This project was funded by award NA1ONMF4720029 from the National Oceanic and Atmospheric Administration’s National Marine Fisheries Service (NMFS), US Department of Commerce. The statements, findings, conclusions, and recommendations are those of the authors
and do not necessarily reflect the views of NMFS or the US Department of Commerce. Special thanks go to FWRI’s Center for Spatial Analysis for resources and support necessary for this
47
project. The first author also received financial support from the Linton Tibbetts Graduate
Fellowship, the Gulf Oceanographic Charitable Trust Endowed Fellowship, and the Southern
Kingfish Association Endowed Fellowship, all from the University of South Florida’s College of
Marine Science.
2.7 References
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Aronson, R.B., Precht, W.F., 1997. Stasis, biological disturbance, and community structure of a Holocene coral reef. Paleobiology 23, 326–346.
Ballantine, D.L., Appeldoorn, R.S., Yoshioka, P., Weil, E., Armstrong, R., Garcia, J.R., Otero, E., Pagan, F., Sherman, C., Hernandez-Delgado, E.A., Bruckner, A., Lilyestrom, C., 2008. Biology and Ecology of Puerto Rican Coral Reefs. Springer.
Banks, K.W., Riegl, B.M., Richards, V.P., Walker, B.K., Helmle, K.P., Jordan, L.K.B., Phipps, J., Shivji, M.S., Spieler, R.E., Dodge, R.E., 2008. The reef tract of continental southeast Florida (Miami-Dade, Broward and Palm Beach counties, USA). In: Riegl, B.M., Dodge, R.E. (Eds.), Coral Reefs of the USA. Springer, pp. 175–220.
Baums, I.B., Johnson, M.E., Devlin-Durante, M.K., Miller, M.W., 2010. Host population genetic structure and zooxanthellae diversity of two reef-building coral species along the Florida Reef Tract and wider Caribbean. Coral Reefs 29, 835–842.
Baumstark, R.D., 2013. Coordinated coral and hardbottom ecosystem mapping, monitoring and management. Florida Fish and Wildlife Conservation Commission, St. Petersburg, FL. Causey, B.D., Dodge, R.E., Jaap, W.C., Banks, K., Delaney, J., Keller, B.D., Spieler, R., 2002.
Florida. In: The State of Coral Reef Ecosystems of the United States and Pacific Freely Associated States: 2002. NOAA, Silver Spring, MD, pp. 101–118.
Colella, M.A., Ruzicka, R.R., Kidney, J.A., Morrison, J.M., Brinkhuis, V.B., 2012. Cold-water event of January 2010 results in catastrophic benthic mortality on patch reefs in the Florida Keys. Coral Reefs 31, 621–632.
Davis, G.E., 1982. A century of natural change in coral distribution at the Dry Tortugas—a comparison of reef maps from 1881 and 1976. Bull. Mar. Sci. 32, 608–623.
Dustan, P., 1985. Community structure of reef-building corals in the Florida Keys USA
Carysfort Reef Key Largo and Long Key Reef Dry Tortugas. Atoll Res. Bull. 288, 1–29. Federal Register, 2006. Endangered and Threatened Species: Final Listing Determinations for
Elkhorn and Staghorn Coral; Final Rule, 71, 89. National Marine Fisheries Service, National Oceanic and Atmospheric Administration.
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Gladfelter, W.B., 1982. White-band disease in Acropora palmata—implications for the structure and growth of shallow reefs. Bull. Mar. Sci. 32, 639–643.
Gladfelter, W.B., Gladfelter, E.H., Monahan, R.K., Ogden, J.C., Dill, R.F., 1977. Environmental Studies of Buck Island Reef National Monument. West Indies Laboratory, Fairleigh Dickinson University, St. Croix, USVI, St. Croix, US Virgin Islands.
Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecol. Modell. 135, 147–186.
