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Doing science underwater: measuring seagrass ecosystems

January 3, 2026 | Hydrodynamics, Instruments, Photos, Seagrass ecology

Seagrass ecosystems are often described with numbers, figures, and models in research articles. But behind every dataset, there are people working underwater. Most of my days are spent in seagrass meadows around the world, stunningly beautiful places, but physically demanding. There’s moving water, limited visibility, finite air in the tank, and shifting tides. Instruments have to be carried, deployed, and pulled up again, and you just hope they’ve captured something you can actually trust.

Working in those conditions doesn’t just affect the work, it changes how I think about the data. Every light curve, sediment core, or current measurement comes from a chain of real-world decisions: which spot to sample, how long to leave a sensor, what compromises to make when things don’t go according to plan. This article grew out of that experience. It pulls together the measurements I use most, light, sediments, hydrodynamics, meadow structure, fauna, herbivory, and restoration, and shows not just how to collect them, but how they fit together to tell the story of a living ecosystem.

If you’re working in seagrass meadows, planning fieldwork, or just curious about what happens underwater, this guide is for you. It’s a practical reference, grounded in experience, written for anyone who knows that the best data isn’t just collected, it’s earned.

Seagrass meadows are often described as simple habitats, composed of plants quietly photosynthesizing beneath the surface. Anyone who has worked in seagrass bed knows how misleading that image is. Seagrass ecosystems are shaped by constant light changes, moving water, mobile sediments, animals, and feedbacks that operate across minutes to decades. Understanding how these systems function requires more than knowing what to measure; it requires knowing how those measurements actually happen underwater, and what they really mean once you are back on land looking at the data.

This article is written from that perspective. It is informed by years of fieldwork in seagrass meadows, walking into intertidal beds at low tide, diving in subtidal meadows across the world with limited visibility, deploying and recovering instruments, losing sensors due to storms, cleaning fouled loggers, and adapting sampling designs to conditions that rarely match the plan. Many of the most important decisions in seagrass research are made in the field, yet they are often reduced to a single line in a methods section. This post exists to unpack what sits behind those lines.

Rather than presenting a catalogue of methods, this blog brings together the why, how, and what it tells us across key components of seagrass ecosystems: light, sediments, hydrodynamics, meadow structure, fauna, herbivory, and restoration. These elements are deeply interconnected. Light controls productivity, hydrodynamics shape sediments and canopy structure, sediments regulate nutrients and carbon storage, fauna mediate trophic and biogeochemical processes, and restoration and grazing alter feedback loops that can either stabilize or destabilize a meadow. Measuring any one component in isolation risks missing the system-level story.

This is not a review paper, and it is not meant to replace methods manuals or primary literature. Instead, it sits in the space between them. What you will find here are field-tested approaches, common analytical frameworks, key equations, and practical considerations that rarely make it into publications but strongly influence data quality and interpretation. The goal is to help readers connect measurements to processes, and tools to ecological meaning.

How to use this article: Each section can be read independently, depending on your interests or field needs. Basic equations are included as analytical reference points, and practical tips highlight common pitfalls and trade-offs encountered during underwater work. Whether you are a student planning your first field campaign, a researcher expanding into new measurements, or a coastal manager interpreting monitoring data, this post is intended as a practical, systems-based reference for working in seagrass meadows.

Underwater light: Quantifying irradiance

Light availability is the primary factor controlling seagrass distribution, depth limits, productivity, and survival. Even modest reductions in underwater light can constrain photosynthesis, leading to reduced growth, increased mortality, and shallower depth limits, a relationship formalized by Dennison et al. (1993). Because underwater light is shaped by depth, turbidity, wave action, and canopy structure, quantifying the light environment is fundamental to understanding seagrass ecosystem functioning.

Light in coastal waters is highly dynamic. Daily and seasonal cycles, wave-driven sediment resuspension, currents, epiphyte growth, and changes in water clarity can cause strong variability over timescales ranging from minutes to months. Capturing this variability requires in situ measurements that reflect the conditions experienced directly by seagrass leaves and the sediment surface. Common research questions include:

How much light reaches the canopy?
How turbidity and epiphytes reduce irradiance?
Which light thresholds define seagrass depth limits or restoration success?

