Geology topics

Randy Dettmers

Publications and source records attributed to Randy Dettmers.

5 recordsLinked to original sources

USGS2020

Threshold responses of songbirds to forest loss and fragmentation across the Marcellus-Utica shale gas region

Context Since 2005, unconventional gas develop[1]ment has rapidly altered forests across the Marcellus[1]Utica shale basin in the central Appalachian region of the eastern United States, an area of high conservation value for biodiversity. Much is still unknown about ecological impacts of associated land cover change. Objectives Our goal was to identify threshold responses among bird species and habitat guilds to (1) overall forest loss and fragmentation in affected landscapes, and (2) distance from anthropogenic landscapes, and (2) distance from anthropogenic disturbance, both related and unrelated to shale gas. Methods We conducted 2589 bird surveys at 190 sites across this region, and quantified community[1]level and species-specific thresholds relating to forest cover and distance to anthropogenic disturbance, using Threshold Indicator Taxa Analysis (TITAN). Results Forest interior species decreased abruptly in abundance and frequency of occurrence above a threshold of 17.0% overall forest loss, while early successional and synanthropic species increased abruptly above 30.5–36.5% forest loss, respectively. Broad quantile intervals around responses to distance from anthropogenic disturbance suggest these were not sharp threshold responses, but more gradual or linear responses. Among forest interior species evaluated, 48.1% increased in abundance farther from shale gas development, while 55.6% of early successional and synanthropic species decreased. Conclusions We found evidence of avian threshold responses to overall forest loss and fragmentation in affected landscapes across the Marcellus-Utica shale region. Our results suggest that efforts to avoid shale gas development in regional core forests—particularly those still retaining C 83% forest cover—can reduce negative effects on area-sensitive, forest interior dependent species

Maryland, New York, Ohio, Pennsylvania, West Virgi↗

USGS2017

The Partners in Flight handbook on species assessment Version 2017

Partners in Flight (PIF) is a cooperative venture of federal, state, provincial, and territorial agencies, industry, non-governmental organizations, researchers, and many others whose common goal is the conservation of North American birds (www.partnersinflight.org). While PIF has focused primarily on landbirds, it works in conjunction with other bird partners to promote coordinated conservation of all birds. PIF follows an iterative, adaptive planning approach that develops a sound scientific basis for decision-making and a logical process for setting, implementing, and evaluating conservation objectives (Pashley et al. 2000, Rich et al. 2004, Berlanga et al. 2010). The steps include: 1. Assessing conservation vulnerability of all bird species; 2. Identifying species most in need of conservation attention at continental and regional scales; 3. Setting of numerical population objectives for species of continental and regional importance; 4. Identifying conservation needs and recommended actions for species and habitats of importance; 5. Implementing strategies for meeting species and habitat objectives at continental and regional scales; 6. Evaluating success, making revisions, and setting new objectives for the future. The 2017 PIF Handbook on Species Assessment (2017 PIF Handbook) documents assessment rules and scores used in the Partners in Flight Landbird Conservation Plan: 2016 Revision for Canada and Continental United States (Rosenberg et al. 2016) and The State of North America’s Birds 2016 (NABCI 2016). It updates previous versions of the handbook (Panjabi et al. 2012, 2005, 2001) developed to accompany other PIF applications including Saving Our Shared Birds: Partners in Flight Tri-National Vision for Landbird Conservation (Berlanga et al. 2010) and the North American Landbird Conservation Plan (Rich et al. 2004). All current and past scores, data sources, and other related information are contained in databases hosted by the Bird Conservancy of the Rockies. Scores can be viewed online and downloaded as excel files, including archived versions (http://pif.birdconservancy.org/acad). The current accompanying Avian Conservation Assessment Database (ACAD) holds assessment scores and data for all 1585 native and 18 well-established non-native bird species found in mainland North America south to Panama plus adjacent islands and oceans. The taxonomy follows the American Ornithological Society’s 7th Edition Checklist of North and Middle American Birds, including updates though the 57th supplement, published in 2016 (http://checklist.aou.org/). The ACAD builds on archived PIF databases that hosted only data on the 882 landbirds native to Canada, USA and Mexico. This handbook is presented in two principal sections. Part I details the factors and scoring used by PIF to assess the vulnerability of species at continental and regional scales (i.e. step 1 of the planning approach above). Each assessment factor is based on biological criteria that evaluate distinct components of vulnerability throughout the life cycle of each species across its range. Part II describes the process of how the factors and the corresponding scores can be combined to highlight conservation needs (i.e. step 2 of the planning approach above). Both the scores and the process have evolved over time (Hunter et al. 1992, Carter et al. 2000, Panjabi et al. 2001, 2005, 2012) and continue to be updated in response to external review (Beissinger et al. 2000), broad partner expertise, and the emergence of new data and analytical tools.

