Content uploaded by Simon James Pittman
Author content
All content in this area was uploaded by Simon James Pittman
Content may be subject to copyright.
Proceedings of the 63
rd
Gulf and Caribbean Fisheries Institute November 1 - 5, 2010 San Juan, Puerto Rico
Importance of Seascape Complexity for Resilient Fish Habitat and Sustainable Fisheries
SIMON J. PITTMAN
1,2
*, BRYAN COSTA
1
, CHRISTOPHER F.G. JEFFREY
1
,
and CHRIS CALDOW
1
1
Biogeography Branch, Center for Coastal Monitoring and Assessment, National Oceanic and Atmospheric Administration,
Silver Spring, Maryland 20910 USA.
2
Marine Science Center, University of the Virgin Islands, 2 John Brewers Bay, St.
Thomas, VI00802, U.S. Virgin Islands. *simon.pittman@noaa.gov.
ABSTRACT
Seascape ecology studies indicate that the spatial arrangement of habitat types and the topographic complexity of the seascape
are major environmental drivers of fish distributions and diversity across coral reef ecosystems. Impairment of one component of an
ecologically functional habitat mosaic and reduction in the architectural complexity of coral reefs is likely to lower the quality of
habitat for many fish including important fished species. Documented declines in coral cover and topographic complexity are
reported from a decade of long-term coral reef ecosystem monitoring in SW Puerto Rico. To examine broader scale impacts we use
“reef flattening scenarios” and spatial predictive modeling to demonstrate how declining seascape complexity will lead to
contractions and fragmentation in the local spatial distribution of fish. This change may result in impaired connectivity, cascading
impacts to ecological functioning and reduced resilience to environmental stressors. We propose that a shift in perspective is needed
towards a more holistic and spatially-explicit seascape approach to ecosystem-based management that can help monitor structural
change, predict ecological consequences, guide targeted restoration efforts and inform spatial prioritization in marine spatial
planning.
KEY WORDS: Topographic complexity, seascape ecology, predictive modeling
Importancia de la Complejidad del Paisaje Marino para el Habitat Resistente
de Peces y las Pesquerías Sostenibles
PALABRAS CLAVE: Complejidad del paisaje marino, ecología marina, modelos de predicción
Importance de Complexité de Paysage Marin pour L'habitat Resilient
de Poissons et la Pêche Soutenable
MOTS CLÉS: Complexité de paysage marin, l’écologie marine, modélisation prédictive
INTRODUCTION
Coral reef ecosystems exhibit complex spatial
heterogeneity in physical structure across a range of spatial
scales (Hatcher 1997). Studies that have applied a
multiscale landscape ecology approach have demonstrated
that the composition and spatial configuration of two-
dimensional seascape mosaics (Grober-Dunsmore et al.
2008, Huntington et al. 2010), as well as, the three-
dimensional terrain morphology are important drivers of
the distribution, abundance, and behavior of marine
organisms (Pittman and Brown 2011). Although more
studies are now incorporating two-dimensional models of
the seascape in marine ecology (such as thematic benthic
habitat maps), these are comprised of discrete patch types
and sharp discontinuities representing seascape heteroge-
neity only in the horizontal dimension, essentially, “a
flatscape”. Yet, the three-dimensional topographic
complexity of the seascape rarely is modeled at spatial
scales that are operationally meaningful for management
frameworks. In recent years, however, airborne remote
sensing techniques are providing highly accurate and
relatively fine resolution spatial representations of three-
dimensional seafloor structure (Brock et al. 2004).
Techniques such as bathymetric LiDAR (Light Detection
and Ranging) provide high resolution digital bathymetry
from which vertical seafloor complexity can be quantified
at multiple spatial scales. The seafloor heterogeneity can
then be analysed as a terrain using tools more typically
applied in terrestrial geomorphology and industrial
meterology, where measures of troughs and peaks and
anomalies in surface roughness are important. Using a
wide range of surface metrics, Pittman et al. (2009) and
Pittman and Brown (2011) demonstrated the utility of
LiDAR bathymetry (and lidar derived vertical seafloor
complexity) for predicting the distribution and abundance
of fish and corals in southwest Puerto Rico.
However, structurally complex coral reefs are proving
vulnerable in the face of rapid environmental change.
