Seagrass Distribution Research Articles

Distribution of Seagrass in North Sulawesi: A Review

Seagrasses are crucial coastal ecosystems that provide a multitude of ecological and economic benefits. The distribution of seagrass meadows plays a pivotal role in maintaining marine biodiversity, supporting fisheries, and contributing to carbon sequestration. Seagrass meadow ecosystems are important in the coastal environment. This ecosystem functions ecologically in the water as a habitat for various species of marine life, and has the ability to bind soil and sediment and dampen waves so that it helps protect the coast from erosion and storms. Seagrass ecosystems play a role in nutrient circulation, carbon storage, and play an important role in climate change mitigation. In addition, these ecosystems can potentially serve as a source of antioxidant compounds and bioindicators of aquatic environmental health. The coast of North Sulawesi has the potential for the existence of this ecosystem so it is necessary to assess the potential of seagrasses that are important in the coast of North Sulawesi Province. There are reportedly 10 seagrass species that have been identified in coastal North Sulawesi, consisting of Hydrocharitaceae and Cymodoceaceae families, namely Enhalus acoroides, Thalassia hemprichii, Halophila ovalis, Syringodium isoetifolium, Halodule uninervis, Cymodocea serrulata, Cymodocea rotundata, Thalassodendron ciliatum, Halophila spinulosa, and Halodule Pinifolia. Keywords: Seagrass, North Sulawesi, Bioindicator, ecosystem

Mapping Seagrass Biodiversity Indicators of Pari Island using Multiple WorldView-2 Bands Derivatives

Comprehensive information on seagrass biodiversity indicators, such as species composition, percentage cover, and biomass carbon stock, remains limited across various regions globally. Mapping these indicators using remote sensing images requires extracting maximum information from the input images to achieve effective results. This study aims to map seagrass distribution, percent cover (PC), and aboveground carbon stock (AGC) as biodiversity indicators in the optically shallow waters surrounding Pari Island. We integrate WorldView-2 (WV2) derivatives, field seagrass data, and RF classification and regression algorithms to accomplish this objective. The WV2 image derivatives encompass surface reflectance bands, band ratios, mean and variance co-occurrence texture bands, and principle component bands. These inputs are used individually and collectively for mapping, employing a random forest algorithm trained with field seagrass data. Our results demonstrate that the most accurate benthic habitat map achieves an overall accuracy (OA) of 65.2%, with a user's accuracy of 65.2% and a producer's accuracy of 72.8% for the seagrass-dominated class. Seagrass PC mapping yields a root mean square error (RMSE) of 17.1%, with an average PC of 47.4 ± 9.9%. Seagrass AGC mapping achieves an RMSE of 5.0 g C m-2, with an average AGC range of 6.2 – 29.1 g C m-2, estimating the study area's aboveground biomass carbon stock at 27.9 tons C. Combined inputs produce the most accurate results for all biodiversity indicators, emphasizing the importance of utilizing combined bands from SR band derivatives to maximize information input for training mapping algorithms, instead of using derivative bands individually or as replacements for the initial SR bands.
 Keywords : Seagrass; Biodiversity; Mapping; WorldView-2; Pari Island
 
 Copyright (c) 2023 Geosfera Indonesia and Department of Geography Education, University of Jember
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Status and conservation challenges of the second-largest seagrass bed in India: Chilika lagoon.

Studies related to seagrass ecology, conservation, and management are sparse and highly biased in India. Even though the geographical distribution of seagrass is diverse in India, about 74% of the scientific publications have been made from Palk Bay and the Gulf of Mannar from Tamilnadu. Chilika, the largest lagoon in Asia, harbors the second largest seagrass meadow in India 22% of the total. The lagoon acts as a potential blue carbon stock and helps in thriving a rich floral and faunal biodiversity. However, the critical role of seagrass in this unique lagoon ecosystem is still poorly understood. This review is aimed at synthesizing the published literature about seagrass in Chilika. We believe this information would encourage more in-depth and diverse seagrass studies in the region and identify future priority areas for research. A total of seven species have been recorded from 169.2 sq. km of seagrass patch in Chilika. For the last two decades, no significant signs of decline in seagrass beds from this lagoon have been reported. Still, various natural and anthropogenic stressors could put this unique ecosystem under severe stress. Moreover, lax enforcement of existing legislation and a general lack of knowledge among the stakeholders about their ecosystem services can be significant impediments to their conservation. More targeted research on Chilika seagrass in changing climate regimes and their sustainable intensification is the need of the hour.

