Impact of Geographical and Environmental Structures on Habitat Choice, Metapopulation Dynamics and Genetic Structure for Hazel Grouse (Bonasa Bonasia)
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 314
Impact of Geographical and Environmental Structures on Habitat Choice, Metapopulation Dynamics and Genetic Structure for Hazel Grouse (Bonasa bonasia)
JONAS SAHLSTEN
ACTA UNIVERSITATIS UPSALIENSIS ISSN 1651-6214 UPPSALA ISBN 978-91-554-6910-8 2007 urn:nbn:se:uu:diva-7911 !" # $ %&&$ '()&& * " * * +" ", !" - ",
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You gotta follow that dream wherever that dream may lead you
-Bruce Springsteen
For you! Rebecca, André & Jennie List of papers
This thesis is based on the following papers, which are referred to by their roman numerals:
I Sahlsten J. Predicting suitable habitats for Hazel Grouse (Bonasa bonasia) in a boreal region using niche factor analysis: A case study in south-central Sweden. (Submit- ted manuscript).
II Sahlsten J., Höglund J. and Rapaport E. Landscape shape and land cover structure as determinants of pre- ferred areas by Hazel Grouse (Bonasa bonasia). (Manu- script).
III Sahlsten J., Wickström F. and Höglund J. Hazel Grouse population dynamics in a fragmented landscape: A metapopulation approach. (Manuscript).
IV Sahlsten J., Thörngren H. and Höglund J. Inference of Hazel Grouse population structure using multilocus data: A landscape genetic approach. (Manuscript).
Cover photo by: Danielle Occhiato©
Contents
Introduction...... 8 Methods ...... 11 The species ...... 11 Study area...... 12 Fieldwork ...... 13 GIS and ENFA ...... 14 Incidence Function Model...... 17 Landscape genetics...... 20 Results and discussion ...... 22 Census ...... 22 Ecological Niche Factor Analysis ...... 22 Patch Structure ...... 23 Incidence Function Model...... 25 Landscape Genetics...... 27 Sammanfattning på svenska...... 31 Acknowledgements...... 33 References...... 35 Abbreviations
ENFA Ecological Niche Factor Analysis HSM Habitat Suitability Map IFM Incidence Function Model GIS Geographic Information System EGV Ecogeographical Variable Introduction
At present degradation and fragmentation of suitable habitats is one of the main reasons to why species decline and goes extinct. Therefore, it is a ma- jor concern to find and develop methods and models to make estimations and predictions of the dynamics driven by the landscape species face. In conservation or management strategies it is fundamental to identify suitable habitats and to determine geographic and geometric distribution of these areas (Wiens 1989, Keitt et al. 1997). In the last decades GIS (geographic information system) and remote sensing has become more readily available and studies of very large or remote areas has been simplified. Ecological niche factor analysis (ENFA) is a recently developed method to find suitable habitats at large scales and to compute habitat suitability maps (HSM) (Hir- zel et al. 2002a,b., Hirzel and Arlettaz 2003). This multivariate method, based on Hutchinson’s concept of ecological niche (Hutchinson 1957), was used to determine distribution of areas suitable for hazel grouse (Bonasa bonasia) in Uppland, south-central Sweden. The ENFA approach revealed that suitable areas in the studied region have a patchy distribution and it ind- cates that good habitat is available for hazel grouse in this region. Although the size and inner structure or content of patches is important, the geometric shape of patches may be equally important to determine the potential incidence of a species in a particular patch (Hargis et al. 1998; Hel- zer and Jelinski 1999). Size of a patch is often considered as linearly corre- lated with population size or species diversity (Martin and McComb 2003). The shape of a patch in terms of the perimeter-area ratio may, however, be more important when considering the suitability of patches (Collinge and Palmer 2002). Hence, different GIS applications and analyses were used in order to determine features of suitable areas, in terms of patch shape and land cover. At the studied resolution and extent the results indicate that hazel grouse is tied to coniferous forest and that they avoid open space. The pre- ferred composition and structure of land cover seem, however, to differ be- tween scales. Analyses of geometric measures indicate that size and espe- cially shape is important factors in the context of incidence of hazel grouse. This implies that consideration needs to be taken at more than one level of geographical scale in planning and management of hazel grouse.
