It's about time: A synthesis of changing phenology in the Gulf of Maine ecosystem

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Staudinger, M. D., Mills, K. E., Stamieszkin, K., Record, N. R., Hudak, C. A., Allyn, A., Diamond, A., Friedland, K. D., Golet, W., Henderson, M. E., Hernandez, C. M., Huntington, T. G., Ji, R., Johnson, C. L., Johnson, D. S., Jordaan, A., Kocik, J., Li, Y., Liebman, M., Nichols, O. C., Pendleton, D., Richards, R. A., Robben, T., Thomas, A. C., Walsh, H. J., & Yakola, K. It's about time: A synthesis of changing phenology in the Gulf of Maine ecosystem. Fisheries Oceanography, 28(5), (2019): 532-566, doi: 10.1111/fog.12429.The timing of recurring biological and seasonal environmental events is changing on a global scale relative to temperature and other climate drivers. This study considers the Gulf of Maine ecosystem, a region of high social and ecological importance in the Northwest Atlantic Ocean and synthesizes current knowledge of (a) key seasonal processes, patterns, and events; (b) direct evidence for shifts in timing; (c) implications of phenological responses for linked ecological‐human systems; and (d) potential phenology‐focused adaptation strategies and actions. Twenty studies demonstrated shifts in timing of regional marine organisms and seasonal environmental events. The most common response was earlier timing, observed in spring onset, spring and winter hydrology, zooplankton abundance, occurrence of several larval fishes, and diadromous fish migrations. Later timing was documented for fall onset, reproduction and fledging in Atlantic puffins, spring and fall phytoplankton blooms, and occurrence of additional larval fishes. Changes in event duration generally increased and were detected in zooplankton peak abundance, early life history periods of macro‐invertebrates, and lobster fishery landings. Reduced duration was observed in winter–spring ice‐affected stream flows. Two studies projected phenological changes, both finding diapause duration would decrease in zooplankton under future climate scenarios. Phenological responses were species‐specific and varied depending on the environmental driver, spatial, and temporal scales evaluated. Overall, a wide range of baseline phenology and relevant modeling studies exist, yet surprisingly few document long‐term shifts. Results reveal a need for increased emphasis on phenological shifts in the Gulf of Maine and identify opportunities for future research and consideration of phenological changes in adaptation efforts.This work was supported by the Department of the Interior Northeast Climate Adaptation Science Center (G14AC00441) for MDS, AJ, and KY; the National Science Foundation's Coastal SEES Program (OCE‐1325484) for KEM, ACT, MEH, and AA; the National Aeronautics and Space Administration (NNX16 AG59G) for ACT, KEM, NRR, and KSS; the USGS Climate Research and Development Program for TGH; National Science & Engineering Research Council of Canada, University of New Brunswick, Environment Canada, Sir James Dunn Wildlife Research Centre, and New Brunswick Wildlife Trust Fund for AD. We also thank the Regional Association for Research on the Gulf of Maine for support, and the Gulf of Maine Research Institute for hosting and providing in kind resources for a two day in‐person workshop in August 2016. We greatly appreciate contributions from K. Alexander, G. Calandrino, C. Feurt, I. Mlsna, N. Rebuck, J. Seavey, and J. Sun for helping shape the initial scope of the manuscript. We thank J. Weltzin and two anonymous reviewers for their constructive comments. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the views of the Northeast Climate Adaptation Science Center, U.S. Geological Survey, National Oceanographic and Atmospheric Administration, Fisheries and Oceans Canada or the US Environmental Protection Agency. This manuscript is submitted for publication with the understanding that the United States Government is authorized to reproduce and distribute reprints for Governmental purposes. None of the authors have conflicts of interest to declare in association with the contents of this manuscript

Changes in BMI During the COVID-19 Pandemic.

