TERRESTRIAL INFLUENCES ON THE MACROINVERTEBRATE BIODIVERSITY OF TEMPORARY WETLANDS
Michael A. Plenzler
A Dissertation
Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
December 2012
Committee:
Dr. Helen Michaels, Advisor
Dr. Enrique Gomezdelcampo Graduate Faculty Representative
Dr. Jeff Miner
Dr. Karen Root
Dr. Amy Downing ! ii!
ABSTRACT
Dr. Helen J. Michaels, Advisor
Vernal pools are temporary wetlands and local-scale biodiversity hot spots for a variety of amphibians, macroinvertebrates, and plants because their seasonal drying prevents the establishment of predatory fish populations. Vernal pools are often of conservation concern because of the amphibian populations; however, the emphasis on these organisms often eclipses the macroinvertebrates, which are important predators, prey, and nutrient cyclers in wetlands and the surrounding habitat. Hydroperiod and water chemistry are thought to be the primary regulators of vernal pool macroinvertebrates, but the surrounding habitat also affects these organisms. Specifically, canopy cover and forest composition can alter the autochthonous and allochthonous carbon sources for wetland food webs. My research objectives were to understand how variations in these factors affect macroinvertebrate diversity and community composition.
In 2009, I conducted a field survey of fifteen vernal pools that varied in area, depth, hydroperiod, canopy and surrounding land use. I measured several habitat conditions, assessed the biotic communities of these wetlands, and found that canopy cover influenced bottom-up productivity and macroinvertebrate diversity. I used the results of this study to determine how known macroinvertebrate communities respond to variation in canopy cover in mesocosm wetlands.
The low canopy treatments sustained the highest macroinvertebrate abundance, family richness, and Shannon diversity, as greater algal productivity increased resources available to support the macroinvertebrate communities. I conducted a second mesocosm experiment to explore how variation in canopy cover (low or high) and litter species (oak, maple, or a mixture of the two litters) affect vernal pool macroinvertebrates. Macroinvertebrate abundance and family richness were greater under low canopy and with oak litter, but the effects of canopy often depended on ! iii! litter species. Canopy limits the amount of light that reaches a wetland, but litter and its impact on dissolved organic carbon (DOC) limit light transmittance and producer communities.
Because forest composition and structure are changing from anthropogenic activities, these results suggest they will also alter the food webs of vernal pools. However, as some organisms utilize pools considered to be low quality for macroinvertebrates, vernal pools should be protected in a variety of habitat contexts to preserve regional species diversity.
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ACKNOWLEDGEMENTS
I once read that a PhD isn’t a statement of intelligence, but rather one of endurance. In my experience, it’s also one of luck and being surrounded by the right people at the right time. I can’t express the number of trying times I’ve encountered on this journey, but I’ve always been amazed at the number of people standing behind me throughout this process. To that end, there’s no shortage of people deserving some sort of recognition here (and I’ll take my time doing it).
First off, my committee. Thank you for helping me take an off-the-cuff observation about vernal pools and turn it into this project. It’s my sincere hope that you see your fingerprints all over this work, too. You have all pushed me further than I ever anticipated going—or sometimes wanted to go. I can’t honestly remember why I chose to do this degree, but I do feel equipped to go out and change some corner of the world for the better. Dr. Enrique
Gomezdelcampo was a fantastic outside referee who was equal parts informed contributor to this work and a willing combatant with an uppity stranger causing a stir at my conference talk. Dr.
Jeff Miner pulled me into graduate school. I had no idea what I was doing then, but I have a better idea of what I’m doing now thanks to him. His keen understanding of aquatic ecology was instrumental in designing and interpreting the following experiments. By some stroke of luck,
Dr. Amy Downing was invited to give a seminar and helped launch the mesocosm projects.
She’s been a fantastic addition to this work and I suspect she’s a Swiss Army knife of statistical techniques, too, as many of the analyses in this dissertation are modeled from her papers. Dr.
Karen Root’s emphasis on scale and conservation underpins much of my thinking on the need for vernal pool preservation. It’s through my conversations with her that I’ve come to think about these wetlands as metacommunities, where connections between them are critical and that habitat quality is organism-subjective. Finally, in one last act of Stockholm syndrome, my ! v! advisor Dr. Helen Michaels, who essentially allowed me to run free with a project that was a complete turn from anything that had previously gone on in the lab. I have a feeling I’ll be asking people, “What’s your question?” thirty years from now. It would feel glib to wax on about being the scientist I am now because of the years as her student, but it’s true. It’s odd thinking about the lack of Bowling Green in the next stage of my life, but as a testament to our history, I do plan on dropping in on her unannounced beyond publishing this work.
To my dear lab mates…it will be an incredibly weird day when I walk into an office and not see all of your faces or trip over all of your chairs on the way to my desk. Between Ryan
Walsh and Jake Meier, it’s been an odd marriage over the years and with the number of off-the- wall things that we’ve said and done, I think we’d have a hit radio show. Jen Shimola has been kind enough to not alert the authorities or psychologists about us. With everyone trying to finish up at once, we’ve probably recently carped at each other more than was necessary. I’ll miss it no less. Expect random phone calls in the future and countless more baseless thoughts about…well, anything goes. The lab and summer of 2012 were improved by the addition of four undergraduates. I’m sure you all have names, but I never really cared to learn any of them (I’m just kidding, calm down). Paige Arnold, Alyssa Dietz, Jake Sublett, and Alex Hill—at the very least, you were all a wonderful infusion of energy and ideas into the lab. If there was any crowd
I had to be surrounded by during this process, I’m glad it was this one.
None of this work would be possible without help from Dave Bonner (chapter one),
Adam Meeker and Dante D’Avello (chapter two), and Nate Schweitzer (chapter three). I mean that. Thanks for your volunteer labor and the oft-interesting conversations in the field. I can’t emphasize enough just how important your contributions were to this project. Don Schmenk
(Maumee State Forest), Gary Haase and Steve Woods (Kitty Todd), Chris Smalley (Wood ! vi!
County Parks) and Karen Menard (Toledo Metroparks) were critical for helping me locate vernal pools and granting permit access to them. The Ohio Biological Survey, Graduate Student
Senate, and BGSU Department of Biological Sciences Oman Fellowship provided highly appreciated funding for chapters two and three. Many, many, many thanks to all of you.
There are a number of other people who helped me get up the mountain in some way, shape, or form. First off, my family—especially my parents and sister. You guys provided no end of support. It was incredible and I find myself at a loss for words to explain it. Whether it was a place to run away to or a free meal when I had little time to feed myself, it all added up. I don’t know if I’ll ever find a way to adequately express my gratitude, but I’ll be able to start soon by repaying the money I borrowed to help keep the lights on during this whole experience.
For my friends in Toledo, Cleveland, Cincinnati, Columbus, Milwaukee, Chicago, San
Francisco, Pittsburgh, Houston, Boston, Arkansas, and on, and on, and on. Thank you. Thank you for your encouragement, the random visits back to BG, and your infinite patience with my frequent existential crises and other grad school related sacrifices. I plan to make good on my promises to visit you all and with a little bit of luck, might just end up employed near you, too.
You guys rock.
I’d also like to give a special thanks to a number of other BG people. Dr. Rex Lowe provided assistance with identifying algae samples and always had an offbeat story about life as a scientist or BG resident. Dr. Dan Pavuk helped identify bugs and was always good for a friendly conversation. The McKay/Bullerjahn lab was kind enough to provide equipment and space for chlorophyll fluorometry and Ben Beall conducted some flow cytometry that ultimately confirmed my water samples were too old and not properly preserved for such a thing. Bob
Midden and Sarah Jindra provided invaluable experience and help for analyzing nutrient content ! vii! in the water samples. Frank Schemenauer and the greenhouse were the perfect getaway when I got too cagey in the office. A lot of respect is also directed towards the Root and Miner labs.
You always showed up to my talks, but were also fun traveling companions and, in a pinch, free therapy for the rigors of grad school. Likewise, the biology staff is always a friendly and fantastic bunch. I’ve always thought you were the engine that made the department run. At this point, the least I can do is tell the new students to treat you well…as well as not go anywhere near the laminator. The Study Skills Center gave me new professional experiences related to teaching, but also more great people to know in my time here. My landlord made my finances easier to handle by never raising my rent; though, that may also have been related to the gas leak, broken water line, and various instances where my apartment was swamped by something/someone. I could go on.
Finally, a big thanks to Eileen Sawyer, who introduced me to these marvelous little wetlands. My experiences in the vernal pools program became the basis of my dissertation and continued desire to protect these ecological diamonds. At the end of this document, I hope we’ll have moved a little closer in that direction.
This is for all of you.
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TABLE OF CONTENTS
Page
GENERAL INTRODUCTION…………………………………………………………… 1
References………………………………………………………………………… 5
CHAPTER I. TERRESTRIAL HABITAT QUALITY IMPACTS
MACROINVERTEBRATE DIVERSITY IN VERNAL POOLS……………………...… 7
Abstract…………………………………………………………………………… 7 Introduction……………………………………………………….………………. 8 Methods…………………………………………………………….…...………… 10 Results…………………………………………………………………………….. 15 Discussion……………………………………………………………………….... 19 Acknowledgements……………………………………………………………...... 26 Tables……………………………………………………………………………... 27 Figures…………………………………………………………………………….. 31 Appendices………………………………………………………………………... 34 References………………………………………………………………………… 40
CHAPTER II. CANOPY COVER MEDIATES BOTTOM-UP PRODUCTIVITY AND
MACROINVERTEBRATE COMMUNITIES IN TEMPORARY WETLANDS………… 47
Abstract…………………………………………………………………….………. 47 Introduction………………………………………………………………………… 48 Methods…………………………………………………………………………….. 51 Results……………………………………………………………………………… 57 Discussion………………………………………………………………………….. 60 Acknowledgements………………………………………………………………… 68 Tables…………………………..…..……………………………………………… 69 Figures……………………………………………………………………………… 72 Appendices…………………………………………………………………………. 78 References………………………………………………………………………….. 80
CHAPTER III. CANOPY, LITTER, AND MACROINVERTEBRATES: SEEING THE
FORESTED WETLAND FOR THE TREES….……………………………………...... 87
Abstract…………………………………………………………………………….. 87 Introduction………………………………………………………………………… 88 Methods…………………………………………………………………………….. 91 Results……………………………………………………………………………… 100 ! ix!
Discussion………………………………………………………………………….. 107 Acknowledgements………………………………………………………………… 119 Tables………………………………………………………………………………. 120 Figures……………………………………………………………………………… 123 Appendices…………………………………………………………………………. 133 References………………………………………………………………………….. 142
SUMMARY AND GENERAL CONCLUSIONS………………………………………… 151
References………………………………..………………………………………... 156
APPENDIX A. INSTITUTIONAL ANIMAL CARE AND USE COMMITTEE
APPROVAL...... 158
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LIST OF TABLES
Table Page
CHAPTER I
1 The fifteen selected vernal pools, the protected lands they occur on, and their latitude
and longitude coordinates…………………………………………………..……… 27
2 Means and ranges of water chemistry measurements and land cover types present within
a 1 km radius of each vernal pool……………………………..…………….. 28
3 Mean macroinvertebrate abundance per pool, proportion of pools each family
occurred in, and proportion of total macroinvertebrates per family…….…………. 29
5 Eigenvalues and cumulative variance of the four PCs retained for multiple linear
regression analysis…………………………………………………………………. 30
CHAPTER II
1 The initial mesocosm macroinvertebrate community composition, including family
names, abundance, and respective feeding guilds (Merritt and Cummins 1996)…. 69
2 Mean and standard error (±SE) of the biotic and abiotic repeated measures variables
per canopy treatment……………………………………………………….………. 70
3 Mean and standard error (±SE) of the biotic and abiotic variables measured only
at the end of the experiment (August 10)…………………………………………... 71
CHAPTER III
1 The initial mesocosm macroinvertebrate community composition, including family
names, abundance, and respective feeding guilds (Merritt and Cummins 1996). Each
mesocosm initially contained 204 macroinvertebrates from 13 taxonomic families... 120 ! xi!
2 SIMPER analysis results for macroinvertebrate functional groups driving
differences in community structure………………………………………………… 121
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LIST OF FIGURES
Figure Page
CHAPTER I
1 Macroinvertebrate family richness decreases as PC2 (a) and canopy (b)
increases……………………………………………………………………………. 31
2 Shannon diversity decreases as PC2 (a) and canopy (b) increases………………… 32
3 Predator family richness decreases as PC2 (a) and canopy (b) increases………….. 33
CHAPTER II
1 Macroinvertebrate abundance (a) and family richness (b) were greatest in the
low (30%) canopy treatment……………………………………………………… 72
2 The low (30%) canopy treatment had the greatest water conductivity (a) and
temperature (b)……………………………………………………………………. 73
3 Water conductivity was always greatest in the low (30%) canopy and increased
faster in this treatment……………………………………………………………. 74
4 Shannon diversity (a) was higher in the low (30%) and intermediate (55%)
canopy treatments, but green algae abundance (b) was greatest in the
high (73%) treatment……………………………………………………………… 75
5 DOC was lowest in the high (73%) canopy treatment……………………………. 76
6 NMDS diagram showing the changes in macroinvertebrate composition over
the three sampling dates……………………………………………………….….. 77
CHAPTER III
1 Macroinvertebrate abundance (LS means) was greatest in the oak treatments…… 123
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2 Macroinvertebrate family richness (LS means) was greatest in the low canopy (a)
and oak litter (b) treatments……………………………………………………….. 124
3 Periphyton chlorophyll a (LS means) was greatest in the low canopy
oak litter treatment…………………………………………………………………. 125
4 Canopy affected underwater light (LS means) in all litter treatments, but
the differences between canopy treatments were greatest in oak litter……………. 125
5 Three-way interaction between canopy, litter, and time on underwater light……... 126
6 Dissolved oxygen (LS means) was greatest in the oak litter tanks………………... 126
7 Water temperature (LS means) was warmest in low canopy (a) and the oak
litter treatment……………………………………………………………………… 127
8 Water conductivity (LS means) was greatest in low canopy (a) and
with maple litter (b)……………………………………………………………….. 128
9 Dissolved organic carbon (a), phenolics (b), and phosphates (c) were highest
in the low canopy maple treatments……………………………………………….. 129
10 The nMDS ordination showing changes in macroinvertebrate composition
throughout the experiment………………………………………………………… 131
11 The nMDS plots for phytoplankton (a) and zooplatnkton (b) compositions……… 132
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GENERAL INTRODUCTION
Freshwater biodiversity is declining at rates far greater than those of terrestrial ecosystems because of anthropogenic activities that overexploit, pollute, destroy, or otherwise degrade aquatic habitats from local to global scales (Sala et al. 2000; Dudgeon et al. 2006). A clear understanding of the patterns and determinants of taxonomic richness is crucial to slowing and preventing the loss of freshwater biodiversity (Dudgeon et al. 2006; Clarke et al. 2008).
Lakes and wetlands are ideal ecosystems for studying freshwater diversity, as they contain well- delineated biotic communities and often occur along natural and anthropogenic habitat gradients that alter the composition of aquatic biota (De Meester et al. 2005).
The impacts of terrestrial habitat quality are often magnified in small aquatic habitats, where ecosystem productivity is strongly mediated by allochthonous and autochthonous carbon sources (Cole et al. 2006, Rubbo et al. 2006). Among the smallest of these habitats are the vernal pools and other temporary wetlands of North America (Colburn 2004). These local-scale biodiversity hot spots are critical habitats for a variety of amphibians, invertebrates, and plants because their seasonal drying prevents the establishment of predatory fish populations. Because of this trophic release, a single vernal pool can contain a greater diversity of organisms than the surrounding terrestrial environment, such that these wetlands have been termed “keystone ecosystems” (Hunter 2008). The vast majority of these wetlands occur in forested habitats, where trees are the primary contributors to a pool’s food web, but also affect water temperature and flooding duration. Vernal pools tend to be hydrologically isolated from other bodies of water, but may be intermittently connected to them during flooding events and permanently through groundwater. Additionally, these wetlands tend to be small and shallow, which helps ! 2! maintain dissolved oxygen and water temperature, while contributing to the likelihood that they will dry in a given season (Colburn 2004; Calhoun and deMaynadier 2008).
Vernal pools are most often studied because of the various amphibian species that breed within their waters. Since temporary wetlands lack legal protection under the Clean Water Act
(1972), these charismatic organisms have become flagship species for vernal pool conservation and much is known about their physical habitat requirements as a result (e.g., Calhoun and deMaynadier 2008). However, the preponderance of attention given to amphibians is in contrast to vernal pool macroinvertebrate communities, which are less studied. Moreover, while only 27 amphibian species are known to use vernal pools, there are hundreds more invertebrates associated with temporary wetlands. Many of these organisms are temporary wetland specialists with important roles in ecosystem functioning, such as nutrient and energy cycling (Colburn
2004). Additionally, invertebrates are important components of vernal pool food webs, as they are predators and prey of the resident amphibians and can further alter and respond to resource availability in wetlands. Despite this, there is limited information regarding the influences on invertebrate diversity in vernal pools (Colburn et al. 2008), such that most understanding of macroinvertebrate regulators has been inferred from other disciplines in aquatic ecology (Batzer et al. 2004). The overall focus of this dissertation is to understand how aquatic and terrestrial habitat affects the biodiversity of macroinvertebrates in vernal pools.
In this dissertation, I have utilized a field study and two mesocosm experiments to understand how canopy cover and leaf litter quality (species) affect invertebrate diversity. The goals of this research were to: 1) understand the roles of aquatic and terrestrial habitat quality as regulators of macroinvertebrate diversity in vernal pools; 2) evaluate how macroinvertebrates and water quality respond to changes in canopy cover as it relates to de- and reforestation; and 3) ! 3! determine how potential anthropogenic-driven changes in forest composition will affect the quality of wetland habitats and the diversity of their macroinvertebrate communities.
In chapter one, I conducted a field survey of fifteen vernal pools that varied in size, depth, hydroperiod, and canopy cover. I sampled the invertebrate communities and measured several indicators of water quality, habitat structure, carbon inputs, and GIS-informed land use. I found that canopy cover affects many aspects of wetland habitat quality and several measurements of macroinvertebrate diversity increase as canopy decreases. Other factors, such as water chemistry, hydroperiod, and anthropogenic land use, had little to no significant effects on invertebrates. This chapter is formatted for submission to Wetlands.
In chapter two, I conducted a mesocosm experiment to examine how vernal pool invertebrates and water quality respond to gradients in canopy cover. Canopy cover was simulated with shade cloth and each mesocosm contained identical initial macroinvertebrate communities. These assemblages were based on the invertebrate communities observed in chapter one. I monitored the invertebrate communities and water quality from late June to
August 2010 and discovered that low canopy wetlands sustain greater macroinvertebrate abundance and diversity. Intermediate and high canopy wetlands were often not significantly different from each other in many measurements of invertebrate diversity and water quality. To our knowledge, this is the first mesocosm experiment specifically focused on temporary wetlands and one of the few to examine invertebrate community responses to gradients in habitat quality. This chapter is formatted for submission to Oecologia.
Chapter three is a mesocosm experiment that examines how changes in canopy cover and leaf litter quality affect wetland macroinvertebrates. North American forests are shifting from oak- to maple-dominated species and this will alter the litter base of wetland food webs. We ! 4! created a fully factorial mesocosm experiment with pin oak, silver maple, or an oak and maple litter mixture with either high or low canopy. Low canopy wetlands sustained greater invertebrate abundance and diversity, but this relationship was further impacted by litter species.
In particular, pin oak mesocosms had greater diversity, while mixed litter and maple treatments were of poorer quality and often statistically similar to each other in regards to measures of habitat variables. My results demonstrate that forest compositional shifts will greatly alter the biota of wetland communities. This chapter is formatted for submission to Freshwater Science.
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REFERENCES
Batzer, D.P., B.J. Palik, and R. Buech. 2004. Relationships between environmental
characteristics and macroinvertebrate communities in seasonal woodland ponds of
Minnesota. Journal of the North American Benthological Society 23:50-68.
Calhoun, A.J.K. and P.G. deMaynadier. 2008. Science and Conservation of Vernal Pools in
Northeastern North America. CRC Press, Boca Raton, FL.
Clarke, A., R. Mac Nally, N. Bond, and P.S. Lake. 2008. Macroinvertebrate diversity in
headwater streams: a review. Freshwater Biology 53:1707-1721.
Colburn, E.A. 2004. Vernal Pools: Natural History and Conservation. The McDonald and
Woodward Publishing Company, Blacksburg, VA.
Colburn, E.A., S.C. Weeks, and S.K. Reed. 2008. Diversity and ecology of vernal pool
invertebrates. Pages 105-126 in A.J.K. Calhoun and P.G. deMaynadier (editors). Science
and Conservation of Vernal Pools in Northeastern North America. CRC Press, Boca
Raton, FL.
Cole, J.J., S.R. Carpenter, M.L. Pace, M.C. Van de Bogert, J.L. Kitchell, and J.R. Godgson.
2006. Differential support of lake food webs by three types of terrestrial organic carbon.
Ecology Letters 9:558-568.
De Meester, L., S. Declerck, R. Stoks, G. Louette, F. Van De Meutter, T. De Bie, E. Michels,
and L. Brendonck. 2005. Ponds and pools as model systems in conservation biology,
ecology and evolutionary biology. Aquatic Conservation: Marine and Freshwater
Ecosystems 15:715-725.
Dudgeon, D., A.H. Arthington, M.O. Gessner, Z.-I. Kawabata, D.J. Knowler, C. Lévêque, R.J.
Naiman, A.-H. Prieur-Richard, D. Soto, M.L.J. Stiassny, and C.A. Sullivan. 2006. ! 6!
Freshwater biodiversity: importance, threats, status and conservation challenges.
Biological Reviews 81:163-182.
Hunter, M.L. 2008. Valuing and conserving vernal pools as small-scale ecosystems. Pages 1-8 in
A.J.K. Calhoun and P.G. deMaynadier (editors). Science and Conservation of Vernal
Pools in Northeastern North America. CRC Press, Boca Raton, FL.
Rubbo, M.J., J.J. Cole, and J.M. Kiesecker. 2006. Terrestrial subsidies of organic carbon
support net ecosystem production in temporary forest ponds: evidence from an ecosystem
experiment. Ecosystems 9:1170-1176.
Sala, O.E., F.S. Chapin III, J.J. Armesto, E. Berlow, J. Bloomfield, R. Dirzo, E. Huber-Sanwald,
L.F. Huenneke, R.B. Jackson, A. Kinzig, R. Leemans, D.M. Lodge, H.A. Mooney, M.
Oesterheld, N.L. Poff, M.T. Sykes, B.H. Walker, M. Walker, and D. Hall. 2000. Global
biodiversity scenarios for the year 2100. Science 287:1770-1774. ! 7!
CHAPTER I
TERRESTRIAL HABITAT QUALITY IMPACTS MACROINVERTEBRATE DIVERSITY IN
VERNAL POOLS
ABSTRACT
Vernal pools are temporary wetlands that provide habitat for many macroinvertebrate species.
Hydroperiod and water chemistry are traditionally thought to control macroinvertebrate diversity in these wetlands, but their effects are often only detected when examining habitats at opposite ends of hydroperiod and water quality gradients. Canopy cover and other biotic factors may be important macroinvertebrate regulators because they mediate the solar energy available for producer communities and further limit the food and shelter resources for wetland consumers.
We studied fifteen northwest Ohio vernal pools with variable physical habitats (area, depth, hydroperiod, canopy cover, etc.) to evaluate which habitat factors affect macroinvertebrate family richness, Shannon diversity, and total macroinvertebrate abundance in temporary wetlands. We surveyed macroinvertebrate communities and measured pool area, depth, canopy cover, hydroperiod, temperature, water chemistry, leaf litter inputs, algal productivity, pool vegetation, and surrounding land use for each vernal pool. We used principal components regression to determine which environmental measurements predicted macroinvertebrate abundance and diversity. The first four PCA axes explained 65 % of the variation in our environmental variables, with PC1 and PC2 related to water quality and canopy-mediated productivity, respectively. PC2 explained 57 % of the variation in total family richness, 41 % of the variation in Shannon diversity, and 63 % of the variation in predator family richness. Our results suggest that canopy cover is the chief regulator of macroinvertebrate diversity in vernal pools. Decreased canopy cover was associated with increased vegetation abundance, algal ! 8! genera abundance, water temperature, and dissolved oxygen, which are known to increase the abundance, diversity, and performance of wetland organisms. Hydroperiod and water chemistry in this survey may have been too invariant or hospitable to affect macroinvertebrates.
Alternatively, temporary wetland macroinvertebrates may be habitat generalists that are tolerant of a wide range of environmental variation. Although numerous studies have evaluated the effects of habitat on vernal pool macroinvertebrates, these data emphasize the importance of canopy cover as a regulator of diversity and community composition and corroborates results of other studies that have found few associations between invertebrate diversity, hydroperiod, and water chemistry.
INTRODUCTION
Vernal pools and other temporary wetlands contribute to regional biodiversity because of the specialized amphibians and macroinvertebrates that utilize these habitats (De Meester et al.
2005). Although their resident amphibian communities are of conservation concern, over 100 species of invertebrates are also known to use these wetlands (Colburn 2004). Historically, hydroperiod and water chemistry have been implicated as the chief regulators of macroinvertebrate diversity, as seasonal drying can prevent or limit top-down regulation of predaceous organisms, such as fish and salamanders, from exploiting these habitats (Wissinger et al. 1999; Brooks 2000; Colburn 2004). Hydroperiod is the defining characteristic of temporary wetlands; however, broad examinations of wetlands along hydrological gradients often find little evidence of hydroperiod directly affecting invertebrate communities except when comparing wetlands near hydrologic extremes (Wissinger et al. 1999; Batzer et al. 2004). As hydroperiods among vernal pools can vary through time, it is likely that unrelated macroinvertebrate taxa have ! 9! repeatedly evolved mechanisms to survive drying, such that drawdowns may not be a major environmental constraint for species that regularly utilize temporary wetlands (Williams 1996).
