Return to title
Proceedings of 10th International Coral Reef Symposium, 1577-1584 (2006)
Global mapping of factors controlling reef-island formation and maintenance 1*
1,2
3
4
Hiroto SHIMAZAKI , Hiroya YAMANO , Hiromune YOKOKI , Toru YAMAGUCHI , 5 6 7 Masashi CHIKAMORI , Masayuki TAMURA and Hajime KAYANNE 1
Social and Environmental Systems Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan 2 UR 128 CoRéUs, Institute de Recherche pour le Développement, BP A5, 98848 Nouméa Cedex, New Caledonia 3 Center for Water Environment Studies, Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, Ibaraki 316-8511, Japan 4 Department of Archaeology, Keio University, 2-15-45 Mita, Minato, Tokyo 108-8345, Japan 5 Faculty of Informatics, Teikyo Heisei University, 2289-23 Uruido-Otani, Ichihara, Chiba 290-0171, Japan 6 Department of Urban and Environment Engineering, Kyoto University, Yoshida-Honmachi, Sakyo, Kyoto 606-8501, Japan 7 Department of Earth and Planetary Science, The University of Tokyo, 3-7-1 Hongo, Bunkyo, Tokyo 113-0033, Japan *Corresponding author: H Shimazaki FAX: +81-29-850-2572, e-mail:
[email protected]
Abstract Reef islands established on shallow reef flats have significantly high vulnerability to projected environmental changes (e.g., a faster rise in sea level, more frequent heat waves and drought, and more extreme weather events). Understanding the major factors that control the processes of the formation and maintenance of reef islands is of critical importance for providing effective conservation measures. We established a geographic database that comprises data on the factors potentially controlling the geological, ecological, and physical processes of reef-island formation and maintenance. Segmentation analysis on the factors was performed, providing us with a broad view of the range of environmental conditions, which could explain the diversity of reef islands. Further study should quantify the relationship between the evolution of reef islands and the environmental conditions, by combining the existing global environmental dataset and the forthcoming regional data on the size, structure and composition of reef islands. Keywords
GIS, global mapping, reef island
Introduction Reef islands established on shallow reef flats have significantly high vulnerability to projected environmental changes (e.g., a faster rise in sea level, more frequent heat waves and drought, and more extreme weather events). They are generally regarded as the combined products of geological, ecological, and physical processes (Gourlay 1988). However we should note that reef islands have diversity in size, structure and composition (Andréfouët et al. 2001a; Yamano et al. submitted), which is expected to be a
result of differences in the factors controlling the processes of reef-island formation and maintenance. In order to provide effective conservation measures for such islands, a better understanding of the significant controlling factors is of critical importance. Geographic information systems (GIS) enable us to integrate a wide variety of geographic data types originating from many diverse sources (Longley et al. 2001) and allow us to perform various operations on the geographic data. A few researchers have begun to use these tools to model coral reef systems (e.g., Treml et al. 1997; Puotinen 2004a; Puotinen 2004b). GIS analysis and global assessment of the variation in controlling factors would help us generalize the knowledge about reef-island systems gained from site specific studies. However, the lack of a comprehensive dataset of the controlling factors has been an obstacle to doing so, even though global datasets of coral reef distribution and their environment are available in “ReefBase” (Spalding et al. 2001) and Kleypas et al. (1999), respectively. To address this, we compiled a global dataset of the factors potentially controlling the geological, ecological, and physical processes of reef-island formation and maintenance. Relating the factors in a spatial context with a GIS, we identified areas having relatively homogeneous characteristics in terms of these factors. The results would provide a basis for comparative studies on the relationship between the evolution of reef islands and the environmental conditions that potentially affect them, leading to insights about the significant factors that control the processes of the formation and maintenance of reef islands located in various environmental regimes. Geographic Data Collected Reef islands are naturally dynamic and are
1577
Return to title
maintained by sediment production through reef-building organisms in adjacent reef environments and their transport and deposition by waves and currents (Woodroffe 2002). Coral reefs act as natural breakwaters protecting shorelines from erosion and produce a depositional environment suitable for island maintenance. Land plants (e.g., Pemphis acidula and Scavola frutescens) also contribute to preventing shoreline erosion, by binding the constituent materials of reef islands (Chikamori 2001). Thus, the presence and morphological characteristics of reef islands could be a result of interactions between the physical and ecological processes, which control the balance among sediment supply, sediment transport, available space for deposition, and shoreline erosion. Historically, Holocene sea-level changes are suggested as having a significant effect on the formation of reef islands. McLean and Woodroffe (1994) suggested that islands formed after the establishment of reef flats, and this was possibly due to relative sea-level fall in the late Holocene (Schofield 1977; Yamano et al. 2001; Woodroffe and Morrison 2001). Hence, geological information on the Holocene relative sea-level history could be indispensable for a better understanding of the evolution of reef islands (Yamano 2002). We collected data on the factors that potentially affect the manifold processes of reef-island formation and maintenance (Table 1). The data we collected can be categorized into the following four groups: (1) factors affecting the physical processes of shoreline erosion and deposition; (2) factors affecting the ecological processes of the development and zonation of coral reefs; (3) factors affecting the ecological processes of land-plant growth; and (4) base map data representing geographic features such as shoreline, topography, and reef locations. The rationales behind factor selections are described below. Table 1.
