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Initially, we asked whether it was possible to identify corals that are resistant or sensitive to such conditions by compil-ing quantitative measures of their phenotypic traits deter-mined through empirical studies, but we found only weak phenotypic discrimination between ecological winners and losers, or among taxa. To reconcile this outcome with ecological evidence demonstrating that coral taxa are function-ally unequal, we looked beyond the notion that phenotypic homogeneity arose through limitations of empirical data. Instead, we examined the validity of contemporary means of categorizing corals based on ecological success. As an alternative means to distinguish among functional groups of corals, we present a demographic approach using integral projection models (IPMs) that link organismal performance to demographic outcomes, such as the rates of population Abstract Many tropical corals have declined in abun-dance in the last few decades, and evaluating the causal basis of these losses is critical to understanding how coral reefs will change in response to ongoing environmental challenges. Motivated by the likelihood that marine envi-ronments will become increasingly unfavorable for coral growth as they warm and become more acidic (i.e., ocean acidification), it is reasonable to evaluate whether specific phenotypic traits of the coral holobiont are associated with ecological success (or failure) under varying environmental conditions including those that are adverse to survival.
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Mar Biol
DOI 10.1007/s00227-014-2547-y
REVIEW, CONCEPT, AND SYNTHESIS
Evaluating the causal basis of ecological success within the
scleractinia: an integral projection model approach
Peter J. Edmunds · Scott C. Burgess · Hollie M. Putnam · Marissa L. Baskett ·
Lorenzo Bramanti · Nick S. Fabina · Xueying Han · Michael P. Lesser ·
Joshua S. Madin · Christopher B. Wall · Denise M. Yost · Ruth D. Gates
Received: 13 February 2014 / Accepted: 17 September 2014
© Springer-Verlag Berlin Heidelberg 2014
Initially, we asked whether it was possible to identify corals
that are resistant or sensitive to such conditions by compil-
ing quantitative measures of their phenotypic traits deter-
mined through empirical studies, but we found only weak
phenotypic discrimination between ecological winners and
losers, or among taxa. To reconcile this outcome with eco-
logical evidence demonstrating that coral taxa are function-
ally unequal, we looked beyond the notion that phenotypic
homogeneity arose through limitations of empirical data.
Instead, we examined the validity of contemporary means
of categorizing corals based on ecological success. As an
alternative means to distinguish among functional groups of
corals, we present a demographic approach using integral
projection models (IPMs) that link organismal performance
to demographic outcomes, such as the rates of population
Abstract Many tropical corals have declined in abun-
dance in the last few decades, and evaluating the causal
basis of these losses is critical to understanding how coral
reefs will change in response to ongoing environmental
challenges. Motivated by the likelihood that marine envi-
ronments will become increasingly unfavorable for coral
growth as they warm and become more acidic (i.e., ocean
acidification), it is reasonable to evaluate whether specific
phenotypic traits of the coral holobiont are associated with
ecological success (or failure) under varying environmen-
tal conditions including those that are adverse to survival.
Communicated by M. Byrne.
Electronic supplementary material The online version of this
article (doi:10.1007/s00227-014-2547-y) contains supplementary
material, which is available to authorized users.
P. J. Edmunds (*) · L. Bramanti
Department of Biology, California State University, 18111
Nordhoff Street, Northridge, CA 91330-8303, USA
e-mail: peter.edmunds@csun.edu
S. C. Burgess · N. S. Fabina
Center for Population Biology, University of California, Davis,
One Shields Avenue, Davis, CA 95616, USA
S. C. Burgess
Department of Biological Science, Florida State University, 319
Stadium Drive, Tallahassee, FL 32306, USA
H. M. Putnam · C. B. Wall · D. M. Yost · R. D. Gates
Hawaii Institute of Marine Biology, School of Ocean and Earth
Science and Technology, University of Hawai’i, PO Box 1346,
Kaneohe, HI 96744, USA
M. L. Baskett
Department of Environmental Science and Policy, University
of California, Davis, One Shields Avenue, Davis, CA 95616,
USA
L. Bramanti
UMR 8222, LECOB, Observatoire Oceanologique, UPMC,
Banyuls sur mer, France
X. Han
Department of Ecology, Evolution and Marine Biology and the
Coastal Research Center, Marine Science Institute, University
of California, Santa Barbara, CA 93106, USA
X. Han
National Center for Ecological Analysis and Synthesis, 735 State
St., Ste. 300, Santa Barbara, CA 93101, USA
M. P. Lesser
School of Marine Sciences and Ocean Engineering, University
of New Hampshire, Durham, NH 03824, USA
J. S. Madin
Department of Biological Sciences, Macquarie University,
Sydney, Australia
Mar Biol
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growth and responses to environmental stress. We describe
how IPMs can be applied to corals so that future research
can evaluate within a quantitative framework the extent to
which changes in physiological performance influence the
demographic underpinnings of ecological performance.
Introduction
Rising concentrations of atmospheric carbon dioxide (CO2)
from the burning of fossil fuels have resulted in global cli-
mate change (GCC) that has increased global sea surface
temperatures (SST) and perturbed the carbonate chemis-
try of seawater, thereby reducing the surface pH of oceans
[i.e., ocean acidification (OA) (Kelly and Hofmann 2012)].
These changes have many biological consequences known
best for their negative implications, such as the physiologi-
cal stress associated with high temperature (Harley et al.
2006; Hoegh-Guldberg and Bruno 2010; Somero 2010),
and in the marine environment, reduced skeletal accretion
(e.g., calcification) and perturbed respiration and photosyn-
thesis associated with OA (Hofmann et al. 2010; Rodolfo-
Metalpa et al. 2011). The potential implications of these
effects are serious, for within 100 years, atmospheric pCO2
is projected to increase from 39 Pa to between 49.6 and
85.1 Pa (van Vuuren et al. 2011), thereby increasing SST
0.3–2.1 °C (depending on the climate change scenario) and
reducing pH of the open ocean by 0.3 units (Feely et al.
2009; Sokolov et al. 2009; Kirtman et al. 2013). Relatively
little is known of the effects of these conditions on coastal
marine ecosystems, including coral reefs.
OA and elevated temperature are among the most promi-
nent threats to ocean ecosystems (Hughes et al. 2003;
Hoegh-Guldberg et al. 2007), and their interactive effects
may represent an evolutionary impasse to the survival of
tropical reefs as coral-dominated, calcifying systems (Sil-
verman et al. 2009; Wild et al. 2011; Anthony et al. 2011).
While it has rapidly become clear that the responses of cor-
als to OA and thermal stress, individually or interactively,
are not uniform among species (Loya et al. 2001; Pandolfi
et al. 2011; Comeau et al. 2013), progress in understand-
ing the causal basis of this variability has been slow. There
are exceptions to this generality, notably with molecular
genetic tools, for example, being used to clarify cellular
function (Meyer et al. 2011; Miller et al. 2011), host tax-
onomy (Forsman et al. 2009; Stat et al. 2012), and the roles
of Symbiodinium genotypes in affecting holobiont biology
(Hennige et al. 2009; Putnam et al. 2012; Yuyama et al.
2012). There is a clear need for more information in order
to understand the factors promoting coral success in the
face of environmental challenges.
Corals have been categorized into functional groups
based on the performance for at least four decades, with
two of the earliest studies partitioning corals by relative
dependence on autotrophy and heterotrophy (Porter 1976)
and degree of digestive aggression (Lang 1973). These
studies began a period of phenomenological approaches
to differentiating among corals based on phenotypic traits.
This interest has re-emerged in the twenty-first century in
efforts to categorize corals in ways that are insightful to
understanding the causes and consequences of declines in
coral cover (Loya et al. 2001; Darling et al. 2012, 2013),
as well as declines that occurred prior to current concerns
over climate change (Cramer et al. 2012). The renewed
interest initially focused on approaches similar to the r-K
life history classification of Stearns (1977), with the debate
crystallizing around whether coral species can be catego-
rized as “winners” or “losers” (Loya et al. 2001), or display
“weedy” or “non-weedy” life history strategies (Knowlton
2001). This discussion is acquiring sophistication with,
for example, studies partitioning hundreds of coral species
among four life history strategies based on up to 11 fea-
tures (Darling et al. 2012), or categorizing them as gener-
alists or specialists based on the genetic diversity of their
Symbiodinium (Fabina et al. 2012; Putnam et al. 2012).
Other studies have characterized vulnerable and resistant
corals on extant and fossil reefs based on key traits gener-
ated from the opinions of experts (van Woesik et al. 2012),
disease-susceptible and disease-resistant corals based
on mostly categorical traits (Diaz and Madin 2011), and
bleaching-susceptible and bleaching-resistant corals based
on mass transfer effects (van Woesik et al. 2012; see Pat-
terson 1992).
While the aforementioned studies demonstrate that cor-
als can be classified into functional groups, most studies
have relied heavily on categorical data, which overlooks
the resolution that can be obtained from continuous data
available in the primary literature (Edmunds et al. 2011
and reinforced below). More importantly, virtually all
studies of functional groupings of scleractinians provide
no mechanism by which trait values can be scaled across
the complex landscape of organismic biology to affect
population-specific processes such as birth rates, death
rates, longevity, and fecundity. These demographic prop-
erties are the best means through which the ecological
successes of corals can be codified and quantified. With
greater understanding of the causal basis of the afore-
mentioned demographic properties, it should be possible
to construct a mechanistic understanding of the effects of
physical environmental conditions on the growth of coral
populations. Madin et al. (2012a) provide one example in
which demographic traits are linked to coral performance,
and their analysis modeled lifetime reproductive output of
Acropora hyacinthus as a function of the effects of seawa-
ter flow and OA on colony dislodgement, photosynthesis,
and respiration.
Mar Biol
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Here, we advocate a demographic construct for scle-
ractinian corals that provides an explicit means to couple
organismic performance to ecological success. Similar
approaches have been applied in other systems (Violle et al.
