Point–count method for estimating rock ptarmigan spring density in the pyrenean chain

Eur J Wildl Res
DOI 10.1007/s10344-011-0541-y
SHORT COMMUNICATION
Point–count method for estimating rock ptarmigan spring
density in the Pyrenean chain
Evelyn Marty & Marc Mossoll-Torres
Received: 8 October 2010 / Revised: 8 April 2011 / Accepted: 14 April 2011
# Springer-Verlag 2011
Abstract This paper describes a census technique that
gives estimates of rock ptarmigan cocks’ number per unit
area during the breeding season. This method originated
from the need for an effective technique for estimating bird
densities in mountainous and inaccessible zones like the
Pyrenean chain. The bird census was carried out using a
point–count method, which is recommended for uneven
areas. To maximize sampling efficiency, we established
sampling points at <500-m distances, in order to detect a
maximum of calling birds within the sample areas. Birds
localized at distances >250 m were excluded. We postulate
that all birds are recorded in a 250-m radius around the
observer. We carried out counts of calling rock ptarmigan
cocks in the border between the Principality of Andorra and
Ariège Department, France, from April to June during three
consecutive years (2005–2007). The estimated spring density
of 10.4 cocks per 100 ha was higher than densities reported in
literature, in other parts of the Pyrénées and the Alps. Our
study provides a useful reference for future monitoring of this
species in its mountainous distribution range.
Keywords Rock ptarmigan . Lagopus muta pyrenaica .
Point–count method . Density . Pyrenees . Andorra . France
Communicated by C. Gortázar
E. Marty (*)
Fédération Départementale des Chasseurs de l’Ariège,
Le Couloumié,
Labarre 09000 Foix, France
e-mail: fdc09@wanadoo.fr
M. Mossoll-Torres
Asociación para la Conservación del Urogallo (ACU),
Casa Cintet,
Poblet d’Encamp, Principality of Andorra
e-mail: tetras@andorra.ad
Introduction
Climate change during the past century has resulted in a
mean increase in global temperatures by 0.6°C, with
particularly large changes during spring in temperate and
arctic regions of the world (Houghton et al. 2001).
Predictions for the future indicate dramatic impacts on
polar and alpine environments (e.g. loss of biodiversity). In
these environments, many plants and animals have already
responded to this change by advancing their annual cycles
(e.g. migration dates, timing of reproduction; Crick 2004;
Root et al. 2002; Walther et al. 2002). So, high elevation
sites represent very suitable regions for research into the
impacts of climate change on wildlife. In general, these
biotopes are not very rich in biodiversity. Only a few
species breed in alpine and sub-alpine habitats.
The rock ptarmigan Lagopus muta is an arctic-breeding
bird with a Holarctic distribution (del Hoyo et al. 1994;
Johnsgard 1983). It has adapted to the cold environments of
the northern hemisphere, and more than two-dozen subspecies have been recognized (del Hoyo et al. 1994). The
Pyrenean rock ptarmigan L. muta pyrenaica, one of these
subspecies, inhabits mountain ranges in Andorra, France
and Spain situated at elevations of 2,200 m above sea level
(a.s.l.) or higher. The rock ptarmigan primarily breeds in
rocky and stony areas above the tree line. It is the only
year-round resident at this high elevation. Therefore, it
represents an ideal candidate to follow climate changes.
Paradoxically, population trends are largely unknown in many
parts of the rock ptarmigan range (Storch 2007a, b). One
major reason for this seems to be the lack of a suitable
census method. Two techniques have been widely employed
in ground surveys: the line-transect and point–count methods. The choice is generally based on the species behaviour,
nature of the terrain, time and personnel available, number of
Eur J Wildl Res
habitats to be censused and accuracy of the density estimate
required. In another study (Mossoll-Torres and Marty 2011),
we have compared the two methods. We have found that a
stationary observer spends more time searching for birds and
detects more birds (the rock ptarmigan is a visually
inconspicuous species in many circumstances), particularly
in uneven terrain. Stationary observers also have less effect
(noise disturbance) on bird activity. So, we have rejected
the line-transect method, contrary to Pelletier and Krebs
(1997) who found that this method provided good results
with ptarmigan moderate population numbers in Canada.
