Estimating age of mule deer in the field: Can we move beyond broad age categories?

Abstract

Age of individuals is an intrinsic demographic parameter used in the modeling and management of wildlife. Although analysis of cementum annuli from teeth is currently the most accurate method used to age ungulates, the age of live ungulates in the field can be estimated by examining tooth wear and tooth replacement patterns. However, there may be limitations to aging based on tooth wear as the rate of tooth wear likely varies among individuals due to factors such as age, diet, environment, and sex. Our objective was to determine the reliability of estimating age for mule deer based on tooth wear and tooth replacement patterns. We compared ages estimated by tooth wear (collected at time of capture for a statewide monitoring effort) to ages determined from cementum analysis (from teeth collected after mortalities of radio-tracked animals from the monitoring effort). Accuracy was high; ages estimated from tooth wear were within one year of cementum ages >75% of the time when aged by experienced observers. Bias in accuracy for estimates of age was low but slightly biased toward underestimation (i.e., 0.6 years on average)—especially as cementum age increased. Our results indicate that aging mule deer using patterns in tooth wear can be reliable if observers estimating age have experience using this method.

Pitfalls and windfalls of detecting demographic declines using population genetics in long-lived species

“…Age structure is being recognized as an important source of information about long-lived species (Dolan et al, 2023; Holmes & York, 2003). Although age information is rarely readily available for empirical populations, it is sometimes possible to approximate an individual’s age from their phenotype (e.g., turtle scutes and carapace length (Jensen et al, 2018; Wilson et al, 2003), rattlesnake rattles (Heyrend & Call, 1951), tree rings (Shroder, 1980), otoliths (Campana, 1999), tooth wear (Hinton et al, 2023), telomere length (Haussmann & Vleck, 2002), DNA methylation (Nakamura et al, 2023), coloration (Pyle, 1997) etc.) or population monitoring (Eaton & Link, 2011)).…”

Pitfalls and windfalls of detecting demographic declines using population genetics in long‐lived species

“…Age structure is being recognized as an important source of information about long‐lived species (Dolan et al., 2023 ; Holmes & York, 2003 ). Although age information is rarely readily available for empirical populations, it is sometimes possible to approximate an individual's age from their phenotype (e.g., turtle scutes and carapace length (Jensen et al., 2018 ; Wilson et al., 2003 ), rattlesnake rattles (Heyrend & Call, 1951 ), tree rings (Shroder, 1980 ), otoliths (Campana, 1999 ), tooth wear (Hinton et al., 2023 ), telomere length (Haussmann & Vleck, 2002 ), DNA methylation (Nakamura et al., 2023 ), coloration (Pyle, 1997 ) etc. ), or population monitoring (Eaton & Link, 2011 ).…”

“…Age structure is being recognized as an important source of information about long-lived species (Dolan et al, 2023;Holmes & York, 2003). Although age information is rarely readily available for empirical populations, it is sometimes possible to approximate an individual's age from their phenotype (e.g., turtle scutes and carapace length (Jensen et al, 2018;Wilson et al, 2003), rattlesnake rattles (Heyrend & Call, 1951), tree rings (Shroder, 1980), otoliths (Campana, 1999), tooth wear (Hinton et al, 2023), telomere length (Haussmann & Vleck, 2002), DNA methylation (Nakamura F I G U R E 3 Plots show percent detection using temporal sampling for annual model (A = 1) and perennial models (A = 2, 5, 10, and 20) for three bottleneck severities (R, bottleneck severity increases with R). Percent detection is defined as the percent of replicates where we found a significant difference between individuals subsampled from the present and previous timepoints.…”