Seagrass spatial data synthesis from north‐east Australia, Torres Strait and Gulf of Carpentaria, 1983 to 2022

AbstractThe Gulf of Carpentaria and Torres Strait in north‐eastern Australia support globally significant seagrass ecosystems that underpin fishing and cultural heritage of the region. Reliable data on seagrass distribution are critical to understanding how these ecosystems are changing, while managing for resilience. Spatial data on seagrass have been collected since the early 1980s, but the early data were poorly curated. Some was not publicly available, and some already lost. We validated and synthesized historical seagrass spatial data to create a publicly available database. We include a site layer of 48,612 geolocated data points including information on seagrass presence/absence, sediment, collection date, and data custodian. We include a polygon layer with 641 individual seagrass meadows. Thirteen seagrass species are identified in depths ranging from intertidal to 38 m below mean sea level. Our synthesis includes scientific survey data from 1983 to 2022 and provides an important evidence base for marine resource management.

Remote sensing detection of seagrass distribution in a marine lagoon (Swan Lake), China.

Seagrass, a submerged flowering plant, is widely distributed in coastal shallow waters and plays a significant role in maintaining marine biodiversity and carbon cycles. However, the seagrass ecosystem is currently facing degradation, necessitating effective monitoring. Satellite remote sensing observations offer distinct advantages in spatial coverage and temporal frequency. In this study, we focused on a marine lagoon (Swan Lake), located in the Shandong Peninsula of China which is characterized by a large and typical seagrass population. We conducted an analysis of remote sensing reflectance of seagrass and other objectives using a comprehensive Landsat satellite dataset spanning from 2002 to 2022. Subsequently, we constructed Seagrass Index I (SSI-I) and Seagrass Index II (SSI-II), and used them to develop a stepwise model for seagrass detection from Landsat images. Validation was performed using in situ acoustic survey data and visual interpretation, revealing the good performance of our model with an overall accuracy exceeding 0.90 and a kappa coefficient around 0.80. The long-term analysis (2002-2022) of the seagrass distribution area in Swan Lake, generated from Landsat data using our model, indicated that the central area of Swan Lake sustains seagrass for the longest duration. Seagrass in Swan Lake exhibits a regular seasonal variation, including seeding in early spring, growth in spring-summer, maturation in the middle of summer, and shrinkage in autumn. Furthermore, we observed an overall decreasing trend in the seagrass area over the past 20 years, while occasional periods of seagrass restoration were also observed. These findings provide crucial information for seagrass protection, marine blue carbon studies, and related endeavors in Swan Lake. Moreover, our study offers a valuable alternative approach that can be implemented for seagrass monitoring using satellite observations in other coastal regions.

Comparative Assessment of Five Machine Learning Algorithms for Supervised Object-Based Classification of Submerged Seagrass Beds Using High-Resolution UAS Imagery

Knowledge about the spatial distribution of seagrasses is essential for coastal conservation efforts. Imagery obtained from unoccupied aerial systems (UAS) has the potential to provide such knowledge. Classifier choice and hyperparameter settings are, however, often based on time-consuming trial-and-error procedures. The presented study has therefore investigated the performance of five machine learning algorithms, i.e., Bayes, Decision Trees (DT), Random Trees (RT), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM) when used for the object-based classification of submerged seagrasses from UAS-derived imagery. The influence of hyperparameter tuning and training sample size on the classification accuracy was tested on images obtained from different altitudes during different environmental conditions. The Bayes classifier performed well (94% OA) on images obtained during favorable environmental conditions. The DT and RT classifier performed better on low-altitude images (93% and 94% OA, respectively). The kNN classifier was outperformed on all occasions, while still producing OA between 89% and 95% in five out of eight scenarios. The SVM classifier was most sensitive to hyperparameter tuning with OAs ranging between 18% and 97%; however, it achieved the highest OAs most often. The findings of this study will help to choose the appropriate classifier and optimize related hyperparameter settings.

STRUKTUR KOMUNITAS LAMUN PADA PULAU SARANGBAUNG, KABUPATEN NIAS UTARA

Seagrass is a coastal vegetation that has various ecological, social, and economic roles for marine biota and humans. However, until now there is little current information regarding the status and inventory Sarangbaung Island which is administratively located in North Nias Regency and geographically located in the Indian Ocean. This study aims to examine the distribution and status of seagrasses in the waters of Sarangbaung Island, North Nias Regency. The research was conducted in December 2022. Observations of seagrass were carried out using the line and square transect method of 0.5 m x 0.5 m which refers to the seagrasswatch method. In total, 3 species were found during this study namely Syringodium isoetifolium, Cymodocea rotundata, and Cymodocea serrulate. Seagrass density on Sarangbaung Island ranges from 53-728 ind/m2. Meanwhile, seagrass coverage is 82%, which indicated that seagrass was in a good category with rich/healthy conditions.

