The Effect of Biotic and Abiotic Forces on Species Richness

Peter J.T. White

Faculty of Science, Department of Biology

McGill University

Montréal, Québec, Canada

A thesis submitted to McGill University in partial fulfillment of the

requirements of the degree of Doctor of Philosophy

TABLE OF CONTENTS

Table of Contents………………………………………………………………………………………………..….. 2 List of Tables………………………………………………………………………….….……………………………. 5 List of Figures……………………………………………………………………………………….…….…….…….. 8 List of Appendices……………………………………………………………………………………………………. 13 Preface………………………………………………………………………………………………………………….… 14 Thesis Format and Style…………………………………………………………………………………… 14 Contribution of Co-Authors……………………………………………………………………………… 15 Original Contributions to Knowledge……………………………………………………………….. 17 References………………………………………………………………………………………………………. 21 Acknowledgements…………………………………………………………………………………………. 23 Thesis Abstract………………………………………………………………………………………………………… 26 Résumé……………………………………………………………………………………………………………………. 28 General Introduction……………………………………………………………………………………………….. 30 References……………………………………………………………………………………………….……… 45 Chapter 1: Detecting Changes in Forest Floor Habitat after Canopy Disturbances…… 53 Abstract……………………..……………………………………………………………………………………. 54 Introduction…………………………………………………………………………………………………….. 55 Local Consequences of Damage……………………………………………………………….. 55 Landscape and Regional Investigations……………………………………………….…… 56 Habitat Implications of Ice Storms………………………………………………………….… 56 Using Remote Sensing to Measure Habitat Change…………………….…………… 58 Objective………………………………………………………………………………….……………… 59 Method……………………………………………………………………………………………….…………… 59 Study Area………………………………………………………………………………………..……… 59 Satellite Imagery Processing…………………………………………………………………….. 60 VI Calculation…………………………………………………………………………………………… 61 Predicting the Spatial Pattern of CWD Influx……………………………………………. 63 Independent Validation of CWD Predictions…………………………………….……… 64 Methods Evaluation……………………………………………………………………………….… 65 Categorizing CWD Habitat………………………………………………………………………… 66 Results……………………………………………………………………………………………………………… 67 Performance of VIs for Predicting CWD Influx……………………………………..…… 67 Independent Validation of CWD Predictions………………………………….………… 68 Categorization of CWD Influx into Different Habitat Types………………….…… 68 Methods Evaluation………………………………………………………………………….……… 69 Discussion……………………………………………………………………………………………………….. 69 Accuracy of VIs in Predicting Spatial Pattern of Damage…………………..……… 69 CWD Benefits for Mont St Hilaire Species…………………………………………..….… 71 The Benefits of Different Habitat Types for Insects……………………………..…… 72 CWD Influx and Species Movements……………………………………………………..… 74 Conclusion………………………………….……………………………………………………………. 76 Acknowledgements……………………………………………………………….………………………… 77 References………………………………………………………………………………………………………. 78 Figures……………………………………………………………………………………………………………… 89 Tables……………………………………………………………………………………………………………… 92

Linking Statement 1………………………………………………………………………………….………..…… 96 Chapter 2: Human-Disturbance and Caterpillars in Managed Forest Fragments.….…. 97 Abstract……………………………………………………………………………………………………….….. 98 Introduction……………………………………………………………………………………………….……. 99 Trailside Habitat in Forests………………………………………………………………………. 99 An Analogy of Forest Edge Habitat…………………………………………………………... 101 Hypotheses……………………………………………………………….….…….….….….………… 102 Method……………………………………………………………………………………………………………. 103 Study Area……………………………………………………………………………………………….. 103 Trail Index Calculation……………………………………………………………………………… 106 Caterpillar Surveying and Identification…………………………………………………… 107 Site Host Plant Availability……………………………………………………………………….. 108 Statistical Analysis……………………………………………………………………………………. 109 Results…………………………………………………………………………………………………………….. 110 Trail Index and Host Plant Availability……………………………………………………… 110 Random Versus Non-Random Distribution of Caterpillar Species…………….. 111 Discussion……………………………………………………………………………………………………….. 112 Possible Mechanisms of Negative Relationship……………………………………….. 112 Trails Versus Edges…………………………………………………………………………………… 114 Conclusion……………………………………………………………………………………………….. 116 Acknowledgements………………………………………………………………………………….……… 117 References………………………………………………………………………………………………………. 118 Figures……………………………………………………………………………………………………….……. 131 Tables……………………………………………………………………………………………………………… 134 Linking Statement 2………………………………………………………………………………………………… 136 Chapter 3: Testing Two Methods that Relate Herbivorous Insects to Host Plants….... 137 Abstract…………………………………………………………………………………………………………… 138 Introduction…………………………………………………………………………………………………….. 140 Overlooked in Conservation Planning…………………………………………………..….. 140 The Relationship between Hosts and Lepidoptera………………………………..…. 141 Methods………………………………………………………………………………..….….….….…....…… 144 Study Area…………………………………………………………………………..…………………… 144 Caterpillar Survey and Identification………………………………………………………… 146 Controlling for Habitat Disturbance..…………………………………………………..…… 147 Analyses…………………………………………………………………………………………………… 147 Host Plant and Caterpillar Relationships..……………………………….…….….….….. 149 Testing Host Plant-Specific Preferences………………………………….…….….….….. 149 Results………………………………………………………………………………………………………….…. 150 Caterpillar Sampling…………………………………………………………………………………. 150 Host Plant and Caterpillar Relationships………………………………………………….. 151 CAPIr and CAPIa…………..………………………………………………………………………….. 151 Host Plant Importance Relative to Trail Index………………………………………….. 152 Discussion……………………………………………………………………………………………………..… 153 Biodiversity and Conservation….….….…………………………………………………….… 153 Low Richness and Abundance in Invasive Trees……………………………………….. 154 Mechanisms…..….….….….….….….……………………………………………….…..…..…….. 157 Conclusion……………………………………………………………………………………………..… 159

Acknowledgements……………………………………………………………………………………..….. 161 References………………………………………………………………………………………………….…… 162 Figures………………………………………………………………………………………………………..…… 172 Tables………………………………………………………………………………………………………….…… 177 Linking Statement 3………………………………………………………………………………………………… 181 Chapter 4: Intra-Seasonal Relationships between Insect Herbivores and their Hosts. 182 Abstract……………………………………………………………………………………………………….….. 183 Introduction………………………………………………………………………………………………….…. 184 Bottom-Up Effects: Foliar Quality.….….……………………….…….….…………………. 184 Bottom-Up Effects: Foliar Toxins.….….….….….….….….….….………………………… 186 Top-Down Effects.….….….….….….….….….….….….….….….….….….….…………….… 187 Current Gaps in Foliar Quality Research.….….….………….…….….…………………. 188 Tri-Trophic Relationships……………….….….….….….….….….…….….…………………. 191 Objective…………………………………………………………………………………………………. 192 Methods……………………………………………………………………………………………………….…. 193 Study Area…………………………………………………………………………………………….…. 193 Caterpillar Survey and Identification………………………………………………………... 195 Measuring the Bottom-Up Effect…………………………..…..….……………….…..…... 195 Measuring the Top-Down Effect…………………………….….….……………….…..…... 196 Analysis.….….….….….….….….….….….….………………….……………..….……….…..…... 198 Methodological Assumptions…………………………………………………………………... 199 Results……………………………………………………………………………………………….……………. 200 Bottom-Up Foliar Quality……………………….………………………………………………… 201 Top-Down Pressure…………………………………………………………………………………. 201 Seasonality of Bottom-Up Effects.….….…………………………………………………….. 201 Tri-Trophic Relationships.….….….….….…………………….….….……………….…..…... 203 Discussion…………………………………………………………………………………………….….….….. 204 Bottom-Up: Foliar Quality………………….…………………………………….…….………… 204 Bottom-Up: Toxins……………………………..….….……………………………….…………... 207 Top Down: Parasitoids…………………………………………………………………….…....… 208 Conclusion…………………………………………………………………………………….……….… 210 Acknowledgements……………………………………………………………………………………….… 211 References…………………………………………………………………………………………………….… 212 Figures…………………………………………………………………………………………………………….. 223 Tables……………………………………………………………………………………………………………… 228 General Conclusion……………………………………………………………………………………………….… 232 References……………………………………………………………………………………………………………... 237 Appendices……………………………………………………………………………………………………………… 239

LIST OF TABLES

CHAPTER 1:

Table 1 Landsat-5 satellite imagery used in calculation of vegetation indices

for Mont St. Hilaire in 1996 and 1998.

Table 2 Vegetation Indices (VIs) used to calculate forest damage after the

1998 ice storm at Mont St. Hilaire. The VIs incorporated four

wavelengths of light: blue (450 – 520 nm), green (520 – 600 nm), red

(630 – 690 nm) and near infrared (NIR, 760 – 900 nm).

Table 3 The data for each regression model were split into random training

and validation datasets (80/20 ratio, 66/16 plots) to create ten cross

validation trials for each Vegetation Index. The R2 fit of each

validation dataset is given respective of the regression coefficient and

intercept calculated in its training dataset. NDVI provides the best for

both training and validation datasets (R2 = 0.20 and 0.37,

respectively).

CHAPTER 2:

Table 1 Trail Index is a consistent negative predictor of caterpillar abundance

and caterpillar species richness both across and within study

sites.Host plant availability is rarely significantly linked to caterpillar

species abundance or richness, only explaining a large degree of

caterpillar variance at Mont Shefford. Near-significant (*) and

significant (**) p-values are marked.

CHAPTER 3:

Table 1The relationship between host plants and caterpillars shows that (a)

host plant (Shannon’s) diversity is a significant descriptor of

caterpillar (Shannon’s) diversity when Trail disturbance is accounted

2 for (total model adjusted R = 0.45F3,68 = 9.9) but (b)host plant

richness is a non-significant descriptor of caterpillar richness when

Trail disturbance is accounted for (total model adjusted R2 = 0.27,

F3,68 = 20.1).

Table 2CAPIa and CAPIr values for host plant trees (sorted in order of

decreasing CAPIr values) are calculated as the difference between the

observed and the average caterpillar abundances and richness in host

plant tree species (see equations 1 and 2). Greater CAPI values

indicate that a host plant is more preferred by the caterpillar

assemblage.

Table 3Host plant frequencies (combined) explained (a) 21.0% of caterpillar

abundance, (b) 24.9% of caterpillar richness and (c) 27.9% of

caterpillar Shannon’s diversity among quadrats. This was independent

of trail index which, when combined with a F. americana interaction

term, explained (a) 19.8%, (b) 22.3% and (c) 18.7% of the variances.

CHAPTER 4:

Table 1 Foliar nutrient properties in broadleaf trees in southern Quebec and

Ontario in early June, early July and early August. Foliar nutrient data

are averaged across 34 different common deciduous tree species.

This table is adapted from Ricklefs and Matthew’s (1982), Table 3.

Table 2 A list of trees sampled (where n > 10) across the 72 study sites, and

their associated caterpillar richness and abundance.

Table 3Parasitoid pressure on host plants in August.

Table 4 Emergence records for parasitoids in Ontario and Quebec

documented by Natural Resources Canada, 1937-1949.

LIST OF FIGURES

CHAPTER 1:

Figure 1 Coarse woody debris influx at Mont St. Hilaire resulting from the

1998 ice storm. Mean coarse woody debris influx was 1.7 kg/m2

(standard deviation = 0.97 kg/m2), for a total estimated input of 16.8

metric tons per hectare. Coarse woody debris input values are

spatially correlated up to 450 meters.

Figure 2 The predicted biomass of coarse woody debris resulting from the

1998 ice storm is positively correlated to the amount of young

(category 1 and 2) coarse woody debris that was measured in the

summer of 2008 at 18 randomly located sites across Mont St. Hilaire.

Figure 3 Concentrations of coarse woody debris at Mont St. Hilaire in (a) dry

shaded habitat, (b), moist shaded habitat, (c) sun-exposed habitat

and (d) wet habitat.

CHAPTER 2:

Figure 1 We sampled at four sites in the St. Lawrence River valley of southern

Quebec, Canada (Figure adapted from Atlas of Canada 2010), each

progressively farther from the center of Montreal (dashed bound).

Urban development is highest in the western sites, giving way to

farmlands eastward in the St-Lawrence Lowland, and then extensive

forests in the Appalachian Highlands

Figure 2 Trail index calculation for a given pixel p at distance d from trail t

that has a width w. Index shown untransformed (a) and log-

transformed (b).

Figure 3 Trail index across our four study sites: (A) Mont Royal, (B) Mont St.

Bruno, (C) Mont St. Hilaire and (D1, D2, D3) Mont Shefford. The

geospatial arrangement in this Figure does not reflect the regional

geolocations of the sites (see Figure 1).

CHAPTER 3:

Figure 1 Caterpillars were collected from four sites in the St. Lawrence River

valley of southern Quebec, Canada (Figure adapted from Atlas of

Canada 2010). The matrix surrounding each site isdominated by

agricultural lands and urban development with the exception of Mont

Royal, which is a forest fragment in an exclusively urban setting.

Figure2There was no relationship between the number of caterpillars

reported to use a given host plant and either (a) CAPIr or (b) CAPIa

scores. These relationships are expected to be positive as a host

plant’s acceptability should be indicative of the caterpillar assemblage

preference of that host plant relative to other host plants in the

community.

Figure3 A host plant replacement simulation for (a) caterpillar species

richness and (b) caterpillar richness in the Mont Royal forest

fragment. In these simulations F. grandifolia and A. pensylvanicum

were substituted for A. platanoides and R. cathartica (dashed line)

and O. virginiana was substituted for F. americana (dotted line). The

substituted species were chosen because they had high CAPIr and/or

CAPIa scores and commonly share the same general canopy position

as the species they replace. Replacement of invasive species with O.

virginiana and A. pensylvanicum resulted in an increase of 30% in

caterpillar species richness and 40% in caterpillar abundance.

Replacement of F. americana with F. grandifolia resulted in an

increase of 18% in caterpillar species richness and 37% in caterpillar

abundance.

CHAPTER 4:

Figure 1 Caterpillars were collected from four sites in the St. Lawrence River

valley of southern Quebec, Canada (Figure adapted from Atlas of

Canada 2010) at the northern edge of the deciduous forest biome in

eastern North America. The matrix surrounding each site isdominated

by agricultural lands and urban development with the exception of

Mont Royal, which is a forest fragment in an exclusively urban setting.

Forest patches across the region are shown in dark gray, urban areas

in light gray (including the City of Montreal at the left side of the

pane).

Figure 2 Average foliar qualities at quadrats in the months of June (Je), July

(Jy) and August (Au) for (a) % water content (b) % nitrogen content,

(f) % lignin content and (g) % polyphenol content. Bars represent

standard error. ANOVAs between months are significant for all foliar

qualities at p < 0.001. Average foliar qualities for each quadrat were

weighted based on the proportion of the total basal area occupied by

each sampled host plant species in the quadrat. Host plant-specific

foliar qualities were taken from Ricklefs and Matthew (1982).

Figure 3Regression tree analyses of the determinants of caterpillar

richness (left hand panels) and abundance (right panels) at the

quadrat level. Analyses are shown for June (a, b), July (c, d) and

August (e, f). The variables shown are % Phosphorus content

(P), % Polyphenol content (Phenol),% Fiber content (Fiber) and

% Lignin content (Lig). Water content, Nitrogen content and

Toughness were included, but were not significant. The clause

presented at each node is the condition corresponding to the

left hand fork (the right hand fork would be the opposite

condition). Each clause is paired with an R2 value associated

with that node in brackets; this value is equal to the complexity

parameter of the node. At each terminus the average caterpillar

richness or abundance is given (depending on the tree) along

with the number of quadrats that satisfy the conditions of the

fork (in brackets). Trees are pruned and show splits

corresponding to R2> 0.05.

Figure 4 Regression tree analyses for the determinants of caterpillar

richness (a) and abundance (b) for the month of August. These

trees were created using the same data as for Figure 3e and 3f

(respectively) but with the addition of parasitoid pressure data

for each quadrat. In (a) parasitoid pressure supersedes % fiber

content as the most important determinant of caterpillar

richness. In (b) parasitoid pressure is less important and

supersedes % polyphenol content as the primary determinant of

caterpillar abundance.

APPENDICES

CHAPTER 2:

Appendix A1 We surveyed 36 macrolepidopteran moth species across the

four sites in our study region Mount Royal (R), Mont St. Bruno (B),

Mont St. Hilaire (H) and Mont Shefford (S). All species IDs were based

on 5th or 6th instar larvae identified using Wagner (2005).

Appendix A2 Micromoth distribution.

Appendix A3 Complete list of host plant species documented in vegetation

surveys.

Appendix A4Randomness was tested using randomization goodness-of-fit

tests. P-values were calculated using 10,000 replicates of

randomization.

CHAPTER 3:

AppendixB1 Caterpillar collections were made from 38 host plant trees in 72

quadrats across the four study sites.

AppendixB2 A record of the macrolepidoptera and microlepidoptera

morphospecies that were surveyed. Microlepidoptera morphospecies

were given unique alphanumeric designations and subsequent

individuals were verified with digital images. All macrolepidoptera

were identified using Wagner (2005) and Handfield (1999).

PREFACE

Thesis Format and Style

This thesis is in a manuscript-based format, consisting of a set of four papers.

The first chapter explores the geospatial pattern of damage and biological implications of the 1998 ice storm on an old-growth forest. The second, third and fourth chapters are focused on how disturbance and habitat quality impact caterpillar assemblage richness and abundance. All four chapters focus on forest fragments found in the same area of the St. Lawrence floodplain in southern

Quebec (i.e. the Monteregié). This thesis was initially intended to have a remote- sensing and GIS theme in all four chapters but after NSERC funding opportunities became available in the spring of 2008 the thesis evolved to centre on lepidopteran ecology and extensive field work was conducted in 2008 and 2009.

Thus, the first chapter has a different focus than the final three chapters that comprise a cohesive unit. The first chapter, however, does link with the second chapter through the themes of natural disturbances (Chapter 1) and human- based disturbances (Chapter 2) in forest fragment communities.

Contributions of Co-Authors

Each of the four chapters was prepared as a manuscript for publication in a peer

reviewed journal, generally focusing on biological reserve management for the

conservation of biological diversity. I received valuable guidance and intellectual

contributions from my supervisors B. McGill and M.J. Lechowicz. I was, however, the primary person designing the research questions, organizing and conducting the data collection, computing statistical analyses and writing the research for each chapter. I had two research assistants – R. MacKenzie and M. VonButtlar who helped me collect the woody debris, vegetation and Lepidoptera data used throughout this thesis.

Chapter 1: M.J. Lechowicz and B. McGill were both valuable intellectual contributors. M.J. Lechowicz provided several rounds of editorial comments and suggestions as the manuscript was in varying stages of preparation. B. McGill provided very valuable insights and advice for the statistical procedures that were used. They both played key roles in sharpening my research focus and suggesting that the research focus on biotic implications. Prior coarse woody debris collections by M. Hooper in 1998were also instrumental in allowing a

geospatial model to be computed.

Chapter 2: M.J. Lechowicz and B. McGill played similar roles as in Chapter

Chapter 3: This will be a single-authored manuscript. M.J. Lechowicz

provided comments on the manuscript and encouraged me to think about the

implications of my findings, particularly encouraging me to explore a post hoc

invasive-species replacement exercise. B. McGill was an invaluable consultanthelping me construct the conceptual arguments I use in this chapter.

He also provided several rounds of review before it was finalized.

Chapter 4: This will also bea single-authored manuscript. B. McGill played

a similar role as in chapter 3. He also encouraged me to use many of the

statistical methods that were chosen.

Original Contributions to Knowledge

Chapter 1:

There has been much research on the impacts of the 1998 ice storm (and similar storms) on broadleaved forests. The bulk of this research has explored either the geophysical correlates to damage or the local biotic implications. For example,

there is a wide array of studies that have mapped out the pattern of ice storm damage (King et al. 2005, Millward and Kraft 2004) or explored the topographic correlates to damage (Vandyke 1999) at a landscape orregional scale. These types of studies are valuable, but do not allow us to understand the large-scale biotic impacts resulting from amass coarse woody debris influx. Thus, the first original contribution to knowledge is the geospatial mapping of different types of coarse woody debris habitat (which support different biotic communities) across a landscape using freely available remote sensing imagery. Second, it has often been assumed that the Normalized Difference Vegetation Index (NDVI) is the best index to use in ecological studies to measure changes in forest canopy cover but tests of this assumption are rare. I provide empirical evidence to support this claim by showing that NDVI outperforms six other popular remotely sensed vegetation indices in a cross validation analyses predicting coarse woody debris influx resulting from canopy damage.

Chapter 2:

The result of human disturbances on forest communities is an important conservation concern. One of the primary ways that humans impact natural communities is through the presence of recreational hiking trails. The impact of such trails has been explored for birds (Miller and Hobbs 2000), small mammals

(Meaney et al. 2002) and ground-dwelling beetles (Grandchamp et al. 2000)

(among other taxa), but it has never been considered for forest-dwelling caterpillar assemblages. In this chapter I show that there is a consistent and negative relationship between recreational trail presence and caterpillar assemblage richness and abundance. Furthermore, to accomplish this analysis I develop a simple index that quantifies the effect of trails on forest habitat as a function of trail width and trail proximity. This is a simple and effective tool that can be readily applied to other forest conservation areas to quantify a geospatial index of trail impact. The importance of this research extends to forest reserve management where trails are generally viewed as positive management tools because they direct and control the flow of pedestrian traffic. While the research in this chapter certainly does not suggest the elimination of trails, it suggests that trails need to be limited and managed to maximize the richness and abundance of forest-dwelling caterpillar assemblages.

Chapter 3:

Insect herbivores have been linked to their host plants in two different ways depending on whether the unit of examination is an entire insect herbivore assemblageor an individualinsect herbivore species. Insect herbivore assemblage richness is often tied to host plant richness under the premise that a species-rich host plant assemblage provides a wider array of food and structural resources than a species-poor one (Haddad et al. 2001, Summerville and Crist 2004). This is contrasted by studies of individual insect herbivore species that often tie insect herbivores to host plants based on plant identity – some host plants are highly preferred while others are not (e.g.Delisle and Hardy 1997, Liebhold et al.

1995).Consumption of preferred host plant foliage often results in greater fecundity and developmental gains. It is unknown how host plant preferences at the individual species level scales up to an entire insect herbivore assemblage. In this chapter I test between these two relationships that posit: (1) insect herbivore assemblage richness is driven by host plant richness and (2) insect herbivore assemblage richness is driven by the presence of highly preferred host plants.I demonstrate that the latter is a much stronger descriptor of insect herbivore richness than the former. In addition, I showed thatinvasive host plants are strongly negatively linked to insect herbivore assemblage richness and abundance. A host plant replacement simulation showed that insect herbivore assemblage richness and abundance could increase by 30-40% if invasive host plants were replaced with functionally comparable native host plants. This

provided an empirical basis for continued and stronger forest management to

eliminate and restrict the establishment of invasive plants.

Chapter 4:

Broadly speaking, there are two theories to explain the distribution of caterpillar species among host tree species. The first is that caterpillar species are distributed with respect to their foliar nutrient needs; the second is that caterpillar species are distributed so as to minimize their chances of coming into

contact with their parasitoid enemies (Lill 2001). Certain caterpillar species have also been documented to feed on foliage high in polyphenol content to sequester toxins as a defense against parasitism (Turlings et al. 1995). Previous research has documented that a change in distribution strategies in certain caterpillar species may occur corresponding to the phenology of parasitoids (Lill et al. 2002). However these two pressures (bottom-up and top-down) have only

rarely been considered in a seasonal context and almost never in the context of

an entire caterpillar assemblage. My research in this chapter indicated that

caterpillars are distributed amongst host plants with respect to foliar quality

early in the season but that late in the season parasitoid pressure supersedes the

importance of foliar qualityas an explanation for caterpillar richness and

abundance patterns. In the caterpillar assemblage I examined, there was no

evidence to suggest that polyphenol sequestration plays a large role in impacting

caterpillar host plant choices.

References

Delisle, J. and Hardy, M. 1997. Male larval nutrition influences the reproductive

success of both sexes of the Spruce Budworm, Choristoneura fumiferana

(Lepidoptera: Tortricidae). - Functional Ecology 11: 451-463.

Grandchamp, A. C., Niemelä, J. and Kotze, J. 2000. The effects of trampling on

assemblages of ground beetles (Coleoptera, Carabidae) in urban forests

in Helsinki, Finland. - Urban Ecosystems 4: 321-332.

Haddad, N. M., Tilman, D., Haarstad, J., Ritchie, M. and Knops, J. M. N. 2001.

Contrasting effects of plant richness and composition on insect

communities: a field experiment. – The American Naturalist 158: 17-35.

King, D. J., Olthof, I., Pellikka, P. K. E., Seed, E. D. and Butson, C. 2005. Modelling

and mapping damage to forests from an ice storm using remote sensing

and environmental data. - Natural Hazards 35: 321-342.

Liebhold, A. M., Gottschalk, K. W., Muzika, R., Montgomery, M. E., Young, R.,

O'Day, K. and Kelley, B. 1995. Suitability of North American Tree Species

to the Gypsy Moth: A Summary of Field and Laboratory Tests. United

States Department of Agriculture, Northeastern Forest Experiment

Station, Radnor, PA

Lill, J. T. 2001. Selection on herbivore life-history traits by the first and third

trophic levels: the devil and the deep blue sea revisited. - Evolution 55:

2236-2247.

Lill, J. T., Marquis, R. J. and Ricklefs, R. E. 2002. Host plants influence parasitism

of forest caterpillars. - Nature 417: 170-173.

Meaney, C. A., Ruggles, A. K., Clippinger, N. W. and Lubow, B. C. 2002. The

impact of recreational trails and grazing on small mammals in the

Colorado Piedmont. - Prairie Naturalist 34: 115-136.

Miller, J. R. and Hobbs, N. T. 2000. Recreational trails, human activity, and nest

predation in lowland riparian areas. - Landscape and Urban Planning 50:

227-236.

Millward, A. A. and Kraft, C. E. 2004. Physical influences of landscape on a large-

extent ecological disturbance: the northeastern North American ice

storm of 1998. - Landscape Ecology 19: 99-111.

Summerville, K. S. and Crist, T.O. 2004. Forest moth taxa as indicators of

lepidopteran richness and habitat disturbance: a preliminary assessment.

- Biological Conservation 116: 9-18.

Turlings, T. C., Loughrin, J. H., McCall, P. J., Röse, U. S., Lewis, W. J. and

Tumlinson, J. H. 1995. How caterpillar-damaged plants protect

themselves by attracting parasitic wasps. - Proceedings of the National

Academy of Sciences 92: 4169-4174.

Vandyke, O. 1999. A literature review of ice storm impacts on forests in Eastern

North America. SCSS Technical Report #112. - Ontario Ministry of Natural

Resources, Southcentral Sciences Section, pp. 1-29.

