AQUATIC AND TERRESTRIAL VEGETATION INFLUENCE
LACUSTRINE DRAGONFLY (ORDER ODONATA)
ASSEMBLAGES AT MULTIPLE LIFE STAGES
By
Alysa J. Remsburg
A dissertation submitted in partial fulfillment of
the requirements for the degree of
Doctor of Philosophy
(Zoology)
at the
UNIVERSITY OF WISCONSIN – MADISON
2007
i ACKNOWLEDGMENTS
Reflecting on the contributions of my colleagues and friends during my graduate studies gives me a strong sense of gratitude for the community of support that I have enjoyed. The people who surround and support me deserve more thanks than I can describe here. Friends and family have supported my graduate studies by generously accommodating my tight schedule and warmly offering encouragement throughout the process.
Monica Turner guided my graduate studies in numerous ways. It was her trust in my abilities and willingness to learn about a new study organism that first made this research possible. She encouraged me to pursue the research questions that most interested and inspired me, although it meant charting territory that was new to both of us.
Monica served as the ideal mentor for me by requiring clear communication, modeling an efficient and balanced work ethic, providing critical reviews, and listening compassionately.
This research benefited from the expertise and generosity of outstanding
Wisconsin ecologists. Members of my graduate research committee, Steve Carpenter,
Claudio Gratton, Tony Ives, Bobbi Peckarsky, and Joy Zedler, all offered useful suggestions and critiques on experimental design, pressing research questions, and the manuscripts. Cecile Ane provided additional statistical advice and smiles. Bill Smith,
Bob DuBois, and Robert Bohanan answered (or reassured me that I should try to answer) many questions about field methods, Odonata biology, and species identification.
ii The kindness of my colleagues encouraged me to ask frequently for help or second opinions. The graduate students and post-doctoral researchers working with
Monica have been great friends, reviewers, trouble-shooters, cooks, and sharers of table space. Coworkers at the Center for Limnology’s Trout Lake research station introduced me to aquatic communities and taught me how to launch a boat. Trout Lake station graduate students and staff not only shared their field equipment, data, and study sites, but also lured me away from the bug-picking table when I needed breaks.
My decision to research odonates was made, in part, because I hoped it would attract and enable many undergraduate students to participate in the research. Neither the odonates nor the students have let me down. I enjoyed and benefited from working in the field with Jasmine Batten, Bridget Henning, Melissa Theis, and Phil Winkler. Students who diligently counted labial palp setae in the lab with me for species identification were
Josh Estreen, Josie Iacarella, Debbie Hong, Anh Duong, and Cecilia Leugers. I thank each of them for helping to investigate the nitty-gritty details that underlie much of ecological research.
Anders Olson, my outstanding field assistant and coauthor for the South Africa research, deserves many thanks. Anders helped design and conduct the field experiments on hot
South African days (including re-hanging shade cloth after the ‘Cape Doctor’ blew through, fixing flat bike tires, and countless other efforts). On all of my research chapters, he offered detailed reviews and patiently listened to practice presentations. In addition to ecological contributions, the moral support and understanding he provided have ultimately helped me to reach this stage with peace of mind. We are blessed to have each other as coauthors in life.
iii
TABLE OF CONTENTS
Abstract of dissertation vi
I. Dissertation introduction 1
Literature Cited 4
II. Aquatic and terrestrial drivers of dragonfly (order Odonata)
assemblages within and among north-temperate lakes 7
Abstract 7
Introduction 8
Methods
Study Area 12
Aquatic and terrestrial drivers of Odonata larval density 13
Effects of riparian vegetation on adult abundance 18
Macrophyte effects on larval densities within sites 20
Results
Aquatic and terrestrial drivers of Odonata larval density 22
Effects of riparian vegetation on adult abundance 24
Macrophyte effects on larval densities within sites 25
Discussion 26
Acknowledgments 31
Literature cited 32
Tables 42
Figures 54
iv Appendix 63
III. Gomphidae densities among three life stages: Oviposition site
selection overrides post-recruitment spatial variation 66
Abstract 66
Introduction 67
Methods
Study sites 72
Odonata surveys 72
Habitat variables 74
Data analysis 75
Results 78
Discussion 79
Acknowledgments 85
Literature Cited 86
Tables 95
Figures 101
Appendix 107
IV. Shade alone from invasive riparian trees reduces dragonfly
abundance 108
Abstract 108
Introduction 109
Methods
Study area 111
v Field methods 112
Statistical methods 114
Results 116
Discussion 117
Acknowledgments 119
Literature Cited 120
Figures 125
vi AQUATIC AND TERRESTRIAL VEGETATION INFLUENCE LACUSTRINE
DRAGONFLY (ORDER ODONATA) ASSEMBLAGES AT MULTIPLE
LIFE STAGES
Alysa J. Remsburg
Under the supervision of Professor Monica G. Turner
At the University of Wisconsin-Madison
Abstract
Understanding how animals respond to habitat structure is a fundamental
objective in ecology, but is particularly challenging when the animals require distinct habitats for different life stages. Although the majority of animals have spatially
segregated life stages, research on habitat associations has generally been restricted to
only one of the life stages. The relative importance of aquatic and terrestrial habitat
structure is not well known for the order Odonata (dragonflies and damselflies).
In northern Wisconsin (USA) lakes, housing development contributes to
heterogeneity in riparian and littoral vegetation structure. I surveyed odonate larval
assemblages at 41 sites across 17 lakes. Based on mixed-effects multiple regressions,
model selection identified site-level littoral macrophyte abundance as a key driver of
larval odonate species richness, and riparian wetland plant abundance as the best
predictor for odonate density. Subsequent field experiments on larval predation and adult
site selection helped explain these patterns. Additional surveys of the most abundant
family (Gomphidae) at 22 lake sites indicated that local larval densities depend most on
vii recruitment, which I estimated from adult densities during the previous year. Densities of emergent Gomphidae skins (exuviae) were most related to densities of the later-instar
(second-year) larvae, further suggesting that larval survivorship and movement are less variable spatially than recruitment from the previous life stage.
Field experiments conducted at two South African lakes demonstrated how
riparian tree structures alter adult odonate abundances. Riparian shade reduced the
abundance of odonates at these potential breeding sites. Perch structures, added to
separate experimental plots, supported locally higher adult abundances, but dragonflies
were not sensitive to perch structure density or diversity. Thus shade is the critical
habitat component that should be addressed for odonate conservation in South Africa.
Collectively, this research describes the role of habitat structure during multiple
life stages. Field experiments demonstrate that generalist predators are sensitive to
vegetation structure. The results suggest that riparian habitat selection by animals with
complex life cycles can influence aquatic communities.
1 CHAPTER I
Dissertation introduction
Understanding how habitat structure influences animal distributions is an important challenge faced by both ecologists and land managers. Natural and anthropogenic disturbances frequently alter vegetation physical structure. Fires, pest outbreaks, and land-use changes all alter foliage cover. By contrast, invasive plant species can add dense foliage layers having different architectures from native plant communities. Vegetation structure directly affects animal densities by controlling physical habitat attributes: microclimate, visibility, and permeability. Identifying which habitat components are most infuential for animal diversity and density can facilitate effective habitat conservation or restoration.
Researchers have long investigated how vegetation structure affects animal diversity. Several empirical studies suggest that species richness of arthropods (Lawton
1983, Gardner et al. 1995, Halaj et al. 2000, Langellotto and Denno 2004), birds
(MacArthur and MacArthur 1961, Karr and Roth 1971), rodents (M'Closkey 1976), and lizards (Pianka 1967) increase with greater vegetation complexity. From a theoretical perspective, increasing diversity of habitat structure was one of several hypotheses proposed for the accumulation of more species with increased sampling area (Connor and
McCoy 1979, McGuinness 1984).
Vegetation structure can influence trophic interactions of Odonata species
(dragonflies and damselflies) in water or on land (e.g., Crowder and Cooper 1982,
Wissinger 1988, Diehl 1992). The > 5000 Odonata species worldwide each have a
2 unique spatial, temporal, and/or behavioral niche, but all members of this order are
generalist predators. Adults and larvae both detect prey visually, although larvae also
rely somewhat on mechanoreceptors (Gaino and Rebora 2001, Rebora et al. 2004, Olberg
et al. 2005). Many larvae fall prey to fish or other odonates, including conspecifics
(Wright 1946, Kennedy 1950, Larochelle 1977, Crowder and Cooper 1982, Johnson 1991,
Momot 1995, Stoks and McPeek 2003). Emerging larvae are often consumed at the
water’s edge by birds (Orians and Horn 1969, Wissinger 1988). Predators of adult
odonates include larger odonates, frogs (particularly for ovipositing females), web-
building spiders (especially on weaker, young damselflies), and the most agile bird
species (Corbet 1999).
Considerable research on the biology and behavior of odonates contributes to
their utility as model organisms for animals with complex life cycles (Corbet 1999), but
knowledge of their habitat requirements is lacking. Odonates and other animals with spatially separated life stages may be sensitive to habitat features in either of the ecosystems inhabited during their life history. Although 66-96 % of the lifespan of temperate-range Odonata species is spent in the aquatic larval stage, the terrestrial adult stage is critical for reproduction and habitat selection. Several studies on odonates have investigated how habitat features in either their aquatic or terrestrial life stages can influence adults (McKinnon and May 1994, Carchini et al. 2003, Foote and Hornung
2005, Ward and Mill 2005) or larvae (Diehl 1992, Sahlen 1999, Burcher and Smock
2002), but the relative importance of aquatic and terrestrial habitat structure has not been investigated. Only a few previous authors (Meek and Herman 1991, Wildermuth 1993,
3 De Marco and Resende 2004) have experimentally tested Odonata oviposition site
choices, leaving many questions about the role of riparian land cover unanswered.
In the first research chapter that follows, I address how odonate larval
assemblages vary in response to aquatic and terrestrial habitat features in and around
northern Wisconsin (USA) lakes (Chapter II, with M.G. Turner). The study was
designed to investigate the relative importance of both natural and anthropogenic drivers of odonate assemblages. Following a survey of odonate larvae among and within 17 lakes, I conducted two field experiments to investigate why riparian wetland vegetation and littoral macrophytes were associated with the highest density and diversity of odonates, respectively. This research describes the role of vegetation structure during multiple life stages that span the aquatic-terrestrial ecotone.
The next research chapter focuses on population dynamics of the odonate family that was most abundant in northern Wisconsin lakes, Gomphidae. Using surveys of three dragonfly life stages that I conducted in 2005 and 2006, I incorporate both recruitment from previous life stages and habitat parameters into predictive models. This research demonstrates how population demographics from one life stage carry over to subsequent stages and generations, even when they occur in separate ecosystems.
In the final chapter, I test how invasive riparian trees in South African lakes affect adult dragonfly riparian site use (Chapter IV, with A.C. Olson and M.J. Samways).
Through field experimentation, I isolated the effects of riparian shade and understory perch structures on dragonfly abundance. The results can inform conservation efforts for several odonate species that have been critically imperiled by the invasive trees. This
4 study also contributes to the small but important body of literature investigating how
generalist predators use vegetation structure.
Literature Cited
Burcher, C. L., and L. A. Smock. 2002. Habitat distribution, dietary composition and life history characteristics of odonate nymphs in a blackwater coastal plain stream. American Midland Naturalist 148:75-89.
Carchini, G., M. Di Domenico, T. Pacione, A. G. Solimini, and C. Tanzilli. 2003. Species distribution and habitat features in lentic Odonata. Italian Journal of Zoology 70:39-46.
Connor, E. F., and E. D. McCoy. 1979. Statistics and Biology of the Species-Area Relationship. American Naturalist 113:791-833.
Corbet, P. S. 1999. Dragonflies: Behavior and Ecology of Odonata. Cornell University Press, Ithaca, NY.
Crowder, L. B., and W. E. Cooper. 1982. Habitat structural complexity and the interaction between bluegills and their prey. Ecology 63:1802-1813.
De Marco, P., and D. C. Resende. 2004. Cues for territory choice in two tropical dragonflies. Neotropical Entomology 33:397-401.
Diehl, S. 1992. Fish predation and benthic community structure - the role of omnivory and habitat complexity. Ecology 73:1646-1661.
Foote, A. L., and C. L. R. Hornung. 2005. Odonates as biological indicators of grazing effects on Canadian prairie wetlands. Ecological Entomology 30:273-283.
Gaino, E., and M. Rebora. 2001. Apical antennal sensilla in nymphs of Libellula depressa (Odonata : Libellulidae). Invertebrate Biology 120:162-169.
Gardner, S. M., M. R. Cabido, G. R. Valladares, and S. Diaz. 1995. The Influence of Habitat Structure on Arthropod Diversity in Argentine Semiarid Chaco Forest. Journal of Vegetation Science 6:349-356.
Halaj, J., D. W. Ross, and A. R. Moldenke. 2000. Importance of habitat structure to the arthropod food-web in Douglas-fir canopies. Oikos 90:139-152.
Johnson, D. M. 1991. Behavioral ecology of larval dragonflies and damselflies. Trends in Ecology & Evolution 6:8-13.
5 Karr, J. R., and R. R. Roth. 1971. Vegetation structure and avian diversity in several New World areas. American Naturalist 105:423-&.
Kennedy, C. H. 1950. The relation of dragonfly-eating birds to their prey. Ecological Monographs 20:103-143.
Langellotto, G. A., and R. F. Denno. 2004. Responses of invertebrate natural enemies to complex-structured habitats: a meta-analytical synthesis. Oecologia 139:1-10.
Larochelle, A. 1977. Les amphibiens et reptiles comee predateurs des odonates (premiere note bibliographique). Cordulia 3:81-84, reviewed in Corbet 1999
Lawton, J. H. 1983. Plant Architecture and the Diversity of Phytophagous Insects. Annual Review of Entomology 28:23-39.
M'Closkey, R. T. 1976. Community Structure in Sympatric Rodents. Ecology 57:728-739.
MacArthur, R. H., and J. W. MacArthur. 1961. On bird species diversity. Ecology 42:594-598.
McGuinness, K. A. 1984. Equations and Explanations in the Study of Species Area Curves. Biological Reviews of the Cambridge Philosophical Society 59:423-440.
McKinnon, B., and M. L. May. 1994. Mating habitat choice and reproductive success of Pachydiplax longipennis (Burmeister) (Anisoptera: Libellulidae). Advances in Odonatology 6:59-77.
Meek, S. B., and T. B. Herman. 1991. The influence of oviposition resources on the dispersion and behavior of calopterygid damselflies. Canadian Journal of Zoology-Revue Canadienne De Zoologie 69:835-839.
Momot, W. T. 1995. Redefining the role of crayfish in aquatic systems. Reviews in Fisheries Science 3:33-63.
Olberg, R. M., A. H. Worthington, J. L. Fox, C. E. Bessette, and M. P. Loosemore. 2005. Prey size selection and distance estimation in foraging adult dragonflies. Journal of Comparative Physiology a-Neuroethology Sensory Neural and Behavioral Physiology 191:791-797.
Orians, G. H., and H. S. Horn. 1969. Overlap in foods and foraging of four species of blackbirds in the potholes of central Washington. Ecology 50:930-938.
Pianka, E. R. 1967. Lizard species diversity. Ecology 48:333-351.
Rebora, M., S. Piersanti, and E. Gaino. 2004. Visual and mechanical cues used for prey detection by the larva of Libellula depressa (Odonata Libellulidae). Ethology Ecology & Evolution 16:133-144.
6 Sahlen, G. 1999. The impact of forestry on dragonfly diversity in central Sweden. International Journal of Odonatology 2:177-186.
Stoks, R., and M. A. McPeek. 2003. Predators and life histories shape Lestes damselfly assemblages along a freshwater habitat gradient. Ecology 84:1576-1587.
Ward, L., and P. J. Mill. 2005. Habitat factors influencing the presence of adult Calopteryx splendens (Odonata : Zygoptera). European Journal of Entomology 102:47-51.
Wildermuth, H. 1993. Habitat selection and oviposition site recognition by the dragonfly Aeshna juncea (L.): an experimental approach in natural habitats (Anisoptera: Aeshnidae). Odonatologica 22:27-44.
Wissinger, S. A. 1988. Spatial-distribution, life-history and estimates of survivorship in a 14-species assemblage of larval dragonflies (Odonata, Anisoptera). Freshwater Biology 20:329-340.
Wright, M. 1946. The economic importance of dragonflies (Odonata). Journal of the Tennessee Academy of Science 21:60-71.
7 CHAPTER II
Aquatic and terrestrial drivers of dragonfly (order Odonata) assemblages within
and among north-temperate lakes1
Abstract
The physical structure of vegetation influences animal interactions, movement,
and thermoregulation. Vegetation structure may be a good indicator of the habitat
requirements of generalist predators such as dragonflies and damselflies (order Odonata).
Odonates utilize both aquatic and terrestrial habitats during larval and adult life stages,
respectively, but the relative importance of vegetation in each of these habitats remained
untested prior to this study. We investigated how several aquatic and riparian habitat
variables affect odonate larvae from 41 sites (each 30 m in shoreline length) on 17 lakes
in northern Wisconsin (USA). We used principal components analyses to reduce
multiple habitat variables to two lake-level axes (lake size and amount of adjacent
wetland), two site-level littoral axes (macrophyte abundance and substrate type), and two
site-level riparian axes (foliage height diversity and length of wetland vegetation).
Multiple linear regression indicated that density of the most abundant odonate family,
Gomphidae, was positively related to the length of shorelines with tall, herbaceous
wetland plants adjacent to the water (adjusted R2 = 0.48), whereas species richness was
positively correlated with abundance of littoral macrophytes (adjusted R2 = 0.34).
To investigate whether riparian wetland plants affected larval densities via
recruitment from the adult stage, we compared adult odonate behaviors in response to
1 Coauthor Monica G. Turner
8 two riparian vegetation treatments. Damselfly abundance was higher where we placed
potted wetland plants than at manicured lawns without tall vegetation.
