A habitat model for Parus major minor using a logistic regression model for the urban area of Osaka, Japan

被引:17
|
作者
Hashimoto, H [1 ]
Natuhara, Y
Morimoto, Y
机构
[1] Kyoto Univ, Grad Sch Agr, Lab Landscape Architecture & Environm Design, Sakyo Ku, Kyoto 6068502, Japan
[2] Osaka Prefecture Univ, Grad Sch Agr & Biol Sci, Osaka 5998531, Japan
[3] Kyoto Univ, Grad Sch Global Environm Studies, Sakyo Ku, Kyoto 6068502, Japan
关键词
habitat model; urban parks; birds; Parus major minor;
D O I
10.1016/j.landurbplan.2003.10.020
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
We developed a habitat model for Great Tits, Parus major minor, in an urban area of Osaka, Japan. Although Great Tits play an important role in the urban food web, there are few in most of Osaka and downtown Tokyo, areas that have low vegetation cover. We derived a habitat model for Great Tits using a logistic regression model with GIs. A bird survey was conducted twice in 85 urban parks using the line census method in the breeding season, May-July 2000. A GIs base map was created from aerial photographs. The following variables were measured for each park from public records and GIs for predicting the habitat: park properties (age, distance to the nearest mountains, distance to the nearest forest of more than 10 ha), the number of other habitats occupied by the tits within 500 m and 1 km and area of tree cover (within it and surrounding parks). Number of nearby habitats and distance from the nearest mountains and forest of more than 10 ha are variables related to the regional populations of the tits. Area of tree cover was measured for five radii (50, 100, 150, 200, 250 m) from park centers, and a logistic regression model was calculated for each of these radii. Variables were selected by a stepwise method. The best fitting model of the five models was selected using Akaike's Information Criteria. In the bird survey, the tit was recorded in 12 parks. The areas of parks occupied by the tit were 0.56-136.0 ha (mean: 26.0 ha +/- 42.06 S.D., n = 12). Variables selected in the best fitting model were area of tree cover in a radius of 250 m from park center and the number of other habitats within 1 km as positive factors. This model shows that area of tree cover within a certain range and number of nearby populations are the keys to the distribution of Great Tits. From this model obtained, 6.0 ha (31%), 4.0 ha (20%), 2.6 ha (13%) and 1.8 ha (9%) of tree area are needed to achieve probability of 0.5 in a radius of 250 m when numbers of other habitats within 1 km was 0-3, respectively. A realistic target figure is 10% of tree cover throughout urban areas to create an ecologically sustainable city. (C) 2003 Elsevier B.V. All rights reserved.
引用
收藏
页码:245 / 250
页数:6
相关论文
共 50 条
  • [31] 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)
  • [32] A logistic regression model for explaining urban development on the basis of accessibility: a case study of Naples
    Borzacchiello, Maria Teresa
    INTERNATIONAL JOURNAL OF ENVIRONMENT AND SUSTAINABLE DEVELOPMENT, 2009, 8 (3-4) : 300 - 313
  • [33] Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model
    Lu, Pan
    Wang, Hao
    Tolliver, Denver
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2019, 2019
  • [34] Mineral Potential Mapping Using a Conjugate Gradient Logistic Regression Model
    Lin, Nan
    Chen, Yongliang
    Lu, Laijun
    NATURAL RESOURCES RESEARCH, 2020, 29 (01) : 173 - 188
  • [35] PREDICTING POSTOPERATIVE NAUSEA AND VOMITING USING A LOGISTIC-REGRESSION MODEL
    SAMRA, G
    LITTLEJOHN, I
    BROOMHEAD, C
    TONER, C
    POWNEY, J
    PALAZZO, M
    EVANS, S
    STRUNIN, L
    BRITISH JOURNAL OF ANAESTHESIA, 1994, 72 (04) : P488 - P488
  • [36] Linking Building Attributes and Tornado Vulnerability Using a Logistic Regression Model
    Egnew, Alyssa C.
    Roueche, David B.
    Prevatt, David O.
    NATURAL HAZARDS REVIEW, 2018, 19 (04)
  • [37] Classification of array CGH data using smoothed logistic regression model
    Huang, Jian
    Salim, Agus
    Lei, Kaibin
    O'Sullivan, Kathleen
    Pawitan, Yudi
    STATISTICS IN MEDICINE, 2009, 28 (30) : 3798 - 3810
  • [38] Parental Vaccine Acceptance: A Logistic Regression Model Using Previsit Decisions
    Lee, Sara
    Riley-Behringer, Maureen
    Rose, Jeanmarie C.
    Meropol, Sharon B.
    Lazebnik, Rina
    CLINICAL PEDIATRICS, 2017, 56 (08) : 716 - 722
  • [39] Using a logistic regression model to delineate channel network in southeast Australia
    Sun, X. Y.
    Thompson, C. J.
    Croke, B. F. W.
    19TH INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION (MODSIM2011), 2011, : 1916 - 1922
  • [40] Using the Bayesian Logistic Regression Model to Determine the Relationship of Demographics and Hyperaldosteronism
    Bartolucci, A. A.
    Singh, K. P.
    Bae, S.
    21ST INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION (MODSIM2015), 2015, : 1628 - 1632