Human activity in the coastal zone - combined with
hurricanes, bio-erosion, disease and thermal stress - have
resulted in region-wide loss and degradation of biogenic
structure created by reef forming scleractinian corals
(Hughes et al. 2003, Gardner et al. 2003). Furthermore, the
loss of structure and any recovery from loss may be
compounded by ocean acidification, with as yet unknown
impacts for Caribbean coral reefs. A pan-Caribbean meta-
analysis using data on coral reef rugosity estimated that
coral reef complexity had declined by more than 50% since
its 1960’s levels (Alvarez-Filip et al. 2009) (Figure 1). A
concurrent decline in the abundance of a wide range of fish
species across the region has also occurred that is thought
to be partly a result of habitat degradation (Paddack et al.
Pittman, S.J. et al. GCFI:63 (2011) Page 421
2009). These declines are likely to have triggered cascad-
ing impacts throughout the ecosystem (Cheal et al. 2008),
adding fresh impetus to the urgent need to understand
broad-scale environmental correlates, such as topographic
complexity that influence species distributions across
tropical seascapes (Pittman and Brown 2011).
The ecological significance of LiDAR derived
seafloor complexity to marine fish provides the key to a
new cost-effective tool for forecasting and hindcasting
some impacts to fish from changes to the surface complexi-
ty of coral reef ecosystems. The ability to predict impacts
will support the development of realistic expectations for
recovery and restoration for coral reef areas that are either
accreting or eroding and will help anticipate the effects of
reductions in habitat suitability for commercially important
food fish. Here we highlight the importance of topographic
complexity in maintaining intact coral reef ecosystems in
SW Puerto Rico using data collected by the National
Oceanic and Atmospheric Administration’s Biogeography
Branch. Using in-situ monitoring data we show a decline in
both live coral cover and the structural complexity of coral
reefs in SW Puerto Rico during the past decade. Then
using simulated flattening of surface complexity for the
entire study area, we predict and map the impact that
declining complexity will have on habitat suitability for
positive influence of structural complexity on marine
faunal distributions and ecological processes, but the
majority of evidence comes from relatively fine-scale
studies conducted across meter and sub-meter spatial
scales. Recent evidence demonstrates that high resolution
(1 - 4 m) measures of topographically complexity collected
across tropical seascapes (100s - 1000s meters), also
provides powerful predictive capability for modeling
broader scale spatial patterns of biodiversity and species
distribution. In SW Puerto Rico, Pittman et al. (2009)
compared a wide range of measures of topographic
complexity derived from LiDAR bathymetry and found
that the “slope of the slope” (a first derivative of slope)
contributed most to models of fish species richness and
distributions of individual species. Models and mapped
predictions for individual species were subsequently
refined by inclusion of the statistical interactions between
slope of the slope and the geographical location across the
insular shelf (distance to shore & shelf) in SW Puerto Rico
(Pittman & Brown 2011). For example, in SW Puerto
Rico, high habitat suitability for Stegastes planifrons
(threespot damselfish) increased almost linearly with
increasing complexity, although spatially the species was
restricted by the interaction with depth and by cross-shelf
location. Similarly, studies from Jamaica, Belize, Cayman
Islands, Florida and Bahamas demonstrated that the
availability of shallow water topographically complex
microhabitats were the most important proximal controls
on S. planifrons distribution and abundance (Precht et al.
2010). Threshold effects are also evident where, below a
certain level of complexity, the habitat becomes sub-
optimal for a species and can no longer support its
occurrence at that location. Identifying these critical
threshold values and describing the associated precursor
conditions that lead to tipping points will be crucial for
anticipating the ecological consequences of eroding and
collapsing coral reefs.
METHODS
Study area
The coral reef ecosystems of the insular shelf of
southwestern Puerto Rico (Figure 2) exist as a spatial
mosaic of habitat types dominated by coral reefs, seagrass-
es, mangroves and patches of sand. The seafloor is highly
heterogeneous in assemblage composition and topographic
structure resulting in a diverse and productive fish
community, with important ecological, economic and
cultural value. Like many Caribbean coral reef ecosystems
the study area has experienced environmental changes on
land and sea that have resulted in loss of structural and
functional integrity. The environmental history and
ecology of the region was documented in Pittman et al.
(2010).
Figure 1. Changes in reef rugosity across the Caribbean
between 1969 and 2008. Steepest decline occurred be-
tween 1969-1985 and rugosity after the mid-2000s was at
the lowest levels recorded in the time series (Adapted from
Alvarez-Filip et al. 2009).
two species of fish associated with Caribbean coral reefs.
