実行環境

sessionInfo()
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22621)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Japanese_Japan.utf8  LC_CTYPE=Japanese_Japan.utf8   
## [3] LC_MONETARY=Japanese_Japan.utf8 LC_NUMERIC=C                   
## [5] LC_TIME=Japanese_Japan.utf8    
## 
## attached base packages:
## [1] parallel  graphics  grDevices utils     datasets  stats     methods  
## [8] base     
## 
## other attached packages:
##  [1] rgdal_1.6-7         ggmap_3.0.2         plotly_4.10.1      
##  [4] viridis_0.6.4       car_3.1-2           carData_3.0-5      
##  [7] posterior_1.4.1     fields_15.2         viridisLite_0.4.2  
## [10] spam_2.9-1          lemon_0.4.6         ggsci_2.9          
## [13] kableExtra_1.3.4    DT_0.27             patchwork_1.1.2    
## [16] ggrepel_0.9.2       GGally_2.1.2        ggnewscale_0.4.9   
## [19] geoR_1.9-2          gt_0.9.0            bayesplot_1.10.0   
## [22] ggeffects_1.1.4     cmdstanr_0.5.3      rstan_2.26.23      
## [25] StanHeaders_2.26.28 brms_2.18.0         Rcpp_1.0.11        
## [28] gstat_2.1-1         gamm4_0.2-6         lme4_1.1-34        
## [31] mgcv_1.8-41         nlme_3.1-160        INLA_23.04.24      
## [34] foreach_1.5.2       Matrix_1.6-1        NipponMap_0.2      
## [37] sf_1.0-14           sp_1.5-1            fontregisterer_0.3 
## [40] systemfonts_1.0.4   extrafont_0.18      data.table_1.14.6  
## [43] see_0.7.5.5         report_0.5.7.4      parameters_0.20.3  
## [46] performance_0.10.3  modelbased_0.8.6.3  insight_0.19.1.4   
## [49] effectsize_0.8.3.6  datawizard_0.7.1.1  correlation_0.8.4  
## [52] bayestestR_0.13.1   easystats_0.6.0.8   scales_1.2.1       
## [55] lubridate_1.9.2     forcats_1.0.0       stringr_1.5.0      
## [58] dplyr_1.1.2         purrr_1.0.2         readr_2.1.4        
## [61] tidyr_1.3.0         tibble_3.2.1        ggplot2_3.4.3      
## [64] tidyverse_2.0.0     MASS_7.3-58.1       knitr_1.44         
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2           tidyselect_1.2.0     htmlwidgets_1.6.2   
##   [4] grid_4.2.2           munsell_0.5.0        codetools_0.2-18    
##   [7] units_0.8-1          miniUI_0.1.1.1       withr_2.5.0         
##  [10] Brobdingnag_1.2-9    colorspace_2.0-3     rstudioapi_0.15.0   
##  [13] stats4_4.2.2         Rttf2pt1_1.3.12      labeling_0.4.3      
##  [16] emmeans_1.8.8        RgoogleMaps_1.4.5.3  splancs_2.01-44     
##  [19] mnormt_2.1.1         bit64_4.0.5          farver_2.1.1        
##  [22] bridgesampling_1.1-2 coda_0.19-4          vctrs_0.6.3         
##  [25] generics_0.1.3       TH.data_1.1-2        xfun_0.39           
##  [28] timechange_0.1.1     R6_2.5.1             markdown_1.8        
##  [31] bitops_1.0-7         cachem_1.0.6         reshape_0.8.9       
##  [34] vroom_1.6.0          promises_1.2.0.1     multcomp_1.4-25     
##  [37] gtable_0.3.4         processx_3.8.0       sandwich_3.0-2      
##  [40] MatrixModels_0.5-2   rlang_1.1.1          splines_4.2.2       
##  [43] lazyeval_0.2.2       extrafontdb_1.0      checkmate_2.1.0     
##  [46] inline_0.3.19        yaml_2.3.7           reshape2_1.4.4      
##  [49] abind_1.4-5          threejs_0.3.3        crosstalk_1.2.0     
##  [52] backports_1.4.1      httpuv_1.6.7         tensorA_0.36.2      
##  [55] tools_4.2.2          tcltk_4.2.2          bookdown_0.35       
##  [58] ellipsis_0.3.2       jquerylib_0.1.4      RColorBrewer_1.1-3  
##  [61] proxy_0.4-27         plyr_1.8.8           base64enc_0.1-3     
##  [64] classInt_0.4-8       ps_1.7.2             