Abstract
Development times and survivorship of Lucilia sericata (Meigen, 1826) were measured for different constant temperatures. In addition, lower threshold (tL) and thermal constant (Kt) were estimated for a local population. The estimated development time calculated from mean or minimum development time induces a bias that may be reduced using a model of intrinsic variability. In order to quantify variation in emergence rate during the development time, a simulation method was developed by adapting the Regniere’s method. The adapted model allowed accurate prediction of the emergence profile of L. sericata at constant temperature in the linear portion of the development curve. We believe that the application of this method in forensic entomology cases could increase the reliability of the conclusions of an entomological Post Mortem Interval (PMI) estimation.
Highlights
Development duration of Lucilia sericata (Meigen, 1826) (Diptera: Calliphoridae), as for many other insects, depends on temperature
We described an adaptation of Régnière’s method to simulate intrinsic variability on L. sericata development speed
At 35°C, the percentage of emergence starts to decrease, and at 37.5°C no pupae eclosed (Table 1). In this temperature range (12.5-30°C), the median development times of Lucilia sericata have a linear relationship with temperature (R2=0.99) (Figure 1)
Summary
Development duration of Lucilia sericata (Meigen, 1826) (Diptera: Calliphoridae), as for many other insects, depends on temperature. Even at a chosen temperature, a large variability in development times is observed within published data [2,3,4,5,6] This variability is largely due to inter-individual competition [7, 8], food type [9, 10] or nyctemeral rhythm [6, 11]. The given development time are mainly based on summary statistics such as minimum [3, 12], median [5, 13, 14], mean [2, 15], maximum [3] or percentage [1, 4, 16] The use of these different sampling methods and types of synthetic parameters could bias the accuracy of the final published development data [14]. Wells and Lamotte [17] promote the use of full distributions instead of summary values
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