Department of Physics, Chemistry and Biology
Master Thesis
Spatial pattern of occurrence of eleven epiphytic lichen
species in a heterogeneous landscape
Usman Haider Muhammadi
LiTH-IFM- Ex--11/2441--SE
Supervisor: Per Milberg, Linköpings universitet Examiner: KarinTonderski, Linköpings universitet
Department of Physics, Chemistry and Biology Linköpings universitet
2 Rapporttyp Reportcategory Licentiatavhandling x Examensarbete C-uppsats x D-uppsats Övrig rapport _______________ Språk Language Svenska/Swedish x Engelska/English ________________ Titel Title:
Spatial pattern of occurrence of eleven epiphytic lichen species in a heterogeneous landscape
Författare
Author: Usman Haider Muhammadi
ISBN
LITH-IFM-A-EX--—11/2441—SE
__________________________________________________ ISRN
__________________________________________________
Serietitel och serienummer ISSN Title of series, numbering
Handledare
Supervisor:Per Milberg Ort
Location: Linköping
Nyckelord Keyword:
Key words: old oaks, spatial scales, spatial pattern, red-listed lichens, oak density, species
Datum
Date
2011-06-03
URL för elektronisk version
Sammanfattning Abstract:
Oaks (Quercus robur) are an important substrate for many epiphytic lichens, and with increasing age the bark of oaks becomes suitable for red-listed species. These species may respond to environmental and landscape factors differently, and at different spatial scales. We tested the effect of tree, environmental and land use factors on the occurrence and richness patterns of lichens species at various spatial scales (circles with radius ranging from 28 to 1225 m), in a heterogeneous landscape in South Eastern Sweden. The occurrence patterns of Cliostomum
corrugatum and Chaenotheca phaeocephala were best explained by the density of oaks within
radii of 400 and 302 m, respectively. In contrast, Ramalina baltica was best explained at smaller scale (263 m) as was species richness (302 m). This study shows that the most important factor for the occurrence and richness patterns of lichens was oak density at almost all the considered scales. Tree circumference also positively affected all four response variables.
Avdelning, Institution
Division, Department
Avdelningen för biologi
Content
1 Abstract………. 4
2 Introduction……… 4
3 Material and Methods………. 5
3.1 Study area and landscape data……….. 5
3.2 Field work and environmental data………... 7
3.3 Study species……… 7
3.4 Data analysis………. 8
4 Results……… 9
4.1 Oak density……… 9
4.2 Land use variables……… 9
4.3 Tree variables……… 10
5 Discussion……….. 11
5.1 Effect of oak density………. 11
5.2 Effect of Land use………. 11
5.3 Effect of tree variables……….. 12
6 Conclusion……….. 12
7 Acknowledgements………. 13
8 References………... 13
1. Abstract
Oaks (Quercus robur) are an important substrate for many epiphytic lichens, and with increasing age the bark of oaks becomes suitable for red-listed species. These species may respond to environmental and landscape factors differently, and at different spatial scales. We tested the effect of tree, environmental and land use factors on the occurrence and richness patterns of lichens species at various spatial scales (circles with radius ranging from 28 to 1225 m), in a heterogeneous landscape in South Eastern Sweden. The occurrence patterns of Cliostomum corrugatum and Chaenotheca phaeocephala were best explained by the density of oaks within radii of 400 and 302 m, respectively. In contrast, Ramalina baltica was best explained at smaller scale (263 m) as was species richness (302 m). This study shows that the most important factor for the occurrence and richness patterns of lichens was oak density at almost all the considered scales. Tree circumference also positively affected all four response variables.
Key words: old oaks, spatial scales, spatial pattern, red-listed lichens, oak density, species occurrence, richness
2. Introduction
Old growth trees are of particular interest for conservation, since they are an important substrate for a number of organisms from several taxonomic groups, e.g. epiphytic lichens and bryophytes and many insects (Ek and Johannesson 2005, Lättman et al. 2009). Oak (Quercus robur) is host to a diverse lichens flora (Johansson et al. 2009) and with increasing age the bark of oaks becomes suitable for rare and threatened species (Paltto et al. 2010). In general, habitat fragmentation and increasing isolation of habitat patches may lead to a decline in species richness (Steffan-Dewenter et al. 2002, Löbel et al. 2006, Ranius et al. 2008). The decline of old oaks in the landscape may be the main reason that many epiphytic lichens only exist in remnant populations (Johansson et al. 2009, Scheidegger and Werth 2009). A number of epiphytic lichens associated with old oaks are now being threatened and red listed (Ranius et al. 2008, Paltto et al. 2010).
