Abiotic and biotic context influence species interactions in coastal vegetated ecosystems
by Althea F. P. Moore
M.S. in Marine Science, The College of William & Mary B.A., Earlham College
A dissertation submitted to
The Faculty of the College of Science of Northeastern University in partial fulfillment of the requirements for the degree of Doctor of Philosophy
June 17, 2016
Dissertation directed by
A. Randall Hughes Assistant Professor of Marine and Environmental Science
Dedication
For my mother and father, who nurtured my curiosity.
For Jane and Miles, who got me through the worst of it.
For Chris, who makes everything better.
ii Acknowledgements
I sincerely thank the many people who have assisted me in completing my dissertation.
First and foremost I thank my advisor Randall Hughes, who guided my research projects, supported me, and provided thorough and timely feedback in a manner that seemed almost super human. I thank my committee members Catherine Gehring, Brian Helmuth, Rebeca Rosengause, and Steve Vollmer for their feedback on my research. I would particularly like to thank Cathrine
Gehring for getting me started on fungal methodology and for her kind and helpful feedback and advice throughout my PhD. I also thank my previous committee at Florida State University including Brian Inouye, Don Levitan, Tom Miller, and Kevin Speer, for their assistance and feedback early in my PhD. I also thank my collaborators Donna J. Devlin and C. Edward Proffitt for making our experiment possible.
Many people and institutions have been important to the success of my PhD. Tarik
Gouhier helped me with statistical analyses on many occasions, always taking the time to explain complex statistical issues. Jarrett Byrnes also answered some of my most pressing statistics and
R questions via social media. A number of other people have given me feedback that has helped me to progress with my research, including Torrance Hanley, David Kimbro, and Kathleen
Lotterhos. Ecology is a team sport and I thank the many people who have helped me to collect data in the field and lab, including Ryan Coker, Ashley Dillon, Emily Field, Emily Goetz, Austin
Heil, Erika Holdridge, Cheston Peterson, Will Ryan, Tanya Rogers, Forest Schenk, Meagan
Tonry, and Robyn Zerebecki. My work would not have been possible without institutional support and/or field access from Florida State University Coastal and Marine Laboratory, St.
Joseph Bay Buffer Preserve, St. Mark’s National Wildlife Refuge, Harbor Branch
iii Oceanographic Institute, and Northeastern University Marine Science Center. I also thank the
National Science Foundation for funding.
I thank my scientist friends and colleagues who have given me their support, both intellectual and otherwise, during my PhD, including Zach Boudreau, Becca Certner, Alex
Forde, Josh Grinath, Elise Gornish, Tanya Rogers, Abigail Pastore, Will Ryan, Forest Schenk,
Jessica Torossian, and Robyn Zerebecki. Jen Elliott and Kylla Benes helped me through the last stages of finishing my dissertation. Chris Wilhelm has supported me in many ways during this time, through his partnership, his confidence in me, and the ever so many dinners he has cooked for me over these years. I also thank my extended family members, who have helped me to grow and shown me their support in many ways during my PhD. In particular, I thank Jane Pipik and
Miles Smith, who have done so much to support me in these last few years as I have finished my
PhD work. I thank my parents, Patricia Pipik Moore and Thomas Moore, who set me on the path to discover and understand the natural world. I thank Mary Pipik for years of support and encouragement. I thank Bob Pipik and Paula Willey, as well as Milo and Ezra, for giving me a great place to stay on my long east coast drives and for always being interested in my work.
Many others have shown me wonderful support and encouragement, especially Joe Pipik and
Jeannie Wall, Anne Wisniewski, Therese and Keighry McClelland, Jim, Dee and Sophie Pipik,
Nancy Pipik, Shannon Pipik, S. L. McCarthy, Peter McCarthy and Donna Socha. I also thank the wonderful teachers I have had in my life who have been instrumental in leading me to this point.
In particular, I thank Judy Duke, who helped me to discover my love of science and Brent Smith and Leslie Bishop, who turned me into an ecologist. My former supervisors Mark Johnson and
J. Emmett Duffy helped me to grow as a researcher.
iv Abstract of Dissertation
The outcomes of species interactions can change depending on the particular conditions under which they occur, including both abiotic and biotic factors, such as physical stress and species traits. For example, the traits of interacting species and individuals can influence amelioration of environmental conditions. Further, the response of individuals to environmental context can depend on traits determined by source population, including genetic background and maternal investment. These different aspects of context dependency are a challenge to predicting and understanding the consequences of species interactions and distributions. In this dissertation I address three aspects of environmental context. In Chapter one, I examine how abiotic factors influence plant-fungal symbiosis in an intertidal salt marsh using a field manipulation increasing resource availability (sediment nutrient concentrations) and physical stress (sediment salinity). I examined the response of the dominant marsh grass Spartina alterniflora and the abundant yet poorly understood dark septate endophyte (DSE) fungi that colonize the roots of S. alterniflora.
As expected, plants grew more with nutrient addition, yet the positive effect on plant percent cover was reduced by salt addition. Increased resource availability decreased colonization by
DSE hyphae, but it did not influence fungal reproductive structures, which were marginally increased when elevated nutrients and increased salinity were combined. These results are consistent with the still controversial view that plant-DSE interactions are based in part on enhanced nutritional condition of plants by fungi. In Chapter two, I use a bidirectional approach to examine a facilitative interaction and the species traits and environmental conditions that contribute to partner benefits. Using a field survey and three complementary field experiments, I characterized an association between the salt marsh forb Limonium carolinianum and ribbed mussel Geukensia demissa. Both species benefited from the association in terms of growth and
v survival, yet the benefits were asymmetrical, with mussels benefitting more consistently and on a shorter timescale. Limonium ameliorated predation and high temperature conditions for mussels, whereas mussels enriched sediment organic matter, which may alleviate nutrient limitation for
Limonium. Both interacting species had positive effects on traits that might serve to enhance the stability of the interaction through time. In Chapter three, I investigated how population identity of mangrove seedlings interacts with the biotic and abiotic environment to influence survival and morphology. I planted Avicennia germinans (black mangrove) propagules from 6 source locations into two common garden experiments. One experiment site is mangrove dominated and in the middle of the mangrove range in FL, while the other is a salt marsh dominated area at the northern edge of the mangrove range. Maternal investment in terms of propagule size was important for survival and growth, however this effect was stronger at the northern site.
Seedlings survived better and grew taller at the more southern site. Seedlings traits differed by source location at the two field experiments, with seedlings from different sources having variable numbers of branches through time and differing in number of leaves, depending on experiment site. The results suggest that success of range-expanding mangroves will depend on an interaction between source population and local environmental conditions. The results from this dissertation highlight the influence of the biotic and biotic context on species interactions and individual responses to environmental conditions. Further study of such dynamics will strengthen our ability to predict and understand ecosystem functions as these contexts change due to anthropogenic influence.
vi Table of Contents
Dedication ...... ii
Acknowledgements ...... iii
Abstract of Dissertation ...... v
Table of Contents ...... vii
List of Figures ...... ix
List of Tables ...... x
Introduction ...... 1
Literature Cited ...... 9
Chapter 1: Plant-fungal symbiosis responds to experimentally increased resource availability and physical stress in a salt marsh
Abstract ...... 15
Introduction ...... 16
Methods ...... 20
Results ...... 26
Discussion ...... 28
Acknowledgements ...... 33
Literature cited ...... 34
Tables and Figures ...... 44
Supplemental materials ...... 48
Chapter 2: Asymmetrical facilitation ameliorates environmental conditions through positive feedback in partner traits
vii Abstract ...... 52
Introduction ...... 53
Methods ...... 55
Results ...... 61
Discussion ...... 65
Acknowledgements ...... 69
Literature cited ...... 70
Tables and Figures ...... 74
Supplemental materials ...... 79
Chapter 3: Interactive effects of environmental setting and source location on mangrove seedling establishment
Abstract ...... 90
Introduction ...... 91
Methods ...... 94
Results ...... 98
Discussion ...... 102
Acknowledgements ...... 106
Literature cited ...... 107
Tables and Figures ...... 113
Supplemental materials ...... 127
viii List of Figures
Chapter 1
Figure 1. Effects of experimental treatments on mean percent occurrence of DSE hyphae and DSE microsclerotia on S. alterniflora roots ...... 44 Figure 2. Effects of experimental treatments on mean S. alterniflora stem density, stem height, and percent cover ...... 45 Figure 3. Effects of experimental treatments on mean S. alterniflora leaf tissue C:N content ...... 46 Figure 4. Relationship between % DSE hyphae colonization and growth of S. alterniflora stems ...... 47
Chapter 2
Figure 1. Relationship between Limonium percent cover and mussel density in the field ...... 74 Figure 2. Plant and mussel responses in Experiment 1 ...... 75 Figure 3. Effects of Limonium size on mussels in Experiment 1 ...... 76 Figure 4. Mussel response to treatments in Experiment 2 ...... 77 Figure 5. Mean daily maximum temperature by treatment in Experiment 2 ...... 78 Figure S1. Effects of mussel density on percent sediment organic matter at field sites ...... 85 Figure S2. Change in the number of Limonium leaves per patch by mussel density in plots with naturally occurring Limonium and mussels monitored for one year ...... 86 Figure S3. Percent sediment organic matter in plots during month two of Experiment 1 ...... 87 Figure S4. Limonium response to treatments in Experiment 2 ...... 88 Figure S5. Effects of initial mussel size on mussel growth and condition index in Experiment 2 ...... 89
Chapter 3
Figure 1. Map of Florida indicating locations of experiment sites and A. germinans propagule collection sites ...... 121 Figure 2. Vegetation differences between Indian River Lagoon (IRL) and St. Joseph Bay (SJB) experiment sites ...... 122 Figure 3. Seedling survival by propagule mass, origin site, and experiment site ...... 123 Figure 4. Influence of propagule mass on seedling traits over three years (2013-2015) ...... 124 Figure 5. Influence of Experiment site and origin site on seedling traits over three years ...... 125 Figure 6. Seedling traits at IRL experiment only, including all origin sites ...... 126 Figure S1. Mean temperatures and maps showing locations of weather stations in Fort Pierce and Apalachicola in relation to experiment sites ...... 131
ix List of Tables
Chapter 1
Table S1. Results from mixed models including the fixed effects of treatments and their interaction on % colonization by DSE hyphae and % abundance of DSE microsclerotia ...... 48 Table S2. Results from a MANOVA testing the response of multiple plant variables to experimental treatments ...... 49 Table S3. Results from mixed models testing the effects of experimental treatments on each plant response variable analyzed separately ...... 50 Table S4. Results from mixed models testing the effects of experimental treatments on leaf C:N content ...... 51
Chapter 2
Table S1. Survival of Limonium and mean density of mussels in Experiment 1 ...... 79 Table S2. Results from field survey analyses ...... 80 Table S3. Results for Limonium responses in Experiment 1 ...... 81 Table S4. Results for mussel responses in Experiment 1 ...... 82 Table S5. Results for plant and mussel responses from Experiment 2 ...... 83 Table S6. Comparison of effects of species interaction and mimics on plants and mussels across both field experiments ...... 84
Chapter 3
Table 1. Mean propagule mass by origin site ...... 113 Table 2. Results from logistic regression of seedling survival ...... 114 Table 3. Results from Cox Proportional Hazards analysis ...... 115 Table 4. Results from PERMANOVA across three years ...... 116 Table 5. Results from mixed linear models testing the effects of experimental factors on seedling traits ...... 117 Table 6. Multiple comparisons of leaves and branches per seedling by seedling origins and year ...... 118 Table 7. Results from mixed linear models at the IRL site only, including all seedlings origin sites ...... 119 Table 8. Multiple comparisons from mixed linear models at the IRL site only, including all seedlings origin sites ……...... 120 Table S1. Numbers of seedlings at the beginning of the experiment by experiment site, origin, and maternal tree ...... 127 Table S2. Results from a linear model comparing percent cover of marsh vegetation at the two experiment sites ...... 128 Table S3. Results from PERMANOVA testing differences across all A. germinans adult size characteristics between the two experiment sites ...... 129 Table S4. Differences in mean annual and mean annual minimum temperatures at weather stations in Apalachicola and Fort Pierce ...... 130
x Introduction
Species interactions exist along a continuum from positive to negative outcomes for interacting partners (Johnson et al. 1997). The outcome of an interaction will generally depend on the relative importance of costs and benefits of the association for both partners (Miller 1994,
Lin et al. 2012). For example, plant-fungal resource exchange symbioses can range from mutualism to parasitism, depending on partner characteristics and environmental conditions, such as resource availability (Johnson et al. 1997, Hoeksema et al. 2010). Similarly, interacting plants may be simultaneously competing for resources and facilitating one another by ameliorating environmental conditions, with outcomes ranging from positive to negative
(Pugnaire et al. 1996, Holzapfel and Mahall 1999). The relative importance of positive and negative components of an interaction can depend on environmental conditions: as abiotic stress increases, facilitation is expected to be more important relative to competition (Bertness and
Callaway 1994). Thus the outcomes of species interactions can change depending on the particular conditions under which they occur, including both abiotic factors, such as physical stress (Menge and Sutherland 1987), and biotic factors, such as species traits (Mcgill et al. 2006).
Abiotic factors can affect the nature of species interactions in important ways. For instance, abiotic changes that increase resource availability can ameliorate competition between plants (Tilman 1994) but can also shift resource exchange mutualisms toward negative outcomes
(Hoeksema et al. 2010). Resource exchange mutualisms between plants and arbuscular mycorrhizal fungi (AMF) or other root-associated fungi are more likely to be mutualistic when limiting soil nutrients are low, such that fungal acquisition of nutrients benefits plants (Johnson et al. 2014). Physical stress is also expected to increase the importance of positive species
1 interactions (Bertness and Callaway 1994), potentially affecting ecosystem properties such as local diversity (Hacker and Bertness 1999). Interactions with fungal symbionts can increase plant tolerance to environmental stressors such as toxins, salinity, and heat (Redman et al. 2002,
Rodriguez et al. 2008, Li et al. 2011). However, physical stress may also cause the break-down of mutualism (Juniper and Abbott 2006, Kiers et al. 2010). Thus, a complex interplay between interacting partners and abiotic factors can ultimately determine the strength and sign of important species interactions.
The outcomes of species interactions can also depend on biotic context, such as the particular characteristics of the species or individuals involved (Wootton and Emmerson 2005,
Bolnick et al. 2011). For example, facilitative interactions between plants have strong negative components and high fitness costs for partners due to competition (Schöb et al. 2014), whereas facilitation between species with limited overlap in resource use such as plant-animal interactions may be less influenced by competition, making net positive outcomes more likely
(Bulleri 2009). The amount or morphology of structures that species provide for one another can also influence interactions. The height and density of mangrove root structures strongly influences algal presence, controlling a facilitation cascade benefitting epifaunal mollusks
(Bishop et al. 2013). In addition, the specific traits of plants can greatly influence their ability to ameliorate conditions for other species (Maestre et al. 2009). For example, alpine cushion plants with larger and more compact canopies can be better facilitators, apparently because this morphology enhances nutrient and moisture accumulation and reduces stress from low temperatures (Schöb et al. 2013). Biotic contexts, including individual and species traits, have clear consequences for a range of species interactions.
2 Individuals may also differ in their response to the biotic and abiotic environment based on genetic background (Via and Lande 1985, Kawecki and Stearns 1993). Individuals may encounter a range of environmental conditions if they disperse across an environmentally heterogeneous landscape, and those encountering habitats they are poorly adapted to may experience high post-settlement mortality, determining local abundances (Marshall et al. 2010).
The ability to respond to conditions through phenotypic plasticity depending on genetic background can also determine local success (Levins 1968). Understanding how species respond across variable environments based on organismal traits can provide information important for understanding and predicting the causes and effects of species distributions (Gaston 2009). For example, performance of plants at range edges can depend on genetic background, based on selection resulting from thermal differences between the source population and planting locations at the range edge (Grady et al. 2011). Differing responses to novel environmental conditions with climate change will likely have significant effects on future species distributions
(Ikeda et al. 2013).
