USGS Awards $2.7 Million in Stimulus Funding to Improve the Detection of Changes in the Earth’s Crust

The U.S. Geological Survey has awarded $2.7 million in cooperative agreements under the American Recovery and Reinvestment Act to the University of California, Berkeley; Central Washington University; University of California, San Diego; and UNAVCO, Inc., to improve networks that detect minute changes in the earth’s crust caused by faulting in earthquake-prone regions.

Monitoring these small changes (undetectable except through the methods of advanced geodesy) is an integral part of assessing the likely rate of large earthquakes. For optimal performance in real time, many existing monitoring stations need modern sensors and improved communication systems. Funds provided through six cooperative agreements will improve monitoring capabilities by replacing obsolete sensors that may be more than 10 years old and by upgrading communications so that real-time data streams are more reliable or possible for the first time. These funds will create or preserve jobs relating to contract work and equipment manufacturing.

“These improvements in advanced geodesy will enhance the ability of the U.S. Geological Survey and its cooperators to monitor in real-time how strain is building across hazardous faults,” said David Applegate, senior science advisor for earthquake and geologic hazards.

The American Recovery and Reinvestment Act passed earlier this year included $3 billion to the Department of the Interior. Of that amount, $140 million in funding is being used by the USGS to fund projects meeting Recovery Act goals

The Recovery Act funds are part of a stimulus package that is an important component of the President’s plan to jumpstart the economy and put a down payment on addressing long-neglected challenges so the country can thrive in the 21st century. Under the Recovery Act, Interior is making an investment in conserving America’s timeless treasures — our stunning natural landscapes, our monuments to liberty, the icons of our culture and heritage — while helping American families and their communities prosper again. Interior is also focusing on renewable energy projects, the needs of American Indians, employing youth and promoting community service.

“With its investments of Recovery Act funds, the Department of the Interior and its bureaus are putting people to work today to make improvements that will benefit the environment and the region for many years to come,” said Secretary of the Interior Ken Salazar.

Secretary Salazar has pledged unprecedented levels of transparency and accountability in the implementation of the Department’s economic recovery projects. The public can follow the progress of each project on RECOVERY.GOV and on the Department of the Interior, Recovery Investments website. Secretary Salazar has appointed a Senior Advisor for Economic Recovery, Chris Henderson, and an Interior Economic Recovery Task Force to work closely with Interior’s Inspector General and ensure that the recovery program is meeting the high standards for accountability, responsibility and transparency set by President Obama.

[Source: USGS press release]

Applying Spatial Conservation Prioritization Software and High-resolution GIS Data to a National-scale Study in Forest Conservation

Forest Ecology and Management, 258 (11), p.2439-2449, Nov 2009

Lehtomaki, J. / Tomppo, E. / Kuokkanen, P. / Hanski, I. / Moilanen, A.

“We apply a recently developed conservation prioritization method (Zonation algorithm) to a national-scale conservation planning task. The Finnish Forest and Park Service (Metsähallitus) was given the mandate to expand the current protected areas in southern Finland by 10 000 ha. The question is which areas should be selected out of the total area of 1 760 000 ha. The data available include a nation-wide GIS data set describing general features of forests at the resolution of 25 m × 25 m for entire Finland and another data set about biodiversity features within the current state-managed conservation areas. Ecologically, the key information includes forest age and the volume of growing stock for 20 forest types representing different productivity classes and dominant tree species. Our analysis employs four different connectivity components to identify forest areas that are (i) locally of high quality and internally well connected, (ii) well connected to surrounding high-quality forests, (iii) well connected to existing conservation areas, and (iv) large enough to allow efficient implementation. Expert evaluation of the results suggested that the present quantitative analysis was helpful in identifying areas with high conservation value systematically across southern Finland. Our analysis also showed that the highest forest conservation potential in Finland is located on privately owned land. The present techniques can be applied to many large-scale planning and management projects.”

Modelling and Mapping Agricultural Opportunity Costs to Guide Landscape Planning for Natural Resource Management

Ecological Indicators, In Press, Available online 24 March 2009

B.A. Bryan, D. King, J.R. Ward

“On-farm actions to better manage natural resources often involve an opportunity cost associated with foregone agricultural production. Spatial information on agricultural opportunity costs is a key indicator that has been demonstrated to increase the cost-effectiveness of environmental investment through spatial targeting. In this paper we develop a method for calculating expected profit as a more robust spatial measure of economic rent accruing from agricultural land and indicator of opportunity cost for use in landscape and planning for natural resource management. We apply this method to the Lower Murray region in southern Australia. Agricultural profit is calculated for three farming system phases (cereals, legumes, and grazing) by census zones based on agricultural statistics and cost of production information within a GIS environment. Zonal profit layers are smoothed using pycnophylactic (mass preserving) interpolation. Farming system rotations are quantified as a set of continuous spatial probability layers for each phase using a moving window kernel density technique based on existing land use data and these probability layers are used in a weighted allocation of expected profit across the landscape. The expected profit layer provides a high spatial resolution description of opportunity costs associated with natural resource management over the Lower Murray region suitable for input into systematic landscape planning analyses. Validation of the opportunity cost layer by field survey identified both random and systematic error. Interpretation of systematic error highlighted the need to augment pycnophylactic interpolation techniques with consideration of covariates of profit such as rainfall for better estimation in areas with high profit gradients.”

