Genetic structure of a racoon population in Müritz National Park – a result .
Beiträge zur Jagd- und Wildforschung, Bd. 36 (2011) 531–537 NIKO BALKENHOL, Berlin; BERIT A. KÖHNEMANN, Tharandt; SUSANNE GRAMLICH, Landau;FRANK-UWE MICHLER, Tharandt Genetic structure of a raccoon population (Procyon lotor) in Müritz
National Park – a result of landscape resistance or space-use behaviour?

Schlagworte/key words: Waschbär, raccoon, Procyon lotor, landscape genetics, microsatellites, landscape resistance, Brownian bridge, utilization distribution overlap index, Mantel test Introduction
Despite the current popularity of landscape genetics, genetic population structure can ac- Understanding the mechanisms shaping the ge- tually be infl uenced by a multitude of factors netic population structure is important for ad- other than landscape heterogeneity, for exam- dressing many questions in evolution, ecology, ple space-use behaviour or reproductive strate- and practical wildlife management (e.g., EPPS gies. While some of these infl uences might be et al. 2007, SEGELBACHER et al. 2010). Current landscape-dependent, others are intrinsic (i.e., studies often focus on testing for landscape- landscape-independent) and highly species- genetic relationships, because the heterogene- specifi c. Thus, in order to fully understand ge- ity of the environment can infl uence the oc- netic structures arising in natural wildlife popu- currence, abundance, dispersal, and thus, gene lations, studies should not only focus on testing fl ow, in plants and animals (MANEL et al. 2003, for landscape-genetic relationships, but also consider alternative causes behind observed ge- Among the most popular approaches for such studies is the statistical comparison of genetic Here, we compare the effects of landscape re- and ‘effective’ landscape distances. While the sistance versus socially-induced space-use be- genetic distances measure how close individual haviour on genetic structures in a population of animals are genetically, the effective distances estimate how close they are in the landscape, First, we test whether genetic structure in the while accounting for potential movement bar- raccoon population has been impacted by land- riers or varying landscape resistances to gene scape resistances, which we estimated from fl ow. If a signifi cant association is found be- movement paths gathered from telemetry data. tween these two distances, it is concluded that Second, we test whether genetic structure is as- landscape resistance, as estimated from the ef- sociated with territorial behaviour of raccoons, fective distances, infl uences gene fl ow and re- which we measured through an index of home Beiträge zur Jagd- und Wildforschung, Bd. 36 (2011) Material & Methods
should lead to wide Brownian bridges with low intensity-of-use values. Brownian bridge calcu- The study was conducted in the Müritz national lations were conducted in the R statistical envi- park in northwest Germany, and was part of an ronment using the adehabitat package (CALENGE intensive effort to study the life-history of rac- coons in this region (www.projekt-waschbaer.
To calculate effective distances among individ- de). Raccoons were captured and fi tted with te- ual raccoons, we had to defi ne landscape resist- lemetry collars within the ‘Serrahn’ part of the ance for the entire sampling region, but Brown- park. Detailed information on the study area, ian bridges were only calculated along the capturing and handling of racoons can be found movement paths of individual animals. Thus, in KÖHNEMANN & MICHLER (2009). From each we constructed a combined Brownian bridge captured raccoon, we sampled either a small tis- layer by adding up intensity-of-use values from sue sample, or hair for genetic analyses. Sam- all individuals. We then used the resulting layer ples were genotyped at ten microsatellite loci. to estimate the contribution of different land- The genetic analyses are described in detail by scape variables to movement resistance. For GRAMLICH et al. (this issue). To estimate genetic this, we used GIS data available for the national population structure, we calculated a genetic park at a 1:10,000 scale. These data include dif- distance among all individuals. Specifi cally, ferent habitat classes that we here grouped into we chose the kinship coeffi cient of RITLAND four general habitat types of potential relevance (1996), where higher values indicate higher ge- for raccoons. We distinguished forested habitat, netic relatedness. The kinship coeffi cient was riparian areas, agricultural fi elds and human calculated in software SPAGeDi 1.3 (HARDY & settlements, and created a GIS-layer for each variable that quantifi ed the distance of each 100 meter cell in the landscape to the closest edge of each of these four habitat types. This distance- Effects of landscape resistance
