Future forest composition under a changing climate and adaptive forest management in southeastern Vermont, USA

Abstract

Global environmental change represents one of the greatest challenges facing forest resource managers today. The uncertainty and variability of potential future impacts related to shifting climatic and disturbance regimes on forest systems has led resource managers to seek out alternative management approaches to sustain the long-term delivery of forest ecosystem services. To this end, forest managers have begun incorporating adaptation strategies into resource planning and are increasingly utilizing the outcomes of forest landscape simulation and climate envelope models to guide decisions regarding potential strategies to employ. These tools can be used alongside traditional methods to assist managers in understanding the potential long-term effects of management and climate on future forest composition and productivity. This study used a spatially explicit forest landscape simulation model, Landis-II, to examine and evaluate a range of long-term effects of current and alternative forest management under three projected climate scenarios within a 50,000-hectare forested landscape in southeastern Vermont, USA. Multiple scenarios were examined within this mixed ownership landscape, allowing for an evaluation of the influence of management and climate on future forest conditions in the region. These simulations indicate that landuse legacies and the inertia associated with long-term forest successional trajectories are projected to be an important driver of future forest composition and biomass conditions for the next 100 years. Nevertheless, climate is projected to have a greater influence on species composition and aboveground biomass over the next two centuries, with forests containing a greater abundance of species from more southerly regions and lower levels of aboveground biomass, resulting in shifts in the future provisioning of ecosystem services.

Determinants of rural household livelihood dependence on non-timber forest products: A case study from Inanda Community, KwaZulu-Natal, South Africa

“…The relatively higher damages are predicted to be high in the forest and agricultural sectors in sub-Saharan Africa because the regions already endure high heat and precipitation (Ofoegbu et al, 2017;Makondo and Thomas, 2018). Thus, the forests regenerated today will have to cope with the future climate conditions of at least several decades, often even more than 100 years (Fekete et al, 2017;Krofcheck et al, 2018;Nevins et al, 2021).…”

Modeling climate-smart forest management and wood use for climate mitigation potential in Maryland and Pennsylvania

“…Balancing trade-offs and opportunities by managing forest landscapes as a heterogeneous patchwork provides a potential pathway to ensure forest ecosystems remain future carbon sinks (Wear and Coulston, 2015) while optimizing the co-benefits received from forests varying by age, structural complexity, and management goals (Nevins et al, 2021). These co-benefits include C storage and sequestration, habitat creation, reduced risk of disturbance, and enhanced resilience and adaptive capacity from both stand-to landscape-level management goals (Littlefield and D' Amato, 2022) as well as optimizing both short-and long-term mitigation potential (Petersson et al, 2022;Schulze et al, 2022).…”

Projecting future aboveground carbon sequestration rate of alpine forest on the eastern Tibetan Plateau in response to climate change

“…Assessing the long-term impact of climate change on the carbon sequestration capacity of forests is a challenge, which usually requires a large spatial and temporal scale to be represented, and the long-term complexity of climate change impacts on forest carbon stocks makes changes in forest vegetation carbon with a significant lag ( Nevins et al., 2021 ). Most recent studies on carbon sinks in forest ecosystems on the Tibetan Plateau in response to long-term climate change have been based on model simulations and remote sensing monitoring ( Dong and Ni, 2011 ; Zhao et al., 2014 ; Sun et al., 2022 ).…”