Gender Differences In Space-Use Patterns And Microhabitat Characteristics Of Southern Flying Squirrel (Glaucomys Volans) In Northeastern Iowa
Bibliographic record
Abstract
Southern flying squirrel (Glaucomys volans) is common throughout the eastern deciduous forests of the United States, southern Canada, Mexico, and Central America. However, within the state of Iowa G. volans currently is listed as a “species of special concern.” This status is due to general loss of local habitat and lack of information about the species within the state. The state of Iowa has lost a majority of its native land cover over the past century due to intensive agricultural practices. Most native forests have been reduced drastically. The majority of habitat that would be suitable for southern flying squirrel has been fragmented or destroyed. These combined factors have led to the current listing of southern flying squirrel as a species of special concern within the state of Iowa. I studied southern flying squirrel at two sites in northeastern Iowa; the Mines of Spain State Recreational Area (MoSRA) and Wolter Property. The majority of my research was done at MoSRA. These sites were located in Dubuque and Clayton counties. Beginning in the summer of 2012 and continuing in the summers of 2014 and 2015 male and female southern flying squirrel were fitted with radio transmitters. Both male and female southern flying squirrels were tracked subsequently by using radio telemetry techniques. During the course of this research 11 males and 15 females were fitted with radio transmitters. Tracking results were variable; while some individuals (1 male and 3 females) yielded only a few locations, others were successfully tracked for up to two months. Home range area varied from 2.4 ha to 71.1 ha. Home ranges were larger for males than for females (P-value = 0.048). Males showed more variation in their range size as well. This variation possibly is due to the high degree of fragmentation within this habitat. Comparisons between my study home range sizes in other portions of southern flying squirrel range showed significant differences. Studies where southern flying squirrel home ranges were measured in contiguous forest habitat were smaller than those measured in my study. Home ranges of southern flying were used to determine microhabitat selection. After determining home range boundaries habitat was sampled both habitat within home ranges (Used) and outside of home ranges (Available). These points were selected by using stratified random sampling design. These data were then used to determine if there is specific microhabitat selection by this species and if so what habitat variables they respond to most strongly. Habitat variables that were significant for explaining the presence of southern flying squirrel were distance-to-nearest-neighbor (distance between trees), tree height, litter depth, and forb cover. Tree species were not significant in explaining presence of southern flying squirrel. Forest structure, not forest community, appeared to be more critical in predicting the habitat of southern flying squirrel. These data hopefully will yield a better understanding of space-use and ecology at a landscape level for the southern flying squirrel in northeastern Iowa. Currently, it is not understood how southern flying squirrel respond to forest characteristics in northeastern Iowa. Understanding movement patterns and habitat associations becomes vital should this species be listed as threatened or endangered within the state of Iowa.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".