Trapping to prove area freedom

biosecurity built on science

Trapping to prove area freedom

Cooperative Research Centre for National Plant Biosecurity

Francis De Lima & Shirani Poogoda Department of Agriculture and Food Western Australia


Project objectives

•reduce monitoring cost

•validate for market access


Methods Dynamic trapping – trap in attractive hosts

Static trapping – trap in fixed grids

Number of Sites – 201 (Donnybrook, Manjimup, Pemberton, Kununurra) Site characteristics 1. Fly density/trap/week = 0; > 0 < 1; > 1 < 2; > 2 2. no control of fruit fly 3. range of alternate hosts Data collected: fly numbers host phenology climate


Orchard Habitats: Citrus / Deciduous

No winter carryover in “Allee” populations:

Manjimup, Pemberton Orchards

Winter survival in Donnybrook


10 Hosts spp monitored by a single dynamic trap vs. 2 Hosts spp by a static trap

Nashi

Pear D3 Nectarine

D1

Plum Nectarine

D7

Peach Apricot

D6 Citrus

Apricot Apple Apple Lemon D5 Plum

S3

Citrus S1/S2 Olive

Apricot Apple D4

Apple Apple Apple D2

Plum Plum Plum Plum Plum Plum Apple

Plum Apple


Donnybrook - Male flies in male Traps (2009)

-0.5

0

0.5

1

1.5

2

2.5

3

15-Ja

n-09

29-Ja

n-09

12-F

eb-09

26-F

eb-09

12-M

ar-09

26-M

ar-09

09-A

pr-09

23-A

pr-09

07-M

ay-09

21-M

ay-09

04-Ju

n-09

18-Ju

n-09

Collection date

Ave

rage

Fly

num

ber p

er tr

ap

per w

eek

DynamicStatic

(Linear Mixed Model with loge (count+1) transformed data). On average Dynamic traps captured 0.79 flies and Static traps captured 0.32 flies (P<0.001). Interaction: trapping method x date of collection (P<0.001) indicated greater efficiency of dynamic trap when fly numbers were high >1fly/trap/week. Data proves that 40-50 dynamic traps are required for every 100 static traps to provide an equivalent estimate of the population of MFF {0.50 (50%) in 2008 & 0.40 (40%) in 2009}.


(General Linear Mixed Model with loge (count+1) transformed data) Host type had a significant effect on Average fly number (after adjustment for date effects (P<0.001)) With pair-wise comparisons (5%LSD): peach > pear > nectarine > others (lemon, apple, fig, orange, grapefruit) Nectarine and plum> orange and grape fruit

Host effect on average number of male flies

0

1

2

3

4

Peach

Pear

Aprico

t

Nectar

ine Plum

Manda

rin

Lemon

Apple Fig

Orange

Grapefr

uit

Host type

Num

ber o

f flie

s pe

r tra

p pe

r w

eek


Fly numbers at Donnybrook Site 232008 - 2010

0

0.5

1

1.5

2

2.5

Jul-0

7

Septem

ber

Novem

ber

Janu

aryMarc

hJu

ne

Augus

t

Octobe

r

Decem

ber

Februa

ryMay Ju

ly

Octobe

r

Decem

ber

Februa

ryApri

l

Collection time

Log(

Fly

num

ber+

1)/ f

ortn

ight

Static Dynamic

High numbers show richness of hosts in dynamic trap


Results Summary Dynamic monitoring method is: • more effective (breeding pop is detected earlier and at lower

threshold) • provides more valuable decision making data

(ecology, biology, phenology) • requires less time • requires less labour It is also a good Template for: • proving Area Freedom • proving Areas of Low Pest Prevalence


Discussion Why use a trap GRID when fruit flies are not UNIFORMLY distributed?