Seafloor Terrain Complexity as an Important Spatial
Predictor for Coral Reef Ecosystems
It is generally accepted as axiomatic in ecology that
within a region, environments with greater architectural
complexity support higher species richness and higher
abundance for certain species than nearby environments
with low complexity (MacArthur and MacArthur 1961).
Coral reef ecosystems have been shown to exemplify the
Page 422 63
rd
Gulf and Caribbean Fisheries Institute
Underwater Survey Methods for Fish and Benthic
Structure
Underwater visual surveys of fish and benthic habitat
were conducted semi-annually (Jan/Feb and Sept/Oct)
across the insular shelf at La Parguera (322 km
2
) between
2001 and 2008 as part of a broader long-term monitoring
program. Survey sites (n = 1,018) were selected using a
stratified-random sampling design whereby sites were
randomly located within two mapped strata (i.e., hardbot-
tom and softbottom) derived from National Oceanic and
Atmospheric Administration's nearshore benthic habitat
map.
Fish surveys were conducted within a 25 m long and 4
m wide (100 m
2
) belt transect deployed along a randomly
selected bearing (0 - 360°). Constant swimming speed was
maintained for a fixed duration of fifteen minutes to
standardize the search time. Abundance data for five
common species were converted to presence-only data.
Fish data are available online at http://www8.nos.noaa.gov/
biogeo_public/query_main.aspx.
To conduct benthic habitat surveys and collect
percentage cover data on scleractinian corals, an observer
placed a 1 m
2
quadrat at five random locations along the
fish transect. The quadrat was divided into 100 smaller
squares (10 x 10 cm). Corals were identified to genus (and
species where possible) and percent cover was estimated to
the nearest 0.1 %. Rugosity was measured with a six meter
chain (1.3 cm chain link) draped over the contoured surface
at two positions along the fish transect. The straightline
horizontal distance was measured with a tape. An index of
rugosity was calculated as the ratio of contoured surface
distance to linear distance, using R = 1-d/l, where d is the
contoured distance and l is the horizontal distance (6 m).
Chain-and-tape rugosity was only measured over hardbot-
tom sites in the study area.
Spatial Predictive Modeling
Fish species occurrence (or species presence) data
from underwater visual surveys was linked statistically to a
suite of spatial predictors derived from LiDAR bathymetry
following the multiscale analytical approach of Pittman
and Brown (2011). Topographic complexity was modeled
as the slope of the slope averaged in a 25 m radius moving
window across the entire study area from the landward
fringe to the shelfedge. MaxEnt (Maximum Entropy
Distribution Modeling) software (Phillips et al 2006;
Phillips and Dudik 2008) was used to model and map
spatial predictions as probabilities of species presence.
Using MaxEnt we exploit the strength of the fish-terrain
relationship to develop preliminary and exploratory models
of species distributions under varying scenarios of reef
flattening. Using GIS tools, our slope of the slope layer
was uniformly reduced across the entire terrain by 25 % to
represent the estimated decadal decline for SW Puerto
Rican coral reefs, and 50 % approximating Caribbean-wide
declines since the 1960s. This was used as a proof-of-
concept for our initial forecasting experiments. Predictions
of high habitat suitability (using consistent probability
threshold for each scenario) were then re-mapped for two
common fish species: i) a herbivorous scraper, Scarus
taeniopterus (Princess parrotfish); and ii) an indicator of
live coral cover, Stegastes planifrons (threespot damsel-
fish). Mapped predictions were overlain and examined for
differences in spatial patterning and area of suitability
habitat under different reef flattening scenarios was
quantified and compared to measure the change.
RESULTS
Changes to Coral Reef Structure in SW Puerto Rico
and the Wider-Caribbean
Underwater visual census from 572 sites where
benthic cover had been estimated semi-annually over a
seven year period (2001 - 2007) revealed significant
declines in live coral cover (Figure 3). Monitoring beyond
2007 showed continued decline to < 4% mean live coral
cover across the region by 2010. At these same sites,
measures of surface complexity using the chain-tape
method indicated that coral reefs had “flattened” signifi-
cantly in the past 10 years by an estimated 25 % (Figure 4).
