Arctic chironomids of the northwest North Atlantic reflect environmental and biogeographic gradients
Bibliographic record
Abstract
Abstract Aim While we understand broad climate drivers of insect distributions throughout the Arctic, less is known about the role of spatial processes in determining these relationships. As such, there is a need to understand how spatial controls may influence our interpretations of chironomid environment relationships. Here, we evaluated whether the distribution of chironomids followed spatial gradients, or were primarily controlled by environmental factors. Location Eastern Canadian Arctic, Greenland, Iceland. Taxon Non‐biting midges (Chironomidae). Methods We examined chironomid assemblages from 239 lakes in the western North Atlantic Arctic region (specifically from the Arctic Archipelago of Canada, two parts of west Greenland (the southwest and central west) and northwest Iceland). We used a combination of unconstrained ordination (Self Organizing Maps); a simple method with only one data matrix (community data), and constrained ordination (Redundancy Analysis); a canonical ordination with two datasets where we extracted structure of community related to environmental data. These methods allowed us to model chironomid assemblages across a large bioregional dimension and identify specific differences between regions that were defined by common taxa represented across all regions in high frequencies, as well as rare taxa distinctive to each region found in low frequencies. We then evaluated the relative importance of spatial processes versus local environmental factors. Results We find that environmental controls explained the largest amount of variation in chironomid assemblages within each region, and that spatial controls are only significant when crossing between regions. Broad‐scale biogeographic effects on chironomid distributions are reflected by the distinct differences among chironomid assemblages of Iceland, central‐west Greenland, and eastern Canada, defined by the presence of certain common and low‐frequency, rare taxa for each region. Environmental gradients, especially temperature, defined species distributions within each region, whereas spatial processes combine with environmental gradients in determining what mix of species characterizes each broad and geographically distinct island region in our study. Main conclusions While biogeographic context is important for defining interpretations of environmental controls on species distributions, the primary control on distributions within regions is environmental. These influences are fundamentally important for reconstructing past environmental change and better understanding historical distributions of these insect indicators.
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 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".