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Abstract Ecosystem models offer a rigorous way to formalize scientific theories and are critical to evaluating complex interactions among ecological and biogeochemical processes. In addition to simulation and prediction, ecosystem models are a valuable tool for testing hypotheses about mechanisms and empirical findings because they reveal critical internal processes that are difficult to observe directly.However, many ecosystem models are difficult to manage and apply by scientists who lack advanced computing skills due to complex model structures, lack of consistent documentation, and low-level programming implementation, which facilitates computing but reduces accessibility.Here, we present the ‘pnetr’ R package, which is designed to provide an easy-to-manage ecosystem modeling framework and detailed documentation in both model structure and programming. The framework implements a family of widely used PnET (net photosynthesis, evapotranspiration) ecosystem models, which are relatively parsimonious but capture essential biogeochemical cycles of water, carbon, and nutrients. We chose the R programming language since it is familiar to many ecologists and has abundant statistical modeling resources. We showcase examples of model simulations and test the effects of phenology on carbon assimilation and wood production using data measured by the Environmental Measurement Station (EMS) eddy-covariance flux tower at Harvard Forest, MA.We hope ‘pnetr’ can facilitate further development of ecological theory and increase the accessibility of ecosystem modeling and ecological forecasting. 

Conservation easements are voluntary legal agreements designed to constrain land-use activities on private land to achieve conservation goals. Extensive public and private funding has been used to establish 'working forest' conservation easements (WFCE) that aim to protect conservation values while maintaining commercial timber production. We use variation in the timing and location of easements to estimate the impacts of WFCEs in Maine from a 33-year time-series of forest loss and harvesting. We find that WFCEs had negligible impacts on an already low rate of forest loss. Compared to matched control areas, easements decreased forest loss by 0.0004% yr−1 (95% CI: −0.0008, to −0.00003%) the equivalent of 3.17 ha yr−1 (95% C.I.: 1.6, to 6.7 ha yr−1) when scaled to the 839 142 ha of total conserved area. In contrast, WFCEs increased the rate of harvesting by 0.37% yr−1 (95% CI: 0.11%–0.63%), or 3,105 ha yr−1 (95% C.I.: 923–5,287 ha yr−1) when scaled to the conserved area. However, more recently established easements contained stricter restrictions on harvest practices and stricter easements reduced harvest by 0.66% yr−1 (95% CI: −1.03, −0.29). Our results suggest that future easements could be more effective if they were targeted to higher risk of loss areas and included additional provisions for harvest restrictions and monitoring. 

Human activities have had complex, long-term impacts on forest function across New England—a trend expected to continue. To assess these impacts, we conducted a regional-scale modeling study using the PnET-CN-daily model, simulating multiple scenarios that reflect projected changes in land cover, climate, and air quality. The results suggest that while New England will continue to serve as a regional carbon sink, carbon accumulation in the southern portion of the region will slow and may shift to a net carbon source due to aggressive urban expansion. Carbon dioxide (CO₂) fertilization and carbon loss associated with urbanization are the dominant factors controlling future carbon dynamics. However, CO₂fertilization may diminish over time due to nutrient limitations, while rising temperatures are expected to accelerate soil decomposition, further increasing carbon loss. The forecasts also show that urbanization will increasingly affect ecosystem nitrogen storage. Climate change and CO₂fertilization along with declining nitrogen deposition from decreases in fossil fuel use are projected to drive nitrogen oligotrophication—slowing forest growth and becoming more severe as nitrogen inputs decrease. In addition, urbanization and climate change are expected to substantially reduce snowpack and shorten snow cover duration in southern New England, with potential consequences for regional water dynamics. These trends highlight the need to integrate future climate, air quality, and land-use projections into forest management strategies for both urban and rural ecosystems. 

Temperate forests are the most fragmented forest biome, yet current understanding of fragmentation effects on ecosystem processes, such as carbon (C) cycling, is rooted in tropical forest research. We review the effects of persistent fragmentation on temperate forest ecosystem processes and quantify the extent to which the US national forest inventory and land‐cover maps represent forest edge area. We found systematic underrepresentation of forest edges across all methods. As compared with very high resolution (1 m) maps, conventional 30‐m resolution forest cover maps underestimated forest edge area by 16.4%, on average. Accounting for all forest edge area and edge effects on forest structure and growth resulted in a 14.8% median increase in aboveground forest C estimates, with 23.8% and 74.2% increases in agriculturally and urban dominated counties, respectively. We conclude by proposing improvements to forest inventories, maps, and models to better represent the fragmented temperate forest landscape. 

