Some good books that I would personally recommend are:

• Hilborn & Mangel (1997) The Ecological Detective: confronting models with data. Princeton University Press.

  This one is more about statistics with ecological examples, but there is nothing wrong about that. This would give a good flavour of how statsistics could be used in ecology. Note the date; it won't cover some of the more recent developments or applications.

• M. Henry H. Stevens (2009) A Primer of Ecology with R. Springer.

  Perhaps too basic and not particularly on anything spatial, but it covers the various topics that we'd teach ecologists and illustrates the ecological theory and models with R code.

• B. M. Bolker (2008) Ecological Models and Data in R. Princeton University Press.

  I love this book. It covers topics you will be familiar with given your stats background but applied in an ecological context. Emphasis on fitting models and optimising them from basic principles using R code.

• James S. Clark (2007) Models for Ecological Data: an introduction. Princeton University Press.

  Don't be put off by the "introduction" in the title; this is anything but an introduction. Broad coverage, lots of theory, emphasis on fitting models by hand employing Bayesian approaches (the R lab manual companion discusses writing your own Gibbs samplers for example!)

Not a book, but I'll add this as you specifically mention your interest in Gaussian Processes. Take a look at Integrated Nested Laplace Approximation (INLA), which has a website. It is an R package and has lots of examples to play with. If you look at their FAQ you'll find several papers that describe the approach, particularly:

H. Rue, S. Martino, and N. Chopin. Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society, Series B, 71(2):319{392, 2009. (PDF available here).

Some good books that I would personally recommend are:

• Hilborn & Mangel (1997) The Ecological Detective: confronting models with data. Princeton University Press.

  This one is more about statistics with ecological examples, but there is nothing wrong about that. This would give a good flavour of how statistics could be used in ecology. Note the date; it won't cover some of the more recent developments or applications.

• M. Henry H. Stevens (2009) A Primer of Ecology with R. Springer.

  Perhaps too basic and not particularly on anything spatial, but it covers the various topics that we'd teach ecologists and illustrates the ecological theory and models with R code.

• B. M. Bolker (2008) Ecological Models and Data in R. Princeton University Press.

  I love this book. It covers topics you will be familiar with given your stats background but applied in an ecological context. Emphasis on fitting models and optimising them from basic principles using R code.

• James S. Clark (2007) Models for Ecological Data: an introduction. Princeton University Press.

  Don't be put off by the "introduction" in the title; this is anything but an introduction. Broad coverage, lots of theory, emphasis on fitting models by hand employing Bayesian approaches (the R lab manual companion discusses writing your own Gibbs samplers for example!)

Not a book, but I'll add this as you specifically mention your interest in Gaussian Processes. Take a look at Integrated Nested Laplace Approximation (INLA), which has a website. It is an R package and has lots of examples to play with. If you look at their FAQ you'll find several papers that describe the approach, particularly:

H. Rue, S. Martino, and N. Chopin. Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society, Series B, 71(2):319{392, 2009. (PDF available here).

Some good books that I would personally recommend are:

• Hilborn & Mangel (1997) The Ecological Detective: confronting models with data. Princeton University Press.

  This one is more about statistics with ecological examples, but there is nothing wrong about that. This would give a good flavour of how statsistics could be used in ecology. Note the date; it won't cover some of the more recent developments or applications.

• M. Henry H. Stevens (2009) A Primer of Ecology with R. Springer.

  Perhaps too basic and not particularly on anything spatial, but it covers the various topics that we'd teach ecologists and illustrates the ecological theory and models with R code.

• B. M. Bolker (2008) Ecological Models and Data in R. Princeton University Press.

  I love this book. It covers topics you will be familiar with given your stats background but applied in an ecological context. Emphasis on fitting moedls and optimising them from basic principles using R code.

• James S. Clark (2007) Models for Ecological Data: an introduction. Princeton University Press.

  Don't be put off by the "introduction" in the title; this is anything but an introduction. Broad coverage, lots of theory, emphasis on fitting models by hand employing Bayesian approaches (the R lab manual companion discusses writing your own Gibbs samplers for example!)

Not a book, but I'll add this as you specifically mention your interest in Gaussian Processes. Take a look at Integrate Nested Laplace Approximation (INLA), which has a website. It is an R package and has lots of examples to play with. If you look at their FAQ you'll find several papers that describe the approach, particularly:

H. Rue, S. Martino, and N. Chopin. Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society, Series B, 71(2):319{392, 2009. (PDF available here).

Some good books that I would personally recommend are:

• Hilborn & Mangel (1997) The Ecological Detective: confronting models with data. Princeton University Press.

  This one is more about statistics with ecological examples, but there is nothing wrong about that. This would give a good flavour of how statsistics could be used in ecology. Note the date; it won't cover some of the more recent developments or applications.

• M. Henry H. Stevens (2009) A Primer of Ecology with R. Springer.

  Perhaps too basic and not particularly on anything spatial, but it covers the various topics that we'd teach ecologists and illustrates the ecological theory and models with R code.

• B. M. Bolker (2008) Ecological Models and Data in R. Princeton University Press.

  I love this book. It covers topics you will be familiar with given your stats background but applied in an ecological context. Emphasis on fitting models and optimising them from basic principles using R code.

• James S. Clark (2007) Models for Ecological Data: an introduction. Princeton University Press.

  Don't be put off by the "introduction" in the title; this is anything but an introduction. Broad coverage, lots of theory, emphasis on fitting models by hand employing Bayesian approaches (the R lab manual companion discusses writing your own Gibbs samplers for example!)

Not a book, but I'll add this as you specifically mention your interest in Gaussian Processes. Take a look at Integrated Nested Laplace Approximation (INLA), which has a website. It is an R package and has lots of examples to play with. If you look at their FAQ you'll find several papers that describe the approach, particularly:

H. Rue, S. Martino, and N. Chopin. Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society, Series B, 71(2):319{392, 2009. (PDF available here).