Mapping the microscale variability of microphytobenthos: Development of a hyperspectral imaging method

Microphytobenthos (MPB) is a grouping of microbial benthic phototrophs that inhabit the sediments of coastal regions. There are clear indications that MPB distributions exhibit profuse spatio-temporal variability on the scale of milli- to centi-meters, which corresponds to the spatial span of their proximal habitats. This microscale variability in MPB distributions escapes detection by most traditional measurement techniques. Analysis of the spatio-temporal aspects of MPB ecology is limited due to the inability of current methods to capture the MPB distributions with high spatial and temporal resolution. This doctoral study identifies a methodological gap in our ability to measure in situ MPB distributions at the microscale and attempts to rectify it through the development of a field instrument and measurement protocol that utilize hyperspectral imaging technology. The ecological applications of the novel ability to measure and visualize full-field patterns of MPB distribution with a high temporal resolution are explored in subsequent studies

Which predictive uncertainty to resolve? Value of information sensitivity analysis for environmental decision models

Unidad de excelencia María de Maeztu CEX2019-000940-MUncertainties in environmental decisions are large, but resolving them is costly. We provide a framework for value of information (VoI) analysis to identify key predictive uncertainties in a decision model. The approach addresses characteristics that complicate this analysis in environmental management: dependencies in the probability distributions of predictions, trade-offs between multiple objectives, and divergent stakeholder perspectives. For a coral reef fisheries case, we predict ecosystem and fisheries trajectories given different management alternatives with an agent-based model. We evaluate the uncertain predictions with preference models based on utility theory to find optimal alternatives for stakeholders. Using the expected value of partially perfect information (EVPPI), we measure how relevant resolving uncertainty for various decision attributes is. The VoI depends on the stakeholder preferences, but not directly on the width of an attribute's probability distribution. Our approach helps reduce costs in structured decision-making processes by prioritizing data collection efforts

High Net Primary Production of Mediterranean seagrass (Posidonia oceanica) Meadows Determined with Aquatic Eddy Covariance

We report primary production and respiration of Posidonia oceanica meadows determined with the non-invasive aquatic eddy covariance technique. Oxygen fluxes were measured in late spring at an open-water meadow (300 m from shore), at a nearshore meadow (60 m from shore), and at an adjacent sand bed. Despite the oligotrophic environment, the meadows were highly productive and highly autotrophic. Net ecosystem production (54 to 119 mmol m-2 d-1) was about one-half of gross primary production. In adjacent sands, net primary production was a tenth- to a twentieth smaller (4.6 mmol m-2 d-1). Thus, P. oceanica meadows are an oasis of productivity in unproductive surroundings. During the night, dissolved oxygen was depleted in the open-water meadow. This caused a hysteresis where oxygen production in the late afternoon was greater than in the morning at the same irradiance. Therefore, for accurate measurements of diel primary production and respiration in this system, oxygen must be measured within the canopy. Generally, these measurements demonstrate that P. oceanica meadows fix substantially more carbon than they respire. This supports the high rate of organic carbon accumulation and export for which the ecosystem is known

The response of seagrass (Posidonia oceanica) meadow metabolism to CO2-levels and hydrodynamic exchange determined with aquatic eddy covariance

We investigated light, water velocity, and CO2 as drivers of primary production in Mediterranean seagrass (Posidonia oceanica) meadows and neighboring bare sands using the aquatic eddy covariance technique. Study locations included an open-water meadow and a nearshore meadow, the nearshore meadow being exposed to greater hydrodynamic exchange. A third meadow was located at a CO2 vent. We found that, despite the oligotrophic environment, the meadows had a remarkably high metabolic activity, up to 20 times higher than the surrounding sands. They were strongly autotrophic, with net production half of gross primary production. Thus, P. oceanica meadows are oases of productivity in an unproductive environment. Secondly, we found that turbulent oxygen fluxes above the meadow can be significantly higher in the afternoon than in the morning at the same light levels. This hysteresis can be explained by the replenishment of nighttime-depleted oxygen within the meadow during the morning. Oxygen depletion and replenishment within the meadow do not contribute to turbulent O2 flux. The hysteresis disappeared when fluxes were corrected for the O2 storage within the meadow and, consequently, accurate metabolic rate measurements require measurements of meadow oxygen content. We further argue that oxygen-depleted waters in the meadow provide a source of CO2 and inorganic nutrients for fixation, especially in the morning. Contrary to expectation, meadow metabolic activity at the CO2 vent was lower than at the other sites, with negligible net primary production

Ocean Acidification Changes Abiotic Processes but Not Biotic Processes in Coral Reef Sediments