Although water chemistry is often assumed to influence macroinvertebrates, its effects are context-dependent. Nutrient levels (Campeau et al. 1994; Gabor et al. 1994) and salinity (Euliss et al. 1999; Lovvorn et al. 1999) limit macroinvertebrates, but these patterns are most evident in habitats with extreme variation in water chemistry and are often associated with anthropogenic activities (Euliss et al. 1999; Batzer et al. 2004). Water chemistry may be too invariant to affect invertebrates among geographically proximate wetlands, but may impact invertebrate assemblages on landscape scales, where bedrock and other geological factors can substantially alter wetland chemistry (Batzer et al. 2004; Nicolet et al 2004).
At smaller scales, environmental gradients involving biotic factors may influence wetland invertebrate communities. Specifically, bottom-up processes mediated by the surrounding forest can alter allochthonous and autochthonous inputs and influence macroinvertebrate diversity in vernal pools. Similar to stream and riparian ecosystems, terrestrial habitat composition and quality can impact wetland productivity by altering carbon and shelter resources available to macroinvertebrates (Wallace et al. 1997; Batzer et al. 2000; Palik et al. 2001; Battle and
Golladay 2001; Colburn 2004) and further dictate the presence of predator fauna that require sufficient terrestrial habitat for their life histories and impose top-down pressures on pool communities (Colburn et al. 2008; Mitchell et al. 2008). Canopy cover has been increasingly identified as an important regulator of wetland organisms because it mediates primary productivity and algal community composition (Skelly et al. 1999; Halverson et al. 2003; Werner et al. 2007; Williams et al. 2008; Binckley and Resetarits 2009). Canopy is often only considered as a limiting factor for small numbers of taxa (e.g., amphibians and beetles) and ! 10! rarely for large suites of species (Batzer et al. 2004), but has yet to be directly examined for vernal pool macroinvertebrates. Additionally, canopy influences leaf litter inputs in wetlands, which can shift productivity from algal to microbial communities (Rubbo and Kiesecker 2004) and further affect macroinvertebrate diversity (Battle and Golladay 2001).
Northwest Ohio provides the ideal stage to assess the relative importance of the various potential aquatic and terrestrial regulators of macroinvertebrate biodiversity in vernal pools.
Historic glacial activity in the region has created a variety of swamp forests, oak woodlands, savannas, and prairies collectively known as the Great Black Swamp and the Oak Openings region (Brewer and Vankat 2004; ODNR Division of Natural Areas and Preserves 2008). This fine scale edaphic and hydrologic heterogeneity has created vernal pools in various terrestrial habitat contexts, leaving these wetlands rather unique reservoirs of biodiversity when compared to others in eastern North America. The landscape variation provides an opportunity to examine the relative roles of terrestrial and aquatic habitat factors as determinants of macroinvertebrate biodiversity before regional land cover changes due to anthropogenic forces increase the already substantial habitat loss for vernal pools and other aquatic habitats (e.g., Schetter and Root 2011).
This study (1) characterizes macroinvertebrate communities in a subset of northwest Ohio vernal pools; (2), measures a suite of aquatic and terrestrial environmental factors associated with vernal pool habitat; and (3) examines relationships between these habitat factors and invertebrate abundance and diversity.
METHODS
Study Area and Vernal Pool Selection
We conducted our survey of vernal pools in Fulton, Lucas, and Wood counties in
Northwest Ohio. These areas harbor post-glacial deposits of sandy and clayey soils oriented in a ! 11! southwest-to-northeast direction, collectively known as the Oak Openings and Great Black
Swamp (Brewer and Vankat 2006). All wetlands occurred on protected lands including Bowling
Green State University’s Steidtmann Woods, Kitty Todd Nature Preserve (The Nature
Conservancy), Maumee State Forest (Ohio Department of Forestry), Oak Openings Metropark
(Metroparks of the Toledo Area), or Rudolph Savanna (Wood County Park District) (Table 1).
In April 2009, we contacted local land managers to locate vernal pools that varied in area, depth, hydroperiod, and terrestrial habitat context (forested or nonforested). Although variable hydroperiods might imply that these are seasonal wetlands as opposed to vernal pools, all identified wetlands reliably form in early spring and dry throughout their basin during the growing season sensu Zedler (2003). We selected pools that spanned a variety of areas and depths such that area and depth did not correlate among all pools, including some sites with large surface areas and shallow depths and some with limited area and relatively deep water. No official record of vernal pool locations existed for Steidtmann Woods, thus all pools in this site were located manually. Initially, we identified 22 potential study pools that would hold water in
May 2009 when field sampling would occur, but reduced our sample size to 15 in by arbitrarily eliminating wetlands that were similar to others in terms of physical habitat.
Field and Laboratory Procedures
All vernal pools were surveyed from May 19 to May 22, 2009. We measured the length and width of the flooded portion of each pool at its longest axes and estimated vernal pool area as an ellipse to the nearest m2. Average water depth was measured using a meter stick placed in the three deepest parts of each pool’s basin. Although all pools had water in March, hydroperiod was determined as the number of days from May 1 until surface water was absent from the pool’s basin. Canopy cover was measured in the center of each wetland using a spherical ! 12! densiometer (Model-C, Forest Densiometers, Bartlesville, OK). Each reading was taken 1 m above the water in four cardinal directions and included canopy cover above the basin and surrounding terrestrial habitat openness. The wetland center was defined as the intersection of the length and width lines used to estimate pool area. At the one-quarter and three-quarters points of these lines and the pool’s center, we collected submerged leaf litter samples occurring within a 0.25 m2 area, which were frozen and later dehydrated, weighed and identified to species.
We recorded total litter mass, the mass of each litter species, and the mass of unidentifiable litter fragments. At these sampling points, we measured litter depth present above the soil and removed leaf litter prior to collecting a 15 cm soil core (two-cm wide) to assess soil organic material via the loss on ignition (LOI) method (Allen 1989). Water conductivity, dissolved oxygen, and pH were also measured at these locations using a sensION156 multiparameter probe
(Hach, Inc.) and averaged for the entire pool. All water measurements were recorded between
11:00 AM and 3:00 PM under full sun and 26 °C mean air temperature. Water samples were collected in 1 L bottles and preserved for later analysis of dissolved organic carbon, NH3, NH4, total nitrogen, PO4, total phosphorus, and phenolic compounds. A subsample of this water was analyzed for chlorophyll a content using the Welschmeyer method (1994). Algal and zooplankton samples were collected by towing a 60-!m mesh plankton net (12.7 cm diameter) five meters through the center of each pool just below the surface. The samples were preserved in 10 % formalin and later quantified to genus with a plankton wheel (zooplankton) or
Sedgewick-Rafter cell (algae). Any filamentous or multicellular algae were considered to be one cell. Pools were georeferenced within 30 m using a handheld GPS (Garmin GPS III Plus) and entered into ArcMap 9.3 (ESRI 2008) with soil and land cover data from the Lake Erie GIS Map
Viewer (Ohio Department of Natural Resources). The land cover data is from a 2003 Ohio ! 13!
Environmental Protection Agency survey that classified the state of Ohio into categories of deciduous forest, evergreen forest, cropland, pasture, commercial/industrial/transportation, residential space, urban areas, bare land/mines, open water, woody wetlands, and herbaceous wetlands (Frohn 2005). We recorded the soil series present at the GPS coordinates and created 1 km buffers around each pool to calculate the area (in sq. km) of each land cover type within the buffer zone to determine recent land use around each wetland.
Macroinvertebrates were surveyed using a time-limited sampling technique (Nicolet et al.
2004). Prior to sampling, we identified and estimated the types and amounts of microhabitats
(e.g., shallow water, coarse woody debris, emergent vegetation, etc.) a vernal pool. Each wetland was then sampled for a total of three minutes, which was partitioned according to the proportions of the microhabitat types in a pool. Macroinvertebrates were sampled with a 800 !m
D-frame net. The net contents were emptied after each sweep. Captured macroinvertebrates were condensed into a single sample and preserved in 95% ethanol for later identification to family level and calculation of Shannon diversity and total macroinvertebrate abundance per pool. We also assessed the macroinvertebrate functional composition of each pool by determining the abundance and family richness of macroinvertebrate functional groups (grazers, shredders, filter feeders, and predators (Merritt and Cummins 1996). Vernal pool vegetation was sampled within two weeks of macroinvertebrate collection by establishing two one-meter-wide transects through and parallel to the flooded area of a pool’s long axis and extending three meters into the surrounding dry portion. Because most plants lacked reproductive structures or other distinguishing features, vascular plant taxa sampled along transects were identified to morphotype. All stems in transects were counted to determine vegetation density and morphotype richness in each pool. ! 14!
Statistical Analyses
Data transformations were applied before statistical analysis to reduce heteroscedasticity
(Zar 1999). Canopy cover and organic material were logit transformed. DOC, NH3, NH4, total nitrogen, PO4, and total phosphorus were ln(X) transformed. Total phenolic compounds were ln
(X + 0.5) transformed. Total algal genera, algal density, zooplankton density, total macroinvertebrate abundance, macroinvertebrate family richness, and macroinvertebrate abundance and family richness per functional group!"ere square root transformed. Because exploratory data analysis indicated that many of our variables were moderately correlated (data not shown), we used principal components regression (Quinn and Keough 2002) to determine which environmental measurements predicted Shannon diversity, macroinvertebrate family richness, and total macroinvertebrate abundance. First, we performed principal component analysis (PCA) on water depth, pool area, canopy cover, hydroperiod, dissolved oxygen, temperature, water chemistry (conductivity, pH, DOC, NH3, NH4, total N, PO4, total P, and total phenolic compounds), chlorophyll a content, litter depth and litter variables (including total litter mass, unidentified litter fragment mass, and the mass of the five most common species: pin oak, silver maple, cottonwood, red maple, and white oak), vegetation richness and density, soil organic material, zoo- and phytoplankton density, total algal genera, and all land cover types.
We retained the first four principal components because they explained 65% of the variation in our dataset with only small increases (< 5%) in explained variation for each remaining component. The four principal components were used as predictor variables to fit multiple linear regression models for Shannon diversity, macroinvertebrate family richness, and total macroinvertebrate abundance. Variables were considered to load strongly on a principal component if their loading coefficient was greater than 0.60 or less than -0.60 and moderately if ! 15! the coefficient was between 0.40 – 0.60 or -0.40 – -0.60 (sensu Quinn and Keough 2002). We used forward and backward stepwise procedures to select which components to include in our multiple regression models (based on lowest AIC score, data not presented). Additionally, we further explored whether canopy cover affected macroinvertebrate abundance, family richness,
Shannon diversity, and functional groups with univariate linear regressions. All statistical analyses were conducted with JMP Statistical Discovery Software 10 (SAS Institute Inc., Cary,
NC). We considered " = 0.05 to be significant for all tests.
RESULTS
Mean vernal pool area was 2184 m2 (range: 555 – 4500 m2) and water depth was 0.40 m
(range: 0.19 – 0.86 m). The vernal pools were flooded for an average of 70 days (range: 38 to
119 days). Six pools held water in July, but only two remained flooded through August. The densiometer indicated that canopy cover ranged from 11 to 94 % shade, with an average of 60 % canopy. Mean vernal pool water temperature was 19 °C (range: 14 – 28 °C) and DO ranged from 1.2 to 6 mg/L (mean: 2.8 mg/L). Several water chemistry variables varied among ponds by one or two orders of magnitude, including conductivity, DOC, NO3, NH4, total N, PO4, and total
P (Table 2a, Appendix 1A).
Vernal pool vegetation density ranged from 0.5 to 82.5 stems per m2. We found 20 distinctly different vascular plant morphotypes in the pools within the flooded portion of the wetland (range: 5 – 25 plant types). Pools with the densest vegetation were generally dominated by herbaceous plants or Cephalanthus occidentalis (buttonbush). Thirty distinct algal genera were present in the pools, with a mean of 9.6 genera per pool (range: 4 – 20). Eunotia lunaris and filamentous species were 42% and 30% of all observed algae (Appendix 1B). Algal density ranged from 55 to 457 cells/mL (mean: 258.8 cells/mL). ! 16!
The vernal pools contained an average of 164.8 g of leaf litter dry mass per m2. The five most dominant litter species were Quercus palustris (pin oak), Acer saccharinum (silver maple),
Populus deltoides (eastern cottonwood), Quercus alba (white oak) and Acer rubrum (red maple) and had means of 57.9, 33.3, 16.4, 10.5, and 4.5 g/m2, respectively. A mean of 25.3 g/m2 comprised of unidentifiable litter fragments. Other minor litter species (generally less than 2 g/m2 across all pools) were Quercus rubra (red oak), Quercus bicolor (swamp white oak), Acer saccharum (sugar maple), Ulmus americana (American elm), Platanus occidentalis (American sycamore), Fraxinus americana (white ash), Pinus strobus (eastern white pine), and Salix spp.
(willow). Mean litter depth was 1.72 cm (range: 0.44 – 4.66 cm). Mean soil organic content was
8.51 % (range: 0.95 – 22.4 %).
Cropland and woody wetlands were the largest land covers near the vernal pools and were present within 1 km of all wetlands. Deciduous forests and residential areas occurred near eight of the vernal pools, but evergreen forests, open water, commercial lands, bare ground/mines, and herbaceous wetlands occurred near seven or fewer wetlands (Table 2b, Appendix 1A). The vernal pools occurred on Granby (n = 10 pools), Wauseon (n = 3), or Udorthents (n = 2) soils and were generally poorly drained (Natural Resources Conservation Service 2012).
We collected 4,041 macroinvertebrates from 24 families from the vernal pools. The pools had an average of 11.4 families (range: 4 - 20). While no wetland contained all families,
Planorbidae, Dytiscidae, Chironomidae, and Limnephilidae were captured in at least 80 % of all pools. Others were unique to particular pools, as Tipulidae and Naididae were only captured in one pool and Belostomatidae and Syrphidae were captured in separate wetlands.
Macroinvertebrate abundance was dominated by a few families, including: Sphaeriidae (31.7 % of all macroinvertebrates), Chaoboridae (17.1 %), Chironomidae (13.7 %), Physidae (10.3 %), ! 17! and Libellulidae (5.6%) (Table 3). Mean Shannon diversity of the pools was 1.43, but values ranged from 0.57 to 2.45.
Principal components analysis reduced our dataset of 39 variables into four retained principal components that explained 65 % of the total variation in our dataset (Table 4). The weights and loadings of the four PCs are presented in Appendix 1C. Hydroperiod, pool depth,
DOC, NH3, NH4, total N, PO4, total P, phenolics, chlorophyll a, and commercial and bare land covers have high loadings on PC1 (24% of variation in dataset), suggesting this component primarily reflects hydroperiod and water quality. PC2 accounted for 21% of the variation in our data and collected together biotic habitat quality, as canopy cover, DO, temperature, vegetation abundance and density, algal genera, and the woody wetland and open water land covers load strongly on this component. Few variables loaded strongly with any individual remaining component. Q. palustris litter mass correlated strongly with PC3 (11% of variation), while conductivity and pH correlated with PC4 (9% of variation). Within these four components, moderately correlated variables were often related to leaf litter quantity or quality (litter depth, litter mass, A. rubrum mass, P. deltoides mass, and organic matter) and land use (deciduous and evergreen forest, residential areas, and herbaceous wetlands).
Multiple linear regression indicated that macroinvertebrate family richness decreased as
PC2 increased (p = 0.0007, adj. r2 = 0.57, Figure 1a). Likewise, Shannon diversity decreased as
PC2 increased (p = 0.006, adj. r2 = 0.41, Figure 2a). Of the macroinvertebrate functional groups, predator family richness decreased as PC2 increased (p = 0.0002, adj. r2 = 0.63, Figure 3a), while no other measures of functional composition were significantly related to the PCs. Thus, decreases in variables with strong positive correlations to PC2 (canopy cover and open water) and increases in variables with strong negative correlations to PC2 (temperature, DO, plant ! 18! types, vegetation density, algal genera, and woody wetlands) increase macroinvertebrate family richness, Shannon diversity, and predator family richness. The factors with moderate loadings on PC2 may further impact vernal pool macroinvertebrates. The deciduous and evergreen forest land cover variables loaded positively on PC2 and are likely related to pool canopy cover, as canopy cover will decrease as forest cover decreases. We further analyzed the relationship between canopy and invertebrates because this variable loaded strongest on PC2. Linear regression analysis indicated that canopy cover affected macroinvertebrate diversity, as family richness (p = 0.0006, adj. r2 = 0.58, Figure 1b), Shannon diversity (p = 0.016, adj. r2 = 0.32,
Figure 2b), and predator family richness (p < 0.0001, r2 = 0.76, Figure 3b) decreased as canopy cover increased. Canopy did not affect total macroinvertebrate abundance or the other functional groups (all p > 0.05). The negative loading of pool area suggests that larger pools can support more family richness, Shannon diversity, and predator family richness. The remaining variables were related to leaf litter (litter depth, total litter mass, and A. rubrum mass) and loaded positively with PC2. As increases in PC2 decreased family richness, Shannon diversity, and predator family richness, an increase in these litter factors may negatively impact vernal pool macroinvertebrates.
No principal components explained total macroinvertebrate abundance in vernal pools
(multiple regression, p > 0.05). PC1, PC3, and PC4 did not significantly predict macroinvertebrate abundance, family richness, Shannon diversity, and functional groups, despite explaining 24, 11, and 9 % of the variation in our environmental variables, respectively. These components were chiefly related to all water chemistry measurements and Q. palustris litter mass and further isolate PC2 (wetland productivity) as the primary factor affecting vernal pool macroinvertebrates. ! 19!
DISCUSSION
Hydroperiod and water chemistry are thought to be the chief regulators of macroinvertebrates in vernal pools and other temporary wetlands, but these trends are often only most noticeable when comparing macroinvertebrate communities at opposite ends of habitat gradients (Batzer et al. 2004). Alternatively, canopy cover affects vernal pool producer communities that provide carbon and shelter resources for wetland organisms (Werner and
Glennemeier 1999; Skelly et al. 2002). Additionally, wetlands with reduced amounts of canopy often have warmer water temperatures and greater dissolved oxygen, which can contribute to the faster growth and development of macroinvertebrates (Fairchild et al. 2003). Similar to Batzer et al. (2004), our research suggests that canopy cover, not hydroperiod or water chemistry, most significantly affects macroinvertebrate diversity.
Canopy cover is linked to the abundance, distribution, diversity, and performance of several aquatic species, including amphibians (Werner et al. 2007), coleopterans (Binckley and
Resetarits 2009), gastropods (Palik et al. 2001), and odonates (Corbet 1999). The differences in primary productivity between open- and closed-canopy wetlands likely drive the greatest responses in macroinvertebrate communities. Open-canopy wetlands have greater primary productivity and altered algal and vegetation compositions that are food sources, oviposition sites, and refuges for macroinvertebrates (Werner and Glennemeier 1999; Colburn 2004). The most responsive taxa are often algivores, such as Physidae and Haliplidae (Palik et al. 2001), but predatory coleopterans and odonates may utilize open-canopy wetlands because of greater prey resources and to oviposit on vegetation substrates (Cummins and Merritt 2001; Binckley and
Resetarits 2009). Aquatic organisms may use canopy to evaluate habitat quality, as it creates favorable biotic and abiotic conditions in forested wetlands (Binckley and Resetarits 2007). ! 20!
This appears to apply to a wide range of vernal pool macroinvertebrate taxa, as greater canopy cover was associated with decreased vegetation density, plant morphotypes, and algal genera and reduced macroinvertebrate diversity and predator family richness in our study.
Cooler water temperatures and lower dissolved oxygen were associated with greater canopy cover and are known to inhibit invertebrate growth (Webb and Mitsch 2001; Fairchild et al. 2003). While these factors may contribute to our observed patterns in macroinvertebrate diversity, they are considered to be less important than resource availability (Schiesari 2006) and may be a secondary benefit associated with open-canopy wetlands. Canopy can also restrict water reflectance and make the wetland less visible to actively dispersing invertebrates (Bernath et al. 2002). Binckley and Resetarits (2007) question whether some invertebrates perceive closed-canopy ponds as low quality habitat or not as habitat at all; however, our study was conducted in late May, when diversity peaks just prior to full leaf out (Colburn 2004). The forested pools still would have been somewhat visible to migrating invertebrates, but the diversity responses to the incompletely developed forest canopy suggest some macroinvertebrates do use canopy to evaluate habitat quality (e.g., Binckley and Resetarits
2007).
Vernal pool area had a moderate negative loading on PC2, indicating that increases in area may also increase richness and diversity. This relationship would be expected, according to classic island biogeography theory (MacArthur and Wilson 1967), but is not always strongly detected in temporary wetland studies (Brose 2003; Batzer et al. 2004; Studinski and Grubbs
2007). Larger pools often have open canopies, but this relationship is not absolute if pools are shallow or the hydrology allows large trees to grow in the wetland basin (Colburn 2004). This appears to be the case in our study, as the largest pool had 88 % canopy cover. Larger pools may ! 21! have a higher microhabitat complexity within and around the wetland to support greater macroinvertebrate diversity (Studinski and Grubbs 2007), but this relationship is weak in our study when compared to the factors associated with wetland productivity. However, these interpretations between macroinvertebrates and pool size must be tentative, as our sampling strategy was not weighted for pool area. We might have under-sampled the largest pools relative to the smallest and thus may be underestimating the effects of area on macroinvertebrate diversity.
Hydroperiod did not affect vernal pool macroinvertebrate abundance or diversity. This is contrary to Euliss et al. (1999), Brooks (2000), Tarr et al. (2005) and Stewart and Downing
(2008), who all found an effect of hydroperiod on macroinvertebrate communities in seasonal wetlands. Hydroperiod restricts the presence of larger predatory fauna, such as fish and certain amphibians, which require permanent to semi-permanent habitats for their life histories (Smith and Van Buskirk 1995; Skelly et al. 1999). Alternatively, hydroperiod may only affect macroinvertebrates for wetlands near hydrological extremes (i.e., permanent ponds versus those flooded for less than one month), as Batzer et al. (2004) and Studinski and Grubb (2007) found no effects of hydroperiod along narrower wetland flooding gradients. The lack of consensus on hydroperiod is surprising, as it is considered to be one of the defining characteristics of vernal pools (Colburn 2004). The flooding duration of our study pools may have not been variable enough to affect macroinvertebrate communities, but other physical characteristics, such as canopy cover, may have an overriding affect on hydroperiod. Open-canopy pools tend to have longer hydroperiods because of reduced evapotranspiration (Skelly et al. 1999), but one of our shaded pools held water in July and another lasted through August. These sites had either average or poor diversity (11 and 4 macroinvertebrate families captured in these pools, ! 22! respectively) and may be further evidence that hydroperiod mainly excludes predatory fish, while canopy-mediated productivity regulates macroinvertebrate diversity. Although our study spanned only one season and pool hydrology fluctuates annually (Leibowitz and Brooks 2008), the ability to overcome drying has evolved repeatedly in several unrelated wetland taxa
(Williams 1996). Any species eliminated by an unexpected drawdown may annually persist if it has dormant stages in the soil or there are nearby source populations to recolonized the pool the following season (Colburn 2004). Ultimately, while hydroperiod may be the defining characteristic of seasonal wetlands, it may not be a significant environmental constraint for macroinvertebrates adapted to these habitats (Batzer et al. 2004).
Overall, macroinvertebrate abundance and diversity were not affected by variation in water chemistry, even though our measurements of water chemistry could differ as much as two orders of magnitude among vernal pools. Similar to hydroperiod, macroinvertebrates may only respond to water chemistry when examining extremes in chemical conditions (e.g., salinity or nutrients)
(Gabor et al. 1994; Euliss et al. 1999; Batzer et al. 2004). Geographic extent may also affect macroinvertebrate responses to water chemistry, as Nicolet et al. (2004) observed that pH and alkalinity were the dominant factors shaping temporary pond macroinvertebrate communities across the United Kingdom, but other environmental gradients may also emerge at smaller scales. Our research was conducted at a smaller scale and is consistent with this view, as local- scale measurements of canopy had the greatest effect on macroinvertebrate diversity and soil pH, salinity, and fertilizer inputs are likely less spatially variable. Likewise, Batzer et al. (2004) and
Oertli et al. (2008) found little effect of water chemistry on macroinvertebrate communities when examining temporary wetlands at spatial scales similar to ours, indicating our results may be expected given the scope of our study. However, our water samples were collected in late spring ! 23! and the effects of chemistry may have faded or producers and consumers may have already utilized the nutrients. Interestingly, water chemistry may have limited macroinvertebrates in the
Horsetrail pool, which contains an active horse trail and was where we collected Syrphidae larvae. These organisms are tolerant of polluted waters (Merritt and Cummins 1996) and while none of the dissolved nutrients were highest in this pool, dissolved oxygen was the lowest (1.2 mg/L). Although we can only speculate whether horse feces are degrading this wetland, it potentially represents a pool with atypical nutrient inputs where water chemistry can affect macroinvertebrates.
Some land cover classifications had moderate to strong effects on vernal pool invertebrate communities. Macroinvertebrate diversity decreased with greater deciduous and evergreen forest, but this relationship is likely associated with canopy cover (greater forest cover creates greater canopy cover). Open water had a negative impact on diversity, which is surprising, as permanent water bodies are propagule sources for some vernal pool macroinvertebrates and habitat for others during dry periods (Colburn 2004). Lesser distance to permanent water sources may increase the likelihood that fish and large amphibians (e.g., Rana catesbeiana) encounter vernal pools; however, this should be interpreted with caution, as open water was only present near seven pools and was never more than 0.04 km2 within the one km buffer zones.
Further understanding of local pool densities and distances to nearest pools are necessary to evaluate source/sink dynamics among macroinvertebrate communities. Greater cover by woody wetlands supports diversity and may indicate areas of high vernal pool density, where source- sink dynamics could help maintain taxa persistence through immigration to our study pools (e.g.,
Urban 2004). The weak positive effect of cropland on macroinvertebrate diversity is counterintuitive to the destructive acts associated with agriculture on vernal pool organisms ! 24!
(Euliss and Mushet 1999; Colburn 2004). Tangen et al. (2003) found no significant effect of human activities on macroinvertebrate communities in temporary wetlands, but also noted the long agricultural history of their study sites and the presence of resilient invertebrates in their surveys. Agriculture was present near all of our wetlands and can affect water conductivity
(Carrino-Kyker and Swanson 2007), but all pools occurred in preserved areas where the surrounding terrestrial habitat could serve as a buffer from any pollutants or other anthropogenic disturbances. Thus, agriculture may have minimal effect on vernal pools in fragmented landscapes, if the wetlands are either in protected lands or woodlots unsuitable for agricultural conversion. Wetlands in different terrestrial contexts may have more profound responses to surrounding land use where anthropogenic activities may impact invertebrate communities.
