Project

Development of biologically-based models in environmental risk assessment to assess the impact of chemicals and pathogenic fungi on amphibian and reptile populations

Acronym
AMPHIBED
Code
41M06722
Duration
17 January 2022 → 17 July 2025
Funding
European funding: various
Promotor-spokesperson
Research disciplines
  • Agricultural and food sciences
    • Veterinary conservation medicine, preventive medicine and hygiene
    • Veterinary pharmacology and toxicology
    • Zoological medicine
Keywords
risk assessment fungi chemicals amphibian reptile
 
Project description

The present proposal aims at progressing in the evaluation of risks associated with multiple stressors, namely pesticides and pathogens, for European amphibians and reptiles through the development, calibration and validation of biologically-based models that allow for extrapolating individual- and, eventually, field-level effects. With this purpose, we will compile and analyse the available information regarding toxicity of pesticides to amphibians and reptiles, impact of the most important diseases to herpetofauna, and physiological parameters useful to develop DEB-TKTD models. In addition, we will perform field monitoring of diseases in amphibians and reptiles from two of the biodiversity hotspots of European  herpetofauna, Italy and Spain, including a characterization of the sites for further mapping and analysis of pathogen distribution across the two countries. With the information  compiled from the public information resources and from the field monitoring, we will run a series of case studies to calibrate and/or validate the models for the assessment of risks associated with single and multiple chemicals, diseases and multiple stressors. Whereas those case studies will be run with previously available information, we will aim at building a limited number of them through newly generated information that will result from the execution of ad hoc designed laboratory experiments. This approach will guarantee that, for some of the cases, model validation is achieved by means of high-quality data.