A mechanistic approach - LazioInnova · A mechanistic approach Luigi Ponti ... AR-VR Workshop,...
Transcript of A mechanistic approach - LazioInnova · A mechanistic approach Luigi Ponti ... AR-VR Workshop,...
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Agroecosystem modeling A mechanistic approach
Luigi Ponti
www.enea.it www.casasglobal.org
AR-VR Workshop, Roma
Wed 25 Oct 2017
blog.iese.edu
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GlobalChangeBiology project was the first Marie Curie IRG hosted by ENEA
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GlobalChangeBiology project was the first Marie Curie IRG hosted by ENEA
Host
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GlobalChangeBiology project was the first Marie Curie IRG hosted by ENEA
Host
Collaboration
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GlobalChangeBiology project was the first Marie Curie IRG hosted by ENEA
![Page 6: A mechanistic approach - LazioInnova · A mechanistic approach Luigi Ponti ... AR-VR Workshop, Roma. Wed 25 Oct 2017 . blog.iese.edu . GlobalChangeBiology project was the . first](https://reader034.fdocuments.us/reader034/viewer/2022042303/5ece2e86ee11c142a623e017/html5/thumbnails/6.jpg)
GlobalChangeBiology project was the first Marie Curie IRG hosted by ENEA
More information on the project globalchangebiology.blogspot.it
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GlobalChangeBiology project was the first Marie Curie IRG hosted by ENEA
More information on the project globalchangebiology.blogspot.it
Scientific consortium casasglobal.org enables continued ongoing collaboration
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Analogy among ecological processes may help deal with system complexity
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Analogy among ecological processes may help deal with system complexity
Processes like predation play by similar rules in all ecosystems
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Analogy among ecological processes may help deal with system complexity
Processes like predation play by similar rules in all ecosystems
Purves et al. 2013, https://doi.org/10.1038/493295a
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Analogy among ecological processes may help deal with system complexity
quecome.com
Processes like predation play by similar rules in all ecosystems
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Same model describes species biology across trophic levels
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Same model describes species biology across trophic levels
Gutierrez 1996, https://goo.gl/ejXBWX
growth
wastage reproduction
consumption
water, minerals, CO2
grape
SUN
BIOLOGICAL respiration
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Same model describes species biology across trophic levels
growth
wastage reproduction
consumption
water, minerals, CO2
respiration
growth
reproduction egestion
consumption
grape pests
SUN
BIOLOGICAL respiration
Gutierrez 1996, https://goo.gl/ejXBWX
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Same model describes species biology across trophic levels
growth
wastage reproduction
consumption
water, minerals, CO2
respiration
growth
reproduction egestion
consumption
grape pests natural enemies
SUN
BIOLOGICAL respiration
Gutierrez 1996, https://goo.gl/ejXBWX
![Page 16: A mechanistic approach - LazioInnova · A mechanistic approach Luigi Ponti ... AR-VR Workshop, Roma. Wed 25 Oct 2017 . blog.iese.edu . GlobalChangeBiology project was the . first](https://reader034.fdocuments.us/reader034/viewer/2022042303/5ece2e86ee11c142a623e017/html5/thumbnails/16.jpg)
Same model describes species biology across trophic levels including the economic one
growth
wastage reproduction
consumption
water, minerals, CO2
respiration
growth
reproduction egestion
consumption
overhead
farmer
savings
profit wastage
supplies
x $
grape pests natural enemies
farmer
SUN
BIOLOGICAL
ECONOMIC ECONOMIC
taxes
respiration
Gutierrez 1996, https://goo.gl/ejXBWX
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This ecosystem modeling approach is known as Physiologically Based Demographic Modeling (PBDM)
growth
wastage reproduction
consumption
water, minerals, CO2
respiration
growth
reproduction egestion
consumption
overhead
farmer
savings
profit wastage
supplies
x $
grape pests natural enemies
farmer
SUN
BIOLOGICAL
ECONOMIC ECONOMIC
taxes
respiration
Gutierrez 1996, https://goo.gl/ejXBWX
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Why complexity is a problem The scientific point of view
What does it mean in practice
How complexity hinders analysis
How the PBDM approach may help
A realistic geospatial info layer
Agroecosystem modeling A mechanistic approach
blog.iese.edu
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Why complexity is a problem The scientific point of view
What does it mean in practice
How complexity hinders analysis
How the PBDM approach may help
A realistic geospatial info layer
Agroecosystem modeling A mechanistic approach
blog.iese.edu
![Page 20: A mechanistic approach - LazioInnova · A mechanistic approach Luigi Ponti ... AR-VR Workshop, Roma. Wed 25 Oct 2017 . blog.iese.edu . GlobalChangeBiology project was the . first](https://reader034.fdocuments.us/reader034/viewer/2022042303/5ece2e86ee11c142a623e017/html5/thumbnails/20.jpg)
IPCC clearly points to ecosystem complexity as a major barrier to assess climate impacts
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IPCC clearly points to ecosystem complexity as a major barrier to assess climate impacts
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IPCC clearly points to ecosystem complexity as a major barrier to assess climate impacts
Uncertainty in predicting the response of terrestrial and freshwater ecosystems to climate and other perturbations […] remains a major impediment to determining prudent levels of permissible change.
