The aim of this thesis is study spatial distribution of different groups from different perspectives and to analyse the different approaches to this
problem.
We move away from the classical approach, commonly used by ecologists, to more complex solutions, already applied in several disciplines.
We are focused in applying advanced modelling techniques in order to understand species distribution and species behaviour and the
relationships between them and environmental factors and have used first the most common models applied in ecology to move then to more
advanced and complex perspectives.
From a general perspective and comparing the different models applied during the process, from MaxEnt to spatio-temporal models with
INLA, we can affirm that the models that we have developed show better results that the already built. Also, it is difficult to compare between
the different approaches, but the Bayesian approach shows more flexibility and also the inclusion of spatial field or the latent spatio-temporal
process allows to include residuals as a proxy for unmeasured variables.
Compared with additive models with thin plate splines, probably considered one of the greatest methods to analyse species distribution models
working with presence-absence data, comparable to MaxEnt, CART and MARS, our results show a better fit and more flexibility in the design.
As a natural process we have realised that the Bayesian approach could be a better solution or at least a different approach for consideration.
The main advantage of the Bayesian model formulation is the computational ease in model fit and prediction compared to classical
geostatistical methods. To do so, instead of MCMC we have used the novel integrated nested Laplace approximation approach through the
Stochastic Partial Differential Equation (SPDE) approach. The SPDE approach can be easily implemented providing results in reasonable
computing time (comparing with MCMC). We showed how SPDE is a useful tool in the analysis of species distribution. This modelling could
be expanded to the spatio-temporal domain by incorporating an extra term for the temporal effect, using parametric or semiparametric
constructions to reflect linear, nonlinear, autoregressive or more complex behaviours.
We can conclude that spatial and spatio-temporal Bayesian models are a really interesting approach for the understanding of environmental
dynamics, not only because of the possibility to develop and solve more complex problems but also for the easy understanding of the
implementation processes.The aim of this thesis is study spatial distribution of different groups from different perspectives and to analyse the different approaches to this
problem.
We move away from the classical approach, commonly used by ecologists, to more complex solutions, already applied in several disciplines.
We are focused in applying advanced modelling techniques in order to understand species distribution and species behaviour and the
relationships between them and environmental factors and have used first the most common models applied in ecology to move then to more
advanced and complex perspectives.
From a general perspective and comparing the different models applied during the process, from MaxEnt to spatio-temporal models with
INLA, we can affirm that the models that we have developed show better results that the already built. Also, it is difficult to compare between
the different approaches, but the Bayesian approach shows more flexibility and also the inclusion of spatial field or the latent spatio-temporal
process allows to include residuals as a proxy for unmeasured variables.
Compared with additive models with thin plate splines, probably considered one of the greatest methods to analyse species distribution models
working with presence-absence data, comparable to MaxEnt, CART and MARS, our results show a better fit and more flexibility in the design.
As a natural process we have realised that the Bayesian approach could be a better solution or at least a different approach for consideration.
The main advantage of the Bayesian model formulation is the computational ease in model fit and prediction compared to classical
geostatistical methods. To do so, instead of MCMC we have used the novel integrated nested Laplace approximation approach through the
Stochastic Partial Differential Equation (SPDE) approach. The SPDE approach can be easily implemented providing results in reasonable
computing time (comparing with MCMC). We showed how SPDE is a useful tool in the analysis of species distribution. This modelling could
be expanded to the spatio-temporal domain by incorporating an extra term for the temporal effect, using parametric or semiparametric
constructions to reflect linear, nonlinear, autoregressive or more complex behaviours.
We can conclude that spatial and spatio-temporal Bayesian models are a really interesting approach for the understanding of environmental
dynamics, not only because of the possibility to develop and solve more complex problems but also for the easy understanding of the
implementation processes
