linking physiology, temperature, and recruitment: a
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University of South Carolina University of South Carolina
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Theses and Dissertations
2018
Linking Physiology, Temperature, And Recruitment: A Classic Linking Physiology, Temperature, And Recruitment: A Classic
Competitive Story Rewritten By Climate Competitive Story Rewritten By Climate
Maeve Kerrigan Snyder University of South Carolina
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Recommended Citation Recommended Citation Snyder, M. K.(2018). Linking Physiology, Temperature, And Recruitment: A Classic Competitive Story Rewritten By Climate. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/4691
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LINKING PHYSIOLOGY, TEMPERATURE, AND RECRUITMENT: A CLASSIC COMPETITIVE STORY REWRITTEN BY CLIMATE
by
Maeve Kerrigan Snyder
Bachelor of Science Coastal Carolina University, 2014 �
Submitted in Partial Fulfillment of the Requirements �
For the Degree of Master of Science in
Biological Sciences
College of Arts and Sciences
University of South Carolina�
2018
Accepted by:
Thomas J. Hilbish, Director of Thesis
David S. Wethey, Reader
Jesus Pineda, Reader
Cheryl L. Addy, Vice Provost and Dean of the Graduate School
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© Copyright by Maeve Kerrigan Snyder, 2018 All Rights Reserved
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ACKNOWLEDGEMENTS
I would like to extend my sincere and deep gratitude first and foremost to Dr.
Jerry Hilbish, without whose encouragement, patience, and mentorship, this work would
not have been possible. Through opportunities and confidence in my ability, Dr. Hilbish
has enabled me to grow and improve as a researcher and an individual. I am also
appreciative to Dr. David Wethey and Dr. Jesus Pineda for their guidance, input, and
inspiration. Amy Zyck, Jesse Turner, Sophia Sudak, and Cameron Good contributed
enormously to this work as research and field assistants. I am additionally grateful to
Rachel Steward, Mike Bramson, Rhiannon Rognstad, and Sam Crickenberger for their
contributions and advice. Special thanks to my friends and family for their loving
support. This work was funded by NASA (NNX11AP77G) and NSF (OCE 1129401)
grants to DSW and TJH and a Magellan Voyager Grant from the University of South
Carolina Office of Undergraduate Research to JMT.
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ABSTRACT
Climatic changes have the potential to alter population and community dynamics,
ultimately influencing the biogeographic distributions of species. For many organisms,
reproduction is physiologically tied to temperature. We tested the hypothesis that a
physiological mechanism for reproductive failure in the acorn barnacle, Semibalanus
balanoides, would result in predictable patterns of larval recruitment over broad
geographic scales. Recruitment density was predicted to be dependent on the duration of
permissive temperatures (< 10oC). for successful reproduction in adult populations of S.
balanoides. We found that temperature was a reliable predictive variable for recruitment
densities throughout our study region. Post-recruitment processes were also considered,
specifically the competition between Semibalanus balanoides and Chthamalus montagui,
described in Joseph Connell’s 1961 experiment. The competitive hierarchy outlined in
this experiment is not consistently observed throughout the range overlap of the two
species. Growth and mortality were found to differ dependent on species and latitude,
indicating that climate mediates this classic ecological system. Our results provide useful
knowledge for refining models of biogeographic shifts. A consistent failure of larval
supply and a breakdown of competitive advantage could accelerate the pace of predicted
range contraction for S. balanoides. Further investigation of how environmental variables
interact with physiology and ecological processes is necessary for accurate predictions of
climate change effects.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS ................................................................................................... iii
ABSTRACT ............................................................................................................................ iv
LIST OF TABLES.................................................................................................................. vi
LIST OF FIGURES ............................................................................................................... vii
LIST OF ABBREVIATIONS .............................................................................................. viii
CHAPTER 1: PREDICTING RECRUITMENT SUCCESS FROM A TEMPERATURE – BASED PHYSIOLOGICAL MECHANISM ACROSS A BROAD LATITUDINAL RANGE .....................................................................................................................................1
1.1 INTRODUCTION .................................................................................................2
1.2 METHODS.............................................................................................................9
1.3 RESULTS ............................................................................................................ 15
1.4 DISCUSSION ..................................................................................................... 19
CHAPTER 2: REVISITNG CONNELL: A CLASSIC COMPETITIVE SYSTEM LINKED TO CLIMATE ....................................................................................................... 38
2.1 INTRODUCTION .............................................................................................. 39
2.2 METHODS.......................................................................................................... 44
2.3 RESULTS ............................................................................................................ 49
2.4 DISCUSSION ..................................................................................................... 51
LITERATURE CITED ......................................................................................................... 66
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LIST OF TABLES
Table 1.1 Sampling period by region for 2015 and 2016. .................................................. 27
Table 1.2 Summary statistics of salinity time series for all sites ........................................ 28
Table 2.1 Subquadrats and corresponding experimental treatments .................................. 60
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LIST OF FIGURES
Figure 1.1 Study region and sites (in blue) on the west coast of the United Kingdom ..... 29 Figure 1.2 Time series of hourly air temperature, daily sea surface temperature, and composite air and SST for both study years ........................................................................ 30 Figure 1.3 Relationship between low tide CFSR air temperature and iButton temperature at Whitsand Bay ..................................................................................................................... 31 Figure 1.4 Figure 4. Relationship between combined SST and adjusted - air temperature and iButton temperature at Whitsand Bay ........................................................................... 32 Figure 1.5 Cumulative days below 10oC using composite air and sea surface temperature across the latitudinal gradient of the study sites for both sample years ............................. 33 Figure 1.6 Number of days below 10oC using composite air and sea surface temperature during the embryonic brooding period using composite temperature vs. recruitment density of S. balanoides at all sites ....................................................................................... 34 Figure 1.7 Early juvenile mortality of Semibalanus balanoides. Recruitment density sampled in May 2016 vs. recruitment density in June 2016 ............................................... 35 Figure 1.8 Log – transformed chlorophyll a concentration vs. recruitment density ......... 36 Figure 1.9 Averaged daily salinity data vs. recruitment density at each site ..................... 37
Figure 2.1 Experimental groupings for measurements of mortality and growth rate ....... 61 Figure 2.2 Average proportional cover of SB and CM across sites. Sites 1 – 18 are southwest England. Sites 18 – 23 are Wales. Sites 23 – 28 are Scotland .......................... 62
Figure 2.3 Percent cover analysis of simulated SB recruitment failure ............................. 63 Figure 2.4 Mortality (mean ± standard error) of SB and CM across latitude in isolation and interspecific groupings ................................................................................................... 64 Figure 2.5 Growth rate (mean ± standard error) of SB and CM across latitude in isolation and interspecific groupings ................................................................................................... 65
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LIST OF ABBREVIATIONS
CM .......................................................................................................... Chthamalus montagui
SB ....................................................................................................... Semibalanus balanoides
SST ....................................................................................................Sea Surface Temperature
UK ...................................................................................................................United Kingdom
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CHAPTER 1
PREDICTING RECRUITMENT SUCCESS FROM A TEMPERATURE – BASED
PHYSIOLOGICAL MECHANISM ACROSS A BROAD LATITUDINAL RANGE1
1 Maeve K. Snyder, David S. Wethey, Rhiannon L. Rognstad, Jesse M. Turner, Thomas J. Hilbish. Submitted to Marine Ecology Progress Series, 10/2/17.
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1.1 INTRODUCTION
Warming sea surface temperatures as a result of anthropogenic climate change
have the potential to cause dramatic shifts in the distributions, dynamics, and community
structures of intertidal organisms (Southward et al. 1995, Hawkins et al. 2008, Wethey et
al. 2011). Furthermore, a major goal of ecology is to understand the drivers that limit
biogeographic distributions. Numerous models have attempted to forecast future species
distributions using current distribution patterns and IPCC (Intergovernmental Panel on
Climate Change) future climate scenarios. This is of particular interest due to the
potential consequences of range migrations, contractions, and expansions, but the
specifics of these changes remain unclear for the populations of many organisms
(Helmuth et al. 2006, Burrows et al. 2014).
Increasingly, research has emphasized the need to consider physiological and
ecological mechanisms when predicting the effects of climate change on organisms and
ecosystems (Woodin et al. 2013, Rapacciuolo et al. 2014, Deutsch et al. 2015). Since
reproduction and larval survival are often sensitive stages in the life history of many
organisms (Thorson 1950, Pechenik 1999), information about how these stages are
affected by environmental variables may prove to be useful for improving ecological
forecasting models. A mechanistic understanding of how temperature interacts with
reproductive physiology and early life history could be a powerful predictor of climate
change effects on biological systems. In this thesis, the role of climate was investigated
for two processes of ecological influence and theoretical importance in rocky intertidal
systems – recruitment and competition. Research in this area will improve understanding
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of population and community structure and will give insight into how a changing climate
may alter interactions and distributions.
Rocky intertidal ecosystems are frequently used for field-based experiments and
have contributed disproportionately to the development of theory in population and
community ecology (Connell 1961, Paine 1966, Dayton 1971). These habitats are suited
to experimental studies of ecology in a number of ways. They experience steep gradients
of environmental conditions and can provide researchers with easy access during low
tides. The rocky intertidal is also home to many sessile or, if mobile, slow-moving
organisms that can easily be marked, tracked, and manipulated in experimental
treatments. (Menge & Branch 2001). The fauna of rocky intertidal habitats in the United
Kingdom (UK) have been model organisms for many such studies of ecology.
Furthermore, the extensive climatic and biogeographic records maintained in the UK help
with the construction of correlational patterns between climate and biogeographic trends.
The United Kingdom is a region of biogeographic overlap and allows the
coexistence of several southern and northern species (Crisp & Southward 1958, Herbert
2003). Populations of sessile invertebrates, particularly barnacles, found in the rocky
intertidal system of the UK therefore provided a model system to investigate the
questions addressed in this thesis. Barnacles are marine crustaceans found worldwide as
members of subtidal, intertidal, and fouling communities. Adult barnacles live in calcium
carbonate shells that are permanently cemented to a substrate. They are able to disperse
and colonize new locations through a planktonic early life history stage (Thorson 1950).
