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Invasive forest insects have significant direct impacts on forest ecosystems and they are also generating new risks, uncertainties, and opportunities for forest landowners. The growing prevalence and inexorable spread of invasive insects across the United States, combined with the fact that the majority of the nation's forests are controlled by thousands of autonomous private landowners, raises an important question: To what extent will private landowners alter their harvest practices in response to insect invasions? Using a quasi‐experimental design, we conducted a causal analysis to investigate the influence of the highly impactful emerald ash borer (EAB) on (1) annual probability of harvest; (2) intensity of harvest; and (3) diameter of harvested trees, for both ash and non‐ash species on private land throughout the Midwest and mid‐Atlantic regions of the United States. We found that EAB detection had a negative impact on annual harvest probability and a positive impact on harvest intensity, resulting in a net increase in harvested biomass. Furthermore, our estimates suggest that EAB detection will influence private landowners to harvest greater quantities of ash, relative to non‐ash species. We also found that harvested trees in EAB‐infested areas had smaller diameters, on average, compared with those unaffected by EAB. These results can help policymakers, forest managers, and extension programs to anticipate and better advise landowners and managers about their options and the associated outcomes for forests. [ABSTRACT FROM AUTHOR]

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 free Plain Language Summary can be found within the Supporting Information of this article. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

Background: Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information., Objective: In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19., Methods: A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI) and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using a stand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart rate variability metrics from each individual's activity-matched baseline values, and scores exceeding a predefined threshold were checked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acute respiratory health anomalies., Results: The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected, of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had an increase in health risk score., Conclusions: We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI and step count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living conditions., (©Pooja Gaur, Dorota S Temple, Meghan Hegarty-Craver, Matthew D Boyce, Jonathan R Holt, Michael F Wenger, Edward A Preble, Randall P Eckhoff, Michelle S McCombs, Hope C Davis-Wilson, Howard J Walls, David E Dausch. Originally published in JMIR Formative Research (https://formative.jmir.org), 07.08.2024.)