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Assessing fish abiotic requirements by ecological niche modelling to design (new) fish species communities for tomorrow’s aquaculture

ABG-94156 Stage master 2 / Ingénieur 6 mois 554.40 €
22/10/2020
Université de Lorraine
Vandoeuvre-Lés-Nancy Grand Est France
  • Biologie
  • Agronomie
  • Ecologie, environnement
ecological niche modelling, species distribution modelling, geographic information system, fish, climate changes, aquaculture
30/11/2020

Établissement recruteur

The Domestication in Inland Aquaculture team works on the sustainable development of aquaculture. Our works aims to foster the fish production diversification thanks to new species domestication. We study (i) the domestication consequences on fish biology and (ii) the domestication process through interspecific and interpopulational comparative approaches in order to improve the fish domestication.

Description

Assessing fish abiotic requirements by ecological niche modelling to design (new) fish species communities for tomorrow’s aquaculture

 

 

Internship proposal for engineering or master student (master 2, only)

 

 

Supervisors – Thomas Lecocq (Ph-D, associate professor) and Marielle Thomas (Ph-D, associate professor)

Research unit - Research Unit Animal and Animal Product Functionality (UR AFPA), team: Domestication in Inland Aquaculture (DAC), Université de Lorraine, Faculté des Sciences et Technologies, Boulevard des Aiguillettes BP 70239, F-54506 Vandœuvre-lès-Nancy, France

Internship duration - 6 months

Internship localization - UR AFPA, Vandœuvre-lès-Nancy, France. Depending on the evolution of the sanitary crisis, a part of the internship could be performed remotely but, as far as possible, this solution will be avoided.

Internship grant - 554.40 €/ month.

Context – Aquaculture is the farming of aquatic organisms through several different practices (i.e. monoculture/polyculture, outdoor/indoor, flow-through systems/recirculated systems and extensive/intensive). Since the 1960s, aquaculture production has rapidly expanded and exponentially grown worldwide as far as to provide more than 50% of the world’s aquatic food consumption nowadays. Aquaculture has thus become a key factor in human food security. However, it is facing major challenges that will be even more important in the next decades.  Indeed, sustainability and resilience of current aquaculture production raises concerns. For instance, intensive monoculture based on few species has often (i) significant environmental negative impact, (ii) low ability to withstand competition and disease/pest attacks, and (iii) a low adaptation potential facing to changes in environmental and socio-economical contexts.

Polyculture in fish production is a long-standing practice, which has been somewhat disregarded in recent aquaculture development in some parts of the world, especially in a production intensification option. Yet, it can avoid some of the drawbacks of fish monoculture. Indeed, there is growing evidence that species diversity contributes to the production system resilience in the context of economic changes, improves farm inputs’ utilization, and decreases the amount of waste by the recycling of co-products of some taxa by the other co-farmed species. Nevertheless, such benefits can be reached only when a relevant combination of species is used. This can be achieved by designing fish community based on species compatibility (i.e. species can live in the same farming environment without detrimental interactions) and complementarity (i.e. complementary use of available resources and/or commensalism/mutualism). This one of the aims of the SEPURE project (i.e. « Nouvelles stratégies de construction et de conduite de systèmes de production en étang pour une pisciculture durable ») in which scientists (6 research units, INRAE) and fish farmers work together to achieve sustainable and efficient aquaculture.

Work – Evaluating species compatibility requires first to assess whether different species can live in the same abiotic environment (e.g., by considering pH, temperature, water current, etc.). The engineering or master student will perform this assessment with the researchers of the University of Lorraine. She/he will develop ecological niche modelling (ENM) for 50 fish species that have been pointed out by aquaculture stakeholders as relevant for the tomorrow’s fish farming in outdoor production. First, she/he will be involved in the selection of relevant abiotic parameters for fish ENM by considering parameters available in databases. Second, the student will develop abiotic parameter and species occurrence datasets through geographic information system (GIS) application. Third, she/he will analyze these datasets to determine the niche position and niche breadth for each species via outlying mean index (OMI) analyses. Multivariate analyses (e.g., MANOVA) will be used to test for significant differences in the mean niche values of species. This step will provide useful insights on the species compatibility. Fourth, ENM will be developed (e.g., boosted regression tree method) for each species. These ENMs will be projected across Europe or the World (depending on the species considered) for current and future climate. This step will provide pieces of information about how the species compatibility could change according to fish farming locations and climate changes. The last step will be to determine what fish community will be the best for fish farming under a given abiotic niche.

References

  1. Lecocq, T., Harpke, A., Rasmont, P., and Schweiger, O. (2019). Divers. Distrib. 25, 1088–1100. doi:10.1111/ddi.12916.
  2. Segurado, P., Branco, P., Jauch, E., Neves, R., and Ferreira, M. T. (2016). Sci. Total Environ. 562, 435–445. doi:10.1016/j.scitotenv.2016.03.188.
  3. Pandit, S. N., Maitland, B. M., Pandit, L. K., Poesch, M. S., and Enders, E. C. (2017). Sci. Total Environ. 598, 1–11. doi:10.1016/j.scitotenv.2017.0228.

Profil

Profile - We are looking for highly motivated and rigorous engineering or master student (master 2, only). The candidate is expected (i) to search, read, and understand scientific literature including in English, (ii) to have team skills, a sense of responsibility, and (iii) to have good skills in GIS and core skills in statistical data analyses (in R language).

Prise de fonction

Dès que possible
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