Doctoral dissertation 2004 Predator-prey interactions of raptors in an arctic community

Predator-prey interactions of raptors in
an arctic community
Doctoral dissertation 2004
Jesper Nyström
Department of Zoology
Stockholm University
SE-106 91 Stockholm
Sweden
E-mail [email protected]
© Jesper Nyström
ISBN 91-7265-950-5
Art director Johan Lind
Jannes Snabbtryck Kuvertproffset HB, Stockholm
Abstract
This thesis concerns the predator-prey interactions of three raptor species in
a Swedish arctic community: the gyrfalcon (Falco rusticolus), the roughlegged buzzard (Buteo lagopus) and the golden eagle (Aquila chrysaetos).
The gyrfalcon behaved like a highly specialised ptarmigan (Lagopus spp.) predator. Gyrfalcon’s functional response to ptarmigan was close to density inde-
pendent, and ptarmigan remained the dominating prey even in areas with the
lowest ptarmigan density. The gyrfalcon did not respond functionally to
microtine rodents (i.e. lemmings and voles) and it was clear that the gyrfalcon did not use microtines as an alternative prey category to ptarmigan. As
the gyrfalcons did not switch to any alternative prey when ptarmigan was
scarce, their reproductive success seemed to be directly dependent on the
amount of ptarmigan available in the breeding territories. Of the two ptarmigan species in the study area, rock ptarmigan (L. mutus) dominated gyrfalcon’s diet. Locally, the proportion of rock ptarmigan in gyrfalcons’ diets
showed a positive relationship to the expected availability of rock ptarmigan
in the breeding territories, indicating a density dependent utilisation.
The rough-legged buzzard behaved like a highly specialised microtine rodent predator and Norwegian lemming (Lemmus lemmus) was its preferred
microtine species. The buzzards showed a type 2 functional response to
lemmings. Surprisingly though, they also had a type 3 functional response to
grey-sided voles (Clethrionomus rufocanus). We present an optimal diet
model where a central place forager, during good food conditions, benefits
from partial prey preference, which renders separate functional responses to
each prey category. We discuss how the double functional responses of the
buzzard affect the population dynamics of sympatric vole species, on both
temporal and spatial scales.
The golden eagle behaved like a generalist predator, and it preyed on all
major prey categories in the study area: microtines, ptarmigan, mountain
hare, (Lepus timidus) and reindeer (Rangifer tarandus). It seemed to respond
functionally to microtine rodent fluctuations with an increased consumption
of lemmings during a peak year in the microtine rodent cycle. The golden
eagle showed a numerical response to its main prey, the ptarmigan.
Ptarmigan, microtine rodents and hares seemed to have synchronized
population fluctuations in the study area. Such synchronized population fluctuations are believed to be generated by predation. Although the three rap-
4
tors are the main predators of their community, their predation patterns fail
to explain the observed prey population dynamics in the study area.
5
-Our understanding on any basic ecological problem depends on the choice
of an easy animal to study. No one who
was interested solely in the general
principles of population regulation
would choose to work on diurnal birds
of prey. Compared to most other birds,
raptors usually nest at low densities, often in inaccessible and remote places;
and in many areas they are also liable
to be shot or interfered with by our fellow man. All this helps to make for samples that to the statistician seems hopelessly small and hard to evaluate
(Newton 1979).
-Tell me about it! (Nyström 2004)
6
List of Papers
I. Nyström, J., Ekenstedt, J., Engström, J. and Angerbjörn, A.
(2004) Gyrfalcons ptarmigan and microtine rodents in northern
Sweden. (Provisionally accepted in IBIS)
II. Nyström, J., Dalén, L., Ekenstedt, J., Angleby, H., Angerbjörn,
A. (2004) The effect of local prey availability on gyrfalcon predation: DNA analysis on ptarmigan remains at nest sites. (Submitted to Molecular Ecology)
III. Nyström, J., Hellström, P. and Angerbjörn, A. (2004) Functional
responses generated by spatial variation in prey density: buzzards
versus rodents. (Manuscript)
IV. Nyström, J., Ekenstedt, J., Angerbjörn, A., Thulin, L. and Dalén,
L. (2004) Golden eagles on the Swedish arctic tundra - diet and
breeding success in relation to prey fluctuations. (Manuscript)
7
Abstract ...........................................................................................................4
List of Papers ..................................................................................................7
Introduction ................................................................................................9
What is a predator-prey relationship?....................................................9
The components of a predator-prey relationship ...................................9
Some characteristic predators and their predator-prey relationships...11
Predator-prey relationships in the arctic environment .........................12
The aim with this study ............................................................................13
Description of the community..................................................................14
The study area......................................................................................14
The predators .......................................................................................14
The prey...............................................................................................15
Study design .............................................................................................16
Predator populations ............................................................................16
Predator diets .......................................................................................16
Prey populations ..................................................................................17
Functional response .............................................................................18
Numerical response .............................................................................18
Brief summary of papers ..........................................................................19
Paper I..................................................................................................19
Paper II ................................................................................................19
Paper III ...............................................................................................20
Paper IV...............................................................................................21
Discussion ................................................................................................21
Study design ........................................................................................21
The gyrfalcon.......................................................................................23
The rough-legged buzzard ...................................................................24
The golden eagle..................................................................................25
Competition and community structure ................................................26
Acknowledgements, tack till!...................................................................30
References ................................................................................................32
Appendix ..................................................................................................38
8
Introduction
What is a predator-prey relationship?
In what way and to what extent, prey population dynamics is affected by
predation is a central question in ecology. Researchers have drawn different
conclusions depending on what species and communities they have studied.
Some have claimed that predators have little effect on their prey (Errington
1946), whereas others have concluded that predation leads to stable equilibriums of both predator and prey populations (Erlinge et al. 1984; Reid et al.
1997). A prey population can also have two stable states, where one state is
controlled by predation and the other by resources (Sinclair et al. 1990; Pech
et al. 1992). In some cases the predation pressure can be strong enough to
create chaotic population fluctuations (Hanski et al. 1993), or dynamic
enough to create continuous prey population cycles (Korpimäki & Krebs
1996; Hanski et al. 2001). Predators can thus affect the dynamics of the prey
population in different ways, but the opposite is also true, namely that prey
availability will affect the growth rate of the predator population. This dynamic interaction between the growth rates of predator and prey populations
are commonly referred to as a predator-prey relationship.
The components of a predator-prey relationship
A predator-prey relationship is defined by its functional and numerical response (Solomon 1949). The functional response describes the relationship
between per-capita kill rate and prey density. The number of prey killed per
unit time is a function of “search time”, i.e. the time required to locate a suitable prey and “handling time”, the time associated with capture and feeding
activities. These time components are in conflict, with handling time forcing
the functional response to level out at high prey densities and search time
determining the rate of approach to that level (Messier 1995). Holling (1959)
categorised the functional response as being of three general types:
A type 1 functional response shows a linear relationship between predator
attack rate and prey density up to a certain threshold, and then it levels out
abruptly (figure 1) The type 1 functional response is unusual among vertebrate predators (Solomon 1949), but it does occur (e.g. Fuller 1989; Korpimäki & Norrdahl 1991b; Nielsen 1999). A type 2 functional response
curve shows a concave shape (type 2a, figure 1). Under some circumstances
the type 2 functional response can produce an inverse density dependent
predation pressure with a greater proportion of the prey population killed at
lower densities (type 2b, figure 1). This can have a destabilising effect on the
9
prey population (Andersson & Erlinge 1977; Fryxell & Lundberg 1994). The
type 2 functional response is common among vertebrate predators (e.g. Dale
et al. 1994; Messier 1995; O'Donoghue et al. 1998; Angerbjörn et al. 1999;
Redpath & Thirgood 1999). The type 3 functional response curve has a sigmoid shape (figure 1). This specific shape has been given different explanations, such as prey switching in generalist predators (Murdoch 1969), a
gradually increase in predator efficiency (Tinbergen 1960) or the presence of
a prey refuge (Taylor 1984). Regardless of what mechanism causing this
specific shape, the type 3 functional response offers a density-dependent
force with the capacity to regulate and stabilise the prey population (Oaten &
Murdoch 1975; Hassel & Comins 1978; Nunney 1980).
The numerical response describes how prey availability affects the growth
rate of a predator population. Prey availability in a given area affects the
predator’s emigration and immigration rates, to -or from that specific area.
These processes can be relatively rapid and allow certain predators to track
changes in prey densities with little time delays (Korpimäki & Norrdahl
1991a). Prey availability is also likely to affect the natality and mortality
rates in the predator population. In general, this numerical response shows a
time lag in relation to the fluctuations of the prey population (Murdoch &
Oaten 1975; Taylor 1984).
