Predicting the distribution of Montastraea reefs using wave exposure

Chollett, Iliana; Mumby, Peter J.

doi:10.1007/s00338-011-0867-7

Cited by 80 publications

(72 citation statements)

References 64 publications

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“…The approach used here, based in wave theory, excludes the influence of any other effects on the wave climate (i.e., tides, and swell arising from distant sources) that are not generated by the local wind. Although an approximation of wave patterns in shallow areas, simple methods based on the configuration of the coastline and wind patterns have repeatedly shown to be sufficient predictors of spatial variation in coastal communities (Chollett and Mumby 2012;Harborne et al 2006). Here we measured fetch using the global, selfconsistent, hierarchical, high-resolution shoreline database (GSHHS version 1.5, Wessel and Smith 1996), and wind speed and direction were acquired from the QuikSCAT (NASA) satellite scatterometer from 1999 to 2008.…”

Section: Methodsmentioning

confidence: 99%

“…For this study we are assessing a large region (thousands of kilometers) with high variability in wind distribution and many open, fetchunlimited areas. For those reasons we made two modifications to the original method: By specifying the shift between equations for ''fetch-limited'' and ''fully developed'' seas, because for a given wind speed and a long fetch there is a fixed height to which a wave can grow (Chollett and Mumby 2012) and by including spatial variability in wind fields using gridded wind data. Additionally, we calculated daily wave exposure and then produced an average for the entire time period, instead of using the average wind speed in each of the main directions (Ekebom et al 2003).…”

Section: Methodsmentioning

confidence: 99%

“…This approach allows inclusion of strong, sporadic winds, which have a disproportionate influence on resulting wave patterns and would be missed otherwise. A detailed description of the overall method and equations can be found in Chollett and Mumby (2012).…”

Section: Methodsmentioning

confidence: 99%

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Physical environments of the Caribbean Sea

Chollett

Mumby

Muller‐Karger

et al. 2012

Limnology & Oceanography

103

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The Caribbean Sea encompasses a vast range of physical environmental conditions that have a profound influence on the organisms that live there. Here we utilize a range of satellite and in situ products to undertake a region-wide categorization of the physical environments of the Caribbean Sea (PECS). The classification approach is hierarchical and focuses on physical constraints that drive many aspects of coastal ecology, including species distributions, ecosystem function, and disturbance. The first level represents physicochemical properties including metrics of satellite sea surface temperature, water clarity, and in situ salinity. The second level considers mechanical disturbance and includes both chronic disturbance from wind-driven wave exposure and acute disturbance from hurricanes. The maps have a spatial resolution of 1 km 2 . An unsupervised neural network classification produced 16 physicochemical provinces that can be categorized into six broad groups: (1) low water clarity and low salinity and average temperatures; (2) low water clarity but average salinity and temperature, broadly distributed in the basin; (3) low salinity but average water clarity and temperature; (4) upwelling; (5) high latitude; and (6) offshore waters of the inner Caribbean. Additional mechanical disturbance layers impose additional pattern that operates over different spatial scales. Because physical environments underpin so much of coastal ecosystem structure and function, we anticipate that the PECS classification, which will be freely distributed as geographic information system layers, will facilitate comparative analyses and inform the stratification of studies across environmental provinces in the Caribbean basin.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Physical environments of the Caribbean Sea

Chollett

Mumby

Muller‐Karger

et al. 2012

Limnology & Oceanography

103

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“…Coastline data were obtained from the Global Self-consistent, 147 Hierarchical, High-resolution, Shoreline (GSHHS v 2.2) database, which provides global 148 coastline at 1:250,000 scale (Wessel and Smith, 1996). From these data, wave exposure 149 was calculated using the methods based on wave theory (after Chollett et al 2012) for 32 150 fetch directions (equally distributed through 360°). Total wave exposure (summed over all 151 directions) was calculated in R using the packages maptools (Bivand and Lewin-Koh, 152 2016), raster (Hijmans, 2016), rgeos (Bivand and Rundel, 2016), and sp (Bivand et al, 153 2013 165 sequence.…”

Section: Sample Collection and Environmental Conditionsmentioning

confidence: 99%

Using high-throughput sequencing of ITS2 to describe Symbiodinium metacommunities in St. John, U.S. Virgin Islands

