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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. 

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. 

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. 

Extreme Mass Ratio Inspirals (EMRIs) are one of the key sources for future space-based gravitational wave interferometers. Measurements of EMRI gravitational waves are expected to determine the characteristics of their sources with sub-percent precision. However, their waveform generation is challenging due to the long duration of the signal and the high harmonic content. Here, we present the first ready-to-use Schwarzschild eccentric EMRI waveform implementation in the frequency domain for use with either graphics processing units (GPUs) or central processing units (CPUs). We present the overall waveform implementation and test the accuracy and performance of the frequency domain waveforms against the time domain implementation. On GPUs, the frequency domain waveform takes in median 0.044 s to generate and is twice as fast to compute as its time domain counterpart when considering massive black hole masses $\ge 2\times 10^6{\text{M}}_{\odot }$and initial eccentricitiese₀> 0.2. On CPUs, the median waveform evaluation time is 5 s, and it is five times faster in the frequency domain than in the time domain. Using a sparser frequency array can further speed up the waveform generation, reaching up to 0.3 s. This enables us to perform, for the first time, EMRI parameter inference with fully relativistic waveforms on CPUs. Future EMRI models, which encompass wider source characteristics (particularly black hole spin and generic orbit geometries), will require significantly more harmonics. Frequency domain models will be essential analysis tools for these astrophysically realistic and important signals. 

Abstract: This work presents a novel technique to physically clone a ring oscillator physically unclonable function (RO PDF) onto another distinct FPG A die, using precise, targeted aging. The resulting cloned RO PDF provides a response that is identical to its copied FPGA counterpart, i.e., the FPGA and its clone are indistinguishable from each other. Targeted aging is achieved by: 1) heating the FPGA using bitstream-Iocated short circuits, and 2) enabling/disabling ROs in the same FPGA bitstream. During self heating caused by short-circuits contained in the FPGA bitstream, circuit areas containing oscillating ROs (enabled) degrade more slowly than circuit areas containing non-oscillating ROs (disabled), due to bias temperature instability effects. This targeted aging technique is used to swap the relative frequencies of two ROs that will, in turn, flip the corresponding bit in the PUF response. Two experiments are described. The first experiment uses targeted aging to create an FPGA that exhibits the same PUF response as another FPGA, i.e., a clone of an FPGA PUF onto another FPGA device. The second experiment demonstrates that this aging technique can create an RO PUF with any desired response.