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Record W2885757328

Development of an inventory practice for rare lichen species within Thousand Islands National Park

2018· article· en· W2885757328 on OpenAlexfundaboutno aff
Christopher Helmeste

Bibliographic record

VenueTSpace · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany, Ecology, and Taxonomy Studies
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsNational parkGeographyLichenArchaeologyEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Urbanization, forest fragmentation and climate change have been accelerating the decline of many lichen species populations, especially those which are most sensitive to subtle environmental changes that disrupt ecosystem equilibrium. Due to the many specific habitat requirements of these lichens, they have shown promise as bioindicators for niche habitat areas with unique site characteristics likely important for a variety of red list species. Locating species at risk and bioindicator lichens may therefore in turn reveal areas of high ecological importance where conservation efforts should be increased. Species at risk (SAR) lichen searches are organized by Environment Canada, however efforts have been mainly limited to those with more expertise in lichenology due to the difficulty with lichen field identification as well as limitations with lichen habitat suitability models. It is proposed that the establishment of a rare lichen inventory practice in Canadian national parks would increase the search efforts for lichen species at risk as well as bioindicator species which have high potential for identifying important habitat areas for conservation and ecological integrity monitoring. A rare lichen inventory practice for Thousand Islands National Park (TINP) Resource Conservation staff was developed, focussing on rare lichen species with distribution ranges that envelope the park boundaries. Usnea sp., Lobaria pulmonaria, Physconia subpallida, Leptogium rivulare, Leptogium corticola, Teloschistes chrysophthalmus, and Heterodermia hypoleuca were selected based on their SAR status in Ontario, bioindicator potential and ease of field identification. A lichen guide was compiled, highlighting distinguishing characteristics, significant habitat features, notable Ecological Land Classifications (ELCs), chemical tests, and search tips for each selected lichen species. Calcareous soil, moist deciduous forest and older growth trees were found to be common habitat requirements for many of the selected lichen species and predictive mapping was prepared for Hill Island and Grenadier Island (two locations frequented by TINP Resource Conservation staff) to show how these features could be combined to highlight priority search areas. Hill Island and Grenadier Island were found to contain the majority of the calcareous ELC plots within the TINP boundary and therefore would make excellent preliminary inventory search areas. It is highly recommended that TINP Resource Conservation staff use the enclosed lichen guide to increase familiarity with the selected lichen species and their potential habitat areas at the beginning of each monitoring season. Periodic inventory searches should be conducted in high priority regions and any encounters with the select lichen species should be documented (species name, substrate, date, general location, GPS coordinate, photo). It is highly recommended that other national parks adopt a similar rare lichen inventory practise using customized sets of SAR and bioindicator species relevant to each region to increase overall search efforts of rare lichens, to indicate habitat areas of special interest and to contribute to the global database of rare lichen population distributions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.077
GPT teacher head0.311
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes2
Has abstractyes

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