Factors affecting the distribution, abundance and...
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1 Factors affecting the distribution, abundance and condition of an invasive freshwater bivalve in a thermal plume Rowshyra A. Castañeda Department of Biology McGill University, Montreal Submitted December 2012 A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Master of Science. © Rowshyra A. Castañeda 2012
Transcript of Factors affecting the distribution, abundance and...
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Factors affecting the distribution,
abundance and condition of an invasive
freshwater bivalve in a thermal plume
Rowshyra A. Castañeda
Department of Biology
McGill University, Montreal
Submitted December 2012
A thesis submitted to McGill University in partial fulfillment of the
requirements of the degree of Master of Science.
© Rowshyra A. Castañeda 2012
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TABLE OF CONTENTS
ABSTRACT...........................................................................…………….…..4
RÉSUMÉ.............................................................................................………5
PREFACE...................................................................................................…6
Contributions of Authors............................................………...……….6
Acknowledgements..............................................………….…............7
LIST OF TABLES............................................................................................9
LIST OF FIGURES.....................................................................................…10
CHAPTER 1: Physical factors affecting the invasion success of the
Asian clam (C. fluminea): A global synthesis....……………………......12
Abstract.............................................................................................13
Introduction..........................................................................….….....13
Methods............................................................................................18
Results..............................................................................................21
Discussion.........................................................................................22
Conclusion........................................................................................25
Figures..................................................................................……….27
References........................................................................................30
LINKING STATEMENT........................................................................…........34
CHAPTER 2: Factors affecting the distribution, abundance and
condition of an invasive bivalve (Corbicula fluminea) along an
artificial thermal gradient in the St-Lawrence River...............………...35
Abstract.............................................................................................36
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Introduction.........................................................................…...........37
Methods............................................................................................40
Results..............................................................................................45
Discussion.........................................................................................49
Conclusions......................................................................................56
Tables...............................................................................................58
Figures..............................................................................................62
References........................................................................................70
GENERAL CONCLUSIONS..............................................................................75
APPENDIX 1………………………………………………..……………………78
APPENDIX 2……………………………………………………………………...83
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ABSTRACT
The Asian clam, Corbicula fluminea, is a freshwater bivalve that has
recently invaded artificially heated waters downstream of the Gentilly-2
nuclear power plant in the St. Lawrence River. C. fluminea is one of the
world’s most invasive molluscs, owing to its ability to rapidly establish
dense populations in new areas. Its physiological requirements have
apparently restricted its global distribution to waterbodies whose
temperatures remain above 2ºC throughout the year; however, recent
invasions suggest that the clam may be adapting to lower temperatures.
Using published data, I have identified patterns of the distribution and
population densities of C. fluminea in artificially heated and natural (non-
heated) waterbodies. Densities of C. fluminea populations do not differ
between artificially heated and non-heated waters, but exhibit a positive
trend with latitudinal distance such that peak densities occur in middle
latitudes. The occurrence of C. fluminea in United States rivers below
40oN is positively correlated with human population density. At local
scales within the St. Lawrence River, temperature, flow velocity, turbidity
and depth were identified as factors that affect the distribution and density
of C. fluminea. Furthermore, the clam was restricted to sites within the
thermal plume of the Gentilly-2 power plant, and its body condition and
reproductive status varied in time and space. The presence of C. fluminea
in St. Lawrence River raises the question of whether the species can use
the thermal plume to adapt to colder conditions and spread further in the
river, especially as warming trends continue.
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RÉSUMÉ
La petite corbeille d’Asie, Corbicula fluminea, est un bivalve subtropical
d’eau douce qui a récemment envahi la panache thermique de la centrale
nucléaire Gentilly-2 (CNG2) du fleuve Saint-Laurent. C. fluminea est un
des mollusques les plus envahissant du monde, en raison de sa capacité
rapide à former de nouvelle population dense. Ses besoins
physiologiques semblent limiter sa distribution mondiale à des plans d’eau
maintenant une temperature de plus de 2ºC au courant de l’année;
néanmoins, des envahissements récents suggèrent que la palourde
s’adapte à des températures plus basses. Utilisant des donnés publiés,
j’ai identifié des types de distribution et de densité de C. fluminea dans
des eaux chauffés artificiellement et non-chauffés. Les densités de C.
fluminea ne diffèrent pas entre les deux types d’eau, mais démontrent une
relation positive avec la distance latitudinal, où les densités maximales
sont atteintes aux latitudes centrale. La présence de C. fluminea dans les
rivières Américaines se situant au sud du 40ºN est positivement corrélée
avec la densité de la population humaine. À l’échelle locale du fleuve
Saint-Laurent, la température, le courant d’eau, la turbidité et la
profondeur ont été identifiés comme facteurs affectant la distribution et la
densité de C. fluminea. De plus, la palourde était limitée aux sites du
panache thermique de la CNG2, et sa condition et son statut réproductif
varient en temps et espace. La présence de C. fluminea dans le fleuve
Saint-Laurent soulève des questions sur la possibilité de l’espèce d’utiliser
la panache thermique afin de s’adapter à des conditions plus froides
surtout si les tendances de réchauffement continuent.
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PREFACE Contributions of Authors
The research described in this thesis reflects my own independent work,
supervised by Dr. Anthony Ricciardi of McGill University and conducted in
consultation with Dr. Anouk Simard of the Ministère des ressources
naturelles et de la faune (Quebec). Dr. Ricciardi provided guidance with
the research design and analysis described in both chapters. Dr. Simard
provided valuable advice and long-term temperature data for the field
study described in Chapter 2; as such, both Dr. Ricciardi and Dr. Simard
will be co-authors for the manuscript when it is submitted for publication.
I conducted the literature review, statistical synthesis and interpretation of
results for Chapter 1. I planned and conducted the field sampling,
laboratory work, and data analysis for Chapter 2. I wrote all sections of
this thesis, and Dr. Ricciardi provided editorial comments throughout.
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ACKNOWLEDGEMENTS
First and foremost, I would like to thank my supervisor, Dr. Anthony
Ricciardi, for all the valuable advice and support throughout the duration of
my thesis. His passion for science and incredible knowledge of all things
aquatic kept me motivated, driven and inspired.
Thanks to my committee members, Dr. Irene Gregory-Eaves and
Dr. Frédéric Guichard for their helpful ideas and constructive criticism
while designing my research project. I would also like to thank my
collaborator, Dr. Anouk Simard from the Ministère des Ressources
Naturelles et de la Faune (MRNF), for facilitating my research and
providing me with valuable data and resources.
I am grateful to Hydro-Québec, and employees at the Gentilly-2
Nuclear Power Plant, specifically M. Stéphan Chapdelaine and M. Yves
Roy, for granting me access to my study site and for all the
technical/logistical help.
I was lucky to have worked with a very supportive and friendly lab.
Thanks to Suncica Avlijas who taught me almost everything in the field,
without her incredible knowledge I would not have been able to complete
my field work. I had an incredible field and lab team (in alphabetical
order): Kara Lynn Beckman, Natasha Dudek, Etienne Lafortune, Charlotte
Lapeyre, Ian Perrera, Christopher Shea and Isabel Tom. I was also
fortunate to have Katie Pagnucco and Andrea Reid who provided great
insight from the beginning of my research up to the very end; and all other
Ricciardi lab members who kept me motivated and always had time for
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me: Kayla Hamelin, Ahdia Hassan, Josie Iacarella, Lisa Jones, Jordan
Ouellette-Plante and Emilija Cvetanovska. A special thanks to Rebekah
Kipp for all her help and training with invertebrate identification. I am also
very grateful for all the statistical advice and QGIS training I received from
Dr. Guillaume Larocque at QCBS.
I would also like to thank all my friends at McGill University and
especially the BGSA (Biology Graduate Student Association) for keeping
my spirits high with all the organized events, get togethers and good
laughs.
Last but not least, I would like to thank my parents, Maria Barreiro
and Ashley Castañeda, for their continued love and support throughout my
whole academic career. Also, to my brother, Cyruss Castañeda and
sister, Cleoshyra Castañeda, for their encouragement. Finally, a huge
thank you to my best friend, Jérôme Janiak, who always knows the right
thing to say and pushes me to be a better person every day.
I would like to dedicate my thesis to my little brother, Spartycuss
Castañeda, who inspires me daily with his optimism, love and comedy.