8 If suitable areas are found to have a patchy distribution the species popula- tion dynamics may be assumed to fit a metapopulation (Hanski and Gilpin 1997). In a metapopulation modelling approach, the assumptions is made that the assembly of sub-populations have reached a steady-state in terms of colonization and extinction events (Hanski and Gilpin 1997). A stochastic, spatially realistic, occupancy model based on a first order linear Markov chain modelling describing the probability of occupancy for each single habitat patch (Hanski 1994, Hanski and Gilpin 1997) was used to simulate population dynamics of hazel grouse under different area measurement sce- narios. This model is denoted the incidence function model (IFM) and one basic assumption in the IFM is that the size of a sub-population is correlated to the size of the patch and extinction probability is correlated to the size of the population (Kindvall and Ahlén 1992, Hanski et al. 1996). However, the area alone could be a deceptive measures due to that some parts of the area having limited suitability due to edge effects. The relationship between pe- rimeter and area will determine the amount of impact on a patch. In order to analyze the impact of edges on occupancy levels and capacity, three differ- ent edge-zones were implemented into the incidence function model and pair -wise compared to a patch network where edges were not included (hereafter called the basic area). The results indicate that a landscape with high perime- ter length (i.e. high amount of edges) seems to be more sensitive to changes. Although number of patches was significantly lower when edges were con- sidered, no evidences were found for decreasing proportion occupied patches. In addition to resources within patches, a recommended strategy to favour hazel grouse in this region should be to preserve Euclidian shaped (Mandlebrot 1983) patches with a homogenous interior. Dispersal opportuni- ties should be considered by proper configuration of patches and land use planning should be at a scale that incorporates more than one or a few sub- populations. To understand the dynamics at a local scale it is also important to under- stand the pattern and processes of gene flow at a large scale (Manél et al. 2003). Spatial heterogeneity affects, among other things, dispersal and con- sequently gene flow (Holderegger et al. 2006). In conservation and man- agement of species, it is important to define groups of individuals as conser- vation or management units (Bonin et al. 2007). However, most previous methods used require a priori defined populations, when analysing the data (Manél et al. 2003). The concept of landscape genetics has recently emerged from combinations of molecular techniques, statistical tools and landscape ecological theories (Manél et al. 2003). The landscape genetic approach is advantageous due to that the individual is the studied unit and it does not require discrete populations in advance. With a Bayesian clustering approach it is possible to assign genotyped individuals of unknown origin to specific populations (Pritchard et al. 2000). Using the landscape genetic approach we tried to make inferences about the number of populations and the population
9 of origin from sampled hazel grouse individuals of unknown origin. Parame- ters were approximated with Markov Chain Monte Carlo (MCMC) methods (Pritchard et al. 2000). Tissue samples for analysis of microsatellite DNA were taken from col- lected geo-referenced hazel grouse wings (n = 613). With the geo-referenced individual as operational unit multi-locus genotypes were analyzed using Geneland, a computer package in R, (Guillot et al. 2005a,b) to infer the number of hazel grouse populations in northern Sweden. The geographical locations of genetic discontinuities between inferred populations were sup- posed to be detected through so-called coloured Poisson-Voronoi tessellation (Dupanloup et al. 2002). There is evidence of a population structure with a basic north-south divide. However, no evidence was found that geographic and environmental structures affected gene flow and dispersal patterns for the hazel grouse. This may suggest that the boreal taiga forests of northern Sweden are good habitats for the species.
In studies of patch occupancy dynamics, such as metapopulation models, dispersal is a crucial factor (Schumaker 1996, Hanski 2003). The available knowledge of dispersal is, however, often sparse and becomes a limiting factor when modelling movement patterns or patch occupancy. At large scales it is a huge task to estimate dispersal using mark-recapture methods, due to the large number of individuals necessary to catch and monitor. This problem may be overcome by using molecular genetic techniques (Waser and Strobeck 1998). In studies of genetic distribution pattern, migration, and genetic flow or dispersal distances neutral markers have great potential (Holderegger et al. 2006). Estimation of neighbourhood size (Sumner et al. 2001) was made using estimates of a kinship coefficient and Roussets’s a, (Rousset 1997, 2000). With knowledge of neighbourhood size it was possi- ble to make inference on dispersal distance. Results from the genetic ap- proach indicate that there is a spatial autocorrelation of both kinship- and Rousset’s a coefficient. Neighbourhood size estimations indicate a dispersal distance in the range of 900-1500 meters. This dispersal distance is compa- rable with results from an ecological approach in a similar forest structure (Swenson 1991c). This approach is certainly enhances the knowledge of population structure and dispersal patterns of hazel grouse within the study area.
10 Methods
The species The hazel grouse (Bonasa bonasia) is a small (375-400 grams) grouse spe- cies distributed throughout the Palaearctic boreal, temperate and mountain- ous forest from the western coast of Norway to north-eastern Siberia. Southwards the distribution extends into central France, former Yugoslavia, North Korea and the Japanese island of Hokkaido. The southern limit of the species mostly parallels with the southern border of the boreal forest. In parts of Europe and parts of eastern Asia the hazel grouse also occurs in deciduous temperate forests and mountainous forests south of the boreal zone (Storch 2000). Population trends have not been quantified, but according to hunting bags there is no indication of a population decline in Sweden (Swedish As- sociation for Hunting and Wildlife Management). In Scandinavia hazel grouse is found in old growth spruce forest with a dense bush layer and ac- cess to deciduous patches e.g. marshland and along streams (Saari et al. 1996, Swenson 1991a,b, Åberg et al. 2000a.b, Åberg et al. 2003). The main winter food resource is catkins and buds from alder (Alnus glutinosa) and birch (Betula spp.). Herbs and berries is the most preferred food source dur- ing spring and summer (Swenson 1991a, Swenson 1993). They inhabit ap- proximately the same area through winter and spring. Home ranges are ap- proximately 40 ha (18-80 ha) and the areas are significantly larger in sum- mer than in other seasons (Swenson 1991a, Swenson 1993). There is no evi- dence for seasonal long distance migration. They are mostly monogamous and live in territories that they defend for access to food resource and mat- ing. Territory size is different between the sexes, males: average 16.4 ha, females: average 22.5 ha (Swenson 1993). Hazel grouse has limited disper- sal ability and a mean movement of only 340 meters was found by Swenson (1993). Juveniles show a relatively short dispersal distance (median 800 meters) (Swenson 1991c). Dispersal distance for post-juvenile hazel grouse’s was somewhat longer (~ 4 km) in the south-eastern French Alps (Montadert and Léonard 2006).
11 Study area The study area (fig. 1) is situated in Uppland, south-central Sweden (59q - 60qN - 17q - 18qE); and is a flat landscape (highest altitude 118 a.s.l.) that for approximately ten thousand years ago was below the sea level of a shal- low inland sea. The area was mainly selected for two reasons. First, the area is fragmented from anthropogenic influence by a variety of land uses (active forestry management and agriculture vs. nature preserves vs. housing). It was assumed based upon past studies that this type of fragmentation would impact hazel grouse movement between suitable habitats and thus affect the hazel grouse population distribution (Swenson 1991a, Saari et al. 1996 and Åberg et al. 2003). Second, the geological history in the region has designed a “naturally” fragmented landscape with different land cover types (forests vs. bogs vs. lakes and ponds). In Uppland the area of productive forests is 389 000 ha, the area of impediment is 48 000 ha and there is 28 000 ha of older (>60 year) deciduous forest, which is approximately 7.2 percent of the total area. Within Uppland there is some lack of old-growth forest. Only 2.6 percent of the forest area is older than 120 years, compared to Sweden in general that has about 11.5 percent of the forest older than 120 years. The productive forest consists of 81.1 percent coniferous trees and 17 percent deciduous trees (Skogsvårdsstyrelsen, 2004). For the genetic approach samples collected from the northern part of Swe- den were used. Approximately 67°-75° N – 13°-19° E was covered. This is the main taiga region of Sweden and compared to the southern part of Swe- den the forests are generally less fragmented.