prettyunits_1.1.1   
##  [67] zoo_1.8-11           magrittr_2.0.3       spacetime_1.3-0     
##  [70] colourpicker_1.3.0   mvtnorm_1.1-3        matrixStats_0.63.0  
##  [73] archive_1.1.5        hms_1.1.3            shinyjs_2.1.0       
##  [76] mime_0.12            evaluate_0.21        xtable_1.8-4        
##  [79] shinystan_2.6.0      jpeg_0.1-10          gridExtra_2.3       
##  [82] rstantools_2.3.1.1   compiler_4.2.2       maps_3.4.1          
##  [85] KernSmooth_2.23-20   crayon_1.5.2         minqa_1.2.5         
##  [88] htmltools_0.5.4      later_1.3.0          tzdb_0.3.0          
##  [91] RcppParallel_5.1.6   DBI_1.1.3            boot_1.3-28         
##  [94] cli_3.6.1            dotCall64_1.0-2      igraph_1.3.5        
##  [97] pkgconfig_2.0.3      sn_2.1.1             numDeriv_2016.8-1.1 
## [100] xml2_1.3.5           svglite_2.1.1        dygraphs_1.1.1.6    
## [103] QuickJSR_1.0.5       bslib_0.5.1          webshot_0.5.5       
## [106] estimability_1.4.1   rvest_1.0.3          distributional_0.3.2
## [109] callr_3.7.3          digest_0.6.31        rmarkdown_2.24      
## [112] intervals_0.15.4     shiny_1.7.5          gtools_3.9.4        
## [115] nloptr_2.0.3         lifecycle_1.0.3      jsonlite_1.8.4      
## [118] fansi_1.0.3          pillar_1.9.0         lattice_0.20-45     
## [121] loo_2.6.0            httr_1.4.7           fastmap_1.1.1       
## [124] pkgbuild_1.4.2       survival_3.5-5       glue_1.6.2          
## [127] xts_0.12.2           FNN_1.1.3.2          png_0.1-8           
## [130] shinythemes_1.2.0    iterators_1.0.14     bit_4.0.5           
## [133] class_7.3-20         stringi_1.7.12       sass_0.4.5          
## [136] e1071_1.7-12

References

Barbraud C, Weimerskirch H (2006) Antarctic birds breed later in response to climate change. Proc Natl Acad Sci U S A 103:6248–6251
Blangiardo M, Cameletti M (2015) Spatial and spatio-temporal bayesian models with R - INLA. John Wiley & Sons
Bouriach M, Samraoui F, Souilah R, et al (2015) Does Core-Periphery gradient determine breeding performance in a breeding colony of white storks ciconia ciconia? AORN J 50:149–156
Carroll R, Lawson AB, Faes C, et al (2015) Comparing INLA and OpenBUGS for hierarchical poisson modeling in disease mapping. Spat Spatiotemporal Epidemiol 14-15:45–54
Chang W (2018) R graphics cookbook: Practical recipes for visualizing data. “O’Reilly Media, Inc.”
Crozier LG, Scheuerell MD, Zabel RW (2011) Using time series analysis to characterize evolutionary and plastic responses to environmental change: A case study of a shift toward earlier migration date in sockeye salmon. Am Nat 178:755–773
Cruikshanks R, Lauridsen R, Harrison A, et al (2006) Evaluation of the use of the sodium dominance index as a potential measure of acid sensitivity
Dale MRT, Fortin M-J (2014) Spatial analysis: A guide for ecologists. Cambridge University Press
Durbin J, Koopman SJ (2012) Time series analysis by state space methods. OUP Oxford
Etheridge DM, Steele LP, Francey RJ, et al (1998) Atmospheric methane between 1000 AD and present: Evidence of anthropogenic emissions and climatic variability. Journal of
Fukuda Y, Manolis C, Saalfeld K, Zuur A (2015) Dead or alive? Factors affecting the survival of victims during attacks by saltwater crocodiles (crocodylus porosus) in australia. PLoS One 10:e0126778
Ga L, Fox MJ Statistical approaches to the problem of phylogenetically correlated data. In: Sosa VJ, Fox GA, Negrete-Yankelevich S (eds) Ecological statistics: Contemporary theory and application.
