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Data from: Cold and hungry: combined effects of low temperature and resource scarcity on an edge-of-range temperate primate, the golden snub-nose monkey

Citation

Hou, Rong et al. (2020), Data from: Cold and hungry: combined effects of low temperature and resource scarcity on an edge-of-range temperate primate, the golden snub-nose monkey, Dryad, Dataset, https://doi.org/10.5061/dryad.73n5tb2tz

Abstract

Both biotic and abiotic factors play important roles in influencing ecological distributions and niche limits. Where biotic and abiotic stressors co-occur in space and time, homeostatic systems face a different category of challenge in which stressors compound to impose a challenge that is greater than the sum of the separate factors. We studied the homeostatic strategies of the golden snub-nosed monkey (Rhinopithecus roxellana), a species living in temperate deciduous forests at the edge of the global distribution range for folivorous primates, to cope with the co-occurrence of cold temperatures and resource scarcity during winter. We discovered that in winter the monkeys experience a dietary energy deficit of 101 kJ/mbm·day-1 compared with calculated needs, despite increased feeding. This is partly offset by behavioral changes (reduced locomotion and increased resting) and reducing skin temperature by an average of 3.2 oC through a cutaneous vasoconstriction to decrease heat loss. However, their major strategy is ingesting surplus energy and accumulating fat reserves when food was not limiting during summer and autumn. Their 14% of body mass lost over the winter represented an energy yield of 102 kJ/mbm·day-1, which closely matched the calculated winter energy deficit of 101 kJ/mbm·day-1. However, the latter value assumes that all the 75.41kJ/mbm·day-1 of protein ingested in winter was available for energy metabolism. This is almost certainly an over-estimate, suggesting that the study population was in negative energy balance over the study period. Our study therefore suggests that despite its suit of integrated homeostatic responses, the confluence of low temperatures and resource limitation during winter places this edge-of-range primate close the threshold of what is energetically viable. It also provides a framework for quantitative models predicting the vulnerability of temperate primates to global change.

Methods

Study Site

Our study was conducted in Zhouzhi National Nature Reserve on the northern slope of the Qinling Mountains, which is the northernmost edge of R. roxellana’s range (107°45´-108°18´E, 33°42´-33°54´N, 56.39 km2). The area is 90.5% forest, primarily deciduous broadleaf, mixed deciduous broadleaf, and conifer forests (Li and He 2007). Our focal group (GNG group) has been studied since the 1999 and uses 2,250 ha that covers an elevational gradient from 1,380–2,974 m (Li et al. 2000). There were 12-14 one male with multi-female units and an all-male band (24-36 individuals), totaling 146-159 individuals.

Even though our aim was to examine the nutritional budget of R. roxellana in a situation where resource scarcity coincided with high energy demand, we nonetheless provided a low level of supplementary foods (30g freshly sliced radish and 60g dry corn per individual per day) in April and May and between October and December (Hou et al. 2018). This maintained a provisioning cycle they had experienced since 2001, albeit at a reduced level, avoiding a situation where an abrupt change affected the behavior and observability of the monkeys. So doing helped ensure that our results are comparable to the earlier study Guo et al. (2018), which were similarly provisioned but to a higher level.

We used a meteorological monitoring system (CR200X, USA) located at 1,600 m to record the temperature in the group’s core area. The China Meteorological Administration (C.M.A. 2012) views the winter as starting when the daily average temperature (DAT) is below 10 oC for 5 days and ends on the first day the DAT exceed or equal 10 oC for 5 days. Similarly, the summer is defined as starting when the DAT exceeds 22 oC for 5 days and ends on the first day the DAT is equal to or less 22oC for 5 days. The in between periods are spring and autumn. Over the last seven years (May 2011 to April 2018) winter lasted 163.8 ± 6.8 days (M ± SD, mainly from middle October to March). The average daily temperature in winter was 2.4 ± 0.6 oC (range from -15.8 to 13.6 oC). The monthly average minimum temperature was below 0oC from late November to early March (totally 113 days, Fig. 1).

We selected 25 food species that previous research indicated were frequently eaten (constituting 88.7% of the overall diet (Hou et al. 2018)) and recorded the abundance of leaves, fruits, seeds, and buds on a scale of 0-4. We calculated a seasonal Food Availability Index (FAI) by multiplying this phenology score (mean score of three months) by the density of each species (number of stem/ha; Fig. 1) and summed this score across species.

Behavioural Data

We collected feeding data for one year (September 2014 to August 2015) for adult males, lactating females, non-lactating females, and juveniles. Golden snub-nosed monkeys are strictly seasonal breeders that reproduce every second year. Females conceive in autumn (1-2 months after the peak of food availability), gestating during winter and giving birth from mid-March to May (2-4 months before the peak of the food availability) (Xiang et al. 2017). Each day one individual was followed from dawn to dark. We collected 80 full-day follows (10.14 ± 0.04 hr per day, M ± SE), including 20 days per season, and 5 days for each of the four categories of individuals per season. For each feeding bout, we recorded plant species, part (e.g., leaf with petiole or not, bark with periderm or not), and start-stop time. In general, we defined a feeding patch as a single tree or liana (woody vines that twine the trees); however, when animals fed on fallen oak seeds, an important food items from late autumn to early spring, the area below the canopy was regarded as a food patch.