Photosynthesis and whole-plant carbon balances of warm affinity Halophila nipponica and cold affinity Zostera marina in relation to water temperature rise: implication for future geographic distribution

Climate change associated sea surface temperature rise could significantly affect the physiology, and consequently geographic distribution of seagrasses. Photosynthetic and respiratory responses of warm affinity Halophila nipponica and cold affinity Zostera marina to a change in water temperature (7–26°C) were investigated to estimate whole-plant carbon balance for prediction of the future geographic distributions of these seagrasses in the northwestern Pacific region. It was hypothesized that H. nipponica and Z. marina showed distinctly different patterns of carbon balance with changes in water temperature. Photosynthetic and respiratory rates of H. nipponica and Z. marina generally increased with increasing water temperature, except for Ic and Ik of H. nipponica and α of Z. marina. H. nipponica showed high Pmax and α values, which could support a greater respiratory demand related to the higher proportion of non-photosynthetic tissues (~67%), compared with Z. marina (~34%). The whole-plant carbon balance of Z. marina remained positive throughout the experiment, although it decreased to nearly zero during the high water-temperature period. H. nipponica exhibited a negative carbon balance during winter and early spring, which corresponded with severely limited growth at water temperatures< 15°C. These results suggest that increases in water temperature, particularly during winter, in relation to continuous climate change, could induce substantial changes in the seagrass ecosystem structure and corresponding changes in coastal ecosystem services in the temperate coastal waters of the northwestern Pacific region. The results of this study will provide valuable information on the effective management and conservation of coastal and estuarine ecosystems under ongoing climate change.

Predictive Mapping of Mediterranean Seagrasses-Exploring the Influence of Seafloor Light and Wave Energy on Their Fine-Scale Spatial Variability

Seagrasses are flowering plants, adapted to marine environments, that are highly diverse in the Mediterranean Sea and provide a variety of ecosystem services. It is commonly recognized that light availability sets the lower limit of seagrass bathymetric distribution, while the upper limit depends on the level of bottom disturbance by currents and waves. In this work, detailed distribution of seagrass, obtained through geoacoustic habitat mapping and optical ground truthing, is correlated to wave energy and light on the seafloor of the Marine Protected Area of Laganas Bay, Zakynthos Island, Greece, where the seagrasses Posidonia oceanica and Cymodocea nodosa form extensive meadows. Mean wave energy on the seafloor was estimated through wave propagation modeling, while the photosynthetically active radiation through open-access satellite-derived light parameters, reduced to the seafloor using the detailed acquired bathymetry. A significant correlation of seagrass distribution with wave energy and light was made clear, allowing for performing fine-scale predictive seagrass mapping using a random forest classifier. The predicted distributions exhibited >80% overall accuracy for P. oceanica and >90% for C. nodosa, indicating that fine-scale seagrass predictive mapping in the Mediterranean can be performed robustly through bottom wave energy and light, especially when detailed bathymetric data exist to allow for accurate estimations.

The natural capital of seagrass beds in the Caribbean: evaluating their ecosystem services and blue carbon trade potential.

Seagrass beds provide tremendous services to society, including the storage of carbon, with important implications for climate change mitigation. Prioritizing conservation of this valuable natural capital is of global significance, and including seagrass beds in global carbon markets through projects that minimize loss, increase area or restore degraded areas represents a mechanism towards this end. Using newly available Caribbean seagrass distribution data, we estimated carbon storage in the region and calculated economic valuations of total ecosystem services and carbon storage. We estimated the 88 170 km2 of seagrass in the Caribbean stores 1337.8 (360.5-2335.0, minimum and maximum estimates, respectively) Tg carbon. The value of these seagrass ecosystems in terms of total ecosystem services and carbon alone was estimated to be $255 billion yr-1 and $88.3 billion, respectively, highlighting their potential monetary importance for the region. Our results show that Caribbean seagrass beds are globally substantial pools of carbon, and our findings underscore the importance of such evaluation schemes to promote urgently needed conservation of these highly threatened and globally important ecosystems.

