A sequential Monte Carlo approach for marine ecological prediction

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A Bayesian approach to state and parameter estimation in a Phytoplankton-Zooplankton model (2010, methodology): “2003) and Markov Chain Monte Carlo (MCMC) algorithms (Dowd 2006; Dowd 2007; Arhonditsis et al. 2008).”
Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo (2007, methodology): “However, these have not been widely applied except as part of more comprehensive SIR algorithms (Gilks and Berzuini, 2001; Lee and Chia, 2002; Dowd, 2006).”
Assimilation of remote sensing into crop growth models: Current status and perspectives (2019, methodology): “A number of implementations of these filters are straightforward extensions of the basic MCMC algorithms (Dowd, 2006, 2007), while others are more directly based on importance sampling (van Leeuwen, 2009; Ristic et al., 2004; Arulampalam et al., 2002).”
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi (2013, background): “Examples are numerous, but include marine biogeochemistry (Dowd 2006; Jones, Parslow, and Murray 2010; Dowd 2011; Parslow, Cressie, Campbell, Jones, and Murray 2013), ecological population dynamics (Wikle 2003; Newman, Fernández, Thomas, and Buckland 2009; Peters, Hosack, and Hayes 2011; Hosack, Pet”