Within littoral sites, we confirmed that larvae from the clasper and sprawler behavioral guilds were most abundant in macrophyte patches. However, when we
tethered larvae in and outside of macrophyte patches to compare predation risk, we found
no difference. Our key findings indicate that odonate larvae may be influenced by vegetation structure in both the aquatic and adjacent riparian habitats, demonstrating how
animals with complex life histories link aquatic and terrestrial communities.
Introduction
Understanding how vegetation structure affects animal abundance and diversity
has long been a fundamental objective in ecology (e.g., MacArthur 1965, Karr and Roth
1971). The physical structure of vegetation influences predator-prey interactions,
thermoregulation, landscape permeability (ease of movement), and mate detection.
Vegetation structure can describe habitat suitability for some predacious species better
than more time-intensive habitat descriptions such as plant species composition (e.g.,
Pianka 1967, Brose 2003, De Souza and Martins 2005, Muntifering et al. 2006). While early studies of habitat structure focused on terrestrial systems, more recent work has demonstrated that animals in aquatic and marine ecosystems also depend on physical structure. For example, fish and invertebrate communities exhibit greater diversity and abundance in habitats with structure provided by coral, macrophytes, or fallen trees (e.g.,
Werner et al. 1983, Carr 1991, Tupper and Boutilier 1995, Cheruvelil et al. 2002, Sass
2004, Milner and Gloyne-Phillips 2005).
9 Animals that use multiple habitats for different behaviors or life stages may
respond to vegetation structure in each of the habitats they occupy. A few studies have compared the importance of aquatic and terrestrial habitat features for animals that inhabit both of these ecosystems, although most of these focused on amphibians (Marnell
1998, Scribner et al. 2001, Campbell et al. 2004, Ficetola et al. 2004). Research
investigating how Odonata (dragonfly and damselfly) species respond to both aquatic and
terrestrial habitat structure is lacking. The long aquatic phase (one to four years) of many odonates in temperate regions may increase the importance of aquatic habitat features for odonates relative to amphibians and other aquatic insects with shorter aquatic phases.
Land-water ecotones are often structurally complex and provide critical habitat for many animals (Decamps et al. 2004). Structural complexity arises from the combination of abiotic characteristics (e.g., substrate, slope), littoral vegetation, and riparian vegetation. Human activity also interacts with natural drivers of structural
complexity. Housing development is expanding rapidly in many riparian areas (Burger
2000, Hansen et al. 2005), including the upper Midwest, USA (Schnaiberg et al. 2002,
Gonzalez-Abraham et al. 2007), where it leads to simplification of vegetation structure by
removal of the shrub layer, floating macrophytes, and coarse wood. These structural
changes may have consequences for a variety of animals in both the riparian and littoral
(near-shore aquatic) zones (Bryan and Scarnecchia 1992, Schindler et al. 2000, Lindsay
et al. 2002, Woodford and Meyer 2003, Henning and Remsburg in review). The relative
influence of natural and anthropogenic drivers on Odonata communities is not well
known.
10 Odonates require both aquatic and terrestrial habitats during their complex life cycle. However, the role of the terrestrial habitat in determining odonate abundance and diversity remains unclear. Odonata species generally show little response to particular plant species (terrestrial or aquatic), but diversity and abundances of many species correlate positively with local vegetation structure (Buchwald 1992, Foote and Hornung
2005). Shoreline vegetation may provide adults with important perching structures for thermoregulation, foraging, territory defense, mate attraction, copulation, nocturnal roosting, or protection from adverse weather (Buchwald 1992, Wildermuth 1993,
McKinnon and May 1994, Rouquette and Thompson 2007). Several researchers in pond and river ecosystems have documented lower odonate diversity and density at sites with reduced riparian vegetation structure, although these studies focused mostly on the adult stage (Table 1). To our knowledge, effects of lakeshore vegetation on Odonata larval assemblages have remained untested. If community controls in lakes behave similarly to other systems, we would expect lower odonate diversity and density at sites with fewer vegetation layers and plant types in the riparian zone. Odonate populations in lakes, however, could be controlled more by fish predation than those in pond and river systems if fish densities in littoral zones are higher in lakes.
Vegetation and other physical structures can interact with top-down controls on odonate larvae or on odonate tenerals (immature adults that have recently emerged from the water) to influence population sizes. Riparian vegetation, for example, may harbor both emerging odonates and their predators (Duffy 1994, Whitaker et al. 2000). In the water, mesocosm experiments have shown that odonate larvae find refuge from predators in aquatic macrophytes (Crowder and Cooper 1982, Thompson 1987, Dionne et al. 1990,
11 Diehl 1992). However, unlike in enclosed experiments, local predator densities in lakes
also tend to increase where macrophytes offer protection or higher prey densities (Werner
et al. 1983, Weaver et al. 1997, Gamboa-Perez and Schmitter-Soto 1999). Littoral coarse
wood or mucky substrates may also provide refugia from predators for some larval
odonate species (Schmude 1998, Alzmann et al. 1999, Burcher and Smock 2002). In
addition to these drivers of local habitat heterogeneity, predation on odonates can vary
among water bodies as a result of water chemistry or fish communities (Johnson and
Crowley 1980, McPeek 1990, Johansson and Brodin 2003).
We surveyed odonates across a wide range of lake and site conditions to address
the following research question: (1) How do odonate abundance and community
composition vary with habitat structure both among and within lakes? We measured
habitat variables that could directly or indirectly affect Odonata mortality, thermoregulation, perching, territory selection, or emergence. Among lakes, we expected that odonate abundance and species richness would decline with increasing fish
abundance, especially the abundance of insectivorous fish; lake depth; and water clarity
(Aksnes and Giske 1993, Davies-Colley and Smith 2001). However, we also
hypothesized that lakes with a greater perimeter-to-area ratio and morphometry index
might be associated with more bays and shallow littoral zones that would provide
odonates with more littoral oviposition areas. Because lakes with a higher specific
conductance correlate with higher primary productivity and invertebrate density, these
lakes were also expected to support more odonates (Hrabik et al. 2005). We anticipated
that odonate density and richness would correlate negatively with lake measures of
development because residential development corresponds with reduced littoral and
12 riparian habitat complexity. At the site scale (within lakes), we expected greater odonate density and richness at sites having more abundant riparian understory vegetation, greater macrophyte abundance, lower slopes, and wetland vegetation.
Isolating the processes that underlie patterns in community ecology calls for experimental research (Agrawal et al. 2007). We conducted field experiments designed to explore the potential mechanisms underpinning the relationships we observed in our comparative study. These experiments addressed two additional research questions: (2)
Does riparian wetland vegetation influence the use of riparian sites by adult dragonflies?
Because adult odonates may use riparian vegetation as cues of littoral quality or perch on these structures, we expected that riparian sites with tall wetland plants added would have higher odonate abundances and oviposition occurrences than riparian lawn sites lacking tall vegetation. Our last question was (3) Does macrophyte density affect predation on
odonate larvae in lakes? Variation in predation rates is one factor that could result in
spatially variable odonate densities. We anticipated that predation might decrease with
macrophyte density because the aquatic vegetation would reduce visual detection of
larval odonates by fish.
Methods
Study area
We conducted this study in Vilas County in northeastern Wisconsin, USA, in the
center of the Northern Highlands Lake District (see Peterson et al. (2003) for details on
NHLD). Curtis (1959) classified the vegetation as northern wet forest and boreal forest.
The forested landscape is interrupted by a high density of glacial lakes (over 1300 lakes
13 in the 2639 km2 county), which cover 13 % of the NHLD surface area (Black et al. 1963).
Lakeshores in the area have experienced unprecedented lakeshore housing development
(Schnaiberg et al. 2002, Gonzalez-Abraham et al. 2007), thereby altering spatial heterogeneity of habitat components in the lake-land ecotone.
For the first research question, we surveyed larval Odonata assemblages at 41 lake sites (Appendix 1), and collected lake- and site-level environmental information.
We defined sites as 30-m segments along the shoreline contour, including the adjacent littoral zone to a water depth of 1 m and the riparian zone 6 m inland from the ordinary high water mark. Sites were distributed among 17 lakes in Vilas County, Wisconsin
(Table 2); the number of sites per lake ranged from one to five. Of sites on the same lakes, we selected sites with qualitative differences in littoral or riparian vegetation structure. Because housing development contributed to the riparian vegetation heterogeneity, we selected half of the sites (21) from developed areas and the rest from undeveloped, forested areas. All sites were separated from each other by at least 120 m
(with only two pairs of sites < 500 m apart).
Aquatic and terrestrial drivers of Odonata larval density and richness
At each of the 41 sites, we collected odonate larvae samples with a D-frame net to scoop benthic material along four 16-m contour transects that paralleled the shore. We centered transects within the 30 m site length and placed them at 0.25-, 0.5-, 0.75-, and
1.0-m water depths; transect distance from shore varied with littoral slope at each site.
On one day per site during June – July of 2004, we performed 20 benthic scoops (4 in
14 each of the two shallower transects and 6 in each of the two deeper transects) covering 12
m2 at each site. Despite differences in emergence dates, the semivoltine life history of
most odonates in northern Wisconsin lakes (except some Zygoptera and smaller
Libellulidae, R. DuBois, personal communication) suggests presence of most larval populations throughout the year at our sites. Larvae were sorted from benthic material by hand, preserved in 70% ethanol, and identified to species or family (for suborder
Zygoptera) using the keys of Needham et al. (2000) and Westfall and May (1996).
To characterize lake-level fish abundance, human activity, vegetation, and water chemistry, we considered 12 variables for each of the 17 lakes in this study: catch-per- unit-effort (CPUE) of total fish and insectivorous fish (based on eight nights of boom- style electrofishing, 24 minnow nets, and 24 crayfish traps per lake in 2001, 2002, or
2003), number of buildings within 100 m of the lake, density of buildings (number of buildings within 100 m divided by lake perimeter), maximum depth, perimeter, area, specific conductance (dissolved ionic strength), secchi depth (water clarity), morphometry (a dimensionless unit: lake perimeter divided by the perimeter of a circle with the same area as the lake), length of lake perimeter in wetlands, and percent of perimeter in wetlands (Table 3). S.R. Carpenter and colleagues (2006) compiled all of
the lake-level data from 2001-2004.
The nine littoral habitat variables recorded at each site were: slope to 1 m depth;
number of submerged logs > 5 cm in diameter (coarse wood) at 0.5 m depth (Marburg et
al. 2006); sand, rock, gravel, and muck substrate dominance categories at 0.5 m depth
(Marburg et al. 2006); mean macrophyte species richness, percent cover, and frequency
of emergents (Table 3). Macrophyte species and percent cover (in the littoral zone out to
15 2 m depth) were recorded along one transect perpendicular to the shoreline at the center of each site (Alexander 2005).
We recorded 10 riparian variables for each site: slope, canopy cover category
(sparse, moderate, or full), length of site with shrubs or saplings (of maximum height
0.75 – 2 m) present, length of site with wetland herbaceous plants > 0.5 m tall (Carex,
Typha, and/or Iris species; hereafter termed ‘tall wetland plants’), minimum distance of vegetation from water’s edge, mean structural diversity, mean foliage height diversity, maximum understory plant height (up to 2.5 m), and percent cover of both shrubs and herbaceous plants within 0.5 m of the ordinary high water mark (Table 3). We estimated mean structural diversity and mean foliage height diversity from a point-intercept method
(Mueller-Dombois and Ellenberg 1974) at 18 random locations per site where we held
2.5 m-tall poles (2.5 cm in diameter) perpendicular to the ground. For each pole, we calculated structural diversity by summing the number of different structural categories
(out of 11: trunk > 3 cm diameter, woody stem, dead woody steam, herbaceous stem, moss, fungus, rock, wood chips, foliage or flower, permanent human structure, other non- natural object ) intersected by the pole. We defined foliage height diversity as the number of vertical layers (out of 10) on the pole intersected by plants. All vegetation data other than shoreline percent cover variables refer to the area within 6 m of the ordinary high water mark but outside of the water.
We used mixed-effects multiple linear regression models to compare the effects of lake- and site-level predictors on total odonate density and richness (both square-root transformed to meet normality assumptions) across the 41 sites. We also constructed mixed-effects linear regressions to evaluate predictors of densities for the most common
16 larval family, Gomphidae. Because all odonate families other than Gomphidae were too
rare to be distributed normally, we used mixed-effects logistic models, including a fixed-
effects term for area sampled, to compare habitat effects on presence or absence of the
Zygoptera (damselfly) suborder and the four other Anisoptera (dragonfly) families observed. For linear and logistic models, we assigned lake as a random-effect term because effects of specific lakes were not of interest, yet sites on the same lakes were not independent of each other. Analyses were completed using R v.2.4.1 software (R Core
Development Team, Vienna, Austria) including the nlme package for mixed-effects models.
To reduce the 12 lake, 9 littoral, and 10 riparian predictor variables to a smaller set of orthogonal variables, we conducted separate principal components analyses (PCA, using PC-ORD 4 software, Gleneden Beach, OR) on each of these variable categories.
Because many lake, littoral, and riparian variables correlated strongly within the three categories, conducting three separate PCA’s enabled us to maximize the variation in site predictors summarized by the PCA scores. PCA’s were based on centered, standardized matrices of Pearson correlation coefficients. Pair-wise scatterplots within the three categories indicated continuous distributions and linear relationships among variables.
After examining PCA eigenvalues and habitat variable correlations with the PCA scores
(Table 3), we chose to use the first two PCA axes from each category of variables in the mixed-effects multiple linear regressions of larval densities. Scores from the three separate PCA processes were generally not correlated with each other (Pearson correlation coefficients < 0.3), except for the first lake axis and first riparian axis (r = -
0.41).
17 We used an information theoretic approach to compare alternative linear models
containing zero to six of the six PCA scores. Unlike hypothesis testing, model selection
using information theory does not require use of arbitrary cutoff values (such as α =
0.05), nor does it involve the Bonferroni problems of multiple tests (Burnham and
Anderson 2002). The 64 candidate models (including permutations for all possible
combinations) were ranked according to second-order criterion Akaike’s Information
Criterion (AICc) values (Sugiura 1978). AICc has a bias correction factor to help prevent
over-parameterized models when sample sizes are small. Selection of the “best”
predictive models based on AICc is more closely related to the sample size than use of
hypothesis testing or Bayseian information criterion (Burnham and Anderson 2002).
Relative empirical support for a model i is indicated by ∆i, the difference between AICc for model i and the smallest AICc of the candidate models. Strength of evidence for each of the candidate models can be compared using Akaike weights, wi, the log likelihood of model i divided by the sum of log likelihoods for all candidate models (Burnham and
Anderson 2002). Information theory suggests that a dataset may equivalently support multiple predictive models (particularly if ∆i <2). We used all of the candidate models to
estimate the relative importance of each predictor by summing the Akaike weights (∑w)
over all models in which a variable appeared (Burnham and Anderson 2002).
We present adjusted R2 values for linear regressions, but the maximum likelihood
model fitting method employed for our mixed-effects models could not be used to
calculate the R2 values. We therefore calculated the adjusted R2 values from fixed-effects
models (with the parameters selected from mixed-effects models) where lake was
included as a fixed effect. We also used lake as a fixed effect to estimate pseudo-R2
18 values (one minus the ratio of the model’s log-likelihood to the null model’s log-
likelihood) for the logistic regression models (Menard 2000).
Effects of riparian vegetation on adult abundance
To investigate whether adult site use helps explain larval distributions within
lakes, we recorded adult Odonata behavioral responses to riparian vegetation treatments
at two location blocks. One block had very dense aquatic macrophytes (‘dense-
macrophyte block’), while the other block had very few aquatic macrophytes (‘sparse-
macrophyte block’). Both blocks were on bays in the same lake (Little Saint Germain),
had stone seawalls separating trimmed lawns from the water, similar aspect, and riparian
slopes near zero.
The two riparian vegetation treatments applied to each block were: trimmed lawns
with 20 dwarf cattail (Typha minima) plants in pots added at the shoreline (‘Typha’
plots), and trimmed lawns without potted or other tall plants (‘no Typha’ plots). We chose the dwarf cattails, which were 0.6 m in height, because they were readily available at nurseries and closely resembled the height and rigidity of native sedges. Ten meters separated the two treatment plots; each plot spanned 15 m along the shoreline and 2 m inland. We replicated both treatments on 21 days at the dense macrophyte block and on
19 days at the sparse macrophyte block. Although this design may have allowed repeated observations of some individuals, we believe that separate observation days could be treated as experimental replicates because adult odonates move away from the shoreline
to roosting sites each evening (Askew 1982, Bried and Ervin 2006). Most of the genera
we observed probably do not demonstrate site attachment across multiple days (Jacobs
19 1955, Logan 1971). At both blocks, placement of the two treatments alternated by day
between the two plots. We thereby separated Typha treatment effects from littoral or
location-specific influences.
On each observation day, we set up the Typha treatments at least three hours prior
to recording odonate behaviors at both treatments within the same block. In order to
capture the period of greatest odonate activity, observations took place from 25 June to
18 August 2006, between 12:00 and 16:00, on days with relatively low wind and cloud
cover. A single observer (AJR) stood at one corner of the plot in order to survey odonates in the whole plot simultaneously, including those over the water adjacent to the riparian plot (within approximately 5 m of shore). To improve detection of the less active
odonates perched on shore, the observer also walked slowly across the plot two times per
observation period. We recorded the total number of individuals present and whether or
not each genus oviposited within the plot during ten minutes of observation. Using
binoculars to verify species would have precluded comprehensive observations across the
whole plot. The observer estimated relative odonate abundance within the plot by
counting all individuals visible at the instant when the ten-minute timer sounded.
We conducted blocked mixed-effects ANOVAs to compare Zygoptera and
Anisoptera abundance estimates among treatments, where observation day was treated as
a random effect. We pooled responses by suborder because odonate abundances were
low overall, and individuals within the same suborder seemed to behave similarly. We
tested effects of the Typha treatment on oviposition location choice only at the dense-
macrophyte block (where oviposition frequency was highest), using McNemar’s Chi-
squared tests (Argesti 2002). McNemar’s test compared the number of days when
20 oviposition was observed at the Typha treatment but not at the other treatment with the
number of days that oviposition was observed at the no-Typha treatment but not at the
Typha treatment.