12 Figure 1. The study area located in Uppland, south-central Sweden. In the enlarge- ment the sub-areas censused for hazel grouse is shown within a landscape of forest, agriculture and urban settlement.
Fieldwork Fieldwork was conducted in two separate periods, although by the aid of quite similar techniques. The first period it was conducted in thirty three sub- areas of 1 km2 each during three consecutive seasons, autumn 2002, spring 2003 and in autumn 2003. In the spring the census were made from mid- April to mid-May, which coincide with mating season. In the autumn the census range from mid-September to mid-October, this period coincide with juvenile dispersal (Swenson 1991a). This was followed by a census con- ducted from early April to late May 2006 within patches obtained by an eco- logical niche factor analysis (Hutchinson 1957, Hirzel et al. 2002a) covering a total of 9862.9 hectares. Both censuses followed the method described by Swenson (1991b). Hence, the patches were searched for hazel grouse by walking transects that were 150 meters from each other. At every 150 me- ters, stops were made for six minutes to lure hazel grouse with a hunting
13 whistle-pipe. Frequency of whistling was approximately every 30 seconds. The census was continued throughout the day although Swenson (1991b) found a lower response frequency during mid-day. However, the census was discontinued when there was heavy rain or if wind speed moved medium sized branches, because it may be more difficult to detect responses. With this method Swenson (1991b) found a mean response accuracy of 82 ± 7.0 % for males and the accuracy was similar for spring (80 ± 8.5 %) and au- tumn (86 ± 3.5 %). The most obvious response is song, but the response may also be in form of flutter jumping, flutter flying or silent approach. If any of the above responses were noticed, the coordinates (in Swedish grid RT90) for that location were saved on a GPS receiver. Within patches where no respond were noticed the patch were considered as absent. For the second census in 2006 fresh droppings were considered as presence of hazel grouse at some occasions even though no response was noticed. In addition, when accuracy could be validated, data from birdwatchers were used.
GIS and ENFA ENFA (ecological niche factor analysis) is a multivariate approach that can be used to study the geographic distribution of species (Hirzel et al. 2002a). The method requires presence data only and is built on Hutchinson’s concept of the ecological niche (Hutchinson 1957). The base of this approach is a comparison between the multidimensional space of the ecogeographical variables that the species occupy and the multidimensional space of vari- ables that describe the whole study area. In stepwise logistic regression vari- ables need to be fairly uncorrelated and consequently one of two correlated variables are removed and no longer in the model. In ENFA eigenvalues and eigenvectors indicate how much information that is explained by the factors and from that information it is possible to make decisions on whether to keep a variable or not. Thus, ENFA is one method where the measurement has an ecological meaning (Hirzel et al. 2002a, Mandleberg 2004). In order to get input to ENFA and HSM computation two types of maps are needed. Firstly, a number of quantitative maps that contains the ecogeographical variables (EGV) and secondly a species map containing the species distribution. EGV maps can easily be derived from satellite images and the species map is de- rived from census data. To create the species map a list of presence coordi- nates were imported from Microsoft Excel™ to Biomapper (Hirzel et al. 2002b) via the module “Converter” to create a binary species map. Further- more, around each presence point an approximated radius of hazel grouse movement (150 meters) was made with the CircAn-module in Biomapper to cover a buffer around each presence point. To locate suitable habitat patches in the present study area an unclassified Landsat 7 ETM+ image (Acquisition date: 2002/08/04, path/row: 193/18,
14 Zone: 34N) was used. Each band in a Landsat scene reflects a different wavelength interval (table 1) and although specific variables from each band cannot be distinct from an unclassified image, the bands can be used in an analysis like ENFA to find suitable areas in terms of geometric measures and geographic distribution.
Table 1. Bands from Landsat 7 ETM+ used as Ecogeographical Variable maps in the ENFA with example of applications of the separate bands. Ȝ is the reflected wave- length of electromagnetic radiation. Band Ȝ (ȝm) Example of application 1 0.45-0.52 Separate soil/vegetation, Deciduous/Coniferous 2 0.52-0.60 Estimation of vegetation state of health 3 0.63-0.69 Separating types of vegetation 4 0.76-0.90 Estimation of biomass, separation of land/water 5 1.55-1.75 Estimation of water in vegetation and soil 7 2.08-2.35 Separation between different bedrocks
Marginality and specialization is two important features in the ENFA ap- proach. Marginality is formally defined as the absolute difference between the global and the species mean distribution of variables. If marginality is close to one it is an indication that the species is found in a different habitat compared to what is available on average in the area. Specialization is for- mally defined as the ratio between the standard deviation of the global land cover distribution and the standard deviation of the land cover within the species distribution. Specialization express if the species utilize a narrow niche within the available habitat in the area, and this is indicated with a result of specialization above one. Although, the inner structure or content of patches might be important, the size and geometric shape of patches may be equally important to deter- mine potential incidence of a species in a particular patch (Helzer and Jelinski 1999). Size alone is often considered as linearly correlated with population size or species diversity (Martin and McComb 2003). The shape in terms of perimeter-area ratio may, however, be more important when con- sidering suitability of patches (Collinge and Palmer 2002). Often analyses in terms of the structure of the landscape are performed as if the landscape constitutes Euclidian structures, but landscapes mostly exhibit non-Euclidian density and perimeter to area relations (Burrough 1981; Krummel et al. 1987). The Euclidian shape is a term used in Mandelbrot (1983) to denote all of standard geometry (i.e. perfect circles, squares, rectangles and other regu- lar shapes). An irregular or convoluted shape, i.e. geometrically “amor- phous” is denoted non-Euclidian. With a non-Euclidian structure of the land- scape, it is important to consider the structure in terms of shape, especially
