Download Population dynamics of small game

Population dynamics of small game
Pekka Helle
Natural Resources Institute Finland
Luke
Oulu
Populations tend to vary in size temporally, some
species show more variation than others
Depends on degree of specialization, and other
life-history characteristics
Changes can be short-term and/or long-term
Populations can be stable or nearly so, fluctuations can be erratic or cyclic
Basic concepts include:
- Population growth rate
- Carrying capacity of environment
- Density dependence
- Marriage of temporal and spatial density variation
Short-term dynamics
Long-term trends
How much is ’normal’
variation; how many
years make a trend?
• Population growth model (in its simplest form)
New population size = old size + births - deaths +
immigrated – emigrated
N(t+1) = N(t)e r [1 - N(t)/K]
N = population size
t = time
r = growth rate
K = carrying capacity
”There are three kinds of mathematicians:
Those who can count and those who cant.”
Anon.
- Population cycles have
been an object of interest
for a long time.
- Many northern species
show cyclic fluctuations.
- North America and Siberia: 10-year year cycles
typical
- 3-4-year cycles common
in Europe especially, but
existing also elsewhere
- Some populations may
be cyclic but some not in
the same species
Hudson Bay Company’s records (redrawn from Butler 1953)
Average Capercaillie, Black grouse and Hazel
grouse densities in Finland during 1963-1988
18
16
14
12
Caper
Black
Hazel
10
8
6
4
2
0
1960
1970
1980
1990
2000
2010
During 1960s-80s: 6-7 years cycles prevailed
Lindström, Jan 1994: Modelling grouse population
dynamics. PhD thesis, Univ. Helsinki.
1960
70
80
90
2000
Nt = a + b1(t) + b2 cos(t) + b3 sin(t),
where a – constant, b1 – captures the trend, b2 and
b3 together with the trigonometric functions allow for
the part of population fluctuation
Annual bag of black grouse in SW Finland
during 1897–1930
What is the population ecological explanation
of sin and cosine functions? Of course, none!
During increasing phase:
- females older than average, producing more
offspring
- females lay more eggs
- females (also/especially old) probably in better
physical condition (why is that? spring food, weather,
’history’ (year of birth?), better incubators, better
in guarding a brood, selecting habitats with less
predators), other behavioural responses to predation?
During decreasing phase:
- factors opposite
Elements needed in Finnish grouse cycles:
- delayed density dependence
- dampening dynamics: random hits are needed
- spatial synchrony of populations
Reasons:
- intrinsic factors; age structure of population
- weather effects (did)
- predation (dd)
- parasites, diseases (dd)
- etc.
- probably a combination of several
factors
What could ’a random hit’ be?
Weather conditions during egg-laying period and
(especially) during early brood season
Predation – especially during vole population low
Diseases
Parasites
A combination of these (and unknown) factors
(In addition, population age structure is playing
(at least some) role in cyclic fluctuations)
Why did the cycles disappear (hypotheses only):
- Species densities decreased below a critical threshold due
to various reasons (increased predation, lowered habitat
quality etc.) to start a new growth
- Decreased densities: fewer observations produce more
noise to the data
- Simulations suggest that minor changes in parameters
may alter dynamics: either shortening or lengthening cycles;
they may easily disappear – and come back as well (*)
- If dispersal is needed to maintain spatial synchrony,
it may have become weaker due to e.g. habitat fragmentation
18
Nation-wide averages
- problem of spatial synchrony
16
14
12
10
Caper
Black
Hazel
8
Willow
6
4
2
0
1960
1970
1980
1990
2000
2010
2020
Black grouse 1964-2015
Autocorrelation function ACF
1964-2015 data into three periods of equal length
----> 1964-1980, 1981-1997, 1998-2015
Within each period
correlations of densities are calculated for different
time lags
Time lag 1: year 1 vs 2, 2 vs 3, 3 vs 4 and so on …
Time lag 2: year 1 vs 3, 2 vs 4, 3 vs 5 and so on …
Time lag 3: year 1 vs 4, 2 vs 5, 3 vs 6 and so on …
And so on…
Grouse and
voles and their
population
dynamics are
connected via
common
predators
(alternative
prey
hypothesis,
e.g. Angelstam)
Asko Kaikusalo
Population cycles of rodents in Kilpisjärvi, Finnish
Lapland during 1946-1982
Laine & Henttonen 1983
Vole density in Pallasjärvi (Henttonen, unpubl.)
35
Forests
30
Density index
25
Mires
My gla
My rut
My ruf
Mi agr
Mi oec
Lem
20
15
10
5
0
1970 1975 1980 1985 1990 1995 2000 2005 2010
Grey-sided
vole in
northern
Sweden
Hörnfelt 2004: Long-term decline in numbers of cyclic voles in
boreal Sweden. – Oikos 107: 376-392.
Ims et al. 2008: Collapsing population cycles. – Trends in Ecology
and Evolution 23: 79-86.
During the past two decades, cycles of voles, forest grouse and
forest insects have been fading out in Europe.
Grouse cycles have gone (voles
also) (signs of come back?)
Spatial synchrony has disappeared
(not grouse only)
Hypotheses:
Present densities too low …
Dispersal weaker due to …
Climate change .. ?
How to proceed: correlations
maybe misleading, large-scale
experiments not possible ?
