The Ecology and Evolutionary Biology (EEB) Department at the University of Arizona in collaboration with researchers at the Lawrence Berkeley National Laboratory is advertising for a postdoctoral position in developing and advancing trait-based ecology, theory (including Trait Driver Theory), and remote sensing to start as early as fall 2020. The position is part of a Harnessing the Data Revolution National Science Foundation project which will be forecasting near-term biodiversity and ecosystem responses to changes in climate and land use. The position will work closely with collaborators Haruko Wainwright, Jon Norberg, and Van Savage.
These forecasts will evaluate the resilience of ecosystems under changing climate as well as – along with input from conservation stakeholders – will assess how differing conservation decisions can minimize the impacts of global change responses.
An ultimate goal of the project is to organize a Biodiversity Forecasting Institute (BioFI) to develop an automated pipeline to ingest new incoming remote sensing, species abundance, and trait data to assess changes in biodiversity and associated changes (net ecosystem productivity and evapotranspiration) in order to forecast how biodiversity and ecosystems respond to perturbations (temperature, drought, mortality events). The project will serve these to end-users to enable a near-real time local to global scale forecasting workflow to provide best-available predictions at any given time in order to understand ecosystem impacts and to inform conservation decisions.
As the effects of climate and land use change take place at an increasing pace, we urgently need tools for communicating and planning for the future of biodiversity and the future of our world. This project is motivated by the realization that more powerful tests of biodiversity theories need to move beyond species richness and explicitly focus on mechanisms that generate diversity, and that these can be attained via community trait composition. By combining the project consortium’s strengths in remote sensing, global change ecology and trait-based theory development, BioFI offers a unique and powerful framework to advance and improve empirical global change ecology, ecological theory via the synthesis of theory and exp
eriments in ecology. Specifically, this BioFI postdoc will integrate the outlines of a more predictive theory – scaling from traits to communities to ecosystems – that has begun to emerge over the last decade. The goal is to validate, apply, and improve this theory using remote sensing and field data from our global change research projects and observational data collected through our collaborative networks. BioFI will offer (i) efforts to work with remote sensing, computer, and earth system scientists as well as with ecologists to integrate the use of the theoretical, conceptual frameworks in trait based ecology; (ii) development of theoretical expertise and large scale approaches to assess biodiversity tipping points and impacts on ecosystem productivity, evapotranspiration, and resiliency; (iii) unique student opportunities for national and international collaboration and research experiences. The proposed synthesis of theory and experimental data will inform how global change influences biodiversity and ecosystem services important to society.
The mission of BioFI will focus on the following guiding goals:
- Goal 1 – Through community co-design, iteratively improve forecasts and guide data integration by assessing new theory and by using high spatial and temporal resolution remote sensing data.
- Goal 2 – Work with a broad set of biodiversity scientists and environmental engineers to develop new computational tools and machine learning workflows to better predict current and future shifts in abundance and geographic distributions. Additional external collaborators include Van Savage from UCLA and Jon Norberg from Stockholm University, Sweden.
- Goal 3 – Address key engineering challenges to integrate diverse remote sensing and other types of data into differing conservation decisions, including new predictors, extreme climatic events, and remotely sensing biomass into assessments of global change impacts.
- Goal 4 – Automate a near-term biodiversity forecasting pipeline to ingest new incoming data, update forecasts, and serve these to end-users to enable near-real time forecasting updates and promote the co-production of management recommendations based on the best-available predictions.
This BioFI postdoc will play a key role in developing, executing and coordinating BioFI research and educational activities and interact with several other BioFI postdocs working on various components of the above goals.
Specifically, the successful candidate will:
- Collate data from a global network of observational and big data streams in close collaboration with project partners
- Develop his/her own research questions and hypotheses related to the overall project
- Analyze data and write and publish research papers
Qualifications and personal qualities:
- PhD in related field
- The successful candidate should have a background that gives a good understanding of at least one of the following trait-based ecological theory, remote sensing, functional ecology, eco/biodiversity informatics.
- Have experience in global change ecology, ecology, and or informatics as well as a relevant statistical and/or programming background, and critical thinking and writing skills. Experience in remote sensing, modelling, vegetation monitoring, and/or plant trait analyses is an advantage.
- Research is a team effort, and all project members must be prepared to contribute to common aspects of empirical and analytical work and assist others with their projects in addition to working on their own research program.
- Be able to work independently, but also have good collaborative skills as the postdoc will be part of a highly integrated project.
- Proficiency in both written and oral English
To apply please provide a CV, a short 1 page statement why you believe this position best matches your career development, and the names of three references. To apply online, The University of Arizona had switched over their hiring software to a new website as of January 2020. To access the website click here. You should be able to find the job posting there by searching for “req789”. Here is also a PDF that walks you through how to apply. Due to COVID19 University delays we expect to officially start hiring processes at the end of June 2020. Please contact Brian J. Enquist and/or Haruko Wainwright for more information