The major drivers of lichen species distribution at the landscape scale are
factors such as stand age, density of trees and landscape composition (Juriado et al. 2003, Bolliger et al. 2007). The long-term survival of species depends on the spatial configuration of suitable substrates and the distance the species are likely to move (Ranius et al. 2008, Snäll et al. 2004). Looking at the species
occupancy patterns in small and large scale (Rahbek 2005, Johansson et al. 2010), it may be possible to analyse the dispersal ability and population
dynamics of species (Lättman et al. 2009, Löbel et al. 2006, Marc et al. 2004, Sillet et al. 2000). Oak lichen species seem to be sensitive to habitat
fragmentation due to poor dispersal ability (Svoboda et al. 2010, Hedenås and Ericson 2000). In addition to the dispersal ability of epiphytic lichens, other factors such as substrate dynamics affect their establishment (Johansson 2008). For instance, a large circumference with rough bark is considered to have a strong effect on lichens occupancy (Juriado et al. 2003). Moreover, for the succession of lichens, sun exposure and habitat availability can also be considered as influential factors (Juriado et al. 2003, Johansson et al. 2009).
In addition, the occurrence of lichen species dependent on old oaks increased with increasing density of oaks in a study in southeastern Sweden (Paltto et al. 2010). Red listed lichens were strongly affected by the density of oaks in the surrounding landscape at a radius of 2 km. Species respond to environmental factors differently at different spatial scales, yet many studies have considered several factors to identify the spatial patterns of species (Holland et al. 2004, Snäll et al. 2003, Paltto et al. 2006). For example, the occurrences patterns of 13 long-horned beetles associated with deadwood were affected by the forest cover at different spatial scales (with radii 20 to 2000m) (Holland et al. 2004). For oak-dependent lichens, Ranius et al. (2008) recorded that the distribution patterns of lichens were best explained with connectivity in the surrounding landscape at a radius of 64 km. The probability of occurrence may also be affected by the land use types in the vicinity of a tree, e.g. the amount of agricultural, urban or forested landscape (Styers et al. 2010, Svoboda et al. 2010). Consequently, multiple factors may affect the spatial patterns of lichen species occurrence at different scales (Pinho et al. 2008).
The aim of this study was to investigate the spatial distribution of eleven epiphytic lichens species preferring large old-oaks in a heterogeneous
landscape, and to identify the spatial scales, in the range of 30 to 1200 m, at which different factors affect species occurrence. The study was conducted in one of the few remaining landscapes in Northern Europe with a high density of old oaks: the province of Östergötland, southeastern Sweden (Antonsson and Wadstein 1991). This area, with its mix of semi-natural grasslands, arable fields, forests, lakes and urban areas, is known as an important area for oak-dependent organisms including lichen species (Ek and Johannesson 2005, Johansson 2008, Johansson et al. 2009).
3. Material and methods
3.1. Study area and landscape data
The study area is located in the province of Östergötland, southeastern Sweden. Östergötland is considered as an important oak area in northern Europe. Today,
this area has one of the largest abundances of large oaks, many nature reserves with high biodiversity and is considered nationally and internationally important for the conservation of biodiversity associated with oak environments (Paltto et al. 2010).
The study area, heterogeneous in terms of different landscape types, consisted mainly of forest 37%, arable land 35%, housing 6.4% and water 2.4%. All the large oaks (>3.1 m in circumference) had previously been mapped by the local environmental authorities (data previously used in Paltto et al. 2010, Bergman et al. submitted). In the present study, these data were used to select oaks, between 3.1 m and 4.1 m in circumference, as evenly distributed as possible. The study area, a 20 km * 20 km square, was divided into 400 grid squares, each grid square was 1 km2 (Figure 1). We aimed to select one oak per grid square and, if given a choice, the one closest to the middle of the grid square were selected. Not all grid squares contained trees of suitable size. In total, 213 oaks were selected for field work. During the field work, it was not always possible to locate the targeted tree, based on coordinated and data on circumference, in which case another tree of appropriate size was chosen.