Larch budmoth Zeiraphera diniana
Esper et al. 2007: 1200 years of regular outbreaks in alpine
insects. – Proc. Biol. Sci. 274: 671-679.
A lot of evidence that fading of cycles is due to recent climate
warming. A study from black grouse in Central Finland
supports this idea (Ludwig 2007, Ph. D., University of
Jyväskylä)
But …
A study from southern Finnish voles shows the opposite …
(Brommer et al. 2010: Global Change Biology 16: 577-586).
Field vole – open circle
Bank vole – filled diamond
SPATIAL ASPECTS
Spatial synchrony in grouse populations1964-2015
15 game management districts
All pair-wise correlations calculated (105)
Mean value is used to describe average regional
synchronism
Sliding time window technique
Synchrony in 10 years periods
1. 1964-1973
2. 1965-1984
..
..
43. 2006-2015
year
EH
1964
ES
7.00
KY
KAI
6.83
6.26
KS
3.70
LA
10.71
OU
4.37
PO
10.50
PH
16.36
PK
10.12
PS
7.25
RP
8.22
SA
4.00
UU
10.08
VS
6.46
5.01
1965
7.24
7.94
6.86
7.14
10.70
4.73
15.86
18.03
9.70
8.75
13.31
14.28
7.25
5.45
3.51
1966
7.94
11.98
8.57
12.54
11.92
5.55
14.04
23.01
10.80
8.02
14.86
16.60
8.91
8.26
8.26
1967
6.44
13.85
9.48
5.24
11.70
5.44
12.13
20.11
9.23
8.00
12.41
13.81
9.82
8.23
9.28
1968
5.90
10.36
8.30
5.00
8.71
5.30
6.90
11.16
8.13
6.50
9.06
9.29
7.02
9.93
12.46
1969
5.04
8.64
7.75
8.00
7.05
8.50
8.39
12.92
4.93
7.20
9.51
8.44
7.86
8.84
5.74
1970
3.59
8.05
6.89
9.46
7.66
8.12
8.61
9.16
4.73
7.76
7.03
7.54
7.17
6.09
3.11
1971
3.32
6.57
6.32
6.91
7.02
6.38
6.74
8.93
2.59
7.52
8.45
6.37
6.27
7.01
6.35
1972
3.54
6.21
7.22
6.80
8.65
5.62
7.85
10.67
3.66
6.52
9.22
7.61
7.26
3.99
6.00
1973
3.54
8.27
6.14
7.14
6.31
7.90
8.78
11.24
5.13
9.37
8.87
7.33
8.44
6.41
5.06
1974
3.18
5.16
4.61
5.67
6.61
6.93
9.02
8.70
4.34
6.46
8.04
6.50
7.38
3.27
5.07
1975
3.26
4.45
3.88
4.66
4.92
4.01
5.90
7.35
3.39
5.71
5.15
6.46
7.48
4.57
4.40
1976
1.34
3.90
2.89
3.47
2.62
2.64
4.76
5.26
2.13
2.86
3.54
4.47
4.53
1.86
2.45
1977
1.33
4.38
2.67
5.06
3.91
2.93
4.37
6.06
3.00
3.09
4.72
4.49
4.45
0.90
2.39
1978
2.44
5.03
4.13
4.75
5.72
5.63
5.73
9.93
3.87
4.47
4.84
4.97
5.61
2.26
2.66
…
…
…
…
…
…
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…
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…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
2013
4.10
5.50
6.00
7.20
4.80
4.50
6.00
5.10
4.20
6.30
3.20
1.80
6.20
4.90
4.70
2014
2.70
4.10
4.30
4.50
3.80
3.50
4.30
3.10
3.70
5.00
3.40
2.00
4.00
2.40
3.70
2015
3.30
3.10
3.80
4.60
2.30
4.10
3.30
2.70
3.00
4.00
1.30
1.50
3.50
3.30
2.20
ES
EH
ES
KY
KAI
KS
LA
OU
PO
PH
PK
PS
RP
SA
UU
VS
KY
KAI
KS
LA
OU
PO
PH
PK
PS
RP
SA
UU
VS
UU
Sliding window technique
1964-73
43 ten-year windows
4515 correlations per species
2006-15
1964
2015
Cc
Bg
Hg
Caper
Average
regional
syncrony
vs
mean
density
Black
Hazel
Red fox synchrony, Finland & Russian Karelia
1990-1995
2000-2005
Example: population structure
Capercaillie – adult sex ratio
Proportion of females about 65 %
At hatching: sex ratio 50:50
Male chicks suffer from much higher mortality
than females for several reasons
Population structure
Percentage of females in adult population
Change in sex ratio of adult capercaillie populations
70
65
A
A
60
B
%
C
55
D
50
B
D C
45
64-68
69-73
74-78
79-83
84-88
Helle, Kurki & Lindén 1999, Wildlife Biology
Mean percentages of females
in the adult capercaillie population by game mgmt districts
during 1995–2010
60.6
52.1
55.6
39.6
44.9
47.1
42.4
42.8 40.7
38.1
35.6
40.4
40.9
46.2
54.4
How are temporal and spatial dynamics
interrelated?
Cycles need (necessarily) spatial syncrony –
spatial syncrony does not need cyclicity
If cycling disappers, is it because cycles
disappear or spatial syncrony disappers first?
Landscape structure – and its spatial
characteristics – may play a role
Located observations
National forest inventory data,
satellite-based
GIS techniques
4 km