Geology topics

Falk Huettmann

Publications and source records attributed to Falk Huettmann.

4 recordsLinked to original sources

USGS2018

Assessment of potential risks from renewable energy development and other anthropogenic factors to wintering Golden Eagles in the western United States

Wind and other energy development are expanding rapidly and on an unprecedented scale within the range of the Golden Eagle ( Aquila chrysaetos ) while other anthropogenic-related changes, wildfires, invasive plants, drought, and climate change are altering or destroying native habitats occupied by Golden Eagles. However, the potential effects of these factors on North American Golden Eagle populations are largely unknown and the most recent evidence indicates that the population in the western United States is declining slightly. Impediments to evaluating the potential effects of energy development projects on wintering Golden Eagles include issues of scale and a paucity of available information about eagle winter use areas and ecology. We applied a predictive model of eagle winter distribution developed for Idaho and Montana, to Idaho, Utah, Nevada and eastern Oregon to help identify potential wintering areas and identify risks that occur in those areas. The model identifies ~40% of the four state study area as potentially suitable eagle winter habitat and provides a basis for spatial assessment of possible risk factors to eagles wintering there. We used eBird and Christmas Bird Count citizen science datasets for an independent evaluation of the accuracy of our predictive distribution model. The model was robust, accurately predicting the presence of wintering Golden Eagles significantly more often than expected. We used digital environmental datasets (layers) of potential risk factors, in conjunction with model predicted eagle distribution, to better understand and estimate the extent of risks to the wintering eagle population in the study area. These layers represent available data for some of the factors previously identified as risks in the landscape to wintering Golden Eagles. The majority of predicted eagle wintering areas occurred where there was little habitat fragmentation (<10%). All predicted winter areas contained at least one potential risk factor (e.g., potential for energy development); 39.4% of predicted winter areas contained at least two known risk factors. The greatest number of risks often occurred where the human footprint was highest and where eagles were less likely to occur during winter. Our results can be used to help prioritize field surveys for identifying important Golden Eagle winter areas in the western United States and determine potential locations where energy development is least likely to have negative effects on wintering eagles. Survey efforts can be allocated in consideration of management and conservation objectives based on predicted habitat suitability and risk factors. For example, surveys for areas of high suitability and low risk can identify places to focus management for conservation of eagle winter areas. Further, sites proposed for wind energy development could be reviewed initially based on model predicted eagle wintering areas and then surveyed to determine if permitting for development is appropriate.

Book chapter↗

USGS2018

BioTIME: A database of biodiversity time series for the Anthropocene

Motivation The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km 2 (158 cm 2 ) to 100 km 2 (1,000,000,000,000 cm 2 ). Time period and grain BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. Software format .csv and .SQL.

Global Ecology and Biogeography↗

USGS2013

Location-only and use-availability data: analysis methods converge

This Special Feature arose from a session on a topic of the same name that took place during The Wildlife Society meeting in Kona, Hawaii, from 5 to 10 November, 2011. The purpose of that session and this Special Feature is to compare methods for predictive modelling of species geographical distributions and the modelling of habitat (resource) selection by animals. The predictive modelling of species geographical distributions and the modelling of habitat selection based on the environmental conditions at sites where animals are known to occur are essentially the same problem. Presence-only and used-available data both consist of a sample of locations with known presence of a species or an individual. A separate sample of locations from a study area, with unknown presence (pseudo-absence), is also assumed to exist. The probability or relative probability of presence of a species or individual is modelled and estimated across a certain time implicitly defined by the sampling mechanism, for example, by the time period during which museum specimens or radiotelemetry data were collected. A number of modelling methods have appeared in the literature over the last couple of decades. Many of these methods were made feasible by the availability of geographical information systems (GIS), global positioning system (GPS) radiotelemetry and public online data access initiatives (e.g. global biodiversity information facility). The papers in this Special Feature are intended to present the state of the methodological art in their subject area, with particular attention paid to contrasting the advantages and disadvantages of alternative methods of analysis for data.

Journal of Animal Ecology↗

USGS2010

Distribution and community characteristics of staging shorebirds on the northern coast of Alaska

Avian studies conducted in the 1970s on Alaska’s Arctic Coastal Plain (ACP) indicated that coastal littoral habitats are important to Arctic-breeding shorebirds for staging prior to fall migration. However, relatively little recent, broad-scale, or quantitative information exists on shorebird use of staging areas in this region. To locate possible shorebird concentration areas in the littoral zone of the ACP, we conducted aerial surveys from the southwest end of Kasegaluk Lagoon on the Chukchi Sea to Demarcation Point on the Beaufort Sea during the summers of 2005–07. These surveys identified persistent within- and between-year concentrations of staging shorebirds at Peard Bay, Point Barrow/Elson Lagoon, Cape Simpson, and Smith Bay to Cape Halkett. Among river deltas in the Beaufort Sea, the Sagavanirktok and Kongakut deltas had large concentrations of staging shorebirds. We also collected data on shorebird community characteristics, staging phenology, and habitat use in 2005 and 2006 by conducting land-based surveys at six camps: Kasegaluk Lagoon, Peard Bay, Point Barrow/Elson Lagoon, Colville Delta, Sagavanirktok Delta, and Okpilak Delta. The shorebird community was more even and diverse (evenness E and Shannon Weiner H’ ) along the Beaufort Sea compared to the Chukchi Sea and in 2005 versus 2006. Staging phenology varied by species and location and differed for several species from that reported in previous studies. Our results suggest the existence of three foraging habitat guilds among the shorebird species observed in this study: gravel beach, mudflat, and salt marsh/pond edge. A comparison to data collected in the mid-1970s suggests that these foraging associations are conserved through time. Results from this research will be useful to land managers for monitoring the effects of changing environmental conditions and human activity on shorebirds and their habitats in Arctic Alaska.

Arctic↗