This dissertation examines the interplay between species and the environment in coastal vegetated ecosystems. I address the ways that abiotic and biotic conditions influence species interactions and traits, as well as how species response to the environment varies across populations. Coastal vegetation such as salt marsh plants and mangroves form highly productive ecosystems that are important to the ecology and economy of coastal areas (Pennings and
Bertness 2001, Feller et al. 2010). Salt marsh plants and mangroves act as foundation species
(sensu, Dayton 1972), providing structural habitat for fauna and ameliorating stressful environmental conditions. Salt marsh and mangrove ecosystems also provide a number of economically valuable ecosystem services for humans, including coastal protection, nutrient
3 cycling, waste treatment, food production, raw materials, and recreation (Costanza et al. 1997,
Mcleod et al. 2011, Lee et al. 2014). These highly valuable ecosystem properties are threatened globally by a number of anthropogenic stressors that affect marsh and mangrove vegetation, such as poor water quality and coastal development (Alongi 2002, Gilman et al. 2008, Gedan et al.
2009). The biotic context is also changing in salt marsh and mangrove ecosystems. Marine ecosystems are undergoing rapid climate change (Burrows et al. 2011) and this change is contributing to shifts in species distributions (Pinsky et al. 2013, Burrows et al. 2014).
Mangroves are an example of these shifts, as they are currently expanding their range from the tropics and increasing in abundance in subtropical to temperate areas historically dominated by marsh plants (Osland et al. 2013, Cavanaugh et al. 2014). Thus understanding the influence of biotic and abiotic factors on species interactions and response is of increasing importance in these valuable ecosystems.
In Chapter 1, I examined how abiotic factors influence plant-fungal symbiosis in the intertidal zone. Abiotic conditions can have significant effects on interactions between plants and root-associated fungi. Global change stressors including elevated resources and physical stress can weaken some plant-fungal interactions (Juniper and Abbott 2006, Hoeksema et al. 2010), yet fungal symbionts may also play a significant and underappreciated role in mediating plant responses to these factors (Kivlin et al. 2013). Thus, it is critical to understand how plant-fungal interactions will respond to simultaneous changes in abiotic factors in the field. I conducted a ten-month field experiment to examine a symbiosis between the dominant marsh grass Spartina alterniflora and the abundant yet poorly understood dark septate endophyte (DSE) fungi in response to increased resource availability (sediment nutrient concentrations) and physical stress
(sediment salinity). As expected, plants grew taller and more densely with nutrient addition, yet
4 the positive effect on plant percent cover was reduced by salt addition. Increased resource availability decreased colonization by DSE hyphae, but it did not influence fungal reproductive structures, which were marginally increased when elevated nutrients and increased salinity were combined. In addition, there was a positive relationship between plant shoot growth and root colonization by DSE, suggesting a benefit of the association for the plant. These results are consistent with the still controversial view that plant-DSE interactions are based in part on enhanced nutritional condition of plants by fungi. That DSE hyphae were not negatively influenced by salinity stress also highlights the ability of these fungi to tolerate stressful conditions. If DSE are indeed more stress tolerant than other root-associated fungi and impart similar tolerance to their plant associates, these taxa could be increasingly important with continued global change.
In Chapter 2, I tested how positive interactions ameliorate environmental conditions for interacting species. Amelioration of environmental stress through facilitative species interactions is increasingly important as many anthropogenic stressors are expected to intensify globally (He et al. 2013). Greater emphasis on the bidirectional nature of facilitation and the specific mechanisms creating benefits for associated species are needed to further our understanding of these interactions (Schöb et al. 2014). Using a field survey and three complementary field experiments, I characterized an association between the salt marsh forb Limonium carolinianum and ribbed mussel Geukensia demissa in the northern Gulf of Mexico and tested the underlying mechanisms for each partner species. Both the plant and the mussel benefited from the association in terms of growth and survival, yet the benefits were asymmetrical, with mussels benefitting more consistently and on a shorter timescale. Limonium ameliorated predation and high temperature conditions for mussels, whereas mussels enriched sediment organic matter,
5 which can alleviate nutrient limitation for Limonium. Both interacting species had positive effects on traits that might serve to enhance the stability of the interaction through time:
Limonium both benefitted from and increased mussel density, whereas mussels both allowed
Limonium to grow more leaves and mussels benefitted more from stress amelioration under larger Limonium canopies. Approaches that incorporate bidirectional effects in facilitation including such feedbacks will enhance our understanding of the stability of facilitation through time (Bronstein 2009) and the ability of these interactions to influence species niches and distributions (Bruno et al. 2003, Bulleri et al. 2015).
In Chapter 3, I investigated how population identity interacts with the biotic and abiotic environment to influence survival and morphology. The interaction between individual traits and environmental conditions can have strong evolutionary and ecological consequences (Marshall et al. 2010). Individuals often differ in their plastic responses to environmental conditions depending upon their genetic background (Marais et al. 2013) and maternal provisioning
(Mousseau 1998). I conducted a three-year reciprocal transplant field experiment at two sites in
Florida to test whether survival and growth of mangrove seedlings from different sites vary in their response to environmental conditions, and if so, whether this variation was indicative of local adaptation. I also examined whether the mangrove response depended on maternal provisioning (propagule mass). I planted Avicennia germinans (black mangrove) propagules from 6 source locations into two common garden locations; one experimental site is mangrove dominated and in the middle of the mangrove range on the Atlantic coast of FL (Indian River
Lagoon; IRL), and the other site is a salt marsh dominated area at the northern edge of the mangrove range in the Gulf of Mexico (St. Joseph Bay; SJB). Seedlings survived better at the more southern Indian River Lagoon site, with the exception of seedlings from the source site
6 with the largest propagules, which had high survival across both experiment sites. Seedlings were taller at IRL than at SJB regardless of their origin. Seedlings traits differed by source location at the two field experiments, with origins having variable numbers of branches through time and differing in number of leaves, depending on experiment site. Maternal provisioning in terms of propagule size was important for survival and growth, however this effect was stronger at SJB where seedlings from the origins with the smallest propagules had extremely low survival. In contrast, at IRL these smallest propagules grew into some of the the largest seedlings at that site. The results were not consistent with local adaptation, as seedlings did not survive or grow better closer to their home site. The latitude of seedling origin was also not a good predictor of seedling success. The results highlight the importance of plasticity in mangrove response and suggest that success of range-expanding mangroves will depend on an interaction between source population and local environmental conditions.
The influence of abiotic and biotic context on individual response and species interactions can have significant effects on ecosystem properties and species distributions.
The outcomes of species interactions can be influenced by environmental conditions (Hoeksema et al. 2010) and traits of interacting partners (Maestre et al. 2009). Individuals also differ in their response to abiotic and biotic context, depending on genetic and non-genetic aspects of their origin and source population (Via and Lande 1985, Núñez-Farfán and Schlichting 2001, Germain and Gilbert 2014). Interactions between abiotic factors and biotic context may be important in determining ecosystem properties and future species distributions. For example, positive species interactions such as facilitation can have important roles in structuring ecological communities, influencing species distributions and persistence (Boucher 1985, Bronstein 1994, Jones et al.
1994, Bruno et al. 2003), and increasing biodiversity and consequent ecological functions
7 (Angelini et al. 2015). Abiotic and biotic factors influencing the outcomes of positive interactions affect ecosystems and may determine species distributions, particularly under increasing anthropogenic stress (Sunday et al. 2014, He and Bertness 2014, Bulleri et al. 2015).
Further, the response of species to novel environmental and biotic condition will depend on the traits and evolved tolerances of populations and individuals (Grady et al. 2011, Ikeda et al.
2013), potentially influencing ecosystem properties, such as diversity and production (Whitham et al. 2012, Grady et al. 2013). Further study of the complex interplay between biotic and biotic context on species interactions and individual responses to environmental conditions will strengthen our ability to predict and understand ecosystem functions as these contexts change due to anthropogenic influence. Understanding how context can influence the strength and sign of interactions can make our understanding of interactions and their consequences more complete and predictive (Agrawal et al. 2007, Kimbro et al. 2014). Given the rapid change occurring in many ecosystems due to anthropogenic influence, understanding how individuals and interactions respond to biotic and abiotic conditions and the interaction between the two may be able to inform conservation under changing conditions (Kiers et al. 2010, He et al. 2013).
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14
Chapter 1: Plant-fungal symbiosis responds to experimentally increased resource availability and physical stress in a salt marsh
Abstract
Plant-fungal symbioses can have strong consequences for ecological communities and are sensitive to variation in abiotic factors. While the functions of mycorrhizal fungi are well established, the role of other root-colonizing fungi such as dark septate endophytes (DSE), which lack specialized structures for nutritional transfer, are less clear. DSE are ubiquitous in extreme and stressful ecosystems, and some studies suggest a potential role in plant nutrition, yet the response of DSE to nutrient availability and physical stress has rarely been tested in the field. We conducted a ten-month field experiment to investigate how a symbiosis between the salt marsh plant Spartina alterniflora and DSE fungi responded to increased resource availability (nutrient levels) and physical stress (salinity). Plant stem density and height increased in response to nutrient enrichment, consistent with past experiments in nutrient-limited marsh systems.
Elevated nutrients increased S. alterniflora percent cover but this effect was eliminated with elevated salinity. Increased resource availability decreased colonization by DSE hyphae, but did not influence fungal reproductive structures, which were marginally increased with the combination of elevated nutrients and increased salinity. Our results are consistent with the view that plant-DSE interactions are based in part on enhanced nutritional condition of plants by fungi. In addition, there was a positive relationship between plant shoot growth and root colonization by DSE, suggesting a benefit of the association for the plants. The ultimate outcome of this interaction for plants however, may also depend on ambient stress. That DSE were not
15 negatively influenced by salt addition is consistent with prior research demonstrating the ability of these fungi to tolerate stressful conditions. Our experimental results provide further evidence of the role of root-colonizing fungi in vegetated intertidal ecosystems. Further, they highlight the importance of the poorly understood symbiosis between DSE and plants in intertidal environments.
Introduction
Species interactions, including both diffuse and tightly-coevolved mutualisms, are often dependent on the abiotic context in which they occur (Bertness and Callaway 1994, Saikkonen et al. 1998). Positive interactions such as mutualism are expected to be more important in physically stressful conditions, as described in the stress gradient hypothesis (Bertness and
Callaway 1994). In contrast, increased resource availability can lead to the breakdown of resource exchange mutualisms, such as those between plants and arbuscular mycorrhizal fungi
(hereafter AMF) (Johnson 2009). Both physical stress and nutrient levels are expected to increase in many ecosystems due to anthropogenic influence, potentially altering the outcome of symbioses in conflicting directions (Chamberlain et al. 2014). Given the importance of resource exchange mutualisms to community and ecosystem properties such as plant diversity, composition, and productivity (van der Heijden et al. 1998, 2008) and species distributions (Van der Putten et al. 2010), it is critical to understand how these relationships will respond to simultaneous changes in abiotic conditions resulting from global change (Kiers et al. 2010).
Stressors affecting either hosts or symbionts are expected to have a negative effect on the other party by reducing partner quality (Travis et al. 2006). Physical stress may also weaken the
16 positive effects of mutualism for hosts by reducing the density or growth of symbionts (Juniper and Abbott 2006). Resource exchange mutualisms can change in magnitude and even in direction, shifting from mutualism to parasitism with changing resource availability (Johnson
2009, Chamberlain et al. 2014). For example, associations between plants and AMF or other root-associated fungi are more likely to be mutualistic when soil nutrients are low, such that fungal acquisition of nutrients benefits plants (Thrall et al. 2007, Daleo et al. 2008, Tylianakis et al. 2008, Johnson 2009, Hoeksema et al. 2010, Newsham 2011). Thus, both increased resource availability and physical stress may be expected to cause a breakdown in mutualism. However, simultaneous changes in multiple abiotic factors can have unpredictable non-additive effects
(Crain et al. 2008, Darling and Côté 2008, Daleo et al. 2013), and thus changes in resource availability and physical stress in combination may have unexpected outcomes in the field.
Important to the growing understanding of plant-microbial interactions is the incorporation of abundant and diverse yet poorly understood endophytes (Mandyam and
Jumpponen 2015). Dark septate endophytes (hereafter DSE) are root-colonizing fungi characterized by melanized hyphae, the structures associated with growth and nutrient acquisition, and microsclerotia, thought to be important for asexual reproduction (Read and
Haselwandter 1981, Jumpponen and Trappe 1998). DSE are classified as endophytes because they colonize healthy root tissue (Read and Haselwandter 1981, Jumpponen and Trappe 1998).
DSE form a diverse, polyphyletic group of fungi with broad global distribution (Grünig et al.
2011) that appear to be particularly important in extreme and stressful environments including arid ecosystems (Porras-Alfaro et al. 2008, Lugo et al. 2014), polar regions (Newsham et al.
2009), highly saline environments (Sonjak et al. 2009, Maciá-Vicente et al. 2012), water-logged wetlands (Šraj-Kržič et al. 2006, Weishampel and Bedford 2006, Dolinar and Gaberščik 2010,
17 Kohout et al. 2012) and even subtidal seagrasses (Torta et al. 2014). The melanized structures and asexual reproductive mode of many DSE may represent adaptations for stress tolerance
(Jumpponen and Trappe 1998, Mandyam and Jumpponen 2005).
Despite their ubiquity and potential importance, the effects of DSE on plants are still not well understood (Mandyam and Jumpponen 2015). DSE can variously have negative (Tellenbach et al. 2011, Reininger et al. 2011) or positive (Newsham 1999, Usuki and Narisawa 2007) effects on plant performance, or a continuum of responses that differ by plant species (Haselwandter and
Read 1982, Mandyam et al. 2012) ecotype (Mandyam et al. 2013) fungal strain (Tellenbach et al.
2011), or experimental methodology (Jumpponen and Trappe 1998, Mayerhofer et al. 2012). The benefits that DSE may provide to hosts remain uncharacterized for most plant-DSE symbioses.
DSE can improve nutrient conditions and increase growth of plants when nutrient levels are low or largely comprised of organic nutrients (Newsham 2011), indicating a possible role in the provision of organic nutrients to plants (Caldwell et al. 2000, Upson et al. 2009). However, DSE do not appear to have specialized structures for resource exchange similar to those in AMF
(Peterson et al. 2008), and their role in nutrient provision remains controversial (Mandyam and
Jumpponen 2014). Another possible role for DSE is increasing plant tolerance to environmental stressors such as heavy metal toxins (Li et al. 2011) and pathogens (Tellenbach and Sieber
2012), analogous to beneficial effects of AMF and other endophytes in response to salinity and heat stress (Newsham et al. 1995, Rodriguez et al. 2008). A role for DSE in stress tolerance is further suggested by their prevalence in stressful environments (Read and Haselwandter 1981,
Mandyam and Jumpponen 2005). Collectively, research to date suggests that plant-DSE symbioses may be altered by varying abiotic conditions such as resource and stress levels, yet their response to such factors has rarely been tested in the field (Newsham 2011).
18 Plant-fungal interactions were traditionally considered unimportant in wetland ecosystems because of low sediment oxygen levels, yet recent studies highlight their potentially important consequences. As in terrestrial ecosystems, fungal root symbionts in salt marshes can increase plant productivity (Daleo et al. 2007) and affect competition between plants, determining the relative abundance of different plant species within the intertidal zone (Daleo et al. 2008) These effects may have far-reaching consequences, because salt marsh vegetation contributes significantly to highly valuable ecosystem services for humans, including storm protection, secondary productivity (Barbier et al. 2011) and disproportionately large contributions to carbon sequestration globally (Mcleod et al. 2011). Salt marsh ecosystems are also threatened by a variety of interacting anthropogenic effects that alter physical conditions, such as climate change induced drought and warming, coastal eutrophication, over-grazing, and habitat loss (Gedan et al. 2009, Gedan and Bertness 2009). Furthermore, abiotic stressors including salinity and flooding have a large influence on species interactions and the distribution of dominant vegetation in these ecosystems (Pennings and Bertness 2001). It is unknown whether some of the previously documented plant responses to abiotic factors such as physical stress and resource levels may be mediated by poorly understood microbial interactions.
