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Patch based Approaches

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... equi-distant from one another (e.g. Harrison and Quinn 1989) ... model ... The incidence function model. Where e'=ey' and y'=y/ . Count based Approaches ... – PowerPoint PPT presentation

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Title: Patch based Approaches


1
Patch based Approaches
2
A classification of multi-site scenarios
3
Metapopulation models
  • Most theoretical metapopulation models assume
    that all populations have identical extinction
    rates, and that they are all equi-distant from
    one another (e.g. Harrison and Quinn 1989)
  • But these models are too far removed from
    realities of specific multi-site situations to be
    of practical use for particular species.

4
Incidence function models
  • Developed by Hanski (1991, 1994)
  • Sjögren-Gulve and Ray 1996, Moilanen 1999, 2000
    and Kindvall 2000)

Ilkka Hanski
Atte Moilanen
5
The Incidence function model
  • Use patterns of patch occupancy over time and
    space (incidence) to estimate
  • Ei, the probability of extinction for habitat
    patch i when it is occupied, and
  • Ci, the probability that patch i becomes
    colonized when it is occupied
  • Taking in account the patchs habitat area,
    habitat quality, distance to other populations,
    and other potentially important characteristics

6
The Incidence function model
  • We start assuming specific function forms for
    the effects of the causal factors (patch area and
    quality, proximity of other populations, etc) on
    Ei and Ci.

7
Extinction
e1 x0.5
A area
P(extinction ) in patch i
Area
8
Colonization
M number of migrants
P(colonization) in patch i
y1
Arriving migrants
9
The number of migrants
  • depends on
  • The probability of other patches having extant
    populations, their population sizes (which is
    assumed to be proportional to the area of each
    patch), the rate at which dispersers leave the
    patch, and the distances from each other patch to
    focal patch i.

10
The number of migrants
Mi number of migrants ß per-unit-area migrant
production rate A patch i area Scaled by
parameter b to allow for nonlinearity pj 0
empty and pj1 if occupied a scaling factor
11
Number of migrants
A area
12
The incidence function model
The probability that site i is occupied in any
one site is predicted by
in cases where rescue effects are thought to occur
13
The incidence function model
  • A snap-shot of the pattern of patch occupancy can
    be used to estimate the parameters governing Ci
    and Ei

However, it means assuming that the occupancy
patterns seen in the field are very near their
equilibrium values
14
The incidence function model
Where eey and yy/ß
15
Count based Approaches
16
Information needed
  • Mean population growth rate
  • Variance in population growth rate
  • Covariance in population growth rate
  • Probabilities of movement between populations
  • Estimates of density dependence

17
The California clapper rail
Harding et al 2001
18
The California clapper rail
0.060.790.720.034
Harding et al. 2001
19
Pearson correlation coefficients
20
The transition matrix
  • Determining counts at different sites


21
The transition matrix
  • Determining counts at different sites

(1-d)?Mt da da

da (1-d)?Ft da
da da (1-d)?Lt
dconstant probability of an individual dispersing
aconstant probability of an individual arriving
22
Effect of correlations
With correlations
Without correlations
23
Effect of levels of dispersal
20 move
10 move
5 move
none move
24
Demographic Approaches
25
Coryphantha robbinsorum
26
The basic model
Seeds Small juveniles Large juveniles Adults
A
27
Vital rates
28
The multi-site model
G
29
The multi-site model
0 0 0 f4as4a,mAB
0 0 0 f4as4a,mAC
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 f4as4a,mBA
0 0 0 f4as4a,mBC
0 0 0 0
0 0 0 0
G
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 f4as4a,mcA
0 0 0 f4as4a,mCB
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
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