Acknowledgements

My wife Blythe has stood by me throughout this journey and it would have been

immeasurably more difficult for me to complete this thesis without her loving

and unwavering support. She has brought such a fantastic richness to my life and

has made our marriage a source of refreshment and rest. Over the past five

years this thesis has been a major story that has been written across the pages of

my life; Blythe has been the one to fill those pages with colour.

MENTORS

Throughout this thesis journey I have had had two invaluable mentors: Dr. Marty

Lechowicz and Dr. Brian McGill. I am appreciative of all of the time and effort

they have spent on me. I am very thankful that they chose to invest in me and I

hope that I can live up to their expectations.

EDUCATORS

I would like to the two educators who inspired me to become a scientist. Robert

Cassibo was my grade high school science teacher. He made science come alive in a way that forever changed my perception of the world in which we live.

Without his influence I would not have become a scientist.

Dr. Jeff Houlahan was my undergraduate ecology professor at the

University of Ottawa. Through Jeff’s teaching, ecology became a relevant science and I developed a deep interest in the study of organisms and their

environments. Without his influence I certainly would not have pursued

graduate-level ecology research.

ENCOURAGERS

There were many people who encouraged and motivated me over the course of

my PhD. There are a few who merit special mention. (1) Richard Feldman and I

started our PhDs in the McGill lab at the same time. He has been an academic

brother to me encouraging me when I’ve been frustrated and challenging me to

be a better student and scientist. (2) David Syncox motivates people to achieve

great things. He has been a wonderful friend and has gone out of his way on

innumerable occasions to be a listening ear for my frustrations and to encourage

me towards reaching the goal of thesis completion. (3) I will also mention the

McGill Lab Group who provided many excellent reviews and encouragement for

my research: Sergio Estrada, Julie Messier and John Donoghue.

FAMILY

My family has been very supportive of my decision to pursue a PhD. My mother

Linda often called asking “how things are going” and has been a constant

reminder of how much I am loved by my family. My sister Mags has also very

quick to read my papers and has been full of support. I am also thankful for the rest of my wonderful familyandthe encouragements they provided along the

way – specifically my son Quentin, my dad Richard, my brother Andrew, my

sister-in-law Bethany. my brother-in-law Mike, my father-in-law Gary, my mother-in-law Beth, my brother-in-law Andy, my sister-in-law Steffani, my godmother Sharon, my godfather Bob and my best friend Todd and his wife

Krista.

FINANCIAL SUPPORT

There were several sources of funding that allowed me to complete this degree.

Thank you to Dr. Brian McGill, Dr. Martin Lechowicz and Dr. Richard Tomlinson via Dr. Brian Alters at the Tomlinson Project in University Level Science Education for your funding and support. Thanks also to the National Sciences and

Engineering Research Council of Canada for two years of support.

COLLEAGUES AND FRIENDS

Finally, I have had many colleagues and friends at McGill University and at

Emmaus Anglican Church to whom I owe a debt of gratitude for their friendship, encouragement and intellectual contributions. I dare not try to list them all in fear that I may leave some out. Thanks to all.

Thesis Abstract

One central question in ecology is why some areas have many species and others

have few. Many explanations have been proposed and often theforcesthat drive

species richness are context-dependent. These forces aredivided into two general categories: biotic drivers and abiotic drivers. Biotic drivers are most commonly described in terms as top-down and bottom-up effects while abiotic

drivers are commonly described in terms of climate and habitat disturbance. The

objective of this thesis is to determine how these drivers affect species richness

in terrestrial ecosystems. To test this I examine an insect herbivore assemblage in a disturbed forest fragment landscape in southern Canada.I use geographic information systems techniques to determine the impact of a natural episodic disturbance (i.e. an abiotic natural driver) and a chronic human disturbance (i.e. an abiotic human driver) on forest habitat quality (Chapter 1) and on a forest- dwelling caterpillar assemblage (Chapter 2). I show that ice stormsresult in a heterogeneous pattern of spatial damage across a forest landscape, differing depending on the type of coarse woody debris examined. These different types of coarse woody debris provide habitat for a diversity of taxa. In contrast with natural disturbance, I found that human-based disturbance do not have a positive impact on caterpillar assemblages.Pursuant to this, I explore the concept of habitat quality from the perspective of host plant identity (Chapter 3) and host plant quality (Chapter 4). I found that caterpillar assemblages have strong host plant preferences and that these preferences may depend on quadrat-scale

foliar qualities (i.e. a biotic bottom-up driver)and parasitoid densities (i.e. a biotic top-down driver) at different times in the growing season. This thesis adds to a growing body of literature aimed to better understand the drivers of insect species richnessacross disturbed landscapes. In addition, this thesis develops several management-specific tools for measuring forest disturbance and provides valuable insight into how the selection of different tree species for planting initiatives can have important impacts on forest communities.

Résumé

Les communautés forestières qui habitent les parcelles de forêts qui subsistent aujourd'hui sont affectées par les perturbations ainsi que par la qualité de l'habitat que leur procure les plantes-hôtes. Ces deux phénomènes ont un impact particulièrement important dans les paysages modifiés par l'activité humaine. Développer une meilleure compréhension de ces phénomènes va faciliter la prise de décision et les efforts de conservations visant à préserver et protéger la biodiversité des forêts. L’objectif global de cette thèse est d’étudier les divers aspects reliés à la qualité de l’habitat dans les parcelles forestières des collines montérégiennes du sud-est du Québec, Canada. J’utilise des techniques en système d’information géographique pour déterminer l’impact d’une perturbation naturelle épisodique (tempête de verglas) ainsi qu’une perturbation anthropogénique chronique (sentiers récréationnels) sur la qualité des habitats forestiers (Chapitre 1) et un assemblage de chenilles vivant en forêt

(Chapitre 2). J’ai démontré que les dégâts engendrés par les tempêtes de verglas sont distribués de façon hétérogène à travers le paysage forestier, différant selon le type de débris ligneux grossiers examiné. Ces différents types de débris ligneux grossiers servent d’habitat à divers groupes taxonomiques. J’ai découvert que les perturbations anthropogéniques, au contraire des perturbations naturelles, n’ont pas eu d’impacts positifs sur les assemblages de chenilles vivants en forêt. J’ai également exploré le concept de qualité d’habitat en considérant l'identité de la plante hôte (Chapitre 3) et la qualité foliaire à l'échelle du quadrat (Chapitre 4). J'ai découvert que l'assemblage de chenille

démontre des préférences marquées pour certaines plantes hôtes et que ces préférences peuvent dépendre de différentes qualités foliaires à l'échelle du quadrat à différentes périodes durant la saison de croissance. Cette thèse contribue à la documentation croissante sur les facteurs qui affectent la richesse spécifique des communautés d'insectes vivant dans les forêts des paysages perturbés. De plus, cette thèse propose plusieurs outils spécifiques à la gestion pour évaluer les perturbations en milieu forestier et donne un aperçu de l'impact que peut avoir la sélection de différentes espèces d'arbres sur les communautés forestières lors de l'élaboration d'initiatives de plantation d'arbres.

General Introduction

Since the beginning of modern ecology “species richness” has been one of the

central fociin research. It is the subject of some of the most important ecological theories and has been associated with overall ecosystem health (Rapport et al

1999) and pristine habitat (Brooks et al. 2002). Species richness is also the primary measurement used in conservation biology where the primary goal is to protect species and their natural environments from extinction. Ehrlich and

Ehrlich (1992) argue that there are four reasons why conserving richness should be a primary goal for humankind: (1) it is an ethical responsibility to preserve diverse forms of life on earth, (2) there are recreational and aesthetic valuesof high species richness, (3) there is a wide variety of medical, food and textile resources offered by high species richness, and (4) there is a wealth ofimportant ecological services offered by high species richness such as air filtration, water filtration, nutrient cycle maintenance and soil renewal. Each of these four factors may (arguably) be diminished with significantrichness losses. While similar arguments have been echoed by other authors and are rarely refuted (Ghilarov

2000, Randall 1991), we live in an era where species extinction rates are extraordinarily high, due in large part to human activities (Pimm et al., 1995).

These extinctions are particularly high for insect species (Conrad et al 2006,

Dunn 2005, Thomas et al 2004) and are often due to human-caused landscape- scale habitat destruction for the purposes of timber exploitation, agriculture and landscape development (Brooks et al 2002).

What Controls Richness?

Why are some areas high in species richness while others are impoverished? The variance of richness within and among different habitats has long intrigued ecologists. There have been dozens of different mechanistic drivers of species

richness proposed over the years and the strength of a proposed variable often

seems to be context-dependent (Rosenzweig 1995, Adams 2009). Mechanistic drivers of richness identified at broad geographic scales tend to differ from those identified at small geographic scales (Currie 1991, Hawkins et al 2003); mechanistic drivers identified in disturbed habitat tend to differ from those identified in undisturbed habitat (Armesto and Pickett 1985).At the most general level these drivers fall into one of two categories: biotic mechanisms and abiotic mechanisms.

Biotic mechanisms occur when living things affect the richness of a taxonomic group. These mechanisms can be divided into two sub-categories: top-down forces and bottom-up forces. Top-down forcesare observable when a species or taxonomic group at a higher trophic level affects a species or taxonomic group at a lower tropic level. Herbivores, for example, can suppress the species richness of their food plants (Crawley 1983, Hay 1985, Hunter and

Price 1992) and predators/parasitoids can affect the species richness of their prey (Bruno and Cardinale 2008, Lill et al 2002). Bottom-up forcesare the opposite and are observable when a species or taxonomic group at a lower trophic level affects a species or taxonomic group at a higher trophic level. For example, grassland plots with many plant species can support more herbivorous

insect species than plots with few plant species (Siemann et al. 1998) and systems rich in prey can often support more predator species than those that are poor (Rosenzweig 1995). The direction of the effect (i.e. top-down vs. bottom- up) often seems to be very system-dependent and opposing effects can be observed in analogous trophic structures– e.g. in some systems the bottom-up effect of plants on herbivores is dominant and in others the top-down effect of herbivores on plants is dominant. A third categoryof driving biotic forces exists where regulation within a trophic level occurs through competition or density- dependence. However this is more commonly observedat the species population level when measuring species abundance and performance rather than at the assemblage or community level when measuring species richness (e.g.

Antonovics and Levin 1980, Ostfeld et al 2003).

Abiotic mechanisms occur when non-living things affect the richness of a taxonomic group. Theseoften include climate variables like temperature, precipitation, solar radiation and habitat modification variables likefragmentation, isolation and habitat loss. At very large spatial scales precipitation and energy are strong correlates to species richness (Currie 1991,

Hawkins et al. 2003). At smaller spatial scales disturbance events can play more important roles. Human disturbance through habitat modification and destruction generally results in species richness loss (e.g. Andren 1994, Helm et al. 2006, Ross et al. 2002) whereas natural disturbances like fire and severe weather events can result in species richness gains or losses depending on the

species group and habitat affected (e.g. species richness gain –Shafi and

Yarranton1973, Facio 2003, Lafon 2004, Moretti et al 2004; species richness loss

– Nekola 2002, Saint-Germain and Mauffette 2001, Swengel 2006).

Thesis Objective

The objective of this thesis is to examine how biotic forces (top-down and

bottom-up effects) and abiotic forces (natural and human disturbances) drive terrestrial species richness. Understanding how these factors drive richness is particularly important in disturbed landscapes where native species richness is declining and extinctions are common. Since the direction and magnitude of these driving factors can be context-dependent, I aim to analyze them using a single taxonomic group in a specific ecological habitat type. The criteria I use for selecting a taxonomic group are: (1) it must be a group occupying mid-trophic levels and therefore subject to both bottom-up and top-down pressure, (2) it must be broad enough to be ecologically important to ecosystem function in disturbed landscapes, (3) it must be narrow enough to occupy one, roughly homogeneous, ecological guild, (4) there must be a plausible mechanism by which it can be affected by both natural and human disturbances. The criteria I use for selecting an ecological habitat are: (1) it must be subjected to periodic or chronic human disturbance, (2) it must be subjected to natural disturbance events, preferably where a recent large disturbance event has occurred, and (3) it must have high conservation value.

Taxonomic Group of Investigation

One ecologically important mid-trophic level taxon that is often affected by

bottom-up and top-down effects is insect herbivores. It isone of the most

species-rich guilds in terrestrial ecosystems and is ecologically important for

many reasons. First, many insect species are indicators thatreflect habitat quality

and the impacts of environmental disturbances (Summerville et al. 2004, New

2004). Second, insect herbivores play critical roles in terrestrial ecosystems as food sources for bats (Goiti et al. 2009), birds (Murray et al. 1980), small mammals (Whitaker 1966), reptiles (Hamilton and Pollack 1956), amphibians

(Moore and Stickland 1954) and other insects (Lill and Marquis 2001). Third, theyalso play a crucial role in ecosystem function by converting leaves into nutrient-rich frass that is readily consumed by detritivores and soil-dwelling organisms (Schowalter et al. 1986). Therefore, in many ways, the health of an ecosystem is tied to the species richness of the insect herbivores assemblage living therein. They are also commonly impacted by both bottom-up (Siemann et al. 2008) and top-down (Lill et al. 2002) effects.

Conservation initiatives focusing on insects havetraditionallyusedone of two approaches: a single-species approach,or an assemblage approach. In the single-species approach, a threatened species is identified and steps are recommended to improve its survival chances. This could include increasing the abundance of important host plants or trying to control predator populations

(e.g. Schultz and Dlugosch 1999).Onedrawback of the single-species approach is

that it is limited to species whose natural history is well known. This is

particularly true for insect herbivores because, as a taxon, their natural history is

very poorly known (New 2004). In fact, it is suggested that the majority of insect

species in the world is currently undiscovered, let alone well-described (Gaston

1991). While most of these undiscovered species are in tropical ecosystems, the

majority of well-studied species in temperate habitats tend to be defoliators

capable of causing significant economic and aesthetic damage (e.g. the gypsy

moth, Lymantria dispar) or species with bright colouration (e.g. the monarch

butterfly, Danaus plexippus). Temperate non-defoliating species and cryptic

species are often overlooked. Because of this narrow spectrum of research it

becomes difficult to makeeffectiveconservation-oriented management decision

based on one (or a few) species when the status of the rest of the assemblage

isunknown.This approach has sometimes been justified by the “umbrella

species” concept, but it is not always clear that thisachieves the desired goal

(Andelman and Fagan 2000, Roberge and Angelstam 2004). Instead, the net

result of this approach can be an overly large management investment for the

benefit of few species. Conversely, an assemblage-based approach focuses on

identifying, conserving and protectinghabitat that is high in species richness(New

2004). This requires less knowledge of the natural history of specific species because the variable of interest isspecies richnessin an assemblage rather than abundance in a species. It asks: “What characteristics of the habitat are

synonymous with high richness?” rather than “What characteristics of the

habitat are synonymous with high abundance in one species?” The assemblage-

based approach can be more effective on the whole but it requiresa context-

specific understanding of how assemblage richness responds to driving

mechanisms.

Geographic Extent of Study

There are dozens of different ecosystems that host insect herbivores and many

fit the desired criteria of being impacted by both human and natural

disturbances. One such ecosystem that meets both criteria is the broadleaved forest of the Mixedwood Plains (MWP) in southern Canada. Historically this forest covered the entirety of the MWP but over the past century ithas been

reduced to isolated fragments in a matrix of agricultural, industrial and urban

development; less than 15% the area is currently forested(Allen 2001). The small

amount of forest that remains is important habitat for threatened species yet

continues to be at risk from human landscape modification(Environment Canada

2007, 2010). Foremost amongst these remnant fragments are those located in an areaof southern Quebec known as the Montérégiewhere less than 1% offorested land is protected(Ministère des Ressources Naturelles 2002).Many of these protected forest fragments tend to occupy the hilltops of the Monteregian

Hills, a set of nine laccolith-based hills rising up from the floodplain.

Conservation on these hills has become increasingly challenging with the occurrence of episodic and chronic, human and natural disturbances across the

MWP landscape. Although these hilltop forest fragments have escaped widespread deforestation, privately owned lots within the fragments have been subjected to residential development with associated vehicle access roads and hiking trails (CantonShefford 2010, Centre de la Nature Mont Saint Hilaire 2007,

Les Amis De La Montagne 2008, Parcs Quebec 2010, Parcs Quebec 2010).

Smaller-scale cutting (and subsequent replanting) and the periodical establishment of sugarbushes (resulting in the selective removal of all but sugar maple trees) has also occurred over the past century and has changed the forest tree species composition. Many of these disturbance events occurred before the

forests on these hills were granted protected status. Additionally, periodic ice

storms that damage canopy trees have left legacies of coarse woody debris on

the forest floor, changing habitat quality for the forest insect community

(Hooper et al. 2001).

The persistence of forest habitat on these hills in a landscape of agricultural development is tied tohistoric events. Up until 10,000 ypb the

Champlain Sea covered much of the St. Lawrence floodplain region; when it receded (c. 8,000 ybp) it left nutrient-rich sediment on the floodplain while the hilltops (which were islands in the Sea) remained comparatively nutrient-poor.

Because of this sub-optimal soil quality and because of their challenging topography, the hills remained (in part or in whole) undeveloped when the rest

of the floodplain was converted for human-use throughout the 20th century.

Forest fragments on three of these hills (Mont Royal, Mont St. Bruno and Mont

St. Hilaire) are currently protected (to varying degrees) with mandates to

preserve the diversity within their forests for conservation targets and forthe enjoyment of the general public. A fourth Hill (Mont Shefford) has designated conservation areas without explicit conservation mandates.

Four Approaches to Determine Drivers of Richness

The Monteregian forest fragments present an ideal ecosystem in which to study

the effects ofbiotic and abiotic forcesoninsect species richness. I use four diverse

approaches to examine this relationship. First, I approach it by using remote

sensing and GIS tools to examine the consequences of an ice-storm disturbance

(i.e. a natural abiotic driver) on saproxylic insects. Since there is a direct link

between high quality habitat and guild species richness, determining the habitat

consequences of a major natural disturbance is an important step for making conservation-oriented management decisions. In addition, I develop and test assessment tools to help quantify habitat quality and disturbance impact. The last major ice stormdisturbance in my study region occurred in 1998 when more than 100 mm of ice fell on Monteregian forest fragments. Various canopydamage models have been developed to gauge the impact of such events (e.g.

Olthof et al. 2004, Proulx and Greene 2001, Stueve et al. 2007), but there is a need to better connect forest damage with ecologically meaningful habitat

changes.In Chapter 1 I usesatellite-derived remotely sensed vegetation indices to

examine how this storm changed coarse woody debris habitat at Mont St.

Hilaire, a hill in the MWP of Quebec. I develop geospatial data to document the

influx of different types of coarse woody debris that are important saproxylic

insect communities.

The research goals of Chapter 1 are stated in the form of objectives rather

than hypotheses. They are as follows:

1. To model the biotic changes resulting from coarse woody debris influx

following a major canopy disturbance.

2. To use remotely sensed satellite images and geographic information

systems to study the spatial heterogeneity, volume and connectivity of

coarse woody debris habitat following a major canopy disturbance.

3. To test the performance of seven different vegetation indices in

predicting habitat change after a major canopy disturbance.

Second, I approach the question of how abiotic and biotic forces affect insect

species by examining howman-made recreational trails (i.e. a human abiotic

driver) impact forest-dwelling Lepidoptera assemblages. The biggest form of

human impactwithin forest fragments is chronic disturbance fromrecreational

hiking trails. Trails are particularly compelling to research because they are a

type of abiotic disturbance that can be controlled by management decisions

within forested parks and reserves. This sets them apart from other types of

disturbance that are either uncontrollable (e.g. ice storms) or extremely difficult

to control without more powerful conservation mandates (e.g. to stop

landscape-scale deforestation). There are often significant habitat modifications caused by the introduction of trails in forests. They are associated with high levels of vegetation trampling, a higher abundance of disturbance-tolerant plants, soil compaction, water drainage changes, light level changes, wind changes and temperature changes. As an analogy to trails, forest edge habitat has often been observed to have a higher than expected Lepidopterarichness due to a mixed assemblage – part forest-dwelling Lepidoptera species, part open-habitat Lepidoptera species. Trails have been positively, negatively, or neutrally associated with population abundances and species richness in certain taxa (e.g. birds: Miller 1998), but rarely linked to insect herbivore assemblages. A greater understanding of the impact of trails on Lepidoptera assemblages can be used to better protect and preserve richness in disturbed forest fragments.

The hypotheses tested in chapter 2 are:

1. Trailside habitat will be beneficial for caterpillar richness and abundance.

2. The availability of acceptable host plants will be a determinant of

caterpillar richness and abundance.

Third, I approach the question of how abiotic and biotic forces affect insect species by examining how tree species richness and identity (i.e. biotic bottom- up mechanisms) drive Lepidoptera richness and abundance within forest

fragments. In studies of single insect herbivore species, insect performance and

distribution are strongly tied to host plant identity, indicating that some host

plants are more preferred than others. This is contrasted by studies of insect

herbivore assemblages that often equate high insect assemblage richness to high tree species richness. While it is well known that single insect herbivore species

(even polyphages)can have strong host plant preferences (Liebhold et al. 1995,

Maufette et al. 1983, Wint 1983) it is not known how well the host preferences of individual species scales to the assemblage level. The claim that higher host plant richness leads to higher insect herbivore richness is rooted in the assumption that richer communities have a greater diversity of foliar nutrients and a more structurally heterogeneous set of resources. This foliar and structural diversity would theoretically allow more insect herbivore species to coexist. On the other hand, comparisons of the hosting capabilities of trees show that some hosts are more highly preferred than others (Barbosa et al. 2000, Moran and

Southwood 1982). Based on this, it is possible that the insect richness in a forest stand depends on having a high volume of preferred hosts. This assemblage-level

“preferred-host” concept may be especially applicable for Lepidoptera in the

MWP forests where a vast majority of species are polyphagic (Handfield1999). It is therefore possible for one or few hosts to be preferred by the majority of the assemblage.These two concepts are not necessarily mutually exclusive, but if the second outperforms the first it has important implications for forest management and suggests a revision in our understanding of the relationships

between insect herbivore assemblages and their environment. Does an

abundance of preferred host plants correlate to high insect herbivore richness or

is insect herbivore richness more closely related to host plant richness? In

chapter 3 I test between these two possibilities.

Hypotheses:

1. The diversity and richness of a Lepidoptera assemblage is locally driven

by host plant richness.

2. Because host plant choice are made by individual Lepidoptera species,

the relationships between the Lepidoptera assemblage and host plants

will be accurately described as a function of host plant identity and

abundance (i.e. host plant preferences are cumulative).

Fourth, I approach the question of how abiotic and biotic forces affect insect species by examining how top-down and bottom-up pressures(i.e. two biotic

drivers) on insect herbivore assemblages vary over the course of a growing

season. The impacts of tri-trophic relationships on insect herbivores have been

well studied (Mayhew 1997), but never for an entire assemblage in specific

temporal windows over the course of a growing season. Individual insect

herbivorespecies often discriminate between available host trees based on

certain foliar nutrient qualities that change with leaf age (Feeny 1970, Ricklefs

and Matthew 1982). A host plant might therefore be well usedearly in the

season but be relatively unused late in the season due to a loss of nutritive value.

Top-down pressure applied by predators and parasitoids (Lill et al. 2002) complicates the seasonal relationship between insect herbivores and tree-

use.This top-down pressure is often more intense later in the growing season. A

full understanding of the drivers of insect herbivore richness needs to take into

account the both bottom-up and top-down pressurein the context of a tri-

trophic relationship.In Chapter 4 I examine intra-seasonal tri-trophic

relationships for a Lepidoptera assemblage in MWP forest fragments. The key

foliar nutrient properties that have been linked as drivers of insect herbivore

performance are nitrogen content, water content, phosphorus content, toxin

content, fiber content, lignin content and leaf toughness. The nutrients among

these that are positively linked to herbivore performance – nitrogen, phosphorus

and water – tend to peak early in the season. The other nutrients peak in the middle or at the end of the season. Insect herbivore host preferences often

correlate to host plant quality but this can be disrupted by high parasitoid abundances (Gratton and Denno 2003). Parasitoids are not typically evenly distributed among all host plants – some have high parasite loads (enemy- packed space) while others have low parasite loads (enemy-free space). Host plant choices for insect herbivores thus become a delicate balance of minimizingexposure to parasitoids by avoiding enemy-packed space while maximizing the nutritive quality of the foliage consumed. Two important questions have arisen in trying to better understand the drivers of insect herbivore assemblage richness: (i) to what extent does bottom-up (foliar

nutrient quality) and top-down (parasitoid-presence) variables impact herbivore richness and (ii) how do the nature of bottom-up and top-down effects change from the beginning to the end of a growing season?

Hypothesis:

1. The impact of top-down and bottom-up pressures will vary over the

course of the growing season. Specifically:

a. Foliar nitrogen content, water content and phosphorus content

should be the primary drivers of Lepidoptera assemblage richness

and abundance early in the season.

b. Foliar toughness, fiber content and lignin content should be the

primary drivers of Lepidoptera assemblage richness and

abundance late in the season.

c. Parasitoid pressure should have a significant impact on

Lepidoptera assemblage richness and abundance late in the

season.

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CHAPTER 1: Detecting Changes in Forest Floor Habitat after Disturbance

Peter J.T. White1, Brian J. McGill2 and Martin J. Lechowicz1

1 McGill University, Department of Biology

1205 Dr. Penfield Ave., Montreal, Quebec

H3A 1B1, Canada

2 University of Maine, School of Biology & Ecology

Deering Hall 303, Orono, ME

04469, U.S.A.

Abstract

A massive ice storm hit northeastern North America in 1998, dropping more than

100 mm of freezing rain at its epicenter in southern Quebec, Canada. There has

been extensive study of which trees and areas received the most damage, but the biodiversity consequences of this damage at landscape scales have not received much attention. We assessed the effectiveness of seven remotely- sensed vegetation indices - NDVI, EVI, DVI, RDVI, ARVI, NDVIgreen and VARI - for modeling the coarse woody debris (CWD) influx in an old growth forest reserve at the storm’s epicenter; NDVI was the best predictor of CWD influx. We categorized the geospatial CWD predictions from the NDVI-derived model to map the spatial distribution of sun-exposed, moist-shaded, dry-shaded and wet

CWD microhabitats on the forest floor. Moist-shaded, dry-shaded and wet patches of CWD were large and well connected, but sun-exposed patches were small and sparse. Since these microhabitats affect the distribution and abundance of saproxylic insects, wood-rotting fungi, salamanders, birds, small burrowing mammals and plant species dependent on nurse-logs for establishment, the CWD influx from the 1998 ice storm may have revitalized local populations of these taxa through increased habitat availability as well as increased dispersal within the reserve.

Introduction

A catastrophic ice storm hit northeastern North America from January 4th to 10th,

1998. Southern Quebec was most heavily impacted with some areas receiving in

excess of 100 mm of freezing rain (Milton and Bourque 1999). The ice storm is considered the worst ever recorded in Canada (Milton and Bourque 1999,

Vandyke 1999). The Quebec Ministry of Natural Resources estimated that 12% of trees in southern Quebec had more than 75% of their canopy destroyed and were unlikely to survive (Irland 1998).

