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Electronic Theses, Treatises and Dissertations The Graduate School

2010 The Role of Environment and Genetics in the Demography of Introduced and Natural Populations of the Endangered Shrub Conradina Glabra Jason Bladow

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COLLEGE OF ARTS AND SCIENCES

THE ROLE OF ENVIRONMENT AND GENETICS IN THE DEMOGRAPHY OF

INTRODUCED AND NATURAL POPULATIONS OF THE ENDANGERED SHRUB

CONRADINA GLABRA

By

JASON BLADOW

A Thesis submitted to the Department of Biological Science in partial fulfillment of the requirement for the degree Master of Science

Degree Awarded: Fall Semester 2010 The members of the committee approve the thesis of Jason Bladow defended on October 18th, 2010.

______Alice Winn Professor Directing Thesis

______Nora Underwood Committee Member

______Austin Mast Committee Member

Approved:

______P. Bryant Chase, Chair, Department of Biological Science

The Graduate School has verified and approved the above-named committee members.

ii ACKNOWLEDGEMENTS

I would like to thank all of the individuals involved in the completion of my research. Thank you to Austin Mast and Nora Underwood whose comments have helped improve this document. Special thanks belongs to my advisor Alice Winn for the opportunity to expand my scientific expertise and the time she graciously spent making suggestions to improve documents and presentations. The Florida Native Plant Society supplied a grant for this research and without which this research could not have taken place. Thank you very much. Credit belongs to David Prentice of The Nature Conservancy, and Tova Spector of the Florida Department of Environmental Protection for allowing me the privilege to study Conradina glabra. I am grateful to the staff of The Nature Conservancy, Torreya State Park, and the Florida Department of Environmental Protection for their assistance with my research. I would also like to thank Doria Gordon of the Nature Conservancy for providing old data from the previous introductions of Conradina glabra as well as census data from successive years.

iii TABLE OF CONTENTS

List of Tables ...... v

List of Figures...... vi

Abstract...... viii

1. INTRODUCTION ...... 1

2. A DEMOGRAPHIC ANALYSIS OF INTRODUCED AND NATURAL POPULATIONS OF CONRADINA GLABRA ...... 2

Introduction...... 2 Method ...... 4 Results...... 12 Discussion...... 24

3. THE EFFECTS OF INBREEDING ON THE DEMOGRAPHY OF AN ENDANGERED PLANT CONRADINA GLABRA ...... 29

Introduction...... 29 Methods...... 31 Results...... 35 Discussion...... 39

4. CONCLUSION...... 43

APPENDIX A...... 44

APPENDIX B ...... 45

REFERENCES ...... 47

BIOGRAPHICAL SKETCH ...... 51

iv LIST OF TABLES

Table 1.1 The Stage Classes of C. glabra...... 6

Table 1.2 Regression equations used to calculate fecundity from plant size...... 8

Table 1.3 The number of stage classes and the breaks between them as determined by the Vandermeer-Moloney algorithm...... 12

Table 1.4 Estimates of lambda and bootstrapped 95% confidence limits for six study populations...... 14

Table 1.5 Means for percent soil moisture and the proportion of area occupied by vegetation, bare ground, and litter at each population...... 23

v LIST OF FIGURES

Figure 1.1 Life cycle of Conradina glabra...... 7

Figure 1.2 Transition matrices for three introduced and three natural populations...... 13

Figure 1.3 Aggregate matrices combining the three populations from each site...... 14

Figure 1.4 The frequency of the predicted stage class distribution, compared with the actual distribution...... 15

Figure 1.5 The stage class frequency distribution for each of six populations of C. glabra at the first census...... 16

Figure 1.6 Sensitivity of Lambda to each element of the transition matrix for each population ...... 18

Figure 1.7 The summed elasticity for each transition type for each population...... 19

Figure 1.8 Sensitivity of Lambda to each matrix element for aggregate matrices for each type of population...... 20

Figure 1.9 Elasticity values for each element of the aggregate matrices for each type of population...... 21

Figure 1.10 Life Table Response Experiment results...... 22

Figure 2.1 The average number of seeds per flower produced by each pollination treatment...... 36

Figure 2.2 The germination rate for inbred and outcrossed seeds for each population ...... 37

Figure 2.3 Matrices for naturally pollinated plants for each population and the same matrices with fecundity values recalculated to reflect the effect of inbreeding depression...... 38

Figure A.1 The LTRE matrix resulting from the analysis...... 44

Figure A.2 The difference matrix resulting from the subtraction of the natural aggregate matrix from the introduced aggregate matrix ...... 44

Figure A.3 The sensitivity matrix calculated for the mean matrix of the aggregate natural matrix and the introduced matrix...... 44

vi

Figure B.1 Sensitivity matrices for the inbred matrices and original unmodified demographic matrices...... 45

Figure B.2 Original elasticity matrix values for each transition types as well as predicted sensitivity values for the modified matrices reflecting the effects of total selfing...... 46

vii ABSTRACT

Biodiversity continues to decline as many species face extinction. One way to mitigate possible extinction is to introduce new populations of a species to locations that the species does not currently occupy. Such introductions can sometimes fail due to environmental factors or to genetic problems that may arise from small population size. Monitoring introduced populations and collecting data can identify reasons for the success or failure of an introduction and provide valuable information for future management efforts. To evaluate the success of introductions, demographic models can be constructed and analyzed to determine if populations are growing and to identify parts of the life cycle that contribute most to the population growth rate. Conradina glabra is an endangered shrub that was introduced to three sites at the Apalachicola Bluffs and Ravines Preserve in Liberty County, Florida 1991. Three introduced populations and three natural populations were censused in 2009 and 2010 to construct stage-structured demographic models to project the current growth rates of the introduced populations and to compare them to natural populations. Sensitivity and elasticity analyses were performed to determine the importance of individual rates of growth, survival, and seed production to the overall success of each population. A Life Table Response Experiment analysis was conducted to determine how much differences in each of these vital rates contributed to the observed difference in the growth rate between introduced and natural populations. All of the populations examined were projected to grow or remain stable (λ ≥ 1) indicating that the introduced and natural populations are projected to be successful. As a whole, introduced populations grew faster (λ = 1.052) than natural populations (λ = 1.004). Stasis of large plants, or the proportion of large plants that survived and remained in the same stage class from one year to the next, was the most important vital rate in maintaining the population growth rate for both introduced and natural populations. Greater growth in early life stages and greater fecundity contributed to the greater population growth rate at introduced populations, while increased regression of larger plants to smaller plants and the stasis of young individuals contributed to the lower growth rate of natural populations.

viii A second experiment looked for evidence of inbreeding depression in C. glabra. Flowers in one natural and one introduced population were hand selfed or outcrossed to estimate inbreeding depression in seed set, seed weight, and germination rate. Inbreeding depression in the number seeds produced per flower and percentage seed germination was significant. Outcrossed flowers produced 1.5 seeds on average, compared to 1.04 seeds per flower from selfed crosses. Seeds from outcrossed flowers germinated at a rate of 12% while seeds from selfed flowers germinated at a rate of 4.1%. There was no evidence of inbreeding depression in seed mass. The effects of inbreeding were incorporated into demographic models for the introduced and natural populations that assumed that all seed production was a result of selfing. This hypothetical scenario was compared to the models for unmanipulated populations to examine the potential effects of extreme inbreeding on the demography of C. glabra. When demographic models included the effects of inbreeding depression, the population growth rate became less than one, indicating that populations would decline if they become entirely selfing. At present, all populations are projected to remain stable or grow, although the timeframe over which data were collected was short relative to the lifetime of individuals, and the predicted success of populations is conditional upon the assumption that estimates of the vital rates accurately reflect long-term rates of growth, survival, and reproduction. Inbreeding depression can occur in these populations and is a threat that should be considered when introducing populations, even though at this time, C. glabra populations are growing despite the potential for inbreeding depression.

ix CHAPTER 1

INTRODUCTION

Increasing habitat destruction has reduced the population sizes of countless species (Falk 1992, Schemske et al. 1994). Small populations face a greater risk of extinction than larger populations (Falk 1992, Schemske et al. 1994). These populations are at risk for two important reasons. Small populations have a greater likelihood of perishing due to environmental stochasticity as there is a greater risk that all individuals will perish in a given year (Caswell 2001, Menges 2008). Small populations can also go extinct due to genetic factors (Schemske 1994, Menges 2008). Inbreeding depression results when closely related individuals interbreed, resulting in offspring with reduced fitness (Husband and Schemske 1996). These problems haunt the conservation of rare and endangered species whose population numbers are low. Conservation efforts are focused on the protection of existing habitat and also on the creation of new populations of endangered species (Falk 1992). An introduction takes place when a new population is established beyond the known historical range of species, usually occurring when a species is very narrowly distributed (Guerrant 2007, Menges 2008). Since introductions are created from natural populations of endangered species, introduced populations will likely be small and not very genetically diverse (Falk 1992, Schemske et al 1994, Guerrant 2007). Despite these problems conservation efforts often do not incorporate scientific research into the introduction process, or stop collecting data after only a few years. This study aims to reassess the status of Conradina glabra, an endangered mint endemic to the Florida panhandle and examine introduced populations nearly 20 years after their initial introduction. The analysis involves two parts, including the creation of a demographic model to analyze the current success of introduced populations and compare and contrast them with natural populations. The second part of the analysis examines the success of the introduced populations in light of possible inbreeding depression that could be ongoing within the population.

