The document summarizes Anne Duputié's presentation on models in evolutionary ecology. It discusses several existing conceptual models for how species respond to environmental changes through migration or adaptation. It then presents a model developed by Duputié et al. that examines how genetic correlations between traits can impact a species' ability to shift its range in response to a changing environment. The model incorporates factors like genetic variance, an environmental gradient, migration rates, and spatial selection pressures.
Fixed Action Pattern (FAP) is a series or sequence of acts that occur behaviorally in animals. it is also known as instinctive behaviour as it is determined by gene of an organism and exhibited automatically without having any prior experience.
Module 5 - EN - Promoting data use III: Most frequent data analysis techniques Alberto González-Talaván
This presentation builds on experiences and presents the most frequently taught ecological niche modelling techniques, so that Node managers can organize successful training and dissemination sessions on this topic.
It was prepared by Anne Sophie Archambeau from GBIF France, with input from Dag Endresen from GBIF Norway.
Fixed Action Pattern (FAP) is a series or sequence of acts that occur behaviorally in animals. it is also known as instinctive behaviour as it is determined by gene of an organism and exhibited automatically without having any prior experience.
Module 5 - EN - Promoting data use III: Most frequent data analysis techniques Alberto González-Talaván
This presentation builds on experiences and presents the most frequently taught ecological niche modelling techniques, so that Node managers can organize successful training and dissemination sessions on this topic.
It was prepared by Anne Sophie Archambeau from GBIF France, with input from Dag Endresen from GBIF Norway.
Article 4 Apes in a changing world - the effects of global warmin.docxfredharris32
Article 4: Apes in a changing world - the effects of global warming on the behaviour and distribution of African apes J. Lehmann et al. Global warming and ape biogeography.
Sourse: Lehmann, Julia, Amanda H. Korstjens, and Robin I. M. Dunbar. "Apes In A Changing World - The Effects Of Global Warming On The Behaviour And Distribution Of African Apes J. Lehmann Et Al. Global Warming And Ape Biogeography." Journal Of Biogeography 37.12 (2010): 2217-2231. Academic Search Premier. Web. 7 Feb. 2015.
O R I G I N A L
A R T I C L E
Apes in a changing world – the effects
of global warming on the behaviour
and distribution of African apes
Julia Lehmann1,2*, Amanda H. Korstjens1,3 and Robin I. M. Dunbar1,4
1British Academy Centenary Research Project,
School of Biological Sciences, Crown Street,
University of Liverpool, Liverpool L69 7ZB,
UK,
2
Department of Life Sciences, Roehampton
University, London SW15 4JD, UK,
3
Conservation Sciences, Bournemouth
University, Poole BH12 5BB, UK, 4Institute of
Cognitive and Evolutionary Anthropology,
University of Oxford, Oxford OX2 6PE, UK
*Correspondence: Julia Lehmann, Life Science
Department, Holybourne Avenue, Roehampton
University, London SW15 4JD, UK.
E-mail: [email protected]
A B S T R A C T
Aim In this study we use a modelling approach to identify: (1) the factors
responsible for the differences in ape biogeography, (2) the effects that global
warming might have on distribution patterns of African apes, (3) the underlying
mechanisms for these effects, and (4) the implications that behavioural flexibility
might be expected to have for ape survival. All African apes are highly
endangered, and the need for efficient conservation methods is a top priority. The
expected changes in world climate are likely to further exacerbate the difficulties
they face. Our study aims to further understand the mechanisms that link climatic
conditions to the behaviour and biogeography of ape species.
Location Africa.
Method We use an existing validated time budgets model, derived from data on
20 natural populations of gorillas (Gorilla beringei and Gorilla gorilla) and
chimpanzees (Pan troglodytes and Pan paniscus), which specifies the relationship
between climate, group size, body weight and time available for various activities,
to predict ape distribution across Africa under a uniform worst-case climate
change scenario.
Results We demonstrate that a worst-case global warming scenario is likely to
alter the delicate balance between different time budget components. Our model
points to the importance of annual temperature variation, which was found to
have the strongest impact on ape biogeography. Our simulation indicates that
rising temperatures and changes in rainfall patterns are likely to have strong
effects on ape survival and distribution, particularly for gorillas. Even if they
behaved with maximum flexibility, gorillas may not be able to survive in most of
their present habitat ...
A R T I C L E SCaptivity for Conservation Zoos at a Cross.docxransayo
A R T I C L E S
Captivity for Conservation? Zoos at a Crossroads
Jozef Keulartz
Accepted: 22 February 2015 / Published online: 13 March 2015
� The Author(s) 2015. This article is published with open access at Springerlink.com
Abstract This paper illuminates a variety of issues that speak to the question of
whether ‘captivity for conservation’ can be an ethically acceptable goal of the
modern zoo. Reflecting on both theoretical disagreements (animal protectionists vs.
wildlife conservationists) and practical challenges (the small percentage of endan-
gered species actually exhibited in zoos, disappointing success of reintroduction
programs), the paper explains why the ‘Noah’s Ark’ paradigm is being replaced by
an alternative ‘integrated approach.’ It explores the changes in the zoo’s core tasks
that the new paradigm implies. And it pays special attention to the changes that
would have to be made in zoos’ collection policies: connection with in situ projects,
emphasizing local species and the local biogeographical region, exchange of ani-
mals among zoos and between zoos and wildlife, and a shift towards smaller spe-
cies. Finally the question will be addressed whether the new paradigm will achieve a
morally acceptable balance between animal welfare costs and species conservation
benefits.
