Understanding the Complexity of Coastal Ecosystems
Coastal ecosystems are rapidly changing due to a variety of human-induced stressors, including global warming, rising sea levels, changing circulation patterns, sea ice loss, and ocean acidification. These changes, in turn, alter the productivity and composition of marine biological communities. Additionally, regional pressures associated with growing human populations and economies result in changes to infrastructure, land use, natural resource extraction, pollution, and eutrophication.
Understanding biodiversity is fundamental to assessing and managing human activities that sustain ecosystem health, services, and mitigate humanity’s impacts. Remote-sensing observations provide rapid and synoptic data for assessing biophysical interactions at multiple spatial and temporal scales, making them useful for monitoring biodiversity in critical coastal zones. However, many challenges remain due to complex bio-optical signals, poor signal retrieval, and suboptimal algorithms.
The Marine Biodiversity Observation Network (MBON) was established to address these challenges and develop a scalable and transferable observational model for detecting biodiversity and marine habitat variability. MBON’s research highlights four approaches in remote sensing that complement its efforts to quantify plankton community composition, map and monitor foundation species, identify dynamic pelagic habitats, and inform species distribution models.
Leveraging Satellite Remote Sensing for Biodiversity Monitoring
Satellite remote sensing, particularly ocean color, can contribute to monitoring biological patterns and processes by providing information on biomass or dominant taxa of lower trophic levels. Ocean color instruments measure ultraviolet, visible, and near-infrared light at the top of Earth’s atmosphere, and atmospheric correction algorithms are applied to obtain remote-sensing reflectances (Rrs), a measure of the color of Earth’s surface.
These Rrs are then related to biogeochemical quantities of interest using statistics or descriptive models. In marine environments, common biogeochemical quantities include chlorophyll a (Chl-a), the concentration of particles (inorganic and organic, including phytoplankton), dissolved colored molecules, and seawater. All these constituents alter light through absorption and/or scattering across different wavelengths.
The physical, biological, geological, and chemical compositions of coastal and marine habitats are complex, and different processes and materials affect electromagnetic radiation differently. Measuring this complexity requires sensors with different spatial, temporal, and spectral resolutions. Thus, a multi-sensor approach is often required for ecological studies to contend with the hydrodynamic and bio-optical complexity of coastal zones.
Addressing Challenges in Coastal Environments
Coastal zones have many different constituents that continuously and often rapidly change in colors and turbidity conditions, making it challenging to measure their complexity. Coastal systems are highly dynamic and contain fine-scale features, including tidal exchange, upwelling, riverine/freshwater inputs, interaction between water and bathymetry, and complex coastline shapes. Some locations are also subject to sea ice dynamics.
Coastal ecosystems are productive, with high particle and dissolved organic matter loads that attenuate light in the water, further reducing the signal of interest leaving the water. Both benthic and plankton producers fuel coastal food webs and can drive biodiversity patterns, but it is difficult to separate these signals without high spectral measurements over long time periods.
Nearshore suspended sediments are bright and can swamp sensitive ocean color sensors, affecting both in-water algorithms and atmospheric corrections. Dynamic and diverse airborne inputs near land, including dust aerosols and pollution, make atmospheric correction challenging. Water masses with different optical constituents may mix at multiple scales and be advected from their origins, and many global ocean color algorithms assume an average or constant water mass composition, which limits their direct application in complex coastal zones.
MBON’s Approach to Addressing Biodiversity Monitoring Challenges
MBON’s science seeks to address these challenges in order to meet current needs while bridging between current observational capacity and future sensors. The network uses four methods for measuring biodiversity, three of which are directly synoptic:
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Deriving information on phytoplankton community composition from remote-sensing reflectance spectra, making the general assumption that primary producer community composition is related to optically discernible pigments or cell structures.
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Mapping and quantifying the spatial distribution and temporal dynamics of foundation species, such as kelp forests, coral reefs, and forage species that structure populations and communities.
