Application and use of insect phenology modeling for invasive … 40pm Kroschel.pdf · White grubs...

11 – 14 October, 2 0 1 0

WorkshopPotential Invasive Pests Workshop

M. Sporleder, J. Kroschel , R. Simon, H. Tonnang, P. Carhuapoma, J.C. Gonzales, H. Juarez, CIP-Agroecology/IPM Group and Research Informatics Unit, Lima, Peru

Application and Use of Insect Phenology Modeling for Invasive

Species for Regional and Global Risk Assessments under Future Climate

Change Scenarios

Introduction• Invasive potato pest species• Expected impacts of CC to pests• Observed and modeled effects of CC to pests• Temperature-based phenology modeling

Methodology

Outline

Methodology• Data collection and data analysis using ILCYM software

Results• Model parameterization and validation• Risk mapping worldwide and for the Andean region under different

climate change scenarios

Conclusions

Important potato pests in the

Tropics and SubtropicsThe Potato Tuber Moth complexPhthorimaea operculella (CPTM)Symmetrischema tangolias (APTM)Tecia solanivora (GPTM)

Lep.; Gelechiidae

Andean Potato WeevilPremnotrypes suturicallus

Col.; Curculionidae

Leafminer flyLiriomyza huidobrensis Dipt.; Agromyzidae

Bud midgeProdiplosis longifila Dipt.; Cecidomyiidae

AphidsMyzus persicae Homopt.; Aphididae

Flee beetlesEpitrix spp. Col.; Chrysomelidae

Potato Tuber MothPhthorimaea operculella (PTM)

• Invasive species; reported from more than 90 countries

• Adapted to wide range of different climates and agroecologies

• Yield losses due to leaf and tuber infestation at harvest

• Storage pest in tropical and subtropical countries

Potato production areas and PTM distribution

Phthorimaea operculellaSymmetrischema tangolias

Tecia solanivora

Framework for IPM development and use in potato

Adoption of IPM in potato by different types of farmers

- On-farm research, adaptation, FFS, extension

Biological control

- Conservation- Augmentation

Cultural

- Seed- Planting date

Biorational Biotechnology

- Pheromones - Attract-and-kill

- Bt-potato

Chemical

- Low toxicity- no effects on

Compatibility of control methods for all pests!!!

Ecological infrastructure

- Fauna inventory and functional diversity- Agro-ecosystems characteristics- Bioindicators for stability / degradation

Pest population ecology

- Population dynamics (abiotic/biotic)- Pest severity- Control thresholds

Communication/behavior

- Migration/distribution- Sex pheromones

Insect modeling & Climate Change

- Augmentation - Classical- Biopesticides

- Planting date- Rotation- …

- Attract-and-kill - no effects onnon-targets

• Range expansion of existing pests and invasion by new pests.

• Accelerated pest development leading to more pest cycles per season.

• Disruption of the temporal and geographical synchronization of pests

Impacts of CC on Pests

An increase in extreme climate events, changes in moistureconditions, temperature rise , elevated CO 2 concentrations, areexpected to magnify pest pressure on agricultural systems t hrough:

• Disruption of the temporal and geographical synchronization of pests and beneficial insects that increase risk of pest outbreaks.

• Promotion of minor pests to primary pests brought about by reduction in host tolerance and changes in landscape characteristics and land-use practices.

• Increase damage potential from invasive alien species.

• Susceptibility to pests increases in drought stressed plants.

(adapted from Padgham, 2009; Discussion paper - The World Bank)

Observed and modeled effects of CC on insect pests

Pest Climate DimensionCropping

System/RegionReference

INSECTS

Brown locust(Locustana pardalina)*

Increased outbreaks during ENSO events.

Crop/rangeland, S-Africa

Todd et al. 2002

Leaf miner (Caloptilia sp.) Range expansion projected. Coffee, Brazil Magrin et al. 2007

Southern pink bollworm(Pectinophora gossypiella)

Increased winter tempera-tures predicted to increase range and damage potential.

Cotton, SW-US Gutierrez et al. 2006

Southern pine beetle(Dendroctonus frontalis)

Increased winter/spring tem-peratures predicted to in-crease range and damagepotential.

