ESTIMATION OF POPULATION GROWTH AND EXTINCTION PARAMETERS FROM NOISY DATA

By Steven Lindley. 2003

85 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fd104ec3b3a6266781c9ea51b3b771c1e1c62507

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. Elizabeth E. Holmes
2. J. Thorson
3. Mark L. Taper
4. Eric J. Ward
5. Subhash R. Lele
6. José Miguel Ponciano
7. David F. Staples
8. Brian Dennis

In Global and Planetary Change

1. Keith R. Hayes
2. J. Thorson
3. Brian Dennis
4. Eric J. Ward
5. Gareth William Peters
6. Geoffrey R. Hosack
7. Benjamin M. Bolker
8. Elizabeth E. Holmes

In Statistical and Nonlinear Physics

1. Brian Dennis
2. Gareth William Peters
3. Geoffrey R. Hosack
4. Keith R. Hayes
5. Benjamin M. Bolker
6. Eric J. Ward
7. Elizabeth E. Holmes
8. L. S. Mills

Citations in the corpus

• Ecological Models and Data in R (2008, background): “) for the growth of species of conservation concern in the presence of both observation and measurement error (Lindley, 2003).”
• Framework for Assessing Viability of Threatened and Endangered Chinook Salmon and Steelhead in the Sacramento–San Joaquin Basin (2007, methodology): “The simplest useful population viability assessments are based on the random-walk-with-drift model (Dennis et al. 1991), and can be extended to account for observation error (Lindley 2003); we use this model where possible in this paper.”
• Ecological non-linear state space model selection via adaptive particle Markov chain Monte Carlo (AdPMCMC) (2010, methodology): “Studies that do incorporate process error and observation noise into ecological inference sometimes use very special cases of linear state and observation equations [Lindley, 2003, Williams et al., 2003, Linden and Knape, 2009].”
• A better way to estimate population trends (2009, methodology): “The EGSS model was proposed by Holmes (2001), and parameter estimation was studied by Lindley (2003), Staples et al. (2004) and Dennis et al. (2006).”
• ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR (2006, methodology): “Holmes proposed a regression method for estimating model parameters; Lindley (2003) observed that ML estimates for the model could be computed with the Kalman filter.”
• Inferring spatial structure from time‐series data: using multivariate state‐space models to detect metapopulation structure of California sea lions in the Gulf of California, Mexico (2010, unknown): “The advantage of using state-space models is that the total variance in a time series of observations can be partitioned into observation and process variance (Holmes 2001; de Valpine & Hastings 2002; Lindley 2003; Dennis et al. 2006).”
• Complexity is costly: a meta‐analysis of parametric and non‐parametric methods for short‐term population forecasting (2014, methodology): “…; ~ 0, Observation (or ‘data model’) equation: ; ~ 0, While the process model is a random walk, the total variance is broken up into a process component (representing natural stochasticity) and observation error component (resulting from imperfect observations and sampling error) (Lindley 2003).”