Simulated Flattening of Coral Reef Complexity to
Model and Map Impacts to Fish
When the predictions from models using variable
levels of flattening were overlain in a GIS, visual examina-
tion of differences in the spatial patterning of high
suitability habitats for both species indicated a clear
contraction in range as terrain complexity declined. In
addition, the suitable habitat became more fragmented
Figure 2. Study area of SW Puerto Rico showing LiDAR
derived bathymetry across the insular shelf and the
locations of stratified-random biological survey sites
conducted between 2001 and 2008 by NOAA’s Biogeogra-
phy Branch.
Pittman, S.J. et al. GCFI:63 (2011) Page 423
leaving patches that were small islands of suitable habitat
surrounded by large expanses of sub-optimal areas (Figure
5). Suitable habitat for Princess parrotfish (Scarus
taeniopterus) contracted by 30 % with a 25 % flattening of
terrain complexity, and as much as 66 % was lost when
terrain was flattened by 50 %. With 25 % flattening,
habitat was lost from the edges of large contiguous patches
of suitable coral reef, probably where structure had already
existed near the lower thresholds of suitability. With a 50
% flattening patches of suitable habitat fragment even
more and few large contiguous patches remain, whereas
the number of small patches with relatively small interiors
of habitat increased across the seascape.
When impacts to threespot damselfish (Stegastes
planifrons) habitat suitability were assessed with a 25 %
flattening of terrain complexity, model comparison
revealed a 56 % loss of suitable habitat. Considerable
heterogeneity was observed in the patterns of loss, with
some clustering of high loss areas along many of the
shallow fringing reef slopes around mid-shelf islands and
the coral reefs between the Laurel, San Cristobal, and El
Palo reefs. Species-specific differences in the magnitude of
lost habitat (i.e. higher for threespot damselfish under a 25
% flattening scenario) are likely to relate to the specificity
for habitat requirements determined by a species’, relative
position along the specialist-generalist gradient of habitat
use, and how close the existing structural complexity is to
the threshold of a particular species. A small decline in
complexity would be expected to have a greater impact on
habitat suitability for areas with complexity already near
the tipping point for unsuitable habitat for a particular
species. See also color figures of predictions for entire
study area in non-print electronic version of the manuscript
(Figures 1 & 2 Appendix 1).
DISCUSSION AND FUTURE DIRECTIONS
Understanding the ecological consequences of losing
structural complexity of coral reefs is a crucial knowledge
gap in our understanding of impacts to coral reef ecosys-
tems (Wilson et al. 2010). Long-term monitoring data
collected over multiple years across a wide range of coral
reef habitat types has provided an early warning of broad
scale declines in the structural complexity of coral reefs in
SW Puerto Rico. This structural decline was likely
exacerbated by the longer-term trends of declining
Figure 3. Trend in mean live coral cover (%) for the SW
Puerto Rico study area estimated from semi-annual
surveys conducted between 2001 and 2008 by NOAA’s
Biogeography Branch (source: Pittman et al. 2010).
Figure 4. Decline in rugosity of coral reefs in
the SW Puerto Rico study area over a ten year
period (2001 and 2010) based on chain-tape
measurements conducted by NOAA’s Biogeog-
raphy Branch.
Figure 5. Predicted habitat suitability for threespot
damselfish (Stegastes planifrons) across a subset of the
SW Puerto Rico study area using i.) Unaltered LiDAR-
derived topographic complexity; and ii) Numerically
flattened topographic complexity to simulate 10 year
declines for coral reefs in SW Puerto Rico. MaxEnt was
used for modeling predictions.
Page 424 63
rd
Gulf and Caribbean Fisheries Institute
impacts to topographic complexity will vary by depth,
distance to shore, type and intensity of human activities,
coral community composition and possibly even patch
characteristics. Studies by Alvarez-Filip et al. (2011b)
showed that annual rates of change in reef complexity
varied significantly between coral reefs across the
Caribbean. Yet patterns of change can also be counterintu-
itive. For example, Alvarez-Filip et al. (2011a) found that
coral reef topographic complexity (measured as rugosity)
had declined more in marine protected areas than in
comparable unprotected areas in the Caribbean. The
authors speculate that bioerosion from increasing herbivo-
rous fish populations may have been the cause.
