Project Description: Interacting Impacts of Multiple Pathogens on Population Dynamics of an Endangered Amphibian

Both the size and structure of most animal and plant populations are in constant flux, reacting to multiple, changing forces in their environment (Birch and Andrewartha 1941; Davidson and Andrewartha 1948b; Davidson and Andrewartha 1948a; Peter J. Hudson 2002). These forces include both abiotic and biotic factors and both positive and negative impacts on population growth and numbers (Uvarov 1931; Smith 1935; Andrewartha and Birch 1954; Lack 1954). For a population to persist, some periods of population growth must occur, but this growth is often countered by a constellation of natural and unnatural impacts. In spite of the complexity of the forces governing population and dynamics, the vast majority of ecological studies focus on only one or two of the many factors that interact to determine population growth rates (Blaustein and Kiesecker 2002). While this simplification can be essential in understanding the nuances of population regulation, it can also be an impediment to a broader understanding of when and where a species will flourish versus perish. My work aims to build a more synthetic understanding of the multiple factors controlling populations of a rare amphibian, with an emphasis on the interactions between multiple pathogens and a suite of biotic and abiotic conditions that together determine population health and persistence. Control of population growth is of general interest in ecology, but this topic takes on special importance for endangered species. With recent rates of extinction accelerating in wild populations, it is essential to understand the factors responsible for population declines (Singh 2002; Stuart et al. 2004). Yet in surprisingly few cases do we have anything approaching a full understanding of these factors, their relative importance, or their interactions (Blaustein and Kiesecker 2002). Amphibian populations are declining at a greater rate than any other vertebrate group on the planet (Stuart et al. 2004), with a rate of extinction about 200 times their historical background extinction rate (McCallum 2007). A variety of factors are likely contributors to amphibian declines, including habitat loss and fragmentation (Blaustein et al. 1994; Green 1997; Corn 2000), compromised water quality (Berrill 1994; Harris 1998; Hayes 2002; Brodman 2003; Houlahan J.E. 2003), introduced predators (Fisher 1996; Lawler 1999; Knapp et al. 2007), and emerging infectious diseases (Daszak 1999; Blaustein and Kiesecker 2002). It is unlikely that any single factor is responsible for the wholesale losses of multiple amphibian species; more likely is that some or all of these factors are working in concert, each contributing differently to declines in different populations (Blaustein and Kiesecker 2002; Collins and Storfer 2003; Wilcox 2006). While many studies have looked at these factors separately (Hecnar 1995; Knapp 2000; Hayes 2002; Johnson 2003), extremely few have looked at two or more impacts to gain an understanding of how they work together and which play the greatest role in declines (but see Kiesecker 2002; Relyea 2004). This tendency to focus narrowly on one causal factor is especially evident in the field of disease ecology, where the role of pathogens in governing the ecological distributions, population dynamics, and community structures of host species is increasingly clear (Dobson 1992; McCallum 1995). Disease and parasite effects are predicted to be especially strong for species with small populations and/or restricted ranges (Anderson et al. 1986) and when acting synergistically with other stressors (Blaustein and Kiesecker 2002; Blaustein 2003; Johnson 2004; Kiesecker 2004). Further, when pathogens are not operating in a density dependent fashion, as is the assumption in most classic disease models (e.g., Anderson and May 1972), they can be powerful drivers of host extinctions (de Castro and Bolker 2005). To date, most studies of wildlife disease focus on only a single pathogen and its hosts (Grenfell et al. 2003). The area of investigation of multiple-pathogens on a host population has begun to grow in the past several years and is ripe for development (Grenfell et al. 2003). My work will provide unique insights to the role and interactions of multiple pathogens on a wild host population, how pathogen effects