Factors Affecting the Physical Processes The first group consisted of data on tide, wind, wind wave and swell that produce currents or water circulation (Andrews and Pickard 1990), and on tropical storms that can transport coral rubble to form reef islands (e.g., Maragos et al. 1973; Scoffin 1993). They were considered to be major factors affecting the physical processes of shoreline erosion and deposition. Water circulation determines the dispersal of materials in coral reefs (Yamano et al. 1998). The importance of wind on circulation in coral reefs has been shown in a deep atoll lagoon (Atkinson et al. 1981) and in platform reefs (Frith 1981; Frith and Mason 1986; Pickard 1986). Data on significant wave height has been used to calculate water flow over a reef crest (Tartinville and Rancher 2000) and to estimate water renewal time for atoll lagoons (Andréfouët et al., 2001b). For fringing reefs, wave over-topping on the reef crest and the induced inflow is considered to be the main driving force of circulation in Caribbean reefs (Roberts et al. 1975; Roberts et al. 1992), while tide is thought to be the main driving force in fringing reefs in the Great Barrier Reef because of the large tidal range (Parnell 1988). Recent numerical modeling studies have considered the transfer of momentum caused by wave over-topping (Prager 1991; Wolanski et al. 1994). High winds, large waves, storm surges and heavy rainfall associated with severe tropical storms have catastrophic impacts on coral reefs and islands in the short-term (Stoddart 1971). However, storms are also necessary for the long-term replenishment of sediment on the shorelines in atoll environments (Sherwood and Howorth 1996), through the processes of delivery and removal of the coral rubble and fragments on the reef flat (Bayliss-Smith 1988; Blumenstock 1958; Hubbard 1992; Maragos et al. 1973; Scoffin 1993).
List of the collected data. Spatial
Category
Temporal
Factor name Coverage
Resolution
Coverage
Resolution
Data name
Data provider ECMWF
Wind wave (direction, hight, period)
36°N-36°S
1.5° GRID
1990/01/01-1999/12/31
6 hours
ERA-40
Primary swell (direction, hight, period)
36°N-36°S
1.5° GRID
1990/01/01-1999/12/31
6 hours
ERA-40
ECMWF
Tidal range
Global
0.5° GRID
---
---
NAO.99b
Matsumoto et al (2000) NOAA NODC
Nutrients (Nitrate)
Global
1.0° GRID
1772-2001
Monthly
WOA01
Nutrients (Phosphate)
Global
1.0° GRID
1772-2001
Monthly
WOA01
NOAA NODC
Nutrients (Silicate)
Global
1.0° GRID
1772-2001
Monthly
WOA01
NOAA NODC NOAA NODC
Oceanographic
Salinity
Global
1.0° GRID
1772-2001
Monthly
WOA01
Sea surface temperature
Global
9.28 km GRID
1985-1997
5 days mean
Pathfinder SST
NASA JPL
Surface wind
Global
2.5° GRID
1980-1995
Monthly climatology
GGUAS
NOAA NCDC
Surface air temperature
Global
2.5° GRID
1980-1995
Monthly climatology
GGUAS
NOAA NCDC
Surface solar irradiance
Global
1.0° GRID
1983/07 - 1991/06
Monthly
SeaWiFS Solar
NASA GISS
Precipitation
Global
2.5° GRID
1979-2003
Monthly
GPCP Ver.2
GPCP
Indo-Pacific
Points
1945-2002
6 hours
Best Track
JTWC
Atlantic
Points
1851-2003
6 hours
HURDAT
NOAA NHC
Pacific
Polygons
---
---
Climatological
Tropical storm Dickinson (2003)
Geological
Late Holocene sea-level change history
Biogeographical
Distribution of foraminifera
Global
Points
---
---
Reef location
Global
Points
---
---
Reef locations
ReefBase
Shoreline
Global
Polylines
---
---
GSHHS Ver.1.2
Univ. of Hawaii
Topograpy
Global
1/30° GRID
---
---
ETOPO2
NOAA NGDC
Base map
1578
Langer & Hottinger (2001)
Return to title
Factors Affecting the Ecological Processes of Coral Reefs The second group consisted of data on tide, wind wave, swell, tropical storm, surface solar irradiance, water attenuation coefficient of light, sea surface temperature, nutrient concentrations, carbonate ion concentrations, and salinity. These factors were considered to have an impact on reef-island formation and maintenance through the effect on the development of coral reefs and the amount of supply of bioclastic materials forming reef islands. Data on solar irradiance at the surface, water attenuation coefficient of light, sea surface temperature, nutrient concentrations, carbonate ion concentrations, and salinity were compiled to evaluate the potential for coral reef development, according to the method described by Kleypas et al. (1999). Wind wave and swell were considered important because wave energy is a major constraint on the development of coral reefs and the susceptibility of reef framework to the storm breakage varies due to wave energy regimes (Yamano et al. 2003). Tropical storms have the potential to affect large areas of reef with structural changes, that while patchy, can require decades to centuries for recovery (Scoffin 1993; Harmelin-Vivien 1994; Van Woesik 1994). The tide limits the development of reef flats by defining the water levels of Mean Low Water Neaps (MLWN) and Mean Low Water Springs (MLWS) (Kleypas and Hopley 1992). Distribution of reef-building organisms on the reef flat is of critical importance to sediment production. Usually the major constituents of reef islands are benthic foraminifera, calcareous algae, hermatypic corals, and