Macrophyte effects on larval densities within sites
To evaluate macrophyte effects on odonate larval densities at a finer scale (5 – 10
m2) than the site-level surveys, we sampled larvae at 18 microsites from nine lakes (Table
3) in July 2006. We chose nine microsites that had abundant submerged macrophytes
and nine adjacent microsites that completely lacked macrophytes. The macrophyte and
non-macrophyte microsites, paired by lake, were separated by 4 to 15 m (except for one
pair separated by 40 m across a bay). Paired microsites had similar substrates and
distances from structures such as docks. We used a D-net to collect benthos enclosed by
a cylinder (i.e., trash can with the bottom cut off) placed at five adjacent subsamples at
0.3 m depth and five subsamples at 0.5 m depth. The cylinder protocol ensured that
larvae within each subsample could not move out of the area during sampling because it
extended from the substrate to the water’s surface. By pooling the 10 subsamples, we
sampled all larvae in 7 m2 of benthos at each microsite.
We compared densities of three larval behavior guilds among the 18 microsites with and without macrophytes using pairwise Wilcoxon tests. Behavioral guilds provided more informative response variables than families for this experiment because densities of some families were very low within the (relatively small) microsite sample areas. Of the behavioral guilds, burrowers hide amid a layer of detritus or under the top
21 layer of sand, while sprawlers lie flat on detritus, and claspers climb on the leaves, stems,
and roots of macrophytes (Table 4).
In August 2006 we conducted a predation experiment at 42 microsites from 21
lakes (including the 18 microsites discussed above; Table 3). Microsites were paired by
lake as in the surveys above. Macrophyte structures at the microsites varied among lakes,
so we recorded macrophyte species and one of four macrophyte biomass categories
(none, low, moderate, or high) for each microsite based on visual estimates.
At each microsite, we used Superglue® to attach monofilament fishing lines 30-40
cm in length onto the abdomens of eight live larvae (in later instars) collected from
nearby lakes or ponds. We used a variety of Anisoptera species and larval instars,
although most larvae were Cordulia shurtleffi 2 cm in length. We controlled for variations in larval behavior and predator appeal among different odonate species by
matching larval species and sizes (range: 15 – 30 cm length) for paired microsites within
the same lake. Laboratory observations indicated that fish predation occurred on tethered
larvae, resulting in empty lines where predation had occurred. We also verified in the
laboratory that larvae never became unglued from the fishing lines. These tethered larvae
were attached just above the microsite benthos by tying fishing lines to submerged metal
stakes. We retrieved tethered larvae remaining after 3.5 - 6 hours of exposure to
predation at all microsites (timing paired by lake). All tethering experiments occurred
during the afternoon (between 12:00 and 19:00), when fish densities in macrophytes are
likely greatest (Hall et al. 1979, Gliwicz et al. 2006). We recorded larval mortality
(proportion of larvae missing after predator exposure) as an estimate of relative predation
rates.
22 For comparisons of tethered larval mortality among 42 microsites with different
categories of macrophyte structure, we conducted an analysis of variance (ANOVA) with lake added as a random effect. Because variance partitioning indicated that a large portion of the variance occurred at the lake level, we also used mixed-effects linear regressions to test effects of secchi depth and conductance at the lake level on larval mortality (fish CPUE for these lakes was not available). As in the first research question, we completed all analyses using R (version 2.4.1) software.
Results
Aquatic and terrestrial drivers of Odonata larval density and richness
We found larvae from 27 Odonata species at the 41 study sites (Table 4). Species richness ranged from 0 to 10 species per (12 m2) site (mean = 5.2 species, SD = 3.4
species). Gomphus spicatus was the most abundant (mean = 0.45 larvae m-2, SD = 0.86
larvae m-2) and frequently-occurring species (present at 67 % of the sites). Gomphus,
which included three other species (G. exilis, lividus, and fraternus), was by far the most
prevalent genus (occurring at 86 % of the sites). The Gomphidae family occurred at 87.8
% of sites, while Zygoptera, Macrominae, Cordulinae, Libellulinae, and Aeshnidae
occurred at 44, 34, 22, 20, and 17 % of sites, respectively.
Total variance summarized by the first two axes of the PCA’s were 52.3 % from
the 12 lake-level variables (axis 1 = 30.4 %, axis 2 = 22.0 %), 53.9 % from the 9 site-
level littoral variables (axis 1 = 34.9 %, axis 2 = 19.0 %), and 56.3 % from the 10 site-
level riparian variables (axis 1 = 37.8 %, axis 2 = 18.6 %; Table 3). The third lake-level
axis represented 14.6% of the variance and correlated almost exclusively with secchi
23 depth (r = -0.9). Correlations of odonate response variables with secchi depth did not
suggest significant relationships, so we did not add the third lake axis to the set of
candidate models.
The six PCA axes that we chose for inclusion in the multivariate regressions have
straightforward interpretations. The first PCA axis representing lake-level variables
(hereafter ‘lake size’) correlates positively with lake area, lake perimeter, number of
buildings, and specific conductance, while the second lake PCA axis (hereafter ‘lake
wetlands’) correlates positively with the length of wetlands bordering the lake and negatively with insectivorous fish CPUE (Fig. 2a, Table 3).
The first PCA axis of littoral variables (hereafter ‘littoral macrophytes’) correlates positively with macrophyte richness, macrophyte percent cover, and frequency of emergent macrophytes and negatively with rocky substrate dominance. The second littoral PCA axis (hereafter ‘littoral muckiness” correlates positively with mucky substrate dominance and negatively with sandy substrate dominance (Fig. 2b, Table 3).
The first riparian axis (hereafter ‘riparian structural complexity’) correlates positively with foliage height diversity, length of site with shrubs, structural diversity, and canopy cover estimate. Finally, the second riparian axis (hereafter ‘riparian tall wetland plants’) correlates most negatively with the length of sites bordered by tall wetland herbaceous plants and shoreline percent cover of herbaceous plants (Fig. 2c;
Table 3).
More of the variance in density and richness of odonates occurred at the site
level than lake level, with all of the variance in Gomphidae densities occurring at the site
level (Table 5). The information theoretic approach for model selection allowed us to
24 assess the importance of predictor variables across all possible models, rather than only from a somewhat arbitrary “best” model. Density of all odonates combined was best explained by increasing riparian tall wetland plants, followed by increasing littoral macrophytes (Tables 6-7). Importance of the tall wetland plant axis was even greater for the Gomphidae family, and the model fit was better (Tables 6-7; Fig. 3).
The strongest predictor for Odonata species richness was increasing littoral macrophyte abundance (Table 7). This was also the best predictor for presence of both
Zygoptera and Libellulinae (Table 7). Littoral “muckiness” was the best predictor for presence of both Aeshnidae and Cordulinae (Table 7).
Effects of riparian vegetation on adult abundance
We observed adult odonates from 15 different genera at the experimental plots, but only Enallagma (Zygoptera: Coenagrionidae), Leucorrhinia, and Libellula spp.
(Anisoptera: Libellulinae) were present on > 50 % of the observation days (96 %, 65 % and 56 %, respectively). Gomphidae adults were observed at these plots in much lower abundances than Libellulidae species, possibly because they spend more time perched in treetops and are less active by late July. Anisoptera abundance was greatest at the high- macrophyte block and did not differ significantly with Typha treatment (p > 0.1; Fig. 4a).
Zygoptera abundance, however, was significantly higher at the riparian Typha treatments
(Table 8; Fig. 4b). We recorded oviposition for Anisoptera and Zygoptera on only 11 and 6 days, respectively. Within each observation day, oviposition for both suborders occurred more frequently at plots with the Typha treatment than plots without Typha,
25 although these differences were not significant (McNemar’s chi-squared tests p > 0.1;
Table 8).
Macrophyte effects on larval densities within sites
At the 18 paired microsites, we found most of the same species as in our 2004
survey, but also three new Libellulids: Leucorhinia hudsonica, L. proxima, and Tramea
Carolina (Table 4). Unlike the broad-scale study, the most abundant species was
Epitheca cynosura (mean = 0.45 larvae m-2, SD = 1.0 larvae m-2), even though it only
occurred at macrophyte microsites (Fig. 5). Gomphus species occurred at 67 % of the microsites.
We found many more odonates in areas with dense macrophytes (mean = 5.2 larvae m-2, SD = 3.8 larvae m-2) than those without (mean = 0.8 larvae m-2, SD = 1.0
larvae m-2; Wilcoxon rank sum test W = 74, p = 0.0035). Clasper and sprawler guilds
were most abundant in dense macrophyte areas, while the burrower larval guild did not
differ significantly with macrophytes (Fig. 6). We also observed slightly higher species
richness at the macrophyte microsites (mean = 5.22 Anisoptera species, SD = 3.35) than
those without (mean = 2.11 Anisoptera species, SD = 1.83; Wilcoxon rank sum test W =
63, p = 0.05).
Tethered larvae mortality ranged from 0 to 100 % among the 42 microsites (mean
= 53 %, SD = 32 %). On separate occasions, we observed two bluegills and one small painted turtle feeding on tethered larvae in the field. Mortality did not vary with macrophyte biomass category (F1,20 = 1.19, p = 0.3), nor with macrophyte presence
(paired t-test: t20 = -1.13, p = 0.3). Microsites with macrophytes present had mean larval
26 mortality of 47 % (SE = 6.9 %), and microsites with no macrophytes had a mean mortality of 56 % larvae (SE = 7.5%). A large portion of the total variance in tethered
larval mortality (39.1 %) occurred at the lake level, but did not correlate significantly
with lake clarity or conductance (p > 0.1).
Discussion
Riparian vegetation influences larval odonate assemblages in the adjacent littoral
ecosystem. Existing literature on ecological links from riparian landscapes to aquatic
communities has focused on detrital, nutrient, and structural subsidies (Wallace et al.
1997, Nakano et al. 1999, Bastow et al. 2002, Sass 2004, Baxter et al. 2005, Roth et al.
2007). Our results (both here and in Chapter III) demonstrate that animals with complex
life histories provide another important link from terrestrial to aquatic ecosystems via
habitat selection. These findings also add an aquatic dimension to the growing body of
research, previously focused on agricultural systems, investigating how vegetation
structure in one ecosystem affects insect abundances in an adjacent system (Langellotto
and Denno 2004, Tscharntke et al. 2005, Bianchi et al. 2006). Site-level riparian
vegetation was, in fact, a better predictor of total larval Odonata density, Gomphidae
density, and Macrominae presence than site- or lake-level aquatic habitat features. In
particular, larval density of the most abundant family, Gomphidae, increased with the
amount of tall wetland plants adjacent to – but not in – the lakes (Table 7; Fig. 3). Other
studies have linked adult odonates to riparian sedge or rush abundance (Van Buskirk
1986, Clark and Samways 1996, Foote and Hornung 2005, Hofmann and Mason 2005b,
27 Rouquette and Thompson 2007), but this is the first demonstration of a correlation
between odonate larvae and terrestrial herbaceous vegetation.
The larvae’s stronger relationship with tall wetland vegetation than with riparian
shrubs may reflect the role of the plant structure. Plants included in this category (Carex,
Typha, and Iris spp.) are all rigid and provide stable vertical structures on which
emerging larvae complete hardening of the wings (personal observation). However,
Gomphidae (unlike most other Anisoptera families) can also emerge on horizontal
surfaces (Eda 1963). Whether availability of suitable emergence structures significantly
influence odonate population abundances remains to be tested.
Most larvae are unlikely to move more than 20 m from the littoral site where eggs
were laid (Ubukata 1984, Schaffner and Anholt 1998, but see Alzmann et al. 1999), so the correlations between larvae and riparian vegetation may also reflect adult habitat selection as a consequence of larval recruitment (Chapter III). Adults may prefer the rigid wetland plants for perching because of their height or open structure that facilitates
thermoregulation (May 1976, Pezalla 1979). Other authors’ observations of adult odonates (from other families) have indicated strong correlations between perch locations
and presence of sedges or rushes (Van Buskirk 1986, Clark and Samways 1996, Foote
and Hornung 2005). As with all oviparous species, it is advantageous for odonates to lay
eggs at sites where they expect high survivorship of eggs and larvae (Storch and Frynta
1999). Because adults cannot assess all aspects of aquatic habitat suitability for larvae
(Wildermuth 1993), several authors have hypothesized that adult odonates use emergent
aquatic plants or riparian plants as proximate cues of macrophyte, substrate, prey
availability, or predator conditions in littoral sites (Buchwald 1992, Wildermuth 1992,
28 McKinnon and May 1994). Male and female odonates split their time at riparian sites between foraging and mating behaviors (e.g., Parr 1983), so riparian vegetation could also affect adult abundances by correlating with prey densities (Baird and May 1997,
Whitaker et al. 2000, Garono and Kooser 2001, Henning and Remsburg in review).
These hypotheses led us to the potted Typha experiment.
Adult riparian site use generally corresponds with oviposition locations (Corbet
1999). Thus the trends recorded for higher adult abundance and oviposition at treatments with only 20 potted Typha (Table 9, Fig. 4) suggest that site selection by adults may have led to higher Gomphidae densities at sites with more wetland vegetation (Fig. 3). The rarity of oviposition events indicates that it would be difficult to demonstrate oviposition preferences without significantly longer observation periods. Previous researchers have added artificial perches to experimental sites to increase local adult odonate densities for other experiments or to compare perch heights (Wolf and Waltz 1988, Rehfeldt 1990,
Baird and May 1997, May and Baird 2002, De Marco and Resende 2004), but this was the first study (that we know of) to test directly whether placement of perch structures affects adult site use or oviposition behavior.
Aeshnidae were most likely to occur at sites with greater littoral muckiness and riparian tall wetland plants (Table 7). Because these are endophytic species, we expected that littoral macrophytes would be the most important predictor of their presence.
Unfortunately, our data from the paired microsites could not address this question because we observed so few Aeshnidae larvae in those surveys. However, coarse wood, an additional oviposition substrate for some Aeshnidae (Mead 2003), also correlated positively with the littoral muckiness axis that explained most of the variation in
29 Aeshnidae presence (Tables 6-7). In addition, odonates likely make use of floating
macrophytes that shift among lake sites through time, which the single macrophyte record per site that we used would not reflect. As the strongest fliers of all odonates,
Aeshnidae may also respond to littoral macrophytes at a larger spatial or temporal scale
than the other odonates.
We found increasing species richness at sites with more littoral macrophytes,
which corresponds with more Zygoptera and Libellulinae occurrences at those sites
(Table 7). These results concur with previous studies of odonate assemblages in lakes:
compared to other biotic and abiotic variables, larvae showed the strongest associations
with macrophyte biomass (Weatherhead and James 2001, Michaletz et al. 2005, Osborn
2005). In addition to zygopterans’ requirement of emergent macrophytes for endophytic
oviposition, both Zygoptera and Libellulinae fall into the clasper and sprawler guilds that
use macrophytes as larvae (Fig. 6). Even though sprawlers generally do not climb
directly in macrophytes, they probably rely on macrophytes and the associated detritus
for cover. Finally, all Zygoptera and Libellulinae species are perchers as adults, so
emergent macrophytes may also serve as important perching structures for adults at
oviposition sites (McKinnon and May 1994, Switzer and Walters 1999, De Marco and
Resende 2004).
Clasper and sprawler larval densities were highest at microsites with dense macrophytes (Fig. 6), but predation risk for tethered larvae did not help explain this pattern. Our findings differ from mesocosm predation experiments showing the importance of macrophytes in predator avoidance (Crowder and Cooper 1982, Thompson
1987, Dionne et al. 1990, Diehl 1992). Tethering can interfere with organisms’ predator
30 avoidance behaviors (Curran and Able 1998, Kneib and Scheele 2000), but field and aquarium observations gave us no reason to suspect that effects of tethering on larval behavior differed among microsite treatments. Tethered larvae appeared capable of burrowing or climbing up macrophytes, depending on their behavioral guild. Although we used odonate larvae paired by size and genus, future tethering experiments conducted exclusively with clasper larvae would have a higher likelihood of detecting predation risk differences – if they exist - in the field. It is likely that larvae do indeed find refuge from predators in dense macrophytes, but that higher predator densities within macrophytes
(Werner et al. 1983, Weaver et al. 1997, Gamboa-Perez and Schmitter-Soto 1999) and fish community differences among lakes obscured this relationship in our field experiment. On the other hand, competition or foraging behaviors of odonate larvae may help explain their associations with macrophytes to a greater extent than predator avoidance (Johansson 1991, Schmude 1998, Elkin and Baker 2000, Hofmann and Mason
2005a).
Our findings that both riparian and littoral vegetation structure influence lentic
Odonata assemblages have several conservation implications. Although some of the habitat factors that influence local odonate assemblages (e.g., substrate, lake size) are not influenced by anthropogenic land use, our surveys suggest that site-level littoral and riparian plant abundances affect odonate assemblages. The largest percentages of the variance in Gomphidae density and species richness occurred at the site rather than lake level (Table 5). As a consequence of the vegetation simplification and removal of macrophytes and coarse wood that often accompanies lakeshore development (Racey and
Euler 1982, Radomski and Goeman 2001, Elias and Meyer 2003, Marburg et al. 2006),
31 odonate abundances and diversity could decline. Most lakeshore homeowners value wildlife such as dragonflies (personal observation), so the result that site-level vegetation structure affects abundance and diversity of these charismatic insects could help motivate homeowners to maintain or restore wetland and littoral vegetation. Our results also suggest that rusty crayfish (Orconectes rusticus) invasion, which leads to severe macrophyte reduction in north-temperate lakes (Wilson et al. 2004), may indirectly reduce odonate diversity and density.
As predators of smaller invertebrates throughout their complex life cycle,
Odonata abundances can influence aquatic and riparian communities. Because larval odonates are often the dominant invertebrate predators in freshwater littoral zones, they can significantly alter benthic communities (Thorp and Cothran 1984, Johnson et al.