We examined a symbiosis between the dominant grass Spartina alterniflora and DSE fungi in response to changes in nutrient availability (sediment nutrient concentrations) and physical stress (sediment salinity). These variables are likely to alter plant-fungal symbioses, and they are also changing in coastal ecosystems in response to anthropogenic forcing. We conducted a ten-month field experiment testing both S. alterniflora and fungal responses to the independent and combined effects of nutrient availability and physical stress. We hypothesized that S. alterniflora would respond positively to nutrient elevation and negatively to increased salinity,
19 consistent with previous studies (Silliman and Zieman 2001, Moon and Stiling 2002). We further hypothesized that root-associated DSE fungal hyphae would decrease in abundance with elevated nutrients due to reduced investment in the symbiosis by one or both partners. We expected that DSE hyphae might also decrease in response to high salinity, since this stress could have negative effects on both host and symbiont. We expected that the abundance of DSE microsclerotia would respond similarly to nutrient levels, but would increase with salinity stress, due to the potential role of these structures in escaping stressful conditions (Jumpponen and
Trappe 1998). Although plants can have diverse responses to DSE (Mandyam and Jumpponen
2015), we expected that the relationship between plant growth and DSE abundance would be positive, due to the potential benefits of these symbionts in a stressful and nutrient limited ecosystem.
Methods
Study ecosystem
Spartina alterniflora is one of the most common and abundant marsh plants along the
Atlantic and Gulf coasts of the United States (Stout 1984, Wiegert and Freeman 1990) and is responsible for a large proportion of primary production in U.S. Atlantic coast salt marshes
(Dame 1989). S. alterniflora is non-mycorrhizal, with an absence of AMF on S. alterniflora roots in the field even where AMF are present on other co-occurring salt marsh plants (Cooke and
Lefor 1990) (Hoefnagels et al. 1993, Daleo et al. 2008), but see Burcham et al. (2012). In addition, attempts to inoculate S. alterniflora with AMF have resulted in no fungal colonization
(Hoefnagels et al. 1993, Pratt-Zossoungbo and Biber 2009) or sparse colonization of roots and no
20 arbuscule formation (McHugh and Dighton 2004). Thus, S. alterniflora may be less susceptible to AMF infection, despite the fact that several of its congeners are known to form associations with AMF (Cooke et al. 1993, Burke et al. 2002, McHugh and Dighton 2004). Despite being non-mycorrhizal, we have found that S. alterniflora roots do harbor symbiotic DSE fungi (see
Results).
Changes in nutrient and salinity levels due to anthropogenic influence can have strong negative consequences for marsh ecosystems. Both salinity and nutrient availability vary within and among natural S. alterniflora populations (Bertness 1991, Levine et al. 1998, Pennings and
Bertness 2001), and they influence plant morphology and plant-plant species interactions (Levine et al. 1998, Silliman and Zieman 2001, Moon and Stiling 2002). Although increased nutrient availability enhances marsh plant growth on a small scale (Sullivan and Daiber 1974, Osgood and Zieman 1993, Silliman and Zieman 2001), on a large scale, eutrophication can cause a dramatic decrease in vegetation through the physical collapse of marsh creeks (Deegan et al.
2012) and can intensify negative effects of sea level rise (Crain et al. 2008). Drought can cause increases in soil salinity in salt marshes that may contribute to marsh die-off (Mckee et al. 2004), particularly in combination with biotic stressors such as over-grazing (Silliman et al. 2005).
Salinity stress reduces photosynthetic rates and biomass accumulation in S. alterniflora (Bertness et al. 1992), can alter microbial activity (Morrissey et al. 2014), and can reduce the growth benefits of elevated nutrients (Mendelssohn and Morris 2000). Although nutrient and salinity levels have well-documented effects on marsh vegetation, their effects on fungi and plant-fungal interactions in these systems are not well understood. For instance, salinity can decrease AMF colonization of marsh plants (Carvalho et al. 2003), or have no effect (Bach Allen and
21 Cunningham 1983, Carvalho et al. 2004), and the effects of salinity on plant-DSE interactions remain virtually unexplored.
We conducted a field experiment from August 2012 to June 2013 testing the independent and combined effects of sediment nutrient and salt additions on a plant-fungal symbiosis in St.
Joseph Bay, a shallow, protected bay on the Gulf coast of Florida. Marshes here are dominated by Spartina alterniflora in lower and mid intertidal and Juncus roemerianus at higher tidal heights. Our experiment was conducted in the zone dominated by S. alterniflora, with low abundances of Batis maritima and Salicornia bigelovii in some plots. Dark septate endophyte
(DSE) fungi are common on S. alterniflora roots at the field site, while arbuscular mycorrhizal fungi (AMF) have not been observed (A. Moore, pers. obs.).
Experimental design and physical measurements
The experiment included four treatments (N=10 per treatment): salt addition, nutrient addition, salt and nutrient addition, and procedural control. The treatments were renewed once per month for the duration of the experiment. We arranged the experimental plots in spatial blocks with 4 plots (1 per treatment) in each block. Plots were 0.5 m x 0.5 m and separated by
1m, with 2 m between blocks.
We added the per plot equivalent of 100 g / m2 pelletized fertilizer (Osmocote brand, 19,
6, 12, % NPK) to the nutrient addition plots at monthly intervals, as these levels have previously been shown to increase nutrient levels in marsh sediments and stimulate increased plant growth
(Silliman and Zieman 2001). We added the equivalent of 71.8 g / m2 NaCl (kosher salt) each month in salt addition plots, an amount sufficient to cause a pulsed elevation in sediment pore water salinity (see Results). The use of kosher salt follows previous methodology in salt marshes
22 (Moon and Stiling 2002). We wrapped nutrient pellets and salt in nylon fabric and added each to
15 ml plastic conical tubes drilled with 16 holes of 1.6 mm diameter, which we buried in the sediment. This method allows for gradual release of treatments over time (Silliman and Zieman
2001). Each plot received eight tubes, four of which contained nutrient pellets and/or salt in the addition treatments. Procedural control plots received the same number of empty tubes to account for disturbance to the plots. We measured the salinity of syringe-extracted pore water in the center of each plot at the beginning and end of the experiment using a refractometer. In addition, we measured pore water salinity in the same manner within 24 hours of the treatment implementation in 5 locations per plot in June 2013.
Fungal and plant measurements
We quantified S. alterniflora abundance in all plots at the beginning and end of the experiment to characterize treatment effects. We visually estimated S. alterniflora percent cover using a gridded quadrat within each 0.5 m x 0.5 m experimental plot and measured the height of five stems within each plot. Stem density was counted within a 0.25 m x 0.25 m quadrat within each plot. We also determined percent cover of the other marsh plants present in some of our plots, Batis maritima and Salicornia bigelovii.
We collected three root samples from the center of each plot to estimate fungal abundance at the end of the experiment. Due to the melanized appearance of DSE fungal structures, they were easier to visualize in roots that were cleared but not stained (Mandyam et al
2013). Thus, root samples were cleared using a procedure modified from Koske and Gemma
(1989) by soaking roots in heated 10% KOH for 30 minutes and unheated 5% KOH for 12 hours or until the tissue was cleared. We determined percent colonization of root-associated fungi
23 using the magnified intersections method at 400 x magnification to examine samples for DSE hyphae and microsclerotia (McGonigle et al. 1990). We quantified the colonization by hyphae and microsclerotia separately to determine whether these structures would show a similar response to treatments. Root subsamples from each plot were stained using the ink and vinegar method (Vierheilig et al. 1998) and visually analyzed in the same manner, to test for the presence of arbuscular mycorrhizal fungi (AMF), or other fungal structures revealed only by staining. No
AMF were observed on any stained slides, consistent with previous assessments that S. alterniflora lacks AMF (Hoefnagels et al. 1993, Daleo et al. 2008).
We examined the effects of nutrient and salt addition on S. alterniflora leaf nutrient
(C:N) content to determine how treatments affected nutrient condition of plants. We examined N because it is typically the limiting nutrient in S. alterniflora salt marshes (Osgood and Zieman
1993). For tissue analysis, we collected the youngest leaf from one S. alterniflora plant in each plot, rinsed it with deionized water in the field and preserved it by freezing. We dried the leaves for at least 48 hours at 60ºC and ground them to a fine powder. We then determined the proportion of C:N in the tissue using a Thermo FlashEA 1112 NC Soil Analyzer.
Plant growth and fungal colonization
We marked three S. alterniflora stems per plot in a subset of three experimental blocks and measured the growth in height (cm day-1) of these stems during the last 40 days of the experiment. Root samples used for analysis of DSE abundance were collected directly from the shoots that we marked and measured for growth. This design allowed us to connect above ground growth rates to fungal colonization associated with the same shoots.
24 Statistical analyses
All analyses were conducted using R (version 3.1.3, R Core Team 2015). To test whether the salinity addition treatment elevated pore water salinity, we used a one-tailed Welch two- sample t test to compare plots with and without added salt, using the t.test function in the base statistics package in R. The effect of nutrient addition on plant tissue nutrient content was analyzed using linear mixed models (LMM), including nutrient and salinity treatments as fixed factors and experimental block as a random factor, using the lmer function in the lme4 package
(Bates et al. 2015). We tested the response of multiple plant variables to treatments with a
MANOVA using the manova function (R base statistics package), including nutrient and salinity manipulations and their interaction as fixed factors and experimental block as a random factor.
Because the MANOVA showed significant effects, we tested the effects of experimental treatments on individual response variables, including abundance of fungal structures and S. alterniflora abundance and traits, using the same model in LMM. Post hoc multiple comparisons were made using Tukey’s HSD using the function glht in the multcomp package (Hothorn et al.
2008). In models with S. alterniflora as a response variable, we also included measurements of S. alterniflora from the beginning of the experiment as a covariate. Proportional response variables including percent colonization of DSE structures and percent cover of S. alterniflora were logit transformed before analysis (Warton and Hui 2011) using the logit function in the car package
(Fox and Weisberg 2010). We analyzed effects of treatments on Batis maritima and Salicornia bigelovii in the same manner as S. alterniflora responses. All LMM analyses were performed in
R (version 3.1.1) using the lmerTest package using Satterthwate approximations to calculate p values. To examine the relationship between fungal colonization and plant performance in the
25 field, we used a linear model to test the effect of percent DSE hyphal colonization on the growth of marked S. alterniflora stems across treatments.
Results
Physical measurements
Salinity was only slightly higher in salt addition treatments at the end of the experiment and this difference was not significant (Welch t test, t = -1.261, df = 37.7, p-value = 0.11, ambient salinity mean 30.9 ± 0.6, elevated salinity mean 31.8 ± 0.5). However, there was a pulsed increase in salinity within 24 hours of salt addition (Welch t test, t = -2.176, df = 5.6, p- value = 0.04, ambient salinity mean 43.3 ± 1.1 ppt, elevated salinity mean 52.5 ± 4.1 ppt) and salinity in some salt addition plots reached 90 ppt during this time frame. Salinity levels above
20 ppt reduce S. alterniflora growth, and the upper limits of salinity tolerance have been found at
60 ppt or 75 ppt (Mendelssohn and Morris 2000, and references therein), so we expect that the elevated salinity in our experiment was high enough to act as a pulsed stressor.
Fungal and plant responses
DSE structures were present in roots from all experimental plots; in contrast, AMF were not observed in any sample. As we hypothesized, nutrient addition resulted in lower DSE hyphal colonization on S. alterniflora roots at the end of the experiment compared to the ambient nutrient treatments (Nutrient addition, F1,27 = 5.02, P = 0.03; Fig. 1A). There was no effect of salt addition on hyphal colonization (Table S1). In contrast, DSE microsclerotia colonization was not significantly influenced by nutrient addition (Table S1) but instead was marginally higher in
26 treatments with increased salinity (Salt addition, F1,27 = 3.20, P = 0.08; Fig. 1B). Microsclerotia were most abundant in plots with both elevated salinity and nutrients (Fig. 1B), though the interaction between nutrients and salinity was not significant (Table S1).
Multivariate analysis of variance (MANOVA) on S. alterniflora stem height, density, and percent cover revealed an interactive effect of nutrient and salt addition (Nutrient addition x Salt addition, F = 2.950, DF= 1, Pillai’s trace= 0.21, P=0.05; Table S2). Additionally, there was a significant effect of DSE hyphal abundance when added as a covariate (Percent DSE hyphae, F=
2.88, DF= 1, Pillai’s trace= 0.22, P=0.04; Table S2). When analyzed independently, S. alterniflora in nutrient addition plots had higher stem density (Nutrient addition, F1, 26 = 16.92, P
= 0.0003; Fig. 2A) and stem height (Nutrient addition, F1, 26 = 8.12, P = 0.008; Fig. 2B) than at ambient nutrient levels. Contrary to expectation, neither stem density nor height was influenced by salt addition or the interaction between nutrient and salt addition (Table S3). However, the effects of nutrient addition on final percent cover of S. alterniflora depended on salt addition
(Nutrient addition x Salt addition, F1, 26 = 8.95, P = 0.006; Fig. 2C): S. alterniflora percent cover was higher with added nutrients, but this benefit was reduced by salt addition. The abundances of
Batis maritima and Salicornia bigelovii were not significantly related to any treatment (data not shown).
S. alterniflora leaves had a higher proportion of N in treatments with experimentally increased salinity, as evidenced by their significantly lower C:N ratio (Salt addition, F1, 27 = 5.43,
P= 0.028, Fig. 3). However, added nutrients only marginally decreased leaf C:N ratio (Nutrient addition, F1, 27 = 3.19, P= 0.085, Fig. 3).
Plant growth and fungal colonization
27 Nutrient addition significantly increased the mean daily stem growth of marked S. alterniflora stems (Nutrient addition, F1, 6 = 16.09, P = 0.007, Ambient nutrients: 0.018 ± 0.017,
Elevated nutrients: 0.084 ± 0.016). Fungal abundance was also a predictor of S. alterniflora growth: increased growth of marked stems was associated with greater colonization by DSE
2 hyphae across treatments (percent colonization DSE hyphae, F1, 10 = 6.65, P= 0.02, R =0.34; Fig.
4).
Discussion
The positive response of S. atlerniflora to elevated nutrients was consistent with our expectations based on prior research (Mendelssohn 1979, Smart and Barko 1980, Hopkinson and
Schubauer 1984, Silliman and Zieman 2001): elevated nutrients generally increased the abundance of S. alterniflora vegetation, although the magnitude of this effect sometimes depended on the salt addition. Salinity stress can decrease S. alterniflora growth (Bertness et al.
1992, Daleo et al. 2015), yet we only observed marginally reduced percent cover when nutrients and salinity were both elevated. The MANOVA across all plant responses showed a significant interaction between salt and nutrient addition, which suggests that the overall importance of these combined effects for plant growth may be more detectable in multivariate space.
Osmoregulation in S. alterniflora requires the production of nitrogen-based compounds, yet salt itself competitively inhibits N uptake, a dynamic which can ultimately increase the amount of N uptake required to increase growth at higher salinities (Mendelssohn and Morris 2000). Due to the complicated physiological interaction between these factors, high salinity can depress the growth effects of nutrients (Haines and Dunn 1976), consistent with our results. The increased N
28 content of S. alterniflora leaf tissue we observed in elevated salinity treatments is consistent with previous findings and demonstrates the role of nitrogen rich proteins in osmoregulation (Moon and Stiling 2004, Brown et al. 2006, Jimenez et al. 2012). Salt addition caused a greater increase in leaf N content than did nutrient addition, perhaps because at higher nutrient levels plants were better able to utilize N for growth of new structures rather than retaining N in the leaves for osmoregulation.