Hallock, P., Schlager, W., 1986. Nutrient excess and the demise of coral reefs and carbonate platforms. Palaios 1, 389–398.
Harrison, P.L., Wallace, C.C., 1990. Reproduction, dispersal and recruitment of scleractinian corals. In: Dubinsky, Z. (Ed.), Coral Reefs. Ecosystems of the World 25. Elsevier, Amsterdam, New York, pp. 133–207.
Hemond, E.M., Vollmer, S.V., 2010. Genetic diversity and connectivity in the threatened Staghorn coral (Acropora cervicornis) in Florida. PLoS One 5.
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Jaap, W.C., 1985. An epidemic zooxanthellae expulsion during 1983 in the lower Florida Keys coral reefs: hyperthermic etiology. In: Proceedings of the 5th International Coral Reef Congress, Tahiti, pp. 143–148.
Jaap, W.C., Lyons, W.G., Dustan, P., Halas, J.C., 1989. Stony Coral (Scleractinia and
Milleporina) Community Structure at Bird Key Reef, Ft. Jefferson National Monument, Dry Tortugas, Florida. Florida Marine Research Publications, pp. 1–31.
Kendall, M.S., Monaco, K.R., Buja, J.D., Christensen, J.D., Kruer, C.R., Finkbeiner, M., Warner, R.A., 2001. Methods Used to Map the Benthic Habitats of Puerto Rico and the U.S. Virgin Islands. National Oceanic and Atmospheric Administration (NOAA), Silver Spring, MD, p. 46.
Klitgord, K.D., Popenoe, P., Schouten, H., 1984. Florida: a Jurassic transform plate boundary. J. Geophys. Res. 89, 7753–7772.
Lang, J.C., Lasker, H.R., Gladfelter, E.H., Hallock, P., Jaap, W.C., Losada, F.J., Muller, R.G., 1992. Spatial and temporal variability during periods of recovery after mass bleaching on western Atlantic coral reefs. Amer. Zool. 32, 696–706.
Lessios, H.A., 1988. Mass mortality of Diadema antillarum in the Caribbean—what we have learned. Annu. Rev. Ecol. Syst. 19, 371–393.
Lirman, D., Fong, P., 1996. Sequential storms cause zone-specific damage on a reef in the northern Florida reef tract: Evidence from Hurricane Andrew and the 1993 storm of the century. Florida Scientist 59, 50–64.
Lirman, D., Schopmeyer, S., Manzello, D., Gramer, L.J., Precht, W.F., Muller-Karger, F., Banks, K., Barnes, B., Bartels, E., Bourque, A., Byrne, J., Donahue, S., Duquesnel, J., Fisher, L., Gilliam, D., Hendee, J., Johnson, M., Maxwell, K., McDevitt, E., Monty, J., Rueda, D., Ruzicka, R., Thanner, K., 2011. Severe 2010 cold-water event caused unprecedented mortality to corals of the Florida reef tract and reversed previous survivorship patterns. PLoS One 6.
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Mayor, P.A., Rogers, C.S., Hillis-Starr, Z.M., 2006. Distribution and abundance of elkhorn coral,
Acropora palmata, and prevalence of white-band disease at Buck Island Reef National
Monument, St. Croix, US Virgin Islands. Coral Reefs 25, 239–242.
Miller, S.L., Chiappone, M., Rutten, L.M., Swanson, D.W., 2008. Population status of Acropora corals in the Florida Keys. In: 11th International Coral Reef Symposium 2, pp. 781–785. Pitts, P.A., 1999. Effects of summer upwelling on the abundance and vertical distribution of fish
and crustacean larvae off central Florida’s Atlantic coast. J. Exp. Mar. Biol. Ecol. 235, 135–146.
Pitts, P.A., Smith, N.P., 1997. An investigation of summer upwelling across central Florida’s Atlantic coast: the case for wind stress forcing. J. Coast. Res. 13, 105–110.
