Introduction

Seasonal changes in temperature, rainfall, and food availability are dominant ecological forces shaping animal physiology, behaviour, and survival (Demas & Nelson, 1998; Tonkin et al., 2017). Species have evolved diverse strategies to cope with such environmental variation (Varpe, 2017), typically classified into behavioural, morphological, and physiological adaptations (Taff et al., 2024; Fuller et al., 2020). Behaviourally, animals may alter their activity patterns, feeding ecology, or migrate to track resources (Winger et al., 2019). For example, Japanese macaques (Macaca fuscata) mitigate the winter cold by bathing in local hot springs (Hanya, 2004; Takeshita et al., 2018), while geladas (Theropithecus gelada) living at high altitudes subsist on abundant but low-quality grasses (Dunbar, 1998). Morphological adaptations to environmental variation include changes in coat insulation and coloration (Asres & Amha, 2014). Physiological adaptations include endocrine changes, which are central to regulating metabolism, thermoregulation, and energy allocation in fluctuating environments (Dufty et al., 2002). Monitoring hormone patterns therefore provides a valuable means of assessing how animals physiologically adapt to seasonal change (Scherbarth & Steinlechner, 2010). In particular, thyroid hormones play a key role in maintaining homeostasis in mammals, regulating metabolism, growth, thermoregulation, and seasonal physiological transitions, such as moulting, migration, and reproduction (Schmid & Kappeler, 2005; Shinomiya et al., 2014). These hormones are produced via the hypothalamic–pituitary–thyroid (HPT) axis, which responds to variation in temperature, resource availability, and energetic demands (Behringer et al., 2018). Activation of the HPT axis increases secretion of triiodothyronine (T3; Behringer et al., 2018; Deschner et al., 2020), the biologically active form, and thyroxine (T4), which is largely converted peripherally (Wasser et al, 2010).

Seasonal changes in T3 have been reported across diverse taxa, including sea lions, seabirds, and small mammals (du Dot et al., 2009; Benowitz-Fredericks et al., 2013; Rimbach et al., 2017). Reproductive stages such as gestation and lactation can further elevate T3 due to increased energetic demands (Rangel-Negrín et al., 2018). Noninvasive endocrine monitoring provides a reliable proxy for circulating hormone patterns without disturbing the animals, by quantifying hormone metabolites excreted in feces or other matrices (Kersey & Dehnhard, 2014). Non-invasive thyroid hormone monitoring has been successfully implemented to monitor metabolic profiles in several primate species, such as yellow baboons (Papio cynocephalus; Gesquiere et al, 2018) in which T3 metabolites were successfully measured and showed decreased levels during the dry season. This indicates this hormone may be used in a more metabolism-based role; in contrast, mantled howlers (Alouatta palliata) and Japanese macaques showed increased fTM levels during the cold seasons indicating a thermoregulatory role (Thompson et al., 2017). Patterns in fTM concentrations influenced by reproductive status have been observed in other primates. These reproductive influences may be related to the energy required for different stages, for instance, males requiring more energy during the mating season, or the metabolically demanding lactating period for females increasing fTM concentrations. For example, in Verreaux’s sifaka (Propithecus verreauxi; Byun et al., 2025), males invest significant energy in preparing for and during the mating season (Gittleman & Thompson, 1988). Barbary macaques show elevated fTM levels one month prior to the mating season (Cristóbal-Azkarate et al., 2016). Therefore, this is another aspect to consider when approaching a longitudinal study.

Because differences in species, age, and sex can affect hormone metabolism and excretion, enzyme immunoassays must be validated before they can reliably measure hormone levels in a new taxon (Sheriff et al., 2011). Physiological validations use controlled stimulation, such as administering thyroid-stimulating hormone (TSH), to induce a predictable endocrine response, allowing confirmation that fecal metabolite measures accurately reflect hormonal levels (Behringer & Deschner, 2017). Although this approach provides strong evidence of biological relevance, it may be invasive or logistically difficult in some field settings (Touma & Palme, 2005; Behringer & Deschner, 2017). Therefore, scientists have been successfully using non-invasive measures to collect samples that can be used for validations (for instance, faeces and urine; Palme, 2005; Wasser et al., 2010) for any species.

