A process-based guide to data collection in plant health

A process-based guide to

data collection in plant health

Luigi Ponti

utagri.enea.it

EFSA, Parma

Wed 2 April 2014

A process-based approach is key

to managing pests effectively

Gutierrez & Ponti 2013

Cost of

management

(millions of US

dollars)

Biological information available

The biology matters



Why the biology matters

North American examples

A process-based approach

Physiological analogies

Work in progress

Tuta absoluta

Pink bollworm cost 300 million $, yet in the Central

Valley of California weather is the limiting factor

Gutierrez & Ponti 2013a

Introduced

to California

in the late 60s

http://dx.doi.org/10.1603/EN12018

Mediterranean fruit fly cost >450 million $ as

threat to California agriculture was overestimated


First detected

in California

in 1975


Medfly

pupae

(103)

Large-scale monitoring and eradication since 1975

with no knowledge of the potential distribution

Gutierrez & Ponti 2011

http://dx.doi.org/10.1007/s10530-011-9937-6

Planned eradication (worth 100 million $) for

light brown apple moth was abandoned


USDA: risk of establishment

(Fowler et al. 2009)

First detected

in California

in 2007








Work in progress

Tuta absoluta

The regional pest status of species is affected by

many factors difficult to separate and quantify

Abiotic

(x,y) Coordinates

Biotic

Geography Organic matter, soil type, topography, etc.

Weather data

Solar radiation, temperature, rainfall, etc.

Models

Natural enemies Pests Plants

H2O and N

Geographic scale

Physiological analogy among trophic

levels is a powerful conceptual tool

Purves et al. 2013

Processes like predation play

by similar rules in all ecosystems

http://dx.doi.org/10.1038/493295a

All organisms are consumers with common

pattern of resource acquisition and allocation

Respiration

costs

r(.)

Egesta

Assimilated

food

Growth or

reproduction

Conversion

costs

(1 - )

(1 - )

Demand

Hunger

Food in gut

Supply

Same model can be used in all trophic levels,

each level supplies resource to the next

Sun

Plant

Growth

Respiration

Wastage

Reproduction

Minerals,

CO2, H2O Herbivore

Gr.

Resp.

Egestion

Repr.

Carnivore

Gr.

Resp.

Egestion

Repr.

Same model describes species biology across

trophic levels including the economy of humans

Ponti et al. 2014

growth

respiration

wastagereproduction

consumption

water, minerals, CO2

respiration

growth

reproductionegestion

consumption

overhead

farmer

savings

profitwastage

supplies

x $

olive olive fly natural enemies

farmer

SUN

SOURCE

PRODUCTIO

N

CONSUM

PTION

BIOLOGICAL

ECONOMIC

BIOLOGICAL

ECONOMIC

taxes

http://dx.doi.org/10.1073/pnas.1314437111

Sub-models of biological process

are the same for all trophic levels

Physiologically based modeling

PBDM




PBDM

Based on physiological analogies




PBDM

Based on physiological analogies

Same process sub-models

Temperature

1 / days

Rate of development

Temperature

1 / days

Scalar for

developmental

time

Nutrition

1

Rate of development Effect of nutrition

Temperature

1 / days

Scalar for

developmental

time

Nutrition

1


Eggs

per female

per day

Age

Age-specific

fecundity

Temperature

1 / days

Scalar for

developmental

time

Nutrition

1


Eggs

per female

per day

Age

Scalar for

eggs per

female

Temperature

Age-specific

fecundity

T effect on fecundity 1

Temperature

1 / days

Scalar for

developmental

time

Nutrition

1


Temperature

Proportion

dying per day

T effect on mortality

Eggs

per female

per day

Age

Scalar for

eggs per

female

Temperature

Age-specific

fecundity


Temperature

1 / days

Scalar for

developmental

time

Nutrition

1


Day length

Proportion

diapause

per day

Temperature

Proportion

dying per day

T effect on mortality Diapause

induction

Eggs

per female

per day

Age

Scalar for

eggs per

female

Temperature

Age-specific

fecundity


1( )t )(2 t i t( ) k t( )

N1(t) x0(t) y(t) N2(t) Ni(t) Nk(t)

Birth

Death

…

2 ( )r t1( )ir t

1( )kr t1( )r t

Population dynamics adds more realism (and density)

via age structure, distributed delay, and attrition

k = 30

k = 10

k = 5

k = 1

Developmental time

Frequency

of maturation

times

GIS integration occurs at the population level,

factors are modeled on a per-capita basis

x,y

Individual

Population

Area

Region

Biology

Geographic distribution

Gutierrez, Ponti & Gilioli 2010

http://www.worldscientific.com/worldscibooks/10.1142/p755







Work in progress

Tuta absoluta

PBDM clearly identifies data gaps in the biology

of T. absoluta and guides data collection

Ponti et al. submitted

As of Nov 2013



Luigi Ponti1,2, Gianni Gilioli2,3,

Antonio Biondi4, Nicolas Desneux4,

Andrew Paul Gutierrez2,5

1 Laboratorio Gestione Sostenibile degli Agro-ecosistemi (UTAGRI-ECO), Agenzia

nazionale per le nuove tecnologie, l’energia e lo sviluppo economico sostenibile (ENEA),

Centro Ricerche Casaccia, Via Anguillarese 301, 00123 Roma, Italy, [email protected]

2 Center for the Analysis of Sustainable Agricultural Systems Global, Kensington, CA

94707-1035, USA, http://casasglobal.org/

3 DMMT, University of Brescia, Viale Europa 11, 25123 Brescia, Italy

4 French National Institute for Agricultural Research (INRA), UMR1355-ISA, 400 Route des

Chappes, 06903 Sophia-Antipolis, France

5 College of Natural Resources, University of California, Berkeley, CA 94720-3114, USA

Joint EFSA‐EPPO Workshop “Data collection and information sharing in plant health”, EFSA premises, Parma, 1‐2‐3 April 2014

mailto:[email protected]

http://casasglobal.org/

A process-based guide to data collection in plant health

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