Institution
Colorado State University
Education•Fort Collins, Colorado, United States•
About: Colorado State University is a education organization based out in Fort Collins, Colorado, United States. It is known for research contribution in the topics: Population & Radar. The organization has 31430 authors who have published 69040 publications receiving 2724463 citations. The organization is also known as: CSU & Colorado Agricultural College.
Topics: Population, Radar, Poison control, Laser, Soil water
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Colorado State University1, University of Georgia2, Landcare Research3, Canadian Forest Service4, Agriculture and Agri-Food Canada5, Queen Mary University of London6, Macquarie University7, Academy of Sciences of the Czech Republic8, University of Jena9, University of Giessen10, Kenya Methodist University11, Forestry Tasmania12, Institut de recherche pour le développement13, University of Lethbridge14, Cardiff University15, Universiti Malaysia Sabah16, International Center for Tropical Agriculture17, Pontifical Xavierian University18, Xishuangbanna Tropical Botanical Garden19, University of Puerto Rico20
TL;DR: Inclusion of soil animals will improve the predictive capabilities of region- or biome-scale decomposition models, soil animal influences on decomposition are important at the regional scale when attempting to predict global change scenarios, and the statistical relationship between decomposition rates and climate, at the global scale, is robust against changes in soil faunal abundance and diversity.
Abstract: Climate and litter quality are primary drivers of terrestrial decomposition and, based on evidence from multisite experiments at regional and global scales, are universally factored into global decomposition models. In contrast, soil animals are considered key regulators of decomposition at local scales but their role at larger scales is unresolved. Soil animals are consequently excluded from global models of organic mineralization processes. Incomplete assessment of the roles of soil animals stems from the difficulties of manipulating invertebrate animals experimentally across large geographic gradients. This is compounded by deficient or inconsistent taxonomy. We report a global decomposition experiment to assess the importance of soil animals in C mineralization, in which a common grass litter substrate was exposed to natural decomposition in either control or reduced animal treatments across 30 sites distributed from 43°S to 68°N on six continents. Animals in the mesofaunal size range were recovered from the litter by Tullgren extraction and identified to common specifications, mostly at the ordinal level. The design of the trials enabled faunal contribution to be evaluated against abiotic parameters between sites. Soil animals increase decomposition rates in temperate and wet tropical climates, but have neutral effects where temperature or moisture constrain biological activity. Our findings highlight that faunal influences on decomposition are dependent on prevailing climatic conditions. We conclude that (1) inclusion of soil animals will improve the predictive capabilities of region- or biome-scale decomposition models, (2) soil animal influences on decomposition are important at the regional scale when attempting to predict global change scenarios, and (3) the statistical relationship between decomposition rates and climate, at the global scale, is robust against changes in soil faunal abundance and diversity.
425 citations
TL;DR: In this paper, the authors estimate the location, extent, and trends in expansion of the wildland-urban interface (WUI) in the continental United States by determining the intersection of housing density classes computed from refined US Census data with a map of wildfire hazards.
425 citations
University of Edinburgh1, Aarhus University2, University of Colorado Boulder3, Institute of Arctic and Alpine Research4, National Ecological Observatory Network5, Smithsonian Institution6, Lund University7, VU University Amsterdam8, University of Lapland9, Northern Arizona University10, Bigelow Laboratory For Ocean Sciences11, University of British Columbia12, University of Washington13, Grand Valley State University14, Swiss Federal Institute for Forest, Snow and Landscape Research15, Max Planck Society16, University of Zurich17, Université de Sherbrooke18, University of Greifswald19, University of Parma20, Memorial University of Newfoundland21, Université du Québec à Trois-Rivières22, University of Gothenburg23, Leiden University24, University of California, Riverside25, Qatar University26, Mississippi State University27, University of Barcelona28, Umeå University29, Utrecht University30, University of Alaska Anchorage31, Adam Mickiewicz University in Poznań32, Wageningen University and Research Centre33, Alaska Department of Fish and Game34, University of Tromsø35, University of Vienna36, University of Copenhagen37, Helmholtz Centre for Environmental Research - UFZ38, University of Oulu39, Spanish National Research Council40, Queen's University41, Saint Mary's University42, Oak Ridge National Laboratory43, University of Aberdeen44, University of Saskatchewan45, Vrije Universiteit Brussel46, University of Victoria47, Swiss Federal Institute of Aquatic Science and Technology48, Norwegian University of Science and Technology49, Research Institute for Nature and Forest50, Florida International University51, Moscow State University52, University of Alaska Fairbanks53, University of Waterloo54, Laval University55, Deakin University56, University of Bonn57, United States Forest Service58, Simon Fraser University59, University of Iceland60, University Centre in Svalbard61, United States Fish and Wildlife Service62, Colorado State University63, University of Texas at El Paso64, University of Stirling65, University of Innsbruck66, Rocky Mountain Biological Laboratory67, University of Oxford68, Pacific Northwest National Laboratory69, University of Camerino70, University of Insubria71, University of New South Wales72, University of Manchester73, National University of Cordoba74, University of Arizona75, Santa Fe Institute76, Harvard University77, King Juan Carlos University78, Estonian University of Life Sciences79, Kyoto University80, World Agroforestry Centre81, Radboud University Nijmegen82, Forschungszentrum Jülich83, Macquarie University84, University of Regensburg85, University of Minnesota86, University of Sydney87, Santa Clara University88, Algoma University89, Komarov Botanical Institute90, University of Wisconsin–Eau Claire91
TL;DR: Biome-wide relationships between temperature, moisture and seven key plant functional traits across the tundra and over time show that community height increased with warming across all sites, whereas other traits lagged behind predicted rates of change.
