Background
Within
ICES, a lot of data is collected on abundances of marine organisms, for example in trawl
surveys, acoustic surveys, and visual surveys. Generally
these data are collected over a large number of locations, and the spatial and
temporal heterogeneity of the marine habitat causes spatial and temporal
variation in the observed abundances.
Analyses of such data may reveal abundance trends of marine populations, but
the concurrent effects of abiotic factors, gear selectivity, and spatial and
temporal variation is challenging. Bayesian inference by means of Integrated
Nested Laplace Approximation (INLA) has proven to be a powerful numeric
technique to analyse spatial and temporal variation in combination with other
covariates. INLA is available as a package in R. Inlabru is an extension
to INLA specifically designed to make it easier to work with spatial data and
to fit models with complex observation processes.
Objectives
In this course, we aim to teach ecologists and stock assessors how to analyse
spatial data as it is often collected in marine research. Attendees will gain
an understanding of the relevant statistical concepts/methods and how to
implement them using the INLA/inlabru software. Examples will be based on
fitting models to observations to investigate spatial and temporal correlations
and possible associations with abiotic factors using real data, as
extracted from, for example, DATRAS database.
The objective is to teach all participants how to independently analyse spatial
data collected at sea using INLA/inlabru and to understand the statistical
underpinnings of their models.
Participants
This course is aimed at anyone
who desires improving analyses of data collected at sea, including those data
that are used as inputs to fishery stock assessments. This includes survey planning, marine mammal, seabird, and stock assessment
working group members.
Level
Participants should be familiar with the R environment, and
general statistical approaches for modelling such as regression, analysis of
covariance, and general linear models.
