Preliminary evaluation of sampling strategies to estimate the species richness of diurnal, terrestrial birds using Monte Carlo simulation

被引:8
|
作者
Neave, HM [1 ]
Cunningham, RB [1 ]
Norton, TW [1 ]
Nix, HA [1 ]
机构
[1] AUSTRALIAN NATL UNIV,GRAD SCH,STAT CONSULTING UNIT,CANBERRA,ACT 0200,AUSTRALIA
关键词
sampling strategies; Monte Carlo simulation; gradsect; species richness; diurnal terrestrial birds; south east Australia;
D O I
10.1016/S0304-3800(96)00016-6
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Various environmental stratifications for estimating the total bird species richness across the south east region of Australia were evaluated using Monte Carlo simulation techniques. This was possible because of the availability of gee-referenced point data for the birds observed in the region that could be generally associated with a set of primary environmental attribute data. The bird data set consisted of a total of 173 species recorded from 1,075 sites. Several stratification options were assessed including: random sampling; sampling within gradsects positioned to span some of the region's major environmental gradients; sampling within stratifications based on climate attributes; and sampling within a stratification based on climate and substrate variables. Variation in Sample size was the most important factor affecting estimates of bird species richness. Several limitations associated with the origins of the bird data set and the distribution of the bird sites across the region dictated what could be achieved by the simulation study. We discuss some of the problems and limitations associated with the use of existing data sets to investigate biological issues at a regional scale. (C) 1997 Elsevier Science B.V.
引用
收藏
页码:17 / 27
页数:11
相关论文
共 50 条
  • [41] Economic comparison of five triptan strategies for migraine headaches using Monte Carlo simulation
    Bell, CF
    Hu, X
    Markson, L
    VALUE IN HEALTH, 2003, 6 (03) : 278 - 278
  • [42] Evaluation of Plasma Characteristics by Using Particle Monte-Carlo (PMC) Simulation
    Samsung Electronics Co. Ltd., Memory Etch Technology Team, Hwaseong-si, Korea, Republic of
    不详
    IEEE Int Conf Plasma Sci,
  • [43] Performance evaluation of a compartmented cooling coil using the Monte Carlo simulation approach
    Maheswaran, Uma
    Sekhar, S. C.
    JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME, 2007, 129 (09): : 1286 - 1290
  • [44] A performance evaluation on monte carlo simulation for radiation dosimetry using cell processor
    Chow, James C. L.
    JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING, 2011, 11 (1-2) : 1 - 12
  • [45] Evaluation of Shrinking Direction of Movement Using Monte Carlo Simulation in A Rectangular Slab
    Udoye, N. E.
    Dare, A. A.
    Fayomi, O. S. I.
    Uguru-okorie, D. C.
    2ND INTERNATIONAL CONFERENCE ON SUSTAINABLE MATERIALS PROCESSING AND MANUFACTURING (SMPM 2019), 2019, 35 : 1370 - 1374
  • [46] Tolerance-based process plan evaluation using Monte Carlo simulation
    Huang, SH
    Liu, Q
    Musa, R
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2004, 42 (23) : 4871 - 4891
  • [47] Evaluation of transmission detector model using Monte Carlo simulation of VMAT delivery
    Johnson, D.
    Thwaites, D.
    Cosgrove, V.
    Weston, S.
    RADIOTHERAPY AND ONCOLOGY, 2016, 119 : S695 - S695
  • [48] MEASUREMENT UNCERTAINTY EVALUATION USING MONTE CARLO SIMULATION: APPLICATIONS WITH LOGARITHMIC FUNCTIONS
    Sousa, Joao A.
    Ribeiro, Alvaro S.
    Costa, Carlos O.
    Castro, Manuel P.
    ADVANCED MATHEMATICAL AND COMPUTATIONAL TOOLS IN METROLOGY VII, 2006, 72 : 335 - +
  • [49] CAPACITY PLANNING WITH FLOW AND RELIABILITY EVALUATION USING MONTE-CARLO SIMULATION
    SU, CT
    WU, TS
    LEE, TH
    HUANG, CL
    IEEE TRANSACTIONS ON RELIABILITY, 1986, 35 (05) : 518 - 522
  • [50] Evaluation of measurement uncertainty based on the propagation of distributions using Monte Carlo simulation
    Cox, M
    Harris, P
    Siebert, BPL
    MEASUREMENT TECHNIQUES, 2003, 46 (09) : 824 - 833