2007), for example, phytoplankton (Litchman and Klaus-
meier 2008) and bighorn sheep (Coulson et al. 2005), but
have received little attention in studies of scleractinians (but
see Burgess 2011; Madin et al. 2012a, b). Coupling physi-
ological phenotypes to demographic properties is central
to understanding the mechanistic basis of ecological suc-
cess and, in the case of scleractinians, to predict which cor-
als might function as ecological winners when faced with
anthropogenic assaults. Arguably, understanding the causal
basis of ecological success (and failure) among scleractin-
ians on contemporary reefs is the most important objective
to advance efforts to forecast the structure and function of
coral reefs in the future.
We have structured our paper into two parts. First, we
outline our efforts using existing continuously distrib-
uted data from the primary literature to characterize the
phenotypes of scleractinian holobionts (i.e., the animal
host plus the consortia of single-celled taxa they contain,
including Symbiodinium dinoflagellates), and in so doing,
underscore the current limitations to accomplishing this
goal. Second, we describe how the well-developed tools
of integral projection models (IPMs) can provide insights
into trait-based explanations of ecological success. In
conclusion, we identify key research areas critical to
understanding and projecting coral assemblages in a
future differing from recent times in a variety of physical
conditions.
Step 1: Assessing contemporary data
After decades of limited attention, the ecophysiology of
tropical scleractinians has become a focus of research
attention. In the 1970s and early 1980s, there was strong
interest in coral ecophysiology (Muscatine et al. 1981;
Dubinsky et al. 1984; Gladfelter 1985), and toward the
end of this period, widespread coral bleaching maintained
interest in this discipline (Glynn 1993; Gates and Edmunds
1999). Although attention waned in the 1990s, the ecophys-
iology of tropical reef corals is now being studied in great
detail to evaluate the effects of GCC and OA on reef corals
(Gattuso et al. 1998; Hofmann and Todgham 2010; Lesser
2013). Consequently, there is nearly a century of legacy
data describing the ecophysiology of corals, with quan-
titative studies beginning as early as the 1920s (Vaughn
1914; Yonge and Nicholls 1930; Wellington et al. 2001).
Not unsurprisingly, however, a century of research spans a
wide range of methodological and technological sophisti-
cation, as well as paradigm shifts in comprehension of the
functional biology of this taxon (Lesser 2004; Davy et al.
2012).
One of the most profound changes in understanding of
the biology of tropical reef corals has involved the discov-
ery of high genetic diversity among their Symbiodinium
symbionts (Rowan and Powers 1991; LaJeunesse et al.
2010; Stat et al. 2012) and an expansion of the notion of
symbiosis in the Scleractinia to embrace microbes (Lesser
et al. 2004; Apprill et al. 2009). These symbionts can have
a striking effect on the physiology of the holobiont (Lesser
et al. 2004; Jones et al. 2008; Putnam et al. 2012), and
through changes in their genetic assemblages, can play
important roles in the capacity of corals to improve their
tolerance of environmental stress (Jones et al. 2008; Bas-
kett et al. 2009; Gates and Ainsworth 2011). In the present
study, we address the advantages to be gained by applying
IPMs to reef corals, and do so by focusing on the physi-
ology and ecology of the holobiont as an emergent prop-
erty of its interactions with symbionts. This should not be
construed to mean that variation in the genetic variants of
the Symbiodinium (or microbial flora) is unimportant in the
application of IPMs to corals, rather it recognizes the cur-
rent state of empirical research necessary to achieve this
goal. We note however that the effects of varying Symbiod-
inium type ultimately can be included in the IPM construct,
essentially in the same way as any other variable that is
important in determining demographic traits.
In 2009, we first became interested in the ecophysiol-
ogy of reef corals when we sought empirical data to inform
dynamic energy budget (DEB) models for scleractinians
(Muller et al. 2009), and to test the aspects of coral biology
that have become deeply engrained in the fabric of this dis-
cipline (e.g., depth-dependent reductions in growth rates)
(Edmunds et al. 2011). Our initial effort included data for
73 species from 126 studies, yet it provided only weak sup-
port for apparently well-established patterns of variation
in coral phenotypes among differing physical conditions
and dissimilar taxa (Edmunds et al. 2011). Given the well-
established differences we wished to test for general appli-
cation, it seemed unlikely that the results of our analyses of
compiled data reflected ecological reality. Rather, we sus-
pected that our null results were a product of methods that
differed among studies, as well as of outdated perspectives
of the ways in which biological properties might differ
among functionally dissimilar groups of corals. Outdated
perspectives are common in older literature, because, for
example, early studies overlooked the importance of seawa-
ter flow to coral biology (Patterson 1992) and the genetic
variation hidden within Symbiodinium symbionts (Pochon
and Gates 2010). We returned to compiling ecophysiologi-
cal data for scleractinians in 2010 when we sought to eval-
uate the fate of corals in warmer and more acidic seas, and
our compilation supported the hypothesis that some corals
Mar Biol
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are functioning as ecological winners while others around
them are less successful (i.e., are losers) (sensu Loya et al.
2001). Further, a trait-based analysis of coral performance
over ecological (i.e., on extant reefs) and geological time
(i.e., the fossil record) revealed that the evolutionary fate
of coral species was largely independent of taxon. We
inferred, therefore, that the fate of corals was more strongly
dependent on holobiont phenotypes than taxonomy, and
subsequently implemented a modeling effort to evaluate the
features of winning and losing corals in a phenotype-based
construct (Edmunds et al. 2014).
The present paper originated as an effort to use empiri-
cal data describing coral phenotypes to codify our general
phenotype-based model projecting present-day reefs into
a future of warmer and more acidic seas (Edmunds et al.
2014). Conceptually, we intended to select corals identified
as ecological winners or losers based on changes in their
absolute and relative abundance on contemporary reefs
between 1981 and 2010 (Edmunds et al. 2014), and then
define their phenotypes based on continuously distributed
measurements of select traits. Our objective was to use the
ecological categories and their corresponding phenotypic
properties as parameter values in a population model from
which we could evaluate emergent properties of the popu-
lation. The phenotypic properties of corals were defined by
12 traits that are widely available in peer-reviewed litera-
ture: calcification (µmol CaCO3 cm2 h1), chlorophyll-a
content (µg cm2), linear extension (mm year1), lipid con-
tent (mg cm2), mitotic index (%), polyp density (polyps
cm2), protein (mg cm2), Symbiodinium density (cells
cm2), tissue thickness (mm), total biomass (mg cm2),
dark aerobic respiration (µmol O2 cm2 h1), and maxi-
mum rate of photosynthesis (µmol O2 cm2 h1) (Table 1).
We used these data to assess phenotypic differences among
groups of corals exemplifying the functional group concept
for this taxon (e.g., Loya et al. 2001). We first contrasted
massive Porites spp. and Acropora spp. that represent the
concept of ecological winners and losers, respectively
(Loya et al. 2001; van Woesik et al. 2011), and rejected the
null hypothesis of no difference between taxa for six traits
(biomass, tissue thickness, linear extension, photosyn-
thesis, chlorophyll-a, and calcification; t > 2.222, df 9,
P 0.034); six additional traits did not differ between
these genera (t 0.917, df 44, P 0.128). The weak
phenotypic discrimination among coral taxa that have been
extensively studied for the select traits was revealed when
they were clustered based on similarities generated from
all 12 traits (Fig. 1). Hierarchical clustering was conducted
(with Primer 6 software) using Gower similarity of group
averages based on maximum standardized mean trait val-
ues across genera. Similarity profile permutations tests
(SIMPROF) identified no statistically significant clusters
(π = 1.315, P = 0.8). Even though the database had grown
4.6-fold for all records of the aforementioned traits com-
pared to our previous work (Edmunds et al. 2011; Elec-
tronic Supplementary Material 1) and is now focused on 6
genera, we were unable to show that ecologically distinct
taxa differed in terms of their multivariate phenotypes.
The inability to link empirical trait values to ecological
success in reef corals prompted a re-evaluation of the crite-
ria used to define winning and losing corals, and the utility
of linear reasoning to couple trait values with performance.
Table 1 Compilation of 12 phenotypic traits that are widely available in peer-reviewed literature for the six most commonly studied genera
Traits are calcification (µmol CaCO3 cm2 h1), chlorophyll-a content (µg cm2), linear extension (mm year1), lipid content (mg cm2), mitotic
index (%), polyp density (polyps cm2), protein (mg cm2), Symbiodinium density (×106 cells cm2), thickness (mm), biomass (mg cm2), dark
aerobic respiration (µmol O2 cm2 h1), and maximum rate of photosynthesis (µmol O2 cm2 h1) for Acropora, Goniastrea, Orbicella (for-
merly Montastraea), Montipora, Pocillopora, and Porites. Trait values (mean ± SE [n]) were used to support a cluster analysis (Fig. 1) illustrat-
ing similarities among these genera
Trait Acropora Goniastrea Orbicella Montipora Pocillopora Porites
Calcification 0.56 ± 0.12 (28) 0.41 ± 0.03 (2) 0.35 ± 0.12 (2) 0.20 ± 0.05 (6) 0.39 ± 0.04 (26) 0.29 ± 0.03 (53)
Chlorophyll-a2.23 ± 0.46 (9) 14.78 ± 4.50 (6) 10.23 ± 1.13 (30) 18.38 ± 4.99 (9) 5.08 ± 1.17 (14) 8.08 ± 1.62 (23)
Linear extension 109.5 ± 17.7 (19) 6.0 ± 0.8 (8) 6.6 ± 0.6 (17) 17.7 ± 6.8 (5) 50.0 (1) 12.1 ± 0.5 (127)
Lipid content 2.56 ± 0.71 (4) 8.00 ± 3.00 (2) 2.04 ± 0.24 (2) 4.43 ± 1.21 (3) 0.17 (1) 5.44 (1)
Mitotic index 2.68 ± 0.72 (10) 0.28 (1) 4.43 ± 0.66 (9) 1.20 ± 0.30 (2) 2.59 ± 1.81 (2) 3.52 ± 0.56 (10)
Polyp density 70.2 ± 18.3 (6) 5.3 ± 1.6 (3) 3.9 ± 1.0 (11) 158.5 (1) 71.2 ± 15.7 (5) 65.2 ± 2.8 (32)
Protein 3.70 ± 0.30 (2) 5.05 ± 0.35 92) 2.40 ± 1.36 (6) 3.83 ± 0.81 (6) 0.26 ± 0.04 (8) 2.47 ± 0.87 (8)
Symbiodinium density 6.65 ± 4.44 (32) 2.66 ± 0.90 (6) 2.69 ± 0.40 (13) 1.55 ± 0.38 (8) 0.97 ± 0.28 (6) 5.98 ± 3.41 (14)
Tissue thickness 1.60 ± 0.30 (3) 3.20 ± 0.40 (2) 0.24 ± 0.01 (11) 0.90 (1) 0.65 ± 0.25 (2) 5.26 ± 0.26 (47)
Tissue biomass 3.08 ± 0.91 (8) 16.10 (1) 7.68 ± 0.77 (24) 7.48 ± 0.48 (2) 1.83 ± 0.33 (2) 12.51 ± 2.03 (8)
Respiration 0.37 ± 0.09 (7) 0.74 ± 0.04 (2) 0.79 ± 0.15 (26) 0.82 ± 0.27 (3) 1.68 ± 0.71 (4) 0.62 ± 0.12 (8)
Photosynthesis 1.00 ± 0.11 (5) 3.24 ± 0.07 (2) 2.61 ± 0.34 (19) 3.30 ± 0.34 (3) 4.75 ± 1.69 (4) 2.55 ± 0.55 (6)
Mar Biol
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This re-evaluation identified four important constraints on
progress toward characterizing ecological success in corals,
or more generally, phenotypically characterizing ecologi-
cally meaningful functional groupings of corals:
1. Defining winners and losers based on changes in abun-
dance (percent cover or number of colonies) provides a
poor indicator of ecological performance measured in a
demographic currency.
