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Spatial pattern metrics have routinely been applied to characterize and quantify structural features of terrestrial landscapes and have demonstrated great utility in landscape ecology and conservation planning. The important role of spatial structure in ecology and management is now commonly recognized, and recent advances in marine remote sensing technology have facilitated the application of spatial pattern metrics to the marine environment. However, it is not yet clear whether concepts, metrics, and statistical techniques developed for terrestrial ecosystems are relevant for marine species and seascapes. To address this gap in our knowledge, we reviewed, synthesized, and evaluated the utility and application of spatial pattern metrics in the marine science literature over the past 30 yr (1980 to 2010). In total, 23 studies characterized seascape structure, of which 17 quantified spatial patterns using a 2-dimensional patch-mosaic model and 5 used a continuously varying 3-dimensional surface model. Most seascape studies followed terrestrial-based studies in their search for ecological patterns and applied or modified existing metrics. Only 1 truly unique metric was found (hydrodynamic aperture applied to Pacific atolls). While there are still relatively few studies using spatial pattern metrics in the marine environment, they have suffered from similar misuse as reported for terrestrial studies, such as the lack of a priori considerations or the problem of collinearity between metrics. Spatial pattern metrics offer great potential for ecological research and environmental management in marine systems, and future studies should focus on (1) the dynamic boundary between the land and sea; (2) quantifying 3-dimensional spatial patterns; and (3) assessing and monitoring seascape change.
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Mar Ecol Prog Ser
Vol. 427: 219232, 2011
doi: 10.3354/meps09119
Published April 12
Landscape ecology has been widely applied in the
terrestrial environment to understand the relation-
ships between spatial patterns and ecological pro-
cesses at a range of spatial and temporal scales (For-
man & Godron 1986, Turner 1989, Wiens 2002). In
landscape ecology, the scientific study of spatial pat-
terning requires the quantification of the structural
geometry of landscapes (Gustafson 1998). To address
this task, landscape ecologists have developed spatial
tools and spatial pattern statistics specifically to quan-
tify the geometric properties in mapped surfaces.
There now exists a wide range of metrics for the
© Inter-Research 2011 ·*Email:
Quantifying seascape structure: extending terrestrial
spatial pattern metrics to the marine realm
Lisa M. Wedding
1, 2,
, Christopher A. Lepczyk
, Simon J. Pittman
2, 4
Alan M. Friedlander
, Stacy Jorgensen
University of Hawaii at Manoa, Department of Geography, Saunders Hall 445, Honolulu, Hawaii 96822, USA
National Oceanic & Atmospheric Administration Biogeography Branch, 1305 East West Highway, Silver Spring,
Maryland 20910, USA
University of Hawaii at Manoa, Department of Natural Resources and Environmental Management, 1910 East-West Road,
Honolulu, Hawaii 96822, USA
Marine Science Center, University of the Virgin Islands, 2 John Brewers Bay, St. Thomas, US Virgin Islands 00802, USA
US Geological Survey Hawaii Cooperative Fisheries Research Unit, 2538 McCarthy Mall, Honolulu, Hawaii 96822, USA
ABSTRACT: Spatial pattern metrics have routinely been applied to characterize and quantify struc-
tural features of terrestrial landscapes and have demonstrated great utility in landscape ecology and
conservation planning. The important role of spatial structure in ecology and management is now
commonly recognized, and recent advances in marine remote sensing technology have facilitated the
application of spatial pattern metrics to the marine environment. However, it is not yet clear whether
concepts, metrics, and statistical techniques developed for terrestrial ecosystems are relevant for
marine species and seascapes. To address this gap in our knowledge, we reviewed, synthesized, and
evaluated the utility and application of spatial pattern metrics in the marine science literature over
the past 30 yr (1980 to 2010). In total, 23 studies characterized seascape structure, of which 17 quan-
tified spatial patterns using a 2-dimensional patch-mosaic model and 5 used a continuously varying
3-dimensional surface model. Most seascape studies followed terrestrial-based studies in their search
for ecological patterns and applied or modified existing metrics. Only 1 truly unique metric was found
(hydrodynamic aperture applied to Pacific atolls). While there are still relatively few studies using
spatial pattern metrics in the marine environment, they have suffered from similar misuse as reported
for terrestrial studies, such as the lack of a priori considerations or the problem of collinearity between
metrics. Spatial pattern metrics offer great potential for ecological research and environmental man-
agement in marine systems, and future studies should focus on (1) the dynamic boundary between
the land and sea; (2) quantifying 3-dimensional spatial patterns; and (3) assessing and monitoring
seascape change.
KEY WORDS: Seascape ecology · Landscape indices · Landscape metrics · Seascape structure ·
Spatial pattern metrics · Spatial scale
Resale or republication not permitted without written consent of the publisher
Contribution to the Theme Section ‘Seascape ecology’
Mar Ecol Prog Ser 427: 219232, 2011220
examination of relationships between spatial struc-
ture, ecological function, and landscape change
(Gustafson 1998). Spatial pattern metrics have been
classified broadly into 3 categories that quantify: (1)
landscape composition, e.g. the abundance and vari-
ety of patch types, without reference to spatial attrib-
utes of the geo metry; (2) configuration, e.g. the spatial
arrangement of individual patches and mosaics of
patches; and (3) fractal dimension, e.g. the shape
complexity of a patch or landscape (Turner et al. 2001,
Mandelbrot 1982) (Table 1). Spatial pattern metrics
provide a consistent method with which to compare
landscape structure and to monitor change at a range
of spatial scales, thus providing ecologists and
resource managers with a suite of tools that have con-
tributed to effective management decisions in conser-
vation and planning (Botequilha Leitão et al. 2006).
Computer software has been produced by landscape
ecologists and statisticians to facilitate the application
of metrics. The most widely used landscape metric
applications are the software packages FRAGSTATS
.html) and Patch Analyst (
~rrempel/ecology/). Spatial pattern metrics can be
quantified for both vector-based and raster-based
maps (Fig. 1).
Like landscape ecology, the marine counterpart,
seascape ecology, focuses on the causes and conse-
quence of spatial patterning (Hinchey et al. 2008, Li &
Mander 2009), including implications of human activ-
ity (Costanza et al. 1990). Seascapes have been repre-
sented using several different conceptual models with
varying cartographic properties (i.e. spatial and the-
matic resolution). The ‘patch-matrix’ model is a com-
mon representation of seascape structure based con-
ceptually upon island biogeography theory, where the
map classification is binary with focal ‘high quality’
patches embedded in a matrix of ‘lower quality’ habi-
tat (Fig. 2A). The focus of this patch-matrix model has
been on patch attributes such as area (i.e. species area
relationships), biotic response to patch edges, perime-
ter:area ratios, patch shape, and inter-patch distances
or isolation (Fig. 2B). More recently, entire mosaics of
patches have also been examined to assess the effect of
the seascape surrounding a focal patch, thereby pro-
viding information on the patch context (Brennan et
al. 2002).
Conclusions on the suitability of landscape ecology
concepts and techniques to marine ecosystems vary
among studies, with some evidence that patch and
seascape structure such as edges, patch size, and the
spatial configuration and composition of patch mosaics
have a sig nificant influence on marine organisms
(Grober-Dunsmore et al. 2009, Boström et al. 2011, this
Theme Section). In contrast, for seagrass ecosystems,
where the majority of research has been conducted,
results from patch level studies have been equivocal
and highly variable among species and eco systems
(Boström et al. 2006).
In general, landscape ecology concepts developed
and evaluated primarily for terrestrial environments
have been used in marine studies on the assumption
Table 1. Summary of commonly used metrics for quantifying landscape pattern from 2D categorical maps arranged into 3 broad
categories following Turner et al. (2001) and McGarigal et al. (2002). Algorithms and descriptions of mathematical formulas are
provided in McGarigal et al. (2002)
Metric Level Type Description
(1) Landscape composition (quantifies type of landscape cover type present and relative amount)
Proportion Mosaic Structural Proportion of landscape occupied by cover type
Richness Mosaic Structural Number of patch types composing the mosaic
Evenness Mosaic Structural Relative abundance of different patch types
Diversity Mosaic Structural Composite measure of richness and evenness
(2) Spatial configuration (quantifies the spatial arrangement and orientation of patches/mosaic)
Contagion Mosaic Structural Distinguishes between overall clumped or dissected
mosaic patterns
Patch area Patch-based Structural Total area of patch
Patch perimeter Patch-based Structural Perimeter of a patch
Perimeter:area ratio Patch-based Structural Index of patch shape complexity
Connectivity Patch-based Functional Average distance between patches
Proximity index Patch-based Structural Degree to which patches in landscape are isolated from
other patches
Area-weighted Patch-based Structural Frequency distribution of patch sizes
average patch size
Core area Patch-based Structural Area unaffected by the edge of the patch
(3) Fractal dimension (quantifies the shape complexity of a patch or landscape)
Mean patch fractal dimension Patch-based Structural Average patch shape complexity
that the approach is equally applicable to marine spe-
cies and habitats. The rationale for this assumption is
that some generalities will exist in the organism and
community response to structural patterns whether
they are in water or in air. However, it is not yet known
whether some of the fundamental differences between
terrestrial and marine systems may affect the transfer
of techniques from land to sea. Further, in landscape
ecology studies that have applied spatial pattern met-
rics, the approach is often exploratory and the selec-
tion of metric(s) and the spatial scale of analyses are
typically unsupported by ecological rationale. The lack
of guidelines on utilizing and implementing landscape
metrics in the marine environment presents a notable
knowledge gap that requires urgent attention to sup-
port future applications of metrics to seascapes.