Semibalanus balanoides is a cold-water species occurring throughout the UK but
reaching a southern range limit in southwest England. S. balanoides and other barnacles
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reproduce with internal fertilization followed by an embryonic brooding period, in which
developing embryos are held within the shell cavity of adult individuals until they
develop into larvae that are competent to be released into the water column as
zooplankton. Chthamalus montagui is a warm water species found throughout the
western coast of Britain and further south in Europe (Barnes 1957, Crisp & Southward
1958). S. balanoides and C. montagui have staggered recruitment seasons: S. balanoides
fertilizes gametes in late Autumn, broods embryos during the winter months, releases
larvae into the plankton in late winter/early spring, and recruit to the shoreline in April –
June (Crisp & Clegg 1960, Crickenberger & Wethey 2017) C. montagui follows this
same procession with the timing offset so that they commonly recruit in late summer –
early fall (Jenkins 2005). Hereafter, Semibalanus balanoides will be referred to as SB and
Chthamalus montagui as CM.
Climate effects on populations have been observed in a multitude of species and
ecosystems, but rocky intertidal may be especially sensitive to these changes due to their
combined exposure to aquatic and terrestrial stresses (Mieszkowska et al. 2006; Helmuth
et al. 2006, Hawkins et al. 2008). Species may manifest climate change effects in a
multitude of ways. For example, phenological shifts are changes in the timing of periodic
life cycle events, such as blooming, migrations, or reproductive events (Memmott et al.
2007, Schlüter et al. 2010). Organisms may also respond to changing climate through
range shifts, genetic shifts, or community shifts (Root et al. 2003). Many intertidal
species in southwest England have changed in abundance since the 1980s (Crisp &
Southward 1958, Herbert et al. 2003, Mieszkowska et al. 2006). Warm water species
(e.g., Chthamalid barnacles, Patella depressa and B. perforatus) have increased while
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cold-water species have decreased in abundance (e.g., SB and Patella vulgata)
(Mieszkowska et al. 2006, Southward 1991, Southward et al. 1995, Helmuth et al. 2006).
However, these changes in abundance could be attributed to a variety of mechanisms and
ecological processes.
To link the influence of climate on ecology, an important focus is physiological
processes that are sensitive to temperature (Woodin et al. 2013). It has been increasingly
recognized that physiology is a vital consideration in biogeographic forecasting. Many
studies of forecasted distribution changes are correlational in nature. The merit of
collecting data on physiological relationships lies in the ability to mechanistically justify
biogeographic models (Woodin et al. 2013, Rapacciuolo et al. 2014). However, the
complexity of relating physiological constraints to large-scale consequences remains a
challenge. Physiological limits may scale up to population level effects in a number of
ways – some acutely fatal, others weaker but persistent. Some organisms are limited by
thermal maxima (Jansen et al. 2007), however physiological stresses may also depend on
the length, frequency, and magnitude of exposure to stressful levels of an environmental
variable, not to mention the state, age, and genetics of the organism (Lima & Wethey
2012, Crickenberger & Wethey 2017). A further complication is that many
environmental conditions act concurrently. Intertidal organisms are often thought to be
more resistant to extremes due to the large fluctuations in their habitat. However,
depending on the organism, it is possible populations may be more sensitive to changes
because they are already near their physiological limits (Findlay et al. 2009). Since
barnacles are immobile following settlement, they are susceptible to even short-term
periods of acutely unfavorable conditions of the environment. Determining the most
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important manner that climate influences organisms, whether it is the magnitude,
duration, or frequency of climatic events, whether it is temperature extremes (maxima or
minima), and the manner of physiological effect that is elicited remains an intricate
challenge to ecologists.
Since reproduction and larval survival are often sensitive stages in the life history
of many organisms, information about how these stages are affected by environmental
variables may prove to be useful for improving ecological forecasting models (Thorson
1950). A mechanistic understanding of how temperature interacts with reproductive
physiology and early life history could be a powerful predictor of climate change effects
on biological systems. Most sessile marine organisms rely on a larval planktonic stage for
dispersal and population connectivity. This life history strategy necessitates that larvae
successfully recruit from a planktonic larval stage into the adult population. Studying the
dynamics surrounding recruitment is crucial to understanding the ecology of such
organisms (Thorson 1950, Strathmann 1993, Nathan 2001). Recruitment is affected by
conditions at global, regional, and local spatial scales (Menge & Sutherland 1987,
Svensson et al. 2006). Recruitment has been described as “setting the stage” on which
post-settlement processes, such as competition and predation, play out (Menge 2000,
Underwood & Keough 2001).
Connecting physiology to biogeography can be approached by working
backwards to find a mechanistic explanation for an observed pattern of range shift or by
applying a known physiological limit to test hypotheses about whether it can cause large-
scale patterns. Using the latter approach, we designed an experiment to empirically test
the physiological model’s predicted effects on broad-scale patterns of recruitment in the
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barnacle Semibalanus balanoides. SB is a boreo-artic species and its reproduction is
physiologically linked to temperature (Southward & Crisp 1954, Crisp 1964, Rognstad &
Hilbish 2014). It broods embryos within its shell during the winter months and this
process is inhibited if sea surface temperature (SST) exceeds 10oC during brooding
(Rognstad & Hilbish 2014). In the United Kingdom, larval release occurs in the months
of March and April; recruitment primarily occurs during May and June (Barnes 1962,
Hawkins & Hartnoll 1982, Kendall et al. 1985). Populations of SB occur throughout the
northeast Atlantic, including the west coast of the United Kingdom. However, the species
reaches its southern range limit in several locations throughout Europe, including
southwest England. In this region, populations of SB are transient and have been shown
to fluctuate with yearly temperature trends (Southward & Crisp 1954, Rognstad et al.
2014). Range extension occurs with high-density recruitment levels following cold
winters where temperature was permissive to reproduction (below 10oC) for greater than
6 weeks (Rognstad et al. 2014). Recruitment is weak or fails after warmer winters.
Here, we specifically test the hypothesis that broad geographic variation in winter
air and sea surface temperatures creates regional variation in larval recruitment that can
be predicted by the cold-temperature threshold model of Rognstad et al. (2014). We
predict that high recruitment will occur in regions where winter temperatures are
sufficiently cold (below 10oC) for greater than six weeks and recruitment will be weak in
warmer regions that do not meet this threshold. However, it is well known that
recruitment is the compilation of complex processes and, consequently, success in
surviving the multiple population bottlenecks during larval development and planktonic
dispersal may obscure any effect of brooding-stage temperature (Pineda et al. 2009,
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Crickenberger & Wethey 2017). We test the hypothesis that recruitment can be predicted
from winter temperatures in SB on the west coast of the United Kingdom, despite this
complexity. The English Channel has already experienced notable climatic warming and
several corresponding changes in its marine communities (Southward et al. 1995, Lima &
Wethey 2012). Understanding the drivers underlying the reproduction and recruitment of
SB will provide insight into the likelihood of their persistence in the current extent of
their range. It will also shed light onto the question of how other marine organisms that
are physiologically linked to temperature may respond to a warming ocean.
Since scientists can make predictions about what is expected to happen
biologically as a result of climate change, it is possible to generate null models and test
hypotheses about whether observed patterns of biological change are probabilistically
explained by climate change (Root et al. 2003, Parmesan & Yohe 2003). Climate change
may have a synergistic effect of modification on these sites since an additional source of
variation in the outcome of density manipulations is differences in recruitment between
sites and between years (Sutherland 1978). The English Channel has already experienced
notable climatic warming and several corresponding changes in its marine communities
(Southward et al. 1995, Lima & Wethey 2012). Understanding the drivers underlying the
reproduction and recruitment of SB will provide insight into the likelihood of their
persistence in the current extent of their range. It will also shed light onto the question of
how other marine organisms that are physiologically linked to temperature may respond
to a warming ocean.
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1.2 METHODS
Data Collection
Recruitment was sampled at 30 intertidal sites along the west coast of the United
Kingdom (Figure 1.1). Sites were spaced at approximately 50 km intervals, based on
estimated dispersal distance for barnacle larvae (Southward 1967, Rognstad et al. 2014).
Five quadrats (7.5cm x 7.5 cm) were established at the mid-tidal level in 2014 and 2015
at each site. The quadrats were scraped completely free of barnacles during May to June
of 2014 (following settlement), which allowed the measurement of de novo recruitment
in subsequent years. Each quadrat was photographed using an Olympus TG-4 digital
camera. Sampling occurred during spring (May to June) 2015 and spring (May to July)
2016 (Table 1.1). In 2016, all sites were sampled twice (once in May and once in June).
Because it was logistically impossible to sample all sites at the same time, we compared
photographs taken in May 2016 to those from June 2016 to address two questions: 1) Did
appreciable recruitment occur following the earliest sampling events? 2) Does early
juvenile mortality obscure the pattern of recruitment?
Image Analysis
The density of new recruits was analyzed using the open source software Image J
(Abramoff et al. 2004). New recruits were identified and quantified from scrapes that
were created the year prior (after the end of Semibalanus balanoides recruitment for that
year). The edge of the square quadrat frame was used to calibrate the ImageJ
measurement tool. To eliminate edge effects, an area of 30.25 cm2 was delineated in the
center of each quadrat, and this area was analyzed for recruitment density. Density was
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measured in all five quadrats for each site, each year. All newly recruited barnacles were
identified based on plate number and shape as described by Drévès (2001). Individuals of
S. balanoides could be confidently differentiated from other common intertidal species in
the region, including Chthamalus stellatus, C. montagui, and Austrominius (=Elminius)
modestus (Southward 1976).