2. The relationships between fine-scale physiological
traits, coarse-scale coral characteristics (e.g., differ-
ences among genera or morphologies), and ecological
responses are complex and nonlinear.
3. There is no theoretical construct for scleractinians to
inform a mapping of fine-scale physiological traits
onto coarse-scale coral characteristics, particularly in
the context of multivariate physical forcing.
4. Synthesis of phenotypic data for scleractinians is
impeded by a lack of more uniform methodology, com-
mon units, and effective model taxa that can be used to
generate continuously distributed values of physiologi-
cal traits.
Step 2: A demographic approach for coupling
organismic performance to ecological success in the
scleractinia
The benefits of a demographic approach to identifying
winning and losing corals
The extent to which scleractinian corals achieve ecologi-
cal success (i.e., win) or failure (i.e., lose) ultimately will
be reflected in their population dynamics. Therefore, prin-
ciples of population persistence derived from population
models (Caswell 2001) can be applied to this task. Specifi-
cally, over ecological time, populations of winning and los-
ing corals should be defined by population growth rates that
are above and below replacement, respectively. Each adult
must, on average, replace itself with one offspring during
its lifetime in order to function as a “winner.
Models of coral populations often have derived popula-
tion growth from age- or stage-structured representation
of population dynamics using (standard or modified) Les-
lie matrices, which contains information on age- or stage-
dependent survival and reproduction (e.g., Hughes 1984;
Fong and Glynn 1998, 2000; Hughes and Tanner 2000;
Edmunds and Elahi 2007). In Leslie matrices, the param-
eter defining population growth without density depend-
ence is given by the dominant eigenvalue of the matrix,
denoted as λ (Caswell 2001). Once the population achieves
a stable age/stage distribution (indicated by the eigenvector
corresponding to the eigenvalue λ), the population grows or
shrinks by a constant factor (i.e., λ) at each time interval.
In a currency that is mechanistically related to “ecological
success,” winning corals can therefore be defined rigor-
ously by λ > 1 and losing corals by λ 1 (Caswell 2001).
A demographic approach to defining ecological success
offers advantages over common measures of abundance
(like percentage cover), which are related only loosely
to demographic processes (Hughes and Tanner 2000;
Edmunds and Elahi 2007; Darling et al. 2013). Stable
coral cover can, for example, hide impending population
decline (Hughes and Tanner 2000), and categorizing corals
based on changes in cover (Loya et al. 2001; Edmunds et
al. 2014) has the potential to generate functional groupings
with equivocal ecological relevance.
The potential utility of a demographic approach to com-
paring ecological success among coral species can be seen
in other biological systems where similar approaches have
been applied. λ has a strong history as a means to evalu-
ate population performance and viability (Caswell 2001),
with examples coming from many taxa as diverse as grizzly
bears (Mace and Waller 1998), whales (Fujiwara and Cas-
well 2001), spotted owls (Noon and Biles 1990), ungulates
(Coulson et al. 2005), sea turtles (Crowder et al. 1994),
precious octocorals (Bramanti et al. 2009), Tasmanian dev-
ils (Lachish et al. 2007), and plants (Ramula et al. 2008;
Crone et al. 2011). In terrestrial plants, for example, much
demographic data are available to assess the patterns and
process of population growth. Buckley et al. (2010) synthe-
sized demographic models having both spatial and tempo-
ral replication from 50 species, with multiple populations
(2) per species and multiple matrices (2) per popu-
lation. They identified the species for which population
growth rates declined through time, as well as the sources
100
90
80
70
60
50
Acropor
a
P
ocillopor
a
Goniastre
a
Montipor
a
Montastraea
Porites
Similarity
Fig. 1 Hierarchical clustering dendrogram based on mean values for
12 traits obtained for 6 coral genera that have been reported in peer-
reviewed literature and linked to stress responses (Online Supplemen-
tary Material 1). Hierarchical clustering is based on Gower similarity
of group means of maximum standardized mean trait values across
genera (PRIMER v6; Clarke and Gorley 2006) where nodes show
similarity groupings
Mar Biol
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of variation in population growth rates among species.
Temporal variation in population growth rates was mostly
due to variation in post-seedling survival (rather than adult
fecundity), herbivory, and fire (Buckley et al. 2010). An
analysis such as that utilized by Buckley et al. (2010) could
be used to good effect with tropical reef corals, specifically
to identify ecological winners and losers in communities
exposed to disturbances such as storms, predatory sea stars,
thermal stress, and OA.
Compared to research in other systems, demographic
studies on corals are rare. Only a handful of studies have
quantified λ (e.g., Hughes 1984; Hughes and Tanner 2000;
Edmunds and Elahi 2007; Edmunds 2011; Hernández-
Pacheco et al. 2011; Madin et al. 2012b), despite long-
standing efforts to promote demographic analyses of this
important taxon (Connell 1973; Hughes and Jackson 1985;
Hughes 1996). The implications of the scarcity of studies
on the demography of scleractinian corals are now being
felt acutely as biologists focus on determining which corals
might function as winners or losers, as well as the causal
basis of these outcomes, in an era of strong effects of GCC
and OA (Hoegh-Guldberg 2012). While there are several
studies that associate ecological success with mean trait
values (Darling et al. 2012, 2013), or model the influence
of environmental and biological traits on fecundity (Madin
et al. 2012a), most efforts have favored phenomenological
links among the functional levels and have not explicitly
addressed the conditions favoring population persistence
(e.g., those involving λ).
Integral projection models (IPMs) for corals
A promising way to integrate organismal-level perfor-
mance with population-level outcomes is through an inte-
gral projection model (IPM [Easterling et al. 2000; Coul-
son 2012]). IPMs evaluate the role of continuous traits in
driving population dynamics and create the potential to
scale up the effects of GCC and OA on individual-level
performance to evaluate population-level consequences.
IPMs are an extension of discrete time, discrete age/stage
models based on the Leslie matrix. While Leslie matrices
are based on discrete classes, IPMs accommodate continu-
ous classes or states (e.g., continuously distributed size) in
a predictive framework (in discrete time, as in the Leslie
matrix). IPMs share many of the features that have made
matrix projection models popular: estimation of population
growth (λ), state-specific reproductive values, the stable
population phenotypic distribution, and identification of the
parameters to which λ is most sensitive. Furthermore, IPMs
are a better representation of transient dynamics than tradi-
tional discrete matrix models, because demographic rates
change in a gradual, rather than abrupt, manner across an
organism’s life history. IPMs also perform better for small
datasets (<300 individuals) than traditional matrix models
because they require fewer parameters to describe vital
rates of a population’s growth, which are integral in the
calculation of λ (Ramula et al. 2008). To date, IPMs have
not been applied widely to scleractinians (but see Burgess
2011; Madin et al. 2012b), or to other “corals” (i.e., octoc-
orals, Bruno et al. 2011).
We describe how IPMs can be used to identify winning
and losing corals as well as the physiological traits driving
these ecological outcomes (Fig. 2; Box 1). The IPM is a
relatively well-developed technique, and it is not our goal
to provide a comprehensive description of the theory and
mechanics of IPMs. Furthermore, the flexibility of con-
structing the IPM means that we would do it injustice if
we set about providing a simple “recipe.” Therefore, we
assume the reader has some familiarity with IPM meth-
odology and assumptions (e.g., as described in Easterling
et al. 2000; Ellner and Rees 2006, 2007; Rees and Ellner
2009; Coulson 2012); without this basic knowledge, the
flexibility of IPMs may give the wrong impression that
they are complicated. Finally, we note that any difficul-
ties involved with obtaining the necessary data for apply-
ing IPMs to corals should not detract from the importance
of assessing winning and losing corals in a demographic
framework.
The simplest representation of an IPM involves a
description of the number of individuals n(y,t + 1) at
time t + 1 with a given state y as a product of the num-
ber of individuals n(x,t) at time t with state x and a kernel
k(y,x,θ(t)) representing all possible transitions from state x
(in time t) to state y (in time t + 1) under the environment
θ(t), integrated over all states x:
The kernel, k(y,x,θ(t)), is analogous to the projection
matrix (e.g., Leslie matrix). While there is flexibility in
the mathematical definition, it is typically expressed as
the fecundity f(x,y,θ(t)) of individuals of state x producing
those of state y plus the product of the survival s(x,θ(t)) of
those in state x and the growth g(x,y,θ(t)) from state y to
state x:
θ(t) describes the environment as it affects growth,
fecundity, and survivorship. In essence, the functions
describe how individuals with states enter the popula-
tion (through birth and immigration), leave the population
(through death and emigration), and how the state of an
individual changes though time (e.g., growth from one time
step to the next causing a transition between size classes).
