MultiSeaSpace mainly focused in the following 5 developments:
1 - Inference of species interactions through spatial latent fields in inlabru: after several months of simulation based validation of the method proposed in the academic literature to infer spatial species interaction through latent fields, I did not obtain reliable results using the method. Spatial latent fields, as most latent fields, are very flexible modeling structures. After careful considerations, I concluded that the use of spatial latents fields (and potentially other latent fields not investigated here) are prone to inferring false positive interactions among species.
2 - Fishery survey data integration in inlabru: I performed a simulation study to validate the model design, which promptly demonstrated to be successfull. Then I tested three different case studies, a red mullet and a european hake case study in the Mediterranean combining trawl survey and trawl commercial catch data, and a common sole case study in the Bay of Biscay combining trawl survey and trammel net commercial catches. The first two case studies turned out to be unsuccessful due the the differences in catchability coefficients between the scientific survey gear and the commercial fishing gear. The common sole case study showed consistent patterns and trends, thus it was used to prepare a scientific manuscript that is now under review in the ICES journal of Sea Research. All the code is available in github.
3 - Analysis of telemetry data using Log-Gaussian Cox Processes in inlabru: I compared three different methods to infer species habitat use through telemetry data. The use of pseudo-absences to model the data using a binomial distribution; using a Poisson discretization of the data; and using a spatially continuous LGCP model. The latter two methods produce very similar results to that of the traditional binomial model using pseudo-absences, but without the burden of having to carefully create pseudo-absences to model the data. In addition, the binomial modelling approach is known (and we also saw this) to be affected by the disposition of the pseudo-absences in the data, which does not hapen with the Poisson and LGCP approaches. Lastly, between the LGCP and Poisson approximation, the LGCP requires less steps to do the modelling because we don't need to aggregate observations in a grid or voronoy discretization to fit the model.
4 - Accommodating fishery extreme catch events in fisheries distribution models: Fishery data is characterised, among other things, by a large number of zeros as well as sporadic extremely high catch abundances. While zero inflation has been tackled in a number of ways, extreme catches has generally been either neglected or windsorized in fishery models. Within this framework, we have proposed a spliced model that not only tackles zero inflation, but also accommodates extreme observations by means of a pareto distribution.
5 - Analysis of fishery acoustic data through Marked Point Processes in inlabru: This is pretty much work in progress. The conceptual design and simulation testing is ready for both 2D and 3D models. The modelling of real data has commenced using a 2D Marked Point Process model that we may further develop into a 3D model if the 2D model proves to be useful. The modeI expects to provide a more realistic modeling of the process behind acoustic data, and therefore a better estimation of uncertainty in abundance estimates.