to-nearest-habitat approach is often used in hab-itat selection studies, and accounts for possible on the genetic population structure
inaccuracies in the GIS-data (CONNER & PLOW- To test the hypothesis that landscape heteroge- MAN 2001). We next constructed different mod- neity infl uences genetic structures, we calculat- els to explain the Brownian bridge layer as a ed effective distances among all raccoons. For function of the four explanatory landscape vari- this, we had to model the resistance of the land- ables. Since the Brownian bridge layer is spa- scape for raccoon movement and gene fl ow. tially autocorrelated, we could not use standard Modelling such resistance layers is often done regression for this step. Rather, we used spatial based on expert-opinion or ‘best guesses’. Al- autoregressive models, which are an analogue ternatively, independent (i.e., non-genetic) data of linear regression, but account for the spatial can be used to defi ne landscape resistances em- autocorrelation of the dependent data. We used pirically. Here, we chose the latter approach and an information-theoretic approach to compare quantifi ed landscape resistances based on the all possible models that can be constructed with telemetry data gathered for individual racoons. four independent variables (N = 14). Spatial au- Specifi cally, we quantifi ed individual move- toregressive models were calculated in software ments using so-called Brownian bridges, which SAM (RANGEL et al. 2010) and the best model account for the movement speed of individuals was chosen based on lowest AIC values cor- (HORNE et al. 2007). This approach has previ- rected for small sample sizes (AICc, BURNHAM ously been used to defi ne movement corridors & ANDERSON 2002). Parameters of this model for mule deer (Odocoileus hemionus) in Wis- were then used to estimate a resistance layer for consin, USA (SAWYER et al. 2009). In low re- the entire study region. This layer refl ects the sistance landscapes, movements should be rela- resistance of the landscape to raccoon move- tively fast and linear, so that Brownian bridges ments as estimated from the telemetry data. become narrow and have high intensity-of-use Finally, effective distances among individual values. In contrast, high resistance landscapes racoon home range centres were calculated Genetic structure of a racoon population in Müritz National Park – a result .
from this GIS-layer using software Circuitscape between individuals that are more closely re- (MCRAE & BEIER 2007. This software estimates lated). If both tests yield insignifi cant results, the effective resistance among sampling loca- this would support the null hypothesis of no tions based on all possible pathways between landscape or social infl uences. Mantel statistics were calculated in the R package ecodist (GOS-LEE & URBAN 2007) using 9,999 permutations to assess signifi cance.
Effects of space use behaviour
on the genetic population structure

To test the hypothesis that space-use behaviour infl uences genetic structure of raccoons, we In total, 141 individuals were successfully quantifi ed territoriality by calculating the over- genotyped and available for genetic data analy- lap of home ranges among individual racoons. ses. See GRAMLICH et al. (this issue) for basic Specifi cally, we estimated the utilization distri- population genetic summary statistics. Telem- bution overlap index (UDOI) for 95 % kernel etry data could be gathered for a subset of 69 home ranges using the adehabitat package in individual raccoons (32 females and 37 males). R. UDOIs are an alternative to the overlap sta- The best model explaining movement patterns tistics used by MUSCHIK et al. (this issue), and of Brownian bridges involved distance to for- the index has been recommended by FIEBERG est habitat (dFor) and distance to agricultural & KOCHANNY (2005), because it accounts for areas (dAgr) and accounted for approximately varying intensities of use within shared home 36.5 % of the variation (Table 1). While the full range areas. The result of this analysis is a pair- model involving all four landscape variables wise data matrix that shows the intensity of explained a slightly higher amount of the varia- space-sharing among all individual raccoons. A tion (R² = 0.366), it was not the most parsimoni- UDOI-value of zero indicates that two raccoons ous model with an AICc value of 6.95 (Table 1). have no home range overlap, while increasing All other models had even higher AICc values values indicate that two individuals share larger and explained less variation (data not shown).
parts of their home ranges with higher intensity.