molluscs. Of these, benthic foraminifera are suggested to be the most important contributor to the sand mass of some reef islands (Yamano et al. 2000). The tide induces water exchange, which influences the cycling of nutrients within the coral reef ecosystems and the input of additional nutrients from ocean waters, and affects the growth and zonation of reef-building organisms inhabiting shallow reef flats (Pugh and Rayner 1981). Wave energy also induces the present-day ecological zonation of coral reefs (Geister 1977; Done 1983; Hubbard 1988; Grigg 1998). Factors Affecting the Ecological Processes of Land Plant Growth The third group consisted of data on surface solar irradiance, surface air temperature, and precipitation because they were considered to be major factors influencing the growth of land plants, which contribute to protect reef islands from erosion by binding sand and soil. Hence, considering the factors affecting the ecological processes of the land-plant growth is important for evaluating the potential of a natural land protection system. Of the data we collected, that on precipitation might be a key parameter in coral reef areas because the broad-scale distribution of flora on reef islands is suggested to have a close relation to the amount of precipitation (Stoddart 1992).
Base Map Data The fourth group contained data on shoreline, topography, and reef locations, which were available online from the appropriate centers of processing and dissemination (Table 1). These data were used as base data when mapping the factors potentially affecting reef-island formation and maintenance and the parameters derived from them. Data Source and Processing Data for our geographic database were predominately obtained in digital format, but also in analog format like a published paper map, which needed to be digitized before being added to the database. The data encoded in digital format were converted into geographic data in a form suitable for use in the geographic database while maintaining original spatial and temporal resolutions. As needed, summary statistics (e.g., mean and standard deviation of observations for each month, over several years) were derived from source data and stored in the geographic database. Although the original values could be redundant, the reformatted version of source data was kept stored in the database to cope with changes in future applications. The source and processing methods of the data we collected are explained below. Wave Energy Flux We obtained the data on the significant wave height H (m), mean wave period T (s) and mean wave direction D (degree from the North) for each of the wind wave and the swell values for the period 1990 to 1999 from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-analysis (ERA-40) wave model product. The data we obtained had a spatial resolution of 1.5*1.5 degree grid and a temporal resolution of 6-hour. From the obtained data, we derived mean energy flux P (W/m) for each of the wind wave and the swell values using equation (1). P = EC g =
ρg 2 2 , H T 32π
(1)
where E is energy density (J/m2); Cg is group velocity (m/s); ρ is mass density of water (kg/m3); and g is gravitational acceleration (m/s2). Then we summarized the energy flux of each of the wind wave and the swell values by computing mean and standard deviation for the entire period, year, and month of the year. We also summarized mean wave direction of each of the wind wave and the swell values by calculating mean and angular deviation (Zar 1999) for the entire period, year, and month of the year. Tropical Storms We collected the track data of tropical storms including typhoons, cyclones and hurricanes, from which we derived parameters to obtain a synoptic view of the effect of storms on reef islands. The storm track data was taken from the official archives provided by the
1579
Return to title
NOAA/National Hurricane Center (NHC) for Atlantic storms, and by the U.S. Navy’s Joint Typhoon Warning Center (JTWC) for all other storms. The data consisted of the 6-hourly center locations and maximum sustained wind speeds for all tropical storms over the past 50 years. We first refined the track data by removing the inconsistent or overlapped records. We then estimated hourly center locations and maximum sustained wind speeds by interpolation; although various interpolation methods could be employed for this purpose, we used cubic spline method (Bartels et al. 1998) in order to obtain smooth interpolation. Finally, we summarized hourly storm data at a spatial resolution of 0.5*0.5 degree grid by calculating the occurrence frequency and accumulated Wind Intensity Index (WII) of the storms within a specific range from each grid point. Although it might not necessarily be appropriate to fix the range size in summarizing data, we fixed it to 200 km, by considering the distances between reef islands and the storms having brought significant impacts on reef islands (e.g., Maragos et al. 1973). The WII was an index of storm intensity, which was tentatively defined in this study in order to consider the asymmetry of wind field. In general, the strongest winds in a storm are found on the right (left) side with respect to the storm’s motion in the Northern Hemisphere (Southern Hemisphere), because the motion of the storm also contributes to its swirling winds (Holland 1980). The WII was calculated by equation (2). WII = Vmax × (cos(α − β + (π / 2) × δ ) + 2) ,