The vulnerability and resilience of seagrass ecosystems to marine heatwaves in New Zealand: a remote sensing analysis of seascape metrics using PlanetScope imagery

AbstractSeagrasses are foundation species that provide ecosystem functions and services, including increased biodiversity, sediment retention, carbon sequestration, and fish nursery habitat. However, anthropogenic stressors that reduce water quality, impose large‐scale climate changes, and amplify weather patterns, such as marine heatwaves, are altering seagrass meadow configurations. Quantifying large‐scale trends in seagrass distributions will help evaluate the impacts of climate drivers on their functions and services. Here, we quantified spatiotemporal dynamics in abundances and configurations of intertidal and shallow subtidal seagrass (Zostera muelleri) meadows in 20 New Zealand (NZ) estuaries that span a 5‐year period (mid/late 2016–early 2022) just before, during and after the Tasman Sea 2017/18 marine heatwave, the warmest summer ever recorded in NZ. We used high‐resolution PlanetScope satellite imagery to map interseasonal seagrass extent and quantify seascape metrics across 20 estuaries along a latitudinal gradient spanning 12° in NZ. We also explored the association of changes in seagrass metrics with satellite‐derived predictors such as sea surface temperature (SST), SST anomaly (SSTa), water column turbidity, and nutrient concentration. Our analyses revealed that NZ seagrass meadows varied in areal extent between years and seasons, but with no clear patterns over the 5‐year period, implying resilience to large‐scale stressors like the 2017/18 marine heatwave. Small‐scale patterns were also dynamic, for example, patch sizes and patch configurations differed across estuaries, seasons, and years. Furthermore, seagrass patches expanded in some estuaries with increasing SST and SSTa. These results highlight dynamic seagrass patterns that likely affect local processes such as biodiversity and carbon sequestration. Our analyses demonstrate that a combination of high‐resolution satellite remote sensing and seascape metrics is an efficient and novel approach to detect impacts from anthropogenic stressors, like eutrophication and climate changes, and climate extremes like cyclones and heatwaves.

Dramatic loss of seagrass Zostera marina L. suitable habitat under projected climate change in coastal areas of the Bohai Sea and Shandong peninsula, China

Seagrass is an important foundational species in marine ecosystems that plays an important role in providing ecosystem services. However, seagrass distribution and suitable habitat have rapidly declined in recent decades because of climate change and human-mediated disturbances. To achieved better conservation and management of the seagrass Zostera marina, an important representative seagrass in northern China, studies must investigate how this species responds to climate change. Here, we used an ensemble species distribution model to forecast the potential distribution and future suitable habitat change for the eelgrass Z. marina in coastal areas of the Bohai Sea and Shandong Peninsula, China. Two future climate scenarios, RCP 4.5 and RCP 8.5, were considered, which represent stable and high levels of greenhouse gas emissions, respectively. The ensemble model accurately predicted the current Z. marina distribution and performed better than the single-algorithm methods of random forest, generalized boosting model and maximum entropy according to performance matrices (AUC and TSS). The distance to land (78.50 ± 3.51%) and the maximum sea surface temperature (11.90 ± 0.25%) were the most important variables determining the distribution of Z. marina, while the contributions of other variables, maximum sea surface current velocity (3.80 ± 0.33%), minimum surface salinity (2.15 ± 0.22%) and water depth (1.74 ± 0.24%) were relatively low. Under future climate scenarios, the suitable habitat of Z. marina was predicted to decrease by more than half by the 2050s, whereas under the pessimistic greenhouse gas emission scenario (RCP 8.5), suitable Z. marina habitat was predicted to decrease by nearly 80% by the end of this century. The loss of Z. marina seagrass beds will have important ecological effects on the marine ecosystem. Our results not only provide valuable information to assist in identifying potential Z. marina meadows, but also have important implications for guiding conservation and management of Z. marina in the northern coastal region of China.

Effects of Shading on the Growth and Carbon Storage of Enhalus acoroides

Light intensity is one of the main factors determining the growth and distribution of seagrasses, but seagrasses differ in their responses to changes in the light environment, resulting in inconsistent adaptation. To investigate the effect of light reduction on Enhalus acoroides (L. f.) Steud., we simulated different light intensities by setting up in situ shade shelters with three light environments: full light (CK), moderate shading (MS) and high shading (HS), and investigated the growth response and adaptation mechanism of E. acoroides to a low-light environment. The results showed that the leaf length and leaf width of E. acoroides decreased in the low-light environment. Plant density, biomass, and chlorophyll content (Chl) decreased significantly with the prolongation of shading. In addition, the sediment carbon content of seagrass beds was significantly reduced in the shading treatments compared to the full-light treatment. After the restoration of light, the chlorophyll content of E. acoroides increased compared to that in the shading period, but its leaf morphology, plant density and biomass did not return to the level of full light treatment. Our study highlights that long-term light reduction leads to a significant reduction in seagrass biomass and its sediment carbon content, which in turn, may reduce the carbon storage capacity of seagrass beds.

