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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. 

Abstract Land protection, whether public or private, is often controversial at the local level because residents worry about lost economic activity. We used panel data and a quasi‐experimental impact‐evaluation approach to determine how key economic indicators were related to the percentage of land protected. Specifically, we estimated the impacts of public and private land protection based on local area employment and housing permits data from 5 periods spanning 1990–2015 for all major towns and cities in New England. To generate rigorous impact estimates, we modeled economic outcomes as a function of the percentage of land protected in the prior period, conditional on town fixed effects, metro‐region trends, and controls for period and neighboring protection. Contrary to narratives that conservation depresses economic growth, land protection was associated with a modest increase in the number of people employed and in the labor force and did not affect new housing permits, population, or median income. Public and private protection led to different patterns of positive employment impacts at distances close to and far from cities, indicating the importance of investing in both types of land protection to increase local opportunities. The greatest magnitude of employment impacts was due to protection in more rural areas, where opportunities for both visitation and amenity‐related economic growth may be greatest. Overall, we provide novel evidence that land protection can be compatible with local economic growth and illustrate a method that can be broadly applied to assess the net economic impacts of protection. 

Abstract Invasive forest insects can induce tree mortality in two ways: (a) by directly harming trees; or (b) by influencing forest owners to pre‐emptively harvest threatened trees. This study investigates forest owners’ intentions to harvest trees threatened by invasive insects.Our first objective is to identify and characterize agent functional types (AFTs) of family forest owners in the northeastern United States using a set of contingent behaviour questions contained in a mail survey. We establish AFTs as a form of dimension reduction, effectively casting landowners into a typology in which each type (AFT) has distinct probabilities of tree harvesting in response to forest insects. Our analysis identifies three functional types of landowners: ‘Cutters’ (46% of respondents; high intent to harvest trees impacted by invasive forest insects), ‘Responsive Cutters’ (42% of respondents; intent sensitive to insect impact severity), and ‘Non‐cutters’ (12% of respondents; low intent to cut).Our second objective is to model AFT membership to predict the distribution of AFTs across the landscape. Predictors are chosen from a set of survey, geographic and demographic features. Our best AFT‐prediction model has three predictor variables: parcel size (hectares of forest), geographical region, and town‐level forested fraction. Application of the model provides a high‐resolution probability distribution of AFTs across the landscape.By coupling human and insect behaviour, our results allow for holistic assessments of how invasive forest insects disturb forests, inclusive of the management response to these pests. A freePlain Language Summarycan be found within the Supporting Information of this article.