(2)
where Vmax is maximum sustained wind speed, α is the azimuth from a grid point to the storm center, β is direction of the storm’s motion, and δ is 1 for the storms in the Northern Hemisphere and is -1 for the storms in the Southern Hemisphere. Tidal Range We calculated the range of astronomical tide at a spatial resolution of 0.5*0.5 degree grid using the data on harmonic constants of the four major component tides (i.e., M2, S2, K1 and O1). The harmonic constants used in the calculation were obtained from the tidal prediction system “NAO.99b” (Matsumoto et al. 2000). Surface Solar Irradiance The NASA/Goddard Institute for Space Studies (GISS) calculated the original daily and the monthly mean surface solar irradiance on 2.5*2.5 degree grid for the period July 1983 to June 1991, using a fast atmospheric radiative transfer algorithm (Bishop and Rossow 1991). The data we obtained was a spatially interpolated version of the original monthly mean product at a spatial resolution of 2.5*2.5 degree grid to 1.0*1.0 degree grid. The interpolated data was distributed from the Distribute Active Archive Center (DAAC) at the NASA/Goddard Space Flight Center (GSFC). Using the monthly data for the period July 1983 to June 1991, we derived climatologies (mean and
standard deviation) of the surface solar irradiance by summarizing the data for the entire period, year, and month of the year. Surface Air Temperature and Wind The data we obtained was monthly climatologies (mean and standard deviation) for the atmosphere represented on 2.5*2.5 degree grid for the period 1985 to 1995. The data was distributed from the NOAA/National Climatic Data Center (NCDC) as the product named “The Global Gridded Upper Air Statistics (GGUAS) Version 1.1”. The source of GGUAS dataset was the semidiurnal analyses for the period 1985 to 1995, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). Using the monthly climatologies data for the period 1985 to 1995, we derived mean and standard deviation values of the surface air temperature for the entire period. Precipitation Rate The data we obtained was the product named “The Global Precipitation Climatology Project Version 2 Combined Precipitation Data Set (GPCP Version 2 Data Set)”, which was developed and computed by the NASA/Goddard Space Flight Center's Laboratory for Atmospheres as a contribution to the GEWEX Global Precipitation Climatology Project, and was distributed from the NOAA/National Climatic Data Center (NCDC). The dataset contains monthly, 2.5*2.5 degree gridded fields of two products: the combined satellite-gauge precipitation estimate; and the combined satellite-gauge precipitation error estimate. It covers the 21-year period January 1979 through the delayed present. Using the monthly data for the period January 1979 to December 2003, we derived climatologies (mean and standard deviation) of the precipitation rate (mm/d) by summarizing the data for the entire period, year, and month of the year. Sea Surface Temperature (SST) The data we obtained was the pentad (5-day) climatology on a 9.28 km grid for the period 1985 to 1997. The data was distributed from the Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the NASA/Jet Propulsion Laboratory (JPL) as the product named “AVHRR Pathfinder Global 9km SST Climatology (JPL)”. The product was derived from the highest quality SST data estimated from AVHRR Pathfinder Oceans Data by the method of spatial and temporal Gaussian interpolation (Casey and Cornillon 1999). Using the pentad climatology data for the period 1985 to 1997, we derived mean and standard deviation values of SST for the entire period, year, and month of the year. Nutrient Concentrations and Salinity The annual and monthly (or seasonal) statistics (mean and standard deviation) on the nutrient elements and salinity for the 0-10 meter depth layer of the ocean represented on 1.0*1.0 degree grid were obtained from
1580
Return to title
“The World Ocean Atlas 2001 (WOA01)”. The WOA01 was prepared by the NOAA/National Oceanographic Data Center (NODC) and its detailed description has been published elsewhere (Conkright et al. 2002). Although the WOA01 is based on all data submitted to the NOAA/NODC up to August 2001 from 1772, the period of time with available data varies depending on the elements and the locations. Late Holocene Sea-Level History Data on the spatial variation of the timing of the relative sea-level fall in the late Holocene was obtained from a paper map prepared by Dickinson (2003). The paper map shows geographic locations of major islands in Pacific Oceania, and indicates spatial extents of the island groups that were considered to have experienced the relative sea-level fall at the same timing in the late Holocene. We first converted the paper map to a digital image by scanning. We then geocoded the imagery map with reference to a high-resolution shoreline data named “Global Self-consistent Hierarchical High-resolution Shorelines (GSHHS) version 1.2.” The methods of processing and assembling GSHHS data were described by Wessel and Smith (1996). Finally, we generated the vector format (polygon) data delineating boundaries of island groups that were
