Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward

Abstract

In the era of big data, ecologists are increasingly relying on computational approaches and tools to answer existing questions and pose new research questions. These include both software applications (e.g., simulation models, databases and machine learning algorithms) and hardware systems (e.g., wireless sensor networks, supercomputing, drones and satellites), motivating the need for greater collaboration between computer scientists and ecologists. Here, we outline some synergistic opportunities for scientists in both disciplines that can be gained by building collaborations between the computer science and ecology research communities, with a focus on the benefits to ecology specifically. We also identify past contributions of computer science to ecology, including high‐frequency environmental sensor technology, advanced supercomputing capacity for ecological modeling, databases for long‐term and high‐frequency datasets, and software programs for ecological analyses, to anticipate future potential contributions. These examples highlight the power and potential for further integration of computer science technology and ideas into the ecological research community. Finally, we translate our own experiences working together as a team of computer scientists and ecologists over the past decade into a conceptual framework with recommendations for supporting productive collaborations at the interface of the two disciplines. We specifically focus on how to apply best practices of team science for bridging computer science and ecology, which we advocate will substantially benefit ecology long‐term.

Applications for deep learning in ecology

“…Ecologists will then have to acquire or have access to good programming and/or mathematical skills and tools. While this might seem scary at first, we believe that there is one simple solution to this challenge: collaboration across disciplines (Carey et al, ). A stronger interaction between computer scientists and ecologists could also lead to new synergies and approaches in data classification and analyses, providing new insights for fundamental and applied research in ecology.…”

Mapping citizen science contributions to the UN sustainable development goals

“…FreshWaterWatch has a global water quality database based on the contributions made by 8,000 + citizen scientists for more than 2,500 water bodies (freshwaterwatch.thewaterhub.org, 2019). Other projects, including Secchi Dip-In (Bigham Stephens et al 2015), Lake Observer (Carey et al 2019), Citclops (Busch et al 2016), Opal Water Survey (Rose et al 2016) and many more, could also inform this indicator. Given the potential of citizen science to support 6.3.2 and the large data gaps that exist, the custodian agency, UN Environment, can play a more active role by encouraging countries to apply citizen science to monitor water quality, and by providing guidelines for initiating and implementing citizen science projects at a local or national level.…”

Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

“…Our team found that computer science expertise was needed throughout the development and implementation of our forecasting system workflow, hardware, and software. Creating an iterative forecasting cycle running on a daily time step required close collaboration, trust, and a shared understanding of terminology and best practices from both freshwater ecology and computer science (following the recommendations of Carey et al 2019).…”