Figure 1.Functional response curves
10
Animals are expected to forage in an optimal manner and thereby increase
their fitness relative to conspecifics. Optimal diet theory states that, “a predator should always prefer the most profitable prey” (Krebs & Clutton-Brock
1990; Smith & Smith 2001). Preference for a certain prey category means
that the prey category is utilised to a greater extent than expected from its
abundance compared to other potential prey species (Taylor 1984). An optimal forager is expected to show such preference for the prey that yields the
highest net energy gain (Charnov 1976; Lucas 1983). Preference can alter
the shape of a functional response curve. This is illustrated in figure 1 where
the curves 2a and 2b could represent the response to the same prey category,
with the difference that 2b describes the attack rate by the predator with the
greatest prey preference. In general, a predator with a strong prey preference
is more likely to show a type 2 functional response than a type 3. This depends on the fact that the attack rate on the main prey is expected to be independent on the density of the alternative prey (Krebs & Clutton-Brock
1990). Therefore, prey switching and subsequently an s-shaped functional
response curve should occur only at the lowest densities of the main prey.
Some characteristic predators and their predator-prey
relationships
The combined effect of a predator’s functional and numerical response is
referred to as the total response. Combining all different functional and numerical responses produces a large set of total responses, with a range of
different effects on the prey population (e.g. Messier 1995). However, some
total responses have received special attention as they predict specific prey
population patterns that have been observed in the wild. In an insightful paper Andersson & Erlinge (1977) used the total response to classify common
predators into three major categories: resident specialists, nomadic specialists and resident generalists.
A resident specialist predator is expected to have a destabilising effect on
a prey population. Its total response comprises a one-sided diet expected to
result in a type 2 functional response, and a numerical response dominated
by changes in natality/mortality rates causing a time lag between the growth
rates of prey and predator populations. This combination can produce a disproportionately high predation pressure when prey is scarce, accelerating the
decrease rate of the prey population. As specialist predators are expected to
be reluctant to switch to alternative prey as their main prey species decline,
they are bound to decrease in numbers some time after their prey population
declines. This can produce Lotka-Volterra like dynamics of predators and
prey.
11
Some raptor and owl species are classified as nomadic specialist predators (Korpimäki & Norrdahl 1991a; Korpimäki 1994; Wiklund et al. 1999).
Their total response is characterised by a fast numerical response. Local prey
density triggers the emigration and immigration rates of these birds
(Andersson & Erlinge 1977), resulting in a density dependent distribution of
the predators. This can have a stabilising effect on the local prey populations, but also synchronise prey populations on a larger scale (Ims & Andreassen 2000).
Resident generalist predators have a stabilising effect on prey populations
(Erlinge et al. 1984). The total response is dominated by a density dependent functional response, often described as a type 3. As the predators are resident, the prey
population experience this stabilising predation pressure throughout the year. A
stable prey population also means a stable predator population, and thus their numerical response is expected to be relatively weak.
Predator-prey relationships in the arctic environment
One of the striking features of the arctic community is the cyclic population
fluctuations of some of its most important herbivore species. Well known is
the “lemming cycle”, i.e. the population cycle of microtine rodents with a
periodicity of three to four years (Laine & Henttonen 1983; Norrdahl 1995;
Chitty 1996; Angerbjörn et al. 2001). In northern Fennoscandia these cycles
also include some bird species of Tetraonidae, as sympatric bird and microtine populations show synchronised population fluctuations (Hörnfeldt
1978; Hörnfeldt et al. 1986; Lindström et al. 1987; Moss & Watson 2001).
Several different theories have been put forward to explain the microtine
rodent cycle. It has been suggested that intraspecific interactions concerning
competition and social behaviour cause the cycles (Watson & Moss 1979;
Charnov & Finerty 1980; Chitty 1996; Oli & Dobson 2001). It has also been
proposed that food quality or quantity could generate the cycles (Lack 1954;
Laine & Henttonen 1983; Seldal et al. 1994; Hansen et al. 1999). However,
the majority of papers concerning prey population cycles as well as synchronised population fluctuations of sympatric small herbivore species, regard
them as consequences of dynamic predator-prey relationships (Andersson &
Erlinge 1977; Finerty 1980; Angelstam et al. 1984; Angelstam et al. 1985;
Henttonen et al. 1987; Lindström et al. 1987; Marcström et al. 1988; Ims &
Steen 1990; Hanski et al. 1991; Akcakaya 1992; Small et al. 1993; Boutin
1995; Hanski & Korpimäki 1995; Norrdahl 1995; Korpimäki & Krebs 1996;
Krebs 1996; Hanski et al. 2001).
For boreal communities of Fennoscandia, many researchers claim that the
main predator causing the four-year population cycle of microtine rodents is
the least weasel, Mustela nivalis (Hanski et al. 1993; Korpimäki 1993; Sundell et al. 2000; Hanski et al. 2001). On the open tundra region, the arctic
fox (Alopex lagopus) is another important vole specialist predator
12
(Angerbjörn et al. 1999). In general, the ratio between specialist and generalist predators increases with increasing latitudes in Northern Europe (Hanski
et al. 1991). From this follows that the northern communities are expected to
be dominated by specialist predator species, and their total effect should thus
be destabilising and generate prey population cycles
The aim with this study
This thesis concerns three raptor species in a Swedish arctic community: the
gyrfalcon, the rough-legged buzzard and the golden eagle. I have studied
their diet choice and their functional and numerical responses to their main
prey species. Further I have investigated to what extent their predator-prey
relationships overlap, and how their predation patterns interact with the main
community processes in the study area.
Figure 2. Map over the main study area and the area where rough-legged buzzards were studied (Ritsem). Park borders, greater water bodies and altitude curves of 1000 and 1500 masl are shown
13
Description of the community
The study area
The field work was performed in a study area situated in three adjacent national parks: Padjelanta, Sarek and Stora Sjöfallet (Norrbotten county 66°N,
17°E, figure 2). The main area is approximately 5200 km2. The western part
of the area comprises the highland plateau of Padjelanta which surrounds the
lakes Virihaure and Vastenjaure. Although some parts are known for its unusually rich flora, the main vegetation is dry heath with an increasing element of willow brushes (Salix sp.) in more moist areas. The central part of
the study area has more of an alpine character, as it is dominated by the
mountain massif of Sarek. It extends up to 2000masl and is transacted by
deep river valleys, of which Rapadalen is the greatest. The great lake Akkajaure in Stora Sjöfallet constitutes the eastern border of the study area. The
study area lacks infrastructure other than hiking trails, small tourist cabins
and some Saami settlements with few inhabitants. Tourist activities are
mostly confined to the summer period. Snow mobiles are forbidden as is
small game hunting. The study area is part of traditional reindeer herding
grounds. The yearly mean temperature is 0°C and the area is snow-covered
225 days per year.
The predators
The gyrfalcon is an arctic species with an almost circumpolar distribution
(Cade 1960). For Nordic, inland populations, ptarmigan species are the most
important prey category (Hagen 1952; Pulliainen 1975; Langvatn 1977;
Langvatn & Moksnes 1979; Lindberg 1983; Huhtala et al. 1996; Nielsen
1999). Adult gyrfalcons are resident and stay in their territories all year.
Breeding activities start in February and the eggs are laid in April. The
chicks hatch after 29-35 days of incubation and are fledged at the end of
June. They are fully self supportive one month later (Cade 1960; Cramp &
Simmons 1980; Danielsson & Bondestad 1999). The gyrfalcon breeding
population in the study area was small. Usually less than 10 pairs tried to
breed each year, with an average success of six pairs. The gyrfalcons rarely
established new territories and the most frequently used territories were situated in Padjelanta, though Sarek held some relatively successful pairs.
The rough-legged buzzard breeds in mountain regions, boreal taiga and
on arctic tundra (Cramp & Simmons 1980). Globally it is not a threatened
species and the Swedish population was estimated to 7000 in the early
1980’s (Nilsson 1981). The rough-legged buzzard is migratory and arrives
from its temperate winter habitats in May. The eggs are laid in June and
hatch after 31 days. The chicks are fledged 40-43 days later (Cramp & Simmons 1980). The rough-legged buzzards main prey consists of small mam14
mals in most parts of its range (Cramp & Simmons 1980). During a peak
year in the microtine rodent cycle (2001) the rough-legged buzzards bred in
relatively high densities in their traditional territories in the north-eastern
part of the study area (Ritsem in Stora Sjöfallet, figure 2). After that peak
they have been virtually absent. As I write this, however, we approach a new
peak and I expect the buzzards to respond accordingly.