The functions can be generated from statistical models fit
to empirical data and, therefore can be linear, nonlinear,
(1)
n
(y,t
+
1)
=
k(y,x,θ(t))n(x,t)dx
.
(2)
k(y,x,θ(t)) =f(x,y,θ(t)) +s(x,θ(t))g(x,y,θ(t)).
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n(y, t + 1) = [k(y,x,θ(t))n(x,t)dx
Fecundity (f)
Population size (n)
Colony size at t (x)
Survival (s)
Colony size (y)
Population size (n)
n(y, t + 1) =
= [f(y,x,θ(t))* + s(x,θ(t))g(x,y,θ(t))]n(x,t)dx
θLow
θHigh
y = function(x,θ(t), a,b,c...)
Biomass (a)
Protein content (b)
Symbiodinium clade (c)
etc.
Colony size (y)
Physiological and
functional attributes
Kernel elements
and population size
distribution
IPM
}
}
}
Colony size at t (x) Colony size at t (x) Colony size at t (x)
Colony size at t+1 (y)
States (y)
• colony age
• colony size
• no. of polyps
Physiological traits
(a, b, c .... etc.)
• chlorophyll-a
• tissue biomass
• dark aerobic respiration
• protein content
• lipid content
Symbiodinium density
Symbiodinium clades
Environmental factors (θ)
• temperature
• pCO2
• storm frequency and intensity
• predator density
• depth
}
Focal Inputs
θLow
θHigh
θLow
θHigh
θLow
θHigh
P
opulation gro
wth (λ)
θLow
θHigh
Trait (a, b, c ... etc.)
Demographic analyses
}
Including
• sensitivities of λ to kernal parameters
• elasticities of λ to kernal parameters
Fig. 2 Schematic illustrating the application of an integral projec-
tion model (IPM) to corals to link holobiont physiology, individual
colony-level performance (i.e., survival, growth, and fecundity), and
population-level (demographic) outcomes (i.e., population growth,
λ) as a function of environmental factors (θ). The population is struc-
tured by colony states [y] (e.g., colony size), which vary among indi-
vidual corals. The colony state of each individual determines the indi-
vidual’s fecundity (f), growth/fission (g), and survival (s), generating
the kernel elements (k(y,x,θ(t))) of the IPM. In this example, physi-
ological traits [a, b, c, etc.], and environmental factors [θ]), determine
vital demographic rates (i.e., kernel elements) through their effects on
the colony states. Physiological traits can affect the multiple kernel
elements independently or interactively through multiple pathways.
Shown here for clarity is just the effect of attributes such as biomass,
protein content, and Symbiodinium clades on colony growth. We dis-
play the relationships in each kernel for two sets of environmental
conditions (θhigh and θlow, e.g., high and low water temperature) for
illustration, but environmental conditions can be discrete or continu-
ous. Kernel elements are used to evaluate population growth (λ), from
low population density, as a function of environmental conditions and
physiological traits (“Demographic analyses”). Additional analyses
can determine the sensitivity and elasticity of the population growth
factor to changes in the parameters defining the kernels; refer to text
and Box 1 for further details. *This term is replaced with r(x,y) in an
open population. a, b, c,.. etc., a variety of physiological traits and
functional attributes that can affect demographic rates, n number of
colonies, y size of colony at time (t) t + 1, x = size of colony at time t
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additive, or nonadditive, or derived from mechanistic mod-
els (e.g., DEBs, Edmunds et al. 2011) explicitly modeling
how energy is converted into growth and fecundity (Fig. 2).
Growth can be measured in ways most relevant to the
morphology and physiology of the study species, and can
include linear extension, change in surface area, increase in
biomass, or calcification. The fecundity kernel incorporates
all the processes from larval release to recruitment.
In the mathematical terms implementing how the envi-
ronment affects growth, fecundity, and survivorship, previ-
ous representations of IPMs (e.g., Rees and Ellner 2009)
have implemented θ(t) as stochastic environmental vari-
ation based on the distribution of data around growth,
fecundity, and survivorship relationships. To understand
the response of corals to future environmental change,
however, θ(t) might represent a predictably changing envi-
ronmental variable that influence these kernel elements.
For example, if θ(t) is a representation of OA (e.g., pCO2),
then it might alter the growth rate through time, while θ(t)
representing temperature might affect how a temperature-
dependent change in symbiont density or genetic compo-
sition influences bleaching susceptibility. Under multiple
environmental changes, θ(t) then becomes a vector where
each element represents a different aspect of the environ-
ment. To illustrate this overall approach, we provide an
example that connects coral size distribution dynamics to
OA in Box 1.
Incorporating physiology into a coral IPM
Coral physiological characteristics (Table 1), or any other
types of coral characteristics that connect environmental
change to coral colony performance, can enter into the IPM
in a number of ways (Box 1 is just one example). For phys-
iological traits that vary among populations or species, the
trait for a given population or species might drive the shape
of the IPM kernel (e.g., faster growth for corals in habi-
tats allowing faster calcification) and allow comparison of
expected coral dynamics among populations or species. In
this case, the physiological trait is a property of the whole
population (or species) and it modifies the parameters that
define the kernel elements for that population. For exam-
ple, coral populations in shallow water may have a different
genetic compliment of Symbiodinium than coral popula-
tions in deeper water. If Symbiodinium composition alters
the slope of the colony growth function (or any other func-
tion in the kernel elements), then the effects of changing
Symbiodinium composition on the dynamics of multiple
populations (or species) can be predicted.
For traits that vary continuously within populations (i.e.,
among individuals), the physiological trait can enter into
the IPM in a number of ways. The trait itself might be a
component of the state y, in addition to size, that directly
affects survival, growth, or fecundity, such that the model
captures the joint distribution of colony size and that trait
in the population. In such a case, for example, survival,
growth, or fecundity might vary depending on the popu-
lation density or genetic variation in the endosymbiotic
Symbiodinium and, therefore, would also be influenced by
interactive effects with a variety of other factors includ-
ing seawater temperature and colony size. Another way in
which the physiological trait can enter into the IPM is by
its indirect affects with colony size. For example, Madin et
al. (2012a, b) modeled a size-structured coral population
with environment-dependent reductions in calcification that
reduced skeletal density, which in turn decreased the sur-
vival of larger colonies due to dislodgment during storms
(i.e., survival was a function of colony size, given its skel-
etal density). Finally, for traits that vary both within and
among populations (as is the case for the traits in Table 1),
then a combination of the two approaches is feasible (e.g.,
dynamically following Symbiodinium density within popu-
lations as part of state y, where the maximum density might
vary with species).
How physiological characteristics are described in the
function of the IPM depends on the research question and
the extent of the basic knowledge of the physiology of
the study species. The IPM is flexible enough to handle
many different configurations of the pathways by which
physiology affects growth, survival, or fecundity, and has
the potential to consider the effects of the host and Sym-
biodinium (including genetic variation in these algae)
independently.
Some considerations in applying IPMs to corals
There are several issues that need to be considered when
applying IPMs to corals, but these issues have solutions
that render IPM approaches highly attractive for corals.
As with previous coral matrix models (Hughes and Tan-
ner 2000; Fong and Glynn 1998, 2000; Edmunds and Elahi
2007), the growth function g(x,y,θ(t)) in a coral IPM needs
to also account for fragmentation and fission that occur
in many coral species. The implication is that the growth
function will have to allow for negative growth, and the
individuals (ramets) arising from fragmentation will have
to be added to n(y,t + 1).
Another particularly important issue to consider is the
spatial scale at which inferences regarding winning and
losing corals are to be made in relation to the spatial scale
of larval dispersal. This determines whether a population is
closed (e.g., where input into the local population is linked
directly to reproductive output of the population) or open
to immigration from other sources (where local recruitment
is uncoupled from local reproductive output). The extent to
which a population is open or closed to larval input from
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other populations will determine whether fecundity needs
to be estimated, and how local fecundity is linked to local
recruitment. Previous applications of IPMs to plants and
ungulates have not included dispersal, so the study popula-
tions were considered “closed.” In contrast, at the spatial
scale of a local coral reef (i.e., 20 km [Mittelbach et al.
2001]), or a typical coral field study, most coral popula-
tions might be considered “open,” or at least partly open, to
immigration of larvae from other nearby reefs.
Most previous applications of matrix models to coral
populations assumed that recruitment into the popula-
tion was uncoupled from fecundity (Hughes and Tanner
2000; Edmunds and Elahi 2007). In such cases, the pro-
jection matrix omitted fecundity and just included transi-
tions between size classes (i.e., survival and growth) with
recruitment included as a constant (Box 1), whose value
is determined empirically in the field, and can be space-
dependent (Roughgarden et al. 1985). Madin et al. (2012a,
b) applied IPM to both open and closed coral populations
(see also Box 1). In summary, in a closed population, the
fecundity kernel needs to be estimated; in an open popu-
lation, the fecundity kernel is replaced by the recruitment
kernel, which describes the number of recruits of a given
size into the population. Importantly, the IPM framework
can describe a “semi-open” population with emigration
and immigration (Coulsen 2012), although this adjust-
ment increases the quantity of data required for the model.
Ideally, some estimate of local larval retention should be
obtained (see Burgess et al. 2014 for more details).
