Native and Invasive Insect Herbivory and Precipitation Explain Variation in Vital Rates of an Endangered Plant, Opuntia stricta

Na#ve and Invasive Insect Herbivory and Precipita#on Explain Varia#on in Vital Rates of an Endangered Plant, Opun%a stricta

Kristen E. Sauby1, John Kilmer2, Robert D. Holt3, Travis D. Marsico2

Introduc#on We hypothesized that na@ve and invasive insects might differ in their impacts on na@ve host plants. The impact of herbivory on plants may be manifested in different ways, depending on which part of the plant’s life history is affected. Our study system consists of the invasive South American cactus moth, Cactoblas)s cactorum (Berg) and the specialist na@ve bug, Chelinidea vi4ger McAtee (Figure 1). Both aOack popula@ons of na@ve prickly pear cac@ (genus Opun)a) in Florida. Objec#ve: To quan@fy the rela@ve importance of herbivory (invasive and na@ve) and abio@c condi@ons in explaining varia@on in demographic rates of O. stricta.

Methods We surveyed six sites in the Florida Panhandle for popula@ons of Opun)a stricta (Haw.) Haw. (Figures 2 and 3, respec@vely). We found popula@ons at Henderson Beach State Park (HBSP), Mexico Beach, and St. Andrews State Park (SASP). At each of those three loca@ons, approximately 14-‐20 plants of each species were individually tagged and resurveyed at least twice a year for five years (2009-‐2014). We used generalized linear mixed models (“lme4” package) in R (version 3.1.1) to quan@fy the rela@ve ability of different variables to explain varia@on in three vital rates of O. stricta: Rela@ve Growth Rate, Probability of Fruit Produc@on, and Fruit Abundance. We performed model selec@on using Akaike Informa@on Criterion (AIC) to determine the best model for each of the rates. For each best model, we calculated confidence intervals using likelihood profiles and likelihood ra@o tests.

-‐ Plant size is measured as the number of segments -‐ Rela#ve Growth Rate = (Size,t – Size,t-‐1)/(Size,t)/(Number of days between surveys), from Paine, CET, et al., 2012, Methods Ecol. Evol. 3:245-‐256. -‐ Precipita#on: total rainfall (cm) within the 12 months prior to the sampling date -‐ Rela#ve Growth Rate, ln(Plant Size),t-‐1, and Precipita#on were standardized by centering values on the mean and then dividing by 2 standard devia@ons

Future Direc#ons We will quan@fy the rela@ve importance of abio@c and bio@c variables in explaining survival of O. stricta. We will also incorporate addi@onal abio@c variables into the analysis (number of days with rain, maximum and minimum temperature). Finally, we will build a popula@on model to es@mate the effect of the insect herbivores on popula@on dynamics.

Acknowledgements Florida State Parks and Nokuse Planta@on provided access to sites and allowed for the collec@on of plant and insect material. Meghan Foard assisted with data collec@on and Anastasia Cooper and Emily Mizell helped with data collec@on and data entry. Gary N. Ervin provided financial support (through support from USGS grants [04HQAG0135 and 08HQAG0139], a USDA grant [2007-‐55320-‐17847], and Mississippi State University funding). T.D. Marsico received funding from Arkansas State University and the Arkansas Biosciences Ins@tute. A. Cooper received a travel grant from the Arkansas State University Molecular BioSciences Program to collect data.

[email protected], Department of Biology, University of Florida; 2Department of Biological Sciences, Arkansas State University; 3Department of Biology, University of Florida

Results Varia@on in Rela#ve Growth Rate was not well explained by models containing precipita@on and plant size. The presence of invasive moth and na@ve bug at the previous @me step had a slightly nega@ve effect on rela@ve growth rate (Figures 4 and 5). Probability of Fruit Produc#on was posi@vely affected by rela@ve growth rate and the presence of the na@ve bug and plant size at the previous @me step. The presence of the invasive moth interacted with plant size at the previous @me step to posi@vely affect the probability of fruit produc@on. In contrast, the na@ve bug and plant size at the previous @me step interacted nega@vely to affect the probability of fruit produc@on. Precipita@on also nega@vely affected the probability (Figures 6 and 7). Fruit abundance was posi@vely affected by precipita@on and plant size at the previous @me step. The presence of the na@ve bug at the previous @me step had a small, nega@ve effect. Also, invasive moth presence interacted with precipita@on to nega@vely affected fruit abundance (Figures 8 and 9).

Mexico Beach

Nokuse Plantation Torreya State Park

Big Lagoon State Park

St. Andrew's State Park

Henderson Beach State Park

0 25 50 75 10012.5Kilometers

Figure 2. Sites surveyed in the Florida panhandle.

Figure 1. (a) The invasive cactus moth and (b) its damage; the (c) na@ve bug and (d) its damage.

(c) (a)

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Figure 6. Plant size at the current @me step as a func@on of plant size at the previous @me step.

Figure 3. Opun)a stricta.

Results Rela#ve Growth Rate

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Invasive Moth Presence, t−1Native Bug Presence, t−1

−0.20 −0.15 −0.10 −0.05 0.00Value

Varia

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Figure 4. Fixed effect es@mates from the best linear mixed model (selected among a set of candidate models using AIC) and 95% confidence intervals. Site was also included in the model as a random effect.

Figure 5. Rela@ve Growth Rate in the presence/absence of insect herbivores.

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Figure 8. Fixed effect es@mates from the best generalized linear mixed model (Poisson family; model selected among a set of candidate models using AIC) and 95% confidence intervals. Site and plant ID were also included in the model as a random effects.

HBSP Mexico Beach SASP

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Figure 9. Rela@onship between (a) precipita@on and fruit abundance and (b) plant size and fruit abundance in the presence/absence of insect herbivores.

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Probability of Fruit Produc#on HBSP Mexico Beach SASP

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ln(Plant Size), t-1 Figure 7. Rela@onship between plant size and fruit produc@on probability in the presence/absence of insect herbivores.

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Invasive Moth Presence, t−1Invasive Moth Presence, t−1 x ln(Plant Size), t−1

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Figure 6. Fixed effect es@mates from the best generalized linear mixed model (binomial family; model selected among a set of candidate models using AIC) and 95% confidence intervals. Site and plant ID were also included in the model as a random effects.