15 with management goals that consider connectivity and flow of genes, organ- isms and energy (Milne 1988; Jorge and Garcia 1997). There are different approaches available to measure fractals and shapes of landscapes objects (Mandelbrot 1983; Milne 1988; Jorge and Garcia 1997; Li 2000). Perimeter- area ratio is a simple measure of shape for a two-dimensional object. A patch with high perimeter-area ratio (P/A) i.e. convoluted edge; need to be larger to have the same perimeter-area ratio value compared to a circular shaped patch. This implies that a patch with high perimeter-area ratio has more edge, and a high proportion of edge may be negative for many species (Wil- cov 1985; Andrén and Angelstam 1988). The perimeter-area ratio was calcu- lated for each sub-area to determine shape pattern. Due to the relationship between perimeter and area, the perimeter-area ratio is more accurate than just using the area only if one considers the best fitted parameter to deter- mine species diversity or a species incidence in a patch (Heltzer and Jelinski 1999). In order to analyze the incidence of hazel grouse in relation to the size and shape of potential areas of habitat within the sub-areas, the land cover that contained coniferous or mixed forest within a digital map (Swedish Land cover Map (SMD)) with a 30 meter/pixel resolution were merged to one single category and reclassified to a binary map. Forest area and perimeter was calculated for each sub-area from the binary map. Each sub-area was also checked for presence of hazel grouse. To determine features of suitable areas, in terms of land cover, SMD- landcover data maps were used with the ArcMap™ software (Environmental System Research Institute, Inc.). From this data land cover and use was ex- tracted from three different scales. The first scale was set to one kilometre square, which coincide with the censused sub-areas. To make an approxima- tion of area covered in daily movement by hazel grouse, mean net movement per day (Swenson 1991a) combined for male and females in May, Septem- ber and October was used (250 meters). Furthermore, the area of a buffer zone with radius of 250 meter coincides with an approximation of a hazel grouse territory size. Presence points within the study area from the bird watchers and from the census of hazel grouse (n = 46) were buffered with 250 meters radius. For comparison an equivalent number (n = 46) of random points were generated and buffered with 250 meter radius. Furthermore, to cover the immediate vicinity of the area where hazel grouse stays, the forty- six presence points and the equal number of generated random points were buffered with a radius of 50 meters. The correlation of hazel grouse presence and land cover within the sub-areas was analysed with single term deletion GLM. The buffer zones were treated different due to no available absence data. Thus, the correlation for buffer zones was analyzed with deviance table with single term deletion. Using logistic regression potential correlation be- tween presence of hazel grouse and area, perimeter and perimeter-area ratio were determined.
16 Incidence Function Model With the patchy distribution of preferred patches and the fact that this is an area were hazel grouse has been established for long time and although the landscape is not in status quo, no major alteration of patch configuration has taken place in present days. It can thus be assumed that populations of hazel grouse have reached a quasi-steady state of colonization and extinction events. With these assumptions it is possible to model hazel grouse dynam- ics in the area as a metapopulation. The data set used in the model sets up the initial configuration of the patches and the initial patch occupancies in the study area (fig.3).
Figure 3. Initial occupancy level of the basic patch network according to hazel gro- sue census results. Filled circles indicate presence and unfilled circles indicate ab- sence. The size of the circles corresponds to relative patch size and the coordinates are given in RT90 corresponding to distance in meters.
17 One of the more realistic models for a metapopulation is the Incidence function model (Hanski and Gilpin 1997). This model is derived from a linear-first order Markov chain of presence or absence of a species in a habitat patch (Hanski 1994). It is possible to apply this model using presence-absence data from one survey and with few numbers of known parameters (Hanski 1994, Hanski et al. 1996). It is also pos- sible to make predictions of the impact on metapopulation dynamics from varying area related measures of patch structure. The mathemati- cal structure of the model involves probability of colonization and extinction.
Ci J i (1) Ci Ei
Where Ci is probability of colonisation in patch i and Ei is probability of extinction of patch i. Incidence Ji of patch i is firstly defined in terms of ex- tinction
e 1/x Ei x if Ai > e (2) Ai
1/x Ei 1 if Ai e (3)
Thus, probability of extinction is indirectly determined by patch area (Ai) and by environmental stochasticity (x). Secondly, incidence is defined by probability of colonisation (Ci)
M i Ci 2 (4) M i y
Where Mi is migration and y is an estimated constant that reflects how fast colonization probability reaches unity with increasing number of immi- grants. Migration may also be expressed as in terms of connectivity between patches (Si).
Mi = ȕSi (5)
Where; S p e Ddij A (6) i ¦ j j
18 where pj is one for presence and zero for absence of the patch from where a potential migrant comes. Alpha (Į) is a constant setting the survival rate for migrants over the Euclidian distance between patch i and patch j (dij), or the inverse of dispersal distance ability. Area (Aj) reflects the emigration fre- quency from patch j to patch i. ß is a constant that stands for a number of components and practically remains unknown.