We used Shannon-Wiener diversity index (H’) to characterize the diversity of food items eaten each season (Krebs 1989) using the top 20 food species each season, based on the proportion of consumption time. We quantified the time allocation of individuals to different activities to determine activity budgets. The activities considered were feeding, moving, resting, and other activities. We determine daily path length (DPL) only of adults, as juveniles were not identified individually. We are able to be within 3 to 10 m of focal adults when they were feeding, resting, and moving, which facilitated individual recognition and permitted accurate estimation of location with the GPS (Garmin, GPSMAP 63SC, Garmin Ltd., USA).

Body Mass and Temperature

All the monkeys are fully habituated, which enabled us to take thermal images from a distance of 3 to 5m. To determine if animals lost body mass over the winter, we weighed individually recognizable animals with a platform scale (accuracy, 0.02kg; EM-60KAL, A&D, Japan, Fig. 2a) over two weeks shortly after the start of winter (early December; Fig. 1) and in two weeks shortly after the start of spring (in early April). We lured the monkeys onto the platform of the scale using a small amount of corn, enabling us to record their body mass. We were unable to individually recognize juveniles, so the same juvenile may have been weighed more than once. We collected 188 body weights (adult males, n = 40; adult females, n = 80; juveniles, n = 68) with the two sampling periods having equal sample sizes. We estimated the energy value of decreased fatty tissues by multiplying the body mass loss by 37 kJ/g (energetic conversion factor for fat). It is difficult to measure animal’s internal temperature in the wild, so we used a thermal infrared imager (FLIR T640, USA) to assess monkeys’ facial skin temperature (Tfs) (Fig. 2b). We measured Tfs from recognizable adults from a distance of about 2 m in mid-December (winter) and mid-April (spring), respectively. To avoid any potential bias of Tfs due to the time of day and differences in activity levels, we measured the monkeys’ Tfs from 10:00 a.m. to 12:00 p.m. when monkeys were resting and inactive. Samples were taken once a day for a two-week period per season. Temperature data were assessed using FLIR R&D analysis software using only high-quality images (front view and near-distance).

Diet

We used the standard techniques to collect the food samples (Rothman et al. 2012), and analyze the foods nutrients and calculate the energy values (van Soest 1994), as used in our previous studies (Guo et al. 2018, Hou et al. 2018).

Total non-structural carbohydrates (TNC) was calculated by subtracting protein, lipids, NDF and ash from total dry mass (Rothman et al. 2012). Non-protein energy (NPE) was calculated using the summation of TNC, NDF, and lipids. Daily energy intake (DEI) was calculated through summation of all foods eaten during a day. To standardise the difference among different age-sex classes, we divided our calculated results by the individual’s metabolic body mass (mbm = M0.762, where M is body mass in kg (Nagy 1994)). Body mass for age/sex classes was based on average measurement for adult individuals and juveniles in winter and spring and we used the averaged body mass in spring and winter to represent the body mass in summer and autumn.

Energy expenditure

We evaluated energy budgets using two different indices: average daily metabolic need (ADMN) and daily energy expenditure (DEE). ADMN had been used by DaSilva (1992) to estimate the energy budget of Colobus polykomos (see also (Wasserman and Chapman 2003)). DEE was evaluated following the regression equation recommended by Pontzer et al. (2014) , who formulated the relationship between DEE (using the doubly labelled water technique) and body mass for 17 primate species and derived the following equation:

y=0.69x+2.09, r2 = 0.97

where y indicates log10DEE (kcal/day), x indicates log10 body mass (kg). We did not distinguish body weight difference between lactating and non-lactating female.

The primates used by Pontzer et al. (2014) occupy tropical and subtropical regions, so we calculated the DEE only for warm seasons (spring, summer, and autumn). For temperature-living primates, the DEE in winter (DEEW) should take into account the additional daily energetic costs of thermoregulation (ADECT) and baseline DEE (calculated from the above equation). The difference value of the ADECT between winter and spring was measured as 329kJ/mbm (Guo et al. 2018).

Average daily metabolic need (ADMN) was estimated using the follow equation:

ADMN=130*W0.76224*S+BMR*0.23884624*24-S+T*d*W*4.1868

where W = body mass (kg), S = number of active hours each day, T = travel costs (kcal/kg*km) = 0.1 (10)1.67*W-0.126, d = daily path length, DPL (km), we used seasonal average DPL to represent d, Basal Metabolic Rate (BMR) was estimated by the following equation: BMR = 70 * 4.1868kJ/ kcal* body mass0.762. The ADMN values calculated through the above equation are used in warm seasons (spring, summer and autumn), while the ADMN in winter (ADMNW) should take the additional daily energetic costs of thermoregulation (329 kJ/mbm) and baseline ADMN in winter (calculated from above equation) into account.

We considered the energetic costs of pregnancy and lactation for females when calculating ADMN and DEE. The costs of pregnancy and lactation individuals have been considered to increase energy expenditure, so, we multiply 1.25 and 1.5 in calculating the ADMN and DEE, respectively (Vogel, et al. 2017).

Usage Notes

no missing values in the upload dataset.

Funding

Strategic Priority Research Program of the Chinese Academy of Sciences, Award: XDB31020302

National Key Programme of Research and Development, Ministry of Science and Technology, Award: 2016YFC0503200

National Nature Science Foundation of China, Award: 31870396

National Nature Science Foundation of China, Award: 31730104

National Nature Science Foundation of China, Award: 31672301

National Nature Science Foundation of China, Award: 31872247

National Nature Science Foundation of China, Award: 31572278

Natural Science Foundation of Shaanxi Province, Award: 2018JC-022

Natural Science Foundation of Shaanxi Province, Award: 2016JZ009

Strategic Priority Research Program of the Chinese Academy of Sciences, Award: XDB31020302