1

CHAPTER 2

A DEMOGRAPHIC ANALYSIS OF INTRODUCED AND NATURAL POPULATIONS OF CONRADINA GLABRA

Introduction

Biodiversity is declining (Falk 1992, Schemske et al. 1994.). Extinction threatens many species. Fragmentation of existing habitat compounds the habitat lost by human development. Between climate change and anthropogenic effects the rate of species loss is projected to continue to accelerate (Travis 2003). Conservation offers a way to preserve many species and ecosystems for generations to come. The aim of conservation is to preserve biological diversity in the face of an uncertain future. But the role conservation plays is one that is passive, at best maintaining the current conditions present within the landscape (Menges 2008). By contrast, restoration is an active process that involves returning a degraded ecosystem to a natural state. What natural state an ecosystem should be restored to is a topic of much debate, but the total restoration of an area involves restoring and integrating the species assemblage along with the functions and processes of the natural ecosystem. Successfully restoring an area is a work of many years. But restoration is not limited to large projects spanning decades, there are also restoration programs designed to focus on a single species of plant or animal. With 22% of the vascular plant species in the United States listed as a concern for conservation (Falk 1992), it becomes important to implement strategies to insure the survival and well being of these species. While reserves have been established, some species are so rare, even extirpated from the wild, that reintroduction is the only way to save them (Guerrant 1996). Reintroduction is the process of restoring a species to a location it is known to have inhabited historically (Menges 2008), but for small populations of critically endangered or narrowly distributed species, introductions are more common. An introduction involves the establishment of a species in an area where

2 it is not known to exist (Menges 2008). These restoration practices have the potential to restore both plants, such as the Mauna-Loa silversword (Argyroxiphium kauense) (Friar et al 2001), Sargent’s cherry palm (Psuedophoenix sargentii) (Maschinski 2007), and animals like the gray wolf (Canis lupus) (Mladenoff 1998) Not all introductions succeed. Often success is ephemeral, particularly for plants. The species may persist for years and disappear. For example, Malheur wire-lettuce (Stephanomeria malheurensis), an herbaceous annual, was originally reintroduced in 1987 to southeastern Oregon and plants persisted at the site for many years, despite declining numbers (Guerrant 1997). As of 2004 the population had gone extinct, though seeds were collected to try reintroductions at a later time (Guerrant 2007). Success is often evaluated with respect to the short term, and populations may not always persist even when projections are initially favorable. At least two factors can contribute to the failures of introduction. They may fail due to problems of an environmental nature, or they may fail from genetic problems stemming from small population sizes (Menges 2008). Either factor can cause a decrease in rates of survival and recruitment, resulting in extinction. The construction and analysis of a demographic model based on measures of individual survival and reproduction provides the foundational knowledge needed to guide recovery efforts. Schemske et al. (1994) proposed conducting detailed demographic studies for populations of rare or threatened species to determine the expected population growth rate as an index of population health. Demographic models can account for environmental stochasticity (Caswell 2001) and be modified to reflect expected changes in populations based on mating system or other factors (e.g., Le Corff and Horvitz 2005). These models can identify factors likely to contribute to the success or failure of an introduction, and with enough data, can inform management decisions for the future. Demography is most often used to assess the fate of populations and devise management strategies. Demographic models can forecast the growth rate of a population from measures of current individual survival and reproduction (Caswell 2001). Sensitivity and elasticity analyses offer a method for projecting how changes in these vital rates of individuals can lead to changes in the population growth rate (Caswell

3 2000). Demographic models can make use of a Life Table Response Experiment, or LTRE, to analyze how differences in the vital rates between populations contribute to differences in their respective growth rates (Caswell 2000). These analyses offer a way to assess and monitor species restoration projects in a way that offers insight from both failure and success. Some work has dealt with the long term health of introduced populations of rare species, but little work has compared the population dynamics of restored introduced populations with natural populations (Maschinski 2007, Colas 2008). Conradina glabra, an endangered shrub of the mint family (Lamiaceae), offers a chance to assess the success of an introduction nearly twenty years after it was initiated. C. glabra was originally introduced at three sites in the Apalachicola Bluffs and Ravines preserve in 1991 (Gordon 1996). An analysis of early success focused on whether prescribed burning increased seedling recruitment in introduced populations, and served to establish protected populations of C. glabra when all the other populations were on private land (Gordon 1996). I constructed and analyzed demographic models for three introduced and three natural populations of Conradina glabra to determine if the introduced populations are growing, which vital rates are most important to the success of natural and introduced populations of C. glabra, and if there are environmental factors that contribute to differences in growth rate among populations.

Methods

Study Species

Conradina glabra Shinners (Lamiaceae), commonly known as Apalachicola rosemary, is endemic to the Florida panhandle with an extremely limited geographic range. This federally endangered mint is found solely in Liberty County Florida (Gray 1965). C. glabra is a woody perennial shrub capable of growing up to 1 meter tall and 1.5 meters wide. Flowering occurs between March and May and produces up to 4 nutlets per flower. These fruits are light brown and vary from 1.5-2.5mm in length. C. glabra can reproduce asexually when branches are buried or by stoloniferous growth (Gray

4 1965). Herbivores forage minimally on C. glabra, though seed eating beetle larvae infest about 10% of flowers at the introduced populations (J. Bladow, personal observation). C. glabra was originally introduced in 1991 to the Apalachicola Bluffs and Ravines from sites on adjacent properties. Genotypes from 50 different parents were collected as cuttings, raised at Bok Tower Gardens in Lake Wales Florida and 432 plants were planted at three different sites for a total of 1296 plants (Gordon 1996). C. glabra was monitored at a basic level for a period of 11 years before monitoring ceased and the populations were considered successful (unpublished data TNC).

Study Area

This study was carried out in six populations of C. glabra in northern Liberty County, Florida. Three populations were introduced 19 years ago (Gordon 1996) into habitat approximating sandhill, currently undergoing restoration from a pine plantation at the Apalachicola Bluffs and Ravines Preserve. One population surrounds a windrow, a manmade berm resulting from logging, upon which oak (Quercus virginiana) grows abundantly. This population was most recently burned in 2009. Another introduced population persists in an open sandy meadow with young longleaf pine trees (Pinus palustris) and turkey oak (Quercus laevis) as dominant cover. This small population was most recently burned in June 2008 along with the third population. The last introduced population also surrounds a windrow, but C. glabra is more widely spread among the wiregrass (Aristida stricta) in a site with less canopy cover. Three other populations were selected for comparison from natural populations at Torreya State Park. These populations represent a variety of locations within a sand pine (Pinus clausa) plantation habitat with substantial canopy cover, including a population in the middle of the plantation, a roadside population under partial shade from Pinus clausa, and a population in the middle of an open sandy area. Natural populations have not been burned in the last 20 years.

Model Construction

5 I measured size, survival, and reproduction in the six populations and modeled them with stage structured demographic matrix models. For the introduced populations I marked all individuals at each population with unique identifiers for a total of 947 individuals. Natural populations were too large to sample exhaustively, so I randomly placed a 5 meter wide transect through the landscape containing 200 individuals at each site, for a total of 600 individuals. I measured the height and circumference of each plant at each of two censuses, separated by one year. The size of each plant was estimated from its height and circumference as the volume of a cylinder. New recruits were marked and measured at the second census. The marked individuals from each population were divided into stage classes for stage structured matrix modeling using the Vandermeer-Moloney algorithm (Table 1.1). This algorithm selects the boundaries between stage classes by simultaneously minimizing the distribution error, which results from aggregating individuals with different vital rates, and sampling error, which arises from having too few individuals in a stage class (Caswell 2001). The first stage was fixed for all populations because plants smaller than 3000 cm3 do not produce fruits. The algorithm added 2-4 stages that are unique to each population.

Table 1.1. The stage classes of C. glabra. Each name corresponds to the volume in cubic centimeters of the plant. Stage Volume (cm3) 1 Non-reproductive Juvenile 0 – 3000 2 Small Adult 3000 - 16000 3 Medium Adult 16000 - 41000 4 Large Adult 41000-591000 5 Gargantuan Adult 591000+

A life cycle graph is depicted in Figure 1 with transitions identified by their corresponding letters.

6

Figure 1.1. Life cycle of Conradina glabra. Each letter and number corresponds to the matrix transition the lines represent. S transitions are stasis, G is for growth transitions, F represents fecundity and R is for regression.