The golden eagle breeds in boreal to warm temperate zones of North
America, Europe, Asia and northern Africa (Cramp & Simmons 1980). In
Sweden, it inhabits mainly the mountains and taiga forests from 61° N to
69°N. The Swedish population was estimated to 400 pairs during the eighties, and established pairs are mostly resident (Tjernberg 1983a). The golden
eagle has a big food niche. In the Nordic countries it preys to a large extent
on tetraonid birds. Other important preys are mountain hare (Lepus timidus)
and reindeer (Rangifer tarandus). In addition to these main prey categories,
at least 30 bird species appear in smaller amounts in the diet. The golden
eagle also preys on several other predatory species such as mustelids (Mustelidae), owls, raptors and foxes (Tjernberg 1981; Hogstrom & Wiss 1992
and references therein; Sulkava et al. 1999). In Sweden, egg-laying starts in
late March or early April (Tjernberg 1983a), and the fledgling period is 6080 days (Cramp & Simmons 1980). There were 12-18 occupied golden eagle
territories in the study area each year. The majority contained single eagles,
and thus the breeding frequency was very low (1-5 pairs each year). The
most frequently used territories were situated in the southern parts of the
study area, with some additional good territories in the centre of Sarek.
The prey
The major prey species of the community in the study area are microtine
rodents, ptarmigan birds, mountain hares and to some extent reindeer. The
most common microtine rodent species are the grey-sided vole, the Norwegian lemming and the field vole (Microtus agrestis). There are two ptarmigan species in the study area, willow ptarmigan (L. lagopus) and rock ptarmigan. They are the only members of the Tetraonidae family, as the bigger
species such as capercaillie (Tetrao urogallus) and black grouse (T. tetrix)
do not occur in an open tundra environment such as the study area. Mountain
hares are rarely seen in the study area, though dropping densities indicate
that they are present in most areas.
15
Study design
Predator populations
The populations of gyrfalcon and golden eagle in the main study area (figure
1)
were
surveyed
by
Projekt
Jaktfalk
Norrbotten
(http://w1.907.telia.com/~u90703421/falk/) from 1996 to 2003. During the
period March to May, the study area was thoroughly searched for occupied
territories. Occupied territories were closely monitored to see if they contained breeding pairs. During June to July the occupied territories were revisited to check the outcome of the breeding attempts. In case of a successful breeding attempt, the number of chicks was counted. The rough-legged
buzzards were mainly studied during 2001. Their traditional breeding sites
around Ritsem in Stora Sjöfallet (Broo & Lindberg 1981, figure 2) were
searched through, and breeding pairs were monitored during the entire
breeding season.
Predator diets
For gyrfalcons and golden eagles, prey remains for dietary analyses were
collected at the last visit to the nest sites. The prey remains were collected
from the nest and the nest shelve, and from perches and roosts in the vicinity
of the nest shelve. For the rough-legged buzzards, prey remains were collected infrequently from May and onwards. In Late September after the
breeding season, all buzzard breeding territories were revisited and thoroughly searched for prey remains. Prey remains from golden eagles and
rough-legged were dominated by regurgitated pellets, whereas prey remains
from gyrfalcons were dominated by skeletal parts.
The batches of prey remains from the different breeding events were analysed separately, allowing diet comparisons between different raptor families. We used mostly teeth morphology to identify the species of small rodents. Feathers could be identified to species in most cases, and skeletal remains were identified to order or genus level. We calculated the minimum
number of specimens for each prey category. These figures were divided
with the total prey number in order to yield the proportions of different prey
in the diets. When convenient, these proportions were multiplied with the
weight of the prey species, yielding proportions of prey biomass.
Skeletal remains from ptarmigan birds are difficult to identify to species
level. The best cues come from the metatarsus that significantly differs in
length between rock and willow ptarmigan (Myrberget 1977). However,
these are fragile bones and few intact metatarsi are found in the prey remains. Therefore we developed a PCR protocol for species identification,
based on DNA from the ptarmigan bones. This method utilises two speciesspecific primers in combination with one general primer. The species16
specific primers were designed to anneal at different distances from the general primer, and thus resulting in PCR products of different size depending
on which species the DNA extract originated from. The PCR products are
then run in an agarose gel, where the travel speed determines whether they
originated from rock ptarmigan or willow ptarmigan (for details, see Dalén
et al. 2004 and paper II and IV).
Prey populations
Ptarmigan and hare densities in the main study area (figure 2) were estimated from dropping counts. The dropping counts were performed during
walked transects with a fixed strip width of one meter in each direction, and
thus the strip was two meters wide (for details, see paper I). In order to estimate prey availability for separate gyrfalcon and eagle families, dropping
counts were performed in separate breeding territories. Three to eight transects were performed inside each breeding territory, with an effort of 10-25
km per territory. The strip transects extended from the nest site and outwards
and were distributed in order to cover all major habitats within the breeding
territory. In order to follow the prey population fluctuations in the study area
over the entire period (1998-2003), we needed yearly estimates of the prey
populations. This was achieved by pooling all results from dropping counts
in the territories with additional counts from other parts of the study area.
This gave more representative estimates of the yearly prey population sizes.
During all transects, we also recorded observations on ptarmigan and hares.
The results from the dropping counts were presented as number of droppings
per km and were used only as relative measures of prey availability. Thus
the actual sizes of the ptarmigan and hare populations in the territories, and
in the study area as a whole, were unknown.
We used some different methods for estimating microtine rodent densities. In paper I and IV (trapping events performed in the main study area,
figure 2) we used trapping protocols for microtine rodents in accordance to
Myllymäki (1971a; 1971b). Common snap traps were placed in squares
with a side of 15 m. At each corner, three snap traps were placed within a
distance of two meters from the corner. Five such squares were aligned 35 m
apart yielding 60 traps within one trap line. The traps were baited with raisins and each trapping event lasted for 48 hours. The trap lines were placed
in habitats that offered a protected environment of rock cavities, as well as a
rich food supply of grasses and herbs for the microtine rodents. All trapping
events were performed in July. We used this trapping protocol to estimate
microtine rodent densities in gyrfalcon and golden eagle breeding territories.
We placed two -to three trap lines (240-360 trap nights) in each territory. As
we also wanted to estimate the size of the yearly microtine population, all
trapping events from the breeding territories were pooled together with additional trapping events from the study area each year.
17
In paper III we estimated microtine rodent densities inside separate roughlegged buzzard territories. Here we used a trapping protocol designed by
Krebs et al. (2003). We used two parallel trap lines, 100 m apart, placed less
than 500 m from the nest. Each trap line had 20 stations placed at 15 m intervals. We placed three traps at each station within a radius of three meters,
preferably near burrows or on runways. A trapping session lasted for 48
hours per territory (240 trap nights). We checked the trap lines twice a day,
once in the morning and once in the evening. All trapping events were performed in mid-July.
Functional response
We investigated the functional responses of gyrfalcon and rough-legged
buzzard to their main prey categories. We used the diet of separate breeding
raptor families (i.e. prey proportions based on the total number of prey remains found after one breeding event) as unit for the functional response
analyses. This means that one data point in a functional response curve represented the diet proportion of a certain prey category, plotted against that
prey category’s density in the breeding territory of the raptor family in question. We analysed if the variance in the data set could be explained by functions representing functional response curves (i.e. type 1, type 2, and type 3,
paper I and III). The function explaining most of the observed variation in
the data set determined what functional response the raptor had to the prey
category in question.
Numerical response
We investigated the numerical responses of gyrfalcons and golden eagles to
their main prey categories. For gyrfalcon, the densities of ptarmigan and
microtine rodents in a set of breeding territories were compared to the number of chicks fledged in the same set of territories. This is referred to as the
reproductive response. Further, the number of occupied territories and the
number of successful breeding attempts each year were compared to the
yearly prey densities of microtines and ptarmigan in the study area. This is
referred to as the gyrfalcons aggregative response.
For golden eagles, the number of occupied territories and the number of
successful breeding attempts each year were compared to the yearly prey
densities of microtines, hares and ptarmigan.
18
Brief summary of papers
Paper I
This paper deals with the predation pattern of the breeding population of
gyrfalcon in the study area. We wanted to investigate the gyrfalcons general
diet, and how availability of the main prey species affected its functional and
the numerical responses. We also investigated how the gyrfalcon responded
to a peak in the microtine rodent cycle (2001) when lemmings were present
throughout the study area.
The diet of the gyrfalcon showed that it was a highly specialised ptarmigan predator. The gyrfalcons showed a weak functional response to ptarmigan densities in the breeding territories, as a 21- fold variation in ptarmigan
densities only caused a 10% shift in the diets (paper I, figure 1). It was clear
that no prey switches to alternative prey could be detected, and ptarmigan
remained the main prey even in territories with the lowest ptarmigan abundance. Further, the gyrfalcon did not respond functionally to the peak year in
the microtine population cycle, and the microtine diet proportion remained
low over the entire period. The gyrfalcon population showed no aggregative
response to ptarmigan population fluctuations, but they showed a reproductive response to ptarmigan densities in the breeding territories, as territories
with low ptarmigan densities produced significantly lower numbers of offspring (paper I, Fig 2). For a specialist predator, which does not utilise alternative prey when the main prey species decline, a direct effect on the breeding success seems as the logical result. The total response of the gyrfalcon
could generate ptarmigan population cycles, however, the observed prey
population dynamics in the study area do not confirm this.