Fig. 1. Spatial distribution of oaks at the study site located in Östergötland, Southeastern, Sweden. Black is target trees (N=213), searched for lichens, while open circles are other large oaks (N approx 3,500) previously recorded in a survey.
For each target tree, the surrounding oak density was calculated, using the above mentioned oak survey data, at twenty-eight different spatial scales (radii
of 28-1225 m). The proportion of different land uses, including arable fields, forest, housing and water was also collected within the same radii, for each target tree. The package ArcGIS9 was used to analyse the data and also for a map of the study area.
3.2. Field work and environmental data
Field work was conducted between July and October in 2010 when the 213 selected oaks were searched for eleven lichen species that are known to be strongly associated with large oaks. For each tree, the entire trunk up to 150 cm above the ground level was carefully searched for lichen species occurrence (presence and absence)using a convex lens.
In addition, three variables describing the tree and it’s near surrounding were recorded: circumference at breast height (using a measuring tape); sun exposure and density of trees of shrubs near the oak. Sun exposure was recorded by estimating the proportion of light availability through the canopy. This was done by estimating the visible portion of the sky that lighted up the bark. The area considered was first divided into North-South as midpoint, and the
proportion of clear sky calculated through visual assessment (Johansson et al. 2009). The nearby density of other trees and shrubs was estimated around each tree at 5 m distance from the margin of the canopy of each target tree.
Table 1.Following variables explaining the occurrence and richness patterns of lichen species on oaks
Tree variables Mean Median SD Minimum Maximum Circumference
(meter)
352 348 28.77 310 410
Sun exposure 47% 44% 0.209 5% 90%
Density of trees & shrubs near oak
62% 65% 0.192 0% 94%
3.3. Study species
The selected lichen species differ in their frequency and abundance (Table 2). For most of these, it has been confirmed that they are very rare outside ancient oaks (Ranius et al. 2008). Six of these species are near threatened species according to IUCN and three species are vulnerable to extinction (Table 2; Gärderfors 2010). These species have been used as indicator of ecological
continuity of old oaks (Johansson et al. 2010). One lichen, however, Crysothrix candelaris, is relatively common, and widely distributed also on younger oaks. All these lichen species most likely live on rough bark, and are negatively affected by shaded bark (Paltto et al. 2010).
Table 2. Details of the studied lichen species status and occurrence on 213 investigated old oaks in Östergötland 2011. Status according to (Gärderfors 2010).
Species Status No. of occurrences
Chrysothrix candelaris Common 210
Chaenotheca phaeocephala Near threatened 142
Cliostomum corrugatum Near threatened 33
Ramalina baltica Near threatened 37
Calicium adspersum Vulnerable 16
Calicium quercinum Vulnerable 2
Schismatomma pericleum Near threatened 1
Schismatomma decolorans Near threatened 0
Caloplaca lucifuga Near threatened 0
Lecanographa amylacea Vulnerable 12
Sclerophora coniophaea Vulnerable 3
3.4. Data analysis
Effects of the oak density variable, tree variables and land use variables of each species were analyzed with a binomial generalized linear model (GLM) with logit link function (equivalent to logistic regression analysis). Furthermore, we used GLM with logarithmic link function and poisson distribution of errors (log-linear regression analysis) to analyze the effect of the same variables on lichen species richness of oaks (cf. Paltto et al. 2010). Species richness was defined as the number of the eleven lichen species per oak. Individual GLMs were conducted for three lichens species considered to be appropriately frequent for the analysis at stake (C. phaeocephala, C. corrugatum, R. baltica). One lichen (C.candelaris) was excluded because it was too frequent, the others for being too rare (Table 2).
We used multiple regression models to assess the relative importance of the three variables describing the tree (circumference, sun exposure and density of trees and shrubs near oak), one landscape variable (oak density) and four land use types (consisted of forest, water, housing and arable fields). We ran separate
series of analyses for each of the most frequent lichens, as well as for species richness, and did so for each of 28 different spatial scales.