Vernal pools near urban areas have shallower depths (Carrino-Kyker and Swanson 2007) and altered microbial communities (Carrino-Kyker et al. 2011). Other physiochemical factors (pH and NO3) associated with anthropogenic activities also affect vernal pool microbes (Carrino-
Kyker and Swanson 2008; Carrino-Kyker et al. 2012) and further alter resources for macroinvertebrates in urban pools. A better evaluation of agricultural impacts may involve comparing pool communities occurring in both of these terrestrial habitat contexts.
Although we were able to explain patterns of macroinvertebrate family richness and
Shannon diversity, no principal components significantly accounted for the variation in total macroinvertebrate abundance. Numerous studies have experienced difficulties finding relationships between temporary wetland macroinvertebrates and their environment (Wissinger et al. 1999; Battle and Golladay 2001; Tangen et al. 2003; Batzer et al. 2004; Studinski and
Grubbs 2007; Shieh and Chi 2010). The most successful temporary wetland macroinvertebrates may be habitat generalists that are tolerant to a wide range of environmental variation (Williams ! 25!
1996; Batzer et al. 2004). Our water chemistry measurements support this, as NO3, NH4, total N,
DOC, PO4, total P, and phenolics can vary by one-to-two orders of magnitude without having any detectable effects on macroinvertebrate abundance. Any variation in presence or absence of taxa may result from random colonization events of wetland habitats (Batzer and Sion 1999;
Batzer et al. 2004). Accordingly, weak relationships between macroinvertebrates and their environment would be expected (Batzer et al. 2004). Additionally, few numeric responses may be expected, as they do not account for size differences among different taxa (e.g., odonates and chaoborids). Thus, a more useful metric for future studies may be total macroinvertebrate biomass.
We observed some weak, but potentially meaningful, relationships between litter inputs and macroinvertebrate diversity. Litter depth, average litter mass, A. rubrum mass, and litter fragment mass had moderate loadings on our PCs and may indirectly impact macroinvertebrates through their effects on habitat quality and producer communities. Leaf detritus is an important food source for some macroinvertebrates in vernal pools (Colburn 2004), but decomposition reduces oxygen in stagnant waters (Magnusson and Williams 2006) and soluble toxins present in litter could stress macroinvertebrates (Batzer and Palik 2007). Compositional shifts in leaf litter can alter bacterial and primary producer composition in temporary wetlands and generate bottom-up affects in wetland food webs (Rubbo and Kiesecker 2004). Although water chemistry did not affect macroinvertebrate diversity, it did load strongly with chlorophyll a content and may lend support to the relationship between litter and producers. Future work should examine how leaf litter and other factors, such as canopy cover, interact to affect wetland producers and the macroinvertebrate communities present in these habitats. In conclusion, many wetland taxa are known to respond to canopy, but our study is unique as it demonstrates that canopy cover, as ! 26! opposed to hydroperiod and water chemistry, is strongly associated with vernal pool macroinvertebrate diversity. Hydroperiod may act as a selective sieve for determining which organisms can live in a vernal pool, but canopy affects pool productivity and subsequently the diversity of organisms sustained by a particular wetland. Although allochthonous carbon is considered to be the base of vernal pool food webs, our work indicates that autochthonous carbon also contributes to bottom-up productivity in these wetlands.
ACKNOWLEDGEMENTS
This project would not have been possible with out field assistance from D. Bonner and K.
Michalson. We also thank G. Haase (The Nature Conservancy), D. Schmenk (Maumee State
Forest), C. Smalley (Wood County Parks), and K. Menard (Toledo Metroparks) for field site access. Financial support was provided to M. Plenzler through a BGSU graduate assistantship.
R. Walsh, J. Meier, J. Shimola, L. Blair, P. Arnold, A. Dietz, J. Sublett, J. Miner, A. Downing,
K. Root, and E. Gomezdelcampo aided earlier versions of this manuscript. R.M. McKay provided equipment and supplies for fluorometic analyses.
! 27!
TABLES
Table 1. The fifteen selected vernal pools, the protected lands they occur on, and their latitude and longitude coordinates.
Pool Name Land Management Group Latitude Longitude Ohio BGSU Steidtmann Woods 41°18'28.25"N 83°39'10.47"W Long Pool BGSU Steidtmann Woods 41°18'25.57"N 83°39'31.72"W Rudolph Wood Co. Parks 41°18'15.66"N 83°39'49.04"W Hite Maumee State Forest 41°30'25.74"N 83°52'46.16"W Horsetrail Maumee State Forest 41°32'17.43"N 83°51'51.46"W CoRd4 Maumee State Forest 41°31'34.62"N 83°56'16.86"W Hahn TNC Kitty Todd 41°37'38.00"N 83°47'30.33"W Nelson TNC Kitty Todd 41°37'31.91"N 83°47'36.66"W Horseshoe TNC Kitty Todd 41°37'22.17"N 83°47'18.65"W Pig Farm TNC Kitty Todd 41°37'15.14"N 83°47'45.61"W CuJo TNC Kitty Todd 41°36'28.31"N 83°48'16.71"W Turtle Prairie TNC Kitty Todd 41°36'59.38"N 83°48'30.77"W Sandpits Toledo Metroparks 41°33'19.25"N 83°50'6.86"W Wood Frog Toledo Metroparks 41°33'16.86"N 83°50'5.19"W
! 28!
Table 2. Means and ranges of water chemistry measurements (a) and mean area and range of land cover types present within a 1 km radius of each vernal pool (b). Untransformed water quality data are presented to display the high variability of some measurements. The land cover information was collected from the 2003 Ohio EPA land cover survey. a. Water chemistry Mean Range pH 6.2 5.89 – 6.82 Conductivity (µS/cm) 208 54 – 528 NO3 (µg/L) 37.5 7.3 – 102.2 NH4 (µg/L) 136.6 15.8 – 283.6 Total N (mg/L) 1.5 0.2 – 5.6 DOC (mg/L) 28.2 5.4 – 78.4 PO4 (µg/L) 181.3 55.3 – 415.4 Total P (µg/L) 281.9 76.1 – 663.4 Phenolics (mg/L) 5.7 0 – 16.4 b. Land Cover Mean (km2) Range (km2) Deciduous forest 0.47 0 – 1.89 Evergreen forest 0.10 0 – 0.60 Cropland 1.24 0.12 – 2.24 Open water 0.01 0 – 0.04 Residential 0.06 0 – 0.49 Commercial 0.05 0 – 0.28 Bare/Mines 0.004 0 – 0.05 Herb. wetland 0.02 0 – 0.24 Woody wetland 1.16 0.47 – 1.29
! 29!
Table 3. Macroinvertebrate functional feeding groups, mean macroinvertebrate abundance per pool, proportion of pools each family occurred in, and proportion of total macroinvertebrates per family.
Class Family Functional Group Mean/Pool Occurrence Prop. Inverts Bivalvia Sphaeriidae Filterer 85.4 0.53 31.7 Clitellata Glossiiphonidae Predator 0.5 0.13 0.2 Clitellata Naididae Grazer 0.7 0.07 0.2 Insecta Aeshnidae Predator 0.4 0.13 0.2 Insecta Belostomatidae Predator 0.1 0.07 0.1 Insecta Chaoboridae Predator 45.9 0.53 17.1 Insecta Chironomidae Grazer 37.0 0.93 13.7 Insecta Corixidae Collector 3.2 0.27 1.2 Insecta Culicidae Filterer 2.1 0.66 0.8 Insecta Dytiscidae Predator 5.3 0.93 1.9 Insecta Gerridae Predator 0.4 0.20 0.1 Insecta Haliplidae Shredder 0.7 0.13 0.2 Insecta Hydrophilidae Predator 1.3 0.33 0.5 Insecta Lestidae Predator 10.4 0.73 3.9 Insecta Libellulidae Predator 15.1 0.66 5.6 Insecta Limnephilidae Shredder 9.1 0.80 3.4 Insecta Notonectidae Predator 2.4 0.27 0.9 Insecta Syrphidae Collector 0.2 0.07 0.1 Insecta Tipulidae Shredder 0.2 0.07 0.1 Malacostraca Asellidae Shredder 6.7 0.60 2.5 Malacostraca Astacidae Collector 0.4 0.40 0.1 Mollusca Lymnaeidae Grazer 0.7 0.33 0.2 Mollusca Physidae Grazer 27.8 0.60 10.3 Mollusca Planorbidae Grazer 13.4 0.80 5.0 ! 30!
Table 4. Eigenvalues, variance explained, and cumulative variance of the four principal components (PCs) retained for multiple linear regression analysis.
PC1 PC2 PC3 PC4 Eigenvalue 9.14 8.00 4.18 3.43 Var. Explained 0.24 0.21 0.11 0.09 Cumulative Var. 0.24 0.45 0.56 0.65
! 31!
FIGURES
" 5
4
3
2
1
0 SQRT(Family Richness) SQRT(Family -8 -6 -4 -2 0 2 4 6 Lower DO, temp., algal PC2 genera, vegetation density, Greater canopy cover and woody wetlands (GIS) and open water (GIS)
+ (!
'!
&!
%!
$! SQRT(Family Richness) SQRT(Family #! )$*(! )$! )#*(! #! #*(! $! $*(! Logit(Canopy Cover)
Figure 1a,b. (a) Macroinvertebrate family richness decreases as PC2 increases (p = 0.0007, r2 =
0.57; y = -0.16 X PC2 + 3.33). As PC2 increased, canopy cover and open water increased, while dissolved oxygen, water temperature, algal genera, vegetation density, and woody wetlands decreased. (b) As canopy cover increased, macroinvertebrate family richness decreased (p =
0.0006, r2 = 0.58; Richness = 12.56 – 4.22 X Canopy Cover).
! 32!
" 3
2
1 Shannon Diversity 0 -8 -6 -4 -2 0 2 4 6 Lower DO, temp., algal PC2 genera, vegetation density, Greater canopy cover and woody wetlands (GIS) and open water (GIS)
+ &!
%!
$! Shannon Diversity
#! )$*(! )$! )#*(! #! #*(! $! $*(! Logit(Canopy Cover)
Figure 2a,b. (a) Shannon diversity decreases as PC2 increases (p = 0.006, r2 = 0.41; y = -0.11 X
PC2 + 1.43). As PC2 increased, canopy cover and open water increased, while dissolved oxygen, water temperature, algal genera, vegetation density, and woody wetlands decreased. (b)
Shannon diversity also decreased as canopy cover decreased (p = 0.016; r2 = 0.32; Shannon =
1.54 – 0.41 X Canopy Cover). ! 33!
" 4
3
2
1
0 SQRT(Predator Families) Families) SQRT(Predator -8 -6 -4 -2 0 2 4 6 Lower DO, temp., algal PC2 genera, vegetation density, Greater canopy cover and woody wetlands (GIS) and open water (GIS)
+ '!
&!
%!
$!
SQRT(Predator Families) Families) SQRT(Predator #! )$*(! )$! )#*(! #! #*(! $! $*(! Logit (Canopy Cover)
Figure 3a,b. (a) Predator family richness decreases as PC2 increases (p = 0.0002, r2 = 0.63; y = -
0.14 X PC2 + 1.99). As PC2 increased, canopy cover and open water increased, while dissolved oxygen, water temperature, algal genera, vegetation density, and woody wetlands decreased. (b)
Predator families decreased as canopy cover increased (p < 0.0001, r2 = 0.76; Predators = 2.16 –
0.61 X Canopy Cover).
! 34!
APPENDIX 1A
Means (± 1 SE) of variables that loaded strongly on principal component 2, water chemistry measurements, and land cover types within 1 km2 of each vernal pool.
Variables with strong loadings on PC2 Pool Canopy Pool area Algal Temp. (°C) DO (mg/L) # Plant Veg. Name cover (%) (m2) genera morphotypes density (#/mL) (#/m2) Ohio 90.9 (2.5) 1647 4 18.9 (0.8) 1.9 (0.7) 6 2.7 Long Pool 88.8 (6.5) 4500 4 17.7 (0.2) 2.5 (1.3) 14 16.4 Rudolph 86.2 (2.2) 740 5 18 (0.2) 1.5 (1.0) 10 3.7 Hite 93.8 (1.0) 2268 7 14.5 (0.1) 3.9 (0.2) 8 12.8 Horsetrail 91.9 (2.2) 1849 6 14.1 (0.1) 1.2 (1.0) 5 9.2 CoRd4 89.3 (4.0) 975 8 19.3 (0.4) 2.0 (1.9) 9 11.7 Hahn 38.5 (5.0) 1508.8 12 27.2 (1.3) 3.4 (0.7) 19 21.7 Nelson 34.6 (3.4) 2988 13 28.6 (1.0) 5.0 (0.8) 25 26 Lone Dune 35.6 (2.2) 2112 8 20.8 (0.1) 1.8 (1.0) 20 27.7 Horse- shoe 12.5 (0.8) 2525 17 25.8 (1.0) 3.4 (0.2) 14 22.2 Pig Farm 10.9 (0.6) 2866.7 20 21.1 (0.1) 6.0 (0.1) 25 82.5 CuJo 68 (8.0) 3010 8 18.6 (1.1) 3.4 (1.1) 25 29 Turtle Prairie 46.5 (6.5) 2600 12 16.9 (0.3) 1.5 (2.0) 19 12.2 Sandpits 49.6 (3.2) 2614 10 16.5 (0.2) 3.7 (1.4) 12 18.8 Wood Frog 93 (1.0) 555 10 15.6 (0.1) 1.3 (1.1) 5 0.5
! 35!
Water chemistry measurements Pool pH Conductivity NO3 NH4 Total N DOC PO4 Total P Phenolics Name (µS/cm) (µg/L) (µg/L) (mg/L) (mg/L) (µg/L) (mg/L) (mg/L) Ohio 6.32 (0.1) 412 (24.2) 75 446 1.976 48.02 275.1 500 9.9 Long Pool 6.25 (0.1) 172.1 (6.6) 95.4 128.6 1.758 37.82 162 218.1 7.8 Rudolph 6.45 (0.2) 171.3 (1.5) 10.7 58.4 0.64 15.37 96.5 159.9 1.5 Hite 6.17 (0.2) 125.4 (1.2) 13.9 150.2 1.236 28.98 415.4 586.2 4.7 Horsetrail 5.89 (0.3) 93.9 (6.4) 27 199.6 1.656 29.72 296.7 418.6 7.9 CoRd4 5.97 (0.1) 123.2 (0.1) 50.7 113.3 1.581 37.86 197.5 308.9 7.2 Hahn 6.32 (0.01) 153.9 (2.2) 15.6 138.2 1.89 33.04 155.9 252.1 6.1 Nelson 6.42 (0.01) 149.7 (21.4) 22.1 131.8 0.864 19.7 74.2 125.9 1.6 Lone 4.94 Dune (0.01) 79.7 (2.1) 45.2 283.6 5.568 78.39 368.7 663.4 16.4 Horseshoe 6.02 (0.1) 54 (0.1) 57.7 128.9 1.248 10.21 334.5 464.1 0.81 Pig Farm 6.5 (0.1) 135.6 (1.0) 11.4 15.8 0.226 5.38 76.4 107.8 0 CuJo 6.57 (0.1) 408.3 (15.3) 102.2 92 1.362 25.09 59.9 114.9 3.9 Turtle 6.82 Prairie (0.7) 528.3 (31.5) 19.2 110.2 1.464 36.66 84.2 142.4 6 Sandpits 6.19 (0.1) 109.3 (0.3) 9.1 20.6 0.37 7.04 55.3 76.1 0 Wood 6.44 Frog (0.2) 400.3 (2.1) 7.3 32.5 0.358 9.24 67.2 91.1 0.21
! 36!
Amount of each land cover type (km2) within a 1-km radius of each vernal pool Pool Deciduous Evergreen Crop Open Reside Comme Bare Herb. Woody Name forest forest land water -ntial -rcial wetland wetlands Ohio 0.0606 0 2.2434 0.0260 0 0.2815 0 0 0.5297 Long Pool 0.1781 0 2.2116 0.0416 0.0466 0.0819 0 0 0.5814 Rudolph 0.3102 0 1.8605 0.0103 0.4894 0 0 0 0.4706 Hite 1.8277 0.1998 0.1819 0.0504 0.1683 0 0 0 0.7130 Horsetrail 1.0656 0.6075 0.8252 0 0.0106 0 0 0 0.6322 CoRd4 0.0117 0.2970 1.4612 0.0189 0.0567 0 0 0 1.2957 Hahn 0 0 1.2119 0 0 0 0 0 1.9293 Nelson 0 0 1.1712 0 0 0 0 0 1.9701 Lone 0.2492 Dune 0 0 1.3511 0 0.1111 9 0.0513 0.0653 1.3132 Horseshoe 0 0 1.4886 0 0 0 0 0 1.6526 Pig Farm 0 0 1.5712 0 0 0 0 0 1.5670 CuJo 0 0 1.6314 0 0.0264 0.1219 0.0133 0.00004 1.3481 Turtle Prairie 0 0 1.0770 0 0.0366 0.0240 0 0.2430 1.7605 Sandpits 1.7770 0.2279 0.2988 0.0297 0 0 0 0 0.8078 Wood Frog 1.8863 0.2423 0.1216 0.0297 0 0 0 0 0.86116
! 37!
APPENDIX 1B
Thirty distinct algal genera were present in the pools. Eunotia lunaris and filamentous green algae were the most common algae and comprised 42 and 30 % of all observed algae. The following table lists all 30 genera, the type of each algae (diatom, chrysophyte, green, filamentous green, or blue-green) and the mean number of cells/mL per pool.
Algae genera Type Mean number of cells/mL/pool Amphora Diatom 0.93 Asterionella Diatom 0.73 Caloneis Diatom 0.07 Cocconeis Diatom 0.47 Cyclotella Diatom 1.6 Cymbella Diatom 0.13 Diatoma Diatom 0.13 Eunotia lunaris Diatom 109.3 Fragilaria Diatom 7.87 Gomphonema Diatom 7.13 Navicula Diatom 12.2 Nitzschia Diatom 1.27 Pinnularia Diatom 0.27 Synedra Diatom 6.47 Tabellaria Diatom 0.07 Unknown genera Diatom 0.4 Dinobryon Chrysophyte 0.13 Closterium Green 3.06 Cosmarium Green 0.07 Desmidium Green 0.73 Staurastrum Green 0.47 Volvox Green 1.0 Chlorococcum Green 0.40 Cladophora Fil. Green 30.4 Microspora Fil. Green 30.3 Oedogonium Fil. Green 5.0 Ulothrix Fil. Green 0.93 Uronema Fil. Green 10.8 Oscillatoria Blue-green 0.27 Unknown genera Blue-green 0.07
! 38!
APPENDIX 1C
The weights (eigenvalues – “Ev”) and loadings (correlations – “PC”) of our environmental variables on the four principal components. Loading scores marked in bold indicate strong correlations (> 0.60) with corresponding principal component. Moderate loading scores (0.40 – 0.60) are underlined. Thirty-five variables were at least moderately correlated (0.4 – 0.6) with one of the four PCs. Of those variables, pool area, litter depth, total litter mass, P. deltoides mass, A. rubrum mass, Q. alba mass, soil organic content, and the deciduous, evergreen, cropland, residential, and herbaceous wetland land covers did not load strongly (correlation > 0.60) on the four PCs. Acer saccharinum mass, litter fragment mass, algal density, and zooplankton density were the only variables without moderate or strong loadings on the four retained PCs.
Variable Ev1 Ev2 Ev3 Ev4 PC1 PC2 PC3 PC4 Area 0.057 -0.159 -0.059 -0.080 0.177 -0.450 -0.121 -0.151
Depth -0.216 0.074 -0.129 0.126 -0.669 0.210 -0.265 0.237
Logit (Canopy -0.009 0.313 0.000 -0.166 -0.027 0.888 0.001 -0.312 Cover) Hydroperiod -0.229 0.066 -0.127 0.180 -0.710 0.186 -0.263 0.338
Temperature 0.073 -0.236 0.043 0.175 0.226 -0.670 0.089 0.330
DO -0.074 -0.260 -0.105 0.065 -0.229 -0.737 -0.215 0.122
Litter depth -0.151 0.195 0.089 -0.016 -0.468 0.552 0.184 -0.030
pH -0.178 -0.077 0.130 -0.341 -0.552 -0.219 0.268 -0.641
Conductivity -0.015 0.027 0.232 -0.394 -0.047 0.076 0.478 -0.741
Chl a content 0.241 0.053 0.091 0.226 0.746 0.151 0.188 0.425
ln(DOC) 0.275 0.117 0.043 -0.126 0.851 0.333 0.088 -0.237
ln(NO3) 0.235 -0.007 -0.051 -0.082 0.729 -0.021 -0.104 -0.154
ln(NH4) 0.284 0.097 -0.051 -0.072 0.881 0.275 -0.106 -0.135
ln(Total N) 0.299 0.074 -0.012 -0.010 0.926 0.211 -0.024 -0.019
ln(PO4) 0.213 0.111 -0.254 0.104 0.661 0.315 -0.522 0.195
ln(Total P) 0.244 0.103 -0.207 0.087 0.756 0.292 -0.427 0.164
ln(Phenolics) 0.278 0.113 -0.002 -0.145 0.862 0.320 -0.005 -0.273
! 39!
Variable Ev1 Ev2 Ev3 Ev4 PC1 PC2 PC3 PC4 Litter mass -0.027 0.167 0.248 0.256 -0.084 0.475 0.511 0.482
Q. palustris -0.048 0.095 0.406 0.196 -0.150 0.270 0.836 0.368
A. saccharinum 0.090 0.091 -0.142 -0.204 0.280 0.257 -0.293 -0.384
P. deltoides -0.162 0.106 0.281 0.204 -0.502 0.299 0.578 0.383
A. rubrum -0.138 0.202 -0.084 0.043 -0.426 0.573 -0.173 0.080
Q. alba 0.167 0.020 -0.118 0.265 0.517 0.057 -0.243 0.497
Litter fragments 0.049 0.111 0.053 0.207 0.153 0.315 0.109 0.389
Logit (Org. Mat.) 0.091 -0.051 0.266 0.084 0.282 -0.146 0.548 0.158
Total plant types 0.055 -0.305 0.131 0.005 0.171 -0.865 0.271 0.010
Veg. Density -0.025 -0.289 -0.068 0.060 -0.076 -0.819 -0.140 0.113
Algal density 0.073 -0.086 -0.178 0.125 0.227 -0.244 -0.366 0.236
Algal genera -0.103 -0.301 -0.099 0.073 -0.318 -0.853 -0.203 0.138
Zooplankton 0.049 -0.088 -0.174 0.122 0.151 -0.248 -0.358 0.230
Deciduous forest -0.188 0.199 -0.143 0.049 -0.582 0.563 -0.294 0.093
Evergreen forest -0.077 0.179 -0.285 -0.040 -0.239 0.507 -0.587 -0.076
Cropland 0.167 -0.073 0.166 -0.042 0.517 -0.206 0.341 -0.078
Open water -0.084 0.229 -0.092 -0.033 -0.261 0.650 -0.189 -0.062
Residential -0.005 0.130 0.209 0.150 -0.015 0.368 0.430 0.283
Commercial 0.231 0.057 0.147 -0.004 0.716 0.162 0.303 -0.007
Bare/Mines 0.199 -0.010 0.103 0.228 0.617 -0.029 0.213 0.429
Herb. Wetland 0.055 -0.063 0.148 -0.248 0.172 -0.178 0.306 -0.467
Woody wetland 0.034 -0.290 -0.003 -0.015 0.106 -0.823 -0.007 -0.028
! 40!
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CHAPTER II
CANOPY COVER MEDIATES BOTTOM-UP PRODUCTIVITY AND
MACROINVERTEBRATE COMMUNITIES IN TEMPORARY WETLANDS
ABSTRACT
Gradients in canopy cover influence the abundance, diversity, and performance of several organisms in temporary forested wetlands and other small aquatic habitats. Generally, these factors are greater in open canopy wetlands, where primary production, dissolved oxygen, and water temperature are higher. While many studies of canopy cover focus on amphibians, macroinvertebrate communities are also affected by canopy and represent important predators, prey, and nutrient cyclers in aquatic and terrestrial habitats. As anthropogenic and natural processes alter forest cover, understanding the mechanisms of how organisms respond to canopy gradients is important for predicting how landscape-level changes will affect aquatic systems.
We designed a mesocosm experiment to assess how macroinvertebrate assemblages respond to treatments of low (30%), intermediate (55%), and high (73%) shade and predicted that shade would alter resource quality for macroinvertebrates and macroinvertebrate abundance and diversity would be greatest in low shade. We quantified macroinvertebrate abundance, family richness, and Shannon diversity, chlorophyll a content, dissolved oxygen, conductivity, pH, and water temperature every two weeks. Additionally, we determined phyto- and zooplankton abundances and DOC, NO3, NH3, and PO4 concentrations in the mesocosms, as well as examined whether invertebrate community structure differed among the shade treatments. We found that the low shade mesocosms sustained the greatest macroinvertebrate abundance and diversity, while differences among canopy treatments were likely mediated by primary productivity. Conductivity and water temperature were also highest in the low shade treatment, ! 48! and may represent secondary effects of open canopy habitats. After eight weeks, predators were more abundant in the low shade mesocosms, but invertebrate community structure did not significantly differ among treatments. DOC was highest in the low and intermediate mesocosms, but we detected no other effects of shade on the other nutrients, pH, or DO. Our study is the first to experimentally examine how canopy affects a diverse assemblage of wetland macroinvertebrates. Our data suggests reforestation may negatively impact the abundance and diversity of many wetland macroinvertebrates. Open canopy pools must be maintained to sustain regional aquatic species diversity; however, conservation efforts should also protect closed canopy wetlands, as they are beneficial for other species and wetland metacommunities.
INTRODUCTION
Anthropogenic and natural processes are changing global forest cover and subsequently altering species assemblages embedded in forested landscapes (Ellison et al. 2005).
