Ecological Models and Data in R

By Benjamin M. Bolker. 2008

1,780 citations recorded by the source corpus.

Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/1a0fc0abb90822ca0c5a88dd993e55ec0f8cef06

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Citations in the corpus

Forest Roads Facilitate the Spread of Invasive Plants (2009, methodology): “To address the question of whether patch expansion was more rapid on the roadside than in the forest, we use Poisson regression, which is more suitable for count data not expected to be extremely high (Bolker 2008), to compare the number of cells occupied in roadside vs. nonroadside habitat.”
A protocol for conducting and presenting results of regression‐type analyses (2016, background): “…(2002), Dalgaard (2002), Crawley (2012) and in textbooks containing detailed R code for more specific topics, for instance Pinheiro & Bates (2000), Bolker (2008), and Zuur et al. (2009), Zuur, Hilbe & Ieno (2013) for mixed modelling; and Wood (2006) and Zuur (2012), Zuur, Saveliev & Ieno 2014)…”
Estimating demographic models for the range dynamics of plant species (2010, methodology): “Interestingly, the statistical framework used to fit phenomenological models provides wellestablished solutions to these questions of model selection and parameter estimation (Burnham & Anderson, 1998; Bolker, 2008).”
For testing the significance of regression coefficients, go ahead and log‐transform count data (2015, methodology): “For GLMs, linearity can be corrected without changing scedasticity by using a different link function (McCullagh & Nelder 1989; Bolker 2008).”
Testing and recommending methods for fitting size spectra to data (2017, methodology): “Bolker (2008) states that such types of best-fit estimates require © 2016 Her Majesty the Queen in Right of Canada.”
Predicting potential distributions of invasive species: where to go from here? (2010, background): “The results of such theoretical mechanistic models show qualitative hints and trends and can be generalized within the framework of ‘a priori’ assumptions (Bolker, 2008).”
Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference (2018, background): “sampling (Hobbs and Hilborn, 2006), but such an interpretation is contentious 518 (Richards, 2005; Bolker, 2008; Claeskens and Hjort, 2008).”
Bamboo overabundance alters forest structure and dynamics in the Atlantic Forest hotspot (2012, methodology): “We fitted the Gamma probability density function (PDF) to the dbh distribution of each type of forest using maximum likelihood techniques and then compared the fits based on their parameter estimates (Burnham and Anderson, 2002; Bolker, 2008).”
Dispersal of the emerald ash borer, Agrilus planipennis, in newly‐colonized sites (2009, methodology): “We fit three models including the standard Ricker function (RF), the negative exponential function (NEF) and the inverse power function (IPF) (Table 1) to the data collected from Site 1 using a maximum likelihood approach, as described by Bolker (2009).”
Intercropping legumes and cereals increases phosphorus use efficiency; a meta-analysis (2020, methodology): “Power of the statistical analysis The power of a data analysis depends on the differences one aims to detect, the variability in the data, and the size of the data set (Bolker 2008).”
South‐western Atlantic reef fishes: Zoogeographical patterns and ecological drivers reveal a secondary biodiversity centre in the Atlantic Ocean (2018, methodology): “GLMMs can handle binomial distributions such as the presence–absence data (Bolker, 2008) and have already been used to assess drivers of reef fish species distribution over marine barriers and association with flotsam (Luiz et al., 2012, 2013).”
Carnivore conservation at the crossroads (2009, background): “…that carnivore science and conservation could benefit greatly from a move towards studies that are based on biological models generated a priori and tested against rigorously collected field data using modern likelihood-based or Bayesian inferential frameworks (Bolker, 2008; Royle & Dorazio, 2008).”
Manual for building tree volume and biomass allometric equations: from field measurement to prediction (2012, neutral): “This method is known in the literature as providing “population prediction intervals” (Bolker, 2008; Paine et al., 2012).”
Functional composition controls invasion success in a California serpentine grassland (2010, background): “This nonlinear model is mechanistically compatible with a situation in which resource availability to an invader individual decreases proportionally to 1 ⁄ (functional diversity) (Bolker 2008, pp. 90–92).”
Bottom‐up versus top‐down control of tree regeneration in the Białowieża Primeval Forest, Poland (2010, methodology): “Although technically quasi-likelihoods (as used in ourmodels) are not real likelihoods, the use of F-test-based likelihood ratio tests is considered appropriate to simplify quasipoisson models (Crawley 2002; Venables&Ripley 2002; Bolker 2008).”
Model selection for ecologists: the worldviews of AIC and BIC. (2014, background): “One of the paradoxes of model selection is that almost all research is based on type B simulations (Breiman 2001), but most statisticians, and even ecologists (e.g., Bolker 2008, Scheiner and Willig 2011) love to quote George Box: ‘‘All models are wrong, but some are useful.’’”
SIMR: an R package for power analysis of generalized linear mixed models by simulation (2016, methodology): “Monte Carlo simulation is a ﬂexible and accurate method appropriate for realistic ecological study designs (Bolker 2008; Johnson et al. 2015).”
Increased temperature alters feeding behavior of a generalist herbivore (2013, background): “…of an eff ect of temperature and an interaction between PC 3 and temperature (p 0.073 for both; Supplementary material Appendix A1 Table A7), low power ( β 0.321, Monte Carlo power simulation, Bolker 2008) may have inhibited our ability to detect a statistically signifi cant interaction at α 0.05.”