Porter, J.W., Battey, J.F., Smith, G.J., 1982. Perturbation and change in coral-reef communities. Proc. Natl. Acad. Sci. USA 79, 1678–1681.
Porter, J.W., Dustan, P., Jaap, W.C., Patterson, K.L., Kosmynin, V., Meier, O.W., Patterson, M.E., Parsons, M., 2001. Patterns of spread of coral disease in the Florida Keys. Hydrobiologia 460, 1–24.
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Rogers, C.S., Muller, E.M., Spitzack, A., Miller, J., 2008b. The future of coral reefs in the US Virgin Islands: is Acropora palmata more likely to recover than Montastraea annularis complex? In: 11th International Coral Reef Symposium.
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3 Species Distribution Model Data Compilation and Processing
3.1 Abstract
The compilation and processing steps used to amass relevant reef ecosystem data for
spatial modeling and mapping purposes is often time consuming and tedious. The purpose of this
chapter is to describe the spatial data compiled and the processing steps taken to format the data.
The primary use of the data described here is for a study that identifies suitable restoration sites
for the staghorn coral, Acropora cervicornis, using a species distribution modelling (SDM)
approach. At the beginning of the project, a series of stakeholder workshops were held to
identify the most important ecological and environmental data relevant to A. cervicornis
distributions. The final data layers created in this study include 1 km2 raster representations of:
region, reef type, distance from shore, depth, slope, sea bottom temperature, and light
availability. The A. cervicornis monitoring data used as the response variable was also analyzed
and formatted for use in a SDM. The data layers described represent the currently best available
environmental data to use for A. cervicornis SDMs. This study confirms that there is no shortage
of spatial data available for the creation of an A. cervicornis SDM, and the data sets described
can also be used for prediction of distributions of other shallow water hermatypic coral species
throughout the Florida Reef Tract.
3.2 Introduction
The task of gathering and formatting data for the purpose of geographic analysis can
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formatting data is also one of the most important tasks, and sets a project up for success. The
purpose of this chapter is to describe the compilation and processing steps used to compile
relevant reef ecosystem data to use for multiple spatial modeling and mapping purposes. The
primary use of the data described in the following sections is for a study that identifies suitable
restoration sites using a species distribution modeling (SDM) approach (See Table 3.1 for all
abbreviations and acronyms). There are a variety of other uses for the compiled data, however
the specific steps required to format the data for SDM development are described herein.
Two types of data are essential to a SDM, responses and predictors. Response data are
generally observations of species occurrence, and predictor data are the environmental or
ecological data that are believed to contribute to the likelihood of a species existing in a certain
location (Guisan and Zimmermann 2000; Guisan and Thuiller 2005; Elith and Leathwick 2009).
Prediction performance of a SDM is enhanced when knowledge of ecological processes is
incorporated in model development, as opposed to simply integrating any and all available data
(Austin 2002). Therefore, by taking the time to develop species-specific predictor data to be used
in model development, the resulting SDM will be more robust and will provide more informative
ecological information.
Model predictors can be grouped into three categories: resource, direct and indirect
gradients. Resource gradients are matter and energy consumed by plants or animals. Examples of
resource gradients include nutrients, water, light for photosynthesis, and food. Direct gradients
are environmental parameters with physiological importance, but are not consumed, such as temperature and pH. Indirect gradients have no direct physiological relevance for a species’ performance, but may replace a combination of different resources and direct gradients in a
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Examples of indirect gradients include slope, depth, and habitat type. In the marine system,
depth is an indirect proxy for several direct and resource gradients: temperature and its
variability, water motion, salinity, and light (Elith and Leathwick 2009). The use of strictly indirect predictors limits a model’s transferability, because in different regions, the same indirect parameters may have very different direct and resource gradient correlations (Guisan and
Zimmermann 2000). While models based on resource and direct gradients have been found to be
the most robust and widely applicable (Austin 2002), indirect gradient data are essential when
direct and resource gradient data are unavailable.
