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
  • [11] Prediction of Aviation Accidents using Logistic Regression Model
    Mathur, Priyam
    Khatri, Sunil Kumar
    Sharma, Mayank
    2017 INTERNATIONAL CONFERENCE ON INFOCOM TECHNOLOGIES AND UNMANNED SYSTEMS (TRENDS AND FUTURE DIRECTIONS) (ICTUS), 2017, : 725 - 728
  • [12] Modelling Job Satisfaction Using a Logistic Regression Model
    Cavusoglu, Behiye
    Cek, Kemal
    Eyupoglu, Serife Z.
    INTERNATIONAL CONFERENCE ON ANALYSIS AND APPLIED MATHEMATICS (ICAAM 2020), 2021, 2325
  • [13] Extroversion Text Classification Model Using Logistic Regression
    Peetaneelavat, Sakolwan
    Boonsiri, Somjai
    INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER SCIENCE, 2022, 17 (04): : 1553 - 1561
  • [14] A Patient Care Predictive Model using Logistic Regression
    Patel, Harkesh J.
    Saini, Jatinderkumar R.
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (12) : 623 - 630
  • [15] Multiple Logistic Regression Model of Signalling Practices of Drivers on Urban Highways
    Puan, Othman Che
    Ibrahim, Muttaka Na'iya
    Zakaria, Rozana
    INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2014 (ICOMEIA 2014), 2015, 1660
  • [16] Urban Growth in the Bucharest Metropolitan Area: Spatial and Temporal Assessment Using Logistic Regression
    Kucsicsa, Gheorghe
    Grigorescu, Ines
    JOURNAL OF URBAN PLANNING AND DEVELOPMENT, 2018, 144 (01)
  • [17] Sovereign Credit Ratings Analysis Using the Logistic Regression Model
    Takawira, Oliver
    Mwamba, John W. Muteba
    RISKS, 2022, 10 (04)
  • [18] A Model to Predict Breast Cancer Survivability Using Logistic Regression
    Nourelahi, Mehdi
    Zamani, Ali
    Talei, Abdolrasoul
    Tahmasebi, Sedigheh
    MIDDLE EAST JOURNAL OF CANCER, 2019, 10 (02) : 132 - 138
  • [19] Using the Analysis of Logistic Regression Model in Auditing and Detection of Frauds
    Boztepe, Engin
    Usul, Hayrettin
    KHAZAR JOURNAL OF HUMANITIES AND SOCIAL SCIENCES, 2019, 22 (03): : 5 - 23
  • [20] Predictive model for areas with illegal landfills using logistic regression
    Luis Lucendo-Monedero, Angel
    Jorda-Borrell, Rosa
    Ruiz-Rodriguez, Francisca
    JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT, 2015, 58 (07) : 1309 - 1326