Novel contributions in Ecology
(WP1)
Benoit Gauzens & Cendrine Mony
Review, March 20, 20141
Content
Ø  Working within the Diversify consortium
Ø  Exchanges with the ecological board
Ø  Ecological projects undertaken within Diversify
WP1: Ecological Modelling
Ø  Task 1.1. Ecological board coordination
-> Organization of workshops 1 & 2
Ø  Task 1.2. Knowledge transfer for diversity and specialization synthesis
-> Literature surveys and meetings
Ø  Task 1.3. Knowledge transfer for reasoning and adaptation
mechanisms
-> Literature surveys and meetings
Working within the Diversify consortium
Knowledge on ecological
processes
Computer
scientists
Ecologists
Bipartite graphs
Ecological processes
Measuring diversity
Measuring resilience
…
Working within the Diversify consortium
Knowledge on ecological
processes
Ecologists
Computer
scientists
New questions in ecology
How implementing diversification processes in graphs ?
What type of plastic responses to environmental variations ?
(…)
Exchanges with the ecological board
Involvement of the ecological board in WP1
B. Kunin, Univ. Leeds
C. Melian, EAWAG M. Hutchings, Univ. Sussex
E. Thébault, CNRS
Project meeting (July 2-6tth, Oslo)
Invited stay (Oct. 17-18th, Rennes)
Invited stay (Feb. 3 & 4th, Rennes)
Exchanges with the ecological board
Involvement of the ecological board in WP1
B. Kunin, Univ. Leeds
C. Melian, EAWAG M. Hutchings, Univ. Sussex
E. Thébault, CNRS
Application 1
Application 2
Methodological issues on graphs
Review on ecological graphs
Conceptual Framework and main goals
The importance of graphs in Ecology
Ø  A set of nodes and edges that can depict any set of components
interacting together
Little Rock Lake food-web
(Martinez, 1991)
Co-occurrence of soil
taxonomic units
(Barerán et al., 2011)
Landscape graph of a
carabid species
(Vasas et al., 2009)
Conceptual Framework and main goals
The importance of graphs in Ecology
Metacommunities
Communities
Individuals
Landscape graphs
Biogeography
Species networks
Co-occurrence
Mating graphs
Social graphs
Architectural graphs
Conceptual Framework and main goals
Complexity vs. Stability
Ø  Graph structure may determine the system stability to local
environmental disturbance Architectural graphs -> Plant structure vs. performance
Species graphs -> Complexity Stability debate (May 1972, Allesina and
Tang 2012, Thébault and Fontaine 2010)
Landscape graphs -> Connectivity vs. species conservation Conceptual Framework and main goals
Complexity vs. Stability
Ø  Positive impact of evolution
-  Species diversification and evolution increase stability
- Adaptation of species behaviour has stabilising effects
Loeuille & Loreau, 2005; Kondoh, 2003;
Heckmann et al., 2012
Conceptual Framework and main goals
Main questions
Ø  Adaptivity in food webs
What functional implications?
Which response to global warming?
Conceptual Framework and main goals
Main questions
Ø  Local plasticity in architectural graphs in response to
heterogeneous environments
What response ?
In which environmental conditions ?
Application 1: Adaptivity in trophic food webs Introduction Size of prey
(Handling time)-1
Energy intake
Modelling studies in food webs: invariant trophic relationships
≠
Optimal foraging theory Size of prey
Brose et al. 2008, Petchey et al. 2008
Application 1: Adaptivity in trophic food webs Introduction Ø  Foraging increases stability in food webs
With foraging
Persistence
Without foraging
Connectance
Connectance
Kondoh, 2003
Application 1: Adaptivity in trophic food webs Introduction Persistence
Ø  Foraging is dependent on web
allometric structure
Allometric structuration
Ø  Foraging increases stability in food webs
Foraging effort
Kondoh, 2003
Application 1: Adaptivity in trophic food webs Introduction Ø  Predation impacts species size at short term (days)
Rutilus rutilus
Abramis brama
Daphnia
F0: no fishes
F1…3: increasing fish biomass Borcic et al. 1998
Application 1: Adaptivity in trophic food webs Main question
What are the consequences of size adaptation on food web functionality ?
