Laboratoire Interdisciplinaire des Environnements Continentaux (LIEC) CNRS – UMR 7360 Campus Bridoux – Rue du Général Delestraint – 57070 Metz (France)

18-month Postdoctoral Researcher Position on Macrophytes-based diagnostic methods in rivers

A post-doctoral position is available in the group “Ecologie du Stress” (ECoSe) of the Interdisciplinary Laboratory for Continental Environments (LIEC; CNRS UMR 7360 – http://liec.univ-lorraine.fr/), University of Lorraine, as a part of the ODM2C scientific project on multi-metric-based ecological diagnostic tools in rivers and streams at large spatial scale. The project is supported by the French Biodiversity Agency (OFB).

Research area:

* Development and characterization of a novel ecological diagnostic tool based on the aquatic macrophytes for assessing specific risks of river degradation (including water quality and habitat degradation stressor types) under multiple pressure scenario.
Efficiently evaluating the global effect of pressures on stream communities needs often to select a combination of generalist metrics, which are much more robust than specialist ones in their diagnostic especially when several pressures co-occur. As a result, they poorly feed the ecological diagnostic in precise information on the exact nature of pressures affecting local communities, because too generalist. Aquatic ecosystems are indeed threatened by multiple pressures that interact in complex ways, impairing both water quality and habitat condition. Their impacts are complex to predict. Our main objective has been to develop trait-based models, able to identify the pressures really involved in community impairment.
Large scale biological trait-based diagnostic tools for assessing specific risks of stream degradation, using benthic macroinvertebrates (Mondy and Usseglio- Polatera 2013), benthic diatoms (Larras et al. 2017) or fishes (Dézerald et al., 2020) have been already developed in the host team.

These models have been mainly based on trait-based metrics, including the utilization of trait categories, the abundance and richness of functional groups and metrics describing trait specialization and diversity within assemblages. They were developed for various pressure categories related to water quality (including organic matter, nutrients, pesticides and other micropollutants) and habitat degradation (like straightening, urbanization in the corridor, catchment anthropization or clogging risk).

Based on random forests, these models have the ability to handle non-linear relationships between predictors and response variables, to integrate complex interactions among a large number of predictors and to generate good predictions with their associated probabilities (Prasad et al., 2006; Rahmati et al., 2019). Used simultaneously, they have allowed, for example, to profitably support the ecological assessment of French wadeable rivers in a multi-pressure context at large spatial scale and over a long period (Alric et al., 2021). We would like to extend this approach to the macrophytes assemblages, i.e. the last Biological Quality Element (BQE) routinely used in the ecological survey of rivers in a WFD context, believed to provide original informations on the nature and intensity of river impairment.

* Large scale validation of the novel diagnostic tool, via between-sites comparison of macrophyte data collected with standardized, well established sampling protocols (about 2000 sampling sites have been surveyed, in the five European biogeographic regions covered by the study).

* Possible inclusion of the macrophytes-based diagnostic tool in a multi-BQE diagnostic tool, aggregating information provided by each of the four Biological Quality Elements.

The successful candidate will closely collaborate with the project coordinator and other colleagues – ecologists or biostatisticians – of the host laboratory (LIEC; Interdisciplinary Laboratory for Continental Environments; https://liec.univ-lorraine.fr/) and of the INRAe Center at Bordeaux (UR EABX, Aquatic Ecosystems and Global Change; https://www6.bordeaux-aquitaine.inrae.fr/eabx_eng/EABX). He will also interact with national partners involved in such topics (e.g. the French Biodiversity Agency (OFB), Water Agencies, and the Ministry of Ecology).

Cited references:

Alric B, Dézerald O, Meyer A, Billoir E, Coulaud R., Larras F, Mondy CP, Usseglio-Polatera P (2021). How recent diatom-, invertebrate- and fish-based tools can support the ecological diagnosis of rivers in a multi-pressure context: temporal trends over the past two decades in France. Science of the Total Environment, 762, 143915.
https://doi.org/10.1016/j.scitotenv.2020.143915
Dézerald O, Mondy CP, Dembski S, Kreutzenberger K, Reyjol Y, Chandesris A, Valette L, Brosse S, Toussaint A, Belliard J, Merg ML, Usseglio-Polatera P (2020). A diagnosis-based approach to assess specific risks of river degradation in a multiple pressure context: insights from fish communities. Science of the Total Environment; 734, 139467.
https://doi.org/10.1016/j.scitotenv.2020.139467
Larras F., Billoir E., Gautreau E., Coulaud R., Rosebery J., Usseglio-Polatera P. (2017). Assessing anthropogenic pressures on streams: a random forest approach based on benthic diatom communities. Science of the Total Environment, 586, 1101-1112.
https://doi.org/10.1016/j.scitotenv.2017.02.096
Mondy CP & Usseglio-Polatera P (2013). Using conditional tree forests and life history traits to assess specific risks of stream degradation under multiple pressure scenario. Science of the Total Environment, 461/462, 750-760.
https://doi.org/10.1016/j.scitotenv.2013.05.072
Prasad AM, Iverson LR, Liaw A (2006). Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9,181–199.
https://doi.org/10.1007/s10021-005-0054-1
Rahmati O, Choubin B, Fathabadi A, Coulon F, Soltani E, Shahabi H, Mollaefar E, Tiefenbacher J, Cipullo S, Ahmad, BB, Tien Bui D (2019). Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods. Science of the Total Environment, 688, 855–866.
https://doi.org/10.1016/j.scitotenv.2019.06.320

Starting date: Preferably on December 1st, 2021

Duration: 18 months

Required qualifications:
• PhD in ecology or biostatistics with good training in descriptive/inferential statistics utilization on large data sets. Applications are particularly encouraged from candidates with expertise in field sampling; macrophyte ecology, trait-based approaches and interest in multivariate analyses, null models, random forest models, and/or Bayesian statistics. A very good background in R software utilization is necessary. A solid experience in geographic information system (GIS) software utilization will be appreciated.
• Dynamic, enthusiastic and autonomous character
• Knowledge of spoken French (or at least enthusiasm to learn French) is essential for contacts with representatives from French administrations. Excellent communication skills are essential.

To apply: Interested applicants are requested to send a detailed letter describing their motivation and competences, updated CV, list of publications, addresses of 2 potential referees, and a short summary of their PhD thesis (2 pages max). Applications should be sent by e-mail at philippe.usseglio-polatera@univ-lorraine.fr, before November 10th, 2021.

Supervising scientists:
Pr. Philippe Usseglio-Polatera, LIEC, CNRS UMR 7360, ECoSe group, University of Lorraine, Metz, France.
Dr. Elise Billoir, LIEC, CNRS UMR 7360, ECoSe group, University of Lorraine, Metz, France (elise.billoir@univ-lorraine.fr).

Le contenu de cette offre est la responsabilité de ses auteurs. Pour toute question relative à cette offre en particulier (date, lieu, mode de candidature, etc.), merci de les contacter directement. Un email de contact est disponible: philippe.usseglio-polatera@univ-lorraine.fr

Pout toute autre question, vous pouvez contacter sfecodiff@sfecologie.org.