Local Consequences of Damage

Much of the ecological research about the 1998 ice storm has focused on local correlates of forest damage and the structural consequences of damage. Local topography has been consistently linked to damage with specific elevations

(Irland 2000, Weeks et al. 2009), slope angles (Bragg et al. 2003, Rhoads et al.

2002) and slope aspects (Bragg et al. 2003) all emerging as significant descriptors. The reported species-specific impacts of ice on trees have varied

from study to study (Vandyke 1999) and from locality to locality (Brommit et al.

2004, Dugay et al. 2001, Hooper et al. 2001, Irland 1998, Rhoads et al. 2002). The

damage caused by ice storms has been found to accelerate, slow, or have no

impact on succession depending on the specific forest stand being examined

(Boerner et al. 1988, Brommit et al. 2004, DeSteven et al. 1991, Takahashi et al.

2007).

Landscape and Regional Investigations

The landscape and regional scale impacts of ice storms have been best studied

by blending satellite reflectance data, digital topography data, and ice accretion

data (King et al. 2005, McNab and Roof 2006, Millward and Kraft 2004, Olthof et al. 2004, Stueve et al. 2007). For the 1998 ice storm, Millward and Kraft (2004) used the Normalized Difference Vegetation Index (NDVI) to map canopy damage in a 2000 km2 forest in the eastern Adirondack region of New York State. They reported a complex and heterogeneous damage pattern, spatially autocorrelated at distances below 300 m. Olthof et al. (2004) used pre- and post- storm remotely sensed forest reflectance to create a regional damage map for eastern

Ontario; this study is also documented in King et al. (2005). Their damage models discriminated between areas of low and high damage, identifying forest areas in need of management intervention. These few regional and landscape scale studies stand out in a literature dominated by studies at much smaller scales, often focusing on no more than several dozen plots.

Habitat Implications of Ice Storms

The biotic implications of a large coarse woody debris (CWD) influx that necessarily follows a major ice storm have rarely been considered on a landscape scale, even though a diverse and abundant CWD landscape can have profound positive impacts on forest diversity (Harmon et al. 1986). CWD created in different moisture, heat and sunlight environments can support significantly unique assemblages of saproxylic (CWD loving) insects. For example, it is common for many bark beetles to prefer sun-exposed CWD over shaded CWD.

The warmer habitat is often a more inviting micro climate, and the sun-exposure

can lead to a quicker volatization of tree host compounds making the substrate

more preferable for many species (seeBouget and Duelli 2004 for a review). In

wet and moist habitats, the quick colonization of brown rot and white rot fungi

can modify the lignin, cellulose and hemi-cellulose concentrations in decaying

CWD making it favourable habitat for a different beetle assemblage (Kaila et al.

1994, Yee et al. 2004). Many saproxylic hoverflies also prefer to breed in very

moist decaying wood (Rotheray and Stuke 1998). Yet, both of these assemblages are usually different from the saproxylic assemblage that uses waterlogged and floating CWD (Braccia and Batzer 2001). Aquatic insects (mainly in freshwater systems) use CWD as a feeding platform, food source, refuge from predation and substrate for oviposition (Cranston and McKie 2006, Harmon et al. 1986).

The size, areal density and connectivity of CWD habitat are also important to consider. Large pieces of CWD decay at a significantly slower rate than small pieces (Vanderwel et al. 2006) and consequently provide a more stable habitat, which can be very important for taxa with limited dispersal abilities (Yee et al. 2004). Species richness and population abundance can be higher for small mammals, woodpeckers, herptofauna, fungi and plants in areas with a high volume of CWD (Keisker 2000). Furthermore, the connectivity of

CWD patches can be vitally important as CWD can support more species when it is well connected compared to when it has patchy or clumped distribution

(McMillan and Kaufman 1995, Schiegg 2000).

Using Remote Sensing to Measure Habitat Change

NDVI is the most popular remotely sensed vegetation index (VI) used in ecological geospatial research, but there is often little rationale given for this choice (cf.Birky 2001, Millward and Kraft 2004, Newton 2007, Pettorelli et al.

2005 among many others). NDVI was originally proposed by Rouse (Rouse 1973,

Rouse et al. 1974) and has a wide array of applications (Bannari et al. 1995); it is positively correlated to the Leaf Area Index (Fassnacht et al. 1997) and leaf

biomass (Birky 2001) in temperate forests. In addition to NDVI, there have been

more than 40 other VIs developed since the early 1970s (Bannari et al. 1995).

Many of these indices were designed with specific purposes in mind, for

example, to delineate low versus very low levels of vegetation (Richardson and

Everitt 1992) or high versus very high levels of vegetation (Huete et al. 2002).

Since red light is typically absorbed by chlorophyll and the near infrared light is

reflected by foliage (Bannari et al. 1995), NDVI is a logical choice for measuring

vegetation levels. But relying on these two wavelengths alone may omit

important information: green light can be used to describe soil properties, blue

light can be used to account for atmospheric abnormalities that may be present

when using imagery from different dates. Because of these properties, it has

been suggested that complete analyses should test and/or incorporate the use

of more than one VI (Bannari et al. 1995, Liu et al. 2007, Payero et al. 2004).

Objective

The goal of this paper is to use remotely sensed satellite images and geographic information systems to study the spatial heterogeneity, volume and connectivity of CWD habitat resulting from the 1998 ice storm at Mont St. Hilaire, a deciduous old-growth forest landscape; the secondary goal is to test the performance of seven different VIs in predicting habitat change after the ice storm. In this paper we use remotely sensed vegetation indices calculated from remotely sensed images, topography data and field measurements to create and validate a predictive landscape-level CWD influx model. Using hydrology data, topography data and canopy cover data we then categorize the influx model into four different CWD habitat types: sun-exposed, dry-shaded, moist-shaded and wet. Finally, we discuss the results and implications of this CWD influx for forest biodiversity.

Methods

Study Area

Mont St. Hilaire is a laccolith-based hill situated on the St. Lawrence Floodplain,

30 km east of Montreal, Quebec (45°33'N; 75°09'W). Its annual average daily temperature is 6 oC fluctuating from a 21 oC monthly average in August to a -11 oC monthly average in January. On average, the region receives 836 mm of rain and 2140 mm of snow each year with 111 and 47 rainfall and snowfall days respectively (National Climate Data and Information Archive 2008). Mont St.

Hilaire is topographically diverse in elevation, slope steepness and slope aspect

and covered in 10 km2 of old growth forest, primarily composed of Acer saccharum and Fagus grandifolia with significant numbers of Quercus rubra,

Fraxinus americana,Tsuga canadensis spp. and Pinus strobus among other species (Arii 2004).

Satellite Imagery Processing

To model CWD input, weused multiband Landsat-5 Thematic Mapper (TM) imagery downloaded from the USGS EROS EarthExplorer (USGS 2007) for the years 1996 and 1998 (Table 1). Satellite imagery was selected from August to ensure that trees were at full leaf flush at the time of observation; scenes from

1997 could not be used due to high cloud cover in available images.

Prior to the calculation of VIs, the Landsat imagery for each band was converted from digital numbers into radiometrically-corrected planetary reflectance (ρ) units using the following equation (adapted fromChandler and

Markham 2003):

[1]

Gain and offset are band-specific rescaling values, Esunλ is the mean solar

exoatmospheric irradiance, d is the earth-sun distance, and θs is the solar zenith angle.

To account for atmospheric differences between the two images, we corrected satellite reflectance data using a relative radiometric normalization and atmospheric correction procedure (Hajj et al. 2008). Each band in the 1998

data was transformed to match the atmospheric conditions of the 1996 data

using a linear regression equation derived by calculating the change in 150,497

invariant targets (i.e. pixels where no change occurred between years). All

invariant targets were pixels between 60 and 160 km north of the ice storm

epicenter where ice accretion was less than 5 mm and unlikely to cause

canopydamage (Proulx and Greene 2001).

VI Calculation

We chose six additional VIs to compare to NDVI in ability to model change in

CWD input from pre to post storm (Table 2). The additional VIs span the blue, green, red and infrared reflectance spectrum but were analogous to NDVI in the sense that (a) they are all simple ratio-based VIs and (b) they do not require any

accessory parameters to compute. For all of these indices, large localized

changes in VI values should correlate with canopy-level damage and gap

formation that characteristically generate large amounts of CWD. The Enhanced

Vegetation Index (EVI) was proposed as an improvement to the NDVI because it

is less prone to saturate in high biomass regions (Huete et al. 2002) and is less

affected by certain atmospheric and soil conditions that may degrade the NDVI

signal (Liu and Huete 1995). The Difference Vegetation Index (DVI) offers more precision when measuring sparsely vegetated surfaces (Liu et al. 2007,

Richardson and Everitt 1992). We included it in our model because heavy disturbance could uncover sparsely vegetated forest floor that may be better

measured with DVI. The Renormalized Difference Vegetation Index (RDVI) is an

index that combines the benefits of NDVI and DVI; it performs well in both dense

canopy and sparse vegetation (i.e. canopy gap) conditions (Roujean and Breon

1995). The Atmospherically Resistant Vegetation Index (ARVI) is a newer version of NDVI that takes into account and corrects for atmospheric light-scattering

effects on the red band (Bannari et al. 1995, Kaufman and Tanre 1992). The ARVI

accomplishes this correction by subtracting the difference in red and blue bands

from the red band before final Index calculation. A parameter used in its

calculation, γ, is an atmospheric self-correcting factor and is generally set to 1.0

for most remote sensing applications (Kaufman and Tanre 1992). The Green

Normalized Difference Vegetation Index (NDVIgreen), has been found to be very

sensitive to leaf chlorophyll levels (Gitelson and Merzlyak 1998) and can be less

prone to atmospheric interference found in the red band of normal NDVI

(Wolter et al. 1995). Finally, using red, green and blue bands, the Visible

Atmospheric Resistant Index (VARI) was designed to be very resistant to

atmospheric effects, employing the use of the blue-band similar to ARVI

(Gitelson et al. 2002). Furthermore, this index omits near infrared light making it

less affected by leaf orientation and soil moisture fluctuations than other indices

(Zhan et al. 2007).

We calculated the change in each index from 1996 to 1998 using the following

equation:

[2]

Predicting the Spatial Pattern of CWD Influx

In the summer of 1998 Hooper et al. (2001) recorded the biomass of freshly

fallen CWD at 103 plots across Mont St. Hilaire. We used these data as reference

points to make our spatial predictions of CWD influx across the mountain. To

reduce the effect of unwanted spatial autocorrelation in the Hooper et al. data, we omitted plots that were less than 30 m apart. Ice storms do not have highly spatially contagious damage because most tree damage comes from direct ice loading originating from storm-based precipitation (a contrasting example of this is tree damage caused by fire or pests/pathogens where damage on one tree results in damage on some or all of the surrounding trees). The exception to this is collateral damage occurring when an ice loaded tree falls, damaging trees in its immediate vicinity (Rhoads et al. 2002).However, given that the occurrence of large tree fall at Hooper’s sites was very low, we decided that a 30 m buffer would sufficiently minimize spatial autocorrelation of collateral damage. This filtering process left 82 of 103 plots.

We used cross validation to test the ability of each VI to describe the influx of CWD. In each cross validation case, 20% of plots were used as holdouts to validate regression importance. This process was repeated ten times for each

VI with a different set of holdouts used for each regression computation. The average regression coefficient, intercept and R2 were computed to describe the

properties and predictive power of each VI. We tested the VIs as individual

regressions because of the high degree of collinearity between indices. We chose the highest performing VI (based on R2) to make our spatial map of CWD influx using the average relationship predicted by the 10 training datasets of that VI.

Independent Validation of CWD Predictions

In the summer of 2008, we chose eighteen new plots at random locations on

Mont St. Hilaire to validate our mountain-wide CWD data predictions. At each plot we measured CWD greater than 75 mm in diameter in two perpendicular intersecting 60m x 60m transects. Each piece was scored into one of five decay classes as defined by Canada’s National Forest Inventory Ground Sampling

Guidelines (Canadian ForestService 2008). CWD in the youngest of these classes

(i.e. category 1 and 2) are most likely to represent wood dating back to the 1998 ice storm (Vanderwel et al. 2006, Zielonka 2006). As such, we estimated the total

CWD volume at each site as the sum of diameters of CWD in both categories 1 and 2.

Methods Evaluation

In our method of computing CWD influx predictions we had to scale the CWD values measured by Hooper et al. (2001) in 113 m2 plots to a 30 m x 30 m (900 m2) scale to correspond to the resolution of Landsat-5 TM imagery. To do this, we superimposed 30 m x 30 m plots centered on the original Hooper et al. plots and assigned each one the CWD value corresponding to the smaller 113 m2 plot

within it. Although this rescaled the CWD data to the desired resolution, the original plots were not georeferenced with Landsat-5 imagery in mind and thus the superimposed 30 m x 30 m pixels corners did not align with the 30 m x 30 m

VI data pixels. To adjust for this, we recalculated VI values for each superimposed pixel as a weighted mean of the four overlapping VI pixels based on the proportion of overlap.

To calculate the impact of this upscaling on the statistics of our models, we mimicked the procedure using high resolution (1 m) multiple-return Light

Detection and Ranging (LIDAR) data collected in 2003 (collection method described in below). With these data we created a high resolution binary map of canopy cover (tree height > 10 m) (Popescu et al. 2002, St-Onge and Achaichia

2001) and calculated the actual canopy cover at each of our 82 test plots in a 6 m radius (113 m2). The high resolution canopy cover data was then converted to 30 m x 30 m resolution with the same spatial referencing used in our VI data. Using weighted means based on the proportion of overlap, we then calculated the average canopy cover at each superimposed 30 m x 30 m site for a correlation comparison. This gave us an empirically-driven approximation of how the scaling and weighted scaling of canopy data and VI data (respectively) affected the statistical fit of our models.

Categorizing CWD Habitat

We divided our mountain-wide CWD prediction data into four categories

denoting areas likely to contain dry-shaded CWD, moist-shaded CWD, sun-

exposed CWD and wet CWD.First, we created a watershed map of Mont St.

Hilaire by categorizing ponds and the area within 15 m buffer of stream line

features as wet habitat most likely to contain wet CWD. We then used high

resolution LIDAR data to create a canopy density map across Mont St. Hilaire. In

May of 2003 multiple-return LIDAR data were collected for the reserve from a

Piper Navajo C-GOVX aircraft from a height of 1300m (at a repetition rate of

50000 Hz, a scanning frequency of 37Hz and a scanning range +/- 15o).The total

number of above-ground LIDAR hits was divided by the total number of LIDAR

hits (above-ground plus last return) in 30 m x 30 m pixels to compute a canopy

density map measuring light penetration to the ground layer. Sun-exposed

habitat was categorized as non-wet habitat pixels with less than 75% canopy

density. Dry-shadedhabitat was categorized as non sun-exposed and non-wet

habitat pixels on slopes of a 20 degree incline or more (i.e. high drainage). Moist-

shaded habitat was categorized as non-wet habitat pixels with greater than 80%

canopy density, on slopes with less than a 10 degree incline. Slopes with an

inclination of between 10 and 20 degrees were filtered out, as were canopy

densities between 75% and 80%.We calculated the number of patches, average

patch size and average (Euclidian) interpatch distances for each CWD habitat

type using Fragstats (McGarigal et al. 2002).Slope steepness and slope aspect for

Mont St. Hilaire were derived from the Canadian Digital Elevation Data

(GovernmentofCanada 2000). Topography variables were scaled from a resolution of 22.5 m to a resolution of 30 m to match the TM satellite imagery.

Results

Performance of VIs for Predicting CWD Influx

NDVI performed the best for both training and validation in our cross-validation

trials (Table 3). It explained an average of 20% of the variance in training

datasets and 37% of the variance in validation datasets. NDVIgreen was a close

second, explaining 19% and 35% of the variance in training and validation

datasets (respectively). The poorest performing VI was VARI, explaining 4% and

19% of the variance in training and validation sets (respectively). The average

relationship between NDVI and CWD in our training datasets was:

[3]

Applying this relationship to hill-wide NDVI data, we created a CWD influx

prediction map for Mont St. Hilaire (Figure 1). On average, the 1998 storm

created an influx of 1.7 kg/m2 (standard deviation = 0.97 kg/m2) of CWD across

Mont St. Hilaire (16.8 metric tons per hectare); 16057 tons of CWD were

produced across the entire hill.

Independent Validation of CWD Influx Predictions

The 18 independent validation sites were predicted, based on fitting a regression

to 1998 data, to gain between 0.75 kg/m2 and 3.2 kg/m2 of CWD from the ice

storm (mean = 1.6 kg/m2, standard deviation = 0.65 kg/m2). There was a

significant positive relationship between the measured amount of young CWD

and the predicted biomass of CWD across independent validation sites collected

in 2008 (Figure 2, R2 = 0.32, p < 0.02).

Categorization of CWD Influx into Different Habitat Types

In total, 59.5% of the Mont St. Hilaire was categorized as likely to contain sun-

exposed, dry shaded, moist shaded, or wet (Figure 3). The remaining 40.5% was

in pixels with intermediate topography or canopy cover and as such could not be

assigned to one of the four habitat types. There were 334 tons of sun-exposed

CWD across 22 hectares of sun-exposed habitat (average = 1.5 kg/m2). This

habitat was arranged in 130 patches (average area = 1700 m2); the average

interpatch distance was 128 m (standard deviation =95 m). There were 4559

tons of dry-shaded CWD across 310 hectares of dry-shaded habitat (average =

1.4 kg/m2). This habitat was arranged in 63 patches (average area = 48000 m2);

the average interpatch distance was 77 m (standard deviation = 32 m). There

were 2851 tons of moist-shaded CWD across 150 hectares of moist-shaded

habitat (average = 1.9 kg/m2). This habitat was arranged in 163 patches (average

area = 9100 m2); the average interpatch distance was 83 m (standard deviation =

34 m). There were 1057 tons of wet CWD across 70 hectares of wet habitat

(average = 1.5 kg/m2). This habitat was arranged in 71 patches (average area =

9500 m2); the average interpatch distance was 74 m (standard deviation = 65 m).

Methods Evaluation

According to the LIDAR canopy cover analysis, among the 82 training/validation

sites the maximum canopy cover at a 113 m2 resolution was 91%; the minimum canopy cover was 2%; the mean percent cover was 32% (standard deviation =

20%). When canopy cover values were recalculated based on a geographically weighted mean at a 900 m2 resolution, the maximum canopy cover among the

82 training/validation sites was 68%; the minimum percent cover was 4%;the mean percent cover was 37% (standard deviation = 13%). These two datasets were significantly positively correlated with a slope of 1.0 (p < 0.001, R2 = 0.44).

Discussion

Accuracy of VIs in Predicting Spatial Pattern of CWD

The use of VIs to map the CWD influx in a forest following an ice storm is largely

unexplored. The CWD influx we calculated (16.8 tons / ha) is very similar to the

Hooper et al. (2001) estimate of CWD increase at St. Hilaire following the ice

storm (19.9 tons / ha). Our NDVI validation dataset produced a moderate R2 value (a mean R2 of 0.37) but the Methods Evaluationsection indicates that the

correlation between data at 113 m2 and 900 m2 had an R2 = 0.44 (i.e. 44% of the

variance explained). Thus, even if the prediction data at 113 m2 was perfect (as in the case of our LIDAR Methods Assessment), 56% of the explanatory power in

our prediction models is likely to be lost by changing resolution from 113 m2 to

900 m2. In this context, our predictive NDVI-based model performed very well,

accounting for 84% of the possible variance (i.e. 37% out of a projected possible

44%). The validity of this relationship was further supported by the relationship

between the predicted and observed CWD at our 18 independent sample sites

(R2 of 0.32). The unexplained variance in this relationship (68%) is in part

attributed to the accuracy of our NDVI prediction model (as explained above),

but it can also be attributed to the difficulty in determining the precise temporal

origin of a piece of CWD found in on the forest floor. Site microclimate

conditions like moisture, standing water, humidity and wind can slow or

accelerate decomposition (Harmon et al. 1986), weakening the relationships

apparent in our data. Damaged trees also can die but remain upright as snags

which can decay at different rates than fallen CWD depending on the ground-

layer microclimate conditions (particularly moisture) and the relative presence of

saproxylic insects, microbes, birds and fungus contributing to decomposition and

mechanical breakdown (Harmon et al. 1986). Pieces of CWD of different sizes

and different wood densities also decay at different rates (Enrong et al. 2006,

Harmon et al. 1986).

CWD Benefits for Mont St. Hilaire Species

The massive influx of CWD provides extra habitat for a diversity of taxa across the Mont St. Hilaire reserve. Perhaps the most obvious taxa to benefit from the habitat increase are those that directly contribute to the decomposition of CWD

– white rot fungi, brown rot fungi and wood boring saproxylic insects – which increase in abundance and diversity with increasing amounts of CWD (Jonsell et al. 1998, Kaila et al. 1997, Kaila et al. 1994, Yee et al. 2004). As CWD goes through escalating stages of decay large downed logs become important nursery habitat for seedlings, herbs and ferns that may otherwise be unable to colonize the forest floor (Clark et al. 1999). Throughout the decay process, CWD is also important habitat for many vertebrate species. Among Mont St. Hilaire mammals, the Woodland Jumping Mouse (Napeozapus insignis) and the

Southern Red-backed Vole (Clethrionomys gapperi), are known to use CWD shelter habitat rather than relying primarily on tunnel-digging.Similarly, the Red- backed Salamander (Plethodon cinereus) is dependent on CWD, particularly in its juvenile amphibian stages (Richmond and Trombulak 2009). Insectivorous bird diversity is higher in heavily storm-damaged areas, but in many cases this is likely an indirect response of bird populations to an increased overall abundance of insects (Faccio 2003, Torgersen and Bull 1995). Because CWD directly benefits some forest-dwelling species more than others, we expect many of these biotic impacts to be spatially aligned with the areas of the reserve there the highest storm impact occurred. Our results show that the biggest concentrations of CWD influx are the eastern and southeastern slopes of the mountain and the low-lying area west of Lac Hertel (Figure 1). In addition, there are pockets of relatively high

CWD influx scattered elsewhere throughout the reserve.

The creation of highly-connected CWD networks across the mountain can

have additional impacts on some forest-dwelling species.Our results indicated

that approximately 50% of the mountain received more than 1.7 kg/m2 of CWD

influx and nearly 85% of the mountain received at least 0.7 kg/m2. These networks can act as transportation corridors for small mammals and saproxylic insects within and between habitat patches allowing an increase in both foraging territory size and dispersal distances (McMillan and Kaufman 1995, Schiegg

2000, Schiegg 2000).

The Benefits of Different Habitat Types to Insects

The high volume of CWD across a clear moisture gradient at Mont St. Hilaire may further benefit saproxylic habitat specialist species. In total, we documented

4893 tons of new CWD in dry habitat (sun-exposed and shaded) and 3908 tons of new CWD in moist and wet habitat. Ødegard (2006) showed that saproxylic insects were more diverse and abundant in wet forest stands than in dry forests stands. The very high volume of CWD in wet, moist and dry habitat at Mont St.

Hilaire may encourage the persistence of this gradient. The baseline volume of

CWD expected in old growth forests similar to Mont St. Hilaire is about 20 tons per hectare (Muller 2003). If this baseline were combined with our calculation of

8800 tons of new CWD in 552 hectares of moisture-defined habitat it yields

~20000 tons of CWD at St. Hilaire in habitats that can be defined by moisture content. It is unknown if this incredible volume of CWD results in a wider

moisture gradient or merely a taller distribution tightly clustered around the

mean. Continued research on saproxylic distributions, diversity and CWD

moisture conditions at Mont St. Hilaire is needed to shed more light on these

patterns.

The sparse sun-exposed habitat on the mountain (22 ha) and its

comparatively low cumulative CWD volume (334 tons) suggests that the 1998 ice

storm may not have had a profound long-term impact on saproxylic insects

specific to these habitats. Generally speaking, the richness and abundance of

sun-loving saproxylic beetles tends to vary with gap size. Larger gaps support

more species and more individuals due to the increase in sun-exposure, wood

volume, structural diversity and microhabitat diversity that they provide (Bouget

2005). In Sweden, 131 of 446 red-listed (i.e. endangered or threatened)

saproxylic coleoptera species exclusively used sun-exposed CWD habitat rather

than semi-shaded or shaded habitat (Jonsell et al. 1998). Bouget (2005) suggests

that gaps need to be a minimum of 0.50 ha in size in order to support

sustainable populations of habitat-specific saproxylic insect populations. Since

the average sun-exposed patch size (i.e. gap size) we documented was 0.17 ha, the Mont St. Hilaire open habitat patches resultant from the storm may be too small to support these types of saproxylic populations.

This small patch-size for sun-exposed CWD is radically different from what persisted in the summer immediately following the storm. Arii and

Lechowicz (2007) showed that the opening of the canopy cover at Mont-St

Hilaire increased twofold compared to the pre-storm canopy cover measures

(from an average of 11% to 22%). However, they and others also show that this canopy opening is short lived, and that the canopy recovers to pre-storm conditions within 3 to 7 years of disturbance (Arii and Lechowicz 2007, Beaudet et al. 2007, Darwin et al. 2004). It is unclear whether or not a short-term burst of sun-exposed CWD habitat like this can result in significant benefits for sun-loving saproxylic species. The LIDAR data from 2003 shows that the canopy at Mont St.

Hilaire is very closed. In fact, at a 30m resolution, only two pixels (0.18 ha) had a canopy density of less than 50% and an additional nine pixels (0.81 ha) had a canopy density of less than 60%.

CWD Influx and Species Movements

In terms of dispersal, the increase in CWD at Mont St. Hilaire is more likely to benefit animal populations within the mountain than to encourage extra- mountain colonization from other forest fragments. Mont St. Hilaire is an isolated forest habitat situated within a matrix of agriculture, urban settlement and roads. The closest large forested areas are Mont Rougemont (15 km2 of forest, 7 km to the southeast) and Mont St. Bruno (4 km2 of forest, 9 km to the west). There are several smaller fragments within 2-3 kilometers of the reserve, but it is improbable that these small fragments would act as source populations for colonization to Mont St. Hilaire. Dispersal distances of a few to several kilometers are unrealistic for most vertebrates that use CWD (Bowman et al.

2002) – not to mention the extra difficulty they face of dispersing through suboptimal (matrix) habitat. This is almost certainly true for salamanders, which are not known as long-distance dispersers. For flightless saproxylic insects, colonization over distances greater than 500 m is uncommon (den Boer 1970).

Even for full-winged beetles, depending on air currents, weather conditions and matrix properties, dispersal of more than a few kilometers is rare (Rainus 2006).