Keywords Future zoo � Animal welfare � Species conservation � Metapopulation
management
Introduction
Today, the animal world is under severe attack as a result of two strongly
interconnected global processes. On the one hand, global environmental changes
J. Keulartz (&)
Emeritus Professor of Environmental Philosophy, Radboud University, Nijmegen, The Netherlands
e-mail: [email protected]
J. Keulartz
Wageningen University, Wageningen, The Netherlands
123
J Agric Environ Ethics (2015) 28:335–351
DOI 10.1007/s10806-015-9537-z
such as climate change, land use and land cover change, deforestation and
desertification have a disruptive impact on plant and animal life. Entire populations
are being confronted with the alternative to abandon their original habitat or to go
extinct. On the other hand, globalization causes massive dislocations of entire
populations. As trade, travel, transport and tourism boom, the world is becoming
more and more borderless and, by the same token, it is becoming increasingly
vulnerable to invasive species. Since globalization took off, more plants and animals
have become globetrotters than ever before (Keulartz and Swart 2012).
Because animals are constantly on the move worldwide as a result of these global
processes, traditional in situ (place-based) conservation methods seem no longer
sufficient to save threatened species (Sandler 2012). The magnitude of anthro-
pogenic environmental stress from bioinvasion, habitat fragmentation, nitrogen
deposition, biodiversity loss, and, above all, climate change, makes it unavoidable
to replace the hands-off approach that has guided mainstream species con.
Living organisms (even human) evolve to match with the climate or not and geo...MdAbdulAhad26
Darwin and his followers’ belief is that plants and animals are dispersed away from their places of origins and then became
subsequently modified to match with the environment. Therefore, climate plays the key role both for the evolution of
organisms (even human) and their geographical distribution. However, living organisms are beautifully adapted with the
climate. Furthermore, they migrate to a safe place with the changing climate; if it is not possible, they simply extinct.
Cosmopolitan animals, and also places having the same climate do not possess the same type of fauna, which opposes the
effect of climate on the evolution. If climate has an effect on the evolution, then only six animals and plant species could be
found according to the six climatic zones. Additionally, seven theories of evolution are formulated without the effects of
environment on the evolution. Again, evolution is a continuous process but there is no record that living organism has evolved
by the effects of the environment. Therefore, living organisms (even human) not evolve to match with the climate. As the
evolution of organisms and their geographical distributions are interrelated and vice versa. So, geographical distributions are
opposite to Darwin’s theory. Gaia theory and Croizat’s views oppose the Darwin’s vision about evolution and his
biogeography.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Article 4 Apes in a changing world - the effects of global warmin.docxfredharris32
Article 4: Apes in a changing world - the effects of global warming on the behaviour and distribution of African apes J. Lehmann et al. Global warming and ape biogeography.
Sourse: Lehmann, Julia, Amanda H. Korstjens, and Robin I. M. Dunbar. "Apes In A Changing World - The Effects Of Global Warming On The Behaviour And Distribution Of African Apes J. Lehmann Et Al. Global Warming And Ape Biogeography." Journal Of Biogeography 37.12 (2010): 2217-2231. Academic Search Premier. Web. 7 Feb. 2015.
O R I G I N A L
A R T I C L E
Apes in a changing world – the effects
of global warming on the behaviour
and distribution of African apes
Julia Lehmann1,2*, Amanda H. Korstjens1,3 and Robin I. M. Dunbar1,4
1British Academy Centenary Research Project,
School of Biological Sciences, Crown Street,
University of Liverpool, Liverpool L69 7ZB,
UK,
2
Department of Life Sciences, Roehampton
University, London SW15 4JD, UK,
3
Conservation Sciences, Bournemouth
University, Poole BH12 5BB, UK, 4Institute of
Cognitive and Evolutionary Anthropology,
University of Oxford, Oxford OX2 6PE, UK
*Correspondence: Julia Lehmann, Life Science
Department, Holybourne Avenue, Roehampton
University, London SW15 4JD, UK.
E-mail: [email protected]
A B S T R A C T
Aim In this study we use a modelling approach to identify: (1) the factors
responsible for the differences in ape biogeography, (2) the effects that global
warming might have on distribution patterns of African apes, (3) the underlying
mechanisms for these effects, and (4) the implications that behavioural flexibility
might be expected to have for ape survival. All African apes are highly
endangered, and the need for efficient conservation methods is a top priority. The
expected changes in world climate are likely to further exacerbate the difficulties
they face. Our study aims to further understand the mechanisms that link climatic
conditions to the behaviour and biogeography of ape species.
Location Africa.
Method We use an existing validated time budgets model, derived from data on
20 natural populations of gorillas (Gorilla beringei and Gorilla gorilla) and
chimpanzees (Pan troglodytes and Pan paniscus), which specifies the relationship
between climate, group size, body weight and time available for various activities,
to predict ape distribution across Africa under a uniform worst-case climate
change scenario.
