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Relations among territoriality, abundance and habitat suitability are fundamental to the ecology of many animal populations. Theory suggests two classes of possible responses to increasing abundance in territorial species: (1) the ideal free distribution (IFD), which predicts smaller territory sizes and decreased fitness as individuals adaptively pack into suitable habitats, and (2) the ideal despotic distribution (IDD), which predicts stable territory sizes and fitness in preferred habitats for dominant individuals and increased use of marginal habitats, reduced fitness and changes in territory sizes for subordinate individuals. We analysed the territory sizes and locations of seven migratory songbird species occupying a 10‐ha plot in the Hubbard Brook Experimental Forest, New Hampshire, USA over a 52‐year period. Species varied in abundance over years from twofold to 22‐fold, and all species displayed clear patterns of habitat preference within the study plot. Consistent with IFD, and contrary to IDD, territory sizes decreased with local abundance for all species, irrespective of habitat preferences. There was at least a twofold variation in territory size within years. Conformity of territory size to predictions of the IFD argues for the efficacy of territorial defence in songbirds and has general consequences for population dynamics. 

Abstract Boreal forests of Alaska and Western Canada are experiencing rapid climate change characterized by higher temperatures, more extreme droughts, and changing disturbance regimes, resulting in forest mortality and composition changes. Mechanistic models are increasingly important for predicting future forest trends as the region experiences novel environmental change. Previously, many process-based models have generated starting conditions by ‘spinning up’ to equilibrium. However, setting appropriate initial conditions remains a persistent challenge in using mechanistic forest models, where stochastic events and latent parameters governing tree establishment have long-lasting impacts on simulation outcomes. Recent advances in remote sensing analysis provide information that can help address this issue. We updated an individual-based gap model, the University of Virginia Forest Model Enhanced (UVAFME), to include initial conditions derived from aerial and satellite imagery at two locations. Following these updates, material legacies (e.g. trees, seed banks, soil organic layer) allowed new forest types to persist in UVAFME simulations, landscape-level forest heterogeneity increased, and forest-wide biomass estimates increased. At both study sites, initialization from remotely sensed data had a strong impact on forest cover and volume. Climate change impacts were simulated decades earlier than when the model was ‘spun up’. In Alaska’s Tanana Valley State Forest, warmer climate scenarios drove deciduous expansion, increased drought stress, and resulted in a 28% decrease in overall biomass by 2100 between historical and high emissions climate scenarios. At a lowland site in Northern British Columbia, lodgepole pine(Pinus contorta)remained dominant and became more productive with exogenous climate forcing as temperature, nutrient, and flooding limitations decreased. These case studies demonstrate a new framework for forest modeling and emphasize the advantages of integrating remotely sensed data with mechanistic models, thereby laying groundwork for future research that explores near-term impacts of non-stationary ecological change. 

Abstract Modeling Arctic-Boreal vegetation is a challenging but important task, since this highly dynamic ecosystem is undergoing rapid and substantial environmental change. In this work, we synthesized information on 18 dynamic vegetation models (DVMs) that can be used to project vegetation structure, composition, and function in North American Arctic-Boreal ecosystems. We reviewed the ecosystem properties and scaling assumptions these models make, reviewed their applications from the scholarly literature, and conducted a survey of expert opinion to determine which processes are important but lacking in DVMs. We then grouped the models into four categories (specific intention models, forest species models, cohort models, and carbon tracking models) using cluster analysis to highlight similarities among the models. Our application review identified 48 papers that addressed vegetation dynamics either directly (22) or indirectly (26). The expert survey results indicated a large desire for increased representation of active layer depth and permafrost in future model development. Ultimately, this paper serves as a summary of DVM development and application in Arctic-Boreal environments and can be used as a guide for potential model users, thereby prioritizing options for model development. 