Range of size evolution
Mechanisms
Convergence toward steady state
Generation of realistic food web structure
underlying food
webs assemblage
Impact on stability
Response to temperature increase
Functional
properties
Application 1: Adaptivity in trophic food webs Methods
Ø  Use of an allometric model
Yodzis & Innes 1992, Heckmann et al. 2012
Application 1: Adaptivity in trophic food webs Methods
Ø  Use of an allometric model
Ø  Species are defined by mean size and variability (normal law)
Predator
Preys
103
106
Size
Yodzis & Innes 1992, Heckmann et al. 2012
Application 1: Adaptivity in trophic food webs Methods
Ø  Use of an allometric model
Ø  Species are defined by mean size and variability (normal law)
Eats on (10-3 predator prey size ratio, Brose et al. 2006)
Predator
Preys
103
106
Size
Yodzis & Innes 1992, Heckmann et al. 2012
Application 1: Diversification in trophic food webs Methods
Size
Greater individuals consumed => decrease of species size
Smaller individuals consumed => increase of species size
Application 1: Diversification in trophic food webs Preliminary results
Ø Food web persists only with unrealistic parameter values without size
adaptivity
In agreement with results from Heckmann et al. 2012
23
Application 1: Diversification in trophic food webs Next steps
Ø  Implementation of the adaptivity rules
Ø  Comparison of the resulting topology to empirical results
Ø  Implementation of temperature effects
24
Application 2: Local plasticity in architectural graphs
Introduction: Plasticity in clonal networks
Ø  Due to their sessile life, plants face high temporal variability
and spatial heterogeneity in environmental conditions
Ø  Modular vertical and horizontal growth forms Ø  High plasticity at the local scale
Application 2: Local plasticity in architectural graphs
Introduction: The particular case of clonal plants
ramet
spacer
connection
Elytrigia repens
Application 2: Local plasticity in architectural graphs
Introduction: Foraging vs. Specialization
Hutchings & de Kroon, 1994; Kroon &
Hutchings, 1995; Alpert & Stuefer, 1997
Application 2: Local plasticity in architectural graphs
Local plastic response
Foraging behavior
Ramet specialization
(division of labour)
Hutchings & de Kroon, 1994; Kroon &
Hutchings, 1995; Alpert & Stuefer, 1997
Application 2: Local plasticity in architectural graphs
Local plastic response
Network function
Foraging behavior
Modification of the connexion traits
Spacer length
Branching degree
Branch orientation
Ramet specialization
(division of labour)
Modification of the ramet traits
Root:Shoot ratio
Space
exploration
Network
Performance
Space
exploitation
Hutchings & de Kroon, 1994; Kroon &
Hutchings, 1995; Alpert & Stuefer, 1997
Application 2: Local plasticity in architectural graphs
Environmental heterogeneity
nb of bad patches
patch size
patch contrast
Application 2: Local plasticity in architectural graphs
Main questions
patch size
Foraging
Plasticity type ?
patch contrast
Specialization
Application 2: Local plasticity in architectural graphs
Hypothesis
Patch size
?
Foraging
?