In practice, we usually measure underwater Photosynthetically Active Radiation (PAR) at two depths above the canopy using submerged light loggers (e.g., HOBO, Apogee, LICOR, Oddysey). These are often paired with fluorometers (to estimate chlorophyll a) and/or turbidity or optical backscatter sensors (e.g., C3-Turner Designs) to help identify the main drivers of light attenuation, such as sediment resuspension or phytoplankton blooms. For instance, elevated turbidity with low chlorophyll a is consistent with resuspended sediments, whereas elevated chlorophyll a suggests turbidity driven by phytoplankton biomass. Simple tools such as Secchi disks remain useful for rapid assessments, but for long-term dataseries, deploying dataloggers are needed to resolve ecologically meaningful variability. Sensor placement relative to canopy height and the sediment surface is critical, as small vertical differences can lead to large changes in measured irradiance.

Light attenuation with depth is commonly analyzed using the Beer-Lambert law:

\( E_z = E_0 e^{-K_d z} \)

where \(E_z\) is the irradiance at depth \(z\) (\(\mu\)mol photons m\(^{-2}\) s\(^{-1}\)), \(E_0\) is the irradiance just below the water surface, \(K_d\) is the vertical light attenuation coefficient (m\(^{-1}\)) describing how light is reduced with depth, and \(z\) is water depth (m).

The attenuation coefficient \(K_d\) can be estimated from measurements at multiple depths:

\( K_d = – \frac{\ln \!\left(E_z / E_0\right)}{z} \)

Practical considerations in the field include: deploying paired PAR sensors separated by a known vertical distance to resolve light gradients, see more details in the Blog post ‘Instrument deployment at sea’ ; regularly cleaning and calibrating sensors to minimize fouling effects; pairing irradiance measurements with turbidity and suspended sediment data to identify drivers of light limitation; using time-series data to calculate daily or seasonal light integrals for productivity estimates; and removing night-time measurements prior to analysis!!!

Dennison WC, et al. (1993) Assessing water quality with submersed aquatic vegetation. BioScience, 43(2), 86-94
Duarte CM (1991) Seagrass depth limits. Aquatic Botany, 40(4), 363-377

Measuring sediment properties: the story underneath

Sediments form the biogeochemical engine of seagrass meadows. Beneath the canopy, sediments regulate carbon sequestration, nutrient recycling, oxygen availability, and the chemical conditions experienced by roots and rhizomes. Grain size, porosity, and organic matter content influence how sediments store carbon and nutrients, while microbial processes control whether sediments act as sinks or sources of greenhouse gases and dissolved nutrients. Understanding sediment properties is therefore essential for interpreting seagrass productivity, hydrodynamic exposure, carbon sequestration, oxygen/sulphide conditions and habitat suitability for restoration.

Because these processes operate belowground and across multiple spatial and temporal scales, sediment research rarely relies on a single measurement. Instead, researchers combine physical characterization, porewater chemistry, and experimental approaches to capture the coupled physical, chemical, and biological dynamics that shape sediment functioning and its response to disturbance or restoration.

Sediment cores: Physical structure and carbon storageSediment cores provide the primary window into the vertical structure of seagrass sediments. Extracting intact cores in dense meadows, where roots and rhizomes bind the sediment, requires careful technique and diver control, but yields critical information on how sediments are formed and maintained. Core analyses address questions such as:

How much organic carbon and nitrogen are stored in seagrass sediments?
How stable these stocks are over time?
How sediment properties differ between natural and restored meadows or along hydrodynamic gradients?

From sectioned cores, researchers quantify grain size, bulk density, porosity, and organic matter content, often paired with elemental and stable isotope analyses (δ¹³C, δ¹⁵N) to trace carbon sources and diagenetic processes.