Results We demonstrate that a worst-case global warming scenario is likely to
alter the delicate balance between different time budget components. Our model
points to the importance of annual temperature variation, which was found to
have the strongest impact on ape biogeography. Our simulation indicates that
rising temperatures and changes in rainfall patterns are likely to have strong
effects on ape survival and distribution, particularly for gorillas. Even if they
behaved with maximum flexibility, gorillas may not be able to survive in most of
their present habitat ...
A R T I C L E SCaptivity for Conservation Zoos at a Cross.docxransayo
A R T I C L E S
Captivity for Conservation? Zoos at a Crossroads
Jozef Keulartz
Accepted: 22 February 2015 / Published online: 13 March 2015
� The Author(s) 2015. This article is published with open access at Springerlink.com
Abstract This paper illuminates a variety of issues that speak to the question of
whether ‘captivity for conservation’ can be an ethically acceptable goal of the
modern zoo. Reflecting on both theoretical disagreements (animal protectionists vs.
wildlife conservationists) and practical challenges (the small percentage of endan-
gered species actually exhibited in zoos, disappointing success of reintroduction
programs), the paper explains why the ‘Noah’s Ark’ paradigm is being replaced by
an alternative ‘integrated approach.’ It explores the changes in the zoo’s core tasks
that the new paradigm implies. And it pays special attention to the changes that
would have to be made in zoos’ collection policies: connection with in situ projects,
emphasizing local species and the local biogeographical region, exchange of ani-
mals among zoos and between zoos and wildlife, and a shift towards smaller spe-
cies. Finally the question will be addressed whether the new paradigm will achieve a
morally acceptable balance between animal welfare costs and species conservation
benefits.
Keywords Future zoo � Animal welfare � Species conservation � Metapopulation
management
Introduction
Today, the animal world is under severe attack as a result of two strongly
interconnected global processes. On the one hand, global environmental changes
J. Keulartz (&)
Emeritus Professor of Environmental Philosophy, Radboud University, Nijmegen, The Netherlands
e-mail: [email protected]
J. Keulartz
Wageningen University, Wageningen, The Netherlands
123
J Agric Environ Ethics (2015) 28:335–351
DOI 10.1007/s10806-015-9537-z
such as climate change, land use and land cover change, deforestation and
desertification have a disruptive impact on plant and animal life. Entire populations
are being confronted with the alternative to abandon their original habitat or to go
extinct. On the other hand, globalization causes massive dislocations of entire
populations. As trade, travel, transport and tourism boom, the world is becoming
more and more borderless and, by the same token, it is becoming increasingly
vulnerable to invasive species. Since globalization took off, more plants and animals
have become globetrotters than ever before (Keulartz and Swart 2012).
Because animals are constantly on the move worldwide as a result of these global
processes, traditional in situ (place-based) conservation methods seem no longer
sufficient to save threatened species (Sandler 2012). The magnitude of anthro-
pogenic environmental stress from bioinvasion, habitat fragmentation, nitrogen
deposition, biodiversity loss, and, above all, climate change, makes it unavoidable
to replace the hands-off approach that has guided mainstream species con.
Living organisms (even human) evolve to match with the climate or not and geo...MdAbdulAhad26
Darwin and his followers’ belief is that plants and animals are dispersed away from their places of origins and then became
subsequently modified to match with the environment. Therefore, climate plays the key role both for the evolution of
organisms (even human) and their geographical distribution. However, living organisms are beautifully adapted with the
climate. Furthermore, they migrate to a safe place with the changing climate; if it is not possible, they simply extinct.
Cosmopolitan animals, and also places having the same climate do not possess the same type of fauna, which opposes the
effect of climate on the evolution. If climate has an effect on the evolution, then only six animals and plant species could be
found according to the six climatic zones. Additionally, seven theories of evolution are formulated without the effects of
environment on the evolution. Again, evolution is a continuous process but there is no record that living organism has evolved
by the effects of the environment. Therefore, living organisms (even human) not evolve to match with the climate. As the
evolution of organisms and their geographical distributions are interrelated and vice versa. So, geographical distributions are
opposite to Darwin’s theory. Gaia theory and Croizat’s views oppose the Darwin’s vision about evolution and his
biogeography.
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Anne Duputié | MEE 2013 | Modelling range shifts in dynamic environments – How can evolution enter the stage?
1. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Modelling range shifts in dynamic environments –
How can evolution enter the stage?