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LIST OF TABLES
Chapter 2: Table 1: Environmental variables recorded at sampling sites inside and
outside the plume of the Gentilly-2 Nuclear Power Plant. Outside the plume defined as upstream ambient water temperature; inside plume defined as water temperatures +1ºC above ambient. Mean ± S.E. (Min, Max)..........................................................................................................58
Table 2: Comparison (using ANOVA and Tukey’s post-hoc tes) between
the different environmental variables in the canal and ROP in the early and late summer of the G2NPP thermal plume. Mean ± S.E. (Min, Max)........59
Table 3: Summary of the generalized linear mixed models and their
corresponding AICc values predicting the abundances and occurrence of Corbicula fluminea for the summer sampling with site, grab and time as random factors……………………………………………………………........60 Table 4: Results from the generalized linear mixed model with the lowest AICc score, using variable/fixed effects and random factors site, grab and time unique to Model 4, AICc = 468.8…………………………………….....61
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LIST OF FIGURES
Chapter 1: Figure 1: Density of C. fluminea along a latitudinal gradient. Open circles
are densities in non-heated waters. Closed circles are densities in heated waters. Reference for each data point can be found in Appendix 1................................................................................................................27 Figure 2: (a) Density of C. fluminea in artificially heated (958.1 ± 372.0 (52.0,4050.0) m-2) and non-heated (874.0 ± 825.6 (8.0, 2991.0) m-2) American waterbodies (one-way ANOVA, p > 0.1). (b) Density of C. fluminea in European (754.4 ± 264.0 (6.23, 5000.0) m-2) and North American (1868.0 ± 867.6 (20.0, 29951.0) m-2) waterbodies (one-way ANOVA, p > 0.1). Reference for each data point can be found in Appendix 1................................................................................................28 Figure 3: Probability of C. fluminea occurring in a water body surrounded by varying human population densities. Line fitted by least-squares regression: y = 13.2/(1+(x/327)) (r2 = 0.23, p = 0.003)..…….....................29 Chapter 2: Figure 1: Gentilly-2 Nuclear Power Plant thermal plume - Sampled sites and Asian Clam distribution.......................................................................62 Figure 2: a) Average temperature sampled in August along the thermal
gradient. Line fitted by least-squares regression: y = 24.548 + 2050.619/x, r2 = 0.975, p < 0.001. b) Average temperature recorded hourly between October 2011 and May 2012, against distance from the heat source. Line fitted by least-squares regression: y = -3.394ln(x) + 30.776, r2= 0.953, p < 0.001. c) Average flow velocity sampled in August along the thermal gradient. Line fitted by least-squares regression: y = -0.0832ln(x) + 0.7091, r2 = 0.513, p < 0.01. Error bars represent standard error; where they are not visible, the error is smaller than the size of the marker………………………...................................................…….....…....63 Figure 3: (a) Average daily temperature in the canal. Temperature was recorded hourly by temperature loggers between October 11, 2010 and August 2, 2012. The solid black line represents the temperature threshold for reproduction (15ºC, RT), the dashed line represents the critical temperature for metabolic processes (30ºC, CM), the solid grey line represents lower temperature tolerance (2ºC, LT), the dotted line represents the upper temperature tolerance (36ºC, UT). Black arrows show times that G2NPP reactor was stopped for maintenance or other emergencies. (b) Mean daily water temperature from October 26, 2010 to May 20, 2011. The black solid line represents average daily temperatures
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in the plume at 1.5km from the discharge point. The grey line represents average daily temperature at the end of the plume at 4.5 km downstream from the discharge point. The dashed line represents the lower temperature tolerance (LT) for C. fluminea at 2ºC.....................................64 Figure 4: Relationship between the log-transformed C. fluminea density (clams m-2) and distance from the discharge source (m). Line fitted by least-squares regression: y = -0.0006x + 3.693, r2 = 0.744, p < 0.001.....65 Figure 5: Log10(C. fluminea density m-2) in relation with a) temperature (ºC), line fitted by least-squares regression: y = 0.3147x - 6.5872,
r2=0.571, p<0.001; b) Depth (m), line fitted by least-squares regression: y = -0.4027x + 2.8938, r2= 0.3011, p<0.001. c) Water transparency (cm), line fitted by least-squares regression: y = -0.0605x + 5.1586, r2= 0.4306, p<0.001.....................................................................................................66 Figure 6: Log10transformed dry tissue weight (g) versus log10transformed shell length (mm) for Corbicula fluminea adults (>6mm), in the Canal and ROP in June and August. Lines fitted by least-squares regression: 1) June Canal: log10mass = 3.16 log10length - 5.32, r2= 0.95, p < 0.001; 2) June ROP: log10mass = 3.30 log10length - 5.45, r2= 0.74, p < 0.001; 3) August Canal: log10mass = 3.12 log10length - 5.34, r2= 0.77, p < 0.001; 4) August ROP: log10mass = 2.99 log10length - 5.11, r2= 0.83, p < 0.001.67
Figure 7: The biomass (dry tissue weight g/m2) of adult C. fluminea along the thermal gradient in a) June, line fitted by least-squares regression: y = 3137.6/x + 6.879, r2=0.80, p < 0.001. b) August, no relationship between the variables, p>0.05. The mean shell length (mm) of C. fluminea along the thermal plume in c) June, line fitted by least-squares regression: y = 68.463/x + 4.817, r2 = 0.635, p = 0.002; d) August, no relationship between the variables, p>0.05. The error bars represent standard error, where the error bars are not visible, the error is smaller than the marker size.…......68 Figure 8: Frequency histogram of C. fluminea’s size classes in the canal in June: a) canal, b) Section 2 c) Section 3, d) Section 4; and in August: e) canal, f) Section 2 g) Section 3, h) Section 4. Each peak in the figure represents a potential cohort.....................................................................69
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CHAPTER 1
Physical factors affecting the invasion success of the
Asian clam (C. fluminea): A global synthesis
Rowshyra A. Castañeda1 and Anthony Ricciardi1
1 Redpath Museum, McGill University, 859 Sherbrooke Street West, Montreal, QC H3A 0C4, Canada
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ABSTRACT
The Asian clam, Corbicula fluminea, is regarded as one of the world’s
most invasive freshwater molluscs, having invaded North America, South
America, Europe and northern Africa. Its reproductive capacity, early
maturation and high growth rate allow it to rapidly colonise and form dense
local populations. Here, I review the literature to identify large-scale
patterns in distribution, density and establishment success of C. fluminea.
Its distribution and abundance in Europe, North and South America were
analysed in natural and artificially heated waterbodies across a latitudinal
gradient. The population density of C. fluminea was weakly correlated
with latitudinal distance of the invaded waterbody from the equator.
Population densities did not differ between natural and artificially heated
waterbodies in the Americas, nor between European and North American
waterbodies, despite having invaded European waterbodies more
recently. The probability of establishment in North American rivers was
positively correlated with human population density in the basin and the
number of endangered species in the river, and negatively correlated with
land use (% agriculture). Increasing invasions of north temperate
waterbodies suggest that the species is adapting to winter conditions or
that climate change has rendered such habitats more hospitable.
GENERAL INTRODUCTION
Introductions of invasive nonindigenous species into novel
environments are unprecedented in their impact, magnitude, and
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frequency on a global scale (Vitousek et al. 1997; Ricciardi 2007).
Regardless of whether these introductions are intentional (e.g. for sport
fishing, biological control) or unintentional (e.g. ballast water release,
aquaculture escapees), invasive species can alter food webs and
ecosystem functioning, and contribute to the extinction of native flora and
fauna (Zaret and Paine 1973; Vitousek et al. 1997; Clavero and García-
Berthou 2005). Moreover, it is estimated that the global damage inflicted
by invasive species is $1.4 trillion per annum (Pimentel et al. 2001), which
exceeds that of all natural disasters combined (Ricciardi et al. 2011).
Invasions are particularly prevalent in aquatic environments (Ruiz et al.
2000; Ricciardi 2006). Freshwater ecosystems appear quite vulnerable to
invasion, owing to extensive anthropogenic disturbance and dispersal
opportunities created by human vectors operating at multiple spatial
scales (Ricciardi 2006). As such, understanding the impacts and
managing the risk of invasive species are both crucial to freshwater
conservation (Dudgeon et al. 2006; Arthington et al. 2010).
The ability of a nonindigenous species to establish a sustainable
population in a new system depends on multiple factors, including the
physiological requirements of the species and the biotic and abiotic
conditions of the recipient environment (Ricciardi and Rasmussen 1998;
McMahon 2002). The climate-matching hypothesis posits that
establishment is more likely if the climate of the invaded region is similar to
that of the donor region (Bomford et al. 2010), which explains why many
tropical and subtropical species fail to invade temperate regions even
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when given ample opportunity (Wiens and Graham 2005). For example,
there have been numerous confirmed reports of tropical fishes (e.g.
Piranha, Pacu, and Suckermouth Catfish) in the Great Lakes (Leach
2003), suggesting that introductions of such species are common (through
aquarium release), despite the absence of reproducing populations.
However, climate change and human-mediated temperature changes at
the habitat scale (such as thermal discharges from power plants and
industries) may alter physiological barriers to establishment.
Indeed, thermal discharges have facilitated invasive invertebrates
since the use of water cooling systems began, such as during the 18th and
19th centuries in Britain, where naturalists first noticed the presence of
nonindigenous snails (Langford 1990); subsequently, other invasive
invertebrates such as the oligochaete worm Brachiura sowerbyi and the
gastropod Physa acuta were found to dominate benthic community
biomass in heated effluents (Aston 1968; Langford 1983). Artificially
heated waterbodies have also facilitated the range expansion of the Asian
clam, Corbicula fluminea, in North America and Europe (Graney et al.
1980; Ward and Hodgson 1997; Schöll 2000).
The establishment success of a nonindigenous species increases
with propagule pressure – the number and frequency of organisms
introduced to a system (Lockwood et al. 2005). Given that propagule
pressure is correlated with human activity, a larger human population
density around a waterbody may increase the latter’s vulnerability to
invasion. Disturbed areas, such as those that have suffered habitat loss
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and fragmentation, or are subject to extensive land use, are also more
vulnerable to invasions (Vitousek et al. 1997; Davis et al. 2000; Marvier et
al. 2004). Thus, the success of a species introduction is determined by a
combination of biotic and abiotic factors, which can change through time.
A predictive understanding of these factors is necessary for effective
management of high-risk invasive species, and progress toward this goal
may be achieved through experimentation and statistical synthesis of data
from multiple invaded sites at different spatial scales (Ricciardi 2003).
Here, I examine published records of one of the world’s most successful
aquatic invaders, the Asian clam Corbicula fluminea, to identify large-scale
patterns in its distribution and abundance.
The study species
Native to southeast Asia, C. fluminea is a hermaphroditic cross- and
self-fertilizing clam capable of brooding and releasing up to 68 000 shelled
larvae per individual per breeding season (McMahon 1999, 2002; Sousa
et al. 2008). The larvae use byssal threads to anchor to sediments and
rapidly grow through their juvenile period; individuals will reach maturation
after 3 to 12 months, depending on environmental conditions (Prezant and
Chalermwat 1984; McMahon 1999, 2002). The maximum life span is 4
years. Adult survivorship is low (<40% per year) compared to longer-lived
species (Unionidae, >95%) (McMahon 2002).
Its ecological impacts are largely related to its rapid population
growth and high filtration rates (McMahon 1999). Competition with C.