The thick-tailed greater galago (Otolemur crassicaudatus; Nash et al., 1989; henceforth galago) inhabits strongly seasonal environments across central and southern Africa (Nekaris & Bearder, 2007). During the warm, wet season, these nocturnal primates consume insects, fruit, and gum, all of which are abundant; during the cool, dry season, they rely more heavily on gum as insect and fruit availability declines (Bearder, 1974; Hladik, 1979; Clark, 1985; Harcourt, 1986). They are solitary foragers, and females sleep with kin while males sleep alone (Bearder, 1974, Masters et al., 1988). Galagos reduce activity during the colder periods when nutritional intake decreases (unpublished data). Their reproduction is tightly synchronised: mating occurs in June–July, gestation spans the dry months, and lactation coincides with the resource-rich wet season (Eaton et al., 1973; Ehrlich, 1974; Sauther et al., 2024). Once the mating season has ended, females provide all care for offspring, with no contribution from males. There are no significant seasonal differences in glucocorticoids in males, but in females the lactating period had a significant increase in glucocorticoid levels (Long et al., 2021).

We aimed to identify the environmental and reproductive drivers of thyroid hormone activity in galagos using fecal T3 metabolite measurements. We first conducted a preliminary physiological validation (TSH challenge) of an enzyme immunoassay to quantify fTM. We then used the assay to investigate seasonal variation in fTM concentrations across 18 months. Specifically, we determined the ambient temperature and rainfall over the study period as our environmental variables, we measured insect, fruit and gum availability as indicators of resource availability, and we determined reproductive stage by physical examination of the reproductive organs in males and females. Because galagos reduce activity when food resources decline, we tested two hypotheses. First (H1), variation in thyroid activity reflects energetic and resource conditions rather than thermoregulatory demand under cold conditions. Under H1, we predicted that fTM would (P1) increase with ambient temperature, whereas a thermoregulatory role predicts the opposite, and (P2) increase with resource availability (insect abundance, fruit and gum availability, and rainfall as a proxy for productivity). Second (H2), thyroid activity increases with the energetic demands of reproduction. Under H2, we predicted (P3) that fTM would be elevated in females during gestation and lactation relative to males sampled in the same periods.

Methods

Study Site

We conducted fieldwork at Lajuma Research Centre (23° 02’16.2”S, 29°26’35.4”E) in the Soutpansberg Mountains, Limpopo Province, South Africa. The site lies on the windward (south-facing) slope of the range at elevations of 1200 m to 1500 m and is characterized by pronounced seasonality, with warm, wet conditions from October to March and cooler, drier months from April to September (Sauther et al., 2024). Vegetation in the area includes bushveld, mist-belt montane forest, and savannah habitats (Van Wyk & Smith, 2001; Von Maltitz et al., 2003; Hahn, 2017). We sampled galagos across approximately 3 km2 along established transects running northwest to southeast (Phukuntsi et al., 2020). Galagos of both sexes have been observed travelling up to 2 km in the study area (unpublished data), suggesting they do not have strict territories.

Environmental Data

We recorded weather data, as described in Long et al. (2021), from the on-site weather station (Davis Instruments sensor suite linked via wireless to a Davis Pro 2™ console, Measurement and Console Systems CC, Cape Town, South Africa). The Ndlovu Node of the North-Eastern Mountain Observatories project of the South African Environmental Observation Network (SAEON) processed the data into CSV files. The device is situated in a central location on the property providing weather records from this location. The device recorded temperature and rainfall every 30 min. For each month and season, we calculated mean daily high and low temperatures (°C) and cumulative daily and monthly rainfall totals (mm).

Food Availability

To assess insect availability, we set up two light traps near the field campsite from September 2017 to June 2018 (trap 1: 23°02'19.6"S 29°26'34.8"E; trap 2: 23°02'20.4"S 29°26'34.6"E). We positioned the traps approximately 20 m from other light sources, with one lamp <1 m above ground and the second mounted ~3 m high on a tree branch. Both remained in fixed locations throughout the study. We switched on lights before sunset (17:00–19:15) and off before sunrise (04:30–06:00). Each day, we counted and identified captured insects to order level or common name when possible and preserved each specimen in 70% ethanol or 90% isopropanol.

We also quantified dietary components directly from fecal samples. We freeze-dried, dissected, and examined fresh samples collected during monthly trapping sessions (N = 65; August 2017–June 2018) searching for seeds and insect fragments. We counted all seeds and identified the insect remains when possible and measured them volumetrically in a 1.7 mL vial to the nearest 0.1 mL. These measures provided an indicator of monthly variation in fruit and insect consumption.