Simulating the spatial impact of these stressors even
across the local seascape is a complicated challenge with
insufficient information currently available to inform such
as model. Development of proxies, however, would allow
us to compare a range of scenarios to examine resultant
impacts. Integration of data available from detailed
ecological studies and long-term monitoring programs
should enable us to begin to piece together sufficient
information to develop reliable scenarios of structural
change which can then be utilized to predict impacts on a
wide range of marine biota. Such spatial predictions can
then be analyzed using spatial pattern metrics from
landscape ecology to quantify and investigate the losses
and gains and the magnitude of fragmentation in local
patterns of species distributions and biodiversity. Frag-
mentation of suitable habitat may disrupt movement
patterns of individuals, impair metapopulation connectivity
leading to isolated dysfunction patches with reduced
population viability, shifts in community composition and
cascading effects through foodwebs and resiliency of
ecosystems. However, existing exploratory studies
indicate that species with different habitat requirements
and preferences will respond differently to changes in
structural complexity; it will be important to identify any
species-specific sensitivities and critical threshold beyond
which an area no longer provides suitable habitat.
High resolution LiDAR data show strong potential as
a data type that, when combined with comprehensive
underwater visual survey data and analyzed with sophisti-
cated spatial predictive modeling algorithms, can help
determine thresholds in topographic complexity below
which habitat no longer supports viable populations of
specific fish species.
CONCLUSIONS & RECOMMENDATIONS FOR
MANAGEMENT
The SW Puerto Rico study area has experienced
marked deterioration in coral reef health concurrent with
an increase in stressors and a significant decline of
commercially targeted fish, with some local extirpations of
several large-bodied and late maturing species (Jeffrey et
al. 2010, Pittman et al. 2010). It is conceivable, however,
that even if fishing were restricted or excluded in the study
branching acroporid species and a shift to a macroalgal
dominated benthic community (Pittman et al. 2010, Jeffrey
et al. 2010). Together these changes are analogous to
changes detected in a Caribbean-wide analyses of rugosity,
whereby reductions in coral cover were followed by loss of
architectural complexity with little evidence of a time-lag
(Alvarez-Filip et al. 2009 and 2011b). Elsewhere in the
region, shifts in coral dominance from Acropora and
Montastraea spp. to more stress-resistant and lower
complexity species such as Agaricia and Porites spp. has
been documented (Alvarez-Filip et al. 2011b). Where
Montastraea annularis is still an important reef-building
species, growth rates have declined over the past 15 years
(Edmunds and Elahi 2007) and Porites astreoides has
increased (Green et al. 2008). This trend is expected to
have major consequences for fish communities, but the
spatially explicit implications of reducing terrain surface
complexity has not before been examined for Caribbean
species.
Although, the ecological ramifications through the
ecosystem are still largely unknown, we now know that
many fish species and assemblage variables correlate with
LiDAR derived measures of terrain surface complexity,
which provides opportunities to manipulate the three-
dimensional surface structure and investigate correspond-
ing impacts to habitat suitability for fishes. This modeled
relationship provides a cost-effective technique to forecast
(and hindcast) effects of varying surface complexity. Our
proof-of-concept modeling here was a first step in this new
direction, yet developing realistic spatial simulations of
flattening at relevant spatial scales is challenging. The
information required to map the spatial patterns and
processes that influence bioerosion, bleaching and physical
collapse across highly heterogeneous and connected land-
sea ecosystems is still lacking. Neither is information
readily available on the likely rates of change at operation-
ally relevant scales. Nevertheless, evidence that structural
complexity is declining in many regions is mounting.
Ecological impacts will need to be anticipated to ensure
that management actions are well targeted and that
expectations for recovery after protection are ecologically
realistic.
We recognize that more spatially complex scenarios
are required to refine our predictions since a uniform
flattening is likely to be over-simplistic. Depth will likely
be an important consideration, since impacts to deeper
coral reefs may be very different to shallow reefs and
shallow sheltered reefs may be very different than shallow
exposed reefs. Clearly, stressors operate across a hierarchy
of scales. Impacts to coral reef structure from hurricanes,
bioeroders and direct human activities are spatially
heterogeneous processes operating at relatively local
scales, whereas thermal stress and ocean acidification
operate at considerable broader spatio-temporal scales.
Regardless of the type of stressors involved, differential
Pittman, S.J. et al. GCFI:63 (2011) Page 425
region at some point in the future, the role of degraded
benthic habitat must be considered when setting and
communicating expectations for rates of recovery and in
assessing the suitability of habitat for the most vulnerable
and large-bodied fish species. Based on the present
trajectory in ecosystem health and stressors, future habitat
structure in this region may no longer be capable of
offering the necessary ecological functions of food and
refuge that it once did; instead it may be impaired to a
point where only sub-optimal habitat remains. Therefore,
such coral reefs are likely to recover more slowly com-
pared with coral reefs with greater structural integrity.