BACKGROUND AND OBJECTIVES: Experts hypothesized increased weight gain in children associated with the coronavirus disease 2019 (COVID-19) pandemic. Our objective was to evaluate whether the rate of change of child body mass index (BMI) increased during the COVID-19 pandemic compared with prepandemic years. METHODS: The study population of 1996 children ages 2 to 19 years with at least 1 BMI measure before and during the COVID-19 pandemic was drawn from 38 pediatric cohorts across the United States participating in the Environmental Influences on Child Health Outcomes-wide cohort study. We modeled change in BMI using linear mixed models, adjusting for age, sex, race, ethnicity, maternal education, income, baseline BMI category, and type of BMI measure. Data collection and analysis were approved by the local institutional review board of each institution or by the central Environmental Influences on Child Health Outcomes institutional review board. RESULTS: BMI increased during the COVID-19 pandemic compared with previous years (0.24 higher annual gain in BMI during the pandemic compared with previous years, 95% confidence interval 0.02 to 0.45). Children with BMI in the obese range compared with the healthy weight range were at higher risk for excess BMI gain during the pandemic, whereas children in higher-income households were at decreased risk of BMI gain. CONCLUSIONS: One effect of the COVID-19 pandemic is an increase in annual BMI gain during the COVID-19 pandemic compared with the 3 previous years among children in our national cohort. This increased risk among US children may worsen a critical threat to public health and health equity

Rehabilitation versus surgical reconstruction for non-acute anterior cruciate ligament injury (ACL SNNAP): a pragmatic randomised controlled trial

BackgroundAnterior cruciate ligament (ACL) rupture is a common debilitating injury that can cause instability of the knee. We aimed to investigate the best management strategy between reconstructive surgery and non-surgical treatment for patients with a non-acute ACL injury and persistent symptoms of instability.MethodsWe did a pragmatic, multicentre, superiority, randomised controlled trial in 29 secondary care National Health Service orthopaedic units in the UK. Patients with symptomatic knee problems (instability) consistent with an ACL injury were eligible. We excluded patients with meniscal pathology with characteristics that indicate immediate surgery. Patients were randomly assigned (1:1) by computer to either surgery (reconstruction) or rehabilitation (physiotherapy but with subsequent reconstruction permitted if instability persisted after treatment), stratified by site and baseline Knee Injury and Osteoarthritis Outcome Score—4 domain version (KOOS4). This management design represented normal practice. The primary outcome was KOOS4 at 18 months after randomisation. The principal analyses were intention-to-treat based, with KOOS4 results analysed using linear regression. This trial is registered with ISRCTN, ISRCTN10110685, and ClinicalTrials.gov, NCT02980367.FindingsBetween Feb 1, 2017, and April 12, 2020, we recruited 316 patients. 156 (49%) participants were randomly assigned to the surgical reconstruction group and 160 (51%) to the rehabilitation group. Mean KOOS4 at 18 months was 73·0 (SD 18·3) in the surgical group and 64·6 (21·6) in the rehabilitation group. The adjusted mean difference was 7·9 (95% CI 2·5–13·2; p=0·0053) in favour of surgical management. 65 (41%) of 160 patients allocated to rehabilitation underwent subsequent surgery according to protocol within 18 months. 43 (28%) of 156 patients allocated to surgery did not receive their allocated treatment. We found no differences between groups in the proportion of intervention-related complications.InterpretationSurgical reconstruction as a management strategy for patients with non-acute ACL injury with persistent symptoms of instability was clinically superior and more cost-effective in comparison with rehabilitation management

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

LiDAR Utility for Natural Resource Managers

Applications of LiDAR remote sensing are exploding, while moving from the research to the operational realm. Increasingly, natural resource managers are recognizing the tremendous utility of LiDAR-derived information to make improved decisions. This review provides a cross-section of studies, many recent, that demonstrate the relevance of LiDAR across a suite of terrestrial natural resource disciplines including forestry, fire and fuels, ecology, wildlife, geology, geomorphology, and surface hydrology. We anticipate that interest in and reliance upon LiDAR for natural resource management, both alone and in concert with other remote sensing data, will continue to rapidly expand for the foreseeable future

