Re‐constructing nutritional history of Serengeti wildebeest from stable isotopes in tail hair: seasonal starvation patterns in an obligate grazer

Abstract

RationaleNutritional bottlenecks often limit the abundance of animal populations and alter individual behaviours; however, establishing animal condition over extended periods of time using non‐invasive techniques has been a major limitation in population ecology. We test if the sequential measurement of δ15N values in a continually growing tissue, such as hair, can be used as a natural bio‐logger akin to tree rings or ice cores to provide insights into nutritional stress.MethodsNitrogen stable isotope ratios were measured by continuous‐flow isotope‐ratio mass spectrometry (IRMS) from 20 sequential segments along the tail hairs of 15 migratory wildebeest. Generalized Linear Models were used to test for variation between concurrent segments of hair from the same individual, and to compare the δ15N values of starved and non‐starved animals. Correlations between δ15N values in the hair and periods of above‐average energy demand during the annual cycle were tested using Generalized Additive Mixed Models.ResultsThe time series of nitrogen isotope ratios in the tail hair are comparable between strands from the same individual. The most likely explanation for the pattern of 15N enrichment between individuals is determined by life phase, and especially the energetic demands associated with reproduction. The mean δ15N value of starved animals was greater than that of non‐starved animals, suggesting that higher δ15N values correlate with periods of nutritional stress.ConclusionsHigh δ15N values in the tail hair of wildebeest are correlated with periods of negative energy balance, suggesting they may be used as a reliable indicator of the animal's nutritional history. This technique might be applicable to other obligate grazers. Most importantly, the sequential isotopic analysis of hair offers a continuous record of the chronic condition of wildebeest (effectively converting point data into time series) and allows researchers to establish the animal's nutritional diary.

Tracking animal movements using biomarkers in tail hairs: a novel approach for animal geolocating from sulfur isoscapes

“…After hair collection, all hairs from an individual were aligned by root to standardize time zero (i.e. the most recent time), tied together and stored at room temperature [ 33 , 34 ].…”

Differentiating captive and wild African lion (Panthera leo) populations in South Africa, using stable carbon and nitrogen isotope analysis

“…Variation could be due to the influence of local factors such as site, season and equipment function (Jardine and Cunjak 2005). Additionally, as hair isotope composition varies from root to tip, reflecting nutritional changes during growth (Rysava et al 2016), sample selection and the degree of homogenisation can lead to inconsistent measurements between repeat samples (Jardine and Cunjak 2005). It is important to note that the sample sizes used in the inter-laboratory comparison are small, with single sample repeats carried out at both laboratories, making effective statistical analysis challenging.…”

From single steps to mass migration: the problem of scale in the movement ecology of the Serengeti wildebeest

“…Wildebeest aggregate in herds ranging from tens of individuals to up to 400 000 [26]. The highly synchronous calving of Serengeti wildebeest could be an emergent property of the seasonal environment where the cost of reproduction is high but only energetically possible in certain areas and during short periods of time [27], rather than be an adaptive response to predation. Breeding synchrony ultimately leads to all reproductively active females (about 450 000 animals) requiring the same resources at the same time.…”

“…However, from a safety point of view solitary animals are more exposed to predation; it is the balance between food and security operating at different scales and at different times that interact to form volatile fission-fusion dynamics of the herd. Therefore, movement decisions of individuals are influenced by multiple factors including physiology, social interactions, environmental cues, resource availability, memory and predation risk [15,[27][28][29]. Disentangling these competing, hierarchical drivers of movement is a substantial task; however, it is a challenge that must be met in order to develop the evidence-based policy required to protect vital ecosystem services in the region.…”