Sediment carbon stocks are commonly calculated as:

\( \text{Carbon stock} = C_{\text{org}} \times \rho_{\text{bulk}} \times z \),

where \(C_{\text{org}}\) is the organic carbon concentration, \(\rho_{\text{bulk}}\) is bulk density, and \(z\) is the depth interval considered. This approach allows comparison of carbon storage across sites, depths, and restoration stages, provided that core compaction and spatial heterogeneity are carefully accounted for.

Porewater chemistry: Redox conditions and nutrient availability

Porewater measurements are used to quatify the chemical environment experienced by seagrass roots and sediment microbes. Dissolved nutrients, sulfide, oxygen, and redox potential reflect the balance between organic matter decomposition, microbial metabolism, and solute exchange with overlying water. Vertical porewater profiles help identify redox zonation, nutrient regeneration hotspots, and conditions that may limit plant performance or favor sulfide toxicity. Key questions include:

What are the concentrations of dissolved nutrients, sulfides, and other redox-sensitive compounds in the sediment?
How do porewater properties vary with depth, season, or meadow condition?
How does microbial activity influence carbon decomposition and nutrient fluxes?

In practice, we sample porewater using long needles constructed from lab plastic pippetes. We drilled small holes into the pippetes and placed filter inside to avoid clogging with sediment. The pippetes were then connected with PVC tubing to syringes and inserted at defined sediment depths. Careful handling of porewater samples is essential to avoid oxygen contamination, pressure artifacts, or sediment disturbance that can alter measured concentrations. Another method to measure the presence of sulfide in the sediment is silver sticks, which provides rapid assessments.

Diffusive solute fluxes across the sediment-water interface are often estimated using Fick’s first law:

\( J = – \phi D_s \frac{dC}{dz} \),

where \(J\) is the diffusive flux, \(\phi\) is sediment porosity, \(D_s\) is the sediment diffusion coefficient, and \(dC/dz\) is the concentration gradient. Although simplified, this approach is useful to calculate whether sediments act as sources or sinks of nutrients and reduced compounds under different environmental conditions.

Sediment incubations and benthic chambers: Process-based measurements

While cores and porewater profiles describe sediment structure and chemical gradients, incubations and benthic chambers allow direct measurement of sediment processes. These approaches quantify rates of oxygen consumption, nutrient release or uptake, carbon mineralization, and gas exchange under controlled or semi-natural conditions. They are particularly valuable for testing how plants and sediments respond to changes in temperature, light, organic matter inputs, or restoration status. Common research questions include:

How do oxygen consumption, CO₂ release, and nutrient fluxes affect seagrass productivity?
How do changes in temperature, light, or organic matter addition affect carbon cycling?
What is the effect of seagrass restoration on sediment biogeochemistry?

Practical considerations for sediment studies include: minimizing core compaction during extraction; aligning porewater sampling depths with root zones; pairing sediment measurements with hydrodynamic and light data to interpret drivers; accounting for spatial variability through replication; and recognizing that short-term process measurements reflect specific environmental conditions rather than long-term averages.

Fluxes are typically calculated from changes in solute concentration over time and chamber volume:

\( \text{Flux} = \frac{\Delta C}{\Delta t} \times \frac{V}{A} \),

where \( \Delta C / \Delta t \) is the rate of concentration change, \(V\) is chamber volume, and \(A\) is the sediment area enclosed. When deployed *in situ*, benthic chambers integrate natural hydrodynamic and biological conditions, but require careful design to avoid altering flow, light, or sediment structure.

Fourqurean, JW, et al. (2012) Seagrass ecosystems as a globally significant carbon stock. Nature Geoscience, 5, 505-509.
Kennedy H, et al. (2010) Seagrass sediments as a global carbon sink: Isotopic constraints. Global Biogeochemical Cycles, 24, GB4026.
Holmer M & Nielsen SL (1997) Sediment sulfur dynamics related to biomass-density patterns in Zostera marina. Marine Ecology Progress Series, 146, 163-171.
Frederiksen MS & Glud RN (2006) Oxygen dynamics in the rhizosphere of Zostera marina. Limnology and Oceanography, 51(2), 1072-1082.
Glud RN (2008) Oxygen dynamics of marine sediments. Marine Biology Research, 4(4), 243-289.
Borum J, et al. (2005) The potential role of plant oxygen and sulphide dynamics in die-off events of seagrasses. Journal of Ecology, 93(1), 148-158.