considered to have followed the similar history of the relative sea-level fall in the late Holocene, by the heads-up digitizing method using ERDAS IMAGINE 8.7. Biogeography of Foraminifera Data on the biogeography of foraminifera was also obtained from paper maps, prepared by Langer and Hottinger (2000). In the same way as the map of the late Holocene sea-level history, the maps of biogeography of foraminifera were converted into vector format (point) data indicating the locations where the following foraminifera were observed: Marginopora vertebralis; Amphistegina spp.; Calcarina spp.; and Baculogypsina sphaerulata. They are dominant on shallow reef flats and could contribute to reef-island formation (Yamano et al. 2000; Woodroffe and Morrison 2001; Yamano 2002). Example of Application using the GIS Datasets: Segmentation Analysis Segmentation analysis enables classification of a large area into a number of small regions with relatively homogeneous characteristics in terms of specified variables. The resulting classification provides a useful framework for focusing attention, summarizing patterns,
Fig. 1. Spatial variations of variables used in segmentation analysis.
1581
Return to title
and aggregating information (Busby 2002). We performed segmentation analysis to characterize the spatial variation of the present-day environmental conditions of the waters in which reef islands are located, using two different sets of variables (Table 2, Fig 1). The variables included in the first set were derived from data on the factors controlling the physical processes of shoreline erosion and deposition; and the variables in the second set were from data on the factors affecting the ecological processes of land-plant growth. Table 2. The variables used in the segmentation analysis. Set
Target process
1st
Shoreline erosion and deposition
Factor name Wind wave
Primary swell Tropical storm Surface solar irradiance
2nd
Land-plant growth
Surface air temperature Precipitation rate
Variables Mean of energy flux Standard deviation of energy flux Angular deviation of wave direction Mean of energy flux Standard deviation of energy flux Angular deviation of wave direction Sum of WII Mean Standard deviation Mean Standard deviation Mean Standard deviation
Segmentation analysis was implemented by the following steps, using the data sampled from the spatial range between 36.0°N and 36.0°S. (1) Standardize each variable by linearly stretching the data range to between 0 and 255. (2) Use ISODATA algorithm (Ball and Hall, 1965) on the set of the standardized data values to determine the relatively homogeneous clusters in terms of the variables. The number of clusters to be generated was specified to be six, to confirm the correspondence to general six geographic partitions of waters (i.e., the South Pacific Ocean, North Pacific Ocean, South Atlantic Ocean, North Atlantic Ocean, Indian Ocean, and equatorial areas). (3) Calculate the mean and covariance matrix of data values for each cluster. (4) Reclassify the entire area into the set of segment classes, using the maximum likelihood algorithm (Duda and Hart 1973) with the statistic parameters of each cluster. Among the 6 segment classes concerning the physical processes of reef-island formation and maintenance, classes 1 to 4 encompass most atolls, with exceptions of the Chagos Archipelago and Midway Islands, which are located in class 5 and 6 respectively (Fig 2-A). Classes 1 and 4 can be characterized by the relatively high storm intensity and relatively low wave energy flux. Differences between the two classes include the degree to which there are seasonal variations in wind wave direction and swell direction. Class 4 corresponds well with areas dominated by the monsoon. Classes 2 and 3 have similar wave energy flux and storm intensity characteristics, but differ in the degree of seasonal variations of swell direction. Classes 5 and 6 show relatively low storm intensity but are experience moderate wave energy flux. The statistics of variables
for each segment class are summarized in Table 3. Among the 6 segment classes describing ecological processes of land-plant growth, classes 2 to 6 encompass most atolls (Fig 2-B). Class 1 can be characterized by relatively low values of solar irradiance and air temperature. Although class 2 also shows relatively low air temperature, there are differences in the solar irradiance and precipitation rate, compared with class 1. Class 3, which includes the Phoenix Islands in Kiribati, can be characterized by relatively high air temperature and relatively low precipitation rate. This indicates that the area is drier than other class areas. Classes 5 and 6 can be characterized by relatively high air temperature and precipitation rate. The statistics of variables for each segment class are summarized in Table 4.