The observed decrease in DSE hyphal colonization at elevated nutrient levels in our experiment is consistent with a nutrient transfer function for DSE. Further, the nutrient limitation documented in this system suggests that the nutritional role hypothesized for DSE could be beneficial to S. alterniflora. Although controversial, the current evidence in the literature is suggestive of a role for DSE in plant nutrient acquisition, yet the mechanisms remain unclear given the apparent lack of specialized structures for nutrient transfer (Peterson et al. 2008). Field tests manipulating inorganic nutrients have either increased or not affected DSE colonization
(Mandyam and Jumpponen 2008, Dean et al. 2013). In addition, several studies have indicated a benefit of DSE to plant growth and/or nutrient levels when organic nutrients were supplied, but not when inorganic nutrients increased (Newsham 1999, Upson et al. 2009), and a meta-analysis supports the generality of these findings (Newsham 2011). We added inorganic nutrients, which would not be expected to enhance benefits provided by DSE based on these findings. Transfer of labeled carbon from a plant to DSE fungi has been demonstrated in vitro (Usuki and Narisawa
2007), indicating a likely nutritional benefit of the association for DSE as well.
The response of DSE to nutrient addition in our experiment was similar to that observed for AMF, which tend to decrease in abundance when nutrient levels are experimentally increased
(Treseder 2004, Hoeksema et al. 2010). In plant-AMF symbioses, this response is thought to
29 represent a shift of plant-symbiont interactions from positive to negative as nutrients are no longer limiting to the plant and the cost of providing carbon to fungi exceeds the nutritional benefits (Johnson et al. 1997, Johnson 2009, Johnson et al. 2014). AMF abundance may decrease in this instance as a result of reduced investment in the symbiosis on the part of the host, which may be limiting partners that are no longer beneficial (Kiers et al. 2011). Decreased fungal abundance could also reflect lower allocation to hyphal structures associated with nutrient acquisition by the fungi, as nutrients become more readily available (Johnson 2009). Whether due to changes in allocation by the plant or fungal partner, our results suggest that increased nutrient levels may alter partner investment in the plant-DSE symbiosis and lend additional support to the idea that plant-DSE symbioses may function similarly to that of AMF (Jumpponen
2001, Mandyam and Jumpponen 2015).
The positive relationship between stem growth and hyphal colonization by DSE suggests a growth benefit of DSE for Spartina. Because we measured stem growth of the same stems that were analyzed for colonization, our measure of growth was more direct than plot-level plant responses which often do not correlate well with fungal colonization (Fitter 1985). Given that plant responses to DSE fall variously along the spectrum from mutualism to parasitism depending on plant genetic background and DSE species (Mandyam et al. 2013, Mandyam and
Jumpponen 2015) it is interesting that our results were consistent across plots, given that S. alterniflora genotypes and DSE species can both vary within field sites at the spatial scale of our experiment (Grünig et al. 2011, Hughes and Lotterhos 2014). That the relationship between plant growth and fungal colonization was positive across treatments may suggest additional benefits for the plant apart from nutrient transfer, similar to benefits such as stress and disease resistance
30 provided by AMF and other plant endophytes (Newsham et al. 1995, Rodriguez et al. 2009,
Porras-Alfaro and Bayman 2011).
Both S. alterniflora and DSE may benefit from stress alleviation as a result of their association. DSE are hypothesized to enhance plant stress tolerance, both due to their prevalence in stressful environments (Barrow 2003, Mandyam and Jumpponen 2005) and their documented role in plant stress tolerance (Waller et al. 2005, Yuan et al. 2010). That increased salinity did not decrease DSE hyphal colonization may indicate the stress tolerance of DSE taxa in general, or the ability of these fungi to adapt to the salt marsh environment. However, soil salinity levels in our experiment were likely not above the higher range of natural salinity levels for long periods of time and thus may simply be within the tolerances of the DSE at this field site. DSE have been found previously in saline environments (Sonjak et al. 2009, Maciá-Vicente et al.
2012) and it has been suggested that the melanins in DSE structures may provide protection from a variety of stressors (Jumpponen and Trappe 1998). Additionally, association with the host plant may protect DSE from salinity stress: Mohamed and Martiny (2011) found that at higher salinity levels plants were increasingly important to the composition of wetland soil fungi, suggesting a possible role of plant in alleviating stress for fungi. In addition, DSE that are dominant in saline field conditions can be highly sensitive to salt stress in vitro, without the benefit of the host plant
(Maciá-Vicente et al. 2012). In salt marsh ecosystems, plant-fungal associations may indeed be a poorly understood a mechanism of stress tolerance for both partners.
The marginal increase in microsclerotia where nutrients and salinity were elevated may also represent a response to stressful conditions (Ruotsalainen et al. 2007). DSE microsclerotia are thought to be a means of asexual reproduction that may allow DSE to disperse away from poor conditions (Currah et al. 1993). The fact that the highest abundance of these structures was
31 found in the treatment with both elevated nutrients and salinity suggests that salinity only becomes stressful when coupled with weakening of the plant-fungal mutualism at higher nutrient levels. Although the role of microsclerotia in the plant-fungal interaction is not well characterized, evidence from pathogenic fungi suggests that microsclerotia production may be related to fungal virulence. Some related genetic pathways that increase pathogenicity also enhance microsclerotial development, whereas other pathways repress microsclerotial development and may be related to host defense (Klimes et al. 2015). Thus colonization by microsclerotia may be the result of a complex interplay between environmental conditions and host response. That we found different patterns for DSE hyphae and microsclerotia as well as higher overall colonization by microsclerotia than by hyphae also highlights the different roles of these structures. Further study of the differing roles of DSE structures would lead to a more mechanistic understanding of the plant-DSE interaction.
Understanding the response of species interactions to altered physical conditions is of increasing importance due to current anthropogenic influence on multiple abiotic factors. There is mounting evidence that fungal symbionts play a crucial role in mediating the response of plants to global change factors (Kivlin et al. 2013) and predicting outcomes for ecosystem function (Johnson et al. 2013). Global environmental change, including climate change, land use alteration, and nutrient enrichment, has generally weakened mutualisms with plants, potentially leading to community-wide changes to interaction webs (Tylianakis et al. 2008). Further investigation of plant-fungal symbiosis will be crucial to our understanding of how communities and ecosystem services respond to global change (Juniper and Abbott 2006, Johnson 2009,
Hoeksema et al. 2010). In many ecosystems, dominant vegetation plays an important role in providing such services as water purification, carbon storage and climate stability (Costanza et
32 al. 1997, Kremen 2005). For example, enhanced growth of dominant yet non-mycorrhizal marsh plants through DSE association could increase plant productivity, as AMF have been shown to do (Daleo et al. 2007). Such increases in productivity may be expected to enhance biomass- related ecosystem services provided by marsh ecosystems, such as storm protection and carbon storage (Koch et al. 2009, Mcleod et al. 2011). Thus understanding the microbial interactions that mediate response of plants to environmental change are also vital to conserving these valuable functions (Johnson et al. 2013). If DSE are indeed more stress tolerant than AMF and provide some analogous functions for plants, these taxa could be increasingly important if abiotic stress levels increase through global change. Further research into the functions of these poorly described fungal taxa and their role in mediating plant response to environmental conditions will enhance our understanding of ecosystem resilience in the face of global change.
Acknowledgements: We thank Erica Holdridge, Robyn Zerebecki, Austin Heil and Forest
Schenk for assistance in the field and lab. Funding was provided by NSF grant DEB-0928279 to
R. Hughes. We thank Florida State University Marine and Coastal Laboratory and Northeastern
University Marine Science Center for institutional support.
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43
Figure 1. Effects of experimental treatments on mean (±SE) percent occurrence of (A) DSE hyphae and (B) DSE microsclerotia on S. alterniflora roots. Different letters above bars or groups of bars indicate significant differences at p≤0.05 based on LMM or Tukey H.S.D. test where significant interactions between factors occurred. Asterisks mark bars with marginally significant difference at p≤0.08. See text and Table S1 for full statistical results.
44
Figure 2. Effects of experimental treatments on mean (±SE) final traits of S. alterniflora: (A) stem density, (B) stem height, and (C) percent cover. Different letters above bars or groups of bars indicate significant differences at p≤0.05 based on LMM or Tukey H.S.D. test where significant interactions between factors occurred. Asterisks mark bars with marginally significant difference at p≤0.07. See text and Table S2 and S3 for full statistical results.
45
Figure 3. Effects of experimental treatments on mean (±SE) S. alterniflora leaf tissue C:N content. Different letters above bars or groups of bars indicate significant differences at p≤0.05 based on LMM or Tukey H.S.D. test where significant interactions between factors occurred. Asterisks mark bars with marginally significant difference at p≤0.09. See text and Table S4 for full statistical results.
46
Figure 4. Relationship between final % DSE hyphae colonization and mean daily growth of marked S. alterniflora stems attached to analyzed roots. Trend line shows linear relationship between variables with R2=0.34.
47 Chapter 1: Supplemental materials
Table S1. Results from mixed models including the fixed effects of treatments and their interaction on % colonization by DSE hyphae and % abundance of DSE microsclerotia. Models included block as a random factor.
48 Table S2. Results from a MANOVA testing the response of multiple plant variables (S. alterniflora stem height, percent cover, and stem density) to treatments including nutrient and salinity manipulations as fixed factors and experimental block as a random factor and DSE hyphal abundance as a covariate.
49
Table S3. Results from mixed models testing the effects of experimental treatments on each S. alterniflora response variable analyzed separately, including nutrient and salinity treatments as fixed factors and experimental block as a random factor and initial response (stem density, height or percent cover) as a covariate.
50 Table S4. Results from mixed models testing the effects of experimental treatments on leaf C:N content of S. alterniflora, including nutrient and salinity treatments as fixed factors and experimental block as a random factor.
51 Chapter 2: Asymmetrical facilitation ameliorates environmental conditions through positive feedback in partner traits
Abstract
Positive interactions that ameliorate stressful environmental conditions have important effects on ecological communities. Facilitation may be particularly important as anthropogenic stressors increase globally. Our progress in understanding these interactions, however, is currently hindered by a lack of emphasis on the bidirectional nature of facilitation and the specific mechanisms creating benefits for associated species. We characterized an association between the salt marsh forb Limonium carolinianum and ribbed mussel Geukensia demissa in the northern Gulf of Mexico. We used a field survey and three field experiments to establish the effects of the association and identify underlying mechanisms for each partner species. Growth and survival of both plants and the mussels increased in the presence of the other species, yet the benefits appear asymmetrical: Limonium benefitted mussels in both experiments, but we only detected positive effects of mussels on Limonium in the longer-term field experiment. These differential benefits likely reflect variation in the seasonality and/or timescale of the underlying mechanisms. Limonium ameliorated predation and high temperature conditions for mussels, whereas mussels enriched sediment organic matter and thus over time may alleviate nutrient limitation for Limonium. Both interacting species in our study had positive effects on traits that can enhance the stability of the interaction through time. Limonium both benefitted from and increased mussel density, while mussels both allowed Limonium to grow more leaves and benefitted from stress amelioration under larger Limonium canopies. As anthropogenic stressors
52 influence environmental conditions globally, it is increasingly vital to understand the role of facilitation in stress amelioration.
Introduction
Positive species interactions such as facilitation can have important roles in structuring ecological communities, influencing species distributions and persistence (Boucher 1985,
Bronstein 1994, Jones et al. 1994, Bertness and Callaway 1994), increasing biodiversity and consequent ecological functions (Angelini et al. 2015), as well as enhancing the persistence of rare species (Soliveres et al. 2015). Facilitation is expected to be stronger in stressful environments, where positive interactions ameliorating harsh conditions should be relatively more important than competition, as formalized in the stress gradient hypothesis (Bertness and
Callaway 1994). Much of the facilitation literature has focused on plant-plant interactions in stressful habitats, typically characterized by a benefactor plant that ameliorates conditions for the beneficiary species (Callaway 2007, Brooker et al. 2008). Feedbacks between the benefactor and beneficiary can range from negative to positive (Pugnaire et al. 1996, Holzapfel and Mahall
1999), yet relatively few studies have addressed these bidirectional effects (Bulleri 2009, Schöb et al. 2014).
Marine ecosystems have been important in the conceptual development of facilitation research, particularly in intertidal zones where facilitation by plants and sessile invertebrates can ameliorate extreme stresses of these habitats (Menge and Sutherland 1987, Altieri and van de
Koppel 2013). In addition, the marine environment provides opportunities to explore unique aspects of facilitation. Notably, facilitative interactions across trophic levels (such as interactions
53 between plants and invertebrates) are common in marine communities. For example, marine vegetation can reduce temperature and desiccation stress for invertebrates (Nomann and
Pennings 1998, Bortolus et al. 2002, Canepuccia et al. 2007, Dijkstra et al. 2011), provide refuge from predation (Peterson and Heck 2001a), or both (Leonard 2000, He and Cui 2015).
Invertebrates facilitate plant growth through excretion of nitrogenous waste or deposition of organic matter (Bertness 1984, Peterson and Heck 2001b, Bracken et al. 2007, Holdredge et al.
2010) and through indirect effects on plant competitors (Valentine and Duffy 2006).
Expectations about the outcomes of facilitation in stressful environments have often been developed for functionally similar species that use common resources (Brooker et al. 2008,
Maestre et al. 2009). Studies of facilitation across trophic levels can provide insight into the outcomes of facilitation across a greater range of positive and negative interaction components
(Bulleri 2009). Here we examine an interaction between a salt marsh plant and bivalve that have limited overlap in resource use and thus reduced potential for competition. We predicted that benefits would be mutual or commensal across a wide range of environmental conditions, given the probable low cost of the association for both partner species.
In salt marsh communities, the ribbed mussel Geukensia demissa, hereafter referred to as mussel, participates in a facultative mutualism with the dominant salt marsh plant Spartina alterniflora (Bertness 1984, Hughes et al. 2014). In the Gulf of Mexico, mussels are also closely associated with the marsh plant Limonium carolinianum, hereafter referred to as Limonium.
Mussels often occur in dense aggregations within and around the basal rosettes of Limonium, attached to the woody roots and stems of the plants (A. Moore, personal observation). We documented the pattern of association between ribbed mussels and Limonium in the field and hypothesized based on these observations that the Limonium-mussel association would have
54 positive effects on both species. We also expected that the net benefit of mussel presence for
Limonium would vary with mussel density. We tested these hypotheses by manipulating
Limonium and mussel co-occurrence and mussel density in multiple field experiments. We also examined several potential mechanisms underlying the bi-directional facilitation of mussels and
Limonium, including (1) organic matter deposition by mussels alleviating nutrient limitation for
Limonium, (2) shading by Limonium ameliorating temperature stress for mussels, and (3) habitat structure created by Limonium reducing predation on mussels.
Methods
Field survey: We surveyed four field sites on the Gulf coast of Florida, U.S.A., where ribbed mussels and Limonium co-occur. We included two sites in St. Marks National Wildlife
Refuge, Wakulla Beach (30°06′36.4′′N, 084°15′51.7′′W) and St. Mark’s Lighthouse
(30°04'24.4"N, 84°10'42.6"W), and two sites in St. Joseph Bay, SJB GD1 (29°41'04.4",
085°20'24.4") and SJB GD5 (29°41’00.8”, 085°21'08.3"). We surveyed at 10 randomly chosen points along two 25 m transects running parallel to the water in the mid-intertidal (where both species occur) at each site. We estimated percent cover of Limonium and counted the number of mussels present within 0.25 m2 gridded quadrats. We also collected one sediment core at 5 cm depth in the center of each quadrat for sediment organic matter analysis.
We followed changes in plant size (number of leaves) and associated mussel abundance at a single site over time in 25 permanent plots with naturally occurring patches of both species.
In July 2013, we designated and marked five plots with mussels alone, four plots with Limonium alone, and 16 plots with the two species co-occurring at a range of densities (N=5-6 per density;
55 Low: mean= 10.2, range= 3-18; Medium: mean= 38, range= 26-51; High: mean= 131.4, range=
63-180).