In this paper, we evaluated the applicability and efficiency
of a point–count method for estimating breeding densities
of the male rock ptarmigan.
Study area
The study was conducted in the southernmost edges of the
rock ptarmigan distribution range, on the border between
the Principality of Andorra and the Haute Ariège in France,
totalling a surface area of about 39,590 ha (Fig. 1). It
includes all the biotopes used by rock ptarmigans, which
prefer treeless sub-alpine (above 1,800 m a.s.l.) to alpine
areas (up to 3,000 m a.s.l.) and moderate to steep slopes
vegetated with grasses and sparse shrubs, and partly rocky.
The climate is highly changing from year to year. In
February, at 1,640 m, the average minimum is −6.5°C, and
the maximum is 3.8°C, while in July, the average minimum
Fig. 1 Location of sample areas
(in grey) in the census zone
(39,590 ha) of the present study.
The dashed line represents the
frontier between France and
Andorra
is 5.9°C, and the maximum is 20.9°C (Panareda 1984). For
a Pyrenean region, the importance of the rainfall is low
(<1,000 mm per year); there is a Mediterranean influence,
especially valleys exposed to the south. In the alpine zone,
the snow cover generally appears in November or
December, and the snowmelt does not really start until
the month of May.
Methods
Since the pioneering work of Bossert (1977), estimates of
rock ptarmigan densities are based on male counts during
maximal bird-calling activity (Bossert 1980; Boudarel
1988; Favaron et al. 2006; Léonard 1992; Morscheidt
1994; Nopp-Mayr and Zohmann 2008; Novoa and Soler
1989; Sentilles et al. 2004; Zohmann and Wöss 2008). In
early spring, the cocks choose calling locations with
territorial borders following cliffs and ridges. They crowed
frequently just before and after sunrise and are more likely
to be located at this time (e.g. Bossert 1977). In a previous
study, we have found that the calling activities peaked
between 15 and 30 min before sunrise, whereas a maximum
duration of more than 2 h was noted in the study area. So,
for the current study, we performed all visits during
morning hours, between 04:00 and 08:00 local time
(02:00 to 06:00 hours GMT).
Based on the results of research done in 2003–2005, we
determined that weather conditions were critical to the
Eur J Wildl Res
success of surveys. Ptarmigans were most active in sunny
and calm conditions, and also during foggy days, whereas
birds were usually inactive during snowy days (Fig. 2, see
also Mossop 1988). For the current study, counts were only
done on days with appropriate weather conditions.
Furthermore, among the numerous variables that can
potentially influence point–count surveys, the total number
of participating observers is of particular importance in
uneven environments. Based on our experience and on the
patchy distribution of displaying cocks in mountainous
biotopes, it has become clear that a minimum number of
five participants were necessary to cover a sampling area
adequately.
The current surveys were carried out from April to June
in three consecutive years, from 2005 to 2007. Sampling
points were randomly selected in the study area. In each
sampling area (see Fig. 1), distances between stations were
established in such a manner as to maximize the probability
of detecting displaying cocks. The distance between
sampling points depends on how far away a bird can be
detected. It is generally accepted that calling cocks can be
heard up to 1 km away (Bossert 1977; Watson 1972).
Nevertheless, the detection distance varies greatly among
sites, depending on topography, vegetation and weather
conditions, among others. In a first step, the optimal
distance has been arbitrarily fixed at 250 m (see also
Mossoll-Torres and Marty 2006). So, the distribution of the
observers respects a maximum distance of about 500 m. In
the analysis, we determine the distance from the stations
where the number of birds heard begins to decline (the
break point) by plotting the number of individuals detected
in concentric circles around the observers. With this
method, we reject all observations outside of the circle of
radius x (Fig. 1). The number of birds is then determined
seasonal peak activity
calling cocks’ number
14
Results
7
non-optimal
weather conditions
May
stormy event
0
95
by compiling the number of individuals counted within the
circle of radius x, dividing by the area and taking into
account topographic breaks (acoustic barriers). The
calling cock census results were then converted to
density as ind./100 ha.
At the time of the study, the plumage of the male was
mimetic making them difficult to localize (Martin et al.
