The impacts of climate change, energy policy, and traditional ecological practices on future firewood availability for Diné (Navajo) People

These data are part of a data portal that accompanies the special issue ‘Climate change adaptation needs a science of culture,’ published in Philosophical Transactions of the Royal Society B in 2023. To access the data portal, please visit https://doi.org/10.5061/dryad.bnzs7h4h4.

The files consist of the code of an agent-based model (ABM) in a NetLogo, detailed documentation of the ABM in a standard format, and a table of data exported from the simulation experiment reported on in the paper. By downloading the Netlogo file, one could not only rerun the experiment we report on and recreate the data table but toggle parameters or edit the model to explore other dynamics.

Methods
To understand complex emergent patterns in how changes in the supply of woody biomass, increasing levels of demand driven by energy transitions, and adherence to IEK impact the pinyon-juniper woodlands and household energy security for wood haulers, we employ an agent-based model (ABM) approach1,2,3. Using Netlogo4, we constructed the ABM to enable interactions between tree populations and firewood harvesting dynamics where we examine the effect of a range of levels of adherence to the indigenous ecological knowledge-informed (IEK) practice of harvesting only dead trees, as demand and supply varies. The Overview, Design Concepts, and Details summary (ODD) 5,6,7. Key independent variables in the model are IEK (valuation of live-wood), demand (impact of addition or loss of alternate energy streams), and climate-driven supply. Climate-driven supply is estimated as the impact on live-biomass resulting from three possible CO2 emission scenarios; 1) stable supply, 2) supply diminished to an extent possible in a low climate change scenario (the dashed line in figure 1), and 3) supply diminished to the extent possible in a high climate change scenario (the solid line in figure 1). Section 3.1 in the ODD offers greater detail on how climate-change-induced decreases in biomass are operationalized in the ABM. 

The model is trained with empirical observations and community-reported information including which species are the primary focus of firewood harvest and the average amount of wood needed per year8. Validation of the model was conducted by observing the minimum time intervals required for each state to reach equilibrium and choosing those parameters as reasonable for model runs. Additional validation measures involved perturbing variables beyond designed ranges and observing whether model outcomes violated theoretical expectations. Further details about model validation are given in the ODD. Due to stochasticity in woodland initiation, tree recruitment, tree death, and tree growth, 100 model runs are conducted for each combination of the independent variables: 1) level of adherence to IEK, 2) variation in demand for firewood, and 3) variation in firewood supply. The experiment therefore consists of 270 model runs. Outputs from the model runs were compiled into comma-delimited values and provided in the attached data table.

1An, L., Grimm, V., Sullivan, A., Turner II, B. L., Malleson, N., Heppenstall, A., Vincenot, C., Robinson, D., Ye, X., Liu, J., Lindkvist, E., & Tang, W. (2021). Challenges, tasks, and opportunities in modeling agent-based complex systems. Ecological Modelling, 457, 109685. https://doi.org/10.1016/j.ecolmodel.2021.109685

2Kohler, T. A., & Gumerman, G. G. (2000). Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes. Oxford University Press.

3Railsback, S. F., & Harvey, B. C. (2020). Modeling Populations of Adaptive Individuals. Princeton University Press.

4Wilensky, U. (1999). Netlogo [Computer software]. Center for Connected Learning and Computer-Based Modeling. http://ccl.northwestern.edu/netlogo/

5Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S. K., Huse, G., & others. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198(1–2), 115–126.

6Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S. F. (2010). The ODD protocol: A review and first update. Ecological Modelling, 221(23), 2760–2768. https://doi.org/10.1016/j.ecolmodel.2010.08.019

7Grimm, V., Railsback, S. F., Vincenot, C. E., Berger, U., Gallagher, C., DeAngelis, D. L., Edmonds, B., Ge, J., Giske, J., Groeneveld, J., Johnston, A. S. A., Milles, A., Nabe-Nielsen, J., Polhill, J. G., Radchuk, V., Rohwäder, M.-S., Stillman, R. A., Thiele, J. C., & Ayllón, D. (2020). The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism. Journal of Artificial Societies and Social Simulation, 23(2), 7.

8Magargal, K., Yellowman, J., Chee, S., Wabel, M., Macfarlan, S., & Codding, B. (2023). Firewood and energy sovereignty on Navajo Nation. Human Ecology, 51(3). https://doi.org/10.1007/s10745-023-00411-2