1 Ci 2 (7) § y ' · 1 ¨ ¸ © Si ¹ where yƍ = y/ß describes the colonization ability of the focal species. Small yƍ indicates a good colonizer. Substituting expression 2 and 7 into equation 1 gives
1 J (8) i 2 § § y ' · · e 1 ¨1 ¨ ¸ ¸ ¨ ¨ S ¸ ¸ A x © © i ¹ ¹ i
To summarize; required data needed to apply this model is patch areas (Ai), their spatial locations, to calculate the pair wise distances between patches (dij), the presence/absence of the species in the patches in the year of the survey (pi). The remaining parameters y, e and x are estimated from equation 8. The fourth parameter, Į, is species specific and after estimation it will be fixed and the other parameters are estimated in relation to alpha. If knowl- edge about dispersal patterns is insufficient it is possible to estimate alpha from “snapshot” site occupancy. This may not be fully biologically realistic, but it may work as a last way out in lack of dispersal data. The hazel grouse median juvenile dispersal distance has been concluded to be 800 meters (Swenson 1991c). The post-juvenile dispersal distance has also been found to be 4 kilometres (Montadert and Léonard 2006). In the present study alpha equal to 0.25 reflecting a dispersal distance of 4 km was used. Metapopulation capacity is the sum of individual patch contributions to sustain a metapopulation in a landscape and it can be used to predict conse- quences of degradation or loss of habitats for the metapopulation dynamic
19 (Hanski and Ovaskainen 2000, 2003). Capacity of a metapopulation is tech- nically the leading Eigenvalues of a landscape matrix (ȜM) with the elements
xe em mij Ai Aj f dij if i j (9)
mij = 0 if i = j (10)
The first component is reflecting the expected longevity of patch i, which is depending on patch area (Ai) and some scaling of extinction (x) together with em immigration rate (e). The second part Aj f (dij ) correspond to exp(-Įdij)Aj , which is the connectivity component from eq. (6) and it gives the rate at which patch i is colonized by patch j. However, since the absolute values of ȜM is dependent on units of measurement for area and distance it is not pos- sible to conclude if a particular value is high or low. On the other hand, with the assumptions of extinction rate = e/Ai and colonisation rate = Ȉ ji exp(- Įdij)Ajpj(t), different patch networks could be ranked according to their ca- pacity. Hence, via metapopulation capacity and manipulation of area- measurement the capacity of the different patch networks to sustain a viable metapopulation was ranked.
Landscape genetics To study genetic differentiation and population genetic structure at a re- gional scale, geo-referenced tissue samples from northern Sweden (approx. north of Lat 60º N) previously collected and stored at the Swedish Natural History Museum was used. The samples were genotyped at 12 microsatellite loci to quantify genetic diversity and infer the number of populations and dispersal distance for hazel grouse in northern Sweden, and subsequently to locate genetic boundaries and correlate those with geographic structures. For a long time, population geneticists have studied how landscape features has affected genetic variation. However, they have mostly used methods that require pre-defined populations, which may be unfavourable in several as- pects (Manél et al. 2003). From recent development of combinations be- tween molecular techniques, statistical tools and landscape ecological theo- ries, landscape genetics has emerged (Manél et al. 2003). The landscape genetic approach is advantageous it takes the individual as the unit of study and it does not require knowledge of discrete populations in advance. Using a Bayesian cluster approach it is possible to assign genotyped individuals to k populations, where k may be unknown. In this model Hardy-Weinberg equilibrium is assumed and marker loci are assumed to be physically unlinked (Pritchard et al. 2000). By using this approach one tries infer the
20 population of origin for individuals of unknown origin based on differences in allele frequencies between the k populations. (Pritchard et al. 2000). An approximation of unknown parameters is made using Markov Chain Monte Carlo (MCMC) methods. With the geo-referenced hazel grouse individuals as the operational units, multilocus genotypes were analyzed with Geneland, a computer package in R, (Guillot et al. 2005a,b) to infer the number of populations. The spatial locations of genetic discontinuities between these putative populations were supposed to be spatially organized through the so- called coloured Poisson-Voronoi tessellation (Dupanloup et al. 2002). Furthermore, neighbourhood size is often considered as a basic unit of a population (Wright 1943). It is a measure of geneflow per generation in con- tinuous populations. Here, neighbourhood size was estimated via a kinship coefficient and for comparison with Rousset’s a coefficient (Rousset 1997, 2000). With theoretical models of isolation by distance it can be shown that kinship and Rosusset’s a is expected to approximately vary linearly with the logarithm of distance and from these estimates neighbourhood size can be estimated (Rousset 1997, 2000). Thus, using kinship- and Rousset’s a coef- ficients, estimated using SPAGeDi (Hardy and Vekemans 2002), approxima- tions of neighbourhood sizes (NS) were made. With kinship: NS § -(1-F)/b- log, where b-log is the slope of regression which is based on the log-distance (Hardy and Vekemans 2007). With Rousset’s a coefficient neighbourhood size was approximated by the inverse of the slope of the regression, thus: NS § 1/b-log. Neighbourhood size is usually defined as NS = 4ʌDı2, where D is popu- lation density and ı is the mean axial squared distance, the sigma parameter determines how much genetic differentiation increase with distance (Rousset 2000, Sumner et al. 2001). Thus, it is possible to estimate dispersal distance if density and neighbourhood size are known. With available data on male hazel grouse density and information that there were 40 percent more males in a study area located near Grimsö research station in south central Sweden (Swenson 1991) effective density could be estimated. Hence, with estimate of neighbourhood size and effective density dispersal distances, were esti- mated using both kinship- and Rousset’s a.
21 Results and discussion
Census The initial period of censuses resulted in 25 presence positions distributed within 12 of the 33 sub-areas. Census of the 117 patches defined by the ENFA approach resulted in 32 patches occupied. Absence data is always a delicate matter and it may be difficult to verify true absence of a species. The method used here has been determined to have high response frequency (Swenson 1991a,b) and the fact that each sub-area was visited three times imply that no strong error or bias is occurring. If the census is made at the edge of the species range or if the species recently is established in the area presence data may also be biased. This is not the case in this study and hazel grouse is considered sedentary, so noted presence should be considered valid.
Ecological Niche Factor Analysis The eigenvalue (21.455) for marginality imply that randomly chosen cells in the area were over 21 times more dispersed along the first factor axis than cells chosen from the area where hazel grouse were found. The hazel grouse show both some marginality (0.852) and some specialisation (2.637) in this area. The first four factors explained about 92 percent of the information. According to the difference of reflection in wavelengths in the Landsat scene (table 1) the score from the ENFA revealed that hazel grouse chose areas according to water content in the vegetation, the state of vegetation health, kind of vegetation, biomass and relationship between deciduous and conifer- ous trees. They also show high specialization regarding moisture content, vegetation type and biomass (table 2).