Fig. 2. Segmentation of global waters. Table 3. Statistics of variables for each class segmented by the factors affecting physical processes of shoreline erosion and deposition. Class 1 2 3 4 5 6
Wind wave energy flux Mean [W/m] S.D.[W/m] A.D.[º] 3.2 1.7 13.7 3.9 1.7 7.6 2.6 1.4 14.0 3.0 2.2 59.1 6.9 3.9 46.8 11.8 11.9 57.8
Swell energy flux Mean [W/m] S.D.[W/m] A.D.[º] 24.8 6.6 15.8 50.8 9.9 15.4 42.2 9.9 48.8 23.0 7.8 27.3 60.1 14.7 12.1 51.2 29.2 53.4
Storm Wind intensity 118627.5 26223.7 17690.5 98672.7 9264.6 33921.1
Table 4. Statistics of variables for each class segmented by the factors affecting ecological processes of land-plant growth. Class 1 2 3 4 5 6
Surface solar irradiance Mean [W/m2] S.D.[W/m2] 196.453 73.501 228.013 60.738 262.48 32.898 240.228 43.386 234.876 26.563 231.213 35.977
Surface air temperature Mean [W/m2] 19.441 21.638 25.809 26.097 27.724 27.017
S.D.[W/m2] 2.559 1.781 1.05 1.279 0.42 0.891
Precipitation rate Mean [mm/d] S.D.[mm/d] 3.24 1.734 0.996 0.918 1.321 1.601 3.266 2.692 5.841 3.187 5.889 4.602
Concluding Remarks A global dataset detailing factors that potentially control the processes of reef-island formation and maintenance was compiled and stored in a GIS database. Segmentation analysis of the factors was performed, providing us with a broad view of the diversity in environmental conditions. This could explain the diversity of reef islands. Further study should quantify the relationship between the evolution of reef islands and the environmental conditions, by combining the existing global environmental dataset and the forthcoming regional data on the size, structure and composition of reef islands. Successful prediction of how reef islands
1582
Return to title
will respond to global environmental changes (e.g., a faster rise in sea level, more frequent heat waves and drought, and more extreme weather events) depends on our understanding of how reef islands have been affected by the past and present environmental conditions and relationships. Acknowledgements We wish to thank all the producers and distributors of the original data sets we used in this study. This research was financially supported by Global Environment Research Fund of the Ministry of the Environment, Japan (Project No. B15, Principal Investigator: HK). References Andréfouët S, Clareboudt M, Matsakis P, Pagés J, Dufour P (2001a) Typology of atoll rims in Tuamotu Archipelago (French Polynesia) at landscape scale using SPOT HRV images. Int J Remote Sens 22: 987-1004 Andréfouët S, Pagés J, Tartinville B (2001b) Water renewal time for classification of atoll lagoons in the Tuamotu Archipelago (French Polynesia). Coral Reefs 20: 399-408 Andrews JC, Pickard GL. (1990) The physical oceanography of coral-reef systems. In: Dubinsky Z (ed) Coral Reefs: Ecosystems of the World, Vol. 25, Elsevier Science Atkinson M, Smith SV, Stroup ED (1981) Circulation in Enewetak Atoll lagoon. Limnol Oceanogr 26: 1074-1083 Ball GH, Hall DJ (1965) A novel method of data analysis and pattern classification. Stanford Research Institute, Menlo Park, California Bayliss-Smith TP (1988) The role of hurricanes in the development of reef islands, Ontong Java Atoll, Solomon Islands. Geogr J 154: 377-391 Bartels RH, Beatty JC, Barsky BA (1998) Hermite and Cubic Spline Interpolation. San Francisco, CA, Morgan Kaufmann Bishop JKB, Rossow WB (1991) Spatial and temporal variability of global surface solar irradiance, Journal of Geophysical Research 96, 16: 839-858 Blumenstock DI (1958) Typhoon effects at Jaluit Atoll in the Marshall Islands. Nature 182: 1267-1269 Busby JR (2002) Biodiversity mapping and modelling. In: Skidmore A (ed) Environmental modelling with GIS and remote sensing. Taylor & Francis, New York Casey, KS. and Cornillon, P (1999). A comparison of satellite and in situ based sea surface temperature climatologies. Journal of Climate 12: 1848-1863 Chikamori M (2001) Changes in atoll vegetation and human settlement. Shigaku 70: 521-549 Conkright, ME, Locarnini RA, Garcia HE, O’Brien TD, Boyer TP, Stephens C, Antonov JI (2002) World Ocean Atlas 2001: Objective Analyses, Data Statistics, and Figures, CD-ROM Documentation. National Oceanographic Data Center, Silver Spring Dickinson WR (2003) Impact of mid-Holocene