Parameters for the best model estimated land-scape resistance as 0.309 * dFor – 0.127 * dAgr. Thus, landscape resistance decreased Statistical data analysis
with decreasing distance from forests, but in-creased with decreasing distance to agricultural To statistically evaluate the two different hy- fi elds. Effective distances calculated from this potheses, we needed to account for the fact that model did not signifi cantly correlate with the kinship coeffi cients, effective distances and UDOIs are pair-wise data. Thus, we analyzed the data using the Mantel statistic, a widely-used method to assess the signifi cance of cor- Table 1 Coeffi cients of determination (R²) and delta relations between pair-wise data matrices using AICc values for spatial autoregressive models explai-ning raccoon movement paths as a function of habitat permutations (MANTEL 1969). If landscape re- variables. dFor = distance to forest habitat, dAgr = sistance as modelled from the Brownian bridg- distance to agricultural fi elds, dRip = distance to ripa- es has impacted genetic population structure, rian habitat, dSet = distance to human settlement. Only we would expect to see a signifi cant negative the four best models are shown, as all other models had correlation between effective landscape dis- tances and kinship coeffi cients (smaller effec-tive landscape distances should be associated delta AICc
with higher kinship values). Similarly, if social space-use behaviour has infl uenced genetic structure, we should see a signifi cant positive correlation between UDOI values and kinship coeffi cients (increased space-use should occur Beiträge zur Jagd- und Wildforschung, Bd. 36 (2011) genetic distances (p > 0.05; Table 2). In con- dilute effects of some landscape characteristics trast, genetic distances were signifi cantly and on raccoon movement paths. Furthermore, as positively correlated with the home range over- noted by HERMES et al. (this issue), the available lap index UDOI for all data and for females landscape data is relatively coarse-scaled, and (Table 2). However, Mantel tests were only mar- is not suitable to analyze habitat selection at the ginally signifi cant when analyzing only males micro-scale. It is possible that movement paths of raccoons are strongly infl uenced by habitat characteristics at the micro-scale, so that accu- Table 2 Results of Mantel statistics for correlations rately estimating landscape resistance with the between kinship coeffi cients and A) effective landscape distances and B) utilization distribution overlap index. available landscape data is challenging.
P-values are based on 9,999 permutations. This could also be a reason why the varying resistance of the heterogeneous landscape to Data used
raccoon movements did not have a signifi cant effect on the genetic structure of the population. There was no signifi cant correlation between effective landscape distances and the kinship coeffi cients. This suggests that the landscape resistance calculated from the movement data does not refl ect the resistance of the landscape Data used
for effective gene fl ow. At this small scale, genetic exchange among individuals is likely not much affected by the landscape, but rather by space-use behaviour associated with mate choice. This conclusion is further supported by the signifi cant correlations between kinship co-effi cients and home range overlap. According to Discussion
our results, animals share greater parts of their home ranges (i.e., are less territorial) if they Our results suggest that the resistance of the are genetically more closely related. Such pat- landscape to raccoon movements depends on terns have already been observed in other spe- the spatial distribution of forested and agricul- cies, including black bears (Ursus americanus; tural areas. Landscape resistance for raccoons MOYER et al. 2006) and swift foxes (Vulpes decreased within or close to forest habitat, velox; KITCHEN et al. 2005). Interestingly, we while it increased with higher proximity to ag- observed signifi cant socio-genetic relationships ricultural fi elds. These results can partially be for the entire population and females, but only explained by general habitat preferences of rac- marginally signifi cant for males. This suggests coons in the study area. For the studied raccoon that the overall structure of the raccoon popula- population, HERMES et al. (this issue) showed a tion is determined by the spatial distribution of slight avoidance of open areas, including agri- matrilineages. As shown by MUSCHIK et al. (this cultural fi elds. Thus, raccoons traverse through issue), juvenile adults stay in close proximity to open areas less frequently, even though such ar- their mothers home range, and while all male eas do not impose a physical movement barrier. offspring eventually disperses away from the Raccoons also showed a slight avoidance of maternal home range, female offspring often forest habitat, and a clear preference for ripari- stays in relatively close proximity. Thus, related an areas. However, these habitat preferences do females are distributed close in space, leading not seem to infl uence movement paths estimat- to the signifi cant correlations between kinship ed through the Brownian bridges. It is possible and home range overlap. In contrast, GRAMLICH that some of the telemetry relocations where too et al. (this issue) showed that male coalitions far apart in time to accurately estimate intensi- are not composed of genetically close kin, so ty-of-use values for all movement paths. This that no such correlations were observed for would lead to ‘fl at’ Brownian bridges and could males. Overall, these socially-induced space- Genetic structure of a racoon population in Müritz National Park – a result .