Deforestation is globally pervasive and eliminates habitats (Groom and Vynne 2006; Hocking and Semlitsch 2008), but many areas (e.g., the 48 contiguous US states) are in a state of net reforestation (Kauppi et al. 2006) and represent additional forms of habitat alteration. These changes affect aquatic communities embedded in forested landscapes, as they are linked to terrestrial habitats by the exchange of energy and organisms (Skelly et al. 2002). In temporary wetlands and other small aquatic habitats, this is commonly observed through organisms that have initial life stages in the water, but eventually become components of terrestrial food webs
(Colburn 2004). The energy available to these aquatic communities is ultimately limited by the surrounding habitat, which regulates the allochthonous and autochthonous carbon for the system
(Colburn 2004; Rubbo and Kiesecker 2004; Williams et al. 2008). Although habitat gradients are long known to structure ecological communities (Hairston 1949; Whittaker 1956; Johnson ! 49! and Hering 2009), only recently have studies examined how terrestrial habitat gradients affect aquatic communities in small habitats (Werner et al. 2007; Van Buskirk 2011; Plenzler and
Michaels, Chapter one). In particular, the impacts of forest change on canopy cover are of increasing interest in wetland ecology, as canopy limits solar radiation and correlates with various biotic and abiotic factors in the underlying aquatic habitat (Werner and Glennemeier
1999; Batzer et al. 2004; Williams et al. 2008; Binckley and Resetarits 2009).
In forested wetlands, several aquatic organisms respond to canopy gradients, including beetles (Binckley and Resetarits 2007), snails (Palik et al. 2001; Hoverman et al. 2011), and amphibians (Werner et al. 2007). Some species may be open-canopy specialists (Van Buskirk
2005); however, organisms generally increase in abundance, diversity, and performance as canopy cover decreases (Werner et al. 2007; Binckley and Resetarits 2009). Most studies of canopy gradients have been directed towards amphibians (Skelly et al. 1999; Werner and
Glennemeier 1999; Skelly et al. 2002; Werner et al. 2007; Williams et al. 2008; Van Buskirk
2011), but the documented macroinvertebrate responses imply that their community structure and interactions will change, as well (e.g., Williams et al. 2008). Thus, identifying the canopy- related mechanisms that affect macroinvertebrate communities is necessary for understanding how they will respond to subsequent alterations in forest cover around wetlands and other aquatic habitats (Binckley and Resetarits 2007; Williams et al. 2008).
Wetlands with closed canopies have less available sunlight, shortened hydroperiods through increased evapotranspiration, cooler water temperatures, low dissolved oxygen, reduced reflectance, altered algal assemblages, and less herbaceous vegetation (Werner and Glennemeier
1999; Bernáth et al. 2002; Skelly et al. 2002; Colburn 2004; Schiesari 2006; Werner et al. 2007).
Thus, wetland macroinvertebrates may use canopy cover as an indicator of habitat quality to ! 50! avoid these unfavorable conditions (Binckley and Resetarits 2007). Low quality, closed-canopy habitats may only be inhabited when open-canopy wetlands become saturated with other organisms, are numerically rare in a landscape or isolated from source pools in larger metacommunities, possess over-exploitative predators, or are of otherwise degraded quality
(Colburn 2004; Binckley and Resetarits 2005; Binckley and Resetarits 2007; Brodin et al. 2006).
Alternatively, perhaps only a subset of macroinvertebrates regards closed-canopy wetlands as habitat (Binckley and Resetarits 2007), as few can adequately tolerate the associated habitat qualities (Van Buskirk 2011).
Vernal pools are temporary wetlands that host over 100 amphibian and invertebrate species because their seasonal drying prevents the establishment of predatory fish populations
(Colburn 2004). These wetlands lack formal protection under the Clean Water Act (CWA) and are of conservation concern because of the resident amphibians (Colburn 2004; Mahaney and
Klemens 2008). However, the emphasis on amphibians often eclipses the macroinvertebrates, many of which are important predators, prey, and nutrient cyclers in aquatic and terrestrial habitats (Colburn 2004). As forest canopy changes from anthropogenic and natural processes, macroinvertebrates are likely to respond to alterations in resource quality. In our previous field study (Plenzler and Michaels chapter one), we determined that reduced canopy cover was associated with increased macroinvertebrate family richness and Shannon diversity in vernal pools. Communities were likely responding to bottom-up effects, as canopy cover, vegetation density, algal abundance, dissolved oxygen, and temperature were related to measures of macroinvertebrate diversity. Understanding the mechanisms of how canopy affects macroinvertebrate diversity is crucial for predicting vernal pool community responses to changes in terrestrial habitat. Therefore, we designed a mesocosm experiment to assess how ! 51! macroinvertebrate assemblages respond to variation in canopy cover (simulated with shade cloth). We predicted that greater shade cover would reduce productivity and lower resource quality for macroinvertebrates. As a result, macroinvertebrate abundance and diversity would be lowest in high shade treatments.
METHODS
We tested the effects of canopy cover on vernal pool macroinvertebrates using an outdoor mesocosm experiment conducted at the Ecology/Ethology Research Station at Bowling Green
State University, Bowling Green, Ohio, USA. Vernal pools were simulated with 416 L elliptical
(1.35 m length X 0.91 m width, Tuff Stuff model KMT 100) cattle watering tanks that were assigned to a high (73 %), intermediate (55 %), or low (30 %) shade level with four replicates per treatment. Canopy shading was manipulated with shade cloth (73, 55, or 30 %) stitched around 2.54 cm hose line, which served as a lid for the mesocosms to prevent unwanted colonization or tampering from other organisms. These shade levels were chosen because they best approximated the range of canopy values observed in Plenzler and Michaels (Chapter one), but were also consistent with the shade treatments used by Williams et al. (2008) and Earl et al.
(2011) in studies of amphibian responses to canopy and leaf litter in forested wetlands.
Each mesocosm was filled with 379 L of municipal water on June 7 and the water was allowed to dechlorinate for 48 hours. We maintained constant water levels weekly until July 15 by adding dechlorinated water to a marked fill line, but then water was allowed to evaporate from the mesocosms to simulate the seasonal drawdowns of real vernal pools. Mud was collected from five forested (> 60 % canopy cover, as determined by a spherical densiometer) and five non-forested (< 40 % canopy cover) local vernal pools and homogenized.
Approximately 11 L of this mud was mixed into each mesocosm on June 9 to establish microbial ! 52! communities and add Oligochaete decomposers, as well as create a 1 cm thick substrate at the bottom of each tank. Water was also collected from these vernal pools, filtered through a 60-!m mesh net, and homogenized. Each mesocosm then received 4 L of the homogenized water to supplement microbes into the system. Fallen and field overwintered pin and red oak (Quercus palustris and Q. rubra) leaves were collected in May 2010 near the margins of forested vernal pools. The litter was allowed to dry indoors for two weeks, mixed, and then 180 g (the mass of litter expected to cover the benthic area of each tank determined by the average amount of litter dry mass/m2 from Plenzler and Michaels, chapter one) was added to each tank on June 10 to simulate the leaf litter base in vernal pools. This litter mixture was chosen because pin oak was the dominant litter by mass in Plenzler and Michaels (Chapter one). Red oak, while less common in vernal pools, was less than ten percent of the litter mass and was included in the litter mixture because it was highly fragmented and unfeasible to remove from the pin oak. Phyto- and zooplankton were collected with a 60-!m plankton net from five forested and five non- forested vernal pools. The samples were homogenized in a large tub and 1.5 L was added to each mesocosm on June 11 to establish plankton communities.
Macroinvertebrates were introduced to the tanks on June 18, seven days after plankton addition. Macroinvertebrates were collected from the five forested and five non-forested vernal pools, identified to family, and evenly distributed to each mesocosm (Table 1). All macroinvertebrates were collected between June 14 and 18 and held in 19 L aerated fish tanks until they were placed in the mesocosms. The assemblages included the dominant macroinvertebrate families observed in Plenzler and Michaels (Chapter one), but also simulated vernal pool community structure with feeding guilds of grazers, shredders, filter feeders, and predators (Colburn 2004). Initial mesocosm macroinvertebrate abundance was determined from ! 53! the average density of individuals per family of sampled area from Plenzler and Michaels
(Chapter one) and used to estimate the number that would be expected to occupy the volume of a tank. We were generally able to maintain macroinvertebrate abundances within a factor of two from our previous study, but were unable to capture enough Chironomidae. Additionally, we were unable to capture enough Physidae, but compensated by adding more Planorbidae.
Belostomatidae, although infrequent in our 2009 survey, are more common in summer vernal pools (Colburn 2004), and so were added to better replicate the macroinvertebrate communities corresponding to the season of our experiment. In total, each mesocosm contained 209 macroinvertebrates from 12 taxonomic families. We did not control for diversity within families, as it was unfeasible to collect macroinvertebrates from single genera or species.
Although this may have obscured lower taxonomic diversity, similar patterns in species richness, composition, and diversity are often observed at higher taxonomic (e.g., family) levels (Schmidt-
Kloiber and Nijboer 2004). Similarly, macroinvertebrate abundances and assemblages vary among natural vernal pools and only a subset of all pools contain our experimental composition and density. Mesocosm experiments can test how organisms respond to environmental gradients and generate predictions applicable to larger, natural habitats (Resetarits and Fauth 1998). Thus, while our experimental communities are not typical of all vernal pools, we can examine patterns of macroinvertebrate responses to different amounts of shade, as well as the mechanisms affecting divergence in community structure and diversity.
The mesocosms were surveyed on June 25 to confirm that the macroinvertebrates had survived transplantation into the experimental treatments. We subsequently surveyed macroinvertebrate communities on July 8 and 22 and August 10 by sweeping a 0.5 mm mesh dipnet (11 cm diameter) through the sediment, leaf litter, and water column along the sides and ! 54! long central axis of the mesocosm. The dipnet contents were emptied into a 10 L water-filled tub after each sweep and sorted for captured macroinvertebrates. We identified all macroinvertebrates to family and recorded total macroinvertebrate abundance, family richness, and the number of individuals per functional feeding group and calculated Shannon diversity
(based on family level data) for each tank. All captured macroinvertebrates were then returned to the mesocosm.
After sixty days in the mesocosms, phyto- and zooplankton samples were collected on
August 10. A 60-!m mesh plankton net (12.7 cm diameter) was gently inserted into the bottom of the mesocosm to minimize water disturbance and was then pulled to the top of the water column to funnel plankton into a 30 mL glass bottle. The plankton net filtered approximately 10
L of water. Plankton samples were preserved in 5% formalin. We assessed phytoplankton abundance by inverting the sample three times, placing a 0.5 mL aliquot on a gridded microscope slide, and counting the number of algal cells at 400X magnification. We identified phytoplankton to groups of either periphyton or green algae. Filamentous and multicellular algae were considered to be one individual. We used a 15 mL zooplankton wheel to initially quantify cladocera, cyclopoid copepods, and naupliius abundance. The remaining plankton sample was inverted three times and 10 mL was added to the wheel. The zooplankton types were counted at
15X magnification; however, we combined the data into a single measurement of zooplankton abundance because of low individual sample size among the zooplankton types.
We measured water quality within 24 hrs of surveying the macroinvertebrate communities. We recorded water conductivity, dissolved oxygen, pH, and temperature on July 9 and 23 and August 11 between 11:00 AM and 1:00 PM with a YSI 556 MPS (YSI, Inc., Yellow
Springs, Ohio, USA). Three replicate measurements were taken 10 cm below the water surface ! 55! along the long edges and center of the tank to generate average values for the mesocosm. We also collected 475 mL surface water samples for chlorophyll a extractions to estimate phytoplankton biomass. The water samples were covered with foil and stored at 4 °C. All chlorophyll extractions were performed using the Welschmeyer (1994) method within 24 hours of sample collection. Additional 475 mL water samples were collected on August 11 and stored at -15 °C for later analysis of dissolved organic carbon (DOC), nitrate (NO3), ammonia (NH3), and phosphate (PO4). DOC was analyzed using the methods described by Collier (1987). NO3,
NH3, and PO4 were analyzed with a SEAL AQ2 Discrete Analyzer (SEAL Analytical, ltd.) using
USEPA methods 127-A rev. 7, 103-A rev. 7, and 118-A rev. 5, respectively.
Statistical Analyses
Macroinvertebrate abundance and family richness were log transformed to reduce heteroscedasticity and meet assumptions of normality for subsequent analysis (Zar 1999).
Because cladocera, cylclopoid copepod, and nauplius abundance were low across all treatments
(< 6 individuals/mL of water), the abundance of these three taxa were added together to instead give a single measure of zooplankton abundance. Zooplankton and algal abundance were square root transformed. Shannon diversity had unequal variances during the July 8 and 22 macroinvertebrate surveys and we were unable to transform the data to meet the assumptions of subsequent repeated measures ANOVA. Therefore, we only report the results for Shannon diversity for the end of the experiment and treat it as a single-measurement variable.
We used Bonferroni-adjusted repeated measures ANOVA (rmANOVA) to test for effects of shade on macroinvertebrate abundance, macroinvertebrate family richness, chlorophyll a content, pH, dissolved oxygen, water temperature, and conductivity. These data were divided into two groups prior to rmANOVA. Biotic variables consisted of macroinvertebrate abundance, ! 56! family richness, and chlorophyll a content. The Bonferroni-corrected statistical significance was
! = 0.017 for this group. Abiotic variables were pH, dissolved oxygen, temperature, and conductivity. Statistical significance was ! = 0.013 for these variables. We used Tukey post- hoc tests to compare means among shade treatments when significant differences were found with ANOVA.
We used two MANOVAs to test for shade effects on variables measured only at the end of the experiment (abundances of green algae, periphyton, and zooplankton, Shannon diversity,
DOC, NO3, NH3, and PO4). Similar to our repeated measures analyses, the data were divided into biotic and abiotic variables. The biotic MANOVA variables were the abundances of green algae, periphyton, and zooplankton, and Shannon diversity. The abiotic MANOVA variables were DOC, NO3, NH3, and PO4. After finding a significant MANOVA, univariate ANOVAs were performed on the biotic and abiotic variables with Tukey post-hoc tests for any significant univariate effects. Statistical significance for the biotic and abiotic ANOVAs were ! = 0.013.
All rmANOVAs and MANOVAs were conducted with JMP Statistical Discovery Software 10
(SAS Institute Inc., Cary, NC).
We conducted two multivariate analyses to detect changes in invertebrate functional feeding group composition (grazers, shredders, filter feeders, and predators) through time and by shade treatment. First, we used non-metric multidimensional scaling (nMDS) to assess how functional group composition diverged by treatment throughout the experiment. Data were averaged across all four replicates of a shade treatment at each time period. The nMDS was performed with a Bray-Curtis dissimilarity matrix on square root transformed functional group abundances using PAST (Hammer et al. 2001). The program places the data in two- or three- dimensional coordinate systems and uses eleven trials to converge on the solution with the ! 57! lowest stress value. In order to determine whether the differences in functional group composition in the nMDS were statistically significant, we used permutational multiple analysis of variance (PERMANOVA) described by Anderson (2001) and McArdle and Anderson (2001) to test for compositional differences among shade treatments. This test is considered to be widely applicable in biological sciences and can accommodate any dissimilarity measurement
(Quinn and Keough 2002). Our PERMANOVA was calculated on Bray-Curtis coefficients for functional group abundance (square root transformed), but unlike the nMDS, the data were not averaged across replicates within each sampling date to properly test for shade effects on functional group composition. Separate PERMANOVAs were conducted for the three samplings dates to determine whether the Bray-Curtis coefficients significantly differed among shade treatments. Upon finding a significant PERMANOVA, we used pairwise comparisons described by Anderson (2001), McArdle and Anderson (2001), and Quinn and McKeough (2002) to determine which shade treatments significantly differed in invertebrate composition. This analysis was also performed in PAST with Bonferroni adjusted ! = 0.017.!
RESULTS
The initial macroinvertebrate survey on June 25 confirmed that the macroinvertebrates survived transplantation into the mesocosms. We were able to recapture an average of 10 macroinvertebrate families per tank. All families were captured at least once per shade treatment. We found gastropod eggs and dytiscid larvae during the July 8 macroinvertebrate survey, suggesting that some organisms began reproducing in the mesocosms within two weeks.
The biotic rmANOVAs indicated that shade affected macroinvertebrate abundance (F2,9 =
13.8, p = 0.002) and family richness (F2,9 = 8.5, p = 0.008), but not phytoplankton chlorophyll a
(F2,9 = 3.1, p = 0.09). Macroinvertebrate abundance and family richness were greatest in the low ! 58!
shade treatment (abundance: F2,9 = 13.8, p = 0.002; richness: F2,9 = 8.5, p = 0.008; Fig. 1a,b).
Macroinvertebrate abundance and family richness changed over time, as invertebrate abundance increased and family richness decreased throughout the experiment (time effect p < 0.01 for both tests). Survey date did not significantly affect phytoplankton chlorophyll (p = 0.15), while changes in biotic variables over time were similar among shade treatments (shade treatment " time, p > 0.19) (Table 2; Appendix 2A)
Shade affected water conductivity (F2,9 = 203.5, p < 0.0001) and temperature (F2,9 = 9.4, p
= 0.006), but not pH (F2,9 = 2.2, p = 0.17) or DO (F2,9 = 0.6, p = 0.55) (abiotic rmANOVAs).
Conductivity (Fig. 2a) and temperature (Fig. 2b) were greatest under low shade (30 %) and lowest in the high shade (73 %) treatment. DO increased from 4.0 to 5.4 mg/L and conductivity increased from 332 to 368 !S/cm, while temperature decreased from 30 to 24 °C throughout the experiment (time effects, p < 0.0001 for all tests). Water pH increased from 6.3 to 6.5 during the experiment, but was not significant with the Bonferroni correction (p = 0.04). The interaction between shade and time was only significant for conductivity (F4,16 = 21.4, p < 0.0001), as conductivity had the greatest increase in the 30 % treatment (Fig. 3). The time by treatment interaction for DO was p = 0.05, but this relationship was non-significant after the Bonferroni correction. No significant time by shade interactions were found for pH and water temperature
(p > 0.09 for both tests) (Table 2; Appendix 2A)
Shade affected the biotic variables measured only at the end of our experiment
(MANOVA, Wilk’s lambda, F8,12 = 3.2, p = 0.04). Shannon diversity was significantly higher in the low and intermediate shade treatments (F2,9 = 18.5, p = 0.0007) (Fig. 4a), while green algae abundance was greater under high shade than either the low or intermediate treatments (F2,9 =
7.3, p = 0.013) (Fig. 4b). There were non-significant trends for periphyton abundance to be ! 59! greatest in the high shade treatments (55, 64, and 93 cells/mL for the low, intermediate, and high shade treatments, respectively, but p = 0.21), and for zooplankton abundance to be highest in the low shade treatments (12, 10, and 7 individuals/mL in the low, intermediate, and high canopies, respectively, p = 0.05) (Table 3; Appendix 2B).
Shade affected one of the abiotic variables measured at the end of our experiment
(MANOVA, Wilk’s lambda, F8,12 = 3.2, p = 0.03). DOC was higher under the low and intermediate canopies than the high shade treatment (F2,9 = 12.9, p = 0.002) (Fig. 5). NO3 (F2,9 =
2.5, p = 0.14), NH3 (F2,9 = 1.2, p = 0.35), and PO4 (F2,9 = 0.7, p = 0.54) did not significantly respond to shade treatments and did not exhibit any trends with increasing or decreasing shade cover (Table 3; Appendix 2B).
The optimal final solution for the nMDS on macroinvertebrate composition was two- dimensional and had a stress value of 6.57%, indicating a robust solution (Fig. 6). Axis 1 and 2 explained 77.7% and 22.3% of the variation in Bray-Curtis scores, respectively. These axes represent differences in invertebrate composition and points closer to each other in two- dimensional space are compositionally similar to each other. The shade treatments had begun to diverge by the July 8 survey, but were clustered together, indicating the invertebrate communities were still compositionally similar to each other. The intermediate and high shade treatments converged for the July 22 and August 10 surveys, but the low shade treatments remained compositionally distinct throughout the remainder of the experiment.
We used PERMANOVA to test whether there were statistically significant differences in invertebrate community structure at each sampling date in the nMDS. PERMANOVA indicated that macroinvertebrate community composition was similar among all shade treatments on July 8
(F2,9 = 1.6; p = 0.17) and July 22 (F2,9 = 1.8; p = 0.14), but diverged by August 10 (F2,9 = 7.1; p = ! 60!
0.006). Subsequent pairwise comparisons revealed that invertebrate composition in the low shade treatment was different from the intermediate (t = 3.9, p = 0.03) and high shade treatments
(t = 3.1, p = 0.03), but these differences were not significant after the Bonferroni correction. No differences were found between the intermediate and high treatments (t = 0.6, p = 0.73). The main compositional difference between the treatments was that a higher proportion of predators
(9%) were sustained in the low shade tanks (average n = 27 predators), as opposed to only 4% in intermediate and high shade (n = 5 predators in both treatments). Shredder abundance was negligible in all treatments during the final sampling date (< 2 individuals/tank). The low shade treatment also supported approximately twice as many grazers (n = 234) and filter feeders (n =
42) than the intermediate and high mesocosms (n = 108 and 98 grazers and 17 and 18 filter feeders, respectively). However, these differences may be negligible, as grazers were 77, 82 and
80% of all invertebrates in the low, intermediate, and high mesocosms, and filter feeders were approximately 14% of all invertebrates in each shade treatment.
DISCUSSION
Canopy and bottom-up productivity
Anthropogenic and other forces that alter canopy cover will affect the macroinvertebrate communities of vernal pools and other forested wetlands. Deforestation is pervasive throughout the world and can create open canopies over wetlands, but also destroy the surrounding terrestrial habitat that is vital for the complex life cycles of many aquatic organisms (Hocking and Semlitsch 2008). Alternatively, many regions, such as eastern North America, are undergoing a state of net reforestation with subsequent impacts on freshwater species distributions (Werner and Glennemeier 1999; Skelly et al. 1999; Skelly et al. 2005).
Understanding the mechanisms of how organisms respond to canopy cover is critical to ! 61! predicting how such landscape-level changes affect aquatic systems (Skelly et al. 2002; Binckley and Resetarits 2007). To our knowledge, our study is the first to examine how macroinvertebrate family richness and functional composition respond to experimental alterations of wetland canopy cover. It further suggests that small changes to forest cover may cross ecological thresholds (e.g., Dodds et al. 2010) for wetland macroinvertebrate communities, as several characteristics of our intermediate canopy mesocosms were statistically distinct from low canopy tanks and similar to high canopy treatments, despite an average difference of 22 % canopy between treatments. Although only a subset of all vernal pools may contain our starting composition, our results show that the low shade treatment best sustains macroinvertebrate communities. Furthermore, while macroinvertebrate functional group composition did not significantly differ among treatments, our results are similar to Plenzler and Michaels (Chapter one), where predator family richness increased with decreasing canopy cover. Additional replicates may be required to detect community structure responses to canopy. Even though mesocosms cannot fully replicate the complexity of vernal pools (Skelly and Kiesecker 2003), they should yield generalizable predictions for larger scale and natural systems (Resetarits and
Fauth 1998; Williams et al. 2008)
Reductions in canopy cover have been linked with increases in the abundance, diversity, and performance of amphibians (Werner et al. 2007), odonates (Corbet 1999), coleopterans
(Palik et al. 2001; Binckley and Resetarits 2007), and gastropods (Palik et al. 2001; Hall et al.
2007). In chapter one, wetlands with lower canopy were associated with greater primary production, dissolved oxygen, and water temperature, and further supported greater macroinvertebrate family richness, Shannon diversity, and predator family richness. In our experimental manipulations of canopy cover, low canopy mesocoms sustained greater ! 62! macroinvertebrate abundance, family richness and Shannon diversity, but may also support a greater proportion of predatory invertebrates. Many macroinvertebrates may be tolerant of a wide range of environmental variation (Batzer et al. 2004), but it appears that bottom-up pathways are substantial regulators of wetland invertebrate communities and the driver of organism responses to changing forest cover.
Similar to wetland amphibians, many macroinvertebrates require producer communities for food and shelter (Colburn 2004) and wetlands located along canopy gradients differ in the abundance and composition of primary producers (Wetzel 2001; Werner and Glennemeier 1999;
Skelly et al. 2002). In natural wetlands, reductions in canopy promote the growth of macrophytes and algae (Colburn 2004), but our experiment lacked vascular plants, which can increase structural complexity and other nutrients for macroinvertebrate communities (Taniguchi et al. 2003; Burdett and Watts 2009). In the presence of larger predators, vegetation can provide a refuge for macroinvertebrates, but our system lacked such strong top-down interactions (Godoy and Cutihno 2002; Burdett and Watts 2009). Algae and other photosynthetic microbes were the only primary producers in the system. Surprisingly, green algae were least abundant in our low shade treatment, while periphyton abundance and phytoplankton chlorophyll a followed similar, but non-significant, trends. Generally, algal abundance should increase as canopy decreases
(Skelly et al. 2002), but other studies have noted variable algal responses to canopy. Williams et al. (2008) found that low-shade ponds had higher levels of phytoplankton and lower levels of periphyton than high-shade ponds. Phytoplankton and periphyton are important food sources for wetland organisms (Hill et al. 1995; Rubbo and Kiesecker 2004) and may have low abundance in open canopy wetlands where productivity is transferring into higher trophic levels (Feminella and Hawkins 1995; Werner and Peacor 2003). Although the proportion of grazers did not ! 63! significantly differ among our treatments, we captured twice as many grazers in our low shade mesocosms than we did from the intermediate shade tanks, suggesting that open canopy wetlands may sustain a greater abundance of this functional group. Additionally, there was a nonsignificant trend for zooplankton abundance to be higher in the low canopy wetlands, which may have further suppressed indicators of algal productivity in this treatment. We did not measure periphyton chlorophyll a and this algal group may be the mechanism driving grazer abundance. However, the higher number of grazers and lower periphyton abundance in the low canopy mesocosms suggest that periphyton chlorophyll would also be reduced in our low shade treatments. Conversely, such algal responses may be difficult to predict in complex food webs where multiple species and environmental constraints affect top-down and bottom-up interactions in aquatic systems (e.g., Werner and Peacor 2003).