A semi-automated LiDAR-GEOBIA methodology for forest even-aged stand delineation based on a two-stage evaluation strategy

International audienceForest stand delineation is rapidly evolving from traditional photointerpretation to semi-automated GEOBIA techniques. To obtain a good correspondence between image objects and geographic objects, GEOBIA techniques require user decisions considering input data, segmentation algorithms, and classification strategies. GEOBIA applications in forestry have relied mostly on optical remotely sensed data, focusing on the spectral properties of vegetation to identify forest stand boundaries. A limitation of this approach is that optical data have limited sensitivity to forest structural parameters which are the main driver of stand boundaries in even-aged forests. Active sensors such as Light Detection and Ranging (LiDAR) are an alternative, providing a direct estimation of forest structure (e.g. height, density) and potentially leading to more accurate stand maps. In this paper, we propose a semi-automated methodology for even-aged stand delineation using LiDAR data and a two-stage GEOBIA evaluation strategy, combining both unsupervised and supervised evaluation methods to select a suitable segmentation output. The study area is located in the Clear Creek, Selway River & Elk Creek watersheds (~ 54,000 ha) in Northern Idaho (USA), where available LiDAR data was collected in 2009 (Clear Creek watershed) and 2012 (Selway River & Elk Creek). Additionally, a reference dataset of stand-replacing disturbances consisting of yearly clearcut maps compiled from timber harvest records were also available from 1950 as part of the US Forest Service FACTS (Forest ACtivity Tracking System). The proposed methodology involves: (1) image segmentation of several airborne LiDAR metrics using the multiresolution segmentation algorithm implemented on the eCognition software varying consistently the scale, compactness and shape parameters; (2) selection of the best set of parameters for segmentation for each tested LiDAR metric, applying an unsupervised evaluation method based on measures of spatial autocorrelation. This stage ensures that the selected segmentation has the highest possible intra-object uniformity and inter-object heterogeneity; (3) selection of the most suitable LiDAR metric for the segmentation, applying a supervised evaluation method based on measures of area-based dissimilarity, selecting the segmentation with the maximum degree of similarity in size and shape to FACTS reference dataset; and (4) validation using as reference data forest stand perimeters independently derived from visual interpretation. The results show good delineation of even-aged forest, including stands harvested more than 60 years ago that are generally challenging to detect with optical data, because the spectral response of forest canopy saturates at high levels of canopy closure. On a methodological level, the proposed two-stage procedure allows not only accurate image objects delineations but also allows to select the most suitable input data that assure that the image objects are spatially matching with the ground objects. This workflow could be implemented in other studies where different segmentation strategies (e.g., different segmentation algorithms, parameters or resolutions), input data (e.g., Landsat data) or target features (e.g., land cove types) need to be assessed.

A semi-automated LiDAR-GEOBIA methodology for forest even-aged stand delineation based on a two-stage evaluation strategy