use patterns of male and female raccoons af- tieren. Viele derzeitige Studien analysieren aus- fect the spatial-genetic structure of the studied schließlich landschafts-genetische Beziehun- gen, obwohl genetische Populationsstrukturen von einer Vielzahl anderer Faktoren beeinfl usst werden können. Study limitations & conclusions
In der vorliegenden Studie wurde getestet, ob genetische Strukturen innerhalb einer Wasch- It is important to note that we have used only a bärenpopulation von Landschaftsstrukturen, single model of landscape resistance to estimate oder vom räumlichen Sozialverhalten der Tiere effective distances among sampled raccoons, beeinfl usst werden. Hierfür wurden 69 Wasch- because more detailed landscape data was not bären (32 Fähen, 37 Rüden) mit Telemetrie- available for the study area. Other studies have Halsbänder ausgestattet. Zusätzlich wurden compared a much higher number of resistance 141 Waschbären anhand von 10 Mikrosatel- models, which differed in the way landscape re- liten genotypisiert, und genetische Distanzen sistance values were derived, and also used dif- zwischen allen Individuen wurden berechnet. ferent ways for estimating effective distances Besenderungen und genetische Analysen waren from these models (CUSHMAN et al. 2006, SHIRK Teil einer großangelegten Studie zur Lebens- weise von Waschbären im Serrahner Teilge- Thus, it is possible that we simply have not yet biet des Müritz-Nationalparks (Mecklenburg- found an adequate model of functional land- Vorpommern, Deutschland). Bewegungsmuster scape resistance for our study system. There- der besenderten Tiere wurden genutzt, um den fore, future analyses should use more fi ne- Widerstand der Landschaft für Waschbärbewe- scaled landscape data, and use more complex gungen empirisch abzuschätzen. Das so gewon- modelling procedures to quantify landscape nenen Landschaftsmodell wurde verwendet, resistance from the telemetry data. Future stud- um effektive Distanzen zwischen allen beprob- ies should also attempt to increase the spatial ten Waschbären zu berechnen. Diese effektiven extend of the sampling, because landscape- Distanzen wurden statistisch mit den geneti- genetic relationships are often scale-dependent schen Distanzen verglichen. Eine signifi kante Korrelation zwischen beiden Distanzen würde Based on our current analyses, we conclude auf einen Einfl uss der Landschaftsstrukturen that landscape characteristics (i.e., distance to auf den Genfl uss innerhalb der Population hin- forests and agricultural fi elds) affect racoon deuten. Zusätzlich wurde auch das Territorial- movements, but these characteristics do not verhalten der Waschbären über einen Streifge- seem to infl uence the genetic structure of the biets-Überlappungs-Index bestimmt, und dieser studied population. Instead, genetic population wurde ebenfalls mit den genetischen Distanzen structure seems to be infl uenced by the space- use behaviour of related raccoons, particularly Die Ergebnisse zeigen, dass die Bewegungs- muster der besenderten Waschbären von Wald und landwirtschaftlichen Flächen beeinfl uss werden. Der Widerstand der Landschaft für Zusammenfassung
Waschbärbewegungen verringerte sich mit zu-nehmender Nähe zu Wald, und erhöhte sich Genetische Strukturen einer Waschbären-
mit zunehmender Nähe zu landwirtschaftlichen population (Procyon lotor L., 1758) im Flächen. Allerdings beeinfl ussen diese Land-
Müritz-Nationalpark – Landschaftsein-
schaftswiderstände nicht den Genfl uss inner- fl üsse oder barrierefreie Liebe?
halb der Population, denn es wurde keine sig- Einsicht in genetische Populationsstrukturen nifi kante Korrelation zwischen genetischen und und in die Faktoren, von denen diese Struktu- effektiven Distanzen gefunden. Signifi kante ren beeinfl usst werden, ist Grundlage für eine Korrelationen wurden allerdings zwischen ge- Vielzahl von Fragestellung in der Evolution, netischen Distanzen und Streifgebietsüberlap- der Ökologie, und dem Management von Wild- pungen gefunden. Waschbären, die einen hö- Beiträge zur Jagd- und Wildforschung, Bd. 36 (2011) heren Verwandtschaftsgrad aufwiesen, teilten ance to movement decreases with increasing sich größere Gebiete ihrer Streifgebiete. Dieser proximity to forests and decreasing distance to Trend war signifi kant für die Gesamtpopulatio- agricultural fi elds. However, landscape resist- nen, sowie für Fähen, jedoch nicht für Rüden.