Temperature Analysis
NOAA Optimum Interpolation ¼ Degree Daily Sea Surface Temperature
Analysis data (Reynolds et al. 2009) were obtained from the NOAA National Climatic
Data Center (https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-
interpolation/access/) These data were available from each field sample site at a 25 km
spatial scale. Climate Forecast System Reanalysis (CFSR) hourly 23km gridded 2m air
temperature data (Saha et al. 2011) were obtained from the NCAR National Centers for
Environmental Prediction (https://rda.ucar.edu/datasets/ds094.1/). Temperature data were
then imported into RStudio (Version 1.1.414) and a script was developed to identify the
closest marine pixel to each sample site and extract the temperature data for each
location. This method was justified based on Lima & Wethey 2012 (their supplemental
materials), which showed that the nearest pixel to intertidal sites and SST measured
onshore are highly correlated. We considered other temperature metrics, such as
cumulative degree days and temperature maxima. The various representations of
temperature all showed a similar trend across latitude, and it is not likely a different
metric would have altered our results. While temperature extremes are often important
from a biological standpoint, we found that at no time during the embryonic brooding
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period did any site meet or exceed 18 – 20oC for air or SST, which is lethal to developing
embryos (Crisp 1959b). Figure 1.2 presents time series data for air, SST, and composite
temperature data at three sites (representative of regions within the latitudinal gradient).
Since SB is an intertidal species, a composite of hourly air temperature and daily
SST data was created to accurately reflect the temperatures experienced by barnacles at
each site. The composite temperature was assembled using SST data during high tide
periods (>3 m tide height, the mid – tide level of quadrats) and air temperature during
low tide periods (<3 m tide height). Tide predictions were estimated with XTide (Flater
2005). To assess how accurately remotely sensed data reflected the body temperatures of
individuals of SB, SST and air temperature were compared to previously recorded
iButton temperature data. iButtons were cemented to rock surfaces among clusters of SB
at a site in southwest England (Whitsand Bay) from November 1 to January 31 of 2012 –
2013 and remotely sensed data were obtained for the same period. iButtons were wrapped
in Parafilm and embedded in marine epoxy (Z-Spar A-788 Splash Zone Epoxy, Carboline
Co, St. Louis, MO USA) to improve water-resistance and mimic rock reflectivity (Jones
et al., 2012). Marine epoxy also attached the iButtons to rock surfaces. Temperature
readings were recorded by iButtons every two hours during this period. iButton and
barnacle body temperatures are expected to conform closely to the surface temperature of
the rock to which they are attached. We found a close correlation between iButton and
SST while the iButton was submerged. However, during emersion remotely sensed air
temperature was typically colder, and often much colder, than were the temperatures of
the iButtons (Slope = 0.30604; R2 = 0.4222, F1,403 = 294.5, p < 0.0001, Figure1.3). This
discrepancy was greater for colder temperatures and the two measures of temperature
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converged (y = 0.30604 * x + 8.1509, Figure S1). We used the regression between
iButton and air temperature to predict the body temperature of barnacles during emersion.
These data were then combined with the remotely sensed SST during immersion to
estimate a composite daily temperature that a barnacle is expected to experience. The
composite daily temperature was closely correlated to the average daily temperature of
the iButton temperature loggers (Slope = 0.7982; R2 = 0.3763, F 1,698 = 421, p <0.0001). We
used this approach to estimate composite daily body temperatures for SB during the
brooding periods in 2015 and 2016 (see Figure 1.4).
Embryonic brooding period was estimated from recorded dates of fertilization,
development rate, and larval release in previous literature. Timing of fertilization of SB
varies latitudinally, and interacts with temperature, but is generally consistent within sites
and regions (Crisp & Clegg 1960, Crickenberger & Wethey 2017). Within a site, tide
level is the best predictor of fertilization timing, which is a controlled variable in this
study (Crisp 1959a). Barnacles at sites in southwest England have been observed to
fertilize later than in colder regions, and additionally have increased rates of development
(Crisp 1959b). The embryonic brooding period was estimated to be November 1 –
February 28 for sites in Wales and Scotland (Crisp & Clegg 1960, Kendall et al. 1985,
Barnes 1962). Due to the delayed timing of fertilization and increased rate of
development for SB in southwest England, brooding period was estimated to be
November 30 – February 28 for this region (Crisp 1959a, Crisp 1959b, Crisp & Clegg
1960). From the composite of hourly air temperature and daily SST, a daily average was
calculated for each day in the brooding period. The cumulative number of averaged days
that each site spent below 10oC during the brooding period was recorded from these data.
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As per Rognstad et al. (2014), sites were then assigned to 1 of 3 categories for the
duration of permissive temperature for reproduction (below 10°C): (1) greater than 6
weeks (> 6) (2) 4 to 6 weeks (4 - 6) (3) less than 4 weeks (< 4). These categories of
temperature duration were chosen based on observed patterns of temperature influencing
reproductive physiology of SB described in Rognstad et al. (2014). Recruitment densities
were designated as “very low” (< 1 individual cm-2), “low” (1-5 individuals cm-2), and
“high” (> 5 individuals cm-2). Density categories were determined based on recruitment
levels that were observed during years with comparatively long or short durations of
permissive winter temperature. These patterns were observed and described for southwest
England in Rognstad et al. 2014. We predicted that sites corresponding to category 1
would receive high levels of SB recruitment; sites corresponding to category 2 would
receive low recruitment; and sites corresponding to category 3 would receive very low
recruitment
Chlorophyll a Analysis
We additionally investigated whether food supply for larvae could be a stronger
predictor of recruitment at local scales, as predicted by phytoplankton mismatch
hypothesis (Cushing 1990). We hypothesized that low phytoplankton abundance during
the planktonic larval period might explain recruitment failure at sites where the
occurrence of cold winter temperature predicted high recruitment. Inadequate food
supply could result in high mortality during the planktonic stage and uncouple high larval
supply from recruitment at a local scale. Gimenez et al. 2017 found that nutritional
reserves for SB vary spatially and may influence recruitment and early post- settlement
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survival. This suggests that food availability during the planktonic stage may be
consequential even following settlement. We tested this hypothesis using remotely sensed
chlorophyll a concentration as a proxy for food supply of barnacle nauplii. Chlorophyll a
data were obtained from the European Union Copernicus Marine Environment
Monitoring Service (http://marine.copernicus.eu/) and were chosen by locating the
monitoring pixel that was closest to each sample site. For 2015, averaged the mean
chlorophyll a concentration across sites to obtain a single value representing during the
planktonic period (estimated to be between March 1 – May 1 as per Southward & Crisp
1954 and Barnes 1962).
Salinity Analysis
Salinity was another environmental variable that was investigated as a potential
influence on recruitment, particularly at more estuarine sites in the study region. We
hypothesized that at sites that experienced low salinities during the settlement period
(corresponding to an influx of freshwater following large precipitation events), we would
find depressed levels of recruitment. We tested this hypothesis using 2015 daily salinity
data from the European Union Copernicus Marine Environment Monitoring Service
(http://marine.copernicus.eu/). Salinity data were averaged across the embryonic
brooding period for all sites. Time series of salinity during the brooding period were also
visualized to check for extreme minima and concluded the averaged salinity data
accurately represented the salinity across time (Table 1.2). Salinity data were imported
into RStudio (Version 1.1.414) and a script was developed to identify the closest marine
pixel to each sample site.
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Statistical Analysis
Mean recruitment density was obtained from each site by averaging the density
measurements from the five quadrats at each site. A simple linear regression was used to
compare the relationship between cumulative cold days (number of days < 10oC) and
recruitment density (individuals per cm-2 ) against a null hypothesis of constant
recruitment across cumulative cold days. A G-test of independence was used to analyze
the relationship between the categories of cumulative cold days (< 4, 4 - 6, > 6 weeks)
and the predicted category of recruitment density (“very low”, “low”, “high”). For early
juvenile mortality, the trend in density reduction between May and June was fit to a non-
linear model using the Michaelis-Menten equation (y = (b1 * x) / (x * b2))) with b1 = 10
and b2 = 20, based on the visually estimated Vmax and Km of the data. Simple linear
regression was used to test additional hypotheses about variables that may affect
recruitment – chlorophyll a concentration and salinity.
1.3 RESULTS
Recruitment and Brooding Period Temperature
Figure 1.2 presents air and sea surface temperature time series data for the
brooding periods in 2015 and 2016. Remotely sensed air temperature was considerably
more variable and colder than SST. Composite temperatures, which combined SST and
adjusted air temperature, during the periods of immersion and emersion, respectively,
were much less variable than remotely sensed air temperature and were much more
similar to SST. Composite temperatures were used to estimate the number of days below
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10oC experienced by barnacles at each sample site. There was a strong relationship
between days below 10oC and latitude during both 2015 and 2016 (Figure 1.5). Barnacles
at sites in Southwest England frequently experienced fewer than four weeks below 10oC
while sites in Scotland all experienced 6 - weeks or longer below 10oC.
A significant, positive linear relationship was found between cumulative cold
days and recruitment density (R2 = 0.4013, F1,52 = 34.86, p < 0.0001, Figure 1.6). A G-test
of independence revealed a significant effect for the duration of permissively cold
temperature on recruitment density levels (G = 22.744, df = 4, p = 0.00014). At the broad
spatial scale, there was an overall latitudinal gradient of recruitment density based on
duration of permissive temperatures. The majority of sites in southwest England (<51oN
latitude) experienced <4 weeks of permissively cold temperatures (< 10oC) in both years
(Figure 1.5). Comparatively, 2016 was a warmer year than 2015, and all southwest sites
were predicted to have “very low” recruitment, having experienced less than 4 weeks of
permissively cold temperatures. In 2016, some sites were in the predicted ranges for
“low” recruitment. Recruitment densities at these sites were found to be either “low” (<
5 individuals cm-2) or “very low” (< 1 individual cm-2) for all sites in both years. Two sites
in southwest England received virtually no recruitment despite being permissively cold
for nearly six weeks in 2015 (Figure 1.6). There was no apparent relationship between the
cumulative time categories (4 -6 weeks vs. < 4 weeks) in regard to whether recruitment
was “low” or “very low.” However, none of these sites experienced “high” recruitment
densities of SB (>5 individuals cm-2) (Figure 1.6).
