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Record W7000770449

Going North: Inferring Testate Amoeba Habitat Shifts in Response to Climate Change with Ecological Niche Modeling

2023· article· en· W7000770449 on OpenAlexaboutno aff

Bibliographic record

VenueSmith ScholarWorks (Smith College) · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsnot available
Fundersnot available
KeywordsEcological nicheHabitatEnvironmental niche modellingNicheClimate changeAbiotic componentSpecies distributionEnvironmental changeBiogeographyBiodiversity
DOInot available

Abstract

fetched live from OpenAlex

Arcellinida are an ancient clade of single-celled eukaryotes distinguished by their tests (shells). They occupy a variety of habitats, including freshwater and other moist environments like leaf litter and soil. The question of how microbes like Arcellinida are dispersed has long troubled microbiologists. Arcellinida are seen as cosmopolitan morphospecies, but studies have shown the distribution of cryptic species within these lineages may be limited by environmental factors or geographical regions. Understanding Arcellinida distribution patterns is important to interpret their evolutionary history and possible ecological shifts that influenced speciation events. Previous studies often focused on a few sites over a small geographic range, making it difficult to assess broad environmental drivers that govern restricted habitat distributions. Here, we use MaxEnt, a machine learning software that uses presence-absence data to assess Arcellinda through time, space, and geography, to predict preferred ecological niches across North America. We train this model by introducing location data collected from a literature review of sites where Arcellinida have been recorded and environmental layers collected from the global climate data site Worldclim. Based on the abiotic characteristics of the literature observation sites, MaxEnt predicts other habitats across North America where Arcellinida are likely to be found. We then use sets of future data taken from the Coupled Model Intercomparison Project, which predict what environmental conditions will be in the years 2050 and 2070, to predict how Arcellinida habitats will shift as a result of climate change. Preliminary results suggest that current habitats for Arcellinida are concentrated in Northern Mexico, along the West Coast of Canada, and the south of the United States. These niches are predicted to shift increasingly Northwest over time, with a narrowing latitudinal range. This work will help us better understand the most influential environmental conditions for Arcellinida distribution and lays the groundwork for future assessments of paleoclimate. By increasing our understanding of the geographic and evolutionary history of the Arcellinida clade, this project will contribute to our understanding of a major microbial community, and allow us to understand how the environment has and will shape diversity in the future.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.254
Teacher spread0.227 · 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 designSimulation or modeling
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
Published2023
Admission routes1
Has abstractyes

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