Habitat Selection of Temperate Bats at Different Temporal and Spatial Scales
Bibliographic record
Abstract
Habitat loss is one of the major threats to biodiversity. Hence, protecting essential habitat of endangered species is a key conservation measure. Nevertheless, it remain a challenge to understand habitat requirements of animals, as they can be modulated by several factors including seasonality, physiological state, and scale of observation. It is especially challenging to identify key habitats for small species like bats which are hard to locate and track, and for which data are often rare or scattered. My thesis seeks to use a combination of citizen science, governmental data and new technologies to study habitat requirements of bats in Québec (Canada) around different seasonal roosts (“central place”) and over different spatial scales. In Chapter One, I examine the role of landscape composition in the selection of summer and winter roosts in Québec. I used citizen science and data from provincial government surveys to localize roosts and extracted surrounding landscape features at different scales. Summer roosts were associated with human-modified landscapes and landscape elements related to water, whereas winter roosts were associated with forest and not with human-modified landscapes. In Chapter Two, I studied habitat preferences and behaviour of the little brown bat (Myotis lucifugus) at two periods of the year. I used automated telemetry to track bats and looked at behaviour and activity levels in different habitats surrounding a summer roost during the lactation period and around a winter roost during the mating period (known as swarming). Bats frequently returned to the summer roost, but during swarming, bats did not frequently return to the winter roost, suggesting different spatial constraints for habitat use in the surroundings as well as the importance of the frequency of returning to a central place (or not). Bat activity was also not distributed uniformly around both roosts, suggesting selection of certain habitats. Many bat populations in North America have suffered substantial declines from the white-nose syndrome. Protecting remnant populations and their habitat might be one of the few effective conservation measures. Together, those two chapters allow identifying key habitats for bats like water and forest edges and seasonal variation in habitat use behaviour. By integrating multiple research methods, including citizen science, data archives, and emerging technologies (e.g., radiotelemetry methods) we show the possibilities to study habitat selection over multiple period of the annual cycle, even for a small and cryptic mammals, to enhance seasonal management practices
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".