Sites in Wales were highly variable in the duration of permissive brooding
temperatures (Figure 1.5). As in southwest England, 2016 was found to be a warmer year
‘ 17
than 2015 in this region. All sites in Scotland experienced well over six weeks of
permissively cold temperatures both years. Recruitment densities in Wales and Scotland
showed a greater range compared to southwest England, with some sites receiving
virtually no recruitment, up to nearly 25 individuals per cm2 (Figure 1.6). While most
sites in Wales and Scotland met the prediction of “high” recruitment based on duration of
permissive temperatures, four sites were found to have “low” recruitment despite
experiencing well over six weeks of permissive temperatures. Recruitment at two sites in
Wales was predicted to be “very low” due to the short duration of permissive
temperatures, but “high” densities were observed (Figure 1.6).
Early juvenile Mortality
In 2016, we asked whether appreciable recruitment occurred at sample sites late
in the recruitment season (April – June). We also investigated if early juvenile mortality
modified populations over the recruitment season. We found a density – dependent
relationship between early and late recruitment season recruitment density (Figure 1.7).
For sites with high initial recruitment, density-dependent mortality resulted in reductions
in density between the May and June sampling periods. Some sites with initial
recruitment densities >40 individuals cm-2 had density reductions of over 50% (Figure
1.7). At sites in southwest England, however, recruitment densities during May were
typically <5 individuals cm-2 and very similar densities were observed in June, indicating
that there was neither significant mortality or additional recruitment between the two
samples (Figure 1.7). Across all sample sites there were very few that had greater
densities in June compared to May, and the increased densities were never greater than 2
‘ 18
- 3 individuals cm-2 at any site. This indicates that the vast majority of settlement was
complete by the time of initial sampling in May.
Chlorophyll a
There was no apparent relationship between chlorophyll a concentration and
recruitment of SB (R2 = 0.016, F1,24 = 0.385, p = 0.541, Figure 1.8). Indeed, the site with
the highest level of phytoplankton was Great Cumbrae in 2015 which experienced nearly
complete recruitment failure that same year (Figure 1.8). It should be recognized that
food quality may be more important than abundance, and that chlorophyll a may be
uninformative about the quality or type of phytoplankton available for consumption
(Toupoint et al. 2012). Regardless, at present we could not attribute variation in
recruitment to food availability while barnacle nauplii were developing in the plankton.
Salinity
There was no significant relationship between salinity and recruitment of SB (R2 =
0.1182, F1, 24 = 3.216, p = 0.086, Figure 1.9). The majority of sites experienced fully
marine salinity (~35 ppt) throughout the brooding period. Two sites, Great Cumbrae
(GC) and St. Bees (SB) (Figure 1.9) were known to be more estuarine in location. These
sites also had the lowest minima recorded during the planktonic period (Table 1.2). These
sites did show decreased average salinity as well as lower recruitment but there was no
overall relationship present between recruitment and salinity. Ullapool experienced a low
minimum and average salinity as well, but had the highest observed recruitment density.
‘ 19
1.4 DISCUSSION
Recruitment in benthic marine species with planktonic larval dispersal is typically
erratic in both time and space. Pineda et al. (2009) argued that survival between various
stages of larval development may be so variable that recruitment may be effectively
uncoupled from success at any given stage of development. It may, therefore, be
exceptionally difficult to predict large-scale geographic patterns of recruitment. Within
the region of southwest England, Rognstad et al. (2014) demonstrated that annual
variation in winter sea surface temperatures (SST) could be used to predict larval
recruitment of the cold-water barnacle Semibalanus balanoides. At sites where composite
winter temperatures were sufficiently cold for > 6 weeks, there was high recruitment of
SB that was not observed at sites where this cold threshold was not met. These results are
consistent with the findings that warm temperatures above 10oC inhibit embryonic
development in SB (Barnes 1957, Crisp & Clegg 1960, Crisp & Patel 1969, Rognstad &
Hilbish 2014). In this study we found that the same physiological model used by
Rognstad et al. (2014) to predict annual variation in recruitment of SB at their southern
range margin can also be used to predict large-scale patterns of SB recruitment.
As predicted from the physiological model, larval recruitment was “low” or “very
low” (<5 cm-2) at over 90% of sites where temperature was not sufficiently cold for more
than four weeks (Figure 1.6). Winter temperatures were warmer in 2016 than in 2015,
especially in Southwest England (latitudes <51oN), and larval recruitment was
correspondingly lower (~1 cm-2). Across all sample locations, there was a significant
correlation between larval recruitment and the number of days < 10oC (R2 = 0.4013, F1,52 =
34.86, p < 0.0001, Figure 1.6). This explains a large portion of the variation across
‘ 20
latitude but is not the sole source of variation. Latitude was closely related to variation in
recruitment (R2 = 0.7061, F 1,52 = 125, p = 1.93e-15). However, winter temperature and
latitude are related measures (Figure 1.5). Based on a known temperature mechanism for
reproduction, we observed a broad geographic pattern of recruitment for populations of
SB in the United Kingdom.
An interesting test of these results would be the response of SB recruitment to a
severe cold winter. There is observational evidence to support that cold winter seasons
result in increased recruitment of SB, which is likely driven by the physiological
mechanism described here (Crisp 1964, Rognstad et al. 2014). Incorporation of effects
from extreme brooding period temperatures into this temperature response model would
improve the mechanistic basis for use in climate and biogeographic forecasting (Wethey
2011, Thompson et al. 2013). In this study we assumed that the 10oC threshold for high
reproductive output was the same for all populations of SB across the species range.
Assessing geographic variability in this temperature threshold may be important for
improving biogeographic forecasts. Explorations of microclimate effects, especially
during low tide, will be a worthwhile direction for research to further investigate the
ability of this physiological method to drive recruitment.
Our results indicate that larval recruitment was highly correlated with predicted
input into the larval pool as a result of successful brooding of embryos. This seems at
odds with the hypothesis of Pineda et al. (2009) that recruitment may be unpredictable
given the potential for success at one stage of the recruitment process to be decoupled
from success at other stages. However, Pineda et al. (2009) also suggests that variation in
recruitment will depend most strongly upon that stage of the life cycle with the greatest
‘ 21
temporal and spatial variation in survival. In this case, we suggest that successful
brooding of embryos may be the most variable stage, and therefore plays the greatest role
in determining variation in recruitment across a large spatial scale. In Southwest England,
winter temperatures were too warm to permit high larval output by brooding adults,
causing widespread and consistent recruitment failure. In contrast, winter temperatures in
Scotland were consistently permissive to successful larval brooding and yet there was
variation in recruitment among sites and between years. For SB, we suggest modifying
the Pineda model to include this threshold effect for embryonic brooding success. If
larval supply is predicted to be high, it remains possible for recruitment to fail at later
stages of development. However, when larval production is precluded by warm winter
temperatures, the model should reflect that it is very unlikely that success later in
development could compensate for this failure to ultimately yield high recruitment. This
revised model treats larval supply as the determinant for recruitment potential, with high
larval supply as a prerequisite to high recruitment. As our results show, at warm locations
there is low variation in recruitment indicating little evidence of rebounding from low
input into the larval pool. At cold locations, while recruitment was generally high as
predicted, it was nonetheless possible for recruitment to fail.
Early juvenile mortality is another process with the ability to uncouple
recruitment from larval success in the plankton (Pineda et al. 2009). Since density-
dependent mortality of recruited juveniles is strongest shortly after recruitment for SB
(Jenkins et al. 2008), we wanted to assess whether appreciable settlement occurred
following sampling in May and whether density dependent mortality was strong enough
to obscure early patterns of recruitment. We found little evidence for additional
‘ 22
recruitment following the May sampling. There was a decline in abundance of new
recruits in the first few weeks post-settlement, the magnitude of which was strongly
dependent upon initial density (Figure 1.7). The greatest reductions in density occurred at
sites where initial settlement was greatest and, in some cases, declined by as much as
50% (Figure 1.7). These results are consistent with the results of Jenkins et al. 2000,
which showed a density dependent reduction in density in southwest England and Wales
(their Figure 1.7). For years with permissive temperature in southwest England, higher
densities of recruits may result in density dependent effects that are typically present at
higher latitudes. Following rapid growth of new recruits, crushing and crowding are
likely sources of mortality, but we did not test the specific causes of density reduction.
Even with density reductions, sites with higher density early in the recruitment period
still had the greatest densities of SB at the end of the recruitment period. The pattern of
recruitment observed in May samples was retained – sites that received high recruitment
remained as such and vice versa.
While there was a strong broad-scale pattern in recruitment related to brooding
temperature there were nonetheless sites that received very low recruitment despite the
fact that the physiological model predicted high requirement (e.g. St. Bees, Borth, Great
Cumbrae). We investigated three possible explanations for why low recruitment was
observed despite the prediction that recruitment would be high. One possible explanation
may be that there was a mismatch between larval release and the availability of suitable
food in the plankton, which may result in recruitment failure (Barnes 1962, Cushing
1990). Prior attempts to apply the phytoplankton mismatch hypothesis to recruitment data
have shown mixed results (Thackeray 2012). Scrosati & Ellrich (2016) found that a
‘ 23
combination of temperature and remotely sensed chlorophyll a explained 51% of the
variability in a 12-year record of recruitment data for SB in Nova Scotia. Leslie et al.
(2005) found no connection between primary production (also measured by chlorophyll
a) and recruitment of Balanus glandula. However, there was a relationship between
primary production and larval production. This would be another potential mechanism by
which primary production could limit recruitment to investigate in future analyses. Our
cursory investigation into the potential explanatory power of phytoplankton mismatch for
SB recruitment in our sample region yielded no support for this hypothesis (Figure 1.8).
However, models predicting the success of early life history stages for SB found that
phytoplankton mismatch is more likely for populations in the western English Channel
(Crickenberger & Wethey 2017).
There is a plethora of other possibilities that may explain variability in
recruitment level when high recruitment is predicted (e.g. predation, transport, etc.). Our
results, however, suggest a specific hypothesis. We observed that anomalously low
densities of Semibalanus balanoides co-occur with elevated abundance of the invasive
barnacle Austrominius modestus (pers. obs.). A. modestus is known to be tolerant of more
estuarine conditions than many barnacle species native to the United Kingdom, especially
SB (Lawson et al. 2004, Gallagher et al. 2016). This observation suggests that several
sites, especially St. Bees, Great Cumbrae and Borth, may be intermittently inundated by
water from nearby estuarine sources. However, it is unlikely that salinity ever fell low
enough to directly kill the larvae or newly settled recruits of SB. During the settlement
period, the lowest minima (Table 1.2) were likely insufficient to kill or even greatly
impair the motion SB larvae (Barnes 1953, Bhatnagar & Crisp 1965, Rosenberg 1972).