Abiotic habitat conditions
Apart from its effects on primary productivity, canopy can also impose other environmental constraints on aquatic organisms. One of the most fundamental factors is water temperature, which is known to affect the growth and development of amphibians and invertebrates (Fairchilld et al. 2003; Halverson et al. 2003) and may be an indicator of habitat quality for macroinvertebrates (Binckley and Resetarits 2007). Unsurprisingly, water temperature decreased as shade increased, but differences among treatments were often less than
2 °C. This temperature difference was similar to those observed by Williams et al. (2008; 2.4
°C) and Earl et al. (2011; 1.4 °C) in mesocosms and Schiesari (2006; 2.5 °C) and Werner and
Glennemeier (1999; 1.7 °C) in natural ponds, but smaller than those observed for natural wetlands in Skelly et al. (1999; 5 °C) and Plenzler and Michaels (Chapter one; 14 °C). A 1 °C increase in water temperature can reduce amphibian larval periods by one day (Hocking and ! 64!
Semlitsch 2008) and temperature differences of 3 – 5 °C can limit invertebrate recruitment and growth (e.g., mayflies, Sweeney 1993). In general, greater variation in temperature is thought to increase macroinvertebrate diversity (Ward and Stanford 1982), provided thermal conditions do not exceed the tolerances of sensitive species (Sponseller et al. 2001). Vernal pool macroinvertebrates are tolerant to a wide range of habitat variation, (Batzer et al. 2004; Chapter one). Moreover, Schiesari (2006) and Williams et al. (2008) consider modest differences in water temperature to be less important than resource availability for amphibians. Although temperature significantly differed among shade treatments, the narrow range (< 2 °C) of our measurements suggests that macroinvertebrate abundance and diversity are primarily responding to differences in resource quantity and not temperature in our mesocosms.
Despite the lack of direct impacts on macroinvertebrates, water temperature may have influenced productivity and nutrients in our mesocosms. Warmer water is associated with greater primary productivity (Colburn 2004; Kishi et al. 2005) and leaf litter decomposition
(Murphy et al. 1998). Similar to temperature, conductivity was highest in the low shade treatment and has been linked to nutrients leached from decomposing vegetation (Yanoviak
1999). DOC, a measure of food abundance in aquatic systems (Burdett and Watts 2009), may have contributed to these patterns in water conductivity, as it was significantly greater in the low and intermediate shade treatments. However, DOC was highest in the intermediate treatment, which suggests that other nutrients and factors are affecting conductivity. For example, DOC may have been more utilized by invertebrates and algae in low shade, while light limited its uptake in the intermediate treatment. We detected no effects of shade on PO4, NO3, and NH3, but these relationships may have been undetectable if the nutrients affected algal productivity
(Wetzel 2001). Other invertebrate processes (e.g., excretion) may also contribute to DOC and ! 65! elevated conductivity in the low shade treatment. Collectively, decreases in canopy will increase water temperature and litter decomposition, but further understanding the relationship between canopy and conductivity may require a detailed profile of the dynamics of the organic and inorganic nutrients in the system over time.
Canopy cover is known to affect dissolved oxygen (Werner and Glennemeier 1999;
Skelly et al. 2002; Plenzler and Michaels, Chapter one), but we did not detect this relationship in our study. Closed canopy wetlands typically have lower dissolved oxygen because of reduced photosynthesis (Werner and Glennemeier 1999; Schiesari 2006; Van Buskirk 2011). Some aquatic organisms can behaviorally avoid oxygen-poor microhabitats (Skelly et al. 2002), but low oxygen can reduce the survival and development of amphibians (Van Buskirk 2011) and invertebrates (Fairchild et al. 2003). Although some amphibians will increase bobbing for air at
4 ppm DO (Wassersug and Seibert 1975), wetland DO is naturally variable (Werner and
Glennemeier 1999; Skelly et al. 2002) and is often not of concern unless it falls below 2 ppm
(e.g., Williams et al. 2008) or food abundance is limited (Feder and Moran 1985). We did not record diel oxygen fluctuations, but our DO measurements were always above 2 mg/L (ppm), suggesting hypoxia was not a large constraint on the macroinvertebrates. We may have not detected treatment effects on oxygen because of the reduced algal productivity we observed or potentially greater microbial respiration in low shade. Additionally, the lack of forest cover at the study site may have allowed for adequate wind mixing of all mesocosms. However, similar to water temperature, Schiesari (2006) considers oxygen levels to be less important than resource availability for aquatic organisms. Our data supports this, as algal productivity responded to canopy and may have cascaded into the invertebrate communities. Thus, while DO affects ! 66! wetland taxa and is influenced by canopy cover, it may only be a secondary benefit for organisms living in open canopy habitats (Plenzler and Michaels, Chapter one).
Conclusions
Canopy closure through forest succession may represent a form of habitat loss, as open canopy wetlands are important for maintaining aquatic diversity (Binckley and Resetarits 2009).
To increase species diversity, wetland management and restoration practices should consider canopy reduction; however, closed canopy habitats are also critical for regional wetland species assemblages. The abundance and diversity of invertebrate predators (primarily dytiscid beetles) increase as canopy decreases (Batzer et al. 2004; Binckley and Resetarits 2007; Plenzler and
Michaels, Chapter one) and some organisms may avoid predation pressures by utilizing closed canopy ponds (Binckley and Resetarits 2007). Others may have preferred prey in closed canopy wetlands (e.g., Batzer et al. 2004; Mokany et al. 2008), as Earl et al. (2011) found that salamander biomass increased in high shade environments and was potentially driven by greater midge abundance in these treatments. Additionally, while the majority of aquatic organisms may regard closed canopy pools as inferior habitat, they may be of conservation importance where open canopy pools are degraded or of limited abundance (Binckley and Resetarits 2007). Closed canopy wetlands can also serve as rescue sites following local extinctions among vernal pool metacommunities provided there are adequate corridors for species dispersal (Carlson and
Edenhamn 2000; Colburn 2004; Urban 2004). Finally, while there are species considered to be open canopy specialists (Skelly et al. 2005), it is not known whether some prefer closed canopy habitats as a function of resource quality and predator avoidance or the degree to which open and closed-canopy pools differ in organism composition. Consequently, vernal pool and other temporary wetland conservation efforts should emphasize protecting a variety of open and closed ! 67! canopy habitats, as well as the dispersal corridors between them, to maintain regional aquatic species assemblages.
Changes to forest canopy cover will also alter the leaf litter inputs of temporary wetlands.
Similar to canopy cover, leaf litter can generate bottom-up effects in wetlands by altering primary productivity and subsequent biomass and survival of aquatic organisms (Rubbo and
Kiesecker 2004; Mokany et al. 2008; Williams et al. 2008; Earl et al. 2011; Stoler and Relyea
2011). Williams et al. (2008) found that amphibian performance may be largely affected by litter quality in open canopy wetlands. Other abiotic factors, such as DOC (Rubbo and
Kiesecker 2004), pH (Stoler and Relyea 2011), and tannins (Maerz et al. 2005) are also influenced by leaf litter and may have greater impacts on wetland communities than canopy alone. Future studies should continue to address how simultaneous alterations in canopy and leaf litter quantity and quality affect aquatic species abundance, diversity, and performance to understand how wetland communities will respond to natural and anthropogenic habitat change.
In conclusion, the ongoing anthropogenic and natural processes that are altering forest canopy cover will affect temporary wetland organisms. Although macroinvertebrate diversity is generally known to increase as canopy decreases, our study is the first to experimentally assess how wetland macroinvertebrates respond to different levels of canopy cover. Several biotic and abiotic variables are linked to open canopy habitats, but these data show that resource quantity is the primary factor driving the observed patterns in abundance and diversity. However, closed canopy pools are also beneficial for some aquatic species (e.g., salamanders, Earl et al. 2011); thus, temporary wetland conservation efforts should protect a wide range of canopy types to maintain regional biodiversity among wetland metacommunities (e.g., Urban 2004).
! 68!
ACKNOWLEDGEMENTS
We thank A. Meeker and D. D’Avello for field and laboratory assistance with this experiment. The Ohio Biological Survey provided financial support for macroinvertebrate collection and field site access was granted by The Nature Conservancy (S. Woods) and Maumee
State Forest (D. Schmenk). Financial support was also provided to M. Plenzler through a BGSU graduate assistantship. J. Miner and A. Downing aided with experimental design and provided comments on this manuscript. R. Walsh, J. Meier, J. Shimola, L. Blair, P. Arnold, A. Dietz, J.
Sublett, K. Root, and E. Gomezdelcampo also aided with manuscript preparation. R.M. McKay provided equipment for fluorometric analyses. We also thank B. Midden and S. Jindra for supplies and assistance for water nutrient analysis. ! 69!
TABLES
Table 1. Initial mesocosm macroinvertebrate community composition, including family names, abundance, and respective feeding guilds (as defined in Merritt and Cummins 1996).
Class Family Common Name #/Tank Feeding Guild Bivalvia Sphaeriidae Fingernail clams 50 Filter Feeder Insecta Belostomatidae Giant water bugs 2 Predator Insecta Chaoboridae Phantom midges 50 Predator Insecta Chironomidae Bloodworms 5 Grazer Insecta Dytiscidae Diving beetles 4 Predator Insecta Lestidae Speadwing damselflies 5 Predator Insecta Libellulidae Skimmer dragonflies 5 Predator Insecta Limnephilidae Log-cabin caddisflies 6 Shredder Insecta Notonectidae Backswimmers 3 Predator Malacostraca Asellidae Isopods 20 Shredder Mollusca Physidae Pond snails 12 Grazer Mollusca Planorbidae Ram’s horn snails 47 Grazer
! 70!
Table 2. Mean and standard error (± 1 SE) of the biotic and abiotic repeated measures variables per shade treatment at each sampling date.
Biotic variable July 8 July 22 August 10 Invertebrate Abundance Low 105.0 (24.3) 179.5 (31.8) 304.0 (29.1) Intermediate 40.8 (8.5) 93.3 (7.2) 131.8 (10.4) High 52.3 (5.9) 86.5 (10.9) 121.5 (17.2) Family Richness Low 9.0 (0.4) 8.3 (1.0) 8.3 (0.6) Intermediate 8.5 (0.6) 7.0 (0.4) 6.0 (0.6) High 8.0 (0.4) 6.5 (0.3) 5.3 (0.5) Chl a content (µg/L) Low 7.4 (1.1) 6.9 (1.4) 6.1 (4.1) Intermediate 10.2 (2.8) 21.8 (5.3) 33.2 (15.1) High 12.9 (4.1) 31.1 (9.6) 32.1 (12.1) Abiotic variable July 9 July 23 August 11 Conductivity (µS/cm) Low 365.0 (2.6) 382.9 (4.2) 442.3 (6.2) Intermediate 324.3 (1.7) 327.9 (3.7) 355.6 (5.0) High 305.4 (2.6) 298.1 (1.6) 306.4 (2.6) Temperature (°C) Low 30.6 (0.06) 26.1 (0.1) 24.4 (0.1) Intermediate 29.8 (0.2) 26.1 (0.1) 24.0 (0.4) High 29.3 (0.2) 25.6 (0.1) 23.9 (0.2) pH Low 6.4 (0.1) 6.5 (0.1) 6.6 (0.1) Intermediate 6.2 (0.04) 6.4 (0.1) 6.4 (0.2) High 6.2 (0.05) 6.3 (0.04) 6.6 (0.1) DO (mg/L) Low 4.5 (0.5) 2.9 (0.3) 4.5 (0.6) Intermediate 4.5 (0.4) 2.9 (0.4) 5.5 (0.3) High 2.9 (0.3) 2.5 (0.2) 6.3 (0.4)
! 71!
Table 3. Mean and standard error (± 1 SE) of the biotic and abiotic variables measured only at the end of the experiment (August 10). Shannon diversity was measured throughout the experiment, but only the August survey results were analyzed because of unequal variances during the July 8 and 22 surveys.
Biotic variables Treatment Diversity Green algae/mL Periphyton/mL Zooplankton/L Low 1.5 (0.03) 43.8 (10.7) 55.5 (8.3) 12 (1.5) Intermediate 1.3 (0.04) 55.3 (11.0) 69.0 (19.4) 10 (0.9) High 1.1 (0.06) 98.8 (6.3) 94.5 (12.5) 6.8 (1.4) Abiotic variables
DOC (mg/L) NO3 (mg/L) NH3 (mg/L) PO4 (mg/L) Low 5.7 (0.3) 0.2 (0.01) 0.04 (0.002) 0.01 (0.006) Intermediate 6.1 (0.2) 0.1 (0.05) 0.07 (0.04) 0.05 (0.02) High 5.3 (0.08) 0.2 (0.02) 0.03 (0.007) 0.1 (0.1)
! 72!
FIGURES
Figure 1a, b. (a) Macroinvertebrate abundance in response to shade treatment (F2,9 = 13.8, p =
0.002) (b) and family richness in response to shade treatment (F2,9 = 8.5, p = 0.008). LS means for both were greatest in the low (30%) shade treatment. Bars sharing the same letter are not significantly different.
a ,! !!!!!!!"! !!!!#! !!!!!!!#! (!
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#! Low (30%) Intermediate (55%) High (73%)
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#! Low (30%) Intermediate (55%) High (73%)
! 73!
Figure 2a,b. (a) Water conductivity (F2,9 = 203.5, p < 0.0001) and (b) temperature (F2,9 = 9.4, p =
0.006) in response to shade treatment. LS means for both were greatest in the low (30%) shade treatment. Bars sharing the same letter are not significantly different.
a (##! !!!!!!!!!!"! '##! !!!!!!#! !!!!!!!!$!
&##!
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#! Low (30%) Intermediate (55%) High (73%)
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%(! Low (30%) Intermediate (55%) High (73%)
! 74!
Figure 3. Water conductivity (LS means) was always greatest in the low (30%) shade and increased faster in this treatment (shade x time interaction, F4,16 = 21.4, p < 0.0001). Time points
1, 2, and 3 correspond to July 9, 23, and August 11, respectively.
./0!1! 4567897:;"67!1((23! <;=>!1-&23! (##!
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! 75!
Figure 4a,b. (a) Shannon diversity (F2,9 = 18.5, p = 0.0007) and (b) green algae abundance (F2,9 =
7.3, p = 0.013) in response to shade treatment. Shannon diversity was higher in the low (30%) and intermediate (55%) treatments, but green algae were most abundant in the high (73%) treatment. Bars sharing the same letter are not significantly different.
a %! "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"!
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#! Low (30%) Intermediate (55%) High (73%)
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#! Low (30%) Intermediate (55%) High (73%)
! 76!
Figure 5. DOC in response to shade treatment (F2,9 = 12.9, p = 0.002). DOC was lowest in the high (73%) shade treatment. Bars sharing the same letter are not significantly different.
-! "! "! #! ,!
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#! Low (30%) Intermediate (55%) High (73%)
! 77!
Figure 6. nMDS diagram showing the changes in macroinvertebrate composition in response to shade treatment over the three sampling dates. The numbers denote the survey dates, where 1 =
July 8, 2 = July 22, and 3 = August 10.. Each line indicates the trajectory of the macroinvertebrate community composition through time. Invertebrate composition diverged at the August 10 survey (PERMANOVA, F2,9 = 7.1; p = 0.006). Predators were more abundant in the low shade treatment (mean: 27 predators, t = 3.9, p = 0.03) than in the intermediate and high shade tanks (mean: 5 predators in both treatments), but this difference was not significant after the Bonferroni correction.
%! &! ./0!1! 4567897:;"67!1((23! &! '! #*$! <;=>!1-&23!
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! 78!
APPENDIX 2A
Repeated measures ANOVA for biotic and abiotic variables.
Biotic variable Source of variation df F P G-Ga Invertebrate abundance Shade 2, 9 13.8 0.002 Time 2, 8 62.5 < 0.0001 < 0.0001 Time " Shade 4, 16 1.2 0.34 0.25 Family richness Shade 2, 9 8.5 0.008 Time 2, 8 8.3 0.01 0.001 Time " Shade 4, 16 0.9 0.50 0.30 Chl a content (µg/L) Shade 2, 9 3.1 0.09 Time 2, 8 12.5 0.15 0.09 Time " Shade 4, 16 1.8 0.19 0.35 Abiotic variable Conductivity (µS/cm) Shade 2, 9 203.5 < 0.0001 Time 2, 8 368.2 < 0.0001 < 0.0001 Time " Shade 4, 16 21.4 < 0.0001 < 0.0001 Temperature (°C) Shade 2, 9 9.4 0.006 Time 2, 8 1107 < 0.0001 < 0.0001 Time " Shade 4, 16 2.4 0.10 0.07 pH Shade 2, 9 2.2 0.17 Time 2, 8 5.1 0.04 0.03 Time " Shade 4, 16 0.9 0.48 0.41 DO (mg/L) Shade 2, 9 0.6 0.55 Time 2, 8 46.8 < 0.0001 < 0.0001 Time " Shade 4, 16 3.1 0.05 0.002
! 79!
APPENDIX 2B
Univariate ANOVA following the biotic and abiotic
MANOVAs.
Biotic variable df SS MS F P Green algae/mL Shade 2 25.82 12.91 7.3 0.01 Error 9 15.90 1.77 Total 11 41.73 Periphyton/mL Shade 2 10.98 5.49 1.9 0.21 Error 9 26.28 2.92 Total 11 37.26 Zooplankton/L Shade 2 1.69 0.84 4.1 0.05 Error 9 1.86 0.21 Total 11 3.54 Shannon diversity Shade 2 0.35 0.18 18.5 0.0007 Error 9 0.09 0.01 Total 11 0.44 Abiotic variable DOC (mg/L) Shade 2 1.28 0.64 12.9 0.002 Error 9 0.45 0.05 Total 11 1.73 NO3 (mg/L) Shade 2 0.02 0.01 2.5 0.14 Error 9 0.04 0.004 Total 11 0.06 NH3 (mg/L) Shade 2 0.004 0.002 1.2 0.35 Error 9 0.02 0.002 Total 11 0.02 PO4 (mg/L) Shade 2 0.02 0.01 0.66 0.54 Error 9 0.12 0.01 Total 11 0.14
! 80!
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! 87!
CHAPTER III
CANOPY, LITTER, AND MACROINVERTEBRATES: SEEING THE FORESTED
WETLAND FOR THE TREES
ABSTRACT
Anthropogenic activities and natural processes are altering global forest species composition. In eastern North America, forests are shifting from oak- to maple-dominated stands with subsequent changes to forest canopy cover through deforestation and succession.
This will affect resource quantity and quality for the macroinvertebrate communities in temporary wetlands, as their food webs are driven by both allochthonous and autochthonous production. Many of these organisms are important nutrient cyclers in aquatic and terrestrial habitats and further serve as indicators of how aquatic ecosystems will respond to habitat change.
We simulated the effects of changing forest cover and composition on macroinvertebrates in a fully factorial mesocosm experiment and predicted that alterations to canopy cover and litter quality would affect macroinvertebrate communities and aquatic habitat quality. Mesocosms contained the same initial macroinvertebrate composition and were assigned to a pin oak, silver maple, or mixture litter treatment with either high or low shade. We monitored macroinvertebrate abundance, diversity, and functional group composition and assessed environmental factors, plankton communities, and algal biomass from June to August 2011.
Shade and litter affected many of these variables, but the effects of high and low shade were most pronounced in oak litter mesocosms. Most notably, macroinvertebrate abundance and family richness were greatest in low shade and with oak litter, consistent with differences in
DOC and primary productivity in these treatments. At the end of the experiment, grazers and predators were more abundant in the low shade and oak litter mesocosms, while the mixture and ! 88! maple treatments supported grazer and filter feeder food webs in response to altered resource quality. These data suggest leaf litter quality induced a bottom-up trophic effect in the mesocosms, as oak litter supported autotrophic producer-dominated communities and the mixture and maple treatments were probably driven by heterotrophic microorganisms. Other indicators of habitat quality, such as dissolved oxygen and water temperature were greater and phenolics were lower in oak treatments, but these factors are of secondary importance to food resource quality for macroinvertebrates. Light and macroinvertebrate abundance gradually increased in the mixture and maple treatments, suggesting that silver maple litter may slow nutrient processing rates in wetland communities, but autotrophic production may eventually increase. Ultimately, predicted forest compositional shifts will affect wetland macroinvertebrate abundance, diversity, and community structure. Additional studies are required to understand how habitat context and forest compositional changes will affect carbon inputs and wetland macroinvertebrates, as factors governing aquatic systems extend beyond traditional edges used to define these wetlands.
INTRODUCTION
Human activities are changing the species composition of many natural food webs
(Chapin et al. 2000), resulting in alteration in ecosystem functioning (Tilman et al. 1997), productivity (Tilman et al. 2001), and invasion resistance (Dukes 2002). Land-use and climate change are important factors altering species composition in numerous ecosystems (Sala et al.
2000) and are predicted to affect energy flow through food webs (Rubbo and Kiesecker 2004).
These habitat alterations will create novel biological communities and environmental conditions not previously experienced by ecosystems (Millar et al. 2007). Thus, understanding how communities respond to such compositional shifts is an important priority for ecosystem ! 89! protection and allowing communities to adaptively respond to environmental change (Millar et al. 2007).
Deforestation and the loss of foundation species can substantially alter forest assemblages and anthropogenic-driven forest species turnovers are one of the most prominent examples of changes in global species composition (Groom and Vynne 2006). In eastern North America, many areas are in a state of net reforestation (Gerhardt and Foster 2002), but are also rapidly shifting from oak- (Quercus spp.) to maple- (Acer spp.) dominated stands because of fire suppression, climate change, habitat loss, and herbivory (McEwan and Muller 2006; Nowacki and Abrams 2008). This compositional shift is predicted to affect communities embedded in forested ecosystems where allochthonous energy inputs subsidize food webs (Rubbo and
Kiesecker 2004). These impacts are often considered in terms of allochthonous resource quantity (Wallace et al. 1999), but altered energy subsidy quality has been increasingly shown to affect the embedded communities and the abiotic conditions of wetlands (Rubbo and Kiescker
2004; Williams et al. 2008; Earl et al. 2011).
In forested temporary wetland systems, food webs are often supported by allochthonous carbon derived from fallen leaf litter (Bonner et al. 1997). Variations in litter species composition have been associated with abiotic habitat variables and the growth and survival of wetland organisms (Rubbo and Kiesecker 2004; Williams et al. 2008; Stoler and Relyea 2011;
Earl et al. 2011), as resource quality for consumers depends on litter chemistry (Wetzel 2001).
Lignin and nitrogen content affect litter palatability and decomposition (Melillo et al. 1982;
Ostrofsky 1997), while soluble litter can elevate dissolved organic carbon (DOC) and restrict primary production (Karlsson et al. 2009). Furthermore, phenolics can inhibit bacteria, phytoplankton, and periphyton growth (Tuchman et al. 2003) and may be toxic for wetland ! 90! organisms (Maerz et al. 2005; Watling et al. 2011). Therefore, a shift in forest composition and subsequent litter quality of component wetlands should alter the community composition and abiotic conditions of these aquatic ecosystems.
Forest compositional changes can also alter canopy cover above forested wetlands and limit autochthonous energy for these systems (Williams et al. 2008). Many amphibians and macroinvertebrates are known to respond to canopy gradients and usually increase in abundance, diversity, and performance as canopy decreases (Werner et al. 2007; Binckley and Resetarits
2009; Hoverman et al. 2011; Plenzler and Michaels chapter one, Plenzler and Michaels chapter two). Wetland organisms may use canopy cover as an indicator of habitat quality (Binckley and
Resetarits 2007). Closed-canopy wetlands have less available sunlight, shorter hydroperiods through increased evapotranspiration, cooler water temperature, lower dissolved oxygen, altered algal assemblages, and fewer herbaceous refugia and food sources (Werner and Glennemeier
1999; Skelly et al. 2002; Colburn 2004; Schiesari 2006; Werner et al. 2007). Although canopy cover does not always correlate with litter quantity (Williams et al. 2008), it may interact with litter quality and affect wetland productivity and community composition (Williams et al. 2008;
Earl et al. 2011).
Alterations in forest composition and structure will therefore be expected to change both allochthonous and autochthonous resource bases for forested wetlands. The predicted shift from oak to maple will alter the chemical and physical structure of wetland litter (Stoler and Relyea
2011) and may reorganize the base of wetland food webs (Rubbo and Kiesecker 2004).
Similarly, canopy cover affects the organismal abundance and diversity of wetland communities
(Werner et al. 2007 Plenzler and Michaels chapter two), suggesting that forested wetlands may be particularly vulnerable to global and regional habitat change. The simultaneous effects of ! 91! changes in canopy cover extent and leaf litter species on forested wetland amphibians have been recently described (Williams et al. 2008; Earl et al. 2011), but the impacts on the macroinvertebrate communities are relatively unknown (but see Mehring and Maret 2011).
Many of these invertebrates are predators and prey in the aquatic and terrestrial habitats associated with wetlands (Colburn 2004) and represent important aspects of how ecosystems respond to ecological change (De Meester et al. 2005). We manipulated canopy cover (with shade cloth) and litter quality (species) in a fully factorial mesocosm experiment to evaluate how macroinvertebrate community dynamics and aquatic habitat quality respond to future alterations in forest composition. Although mesocosms cannot fully replicate natural wetland conditions
(Skelly and Kiesecker 2003), they can yield generalizable predictions about natural systems
(Resetarits and Fauth 1998). Thus, determining macroinvertebrate responses to forest change through model systems is critical for conserving, protecting, and creating functional wetland habitats for a diverse assemblage of aquatic organisms.
METHODS
The mesocosm experiment was conducted at the Ecology Research Station (ERS) at
Bowling Green State University (Bowling Green, OH, USA) where temporary wetlands were simulated with eighteen elliptical cattle watering tanks (Tuff Stuff model KMT 100; 416 L, 1.35 m length X 0.91 m width). The tanks were randomly assigned to a pin oak (Quercus palustris), silver maple (Acer saccharinum), or mixed pin oak and silver maple litter treatment (hereafter called “mixture”) with either high (73%) or low (30%) shade. These litter species were chosen because they were the most common by mass in a previous field survey of 15 temporary wetlands in northwest Ohio (Plenzler and Michaels chapter one). The shade treatments were identical to the high and low shade treatments from Plenzler and Michaels (Chapter two), but ! 92! were also similar to the shade treatments (77 and 27%) used by Williams et al. (2008) and Earl et al. (2011) in studies of amphibian responses to canopy and leaf litter in forested wetlands.