Juveniles are plants that are too small to reproduce, including new recruits and older plants that have just resprouted. Because of time constraints, the seed bank was ignored, although it may play an important role in the recovery of populations after a fire.

The mathematical formula for a matrix model is given by the general equation:

n(t + 1) = A*n(t)

Where n(t) is a vector of the abundances of individuals in each stage at time = t, and A is a matrix containing probabilities of transitions between stages during one projection interval (Caswell 2001). The A matrix for Conradina glabra corresponding to the life cycle diagram in Figure 1.1 follows:

 S1ij F1ij F 2ij F3ij F4ij     G1ij S2ij R1ij R4ij R6ij  A =  G5 G2 S3 R2 R5   ij ij ij ij ij   G8ij G6ij G3ij S4ij R3ij    G10ij G9ij G7ij G4ij S5ij 

7

Each entry in the matrix reflects the probability of an individual in stage j at time t

making a demographic contribution to stage i at time t+1. Sij is the probability of surviving and remaining in stage i. Gij is the probability of surviving and growing into

the next stage, Rij is the probability of surviving but regressing from a larger stage to a previous stage. Fij is the mean number of seeds produced by individuals that successfully germinate and survive to become new recruits in stage 1 by the next projection interval (Caswell 2001). Stasis was estimated from the census data as the probability that individuals within a stage survive and remain in that stage until the next census. Growth and regression were calculated as the probability of an individual in one stage class transitioning to another stage class at t +1. Fecundity was estimated by selecting 20 plants from each population spanning the size range of C. glabra and estimating the total number of seeds produced by each in one flowering season as a function of total length of branches. For this subset of individuals, I measured the length of one reproductive branch and collected all the seeds produced along it. Then I divided the number of seeds produced by the length of the measured branch and multiplied by the plants total reproductive branch length to estimate the total number of seeds produced by each plant. I regressed the estimate for the total number of seeds produced on volume to obtain a linear relationship whereby an estimate of the total number of seeds produced could be calculated for the rest of the flowering plants in each population (Table 1.2).

Table 1.2. Regression equations used to calculate fecundity from plant size. R2 refers to the percent of variation in seed number each equation explains for each of six populations. Site Equation R2 Introduced 1 y = 1.1327x - 2.9525 0.767 Introduced 2 y = 0.9008x - 2.3272 0.412 Introduced 3 y = 0.7701x - 1.039 0.730 Natural 1 y = 0.7431x - 1.511 0.324 Natural 2 y = 0.9481x - 2.1983 0.584 Natural 3 y = 1.1235x – 3.1691 0.674

8 I used the regression equations to estimate number of seeds produced by each marked plant as a function of volume. I calculated fecundity values in the A matrix by averaging the number of seeds produced by a stage class and multiplying by the number of new recruits divided by the total number of seeds a population produced to estimate the average number of recruits produced by an individual in each stage. . Time Invariant Model Analysis

I determined the asymptotic growth rate of each population from the dominant eigenvalue (λ) for its transition matrix to establish whether each was increasing, decreasing, or stable. I used a bootstrap procedure to generate 10000 matrices by randomly resampling individuals within a population. For each of these matrices the Lambda values were found. A 95% confidence interval for Lambda was calculated from those values. If the bootstrapped confidence interval for Lambda did not include 1, the population growth rate was considered significantly different from 1. If the confidence limits of Lambda for 2 populations did not overlap, their growth rates were considered statistically significantly different. The stable stage distribution for each population was determined from the dominant right eigenvector (w) of the matrix (Caswell 2001). I compared the stable stage distribution to the actual stage distribution for each population with Chi-square tests to determine if populations were at the stable stage distribution. Two prospective analyses of the effects of changes in elements of A on Lambda, sensitivity and elasticity analyses, were conducted for each matrix. Sensitivity analysis quantifies the dependence of Lambda on specific vital rates by calculating the effect of a small absolute change in a matrix element on the projected growth rate from the equation:

 ∂λ  s =   ij  ∂   aij 

Where sij is the sensitivity of Lambda to a given matrix element, aij. In addition to identifying stages and transitions that have large effects on population growth rate,

9 sensitivity is valuable for identifying stages that may be affected by small changes in vital rates due to some future circumstance. It can also identify the importance of transitions that were not observed during the study but that may occur rarely. Elasticity is the proportional change in λ resulting from a proportional change in a vital rate (aij), calculated as:

 a  =  ij  eij  sij   λ 

Elasticities indicate the relative importance of each vital rate to determining the asymptotic growth rate, which can identify life stages most important to conservation. In

contrast to sensitivity, elasticity scales such that the sum of all eij is 1. This is useful because values can be summed for each transition type to compare the elasticity of Lambda to individual transitions or classes of transitions.

Life Table Response Analysis

I used a Life Table Response Experiment (LTRE) analysis to decompose the difference in the overall growth rate (Lambda) for summary matrices for introduced and natural populations into the contributions due to the differences in the individual elements of the transition matrix (Caswell 2001). In contrast to elasticity analysis, which calculates the projected response of Lambda to hypothetical variation in transition rates, an LTRE analysis measures the contribution of observed differences in the transition rate to observed differences in Lambda. The contribution of each element to the observed difference in Lambda is calculated as:

= ×δλ δ LTREij Vij / aij

Where Vij is the standard deviation of the transition rate aij, which is equivalent to the δλ δ difference in aij for an analysis of two matrices. The term /aij is the sensitivity of

10 Lambda to aij evaluated for the matrix that is the mean of the elements of the matrices being compared. For this analysis, I aggregated the individuals at the introduced populations and the individuals at the natural populations to create one projection matrix for each habitat. The LTRE can only make comparisons between matrices based upon the same size parameters, so each of the combined populations was conformed to the same five stages identified by the Vandermeer-Moloney algorithm when used on the introduced population. I then ran an LTRE comparing the two matrices to determine the contribution of each matrix element to the difference in λ for the two types of population. All demographic analyses were conducted with the popbio package for R (Stubben 2007).

Measurement of Environmental Conditions

I collected data on soil moisture and the species composition at all six sites to determine if variation in any of these is related to variation in projected population growth rate. For each population of C. glabra, I selected 20 locations spaced evenly along a randomly placed transect through the marked plants. Soil moisture was measured from samples collected three days after a rain from each location in each population. Each sample was taken with a soil corer to a depth of 15 centimeters and placed in a watertight tin to prevent evaporation. Cores were weighed both wet and dry, to quantify the amount of water in the soil on a dry-mass basis (wet weight – dry weight)/(dry weight). Tins were weighed prior to filling with soil and their weights were subtracted from the calculations. Percent cover of understory vegetation, bare ground, and litter was estimated along the same 100 meter transect, by systematically sampling with a one meter square quadrat every five meters. I conducted a nested ANOVA for the effects of habitat and population nested within habitat for each environmental variable after testing for normality and homogeneity of variance. Environmental variable means were compared among habitats

11 and among populations within habitats and with vital rates for the populations to determine conditions associated with population growth of C. glabra.

Results

Time Invariant Analysis

The Vandermeer-Moloney algorithm identified four breaks in the stage distribution at one introduced population and three at the other two, resulting in five stages classes in one matrix and two four-stage matrices (Table 1.3). For the natural populations, two populations had two breaks, resulting in three stage classes and one population with three breaks had four stage classes.

Table 1.3. The number of stage classes and the breaks between them as determined by the Vandermeer-Moloney algorithm. Significant Chi square indicates departure of actual population distribution from the stable stage distribution predicted by the model. # of Population Type of Stage # Population Classes Breaks Chi Square 1 Introduced 5 0-3000-16000-41000-591000+ p = 7.97 E-06 2 Introduced 4 0-3000-26000-300000+ p = 0.065 3 Introduced 4 0-3000-28000-70000+ p = 0.0001 4 Natural 3 0-3000-210000+ p = 9.75E-13 5 Natural 3 0-3000-16000+ p = 0.202 6 Natural 4 0-3000-5000-55000+ p = 4.36E-05

Fecundity values for natural populations were greater than the introduced populations. Among the introduced populations, fecundity values were highest in introduced population 2 (Figure 1.2). Transitions reflecting growth, particularly the first transition from juveniles to small/medium stage classes, were higher for introduced populations than their natural counterparts. All matrices had large values for stasis across all stage classes though introduced populations generally had higher values than natural populations.