Report↗

USGS2009

Application of models to conservation planning for terrestrial birds in North America

Partners in Flight (PIF), a public–private coalition for the conservation of land birds, has developed one of four international bird conservation plans recognized under the auspices of the North American Bird Conservation Initiative (NABCI). Partners in Flight prioritized species most in need of conservation attention and set range-wide population goals for 448 species of terrestrial birds. Partnerships are now tasked with developing spatially explicit estimates of the distribution, and abundance of priority species across large ecoregions and identifying habitat acreages needed to support populations at prescribed levels. The PIF Five Elements process of conservation design identifies five steps needed to implement all bird conservation at the ecoregional scale. Habitat assessment and landscape characterization describe the current amounts of different habitat types and summarize patch characteristics, and landscape configurations that define the ability of a landscape to sustain healthy bird populations and are a valuable first step to describing the planning area before pursuing more complex species-specific models. Spatially linked database models, landscape-scale habitat suitability models, and statistical models are viable alternatives for predicting habitat suitability or bird abundance across large planning areas to help assess conservation opportunities, design landscapes to meet population objectives, and monitor change in habitat suitability or bird numbers over time. Bird conservation in the United States is a good example of the use of models in large-scale wildlife conservation planning because of its geographic extent, focus on multiple species, involvement of multiple partners, and use of simple to complex models. We provide some background on the recent development of bird conservation initiatives in the United States and the approaches used for regional conservation assessment and planning. We focus on approaches being used for landscape characterization and assessment, and bird population response modeling.

Book chapter↗

USGS1999

A GIS modeling method applied to predicting forest songbird habitat

We have developed an approach for using “presence” data to construct habitat models. Presence data are those that indicate locations where the target organism is observed to occur, but that cannot be used to define locations where the organism does not occur. Surveys of highly mobile vertebrates often yield these kinds of data. Models developed through our approach yield predictions of the amount and the spatial distribution of good-quality habitat for the target species. This approach was developed primarily for use in a GIS context; thus, the models are spatially explicit and have the potential to be applied over large areas. Our method consists of two primary steps. In the first step, we identify an optimal range of values for each habitat variable to be used as a predictor in the model. To find these ranges, we employ the concept of maximizing the difference between cumulative distribution functions of (1) the values of a habitat variable at the observed presence locations of the target organism, and (2) the values of that habitat variable for all locations across a study area. In the second step, multivariate models of good habitat are constructed by combining these ranges of values, using the Boolean operators “and” and “or.” We use an approach similar to forward stepwise regression to select the best overall model. We demonstrate the use of this method by developing species-specific habitat models for nine forest-breeding songbirds (e.g., Cerulean Warbler, Scarlet Tanager, Wood Thrush) studied in southern Ohio. These models are based on species’ microhabitat preferences for moisture and vegetation characteristics that can be predicted primarily through the use of abiotic variables. We use slope, land surface morphology, land surface curvature, water flow accumulation downhill, and an integrated moisture index, in conjunction with a land-cover classification that identifies forest/nonforest, to develop these models. The performance of these models was evaluated with an independent data set. Our tests showed that the models performed better than random at identifying where the birds occurred and provided useful information for predicting the amount and spatial distribution of good habitat for the birds we studied. In addition, we generally found positive correlations between the amount of habitat, as predicted by the models, and the number of territories within a given area. This added component provides the possibility, ultimately, of being able to estimate population sizes. Our models represent useful tools for resource managers who are interested in assessing the impacts of alternative management plans that could alter or remove habitat for these birds.

Ecological Applications↗

USGS1995

Breeding productivity and adult survival in nongame birds

Populations of many North American land-birds, including forest-inhabiting species that winter in the Neotropics, seem to be declining (Robbins et al. 1989; Terborgh 1989). These declines have been identified through broad-scale, long-term survey programs that identify changes in abundance pf species, but provide little information about causes of changes in abundance or the health of specific populations in different geographic locations. Population health is a measure of a population's ability to sustain itself over time as determined by the balance between birth and death rates. Indices of population size do not always provide an accurate measure of population health because population size can be maintained in unhealthy populations by immigration of recruits from health populations (Pulliam 1988). Poor population health across many populations in a species eventually results in the decline of that species. Early detection of population declines allows managers to correct problems before they are critical and widespread. Demographic data (breeding productivity and adult survival) provide the kind of early warning signal that allows detection of unhealthy populations in terms of productivity or survival problems (Martin and Guepel 1993). In addition, demographic data can help determine whether population declines are the result of low breeding productivity or low survival in migration or winter. Breeding productivity data also can help identify habitat conditions associated with successful and failed breeding attempts. Such information is critical for developing habitat- and land-management practices (Martin 1992). Here, we provide examples of the kinds of information that can be obtained by broad-scale demographic studies.

Book chapter↗