Paper II
The diet analysis from paper I showed that ptarmigan was the most important prey category for the gyrfalcon. However, there are two different ptarmigan species in the study area, and in this paper we wanted to investigate
the relative importance of rock and willow ptarmigan as prey. With the help
of DNA analyses, we could identify the ptarmigan species of any bone in our
large prey collection (paper I, table 2). This allowed us to establish the ratio
of rock and willow ptarmigan in the diets originating from separate gyrfalcon breeding events. We found that breeding events originating from the
same territory showed predictable diet proportions of rock and willow ptarmigan during successive years (paper II, table 2). We hypothesized that this
depended on the fact that the territories contained predictable amounts of
rock and willow ptarmigan, and accordingly, the gyrfalcons utilized the two
ptarmigan species in a density dependent manner. However, we could not
19
test this as we did not know the densities of the two prey species in the
breeding territories. Instead, we calculated the proportions of rock and willow ptarmigan habitat in the territories with help of a GIS analysis. The proportion of rock ptarmigan habitat in the territories was compared to the proportion of rock ptarmigan in the diets, in order to investigate whether gyrfalcons utilization of ptarmigan species was density dependent or not.
The DNA analysis showed that rock ptarmigan held a much greater proportion in gyrfalcon’s diet than willow ptarmigan did (paper II, table 2). The
GIS analysis revealed that the study area as a whole held a much greater
proportion of rock ptarmigan habitat than willow ptarmigan habitat (paper II,
figure 2), which explained the dominance of rock ptarmigan in gyrfalcon’s
diet. We found a positive relationship between the proportion of rock ptarmigan in the diets and the proportion of rock ptarmigan habitat in the territories, which confirmed that gyrfalcons preyed on ptarmigan species in a density dependent manner (paper II, figure 2). We use this result to discuss gyrfalcons vulnerability to game hunting, which affects the two ptarmigan species unevenly.
Paper III
The peak in the microtine rodent cycle during 2001 resulted in a relatively
dense population of breeding rough-legged buzzards in the north-eastern part
of the study area (figure 2). This gave us the opportunity to study the predation pattern of a microtine rodent predator during exceptionally good food
conditions. We investigated the general diet and the prey preference of the
rough-legged buzzard. We also investigated its functional response to different microtine rodent species.
Rough-legged buzzards strongly preferred Norwegian lemmings to other
vole species and showed a steep type 2 functional response to lemming density (paper III, figure 1). The same rough-legged buzzards showed a different
functional response to grey-sided voles, best described by a type 3 function
(paper III, figure 1). Double functional responses could be explained by high
prey density, and the fact that breeding raptors behave like central place
foragers (Stephens & Krebs 1986). During these circumstances, the buzzards
can maximise the energy gain from foraging by showing partial prey preferences (Stephens & Krebs 1986), yielding different functional responses to
different prey categories. The sharp rise of the functional response curve to
lemmings was probably an effect of the preference for lemmings. The preference in turn, was probably due to the fact that lemmings are conspicuous
and slow compared to other voles, and therefore easier to detect and catch.
The rough-legged buzzard is often described as a nomadic specialist predator, which due to a fast numerical response can synchronise microtine rodent
populations on a large spatial scale. Our study indicates that similar preda-
20
tion patterns may operate on a more fine-grained spatial scale generated by
functional responses.
Paper IV
Golden eagles in the boreal region of Fennoscandia are often characterized
as generalist predators, incorporating a variety of birds and mammals in their
diet. However, golden eagles in the arctic environment have to cope with
low prey diversity and a fluctuating prey base. How these conditions affect
its predator-prey relationships are poorly studied. We analyzed the diet of
the breeding population in the study area. We also analyzed how the population fluctuations of the main prey species in the study area affected the numerical response of the golden eagle.
The diet analysis of the golden eagle showed that it has a wider food
niche than gyrfalcons and rough-legged buzzards. It preyed on ptarmigan,
hares, reindeer, birds and also on other predatory species (paper IV, table 1).
In contrast to the gyrfalcons, it seemed like the golden eagle responded functionally to the microtine rodent cycle, as more microtines were found in the
diet during the peak year in the microtine population cycle. Ptarmigan, hare
and microtine rodents showed synchronized population fluctuations (paper
IV, figure 1). The number of breeding golden eagle pairs showed a significant relationship to the ptarmigan population fluctuations in the study area
(paper IV, figure 2), whereas the number of occupied territories was unaffected.
Discussion
Study design
The arctic environment is a good place for studying natural predator-prey
relationships, as the prey population cycles provide opportunities for exploring how predators respond to a wide range of prey densities (Boutin 1995).
Further, due to low species diversity, the main community processes are
relatively easy to identify. However, there are several factors that complicate
field studies in such an environment. One factor is the logistics, which precludes field studies requiring bulky or large amounts of equipment. Further,
in a protected environment such as the study area, manipulative experiments
are prohibited, and thus descriptive studies are usually the only option available. And when it comes to raptors as model animals, I refer to the citation
of Ian Newton (1979) on paper 3 in this thesis.
One problem often associated with diet quantification from prey remains
is that pellets tend to overestimate small prey items, whereas skeletal re21
mains typically overestimate larger prey categories (Langvatn 1977; Huhtala
et al. 1996; Redpath et al. 2001). Gyrfalcons prey remains comprised few
pellets, and thus possibly biased towards large prey items such as ptarmigan.
Ptarmigan bones made up the bulk of skeletal parts found. However, ptarmigan bone fragments also made up a substantial proportion of pellet contents.
Therefore, the diet proportions were relatively unaffected by the number of
pellets found in the total batch of prey remains. Pellets dominated the prey
remains from the rough-legged buzzard. Since all major prey categories were
microtine rodents of similar sizes, I feel relatively confident that this diet
analysis was unbiased. Further, it was comparably easy to reach the buzzards
nests, allowing a more careful search for prey remains. The golden eagle diet
analysis caused the biggest problem. Despite a six-year effort, few prey remains were found. At several occasions, I visited nest sites where golden
eagles successfully had raised young without leaving any prey remains. This
was probably due to the fact that golden eagles actively remove prey remains
from their nest sites (Collopy 1983).
DNA identification of prey remains is a useful tool in diet analyses, especially when prey remains are too fragmented to be identified correctly. DNA
identification of prey is also useful when discriminating between species
with similar morphological traits, such as closely related species.
Boutin (1995) discussed the problems associated with functional response
analyses based on relative diet measures. He concluded that predators could
respond to high prey densities by eating only parts of the prey, or by prey
caching. Therefore, even if a prey made up 100% of the diet, it does not necessarily mean that the maximum attack rate is reached. However we found
no indications of prey caching for any of the raptor species. Holling (1959b;
1959a) defined the functional response as the consumption rate of an individual predator over a short period of time. As such, it involves the full
range of foraging behaviours, and has an immediate effect on prey mortality.
Despite this, functional responses of large predators are usually studied on
the same temporal scale as predator population dynamics (i.e. the numerical
response) by pooling the diet of individual predators within a breeding interval, thereby assuming that the diet of individuals do not differ from the
population average (Korpimäki & Norrdahl 1991b; O'Donoghue et al. 1998;
Nielsen 1999; Tome 2003). In comparison to this method, our approach with
breeding territories as unit has several benefits. To start with, it allows for a
detailed study of how individuals vary in their response to prey density,
which is of importance for general foraging theories. Furthermore, our approach is less time consuming since data can be collected over a short time
span. This in turn, reduces the effect of between-year variation in stochastic
factors such as weather. Finally, for a species with chaotic dynamics characterised by extremely low densities between peaks (such as the lemming) our
approach is the only option for research programmes with a finite time period (such as a PhD).
22
The method of line transect sampling of ptarmigan and hare droppings
was developed by us, so there is no other study to make direct comparisons
with. However, Pelliter and Krebs (1997) estimated densities of live ptarmigan with a similar method and concluded that it was accurate. In general,
line transect sampling is regarded as a robust method for bird density estimations (Järvinen et al. 1978; Burnham et al. 1981). Further, Gibbons et al.
(1997) pointed out that droppings from wildfowl are both persistent and
recognisable and thus very suitable for estimating populations of these rather
elusive birds.
Snap trapping is a well-known method for estimating relative abundances
of microtines. However, it should be considered, that capture success can
vary between different microtine species. This could cause biases in calculations such as prey preferences and functional responses (such as in paper
III).
The gyrfalcon
The diet of the gyrfalcon showed that it was a highly specialised ptarmigan
predator. No other prey category seemed to be of any importance for the
gyrfalcon breeding population.
