Quantitative Methods for Ecology and Evolutionary Biology (Cambridge, 2006) - Chapter 6 pot
Quantitative Methods for Ecology and Evolutionary Biology (Cambridge, 2006) - Chapter 6 pot
... Catch (thousands of tons)
1 965 1.78 94
1 966 1.31 212
1 967 0.91 195
1 968 0. 96 383
1 969 0.88 320
1970 0.9 402
1971 0.87 366
1972 0.72 60 6
1973 0.57 378
1974 0.45 319
1975 0.42 309
19 76 0.42 389
1977 ... PðtÞ
5
0
(6: 16)
Include the rule that if E(t þ1) is predicted by Eqs. (6. 16) to be less than 0 then
E(t þ1) ¼0 and that if E(t) ¼0, then E(t þ1) ¼DE
þ
. Iterate Eqs. (6. 15) a...
Quantitative Methods for Ecology and Evolutionary Biology (Cambridge, 2006) - Chapter 1 potx
... undergraduates,
graduate students, post-docs, and even those beyond) who wants to
develop the intuition and skills required for reading the literature in
theoretical and mathematical biology and for doing work in ... (Einum and Fleming used both 28 and
107 days with similar results) when egg mass is x. Einum and Fleming
chose to model S(x)by
SðxÞ¼1 À
x
min
x
a
(1:7)
whe...
Quantitative Methods for Ecology and Evolutionary Biology (Cambridge, 2006) - Chapter 4 pot
... sketch a graph with l on the x-axis
and y ¼l or y ¼Àre
Àl
on the y-axis and look for their intersections and note
that if ¼0 then l ¼Àr).
148 The evolutionary ecology of parasitoids
hosts at ... has the potential of high mortality rates for small
insects, and found that the predictions and experimental results were
once again consistent.
0 2 4 6 8 10 12 14 16 18 20
0
1...
Quantitative Methods for Ecology and Evolutionary Biology (Cambridge, 2006) - Chapter 2 pptx
... then that
N(1) 60 (by reading where the line N ¼50 intersects the curve). We
then go back to the x-axis, for N(1) 60 , we see that N(2) 69 .6; we
then go back to the x-axis for N(2) and obtain N(3). ... 10
–1
–0.8
–0 .6
–0.4
–0.2
0
0.2
0.4
0 .6
0.8
1
(a)
x
y
0 1 2 3 4 5 6 7 8 9 10
–1
–0.8
–0 .6
–0.4
–0.2
0
0.2
0.4
0 .6
0.8
1
(b)
x
y
0
(d)
1 2 3 4 5 6 7 8 9 10
0
0.1
0.2
0.3
0.4
0...
Quantitative Methods for Ecology and Evolutionary Biology (Cambridge, 2006) - Chapter 3 doc
... 0.2 0.3 0.4 0.5 0 .6 0.7 0.8 0.9 1
– 16
–15
–14
–13
–12
–11
–10
–9
–8
–7
6
(a)
p
L( p| 4, 10)
0 0.1 0.2 0.3 0.4 0.5 0 .6 0.7 0.8 0.9 1
– 76
–75
–74
–73
–72
–71
–70
69
68
67
66
p
(b)
L( p| 40, ... 10
Figure 3.9. The sum of squared
deviations for estimate of an
unknown value M by the data
6. 469 4, 5.0 96, 6. 0359,
5.3725, 6. 5354, 6. 5529,
5.7 963 , 3.945, 6. 13 26,
7.592...
Quantitative Methods for Ecology and Evolutionary Biology (Cambridge, 2006) - Chapter 5 ppt
... interest and oppor-
tunity for modeling (Lipp et al. 2002, Hastings 1997, 2001, Hastings
and McKinnon 1998, Hastings and D’Allesandro 2000, Hastings et al.
2002) and opportunities exist for new ... 5 .6. Solutions of various forms of the SIR model. (a) The basic SIR model for an epidemic (b ¼0.005,
v ¼0.3; true for panels b and c); (b) the SIRS model for an endemic disease...
Quantitative Methods for Ecology and Evolutionary Biology (Cambridge, 2006) - Chapter 7 pptx
... can choose appropriate
forms for a(X, t) and b(X, t). In the next chapter, we will discuss this
artistry in more detail, but before we create new art, we need to under-
stand how the tools work.
General ... tjx
c
Þg (7 :60 )
and the procedure from here should be obvious: Taylor expand in
powers of dX and dt and then take the average over dX.
General diffusion processes and the ba...
Quantitative Methods for Ecology and Evolutionary Biology (Cambridge, 2006) - Chapter 8 docx
... (Halley and Iwasa 1998, Diserud and Engen 2000,
Hakoyama and Iwasa 2000, Lande et al. 2003) takes the form
dX ¼ bðX Þdt þ
ffiffiffiffiffi
a
e
p
X dW
e
þ
ffiffiffiffiffiffiffiffiffi
a
d
X
p
dW
d
,wherea
e
and a
d
are the envir-
onmental ... side of Eq. (8 .61 ), and that, for example
Ð
1
0
tu
txx
dt ¼
ðq
2
=qx
2
Þ
Ð
1
0
tu
t
dt ¼ h
xx
and which allows us to rewrite Eq. (8 .61 )as
"
2
h
xx
xh
x
¼
ð
1...
Quantitative Methods for Ecology and Evolutionary Biology (Cambridge, 2006) - References pdf
... 24
two-dimensional 48–58
diffusion
and exponential growth 64 69 , 78
and logistic growth 69 –73, 79
as a random walk 58– 64 , 77–78
in a bounded region 62 64 , 78
in an unbounded region 60 62 , 78
linear ... optimal foraging theory,
evolutionary stable strategies, and quantitative genetics. Evolutionary Ecology,
6, 152– 169 .
Mangel, M. (1998). No-take areas for sustainab...
applied quantitative methods for trading and investment- dunis 2003
... Std. error t -Statistic Prob.
C 8.33E-07 0.000144 0.0057 76 0.9954
AR(1) 0.00 060 0 0.04 061 2 0.014773 0.9882
AR(3) 0.019545 0.0358 86 0.54 463 9 0.5 861
AR (6) 0.018085 0.0318 76 0. 567 366 0.57 06
AR(10) −0.028997 ... 0.052594 −4.54 261 6 0.0000
AR(8) 0.1828 16 0.0 468 78 3.899801 0.0001
AR(9) 0.0 264 31 0. 060 321 0.438 169 0 .66 13
AR(10) −0 .61 560 1 0.0 761 71 −8.081 867 0.0000
M...
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