Anne Duputié
CEFE, Montpellier
2. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Parmesan et al. Nature 1999
Tircis
1940-69
1970-97
1915-39
- Migration
Responses to environmental changes (& lack thereof)
65% of 35 non migrating butterflies have
shifted their range northwards in <100 y
3. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Zhu et al. GCB 2012
Northward shift
Southward shift
Expansion
Contraction
- Migration (or not)
Northernboundarychange(deglatitude)
Southern boundary change (deg latitude)
Responses to environmental changes (& lack thereof)
only 20% of 92 North American tree
species show a northward range shift
(59%: contraction)
4. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Bradshaw & Holzapfel PNAS 2001
1940-69
1970-97
Latitude (corrected for altitude)
Wyeomyia smithii (photo S Gray)
- Migration (or not)
- Adaptation
Criticalphotoperiod(h)
Responses to environmental changes (& lack thereof)
Evolution of critical photoperiod for
entering into diapause (heritable trait,
h²=15-70%) within 25 years
5. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
- Migration (or not)
- Adaptation (or not)
. generation time
. no standing variance left
Responses to environmental changes (& lack thereof)
Drosphila birchii
Fragmented populations;
no available genetic variance to respond
to stress
Hoffmann et al Science 2003
6. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Etterson & Shaw Science 2001
Chamaechrista
fasciculata
- Migration (or not)
- Adaptation (or not)
. generation time
. no standing variance left
. correlations among traits
Distribution of Chamaechrista fasciculata
Responses to environmental changes (& lack thereof)
7. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Etterson & Shaw Science 2001
precocity
number of leaves
Responses to environmental changes (& lack thereof)
Chamaechrista
fasciculata
- Migration (or not)
- Adaptation (or not)
. generation time
. no standing variance left
. correlations among traits
Distribution of Chamaechrista fasciculata
8. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Etterson & Shaw Science 2001
Future optimum
climate shift
by 2035
Responses to environmental changes (& lack thereof)
precocity
number of leaves
Chamaechrista
fasciculata
Distribution of Chamaechrista fasciculata
- Migration (or not)
- Adaptation (or not)
. generation time
. no standing variance left
. correlations among traits
9. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Etterson & Shaw Science 2001
Future optimum
migrationclimate shift
by 2035
Responses to environmental changes (& lack thereof)
precocity
number of leaves
Chamaechrista
fasciculata
Distribution of Chamaechrista fasciculata
- Migration (or not)
- Adaptation (or not)
. generation time
. no standing variance left
. correlations among traits
10. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Etterson & Shaw Science 2001
Future optimum
migration
Adaptation
(no migration)
climate shift
by 2035
Responses to environmental changes (& lack thereof)
precocity
number of leaves
Chamaechrista
fasciculata
Distribution of Chamaechrista fasciculata
- Migration (or not)
- Adaptation (or not)
. generation time
. no standing variance left
. correlations among traits
11. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Etterson & Shaw Science 2001
Future optimum
climate shift
by 2035
Responses to environmental changes (& lack thereof)
precocity
number of leaves
Chamaechrista
fasciculata
Distribution of Chamaechrista fasciculata
- Migration (or not)
- Adaptation (or not)
. generation time
. no standing variance left
. correlations among traits
Phenotype distribution
12. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Etterson & Shaw Science 2001
Future optimum
climate shift
by 2035
Responses to environmental changes (& lack thereof)
precocity
number of leaves
Chamaechrista
fasciculata
Distribution of Chamaechrista fasciculata
- Migration (or not)
- Adaptation (or not)
. generation time
. no standing variance left
. correlations among traits
Phenotype distribution
13. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Etterson & Shaw Science 2001
Future optimum
climate shift
by 2035
Responses to environmental changes (& lack thereof)
precocity
number of leaves
Chamaechrista
fasciculata
Distribution of Chamaechrista fasciculata
- Migration (or not)
- Adaptation (or not)
. generation time
. no standing variance left
. correlations among traits
Phenotype distribution
14. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Etterson & Shaw Science 2001
Future optimum
climate shift
by 2035
Responses to environmental changes (& lack thereof)
precocity
number of leaves
Chamaechrista
fasciculata
Distribution of Chamaechrista fasciculata
- Migration (or not)
- Adaptation (or not)
. generation time
. no standing variance left
. correlations among traits
Phenotype distribution
Correlations among traits
slow down evolution
15. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Modelling species distributions
Observed occurrences
Observed/inferred density
Probability of occurrence
Habitat suitability
16. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Modelling species distributions
Observed occurrences
Observed/inferred density
Probability of occurrence
Habitat suitability
17. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Modelling species distributions
Example: Fagus sylvatica, European beech
18. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Modelling species distributions
Example: Fagus sylvatica, European beech
19. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Environment
Probability of occurrence
Tmax Tmin Prec GDD
…
Modelling species distributions
?
20. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
“Phenomenological”
Environment
Probability of occurrence
Tmax Tmin Prec GDD
…
Modelling species distributionsP(occurrence)
environment
“
21. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
“Phenomenological”
“Process-based”
Environment
Probability of occurrence
Traits:
reaction norms
Growth/
Survival…
(Fitness)
Tmax Tmin Prec GDD
…
Modelling species distributionsP(occurrence)
environment
Trait
environment
22. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
“Phenomenological”
“Process-based”
Environment
Probability of occurrence
Traits:
reaction norms
Growth/
Survival…
(Fitness)
“Conceptual”
Traits:
realised vs
optimum
Fitness
Tmax Tmin Prec GDD
…
Modelling species distributionsP(occurrence)
environment
Trait
environment
Fitness
Matching
trait/optimum
23. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
“Phenomenological”
“Process-based”
Environment
Probability of occurrence
Traits:
reaction norms
Growth/
Survival…
(Fitness)
“Conceptual”
Traits:
realised vs
optimum
Fitness
Ease of calibration
Understanding
Tmax Tmin Prec GDD
…
Modelling species distributionsP(occurrence)
environment
Trait
environment
Fitness
Matching
trait/optimum
Trait evolution
24. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Genetic adaptation and distribution ranges