Considering the issues surrounding landscape met-
rics and their relatively recent rise in marine ecology,
our overarching goal was to assess the potential for
the application of spatial pattern metrics to seascapes.
In order to address this goal, we had 3 key objectives:
(1) determine how many studies have applied spatial
pattern metrics to quantify seascapes; (2) highlight
uniquely marine spatial pattern metrics; and (3) dis-
cuss the importance of considering spatial, temporal,
and thematic resolution.
Definitions of seascape ecology and spatial pattern
metrics. Seascape ecology is the application of land-
scape ecology to the marine environment, and currently
is almost entirely based on concepts and techniques de-
veloped for terrestrial species and habitats (Kneib 1994,
Robbins & Bell 1994). Within the context of this review,
spatial pattern metrics, sometimes referred to as land-
scape metrics or indices, are applied to characterize and
quantify the spatial structure of seascapes. Spatial pat-
tern metrics have evolved from the original need to
quantify the complex spatial hetero geneity represented
in remotely sensed images (both aerial photography and
satellite imagery). There are 2 major types of metrics that
are applied to specific data types (e.g. point data, 2-di-
mensional [2D] categorical maps, and continuously vary-
ing 3D surfaces; Burrough 1981, Legendre & Fortin 1989,
Li & Reynolds 1995, McGarigal et al. 2009). In this paper,
we focused primarily on the quantification of spatial pat-
tern metrics that are applied to 2D maps of the seafloor,
such as benthic habitat maps (e.g. maps with horizontal
patterning, but no vertical dimension). Marine ecologists
are now also applying spatial pattern metrics (terrain
metrics) to continuously varying 3D surface models; thus
some examples are included in this review.
Wedding et al.: Seascape ecology metrics
Fig. 1. Example of multi-
scaled approach to derive sea -
scape metrics using NOAA
Biogeography Branch benthic
habitat maps of St John, US
Virgin Islands; from vector
data (bottom left), using the
increasing radius approach,
and raster data (bottom
right), using the moving
window approach
Literature search and selection. Marine applications
of spatial pattern metrics were sourced primarily from
the ISI Web of Knowledge’s Web of Science (www. /) over a 30 yr period (1980 to 2010)
using relevant key words and search strings (Table 2
and see Table S1 in the supplement at
com/articles/suppl/m427p219_supp.pdf). The asterisk
was used as a wildcard in ISI to allow for singular or
plural words to be identified in the same search. In
addition to these articles from the ISI search, several
supplementary articles were included in the review
from bibliographic lists cited in these ISI articles.
Research articles were examined and only included in
this marine spatial pattern metric review if they met
the following criteria:
(1) The article was published in a peer-reviewed
journal in the English language.
(2) Spatial pattern metric(s) were used to quantify
seascape structure in the article and not just men-
tioned in the text.
(3) Spatial pattern metric(s) were applied to 2D cate-
gorical maps or continuously varying 3D surfaces.
The articles examined were based on a review of a
strictly qualified subset of the literature, and as a
result, the conclusions are relevant specifically to stud-
ies that have applied spatial pattern metrics to marine
environments. The studies were reviewed and attrib-
utes were recorded in a database that included author,
article title, publication year, journal, volume, issue,
key word, landscape pattern metric, quantification,
data representation, data type, minimum mapping unit
(MMU), and extent (Table S2 in the supplement).
Structure of the review and synthesis. Relevant
papers were reviewed to examine (1) the number of
studies that applied spatial pattern metrics to quantify
seascapes; (2) uniquely marine spatial pattern metrics;
and (3) importance of considering spatial, temporal,
and thematic resolution. The results of the literature
search are synthesized and organized by the 2 major
groupings of metrics we have identified (e.g. 2D cate-
gorical maps and continuously varying 3D surfaces).
From the se lected papers and the broader literature on
multivariate ecological modeling, we discuss and high-
light many of the analytical techniques that have been
used effectively to identify the most influential metrics,
and to link this spatial variability to the ecology of spe-
Mar Ecol Prog Ser 427: 219232, 2011222
Fig. 2. Examples of 2 different seascape models. (A) Binary seascape with focal patches (seagrasses) that contrast sharply with the
surrounding homogeneous and potentially ‘hostile’ matrix (bare sand). (B) Marine patch-mosaic model, where the seascape
is spatially and compositionally complex, cannot be simply categorized into discrete binary elements
Table 2. Key words used in ISI literature review for marine
applications of spatial pattern metrics. The asterisk is used in
ISI as a wildcard in order for singular or plural words to be
identified in the same search. Numbers in the left column de-
note the key words used to find studies and are also used in
Table S1 in the supplement
1. ‘landscape metric*’
2. ‘landscape indice*’
1. ‘seascape*’ AND ‘metric*’
2. ‘marine’ AND ‘landscape*’ AND ‘metric*’
3. ‘seascape*’ AND ‘indice*’
4. ‘marine’ AND ‘landscape*’ AND ‘indice*’
5. ‘marine landscape ecology’
6. ‘seascape structure’
cies. The final 2 sections highlight
current knowledge gaps and re -
search questions to help guide future
applications of spatial pattern met-
rics to the marine environment.
Literature review
Marine applications of landscape
The first published ecological stud-
ies using metrics to quantify spatial
patterns emerged in the early 1980s
for terrestrial systems (Romme 1982,
Forman & Godron 1986, Krummel et
al. 1987, O’Neill et al. 1988). In the
marine environment, edge metrics
such as the amount of landwater
interface were recognized as impor-
tant predictors of coastal species dis-
tributions in the late 1980s (Browder
et al. 1989), although the structural
attributes of individual patches had
been considered from at least the
1970s (e.g. speciesarea relationships;
Neigel 2003). However, it was not
until more recently that pattern met-
rics were applied to quantify marine
habitat mosaic composition and con-
figuration (Garrabou et al. 1998,
Andréfouët et al. 2001). Metrics have
now been applied to Antarctic ben-
thos (<10s of m
), and at broader
spatial scales (10s to 100s of m
) to
seagrass, saltmarsh, mangrove, and
coral reef ecosystems (Table 3).
Over the past 30 yr, a total of 556
terrestrial research papers focused
on the subject of landscape metrics
(e.g. based on a ‘landscape metric*’
query in ISI) or indices (e.g. based on
‘landscape indice*’ query in ISI),
compared to 40 marine papers that
contained 1 or more of these search
terms. Of the 40 marine studies, only
23 met our specific selection criteria
that required the actual application
of spatial pattern metrics to quantita-
tively measure seascape structure
(Table S1 in the supplement). Conse-
quently, this review focuses on these
Wedding et al.: Seascape ecology metrics
Table 3. Overview of modeling techniques and relevant attributes in seascape ecology case studies. nMDS: non-metric multidimensional scaling
Source Focal patch type Seascape model Spatial pattern metrics Measure of spatial Variable reduction Ecological
autocorrelation method modeling
Pittman et al. (2004); Seagrasses; 2D patch mosaic %cover; total edge; no. patches; Not measured Cluster analysis; Structural
Queensland, Australia mangroves with vertical patch size; contrast weighted hybrid MDS; equation
attributes in edge density; core area; nearest principal axes modeling
patch classes neighbor; proximity; patch correlation
richness; evenness; contagion;
Manson et al. (2005); Mangroves; 2D patch mosaic Area; perimeter; no. patches; Not measured PCA Regression trees
Queensland, Australia offshore fisheries mean patch area & perimeter & stepwise
of mangroves; length coastline; multiple
area of shallow water regression
Grober-Dunsmore Coral reefs; 2D patch mosaic Patch size; perimeter to area Not measured None Stepwise multiple
et al. (2007); seagrasses ratio; %cover regression &
US Virgin Islands linear regression
Pittman et al. (2007a); Coral reefs; 2D patch mosaic %cover; patch richness; Moran’s I None Regression
Puerto Rico seagrasses; sand & gradient model SD of water depth trees
Pittman et al. (2007b); Mangroves; coral 2D patch mosaic %cover; patch richness; distance Moran’s I nMDS & similiar- ANOVA
Puerto Rico reef ecosystems to shore ity percentages
Drew & Eggleston Seagrass; 2D patch mosaic Habitat area; island perimeter to Not measured None Multiple regression
(2008) mangroves area ratio; no. mangrove patches; with backward
diversity, distance to nearest elimination
major channel, depth contour
Grober-Dunsmore Coral reefs; 2D patch mosaic Patch size; perimeter to area Not measured PCA Stepwise multiple
et al. (2008); seagrasses ratio; %cover; diversity; regression using
US Virgin Islands patch richness principle components
Meynecke et al. (2008); Coastal wetlands; 2D patch mosaic Area; no. patches; patch density; Not measured PCA Stepwise multiple
Queensland, Australia offshore fisheries length coastline; river length; regression using
connectivity index principle components
Mar Ecol Prog Ser 427: 219232, 2011
23 studies, of which 18 quantified spatial patterns from
2D categorical data and 5 applied surface metrics or
morphometrics to continuously varying 3D surfaces
(e.g. seafloor bathymetry from multibeam or light
detection and ranging [LiDAR] data).
