Variation in ocean colour may help predict cod and haddock recruitment

Characteristics of the spring and fall phytoplankton blooms in spawning areas on the Scotian Shelf, Canada, were estimated from remote sensing data. These blooms, along with anomalies in the North Atlantic Oscillation, were used to explain variation in the recruitment of 4 populations of cod and haddock. We tested the effects of the timing of the bloom using the chlorophyll a (chl a) signal, the maximum amount of chl a, the timing of the diatom bloom, and the maximum relative dominance of diatoms on the recruitment (to Age 1) of cod and haddock on the Scotian Shelf. Models were run separately for the effects of the spring and fall blooms. Only 3 of 10 models tested (0-lag) explained significant (80 to 92%) variation in recruitment. However, the performance of these models was not consistent across populations or species, suggesting that generalities about how spring and fall phytoplankton blooms affect recruitment cannot yet be made. The differences among models suggest that fish larvae are probably adapted locally to food production and thus indirectly to the characteristics of the phytoplankton bloom, which in turn are influenced by regional (meso-scale) oceanographic conditions.

The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms

Satellite-derived remote-sensing reflectance (Rrs) can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scale for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Using in situRrs as input to the models, the performance of eleven semi-analytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, is ranked for spectrally-resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking are tested using a Monte-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflects either their immunity to scale errors or instrument noise in Rrs data, or simply that the data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggests that the performance of some semi-analytical algorithms at retrieving chlorophyll is comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also perform with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate-change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.