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fluminea for food resources is considered to be an important factor in
population declines of native unionid mussels and other filter feeding
invertebrates (Gardner et al. 1976; Lauritsen 1986; Cherry et al. 2005;
Cooper et al. 2005; Sousa et al. 2008). In the Potomac River, C.
fluminea’s filtration activity tripled the water clarity, which led to the
resurgence of dense macrophyte beds and associated increases in fish
and waterfowl populations (Phelps 1994). Hence, C. fluminea acts as an
ecosystem engineer in its invaded environment. Its economic impacts are
associated with the entrainment of larvae in water intake pipes and its
ability to thrive in these structures, especially where sand and silt may
accumulate and provide substrate for burrowing clams. Consequently, C.
fluminea is often been found as a fouling organism in the water supply
systems of power plants and industries, causing reductions in efficiency
and power outages, and requiring chronic chemical controls (McMahon
1999), collectively resulting in substantive annual costs (e.g. >$1 billion
USD in the United States; Isom 1986).
The distribution of C. fluminea has been mainly limited to warm
waters in which the temperature does not drop below 2ºC, which is
believed to be its lower tolerance limit (McMahon 1983, 1999, 2002). The
species requires temperatures between 13-19ºC for reproduction,
therefore it is generally confined to waterbodies located below 40º latitude
(Britton and Morton 1979). Above the 40º latitude, it lives in thermal
refuges of artificially heated waters which tend to have more stable
temperatures during the winter (Britton and Morton 1979; Langford 1990).
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However, within the past two decades, surveys have found overwintering
populations in unheated waters, as well as populations in heated effluents
of power plants, further north than previously recorded (French and
Schloesser 1991; Janech and Hunter 1995; Simard et al. 2011).
Given that the impacts of nonindigenous species are a function of
their abundance and, thus, patterns of abundance are useful to risk
assessment (Ricciardi 2003), it is of interest to compare the abundance of
C. fluminea in different areas in which it has been introduced. In both the
Americas and in Europe, I hypothesize 1) that the density of C. fluminea in
non-heated waters is inversely correlated with distance from the equator
while its density in artificially heated waters is independent of latitude; 2)
the density of C. fluminea in non-heated water will be lower from that in
artificially heated waters, which are less subject to fluctuations and can
maintain temperatures within the clam’s optimal range; 3) because C.
fluminea invaded Europe 50 years later than North America, its population
densities in Europe will be smaller on average, owing to lag times in
population growth and adaptation (Crooks and Soulé 1999); and 4) C.
fluminea’s establishment is correlated with human population density and
disturbance in the watershed, owing to elevated propagule pressure and
the ability of the clam to rapidly colonise unstable habitats (McMahon
1999).
METHODS
Population density of C. fluminea
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Density data for C. fluminea were compiled through a literature
search conducted on ISI Web of Knowledge and Google Scholar using the
following search terms: (Corbicula fluminea OR Corbicula manilensis OR
Asian clam OR Asiatic clam) AND (invasion OR first mention OR first
record OR density OR abundance). I harvested all articles that mentioned
either Corbicula fluminea (Müller 1774) or its junior synonym Corbicula
manilensis (Philippi 1844), and that contained density data. This effort
was supplemented by a more focused search of the journal The Nautilus,
which is a primary source of data for C. fluminea in North America. Clam
populations were then classified as being in either artificially heated or
non-heated waters, and GPS points for each study location were obtained
(using Google Earth software version 5.2.1.1588, if the authors did not
provide them). Density data were obtained from a total of 48 different
waterbodies in the Americas (11 artificially heated and 37 non-heated) and
23 waterbodies in Europe (1 artificially heated and 22 non-heated), for a
total of 71 scientific articles. The data were used in a least-squares
regression analysis of density (log10-transformed) of C. fluminea against
the study site’s latitudinal distance from the equator. An ANCOVA
analysis was attempted with density (m-2) as the dependent continuous
variable, latitude as the continuous predictor and non-heated/heated
condition as the categorical fixed factor (Glantz and Slinker 2001);
however, the analysis could not be completed because the data did not
meet the assumption of homogeneity. Density differences between
heated and non-heated waters in the Americas (lack of sufficient data
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prevented such a comparison for Europe), and between American and
European waterbodies, were tested by a one-way ANOVA between non-
heated and heated waters.
Occurrence of C. fluminea
Data on the presence/absence of C. fluminea in rivers across North
America were compiled from Benke and Cushing (2005). The occurrence
of C. fluminea for each water body was classified as 0 for absent and 1 for
present. It is recognised that the absence of a record does not necessarily
imply that the species is absent from the river; however, C. fluminea has
been generally present for several decades and often at conspicuous
densities in North American rivers, whose molluscan fauna are relatively
well-studied, so there are probably few false negatives. Proxies for
disturbance used for this study are percent land use and the reported
number of endangered species within each river basin (IUCN 2010). Data
on C. fluminea occurrence, human population density (km-2), percent land
use (indicated by the sum of %agriculture and %urban development), and
the number of endangered species were collected for a total of 99 rivers
across the United States, from Benke and Cushing (2005). These
independent variables were then related to the establishment success
(presence/absence) of C. fluminea using multiple logistic regression. All
statistical analyses were performed in R (R Development Core Team
2012) and Sigmaplot (Systat Software, San Jose, CA).
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RESULTS
Population density
In North America, a weak positive correlation was found between the
density (log10
-transformed) of C. fluminea and latitudinal distance from the
equator for non-heated waterbodies (least-squares regression, r2=0.257, p
= 0.060). However, a weak negative relationship was found for artificially
heated waterbodies (least-squares regression, r2=0.325, p = 0.067) (Fig.
1). The density of C. fluminea was positively correlated with latitudinal
distance in South America (r2=0.868, p<0.001). There was no such
relationship for European populations (least-squares regression, p>0.1;
Fig. 1)
There was no significant difference in population densities within
non-heated and heated waterbodies in the Americas (ANOVA, DF = 47, p
> 0.1); mean population densities in non-heated and artificially heated
waters were 1874.0 ± 825.6 m-2 and 958.1 ± 372.0 m-2, respectively (Fig.
2a). Contrary to prediction, there was no significant different in densities
between Europe (754.4 ± 264.0 m-2) and North America (1868.0 ± 867.6
m-2) (ANOVA, p>0.1; Fig. 2b).
Occurrence
Multiple logistic regression yielded a model that included human
population density (p = 0.003), number of endangered species (p = 0.024),
and percent land use (p = 0.009). Human population density (Fig. 3) and
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number of endangered species were positively correlated to C. fluminea’s
occurrence. In the model, percent land use was negatively correlated with
occurrence. Given that percent land use was calculated as the sum of
percent agriculture and percent urban development, a separate logistic
regression was performed to analyze the influence of these individual
types of land use. Urban development had no significant effect (p =
0.135), but agricultural development had a negative effect on C. fluminea’s
probability of occurrence (p = 0.0244).
DISCUSSION
Global distribution and density of C. fluminea
C. fluminea’s density in natural (non-heated) waterbodies tends to
increase with latitudinal distance from the equator, contrary to prediction.
No significant relationship was found for clam densities in artificially
heated waters, although there was a negative trend with latitude; it must
be noted that such waterbodies are located primarily above 40º latitude,
where the heated effluent would likely provide a refuge for C. fluminea
throughout the winter months (Britton and Morton 1982; Langford 1990).
Nevertheless, the presence of C. fluminea in heated waters does not
represent a confinement to those habitats but perhaps a preference for
such disturbed states. Population densities in artificially heated and non-
heated waterbodies did not differ significantly, again contrary to prediction.
We hypothesized that population densities would be higher in artificially
heated waterbodies, based on the assumption that the temperature in
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these systems would fluctuate less and remain within the optimal ranges
for clam survival and reproduction, whereas waters that are not thermally
buffered would fluctuate more frequently beyond these optima (Britton and
Morton 1982). Although this assumption may be justified for winter
months, thermal plumes can become drastically warm during the summer;
temperatures may reach or exceed ~40ºC (Benda and Proffitt 1974;
Wellborn and Robinson 1996), which is deleterious to C. fluminea, whose
metabolic processes shut down above 30ºC and survivorship declines
rapidly above 36ºC (McMahon 1999). Confidence in such comparisons is
also limited by the smaller sample size for artificially heated waters (N =
11, versus N=24 for non-heated waterbodies).
Furthermore, there were limited data for South American
waterbodies. The point closest the the equator (Cametá, Brazil) seems to
driving the relationship; we do not consider it an outlier, but a reflection of
scarce data for watersheds in that climate. Water temperatures closer to
the equator may be too high, and the productivity of waterbodies in
northern latitudes may be too low (Caissie 2006), to support large
populations. More data for tropical regions are needed to confirm this
pattern.
Despite C. fluminea’s much earlier colonization of waterbodies in
North America, its population densities in Europe are not significantly
different, reflecting the clam’s capacity to rapidly build dense populations.
Moreover, there was no relationship between density and latitude in
Europe. The presence of heated habitats are not necessary for
24
colonization of northern areas above 50ºN. In fact, C. fluminea has
continued to spread into northern Europe (most recently in Ireland, Caffrey
et al. 2011), perhaps suggesting adaptation to colder temperatures or the
release from thermal constraints as a result of climate change (Muller and
Baur 2011).
In addition to climate, local-scale factors may limit population
densities within, and even throughout, a waterbody; these include physico-
chemical variables such as sediment type, dissolved oxygen, nutrient
concentrations, water flow and local temperature fluctuations (Sousa et al.
2008; Werner and Rothhaupt 2008; Ilarri et al. 2011). Some of these
variables are examined in Chapter 2.
Factors affecting the occurrence of C. fluminea
Data from rivers within the United States, suggests that the
probability of C. fluminea’s occurrence in a given waterbody increases with
human population density and number of endangered species within the
basin. Human population density is a proxy for propagule pressure, and
the number of endangered species in the waterbody is a proxy for
disturbance; both propagule pressure and disturbance are considered
important mediators of establishment success (Davis et al. 2000;
Lockwood et al. 2005; Galil et al. 2007). Surprisingly, however, C.
fluminea occurrence was negatively correlated with land use, apparently
contradicting the presumed facilitative role of disturbance. Further
examination found that this relationship was driven by agricultural (but not
25
urban) development. A major consequence of agricultural development is
nutrient pollution from fertilizer run-off (Benke and Cushing 2005), which
might be expected to increase the productivity of rivers, perhaps
occasionally to deleterious levels. Increased primary productivity might be
expected to drive the population expansion of C. fluminea, but not
necessarily be limiting its establishment success. Agricultural lands are
large, remote and tend to be continuous; thus, population densities are
much lower and public access to waterbodies may be limited; these
characteristics would constrain opportunities for the introduction (and
perhaps even the detection) of C. fluminea.
