MS ENVIRONMENTAL BIOLOGY CAPSTONE PROJECT

1. Impacts of Trout Stocking Practices on Colorado’s Alpine Ecosystems 2. Effects of Trout Presence on Alpine Lake Macroinvertebrate Emergence in Colorado 3. Post-Stocking Survival and Myxospore Evaluation of Whirling Disease Resistant Rainbow Trout Strains 4. A Compromise for the Planned Reservoir Expansion at Bear Creek Lake Park to Combat Denver’s Water Crisi

Evaluation of the Water Footprint of Beef Cattle Production in Nebraska

Data were compiled on feed usage to model the amount of water needed to produce beef in typical Nebraska production systems. Production systems where cows were wintered on corn residue utilized 18% less water than systems utilizing native range as a wintering source, because of water allocations. Therefore, the water footprint (gallons of water required to produce one pound of boneless meat) was decreased by 18%. In addition, increasing the dietary inclusion of distillers grains from 0% to 40% decreased the water footprint in the finishing phase by 29%, again based on water allocation. Utilizing corn residue and distillers grains in Nebraska beef cattle systems decreases the overall water footprint of production. Additionally, the water footprint of the systems analyzed was 80% green water as rain, minimizing the environmental impact of beef production on freshwater use and ecological water balance

Effects of Supplemental SoyPass in Forage-Based Diets Containing Distillers Grains on Performance of Growing Steers

SoyPass was supplemented in two grass hay diets containing 20% or 35% wet distillers grains with solubles (WDGS) to analyze the effects on growing cattle performance. The SoyPass supplement replaced 0, 30, or 60% of dietary WDGS for a total of 6 treatments with a factorial design. Substituting SoyPass into the diet did not affect average daily gain (ADG) of calves; however, calves consuming the 35% WDGS diet gained 31% more than the 20% WDGS treatment calves. Dry matter intake (DMI) and feed to gain (F:G) increased linearly in the 35% WDGS diet with the inclusion of SoyPass. In the 20% WDGS diet, DMI and F:G were maximized when SoyPass replaced 30% of the WDGS and lowest when SoyPass replaced 60% of WDGS. Therefore, SoyPass can replace up to 60% of the WDGS in forage based diets containing 20% WDGS with no adverse effects on performance by appearing to supply needed lysine

Evaluation of the Water Footprint of Beef Cattle Production in Nebraska

Data were compiled on feed usage to model the amount of water needed to produce beef in typical Nebraska production systems. Production systems where cows were wintered on corn residue utilized 18% less water than systems utilizing native range as a wintering source, because of water allocations. Therefore, the water footprint (gallons of water required to produce one pound of boneless meat) was decreased by 18%. In addition, increasing the dietary inclusion of distillers grains from 0% to 40% decreased the water footprint in the finishing phase by 29%, again based on water allocation. Utilizing corn residue and distillers grains in Nebraska beef cattle systems decreases the overall water footprint of production. Additionally, the water footprint of the systems analyzed was 80% green water as rain, minimizing the environmental impact of beef production on freshwater use and ecological water balance

Terrestrial primary production for the conterminous United States derived from Landsat 30 m and MODIS 250 m

Terrestrial primary production is a fundamental ecological process and a crucial component in understanding the flow of energy through trophic levels. The global MODIS gross primary production (GPP) and net primary production (NPP) products (MOD17) are widely used for monitoring GPP and NPP at coarse resolutions across broad spatial extents. The coarse input datasets and global biome‐level parameters, however, are well‐known limitations to the applicability of the MOD17 product at finer scales. We addressed these limitations and created two improved products for the conterminous United States (CONUS) that capture the spatiotemporal variability in terrestrial production. The MOD17 algorithm was utilized with medium resolution land cover classifications and improved meteorological data specific to CONUS in order to produce: (a) Landsat derived 16‐day GPP and annual NPP at 30 m resolution from 1986 to 2016 (GPPL30 and NPPL30, respectively); and (b) MODIS derived 8‐day GPP and annual NPP at 250 m resolution from 2001 to 2016 (GPPM250 and NPPM250 respectively). Biome‐specific input parameters were optimized based on eddy covariance flux tower‐derived GPP data from the FLUXNET2015 database. We evaluated GPPL30 and GPPM250 products against the standard MODIS GPP product utilizing a select subset of representative flux tower sites, and found improvement across all land cover classes except croplands. We also found consistent interannual variability and trends across NPPL30, NPPM250, and the standard MODIS NPP product. We highlight the application potential of the production products, demonstrating their improved capacity for monitoring terrestrial production at higher levels of spatial detail across broad spatiotemporal scales

EEFlux: A Landsat-based Evapotranspiration mapping tool on the Google Earth Engine

“EEFlux” is an acronym for ‘Earth Engine Evapotranspiration Flux.’ EEFlux is based on the operational surface energy balance model “METRIC” (Mapping ET at high Resolution with Internalized Calibration), and is a Landsat-imagebased process. Landsat imagery supports the production of ET maps at resolutions of 30 m, which is the scale of many human-impacted and human-interest activities including agricultural fields, forest clearcuts and vegetation systems along streams. ET over extended time periods provides valuable information regarding impacts of water consumption on Earth resources and on humans. EEFlux uses North American Land Data Assimilation System hourly gridded weather data collection for energy balance calibration and time integration of ET. Reference ET is calculated using the ASCE (2005) Penman-Monteith and GridMET weather data sets. The Statsgo soil data base of the USDA provides soil type information. EEFlux will be freely available to the public and includes a web-based operating console. This work has been supported by Google, Inc. and is possible due to the free Landsat image access afforded by the USGS

Modeling regional-scale wildland fire emissions with the wildland fire emissions information system

As carbon modeling tools become more comprehensive, spatial data are needed to improve quantitative maps of carbon emissions from fire. The Wildland Fire Emissions Information System (WFEIS) provides mapped estimates of carbon emissions from historical forest fires in the United States through a web browser. WFEIS improves access to data and provides a consistent approach to estimating emissions at landscape, regional, and continental scales. The system taps into data and tools developed by the U.S. Forest Service to describe fuels, fuel loadings, and fuel consumption and merges information from the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration on fire location and timing. Currently, WFEIS provides web access to Moderate Resolution Imaging Spectroradiometer (MODIS) burned area for North America and U.S. fire-perimeter maps from the Monitoring Trends in Burn Severity products from the USGS, overlays them on 1-km fuel maps for the United States, and calculates fuel consumption and emissions with an open-source version of the Consume model. Mapped fuel moisture is derived from daily meteorological data from remote automated weather stations. In addition to tabular output results, WFEIS produces multiple vector and raster formats. This paper provides an overview of the WFEIS system, including the web-based system functionality and datasets used for emissions estimates. WFEIS operates on the web and is built using open-source software components that work with open international standards such as keyhole markup language (KML). Examples of emissions outputs from WFEIS are presented showing that the system provides results that vary widely across the many ecosystems of North America and are consistent with previous emissions modeling estimates and products

A General Modeling Framework for Describing Spatially Structured Population Dynamics

Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network‐based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life‐history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network‐based population is modeled with discrete time steps. Using both theoretical and real‐world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network‐based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance that comparative analyses are colored by model details rather than general principles