Photo: Mathilde Poirier

Computational biodiversity science

Biodiversity science progressed in the last fifteen years due to remarkable technical advances in computing power and data acquisition, resulting in large biodiversity databases.

Increasing societal demands for assessment of the status of biodiversity under global changes is pushing ecology and environmental sciences towards a predictive science. Biodiversity monitoring programs implemented to track the impacts of major industrial projects (e.g. hydro-electricity dams, mines) generate massive amounts of information that can be used to predict future impacts of human actions on biodiversity. Other fields of life sciences, such as genomics and medicine, have met increasing data availability by developing computational infrastructure, data pipelines and analytic frameworks, while ecology is comparatively lagging behind.  

Biodiversity science faces the distinct challenge of integrating heterogeneous, disparate and increasingly voluminous datasets. This is particularly true in Canada, a vast country with an impressive south-north gradient and including a large range of environments from the deciduous forests in the south to the arctic tundra ecosystems. Biodiversity data from northern environments, based on scientific or traditional knowledge, can be particularly fragmented. This requires a specific approach to raising data availability and computational literacy among future ecologists.

We propose a training program to accelerate the development of big data approaches to biodiversity science and to make them available to future employers to ensure they support biodiversity management in our country, from the south to the Arctic.

Photos on the left and center: Timothée Poisot, Photo on the right: Markus Spiske | Unsplash

Employers need for training 

Prior to the establishment of the program, we performed a large-scale survey to assess the needs in computational biodiversity science. 

We reached 385 students, university professors, and professionals in the field of biology and ecology to investigate the match between employer’s needs in data management and analysis, and training received in regular academic programs. We further mined 179 job advertisements for biologists to identify the most frequently required skills. 

We found that while training for skills such as communication, fieldwork, and taxonomy is usually well supported by traditional academic programs, there is insufficient coverage for database management, advanced statistical techniques, GIS, scientific programming and computing, all skills widely required in today’s job market. 

The new opportunities offered by unprecedented access to numerical information requires researchers and practitioners to develop these skills but current traditional training programs cannot meet the demand.

Developing a culture of computational biodiversity science and services outside of academia cannot be achieved with one or a few specialized courses; this requires an integrated training program based on a multi-disciplinary network of expertise where students are mentored throughout their studies and constantly in contact with future employers. This is what the program in Computational Biodiversity Science and Services (BIOS2) is about.

Career opportunities for BIOS2 fellows 

The number of employees in the sector of the environment is constantly growing, with an increase of 7.0% between 2010 and 2013, for a total of approximately 730 000 jobs in Canada. These are high-quality jobs, with a much larger fraction of employees with a university degree than the Canadian average (36.9% higher). 75% of the employers expect to recruit new employees over the next two years. Similarly, the federal government is recruiting more biologists and this trend is expected to increase in the next decade because of high demand in the field of biodiversity science. Most of the job offers we evaluated were for leaders and highly qualified personnel, such as director, project manager, coordinator, team leader. The BIOS2 training program is designed to facilitate the transition of the trainees towards non-academic sectors. Internships, problem-solving workshops, continuous training of partners and specific training sessions are all activities that will get fellows in contact with future employers, improve their networking and communication skills and provide them a better understanding of the expectations and roles they will have once in the Canadian workforce.