We measured monthly weights of gum from selected sites to assess gum availability from July 2017 to June 2018. The Vachellia karroo gum tree was most frequented by galagos (unpublished data). This tree expels gum from its bark via cracks, notches or damage to the tree (e.g., by baboons biting open the bark to expose the gum). Galagos feed on the gum, which is the consistency of thick glue and flows slowly from damage to the bark. We selected 13 trees in July 2017 based on the visible presence of gum and where we had observed galagos feeding. We added 23 further trees in August 2017 as observations of galagos feeding activities increased (mean number of trees per month = 23.36 ± 8.88; range 10–36). Once a month, we collected the gum from these gum sources on the same trees to improve consistency; however, some months there was limited gum exudate production. We removed all the gum from trees present from one of the openings in the bark using a sharp knife, avoiding removal of tree bark. We placed the samples in zip lock bags and transferred them to the laboratory. We weighed the samples in grams, then stored them at −4 °C for future nutritional content analysis.

Seasonal fTM Analysis

We identified four seasons in the 18-month study: dry1 (June–September 2017), wet1 (November 2017–February 2018), dry2 (May–July 2018), and wet2 (December 2018). We selected these periods to represent the temperature and rainfall extremes. Temperatures range between 18 °C and 40 °C in the wet period and drop to between 12 °C and 22 °C in the cold dry period. Rainfall is between 300 and 500 mm in the wet period and decreases to approximately 20 mm in the dry period.

We collected 126 fecal samples from 16 adult females (N = 64; mean = 4.00 ± 3.06 samples/individual) and 23 adult males (N = 62; mean = 2.70 ± 2.08 samples/individual). Samples were distributed across dry1 (N = 28), wet1 (N = 36), dry2 (N = 36), and wet2 (N = 26). We used these data to quantify seasonal variation in fecal triiodothyronine metabolite (fTM) concentrations.

Sampling

We collected fecal samples from June 2017 to December 2018, with each sampling period consisting of two consecutive weeks. We followed the standardized trapping method described in Long et al. (2021). Briefly, we secured 20 Havahart® traps to tree branches, approximately 1–2 m above the ground, and baited with banana and peanut butter. We set traps just before sunset (17:00–18:30) and checked them early in the morning (05:00–07:00). We captured a mean of two individuals (range 0–5 individuals) each night, but success depended on the location. We had no adverse events linked to capture during the sampling period. We placed captured individuals in cloth handling bags, then weighed and sexed them, and then scanned for identification using a passive identification transponder (ID100 Trojan, EURO I.D., Weilerswist, Germany). When we were finished recording their information, we released the animal at the capture site. If an individual did not carry a transponder tag, we took the individual back to the field lab, where the veterinarian anaesthetised the animal, and we administered a new transponder.

We collected fresh fecal pellets deposited in the traps into sterile plastic sample bags. We only collected samples that appeared fresh (dark, moist, and soft in texture), and we excluded old or desiccated samples and also those contaminated with urine. We cleaned the traps daily. To minimise the effects of diurnal hormonal variation, we pooled all feces produced by a single individual during one night and treated as a single sample.

Reproductive Stage

We conducted a comprehensive sampling event every 3 months over a 7-day period. During these sessions, a registered veterinarian performed health examinations and assessed reproductive stage. In males, we evaluated mating condition by measuring the right testis (length and width) with a tape measure and calculating testis volume using the formula ⅙π(LW2). Increases in volume typically correspond to the mating season (Møller, 1988). In females, we recorded vulval condition (open = ovulating; closed = not ovulating), vulval length, and nipple prominence (more predominant nipples indicate lactating females). The veterinarian determined pregnancy in females by abdominal palpation.

We defined reproductive periods following Bearder (1974), Masters et al. (1988), and our unpublished observations: premating (males showing aggressive behaviour towards other males; May 2018), mating 1 (males chasing females for mating, June–July 2017), pregnant 1 (when females are pregnant; August–September 2017), early lactating 1 (females have given birth and are lactating and the offspring are young requiring constant attention; November–December 2017), late lactating (the females may still be lactating, with offspring older and not requiring constant attention; January–February 2018), mating 2 (June–July 2018), early lactating 2 (December 2018).

Physiological Validation

To assess whether the selected EIA detects changes in fecal T3 metabolites (fTM) in galagos, we conducted a TSH challenge. Because no captive individuals were available in South Africa, we used one male and female individual from the free-ranging study population. We used a validation approach that has been successfully implemented in several wildlife species (Keech et al., 2010; Wasser et al., 2010; Mondol et al., 2020). We captured both individuals using baited Havahart® traps following a standardized protocol, measured them morphometrically, and a veterinarian examined them to confirm that the female was neither pregnant nor lactating. We transferred each animal to a separate on-site enclosure (350 cm x 120 cm x 230 cm). We shaded the enclosures with a black tarpaulin to reduce heat exposure, lined them with plastic sheeting to facilitate fecal collection, and enriched them with branches and a dark nest box. We provided the individuals with fruit, commercial cat pellets (Catmor®, South Africa), and peanut butter, with water available ad libitum. We allowed them a two-day acclimation period with minimal human disturbance. From the third to the fifth day, we collected fecal samples hourly at night (18h00–06h00) and every two hours during the day (06h00–18h00) to establish baseline fTM levels. At 18h00 on day 5, the veterinarian administered an intramuscular injection of 2 IU/kg of TSH (Sigma Aldrich©, Sigma Aldrich Inc., USA) to both individuals. We continued with sample collection (feces collected every hour during the night and every 2 h during the day) until 18h00 on day 8, after which we released both animals at their original capture locations. We observed the animals during release to ensure they were unharmed. Subsequent captures of these two individuals close to the original capture site during the study assured us that the individuals were not injured or affected by the capture and housing.