Obtaining data for an IPM
To prepare an IPM and use it for the purpose we propose,
it is necessary to (1) identify the traits that contribute most
to coral growth, survival, and reproduction; (2) describe
functions relating such traits to growth, survival, and repro-
duction, as well as their environmental dependencies; and
(3) calculate λ and evaluate how sensitive it is to changes
in the parameters describing the relationships between
traits and vital rates (e.g., Box 1, Online Supplementary
Material 2; see supplement of Ellner and Rees 2006 for
another example with R code). In many cases, fundamen-
tal principles of biology or the basic biology of the Scler-
actinia and their Symbiodinium symbionts can be used to
inform the choice of proximal traits that are informative
with regard to variation in growth, survival, and reproduc-
tion, and whether traits vary across populations or species,
or vary continuously within populations. For instance,
the size of coral colonies, which varies among individu-
als, is a critical feature determining whole-colony fecun-
dity (Hall and Hughes 1996), the probability of dislodge-
ment during storms (Denny et al. 1985; Massell and Done
1993; Madin and Connolly 2006), and the mass transfer
of key metabolites that can affect the availability of ener-
getic resources required for reproduction (Patterson 1992;
Hoogenboom and Connolly 2009). Likewise, it is becom-
ing increasingly clear that genetic variants of Symbiodin-
ium hosted by different individuals, populations, or species
of corals have important roles in determining the fitness of
the holobiont (Putnam et al. 2012). As we describe above,
incorporating into IPMs the physiological consequence for
the holobiont of hosting multiple, dissimilar, or changing
combinations of genetically distinct Symbiodinium spp. is
an important research objective in order to realize the full
potential of these tools. Currently, this objective is beyond
the scope of what can be accomplished with the state of the
empirical and theoretical literature.
Obtaining the data in the field necessary to support an
IPM approach requires an effort similar to that necessary
to monitor permanent areas of reef (Hughes 1996; Bur-
gess 2011; Bruno et al. 2011; Coulson 2012). One critical
difference in comparison with much of the contemporary
monitoring efforts on coral reefs is that the fate of indi-
vidual colonies needs to be recorded, rather than changes
in percent cover of species or groups of species. Monitor-
ing individual colonies is inherently more time-consum-
ing than measuring area (or percentage cover), because
it requires censusing individuals at two or more points in
time. Furthermore, delineating colonies (especially indi-
vidual ramets belonging to a clonal genotype [a genet] that
reflect fragmentation rather than sexual recruitment) will
be more difficult for some species (e.g., Acropora cervi-
cornis and Porites irregularis) than others (e.g., Orbicella
[formerly Montastraea annularis complex, and Acropora
hyacinthus). Indeed, the scarcity of demographic studies on
corals exists, in part, because of the difficulty in attributing
changes in colony size to growth, fusion, fission, recruit-
ment, and partial mortality at the data collection stage. In
a practical sense, the utility of applying IPMs to corals
will be limited to some extent by the growth form of the
study species, which influences how data collected in the
field (such as circumference, length, height, and 2D surface
area) relate to physiologically relevant metrics of size (such
biomass).
Analysis of the IPM: what can an IPM tell us?
Once each element of the IPM kernel has been defined,
numerical representation of the kernel provides a matrix of
conversion from state(s) x to state(s) y that can be treated
in a manner analogous to a Leslie matrix (e.g., Easterling
et al. 2000; Ellner and Rees 2006, 2007; Rees and Ellner
2009; Coulson 2012). Specifically, after discretizing the
continuum of possible states into bins of size Δx and ana-
lyzing the kernel across the matrix of all possible combi-
nations of x and y (defined at the midpoints of their bins),
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the resulting matrix of k(x,y,θ(t))Δx represents the transi-
tion matrix for each time step. The leading eigenvalue (λ)
of this matrix then is the population growth factor, the
corresponding right eigenvector v is the vector of size- or
physiological state-specific reproductive values, and the
corresponding left eigenvector w is the stable population
phenotypic distribution. See Online Supplementary Mate-
rial 2 for a detailed description of this analysis. The eigen-
value and eigenvectors can be interpreted in this way under
the assumption that the population has reached a stable age/
size/phenotype distribution.
Rather than using λ to evaluate “what if” scenarios
(Crone et al. 2011), or to make projections into the future
(i.e., as in traditional matrix models [Hughes and Tanner
2000; Edmunds and Elahi 2007]), the most useful infor-
mation that an IPM reveals is how changes in the relation-
ship between physiology and performance (e.g., survival,
growth, and fecundity) influence long-term population
growth rate (at low density). This is done by perturbing the
model to examine how model predictions vary as model
parameters are altered, with these procedures termed sen-
sitivity analysis (perturbations in absolute units; dλ/dpi for
each parameter pi, given by
v(y1)w(y2)/v,w
for sensitiv-
ity to the transition from state y2 to state y1) and elasticity
analysis (perturbations in proportional units; pidλ/(λdpi),
given by
k(y1,y2)v(y1)w(y2)/(v,w)
for sensitivity to the
transition from state y2 to state y1; Caswell 2001).
Perturbation analysis in previous matrix models or IPMs
in other systems suggests that the patterns of variation in
the demographic parameters contributing to λ are likely to
be more complex (e.g., Franco and Silverton 2004) than the
simple classification of corals into a few dimensions (e.g.,
Darling et al. 2012, 2013). In other words, two coral spe-
cies with similar mean colony growth rates, for example,
may have very different contributions of survival, growth,
and fecundity toward their overall λ. A demographic
approach to link variation in continuous traits to ecologi-
cal success allows for an assessment of whether species
with similar mean trait values have different population
growth rates. Furthermore, some mean trait values related
to competitive ability or stress tolerance, for example, may
be different between two species, but make a similar rela-
tive contribution toward λ in both species (Franco and Sil-
verton 2004; Coulson et al. 2005). Previous analyses on
Soay sheep and Yellowstone wolves, for example (Coul-
son et al. 2010, 2011), show that a wide range of popula-
tion responses is possible depending on which parameter
is perturbed. Furthermore, depending on which parameter
is influenced by environmental change, almost any type of
population change can occur. With Yellowstone wolves,
for example (Coulson et al. 2011), the population growth
rate was more sensitive to changes in the shape and varia-
tion in the growth and trait inheritance function than of the
survival and recruitment function. Furthermore, altering
the mean environment had greater population-level con-
sequences than changing the variability in environmental
conditions. Sensitivity and elasticity analyses can be more
useful at informing management decisions because, as
opposed to the predictions of population numbers that try
to forecast the future, such analyses identify which demo-
graphic processes are most important to the future, and
therefore where management efforts might be most effec-
tive (Crouse et al. 1987; Crone et al. 2011).
IPMs allow questions like “How are population dynam-
ics influenced by reductions in calcification rate” to be
addressed (e.g., Madin et al. 2012b; Box 1), which clearly
is relevant to evaluating the ecosystem-level consequences
of OA. Reductions in calcification rate can reduce skeletal
density and increase the vulnerability of larger colonies to
dislodgment during storms (Madin et al. 2012b). Addition-
ally, depressed calcification also reduces colony growth
rates, which in turn reduces survival and reproductive rate,
since colonies will be smaller, less fecund, and remain in
more vulnerable size classes for longer than under normal
growth rates (Madin et al. 2012b).
Codifying the construct and future research
We do not present novel theory or methods, but instead
advocate the application of emerging quantitative
approaches from other systems (Crone et al. 2011; Coul-
son 2012) to scleractinian corals. We have been motivated
in this effort by the striking changes that have taken place
on tropical reefs, specifically leading to the widespread
reduction in cover of scleractinian corals (Bruno and Selig
2007; Déath et al. 2012), as well as reductions in coral lin-
ear extension, potentially as a consequence of increased
seawater temperature and OA (Déath et al. 2009). These
changes have, in part, fueled a growing emphasis on iden-
tifying the winners and losers among the coral fauna on
contemporary and future reefs in warmer and more acidic
seas (Fabricius et al. 2011). This emphasis has been char-
acterized by limited progress in evaluating the causal basis
of ecological success or failure among coral taxa (Loya
et al. 2001), and therefore provides a compelling context
within which new approaches can be proposed. It is widely
accepted that “weedy” corals will fare better than “non-
weedy” corals on the reefs of tomorrow (Knowlton 2001),
and that thermal resilience will be critical for survival in a
warmer future (Brown and Cossins 2011; Edmunds et al.
2014). These traits, however, have not been evaluated in the
context of impacts on long-term demography such as the
population growth factor λ, nor have they been evaluated
for relative importance against one another. We advocate
the application of a demographic approach, where IPMs are
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just one example [see de Roos and Persson (2012) for other
examples linking individual-level process to population
dynamics] that couple trait-based analyses to demographic
approaches for scleractinian corals, and suggest it can serve
as an effective template for further research. We do not
imply this is the only template that can advance studies of
the causal basis of winning and losing among scleractin-
ian corals on contemporary reefs. Rather, we propose that a
demographic approach is essential to overcome the impasse
to progress in coupling organismic performance to popula-
tion success under future climate change. Given the daunt-
ing prospect of collecting the empirical data necessary to
prepare IPMs for reef corals, it is clear that properly under-
standing the mechanistic basics of future coral community
structure remains difficult and represents a topic where
shortcuts are unlikely to reveal profoundly useful discover-
ies. Coral reef biologists will need to rise to this challenge
in order to make robust progress toward understanding the
future of coral reefs. Such progress has been clearly dem-
onstrated in other biological systems, and there is reason to
expect this success can be transferable to coral reefs.
We hope that by identifying the lack of existing data as
an impediment to illustrating our proposed framework with
an empirical example, we can emphasize that there is much
work to be done in the future. To advance a demographic
approach, we recommend that experimental investigations
of scleractinian corals should focus on three themes:
I. Given the complexity of the hierarchical studies we are
advocating, it will be increasingly important to focus
initial efforts on coral species for which comprehen-
sive data can be obtained. The construction of multi-
factorial analyses of response variables coupled to λ
is exceptionally challenging and would benefit from a
major research initiative supported through different
laboratories. The judicious selection of study species
may facilitate access to a large quantity of legacy data
that could accelerate progress in the construct illus-
trated herein. As model species become better studied,
the taxonomic breadth of the analyses can be expanded
to test other species for traits favoring greater capac-
ity to respond in favorable ways to environmental chal-
lenges.
II. Our perusal of the literature in support of Step 1 of this
paper underscored the difficulty faced with legacy data.
Some of the limitations associated with these data can
be solved by careful attention to measurement units
and appropriate normalization. We have found data on
a percentage scale among the most difficult to combine
in synthetic analyses, and for this reason discourage
the use of this scale. Associated with the quality of data
that can be mined from legacy studies is the problem
of accessing records, and we therefore recommend
the establishment of a global open-access database for
coral physiological data (e.g., www.coraltraits.org).