Forests, S-US Gan 2004

White fly(Bemisia tabaci, and B. afer)*

Increased outbreaks during ENSO events.

Tuber crops, Andean region

Shapiro et al. 2007

Potato tuber moth(Phthorimaea operculella)

Temperature increase of 2°C–3°C expands distribution by 400–800 km to the north and accelerates damage intensity .

Potato, globallySporleder et al. 2007

(Padgham 2009)

Pest infestation in potato/sweetpotato in the Cañete Valley of

Peru before, during and after the El Niño year in 1997.

1 = absent; 2 = slight; 3 = medium; 4 = severe; 5 = extreme infestation. - = no information

Name Scientific name Degree of infestation

Potato 1996 1997 1998

Leafminer fly Liriomyza huidobrensis 4 2 4

Bud midge Prodiplosis longifila 2 5 3

Broad mite Polyphagotarsonemus latus 3 4 3

Potato tuber moth Phthorimaea operculella 2 3 2

(Cisneros and Mujica 1999)

Potato tuber moth Phthorimaea operculella 2 3 2

Soybean Looper Pseudoplusia includens 1 3 1

SweetpotatoWhite fly Bemisia tabaci 2 4 5

Leafhopper Empoasca spp . 2 3 4

Aphids Myzus persicae 2 3 3

Red spider mite Paraponychus incanus 2 3 2

Sweet potato weevil Euscepes postfasciatus 3 4 4

White grubs Bothynus spp. / Anomala spp. 3 4 4

Yields (in t or quintal for cotton) in different crops, Cañete

Valley, Peru, before, during and after the El Niño year in 1997

and related yield losses (%) due to increased pest severity

Crop 1996 1997 1998Yield loss

(1996-1998)

Potato 27 (18-35) 12 (1.5-21) 15 (5-20) 55

Sweetpotato 28 (10-40) 16 (2-40) 15 (2-20) 54Sweetpotato 28 (10-40) 16 (2-40) 15 (2-20) 54

Cotton 50 (45-110) 25 (1-60) 35 (10-70) 50

Maize - 6.5 (5-8) 3.5 (2-4) 46

Citrus - 60 24 (14-30) 60

Asparagus 8 7 2 70

(Cisneros and Mujica 1999)

Real system Model

Abstraction

Models as analytical tools

Interpretation

A model is a tool. It should not be considered as the ultimate objective in ecological studies.

No model is perfect but it can be very useful.

• Phytosanitary pest risk assessments / Climate change

• Integrated Pest Management

Temperature-based insect phenology modeling

Analytical tools for predicting, evaluating and und erstanding the dynamics of insect populations in agroececosyst ems.

• Integrated Pest Management

- Classical biocontrol: identification of potential release sites for parasitoids

- Simulation of field performance of biopesticides and application frequencies

Insect Life Cycle Modeling (ILCYM)

• Collecting life cycle data under constant and fluctuating temperature regimes

• Analyzing the data using ILCYM

- Fitting functions for describing temperature-driven development processes under

Software package for developing temperature-based i nsect phenology models with applications for regional and global pest risk assessments and mapping .

driven development processes under constant temperatures, i.e. development rate, mortality and reproduction (“Model Builder”)

- Validating the model using life table data established under fluctuating temperature

• Generate risks maps using ILCYM in an GIS environment- Establishment index- Generation index- Activity index (www.cipotato.org/ilcym)

� ILCYM offers advanced modeling techniques for insect populations and tools for analyzing models; users do not need to be experts in the field.

� ILCYM guides the user interactively through the steps

Use of ILCYM

� ILCYM guides the user interactively through the steps developing a phenology/population model and facilitates spatial pest risk mapping.

� User do not need to learn programming languages.

� However, ILCYM restricts users to certain model designs and might not provide solutions for every problem.