on different life history stages interact, and how different spatial and temporal patterns translate to differential regulation for host populations. Through my dissertation, I am investigating how interactions between amphibian diseases and other threats affect populations at the local and metapopulation levels, and more broadly, I am developing and investigating approaches to the quantification and prediction of interacting factors on population dynamics. As I describe in the System Description below, I have chosen as the focal points of my work a set of three pathogens and several key environmental stressors that are especially relevant for California red-legged frogs (Rana draytonii) and their co-occurring amphibian species (Figure 1). Each pathogen has a different spatial and temporal pattern of interactions with their common host and each also affects a different set of host life stages. While tackling this set of varied interactions is an ambitious task, I can base my work on considerable background knowledge of the study system, and will use well-established modeling methods to synthesize my results. Additionally, many of the parameters needed to understand population-level dynamics of these diseases can be determined directly from field data and simple experiments. The three key questions that I am attempting to answer in this work are: 1) Do the three pathogens act synergistically in their effects on hosts, or will some limit the population-level consequences of others? 2) Which biotic and abiotic factors most critically mediate the intensity of infection and virulence for each pathogen? 3) To what extent is the presence of multiple, co-occurring amphibian or fish hosts important in modifying host-pathogen interactions in this system? Background: Diseases and Other Threats to Amphibian Populations In spite of the widespread view that pathogens have been critical drivers of many amphibian declines, few attempts have been made to directly quantify population-level effects of pathogens on amphibian hosts. For example, a theoretical epidemic model of Batrachochytrium dendrobatidis (Bd), the agent causing chytridiomycosis in many amphibian populations, and a single host was developed by Emmert and Allen (Emmert and Allen 2004). Using SIR and SI models, they predict complex deterministic dynamics, but their work does not incorporate temporal variability, which may greatly modify the outcomes. Further, the parameter values are not tied to any species nor based on actual values grounded in field or lab studies, thus the outcomes of the models are of limited value in predicting real population dynamics in the field. Another study exploring the effects of Bd applies SI models a population of Rana muscosa (Briggs et al. 2005). Demographic parameters were based on field and lab estimates, while Monte-Carlo simulations were used to investigate a range of transmission parameter values due to a lack of field data (Briggs et al. 2005). The key findings in this study make intuitive sense: population persistence is highly unlikely if infection always leads to death, recovery between tadpole and metamorph stage decreases the impact on the population, and population persistence depends on the survival of some post-metamorphic individuals (Briggs et al. 2005). A literature search did not reveal any attempts to model the effects of Ranavirus (Rv) or Ribieroia ondatrae (Ro), my other focal pathogens. Interest in understanding the dynamics of multi-pathogen models for wildlife is on the rise (VanBaalen and Sabelis 1995; Dobson and Foufopoulos 2001; Dawes and Gog 2002; Allen et al. 2004; Kirupaharan and Allen 2004; Abu-Raddad and Ferguson 2005; Allen and Kirupaharan 2005). While some recent studies focus on a particular suite of pathogens (Dobson and Foufopoulos 2001; Allen and Kirupaharan 2005), to date their models have not been based on parameter values estimated with field or lab data. In sum, these models offer a basis for thinking about multi-pathogen models for wildlife populations, but have not been developed for real

Figure 1: Study Schematic

Figure 3: Freshwater Sites

populations or with the limits of field data collection in mind. I will base my work on existing model forms, but I will modify them to incorporate field-estimated parameter values to provide insight on the dynamics of my focal species. One of the most active and important areas of work on amphibian declines concerns the interactions of disease and other stressors. Host-pathogen dynamics involve a variety of host defenses and pathogen counter offenses that are both impacted by complex environmental interactions (Tinsley 1995; Dybdahl 1998; Rollins-Smith et al. 2002; Thrall 2002; Davidson et al. 2007). Seasonality, temperature, and humidity are a few of the environmental factors that can influence spatial and temporal dynamics of host-pathogen relationships (Weng et al. 2002; Berger et al. 2004; Piotrowski et al. 2004; Carey et al. 2006; Pounds et al. 2006; Rowley 2006; Bosch et al. 2007; Kriger and Hero 2007). Contaminants (Kiesecker 2002; Linzey et al. 2003; Taylor 2004; Forson and Storfer 2006; Davidson et al. 2007), eutrophication (Johnson 2004), climate change (Pounds et al. 2006; Bosch et al. 2007), predators (Taylor 2004; Johnson et al. 2006), and intra- and inter-specific competitors (Parris and Cornelius 2004) are also implicated in changing host-pathogen dynamics. One example, the interaction between pathogenicity of Bd and temperature patterns, has proven difficult to sort out, but ultimately fruitful. Temperature ranges in broad scale studies of patterns of Bd have led to some insights (Pounds et al. 2006; Bosch et al. 2007), but also no clear patterns have emerged in another broad scale study (Alexander 2001). Studies at a finer scale have led to a much clearer understanding of the interaction of temperature and Bd in specific species (Woodhams 2003; Berger et al. 2004; Drew et al. 2006; Rowley 2006). Thus, understanding the role a pathogen plays in host populations reveals only a portion of the story; exploring the complexity of host-pathogen dynamics and the environmental interactions can help understand the role of pathogens in real populations. System Description