Sample Storage and Extraction

We placed all fecal samples that we collected during the validation and seasonal study into a microcentrifuge tube and stored them in a −4 °C freezer at the field site. We transported the frozen samples to the National Zoological Gardens, South African National Biodiversity Institute (Pretoria, South Africa), for processing. We lyophilised, pulverised, and sieved samples through a thin mesh to remove non-fecal matter (Fieß et al., 1999). We extracted approximately 50–55 mg of dried fecal powder by adding 1.5 mL of 80% ethanol, vortexed for 15 min, and centrifuging at 1500 x g for 10 min (Wasser et al., 1993). We stored the resulting supernatant at −20 °C until hormone analysis. We conducted all EIA analyses at the Endocrine Research Laboratory, University of Pretoria.

Enzyme Immunoassay

We selected 54 fecal samples for analysis from the two individuals: preinjection: N = 20 (female: N = 10; male: N = 10) and postinjection: N = 34 (female: N = 17; male: N = 17). We selected samples with a viable volume of feces, and equal spacing before and after the injection hour, so that we could accurately estimate baseline values and capture the peak fTM level. We determined total fTM levels using the DetectX® Triiodothyronine Enzyme Immunoassay Kit (Arbor Assays™, USA), following the manufacturer’s instructions. Cross reactivities of the T3 antibody were 100% with T3 (L-triiodothyronine), 0.88% with thyroxine (T4), and 0.1% with reverse T3 (3,3’,5’-triiodo-L-thyronine).

We then used the assay to quantify fTM concentrations for the seasonal analysis. The intra-assay coefficients of variation (CV) ranged from 5.5% to 6.7% and the inter-assay CV ranged from 11.6% to 14.7%. The assay sensitivity of the triiodothyronine EIA was 37.6 pg/mL and limit of detection was determined as 46.6 pg/mL.

Statistical Analysis

We performed all statistical analyses in R (4.6.1; R Core Development Team, 2026). We report results as mean ± standard deviation unless otherwise specified and set statistical significance at P ≤ 0.05. We expressed fTM concentrations as nanograms per millilitre for dry weight of fecal matter (ng/g DW).

fTM Assay Validation

We determined a preinjection baseline from preinjection samples using an iterative procedure (Brown et al., 1994; Scheun et al., 2016; Long et al., 2021) in which values exceeding the mean + 1.5 SD were sequentially excluded, and the mean and SD were recalculated until no further values were removed and produced the baseline value for each individual; the resulting mean + 1.5 SD defined the elevation threshold. We determined the peak percentage response for each individual by dividing the peak fTM value postinjection by the calculated baseline and multiplying by 100 (Scheun et al., 2018). Owing to the small sample size (N = 2), we did not perform any formal statistical tests; instead, we compared pre- and postinjection values descriptively including the number of samples exceeding the threshold before and after injection.

Seasonal Analysis

We determined mean seasonal ambient temperature and rainfall for each of the four sampling periods (dry1, wet1, dry2, wet2). We evaluated seasonal differences in ambient temperature and rainfall by using Kruskal–Wallis tests, followed by post-hoc Dunn’s tests where appropriate.

Owing to right-skewed distributions, we also compared seasonal insect population counts, seed counts, and gum volumes by using Kruskal-Wallis tests followed by post-hoc Dunn’s tests when we detected significant differences. We tested for seasonal changes in testes volume, nipple length, and vulva length by using Kruskal-Wallis tests and post-hoc Dunn’s tests.

We summarised fTM concentrations (mean ± SD) by sex and sampling period to describe seasonal patterns; inferential tests of sex and environmental effects were conducted with the mixed models described below.

Linear Mixed Models

We tested the predictions with two linear mixed models, fitted with the “lmer” function in the lme4 package (Bates et al., 2015). We ln-transformed fTM concentrations to meet the model assumptions of normality and homoscedasticity. Both models included individual identity as a random intercept to account for repeated sampling of the same individuals (16 females, 23 males).