Experiment 1. Testing mutual benefits of interaction: This experiment was conducted at Wakulla Beach, St. Marks National Wildlife Refuge, FL. The marsh vegetation is dominated by smooth cordgrass (Spartina alterniflora), which is intermixed with our focal species,
Limonium carolinianum. Other plant species include pickleweed (Salicornia virginica) and needlerush (Juncus roemerianus). Wakulla Beach harbors high densities of mussels relative to other sites in the FL Gulf coast region (Fig. 1).
We manipulated plant and mussel presence and mussel density in the field from July
2012 to August 2013. The experiment consisted of six blocks with twelve plots each, including three replicates of each treatment in a randomized blocked design. Treatments included:
Limonium alone, mussels alone at one of three mussel densities (5, 20, or 50 individuals per plot), and Limonium with mussels together at one of three mussel densities (5, 20, or 50 individuals per plot). We also included a procedural control treatment consisting of un- manipulated plants with naturally occurring mussels. Un-manipulated treatments were selected to have mussel densities comparable to the three controlled density treatments and were replicated in the same manner as the manipulated treatments.
To create the experimental treatments, we haphazardly collected Limonium patches of
0.02 m2 area with naturally occurring mussels attached. We removed mussels from plants to create plant and mussel alone treatments and replanted mussels on plants to create the treatments with plants and mussels together. The un-manipulated controls were maintained and handled similarly to the manipulated treatments, except that the mussels were not removed. After the
56 treatments were established, patches were replanted in the field in naturally occurring vegetation within 14 days of collection.
We examined the effect of our treatments on the survival, growth, and reproduction of
Limonium. At the beginning of the experiment (July 2012), we measured the number of
Limonium leaves within each patch. We then censused Limonium survival and the number of flowering stems per plant in July 2012, August 2012, April 2013, and in August 2013. At the end of the experiment (August 2013), we harvested the experimental plots and again counted the number of leaves per plant.
We measured changes in the abundance and size of mussels in response to our experimental treatments. At the beginning and end of the experiment, we counted and measured the shell width of live mussels to the nearest ± 0.01 mm. Although the final size measurements do not correspond to initial measurements of particular individuals, they do provide information about changes in size structure over the course of the experiment. We also placed small plastic mussel recruit collectors in all plots to measure recruitment in experimental treatments.
In August 2012, there was a tropical storm that caused significant mussel mortality due to smothering of our plots by seagrass wrack. We non-destructively counted plant presence and mussel density in the experimental plots immediately after this disturbance to quantify plant survival and changes in mussel abundance in our treatments (Table S1). At this time we also collected sediment cores at 5 cm depth in each experimental plot for sediment organic matter
(SOM) analysis to determine whether mussels affected Limonium through the input of nutrients to the sediment. To determine SOM, we dried the sediment at 105ºC for at least 3 hours and combusted the sediment at 525ºC for 3 hours, and percent mass lost.
57 Experiment 2. Testing mechanisms of benefit: We used a subsequent manipulative field experiment at the same site to test potential mechanisms underlying the Limonium-mussel facilitation: (1) mussels benefit Limonium through organic matter deposition that alleviates nutrient limitation; and (2) Limonium benefits mussels via habitat amelioration by the plant canopy. We included the following treatments (N=10): intact Limonium; mussels alone; intact
Limonium and mussels; Limonium belowground (roots and woody shoots) and mussels; artificial
Limonium canopy and mussels; intact Limonium with nutrient addition. We used a randomized blocked design with 10 blocks for a total of 60 plots in the experiment. Plots within blocks were separated by 1 m, with 2 m between blocks. All plots were parallel to the marsh edge at the same tidal elevation. We added 24 mussels to each treatment with mussels, which was similar to mean mussel abundance in patches of the same area from the previous field experiment. We also controlled mussel size distribution across plots to mimic that of naturally occurring mussel patches. We initiated the experiment in July 2014 and continued the treatments for seven weeks.
Mussels that died within the first 24 hours of the experiment were replaced.
We created our experimental treatments as in the first experiment, with the following exceptions. To create the Limonium below ground treatment, we removed the leaves from the woody shoots and roots of the plants. The leaves did not re-grow during the course of the experiment. We created the artificial plant canopy treatment using leaf mimics made from thin, flexible foam attached to metal garden staples using cable ties to create shading similar to leaves, without the mussel attachment substrate provided by Limonium root and shoot structure. The artificial canopy consisted of 3 bunches of 12 artificial leaves each. Each artificial leaf was approximately 8 cm long, equivalent to the average length of Limonium leaves. We created the nutrient addition treatment by adding the per plot equivalent of 100 g / m2 pelletized fertilizer
58 (Osmocote brand, 19, 6, 12, % NPK) to the elevated nutrient plots at the initiation of the experiment, as these levels have previously been shown to increase plant growth in the marsh
(Silliman and Zieman 2001). We wrapped nutrient pellets in nylon fabric and added to 15 ml plastic conical tubes drilled with 16 holes of 1.6 mm diameter, which we buried in the sediment, allowing for gradual release of nutrients over time (Silliman and Zieman 2001). We placed empty tubes in plots without nutrients to account for any disturbance to the plots. To determine whether mussels benefit from temperature amelioration due to shading by plants, we placed temperature loggers (iButton, Maxim Integrated Products, Inc) inside of empty mussel shells filled with clear silicone caulking (Helmuth and Hofmann 2001, Jost and Helmuth 2007). Mussel mimic loggers were partially buried at the sediment surface, attached to small metal posts. We collected temperature data at 10-minute intervals for 4 weeks in a subset of blocks (N=8-9 per treatment).
At the beginning and end of the experiment, we counted the number of Limonium leaves and leaf length, which we estimated from the base of the plant at three points across the canopy.
At the end of the experiment, we measured and counted flowering stems produced during the experiment. We tagged all mussels used in the experiment so that we could follow individuals through time. At the beginning and end of the experiment, we measured shell length to ± 0.01 mm using electronic calipers. We also measured growth of mussels over the course of the experiment by filing a small (~ 1 mm) notch in the shells of 12 mussels per plot (2-4 per size class) and measuring shell length from apex to notch and from apex to growing edge at the experiment’s end (Ekaratne and Crisp 1982, McQuaid and Lindsay 2000). At the end of the experiment we removed all mussels and preserved them by freezing. We quantified the condition index (CI) of all mussels recovered from the experiment as: [dry tissue mass/(tot wet mass –
59 shell mass)] x 1000, following previous work with ribbed mussels (Crosby and Gale 1990,
Chintala et al. 2006).
Predation trial: We hypothesized that Limonium may protect mussels from predation by visually obscuring mussels under the plant canopy and/or by providing a secure attachment site.
To test this hypothesis, we tethered mussels (N=7 per treatment) at the Wakulla Beach field site under a naturally occurring Limonium plant or in a paired plot with S. alterniflora at the same tidal height (at least 10 cm away from the nearest Limonium plant). Mussels were tethered by affixing fishing line to one shell valve with marine epoxy (Splash Zone 2 part epoxy A 2788).
We measured water depth in each plot at high tide. We examined the tethered mussels every 24 hours for 7 days to determine survival.
Data analysis: Survey: We analyzed the effects of site and Limonium abundance on mussel densities with Analysis of Covariance (ANCOVA) and regression, using the lm function in the R base statistics package. We used regression with the lm function to analyze the effect of plant size on mussel density and retention in naturally occurring Limonium patches.
Experiments: For data from both experiments, we analyzed the effects of experimental treatments on all continuous metrics, including size, growth, and change in number, using linear mixed models (LMM) with experimental block as a random variable. We analyzed final plant size as number of leaves using initial number of leaves as a covariate to account for initial size.
During Experiment 1 there was significant mussel mortality early in the experiment across all treatments (Fig. A1), so we analyzed Limonium responses based on the presence and density of mussels immediately after this mortality event (August 2012). We analyzed the effects of experimental treatments on survival of Limonium plants in Experiment 1 and mussels in
Experiment 2 using generalized linear mixed models (GLMM) with a binomial distribution,
60 including experimental block as a random variable. Counts of Limonium flowering stems were analyzed using GLMM with a Poisson distribution. P-values for GLMM models were determined using the Anova function in the car package in R. We made multiple comparisons for mixed models with Tukey H.S.D. tests, using the glht function in the multcomp package.
We analyzed the proportion of initial mussels per plot remaining at the end of Experiment
1, which represents a combination of survival and recruitment, with initial mussel density as a covariate. We logit transformed the proportion of mussels remaining before analysis using the logit function in the car package. We used the mussels that settled in our recruitment collectors to estimate a maximize size for mussel recruits in Experiment 1, and then we counted all mussels less than or equal to that size in the collectors or the plot to estimate total recruitment. During
Experiment 2, no plants died and there was negligible recruitment by mussels (n=4 individuals).
Mussel survival responses for Experiment 2 were summed by plot, with the exception of analyses including initial size of individuals, which were analyzed with initial size and treatment as fixed factors and plot and block as a random factor. We analyzed mussel survival in response to experimental treatments using GLMM with a binomial distribution. Survival of tethered mussels in the field predation trial was also analyzed using GLMM with a binomial distribution, with water height included as a random variable. We also used Kaplan-Meier curves to compare with and without Limonium plants using the survfit function in the survival package in R.
Results
Field survey: There was a significant relationship between mussel density and Limonium percent cover in the field, but the shape of this relationship varied across field sites (Limonium cover x
61 Site, F3,51=3.103, P=0.032; Fig. 1A, B, Table S2). At three of the four sites, the number of
2 mussels increased linearly with Limonium cover (F1,58 5.65, P=0.02, R =0.07, y=1.17 + 0.02x,
Fig 1B) and a quadratic term was not significant (P>0.595) so was removed from the model. The fourth site (Wakulla Beach) had much higher mussel densities, and mussel numbers peaked at
2 2 intermediate Limonium cover (F1,17=9.1, P= 0.008, R =0.35, y= -4.20759 + -0.06420x , Fig 1A).
The relationship between mussel density and sediment organic matter (SOM) also varied by field site (site x mussels, F3,722.86, P=0.04, Fig. S2). At Wakulla Beach, where mussel densities were higher, there was a positive linear relationship between mussel density and SOM (F1,18=7.39,
P=0.01, y=2.26 + 0.006x, R2=0.25, Fig.S2); however, mussels were not a significant predictor of
SOM (P> 0.27) across the other three sites.
At the Wakulla Beach plots that we followed through time, larger Limonium patches
2 initially had more associated mussels (Fig. 1C; F1,23 = 19.79, P=0.0002, R =0.44, Table S2), and they retained higher mussel numbers over the one-year survey period (F1, 23=10.12, P=0.004,
R2=-0.28). When we removed one very large plant that died from the analysis, there was no relationship between plant growth (measured as the change in number of leaves) and initial
2 mussel density (F1,220.07, P=0.90, R =-0.04, Fig. S2).
Experiment 1. Testing mutual benefits of interaction
Plant responses: There was significant mortality of mussels in first two months of the experiment, so we used mussel densities at the month two (August 2012) sampling date as the predictor of plant responses, rather than initial mussel density. Limonium survival at the end of the experiment was marginally higher in plots with higher densities of mussels remaining in month two, both across all treatments (Chi Sq=3.43, DF=1, P=0.06, Fig 2A, Table S3) and in the
62 subset of treatments in which mussels were directly manipulated (Chi Sq=3.25, DF=1, P=0.07).
Mussels had a positive effect on Limonium size over the course of the experiment: plants with higher mussel densities in month two retained or grew more leaves during the experiment across all treatments (F1,51=4.65, P=0.04, Fig. 3C), and particularly in manipulated treatments
(F1,33=14.54, P=0.0006). Neither mussel presence nor density in month two predicted Limonium flowering stem production, but mussels did matter at the end of the experiment, which coincided with flowering: plants across all treatments grew more flowering stems when mussels were present (Chi Sq.=12.31, DF=1, P=0.0004; Fig. 2B) or at higher densities (Chi Sq.=12.11, DF=1,
P=0.0005) at the time of flowering. In manipulated treatments, mussel presence (Chi
Sq.=10.669, DF=1, P=0.001), but not density, at the end of the experiment led to increased number of flowering stems.
Mussel responses: For consistency, we used Limonium presence in month two (August
2012) as our predictor of mussel responses whenever possible. Mussel densities remained higher over the course of the experiment in plots with Limonium present in month two, both including all treatments (F1,50=22.45, P<0.00001, Fig 2C, Table S4), and in manipulated treatments only
(F1,33=7.24, P=0.011). Plots with Limonium present in month two had higher recruitment of juvenile mussels (Chi Sq. =22.41, DF=1, P<0.00001, Fig. 2D). Larger Limonium (i.e., plants with more leaves at the end of the experiment) had a greater proportion of mussels remaining per plot
(F1,51=54.98, P<0.00001, Fig. 3A) and more mussel recruits (Chi Sq.=80.469, DF=1, P<
0.00001, y= -0.37 + 0.03x, Fig. 3B).
Sediment organic matter: SOM in month two of the experiment was significantly higher in plots where mussels were present (Welch two sample test, t = -3.32, DF = 57.50, P = 0.002;
63 absent= 6.104 ±0.263, present= 7.323± 0.256, Fig. S3A). There was also a non-significant trend toward increased SOM with higher mussel density in month two (F1,69=3.216, P=0.08, Fig. S3B).
Experiment 2. Testing mechanisms of benefit
Plant responses: Leaf length and flowering stem length, but not the final number of leaves or flowering stems (Fig S4A, C, Table S5), varied across treatments (leaf length:
F2,27=3.26, P=0.05; flowering stem length: F2,18=4.44, P=0.02; number of leaves: F1,25=0.42,
P=0.66). Specifically, leaves were significantly longer in the nutrient addition treatment (mean=
8.52 ± 0.58) compared with Limonium alone (mean= 6.98 ± 0.30), but neither of those was different from Limonium with mussels (mean= 7.63 ± 0.33, Fig. S4B). Flowering stems in the nutrient addition treatment (mean= 16.67 ± 2.14) were significantly longer than those in treatments with Limonium alone (mean= 10.46 ± 1.78) and Limonium with mussels
(mean=10.97±1.10, Fig S4C).
Mussel responses: Mussels were more likely to survive in treatments with a natural or artificial canopy (Chi Sq=21.88, DF=1, P<0.0001, Fig. 4A, Table S5). Mussels also grew more in treatments with canopies (F3,22= 5.64, P=0.005, Fig 4B). However, initial plant size did not influence mussel survival (P=0.835) or growth (P=0.883). Smaller mussels grew more
(F1,202=21.17, P<0.0001, Fig S5A) relative to larger mussels, and they also had lower condition index (F1,488=57.72, P<0.0001, Fig S5B); despite this difference, condition index did not vary by treatment (P=0.50). Smaller mussels were also significantly less likely to survive (Chi Sq=
34.97, DF=1, P<0.0001), but this effect did not differ by treatment (P>0.19). Mussel recruitment during this experiment was negligible (n=4 individuals).
64 Temperature: The average daily maximum temperature differed significantly by treatment (F5,35=14.00, P<0.0001; Fig. 5A). Treatments with a real or artificial plant canopy maintained lower daily maximum temperatures compared with those lacking a canopy (Fig. 5A), mirroring patterns of mussel survival (Fig. 4A, 5B).
Predation Trial
Tethered mussels were more likely to survive under Limonium plants than in adjacent
Spartina alterniflora (Chi Sq1=3.7957, P=0.05). Kaplan-Meier cumulative hazard curves were not significantly different between the two groups (Chi Sq= 0.9, DF=1, P= 0.35).
Discussion
We experimentally demonstrated that ribbed mussels and Limonium benefit one another.
