drmr package

  • DRMs explicitly incorporate demographic processes that drive range dynamics (Pagel and Schurr 2012). They allow for linking environmental drivers directly to specific processes and have the potential for more robust forecasting under novel conditions.

  • drmr goal: Bridge the gap between DRM potential and practical application.

  • Check out online.

Spoiler: Comparison to an SDM

More spoilers

Species Model RMSE IS (80%)
Summer flounder DRM 9.49 7.83
SDM 13.20 11.80
Red-bellied woodpecker DRM 4.49 8.68
SDM 6.86 6.20

What about FinRisk?

  • Key Environmental Drivers: NEA Mackerel distribution is primarily driven by SST, prey availability, and stock size. Mackerel density peaks around \(8.5\) to \(12^\circ\) (Ono et al. 2024) with no ocurrences registered when temperature is below \(4.8^\circ\) (Olafsdottir et al. 2019)

  • Data Availability: Data available from various surveys (Egg Survey for spawning/SSB; Bottom Trawl for juveniles). No single survey offers complete coverage of all life stages or the entire distribution annually.

  • IESSNS: The International Ecosystem Summer Survey in the Nordic Seas (IESSNS) is likely the most appropriate for adult summer feeding distribution, providing annual, age-disaggregated abundance indices and tracking distribution.

More about data availability

Survey Distribution Start Age structure
IESSNS Summer feeding 2010 Age and size composition (Age 3+)
BTS Overwinter 1965 Age-0 abundance index
Egg Survey Spawning 1992 No age structure

What’s next?

  • Preliminary results for FinRisk September meeting

  • Taylor the package code to the NEA species

  • Simulation based inference: Neural Bayes Estimators (Sainsbury-Dale et al. 2024)

  • Coordinate how (and what) the results would be more useful for the translational tools and the financial risk analyses —

References

Olafsdottir, A. H., Utne, K. R., Jacobsen, J. A., Jansen, T., Óskarsson, GuJ., Nøttestad, L., Elvarsson, B., Broms, C., and Slotte, A. (2019), “Geographical expansion of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic Seas from 2007 to 2016 was primarily driven by stock size and constrained by low temperatures,” Deep Sea Research Part II: Topical Studies in Oceanography, 159, 152–168. https://doi.org/10.1016/j.dsr2.2018.05.023.
Ono, K., Katara, I., Eliasen, S. K., Broms, C., Campbell, A., Santos Schmidt, T. C. dos, Egan, A., Hølleland, S. N., Jacobsen, J. A., Jansen, T., Mackinson, S., Mousing, E. A., Nash, R. D. M., Nikolioudakis, N., Nnanatu, C., Nøttestad, L., Singh, W., Slotte, A., Wieland, K., and Olafsdottir, A. H. (2024), “Effect of environmental drivers on the spatiotemporal distribution of mackerel at age in the Nordic Seas during 2010-20,” ICES Journal of Marine Science, 81, 1282–1294. https://doi.org/10.1093/icesjms/fsae087.
Pagel, J., and Schurr, F. M. (2012), “Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics,” Global Ecology and Biogeography, 21, 293–304. https://doi.org/https://doi.org/10.1111/j.1466-8238.2011.00663.x.
Sainsbury-Dale, M., Zammit-Mangion, A., and and, R. H. (2024), “Likelihood-free parameter estimation with neural bayes estimators,” The American Statistician, ASA Website, 78, 1–14. https://doi.org/10.1080/00031305.2023.2249522.