Abstract

Biophysical models have become the ‘go-to’ tool for predicting the dispersive trajectories of planktic marine organisms, and are used to design Marine Protected Areas (MPAs), identify pathways of invasion, and understand metapopulation dynamics and biogeography. Yet, despite this relatively long history of development, continued technological advancement and increased usage, models continue to often fail to predict patterns in nature. As biophysical models are able to accurately predict the dispersal of abiotic particles, it is argued that it is how we incorporate larval behaviour (sensu vertical swimming) in models that may be decoupling predictions of dispersal and species distribution patterns. Yet, despite the recognised importance of vertical distribution/position to dispersal by advection, especially in smaller organisms, there is currently no general consensus of how, what, and when behaviours can and should be included in models, perhaps because the drivers of larval behaviour are inherently complex and as yet, not fully understood (Chapter 1). The typical approach is to parameterise behaviours as ‘rules’ based on laboratory observed responses to cues, but it this approach appropriate given the complexity of larval decision-making in the presence of multiple cues in nature in comparison to single cue responses in controlled environments? In Chapter 2 I explored the movements larvae must undertake to achieve the vertical distribution patterns observed in nature. Results suggest that behaviours are not consistent with those described under the Tidal Vertical Migration (TVM) hypothesis, instead, showing a need for swimming speed and direction to vary over the tidal cycle -- with upward swimming needing to be 2.5x faster than downwards swimming and a change in direction from upwards to downwards needing to occur around the midpoint of the flood tide - and low model compatibility during the ebb tide. Next, I looked to identify the environmental drivers of larval vertical distribution during the ebb tide, where the model compatibility of Chapter 2 was low. Explored external drivers (density, salinity, temperature, turbulence) explained very little of the vertical distribution patterns of the larvae, however, results suggest differential usage of environmental cues based on ontogenetic stage, and vertical distribution patterns observed differed from previous observations of a similar species at a different location. Finally, I presented a framework for assessing how behavioural parameterisation can influence dispersal trajectories in marine systems (Chapter 4), comparing a novel approach of reverse engineering larval swimming from in-situ observations (Chapter 2: REVM behaviours) against simulations adopting passive dispersal, and particles attributed a tidal vertical migration (TVM) signature. Results highlight how the implementation of behaviour within biophysical models can lead to fundamentally different dispersal outcomes, and specifically, that the inclusion of vertical migration behaviour is a mechanism that significantly reduces dispersal distances, but depending on the approach to implementation can lead to fundamental differences in dispersal direction. This thesis makes significant steps towards improving the parameterisation of behaviour within dispersal models by considering larval movement as a manifestation of behaviour influenced by the larva’s in-situ environment. The methodologies and analytical techniques designed and applied within the data chapters can be applied to any species with a planktonic dispersal phase in any location, and provide an important step towards improving the biological ‘realism’ of behavioural parameterisation in dispersal models in the absence of an understanding of the complex drivers of active larval movement

Document Type

Thesis

Publication Date

2021-01-01

DOI

10.24382/753

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