Multi-Species Occupancy Model Converted to JSDM

[Joshua Hubbell]

Hi all,

Has anyone in this group converted a MSOM to a JSDM using the JAGS or NIMBLE languages?  None of the sites in my dataset received multiple visits, so I cannot use a multi-species occupancy model. I know this can be done, and than in general, it would involve removing  the detection submodel from the broader multi-species model. I tried to do this in JAGS (i.e., remove the detection portion of the model, and subsequently all detection parameters), but continued to receive error after error.  I also need to include three random effects within the model. Therefore, I'm current running a JSDM using the "HMSC" package (which is one the few JSDM packages that allows for the inclusion of random effects as far as I can tell), but its now been 4 weeks, and the model has yet to converge! Ultimately, I think the interaction between the dataset's size (230 sites, 280 species) and model complexity (3 random effects, 7 predictors) is causing the model's run-time to take way too long. I know that model would run much faster using NIMBLE (or potentially JAGS), I just don't know how to remove the detection submodel from a traditional multi-species model (e.g., DR model in AHM Vol. 1) so it could be converted into a JSDM.

Thanks,

Josh

[Marc Kery]

Dear Josh,

please see this paper: https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecy.2754

Joint species distribution models with species correlations and imperfect detection - Tobler - 2019 - Ecology - Wiley Online Library Spatiotemporal patterns in biological communities are typically driven by environmental factors and species interactions. Spatial data from communities are naturally described by stacking models for all species in the community. esajournals.onlinelibrary.wiley.com

Also, for a detection-naive approach, you should be able to directly use Francis Hui's boral package (here, and then scroll down: https://francishui.netlify.app/)

Best regards  -- Marc

Francis K.C. Hui Model selection and averaging in complex, correlated data settings This topic will focus on the development and study of computational efficient and statistically rigorous approaches for model/feature selection in complex, correlated data settings. francishui.netlify.app

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From: hmec...@googlegroups.com <hmec...@googlegroups.com> on behalf of Joshua Hubbell <jhubbel...@gmail.com>
Sent: Friday, August 5, 2022 17:58
To: hmecology: Hierarchical Modeling in Ecology <hmec...@googlegroups.com>
Subject: Multi-Species Occupancy Model Converted to JSDM

 

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