1. Constraints to adaptation ?
a conceptual model of trait adaptation on a shifting gradient
2. Evolution of trait reaction norms
a process-based model of tree distribution ranges
25. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Factors limiting distribution ranges:
Topography
Biotic interactions
Demography
Adaptation
Migration
1. Responses to environmental changes: existing conceptual models
Brown AmNat 1974
Grinnell Auk 1917
Mimura & Aitken JEB 2009
« Fundamental » niche
Realised niche
Svenning & Skov EcolLett 2007
26. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Factors limiting distribution ranges:
Topography
Biotic interactions
Demography
Adaptation
Migration
1. Responses to environmental changes: existing conceptual models
Brown AmNat 1974
Grinnell Auk 1917
Mimura & Aitken JEB 2009
« Fundamental » niche
Realised niche
Svenning & Skov EcolLett 2007
27. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Breeder’s equation: R=h² S
Available genetic variance
Selection strength
1. Responses to environmental changes: existing conceptual models
Fitness
(intrinsicgrowthrater)
Mean trait z
Selection
gradient
Model:
- One species
- Quantitative trait evolves
28. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Fitness
(intrinsicgrowthrater)
Mean trait z
Selection
gradient
Model:
- One species
- Quantitative trait evolves
- Environmental gradient
Fitness r
1. Responses to environmental changes: existing conceptual models
29. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Model:
- One species
- Quantitative trait evolves
- Environmental gradient
- Variable population density
Fitness r
Space
Density
Coupling demography/adaptation
1. Responses to environmental changes: existing conceptual models
Coupling demography/adaptation
30. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Model:
- One species
- Quantitative trait evolves
- Environmental gradient
- Variable population density
Fitness r
Space
Density
Coupling demography/adaptation
1. Responses to environmental changes: existing conceptual models
31. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Some results of this type of models:
- No spatial heterogeneity:
Maximal speed of environmental change (Lynch & Lande 1993)
- Can be generalised to several traits (Gomulkiewicz & Houle AmNat 2009)
0 2
1
2
2 2
G G
c
S e S
V V
k k r
V N V
too little genetic variance
or too weak selection
low fecundity
small population
Extinction if:
1. Responses to environmental changes: existing conceptual models
32. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Some results of this type of models:
- No spatial heterogeneity
- Spatial heterogeneity only (Kirkpatrick & Barton AmNat 1997)
1. Responses to environmental changes: existing conceptual models
Adaptation depends on VG and migration
Meantrait
optimum
realised
Space
33. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
1. Responses to environmental changes: existing conceptual models
Adaptation depends on VG and migration
Wider distribution for intermediate
migration rates
Space
MeantraitDensity
optimum
realised
Space
Some results of this type of models:
- No spatial heterogeneity
- Spatial heterogeneity only (Kirkpatrick & Barton AmNat 1997)
34. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Some results of this type of models:
- No spatial heterogeneity
- Spatial heterogeneity only
- Spatial and temporal heterogeneity (Pease et al Ecology 1989)
1. Responses to environmental changes: existing conceptual models
Space
MeantraitDensity
optimum
realised
Space
35. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Some results of this type of models:
- No spatial heterogeneity
- Spatial heterogeneity only
- Spatial and temporal heterogeneity (Pease et al Ecology 1989)
1. Responses to environmental changes: existing conceptual models
Space
MeantraitDensity
optimum
realised
Space
Clines move as the environment changes.
If persisting, the species shifts its range at
the speed of the environmental change,
with a lag.
36. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Some results of this type of models:
- No spatial heterogeneity
- Spatial heterogeneity only
- Spatial and temporal heterogeneity
what about genetic constraints in
heterogeneous environments?