1995). Only about 15.20% of calling cocks were observed
directly (or with the aid of binoculars) by observers. For
the remaining calling cocks, direction and putative
position were noted on a field book. As the method
depends on how the distance between the bird and the
observer can be accurately measured, precise localization
of the displaying cock territories used by birds was crucial.
At the end of the experiment, indices of bird presence
(footprints or trails on the snow cover, feathers or faecal
deposits) were searched out by each observer. The position
of the displaying cock territories found was then measured
by GPS and telemeter and reported on a 1/25,000 map.
Positions of birds and observers were then mapped, and
exact distances between recorded positions were later
calculated using a computer programme (GIS). Beyond
approximately 500 m from the observer, the exact position
of cock territories was difficult to localize in uneven
terrain.
In each sampling area, we counted territorial male
ptarmigans at a minimum of five sampling points,
depending on personnel available. When multiple counts
have been done, the maximum counts recorded from all
visits were taken as the species abundance at a particular
sampling point. After that, a nonlinear regression analysis
was used to fit a predictive equation between birds’
density and distance from observer. Finally, the yearly
abundance of ptarmigan cocks in the study area was
calculated as the mean of point abundances.
We used non-parametric tests in our analyses because
densities of birds between the sample units were not
normally distributed (Sokal and Rohlf 1995). All the
statistical analyses were performed with XLSTAT 2009
(Addinsoft, USA).
105
115
125
135
145
155
165
Julian date (from April 6th to June 12th)
Fig. 2 The calling cocks’ number over the entire breeding season in
the same sampling area (data from a previous work done in 2004)
Between 2005 and 2007, among 69 sampling days
totalizing 418 sampling points, we selected 23 surveys
(197 sampling points) from April 6 to June 12. Finally,
during this 3-year survey, we detected the presence of
407 rock ptarmigan calling cocks during days of peaking
activity. Among them, due to multiple counts and
difficulties in precisely determining the exact position of
displaying cock territories (see above), 122 birds were
excluded from our database. Table 1 gives the detailed
Eur J Wildl Res
0
Animal - Observer Distance (m)
results for the 3 years. Up to seven calling cocks were
detected per sampling point (one observer). Frequency
distributions revealed no significant difference between
years (two-sample Kolmogorov–Smirnov test: D=0.375
and p=0.660 for 2005 vs. 2006, D=0.375 and p=0.622 for
2006 vs. 2007, D=0.250 and p=0.970 for 2005 vs. 2007).
Plotting the number of birds with distances from the
observers (Fig. 3), we found a gradual decline in our ability
to detect displaying cocks. In a first step, mainly subjective,
we rejected all observations outside the circle of a radius of
250 m to estimate male densities. With this procedure, for
the 3 years combined, the mean annual density is 10.4
cocks per 100 ha.
Bird densities between the 3 years were compared by
Kruskal–Wallis analysis of variance (H). Test results
indicated that no significant difference in bird repartition
was observed between years (Fig. 4; Kruskal–Wallis test:
H=0.070, df=2, p=0.965). These results attest to the
reproducibility of the census method used.
20
0 - 50
50 - 100
100 - 150
150 - 200
200 - 250
250 - 300
300 - 350
350 - 400
400 - 450
450 - 500
500 - 550
550 - 600
600 - 650
650 - 700
700 - 750
750 - 800
800 - 850
40
60
80
100
70.34%
Break Point
(mainly subjective)
N = 408 cocks
0
20
40
60
80
100
Number of Calling Cocks
Fig. 3 Plot of the number of cocks per circular band around observers
in the study area (2005–2007). A decline in the number of cocks heard
occurs at the fifth circle (250 m)
Our data have indicated that the territorial male counts are
affected by several environmental variables including time
of year, time of day and current weather conditions. We
have found that the number of cocks registered by four
observers notably differed between days, ranging from 0 to
14 cocks for the same sampling area (see Fig. 2). The
absence of calling bird activity the 2 days following stormy
events is of special concern for our purpose. So, weather
conditions, particularly after stormy events, are particularly
detrimental and can greatly affect counting results. Counts
should only be attempted when conditions are excellent
(sunny or foggy days with no wind) from 1 h before to half
an hour after sunrise. Furthermore, depending on available
Table 1 Number of observers vs. number of cocks detected for
3 years
Number of
cocks
Nb. Obs.-2005
0
1
2
3
4
5
6
7
Total
9
12
16
10
8
3
2
1
61
Nb. Obs. Number of Observators
Nb. Obs.-2006
27
17
19
11
6
4
1
0
85
Nb. Obs.-2007
8
7
12
12
6
2
3
1
51
participants, each sampling site should be counted at least
twice during the optimum period (around mid-May).