We chose to present the results in two ways. First, we plotted the partial
regression coefficients for the full models for each radius. Second, we used the Akaike information criterion (AIC) to select the best model for each radius, then compiling the best models in tables that indicated positive or negative effect of the selected variables.
4. Results
Lichen species richness varied between 0 and 7 species per tree (mean=2.14; median= 2). The oak density varied greatly, especially at the large scales,
reflecting the aggregation of oaks in part of the study area seen in Figure 1. The strongest model (highlighted in bold) are considered as the “characteristic scales” explaining the effect of landscape and tree variables (Table 3-6). 4.1. Oak density
The occurrence patterns of three lichen species and species richness on oaks was predicted by oak density at different spatial scales. The occurrence pattern of C.corrugatum was best explained by the oak density at a scale of 400 m (Table 3) and C. phaeocephala at slightly smaller scale (302 m; Table 4). R. baltica was best predicted by the oak density at 263 m (Table 5). The richness pattern of lichens was best explained by the oak density at 302 m (Table 6). Comparing the full models, it is apparent that oak density was the most
important of the variables compared, and that it exerts a strong influence over all the scales considered here (Fig. 2).
4.2. Landuse variables
The occurrences of two of the three lichens analysed were correlated with the land use variables, but in different ways. For C. Corrugatum, water had a positive effect while housing had a negative effect on the occurrence patterns (Table 3). In contrast, R. baltica was positively affected by the proportion of arable fields (Table 5).
Figure 2. Occurrence and species richness of lichen species on 213 oaks as affected by oak density and land use (water, forest, housing and arable field) and tree variables (circumference, sun exposure and trees and shrubs near the oak) at increasing spatial scales (radii of 28 -1225 m around target oaks). Y-axis shows partial regression coefficients.
4.3. Tree variables
The species richness of lichens on oaks, and the occurrence patterns of C. corrugatum and C. phaeocephala and R. baltica were all positively affected by tree circumference (Table 3-6).Sun exposure positively affected the occurrence pattern only for C. phaeocephala and R. baltica (Table 4 and 5). Only one species, C. phaeocephala, was negatively affected by the density of trees and
shrubs near the oak (Table 4), as was also the species richness (Table 6). As the tree variables were identical over all scales, their explanatory power varied only marginally with radius (Figure 2).
5. Discussion
There are several factors affecting the occurrence of lichens (Pinho et al 2008), but their relative contribution is difficult to assess. We investigated the
occurrence and richness patterns of lichens in a heterogeneous landscape, and therefore take land use (forest, arable, housing and water), as well as tree
variables (circumference, sun exposure, trees and shrubs near oak) into account parallel with oak density the variable of main interest. We found that the
variables apparently had different effects on species richness and the occurrence of the three lichens species analyzed. The outcome of the full models, involving land use, tree factors and oak density, show the relative importance of the
factors and spatial dependence of these factors (Figure 2). 5.1. Effect of oak density
Increasing oak density in the surrounding of an oak increased the number of lichen species recorded. As expected for both species occurrence and species richness, every lichen species was positively related to the density of oaks. The study of Ranius et al. (2008) also showed that both occurrence and richness of lichens were positively related to the density of oaks within the parish area in a larger landscape (median size of 51.9 km2).Moreover, the occurrence of lichen species increased with an increasing amount of a habitat, i.e. oaks, in both small and large landscapes (Paltto et al. 2010, Ranius et al. 2008).
Paltto et al. (2010) evaluated at what spatial scales, of eight ranging from 500 m to 7 km, the occurrence of lichen species was best predicted by the density of oaks, using some different oak size cutoffs. They found that the occurrence of C. corrugatum was best predicted by oak density within the small landscape size (<500 m), while corresponding scales for C. phaeocephala and R. baltica were 2 km and 5 km, respectively. However, according to our finding, only C. corrugatum was best explained at relatively same scale (400m), but C.
phaeocephala and R. baltica were best explained at smaller scale 302m and 263m respectively.