International audienceForest stand delineation is rapidly evolving from traditional photointerpretation to semi-automated GEOBIA techniques. To obtain a good correspondence between image objects and geographic objects, GEOBIA techniques require user decisions considering input data, segmentation algorithms, and classification strategies. GEOBIA applications in forestry have relied mostly on optical remotely sensed data, focusing on the spectral properties of vegetation to identify forest stand boundaries. A limitation of this approach is that optical data have limited sensitivity to forest structural parameters which are the main driver of stand boundaries in even-aged forests. Active sensors such as Light Detection and Ranging (LiDAR) are an alternative, providing a direct estimation of forest structure (e.g. height, density) and potentially leading to more accurate stand maps. In this paper, we propose a semi-automated methodology for even-aged stand delineation using LiDAR data and a two-stage GEOBIA evaluation strategy, combining both unsupervised and supervised evaluation methods to select a suitable segmentation output. The study area is located in the Clear Creek, Selway River & Elk Creek watersheds (~ 54,000 ha) in Northern Idaho (USA), where available LiDAR data was collected in 2009 (Clear Creek watershed) and 2012 (Selway River & Elk Creek). Additionally, a reference dataset of stand-replacing disturbances consisting of yearly clearcut maps compiled from timber harvest records were also available from 1950 as part of the US Forest Service FACTS (Forest ACtivity Tracking System). The proposed methodology involves: (1) image segmentation of several airborne LiDAR metrics using the multiresolution segmentation algorithm implemented on the eCognition software varying consistently the scale, compactness and shape parameters; (2) selection of the best set of parameters for segmentation for each tested LiDAR metric, applying an unsupervised evaluation method based on measures of spatial autocorrelation. This stage ensures that the selected segmentation has the highest possible intra-object uniformity and inter-object heterogeneity; (3) selection of the most suitable LiDAR metric for the segmentation, applying a supervised evaluation method based on measures of area-based dissimilarity, selecting the segmentation with the maximum degree of similarity in size and shape to FACTS reference dataset; and (4) validation using as reference data forest stand perimeters independently derived from visual interpretation. The results show good delineation of even-aged forest, including stands harvested more than 60 years ago that are generally challenging to detect with optical data, because the spectral response of forest canopy saturates at high levels of canopy closure. On a methodological level, the proposed two-stage procedure allows not only accurate image objects delineations but also allows to select the most suitable input data that assure that the image objects are spatially matching with the ground objects. This workflow could be implemented in other studies where different segmentation strategies (e.g., different segmentation algorithms, parameters or resolutions), input data (e.g., Landsat data) or target features (e.g., land cove types) need to be assessed.

Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest

This study compared aspatial and spatial methods of using remote sensing and field data to predict maximum growing season leaf area index (LAI) maps in a boreal forest in Manitoba, Canada. The methods tested were orthogonal regression analysis (reduced major axis, RMA) and two geostatistical techniques: kriging with an external drift (KED) and sequential Gaussian conditional simulation (SGCS). Deterministic methods such as RMA and KED provide a single predicted map with either aspatial (e.g., standard error, in regression techniques) or limited spatial (e.g., KED variance) assessments of errors, respectively. In contrast, SGCS takes a probabilistic approach, where simulated values are conditional on the sample values and preserve the sample statistics. In this application, canonical indices were used to maximize the ability of Landsat ETM+ spectral data to account for LAI variability measured in the field through a spatially nested sampling design. As expected based on theory, SGCS did the best job preserving the distribution of measured LAI values. In terms of spatial pattern, SGCS preserved the anisotropy observed in semivariograms of measured LAI, while KED reduced anisotropy and lowered global variance (i.e., lower sill), also consistent with theory. The conditional variance of multiple SGCS realizations provided a useful visual and quantitative measure of spatial uncertainty. For applications requiring spatial prediction methods, we concluded KED is more useful if local accuracy is important, but SGCS is better for indicating global pattern. Predicting LAI from satellite data using geostatistical methods requires a distribution and density of primary, reference LAI measurements that are impractical to obtain. For regional NPP modeling with coarse resolution inputs, the aspatial RMA regression method is the most practical option

www.mdpi.com/journal/remotesensing Review LiDAR Utility for Natural Resource Managers

Abstract: Applications of LiDAR remote sensing are exploding, while moving from the research to the operational realm. Increasingly, natural resource managers are recognizing the tremendous utility of LiDAR-derived information to make improved decisions. This review provides a cross-section of studies, many recent, that demonstrate the relevance of LiDAR across a suite of terrestrial natural resource disciplines including forestry, fire and fuels, ecology, wildlife, geology, geomorphology, and surface hydrology. We anticipate that interest in and reliance upon LiDAR for natural resource management, both alone and in concert with other remote sensing data, will continue to rapidly expand for the foreseeable future