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One of the objectives of the environmental monitoring programs of transmission power lines is to quantify bird mortality. To account for carcass removal, these programs typically include field experiments which allow to obtain data on the persistence time of the carcass in the field until removal. In this study, we aim to estimate the removal bias correction factor, considering the carcass size, the season, and the location of power line projects, eliminating the need for field trials in every new project. To achieve this goal, we used the Integrated Nested Laplace Approximation (INLA) method combined with the Stochastic Partial Differential Equations (SPDE) approach to model the probability of persistence considering both fixed (carcass size and season) and random (geographic location and project) effects. The results allowed to analyze the variation in space of bird carcass persistence and to create a tool for common users to estimate the removal correction factor for a specific location as a function of the covariates considered, in mainland Portugal. However, further improvement is required as model predictions are still unreliable in large portions of the national territory. We discuss the model limitations and offer directions for future work.

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An Approach for Predicting Spatially Indexed Carcass Persistence Probability to Estimate Bird Mortality at Power Lines

  • 会议论文

  • 发表日期：

    2025年

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  • Ema Biscaia,
  • Joana Bernardino,
  • Regina Bispo

摘要

One of the objectives of the environmental monitoring programs of transmission power lines is to quantify bird mortality. To account for carcass removal, these programs typically include field experiments which allow to obtain data on the persistence time of the carcass in the field until removal. In this study, we aim to estimate the removal bias correction factor, considering the carcass size, the season, and the location of power line projects, eliminating the need for field trials in every new project. To achieve this goal, we used the Integrated Nested Laplace Approximation (INLA) method combined with the Stochastic Partial Differential Equations (SPDE) approach to model the probability of persistence considering both fixed (carcass size and season) and random (geographic location and project) effects. The results allowed to analyze the variation in space of bird carcass persistence and to create a tool for common users to estimate the removal correction factor for a specific location as a function of the covariates considered, in mainland Portugal. However, further improvement is required as model predictions are still unreliable in large portions of the national territory. We discuss the model limitations and offer directions for future work.