Data collection

•• OvipositionOviposition•• Survival timeSurvival time

(male and female)(male and female)

•• Development timeDevelopment time•• MortalityMortality

Constant Constant temperaturestemperatures

Life table data (complete)

Each column Each column represent one

individual

Each line represent one

evaluation interval (days)

Instead of “numbers” for

oviposited eggs or “M” for

Life table data (incomplete)

eggs or “M” for males you

indicate “A” for adult or “F” and

“M” for “female” and

“male”

Model Implementation

EI

Development to the next stage

ReproductionTemp

Dev

.rat

e

Dev

elop

men

t

Tem perature

Ovi

posi

tion

normalized age

Pro

por

tion

of p

roge

ny

EIE..E1E0 LIL..L1L0PIP..P1P0

AfIAf..Af1Af0

Mortality

Temperature

Mo

rtalit

y

0 1normalized age

Variation of Development Time

Functions generated in ILCYM

Development Rate


Mortality


Total oviposition


Age-dependent oviposition frequency


Accounting for within year variation:

1 Net reproduction rate: R0 (♀/♀)

Mean generation time: T (days)

Life table parameters or risk indices

2

Intrinsic rate of increase: rm

Finite rate of increase: λλλλ = erm

Doubling time: Dt = Ln(2)/rm

Immature survival rates

Development times of specific life-stages

Simulated life table parameters

Net reproduction rate Mean generation time Intrinsic rate of increase

Simulated life table parameters

50

100

Num

ber

of e

ggs

(♀/♀

)

50

100

150

Tim

e (d

ays)

0.08

0.06

0.04

0.02

r m

( )T

RoLnrm =

∑∑=

ml

mlxT

∑ ×= mlRo

1.25

1.20

1.15

1.10

1.05

1.00

0.95

0.90100 20 30 40

Finite rate of increase Doubling time

Temperature °C

0 10 20 30Num

ber

of e

ggs

0 10 403020

Temperature °C Temperature °C

0 10 20 30 400.00

0

150

100

50

0 10 20 30 40

Temperature °C

Dt (

days

)

Temperature °C

λ

( )mr

LnDt

2=rme=λ

Model validation

Tem

pera

ture

(°C

)

Indiv

iduals 70

8090

100

25

30

35

40

Huancayo 2nd. cycle

LarvaPupa Adults

Eggs

Days

Tem

pera

ture

(

Indiv

iduals

010203040506070

0 10 20 30 40 50 60 70 80 90 100 1100

5

10

15

20

25Larva

Pupa Adults

60708090

100

25

30

35

40

Egypt 2nd. cycle

LarvaEggs

Model validation

Tem

pera

ture

(°C

)

Indiv

iduals

0102030405060

0 10 20 30 400

5

10

15

20

25Larva

PupaAdults

Eggs

Days

Tem

pera

ture

(

Indiv

iduals


20

25

30

35Te

mpe

ratu

re

Min

Max

0

5

10

15

20

0 4 8 12 16 20 24

Time steps

Tem

pera

ture

Min

Min


1) Present scenario:interpolated temperatures [spatial resolution of about 1 km2] (NOAA) described by: Hijmann et al. (2005)

2) 2050 scenario: atmospheric general circulation model (GCM) described by: Govindasamy et al. (2003)

�� linked with geographic information systems (DIVA-GIS)

Geographic simulation in ILCYM

Establishment risk index (Survival risk index):

Index = (1-xEgg) ×××× (1-xLarva) ×××× (1-xPupa)

x is percentage of days a specific life-stage does not survive

Generation Index:

Index =

1

2

Risk indices

/365∑ xTIndex =

the average number of generations within one year

Activity Index:

Index = Log ∏∏∏∏ finite rate of population increase (λλλλ)

this index not only highlights the risk of establishment but

also the spread potential

3

365

/365∑ xT

Establishment risk index

Generation index

Activity index

CC - Expected temperature increase until 2050

Govindasamy, B., P. B. Duffy, J. Coquard, 2003.High-resolution simulations of global climate, part 2: effects of increased greenhouse cases. Climate Dynamics 21: 391–404.

Global risks of potato tuber moth in potato:

2000 - 2050

Absolute generation index change due to

effect of climate change: 2000 - 2050

Expansion to the North and to higher altitudes!

Risk of potato tuber

moth in Peruvian

highlands

� Total potato production area in Peru in 2007: 267,000 ha.

� Potato tuber moth is � Potato tuber moth is reported from the coast and Inter-Andean valleys.

� It is estimated that today 120,398 ha of potato are affected by the potato tuber moth (45% of the total potato area).

