Trapping to prove area freedom Francis De Lima & Shirani Poogoda Department of Agriculture and Food Western Australia
biosecurity built on science Cooperative Research Centre for National Plant Biosecurity
Project objectives
•reduce monitoring cost •validate for market access
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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 biosecurity built on science
Orchard Habitats: Citrus / Deciduous
Winter survival in Donnybrook No winter carryover in â&#x20AC;&#x153;Alleeâ&#x20AC;? populations: Manjimup, Pemberton Orchards biosecurity built on science
10 Hosts spp monitored by a single dynamic trap vs. 2 Hosts spp by a static trap Nashi Pear D3
Nectarine D1
Apricot
Apple
Apple
Plum Peach Nectarine Apricot D7
D6
Citrus
Lemon
S1/S2
D5
Plum S3
Apricot
Apple
Apple
Plum
Plum
D4
Plum
Citrus
Apple
Apple
Plum
Plum
Olive
D2
Plum Plum Apple Apple
biosecurity built on science
3
Dynamic 2.5
Static
2 1.5 1 0.5 0 -0.5 15 -J an -0 9 29 -J an -0 9 12 -F eb -0 9 26 -F eb -0 9 12 -M ar -0 9 26 -M ar -0 9 09 -A pr -0 9 23 -A pr -0 9 07 -M ay -0 9 21 -M ay -0 9 04 -J un -0 9 18 -J un -0 9
Average Fly number per trap per week
Donnybrook - Male flies in male Traps (2009)
Collection date (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}.
biosecurity built on science
4 3 2 1
it pe fru
G ra
ng e O ra
Fi g
pl e Ap
m on Le
rin da
M an
ar in ec t
N
Pl um
e
t ric o Ap
ar Pe
ac h
0
Pe
Number of flies per trap per week
Host effect on average number of male flies
Host type (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
biosecurity built on science
High numbers show richness of hosts in dynamic trap
Fly num bers at Donnybrook Site 23 2008 - 2010 Dynamic
2.5 2 1.5 1 0.5
Ju n Au e gu O st ct D obe ec r em Fe ber br ua ry M ay Ju O ly ct ob D ec e r em Fe ber br ua ry Ap ri l
0
J Se ul-0 7 pt em b N ov er em b J a er nu ar y M ar ch
Log(Fly number+1)/ fortnight
Static
Collection time
biosecurity built on science
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) • •
less time requires less labour requires
It is also a good Template for: • •
Area Freedom proving Areas of Low Pest Prevalence proving
biosecurity built on science
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 biosecurity built on science
Stage
Threshold (oC)
Daydegrees
Egg-larva
10.0
176.6
Pupa
11.6 15.7
138.6 60.4
Population Lifecycle 2-4 days
Donnybrook
28-34 days
12-14 days Eggs
Preoviposition
14-16 days
2-4 days
Summer
Adults
Winter
Aug
Sep
Oct
fly emerging
Nov
Dec
1st
Jan
Feb
2nd
3rd gen
Mar
Apr
4th gen
May
Jun
Jul
Aug
Sep
12-14 days
Oct
Nov
28-34 days
14-16 days
Summer
Dec
Jan
Feb
Mar
5th gen Overwintering
Au Sep Oct No Dec Jan Feb Ma Ap Ma Jun Jul Au Sep Oct No Dec Jan Feb Ma Donnybrook & Manjimup Town
Navel Valencia Mandarin Grapefruit Apple Pear Apricot Plum Nectarine Peach Fig Loquat RISK
Adelaide City
Pemberton Manjimup & Adelaide Orchards biosecurity built on science
L
M
H
Average of 10yr 째C temperature data 1996-2005
Medfly survives in Manjimup Town
GT
120
GT
GT
GT
100 80
Non Infesting Phase
Period of High Infestation Risk
Dispersal Phase
60 40 20
0 Au
S
Eggs
O
N
D
J
F
M
A
My
J
Jy
Adults biosecurity built on science
The derived thermal time model (De Lima 2007) for the complete life cycle is: n 0, tmi ≤ θ 1 TTS 1 = ∑ (t − θ 1) ⋅ δ 1 with δ 1 = i =1 1, tmi > θ 1 1
mi
TTS 2 =
n2
∑ (t
mi
− θ 2) ⋅ δ 2
with
0, tmi ≤ θ 2 δ2= 1, tmi > θ 2
with
0, tmi ≤ θ 3 δ3= 1, tmi > θ 3
i = ( n1 +1)
TTS 3 =
n3
∑ (t
i = ( n 2 +1)
mi
− θ 3) ⋅ δ 3
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
biosecurity built on science
Validating the model Using the measured data in a time series to describe the information about the dynamics of the system.
{x
m N +k
k-steps
}=
1 m ) B∈ ( x NN
xm N-2
xm N-1
xm N
m x ∑ NN + k
m m ∈B∈ ( x NN ) X NN xm N+1
xm N+2
Pop. N
biosecurity built on science
Pe r M an t h jim up Ha Do r nn vey yb Pe roo k m be Ku rto nu n nu rr Be a na Be lla nd ig o Ta rtu r Ec a uc Sw ha an Hi ll Te Mil du nn ra an tC Al re iC ek ur in Al g ic eS NT Hi prin lls gs to nN S Br ok W en Lo Hil l xt on Le S ns A w Po o rtA od ug us Ad ta el La a un ide ce st on
Days Below 6째C 60
CLIMATE Medfly: mortality due to low temperature
50
40
LD99
30
LD50
20
10
0
biosecurity built on science
Pe rt M an h jim up H a D on rve y ny br oo k B en al la B en di go Ta rt ur Ec a uc Sw ha an H ill M Te ild nn ur an a tC A l i C re e k ur i ng H ill N st on T N SW B ro ke n Lo Hil l xt on Le S ns A P o wo od rt A ug A us lic ta eS pr in K un gs un ur ra A de la La un ide ce Pe sto n m be rt on
Hours above 35째C
Medfly: Mortality due to high temperature
80
CLIMATE
70
LD99
60
50
LD50
40
30
20
10
0
biosecurity built on science
biosecurity built on science
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
biosecurity built on science
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)
biosecurity built on science
Thank you
ď&#x201A;§ For more information, please email francis.delima@agric.wa.gov.au
biosecurity built on science