Application to 2D seascapes
The majority (78%) of seascape studies quantified
metrics based on a patch-mosaic model representing 2D
seascape structure. Of the 18 seascape metric studies
based on 2D data, 7 were conducted in estuarine, man-
grove, and seagrass communities (Turner et al. 1999,
Manson et al. 2003, 2005, Pittman et al. 2004, Slee man
et al. 2005, Drew & Eggleston 2008, Meynecke et al.
2008), 6 studies were conducted in coral reef ecosys-
tems (Andréfouët et al. 2001, Grober-Dunsmore et al.
2007, Pittman et al. 2007a,b, Grober-Dunsmore et al.
2008, Prada et al. 2008), 2 studies in Antarctic benthic
communities (Teixido et al. 2002, 2007), and the re -
maining 2 in the subtidal zones of Mediterranean rocky
shores (Garrabou et al. 1998, 2002).
Multiple spatial pattern metrics were applied to quan -
tify landscape composition, and contagion spatial con-
figuration (patch-based and mosaic), and patch com-
plexity (patch-based) (Table 4; Table S2 in the
supplement). Specifically, 10 of the studies applied
metrics at the patch level (e.g. individual patch types)
and 7 to entire seascape mosaics comprising multiple
patch types. Nine metrics measuring landscape com-
position were applied, with patch area being the most
prevalent metric (n = 5), followed by habitat diversity
and richness (n = 3) and evenness (n = 2).
In total, 24 different spatial pattern metrics were
applied to quantify the spatial arrangement, orienta-
tion, or shape of seascape patches. Most metrics were
standard pattern metrics from terrestrial landscape
Table 4. Summary of 2D spatial pattern metrics applied in the reviewed literature. ‘Contagion’ quantifies the level of clumping or
aggregation in landscape elements. *Spatial pattern metrics adapted or developed uniquely for seascape ecology studies
Spatial pattern metric No. of studies Major habitat type in study
Landscape composition — 9 metrics (of 7 studies using composition)
Habitat area 5 Coral reef, Antarctic benthic, mangrove
Habitat diversity 3 Coral reef, Antarctic benthic, mangrove
Patch richness 3 Coral reef, Antarctic benthic
Habitat richness 2 Coral reef
Evenness 2 Coral reef, Antarctic benthic
Habitat perimeter 2 Mangrove
Patch diversity 1 Coral reef
Percent cover 1 Coral reef
Mean depth 1 Coral reef
Spatial configuration (contagion) — 2 metrics (of 2 studies using contagion)
Interspersion 1 Antarctic benthic
Contagion index 1 Intertidal
Spatial configuration (patch-based) — 22 metrics (of 10 studies using configuration)
Patch mean size 4 Intertidal, coral reef, mangrove
Number of patches 4 Mangrove, intertidal
Perimeter:area ratio 3 Coral reef, mangrove
Mean patch area 2 Mangrove, seagrass
Mean patch perimeter 2 Mangrove, seagrass
Mean shape index 2 Intertidal, mangrove
Area weighted mean shape index 1 Intertidal
Landscape shape index 1 Intertidal
Patch size standard deviation 1 Intertidal
Patch size coefficient of variation 1 Intertidal
Total edge 1 Intertidal
* Coral habitat intersecting boundary/ 1 Coral reef
Coral habitat inside boundary
Patch size variability 1 Intertidal
Patch shape 1 Intertidal
* Distance to nearest feature 1 Mangrove
(e.g. prop root)
Coefficient of variation 1 Mangrove
Mean proximity index 1 Mangrove
* Mangrovewater interface 1 Mangrove
* Length of coastline 1 Mangrove
Fractal dimension 1 Seagrass
Nearest neighbor 1 Seagrass
* Hydrodynamic aperture 1 Coral reef
Wedding et al.: Seascape ecology metrics
ecology (e.g. contagion, perimeter:area ratio, inter-
spersion), with 5 being adapted specifically to the
marine environment. The 1 truly unique marine metric
we encountered was hydrodynamic aperture (total
aperture and degree of aperture) developed to mea-
sure the morphological openings in the carbonate rims
of Pacific atolls. Apertures are channels that allow
water, nutrients, and biological exchanges between
the ocean and the interior lagoon environments of
atolls (Andréfouët et al. 2001, 2003). In addition,
unique derivatives of commonly used terrestrial edge
metrics have been developed for coastal wetlands,
such as the linear extent of the mangrovewater inter-
face (Manson et al. 2003), and the marshwater inter-
face used as a predictor of brown shrimp production in
Louisiana saltmarshes (Browder et al. 1989). Bartholo -
mew et al. (2008) developed an edge metric that quan-
tified the ratio between marine reserve boundary that
intersected coral reefs and the area of coral reefs
within marine reserves. This metric provided a proxy
for boundary permeability to examine the influence of
reserve boundary placement on the retention potential
of recovering exploited fish populations.
Mean patch size and number of patches (n = 4) were
the most commonly applied metrics used to quantify
seascapes, followed by perimeter to area ratio (n = 3),
mean patch area, mean patch index, and mean patch
perimeter (n = 2). Of the marine applications of pattern
metrics, 67% involved an evaluation of the relation-
ships between ecological patterns in the marine envi-
ronment. Garrabou et al. (1998) characterized the spa-
tial dynamics of mosaics of colonizing organisms on
Mediterranean rocky shores using digital photographs
and GIS to map benthic communities at relatively fine
spatial scales (310 cm
plots). At a broader scale, Mey-
necke et al. (2008) characterized the coastal seascape
in Queensland, Australia, and applied metrics to deter-
mine the links between seascape structure and off-
shore fisheries productivity. In coral reef ecosystems,
studies focused primarily on the influence of seascape
structure on coral reef fish assemblages, trophic guilds,
and species of concern. Two studies in the Caribbean
explored the linkages between mangroves (Pittman et
al. 2007a) and seagrass habitat (Grober-Dunsmore et
al. 2007) for fish species and assemblages.
Seventeen percent of the studies applied metrics to
monitor spatial dynamics across a range of temporal
scales. Garrabou et al. (2002) utilized pattern metrics to
monitor change of benthic communities on rocky sub-
tidal substratum over a 2 yr period. Manson et al.
(2003) applied 7 spatial pattern metrics to document
change in mangrove communities from vegetation
maps over a 25 yr period. In Antarctic benthic commu-
nities, Teixido et al. (2007) applied 2 metrics (class area
and number of patches) to measure benthic community
change across a gradient of disturbance due to iceberg
scouring. Unlike terrestrial studies where change over
time is a prominent component of studies, seascape
studies have not pursued this to any notable degree,
and this is an area of research that has great potential
to expand in the future.
Application to 3D seascapes
Of the 5 seascape metric studies based on continu-
ously varying 3D surfaces (e.g. LiDAR or multibeam
derived bathymetry), 4 were conducted in coral reef
ecosystems and the other study was carried out in
shale beds off the coast of California (Table 5). The
most commonly applied morphometric was rugosity
(n = 3), followed by slope and mean depth (n = 2).
Overall, 8 morphometrics were applied, of which 7
were used to quantify habitat complexity in the marine
environment. For example, Wedding & Friedlander
(2008) found that variance in depth (within a 75 m
radius) explained most of the variation in numerical
abundance and species richness compared to other
spatial pattern metrics applied to continuously varying
3D surface data. Pittman et al. (2009) compared 8 mor-
phometrics at multiple spatial scales to identify the
best predictors of fish and coral species richness and
abundance. Slope of the slope, a measure of the habi-
tat complexity, emerged as the most influential spatial
predictor for a wide range of coral reef associated fau-
nal species (Pittman et al. 2009).
Influence of spatial, thematic, and temporal
resolution on pattern metrics
The 2 main components of scale, viz. grain (e.g. spa-
tial resolution of the data) and extent (e.g. geographic
area of the study site), have been well studied and are
Table 5. Summary of 3D spatial pattern metrics applied in the
reviewed literature. Of 5 studies in total, the major habitat
types to which the metrics were applied were coral reef, man-
grove, and seagrass. See Pittman et al. (2009) for description
of common 3D spatial pattern metrics
Spatial pattern metric No. of studies
Landscape composition — 8 metrics
Rugosity 3
Slope 2
Mean depth 2
Variance in depth 1
Standard deviation of depth 1
Slope of slope 1
Plan curvature 1
Fractal dimension 1
Mar Ecol Prog Ser 427: 219232, 2011
known to affect the behavior of individual spatial pat-
tern metrics, and therefore, the understanding of eco-
logical relationships (Urban et al. 1987, Wiens 1989).
For instance, as grain is increased with an unchanging
extent, the number of patches in the landscape
decreases (Lepczyk et al. 2007). Another often
neglected map characteristic that can influence the
results from spatial pattern metrics is the thematic res-
olution, e.g. the amount of detail in a map represented
by the number of classes (Kendall & Miller 2008, Cas -
tilla et al. 2009, Kendall et al. 2011, this Theme Section).
