A Model for using Environmental Data‐Driven Inquiry and Exploration to Teach Limnology to Undergraduates

Carey, Cayelan C.; Gougis, Rebekka Darner; Klug, Jennifer L.; O’Reilly, Catherine M.; Richardson, David C.

doi:10.1002/lob.10020

Cited by 17 publications

(24 citation statements)

References 8 publications

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“…Graduate students increasingly need to be able to analyze high‐frequency datasets early on in their dissertation research (Read et al., ), and thus the lack of training in computational literacy skills at the undergraduate level could hinder progress in ecology graduate education. Given that a recent survey of undergraduate ecology students found that the majority of respondents expect that they will need quantitative, data management, and/or database skills for their future careers (Carey, Gougis, Klug, O'Reilly, & Richardson, ), it is clear that many students are interested in receiving greater computational literacy training. Importantly, programming is increasingly being incorporated in undergraduate curricula across many STEM (science, technology, engineering, and mathematics) fields (e.g., Lawson, Szajda, & Barnett, , National Research Council ; Wright, Provost, Roecklein‐Canfield, & Bell, ), indicating that ecological instruction needs to evolve to keep pace with related disciplines.…”

Section: Powermentioning

confidence: 99%

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Farrell

Carey

2018

Ecology and Evolution

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Environmental research requires understanding nonlinear ecological dynamics that interact across multiple spatial and temporal scales. The analysis of long‐term and high‐frequency sensor data combined with simulation modeling enables interpretation of complex ecological phenomena, and the computational skills needed to conduct these analyses are increasingly being integrated into graduate student training programs in ecology. Despite its importance, however, computational literacy—that is, the ability to harness the power of computer technologies to accomplish tasks—is rarely taught in undergraduate ecology classrooms, representing a major gap in training students to tackle complex environmental challenges. Through our experience developing undergraduate curricula in long‐term and high‐frequency data analysis and simulation modeling for two environmental science pedagogical initiatives, Project EDDIE (Environmental Data‐Driven Inquiry and Exploration) and Macrosystems EDDIE, we have found that students often feel intimidated by computational tasks, which is compounded by the lack of familiarity with software (e.g., R) and the steep learning curves associated with script‐based analytical tools. The use of prepackaged, flexible modules that introduce programming as a mechanism to explore environmental datasets and teach inquiry‐based ecology, such as those developed for Project EDDIE and Macrosystems EDDIE, can significantly increase students’ experience and comfort levels with advanced computational tools. These types of modules in turn provide great potential for empowering students with the computational literacy needed to ask ecological questions and test hypotheses on their own. As continental‐scale sensor observatory networks rapidly expand the availability of long‐term and high‐frequency data, students with the skills to manipulate, visualize, and interpret such data will be well‐prepared for diverse careers in data science, and will help advance the future of open, reproducible science in ecology.

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Section: Powermentioning

confidence: 99%

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Farrell

Carey

2018

Ecology and Evolution

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“…, Carey et al. , Touchon and McCoy , Klug et al. , Farrell and Carey ), we suggest that one previously overlooked benefit to such training is that computational ecologists are more likely to recognize the benefits of, and successfully engage in, collaborations with computer scientists.…”

Section: Enabling Greater Collaboration Among Ecologists and Computermentioning

confidence: 78%

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

et al. 2019

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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.

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“…Further, GLEON science has demonstrated that the choice of physical model can have a large and significant influence on estimates of gas exchange across lakes spanning a productivity gradient , in some cases resulting in a switch from a lake being considered net autotrophic versus heterotrophic. Regarding the important role of lakes and reservoirs in the global carbon cycle, variation in dissolved organic carbon among lakes can help explain their thermal responses to external energy inputs (Read and Rose 2013) and has important implications for how lakes respond as sentinels of climate change (Williamson et al 2014, O'Reilly et al 2015. While dozens of papers using more than one lake can be cited within the GLEON context, the aforementioned studies exemplify the collaborative nature of GLEON through data sharing (http://gleon.org/data).…”

Section: Comparative Studiesmentioning

confidence: 99%

“…Understanding OC dynamics in bog lakes, where much of the OC in northern hemispheres is stored, requires a much better understanding of how their hydrology differs from almost all other lake ecosystems (Watras et al 2013). The future of water quality in lakes and reservoirs will require a better understanding of both the climatic and the nutrient effects on cyanobacteria (Brookes and Carey 2011, Carey et al 2012b, Cottingham et al 2015, O'Reilly et al 2015, Schaeffer et al 2015 as well as how invasive species affect biodiversity and ecosystem function. Macroscale science questions will require high-performing collaborative research teams ) and scientists with skills in managing and analyzing big data (Porter et al 2012).…”

Section: Theory and Synthesismentioning

confidence: 99%

“…The network includes lakes with instrumented sites ranging from <1 ha (Trout Bog, WI) to thousands of hectares (Lake Rotorua, NZ); eutrophic lakes (Lake Mendota, WI), oligotrophic lakes (Lake Sunapee, NH), and brown-stained lakes (Lac Feagh, Ireland); large shallow lakes (Taihu, China) and deep lakes (Lake Tanganika, Africa); and alpine lakes (Alpine Lake Observatory, France), subtropical lakes (Yuan Yang Lake, Taiwan), and lakes from Antarctica (Bonnie Lake). This diversity provides opportunities to develop generalized understanding across large ecosystem gradients (e.g., Read et al 2012, Solomon et al 2013, O'Reilly et al 2015 and potentially develop globally relevant relationships .…”

Section: Integrating Network Of People Ecosystems and Data To Advamentioning

confidence: 99%

See 1 more Smart Citation

Networked lake science: how the Global Lake Ecological Observatory Network (GLEON) works to understand, predict, and communicate lake ecosystem response to global change

2016

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The Global Lake Ecological Observatory Network (GLEON) has built an international, grassroots network of scientists and citizens, data, and lake observatories to advance understanding of lake ecosystems. Through careful attention to the professional needs and aspirations of a community, GLEON has formed as its foundation the trust and respect essential to product-based network science. As a consequence, GLEON is making significant advancements in lake ecosystem understanding through all "five legs of the table that support scientific understanding"-natural history, multiscale data, experiments, theory, and comparative studies-with particular emphasis on multiscale data and comparative studies. Technical products, such as cyberinfrastructure in support of network data and operations, software tools for calculating lake physical metrics (e.g., thermocline depth, buoyancy frequency, Schmidt stability), and lake metabolism, as well as ecosystem-scale numerical simulation software, have derived from GLEON collaborations and have become community resources catalyzing interdisciplinary science. Education and outreach initiatives have served to engage citizens from outside the traditional boundaries of academia directly in research. Moreover, these cross-boundary collaborations have provided essential links to lake and reservoir stakeholders who have informed how science is prioritized and communicated within GLEON. As a grassroots network, GLEON derives its momentum, flexibility, and impact from its talented members, who are committed to the future sustainability of lakes and reservoirs and the services they provide.

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A Model for using Environmental Data‐Driven Inquiry and Exploration to Teach Limnology to Undergraduates

Cited by 17 publications

References 8 publications

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

Power, pitfalls, and potential for integrating computational literacy into undergraduate ecology courses

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

Networked lake science: how the Global Lake Ecological Observatory Network (GLEON) works to understand, predict, and communicate lake ecosystem response to global change

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