127 research outputs found
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Evidence for and Mechanisms of Ecosystem Transformation In The Great Basin of The Western United States
Ecosystem transformations are difficult to reverse and often create novel ecosystems. These systems can be maintained by stabilizing feedbacks driven by external or internal mechanisms. Increasing our understanding of these feedback components and how they relate to each other is crucial to know if and when they can be reversed. We studied the sagebrush biome in the western United states. Here, cheatgrass invasion and fire are converting vast areas of shrublands into grasslands, which may be two alternative stable states. We aimed to understand the components of positive feedback mechanisms that maintain these two states, and the impacts that conversion from shrubland to grassland has on ecosystem function.
In chapter one the connectivity of fuel influenced the burn severity of fire, which favored fire-tolerant annuals in the seedbank. In chapter two invasion and the loss of shrubs and perennial grasses by fire changed the system from a sink to a source of soil carbon and nitrogen. In chapter three, we constructed a fire history atlas to isolate the effect of time since fire by removing the effect of repeated fires. There was little evidence of recovery towards the pre-fire state, and instead livestock grazing and cheatgrass abundance interacted to maintain the grass-dominated state. In chapter four we created an allometric equation to calculate biomass from cover estimates that can be applied to broad-scale models of cheatgrass abundance.
Overall, we document self-reinforcing mechanisms of an alternative stable state, over several decades. We demonstrate how interannual climate variability creates initial or sustaining conditions that can alter longer-term trends and interannual oscillations in ecosystem functions. Finally, there are important management implications of this work, as cheatgrass dominance can present a sustained and novel ecosystem type with few opportunities to restore to native shrubland. Cheatgrass-dominated landscapes in the U.S. Great Basin offer a critical view into how novel alternative stable vegetation states can be initiated through disturbance and sustained through self-reinforcing mechanisms. Global changes such as climate warming, expanding land use, and invasive species present novel disturbance regimes and species assemblages. This study provides key insights into what prompts and sustains ecosystem transformation in drylands.</p
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Cover-based allometric estimate of aboveground biomass of a non-native, invasive annual grass (Bromus tectorum L.) in the Great Basin, USA
Cheatgrass (Bromus tectorum L.) presence in the Great Basin is associated with an increase in fire frequency andsize, likely due to increased spatial continuity of fine fuel biomass. Measurements of the extent and cover ofcheatgrass are steadily improving, but the strength of the relationship between cover and aboveground biomass(AGB) is unclear. An allometric equation that can reliably convert cover to AGB of cheatgrass would allow forimproved incorporation of regional estimates of cover into models of fire activity, carbon storage, and net pri­mary productivity, all of which rely on biomass. We measured cover and AGB of cheatgrass at 60 locations in thenorth-central Great Basin and used these measurements to model the relationship. We found a strong, linearrelationship between the percent cover and AGB, which was improved after square root transformation of bothcover and AGB, and after incorporating the number of days after peak NDVI that the biomass and cover weremeasured. These results show that AGB of cheatgrass can be reliably estimated from cover. It is likely thatallometric equations based on cover will be effective for other grass species, but care must be taken to account forphenology (e.g., peak NDVI) in the estimation.</p
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Six central questions about biological invasions to which NEON data science is poised to contribute
Biological invasions are a leading cause of rapid ecological change and often present a signifi-cant financial burden. As a vibrant discipline, invasion biology has made important strides in identifying,mapping, and beginning to manage invasions, but questions remain surrounding the mechanisms bywhich invasive species spread and the impacts they bring about. Frequent, multiscalar ecological monitor-ing such as that provided through the National Ecological Observatory Network (NEON) can be an impor-tant tool for addressing some of these questions. We articulate a set of major outstanding questions ininvasion biology, consider how NEON data science is positioned to contribute to addressing these ques-tions, and provide suggestions to help equip a growing contingent of NEON data users in solving invasionbiology problems. We demonstrate these ideas through four case studies examining the mechanisms ofplant invasions in the U.S. Intermountain West. In Case Study I, we evaluate the relationships betweennative species richness, non-native species richness, and probability of invasion across scales. In Case Stud-ies II and III, we explore the relationship between environmental factors and non-native species presenceto understand invasion mechanisms. Case Study IV outlines a method for improving the ability to distin-guish invasive plants from native vegetation in remotely sensed data by leveraging temporal patterns ofphenology. There are many novel elements in the NEON sampling design that make it uniquely poised toshed light on the mechanisms that can help us understand invasibility, prediction, and progression, as wellas on the variability, longevity, and interactions of multiple invasive species’ impacts. Thus, knowledgegained through analysis of NEON data is expected to inform sound decision-making in unique ways formanagers of systems experiencing biological invasions.</p