When thematic maps (e.g. benthic habitat maps) are
used to represent structure in the marine environment,
issues related to map accuracy, cartographic bias, error
propagation, and uncertainty become increasingly
important and must be assessed (Lunetta et al. 1991,
Hess 1994, Shao & Wu 2008) (Figs. 3 & 4). Remotely
sensed data are available in a broad range of spatial
resolutions, and the resolution of the imagery used to
derive spatial pattern metrics can affect the results of
the subsequent analysis (Manson et al. 2003). Within
the subset of seascape papers reviewed, the geo-
graphic extent ranged from a photo quadrat at 310 cm
(Garrabou et al. 1998) to an estuarine region that
extended along the entire coast of Queensland, Aus-
tralia (Meynecke et al. 2008). Spatial resolution of the
data ranged from a vector data set with an MMU of
(Garrabou et al. 2002) to a raster data set with a
pixel size of 10 m (Meynecke et al. 2008). Prada et al.
(2008) explored the effects of changing the grain size
(e.g. 4 m
and 400 m
MMU) of habitat maps on 7 com-
monly used pattern metrics and found habitat richness
to be the only metric that remained constant. Kendall &
Miller (2008) found that changing the spatial resolution
of benthic habitat maps (100 m
to 4048 m
resulted in disproportionate changes in the area,
perimeter, and other values among feature types, but
had little effect on the relationship between seascape
structure and fish community composition (Kendall
& Miller 2010). Subsequently, however, species level
analyses by Kendall et al. (2011) found that different
resolution maps changed the strength of correlations
for several fish associated with coral reef edges and
sandy areas, but results were consistent regardless of
map resolution for comparisons involving area of sea-
grass and habitat diversity.
The Caribbean seascape ecology studies reviewed
(Grober-Dunsmore et al. 2007, 2008, Pittman et al.
2007a,b) used existing maps with predetermined car-
tographic characteristics including spatial and the-
matic resolution. Thematic resolution, as well as the
quality and resolution of the imagery from which the
map was derived, can have important impacts on the
quantification of patch or habitat diversity. This is par-
ticularly important in studies of biodiversity patterns.
Habitat diversity in terrestrial systems has been found
to be positively correlated with animal species diver-
sity, which is consistent with the ‘habitat heterogeneity
hypothesis’ (Tews et al. 2004). In contrast, habitat rich-
ness and diversity of seascapes have not emerged as
important explanatory variables for faunal diversity at
the spatial scales examined in seascape ecology
studies. This important difference be -
tween marine and terrestrial studies has
not yet been sufficiently addressed in eco -
logy and requires more detailed compara-
tive and multi-scale analyses. In addition,
very few studies (marine or terrestrial)
have used diversity metrics such as taxo-
nomic diversity and distinctness (Clarke &
Warwick 1999) to quantify seascape and
landscape habitat diversity. Taxonomic
indices account for diversity across hierar-
chical levels of classification that can be
equally applicable to a benthic map classi-
fication as to a multi-species community.
For instance, weightings can be assigned
to different levels of a map classification,
such that 4 classes from the same level in
the hierarchy (e.g. sparse seagrass, dense
seagrass, macroalgae, algal turf – all
marine plants) would be less taxonomi-
cally diverse than 4 more structurally dif-
ferent classes (e.g. boulders, patch reef,
seagrass, sand). Conventional diversity
metrics such as patch richness and Shan-
Fig. 3. Three types of problems in landscape analysis with pattern metrics:
conceptual flaws, improper uses, and inherent limitations of landscape in-
dices. Each type manifests in several forms that overlap with the other types.
Modified from Li & Wu (2004)
Wedding et al.: Seascape ecology metrics
non diversity would assign an equal score to these
structurally and functionally different seascapes. Future
studies in landscape ecology should examine a range
of diversity metrics with consideration given to the
functional relevance of the thematic resolution of any
given habitat map.
Data considerations and analytical techniques
Potential misuses of pattern metrics can easily arise
for 2 main reasons: (1) quantifying patterns without
considering ecological processes and causal relation-
ships, and (2) failing to deal with caveats of correla-
tion analysis (Li & Wu 2004). The first reason is self-
explanatory and an important consideration for all
areas of ecology. The second reason requires some
explanation and discussion of analytical solutions. Data
in landscape ecology, particularly spatial pattern met-
rics, are typically non-normally distributed, exhibit
multicollinearity, spatial autocorrelation, and often in-
clude irrelevant variables. Multicollinearity has im -
plications for certain statistical modeling techniques,
such as multiple regression (Graham 2003). Multi-
collinearity occurs because many of the metrics share
some component (often geometric) derived from a core
suite of interrelated measures such as patch area, edge
length, shape, and inter-patch distance to quantify dif-
ferent attributes of spatial pattern often resulting in
strong collinearity (positive and negative) between
metrics (Li & Reynolds 1993, Riitters et al. 1995). None -
theless, similar metrics can still capture slightly differ-
ent attributes of spatial structure, and a single metric
may not capture sufficient structural variability (e.g.
spatial arrangement and composition) to explain com-
plex organism responses to spatial patterning (Cush-
man et al. 2008). Much effort has been directed toward
finding parsimony amongst the wealth of spatial pat-
tern metrics available (Riitters et al. 1995, Gustafson
1998, Cardille et al. 2005). Cushman et al. (2008) exam-
ined 49 class-level metrics and 54 landscape-level met-
rics applied to 3 geographically distinct regions and
identified a reduced set of metrics that consistently de-
scribed the major attributes of landscape configuration.
Exploratory analyses can be crucial to identifying a
suite of potentially important patterns through correla-
tive techniques as a precursor to refining the subse-
quent steps toward explicitly determining causality.
Although the pattern-pattern approach is often criti-
cized in science, it is clear that progress in ecology can
be accelerated by first identifying and describing pat-
terns (Underwood et al. 2000). We focus here on a brief
review of multivariate statistical techniques that have
been developed to increase interpretability of pattern-
pattern relationships from analysis of complex multi-
scale ecological data sets (Table 3). We do not include
linear regression, although we recognize its utility for
modeling in landscape ecology, where it is sometimes
used as a secondary step after orthogonal decomposi-
tion of multivariate data through ordination tech-
niques. Our focus is not on the details of the algorithms
themselves, but rather on highlighting the applications
of the techniques in landscape ecology.
Ordination is a family of techniques that reduces
high dimensionality data into fewer variables, each of
which represents a continuum or gradient in the data
Fig. 4. Factors potentially influencing accuracy during the steps involved in seascape mapping, characterization, and quantification
Mar Ecol Prog Ser 427: 219232, 2011228
that may be visualized in a 2D or 3D plot. The deriva-
tive variables are composites of environmental data
and can be used as predictor variables in ecological
modeling. Principle components analysis (PCA) has
frequently been used to reduce the large number of
sometimes collinear pattern metrics into a more parsi-
monious suite of variables (McGarigal & McComb
1995, Cushman et al. 2008). PCA has been used to
reduce the dimensionality of the multivariate data and
to describe seascape structure based on the size and
significance of the component loadings. For example,
Meynecke et al. (2008) regressed 3 principal compo-
nents (PCs), representing independent gradients in
coastline characteristics and seascape composition and
connectivity, against reported catch of individual fish
and crustacean species, to highlight the importance of
wetland connectivity. However, Grober-Dunsmore et
al. (2008) regressed PCs of seascape structure against
reef fish variables and explained less variability than
did individual pattern metrics. PCA is a useful tool, but
is influenced by sample size and assumes that the suite
of variables change linearly along underlying gradi-
ents (Gauch 1982). In addition, non-linearity and inclu-
sion of many collinear variables can result in distorted
ordinations using standard PCA (McGarigal & Cush-
man 2000).
Non-metric multidimensional scaling (nMDS) is an
ordination technique that does not assume linearity and
uses a similarity matrix rather than a correlation or co-
variance matrix and where samples are ranked accord-
ing to their similarity. Pittman et al. (2007b) applied
cluster analysis and nMDS using Plymouth Routines in
Multivariate Ecological Research (PRIMER) to charac-
terize seascape types from a small selection of pattern
metrics that measured seascape composition (abun-
dance of patch types and overall patch richness). Simi-
larity percentages (SIMPER) were calculated to quan-
tify the similarities and dissimilarities of the seascape
structure within and between seascape types. Canoni-
cal correspondence analysis (CCA; Jongman et al.
1995), a hybrid of ordination and multiple regression,
has been used effectively in explaining patterns of vari-
ation in organism distributions. CCA performs well
with non-orthogonal and collinear gradients, making it
suitable for complex landscape eco logy analyses
(Cushman & McGarigal 2002), and has been used as a
secondary step in the variable selection process to cal-
culate the statistical significance when a variable is
added into a model (Cushman & McGarigal 2004).