5.2. Effect of land use
In the effect of land use factors, we found that the amount of water positively affected the occurrence of C. corrugatum, but cannot think of a biological mechanism. Instead, we believe that this relationship is a consequence of the prevalence of this species in the south-eastern corner of the study area, which is also where most lakes and wetlands are present. C. corrugatum was negatively
affected by housing (Table 3). One possible reason for the negative effect is that lichen species are recognized as being very sensitive to air pollution and
therefore should not flourish in urban and sub urban areas (Gombert et al. 2004, Svoboda et al. 2010). In addition, air pollution and emission of chemicals (NO2
and NH3) from roads traffic may affect the metabolism of the species through
changes the substratum properties (Dymytrova 2009, Frati et al. 2009, Marmor 2007). Other environmental stress, for example pest and disease might be more susceptible to species occurrence, which is why urban areas may have less occupancy of lichen than forest-dominated areas (Styers 2010).
For the occurrence of R.baltica, only the amount of arable field was found to have a (positive) effect. A previous study has found that the mean species richness of lichens was higher in arable land than in oak forest stands
(Nascimbene and Marini 2010), and dust from roads or arable land can also be beneficial.
5.3. Effect of tree variables
Our results showed that both the probability of occurrence of C. corrugatum,
C.phaeocephala, R. baltica and the species richness were positively affected by
the oak circumference. Although this was expected, as the species were selected for being confined to large oaks, we found the strength of the patterns surprising as the circumference in the target trees ranged only from 3.1-4.1 m. Despite this, circumference was the second most important factor that affected
positively on richness and the occurrence of three individual lichens. Compared to a previous study, the probability of lichen species occurrence increased with increasing tree size (Johansson et al. 2009), but that study included all oaks, not just the large ones. Several studies have found the close positive relationship between tree age and lichens diversity (Svoboda et al. 2010). According to Jurado et al. (2009) tree and stand variables affect the diversity of species, however tree stand variables had more pronounced effect than variables describing the individual trees. Similarly, for the factor describing tree and shrubs near the oaks, we found a negative effect on the occurrence of C. phaeocephala and on the total lichens richness of each oak (Table 5).The majority of lichens seems to prefer sun exposed bark rather than shaded bark (Paltto et al. 2010, Johansson et al. 2009) which is confirmed also in our study, but only for two species (C. phaeocephala and R. baltica) and with a relatively weak pattern.
6. Conclusion
This study focused on how the occurrence and richness of red-listed lichens is affected by different land use and tree factors. It may be difficult to determine the effect of any particular factor on lichens species. According to our results, oak density was the single most important factor for the studied three species
and the total lichens richness. So, the density of old oak trees within the surroundings of an old oak seems to be important for many red-listed species. This work also highlighted the importance of the spatial scale for understanding the occurrence of epiphytic lichens. In conservation, it is important to make predictions of species patterns on occurrence and richness and to do this at an appropriate spatial scale. The basic concept of this project can also be used on other groups of organisms including insects, fungi, bryophytes etc.
7. Acknowledgements
I would like many thanks to my supervisor Professor Per Milberg and Co- supervisor Dr Karl-Olof Bergman for all their untiring effort, support and guidance. Thanks to Lars Westerberg for his kind help in measuring the land use data.
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Table: 3 Oak density, tree and land use variables explaining the
Positive/negative effect on the occurrence patterns of C. corrugatum within spatial scales (28-1225 m).