III. We believe the approach we advocate has the potential
to advance the identification of demographically suc-
cessful taxa among the scleractinian fauna of contem-
porary coral reefs. This process is critical if we are to
understand in what form the reefs of the future will
exist, and what functional attributes will characterize
the ecological goods and services provided by these
ecosystems. The potential of this approach will only be
realized if physiological studies are designed with an
eye to inform the causal basis of demographic rates.
Box 1
Elements of an IPM for corals
Here, we provide an example functional form for fitting
data to construct a coral IPM. This example is included
for illustration, and the exact functional form of the IPM
might vary with the coral and environmental factor(s)
under consideration. First, we indicate how a basic coral
IPM can be constructed for a stable environment, and then
indicate how this model might extend to include a variable
environmental factor. In Electronic Supplementary Mate-
rial 2, we indicate the numerical tools for analyzing such
an IPM.
Basic IPM
A coral IPM requires data relating colony size in 1 year
to size (through growth, stasis, or shrinkage) and survival
probability and contribution of offspring to the popula-
tion in the following year (Eq. 2). Growth and mortality
relationships can be calculated by measuring survival and
changes in colony size in consecutive years. Growth is cap-
tured best with a power function because it is multiplica-
tive (Fig. 3a), which also means that the IPM will operate
more effectively on log-transformed size data, resulting in
a linear function for the probability of growing from x to y
during the year:
Size-independent survival processes can be captured
as probabilities with error, b + ε (dashed line, Fig. 3b),
whereas size-dependent survival processes can be captured
as a logistic function (solid curve, Fig. 3b). Combining the
two gives
g
(x,y)
=
1
σ2π
e(y(mx+c))
2
2σ2
s(x)=(b+ε) logit1(mx +c+ε).
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while colony fecundity is typically a function of size (i.e.,
number of polyps; Hall and Hughes 1996), many coral spe-
cies are broadcast spawners, and so contributions to the
recruitment from inside and outside the population are dif-
ficult to estimate. If modeling the population as an open
system where recruitment is constant independent of the
local population, the IPM intrinsic growth rate (λ) meas-
ured in the absence of this recruitment will indicate popula-
tion decline, because the population has no intrinsic capac-
ity to sustain itself. This rate of population decline can be
used as a common currency when comparing environmen-
tal change scenarios. However, if the outside recruitment
rate q can be estimated and/or expected to be associated
with environment, then the population can be projected
through time until it reaches a stable growth factor and size
distribution for different environmental scenarios, using
Then, the relative cover (i.e., the sum of colony areas)
of populations for different scenarios can be contrasted,
providing another common currency. This relative cover
approach is problematic, because it compares populations
that are limited by recruitment, but does not incorporate
density-dependent processes, such as competition.
When modeling the population as a closed system
where all recruitment depends on the local popula-
tion size, the IPM intrinsic growth rate is a currency
for the propensity for the populations to recover from
low abundance such that density-dependent factors are
negligible (e.g., following a storm, bleaching episode,
or COTS outbreak). This definition of λ is equivalent
to population (or engineering) resilience (Madin et al.
2012a, b) and makes no assumptions about the onset to
density-dependent processes as space on the reef satu-
rates. Closed system modeling can be justified if inter-
connected populations all tend to occupy similar habi-
tats and environmental changes operate at scales larger
than the meta-population, and therefore affect all popu-
lations similarly. In this case, a closed meta-population
model will provide an approximation for local dynamics.
Assuming a constant environment, an individual’s con-
tribution to recruitment q (recruits per unit colony area)
can then be varied until the IPM stable size distribution
(first eigenvector) best fits the empirical size distribution
(Fig. 3c, d).
r
(x,y)=
qx if x>recruitment size and y
recruitment size
0 if xrecruitment size
Fig. 3 Example relationships
describing the ways by which
size translates into organismic
and demographic properties
required in the preparation of
an IPM: a growth, b survival,
c population density, and d
recruitment
Size at time t
Size at time t + 1
-3
-2
-1
0
1
-3
-2
-1
0
1
a
Size, log
Survival p
-3
-2
-1
0
1
0.0
0.2
0.4
0.6
0.8
1.0
b
Size, log
Density
-3
-2
-1
0
1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
c
Size, log
Recruits per m2 coral
-3
-2
-1
0
1
0
5
10
15
20
25
30 d
Mar Biol
1 3
Once the recruitment parameter q has been estimated,
the stable population growth factor (λ) can be calculated.
In reality, recruitment within most coral populations will
lie between the extremes of complete independence or
dependence on local demography. Altering the recruitment
function accordingly can model such a system where the
data are available for parameterization.
Integrating environmental effects on physiological traits
Environmental variables (e.g., ocean pH) that influence
physiological traits (e.g., calcification and cellular chemis-
try) can be manipulated experimentally to determine their
effects on different demographic rates (e.g., growth, mortal-
ity, and fecundity). In some cases, the effect of environment
on demographic rates can be estimated mechanistically
(e.g., storm intensity on mechanical survival probability,
Madin and Connolly 2006). Mechanistic effects are pref-
erable, because they can be expected to operate similarly
in novel environments (i.e., environments not considered in
manipulative experiments) (Kearney et al. 2010).
For an example, we explore the effects of decreasing
calcification rates in the future, a physiological trait that is
responsive to increasing in sea surface temperature (SST)
and decreasing aragonite saturation state (Ωarag) due to OA
(Anthony et al. 2008). For brevity, we illustrate the IPM
approach using one of the scenarios modeled by Madin et
al. (2012a), in which declining calcification impacts growth
rate (i.e., material density is not affected, and therefore the
mechanical integrity of coral skeleton and reef substrate
is constant). Figure 4a shows how calcification rate is
expected to change for two existing relationships of calcifi-
cation with SST and Ωarag (Anthony et al. 2008; Silverman
et al. 2009), at least based on existing estimates of SST and
Ωarag for future pCO2 stabilization scenarios (Cao and Cal-
deira 2008). Relative changes in future calcification rate are
applied directly to mean growth probability (i.e., the inter-
cept c in the growth function):
The population growth factor (λ) is plotted for the two coral
calcification response scenarios as a function of stabilized
atmospheric pCO2 (Fig. 4b). Confidence intervals (shaded
bands around the lines) reflect many sources of uncertainty,
primarily the fitted recruitment parameter q. This coral species
is predicted to become a “loser” for the red-colored calcifica-
tion response scenario when pCO2 levels reach approximately
500 ppm; it is predicted to remain a “winner” on average for
the black-colored calcification response scenario.
Acknowledgments This work was conducted as part of the “Tropi-
cal coral reefs of the future: modeling ecological outcomes from the
analyses of current and historical trends” Working Group (to RDG
and PJE) funded by NSF (Grant #EF-0553768), the University of
California, Santa Barbara, and the State of California. We acknowl-
edge additional support from NSF (OCE 04-17413 and 10-26851 to
PJE, DEB 03-43570 and 08-51441 to PJE, OCE 07-52604 to RDG),
and the US EPA (FP917199 to HMP). This is a contribution of the
Moorea Coral Reef, Long-Term Ecological Research site, SOEST
(9205), Hawaii Institute of Marine Biology (1063), and California
State University, Northridge (221).
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... Together, the vitality of individuals (i.e., growth, maintenance, and reproduction rates) determines the population growth rate after a coral-bleaching event. Therefore, life history and demographic models may best connect coral physiology with ecology (Cant et al., 2020;Edmunds et al., 2014). There is also considerable value in collaborative efforts across regions and ocean basins. ...
... Quantifying the effects of thermal stress on demography will enhance the predictability of population and community trajectories. (Cant et al., 2020;Cornwell et al., 2021;Edmunds et al., 2014) Heritability Genes to populations How heritable is acclimatization and what is the role of epigenetics in shaping adaptation to thermal stress? ...
... Such tools include surveys using drones and autonomous underwater vehicles, and recent advances in photogrammetry (e.g., structure-frommotion) to generate orthomosaics, which will be especially powerful when combined with developments in robotics, artificial intelligence, and machine learning (Dornelas et al., 2019;Roach et al., 2021b;Yuval et al., 2021). If collected repeatedly, these high-resolution threedimensional reef-scale archives can provide population-level vital rates used in demographic models to make predictions of population trajectories under different environmental conditions (Cant et al., 2020;Edmunds et al., 2014). These population trajectories will provide further insight when placed into context with bio-geophysical oceanographic models, some of which are readily accessible (Thompson et al., 2018). ...
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The global impacts of climate change are evident in every marine ecosystem. On coral reefs, mass coral bleaching and mortality have emerged as ubiquitous responses to ocean warming, yet one of the greatest challenges of this epiphenomenon is linking information across scientific disciplines and spatial and temporal scales. Here we review some of the seminal and recent coral-bleaching discoveries from an ecological, physiological, and molecular perspective. We also evaluate which data and processes can improve predictive models and provide a conceptual framework that integrates measurements across biological scales. Taking an integrative approach across biological and spatial scales, using for example hierarchical models to estimate major coral-reef processes, will not only rapidly advance coral-reef science but will also provide necessary information to guide decision-making and conservation efforts. To conserve reefs, we encourage implementing mesoscale sanctuaries (thousands of km2 ) that transcend national boundaries. Such networks of protected reefs will provide reef connectivity, through larval dispersal that transverse thermal environments, and genotypic repositories that may become essential units of selection for environmentally diverse locations. Together, multinational networks may be the best chance corals have to persist through climate change, while humanity struggles to reduce emissions of greenhouse gases to net zero.
... Although limited, the application of demographic theory within coral research is not a novel concept (Edmunds et al. 2014). Structured population models offer insight into how the state of individuals (typically their size, age, or developmental stage ;Caswell 2001) arbitrates their survival and reproduction and how these fitness components in turn shape the overall responses of populations to local biotic and abiotic conditions (Box 1; Benton et al. 2006 Understanding how patterns in the survival, progression (i.e. ...
