Carbon Stocks and Climate Change: Management Implications in Northern Arizona Ponderosa Pine Forests

Simulating forest resilience: A review

AIM

Simulation models are important tools for quantifying the resilience (i.e., persistence under changed environmental conditions) of forest ecosystems to global change. We synthesized the modelling literature on forest resilience, summarizing common models and applications in resilience research, and scrutinizing the implementation of important resilience mechanisms in these models. Models applied to assess resilience are highly diverse, and our goal was to assess how well they account for important resilience mechanisms identified in experimental and empirical research.

LOCATION

Global.

TIME PERIOD

1994 to 2019.

MAJOR TAXA STUDIED

Trees.

METHODS

We reviewed the forest resilience literature using online databases, selecting 119 simulation modelling studies for further analysis. We identified a set of resilience mechanisms from the general resilience literature and analysed models for their representation of these mechanisms. Analyses were grouped by investigated drivers (resilience to what) and responses (resilience of what), as well as by the type of model being used.

RESULTS

Models used to study forest resilience varied widely, from analytical approaches to complex landscape simulators. The most commonly addressed questions were associated with resilience of forest cover to fire. Important resilience mechanisms pertaining to regeneration, soil processes, and disturbance legacies were explicitly simulated in only 34 to 46% of the model applications.

MAIN CONCLUSIONS

We found a large gap between processes identified as underpinning forest resilience in the theoretical and empirical literature, and those represented in models used to assess forest resilience. Contemporary forest models developed for other goals may be poorly suited for studying forest resilience during an era of accelerating change. Our results highlight the need for a new wave of model development to enhance understanding of and management for resilient forests.

Hundred year projected carbon loads and species compositions for four National Forests in the northwestern USA

BACKGROUND

Forests are an important component of the global carbon balance, and climate sensitive growth and yield models are an essential tool when predicting future forest conditions. In this study, we used the dynamic climate capability of the Forest Vegetation Simulator (FVS) to simulate future (100 year) forest conditions on four National Forests in the northwestern USA: Payette National Forest (NF), Ochoco NF, Gifford Pinchot NF, and Siuslaw NF. Using Forest Inventory and Analysis field plots, aboveground carbon estimates and species compositions were simulated with Climate-FVS for the period between 2016 and 2116 under a no climate change scenario and a future climate scenario. We included a sensitivity analysis that varied calculated disturbance probabilities and the dClim rule, which is one method used by Climate-FVS to introduce climate-related mortality. The dClim rule initiates mortality when the predicted climate change at a site is greater than the change in climate associated with a predetermined shift in elevation.

RESULTS

Results of the simulations indicated the dClim rule influenced future carbon projections more than estimates of disturbance probability. Future aboveground carbon estimates increased and species composition remained stable under the no climate change scenario. The future climate scenario we tested resulted in less carbon at the end of the projections compared to the no climate change scenarios for all cases except when the dClim rule was disengaged on the Payette NF. Under the climate change scenario, species compositions shifted to climatically adapted species or early successional species.

CONCLUSION

This research highlights the need to consider climate projections in long-term planning or future forest conditions may be unexpected. Forest managers and planners could perform similar simulations and use the results as a planning tool when analyzing climate change effects at the National Forest level.

Diné kinship as a framework for conserving native tree species in climate change

Climate change affects all ecosystems but despite increasing recognition for the needs to integrate Indigenous knowledge with modern climate science, the epistemological differences between the two make it challenging. In this study, we present how Indigenous belief and knowledge system can frame the application of a modeling tool (Climate-Forest Vegetation Simulator). We focus on managing forest ecosystem services of the Diné (Navajo) Nation as a case study. Most Diné tribal members depend directly on the land for their livelihoods and cultural traditions. The forest plays a vital role in Diné livelihoods through social, cultural, spiritual, subsistence, and economic factors. We simulated forest dynamics over time under alternative climate change scenarios and management strategies to identify forest management strategies that will maintain future ecosystem services. We initialized the Climate-Forest Vegetation Simulator model with data from permanent plots and site-specific growth models under multiple management systems (no-management, thinning, burning, and assisted migration planting) and different climate scenarios (no-climate-change, RCP 4.5, RCP 6.0). Projections of climate change show average losses of basal area by over 65% by 2105, a shift in tree species composition to drier-adapted species, and a decrease in species diversity. While substantial forest loss was inevitable under the warming climate scenarios, the modeling framework allowed us to evaluate the management treatments, including planting, for conserving multiple tree species in mixed conifer forests, thus providing an anchor for biodiversity. We presented the modeling results and management implications and discuss how they can complement Diné kinship concepts. Our approach is a useful step for framing modern science with Indigenous Knowledge and for developing improved strategies to sustain natural resources and livelihoods.