Python code for Kelp-Urchin stochastic dynamical model
Authors: Phil Wallhead, NIVA (pwa@niva.no), Magnus Norling, NIVA.
This is a python implementation of the model described in Wallhead et al. (2018). It is a stochastical dynamical model including a simple, bulk description of kelp biomass plus either a bulk or age/size-structured urchin population, similar to the model developed by Marzloff et al. (2013), with optional harvesting of both kelp and urchin populations. The code is designed to facilitate large ensemble simulations to assess uncertainty and sensitivity, and to meet the needs to socioeconomic studies focussed on values of urchin harvesting and kelp restoration/harvesting.
References:
Marzloff, M.P., C.R. Johnson, L.R. Little, J.-C. Soulie, S.D. Ling and S.D. Frusher, 2013: Sensitivity analysis and pattern-oriented validation of TRITON, a model with alternative community states: Insights on temperate rocky reefs dynamics. Ecological Modelling, 258:16-32.
Wallhead, P.J., Chen, W., Falkenberg, L., Norling, M., Bellerby, R., Dupont, S., Fagerli, C., Dale, T., Hancke, K., Christie, H., 2018. Annex 2: Urchin harvesting and kelp regrowth in northern Norway under ocean acidification and warming. In: AMAP Assessment 2018: Arctic Ocean Acidification. pp. 79-90 Arctic Monitoring and Assessment Programme (AMAP), Tromsø, Norway.