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IPCC clearly points to ecosystem complexity as a major barrier to assess climate impacts
Uncertainty in predicting the response of terrestrial and freshwater ecosystems to climate and other perturbations […] remains a major impediment to determining prudent levels of permissible change.
![Page 24: A mechanistic approach - LazioInnova · A mechanistic approach Luigi Ponti ... AR-VR Workshop, Roma. Wed 25 Oct 2017 . blog.iese.edu . GlobalChangeBiology project was the . first](https://reader034.fdocuments.us/reader034/viewer/2022042303/5ece2e86ee11c142a623e017/html5/thumbnails/24.jpg)
IPCC clearly points to ecosystem complexity as a major barrier to assess climate impacts
Uncertainty in predicting the response of terrestrial and freshwater ecosystems to climate and other perturbations […] remains a major impediment to determining prudent levels of permissible change. A significant source of this uncertainty stems from the inherent complexity of ecosystems […].
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IPCC clearly points to ecosystem complexity as a major barrier to assess climate impacts
Uncertainty in predicting the response of terrestrial and freshwater ecosystems to climate and other perturbations […] remains a major impediment to determining prudent levels of permissible change. A significant source of this uncertainty stems from the inherent complexity of ecosystems […].
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Why complexity is a problem The scientific point of view
What does it mean in practice
How complexity hinders analysis
How the PBDM approach may help
A realistic geospatial info layer
Agroecosystem modeling A mechanistic approach
blog.iese.edu
![Page 27: A mechanistic approach - LazioInnova · A mechanistic approach Luigi Ponti ... AR-VR Workshop, Roma. Wed 25 Oct 2017 . blog.iese.edu . GlobalChangeBiology project was the . first](https://reader034.fdocuments.us/reader034/viewer/2022042303/5ece2e86ee11c142a623e017/html5/thumbnails/27.jpg)
Climate models are widely used, yet no general ecosystem models exist
Global climate models
No general ecosystem models
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Modeling every organism within an ecosystem is impossible
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Modeling every organism within an ecosystem is impossible
www.shutterstock.com
Standard laptop
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Modeling every organism within an ecosystem is impossible
Standard laptop 100 year simulation
www.shutterstock.com
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Modeling every organism within an ecosystem is impossible
www.shutterstock.com
Standard laptop 100 year simulation One-degree grid cell
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Modeling every organism within an ecosystem is impossible
www.shutterstock.com
Standard laptop 100 year simulation One-degree grid cell Only multicelluar animals
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Modeling every organism within an ecosystem is impossible
Purves et al. 2013, https://doi.org/10.1038/493295a
Standard laptop 100 year simulation One-degree grid cell Only multicellular animals
≈ 47 billion years
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Why complexity is a problem The scientific point of view
What does it mean in practice
How complexity hinders analysis
How the PBDM approach may help
A realistic geospatial info layer
Agroecosystem modeling A mechanistic approach
blog.iese.edu
![Page 35: A mechanistic approach - LazioInnova · A mechanistic approach Luigi Ponti ... AR-VR Workshop, Roma. Wed 25 Oct 2017 . blog.iese.edu . GlobalChangeBiology project was the . first](https://reader034.fdocuments.us/reader034/viewer/2022042303/5ece2e86ee11c142a623e017/html5/thumbnails/35.jpg)
A gap exists between bottom-up and top-down approaches to on-ground ecosystem problems
Rocchini et al. 2015, https://doi.org/10.1177/0309133315574659
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A gap exists between bottom-up and top-down approaches to on-ground ecosystem problems
Field observations (bottom-up, scarce and costly)
Rocchini et al. 2015, https://doi.org/10.1177/0309133315574659
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A gap exists between bottom-up and top-down approaches to on-ground ecosystem problems
Remote sensing, climate models
(top-down, no biology)
Field observations (bottom-up, scarce and costly)
Rocchini et al. 2015, https://doi.org/10.1177/0309133315574659
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A gap exists between bottom-up and top-down approaches to on-ground ecosystem problems
Remote sensing, climate models
(top-down, no biology)
Field observations (bottom-up, scarce and costly)
Ecological niche models
(top-down, correlative)
Rocchini et al. 2015, https://doi.org/10.1177/0309133315574659
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A gap exists between bottom-up and top-down approaches to on-ground ecosystem problems
Remote sensing, climate models
(top-down, no biology)
Field observations (bottom-up, scarce and costly)
Current gap (scale, reliability, etc.)
