Our key objectives in the Pelagic Ocean Living Lab 2 are to:

• Investigate and rank the importance of different plankton-related processes for carbon sequestration,
• Assess their potential changes in the future ocean and develop indicators (e.g., shifts in functional groups) that could predict upcoming changes in these processes.
• Gain a mechanistic understanding of the biological processes underlying carbon sequestration that will allow for extrapolation of the results to undersampled geographic areas and / or seasons.

 
The Living Lab will aim for more operational workflows for zooplankton observations and modelling by providing better representation of a key biological process (carbon sequestration through biological pump) for Earth System Models (ESMs), better understanding of the links between multiple stressors, (functional) biodiversity and biogeochemistry, and developing phytoplankton and zooplankton functional groups as potential indicators for abrupt changes (ecosystem tipping point).

The Pelagic Ocean Living Lab 2 is collaborating closely with the Pelagic Ocean Living Lab 1 and the Marine Organic Carbon Atlas Living Lab. 

Main EOVs studied in the Living Lab:
(Click on the EOV name to access the corresponding GOOS EOV Specification Sheet if available)

Zooplankton biomass and diversity

Task on “Validation of emerging technologies” 

Lead partner: UNIPI

Contributing partners: DTU, UU, CIIMAR, MOi

In this task we perform calibration and validation of emerging biodiversity observation technologies to sense EOVs through direct comparisons with traditional sampling methods, with the aim of increasing their spatial coverage and sampling frequency. The task focuses on cabled camera networks, eDNA sampling, Argo floats and acoustics, and how modelling analyses can add value to these observations. Recommendations for improved sampling protocols will also be provided. 

Task on “Evaluating the power of EOVs under scenarios of biodiversity change” 

Lead partner: UNIPI

Contributing partners: DTU, UU

In this task we assess the power of statistical models involving EOVs and their supporting, complementary and sub-variables under different scenarios of biodiversity change. This is because different EOVs may have different ability to capture changes such as steep vs. gradual decline or loss of resilience, depending on their inherent level of variability against which the signal must be identified. The task will generate power curves for different combinations of effect sizes and sampling designs that will ultimately guide the operational use of EOVs.

Task on “Predicting key ecosystem services: biomass and food web dynamics”

Lead partner: MOi

Contributing partners: DTU 

In this task we model the distribution and biomass of main mesozooplankton and micronekton functional groups under different IPCC climate change projections to validate ocean ecosystem models, specifically by predicting habitats, behaviours and population  dynamics of their predator species and by adding gelatinous (salps) and the mesopelagic migrant and non-migrant groups.

Task on “Predicting key ecosystem services: carbon sequestration in the open ocean”

Lead partner: DTU

Contributing partners: MOi, IOPAN

This task will use historical and newly connected in situ data in combination with state-of-the-art models to better quantify the processes that contribute to export flux and carbon sequestration; and recommend how these processes could be best measured within the EOV framework. Main focus of the task will be on 1) phyto- and zooplankton functional diversity and biomass, 2) vertical migration of zooplankton, mesopelagic fish and invertebrates, 3) feeding and respiration rates of epi- and mesopelagic copepods and 4) effect of multiple stressors on the behaviour and metabolic rates of functional groups of zooplankton and mesopelagic organisms. 

Task on “Coordination of the living labs to demonstrate an operational workflow in different settings” 

Lead partners: IOPAN, UU

Contributing partners: All partners

This task coordinates across all the focal living labs to iteratively test the ability to

connect all the components of the workflow by responding to the Blueprint’s guiding questions in particular settings. Feedback from the living labs will be collected at various stages and used to improve the final Blueprint. 

Task on “Development of new EOV data products and associated roadmaps”

Lead partner: IOPAN

Contributing partners: All partners

This task develops roadmaps and implementation plans towards establishing new biological data products critical for advancing ecosystem, biodiversity and climate projections and global assessments. Roadmaps will be co-created with relevant stakeholders, in particular the modelling communitie and key data integrators. In the process we will test the applicability of the Blueprint workflow specifically for data product development. Substantial data rescue effort targeting publication of long-term biological observations from the Arctic region will be undertaken to deliver a pilot demonstration of the Marine Organic Carbon Atlas. Incorporating remote sensing and model estimates of marine organic carbon stocks and fluxes will help better understand and model the links between biology, biodiversity, biogeochemistry and climate.

Task on “Demonstrated advancement in BioEco forecasting” 

Lead partner: MOi

Contributing partners: IOPAN, DTU

This task will contribute to decreased uncertainty in biology and ecosystem model projections to facilitate regional and global assessments. Particular examples of this are the leatherback turtle model and the trait-based approach to modelling unicellular and multicellular life in the ocean. In parallel, we will collaborate with international expert working groups to further the development of a protocol for scientific validation of BioEco model forecasts.

Task on “Establishing early-warning indicators, detecting and assessing impacts of compound events and tipping points”

Lead partner: UNIPI

Contributing partners: MOi, DTU, CIIMAR, UU, IO PAN

Some of the main challenges addressed in the different focal living labs are identifying and predicting nonlinear changes (such as tipping points), potentially caused by combined effects of different forcing factors. In addition to classical indicators of loss of resilience based on time series, this task considers spatial early warning signals that rely on short-term observations that can be easily obtained and updated. We will recommend and test the performance of a set of potential early-warning indicators. The focus will be on exploring indicators from cable camera observations relevant to detecting changes in macroalgae and seagrass; and new functional plankton diversity indicators derived from a combination of

trait-based models and historical observations.