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Research about Wind Cave National Park

Source-linked reports with geographic coverage including Wind Cave National Park.

9 recordsLinked to original sources
USGS2022

Supplemental vegetation monitoring plots at Wind Cave National Park to accelerate learning of the Annual Brome Adaptive Management (ABAM) model

The Annual Brome Adaptive Management (ABAM) project is a consortium of seven parks in the Northern Great Plains (NGP) working together to better understand how to control invasive annual grasses (including Bromus species) through an adaptive management approach. This approach is supported by a quantitative model that uses current data from standardized vegetation monitoring plots in all seven parks to annually update the model’s parameters and predictions regarding the effects of different management actions on invasive annual grasses and other components of the mixed-grass prairie plant community. This updating of the model is called “learning.” The ABAM model includes treatments in which the herbicides indaziflam and imazapic are applied alone or in combination with or without a prescribed fire preceding or following their application. However, the original ABAM model did not have field data for the effects of those treatments on target invasive annual grasses and other components of the vegetation in conditions like those that frequently occur in ABAM parks (i.e., ungrazed). The purpose of this study is to increase the amount of information about these treatments and therefore accelerate the rate of learning accomplished in the adaptive management cycle.

South Dakota↗
USGS2021

Spatial network clustering reveals elk population structure and local variation in prevalence of chronic wasting disease

Spatial organization plays prominent roles in disease transmission, genetics, and demography of wildlife populations and is therefore an important consideration not only for wildlife management, but also for inference about populations and processes. We used hierarchical agglomerative clustering of a spatial graph network to partition Wind Cave National Park (WICA) into five regions used by 163 female elk ( Cervus elaphus ) marked with global positioning system collars during 2005–08 and 2011–13. We grouped elk based on differential use of the five regions, developed a priori models for inter-group variation in the occurrence of chronic wasting disease (CWD), and used Akaike's information criterion to compare models and stratify regions. Previous descriptions of elk population structure, which have been based on social contact or overlap of individual ranges, have distinguished spatially disjunct population subsets. Constructing hierarchical partitions of the landscape enabled us to also discern and describe overlapping and nested subsets. During 2016–18, apparent park-wide prevalence of CWD was 0.18 (90% CI = [0.146, 0.182]); however, prevalence within three spatial strata used primarily by different elk ranged from 0.03 ([0.008, 0.074]) to 0.29 ([0.211, 0.375]). In context with published estimates of recruitment, predation, and anthropogenic mortality, such differences in prevalence equate to increasing local abundance of elk in southwestern WICA, stable to declining abundance in the west/northwest, and rapidly declining abundance in the east. Despite the modest size of WICA (11,357 ha), park-wide averages conflate effects of elk distribution and disease, obscuring spatial patterns with profound implications for study and management of elk and CWD. Graph networks have been used widely in ecology to describe such phenomena as social relationships, connectivity of habitat patches, animal movements, and the spread of disease. Extension to partitioning of geographic range is straightforward but entails different considerations. We discuss allocation of sampling effort, construction of an initial partition, specification of a model for graph cohesion, selection of a clustering algorithm, and identification of useful partitions.

North Dakota↗
USGS2021

Climate change scenario planning for resource stewardship at Wind Cave National Park

This report explains scenario planning as a climate change adaptation tool in general, then describes how it was applied to Wind Cave National Park as the second part of a pilot project to dovetail climate change scenario planning with National Park Service (NPS) Resource Stewardship Strategy development. In the orientation phase, Park and regional NPS staff, other subject-matter experts, natural and cultural resource planners, and the climate change core team who led the scenario planning project identified priority resource management topics and associated climate sensitivities. Next, the climate change core team used this information to create a set of four divergent climate futures—summaries of relevant climate data from individual climate projections—to encompass the range of ways climate could change in coming decades in the park. Participants in the scenario planning workshop then developed climate futures into robust climate-resource scenarios that considered expert-elicited resource impacts and identified potential management responses. Finally, the scenario-based resource responses identified by park staff and subject matter experts were used to integrate climate-informed adaptations into resource stewardship goals and activities for the park's Resource Stewardship Strategy. This process of engaging resource managers in climate change scenario planning ensures that their management and planning decisions are informed by assessments of critical future climate uncertainties.

