Rainfall (mmH2O). 20. 40. 60. 80. 100. 120. A S O N D J F M A M J J A S. 10. 15. 20. 25 ... saving. Resting metabolism. MADAGASCAR. Tropique du Capricorne.
A statistical approach Chronic food restriction and seasonal modulations of daily heterothermia and locomotor activity in a Malagasy primate: Microcebus murinus Giroud S., Blanc S., Aujard F., Bertrand F., Gilbert C., Perret M. 2007
Abiotic factors Input or energy contribution
Output or energy expenditure
Adaptive strategies
Maintenance of energy balance Survival of the individual Energy imbalance
Species survival
Madagascar: contrasted climate and habitats Habitat of rain forests Rainfall ~ 2000/4000 mm/yr Temperature ~ 24-28°c
MADAGASCAR
Habitat of dry deciduous forests Rainfall ~ 700/1200 mm/yr Temperature ~ 24-27°c
Habitat of spiny desert forests Rainfall ~ 500 mm/yr Temperature ~ 18-25°c
Tropique du Capricorne
Predictable seasonal environment
Trophic resource variability
MADAGASCAR
Rainfall (mmH2O) Minimal T °C
Resources
35
120
100
30
Cold and dry season
80
25 20
60 15 40
10
20
Tropique du Capricorne
A S
O
N D
J
F
M
A M J
J
A S
Unpredictable environment El Niño phenomenon
Rainfall (mmH2O) Resources
Minimal T °C
120
35
100
30 25
80
20 60 15 40
10
20
A S
O
N D
J
F
M
A M J
Species survival
J
Severe food shortage during several months in summer
A S
Unusual pressure during reproductive season
MADAGASCAR
Grey mouse lemur - Microcebus murinus Heterothermic primate Seasonal fattening Body mass Resting metabolism
% of variations
140 120
Energy saving
100 80 60
Tropique du Capricorne
Distribution area of Microcebus murinus
Ecological constraints
Sexual rest
Season of reproduction
Dry season
Seasonal variations of several biological parameters in percentage of the annual average
Adaptive mechanisms
Face to a chronic food shortage, what are seasonal thermo-modulation and behavioural energy-saving responses of M. murinus ?
Experimental protocol - Recording of body temperature and activity level - 12 summer-adapted male individuals Long Photoperiod (LP) Logger
- 12 winter-adapted male individuals Short Photoperiod (SP) Telemetry
Control period
Calorie restriction (CR)
2 weeks
5 weeks
Stable energy balance
40% or 80%
Daily body temperature and activity parameters Initiation time Active period
38
Hypothermia
Average Tb
100
Duration
37 60 36
Average Tb
LA
40
Minimal Tb
35
Occurrence time
20
LA 0
34
12
14
16
18
20
Dark phase
22
24 2 Hours of day
4
6
8
Light phase
10
12
Locomotor activity (a.u.)
Body temperature (°c)
80
Daily temperature and activity parameters Initiation time Active period
38
Hypothermia phase
100
37 60 36 40
Minimal Tb
35
20
0
34
12
14
16
18
20
Dark phase
22
24 2 Hours of day
4
6
8
Light phase
10
12
Locomotor activity (a.u.)
Body temperature (°c)
80
Initiation time of the hypothermia 0
- Data with large variability
Duration (min)
-60
→ Weighted moving average to highlight the general trend of the evolution
-120
-180
- Distribution not normal
-240
→ parametric tests were not applicable
-300
Body temperature (°c)
Minimal body temperature
- Non-linear evolutions
36
→ ANOVA not suitable (parametric test, linear trend) → Non-Linear General Model (NLGM) used
34 32 30 28 26 0
5
10
15
20
Time (days)
25
30
LP40 SP40 LP80 SP80
Body temperature (°c)
NLGM on Statistica
36 34 32
Effect
30
DF
Wald Stat
PP
1
1795
0.000000
CRi
1
4
0.038918
CRd
30
352
0.000000
PP*CRi
1
11
0.000812
PP*CRd
30
137
0.000000
CRi*CRd
30
11
0.999297
7
0.999998
28 26 0
5
10
15
20
25
Time (days)
LP40 SP40 LP80 SP80
General evolution in a whole model
30
Minimal Tb - test all the effects Distribution: GAMMA
SigmaPlot Statistical adjustment of the 2-part linear evolution
Minimal body temperature
- Significability of each slope was tested by non-parametric ANOVA of Friedman (*)
Body temperature (°c)
36 34 32
- Times of rupture and slopes were compared by nonparametric ANOVA between groups and by Mann-Whitney U-test between 2 groups in particular
30 28 26 0
5
1012 15
20
Time (days)
25
24
30
Slope 1 (°C/day)
Time of rupture (day)
Slope 2 (°C/day)
LP40
-0.13 ± 0.01*
8±2
NS → stable value 34.7 ± 0.4 °C
LP80
-0.13 ± 0.01*
24 ± 1
NS → stable value 32.9 ± 0.6 °C
SP40
-0.67 ± 0.13*
13 ± 2
NS → stable value 27.6 ± 0.8 °C
SP80
-0.74 ± 0.08*
12 ± 2
NS → stable value 27.8 ± 0.6 °C
Summary… - Biological data need statistical tools - Advanced statistical tools (NLGM) are required to explain the complexity of the biological responses
Acknowledgements - All the volunteers in this experiment… - Martine Perret and Stéphane Blanc, my PhD supervisors - The staff of Brunoy’s lab - Myriam and Frédéric Bertrand for their help in statistic analysis
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