Radius (m)
Oak
density
Tree variables Land use
AIC-value P Sun Exposure Density of trees & shrubs near oak Circum-ference
Forest Water Housing Arable Field 28 + + 179.84 0.00730 32 + + 179.46 0.00604 37 + + 178.22 0.00326 42 + + 178.85 0.00446 49 + + 177.24 0.00200 56 + + 173.59 0.00032 65 + + 173.27 0.00027 74 + + 172.55 0.00019 86 + + 172.22 0.00016 99 + + 173.71 0.00034 113 + + 174.31 0.00046 130 + + 174.19 0.00043 150 + + + - 173.85 0.00054 173 + + + - 172.11 0.00024 199 + + + - 169.18 0.00006 229 + + + - 164.78 0.00001 263 + + + - 162.76 <0.001 302 + + + - 162.47 <0.001 348 + + + - 161.34 <0.001 400 + + + - 160.58 <0.001 460 + + - 161.93 <0.001 529 + + + - 163.98 0.00001 609 + + - 164.24 <0.001 700 + + - 164.88 0.00001 805 + + - 165.88 0.00001 926 + + - 167.05 0.00002 1065 + + - 167.93 0.00003 1225 + + - 168.62 0.00004
Table: 4 Oak density, tree and land use variables explaining the
positive/negative effect on the occurrence patterns of C. phaeocephala within spatial scales (28-1225m)
Radius (m)
Oak density
Tree variables Land use
AIC-value P Sun Exposure Density of trees & shrubs near oak Circum-ference
Forest Water Housing Arable Field 28 + + 264.44 0.00208 32 + + 263.55 0.00137 37 + + 263.46 0.00131 42 + + 263.62 0.00141 49 + + 263.89 0.00160 56 + + - + 263.90 0.00172 65 + + + 262.62 0.00097 74 + + + 261.85 0.00068 86 + + + 262.05 0.00067 99 + + + 260.58 0.00038 113 + + + 259.22 0.00021 130 + + + 256.83 0.00007 150 + + + 254.96 0.00002 173 + + + 253.85 0.00001 199 + + + 252.23 0.00001 229 + + + 253.42 0.00001 263 + + + 253.44 0.00001 302 + + - + 252.12 0.00001 348 + + - + 253.05 0.00001 400 + + + 253.48 0.00001 460 + + + 252.92 0.00001 529 + + + 254.01 0.00002 609 + + + 253.70 0.00002 700 + + - + 252.46 0.00001 805 + + + 254.93 0.00003 926 + + + 258.53 0.00015 1065 + + - + 258.91 0.00020 1225 + + - + 258.45 0.00016
Table: 5 Oak density, tree and land use variables explaining the
positive/negative effect on the occurrence patterns of R. baltica within spatial scales (28-1225m)
Radius
(m) Oak density Tree variables Land use AIC-value P Sun Exposure Density of trees & shrubs near oak Circum-ference
Forest Water Housing Arable Field 28 + + 190.537 0.00229 32 + + + 189.467 0.00163 37 + + + 190.112 0.00221 42 + + + 188.264 0.00093 49 + + + 189.583 0.00173 56 + + + 186.123 0.00034 65 + + + 186.001 0.00032 74 + + + + 186.602 0.00050 86 + + + + 185.171 0.00027 99 + + + + 184.586 0.00021 113 + + + + 183.771 0.00014 130 + + + + 183.500 0.00013 150 + + + + 183.489 0.00013 173 + + + + 183.341 0.00012 199 + + + + 182.491 0.00008 229 + + + + 182.393 0.00007 263 + + + + 181.509 0.00005 302 + + + + 181.588 0.00005 348 + + + + 182.781 0.00008 400 + + + + 182.751 0.00008 460 + + + + 183.445 0.00011 529 + + + + 185.531 0.00029 609 + + + + 186.426 0.00044 700 + + + + 186.749 0.00051 805 + + + + 187.048 0.00059 926 + + + 186.236 0.00041 1065 + + + 187.186 0.00062 1225 + + + 187.664 0.00070
Table: 6 Oak density, tree and land use variables explaining the
positive/negative effect on the occurrence patterns of species richness within spatial scales (28-1225m)
Radius (m)
Oakdensity Treevariables Land use
AIC-value P Sun Exposure Density of trees & shrubs near oak
Circumference Forest Water Housing ArableField
28 + 659.99 0.07352 32 + + 658.36 0.03129 37 + + 658.95 0.04100 42 + + 658.67 0.03606 49 + + 658.86 0.03936 56 + + 654.61 0.00558 65 + + 653.88 0.00398 74 + + 653.60 0.00349 86 + + 652.09 0.00172 99 + + 652.14 0.00175 113 + + 651.47 0.00128 130 + + 650.37 0.00076 150 + + 649.35 0.00047 173 + + 648.66 0.00034 199 + + 647.61 0.00020 229 + + 647.95 0.00024 263 + + 647.12 0.00016 302 + - + 646.15 0.00010 348 + - + 646.56 0.00012 400 + - + 646.64 0.00013 460 + + 647.49 0.00019 529 + + 649.26 0.00045 609 + + 649.01 0.00040 700 + - + 649.33 0.00053 805 + - + 650.52 0.00090 926 + - + 651.74 0.00157 1065 + - + 652.02 0.00178 1225 + - + 652.06 0.00181
