Zooplankton in the Southern Ocean from the continuous plankton recorder: Distributions and long-term change

Pinkerton, Matthew H.; Décima, Moira; Kitchener, John A.; Takahashi, Kunio; Robinson, Karen; Stewart, Robert A.; Hosie, Graham W.

doi:10.1016/j.dsr.2020.103303

Cited by 47 publications

(82 citation statements)

References 89 publications

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“…They also tend to happen on timescales where any follow-up sampling may be years later. One notable exception of a long term, extensive monitoring scheme is the SCAR Southern Ocean Continuous Plankton Recorder Survey was established in 1991 by the Australian Antarctic Division to map the spatialtemporal patterns of plankton biodiversity and use the sensitivity of plankton to environmental change as early warning indicators of the health of the Southern Ocean (Hosie et al, 2003;Pinkerton et al, 2020). The current dataset comprises over 47,600 segments, each representing the laboratory analysis of zooplankton samples within water filtered by a Continuous Plankton Recorder while it travelled approximately five nautical miles.…”

Section: Building the System Monitoringmentioning

confidence: 99%

From Data to Marine Ecosystem Assessments of the Southern Ocean: Achievements, Challenges, and Lessons for the Future

et al. 2021

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Southern Ocean ecosystems offer numerous benefits to human society and the global environment, and maintaining them requires well-informed and effective ecosystem-based management. Up to date and accurate information is needed on the status of species, communities, habitats and ecosystems and the impacts of fisheries, tourism and climate change. This information can be used to generate indicators and undertake assessments to advise decision-makers. Currently, most marine assessments are derivative: reliant on the review of published peer-reviewed literature. More timely and accurate information for decision making requires an integrated Marine Biological Observing and Informatics System that combines and distributes data. For such a system to work, data needs to be shared according to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), use transparent and reproducible science, adhere to the principle of action ecology and complement global initiatives. Here we aim to provide an overview of the components of such a system currently in place for the Southern Ocean, the existing gaps and a framework for a way forward.

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Section: Building the System Monitoringmentioning

confidence: 99%

From Data to Marine Ecosystem Assessments of the Southern Ocean: Achievements, Challenges, and Lessons for the Future

et al. 2021

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“…To characterize variability in the New Zealand marine environment, spatial environmental and biotic variables were collated at a 1 km 2 grid resolution, with each spanning the breadth of the New Zealand EEZ (Table 1 and Supplementary Table 1, further details are available in Stephenson et al, 2020a). In addition to these variables, spatial estimates of various zooplankton densities (inferred prey for basking shark) (Pinkerton et al, 2020) were used as biological predictors in the models (Supplementary Table 1). Estimates of zooplankton densities did not cover the entire New Zealand EEZ (Supplementary Figures 1.9-1.15).…”

Section: Environmental and Biotic Predictor Variablesmentioning

confidence: 99%

“…Chlorophyll-a concentration Further details for each environmental variable are available in Stephenson et al (2020a) and details on the biotic variables are available in Pinkerton et al (2020). All other environmental and biotic predictor variables are found in the Supplementary Table 1.…”

Section: Chl-amentioning

confidence: 99%

“…BRT models were fitted with a Bernoulli error distribution, a tree complexity of 2, a learning rate of 0.01 (with parameters selected so as to fit trees for each bootstrapped model), a bag fraction of 0.7 and random 10-fold cross evaluation following recommendations from Leathwick et al (2006) and Elith et al (2008). The BRT method has been widely used in ecological applications and has performed well in previous studies of fish, zooplankton, and cetacean distributions in New Zealand (Leathwick et al, 2006;Compton et al, 2013;Pinkerton et al, 2020;Stephenson et al, 2020b).…”

Section: Boosted Regression Tree Modelsmentioning

confidence: 99%

“…In areas with high uncertainty, such as Cook Strait, the northern Chatham Rise, and Foveaux Strait, few basking shark sightings where available and uncertainty might be linked to low sample size. Uncertainties regarding the most northern predictions of habitat suitability (north of 40 • S) may, in part, be explained instead by a lack of information on copepod density north of 40 • S (Pinkerton et al, 2020), but is more likely associated with a lack of basking shark data. Despite a lack of surface sightings around northern New Zealand, there is some evidence of basking shark captures in deepwater trawl fisheries off central Hawke's Bay.…”

Section: Basking Shark Habitat Suitability In New Zealandmentioning

confidence: 99%

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Drivers of Spatial Distributions of Basking Shark (Cetorhinus maximus) in the Southwest Pacific

Finucci

Duffy²,

Brough

et al. 2021

Front. Mar. Sci.

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Basking sharks (Cetorhinus maximus) were widely reported throughout New Zealand waters. Once commonly observed, and sometimes in large numbers, basking sharks are now infrequently reported. Basking shark observations are known to be highly variable across years, and their distribution and occurrence have been shown to be influenced by environmental predictors such as thermal fronts, chl-a concentration, and the abundance of prey (zooplankton). Little is known of basking sharks in the South Pacific and more information on distribution, habitat use, and migratory patterns is required to better understand the species’ regional ecology. Here, we used bootstrapped Habitat Suitability Models [HSM, ensembled from Boosted Regression Tree (BRT) and Random Forest (RF) models] to determine the drivers of basking shark distribution, predict habitat suitability and estimated uncertainty in the South Pacific for the first time. High−resolution environmental (1 km2 grid resolution) and biotic data, including inferred prey species, and all available basking shark records across New Zealand’s Exclusive Economic Zone (EEZ) were included in the ensemble HSMs. The most influential driver of modeled basking shark distribution was vertical flux of particulate organic matter at the seabed, which may indicate higher levels of primary production in the surface ocean and higher prey density in the mesopelagic zone and at the seafloor. The BRT and RF models had good predictive power (AUC and TSS > 0.7) and both models performed similarly with low variability in the model fit metrics. Areas of high basking shark habitat suitability included the east and west coasts of the South Island, Puysegur Ridge, and Auckland Island slope. The outputs produced here could be incorporated into future management framework for assessing threat and conservation needs (e.g., spatially explicit risk assessment) for this regionally protected species, as well as providing guidance for future research efforts (e.g., areas of interest for sampling).

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Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle

Clerc

Bopp²,

Benedetti

et al. 2022

Preprint

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Zooplankton in the Southern Ocean from the continuous plankton recorder: Distributions and long-term change

Cited by 47 publications

References 89 publications

From Data to Marine Ecosystem Assessments of the Southern Ocean: Achievements, Challenges, and Lessons for the Future

From Data to Marine Ecosystem Assessments of the Southern Ocean: Achievements, Challenges, and Lessons for the Future

Drivers of Spatial Distributions of Basking Shark (Cetorhinus maximus) in the Southwest Pacific

Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle

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