Data from Burke and Goulet (1998) show that the diversity of native beetle species in deciduous forest fragments is linked forest fragment size, particularly for patches that are isolated at distances of more than 2 km from source populations. Among animals, the obvious exception to dispersal limitation in this case are avifauna where the distance between Mont St. Hilaire and surrounding habitat patches is within the natal and breeding dispersal ranges of many bird species, particularly migrants (Greenwood and Harvey 1982, Paradis et al. 1998).

Within the Mont St. Hilaire, many species dependent on CWD as habitat won’t necessarily be constrained to the CWD habitat types as we have defined them; for species that are constrained to these specific habitats, our analyses suggests that within-mountain dispersal is a strong possibility. The average distance between moist-shaded debris patches (83 m) and dry-shaded CWD patches (77 m) are manageable dispersal distances for many forest-dwelling species. In addition, because the matrix-habitat in these cases is often very similar to preferred-habitat (i.e. old-growth temperate forest habitat) successful migration from one patch to another may occur more frequently.

Conclusion

We have shown that the geospatial patterns of ice storm damage can be accurately predicted across forest landscapes using remotely sensed NDVI. The

1998 ice storm provided a large influx of CWD, which provided an array of CWD

habitats across local environmental gradients. Although canopy cover has long

since returned to pre-storm levels, the complex geospatial CWD pattern that was produced across the Mont St. Hilaire shows a change in forest floor habitat dynamics that favour deadwood-reliant fungi, insects, birds, small mammals and

salamanders.

Acknowledgements

We are very grateful to M.C. Hooper and K. Arii who conducted the bulk of the

data collection and conducted many of the initial Mont St. Hilaire 1998 ice storm

studies. We would like to thank T. Work, M. Kalacska, R. Feldman, J. Messier and

S. Estrada for their helpful reviews and suggestions. M. VonButtlar and R.

MacKenzie helped with the deadwood collection data and L. Herzig helped with

the LIDAR data compilation and computations. This research was made possible by funds provided by the Natural Sciences and Engineering Research Council of

Canada; PW would also like to thank Richard Tomlinson, T-PULSE and B. Alters for generously providing supplementary funding opportunities.

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Figure 1Coarse woody debris influx at Mont St. Hilaire resulting from the 1998 ice storm. Mean coarse woody debris influx was 1.7 kg/m2 (standard deviation =

0.97 kg/m2), for a total estimated input of 16.8 metric tons per hectare. Coarse woody debris input values are spatially correlated up to 450 meters.

Figure 2The predicted biomass of coarse woody debris resulting from the 1998

ice storm is positively correlated to the amount of young (category 1 and 2)

coarse woody debris that was measured in the summer of 2008 at 18 randomly located sites across Mont St. Hilaire.

Figure 3Concentrations of coarse woody debris at Mont St. Hilaire in (a) dry shaded habitat, (b), moist shaded habitat, (c) sun-exposed habitat and (d) wet habitat.

Table 1Landsat-5 satellite imagery used in calculation of vegetation indices for

Mont St. Hilaire in 1996 and 1998.

Scene Identifier Acquisition Sun Elevation Sun Azimuth Date LT50140281996218XXX02 8/05/1996 51.0 o 128.4 o

LT50140281998239XXX02 8/27/1998 48.5 o 142.6 o

Table 2Vegetation Indices (VIs) used to calculate forest damage after the 1998 ice storm at Mont St. Hilaire. The VIs incorporated four wavelengths of light: blue (450 – 520 nm), green (520 – 600 nm), red (630 – 690 nm) and near infrared (NIR, 760 – 900 nm).

Name Equation

Normalized Difference Vegetation Index (NDVI)

Enhanced Vegetation Index (EVI)

Difference Vegetation Index (DVI)

Renormalized Difference Vegetation Index (RDVI)

Atmospherically Resistant Vegetation Index (ARVI)

Green Normalized Difference Vegetation Index (NDVIgreen)

Visible Atmospheric Resistant Index (VARI)

Table 3The data for each regression model were split into random training and

validation datasets (80/20 ratio, 66/16 plots) to create ten cross validation trials for

each Vegetation Index. The R2 fit of each validation dataset is given respective of the regression coefficient and intercept calculated in its training dataset. NDVI provides the best for both training and validation datasets (R2 = 0.20 and 0.37, respectively).

Training Dataset R2 Validation Dataset R2 Cross Validation NDVI EVI DVI RDVI ARVI NDVI VARI NDVI EVI DVI RDVI ARVI NDVI VARI Trial # green green

1 0.15 0.08 0.03 0.09 0.10 0.14 0.02 0.56 0.54 0.54 0.57 0.53 0.61 0.30 2 0.07 0.02 0.01 0.02 0.03 0.04 0.02 0.69 0.69 0.71 0.74 0.55 0.70 0.10 3 0.21 0.13 0.10 014 0.18 0.19 0.03 0.34 0.28 0.28 0.32 0.16 0.45 0.24 4 0.24 0.16 0.13 0.18 0.21 0.28 0.08 0.19 0.06 0.14 0.16 0.07 0.02 0.00 5 019 0.11 0.10 0.14 0.13 0.19 0.04 0.38 0.31 0.25 0.34 037 0.35 0.12 6 0.23 0.16 0.14 0.18 0.20 0.22 0.03 0.33 0.18 0.05 0.11 0.04 0.25 0.36 7 0.29 0.18 0.15 0.22 0.23 0.32 0.03 0.20 011 0.14 0.17 0.18 0.03 0.24 8 0.24 0.15 0.13 0.18 0.20 0.22 0.04 0.24 0.23 0.16 0.21 0.18 0.25 0.18 9 0.24 0.17 0.14 0.19 0.17 0.23 0.03 0.28 0.14 0.08 0.15 0.25 0.25 0.22 10 0.16 0.09 0.05 0.09 0.09 0.11 0.04 0.46 0.41 0.40 0.47 0.43 0.54 0.15 Average 0.20 0.13 0.10 0.14 0.15 0.19 0.04 0.37 0.30 0.28 0.32 0.28 0.35 0.19

Linking Statement 1

In Chapter 1 I examined the geospatial and biotic impacts of an abiotic natural disturbance on a remnant forest. I generated detailed biotic impact data and it was evident that the patterns of influx for different types of coarse woody debris were highly heterogeneous. The long-term impact of this will be variable for different saproxylic guilds and other taxa reliant on specific forms of coarse woody debris. Remnant forest fragments are subjected to many forms of disturbance, both natural and human in origin. To best understand how disturbances shape forest communities the second category of abiotic disturbances, human-caused, needs to be examined. In Chapter 2 I explore this by analyzing the relationship between recreational trails and a forest insect assemblage.

CHAPTER2: Human-Disturbance and Caterpillars in Managed Forest Fragments

Peter J.T. White1, Brian J. McGill2 and Martin J. Lechowicz1

1 McGill University, Department of Biology

1205 Dr. Penfield Ave., Montreal, Quebec

H3A 1B1, Canada

2 University of Maine, School of Biology & Ecology

Deering Hall 303, Orono, ME

04469, U.S.A.

*This chapter was first published by the above authors in Biodiversity and

Conservation (2011)

Abstract

The impact of forest-edge habitat on caterpillar assemblages has been well-

studied, but the impact of trailside habitat has rarely been considered. We

surveyed caterpillar populations in relation to recreational trails in forest

fragments in southeastern Quebec, Canada. We found a consistent negative

relationship between trails in the forest and both the abundance and species

richness of caterpillars within and among forest fragments. Conversely, caterpillar presence was not related to the nearby presence of favourable host trees.We suggest that the negative effect of trails may be due to increased predation pressure in trailside habitat and to conditions that make trailside habitat less preferable for oviposition. These results underscore the importance of managing trails to limit the amount of intra-forest disturbance inforest fragment remnants.

Introduction

The temperate deciduous forests of eastern North American have been

subjected to widespread habitat destruction over the past two hundred years

(Drushka 2003, Hannah et al. 1995). The forest fragments that remain have high

levels of edge-related effects that can have a significant impact on many forest-

dwelling organisms(Alverson et al. 1988, Murica 1995, Wilcove 1985). Habitat

destruction has been particularly widespread in the St. Lawrence Valley of

southeastern Quebec, Canada where most of the historic mixedwood forests

have been cleared in favour of agricultural development. The few forest

fragments that remain are critical habitat for many forest-dwelling species

(Warman et al. 2004).Since many of these remnant forest fragments are in close

proximity to urban areas they experience high volumes of human traffic on both designated and unofficial hiking trails.

Trailside Habitat in Forests

Much has been made of the necessity to protect forest fragments from external threats (such as cutting and isolation), but there has been comparatively little focus on internal threats to forest fragments such as recreational hiking or walking trails.Trailside habitat is distinct from forest-edge habitat because the latter marks a transition to a different habitat type (e.g. agriculture, open field, urban area, etc.) whereas the former often does not. For many taxa, trailside communities differ significantly in species composition from forest interior

communities, the formeroften characterized by a high proportion of early-

succession, disturbance-tolerant and invasive species (Dickens et al. 2005, Hall and Kuss 1989). Trails can facilitate soil-compaction via trampling that adversely

affects root development and the growth of trailside woody plants (Bhuju and

Ohsawa 1998). Trails can also lead to increased soil erosion, muddiness and

vegetation trampling in trailside habitat (Bhuju and Ohsawa 1998, Dale and

Weaver 1994, Farrell and Marion 2001).

The few studies that have examined the impact of trailside habitat on

forest-dwelling species often have uncovered a pattern similar to what is found

associated with edge habitat. Recreational trails generally have a negative effect

on small-mammal populations (Boyle and Samson 1985, Meaney et al. 2002,

Sauvajot et al. 1998) – a relationship that is typically mirrored by small mammal

populations in forest-edge habitat (Bayne and Hobson 1998, Miller and Hobbs

2000, Wolf and Batzli 2004); but see also (Anderson et al. 2003). Certain bat

species are well known to prefer both forest-edge and trailside habitat over

forest-interior habitat (Krusic et al. 1996, Patriquin and Barclay 2003). Many

carabid beetles often favour heavily-trampled trailside habitat over forest-

interior habitat (Grandchamp et al. 2000, Raymond et al. 2002), a similar pattern

occurring in forest-edge habitat (Magura 2002, Magura and Tothmeresz 1997,

Molnár et al. 2001; but see alsoDavies and Margules 1998). Among birds, many

disturbance-tolerant species tend to favour trailside habitat over forest-interior

habitat (Miller et al. 1998) in contrast to forest-edge habitat where the overall

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species richness and abundance of the bird assemblage can be significantly

higher than the forest-interior (Best et al. 1990, Strelke and Dickson 1980).

An Analogy of Forest Edge Habitat

The impact of forest-edge habitat on forest-dwelling moth populations has also been well-examined, but little research exists on the effects of trailside habitat.

Insight from edge-effect literature on forest-dwelling moths can help inform an a priori hypothesis about the impact of trails. For example, in boreal forest habitat,

Mönkkönen and Mutanen (2003) found no difference in moth species richness or abundance in edge versus non-edge (interior) habitat, which may suggest that trails could have little or no impact. Conversely, in tropical areas, Arctiidae moths have been found to be significantly more species rich in recovering secondary forests (edge-like habitat) compared to mature forest (non edge-like habitat)(Fiedler et al. 2007, Noske et al. 2009). Noske et al (2009) argue that this pattern may be because recovering forest stands tend to have more habitat niches than mature forest stands – a feature that could certainly be applicable to trailside habitat. In temperate regions, Summerville and Crist (2004) show that small forest fragments can have higher than expected moth richness in cases where there is a high host plant richness to offset the loss of forest area. A similar pattern could be predicted for trailside habitat especially if there is an increase in host plant richness driven by an increase in disturbance-tolerant plants. This higher-than-expected richness typically results from a greater

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proportion of matrix-dwelling moths, sometimes at the expense of the forest-

dwellers (Summerville 2004). Fortin and Maufette (2001) found better caterpillar performance (bigger pupae, higher larval and pupal survivorship) and larger egg masses in edge habitat and connected their findings to an increased nutritional quality of edge trees. Roland (1993) uncovered higher caterpillar abundance at forest edges but suggested that the pattern may be due to an edge-associated decrease in top-down parasitoid or pathogen pressure. Although there are a plethora of proposed mechanisms (niche availability, host plant richness, host plant nutritional quality, parasitoid/pathogen pressure), the general trend shows either a neutral effect or a positive effect of edge or edge-like habitat on caterpillar species richness and abundance. Although many of these mechanisms could act as described in trailside habitat, no studies to our knowledge have examined the pattern of caterpillar richness or abundance associated with forest trails.

Hypotheses

In this study we therefore test two hypotheses about the drivers of caterpillar assemblages in relation to trailsides within forest fragments:

1. Trailside habitat is beneficial to caterpillar assemblages.

We pose this hypothesis based on the set of forest-edge literature showing that there is a positive relationship between forest-edge habitat and moth species

(richness or abundance). Presuming that trailside habitat is analogous to forest-

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edge habitat, we predict caterpillar species richness and abundance to be higher

in trailside habitat than in forest-interior habitat.

2. Host plant availability determines caterpillar species richness and

abundance.

Because the forest fragments we examine in this study are intensively managed

we decided to put the impact of trails in a habitat context, to control for the

impact of habitat suitability. We therefore examine whether caterpillar

occurrence is related to host plant availability (i.e. the quantity of host plants at

a given quadrat) and predict that quadrats with higher host plant availability will

have more diverse and abundant caterpillar assemblages.

Methods

Study Area

We studied caterpillar assemblages associated with the Monteregian Hills

(Feininger and Goodacre, 1995) in the St. Lawrence River valley in southeastern

Quebec, Canada (45°30'N, 73°30'W to 45°24'N, 72°35'W; Figure 1).During

presettlement times all these Monteregian Hills would have had broadly similar

forests embedded in a more or less continuously forested landscape (Richard

and Grondin 2009), but now they exist as a series of large remnant forest

fragments isolated in the developed landscape.We established eighteen 400 m2

(20 m x 20 m) quadrats in the forests at each of four sampling sites on

Monteregian Hills.

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Our first site was at Parc Mont Royal - an urban park in middle on

Montreal, a city of 3.5 million inhabitants. A much-disturbed secondary

broadleaf deciduous forest comprises about 100 ha of the 190 ha park on this

Monteregian Hill in city centre. Many trees were cut in the 1950s and early

1960s (Brunel et al. 2005) and more than 70,000 trees were subsequently

replanted up to the early 1990s in an attempt to restore the forest. Currently,

the park has more than 110,000 trees and more than 80 species of woody plants

(Brunel et al. 2005).

Our second site was at Parc National du Mont St. Bruno – a protected

provincial park in the eastern suburbs of greater Montreal. The forest at Mont

St. Bruno is a broadleaf deciduous forest covering more than 500 ha of the 790

ha park. Aerial photography records indicate that 60 or more ha of forest were

cut prior to the 1940s in the northern part of the park, and subsequently

replanted or allowed to regrow (National Air Photo Library 1950). The forest has

more than 85 species of woody plants.

Our third site was at the Gault Nature Reserve on Mont St. Hilaire – a protected park and UNESCO Biosphere Reserve 38 km east of Montreal. The forest at Mont St. Hilaire is an old-growth broadleaf deciduous forest covering

most of the 1000 ha reserve. This site has a long history of protection dating

back to the 1600s (Arii 2004, Maycock 1961), and is recognized as a biodiversity

hotspot not only for vascular plants (Holland 1980, Karst and Lechowicz 2007),

but also moths and butterflies (Handfield 1999), mammals (Grant 1976,

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Humphries et al. 2003), birds (Harris and Lemon 1974, Quellet 1967), and

herpetofauna(Denman and Lapper 1964).

Our fourth site was at Mont Shefford – one of the easternmost

Monteregian Hills and well outside greater Montreal. At the time of this study different parts of the site nonetheless were in varying states of disturbance and urban development. Numerous small houses and some maple sugar bushes occur along several paved two-lane roads that run through the centre of Mont

Shefford and there is a ski-hill on the northern slope as well. We conducted caterpillar sampling at three sub-sites around the hill. Six quadrats were in a 100 ha semi-disturbed patch of forest on the west side of Mont Shefford and six quadrats in a 25 ha patch of forest on the east side set aside as a forested community park. A quarter of the former site was a sugar bush and there were numerous trails used by deer-hunters throughout the area. The community park had several trails spread out within the park, but off-trail use was discouraged.

Six quadrats were in a third sub-site, Parc de la Yamaska, a provincial park just north of Mont Shefford. Our quadrats were in the east-portion of the 450 ha semi-disturbed park that has several trails and a high volume of pedestrian traffic (ParcsQuebec 2010).

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Trail Index Calculation

We created a geospatial variable called trail index to measure the impact of trails

at every 1 m2 pixel p across an entire site:

n w [1] t trail index p = ∑ t=1 d pt

where the width w (in meters) of trail tis divided by distance dpt , the distance

between a given pixel p (at a 30 meter resolution) and trail t in meters. Trail

index was calculated only for pixels that had at least one dpt less than 50 meters, the distance at which a given pixel p is considered to be forest interior habitat

(Matlack 1993). Thus, for each pixel p, trail index is calculated as the cumulative impact of all ntrails within 50 meters of that pixel.Pixels where all dptdistances were greater than 50 meters were given a trail index value of zero. In our data trail index values scale from 0.02 (a pixel 50 meters away from a 1 meter trail) to

6.0 (a pixel 1 meter away from a 6 meter trail) for each trail (Figure2).

We created a map of trail index for each of the four study sites based on

documented trail data (Bossé 2005, CantonShefford 2010,

CentredelaNatureMontSaintHilaire 2007, LesAmisDeLaMontagne 2008,

ParcsQuebec 2010, ParcsQuebec 2010).There were no existing trail data for our

first sub-site at Mont Shefford, so we georeferenced the trails that we observed

adjacent to our six study quadrats in this area. Finally, we also calculated an

index of overall trail impact for each 400 m2 quadratby averaging the trail index

values of the pixels within the given 20 m x 20 m quadrat boundary.

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Caterpillar Surveying and Identification

Most studies examining moths in ecological settings use light traps to capture adult morphs, which yields large sample sizes from a wide area that can encompass multiple habitats. Beck and Linsenmair(2006)calculate that the attraction radius of light trapping is typically around 15 meters, although this may vary between an open and dense forest understory (but see alsoBaker and

Sadovy 1978). For the purposes of surveying moth populations adjacent to recreational trails, caterpillar sampling via tree-beating(Futuyma and Gould

1979)is a more direct measure of the association between individuals and the trailside trees.We therefore focus our sampling on the caterpillar life-stage to ensure that the species we document are forest-dwelling rather than migrant from any adjacent matrix-habitat

Caterpillars were collected at 18 quadrats at each of the four study sites.

At each of the 72 quadrats, ten sugar maple (Acer saccharum) trees between 3 and 10 cm dbh were sampled by striking the bole and lower branches of each tree ten times with a 20 oz, 30" aluminum baseball bat and catching dislodged caterpillars on a sheet. Caterpillar collections were made three times at each quadrat (between June 1 and June 6, July 4 and July 9 and August 3 and August 6 in 2009), yielding a total of 21600 tree-strikes (10 strikes/tree x 10 trees/plot x

18 quadrats/site x 4 sites/study region x 3 survey windows).

Macrolepidopteran moth caterpillars were identified to species with a dissecting microscope using Wagner’s Caterpillars of Eastern North America Field

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Guide(2005). Microlepidopteran moths were counted and identified only to

morphospecies for lack of an accurate identification guide. Macrolepidopteran

moths collected in early instars were reared so that positive identifications could

be made. Flightless moth species (with wingless or flight-limited adult female

morphs) were retained in our dataset because of their ability to select host

plants by tree-to-tree movement of late instars and aerial ballooning of first instars (Barbosa et al. 1989, Bell et al. 2005). In our data these included

Lymantria dispar, Alsophila pometaria, Operophtera bruceata, Phigalia titea,

Orgyia definita, and Orgyia leucostigma.

Site Host Plant Availability

All trees greater than 1 cm diameter at breast height (dbh) were counted and

identified to species at each quadrat. Host plant preferences for macro-

Lepidoptera were taken from Handfield’s Le Guide des Papillons du

Quebec(Handfield 1999) and cross-referenced with Wagner’s account (2005).

Host plant availability was calculated for each quadrat using the basal area

measurement of each quadrat tree as an index of foliage biomass (Tucker et al.

1993). Specifically, the host plant availability of each quadrat was calculated as:

m n ∑∑bij [2] hostplant availability = i=1 j=i m

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where bij is the basal area in meters-squared for the jth of ni host plant species at a quadrat for the ith among m macromoth caterpillar species observed we identified in our study region.

Statistical Analyses

We used a general linear model (SPSS Inc. 2000) to examine how host plant availability and trail index relate to caterpillar richness and abundance across quadrats:

[3] caterpillar species = trail index + hostplant availability

Trail index, caterpillar species richness and caterpillar abundance were 1+ log10

transformed to improve normality.

We also tested the probability of random distribution of caterpillars

across our study quadrats for any caterpillar species found at 20% or more of the

quadrats. For each of these caterpillar species we calculated the species-specific

host plant availability at each quadrat. We then categorized quadrat host plant

availability into one of five equal-interval categories: Very Low: 0-0.250 m2, Low:

0.251-0.500 m2, Medium: 0.501-0.750 m2, High: 0.751-1.00 m2 and Very High: >

1.00 m2. The expected occupancy of each species in each category was

calculated as the total number of quadrats a given species was observed to

occupy multiplied by the proportion of quadrats that fell into each of the above

host plant availability categories.Because the distribution of a given species

could incorporate as few as 15 quadrats (20% of 72), we used a randomization

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test for goodness-of-fit (McDonald 2009) to see whether abundant caterpillar

species were non-randomly distributed with respect to site host plant availability. P-values were calculated based on 10,000 replicates of distribution for each caterpillar across the five categories.

Results

We collected 75 species across our four sites, 36 macrolepidopteran moths (420

individuals, Appendix A1) and 39 microlepidopteran moths (253 individuals,

Appendix A2). Of these, 21 species were collected on Mont Royal (11 macro, 10

micro), 46 on Mont St. Bruno (24 macro, 22 micro), 37 species on Mont St.

Hilaire (20 macro, 17 micro) and 43 species on Mont Shefford (21 macro, 22

micro).

Trail Index and Host Plant Availability

Trails were most prevalent across Mont Royal where 95% of the forested area

had non-zero trail index values (Figure 3); the average trail index was 0.164

across the entire site and 0.188 when averaged among quadrats. Trails were

least prevalent at the Mont St. Hilaire site where only 29% of forested area had

non-zero trail index values; the average trail index was 0.021 across the entire

site and 0.015 when averaged among quadrats. The Mont St. Bruno and Mont

Shefford sites were intermediate in terms of trail presence with 56% and 36% of forested areas having non-zero trail index values, respectively.Trail indices were

0.047 averaged across St. Bruno and 0.042 averaged across Shefford; trail index

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values when averaged among quadrats were 0.028 for St. Bruno and 0.038 for

Shefford.

Average host plant availability for macromoths across all quadrats at each site ranged from 0.11 m2 to 1.34 m2 basal area (mean = 0.63 m2, standard deviation = 0.24) (see Appendix A3 for complete list of trees surveyed). Average host plant availability did not vary by site (ANOVA among sites, p = 0.80). Across all quadrats, trail index was a significant (negative) predictor of caterpillar richness and abundance; site host plant availability was a near-significant

(positive) predictor of caterpillar richness and abundance (Table 1). Performing regression analyses on a site-by-site basis, trail index remained significant at all four sites whereas host plant availability was significant only at the Mont

Shefford (Table 1).

Random Versus Non-Random Distribution of Caterpillar Species

Seven of the observed 36 macrolepidopteran moth species were present at more than 20% of the study quadrats: Cyclophora pendulinaria, Itame pustularia,

Lambdina fiscellaria, Lithophane antennata, Lymantria dispar, Melanolophia canadaria and Morrisonia latex. Of these, only I. pustularia had a distribution across quadrats significantly different from random expectation (p = 0.0094,

Appendix A4).I. pustularia occurred at high host plant availability quadrats more frequently than expected and low host plant availability quadrats less frequently than expected.

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Discussion

We show a consistent negative relationship between trail presence and

caterpillar abundance and richness. This relationship was present both within and among the four forest fragments we examined. This relationship is contrary to our hypothesis that trailside habitat would be associated with an increase in caterpillar abundance and/or richness. We also did not find evidence that host plant availability was limiting caterpillar abundance or species richness across our sites. There was no consistent link between host plant availability and caterpillar abundance or richness across quadrats, and only one of the seven most abundant caterpillar species was distributed across quadrats nonrandomly in relation to host plant availability.

Possible Mechanisms of Negative Relationship

A few mechanisms may interact to explain the significant negative relationship between trails and caterpillars. First, changes in trailside microsite conditions may make potential host plants less attractive to gravid female moths (resulting in fewer caterpillars). Often, host selection by moths occurs via chemodetection of volatile organic compounds (VOC) emanating from candidate host plants

(Mphosi 2007, Pophof et al. 2005, Shields and Hildebrand 2001). Changes in habitat temperature and moisture can result in changes to tree VOC emission levels (Tollsten and Müller 1996) potentially making them more or less attractive for oviposition. The assortment of unique habitat conditions at trailside habitat

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(e.g. higher understory light levels, muddier terrain, more compact soil and more

understory herbaceous vegetation) could therefore result in trailside trees

having different VOC levels that attract fewer moths than interior-forest trees.

This mechanism could also apply to species whose females are flightless or flight-

limited as adults, resulting in redispersal by ballooning in early instars or inter-

tree migration in later instars in Alsophila pometaria, Operophtera bruceata,

Phigalia titea, Orgyia definita, Orgyia leucostigma and Lymantria dispar.

Additionally, increased light levels in trailside habitat may lead to higher leaf

toughness levels which can decrease host plant suitability for caterpillars

(Choong 1996, Feeny 1970).

Secondly, the aversion to trailside host trees may in part be driven by top-down predation or parasitism. In our system, insectivorous birds, bats, omnivorous rodents, coleopteran beetles and parasitic hymenoptera are major caterpillar predators that have been observed to play a role in regulating caterpillar populations (Barbosa et al. 2001, Grushecky et al. 1998, Lill et al.

2002, Medina and Barbosa 2002, Sanz 2001). While trailside habitat does not always benefit small mammal populations, caterpillar populations could experience high predation and parasitism rates from disturbance-tolerant

(insectivorous) coleoptera, birds, bats and (parasitic) hymenoptera (Grandchamp et al. 2000, Holzschuh et al. 2009, Krusic et al. 1996, Miller et al. 1998).

Thirdly, it is possible that overwintering egg and larval mortality is higher in trailside habitat due to lethal microclimatic conditions. High winter winds,

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trampling and tracking for cross-country skiing all can remove insulating snow

cover along trails (Leonard 1972) and the resulting exposure to extreme freezing

conditions can lead to high rates of egg and larva death (Brokerhof et al. 1993,

Cooke and Roland 2003, Layne and Peffer 2006, Waggoner 1985). This may seem

like an unlikely explanation, particularly as edge habitat (which would be

exposed to similar conditions) has been found to have high egg masses, larval

and pupal densities in some caterpillar species (Bellinger et al. 1989, Fortin and

Mauffette 2001, Roland 1993) – but survivorship in leeward versus windward

edges has not been tested. If certain trailside habitats experience significantly

harsher wintertime conditions then springtime caterpillar populations may be

affected.