... The repeated survey of individual colonies, therefore, allows for documenting how the size of individuals regulates their survival, development, and reproductive contribution over time, and how this is shaped by changing environmental conditions (Boyce et al. 2006;Ehrlén et al. 2016). By condensing these temporal observations from across multiple tagged colonies, population ecologists can then explore how the individual-level vital rates subsequently underpin the characteristics of populations and define their capacity for tolerating various environments Crucially, although more time consuming, demographic approaches transcend the correlative techniques previously used for evaluating the viability of coral populations and communities, and for predicting their resilience to future climatic stressors (Edmunds et al. 2014;Edmunds and Riegl 2020). State-structured demographic approaches enable the quantification of the relationship between environmental conditions and population-level characteristics. ...
... Complexities in the modelling approaches used to explore the dynamics of natural populations have resulted in these techniques remaining largely overlooked within coral research (Edmunds et al. 2014). Indeed, parametrising the demographic models needed to quantify population characteristics requires considerable amounts of data (Ellner et al. 2002). ...
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Coral communities are threatened by an increasing plethora of abiotic and biotic disturbances. Preventing the ensuing loss of coral coverage and diversity calls for a mechanistic understanding of resilience across coral species and populations that is currently lacking in coral reef science. Assessments into the dynamics of coral populations typically focus on their long-term (i.e. asymptotic) characteristics, tacitly assuming stable environments in which populations can attain their long-term characteristics. Instead, we argue that greater focus is needed on investigating the transient (i.e. short-term) dynamics of coral populations to describe and predict their characteristics and trajectories within unstable environments. Applying transient demographic approaches to evaluating and forecasting the responses of coral populations to disturbance holds promise for expediting our capacity to predict and manage the resilience of coral populations, species, and communities.
... The physiological and biochemical data can enter demographic models either at the individual level or at the level of populations or species (Edmunds et al. 2014). The design of the data collection depends on the data type needed, and this in turn depends on the question at hand. ...
... In many cases, population-or species-level data will be sufficient. Nevertheless, both between-and within-individual variation may affect vital rates and individual-level data are needed if those effects are of interest (Edmunds et al. 2014). In such a case, the sampling design should aim to accurately quantify within-species biomarker variability in space and time, with random selection of individuals and their repeated sampling over time, ideally over their lifetimes, being the preferred sampling methods (Hõrak and Cohen 2010;Violle et al. 2012). ...
... Biochemistry can also provide useful information in invertebrates. In corals, for example, biochemical data, including calcification and content of chlorophyll, lipids, and proteins, have been proposed to inform demographic modelling of ecological performance of coral reefs (Edmunds et al. 2014). ...
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Physiological and biochemical traits hold great promise for demographic research as potential proxies (biomarkers) of various biotic and environmental variables that determine individual fitness and ultimately demographic rates. Integrating such biomarkers into demographic models can thus provide insights into drivers of population dynamics or increase predictive power of the models by refining estimation of vital rates. Biomarkers also represent promising means to characterise population structure and dynamics on much shorter time-scales compared to classical demographic approaches. Functional traits further emerge as direct targets of conservation efforts directed towards conserving functional diversity. Yet, biomarkers and functional traits remain underutilised in demography and population ecology, indicating that their benefits still await wider recognition. This chapter briefly reviews the most prominent physiological and biochemical traits (e.g. metabolic rates, hormones, oxidative stress markers, telomeres) that may be of interest in animal and plant demographic research, including the methods for collection, storage, and analysis, and the criteria to be met before the trait is validated as a biomarker. Hopefully, this effort will stimulate further integration of physiological and biochemical data into demographic framework.
... These models, however, use discrete life stage or size classes and have been recently improved by the development of integral projections models (IPMs) that allow more flexibility in model construction and use continuous sizes-thus avoiding sensitivities stemming from the choice of discrete life stages or size classes (Coulson, 2012;Easterling et al., 2000;Edmunds et al., 2014;Ellner & Rees, 2006;Merow et al., 2014;Rees et al., 2014). Given their strengths, IPMs are being increasingly implemented in studies of coral populations and have been instrumental in: (a) studying coral population responses to the impacts of disease (Bruno et al., 2011), (b) detecting recovery from disturbances (Kayal et al., 2018), (c) determining responses to restoration (Montero-Serra et al., 2018), (d) examining population responses to environmental changes (Cant et al., 2020;Edmunds et al., 2014;Elahi et al., 2016;Madin et al., 2012) and (e) assessing the viability and dynamics of populations (Precoda et al., 2018;Scavo Lord et al., 2020). ...
... These models, however, use discrete life stage or size classes and have been recently improved by the development of integral projections models (IPMs) that allow more flexibility in model construction and use continuous sizes-thus avoiding sensitivities stemming from the choice of discrete life stages or size classes (Coulson, 2012;Easterling et al., 2000;Edmunds et al., 2014;Ellner & Rees, 2006;Merow et al., 2014;Rees et al., 2014). Given their strengths, IPMs are being increasingly implemented in studies of coral populations and have been instrumental in: (a) studying coral population responses to the impacts of disease (Bruno et al., 2011), (b) detecting recovery from disturbances (Kayal et al., 2018), (c) determining responses to restoration (Montero-Serra et al., 2018), (d) examining population responses to environmental changes (Cant et al., 2020;Edmunds et al., 2014;Elahi et al., 2016;Madin et al., 2012) and (e) assessing the viability and dynamics of populations (Precoda et al., 2018;Scavo Lord et al., 2020). The IPM framework allows for a simple and flexible incorporation of demographic processes relative to the size of coral colonies. ...
... As we did not observe mortality among the largest colonies in this study (only one colony with a diameter larger than 10 cm, which is 2.3 on natural log scale, went through whole-colony mortality), the logistic regressions of survival can reach the asymptote of 1, effectively predicting immortality (Merow et al., 2014). Therefore, to limit this implausible biological condition, we introduced a size-independent mortality probability of 1% following Edmunds et al. (2014) andPrecoda et al. (2018). ...
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Increases in the frequency and intensity of acute and chronic disturbances are causing declines of coral reefs worldwide. Although quantifying the responses of corals to acute disturbances are well documented, detecting subtle responses of coral populations to chronic disturbances are less common, but can also result in altered population and community structures. We investigated the population dynamics of two key reef‐building Merulinid coral species, Dipsastraea favus and Platygyra lamellina, with similar life‐history traits, in the Gulf of Eilat and Aqaba, Red Sea from 2015 – 2018, to assess potential differences in their population trajectories. Demographic processes, which included rates of survival, growth, reproduction, and recruitment were used to parameterize integral projection models and estimate population growth rates and the likely population trajectories of both coral species. The survival and reproduction rates of both D. favus and P. lamellina were positively related to coral‐colony size, and elasticity analyses showed that large colonies most influenced population dynamics. Although both species have similar life‐history traits and growth morphologies and are generally regarded as ‘stress‐tolerant’, the populations showed contrasting trajectories — D. favus appears to be increasing whereas P. lamellina appears to be decreasing. As many corals have long life‐expectancies, the process of local and regional decline might be subtle and slow. Ecological assessments based on total living coral coverage, morphological groups, or functional traits, might overlook subtle, species‐specific trends. However, demographic approaches capable of detecting subtle species‐specific population changes can augment ecological studies and provide valuable early‐warning signs of decline before major coral loss becomes evident.
... Our primary goal was to develop a model that captured the spatiotemporal dynamics of community composition in coral reefs as component coral and algal species responded to inter-species competitive interactions and external disturbances. Our trait-based and demographic approaches provided a combination that yielded better predictions and a better understanding of coral ecosystem dynamics relative to single-component models (Edmunds et al., 2014;Salguero-Gó mez et al., 2018;Violle et al., 2007). The spatial structure we imposed-a grid of 1 cm 2 agents that collectively comprise a sizeable reef (tens of m 2 ) as inspired by previous models (e.g. ...
... population percentage cover, recruitment rates, colony size distributions, rugosity) emerge from agent-related processes happening at the lower scales (e.g. conversion of a barren ground agent to a new coral recruit agent, dislodgement of a single colony); (ii) a functional trait-based approach (Madin et al., 2016b;McGill et al., 2006)-species diversity and dynamics are determined by mechanistically linked trait-process associations; (iii) a demographic approach (Edmunds et al., 2014;Tuljapurkar and Caswell, 1997)-the dynamics of a population depends on its demographic structure (e.g. colony size distribution) because the size of a colony influences its capacity to reproduce, compete and resist disturbances. ...
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The complexity of coral-reef ecosystems makes it challenging to predict their dynamics and resilience under future disturbance regimes. Models for coral-reef dynamics do not adequately account for the high functional diversity exhibited by corals. Models that are ecologically and mechanistically detailed are therefore required to simulate the ecological processes driving coral reef dynamics. Here, we describe a novel model that includes processes at different spatial scales, and the contribution of species’ functional diversity to benthic-community dynamics. We calibrated and validated the model to reproduce observed dynamics using empirical data from Caribbean reefs. The model exhibits realistic community dynamics, and individual population dynamics are ecologically plausible. A global sensitivity analysis revealed that the number of larvae produced locally, and interaction-induced reductions in growth rate are the parameters with the largest influence on community dynamics. The model provides a platform for virtual experiments to explore diversity-functioning relationships in coral reefs.
... Therefore, the physiological patterns mediating coral growth under OA and leading to different growth responses are not fully understood, despite an extensive body of literature characterizing physiological changes in corals under OA. Additionally, OA studies characterizing coral growth in colony surface area and volume are few, despite the advantage of providing ecologically relevant information of realized growth and occupied space (Edmunds, 2007;Pratchett et al., 2015) that could inform coral demography projections (Edmunds et al., 2014;Kayal et al., 2019) and modeling of OA impacts across large spatio-temporal scales (Evensen et al., 2021). ...