I am conducting this work along the Central Coast of California, focusing on the Elkhorn Slough area (Figure 2), where I have worked for the past four years. Lands around Elkhorn Slough are primarily residential, agricultural, or conserved. About forty freshwater

ponds are scattered throughout the landscape (Figure 3), encompassing a range of sizes, depths, and other habitat characteristics (D'Amore et al. in review). These ponds harbor my host focal species, three pond-breeding frogs: Rana draytonii (California red-legged frog), Rana catesbeiana (American bullfrog), and Pseudacris regilla (Pacific treefrog). R. draytonii is a large native ranid frog that has been extirpated from over 70% of its range (Fellers 2005). P. regilla is smaller native pond-breeding frog that is abundant throughout its range (Rorabaugh and Lannoo 2005). R. catesbeiana is an introduced ranid that poses a threat to native amphibians through predation, competition, and shared pathogens (Daszak et al. 2004; Casper and Hendricks 2005). Three serious amphibian pathogens have been confirmed in amphibians in this region, the fungus Batrachochytrium dendrobatidis (Bd), a parasitic flatworm Ribeiroia ondatrae (Ro), and a virus in the genus Ranavirus (Rv) (Hemingway, unpublished data). Bd inhabits the keratinized mouthparts of larval amphibians and skin of adults (Berger et al. 1998), Ro causes limb

Figure 2: Study Region

deformities in metamorphic amphibians (Sessions 1990; Johnson 1999; Stopper 2002), and Rv causes mass mortality events in larval and metamorphic amphibians (Wolf et al. 1968; Chinchar 2002) (see Table 1 for more details).

Table 1: Comparison of Focal Pathogens (see References for citations)

Ribieroia ondatrae (Ro)

Batrachochytrium dendrobatidis (Bd) Ranavirus (Rv)

Type of Pathogen Trematode parasite Chytrid fungus Virus Transmitted Between

Amphibians? No1 Yes2 Yes2, 3

Amphibian Life Stage Affected/Infected Tadpole1,4,5,6,7 Tadpole, Metamporph,

Adult7,8 Tadpole, Metamorph,

Adult2,9,10

Life-stage Associated with Mortality

Larvae, Metamorph1,2,4,6,7 Metamorph, Adult2,8,9 Larvae, Metamorph2,9,10

Sublethal Effects Subcutaneous cysts5,11 Decreased growth rates12,13 Percent Mortality <5% to >50%5 <=100%8,9,14,15,16 <=100%2,9,10,17

Carriers N/A Bullfrogs18

Intraspecific adults, Bullfrogs, Tiger

Salamanders, Fish2,19,20,21,22

Potential Interactions Nutrients,

contaminants, predators,

seasonality5,7,23,24,25,26

Temperature, seasonality, predators, contaminants, antimicrobial peptides

13,16,27,28,29,30,31,32,33,34,35,36,37,38,3

9,40,41,42,43

Contaminants, host density-dependence,

seasonality, temperature, antimicrobial

peptides9,17,19,44,45,46,47

Detection Method

Visual Identification of Limb Malformat

ion or Histology6,

11

Visual Detection of Tadpole Mouthpart

Malformation, Histology, or qPCR48,49,50

Visual Detection of Edema and Hemorrhagi

ng Symptoms,

Histopathology, PCR17,51,52,53

Of key importance for my study is the ease of detection of the three pathogens in this

threatened host population. As noted in Table 1, symptoms from all three pathogens can sometimes be detected visually, but not all infected individuals are symptomatic. Samples to test for Bd can be taken by gently swabbing the skin of adult amphibians and the mouthparts of tadpoles (Boyle et al. 2004), while samples for Rv are collected by taking a small tail clip from tadpoles (Greer and Collins 2007) and toe clip from adults (St-Amour and Lesbarreres 2007). Bd and Rv DNA can then be detected using regular or quantitative Polymerase Chain Reaction technology (Boyle et al. 2004; Greer et al. 2007; St-Amour and Lesbarreres 2007). Alternatively, the amphibian can be sent to a veterinary pathologist for histopathology (Densmore and Green 2007), but this is less favorable, particularly with a declining host population, as it involves sacrifice of the animal. Thus, I will take advantage of the technologies that allow pathogen detection without sacrifice for the majority of my data collection.