Trails Versus Edges

Many of the potential mechanisms driving reduced caterpillar presence in

trailside habitat could arguably be applied to forest-edge habitat, yet forest-edge habitat often shows a positive rather than negative relationship with caterpillar

abundance and richness. There are, however, two fundamental differences

between forest-edge habitat and trailside habitat that could, in this case,

account for the difference. In forest-edges moth populations transition from

forest-interior species to edge-tolerant species as the habitat type transitions

from undisturbed forest interior to disturbed successional edge-habitat (Fiedler

et al. 2007, Noske et al. 2009). In many cases the abundance and richness of the

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edge-dwelling moth assemblage is augmented by species from an adjacent soft

matrix (e.g.Ricketts et al. 2001). For moths, a soft matrix would be agricultural

habitat with sparse tree and shrub cover (Fischer et al. 2005). This type of

habitat is not necessarily hostile to forest-dwelling moths, but generally lacks the

host plants required to support stable populations. Similarly, for open-field moth

species, a soft matrix is a forest-edge habitat that has a low abundance of

suitable host- or food-plant species (Summerville and Crist 2003). Therefore,

because forest-edge habitat is flanked by two distinct moth assemblages (forest-

dwelling and field-dwelling) that often occupy different habitat niches,

abundance and richness tend to be higher. In contrast, trails that cut through

forest interior habitat do not mark a transition to open field soft-matrix

conditions. This was documented in a tropical forest system by Fiedler et al.

(2007) who showed that Geometrid and Pyraloid moth richness decreased in

edge habitat whereas Arctiid or Sphingid moth richness often increased.

A second consideration is that thesurvey methods we used in this study are not the standard light-trapping methods most often adopted for Lepidoptera population studies in forest-edge habitat. Our tree beating methodology gives the advantage of surveying caterpillar populations at very specific locations in proximity to trails and allows us to control for microhabitat (host plant) characteristics. Gaston (1988) summarizes the many limitations of light trapping, including the inability to associate individual moths to specific microhabitats.

This makes it very difficult to sample edge populations independent of matrix-

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populations. Furthermore, the sampling effectiveness of light-traps deployed

across a large region can be quite variable due to fluctuations in wind, cloud

cover, moonlight and temperature from one site to another (McGeachie 1989,

Yela and Holyoak 1997).

Conclusion

The strong negative association of trails with forest-dwelling moth richness and abundance suggests that management of forest fragments should focus on limiting proliferation of recreational-trails as a primary conservation activity. In our dataset, the host plant availability of a given site was rarely important in predicting moth richness or abundance, suggesting that tree abundance and richness maintenance could be designated a second-tier priority in Monteregian forest fragments. We don’t suggest that a healthy tree assemblage is not important for forest communities but rather that limiting intra-forest disturbance due to trail networks may be more important. Ours is not the first study to document the negative impact of recreational trails on diverse organisms in forest communities – significant and negative impacts have been widely reported (e.g.Bhuju and Ohsawa 1998, Dale and Weaver 1974, Dickens et al. 2005, Farrell and Marion 2001, Hall and Kuss 1989, Leung and Marion 1999,

Miller et al. 1998, Wilson and Seney 1994). Ours is however one of the first to document a negative relationship specifically associated with moths, a popular indicator species of forest disturbance (New 1997, Summerville et al. 2004).

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Leung and Marion (1999) note that closemonitoring of trail networks is needed

in places where human disturbance is present and that pedestrian traffic should be limited to few well-defined trails. Both boardwalks and physical barriers can be effective for directing and minimizing the impact of pedestrian traffic on natural habitats (Doucette and Kimball 1990, Zhou and Tachibana 2004) but they

can be costly to construct and maintain. However, forest fragments that have a

high degree of human visitation – particularly those fragments that are managed

with a mandate to protect wildlife and biodiversity for future generations – may

require these types of extra measures to ensure that trails have a minimum

impact on the surrounding habitat.

Acknowledgements

We would like to thank R. Feldman, J. Messier and S. Estrada for their helpful reviews and suggestions. M. VonButtlar and R. MacKenzie helped with the data collection. This research was made possible by funds provided by the Natural

Sciences and Engineering Research Council of Canada. We would also like to thank D. Rodrigue and A. Mochon from Parcs Québec for their help at Parc

National du Mont-Saint-Bruno and Parc National de la Yamaska.

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Figure 1We sampled at four sites in the St. Lawrence River valley of southern Quebec, Canada (Figure adapted from Atlas of Canada 2010), each progressively farther from the center of Montreal. Urban development is highest in the centre of the Montreal Metropolitan Area decreasing towards the edges of the dashed boundary that marks the geographical limits of greater-Montreal. The urban development gives way to farmlands eastward in the St-Lawrence Lowland, and then extensive forests in the Appalachian Highlands.

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Figure 2Trail index calculation for a given pixel p at distance d from trail t that has a width w. Index shown untransformed (a) and log-transformed (b).

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Figure 3Trail index across our four study sites: (A) Mont Royal, (B) Mont St. Bruno, (C) Mont St. Hilaire and (D1, D2, D3) Mont

Shefford. The geospatial arrangement in this Figure does not reflect the regional geolocations of the sites (see Figure 1).

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Table 1Trail Index is a consistent negative predictor of caterpillar abundance and

caterpillar species richness both across and within study sites.Hostplant availability is rarely significantly linked to caterpillar species abundance or richness, only explaining a large degree of caterpillar variance at Mont Shefford. Near-significant

(*) and significant (**) p-values are marked.

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Dependent Variable Independent Variable Site Standardized Partial R2 p-value Coefficient Caterpillar Trail Index -0.66 0.434 1.8 x 10-10** All Abundance Site Host Plant Availability 0.17 0.030 0.054* Caterpillar Species Trail Index -0.64 0.416 5.7 x 10-10** All Richness Site Host Plant Availability 0.17 0.030 0.055* St. Bruno -0.592 0.315 0.019** St. Hilaire -0.697 0.465 0.002** Trail Index Royal -0.473 0.223 0.054* Caterpillar Shefford -0.450 0.196 0.038** Abundance St. Bruno 0.214 0.042 0.357 St. Hilaire -0.356 0.122 0.073* Site Host Plant Availability Royal -0.005 0.001 0.982 Shefford 0.570 0.322 0.011** St. Bruno -0.498 0.223 0.056* St. Hilaire -0.520 0.259 0.035** Trail Index Royal -0.479 0.228 0.052* Caterpillar Species Shefford -0.368 0.131 0.120* Richness St. Bruno 0.189 0.032 0.442 St. Hilaire -0.222 0.047 0.340 Site Host Plant Availability Royal 0.059 0.004 0.798 Shefford 0.453 0.199 0.060*

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Linking Statement 2

In Chapter 2 I showed that human disturbance associated with trails is negatively related to caterpillar richness and abundance in forest fragments. I developed a simple and effective index to measure the impact of trails. This indexcomplements the NSDVI-based remote-sensing ice storm impact framework developed in Chapter 1 to provide a management toolkit for holistically monitoring disturbances in remnant forest fragments.The next question that needs to be addressed relates to the biotic forces that affect species richness. Specifically, bottom-up effects from host plants. Both ice storms and recreational trails impact habitat quality, but for phytophagous insects “habitat quality” is highly dependent on the availability of suitable host plants. The methods used in Chapter 2 did not identify acceptable host plant abundance as a consistent determinant of caterpillar richness or abundance. In Chapter 3 I examine this in greater detail and expand my sampling regime to include a diversity of host plants at each quadrat (rather than just Acer saccharum). This will allow me to directly test the impact of different host plant species and overall host plant richness on caterpillar richness and abundance.

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CHAPTER 3: Testing Two Methods that Relate Herbivorous Insects to Host Plants

Peter J.T. White1

1 McGill University, Department of Biology

1205 Dr. Penfield Ave., Montreal, Quebec

H3A 1B1, Canada

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Abstract

Insect herbivores are integral to terrestrial ecosystems. They provide essential food for higher trophic levels and aid in nutrient cycling. In general, research tends to relate individual insect herbivore species to host plant identity where a species will show preference one host over another. In contrast, insect herbivore assemblages are often related to host plant richness where an area with a higher richness of hosts will also have a higher richness of herbivores. In this study I test the ability of these two approaches (host plant identity/abundance vs. host plant richness) to describe the diversity, richness and abundance of an herbivorous Lepidoptera assemblage in temperate forest fragments in southern Canada. Many of these fragments are intensively managed in an effort to maximize the protection and preservation of biodiversity while simultaneously allowing for recreational use. Analyses indicated that caterpillar diversity, richness and abundance were better described by quadrat-scale host plant identity and abundance than by host plant richness. Most host plant-herbivore studies to date have only considered investigating host plant preferences at a species level; this type of assemblage level preference I show has been rarely considered. In addition, host plant replacement simulations indicate that increasing the abundance of preferred host plants could increase Lepidopterarichness and abundance by as much as 30% and 40% (respectively) in disturbed remnant forest fragments. This differs from traditional thinking that suggests higher levels of insect richness can be best obtained by maximizing plant richness. Host plant species that are highly preferred by the forest-dwelling caterpillar assemblage should be given special

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management and conservation considerations to maximize biodiversity in forest communities.

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Introduction

Lepidoptera are very important in forest ecosystems. They are an intricate link between forest foliage and higher trophic levels. As larva (caterpillars) and as pupa, they are components of forest food webs providing an essential food source for birds, small mammals, snakes, amphibians and other insects(Hamilton and Pollack 1956, Lill et al. 2002,

Moore and Strickland 1955, Murakami and Nakano 2000, Vasconcelos et al. 1996,

Whitaker 1966). As adult moths, they are food for bats and birds (Goiti et al. 2009, Murray et al. 1980) and can be important flower pollinators (Pellmyr et al. 1996).Being herbivores in their larval life stage,Lepidoptera play a critical role in forest nutrient cycling, converting nutrient-rich leaves into nutrient-rich feces (either their own or those of a predator) that are easily digestible by soil organisms (Schowalter et al. 1986).

Overlooked in Conservation Planning

Even though forest-dwelling Lepidoptera play a central role in forest processes they are often overlooked in conservation planning (New 2004). This is in part because the natural history of most forest-dwelling Lepidoptera is very poorly known. In temperate regions research has focused on species capable of outbreak conditions like the spruce budworm (Choristoneura occidentalis), the gypsy moth (Lymantria dispar), the forest tent caterpillar (Malacosome dispar) or the fall webworm (Hyphantria cunea). More cryptic species and virtually all microlepidopteran species have been largely ignored.

Acknowledging that individual species-targeted conservation management of Lepidoptera is often not possible, New (2004) suggests that an assemblage level approach could make it

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easier to make conservation-oriented management decisions leading to the protection of entire lepidopteran assemblages and their natural habitats.

The Relationship between Hosts and Lepidoptera

The goal of this study is to determine how host plants richness, abundance and identity determine Lepidoptera assemblage richness in temperate deciduous forests.

Across terrestrial ecosystems, theory has often focused on the richness of host plants driving the richness of Lepidoptera. This builds off the theory that richness at one trophic level determines the richness of the trophic level above (Andow 1991, Rosenzweig 1995).

This means that in terrestrial ecosystems host plant richness would be a logical driver of insect herbivore richness. Indeed, this is what is often reported. In fields, plots with more forb, grass, legume and woody shrub species have been found to support higher insect herbivore richness and abundance than plots with less plant richness (Haddad et al. 2001,

Siemann et al. 1998). A similar pattern has been observed in forest ecosystems where forest fragments with many tree species sustain higher insect herbivore richness than those with few (Summerville and Crist 2004). Along a successional gradient, insect herbivore richness can be tightly linked to plant species richness in young communities but more tightly linked to structural diversity in old communities (Southwood et al. 1979). The proposed mechanism to explain thesetypes of relationships is that a richer or more diverse host plant community provides more diverse foliar resources and more diverse structural resourcesthan a less diverse host plant community, allowing it to meet the physiological

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and niche demands of more insect herbivore species (Lawton 1983, Murdoch 1972,

Siemann 1998).

The claim that host plant richness drives insect herbivore richness is problematic for two reasons. First, there are many exceptions to this relationship - especially when other factors are tested alongside host plant richness as competing explanatory variables. These factors include site-specific soil nutrient conditions (Hartley and Jones 2003), primary productivity and topography (Hawkins and Porter 2003), and habitat disturbance (Kruess and Tscharntke 2002). In addition, significant differences between insect herbivore richness have been observed among co-occurring host plants. For example,when the black willow tree (Salix nigra)and the box elder tree (Acer negundo) co-occur, the former tends to host a richer and more abundant Lepidoptera assemblage than the latter (Barbosa et al.

2000). Similarly, when the Norway maple (Acer platanoides) and sugar maple (Acer saccharum) co-occur, the former experiences significantly less insect herbivory than the latter (Cincotta et al 2009).These types of results seem to indicate that higher abundances of certain host plants in forest stands may be more important in facilitating a diverse and abundant insect herbivore assemblage. To my knowledge this assertion not been formally tested.

Second, the claim that host plant richness drives insect herbivore richness is actuallydisconnected from the mechanisms that drive individual insect herbivore species abundance and distribution. For individual insect herbivore species the relationship with host plants is typically described in terms of host plant identity and host plant abundance rather than host plant richness (Thompson and Pellmyr 1991). For example, both the gypsy

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moth (Lepidoptera: Lymantria dispar) and the winter moth (Lepidoptera: Operophtera brumata) are broad generalists but they tend to have faster developmental rates and higher population abundances when they feed on a select set of preferred host plants

(Barbosa 1978, Liebhold et al. 1995, Maufette et al. 1983, Tikkanen et al 1999, Wint 1983).

Many other similar examples exist (e.g. Busching and Turpin 1977, Capinera 1978, Deslile and Hardy 1997, Karban and English-Loeb 1997, Wiklund 1981). These types of preferences are usually driven by host plant-specific foliar nutrient qualities or natural enemy densities, which both have significant impacts on insect herbivore performance and survival (Hunter and Price 1992, Scriber and Slansky 1981, Thompson and Pellmyr 1991). Insect herbivore assemblage richness in a given locale is the culmination of these kinds of host plant choices made by individual species based on the host plants that are present. Since the individual choices are usually made based on host plant identity, insect herbivore richness may be best modeled by taking host plant identity into account.

In this study I perform two tests to determine how host plants drive insect herbivore assemblage richness in temperate forests.First,I test whether caterpillar diversity and richness are related to host plant richness. Based on the aforementioned studies (and rationale), an increase in host plant species diversity or richnessis expected to be proportional to an increase in insect species diversity or richness. Second, I test whether caterpillar richness, diversity and abundance are more accurately described by the abundance of specific host plants. Both positive and negative host plant associations may be expected, synonymous to host plant choices made by individual insect herbivore species and indicative of the presence of preferred and non-preferredhosts. Given that

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relationships between host plant identity and individual insect herbivore species are often evident, it is reasonable to test whether the cumulative selections made across all species results in detectable preference patterns at the assemblage level. I test these relationships with a Lepidoptera assemblage in forest fragments of the mixed wood plains in the St.

Lawrence River valley of southeastern Canada. This area has historically experienced widespread forest habitat destruction; 85% of the original landscape has been cleared of old-growth forest in favour of agricultural, industrial and urban development (Allen 2001,

Drushka 2003). This type of widespread habitat loss and associated habitat fragmentation can have significant detrimental effects on both generalist and specialist species (Bender et al. 1998). With this in mind, one of the biggest challenges is to manage forest fragments in a way that benefits forest-dwelling species assemblages, maximizing and preserving species richness in intensively developed landscapes.

Methods

Study Area

The forest fragments I studied were associated with the Monteregian Hills (Feininger and

Goodacre, 1995), in the St. Lawrence River valley in southeastern Quebec, Canada

(45°30'N, 73°30'W to 45°24'N, 72°35'W; Figure 1). During pre-settlement times all these

Monteregian Hills would have had broadly similar forests embedded in a more or less continuously forested landscape (Richard and Grondin 2009), but now exist as a series of large forest remnants isolated in the developed landscape. I established eighteen 400 m2

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(20 m x 20 m) quadrats in the remnant forests at each of four sampling sites on

Monteregian Hills.

The first site was at Parc Mont Royal - an urban park in the middle of Montreal, a city of 3.5 million inhabitants. The forest at the park was cut in the 1950s and early 1960s

(Brunel et al. 2005) and was subsequently reforested up until the early 1990s. The land area of the park is 190 ha, ~100 ha of which is forested.

The second site was at Parc National du Mont St. Bruno – a protected provincial park in the eastern suburbs of greater Montreal. The forest at Mont St. Bruno is a broadleaf deciduous forest covering more than 500 ha of the 790 ha park. Aerial photography records indicate that 60 or more hectares of forest were cut prior to the

1940s in the northern part of the park, and subsequently replanted or allowed to regrow

(National Air Photo Library 1950). The forest has more than 85 species of woody plants.

The third site was at the Gault Nature Reserve on Mont St. Hilaire – a protected park and UNESCO Biosphere Reserve, 38 km east of Montreal. The forest at Mont St.

Hilaire is an old-growth broadleaf deciduous forest covering most of the 1000 ha reserve.

This site has a long history of protection dating back to the 1600s (Arii 2004, Maycock

1961).

The fourth site was at Mont Shefford – one of the easternmost Monteregian Hills,

70 km east of Montreal. At the time of this study different parts of the site were in varying states of disturbance and urban development. I conducted caterpillar sampling in three sub-sites around the hill. Six quadrats were in a 100 ha semi-disturbed patch of forest on the west side of the Mont Shefford and six quadrats in a 25 ha patch of forest on the east

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side of the hill set aside as a forested community park. A quarter of the former site was a sugar bush and there were numerous trails used by deer-hunters throughout the area. The community park had several recreational trails, but off-trail use was discouraged. Six quadrats were in a third sub-site, Parc de la Yamaska, a provincial park just north of Mont

Shefford.

Caterpillar Survey and Identification

Caterpillars were collected at 18 quadrats at each of the four study sites. Prior to caterpillar surveying a vegetation analysis indicated that sugar maple (Acer saccharum) was the most abundant host plant across sites and was the only host plant that was present at every quadrat. Therefore, at each of the 72 quadrats, ten sugar maples and up to 10 of all other tree species between 3 and 10 cm dbh were sampled for caterpillars (Appendix B1). This sampling method was chosen to survey a representative proportion of host plants among quadrats. When a given host plant species was abundant in excess of 10 individuals, sample trees were chosen at random. Each sample tree was surveyed by striking the bole and lower branches ten times with a 20 oz, 30" aluminum baseball bat and catching dislodged caterpillars on a 1 m2 sheet. Caterpillar collections were made three times at each quadrat (between June 1 and June 6, July 4 and July 9, August 3 and August 6 in

2009). This resulted in a total of 2090 sampled trees and 62700 total tree-strikes (2090 trees x 10 tree-strikes per tree x three caterpillar collection periods).

Macrolepidopteran moth caterpillars were identified to species with a dissecting microscope using Wagner’s Caterpillars of Eastern North America Field Guide(2005).

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Microlepidopteran moths were counted and identified only to morphospecies for lack of an accurate identification guide. Macrolepidopteran moths collected in early instars were reared so that positive identifications could be made.

Controlling for Habitat Disturbance

Recent investigations have shown that forest-dwelling caterpillar assemblages are sensitive to intra-habitat disturbances. Any investigation of herbivore-host plant relationships should therefore take this into account. White et al.(2011) showed that there is a consistent negative relationship between recreational trail presence and caterpillar richness in forest fragments in southeastern Quebec, Canada. They suggested that this relationship may be due to increases in caterpillar parasitism/predation and/or changes in trail-side conditions that make trail-side habitat less suitable for caterpillars.Non-native tree species introductions are sometimes correlated to management and can have a negative impact on caterpillar species richness and abundance. To control for these effects

I used a variable called Trail Index to measure the impact of trails at each quadrat. Trail index values for the quadrats in this study were described and calculated in White et al.

(2011).

Analyses

I performed a two-run stepwise (forwards) multiple regression to determine the impacts of trail index and host plant frequencies on caterpillar abundance in the sampled quadrats. This type of analysis is very useful when a large number of independent variables

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are used and the goal is to eliminate variables of marginal (or non-) significance. While it lacks the sophistication of other multivariate statistical methods (e.g. ordination or regression trees), one advantage it provides is that the final model it computes is independent of insignificant variables. It assumes a Gaussian distribution of the model residual values; collinearity in independent variables can be tested with interaction terms.

The decision threshold to include a given independent variable in each step of each regression was based on p < 0.05. In the first run-through of the stepwise regression, I used

25 candidate independent variables to explain the variance in (log10 transformed) caterpillar abundances in the 72 study quadrats. This suite of 25 independent variable consisted of the host plant frequencies (24 species, Appendix A1) and the trail-index value of each quadrat. I only included host plants that were present at more than two quadrats, resulting in the exclusion of 13 of the original 38 host plant species. Acer saccharum was also excluded from the analysis because it is ubiquitous throughout the study region and I wanted to focus on the impact that additional host plant species had on quadrat caterpillar abundance. After the first run-through of the stepwise regression, I conducted a second run-through using the independent variables selected in the first run-through and the interactions between each of these variables and Trail Index. The standard coefficients and partial R2 values of the remaining independent variables were then calculated.I conducted two identical analyses using caterpillar species richness and caterpillar Shannon’s diversity

(Shannon and Weaver 1949) as the dependent variables in place of caterpillar abundance.

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Host Plant and Caterpillar Relationships

I used two simple multiple regressions to test significance and strength of the relationship between host plants and caterpillars among quadrats. The first compared caterpillar richness to host plant richness and Trail Index (log10 + 1 transformed); the second compared caterpillar Shannon’s diversity to host plant Shannon’s diversity and Trail Index

(log10 + 1 transformed). Interaction terms were included in both models to determine whether there was a relationship between independent variables.

Testing Host Plant-Specific Preferences

The preference of each tree species by the caterpillar assemblage was calculated as a

Caterpillar Assemblage Preference Index (CAPIr) which measures the observed caterpillar richness in j trees of host plant species i minus the average caterpillar species richness in j trees drawn at random from the entire host plant-caterpillar dataset. It is calculated as:

1000 ∑{r} j [1] CAPIr = R − n=1 i i 1000 where a given tree species i has a cumulative caterpillar species richness R summed across j trees that were surveyed. In this calculation, r is the caterpillar species richness in a subset of j individuals selected at random (with replacement) from the entire dataset of all

2090 host plant samples from all host plant species; 1000 subsets of rwere selected and averaged. CAPIr is essentially the actual caterpillar species richness in host plant species i with j individuals minus the average (i.e. expected) caterpillar species richness found in j trees. Thus, if a host plant species with j trees has a CAPIr value of x, it would be said to

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support x more (or less if x is negative) caterpillar species than would be found if a random sample of j trees was sampled from the set of all trees.

Similarly, the Caterpillar Assemblage Preference Index in terms of caterpillar abundance (CAPIa) computes the observed caterpillar abundance in j trees of host plant species i minus the average caterpillar abundance in j trees drawn at random from the entire host plant dataset. It is calculated as:

1000 ∑{a} j [2] CAPIa = A − n=1 i i 1000

where A and a are the abundances of host plant species i and of a random tree subset with j individuals respectively.

For the purposes of this study, the acceptabilityof a host plant species is defined as the number of caterpillar species in an assemblage that are documented to use it. Host plant acceptability measures were taken from Wagner (2005) and Handfield (1999). Host plant acceptability was correlated to CAPIr and CAPIa to test whether caterpillars are distributed relative to the occurrence of acceptable host plants.

Results

Caterpillar Sampling

I collected 1896 caterpillars including 53 macrolepidoptera species (1305 individuals) and

56 microlepidopteran morphospecies (591 individuals) (Appendix B2; Appendix A3 for botanical authorities) from 38 different host plant tree species (2090 total trees). The five

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most common trees among quadrats (Acer saccharum, Fagus grandifolia, Fraxinus americana, Acer pensylvanicum and Ostrya virginiana) yielded 81% of caterpillar catches and high levels of caterpillar richness (Appendix B1).

Host Plant and Caterpillar Relationships

There was a significant positive relationship between host plant Shannon’s diversity and caterpillar Shannon’s diversity (Table 1a). Trail Index was also a significant descriptor; the total model 45% of the variance in caterpillar diversity. Host plant richness however was not significantly related to caterpillar richness (Table 1b). Trail Index remained a significant descriptor with the total model explaining 27% of the variance in caterpillar richness.

CAPIr and CAPIa

CAPIr values ranged between 7.9 (Prunus serotina) and -11.6 (Rhamnus cathartica); CAPIa values ranged between 121.5 (Fagus grandifolia) and -99.7 (Fraxinus americana).

Increasingly positive CAPI scores indicate that a host plant is used by caterpillars more than average; increasingly negative CAPI scores indicate that a host plant is used by caterpillars less than average (Table 2). Host plant acceptability documented by Wagner (2005) and

Handfield (1999) did a very poor job explaining the variance in CAPIr and CAPIa (Figure

2a,b). This indicates that there is no detectable connection between acceptable host plants and preferred host plants in my study system.

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Host Plant Importance Relative to Trail Index

For caterpillar abundance, Acer pensylvanicum, Ostrya virginiana and Fagus grandifolia had the highest partial R2 values in the step-wise regression. Host plant frequencies accounted for 21.0% of the variance in caterpillar abundance independent of Trail Index which accounted for 19.8% of the variance; this includes a significant interaction between

Trail Index and F. americana frequency (Table 2a). The combination of independent variables accounted for an additional 20.5% of the variance (total R2 of model = 0.61).

For caterpillar species richness, host plant frequencies of Acer pensylvanicum and

Ostrya virginiana accounted for a combined 24.9% of the variance in caterpillar species richness among quadrats (Table 2b). Trail Index accounted for an additional 22.3% of the variance; this includes a significant interaction between Trail Index and F. americana frequency. The combination of independent variables accounted for an additional 11.5% of the variance (total R2 of model = 0.59).

For caterpillar Shannon’s diversity, host plant frequencies of Acer pensylvanicum,

Ostrya virginiana and Ulmus americana accounted for 27.9% of the variance in caterpillar diversity (Table 2c). Trail Index accounted for an additional 18.7% of the variance (this again includes a significant interaction between Trail Index and F. americana frequency); the combination of independent variables accounted for an additional 9.4% of the variance

(total R2 of model = 0.56).

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Discussion

Host plant identity and abundance were statistically significant and strong predictors of caterpillar richness. This was contrasted by host plant diversity that had a statistically significant – but very weak – effect on caterpillar diversity. Tree richness and caterpillar richness were unrelated. These results are in sharp contrast with the idea that host plant richness drives insect herbivore richness (Lawton 1983, Southwood et al. 1979). Instead myresults indicate that host plant identity and abundance are more appropriate measures for explaining insect herbivore assemblage diversity, richness and abundance. This is a novel result that has not yet been described in analyses examining the relationship between host plants and insect herbivores.