South Dakota↗
USGS2018

Chronic wasting disease detection and mortality sources in semi-protected deer population

Surveillance for wildlife diseases is essential for assessing population dynamics of ungulates, especially in free-ranging populations where infected animals are difficult to sample. Chronic wasting disease (CWD) is an emerging infectious disease of concern because of the potential for substantial negative effects on populations of cervids. Variability in the likelihood that CWD is detected could invalidate traditional estimators for prevalence. In some instances, deer located after death cannot be tested for infectious diseases, including CWD, because of lack of availability or condition of appropriate tissues. We used various methods to detect infectious diseases that could cause mortality for deer Odocoileus spp. residing in Wind Cave National Park, South Dakota, USA, and we report survival estimates for animals in this population. We included 34 monthly encounters of deer resightings and 67 mortalities. We tested live deer by tonsillar biopsy for CWD and estimated pooled prevalence (mean ± SE) at 5.6 ± 3.0% over the three-year study. Live deer potentially had exposure to several infectious diseases, including bluetongue, epizootic hemorrhagic disease, bovine viral diarrhea, West Nile virus, and malignant catarrhal fever, but no apparent morbidity or mortality from those diseases. We tested survival and influence of covariates, including age and sex, using known-fate analysis in Program MARK. Those data best supported a model with time-invariant encounter probability and an annual survival of 72.8%. Even without direct pressure from hunting within the park, average life expectancy in this population was 3.2 years. Only 68% of mortalities contained sufficient material for CWD sampling (because of predation and scavenger activity) and >42% of these were CWD-positive. These findings underscore the possible biases in postmortem surveillance estimates of disease prevalence because of potential for subclinical infected animals to be removed by predators and not tested.

South Dakota↗
USGS2015

Application of MC1 to Wind Cave National Park: Lessons from a small-scale study: Chapter 8

MC1 was designed for application to large regions that include a wide range in elevation and topography, thereby encompassing a broad range in climates and vegetation types. The authors applied the dynamic global vegetation model MC1 to Wind Cave National Park (WCNP) in the southern Black Hills of South Dakota, USA, on the ecotone between ponderosa pine forest to the northwest and mixed-grass prairie to the southeast. They calibrated MC1 to simulate adequate fire effects in the warmer southeastern parts of the park to ensure grasslands there, while allowing forests to grow to the northwest, and then simulated future vegetation with climate projections from three GCMs. The results suggest that fire frequency, as affected by climate and/or human intervention, may be more important than the direct effects of climate in determining the distribution of ponderosa pine in the Black Hills region, both historically and in the future.

South Dakota↗
USGS2013

Climate change and fire effects on a prairie-woodland ecotone: projecting species range shifts with a dynamic global vegetation model

Large shifts in species ranges have been predicted under future climate scenarios based primarily on niche-based species distribution models. However, the mechanisms that would cause such shifts are uncertain. Natural and anthropogenic fires have shaped the distributions of many plant species, but their effects have seldom been included in future projections of species ranges. Here, we examine how the combination of climate and fire influence historical and future distributions of the ponderosa pine–prairie ecotone at the edge of the Black Hills in South Dakota, USA, as simulated by MC1, a dynamic global vegetation model that includes the effects of fire, climate, and atmospheric CO 2 concentration on vegetation dynamics. For this purpose, we parameterized MC1 for ponderosa pine in the Black Hills, designating the revised model as MC1-WCNP. Results show that fire frequency, as affected by humidity and temperature, is central to the simulation of historical prairies in the warmer lowlands versus woodlands in the cooler, moister highlands. Based on three downscaled general circulation model climate projections for the 21st century, we simulate greater frequencies of natural fire throughout the area due to substantial warming and, for two of the climate projections, lower relative humidity. However, established ponderosa pine forests are relatively fire resistant, and areas that were initially wooded remained so over the 21st century for most of our future climate x fire management scenarios. This result contrasts with projections for ponderosa pine based on climatic niches, which suggest that its suitable habitat in the Black Hills will be greatly diminished by the middle of the 21st century. We hypothesize that the differences between the future predictions from these two approaches are due in part to the inclusion of fire effects in MC1, and we highlight the importance of accounting for fire as managed by humans in assessing both historical species distributions and future climate change effects.