CONCLUSION
Mean densities of C. fluminea appear remarkably consistent
worldwide, suggesting that the species is capable of growing and
reproducing quickly across a broad range of environments, and that a
common set of factors is influencing its abundance. Interregional
comparisons of abundance are limited by the scarcity of data from tropical
regions and from artificially heated waterbodies outside of North America.
Nonetheless, some large-scale patterns are apparent. Population
densities of C. fluminea tend to be greater in mid-latitudinal regions, where
temperatures are optimal. The probability of C. fluminea’s occurrence in a
given waterbody increases with human population density. The potential
role of agricultural development as a limiting factor in the establishment
success of the species needs to be explored further. Finally, a future
26
consideration is how latitudinal patterns will be altered by climate change.
Artificially heated waterbodies in north temperate latitudes could
potentially facilitate further range expansion of C. fluminea, as warming
trends remove thermal barriers to the spread of these refuge populations.
27
Figure 1: Density of C. fluminea along a latitudinal gradient. Open circles are densities in non-heated waters. Closed circles are densities in heated waters. Reference for each data point can be found in Appendix 1.
28
Figure 2: (a) Density of C. fluminea in artificially heated (958.1 ± 372.0 (52.0,4050.0) m-2) and non-heated (874.0 ± 825.6 (8.0, 2991.0) m-2) American waterbodies (one-way ANOVA, p > 0.1). (b) Density of C. fluminea in European (754.4 ± 264.0 (6.23, 5000.0) m-2) and North American (1868.0 ± 867.6 (20.0, 29951.0) m-2) waterbodies (one-way ANOVA, p > 0.1). Reference for each data point can be found in Appendix 1.
Den
sity (
m-2
)
(a)
Den
sity (
m-2
)
(b)
29
Figure 3: Probability of C. fluminea occurring in a water body surrounded by varying human population densities. Line fitted by least-squares regression: y = 13.2/(1+(x/327)) (r2 = 0.23, p = 0.003).
Pro
bab
ilit
y o
f occ
urr
ence
Human population density (km-2)
30
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34
LINKING STATEMENT
In Chapter 1, I used literature data to explore large-scale patterns in the
distribution and abundance of C. fluminea. The population density of C.
fluminea tends to be positively correlated across latitudes such that
densities are plateau in the mid-latitudinal range in which the reported
optimal growing temperatures of the species are realized. Surprisingly,
there was no difference in mean density between populations in non-
heated and artificially heated waterbodies; however, the presence of
artificially heated waters has facilitated the C. fluminea’s northern range
expansion. European densities were not significantly different from those
in North America, despite having been invaded more recently. Chapter 2
examines the influence of physico-chemical variables on the local
distribution, abundance and condition of a northern population of C.
fluminea across an artificial thermal plume in the St. Lawrence River.
35
Chapter 2
Factors affecting the distribution, abundance and condition
of an invasive bivalve (Corbicula fluminea) along an
artificial thermal gradient in the St. Lawrence River
Rowshyra A. Castañeda1, M. Anouk Simard2 and Anthony Ricciardi1
1 Redpath Museum, McGill University, 859 Sherbrooke Street West, Montreal, QC H3A 0C4, Canada
2Ministère des Ressources naturelles et de la faune, Service de la biodiversité et des maladies de la faune, 880 chemin Sainte-Foy, 2e étage, Québec, QC G1S 4X4 Canada
36
ABSTRACT
The Asian clam Corbicula fluminea has been introduced to lakes and
rivers worldwide, but its physiological requirements are thought to restrict
its distribution to waters whose temperatures exceed 2ºC. In north
temperate areas of North America, C. fluminea occurs primarily in
artificially heated waterbodies. In November 2009, C. fluminea was
discovered in the St. Lawrence River in the thermal discharge plume of the
Gentilly-2 nuclear power plant. In summer 2011, C. fluminea’s distribution,
abundance and condition was sampled in a section of the river around the
power plant; the species occurred as far downstream as 5.5 km, and its
density declined along this distance. A generalized linear mixed model
identified environmental predictors of C. fluminea’s local population
density: depth had a negative effect on C. fluminea density, whereas
temperature, turbidity and flow velocity had positive effects, with
temperature having the most influence. The mean body condition of
clams generally declined downstream; however, in August, when
temperatures exceeded 30ºC in the discharge canal, body condition was
greatest outside the canal at 1.5 km downstream of the discharge source.
Size histograms and biomass measurements indicate that clams are
larger and older at sites closer to the discharge source.
37
INTRODUCTION
The Great Lakes–St. Lawrence River basin is being transformed by
invasive nonindigenous species that have altered its water quality,
biodiversity and food webs (Ricciardi 2006). Over 60 nonindigenous
species have been reported in the St. Lawrence River, and most of these
are assumed to have been introduced to the basin through ballast water
discharge from transoceanic ships (de Lafontaine & Costan 2002;
Ricciardi 2006). Human activities not only disperse species but also
disturb and alter habitats, increasing their vulnerability to invasions (Hobbs
and Huenneke 1992; Moyle and Light 1996). A particular example is river
modification by thermal effluent from power plants (Langford 1990).
Temperature affects the growth, reproduction and the distribution of
aquatic organisms (Sorte et al. 2013), and modulates the productivity and
water quality of lakes and rivers (Caissie 2006). The spread of non-native
aquatic species, in particular, is expected be strongly influenced by climate
change (Sorte et al. 2013). The thermal requirements of subtropical and
tropical species prevent most of them from becoming established in
temperate environments, even where they are introduced frequently
(Coutant 1977; Leach 2003; Bomford et al. 2010). Artificially heated
waters provide subtropical species with a refuge from winter temperatures
and aid their northern range expansion. Conceivably, these refuge
populations could expand into natural environments as temperatures
continue to rise.
38
In November 2009, the Asian clam, Corbicula fluminea – a
subtropical species and one of the world’s most invasive freshwater
bivalves – was discovered in the thermal plume of the Gentilly-2 nuclear
power plant (hereafter known as G2NPP) in Bécancour, Québec (Simard
et al. 2011). This is the first record of C. fluminea for the St. Lawrence
River and the coldest habitat in which it has been found. The thermal
discharge of G2NPP prevents the formation of ice in this area of the river
by maintaining water temperatures at ~10ºC above ambient throughout
the winter (Langlois and Vaillancourt 1990; Alliance Environnement Inc
2005). A frequently cited study suggests that the species cannot survive
prolonged exposure to temperatures below 2ºC (Mattice and Dye 1975),
contributing to the conventional view that C. fluminea can exist only in
artificially heated waters in temperate regions (McMahon 1983; but see
McMahon 1999). However, a population has been recently discovered in
Lake George, N.Y. (Meg Modley, Lake Champlain Basin Program, pers.
comm.) and previously in a Michigan River, where they must survive
freezing winter temperatures (Janech and Hunter 1995). Thus, it is
conceivable that C. fluminea could spread within the St. Lawrence River,
particularly as winter temperatures continue to increase with climate
change (Hudon et al. 2010).
Corbicula fluminea’s invasion success has been attributed to its
high growth rate, high reproductive output as a self-fertilizing
hermaphrodite, and ability to colonize disturbed habitats (McMahon 2002).
It can cause substantive ecological and economic impacts (McMahon
39
1999). Being an efficient filter-feeder, it can clear suspended particles from
the water column, thereby increasing transparency and macrophyte
growth – habitat changes that affect both benthic and pelagic communities
(Lauritsen 1986; Phelps 1994). Furthermore, because of its pedal-feeding
and filter-feeding activities, the tissues of C. fluminea sequester heavy
metals and other contaminants, which can be released into the water
column following mass mortality events (boom-bust cycles) or may be
transferred to molluscivores (Robinson and Wellborn 1988; Inza et al.
1997; McMahon 1999; Liao et al. 2008). Rapid accumulations of clams in
water intake systems cause municipal and industrial biofouling problems
that incur annual costs on the order of billions of dollars in the U.S. (Isom
1986).
The thermal plume produced by G2NPP offers an excellent outdoor
laboratory to compare life history and population dynamics of C. fluminea
along a thermal gradient. To understand how this recently discovered
population is coping under this temperature regime, we examined the
distribution, abundance, condition and life-history traits at sites inside and
outside the thermal plume.
We tested predictions that 1) C. fluminea’s distribution is restricted
to the thermal plume, and 2) the age, body condition, biomass and
reproductive capacity of clams were inversely correlated with distance
from the discharge source. Because C. fluminea requires 15-16ºC for
reproduction, and suffers high mortality as temperatures approach 2ºC
(McMahon 1999), it was not expected to survive or reproduce outside the
40
plume. Although temperature was expected to be the major factor limiting
the growth and reproduction of C. fluminea, its local abundance was also
expected to be affected by other factors including sediment particle size
and flow velocity; C. fluminea tends to reach higher densities in sandy or
gravel substrates (McMahon 1983; McMahon 1999; Schmidlin and Baur
2007) and is thought to be restricted to shallow, flowing, and well-
oxygenated areas (McMahon 1983).
METHODS
Study site
The study site is a section of the St-Lawrence River surrounding the
G2NPP at Bécancour, Quebec (46°23'42.51"N, 72°21'23.53"W). The
G2NPP has been in operation and functioning at 50-100% of its maximum
capacity since 1983, throughout which it has experienced random reactor
stoppages for varying amounts of time (Langlois and Vaillancourt 1990).
Its predecessor, the Gentilly-1 power plant, had been in operation during
1971-1973 in the same area. Environmental assessments show that a
thermal plume extends at least 4.0 km downstream from the power plant.