Environmental Model (Predictions 1 and 2)

Fixed effects were ambient temperature (P1), and rainfall, insect abundance, seed counts, and gum availability (P2), with sex as a covariate. Rainfall, insect abundance, seed counts, and gum availability were right-skewed and contained zeros, so we ln(x + 1)-transformed them before analysis. A positive effect of temperature supports a metabolic role for T3, whereas a negative effect would indicate a thermoregulatory role. Food availability data were not available for December 2018 (wet2), so this model was fitted to 100 samples from 34 individuals. Because food data were unavailable for December 2018, we also fitted a model with temperature, rainfall, and sex only to all samples.

Reproductive Model (Prediction 3)

Fixed effects were reproductive stage, sex and their interaction (sex*reproductive stage), with early-lactating 1 as the reference stage. Because reproduction in this species is seasonally synchronised, reproductive stage is confounded with environmental conditions. Males, which do not gestate or lactate, therefore act as a within-period control. A reproductive effect predicts a sex × stage interaction, with higher fTM in females than males during gestation and lactation. A parallel change in both sexes instead indicates an environmental driver. If the interaction was not significant, we removed it and tested the main effects of stage and sex in the additive model. We compared the sexes within each reproductive stage (interaction model) and the reproductive stages with each other (additive model) using estimated marginal means with Tukey adjustment (emmeans package; Lenth, 2024).

We fitted models by maximum likelihood (REML = FALSE) so that nested models could be compared. We tested each fixed effect with likelihood ratio tests comparing the full model with a reduced model lacking that term (“lrtest” function, lmtest package; Zeileis & Hothorn, 2002). We calculated 95% confidence intervals with the 'confint' function (Wald method). We report marginal and conditional R2 (Nakagawa & Schielzeth, 2013) as measures of goodness of fit. We checked for multicollinearity with variance inflation factors (car package; Fox & Weisberg, 2019); all VIFs were < 3, so no predictors were removed (Zuur et al., 2010). We assessed model assumptions by visual inspection of residual plots and checked normality and homoscedasticity with the performance package (Lüdecke et al., 2021).

We present seasonal differences in climate, food availability, and reproductive indicators descriptively to characterise the study periods (Online Resource 1, Tables S1–S2); we did not use them to test predictions.

Ethical Statement

All procedures were approved by the South African National Biodiversity Institute (SANBI) National Zoological Gardens Research and Ethics Committee (18/26) and the University of Pretoria Animal Ethics Committee (V037-17). Ethical approval covered the biological validation, animal capture and housing, and the seasonal analysis. The authors declare that they have no conflict of interest.

Data Availability

The dataset analysed during this study and the R code used for the analyses are available as Online Resource 2 and Online Resource 3.

Results

Physiological Validation

We measured fecal T3 metabolite (fTM) concentrations in one male and one female from 24 h before to 27 h after TSH injection (Fig. 1). In the female, fTM exceeded the elevation threshold (mean + 1.5 SD = 0.23 ng/g DW) in six of ten postinjection samples, forming a sustained elevation between 3 and 15 h after injection that peaked at 6.5 h compared with two of ten preinjection samples (both 23–24 h before injection). In the male, fTM exceeded the threshold (mean + 1.5 SD = 0.19 ng/g DW) in two of ten postinjection samples, with a peak at 10.5 h, but also in four of ten preinjection samples, and the postinjection peak was of similar magnitude to the largest preinjection values (Fig. 1).

Fig. 1
Fig. 1
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Time series of fecal triiodothyronine metabolite (fTM) concentrations for one male and one female thick-tailed greater galagos (Otolemur crassicaudatus) in a thyroid stimulating hormone (TSH) stimulation test (pre- and postinjection) from 28 May to 2 June 2018 at Lajuma Research Centre, Limpopo, South Africa. Solid vertical line indicates the time of injection (hour 0). The dotted horizontal line indicates the baseline (mean) values determined for both individuals. The shaded region indicates the baseline threshold (mean + 1.5 SD). Black markers indicate postinjection samples above the threshold (mean + 1.5 SD).

Seasonal Analysis

The four sampling periods differed in weather and food availability (Online Resource 1, Table S1). Mean daily ambient temperature was higher in both wet periods than in both dry periods and differed between dry1 and dry2, whereas rainfall was highest in wet2 and lowest in dry2 (Online Resource 1, Fig. S1). We captured 5017 insects in the light traps and recovered 2032 seeds from 65 fecal samples, 71% of which were from fig (Ficus sp.) and Simmondsia chinensis fruits. Insect availability varied across seasons, whereby wet2 had the greatest abundance of insects and dry1 and dry2 were significantly lower in abundance than both wet1 and wet2 (Online Resource 1, Fig. S2). Seed counts were highest in wet1 and lowest in dry1, and gum availability did not differ among periods (Online Resource 1, Fig. S2).