‘ 24
While we did not find a significant relationship between daily salinity data averaged
across the recruitment period, two sites (Great Cumbrae and St. Bees) that were noted for
having anomalously low recruitment were found to have lower salinity. It is possible that
SB may be physically excluded from these sites located near estuaries when freshwater
discharge is high. Estuarine water, carrying larvae of A. modestus, may displace marine
water (carrying larvae of SB). Boundaries between water masses, such as an
estuarine/marine front can influence dispersal and recruitment of marine organisms; the
role of fronts was not tested but may have been another driver of recruitment outcomes
(Woodson et al. 2012). Testing this hypothesis would require high-resolution sampling of
plankton communities and salinity levels along a transect of the proposed
estuarine/marine front.
For species with limited dispersal ability, shifting zones of physiological tolerance
may lead to complete extermination (Pearson & Dawson 2003, Araújo & Guisan 2006).
The majority of SB populations in the southwest are dependent on colonization from sites
east of a headland called Start Point in the English Channel, where SST is typically
colder. The lifespan of SB is three years (Southward & Crisp 1954, Wethey 1985).
Several successive years of non-permissive winter temperatures could result in the
extirpation of SB from much of its range in Devon and Cornwall (Gilg & Hilbish 2003,
Rognstad et al. 2014). SB has already been predicted to become locally extinct in
southwest England by 2050 (Poloczanska et al. 2008). Moreover, continued warming will
have negative consequences to populations of SB on the western coast of the United
Kingdom. Wethey et al. 2011 found that SST permissive to SB during the brooding
season might significantly contract northward over the next century. Based on their
‘ 25
analysis, it seems likely that winter temperatures that favor high brooding success will no
longer occur in southwest England by 2100.
Sites in southwest England had high levels of open space available to recruiting
Semibalanus balanoides. However, recruitment densities at these sites were low in the
years of our study, and the majority of space in sample quadrats remained open. At sites
with little open space, SB often smothers other barnacles. This does not seem to affect
community composition at southern sites where recruitment of SB on adult barnacles or
mussels was rarely observed. Competition is an important ecological process for shaping
community structure in the rocky intertidal; the competitive hierarchy between SB and
CM is a classic illustration of the realized and fundamental niche (Connell 1961). If
population smothering is an important mechanism of interspecific competition for SB,
poleward movement of warm temperatures inhospitable to reproduction may lead to
progressively thinning larval at a retreating range edge. Loss of a density driven
competitive mechanism may make SB more vulnerable to competition for space with
other intertidal organisms and accelerate their disappearance at southern range limits.
Climate change has the potential to dramatically re-structure ecosystems, which
could result in negative consequences for biodiversity (Pereira et al. 2010). Furthermore,
range expansions and contractions can drastically alter community functioning. A shift
from a rocky intertidal zone dominated by SB to one dominated by CM has implications
for biodiversity and trophic effects. The body structure of SB is more rugose, which may
promote the recruitment of other intertidal species, such as algae and mussels. SB is also
a preferred prey species for several intertidal predators, such as dogwhelks (Nucella spp.)
(Crothers 1985, Burrows & Hughes 1991).
‘ 26
Our study highlights the importance of considering climate change effects on the
organismal level. Physiological thresholds that play a disproportionately large role in
determining species distribution may amplify the effect of small changes in temperature.
Looking at the community or ecosystem level may lead to broad assumptions that are
invalidated by the specific biology of the organisms in question. It is particularly
important to examine the larval stages of organisms because they are usually more
sensitive than adults, occupy very different environments than adults, and are necessary
for dispersal and population connectivity (Byrne 2011, 2012). Anthropogenic effects such
as climate change are often measured in terms of effects on adult populations (Thomas et
al. 2004), but it will be equally important for conservation scientists and managers to
consider factors of population persistence such as fecundity, recruitment, and
connectivity. Understanding the dynamics of these processes will be vital to the
conservation of all species that have a life history that couples planktonic and sessile
stages (Hughes et al. 2000). Overall, we find a need to increasingly incorporate natural
history and physiology into models forecasting distribution shifts as a result of climate
change. Understanding the interplay of complex physical and ecological processes will
allow conservation managers to predict and plan for changing biological communities.
‘ 27
Figures
Table 1.1 Sampling period by region for 2015 and 2016 (All dates are presented as Month/Day/Year).
Region Sampling Period Southwest England 5/16/2015 - 6/19/15 Wales 6/28/15 - 7/2/15 Scotland 7/3/15 - 7/13/15 Scotland 5/6/2016 - 5/10/16 Wales 5/11/16 - 5/16/16 Southwest England 5/17/2016 - 6/9/16 Wales 6/20/2016 - 6/22/16 Scotland 6/24/2016 - 6/27/16
‘ 28
Table 1.2 Summary statistics of salinity times series for all sites (All data are measured in ppt). Site Min Max Mean StD Ullapool 28.40 32.85 31.57 0.97 Glenbrittle 33.40 33.96 33.69 0.13 Great Cumbrae 25.89 32.34 29.94 1.63 St. Bees 30.76 32.14 31.43 0.33 Porth Trecastell 34.32 34.55 34.44 0.06 Borth 32.88 33.32 33.04 0.12 St. David's Head 34.43 34.58 34.51 0.03 Manorbier 32.72 33.72 33.27 0.22 Port Quin 34.53 34.90 34.76 0.09 Trevaunance 35.11 35.16 35.14 0.01 Portreath 35.08 35.17 35.15 0.01 Cape Cornwall 35.13 35.17 35.15 0.01 Michael's Mount 35.13 35.18 35.16 0.01 Poldhu 35.14 35.17 35.16 0.01 Kennick Sands 35.12 35.16 35.14 0.01 Maenporth 35.08 35.12 35.10 0.01 Hemmick Beach 35.02 35.10 35.06 0.02 Pendower 34.87 35.07 34.99 0.05 Whitsand Bay 34.78 35.01 34.90 0.07 Mothecomb 34.94 35.05 35.00 0.03 Maidencombe 34.82 34.98 34.92 0.04 Porthmeor NA NA NA NA Lansallos NA NA NA NA Wembury NA NA NA NA Hope Cove NA NA NA NA Dartmouth NA NA NA NA
‘ 29
Figure 1.1 Study region and sites on the west coast of the United Kingdom. Inset box: Sites in southwest England (Devon and Cornwall). The cities of Plymouth and Exeter are shown for reference. Designated sites are: GB = Glenbrittle, GC = Great Cumbrae, SB = St. Bees, PT = Porth Trecastell, BT = Borth, MB = Manorbier, WB = Whitsand Bay, MC = Maidencombe. SP designates the Start Point headland.
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‘
Figure 1.2 Time series of hourly air temperature, daily sea surface temperature, and composite air and SST for both study years. Representative sites for regions were 1) Southwest England = Whitsand Bay 2) Wales = Porth Trecastell 3) Scotland = Glenbrittle
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‘ 31
Figure 1.3 Relationship between low tide CFSR air temperature and iButton temperature at Whitsand Bay. Red, solid line is line of best fit. Dashed, black line is a 1:1 line. Red line represents a line of best fit (Slope = 0.30604; R2 = 0.4222, F1,403 = 294.5, p < 0.0001). Dashed, black line is a 1:1 line.
0
5
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−10 0 10Air Temperature (°C)
iBut
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‘ 32
Figure 1.4 Relationship between combined SST and adjusted - air temperature and iButton temperature at Whitsand Bay. Red line represents line of best fit (Slope = 0.7982; R2 = 0.3763, F1, 698 = 421, p <0.0001). Dashed, black line is a 1:1 line.
0
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iBut
ton
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‘ 33
Figure 1.5 Cumulative days below 10oC using composite air and sea surface temperature across the latitudinal gradient of the study sites for both sample years. Red, dashed lines indicate the breaks between Category 1 (<four weeks), Category 2 (four to six weeks), and Category 3 (>six weeks).
0
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50 52 54 56 58Latitude (°N)
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s Be
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‘
Figure 1.6 Number of days below 10oC using composite air and sea surface temperature during the embryonic brooding period using composite temperature vs. recruitment density of S. balanoides at all sites (mean ± standard error). Red, dashed lines indicate the breaks between Category 1 (<four weeks), Category 2 (four to six weeks), and Category 3 (>six weeks). A = St. Bees, B = Borth, C = Great Cumbrae (R2 = 0.4013, F 1,52 = 34.86, p < 0.0001).
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‘ 35
Figure 1.7 Early juvenile mortality of Semibalanus balanoides. Recruitment density sampled in May 2016 vs. recruitment density in June 2016. The Michaelis-Menten curve is shown with a red line and a 1:1 line is shown for comparison with a black, dashed line. Each data point is color-coded by latitude of the sample site.
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‘ 36
Figure 1.8 Log – transformed chlorophyll a concentration vs. recruitment density at each site (R2 = 0.016, F1,24 = 0.385, p = 0.541). GC = Great Cumbrae.
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37
Figure 1.9 Averaged daily salinity data vs. recruitment density at each site (R2 = 0.1182, F1,24 = 3.216, p = 0.0855). GC = Great Cumbrae, SB = St. Bees.
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38
CHAPTER 2:
REVISITING CONNELL: A CLASSIC COMPETITIVE SYSTEM LINKED TO
CLIMATE2
2 Maeve K. Snyder, David S. Wethey, Thomas J. Hilbish. To be submitted.
39
2.1 INTRODUCTION
Competition is a driving force of community structure across ecosystems (Connell
1961a, Dayton 1971, Menge 1976, Connell 1978). Competitive interactions are especially
well known in intertidal areas because organisms in these systems often occur in high
densities and are sessile or slow moving. For this reason, studies carried out in the
intertidal have had a large role in shaping theory around ecology and biodiversity at
several levels of organization. As previously shown, a physiological for recruitment
success is a useful predictor of population dynamics across a latitudinal gradient.
