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M/W CDD PhD « 4D modeling of collision risks for large birds of prey with electric lines or windfarms" »

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Date Limite Candidature : jeudi 4 novembre 2021

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General information

Reference : UMR5175-AURBES-009
Date of publication : Thursday, October 14, 2021
Scientific Responsible name : Besnard Aurélien
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 December 2021
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

The present research project therefore proposes to develop models to predict which areas present a high risk of negative impact on large raptor populations when new infrastructures are created. The results of these models will be developed into an operational tool for infrastructure planners, local authorities or protected areas, and the approach will be replicable for other large raptors and gliders in general. The general principle of the model will be based on an iterative process whose successive stages would be:
i) A 4D space use model (geographical space and altitude combined with temporal changes, in particular seasonal changes) based on the mutualization of a set of data already available for a "test" species,
ii) A prediction of the sectors at risk resulting from this modeling to allow avoidance or even micro-siting (adjustment of the position of an infrastructure),
iii) An evaluation of the quality of the predictions of this model through the equipment of additional birds through some development projects (all of them requiring an authorization from the CRBPO, co-supervisor of the present project), and
iv) An improvement of the model predictions as new data are collected (e.g. by allowing or requesting the fitting of birds in areas where the model predictions seem to be less accurate). This work should both maximize the use of available data by pooling them on a national scale (or even internationally, once the proof of concept is established), and reduce the need to equip new individuals once the risk predictions are considered reliable (i.e. equipping a few individuals on a local scale would be useless because it would not improve the reliability and accuracy of the predicted risk).
This approach has the advantage of making the global approach more respectful of the animals (ethics), of increasing the scientific relevance (pooling of data allowing a greater generalization of results), but also of reducing the budgets for the operators by avoiding to equip more birds than necessary.
The different previous steps will provide a model allowing to predict the use of the habitat in 2D, the use of the airspace in 3D and its temporal variations (seasonal effects, i.e. in 4D). The results of this model will be mobilized by users via a web application that will give access to the predictions for the local situation defined by the user, in cartographic form. This web application will be freely available and can therefore be consulted before the elaboration of infrastructure development projects (see Avoidance and Reduction).

The recruited PhD student will be in charge of the concrete implementation of this project (modeling and interface development). To do this, he will have to manipulate spatialized data from GPS monitoring of test species but also environmental covariates at several spatial and temporal scales. For the test of the replicability of the approach to another species as well as for the creation of the web interface, the PhD student will have the support of Master 2 trainees (one from an ecology background and one from a computer science background), whom he will have to co-supervise. The PhD student will actively participate in field campaigns to equip new individuals with GPS transmitters.


Master's degree in ecology with recognized skills in statistical ecology, including species distribution models and habitat analysis. Motivation for operational research and partnerships with multiple stakeholders. Ability to work in the field.
Experience of field work

Work Context

Location: UMR5175 Centre d'Ecologie Fonctionnelle et Evolutive – Montpellier ; within the team HAIR under the direction of Aurélien Besnard.

Field work necessary
In a context of global climate change and the fight against it by governments, the use of renewable energy is the preferred way to make an energy transition that consumes less fossil fuels (https://www.gouvernement.fr/action/la-transition-energetique-pour-la-croissance-verte). It is within the framework of this strategy that wind farms, as well as photovoltaic panel farms, are booming. Their numbers have been increasing strongly in France in recent years. To quote some figures, in 2020, there were 8500 wind turbines on the French territory producing 17.5 MW. The law of Pluriannual Programming of Energy (PPE 2019-2023) plans to double this production capacity by 2024. Worldwide, 651 GW were produced by wind turbines as of January 1, 2020, led by China and the United States. However, this transition to renewable energies, and in particular to wind power, has consequences on the environment and particularly on biodiversity. Birds and chiropterans are the main taxonomic groups negatively impacted by these infrastructures.
Indeed, these infrastructures represent barriers for which large birds do not have an adapted behavioral response, and which often prove fatal in case of contact. When they concern long-lived species such as large raptors, induced mortality has a major impact on the dynamics of these populations since these dynamics are very sensitive to variations in adult survival rates. Large raptors are also among the species classified as the most frequently impacted by collisions. These infrastructures also have indirect consequences on the populations by reducing the available habitat and by modifying the access to vital spaces.
Because of these risks, planners and project developers must, for these protected species, place themselves within the framework of the "Avoid-Reduce-Compensate" sequence. However, for such species occupying very large home ranges, it is complex to identify, by simple field visits, both the areas of least impact for "Avoid" but also the high-risk areas for "Reduce" (e.g. by equipping wind turbines with scaring devices) or "Compensate". Understanding how populations of species that use airspace exposed to anthropogenic infrastructures use this space and understanding how this infrastructures influence this use of space is therefore crucial for predicting the impact of future developments and mitigating these impacts, in particular through a layout that minimizes the risks of collision or alteration of the aerial vital space (Avoid) or by prioritizing the fitting of existing infrastructure with scaring devices (Reduce).
For the last twenty years, the risk of collision mortality has been studied extensively for various species, particularly raptors, which are particularly sensitive because of their flight behavior. These studies are very often based on body counts to estimate a mortality rate and model the risk of collision. However, authors have shown that these models are difficult to generalize on a large scale because of the type of parameters used, which are very context-dependent. They advocate the use of 3D telemetry data, an increasingly affordable technology, to finely study collision risk. The number of studies using such data to model, or even predict, the risk of large raptor collisions with electric lines and windfarms is growing rapidly. However, existing studies are based either on habitat and topography data or on aerology data, but no model currently combines these two major determinants of airspace use by large birds. Understanding and predicting the risk of bird collisions with wind turbines (or power lines) requires an understanding of how birds use space in four dimensions: three spatial dimensions (where and at which altitude?), and how they vary over time (when?).

The answer to these questions requires a modeling work which will be carried out by the PhD student recruited over three years and whose financing is supported by the ADEME within the framework of the call for projects "R&D Sustainable Energy Edition 2020/2021 ».

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