Post on 12-Jun-2018
Complex Adaptive Systems at the Interface of Ecology and Evolution
Ulf DieckmannEvolution and Ecology ProgramInternational Institute for Applied Systems AnalysisLaxenburg, Austria
Two Common Misperceptions
Evolution is always slowOn the contrary, rapid contemporary evolution is widespread, in particular in response to anthropogenic environmental change
Evolution is always optimizingOn the contrary, frequency-dependent selection is ubiquitous, implying that population-level features will rarely get optimized by evolution
Eco-Evolutionary Models
Derive species- or ecosystem-level predictions from individual-level processes
Forecast rapid evolution
Identify evolutionary mechanisms
Explain ecological structures
Ecosystem
Levels of Complexity
CommunityAnthropogenic
impacts:HarvestingPollution
EutrophicationClimate change
…
Stakeholders
Population
IndividualDem
ogra
phy &
Ada
ptat
ion Regulation & Selection
Evolutionary responses of stocks to modern fishing pressures are inevitable
Significant evolution can occur within just a few generations
Evolutionary changes are not necessarily beneficial
Evolutionary changes will often be difficult to reverse
The Overlooked Evolutionary Dimension
Which Traits Are at Risk?
Age and size at maturation Reproducing late is impossible
Reproductive effort Saving for future seasons is futile
Growth rate Staying below mesh size prolongs life
Morphology and behavior Avoiding fishing gear is advantageous
Overview of Case Studies 1/3
American plaice
Bluegill
Argentine
Atlantic silversideAtlantic herring Atlantic salmon
Atlantic cod
Brook troutBanded spiny lobster
Overview of Case Studies 2/3
Coho salmon
Largemouth bass Mozambique tilapia
Common whitefish
Guppy Haddock
Common carp
Grayling
Lake whitefish
Overview of Case Studies 3/3
Witch flounder Yellowtail flounder
Plaice
Small yellow croaker
Sole
Pink salmon Rainbow trout
Sockeye salmonRed porgy
© Google Earth
Feeding groundsBarents Sea,mature & juvenile fishSpawning groundsNorwegian coast,only mature fish
With a catch of 400,000 tonnes per year, Northeast Arctic cod is one of the most important European fish stocks
Northeast Arctic Cod: Stock Structure
Northeast Arctic Cod: Fishing History
Fishing along the Norwegian coast has been intensive for centuries
Trawling in the Barents Sea started in the 1920s and reached its current high level around 1960
This devalues the later parts of anindividual’s lifespan
Evolution of earlier maturationat smaller size is thus expected
A certain combination of age and size at maturation can be observed if:1. Fish survive until that
age and size2. Fish grow to that
age and size3. Fish mature at that
age and size
Maturation Schedules (PMRNs)
Age
Size
Environmentalvariation
in growthand survival
Maturation probability
10%
50%
90%
Leng
th at
50%
mat
urat
ion
prob
abilit
y at a
ge 7
(cm
)
1930 1970 200570
100
90
80
This shift in maturation schedule contributes to a drop in maturation age from 9-10 years to 6-7 years and reduces initial egg production by about 50%
Until about 1970
Today
Northeast Arctic Cod: Evolutionary Change
Eco-Genetic Models
Eco-genetic models are process-based and designed to incorporate salient
Ecological detailtogether with
Genetic detailin the context of apopulation’s fulllife cycle
Northeast Arctic Cod: Model Details
Demography Linear growth before maturation Growth increments of mature
individuals depend on size and gonadosomatic index
Growth increments correlate negatively with stock biomass
Growth increments vary between individuals
Constant natural mortality Density-dependent newborn
mortality Density-dependent cannibalism on
age classes 1 and 2 (optional) Linear probabilistic maturation
reaction norm of constant width Fecundity depends allometrically on
size
Fishing mortality
00.10.20.30.40.50.60.70.80.9
1
1932
1936
1940
1944
1948
1952
1956
1960
1964
1968
1972
1976
1980
1984
1988
1992
1996
Feeding groundsSpawning grounds
Historical fishing:Ffeeding = 0.05 and Fspawning = 0.2
Current fishing:Ffeeding = 0.4 and Fspawning = 0.3
Size selectivity of fishing gear taken into account
0 100Time (years)
Currentfishing
Age a
t mat
urat
ion
(yea
rs)
12
10
8
6
4
2
0
Historicalfishing
Eco-genetic model of Northeast Arctic cod
Fast Pace of Evolutionary Decline
ca. 40 yearsTo
day
Age a
t mat
urat
ion
(yea
rs)
Historicalfishing
0 100Time (years)
12
10
8
6
4
2
0
Currentfishing
Eco-genetic model of Northeast Arctic cod
Slow Pace of Evolutionary Recovery
Toda
y
ca. 250 years
0 Population crash
EmploymentYieldProfit
Zoneof new
consensus
Zone of traditional fisheries
management
Fishing effort
Bene
fits (
utilit
y)
Ecosystem preservation
Hilborn 2007: “Zone of New Consensus”
Integrated Assessment
1. Biological modelNortheast Arctic codBarents sea capelin
2. Socio-economic modelFleet costs, revenues, and effort-employment relationships estimated from the profitability surveys of the Norwegian Fisheries Directorate
3. Stakeholder modelHeterogenous preferences
Yield Employ-ment Profit SSB
Industrial fishers 0.3 0 0.7 0
Artisanal fishers 0.5 0.1 0.1 0.3
Employment-oriented society 0.2 0.5 0 0.3
Profit-oriented society 0.2 0 0.6 0.2
Conservationists 0.1 0.2 0.2 0.5
Stakeholder Preferences
Area of joint satisfaction: Consensus most likely
Stakeholder A Stakeholder B
Mapping the Zone of Consensus
Harvest proportion (%)
Mini
mum
size
(cm
)
Capelin Cod
0 20 40 60 80 100 0 20 40 60 80 100
50
100
150
5
1
0
1
5
20
Status quo
70%
90%
Mapping the Zone of Consensus
Assessing Food-Web Stability
Classical approach: Study linear food-web dynamics and randomize formal parameters (May 1972)
Better approach: Study nonlinear food-web dynamics, calculate equilibrium, linearize around equilibrium, and randomize ecological parameters
Even better approach: Study non-linear food-web dynamics, avoid costly equilibrium calculation, linearize around unknown equilibrium, ecologically interpret resulting parameters, and randomize these (generalized modeling; Gross and Feudel 2006)
First Structure-Stability Rule
Correlation of stability with number ofpredator species preying on focal species
Trophic position of focal prey
Food-web stability is enhanced when
Intermediate prey are utilized by
many predators
Second Structure-Stability Rule
Correlation of stability with number ofprey species predated upon by focal species
Trophic position of focal predator
Food-web stability is enhanced when
Top predators utilize many prey
Example: Bivariate Shared Trait Space
Unimodal carrying capacity
Strength of competition attenuates with trait difference
Two traits, of relevance for community dynamics and biological invasions
Gradual Evolution with Radiation
Fitness
Bright colors: positive fitness; dark colors: negative fitness
Perspectives
The field of theoretical evolutionary ecology is undergoing exciting developments. Some key research frontiers:
Rapid anthropogenic evolution Derivation of fitness from its ecological
underpinnings Calibrated and process-based eco-evolutionary
models Functional traits and ecosystem dynamics