During the 3-year survey, estimated spring densities
varied from 9.06 to 10.7 calling cocks per 100 ha (Fig. 4).
Lower densities are generally recorded in other parts of
the rock ptarmigan distribution range in mountainous
areas (e.g. Bossert 1977; Desmet 1988; Favaron et al.
2006; Zohmann and Wöss 2008). In the Pyrenees, the
same tendency is observed. Thus, Morscheidt (1994)
reported densities from 2.9 to 3.75 calling cocks per
100 ha in the “Réserve domaniale du Mont Vallier, Ariège,
France”, whereas Sentilles et al. (2004) estimated male
Fig. 4 Box plots of calling cock
density estimated for the three
surveyed years. The horizontal
line represents the median,
whereas the circle indicates the
mean. The horizontal ends of
the box plot represent the
25th and 75th percentiles. The
asterisk indicates data value
outside fences
25
calling cocks’ density (number/100 ha)
Discussion
24.11
20
2006
(n = 10)
15
10
5
2005
(n = 7)
0
2007
(n = 6)
Eur J Wildl Res
densities of about 4 individuals per 100 ha on average at
the Massif du Canigou-Puigmal, Pyrénées Orientales,
France. Our estimations are clearly higher than the
estimates of all other studies. Several factors could explain
this difference.
Firstly, there was a really significant effect of the method
used to calculate the surface covered by observers. It seems
that all other surveys in mountainous environments are
unlimited-radius counts, i.e. record all displaying cocks
regardless of their distance from the observer. In our study,
on average, each observer effectively detects birds in an
11-ha surface. Nevertheless, the effective detection radius
or EDR is fundamental as it determines both the distance
between the observers and the size of the census area
(Hörnell and Willebrand 1998). One of the main learning
aspects of this assignment has been the use of an EDR
of 250 m. In our study, only 4.4% of the ptarmigans
(18 calling birds) we detected were measured to have
been >500-m distant from the observer. In our case,
selection of a break point distance beyond which there
is a large fall in observations was problematic. Our
results showed that up to 70% of calling birds (287
cocks) are recorded under 250 m around the observers
(see Fig. 3). Between distances of 250 and 300 m, the
birds’ number decreased from 58 cocks (band 5: 200 to
250 m) to 36 cocks (band 6: 250 to 300 m), corresponding
to a 37.9% decrease. Furthermore, it seems our data could
be adjusted to a theoretical model (see Fig. 3). It became
evident when we plotted the density of calling cocks per
100 ha vs. distances from the observers. We could
superimpose a mathematical function to describe the
relationship between distance and density of birds. Fig. 5
shows the observed data and the resulting regression fit,
performed using built-in functions from XLSTAT. The
relationship had R2 =0.99. A sharp decrease is evident
between 150 and 200 m. Our data suggest that an
integrated approach based on a detection function is, as a
consequence, applicable. This function, used to compute
Summary and conclusion
Rock ptarmigans are difficult to study because of their
uneven habitats that are often difficult to access. Estimates
of true abundance were yet problematic because of the
lack of quantitative information on factors that might
affect the detection probability of birds. So, obtaining
information about spatial and temporal differences in
detection probability is a priority for researchers implicated
in the monitoring and conservation of this bird. The aim
at that time was mainly to design a standardized method
that could authorize comparisons between years and
16
Numbers of cocks per 100 ha
Fig. 5 Relationship between
density of calling birds per
100 ha and distance from the
observer for the 3-year survey.
The dashed line indicates the
best-fit theoretical model as
determined by statistical
analysis (with XLSTAT 2009)
probability of detection, compensates for the fact that the
calling birds’ detectability decreases with increasing
distance from the observers. At that time, more data are
necessary to adjust this function and to precisely
determine the exact position of the break point (e.g. by
using an equidistance between circles of 20 m).
