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Inter-comparison of population models for the calculation of radiation dose effects on wildlife
Authors:Jordi Vives i Batlle  Tatiana G Sazykina  Alexander Kryshev  Luigi Monte  Isao Kawaguchi
Institution:1. Belgian Nuclear Research Centre (SCK?CEN), Boeretang 200, 2400, Mol, Belgium
2. Research and Production Association “Typhoon”, 4 Pobedy Str., Obninsk, Kaluga Region, 249038, Russia
3. ENEA, Agenzia Nazionale per le Nuove Tecnologie, l’energia e lo Sviluppo Economico Sostenibile, Via P. Anguillarese, 301, 00100, Rome, Italy
4. Research Centre for Radiation Protection, National Institute of Radiological Sciences, 4-9-1, Anagawa, Inage, Chiba, 263-8555, Japan
Abstract:An inter-comparison of five models designed to predict the effect of ionizing radiation on populations of non-human wildlife, performed under the IAEA EMRAS II programme, is presented and discussed. A benchmark scenario ‘Population response to chronic irradiation’ was developed in which stable generic populations of mice, hare/rabbit, wolf/wild dog and deer were modelled as subjected to chronic low-LET radiation with dose rates of 0–5?×?10?2 Gy?day?1 in increments of 10?2 Gy?day?1. The duration of exposure simulations was 5?years. Results are given for the size of each surviving population for each of the applied dose rates at the end of the 1st to 5th years of exposure. Despite the theoretical differences in the modelling approaches, the inter-comparison allowed the identification of a series of common findings. At dose rates of about 10?2 Gy?day?1 for 5?years, the survival of populations of short-lived species was better than that of long-lived species: significant reduction in large mammals was predicted whilst small mammals survive at 80–100?% of the control. Dose rates in excess of 2?×?10?2 Gy?day?1 for 5?years produced considerable reduction in all populations. From this study, a potential relationship between higher reproduction rates and lower radiation effects at population level can be hypothesized. The work signals the direction for future investigations to validate and improve the predictive ability of different population dose effects models.
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