Abstract: The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature-trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming.
425 citations
TL;DR: A new cloudy boundary layer single-column model is presented, designed to be flexible enough to represent a variety of cloudiness regimes—such as cumulus, stratocumulus, and clear regimes—without the need for case-specific adjustments.
Abstract: A new cloudy boundary layer single-column model is presented. It is designed to be flexible enough to represent a variety of cloudiness regimes—such as cumulus, stratocumulus, and clear regimes—without the need for case-specific adjustments. The methodology behind the model is the so-called assumed probability density function (PDF) method. The parameterization differs from higher-order closure or mass-flux schemes in that it achieves closure by the use of a relatively sophisticated joint PDF of vertical velocity, temperature, and moisture. A family of PDFs is chosen that is flexible enough to represent various cloudiness regimes. A double Gaussian family proposed by previous works is used. Predictive equations for grid box means and a number of higherorder turbulent moments are advanced in time. These moments are in turn used to select a particular member from the family of PDFs, for each time step and grid box. Once a PDF member has been selected, the scheme integrates over the PDF to close higher-order moments, buoyancy terms, and diagnose cloud fraction and liquid water. Since all the diagnosed moments for a given grid box and time step are derived from the same unique joint PDF, they are guaranteed to be consistent with one another. A companion paper presents simulations produced by the single-column model.
425 citations
TL;DR: In this paper, the operational Cloudsat hydrometeor detection algorithm is described, difficulties due to surface clutter are discussed, and several examples from the early mission are shown, and a preliminary comparison of the CloudsAT hydrometric detection algorithm with lidar-based results from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is also provided.
Abstract: In late April 2006, NASA launched Cloudsat, an earth-observing satellite that uses a near-nadir-pointing millimeter-wavelength radar to probe the vertical structure of clouds and precipitation. The first step in using Cloudsat measurements is to distinguish clouds and other hydrometeors from radar noise. In this article the operational Cloudsat hydrometeor detection algorithm is described, difficulties due to surface clutter are discussed, and several examples from the early mission are shown. A preliminary comparison of the Cloudsat hydrometeor detection algorithm with lidar-based results from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is also provided.
424 citations
Authors
Showing all 31766 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Mark P. Mattson | 200 | 980 | 138033 |
| Stephen J. O'Brien | 153 | 1062 | 93025 |
| Ad Bax | 138 | 486 | 97112 |
| David Price | 138 | 1687 | 93535 |
| Georgios B. Giannakis | 137 | 1321 | 73517 |
| James Mueller | 134 | 1194 | 87738 |
| Christopher B. Field | 133 | 408 | 88930 |
| Steven W. Running | 126 | 355 | 76265 |
| Simon Lin | 126 | 754 | 69084 |
| Jitender P. Dubey | 124 | 1344 | 77275 |
| Gregory P. Asner | 123 | 613 | 60547 |
| Steven P. DenBaars | 118 | 1366 | 60343 |
| Peter Molnar | 118 | 446 | 53480 |
| William R. Jacobs | 118 | 490 | 48638 |
| C. Patrignani | 117 | 1754 | 110008 |