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Combining biophysical information from multiple sensors to identify features, like fronts, eddies, or habitat patches, which can map the quality and geographic extents of pelagic habitats.
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Utilizing relationships among remotely sensed biophysical variables and occurrences or abundances of tagged or surveyed organisms to map species distributions and biodiversity patterns.
These methods assume, to some degree, that primary producer community structure relates to the structure of higher trophic level communities and that phenological changes in primary producer communities propagate throughout food webs.
Advancing Phytoplankton Community Composition Monitoring
Phytoplankton form the base of the oceanic food web, providing food for ecologically and commercially important fisheries, and fueling the exchange of carbon between the ocean and the atmosphere. Understanding and monitoring changes in plankton abundance and structure is a vital component of coastal ecosystem monitoring and management.
Phytoplankton species can be classified into several types based on a mixture of heuristics, including size, geochemical function, and taxonomic groups. While broad classifications allow detection through multi-spectral methods, there are challenges in all classification types, as groupings are often overly broad and misclassified.
Several algorithms have been developed to relate the optical constituents, spectra shape, and pigments of various plankton groups to multispectral remote sensing. These include methods for identifying single bloom-forming species, multi-species algorithms involving dominant taxa, and community composition algorithms that consider phytoplankton community size distribution.
However, the longer time series that are critical for quantifying baselines and trends may only focus on imprecise classifications that characterize polyphyletic differences through marker pigments or coarse differences in spectra. Obtaining the taxonomic details of microorganisms has traditionally relied on costly and time-consuming genetic and microscopy methods, with expertise in the latter becoming increasingly rare.
Advancing Monitoring of Foundation Species and Dynamic Habitats
In addition to monitoring phytoplankton community composition, MBON’s research is also focused on mapping and quantifying the spatial distribution and temporal dynamics of foundation species, such as kelp forests, coral reefs, and forage species that structure populations and communities.
The network also combines biophysical information from multiple sensors to identify features like fronts, eddies, or habitat patches, which can map the quality and geographic extents of pelagic habitats. This approach utilizes relationships among remotely sensed biophysical variables and occurrences or abundances of tagged or surveyed organisms to map species distributions and biodiversity patterns.
These methods are essential for understanding the relationships between primary producer community structure and the structure of higher trophic level communities, as well as how phenological changes in primary producer communities propagate throughout food webs.
Integrating Satellite Remote Sensing into Marine Ecosystem Management
Satellite remote sensing is critical to monitoring essential ocean and biodiversity variables for management and policy-ready collections that adhere to common standards and dissemination. The Global Ocean Observing System has created the system of Essential Ocean Variables (EOVs), several of which can be observed synoptically from space, including sea surface temperature, sea surface height and currents, salinity, and ocean color.
The Essential Biodiversity Variables (EBVs) build on these EOVs to provide information on several dimensions of biodiversity, including genetic composition, species populations, species traits, community composition, ecosystem functioning, and ecosystem structure. MBON’s research contributes to the ability to link observations of ocean biodiversity to this global framework of marine ecosystem management and policy.
Conclusion: Unlocking the Potential of Networked Sensor Ecosystems
Satellite remote sensing provides synoptic time series to monitor changing ocean conditions over space and time. Within the US MBON network, multi-platform satellite remote sensing is being used at local to global scales to define plankton groups, identify the extent, composition, and functioning of foundation species, and classify dynamic features and seascape habitats.
Ongoing and future work will integrate high taxonomic resolution sensors and hyperspectral optics to better resolve the rich multitrophic level diversity of coastal ecosystems. As part of an integrated network, satellite remote sensing provides oceanographic context, a means to scale in situ measurements of species to communities and broader spatial scales, and a critical tool for understanding the response of marine biodiversity to its ever-changing environment.
By leveraging the power of networked sensor ecosystems and advanced remote sensing technologies, MBON’s research is paving the way for more comprehensive, reliable, and actionable insights into the complex dynamics of coastal environments. This knowledge is essential for sustainable management and conservation of these vital ecosystems in the face of mounting human-induced stressors.
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