Biodiversity and Conservation

My results suggest that reserve management should adopt an approach that identifies and promotes high biodiversity host plants. This is in contrast with other popular approaches such as maximizing stand structural complexity, maximizing floral biodiversity and using natural disturbance regimes (Battles et al 2001, Lindenmayer et al. 2006). Niemela and

Neuvonen (1981, Neuvonen and Nimela 1983) suggested that the most important host plants in temperate forests for Lepidoptera biodiversity are those with the highest abundance. This is true in a static sense - in most northeastern broadleaved forests the sugar maple (Acer saccharum) is the most important for insect herbivores. By virtue of being the most abundant tree it hosts the highest insect herbivore species richness. But this is a narrow view that does not take into account low abundance host plants. In

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situations where reserve management has a mandate to manage tree relative abundances to maximize overall forest health and biodiversity, a more nuanced approach is warranted.

For the range of host plants in northeastern deciduous forest, this suggests that restoration and replanting efforts should include black cherry (Prunus serotina), serviceberry (Amelanchier spp.), mountain maple (Acer spicatum), striped maple (Acer pensylvanicum) and yellow birch (Betula alleghaniensis). In regions where different forest types persist, a system-specific analysis of tree hosting abilities should be conducted to identify high and low biodiversity host plants. Pair-wise host plant comparisons can be useful for this purpose and have been conducted for many common plants (Barbosa et al

2000, Cincotta et al 2009). That said, there is evidence to suggest that herbivore-hosting capabilities of trees can be conserved across large geographic scales. Moran and

Southwood (1982) found that the relative species richness of insect herbivores and insect predators were very similar on five tree taxa present in both the United Kingdom and

South Africa. This might suggest that the preference indices I calculated for broadleaved deciduous forests in southern Canada may be broadly applicable to maple-dominated broadleaved and mixedwood forests across northeastern North America.

Low Caterpillar Richness and Abundance in Invasive Trees

My results also indicate that invasive trees may be problematic in deciduous broadleaved forests. The impoverished caterpillar assemblages found on Acer platanoides (Norway maple) and Rhamnus cathartica (European Buckthorn) add to a growing body of evidence showing that non-native host plants are a detriment to forest insect assemblages. A.

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platanoides was introduced in the late 1700s (Spongberg 1990) and has periodically been planted for forest restoration (Larson 1996, Webb and Kaunzinger 1993). However, it often outcompetes native tree species and is able to invade intact woodlands (Bertin et al. 2005,

Wyckoff and Webb 1996). My results reinforce the trend identified by Cincotta et al (2009) showing that A. platanoides is not a favoured host plant of forest insect herbivores. Their results compared A. platanoides to A. saccharum, whereas I show it in relation to other common sub-dominant host plant trees where it ranked 21st among 24 tree species for both CAPIr and CAPIa (Table 3). Similarly, Rhamus cathartica ranked 24th and 22nd in CAPIr and CAPIa, respectively. Research has shown that Rhamnus cathartica is a detriment to forest communities as it modifies soil nitrogen conditions, reduces leaf litter levels, propagates the spread of invasive species, is not consumed by many native herbivores and has allopathic effects on native trees (Heneghan et al. 2004, Knight et al. 2007). While R. cathartica can be beneficial for sustaining insect populations in disturbed and urban settings (VanVeldhuizen et al. 2005) its negative association with forest-dwelling moth populations give further reason for its control in North American forest fragments

(Gassmann 2005, Moriarty 2005). Curiously, Fraxinus americana also had markedly low

CAPIr and CAPIa scores, ranking 22nd and 24th (respectively) among 24 tree species. Species in the genusFraxinustend to be higher than average in terms of leaf toughness (Ricklefs and

Matthew 1982), support high caterpillar parasitoid loads(Lill et al. 2002) and can have prohibitively toxic phenolic compounds (i.e. in the case of the closely related Fraxinus pennsylvanica;Markovic et al. 1996). Despite these, they are however documented as widely used host plants by caterpillar species (Handfield 1999, Karban and Ricklefs 1984,

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Wagner 2005). The step-wise regression analysis indicated that F.americana has a strong association with trails at my study sites which when combined with these other deterrents may have resulted in the low CAPI scores.

From the point of view of forest management for biodiversity conservation, the full impact of A. platanoides and R. cathartica on forest dwelling moth assemblages can be enumerated with a host plant replacement simulation. At Mont Royal, caterpillar surveys included a total of 629 sampled host plants, the most abundant being A. saccharum (180 trees surveyed), F. americana (102 trees surveyed), R. cathartica (48 trees surveyed), T. americana (48 trees surveyed) and A. platanoides (39 trees surveyed); the remaining 212 surveyed trees were made up of 26 different host plant species. Caterpillar species richness and abundance collector curves can be created (using the second half of equations [1] and [2], respectively), first using all trees at Mont Royal and second by replacing the data collected from A. platanoides and R. cathartica on Mont Royal with data collected from O. virginiana and A. pensylvanicum from other locations (Figure 3). This results in an increase of 30% in caterpillar species richness and 40% in caterpillar abundance. In this exercise, O. virginiana was chosen as a replacement for A. platanoides because it has a similar abundance (averaged per site) and average height (5.6 meters versus 5.5 meters). A. pensylvanicum was chosen as a replacement for R. cathartica because these species are both often subdominant trees associated with disturbed areas

(sun-loving) and are relatively similar in average height (5.4 meters versus 3.8 meters).A similar replacement simulation can be run for F. americana. While native, it has a CAPIr and CAPIa scores were amongst the lowest in the host plant data set. When the caterpillar

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data collected from 102 F. americana surveyed at Mont Royal are replaced with data from

F. grandifolia (both are common canopy-contributing species and have similar average heights; 7.4 meters versus 6.9 meters) caterpillar species richness increases 18%, abundance 37% (Figure 3).

Mechanisms

Given the important implications of these results, it is worthwhile considering the ecological mechanisms that may be driving the observed relationships. Since host plant choices are made by individual Lepidoptera species (and scale up to the assemblage level), any driving mechanism explaining the assemblage-wide pattern would need to be operational at the individual species level. The most well established factors used to explain host plant choices amongst individual species are host plant foliar quality (Feeny

1970, Matteson 1980, Buse et al. 1998), and the presence of natural enemies (Hunter and

Price 1992, Siemann et al. 1998, Lill 2001).

Nitrogen has been identified as one of the preeminent foliar nutrients associated with insect herbivore host plant selection and performance (Matteson 1980). This makes it a prime foliar candidate to explain the host plant preferences I observed. One of the most preferred host plant species in my study, Ostrya virginiana,is documented as having moderate to high levels of foliar nitrogen compared to other broad-leaved species

(Mertzger 1990). However, the widely preferred host plant Acer pensylvanicum has a moderate to low level of foliar nitrogen (Zehnder et al. 2009) similar to that of the avoided host plant Fraxinus americana (Abrams and Mostoller 1995, Côté et al. 2002). One of the

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most nitrogen-rich host plant species Betula papyrifera(Abrams 1998) was also has lower than average CAPIr and CAPIa values (Table 3). This would be consistent with the findings of Karban and Ricklefs (1984) who found no relationship between foliar nutritional quality and caterpillar species richness and abundance in broad-leaved deciduous caterpillar communities. Although a simple relationship between CAPI values and foliar nitrogen content does not seem apparent in this case, other foliar variables such as water content

(Scriber and Slansky 1981) and foliar toxins (Gatehouse 2002) have also been commonly cited as determinants of insect herbivore performance and distribution. In an in-depth analysis of host plant-insect interactions, Futuyma and Gould (1979) concluded that the variation in insect populations among hosts is likely due to a multiplicity of plant leaf chemical variables.

Top-down pressure from parasitoids can also have a significant impact on oviposition host choices of adult Lepidoptera (Karban and English-Loeb 1997, Thompson and Pellmyr 1991). Lill et al. (2002) documented parasitoid-host plant-caterpillar interactions in common host plant genera and showed that the genera Fraxinus and Acer were associated with higher than expected caterpillar parasitoid loads while the genus

Ulmus with lower than expected caterpillar parasitoid loads. They did not include Ostrya or

Fagus in their analyses. The positive association between Fraxinus and caterpillar parasitoids could explain the exceptionally low Fraxinus americana CAPIr and CAPIa values.

However, Ulmus americana alsohad lower-than average CAPIr and CAPIa values, even though this genus is a documented predator-reduced space. This discrepancy could indicate that parasitism is not the primary driving mechanism of caterpillar assemblage

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richness in Ulmus hosts in my study region. The wide range in CAPIr and CAPIa values for different Acer species in my data further suggest that a genus-level parasitoid-control of caterpillar assemblages may not be the dominant driver impacting caterpillar host-plant preferences. For example, A. pensylvanicum and A. spicatum had significantly positive

CAPIr values (6.1 and 6.4 respectively) but A. rubrum and A. platanoideshad significantly negative CAPIr values (-6.1 and -9.7 respectively). Similarly, A. pensylvanicum and A. spicatum had significantly positive CAPIa values (44.5 and 14.6) but A. rubrum and A. platanoides had significantly negative CAPIa values (-10.3 and -22.3 respectively). If parasitoid regulation of caterpillars was occurring at the host plant genus level, then species within a given genera would be expected to have roughly similar CAPIr and CAPIa values. While it is still possible that host plant caterpillar parasitoid loads may play a role in driving caterpillar assemblage richness among the other host plant genera, it seems implausible that they are the sole driving mechanism determining caterpillar species richness and abundance among Acer or Ulmus host plants in my study.

Conclusion

Understanding the important determinants of insect assemblage richness and abundance in remnant forest fragments can improve management and conservation efforts. In a landscape where pristine forest habitat is rare, conservation-based management should attempt to maximize and maintain the richness in the forest fragments that remain.In this study, I have shown that the richness and abundance of an insect herbivore assemblage can be more effectively described in terms of host plant identity and host plant abundance.

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Describing the insect herbivore assemblage in these terms is more consistent with single- species studies that show insect herbivore host choice is often a function of host plant identity rather than quadrat- or stand-scale host plant richness. In the system I examined, it does not appear as though top-down parasitoid control is the driving force behind host choice at the herbivore assemblage level (Lill et al. 2002). It also seems unlikely that host plant foliar nitrogen content is driving assemblage level host choice. Given that neither of these two mechanisms seem dominant, it is possible that the assemblage-level host plant selections are driven by a complex interaction of multiple foliar nutrient properties and top-down pressure (Mayhew 1997). It is also possible that there is a third factor (e.g. historical disturbance events) driving both the relative abundance of community host plant species and Lepidoptera assemblage host plant occupancy. In a direct conservation application of my results, host plant replacement simulations indicate that planting preferred host plants in the place of non-preferred host plants could result in a profound impact on insect herbivore assemblage richness and abundance. At a broader level, these results call for a shift in conservation management principles where some emphasis should be placed on identifying and protecting high value host plants that are synonymous with high levels of insect herbivore richness and abundance.

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Acknowledgements

I would like to thank P. Peres-Neto, C. Buddle, K. Summerville, J. Donoghue, R. Feldman and J. Messier for their important reviews. BothM.J. Lechowicz and B. McGill played integral roles commenting on drafts of this MS throughout its creation. I am grateful to M.

VonButtlar and R. MacKenzie for help with data collection andam indebted to both D.

Rodrigue (Supervisor, Conservation and Education Service at Parc du Mont St. Bruno) and

A. Mochon (Supervisor, Conservation and Education Service at Parc de la Yamaska) for their help in selecting research locations within their respective parks. This research was made possible by funds provided by the Natural Sciences and Engineering Research Council of Canada.

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Mont RoyalMont St. Bruno Mont St. Hilaire Mont Shefford

Downtown CANADA Montreal 10 km

U.S.A. U.S.A.

Figure 1 Caterpillars were collected from four sites in the St. Lawrence River valley of southern Quebec, Canada (Figure adapted from Atlas of Canada 2010). The matrix surrounding each site isdominated by agricultural lands and urban development with the exception of Mont Royal, which is a forest fragment in an exclusively urban setting. The dashed boundary represents the Montreal

Metropolitan Community (MMC). Urban development is very dense in downtown Montreal decreasing towards the MMC boundary.

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Figure 2There was no relationship between the number of caterpillars reported

to use a given host plant and either (a) CAPIr or (b) CAPIa scores. These relationships are expected to be positive as a host plant’s acceptability should be indicative of the caterpillar assemblage preference of that host plant relative to other host plants in the community.

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Figure 3A host plant replacement simulation for (a) caterpillar species richness

and (b) caterpillar richness in the Mont Royal forest fragment. In these

simulations F. grandifolia and A. pensylvanicum were substituted for A.

platanoides and R. cathartica (dashed line) and O. virginiana was substituted for

F. americana (dotted line). The substituted species were chosen because they had high CAPIr and/or CAPIa scores and commonly share the same general canopy position as the species they replace. Replacement of invasive species with O. virginiana and A. pensylvanicum resulted in an increase of 30% in caterpillar species richness and 40% in caterpillar abundance. Replacement of F.

americana with F. grandifolia resulted in an increase of 18% in caterpillar species

richness and 37% in caterpillar abundance.

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Table 1 The relationship between host plants and caterpillars shows that (a) host

plant (Shannon’s) diversity is a significant descriptor of caterpillar (Shannon’s)

diversity when Trail disturbance is accounted for (total model adjusted R2 =

0.45F3,68 = 9.9) but (b)host plant richness is a non-significant descriptor of

caterpillar richness when Trail disturbance is accounted for (total model adjusted

2 R = 0.27, F3,68 = 20.1).

a) Caterpillar Shannon’s diversity = host Shannon’s Diversity + trail index + interactions Independent Variable Standardized t-value p-value Coefficient Host Shannon’s Diversity 0.32 2.2 0.035 Trail Index -2.7 -3.0 0.0037 Host x Trail Interaction 0.29 1.1 0.30 b) Caterpillar Richness = host plant richness + trail index + interactions Independent Variable Standardized t-value p-value Coefficient Host Richness 0.11 0.34 0.51 Trail Index -1.9 -2.2 0.032 Host x Trail Interaction 0.010 0.034 0.97

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Table 2CAPIa and CAPIr values for host plant trees (sorted in order of decreasing

CAPIr values) are calculated as the difference between the observed and the average caterpillar abundances and richness in host plant tree species (see equations 1 and 2). Greater CAPI values indicate that a host plant is more preferred by the caterpillar assemblage.

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# of Trees Observed Average Observed Average Host Plant Species CAPIa CAPIr Surveyed AB AB* SR SR* Prunus serotina 19 38 16.5 21.5† 17 9.1 7.9† Amelanchier arborea 35 58 30.0 28.0† 21 14.6 6.4† Acer spicatum 20 32 17.4 14.6† 16 9.6 6.4† Acer pensylvanicum 206 221 176.4 44.6† 52 45.9 6.1† Betula alleghaniensis 10 17 8.5 8.5† 11 5.1 5.9† Cratageus spp 19 15 16.5 -1.5 11 9.1 1.9 Carya cordiformis 15 9 12.9 -3.9 9 7.4 1.6 Ostrya virginiana 145 239 124.0 115.0† 39 38.0 1.0 Carpinus caroliniana 26 21 19.1 1.9 10 10.3 -0.3 Malus pumila 11 4 9.4 -5.4 4 5.6 -1.6 Fagus grandifolia 229 317 195.5 121.5† 46 48.4 -2.4 Cornus alternifolia 13 3 11.2 -8.2 3 6.5 -3.5 Ulmus americana 35 19 30.0 -11.0 11 14.6 -3.6 Prunus nigra 11 3 9.4 -6.4 2 5.6 -3.6‡ Betula papyrifera 18 5 15.6 -10.6‡ 5 8.7 -3.7 Tsuga canadensis 33 25 28.3 -3.3 10 14.0 -4.0 Acer rubrum 12 0 10.3 -10.3‡ 0 6.0 -6.0‡ Rhus typhina 15 4 12.9 -8.9‡ 1 7.4 -6.4‡ Tilia americana 66 24 56.7 -32.7‡ 16 23.1 -7.1‡ Quercus rubra 34 11 29.1 -18.1‡ 6 14.3 -8.3‡ Acer platanoides 39 11 33.3 -22.3‡ 6 15.7 -9.7‡ Fraxinus americana 228 95 194.7 -99.7‡ 38 48.3 -10.3‡ Prunus virginiana 55 26 47.4 -21.4‡ 10 20.5 -10.5‡ Rhamnus cathartica 48 11 41.1 -30.1‡ 7 18.6 -11.6‡ * Average SR and AB values are based on the sample size (# of trees surveyed) for a given host plant. **Mostly composed of a mixture of Cratageus punctata Jacq. and Cratageus mollis Scheele and their hybrids. † CAPIa or CAPIr greater than 1 standard deviation above mean expected value. ‡ CAPIa or CAPIr greater than 1 standard deviation below mean expected value.

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Table 3Host plant frequencies (combined) explained (a) 21.0% of caterpillar

abundance, (b) 24.9% of caterpillar richness and (c) 27.9% of caterpillar

Shannon’s diversity among quadrats. This was independent of trail index which, when combined with a F. americana interaction term, explained (a) 19.8%, (b)

22.3% and (c) 18.7% of the variances.

a) Caterpillar Abundance = host plant frequencies + trail index + interactions Independent Variable Standardized Coefficient Partial R2 Acer pensylvanicum 0.369 0.108 Ostrya virginiana 0.304 0.083 Fagus grandifolia 0.168 0.019 Log 10 (Trail Index) -0.610 0.198 Log10 (Trail Index) * F. americana 0.497 Total Model 0.613 b) Caterpillar Richness = host plant frequencies + trail index + interactions Independent Variable Standardized Coefficient Partial R2 Acer pensylvanicum 0.415 0.150 Ostrya virginiana 0.331 0.099 Log 10 (Trail Index) -0.636 0.223 Log10 (Trail Index) * F. americana 0.556 Total Model 0.590 c) Caterpillar Shannon’s Div = host plant frequencies + trail index + interactions Independent Variable Standardized Coefficient Partial R2 Acer pensylvanicum 0.453 0.175 Ostrya virginiana 0.321 0.094 Ulmus americana 0.189 0.010 Log 10 (Trail Index) -0.574 0.187 Log10 (Trail Index) * F. americana 0.527 Total Model 0.560

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Linking Statement 3

In Chapter 3 I showed that there is a strong effect of bottom-up forces on insect herbivore richness. This mechanism was strongest when described as assemblage-level preference for specific host plants. Host plant species richness was a poor predictor of insect herbivore species richness. In addition I showed that the replacement of non-native host plants could have significant positive impacts on caterpillar richness and abundance. While this pattern is described in an exclusively “bottom-up” perspective, it is possible that both bottom-up and top-down forces are at work. Bottom-up forces can be described as the quality of foliage – often expressed as the concentration of foliar nutrients. Top-down forces can be described as the relative occupancy of enemies in prospective host plants (these enemies more often tend to be parasitoids in the case of Lepidoptera). Both of these forces may be highly seasonal in their nature. Foliar quality is usually highest in the beginning of the growing season when parasitoid abundance is low. Parasitoid abundance is often high in the latter half of the growing season when foliar quality is low.

The question of how foliar quality impacts caterpillar assemblages thus has an intra-seasonal temporal component. In Chapter 4 I relate the intra-seasonal changes in quadrat-scale foliar quality (biotic bottom-up driver) to caterpillar richness and abundance, andI take parasitoids (biotic top-down driver) into consideration late in the growing season (August) to examine their impact.

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CHAPTER4: Intra-Seasonal Relationships between Insect Herbivores and their

Hosts

Peter J.T. White1

1 McGill University, Department of Biology

1205 Dr. Penfield Ave., Montreal, Quebec

H3A 1B1, Canada

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Abstract

Research has often linked host plant foliar qualities to insect herbivore distribution and performance but rarely have these factors been related to the richness or abundance of an entire caterpillar assemblage or examined in a seasonal context. This “bottom-up” effect can often be complicated by a “top- down” effect driven by the presence of natural enemies. The purpose of this research is to examine how a caterpillar assemblage in temperate broadleaved forest fragments is related to quadrat-scale foliar properties in three sampling windows within a growing season (early June, early July and early August). In addition, I test whether top-down parasitoid pressure supersedes foliar quality as the prime predictor of caterpillar assemblage richness and abundance in the third (August) sampling window. A regression tree analysis suggests that quadrat-scale foliar polyphenol content was important for caterpillar richness and abundance in June and August as a negative control. Foliar phosphorus was the primary predictor for the month of July. Parasitoid pressure superseded polyphenol content as the primary predictor of caterpillar richness in August.

Since the foliar quality data, the parasitoid data and the caterpillar assemblage data I use come from different sources, my results should be interpreted with caution. However, there is good evidence to suggest that foliar quality plays an important role in affecting caterpillar assemblages early in the growing season, but that the nature of this effect differs throughout the growing season and can depend on parasitoid presence.

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Introduction

Determining the factors that affect insect herbivore distribution and performance has been an important topic in ecological research over the past four decades (Scriber 1978, Scriber 2010, Strong et al 1984, Wint 1983). These factors mostly fall into two categories: (i) bottom-up effects that pertain to the nutritional or structural quality of foliar resources and (ii) top-down effects that pertain to natural enemies finding and killing (or parasitizing) insect herbivores

(Hunter and Price 1992, Mayhew 1997). In recent years, research has aimed at better understanding how these two forces shape insect herbivore communities given their high diversity (Wilson 1987) and the essential role they play in food web structure (Murakami and Nakano 2000, Vasconselos et al. 1996, Goiti et al.

2009) and ecosystem nutrient cycling (Schowalter et al. 1986). In this study, I hope to address two important gaps in the understanding of these tri-trophic relationships – namely (i) the effect of top-down and bottom-up forces on entire insect herbivore assemblages and (ii) the intra-seasonal variation in the nature and magnitude of top-down and bottom-up forces.

Bottom-Up Effects: Foliar Quality

A bottom-up effect occurs when the distribution or performance of insect herbivores is controlled or limited by aspects of foliar quality (Power 1992) – often defined by the concentration of specific nutrients present in edible foliage

(Amwack and Leather 2000, Forkner and Hunter 2000). While different insect

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herbivore species have different nutrient needs, foliar properties such as high

nitrogen content and high water content are generally identified as positive

controls of insect herbivore performance and distribution; high toughness and

polyphenol (toxin) content are generally identified as negative controls (Amwack

and Leather 2002, Choong 1996, Feeny 1970, Matteson 1980, Schowalter et al.

1986, Scriber 1977, Scriber 1878, Scriber and Slansky 1981). Feeny (1970) was

among the first to describe this relationship for Lepidoptera larva (caterpillars)

when he showed that Winter Moth (Operophtera brumata) early season

herbivory was limited by available foliar nitrogen content but late season

herbivory was limited by foliar tannin (toxin) content. Subsequent experiments

also identified that foliar water content is an important driver of caterpillar

performance (development times, weights, etc.) and caterpillar distribution (host

plant choices) (Amwack and Leather 2002, Scriber and Slansky 1981). A third

foliar quality, phosphorus, has more recently been identified as important to

insect herbivore development (Perkins et al. 2004, Woods et al. 2002). While

phosphorus is not generally considered of primary importance (like nitrogen or

water content) its impact has a strong mechanistic basis for being a positive

control as it is used in rRNA formation, which may be very important for fast-

growing organisms like caterpillars (Elser et al 1996). Of the negative control foliar nutrient controls, high foliar “toughness” can cause insect herbivores to eat foliage at a slower rate and interfere with gut processes (Clissold et al. 2009).

Slower development times may also make them vulnerable to predators and

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parasitoids for longer. Toughness is often described as a function of foliar lignin

content and foliar fiber content; when these are present in high concentrations,

they can result in host plant avoidance by gravid females (Rausher 1981 Slansky

and Scriber 1981, Schowalter et al. 1986).

Bottom-Up Effects: Foliar Toxins

Another class of foliar nutrients, toxins (polyphenols, alkaloids and terpenes) have been both positively and negatively linked to host plant selection depending on the scenario. Feeny (1970) identified three mechanisms by which these types of toxic compounds can negatively affect caterpillars: (1) by acting as a repellent to discourage oviposition, (2) by interfering with protein synthesis and functioning and (3) by acting as a direct toxic compound. A fourth mechanism of impact has since been observed in cases where toxic compounds act as attractants to herbivore enemies, particularly parasitic wasps (DeMoraes et al 1998, Hoballah and Turlings 2001). Empirical tests have often shown diminished larval growth rates, lower larval weight, higher larval mortality and lower overall fitness associated with feeding on foliage high in toxin content (for reviews see: Amwack and Leather 2002, Pasteels et al. 1983, Schowalter et al

1986, Scriber and Slansky 1981). The ability of some insect herbivores to sequester plant toxins as a defense against predators and parasitoids is often juxtaposed to this (Blum 1983, Duffey 1980, Nishida 2002). For example, the wooly bear caterpillar (Grammia geneura) can increase its toxicity to natural

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enemies by feeding on a diet highin pyrrolizidine alkaloids (Singer 2004a). This

can be at the expense of feeding on host plants that result in faster

development, but can be an advantageous behaviour where enemies are

common. The eastern tent caterpillar (Malacosoma americanum) can sequester

foliar hydrocyanic acid found in Prunus host plants (e.g. black cherry, P. serotina

and choke cherry, P. virginiana), making their active defense against predators

more effective (i.e. a more toxic regurgitation when attacked) (Peterson et al.

1987). There are many other such examples in the literature (e.g. Boppré 1990,

Bowers 1992, del Campo et al. 2005, Marsh and Rothschild 1974, Moore et al.

1990) but Pasteels et al. (1983) contend that this phenomenon occurs far more

frequently in monophagic and oligophagic insect herbivores than in polyphagic

herbivores. To my knowledge this assertion has not been verified and since the

ecology and life history of many insect herbivore species is poorly known (with

the exception of well-documented defoliators), it is difficult to formally test.

Top-Down Effects

A top-down effect occurs when the distribution or performance of insect herbivores is controlled or limited by a natural enemy – often a predator or a parasitoid (Hunter and Price 1992). Top-down effects can play a direct role in

regulating insect herbivore population size (Holmes et al. 1979, Hooks et al.

2003, Mols and Visser 2002, Sanz 2001) and their presence may force insect

herbivores to make sub-optimal host plant choices in search of “enemy free”

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space (Jeffries and Lawton 1984, Lill et al. 2002). This type of relationship has also been framed in the context of density-dependence – a high density of caterpillars (presumably on a high quality host plant) can become a magnet for would-be predators and parasitoids (Mayhew 1997). Because of this, some caterpillar species change their choice behaviour in favour of lower quality hosts.

