Suchergebnisse

Abstract Context The ability to accurately estimate age of animals is important for both research and management. The two methods for age estimation in ungulates are tooth replacement and wear (TRW) and cementum annuli (CA). Errors in estimated TRW ages are commonly attributed to environmental conditions; however, the influence of environmental variables on tooth wear has not been quantified. Further, the performance of CA in environments with weak seasonality has not been thoroughly evaluated. Aims The study had the following three goals: identify environmental and morphological factors that influenced estimated ages, quantify accuracy of TRW and CA, and develop TRW ageing criteria that minimise error. Methods We used data from harvested (n = 5117) and free-ranging, known-age white-tailed deer (n = 134) collected in southern Texas, USA, to quantify environmental and morphological influences on estimated TRW ages, and assess biases in both methods. Key results We observed substantial variation in age estimates for both TRW and CA. Soil, drought and supplemental nutrition had minor effects on tooth wear, insufficient to alter age estimates by ≥1 year. Body mass and antler size influenced age estimates for TRW only for extreme outliers. Both methods were biased and tended to under-estimate ages of adult deer, especially TRW. Wear on the first molar was most correlated with the known age (r2 = 0.78) and allowed biologists to correctly place known-age deer into age classes of 2, 3–5, and ≥6 years old 72%, 73% and 68% of the time, an improvement compared with the 79%, 48% and 28% accuracy from pooled TRW. Conclusions We observed substantial inter- and intra-individual variation in tooth-wear patterns that became more pronounced in older deer. Individual variation had a greater influence on TRW ages than did environmental covariates, whereas CA ages appeared unaffected by environment. Although variable, age estimates were ±1 year of the true age 87% and 93% of the time for TRW and CA respectively. Implications Managers, ecologists and epidemiologists often incorporate ages into population models. The high inter-individual variation in estimated ages, the tendency to underestimate ages of older deer, and the ageing method need to be considered.