Faunal communities: Epifauna, infauna, and fish

Seagrass meadows are structured not only by plants and sediments but also by the animals that inhabit them. Faunal communities integrate the effects of sediment, water movement, light, and canopy structure, serving as sensitive indicators of ecosystem health, habitat complexity, and restoration outcomes. Epifauna, infauna, and fish respond differently to environmental gradients, so sampling must be tailored to their ecology and behavior. Together, they provide information on trophic interactions, nutrient cycling, and ecosystem resilience.

Epifauna: Life on the seagrass leaves

Epifauna live on seagrass leaves and in the water column immediately above the canopy, and include small crustaceans, gastropods, and juvenile stages of many taxa. Their close association with canopy structure makes them highly responsive to changes in shoot density, leaf length, and epiphyte load. Common research questions include:

How does canopy density and height influence epifaunal abundance and diversity?
Do restored meadows support epifaunal communities comparable to natural systems?
How does epifauna contribute to trophic transfer and nutrient cycling?

Epifauna are typically sampled using hand-held nets placed directly over the canopy, enclosure nets that isolate a known area, or tent-shaped passive traps that capture mobile organisms moving upwards within the meadow. Samples are usually rinsed through fine-mesh sieves (commonly 250-500 µm) to separate fauna from plant material and sediment. Subsequent taxonomic identification under a microscope is often the most time-consuming step, requiring expertise and substantial processing time, particularly for diverse crustacean assemblages.

Practical tips include standardizing sampled area or volume, and pairing epifaunal data with canopy structure measurements (e.g., number of shoots, leaf length, Surface area) to aid interpretation. If damage to the meadow is not allowed then enclosure nets work well, otherwise hand nets are more time efficient underwater.

Community structure is frequently summarized using diversity indices such as the Shannon-Wiener Index:

\( H’ = – \sum_{i=1}^{S} p_i \ln(p_i) \),

where \(p_i\) is the proportional abundance of species \(i\) and \(S\) is total species richness. The Shannon-Wiener index is widely used because it combines species richness and evenness into a single value, making it simple to compare communities, but it is not always the most informative metric because it can mask changes in rare species, functional traits, or community composition that are ecologically important. Alternative or complementary approaches include species richness, Pielou’s evenness, functional trait analyses, multivariate ordinations (e.g. nMDS, PCA), and PERMANOVA, which are often better suited for comparing communities across environmental gradients or restoration stages.

Infauna: Organisms living within the sediment

Infauna live within the sediment matrix and include polychaetes, bivalves, echinoderms, and other benthic organisms that strongly influence sediment mixing, oxygen penetration, and nutrient fluxes. Scientific questions typically include:

How does infaunal community structure vary across sediment types and meadows, depth gradients or seasons?
What role does infauna play in sediment biogeochemistry and carbon cycling?
How do disturbance and restoration alter infaunal assemblages?

Infauna are collected using sediment corers of known diameter and depth, ensuring consistent sampling volume. Samples are washed through coarse to fine sieves (typically 500 µm or 1 mm), preserved, and later identified in the laboratory. As with epifauna, taxonomic identification is labor-intensive, often representing the largest investment of time in infaunal studies.

Data analysis commonly includes abundance, biomass, and functional traits, alongside diversity metrics. Infaunal studies increasingly rely on functional diversity indices, bioturbation potential indices, and multivariate community analyses, which better capture ecosystem functioning than species counts alone.