In the canal area located at the discharge source, water temperatures are
13-18ºC higher than ambient and this difference attenuates downstream
and varies over the year (Alliance Environnement 2005). For the
population size structure of this study, we divided the plume area into four
sections defined by distance and temperature; each section included three
41
transects and three sample sites per transect, yielding nine sites per
section. The first section was the 600m discharge canal; the second,
third, and fourth sections were 600–1450 m, 1450–2600 m, and 2600–
4000 m downstream of the discharge source, respectively.
Distribution and abundance of Corbicula and environmental variables
In October 2010, we deployed temperature loggers (thermographs)
along the thermal plume that recorded the water temperature hourly until
we retrieved them retrieved in May 2011.
During summer 2011, we established 36 sampling stations along
the previously defined 4.0-km section of the plume (Fig. 1). These were
located along 12 transects perpendicular to the shore, with three stations
per transect. It was difficult to keep the distance between transects and
between sites consistent, because sites were selected based on their
accessibility throughout the entire summer. In addition to these, we
sampled 20 stations outside the plume (7 upstream, 6 downstream and 7
north of the plume; see Appendix 3 for station coordinates). Because the
phenology of C. fluminea is mediated largely by temperature (e.g. biannual
or single reproductive periods), sampling was conducted in June and
August (McMahon 1999). At each site, we took three ponar grabs off the
side of a boat, yielding 168 samples. We sieved the collected sediment
through a 500µm mesh and preserved the invertebrates in 75% ethanol.
In the field, we removed all visible clams from the sediment and placed
them in a separate vial filled with 75% ethanol. At each sample site, we
42
measured temperature (ºC), conductivity (µs/m) and dissolved oxygen
(mg/L) using a digital YSI (Pro 2030) meter. We used a digital flowmeter
(Swoffer model 3000) to determine the flow (m/s) and a Secchi tube to
measure transparency (cm) at each site. We noted the presence or
absence of macrophytes, and determined depth (m) with a depth sounder.
Prior to sieving, we examined the sediment size for each ponar grab by
placing the sediment evenly in a tray and visually determined the percent
coverage of each substrate type through a pre-marked 0.0625m2 quadrat
(Jones and Ricciardi 2005). These measurements were multiplied by their
respective phi-values (φ = -log2(average diameter, in mm)) of each
substrate type and then summed to give the mean sediment size per site
(following Mellina and Rasmussen 1994; Jones and Ricciardi 2005).
Subsequently, we sorted the preserved samples under a dissecting
microscope, where all macroinvertebrates were separated from the
sediment. All three grabs were sorted for the early summer sampling;
however, owing to time constraints, only two grabs per site were sorted for
the late summer sampling. We measured and counted all Asian clams
from the sorted grabs.
The environmental factors were compared between sites inside and
outside the thermal plume using a one-way ANOVA, in June and August.
Additionally, we compared the same factors between the canal and the
ROP, using ANOVA and Tukey’s post-hoc test. To determine which
environmental variables explained variation in local abundances we fitted
a generalized linear mixed model (GLMM) using R (R Development Core
43
Team 2012). As we took multiple grabs per site at two different time
periods, we included ‘site’ and ‘time of sampling’ as random effects (Bolker
et al. 2009; Zuur et al. 2009). The fixed effects used in the model include
temperature (ºC), depth (m), dissolved oxygen concentration (mg/L),
specific conductivity (µs/m), transparency (cm), flow velocity (m/s),
sediment size (phi-scale) and the presence/absence of macrophytes as a
factor. We did not include conductivity and transparency in the same
model, because these two variables were correlated (Pearson correlation,
r = 0.6, p < 0.001). To account for overdispersion, we included individual
observations (i.e. each grab) as a random effect (Bolker et al. 2009). We
used Akaike’s Information Criterion (AIC) and the information-theoretic
approach to achieve the most parsimonious and biologically relevant
inferences about the roles of these environmental factors. The model with
the lowest AICc score and highest Akaike weight (ωi) were assumed to be
the best suited to explain trends in the data. Models with a difference in
AICc (∆i) less than 2 are considered equivalent (Burnham and Anderson
2002). To assess the differences in AIC scores and ‘a priori’ hypotheses
we tested ten models using the ‘drop1’ function in R (Bolker et al. 2009).
We dropped variables from the model if they showed no substantive
change in AIC values (Bolker et al. 2009).
Body condition, reproductive status, and size structure of clams
All adult C. fluminea (> 6mm) were kept in 75% ethanol for a
maximum of two weeks prior to examination of their body condition. We
44
measured the length, width and height of the clams to the nearest 0.01
mm using digital vernier calipers (model 47257). We dissected each clam
and noted the presence/absence of brooding larvae in the gills to
determine reproductive status (Britton and Morton 1982). The tissue was
removed by scraping it off the shell with a scalpel, and we measured its
preserved wet weight to the nearest 0.0001g. The clams’ tissue and shells
were placed in an oven at 70ºC for 24 hours, prior to being weighed to
obtain their dry mass (Cataldo et al. 2001).
We compared the condition of Corbicula i) inside the canal versus
along the plume, and ii) in June versus August, by a two-factor analysis of
covariance (ANCOVA) with dry weight as the dependent variable, shell
length as the covariate, and site and time as factors (Glantz and Splinker
2001, SPSS Statistics 20). We aimed to used restricted size classes of
the clams to maximize overlap between all sites; however, owing to the
difference in population dynamics between the canal and rest of the plume
(ROP), it was difficult to obtain sufficient numbers of clams of the same
size class. The early summer samples consisted of 32 clams (7–28mm)
for the canal site, and 45 clams (6–12mm) for the plume site. The late
summer samples consisted of 131 clams (6–12mm) for the canal site, and
73 clams (6–13mm) for the plume site. The assumptions of normality,
homogeneity of regression slopes, and homogeneity of variance were
tested and supported. Contingency tests on the proportion of gravid clams
were done to test differences in reproductive status between sampling
periods (June versus August) and location (inside or outside the canal).
45
Lastly, the maximum shell length and biomass were regressed against
distance from the discharge source.
Juvenile clams (< 6 mm length) were measured under a dissecting
microscope using a stage micrometer, whereas all larger clams were
measured using digital calipers. Size class frequencies were graphed and
the peaks of histograms were identified as cohorts (Schmidlin and Baur
2007). The size-frequency distribution for populations of each of these
sections was measured twice (in June and August).
Results
Differences in environmental variables inside, outside and along the discharge plume
The one-way ANOVA showed that environmental variables differed
significantly inside and outside the plume in June (Table 1). Depth and
dissolved oxygen were lower, whereas temperature and specific
conductivity were higher, inside the plume. In August, temperature
remained higher in the plume and was the only environmental variable that
differed from outside the plume. The canal and the rest of the plume
(ROP) sites were analyzed separately, as they differed in all environmental
variables except depth (ANOVA, Table 2). Temperature in the canal in
August was significantly higher than temperatures recorded in the ROP at
any time during the year (p < 0.001 ANOVA; Tukey’s post hoc tests; Table
2).
46
During late summer sampling, temperature and flow velocity were
the only physical variables correlated with distance downstream from the
discharge source (Figs. 2a & 2c). We confirmed a similar negative
relationship between temperature and distance from the discharge source
using thermograph data from October 11, 2010 to August 2, 2011 (Fig. 2b).
Data obtained from the temperature loggers inside the canal registered
average daily temperature of 17.4 ± 0.4ºC (October 11, 2010 - August 2,
2011), and 13.8 ± 0.1 ºC during winter months (December 15 - March 31;
Fig. 3a). Average daily temperatures in the canal remained within the
tolerance range of C. fluminea throughout most the year (N=296 days),
except for 39 days in which it was above 30ºC. Sharp declines in
temperature (by 8-11ºC) occurred periodically when the reactor was turned
off for maintenance. Outside the canal, at 1.5 km from the discharge
source in the thermal plume, we measured average daily temperature of
4.0 ± 0.2ºC from October 26, 2010 to May 20, 2011; but temperatures
were ≤2ºC during 49 of the 207 recorded days. At the extreme end of the
plume (4.5 km from the discharge source), we measured average daily
temperature of 3.4 ± 0.2 ºC, but temperatures were ≤2ºC during 115 days
of the 207 (Fig. 3b).
Population density of Corbicula
Sampling in June revealed a population density of 275.9 ± 146.3
clams m-2 across all sites within the plume. The canal area supported a
higher density (832.9 ± 113.2 m-2) than the rest of the 4-km plume (90.3 ±
47
10.8 m-2). We found only a single individual (a juvenile) outside the pre-
defined plume, 5.5 km downstream from the discharge source (Fig. 1). In
August, Corbicula’s population density (1618.3 ± 650.5 m-2 across all
sampling sites) was nearly 6-fold higher than in June, with 13 times as
many clams in the canal (5270.9 ± 2211 m-2) than in the ROP (400.8 ±
173.2 m-2). No individuals were collected outside the plume area (Fig. 1).
The population density of C. fluminea decreased linearly with distance
downstream from the discharge source (Fig. 4).
Of the ten multivariate models tested on ‘a priori’ hypotheses, three
are considered supported (∆i < 2, Table 3) and the best model (ωi = 0.390)
included temperature, depth, transparency and flow velocity (‘Model 4’;
Table 4). According to Model 4, the abundance of the clams is positively
related to flow velocity and temperature, while, depth and transparency
are negatively related (Fig. 5).
Body condition and population size structure
The dry mass-shell length relationships for clams collected in the
canal did not differ between sampling periods (ANCOVA, F1,276 = 0.251, p
= 0.617), but did differ with the ROP (ANCOVA, F1,276 = 5.827, p = 0.016)
(Fig. 6). We obtained a significant interaction between site and time
(ANCOVA, F1,276 = 6.995, p = 0.009). At a standard clam size of 8.3 mm,
the dry tissue weight for the clams in the canal and the ROP were identical
in June (~0.0038g, ANCOVA, F1,74=0.003, p=0.953). By contrast, in
August, clams in the ROP were in better condition than those in the canal
48
(i.e., 0.0044 and 0.0034 g, respectively, ANCOVA, F1,201=28.129, p <
0.01).