We gathered pin oak and silver maple litter from forested and urban areas soon after leaf senescence in November 2010. We sorted the litter to species and allowed it to dry indoors for four weeks. In January 2011, the litter was transferred into outdoor tubs at the ERS to simulate the natural overwintering conditions experienced by fallen leaves. We allowed precipitation to collect in the tubs to age the litter until late May 2011 when we initiated the mesocosm experiment.
The mesocosms were filled with 379 L of tap water on May 27 and allowed to dechlorinate for 48 hours. We maintained constant water levels every week until July 7 by adding dechlorinated tap water to a marked fill line and ceased after this date to simulate the seasonal drawdowns of forested wetlands; however, July and August precipitation events prevented net water loss from the mesocosms. We collected mud from five forested (greater than 60 % canopy cover, as determined by a spherical densiometer) and five non-forested (less than 40 % canopy) wetlands in northwest Ohio and mixed it in an outdoor tub to homogenize the sediment. Eleven liters of the sediment were distributed into each mesocosm on May 30 to establish microbial communities and Oligochaete decomposers and create a one-cm thick sediment substrate at the bottom of each tank. We also collected and mixed water from the five forested and five non-forested vernal pools, filtered it through a 60-!m mesh net, and added four liters of this water to introduce additional microbes into the mesocosms. 180 g (the mass of litter expected to cover the benthic area of each tank determined by the average amount of litter dry mass/m2 from Plenzler and Michaels, chapter one) of the aged leaf litter was added to mesocosms on May 31. We gathered phyto- and zooplankton from the forested and non-forested ! 93! vernal pools by towing a 60 !m plankton net 10 m through the center of each wetland. The plankton samples were homogenized in a large tub and 1.5 L was added to each tank on May 31 to introduce plankton communities into the mesocosms.
Each mesocosm contained the same initial macroinvertebrate community composition
(Table 1). Macroinvertebrates were collected from May 30 to June 3 from forested and non- forested vernal pools, identified to taxonomic family, and held in 19 L aerated fish tanks until they were distributed into the mesocosms on June 3. This assemblage included the dominant invertebrate families observed in the temporary wetland survey (Plenzler and Michaels chapter one) and included common wetland grazer, shredder, filter feeder, collector, and predator functional feeding guilds (Merritt and Cummins 1996; Colburn 2004). In total, each mesocosm contained 204 macroinvertebrates from 13 taxonomic families. We did not control for size or diversity within families because it was unfeasible to collect invertebrates from a single instar or lower taxonomic levels; however, many invertebrate families were only collected from particular microhabitats within certain wetlands, suggesting they may have been from the same genus, species, or larval brood. This experimental design may obscure lower taxonomic responses, but similar patterns are often observed at higher taxonomic levels (Schmidt-Kloiber and Nijboer
2004). Similar to Plenzler and Michaels (Chapter two), mesocosm macroinvertebrate abundance was based on field data from Plenzler and Michaels (Chapter one) by estimating the average density of individuals per family that would be expected to occupy the volume of a tank. We were generally able to maintain macroinvertebrate abundances within a factor of two from the previously documented field survey abundances in Plenzler and Michaels (Chapter one), and our community composition was similar to that of our previous mesocosm experiment examining the effects of canopy cover on wetland macroinvertebrates (Plenzler and Michaels chapter two). ! 94!
Notably, our tanks contained twice as many Chironomidae (n = 10/mesocosm in this experiment) and fewer Planorbidae (n = 30/mesocosm) than the previous experiment. Additionally, we were able to increase feeding guild functional diversity in our mesocosms by including Corixidae
(collectors) in the initial invertebrate communities. Although only a subset of all forested wetlands will contain our experimental assemblage, as invertebrate communities vary spatially and temporally among aquatic habitats (Colburn 2004), mesocosm experiments can generate broadly applicable predictions of how organisms respond to environmental change (Resetarits and Fauth 1998). Thus, our experimental community allowed us to examine how macroinvertebrates respond to habitat quality changes associated with shifts in forest composition.
We initially surveyed the mesocosms on June 8 (five days after macroinvertebate inoculation) to determine whether the macroinvertebrates survived transplantation into the experimental treatments. We surveyed the macroinvertebrate communities on June 22 (day 23),
July 19 (day 53), and August 24 (day 85) by sweeping a 0.5 mm mesh dipnet (11 cm diameter) through the leaf litter and water column along the sides and long central axis of the mesocosm.
In total, the dipnet was swept through approximately 39 L of water. The dipnet contents were emptied into a 10 L tub filled with mesocosm water. We identified all captured macroinvertebrates to family and returned them alive to their respective mesocosm. We determined macroinvertebrate abundance, family richness, and the number of individuals per functional feeding group and further used the family level data to calculate Shannon diversity for each tank.
We assessed water quality on June 23, July 20, and August 25 (within 24 hours of surveying the macroinvertebrate communities). We measured water conductivity, dissolved ! 95! oxygen (DO), pH, and water temperature between 11:00 AM and 1:00 PM with a YSI 556 MPS
(YSI, Inc., Yellow Springs, Ohio, USA) by taking three replicate measurements 10 cm below the water surface along the long edges and center of each tank to generate average values for each mesocosm. We also measured underwater light availability (LI-COR Quantum Sensor, model
189, LI-COR Biosciences, Lincoln, NE) beneath the shade cloth lids and 10 cm below the water surface at these three locations to create averages for each mesocosm.
We measured periphyton and phytoplankton chlorophyll a content (a measure of algal productivity) on the days we assessed mesocosm water quality. A 7.5 " 2.5 cm glass slide was suspended 10 cm below the water for periphyton colonization. The slide was positioned away from the side of the mesocosm to limit grazing from snails. Periphyton was scraped off the slide into a 30 mL jar filled with 10 mL of deionized water and analysis of chlorophyll a was performed within 24 hours of sample collection using the Welschmeyer (1994) method.
Chlorophyll a values were divided by 18.75 cm2 to express periphyton biomass in µg chlorophyll a/cm2. We also collected a 475 mL surface water sample from each mesocosm to determine phytoplankton chlorophyll a using the Welschmeyer (1994) method. Phytoplankton chlorophyll was expressed in µg/mL. All water samples were stored in the dark at 4 °C prior to chlorophyll extractions.
Phyto- and zooplankton samples were collected on August 24 after 85 days in the mesocosms. A 60-!m mesh plankton net (12.7 cm diameter) was gently inserted to the bottom of each mesocosm to minimize water disturbance and was then pulled to the top of the water column to funnel plankton into a 30 mL opaque glass bottle. The plankton net filtered approximately 10 L of water and was rinsed after sampling each tank. Plankton samples were preserved in 5% formalin. We determined phytoplankton abundance by mixing the sample and ! 96! placing a 0.5 mL aliquot on a gridded microscope slide. We counted the number of algal cells at
400X magnification and identified phytoplankton to genus. All filamentous and multicellular algae were considered to be one individual. The remaining sample was again inverted three times and 10 mL was added to a zooplankton wheel. All zooplankton (cladocerans, cyclopoid copepods, nauplii, and rotifers) were counted and identified to genus at 40X magnification.
On August 25, we collected 475 mL water samples and stored them in the dark at -15 °C for later analysis of dissolved organic carbon (DOC), nitrate (NO3), ammonia (NH3), phosphate
(PO4), and phenolic compounds. Prior to freezing, all samples were passed through a 0.2 µm membrane filter to remove microorganisms. DOC was determined through spectrophotometry using the methods described by Collier (1987). We used a SEAL AQ2 Discrete Analyzer (SEAL
Analytical, ltd.) to determine NO3, NH3, PO4, and phenolics using USEPA methods 127-A rev.
7, 103-A rev. 7, 118-A rev. 5, and 117-A rev. 6, respectively.
Litter decomposition was monitored by placing a mesh bag of known litter mass in each mesocosm and weighing the remaining contents at the end of the experiment. Each bag contained approximately three grams of leaf litter particular to a tank. The bags in the mixed litter mesocosms consisted of 1.5 g pin oak and 1.5 g silver maple litter. On August 26, the bags were removed from the tanks, dried at 60 °C for 48 hours, and the litter was weighed to determine the remaining proportion of the initial mass.
Statistical Analyses
Repeated measures analyses
We used Bonferroni-adjusted two-way repeated measures ANOVA (rmANOVA) to test for effects of shade, leaf litter, and shade " litter interactions on macroinvertebrate abundance, macroinvertebrate family richness, Shannon diversity, phytoplankton chlorophyll a, periphyton ! 97! chlorophyll a, pH, DO, water temperature, conductivity, and underwater light. Prior to rmANOVA, these data were divided into two groups of biotic and abiotic variables measured on
June 22/23, July 19/20, and August 24/25. Biotic variables analyzed were macroinvertebrate abundance, family richness, Shannon diversity, phytoplankton chlorophyll a, and periphyton chlorophyll a. The abiotic variables were underwater light, DO, water temperature, pH, and conductivity. Statistical significance for both analyses was set at ! = 0.01 for this group.
Student’s t-tests and Tukey’s post-hoc comparisons of least square means determined where significant differences occurred in shade, litter, and interaction terms. Macroinvertebrate abundance and family richness were square root transformed to reduce heteroscedasticity (Zar
1999). All data were tested for normality prior to statistical analyses.
Nutrients and litter decomposition
We used a two-way MANOVA to test for shade and litter effects on variables measured once during the experiment (DOC, NO3, NH3, PO4, phenolics, and litter decomposition). After finding a significant MANOVA, we used two-way ANOVAs with comparisons of least squares means between treatment groups and a Bonferroni-corrected significance of ANOVAs was ! =
0.008. Litter decomposition was logit transformed to reduce heteroscedasticity (Zar 1999). All rmANOVA and MANOVA tests were conducted with JMP Statistical Discovery Software 10
(SAS Institute Inc., Cary, NC).
Macroinvertebrate community structure
We used non-metric multidimensional scaling (nMDS) to visually represent how functional group composition (grazers, shredders, collectors, filterers, and predators) diverged by treatment throughout the experiment. The nMDS uses information on the number of functional groups and the abundance of individuals in each group to assess community similarity in two- or ! 98! three-dimensional space. The final model is the one with the lowest stress value, which serves as a “goodness of fit” and is ideally less than 0.1 (Clarke 1993; Quinn and McKeough 2002).
Points closer to each other in the nMDS are considered to be compositionally similar. In our nMDS, data were first square root transformed and averaged for each shade and litter combination to generate a mean community composition per treatment at each survey date. The nMDS was then performed with a Bray-Curtis dissimilarity matrix on the functional group compositions using PAST (Hammer et al. 2001).
A two-way permutational multivariate analysis of variance (PERMANOVA) was used to determine whether the differences in functional group composition were significantly different from each other. PERMANOVA (Anderson (2001; 2005; McArdle and Anderson 2001) is considered to be widely applicable to biological experiments, as it can accommodate any dissimilarity matrix (Quinn and Keough 2002). The PERMANOVA technique calculates a distance-based F statistic for each model term (shade, litter, and the interaction) and utilizes a permutation procedure to calculate the probability that treatments differ from each other
(Anderson and ter Braak 2003). Our PERMANOVA used 9999 permutations and was calculated on Bray-Curtis coefficients for square root transformed macroinvertebrate abundance per functional group, but unlike the nMDS, the data were not averaged across replicates within each sampling date in order to properly test for shade, litter, and interaction effects on functional group composition. Separate PERMANOVAs were conducted for the three sampling dates to determine whether the treatments affected macroinvertebrate composition. Bonferroni-corrected pairwise comparisons determined which macroinvertebrate communities were different from each other. The two-way PERMANOVAs were performed using PERMANOVA version 6
(Anderson 2005), followed by the Similarity Percentage (SIMPER, Clarke 1993) routine to ! 99! assess which functional groups were primarily responsible for any significant differences in functional group composition among treatments. SIMPER determines the contribution of a functional group to the average Bray-Curtis values for litter and shade treatments. This analysis was conducted on Bray-Curtis values for macroinvertebrate abundance (square root transformed) per functional group. All SIMPER analyses were conducted in PAST (Hammer et al. 2001).
Plankton abundance and composition
We used a two-way MANOVA to determine whether the total density and richness of phytoplankton and zooplankton differed among the shade and litter treatments. Subsequent two- way ANOVAs (Bonferroni adjusted ! = 0.013) further tested for significant differences among treatments in phyto- and zooplankton samples. Compositional differences in the phytoplankton and zooplankton samples were assessed with separate nMDS analyses. Similar to the macroinvertebrate communities, the data were square root transformed and averaged across shade and litter treatments, but any genera that occurred in two or fewer mesocosms were eliminated to limit the effects of rarity on the procedures (Clarke and Green 1988). Following each nMDS, the phytoplankton and zooplankton communities were analyzed with separate two- way PERMANOVAs, with 9999 permutations and calculations based on Bray-Curtis scores of the square root transformed abundances. Significant PERMANOVAs were then followed with pairwise comparisons to determine whether the phytoplankton or zooplankton communities were significantly affected by shade, litter, or the interaction term. For all significant pairwise comparisons, SIMPER determined which phytoplankton or zooplankton taxa were responsible for compositional differences among shade and litter treatments.
! 100!
RESULTS
The June 8 initial macroinvertebrate survey confirmed that macroinvertebrates had survived transplantation into the mesocosms. We captured an average of 11 macroinvertebrate families per tank and all families were captured at least once in each shade and litter combination. During the June 22 survey, we discovered gastropod eggs and dytiscid larvae, which suggested that some macroinvertebrates began reproducing in the mesocosms early in the experiment.
Biotic responses
Macroinvertebrate abundance was affected by shade (F1,12 = 44.8, p < 0.0001), as an average of 96 and 57 macroinvertebrates per mesocosm survey (three dipnet sweeps) were found in the low and high shade treatments, respectively (Appendix 3A, 3B). Macroinvertebrate abundance was significantly greater with oak (x̄ = 138 invertebrates) than either the mixture (x̄ =
51) or maple (x̄ = 39) treatments (F2,12 = 149.7, p < 0.0001). However, macroinvertebrate abundance was only affected by shade in the oak litter monocultures (shade " litter interactions,
F2,12 = 13.3, p = 0.0009, Fig. 1). Repeated measures ANOVA revealed a significant effect of time, as average abundance increased from 27 to 119 invertebrates throughout the experiment
(F2,11 = 124.3, p < 0.0001). Macroinvertebrate abundance increased faster in the low shade (time
" shade interaction, F2,11 = 8.9, p = 0.005) and oak litter treatments (time " litter interaction, F4,22
= 26.1, p < 0.0001), but the three-way interaction between time, shade, and litter was not significant (F4,22 = 4.2, p = 0.012).
Macroinvertebrate family richness was greater in the low shade treatment (F1,12 = 10.9, p
= 0.006). On average, 7 families were captured in the low treatments and 6 from the high shade tanks (Fig. 2a). Family richness was highest in the oak treatments (n = 8 families), but the ! 101!
mixture (n = 6) and maple (n = 5) mesocosms did not differ from each other (F2,12 = 18.2, p =
0.0002, Fig. 2b). In general, family richness was only higher under low shade in the litter monocultures, but the interaction between shade and litter was not significant after the
Bonferroni correction (F2,12 = 4.3, p = 0.04). Family richness did not change over time, nor were any time by treatment interactions significant (p > 0.29 for all tests). At the end of the experiment (August 24), some macroinvertebrate families were not captured in the tanks.
Notonectidae (backswimmers) were not present in the low shade maple tanks or any high shade treatments. Libellulidae (skimmer dragonflies) and Corixidae (water boatmen) were not detected in the high shade mixture treatment or either maple treatments. Dytiscidae (diving beetles) were absent from the high shade mixture and maple tanks, while Physidae (pond snails) and Lestidae
(spreadwing damselflies) were not captured in the high shade maple treatments. Shannon diversity was not affected by shade (F2,12 = 0.56, p = 0.47), litter (F2,12 = 3.3, p = 0.07), or the interaction between these terms (F2,12 = 2.4, p = 0.13). Throughout the experiment, Shannon diversity decreased across all treatments (time, F2,11 = 203.9, p < 0.0001), but there were no significant two- or three-way interactions with shade and litter (p > 0.03 for all tests) (Appendix
3A, 3B).
Our measurements of algal chlorophyll a had different responses to the shade and litter treatments. Phytoplankton chlorophyll a was unaffected by shade (F1,12 = 3.7, p = 0.08), litter,
(F2,12 = 0.16, p = 0.86), or the interaction between these treatments (F2,12 = 0.67, p = 0.53,
Appendix 3A). Phytoplankton chlorophyll a increased from June (average: 2.20 µg/L) to July
(3.27 µg/L), but decreased in August (2.57 µg/L) (F2,11 = 7.1, p = 0.01, Appendix 3B).
Additionally, the temporal responses did not differ among shade or litter treatments (all two- and three-way interactions p > 0.66). The low shade treatments tended to have greater periphyton ! 102! chlorophyll a (2.21 and 1.26 µg/cm2 in low and high shade, respectively, Appendix 3B), but this relationship was not significant after the Bonferroni adjustment (F1,12 = 5.7, p = 0.03).
Periphyton chlorophyll was also greater with oak litter (3.79 µg/cm2), while the mixture (0.67
2 2 µg/cm ) and maple (0.76 µg/cm ) treatments were not different from each other (F2,12 = 26.9, p <
0.0001). The interaction between shade and litter indicated that periphyton biomass was greatest in low shade mesocosms with oak litter (F2,12 = 8.5, p = 0.005, Fig. 3). Periphyton chlorophyll
2 2 decreased from 3.42 µg/cm to 0.48 µg/cm throughout the experiment (F2,11 = 18.5, p = 0.0003); however, these changes were largely affected by the oak litter treatment, where chlorophyll decreased from 8.1 µg/cm2 (June) to 0.53 µg/cm2 (August), while the other litter treatments had
2 less than 1.3 µg/cm at each sampling date (time " litter interaction, F4,22 = 6.5, p = 0.001).
There were no significant interactions between time and shade or time, litter, and shade (both p >
0.16, Appendix 3A).
Abiotic responses
Shade and litter affected underwater light. Low shade tanks had an average light value of
656.3 µE/m2/sec, while light in high shade tanks was significantly reduced to 217.2 µE/m2/sec
2 (F1,12 = 1074.5, p < 0.0001). Light was highest with oak litter (695.4 µE/m /sec), but the mixture
(373.7 µE/m2/sec) and maple (241.0 µE/m2/sec) treatments also significantly differed from each other (F2,12 = 405.8, p < 0.0001, Appendix 3A). The effects of shade were most pronounced in the oak treatment (shade " litter, F2,12 = 108.0, p < 0.0001, Fig. 4). Light was significantly lower
2 2 2 in July (410.3 µE/m /sec) than in June (444.1 µE/m /sec) or August (455.7 µE/m /sec) (F2,11 =
16.1, p = 0.0005). From June to August, light increased from 647.6 to 702.7 µE/m2/sec in low shade and decreased from 240.7 to 208.7 µE/m2/sec in the high shade treatments (time " shade interaction, F2,11 = 10.0, p = 0.003). Light in the oak tanks decreased from 774.5 to 610.2 ! 103!
µE/m2/sec during the experiment, while the mixture and maple treatments generally increased
(litter " time, F4,22 = 15.7, p < 0.0001, Appendix 3B). There was a significant three-way interaction between time, shade, and litter on underwater light (F4,22 = 10.8, p < 0.0001).
Regardless of shade, light decreased in oak treatments; however, in the mixture and maple treatments, light increased in low shade and decreased in high shade (Fig. 5, Appendix 3A, 3B).
Oak litter tanks had higher DO than the mixture or maple treatments (F2,12 = 49.7, p <
0.0001, Fig. 6). Shade did not affect DO (p = 0.07) and the interaction between shade and litter was not significant (p = 0.11). DO generally increased from 2.8 to 3.9 mg/L throughout the experiment (F2,11 = 105.4, p < 0.0001), but this pattern was driven by the mixture and maple treatments, as DO in oak tanks fluctuated little from June to August (time " litter interaction,
F4,22 = 4.7, p = 0.007). After the Bonferroni adjustment, there were no significant interactions between time and shade, or time, shade, and litter (both p > 0.012) (Appendix 3A, 3B).
Water temperature was significantly warmer in low shade tanks (average 24.8 °C) than those with high shade (24.1 °C; F1,12 = 30.4, p < 0.0001, Fig. 7a). Additionally, water temperature was approximately 1 °C warmer in oak litter mesocosms (F2,12 = 18.0, p = 0.0002,
Fig. 7b) and peaked in July across all treatments (26.2 °C; F2,11 = 517.3, p < 0.0001, Appendix
3B). June and July water temperature differences were less than 0.5 °C between low and high shade tanks; however, in August, low shade tanks were 1.4 °C warmer than high shade mesocosms (time " shade interaction, F2,11 = 7.3, p = 0.01). The shade " litter, time " shade, and three-way interaction were not significant for water temperature (all p > 0.15, Appendix 3A).
Conductivity was greater in low shade (F1,12 = 70.1, p < 0.0001, Fig. 8a, Appendix 3A) and with maple litter (F2,12 = 42.0, p < 0.0001, Fig. 8b). There was no interaction between shade and litter (p = 0.87). Conductivity increased from 322.2 to 394.4 µS/cm through time (F2,11 = ! 104!
205.0, p < 0.0001, Appendix 3B), but these increases were faster in the low shade treatments
(time " shade, F2,11 = 32.3, p < 0.0001). Neither the time " litter or the three-way interaction were significant after Bonferroni adjustment (p > 0.03, Appendix 3A). Water pH was significantly lower in maple tanks (F2,12 = 6.5, p = 0.01), but pH among litter treatments varied little (mean: maple = 6.9, mixture and oak = 7.0). Average pH increased from 6.9 to 7.0 from
June to August (F2,11 = 24.6, p < 0.0001), but this pattern was driven by the mixture and maple treatments, as mean oak tank pH never deviated from 7.0 (time " litter, F4,22 = 4.5, p = 0.008).
Shade and all other interactions did not affect pH (all p > 0.52, Appendix 3A, 3B).
Nutrients and litter decomposition
The two-way MANOVA for DOC, NO3, NH3, PO4, phenolics, and litter decomposition was highly significant (Wilks’ F30,30 = 6.5, p < 0.0001) and indicated that these factors were affected by shade (F6,7 = 22.3, p = 0.0003), litter (F12,14 = 14.6, p < 0.0001), and the interaction term (F12,14 = 4.3, p = 0.006). At the univariate level, the treatments significantly affected DOC, phenolics, and PO4 (all p < 0.0001, Appendix 3C). DOC and phenolics were greatest in high shade (DOC: F1,12 = 130.6, p < 0.0001; phenolics: F1,12 = 137.5, p < 0.0001) and with maple litter
(DOC: F2,12 = 238.1, p < 0.0001; phenolics: F2,12 = 120.7, p < 0.0001). The effects of shade were most pronounced in tanks with maple litter (shade " litter interactions, both p < 0.0001, Fig.
9a,b). PO4 was also greatest in high shade (F1,12 = 5.8, p = 0.03) and with maple litter (F2,12 =
30.5, p < 0.0001), but shade only affected PO4 in the maple treatments (shade " litter interactions, p = 0.02, Fig. 9c). NO3 was unaffected by shade or litter (p = 0.43). These treatments affected NH3 and litter decomposition at the ! = 0.05 level; however, these relationships were not significant after the Bonferroni correction (NH3: p = 0.013, decomposition: p = 0.03). NH3 was higher in the low shade treatments (mean: low shade = 0.18 ! 105! mg/L, high shade = 0.02 mg/L) and a greater percentage of oak litter remained at the end of the experiment (mean: oak = 59% remaining, mixture = 37%, maple = 27%; Appendix 3C).
Macroinvertebrate community structure
The nMDS for macroinvertebrates was two-dimensional and had a stress value of 0.76, indicating that the ordination was robust and the data were suitable for interpretation (Clarke
1993). Axis 1 and 2 explained 76.13% and 22.58% of the variance in Bray-Curtis scores and represent differences in macroinvertebrate composition. Points closer to each other are considered to be compositionally similar (Figure 10). The points were clustered together for the
June 22 survey, suggesting the communities had not yet diverged from each other. By July 19, the communities in oak monocultures had diverged and clustered within shade treatments, which persisted through the August 24 survey; however, the maple and mixture treatments remained compositionally similar to the June 22 sampling date. These treatments diverged by August 24, but only high shade tanks with maple litter were compositionally distinct, while mixture and low shade maple tanks were similar to the July and August high shade tanks with oak litter.
The PERMANOVAs confirmed that there were significant differences in macroinvertebrate composition at each sampling date (Appendix 3D). On June 22, only shade affected macroinvertebrate communities (F1,9 = 7.6, p = 0.001). Subsequent pairwise comparisons confirmed that the low and high canopies were significantly different from each other (t = 2.6, p = 0.004). The SIMPER analysis indicated that predators and grazers were the most important discriminating functional groups (Table 2a). Predators were more abundant in the low shade tanks (low shade: mean = 8; high shade: mean = 1), while grazers were more common in the high shade mesocosms (low shade: mean = 5; high shade: mean = 8). The July
19 PERMANOVA indicated macroinvertebrate communities shifted with shade (F1,9 = 6.1, p = ! 106!
0.013) and litter (F2,9 = 24.5, p = 0.0001). There was no significant interaction between shade and litter (p = 0.28). The post hoc pairwise comparisons were unable to detect significant differences between shade treatments (t = 1.2, p = 0.23), but the oak litter treatments were different from the mixture (t = 5.0, p = 0.001) and maple (t = 4.7, p = 0.001) tanks. No compositional differences existed between the mixture and maple treatments (t = 1.3, p = 0.2).
SIMPER indicated that grazers largely contributed to these differences among litter treatments
(Table 2b), as there was an average of 169 in the oak treatments, but only 12 and 8 in the mixture and maple tanks, respectively. The August 24 PERMANOVA also indicated macroinvertebrates differed with shade (F1,9 = 7.7, p = 0.001) and litter (F2,9 = 7.1, p = 0.001). Pairwise comparisons confirmed the shade treatments differed from each other (t = 2.0, p = 0.04). SIMPER indicated the differences in shade treatments were driven by grazers and filter feeders (Table 2c). There were averages of 110 grazers and 10 filterers in low shade compared to 71 grazers and 5 filter feeders in high shade tanks. Among litter treatments, only the oak and maple monocultures were different from each other (t = 2.6, p = 0.01). The SIMPER analysis showed these differences were largely governed by grazers and predators, which were both more abundant in the oak treatment (mean: oak = 156 grazers and 6 predators; mean: maple = 44 grazers and 1 predator).