Table 3.1: Summary of abbreviations and acronyms
Acronym Definition
SDM Species Distribution Model(ing) PCS Projected Coordinate System UFRTM Unified Florida Reef Tract Map LiDAR Light Detection and Ranging SST Sea Surface Temperature SBT Sea Bottom Temperature PAR Photosynthetically Available Radiation CREMP Coral Reef Evaluation and Monitoring Project OOB Out-of-bag
DIN Dissolved Inorganic Nitrogen TP Total Phosphorus GAM Generalized Additive Model FRT Florida Reef Tract
3.3 Methods
3.3.1 Stakeholder Workshops
At the beginning of the project, two stakeholder workshops were held to inform the data
analysis and modeling approach. A main discussion point was identification of the most
important ecological and environmental data relevant to Acropora spp. distributions, as well as
the availability of such data. Each workshop hosted 20-30 participants including researchers,
nursery supervisors, managers, and other relevant coral-restoration experts, thus providing a
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most important considerations when selecting data used in the modeling process is that the data
are ecologically relevant to the species of interest (Elith and Leathwick 2009). The expert
opinion provided in these stakeholder workshops guided the effort to select such data used to
predict A. cervicornis distributions. The results of these discussions dictated the data layers
created and further discussed in this document.
Restoration efforts were a core focus during the stakeholder engagements. Given the
choice of focusing the restoration modeling efforts on all stony coral species combined, or to
limit the focus to a few target species, it was recommended that focus should be concentrated on
A. cervicornis. Currently, a majority of restoration efforts within the Florida Reef Tract (FRT)
have targeted A. cervicornis and A. palmata (Johnson et al. 2011; Young et al. 2012). While the
following data layers were developed with A. cervicornis modeling in mind, they are all
appropriate and applicable to other shallow water stony coral species on the Florida Reef Tract.
3.3.2 Study Area
The combination of a broad, shallow continental shelf and warm waters of the Gulf Stream uniquely suit Florida’s benthic habitat for the support of coral reef ecosystems (Jaap and Hallock 1990; Andrews et al. 2005). The study area included the Florida Reef Tract, from Martin
County through the Dry Tortugas, with the exception of Biscayne Bay due to limited in situ
observations in Biscayne Bay (
Figure 3.5). For each data layer described below, a boundary layer was used to clip the data to the
appropriate geospatial extent.
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One of the most important tasks involved in building a SDM is the determination of an
appropriate spatial scale (Wiens 2002). For sessile organisms, such as coral species, finer spatial
resolution data often provide better predictions in SDMs (Guisan and Thuiller 2005). There is no
single natural scale at which ecological patterns should be studied (Levin 1992). Instead,
appropriate scales are determined by the study goals, the system and data availability (Elith and
Leathwick 2009).
The spatial resolution of all layers in this study was set to 1 km2, as designated by the
coarsest resolution of the data sets used. All data layers were projected to the Albers projected
coordinate system (PCS Albers) and converted to raster format, with the exception of the A.
cervicornis observation point data, which remained in vector point format, also projected in PCS
Albers. Data layers used in this study are summarized in Table 3.2
Table 3.2: Summary of data layers and brief descriptions
Data Layer Format Description
Region Raster Seven regions of the Florida Reef Tract Reef type Raster Dominant reef type within 1 km2 cell
Distance from Shore Raster Distance from shore at 1 km increments
Depth Raster Depth interpolated from LiDAR and point bathymetric data Slope Raster Slope derived from depth layer
SBT High Raster Number of weeks above 30º C from 2008 – 2011 SBT Low Raster Number of weeks below 20º C from 2008 – 2011 Light380 Raster Percent total light at 380 nm available at depth Light488 Raster Percent total light at 488 nm available at depth
A. cervicornis
survey data Point
Long term trends from repeat A. cervicornis survey data, categorized by presence/absence trends
3.4 Data Layer Methods and Results