Thalassia Testudinum Classification Essay

1. Introduction

Seagrass meadows are an intricate link between inland and marine ecosystems. In subtropical and tropical estuaries and coastal lagoons, large contiguous seagrass meadows support a range of ecosystem services including nutrient cycling, nursery grounds for many fish and crustacean species, food for endangered large grazers, such as dugong and turtles, and coastal protection by sediment accretion and stabilization [1,2,3]. Seagrasses are productive carbon fixers [4,5], which places their economic value among the highest of the world’s ecosystems [6]. Seagrasses are threatened by a variety of upstream processes, and the loss or reduction in the capacity of seagrasses to perform ecosystem services will influence the balance of adjoining ecosystems, such as coral reefs. Conservation of seagrasses requires management that addresses the spatially and temporally variable nature of hydrologic discharges at coasts.

Seagrass meadows grow at the nexus of terrestrial and marine environments and are impacted by highly dynamic anthropogenic and natural factors. Despite their environmental and economic significance, seagrass populations are threatened worldwide by coastal development and eutrophication and may be nearing a crisis with respect to global sustainability [7]. The land-water interaction along coasts is influenced by freshwater networks where hydrologic impacts to seagrass ecosystems include increases in nutrients such as nitrogen and phosphorus occurring from industrial or agricultural sources [8], sediment discharge from watershed deforestation and mangrove clearing [8], and disruption of the natural salinity regime [2,9]. Without appropriate management, widespread loss of seagrass habitats is predicted to continue [10], especially given continued development of coastal lands.

Researchers and managers would benefit from maps showing temporal changes in the density and distribution of seagrass cover to help inform decisions for minimizing negative impacts to seagrass resources. Continued study of coastal landscape dynamics with accurate, quantitative measurements of areal extent and density of seagrasses is needed to better understand the mosaic of their distribution (degree of patchiness, gap dynamics, habitat edge type, and connectivity) in conjunction with the temporal and spatial variability of hydrologic inputs to coastal areas [7]. Characterizing these dynamics from a synoptic, repeatable perspective with remotely sensed data [11] can strongly augment more accurate point measures of change derived in-situ [12].

Long-term, global archives of satellite multispectral imagery are now readily accessible; yet, the data are not widely used for thematic mapping of benthic cover [13]. Intensive field and laboratory data collection campaigns have been undertaken to calibrate satellite-based retrospective benthos mapping and to validate products used for time series analysis, but the amount of research exploiting satellite archives could be increased if less costly benthic mapping methodologies were identified. The archive of the Landsat 5 Thematic Mapper (TM) sensor offers multispectral imagery at 30 m spatial resolution at 16-day intervals from 1984 to 2011 and currently is an underutilized global resource for long-term data on coastal environments. The long and consistent record provides an opportunity for scientists to retrieve information from periods of time where no other forms of quantitative data are available, making the archive especially valuable in understanding seagrass habitats and coastal dynamics.

Approaches for mapping seagrasses in optically shallow water bodies have evolved from visual interpretation of aerial photography to semi-automated mapping from high resolution airborne or satellite image datasets in association with field-survey and hydro-optical data [14]. A relatively simple and widely used approach for creating seagrass maps is image-specific, pixel-based supervised classification where training pixels are selected to represent each of the classes being mapped and an algorithm matches the spectral properties of image pixels to the most similar, pre-defined, class. Whereas field data are required to ensure appropriate selection of training pixels, this method may be carried out without additional hydro-optical data from the field or lab.

Maps produced from Landsat data and pixel-based supervised classification have been demonstrated to appropriately represent the spatial characteristics of seagrass meadows. Seagrass cover was mapped in Moreton Bay, Australia, by Roelfsema et al. [15] with training pixels for five different seagrass cover classes extracted from a Landsat 5 image of the Bay. Results showed that >75% of the Bay was mapped with high categorical reliability. Wabnitz et al. [16] tested the feasibility of achieving large-scale seagrass mapping for the Wider Caribbean region with limited ground truth data, obtaining an average overall accuracy of 68% across sites for the three-class scheme. Pu et al. [17] mapped seagrass along the western coast of Florida using Landsat 5 data to calculate depth-invariant bands and achieved a 93% and 66% overall accuracy for their three-class and five-class schemes, respectively. However, the applicability of the pixel-based classifier in producing a time series of benthic map products was not tested in these studies.

Time series maps of seagrass abundance have recently been produced from Landsat 5 data and pixel-based classification [18,19,20] using image-specific training areas so that each date in the series is associated with a unique classifier. The “quality” and relevance of classes derived from satellite data are variable across images [21] so that transferring pixel-based definitions of class spectra to other images has been considered impractical [11]. Constraining class definitions to the image in which training pixels are identified, however, limits the resulting multi-temporal analysis to dates where in-situ or other ground truth source is available for the selection of calibration data.