22 Table 2. Variance explained by the first four factors and coefficient values for the EGVs. The EGVs are sorted by decreasing absolute values on the marginality factor. Because the Landsat scene is unclassified, negative values on this factor can be interpreted as that hazel grouse preferring areas with a lower mean of that feature compared to the whole study area. In the specialization factors sign of coefficients has no meaning, higher absolute values indicate higher specialization of that EGV. EGV Marginality Spec.1. Spec.2. Spec.3. (51%) (19%) (13%) (9%) Estimation of water -0.49 -0.82 -0.63 0.86 content in vegetation and soil Estimation of vegetation -0.44 0.29 0.41 -0.32 state of health Separation between -0.44 0.41 -0.45 -0.37 different bedrocks Separating types -0.39 0.25 0.35 -0.16 of vegetation Separate soil/vegetation, -0.36 -0.11 0.27 0.02 Deciduous/Coniferous Estimation of biomass, -0.31 -0.09 0.18 -0.01 separate of land/water
The ENFA revealed that hazel grouse in this region is a species that live in a patchy environment. At a large scale it is important to preserve the possibil- ity for hazel grouse to disperse between different patches suitable for settle- ment by considering the connectivity and patch configuration. The method gives the distribution of such areas and also gives a framework for other approaches, such as metapopulation- or niche differentiation models. This presence-only approach is therefore highly attractive when demographic data are lacking and time is restricted. For hazel grouse in the present study area the result here could imply metapopulation dynamics in the area, which will have consequences for management.
Patch Structure Coniferous forest higher than 15 meters not growing on lichen (t = -2.4978, d.f = 25.496, p-value = 0.01929) and pasture (t = 2.6148, d.f = 20.087, p- value = 0.01655) was significantly correlated with incidence of hazel grouse. At the buffer scale with 250 meters radius, there was a positive correlation between coniferous forest on lichen ground and incidence of hazel grouse. Clear cuts and lakes and ponds covered with vegetation were negatively correlated with incidence and there was a tendency of negative correlation for agriculture land and pastures (table 3). Except for a tendency of “Conif-
23 erous not on lichen over 15 meters high” none of the land cover variables were correlated with incidence of hazel grouse at the buffer with 50 meter radius. Hazel grouse seem, however, to avoid areas containing agricultural land, clear cuts, forests at early regeneration state and pastures (table 3).
Table 3. Result from of deviance table analysis with single term deletion for buffer zones with 50 meter and 250 meter radius respectively. Buffer 50 meters Df Deviance AIC LRT P
Buffer 250 meters
Analysis of geometric measures shows that high perimeter was negative for presence of hazel grouse and large area was positive for the presence of ha- zel grouse. Perimeter-area ratio was significantly lower for sub-areas with incidence of hazel grouse than areas lacking hazel grouse and perimeter-area ratio also shows the best fitted model and highest relative importance. Con- cluding remarks would be; in order to favour hazel grouse, patches need to have large enough forest areas and small patches need to be shaped with minimum amount of perimeter-length (i.e. edge-zones). This will be accom- plished if planning is concentrated on creation of patches that imitate shapes of natural fragmentation. Within patches suitable according to size and shape it is necessary to preserve core areas of old growth coniferous forest includ- ing preferred vegetation structure. Moreover, it should be important to con- sider cumulative effects from clear cuts and pastures in the vicinity of lakes and ponds.
24 Incidence Function Model The simulation of the different patch networks resulted in significantly lower number of occupied patches in the patch network reduced by a 100 meter edge-zone. There was no significant difference of occupancy among the other networks. Considering the proportion of occupied patches no signifi- cant difference was found (fig. 3).
Figure 3. Resulting occupancy levels from simulation with IFM. a) Occupancy in number of patches occupied. b) The occupancy level in proportion of the patch- network occupied.
25 The metapopulation capacity analysis shows that the landscape with the patch configuration where area had been reduced by an edge-zone of fifty meters was more sensitive to patch destruction (fig 4). The removal of im- portant patches implies a sharp decrease of capacity.
Figure 4. According to capacity, the patch network reduced by an edge-zone (50 meters) show higher sensitivity to loss of patches a) Capacity of the basic patch network. b) Capacity of the patch network reduced by a 50 meter edge zone. c) Box plot, where basic area network has significantly higher capacity in comparison to the area reduced networks.
This study was an attempt to apply the model to hazel grouse with lack of some knowledge that might be difficult to obtain in any bird or mammal species. It is important that models and methods are possible to apply on species even if it is difficult to obtain data about colonization or extinction events or when time is lacking to collect this knowledge, which often is the case for species under conservation effort. The area measurements used in this study is not claimed to be optimal, rather it is an indication that the measure of area as an estimation of extinc- tion probability should be considered carefully. It should also be mentioned
26 that the effect of perimeter-area relationship probably have a threshold where the area become large enough to sustain a population with no regards to the convolution of its edges. But, then on the other hand, with such large patches in a system the metapopulation approach is probably not appropriate. Rather a source-sink or mainland-island system would be more appropriate.
Landscape Genetics Expected heterozygosity was 0.561 ± 0.066 SD and the observed heterozy- gosity was 0.466 ± 0.007 SD. Using Jack-knife estimators over loci mean FIS was estimated to 0.1632 ±0.0545 SE. The numbers of alleles was 10.5 ± 6.04 SD. The levels of genetic diversity reported in this study suggests that Swed- ish hazel grouse have substantial levels of genetic diversity and do not suffer from loss of genetic diversity compared to other grouse species. There was a significant FIS (deviation from Hardy-Weinberg expectations) in the entire sample. This is most likely explained by a Wahlund effect (Wahlund 1928). The spatial autocorrelation for coefficients of Rousset’s a and kinship was as expected increasing and decreasing, respectively with geographic distance (fig. 5). Neighbourhood size estimated with kinship was 158.27 (117.52- 261.63) and from Rousset’s a neighbourhood size was estimated to 62.85 (39.78-149.64). Effective population size in the study area (195 hectares) of Swenson (1991) was estimated to Ne = 10.72 corresponding to a De = 5.5. 2 Solving NS = 4ʌDeı for the mean axial parent-offspring dispersal distance V on both kinship and Rousset’s a coefficients yielded an estimate of 1514 meters (1304-1946) and 954 meters (759-1472) respectively.
