Native and Invasive Insect Herbivory and Precipitation Explain Variation in Vital Rates of an...

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Poster presented at the Graduate Student Research Day at University of Florida on 28 October 2014.

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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.  

1ksauby@ufl.edu,  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)  

(b)   (d)  

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  

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

−0.20 −0.15 −0.10 −0.05 0.00Value

Varia

ble

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.  

●●

−2

−1

0

Absent PresentNative Bug

Rel

ative

Gro

wth

Rat

e

●●

−2

−1

0

Absent PresentInvasive Moth

Rel

ative

Gro

wth

Rat

e

Fruit  Abundance  

Invasive Moth Presence, t−1Invasive Moth Presence, t−1 x Precipitation

ln(Plant Size), t−1Native Bug Presence, t−1

Precipitation (12 month sum)

−2 −1 0 1Value

Varia

ble

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

0

10

0 50 100 150 200 0 50 100 150 200 0 50 100 150 200

Invasive MothAbsentPresent

HBSP Mexico Beach SASP

−5

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5

10

0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5

Native BugAbsentPresent

Total Annual Precipitation (cm)

ln(Plant Size), t-1

Ln(N

umbe

r of

Fru

it)

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.  

(a)

(b)

Probability  of  Fruit  Produc#on  HBSP Mexico Beach SASP

0.00

0.25

0.50

0.75

1.00

−1 0 1 −1 0 1 −1 0 1

Invasive MothAbsentPresent

HBSP Mexico Beach SASP

0.00

0.25

0.50

0.75

1.00

−1 0 1 −1 0 1 −1 0 1

Native Bug AbsentPresent

Pro

babi

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of F

ruit

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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.  

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

ln(Plant Size), t−1Native Bug Presence, t−1

Native Bug Presence, t−1 x ln(Plant Size), t−1Precipitation (12 month sum)

Relative Growth Rate

−10 0 10 20Value

Varia

ble

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.  

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