Ecological niche models
(top-down, correlative)
Rocchini et al. 2015, https://doi.org/10.1177/0309133315574659
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Remote sensing, climate models
(top-down, no biology)
Field observations (bottom-up, scarce and costly)
Ecological niche models
(top-down, correlative)
Rocchini et al. 2015, https://doi.org/10.1177/0309133315574659
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Remote sensing, climate models
(top-down, no biology)
Field observations (bottom-up, scarce and costly)
(biological processes linked explicitly to environmental drivers)
Ecological niche models
(top-down, correlative)
Rocchini et al. 2015, https://doi.org/10.1177/0309133315574659
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Remote sensing, climate models
(top-down, no biology)
Field observations (bottom-up, scarce and costly)
PBDMs, physiologically based demographic models
(biological processes linked explicitly to environmental drivers)
Ecological niche models
(top-down, correlative)
PBDMs link biological processes explicitly to their environmental drivers (vs. proxies)
Rocchini et al. 2015, https://doi.org/10.1177/0309133315574659
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Remote sensing, climate models
(top-down, no biology)
Field observations (bottom-up, scarce and costly)
PBDMs, physiologically based demographic models
(biological processes linked explicitly to environmental drivers)
Ecological niche models
(top-down, correlative)
test
PBDMs link biological processes explicitly to their environmental drivers (vs. proxies)
Rocchini et al. 2015, https://doi.org/10.1177/0309133315574659
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Remote sensing, climate models
(top-down, no biology)
Field observations (bottom-up, scarce and costly)
PBDMs, physiologically based demographic models
(biological processes linked explicitly to environmental drivers)
Ecological niche models
(top-down, correlative)
test
drive
PBDMs link biological processes explicitly to their environmental drivers (vs. proxies)
Rocchini et al. 2015, https://doi.org/10.1177/0309133315574659
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Same model describes species biology across trophic levels including the economic one
Gutierrez et al. 2015, https://doi.org/10.1186/s12302-015-0043-8
growth
wastage reproduction
consumption
water, minerals, CO2
respiration
growth
reproduction egestion
consumption
overhead
farmer
savings
profit wastage
supplies
x $
cotton pests natural enemies
farmer
SUN
BIOLOGICAL
ECONOMIC ECONOMIC
taxes
respiration
![Page 46: A mechanistic approach - LazioInnova · A mechanistic approach Luigi Ponti ... AR-VR Workshop, Roma. Wed 25 Oct 2017 . blog.iese.edu . GlobalChangeBiology project was the . first](https://reader034.fdocuments.us/reader034/viewer/2022042303/5ece2e86ee11c142a623e017/html5/thumbnails/46.jpg)
Factors are modeled on a per-capita basis, GIS integration occurs at the population level
x,y
Individual
Population
Area
Region
Gutierrez et al. 2010, https://doi.org/10.1142/9781848166561_0012
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Factors are modeled on a per-capita basis, GIS integration occurs at the population level
x,y
Individual
Population
Area
Region
Gutierrez et al. 2010, https://doi.org/10.1142/9781848166561_0012
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Factors are modeled on a per-capita basis, GIS integration occurs at the population level
x,y
Individual
Population
Area
Region
Gutierrez et al. 2010, https://doi.org/10.1142/9781848166561_0012
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Factors are modeled on a per-capita basis, GIS integration occurs at the population level
x,y
Individual
Population
Area
Region
Biology
Geographic distribution
Gutierrez et al. 2010, https://doi.org/10.1142/9781848166561_0012
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PBDMs become a realistic biological layer having same time/space coverage of driving info layers
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
Gutierrez et al. 2010, https://doi.org/10.1142/9781848166561_0012
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Projected 1.8 °C warming shows a wide range of effects on grape and its major insect pest
Change in yield (g dry matter per vine) Change in pest (n per vine per year)
Gutierrez et al. 2017, https://doi.org/10.1111/afe.12256
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Turning climate-related information into added value for European food systems
Upcoming Horizon 2020 project under topic SC5-01-2016-2017 https://goo.gl/wqztJa
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Why complexity is a problem The scientific point of view
What does it mean in practice
How complexity hinders analysis
How the PBDM approach may help
A realistic geospatial info layer
Agroecosystem modeling A mechanistic approach
blog.iese.edu
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Agroecosystem modeling A mechanistic approach
Luigi Ponti
AR-VR Workshop, Roma
Wed 25 Oct 2017
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Luigi Ponti1,2, Andrew Paul Gutierrez2,3, Massimo Iannetta1 1 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] (LP); [email protected] (MI)
2 Center for the Analysis of Sustainable Agricultural Systems Global (CASASA Global), Kensington, CA 94707-1035, USA, http://casasglobal.org
3 College of Natural Resources, University of California, Berkeley, CA 94720-3114, USA, [email protected]
Workshop “La realtà aumentata e virtuale in agricoltura: sfide e strumenti per il sostegno di progetti innovativi”,
25 October 2017, Roma, Italy
Ponti, L., Gutierrez, A.P., Iannetta, M., 2016. Climate change and crop-pest dynamics in the Mediterranean Basin. ENEA Technical Report, 27: 18 pp.
http://hdl.handle.net/10840/8042
Further info
L’approccio alla simulazione degli agroecosistemi nel progetto ENEA GlobalChangeBiology