FITTING POPULATION MODELS INCORPORATING PROCESS NOISE AND OBSERVATION ERROR

By P. de Valpine, Alan Hastings. 2002

444 citations recorded by the source corpus.

Subfields: Ecology, Genetics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/9c0bbc3fad6570b43af061582ac6f5d57491aaf2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. James S. Clark
2. Michael Schaub
3. Olivier Gimenez
4. Byron J. T. Morgan
5. Steve P. Brooks
6. OTTAR N. Bjørnstad
7. Mark L. Taper
8. José Miguel Ponciano

In Genetics

1. Byron J. T. Morgan
2. Brian Dennis
3. Richard Parker
4. J. Sabo
5. Michael Schaub
6. Steve P. Brooks
7. Ruth King
8. Mark L. Taper

In Global and Planetary Change

1. M. Emmerson
2. J. Timothy Wootton
3. OTTAR N. Bjørnstad
4. Thompson Hobbs
5. Ulf K. Mertens
6. Ray Hilborn
7. Byron J. T. Morgan
8. J. Sabo

Citations in the corpus

• Bayesian inference in ecology (2004, background): “The studies by Strong et al. (1999) and de Valpine & Hastings (2002) are the only examples I have found where ecologists explicitly rejected a Bayesian method because it considered the parameters to be random variables and not a reflection of a fixed reality.”
• Integrated population models facilitate ecological understanding and improved management decisions (2018, background): “For estimates of population size, this typically involves a state-space model (de Valpine and Hastings 2002) to separate true variation in population size (i.e., process variation) from observation error (i.e., noise; advanced models can also accommodate bias; Schmidt et al. 2015).”
• How to analyze long-term insect population dynamics under climate change: 50-year data of three insect pests in paddy fields (2005, methodology): “Numerical integration or simulation will be required if we incorporate nonlinear terms into the equation (Kitagawa 1987; Durbin and Koopman 2001; deValpine and Hastings 2002).”
• MEASUREMENT OF INTERACTION STRENGTH IN NATURE (2005, methodology): “Ives et al. (2003) and others (e.g., de Valpine & Hastings 2002, Pascual & Karieva 1996) present methods to incorporate measurement error into time series analyses.”
• Alternatives to statistical hypothesis testing in ecology: a guide to self teaching. (2006, background): “…(Gelman et al. 1995), and have recently been extensively used in fisheries (Hilborn and Liermann 1998, Myers 2001, Myers et al. 2001, 2002, Dorn 2002) and ecology (Bender et al. 1998, Connor et al. 2000, Schmitz et al. 2000, Chalfoun et al. 2002, Fahrig 2002, Hillebrand and Cardinale…”
• Recent advances in ecological stoichiometry: insights for population and community ecology (2005, background): “For example, DeMott and Tessier (2002) considered several alternative explanations for among-lake differences in somatic growth of Daphnia .”
• Efficacy of simple viability models in ecological risk assessment: Does density dependence matter? (2004, methodology): “…(Dennis and Taper 1994, Taylor 1995), the bias introduced by sampling error on estimates of process error (Meir and Fagan 1999, Holmes 2001, De Valpine and Hastings 2002), difficulty in separating demographic and environmental sources of stochasticity from count data (En- Manuscript received…”
• BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks (2020, background): “Indeed, there are many instances of research domains where a single model is extensively explored and independently fitted by multiple researches to test scientific hypotheses [54, 58, 45, 10].”
• ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR (2006, methodology): “The state-space model of De Valpine and Hastings (2002) includes the GSS model as a special case.”