In males, testis volume was lower in the late-lactating period than during mating 1 and premating 2 (Online Resource 1, Table S2; Fig. S3). In females, vulva length and nipple length did not differ among stages (Online Resource 1, Table S2; Fig. S4).

Seasonal fTM Concentrations

In both sexes, log-transformed fTM concentrations were highest in the wet periods and lowest in the dry periods (Fig. 2).

Fig. 2
Fig. 2
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Seasonal variation in log-transformed fecal triiodothyronine metabolite (fTM) concentrations in thick-tailed greater galagos (Otolemur crassicaudatus) at Lajuma Research Centre, Limpopo, South Africa between June 2017 and December 2018. Boxplots show medians (line in the boxplot), interquartile ranges, and outliers.

Environmental Predictors of fTM Concentrations

We fitted the environmental model to 100 samples from 34 individuals. The model explained significantly more variation in log-transformed fTM than a null model containing only the random intercept for individual identity (LRT: χ2 = 25.39, df = 6, P < 0.001; marginal R2 = 0.23; Table I). fTM increased with ambient temperature (β = 0.032, LRT: χ2 = 6.59, P = 0.010; Fig. 3a). Additionally, fTM increased with rainfall (β = 0.315, LRT: χ2 = 9.37, P = 0.002; Fig. 3b) but was not related to insect abundance (χ2 = 1.34, P = 0.25), seed counts (χ2 = 2.38, P = 0.12), or gum availability (χ2 = 1.22, P = 0.27). fTM did not differ between the sexes (χ2 = 2.63, P = 0.10). Among-individual variance was estimated as zero (conditional R2 = marginal R2), so repeated sampling of individuals did not affect these estimates. A climate-only model fitted to all 126 samples, including December 2018, gave the same result (temperature: β = 0.046, 95% confidence interval [CI] 0.029–0.064, χ2 = 24.66, P < 0.001; rainfall: β = 0.296, 95% CI 0.141–0.450, χ2 = 13.35, P < 0.001).

Fig. 3
Fig. 3
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Model-predicted log-transformed fecal triiodothyronine metabolite (fTM) concentrations of thick-tailed greater galagos (Otolemur crassicaudatus) in relation to ambient temperature (a) and rainfall (b) from the environmental linear mixed model with all other predictors held at their means, at Lajuma Research Centre, Limpopo, South Africa, June 2017 to July 2018 (N = 100 samples, 34 individuals). Lines show model predictions with 95% confidence bands.

Table I Results of linear mixed models assessing the influences of environmental and reproductive state predictors on log-transformed fecal triiodothyronine metabolite (fTM) concentrations in thick-tailed greater galagos (Otolemur crassicaudatus) at Lajuma Research Centre, Limpopo, South Africa, from June 2017 to December 2018. Both models included individual identity as a random intercept and were fitted by maximum likelihood. β, SE and 95% CI are Wald estimates; LRT is the likelihood ratio χ2 for dropping each term and p values for terms with an LRT are from that test. Reproductive stage estimates are relative to early-lactating 1. Environmental model: N = 100 samples from 34 individuals (December 2018 excluded owing to missing food data); reproductive model: N = 126 samples from 39 individuals
Full size table

Reproductive Stage and fTM

The change in log-transformed fTM across reproductive stages did not differ between the sexes (sex*reproductive stage interaction: LRT χ2 = 1.77, df = 6, P = 0.94; Fig. 4), and female and male fTM did not differ within any reproductive stage (all P > 0.5). Log-transformed fTM varied with reproductive stage (LRT χ2 = 47.26, df = 6, P < 0.001; marginal R2 = 0.32; Table I) similarly in both sexes. fTM concentrations were highest in early-lactating 2, and were significantly higher than in all other stages except late-lactating (Tukey-adjusted: P ≤ 0.03), and lowest during mating 2 and pregnancy (Table II; Fig. 4). fTM did not differ between the sexes (χ2 = 0.05, P = 0.83).

Fig. 4
Fig. 4
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Log-transformed fecal triiodothyronine metabolite (fTM) concentration (mean ± SEM) across reproductive stages of greater galagos (Otolemur crassicaudatus) sampled between June 2017 and December 2018 at Lajuma Research Centre, Limpopo, South Africa, shown separately for females and males.