12 Introduced 1 Natural 1  .0 385 .0 096 .0 100 .0 108 .0 359    .0 311 .0 308 .0 063 .0 029 0   .0 852 .0 156 .0 227    .0 095 .0 295 .0 349 .0 081 0  .0 111 .0 861 6.0      .0 054 .0 231 .0 444 .0 814 6.0   0 .0 019 4.0   0 0 .0 015 .0 017 2.0  Introduced 2 Natural 2  .0 476 .0 433 12.1 90.3     .0 559 .0 136 .0 169 .0 381 .0 533 0 0     .0 294 .0 375 .0 030  0 .0 333 .0 733 .0 667      .0 058 .0 563 .0 925  0 0 .0 067 .0 333 Introduced 3 Natural 3  .0 174 .0 148 .0 137 .0 294  .0 313 .0 022 .0 059 .0 054     .0 530 .0 314 .0 130 .0 037 25.0 .0 375 .0 033 0      .0 181 .0 419 .0 333 .0 156  .0 313 .0 375 .0 766 .0 310      .0 038 .0 152 .0 481 .0 724  0 0 .0 117 .0 655

 S1ij F1ij F2ij F3ij F4ij     G1ij S2ij R1ij R4ij R6ij  Key =  G5 G2 S3 R2 R5   ij ij ij ij ij   G8ij G6ij G3ij S4ij R3ij    G10ij G9ij G7ij G4ij S5ij  Figure 1.2. Transition matrices for three introduced and three natural populations. The key provides a reference for where each transition lies. S refers to stasis transitions along the diagonal axis. F refers to fecundity values along the top row. G refers to growth transitions and R refers to regression transitions.

When aggregated, fecundity and growth were greater in the introduced populations than the natural populations (Figure 1.3). Stasis and regression values were greater for the natural populations with the exception of the S4 transition. The probability of remaining in the fourth stage class was 0.789 for introduced populations and 0.686 for natural populations.

13 Introduced aggregate Natural Aggregate  .0 311 .0 143 .0 132 .0 208 .0 875  .0 610 .0 117 .0 117 .0 120 .0 284      .0 345 .0 210 .0 079 .0 028 0   .0 247 .0 521 .0 237 .0 041 0   .0 143 .0 389 .0 307 .0 107 0   .0 026 .0 319 .0 398 .0 211 0       .0 081 .0 269 .0 465 .0 789 .0 640  .0 039 .0 043 .0 269 .0 686 .0 889  0 0 .0 009 .0 009 24.0   0 0 0 .0 006 .0 111 Figure 1.3. Aggregate matrices combining the three populations from each site.

The projected growth rates for the six populations ranged from 0.964 to 1.254 (Table 1.4). Two introduced populations and one natural population had a Lambda significantly greater than 1, indicating a positive growth rate (Table 4). The other three populations were not significantly different from 1, implying they are not different than stable populations.

Table 1.4. Estimates of Lambda and bootstrapped 95% confidence limits for six study populations. Asterisks indicate the populations with a Lambda significantly greater than 1, indicating growth. Populations that share a letter superscript have Lambdas that are not significantly different.

Lower Limit Upper Limit Time since Introduced of 95% CI Lambda of 95% CI Last Burn A 1 0.968 0.996 1.024 1 Year C 2 1.098 1.254* 1.408 2 Years B 3 1.061 1.094* 1.125 2 Years Natural AB 4 0.943 1.001 1.039 25+ Years B 5 1.027 1.066* 1.097 25+ Years A 6 0.912 0.964 1.007 25+ Years

Growth rates for some populations within each habitat are significantly different from one another (Table 4). One introduced population had a significantly lower Lambda

14 than the other two. One natural population had a growth rate that was significantly greater than one other, but was not significantly different from the third. Chi-square analyses comparing actual distributions of individuals among stages in each population to the stable stage distribution predicted by the model identified one population from each habitat that was not significantly different than the stable stage distribution. All other populations within each habitat were significantly different than predicted by the model (Figure 1.4).

Figure 1.4. The frequency of the predicted stage class distribution, compared with the actual distribution Dark bars are the predicted stage class distribution of each population. Light bars are the observed distribution of individuals within the stages of C. glabra identified by the Vandermeer-Moloney algorithm.

When all six populations are divided into the same five stage classes, it is clear that a larger proportion of the population is represented by individuals in the smallest stage class in introduced populations than in natural populations (Figure 1.5).

15

Figure 1.5. The stage class frequency distribution for each of six populations of C. glabra at the first census.

16 The pattern of sensitivity of Lambda to matrix elements varied considerably among populations, with no consistent difference between introduced and natural populations. In one introduced population, Lambda was most sensitive to variation in the growth of medium individuals to large individuals (G1), and the stasis of large individuals (S4). This population also exhibited a large sensitivity to two regression elements (R2 and R4) (Figure 1.6). The second introduced population had large sensitivity values for growth (G3, G2 and G1) and smaller values for stasis. The final introduced population’s sensitivity values were largely uniform, with stasis of the largest individuals having the greatest sensitivity value (S4). The first natural population emphasized growth of small adults into medium adults (G2) and of juveniles into small adults (G1) as important. Stasis of large individuals (S3) was the most important value for the second natural population, with regression of individuals from larger stages to medium stages (R1) slightly lower. The growth of medium to large individuals (G3) displayed the highest sensitivity in the third natural population with the stasis of large individuals also identified as important (S3) (Figure 1.6).

17

Figure 1.6. Sensitivity of Lambda to each element of the transition matrix for each population. F indicates values of fecundity, S stasis in a stage class, R regression to a smaller stage, and G indicates growth into a larger stage class. For definitions of specific matrix elements, see Figure 1.2.

There were differences among populations in the pattern of elasticity, but these differences were more pronounced between introduced and natural populations (Figure 1.7). Stasis values had the largest summed elasticity for all populations, indicating that surviving in the same stage class is contributing most to Lambda. For introduced populations, the summed elasticity values for growth were consistently greater than their natural counterparts.

18

Figure 1.7. The summed elasticity for each transition type for each population. F = fecundity, S = stasis, R = regression, G = growth.

When the three populations of each type were aggregated by combining all individuals in one matrix, Lambda for the introduced (1.052; 95% CI 1.029 to 1.076) was significantly greater than for natural population (1.004; 95% CI 0.977 to 1.028), for which the growth rate was close to 1 (Table 1.4). The aggregate natural population is stable, but the lack of overlap in the confidence intervals between introduced and natural populations suggests that the introduced populations are growing faster. Lambda for the aggregate of the introduced populations was more sensitive to five stages than Lambda for the natural populations (Figure 1.8). The growth of large plants was most important (G4) as well as the stasis of those large plants (S4). However the regression of plants at from medium to small (R2) and gargantuan to large (R4) were also important as was true in the individual population analysis. Lambda was also sensitive to the fecundity of largest individuals (F3).

19

Figure 1.8. Sensitivity of Lambda to each matrix element for aggregate matrices for each type of population. F = fecundity, S = stasis, R = regression, G = growth. Natural population values are the light bars, Introduced population values are the dark bars.

Stasis of the next to largest stage class (S4) was far and away the most important element for both introduced and natural populations in the elasticity analysis for aggregate matrices, although the stasis values were more evenly distributed among the natural populations. Stasis of the earlier stage classes was more important to Lambda for natural populations (Figure 1.9). Fecundity of the largest plants was more important for introduced populations.

20

Figure 1.9. Elasticity values for each element of the aggregate matrices for each type of population. F = fecundity, S = stasis, R = regression, G = growth. Natural population values are the light bars, introduced population values are the dark bars.

LTRE Analysis

In the LTRE analysis, I identified the contributions of differences in individual matrix elements to the difference in Lambda for the aggregated natural and aggregated introduced populations. Greater stasis of large plants and growth rates at the introduced populations contributed most to the greater Lambda there (Figure. 9). G4, a highly sensitive element for both matrices, was similar for the two populations, and so contributed little to the difference in Lambda. The S4 element, which has the highest elasticity in the both matrices, contributed positively to growth at introduced populations. This is due to the differences in the sensitivity of Lambda to demographic contributions of plants in the fourth stage class rather than large differences in the actual transition value S4 for the two matrices (Appendix A). Greater stasis for the other stages in natural populations, which corresponds to lower rates of transition to larger stage classes, contributed to a smaller value of Lambda. Individuals in natural populations had twice

21 the rate of regression as introduced populations contributing to a lower value of Lambda. Large differences in stasis at earlier stages, contributed to more negative values for the natural populations even though Lambda was not as sensitive to these particular stages (Appendix A).

Figure 1.10. Life Table Response Experiment results. Relative contribution of differences in each element of the transition matrix to the observed greater growth rate for introduced relative to natural populations. Positive values indicate transitions that explain why Lambda is greater at the introduced populations than at natural populations. Negative values are transitions that contribute to lower Lambda at natural populations.