Structural equation modeling
Structural equation modeling (SEM) can provide
accurate and meaningful models in the presence of
multicollinearity by incorporating collinear variables
and non-linear variables explicitly in the model,
rather than excluding them, or combining them into
orthogonal components (Graham 2003). In SEM, the
overriding concept is that a correlation may not imply
causation, but the existence of a causal relationship
implies some correlation. The possibilities can be
tested as competing hypotheses. Models can be built
and visualized using path diagrams to represent the
working hypothesis about the causal relationships
among variables (Shipley 1999). The relative effect of
1 variable is communicated using a standardized path
coefficient analogous to partial regression coeffi-
cients. Parameter estimation is done by measuring
the goodness of fit between the actual data matrix
(correlation or co variance) representing the relation-
ships between variables and the estimated data
matrix of the best fitting model. Pittman et al. (2004)
used SEM and path models to explain spatial patterns
in fish and prawn distributions and diversity as influ-
enced by habitat structure at multiple spatial scales.
SEM was used to decompose correlations into direct
and indirect components and examine the relative
importance of within-patch structure (leaf length,
sediment grain size) versus seascape composition and
configuration (represented by a set of spatial pattern
metrics). Competing models were tested using a
range of goodness of-fit statistics and a final model
selected on the basis of overall performance of the
measures of model fit.
Machine-learning algorithms
Over the past decade, many advanced algorithms
have been developed to efficiently explore and model
complex patterns in complex data (Hastie et al. 2009).
Some of the most successful examples are ‘ensemble’
techniques that use many models developed through
iterative training and testing to learn and improve
upon the errors of predecessors (Elith et al. 2006).
Tree-based ensemble techniques, such as boosted re -
gression trees and random forests have recently been
used to model fishseascape relationships at a range
of spatial scales and to assess the relative importance
of variables, to model interactions between variables,
and to identify ecological thresholds (Leathwick et al.
2006, Pittman et al. 2009, Knudby et al. 2010). These
machine-learning techniques are robust to collinear-
ity and the presence of irrelevant predictors and
therefore do not require prior variable selection or
data reduction. Additional machine-learning tech-
niques that offer great utility include multivariate
adaptive regression splines and neural networks (Lin-
derman et al. 2004).
Wedding et al.: Seascape ecology metrics
Landscape ecology at the boundary of land and sea
Landscape ecology approaches offer great promise
for examining functionally important structural bound-
aries at the landsea interface (Kneib 1994), and can
extend the progress made with understanding and
managing the landwater interface for terrestrial
freshwater environments (Naiman & Decamps 1997).
Tidal wetlands including some saltmarshes and man-
groves, where the landsea interface is dynamic over
the tidal cycle, present a unique challenge for the
application of spatial pattern metrics. Measuring such
dynamic structure would require the application of
metrics over a time series of imagery or quantification
of features that provide a reliable proxy. Adequately
quantifying dynamic patterns, however, may require
new metrics. The easily mapped shallow-water and
semi-terrestrial environments at the landsea inter-
face offer great opportunities to develop, apply, and
test pattern metrics. Several commonly used metrics in
hydrology, such as drainage density and measures of
dendritic network complexity and channel morpho -
logy, as well as patch-based metrics such as marsh
water interface and edge:perimeter ratio have been
applied successfully to examine the spatial ecology
of saltmarshes (Kneib 1991, 1994, Feagin & Wu 2006).
Future remote sensing techniques will increase the
thematic resolution of seascape maps, and new vari-
ables that reveal more detailed spatial patterns in soil
and water conditions across saltmarsh seascapes could
be quantified using spatial pattern metrics. The adap-
tation of conventional landscape indices together with
new metrics that can account for dynamic linear fea-
tures, changing water volumes, wave action, and
edaphic variables may increase the ability of statistical
models to predict the distribution of species and as sem -
blages. Understanding the consequences of changing
spatial patterning will increase our ability to predict
the impact of human modifications to coastal environ-
ments and guide effective restoration activities (Feagin
& Wu 2006, Kelly et al. 2011).
3D seascape structure
Detailed seafloor terrain models are becoming in -
creasingly accessible and reliable with technological
advances and the proliferation of marine remote sens-
ing. The 3D models that result from seafloor acoustic,
laser, and optical mapping provide an opportunity to
examine the relationship between benthic morpho -
logy, including topographic complexity and marine
organisms and communities (Brock & Purkis 2009).
Morphometrics commonly used in geomorphology and
industrial engineering to quantify surface features and
complexity have performed well as predictors of fish
diversity and species distributions across coral reef
seascapes (Wedding et al. 2008, Pittman et al. 2009).
The current limitation with morphometrics, and simi-
larly with many of the spatial pattern metrics, is the
lack of information on the ecological mechanisms that
drive the patternpattern relationships. Nevertheless,
inclusion of morphometrics in the suite of metrics
applied to seascapes will likely offer new insights in
the study of the reciprocal link between pattern and
process (McGarigal et al. 2009).
Quantifying seascape change
Spatial pattern metrics combined with remote sens-
ing data offer a cost-effective suite of spatial tools for
surveillance and monitoring of seascape change. Re -
motely sensed imagery to document change in marine
and coastal habitats over time is becoming increasingly
important as anthropogenic stresses change coastal
environments. Shallow water marine ecosystems such
as seagrass, salt marsh, coral reef, and mangrove sys-
tems are globally threatened with an estimated loss of
30% in the past few decades (Valiela et al. 2009, Way-
cott et al. 2009). Detection of coastal habitat changes
may be greatly enhanced by the application of spatial
pattern metrics because they can provide important
information beyond simple estimates of areal losses
and gains. Habitat change can be a spatially complex
process. Pattern metrics can quantify fragmentation
rates and identify threshold effects or tipping points in
ecosystem function (Grober-Dunsmore et al. 2009). For
example, Manson et al. (2003) applied 7 spatial metrics
to analyze mangrove change in Queensland, Australia,
between 1973 and 1999 and found significantly altered
spatial configuration, with implications for the move-
ment and dispersal of marine fauna. With rapid loss
and alteration of coastal ecosystem structure under-
way, it is now imperative to find cost-effective and reli-
able ways to quantitatively monitor changes and pre-
dict the ecological consequences.
Comparative studies and careful evaluation are re -
quired to support the judicious application of landscape
ecology principles, concepts, and analytical techniques
in the marine environment. Seascape structure (e.g. the
composition and spatial configuration) of the marine
environment is perceived differently, through the
Mar Ecol Prog Ser 427: 219232, 2011230
lens of landscape ecology, than conventional ecology.
The development of spatial pattern metrics unique to
the marine environment should be conducted with a
strong ecological rationale in mind and with an aim to
better understand the linkages between spatial pat-
terns and ecological processes. Although we have fo-
cused entirely on shallow coastal applications, spatial
pattern metrics could potentially offer new insights on
pelagic ecosystems. Oceanic fronts, plankton patches,
and spatial gradients in biophysical conditions across
continental shelves are spatial patterns that have eco-
logical consequences, yet are rarely quantified with
pattern metrics. Seascape ecologists could benefit from
lessons already learned in terrestrial landscape eco -
logy. In particular, more effort should be focused on the
a priori identification of ecologically relevant metrics to
characterize spatial patterns. Research on the quantifi-
cation of spatial patterns in the marine landscape
should develop with an awareness of the evolution of
terrestrial metrics, and with due attention to the limita-
tions and pitfalls that arose as landscape pattern analy-
sis became more widely applied and critically assessed.
Future work on seascape metrics must achieve a bal-
ance between applying the fundamental metrics based
on established terrestrial landscape ecology and ex-
panding the theoretical basis of landscape ecology to
address the unique set of challenges that must be con-
fronted when working in the marine environment.
Acknowledgements. This research was supported by a
National Science Foundation Dissertation Improvement
Grant BCS-1003871, NOAA’s Biogeography Branch, and the
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Submitted: June 10, 2010; Accepted: March 7, 2011 Proofs received from author(s): April 4, 2011
... On the basis of these observations, we characterized the seagrass-dominated habitats with previously defined parameters: the habitat types, the species composition, the seascape properties, the sea urchin density epiphytic cover, and the dead leaves. These elements were integrated as part of the remote sensing of seagrass habitats and their health characterization [24,31,50,52,55,56,[69][70][71][72][73]. We selected nine nearshore sites ( Figure 1) on the basis of the available information on the distributions of the seagrasses, coral reefs, and macroalgal habitats. ...
... Several metrics are available to quantify the fragmentation in seagrass meadows [24,49,73]. We selected indices that allowed for comparison between different landscapes, and that have been evaluated for their ability to change across fragmentation categories [49], following the recommendations by the authors of [24,73]. ...
... Several metrics are available to quantify the fragmentation in seagrass meadows [24,49,73]. We selected indices that allowed for comparison between different landscapes, and that have been evaluated for their ability to change across fragmentation categories [49], following the recommendations by the authors of [24,73]. We applied the patch number (PN) and the patch density (PD) as the primary metrics for the landscape composition, the shape metrics, such as an area-mean-weighted perimeter-to-area ratio, and the connectivity metrics, such as the landscape division index (LD). ...
