Community-based estimates of crustacean zooplankton production rates and ecological efficiencies in the NE Pacific
Community-based estimates of crustacean zooplankton production rates and ecological efficiencies in the NE Pacific
Abstract:
Crustacean zooplankton play a central role in the transfer of energy and can serve as indicators of changing marine ecosystems. However, most attempts to quantify zooplankton production rates are limited to measurements of single species from incubation-based growth rate methods. Here, we use the chitobiase method to estimate community level crustacean zooplankton biomass production rates (BPR) off the west coast of Vancouver Island and along an offshore transect in the subarctic NE Pacific from 2015-2017. Chitobiase is an enzyme released by crustaceans during moulting in an amount proportional to body size. Average BPR values from 2015-2017 displayed considerable spatiotemporal variability, with on-shelf values generally higher than off-shelf values and differences in production rates between spring and early fall. For example, May BPR averaged 32.2±28.5, 64.6±40.0, and 103.3±71.4 mg C m-2 d-1 for 2015-2017 respectively, while September BPR averaged 108.8±72.8, 69.5±32.1, and 60.7±30.4 mg C m-2 d-1. By coupling chitobiase-based measures of crustacean zooplankton productivity with estimates of primary productivity we can directly estimate ecological efficiencies (EE, also referred to as Trophic Transfer Efficiency), the proportion of energy transferred between phytoplankton and zooplankton. Although generally assumed to be about 10%, our data show considerable spatiotemporal variability in EE - from 1-18% off the west coast of Vancouver Island and 0-22% along Line P in the subarctic NE Pacific. The routine coupling of crustacean zooplankton production rates and primary production rates to estimate EE provides insight into how much energy is actually being transferred at the base of the marine food web. Additionally, quantifying spatiotemporal variability in crustacean zooplankton productivity and EE (i.e. instead of simply assuming 10%) will help to better parameterize and constrain ecosystem models.