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Record W3091584324 · doi:10.1002/eco.2255

Ecohydrological controls on lichen and moss CO<sub>2</sub> exchange in rock barrens turtle nesting habitat

2020· article· en· W3091584324 on OpenAlexafffundabout
Danielle T. Hudson, Chantel E. Markle, Lorna I. Harris, Paul Moore, J. M. Waddington

Bibliographic record

VenueEcohydrology · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of AlbertaMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMossLichenEnvironmental scienceEcologyHabitatEcological successionEcosystemGrowing seasonBiology

Abstract

fetched live from OpenAlex

Abstract Lichens and mosses are among the first organisms to colonize the open bedrock of eastern Georgian Bay, Ontario making them essential for primary soil formation and ecosystem succession, while also providing nesting habitat for turtle species‐at‐risk. However, the slow growing nature of lichen and moss makes them vulnerable to ecohydrological stresses caused by climate and land‐use change. In order to better understand how lichen and moss will respond to stressors, we examined which ecohydrological factors (e.g., near‐surface soil moisture and temperature) control the CO 2 exchange of lichen ( Cladonia spp.) and moss ( Polytrichum spp.) on rock barrens, and the time of year growth primarily occurs. Net ecosystem productivity (NEP) was significantly greater in the wet period of the growing season than the dry, with an estimated difference of 0.7 μmol m −2 s −1 for lichen, 2.9 μmol m −2 s −1 for moss, and 2.5 μmol m −2 s −1 for a moss and lichen mix. These findings indicate that the wet portions of the growing season are critical for growth, while lichen and moss have little to no productivity during the dry period. Our results indicate that near‐surface soil moisture is an indicator of the CO 2 exchange of lichen and moss, and this relationship varies among cover types. For the geographical regions where warm, dry conditions are expected to increase in duration and frequency with climate change, lichen and moss NEP will likely decrease, thus limiting the long‐term availability of nesting habitat for turtle species‐at‐risk.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.027
GPT teacher head0.241
Teacher spread0.214 · 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.

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

Citations4
Published2020
Admission routes3
Has abstractyes

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