Biomass of C. fluminea was inversely related to distance from the
discharge source in June (p = 0.002, Fig. 7a), but not in August (p > 0.05,
Fig. 7b). The reproduction of C. fluminea is also influenced by its position
in the thermal plume and by time. There were significantly more brooding
clams in the canal than in the downstream plume area in June, whereas
the opposite is true in August, indicating that clams in the canal reproduce
earlier than elsewhere in the plume (X2=29.21, df=1, p<0.001).
The size-frequency distributions of clams in all four sections of the
river suggest at least 3 cohorts throughout the summer (Fig. 8). In June,
there appears to be a trend of decreasing cohort number from the
discharge source four potential cohorts in the canal, three cohorts in
Sections 2 and 3, and two cohorts in Section 4. No such trend is
discernible in August. In June, the maximum shell lengths (SL) for the
cohorts were 28mm, 14.2mm, 10.6 mm (one outlier at 19.5 mm) and 9.3
mm in the canal and Sections 2, 3 and 4, respectively. Accordingly we
obtained a negative relationship between meanSL and the distance from
the discharge source in June, with a meanSL ranging from 20.75 ± 4.57
mm nearest the discharge source to 7.23 ± 0.85 mm at the edge of the
plume (Fig. 7c). By August, however, the relationship between distance
and meanSL of clams was no longer significant (p>0.05, Fig. 7d). The
maximum shell lengths for each cohort increased by ~3 mm, whereas the
median and minimum SL for the cohorts increased by ~1 mm.
49
DISCUSSION
The thermal plume generated by the G2NPP appears to provide a
refuge habitat for C. fluminea in the St. Lawrence River. This
microenvironment is characterized by a temperature that is consistently
higher than ambient, although other abiotic factors vary during summer.
Throughout the winter months, the temperature in the canal was
consistent and predictable, except when the G2NPP was shut down for
short periods of maintenance. The temperature of the canal never fell to
2ºC (the putative cold tolerance limit of C. fluminea), but frequently
exceeded 30ºC (the clam’s upper tolerance limit). By contrast, the
temperatures logged at 1.5 km and 4.5 km from the discharge source in
the thermal plume were more variable and stochastic; they often fell below
2ºC in winter, but never exceeded 30ºC in the summer.
Our model selection indicates that water temperature is the most
significant predictor of the abundance and distribution of C. fluminea in the
river. Indeed, only few specimens have been discovered outside the
thermal plume (Simard et al. unpublished data). Densities of C. fluminea
were highest inside the discharge canal and declined with distance
downstream; the same negative relationship is found for biomass and
clam size in June, but disappears by August, likely because late summer
temperatures in the canal are above optimal for feeding, growth and
reproduction.
50
During the first survey of the St. Lawrence River population in
November 2009, the density of C. fluminea within the plume was 303 ±
132 m-2, with a peak density of 841 ± 125 m-2 inside the discharge canal;
and no clams were found outside the plume (Simard et al. 2011).
Population density inside the canal appeared much higher in August 2010,
where Simard et al. (2012) obtained a density of 5 339 ± 2 500 m-2 in the
canal and of 3380 ± 1315 m-2 across the entire plume, and during our
preliminary survey conducted in October 2010 revealed a population
density of 2151.8 ± 63.2 m-2 in the canal. In June 2011 (this study), the
population density was 832.9 ± 113.2 m-2 in the canal and 275.9 ± 146.3
m-2 across the entire plume; in August 2011, these densities were 5270.9 ±
2211 m-2 and 1618.3 ± 650.5 m-2, respectively. This pattern suggests that
the population has increased in abundance since the time of its discovery,
although densities vary substantially across seasons.
The limiting effect of temperature and other abiotic variables
We hypothesized that temperature would limit the distribution of C.
fluminea in the river, based on the assumption that C. fluminea cannot
survive below 2ºC (McMahon 1983; McMahon 1999), but this assumption
is derived from a temperature tolerance experiment that was unable to
acclimate the clams to 2ºC (Mattice and Dye 1975). Subsequent studies
and field observations suggest that C. fluminea is able to adapt to
temperatures between 0-2ºC when acclimated to winter conditions (Habel
1970; Janech and Hunter 1995; Kreiser and Mitton 1995; Muller and Baur
51
2011). Intriguingly, in 2010, an overwintering population of C. fluminea
was discovered in a north temperate lake (Lake George, NY) that does not
receive heated effluent, adding further evidence that the species can
tolerate colder temperatures than previously believed (Meg Modley, Lake
Champlain Basin Program, pers. comm.). The recent closure of G2NPP
will allow us to verify whether C. fluminea can survive in the St. Lawrence
River without access to a thermal refuge.
Although C. fluminea tolerate long-term exposure to temperatures
between 2ºC and 34ºC in the laboratory, its filtration rate, oxygen uptake
and reproduction are all significantly depressed when temperatures are
above 30ºC (Habel 1970; Mattice and Dye 1975; Cherry et al. 1980;
McMahon 1983; McMahon 1999). Temperature loggers in the discharge
canal of G2NPP recorded water temperatures above 30ºC for 39 days
between June and the first week of August (time at which loggers were
retrieved); based on temperatures recorded manually during sampling
events, we suspect that it remained in that range for the entire month of
August. Despite the potential physiological stress that high temperatures
might have imposed on C. fluminea, its population density was positively
related with temperature, possibly owing to reproduction that occurred in
the canal in June. The recruits may not have been exposed to the sub-
optimal temperatures long enough to cause mass mortality, but their body
condition, growth and biomass were significantly reduced.
The local abundance of C. fluminea was reduced in deeper or more
transparent waters, but enhanced in areas subject to higher flow velocity.
52
Most studies also suggest that C. fluminea prefers shallow waters (Dresler
and Cory 1980; Bagatini et al. 2007; Brown et al. 2007), although some
studies have observed a positive or an absence of depth effect on the
abundance and distribution of the species (Schmidlin and Baur 2007;
Cooper 2007). The high flows in the plume may contribute to the re-
suspension of organic material from the sediment, providing more
nutrients to the clams. Owing to its high filtration capacity, an abundant C.
fluminea population can reduce water turbidity (and nutrients), thus a
positive correlation between these two variables may be expected (Cohen
et al. 1984; Lauritsen 1986; Phelps 1994). Yet, our study found higher
densities in more turbid waters. We suspect that tidal activity (of up to 1.5
m) in our study area is causing sediment resuspension (Howarth et al.
1996). In addition, agricultural activity in this area of the basin contributes
to the river’s productivity (Hudon and Carignan 2008), which, along with
the tides and flow velocity re-suspending organic material, may allow the
plume to support larger clam populations.
Finally, because C. fluminea is reported to reach higher densities in
well-oxygenated sediments such as sand and gravel (McMahon 1983;
McMahon 1999; Schmidlin and Baur 2007), we expected sediment size to
predict its local abundance. However, we collected clams from a wide
range of sediment types and their abundance was unrelated to sediment
size (p > 0.01). In sum, although several abiotic variables may contribute
to the distribution and abundance of this northern population of C.
fluminea, temperature appears to be the principal limiting factor.
53
Body condition of clams
The body condition of C. fluminea was significantly lower in the
canal in August, indicating exposure to suboptimal conditions. Indeed,
between the months of July and August, the canal experienced over 39
days in which temperatures exceeded 30ºC, a critical limit that is believed
to provoke suspension of growth, feeding and reproduction in C. fluminea
(McMahon 1983). The lower body condition observed in the canal
population in August (in contrast to the ROP, whose body condition
increased from June to August) is consistent with results from studies of
the clam’s temperature tolerances (Bush et al. 1974; Mattice and Dye
1975, McMahon 1999). Interpretations of observed changes in body
condition may be confounded by the loss of gametic tissue during
reproductive events (Williams and McMahon 1986); but this is not the case
here, as temperatures most conducive to the release of pediveligers
(~25ºC) occurred in the ROP in August. Potentially lethal temperatures
(34.4 ± 0.4ºC) were also recorded in the canal during August 2010
(GENIVAR 2011), which suggests that the population in the canal may be
exposed to suboptimal conditions in late summer on an annual basis,
whereas conditions remain favorable in the ROP.
Population structure and growth analysis
The size distribution and the presence of gravid clams suggest a
well-established reproducing population of C. fluminea in the St. Lawrence
54
River. Results obtained from size-frequency histograms confirmed our
initial hypothesis predicting a declining number of cohorts along the length
of the plume, but only in June and not in August. Similarly meanSL was
inversely related to distance from the discharge in June, confirming our
prediction that mean individual age declines downstream, although this
pattern was not observed in August. Indeed, SL at sampling sites closest
to the canal has declined through summer, whereas it has increased in the
ROP. This phenomenon could originate from a die-off of older individuals
promoted by detrimentally high temperatures in the canal. A second
hypothesis is related to differential growth pattern. Given that smaller
clams grow faster than larger ones (McMahon 2002), they may achieve
shell lengths similar to those of older, slow-growing individuals, thus
obscuring cohorts. This would not increase the median shell lengths very
quickly, but it would account for larger maximum shell lengths.
In June, the measured size classes suggest that clams range from
<1 to 4 years old (0.5 – 28.0 mm) in the canal and <1 to 2 years old
immediately downstream in the plume (0.5 – 14.2 mm) (based on
Schmidlin and Baur 2007). We hypothesize that the water temperature
range in the canal (4.1–36.0 ºC) permit growth throughout winter and more
frequent reproductive events, thereby generating a higher number of
cohorts. We suspect that some clams also overwinter in the ROP, but we
cannot exclude the possibility of different growth stages emigrating from
the canal; individuals with a shell length of ≤14 mm may drift downstream
using a mucous or byssal thread when exposed to water current (Prezant
55
and Chalermwat 1984; McMahon 1999), thus skewing the size frequency
histograms for clams at downstream sites by adding intermediate size
classes between peaks. Similarly, drifting may have led to
misinterpretation of the number of cohorts in the ROP.
Low winter temperatures are expected to reduce the fitness of C.
fluminea and subsequently cause die-offs (Muller and Baur 2011).