� It is estimated that due to CC, the potato tuber moth will affect potato at higher

Absolute generation

index change due to

effect of climate

change: 2000 - 2050

potato at higher elevations and will increase the total area of infestation from 120,398 ha to 179,178 ha (67% of the total potato area).

Num

ber

of g

ener

atio

ns p

er y

ear

10

12

14

16

18

12.512

+1.5+2.5

+3.9+4.6

+2.4+2.8

+1.3+2.2

+2.7 10 10+1.2

+2.0

+3.7+3.1

Effect of climate change scenarios on the

number of generations at different locations

Num

ber

of g

ener

atio

ns p

er y

ear

0

2

4

6

8

Peru - San Ramon (800 m) Peru - Huancayo (3250 m) Peru - Arequipa (1440) Yemen - Sana’a (2250) Egypt - Giza (10 m)

2.9+0.4+0.7

+1.0+1.2

6.7+0.8

+1.4+2.4

3.0

7.0

8.0

7.0

+0.9+1.3

PRESENT PREDICTION PRESENT PREDICTION PRESENT PREDICTION PRESENT PREDICTIONPRESENT PREDICTION

0

5

10

15

20

25

30

35

tem

pera

ture

(ºC

)

-5

0

5

10

15

20

25

30

35

tem

pera

ture

(ºC

)

Hermiston, OR (CRB)144 masl

45.84 LatØ 11.9°C => 14.0°C

Huancayo, Peru3283 masl

-12.03 LatØ 11.1°C => 12.7°C

potential activity increase

YearEI: 1 => 1

GI: 2.6 => 3.3AI: 1.7 => 2.9

1338%

Veg. PeriodAI: 0.8 => 1.3

0,95

1,00

1,05

1,10

1,15

0 100 200 300Julian day

finite

rat

e of

incr

ease

-5

0 100 200 300Julian day

-5

0 100 200 300Julian day

0,95

1,00

1,05

1,10

1,15

0 100 200 300Julian day

finite

rat

e of

incr

ease

YearEI: 0.78 => 0.85GI: 4.1 => 5.1AI: 3.8 => 5.7

6523%


665%

AI: 0.8 => 1.3328%

Al Buhayrah, Egypt10 masl30.4 LatØ 21.5°C => 23.8°C

Cochabamba, Bolivia2531 masl-17.45 LatØ 15.3°C => 16.9°C

potential activity increase

0

5

10

15

20

25

30

35

40

tem

pera

ture

(ºC

)

0

5

10

15

20

25

30

35

40

tem

pera

ture

(ºC

)

YearEI: 1 => 1

GI: 5.0 => 5.9AI: 5.9 => 7.7

5730%


0 100 200 300Julian day

0

0 100 200 300Julian day

1,00

1,05

1,10

1,15

1,20

0 100 200 300Julian day

finite

rat

e of

incr

ease

1,00

1,05

1,10

1,15

1,20

0 100 200 300Julian day

finite

rat

e of

incr

ease

AI: 3.2 => 4.1709%

YearEI: 1 => 1

GI: 9.6 => 11.8AI: 13.9 => 16.4

>33000%


951%

Conclusions and challenges

� Phenology modeling and GIS risk mapping might be an appropriate tool and framework for pest risk assess ments and adaptation planning for CC, but: - Uncertainties in climate change predictions.- Worldclim data provide monthly aggregated temperat ure data;

interpolation of weather data.- Regional meteorological data could have higher accu racy. - Regional meteorological data could have higher accu racy. - Other factors: e.g., precipitation.

� Understanding the effects of temperature increase o n key pests and related beneficial insects by using two-s pecies interaction models.

� Developing risk maps for major pests for current an d future scenarios according to temperature indicating major future pest hot spots.

CIP’s Modeling Group

The team is composed by:-specialists in Agroecology/IPM- insect modeling and statistics, and - application of GIS.

RheinhardSimon

HenryTonnang

HenryJuarez

Juan CarlosGonzalo

PabloCarhuapoma

MarcSporleder

Jürgen Kroschel

Application and use of insect phenology modeling for invasive … 40pm Kroschel.pdf · White grubs...

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