Going North: Inferring Testate Amoeba Habitat Shifts in Response to Climate Change with Ecological Niche Modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it