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Record W7055768721

Desert Bighorn Sheep Restoration in Texas: Survival, Population Dynamics, and Habitat

2019· dissertation· en· W7055768721 on OpenAlexaboutno aff

Bibliographic record

VenueOakTrust (Texas A&M University Libraries) · 2019
Typedissertation
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
FundersDepartment Ecosystem Science and Management, Texas A and M University
KeywordsOvis canadensisDesert (philosophy)HabitatPopulationForageBovidaeDistribution (mathematics)Abundance (ecology)
DOInot available

Abstract

fetched live from OpenAlex

Bighorn sheep (Ovis canadensis) once occupied mountain ranges from western Canada\nto northern Mexico in North America. The distribution and abundance of mountain\nsheep in North America have declined from >500,000 historically, to 185,000 in the\n1990s. In Texas, there were 1,000-1,500 desert bighorn (O. c. mexicana) living in 16\nmountains ranges within the Trans-Pecos region during the late 1800s. Declines\nresulted from a combination of factors including competition for forage with domestic\nlivestock, introduced diseases from domestic animals, unrestricted hunting, and\nrestriction of movements by net-wire fencing. By the mid-1940s, bighorn sheep\npopulations were estimated at 35 individuals, and by early 1960s the last Texas native\ndesert bighorn was extirpated. One successful approach to the conservation of large\nmammals has been their translocation into former habitats. While translocation\nstrategies have been successful for many species, translocations of large ungulates can\nbe expensive and time consuming, as well as logistically and politically challenging.\nBeginning in 1957, the Texas Game and Fish Commission brought desert bighorn from\nArizona to a breeding facility to initiate a restoration process. Over the next 4 decades,\na total of 146 desert bighorn were transplanted to Texas facilities from other states.\nThis study was initiated to fill gaps in the autecological knowledge of desert bighorn in\norder to inform management decisions and maximize the potential for long–term\nsuccess of translocated desert bighorn populations. The objectives of this study\nincluded: (1) analysis of survival and cause-specific mortality, (2) assess various\nstrategies to conduct translocations of desert bighorn in Texas using a system modeling\napproach, and (3) evaluation of potential desert bighorn distributions utilizing a\nprobability occurrence distribution model at a landscape scale within the Trans-Pecos\nregion of Texas. Results for the first objective, from the 172 collared individuals a total\nof 57 mortalities was recorded (25 M, 32 F). Causes of mortality were: 27\nundeterminable, 20 by mountain lion predation (Puma concolor), 5 were attributed to\ncontagious ecthyma (parapox orf virus), 1 poached in Mexico, 1 birth complication, 1\ninfection due to a broken jaw, 1 ingestion of toxic vegetation (cloakfern,\nAstrolepis sinuate), and 1 fell from a cliff. For the second objective, results indicated\nthat the number of years required for the population to reach carrying capacity (1) was\nreduced when proportionally more females than males were reintroduced, (2) was\nreduced slightly more by shorter than by longer time lags between the initial and the\nsecond reintroduction, although differences were negligible, and (3) was reduced when\na larger number of animals (representing a larger proportion of carrying capacity) was\nreintroduced. Results for objective 3 showed slope (49.74%) to have the greatest\nvariability explanation followed by elevation (21.26%). The model was able to explain\n95.73% of variability by using 4 variables. Distribution values for slope demonstrated\nselection values ranging from 0.09 to 314, having a median of 56.6 with a lower\nquartile of 38.2 and upper quartile of 76.3. Elevation values showed greater selection\nfor elevations between 1,200 m and 1,600 m having the median of 1,459 m. Elevation\nvalues ranged from 721 m to 2,024 m. In conclusion, reintroductions are increasingly\nused to re-establish populations of threatened species. However, many reintroduction\nattempts have been unsuccessful and the main reasons of failure are seldom\nunderstood. Monitoring should continue to provide the primary tool by which we learn\nabout the success or failure of conservation investments.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.186
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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