Infaunal community structure is typically quantified using the same analytical metrics applied to epifauna, including abundance, biomass, and diversity indices. Species diversity is commonly calculated using the Shannon-Wiener index. Abundance and biomass are usually standardized per unit area (e.g., individuals m^–²) based on the core diameter and penetration depth, allowing direct comparison among sites, depths, and meadow types. Infaunal biomass is often expressed per square meter by combining dry mass with core area, but volume-based metrics (e.g., per m³) may be more appropriate when comparing sediments of different penetration depths.

Fish: Mobile consumers and habitat users

Fish are among the most visible components of seagrass ecosystems and are key indicators of habitat complexity, connectivity, and ecosystem services. Because fish are mobile and behaviorally responsive, sampling methods must carefully account for scale, visibility, and observer effects. Research questions commonly include:

How does seagrass structure influence fish abundance, diversity, and size distribution?
Do restored meadows provide habitat equivalent to natural systems?
How do seagrass meadows function as nursery areas?

Fish are sampled using diver-based visual transects, underwater beach seines in shallow systems, or photogrammetry approaches such as stereo-video transects and static baited or unbaited cameras. Photogrammetry allows accurate length measurements while minimizing diver disturbance, but requires careful calibration and standardized protocols.

Fish biomass is commonly estimated using the length-weight relationship, often referred to as Froese’s length-weight equation:

\( W = a L^b \),

where \(W\) is body weight, \(L\) is total length, and \(a\) and \(b\) are species-specific parameters (commonly sourced from FishBase; Froese & Pauly, 2006). Additional analytical approaches include size-spectrum analysis, biomass per unit area, trophic guild classification, and multivariate community analyses, which help link fish assemblages to habitat structure and ecosystem function.

Gagnon K, et al. (2021) Rapid faunal colonisation and recovery of biodiversity and functional diversity following eelgrass (Zostera marina) restoration. Restoration Ecology, 29(6):e13512.
Orth RJ, et al. (1984) Faunal communities in seagrass beds: A review of the influence of plant structure. Estuaries, 7(4):339-350
Heck KL, et al. (2003) Critical evaluation of the nursery role hypothesis for seagrass meadows. Marine Ecology Progress Series, 253:123-136

Measuring currents and waves

Water motion is a fundamental driver of seagrass ecosystem structure and function. Currents and waves regulate sediment transport, nutrient exchange, oxygen delivery, and the mechanical stress experienced by plants. By interacting with canopy structure, hydrodynamics create feedbacks that influence meadow stability, productivity, and resilience. Even subtle changes in flow regime can alter sediment deposition, resuspension, and biogeochemical fluxes, making hydrodynamic measurements essential for interpreting patterns observed in sediments, fauna, and plant performance.

Unlike light, hydrodynamics are often invisible and spatially heterogeneous, varying over seconds to seasons and across small spatial scales within a meadow. Measuring them underwater requires matching the instrument and deployment strategy to the research question, sampling resolution, replication, and risk. Key questions commonly addressed include:

How does seagrass canopy height and density modify current velocity and turbulence?
How do waves interact with flexible canopies to dissipate energy and stabilize sediments?
What hydrodynamic thresholds lead to sediment erosion, plant uprooting, or meadow loss?
How do hydrodynamic conditions differ between natural and restored meadows?

Measuring currents: Mean flow and turbulence

Currents transport dissolved nutrients, particles, and gases, and control exchange between the sediment, canopy, and overlying water column. Within seagrass meadows, flow is typically attenuated by plant stems and leaves, creating vertical gradients in velocity and turbulence (Nepf 2012). These gradients are central to understanding sediment trapping, oxygen fluxes, and nutrient delivery to leaves and roots.

In practice, Acoustic Doppler Velocimeters (ADV) are widely used to resolve three-dimensional flow and turbulence at fixed points within or above the canopy, while Acoustic Doppler Current Profilers (ADCP) provide vertical profiles of mean flow across the water column. In low-energy or shallow environments, electromagnetic current meters offer robust measurements close to the sediment surface. For comparative studies or broad spatial surveys, simple dissolution-based methods such as plaster balls (clod cards) provide relative estimates of flow exposure, trading precision for replication and safety. Field tips for current measurements:

Position sensors above/below the canopy to capture flow attenuation by the meadow.
Use sampling rates according to the flow scales (seconds for turbulence, minutes for mean flow, hours for waves).
Consider high-resolution instruments at a few sites with simpler methods across many locations.

