Season, decay stage, habitat, temperature and carrion beetles allow estimating the post-mortem interval of wild boar carcasses.

Published online
22 Apr 2024
Content type
Journal article
Journal title
Ecological Solutions and Evidence

Müller, J. & Rietz, J. & Hoermann, C. von & Conraths, F. J. & Benbow, M. E. & Mitesser, O. & Schlüter, J. & Lackner, T. & Reckel, F. & Heurich, M.
Contact email(s)

Publication language


The decay process of animal carcasses is a highly complex succession driven by abiotic and biotic variables and their interactions. As an underexplored ecological recycling process, understanding carrion decomposition associated with pandemics such as African swine fever is important for predicting the rate and post-mortem interval (PMI) variation of wild animal carcasses to improve disease management. To model PMIs of wild boar, we deployed 73 wild boar carcasses in four different forest habitat types throughout a year and monitored the decomposition process, carrion beetles and blow fly larval populations. The 601 single observations were split randomly into 501 training data and 100 validation data. A linear additive mixed model for log-transformed PMI values using the training data identified the decay stage, day of year, ambient temperature during sampling, habitat and prevalence of Oiceoptoma thoracicum (Silphidae) as predictive variables for time since death, but neither the initial body mass nor if a fresh or previous frozen carcass was used. Using the validation data, this model showed a high predictive power for log-transformed PMI values (R2 = 0.80). This study aimed at improving the methodology of estimating the PMI of wild boar carcasses based on important abiotic and biotic environmental factors that can be easily assessed in the field. Using only a small set of predictors, including a conspicuous beetle species, allowed prediction of the mean, minimum and maximum PMI of wild boar carcasses. The strong effects of a few surrogates on PMI in our model suggest that this model can easily be transferred to wider regions of Central Europe by retraining the model with data from a broader environmental space and can thus be instrumental in assessing timing of disease introduction in areas newly affected by emerging diseases such as African swine fever.

Key words