Plankton abundance and composition
Shade and litter did not affect the density or richness of phyto- or zooplankton (Wilks
F20,31 = 1.1, p = 0.43). The nMDS for phytoplankton composition was two-dimensional with a stress value of 0.04. Axis 1 and 2 explained 62.8 and 5.0% of the variation in phytoplankton composition, respectively. With the exception of the high and low shade litter mixture treatments, all phytoplankton communities appeared to be compositionally distinct (Fig. 11a).
However, the two-way PERMANOVA revealed that phytoplankton community structure was ! 107! unaffected by shade (p = 0.71), litter (p = 0.46), or the interaction between shade and litter (p =
0.69, Appendix 3E). The zooplankton nMDS was also two-dimensional, but had a stress value of 0.14, indicating that community structure must be interpreted with caution (Clarke and
Warwick 2001). Axis 1 and 2 explained 39.4 and 21.4% of the variation in zooplankton composition, respectively. Zooplankton in the low shade tanks with either oak or maple litter were compositionally similar, as were those in the high shade oak and low shade mixture tanks.
The high shade maple and mixture treatments appeared to be compositionally distinct from all other treatments (Fig. 11b); however, similar to phytoplankton composition, the subsequent
PERMANOVA was unable to detect any effects of shade (p = 0.41), litter (p = 0.78), or the interaction between shade and litter (p = 0.57) on zooplankton composition (Appendix 3E).
DISCUSSION
Bottom-up effects of canopy and litter quality
Forest compositional changes will alter the abundance, diversity, and community structure of temporary wetland macroinvertebrate communities. Our work indicates these alterations will arise through bottom-up mechanisms, as both canopy cover and DOC from leaf litter restrict the amount of light transmitted to wetland autotrophs. Canopy cover affects the distribution, abundance, and diversity of wetland organisms and is associated with several indicators of habitat quality (Werner and Glennemeier 1999; Skelly et al. 1999; Skelly et al.
2005, Plenzler and Michaels chapter one, Plenzler and Michaels chapter two). Similarly, microbes, fungi, and primary producers utilize leaf litter nutrients and serve as the base of the food web in temporary wetlands (Colburn 2004). Although only a subset of all temporary wetlands will be structured like our macroinvertebrate assemblages and our mesocosms cannot fully replicate the complexity of natural systems (Skelly and Kiesecker 2003), they serve as a ! 108! model for mechanistic understanding of how invertebrate communities may respond to the predicted shifts in forest species composition and associated changes in canopy cover.
Surprisingly, measurements of macroinvertebrates and habitat conditions in the mixture treatments were often similar to the maple tanks, suggesting that as forest dominance transitions from oak-to-maple species (Nowacki and Abrams 2008), increases in silver maple litter may have antagonistic effects on wetland habitat quality. Thus, while mesocosms may overestimate the strength of ecological relationships (Skelly and Kiesecker 2003), our results imply that macroinvertebrate communities may change as maple begins to dominate the litter composition of former oak litter wetlands.
The role of allochthonous carbon has been widely studied in streams (Wallace et al.
1997; Wallace et al. 1999; Wetzel 2001), but there is also growing interest in its importance for lake and forested wetland food webs (Bonner et al. 1997; Wetzel 2001; Rubbo et al. 2008;
Williams et al. 2008; Earl et al. 2011; Stoler and Relyea 2011). Leaf litter is the primary food source for macroinvertebrates in headwater streams (Wallace et al. 1997) and its quantity is crucial for supporting and sustaining secondary production in wetlands (Rubbo et al. 2006;
Batzer and Palik 2007). Although we did not manipulate litter quantity, our experiment demonstrates that litter quality (i.e., species) is also an important regulator of wetland food webs.
The impact of litter on DOC and other nutrients is central to this relationship (Wetzel 2001), as it stimulates microbial and primary production (Jansson et al. 2000; Colburn 2004). Enhancements to these communities further support the development and performance of wetland shredders
(Merritt et al. 2008) and grazers (Skelly and Golon 2003, Rubbo et al. 2008) that process litter into usable forms for other wetland organisms, but also serve as prey for predatory invertebrates and amphibians (Colburn 2004). ! 109!
Litter species with low C:N ratios, such as silver maple (Ostrofsky 1997), are considered to be superior food sources for wetland consumers (Palik et al. 2006). However, while DOC and conductivity (indicators of nutrients leached from decomposing vegetation, Yanoviak 1999) were highest in our maple treatments, macroinvertebrate abundance and family richness were lowest in these conditions. Leaf litter that elevates DOC can shift food webs from autotrophic to heterotrophic production (Jansson et al. 2000) and alter resource quality for wetland consumers
(Rubbo and Kiesecker 2004). In the mixture and maple treatments, higher DOC levels limited light availability for primary producers and may have created an environment conducive for heterotrophic microbes. In contrast, the low DOC in the oak mesocosms promoted the growth of epiphytic algae and thus greater macroinvertebrate abundance and family richness. This is similar to Rubbo and Kiesecker’s (2004) study of the effects of litter type on wetland amphibians and zooplankton, where microbes served as the base of the food web in maple treatments.
Although DOC can attenuate light availability for primary producers (Wetzel 2001), it was lowest in the oak treatments and likely allowed these tanks to support the highest periphyton biomass. In the mixture and maple treatments, higher DOC levels limited light availability for primary producers and created an environment conducive for heterotrophic microbes. Although we did not assess the microbial communities in the mesocosms, both elevated DOC and low DO levels in the maple and mixture tanks are indicators of increased microbial production (Jansson et al. 2000; Rubbo et al. 2008). Additionally, pin oak leaves have greater phosphorous content than silver maple (Ostrofsky 1997), but phosphate was highest in the maple treatments and barely detectable in the oak mesocosms. Phosphates may have been lowest in the pin oak treatment because this litter decomposes slower than silver maple (Ostrofsky 1997); however, our aging process should have conditioned the litter for microbial decomposers (e.g., Rubbo and ! 110!
Kiesecker 2004). Our data suggests the periphyton rapidly assimilated phosphates from the oak litter (e.g., Wetzel 2001), but this nutrient was less utilized in the maple and mixture treatments where light limited algal productivity.
These results indicate that the shift from periphyton to microbial production in the mixture and maple treatments altered producer communities and further affected macroinvertebrate abundance. Periphyton is a high-quality food source for wetland organisms and is associated with the increased performance and production of amphibians and macroinvertebrates (Skelly and Golon 2003; Rubbo and Kiesecker 2004; Binckley and Resetarits
2007; Williams et al. 2008; Plenzler and Michaels chapter one). Thus, the shift from autotrophic to heterotrophic production in the mixture and maple tanks created a reduction in food quantity and quality for macroinvertebrates, as these tanks sustained less invertebrate abundance and family richness. Decreases in canopy can further promote primary production (Werner and
Glennermeier 1999; Skelly et al. 2002; Plenzler and Michaels chapter one), but the impacts of canopy are litter-specific. The effects of low shade on underwater light were dampened with greater maple litter, such that average light in the low shade maple tanks was statistically equivalent to the high shade oak treatments. Periphyton biomass mirrored this pattern, but macroinvertebrate abundance only responded to shade in the oak monocultures. Moreover, in high shade oak tanks, abundance was greater than those of the mixture and maple treatments, suggesting additional primary production had cascaded into the invertebrates. No treatments affected phytoplankton biomass, but filamentous algae appeared to limit light availability in the oak mesocosms in July and August. Interestingly, light and DO increased in the low shade mixture and maple treatments throughout the experiment and may be indicative of diminished heterotrophic production in these tanks (Jansson et al. 2000; Rubbo and Kiesecker 2004). ! 111!
Although we did not measure initial DOC in the mesocosms, the light increases imply DOC was assimilated by microbes, algae, and invertebrates throughout the experiment (Jansson et al. 2000;
Wetzel 2001). We detected no increase in periphyton biomass in response to this phenomenon, but macroinvertebrate abundance gradually increased in these treatments. Leaf litter nutrient loss can be rapid in aquatic systems from leaching and nutrient immobilization (Bärlocher et al.
1978), but our results suggest silver maple litter also alters nutrient processing time in wetland food webs, as productivity transfers more slowly into higher consumers.
Macroinvertebrate diversity and community structure
Reductions in canopy cover increase the diversity of wetland amphibians and macroinvertebrates primarily through bottom-up pathways (Palik et al. 2001; Batzer et al. 2004;
Binckley and Resetarits 2007; Werner et al. 2007; Plenzler and Michaels chapter two). In our experiment, the more productive low shade tanks sustained greater macroinvertebrate family richness, but richness was also greatest in oak tanks, regardless of shade level. While canopy and oak litter are known to affect periphyton productivity (Skelly et al. 2002; Rubbo and
Kiesecker 2004), our results further highlight the importance of periphyton as a high-quality food resource for wetland food webs (Skelly et al. 2002; Colburn 2004; Schiesari 2006; Williams et al. 2008). Algivore (grazer) abundance should closely track the increased periphyton biomass
(Palik et al. 2001; Gjerløv and Richardson 2010) and predator abundance may increase in response to greater prey resources (Colburn et al. 2008). These two functional groups contributed the most to macroinvertebrate compositional differences in our experiment. In particular, pond snails and predatory dragonflies, backswimmers, beetles, and damselflies were absent in high shade maple tanks at the end of the experiment. Although grazers were more abundant in high shade on June 22, the more productive low shade tanks supported more ! 112! predators. We detected no effects of shade in July, but grazers and predators accounted for differences among low and high shade mesocosms in August. In July these two functional groups were most abundant in the oak litter mesocosms, where primary productivity was greatest. Despite low abundances, grazers and filter feeders were the most common macroinvertebrates in the mixture and maple treatments. This suggests the shift from autotrophic production in oak litter to heterotrophic production in the mixture and maple litters will restructure wetland food webs (e.g., Jansson et al. 2000). However, these effects may be temporary, as macroinvertebrate community structure in the oak and mixture treatments no longer differed in August. Additionally, while grazers and predators were more abundant in oak litter, August grazer abundance increased in maple litter and may be further indicative of greater autotrophic production or that productivity cascades more slowly with silver maple.
Notably, macroinvertebrate shredders contributed little to differences in community structure throughout the experiment. Shredders were more abundant in the high shade tanks on
June 22, but their lack of responses to litter treatments is conspicuous, given their role in streams and wetlands (Colburn 2004). Collectively, shredders process leaf litter into fine particulate organic matter (FPOM) that is consumed by other macroinvertebrates (Colburn 2004). Mehring and Maret (2011) observed an increase in shredder growth and development when fed red maple
(Acer rubrum) versus oak leaves because of greater fungal biomass in their maple treatments.
Conceivably, shredder abundance should have been greatest in our silver maple tanks, as its low
C:N content relative to pin oak (Ostrofsky 1997) made it a preferred substrate for microbial growth (Palik et al. 2006). Perhaps shredder responses are primarily observed through biomass changes (e.g., Mehring and Maret 2011); however, there appears to be debate as to whether maple litters are high-quality food sources in wetlands. Rubbo and Kiesecker (2004) associated ! 113! red maple with lower quality food sources for amphibians, but Mehring and Maret (2011) and
Earl and Semlitsch (2012) concluded this litter was high quality for macroinvertebrate shredders
(caddisflies) and wood frogs (Rana sylvatica), respectively. Conversely, Earl and Semlitsch
(2012) also noted that red maple was inferior for southern leopard frogs (Rana sphenocephala).
Palik et al. (2006) found that sugar maple (Acer saccharum) was a poor food source for mosquitoes. In a comparison of red and sugar maple, Stoler and Relyea (2011) demonstrated that sugar maple had higher periphyton production and larger gray tree frog (Hyla versicolor) metamorphs. As litter chemistry can vary widely within plant families (Ostrofsky 1997), its impacts on aquatic communities appear to be specific to both litter species and the wetland organisms under consideration (e.g., Stoler and Relyea 2011).
Effects on water quality
Several other factors associated with litter and canopy may have affected macroinvertebrates in our mesocosms. Phenolics compose a range of plant tannins and other secondary compounds that deter herbivorous organisms (Salminen and Karonen 2011). Tannins make leaf tissues unpalatable or non-nutritious by limiting protein availability for consumers
(Salminen and Karonen 2011) and can also stimulate chemical oxygen demand and reduce DO for wetland organisms (Earl et al. 2012). Secondary compounds can potentially damage respiratory tissues in amphibians (Maerz et al. 2005; Watling et al. 2011) and limit macroinvertebrate biomass in aquatic systems (Kominoski and Pringle 2009). Leaf litter and shade clearly affected the presence of phenolic compounds in our mesocosms, but it is unclear whether phenolics stressed the macroinvertebrates (e.g., Batzer and Palik 2007). Light increases tannin degradation (Maie et al. 2008) and pin oak has greater tannin content than silver maple
(Ostrofsky 1993); however, total phenolics were greater in silver maple treatments likely because ! 114! its lower C:N ratio promoted the faster release of these chemicals into the water (Ostrofsky 1993;
Ostrofsky 1997). Although these compounds could have lowered DO in the mixture and maple tanks, DO was similar in these treatments and phenolics were greatest in maple, suggesting microbes were largely driving DO in the mesocosms. Alternatively, tannins could indirectly affect macroinvertebrates if these chemicals limited microbial biomass (Kominoski and Pringle
2009); however, the maple litter was most decomposed, indicating microbial food sources were probably available for other consumers. Moreover, our phenolic measurements were within the range of our previous study of natural temporary wetlands (0.21 – 16.4 mg/L), where these chemicals did not affect macroinvertebrate abundance or family richness (Plenzler and Michaels chapter one). Ultimately, the type and concentration of phenolics varies widely among plant species (Salminen and Karonen 2011) and wetland organism responses are more dependent on particular phenolic compounds, as opposed to total phenolic content (Watling et al. 2011; Earl et al. 2012). Thus, while phenolics can interact with various wetland organisms, more detailed assays of phenolics and macroinvertebrate responses are necessary to understand these potential relationships.
Aquatic organisms may use canopy cover to evaluate wetland habitat quality (Binckley and Resetarits 2007) because DO, water temperature, and conductivity are higher in low canopy wetlands (Werner and Glennemeier 1999; Williams et al. 2008; Plenzler and Michaels chapter two). Litter quality alters the relationship between these factors and canopy, but our data indicates these impacts are specific to litter type, as Williams et al. (2008) found few interactions between canopy and grass or leaf litter. Generally, resource availability is of primary importance for wetland consumers (Schiesari 2006; Williams et al. 2008), but the hypoxic conditions (< 2 ppm DO) in the maple tanks may have limited macroinvertebrates in June and July. DO is ! 115! naturally variable in wetlands (Werner and Glennemeier 1999) and many temporary wetland organisms are tolerant of a wide range of habitat variation (Batzer et al. 2004) or can behaviorally avoid low DO conditions (Skelly et al. 2002). It is unclear whether low DO caused mortality in the mixture and maple tanks, but it likely served as a substantial habitat constraint for the macroinvertebrates when coupled with the lower resource quality of the maple and mixture tanks (Rubbo and Kiesecker 2004; Magnusson and Williams 2006). However, macroinvertebrate abundance increased as light and oxygen become more available over time in these tanks and abundance increased in the oak mesocosms despite constant DO. Thus, our results suggest macroinvertebrates are more responsive to resource quality, while DO is a byproduct of resource quality that can further constrain invertebrates in maple-dominated wetlands.
Although low canopy wetlands are known to have warmer water temperatures (Williams et al. 2008; Plenzler and Michaels chapter two), it was surprising that leaf litter affected water temperature, as well. Although water temperature differences among litter treatments were less than 2 °C on all survey dates, the oak treatments were warmest, possibly because the DOC in the mixture and maple treatments limited light availability in these mesocosms (e.g., Wetzel 2001).
Water temperature can increase macroinvertebrate diversity (Colburn 2004), but minor variations in water temperature are considered to be less important than resource availability for wetland organisms (Schiesari 2006; Williams et al. 2008; Plenzler and Michaels chapter two). Similarly, pH was lower in the maple monocultures (6.9), but mean pH was only 7.0 in the mixture and oak treatments and unlikely affected the producer or consumer communities in our mesocosms (e.g.,
Stoler and Relyea 2011). Moreover, while leaf litter reliably affects water pH (Magnusson and
Williams 2006; Stoler and Relyea 2011), pH variation in natural temporary wetlands is much ! 116! wider and not always a reliable predictor of macroinvertebrate abundance or diversity (Batzer et al. 2004; Plenzler and Michaels, chapter one).
Plankton abundance and community structure
Shade and litter did not affect the density, richness or community structure of phytoplankton and zooplankton. Similar to the periphyton, phytoplankton chlorophyll a was likely limited by light availability in the mixture and maple mesocosms. Phytoplankton may have not responded to oak litter because its slower nutrient release would permit the dominance of periphyton communities (Sand-Jensen and Borum 1991). Accordingly, these factors may have limited phytoplankton community divergence in our treatments. Alternatively, grazers and predators should have transferred nutrients into the water column (Iwai et al. 2009) to stimulate phytoplankton growth. We have qualitative evidence to suggest light decreased in the oak treatments because of greater phytoplankton abundance (particularly filamentous algae).
Additionally, increased light in the mixture and maple tanks could eventually stimulate algal productivity (e.g., Gjerløv and Richardson 2010), thus a longer experiment may have been required to detect differences in phytoplankton composition. The lack of shade or litter effects on phytoplankton may have limited zooplankton across all treatments, but many zooplankton indirectly utilize allochthonous carbon and may not dramatically respond to variations in carbon inputs (Rubbo et al. 2008). Furthermore, zooplankton responses to litter quality can be impacted by trophic interactions (i.e., competition, Rubbo and Kiesecker 2004) and may be particular to individual litter species and its effects on habitat quality (Stoler and Relyea 2011; Watkins et al.
2011). Ultimately, zooplankton can potentially respond to variations in autochthonous and allochthonous inputs in a myriad of ways. Since they are important food sources for other ! 117! wetland organisms (Colburn 2004), additional studies should address the mechanisms through which litter and canopy alterations affect zooplankton communities.
Conclusions
Macroinvertebrates in temporary wetlands may be particularly vulnerable to forest compositional changes because their abundance and diversity are inextricably linked to the quality and quantity of allochthonous and autochthonous carbon inputs. Canopy and litter interact and affect bottom-up productivity and other indicators of wetland habitat quality. Litter species dictates the strength of these effects (Williams et al. 2008) and can further promote or inhibit primary production regardless of canopy cover (Rubbo and Kiesecker 2004; Stoler and
Relyea 2011). The shift from oak to maple-dominated forests might negatively affect macroinvertebrates by reducing resource quality in temporary wetlands. These effects are not dependent on drastic shifts in litter composition, as many habitat conditions in the mixture treatments were similar to the maple monocultures, indicating small changes to litter composition may cross ecological thresholds (e.g., Dodds et al. 2010) and restructure wetland communities. Others have found decreased primary production and organism performance in oak and maple litter mixtures, suggesting that litter combinations may largely have antagonistic effects on wetland communities (Rubbo and Kiesecker 2004; Stoler and Relyea 2011). Litter species vary within wetlands, but pin oak was twice as abundant as silver maple in our field survey (Plenzler and Michaels chapter one) and this ratio should theoretically decrease as maples become more dominant in east North American forests. Future studies should locate these thresholds both experimentally and in the field to understand how subtle alterations to bottom-up productivity could cascade through wetland communities. There are many other unknowns associated with forest compositional shifts, such as how climate change will modify hydrologic ! 118! regimes (Brooks 2009), alter litter C:N ratios for wetland food webs (Woodward et al. 2010), and to what extent nonnative plant species will impact wetland communities (Cohen et al. 2012). At the same time, habitat quality is context-dependent, as some organisms (e.g., salamanders and caddisflies) prefer wetlands associated with inferior resources for others (Earl et al. 2011;
Mehring and Maret 2011). Additionally, forest compositional shifts will take many years and wetland populations may adapt to these changes (Earl et al. 2012). To our knowledge, this is the first study to examine how wetland macroinvertebrates respond to simultaneous alterations in canopy cover and litter quality. Most importantly, our work demonstrates the complexities of terrestrial habitat impacts on macroinvertebrates, as processes governing temporary wetlands are not always in the aquatic habitat. On an ecosystem level, the shift from oak to maple dominance will potentially induce trophic cascades on wetland food webs and have adverse effects on wetland macroinvertebrate communities. Our results indicate this transition from oak to maple may lead to habitat loss for dragonflies, backswimmers, and water boatmen, which were absent from maple wetlands regardless of canopy at the end of the experiment. Moreover, it may reduce habitat quality for pond snails, damselflies, and beetles in maple wetlands with high shade levels.
Ultimately, this will reduce food availability for other macroinvertebrates and the amphibian species in temporary wetlands. Additional studies are needed to determine the responses of other wetland species of concern; however, as many of these habitats lack formal legal protection
(Mahaney and Klemens 2008), additional emphasis must be placed the creation and preservation of wetland metacommunities and adequate dispersal corridors (e.g., Urban 2004) in a variety of terrestrial contexts to better shield their resident organisms from further anthropogenic-induced environmental changes.
! 119!
ACKNOWLEDGEMENTS
N. Schweitzer provided invaluable field and laboratory assistance with this project.
Financial assistance was provided by the Larry and Linda Oman Graduate Scholarship and the
BGSU department of biological sciences. Field site access for macroinvertebrate collection was granted by Kitty Todd Nature Preserve (The Nature Conservancy, S. Woods) and Maumee State
Forest (D. Schmenk). R. Walsh, J. Meier, J. Shimola, L. Blair, P. Arnold, A. Dietz, J. Sublett, K.
Root, J. Miner, A. Downing, and E. Gomezdelcampo also aided with manuscript preparation. R.
M. McKay provided equipment for fluorometric analyses and R. Lowe aided in algal identification. We also thank B. Midden and S. Jindra for their assistance with the water chemistry analyses.
! 120!
TABLES
Table 1. The initial mesocosm macroinvertebrate community composition, including family names, abundance, and respective feeding guilds (Merritt & Cummins 1996). Each mesocosm initially contained 204 macroinvertebrates from 13 taxonomic families.
Class Family Common name #/Tank Feeding Guild Bivalvia Sphaeriidae Fingernail clams 50 Filter Feeder Insecta Belostomatidae Giant water bugs 2 Predator Insecta Chaoboridae Phantom midges 50 Predator Insecta Chironomidae Bloodworms 10 Grazer Insecta Corixidae Water boatmen 3 Collector Insecta Dytiscidae Diving beetles 4 Predator Insecta Lestidae Spreadwing damselflies 5 Predator Insecta Libellulidae Skimmer dragonflies 5 Predator Insecta Limnephilidae Log-cabin caddisflies 8 Shredder Insecta Notonectidae Backswimmers 3 Predator Malacostraca Asellidae Isopods 20 Shredder Mollusca Physidae Pond snails 14 Grazer Mollusca Planorbidae Ram’s horn snails 30 Grazer
! 121!
Table 2. SIMPER (similarity percentage) analysis results for macroinvertebrate functional groups driving differences in community structure. Variables with high percent contributions contribute more strongly to differences in community structure. On June 22 (a), community structure was only affected by shade. Grazers and predators were most responsible for these differences. Litter affected community structure on July 19 (b). Both shade and litter affected
August 24 (c) community structure. Grazers were the primary group of macroinvertebrates differentiating the communities at these time points. The mean numbers of macroinvertebrates per functional group are presented for shade and/or litter at each sampling date.
a. June 22 – Shade Functional group % Contribution Cumulative % Low High Grazers 19.16 19.16 5 8 Shredders 18.25 37.41 2 5 Filterers 14.03 51.44 9 10 Collectors 6.69 58.13 1 0 Predators 41.87 100.00 8 1 b. July 19 – Litter (Oak and mixture) Oak Mixture Grazers 71.17 71.17 169 12 Shredders 5.53 76.70 3 4 Filterers 5.39 82.09 9 10 Collectors 7.31 89.39 1 0 Predators 10.60 100.00 4 2
Litter (Oak and maple) Oak Maple Grazers 72.11 72.11 169 8 Shredders 4.61 76.72 3 4 Filterers 4.74 81.46 9 8 Collectors 7.80 89.26 1 0 Predators 10.75 100.00 4 1 c. August 24 – Shade Low High Grazers 45.63 45.63 110 71 Shredders 6.42 52.05 3 5 Filterers 22.46 74.51 10 5 Collectors 8.41 82.92 1 0 Predators 17.08 100.00 5 1
! 122!
Litter (Oak and maple) Oak Maple Grazers 56.37 56.37 156 44 Shredders 4.68 61.05 4 3 Filterers 13.64 74.69 7 6 Collectors 8.32 83.02 1 0 Predators 16.99 100.00 6 1
! 123!
FIGURES
Figure 1. Macroinvertebrate abundance (LS means) was greatest in the oak treatments and only affected by shade level in this litter type (F2,12 = 13.3, p = 0.0009). Shaded bars are the high shade (73%) treatments. Bars sharing the same letter are not significantly different.
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Figure 2a,b. (a) Macroinvertebrate family richness (LS means) was greatest in the low shade
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Figure 3. Periphyton chlorophyll a (LS means) was greatest in the low shade oak litter treatment and only responded to shade level in this litter type (F2,12 = 8.5, p = 0.005). Shaded bars are the high shade (73% treatments). Bars sharing the same letter are not significantly different.
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Figure 4. Shade affected underwater light (LS means) in all litter treatments, but the differences between shade treatments were greatest in oak litter (F2,12 = 108.0, p < 0.0001). Shaded bars are the high shade (73%) treatments. Bars sharing the same letter are not significantly different.
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Figure 5. The three-way interaction between shade, litter, and time on underwater light availability (F4,22 = 10.8, p < 0.0001). Light was always highest in the low shade oak treatment, but light increased in the low shade mixture and maple treatments from July to August. Light remained constant in the high shade mixture and maple mesocosms. Regardless of shade, light decreased in the oak tanks.
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0.0001). Shade did not affect DO. Bars sharing the same letter are not significantly different.
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Figure 7a,b. (a) Water temperature (LS means) was warmer in low shade (F1,12 = 30.4, p <
0.0001) and in the (b) oak litter treatment (F2,12 = 18.0, p = 0.0002 ). Bars sharing the same letter are not significantly different.