Determination of Marine Conservation Areas by means of Satellite Imagery and Participatory Planning in Bawean Island, Gresik Regency, East Java

Bawean Island and its marine surrounding hold a lot of potential biotas in its marine ecosystem. To maintain these potential natural resources, it is necessary to make marine conservation efforts, one of which is by means of Bawean marine conservation area determination. This research began with the preparation of image processing to determine the distribution of seagrass and coral reef biota. Furthermore, a groundcheck was carried out to review the condition of the existing ecosystem, the results of which became input for FGD as a form of participatory planning. Categories, types, zones, and subzones of conservation areas are determined based on marine and fisheries regulations. As a result, seagrasses can be found at several points in Bawean waters with a total area of 125.60 ha. The average seagrass closure condition can be considered as moderate, with the most encountered species being Thalassia Hemprichii. Meanwhile, coral reefs distribution is scattered in almost all Bawean marine with a total cover area of 3,037.51 ha. The average live coral reef cover in the observation area reaches 76%, which can be considered as good condition.

Pola Distribusi Tumbuhan Lamun di Perairan Pantai Sejuk Kabupaten Lombok Utara dalam Upaya Pengembangan Modul Ekologi

Seagrass plays an important role in the life of living things. However, the existence of seagrasses in every area is damaged due to pollution and human activities. One of the areas where seagrass has not been studied is Sejuk Beach Waters in North Lombok Regency. This study aims to determine the types of seagrass, dominating species population, population density, distribution pattern, diversity of seagrass, and development of ecological modules. The type of research used is descriptive exploratory, with data collection techniques namely observation and documentation. Data analysis techniques used include: 1) diversity index; 2) dominance index; 3) population density; 4) moricita index; and 5) test the validity of the module. Based on the results of the study it was found that there were 4 types of seagrass in Sejuk Beach Waters, North Lombok Regency, namely; Enhalus acoroides, Syringodium isoetifolium, Cymodocea rotundata, and Halophila minor. The population of seagrass species that dominates the highest is found at station III of 0.2476, the population of seagrass species that dominates at each station is Syringodium isoetifolium. The highest population density was found at station I with a value of 47.1, but the one with the highest population density was Syringodium isoetifolium. The highest seagrass distribution pattern was found at station III with an Id value of 2.9608, the seagrass that had the highest Id value was Syringodiun isoetifolium. The highest diversity of seagrass species was found at station I with an H' value of 0.3637, but the one with the highest diversity was Cyromocea rotundata. Meanwhile, the seagrass that grew the lowest at each observation station was Enhalus acoroides. The results of the study were then made in the form of modules and validated by 3 experts, scores obtained from content/content experts were 80%, linguists were 86%, and visual experts were 90%, these scores stated that the developed module was valid. Then it was tested for readability on 20 students of the Biology Education Study Program, FSTT, Mandalika University of Education semester IV, the scores obtained ranged from 80% -96%, indicating that the developed module was highly legible.

Cryptic marine barriers to gene flow in a vulnerable coastal species, the dugong (Dugong dugon)

AbstractDespite the lack of obvious physical barriers and their ability to travel significant distances, many marine mammals exhibit substantial population structuring over relatively short geographical distances. The dugong (Dugong dugon), the only extant representative of family Dugongidae, is listed as Vulnerable to Extinction globally. We investigated the genetic population structure of dugongs in the shallow coastal waters along >2,000 km of the eastern Queensland coast, including the Great Barrier Reef region. Microsatellite genotypes for 22 loci in 293 dugongs, SNP genotypes based on 10,690 loci in 43 dugongs, and 410 bp mitochondrial control‐region sequences from 639 dugongs were analyzed. Clustering analysis techniques consistently identified an abrupt genetic break in the Whitsunday Islands region (20.3°S), which interrupts an overall pattern of isolation‐by‐distance. Geographic distance was relatively more important than sea‐surface temperature and seagrass distribution in explaining pairwise microsatellite genetic distances. The cause of reduced dispersal across this region is unknown but might relate to an unusual tidal and current mix, termed the “sticky‐water” effect, and/or a break in the geographical distribution of off‐shore seagrass meadows. The genetic structure suggests distinct breeding units north and south of the Whitsunday Islands region for consideration in further developing management plans for Queensland dugongs.

