Species distribution models (SDMs) are essential tools for understanding the spatial dynamics of fish populations. Traditionally, SDMs estimate species abundance, biomass, or occurrence, either for entire populations or specific life stages, such as juveniles and adults. This study introduces a novel length-based spatially explicit SDM designed to estimate length frequency distributions (LFD) in continuous space. By integrating covariate-length and space-length correlations, the model provides a powerful tool for understanding spatial population structure dynamics. We describe the generalised length-based spatially explicit SDM and validate the model through simulation and apply it to a European hake ({\it Merluccius merluccius}, Merlucciidae) case study in the northeastern Atlantic, demonstrating its potential for real-world applications. We follow by discussing the utility of model-based LFD estimates, particularly in the fields of stock assessment, spatial fisheries management, climate change and ecosystem based fisheries management. Finally, we propose a number of model extensions departing from the proposed length-based SDM that could profoundly enhance our understanding of population dynamics and refine future fisheries management models.

Continuous space species distribution models (SDMs) have a long-standing history as a valuable tool in ecological statistical analysis. Geostatistical and preferential models are both common models in ecology. Geostatistical models are employed when the process under study is independent of the sampling locations, while preferential models are employed when sampling locations are dependent on the process under study. But, what if we have both types of data collectd over the same process? Can we combine them? If so, how should we combine them? This study investigated the suitability of both geostatistical and preferential models, as well as a mixture model that accounts for the different sampling schemes. Results suggest that in general the preferential and mixture models have satisfactory and close results in most cases, while the geostatistical models presents systematically worse estimates at higher spatial complexity, smaller number of samples and lower proportion of completely random samples.