To be as objective as possible and increase the capacity for multi-temporal and multi-site comparison of classification results it is necessary to decouple field work and satellite sensor imaging [22] such as through the use of a spectral library. In the spectral library approach, remote sensing reflectance of individual pixels are compared with simulated spectra created using measured values of bottom reflectance and water inherent optical properties [23]. The spectral library method requires specialized equipment to capture the bottom reflectances and optical properties specific to the study site as well as a radiative transfer model to simulate the spectra of varying water columns over different substrates [22,23,24,25,26,27]. Although the spectral library method is objective and repeatable, the high degree of expertise and optical data required to define the library may be unattainable in the near term for many seagrass systems. Further, studies have indicated that image-based classification provides similar or even higher accuracy benthic maps compared to the spectral library method [28,29].

A study by Lyons et al. [30] demonstrated that image-based classification methods are applicable to periods without concurrent in-situ data so that long term seagrass maps over the entire Landsat record can be produced allowing management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes. Instead of pixel-based methods, the object-based supervised classification applied by Lyons et al. [30] was guided by hierarchical rule sets, which achieved an overall classification accuracy of approximately 65%. The thresholds and membership functions in the rule sets were manually adjusted for each image. Although this research illustrated the utility of time-series seagrass maps produced without intensive data collection, the work also indicated a need for mapping methodologies that can improve upon transferability between image dates. Although object-oriented classification can greatly improve accuracy compared with traditional supervised classification, the difficulty in applying the rule-based system for seagrass habitats requires further study to test the appropriateness of object-based classification in the successful extraction of seagrass features [31].

The seagrass meadows mapped in this study are located in Florida Bay, a shallow semi-enclosed estuary in South Florida. The Florida Bay ecosystem has experienced large changes in water quality concurrent with massive die-offs of seagrasses [32]. In 1987 approximately 40 km2 of Thalassia testudinum meadows experienced a major “die-off” in Florida Bay, and that die-off has been followed by smaller (<1 km2) patchy episodes of mortality on an annual basis [1].

Despite tremendous losses suffered in the past 30 years, South Florida still supports roughly 55%–65% of Florida’s seagrass resources and the greatest population densities on the state’s coastline [33]. The Florida Bay seagrass meadows are intricately linked to the reefs of the Florida Keys, a popular tourist destination with approximately 2.5 million visitors annually generating nearly $1.2 billion for the region [32]. Although the Florida Bay seagrass landscape is an invaluable cultural and economic resource, a consensus on the primary cause of seagrass losses there has never been ascertained [33]. Therefore, the study area is representative of coastal systems that require better understanding to characterize, monitor and analyze seagrass landscape dynamics to support resource management decisions.

The underlying motivation for this work was to promote the development of remote sensing techniques that can be easily and objectively applied to the span of Landsat 5 images, including periods for which no ground truth data are available, to encourage greater interpretation of satellite archives for resource management. More specifically, a pixel-based classifier trained using ground truthed pixels compiled from three recent images was applied to a series of older images to test the transferability of the image-based spectral characterizations of classes. This work focused on using widely accessible practices to complete the tasks of: (1) normalizing the various dates of satellite data to ensure comparability of the spectral profiles across dates; and (2) defining seagrass classes that are spectrally separable and ecologically relevant.

Home » Thalassia testudinum (Species code: Tt, Turtlegrass)

Taxonomy [top]


Scientific Name:Thalassia testudinum Banks & Sol. ex K.D.Koenig
Common Name(s):
EnglishSpecies code: Tt, Turtlegrass

Assessment Information [top]

Red List Category & Criteria: Least Concern ver 3.1
Year Published:2010
Date Assessed:2007-03-20
Assessor(s):Short, F.T., Carruthers, T.J.R., van Tussenbroek, B. & Zieman, J.
Reviewer(s):Livingstone, S., Harwell, H. & Carpenter, K.E.
Thalassia testudinum forms extensive dense seagrass beds and is thought to be the most important habitat-forming seagrass species in the Caribbean. This is an abundant species that is relatively robust to disturbance, and the overall population trend is stable. Localized threats to T. testudinum include coastal development, eutrophication and sedimentation, which have contributed to some local declines. This species is listed as Least Concern.

Increased coastal development has the potential to cause more widespread declines. Since this species is a major habitat-forming species that cannot be replaced functionally by another species, its available habitat should be closely monitored.

Geographic Range [top]

Range Description:Thalassia testudinum occurs in the western central Atlantic from Florida, USA to Venezuela, throughout the Gulf of Mexico and the Caribbean Sea. It is also found in Bermuda.
Countries occurrence:
Anguilla; Antigua and Barbuda; Aruba; Bahamas; Barbados; Belize; Bermuda; Bonaire, Sint Eustatius and Saba (Saba, Sint Eustatius); Cayman Islands; Colombia; Costa Rica; Cuba; Curaçao; Dominica; Dominican Republic; French Southern Territories; Grenada; Guadeloupe; Guatemala; Haiti; Honduras; Jamaica; Martinique; Mexico; Montserrat; Nicaragua; Panama; Puerto Rico; Saint Kitts and Nevis; Saint Lucia; Saint Martin (French part); Saint Vincent and the Grenadines; Sint Maarten (Dutch part); Trinidad and Tobago; Turks and Caicos Islands; United States; Venezuela, Bolivarian Republic of; Virgin Islands, British; Virgin Islands, U.S.
FAO Marine Fishing Areas:
Atlantic – western central; Pacific – eastern central
Additional data:
Lower depth limit (metres):10
Range Map:Click here to open the map viewer and explore range.