27 Figure 5. a) Spatial autocorrelation correlogram for estimated kinship within ten distance classes and b) correlogram for the Rousset’s a coefficient with ten distance classes. Error bars represent standard error.
Result from the Monte Carlo Markov Chain model, iterated 100 000 times estimated two differentiated populations. With known number of populations the model were re-ran with a fixed number of 2 populations and Voronoi tessellation of observed genetic data resulted in a maps of posterior probabilities of population membership (fig.
28 6). The map is interpreted as one population coming in from the south meet- ing another population in north at approximately: 6950000-1550000. Several migrants from the southern population are found in the northern population. No migrant from north to south seem to be present in the southern popula- tion. The approximate geographic location of the “contact zone” coincides with many other Scandinavian species, which has been formed by an immi- gration pattern according to the ice masses withdrawal during the end of the latest ice-age (e.g. bears: Taberlet et al. 1995, willow warblers Bensch et al. 2002, shrews Andersson 2004).
Figure 6. Wing sample distribution in northern Sweden to the left and the resulting Voronoi-tessellation map showing genetic boundaries between different areas to the right. In the north-west the mountainous area present and to the south-east the coast line of the Baltic Sea is present.
29 No evidence of any landscape features coinciding with genetic discontinui- ties was found. Rather it seems as if hazel grouse can disperse rather freely in the boreal taiga zone of Sweden. Previous studies (Swenson and Daniel- sen 1995, Åberg et al. 2000a, Sahlsten in progress) have indicated that the hazel grouse is a poor disperser reluctant to travel over open land. However, Montadert et al. (2006) did find that radio-tagged hazel grouse could dis- perse over longer distances than previously thought and also over unsuitable habitat. Given that northern Sweden to a large extent is covered by unbroken forests suitable for hazel grouse, it is perhaps not surprising that we do find evidence of substantial dispersal and no sharp genetic boundaries between the populations. The only physical barriers to gene flow existing within the studied area are several moderately sized rivers flowing west to east from the Scandinavian mountains to the Baltic Sea. However, none of these appear to impose any barriers to gene flow in hazel grouse. This is somewhat surpris- ing since previous studies have suggested that rivers in Scotland can prevent gene flow in red grouse (Lagopus lagopus scoticus, Piertney et al. 1998). In summary we found that there is evidence of a population structure reminiscent of what has been found in many other Scandinavian animals with a basic north-south divide. Genetic distance increased with geographic distance between individuals. However, we could not find any evidence that geographic and environmental structures affected gene flow and dispersal patterns for the forest breeding hazel grouse. This may suggest that the bo- real taiga forests of northern Sweden are good habitats for the species. The results from the genetic approach must be considered unique in the respect that extent of the study in terms of area covered and number of hazel grouse individuals sampled has not been made before this study.
30 Sammanfattning på svenska
De senaste årtiondena har mycket av frågorna inom bevarande och förvalt- ningsstrategier för många arter handlat om degradering och fragmentering av naturliga habitat. Man har funnit att fragmentering troligen är en av de främsta orsakerna till att många arter hotas av utrotning. Därför är det viktigt att utveckla metoder och modeller för bevarande- och förvaltningsstrategier som kan ge information och kunskap om dynamik på en relevant skala. Många arter har en uppdelad spatial utbredning, mycket på grund av att de- ras livsviktiga resurser har en sådan fördelning i landskapet. Att kunna över- vaka arter på en stor spatial skala där flera sub-populationer lever är svårt eller tidskrävande om man ska använda traditionella ekologiska metoder. De senaste årtionden har dock GIS (geografiskt informations system) och fjärr- analys gjort att studier av stora eller svårtillgängliga områden har blivit enk- lare. Studie området som använts för att samla in data till den här avhand- lingen är beläget i Uppland. Där har trettiotre utvalda områden inventerats för att bestämma förekomst av järpe. Resultatet av inventeringen användes sedan som indata vid olika GIS och modelleringstillämpningar. Att definiera och lokalisera lämpliga områden är en grundläggande del av de bevarande och förvaltningsstrategier som används. I detta arbete användes ekologisk nisch faktor analys (ENFA) till att hitta lämpliga områden för järpe (Bonasa bonasia). ENFA är en multivariat metod som grundas på Hutchinson’s be- grepp av fundamental nisch. Resultatet av denna analys pekar på att järpen utnyttjar en nisch som skiljer sig ifrån vad som är generellt tillgängligt i stu- dieområdet. Den har också en relativt snäv nisch jämfört med vad som finns tillgängligt. Storleken av en patch anses ofta vara en viktig faktor när man ska avgöra om ett område är lämpligt eller då man vill estimera diversitet eller storleken av en population. Men när ett landskap är fragmenterat så ökar mängden kantzon, vilket är negativt för många arter. Kantzonen påver- kar hur stor tillgänglig area en patch har och en patch med stor omkrets i förhållande till ytan kommer att påverkas i större utsträckning. Omkrets-area kvoten är således ett mått på formen av en yta och den relationen användes till att analysera den relativa betydelsen av olika geometriska mått. Med syftet att undersöka om det fanns korrelation mellan olika landskaps variab- ler och förekomst av järpe användes inventerings data och digitala kartor till att utföra regressionsanalys. Slutsatsen av denna studie är att järpen är knu- ten till barrskog och att de undviker öppna ytor. Resultaten indikerar dock att det finns en effekt av skala, vilket bör beaktas i förvaltningsstrategier. När en