Accordingly, age (inferred from size) class declines with downstream
distance from the discharge source. Based on observed clam size (10.6
mm) in June, we infer that clams located at sites between 2.6 km to 4km
downstream managed to reach sexual maturity (normally associated with
6-10mm shell lengths; McMahon 1983) within 1-3 months following winter,
despite being exposed to an average winter temperature of 2.1 ± 0.1 ºC
(49 days ≤ 2ºC). However, between 2.6 km to 4km downstream, the
largest individual (9.3 mm) was probably less than a year old, perhaps
reflecting a more limited survival and growth in average winter
temperatures of 1.0 ± 0.1ºC (95% of days ≤ 2ºC). It is possible that our
age estimates (following Schmidlin and Baur 2007) are erroneous, if
environmental conditions are sever enough to limit clam growth in the
ROP and produce older clams of smaller size; in such a case, individuals
of 9.3mm in June may have actually overwintered. Furthermore, the
collection of a 19.5 mm individual, seemingly too large to be accounted for
by downstream migration, suggests that some individuals may be able to
overwinter in this section of the plume.
56
CONCLUSIONS
Corbicula fluminea is considered to be among the world’s most
successful invaders, owing largely to its early maturation, hermaphroditic
self-fertilization, and biannual reproduction – traits which allow populations
to recover quickly after being exposed to harsh environmental conditions
(McMahon 1999). However, its distribution in northern latitudes appears
to have been limited by basic physiological constraints. The establishment
of C. fluminea in the St. Lawrence River has apparently been facilitated by
the thermal plume from the G2NPP, which has provided a refuge habitat
for this subtropical bivalve. Thus far, more than five years after C. fluminea
was introduced, populations of the species have not been reported in
areas of the river beyond the plume.
Remarkably, the plume creates a temperature gradient that
comprises both the upper and lower thermal limits of C. fluminea.
Significant differences in temperature between the canal and ROP are
coupled with physiological and phenological differences in C. fluminea
populations in these river sections. The onset of reproduction (indicated by
the presence of gravid females) occurs earlier in the canal than in the
ROP. In August, condition indices suggest that extreme temperatures in
the canal restrict growth, whereas the condition of clams in the ROP is
maximal. Thus, the environment in the canal is optimal during the winter,
but suboptimal or detrimental in late summer. Similarly, a recent
experimental study in the Rhine River system found that a warm summer
event caused a reduction in body mass and an increase in mortality of C.
57
fluminea; thus, the positive effects of a warmer winter were counteracted
to some extent by the negative effects of higher peak summer
temperatures (Weitere et al. 2009). These results are mirrored along the
thermal gradient in our study system, and are consistent with the observed
general pattern of stronger responses (both positive and negative) by
aquatic animals, particularly non-native species, to climate change (Sorte
et al. 2013).
Within the thermal plume, C. fluminea populations are most
abundant at sites that are relatively warm, shallow, turbid and exposed to
higher flows. Populations vary in density and body condition in different
river sections and at different times of year, corresponding to variation in
temperature. An important question is whether these populations will
persist in future years, as the G2NPP has been shut down permanently in
December 2012. It is conceivable that populations at the northeastern end
of the plume have adapted to suboptimal cold temperatures and, aided by
a progressively warmer climate, will persist and perhaps expand in the
river after the heated water discharges have ceased.
58
Table 1: Environmental variables recorded at sampling sites inside and outside the plume of the Gentilly-2 Nuclear Power Plant. Outside the plume defined as upstream ambient water temperature; inside plume defined as water temperatures +1ºC above ambient. Mean ± S.E. (Min, Max).
Environmental factor Outside plume Inside plume Sites (N) 20 36
June
Depth (m) 4.8 ± 0.7 (1.4, 11.0) * 3.1 ± 0.3 (0.6, 7.6) *
Flow velocity (m/s) 0.1389 ± 0.0311 (0.0120, 0.6350)
0.1274 ± 0.0748 (0.0030, 0.6600)
Macrophyte (presence/absence)
0.30 ± 0.11 (0, 1) 0.31 ± 0.08 (0, 1)
Transparency (cm) 53.7 ± 3.7 (22.4, 78.8) 61.0 ± 4.0 (21.5, 101.0)
Dissolved oxygen concentration (mg/L)
10.01 ± 0.06 (9.63, 10.78) *** 9.30 ± 0.09 (8.10, 11.00) ***
Temperature (ºC) 17.59 ± 0.11 (16.70, 18.60) *** 25.12 ± 0.22 (22.30 28.00) ***
Specific conductivity (µs/m)
214.37 ± 1.66 (195.0, 225.3) ** 235.53 ± 4.25 (182.3, 259.3) **
Sediment (ϕ-scale) 0.3953 ± 1.0190 (-6.2154, 8.3636)
1.2460 ± 0.4806 (-5.2776, 7.3000)
August
Depth (m) 3.77 ± 0.67 (0.82, 10.12) . 2.62 ± 0.33 (0.30, 7.07) .
Flow velocity (m/s) 0.1186 ± 0.0293 (0.0060, 0.5300)
0.1068 ± 0.0228 (0.0010, 0.5850)
Macrophyte (presence/absence)
0.3 ± 0.1 (0, 1) 0.31 ± 0.08 (0,1)
Transparency (cm) 58.38 ± 3.00 (27.5, 87.5) 54.89 ± 2.59 (17.90, 83.80)
Dissolved oxygen concentration (mg/L)
8.34 ± 0.15 (7.42, 10.19) 8.38 ± 0.125 (6.89, 10.41)
Temperature (ºC) 23.9 ± 0.1 (23.3, 24.9) *** 26.8 ± 0.6 (23.4, 33.7) ***
Specific conductivity (µs/m)
252.5 ± 1.3 (237.4, 259.5) 252.8 ± 1.5 (210.9, 263.8)
Sediment (ϕ-scale) 0.3953 ± 1.0190 (-6.2154, 8.3636)
1.2460 ± 0.4806 (-5.2776, 7.300)
a Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05
‘.‘ 0.1
59
Table 2: Comparison (using ANOVA and Tukey’s post-hoc test) between the different environmental variables in the canal and ROP in the early and late summer of the G2NPP thermal plume. Mean ± S.E. (Min, Max).
Environmental variables
June August
Canal ROP Canal ROP
Depth 2.76 ± 0.39 (1.46, 4.79) 3.17 ± 0.44 (0.58, 7.62) 2.32 ± 0.33 (0.94, 4.18) 2.72 ± 0.42 (0.30, 7,07)
Flow 0.156 ± 0.043 (0.003, 0.361) 0.118 ± 0.0316 (0.004, 0.660) 0.237 ± 0.059 (0.027, 0.585) a** 0.063 ± 0.017 (0.001, 0.364)
a**
Macro 0.0 ± 0.0 (0.0, 0.0) 0.41 ± 0.10 (0.0, 1.0) 0.0 ± 0.0 (0.0, 0.0) 0.41 ± 0.10 (0.0, 1.0)
Trans 33.3 ± 6.2 (21.5, 75.9) a***
,b** 70.2 ± 3.4 (22.9, 101.0)
a***
c,d* 52.2 ± 5.0 (38.6, 83.8)
c* 55.8 ± 3.1 (17.9, 73.8)
b**
, d*
DO 8.84 ± 0.083 (8.62, 9.27) a*
, b** 9.45 ± 0.11 (8.1, 11.0)
a*
,c,d*** 7.80 ± 0.28 (7.04, 9.23)
b,e**
, c*** 8.57 ± 0.12 (6.89, 10.41)
d***
,e**
Temp
25.0 ± 0.6 (22.3, 26.9) a*** 25.2 ± 0.2 (23.9, 28.0)
b*** 30.2 ± 1.5 (23.4, 33.7)
a,b,c*** 25.6 ± 0.4 (24.0, 32.1)
c***
Spec. Cond 203.6 ± 8.6 (189.4, 250.2)a,b,c
*** 245.8 ± 2.8 (182.3, 259.3) a*** 253.3 ± 1.0 (248.4, 256.9)
b*** 252.7 ± 2.0 (210.9, 263.8)
c***
Sed -1.514 ± 0.800 (-5.278, 1.714) a,b
** 2.166 ± 0.468 (-2.625, 7.300)
a,c**
-1.514 ± 0.800 (-5.278, 1.714)
c,d**
2.166 ± 0.468 (-2.625, 7.300)
b,d**
Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05
60
Table 3: Summary of the generalized linear mixed models and their corresponding AICc values predicting the abundances and occurrence of Corbicula fluminea for the summer sampling with site, grab and time as random factors.
Model a Model
ID K AICc ∆i ωi
Depth + flow + temp + trans 4 8 468.835 0.000 0.390
Depth + temp + trans 5 7 469.523 0.688 0.277
Depth + DOconc + flow + temp + trans 3 9 470.776 1.941 0.148
Temp + trans 6 6 471.638 2.803 0.096
Depth + DOconc + flow + temp + trans 2 10 472.347 3.512 0.067
Depth + DOconc + flow + macro + sed + temp + trans
1 11 474.548 5.713 0.022
Depth + DOconc + flow + temp 8 8 496.035 27.200 0.000
DOconc + temp + trans 10 8 496.135 27.300 0.000
Temp 7 5 501.982 33.147 0.000
Depth + flow +sed 9 7 533.923 65.088 2.9 E -15 a Depth: water depth (m); DOconc: dissolve oxygen concentration (mg/L); temp:
temperature (ºC); trans: water transparency (cm); flow: flow velocity (m/s); sed: sediment type (phi-scale); macro: presence/absence of macrophytes K: Parameter count includes intercept and variance
61
Table 4: Results from the generalized linear mixed model with the lowest AICc score, using variable/fixed effects and random factors site, grab and time unique to Model 4, AICc = 468.8.
Variable Coefficient Standard Error p-valuea
Depth -0.246 0.131 0.0614 .
Transparency -0.050 0.009 6.77e-09 ***
Flow 1.866 1.074 0.0824 .