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Unmanned automatic systems (UAS) are increasingly being applied as an alternative to more costly time-consuming traditional methods for mapping and monitoring marine shallow-water ecosystems. Here, we demonstrate the utility of combining aerial drones with in situ imagery to characterize the habitat conditions of nine shallow-water seagrass-dominated areas on Unguja Island, Zanzibar. We applied object-based image analysis and a maximum likelihood algorithm on the drone images to derive habitat cover maps and important seagrass habitat parameters: the habitat composition; the seagrass species; the horizontal- and depth-percent covers, and the seascape fragmentation. We mapped nine sites covering 724 ha, categorized into seagrasses (55%), bare sediment (31%), corals (9%), and macroalgae (5%). An average of six seagrass species were found, and 20% of the nine sites were categorized as “dense cover” (40–70%). We achieved high map accuracy for the habitat types (87%), seagrass (80%), and seagrass species (76%). In all nine sites, we observed clear decreases in the seagrass covers with depths ranging from 30% at 1–2 m, to 1.6% at a 4–5 m depth. The depth dependency varied significantly among the seagrass species. Areas associated with low seagrass cover also had a more fragmented distribution pattern, with scattered seagrass populations. The seagrass cover was correlated negatively (r2 = 0.9, p < 0.01) with sea urchins. A multivariate analysis of the similarity (ANOSIM) of the biotic features, derived from the drone and in situ data, suggested that the nine sites could be organized into three significantly different coastal habitat types. This study demonstrates the high robustness of drones for characterizing complex seagrass habitat conditions in tropical waters. We recommend adopting drones, combined with in situ photos, for establishing a suite of important data relevant for marine ecosystem monitoring in the Western Indian Ocean (WIO).
... Spatial pattern metrics have been developed to quantify seascape composition (the abundance and variety of patch types), configuration (the spatial arrangement of patch types) and terrain morphology (e.g. slope, structural complexity) from habitat maps or digital bathymetric models (Lecours et al., 2016;Wedding et al., 2011). In shallow water reefs, these have, amongst others, provided new insights into seascape connectivity (McMahon et al., 2012), species-specific responses to environmental structure (Hitt et al., 2011), the importance of terrain complexity (Wedding et al., 2019) and scale-dependent responses (Kendall et al., 2011). ...
... These areas should form important targets for future surveying and monitoring efforts as well as for fisheries. Monitoring programmes could benefit from incorporating spatial pattern metrics to identify where and when they change and to evaluate possible implications for associated species and habitats (Wedding et al., 2011). All findings on habitat associations of MCE fish at different depth zones uncovered in this study were shared with stakeholders from the region. ...
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Benthic components of tropical mesophotic coral ecosystems (MCEs) are home to diverse fish assemblages, but the effect of multiscale spatial benthic characteristics on MCE fish is not well understood. To investigate the influence of fine‐scale benthic seascape structure and broad‐scale environmental characteristics on MCE fish, we surveyed fish assemblages in Seychelles at 30, 60 and 120 m depth using submersible video transects. Spatial pattern metrics from seascape ecology were applied to quantify fine‐scale benthic seascape composition, configuration and terrain morphology from structure‐from‐motion photogrammetry and multibeam echosounder bathymetry and to explore seascape–fish associations. Hierarchical clustering using fish abundance and biomass data identified four distinct assemblages separated by the depth and geographic location, but also significantly influenced by variations in fine‐scale seascape structure. Results further revealed variable responses of assemblage characteristics (fish biomass, abundance, trophic group richness, Shannon diversity) to seascape heterogeneity at different depths. Sites with steep slopes and high terrain complexity hosted higher fish abundance and biomass, with shallower fish assemblages (30–60 m) positively associated with aggregated patch mixtures of coral, rubble, sediment and macroalgae with variable patch shapes. Deeper fish assemblages (120 m) were positively associated with relief and structural complexity and local variability in the substratum and benthic cover. Our study demonstrates the potential of spatial pattern metrics quantifying benthic composition, configuration and terrain structure to delineate mesophotic fish–habitat associations. Furthermore, incorporating a finer‐scale perspective proved valuable to explain the compositional patterns of MCE fish assemblages. As developments in marine surveying and monitoring of MCEs continue, we suggest that future studies incorporating spatial pattern metrics with multiscale remotely sensed data can provide insights will that are both ecologically meaningful to fish and operationally relevant to conservation strategies. To investigate the influence of benthic seascape structure on MCE fish assemblages in Seychelles, this study surveyed benthic structure and fish assemblages using submersible video transects at mesophotic depths. Spatial pattern metrics measuring benthic habitat composition, configuration and terrain structure were extracted from Structure‐from‐Motion photogrammetry models to quantify fish‐habitat associations. The results revealed depth‐ and site driven grouping of mesophotic fish assemblages that show significant associations with fine‐scale (cm‐m) terrain structure, seascape composition and configuration.
... Studying intertidal habitats as intricate mosaics forming a system rather than individual patches can allow for a better understanding of the complex and dynamic interactions in these environments [1][2][3][4]. Ecosystem services provided by intertidal habitats, such as oyster reefs, salt marshes, and mudflats, are often multifaceted as services may be amplified or hindered by patch configuration. For example, the presence of the Crassostrea virginica (eastern oyster) shell adjacent to a salt marsh can reduce erosion [5], the composition and spatial configuration of intertidal habitats can affect the community structure [6,7], and denitrification services provided by oyster reefs are dependent on habitat context [8]. ...
... In general, results showed that classification accuracy did not change dramatically when coarsening data resolution, which has direct implications for data collection and processing. Results also (1) confirmed that one single scale is not optimal for the study of all habitat types; (2) showed how promising the implementation of multiscale analysis is for coastal habitat classification; and (3) highlighted that fuzzy classifications should be explored to capture the areas of transition among habitat types, which were often the sources of misclassification. Coupling information obtained from sensors with a repeatable workflow that is freely available provides managers with the necessary tools to conduct consistent monitoring and identifies scales and variables of interest for three intertidal habitat types. ...
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Monitoring intertidal habitats, such as oyster reefs, salt marshes, and mudflats, is logistically challenging and often cost- and time-intensive. Remote sensing platforms, such as unoccupied aircraft systems (UASs), present an alternative to traditional approaches that can quickly and inexpensively monitor coastal areas. Despite the advantages offered by remote sensing systems, challenges remain concerning the best practices to collect imagery to study these ecosystems. One such challenge is the range of spatial resolutions for imagery that is best suited for intertidal habitat monitoring. Very fine imagery requires more collection and processing times. However, coarser imagery may not capture the fine-scale patterns necessary to understand relevant ecological processes. This study took UAS imagery captured along the Gulf of Mexico coastline in Florida, USA, and resampled the derived orthomosaic and digital surface model to resolutions ranging from 3 to 31 cm, which correspond to the spatial resolutions achievable by other means (e.g., aerial photography and certain commercial satellites). A geographic object-based image analysis (GEOBIA) workflow was then applied to datasets at each resolution to classify mudflats, salt marshes, oyster reefs, and water. The GEOBIA process was conducted within R, making the workflow open-source. Classification accuracies were largely consistent across the resolutions, with overall accuracies ranging from 78% to 82%. The results indicate that for habitat mapping applications, very fine resolutions may not provide information that increases the discriminative power of the classification algorithm. Multiscale classifications were also conducted and produced higher accuracies than single-scale workflows, as well as a measure of uncertainty between classifications.
... Los estudios basados en unidades de paisaje permiten localizar las áreas más sensibles de los ecosistemas e identificar puntos calientes ("hotspots" en inglés) de diversidad local (Loubersac et al., 1989) y definir límites para esquemas de zonificación (Kenchington y Claasen, 1988). En la actualidad, aún no es claro si los conceptos, técnicas y estadísticas de la ecología del paisaje comúnmente utilizadas para los ecosistemas terrestres son relevantes para las especies y paisajes marinos (Wedding et al., 2011). ...
... Así mismo, otros estudios han involucrado a poblaciones de peces para conocer aspectos de la conectividad en el paisaje marino, identificando unidades ecológicas en diferentes escalas espaciales (Nagelkerken et al., 2000;Cowen y Paris, 2006;Mumby, 2006;Nagelkerken et al., 2008;Arias-González, et al., 2008;Sale y Kritzer, 2008;Appeldoorn et al., 2009;Berkström et al., 2012;McMahon et al., 2012;Rioja-Nieto., 2013). Algunos estudios también han analizado la relación entre el tipo de unidad ecológica y la geometría del paisaje utilizando modelos de 2 o 3 dimensiones, que sirven para identificar cambios temporales en la forma de los parches que componen los hábitats (Böstrom et al., 2011;Wedding et al., 2011). En el Caribe colombiano se ha determinado la distribución de la comunidad coralina en varias de sus áreas coralinas, a través de esquemas de zonación ecológica, en los que se han discutido los factores ambientales que estructuran a las unidades ecológicas (Sánchez, 1995;Díaz et al., 1996a;Díaz et al., 1996b;Díaz et al., 2000;Cendales et al., 2002;Díaz-Pulido, et al., 2004). ...