Flow velocity is commonly calculated as the time-averaged current speed:

\( U = \frac{1}{n}\sum_{i=1}^{n} u_i \),

where \(U\) is the mean current velocity (m s\(^{-1}\)) over the sampling period, \(u_i\) are individual velocity measurements (e.g. from an ADV, ADCP, or current meter), and \(n\) is the number of samples. If velocity components are measured separately in the horizontal directions (which is very common), mean current speed is often calculated as:

\( U = \sqrt{\overline{u}^{\,2} + \overline{v}^{\,2}} \),

where \(\overline{u}\) and \(\overline{v}\) are the time-averaged east-west and north-south velocity components (m s\(^{-1}\)), respectively.

Measuring waves: Oscillatory motion and energy dissipation

In shallow and exposed seagrass meadows, waves often dominate hydrodynamic forcing. Oscillatory wave motion controls sediment resuspension, bed shear stress, and plant movement, particularly during storms. Seagrass canopies can attenuate wave energy, reducing orbital velocities and stabilizing sediments, but this capacity depends on canopy density, flexibility, and meadow extent.

Wave measurements typically rely on pressure sensors or wave loggers, which infer wave properties from pressure fluctuations. These instruments are relatively robust and affordable, allowing spatial replication, but do not resolve wave direction. ADCPs configured for wave analysis provide full directional wave spectra and detailed statistics, but are costly and logistically demanding. For relative assessments of wave-induced motion, accelerometers (e.g., HOBO loggers) can be deployed to compare exposure among sites, recognizing that these data are comparative rather than absolute. Field tips for wave measurements:

Secure instruments carefully to prevent burial or loss during storms.
Correct pressure-derived wave data for depth attenuation.
Pair wave measurements with sediment and canopy data to interpret ecological effects.
Use long-term deployments to capture episodic, high-energy events that drive change.

Analytical context and integration

Hydrodynamic data are often analyzed alongside sediment properties, light availability, and meadow structure to reveal coupled processes. While mean velocities and wave heights are informative, many studies increasingly use integrated metrics such as shear stress, energy dissipation rates, or attenuation coefficients to link physical forcing directly to ecological responses. Multivariate approaches and coupled physical–biological models are also commonly used to explore feedbacks between flow, canopy structure, and ecosystem function. By quantifying both currents and waves, researchers establish the physical template upon which seagrass ecosystems are built, allowing patterns in sediment dynamics, faunal communities, herbivory, and restoration success to be interpreted within a coherent, process-based framework.

Wave exposure can be described using significant wave height (\(H_s\)) and wave period (\(T_p\)), while near-bed orbital velocity (\(U_b\)) links waves directly to sediment dynamics. Orbital velocity can be approximated as:

\( U_b = \frac{\pi H}{T \sinh(kh)} \),

where \(H\) is wave height, \(T\) is wave period, \(k\) is the wave number, and \(h\) is water depth. Reductions in orbital velocity within seagrass meadows provide a quantitative measure of wave attenuation. See more details on how to calculate this on the Blog page, Fluid Dynamics for Benthic Ecology. For the data analysis of long time-series is best to use Matlab or RStudio.

