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
  • [21] CLASSIFICATION MODEL FOR TYPE OF STROKE USING KERNEL LOGISTIC REGRESSION
    Annas, Suwardi
    Poerwanto, Bobby
    Aswi
    Abdy, Muhammad
    Fa'rifah, Riska Yanu
    COMMUNICATIONS IN MATHEMATICAL BIOLOGY AND NEUROSCIENCE, 2022,
  • [22] Modeling the potential distribution of shallow-seated landslides using the weights of evidence method and a logistic regression model:a case study of the Sabae Area, Japan
    Ru-Hua SONG
    Daimaru HIROMU
    Abe KAZUTOKI
    Kurokawa USIO
    Matsuura SUMIO
    International Journal of Sediment Research, 2008, (02) : 106 - 118
  • [23] Modeling the potential distribution of shallow-seated landslides using the weights of evidence method and a logistic regression model: a case study of the Sabae Area, Japan
    Song, Ru-Hua
    Hiromu, Daimaru
    Kazutoki, Abe
    Usio, Kurokawa
    Sumio, Matsuura
    INTERNATIONAL JOURNAL OF SEDIMENT RESEARCH, 2008, 23 (02) : 106 - 118
  • [24] Prediction of postoperative nausea and vomiting using a logistic regression model
    Toner, CC
    Broomhead, CJ
    Littlejohn, IH
    Samra, GS
    Powney, JG
    Palazzo, MGA
    Evans, SJW
    Strunin, L
    BRITISH JOURNAL OF ANAESTHESIA, 1996, 76 (03) : 347 - 351
  • [25] Input Attribution for Statistical Model Checking Using Logistic Regression
    Hansen, Jeffery P.
    Chaki, Sagar
    Hissam, Scott
    Edmondson, James
    Moreno, Gabriel A.
    Kyle, David
    RUNTIME VERIFICATION, (RV 2016), 2016, 10012 : 185 - 200
  • [26] FACTORS AFFECTING OSTEOPOROSIS USING BINARY LOGISTIC REGRESSION MODEL
    Sultan, Maysoon A.
    JP JOURNAL OF BIOSTATISTICS, 2024, 24 (03) : 439 - 448
  • [27] Risk model for first semester student using logistic regression
    Din, A. M.
    PROCEEDINGS OF MECHANICAL ENGINEERING RESEARCH DAY 2017 (MERD), 2017, : 292 - 293
  • [28] EVALUATION OF REGRESSION ANALYSIS AND GRAVITY MODEL IN PHOENIX URBAN AREA
    BRADY, CR
    BETZ, MJ
    JOURNAL OF TRANSPORT ECONOMICS AND POLICY, 1971, 5 (01) : 76 - 90
  • [29] Rapid screening for influenza using a multivariable logistic regression model to save labor at a clinic in Iwaki, Fukushima, Japan
    Nguyen, Vinh Quang
    Abe, Shigeto
    Sun, Guanghao
    Matsuoka, Ayumu
    Nishimura, Hidekazu
    Ishihara, Masayuki
    Matsui, Takemi
    AMERICAN JOURNAL OF INFECTION CONTROL, 2014, 42 (05) : 551 - 553
  • [30] Driving factors of urban sprawl in Giza governorate of the Greater Cairo Metropolitan Region using a logistic regression model
    Osman, Taher
    Divigalpitiya, Prasanna
    Arima, Takafumi
    INTERNATIONAL JOURNAL OF URBAN SCIENCES, 2016, 20 (02) : 206 - 225