Environmental Conditions

Percent soil moisture ranged from 7.7-9.3% (Table 1.5) and was significantly different among populations within a habitat but not between the habitats of natural and introduced populations. The proportion cover by vegetation at the introduced populations ranged from 9% to 44% with each population significantly different from the others. For the natural populations, proportion of vegetation ranged from 20% to 26% and was not significantly

22 different among populations. The proportion cover by vegetation was not significantly different between natural and introduced populations. Percentage cover by bare ground was substantially greater at the introduced populations ranging from 30% to 50%, while natural populations had 10% to 30% bare ground. The introduced population with 50% bare ground had significantly more bare ground than either of the other introduced populations. The natural populations were all significantly different from one another in percentage bare ground. Introduced populations had significantly less ground covered by litter on average than natural populations, ranging from 18% to 39%. Natural populations had large amounts of leaf litter ranging from 36% to 62%. Within each habitat populations were also significantly different for the proportion of litter. Each habitat had one population with significantly lower litter than the other two populations. The composition of the litter was also different at each habitat, consisting mostly of Turkey oak (Quercus laevis) at the introduced populations and of Sand pine (Pinus clausa) at natural populations (Bladow, personal observation). There does not seem to be a clear correlation between any of the measured environmental variables and Lambda.

Table 1.5. Means for percent soil moisture and the proportion of area occupied by vegetation, bare ground, and litter at each population. Asterisks indicate a significant difference between habitats determined by a nested ANOVA. Within a habitat, means that share a superscript do not differ significantly. Lambda from time invariant models is presented for comparison. Significant differences among Lambda were determined by absence of overlap of bootstrapped 95% confidence intervals. Populations that share a letter superscript have Lambdas that are not significantly different.

Percent Proportion Proportion Soil of of Bare Proportion Introduced Moisture Vegetation Ground of Litter Lambda 1 7.741A 0.263A 0.298A 0.387A 0.996A 2 7.992A 0.093B 0.506B 0.335A 1.254C 3 9.218B 0.439C 0.328A 0.176B 1.094B Natural * * * 1 9.272B 0.204A 0.097A 0.623A 1.001AB 2 7.773A 0.2A 0.194B 0.552A 1.066B 3 7.736A 0.261A 0.293C 0.362B 0.964B

23 Discussion

Introduced Conradina glabra populations are currently growing or stable. Both natural and introduced populations of C. glabra were all stable or experienced positive growth over the course of the study. Vital rates that contributed most to Lambda primarily involve the stasis transitions. The stasis of large individuals (S4) is the most important variable when measured by elasticity, but also has a very high sensitivity. In the LTRE analysis stasis of smaller plants and regression contributed to a smaller Lambda for natural populations and higher values for growth and the stasis of the large plants contributed to a larger Lambda at the introduced populations. This study was unable to find correlation between the environmental variables measured and the growth rate of populations. Model analyses suggest that all of the populations of C. glabra that I monitored are currently stable or growing (Table 1.4). Both introduced populations that were burned in 2008 have growth rates significantly greater than 1. The population burned in January 2009 has a Lambda close to 1, but this population may have a higher growth rate next year as over 50 plants resprouted during the year between censuses. The natural roadside population also had a positive growth rate. The natural population in the extremely open habitat and the natural population in the sand pine plantation both had Lambdas close to 1. These populations are stable, though the low mortality rates at the open population may mask the effects of the low observed recruitment there. Assuming these populations will continue to grow or remain stable according to their projected Lambda is not a guarantee. Transition matrix values are assumed to be constant, but it is likely that stochastic events like fires or tree fall will change these values in successive years. In the short term only the populations that meet the stable stage distribution will grow at the predicted rates. Additionally, including resprouting plants from fire as juveniles may artificially inflate the growth rate since these plants are more likely to make the rare growth transitions that skip a stage class. However this bias is likely to be small as the sensitivity and elasticity values for those transitions were greatly outstripped by other values. The effect of choosing stage classes with the Vandermeer-Moloney algorithm was negligible. Populations that had positive growth rates retained their positive growth

24 when coerced to new configurations. Often changes in lambda were small, as the change from four stage classes to five varied lambda by less than five thousandths. The algorithm identified five stage classes for both the aggregate matrices, though the boundaries of those stage classes were different. However, coercing the matrices to one form or another did not have any effect on the results of the LTRE analysis. Caswell (2001) supports the use of these algorithms to delineate stage classes. I agree that they serve a useful purpose in identifying where breaks in stages are likely to occur, but for the specifics of a matrix, fine distinctions are not likely to matter as Doak and Morris argue (2002). All the populations have large summed elasticities for stasis. Natural populations have higher values of elasticity for stasis than their introduced counterparts because new recruits and growth occurred less frequently (Figure 1.7, Figure 1.8). Growth also had higher values for elasticity in introduced populations, possibly because the open terrain and low canopy cover facilitate growth. Age may also factor into the relative importance at each habitat. The introduced populations are nearly 20 years old, but the natural populations may be much older. If much of the sites suitable for establishment are already occupied at natural sites, then growing to claim those spaces becomes less important than maintaining current size. With the exception of the smallest introduced population, the sensitivity analyses identified stasis of the stage with the highest fecundity as the most important to population dynamics (Figure 1.6). Large plants produce the most seeds, and since recruitment will occur more often if every seed has the same chance of establishment, having the largest possible plants would be ideal. But in some populations, gargantuan plants were separated from the large plant class by the Vandermeer-Moloney algorithm (Table 2). Growth of large plants into the gargantuan class (G4) was a highly sensitive value with very low elasticity (Figure 1.8). As these plants have the highest fecundity per individual small changes in the transition rate could have some impact on the future growth of the population. These largest plants appeared less healthy, with more dead branches and withered centers and were few in number (Bladow personal observation). Surviving to the largest stage is rare and the analyses suggest those massive plants are not as important as the multitudes of slightly smaller, but robust plants (Figure 1.9).

25 The LTRE analysis plainly identifies the reasons why Lambda for the introduced populations and the natural populations are different (Figure 1.10). Greater stasis of large plants and growth for the introduced populations contributed to a larger Lambda (Figure 1.2, Figure 1.3). Stasis of younger plants and regression contributed to a lower Lambda for natural populations (Figure 1.2, Figure 1.3). This would suggest that the environment at natural populations is tolerable for C. glabra to survive and maintain a large number of individuals, but perhaps not as conducive for growth or recruitment. The reduction of litter at introduced sites may promote seedling establishment (Table 1.5) and the partial, yet less dense canopy cover probably seems to provide conditions better suited for growth of young plants. Few plants were established in completely open terrain, but often were associated with a nurse plant or the border of a stand of trees (Bladow personal observation). Many reintroductions have not been met with high initial survival or success (Drayton & Primack 2000, Holl & Hayes 2006). Drayton and Primack used eight different perennial species and four different methods of reintroduction to determine the best possible way to introduce species. They encountered low survival rates across many of their populations, but found that reintroducing older plants offered a better chance at survival. Initial introduction of Conradina glabra utilized grown cuttings, and helps to corroborate the consensus many reviewers share that founding populations with older individuals results in higher success rates (Guerrant 2007, Menges 2008). Holl and Hayes (2006) concluded that reintroductions remain tenuous at best after conducting many reintroductions of an annual grassland forb at multiple sites under different disturbance regimes. Exotic grasses played a role in the decline of those introduced populations, in the same way they are attributed to the loss of Malheur wire-lettuce (Stephanomeria malheurensis) populations. Management for all introduced species must consider not just the disturbance regime, but also community composition. C. glabra populations seemed to grow regardless of surrounding vegetation or groundcover (Table 1.5), but other environmental factors like canopy cover or a combination may play an important role in the fate of the populations. Similar to another long lived introduced species, Sargent’s cherry palm (Psuedophoenix sargentii) (Maschinski 2007), C. glabra’s natural populations

26 experienced higher survival than their introduced counterparts. Introduced populations of P. sargentii have not yet reached a reproductive age, while the population structure of C. glabra at introduced populations tends to have a large portion of younger plants which accounts for greater mortality (Figure 1.5). Other species have exhibited higher survival or growth at introduced populations, including Centaurea corymbosa (Colas et al 2008), a perennial plant, and the Nodding Beggarstck (Bidens cernua) (Gratiani et al 2009), an annual. Despite C. corymbosa having higher survival rates in introduced plants, the fecundity of introduced plants was lower than their natural counterparts. Lower fecundity was largely due to the lower amount of flowering plants in the introduced population (Colas et al 2008). My results show Conradina glabra with the opposite trend, having higher survival, but lower fecundity in natural populations. These two trends may in large part be due to the biology of the species, where Conradina glabra continues to flower year after year while Centaurea corymbosa plants typically die after flowering. B. cernua grew larger at introduced populations than at natural populations, but no demographic parameters were measured for this species (Gratiani et al 2009). These results suggest that introduced populations can survive to become successful when provided with suitable sites but does not provide clear answer to why some populations succeed while others fail. Similar to other species from fire dependent habitats, C. glabra had higher elasticity for growth and fecundity in the presence of fire in the introduced populations than in the natural populations where fire has been suppressed (Warton 2003). Also similar to other fire dependent species (Menges 2006), C. glabra experiences positive population growth after prescribed burning. Growth is not limited to areas disturbed by fire. A natural population situated along a dirt road experienced positive growth as well. C. glabra appears to be able to colonize disturbed areas, particularly at natural locations where it often grows in the middle of dirt roads, though the range of seed dispersal remains unknown (Bladow personal observation). C. glabra is performing well in its introduced populations nearly 20 years after reintroduction, but some questions still remain regarding the success of these populations. C. glabra appears to have limited dispersal ability so reintegration into the ecosystem beyond the introduction site may take many more decades. The question of fire return