1987, Van Buskirk 1988). High predation of terrestrial pollinators – including bees, moths, and flies - by adult odonates may even alter riparian plant reproduction (Knight et al. 2005). At critical times, odonates comprise a significant portion of the diets of fish
(Crowder and Cooper 1982, Johnson et al. 1995, Sass 2004) and birds (Orians and Horn
1969, Wissinger 1988), so habitat variables driving odonate abundances could lead to changes in both aquatic and terrestrial food webs.
Acknowledgments
This research was supported financially by NSF Biocomplexity Program DEB-
0083545, North-temperate lakes LTER, an NSF Graduate Research Fellowship, the
Garden Club of America, and the Animal Behavior Society. Tim Kratz, M. Woodford, S. van Egeren, and the University of Wisconsin Trout Lake station staff provided invaluable
32 logistical support. C. Ane offered statistical advice. Thanks also to W. Smith, R.
DuBois, K. Tennessen, and R. Bohanan for sharing their expertise on Odonata behaviors
and taxonomy. B. Peckarsky, A. Ives, J. Zedler, C. Gratton, and S. Carpenter helped guide and critique the manuscript. Field and laboratory assistants included M. Theis, P.
Winkler, B. Henning, J. Batten, J. Estreen, D. Hong, J. Iacarella, C. Leugers, A. Duong,
and A. Olson. Finally, the manuscript was reviewed by A. Olson.
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42
Table 1. Publications demonstrating correlations between Odonata species and riparian vegetation.
Location Life stage, Riparian Shoreline Odonate Reference odonate taxa plant variable distance or response to plant area decreased studied plant variable Italy, ponds Adults, all 50 % cover of 0.5 – 5 km2 ↓ richness Carchini et al. riparian veg. (2003) Canada, Adults, all Shrub and 250 m ↓ abundance Foote and wetlands bulrush height Hornung (2005) New Jersey, Adults, Grass height 5.6 m2 ↓ density McKinnon and pond Pachydiplax May (1994) longipennis Hungary, river Combined Presence of 150 m ↓ richness Muller (2003) stages, all native riparian and aquatic plants Pennsylvania, Adults, Grasses, % cover of Chose different Van Buskirk pond Sympetrum sedges, rushes 0.09 ha pond pond section (1986) rubicundulum South Africa, Adults, all Sedges and 90 m2 ↓ richness Clark and river rushes % cover Samways (1996) UK, river Adults, all Sedge and tall 20 m Altered species Hofmann and grass % cover assemblage Mason (2005b) UK, river Adults and Shade % cover 20 m Altered species Hofmann and larvae, all assemblage Mason (2005b) UK, river Adults, Tree % cover 1.5 km ↑ abundance Ward and Mill Calopteryx (2005) splendens UK, river Adults, Understory 1.5 km ↓ abundance Ward and Mill Calopteryx stem height (2005) splendens Sweden, ponds Larvae, all Logging 1 km2 ↓ richness Sahlen (1999)
Note: A study on how Minnesota forest age affects Anisoptera diversity (Rith-Najarian
1998) was not included because results did not demonstrate statistical
significance.
Table 2. Physical and biological attributes1 of study lakes in northeastern Wisconsin.
Insectiv- 2 Secchi Dataset Perim Area Morpho Wetland Houses Cond. (µ orous All fish Lake 4 5 6 7 depth (m) 9 (km) 3 (ha) -metry perim(%) / km S / cm) fish CPUE 8 CPUE9 Arrowhead T 3.5 40.0 1.6 3.0 45.8 99.0 3.35 1095 1763 Big S 15.2 343.6 2.3 4.8 4.7 130.8 4.26 247 328 Black Oak S 12.0 230.1 2.2 15.7 18.0 50.0 5.25 174 283 Big Arbor Vitae T 16.4 441.5 NA NA NA 117.0 3.35 NA NA Big St. Germain S,M,T 12.8 665.7 1.4 0.5 24.3 90.8 2.59 411 440 Brandy S,M,T 3.5 45.1 1.5 0.0 30.1 166.6 3.06 488 501 Boulder T 11.3 215.8 NA NA NA 71.0 1.83 NA NA Carpenter S 5.6 144.5 1.3 0.9 18.0 23.0 5.50 277 294 Diamond M,T 3.6 50.4 NA NA NA 15.0 7.92 NA NA Erickson T 4.0 47.2 1.6 0.0 0.5 57.0 3.35 NA 292 Found T 6.4 139.3 1.5 2.2 16.6 49 1.52 NA 507 Jag S 5.1 68.9 1.7 14.4 1.4 15.0 3.50 219 219 Johnson T 3.6 34.7 1.7 0.0 26.2 98 1.83 NA NA Laura S 10.6 221.1 2.0 0.0 7.29 112.5 4.13 282 235 Little Arbor Vitae S,M 10.6 221.1 2.0 5.8 7.2 112.5 4.13 109 521 Little St. Germain S,M,T 23.3 402.2 3.3 2.7 19.8 76.5 3.63 238 293 Little Star S 6.4 107.1 1.7 0.0 19.5 93.3 6.01 204 284 Lost T 7.8 223.7 1.5 1.1 26.2 58 1.52 NA 1915 Lynx S 11.2 126.2 2.8 0.7 5.6 23.4 4.44 435 437 Mann T 7.7 103.0 NA NA NA 146 2.44 NA NA Pallette S,M,T 3.6 71.0 1.2 0.0 0 18.9 4.07 298 469 Papoose S 13.3 176.6 2.8 3.9 13.5 110.0 5.75 140 177 Snipe T 6.0 89.6 NA NA NA 25.0 1.22 NA NA Stella T 7.1 36.8 NA NA NA NA NA NA NA Starett T NA 26.7 NA NA NA NA NA NA NA Tomahawk T 48.6 1372.7 NA NA NA NA NA NA NA Towanda S,M,T 6.0 60.6 2.2 24.8 18.9 30.7 2.38 227 302 Trilby T 2.7 37.2 NA NA NA NA NA NA NA
Upper Buckatabon S 13.2 211.4 2.6 12.0 12.6 74.0 3.25 406 424 Upper Gresham M,T 9.3 148.1 NA NA NA NA NA NA NA Vandercook S 3.3 41.3 1.5 0.0 13.8 13.6 3.94 180 188 White Sand S 9.3 304.6 1.5 19.1 5.8 74.0 4.88 100 502 Wild Rice M,T 7.0 158.7 NA NA NA 87.0 3.05 NA NA
Note: NA indicates that data are not available.
1 Data from Carpenter et al. (2006)
2 Identifies the research question included data from each lake: S = larval survey (question 1), M = comparison of larvae among paired microsites (question 3), T = predation experiment using tethered larvae (question 3).
3 Lake perimeter.
4 Lake morphometry is a unit-less index of the lake’s deviance from a circular shape.
5 Percent of lake perimeter in wetlands (Gergel 1996).
6 Number of buildings within 100 m of the shoreline (Schnaiberg et al. 2002) divided by the lake perimeter.
7 Specific conductance (dissolved ionic content) in µ Siemens cm-1.
8 Water clarity increases with greater depths of viewing a secchi plate.
9 Catch per unit effort (CPUE) based on eight nights of electro-fishing, 24 minnow traps, and 24 crayfish traps.
45 Table 3. Summary statistics from 41 sites (17 lakes), abbreviations (used in Fig. 1), hypothesized correlations with Odonata species richness, and Pearson correlation coefficients between habitat variables and the best two axes generated by three separate principal components analysis (PCA) procedures on the three categories of habitat variables.
Pearson correlations Expect. PCA PCA Abbre- Std correl- Axis Axis Habitat descriptors viation Mean dev. Min Max ation 1 2 Lake-level1 Insectivorous fish CPUE 2 predfish 256 105 100 488 - 0.20 -0.73 Total fish CPUE 2 allfish 334 114 177 521 - 0.24 -0.34 Conductance (µSiemens/cm) 3 conduct. 63.60 44.69 13.64 166.59 + 0.67 -0.18 Secchi depth (m) 3 secchi_m 4.36 1.36 2.38 8.05 + -0.16 0.07 Morphometry 3 morpheme 1.9 0.6 1.2 3.3 + 0.60 0.54 Area (ha) 3 area 200.8 166.9 41.3 665.7 - 0.75 -0.14 Maximum depth (m) 3 maxdepth 13.6 6.5 4.3 25.9 - 0.49 0.22 Perimeter (km) 3 perim. 9.0 5.0 3.3 23.3 + 0.87 0.30 Length of wetlands (m) 4 wetlands 554.2 622.1 0.0 1917.9 + 0.15 0.85 Perimeter % with wetlands 4 % wetland 0.1 0.1 0.0 0.2 + -0.15 0.78 Building density (per km) 5 bldg dens 120 115 0 460 - 0.56 -0.40 Number of buildings 5 bldgs 12.4 8.7 0.0 30.1 - 0.89 -0.03
Site-level littoral6 Sand (ordinal variable: 0, 1, 2)7 sand 2 1 0 2 + -0.23 -0.77 Rock (ordinal variable: 0, 1, 2) 7 rock 0 1 0 2 - -0.44 0.21 Gravel (ordinal variable: 0, 1, 2) 7 gravel 1 1 0 2 - -0.63 -0.10 Muck (ordinal variable: 0, 1, 2) 7 muck 1 1 0 2 + 0.53 0.64 Mean macrophyte richness8 macrich 4 2 0 9 + 0.90 -0.09 Mean macrophyte % cover8 macperc 35.0 28.8 0.0 90.0 + 0.86 0.03 Mean frequency of emergent emergent 0.2 0.3 0.0 1.2 + 0.66 -0.28 macrophytes8 Coarse wood (number per 50 m)6 num.logs 2 3 0 13 + -0.03 0.60 Slope (degrees) slope 1.3 0.2 0.9 1.5 - -0.48 0.47
Site-level riparian9 Canopy (ordinal variable: 1, 2, 3) canopy 2 1 1 3 + 0.70 0.03 Slope (degrees) 6 rip.slope 10.3 7.0 0.0 29.0 - 0.35 -0.10 Length of shore with shrubs (m) shrublen 13.3 12.8 0.0 30.0 + 0.86 0.04 Length of shore with tall wetland carex.ty 9.3 11.5 0.0 30.0 + 0.17 -0.84 plants (m)10 Vegetation distance from water (m) rip.veg.di 0.5 0.9 0.0 3.8 - 0.46 0.26
46 Max. understory foliage height (m) maxveg 1.7 0.6 0.2 2.0 + 0.68 -0.25
Mean structural diversity (number of structure types intersected) avg.veg. 2 1 0 4 + 0.73 <0.01 Mean foliage height diversity (number avg.layer 4 2 1 7 + 0.92 -0.08 of layers intercepted) Shore % herbaceous herbaceo 31.0 31.4 0.0 98.0 + 0.12 -0.82 Shore % shrubs shrubper 21.9 31.4 0.0 92.0 + 0.56 0.58
1 The study includes 17 lakes, all in Vilas County, Wisconsin (Table 2).
2 Catch-per-unit-effort (CPUE) based on 8 equivalent nights of electro-fishing, 24
minnow traps, and 24 crayfish traps in 2001-2004 (Carpenter et al. 2006) .
3 Data collected 2001-2003 (Carpenter et al. 2006)
4 Data from Gergel (1996).
5 Schnaiberg et al. (2002).
6Littoral sites consisted of 30 m along the lakeshore contour and the adjacent littoral zone with water depth < 1 m.
7 Littoral substrate dominance and coarse wood abundance estimated at 0.5 m water
depth in 2001-2004 (Marburg et al. 2006).
8 Macrophyte species richness and percent cover estimated in 2001-2003 along a transect
extending 0 to 2 m depth (Alexander 2005).
9Riparian sites were 30 m along the shore by 6 m inland. However, the two shore percent
cover variables were within 0.5 m of the ordinary high water mark.
10 Carex, Typha,or Iris species.
47 Table 4. Species list of all Odonata larvae collected from 59 littoral sites1 in northern
Wisconsin lakes (Table 3), including their behavioral guild classifications .
Larval behavior Adult behavior Clasper Sprawler Burrower Flier Percher Aeshnidae Basiaeschna janata X X Boyeria vinosa X X Gomphidae Arigomphus furcifer X X Dromogomphus spinosus X Gomphus spicatus X X Gomphus exilis X X Gomphus lividus X X Gomphurus fraternus X X Hagenius brevistylus X X Libellulinae Celithemis elisa X X Ladona julia X X X Leucorrhinia hudsonica X X Leucorrhinia proxima X X Somatachlora cingulata X X Tramea carolina X X Cordulinae Cordulia shurtleffIi X X Drorocordulia libera X X Epitheca cynosura X X Epitheca princeps X X Epitheca spinigera X X Macrominae Didymops transversa X X X Macromia illinoiensis X X X Coenagrionidae spp. X X
1 This includes a broad-scale survey of 41 sites (12 m2 of littoral benthos sampled per site) in 2004 and a fine-scale survey of 18 sites (half in macrophytes; 7 m2 of littoral benthos sampled per site) in 2006. Three Libellulinae species (L. hudsonica, L. proxima, and T. carolina) were only observed in 2006; all other species were found in both years.
48 Table 5. Variance (and percent of total variance) occurring at two nested spatial scales for mixed-effects models of Odonata larval abundances.
Scale Odonata density Gomphidae density Odonata richness
Site 0.19 (56.5 %) 0.11 (100.0 %) 0.03 (61.6 %)
Lake 0.12 (34.6 %) 0.00 (0.0 %) 0.01 (29.4 %)
49 Table 6. Relatively equivalent linear and logistic models (based on differences between second-order Akaike scores, ∆AICc) predicting Odonata larval density or presence among 41 Wisconsin lake sites.
Model coefficients Riparian Model Struct- Riparian parameters Fixed- Littoral ural tall AICc effects Lake Lake macro- Littoral complex wetland y- 1 2 ∆AICc weight model fit K size wetlands phytes muck -ity plants intercept Linear regressions adj. R2 Odonata 0.00 0.14 0.43 1 -0.14 1.02 density 0.51 0.11 0.45 2 0.08 -0.11 1.02 1.16 0.08 0.44 1 0.11 1.03 1.59 0.05 0.44 2 -0.09 0.13 1.05 1.63 0.05 0.45 3 -0.07 0.09 -0.1 1.04 1.89 0.05 0.43 2 -0.05 -0.14 1.03
Species 0.00 0.15 0.34 1 0.04 0.60 richness m-2 1.04 0.09 0.30 0 0.60 1.20 0.08 0.34 2 -0.02 0.04 0.60 1.58 0.06 0.32 2 0.04 0.02 0.60 1.99 0.05 0.32 1 -0.03 0.60
Gomphidae 0 0.15 0.48 1 -0.15 0.88 density 1.11 0.09 0.48 2 -0.07 -0.14 0.90 1.15 0.08 0.49 2 0.06 -0.12 0.88 1.76 0.06 3 -0.02 0.02 0.01 0.88
Logistic Pseudo regressions R2 Zygoptera 0.00 0.12 0.37 1 0.38 -2.31 presence 0.81 0.08 0.42 2 045 0.34 -1.99 1.36 0.06 0.44 2 0.39 -0.27 -2.37 1.50 0.05 0.37 2 0.42 0.18 -2.20 1.73 0.05 0.37 0 -2.37
50 Table 6, continued Model coefficients Riparian Model Struct- Riparian parameters Fixed- Littoral ural tall AICc effects Lake Lake macro- Littoral complex wetland y- 1 2 ∆AICc weight model fit K size wetlands phytes muck -ity plants intercept Libellulinae 0 0.11 0.65 1 0.470 -5.91 presence 0.68 0.08 0.56 0 -5.40 0.80 0.07 0.65 2 -0.38 0.62 -6.23 1.46 0.05 0.65 3 -0.61 0.64 -0.41 -6.84
Macrominae 0.00 0.09 0.63 1 0.42 -4.91 presence 0.75 0.06 0.63 2 0.36 0.35 -4.36 1.09 0.05 0.69 2 -0.36 0.50 -5.06 1.20 0.05 0.63 1 0.43 -4.78 1.21 0.05 0.69 3 0.47 -0.50 0.43 -4.23 1.62 0.04 0.63 2 0.41 0.26 -4.75 1.63 0.04 0.63 0 -5.37
Cordulinae 0.00 0.22 0.92 2 0.63 0.94 -5.54 presence 3 1.77 0.09 NA 3 0.54 1.04 -0.26 -6.09
Aeshnidae 0.00 0.14 NA3 3 7.93 5.85 -5.09 -31.65 presence 3 0.15 0.13 NA 3 -21.36 17.03 -13.69 -51.08 3 0.44 0.12 NA 3 -11.66 21.25 -35.25 -215.28 3 1.36 0.07 NA 2 15.22 -17.00 -132.61 3 1.80 0.06 NA 5 1.99 7.50 5.71 2.01
1 Only models with ∆AICc < 2 are shown. ∆AICi is the difference between AICc for model i and the smallest AICc of the candidate models.
2 k = number of fixed-effects parameters in the model
3 Fixed-effects pseudo R2 for these models could not be calculated because there was no variability within lakes.
51 Table 7. Relative importance (sums of Akaike weights) of lake- and site-level parameters1 in predicting Odonata larval densities at 41 Wisconsin lake sites.
Sums of Akaike weights from all candidate models with the predictor (Riparian 1) (Riparian 2) Model parameters (Lake 1) (Lake 2) (Littoral 1) (Littoral 2) “Structural “Tall wetland “Size” “Wetlands” “Macrophytes” “Muckiness” complexity” plants” Linear regressions Odonata density 0.33 0.20 0.54 0.20 0.21 0.63 Species richness 0.29 0.22 0.60 0.21 0.26 0.28
Gomphidae density 0.40 0.20 0.42 0.20 0.22 0.73
Logistic regressions Zygoptera presence 0.23 0.22 0.73 0.33 0.31 0.34 Libellulinae presence 0.38 0.23 0.58 0.22 0.32 0.21 Macrominae presence 0.25 0.45 0.25 0.34 0.59 0.25 Cordulinae presence 0.73 0.23 0.24 0.87 0.32 0.25 Aeshnidae presence 0.34 0.20 0.35 0.95 0.45 0.65
1 The six predictors are synthetic axes derived from three separate principle components
analyses of 12 lake-level, nine site-level littoral, and 10 site-level riparian
variables.