There is strong evidence to support this indirect negative effect of natural enemies on caterpillars. In northeastern North American forests tree genera with high parasitoid loads tend to have fewer caterpillar species than genera with low parasitoid loads (Lill et al. 2002). The pine beauty moth (Panolis flammea) has a higher survival rate on low quality host plants because of the prominence of parasitoids associated with high quality host plants (Leather and Walsh 1993).In field ecosystems, insect herbivore diversity is initially determined by host plant diversity (i.e. at initial community assembly), but it is regulated predator and parasitoid density thereafter (Siemann et al. 1998).

Current Gaps in Foliar Quality Research

There are two gaps in host plant host plant-caterpillar research that need to be addressed. First, the vast literature on foliar quality-caterpillar relationships has focused primarily on how foliar quality impacts single or only a few caterpillar species (e.g.Camara 1997, Clancy and King 1993, Feeny 1970, Hagen and Chabot

1986, Hough and Pimentel 1978, Hunter and Lechowicz 1992, Karban and

English-Loeb 1997, Lill and Marquis 2001, Rausher 1981, Singer et al. 2004a,

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Woods et al. 2002). These types of studies are often of limited utility in

management or conservation contexts because they are difficult to apply to

larger species groups. Important pests such as Alsophila pometaria, Lymantria dispar, Malacosoma disstria, Hyphantria cunea or H. textorare often studied but overall caterpillar assemblages are rarely considered (but see Karban and

Ricklefs 1984). In other contexts, assemblage-based approaches have been very successful in describing factors that affect richness and abundance.For example, in a landscape-disturbance context, assemblage based approaches have demonstrated that species richness in moth assemblages is significantly higher in agricultural fields adjacent to forest habitat compared to those that were distant

(Ricketts et al. 2001). In a forest fragmentation context, an assemblage-based approach was used to show how the host plant composition of a given fragment offset the species-loss expected due to small fragment size (Summerville 2004,

Summerville and Crist 2003). Still other contexts exist (e.g. host plant preferences - White, Chapter 3), but an assemblage-based approach has not yet been used to examine bottom-up and top-down effects on Lepidoptera. An assemblage-based approach also has the advantage of including microlepidopteran species that are rarely studied because their taxonomy is very poorly known, even in well-studied locales.

A second gap in our knowledge is that the impact of foliar quality on caterpillars is not often examined in a seasonal context. This is in part a function of the first gap (studies with one or few species); most species are very short

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lived and it is only possible to study them in their limited seasonal timeframe.

Yet, at the Lepidoptera assemblage level it is well known that significant

composition changes occur over the course of the growing season - to such a

degree that experimental designs have to take this factor into account. Because

of these intra-seasonal compositional changes, it is possible that the balance

between top-down and bottom-up regulators may change as well. It is not well-

known how early season versus late season caterpillars distribute themselves

with respect to foliar qualities. In one rare example, Niemela and Haukioja

(1982) showed that host plant use patterns among Finnish Lepidoptera change

from the beginning to the end of the growing season. They attributed these

changes to the timing of shoot growth patterns throughout the season, but did

not have the data to implicate specific changes in foliar qualities. There are some

examples of single insect herbivore species changing their feeding habits from

early to late in their flight seasons – often tracking changes in host plant quality

(e.g. Feeny 1970, Rausher 1981) – but this falls under the purview of the “first

gap” above (i.e. most studies focus on one or few caterpillar species).

Many tree species in temperate broadleaved forests have very significant

seasonal trends in foliar qualities. Ricklefs and Matthew (1982) documented

foliar qualities of 34 broad-leaved deciduous tree species in southern Canada

and showed that average foliar nitrogen dropped by 19% and average leaf water

content dropped by 14% from early June to August (Table 1). Since nitrogen and

water content levels are often related to faster caterpillar development times

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and larger population sizes (Matteson 1980, Scriber 1977, Scriber and Slansky

1981)a loss of nitrogen- or water-rich host plants could have significant

consequences. Phosphorus, linked to caterpillar development, is 47% lower in

early July than in early June and also 41% lower in early July than in early August

(Table 1). Toughness, lignin content and fiber content also have strong intra-

seasonal variation. Toughness peaks late in the growing season whereas lignin content and fiber content peak mid-season (on average) (Table 1); all three are

lowest early in the growing season.

Like foliar quality, parasitoid (or predator) pressure can be highly

seasonal, peaking multiple times or in the latter half of the growing season

(Correa-Ferreira and Moscardi 1995, Damman 1987, Kato 1994, Liu et al. 2000,

McAuslane et al. 1993, Okada 1989, Peña et al. 1996, Wong et al. 1984). It thus

becomes quite difficult to analyze something like herbivorous caterpillar richness

in a system where both foliar quality and parasitoid pressure are seasonally

variable. For this type of analysis seasonally sensitive tri-trophic models may be

warranted.

Tri-Trophic Relationships

Tri-trophic relationships occur when both bottom-up pressure (effects of foliar

quality) and top-down pressure (effects of natural enemies) impact the

performance or distribution of an herbivorous species (Leibold 1989). While the

strength and significance of top-down and bottom-up forces vary depending on

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the system in question (as discussed in the sections above), many authors argue for the primacy of bottom-up forces pointing out that all levels of the tri-trophic system will cease to exist in the absence of the lowest trophic level (i.e. no host plants = no herbivores = no natural enemies) butnot in the absence of the highest trophic level as herbivores can continue to graze on plants in the absence of natural enemies (Hunter and Price 1992, Power 1992, Siemann et al.

1998). Primacy aside, the seasonal nature of bottom-up foliar quality and top- down natural enemy abundance suggests that tri-trophic relationships should be studied in seasonal contexts. This is particularly appropriate when the unit of examination is a species assemblage and the period of examination is an entire foliar growth-season. In one of the few studies examining tri-trophic relationships in a seasonal context, Kato (1994) showed that a bi-voltine insect herbivore was distributed with respect to foliar quality in the first generation when parasitoids are absent, but distributed with respect to top-down forces

(i.e. parasitoid presence) in the second generation when parasitoids are abundant. Further investigation is required to test if this type of pattern applies to an entire herbivorous assemblage.

Objective

The objective of this study is to use an assemblage-based approach to examine how caterpillar richness and abundance are related to foliar quality over the course of a growing season and, in addition, to determine if top-down parasitoid

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pressure impacts this relationship. The base expectations are that caterpillar richness and abundance will be (i) positively related to foliar nitrogen content, water content and phosphorus content, (ii) negatively related to foliar toxin content, toughness, lignin content and fiber content, (iii) positively related to toxin content in the scenario where assemblage-level toxin sequestration occurs,

and (iv) negatively related to parasitoid pressure. I hypothesize that the relative

effects of these factors will change over the course of the growing season.

Specifically, positive foliar properties (nitrogen, water content and phosphorus)

should play a stronger role determining caterpillar distribution amongst trees

early in the growing season when quality is high; negative foliar properties

(toughness, lignin content, fiber content) should play a stronger role late in the

growing season. Parasitoid pressure should also have a higher impact on

caterpillar richness and abundance late in the growing season when parasitoids

tend to be more common. Given that the impact of foliar polyphenol content

could be either positive or negative it is difficult to predict how the magnitude of

its impact may change over the growing season.

Methods

Study Area

I surveyed caterpillars in four forest fragments located on the Monteregian Hills

located in the St. Lawrence River valley in southeastern Quebec, Canada

(45°30'N, 73°30'W to 45°24'N, 72°35'W; Figure 1). During pre-settlement times

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all these Monteregian Hills would have had broadly similar forests embedded in

a more or less continuously forested landscape (Richard and Grondin 2009).

They now exist as a series of remnant forest fragments isolated in a developed

landscape. Eighteen 400 m2 (20 m x 20 m) quadrats were placed randomly within each of the four sampling sites on Monteregian Hills.

The first site was at Parc Mont Royal - an urban park in the middle of

Montreal, a city of 3.5 million inhabitants. The park has a secondary-growth

forest that covers approximately 100 of 190 total hectares. The second site was

at Parc National du Mont St. Bruno – a protected provincial park in the eastern

suburbs of greater Montreal. The forest at Mont St. Bruno is a secondary-growth

forest that covers more than 500 ha of the 790 ha park. The forests are only

minimally disturbed by a series of trails designated for public use. The third site

was at the Gault Nature Reserve on Mont St. Hilaire – a protected park and

UNESCO Biosphere Reserve, 38 km east of Montreal. The forest at Mont St.

Hilaire is primarily an old-growth broadleaf deciduous forest covering most of

the 1000 ha reserve. This site has a long history of protection dating back to the

17600s (Arii 2004, Maycock 1961). The fourth site was at Mont Shefford – one of

the easternmost Monteregian Hills, 70 km east of Montreal. I conducted

caterpillar sampling in three sub-sites around the hill. Six quadrats were in a 100

ha semi-disturbed patch of forest on the west side of the Mont Shefford and six

quadrats in a 25 ha patch of forest on the east side of the hill set aside as a

forested community park. A quarter of the former site was a sugar bush and

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there were numerous trails used by deer-hunters throughout the area. The community park had several recreational trails, but off-trail use was discouraged.

Six quadrats were in a third sub-site, Parc de la Yamaska, a provincial park just north of Mont Shefford.

Caterpillar Surveys and Identification

Caterpillars were surveyed at each quadrat in three time periods in the growing season of 2009: (i) June 1 to 6, (ii) July 4 to 9 and (iii) August 3 to 6. At each quadrat, ten sugar maples (Acer saccharum) and up to 10 of every other tree species between 3 and 10 cm dbh were sampled for caterpillars (Table 2). Each target tree was sampled by striking the bole and lower branches ten times with a

20 oz, 30" aluminum baseball bat; dislodged caterpillars were caught on a 1 m2 sheet held under the point of striking. All macrolepidopteran caterpillars collected were identified to species with a dissecting microscope using Wagner’s

Caterpillars of Eastern North America Field Guide(2005). Microlepidopteran caterpillars were counted and identified to morphospecies for lack of an accurate identification guide. Any caterpillar that was collected in an early instar was reared to a late instar so that positive identifications could be made.

Measuring the Bottom-Up Effect

I computed the foliar quality of the trees surveyed at each quadrat using seasonal leaf properties reported by Ricklefs and Matthew (1982) for the study

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species in similar forests in southern Canada. From their data, the foliar qualities

I calculated for each quadrat were: percent water content, toughness (measured in grams), percent polyphenol content, percent nitrogen content, percent phosphorus content, percent lignin content and percent fiber content. In quadrats where more than one tree species was present (i.e. all but two quadrats), quadrat foliar qualities were averaged across host plant species weighted by their relative basal areas in a given quadrat. In other words, the caterpillar assemblage is related to an aggregated estimate of foliar quality in each quadrat; this is an approach routinely used in assessing the relationship between foliar traits and ecosystem functions such as decomposition (Kakazou et al 2006). Quadrats that contained host plants undocumented by Ricklefs and

Matthew (1982) were included in the analysis only if three or fewer trees were undocumented. This resulted in foliar quality profiles being constructed for 52 of

72 possible quadrats. The tree species undocumented by Ricklefs and Matthews which resulted in the exclusion of quadrats were: Abies balsamea, Acer platanoides, Acer saccharinum, Acer spicatum, Amelanchier arborea, Crataegus spp., Dirca palustris, Picea glauca, Rhamnus cathartica, Prunus nigra, Robinia pseudoacacia and Tsuga canadensis.

Measuring the Top-Down Effect

An index of parasitoid pressure was created using the Forest Insect

Survey data compiled by Natural Resources Canada. These data record the

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occurrence of several common species of caterpillars sampled from coniferous

and mixed wood forests across Ontario and Quebec between 1937 and 1949.

The focal caterpillar species that Natural Resources Canada provided records for

were: Acleris variana, Archips cerasivorana, Caripeta divisata, Hyphantria cunea,

Lambdina fiscellaria, Nepytia canosaria and Semiothisa granitata (now Macaria

granitata). Each of these species has a set of collection records documenting

caterpillars collected on specific dates from specific host plants at specific

locations. Each record also documented the number of parasitoids (and

sometimes parasitoid taxa or species) that were observed emerging from

collected caterpillars. The parasitoid emergence data associated with these

records were divided into three subsets to match the three sampling periods that I used for my caterpillar collecting; a two week buffer was given around each sampling window for matching parasitoid emergence dates. Parasitoid

emergence data were then pooled across the seven focal caterpillar species so

that a parasitism likelihood score for each host plant could be created, simply calculated as the number of parasitoids observed divided by the number of caterpillars observed (both specific to a given host plant) multiplied by 100 to

give the percentage of samples that were parasitized. There were not enough

caterpillar collection records from June and July to construct a comprehensive

set of host plant specific parasitoid pressure scores, so these months were omitted from the analysis; only 7 of my host plants were sampled by Natural

Resources in the June sampling window and 11 in the July sampling window (see

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Table 2 for a complete list). Finally, quadrat-specific parasitoid pressure values were calculated as the average parasitoid pressure of all trees present in each given quadrat.

Analysis

Quadrat-scale foliar qualities were used in a regression tree analysis

(De’Ath and Fabricus 2000) to describe caterpillar richness and abundance across quadrats in June, July and August. Regression trees are good instruments to use when the dependent variable may not be linearly related to one or more independent variables homogenously throughout its numeric range. For example, when caterpillar richness is low it may have a strong relationship with nitrogen content and water content, but when it is high it may be limited by toughness and lignin. One of the advantages of regression trees is that they require no foreknowledge of the nature of the relationships between dependent and independent variables prior to analysis. In my case, a regression tree analysis is an appropriate choice given that the relationship between caterpillar richness and foliar nutrients is likely complex and non-linear (Futuyama et al 1984, Karban and Ricklefs 1984). Prior to regression tree computation, caterpillar abundance was log10 transformed to reduce the impact that outlier data points had on dataset variance. A second set of regression trees were calculated for August, including August parasitoid pressure values at each quadrat. Regression trees were pruned to omit nodes that had a complexity parameter of less than 0.05.

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Methodological Assumptions

There are two assumptions inherent to the methodology of calculating

parasitoid pressure that should be taken into consideration before examining the

regression tree results. First, I made the assumption that parasitoid host plant pressures in 2009 are similar to those observed between 1937 and 1949. In some areas, forests may have undergone significant compositional changes in the past six decades that may have caused certain parasitoids to adjust their preferred host plant targets for finding caterpillar hosts. For example, the caterpillar species Archips cerasivorana was often found on Prunus (cherry) host plants. If

cherry trees have been significantly reduced in forest fragments over the past 60

years Archips may have adjusted its feeding habits to incorporate a much larger proportion of other host plant species. It is not known, were this the scenario, whether parasitoids that typically parasitize Archips and other Prunus feeders would be associated with Prunus in the same way they were from 1937 to 1949 or if they would become more closely associated with other host plant species.

Even though most of the caterpillar species I analyze in this study are widely polyphagous, changes to caterpillar feeding preferences over the past 60 years could bring the parasitoid-association data into question because it is derived from parasitized caterpillar capture records.

Second, the caterpillar assemblage I draw parasitoid data from consists of only seven species. I am assuming that the parasitoid-host plant associations

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observed amongst these seven species are representative of the parasitoid-host

plant associations present throughout the entire caterpillar assemblage. The

accuracy of both these assumptions is not immediately testable, but is an

important consideration. Extensive searches failed to find additional data with

which to test the questions presented in this chapter. Collecting parasitoid data

myself was not feasible given the timeframe and funding available for this

project. As such, the choices that remained were to conduct the analyses as

described with the understanding that any results may require further testing

before definitive conclusions are drawn, or to leave parasitoids out of the

analysis. I chose the former.

Results

In the June sampling period, surveys yielded 845 caterpillars from 26

macrolepidoptera species and 18 microlepidoptera morphospecies. In the July

sampling period, surveys yielded 489 caterpillars from 25 macrolepidoptera

species and 25 microlepidoptera morphospecies. In the August sampling period,

surveys yielded 562 caterpillars from 32 macrolepidoptera species and 35 microlepidoptera morphospecies. May and June shared 22 of a combined 72

species; July and August shared 26 of 91 combined species; June and August

shared 16 of 95 combined species.

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Bottom-Up Foliar Quality

Quadrat water content, nitrogen content and phosphorus content is highest in

June (Figure 2a, b, c). Both water content and nitrogen content are lowest in

August; phosphorus content is lowest in July. Quadrat toughness, fiber content

and lignin content are lowest in June (Figure 2d, e, f). Both lignin and fiber content peak in July while toughness peaks in August. Average quadrat polyphenol content is lowest in August and highest in July (Figure 2g).

Top-Down Pressure

Parasitoid pressure is highest in Acer saccharum (13.6%), Ulmus americanum

(13.3%) and Tsuga canadensis (11.9%). It is lowest in Acer pensylvanicum (0%),

Cratagus spp (0%) and Fagus grandifolia (0%). These three host plant species also had a very low sample size (of caterpillars) within the Natural Resources

Canada data (Table 3), suggesting that the results may be unreliable. Further testing shows no evidence a significant correlation between the number of caterpillars collected and parasitoid pressure across host plants (i.e. correlating

2 column 2 and column 4 in Table 3: p = 0.19, R = 0.09, F1,19 = 1.85).

Seasonality of Bottom-Up Effects

Regression tree analyses of the June sampling period showed that foliar quality explained 0.41 of the variance in caterpillar species richness and 0.44 of the

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variance in caterpillar abundance among quadrats (Figure 3a, b). The most

important foliar quality in both regression trees was polyphenol content, which

explained 0.33 of the variance in caterpillar richness and 0.44 of the variance in

caterpillar abundance. Quadrats where the polyphenol content was greater than

10.9% (left hand fork of the regression tree 3a, b) had significantly fewer species

and total caterpillars compared to quadrats where polyphenol content was

lower. Phosphorus content explained 0.10 of the variance in the caterpillar

richness regression tree (positive relationship, regression tree 3a).

In the July sampling period, regression tree analyses showed that foliar

quality explained a 0.42 of the variance in caterpillar species richness and 0.44 of

the variance in caterpillar abundance among quadrats (Figure 3c, d). In both

cases, phosphorus content was the most important foliar quality; sites with high phosphorus content (greater than 0.19%) had lower caterpillar richness and abundance than sites where phosphorus content was lower. The similarity between these two regression trees can be attributed to a strong correlation between caterpillar richness and abundance among quadrats in July (R = 0.82,

F1,50 = 2.27, p< 0.0001). Lignin content was a significant variable in both trees, positively linked to caterpillar richness and abundance, but explaining only 0.06 and 0.10 of the variance respectively. Polyphenol content explained an additional 0.06 of the variance in the caterpillar richness tree (Figure 3c).

In the August sampling period, regression tree analyses showed that foliar quality explained 0.30 of the variance in caterpillar species richness and

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0.20 of the variance in caterpillar abundance among quadrats (Figure 3c, d). The most important foliar quality in the caterpillar richness tree was fiber content.

Quadrats where fiber content was less than 28.5% (left hand fork of regression tree 3e) contained fewer caterpillar species than quadrats where fiber content was higher. For caterpillar abundance, quadrats that had high polyphenol content (greater than 10.3%) had significantly fewer species than quadrats where polyphenol content was lower. Polyphenol was additionally important in the caterpillar richness regression tree and provided a second node in the caterpillar abundance regression tree. Lignin content explained a small amount of variance in the caterpillar richness regression tree.

Tri-Trophic Relationship

When parasitoid pressure was added to the August analyses, there were notable changes to both regression trees. Quadrats where parasitoid pressure was greater than 6 % had significantly lower caterpillar richness and abundance than quadrats where parasitoid pressure was lower. In the caterpillar richness regression tree parasitoid pressure explained 0.28 of caterpillar variance (Figure

4a), superseding all of the foliar quality variables that were significant in Figure

3e. In the caterpillar abundance tree parasitoid pressure superseded polyphenol content for the primary regression tree node explaining 0.18 of the caterpillar variance (Figure 4b). Water content and phosphorus content explained smaller proportions of caterpillar abundance (0.10 and 0.06 respectively).

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Discussion

There appears to be a transition in the nature of bottom-up effects over the

course of the growing season. Early in the season, low polyphenol content is the most important variable in regression tree models. In the middle of the summer, foliar phosphorus contentbecomes the most important variable, with low levels corresponding to high levels of caterpillar richness and abundance. By late

summer, polyphenol and fiber content are both important, but when a tri-

trophic relationship is considered top-down parasitoid pressure supersedes both

foliar variables to explain a higher proportion of the variance in caterpillar

richness and abundance. There was no evidence to support the hypothesis that polyphenols are being selected for at the assemblage level, which would be

expected if toxin sequestration for defense was a primary driver of

caterpillarrichness or abundance among quadrats. Instead, the opposite was prevalent (a negative relationship between assemblage richness/abundance and polyphenol content). This set of results should however be interpreted with caution and as a preliminary finding rather than as a definitive test of these relationships across seasons.

Bottom-Up: Foliar Quality

Traditionally, nitrogen and water content have been identified as the more important foliar drivers of caterpillar distribution and performance; the negative effect of phosphorus I identified has received little attention. The insignificance

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of nitrogen and water content in most intra-seasonal models could suggest that nitrogen and water content are not limiting foliar traits for caterpillars in northeastern temperate forests. These variables are often associated with feeding preferences in feeding trials, but the cost associated with redispersal in search of more nitrogen-rich and water rich foliage may outweigh the benefits.

This non-effect of nitrogen and water content was also reported by Karban and

Ricklefs (1984) who examined Lepidoptera assemblages in a similar forest type using the same foliar nutrient data. They concluded that top-down forces (i.e. parasitoids) or historical factors (i.e. that limit geographical distribution) might instead be responsible for Lepidoptera distribution amongst trees. My analysis agrees with this suggestion for the month of August (i.e. late in the growing season) but identified polyphenol content and phosphorus content as the driving nutrient foliar properties for June and July regression trees.

The negative relationship between foliar phosphorus and caterpillars has a few possible explanations. The expectation was for a positive relationship, reflecting increased growth and shorter instar durations observed in some species (Apple et al. 2009, Perkins et al. 2004). One possibility is that foliar phosphorous is linked to other foliar qualities and the negative link described in the regression trees is merely coincidental. To test this, I ran a multiple regression analysis of quadrat foliar qualities (in July) where phosphorus content is a function of lignin content, fiber content and toughness. The results of this regression identify lignin content and toughness as significant positive predictors

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2 of phosphorus content (p < 0.005, adj R = 0.24, F3,48 = 6.22) while fiber content

is a negative predictor. Both toughness and lignin content make leaves less

palatable to caterpillars (Choong 1996, Scriber 1977). Therefore, it is possible that quadrat-selection by Lepidoptera is driven by preference for low toughness and low lignin content and the negative relationship between caterpillars and phosphorus is merely a byproduct. This is exemplified by the foliar qualities of three very common host plants: striped maple (Acer pensylvanicum) hophornbeam (Ostrya virginiana) and white ash (Fraxinus americana). Striped maple and hophornbeam are highly preferred host plants for caterpillars whereas white ash is often avoided (White - Chapter 3); these three host plant species make up nearly 30% of the host plants I sampled. According to Ricklefs and Matthew (1982) the former two species have low toughness (453 g and 539 g respectively) and low phosphorus content (0.12 % and 0.16 % respectively) and the latter species has high toughness (672 g) and high phosphorus content (0.25

%) (all are July values). A similar pattern is evident with lignin content.

Another possible explanation of the observed negative relationship between phosphorus and caterpillars is that the impact of dietary phosphorus is being mediated by another dietary element. Clancy and King (1993) suggest that this may be the case with the spruce budworm and identify dietary magnesium as the likely mediating element. In their research they showed that there was a negative relationship between dietary phosphorus intake and pupal mass and survival among caterpillars at high levels of dietary magnesium intake. At my

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study quadrats the ratio of P to Mg in June is 1.64; in July it is 0.81 reflecting a

38% increase in magnesium and a 32% decrease in phosphorus from June to July

(data not shown). It is not known whether or not (or to what extent) Clancy and

King’s (1993) research on a conifer-feeding caterpillar species would be relevant

to a deciduous-feeding caterpillar assemblage, but the dramatic change in

relative foliar magnesium and potassium I observe among quadrats is consistent

with a magnesium-mediating-phosphorus scenario.

Bottom-Up: Toxins

The negative relationship between the caterpillar assemblage and foliar polyphenol content is an example of where processes applicable to single- species are not scalable to entire assemblages. Foliar polyphenol content levels were derived from data published by Ricklefs and Matthew (1982),who state that the method they used to measure polyphenol content in leaves was “not specific for polyphenols but may also measure flavonoids and other easily oxidized compounds…”Thus, high polyphenol content levels in any two given host plant species may be representative of very different toxic compounds that may be tolerated (or sequestered) by different caterpillar species. While many examples of species-specific toxin sequestration exist (e.g. Barbehenn et al.

2005, Nishida 2002, Marsh and Rothschild 1974, Peterson et al. 1987, Singer

2004),the relationship between an entire insect herbivore assemblage has not yet formally been explored. Most of the positive relationships between

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caterpillars and toxins are very species specific and unrelated Lepidoptera rarely tolerant to the same toxin (Nishida 2002). These types of species- and genera- specifictoxic compound tolerances may explainthe nature of the negative relationship between caterpillars and quadrat-scale polyphenols; caterpillar- toxin relationships do not scale up to entire assemblages. An increase in quadrat- scale “polyphenols” would thus only truly be beneficial for caterpillars if the group of compounds in question could be sequestered by most of the caterpillars in the assemblage, which is not often the case (Pasteels et al. 1983).

Top-Down: Parasitoids

My analyses suggest that late-season top-down control of caterpillar assemblages may be occurring. It was not possible to examine parasitoid data in a seasonal context but the August analyses (Figure 4a, b) showed that top-down parasitoid pressure was negatively associated with caterpillar richnessand abundance late in the growing season. A negative impact of parasitoids or predatorson caterpillars is well established,(Gripenberg and Roslin 2007, Hunter et al. 1997, Preszler and Boecklen 1996, Scheirs and Bruyn 2002) but it has rarely been examined in relation to changes in bottom-up variables throughout a growing season. This raises questions about my June and July analyses– would the regression trees in Figure 3a-d look different if parasitoid data were taken into consideration? The reason that August parasitoids were the only ones included in my analyses was because of the limited parasitoid data within June

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and July caterpillar records. I decided to try to address this question further with a simple post-hoc analysis examining an additional parasitoid dataset provided by Natural Resources Canada that detailed parasitoid records (without host plant associations) from the same period (1937 to 1949). Out of 487 emergence records in this dataset from mid-May to mid-August pooled across 13 parasitoid species, 293 were from the third sampling period (mid-July to mid-August - this includes a sampling window buffer as described in the methods), 173 were from the second sampling period (mid-June to mid-July) and only 21 were from the first sampling period (mid-May to mid-June) (Table 4). Based on the scarcity of parasitoids documented early in the growing season it seems unlikely that parasitoids would play a large role driving caterpillar assemblage richness or abundance in early June. The large increase in parasitoid records from June into

July may be synonymous with an increasing significance of their impact on caterpillar assemblages. Gratton and Denno (2003) showed this type of trend with Prokelisia planthoppers that were distributed with respect to quadrat quality early in the season but with respect to predator (spider) densities late in the season. On an assemblage level, Morais et al (1999) documented a strong seasonal trend in tropical caterpillar abundance and postulated that the significant decline they observed towards the end of the season was likely due to an increase in predator and parasitoid activity. Both of these examples provide interesting parallels to my results and show that there is an empirical basis for an increased importance of late-season top-down effects.