Ensemble-based change detection can improve map accuracies by combining information from multiple datasets. There is a growing literature investigating ensemble inputs and applications for forest disturbance detection and mapping. However, few studies have evaluated ensemble methods other than Random Forest classifiers, which rely on uninterpretable “black box” algorithms with hundreds of parameters. Additionally, most ensemble-based disturbance maps do not utilize independently and systematically collected field-based forest inventory measurements. Here, we compared three approaches for combining change detection results generated from multi-spectral Landsat time series with forest inventory measurements to map forest harvest events at an annual time step. We found that seven-parameter degenerate decision tree ensembles performed at least as well as 500-tree Random Forest ensembles trained and tested on the same LandTrendr segmentation results and both supervised decision tree methods consistently outperformed the top-performing voting approach (majority). Comparisons with an existing national forest disturbance dataset indicated notable improvements in accuracy that demonstrate the value of developing locally calibrated, process-specific disturbance datasets like the harvest event maps developed in this study. Furthermore, by using multi-date forest inventory measurements, we are able to establish a lower bound of 30% basal area removal on detectable harvests, providing biophysical context for our harvest event maps. Our results suggest that simple interpretable decision trees applied to multi-spectral temporal segmentation outputs can be as effective as more complex machine learning approaches for characterizing forest harvest events ranging from partial clearing to clear cuts, with important implications for locally accurate mapping of forest harvests and other types of disturbances. 

Abstract Nature‐based climate solutions (NCS) are championed as a primary tool to mitigate climate change, especially in forested regions capable of storing and sequestering vast amounts of carbon. New England is one of the most heavily forested regions in the United States (>75% forested by land area), and forest carbon is a significant component of climate mitigation policies. Large infrequent disturbances, such as hurricanes, are a major source of uncertainty and risk for policies relying on forest carbon for climate mitigation, especially as climate change is projected to alter the intensity and extent of hurricanes. To date, most research into disturbance impacts on forest carbon stocks has focused on fire. Here, we show that a single hurricane in the region can down between 121 and 250 MMTCO₂e or 4.6%–9.4% of the total aboveground forest carbon, much greater than the carbon sequestered annually by New England's forests (16 MMTCO₂e year⁻¹). However, emissions from hurricanes are not instantaneous; it takes approximately 19 years for downed carbon to become a net emission and 100 years for 90% of the downed carbon to be emitted. Reconstructing hurricanes with the HURRECON and EXPOS models across a range of historical and projected wind speeds, we find that an 8% and 16% increase in hurricane wind speeds leads to a 10.7‐ and 24.8‐fold increase in the extent of high‐severity damaged areas (widespread tree mortality). Increased wind speed also leads to unprecedented geographical shifts in damage, both inland and northward, into heavily forested regions traditionally less affected by hurricanes. Given that a single hurricane can emit the equivalent of 10+ years of carbon sequestered by forests in New England, the status of these forests as a durable carbon sink is uncertain. Understanding the risks to forest carbon stocks from disturbances is necessary for decision‐makers relying on forests as a NCS. 

Ecosystem models offer a rigorous way to formalize scientific theories and are critical to evaluating complex interactions among ecological and biogeochemical processes. In addition to simulation and prediction, ecosystem models are a valuable tool for testing hypotheses about mechanisms and empirical findings because they reveal critical internal processes that are difficult to observe directly. However, many ecosystem models are difficult to manage and apply by scientists because of complex model structures, lack of consistent documentation, and low‐level programming implementation.Here, we present the ‘pnetr’ R package, which is designed to provide an easy‐to‐manage ecosystem modelling framework and detailed documentation in both model structure and programming. The framework implements a family of widely used PnET (net photosynthesis, evapotranspiration) ecosystem models, which are relatively parsimonious but capture essential biogeochemical cycles of water, carbon and nitrogen. We chose the R programming language because it is familiar to many ecologists and has abundant statistical modelling resources. We showcase examples of model simulations and test the effects of phenology on carbon assimilation and wood production using data measured by the Environmental Measurement Station (EMS) eddy‐covariance flux tower at Harvard Forest, MA.We hope ‘pnetr’ can facilitate further development of ecological theory and increase the accessibility of ecosystem modelling and ecological forecasting.