Table II Log-transformed fecal triiodothyronine metabolite (fTM) concentrations by reproductive stage and sex of greater galagos (Otolemur crassicaudatus) sampled from June 2017 to December 2018 at Lajuma Research Centre, Limpopo, South Africa. Values are mean ± SD of natural-log-transformed fTM concentrations (ln fTM; fTM measured in ng/g DW), given the right-skewed distribution of raw hormone concentrations. Sample size (n) is given in parentheses for each sex
Full size table

Discussion

The TSH challenge provided preliminary support for the EIA, and fTM concentrations increased with ambient temperature and rainfall, but not with measures of food availability, and changed in parallel in both sexes across reproductive stages. The TSH challenge provided preliminary support for the use of this EIA in this species: the female showed a sustained postinjection increase in fTM, whereas the male’s postinjection values could not be distinguished from its preinjection variation.

The timing of the female's postinjection peak (6.5 h) aligns with gut passage times reported for the same species (~9 h for an ACTH validation; Long et al., 2021), other strepsirrhines (Galago moholi; ~14 h; Scheun et al., 2015), common marmosets (Callithrix jacchus; 8–24 h; Bahr et al., 2000) and yellow-breasted capuchins (Sapajus xanthosternos; ~8 h; Schaebs et al., 2016). Some species have shorter gut passage times, such as the golden-headed lion tamarin (Leontopithecus chrysomelas, 4–6 h, Fiorini-Torrico et al., 2026) and other callitrichids (2–6 h, Power & Oftedal 1996), demonstrating the variation in gut passage times, highlighting how important these assay validations are for each species. These findings emphasise the importance of species-specific gut transit times when interpreting hormone patterns in free-ranging animals.

TSH-induced increases in fecal T3 metabolites reported in other mammals include those in bottlenose dolphins (44%; Tursiops truncatus; Houser et al., 2021) and Steller sea lions (18–57%; Eumetopias jubatus; Keech et al., 2010). The repeated peaks observed before injection are consistent with the pulsatile and rhythmic pattern of thyroid axis activity. In mammals, TSH is secreted in ultradian pulses superimposed on a circadian rhythm, influencing fluctuations in circulating T3 (Brabant et al., 1990), and within-individual variation in thyroid parameters arises from pulsatile secretion, circadian rhythm and seasonality (van der Spoel et al., 2021). Such variability is expected to propagate into fecal metabolite profiles, which are further influenced by diet, gut passage and individual differences (Palme, 2005; Wasser et al., 2010). A fluctuating preinjection baseline is therefore expected in noninvasive thyroid monitoring. However, these explanations account for the preinjection variation; they do not in themselves show that the postinjection changes were caused by TSH. Comparable fluctuating baselines have been reported in fecal thyroid hormone validations in other primates (Behringer et al., 2023), and challenge-based physiological validations in other taxa likewise identify the response against a variable, individually defined baseline rather than against an assumed quiescent state (Braga Gonçalves et al., 2016; Leach et al., 2024).

The TSH challenge should therefore be regarded as preliminary, supportive evidence for the assay rather than a definitive physiological validation. The challenge involved only one male and one female and had no control (e.g., saline-injected) treatment, so postinjection changes cannot be attributed to TSH with certainty, and the responses of two individuals cannot be generalised to the population. Preinjection fTM concentrations also varied considerably: in the male, several preinjection samples exceeded the threshold and matched the postinjection peak, so its response cannot be distinguished from baseline variation. The female's response is more convincing, because fTM remained above the threshold for most of the 3–15 h postinjection period, whereas her preinjection exceedances were isolated. Other lines of evidence are consistent with the assay detecting biologically relevant variation: the timing of the female's response matches gut passage times measured in the same population (Long et al., 2021), the T3 antibody has low cross-reactivity with thyroxine and reverse T3, and fecal T3 assays have been validated in other primates (Gesquiere et al., 2018; Chen et al., 2021). Nevertheless, a TSH challenge with more individuals and a control treatment, ideally in captive animals, together with analytical validation, is needed before the assay can be considered fully validated for this species, and the seasonal patterns below should be interpreted with this limitation in mind.

We then applied the assay to explore natural fluctuations in thyroid activity across seasons. Consistent with our predictions, fTM concentrations were higher during wet periods and lower during dry periods. In the environmental model, fTM increased with both ambient temperature and rainfall, whereas direct measures of food availability were not significant predictors. This suggests that seasonal climatic conditions are associated with thyroid activity, although, as discussed below, these effects cannot be separated from reproductive stage.