Fonseca MS & Fisher JS (1986) A comparison of canopy friction and sediment movement between seagrass species. Estuarine, Coastal and Shelf Science, 22(4):399-407
Infantes E, et al. (2009) Wave energy and the upper depth limit distribution of Posidonia oceanica. Botanica marinae, 52:419-427
Koch EW (2001) Beyond light: Physical, geological, and geochemical parameters as possible submersed aquatic vegetation habitat requirements. Estuaries, 24(1):1-17
Nepf HM (2012) Flow and transport in regions with aquatic vegetation. Annual Review of Fluid Mechanics, 44:123-14

Quantifying meadow morphology and structure

Seagrass meadow morphology describes the physical structure of the plant canopy and is central to how meadows interact with light, water flow, sediments, and fauna. Most morphological metrics are intentionally simple: they are designed to be repeatable, scalable, and comparable across sites, species, and studies. These measurements form the backbone of condition assessments, restoration monitoring, and process-based studies. Practical tips for field application.

Use quadrat sizes according to plant densities, for example too large quadrats at high densities will be very time consumming. Ensure enough replication.
Measure morphology along transects to capture spatial variability by using measuring tapes.
Pair structural metrics with light and hydrodynamic measurements to interpret functional effects.
In restoration studies, track morphology over time (months, years), not just at a single snapshot.

Shoot density is one of the most widely used indicators of meadow structure. It is directly comparable across sites and over time, sensitive to disturbance, grazing, and recovery, and can be easily integrated with measurements of light, sediment properties, and faunal communities. It is calculated as:

\( D = \frac{N}{A} \),

where \(D\) is shoot density (shoots m\(^{-2}\)), \(N\) is the number of shoots counted within a quadrat, and \(A\) is the area of the quadrat (m²).

Canopy height is the vertical structure of the meadow and strongly influences light attenuation and flow reduction. It is commonly calculated as the mean leaf length:

\( H = \frac{1}{n} \sum_{i=1}^{n} h_i \),

where \(H\) is mean canopy height (cm or m), \(h_i\) are individual leaf or shoot heights, and \(n\) is the number of measurements. Measurements can be taken on the tallest leaf per shoot or as an average of multiple leaves, depending on study objectives.

Percent cover provides a rapid, spatially integrative measure of meadow extent and patchiness:

\( C = \frac{A_v}{A_t} \times 100 \),

where \(C\) is percent cover (%), \(A_v\) is the area occupied by seagrass within a quadrat or image, and \(A_t\) is the total sampled area. Cover is commonly estimated visually in the field or derived from photographs or video frames.

Aboveground/Belowground biomass integrates plant size and density and is often used in productivity and carbon stock assessments. Is calculated as:

\( B = \frac{W}{A} \),

where \(B\) is biomass (g DW m\(^{-2}\)), \(W\) is dry weight of harvested material, and \(A\) is the sampled area.

Studying herbivory with cages: Protecting and exposing seagrass

Herbivory is a key top-down process shaping seagrass structure, productivity, and recovery (Heck and Valentine 2006). Grazers such as fish, sea urchins, turtles, and waterfowl can rapidly remove aboveground biomass, altering canopy height, shoot density, and sediment stability. Quantifying grazing is therefore essential, particularly in disturbed or restored meadows where herbivory can determine success or failure.

Because direct observation of grazing underwater is difficult, herbivory is commonly assessed using cage experiments that selectively exclude grazers. Studies typically compare exclusion cages, partial cages, open cages (procedural controls), and open plots to isolate grazing effects while accounting for cage artifacts such as shading or reduced flow. Cages are deployed over small patches (≈0.25-1 m²) for weeks to months, with mesh size chosen to exclude target herbivores while maintaining water exchange. Non-destructive metrics such as canopy height reduction, leaf area loss, or shoot density change are often used when harvesting is limited.

Identifying the main grazers usually requires complementary observations, including visual surveys, underwater video, bite-mark analysis, or enclosure experiments with known grazer densities. Herbivory rarely acts in isolation: its ecological effects are best interpreted alongside measurements of meadow structure, light availability, hydrodynamics, and sediment conditions.