However, the degree to which patterns of recruitment are later modified is an important
consideration for forecasting population persistence and biogeographic changes. Post-
recruitment processes are highly consequential at the community and ecosystem level
(Findlay et al. 2010). Competition may be the most important determinant of community
structure in harsh environments, such as the intertidal, where abiotic conditions limit the
influence of predators (Menge & Sutherland 1987). Following the definition of Birch
1957, competition occurs when animals (of the same or of different species) utilize a
common resource, which is limited. Or if the resources are not in short supply, animals
seeking the resource nevertheless harm one another in the process (Birch 1957).
Barnacles have been tractable model organisms for several ecological questions
due to their sessile adult stage, small size, dense populations, and intertidal habitat
(Connell 1961, Wethey 1983, Leslie 2005, Jenkins et al. 2008). My thesis re-examines a
classic experimental demonstration of the potential for competition to restructure
communities, Joseph Connell’s 1961 study, “The Influence of Interspecific Competition
and Other Factors on the Distribution of the Barnacle, Chthamalus Stellatus”. In this
40
highly influential paper (as of November 2017: 2,057 citations on Google Scholar and
1,317 citations on Web of Science), Connell describes patterns of recruitment and
competition in two barnacles, Semibalanus balanoides and Chthamalus montagui, at an
intertidal site on the Isle of Great Cumbrae in the Firth of Clyde, Scotland. Connell 1961
demonstrated that both species of barnacles were able to settle through the intertidal zone.
However, through interference competition for space, SB was superior in smothering,
crushing, and undercutting CM and eventually eliminated this species from the mid-
intertidal. In the high intertidal, SB is unable to withstand the physical stress of
desiccation. Its absence creates a refuge for CM resulting in distinct bands of species
zonation in the mid- and high intertidal. The results of this study led to the development
of the fundamental and realized niche concept.
A limitation of Connell 1961, however, is that it represented a single geographic
snapshot of the latitudinal range across which these two species co-exist. Therefore, the
potential for latitudinally varying environmental factors to mediate this interaction was
not considered. Connell 1978 recorded an instance of marine sessile organisms reversing
competitive rankings and remarked, “climates do change gradually, which probably
results in changes in the competitive hierarchy” (p. 1309, Connell 1978). It has been
observed that the intensity of competitive interactions is mediated by physical conditions
that affect the dominant competitor (Wethey 1984). It is therefore important to
characterize the geographic range where a certain interaction can exert influence in a
community, as well as understanding the forces that determine its effect. Based on
observations of population abundances and percent cover in southwest England (within
the mid-intertidal zone where CM is expected to cede space to SB), we investigated the
41
possibility of competitive breakdown. Near its range limit in southwest England, we
hypothesize that the competitive advantage of SB is neutralized or even reversed.
In a similar experiment, Gordon & Knights 2017 examined differences in
mortality and growth rate in individual adult barnacles at two sites near Plymouth,
England. Their results indicated that initial size was an important predictor of competitive
outcome, and suggests that scope-for-growth is an important consideration when using
growth rate as a metric of competitive outcomes (Gordon & Knights 2017). My
experiment expanded this analysis by examining growth and mortality at more sites,
across a latitudinal range, and, importantly, in newly recruited barnacles. We expected
that the majority of consequential interference competition would occur during the
recruitment period, when the greatest amount of growth is occurring.
Investigating the interplay of climate and competition necessitates carrying out
studies across environmental gradients. Patterns of competition need to be repeatedly
investigated under a variety of conditions to fully understand the drivers and
consequences of struggling for limited resources. Connell himself recognized the
importance of repeated studies of field experiments of competition: “Probably the most
direct way to decide how the occurrence or strength of competition varies is to repeat the
field experiment on the same species at a different time or place.” (Connell 1983).
Temporal and spatial variability in competition could have important consequences for
biogeography and ecosystem functioning, and it is problematic to generalize
geographically limited studies of competition across the full extent of a species
distribution. Interspecific competition may not take place at all times or in all locations
between two co-occurring species (Menge 1976). Determining the variables that govern
42
competitive interactions allows for predictions of population and community dynamics.
One of the most tractable approaches to such questions is to search for correlations in
competitive outcomes and environmental conditions.
To identify interspecific competition, there needs to be a significant, reciprocal
response of Species A to a change in density of Species B (Connell 1983). If reducing
density of SB in southwest England did not lead to a change in CM, then we would
conclude that competition is not occurring there. Investigation of competitive interactions
requires researchers to identify the limiting resource for a population as well as a
mechanism for differential capability in exploiting the resource (Underwood 1978).
Wiens 1977 discusses a number of limitations and challenges associated with field
experiments of competition theory. In particular, it is rarely established that the resource
in question is the only or most important limiting factor and that observed differences
between species are related directly or solely to the limiting resource. For intertidal
ecosystems, it is well-established that organisms are predominantly constrained by the
same limiting resource, space (Underwood 1978) Competition may occur through a wide
variety of mechanisms. The means of competition between SB and CM most likely
include one or more of the following descriptions (modified from Schoener 1983):
1) Consumptive competition - some quantity of the limiting resource is consumed or
used up by an individual, prohibiting its use by other individuals.
2) Preemptive competition - space is passively occupied by an organism, causing
other individuals to be unable to occupy that space before it is cleared. This style
of competition occurs most frequently in sessile organisms, like barnacles.
43
3) Overgrowth competition – this is the method of competition described in Connell
1961. It occurs when an individual or individuals grow over, crush, or smother
another individual, which may deprive it of some necessary resource (e.g. light,
food, etc.) or may directly injure or kill the organism.
In order to investigate the ability of temperature to mediate competitive outcomes
across a latitudinal gradient, it was necessary to consider all three potential mechanisms
of competition as potentially consequential for community structure patterns. Therefore,
for our investigation of competitive breakdown at the southern range limit of SB, we
proposed the following mechanistic hypotheses:
1. Differential growth rate due to temperature: At colder latitudes, SB grows faster
and can overgrow or crush neighbouring CM (Connell 1961). It is possible that at
more southern latitudes these rates of growth are reversed and CM can out-grow
and competitively displace SB.
2. Competitive swamping: Reproduction is temperature-dependent in SB (Rognstad
& Hilbish 2014, Rognstad et al. 2014) and larval output is greater in northern
locations than in the south (Snyder et al. in prep.). SB may smother CM with
newly settled larvae (Connell 1961). We observed that smothering is common in
the north but rare in the south. At warmer temperatures in the south SB may not
be able to produce enough larvae to smother CM. Deprived of this mechanism of
competition, SB may no longer be able to out-compete CM at southern locations.
44
3. Space pre-emption: It is possible that if they grow to a sufficient size, adult CM
cannot be displaced by juvenile SB. As a consequence of the latitudinal
differences in reproductive output CM may recruit to open sites more rapidly and
have more time to grow into adults at southern latitudes than in the north.
4. Non-competitive population turnover: It is possible that competition does not play
a significant role in the dominance of CM at southern locations. Both species may
be continuously removed by predation or some other disturbance and CM
eventually occupies open space through its greater reproductive output over SB.
Despite this complexity, it is important to consider mediating effects of climate on
drivers of community structure. If the relationship described in Connell 1961, a textbook
ecological principle, is modified by climatic variables, it suggests that climate disruptions
may be more extensive and difficult to predict than our current approaches can manage.
Refining our knowledge of ecology with climate effects will help improve resilience of
human society, especially industries that rely on natural resources, through improving
decision-making capabilities and promoting a conceptual understanding of organisms as
part of connected systems.
2.2 METHODS
Basis of Competitive Breakdown
To establish a quantitative basis for the observation that competitive exclusion of
CM in the mid-intertidal does not occur in southwest England, a percent cover analysis of
45
the control subquadrats was conducted across the latitudinal gradient (approximately 51
to 58 degrees North) using photos from spring 2015. Percent cover was estimated using
the open source software Image J (Abramoff et al. 2004). The edge of the square quadrat
frame was used to calibrate the ImageJ measurement tool. A grid of dots was placed over
the image of the control subquadrat. Spacing of dots was determined by measuring the
largest barnacle within the control (approximated by eye). The width of this barnacle was
used to set the spacing, so that no barnacle could have two dots fall on it. Each dot was
then assigned to one of the following categories: 1) Open Space 2) SB 3) CM 4) Other
(A. modestus, mussel, algae, etc.)
Experimental Design and Treatments
Five quadrats per site were established at the mid-intertidal level (the same
quadrats used to analyze recruitment densities). The mid-intertidal level is known for
having the strongest influence of interspecific competition (Menge 1976). Experimental
treatments were applied within subquadrat areas of each quadrat (Table …X) Scrapes
were created to allow de novo measurement of recruitment densities, as well as for
controlling pre-emptive settlement of certain species. This is an important consideration
since competitive ability may be size and age specific, rather than species specific
(Connell 1978). Since SB recruits in spring and CM recruits in fall, the timing of a
scrape determines which species will have first access to the cleared space and allows the
testing of the space pre-emption hypothesis as a mechanism of competition. Selective
removal of SB, simulating a year in which SB failed to recruit across all latitudes, also
tested this hypothesis. In this treatment, SB were thinned by using a needle to selectively
46
kill individuals (all individuals present were killed, but some may have re-colonized in
the period following the treatment, hence thinned). The shells of barnacles that are dead
will usually persist on a rock surface for a few days, but will eventually be removed
through wave action or the growth of adjacent barnacles (pers. obs.). Controls were
established concurrently to experimental treatments when quadrats were created so that
they experienced the same environmental conditions as experimental treatments. Controls
were never manipulated in any way.
Percent Cover Analysis of Simulated Recruitment Failure
With the same methods used to analyze percent cover in the control subquadrat,
an analysis of subquadrats 2 and 3 was conducted using photographs from spring 2016.
This analysis served as a preliminary investigation into patterns of space occupation
when CM is reprieved of competition with SB for 1.5 years. In this investigation,
subquadrat 2 served as a control for comparison against the experimental removal of SB
in subquadrat 3.
