A process-based guide to

Transcription

1 A process-based guide to data collection in plant health Luigi Ponti utagri.enea.it EFSA, Parma Wed 2 April 2014

2 A process-based approach is key to managing pests effectively The biology matters Cost of management (millions of US dollars) Biological information available Gutierrez & Ponti 2013

3 A process-based guide to data collection in plant health Why the biology matters North American examples A process-based approach Physiological analogies Work in progress Tuta absoluta

4 A process-based guide to data collection in plant health Why the biology matters North American examples A process-based approach Physiological analogies Work in progress Tuta absoluta

5 Pink bollworm cost 300 million $, yet in the Central Valley of California weather is the limiting factor Introduced to California in the late 60s Gutierrez & Ponti 2013a

6 Mediterranean fruit fly cost >450 million $ as threat to California agriculture was overestimated First detected in California in 1975 Gutierrez & Ponti 2013a

7 Large-scale monitoring and eradication since 1975 with no knowledge of the potential distribution Medfly pupae (10 3 ) Gutierrez & Ponti 2011

8 Planned eradication (worth 100 million $) for light brown apple moth was abandoned First detected in California in 2007 USDA: risk of establishment (Fowler et al. 2009) Gutierrez & Ponti 2013a

9 A process-based guide to data collection in plant health Why the biology matters North American examples A process-based approach Physiological analogies Work in progress Tuta absoluta

10 The regional pest status of species is affected by many factors difficult to separate and quantify Geographic scale (x,y) Coordinates Biotic Abiotic Models Weather data Geography Natural enemies Pests Plants H 2 O and N Solar radiation, temperature, rainfall, etc. Organic matter, soil type, topography, etc.

11 Physiological analogy among trophic levels is a powerful conceptual tool Processes like predation play by similar rules in all ecosystems Purves et al. 2013

12 All organisms are consumers with common pattern of resource acquisition and allocation Supply Demand Hunger Food in gut Assimilated food Growth or reproduction (1 - ) r(. ) (1 - ) Egesta Respiration costs Conversion costs

13 Same model can be used in all trophic levels, each level supplies resource to the next Sun Growth Gr. Gr. Minerals, CO 2, H 2 O Plant Wastage Respiration Reproduction Herbivore Repr. Egestion Resp. Carnivore Repr. Egestion Resp.

14 Same model can be used in all trophic levels, each level supplies resource to the next Sun Growth Gr. Gr. Minerals, CO 2, H 2 O Plant Wastage Respiration Reproduction Herbivore Repr. Egestion Resp. Carnivore Repr. Egestion Resp.

15 Same model can be used in all trophic levels, each level supplies resource to the next Sun Growth Gr. Gr. Minerals, CO 2, H 2 O Plant Wastage Respiration Reproduction Herbivore Repr. Egestion Resp. Carnivore Repr. Egestion Resp.

16 Same model can be used in all trophic levels, each level supplies resource to the next Sun Growth Gr. Gr. Minerals, CO 2, H 2 O Plant Wastage Respiration Reproduction Herbivore Repr. Egestion Resp. Carnivore Repr. Egestion Resp.

17 Same model describes species biology across trophic levels including the economy of humans SOURCE PRODUCTION CONSUMPTION SUN growth growth water, minerals, CO 2 olive olive fly consumption reproduction wastage respiration respiration consumption reproduction egestion natural enemies BIOLOGICAL x $ savings ECONOMIC farmer overhead taxes supplies profit wastage Ponti et al. 2014

18 Sub-models of biological process are the same for all trophic levels Physiologically based modeling PBDM

19 Sub-models of biological process are the same for all trophic levels Physiologically based modeling PBDM Based on physiological analogies

20 Sub-models of biological process are the same for all trophic levels Physiologically based modeling PBDM Based on physiological analogies Same process sub-models

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22 Rate of development 1 / days Temperature

23 1 / days Rate of development Scalar for developmental time Effect of nutrition 1 Temperature Nutrition

24 1 / days Rate of development Scalar for developmental time Effect of nutrition 1 Temperature Nutrition Eggs per female Age-specific fecundity per day Age

25 1 / days Rate of development Scalar for developmental time Effect of nutrition 1 Temperature Nutrition Eggs per female Age-specific fecundity Scalar for eggs per T effect on fecundity 1 per day female Age Temperature

26 1 / days Rate of development Scalar for developmental time Effect of nutrition 1 Temperature Nutrition Eggs per female Age-specific fecundity Scalar for eggs per T effect on fecundity 1 per day female Age Temperature Proportion T effect on mortality dying per day Temperature

27 1 / days Rate of development Scalar for developmental time Effect of nutrition 1 Temperature Nutrition Eggs per female Age-specific fecundity Scalar for eggs per T effect on fecundity 1 per day female Age Temperature Proportion dying per day T effect on mortality Proportion diapause per day Diapause induction Temperature Day length

28 Population dynamics adds more realism (and density) via age structure, distributed delay, and attrition Birth x 0 (t) N 1 (t) r() t 1 N 2 (t) N i (t) N k (t) r () t 2 r () 1 t r () 1 t i k Death y(t) 1( t ) ( t 2 ) i ( t) k ( t) Frequency of maturation times k = 30 k = 10 k = 5 k = 1 Developmental time

29 GIS integration occurs at the population level, factors are modeled on a per-capita basis Region Area Population Geographic distribution Individual x,y Biology Gutierrez, Ponti & Gilioli 2010

30 A process-based guide to data collection in plant health Why the biology matters North American examples A process-based approach Physiological analogies Work in progress Tuta absoluta

31 PBDM clearly identifies data gaps in the biology of T. absoluta and guides data collection As of Nov 2013 Ponti et al. submitted

32 A process-based guide to data collection in plant health Luigi Ponti 1,2, Gianni Gilioli 2,3, Antonio Biondi 4, Nicolas Desneux 4, Andrew Paul Gutierrez 2,5 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, Roma, Italy, luigi.ponti@enea.it 2 Center for the Analysis of Sustainable Agricultural Systems Global, Kensington, CA , USA, 3 DMMT, University of Brescia, Viale Europa 11, Brescia, Italy 4 French National Institute for Agricultural Research (INRA), UMR1355-ISA, 400 Route des Chappes, Sophia-Antipolis, France 5 College of Natural Resources, University of California, Berkeley, CA , USA Joint EFSA EPPO Workshop Data collection and information sharing in plant health, EFSA premises, Parma, April 2014