1. Responses to environmental changes: existing conceptual models
37. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
1. Genetic correlations and range shifts: model ingredients
- One species
- Fitness depends on several traits under stabilizing selection: S
Trait1
Trait 2
Adaptive landscape
S
38. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
1. Genetic correlations and range shifts: model ingredients
- One species
- Fitness depends on several traits under stabilizing selection: S
- Genetic variance: G
Trait1
Trait 2
Adaptive landscape
S
G
39. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
1. Genetic correlations and range shifts: model ingredients
- One species
- Fitness depends on several traits under stabilizing selection: S
- Genetic variance: G
- Environmental gradient, slope b
Trait1
Trait 2
Adaptive landscape
S
G
b
Space
optimum 1
optimum 2
Traitmean
40. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
- One species
- Fitness depends on several traits under stabilizing selection: S
- Genetic variance: G
- Environmental gradient, slope b
- Shifting at speed v
Trait1
Trait 2
Adaptive landscape
S
G
b
Space
optimum 1
optimum 2
Traitmean
1. Genetic correlations and range shifts: model ingredients
41. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
- One species
- Fitness depends on several traits under stabilizing selection: S
- Genetic variance: G
- Environmental gradient, slope b
- Shifting at speed v
- Migration: density-dependent diffusion, σ
Trait1
Trait 2
Adaptive landscape
S
G
bσ
Space
Density
Space
optimum 1
optimum 2
Traitmean
1. Genetic correlations and range shifts: model ingredients
42. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
- One species
- Fitness depends on several traits under stabilizing selection: S
- Genetic variance: G
- Environmental gradient, slope b
- Shifting at speed v
- Migration: density-dependent diffusion, σ
- Spatial selection gradient Sb
Trait1
Trait 2
Adaptive landscape
S
G
b
Sb
σ
Space
optimum 1
optimum 2
Traitmean
Space
Density
1. Genetic correlations and range shifts: model ingredients
43. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
- One species
- Fitness depends on several traits under stabilizing selection: S
- Genetic variance: G
- Environmental gradient, slope b
- Shifting at speed v
- Migration: density-dependent diffusion, σ
- Spatial selection gradient Sb
- G, S, b assumed constant
Trait1
Trait 2
Adaptive landscape
S
G
b
Sb
σ
Space
optimum 1
optimum 2
TraitmeanDensity
Space
1. Genetic correlations and range shifts: model ingredients
44. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
Trait1
Trait 2
Adaptive landscape
S
G
b
Sb
σ
Space
trait z optima
1. Genetic correlations and range shifts: results
45. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
Trait1
Trait 2
Adaptive landscape
S
G
b
Sb
σ
Traits develop clines
Clines often flatter than optima
Space
trait z optima
realised
1. Genetic correlations and range shifts: results
46. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
Trait1
Trait 2
Adaptive landscape
S
G
b
Sb
σ
Traits develop clines
Clines often flatter than optima
Population density is gaussian
Space
trait z optima
fitness r
Space
Space
density n
realised
1. Genetic correlations and range shifts: results
47. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
Trait1
Trait 2
Adaptive landscape
S
G
b
Sb
σ
Traits develop clines, shifting across time
Clines often flatter than optima
Population density is gaussian
Population shifts
Space
trait z optima
fitness r
Space
Space
density n
realised
1. Genetic correlations and range shifts: results
48. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
Trait1
Trait 2
Adaptive landscape
S
G
b
Sb
σ
Traits develop clines, shifting across time
Clines often flatter than optima
Population density is gaussian
Population shifts, with constant lag
Space
trait z optima
fitness r
Space
Space
density n
realised
Ln
1. Genetic correlations and range shifts: results
49. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
Trait1
Trait 2
Adaptive landscape
S
G
b
Sb
σ
Traits develop clines, shifting across time
Clines often flatter than optima
Population density is gaussian
Population shifts, with constant lag
Space
trait z optima
fitness r
Space
Space
density n
realised
Ln
ρ
1. Genetic correlations and range shifts: results
50. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
Trait1
Trait 2
Adaptive landscape
S
G
b
Sb
σ
Traits develop clines, shifting across time
Clines often flatter than optima
Population density is gaussian
Population shifts, with constant lag
Range width constant
Space
trait z optima
fitness r
Space
Space
density n
realised
Ln
ρ
Vn
1. Genetic correlations and range shifts: results
51. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
Trait1
Trait 2
Adaptive landscape
S
G
b
Sb
Traits develop clines, shifting across time
Clines often flatter than optima
Population density is gaussian
Population shifts, with constant lag
Range width constant
Analytical expressions for adaptation &
demography.
Increase when:
Maximal adaptability A = bTS G Sb
G aligned with Sb
Minimal spatial fitness gradient B = bT S b
b aligned with S
1. Genetic correlations and range shifts: results
52. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
Trait1
Trait 2
Adaptive landscape
S
b
Traits develop clines, shifting across time
Clines often flatter than optima
Population density is gaussian
Population shifts, with constant lag
Range width constant
Analytical expressions for adaptation &
demography.
Increase when:
Maximal adaptability A = bTS G Sb
G aligned with Sb
Minimal spatial fitness gradient B = bT S b
b aligned with S
1. Genetic correlations and range shifts: results
53. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
0 2
2 2
c
B A
v r
B
Extinction if change faster than:
Low fecundity Maladapted
migrants
Not enough
adaptation
Slow migration
Tolerance to change:
1. Genetic correlations and range shifts: results
54. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
1. Genetic correlations and range shifts: results
Hinders adaptation Widens range
diffusion σ
Rangewidth
diffusion σ
Criticalspeed
ofchange
Migration:
diffusion σ
Clineslopes
Maximal tolerance for
intermediate dispersal
+ =
0 2
2 2
c
B A
v r
B
Extinction if change faster than:
Low fecundity Maladapted
migrants
Not enough
adaptation
Slow migration
Tolerance to change:
55. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. EcolLett. 2012
Adaptation to change easier when
- genetic variance available in the direction of the (spatial) selection gradient
- optimum changes in a direction under weak stabilising selection.
Counter gradients may appear due to genetic correlations/correlational
selection
The more traits, the more persistence is threatened.