Changes in topographic complexity should not be consid-
ered the only seascape measures of habitat suitability for
fish associated with coral reefs, since fish are highly
mobile and many key species require mosaics of connected
habitat to maintain viable populations. Therefore, resource
management agencies and associated monitoring programs
interested in maintaining or restoring sustainable fisheries
must also consider other components of seascape change,
such as loss of seagrasses and mangroves, which may
reduce the availability of critical resources for fish species
or reduce connectivity across ontogenetic life stages
(Pittman et al 2007, Grober-Dunsmore et al. 2009).
Through interconnectedness, loss and degradation of these
surrounding habitat mosaics can also influence the
suitability of coral reefs as fish habitat independent of
declining coral reef complexity.
Managing Habitat Mosaics and Terrains for Resilient
Ecosystems and Sustainable Fisheries
We propose that a shift towards a more holistic and
spatially-explicit approach to ecosystem-based manage-
ment is needed and that a seascape approach can help
guide targeted restoration efforts and ecologically relevant
spatial prioritization in marine spatial planning. Specifical-
ly, we recommend:
i) A shift in emphasis from monitoring, managing
and restoring individual habitat types to a focus
on protecting and restoring optimal seascape
types based on ecological requirements of species
and communities,
ii) Identifying and prioritizing seascape types that
support high biodiversity, productivity and key
species of concern,
iii) Understanding vulnerability of mosaic integrity to
environmental stressors in order to predict the
consequences of impaired structure for mosaic
function including connectivity,
iv) Utilizing terrain morphology and benthic habitat
maps to predict diversity patterns, map essential
fish habitat including critical life history spaces
such as nursery areas and spawning areas,
v) Identifying tipping points in habitat structure
beyond which abrupt change is expected to help
anticipate impacts and set targets for restoration,
and
vi) Developing management strategies and actions
that reduce threats to structural integrity and
actively help to re-build lost structure.
ACKNOWLEDGEMENTS
We thank Richard Appeldoorn of the University of Puerto Rico for
inviting us to participate in the theme session on Management of Coral
Reef Ecosystems at the 63
rd
GCFI meeting in San Juan, Puerto Rico. We
are grateful to all of our scientific divers for many years of data
collection. Funding for research and conference attendance was provided
by NOAA’s Coral Reef Conservation Program and NOAA’s Biogeogra-
phy Branch.
LITERATURE CITED
Alvarez-Filip, L. Dulvy, N.K., Gill, J.A., Cote, I.M., and A.R. Watkinson.
2009. Flattening of Caribbean coral reefs: region-wide declines in
architectural complexity. Proceedings of the Royal Society Series B
276: 3019-3025.
Alvarez-Filip, L., J.A. Gill, N.K. Dulvy, A.L. Perry, A.R. Watkinson,
and I.M. Côté. 2011a. Drivers of region-wide declines in
architectural complexity on Caribbean reefs. Coral Reefs. Online
First. DOI: 10.1007/s00338-011-0795-6.
Alvarez-Filip, L., I.M. Côté, J.A. Gill, A.R. Watkinson, and N.K Dulvy.
2011b. Region-wide temporal and spatial variation in Caribbean
reef architecture: is coral cover the whole story? Global Change
Biology 17: 2470-2477.
Brock, J.C., Wright, C.W., Clayton, T.D., and A. Nayegandhi. 2004.
Lidar optical rugosity of coral reefs in Biscayne National Park,
Florida. Coral Reefs 23:48-59.
Cheal, A.J., Wilson, S.K., Emslie, M.J., Dolman, A.M., and H.
Sweatman. 2008. Responses of reef fish communities to coral
declines on the Great Barrier Reef. Marine Ecology Progress Series
372: 211–223.
Edmunds, P.J. and R. Elahi. 2007. Demographics of a 15-year decline in
cover of the Caribbean reef coral Montastraea annularis. Ecological
Monographs 77(1): 3-18.
Gardner, T.A., Cote, I.M., Gill, J.A., Grant, A., and A.R.Watkinson.
2003. Long-term region-wide declines in Caribbean corals. Science
301: 958–960.