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Conclusion

An assemblage-based approach has become more popular in studies of forest

Lepidoptera and has been applied to landscape fragmentation questions

(Ricketts et al. 2001, Summerville and Crist 2003), human-disturbance problems

(White et al. 2011) and conservation problems (White - Chapter 3). In this study,

I documented a change in the nature of the bottom-up relationship between

host plant foliar quality and an herbivorous caterpillar assemblage over the

course of a growing season. My results also suggested that late-season top-down

effects might be greater than early-season top-down effects, at least in the case

of caterpillar parasitoids. These results should be treated as preliminary, and a

more direct measurement of bottom-up and top-down variables needs to be

conducted before definitive conclusions can be made. That said, there is good

reason to believe that many of the findings reported in this study are accurate.

First, early season foliage tends to be more nutritious than late season foliage

(Feeny 1970, Ricklefs and Matthew 1980). Host plant rejection and reselection

(by larval ballooning or crawling)is more likely to happen when the likelihood of

finding a high quality host plant is great (Mayhew 1997). Late in the season when

average host quality is lower, caterpillars would be more likely to settle for a

poor quality host because the chances of finding a better host are small. These

observations support a hypothesis that bottom-up control is the predominant

driver in early season tri-trophic relationships. Second, in my study system

parasitoid abundance is very low in June and very high in August. While this in

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itself is not evidence for the primacy of top-down effects in August, the scarcity of parasitoids in June supports a non-top-down (parasitoid) control scenario early in the growing season. While other top-down enemies may factor into the tri-trophic relationship, demand for prey can be considerably higher after predator young become independent, later in the growing season (Adams et al.

1991, Drent and Daan 1980). At the most basic level this study has shown that the nature of top-down and bottom-effects are probably variable across the length of a growing season and that tri-trophic analyses should take this intra- seasonal variance into account.

Acknowledgements

I am very grateful to C. Buddle, P. Peres-Neto, K. Summerville, R. Feldman, J.

Messier, S. Estrada B. McGill and M.J. Lechowicz for comments an early draft;B.

McGill was also instrumental in helping to revise and refine the final draft. The vegetation and Lepidoptera field surveys were conducted with the help of R.

MacKenzie and M. VonButtlar. The parasitoid data was assembled with the help of Isabelle Ochoa at the Canada Forest Service’s Great Lakes Forestry Centre. I would also like to thank A. Mochon (SEPAQ), D. Rodrigue (SEPAQ) and J. Lapalme

(Les Amis de la Montagne) who were consultants for field work planning at Parc de la Yamaska, Parc Mont St Bruno and Parc Mont Royal (respectively). This research was funded by NSERC.

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Mont Royal Mont St. Bruno Mont St. Hilaire Mont Shefford

City of

Montreal

CANADA

U.S.A

Figure 1Caterpillars were collected from four sites in the St. Lawrence River valley of southern Quebec, Canada (Figure adapted from Atlas of Canada 2010) at the northern edge of the deciduous forest biome in eastern North America. The matrix surrounding each site isdominated by agricultural lands and urban development with the exception of Mont Royal, which is a forest fragment in an exclusively urban setting. Forest patches across the region are shown in dark gray, urban areas in light gray (including the City of

Montreal at the left side of the pane).

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a b c d e f g

%Water %Nitrogen %Phosphorus Toughness (g) %Fiber %Lignin %Polyphenol

Figure 2Average foliar qualities at quadrats in the months of June (Je), July (Jy) and August (Au) for (a) % water content , (b) %

nitrogen content, (c) % phosphorus content, (d) toughness (grams), (e) % fiber content, (f) % lignin content and (g) % polyphenol

content. Bars represent standard error. ANOVAs between months are significant for all foliar qualities at p < 0.001. Average foliar

qualities for each quadrat were weighted based on the proportion of the total basal area occupied by each sampled host plant

species in the quadrat. Host plant-specific foliar qualities were taken from Ricklefs and Matthew (1982).

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Figure 3Regression tree analyses of the determinants of caterpillar richness

(left hand panels) and abundance (right panels) at the quadrat level.

Analyses are shown for June (a, b), July (c, d) and August (e, f). The variables shown are % Phosphorus content (P), % Polyphenol content (Phenol),%

Fiber content (Fiber) and % Lignin content (Lig). Water content, Nitrogen content and Toughness were included, but were not significant. The clause presented at each node is the condition corresponding to the left hand fork

(the right hand fork would be the opposite condition). Each clause is paired with an R2 value associated with that node in brackets; this value is equal to the complexity parameter of the node. At each terminus the average caterpillar richness or abundance is given (depending on the tree) along with the number of quadrats that satisfy the conditions of the fork (in brackets). Trees are pruned and show splits corresponding to R2> 0.05.

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Caterpillar Richness Caterpillar Abundance (log10) a b Phenol > 10.9 % (0.31) Phenol > 10.9 % (0.44) | |

June P < 0.24% (0.10) 2.1(9)

4.6 (10) 7.0 (33) 0.54 (9) 1.13 (43)

P > 0.19 % (0.30) P > 0.19 % (0.34) | | c d

July

Lig < 10.5 % (0.10) Lig < 10.5 % (0.06) Phenol > 8.2 % (0.06) 0.83 (31)

0.4 (10) 2.4 (11) 3.8(22) 5.7 (9) 0.12 (10) 0.54 (11)

Fiber < 28.4 % (0.14) Phenol > 10.3 % (0.13) | | e f

Phenol > 9.8 % (0.08) 5.7 (22) August Phenol < 6.2 % (0.07) Lig > 9.2 % (0.08) 2.5 (10) 0.54 (10)

0.92 (36) 2.7 (9) 5.5 (11) 0.69 (19)

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Figure 4Regression tree analyses for the determinants of caterpillar richness (a)

and abundance (b) for the month of August. These trees were created using the

same data as for Figure 3e and 3f (respectively) but with the addition of

parasitoid pressure data for each quadrat. In (a) parasitoid pressure supersedes

% fiber content as the most important determinant of caterpillar richness. In (b)

parasitoid pressure is less important and supersedes % polyphenol content as

the primary determinant of caterpillar abundance.

a b

Parasitoids > 6 % (0.28) Parasitoids > 6 % (0.18) | |

H2O > 51 % (0.10) 1.0 (19)

P < 0.18 % (0.06) 0.93 (1259) 3.5 (35) 6.6 (17) 0.50 (9) 0.74 (15)

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Table 1Foliar nutrient properties in broadleaf trees in southern Quebec and

Ontario in early June, early July and early August. Foliar nutrient data are averaged across 34 different common deciduous tree species. This table is adapted from Ricklefs and Matthew’s (1982), Table 3.

Relationship with Foliar Property June July August Caterpillars Water % 62.8 57.3 54.9 + Nitrogen % 2.33 2.08 1.89 + Phosphorus % 0.23 0.12 0.20 + Polyphenols (%) 5.53 5.68 6.43 +/- Lignin % 10.73 10.48 10.24 - Crude fiber % 26.52 26.24 25.21 - Toughness (g) 484 598 621 -

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Table 2A list of trees sampled (where n > 10) across the 72 study sites, and their associated caterpillar richness and abundance.

Total Total # of Trees Host Plant Tree Species Caterpillar Caterpillar Surveyed Richness Abundance Acer saccharum 720 75 673 Fagus grandifolia 229 46 317 Fraxinus americana 228 38 95 Acer pensylvanicum 206 52 221 Ostrya virginiana 145 39 239 Tilia americana 66 16 24 Prunus virginiana 55 10 26 Rhamnus cathartica 48 7 11 Acer platanoides 39 6 11 Amelanchier arborea 35 21 58 Ulmus rubra 35 11 19 Quercus rubra 34 6 11 Tsuga canadensis 33 10 25 Carpinus caroliniana 26 10 21 Acer spicatum 20 16 32 Prunus serotina 19 17 38 Cratageus spp 19 11 15 Betula papyrifera 18 5 5 Carya cordiformis 15 9 9 Rhus typhina 15 1 4 Cornus alternifolia 13 3 3 Acer rubrum 12 0 0 Malus pumila 11 4 4 Prunus nigra 11 2 3 Betula alleghaniensis 10 11 17

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Table 3 Parasitoid pressure on host plants in August.

Host Plant # of Caterpillars Collected # of Parasitoids Parasitoid Pressure Acer saccharum 127 20 13.6 Ulmus americana 919 141 13.3 Tsuga canadensis 590 80 11.9 Carya cordiformis 225 22 8.9 Prunus virginiana 1333 106 7.4 Fraxinus americana 339 20 5.6 Salix nigra 644 35 5.2 Quercus rubra 200 9 4.3 Prunus serotina 673 27 3.9 Malus pumila 240 8 3.2 Acer negundo 271 9 3.2 Alnus incana 1137 34 2.9 Acer rubra 96 2 2.0 Ostrya virginiana 68 1 1.4 Betula alleghaniensis 81 1 1.2 Betula papyrifera 1223 15 1.2 Amelanchier arborea 335 4 1.2 Tilia americana 199 1 0.5 Acer pensylvanicum 22 0 0 Cratagus spp. 38 0 0 Fagus grandifolia 21 0 0

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Table 4Emergence records for parasitoids in Ontario and Quebec documented by

Natural Resources Canada, 1937-1949.

mid-May to mid-June to mid-July to Parasitoid mid-June mid-Jul mid-August Apanteleshyphantriae 0 1 11 Apantelespolychrosidis 16 21 9 Apecthisontario 0 41 63 Campoplexvalidus 0 1 0 Casinariaeupitheciae 0 0 3 Casinariasemiothisae 0 1 2 Hyposoterfugitivus 0 18 22 Itoplectisconquisitor 2 77 168 Meteorusbakeri 0 0 3 Meteorushyphantriae 1 7 9 Phobocampegeometrae 0 3 0 Pimplapedalis 2 3 3 Total 21 173 293

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General Conclusion

One of the central aims of conservation biology is to better understand the

forces that drive species richness, particularly across fragmented and disturbed

landscapes (Andrén 1997, Fahrig 2003). Forest habitat represents one of the

most modifiedhabitat types in North America (Ricketts and Imhoff 2003) and in

Canada there is no ecozone more imperiled than the Mixedwood Plains in

southern Ontario and Quebec (Gibbs et al. 2009). Because such a small

proportion of the remnant forest fragments are protected,(Ministère des

Ressources Naturelles 2002) there is little preventing the situation from getting

worse in the coming decades. Understanding how biotic and abiotic forces affect

species richness is important for the current and future conservation of species across this landscape. In this thesis I have focused on four facets of abiotic and

biotic forces: (i) the impacts of natural disturbances, (ii) the impacts of human

disturbances (iii) the effects of bottom-up forces, and (iv) the effects of top-

down forces.

Natural ice storm disturbances can have both positive and negative

impacts on the richness in forest communities. There have been remote-sensing

based tools used similar to the one I developed in Chapter 1 (e.g. King et al.

2005, Millward and Kraft 2004), but they have seldom been transformed into

meaningful indices of habitat change relevant to different assemblages of coarse

woody debris dependent guilds. While these previously-developed tools are

useful on many levels, they are insufficient for making species-specific

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predictions about ice storm impacts. Biomonitoring of rare or threatened species

requires special knowledge about the geographical range of suitable habitat

within a reserve. The ability to map the influx of specific types of coarse woody debris accomplishes this for the guilds in question. In addition, I have provided an important empirical example showing that NDVI outperforms other popular vegetation indices for making geospatial predictions of coarse woody debris influx following a major canopy-damaging disturbance. Beyond Mont St. Hilaire, there is now an empirical basis to use pre- and post-storm NDVI imagery to create a base map of damage in other protected forested areas using freely available archived Landsat 5 imagery (USGS 2007).

There has been much research on the impacts of habitat destruction and fragmentation on forest communities (Fahrig 2003) while the impact of intra- forest disturbances like trails are often overlooked. Biodiversity and abundance gains associated with trails are often due to the mixing of forest-dwelling and open-habitat species. This can result in an apparent positive impact of trailside

habitat on overall biodiversity (somewhat analogous to edge-habitat) even

though biodiversity may be declining amongst forest-dwelling species. Strong

research documenting a negative relationship between trails and forest

assemblages (Chapter 2) has been sorely needed to help make empirically-

supported management decisions to limit trails in forest reserves. Many forest

reserve areas have a joint mandate to protect natural habitat while allowing

access (through trails) for the enjoyment of the general public. In light of the

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relationships I uncovered in Chapter 2, these two goals seem to be in direct

conflict. One solution to this conflict is to manage trails in such a way so as to

minimize impacts on trailside habitat. Recommendations to accomplish this goal

are (i) construct boardwalks or physical barriers (i.e. rock walls or fences) to

discourage pedestrians from walking off-trail (Doucette and Kimball 1990, Zhou

and Tachibana 2004), (ii) limit the width of trails thereby increasing cross-trail

canopy cover which will keep wind and temperature conditions in trailside

habitat similar to that of forest-interior habitat, (iii) limit the prevalence of trails

to reduce overall impact. In reserves that have some control over the amount

public access (e.g. Mont St. Hilaire, or Mont St. Bruno and Parc de la Yamaska),

trail traffic could be reduced by setting maximums for daily visitors.

The bottom-up effect of host plants on insect herbivores has been a

popular research area for pest-species capable of outbreak conditions

(e.g.Maufette et al. 1983, Stuart and Polavarapu 1998) but has not been well- examined across an entire species assemblage. For an assemblage, this question can be broken into two parts: (i) how are host plants related to insect herbivore assemblages (Chapter 3), and (ii) what is the mechanism behind host selection

(Chapter 4). My analysis of caterpillars in the Monteregian showed that insect herbivore assemblages are more tightly linked to host plant identity and abundance; host plant richness was a poor predictor of caterpillars.

Furthermore, caterpillars largely avoided non-native plants. The implications of

these results are most applicable to Mont Royal, which has a high abundance of

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non-native host plants (Acer platanoides and Rhamnus cathartica) and a low abundance of highly preferred host plants (A. pensylvanicum and O. virginiana).

Although the eradication of A. platanoides would be impractical because they are often very large trees, it could be feasible to remove the vast majority of R. cathartica, replacing it with either A. pensylvanicum or O. virginiana.

The mechanism behind these host plant preferences may be based on bottom-up foliar quality early in the season (June), but on top-down parasitoid pressure late in the season (August). These two mechanisms (bottom-up foliar nutrient pressure versus top-down parasitoid pressure) have been juxtaposed for a long time in ecology (Lill 2001). Mine is one of the first studies to draw links between these pressures and entire assemblages. The implication of this result is both research-oriented and conservation oriented. It underscores the need to examine tri-trophic relationships within assemblages in a seasonal context, largely because of temporal changes in foliar quality and parasitoidpressure. It also suggests that if there are significant declines in insect herbivore richness, late season parasitoidpressure may be a good factor to investigate as causal.

Overall, the research presented in this thesis converges to single theme; conservation of forest fragment biodiversity in Mixedwood Plains forest fragments can be best achieved through the monitoring and understanding of both abiotic and biotic forces. Two very important management recommendations come from this. The first is to be aware of and manage human-presence in conservation areas. The second is to be aware of and

235

manage habitat quality (when necessary) through control of host plant species

(planting or eradication) and coarse woody debris (protecting rare or important coarse woody debris habitat).

236

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238

Appendix A1We surveyed 36 macrolepidopteran moth species across the four sites in our study region Mount Royal (R), Mont St. Bruno (B), Mont St. Hilaire

(H) and Mont Shefford (S). All species IDs were based on 5th or 6th instar larvae identified using Wagner (2005).

239

Distribution Number of Total # of Family Genus and species Among Sites Host Plants* Individuals Geometridae Lambdina fervidaria BHS 26 70 Lymantriidae Lymantria dispar** RBH 45 59 Geometridae Melanolophia canadaria RBHS 42 52 Noctuidae Lithophane antennata BHS 11 52 Geometridae Itame pustularia RBHS 9 44 Noctuidae Morrisonia latex RBHS 12 20 Geometridae Cyclophora pendulinaria RBHS 14 15 Geometridae Operophtera bruceata RBHS 24 14 Notodontidae Symmerista leucitys H 9 11 Geometridae Plagodis alcoolaria BHS 19 7 Geometridae Hypagyrtis unipunctata RBH 22 6 Lymantriidae Orgyia definite BHS 11 6 Noctuidae Crocigrapha normani BHS 32 6 Noctuidae Lithophane baileyi HS 11 6 Noctuidae Zanclognatha cruralis BS 3 6 Noctuidae Lithophane patefacta BHS 12 5 Noctuidae Morrisonia confuse BH 30 5 Arctiidae Halysidota tessellaris RH 40 4 Noctuidae Zale minerea H 19 4 Notodontidae Clostera spp S 7 4 Geometridae Alsophila pometaria RB 35 3 Geometridae Phigalia titea RB 36 3 Geometridae Ennomos subsignaria BS 29 2 Geometridae Tetracis cachexiata S 37 2 Noctuidae Egira alternans BS 5 2 Noctuidae Elaphria versicolor BS 13 2 Geometridae Lomographa vestaliata B 19 1 Geometridae Pero ancetaria B 4 1 Lasiocampidae Malacosoma disstria B 30 1 Lymantriidae Orgyia leucostigma S 90 1 Noctuidae Acronicta morula B 12 1 Noctuidae Bomolocha baltimoralis H 6 1 Noctuidae Eupsilia vinulenta S 18 1 Noctuidae Orthosia hibisci R 43 1 Nolidae Baileya opthalmica H 7 1 Notodontidae Heterocampa guttivitta Y 38 1 * This is based on Handfield’s account (1999). He notes that some of these species have many more recorded host plants than he has documented. This is non-problematic for our study as all of the tree- species at our study quadrats are counted. ** Non-native species

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Appendix A2 Micromoth distribution.

Species ID Distribution Total # of Among Sites Individuals A302 RBHY 18 A309 R 1 A313 R 1 A315 H 2 A401 B 1 A402 BH 3 A405 B 1 A406 RBHY 46 A411 B 1 A501 HY 4 A502 H 1 A503 HY 5 A601 Y 1 A604 Y 1 A605 Y 1 A606 Y 1 A607 Y 1 J1001 B 2 J1004 RBHY 37 J1005 B 1 J1007 BHY 11 J1008 RBHY 9 J1101 BHY 12 J1103 BY 4 J1106 Y 1 J800 RBHY 29 J804b B 1 J805b BHY 11 J811 BR 2 J901 Y 1 J903 B 6 J905 BY 2 Y1401 Y 1 Y1405 Y 2 Y1406 Y 1 Y702 BH 3 Y703 RBH 24 Y707 R 1 Y708 BH 3

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Appendix A3 Complete list of host plant species documented in vegetation surveys.

Host Plant Species Abies balsamea (L.) Mill. Acer negundo L. Acer nigrum Michx. f. Acer pensylvanicum L. Acer platanoides L. Acer rubrum L. Acer saccharinum (L.) Small Acer saccharum Marsh. Acer spicatum Lam. Alnus incana (L.) Moench ssp. rugosa (Du Roi) R.T. Clausen* Amelanchier arborea (Michx. F.) Fernald Betula alleghaniensis Britt. Betula papyrifera Marsh Carpinus caroliniana Walter Carya cordiformis (Wangenh.) K. Koch Cornus alternifolia L.f. Crataegus spp** Dirca palustris L. Fagus grandifolia Ehrh. Fraxinus americana L. Malus pumila Mill. Ostrya virginiana(Mill.) K. Koch Picea glauca (Moench) Voss Pinus resinosa Aiton Pinus strobus L. Populus deltoides Bartram ex Marsh. Populus grandidentata Michx. Populus tremuloides Michx. Prunus nigra Aiton Prunus serotina Ehrh. Prunus virginiana L. Quercus rubra L. Rhamnus cathartica L. Rhus typhina L. Robinia pseudoacacia L. Salix nigra Marsh. Sorbus aucuparia L. Tilia americana L.

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Tsuga canadensis (L.) Carrière Ulmus americana L. Viburnum lantanoides Michx. Viburnumopulus L. var. americanum Aiton †

* Referred to as Alnus rugosa in this thesis. ** Mostly composed of a mixture of Cratageus punctata Jacq. and Cratageus mollis Scheele and theirhybrids. † Referred to as Viburnum trilobum in this thesis.

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Appendix A4 Randomness was tested using randomization goodness-of-fit tests. P-values were calculated using 10,000 replicates of

randomization.

Caterpillar Species Expected Occurrence Actual Occurrence p-value <0.25 0.26-0.50 0.51-0.75 0.76-1.0 >1.0 <0.25 0.26-0.50 0.51-0.75 0.76-1.0 >1.0 Cyclophora pendulinaria 11.7 2.5 0.4 0.2 0.2 10 3 1 1 0 0.30 Itame pustularia 4.9 3.8 6.3 4.9 5.2 1 0 7 10 7 0.0094 Lambdina fervidaria 1.5 1.9 1.9 5.3 16.5 0 0 1 4 22 0.19 Lithophane antennata 18.3 5.4 3.3 0.8 2.1 22 7 1 0 0 0.20 Lymantria dispar 2.5 2.5 4.2 4.2 16.7 3 5 4 3 15 0.55 Melanolophia canadaria 0.0 1.8 2.2 8.0 20.0 0 3 2 10 17 0.63 Morrisonia latex 0.9 3.8 3.1 3.3 4.9 0 2 3 3 8 0.43

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Appendix B1Caterpillar collections were made from 38 host plant trees in 72 quadrats across the four study sites.

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Quadrat Total Trees Total Caterpillar Total Caterpillar Host plant Occurrence Sampled Abundance Species of Host Plant Acer saccharum 72 720 673 77 Fagus grandifolia 35 229 317 46 Fraxinus americana 38 228 95 38 Acer pensylvanicum 29 206 221 52 Ostrya virginiana 29 145 239 39 Tilia americana 21 66 24 16 Prunus virginiana 11 55 26 10 Rhamnus cathartica 9 48 11 7 Acer platanoides 5 39 11 6 Amelanchier arborea 7 35 58 21 Ulmus rubra 7 35 19 11 Quercus rubra 7 34 11 6 Tsuga canadensis 6 33 25 10 Carpinus caroliniana 4 26 21 10 Acer spicatum 6 20 32 16 Cratageus spp 7 19 15 11 Prunus serotina 8 19 38 17 Betula papyrifera 8 18 5 5 Carya cordiformis 6 15 9 9 Rhus typhina 2 15 4 1 Cornus alternifolia 3 13 3 3 Acer rubrum 4 12 0 0 Malus pumila 3 11 4 4 Prunus nigra 2 11 3 2 Betula alleghaniensis 6 10 17 11 Viburnum lantanoides 3 5 1 1 Sorbus aucuparia 4 4 0 0 Alnus rugosa 1 3 2 2 Viburnum trilobum 1 3 1 1 Abies balsamea 1 2 6 4 Acer negundo 1 2 0 0 Acer saccharinum 1 2 0 0 Dirca palustris 2 2 1 1 Picea glauca 1 1 0 0 Populus deltoides 1 1 0 0 Robina pseudoacacia 1 1 3 2 Salix sp 1 1 1 1

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Appendix B2A record of the macrolepidoptera and microlepidoptera morphospecies that were surveyed. Microlepidoptera morphospecies were given unique alphanumeric designations and subsequent individuals were verified with digital images. All macrolepidoptera were identified using Wagner

(2005) and Handfield (1999).

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Total Microlepidoptera Total Family Genus Species Abundance Morphospecies Abundance Arctiidae Halysidota tessellaris 5 A302 41 Arctiidae Lophocampa caryae 7 A307 1 Drepanidae Euthyatira pudens 1 A308 2 Drepanidae Pseudothyatira cymatophoroides 7 A309 1 Geometridae Alsophila pometaria 6 A312 1 Geometridae Campaea perlata 5 A313 1 Geometridae Cingilia catenaria 1 A315 2 Geometridae Cyclophora pendulinaria 45 A401 1 Geometridae Ectropis crepuscularia 1 A402 6 Geometridae Ennomos subsignaria 8 A404 7 Geometridae Eufidonia notataria 7 A405 1 Geometridae Eutrapela clemataria 3 A406 52 Geometridae Hypagyrtis unipunctata 15 A410 4 Geometridae Itame pustularia 71 A411 2 Geometridae Lambdina fervidaria 183 A501 5 Geometridae Lomographa vestaliata 21 A502 1 Geometridae Melanolophia canadaria 112 A503 9 Geometridae Operophtera bruceata 65 A504 3 Geometridae Paleacrita vernata 7 A505 3 Geometridae Pero ancetaria 3 A601 1 Geometridae Phigalia strigataria 1 A602 1 Geometridae Phigalia titea 9 A604 6 Geometridae Plagodis alcoolaria 10 A605 1 Geometridae Prochoerodes lineola 1 A606 3 Geometridae Tetracis cachexiata 2 A607 2 Geometridae Xanthotype spp 2 J1001 6 Lasiocampidae Malacosoma disstria 6 J1004 60 Lymantriidae Dasychira plagiata 1 J1005 1 Lymantriidae Lymantria dispar 206 J1007 25 Lymantriidae Orgyia definita 15 J1008 29 Lymantriidae Orgyia leucostigma 2 J1101 19 Noctuidae Achatia distincta 3 J1103 11 Noctuidae Acronicta morula 6 J1104 1 Noctuidae Amphipyra pyramidoides 2 J1106 1 Noctuidae Bomolocha baltimoralis 13 J1501 2 Noctuidae Catocala ultronia 2 J1502 1 Noctuidae Crocigrapha normani 26 J703 9 Noctuidae Egira alternans 6 J800 51 Noctuidae Elaphria versicolor 8 J804 5 Noctuidae Eupsilia vinulenta 7 J804b 4 Noctuidae Lithophane antennata 197 J805b 21 Noctuidae Lithophane patafacta 9 J811 6

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Noctuidae Lithophane viridipallens var 10 J901 3 Noctuidae Morrisonia confusa 12 J904 12 Noctuidae Morrisonia latex 54 J905 14 Noctuidae Orthosia hibisci 11 Y1401 8 Noctuidae Orthosia rubescens 12 Y1404 1 Noctuidae Zale minera 9 Y1405 3 Noctuidae Zale unilineata 1 Y1406 1 Noctuidae Zanclognatha cruralis 20 Y1407 1 Nolidae Baileya ophthalmica 20 Y1409 6 Notodontidae Heterocampa guttivitta 5 Y702 13 Notodontidae Symmerista leucitys 44 Y703 97 Y707 10 Y708 11 Y710 2

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