Mussels were more abundant in association with Limonium in natural populations in the field, and our experiments indicate this relationship was due to a combination of increased mussel survival and recruitment. We observed greater recruitment of juvenile mussels to patches with
Limonium during the year-long Experiment 1. Mussels also survived better and grew more under
Limonium or mimic canopies, which created a shaded microhabitat where high summer temperatures were ameliorated. Our predation trials confirmed that Limonium also provides a refuge from predation, lending support to an additional mechanism of increased mussel survival.
At the same time, Limonium benefitted from mussels in terms of survival, growth, and production of flowering stems, most likely due to deposition of organic matter by mussels which alleviates nutrient limitation. The experimental addition of nutrients in Experiment 2 resulted in
65 longer stems and leaves, indicating that Limonium is nutrient limited at this site. However, enhanced growth and survival of Limonium in association with mussels was only evident during our year-long Experiment 1, suggesting that any fertilization benefits to Limonium from mussels operate on a longer timescale than the seven-week Experiment 2.
The specific traits of plants can greatly influence their ability to ameliorate conditions for other species (Maestre et al. 2009). For example, alpine plants with more shading canopies are better facilitators (Michalet et al. 2011, Schöb et al. 2013). The temperature amelioration provided by Limonium may be similarly related to its particular canopy structure, which differs from other marsh plants at our field sites. In particular, the basal rosette of leaves near the sediment surface is very distinct from the leaf morphology of the dominant marsh grass Spartina alterniflora, another known facilitator of mussels (Bertness 1984, Angelini et al. 2015). Spartina alterniflora can actually increase temperature stress for ribbed mussels by buffering airflow and reducing convective cooling (Jost and Helmuth 2007). Thus, Limonium may be a more effective facilitator of ribbed mussels than S. alterniflora at our Florida field site where summer temperatures in our mussel-mimic temperature loggers often exceeded the potentially lethal threshold of 45ºC (Jost and Helmuth 2007).
Positive feedbacks between the traits of interacting species that enhance benefits may serve to stabilize facilitative interactions over time (Bronstein 2009). For example, nutrient enrichment from reef fish aggregations can maintain the highly productive seagrass patches that provide habitat for the fishes, leading to a positive feedback in facilitative effects of the vegetation (Peterson et al. 2013). Both interacting species in our study had positive effects on traits that might serve to enhance the mutual facilitation. Limonium both benefitted from higher densities of mussels and increased mussel density over time, while ribbed mussels benefitted
66 more under larger Limonium canopies, and their presence allowed Limonium to grow more leaves. This positive feedback between partner traits may be important for maintaining the association. A bidirectional approach including such feedbacks will enhance our understanding of the evolutionary stability of facilitation through time (Bronstein 2009).
As with other species interactions, facilitation can be considered along a continuum from completely symmetrical benefits (mutualism) to one-sided benefits (commensalism or parasitism), depending on the balance of positive and negative components of the interaction
(Vellend 2008). Studies that have examined bidirectional effects of facilitative interactions typically focus on plant-plant interactions, which commonly have strong negative components and high fitness costs for partners due to competition (Schöb et al. 2014). The bidirectional benefits we observed between Limonium and mussels generally support the prediction that facilitative plant-animal interactions may be less influenced by competition, making net positive outcomes more likely (Bulleri 2009). However, the peaked relationship between Limonium percent cover and mussel abundance at one field site suggests density-dependent costs of the association. In addition, the benefits of association may be asymmetrical: Limonium consistently benefitted mussels in both experiments, but we only detected positive effects of mussels on
Limonium growth in the longer-term Experiment 1 (Table S6). This discrepancy may result from differences in either the timescale or seasonality of their effects; in particular, alleviation of plant nutrient limitation by mussels may operate on a longer timescale or at a time of year that the shorter second experiment did not capture. Benefits to mussels from temperature amelioration, on the other hand, were most likely to occur during the summer months (Kawai and Tokeshi
2004, Cartwright and Williams 2014) which were included in both experiments. Although both positive and negative components of cross-trophic interactions are likely to play an important
67 role in outcomes of facilitation, these components may depend on mechanisms that operate on different timescales or under different conditions.
The amelioration of physical stress, such as that provided by Limonium for ribbed mussels, is a feature of many facilitative interactions in unfavorable environments, such as intertidal and arid ecosystems (Brooker et al. 2008, He and Bertness 2014). Facilitation may ultimately determine the resilience of some species to increasing anthropogenic stress, including climate change. Many ectotherms, for example, rely on vegetation or other facilitating structure to shelter them from harsh conditions, suggesting that the availability of such facilitators may ultimately determine their response to climate warming (Kearney et al. 2009, Sunday et al.
2014). Facilitators that ameliorate environmental conditions can allow associated species to persist in habitats that would otherwise be too stressful, thus expanding the realized niche of beneficiary species (Bruno et al. 2003, Crotty and Bertness 2015). Niche expansion due to facilitative interactions may determine local persistence and even larger scale geographical ranges if stressful conditions intensity (Bulleri et al. 2015). Although many studies of facilitation propose stress amelioration as a likely mechanism of benefit, few actually measure the physical stressors needed to clearly establish the mechanism (Bulleri 2009). Thus the effects of facilitators on small-scale variability in abiotic conditions is not well described (Bulleri et al. 2015). Given the potential importance of habitat amelioration, more studies measuring the effects of facilitation on physical stress are needed to provide a more mechanistic understanding of these interactions. Further, a bidirectional approach to facilitation will enhance our ability to understand the dynamics and stability of these important interactions (Bronstein 2009, Schöb et al. 2014).
68 Acknowledgments: We thank Robyn Zerebecki, Erica Holdridge, Ryan Coker, Ashley Dillon, and Emily Goetz for assistance in the field and lab. We thank Florida State University Marine and Coastal Laboratory and Northeastern University Marine Science Center for institutional support. This work was supported by National Science Foundation grant DEB-0928279 to ARH.
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73
Figure 1. Relationship between Limonium percent cover and mussel density (A) at Wakulla beach (black circle), the field site with the highest mussel densities and (B) the other three field sites: St. Joseph Bay GD1 (black triangle), St. Joseph Bay GD5 (black circle), St. Mark’s Lighthouse (open triangle). (C) Number of naturally occurring mussels vs number of Limonium leaves per patch in plots at Wakulla Beach. Note the different axes for panels.
74
Figure 2. Plant and mussel responses in Experiment 1. Plant responses: (A) Limonium survival to the end of the experiment by mussel density in month two, across all treatments with Limonium. Plants that survived are shown with black circles and plants that died with white. (B) Mean (±SE) number of flowering stems per plant in plots with and without mussels remaining at the time of flowering. Mussel responses: (C) Proportion of initial mussels remaining at end of experiment and (D) Mean (±SE) number of juvenile mussels that recruited per plot, in plots with and without Limonium present at month two.
75
Figure 3. Limonium size in Experiment 1 had positive effects on (A) the proportion of mussels remaining at the end of the experiment and (B) the number of juvenile mussels recruiting to plots. Mussel density in month two (C) was positively associated with final Limonium size (no. leaves).
76
Figure 4. Mussel responses in Experiment 2. (A) Mean (±SE) proportion of mussels surviving per plot and (B) mussel growth (re-growth of notched shells in mm) as a function of Limonium treatments: artificial canopy (striped bars), intact Limonium plants (dark grey bars), Limonium roots with no canopy (light grey bars) and mussels alone (open bars). Symbols above bars indicate Limonium treatments. Different letters above bars indicate significant differences between treatments (Tukey H.S.D, P<0.05).
77
Figure 5. (A) Mean (±SE) daily maximum temperature (ºC) by treatment: artificial canopy (striped bar), intact Limonium plants (dark grey bar), Limonium roots with no canopy (light grey bar) and mussels alone (open bar), Limonium alone (black bar) and Limonium with nutrient addition (Black bar with white N+ symbol). Symbols above bars indicate Limonium treatments. Different letters above bars indicate significant differences between treatments (Tukey H.S.D, P<0.05). (B) Proportion mussels surviving per plot as a function of mean daily maximum temperature per plot across treatments: artificial canopy (black triangle), intact Limonium plants (black circle), Limonium roots with no canopy (black square) and mussels alone (plus symbol).
78 Chapter 2: Supplemental Materials
Table S1. Survival of Limonium and mean (±SE) density of mussels by treatment during Experiment 1, including initial numbers, month two, and final time points.
79 Table S2. Results from Analysis of Covariance and regression models of field survey data including patterns across all field sites, and Wakulla Beach field site and other field sites analyzed separately. Results from regression models of naturally occurring Limonium and mussel patches at the Wakulla Beach field site, including initial plant size and mussel density as well as change in mussel density and plant size (number of leaves) over one year. The analysis of change in plant size excluded one plant that had died.
80 Table S3. Statistical results for Limonium responses in Experiment 1 using mixed effects models with experimental block as a random factor. Initial treatment was included as an explanatory variable in models with data across all treatments to distinguish between manipulated and un- manipulated treatments, however we removed initial treatment when it was not a significant factor.
81 Table S4. Results from linear mixed models of mussel responses in Experiment 1, including the proportion of initial mussels remaining and number of juvenile mussels recruited to the experiment.
82 Table S5. Effects of experimental treatment on Limonium and mussel responses in Experiment 2 with linear mixed models with treatment as a fixed factor and experimental block as a random factor.
83 Table S6. Comparison of effects of species interaction (in terms of partner presence, size, or density) and mimics (nutrient addition and canopy mimic) on plants and mussels across both field experiments. Significant effects on Limonium and mussel responses are indicated as Sig. while non-significant effects are indicated as n.s. and non-applicable responses with a dash (-).
Effect of Plant Effect of plant-animal mimic or Response interaction Nutrients
Experiment 1 Experiment 2 Experiment 2 Plants
Survival Sig. - n.s. No. leaves per plant Sig. n.s. n.s. Leaf length - n.s. Sig. Flowering stem length - n.s. Sig. No. flowering stems per plant Sig. n.s. n.s.
Experiment 1 Experiment 2 Experiment 2 Mussels
Survival/Change in no. individuals Sig. Sig. Sig. Recruitment Sig. - - Growth - Sig. Sig.
84
Figure S1. Effects of mussel density on percent sediment organic matter at four field sites on the Gulf coast of FL: (A) St. Joseph Bay GD1 (B) St. Joseph Bay GD2, (C) St. Mark’s Lighthouse, and (D) Wakulla Beach. Note different axes in panels.
85
Figure S2. Change in the number of Limonium leaves per patch by mussel density in plots with naturally occurring Limonium and mussels monitored for one year. Plants that died during the year are shown with white circles and surviving plants are in black.
86
Figure S3. Percent sediment organic matter (SOM) in plots during month two of Experiment 1, showing (A) Mean SOM in plots with and without mussels present at month two and (B) SOM by mussel density with treatment shown by symbol, including Limonium and mussels together manipulated (black circle) and un-manipulated (black triangle), Limonium alone (black square), and mussels alone (plus symbols).
87
Figure S4. Limonium response to treatments in Experiment 2, including mean (A) no. leaves per plant, (B) length of leaves per plant (cm), (C) no. flowering stems per plant, and (D) length of flowering stems (cm). Treatments are indicated by different colored bars, including Limonium alone (white), Limonim and mussels together (light gray) and Limonium with nutrients added (dark gray).
88
Figure S5. Effects of initial mussel size on (A) re-growth of notched mussels and (B) mussel condition index in Experiment 2. Symbols indicate treatments: intact Limonium plants (black circle), artificial canopy (black triangle), Limonium roots with no canopy (black square) and mussels alone (plus symbol). Standard error around regression lines is shown in gray.
89 Chapter 3: Interactive effects of environmental setting and source location on mangrove seedling establishment
Abstract
The interaction between individual traits and environmental conditions can have strong evolutionary and ecological consequences. Individuals often differ in their responses to environmental conditions depending upon their genetic background and non-genetic factors such as maternal provisioning. We conducted a three-year reciprocal transplant field experiment at two sites in Florida to test whether survival and growth of mangrove seedlings from different sites vary in their response to environmental conditions, and if so, whether this variation was indicative of local adaptation. We also examined whether the mangrove response depended on propagule mass, an indicator of maternal provisioning. We planted black mangrove (Avicennia germinans) propagules from six source locations into two common garden locations; one experimental site is mangrove dominated and in the middle of the mangrove range in FL (Indian
River Lagoon; IRL), and the other site is a salt marsh dominated area at the northern edge of the mangrove range in the Gulf of Mexico (St. Joseph Bay; SJB). Seedlings survived better at the more southern Indian River Lagoon site, with the exception of seedlings from the source site with the largest propagules, which had high survival across both experiment sites. Seedlings were taller at IRL than at SJB regardless of their origin. Seedling traits differed by source location at the two field experiments, with origins having variable numbers of branches through time and differing in number of leaves, depending on experiment site. Maternal provisioning in terms of propagule size was important for survival and growth, however this effect was stronger
90 at SJB where seedlings from the origins with the smallest propagules had extremely low survival. In contrast, at IRL these smallest propagules grew into some of the largest seedlings.
We found no evidence for local adaptation, as seedlings did not survive or grow better closer to their home site. The latitude of seedling origin was also not a good predictor of seedling success.
These results highlight the importance of plasticity in mangrove response and suggest that success of range-expanding mangroves will depend on an interaction between source population and local environmental conditions. Further research into the response of multiple populations to environmental conditions at different locations within species ranges will help to predict species responses under novel conditions due to dispersal and range expansion.
Introduction
The ability of organisms to colonize novel environments through long distance dispersal or range expansion will depend on genetic and non-genetic influences of their origin site: individuals that arrive in environments poorly matched to their phenotype or genetic background can experience high post-settlement mortality which then determines local abundances (Marshall et al. 2010). For example, performance of plants at range edges can depend on genetic background, based on thermal differences between the source population and planting locations at the range edge (Grady et al. 2011). Non-genetic influences of population origin can also be important. For instance, environmental conditions such as resource availability can determine maternal investment in offspring propagule size, which influences robustness to environmental conditions (Mousseau 1998). Genetic background may also influence future success of plant
91 populations, as many populations will likely be maladapted to the conditions they face due to climate change and range expansion (St Clair and Howe 2007).
Marine ecosystems are undergoing rapid climate change (Burrows et al. 2011) and this change is contributing to shifts in species distributions (Pinsky et al. 2013, Burrows et al. 2014).
Mangroves are currently expanding their range from the tropics and increasing in abundance in subtropical to temperate areas historically dominated by marsh plants (Osland et al. 2013,
Cavanaugh et al. 2014). The factors controlling marsh and mangrove distributions differ: the northern extent of mangrove distribution appears to be controlled primarily by cold intolerance
(Mcmillan and Sherrod 1986), whereas the southern limits of salt marsh plants are thought to be influenced by competition with mangroves (Kangas and Lugo 1990). In Florida, a marsh- mangrove ecotone transitions from mangrove dominant to marsh dominant systems north of St.
Augustine on the Atlantic coast (Williams et al. 2014) and north of Cedar Key on the Gulf coast
(A. Moore, personal observation). The black mangrove, Avicennia germinans, is the most cold tolerant of the mangrove species in Florida, and thus may be expected to expand northward most rapidly (Cook-Patton et al. 2015). Because A. germinans populations have been historically limited by periodic freezes that have reduced or eliminated adult trees in temperate marsh zones
(Sherrod and Mcmillan 1985), the increased warming as a result of climate change may lead to further increases in mangrove abundance in marsh systems. Salt marsh plants and mangroves act as foundation species (sensu, Dayton 1972), providing structural habitat for fauna and ameliorating stressful environmental conditions. Salt marsh and mangrove ecosystems also provide a number of economically valuable ecosystem services for humans (Costanza et al.
1997, Mcleod et al. 2011, Lee et al. 2014). Thus, understanding the response of foundation
92 species such as mangroves to novel environmental conditions may be of particular importance
(Ellison et al. 2005).