To date, no published studies have examined if R. draytonii experience mortality due to infection with Bd, but a closely related species, Rana muscosa, has been shown to be strongly effected (Rachowicz et al. 2006), while bullfrogs are thought to be carriers of the fungus (Daszak et al. 2004). I have observed R. draytonii, P. regilla, and R. catesbeiana tadpoles with limb malformations indicative of Ro, but have observed few adults with them, indicating that limb malformations lead to decreased survivorship (Hemingway, pers. obs.). Further, I have positively identified Ro encysted in the inguinal and tail reabsorption area of malformed P. regilla from my

*These same methods will be applied to P. regilla & R. catesbeiana for question 3.

study sites, as well as dissected them out of Planorbid sp. freshwater snails from the same sites. I have also observed all three species with symptoms of Rv, and I have confirmed these infections in R. draytonii and P. regilla from my sites with collaborator Dr. David Green of the USGS National Wildlife Health Center (Hemingway, unpublished data). In my field sites, I have also observed major die-off events characteristic of Rv for all three of my host species (Hemingway, unpublished data). Proposed Research General Strategy My proposed research builds on five years of field data collection and analysis. My collaborators and I have surveyed ~40 ponds in each of these five years, collecting data on presence and distribution of the three amphibians and also habitat characteristics for each pond site. We have used this information to understand which habitat characteristics best predict site occupancy and breeding for R. draytonii (D'Amore et al. In Review). Further, we have been conducting a mark-recapture study on R. draytonii and have pittagged more than 650 individuals to date. While this ongoing monitoring is a rich platform for my dissertation work, it is not primarily targeted at the questions I am trying to answer now. My overall goal is to determine which of my pathogens are most likely to have strong effects in suppressing R. draytonii populations, if they are likely to operate together in an additive or synergistic fashion, and how best to target further data collection to answer specific questions about these pathogens and the effects of environmental factors in shaping their effects. More specifically, I will address the three questions posed in the introduction through a combination of individual assays and experiments, aimed at understanding the various controls on pathogen effects (Table 2). Importantly, there is no off-season for my work, with one or more frog stages present in ponds year-round. Thus, as with past data collection, I will visit ponds between four and ten times per month over the next two years: unless otherwise noted, all field sampling will be conducted at sampling done during these year-round visits, providing fine-scale temporal resolution on disease incidence and mortality rates. These individual-level data will combine with my ongoing population-level data collection on numbers and habitat characteristics to allow inferences about both individual and population effects of pathogens. I will use both standard statistical analyses and host-disease models to synthesize my results into a unified picture of this complex set of species interactions. It is important to note that my collaborators and I carefully adhere to a set of accepted protocols to avoid being a vector for these pathogens, both between sites and between animals (Speare et al. 2004). Question 1: Do the three pathogens act synergistically in their effects on host survival and health, or will some limit the individual and population-level consequences of others?

To address this question, I need to understand the effect of each of the pathogens on individual R. draytonii and on host population numbers. To estimate virulence, I am using the mark-recapture study to estimate mortality and changes in length to weight ratio for individual frogs in natural settings, rather than depending on traditional experimental infections that involve

sacrifice of animals and artificial effects of lab conditions. Our ongoing mark-recapture work relies on pittagging, but this method only allows marking of R. draytonii that are 5cm or larger, excluding the sensitive metamorphic stage through the first year. To get data on this important stage, I will employ Visual Implant alpha-numeric tags to mark newly-emerging frogs (Buchan et al. 2005) from a sub-sample of our pond sites. These alpha-numeric tags avoid many common tagging problems (e.g., the tag migration in the body seen with elastomer-dye tags) and they are appropriate for marking very small frogs (Buchan et al. 2005).