Herbivory is most often quantified as biomass loss over time, based on differences between protected and grazed plots:

\( G = \frac{(B_c – B_o)}{t} \),

where \(G\) is grazing rate (g DW m\(^{-2}\) d\(^{-1}\)), \(B_c\) is biomass in exclusion cages, \(B_o\) is biomass in open plots, and \(t\) is deployment time. When initial biomass varies, grazing pressure is frequently expressed as proportional loss:

\( G_{\text{rel}} = \frac{(B_c – B_o)}{B_c} \)

Heck KL & Valentine JF (2006) Plant-herbivore interactions in seagrass meadows. Journal of Experimental Marine Biology and Ecology, 330(1):420-436
Gera A, et al. (2013) Combined effects of fragmentation and herbivory on Posidonia oceanica seagrass ecosystems. Journal of Ecology, 101(4):1064-1075
Moksnes, P‑O et al (2008) Trophic cascades in a temperate seagrass community. Oikos, 117(1):763-777

Seagrass restoration: From site selection to functional recovery

Seagrass restoration is most successful when it is treated as a process-based experiment rather than a planting exercise. While initial survival is necessary, long-term success depends on whether restored sites meet the physical, chemical, and biological conditions required for meadow persistence and whether key ecosystem functions, such as habitat provision and carbon storage, begin to recover. Restoration monitoring therefore focuses on diagnosing why plants survive or fail, and how restored meadows transition toward natural systems. Key research and management questions include:

Which light, sediment, and hydrodynamic conditions define suitable sites before planting?
Which physical or chemical constraints (e.g. light limitation, sediment instability, sulfide accumulation) cause post-planting mortality?
How quickly do restored meadows increase habitat complexity, faunal use, and biodiversity?
When restored sediments begin to accumulate blue carbon and at what rate?

In practice, restoration studies integrate a small set of highly diagnostic measurements. Structural metrics such as shoot density, canopy height, patch size, and percent cover are tracked alongside underwater light, temperature, salinity and hydrodynamic exposure. Together, these measurements identify whether failure is driven by physical stress (e.g. erosion, burial), chemical stress (e.g. sulfide toxicity), or biological interactions (e.g. herbivory). Effective restoration monitoring therefore emphasizes diagnostic simplicity, which are repeated measurements of a few key parameters (eg. shoot density), interpreted within the physical and biogeochemical context of the site. When combined with faunal surveys and sediment analyses, restoration becomes a powerful tool for understanding not only how to rebuild seagrass meadows, but which processes ultimately sustain them.

Plant survival is the most commonly metric used to estimat early planting success or failure, calculated from shoot counts:

\( \text{Survival} (\%) = \left( \frac{N_t}{N_0} \right) \times 100 \),

where \(N_0\) is the number of planted shoots and \(N_t\) is the number of surviving shoots at time \(t\). Survival is typically assessed repeatedly over time (e.g. 1, 3, 6, 12, 24, and 36 months), allowing early mortality to be distinguished from longer-term instability.

Beyond survival, simple structural indicators provide strong insight into recovery trajectories. Patch size expansion, increasing canopy height, and rising shoot density indicate improving environmental conditions and the onset of positive feedbacks such as sediment trapping and flow attenuation. These metrics are fast to collect, sensitive to stress, and directly comparable to natural reference meadows.

Orth RJ, et al. (2006) A global crisis for seagrass ecosystems. BioScience, 56(12), 987-996
van Katwijk MM, et al. (2016) Global analysis of seagrass restoration: The importance of large-scale planting. Biological Conservation, 186:92101
Moksnes, et al. (2016) Handbook for restoration of eelgrass (Zostera marina) in Sweden, National guideline. Swedish Agency for Marine and Water Management, Report 2016:9

Behind every dataset of a seagrass ecology research article, there is a story of fieldwork, of logistics, problem-solving, and sometimes, sheer perseverance underwater. The techniques we use are constantly evolving, blending classic tools with new technologies, but their goal remains the same: to understad the processes that sustain seagrass meadows. I hope this post illustrates the practical science to measure seagrass ecosystems and our efforts to protect them. To see how these methods translate into discoveries, please browse my Publication pages. If you are working on similar challenges or are curious to learn more, don’t hesitate to get in touch, I’m always eager to discuss the science beneath the surface.