Ptarmigan density fluctuated both on a temporal and spatial scale; however, these fluctuations had little effect on the predation behaviour of the
gyrfalcon. The functional response to ptarmigan was almost density independent, as ptarmigan remained the dominating prey even in territories with
the lowest ptarmigan density. Rock ptarmigan dominated over willow ptarmigan in the general diet of the gyrfalcon breeding population. It was also
the dominating prey species in the diets originating from separate breeding
events. Furthermore, it seemed like all breeding events originating from the
same territory showed predictable diet proportions of rock and willow ptarmigan during successive years. This could be explained by the habitat composition in the breeding territories, as territories that produced diets containing larger proportions of rock ptarmigan also contained larger proportions of
rock ptarmigan habitat. Thus it seemed as gyrfalcons utilization of the two
prey species was density dependent, and the availability of the two prey species depended on habitat composition.
We had expected the gyrfalcon to compensate for low ptarmigan density
by increasing the consumption of microtines, however no such prey switch
could be detected. The gyrfalcon population did not respond functionally to
the microtine rodent population cycle in the study area. Even during a peak
in the microtine population cycle, the proportion of microtine in the diet
remained low. As gyrfalcons breeding activities start in March, it must rely
on resident prey species throughout the major part of its breeding period, and
ptarmigan constitutes one of the few prey species available in the study area
during this time of the year. This is probably the main reason to the narrow
23
food niche of the gyrfalcon. During a substantial part of the breeding period,
a thick snow layer covers the study area, and the microtines spend this period under the snow cover. This can explain why the gyrfalcons weak consumption of microtines, they were, to a large extent, protected form gyrfalcon predation.
A predator that does not switch to alternative prey during declines in the
main prey density is bound to show a numerical response to the main prey
density fluctuations. This could be seen in the gyrfalcons breeding territories, as we found a positive relationship between the numbers of chicks
fledged, and the ptarmigan density in the same territories. This reproductive
response indicates that the growth rate of the gyrfalcon population lags behind that of the ptarmigan population. The combination of a strong, almost
density independent functional response, and a numerical response with a
time lag (i.e. due to the reproductive response) suggests that the gyrfalcons
total response should generate ptarmigan population fluctuations. This has
been shown for Icelandic gyrfalcons (Nielsen 1999). However, In Fennoscandia, ptarmigan populations are believed to be linked to the microtine
rodent cycle, possibly by predation (Hörnfeldt 1978; Angelstam et al. 1985;
Hörnfeldt et al. 1986). As we found that the number of gyrfalcon breeding
attempts tracked the microtine population fluctuations, it seems like the
predator-prey relationship of gyrfalcon and ptarmigan is influenced by other
community interactions in the study area.
The rough-legged buzzard
The diet of the rough-legged buzzard showed that it behaved like a highly
specialised microtine rodent predator. Three species of microtines made up
the bulk of the general diet, lemming, grey-sided vole and field vole. Lemming was the preferred prey and it was utilised to a much higher degree than
expected from its abundance. The buzzards showed a steep type 2 functional
response to lemming density, but the same rough-legged buzzards showed a
different functional response to grey-sided voles, best described by a type 3
function. The classic prediction from optimal foraging theory is that the inclusion of alternative prey in the diet solely depends on density of the preferred prey ("zero or one rule", see Stephens & Krebs 1986), and therefore,
two simultaneous functional responses should not be possible. There are,
however, many circumstances that can relax the zero to one rule and make
way for a partial prey preference (Berec 2000; Berec & Krivan 2000). Waddington & Holden (1978) claimed that a predator in an environment with
abundant prey should encounter prey simultaneously rather than sequentially. In a central place foraging situation (such as a breeding rough-legged
buzzard, which has to deliver prey at the nest site), a predator that encounters
different prey simultaneously can maximise energy gain by considering both
prey profitability and the travel distance to the nest before an attack. Each
24
prey category should therefore be utilised in relation to its own benefits,
resulting in separate functional responses for the different prey categories.
There are some general characteristics of lemmings that can explain why
they were the preferred prey species. Lemmings are more conspicuous than
both grey-sided and field voles, making the easy to detect. They are also
slow compared to other vole species, making the susceptible to predation
when detected (Andersson 1976).
The predation behaviour of the rough-legged buzzard is commonly discussed in the context of the regional synchrony theory. Nomadic predators
such as the rough-legged buzzard respond to local prey densities by a fast
numerical response and distribute themselves in accordance to prey density,
creating a synchronising predation pressure on microtine populations on a
regional scale (Ims & Steen 1990; Norrdahl & Korpimäki 1996; Ims & Andreassen 2000). Our study did not include a numerical response. However, it
shows that a similar mechanism can operate on a more fine-grained scale,
generated by functional responses. Or results show that a predator can generate different predation pressures on sympatric prey species. More specifically, the predation pattern of the buzzard implies that lemmings should experience a relatively density independent and destabilising predation pressure, whereas grey-sided voles should experience a density dependent and
stabilising predation pressure. This could explain why lemming population
dynamics appear as chaotic in relation to the population dynamics of its
sympatric vole species (e.g. Oksanen & Oksanen 1992).
The golden eagle
The diet of the golden eagle showed that it behaved like a generalist predator. It preyed on all major prey categories of the community: microtines,
ptarmigan, hares and reindeer. For Nordic boreal populations of golden eagles, tetraonid birds constitute one of the main prey categories (Tjernberg
1981; Högström & Wiss 1992; Sulkava et al. 1999). The bigger species of
the family, the black grouse and capercaillie, are not found in an arctic environment, such as in the study area. Despite this fact, the eagles maintained
their preference for tetraonid birds by relaying on the only two members of
the prey category present, rock and willow ptarmigan. In contrast to the gyrfalcons, willow ptarmigan held a greater part in the eagle’s diet than rock
ptarmigan did. This was surprising as we expected rock ptarmigan to be the
most common species in the study area (paper II, figure 2). This could indicate a true preference for willow ptarmigan, however, it could also depend
on the fact that prey remains from the southernmost part of the study area
were somewhat over represented in the general diet. This part of the study
area is probably more suitable for willow ptarmigan, as it contains birch
forests and dense vegetation. The total number of prey remains was too
small in order to detect any diet changes between years, with perhaps, an
25
exception for the proportion of microtine rodents. More than half of the total
number of microtines identified came from 2001, the year when microtine
populations peaked in the study area. The majority of these were lemmings,
and thus it seemed as the golden eagles responded functionally to, at least,
lemming peaks.
The number of occupied territories was unaffected by prey fluctuations in
the study area. This seems to be a common phenomena in golden eagle
populations, and it is usually attributed to territoriality limiting population
growth, even during good food conditions (Brown & Watson 1964; Newton
1979; Tjernberg 1981; Steenhof et al. 1997). It has been shown that the
breeding success of golden eagles in the boreal region of Sweden depends on
a relatively large set of prey, including hares and tetraonid species
(Tjernberg 1983b). In our study area were the prey base is less diverse, the
number of breeding pairs depended solely on the density of the most important prey category, the ptarmigan.
Competition and community structure
Though a predator-prey relationship typically concerns one predator species
and its main prey species, it is not always possible to regard them as single,
self-dependent entities. In an environment with a limited number of preys,
the predators’ food niches will overlap, resulting in interspecific competition
for prey. In order to investigate the competitive interactions between the
three raptors, I recalculated their diets so that they contained comparable
prey categories (table 1). After that, I measured their food niche overlaps
with Shoeners overlap index (Krebs 1989, see appendix). The total diet overlap between the three raptors was 11.7%. This was mainly due to the microtines, which was the only prey category common in all three diets. Pairwise comparisons produced much higher measures. The greatest diet overlap
was between golden eagle and gyrfalcon (59.3%). This depended on the fact
that both species preyed to such large extent on ptarmigan (table 1). The
rough-legged buzzard shared a smaller fraction of its diet with the other two,
it had 14.0% overlap with gyrfalcons and 13.3% with the golden eagle.
These results indicate that golden eagle and gyrfalcon compete for ptarmigan. The number of breeding golden eagle pairs tracked the ptarmigan population fluctuations in the study area. This was not the case for gyrfalcons,
despite their dependence on ptarmigan as prey.
26
Table 1. The diets of gyrfalcon, rough-legged buzzard and golden eagle. Prey categories are expressed as percentage of the total number of prey items
Prey categories
Gyrfalcon
Rough-legged
buzzard
Golden eagle
76.1
9.8
85.9
0.3
0.7
0.0
0.0
0.3
0.1
87.3
0.0
0.0
1.0
0.1
0.1
0.0
0.1
0.0
0.5
1.9
38.3
25.0
63.3
0.9
0.0
0.3
0.6
3.1
0.3
68.5
9.6
1.6
1.6
0.0
12.7
35.4
47.6
14.9
0.2
98.1
10.8
0.6
0.3
0.0
11.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.3
11.4
5.9
1.9
0.3
19.8
Birds
Rock ptarmigan
Willow ptarmigan
Ptamigan total
Duck
Wader
Raptor
Owl
Corvid
Passerine
Bird total
Microtine rodents
Lemming
Grey-sided vole
Field vole
Red-backed vole
Microtine total
Other mammals
Shrew
Reindeer
Mountain hare
Mustelid
Red fox
Other mammals total
As the diet overlap indicated food competition, I hypothesised that the absence of a gyrfalcon numerical response to ptarmigan was a result of competition with golden eagle, and thus, a negative relationship between the two
species breeding successes could be expected. I tested this with a regression
analysis. Figure 3 shows the number of yearly successful breeding pairs of
golden eagles plotted against the yearly number of breeding gyrfalcon pairs.