52
Table 8. Analysis of Variance results from a mixed-effects linear regression of Zygoptera adult abundance (n = 40).
Fixed-effects variables d.f. F p-value Typha treatment 1,51 9.2 0.004 Macrophyte block 1,51 2.8 0.10 Typha x block 1,51 2.8 0.10
53 Table 9. McNemar’s Chi-squared contingency tables showing the number of days when oviposition was observed at treatments with and without potted Typha minima at the shoreline.
a) Anisoptera oviposition
No Typha1 2 Yes No Yes 1 6 No 4 10 b) Zygoptera oviposition
No Typha Yes No Typha Typha Yes 1 4 No 1 15
Typha Typha
1 On all 21 days, observations were made at shoreline plots with and without the Typha treatment.
2 A ‘Yes’ count indicates that oviposition occurred at the specified shoreline treatment.
54 Fig. 1. Locations of 41 Odonata larval survey sites on lakes in Vilas County, Wisconsin.
Fig. 2. Biplots of the first two axes from Principal Components Analyses (PCA) performed on (a) 12 lake-level, (b) 9 site-level littoral, and (c) 10 site-level riparian habitat variables (abbreviations defined in Table 4). Direction and magnitude of the arrows illustrate correlations of each habitat variable with the first two PCA axes. PCA scores for the 41 sites surveyed are shown as circles.
Fig. 3. Gomphidae larval density graphed as a function of the second axis from a principal components analysis of riparian vegetation (Table 4). As the horizontal axis increases, the length of riparian sites having wetland herbaceous vegetation decreases.
Fig. 4. Mean adult dragonfly (a) and damselfly (b) abundance estimates (± 1 SE) within
15 x 7 m experimental plots at two lakeshore home sites in northern Wisconsin (one of the two having dense emergent macrophytes in the littoral zone). Typha treatments, consisting of 20 potted Typha minima placed on the sea walls, were rotated between two plots per site on 21 days during June-August 2006.
Fig. 5. Rank-abundance curves (± 1 SE) for Odonata larval assemblages in lake microsites with and without dense submerged macrophytes.
Fig. 6. Mean densities (± 1 SE) of three larval behavior guilds (Table 2) among paired 7 m2 lake microsites with and without dense submerged macrophytes.
Figure 1.
56 Figure 2.
(a)
57 (b)
58 (c)
59 Figure 3.
2.5 ) -2 2
1.5
1
0.5 sqrt(Gomphidae density m
0 -3 -2 -1 0 1 2 PCA riparian axis 2: Tall w etland plants
60 Figure 4.
(a)
2.5
2
1.5 No Typha Typha 1
0.5 Anisoptera abundance Anisoptera 0 Dense macrophytes Sparse macrophytes
(b)
7 6 5
4 No Typha 3 Typha 2
Zygoptera abundance Zygoptera 1 0 Dense macrophytes Sparse macrophytes
61
Figure 5.
10 No Macrophytes 8 Epitheca cynosura Macrophytes
6
4
2 Mean abundance 0 0 5 10 15 20 25 30 -2 Species rank
62 Figure 6.
4 Macrophytes No macrophytes 3
2
1
0
Odonata larval guild density (per m^2) (per density guild larval Odonata Burrowers Claspers Sprawlers
63 Appendix 1. Geographic locations of 41 study sites surveyed in 2004 for Odonata larvae.
Lake (abbreviation) and site number Latitude Longitude Big (BG) 3 46.14771 -89.75002 Big (BG) 8 46.16294 -89.77978 Black Oak (BO) 1 46.16587 -89.31061 Black Oak (BO) 4 46.15580 -89.30875 Brandy (BR) 6 45.90317 -89.70406 Brandy (BR) 8 45.90784 -89.70465 Big St. Germain (BSG) 1 45.94415 -89.50678 Big St. Germain (BSG) 3 45.92938 -89.50267 Big St. Germain (BSG) 7 45.92993 -89.54494 Carpenter (CR) 2 45.94619 -89.13996 Carpenter (CR) 5 45.93837 -89.15183 Carpenter (CR) 7 45.94616 -89.15234 Carpenter (CR) 8 45.95045 -89.15099 Jag (JA) 1 46.09044 -89.72128 Jag (JA) 4 46.08059 -89.71660 Jag (JA) 5 46.08175 -89.72340 Jag (JA) 7 46.08747 -89.72798 Jag (JA) 8 46.08780 -89.72626 Little Arbor Vitae (LAV) 2 45.91324 -89.60908 Little Arbor Vitae (LAV)7 45.90893 -89.63440 Laura (LLA) 5 46.05154 -89.44090 Laura (LLA) 6 46.05277 -89.44571 Little St. Germain (LSG) 1 45.92841 -89.42837 Little St. Germain (LSG) 4 45.91356 -89.45675 Little Star (LST) 1 46.11925 -89.85830 Little Star (LST) 2 46.11527 -89.85170 Lynx (LX) 1 46.20764 -89.66808 Pallette (PA) 1 46.19405 -89.50623 Pallette (PA) 7 46.19823 -89.51094 Pallette (PA) 8 46.20645 -89.50517 Papoose (PP) 3 46.18217 -89.79484 Papoose (PP) 6 46.18210 -89.81336 Papoose (PP) 7 46.18678 -89.80814 Towanda (TW) 3 45.93572 -89.70187 Towanda (TW) 8 45.94200 -89.71255 Upper Buckatabon (UB) 1 46.02433 -89.33512 Upper Buckatabon (UB) 4 46.00898 -89.34746 Vandercook (VC) 5 45.97960 -89.68855 Vandercook (VC) 6 45.98056 -89.68917 White Sand (WS) 3 46.08429 -89.57942 White Sand (WS) 8 46.09515 -89.60004
Appendix 2. Densities (benthic m-2) of larval Odonata families and genera at 41 lake sites in Vilas County, Wisconsin, including riparian vegetation data
Larval Riparian vegetation characteristics2 Sub- Site1 survey summary order Families3 Common genera s b ru h
s h t
i (m) w canopy h diversity diversity Epitheca Epitheca Hagenius Gomphus Zygoptera Zygoptera Aeshnidae Typha (m) Typha (m) Celithemis Celithemis Cordulinae Cordulinae Gomphidae Libellulinae Libellulinae Macrominae Macrominae from water (m) from water (m) mean structural structural mean total abundance total abundance species richness richness species Dromogomphus engt larval sample area area sample larval l mean foliage height mean vegetation distance vegetation distance length with Carex or with Carex length vegetation height (m) height vegetation BG3 12 1 1 3 30 0 0 2.0 2.72 6.89 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 BG8 9 12 5 2 0 25 0 1.7 1.25 2.25 0.00 0.00 0.00 0.00 0.00 1.31 0.24 0.83 0.24 0.00 0.00 BO1 24 22 12 2 5 0 0 2.0 2.39 3.33 0.04 0.04 0.08 0.17 0.13 0.46 0.33 0.13 0.00 0.08 0.13 BO4 24 46 7 2 2 0 0 0.5 1.89 1.72 0.00 0.00 0.33 0.38 0.08 1.13 0.08 1.00 0.04 0.33 0.08 BR6 12 21 5 2 2 29 0 1.8 2.00 2.56 0.00 0.00 0.00 0.00 0.25 1.50 1.50 0.00 0.00 0.00 0.25 BR8 12 36 7 2 0 15 0 1.8 1.65 2.18 0.17 0.00 1.17 0.17 0.00 1.50 1.50 0.00 0.00 1.17 0.00 BSG1 12 2 2 2 2 0 0 2.0 1.61 2.89 0.00 0.00 0.00 0.00 0.00 0.17 0.17 0.00 0.00 0.00 0.00 BSG3 12 4 4 3 2 0 0 2.0 1.83 2.28 0.08 0.00 0.00 0.00 0.00 0.25 0.17 0.00 0.00 0.00 0.00 BSG7 12 10 6 2 2 0 0 2.0 1.71 2.29 0.17 0.00 0.00 0.00 0.00 0.58 0.17 0.00 0.08 0.00 0.00 CR2 12 50 7 2 4 6 1 2.0 1.94 3.61 0.08 0.00 0.17 0.00 0.00 3.92 3.83 0.00 0.08 0.17 0.00 CR5 12 10 4 2 25 22 0.6 2.0 3.11 5.11 0.00 0.00 0.00 0.00 0.00 0.83 0.75 0.08 0.00 0.00 0.00 CR7 12 5 2 3 0 0 1 0.2 1.29 1.86 0.00 0.00 0.00 0.00 0.00 0.42 0.25 0.17 0.00 0.00 0.00 CR8 12 65 7 2 0 6 0 0.5 1.73 2.33 0.08 0.00 0.00 0.25 0.00 5.08 4.67 0.17 0.00 0.00 0.00 JA1 24 41 10 2 30 1 2.2 2.0 3.56 6.22 0.04 0.00 0.00 0.17 0.00 1.46 1.04 0.00 0.42 0.00 0.00 JA4 16.8 12 7 3 29 2 3.8 2.0 2.61 5.67 0.06 0.00 0.06 0.06 0.00 0.54 0.42 0.12 0.00 0.06 0.00 JA5 12 4 3 3 30 15 0 2.0 2.83 5.17 0.00 0.00 0.00 0.08 0.00 0.25 0.17 0.00 0.08 0.00 0.00 JA7 12 30 9 3 25 5 3.4 2.0 2.72 6.00 0.42 0.00 0.00 0.42 0.00 1.67 0.75 0.17 0.75 0.00 0.00 JA8 12 8 6 3 15 5 2.2 2.0 2.78 5.72 0.00 0.00 0.00 0.00 0.08 0.58 0.42 0.00 0.17 0.00 0.08 LAV2 10.8 14 5 3 2 30 0 2.0 3.28 6.44 0.00 0.09 0.00 0.00 0.83 0.28 0.00 0.00 0.00 0.00 0.83
LAV7 10.8 6 3 3 30 0 0 2.0 2.72 6.50 0.19 0.00 0.00 0.09 0.00 0.19 0.19 0.00 0.00 0.00 0.00 LLA5 12 35 8 2 8 8.4 0 2.0 1.91 3.27 0.08 0.00 0.00 0.33 0.00 2.33 1.17 0.67 0.50 0.00 0.00 LLA6 12 16 5 3 8 9 0.8 2.0 2.17 4.56 0.00 0.00 0.00 0.00 0.00 1.33 0.83 0.25 0.25 0.00 0.00 LSG1 12 36 5 3 5 30 0 2.0 2.94 5.33 0.08 0.00 0.00 0.00 0.83 2.08 2.08 0.00 0.00 0.00 0.83 LSG4 12 9 4 2 0 0 0 0.2 1.41 1.82 0.00 0.00 0.00 0.00 0.25 0.50 0.42 0.00 0.00 0.00 0.17 LST1 12 1 1 1 0 0 0 0.7 1.67 1.17 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.08 0.00 0.00 0.00 LST2 12 2 2 3 30 2 0 2.0 3.17 6.44 0.00 0.00 0.00 0.00 0.00 0.17 0.17 0.00 0.00 0.00 0.00 LX1 16.8 69 16 3 25 29 1.9 2.0 2.39 4.72 0.30 0.00 0.18 0.00 0.30 3.33 1.90 1.43 0.00 0.18 0.00 PA5 9 0 0 3 30 30 0 2.0 0.00 6.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 PA7 12 6 1 3 30 30 0 2.0 2.28 5.17 0.00 0.00 0.00 0.00 0.00 0.50 0.00 0.00 0.50 0.00 0.00 PA8 12 3 3 3 30 10 1 2.0 2.61 4.44 0.00 0.08 0.00 0.00 0.00 0.17 0.08 0.00 0.08 0.00 0.00 PP3 12 4 3 1 0 0 0 0.6 1.72 3.33 0.00 0.00 0.00 0.00 0.00 0.33 0.17 0.00 0.17 0.00 0.00 PP6 9 0 0 1 4 2 0 1.5 1.76 2.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 PP7 12 9 5 3 28 0 0 2.0 2.28 4.44 0.00 0.00 0.00 0.17 0.00 0.75 0.25 0.08 0.42 0.00 0.00 TW3 12 39 9 1 7 7 0.5 2.0 1.75 1.13 0.25 0.00 0.17 0.00 0.00 2.83 2.75 0.08 0.00 0.00 0.00 TW8 12 10 4 2 2 2 0.5 1.8 0.00 2.78 0.00 0.00 0.00 0.00 0.17 0.67 0.67 0.00 0.00 0.00 0.17 UB1 12 5 3 3 20 0.5 0.1 2.0 2.83 6.78 0.00 0.08 0.00 0.00 0.00 0.33 0.25 0.00 0.08 0.00 0.00 UB4 12 2 2 1 2 2 0.2 0.9 1.50 1.50 0.08 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 VC5 12 25 10 3 20 2 0.2 2.0 1.67 3.50 0.08 0.08 0.08 0.00 0.00 1.75 1.42 0.08 0.25 0.08 0.00 VC6 12 51 7 1 2 30 1.2 1.5 2.00 4.39 0.00 0.08 0.00 0.00 0.00 4.17 3.83 0.17 0.08 0.00 0.00 WS3 12 15 6 3 30 0 0.5 2.0 2.72 6.28 0.25 0.00 0.00 0.17 0.00 0.83 0.00 0.67 0.17 0.00 0.00 WS8 12 14 6 2 30 25 0.7 2.0 2.83 5.72 0.25 0.00 0.00 0.25 0.00 0.67 0.08 0.58 0.00 0.00 0.00
1 Lake abbreviations are shown in Appendix 1.
2 Habitat variables are described in Table 3.
3 Species included in each family are listed in Table 4.
66 CHAPTER III
Gomphidae densities among three life stages: Oviposition site selection overrides
post-recruitment spatial variation
Abstract
Many animals have life stages that occupy different habitats. Although
population demographics in one stage can carry over to subsequent – even spatially
separated – life stages, most studies of habitat associations have been restricted to a
single life stage. Dragonflies (Odonata: Anisoptera) require both aquatic and terrestrial habitats for different life stages, but habitat heterogeneity may not contribute equally to the densities of both stages. Although recruitment via adult oviposition establishes initial population sizes of the aquatic larvae, spatial variability in larval survivorship could obscure the expected correlation between adult and larval densities. I used surveys of
Gomphidae larvae, exuviae, and adults from 22 lake sites in northern Wisconsin, USA, to investigate (1) whether the density of each life stage was correlated with that of the preceding life stage and (2) what habitat factors help explain variation in densities at each life stage.
`I found strong positive correlations between densities of first-year larvae and adults from the previous season, as well as between second-year larvae and exuviae later during the same year. Adult densities did not correlate with exuvia densities from the same year. In addition to parameters for the previous life stages, water clarity helped predict larval densities, and riparian wetland vegetation helped predict exuvia densities.
67 The results demonstrate that habitat features affecting adults or larvae can transcend the
aquatic-terrestrial boundary to influence population densities of the subsequent life stage.
Introduction
Ecologists have long described the natural processes that limit population sizes
(e.g., Lindeman 1942, Davidson and Andrewartha 1948, Cole 1954, Lack 1954), but have
only recently begun to examine how spatially explicit population stage structures affect
population dynamics (reviewed by Halpern et al. 2005). The vast majority of
multicellular animals have separate life stages that occur in different habitat types. These
include animals with stages separated by size or age structure (e.g., Ebenman and Persson
1988, Persson and De Roos 2006), as well as animals with complex life cycles, in which
animals undergo a succession of discrete life history phases (e.g., Istock 1967, Moran
1994). Despite the ubiquity of these life stage separations, population studies
incorporating habitat spatial heterogeneity have been mainly restricted to a single life
stage (reviews by Wilbur 1980, Werner et al. 2001). More recent studies have
demonstrated that spatial variation in population demographics at one life stage can affect population sizes in subsequent stages (e.g., Roughgarden et al. 1988, Hellriegel 2000,
Hughes et al. 2000, Mumby et al. 2004, Halpern et al. 2005), but have not included aquatic insects or other animals with longer larval than adult phases. The semi-voltine
(multi-year) life history of many temperate dragonflies (order Odonata, suborder
Anisoptera) could weaken population density correlations that are expected between different life stages. Understanding the relative influence of different life stages on
68 population demographics is critical for predicting how modifications to either habitat could affect animals with spatially separated life stages.
The utility and attraction of land-water interfaces have resulted in human alterations to riparian areas for several millennia, with riparian development continuing to expand (WDNR 1996, Naiman and Decamps 1997, Burger et al. 2004, Hansen et al.
2005). Because biodiversity is often particularly high in these land-water ecotones
(Decamps et al. 2004), ecological consequences of habitat change can be disproportionately high in riparian areas. Lakeshore housing development is expanding rapidly in many areas and is associated with simplification of vegetation structure by removal of the riparian shrub layer, floating macrophytes, and littoral coarse wood
(Racey and Euler 1982, Radomski and Goeman 2001, Elias and Meyer 2003, Marburg et al. 2006). These structural changes correlate with reduced biodiversity and productivity across multiple taxa in both the riparian and littoral (near-shore aquatic) zones (Bryan and Scarnecchia 1992, Schindler et al. 2000, Lindsay et al. 2002, Henning and Remsburg in review).
Although dragonflies require both suitable aquatic habitat for larvae and terrestrial habitat for adults, the local abundances of dragonflies may not depend on each of these habitats equally. As with many animals, dispersal occurs during the more-mobile adult dragonfly stage, whereas survivorship during the more-vulnerable juvenile stage limits population sizes (Johnson 1986, Wissinger 1988). The relative importance of recruitment versus larval survival, however, remains unknown. Dragonfly adults are outstanding fliers, enabling them largely to avoid their avian and amphibian predators and to disperse hundreds of kilometers (Corbet 1999). Recruitment via adult oviposition thus establishes
69 the initial spatial patterns of aquatic insect abundances. Dragonfly larvae, in contrast,
probably do not travel > 20 m in lentic systems because movement makes them more
susceptible to predators (Ubukata 1984, Schaffner and Anholt 1998, but see Alzmann et
al. 1999). Larval survivorship may vary among sites or lakes due to heterogeneity in
post-recruitment processes such as larval movement, competition, and predation
(Forrester 1990, Pfister 1996, Stoks and McPeek 2003). Spatial patterns of local
dragonfly population abundances therefore depend on the relative importance of adult
habitat site selectivity and the spatial heterogeneity of larval survivorship (Fig. 1).
