Durante mucho tiempo no había principios uniformes para la Atribución de nombres a los antibióticos https://antibioticos-wiki.es . Más a menudo se les llama por el nombre genérico o especie del producto, con menos frecuencia-de acuerdo con la estructura química. Algunos antibióticos se nombran de acuerdo con el lugar donde se asignó el producto.
55_balkenhol__genetic_041111_k3.indd
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 negativethe 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
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CONVOCATORIA Y DATOS GENER ALES DEL PROCE SO DE CONTRATACI ÓN S ERVICIO MUNIC IP AL DE AGUA P OTABLE Y AL CANT ARILLADO SANIT ARIO DE C OCHABAMBA - SEMAPA 1. CONV OCATOR IA Se convoca a la p resen tación de p ropuestas para el sig uiente proceso : S ERV ICIO MUNIC IPA L DE AGU A PO TAB LE Y AL CAN TAR ILLA DO S ANIT ARI O DE En tidad Convocan te : CO CHA BAMB A Mo d