Spatializing Social Networks: Using Social Network Analysis to Investigate Geographies of Gang Rivalry, Territoriality, and Violence in Los Angeles

Annals of the Association of American Geographers, 1467-8306, Volume 100, Issue 2, First published 2010, Pages 307 – 326

Steven M. Radil; Colin Flint; and George E. Tita

“Social network analysis is an increasingly prominent set of techniques used in a number of social sciences, but the use of the techniques of social network analysis in geography has been challenged because of a perceived lack of geographic nuance or consideration of spatialities of context in social networks. The concept of social position and the associated technique of structural equivalence in social network analysis are explored as a means to integrate two different kinds of embeddedness: relative location in geographic space and structural position in network space. Using spatialized network data, this article compares the geography of rivalry relations that connect territorially based criminal street gangs in a section of Los Angeles with a geography of the location of gang-related violence. The technique of structural equivalence uses the two different spatialities of embeddedness to identify gangs that are similarly embedded in the territorial geography and positioned in the rivalry network, which aids in understanding the overall context of gang violence. The technique demonstrated here has promise beyond this one study of gang crime as it operationalizes spatialities of embeddedness in a way that allows simultaneous systematic evaluation of the way in which social actors’ positions in network relationships and spatial settings provide constraints on and possibilities for their behavior.”

Scale-dependent Environmental Variables Affecting Red Squirrel (Sciurus vulgaris meridionalis) Distribution

Italian Journal of Zoology, Volume 77, Issue 1 March 2010 , pages 92 – 101

P. C. Rima; M. Cagnin; G. Aloise; D. Preatoni; and L. A. Wauters

“We investigated the effects of habitat fragmentation on the endemic subspecies of red squirrel Sciurus vulgaris meridionalis in the Pollino National Park, Calabria, Southern Italy. Presence/absence of squirrels was monitored using drey (nest) counts in 51 1-ha census plots. Squirrel dreys were found in 16 plots (31%). Patch size was not correlated to squirrel presence. Squirrels were found in patches ranging from 3.19 to 6051 ha. Small-scale forest structure significantly affected the probability of occurrence. The proportion of conifers and average tree height positively predict squirrel presence; furthermore, nest density was positively correlated with high tree species diversity and the proportion of deciduous oaks (Quercus cerris, Q. ilex). Also at the home-range scale the proportion of conifer forest and oak-dominated deciduous forests positively predicted squirrel presence (200-300 m radius). At the even larger scale, corresponding with potential dispersal distances (3000 m radius), landscape parameters indicating a lower degree of fragmentation and proportion of oak seemed to favour squirrel presence. Our results emphasize that multi-scale analyses can enhance our understanding of red squirrel distribution, and that their distribution and abundance were mainly determined by forest structure components, such as food availability, at the home-range scale. We underline the importance of protection, and eventually increasing conifer and deciduous oak woods range in the Pollino National Park for the management and conservation of endemic Calabrian red squirrels.”

Data Analysis of Spatio-Temporal Sensor Data as a Contribution to the Model Analysis for Water Resources

BALWOIS 2010, Ohrid, Republic of Macedonia – 25, 29 May 2010

Sanja Veleva, Kosta Mitreski

“The quality of the information is measured by its accuracy and its relevance over time. Therefore, the process of data analysis of the sensor eco-data is of a great importance to the detection and prediction of the eco-hydrology phenomena. The existing models for data mining do not relate to the continuously changing characteristics of the sensor eco-data. Furthermore, most of the monitoring systems are based on event alert services, which do not answer to the continuous variations of the measured parameters. Our approach embeds the nature of system characteristics into one dynamic model for data mining of continuously changing spatio-temporal characteristics of one eco-hydrology system. The continuously gathered sensor eco-data from the region of Lake Prespa consisted of 320 water samples, among them 224 from the lake gauging stations and 96 from the river gauging stations. Considering the recommendations from the Water Framework Directive (WFD), the sensor eco-data were grouped into three types: physical, chemical and biological, corresponding to their aspect of water quality. All of these types convey the same class definition in the form of value, spatial and temporal information. To define our sensor data mining model we contribute to three segments: outlier analysis, pattern analysis, and prediction analysis. The suggested sensor data analysis model should be of a useful asset in obtaining knowledge for certain aquatic phenomena.”

Spatial Synchrony in Intertidal Benthic Algal Biomass in Temperate Coastal and Estuarine Ecosystems

Ecosystems, Volume 13, Number 2 / March, 2010

Daphne van der Wal, Annette Wielemaker-van den Dool and Peter M. J. Herman

“Microphytobenthos plays a vital role in estuarine and coastal carbon cycling and food webs. Yet, the role of exogenous factors, and thus the effects of climate change, in regulating microphytobenthic biomass is poorly understood. We aimed to unravel the mechanisms structuring microphytobenthic biomass both within and across ecosystems. The spatiotemporal distribution of the biomass of intertidal benthic algae (dominated by diatoms) was estimated with an unprecedented spatial extent from time-series of Normalized Differential Vegetation Index (NDVI) derived from a 6-year period of daily Aqua MODIS 250-m images of seven temperate, mostly turbid, estuarine and coastal ecosystems. These NDVI time-series were related to meteorological and environmental conditions. Intertidal benthic algal biomass varied seasonally in all ecosystems, in parallel with meteorology and water quality. Seasonal variation was more pronounced in mud than in sand. Interannual variation in biomass was small, but synchronized year-to-year biomass fluctuations occurred in a number of disjointed ecosystems. Air temperature explained interannual fluctuations in biomass in a number of sites, but the synchrony was mainly driven by the wind/wave climate: high wind velocities reduced microphytobenthic biomass, either through increased resuspension or reduced emersion duration. Spatial variation in biomass was largely explained by emersion duration and mud content, both within and across ecosystems. The results imply that effects on microphytobenthic standing stock can be anticipated when the position in the tidal frame is altered, for example due to sea level rise. Increased storminess will also result in a large-scale decrease of biomass.”