In coral reefs, sediments play a crucial role in element cycling by contributing to primary production and the remineralization of organic matter. We studied how future ocean acidification (OA) will affect biotic and abiotic processes in sediments from two coral reefs of the Great Barrier Reef, Australia. This was investigated in the laboratory under conditions where water-sediment exchange was dominated by molecular diffusion (Magnetic Island) or by porewater advection (Davies Reef). OA conditions (+ΔpCO2: 170–900 μatm, −ΔpH: 0.1–0.4) did not affect photosynthesis, aerobic and anaerobic organic matter remineralization, and growth of microphytobenthos. However, microsensor measurements showed that OA conditions reduced the porewater pH. Under diffusive conditions these changes were limited to the upper sediment layers. In contrast, advective conditions caused a deeper penetration of low pH water into the sediment resulting in an earlier pH buffering by dissolution of calcium carbonate (CaCO3). This increased the dissolution of Davis Reef sediments turning them from net precipitating (−0.8 g CaCO3 m−2 d−1) under ambient to net dissolving (1 g CaCO3 m−2 d−1) under OA conditions. Comparisons with in-situ studies on other reef sediments show that our dissolution rates are reasonable estimates for field settings. We estimate that enhanced dissolution due to OA will only have a minor effect on net ecosystem calcification of the Davies Reef flat (<4%). However, it could decrease recent sediment accumulation rates in the lagoon by up to 31% (by 0.2–0.4 mm year−1), reducing valuable reef space. Furthermore, our results indicate that high-magnesium calcite is predominantly dissolving in the studied sediments and a drastic reduction in this mineral can be expected on Davis Reef lagoon in the near future, leaving sediments of an altered mineral composition. This study demonstrates that biotic sediment processes will likely not directly be affected by OA. Ensuing indirect effects of OA-induced sediment dissolution on biotic processes are discussed

Kartierung der Mikrovariabilität von Mikrophytobenthos: Entwicklung einer Hyperspektrale Abbildgebungsmethode

Microphytobenthos (MPB) is a grouping of microbial benthic phototrophs that inhabit the sediments of coastal regions. There are clear indications that MPB distributions exhibit profuse spatio-temporal variability on the scale of milli- to centi-meters, which corresponds to the spatial span of their proximal habitats. This microscale variability in MPB distributions escapes detection by most traditional measurement techniques. Analysis of the spatio-temporal aspects of MPB ecology is limited due to the inability of current methods to capture the MPB distributions with high spatial and temporal resolution. This doctoral study identifies a methodological gap in our ability to measure in situ MPB distributions at the microscale and attempts to rectify it through the development of a field instrument and measurement protocol that utilize hyperspectral imaging technology. The ecological applications of the novel ability to measure and visualize full-field patterns of MPB distribution with a high temporal resolution are explored in subsequent studies

Digitizing the coral reef: Machine learning of underwater spectral images enables dense taxonomic mapping of benthic habitats

Abstract Coral reefs are the most biodiverse marine ecosystems, and host a wide range of taxonomic diversity in a complex spatial community structure. Existing coral reef survey methods struggle to accurately capture the taxonomic detail within the complex spatial structure of benthic communities. We propose a workflow to leverage underwater hyperspectral image transects and two machine learning algorithms to produce dense habitat maps of 1150 m2 of reefs across the Curaçao coastline. Our multi‐method workflow labelled all 500+ million pixels with one of 43 classes at taxonomic family, genus or species level for corals, algae, sponges, or to substrate labels such as sediment, turf algae and cyanobacterial mats. With low annotation effort (only 2% of pixels) and no external data, our workflow enables accurate (Fbeta of 87%) survey‐scale mapping, with unprecedented thematic detail and with fine spatial resolution (2.5 cm/pixel). Our assessments of the composition and configuration of the benthic communities of 23 image transects showed high consistency. Digitizing the reef habitat and community structure enables validation and novel analysis of pattern and scale in coral reef ecology. Our dense habitat maps reveal the inadequacies of point sampling methods to accurately describe reef benthic communities

Dense and taxonomically detailed habitat maps of coral reef benthos machine-generated from underwater hyperspectral transects in Curaçao

This dataset contains 248 benthic habitat maps, that were created from 31 underwater hyperspectral images captured with the HyperDiver device in 8 reef sites across the western coastline of Curacao (see https://doi.org/10.3390/data5010019 for information on the acquisition of the transects). The maps were produced by 8 combinations of two semantic labelspaces (detailed and reefgroups), two machine learning classifiers (patched and segmented), and two spectral signals (radiance and reflectance). Maps in the detailed labelspace have each pixel assigned to one of 43 labels, which are taxonomic labels at family, genus and species levels for biotic components of the reef (corals, sponges, macroalgae, etc.), as well as substrate labels (sediment, cyanobacterial mats, turf algae) and survey material labels (transect tape, reference board, etc.). The set of maps in the reefgroups labelspace cluster the labels in the detailed labelspace into 11 classes that describe reef functional groups (i.e. corals, sponges, algae, etc.). All habitat maps were produced with high accuracy (Fbeta 87%), by two different machine learning methods: a random forest ensemble classifier (segmented method) and a deep learning neural network classifier (patched method). The maps are further divided by the signal type from the hyperspectral image that was used, either radiance or reflectance (the latter was calculated with a reference board located at the beginning and end of each transect). These benthic habitat maps can be used to obtain accurate descriptions of the benthic community and habitat structure of coral reef sites in Curacao. The dataset also contains: an assessment of the accuracy and data efficiency of the machine learning methods, a consistency assessment of the mapped regions, a comparison of habitat metrics (class coverage, biodiversity indices, composition and configuration) between habitat maps produced by each method, and an effort-vs-error analysis of sparse sampling techniques on the densely classified maps