Download Climatic influence on Black Grouse population dynamic in Belgian

SYLVIA 39 / 2003 SUPLEMENT
Climatic influence on Black Grouse
population dynamic in Belgian Hautes-Fagnes
Nature Reserve: an update.
Michèle Loneux1, James K. Lindsey2, Marc
Vandiepenbeeck3, Olivier Charlet1, Christine Keulen1,
Pascal Poncin1 & Jean-Claude Ruwet1
1
Unit of Behavioural Biology and Animal Psychology, Zoological Institute, Quai Ed. Van
Beneden 22, B-4020 Liège, Belgium; e-mail: [email protected]
2
Quantitative Methodology, Faculty of Economics, University of Liège, B-4000 Liège, Belgium;
e-mail: [email protected]
3
Institut Royal Météorologique, avenue Circulaire 3, B-1180 Bruxelles, Belgium; e-mail:
[email protected]
Loneux M., Lindsey J. K., Vandiepenbeeck M., Charlet O., Keulen C., Poncin P. & Ruwet J.-C.
2003: Climatic influence on Black Grouse population dynamic in Belgian Hautes-Fagnes
Nature Reserve: an update. Sylvia 39(suppl.): 53–57.
Previous work investigated the population dynamics of the Black Grouse (Tetrao tetrix) in the
Belgian Hautes-Fagnes in relation to climatic influence from 1966 to 1998. Spring censuses of
Black Cocks in the arenas have continued. This contribution analyses the changes in numbers
until 2003 and discuses the results estimated by modelling in relationship to explanatory variables previously pointed out as most relevant to explain the dynamics observed for the last
30 years.
Keywords: modelling, climate, population dynamics, Black Grouse, Tetrao tetrix, Belgium
INTRODUCTION
Spring censuses of lekking Black
Grouse cocks are available for 20 to 30
years in various European areas.
Previous work has shown the relevance
of certain climatic variables to model
the population dynamics of these birds
in Belgium (Loneux et al. 1997, 2000).
The statistically remarkably good fit
(Lindsey 1999) of the model previously
obtained for the Belgian population, has
been tested with success on several
isolated populations in Germany and
the Netherlands (Loneux 2001, 2003).
The concordant significant results for
four “lowland” populations (versus
mountainous population) have stressed
the negative effect of mild and rainy
winters and of rainy brooding and
hatching periods, and the positive effect
of a warm hatching period. The populations concerned are living in protected
areas and have not been subjected to
hunting for about 30 years. Climate
seems thereby to be the main factor
affecting population fluctuations, even
if other factors such as predation or
disturbance or anything else may have
had more influence in the years with
poor fit.
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Loneux M. et al. / Climatic influence on Black Grouse
Investigation of climate development
has shown certain recent trends with
expected long term negative effects on
the breeding success and the winter
survival of the birds: more rain from
November to January, in March and in
May-June and milder winters than previously (Loneux 2001, Loneux & Vandiepenbeeck 2002, 2003). The spring
census (Ruwet et al 1997) is still performed by our team every year in the
Belgian Hautes-Fagnes. Substitute meteorological data were required for those
lacking in 1999 at the main weather
station of the study area. Therefore, we
were able to continue the modelling
analysis for 5 more years, a period with
few birds. Would the same models still
fit or would the influence of climate
have become less important in compari-
son to other factors not used in the
modelling?
METHODS
The analysis uses the amount of rainfall
and the mean minimum temperature to
model fluctuations in yearly cock
censuses, these performed on all arenas
of the Nature reserve (as described in
Ruwet et al. 1997). These variables are
related to specific crucial time periods
in the life cycle of the Black Grouse
(winter time, breeding and hatching
time and autumn) using Poisson multiple regression in the “R” software (a free
S-Plus clone, Ihaka & Gentleman 1996).
The explanatory variables are: (1) previous cock numbers; (2) mean minimal
temperatures during the winter
(1 November to 31 March) and during
Table 1. Explanatory variables and their estimates for the best model calculated for the period
1967–2003 with 3 or 4-week periods. P-values refer to partial effects of each variable.
ppSept – sum of rainfall during September previous year, ppNov – sum of rainfall during
November previous year, ppJan – sum of rainfall during last January, t3wdd.m – minimum
temperature mean over 3 weeks beginning the day of month, pp3wdd.m – sum of rainfall
during 3 weeks beginning the day of month, pp4wdd.m – sum of rainfall during 4 weeks
beginning the day of month, twinter – minimum temperature mean during last winter period
defined from 1 November to 31 March, twinter1 – minimum temperature mean during previous last winter period, no1 – spring cocks number previous year, no2 – spring cocks number
two years before.