Temperature 0.531 0.052 < 2e-16 ***
a Significant codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
62
63
0 1000 2000 3000 4000
Mea
n t
em
pe
ratu
re (
oC
)
22.0
24.0
26.0
28.0
30.0
32.0
34.0
36.0
Distance from discharge source (m)
0 1000 2000 3000 4000
Mea
n f
low
ve
locity (
m/s
)
0.00
0.10
0.20
0.30
0.40
0.50
0 1000 2000 3000 4000
Mea
n t
em
pe
ratu
re (
oC
)
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
Figure 2: a) Average temperature sampled in August along the thermal gradient. Line fitted by least-squares regression: y = 24.548 + 2050.619/x, r2 = 0.975, p < 0.001. b) Average temperature recorded hourly between October 2011 and May 2012, against distance from the heat source. Line fitted by least-squares regression: y = -3.394ln(x) + 30.776, r2= 0.953, p < 0.001. c) Average flow velocity sampled in August along the thermal gradient. Line fitted by least-squares regression: y = -0.0832ln(x) + 0.7091, r2 = 0.513, p < 0.01. Error bars represent standard error; where they are not visible, the error is smaller than the size of the marker.
64
Figure 3: (a) Average daily temperature in the canal. Temperature was recorded hourly by temperature loggers between October 11, 2010 and August 2, 2012. The solid black line represents the temperature threshold for reproduction (15ºC, RT), the dashed line represents the critical temperature for metabolic processes (30ºC, CM), the solid grey line represents lower temperature tolerance (2ºC, LT), the dotted line represents the upper temperature tolerance (36ºC, UT). Black arrows show times that G2NPP reactor was stopped for maintenance or other emergencies. (b) Mean daily water temperature from October 26, 2010 to May 20, 2011. The black solid line represents average daily temperatures in the plume at 1.5km from the discharge point. The grey line represents average daily temperature at the end of the plume at 4.5 km downstream from the discharge point. The dashed line represents the lower temperature tolerance (LT) for C. fluminea at 2ºC.
(a) (b)
65
Figure 4: Relationship between the log-transformed C. fluminea density (clams m-2) and distance from the discharge source (m). Line fitted by least-squares regression: y = -0.0006x + 3.693, r2 = 0.744, p < 0.001.
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Figure 5: Log10(C. fluminea density m-2) in relation with a) temperature (ºC), line fitted by least-squares regression: y = 0.3147x - 6.5872,
r2=0.571, p<0.001; b) Depth (m), line fitted by least-squares regression: y = -0.4027x + 2.8938, r2= 0.3011, p<0.001. c) Water transparency (cm), line fitted by least-squares regression: y = -0.0605x + 5.1586, r2= 0.4306, p<0.001.
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Figure 6: Log10transformed dry tissue weight (g) versus log10transformed shell length (mm) for Corbicula fluminea adults (>6mm), in the Canal and ROP in June and August. Lines fitted by least-squares regression: 1) June Canal: log10mass = 3.16 log10length - 5.32, r2= 0.95, p < 0.001; 2) June ROP: log10mass = 3.30 log10length - 5.45, r2= 0.74, p < 0.001; 3) August Canal: log10mass = 3.12 log10length - 5.34, r2= 0.77, p < 0.001; 4) August ROP: log10mass = 2.99 log10length - 5.11, r2= 0.83, p < 0.001
68
0 1000 2000 3000 4000
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ass (
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a) b)
c) d)
Figure 7: The biomass (dry tissue weight g/m2) of adult C. fluminea along the thermal gradient in a) June, line fitted by least-squares regression: y = 3137.6/x + 6.879, r2=0.80, p < 0.001. b) August, no relationship between the variables, p>0.05. The mean shell length (mm) of C. fluminea along the thermal plume in c) June, line fitted by least-squares regression: y = 68.463/x + 4.817, r2
= 0.635, p = 0.002; d) August, no relationship between the variables, p>0.05. The error bars represent standard error, where the error bars are not visible, the error is smaller than the marker size.
69
b)
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Figure 8: Frequency histogram of C. fluminea’s size classes in the canal in June: a) canal, b) Section 2 c) Section 3, d) Section 4; and in August: e) canal, f) Section 2 g) Section 3, h) Section 4. Each peak in the figure represents a potential cohort.
70
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GENERAL CONCLUSIONS
Globally, C. fluminea continues to expand its northern range. Most
populations found above 40ºN in North America are located in artificially-
heated waterbodies; this pattern does not occur in Europe, where
relatively few populations are in heated waters. Furthermore, C. fluminea
population densities are highest in the mid-latitudinal region of the
Americas, whose climate is within the reported optimal temperature range
of the species (Chapter 1). The presence of C. fluminea in non-heated
waterbodies in northern Europe suggests an adaptation to colder
temperatures. The continued intercontinental transport of this species
offers the opportunity for cold-adapted genotypes to be introduced to
North America. Alternatively, populations currently restricted to thermal
plumes could conceivably adapt to colder temperatures and spread from
these refugia, particularly as winter temperatures become milder under
climate change. In any case, I predict that climate change will cause the
unimodal relationship between C. fluminea density and latitudinal distance
from the equator to shift northwards, thereby altering invasion risks across
the continent.
Because abundance is often highly correlated with impact (Ricciardi
2003), the identification of variables that influence the population density
and distribution of C. fluminea has both theoretical and applied value to
risk assessment. The occurrence of C. fluminea in North American
watersheds was found to be positively related to human population density
(a proxy for propagule pressure) and to the number of recorded
76
endangered species (a proxy for disturbance), and negatively related to
agricultural development (another proxy for disturbance). The role of
agricultural activity as a potential mediator of the susceptibility of
waterbodies to C. fluminea invasion requires further examination; thus far,
research has focused on the roles of nutrient pollution (e.g. runoff from
farmland) and disturbance in the success of plant invasions (e.g. Davis et
al. 2000).
The thermal plume created by the Gentilly-2 nuclear power plant
facilitated the establishment of C. fluminea in the St. Lawrence River, the
northernmost site in the recorded distribution of the species (Simard et al.
2012). An empirical model found that C. fluminea density was highest at
St. Lawrence River sites characterized by higher temperature, turbidity
and flow (Chapter 2). To date, the species appears confined to the
thermal plume heated effluent and declines in density with distance from
the discharge source. In June, population biomass, number of cohorts
and mean shell length also decline downstream across the plume. By
August, this pattern disappears, owing largely to potentially lethal high
temperatures in the canal; the condition indices of C. fluminea are greatest
at sites further downstream, where temperatures are optimal for
reproduction and growth. Thus, the G2NPP thermal plume provides a
unique environment to study the effects of both upper and lower
temperature tolerances of the clam (reviewed by McMahon 1999). The
thermal plume provides optimal conditions in the canal during the winter,
but only outside the canal during the summer; throughout the year, the
77
plume maintains a persistent reproducing population of C. fluminea in the
river. The power plant is scheduled to be shut down permanently at the
end of 2012, re-establishing natural winter conditions for the first time in
over three decades, and prompting the question of whether a remnant
population of C. fluminea will persist. This study provides baseline data
that would allow comparisons of population dynamics and tolerances
before and after the closure of G2NPP and with other invaded rivers.
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78
Appendix 1
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APPENDIX 2 GPS coordinates of sample sites
Inside Plume Outside Plume
Site North West Site North West
1.1 46°23'40.87" 72°20'58.40" U1 46°23'52.07" 72°21'4.93"
1.2 46°23'41.66" 72°20'59.40" U2 46°23'59.70 72°21'7.36"
1.3 46°23'42.56" 72°21'0.51" U3 46°24'4.85" 72°21'22.54"
2.1 46°23'48.15" 72°20'53.25" U4 46°24'10.58" 72°21'6.23"
2.2 46°23'49.40" 72°20'56.34" U5 46°24'22.39" 72°21'9.01"
2.3 46°23'49.90" 72°20'59.46" U6 46°24'25.41" 72°20'58.07"
3.1 46°23'55.36" 72°20'48.30" U7 46°23'56.30" 72°21'24.81"
3.2 46°23'58.22" 72°20'53.71" N1 46°24'31.90" 72°20'36.06"
3.3 46°24'0.70" 72°20'58.66" N2 46°24'36.94" 72°20'14.82"
4.1 46°24'1.73" 72°20'40.56" N3 46°24'43.66" 72°19'48.41"
4.2 46°24'7.00" 72°20'48.45" N4 46°24'48.92" 72°19'28.18"
4.3 46°24'12.55" 72°20'57.80" N5 46°24'56.53" 72°19'10.35"
5.1 46°24'5.36" 72°20'29.88" N6 46°25'6.96" 72°18'53.42" 5.2 46°24'13.10" 72°20'35.70" N7 46°25'15.51" 72°18'25.62"
5.3 46°24'20.39" 72°20'41.25" D1 46°25'11.78" 72°18'5.54"
6.1 46°24'6.59" 72°20'16.20" D2 46°24'57.36" 72°17'56.03"
6.2 46°24'16.23" 72°20'19.11" D3 46°24'38.82" 72°17'45.56"
6.3 46°24'25.20" 72°20'21.88" D4 46°25'19.88" 72°17'31.59"
7.1 46°24'7.41" 72°20'1.96" D5 46°25'5.75" 72°17'23.17"
7.2 46°24'17.84" 72°20'3.44" D6 46°24'48.66" 72°17'12.10"
7.3 46°24'27.43" 72°20'5.18" 8.1 46°24'9.15" 72°19'43.17" 8.2 46°24'20.63" 72°19'44.48" 8.3 46°24'31.98" 72°19'46.25" 9.1 46°24'12.87" 72°19'25.58" 9.2 46°24'23.23" 72°19'25.31" 9.3 46°24'33.30" 72°19'25.19" 10.1 46°24'19.00" 72°19'5.73 10.2 46°24'30.17" 72°19'6.58" 10.3 46°24'42.27" 72°19'7.35" 11.1 46°24'27.07" 72°18'45.32" 11.2 46°24'39.64" 72°18'48.50" 11.3 46°24'51.49" 72°18'52.10" 12.1 46°24'31.56" 72°18'22.06" 12.2 46°24'47.42" 72°18'28.30" 12.3 46°25'3.03" 72°18'34.43"