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The cartography of ecological units at a detailed level requires differentiating them by the associations of coral species, but also by the use of physical and biotic attributes. Remote sensors have limitations to perform this type of discrimination; this is not only due to the spectral response of the coral species, which is very similar, but also to their variation in abundance, which can be considerable within the same ecological unit; the abundance can be so low, that their identification can go unnoticed when interpreting satellite images. In order to provide clues to propose criteria for the delimitation of ecological units, in the present study, and through the use of Bray-Curtis similarity index and multivariate analyzes, spatial distribution patterns of biotic assemblages and their relationship with the geomorphology in the Seaflower Biosphere Reserve were identified and analyzed, both, at the level of reef complexes [Serrana, Roncador, Quitasueño and Providence Island (SRQP)], and in the particular case of San Andrés Island (SAI) coral reefs. In general, spatial distribution trends among the identified biotic assemblages were recognised with respect to geomorphology, when they nested to one or two specific geomorphological units. This shows that the geomorphological units, rather than indicate the presence of a particular ecological unit, provide indications of a series of possibilities. In some cases, the patterns were expressed within the geomorphological units, which suggest the need to carry out analyses at a more detailed geomorphological scale. On the other hand, the increase in the abundance of macroalgae seems to create noise in the identification of ecological units, and that these present a high abundance does not necessarily indicate that the richness or the coral abundance should be low, which implies the need to establish delimitation thresholds. It is concluded that in order to establish criteria for the delimitation of ecological units at higher detail, the spatial distribution patterns of biotic assemblages are indispensable. Consequently, four criteria are proposed for the delimitation of ecological units (1. Biotic, 2. Biotic-Geomorphology-Zoning, 3. Biotic-Cover (Remote sensing), 4. Biotic-Macroalgae), which in addition to including biotic assemblages and geomorphological aspects, they must be complemented with various physical attributes that make up the landscape of these coral areas.
... Consequently, seagrass maps that delineate discrete patches are of great interest to ecologists and natural resources managers because it allows the exploration of meadow spatial structure and configuration, both of which have important ecological implications (Robbins and Bell 1994;Bell et al. 1999;Johnson and Heck 2006;Hensgen et al. 2014). Spatial indices (i.e., patch statistics) from the field of landscape ecology have increasingly been used to assess the configuration of seagrasses and freshwater submerged aquatic vegetation (Frederiksen et al. 2004;Sleeman et al. 2005;Wedding et al. 2011;Yeager et al. 2011;Santos et al. 2018). However, these are typically sensitive to differences in scale (Wu 2004), which makes it important to select an appropriate scale given the ecosystem being studied and the purpose of the analysis. ...
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Context Seagrasses are submerged marine plants that have been declining globally at increasing rates. Natural resource managers rely on monitoring programs to detect and understand changes in these ecosystems. Technological advancements are allowing for the development of patch-level seagrass maps, which can be used to explore seagrass meadow spatial patterns. Objectives Our research questions involved comparing lacunarity, a measure of landscape configuration, for seagrass to assess cross-site differences in areal coverage and spatial patterns through time. We also discussed how lacunarity could help natural resource managers with monitoring program development and restoration decisions and evaluation. Methods We assessed lacunarity of seagrass meadows for various box sizes (0.0001 ha to 400.4 ha) around Cat Island and Ship Island, Mississippi (USA). For Cat Island, we used seagrass data from 2011 to 2014. For Ship Island, we used seagrass data for seven dates between 1963 and 2014. Results Cat Island, which had more continuous seagrass meadows, had lower lacunarity (i.e., denser coverage) compared to Ship Island, which had patchier seagrass beds. For Ship Island, we found a signal of disturbance and path toward recovery from Hurricane Camille in 1969. Finally, we highlighted how lacunarity curves could be used as one of multiple considerations for designing monitoring programs, which are commonly used for seagrass monitoring. Conclusions Lacunarity can help quantify spatial pattern dynamics, but more importantly, it can assist with natural resource management by defining fragmentation and potential scales for monitoring. This approach could be applied to other environments, especially other coastal ecosystems.
... These segment and classify seascapes into areas of similar abiotic conditions that may be associated with a particular biological community. Land-and seascape ecologists have developed a wide range of analytical tools to quantify spatial heterogeneity from patch-mosaic representations as benthic habitat maps, focusing on the composition (patch types and their relative amount) and con guration (the spatial arrangement and orientation of patches) of seascapes (Wedding et al. 2011;Swanborn et al. 2022). ...
Context Seamounts are abundant geomorphological features creating seabed spatial heterogeneity, a main driver of deep-sea biodiversity. Despite its ecological importance, substantial knowledge gaps exist on the character of seamount spatial heterogeneity. Objectives This study aimed to map, quantify and compare seamount seascapes to test whether individual habitats and seamounts differ in geomorphological structuring, and to identify spatial pattern metrics useful to discriminate between habitats and seamounts. Methods We mapped and classified geomorphological habitat using bathymetric data collected at five Southwest Indian Ridge seamounts. Spatial pattern metrics from landscape ecology are applied to quantify and compare seascape heterogeneity in composition and configuration represented in resulting habitat maps. Results Whilst part of the same regional geological feature, seamounts differed in seascape composition and configuration. Five geomorphological habitat types occurred across sites, which within seamounts differed in patch area, shape and clustering, with ridge habitat most dissimilar. Across seamounts, the spatial distribution of patches differed in number, shape, habitat aggregation and intermixing, and outcomes were used to score seamounts on a gradient from low to high spatial heterogeneity. Conclusions Although seamounts have been conceptualised as similar habitats, this study revealed quantitative differences in seascape spatial heterogeneity. As variations in relative proportion and spatial relationships of habitats within seamounts may influence ecological functioning, the proposed quantitative approach help generate insights into within-seamount characteristics and seamount types relevant for representational ecosystem-based management. Further research into biodiversity associations with seascape composition and configuration at relevant spatial scales will help improve metric ecological interpretation.
... A measure of habitat diversity for surveys was determined using Shannon's diversity index, where a higher index value indicates that a greater number of habitat types are present and the proportion of different habitat types is more equal (Wedding et al., 2011). ...
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Aim Temperate marine systems globally are warming at accelerating rates, facilitating the poleward movement of warm‐water species, which are tropicalizing higher‐latitude reefs. While temperature plays a key role in structuring species distributions, less is known about how species’ early life stages are responding to warming‐induced changes in preferred nursery habitat availability. We aim to identify key ecological and environmental drivers of juvenile reef fishes’ distributions in the context of ocean warming. Location South‐eastern Australian coastline from 30 to 37°S. Methods We used a decade of underwater visual census data to uncover latitudinal distribution patterns of juvenile reef fishes and habitats across 1000 km of coastline, from subtropical to temperate latitudes. We modelled how benthic habitat cover, depth, wave exposure and sea surface temperature influence distributions of warm‐water and cool‐water juvenile reef fishes on temperate rocky reefs. Results We found sea surface temperature was typically the most important factor influencing densities of juvenile fishes, regardless of species’ thermal affinity or latitudinal range extent. Juveniles of tropical and subtropical range‐expanding fishes responded more strongly to warmer temperatures and lower wave exposure, while juveniles of temperate species responded more strongly to benthic habitats. Species’ responses to greater availability of temperate reef habitat‐formers such as kelp and other macroalgae contrasted, being positive for temperate and negative for tropical and subtropical juvenile fishes. Main conclusions The availability of both suitable habitat and sea temperatures for species’ early life stages is important considerations when predicting changes in reef fishes’ distributions in the context of ocean warming. Warming‐induced isotherm shifts and feedback loops constraining the persistence of key temperate reef habitat‐formers will favour range‐expanding tropical reef fishes colonizing higher‐latitude reefs, while disadvantaging some macroalgal‐associated resident temperate species. Such varying responses to warming‐induced environmental changes may strongly influence the structure of emerging tropicalized reef assemblages.
... Martino & Able 2003) including the degree of estuarine dependency (Whitfield 2020b). Local context also determines the range of other habitats available in the surrounding seascape, and the processes that link these different habitats into ecosystem mosaics, all of which shape the way that fish use habitats (Boström et al. 2011, Wedding et al. 2011, Litvin et al. 2018, Pittman 2018. This habitat connectivity within estuaries can vary dynamically along both the estuarine-terrestrial gradient (Pollard & Hannan 1994, Colombano et al. 2020) and estuarine-marine gradient (Sheaves & Johnston 2008, Vasconcelos et al. 2012, as well as across the shifting mosaic of a comprehensive estuarine ecotone (Basset et al. 2013). ...
Many complex factors determine the role of estuarine habitats and landscapes in fish growth and survival that ultimately contribute individuals to adult populations. In this chapter, we recognise the diversity of habitats, both those frequently (e.g. submerged aquatic vegetation, mangroves) and infrequently (e.g. shellfish beds, woody debris) evaluated and how these vary in use among life history stages and among estuaries from the tropics to the poles. Some factors that clearly influence habitat diversity and use vary with temperature, salinity, geomorphology, hydrology and niche availability coupled with species‐specific and intraspecific differences in habitat fidelity and landscape context. This diversity of factors hampers our ability to fully determine habitat quality and connectivity requirements but provides opportunities to enhance our understanding with multiple approaches from basic natural history to application of developing techniques.
... This does not detract from the fact that there is an important tradition of ecological cartography in the Mediterranean Sea, in origin mainly by the French [87] and Italian schools [88], but currently also in many other countries. Several of these maps are strictly monothematic, especially aimed at mapping the meadows of the seagrass Posidonia oceanica [89] adopting a binary approach (patch-matrix), while the so-called bionomic (or biocoenotic) maps rely on the patch-mosaic model [90] and take into consideration all the benthic habitats present in the area [91]. In absence of bionomic maps, fishery maps may sometime provide information on the distribution of marine habitats [92]. ...
