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At least 1,171 records · Page 65Linked to original sources
USGS2014

Reply to Efford on ‘Integrating resource selection information with spatial capture-recapture’

1. We proposed ( Methods in Ecology and Evolution , 2013, 4) a model for combining telemetry data with spatial capture–recapture (SCR) data that was vigorously criticized by Efford ( Methods in Ecology and Evolution , 2014, 000, 000). Efford's main claim was that our encounter probability model was incorrect, and therefore our R code and simulation results were wrong. 2. In fact, our encounter probability model is correct under the Poisson point process model that we used as a basis for our integrated model. On the other hand, the basis for Efford's claims clearly rest on the assumption of an alternative model which, while possibly useful, is distinct from that analysed in Royle et al. (Methods in Ecology and Evolution, 2013, 4). 3. A key point of Royle et al. ( Methods in Ecology and Evolution , 2013, 4) was that active resource selection induces heterogeneity in encounter probability which, if unaccounted for, should bias estimates of population size or density. The models of Royle et al. ( Methods in Ecology and Evolution , 2013, 4) and Efford ( Methods in Ecology and Evolution , 2014, 000, 000) merely amount to alternative models of resource selection, and hence varying amounts of heterogeneity in encounter probability.

Methods in Ecology and Evolution↗
USGS1975

Information through color imagery

The color-sensing capability of the human eye is a powerful tool. In remote sensing we should use color to display data more meaningfully, not to re-create the scene. Color disappears with distance, and features change color with viewing angle. Color infrared film lets us apply color with additional meaning even though we introduce a false color response. Although the marginal gray scale on an ERTS (Earth Resources Technology Satellite) image may indicate balance between the green, red, and infrared bands, and although each band may be printed in a primary color, tests show that we are not fully applying the three primary colors. Therefore, contrast in the green band should be raised. For true three-color remote sensing of the Earth, we must find two generally meaningful signatures in the visible spectrum, or perhaps extend our spectral range. Before turning to costly digital processing we should explore analog processing. Most ERTS users deal with relative spectral radiance; the few concerned with absolute radiance could use the computer-compatible tapes or special annotations. NASA (National Aeronautics and Space Administration), which assigns the range and contrast to the ERTS image, controls processing and could adjust the density range for maximum contrast in any ERTS scene. NASA cannot alter processing for local changes in reflective characteristics of the Earth but could adjust for Sun elevation and optimize the contrast in a given band.

Journal of Research of the U.S. Geological Survey↗
USGS1989

NEIC; the National Earthquake Information Center

Mexico was hit by one of the most devastating earthquakes in its history on September 19, 1985 at 7:18 a.m. MDT time. this earthquake, which was centered about 380 kilometers west-southwest of Mexico City, had a surface-wave magnitude of 8.1. In less than a minute, seismic waves from this earthquake had traveled to Mexico City. At this early hour, people were beginning to get ready for the day's work. Without warning, buildings in some sections of the city began to shake violently. A few minutes later, 412 buildings had collapsed and another 3,124 were badly damaged (figure 1). Most communication systems linking Mexico City to the rest of the world were damaged or rendered inoperable. At least 9,500 people were killed, 30,000 were injured and 100,000 were left homeless by this earthquake. According to some unconfirmed reports, the death toll from this earthquake may have been as high as 35,000. this earthquake is estimated to have seriously affected an area of 825,000 square kilometers, caused between 3 and 4 billion dollars in damage, and been felt by 20 million people.

Earthquakes & Volcanoes (USGS)↗
USGS2016

Informing Lake Erie agriculture nutrient management via scenario evaluation

Harmful algal blooms (HABs) have been increasing in extent and intensity in the western basin of Lake Erie. The cyanobacteria Microcystis produces toxins that pose serious threats to animal and human health, resulting in beach closures and impaired water supplies, and have even forced a “do not drink” advisory for the City of Toledo water system for several days in the summer of 2014. The main driver of Lake Erie HABs is elevated phosphorus loading from watersheds draining to the western basin, particularly from the Maumee River watershed (Obenour et al. 2014). Through the 2012 Great Lakes Water Quality Agreement (GLWQA), the U.S. and Canadian governments agreed to revise Lake Erie phosphorus loading targets to decrease HAB severity below levels representing a hazard to ecosystem and human health. New targets limit March-July loadings from the Maumee River to 186 metric tonnes of dissolved reactive phosphorus (DRP) and 860 metric tonnes of total phosphorus (TP) – a 40% reduction from 2008 loads (GLWQA 2016). The Great Lakes region must now determine what policy options are most effective and feasible for meeting those targets. While all sources are important, our focus is on agriculture because it overwhelms other sources. In a conservative ballpark estimate we found that 85% of the Maumee River’s load to Lake Erie comes from farm fertilizers and manures, even though this is only 10% of farmland fertilizer applications (Figure 1). Load targets will not be met without reductions from agriculture. Therefore, the overall goal of this study was to identify potential options for agricultural management to reduce phosphorus loads and lessen future HABs in Lake Erie. We applied multiple watershed models to test the ability of a series of land management scenarios, developed in consultation with agricultural and environmental stakeholders, to reach the proposed targets.

Indiana, Michigan, Ohio↗