To date, I have Bd-swabbed and Rv- and Ro-inspected over 300 marked R. draytonii, with a nearly 20% recapture rate. Additionally, I have swabbed and inspected over 250 juveniles and young of the year that are too small to pittag. To improve detection of asymptomatic or latent infections of Rv in 2008 and 2009 field seasons, I will employ a newly developed, non-lethal technique for the detection of Rv, involving PCR of a small tail clip of 60 larval amphibians per site (the minimum number to obtain a 95% confidence of detection) (Greer and Collins 2007) or a toe clip of up to 60 post-metamorphic amphibians per site (St-Amour and Lesbarreres 2007). To estimate mortality effects, I will construct alternative survival models using program MARK that will allow me to gauge AIC support for different mortality rates due to disease status, and also estimate likelihood-based confidence intervals for these effects.

I am also collecting data on individual, unmarked tadpoles, capturing a sample from each pond where R. draytonii breed, storing them in individual containers in a cooler while I take data pond-side on symptoms, weight, length, body condition, Bd swabbing. After taking the data, I immediately release each tadpole back to the pond. In 2007, I inspected and took swab samples from 153 R. draytonii tadpoles over the 22 ponds that had water for breeding during this dry year. To limit costs, I will confirm a subset of the Bd-infected larvae with qPCR tests and compare those results to the mouthpart malformations to determine if mouthpart malformation can be used as an accurate proxy for infection in this species. Currently, I am working up the Bd swab samples in the lab. I have completed the extraction step on over 600 samples, including these tadpole samples, and I will begin running the qPCR on them in December 2007. When I combine the resulting data with the Rv PCR testing next field season, I will have a very good dataset on incidence of infection. In addition to the R. draytonii tadpoles I sampled during the 2007 field season, I inspected and took an additional 321 Bd samples from P. regilla and 40 samples for R. catesbeiana tadpoles in the ponds where they occurred. I also opportunistically Bd swabbed all adult and juvenile R. catesbeiana and P. regilla. Results from a sample of 30 of my swabs reveal R. draytonii and R. catesbeiana infected with Bd in both sites at relatively high frequencies, in particular about 30% of R. draytonii (Figures 4a, b, and c). While the sample size is low, there also appears to be higher percent of infection at the Elkhorn Ranch ponds compared to the Elkhorn Slough ponds (Figures 4b and c). Importantly for my research, this preliminary data show a high and spatially variable

infection rate, suggesting good statistical power to look at effects of infection in these populations.

Unfortunately, the tadpole sampling will not provide data on mortality rates for R. draytonii for these pathogens. To get estimates of susceptibility and mortality in late-stage tadpoles and young metamorphs due to Rv and Bd in the 2008 field season, I will place 15 Gosner stage 35 to 38 tadpoles in field cages (Rachowicz and Briggs 2007) in each of six ponds and observe them for symptoms of Rv and Bd. At the beginning and the end of the experiment, I will sample each animal for both Rv and Bd. I will observe these animals until 60 days after metamorphosis. Any animals that become symptomatic during the experiment will be sent to Dr. David Green of the USGS National Wildlife Health Center for pathological work to determine infection status, cause of illness, and/or cause of mortality. Asymptomatic animals will be released from the field cages into the pond. The data from this experiment will allow me to estimate rates of infection and mortality for Bd and Rv, which are otherwise impossible to estimate in the field. The data collection described above will provide the information necessary to quantify the effects of the three pathogens on R. draytonii individuals, providing information on mortality, susceptibility, and sublethal effects such as changes in mass. This data can then be used to estimate the effects at the population level for each pathogen. Because the pathogens occur singly at some sites and in combination at other sites, I can also explore be able to estimate how the pathogens operate in concert, using modeling and statistical analyses that incorporate the field-derived data to ground my results. Question 2: What biotic and abiotic factors mediate the intensity of infection and virulence for each pathogen and disease? A variety of environmental variables are likely to impact the dynamics of these three pathogens (Figure 5). I will collect data on the most likely of these environmental drivers and perform a set of statistical analyses to determine the best predictive models with which to characterize pond-level disease patterns.

Data collection will focus on quantifying the intermediate and definitive Ro hosts, water quality data (nitrates, nitrites, phosphates, and ammonia, pH, turbidity, and water temperature), pond characteristics (water depth, pond permanence, proximity to roads and agriculture, pond and upland vegetation), and presence of aquatic predators, including both fish and invertebrates. These are characteristics we have collected for our pond sites for the past five years of monitoring, thus I have an established data collection protocol and baseline data (D'Amore et al. In Review). I am also inspecting a subset of larval amphibians in each pond for the following indicators of stress: (1) intensity of tissue damage due to predation, typically observable in the tail of the larvae and (2) weight and length in conjunction with Gosner developmental stage.

