Sample collection and hormone analysis

A total of 369 fecal samples from 171 individuals were collected over four field seasons: in September-November (late dry season, mating season) 2010 and 2012 and in March-May (late rainy season, non-reproductive) 2011 and 2012. All individuals included in the sample were assumed to be sexually mature (age >6 months), since juveniles of the season were excluded from the rainy season sample. Animals were sampled either within a few hours after trap entry at night, or on the following morning after a night spent in a trap. Capturing induces an acute stress response that is measurable 24-72 h after capture [Hämäläinen et al. 2014b]; hence samples were always collected within the

“baseline” period, (i.e. within 12 h of the first monthly capture) and should be unaffected by the capture event. The time of day of fecal sampling does not influence fGCM levels in the species ([Hämäläinen et al. 2014b], confirmed for the data used in this study). Fresh feces was collected from cleaned traps, avoiding urine contamination (details in [Hämäläinen et al. 2014b]) and stored in ethanol within a few hours of collection.

The methods of collection, extraction and fGCM analysis have been previously validated [Hämäläinen et al. 2014b]. Briefly, approximately 0.2 g of feces (range: 0.07 – 0.36 g) was subsampled and

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homogenized in 2 ml of 80-90% ethanol. The fecal suspension was then vortexed, centrifuged, and the supernatant stored for future hormone analyses. The pellet was dried to a constant mass to obtain an estimate of feces water content. Duplicate aliquots of the fecal extracts were measured as detailed in [Hämäläinen et al. 2014b; Heistermann et al. 2004] using a validated enzymeimmunoassay for 11ß-hydroxyetiocholanolone [Ganswindt et al. 2003], a major metabolite of cortisol in gray mouse lemur feces [Hämäläinen et al. 2014b]. All hormone concentrations are given as ng/g fecal wet weight.

Statistical analyses

Explorative analyses of the data was conducted by visual inspection of the data and smoothers of age and body mass (GAM, R-package mgcv, [Wood 2006]) to examine the shapes of the relationships between fGCM and age or body mass. The relationships were found to be approximately linear and the exploration further indicated that 3-way interactions of sex and season with age or body mass might be present (data not shown). Given our interest in the seasonal patterns of GC across the range of ages and the two sexes, and the potentially non-linear within-season trends, we proceeded to analyse the data separately for the seasons to reduce the complexity of the models.

The influences of sex, age, body mass and within-season variation on fGCM levels were analyzed separately for each season using Linear Mixed Models (LMM, R-package lme4, [Bates et al. 2014]).

The fixed effects structure of each full model included the terms sex, age, body mass and month of sampling, and all associated two-way interaction terms (sex*age, sex*body mass, age*body mass, month*age, month*sex and month*body mass). The nuisance variables year of sampling (2010-2011 or 2012) and feces water content (water%) were added as fixed factors to all models to account for yearly variation and the known influence of fecal water content on fGCM levels in a sample [Hämäläinen et al. 2014b]. Individual identity was included as a random effect to account for repeated sampling of the same individuals.

Due to the modest sample size and substantial “noise” typical for hormone data, we removed non-significant interaction terms (Likelihood Ratio Tests, LRT, P >0.1) one at a time from the full model to reduce over-parameterization and to identify significant effects [Pinheiro and Bates 2000].

Predictions based on the full models indicated similar patterns as the reduced models, and are presented in the electronic supplementary material.

Age and body mass were log-transformed, centered and scaled (mean/SD) prior to the analyses to improve the interpretability of interactions [Schielzeth 2010]. The normality and homogeneity of error assumptions were examined using residual plots for the most complex model for each data set.

Satterthwaite estimation was used to compute P-values (lmerTest-package, [Kuznetsova et al. 2014]).

To gain an estimation of significance for factors with more than 2 levels (month, month*body mass), LRT was used. Marginal and conditional R²-scores [Nakagawa and Schielzeth 2013] were computed using the r.squaredGLMM-function of the MuMIn-package [Barton 2014]. All analyses were performed in R version 3.0.3 [R Development Core Team 2014] and statistical significance was set at P≤ 0.05.