Population [top]

Population:Thalassia testudinum forms extensive dense seagrass beds throughout its range. This is the most important seagrass habitat-forming species in the Caribbean. There have been some declines in seagrass beds in developing areas, particularly in areas of nutrient enrichment and sedimentation. Generally, however, this species still very abundant throughout its range.

Out of 103 published studies on this species, 29 showed an increase in abundance (mix of biomass, aerial extent, or density), 17 decreased and 57 showed no change (Global Seagrass Trajectories Database, Carruthers pers. comm. 2007). The overall population trend is stable.

Caribbean regional biomass ranges from 3.2 g dw/m² (recorded at the latitude 17.8o in the Virgin Islands) to 820 g dw/m² (recorded along the 28.3o latitude on the Florida coast). These findings are summarized in Gacia (1999) and include data from publications ranging from 1959 until 1996.

The T. testudinum population in the Florida Bay covered 90% of mud bank and basins until a massive die off in 1987. Four thousand hectares were completely lost and 24,000 ha were affected due to primary die off caused by higher water temperatures, hypersalinity and increased biomass, leading to high respiration demands. This was followed by secondary die-off from increased turbidity due to increased phytoplankton beginning the fall of 1991. Between 1984 and 1994, mean short shoot density dropped by 22% throughout the Florida Bay and standing crop biomass dropped by 28%. While light attenuation may have been a factor, the patchy distribution of die-off suggests that primary die-off was the principle reason for the decrease in abundance (Hall et al. 1999). Between the spring of 1995 and spring of 1999, despite individual basins in the Florida Bay exhibiting changes in abundance by between 12-100%, the total abundance throughout the Bay exhibited little change due to declines in abundance in the western part of the Bay being offset by increases in abundance in the central and eastern basins (Durako and Hall 2000). Increases in seagrass coverage along with large occurrences of flowering plants were observed during the spring of 2000; however, primary die-off among high density stands was also observed north of Barneys Key with similar symptoms noted to the 1987 die-off (Nuttle et al.  2003).
Current Population Trend:Stable
Additional data:
Population severely fragmented:No

Habitat and Ecology [top]

Habitat and Ecology:Thalassia testudinum is the most abundant seagrass in the Caribbean and forms dense rhizome mats below the sediment, creating extensive meadows on shallow sand or mud substrates from the lower intertidal to a maximum 10-12 m depth.  It has also been reported below 20 m. In Cuba, this species was found to a depth of 14 m and accounted for 97.5% of total angiosperm biomass (190 g/m²). Optimum temperatures for this species range between 20-30°C (Phillips 1960).

Thalassia testudinum typically dominates seagrass vegetation in reef lagoons, where it often coexists with Syringodium filiforme, Halodule wrightii and calcareous rhizophytic green algae belonging to the order Caulerpales, amongst which Halimeda spp. are the most conspicuous members. This species plays an important role in production of sediments (Zieman 1982, UNESCO 1998, Hemminga and Duarte 2000, Green and Short 2003, Larkum et al. 2006). It is typically found in low density in oligotrophic areas and replaced by other species when there are continuous high nutrient inputs (Fourqurean and Rutten 2004).

Thalassia testudium is an important food for green sea turtles and manatees, as well as for a number of other fish and invertebrate species.

The dense beds in the Florida Bay are susceptible to primary die-off, which usually occurs during late summer to early winter. Primary die-off is usually followed by reduced water clarity and increase epiphytic growth, which can lead to secondary die-off among neighbouring local seagrass (Nuttle et al. 2003).

Seeds are viviparous and do not form seed banks. After major eradication, recolonization is dependent on import from seeds from other areas or from vegetative fragments (van Tuessenbroek et al. 2006). Seedling success can be variable, and the generation length of this species has been estimated as eight years (for recruitment).
Generation Length (years):8

Threats [top]

Major Threat(s): The biggest threats to Thalassia testudinum are coastal development, eutrophication, and sedimentation (T. Carruthers, F. Short, B. van Tussenbroek, J. Zieman pers. comm. 2007). Boat traffic, marina development, and sewage pollution from greatly expanded residential and hotel development is a particular problem in Florida. Trawling is also a threat in some parts of the range.

Conservation Actions [top]

Conservation Actions: The range of Thalassia testudinum falls in some Marine Protected Areas (MPAs). In the Caribbean, T. testudinum is included in the 24 fully managed marine protected areas. This species is monitored by the CARICOMP network (Caribbean Coastal Marine Productivity network), including coral reefs and mangroves (Green and Short 2003). Currently, a seagrass management plan is being developed in Bermuda (Sarkis pers. comm. 2007). This species is also listed as Vulnerable (A2a) under the Bermuda Protected Species Act (Sarkis pers. comm. 2007).

This is a major habitat-forming species in the Greater Caribbean and should be monitored (Van Tussenbroek et al. 2006).

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