A preliminary analysis of the tadpole data from 2007 revealed that predator-caused lacerations varied by species (Pearson Chi-square df=2, p<0.000) with R. catesbeiana and P. regilla more likely to be injured than R. draytonii and no significant species x pond relationship

Figure 5: Example of Biotic and Abiotic Influences on Pathogens

(Pearson Chi-square df=2, p<0.060). I suspect this difference may be behavioral, with R. draytonii tadpoles tending to spend more time in vegetation and away from predators, while the other two species are often found basking in waters along the shore and in open waters. I digitized tadpole lengths from field photos, and I have measured about half of the tadpoles sampled. For those samples, I looked at the length to weight ratio by species and found that R. catesbeiana (N= 19) had an average of 1.74 length to weight ratio and a standard deviation of 1.87, R. draytonii (N= 42) had a ratio of 2.69 and a standard deviation of 2.13, P. regilla (N=139) had the largest ratio, 5.98 and standard deviation of 2.64. As the sample grows, it will lend itself to exploration by Gosner stage and relationships to predator composition within each pond (Lecort 1995; Lawler 1999). This data will then be used with disease incidence information to explore the role predator stress may play in rates of infection by the three different pathogens in the wild.

To gather data on temperature relevant to R. draytonii habitat use, I will place ibutton temperature loggers in locations where frogs spend their time, as determined by radio-tracking studies (Fellers and Kleeman 2007, D’Amore unpublished data), to estimate seasonal average temperature ranges for R. draytonii.

I will develop multi-parameter logistic regression models that predict pathogen presence as a function of these environmental variables. For ponds with evidence of a pathogen, I will also build general linear models to predict disease prevalence and virulence as a function of the measured environmental variables. I will use maximum likelihood methods to look at how environmental variables explain the variation in infection and AIC model comparisons to select the most important environmental drivers of disease presence. Question 3: To what extent is the presence of multiple, co-occurring amphibian or fish hosts important in modifying host-pathogen interactions in this system?

R. catesbeiana has been cited as a potential carrier of Bd (Daszak P 2004; Garner et al. 2006) while both R. catesbeiana and P. regilla experience mortality due to Rv (Miller et al. 2007). Fish can also be infected with the same strain of Rv as amphibians (Mao et al. 1999). To gauge how these organisms play a role in disease effects on R. draytonii, I have begun by testing a subset of R. catesbeiana and P. regilla at each pond for Bd and Rv and by estimating Ro infection by examination for limb malformations. I will also monitor for fish die-off events (which have been observed in our previous field surveys) and send samples to Dr. Green for histopathology. Using this field data, I will compare infection rates of amphibians at sites with multiple species and those sites with single species. I will then use maximum likelihood models, in a manner similar to that used in Question 2, to look at how interactions with R. catesbeiana and P. regilla and co-occurring fish species may explain the variation in infection rates for the three pathogens in R. draytonii.

I have performed a preliminary analysis on the first year of tadpole data, using degraded mouthparts as a sign of infection with Bd, hemorrhaging and edema as a sign of infection with Rv, and limb malformations as a proxy for infection with Ro. Mouthpart malformation rates differed between species (Pearson Chi-square df=2, p<0.000), with R. catesbeiana having the greatest proportion of tadpoles with malformed mouthparts, 90%, while R. draytonii and P. regilla had 22% and 19% malformed mouthparts respectively. These differences may be due to life history, with R. catesbeiana metamorphosing after two years compared to one year for the natives, or due to difference in tadpole density or susceptibility. The proportion of each tadpole species with degraded mouthparts was correlated with co-occurring species with degraded mouthparts by pond (Pearson correlation df=6 r=0.795, p=0.02). Sign of Rv infection in tadpoles varied by pond (Pearson Chi-square df=18, p<0.000) with a few ponds hosting the majority of tadpoles with symptoms, as well as by species (Pearson Chi-square df=2, p<0.000), with R. catesbeiana with no cases and P. regilla having a greater proportion of likely infections than R. draytonii. Limb malformation were significant by species (Pearson Chi-square df=2, p<0.000) with R. draytonii and P. regilla much more likely to be malformed than R. catesbeiana, while there did not appear to be a relationship to site (Pearson Chi-square df=2, p<0.190).