Green, D.H., Edmunds, P.J., and R.C. Carpenter. 2008. Increasing
relative abundance of Porites astreoides on Caribbean reefs
mediated by an overall decline in coral cover. Marine Ecology
Progress Series 359:1-10
Grober-Dunsmore, R., Frazer, T.K., Beets, J., Lindberg, W.J., Zwick, P.,
and N.A. Funicelli. 2008. Influence of landscape structure on reef
fish assemblages. Landscape Ecology 23:37–53.
Grober-Dunsmore, R., S.J. Pittman, C. Caldow, M.A. Kendall, T. and
Fraser. 2009. Chapter 14: A landscape ecology approach for the
study of ecological connectivity across tropical marine seascapes.
Pages 493-530 in: I. Nagelkerken (ed.) Ecological Connectivity of
Coral Reef Ecosystems. Springer Publishing, New York, New York
USA.
Hatcher, B.G. 1997. Coral reef ecosystems: how much greater is the
whole than the sum of the parts? Coral Reefs 16: S77-S91.
Hughes, T.P., Baird, A.H., Bellwood, D.R., Card, M., Connolly S.R., et
al. (2003) Climate change, human impacts, and the resilience of
coral reefs. Science 301: 929–933.
Huntington, B.E., M. Karnauskas, E.A. Babcock, and D. Lirman. 2010.
Untangling natural seascape variation from marine reserve effects
using a landscape approach. PLoS ONE 5(8): e12327. doi:10.1371/
journal.pone.0012327
Jeffrey, C.F.G., R. Clark, and S.D. Hile. 2010. Spatial patterns in
benthic composition of nearshore seascapes and implications for
scarid populations and fisheries in La Parguera, SW Puerto Rico.
Proceedings of the 62nd Gulf and Caribbean Fisheries Institute
November 2 - 6, 2009 Cumana, Venezuela.
Page 426 63
rd
Gulf and Caribbean Fisheries Institute
MacArthur, R.H., and J.W. MacArthur. 1961. On bird species diversity.
Ecology 42:594–598.
Paddack, M.J., Reynolds, J.D., Aguilar, C., Appeldoorn, R.S., Beets, J., et
al. 2009. Recent region-wide declines in Caribbean reef fish
abundance. Current Biology 19: 590–595.
Phillips, S.J., Anderson, R.P., and R.E. Schapire. 2006. Maximum
entropy modeling of species geographic distributions. Ecological
Modelling 190: 231–259.
Phillips, S.J., and M. Dudik. 2008. Modeling of species distributions
with Maxent: new extensions and a comprehensive evaluation.
Ecography 31: 161–175.
Pittman, S.J., Hile, S.D., Jeffrey, C.F.G., Caldow, C., Clark, R., Woody,
K., Monaco, M.E., and R. Appeldoorn. 2010. Coral reef ecosystems
of Reserva Natural de La Parguera (Puerto Rico): Spatial and
temporal patterns in fish and benthic communities (2001-2007).
NOAA Technical Memorandum 78.
Pittman, S.J., and K.A. Brown. 2011. Multi-scale approach for
predicting fish species distributions across coral reef seascapes.
PLoS ONE 6(5): e20583.
Precht, W.F., Aronson, R.B., Moody, R.M., and L. Kaufman. 2010.
Changing patterns of microhabitat utilization by the threespot
damselfish, Stegastes planifrons, on Caribbean reefs. PLoS ONE 5
(5): e10835.
Wilson, S.K., Adjeroud, M., Bellwood, D.R., Berumen, M.L., Booth, D.,
Bozec, Y.-Marie, Chabanet, P., Cheal, A., Cinner, J., Depczynski,
M., Feary, D.A., Gagliano, M., Graham, Nicholas, Halford, A.H.,
Halpern, B.S., Harborne, A.R., Hoey, A.S., Holbrook, S.J., Jones,
G.P., Kulbiki, M., Letourneur, Y., De Loma, T.L., McClanahan, T,
McCormick, M.I., Meekan, M.G., Mumby, P. J., Munday, P.L.,
Öhman, M.C., Pratchett, M.S., Riegl, B., Sano, M., Schmitt, R.J.,
and Syms, C. (2010) Crucial knowledge gaps in current understand-
ing of climate change impacts on coral reef fishes. Journal of
Experimental Biology 213: 894-900.