As predicted, fTM concentrations were positively associated with ambient temperature. Animals routinely adjust both behavioural and physiological mechanisms in response to environmental fluctuations, and one possible explanation for our results is that galagos reduce activity and metabolic output during colder periods to conserve energy. Similarly, reduced encounter rates between individuals for this population occur during the cold, dry season, suggesting behavioural energy conservation (Sauther et al., 2024). Similar patterns have been documented in other primates, including the African lesser bushbaby (Galago moholi; Nowack et al., 2013), Japanese macaques (Macaca fuscata; Hanya 2004), Barbary macaques (Majolo et al., 2013), and collared brown lemurs (Eulemur collaris; Donati et al., 2011), all of which decrease their activity in cold weather to reduce thermoregulatory costs. This pattern could suggest that galagos are relying on decreased activity (including feeding) to reserve energy levels and reduce energy expenditure on thermoregulation. In some species, cold exposure can lead to an increase in metabolic rate and elevated thyroid hormone concentrations (McBride et al., 1985; Burger & Denver, 2002), for instance in Japanese macaques and mantled howler monkeys (Thompson et al., 2017). This pattern contrasts with the thermoregulatory response reported in Japanese macaques and mantled howler monkeys, where cold exposure elevates thyroid hormone concentrations to support heat production; instead, the positive association between fTM and ambient temperature and rainfall is consistent with fTM in galagos being tied to metabolic demands linked with resource availability and activity rather than to cold-induced thermogenesis, although we did not measure activity and direct food measures were not related to fTM.

We also found a positive relationship between fTM concentrations and rainfall. Direct measures of food availability (insect abundance, seed counts, and gum availability) were not related to fTM, so our data do not show that resource availability itself drives thyroid activity. One possible interpretation is that rainfall acts as a broader proxy for primary productivity and resource availability than any single food resource we measured, because rainfall supports vegetation growth and, indirectly, the insects and fruit on which galagos feed. If so, lower fTM during dry periods could reflect down-regulation of the HPT axis to reduce metabolic expenditure when food is limited, as described in other vertebrates (Eales, 1988); for example, plasma T3 decreases during reduced-food and hibernation periods in black bears (Ursus americanus; Tomasi et al., 1998). Conversely, elevated T3 during periods of food abundance has been reported in Barbary macaques (Cristóbal-Azkarate et al., 2016), mantled howler monkeys (Dias et al., 2017), and killer whales (Orcinus orca; Ayres et al., 2012). However, we did not measure food intake or energy balance directly, so this mechanism remains to be tested in galagos, for example by relating fTM to individual measures of diet, body condition, or activity.

Because reproduction is seasonally synchronised, climatic conditions and reproductive stage were strongly confounded in this population: the highest fTM concentrations, in both sexes, occurred during the second wet season, which coincided with the second early-lactating stage. Our separate models therefore cannot establish whether environmental or reproductive factors have the greater influence on thyroid activity. Lactation is metabolically costly as mothers are required to produce sufficient milk for several months postpartum (Altmann, 1980; Sauther, 1998; Dufour & Sauther, 2002), and several studies have observed increased feeding during lactation (e.g., Papio cynocephalus, Muruthi et al., 1991; Theropithecus gelada, Dunbar et al., 2002; Pan troglodytes, Thompson et al., 2012). However, fTM changed in parallel in both sexes, and females did not show elevated fTM relative to males during gestation or lactation, suggesting that the seasonal pattern is not driven by female-specific reproductive demands. We cannot exclude that lactation contributes to thyroid activity in females, and experimental or multiyear studies in which reproductive timing varies relative to climate would be needed to separate environmental from reproductive contributions. In other seasonal breeders, such as Barbary macaques, males show increased fTM levels close to the breeding season (Cristóbal-Azkarate et al., 2016), which may suggest a role for T3 in spermatogenesis (Wagner et al., 2009). In female mantled howler monkeys, reproductive state had a major influence on fTM levels, specifically, gestating females showed the highest levels of fTM concentrations, followed by lactating females while the males showed the lowest levels (Dias et al., 2017), highlighting the energetic demands required for the development of the offspring. In contrast, gestating and lactating female baboons showed lower fTM concentrations, which may reflect an energy-saving mechanism (Gesquiere et al., 2018).

Overall, fTM concentrations were highest in warm, wet conditions and did not show female-specific elevations during gestation or lactation, which is more consistent with a metabolic than a thermoregulatory role of thyroid hormones in galagos. These findings highlight that both environmental and reproductive pressures may influence thyroid activity, but the relative contribution likely varies by species and ecological context. More broadly, our results indicate that seasonal variation in thyroid activity in primates need not reflect cold-induced thermogenesis, and that separating climatic from reproductive influences requires study designs in which the timing of reproduction varies independently of season.