In this study, we analyzed territory sizes of seven migratory songbirds occupying a 10-hectare plot in the Hubbard Brook Experimental Forest, New Hampshire, USA over a 52-year period (1969-2021). All species varied in abundance over the duration of the study, some dramatically. Changes in territory sizes were inversely related to changes in abundance within the study plot despite differences in habitat preference, supporting the ideal free distribution. Territory sizes varied two-fold within a year across species. Results contribute to understanding how variation in territory size relates to 1) how habitat use changes with bird abundance and 2) the evolution of territory size. This dataset includes data, R code, and spatial files supporting this study. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Associated datasets in the data catalog: Holmes, R.T., N.L. Rodenhouse, and M.T. Hallworth. 2022. Bird Abundances at the Hubbard Brook Experimental Forest (1969-present) and on three replicate plots (1986-2000) in the White Mountain National Forest ver 8. Environmental Data Initiative. https://doi.org/10.6073/pasta/6422a72893616ce9020086de5a5714cd (Accessed 2023-12-17). Zammarelli, M.B. and R.T. Holmes. 2023. Hubbard Brook Experimental Forest: 10-ha bird plot territory maps, 1969 - 2021 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/df93595ba8df60570d472f6e6f58839e (Accessed 2024-01-11). 

Palms play an outsized role in tropical forests and are important resources for humans and wildlife. A central question in tropical ecosystems is understanding palm distribution and abundance. However, accurately identifying and localizing palms in geospatial imagery presents significant challenges due to dense vegetation, overlapping canopies, and variable lighting conditions in mixed-forest landscapes. Addressing this, we introduce PalmProbNet, a probabilistic approach utilizing transfer learning to analyze high-resolution UAV-derived orthomosaic imagery, enabling the detection of palm trees within the dense canopy of the Ecuadorian Rainforest. This approach represents a substantial advancement in automated palm detection, effectively pinpointing palm presence and locality in mixed tropical rainforests. Our process begins by generating an orthomosaic image from UAV images, from which we extract and label palm and non-palm image patches in two distinct sizes. These patches are then used to train models with an identical architecture, consisting of an unaltered pre-trained ResNet-18 and a Multilayer Perceptron (MLP) with specifically trained parameters. Subsequently, PalmProbNet employs a sliding window technique on the landscape orthomosaic, using both small and large window sizes to generate a probability heatmap. This heatmap effectively visualizes the distribution of palms, showcasing the scalability and adaptability of our approach in various forest densities. Despite the challenging terrain, our method demonstrated remarkable performance, achieving an accuracy of 97.32% and a Cohen's κ of 94.59% in testing. 

In this study, we analyzed territory sizes of seven migratory songbirds occupying a 10-hectare plot in the Hubbard Brook Experimental Forest, New Hampshire, USA over a 52-year period (1969-2021). All species varied in abundance over the duration of the study, some dramatically. Changes in territory sizes were inversely related to changes in abundance within the study plot despite differences in habitat preference, supporting the ideal free distribution. Territory sizes varied two-fold within a year across species. Results contribute to understanding how variation in territory size relates to 1) how habitat use changes with bird abundance, 2) the evolution of territory size, and 3) the role of territoriality in population dynamics. This dataset includes data, R code, and spatial files supporting this study. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Associated datasets in the data catalog: Holmes, R.T., N.L. Rodenhouse, and M.T. Hallworth. 2022. Bird Abundances at the Hubbard Brook Experimental Forest (1969-present) and on three replicate plots (1986-2000) in the White Mountain National Forest ver 8. Environmental Data Initiative. https://doi.org/10.6073/pasta/6422a72893616ce9020086de5a5714cd (Accessed 2023-12-17). Zammarelli, M.B. and R.T. Holmes. 2023. Hubbard Brook Experimental Forest: 10-ha bird plot territory maps, 1969 - 2021 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/df93595ba8df60570d472f6e6f58839e (Accessed 2024-01-11). 

This data set documents the temporal and spatial variation of soil and deadwood moisture, and nearby microclimate, for the a four-month period from June to October 2018. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and main tained by the USDA Forest Service, Northern Research Station.