There seemed to be a negative trend (B=-0.62, r2=0.39, N=6, p=0.18), indicating that the gyrfalcons breeding success is connected to the breeding success of golden eagle. Competitive interactions between golden eagles and
gyrfalcons could contain both interference competition and exploitive competition. Exploitive competition affects both species, whereas the effect of
interference competition should be more notable for the inferior competitor,
the gyrfalcon. These interactions between gyrfalcons and golden eagles need
further studying and it is something I consider for future studies.
27
No. successful golden eagle breeding pairs
7
2001
6
2003
5
1998
2002
4
2000
3
2
1999
1
0
2
3
4
5
6
7
8
9
10
No. successful gyrfalcon breeding pairs
Figure 3. The relationship between the number of successful breeding pairs of
golden eagles and gyrfalcons (1998-2003).
There are many studies discussing the synchronised population fluctuations of microtine rodents, hares and small game species that occur in Fennoscandian communities (Hörnfeldt 1978; Lindström et al. 1987; Lindén
1988; Small et al. 1993). Predation has been proposed as one explanation to
this phenomenon. According to predation theories: predators use the microtines as their main prey, and when the density of the main prey decreases,
the predators switches to alternative prey species such as tetraonid birds and
mountain hares. As the alternative prey will experience a predation pressure
depending on the density of the main prey, the population dynamics of the
main and alternative prey will become linked to each other. If the predators
have sufficient numbers of alternative prey, and a limited numerical response, the result will be stable populations of both predators and prey
(Erlinge et al. 1984; Erlinge 1987). However, if the predator community
28
comprise specialist predators with a delayed numerical response, the result
will be population cycles with a four year period for both main and alternative prey, with predator and alternative prey populations showing a time lag
to the main prey population (Angelstam et al. 1984; Angelstam et al. 1985).
If the predators are less selective, there will be no time lag between main and
alternative prey populations (Norrdahl & Korpimäki 2000).
My results indicate that there exists a microtine rodent population cycle
with a four year period in the study area, and that the populations of ptarmigan and hares are synchronised to this cycle (paper IV, figure 1). If the predation hypotheses are to explain these patterns, the community of the study
area must comprise a set of predators with predation strategies in accordance
to this model. To start with, there must be sufficient numbers of microtine
rodent specialist predators in order for the cycle to occur. The main candidate, the least weasel (see introduction) is however, expected to be rare in an
alpine environment such as the study area (Oksanen et al. 1992), which our
own observations confirm. The arctic and red fox are potential microtine
specialists (Elmhagen et al. 2000; Elmhagen et al. 2002), but they occur in
low numbers in the study area (<10 breeding pairs each year, Love Dalén
pers. communication).
The predation patterns of golden eagle and gyrfalcon do not fit the general
predation hypothesis, as their main prey is ptarmigan. The rough-legged
buzzard shows the correct main prey choice, however, it is a migrating species and only present at high microtine densities. Thus in conclusion, the
community seems to lack the resident specialist predator needed to support
the general predation theory. There are studies that show that lemming cycles in an open tundra environment is mediated by resource-consumer interactions (Oksanen & Oksanen 1992; Turchin et al. 2000). This could very
well apply to the study area, but it does not explain the synchrony between
the three prey populations. In fact, synchronised population fluctuations of
microtine rodents, ptarmigan and hare have not been shown for an arctic
tundra community before, to my knowledge. To identify the processes causing these patterns is a challenging task.
29
Acknowledgements, tack till!
Jag fattar mig kort, ni som känner er bortglömda får höra av er så ska ni få ett
muntligt erkännande, om ni förtjänar det.
Tack till:
•
Zootis. Det har varit en ära att få tillbringa dessa år i en sådan stimulerande
miljö. Det är trångt på zootis, men väldigt högt till tak.
•
Anders Angerbjörn, min handledare. Anders kan vända rådande världsordning till kaos på nolltid. Han sprutar ur sig idéer, och överger dom så fort
som någon håller med honom. Det är inte helt lätt att vara Anders doktorand, men det är sällan tråkigt.
•
Alla fältarbetare som har deltagit i mina fältstudier: Håkan Lundberg, Love
Dalén, Sverker Dalén, Jukka Ikonen, Tobias Sahlman, Susanna Holmgren,
Lena Malmström, Kajsa Winnes, Markus Bergvind, Mikael Nilzén, Fredrik
Dalerum, Gill Telford och Susanna Eschricht. Varför är dom är så många?
Det beror på att alla utom en (tack Lena!) har vägrat att jobba mer än en säsong i mitt projekt.
•
Mina rumskollegor på rum D525: Lisa Jansson, Anders Bergström och
Fredrik Dalerum. På rum D525 råder det flextid. Anders kommer väldigt
tidigt, men så börjar han nicka till redan framåt tolvtiden. Lisa och jag
kommer senare, framåt tio. Sedan tävlar vi om vem som stannar längst. Det
slutar oftast med att jag surfar på motorcyklar och Lisa mailar. Mest flextid
har Fredrik, han dyker bara upp en, till två gånger om året. Men då stannar
han dygnet runt på rummet.
•
Mina medförfattare: Love Dalén som i alla fall nästan står ut med att fältjobba med mig. Johan Ekenstedt som lärde mig regel nummer 1 för snöskoteråkande: -om isen brister, GASA! Peter Hellström, han är inte snabb
(vråk-manus, tre år), men det blir bra.
•
Ulf Norberg som med rätt dålig framgång har försökt lära mig klättra och
fixa mina datorproblem själv.
•
Johan Lind som har hjälpt mig med layouten på denna avhandling, och som
alltid lyckas få mig att känna mig väldigt bra på det jag gör för tillfället.
•
Christer Wiklund. Det är allmänt känt att, om man vill ha tag i Krille så
finns det bara en metod. Stå stilla i korridoren så susar han snart förbi, oftast nedlastad med fjärilsburkar. Trots sitt tempo så hinner han diskutera
och filosofera, samt leverera en hel del coola blues skivor.
30
•
Birgitta Tullberg som har hjälpt mig jättemycket med korrektur och allmän
uppmuntran. Hon tittar alltid in och tycker synd om mig när jag jobbar sent,
och glömmer att det är lika synd om henne eftersom hon ju också är kvar.
Hon får också ett bonus-tack för att hon inte spottar i glaset, så man slipper
skämmas ensam, dagen efter.
•
Hans Temrin som korrekturläst många av mina manus, och som gärna
snackar motorcyklar vid kopiatorn.
•
Bertil Borg för seriös kritik av allt som jag försökt skriva, och som tappert
håller vår bar öppen.
•
Tommy Radesäter, vår prefekt. Jag var lite rädd för honom i början eftersom han påminner lite om Clint Eastwood. Tommy behöver bara hosta lite,
så tystnar hela Zootis.
•
Anette Lorents, Berit Strand och Siw Gustafsson på sekretariatet, som fixar
så man får igen sina utlägg, trots att kvittona består av Same-kråkfötter
skrivna på baksidan av bensinkvitton från OK i Jokkmokk.
•
Gunilla Börjeson, Sonja Schön, och Kristina Tröjer som jobbar på Tovetorp, och som bistår med lakan, tandborstar och allt annat som jag alltid
glömmer att ta med när jag ska dit och jobba.
•
Min mamma Marianne och min styvfar Janne som med förundran och
stolthet har följt min metamorfos från förslappad rockmusiker till målmedveten (nåja) akademiker. Tack för ert stöd
•
Min far Hans, som sväller över av stolthet när han tänker på sin framgångsrika arvsmassa. Akademiker varde vi alla, till slut.
•
Min flickvän Sanna, bara det är ett under
.
31
References
Akcakaya HR (1992) Population-cycles of mammals - Evidence for a ratiodependent predation hypothesis. Ecological Monographs, 62, 119-142.
Andersson M, Erlinge S (1977) Influence of predation on rodent populations. Oikos,
29, 591-597.
Angelstam P, Lindström E, Widén P (1984) Role of predation in short-term population fluctuations of some bird and mammals in Fennoscandia. Oecologia,
62, 199-208.
