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Kwong, Lian Elizabeth

University of British Columbia

Mesozooplankton play a critical role in the World’s oceans, fundamentally linking primary producers to higher trophic levels, including species of commercial importance (e.g., Chinook salmon). Production reflects how much biomass is available in one trophic level to support organisms in the next level. Although estimates of primary production are readily available, those of secondary production (e.g., mesozooplankton) are limited. Due to the variable energy loss between trophic levels at the primary-secondary producer interface, improved representation of mesozooplankton in ecosystem models is essential to understand climate effects on pelagic ecosystems. The objective of this thesis is to develop, test, and implement a tool for estimating mesozooplankton production. As ecological rates (e.g., growth, mortality) scale with size, information on mesozooplankton size structure (normalized biomass size spectra; NBSS) derived using a bench-top laser optic particle counter (lab-LOPC) were paired with empirical growth rate models. To identify the best growth rate model for the region, a comparison study was conducted in Saanich Inlet, British Columbia, where model outputs were compared with chitobiase-derived estimates. I applied the approach to mesozooplankton collected as part of the Fisheries and Oceans Canada (DFO) Line P oceanographic time series between 2017-2019 using mesozooplankton collected with different mesh sizes, and between 1997-2019 using a single mesh. Data were paired with Chinook salmon productivity (Recruits/Spawner; hereafter ln(R/S)) data to build Bayesian Belief Networks (BBNs), which evaluated the association between variables. Findings of this study demonstrate that: (i) the Hirst and Bunker (2003) growth rate model agreed with the biochemical chitobiase approach; (ii) a single net mesh cannot be used to infer whole community size spectra and production, and can potentially lead to over-/underestimations; (iii) NE Pacific NBSS indicate progressively lower production and longer food chains moving offshore; (iv) NBSS are correlated with chlorophyll-a, zooplankton diversity, zooplankton water content, nitrate + nitrite, multivariate El Niño Southern Oscillation index (MEI), and species number; and (v) the associations between chlorophyll-a, mesozooplankton (biomass and production), and Chinook salmon productivity varied considerably across stocks. This research highlights the importance of including alternative hypotheses about linkages between mesozooplankton and higher trophic levels in ecosystem and fisheries models.

Text

2021-10-22

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/80030

Oceanography

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/