Geology topics

Beth E. Ross

Publications and source records attributed to Beth E. Ross.

4 recordsLinked to original sources

USGS2016

The relative contribution of climate to changes in lesser prairie-chicken abundance

Managing for species using current weather patterns fails to incorporate the uncertainty associated with future climatic conditions; without incorporating potential changes in climate into conservation strategies, management and conservation efforts may fall short or waste valuable resources. Understanding the effects of climate change on species in the Great Plains of North America is especially important, as this region is projected to experience an increased magnitude of climate change. Of particular ecological and conservation interest is the lesser prairie-chicken ( Tympanuchus pallidicinctus ), which was listed as “threatened” under the U.S. Endangered Species Act in May 2014. We used Bayesian hierarchical models to quantify the effects of extreme climatic events (extreme values of the Palmer Drought Severity Index [PDSI]) relative to intermediate (changes in El Niño Southern Oscillation) and long-term climate variability (changes in the Pacific Decadal Oscillation) on trends in lesser prairie-chicken abundance from 1981 to 2014. Our results indicate that lesser prairie-chicken abundance on leks responded to environmental conditions of the year previous by positively responding to wet springs (high PDSI) and negatively to years with hot, dry summers (low PDSI), but had little response to variation in the El Niño Southern Oscillation and the Pacific Decadal Oscillation. Additionally, greater variation in abundance on leks was explained by variation in site relative to broad-scale climatic indices. Consequently, lesser prairie-chicken abundance on leks in Kansas is more strongly influenced by extreme drought events during summer than other climatic conditions, which may have negative consequences for the population as drought conditions intensify throughout the Great Plains.

Ecosphere↗

USGS2016

Landscape composition creates a threshold influencing Lesser Prairie-Chicken population resilience to extreme drought

Habitat loss and degradation compound the effects of climate change on wildlife, yet responses to climate and land cover change are often quantified independently. The interaction between climate and land cover change could be intensified in the Great Plains region where grasslands are being converted to row-crop agriculture concurrent with increased frequency of extreme drought events. We quantified the combined effects of land cover and climate change on a species of conservation concern in the Great Plains, the Lesser Prairie-Chicken ( Tympanuchus pallidicinctus ). We combined extreme drought events and land cover change with lek count surveys in a Bayesian hierarchical model to quantify changes in abundance of male Lesser Prairie-Chickens from 1978 to 2014 in Kansas, the core of their species range. Our estimates of abundance indicate a gradually decreasing population through 2010 corresponding to drought events and reduced grassland areas. Decreases in Lesser Prairie-Chicken abundance were greatest in areas with increasing row-crop to grassland land cover ratio during extreme drought events, and decreased grassland reduces the resilience of Lesser Prairie-Chicken populations to extreme drought events. A threshold exists for Lesser Prairie-Chickens in response to the gradient of cropland:grassland land cover. When moving across the gradient of grassland to cropland, abundance initially increased in response to more cropland on the landscape, but declined in response to more cropland after the threshold ( δ=0.096 , or 9.6% cropland). Preservation of intact grasslands and continued implementation of initiatives to revert cropland to grassland should increase Lesser Prairie-Chicken resilience to extreme drought events due to climate change.

Kansas↗

USGS2015

Combined effects of climate, predation, and density dependence on Greater and Lesser Scaup population dynamics

An understanding of species relationships is critical in the management and conservation of populations facing climate change, yet few studies address how climate alters species interactions and other population drivers. We use a long-term, broad-scale data set of relative abundance to examine the influence of climate, predators, and density dependence on the population dynamics of declining scaup ( Aythya ) species within the core of their breeding range. The state-space modeling approach we use applies to a wide range of wildlife species, especially populations monitored over broad spatiotemporal extents. Using this approach, we found that immediate snow cover extent in the preceding winter and spring had the strongest effects, with increases in mean snow cover extent having a positive effect on the local surveyed abundance of scaup. The direct effects of mesopredator abundance on scaup population dynamics were weaker, but the results still indicated a potential interactive process between climate and food web dynamics (mesopredators, alternative prey, and scaup). By considering climate variables and other potential effects on population dynamics, and using a rigorous estimation framework, we provide insight into complex ecological processes for guiding conservation and policy actions aimed at mitigating and reversing the decline of scaup.

Ecological Applications↗

USGS2012

An accessible method for implementing hierarchical models with spatio-temporal abundance data

A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.

PLoS ONE↗