Using Fecal Progestagens and Logistic Regression to Enhance Pregnancy Detection in Wild Ungulates: A Bison Case Study

被引:9
|
作者
Cain, Steven L. [1 ]
Higgs, Megan D. [2 ]
Roffe, Thomas J. [3 ]
Monfort, Steven L. [4 ]
Berger, Joel [5 ]
机构
[1] Natl Pk Serv, Moose, WY 83012 USA
[2] Montana State Univ, Dept Math Sci, Bozeman, MT 59717 USA
[3] US Fish & Wildlife Serv, Bozeman, MT 59718 USA
[4] Smithsonian Conservat Biol Inst, Front Royal, VA 22630 USA
[5] Univ Montana, Div Biol Sci, Wildlife Conservat Soc, Northern Rockies Field Off, Missoula, MT 59812 USA
来源
WILDLIFE SOCIETY BULLETIN | 2012年 / 36卷 / 04期
关键词
bison; fecal; hormones; logistic; non-invasive; pregnancy; pregnancy-specific protein B; regression; Wyoming; Yellowstone;
D O I
10.1002/wsb.178
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Ungulate ecological studies often include components of reproduction because of its demographic importance and the ecological factors affecting it. Pregnancy status, in particular, is key because it represents a starting point for succeeding measurements of vital rates. Here, we present a case study using wild bison (Bison bison), in which we developed a non-invasive method for assessing pregnancy in unmarked, non-handled animals that improves upon existing approaches for wild ungulates. Specifically, we employed a model-based binary logistic-regression approach to estimate the probability of pregnancy predicted by fecal progestagen concentrations quantified from a single, late-gestation scat sample. For 155 observations of 42 marked bison from the Jackson herd in northwest Wyoming, USA during 1997-2005, we used combinations of transrectal uterine palpation and calf status as independent measures of pregnancy to reduce the potential for error inherent in using either measure alone. We evaluated predictive success by calculating mis-prediction rates from leave-one-out cross-validation, and by calculating the percentage of 95% confidence intervals that crossed a pregnant-not-pregnant threshold. Correct predictions, with high confidence, were obtained from a model using year-centered, natural-log-transformed progestagen concentrations, resulting in an overall successful cross-validation pregnancy prediction rate of 93.5%. Our approach will allow practitioners to consider the uncertainty associated with each prediction, thereby improving prediction interpretations. The approach should appeal to practitioners because fecal samples are easily collected and preserved, laboratory procedures are well-documented, and logistic-regression statistical software is readily available. Furthermore, samples can be obtained non-invasively, which reduces cost and potential bias and increases animal safety, human safety, and social acceptability. (C) 2012 The Wildlife Society.
引用
收藏
页码:631 / 640
页数:10
相关论文
共 50 条
  • [21] Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)
    Van den Eeckhaut, M.
    Vanwalleghem, T.
    Poesen, J.
    Govers, G.
    Verstraeten, G.
    Vandekerckhove, L.
    GEOMORPHOLOGY, 2006, 76 (3-4) : 392 - 410
  • [22] Landslide susceptibility mapping using backpropagation neural networks and logistic regression: The Sephidargole case study, Semnan, Iran
    Khamehchiyan, M.
    Abdolmaleki, P.
    Rakei, B.
    GEOMECHANICS AND GEOENGINEERING-AN INTERNATIONAL JOURNAL, 2011, 6 (03): : 237 - 250
  • [23] Estimating Motives for Renting Shared Bicycles Using Multinomial Logistic Regression: A Case Study from the Slovak Republic
    Kubalak, Stanislav
    Mazanec, Jaroslav
    Gogola, Marian
    APPLIED SCIENCES-BASEL, 2024, 14 (22):
  • [24] A Bayesian Case Study in Oncology Phase I Combination Dose-Finding Using Logistic Regression with Covariates
    Bailey, Stuart
    Neuenschwander, Beat
    Laird, Glen
    Branson, Michael
    JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 2009, 19 (03) : 469 - 484
  • [25] Landslide susceptibility mapping by using landslide ratio-based logistic regression: A case study in the southern Taiwan
    Wu Chun-Hung
    JOURNAL OF MOUNTAIN SCIENCE, 2015, 12 (03) : 721 - 736
  • [26] Area Scale Landslide Hazard Assessment Using Logistic Regression: a Case Study in Bailongjiang River Basin, China
    Bai, Shibiao
    Zhang, Fanyu
    Dong, Lanfeng
    Wang, Jian
    THEORY AND PRACTICE OF RISK ANALYSIS AND CRISIS RESPONSE, PROCEEDINGS, 2008, : 395 - +
  • [27] Seismic vulnerability assessment and mapping using frequency ratio and logistic regression: A case study of Gyeongju, South Korea
    Han, J.
    Park, S.
    Kim, S.
    Son, S.
    Lee, S.
    Kim, J.
    EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS X, 2019, 11156
  • [28] Urban Expansion Simulation Based on Various Driving Factors Using a Logistic Regression Model: Delhi as a Case Study
    Salem, Muhammad
    Bose, Arghadeep
    Bashir, Bashar
    Basak, Debanjan
    Roy, Subham
    Chowdhury, Indrajit R.
    Alsalman, Abdullah
    Tsurusaki, Naoki
    SUSTAINABILITY, 2021, 13 (19)
  • [29] Exploring the driving forces of farmland loss under rapidurbanization using binary logistic regression and spatial regression: A case study of Shanghai and Hangzhou Bay
    Xiao, Rui
    Liu, Yue
    Huang, Xin
    Shi, Ruixing
    Yu, Weixuan
    Zhang, Tao
    ECOLOGICAL INDICATORS, 2018, 95 : 455 - 467
  • [30] Comparative Analysis using Multinomial Logistic Regression A Case Study for Financial Control used in Urban Local Bodies in India
    Bhattacharyya, Sidhakam
    Bandyopadhyay, Gautam
    2014 2ND INTERNATIONAL CONFERENCE ON BUSINESS AND INFORMATION MANAGEMENT (ICBIM), 2014,