hydro-isostatic highstand in regional sea level on habitability of islands in Pacific Oceania. Journal of Coastal Research 19: 489-502 Done TJ (1983) Coral zonation: its nature and significance. In: Barnes DJ (ed) Perspectives in Coral Reefs. AIMS, Towns-ville, Australia Duda RO, Hart PE (1973) Pattern classification and scene analysis. John Wiley & Sons, New York Frith CA (1981) Circulation in a platform reef lagoon, One Tree Reef, southern Great Barrier Reef. Proc 4th Int Coral Reef Symp 1: 347-354 Frith CA, Mason LB (1986) Modelling wind driven circulation One Tree Reef, Southern Great Barrier Reef. Coral Reefs 4: 201-211 Geister J (1977) The influence of wave exposure on the ecological zonation of Caribbean coral refs. Proceedings of the Third International Coral Reef Symposium 1: 23-29 Gourlay MR (1988) Coral cays: Products of wave action and geological processes in a biogenic environment. Proceedings of the Sixth International Coral Reef Symposium 2: 491-496 Grigg RW (1998) Holocene coral reef accretion in Hawaii: a function of wave exposure and sea level history. Coral Reefs 17: 263-272 Harmelin-Vivien ML (1994) The effects of storms and cyclones on coral reefs: A review. Journal of Coastal Research 12: 211-231 Holland GJ (1980) An analytic model of the wind and pressure profiles in hurricanes, Monthly Weather Review 108: 1212-1218 Hubbard DK (1988) Controls of modern and fossil reef development: common ground for biological and geological research. Proceedings of the Sixth International Coral Reef Symposium 1: 243-252 Hubbard DK (1992) Hurricae-induced sediments transport in open-shelf tropical systems - an example from St. Croix, U. S. Virgin Islands. J Sediment Petrol 62: 946-960 Kleypas JA, McManus JW, Menez LAB (1999) Environmental limits to coral reef development: Where do we draw the line? Amer. Zool. 39: 146-159 Kleypas and Hopley (1992) Reef development across abroad continental shelf, southern Great Barrier Reef, Australia. Proceedings of the Seventh International Coral Reef Symposium 2: 1129-1141 Langer MR, Hottinger L (2001) Biogeography of selected "larger" foraminifera. Micropaleontology 46: 105-126 Longley PA, Goodchild MF, Maguire DJ, Rhind DW (2001) Geographic Information Systems and Science. John Wiley & Sons, London Matsumoto K, Takanezawa T, Ooe M (2000) Ocean Tide Models Developed by Assimilating TOPEX/POSEIDON Altimeter Data into Hydrodynamical Model: A Global Model and a Regional Model Around Japan, Journal of Oceanography 56: 567-581 Maragos JE, Baines GBK, Beveridge PJ (1973) Tropical cyclone Bebe create a new land formation on Funafuti
1583
Return to title
atoll. Science 181: 1161-1164 McLean RF, Woodroffe CD (1994) Coral Atolls. In: Carter RWG, Woodroffe CD (eds) Coastal evolution: Late quarternary shoreline morphodynamics. Cambridge University Press, Cambridge Parnell KE (1988) The hydrodynamics of fringing reef bays in the Great Barrier Reef Marine Park. Proc 6th Int Coral Reef Symp 2: 503-508 Pickard GL (1986) Effects of wind and tide on upper-layer currents at Davies Reef, Great Barrier Reef, during MECOR (July-August 1984). Aust J Mar Freshwater Res 37: 545–65 Prager EJ (1991) Numerical simulation of circulation in a Caribbean-type backreef lagoon: a preliminary study. Coral Reefs 10: 