27 interval needs to be addressed. Fire frequency has increased since the Nature Conservancy ceased monitoring populations in 2002. Introduced populations of C. glabra went unburned for 7-10 years after introduction, but since 2003 they have been burned every 3-5 years and the number of individuals has declined, by an order of magnitude at one population, even though its projected growth rate is positive. This analysis cannot determine the effects of future fire and assumes these populations are successful so long as the environment does not change. In restored populations this assumption will not hold as prescribed fires kill C. glabra and will change the vital rates. Any future study should consider the impacts of the fire return interval upon C. glabra to reconcile the negative effects of killing grown individuals with the possible benefits from increased seedling emergence and early survival. The violet butterwort (Pinguicula ionatha), an endangered carnivorous plant, experienced discrete differences in vital rates between the years following a fire (Kessler et al. 2008). A longer demographic study including years where the populations are burned at different intervals could identify which conditions best promote the growth rate of the population. Understanding the effect of fire on C. glabra is necessary because natural populations are slated to undergo restoration in the coming years as the sand pine plantations are gradually converted to back to sandhill habitats. This conversion will involve the use of prescribed fire, thus if C. glabra is to survive the restoration process a suitable fire regime should be identified. These introduced populations have survived for 20 years, but continued periodic monitoring will be necessary to ensure the success of this long lived plant so that these experimental populations can serve as an example of successful introductions.

28 CHAPTER 3

THE EFFECTS OF INBREEDING ON THE DEMOGRPAHY OF AN ENDANGERED PLANT CONRADINA GLABRA

Introduction

As land use changes result in increased destruction and fragmentation of natural habitats, formerly large populations have declined (Falk 1992). These small populations face a greater chance of extinction from a variety of threats. Demographic and genetic properties, including inbreeding depression, are two causes of decline, the effects of which are not always independent (Menges 2008). Decline resulting from demography is usually due to environmental stochasticity or environmental quality (Menges 1991a), while genetic problems stem from the lack of genetic variation, which is compounded by the effects of drift and forced inbreeding, which can lead to inbreeding depression (Husband and Schemske 1996). Inbreeding depression is the reduction in fitness of progeny derived from inbreeding relative to those derived from outcrossing (Husband and Schemske 1996). Fitness plays an essential role in demography, so any reduction in fitness through inbreeding will reduce growth rates of the population. Both demographic stochasticity and inbreeding depression have been studied in detail separately, but little work links the effects of inbreeding depression to its consequences at the population level. Problems of small population size are important to consider when introducing or conserving populations of rare and critically endangered taxa. Introduced populations are new populations established where no record of the species exist (Menges 2008). In the case of narrowly distributed species or species on the verge of extinction any new population will by necessity be an introduced one. Introduced populations often have few founders and limited genetic variability, many times because the original sources are small and not genetically diverse to begin with (Guerrant 2007). Limited source material necessitates that introductions will face the same challenges as naturally small populations. Normally outcrossing species when introduced to a new location may lack pollination vectors, or be planted with only a few different mating types. In the case of Florida ziziphus (Ziziphus celata) and the lakeside daisy (Hymenoxys acaulis var. glabra)

29 only one mating type occurs in natural populations (Weekly et al. 2002, DeMauro 1993). Early reintroduction efforts resulted in failure as populations were unable to reproduce sexually. Even if multiple mating strains are included, the level of genetic variation within the population can remain low. Inbreeding depression in these cases will still result in significant limits to the success of the introduction. Inbreeding depression reduces individual fitness, resulting in lowering the chance that a population will persist. A review by Husband and Schemske (1996) showed that on average inbreeding depression can reduce fitness by up to 50%. Inbreeding can be present in any stage of the life cycle, but has the strongest effects early in the lifecycle (Husband and Schemske 1996). Seed production is very susceptible to inbreeding depression as on average selfing decreases seed production by 30% for primarily outcrossing taxa (Husband and Schemske 1996). Seed and seedling weight, as well as seedling survival can be reduced as a result of inbreeding (Oostermeijer 1994). Reduced seed germination has also been implicated as a possible effect of inbreeding in small populations (Menges 1991b). The effects of inbreeding depression are exacerbated by mating system. Plants that are primarily outcrossing will have inbreeding depression on average twice the magnitude of species that regularly self fertilize (Husband and Schemske 1996). Inbreeding depression has been identified as cause for concern in natural rare species (Falk 1992, Menges 2008), and as a potential roadblock to creating new populations of endangered species (Friar et al 2001). Inbreeding has only recently been linked to demography (Le Corff and Horvitz 2005, Steets et al 2007) Le Corff and Horvitz used a demographic model to analyze the dispersal and germination abilities of a tropical perennial shrub, Calathea micans, for inbred and outbred seeds. Steets et al. (2007) performed a study looking at the direct consequences of mating system in the annual Common Jewelweed (Impatiens capiensis), a plant capable of producing cleistogamous flowers, which remain closed and self pollinate, and chasmogamous flowers, which remain open and outcross at a high rate (Steets et al 2007). Two populations were modeled with demographic matrix models and sensitivity analyses identified cleistogamy as contributing significantly to the population’s growth rate. Both studies use matrix models incorporating demographic differences for offspring produced

30 through selfing and outcrossing. This approach can be extended to evaluate the effects of inbreeding depression on the demography of a rare species I combined empirical estimates of inbreeding depression with a demographic matrix model to assess the impact selfing could have on the dynamics of introduced and natural populations of a federally endangered species. Conradina glabra offers a unique opportunity to compare the effects of inbreeding in natural and introduced populations. Conradina glabra is a species with a limited distribution, whose original habitat is unknown: it is currently restricted to sand pine (Pinus clausa) plantations. In 1991, C. glabra was introduced at three sites owned by the Nature Conservancy as part of an effort to ensure the species remained protected. I performed self and outcross pollinations by hand in an introduced and a natural population to estimate inbreeding depression on early life cycle stages. These data were incorporated into a demographic model to determine the potential effects on the population dynamics of this species. This study aims to answer three questions pertaining to the survival of C. glabra in both introduced and natural populations in light of the possibility of inbreeding depression. Does inbreeding depression reduce seed production, seed mass, or seed germination rate? Does inbreeding depression differ for natural and introduced populations? If inbreeding affects seed and seedling traits, how would the demography of a selfing population compare with that of the actual populations?

Methods

Study Species

Conradina glabra Shinners (Lamiaceae), commonly known as Apalachicola Rosemary, is endemic to the Florida panhandle with an extremely limited geographic range. C. glabra is a woody perennial shrub which has been reported to be self incompatible (Kral 1983). While more recent studies have demonstrated that C. glabra is capable of selfing (Kubes 2009), prior reports of self incompatibility suggest that the mating system is biased towards outcrossing. This federally endangered mint is found solely in Liberty County Florida (Gray 1965). C. glabra is capable of growing up to 1 meter tall and 1.5 meters wide. Flowering occurs between March and May and produces

31 up to 4 nutlets per flower. These fruits are light brown and vary from 1.5-2.5 mm in length. C. glabra can reproduce asexually when branches are buried, or by stoloniferous growth (Gray 1965). Herbivore damage to leaves is minimal on C. glabra, though seed eating beetle larvae are present in about 10% of all flowers (Bladow, personal observation).

Cross Pollination

I conducted self and outcrossed hand pollinations of C. glabra at the Bluffs and Ravines Preserve and Torreya State park to quantify inbreeding depression and the demographic consequences of inbreeding. The Bluffs and Ravines Preserve population is the result of an experimental introduction performed in 1990 (Gordon 1996). Populations on Torreya State Park are natural populations which served as the original source for the introduced material. I manipulated 4 flowers along each of 12 branches for 10 plants at one population at the Bluffs and one population at Torreya. At each population, 480 flowers were marked and manipulated for a total of 960 flowers. I removed the anthers from three of the flowers in each set of four to prevent unwanted selfing. One of the three emasculated flower was outcrossed with pollen collected from random individuals throughout the population. A second flower was selfed with pollen collected from same plant. The third was left unpollinated as a control for accidental contamination with self pollen. These three flowers were placed inside a bridal veil enclosure to prevent contamination with unwanted pollen. The fourth flower was not emasculated or bagged and served as a control. Each flower was marked with a dot of acrylic paint on the calyx to identify the treatment to which it belonged. Before maturation of the nutlets, the persistent calyces were glued shut to seal the nutlets inside for subsequent collection and counting. Calyces were collected once the nutlets were mature. I counted the number of nutlets produced by each marked flower, and weighed each individually on a microbalance. Inbred and outbred seeds from each plant were allowed to germinate in separate petri dishes. Sterile sand was placed on the bottom of each dish and water was added weekly to keep the dish moist. Seedlings were counted

32 and removed from the dishes within a week of germination for three months to assess the effect of inbreeding on germination rate. Seed set and seed germination rate are proportion data, so generalized linear models were constructed to analyze the data. A generalized linear model with binomial distribution and a logit link function was created to model seed set as a response variable and pollination treatment, population and the interaction term as the independent variables. Seed germination rate was modeled with a generalized linear model using a binomial distribution with pollination treatment, population and an interaction term as independent variables. Pair wise comparisons were performed using a paired Wilcoxon test between different crossing treatments. Seed weight was log transformed and modeled as a function of pollination treatment, population, and the interaction of those two terms.