Biodiversity is a portmanteau word to indicate the variety of life at all levels from genes to ecosystems, but it is often simplistically equated to species richness; the word ecodiversity has thus been coined to address habitat variety. Biodiversity represents the core of the natural capital, and as such needs to be quantified and followed over time. Marine Protected Areas (MPAs) are a major tool for biodiversity conservation at sea. Monitoring of both species and habitat diversity in MPAs is therefore mandatory and must include both inventory and periodic surveillance activities. In the case of inventories, the ideal would be to census all species and all habitats, but while the latter goal can be within reach, the former seems unattainable. Species inventory should be com-measured to investigation effort, while habitat inventory should be based on mapping. Both inventories may profit from suitability spatial modelling. Periodic surveillance actions should privilege conspicuous species and priority habitats. Efficient descriptor taxa and ecological indices are recommended to evaluate environmental status. While it seems obvious that surveillance activities should be carried out with regular recurrence, diachronic inventories and mapping are rarely carried out. Time series are of prime importance to detect marine ecosystem change even in the absence of direct human impacts.
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Classification and mapping of marine-island landscapes according to an integrated approach not only clarifies the structure and division of natural units of the sea areas but also creates a basis for the orientation of natural resource management resources, environmental protection, and conservation of marine-island biodiversity. The principles of marine-island landscape mapping are to define terminology and establish a classification system based on taxonomy and criteria. This article represents the establishment of marine-island landscape mapping of Nam Yet Island and adjacent water (scaled at 1:10000) through comprehensive work, including marine-island landscape classification, applied GIS - remote sensing, and field investigation in the two years of 2020-2021. Accordingly, the landscape classification system of the Nam Yet Island area includes 1 system, 1 subsystem, 4 classes, 6 subclasses, 10 types, 29 kinds, and 34 forms of landscape (of which 4 island forms and 30 marine forms). The units of the marine-island landscape fully express the natural components, anthropogenic factors, and biotic and abiotic factors in their relationship and interaction with each other. Depending on each corresponding level, the detailed level of components and landscape elements is shown, in which components and biological factors are studied and analyzed most fully. The research results have clarified the characteristics and the law of differentiation of the marine-island landscape in Nam Yet Island, contributing to supplementing knowledge about Truong Sa Islands of Vietnam, which is a scientific basis for resource management and biodiversity conservation, protecting the marine environment, and at the same time supplementing the theoretical basis for the study of the tropical monsoon tropical island and marine landscape, which has not yet been studied in Vietnam.
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Foreword by Richard T.T. Forman, Harvard University (Extract) (…) The gaping lacuna in wise planning is not more knowledge, but rather the scarcity of accessible, informative and (especially) compelling syntheses and handbooks. The authors, from ecology, landscape architecture, and land-use planning, are experts in spatial pattern and landscape metrics, and have blended their expertise into this handy, readable book. Hopefully analogous collaborations will proliferate, with the land around us being the big beneficiary. The authors highlight the importance of absorbing landscape ecology principles and then applying them in spatial planning. That has been, and is, exactly the growing success story in strengthening forestry (e.g., water- shed management), biological conservation (rare species protection), transportation (road ecology), and wildlife management. Landscape metrics, the measures of spatial pattern, are indicators of many human and natural conditions on land, from built-area patterns and built-system flows to the big three habitat issues: loss, degradation, and fragmentation. The pages ahead lucidly portray landscape metrics and their application, effectively sorting a large number in the literature down to a core set of ten. Both opportunities and caveats are explained. For the first time this scientific area is made available and readable. Landscape structure is a key indicator of how the land works for people and for nature. Thus, changing landscape pattern emerges as a convenient “handle” for planners, and for each of us, to improve the planet. (…) In effect, landscape metrics represent a “pre-handle,” or primer, for the planners’ handle for change. (…) The pages in your hand reveal a rich array of insights and an important big-picture perspective. Planners and ecologists, and indeed all who think about changing the land, will be enriched by the exploration ahead.
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Perimeter, surface area, total hydrodynamic aperture, and degree of hydrodynamic aperture are key landscape parameters used to quantify differences in the biological functioning of Tuamotu Archipelago atolls (French Polynesia). In a previous study, these landscape parameters were computed using Satellite pour l'observation de la terre (SPOT) high-resolution visible (HRV) data at 20 m spatial resolution. Since 1999, Tuamotu atolls have been systematically imaged by an array of satellite sensors with a wide range of spatial resolution (from 1 km to 5 m) including the sea-viewing wide field-of-view sensor (SeaWiFS), Landsat enhanced thematic mapper plus (ETM+), and digital photographs taken by astronauts from the International Space Station (ISS). Our goal was to assess the influence of the spatial resolution of SeaWiFS (1 km), ETM+ (30 m), HRV (20 m), and ISS digital photographs (5 m) on the estimation of landscape parameters of Pacific Ocean atolls. Total hydrodynamic aperture and degree of hydrodynamic aperture are the parameters most sensitive to variation in resolution. For the same atoll, the differences between degree of aperture computed from SPOT and Landsat can reach 28%. Conversely, perimeters and atoll surface area estimates are in agreement within 7% using data with resolution from 5 to 30 m. One kilometre resolution SeaWiFS data offer the possibility to rank atolls based on surface area correctly, but only for atolls larger than 70 km2.
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We review the progress made in the emerging field of coastal seascape ecology, i.e. the application of landscape ecology concepts and techniques to the coastal marine environment. Since the early 1990s, the landscape ecology approach has been applied in several coastal subtidal and intertidal biogenic habitats across a range of spatial scales. Emerging evidence indicates that animals in these seascapes respond to the structure of patches and patch mosaics in different ways and at different spatial scales, yet we still know very little about the ecological significance of these relationships and the consequences of change in seascape patterning for ecosystem functioning and overall biodiversity. Ecological interactions that occur within patches and among different types of patches (or seascapes) are likely to be critically important in maintaining primary and secondary production, trophic transfer, biodiversity, coastal protection, and supporting a wealth of ecosystem goods and services. We review faunal responses to patch and seascape structure, including effects of fragmentation on 5 focal habitats: seagrass meadows, salt marshes, coral reefs, mangrove forests, and oyster reefs. Extrapolating and generalizing spatial relationships between ecological patterns and processes across scales remains a significant challenge, and we show that there are major gaps in our understanding of these relationships. Filling these gaps will be crucial for managing and responding to an inevitably changing coastal environment. We show that critical ecological thresholds exist in the structural patterning of biogenic ecosystems that, when exceeded, cause abrupt shifts in the distribution and abundance of organisms. A better understanding of faunal–seascape relationships, including the identifications of threshold effects, is urgently needed to support the development of more effective and holistic management actions in restoration, site prioritization, and forecasting the impacts of environmental change.
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Detection and perception of ecological relationships between biota and their surrounding habitats is sensitive to analysis scale and resolution of habitat data. We measured strength of univariate linear correlations between reef fish and seascape variables at multiple spatial scales (25 to 800 m). Correlation strength was used to identify the scale that best associates fish to their surrounding habitat. To evaluate the influence of map resolution, seascape variables were calculated based on 4 separate benthic maps produced using 2 levels of spatial and thematic resolution, respectively. Individual seascape variables explained only 25% of the variability in fish distributions. Length of reef edge was correlated with more aspects of the fish assemblage than other features. Area of seagrass and bare sand correlated with distribution of many fish, not just obligate users. No fish variables correlated with habitat diversity. Individual fish species achieved a wider range of correlations than mobility guilds or the entire fish assemblage. Scales of peak correlation were the same for juveniles and adults in a majority of comparisons. Highly mobile species exhibited broader scales of peak correlation than either resident or moderately mobile fish. Use of different input maps changed perception of the strength and even the scale of peak correlations for many comparisons involving hard bottom edge length and area of sand, whereas results were consistent regardless of map type for comparisons involving area of seagrass and habitat diversity.
Field data on the species content of plant and animal communities are noisy. Variation in community samples partly reflects interesting variation in environmental and historical factors, and partly reflects random fluctuations in species abundances. Routinely community data are analyzed by eigenvector ordination techniques, such as principal components analysis, reciprocal averaging, and detrended correspondence analysis. It is shown here with simulated community date that ordination selectively recovers patterns affecting several species simultaneously in early ordination axes, while selectively deferring noise to late axes. Eigenvector ordinations thus appear to be effective for reducing noise. This result helps to explain the observation that ordinations of field data are frequently useful even when the percentage of variance accounted for by the first few ordination axes is small. A related conclusion is that rounding of the abundance values of community data sets has little effect on results from ordination, and consequently fairly crude field data are entirely adequate for ordination purposes.
Deciduous forest patterns were evaluated, using fractal analysis, in the U. S. Geological Survey 1: 250,000 Natchez Quadrangle, a region that has experienced relatively recent conversion of forest cover to cropland. A perimeter-area method was used to determine the fractal dimension; the results show a different dimension for small compared with large forest patches. This result is probably related to differences in the scale of human versus natural processes that affect this particular forest pattern. By identifying transition zones in the scale at which landscape patterns change this technique shows promise for use in developing hypotheses related to scale-dependent processes and as a simple metric to evaluate changes on the earth's surface using remotely sensed data.