The resilience of Polar Collembola to climate change
Bibliographic record
Abstract
Polar Collembola have adapted over millions of years to an environment that is changing faster than any other on earth. Globally, Collembola (springtails) are among the most abundant and widely-distributed arthropods and are key components of species-poor polar ecosystems. Understanding the resilience of polar collembola to climate change is therefore an urgent research priority. Here we explore the links between genetic diversity and physiology in shaping the resilience of polar Collembola to climate change. I have reviewed the resilience of polar Collembola considering genetic diversity, behavioural avoidance and physiological tolerances along with an examination of the potential impacts of biotic interactions. I also explored potential recovery dynamics with reference to temperate taxa and colonisation patterns of new habitat exposed by glacial retreat. This review illustrated that polar Collembola exhibit a suite of traits that have enabled their survival in extreme conditions and may serve as pre-adaptations to changing conditions. However, if resistance capacities are insufficient, complete community level recovery following disturbances is exceedingly slow, especially among Collembola that inhabit deeper microhabitats within the soil column (deeper-dwelling). Overall, it appears that deeper-dwelling species that fail to resist climate changes may not recover in ecologically realistic timescales, especially given the projected pace of climate changes. The largest spatial scale study and analysis of the genetic diversity of Collembola from the central Canadian High Arctic location of Cambridge Bay (Ikaluktuktiak) was undertaken to refine species identifications, examine levels of population diversity, and explore the role of geological processes and glacial dynamics in shaping the current Arctic collembolan fauna. I identified 68 Barcode Index Numbers (BINs, as a proxy for species diversity) representing an estimated 43 morphological species, with 29 BINs unique to Cambridge Bay. The geographic linkages between populations across the High Arctic supported hypothesised east to west dispersal patterns in accordance with prevailing ocean currents. The physiology of five of the most abundant surface-active species from the Canadian High Arctic was explored to determine how resistant local species are likely to be to rising temperatures and increasing drought pressure. Some individuals were found to exhibit remarkably high heat tolerances (>40 ℃) with only limited cold tolerance capacities (64 % had supercooling points higher than -10 ℃). Survival rates in response to a desiccation stress were also variable among individuals (range: 1.0-13.5 hrs). This indicated that Arctic Collembola may be pre-adapted to a level of climate warming. I also explored the specific relationship between two populations of the Antarctic collembolan Gomphiocephalus hodgsoni found in the largest ice-free area in continental Antarctica, McMurdo Dry Valleys. I tested whether the genetic variation found between coastal and inland individuals of G. hodgsoni corresponded with differences in physiological tolerances of hot and cold temperatures was tested. Individuals from the population nearest the warmer coastal site had higher upper thermal limits (mean CTₘₐₓ 31.3 ℃) compared to individuals from the more inland population (mean CTₘₐₓ 27.2 ℃). However, these differences in heat tolerance weren’t significant until accounting for microhabitat temperature at time of collection (site + microhabitat at time of collection, p=0.0029). Coastal individuals also had higher mean supercooling points (coastal: -14.3 ℃; inland: -22.6 ℃, p=0.011). Under climate change associated warming warm-adapted populations may have a selective advantage relative to more cold adapted individuals, leading to changes in population genetic structure, a decline in genetic diversity, and associated decline in resilience. Collectively my thesis chapters have identified that the biggest threats to the ongoing survival of polar Collembola are sustained heat stress, desiccation stress, changing biotic interactions, and the arrival and spread of invasive species. Despite this, polar Collembola exhibit considerable levels of genetic diversity and physiological tolerances that may make them pre-adapted to climate change induced environmental changes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".