Abundance and diversity of adult dragonflies increase in riparian areas with high
vegetation structural complexity, although the underlying reasons for these patterns
remain unclear (Van Buskirk 1986, McKinnon and May 1994, Carchini et al. 2003, Foote
and Hornung 2005, Hofmann and Mason 2005, Ward and Mill 2005). Gomphidae larvae
in northern Wisconsin lakes are associated with riparian sites having tall wetland
vegetation such as sedges (Chapter II). Among riparian and littoral habitat
characteristics, adult odonates often demonstrate strongest associations with riparian
sedges and bulrushes (Van Buskirk 1986, Clark and Samways 1996, Foote and Hornung
2005). Mature Gomphidae split their time at riparian sites between reproductive and
foraging behaviors, perching on the ground or on vegetation to watch for prey or
potential mates (May 1976, Mead 2003). The Gomphidae species considered in this
study copulate near lotic or sufficiently oxygenated lentic water and then oviposit directly onto the surface of open water (exophytically). As with other oviparous species, it is advantageous for adults to oviposit at sites with the greatest likelihood for survival of their eggs and larvae (Resetarits 1996). Because adults may not be able to assess all
70 aspects of aquatic habitat suitability, several authors have hypothesized that adult
odonates use emergent aquatic plants or riparian plants as proximate cues of macrophyte,
substrate, water depth, prey, or predator conditions in littoral sites (Buchwald 1992,
Wildermuth 1992 as reviewed by Corbet 1999, McKinnon and May 1994).
Alternatively, foraging odonates may be attracted to higher prey abundances in riparian
areas with high structural complexity (Baird and May 1997).
Larval odonates face high rates of predation by fish or larger odonates, and to a
lesser degree by turtles, frogs, birds, and crayfish (Wright 1946, Kennedy 1950,
Larochelle 1977, Crowder and Cooper 1982, Johnson 1991, Momot 1995, Stoks and
McPeek 2003). Mortality of larval odonates may be as high as 95-99 % (Johnson 1986,
Duffy 1994, Schutte et al. 1998). The Gomphidae larvae in this study hide from predators
by burrowing (burying themselves) in the benthic substrate, but this behavior can also
reduce their foraging efficiency (e.g., Sih 1980, Lima 1998, Alzmann et al. 1999,
Johansson 2000). Burrowing reduces their dependence on macrophytes for cover,
although burrowers may occupy macrophyte microsites (Mahato and Johnson 1991,
Alzmann et al. 1999). While moving from their typical littoral habitats (1- to 2- m water
depths [Remsburg, unpublished data], and up to 6 m depth (Thomas 1965)) onto land for
emergence, larvae may face increased risk of fish predation. The one – two hour process
of larval emergence and wing hardening is a final period of susceptibility to terrestrial predators such as birds, spiders, and ants (Wissinger 1988, Orians and Wittenberger
1991, Duffy 1994, Jakob and Suhling 1999). High wind, rain, waves, or trampling can also significantly reduce rates of successful emergence (Gribbin and Thompson 1990,
Jakob and Suhling 1999).
71 Heterogeneity in aquatic or riparian vegetation structure could interact with trophic or environmental controls on Gomphidae abundances. For example, shoreline vegetation abundance can lead to greater spider abundances (Scheidler 1990, Langellotto and Denno 2004) and greater susceptibility of emerging odonates to damage from wind
(Jakob and Suhling 1999). Lower macrophyte abundances, higher insectivorous fish abundances, or higher water clarity could each lead to higher predation on larvae
(Crowder and Cooper 1982, Thompson 1987, Diehl 1992, Aksnes and Giske 1993) or reduced prey availability for larvae (Alzmann et al. 1999, Bazzanti et al. 2003). Riparian forest understory and aquatic transition plants may provide adults with perching structures for thermoregulation, foraging, territory defense, mate attraction, copulation,
nocturnal roosting, or protection from adverse weather (Buchwald 1992, Wildermuth
1993, McKinnon and May 1994, Rouquette and Thompson 2007).
Based on potential drivers of odonate distributions during the adult, larval, and
emergence stages, I investigated two main research questions: (1) Is there a positive
correlation between the density of one Gomphidae life stage and its previous life stage at
either lake- or within-lake- spatial scales? and (2) What habitat factors help explain
variation in Gomphidae larvae, exuviae, and adult densities? I hypothesized that if larval
densities depend most on recruitment and larvae do not move significant distances, then
first-year larval densities would correlate with adult densities from the previous year. On
the other hand, significant differences in larval survivorship among sites would obscure
the hypothesized relationship between adult abundances and their offspring. Aquatic or
terrestrial habitat features relevant to each life stage may help explain the variation in
Gomphidae densities not accounted for by the previous life stage.
72
Methods
Study sites
I conducted the study at 22 sites on 11 lakes in Vilas County, northeastern Wisconsin
(USA). This mixed-forest landscape contains over 1300 glacial lakes in the 2639 km2 county
(see Peterson et al. 2003 for more details on the region). Most of the study sites were privately owned; 14% were public property. I selected two sites per lake, separated by at least 200 m, with similar aspect, slope, fetch, macrophyte density, benthic substrate, and hydrologic context
(location relative to streams or bays) for each pair (Table 1). Each site spanned 30 m along the shoreline by 5 m inland. The pair of sites on each lake represented opposite extremes of human influence: manicured lawns and forests. Forest sites had a dense layer of shrubs
(mainly Alnus rugosa), saplings (e.g., Betula papyrifera, Thuja occidentalis, Acer rubrum), or wetland herbaceous plants (e.g., Carex retrorsa, Juncus effusus, Sparganium spp.), and were generally undeveloped. Manicured lawns had minimal understory vegetation and were associated with housing inland from the study sites.
Odonata surveys
For a 30-m transect paralleling the shoreline, I collected all Odonata exuviae on shore within approximately 0.5 m of the water’s edge. I collected exuviae on three days per site in 2005 (May 27 - July 12) and four days per site in 2006 (June 9 – July 12). I visited both sites from the same lake during the same day, and completed survey rounds for nearly all sites before beginning the next round of surveys. The last two rounds of
2005 exuviae sampling on one lake (Little Arbor Vitae) took place within four days of
73 each other, but all other survey rounds were separated by at least a week. Exuviae were collected by careful inspection (on hands and knees) of the ground and vegetation for 15
– 45 minutes per survey day. Time required for searching a site exhaustively varied with understory plant density and abundance of odonates. Exuvia and adult surveys in 2006 took place at 21 sites because one of the study sites (on Big St. Germain lake) became inaccessible (due to owner concerns about liability).
Three adult odonate surveys per site (per year) took place between May 31 - July
27 in 2005 and June 15 – July 21 in 2006. I estimated adult odonate densities along the same 30 m shoreline transects using a modified ‘Pollard walk’ (Pollard 1977, Walpole and Sheldon 1999). While walking the transect over a period of two minutes, I counted and identified all adult odonates perched or flying within 2 m around me (but not behind) and up to 3 m above the ground. If an individual clearly flew back and forth through the observation zone, it was only counted once. However, individuals could not be tracked if they flew out of the observation zone and later returned, so observed abundances may be slightly higher than actual abundances. I repeated the two-minute transect walk three or four times on each survey day. I conducted all adult surveys on relatively calm, sunny days between 11:00 and 16:00, when odonate flight activity is greatest (Lutz and Pittman
1970, Moore and Corbet 1990).
I surveyed larval odonates on one occasion per site during the first week of June
2006 in the littoral zone immediately adjacent to the 22 sites. At each site, I used a D- frame net to scoop benthic material along four 16 m contour transects that paralleled the shore. I centered transects within the 30 m site length and placed them at 0.25 m, 0.5 m,
0.75 m, and 1.0 m water-depths; transect distance from shore varied with littoral slope at
74 each site. I placed a 0.95m – diameter cylinder (i.e., a trash can with the bottom cut
off) at 20 subsample locations per site (4 in each of the two shallower transects and 6 in
each of the two deeper transects) and collected four equivalent scoops of benthos within
each subsample. Because the cylinder extended from the substrate to the water’s
surface, this protocol ensured that larvae within each subsample area could not move out of the area while I dipped the D-net (Caudill 2002).
For analysis I combined each site’s 20 subsamples, which together represent a
14m2 area surveyed per site. I separated larvae into two year classes based on head
widths (W. Smith, in prep). The Gomphidae species surveyed generally have two-year
larval phases in Wisconsin, but no sexual size dimorphism. A histogram of head widths
revealed a distribution that was approximately bimodal (Fig. 2). The range of head
widths was 0.5 to 6.0 mm; based on the distribution, I designated all individuals with
head widths less than or equal to 3.0 mm as first-year larvae and larger individuals as
second-year larvae.
Habitat variables
I estimated the relative abundances of riparian tall wetland herbaceous plants (>
0.3 m high; Carex, Typha, and Iris species), and littoral macrophytes among sites by
measuring the proportion of site shorelines (out of 30 m) having these vegetation
categories present within 4 m of the water’s edge. Lake-level variables included Secchi
depth (water clarity), insectivorous fish catch-per-unit-effort (CPUE), and rusty crayfish
(Orconectes rusticus) CPUE. Rusty crayfish density is likely to correlate inversely with
lake-level macrophyte abundance because this invasive species consumes large amounts
75 of macrophytes (Wilson et al. 2004). Lake-level data were compiled by Carpenter et al.
(2006) and Olden et al. (2006).
Data analysis
All analyses focus on Gomphidae species because this was the only group with larvae occurring at > 50 % of the study sites. Previous larval surveys suggested that
Gomphidae is by far the most abundant Odonata family in these lakes (Chapter II). In addition, I focused on only the “burrowers,” excluding one Gomphidae species that falls into the “hider” larval guild, Hagenius brevistylus (Corbet 1999). Co-occurrence of the 6 burrower Gomphidae species observed (Arigomphus furcifer, Dromogomphus spinosus,
Gomphus exilis, G. fraternus, G. lividus, and G. spicatus) appears to be very high
(personal obs.), which follows a pattern of minimal niche separation among many
Gomphidae species (Crowley and Johnson 1982, Burcher and Smock 2002). The
Gomphidae species considered in this study all emerge fairly synchronously in mid-June
(Mead 2003), thus restricting the number of weeks required for exuvia and adult surveys.
The main flight period lasts between two and six weeks for most Gomphidae in this region (Mead 2003).
Adult density estimates were derived from the total number of individuals counted during survey transects divided by the transect length (30 m). I averaged the adult densities from transect walks on all three survey dates in 2006. Surveys in 2005, however, took place over a longer time frame, making adult abundances difficult to compare among lakes that were sampled during different parts of the dragonfly’s flight season. Gomphidae adults were hardly ever present before June 10 or after July 10,
76 2005, so I used data from only the single survey date that took place at each site during
this time period. This required that I exclude two lakes (Lynx and Little Arbor Vitae) from the 2005 adult analyses that were not surveyed between those optimal flight period dates.
I used mixed-effects linear regressions to test effects of adult, larvae, and exuviae densities on densities of the subsequent life stages among sites. I assigned lake as a
random-effect term because effects of specific lakes were not of interest, but dragonfly
densities at sites on the same lakes were not necessarily independent. To compare life
stage densities at the lake level, I averaged Gomphidae densities from both sites at each
lake and then conducted Spearman’s rank correlation tests. Gomphidae densities for all
life stages except 2006 adults were log-transformed to meet normality assumptions of the
test. Because some larval and adult densities were zero, I added the smallest non-zero
density value before taking the log and then an order-of-magnitude constant (McCune
and Grace 2002). Variance partitioning of random effects revealed what proportion of
the total variation in Gomphidae densities occurred at the lake versus site level.
To test the influence of habitat effects on each life stage, I added five habitat
variables individually to the linear regressions that also included previous life stages.
Site-level models used lake as a random-effect parameter, whereas the lake-level models
considered only fixed effects. At the site level, I tested five candidate models for first-
year larval densities: 2005 adult densities alone and combined individually with site
macrophyte abundance, lake Secchi depth, lake insectivorous fish CPUE, and lake rusty
crayfish CPUE. Models for first-year larvae averaged by lake included all of the above
lake-level habitat variables in addition to 2005 adult densities averaged by lake. To
77 predict 2006 exuviae densities at the site level, I considered six candidate models:
second-year larval densities alone and combined individually with lake Secchi depth, lake
insectivorous fish CPUE, lake rusty crayfish CPUE, site development category (forest or
manicured lawn), and site abundance of riparian wetland vegetation. I also tested effects
of the three lake-level predictors (in combination with lake-level larval densities) on
exuviae densities at the lake level. Predictive models for adult abundances in both 2005
and 2006 were completed only at the site level and included four candidate models for
each year: exuviae densities of the same year alone and in combination with site
development, site riparian wetland vegetation, and site macrophytes. In addition, I tested
each of these three habitat variables alone (without the previous life stage) as predictors
of adult densities.
I used an information theoretic approach to compare alternative mixed-effects
habitat models within the set of candidate models for each life stage. In particular, I
ranked models according to Akaike’s Information Criterion (AICc) values. AICc has a
bias correction factor to help prevent over-parameterized models when sample sizes are
small (Burnham and Anderson 2002). Relative empirical support for a model i is
indicated by ∆i, the difference between AICc for model i and the smallest AICc of the
candidate models. Information theory suggests that datasets may equivalently support multiple predictive models (particularly if ∆i < 2).
I used likelihood ratio tests to compare each habitat model with the linear model
that used only the previous life stage to predict Gomphidae densities. I also calculated
adjusted R2 values for all linear regressions, but the maximum likelihood model fitting method employed for the mixed-effects models could not be used to calculate the R2
78 values. Thus adjusted R2 values were calculated from fixed-effects models (using
identical parameters selected from mixed-effects models) where lake was added as a
fixed effect. All analyses were completed using R v.2.4.1 software (R Core Development
Team, Vienna, Austria), including the nlme package for mixed-effects models.
Results
All three life stages had high variability among the 22 sites sampled (Table 2).
Variance partitioning revealed that nearly all of the variance in first-year larval densities occurred at the lake level, whereas second-year larvae, exuviae, and adults each had progressively smaller proportions of the variance occurring at the lake level (Table 2).
First-year larval densities in 2006 were positively correlated with adult densities in 2005, at both the site and lake scales (Table 3; Fig. 3). Exuviae in 2006 were positively correlated with second-year larval densities from earlier in the same season
(Table 3; Fig. 4). Adult densities did not correlate with exuviae densities from the same season in 2005 or 2006.
Of the three aquatic habitat variables added to the site-level model to predict first- year larval densities, Secchi depth was the only variable that contributed significantly in
addition to previous-year adult densities (Table 4). Secchi depth correlated negatively
with larval density, indicating that larval densities were lower in lakes with clearer water
(Fig. 5). From the set of six candidate models for prediction of 2006 exuviae densities,
both macrophyte abundance and riparian tall wetland vegetation improved the model in
addition to 2006 second-year larvae (Table 4). Macrophyte abundances correlated
negatively with exuviae densities, whereas riparian wetland vegetation correlated
79 positively with exuviae densities. For both larvae and exuviae densities at the lake
level, densities of the previous life stage alone served as the best predictor; models that
included habitat variables all had much higher ∆AICc The best predictor of adult
densities in 2005 was riparian wetland vegetation with 2005 exuviae density, although
this model was not significantly better than the model using only the previous life stage
(Table 4). Model selection indicated that macrophyte abundance alone was also
positively correlated with adult densities in 2005, but the model was only marginally
2 significant (fixed-effects adj. R = 0.32, mixed-effects model F1,8 = 4.4, p = 0.07). None
of the habitat variables considered helped predict adult densities in 2006 (Table 4).
Discussion
First-year larval densities varied considerably among survey sites (Table 2); most
of this variation can be attributed to the adult phase during the year that these larvae were
oviposited (Table 4, Fig. 1). Positive correlations between first-year larval densities and
adult densities in the previous year suggest that oviposition site selection controlled the local larval distribution more than spatial variations in survivorship or movement during the first year of the larval phase. The relationship is more significant when considering lake-level averages of adult and larval densities (Table 3, Fig. 3), probably because breeding adult Gomphidae cover broader areas than the 30-m shoreline sites. The strength of this linear relationship is noteworthy because a number of other factors could have obscured the positive linear relationship: measurement error, males present at lakes where they did not copulate, loss of eggs to predation or water currents, larval site selection, or heterogeneity in larval survivorship.
80 The demonstrated relationship between larvae and adults of the previous
generation does not diminish the role of population controls during the odonate larval
phase, but instead suggests that larval population controls have relatively similar effects
among lakes and sites. Thus larval movement and predation on eggs and first-year larvae
apparently did not significantly diminish the population density spatial patterns
established by adult oviposition site selection. Larval mortality or movement, however,
could influence larval densities at a finer spatial scale than the 14 m2 littoral sites I
surveyed. Finally, larval mortality among odonates may be highest during the later
instars because fish prey most heavily on larger odonates (Martin et al. 1991, Duffy 1994,
Schutte et al. 1998). Therefore, to address the complete effects of heterogeneity in larval survivorship or movement, it would be informative to track densities of multiple larval instars during subsequent years.
Instead of supporting relatively homogeneous pressures on odonate larvae at the site level, results of this study may indicate that adults appropriately select sites where larvae are most likely to survive. This process would follow the balanced distribution model, in which discrete habitat patches function simultaneously as sources and sinks for adjacent patches (McPeek and Holt 1992, Doncaster et al. 1997). Odonates are likely candidates for balanced dispersal because they can easily disperse among potential breeding sites. Further, Gomphidae species are apparently not territorial (Corbet 1999), so site selection is largely unconstrained. It is difficult to assess whether adults accurately perceive the heterogeneity in larval site suitability within lakes, but failure of odonates to discern fish versus fishless ponds (McPeek 1989) makes this seem unlikely.