1. Genetic correlations and range shifts: wrap-up
56. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Fixed genetic variance
Fixed, diffusive dispersal
Constrained fitness function
No phenotypic plasticity
Linear gradients shifting at constant speed
1. BUT…
57. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Fixed genetic variance
Fixed, diffusive dispersal
Constrained fitness function
No phenotypic plasticity
Linear gradients shifting at constant speed
Burrows et al. Science 2011
Williams et al PNAS 2007
Non-analogous climates, B2 scenario
Projected temperature changes (°C/decade)
Spatial gradient (°C/km)
1. BUT…
58. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Fixed genetic variance
Fixed, diffusive dispersal
Constrained fitness function
No phenotypic plasticity
Linear gradients shifting at constant speed
use a process-based model to:
- evaluate selective pressures
- take phenotypic plasticity into account
- explicitly model spatial heterogeneity
1. BUT…
59. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Genetic adaptation and distribution ranges
1. Constraints to adaptation ?
a conceptual model of trait adaptation on a shifting gradient
2. Evolution of trait reaction norms
a process-based model of tree distribution ranges
60. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Chuine & Beaubien EcolLett. 2001
2. Evolution of trait reaction norms: the model PHENOFIT
Fitness
Local climate
Phenological traits
ReproductionSurvival
Resistance traits
61. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Chuine & Beaubien EcolLett. 2001
2. Evolution of trait reaction norms: the model PHENOFIT
Bud dormancy
Fitness
ReproductionSurvival
62. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Chuine & Beaubien EcolLett. 2001
2. Evolution of trait reaction norms: the model PHENOFIT
Bud dormancy
Leafing
Flowering
Fitness
ReproductionSurvival
63. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Chuine & Beaubien EcolLett. 2001
2. Evolution of trait reaction norms: the model PHENOFIT
Bud dormancy
Leafing
Flowering
Fruit maturation
Fitness
ReproductionSurvival
64. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Chuine & Beaubien EcolLett. 2001
2. Evolution of trait reaction norms: the model PHENOFIT
Bud dormancy
Leafing
Flowering
Fruit maturation
Leaf
senescence
Fitness
ReproductionSurvival
65. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Chuine & Beaubien EcolLett. 2001
2. Evolution of trait reaction norms: the model PHENOFIT
FrostBud dormancy
Leafing
Flowering
Fruit maturation
Leaf
senescence
Fitness
ReproductionSurvival
66. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Chuine & Beaubien EcolLett. 2001
2. Evolution of trait reaction norms: the model PHENOFIT
Frost
Drought
Bud dormancy
Leafing
Flowering
Fruit maturation
Leaf
senescence
Fitness
ReproductionSurvival
67. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Chuine & Beaubien EcolLett. 2001
2. Evolution of trait reaction norms: the model PHENOFIT
Frost
Drought
Bud dormancy
Leafing
Flowering
Fruit maturation
Leaf
senescence
Fitness
ReproductionSurvival
68. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: calibrating the model
fructification
leafing + senescence
Using time series: phenology & climate
Leafingdate
observed
modelled
Year
Example: sessile oak Quercus petraea
69. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: validating the model
Presence / absence
Observed distribution Fitness simulated by
PHENOFIT (1980-2000)
Using observed distribution ranges
Example: sessile oak Quercus petraea
70. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: model extrapolations
1950-2000 50-year fecundity,
simulated by PHENOFIT
Under scenario A1Fi (“business as usual”)
Example: sessile oak Quercus petraea
71. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
1990-2040
2. Evolution of trait reaction norms: model extrapolations
50-year fecundity,
simulated by PHENOFIT
Under scenario A1Fi (“business as usual”)
Example: sessile oak Quercus petraea
72. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2010-2060
2. Evolution of trait reaction norms: model extrapolations
50-year fecundity,
simulated by PHENOFIT
Under scenario A1Fi (“business as usual”)
Example: sessile oak Quercus petraea
73. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2030-2080
2. Evolution of trait reaction norms: model extrapolations
50-year fecundity,
simulated by PHENOFIT
Under scenario A1Fi (“business as usual”)
Example: sessile oak Quercus petraea
74. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2050-2100
2. Evolution of trait reaction norms: model extrapolations
50-year fecundity,
simulated by PHENOFIT
Under scenario A1Fi (“business as usual”)
Example: sessile oak Quercus petraea
75. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: determine selection gradients
« plastic » date
d=165
d=125
d=102
Method: impose event dates – e.g. leafing date.
76. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: determine selection gradients
« plastic » date
d=165
d=125
d=102
d=166
d=126
d=103
d=164
d=124
d=101
1plasticd d 1plasticd d
Method: impose event dates – e.g. leafing date.
77. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: determine selection gradients
« plastic » date
d=165
d=125
d=102
d=166
d=126
d=103
d=164
d=124
d=101
Fecundity
1plasticd d 1plasticd d
Method: impose event dates – e.g. leafing date.
78. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: determine selection gradients
« plastic » date
d=165
d=125
d=102
d=166
d=126
d=103
d=164
d=124
d=101
Fecundity
1plasticd d 1plasticd d
log fecundity
trait
Method: impose event dates – e.g. leafing date.
79. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: determine selection gradients
Sessile oak
Quercus petraea
European beech
Fagus sylvatica
Fecundity
Selection
gradient
2000
Fecundity:
high
low
Budburst selected
to occur:
later
earlier
80. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2020
Sessile oak
Quercus petraea
European beech
Fagus sylvatica
Fecundity:
high
low
Budburst selected
to occur:
later
earlier
Fecundity
Selection
gradient
2. Evolution of trait reaction norms: determine selection gradients
81. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: determine selection gradients
2040
Sessile oak
Quercus petraea
European beech
Fagus sylvatica
Fecundity
Selection
gradient
Fecundity:
high
low
Budburst selected
to occur:
later
earlier
82. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: determine selection gradients
2060
Sessile oak
Quercus petraea
European beech
Fagus sylvatica
Fecundity
Selection
gradient
Fecundity:
high
low
Budburst selected
to occur:
later
earlier
83. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: determine selection gradients
2080
Sessile oak
Quercus petraea
European beech
Fagus sylvatica
Fecundity
Selection
gradient
Fecundity:
high
low
Budburst selected
to occur:
later
earlier
84. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: determine selection gradients
Sessile oak
Quercus petraea
European beech
Fagus sylvatica
Fecundity
Selection
gradient
Fecundity:
high
low
Budburst selected
to occur:
later
earlier
2100
85. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
2. Evolution of trait reaction norms: determine selection gradients
Temperature
Precipitations
Temperature
Selection for later budburst: western (warmer) part of the range
Sessile oak
Quercus petraea
European beech
Fagus sylvatica
Budburst selected
to occur:
later
earlier
In the climatic (niche) space:
86. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
Budburst date (imposed)
Fecundity
Sessile oak
Quercus petraea
Jan 30 Mar 30 Jun 20
2. Evolution of trait reaction norms: why these patterns?
87. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
Budburst date (imposed)
Fecundity
Sessile oak
Quercus petraea
Jan 30 Mar 30 Jun 20
frost damage
2. Evolution of trait reaction norms: why these patterns?
88. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Duputié et al. in prep
insufficient time to
reach maturation
Budburst date (imposed)
Fecundity
Sessile oak
Quercus petraea
Jan 30 Mar 30 Jun 20
frost damage
2. Evolution of trait reaction norms: why these patterns?
89. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Rutschmann et al. in prep
Method: suppress reaction norm phenology/local climate
Treatments:
plastic population
d=165
d=125
d=102
d=152
d=120
d=96
year1 year 2
200
160
120
80
Resistance
Fitness
Climate
Phenology
2. Evolution of trait reaction norms: where is plasticity beneficial?
90. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Rutschmann et al. in prep
2. Evolution of trait reaction norms: where is plasticity beneficial?
Method: suppress reaction norm phenology/local climate
Treatments:
plastic population
no interannual plasticity
J=165
J=125
J=102
J=152
J=120
J=96
year1 year 2
200
160
120
80
d=145
d=125
d=102
d=145
d=125
d=102
Resistance
Fitness
Climate
Phenology
91. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Rutschmann et al. in prep
2. Evolution of trait reaction norms: where is plasticity beneficial?
advantageous
burdensome
interannual
plasticity
precipitations
temperature
92. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Rutschmann et al. in prep
Précipitations
Température
2. Evolution of trait reaction norms: where is plasticity beneficial?
advantageous
burdensome
interannual
plasticity
imposed budburst date
fecundity
93. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Rutschmann et al. in prep
Précipitations
Température
2. Evolution of trait reaction norms: where is plasticity beneficial?
advantageous
burdensome
interannual
plasticity
imposed budburst date
fecundity
94. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Rutschmann et al. in prep
Précipitations
Température
2. Evolution of trait reaction norms: where is plasticity beneficial?
advantageous
burdensome
interannual
plasticity
imposed budburst date
fecundity
95. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013 Rutschmann et al. in prep
Précipitations
Température
2. Evolution of trait reaction norms: where is plasticity beneficial?
advantageous
burdensome
interannual
plasticity
imposed budburst date
fecundity
96. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Wrap-up
Phenotypic plasticity may translate constraints
Interannual variability on budburst/senescence dates weakly
impacts fitness
+ long-distance gene flow e.g. Kremer et al. 2012
-> reaction norms selected at the scale of the range?
… except at range/niche margins
e.g. Pichancourt & van Klinken 2012
97. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Perspectives
Selection gradients vary over space/time
weak response to climatic change?
Can the evolution of phenology mitigate projections
of range shifts in temperate trees?
Optimal reaction norm
Simulated fitness, t=later
98. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Perspectives
Selection gradients vary over space/time
weak response to climatic change?
Can the evolution of phenology mitigate projections
of range shifts in temperate trees?
Optimal reaction norm
Simulated fitness, t=later
PhD project, O. Ronce/I. Chuine, ED SIBAGHE
response Gβ
99. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Perspectives
Selection gradients vary over space/time
weak response to climatic change?
Can the evolution of phenology mitigate projections
of range shifts in temperate trees?
Optimal reaction norm
realised reaction norm
Simulated fitness, t=later
PhD project, O. Ronce/I. Chuine, ED SIBAGHE
response Gβ
100. Anne Duputié – Models in Evolutionary Ecology – May 23rd, 2013
Thanks!
Isabelle Chuine
François Massol Ophélie Ronce
Alexis Rutschmann
Mark Kirkpatrick