Road ecology of a northern population of badgers (Taxidea Taxus) in British Columbia, Canada
Bibliographic record
Abstract
The badger (Taxidea taxus) is one of British Columbia’s (BC) most endangered mammals, with estimates placing the current provincial population at fewer than 340 adults. Previous research in the province has isolated two main factors contributing to the decline of badgers in BC: (1) a historical and current decline in habitat, and (2) road mortality caused by highways running through badger habitat. This intensive badger radio-telemetry study was initiated in 2007 to document badger ecology and movements in order to investigate why badgers are susceptible to vehicle collisions and possible mitigation methods.\nFrom 2007-2009, a subset (11♀, 5♂) of the badger population in south-central BC were captured and outfitted with VHF radio-tags to track their location and movements. In addition, the systematic collection of hair to identify individuals using DNA was employed to assist in tracking movements, dispersal, and aid in identifying carcasses. Locations for study animals were used to determine fixed kernels (95% FK) for home range analysis. Mean home ranges for males were 253.1 km2 while females had much smaller home ranges of 29.4 km2. In addition to their extremely large home ranges, males displayed extensive movements during summer, while reproductive females maintained the smallest home ranges and restricted their movements during the kit rearing period. Movements of adult females with kits were influenced by time of kit emergence from the den, litter size, and period of time before natal den abandonment.\nNewly emerging GPS technology was used to reveal extensive movements during summer that conventional radio-telemetry had underestimated. Movement rates were five times greater for GPS outfitted badgers versus conventional radio-telemetry estimates [GPS 4,801 m/day (n = 5): VHF radio 840 m/day (n = 16)] suggesting that badger movements and road crossings were underestimated by previous badger ecology studies.\nIn an attempt to document the frequency and ratio of above- and below-grade road crossings, remote motion cameras were placed at existing underpass structures to compare telemetry locations and GPS movements with camera data. Both marked and unmarked badgers were recorded using a variety of structures. Data revealed that both genders and family groups used culverts and livestock underpasses to pass under roads, with 500 mm culverts being used most frequently. Both above- and below-grade road crossings were detected, with frequency varying by individual, time of day, and seasonality.\nAnimals whose home ranges bisected highways were repeatedly observed crossing highways, with half of the study animals succumbing to collisions with vehicles (8 of 16). Due to their large home ranges, all five males in the study eventually crossed highway corridors with four of five males dying on these major roadways. Sites of badger road mortalities were spatially defined to identify areas of concentrated road mortality sites or ‘hotspots’ on major roadways in south-central BC.\nDespite the high percentage of study animal road mortality, DNA data and population modeling suggests an increasing population of badgers in the regional study area. At the present time, the high incidence of road mortality in the study area seems to be negated to some extent by strong recruitment, hence the estimate of increasing population size.\nHome range size and movement rate estimates for badgers in this study are some of the largest reported in BC. These phenomena may be explained by the patchy distribution of resources near the northern range limit of badgers in BC, such as suitable soils for burrowing and distribution of prey species. These limited resources often coincide with human populated areas where development of transportation corridors and agriculture often occur. These human-induced pressures on badger habitat often conflict with seasonal movement patterns of badgers (i.e., breeding season) leading to habitat fragmentation and direct mortality. The ability to increase our understanding of these movement patterns and provide mitigation measures (e.g., road underpasses, land conservation) that reduce direct mortality and increase productivity should allow for the continued existence of this species near their range limit in south-central BC, Canada.\nIn conclusion, I present a focused discussion on how an understanding of the ecology of badgers (particularly movements) may be integrated into habitat protection and restoration plans, including mitigation for current and future road infrastructure projects, thereby reducing impacts on this species.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".