•Rigid grids are not based on science •Many traps are in unattractive hosts •Monitoring costs are higher

Alternative: Dynamic method. Required Knowledge: Fruit phenology (attractive hosts) Fly biology (life cycle) Fly ecology (flight patterns, orientation) Environmental conditions (temperature)

Fruit flies will turn up – better trap them in the areas

they prefer to inhabit


Population Lifecycle

Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

Eggs Adults

Stage Threshold (oC)

Day-degrees

Egg-larva 10.0 176.6

Pupa 11.6 138.6Preoviposition 15.7 60.4

Winter

2-4 days

14-16 days 12-14

days

28-34 days

Summer 2-4 days

14-16 days 12-14

days

28-34 days

Summer

fly emerging 1st 2nd 4th gen 5th gen Overwintering

Au Sep Oct No Dec Jan Feb Ma Ap Ma Jun Jul Au Sep Oct No Dec Jan Feb MaNavel

Valencia Mandarin

GrapefruitApple

PearApricot

PlumNectarine

PeachFig

Loquat

RISK L M H Average of 10yr °C temperature data 1996-2005

3rd gen

Donnybrook

Donnybrook & Manjimup Town Adelaide City

Pemberton Manjimup & Adelaide Orchards


0

20

40

60

80

100

120

Au S O N D J F M A My J Jy

Non -Infesting

Phase

Period of HighInfestation Risk

Dispersal Phase

GT GT GT GT

Eggs Adults

Medfly survives in Manjimup Town


where tmi is the mean temperature at day i. TTS1 is the thermal time sum for the development egg phase, TTS2

for the larvae and TTS3 for the pupae. N is the number of days required to complete each phase respectively

The derived thermal time model (De Lima 2007) for the complete life cycle is:

>≤

=⋅−=

>≤

=⋅−=

>≤

=⋅−=

∑

∑

∑

+=

+=

=

3mi

3mi3

)1(333

2mi

2mi2

)1(222

1mi

1mi1

1111

t,1 t,0

with )(

t,1 t,0

with )(

t,1 t,0

with )(

3

2

2

1

1

θθ

δδθ

θθ

δδθ

θθ

δδθ

n

ni

n

ni

n

i

mi

mi

mi

tTTS

tTTS

tTTS


Validating the model

∑∈∈

+

∈

+ =)()(

1}{mNN

mNN xBX

mkNNm

NN

mkN x

xBx

Using the measured data in a time series to describe the information about the dynamics of the system.

k-steps xm N-2 xm N-1 xm N xm N+1 xm N+2

Pop. N


Medfly: mortality due to low temperature

0

10

20

30

40

50

60

Perth

Manjim

up

Harve

y

Donny

broo

k

Pembe

rton

Kunun

urra

Benall

a

Bendig

o

Tartu

ra

Ecuch

a

SwanHill

Mildur

a

Tenn

antC

reek

AliCur

ingNT

AliceS

pring

s

Hillsto

nNSW

Broke

nHill

Loxto

nSA

Lens

wood

PortA

ugus

ta

Adelai

de

Laun

cesto

n

Day

s B

elow

6°C

LD50

LD99

CLIMATE


Medfly: Mortality due to high temperature

0

10

20

30

40

50

60

70

80

Perth

Manjim

up

Harvey

Donnybro

ok

Benall

a

Bendigo

Tartura

Ecuch

a

SwanHill

Mildura

Tennan

tCree

k

AliCurin

gNT

HillstonNSW

Broke

nHill

LoxtonSA

Lensw

ood

PortAugusta

AliceS

prings

Kununurra

Adelaide

Launce

ston

Pembert

on

Hou

rs a

bove

35°

C

LD50

LD99

CLIMATE


Discussion

Comparative Trap Density - Australia and USDA 400 m (AU) = 25 traps /2.56 km2 1 km (AU) = 4 traps/1 km2

1 mile (USDA)= 4 traps/1 mile2 (2.56 km2) density /1 km2 = 4 (USDA) : 25 (AU)

Dynamic system proves area freedom by trapping in attractive hosts @ 2 traps/1km2


Conclusion

We can reduce Monitoring Costs by: 1. Reducing trap density by 50% (no fixed grid)

2. Improving trap placement (phenology, biology)

3. Reducing trap monitoring frequency (ecology, biology)


Thank you

For more information, please email [email protected]

Trapping to prove area freedom

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