Specialization
Application 2: Local plasticity in architectural graphs
Hypothesis
Patch contrast
? Specialization/ Foraging
Application 2: Local plasticity in architectural graphs
Hypothesis
Difficulty to argue for good hypothesis due to:
Gain vs. Cost of the two types (no experimental or empirical
data) Potential trade-off between the two plastic responses
Benefit of these responses may be relative: degree of the
response vs. spatial scale of the heterogeneity
Things may be disentangled through modelling
Application 2: Local plasticity in architectural graphs
Modelling
CLONAL Elementary unit: the clone Basic metabolism Plant Structural Blue-­‐Print Ontogeny Plas6city Simulations
11 input parameters 2-­‐4 values tested each X Non Plas9c, Foraging, Specializa9on, F+S Environmental Heterogeneity Scenario selec6on Number and size of the patches Patch contrast Literature survey on experimental data Application 2: Local plasticity in architectural graphs
Simulations
CLONAL Elementary unit: the clone Basic metabolism Plant Structural Blue-­‐Print Ontogeny Plas6city 11 input parameters 2-­‐4 values tested each X Non Plas9c, Foraging, Specializa9on, F+S Environmental Heterogeneity Number and size of the patches Patch contrast Network performance Optimization
Application 2: Local plasticity in architectural graphs
Methods: 1. Foraging behavior – Effect of grain size
Length = L0 + µ*[d0,d1]
µ € [0,1]
d0 -> poor
d1 -> rich Total Nb of ramets
Total Biomass
Application 2: Local plasticity in architectural graphs
Methods: 1. Foraging behavior – Effect of patch contrast
dBiomass/dt(ramet) = B0(1-rs)B(t)(1-B(t)/Bmax))
Bm0 -> poor
Bm1 -> rich
B1=Bm0/C
C: Contrast poor vs. rich
Total Nb of ramets
Total Biomass
Application 2: Local plasticity in architectural graphs
Preliminary results: Effect of grain size (Strong contrast)
Fine grain
Total Biomass
Total Biomass
Coarse grain
d0
d1
d0
d1
Application 2: Local plasticity in architectural graphs
Preliminary results: Effect of patch contrast
Strong contrast
Total Biomass
Total Biomass
Low contrast
d0
d1
d0
d1
Application 2: Local plasticity in architectural graphs
Next steps
Ø  Implement 2-D simulations
Ø  Implement Specialization process
Ø  Large-scale simulations with Foraging and Specialization together
Graph simplification information loss and ecological
meaning
Introduction: Grouping species in graphs
Modularity
Trophic groups
Groups of species with rather similar
interactions
Groups of species interacting more
between themselves than other species
Graph simplification information loss and ecological
meaning
Introduction: Grouping species in graphs
Modularity
Trophic groups
Groups of species with rather similar
interactions
Groups of species interacting more
between themselves than other species
- Strong mathematical background
- Few ecological significance
- No clear definition
- Little mathematical background
- Strong ecological significance
Graph simplification information loss and ecological
meaning
Main questions
Ø  Develop a method for trophic group detection
Ø  Analyse of information loss by aggregation
Ø  Comparison of relationships between different clustering
methods
Graph simplification information loss and ecological
meaning
Methods
Ø  Development of a model for trophic group detection in food webs
based on modularity maximisation approaches Difference between proportion of
common trophic links between
species of a group and its random
expectation E
G( E ) = ∑
g =1
1
g
∑ (T (i, j) − T (i, j))
r
i , j∈g
i< j
Gauzens et al. Sub. (arXiv)
Graph simplification information loss and ecological
meaning
Preliminary results
Creteil lake food web: colors set trophic groups, grey square modules
Graph simplification information loss and ecological
meaning
Next steps
Ø  Functional implications of a trophic group structure
Ø  Species traits underlying group compositions
Ø  Link between trophic groups composition and key species
detections
Toward a synthesis on ecological graphs
Main goals
Ø  Need for more general overview on graphs in ecology in order to
detect common patterns to transfer to computer scientists
Common and divergent characteristics
Structure vs. Functionning
Structure vs. Stability
Response to environmental variations
Toward a synthesis on ecological graphs
Main goals
Ø  Find a new methodology for coupling different types of graphs
Spatial, temporal, organisational scales
Available data
Toward a synthesis on ecological graphs
Main goals
Ø  Examples that could be addressed through this coupling:
Conservation issues
Response to climate change
Ecosystem services
(…)
Publications and Communications
Ø  Gauzens et al. 2013 Intermediate predation pressure leads to maximal
complexity in food webs. Intecol, London.
Ø  Gauzens et al. Submitted. Trophic groups and modules: two levels of group
detection in food webs. arXiv
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