Cunning

Gates

Edmunds

2017

Preprint

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Symbiotic microalgae (Symbiodinium spp.) strongly influence the performance and stresstolerance of their coral hosts, making the analysis of Symbiodinium communities in corals (and metacommunities on reefs) advantageous for many aspects of coral reef research.High-throughput sequencing of ITS2 nrDNA offers unprecedented scale in describing these communities, yet high intragenomic variability at this locus complicates the resolution of biologically meaningful diversity. Here, we demonstrate that generating operational taxonomic units by clustering ITS2 sequences at 97% similarity within, but not across, samples collapses sequence diversity that is more likely to be intragenomic, while preserving diversity that is more likely interspecific. We utilize this 'within-sample clustering' to analyze Symbiodinium from ten host taxa on shallow reefs on the north and south shores of St. John, US Virgin Islands. While Symbiodinium communities did not differ between shores, metacommunity network analysis of host-symbiont associations revealed Symbiodinium lineages occupying 'dominant' and 'background' niches, and coral hosts that are more 'flexible' or 'specific' in their associations with Symbiodinium. These methods shed new light on important questions in coral symbiosis ecology, and demonstrate how application-specific bioinformatic pipelines can improve the analysis of metabarcoding data in microbial metacommunity studies. 63 antillogorgium, and potentially other species (Parkinson et al., 2015). Moreover, the 64 position of ITS2 within the tandemly-repeating ribosomal DNA array creates multiple ITS2 65 sequence variants within a single genome (Thornhill et al., 2007) that evolve through 66 concerted evolution (e.g. (Dover, 1986)). In fact, concerted evolution may mask species 68 dominants in multiple derived lineages ( Thornhill et al., 2013). Together, these features of 69 ITS2 complicate the interpretation of intragenomic versus interspecific variation, and 70 preclude its use as a true species-level marker for Symbiodinium. Nevertheless, numerically 71 dominant intragenomic variants of ITS2 are still phylogenetically informative across the 72 genus (LaJeunesse, 2001), and resolve diversity at a functionally and ecologically important 73 level. Moreover, due to the large quantity of existing sequence data for comparative 74 analysis (e.g., (Franklin et al., 2012;Tonk et al., 2013)), and the relative ease of amplifying 75 and sequencing ITS2, it remains an essential and powerful marker for Symbiodinium.76 Therefore, it is important to develop best practices in the bioinformatic analysis and 77 interpretation of ITS2 metabarcoding surveys of Symbiodinium.78 Here, we describe a Symbiodinium metacommunity associated with scleractinians and a 79 Millepora hydrocoral in St. John, U.S. Virgin Islands, with the objectives of: 1) developing an 80 appropriate bioinformatic approach for Symbiodinium ITS2 metabarcoding surveys, and 2) 81 exploring network analysis and statistical applications for such datasets that can advance 8...

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“…Non-directional water motion caused by tides and waves also affects coral development [25,26]. However, the measurements of galvanic corrosion failed to detect any difference in water motion among the mooring areas.…”

Section: Mooring Areasmentioning

confidence: 99%

Coral Distribution on Artificial Structures in Bonaire, Leeward Antilles

Filkovsky¹,

Hoback²

2014

TOMBJ

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Distribution patterns of live scleractinian coral colonies on vertical faces of concrete blocks were compared with distribution patterns of environmental parameters known to affect coral recruitment and growth. Coral coverage was not random across the blocks and among regions of their faces. Variations in colonization did not correspond to differences in substrate type, illuminance, and water motion intensity. Coral distribution significantly correlated with blocks location and faces orientation, and these correlations depended on prevailing current direction. These correlations and dependencies are consistent with the hypothesis that the coral coverage was detrimentally affected by a low quality of water flow from the local marine port.

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Predicting the distribution of Montastraea reefs using wave exposure

Cited by 80 publications

References 64 publications

Physical environments of the Caribbean Sea

Physical environments of the Caribbean Sea

Using high-throughput sequencing of ITS2 to describe Symbiodinium metacommunities in St. John, U.S. Virgin Islands

Coral Distribution on Artificial Structures in Bonaire, Leeward Antilles

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