were 3D-printed arrays of cylinders of varying heights and densities. Artemia nauplii were injected within these arrays, and their distributions over time were recorded with video. The detailed three-dimensional flow fields were quantified using computational fluid dynamics and validated experimentally with particle image velocimetry. To better quantify plankton distributions, we developed an agent-based modeling framework, Planktos, to simulate the movement of plankton immersed within such flow fields. The spatially and temporally varying Artemia distributions were compared across models of varying heights and densities for both the experiments and the agent-based models. The results show that increasing the density of the macrophyte bed drastically increases the average time it takes the plankton to be swept downstream. The height of the macrophyte bed had less of an effect. These effects were easily observed in both experimental studies and in the agent-based simulations.

Modeling the biomechanics of cells on microcarriers in a stirred-tank bioreactor: an ABM-CFD coupling approach

“…Combining multiple modeling approaches in a more comprehensive model can generate novel insights and identify emergent properties in the system under study. Indeed, CFD and ABM have been used together in several diverse multiscale models that simulate disaster responses (Epstein et al, 2011), cell and particle migration through blood vessels (Fullstone et al, 2015;Bhui and Hayenga, 2017;Bhui, 2018;Corti et al, 2020), and movement of zooplankton in complex flow environments (Ozalp et al, 2020). Notably, CFD and ABM per se have never been used together to understand the process dynamics within a bioreactor relevant to cultivated meat.…”

Modeling the biomechanics of cells on microcarriers in a stirred-tank bioreactor

“…Combining multiple modeling approaches in a more comprehensive model can generate novel insights and identify emergent properties in the system under study. Indeed, CFD and ABM have been used together in several diverse multiscale models that simulate disaster responses [32], cell and particle migration through blood vessels [33][34][35][36], and movement of zooplankton in complex flow environments [37]. Notably, CFD and ABM per se have never been used together to understand the process dynamics within a bioreactor relevant to cultivated meat.…”

The Hydrodynamic Characteristics Induced by Multiple Layouts of Typical Artificial M-Type Reefs with Sea Currents Typical of Liaodong Bay, Bohai Sea

“…To date, the most common methods to investigate the hydrodynamic characteristics of artificial reefs relate to numerical simulations or experimental tests conducted within wind tunnel or flumes, where particle image velocimetry (PIV) techniques can be applied [25][26][27][28][29][30][31]. Previous studies conducted by adopting these methods have highlighted a list of factors that can alter the hydrodynamic characteristics of artificial reefs, and this includes the original flow field and the existing terrain, the location of the new artificial reefs placed, the manufacturing materials, the shape and the structure, the opening ratio and the upstream area of the reefs, as well as the number of elements [32][33][34][35][36][37][38].…”