Power to detect trend in short-term time series of bird abundance

被引:0
|
作者
Thogmartin, Wayne E. [1 ]
Gray, Brian R. [1 ]
Gallagher, Maureen [2 ,3 ]
Young, Neal [2 ,4 ]
Rohweder, Jason J. [1 ]
Knutson, Melinda G. [1 ,5 ]
机构
[1] Upper Midwest Environm Sci Ctr, US Geol Survey, La Crosse, WI 54603 USA
[2] Big Muddy Natl Fish & Wildlife Refuge, US Fish & Wildlife Serv, Columbia, MO 65201 USA
[3] Appalachian Partnership Coordinat Off, US Fish & Wildlife Serv, Wise, VA 24293 USA
[4] Natl Resource Conservat Serv, USDA, Warrensburg, MO 64093 USA
[5] Regions 3 & 5 Biol Monitoring Team, US Fish & Wildlife Serv, La Crosse, WI 54603 USA
来源
CONDOR | 2007年 / 109卷 / 04期
关键词
floodplain habitat; Missouri River; point counts; Poisson regression; power; trend estimation; underdispersion;
D O I
10.1650/0010-5422(2007)109[943:PTDTIS]2.0.CO;2
中图分类号
Q95 [动物学];
学科分类号
071002 ;
摘要
Avian point counts for population monitoring are often collected over a short timespan (e.g., 3-5 years). We examined whether power was adequate (power >= 0.80) in short-duration studies to warrant the calculation of trend estimates. We modeled power to detect trends in abundance indices of eight bird species occurring across three floodplain habitats (wet prairies early successional forest, and mature forest) as a function of trend magnitude, sample size, and species-specific sampling and among-year variance components. Point counts (5 min) were collected from 365 locations distributed among 10 study sites along the lower Missouri River; counts were collected over the period 2002 to 2004. For all study species, power appeared adequate to detect trends in studies of short duration (three years) at a single site when exponential declines were relatively large in magnitude (more than -5% year(-1)) and the sample of point counts per year was >= 30. Efforts to monitor avian trends with point counts in small managed lands (i.e., refuges and parks) should recognize this sample size restriction by including point counts from offsite locations as a means of obtaining sufficient numbers of samples per strata. Trends of less than -5% year(-1) are not likely to be consistently detected for most species over the short term, but short-term monitoring may still be useful as the basis for comparisons with future surveys.
引用
收藏
页码:943 / 948
页数:6
相关论文
共 50 条
  • [1] Short-term trend prediction in financial time series data
    Ozorhan, Mustafa Onur
    Toroslu, Ismail Hakki
    Sehitoglu, Onur Tolga
    KNOWLEDGE AND INFORMATION SYSTEMS, 2019, 61 (01) : 397 - 429
  • [2] Short-term trend prediction in financial time series data
    Mustafa Onur Özorhan
    İsmail Hakkı Toroslu
    Onur Tolga Şehitoğlu
    Knowledge and Information Systems, 2019, 61 : 397 - 429
  • [3] Application of Short-term time series forecasting of power consumption
    Huong Phan Dieu
    Lan Huong Phan Thi
    2023 ASIA MEETING ON ENVIRONMENT AND ELECTRICAL ENGINEERING, EEE-AM, 2023,
  • [4] Short-Term Wind Power Prediction with Combination of Speed and Power Time Series
    Heinermann, Justin
    Kramer, Oliver
    KI 2015: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2015, 9324 : 100 - 110
  • [5] Of power and despair in cetacean conservation: estimation and detection of trend in abundance with noisy and short time-series
    Authier, Matthieu
    Galatius, Anders
    Gilles, Anita
    Spitz, Jerome
    PEERJ, 2020, 8
  • [6] Evaluating machine learning in short-term forecasting time series of solar power
    Fraccanabbia, Naylene
    Mariani, Viviana Cocco
    REVISTA BRASILEIRA DE COMPUTACAO APLICADA, 2021, 13 (02): : 105 - 112
  • [7] A study on short-term wind power forecasting using time series models
    Park, Soo-Hyun
    Kim, Sahm
    KOREAN JOURNAL OF APPLIED STATISTICS, 2016, 29 (07) : 1373 - 1383
  • [8] Multilinear-Trend Fuzzy Information Granule-Based Short-Term Forecasting for Time Series
    Li, Fang
    Tang, Yuqing
    Yu, Fusheng
    Pedrycz, Witold
    Liu, Yuming
    Zeng, Wenyi
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2022, 30 (08) : 3360 - 3372
  • [9] COMPARISON OF DIFFERENT TIME SERIES METHODS FOR SHORT-TERM FORECASTING OF WIND POWER PRODUCTION
    Li, Gong
    Shi, Jing
    ES2010: PROCEEDINGS OF ASME 4TH INTERNATIONAL CONFERENCE ON ENERGY SUSTAINABILITY, VOL 2, 2010, : 837 - 843
  • [10] Fuzzy Time Series Methods Applied to (In)Direct Short-Term Photovoltaic Power Forecasting
    Serrano Ardila, Vanessa Maria
    Maciel, Joylan Nunes
    Ledesma, Jorge Javier Gimenez
    Ando Junior, Oswaldo Hideo
    ENERGIES, 2022, 15 (03)