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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 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. 

Understanding the interaction between land ownership, climate conditions, and harvesting strategies is essential for promoting long-term tree species diversity and ensuring sustainable forest ecosystems. This study uses forest inventory, climate, soil and socio-economic data to examine how land ownership types, climate gradients, and soil characteristics influence tree species diversity in Maine, USA. Our results suggest that southern Maine, characterized by milder climate conditions, supports greater tree species diversity compared to colder, boreal-dominated northern regions. Family forest owners, predominantly situated in southern Maine, consistently exhibited the highest diversity, reflecting less intensive management practices. Conversely, industrial and institutional forests concentrated in northern Maine demonstrated lower species diversity, likely driven by uniform, economically driven management practices. Incorporating soil attributes significantly improved the explanatory power of our diversity models. Harvesting activities showed varied impacts on biodiversity. Harvesting effects varied among ownership types: while overall biodiversity changes were minor post-harvest, industrial forests in northern Maine experienced a sustained 7 % decline in species diversity approximately ten years after harvesting, suggesting the need for continued long-term monitoring. Consequently, it is essential to develop management strategies at both the stand- and landscape-levels that effectively balance economic objectives while mitigating long-term biodiversity losses. 

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. 

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.