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Ocean acidification (OA) poses a major threat to calcifying organisms such as reef-building corals, typically leading to reduced calcification rates. Mechanisms to compensate the effects of OA on coral growth may, however, involve processes other than calcification. Yet, the physiological patterns mediating coral growth under OA are not fully understood, despite an extensive body of literature characterizing physiological changes in corals under OA. Therefore, we conducted a three-month laboratory experiment with six scleractinian coral species (Acropora humilis, Acropora millepora, Pocillopora damicornis, Pocillopora verrucosa, Porites cylindrica, and Porites lutea) to assess physiological parameters that potentially characterize growth (calcification, volume, and surface area), maintenance (tissue biomass, and lipid and protein content), and cellular stress (apoptotic activity) response under ambient (pH 7.9) and low pH (pH 7.7). We identified genus- and species-specific physiological parameters potentially mediating the observed growth responses to low pH. We found no significant changes in calcification but species showed decreasing growth in volume and surface area, which occurred alongside changes in maintenance and cellular stress parameters that differed between genera and species. Acropora spp. showed elevated cellular stress and Pocillopora spp. showed changes in maintenance-associated parameters, while both genera largely maintained growth under low pH. Conversely, Porites spp. experienced the largest decreases in volume growth but showed no major changes in parameters related to maintenance or cellular stress. Our findings indicate that growth- and calcification-related responses alone may not fully reflect coral susceptibility to OA. They may also contribute to a better understanding of the complex physiological processes leading to differential growth changes of reef-building corals in response to low pH conditions.
... They also exhibit various colony morphologies, such as massive, branching, columnar, laminar, corymbose, and digitate (Veron, 2000). The different colony morphologies of scleractinians often show diverse symbiodiniaceae density (Thornhill et al., 2011), corallite width (Loya et al., 2001), and tissue thickness (Edmunds et al., 2014). Importantly, these differences may strongly influence coral nutritional ecology parameters, such as energy storage (Pupier et al., 2021), trophic strategies (Radice et al., 2019;Conti-Jerpe et al., 2020), and even response mechanisms to environmental changes (Loya et al., 2001;Hughes et al., 2017), which can affect the evolution of coral communities. ...
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Reef-building corals present various colony morphologies that may greatly influence their nutritional ecology. Fatty acids (FAs) and lipids are important components of corals and have been increasingly used to research the nutritional ecology of corals. In this study, we examined the symbiodiniaceae density, corallite area, total lipid content, and FAs composition of 14 species of corals with different colony morphologies. The results showed that the different colony morphology of coral was significantly correlated with the corallite area but not with the symbiodiniaceae density. Massive corals, with a large corallite area (7.16 ± 6.29 mm ² ), could ingest a high quantity of food, leading to high levels of total lipid content and unsaturated FAs [particularly n-6 polyunsaturated FAs (PUFAs) and monounsaturated FAs]. For branching corals, the total lipid content and saturated FAs (SFAs, 16:0 and 18:0) were significantly positively correlated with the Symbiodiniaceae density, indicating that branching corals are predominantly autotrophic. Moreover, compared with healthy corals, bleached corals consume larger amounts of stored energy (such as lipids and SFAs) to maintain their normal physiological functions. Although bleached corals may obtain PUFAs from heterotrophic assimilation or biosynthesize, the efficiency is too low to sufficiently replenish essential PUFAs in a short time. Overall, massive corals with more initial total lipid content and PUFAs exhibit an advantage under adverse environmental conditions.
... Notably, M. verrilli and P. verrucosa are also known for their lower Symbiodinium density (Edmunds et al., 2014;Putnam & Edmunds, 2011, Coral Trait Database), which may support their high photosynthetic rates. The distinct photosynthetic rates among coral taxa might arise from the different physiological and ecological attributes TA B L E 2 Estimates and 95% credible intervals for fitted parameters based on Bayesian linear models estimating calcification, respiration, and photosynthesis rates according to colony size for six coral species Notes: The coefficients α and β are calculated as metabolic rate = S A , where S A is the coral surface area (cm 2 ) and the metabolic rate is expressed in (mg h −1 ). ...
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Coral reefs provide a range of important services to humanity, which are underpinned by community-level ecological processes such as coral calcification. Estimating these processes relies on our knowledge of individual physiological rates and species-specific abundances in the field. For colonial animals such as reef-building corals, abundance is frequently expressed as the relative surface cover of coral colonies, a metric that does not account for demographic parameters such as coral size. This may be problematic because many physiological rates are directly related to organism size, and failure to account for linear scaling patterns may skew estimates of ecosystem functioning. In the present study, we characterize the scaling of three physiological rates - calcification, respiration, and photosynthesis - considering the colony size for six prominent, reef-building coral taxa in Mo'orea, French Polynesia. After a seven-day acclimation period in the laboratory, we quantified coral physiological rates for three hours during daylight (i.e., calcification and gross photosynthesis) and one hour during night light conditions (i.e., dark respiration). Our results indicate that area-specific calcification rates are higher for smaller colonies across all taxa. However, photosynthesis and respiration rates remain constant over the colony-size gradient. Furthermore, we revealed a correlation between the demographic dynamics of coral genera and the ratio between net primary production and calcification rates. Therefore, intraspecific scaling of reef-building coral physiology not only improves our understanding of community-level coral reef functioning but it may also explain species-specific responses to disturbances.
... Further, multivariate analyses reveal that community structure at these locations is now very similar to that prevailing before the COTS outbreak, indicating that the coral community on the fore reef of Moorea remains remarkably resilient . However, the threats of longer-term drivers have not dissipated, and a program of microcosm-to-mesocosm-to-in situ experiments has been ongoing in the MCR research program to quantity these effects and reveal underlying mechanisms (Holbrook et al. 2008, Comeau et al. 2014, Edmunds et al. 2014b, 2016b. ...
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Ecosystems are changing in complex and unpredictable ways, and analysis of these changes is facilitated by coordinated, long‐term research. Meeting diverse societal needs requires an understanding of what populations and communities will be dominant in 20, 50, and 100 yr. This paper is a product of a synthesis effort of the U.S. National Science Foundation funded Long‐Term Ecological Research (LTER) network addressing the LTER core research area of populations and communities. This analysis revealed that each LTER site had at least one compelling story about what their site would look like in 50 or 100 yr. As the stories were prepared, themes emerged, and the stories were grouped into papers along five themes for this special issue: state change, connectivity, resilience, time lags, and cascading effects. This paper addresses the resilience theme and includes stories from the Baltimore (urban), Hubbard Brook (northern hardwood forest), Andrews (temperate rain forest), Moorea (coral reef), Cedar Creek (grassland), and North Temperate Lakes (lakes) sites. The concept of resilience (the capacity of a system to maintain structure and processes in the face of disturbance) is an old topic that has seen a resurgence of interest as the nature and extent of global environmental change have intensified. The stories we present here show the power of long‐term manipulation experiments (Cedar Creek), the value of long‐term monitoring of forests in both natural (Andrews, Hubbard Brook) and urban settings (Baltimore), and insights that can be gained from modeling and/or experimental approaches paired with long‐term observations (North Temperate Lakes, Moorea). Three main conclusions emerge from the analysis: (1) Resilience research has matured over the past 40 yr of the LTER program; (2) there are many examples of high resilience among the ecosystems in the LTER network; (3) there are also many warning signs of declining resilience of the ecosystems we study. These stories highlight the need for long‐term studies to address this complex topic and show how the diversity of sites within the LTER network facilitates the emergence of overarching concepts about this important driver of ecosystem structure, function, services, and futures.
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The current exposure of species assemblages to high environmental variability may grant them resilience to future increases in climatic variability. In globally threatened coral reef ecosystems, management seeks to protect resilient reefs within variable environments. Yet, our lack of understanding for the determinants of coral population performance within variable environments hinders forecasting the future reassembly of coral communities. Here, using Integral Projection Models, we compare the short- ( i.e. , transient) and long-term ( i.e. , asymptotic) demographic characteristics of tropical and subtropical coral assemblages to evaluate how thermal variability influences the structural composition of coral communities over time. Exploring spatial variation across the dynamics of functionally different competitive, stress-tolerant, and weedy coral assemblages in Australia and Japan, we show that coral assemblages trade-off long-term performance for transient potential in response to thermal variability. We illustrate how coral assemblages can reduce their susceptibility towards environmental variation by exploiting volatile short-term demographic strategies, thus enhancing their persistence within variable environments. However, we also reveal considerable variation across the vulnerability of competitive, stress-tolerant, and weedy coral assemblages towards future increases in thermal variability. In particular, stress-tolerant and weedy corals possess an enhanced capacity for elevating their transient potential in response to environmental variability. Accordingly, despite their current exposure to high thermal variability, future climatic shifts threaten the structural complexity of coral assemblages, derived mostly from competitive coral taxa within highly variable subtropical environments, emulating the degradation expected across global coral communities.
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Rising atmospheric carbon dioxide (CO 2), primarily from human fossil fuel combustion, reduces ocean pH and causes wholesale shifts in seawater car-bonate chemistry. The process of ocean acidification is well documented in field data, and the rate will accelerate over this century unless future CO 2 emissions are curbed dramatically. Acidification alters seawater chemical spe-ciation and biogeochemical cycles of many elements and compounds. One well-known effect is the lowering of calcium carbonate saturation states, which impacts shell-forming marine organisms from plankton to benthic molluscs, echinoderms, and corals. Many calcifying species exhibit reduced calcification and growth rates in laboratory experiments under high-CO 2 conditions. Ocean acidification also causes an increase in carbon fixation rates in some photosynthetic organisms (both calcifying and noncalcifying). The potential for marine organisms to adapt to increasing CO 2 and broader implications for ocean ecosystems are not well known; both are high priorities for future research. Although ocean pH has varied in the geological past, paleo-events may be only imperfect analogs to current conditions.
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Most organisms show substantial changes in size or morphology after they become independent of their parents and have to find their own food. Furthermore, the rate at which these changes occur generally depends on the amount of food they ingest. In this book, André de Roos and Lennart Persson advance a synthetic and individual-based theory of the effects of this plastic ontogenetic development on the dynamics of populations and communities. De Roos and Persson show how the effects of ontogenetic development on ecological dynamics critically depend on the efficiency with which differently sized individuals convert food into new biomass. Differences in this efficiency--or ontogenetic asymmetry--lead to bottlenecks in and thus population regulation by either maturation or reproduction. De Roos and Persson investigate the community consequences of these bottlenecks for trophic configurations that vary in the number and type of interacting species and in the degree of ontogenetic niche shifts exhibited by their individuals. They also demonstrate how insights into the effects of maturation and reproduction limitation on community equilibrium carry over to the dynamics of size-structured populations and give rise to different types of cohort-driven cycles.