Avicennia germinans reproduces via cryptoviviparous propagules that develop large cotyledons while attached to the maternal tree. These propagules have the potential to disperse long distances over water (Rabinowitz 1978b, Nettel and Dodd 2007), but local recruitment may also be common (Sousa et al. 2007). Long distance dispersal may connect populations via gene flow, favoring selection for trait plasticity in A. germinans to respond to a variety of environmental conditions as they disperse across a heterogenous landscape (Levins 1968, Sultan and Spencer 2002). On the other hand, if populations recruit close to home, mangrove seedlings from different source populations may differ in traits and in response to environmental conditions, due to reduced gene flow or local adaptation. However, maternal investment may have a larger influence than other factors related to source location, as seedlings receive large initial reserves through viviparous reproduction (Farnsworth 2000) and A. germinans propagules can vary in size (Alleman and Hester 2011). In addition, larger dispersing propagules of tropical mangrove trees exhibit higher growth rates during seedling establishment (Lin and Sternberg
1995).The ability of seedlings from various source populations to exhibit phenotypic plasticity to novel environmental conditions or withstand them through high maternal investment is of particular interest currently, as these factors will likely influence the speed of range expansion and genetic composition of expanding populations.
We tested how A. germinans seedlings from different source locations responded to different environmental settings on the coast of Florida. We planted propagules from six source sites into common gardens in two experimental sites that differ greatly in abiotic and biotic characteristics. One site is located in the Indian River Lagoon, a mangrove-dominated area
93 within the center of the A. germinans range on the Atlantic coast of Florida, whereas the other site is in St. Joseph Bay, a salt-marsh dominated region at the northern edge of the A. germinans range on the Gulf coast. We examined whether seedling survival or morphology differed by origin site and/or experimental site. Further, we evaluated how maternal provisioning (propagule size) influenced seedling survival and traits over time.
Methods
Experimental site characterization
We conducted a survey to characterize vegetation at each of the common garden experiment sites in July of 2013: St. Joseph Bay, FL (SJB; 29°42'52.27"N, 85°18'25.32"W) and
Indian River Lagoon, FL (IRL; 27°31'55.92"N, 80°20'55.50"W; Figure 1). At each site, we surveyed 5 randomly-selected locations along a 50 m transect parallel to the shoreline running through the approximated midpoint of the vegetated area. At each of these five locations we measured the following metrics on the nearest A. germinans individual: tree height (m), trunk circumference (cm) 10 cm above ground or below the lowest branch (if lower than 10 cm), diameter of the major and minor axes of the canopy area (m2), and density of A. germinans pneumatophores (no. per 0.25 m2). For trees with multiple trunks we used the mean circumference of all trunks. We estimated canopy area by multiplying the two canopy axes. We also characterized marsh vegetation at each sampling point by estimating the percent cover of all plant species present per 0.25 m2 quadrat. Finally, we measured sediment redox potential (mV) and porewater salinity at five points along each transect to characterize abiotic conditions that may be important for intertidal vegetation. To determine whether the two experiment sites
94 differed in their mean annual and mean annual minimum air temperatures, we used publicly available data from The Center for Ocean-Atmospheric Prediction Studies (COAPS) at Florida
State University (http://climatecenter.fsu.edu) from the closest weather stations to our experiment sites (Figure S1): Apalachicola (Station ID: 80211, 29°43'00.12", -085°01'00.12", approx. 29 km from the SJB experiment) and Fort Pierce (Station ID: 83207, 27°25'59.88", -
080°21'00.00" approx. 6 km from the IRL experiment). We included data from the span of years
(1931-2014) that was available for both weather stations.
Experimental planting and measurements
We collected A. germinans propagules in October 2012 from two sites in the Gulf of
Mexico: Stump Hole (SH; 29°40'43.44"N, 85°21'34.10"W) and Cedar Key Cemetery (CK; 29°
83'43.64"N, 83° 2'28.40"W). We also collected from four sites from the Atlantic coast: Cape
Canaveral (CC; 28°56'23.55"N, 80°51'49.14"W); Pine Island (PI; 28°29'37.66"N,
80°43'35.17"W); Harbor Branch (HB; 27°31'55.92"N, 80°20'55.50"W); and Bear Point (BP;
27°25'44.87"N, 80°16'51.61"W). No propagules were collected from the locations of our experimental sites (Figure 1). Propagules were collected from between 6 - 9 maternal trees in each source location to ensure that responses were representative of multiple individuals. At each common garden, propagule planting was fully randomized by propagule source location. Rows of each experiment were two meters apart and plots within rows were separated by one meter.
One propagule was planted within existing vegetation in each plot. Propagules were planted in
November 2012 at both common garden planting sites. We weighed each propagule in the field
(to ± 0.01 g) before planting to account for the effects of initial size on seedling survival and traits. We secured propagules to the sediment surface using two small sections of plastic coated
95 wire, which we removed in month 3 of the experiment, after seedlings had rooted themselves securely. Some propagules at IRL were lost at the beginning of the experiment due to storm activity, such that initial numbers were not equal between the experiment sites (Supplemental
Table S1). We determined survival and measured seedling height (cm) and number of leaves and branches per seedling in the summers of 2013, 2014, and 2015.
Data analysis
All statistical analyses were performed in R (version 3.1.3, R Core Team 2015). We analyzed differences between the two experiment sites in percent cover of marsh vegetation and
A. germinans tree size. We analyzed occurrence of any marsh vegetation in plots using a binomial regression with the glm function in the R base statistics package. To analyze differences in marsh cover, we summed the percent covers of all the marsh species in each quadrat, capped those values at 100 and logit transformed them, a conservative approach with respect to site differences. We used the logit function in the car package (Fox and Weisberg
2010). We used a linear model with the function lm in the R base statistics package to test for differences between the SJB and IRL experiment sites. Marsh species present included Spartina alterniflora, Salicornia virginica, Batis maritima, Limonium carolinianum, Distichlis spicata, and Juncus roemarianus. We used Permutational multivariate analysis of variance
(PERMANOVA) (Anderson 2005) with the adonis function from the vegan package (Oksanen et al. 2015) to analyze the difference in tree size between the two experiment sites based on a multivariate response including tree height, trunk circumference, estimated canopy area, and pneumatophore density. We tested for differences in sediment redox conditions and pore water salinity using Welch two sample t tests with the t.test function in R the base statistics package.
96 We analyzed differences in mean annual air temperature and mean annual minimum air temperature between weather stations near the SJB and IRL experiments, respectively, using the lm and anova functions in the R base statistics package.
Seedling origins differed significantly in their initial propagule mass (ANOVA,
F=101.61, DF=5, P<0.0001, Table 1). Propagules from CK were the largest and SH the smallest and other origins were of intermediate size: HB and CC were larger than BP and PI but the origins within these two pairs did not differ in size (Table 1). As propagule size is known to influence seedling performance (Rabinowitz 1978a, Sousa et al. 2003) we included initial propagule size as a covariate in all seedling analyses to account for the effects of maternal resources on seedling success.
We examined the effects of seedling origin site and experimental transplant site on seedling survival and growth using several approaches. We determined how survival to summer of 2015 differed among treatments using logistic regression with the glm function in the R base statistics package. We analyzed survival patterns over time by comparing Kaplan Meier curves with a log rank test using the survdiff function in the survival package (Therneau and Grambsch
2000) and with a Cox proportional hazards regression (Cox PH), both using the Surv function to create a survival object as the response and the coxph function for the model, both from the
Survival package. To determine proportional hazards by seedling origin we compared seedlings of each origin to CK, the seedling origin with highest survival across the two experiments. Both logistic regression and Cox PH models included experiment site, origin site, propagule mass, experiment site x origin site, and experiment site x propagule mass as explanatory variables.
We determined the effects of experiment and origin on multivariate seedling responses using PERMANOVA across all three sampling dates (Summer of 2013, 2014, 2015) including
97 seedling height (cm), numbers of branches, and leaves per seedling as response variables. We then analyzed each response separately with repeated measures analyses across the three sampled years with an identifier for each seedling as a random factor, using a linear mixed model (LMM) for height and generalized linear mixed models (GLMM) with a Poisson distribution for number of leaves and number of branches per seedling. We used the lmer function for LMM and the glmer function for GLMM, both from the lme4 package (Bates et al. 2015). We calculated type
III sum of squares for models to account for unequal sample sizes using the Anova function in the car package (Fox and Weisberg 2010). We included experiment site, origin site, propagule mass, date, experiment site x origin site, experiment site x propagule mass, experiment site x date, and propagule mass x date as fixed factors. We made multiple comparisons between seedling origins and experimental planting sites using the lsmeans function in the lsmeans package (Lenth 2015) and multcompLetters in the multcompView package (Graves et al. 2015).
For all analysis of seedling traits across experiment sites, we excluded seedlings from PI and SH from the analysis due to almost complete mortality of these source sites at the SJB experiment site. We also completed the same analyses of seedling traits without the effect of experiment using data from within the IRL site only. This allowed us to compare seedlings from all origin sites, including PI and SH.
Results
Site characteristics
The two experiment sites differed in their vegetation characteristics. The SJB experiment site had significantly greater occurrence of marsh vegetation (Chi Sq.= 16.54, DF = 1, P <
98 0.0001) and greater total percent cover of marsh plants in survey plots (F = 51.63, DF = 1, P <
0.0001, Table S2, Figure 2A-B). Existing A. germinans trees were much smaller at the SJB site relative to IRL across all size metrics, including height, trunk circumference, canopy area, and pneumatophore density (Pseudo F = 19.69, DF = 1, R2 = 0.71, P = 0.001, Table S3, Figure 2C-
F). Sediment redox conditions were marginally different between the two sites (t = -2.21, df =
11.14, P = 0.05) indicating lower oxygen levels at IRL (mean redox potential = -91.22 mV ±
46.04 SE) relative to SJB (mean redox potential = 41.71 mV ± 38.89 SE). Salinity was slightly higher at the IRL experiment site (mean salinity = 21.50 ppt ± 0.56 SE) than at SJB (mean salinity = 20.22 ppt ± 0.22 SE), but this difference was not significant (t = 2.11, df = 6.58, P =
0.08). The weather stations near SJB had significantly lower mean annual air temperature compared with the station near IRL (F = 382.45, DF = 1, P < 0.0001, Table S4, Figure S1). The mean annual minimum air temperature was also lower at SJB (F = 172.88, DF = 1, P < 0.0001).
Seedling survival
Seedlings that grew from larger propagules were more likely to survive at both experiment sites (Table 2A, Fig. 2A). There was also an interaction between experiment site and origin site on seedling survival at the end of the experiment (Summer 2015): at IRL seedling survival did not differ significantly by seedling origin, whereas at SJB seedlings from CK had significantly higher survival than seedlings from other source sites (Table 2B, Fig. 2B). Although survival was generally higher at IRL for most origin sites, only seedlings from CC and PI survived significantly better at IRL than SJB (Table 2B). Most of the mortality at SJB occurred in the first few months of the experiment, but mortality at IRL was more evenly split between years, such that their survival curves were significantly different (log rank test, Chi Sq.= 46.9,
99 DF = 1, P < 0.0001, Fig. 2C-D). Survival patterns through time also depended interactively on experiment site and seedling origin (Table 3B, Fig. 2C-D). Notably, seedlings from CK survived well at both experiment sites at all time points. At the SJB experiment, CK survived significantly better over time than seedlings from all other origins except BP. Seedlings from PI survived well at IRL but very poorly at SJB, and seedlings from SH had the lowest survival across experiment sites (Table 3B, Fig. 2).
Seedling traits across experiment sites
Multivariate analysis of all three seedling traits (seedling height, numbers of branches, and leaves per seedling) at the two experiment sites indicated significant independent and/or interactive effects of experiment site, origin site, propagule mass, and time on seedling traits
(Table 4), so we analyzed each trait individually. All analyses of seedling traits across both experiments included seedlings from origin sites CK, HB, CC, and BP, but not seedlings from PI and SH, due to high mortality at the SJB experiment.
Seedlings that began as larger propagules grew more leaves, and this effect was consistent across experiment sites and dates (Table 5, Fig. 3A). However, the effects of experiment site and seedling origin on number of leaves both depended on date (experiment site x date, seedling origin x date, Table 5, Fig. 3A). By later time points, seedlings at the SJB experiment grew more leaves than seedlings at IRL (experiment site x date, Tables 6 and 7, Fig.
4A-B). In addition, seedlings from some origins grew leaves more quickly than others, such that numbers of leaves per seedling diverged over time (seedling origin x date, Tables 6 & 7, Fig. 4A-
B). The number of leaves per seedling also depended interactively on origin site and
100 experimental site: seedlings from all origins except HB grew more leaves at SJB (experiment site x seedling origin, Tables 6 & 7, Fig. 4A-B).
Larger propagules also produced taller seedlings in both experiments, and this influence was consistent through time and across experiment sites (Table 5, Fig. 3B). Seedling height did not differ by origin. Seedlings were taller in IRL, but the degree of this difference varied through time, with greater differences in height as the experiment progressed (experiment site x date,
Table 5, Fig. 4C).
The number of branches per seedling did not differ by experiment site or propagule size.
The number of branches differed by seedling origin, but this effect depended on date, with seedlings from different origins producing and losing branches at different rates (seedling origin x date, Tables 5 & 6, Fig. 4D). The number of branches on seedlings from both CK and CC significantly increased between 2014 and 2015, whereas seedlings from other origins did not differ (Table 6, Fig. 4D).
Seedling traits at IRL experiment site
We also analyzed the effects of origin site, propagule mass, and time on seedling traits within the IRL site only to compare seedlings from all origin sites, which we were unable to do when comparing across experiment sites because of high mortality of some origin sites in SJB.
The number of leaves per seedling differed by origin site, but this effect depended on time. By
2014 seedlings from PI grew more leaves than seedlings from BP, CC, and SH; in 2015, PI maintained those differences and HB grew significantly more leaves than seedlings from BP CC and SH as well (seedling origin x date, Tables 7 & 8, Figure 5A). Propagule mass influenced number of leaves, but this effect also depended on time, as seedlings of different initial sizes
101 grew leaves at different rates (propagule mass x date, Table 7, Figure 6C). Although seedling height differed significantly by seedling origin and increased through time, propagule mass did not influence height at IRL (Table 7, Figure 6A). In fact, the seedlings from origins with the smallest propagules (Table 1) were among the tallest by the last year of sampling (Figure 6A).
Multiple comparisons of height by seedling origin revealed that PI was taller than BP (Z=2.88,
P= 0.045) and CC (Z=3.60, P=0.004), with no other significant differences between origin pairs
(P>0.1). Number of branches per seedling at IRL differed only by date, decreasing slightly in
2014 and increasing greatly in 2015 (Figure 6B).
Discussion
Seedlings generally survived better at the IRL experiment site in the center of the A. germinans range, yet seedlings from CK had high survival across both experiment sites.
Seedlings from SH (near SJB) and PI (near IRL) had very low survival at the SJB site. The fact that seedlings survived better at IRL is likely due to more favorable conditions at this site.
Because it is at a lower latitude, the IRL site experiences warmer air temperatures relative to
SJB, particularly in winter. Cold temperatures can reduce A. germinans survival and damage growing seedlings (Krauss et al. 2008, Pickens and Hester 2011). The dominant vegetation at the two experiment sites may also have influenced seedling performance. Both marsh vegetation and adult mangroves can increase the retention of propagules (Huisman et al. 2009, Peterson and Bell
2015). Competition from marsh plants such as those dominant at SJB can inhibit seedling establishment (Patterson et al. 1993, Simpson et al. 2013), which could contribute to the lower initial survival of propagules at this site. This negative effect of marsh plants is neutralized as seedlings grow into young trees (Guo et al. 2013), and mangroves can even outcompete marsh
102 plants as adults (Kangas and Lugo 1990), though our three-year experimental duration was not sufficient to assess these effects. Although adult mangrove canopies can also limit seedling survival (Rabinowitz 1978a, Ellison and Farnsworth 1993), we observed high survival initially at
IRL. This site has a relatively sparse canopy (A. Moore, personal observation) and little marsh vegetation, potentially enhancing seedling survival and growth at this site.
