ibm meets traditional population ecology ibm behavioural patterns life history variation ...
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IBM meets traditional population ecology
Born Judging environment
Death
Environmental evaluation
Behavioural Allocation
Nursing Parturition Pregnant
Mating
Growth IBM
behavioural patterns
life history variation
population level responses (ecology, genetics)
What are the characteristicsof population-level responsesto different IBM scenarios?
Analytical tools
IBM meets traditional population ecology
Born Judging environment
Death
Environmental evaluation
Behavioural Allocation
Nursing Parturition Pregnant
Mating
Growth IBM
individual behaviour patterns
individual life history variation
population level responses (ecology, genetics)
Matrix populationmodel:
nt+1 = A nt
abundance vector
projection matrix
sensitivityCaswell, H (2001) Matrix population models
IBM meets traditional population ecology
Born Judging environment
Death
Environmental evaluation
Behavioural Allocation
Nursing Parturition Pregnant
Mating
Growth IBM
individual behaviour patterns
individual life history variation
population level responses (ecology, genetics)
Abundance in time andspace:
synchrony
structural dynamics density dependence
Matrix populationmodel:
nt+1 = A nt
abundance vector
projection matrix
sensitivityCaswell, H (2001) Matrix population models
IBM meets traditional population ecology
Analysis of patterns: fox abundance in time and space
IBM space
7
7.5
8
8.5
9
9.5
100 150 200 250 300
time (years)
ab
un
dan
ce
regional
34
IBM time
Many IBM modellers look for cyclic population level responses (?) ...
IBM meets traditional population ecology
Analysis of patterns: fox abundance in time and space
10.4
10.6
10.8
11
11.2
1 11 21 31 41
time (years)
ab
un
dan
ce
regional
time (years)
abu
ndan
ce
-1-0.8-0.6-0.4-0.20
0.20.4
1 11 21 31 41 local
IBM spaceIBM time
10.4
10.6
10.8
11
11.2
1 11 21 31 41
IBM meets traditional population ecology
Analysis of patterns: fox abundance in time and space
time (years)
ab
un
dan
ce
time (years)
abu
ndan
ce
-1-0.8-0.6-0.4-0.20
0.20.4
1 11 21 31 41
-1-0.8-0.6-0.4-0.20
0.20.4
1 11 21 31 41
regional
local
IBM spaceIBM time
IBM meets traditional population ecology
Analysis of patterns: fox abundance in time and space
cross
corr
ela
tion
distance
-1
-0.5
0
0.5
1
0 2 4 6 8 10
spatial fox dynamics IBM space
migration, predation and climate
-1-0.8-0.6-0.4-0.20
0.20.4
1 11 21 31 41
IBM meets traditional population ecology
Analysis of patterns: fox abundance in time and space
time (years)
abu
ndan
ce
local
temporal fox dynamics: fluctuations
lag (years)
corr
ela
tion
corr
ela
tion
lag (years)
24
ACF
-1-0.8-0.6-0.4-0.20
0.20.4
1 11 21 31 41
IBM meets traditional population ecology
Analysis of patterns: fox abundance in time and space
time (years)
abu
ndan
ce
local
corr
ela
tion
lag (years)
spect
rum
frequency (1/years)
22
6 2
24
temporal fox dynamics: fluctuations
-1-0.8-0.6-0.4-0.20
0.20.4
1 11 21 31 41
IBM meets traditional population ecology
Analysis of patterns: fox abundance in time and space
time (years)
abu
ndan
ce
local
temporal fox dynamics: structure
Xt=f (Xt-1, ..., Xt-n)
Xt = 1.14Xt-1 – 0.48Xt-
2
Xt = 0.66Xt-1+0.22Xt-2
autoregression
Royama, T (1992) Analytical population dynamics
Tong, H (1990) Non-linear time series
IBM meets traditional population ecology
Fox abundance in time and space: connecting patterns and processes
intra-specific
inter-specific
inter-specific
Xt=f (Xt-1, ..., Xt-n)fox AR structure:
IBM meets traditional population ecology
Fox abundance in time and space: connecting patterns and processes
Xt=f (Xt-1)intra-specific
inter-specific
inter-specific
fox AR structure:
IBM meets traditional population ecology
Fox abundance in time and space: connecting patterns and processes
Xt=f (Xt-1, Xt-2)intra-specific
inter-specific
inter-specific
fox AR structure:
IBM meets traditional population ecology
Fox abundance in time and space: connecting patterns and processes
Xt=f (Xt-1, Xt-2)intra-specific
inter-specific
inter-specific
fox AR structure:
IBM meets traditional population ecology
Fox abundance in time and space: connecting patterns and processes
intra-specific
inter-specific
inter-specific
Xt=f (Xt-1, Xt-2, Xt-3)
dimension indicates no of trophic interactions
fox AR structure:
-1-0.8-0.6-0.4-0.20
0.20.4
1 11 21 31 41
IBM meets traditional population ecology
Analysis of patterns: fox abundance in time and space
time (years)
abu
ndan
ce
local
Xt = 0.66Xt-1+0.22Xt-2
Xt = 1.14Xt-1 – 0.48Xt-
2
2-dimensional AR models
IBM meets traditional population ecology
Bornholm
Jylland
FynSjælland
-1-0.8-0.6-0.4-0.20
0.20.4
1 11 21 31 41
time (years)
abu
ndan
ce
local
Xt = 0.66Xt-1+0.22Xt-2
Xt = 1.14Xt-1 – 0.48Xt-
2
Indeed, ...
IBM meets traditional population ecology
The interface between IBM and analytical population ecology may ...
... provide information on how populations will behave – in time and space – over a range of IBM scenarios... focus on key variables potentially responsible, otherwise muddled by the numerous IBM variables and their syngistic effects
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