81 Most of the tremendous variation in exuviae densities among sites (Table 2) can
be attributed to site-level densities of the previous life stage (second-year larvae Table 3).
Unlike the relationship between first year larvae and adults, this positive correlation did
not occur at the lake level (Table 3). This finding supports the hypothesis that larvae do not move very far during the weeks before they emerge (Ubukata 1984). Although larval odonate mortality may be highest during later instars (Martin et al. 1991, Duffy 1994,
Schutte et al. 1998), the strength of the site-level correlation among stages also suggests that variation in predation on final-instar larvae among sites is small relative to the variation in recruitment of second-year larvae. I cannot make inferences about predation on teneral dragonflies (immature adults that have recently shed their larval skins), though, because exuviae reflect teneral densities only at the time of emergence, before tenerals are subject to predation.
Absence of correlations between adult Gomphidae densities and corresponding exuviae densities in 2005 or 2006 at the site or lake scales (all p > 0.1) reflects the high mobility and temporal variability of adults. Assuming that adult density estimates were comparable among sites (based on equivalent weather, timing, and survey effort), these results also suggest that Gomphidae do not return to their emergence sites for reproduction (i.e. they lack ‘homing’ or ‘philopatry’). The odonate species demonstrating philopatry depend on temporary lentic environments for larval survival
(Utzeri et al. 1984, McPeek 1990). Studies of marked individuals (e.g., Michiels and
Dhondt 1991, Baird and May 1997, Angelibert 2003) would be necessary to assess site attachment, including philopatry, but have not yet been conducted on adult Gomphidae.
82 Inclusion of the previous life stage in predictive models allowed me to
investigate habitat factors that affect only the life stage modeled (research question 2).
One aquatic habitat variable, Secchi depth, improved models of larval densities (Table 4).
Lower larval densities in lakes with greater water clarity (Fig. 5) may result from improved visibility of odonates to their predators in these conditions (Aksnes and Giske
1993, Davies-Colley and Smith 2001). The negative relationship between macrophyte abundances and exuviae densities (Table 4) probably reflects a sampling bias: I did not sample exuviae from emergent aquatic macrophytes, which can also serve as effective substrates for emergence (Corbet 1999). More notable was the positive relationship between exuviae densities and abundance of riparian wetland vegetation, after densities of second-year larvae are taken into account (Table 4). This result suggests that emerging larvae preferentially seek areas with rigid wetland plants (such as Carex or Iris
species) on the shoreline for emergence, or that more larvae emerge successfully in these
areas. The rigid, vertical structures of wetland vegetation may offer emerging odonates
protection from predators and/or less susceptibility to wind damage than tall grasses.
However, Gomphidae can also emerge on horizontal structures, including the ground
(Corbet 1999), so it remains unclear whether riparian wetland vegetation supports greater
emergence rates. An alternative explanation for the positive relationship with wetland
vegetation is that exuviae may remain intact there longest after emergence, due to
protection provided by the wetland vegetation from weathering or trampling damage.
With or without inclusion of exuviae densities in the models, adult densities did
not relate significantly to the habitat variables measured (Table 4). Littoral macrophytes
were positively associated with higher adult densities in 2005 (Table 4), which may result
83 from adults perching on emergent macrophytes or selecting those sites for oviposition.
To understand use of submerged and emergent macrophytes by adult and emerging
Gomphidae, behavior experiments in the field will be important.
The riparian development category, which distinguished forest versus lawn sites, did not correlate with exuvia or adult odonate densities (Table 4). Previous studies relating adult odonates to riparian vegetation indicated positive relationships between riparian woody vegetation and odonate species richness, rather than abundance (Rith-
Najarian 1998, Sahlen 1999, Hofmann and Mason 2005). Odonate species other than the
Gomphidae considered here may therefore respond to lakeshore development. This study suggests that Gomphidae are more sensitive to water clarity, littoral macrophytes, and riparian wetland vegetation than to clearing of the forest understory for creation of lawns.
However, water clarity, littoral macrophyte abundance, and riparian wetland vegetation abundance can each be reduced by the landscaping practices of riparian homeowners
(Racey and Euler 1982, Radomski and Goeman 2001, Elias and Meyer 2003, Carpenter et al. 2007).
In addition to biological understanding, results of the first research question I addressed provide methodological insights for surveys of odonates and other animals with complex life cycles. Studies of only one life stage may not accurately assess population viability (e.g., Roughgarden et al. 1988, Beck et al. 2001, De Block and Stoks
2005). Most publications on odonate habitat include surveys of only one life stage (but see Muller 2003, D'Amico et al. 2004, Foote and Hornung 2005, Hofmann and Mason
2005), probably because sampling different life stages each have distinct advantages and disadvantages. Larval presence indicates successful oviposition and is relatively stable
84 through time, due to relatively low larval mobility and the multi-year (semivoltine)
larval periods of many temperate species. Unfortunately, density estimates for the
‘clasper’ guild of larvae, which hide among macrophytes and coarse wood, can be
underestimated without severe disturbance to the habitat (personal observation).
Sampling larvae is also more time-consuming than sampling other life stages because it
requires manual searching and separation of larvae from benthic material. Exuviae can
easily be collected by nearly anyone and later identified to species. The main drawback with sampling these fragile exoskeletons is that they remain visible for short – but variable – time periods before rain or trampling destroy them. In addition, phenological differences among species make community-level surveys highly dependent on survey dates. Higher variability observed from exuvia surveys than surveys of second-year larvae (Table 2) likely resulted from this temporal component. As with exuviae, adults can only be sampled during a few weeks following species emergence. Adult behavior
(and therefore conspicuity) also varies considerably with daily weather conditions. An advantage to sampling adult males is ease of identification. Unless copulation or oviposition are observed, though, adult abundances may not correspond with breeding populations. The strong correlation between larvae and exuviae (Table 3) indicates that
Gomphidae exuvia surveys closely represent the densities of larvae in the adjacent littoral zone. Densities of adults may correspond with breeding sites, but cannot serve as a proxy for densities of successfully emerging larvae from the area.
Aquatic insects and amphibians, with both spatially segregated and complex life histories, play a unique ecosystem role by linking their aquatic and terrestrial habitats between life stages. They provide substantial nutrient and prey subsidies to adjacent
85 ecosystems when they transition from water to land during metamorphosis (Likens
1985, Bataille and Baldassarre 1993, Polis et al. 1997, Baxter et al. 2005, Regester et al.
2006). Incorporation of these “spatial subsidies” from external ecosystems has recently led to more complete understandings of organism diversity, abundance, and dynamics in a variety of systems (Power and Dietrich 2002, Polis et al. 2004). Positive correlations demonstrated here between odonate densities and those of the previous life stage suggest an additional process by which animals with complex life histories link terrestrial and aquatic ecosystems: habitat selection by adults affects the local abundances of larvae in the adjacent ecosystem. More generally, the strength of odonate density correlations across terrestrial-to-aquatic and aquatic-to-terrestrial transitions indicates that population dynamics of one life stage carry over to spatially separated life stages and subsequent generations.
Acknowledgments
This research was supported by NSF Biocomplexity Program DEB-0083545,
North-temperate lakes LTER, an NSF Graduate Research Fellowship, the Garden Club of
America, and the Animal Behavior Society. M. Turner and staff at the University of
Wisconsin Trout Lake station provided excellent logistical support. Thanks also to W.
Smith for sharing expertise on Gomphidae behavior. M. Turner, B. Peckarsky, A. Ives, J.
Zedler, C. Gratton, and S. Carpenter helped guide and critique the manuscript. M. Theis,
P. Winkler, B. Henning, D. Hong, J. Iacarella, C. Leugers, and A. Duong helped collect data.
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Table 1. Lake attributes1 of the 11 study lakes in northeastern Wisconsin and three littoral variables shared by the two study sites within each lake.
Wet- Insectiv All Cray- Macro- Cond. Secchi Silt Rock Perim Area Morpho land Houses -orous fish fish phyte Lake 3 (µS / depth dom- dom- (km) 2 (ha) -metry perim / km fish CPUE CPUE dom- cm)5 (m)6 inance inance (%)4 CPUE7 7 8 inance Black Oak 12.0 230.1 2.2 0.2 18.0 50.0 5.25 174 283 6.58 1 0 1 Big St. 12.8 665.7 1.4 0.0 24.3 90.8 2.59 411 440 0.00 1 0 1 Germain Carpenter 5.6 144.5 1.3 0.0 18.0 23.0 5.5 277 294 0.00 1 1 0 Little Arbor 10.6 221.1 2.0 0.1 7.2 112.5 4.13 109 521 12.67 1 1 1 Vitae Little St. 23.3 402.2 3.3 0.0 19.8 76.5 3.63 238 293 0.00 1 1 0 Germain Little Star 6.4 107.1 1.7 0.0 19.5 93.3 6.01 204 284 3.13 0 0 1 Lynx 11.2 126.2 2.8 0.0 5.6 23.4 4.44 435 437 0.00 1 0 1 Papoose 13.3 176.6 2.8 0.0 13.5 110.0 5.75 140 177 28.42 0 0 1 Stormy 7.6 216.0 1.5 0.0 25.0 37.0 6.25 343 345 0.17 1 0 1 Towanda 6.0 60.6 2.2 0.2 18.9 30.7 2.38 227 302 0.00 1 0 1 Upper Buck- 13.2 211.4 2.6 0.1 12.6 74.0 3.25 406 424 0.00 0 0 1 atabon
1 Data from Carpenter et al. (2006).
2 Lake perimeter.
3 Lake morphometry was calculated as the lake perimeter divided by the perimeter of a circle with the same area.
4 Percent of lake perimeter in wetlands (Gergel 1996).
5 Specific conductance (dissolved ionic content) in µ Siemens / cm
6 A measure of water clarity
7 Catch-per-unit-effort (CPUE)
8 Catch-per-unit-effort (CPUE) of an invasive, macrophyte-eating species, rusty crayfish (Orconectes rusticus)
97 Table 2. Sample size, mean, coefficient of variation (CV), and percent of total
variance in densities of three Gomphidae life stages occurring among two 22 sites
and 11 lakes.
Site-level Lake-level Sample variance variance size Mean CV (%) (%) (%) First-year larvae / m2 22 0.54 130 0 100.0 Second-year larvae / m2 22 0.18 99 34.4 58.5 2005 Exuviae / m 22 2.63 250 34.5 58.3 2006 Exuviae / m 21 2.95 130 37.9 54.7 2005 Adults / m 18 0.05 120 64.1 27.3 2006 Adults / m 20 0.01 82 100.0 0.0
98 Table 3. Results from mixed-effects linear models comparing Gomphidae life stage
densities among sites and Spearman rank correlation tests comparing life stage
densities among lakes
Adults Æ 1st year larvae 2nd year larvae Æ exuviae Exuviae Æ adults
Year 2005 Æ 2006 2006 2005 2006
Site Adjusted R2 = 0.50 Adjusted R2 = 0.74 n.s.1 n.s.
F1,8 = 8.9 F1,9 = 18.1
p = 0.02 p = 0.002
Lake Spearman r = 0.91 n.s. n.s. n.s.
p < 0.001
1 n.s. = no significant relationship detected (p >0.1)
99 Table 4. Mixed-effects linear models1 using population and habitat variables to
predict Gomphidae densities at three different life stages2.
3 4 Fixed-effects predictors from mixed-effects models n ∆AICc log p Adj. L4 R2 2006 Larvae st 1 year larvae2006 = adults2005 18 3.3 0.50
st 1 year larvae2006 = adults2005 - lake Secchi 18 0.0 8.9 <0.01 0.50 st 1 year larvae2006 = adults2005 + site macrophytes 18 2.5 1.5 0.22 0.11 st 1 year larvae2006 = adults2005 - lake crayfish 18 4.0 0.07 st 1 year larvae2006 = adults2005 - lake insectiv.fish 18 15.5 0.07
2006 Exuviae nd exuviae2006 = 2 year larvae2006 21 3.6 0.74
nd exuviae2006 = 2 year larvae2006 -site macrophytes 21 0 6.6 0.01 0.86 nd exuviae2006 = 2 year larvae2006 + site wetland plants 21 2.3 4.8 0.03 0.88 nd exuviae2006 = 2 year larvae2006 + site development 21 7.6 0.74 nd exuviae2006 = 2 year larvae2006 - lake crayfish 21 8.4 0.74 nd exuviae2006 = 2 year larvae2006 - lake Secchi 21 8.5 0.74 nd exuviae2006 = 2 year larvae2006 - lake insectiv.fish 21 16.7 0.74
2005 Adults adults2005 = exuviae2005 18 3.7 0.15 adults2005 = exuviae2005 + site wetland plants 18 0.0 0.003 0.90 0.09 adults2005= macrophytes 18 1.6 0.32 adults2005 = exuvia2005+ site macrophytes 18 1.9 3.56 0.06 0.23 adults2005 = site development 18 5.7 0.16 adults2005 = exuviae2005 + site development 18 6.0 0.04 adults2005= site wetland plants 18 9.9 0.20
2006 Adults adults2006= exuviae2006 20 3.0 <0 adults2006= macrophytes 20 0.0 <0 adults2006 = site development 20 1.5 <0 adults2006= exuviae2006 + macrophytes 20 13.8 <0 adults2006 = exuviae2006 + site development 20 15.4 <0 adults2006= site wetland plants 20 24.8 <0 adults2006= exuviae2006 + site wetland plants 20 38.2 <0
1Lake was included as a random effect in each model. Only fixed-effects terms are
shown here.
100 2 All life stage densities, except for 2006 adults, were log-transformed to meet normality assumptions. Abundances of littoral macrophytes, riparian wetland
plants, and rusty crayfish were square-root-transformed.
3 ∆AICc is the difference in Akaike’s second-order criterion values between the
model with the lowest AICc and the stated model.
4 Likelihood ratio value (followed by p-value) from a likelihood ratio test
comparing the stated model with the model using density of the previous life stage
as the only predictor variable. Likelihood ratio tests were only conducted on models
with lower AICc values than the model with the previous life stage as the sole
predictor.
101 Fig. 1. Diagram of the life history of temperate dragonflies in the family
Gomphidae. Dotted lines illustrate the relatively weak effects of survivorship and
site selection on Gomphidae abundances at the designated stages. Note that
transition times are not to scale.
Fig. 2. Distribution of larval Gomphidae head widths in 11 northern Wisconsin
lakes.
Fig. 3. Larval densities of Gomphidae burrowers from nine north-temperate lakes
graphed as a function of adult densities from surveys at the same sites in the
previous year (on one day per lake from June 10 – July 10, 2005).
Fig. 4. Gomphidae exuviae densities as a function of larval densities from the
second year-class sampled earlier during the same year (2006) in 11 north-
temperate lakes.
Fig. 5. First-year larval Gomphidae densities graphed in relation to lake Secchi
depth (water clarity) from 11 north-temperate lakes.
102 Figure 1.
103 Figure 2.
104 Figure 3.
2+log(0.1+adult Gomphidae m-1) in 2005
105 Figure 4.
2+log(0.005+ Gomphidae larvae m-1) in early 2006
106 Figure 5.
Lake Secchi depth (m)
Appendix. Site habitat data and Gomphidae densities1 at three life stages.
Site Site length (m) with vegetation type present 2005 2006 Manicured Riparian Riparian Littoral first-year lawn shrubs wetland plants macrophytes exuviae adults larvae larvae exuviae adults BO10 0 30 NA 3 2.46 0.042 0.21 0.07 2.07 0.006 BO4 1 2 0 0 0.66 0.000 0.07 0.00 1.77 0.000 BSG12 0 30 5 0 31.92 0.078 0.57 0.43 NA NA BSG7 1 2 0 6 5.73 0.133 0.86 0.79 6.13 0.025 CR10 1 2 6 7 0.43 0.017 0.14 0.07 0.30 0.011 CR5 0 25 22 1.5 0.33 0.017 0.29 0.14 1.03 0.011 LAV10 1 0 0 0 0.40 NA 0.00 0.00 0.27 0.013 LAV7 0 30 0 3 2.68 NA 0.50 0.21 1.47 0.017 LSG1 0 5 30 7.5 2.62 0.044 2.93 2.50 4.77 0.011 LSG4 1 0 0 30 0.97 0.233 2.21 2.14 0.43 0.004 LST1 1 0 0 0 0.05 0.022 0.21 0.00 4.20 0.019 LST2 0 30 2 0 0.60 0.025 0.57 0.43 2.27 NA LX1 0 25 29 0 1.98 NA 1.64 1.07 17.50 0.000 LX10 1 0 0 1.5 3.03 NA 1.14 0.79 5.87 0.014 PP10 0 30 NA 0 0.03 0.008 0.00 0.00 1.10 0.011 PP3 1 0 0 0 0.26 0.025 0.07 0.07 1.57 0.011 ST1 0 0 NA 3 0.28 0.000 0.21 0.07 1.67 0.017 ST8 1 0 0 0.3 0.43 0.000 0.36 0.00 3.57 0.006 TW11 0 30 0 3 0.97 0.111 1.64 1.07 2.27 0.033 TW8 1 2 2 3 0.25 0.011 1.57 1.50 2.47 0.000 UB1 0 20 0.5 0 1.60 0.000 0.29 0.21 0.90 0.000 UB4 1 2 2 0 0.18 0.078 0.21 0.21 0.33 0.006
1 Larval densities are per benthic m2; exuvia and adult densities are per shoreline m. Appendix. Site habitat data and Gomphidae densities1 at three life stages.
108 CHAPTER IV
Shade alone reduces adult dragonfly abundance2
Abstract
We investigated how changes to physical habitat conditions caused by nonnative
Acacia trees in riparian areas of South Africa could influence dragonfly (Odonata:
Anisoptera) populations. In this naturally treeless region, the invasive tree canopy creates shade and affects native vegetation. We hypothesized that most breeding odonates select riparian areas (1) without shade, and (2) with high density and diversity of understory perch structures. We conducted two experiments at reservoir shorelines by varying shade and perch structures independently. Dragonfly abundances were lower at sites with high
(75%) or moderate (55%) shade cover than at sites with no shade, and also at bare sand sites compared with sites containing stick perches. Perch stick density and diversity
(variety of stick heights and diameters), however, did not affect dragonfly abundance.