ance to movement does not infl uence genetic Insgesamt weisen diese Ergebnisse darauf hin, population structure, as there was no signifi cant dass die genetische Struktur der untersuchten correlation between effective and genetic dis- Waschbärpopulation nicht von Landschaft- tances. Instead, there was a signifi cant correla- strukturen beeinfl usst wird, sondern von der tion between genetic distances and home range räumlichen Verteilung der Matrilinien, sowie overlap, with genetically more closely-related dem Territorialverhalten der Fähen.
individuals sharing greater parts of their home ranges. This correlation was signifi cant for the total population, as well as for females, but not for males. In sum, these results suggest that genetic struc- Understanding genetic population structure and ture of the studied raccoon population is not in- the mechanisms shaping this structure is impor- fl uenced by landscape heterogeneity, but rather tant for addressing many questions in evolution, by the spatial distribution of matrilineages and ecology, and conservation. Current studies ana- by the territorial behaviour of females. lyzing genetic population structure often focus on testing for landscape-genetic relationships, but genetic structures can actually be infl uenced Literatur
by a variety of other, landscape-independent factors. Here, we test whether genetic structure NDERSON, C.; EPPERSON, B.; FORTIN, M.; HOLDEREGGER, R.; JAMES, P.; ROSENBERG, M.; SCRIBNER, K. & SPEAR, S. of a raccoon population is affected by landscape (2010) – Considering spatial and temporal scale in resistances to raccoon movement, or by social- landscape-genetic studies of gene fl ow. – Molecular ly-induced space-use patterns. Sixty-nine rac- Ecology 19: 3565– 3575.
coons (32 females, 37 males) were fi tted with BURNHAM, K. & ANDERSON, D. (2002) – Model selection and multimodel inference: a practical information-the- telemetry-collars as part of an intensive effort oretic approach. 2nd edn. Springer-Verlag; New York.
to study the life-history of the species in Müritz CALENGE, C. (2007) – Exploring Habitat Selection by Wildlife with adehabitat. – Journal of Statistical Soft- Movement paths of raccoons were used to em- ware 22: 1–19.
pirically estimate the resistance of the land- ONNER, L. & PLOWMAN, B. (2001) – Using Euclidean distance to assess nonrandom habitat use. – In: Radio scape to animal movements as a function of Tracking and Animal Populations (eds. MILLSPAUGH, J. various habitat variables. In addition, 141 rac- & MARZLUFF, J.); pp. 276–292. – Academic Press; New coons were genotyped at ten microsatellite loci, and genetic population structure was estimated CUSHMAN, S.A.; MCKELVEY, K.S.; HAYDEN, J. & SCHWARTZ, M.K. (2006) – Gene fl ow in complex landscapes: test- through an individual-based genetic distance. ing multiple hypotheses with causal modeling. – The Using the empirically-derived landscape resist- American Naturalist 168: 486 – 499.
ance model, we then estimated effective sepa- EPPS, C.; WEHAUSEN, J.D.; BLEICH, V.C.; TORRES, S.G. & ration distances among sampled raccoons, and BRAHSARES, J.S. (2007) – Optimizing dispersal and cor-ridor models using landscape genetics. – Journal of Ap- statistically compared these effective distances plied Ecology 44: 714–724.
with the genetic distances. A signifi cant corre- FIEBERG, J. & KOCHANNY, C. (2005) – Quantifi cation of lation between genetic and effective distances home range overlap: the importance of the utilization would indicate an effect of landscape resistance distribution. – Journal of Wildlife Management 69:
on gene fl ow. Additionally, we also tested for GOSLEE, S.C. & URBAN, D.L. (2007a) – The ecodist Pack- the effects of territoriality (measured through age for Dissimilarity-based Analysis of Ecological an index of home range overlap) on observed Data. – Journal of Statistical Software 22: 1–19.