Image Analysis for Growth Rate and Mortality
Images were analyzed using the open source software Image J (Abramoff et al.
2004). The edge of the square quadrat frame was used to calibrate the ImageJ
measurement tool. The metrics of mortality and growth rate were measured over a one-
year period, from 2015 to 2016. Measurements were taken from barnacles in subquadrats
2 and 3. Since both of these subquadrats were scraped in spring of 2014, C. montagui
settled preemptively into these spaces in fall of 2014. SB then settled in spring of 2015.
47
Therefore, measurements collected in spring 2015 represent 0.5-year-old individuals of
CM and newly recruited SB.
Barnacles were sorted into nine groupings based on growing in situations of
isolation, intraspecific competition, and interspecific competition, at the level of the
individual (Figure 2.1). Since groupings were encountered based on recruitment into the
cleared spaces, not all groupings were present in all quadrats or all sites. A power
analysis was conducted, which showed that approximately 26 individual barnacles
needed to be sampled per quadrat. While this was not possible at every site, there were
sufficient populations across regions to analyze samples from the entire latitudinal range.
Therefore, barnacles were identified and measured from photographs taken at four sites
in spring 2015. The same quadrats were then analyzed from photographs in spring 2016.
If an individual barnacle could not be re-located, it would be marked as deceased. When
a barnacle could be found, opercular length was measured to obtain estimates of growth
rate. All newly recruited barnacles were identified based on plate number and shape as
described by Drévès (2001). Individuals of S. balanoides could be confidently
differentiated from other common intertidal species in the region, including Chthamalus
stellatus, C. montagui, and Austrominius (=Elminius) modestus (Southward 1976).
Competition is often studied in field experiments by changing the densities of
competitors and measuring their response (Connell 1983, Gordon & Knights 2017).
Response may be measured in a number of ways. This analysis examined both growth
rate and mortality. These two response variables were selected to be useful in comparing
across latitude (since temperature varies at this scale) and to test the hypotheses of
competitive mechanism. Mortality was measured in two ways: 1) Change in density over
48
time 2) Individuals were assigned a binary code (0 for dead or 1 for alive) and proportion
of surviving individuals was measured from one year to the next. Barnacles were
identified as dead by the absence of opercular plates. Mortality is a useful measure of
ultimate trends, which are consequential for community structure. Growth rate allowed
the testing of the differential growth rate hypothesis as a mechanism of competition.
Growth rate was measured using opercular length (Wethey 1983, Jenkins et al. 2008,
Gordon & Knights 2017). This measurement is better than basal plate length, which is
highly plastic in response to the rock and other organisms it is in contact with. Opercular
growth rate was measured as initial length – final length.
Statistical Analysis
The results to be presented are preliminary patterns across latitude and will be
expanded with subsequent analysis of all available data. To ensure sufficient statistical
power for these preliminary analyses, all interspecific groupings (5, 6, and 7) and all
intraspecific groupings (3, 4, 8, and 9) were categorized together, forming three
groupings: “alone”, “interspecific”, and “intraspecific.” Data were collected at two sites
in southwest England, one site in Wales, and one site in Scotland. Mortality data were
analyzed with a binomial generalized linear model (GLM). Growth rate was analyzed
with a linear model and ANOVA.
49
2.3 RESULTS
Basis of Competitive Breakdown
We found that in the control subquadrat, there is considerable variation in
proportional occupancy of mid-intertidal space across latitudes (figure 2.2) Percent cover
data show that both species are present throughout the entire sample region (although not
at all sites sampled). Looking at the transition in dominance between SB and CM
specifically (figure 2.2) shows that CM never outnumbers SB in reciprocal densities of
the sort that occur in Scotland - including the Isle of Great Cumbrae, the research site for
Connell 1961. The transition to mid-intertidal dominance seems to occur around site 18,
which corresponds to the transition between southwest England and Wales. In southwest
England, SB fails to competitively exclude CM and they occur in relatively equal
proportions.
Percent Cover Analysis of Simulated Recruitment Failure
The results of a year of simulated SB recruitment failure showed little difference
in the percent cover of the two experimental treatments (with and without SB). Across
latitude, the average proportional cover of each site is roughly the same regardless of
whether SB was allowed to recruit into cleared space or not (figure 2.3).
Mortality
There was enormous variability in the mortality of barnacles growing in isolation,
across latitudes (figure 2.4). Still, at lower latitudes CM has far higher survivorship
(almost all CM individuals survived) than SB (~25% of individuals survive) from 2015 –
50
2016. Survivorship of CM decreases with latitude while survivorship of SB appears to
increase, but with high variability for both species. Confidence intervals for both species
overlap indicating that the data do not support the likelihood of a difference between
mortality of SB and CM growing at high latitudes.
For SB and CM growing in interspecific groupings, patterns of mortality were
more distinct. CM again had higher survivorship and declining survivorship with latitude.
Although it is hard to draw conclusions about patterns with such broad confidence
intervals, averaged mortality at high latitudes was higher for CM in interspecific growing
in interspecific treatments as opposed to isolated individuals, indicating a negative
consequence for CM growing in contact with SB. One of the reasons for the wide
confidence intervals for individuals in isolation is that there is little open space at
northern latitudes and there was a smaller sample size for isolated individuals. In contrast
to SB individuals growing in isolation, SB growing in interspecific treatments showed
decreasing mortality with increasing latitude.
Growth Rate
For barnacles growing in both isolation and interspecific competition treatments,
SB has a much higher growth rate than CM (Figure 2.5). There was no effect of latitude
on growth rate of SB growing in isolation, which stayed constant at ~0.13 cm per year.
For interspecific treatments, however, SB had a lower growth rate overall, with rate
increasing with latitude. SB growth rate seems to be inhibited by interspecific
competition, but is recovered to its isolation level growth rates at the northernmost
latitudes of our study region. Growth rate of CM showed a negative relationship with
51
latitude in both isolation and interspecific competitions. There was no effect of
competitive grouping on CM growth rate.
2.4 DISCUSSION
The results of the percent cover analysis of control subquadrats supported the
personal observations of competitive breakdown across latitude. In the mid intertidal
zone where the expectation based on a known competitive hierarchy would have been the
exclusion of CM, we quantitatively demonstrated SB exists in relatively equal
proportions with CM in southwest England. Although a percent cover analysis is not a
rigorous way to investigate mechanisms of competition, our result suggests that space
preemption is not an influential mechanism relative to competition outcomes, at least for
CM, which is the species that received a preemption advantage in this experiment. While
not identical, there is no apparent shift in the overall pattern between treatments. Wiens
(1977) has proposed that competition might occur only when resources are scarce. I.e. in
southwest England competition is a non-issue. I can test this hypothesis by considering
competition at the level of the individual. However, it appears that at the population scale
it is effectively true that competition is not occurring due to an excess of space.
Comparing the percent cover results of the simulated recruitment failure
treatments to percent cover of control subquadrats shows an interesting pattern. It appears
that in southwest England, SB will eventually come to occupy more space than initially
suggested by the recruitment failure analyses, which are based on patterns of recruitment.
Recruitment lays out patterns of space occupation that are later modified with growth and
competition. It is unclear from this data to what extent growth and competition are
52
important in this shift, but it does show that occupation of space for SB is not reduced
following recruitment.
Declining survivorship of CM with increasing latitude supports the expectation
that SB should come to dominate the mid-intertidal at these latitudes. Furthermore, it
shows that densities of CM at northern latitudes is not the result of patterns of
recruitment. Some mechanism (environmental or biotic interaction) is resulting in greater
mortality in these locations. In southwest England, this mechanism seems to be lost
resulting in near 100% survivorship. This is consistent with the findings of Hyder et al.
2001.
SB survivorship declines with latitude in interspecific groupings. This result is
most likely misleading, in that it seems to indicate that SB are losers in competitive
contests with CM. This result is almost certainly an artifact of the fact that recruitment
supplies sites at northern latitudes with far more SB larvae than can ultimately grow to
occupy the available space, resulting in intense intraspecific competition and density
dependent mortality (figure 2.4). SB still comes to occupy the majority of space in the
mid-intertidal at higher latitudes (figure 2.2), so it remains to be seen if part of this
pattern is a mechanism of competition that varies with latitude.
Furthermore, SB survivorship is far lower in southwest England when growing in
isolation compared to interspecific treatments. This was a somewhat surprising result,
since we expected that having ample space for growth would be advantageous across
latitude. However, it may be that in southwest England, the stresses of environmental
variables may outweigh negative consequences of growing in interspecific competitive
groupings. There may be a positive effect of being in a competitive grouping in
53
southwest England, through creating a moist microclimate that shelters SB from a known
cause of mortality – desiccation. This has been observed in populations of SB in North
America (Bertness 1989).
SB had a higher growth rate regardless of competitive grouping. Part of this result
may be that we compared photographs from spring 2015 to spring 2016, the recruitment
season for SB. Therefore, CM may have had a lower scope-for-growth than SB. This
could be remedied by examining photographs from fall 2015 to spring 2016 to see if
newly recruited CM have a comparable growth rate in the early months following
recruitment. CM showed no effect of competitive grouping on growth rate. SB, however,
had a depressed growth rate in interspecific competition, that was at its lowest in
southwest England. This supports our hypothesis of differential growth rate across
latitude. SB is growing slowest in the region where it has been observed to lose its ability
to competitive exclude CM. There is no effect of latitude for SB growing in isolation,
however.
The possibility of non-competitive population turnover as an explanation for our
observations of patterns of abundance, presents a confounding factor in testing other
hypotheses of competitive mechanisms. Although these are numerous, we hypothesize
that two of the most important variables acting on these species were predation and
desiccation. In the UK, a main predator of barnacle populations in the intertidal is
Nucella lapillus, known as dog-whelks. Dog-whelks are direct developing gastropods that
are known to prey on a variety of species, but preferentially consume S. balanoides and
mussels (Mytilus edulis) (Crothers 1985). Predation has been proposed as the single most
important determinant of biodiversity and community structure in several ecosystems.