Comparison of Satellite Imagery for Identifying Seagrass Distribution Using a Machine Learning Algorithm on the Eastern Coast of South Korea

Seagrass is an essential component of coastal ecosystems because of its capability to absorb blue carbon, and its involvement in sustaining marine biodiversity. In this study, support vector machine (SVM) technologies with corrected satellite imagery data, were applied to identify the distribution of seagrasses. Observations of seagrasses from satellite imagery were obtained using GeoEye-1, Sentinel-2 MSI level 1C, and Landsat-8 OLI satellite imagery. The satellite imagery from Google Earth has been obtained at a very high resolution, and was to be used within both the training and testing of a classification method. The optical satellite imagery must be processed for image classification, throughout which radiometric correction, sunglint, and water column adjustments were applied. We restricted the scope of the study area to a maximum depth of 10 m due to the fact that light does not penetrate beyond this level. When classifying the distribution of seagrasses present in the research region, the recently developed SVM technique achieved overall accuracy values of up to 92% (GeoEye-1), 88% (Sentinel-2 MSI level 1C), and 83% (Landsat-8 OLI), respectively. The results of the overall accuracy values are also used to evaluate classification models.

Providing a framework for seagrass mapping in United States coastal ecosystems using high spatial resolution satellite imagery

Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.

Species‐specific acclimatization capacity of key traits explains global vertical distribution of seagrass species

AbstractAimThe global vertical depth distribution of seagrass species remains poorly understood. Locally, the abundance and distribution of seagrasses is determined by light penetration, but at global levels each seagrass species has very distinct maximum distributional depth ranges, indicating that plant‐associated traits must also influence their specific depth ranges. Seagrass‐specific attributes, such as plant size or architecture, growth or reproductive strategy and their physiological and/or morphological acclimatization potential, have been suggested to be responsible for this variety of vertical distributions. We investigate here whether these species‐specific traits drive differences in the global maximum vertical distribution of seagrasses.LocationGlobal.Time periodPublications between 1982 and 2020.Major taxa studiedSeagrasses (order Alismatales).MethodsWe tested whether the species‐specific maximum vertical distribution of seagrasses can be predicted by (1) their rhizome diameter (a proxy for plant size); (2) their functional resilience (growth/reproductive strategy); or (3) their acclimatization capacity. For the last aspect, we used a systematic review followed by meta‐analytical approaches to select key seagrass traits that could potentially acclimatize to extreme light ranges across different seagrasses.ResultsWe found that vertical distribution is best explained by the species‐specific acclimatization capacity of various seagrass traits, including saturation irradiance (physiological trait), leaves per shoot (morphological trait) and above‐ground biomass (structural trait). In contrast, our results indicate no predictive power of seagrass size or growth/reproductive strategy on the vertical distribution of seagrasses.Main conclusionsAcross the globe, the ability of seagrass species to thrive at a wide range of depths is strongly linked to the species‐specific acclimatization capacity of key traits at different organizational levels.

Annual variation in leaf canopy across a seagrass distribution range is modulated by local environmental variation

ABSTRACT Seagrasses create three-dimensional habitats (i.e. meadows) of paramount conservation relevance, which are distributed across large spatial scales, under varying environmental conditions. Cymodocea nodosa (Ucria) Ascherson is a warm-temperate seagrass, distributed along the entire Mediterranean and the adjacent Atlantic Ocean. As with many seagrasses, this species shows an annual (seasonal) pattern in canopy vitality, with maximums in leaf canopy structure (e.g. shoot density and leaf length) in spring-summer, while canopy leaf structure is reduced in winter. Since this seagrass experiences varying environmental (e.g. thermal) scenarios across its distribution range, we tested whether annual (seasonal) variation in the leaf canopy structure, in terms of shoot density, leaf length and above-ground biomass, are modulated by local environmental (here, thermal) variation. Annual variation in the shoot density of C. nodosa across its distribution range was not predicted by local environmental regimes (thermal regimes, in terms of variation in sea surface temperature, SST). However, annual variation in leaf length and biomass was significantly predicted by variation in local thermal regimes. A larger annual environmental variation, here in terms of SST, was connected with a more marked seasonal variation in leaf canopy structure, both in terms of leaf length and biomass. If a major effect of climate change is an increase in environmental variation, an increase in the annual leaf canopy variation for C. nodosa meadows along its distribution range might be expected.