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R

In the rainy season model, the only interaction term remaining after model simplification was month*body mass (Table 1, Fig 1a). In the dry season model, the interaction terms sex*age and sex*body mass were retained. The main effects of the terms sex, age, month, body mass and the control variables water% and year of sampling were retained in the models regardless of their significance (Table 1).

Results from the season-specific models indicate that fGCM-levels were higher at old age in the dry season as predicted based on the coping hypothesis. However, this relationship was much stronger in females, as indicated by the interaction term sex*age (Table 1, Fig 1b). A positive association was found between body mass and fGCM levels in males in dry season, whereas the trend for females was negative, as indicated by the interaction term sex*age (Table 1, Fig 2b). In the rainy season, the term month*body mass indicated that body mass was negatively associated with fGCM in March and May, but in April, this relationship was leveled or even reversed (Table 1, Fig 2a). The nuisance variables water% and year of sampling had statistically significant effects in each season.

Table 1. Predictors of fGCM in the dry and the rainy season based on the best model after exclusion of non-significant interaction terms.

Dry ^a Rainy ^b

β SE t P β SE T P

Intercept 6.522 0.468 13.945 <0.001 7.694 0.672 11.450 <0.001 Water% -1.451 0.444 -3.269 0.001 -4.028 0.724 -5.565 <0.001 Year 0.309 0.144 2.153 0.033 0.867 0.249 3.482 <0.001 Month2 ^c -1.134 0.140 -0.962 0.338 ^d -0.616 0.250 -2.462 0.015 Month3 ^c 0.080 0.245 0.327 0.744 ^d -0.066 0.344 -0.192 0.847 Sex (ref. female) 0.416 0.332 1.253 0.212 0.201 0.242 0.832 0.409 Age 0.760 0.187 4.069 <0.001 0.128 0.176 0.724 0.472 Body mass -0.435 0.341 -1.274 0.205 -0.521 0.291 -1.789 0.075 Sex:Age -0.667 0.221 -3.024 0.003

Sex:Body mass 0.812 0.384 2.117 0.036

Month2:Body mass 0.762 0.282 2.698 0.008 ^e

Month3:Body mass -0.087 0.395 -0.220 0.826 ^e

a N= 170/126, final model R²_marginal= 0.243, R²conditional= 0.560

b N= 204/76, final model R²_marginal= 0.300, R²conditional = 0.507

c Month within season: Reference value 1=March & September, 2=April & October, 3=May &

November

d Month LRT: X₂²= 1.098, P= 0.578

e Month*body mass LRT: X₂²= 12.187, P=0.002

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Figure 1: Age did not influence on fGCM-levels of wild Microcebus murinus in the (a) rainy season, but was positively associated with fGCM in (b) dry season in the females (sex*age interaction, P =0.003).

Solid lines indicate prediction lines from the final model for year 2012 (predictions for 2010/2011 have slightly lower values but lines are parallel), and dotted lines indicate associated 95% confidence intervals. Data points are shown for both years. The predictions are based on scaled and centered age but corresponding chronological age in years is presented on x-axis. Note log scale used on y-axis.

121 Figure 2: Body mass as a predictor of fGCM-levels. A significant interaction of month*body mass (P = 0.002; prediction lines shown only for females, the patterns are similar for both sexes) was found in (a) the rainy season, and in (b) the dry season an interaction of sex*body mass was significant (P = 0.036). All data points are shown, prediction lines are only for year 2012 (predictions for 2010/2011 have slightly lower values but lines are parallel). Solid lines indicate predictions from the best models for each season, and dotted lines show the associated 95% confidence intervals. The predictions are based on scaled and centered body mass but corresponding untransformed body mass (grams) is presented on x-axis. Note log scale used on y-axis.

D

In this study, we tested the coping hypothesis of aging, evaluating whether there are physiological indications that the performance of aged individuals in a natural population of gray mouse lemurs is impaired during energetically demanding environmental conditions. In support of this hypothesis, we found a sex-specific, seasonal age effect, with old females showing significantly higher stress hormone (GC) levels than young females in the dry season, when intrinsic and extrinsic factors both may increase the allostatic load. No age effect was found in males in the dry season or in either sex in the non-reproductive, rainy season, when food availability is high.