54
The relative importance of competition and predation at different trophic levels to
determining community structure remains a tug of war in ecological theory (Paine 1971,
Menge & Sutherland 1976).
Because barnacles occupy a relatively intermediate trophic level, predation and
competition may play a relatively equal role in structuring their communities (Menge &
Sutherland 1976). It should also be noted that when environmental conditions are severe
enough, intertidal communities may be released from biological stresses from mobile
predators and competitors, and become composed entirely of organisms capable of
withstanding mechanical stresses (Menge & Sutherland 1987). The relative influence of
competition, predation, and physical stress shifts with several variables that may
characterize any one site. An experiment was carried out to provide proof of concept and
preliminary data to investigate effects of predation and desiccation. Overall, this
experiment represents only a limited view of how these factors affect populations of SB
and CM because the intensity of predation and desiccation certainly vary across our
entire study region. Furthermore, these two effects (predation and desiccation) are likely
to have a significant interaction. For example, Menge & Sutherland 1976 found that
shaded sites in their experiment of had increased predation of SB by Thais snails, most
likely due to the mobile predators seeking moist refuges during low tide. An alternative
hypothesis about the influence of predators is presented in Paine 1971 – in which
predators preferentially prey on a dominant competitor. This would seem to be the case
for our system where Nucella preferentially consumes SB over CM. However, there is no
evidence that they are the preferred prey because they are the dominant competitor and
not some other aspect of their biology. Understanding the interplay between competition
55
and predation will be important for making predictions about how these communities will
respond to climate change. Ecological processes rarely act in isolation and it is prudent to
avoid testing hypotheses that assume only one variable is responsible for an observation
(Quinn & Dunham 1983, Connell 1983).
Recruitment may have played a role in creating these patterns of competition. The
magnitude of recruitment is known to be influential on other ecological processes that
influence community structure – environmental conditions, predation, and competition
(Menge & Sutherland 1987, Hyder et al. 2001, Svensson et al. 2006). For example, we
have observed that barnacles may or may not recruit in sufficient densities to create a
moist microclimate through the complex topography of their shells, and suffer
desiccation mortality as a consequence. Furthermore, higher recruitment densities may
create a foraging landscape that benefits mobile predators, such as Nucella spp. It has
been suggested that competition may only be an important driver of community structure
when populations are near their carrying capacity (Menge & Sutherland 1976,
Underwood & Keough 2001). Low recruitment densities at warmer sites, due to a
physiological mechanism for embryo brooding, may therefore interfere with the
possibility for recruitment-dependent mechanisms of competition to act in ways that are
consequential to community structure. In that scenario, SB may be at a greater
disadvantage at its range limit in southwest England population predictions based on
thermal tolerances of adults or embryos may suggest.
There are, however, a number of ecological processes that have not been
controlled in our experiments and may be exerting meaningful influence. Disturbances
are known to play a role in structuring intertidal communities (Dayton 1971, Connell
56
1978, Menge & Sutherland 1987) and may interrupt competition and preserve diversity.
While we have made no observations to suggest this is the case, we have not measured
variability in disturbances across our sites, we are confident this does not explain our
results. Many varieties of disturbance (especially mechanical removal by waves and
rocks) would be expected to affect SB and CM equally. Additionally, our study has
focused on temperature as an environmental stressor, but such stressors do not occur in
isolation. An additional variable that was of interest when considering multiple stressors
that act on this competition system was ocean acidification. Findlay et al. 2010 discusses
findings that indicate ocean acidification could negatively affect population dynamics of
SB at its southern range limit and accelerate local extinction in this region. The interplay
of these effects with competitive outcomes would be an interesting avenue for further
research.
It is also important to consider evolutionary processes across a species range.
Organisms typically occur at reduced densities at range edges compared to within the
core of a range (Weiss-Lehman et al. 2017). Therefore, selection pressures may favor
traits that confer an advantage in these different density environments. Differing selective
forces (challenging environmental variables, decreased probability of finding a mate,
different foraging landscape) at range edges may force trait trade-offs that lower
competitive ability (Weiss-Lehman et al. 2017). Ecological processes at range limits may
prove difficult to study because of the stochastic evolutionary processes that act on these
populations. The majority of studies of eco-evolutionary feedbacks have focused on
expanding range edges, neglecting empirical studies of these effects at contracting edges
(Bridle & Vine 2006, Burton et al. 2010, Hargreaves & Eckert 2014, Fronhofer &
57
Altermatt 2015). Continuing work on this aspect of biogeography will be important for
future studies but is not addressed in this experiment.
Measuring the influence of interspecific competition in determining distribution,
abundance, and resource use of species in communities is a challenge due to the number
of other factors that can play a role, such as symbioses, abiotic factors, disturbances, etc.
One limitation of our experimental approach is that we were only able to manipulate
densities below the levels in which they were naturally occurring in the sample sites.
However, the densities we encountered during the time period of the experiments was not
reflective of the full range of population densities that have been known to occur at these
locations. Another uncontrolled variable in our experiment was how sheltered vs. high
energy shorelines affect predation, competition, and ultimately community structure
(Menge & Sutherland 1987). We have not measured the degree of mechanical stress
occurring across sample sites.
As it has been noted before in other systems (Connell 1983, Woodin et al. 2013),
it is limiting to measure ecological processes in only one point in space and time. More to
the point, it is misleading to draw general conclusions about community dynamics from
any such case study of competition. This reassessment of Connell 1961 prompts the
question of how many foundational concepts of ecology are less widely applicable than
thought, due to interplay with physical characteristics with biotic interactions. Climate
variability and frequency and amplitude of extreme events are important aspects of
climate change (Thompson et al. 2013). This result further muddies the waters of
predicting how species and ecosystems may respond to rapid environmental change as a
result of climate change. However, it is likely that predictions founded on assumptions
58
that the interactions within ecosystems (competition or otherwise) will remain stable
through rapid environmental changes, are inaccurate. There is a need for increased study
of ecological interactions across physical and latitudinal gradients and how
environmental variables interact with trait – mediated effects (Woodin et al. 2013).
Competition is a crucial ecological consideration in this regard because it has been
implicated in the creation or maintenance of biodiversity (Pianka 1966, Menge &
Sutherland 1976, Underwood 1978, Connell 1978, Lubchenco 1978, Underwood 1978).
Through competition, the rocky intertidal of the UK is highly structured and allows the
coexistence of SB and CM. Changes in competitive hierarchies due to climate could flip
community dynamics from coexistence to competitive exclusion, leading to local
extinctions.
Overall, our results suggest an accelerated pace of range contraction for SB.
Through an interaction modification (Wootton 1993), SB has lost the competitive edge
that promotes its presence in the mid-intertidal at colder latitudes. Range contraction of
boreal species is likely to be a consistent pattern in marine environments. For industries
that depend on natural resources, it is crucial to be able to predict where organisms will
occur in the future. Besides helping society predict and adapt to changes in the
distributions of commercially important species, building models to incorporate climate-
mediated processes such as recruitment and competition will lead breakthroughs in
understanding of evolution and ecology. Applying paleoclimate data may provide
insights into ecological processes throughout geologic time and shed light on
evolutionary history. Furthermore, these results reinforce the complexity of ecological
processes and the need to conduct experiments across a range of spatial and temporal
59
scales. Ecologists should be circumspect in the broad application of ecological theory,
such as outlined in Connell’s iconic 1961 experiment, particularly in the face of changing
climate
Climate envelope models have been a standard approach to forecasting
biogeographic responses to climate change (Pearson & Dawson 2004, Araújo & Guisan
2006, Helmuth et al. 2006, Poloczanska et al. 2008, Woodin et al. 2013). However, they
have several limitations, such as a failure to consider physiologically sensitive stages
across the life span of an organism, dispersal abilities, direct- and in-direct effects of
species interactions, and the mediating effects of physical conditions on those
interactions. Failing to incorporate details of natural history and organismal biology often
limits the broad scale predictive power of ecological theory (Tewksbury et al. 2014).
Models that include mechanistic predictors incorporating physiologically sensitive life
history stages and trait-mediated ecological processes are likely to improve forecasting of
biogeographic response to climate change (Wethey et al. 2011, Wethey & Woodin 2008).
60
Table 2.1 Subquadrats and corresponding experimental treatments.
Subquadrat Number Treatment
1 Control. Unmanipulated.
2 Scraped spring 2014. All species recolonize.
3 Scraped spring 2014. S. balanoides selectively removed to simulate a year of recruitment failure.
4 Scraped spring 2015. All species recolonize.
5 Scraped fall 2015. All species recolonize.
6 Scraped spring 2016. All species recolonize.
Figure 2.1 Experimental groupings for measurements of mortality and growth rate.
61
62
Figure 2.2 Average proportional cover of SB and CM across sites. Sites 1 – 18 are southwest England. Sites 18 – 23 are Wales. Sites 23 – 28 are Scotland.
Figure 2.3 Percent cover analysis of simulated SB recruitment failure.
63
Figure 2.4 Mortality (mean ± standard error) of SB and CM across latitude in isolation and interspecific groupings. Shown with 95% confidence intervals.
SpeciesChthamalusSemibalanus
50.0 52.5 55.0 57.5 60.0
0.25
0.50
0.75
1.00
Latitude
Perc
ent S
urvi
val
SpeciesChthamalusSemibalanus
50.0 52.5 55.0 57.5 60.0
0.25
0.50
0.75
1.00
Latitude
Perc
ent S
urvi
val
SpeCS
Alone Interspecific
64
Figure 2.5 Growth rate (mean ± standard error) of SB and CM across latitude in isolation and interspecific groupings. Shown with 95% confidence intervals.
SpeciesChthamalusSemibalanus
Alone Interspecific
50.0 52.5 55.0 57.5 60.00.00
0.05
0.10
0.15
Latitude
Gro
wth
Rat
e (c
m p
er y
ear)
SpeciesChthamalusSemibalanus
50.0 52.5 55.0 57.5 60.00.00
0.05
0.10
0.15
Latitude
Gro
wth
Rat
e (c
m p
er y
ear)
SpecieChthSem
65
66
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