Angelstam P, Lindström E, Widén P (1985) Synchronus short-term population fluctuations of some bird and mammals in Fennoscandia - occurrence and distribution. Holarctic Ecology, 8, 285-298.
Angerbjörn A, Tannerfeldt M, Erlinge S (1999) Predator-prey relationships: arctic
foxes and lemmings. Journal of Animal Ecology, 68, 34-49.
Angerbjörn A, Tannerfeldt M, Lundberg H (2001) Geographical and temporal patterns of lemming population dynamics in Fennoscandia. Ecography, 24,
298-308.
Berec L (2000) Mixed encounters, limited perception and optimal foraging. Bulletin
of Mathematical Biology, 62, 849-868.
Berec L, Krivan V (2000) A mechanistic model for partial preferences. Theoretical
Population Biology, 58, 279-289.
Boutin S (1995) Testing predator-prey theory by studying fluctuating populations of
small mammals. Wildlife Research, 22, 89-100.
Broo B, Lindberg P (1981) Ritsemområdets exploatering - effekter på faunan, främst
rovfåglarna. Statens Naturvårdsverk, Stockholm.
Brown LH, Watson A (1964) The golden eagle in relation to its food supply. Ibis,
106, 78-100.
Burnham KP, Anderson DR, Laake JL (1981) Line transect estimation of bird population density using a Fourier series. Studies in Avian Biology, 6, 466-482.
Cade TJ (1960) Ecology of the peregrine and gyrfalcon populations in Alaska. In:
University of California Publications in Zoology (eds. Furgason WH, Bullock TH, Cowles RB), pp. 151-269. University of California press, Berkley
and Los Angeles.
Charnov EL (1976) Optimal foraging: attack strategy of a mantid. American Naturalist, 110, 141-151.
Charnov EL, Finerty JP (1980) Vole population cycles: a case of kin-selection?
Oecologia, 45, 1-2.
Chitty D (1996) Do lemmings commit suicide? Beautiful hypotheses and ugly facts.
Oxford university press, Oxford.
Collopy MW (1983) A comparison of direct observations and collections of prey
remains in determinig the diet of golden eagles. Journal of Wildlife Management, 47, 360-368.
Cramp S, Simmons KEL (1980) Handbook of the birds of Europe the Middle East
and North Africa. In: The birds of the western Palearctic (ed. Cramp S).
Oxford University press, Oxford.
Dale BW, Adams LG, Bowyer RT (1994) Functional-response of wolves preying on
Barren-Ground caribou in a multiple-prey ecosystem. Journal of Animal
Ecology, 63, 644-652.
32
Dalén L, Götherström A, Angerbjörn A (2004) Identifying species from pieces of
faeces. Conservation Genetics, 5, 109–111.
Danielsson L, Bondestad L (1999) Jaktfalk i den Svenska fjällkedjan, p. 11. Länstyrelsen, Västerbottens län, Umeå.
Elmhagen B, Tannerfeldt M, Angerbjorn A (2002) Food-niche overlap between
arctic and red foxes. Canadian Journal of Zoology-Revue Canadienne De
Zoologie, 80, 1274-1285.
Elmhagen B, Tannerfeldt M, Verucci P, Angerbjorn A (2000) The arctic fox
(Alopex lagopus): an opportunistic specialist. Journal of Zoology, 251,
139-149.
Erlinge S (1987) Predation and noncyclicity in a microtine population in southern
Sweden. Oikos, 50, 347-352.
Erlinge S, Göransson G, Högstedt G, et al. (1984) Can vertebrate predators regulate
their prey? American Naturalist, 123, 125-133.
Errington PL (1946) Predation and vertebrate populations. Quarterly review of Biology, 21, 144-245.
Finerty JP (1980) The population ecology of cycles in small mammals. Yale university press, London.
Fryxell JM, Lundberg P (1994) Diet choice and predator-prey dynamics. Evolutionary Ecology, 8, 407-421.
Fuller TK (1989) Population-dynamics of wolves in North-Central Minnesota. Wildlife Monographs, 1-41.
Gibbons DW, Hill DA, Sutherland WJ (1997) Birds. In: Ecological census techniques, a handbook (ed. Sutherland WJ), pp. 227-255. Cambridge University press, Cambridge.
Hagen Y (1952) The gyr-falcon (Falco r. rusticolus L.) in Dovre, Norway. Skrifter
utgitt av det Norske Videnskaps-Akademi. I. Mat.-Naturv., Klasse 4, 1-37.
Hansen TF, Stenseth NC, Henttonen H, Tast J (1999) Interspecific and intraspecific
competition as causes of direct and delayed density dependence in a fluctuating vole population. Proceedings of the National Academy of Sciences of
the United States of America, 96, 986-991.
Hanski I, Hansson L, Henttonen H (1991) Specialist predators, generalist predators
and the microtine rodent cycle. Journal of Animal Ecology, 60, 353-367.
Hanski I, Henttonen H, Korpimaki E, Oksanen L, Turchin P (2001) Small-rodent
dynamics and predation. Ecology, 82, 1505-1520.
Hanski I, Korpimäki E (1995) Microtine rodent dynamics in northern Europe - parameterized models for the predator-prey interaction. Ecology, 76, 840-850.
Hanski I, Turchin P, Korpimäki E, Henttonen H (1993) Population oscillations of
boreal rodents - Regulation by mustelid predators leads to chaos. Nature,
364, 232-235.
Hassel MP, Comins HN (1978) Sigmoid functional responses and population stability. Theoretical Population Biology, 14, 62-67.
Henttonen H, Oksanen T, Jortikka A, Haukisalmi V (1987) How much do weasels
shape microtine cycles in the northern Fennoscandian Taiga. Oikos, 50,
353-365.
Hogstrom S, Wiss LE (1992) Diet of the Golden Eagle Aquila-Chrysaetos (L) in
Gotland, Sweden During the Breeding-Season. Ornis Fennica, 69, 39-44.
33
Holling CS (1959a) The components of predation as revealed by a study of smallmammal predation of the European pine sawfly. Canadian Entomologist,
91, 293-320.
Holling CS (1959b) Some characteristics of simple types of predation and parasitism. Canadian Entomologist, 7, 385-398.
Huhtala K, Pulliainen E, Jussila P, Tunkkari PS (1996) Food niche of the gyrfalcon
Falco rusticolus nesting in the far north of Finland as compared with other
choices of the species. Ornis Fennica, 73, 78-87.
Högström S, Wiss LE (1992) Diet of the Golden Eagle Aquila-Chrysaetos (L) in
Gotland, Sweden During the Breeding-Season. Ornis Fennica, 69, 39-44.
Hörnfeldt B (1978) Synchronus population fluctuations in voles, small game, owls
and tularaemia in northern Sweden. Oecologia, 32, 141-152.
Hörnfeldt B, Löfgren O, Carlsson B-G (1986) Cycles in voles and small game in
relation to variations in plant production indices in Northern Sweden.
Oecologia, 68, 496-502.
Ims RA, Andreassen HP (2000) Spatial synchronization of vole population dynamics by predatory birds. Nature, 408, 194-196.
Ims RA, Steen H (1990) Geographical synchrony in microtine population-cycles - A
theoretical evaluation of the role of nomadic avian predators. Oikos, 57,
381-387.
Järvinen O, Väisänen R, Enemar A (1978) Efficiency of the line transect method in
mountain birch forest. Ornis Fennica, 55, 16-23.
Korpimäki E (1993) Regulation of multiannual vole cycles by density-dependent
avian and mammalian predation. Oikos, 66, 359-363.
Korpimäki E (1994) Rapid or delayed tracking of multiannual vole cycles by avian
predators. Journal of Animal Ecology, 63, 619-628.
Korpimäki E, Krebs CJ (1996) Predation and population cycles of small mammals A reassessment of the predation hypothesis. Bioscience, 46, 754-764.
Korpimäki E, Norrdahl K (1991a) Do breeding nomadic avian predators dampen
population fluctuations of small mammals. Oikos, 62, 195-208.
Korpimäki E, Norrdahl K (1991b) Numerical and functional responses of kestrels,
short-eared owl, and long-eared owls to vole densities. Ecology, 72, 814826.
Krebs CJ (1989) Ecological methodology. Harper and Row, New York.
Krebs CJ (1996) Population cycles revisited. Journal of Mammalogy, 77, 8-24.
Krebs CJ, Danell K, Angerbjorn A, et al. (2003) Terrestrial trophic dynamics in the
Canadian Arctic. Canadian Journal of Zoology-Revue Canadienne De Zoologie, 81, 827-843.
Krebs JR, Clutton-Brock T (1990) Foraging theory. Princeton University Press,
New Jersey.
Lack D (1954) The natural regulation of animal numbers. Oxford university press,
London.
Laine K, Henttonen H (1983) The role of plant production in microtine cycles in
northern Fennoscandia. Oikos, 40, 407-418.