Modifying the Demographic Model

A demographic model for calculating the effects of inbreeding depression was derived from models for unmanipulated populations described in Chapter 1. The A matrix for both the natural and introduced populations is of the form:

 S1ij F1ij F2ij F3ij F4ij     G1ij S2ij R1ij R4ij R6ij  A =  G5 G2 S3 R2 R5   ij ij ij ij ij   G8ij G6ij G3ij S4ij R3ij    G10ij G9ij G7ij G4ij S5ij 

The A matrix was modified by changing the fecundity values to reflect the effects of inbreeding depression on seed set and seed germination. Changes were applied to the F values listed in the matrix. The first matrix value was not modified as it reflects the stasis of small plants that do not produce seeds rather than reproduction by seed. Total seed set of the unmanipulated populations was assumed to be completely outcrossed. I modified the number of outcrossed seeds for each reproductive stage class by multiplying by the

33 average number of seeds per inbred flower divided by the average number of seeds per outbred flower. I further reduced fecundity by lowering the germination rate. I first assumed that the rate in the original model was based entirely on outbred seeds. I multiplied this rate by the relative germination rate of inbred seeds to outbred seeds observed in the lab. I used this calculation instead of the absolute germination rates observed in the lab because lab rates greatly exceeded the derived rates of germination in the field. I determined the growth rate of each population as the dominant eigenvalue (λ) of its transition matrix to establish whether each was increasing, decreasing, or stable. These matrices were bootstrapped 10000 times to generate 95% confidence intervals for lambda. Sensitivity and elasticity analyses examining the effects of changes in elements of A on Lambda were conducted for each matrix. Sensitivity analysis quantifies the dependence of Lambda on specific vital rates by calculating the effect of a small absolute change in a matrix element on the projected growth rate from the equation:

 ∂λ  s =   ij  ∂   aij 

Where sij is the sensitivity of Lambda of to a given matrix element, aij. In addition to identifying stages and transitions that have large effects on population growth rate, sensitivity is valuable for identifying stages that may be affected by changes in vital rates due to some future circumstance. It can also identify the importance of unobserved transitions that may occur rarely. Elasticity is the proportional change in λ resulting from a proportional change in a vital rate, calculated as:

 a  =  ij  eij  sij   λ 

Elasticities indicate the relative importance of each vital rate to determining the asymptotic growth rate, which can identify life stages most important to conservation. In

34 contrast to sensitivity, elasticity scales from 0 to 1. This is useful because values can be summed for each transition type to discover which type of transition is the most important.

Results

Cross Pollination

The generalized linear model for seed set comprised three terms including an effect of treatment type, an effect of population, and the interaction of population and treatment. The model for seed set was simplified by eliminating the interaction after performing an F test. Reducing the model to main effects was not significantly different from the overall fit. The effect of population was also not significant and was dropped from the model, leaving treatment as the only independent variable. The effect of treatment was significant (p = .0023), indicating a difference in the seed set between outcrossed and selfed flowers The difference in the number of seeds produced by outbred flowers compared to inbred flowers in C. glabra demonstrates the effect of inbreeding on the number of seeds produced by a flower. Selfed flowers produced a mean of 1 seed per flower at the introduced population and 1.075 seeds per flower at natural populations (Figure 2.1). Outcrossed flowers produced 1.55 seeds per flower at the introduced populations and 1.47 seeds per flower at the natural populations. Control flowers produced 1.45 seeds at the introduced population and 1.29 seeds at the natural population. Some emasculated flowers produced seeds, (0.21 seeds at the introduced population and 0.24 seeds per flower at the natural population) indicating some contamination by accidental pollination by other flowers in the bag.

35

Figure 2.1 The average number of seeds per flower produced by each pollination treatment. Self = mean number of seeds produced from inbred flowers, Outcross = mean number of seeds produced from outcrossed flowers, Control = unmanipulated flower with mean number of seeds resulting from natural pollination, Emas = Anthers removed. Error bars show 1 standard error.

The generalized linear model included a treatment effect, an effect of population and the interaction of population and treatment. Simplifying the model for seed germination by dropping the interaction term was not significantly different from the model including treatment, population and the interaction. Upon dropping the interaction both the effect of treatment (p = .0017) and population (p = .0022) became significant. Outcrossed seeds had a greater percent germination than inbred seeds at each population (Figure 2.2). Outcrossed seeds at the introduced population germinated 7.0% of the time while inbred seeds germinated at a rate of 1.1%. The natural population’s seeds germinated at a rate of 16.1% for outcrossed seeds and at a rate of 6.6% for inbred seeds. The model found a significant effect between populations (p = .0022) indicating that seeds from the introduced population germinated less often. There was also a significant effect of cross type (p = .0017) supporting evidence of inbreeding as selfed seeds germinated less often than introduced seeds.

36

Figure 2.2. The germination rate for inbred and outcrossed seeds for each population.

The linear model for seed weight contained a term for treatment type, population and the interaction of treatment and population. There was not a significant difference in mean seed mass for inbred seeds and outcrossed seeds. The interaction and the effect of population were also not significant. Mean seed mass for outbred seeds was 0.129 grams while inbreed seed mass was 0.125 grams. A one way ANOVA with two treatments found that when comparing both populations of inbred and outbred seeds p = 0.829. When compared separately the p value was lower for the introduced population p = .496 compared to p = .625 for the natural population.

Modifying the Demographic Model

Reducing the seed number and rate of germination reduces the fecundity values of the matrices modified by inbreeding (Figure. 2.3)

37

Introduced Unmanipulated Natural Unmanipulated  .0 385 .0 096 .0 100 .0 108 .0 359    .0 311 .0 308 .0 063 .0 029 0   .0 852 .0 156 .0 227    .0 095 .0 295 .0 349 .0 081 0  .0 111 .0 861 6.0      .0 054 .0 231 .0 444 .0 814 6.0   0 .0 019 4.0   0 0 .0 015 .0 017 2.0 

Introduced Selfing Natural Selfing  .0 385 .0 062 .0 061 .0 027 .0 090    .0 311 .0 308 .0 063 .0 029 0   .0 852 .0 058 .0 049    .0 095 .0 295 .0 349 .0 081 0  .0 111 .0 861 6.0      .0 054 .0 231 .0 444 .0 814 6.0   0 .0 019 4.0   0 0 .0 015 .0 017 2.0 

Figure 2.3. Matrices for naturally pollinated plants for each population and the same matrices with fecundity values recalculated to reflect the effect of inbreeding depression. Fecundity values are the top row with the exception of the first value, as the juvenile stage does not produce seeds.

The growth rates for the inbred populations are below 1. The introduced inbred population has a lambda of 0.949 and a 95% confidence interval of 0.9-0.96. The natural inbred population has a lambda of 0.950 with a 95% confidence interval of 0.89-0.98. Neither confidence interval includes 1 indicating populations that would decrease over time. The confidence interval for the inbred natural population overlaps with the confidence interval from the unmanipulated natural population of 0.943-1.039. The confidence interval for the introduced inbred population does not overlap the confidence interval from the unmanipulated introduced population of 0.968-1.024, indicating that complete inbreeding would significantly reduced the growth rate of the introduced population. There were no effects of inbreeding on the sensitivity or elasticity of the stages (Appendix B).