Trait plasticity in response to environmental conditions is generally thought to be adaptive for organisms such as mangroves that have the potential for long distance dispersal and thus may encounter a variety of habitats (Levins 1968, Sultan and Spencer 2002). We found evidence for plasticity in seedling traits across our experimental sites in some, but not all, traits.
For example, height was environmentally controlled, with seedlings growing taller at IRL regardless of origin. Leaves per seedling also differed by experiment site, but the magnitude of this site effect varied by origin: CC, BP, and CK produced more leaves at SJB, whereas those from HB produced many leaves at both sites. Plasticity in leaf production is also supported by previous research on A. germinans, which changes leaf production and traits in response to nutrient levels (Simpson et al. 2013) and temperature (Cook-Patton et al. 2015). In contrast to the experimental site effects on height and leaves per seedling, production of branches was determined solely by seedling origin.
Genetic or non-genetic factors that affect juvenile provisioning, including maternal resource availability, can have an important influence in shaping offspring traits (Schmid and
Dolt 1994, Sultan 1996, Yanagi and Tuda 2010, Beldade et al. 2012, Germain and Gilbert 2014) and determine response to environmental conditions (Plaistow et al. 2006, Gagliano and
McCormick 2009). For A. germinans, prior work found that larger propagules produced taller seedlings with more leaves and more branches, but propagule size did not affect establishment
103 success (Sousa et al. 2003). In our study, maternal provisioning in terms of propagule mass was important for seedling survival across experiment sites and dates, perhaps because the experiment was conducted in the field, where more factors can influence establishment success.
Seedlings from PI and SH, which had the smallest propagules, survived very poorly at SJB: all the PI seedlings died and only two SH seedlings remained in 2015. Propagule mass also influenced seedling height and number of leaves, but not branching, across both experimental sites. Interestingly, when we analyzed trait responses at the IRL site alone, to include seedlings from the origin sites with the smallest propagules, we found that propagule mass was not related to seedling height and its influence on leaf production varied by date. Despite their diminutive propagules, seedlings from PI and SH were among the tallest at the IRL site, and PI was also one of the most productive in terms of leaves. These results are consistent with the prediction that maternal investment will be more beneficial for offspring in poorer environments (Fox and
Mousseau 1996). However the outcomes of greater maternal investment for offspring are context dependent (Semlitsch and Gibbons 1990) with lower investment providing an advantage in some settings (Gómez 2004) or a greater similarity between maternal and offspring environments enhancing fitness (Sheriff and Love 2012, Moore et al. 2015). While initial size did have a strong influence on seedling success and performance in our experiment, differences between seedling origins are not entirely attributable to initial size, suggesting that other genetic differences between populations may influence their response to different environmental conditions.
One might expect that seedling origin response to environmental setting at the two experiment sites would show some patterns of local adaptation, with seedlings from origins closer to an experiment performing better there. We did not observe patterns suggesting that seedlings from closest to home had any advantage. Seedlings from HB and BP origin sites
104 planted at the IRL experiment were in close proximity to home, but did not perform better at this site than seedlings from other origins. Seedlings from SH, the closest site to SJB, survived very poorly there relative to IRL. Given A. germinans’ potential for long distance dispersal across water (Rabinowitz 1978b, Dodd and Afzal Rafii 2002), gene flow from adjacent areas may tend to prevent strong adaptation to local conditions or the scale of adaptation may depend on ocean currents, rather than geographic distance per se (Kennedy et al. 2016). Alternatively, existing genetic differentiation among A. germinans populations may not be adaptive. For example, the poor performance of the SH range edge population may be due in part to small population size or low connectivity leading to low genetic diversity, as has been shown for other mangrove edge populations (Arnaud-Haond et al. 2006). Further, it is not uncommon for plant populations to be maladapted to their home location and many would actually thrive by escaping local conditions
(Hereford 2009). Local maladaptation may be the result of genetic drift (Crespi 2000), or an inability to outpace co-evolving enemies with shorter life spans (Engelkes et al. 2008), both of which may influence mangroves.
In addition to providing no evidence of local adaptation, our results suggest that source latitude is not necessarily a good predictor of A. germinans seedling success. Seedlings from the higher latitude origins did not consistently perform better at SJB, our higher latitude experiment.
While provenance latitude can be a significant predictor of success at range edges (Grady et al.
2011), environmental differences that determine local success of a species may act more as a mosaic of factors across the landscape that do not necessarily scale with latitude (Kroeker et al.
2016). Maternal investment may also be more important than source latitude, given that CK, the origin with the highest performance at SJB in terms of survival and growth, also had significantly larger propagules than any of the other origin sites. Large seeds can contribute to
105 success of plants outside their native range (Hierro et al. 2012) and similar dynamics could also influence range expansion. If larger mangrove propagules disperse to the expanding edges of the mangrove range, they may accelerate the rate of range expansion.
Responses to changing climate can be population specific, and the response of different populations to climate change will influence future species distributions based on genetic differences (Ikeda et al. 2013). The genetic background of foundation species can influence the diversity and composition of other community members (Genung et al. 2011, Whitham et al.
2012) such that the particular individuals and populations contributing to expanding edges of ranges may determine important ecosystem properties such as diversity-productivity relationships (Ikeda et al. 2013). Thus the genetic background and attributes of populations that contribute to the expanding range edges may influence properties of these novel ecosystems.
Further research into the response of multiple populations to environmental conditions at range edges will help to predict the speed and consequences of range expansion.
Acknowledgements: We thank Ryan Coker, Emily Field, Erika Holdridge, Meagan Tonry, and
Robyn Zerebecki, who provided assistance in the field and laboratory. We thank Florida State
University Coastal and Marine Laboratory and Florida Atlantic University and Harbor Branch
Oceanographic Institute for institutional support. We also thank the St. Joseph Bay Buffer
Preserve for facilities and support.
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112 Table 1. Mean and SE of propagule mass (g) by origin site arranged from largest to smallest average propagule size. Different comparison letters indicate groups that are significantly different at P<0.05.
Comparison Origin site Mean SE letters CK 3.52 0.13 c
HB 2.51 0.10 b
CC 2.43 0.07 b
BP 1.83 0.09 a
PI 1.47 0.07 a
SH 0.91 0.05 d
113 Table 2. Results from logistic regression of seedling survival at the final time point, Summer 2015. (A) Analysis of deviance table. P-values were calculated with type II sum of squares. P- values for significant factors at P<0.05 are shown in bold. (B) Multiple comparisons by experiment and origin. Different comparison letters indicate groups that are significantly different at P<0.05.
114 Table 3. Results from Cox Proportional Hazards analysis. (A) Analysis of deviance table with results for each factor. (B) Proportional hazards comparing levels of Experiment and Origin to CK, seedling origin with highest survival across the two experiments. P-values for significant factors at P<0.05 are shown in bold.
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Table 4. Results from Permutational multivariate ANOVA (PERMANOVA) across all three time points (Summer of 2013, 2014, 2015) including seedling height (cm), and numbers of branches and leaves per seedling as response variables. Seedlings from origin sites PI and SH were excluded from the analysis due to almost complete mortality at SJB experiment. P values for significant factors at P<0.05 are shown in bold.
2 Factor MS DF Pseudo F R P
Experiment Site 1.83 1 70.83 0.09 0.001 Propagule mass 1.71 1 65.88 0.08 0.001 Origin Site 0.15 3 5.66 0.02 0.001 Date 3.16 2 121.86 0.29 0.001
Experiment Site x Propagule mass 0.21 1 7.93 0.01 0.001 Experiment Site x Origin Site 0.03 3 1.33 0.005 0.001 Origin Site x Date 0.10 6 3.87 0.03 0.001 Propagule mass x Date 0.09 2 3.32 0.008 0.03 Experiment Site x Date 0.46 2 17.75 0.04 0.001 Residuals 0.03 354 0.43
116 Table 5. Results from mixed linear models testing the effects of experimental factors on seedling height (cm), and number of leaves and branches per seedling. P-values were calculated with type III sum of squares, due to unequal sample sizes following mortality. Seedlings from origin sites PI and SH were excluded from the analysis due to almost complete mortality at SJB experiment. P-values for significant factors at P<0.05 are shown in bold.
117 Table 6. Multiple comparisons of leaves and branches per seedling by seedling origins and year. Seedlings from origin sites PI and SH were excluded from the analysis due to almost complete mortality at SJB experiment. Different comparison letters indicate groups that are significantly different at P<0.05.
118 Table 7. Results from mixed linear models at the IRL site only, including all seedlings origin sites, testing the effects of experimental factors on seedling height (cm), and number of leaves and branches per seedling. P-values were calculated with type III sum of squares, due to unequal sample sizes following mortality. Seedlings from origin sites PI and SH are included here but were excluded from the analyses comparing experiment sites due to almost complete mortality at SJB experiment. P-values for significant factors at P<0.05 are shown in bold.
Response Factor Chi. Sq. DF P
Seedling Height (cm) Origin Site 16.98 5 0.005 Date 128.17 2 <0.0001 Propagule mass 1.24 1 0.27 Origin Site x Date 8.60 10 0.57 Propagule mass x Date 0.31 2 0.86
No. Leaves per seedling Origin Site 26.46 5 0.0001 Date 291.31 2 <0.0001 Propagule mass 5.12 1 0.024 Origin Site x Date 136.90 10 <0.0001 Propagule mass x Date 11.97 2 0.003
No. Branches per seedling Origin Site 1.73 5 0.884 Date 15.45 2 0.0004 Propagule mass 0.00 1 0.972 Origin Site x Date 14.05 10 0.171 Propagule mass x Date 1.12 2 0.572
119 Table 8. Multiple comparisons from mixed linear models at the IRL site only, including all seedlings origin sites, testing the effects of experimental factors on number of leaves per seedling. Seedlings from origin sites PI and SH are included here but were excluded from the analyses comparing experiment sites due to almost complete mortality at SJB experiment. Different comparison letters indicate groups that are significantly different at P<0.05. Seedling height differed by seedling origin and multiple comparisons revealed that PI was taller than BP (Z=2.88, P= 0.045) and CC (Z=3.60, P=0.004), with no other significant differences between origin pairs (P>0.1).
Response: No. Leaves per seedling Comparison Year Origin letters 2013 BP abcd CC abcd CK abef HB aceg PI abcdh SH abcd 2014 BP abcd CC ab CK abefi HB bdfijk PI efgjl SH abcd 2015 BP abcdefgj CC abcd CK cdghjklm HB hlm PI ikm SH abcd
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Figure 1. Map of Florida indicating locations of experiment sites and A. germinans propagule collection sites (marked with pins). The St. Joseph Bay experiment site (SJB; 29°42'52.27"N, 85°18'25.32"W) is at the northern edge of the mangrove range on the Gulf of coast of FL, whereas the IRL site (27°31'55.92"N, 80°20'55.50"W) is within the center of the A. germinans range in FL. Propagules were collected from two sites on the Gulf coast: Stump Hole (SH; 29°40'43.44"N, 85°21'34.10"W) and Cedar Key Cemetery (CK; 29° 83'43.64"N, 83° 2'28.40"W). We also collected from four sites from the Atlantic coast: Cape Canaveral (CC; 28°56'23.55"N, 80°51'49.14"W); Pine Island (PI; 28°29'37.66"N, 80°43'35.17"W); Harbor Branch (HB; 27°31'55.92"N, 80°20'55.50"W); and Bear Point (BP; 27°25'44.87"N, 80°16'51.61"W). Insets show experiment sites and closest collection sites. Image is courtesy of Google Earth, 2016.
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Figure 2. Vegetation differences between Indian River Lagoon (IRL) and St. Joseph Bay (SJB) experiment sites, including (A) marsh vegetation (% cover of all marsh species per quadrat), (B) proportion of quadrats with marsh vegetation, as well as several metrics of A. germinans tree size: (C) Height (m), (D) trunk circumference (E) Estimated canopy area, and (F) pneumatophore density.
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Figure 3. Seedling survival by propagule mass, origin site, and experiment site. (A) Proportion seedlings surviving at the last sampling point (2015) across seedlings for each origin site in each experiment by mean propagule mass (g). Each point represents one origin site at one experiment. (B) Proportion of seedlings surviving in 2015 from each origin site in the IRL and SJB experiments. (C-D) Kaplan Meier survival curves showing the proportion of seedlings surviving by origin in each year at IRL (C) and SJB (D). Seedling origins in figure key are ordered by mean propagule size.
123
Figure 4. Influence of propagule mass on seedling traits over three years (2013-2015) including (A) number of leaves per seedling and (B) Seedling height (cm). Each point represents one seedling.
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Figure 5. Influence of Experiment site and origin site on seedling traits over three years (2013- 2015). Number of leaves per seedling by seedling origin site, at the two experiment sites, (A) IRL and (B) SJB. (C) Seedling height (cm) across seedling origin sites at IRL and SJB experiments. (D) Number of branches per seedling by seedling origin across the two experiment sites. Different lowercase letters above means in panels A-C indicate multiple comparisons by experiment site and year across all seedling origins for number of leaves per seedling and seedling height. Multiple comparisons by seedling origin and year for number of leaves and branches per seedling are listed in Table 5. Seedling origins in figure key are ordered by mean propagule size. Seedlings from SH and PI were included in figures although they were excluded from analyses of seedling traits between experiment sites due to high mortality at SJB.
125
Figure 6. Seedling traits at IRL experiment only, including all origin sites, showing (A) Seedling height by origin, (B) number of branches per seedling by year and (C) number of leaves per seedling by propagule mass (g). Seedling origins in figure key are ordered by mean propagule size.
126 Chapter 3: Supplemental Materials
Table S1. Numbers of seedlings at the beginning of the experiment by experiment site, origin, and maternal tree. The numbers initially planted for each origin and maternal tree were equal at both experiment sites. However, storm activity at IRL in the first month of the experiment washed away an experimental row, leaving the initial numbers represented in this table.
127 Table S2. Results from a linear model comparing percent cover of marsh vegetation at the two experiment sites, SJB and IRL. Percent cover was summed over all marsh species and then capped at 100 and logit transformed before analysis.
Factor MS Df F P
Site 205.90 1 51.63 <0.0001 Residuals 3.99 37
128 Table S3. Results from Permutational multivariate ANOVA (PERMANOVA) testing differences in Experiment sites (SJB and IRL) across all four Avicennia germinans size characteristics as response variables: tree height (m), estimated canopy area (m), trunk circumference (cm), and pneumatophore density (no. per 0.25 m2) . P value in bold indicates significant factor at P<0.05.
2 Factor MS DF Pseudo F R P
Site 1.04 1 19.69 0.71 0.001 Residuals 0.05 8 0.29 Total 9
129 Table S4. Differences in mean annual and mean annual minimum temperatures at weather stations Apalachicola (near the SJB experiment) and Fort Pierce (near the IRL experiment) from 1931-2014. Results from linear models. P-value in bold indicates significant factor at P<0.05.
Response Factor MS F DF P
Mean annual temp. Site 296.522 382.45 1 <0.0001 Residuals 0.775 166 Mean annual minimum temp. Site 207.245 172.88 1 <0.0001 Residuals 1.199 166
130 Figure S1. Locations of two weather stations, Apalachicola and Fort Pierce (near SJB and IRL sites, respectively), and temperature data from these stations. Each map shows one weather station and its proximity to field sites including (A) the SJB experiment site, closest seedling origin site, and Apalachicola (Station ID: 80211, 29°43'00.12", -085°01'00.12", approx. 29 km from the SJB experiment) and (B) the IRL experiment site, closest seedling origin sites, and Fort Pierce (Station ID: 83207, 27°25'59.88", -080°21'00.00" approx. 6 km from the IRL experiment). Map images are courtesy of Google Earth, 2016. Bar plots show mean ±SE of (C) mean annual air temperatures and (D) mean annual minimum air temperatures (ºC) at the two weather stations from 1931-2014.
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