Secondly, time of year, weather and daytime also had
significant effects on male density estimates (see above).
We also found that differences between years in snow
cover and mean temperatures strongly affect bird activity
(unpublished data). We also pointed out a net reduction
in bird activity in days following stormy events. We have
attributed this fact to the birds’ need to rebuild their
energy levels (the ptarmigans sighted were feeding). The
situation could be as with the willow ptarmigan in which
there are peaks of activity in the displaying season when
the territories are established and when a female chooses
a male for mating (Hörnell and Willebrand 1998). So,
repeated counts from day to day are necessary in order to
obtain a sound estimation of male density in the study
sites. Nopp-Mayr and Zohmann (2008) lead to the same
conclusion. Furthermore, the timing of the census count
was adapted in order to gather maximum calling activity,
from 1 h before to half an hour after sunrise.
Current density of calling cocks
12
Theoretical model: Y = pr1 + (pr4 - pr1)/(1 + (X1/pr3)^pr2)
with:
8
pr1 = 0.165115679582205
pr2 = 7.02365026112858
pr3 = 193.41250617195
pr4 = 14.9830580213847
4
0
0
100
200
300
400
500
Distance from observers X1 (m)
700
900
Eur J Wildl Res
regions. In this report, we have presented data from a
point–count method applied to uneven habitats of the
rock ptarmigan in the Pyrenean chain during three
consecutive years. The results obtained have led to an
updating of the census conducting methods. Our data
support some general ideas of the applicability of this
method:
Landry Riba, Jordi Sola and Jose-Maria Sanchez from the Andorran
Government. We are enormously grateful to all those who have
accompanied us throughout this experience and, in particular, Thomas
Razat, Jean Bouilleau, Régis Didier, Gaèl Aleix Mata and Nicolas
Archer for her patience in translating our texts.
1. The locations of sampling areas should be selected at
random, with a minimum of five observers (5 sampling
points per sampling area).
2. As weather conditions are critical to the success of
surveys, counts might be limited to days of calm
weather (sunny or foggy days). All counts may be
conducted in the morning starting approximately 1 h
before sunrise and ending one half hour after.
3. As there was considerable variability between days and
observers, repeated surveys should be conducted at
each sampling point.
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Mossoll-Torres M, Marty E (2011) Précisions apportées au protocole
de dénombrement des mâles chanteurs de lagopède alpin. Faune
Sauvage (in press)
Estimation of bird density from point counts requires a
fixed-sampling radius that describes the relationship
between bird detectability and distance. Our data indicate
that all the male ptarmigans could not be identified with
certainty at distances >250 m or less (probably between
150 and 200 m), leading to a first estimation of a density
of about 10.4 cocks per 100 ha. Taking into account an
EDR reduction of 50 to 100 m, estimation of territorial
rock ptarmigan cocks’ density could increase to about 14
individuals per 100 ha (see Fig. 5), far away from densities of
four cocks per 100 ha generally advanced in literature by
specialists for the Pyrenean chain (e.g. Canut et al. 2004;
Novoa 2008; Vallance 2007). Another possibility consists in
modelling the decline in observations and estimating the
number missed (programme distance: Buckland et al. 1993;
Thomas et al. 2010).
Our data show that surveys of calling rock ptarmigans
in spring without estimations of detectability give rise to
underestimations of the true density of birds. The
generalization of our methodology throughout the Pyrenean
chain will bring precision to the Pyrenean population size and,
in particular, will serve as a revision of the current distribution
area of this species. In the context of global warming, the
number of birds could decrease and even disappear on a local
scale on the edge of the distribution areas, especially in the
isolated massifs on the north- and south-facing slopes of the
chain. Future investigations must give priority to these
marginalised sectors.
Acknowledgements This study was entirely financed by the
Andorran Government and the Fédération Départementale des
Chasseurs de l’Ariège (the hunting federation of Ariège, France). We
are grateful to Dr. Philippe Mourguiart for his statistical assistance and
for valuable comments on this manuscript. We would like to thank
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