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Figure 8a, b. Water conductivity (LS means) was greatest in low shade (F1,12 = 70.1, p < 0.0001) and with (b) maple litter (F2,12 = 42.0, p < 0.0001). Bars sharing the same letter are not significantly different.
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Figure 9a – c. (a) Dissolved organic carbon (F2,12 = 36.1, p < 0.0001), (b) phenolics (F2,12 = 51.8, p < 0.0001), and (c) phosphates (F2,12 = 5.5, p = 0.02) (c) were highest in the low shade maple treatments. Shade did not affect DOC, phenolics, or PO4 in the oak treatments, but DOC and phenolics in mixture treatments were higher with high shade. LS means are presented. Shaded bars are high shade (73%) treatments. Bars sharing the same letter are not significantly different.
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Figure 10. The nMDS ordination showing changes in macroinvertebrate composition throughout the experiment. The numbers denote the survey dates (1 = June 22, 2 = July 19, and 3 = August
24). Open markers correspond to low shade (30%) and closed markers correspond to high shade
(73%) treatments. Oak litter treatments are indicated by circles, mixtures by squares, and maple tanks by triangles. Points closer to each other in the nMDS are considered to be compositionally similar. Canopy affected macroinvertebrate community structure on June 22 (PERMANOVA
F1,9 = 7.6, p = 0.001), as predators were more abundant in low shade tanks and grazers were more abundant in high shade mesocosms. On July 19, grazers were most abundant in oak tanks
(litter, PERMANOVA F2,9 = 24.5, p = 0.0001). Community composition was affected by both canopy (PERMANOVA F1,9 = 7.7, p = 0.001) and litter (F2,9 = 7.1, p = 0.001). Grazers and filter feeders were more abundant in low shade, while the oak treatments had greater grazer and predator abundance.
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Figures 11a,b. (a) The nMDS plots for phytoplankton and (b) zooplankton composition on
August 24. Points closer to each other in the nMDS are considered to be compositionally similar. Open markers indicate low shade (30%) and closed markers are high shade (73%) treatments. Circles correspond to oak, squares to the litter mixture, and triangles to the maple mesocosms. Shade appeared to affect both phtyo- and zooplankton composition, but these patterns were not substantiated by PERMANOVA.
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! 133!
APPENDIX 3A
Two-way repeated measures ANOVA results for the biotic and abiotic variables.
Biotic variable Source of variation df F P G-Ga Invert. abundance Between subjects Shade 1, 12 74.2 < 0.0001 Litter 2, 12 149.8 < 0.0001 Shade " Litter 2, 12 13.3 0.0009 Within subjects Time 2, 11 124.3 < 0.0001 < 0.0001 Time " Shade 2, 11 8.9 0.005 0.03 Time " Litter 4, 22 26.1 < 0.0001 < 0.0001 Time " Shade " Litter 4, 22 4.1 0.012 0.12 Family richness Between subjects Shade 1, 12 10.9 0.006 Litter 2, 12 18.2 0.0002 Shade " Litter 2, 12 4.3 0.04 Within subjects Time 2, 11 0.3 0.75 0.69 Time " Shade 2, 11 0.3 0.71 0.66 Time " Litter 4, 22 1.3 0.29 0.47 Time " Shade " Litter 4, 22 0.6 0.64 0.50 Shannon diversity Between subjects Shade 1, 12 0.6 0.47 Litter 2, 12 3.3 0.07 Shade " Litter 2, 12 2.4 0.13 Within subjects Time 2, 11 203.9 < 0.0001 < 0.0001 Time " Shade 2, 11 1.0 0.41 0.27 Time " Litter 4, 22 1.3 0.30 0.49 Time " Shade " Litter 4, 22 3.3 0.03 0.11 Phytoplankton chl Between subjects Shade 1, 12 3.7 0.08 Litter 2, 12 0.2 0.86 Shade " Litter 2, 12 0.7 0.53 Within subjects Time 2, 11 7.1 0.01 0.21 Time " Shade 2, 11 0.4 0.66 0.78 Time " Litter 4, 22 0.2 0.91 0.83 Time " Shade " Litter 4, 22 0.3 0.88 0.72 ! 134!
Periphyton chl Between subjects Shade 1, 12 5.7 0.03 Litter 2, 12 26.9 < 0.0001 Shade " Litter 2, 12 8.5 0.005 Within subjects Time 2, 11 18.5 0.0003 < 0.0001 Time " Shade 2, 11 1.1 0.35 0.39 Time " Litter 4, 22 6.5 0.001 < 0.0001 Time " Shade " Litter 4, 22 1.8 0.16 0.17 Abiotic variable pH Between subjects Shade 1, 12 0.1 0.77 Litter 2, 12 6.5 0.01 Shade " Litter 2, 12 0.02 0.98 Within subjects Time 2, 11 24.6 < 0.0001 < 0.0001 Time " Shade 2, 11 0.3 0.72 0.69 Time " Litter 4, 22 4.5 0.008 0.001 Time " Shade " Litter 4, 22 0.8 0.52 0.43 DO Between subjects Shade 1, 12 3.9 0.07 Litter 2, 12 49.7 < 0.0001 Shade " Litter 2, 12 2.7 0.11 Within subjects Time 2, 11 105.4 < 0.0001 < 0.0001 Time " Shade 2, 11 6.1 0.02 0.002 Time " Litter 4, 22 4.7 0.007 0.0005 Time " Shade " Litter 4, 22 4.1 0.012 0.12 Conductivity Between subjects Shade 1, 12 70.1 < 0.0001 Litter 2, 12 42.0 < 0.0001 Shade " Litter 2, 12 0.14 0.87 Within subjects Time 2, 11 204.9 < 0.0001 < 0.0001 Time " Shade 2, 11 32.3 < 0.0001 < 0.0001 Time " Litter 4, 22 3.2 0.03 0.004 Time " Shade " Litter 4, 22 1.6 0.21 0.35
! 135!
Temperature Between subjects Shade 1, 12 30.4 0.0001 Litter 2, 12 18.0 0.0002 Shade " Litter 2, 12 2.2 0.15 Within subjects Time 2, 11 517.3 < 0.0001 < 0.0001 Time " Shade 2, 11 7.3 0.01 0.004 Time " Litter 4, 22 0.7 0.62 0.44 Time " Shade " Litter 4, 22 1.7 0.19 0.19 Light Between subjects Shade 1, 12 1074.6 < 0.0001 Litter 2, 12 405.8 < 0.0001 Shade " Litter 2, 12 108.0 < 0.0001 Within subjects Time 2, 11 16.1 < 0.0001 0.0002 Time " Shade 2, 11 10.0 0.003 0.0002 Time " Litter 4, 22 15.7 < 0.0001 < 0.0001 Time " Shade " Litter 4, 22 10.8 < 0.0001 < 0.0001
! 136!
APPENDIX 3B
Mean (± SE) values of biotic (a) and abiotic (b) variables from the rmANOVAs. Mean values are untransformed and presented for individual shade and litter treatments. LS means are presented for shade and litter treatments at each sampling date and across the entire experiment. a. Biotic variable June 22 July 19 August 22 Experiment Invert. abundance Time 27.1 (2.2) 83.7 (22.6) 119.4 (16.9) 76.7 (10.6) Shade Low 29.0 (3.6) 109.6 (41.4) 150.4 (27.7) 96.3 (18.8) High 25.2 (2.7) 57.8 (17.2) 88.3 (14.0) 57.1 (8.8) Litter Oak 33.2 (3.5) 197.7 (36.2) 188.2 (32.1) 139.7 (23.8) Mixture 19.5 (2.1) 29.7 (1.8) 104.5 (9.2) 51.2 (9.7) Maple 28.7 (3.9) 23.7 (2.9) 65.5 (15.0) 39.3 (6.7) Family richness Time 6.3 (0.5) 6.7 (0.5) 6.4 (0.6) 6.5 (0.3) Shade Low 6.9 (0.7) 7.3 (0.8) 7.3 (0.8) 7.2 (0.4) High 5.8 (0.5) 6.0 (0.5) 5.4 (0.6) 5.7 (0.3) Litter Oak 7.8 (0.8) 8.5 (0.8) 8.5 (0.8) 8.3 (0.5) Mixture 5.2 (0.6) 6.2 (0.5) 5.7 (0.7) 5.7 (0.3) Maple 6.0 (0.6) 5.3 (0.6) 5.0 (0.9) 5.4 (0.4) Shannon diversity Time 1.5 (0.1) 0.71 (0.04) 0.84 (0.1) 1.03 (0.1) Shade Low 1.6 (0.1) 0.68 (0.04) 0.93 (0.2) 1.06 (0.1) High 1.51 (0.1) 0.73 (0.1) 0.75 (0.1) 1.0 (0.1) Litter Oak 1.75 (0.1) 0.88 (0.04) 0.95 (0.1) 1.19 (0.1) Mixture 1.37 (0.1) 0.64 (0.03) 0.82 (0.1) 0.94 (0.1) Maple 1.52 (0.1) 0.59 (0.04) 0.75 (0.2) 0.95 (0.1) Phyto. Chl. a (µg/L) Time 2.20 (0.4) 3.27 (0.3) 2.57 (0.6) 2.68 (0.3) Shade Low 2.63 (0.6) 3.98 (0.5) 3.21 (1.0) 3.27 (0.4) High 1.77 (0.4) 2.55 (0.4) 1.94 (0.5) 2.09 (0.3) Litter Oak 2.55 (0.5) 3.53 (0.6) 2.68 (1.1) 2.92 (0.5) Mixture 2.34 (0.8) 3.09 (0.6) 2.17 (1.3) 2.53 (0.5) Maple 1.7 (0.6) 3.19 (0.6) 2.87 (0.5) 2.58 (0.3)
! 137!
Peri. chl. a (µg/cm2) Time 3.42 (1.0) 1.40 (0.5) 0.39 (0.1) 1.74 (0.4) Shade Low 4.20 (1.7) 1.94 (0.9) 0.50 (0.1) 2.21 (0.7) High 2.65 (1.0) 0.87 (0.2) 0.27 (0.1) 1.26 (0.4) Litter Oak 8.12 (1.6) 2.72 (1.2) 0.53 (0.2) 3.79 (1.0) Mixture 0.82 (0.3) 0.92 (0.4) 0.26 (0.1) 0.67 (0.2) Maple 1.33 (0.7) 0.57 (0.1) 0.37 (0.1) 0.76 (0.2) b. Abiotic variable June 22 July 19 August 22 Experiment Light (µE/m2/sec) Time 444.1 (83.6) 410.3 (77.8) 455.7 (67.8) 436.7 (43.5) Shade Low 647.6 (402.3) 618.5 (116.3) 702.7 (60.0) 656.3 (60.4) High 240.7 (37.4) 202.1 (36.6) 208.7 (26.1) 217.2 (19.0) Litter Oak 774.5 (174.2) 701.6 (397.9) 610.2 (136.4) 695.4 (87.5) Mixture 351.7 (73.1) 336.7 (77.1) 432.8 (112.6) 373.7 (49.5) Maple 206.1 (28.3) 192.8 (42.8) 324.1 (85.1) 241.0 (34.2) DO (mg/L) Time 2.8 (0.4) 2.4 (0.2) 3.9 (0.8) 3.1 (0.2) Shade Low 3.4 (0.5) 2.4 (0.6) 3.9 (0.3) 3.2 (0.2) High 2.3 (0.5) 2.4 (0.4) 3.9 (0.3) 2.9 (0.3) Litter Oak 4.7 (0.4) 3.4 (0.3) 4.7 (0.3) 4.3 (0.2) Mixture 2.2 (0.3) 2.0 (0.1) 3.7 (0.3) 2.6 (0.2) Maple 1.1 (0.4) 1.8 (0.1) 3.4 (0.2) 2.3 (0.3) Temperature (°C) Time 22.5 (0.2) 26.2 (0.2) 24.7 (0.2) 24.4 (0.2) Shade Low 22.6 (0.3) 26.4 (0.2) 25.4 (0.1) 24.8 (0.3) High 22.3 (0.2) 25.9 (0.3) 24.0 (0.3) 24.1 (0.3) Litter Oak 23.1 (0.3) 26.7 (0.4) 25.2 (0.2) 25.0 (0.4) Mixture 22.1 (0.2) 25.9 (0.2) 24.1 (0.5) 24.0 (0.4) Maple 22.2 (0.1) 25.8 (0.2) 24.8 (0.3) 24.3 (0.4) Conductivity (µS/cm) Time 322.2 (10.1) 366.9 (13.5) 394.4 (18.0) 361.2 (9.1) Shade Low 343.2 (13.4) 404.6 (15.3) 445.7 (21.1) 397.8 (12.5) High 301.4 (12.2) 329.2 (13.7) 343.1 (16.7) 324.5 (8.6) Litter Oak 282.4 (11.9) 321.0 (17.4) 329.7 (22.9) 311.0 (10.9) Mixture 319.4 (9.3) 365.7 (19.7) 404.6 (25.2) 363.2 (13.4) Maple 365.0 (11.5) 414.0 (17.8) 448.8 (26.6) 409.3 (13.5) ! 138! pH Time 6.9 (0.02) 7.0 (0.01) 7.0 (0.01) 7.0 (0.01) Shade Low 6.9 (0.03) 7.0 (0.01) 7.0 (0.02) 7.0 (0.02) High 6.9 (0.02) 7.0 (0.02) 7.0 (0.02) 7.0 (0.01) Litter Oak 7.0 (0.03) 7.0 (0.01) 7.0 (0.03) 7.0 (0.01) Mixture 6.9 (0.01) 7.0 (0.01) 7.0 (0.01) 7.0 (0.02) Maple 6.8 (0.02) 6.9 (0.02) 7.0 (0.03) 6.9 (0.02)
! 139!
APPENDIX 3C
Results from the two-way ANOVAs following the two-way MANOVA and mean (± SE) values of each variable among shade and litter treatments
Variable df SS MS F P DOC Model 5 249.0 49.8 135.8 < 0.0001 Error 12 4.40 0.37 Total 17 253.4 NO3 Model 5 0.14 0.03 1.1 0.43 Error 12 0.31 0.03 Total 17 0.44 NH3 Model 5 0.23 0.05 4.7 0.013 Error 12 0.12 0.01 Total 17 0.35 PO4 Model 5 81.37 16.3 15.6 < 0.0001 Error 12 12.54 1.04 Total 17 93.91 Phenolics Model 5 62.64 12.5 96.5 < 0.0001 Error 12 1.56 0.13 Total 17 64.20 Remaining litter Model 5 6.84 1.37 3.9 0.02 Error 12 4.20 0.35 Total 17 11.04
Treatment DOC NO3 NH3 PO4 Phenolics Decomposition (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (% remaining) Shade Low 6.45 0.12 0.18 1.13 1.32 40.30 (0.7) (0.1) (0.1) (0.6) (0.2) (6.3) High 9.71 0.18 0.02 2.29 3.31 41.63 (1.5) (0.03) (0.01) (0.95) (0.8) (5.3) Litter Oak 4.03 0.25 0.03 0.02 0.67 58.86 (0.5) (0.1) (0.01) (0.01) (0.1) (4.5) Mixture 8.60 0.07 0.15 0.77 2.37 37.46 (1.9) (0.03) (0.1) (0.1) (0.4) (3.3) Maple 11.61 0.14 0.11 4.33 3.90 26.6 (1.4) (0.04) (0.1) (1.0) (0.1) (5.1) ! 140!
APPENDIX 3D
Permutational multivariate analysis of variance (PERMANOVA) of macroinvertebrate community structure for the June, July, and August sampling dates. Both permutation and adjusted p-values are presented.
June 22 Source df SS MS F P(perm) P(MC) Shade 1 1687.62 1687.62 7.63 0.0013 0.0017 Litter 2 902.32 451.16 2.04 0.0860 0.0997 Interaction 2 474.66 237.33 1.07 0.3800 0.3834 Residual 12 2653.17 221.10 Total 17 5717.77
July 19 Source df SS MS F P(perm) P(MC) Shade 1 875.18 875.18 6.05 0.0131 0.0110 Litter 2 7086.36 3543.18 24.5 0.0001 0.0001 Interaction 2 390.83 195.41 1.35 0.2811 0.2838 Residual 12 1735.19 144.60 Total 17 10087.55
August 24 Source df SS MS F P(perm) P(MC) Shade 1 1308.12 1308.12 7.60 0.0010 0.0020 Litter 2 2417.33 1208.67 7.10 0.0010 0.0010 Interaction 2 719.52 359.76 2.11 0.0950 0.1080 Residual 12 2041.71 170.14 Total 17 6486.69
! 141!
APPENDIX 3E
The PERMANOVA for phytoplankton composition.
Source df SS MS F P(perm) P(MC) Shade 1 1698.51 1698.51 0.73 0.7057 0.6270 Litter 2 4678.38 2339.19 1.01 0.4628 0.4388 Interaction 2 3755.73 1877.87 0.81 0.6409 0.6409 Residual 12 27776.56 2314.71 Total 17 37909.18
The PERMANOVA for zooplankton composition.
Source df SS MS F P(perm) P(MC) Shade 1 1839.35 1839.35 1.04 0.4054 0.3911 Litter 2 2094.97 1047.49 0.60 0.7751 0.7463 Interaction 2 3032.95 1516.48 0.86 0.5655 0.5397 Residual 12 21096.91 1758.08 Total 17 28064.19
! 142!
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SUMMARY AND GENERAL CONCLUSIONS
In this dissertation, I have utilized a field study and two mesocosm experiments to understand how canopy cover and leaf litter species affect macroinvertebrate diversity in vernal pools. Canopy gradients and litter quality are of increasing interest to ecologists, as they influence the autochthonous and allochthonous resources available to wetland food webs.
Vernal pools and other temporary wetlands have been of conservation significance because of their resident amphibian populations, but there are hundreds of macroinvertebrate species in these habitats that are important predators, prey, and nutrient cyclers in the wetland and upland, as well (Colburn 2004). Hydroperiod and water chemistry are considered to be the strongest regulators of macroinvertebrate abundance and diversity in temporary wetlands, but these patterns are most often observed when comparing wetlands with extreme variation in habitat quality (Batzer et al. 2004). This dissertation advances the understanding of vernal pool macroinvertebrates by postulating and demonstrating that canopy cover and litter quality govern macroinvertebrate communities. In general, abundance and diversity increase with wetland resource quantity and quality as canopy decreases, but the strength of this relationship is influenced by leaf litter, which determines the nutrients available to producer communities and can trigger bottom-up cascades in wetland food webs. Concomitantly, there are wetland organisms that preferentially utilize habitats that may be of low quality for others. Thus, this dissertation also indicates that vernal pool conservation efforts must protect wetlands in a variety of habitat contexts, as well as create and maintain vernal pool metacommunities to protect regional species diversity.
In chapter one, I conducted a field study of fifteen vernal pools that varied in physical habitat (size, depth, hydroperiod, canopy cover, land use, etc.). I surveyed macroinvertebrate ! 152! abundance, family richness, and Shannon diversity and measured several variables of water quality, habitat structure, leaf litter inputs, and GIS-based land use. Canopy cover was the chief regulator of macroinvertebrate diversity, as decreased canopy was associated with greater resource quantity and quality (vegetation and algal genera abundance), as well as warmer water temperature and higher dissolved oxygen. Despite substantial variation among the pools, hydroperiod and water chemistry did not affect the macroinvertebrates. Many temporary wetland organisms may be tolerant to variation in water chemistry, while hydroperiod may only act as a selective sieve for which invertebrates can live in vernal pools. Although allochthonous carbon is considered to be the base of vernal pool food webs, my work advances the understanding of these wetlands, as it indicates that autochthonous carbon is also a significant factor governing macroinvertebrate diversity in these wetlands.
I further investigated the relationship between canopy cover and macroinvertebrates in chapter two. In open canopy wetlands, primary production, dissolved oxygen, and water temperature are higher and the abundance, diversity, and performance of many organisms increase with these factors. As anthropogenic and natural processes are changing global forest cover, it is important to understand the mechanisms through which canopy cover affects macroinvertebrates. We designed a mesocosm experiment to examine how habitat quality and known macroinvertebrate compositions respond to variations in canopy cover. Throughout summer 2010, we monitored macroinvertebrate abundance, family richness, and functional group composition, as well as water quality and primary productivity. Low canopy mesocosms sustained the greatest macroinvertebrate abundance and diversity. Although we detected few differences in primary productivity among the treatments, we suspect that it was higher in the low canopy treatments, but had cascaded into the macroinvertebrate communities. Additionally, ! 153! there was a nonsignificant trend for greater predator abundance in the low canopy tanks. Water temperature and dissolved oxygen were greater under low canopy and are known to benefit wetland organisms. However, these factors are of secondary importance to resource quality for wetland organisms and their differences among canopy treatments were likely too small to substantially affect the macroinvertebrates (Williams et al. 2008). This project is the first to experimentally examine how multiple wetland macroinvertebrates respond to different levels of canopy cover and indicates that ongoing changes to forest cover will impact macroinvertebrate abundance and diversity. This is of particular concern for eastern North America, where many areas are in a state of net reforestation (Kauppi et al. 2006; Williams et al. 2008). Throughout this transition, open canopy pools must be protected and maintained, as they are critical for sustaining regional macroinvertebrate biodiversity.
Changes to forest cover are likely to be accompanied by alterations in forest composition.
Because of climate change, altered disturbance regimes, and habitat loss, forests are shifting from oak- to maple-dominated tree species (Nowacki and Abrams 2008; McEwan et al. 2011).
This will change the leaf litter quality and potentially the food webs of temporary wetlands. I utilized a second mesocosm experiment in chapter three to understand how simultaneous alterations in canopy cover and leaf litter quality will affect macroinvertebrates. The mesocosms had either high or low canopy and aged pin oak, silver maple, or a mixture of these two litters.
The litters were chosen because they were the most dominant species observed in chapter one, but are also representative of species affected by the predicted oak-to-maple shift. Similar to chapter two, the mesocosms contained a known initial macroinvertebrate composition that was monitored throughout summer 2011. I also assessed underwater light, dissolved oxygen, conductivity, pH, temperature, phytoplankton, and periphyton biomass throughout the season ! 154!
and determined litter decomposition, DOC, NH3, NO3, PO4, phenolics, and zooplankton abundance and composition at the end of the experiment. Macroinvertebrate abundance, family richness, and several measures of habitat quality were often greatest under low canopy and with oak litter. Most of these patterns were driven by bottom-up productivity, as canopy limits the amount of light that reaches a wetland, but litter quality can further limit light transmittance in the water and induce trophic cascades on wetland food webs (Rubbo and Kiesecker 2004). This relationship is controlled by the C:N ratios in litter, which affect decomposition and the release of DOC and other nutrients into the water (Ostrofsky 1997). In particular, elevated DOC can limit light availability, stimulate microbial communities, and shift food webs from autotrophic to heterotrophic production. This was detected in the mixture and maple mesocosms, which were dominated by grazers and filter feeders, while the oak food webs were driven by periphyton and supported a greater abundance of grazers and predators. This suggests the oak-to-maple shift will reduce resource quality for temporary wetland macroinvertebrate diversity. Additionally, the habitat features of mixture and maple treatments were often similar, indicating that small changes in litter composition could drastically affect temporary wetland food webs. However, the effects of silver maple litter may only be temporary, as light levels increased in the mixture and maple tanks and may have promoted greater autotrophic production at the end of the experiment. This experiment is the first to examine how macroinvertebrate communities are affected by simultaneous alterations to canopy cover and litter quality. While many organisms respond to canopy gradients, this work also indicates these patterns are litter-dependent, as many features of low canopy maple mesocosms were similar to high canopy oak treatments.
In conclusion, this research used field and experimental ecology to address how terrestrial-mediated processes affect macroinvertebrate communities in vernal pools. Most ! 155! importantly, this work contributes to a growing body of knowledge of how canopy cover and leaf litter quality impact temporary wetland organisms. Moreover, as most of these studies have focused on the charismatic amphibian communities, this dissertation provides a much-needed examination of how macroinvertebrates respond to these factors. There are numerous research avenues that could potentially follow this dissertation, from assessing the impacts of invasive or other native litter species on wetland communities and further determining how other habitat factors (e.g., hydroperiod, depth, and anthropogenic activities) will interact with canopy and litter quality. Additionally, emerging evidence suggests some wetland species (salamanders and caddisflies) may preferentially utilize habitats considered to be low quality for other organisms
(Earl et al. 2011; Mehring and Maret 2011). Future studies should investigate whether there are other organisms that display similar behaviors. I also detected the presence of ecological thresholds with respect to changes in canopy cover and litter. In order to fully understand how alterations to resource quantity and quality will affect wetland communities, additional work is necessary to more explicitly determine where these thresholds occur, as well as how they alter autotrophic and heterotrophic production for macroinvertebrates. Ultimately, this research and all other potential projects hinge on preserving temporary wetlands. These habitats often lack formal legal protection and are most susceptible to loss during their dry phase. As a result, continued habitat loss represents a threat to both regional species diversity, as well as improving our knowledge of ecological phenomena. Thus, greater awareness of these wetlands must be brought to the general public and policymakers, alike, to ensure the long-term ecological value and importance of vernal pools.
! 156!
REFERENCES
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Rubbo, M. J. and J. M. Kiesecker. 2004. Leaf litter composition and community structure:
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tadpole performance across forest canopy treatments. Oecologia 155:377-384. ! 158!
APPENDIX A
INSTITUTIONAL ANIMAL CARE AND USE COMMITTEE APPROVAL
Office of Research Compliance 309A University Hall Bowling Green, OH 43403 Phone: (419) 372-7716 Fax: (419) 372-6916 E-mail: [email protected]
February 22, 2011
Dr. Helen Michaels Biological Sciences Bowling Green State University
Re: Annual Renewal of IACUC Protocol 09-004
Title: The Influences of Different Carbon Sources on the Biodiversity of Vernal Pools
Dear Dr. Michaels:
On February 21, 2011 the annual renewal for the above referenced protocol was approved after review by the IACUC. This renewal expires on February 20, 2012. Please consult with the staff of the Animal Facility about any special needs you might have to continue with this project.
Comments: You have used 26 Gray Tree Frogs and are approved for 30. Please submit an addendum request form to increase the number of approved animals.
Sincerely,
Hillary Harms, Ph.D. IACUC Administrator