Hopkins WD, Reamer L, Mareno MC, Schapiro SJ (2015) Genetic basis in motor skill and hand preference for tool use in chimpanzees (pan troglodytes). Proc Biol Sci 282:20141223
Irl SDH, Harter DEV, Steinbauer MJ, et al (2015) Climate vs. Topography – spatial patterns of plant species diversity and endemism on a high‐elevation island. J Ecol 103:1621–1633
Kruschke J (2014) Doing bayesian data analysis: A tutorial with r, JAGS, and stan. Academic Press
Ligas A (2008) Population dynamics of procambarus clarkii (girard, 1852) (decapoda, astacidea, cambaridae) from southern tuscany (italy). Crustaceana 81:601–609
Lindgren F, Rue H (2015) Bayesian spatial modelling with R-INLA. J Stat Softw 63:1–25
Lindgren F, Rue H, Lindström J (2011) An explicit link between gaussian fields and gaussian markov random fields: The stochastic partial differential equation approach. J R Stat Soc Series B Stat Methodol 73:423–498
Mauritzen M, Belikov SE, Boltunov AN, et al (2003) Functional responses in polar bear habitat selection. Oikos 100:112–124
McElreath R (2020) Statistical rethinking : A bayesian course with examples in R and STAN, 2nd Edition. Chapman; Hall/CRC
Ntzoufras I (2011) Bayesian modeling using WinBUGS. John Wiley & Sons
Petty SK, Zuckerberg B, Pauli JN (2015) Winter conditions and land cover structure the subnivium, a seasonal refuge beneath the snow. PLoS One 10:e0127613
Pierce GJ, Santos MB, Smeenk C, et al (2007) Historical trends in the incidence of strandings of sperm whales (physeter macrocephalus) on north sea coasts: An association with positive temperature anomalies. Fish Res 87:219–228
Ravishanker N, Raman B, Soyer R (2022) Dynamic time series models using R-INLA: An applied perspective. https://ramanbala.github.io/dynamic-time-series-models-R-INLA/; CRC Press
Reed JM, Elphick CS, Ieno EN, Zuur AF (2011) Long-term population trends of endangered hawaiian waterbirds. Popul Ecol 53:473–481
Simpson DP, Rue H, Martins TG, et al (2014) Penalising model component complexity: A principled, practical approach to constructing priors. Stat Sci 32:1–28
Smeenk C (1997) Strandings of sperm whales physeter macrocephalus in the north sea: History and patterns
Sofaer HR, Sillett TS, Langin KM, et al (2014) Partitioning the sources of demographic variation reveals density-dependent nest predation in an island bird population. Ecol Evol 4:2738–2748
Steidl RJ, Griffin CR, Niles LJ (1991) Contaminant levels of osprey eggs and prey reflect regional differences in reproductive success. J Wildl Manage 55:601–608
Sturrock AM, Hunter E, Milton JA, et al (2015) Quantifying physiological influences on otolith microchemistry. Methods Ecol Evol 6:806–816
Timi JT, Lanfranchi AL, Etchegoin JA, Cremonte F (2008) Parasites of the brazilian sandperch pinguipes brasilianus cuvier: A tool for stock discrimination in the argentine sea. J Fish Biol 72:1332–1342
Wickham H, Grolemund G (2016) R for data science: Import, tidy, transform, visualize, and model data. “O’Reilly Media, Inc.”
Zuur AF (2017) Beginner’s guide to spatial, temporal and Spatial-Temporal ecological data analysis with R-Inla: Using glm and glmm volume I. Hightland Statistics Ltd.
Zuur AF (2012) A beginner’s guide to generalized additive models with R. Highland Statistics, Newburgh, Scotland
Zuur AF (2009) Mixed effects models and extensions in ecology with R. Springer, New York, NY
Zuur AF, Hilbe JM, Leno EN (2013) A beginner’s guide to GLM and GLMM with r: A frequentist and bayesian perspective for ecologists. Highland Statistics
Zuur AF, Ieno EN (2016) Beginner’s guide to zero-inflated models with R. https://www.highstat.com/Books/BGS/ZIM/pdfs/TOCOnly.pdf; Highland Statistics
Zuur AF, Ieno EN, Smith GM (2007) Analyzing ecological data. Springer New York
健太郎大東 (2010) 線形モデルから一般化線形モデル(GLM)へ. 雑草研究 55:268–274
岡田謙介, 大久保街亜 (2012) 伝えるための心理統計: 効果量・信頼区間・検定力. https://core.ac.uk/download/pdf/71788227.pdf; 勁草書房
村上大輔 (2022) Rではじめる地理空間データの統計解析入門. 講談社
松村優哉, 湯谷啓明, 紀ノ定保礼, 前田和 (2021) RユーザのためのRstudio[実践]入門 tidyverseによるモダンな分析フローの世界 改訂2版. 技術評論社
松浦健太郎 (2016) Stan と R でベイズ統計モデリング (wonderful R 2). 共立出版
馬場真哉 (2019) R と stan ではじめるベイズ統計モデリングによるデータ分析入門. 講談社
馬場真哉 (2018) 時系列分析と状態空間モデルの基礎 : RとStanで学ぶ理論と実装. プレアデス出版