38 Discussion

I found evidence of inbreeding depression in two of the three life cycle stages measured in two populations of C. glabra. Flowers that were selfed produced significantly fewer seeds than those pollinated by outcrossing. Outcrossed seeds at both the natural and introduced populations produced the most seeds per flower. Control treatments were not significantly different from selfed or outcrossed treatments, suggesting that at each population naturally pollinated flowers are both outcrossed and selfed. The emasculated control was significantly different from zero, due to passive contamination from other nearby flowers within the bridal veil enclosure. The contamination biases the results of the selfed and outcrossed treatments and may artificially inflate the number of seeds produced by crossing treatments, but doesn’t affect the significant difference between the two treatments. Seed germination rates were also lower in seeds resulting from selfing (Figure 2.2). Seed mass was not significantly different between populations. Though seed mass plays a role in the survival of seedlings of some species (Westoby et al 1996), in C. glabra there is no evidence at either population that seed weight differed for outcrossed and inbred seeds. Introduced and natural populations responded similarly to the effects of inbreeding. Introduced populations produced the same number of seeds per flower as natural populations. Seed germination rates were significantly lower for introduced populations. However the magnitude of inbreeding for seed germination was not different as the interaction between cross type and population in the model would have been significant if this was the case. This suggests that there is not a greater cost to inbreeding at introduced populations. Demographic models of these populations incorporating the effects of inbreeding produced a lambda significantly less than 1. This suggests that if both populations were entirely composed of selfing individuals, the populations would decline due to reduced fecundity. The inbreeding matrix for the introduced population has a lambda that is outside the confidence interval for the original population. The inbreeding matrix for the natural population has some overlap with the original unmodified matrix, so while inbreeding affects the population and reduces the population’s growth rate it may not

39 significantly differ from the original population projections. This could possibly be due to the very low germination rate from selfed seeds at introduced populations. Accounting for inbreeding depression in C. glabra with a model, suggests that even though populations were founded with only fifty genotypes, the potential harm of inbreeding may be mitigated by outcrossing in these populations. However, my study only looks at early stages of the life cycle. Further detrimental effects of inbreeding can occur throughout the lifecycle, perhaps resulting in different rates of survival for inbred and outbred progeny (Husband and Schemske 1994). It is also worth noting that inbreeding depression is often stronger under harsher conditions (Armbruster & Reed 2005). Though seeds were crossed and matured in the field, germination took place under laboratory conditions. Hence the effects of inbreeding regarding germination rate may be stronger in the field. Although the relationship of the earliest founders to one another is unknown, these fifty genotypes were distinct enough that crossing between their progeny may not have resulted in a decline of the population growth rate under the current rates of self and cross pollination. The demographic model accounting for inbreeding depression still has many applications even though it may not always be applicable to a population. The model might suggest the dynamics of a population begun with only a small number of distinct genotypes. The model also reflects the possible consequences of the loss of pollinators, whether through climate change or another catastrophe. This model is of importance for describing C. glabra populations in the event of a cataclysmic fire. Such a population might attract few pollinators and be composed of the few genotypes capable of withstanding such an event. This study lends support to the conclusions of Chapter 1, regarding the success of C. glabra introductions. While some demographic variables remain unknown, particularly the response of the species to fire, some of the potential genetic consequences of inbreeding do not seem to be a factor. Though inbreeding is often best observed in early life traits, for C. glabra, potential defects from inbreeding may be the most detrimental for large plants. The elasticity of stasis for large plants is the most important value to the growth rate of the population (Chapter one, Figure 8), so any observed increase in regression of large plants or of the survival of those large plants may have a

40 greater impact on the growth rate of a population than the reduction of fecundity. Avoiding inbreeding depression should be considered when creating introduced populations. Primarily outcrossing taxa are at a greater risk for inbreeding depression to adversely affect population dynamics (Husband and Schemske 1996). Any attempt at reintroduction needs to consider the mating system of the target species. Plants that are primarily outcrossing ought to be subjected to screening to ascertain the potential for inbreeding depression to negatively affect conservation efforts. Friar et al (2001) provides an example in screening the Mauna Loa silversword (Argyroxiphinium kauense) in Hawaii for evidence that sufficient genetic variation exists within a population prior to undertaking reintroduction efforts (Friar et al 2001). Potential for inbreeding depression also means that the source material for populations should be genetically diverse (Guerrant 2007). However it is possible that maximizing genetic variation involves collecting founders from multiple populations. When founders from locally adapted populations are mixed, outbreeding depression can occur. Outbreeding depression is the reduction of fitness in progeny bred between two populations relative to offspring derived from within population crosses (Lynch 1991). Outbreeding depression has been seen in rare plants with small populations (Fisher 1997). The potentially conflicting effects of inbreeding and outbreeding depression present a quandary for species introductions. Genetic variation should be maximized, but limited in scope to the areas from which it is collected. The study here has shown that 50 founding genotypes were sufficient to ensure the survival of the populations for nearly 20 years. For outcrossing plants, 50 genotypes may set a lower boundary for initial population creation, based on limited source material and budget constraints, but empirical data will be needed to be collected to determine just how many genotypes will need to be collected to successfully establish reintroduced populations. Further work will be needed to identify the optimal number of genotypes a population needs to survive without resulting in inbreeding depression. A series of populations with different numbers of genetic founders could be investigated over the course of many years with the aim of detecting inbreeding depression. Studies of this nature would provide practical guidance to the establishment of new populations with

41 regard to the amount of genetic diversity necessary to create thriving new populations of endangered plants. In the case of Conradina glabra, work still needs to be done to determine how the plant responds to fire, as this will help inform management. Further research on the effects of inbreeding throughout the lifecycle would enable further refinement of the demographic model to see if detrimental effects of inbreeding arising later in life contribute to a decline in the population growth rate than early life effects

42 CHAPTER 4

CONCLUSION

Demographically Conradina glabra is doing well. All of the populations examined were projected to grow or remain stable indicating that the introduced and natural populations were currently successful (Table 1.4). As a whole reintroduced populations grew faster (λ = 1.052) than natural populations (λ = 1.004), which indicates that fire may play a role in increasing recruitment. However the stasis of large plants was the most important vital rate in maintaining the population growth rate for both reintroduced and natural populations. This odd dilemma means that a period of rest between burns is necessary to ensure plants can grow and survive in the large stage class for a few years before being destroyed by fire. Genetically, C. glabra has the potential for trouble. The GLMs for seed set and seed germination found a significant effect of pollination treatment (p = .0023, p = .0017), indicating that inbreeding depression affects seed set and seed germination rates in C. glabra. When this data was incorporated into a demographic model the population growth rate became less than one (λ ≤ 1), indicating decline in the event the populations become entirely selfing. This could indicate potential problems that may arise if population sizes are reduced to where they no longer attract pollinators perhaps due to a catastrophic fire. Fire is necessary to keeping the sandhill ecosystem intact (Myers 1990). Conradina glabra as part of that ecosystem dies to fire, but is expected to experience enhanced population growth and recruitment because of fire (Gordon 1996). A balance must be struck to ensure the survival of C. glabra if this is indeed the case. A quantitative study is necessary to determine what the optimal fire return for C. glabra is in light of its precarious situation. A long term plan is necessary when approaching introductions to ensure populations survive and grow to become successful parts of the ecosystem. Introductions are not fire and forget ways to save species, they require diligent work and adequate data to determine how to achieve success in the preservation of species.

43 APPENDIX A

− .0478 .0033 .0024 .0279 .0022   − − −   .0194 .0489 .0314 .0049 0   .0242 .0115 − .0189 − .0424 0     .0099 .0425 .0468 .0486 − .0014  0 0 .0028 .0020 .0010 

Figure A.1. The LTRE matrix resulting from the analysis. This matrix is graphed in Figure 1.10.

− .2998 .0265 .0151 .0884 .5909   − − −   .0980 .3117 .1576 .0126 0   .1169 .0701 − .0908 − .1034 0     .0418 .2269 .1961 .1038 − .2489  0 0 .0088 .0031 .1289 

Figure A.2. The difference matrix resulting from the subtraction of the natural aggregate matrix from the introduced aggregate matrix.

.1594 .1262 .1606 .3152 .0037   .1980 .1567 .1995 .3916 0  .2067 .1636 .2082 .4087 0    .2367 .1874 .2384 .4680 .0056  0 0 .3238 .6355 .0075 Figure A.3. The sensitivity matrix calculated for the mean matrix of the aggregate natural matrix and the introduced matrix.

44 APPENDIX B

Figure B.1. Sensitivity matrices for the inbred matrices and original unmodified demographic matrices. The top matrices are the sensitivity values from the modified demographic models reflecting the effects of total selfing. Below them are their respective sensitivity values from the original matrices. F = fecundity, S = stasis, R = regression, G = growth.

45

Figure B.2. Original elasticity matrix values for each transition types as well as predicted sensitivity values for the modified matrices reflecting the effects of total selfing. F = fecundity, S = stasis, R = regression, G = growth.

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50 BIOGRAPHICAL SKETCH

Jason Bladow was born on May 28, 1987 in Bremerton, Washington. He graduated Fort Collins High School in Fort Collins, Colorado in 2005. After high school he attended Florida State University and graduated with a B.S. in Biological Science in the winter of 2007. During the summer of 2007 he worked as Student Conservation Association intern at the Indiana Dunes National Lakeshore, instilling in him love for conservation and an appreciation of the hard work it required. In 2008 he worked as an SCA intern at Big Cypress National Preserve until the summer when he was hired as a Biological Science Technician at the Preserve. Jason returned to Florida State in the fall of 2008 to begin a Master’s degree in order to learn how to apply scientific principles to the conservation of rare and endangered species.

51