GRAMLICH, S.; MICHLER, F.U.; KÖHNEMANN, B. & SCHULZ, Results suggest that raccoon movements in the H. (2011) – Mater semper certa? – Molekularbiologi-sche Analyse einer Waschbärenpopulation (Procyon study area are infl uenced by forested and ag- lotor Linné; 1758) im Müritz-Nationalpark. – Beitr.
ricultural habitats, and that landscape resist- Jagd- u. Wildforsch. 36: 521–530.
Genetic structure of a racoon population in Müritz National Park – a result .
HARDY, O. & VEKEMANS, X. (2002) – SPAGeDi: a versatile SAWYER, H.; KAUFFMAN, M.; NIELSON, R. & HORNE, J. computer program to analyse spatial genetic structure at (2009) – Identifying and prioritizing ungulate migra- the individual or population levels. – Molecular Ecolo- tion routes for landscape-level conservation. – Ecologi- gy Notes 2: 618– 620.
cal Applications 19: 2016–2025.
HERMES, N.; KÖHNEMANN, B.A. & MICHLER, F.-U. (2011) SEGELBACHER, G.; CUSHMAN, S.A.; EPPERSON, B.K.; FOR- – Radiotelemetrische Untersuchungen zur Habitatnut- TIN, M.J.; FRANCOIS, O.; HARDY, O.; HOLDEREGGER, R.; zung des Waschbären (Procyon lotor Linnaeus; 1758) TABERLET, P.; WAITS, L.P. & MANEL, S. (2010) – Appli- im Müritz-Nationalpark.Beitr. Jagd- u. Wildforsch.
cations of Landscape Genetics in Conservation Biolo- 36: 557–572.
gy: Concepts and Challenges. – Conservation Genetics HOLDEREGGER R. & WAGNER H.H. (2008) – Landscape Ge- 11: 375–385.
netics. – BioSciences 58: 199–207.
SHIRK, A.J.; WALLIN, D.O.; CUSHMAN, S.A.; RICE, C.G. & HORNE, J.; GARTON, E.; CRONE, S. & LEWIS, J. (2007) – WARHEIT, K.I. (2010) – Inferring landscape effects on Analyzing animal movements using Brownian bridges. gene fl ow: a new model selection framework. – Mo- – Ecology 88: 2354 – 2363.
lecular Ecology 19: 3603–3619.
KITCHEN, A.; GESE, E.; WAITS, L.; KARKI, S. & SCHAUSTER, E. (2005) – Genetic and spatial structure within a swift
fox population. – Animal Ecology 73: 1173 –1182.
ÖHNEMANN, B.A. & MICHLER, F.-U. (2009): Sumpf- und Moorlandschaften der nordostdeutschen Tiefebene – Idealhabitate für Waschbären (Procyon lotor L.; 1758)
in Mitteleuropa? – Beitr. Jagd- u. Wildforsch. 34: 511–
MANEL, S.; SCHWARTZ, M.K.; LUIKART, G. & TABERLET, P. (2003) – Landscape genetics: combining landscape eco- logy and population genetics. – Trends in Ecology and Evolution 18: 189 –197.
MANTEL, N. (1967) – The detection of disease clustering and a generalized regression approach. – Cancer Re-
search 27: 209 –220.
MCRAE, B.H. & BEIER, P. (2007) – Circuit theory predicts gene fl ow in plant and animal populations. – Proceed- ings of the National Academy of Sciences of the USA
104: 19885 –19890.
Institut für Forstzoologie und Forstbotanik MOYER, M.A.; MCCOWN, J.W.; EASON, T.H. & OLI M,.K. (2006) – Does genetic relatedness infl uence space use pattern? A test on fl orida black bears. – Journal of Mam- malogy 87: 255–261.
MUSCHIK, I.; KÖHNEMANN, B.A. & MICHLER, F.-U. (2011) – Entwicklung des Raum- und Sozialverhaltens juveniler Waschbären (Procyon lotor L.) – im Müritz-National-
park. – Beitr. Jagd- u. Wildforsch. 36: 573–585.
RANGEL, T.; DINIZ-FILHO, A. & BINI, L. (2010) – SAM: a comprehensive application for Spatial Analysis in Ma- croecology. – Ecography 33: 46 –50.
RITLAND, K. (1996) – A marker-based method for infer- ences about quantitative inheritance in natural popula- tions. – Evolution 50: 1062–1073.

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