177-182 Pugh DT, Rayner RF (1981) The tidal regimes of three Indian Ocean atolls and some ecological implicatons. Estuarine, Coastal and Shelf Science 13: 389-407 Puotinen M (2004a) Mapping tropical cyclone disturbance of the Great Barrier Reef. Journal of The Australian Map Circle Inc. The Globe 55: 31-41 Puotinen, ML (2004b). Tropical cyclones in the Great Barrier Reef Region, 1910-1999: A first step towards characterising the disturbance regime. Australian Geographical Studies 42: 378-392 Roberts HH, Murray SP, Suhaida JN (1975) Physical processes in a fringing reef system. J Mar Res 33-2: 233-260 Roberts HH, Wilson PA, Lugo-Fernandez A (1992) Biologic and geologic responses to physical processes: examples from modern reef systems of the Caribbean-Atlantic region. Cont Shelf Res 12-7/8: 809-834 Schofield (1977) Effect of the late Holocene sea-level fall on atoll development. N Z J Geol Geophys 20: 531-536 Scoffin TP (1993) The geological effects of hurricanes on coral reefs and the interpretation of storm deposits. Coral Reefs 12: 203-221 Sherwood A, Howorth R (1996) Coasts of Pacific Island. SOPAC Miscellaneous Report 222 Spalding MD, Ravilious C, Green EP (2001) World Atlas of Coral Reefs. University of California Press, Berkeley Stoddart DR (1971) Coral reefs and catastrophic storms. In: Steers JA (ed) Applied Coastal Geomorphology, M.I.T. Press, Cambridge Stoddart DR (1992) Biogeography of the Tropical Pacific. Pacific Science 46: 276-293 Tartinville B, Rancher J (2000) Wave-induced flow over Mururoa Atoll Reef. Journal of Coastal Research 16: 776-781 Treml E, Colgan M, Keevican M (1997) Hurricane disturbance and coral reef development: A geographic information system (GIS) analysis of 501 years hurricane data from the Lesser Antilles. Proceedings of the 8th International Coral Reef Symposium 1: 541-546 Van Woesik R (1994) Contemporary disturbances to coral communities of the Great Barrier Reef. Journal
of Coastal Research 12: 233-252 Wessel P, WHF Smith (1996) A Global Self-consistent, Hierarchical, High-resolution Shoreline Database. Journal of Geophysical Research 101 #B4: 8741-8743 Wolanski E, Delesalle B, Dufour V, Aubanel A (1994) Modeling the fate of pollutants in the Tiahura Lagoon, Moorea, French Polynesia. 11th Australasian Conf Coastal and Ocean Engineering Woodroffe CD (2002) Coasts: form, process and evolution. Cambridge University Press, Cambridge Woodroffe CD, Morrison RJ (2001) Reef-island accretion and soil development on Makin, Kiribati, central Pacific. Catena 44: 245-261 Yamano H (2002) Sensitivity of reef flats and reef islands to sea-level change. Proceedings of the 9th International Coral Reef Symposium 1193-1198 Yamano H, Abe O, Matsumoto E, Kayane H, Yonekura N, Blanchon P (2003) Influence of wave energy on Holocene coral reef development: an example from Ishigaki Island, Ryukyu Islands, Japan. Sedimentary Geology 159: 27-41 Yamano H, Kayane H, Yonekura N (2001) Anatomy of a modern coral reef flat: a recorder of storms and uplift in the late Holocene. Journal of Sedimentary Research. 71: 295-304 Yamano H, Kayanne H, Yonekura N, Nakamura H, Kudo K (1998) Water circulation in a fringing reef located in a monsoon area: Kabira Reef, Ishigaki Island, Southwest Japan. Coral Reefs 17: 89-99 Yamano H, Miyajima T, Koike I (2000) Importance of foraminifera for the formation and maintenace of a coral sand cay: Green Island, Australia. Coral Reefs 19:51-58 Yamano H, Yamaguchi T, Chikamori M, Kayanne H, Yokoki H, Shimazaki H, Tamura M, Watanabe S, Yoshii S (submitted) Satellite-based typology to assess stability and vulnerability of atoll islands: a comparison with archaeological data. Proc 10th Int Coral Reef Symp Zar JH (1999) Biostatistical Analysis 4th ed. Prentice-Hall, New Jersey
1584