These experimental results indicate that shade alone directly affected adult odonate habitat selection. This study isolates which aspects of habitat change affect insect site selection and can inform conservation strategies.
Keywords: Odonata, riparian buffer, habitat structure, shade, insect behavior, habitat selection
2 Coauthors Anders C. Olson and Michael J. Samways
109 Introduction
Insects and many other animals respond behaviorally to physical habitat conditions. Tree canopies influence these physical habitat conditions in part via light interception, which affects local climate and visibility (Endler 1993, Thery 2001). In addition, tree canopies may also influence insect behavior indirectly via effects on understory vegetation and prey composition. Changes to canopy structure from riparian development, logging, and invasion by non-native plants can thus strongly influence insect habitat use.
In South Africa, most riparian corridors had no indigenous trees and were open until Australian black wattle (Acacia mearnsii) and long-leaved wattle (A. longifolia) invaded and formed dense canopies (Gorgens and van Wilgen 2004). Numerous plants and insects are likely sensitive to the shading effects of invasive Acacia (Breytenbach
1986, Richardson et al. 1989, Samways 2006), but experimental research is needed to isolate direct and indirect effects of shade. Our experiments independently test effects of shade intensity and perch availability on South African adult dragonfly (suborder
Anisoptera) riparian site use. Understanding which components of habitat disturbance are most influential for odonates can inform conservation strategies.
Observational studies suggest that many adult Odonata (dragonfly and damselfly) species avoid shaded areas (Pezalla 1979, McKinnon and May 1994, Chwala and
Waringer 1996, Clark and Samways 1996, Painter 1998, Kinvig and Samways 2000,
Samways and Taylor 2004, Samways et al. 2005, Ward and Mill 2005). Their behavior may reflect thermoregulation requirements. Insects and other ectothermic animals are particularly sensitive to the variation in microclimates produced by vegetation (e.g., May
110 1976, McGeoch and Samways 1991, Downes and Shine 1998, Arnan et al. 2007,
Valentine et al. 2007). Lighting can also play an important role in habitat selection (e.g.,
Davies 1978, McCall and Primack 1992, Grundel et al. 1998, Bernath et al. 2002, De
Cauwer et al. 2006, Reinhardt 2006) because insects that fly during the day orient almost entirely based on visual cues (Lehrer 1994, Dafni et al. 1997, Egelhaaf and Kern 2002,
Olberg et al. 2005).
Adult odonates in the “percher” behavioral guild may require riparian understory vegetation because they guard breeding territories, thermoregulate, and watch for prey from plant perches (Corbet 1999). Previous studies indicate greater abundances of adult dragonflies in areas with taller wetland plants (McKinnon and May 1994, Ueda 1994,
Clark and Samways 1996, De Marco and Resende 2004, Foote and Hornung 2005, Ward and Mill 2005), perhaps because they serve as perch structures. In a few studies, researchers added artificial stick perches to their study sites to manipulate dragonfly abundances for other behavioral questions (Wolf and Waltz 1988, Rehfeldt 1990, Baird and May 1997, May and Baird 2002), although no research has specifically tested effects of perch stem density for odonate populations. We hypothesized that riparian sites with higher densities of perch structures (sticks we erected in the sand) would have greater odonate abundances than sites with fewer or no perch structures.
The distribution of plant heights or forms (structural diversity) may change independently of plant density. Odonata likely respond more to structural diversity than to the diversity of plant species (Buchwald 1992, Corbet 1999, Foote and Hornung 2005).
Taller perch structures can facilitate territory guarding, so perching height is often proportional to body size (Corbet 1999, De Marco and Resende 2004). Odonate species
111 (namely damselflies, suborder Zygoptera) also select perch structures based on stem diameter (Askew 1982, Rouquette and Thompson 2007). Thus, we expected that sites with a variety of stick diameters and heights (i.e. higher stem diversity) support higher dragonfly species richness and abundance than sites with uniform sticks.
In addition, we hypothesized that perch stick location relative to the water’s edge affects Odonata behavior. Specifically, we expected that territorial male dragonflies would select perch sticks closest to the water, perhaps to intercept females en route to oviposition sites (Van Buskirk 1986, Switzer and Walters 1999). Testing whether odonates use structures farther from the water can inform land managers seeking to establish riparian buffer widths and manage for wildlife diversity. Thus our research questions were: 1) Do dragonflies avoid shaded zones? and 2) Do density, diversity, or position of perch structures affect dragonfly site use?
Materials and methods
Study area
Separate shade and perch stick experiments were conducted at two small reservoirs with little shoreline vegetation on Vergelegen Estate, Western Cape, South
Africa. Both reservoirs receive water from the Hottentots-Holland Mountains and each is
~2 ha in area. By the end of the dry summer, when field experiments began, water levels in the reservoirs had dropped so that bare shorelines about 15 m wide were exposed around the margins of both reservoirs. These shorelines lacked tall vegetation, providing relatively homogeneous field sites where we could experimentally isolate the structural
112 variables of interest: shade levels, perch stick density, perch stick diversity, and perch stick distance from water.
Field methods
Although a fully crossed experimental design of shade level, perch diversity, and perch density would have been informative, space and time constraints required us to test these factors in two separate field experiments. We established twenty shade plots, with four shade levels replicated randomly within five complete blocks. Distances of 8 to 20 m separated the blocks. All treatment plots (2.8 x 2.8 m each) were separated by at least
5 m, and were 1 m away from the water’s edge (although the water level receded to a distance of about 3 m from the shade treatments during the study period). We suspended three types of commercial plant nursery shade cloth 2 m off the ground with wooden poles (about 10 cm in diameter) and nylon ropes. We chose this height because it was above normal anisopteran flight routes. Shade cloths were solid green or black and provided three levels of sunlight interference: 30 %, 55 % and 75 % shade. Control treatments, which we refer to as 0 % shade, were set up with the same pole and rope structures, but no cloth. Shade levels were verified using a standard light meter to quantify percent of full sunlight available under each shade cloth treatment at approximately the same time of day for all treatments. We measured light levels at a typical odonate perching height of 20 cm above the ground. Nearby invasive Acacia trees created 96-97% shade at the same height. To standardize structures and facilitate observations in the plots, we trimmed any vegetation present, and erected 16 sticks (2 per
113 m2) of equivalent sizes (6 - 10 mm diameter; 0.4 - 0.5 m tall) in a grid pattern beneath the
shade cloth.
At the other reservoir, we used regular grids of Eucalyptus sticks to mimic two
structural attributes of understory shoreline vegetation: stem diversity and stem density.
We replicated each of four stick density x stick diversity treatments plus a control
treatment at random within four blocks, giving a total of 20 plots for the perch stick
experiment. We separated each plot by 5 m and each block by 5 - 10 m. Within these
plots (3 x 2.5 m), we varied stick density by erecting two different numbers of sticks in
uniform grids. High-density stick treatments contained 30 sticks (4 per m2); low-density treatments had 9 sticks (1.2 per m2). The third level of stick density, control plots,
contained no sticks.
Additionally, we created low stick diversity at half of the treatments by using
sticks of all the same diameter and height, and high stick diversity at the other half of
treatments by including an even mixture within plots of ‘tall thin’, ‘tall stout’, ‘short thin’, and ‘short stout’ sticks. The ‘thin’ sticks were 2 – 6 mm in diameter, while ‘stout’ sticks were 15 – 20 mm in diameter. All sticks were clearly taller than any other vegetation emerging in the plots: ‘tall’ sticks were 0.6 m high and ‘short’ sticks were about 0.2 m high. Average odonate perch heights vary between 0 and 0.75 m (May 1976,
Nomakuchi 1992, McKinnon and May 1994, Reinhardt 1999). For the low perch diversity treatments (where all sticks within a plot were of the same size), each block contained a different stick size (from the four height and diameter combinations).
Based on average dragonfly densities and logistical constraints, we recorded dragonfly observations in all shade treatments for ten-minute periods and perch stick
114 treatments for five-minute periods. Two observers collected data only on 11 calm, sunny days during February – April 2005 between 9h45 and 15h45, the hours when odonates are most active. Although the shadow of shadecloths moved somewhat throughout the course of the day, we observed odonates only within the shaded portions of each plot.
We observed the same plot portions (measurable from the regular grid of stick perches) for the same time period within each location block. Observation periods began one minute after the observers became situated in front of each plot. Observers sat on shore 2 m inland from the edge of each plot, at the center of each plot’s shoreline width. We saw no evidence of altered dragonfly behaviors when observers were at least a meter away from perch locations. Although changing weather conditions prevented us from observing all location blocks on each of the 11 days, we completed observations on all treatments within a block each day (for both experiments).
During each observation period, we recorded the number and species of dragonflies perching in the plot, investigating the plot (flying slowly), and passing (flying quickly) through the plot. We also recorded the number of dragonflies in the plot at the instant when a timer signaled the end of the observation period, which we used as a measure of dragonfly relative abundance per plot. Abundance estimates were mainly comprised of individuals perching within treatments. Size of the plots enabled observers to count all anisopterans in each plot during a single glance. Species present were easy to distinguish on the wing. At the perch stick experiment, we also noted where perching occurred from one of two distance-from-water categories (1-2.5 m or 2.5-4 m inland).
Statistical methods
115 Single-factor randomized-block Analysis of Variance (ANOVA) tests were used
to compare mean dragonfly relative abundance (for all species combined) among shade
treatments, which was coded as an ordered variable. We also assessed habitat selectivity
for perching and investigative flight behaviors by dividing each of these variables by the total number dragonfly entries into the given plot. Based on Bonferroni corrections for conducting three separate tests, we assessed significance at p < 0.02. After finding significant differences among treatments in Anisoptera perch selection, we conducted the same ANOVAs on perch selection for the two most common species. Bonferroni corrections for conducting these two additional tests (protected under the total Anisoptera perch selection test) resulted in assessment of significance at p<0.025. We used the means of response variables across all 11 observation days for each treatment (n = 20 for both perch and shade experiments). Response variables were square-root transformed when necessary to fulfill normality and homoscedasticity assumptions. Using Tukey’s
Honest Significant Difference (HSD) tests, we conducted post-hoc pairwise comparisons among treatment means.
We compared Anisoptera abundance, richness, investigative flights, and perch selection among the two perch stick diversity and the high- and low-density treatments
(excluding the control) with two-factor randomized-block ANOVAs. We subsequently compared response variables among all three density treatments (including the control) with one-way blocked ANOVAs. The Bonferroni-corrected rejection level was p <
0.0125 because we repeated these ANOVAs on four separate response variables. As in the shade experiment, we conducted the same one-way blocked ANOVAs on perch selection for the two most common species after finding significant differences among
116 stick density treatments. Bonferroni corrections for conducting these two additional tests
(protected under the total Anisoptera perch selection test) resulted in assessment of
significance at p < 0.025. To test whether dragonflies perched more than expected at
random on the row of sticks closest to the water, we used a two-sided t-test (n = 20). All
analyses were conducted using R 2.2.0 software (R Development Core Team, 2005,
Vienna, Austria).
Results
Anisoptera species recorded within the plots included three Libellulidae
(Trithemis dorsalis, T. arteriosa, Pantala flavescens), one Aeshnidae (Anax imperator),
and one Gomphidae (Paragomphus sp.). One zygopteran species (Atricallagma
glaucum) was also occasionally observed in the plots, but was not included in the dataset
because their locations proved too difficult to track reliably in conjunction with
anisopterans. The two Trithemis species were by far the most abundant odonates within the experimental plots (77 and 95 % of individuals perching in shade and perch experiment plots, respectively).
Anisoptera mean perch selection decreased from 0.5 to 0 perches per plot entry as shade cover increased from 0 % to 75 % shade (F3,12 = 13.7, p = 0.0004; Fig. 1).
Specifically, mean perch selection for both species tested was higher at the plots with no
or low (30 %) shade than at the 75 % shade plots (T. arteriosa: F3,12 = 12.9, p = 0.0005;
T. dorsalis: F3,12 = 14.9, p = 0.0002; pairwise differences based on Tukey’s HSD tests
with p <0.05). Mean anisopteran abundances followed the same pattern (F3,11 = 24.7, p <
0.0001), excluding abundance estimates from an outlier zero-shade plot that had no
117 odonates present during the relative abundance counts. Dragonflies appear to have
avoided this particular plot more than other zero-shade plots, although we have no data to
suggest why. Mean anisopteran investigative (slow) flights did not differ by shade
treatment (p > 0.1).
Based on the perch stick experiment two-way ANOVA, mean Anisoptera
abundance, richness, investigative flights, and perch selection did not differ with perch
diversity or density. The one-way ANOVA comparing three stick densities revealed that
mean dragonfly abundance was 2.5 to 2.7 times higher at the stick treatments than at
control plots without sticks (F2,14 = 6.8, p = 0.009), but there were no significant differences between plots with high and low stick densities (Fig. 2). Perch selection followed the same pattern, with the mean perches per entry 1.6 times higher at stick treatments than at the control plots (F2,14 = 6.3, p = 0.01). By species, T. dorsalis perch
selections were lower at control plots than at plots with high or low stick densities (F2,14 =
5.6, p = 0.02; pairwise differences based on Tukey’s HSD tests with p <0.05), while T. arteriosa perch selection did not differ with stick density (p = 0.1). Based on perch location data across all five treatments, dragonflies perched more frequently (63 % of observed perches) in the half of the treatment closest to water (t = 90.8, p < 0.0001).
Discussion
Results from the shade experiment provide strong evidence for shade avoidance behavior by the two most abundant anisopterans around the reservoirs. The three shade treatments had identical structures except for cloth weave density, but the control treatment lacked shade cloth completely. Under the assumption that presence of
118 shadecloth could inhibit dragonflies from entering plots rather than their decision to perch there, we considered whether dragonflies responded adversely to the artificial screen structures rather than shade alone. The perch selection response adjusts for potential alteration of behavior by screen structures because it standardizes by the total number of entries into a plot. Additionally, we recorded lower perch selection at the 75 % shade treatment than at the 30 % shade treatment, which had no differences in structure. After controlling for effects of structure per se, we thus conclude that shade alone can have a significant inhibitory effect on the behavior of these species.
Whether shade limits prey availability, mate attraction, hunting effectiveness, or thermoregulation remains to be tested. Vegetation structure can simultaneously influence trophic interactions through several of these mechanisms (e.g., Coll et al. 1997). Prey availability seems less likely to control the distribution of adult odonates around water bodies because they are mobile, generalist predators. Based on rapid flight paths following shade boundaries (Reinhardt 2006), odonates may distinguish shade boundaries by sight rather than solely by temperature.
Presence of perch sticks increased odonate abundances, but contrary to our hypotheses, the density, size, height, and heterogeneity of perch sticks set up in unshaded areas had little effect on odonate abundances. Examination of a broader range of stem varieties and densities, though, may reveal additional odonate perch selection preferences. The only measurable perch preferences we observed were for sticks closest to the water. Preference for perch sites within 1 m of the water’s edge concurs with previous odonate behavior studies (Ward and Mill 2005). Proximity to suitable oviposition areas likely explains this perching behavior for male dragonflies (Van
119 Buskirk 1986), although prey density gradients could also be investigated with additional
field studies. Our results suggest that understory perch structures only affect odonate
habitat selection when they are completely absent.
Behavioral studies are critical for effective wildlife conservation because animal
behavior is an important determinant of species’ habitat use and distributions (Anthony
and Blumstein 2000, Linklater 2004, Reed 2004). Odonata in this study exemplify how
plants can directly influence animal behavior and abundance via altered light conditions.
Further, our results suggest that creation of shaded rather than open habitats by the
introduced Acacia have a direct negative influence on adult odonate abundances.
Because body size and pigmentation likely influence thermoregulation behavior (May
1976, De Marco and Resende 2002), other Odonata species with similar morphologies
may exhibit similar responses to shade. Vulnerability and scarcity of threatened
populations often necessitates behavioral research on related species.
Experimental isolation of physical habitat conditions that affect insect behavior
and distributions can inform conservation strategies. Knowledge that shade affects
odonates directly, rather than via reductions of other habitat structures, suggests that removal of alien tree canopies could enhance populations of indigenous dragonflies. The
South African national Working for Water Programme, devoted to removal of alien riparian trees, has improved wildlife habitat by returning many riparian areas to sunlit conditions (Samways and Grant 2006). Our results suggest that invasive tree growth and other forest canopy disturbances influence riparian site use by adult dragonflies.
Acknowledgments
120 G. Wright and the staff of Vergelegen offered invaluable support and access to a
large natural area currently undergoing restoration. Thanks to M.G. Turner, J.B. Zedler,
C. Gratton, A.R. Ives, S.R. Carpenter, B. Peckarsky, and two anonymous readers for
careful reviews of the manuscript. Funding was provided by a National Science
Foundation Graduate Research Fellowship awarded to the primary author. The
Stellenbosch University Department of Conservation Ecology and Entomology supplied
material and logistical support.
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125 Tables
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Figure 1. Mean count of perching Anisoptera (corrected by the total number of anisopteran entries) within each shade treatment (n = 20). Different letters indicate significant differences based on Tukey’s honest significant difference tests (p < 0.05).
Error bars represent two standard errors above the mean and two standard errors below the mean.
Figure 2. Mean Anisoptera abundance associated with three types of stem treatments.
The control was without sticks, while high and low stem density treatments had 30 and 9 sticks erected, respectively. Different letters indicate significant differences based on
Tukey’s honest significant difference tests (p < 0.05); error bars show two standard errors both above and below the mean.
126 Figure 1.
y 0.8 r A AB BC C
ent 0.6 ot pl 0.4
hes per 0.2 c r e
P 0 02030 55508750 Percent shade
127 Figure 2
1.6 A B B ent m t 1.2 ea r
0.8
0.4 bundance per t A
0 control high low Stem density