Null deviance: 935.420 on 35 degrees of freedom, residual deviance: 72.532 on 23 degrees of
freedom, AIC: 306.63.
coefficients
variables
estimate
std. error
(intercept)
no1
no2
twinter
twinter1
ppSept
pp4w25.5
pp4w19.5
tm3w16.6
pp3w01.6
tm3w10.6
ppNov
ppJan
2.6713332
0.0127892
–0.0034454
–0.1069434
–0.0545302
–0.0014445
0.0058277
–0.0022788
0.1256919
–0.0068224
–0.0453070
–0.0009791
0.0008791
0.2301569
0.0009492
0.0009137
0.0201825
0.0231992
0.0004157
0.0022253
0.0012962
0.0253113
0.0012778
0.0326375
0.0004799
0.0003593
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z-value
P-value
11.607
13.474
–3.771
–5.299
–2.351
–3.475
2.619
–1.758
4.966
–5.339
–1.388
–2.040
2.446
<0.001
<0.001
<0.001
<0.05
<0.01
<0.02
<0.10
<0.001
<0.001
<0.20
<0.10
<0.05
SYLVIA 39 / 2003 SUPLEMENT
three or four week periods while brooding and hatching and; (3) the total rainfall during three or four week periods
while brooding and hatching and
during the autumn and winter months,
all for the year before each census.
RESULTS
The best model from 1967 to 2003 is
exactly the same as that till 1999: the
explanatory variables are the same
and related to the same time periods
(Table 1).
The modelling produces estimated
cock numbers very close to the values
from the census (Fig. 1).
winters, which have negative effect on
the spring cocks number.
DISCUSSION
The best model for 30 years in Belgium
is still the same for 34 years, confirming
the strong effect of the winter, brooding
and hatching time climate, and the
special winter metabolism of the
species, adapted to cold and snowy
winter rather than mild and rainy ones.
The poor quality of winter food does
not compensate the energy costs increased by wetting of bird’s plumage
and the impossibility to rest in an isolated
snow-burrow.
Fig. 1. Models for spring numbers of Black Grouse cocks in the Belgian Hautes-Fagnes. Plot of
the numbers on the arenas (N observed) and their fitted values from the model for 1966 to
1998 and updated to 2003. In these models, certain minimum mean temperature and rainfall
variables cover 3 or 4-week periods in May, June, or July, with overlap.
This update confirms the previous
models and conclusions:
(1) Local climate fluctuations in rainfall and minimum temperature explain
very well the population fluctuations
since the end of the 1960s. (2) The
species, a ground nester, suffers from
rain during the breeding months. (3)
High minimum temperature has a positive effect during the rearing period. (4)
This homeotherm animal does not
appreciate rainy autumns and mild
But the model accuracy decreases in
recent years (Table 2, sum of squared
residuals by 12 years). In fact, the climatic variables taken in the model give less
accurate predictions during the last
4-year period (Loneux & Lindsey 2003).
Very low bird numbers are involved and
are subject to fluctuation. Again, either
(1) other, non-climatic, explanatory variables have become more important
regarding the small level of the population (predation pressure, disturbance
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Loneux M. et al. / Climatic influence on Black Grouse
Table 2. Table of the sum of squared residuals (proportion of residual deviance) calculated by 12 years for the model using 3 or
4-week periods variables. The higher the
sum, the less accurate the model.
period
sum of squared residuals
1968–1979
1980–1991
1992–2003
6.55
18.57
47.41
pressure, habitat perturbation), (2)
poorly estimated years correspond to
exceptional values of the explanatory
variables, (3) the real breeding period
has shifted with respect to the mean
period calculated for 30 years, or (4)
maybe both together.
CONCLUSIONS
This update confirms the decisive effect
of climate, but also emphasises the variability of the relation in the last period,
due to the last 4 years, when small
numbers are involved.
Global warming and population insularity are not favourable to long-term
survival of Black Grouse outside the
strictly northern and mountain areas.
In any case, the medium term survival of Black Grouse at the western limits
of its range (“lowland” areas) depends
especially on good management of
quality, reasonably sized, and quiet
habitat. Such ‘good’ management
should allow the Black Grouse to hide
and escape predation and energy-cost
disturbances, to protect from bad
weather, to find good nest sites and
good food at the right time and thus to
improve its survival as young or adult.
Improvement of habitat quality and
carrying capacity appear to be the most
urgent measures to promote conservation of the species. This should also
benefit the biodiversity associated with
the presence of Black Grouse (Müller &
Kolb 1997, Kolb 2001).
56
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