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Record W2896183851 · doi:10.1002/lob.10282

The World's Freshwater Laboratory Turns Fifty

2018· article· en· W2896183851 on OpenAlexaffabout
Sumeep Bath

Bibliographic record

VenueLimnology and Oceanography Bulletin · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsInternational Institute for Sustainable Development
Fundersnot available
KeywordsLimnologyCuriosityGovernment (linguistics)AsideFreshwater ecosystemAquatic ecosystemGeographyEcosystemEnvironmental ethicsEcologyOceanographyGeologyPsychology

Abstract

fetched live from OpenAlex

For all of those who have built their career, maintained a professional interest, or merely a passing curiosity in the aquatic sciences, the words “Experimental Lakes Area” evoke many things—revolutionary science, an honorable legacy, incredible vistas, and critical limnological findings. For some, it also represents a significant part of their educational experience, professional development—and for a lucky few, it is home. It is all of those multiple facets of the world's freshwater laboratory that we are celebrating this year as it hits the big 5-0. The International Institute for Sustainable Development-Experimental Lakes Area (IISD-ELA) is wholly unique in the world of limnology and, indeed, in the world of field science more broadly. It is a real-world laboratory—a series of lakes (and their watersheds) on which scientists and researchers can conduct experiments to determine the impact of contaminants and threats to freshwater supplies (Fig. 1). Located in a sparsely populated region of northwestern Ontario, Canada, the lakes in the region are not affected by human impacts. By manipulating these small lakes, scientists are able to examine how all aspects of the ecosystem—from the atmosphere to fish populations—respond. Findings from these real-world experiments are often much more accurate than those from research conducted at smaller scales, such as in laboratories. Those legendary 58 lakes and their watersheds are turning 50 this year. Or rather, 2018 marks 50 years since these lakes were set aside by the Government of Canada for the then-revolutionary approach of whole-ecosystem experimentation. Let us take a quick look back through the major stages of IISD-ELA's first 50 years. More information about many of the studies mentioned below is available at https://www.iisd.org/ela/blog/research-highlights/. Back in the mid-1960s, many lakes in North America were suffering from toxic and unsightly algal blooms. Researchers at the Freshwater Institute in Winnipeg prioritized the issue, and, throughout 1966 and 1967, set about scouring northern Manitoba and northern Ontario for a cluster of isolated lakes on which the issue could be explored. A total of 463 lakes were surveyed in total. In 1968, the Government of Canada ultimately selected 46 remote lakes in northwestern Ontario as the Experimental Lakes Area. Ultimately many long-term experiments were conducted on harmful algal blooms, or “eutrophication,” which determined that phosphorus (as opposed to nitrogen or carbon) was the key factor in the development of those harmful algal blooms (Fig. 2). But the impact did not stop there. These findings went on to inform and rewrite policy around the world, ultimately resulting in the banning of phosphates in detergents internationally—all in order to mitigate the impacts of eutrophication (see Higgins et al. (2017) for a comprehensive review). In our 50th year, our work on eutrophication and the role of respective nutrients continues strong. Algal blooms may have proven our raison d’être, but since 1968, the site has grown in size and scope, and has intentionally evolved its research portfolio to respond to the pressing freshwater issues of the time. As we moved into the 1970s, public imagination was captured by the concept of acid rain—rain that is slightly acidified when nitrogen oxide or sulfur dioxide gasses are released into the atmosphere, primarily from the burning of fossil fuels. Once this acid rain lands on earth, it can do anything from acidify lakes and rivers to dissolve infrastructure and buildings. In order to mimic the acidity of the rain that was falling on freshwater ecosystems at the time, researchers at IISD-ELA introduced minute amounts of sulfuric acid into an experimental lake (Lake 223) in order to reduce the pH from about 6.8 to about 5.0 over the 7-year experiment. Among the many effects found were reduced body condition (how “fat” a fish is) and low breeding success in white suckers and lake trout, and the near extinction of fathead minnows. In addition, they found that crayfish populations crashed and that Mysis—a small but important freshwater shrimp—disappeared completely from the lake. In fact, in our 50th year, researchers just reintroduced Mysis to that lake to see what impacts it could have. In the early 1990s, as sources of renewable energy were becoming more popular, researchers set about to explore the relationship between hydroelectric reservoir creation and the production of greenhouse gases (GHGs). A strong argument for the development of hydroelectric power has been that it reduces GHG emissions, such as carbon dioxide and methane, but researchers set out to investigate how valid those assumptions were. We conducted two experiments whereby we intentionally flooded lakes, mimicking the development of reservoirs and dams. We found that both carbon dioxide and methane, an especially potent greenhouse gas, were produced in higher levels after flooding, suggesting that reservoirs can be sources of GHGs. We also discovered that flooding creates conditions that favor and increase the conversion by bacteria of mercury existing in flooded soils and vegetation to its toxic form of methyl mercury. As we moved into the twenty-first century, our focus turned to how mercury can build up in fish populations. From 2011 to 2007, we intentionally added small amounts of traceable mercury to a lake to see how it moved through the ecosystem and food web. Predictably, the amount of mercury found in the fishes increased. When we stopped adding mercury, the amount found in fishes decreased, suggesting that reducing the amount of mercury that enters the atmosphere may have a significant impact on the amount of mercury that ends up in fish (and therefore humans). This is good news, and bodes well for the impact of the Minamata Convention on Mercury—an international treaty designed to reduce the amount of mercury emitted internationally, on which the research at IISD-ELA was influential. At the beginning of the 2010s, the Government of Canada announced its intentions to no longer fund the site. This resulted in a nonprofit think tank based in Winnipeg—the International Institute for Sustainable Development (IISD)—assuming operation of the site. It also signaled a new era for the newly minted IISD-ELA, with a ramped up research portfolio, and a greater focus on public education, community outreach, and communication (Fig. 2). In 2018, two major projects kicked off during the 50th year to explore the impact of oil spills on freshwater systems. While clearly a response to the current significant dependence that North America has on oil and its transportation, these projects—collaborative efforts involving governments, industry, universities, local communities, and the public—are a true product of the new IISD-ELA (Fig. 3). Ever since 2014 signaled our new era, we have been figuratively and literally opening our doors to the world, and engaging with new audiences and groups who had not previously been reached by the work at the site. We are also actively seeking collaboration with a broader range of researchers and scientists across the globe, who can bring fresh and interesting perspectives to our existing work, as well as proposing new ideas for whole-lake experimentation. If you would like to propose work, please contact Vince Palace at vpalace@iisd-ela.org. And to learn more about the world's freshwater laboratory, and how it is celebrating its 50th year, visit www.iisd.org/ela.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.191
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2018
Admission routes2
Has abstractyes

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