31 art är fördelad som sub-populationer i ett fragmenterat landskap med migra- tion mellan dessa populationer är det möjligt att dynamiken följer en meta- populationsdynamik. I denna studie användes Incidence Function Model (IFM), vilket är en patchockupans modell, till att modellera metapopula- tionsdynamiken i landskapet. Målet var att estimera ockupansnivå och kapa- citeten av ett landskap att stödja en metapopulation. Mängden kantzon ver- kar vara en viktig faktor och formen av patcher borde vara ett bättre mått vid estimering av populationer. Därför simulerades IFM med fyra olika area- scenario. Resultatet av simuleringarna indikerar att omkrets-area relaterade mått inte påverkar proportionen ockuperade patcher. Däremot verkar kapaci- teten att hålla en metapopulation långsiktigt vid liv vara beroende av vilket area mått som används. Omkrets-area måttet i kombination med kapaciteten bör vara ett viktigare mått om dynamiken på landskapsskala undersöks. Landskapsgenetik har uppstått under senare tid genom en kombination av genetik, statistik och landskapsekologiska teorier. Det landskapsgenetiska fältet ger möjligheter till att öka kunskapen om genflöde, populationsstruktu- rer och vilka geografiska karaktärer som påverkar dessa. För att öka kunska- pen om situationen för järpen i den norra regionen av Sverige analyserades 613 prov av geo-refererade individer. Nivåerna av genetisk diversitet tyder på att järpen inte har drabbats av förlust av genetisk diversitet jämfört med andra skogshöns. Det fanns dock en signifikant avvikelse från Hardy- Weinberg jämvikt. Det kan troligen förklaras av en Wahlund effekt. Spatial autokorrelation av distansklasser och släktskapskoefficient samt Rousset’s a pekar också på en genetiskt distinkt struktur. De spridningsavstånd som es- timerats motsvarar till viss del uppskattade spridningsavstånd från tidigare ekologiska studier. Genom att använda en Bayesiansk kluster metod och Monte Carlo Markov Chain assignement kunde populationsstrukturen defi- nieras i geografiska termer. Den geografiska fördelningen av genetiska bar- riärer funna genom dessa metoder tyder på att järpen är uppdelad i en nordlig och en sydlig population. Denna struktur är möjligen ett resultat av invand- ring i samband med att den senaste istiden gick mot sitt slut. Sammanfatt- ningsvis; det finns bevis för en populationsstruktur som påminner om de tidigare funna strukturerna för många andra Skandinaviska arter, med en nord-syd uppdelning. Genetisk distans mellan individer ökade med geogra- fisk distans mellan individer. Det fanns dock inga tecken på att geografiska eller miljömässiga strukturer påverkade genflöde och spridningsmönster för järpen. Detta innebär troligen att den boreala taiga skogen i norra Sverige i allmänhet är bra habitat för arten.
32 Acknowledgements
So, now I am sitting here writing on this thesis and thinking about people nearby and faraway to whom I would like to send my thanks and regards. Fortunately, I have this page to my help. To start with I would like to give my supervisor Jacob Höglund lots and lots of thanks for giving me this op- portunity, and of course also for all the support and discussions along the way. You have been like my indispensable second in this “fight”. Thank you also to Markku Pyykkönen and Eric Rapaport for advices and guidance with my GIS work. I would like to thank Johan Nilsson, Mathilda Åkerlind, Anna-Sara Liman, Martin Kellner, Johan Rodhén, Fredrik Wickström, An- dreas Rudh and Robert Ekblom for assistance in the field. Hanna Thörngren, to you I say thanks for excellent work in the lab and for your patience with all my repeated and stupid questions. I am also grateful to all the people at the department that made my days interesting and funny in many different ways. Many thanks, Martina Schäfer, my room-mate, for good company and talks about GIS, horses, and other stuff. Jobs Karl Larsson, you thought me some French and some other useful things when we were waiting on the flight home in Toulouse. Magdalena, thanks for the GIS conversations, by the way you is also a funny one. Andreas Rudh, you have been very helpful during my early lab-work. But, most of all I would like to thank you for making me look good by the fishing stream. Marianne, you need a special thank for the tremendous work you do at the department, everything from surprise cakes at the coffee breaks to total control over the situation at hand. Now, I would like to address some thanks to my family and friends back home. I pay my regards to my dad, Billy, thank you for showing interest and for giving me inspiration thru your own strong character. Mom, you put up with me when I came home with parts of the nature, which gave me the space to grow interest. To my brothers Dennis, Mathias and Fredrick with families I say thank you for taking care of us when we came down to the other side of this country. From the secret garden in my heart I would like to address a thank you to my deceased dog Amanda I loved you very much, we had many good talks and you gave me a reason to find out what we actually saw on our walks in the “nature”. And, yes Cuanto to you as well old fellow. My wife Jennie, you have helped me in so many ways and if anyone accuse you for starting all this studying stuff and I was the judge; I find you guilty. Thank you! Finally, my loving thanks to Rebecca and André for giving me something else to think about now and then. You are the love of my life and
33 you can make a semi-old man feel young and childish any time. You are wonderful!
Financial support was given by Stiftelsen för zoologisk forskning and from FORMAS (forskningsrådet för miljö, areella näringar och samhällsbyggan- de).
34 References
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38
Acta Universitatis Upsaliensis Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 314
Editor: The Dean of the Faculty of Science and Technology
A doctoral dissertation from the Faculty of Science and Technology, Uppsala University, is usually a summary of a number of papers. A few copies of the complete dissertation are kept at major Swedish research libraries, while the summary alone is distributed internationally through the series Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology. (Prior to January, 2005, the series was published under the title “Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology”.)
ACTA UNIVERSITATIS UPSALIENSIS Distribution: publications.uu.se UPPSALA urn:nbn:se:uu:diva-7911 2007