Langvatn R (1977) Characteristics and relative occurrence of remnants of prey
found at nesting places of gyrfalcon Falco rusticolus. Ornis Scandinavica,
8, 113-125.
Langvatn R, Moksnes A (1979) On the breeding ecology of the gyrfalcon Falco
rusticolus in central Norway. Fauna Norvegica Series C., Cinclus 2, 27-39.
34
Lindberg P (1983) Relations between the diet of Fennoscandian peregrines Falco
peregrines and organochlorines and mercury in their eggs and feathers,
with a comparison to the gyrfalcon Falco rusticolus PhD thesis, University
of Gothenburg.
Lindén H (1988) Latitudinal gradients in predator-prey interactions, cyclicity and
synchronism in voles and small game populations in Finland. Oikos, 52,
341-349.
Lindström E, Angelstam P, Widen P, Andren H (1987) Do predators synchronize
vole and grouse fluctuations - an experiment. Oikos, 48, 121-124.
Lucas JR (1983) The role of foraging time constraints and variable prey encounter in
optimal diet choice. Amercian Naturalist, 122, 191-209.
Marcström V, Kenward RE, Engren E (1988) The impact of predation on boreal
tetraonids during vole cycles - an experimental study. Journal of Animal
Ecology, 57, 859-872.
Messier F (1995) On the functional and numerical response of wolves to changing
prey density. In: Ecology and Conservation of Wolves in a Changing World
(eds. Carbyn LN, Fritss SH, Seip DR), pp. 187-197. Canadian circumpolar
institute, Edmonton.
Moss R, Watson A (2001) Population cycles in birds of the grouse family (Tetraonidae). In: Advances in Ecological Research, Vol 32, pp. 53-111. Academic
press LTD, London.
Murdoch WW (1969) Switching in general predators: experiments on predator
specificity and stability of prey populations. Ecological Monographs, 39,
336-354.
Murdoch WW, Oaten A (1975) Predation and population stability. In: Advances in
Ecological Research (ed. MacFayden A), pp. 2-130. Academic press, London.
Myllymäki A, Paasikallio A, Häkkinen U (1971a) Analysis of a "standard trapping"
of Microtus agrestis (L.) with triple isotope marking outside the quadrat.
Annales Zoologici Fennici, 8, 22-34.
Myllymäki A, Paasikallio A, Pankakoski E, Kanervo V (1971b) Removal experiments on small quadrats as a means of rapid assessment of abundance of
small mammals. Annales Zoologici Fennici, 8, 177-185.
Myrberget S (1977) Artbestemmelse av ryper ved mål og vekt av bein. Sterna, 16,
231-236.
Newton I (1979) Population ecology of raptors. Buteo books, Vermilion.
Nielsen OK (1999) Gyrfalcon predation on ptarmigan: numerical and functional
responses. Journal of Animal Ecology, 68, 1034-1050.
Nilsson SG (1981) Det Svenska rovfågelbeståndets storlek. Vår fågelvärld, 40, 249262.
Norrdahl K (1995) Population-cycles in Northern small mammals. Biological Reviews of the Cambridge Philosophical Society, 70, 621-637.
Norrdahl K, Korpimäki E (1996) Do nomadic avian predators synchronize population fluctuations of small mammals? A field experiment. Oecologia, 107,
478-483.
Norrdahl K, Korpimäki E (2000) Do predators limit the abundance of alternative
prey? Experiments with vole-eating avian and mammalian predators.
Oikos, 91, 528-540.
35
Nunney L (1980) The influence of the type 3 (sigmoid) functional response upon
stability of predator-prey difference models. Theoretical Population Biology, 18, 257-278.
Oaten A, Murdoch WW (1975) Functional response and stability in predator-prey
systems. American Naturalist, 709, 289-297.
O'Donoghue M, Boutin S, Krebs CJ, Zuleta G, Murray DL (1998) Functional responses of coyotes and lynx to the snowshoe hare cycle. Ecology, 79, 11931208.
Oksanen L, Oksanen T (1992) Long-term microtine dynamics in north Fennoscandian tundra: the vole cycle and the lemming chaos. Ecography, 15, 226236.
Oksanen T, Oksanen L, Norberg M (1992) Habitat Use of Small Mustelids in North
Fennoscandian Tundra - a Test of the Hypothesis of Patchy Exploitation
Ecosystems. Ecography, 15, 237-244.
Oli MK, Dobson FS (2001) Population cycles in small mammals: The alphahypothesis. Journal of Mammalogy, 82, 573-581.
Pech RP, Sinclair ARE, Newsome AE, Catling PC (1992) Limits to predator regulation of rabbits in Australia - evidence from predator-removal experiments.
Oecologia, 89, 102-112.
Pelletier L, Krebs CJ (1997) Line-transect sampling for estimating ptarmigan
(Lagopus spp.) density. Canadian Journal of Zoology, 75, 1185-1192.
Pulliainen E (1975) Choice of prey by a pair of gyrfalcons Falco rusticolus during
the nesting period in Forest-Lapland. Ornis Fennica, 52, 19-22.
Redpath SM, Clarke R, Madders M, Thirgood SJ (2001) Assessing raptor diet:
Comparing pellets, prey remains, and observational data at hen harrier
nests. Condor, 103, 184-188.
Redpath SM, Thirgood SJ (1999) Numerical and functional responses in generalist
predators: hen harriers and peregrines on Scottish grouse moors. Journal of
Animal Ecology, 68, 879-892.
Reid DG, Krebs CJ, Kenney AJ (1997) Patterns of predation on noncyclic lemmings. Ecological Monographs, 67, 89-108.
Seldal T, Andersen K-J, Högstedt G (1994) Grazing-induced proteinase inhibitors: a
possible cause for lemming population cycles. Oikos, 70, 3-11.
Sinclair ARE, Olsen PD, Redhead TD (1990) Can predators regulate small mammal
populations? Evidence from house mouse outbreaks in Australia. Oikos, 59,
382-392.
Small RJ, Marcström V, Willebrand T (1993) Synchronous and nonsynchronous
population fluctuations of some predators and their prey in Central Sweden.
Ecography, 16, 360-364.
Smith RR, Smith TM (2001) Concepts of predation. In: Ecology and field biology
(eds. Smith RR, Smith TM), pp. 264-283. Benjamin Cummings, San Francisco.
Solomon ME (1949) The natural control of animal populations. Journal of Animal
Ecology, 18, 1-35.
Steenhof K, Kochert MN, MCDonald TL (1997) Interactive effects of prey and
weather on golden eagle reproduction. Journal of Animal Ecology, 66, 350362.
Stephens DW, Krebs JR (1986) Foraging theory. Princeton University Press, New
Yersey.
36
Sulkava S, Huhtala K, Rajala P, Tornberg R (1999) Changes in the diet of the
Golden Eagle Aquila chrysaetos and small game populations in Finland in
1957-96. Ornis Fennica, 76, 1-16.
Sundell J, Norrdahl K, Korpimäki E, Hanski I (2000) functional response of the least
weasel, Mustela nivalis nivalis. Oikos, 90, 501-508.
Taylor RJ (1984) Predation. Chapman and Hall Ltd, New York.
Tinbergen L (1960) The natural control of insects in pinewood -factors influencing
the intensity of predation by songbirds. Archives Neerlandaises de Zoologie, 13, 266-336.
Tjernberg M (1981) Diet of the golden eagle Aquila chrysaetos during the breding
season in Sweden. Holarctic Ecology, 4, 12-19.
Tjernberg M (1983a) Breeding ecology of the golden eagle, Aquila chrysaetos (L.),
in Sweden PhD, Swedish University of Agricultural Sciences.
Tjernberg M (1983b) Prey abundance and reproductive success of the golden eagle
Aquila chrysaetos in Sweden. Holarctic Ecology, 6, 17-23.
Tome D (2003) Functional response of the Long-eared Owl (Asio otus) to changing
prey numbers: a 20-year study. Ornis Fennica, 80, 63-70.
Turchin P, Oksanen L, Ekerholm P, Oksanen T, Henttonen H (2000) Are lemmings
prey or predators? Nature, 405, 562-565.
Waddington KD, Holden LR (1978) Optimal foraging: on flower selection by bees.
The American naturalist, 114, 179-196.
Watson A, Moss R (1979) Population cycles in the Tetraonidae. Ornis Fennica, 56,
87-109.
Wiklund CG, Angerbjörn A, Isakson E, Kjellen N, Tannerfeldt M (1999) Lemming
predators on the Siberian tundra. Ambio, 28, 281-286.
37
Appendix
Shoener’s overlap index
⎡ n
⎤
Pjk= ⎢∑ (minimum Pij , Pik )⎥100
⎣
⎦
Where
Pjk= Percentage overlap between species j and k, Pij,
Pij, Pik= Proportions resource i is of the total resources used by
species j and k
n= Total number of resource states
38
39