Decomposing trends in Swedish bird populations using generalized additive mixed models

被引:56
作者
Knape, Jonas [1 ]
机构
[1] Swedish Univ Agr Sci, Dept Ecol, Box 7044, S-75007 Uppsala, Sweden
基金
瑞典研究理事会;
关键词
bird population; bird survey; generalized additive mixed model; generalized additive model; monitoring programme; population; population change; population management; population trend; trend; FARMLAND BIRDS; BIODIVERSITY; INSECT; COMMON;
D O I
10.1111/1365-2664.12720
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Estimating trends of populations distributed across wide areas is important for conservation and management of animals. Surveys in the form of annually repeated counts across a number of sites are used in many monitoring programmes, and from these, nonlinear trends may be estimated using generalized additive models (GAM). I use generalized additive mixed models (GAMM) to decompose population change into a long-term, smooth, trend component and a component for short-term fluctuations. The long-term population trend is modelled as a smooth function of time and short-term fluctuations as temporal random effects. The methods are applied to analyse trends in goldcrest and greenfinch populations in Sweden using data from the Swedish Breeding Bird Survey. I use simulations to investigate statistical properties of the model. The model separates short-term fluctuations from longer term population change. Depending on the amount of noise in the population fluctuations, estimated long-term trends can differ markedly from estimates based on standard GAMs. For the goldcrest with wide among-year fluctuations, trends estimated with GAMs suggest that the population has in recent years recovered from a decline. When filtering out, short-term fluctuations analyses suggest that the population has been in steady decline since the beginning of the survey. Simulations suggest that trend estimation using the GAMM model reduces spurious detection of long-term population change found with estimates from a GAM model, but gives similar mean square errors. The simulations therefore suggest that the GAMM model, which decomposes population change, estimates uncertainty of long-term trends more accurately at little cost in detecting them.Policy implications. Filtering out short-term fluctuations in the estimation of long-term smooth trends using temporal random effects in a generalized additive mixed model provides more robust inference about the long-term trends compared to when such random effects are not used. This can have profound effects on management decisions, as illustrated in an example for goldcrest in the Swedish breeding bird survey. In the example, if temporal random effects were not used, red listing would be highly influenced by the specific year in which it was done. When temporal random effects are used, red listing is stable over time. The methods are available in an R-package, poptrend. Filtering out short-term fluctuations in the estimation of long-term smooth trends using temporal random effects in a generalized additive mixed model provides more robust inference about the long-term trends compared to when such random effects are not used. This can have profound effects on management decisions, as illustrated in an example for goldcrest in the Swedish breeding bird survey. In the example, if temporal random effects were not used, red listing would be highly influenced by the specific year in which it was done. When temporal random effects are used, red listing is stable over time. The methods are available in an R-package, poptrend.
引用
收藏
页码:1852 / 1861
页数:10
相关论文
共 50 条
[41]   Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model [J].
Sun-Joo Cho ;
Kristopher J. Preacher ;
Haley E. Yaremych ;
Matthew Naveiras ;
Douglas Fuchs ;
Lynn S. Fuchs .
Behavior Research Methods, 2024, 56 :2094-2113
[42]   Modeling variability in treatment effects for cluster randomized controlled trials using by-variable smooth functions in a generalized additive mixed model [J].
Cho, Sun-Joo ;
Preacher, Kristopher J. ;
Yaremych, Haley E. ;
Naveiras, Matthew ;
Fuchs, Douglas ;
Fuchs, Lynn S. .
BEHAVIOR RESEARCH METHODS, 2024, 56 (03) :2094-2113
[43]   Hawaiian forest bird trends: Using log-linear models to assess long-term trends is supported by model diagnostics and assumptions (reply to Freed and Cann 2013) [J].
Camp, Richard J. ;
Pratt, Thane K. ;
Gorresen, P. Marcos ;
Woodworth, Bethany L. ;
Jeffrey, John J. .
CONDOR, 2014, 116 (01) :97-101
[44]   RETRACTED: Modeling and Fault Monitoring of Bioprocess Using Generalized Additive Models (GAMs) and Bootstrap (Retracted article. See vol. 21, pg. 270, 2013) [J].
Zheng Rongjian ;
Zhou Lincheng ;
Pan Feng .
CHINESE JOURNAL OF CHEMICAL ENGINEERING, 2012, 20 (06) :1180-1183
[45]   Emissions, meteorological and climate impacts on PM2.5 levels in Southern California using a generalized additive model: Historic trends and future estimates [J].
Gao, Ziqi ;
Ivey, Cesunica E. ;
Blanchard, Charles L. ;
Do, Khanh ;
Lee, Sang-Mi ;
Russell, Armistead G. .
CHEMOSPHERE, 2023, 325
[46]   Quantifying Temporal Trends in Fisheries Abundance Using Bayesian Dynamic Linear Models: A Case Study of Riverine Smallmouth Bass Populations [J].
Schall, Megan K. ;
Blazer, Vicki S. ;
Lorantas, Robert Ni. ;
Smith, Geoffrey D. ;
Mullican, John E. ;
Keplinger, Brandon J. ;
Wagner, Tyler .
NORTH AMERICAN JOURNAL OF FISHERIES MANAGEMENT, 2018, 38 (02) :493-501
[47]   Predicting West Nile virus transmission in North American bird communities using phylogenetic mixed effects models and eBird citizen science data [J].
Kain, Morgan P. ;
Bolker, Benjamin M. .
PARASITES & VECTORS, 2019, 12 (01)
[48]   Age- and region-specific hepatitis B prevalence in Turkey estimated using generalized linear mixed models: a systematic review [J].
Toy, Mehlika ;
Onder, Fatih Oguz ;
Woermann, Tanja ;
Bozdayi, A. Mithat ;
Schalm, Solko W. ;
Borsboom, Gerard J. ;
van Rosmalen, Joost ;
Richardus, Jan Hendrik ;
Yurdaydin, Cihan .
BMC INFECTIOUS DISEASES, 2011, 11
[49]   Site-specific prediction of West Nile virus mosquito abundance in Greater Toronto Area using generalized linear mixed models [J].
Yoo, Eun-Hye .
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2014, 28 (02) :296-313
[50]   Climate sensitive tree growth modelling of Myrocarpus frondosus in Southern Brazil using tree-ring time series and distributed generalized additive lag models [J].
Schons, Cristine Tagliapietra ;
Nothdurft, Arne ;
Ritter, Tim ;
Fleig, Frederico Dimas .
FORESTRY, 2024, 98 (02) :278-292