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FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001-2019)
Harnessing the fire data revolution, i.e., the abundance of information from satellites, government records, social media, and human health sources, now requires complex and challenging data integration approaches. Defining fire events is key to that effort. In order to understand the spatial and temporal characteristics of fire, or the classic fire regime concept, we need to critically define fire events from remote sensing data. Events, fundamentally a geographic concept with delineated spatial and temporal boundaries around a specific phenomenon that is homogenous in some property, are key to understanding fire regimes and more importantly how they are changing. Here, we describe Fire Events Delineation (FIRED), an event-delineation algorithm, that has been used to derive fire events (N = 51,871) from the MODIS MCD64 burned area product for the coterminous US (CONUS) from January 2001 to May 2019. The optimized spatial and temporal parameters to cluster burned area pixels into events were an 11-day window and a 5-pixel (2315 m) distance, when optimized against 13,741 wildfire perimeters in the CONUS from the Monitoring Trends in Burn Severity record. The linear relationship between the size of individual FIRED and Monitoring Trends in Burn Severity (MTBS) events for the CONUS was strong (R2 = 0.92 for all events). Importantly, this algorithm is open-source and flexible, allowing the end user to modify the spatio-temporal threshold or even the underlying algorithm approach as they see fit. We expect the optimized criteria to vary across regions, based on regional distributions of fire event size and rate of spread. We describe the derived metrics provided in a new national database and how they can be used to better understand US fire regimes. The open, flexible FIRED algorithm could be utilized to derive events in any satellite product. We hope that this open science effort will help catalyze a community-driven, data-integration effort (termed OneFire) to build a more complete picture of fire.</p
Ten simple rules for working with high resolution remote sensing data
Researchers in Earth and environmental science can extract incredible value from high- resolution (sub-meter, sub-hourly or hyper-spectral) remote sensing data, but these data can be difficult to use. Correct, appropriate and competent use of such data requires skills from remote sensing and the data sciences that are rarely taught together. In practice, many researchers teach themselves how to use high-resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. In order to implement a consistent strategy, we outline ten rules with examples from Earth and environmental science to help academic researchers and professionals in industry work more effectively and competently with high-resolution data
The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2
Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age 6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score 652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701
Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community
It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellationâacross existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in humanâenvironmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the communityâs use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building
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FIRED FINLAND
This is event- and daily-level polygons for the Fire event delineation (FIRED) product for Finland from November 2001 to January 2022. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v006/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. Firedpy (www.github.com/earthlab/firedpy) is an algorithm that converts these rasters into events by stacking the entire time series into a spatial-temporal data cube, then uses an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. This particular dataset was created using a spatial parameter of 5 pixels and 11 days. The primary benefit to this dataset over others is the ability to calculate fire spread rate. For each of these products (events and daily) the event identification numbers are the same, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event. </p
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FIRED SENEGAL
This is event- and daily-level polygons for the Fire event delineation (FIRED) product for SENEGAL from November 2000 to July 2021. It is derived from the MODIS MCD64A1 burned area product (see https://lpdaac.usgs.gov/products/mcd64a1v006/ for more details). The MCD64A1 is a monthly raster grid of estimated burned dates. Firedpy (www.github.com/earthlab/firedpy) is an algorithm that converts these rasters into events by stacking the entire time series into a spatial-temporal data cube, then uses an algorithm to assign event identification numbers to pixels that fit into the same 3-dimensional spatial temporal window. This particular dataset was created using a spatial parameter of 5 pixels and 11 days. The primary benefit to this dataset over others is the ability to calculate fire spread rate. For each of these products (events and daily) the event identification numbers are the same, but the event-level product has only single polygons for each entire event, while the daily product has separate polygons for each date per event. See the accompanying metadata files for the statistics provided by each data set. See the associated paper for more details on the methods and more:Balch, J.K.; St. Denis, L.A.; Mahood, A.L.; Mietkiewicz, N.P.; Williams, T.M.; McGlinchy, J.; Cook, M.C. FIRED (Fire Events Delineation): An Open, Flexible Algorithm and Database of US Fire Events Derived from the MODIS Burned Area Product (2001–2019). Remote Sens. 2020, 12, 3498. https://doi.org/10.3390/rs12213498</p
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