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Record W3000676500 · doi:10.1111/fwb.13477

Multitrophic biodiversity patterns and environmental descriptors of sub‐Arctic lakes in northern Europe

2020· article· en· W3000676500 on OpenAlexaff
Danny C. P. Lau, Kirsten Christoffersen, Jaakko Erkinaro, Brian Hayden, Jani Heino, Seppo Hellsten, Kerstin Holmgren, Kimmo K. Kahilainen, Maria Kahlert, Satu Maaria Karjalainen, Jan Karlsson, Laura Forsström, Jennifer Lento, Marit Mjelde, Jukka Ruuhijärvi, Steinar Sandøy, Ann Kristin Schartau, Martin‐A. Svenning, Tobias Vrede, Willem Goedkoop

Bibliographic record

VenueFreshwater Biology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and biodiversity studies
Canadian institutionsUniversity of New Brunswick
FundersNatureAcademy of Finland
KeywordsArcticBiodiversityGeographyEcologyThe arcticEnvironmental scienceBiologyOceanographyGeology

Abstract

fetched live from OpenAlex

Abstract Arctic and sub‐Arctic lakes in northern Europe are increasingly threatened by climate change, which can affect their biodiversity directly by shifting thermal and hydrological regimes, and indirectly by altering landscape processes and catchment vegetation. Most previous studies of northern lake biodiversity responses to environmental changes have focused on only a single organismal group. Investigations at whole‐lake scales that integrate different habitats and trophic levels are currently rare, but highly necessary for future lake monitoring and management. We analysed spatial biodiversity patterns of 74 sub‐Arctic lakes in Norway, Sweden, Finland, and the Faroe Islands with monitoring data for at least three biological focal ecosystem components (FECs)—benthic diatoms, macrophytes, phytoplankton, littoral benthic macroinvertebrates, zooplankton, and fish—that covered both pelagic and benthic habitats and multiple trophic levels. We calculated the richness relative (i.e. taxon richness of a FEC in the lake divided by the total richness of that FEC in all 74 lakes) and the biodiversity metrics (i.e. taxon richness, inverse Simpson index (diversity), and taxon evenness) of individual FECs using presence–absence and abundance data, respectively. We then investigated whether the FEC richness relative and biodiversity metrics were correlated with lake abiotic and geospatial variables. We hypothesised that (1) individual FECs would be more diverse in a warmer and wetter climate (e.g. at lower latitudes and/or elevations), and in hydrobasins with greater forest cover that could enhance the supply of terrestrial organic matter and nutrients that stimulated lake productivity; and (2) patterns in FEC responses would be coupled among trophic levels. Results from redundancy analyses showed that the richness relative of phytoplankton, macrophytes, and fish decreased, but those of the intermediate trophic levels (i.e. macroinvertebrates and zooplankton) increased with decreasing latitude and/or elevation. Fish richness relative and diversity increased with increasing temporal variation in climate (temperature and/or precipitation), ambient nutrient concentrations (e.g. total nitrogen) in lakes, and woody vegetation (e.g. taiga forest) cover in hydrobasins, whereas taxon richness of macroinvertebrates and zooplankton decreased with increasing temporal variation in climate. The similar patterns detected for richness relative of fish, macrophytes, and phytoplankton could be caused by similar responses to the environmental descriptors, and/or the beneficial effects of macrophytes as habitat structure. By creating habitat, macrophytes may increase fish diversity and production, which in turn may promote higher densities and probably more diverse assemblages of phytoplankton through trophic cascades. Lakes with greater fish richness relative tended to have greater average richness relative among FECs, suggesting that fish are a potential indicator for overall lake biodiversity. Overall, the biodiversity patterns observed along the environmental gradients were trophic‐level specific, indicating that an integrated food‐web perspective may lead to a more holistic understanding of ecosystem biodiversity in future monitoring and management of high‐latitude lakes. In future, monitoring should also focus on collecting more abundance data for fish and lower trophic levels in both benthic and pelagic habitats. This may require more concentrated sampling effort on fewer lakes at smaller spatial scales, while continuing to sample lakes distributed along environmental gradients.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.993

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.170
Teacher spread0.157 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations34
Published2020
Admission routes1
Has abstractyes

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