South Dakota↗
USGS2013

Vegetation projections for Wind Cave National Park with three future climate scenarios: Final report in completion of Task Agreement J8W07100052

Introduction The effects of climate change on the natural resources protected by Parks will likely be substantial, but geographically variable, due to local variation in climate trajectories and differences among ecosystems in their vulnerability to climate change. The projections of general circulation models (GCMs) indicate the possible magnitude and direction of future climate change for a region, but the utility of these projections for more local scales, those of individual National Park Service (NPS) units, are more uncertain because the coarse-scale GCMs lack much of the topographic detail that alters local climates. In addition, complex, interacting effects of temperature, precipitation, atmospheric CO 2 concentrations, fire, and herbivores on the vegetation that is the foundational natural resource of many NPS units present challenges in assessing the effects of projected future climates on plant and animal assemblages managed by the NPS. In spring 2009, Wind Cave National Park (WICA) served as a case study in a workshop assessing the use of scenario planning as a tool for park management planning in the face of rapidly changing climate. One outcome of the workshop was the recognized need for quantitative models to better understand the range of possible vegetation changes under different future climates and management decisions. This report addresses this need; it describes our adaptation of a dynamic global vegetation model (DGVM) to WICA vegetation and the resulting projections of future vegetation under three future climate scenarios and 11 management scenarios determined by Park natural resource managers. Wind Cave National Park lies along a narrow transition zone between the ponderosa pine (Pinus ponderosa) forests of the Black Hills and the mixed grass prairie that once extended with few interruptions over the lower, gentler terrain, subject to warmer, drier climate to the east and south of the Park. The location and character of this transition is strongly influenced by fire frequency and intensity (Brown and Sieg 1999). Furthermore, the mixed grass prairie occupies a broader transition zone between eastern tallgrass prairie and the shortgrass prairie of the western Great Plains. The dominance of species characteristic of these two prairie types varies with soil moisture availability, evaporative demand, and recent grazing history (Cogan et al. 1999). In addition, Wind Cave lies near the midpoint of a long gradient of C 3 (cool season) grass dominance to the north and C 4 (warm season) grass dominance to the south. The ecotonal position of WICA may make it particularly sensitive to climate change. For example, small changes in fire frequency and/or intensity and the vigor of trees vs. grass could dramatically shift the proportions of these two lifeforms. The Park hydrology is also sensitive to changes in the balance between infiltration of precipitation and evapotranspiration, as on average, only a small fraction of annual precipitation reaches the deeper soil layers that feed permanent streamflow. The resources at risk at Wind Cave NP include the Cave itself, as well as small backcountry caves, a genetically important bison herd, and other prairie species including the black-tailed prairie dog and endangered black-footed ferrets. All of these resources will be directly affected by climate change impacts on vegetation and hydrology. Natural resource management challenges at WICA are substantial, diverse, and intertwined. Aboveground, the park has been recognized as exemplary for its high quality vegetation (Marriot et al. 1999), though the park is relatively small for the diversity of vegetation types and species that it supports. Even without a changing climate, maintaining the integrity of the plant communities is complicated by the park’s legislated responsibility to maintain viable populations of bison, elk and pronghorn. In addition, the federally endangered black-footed ferret was recently re-introduced to the park. This species requires large extents of prairie dog towns for prey and habitat. Prairie dogs impact vegetation by constant clipping, grazing and soil disturbance, thereby affecting plant composition and productivity. Moreover, naturally high interannual climate variability and the strong influence of precipitation on grass productivity in this region combine to yield high interannual variability in the amount of forage available for the wildlife that the park is tasked to maintain. Finally, fire, which is now primarily controlled by WICA and NPS Northern Great Plains fire management programs, is intertwined with all other natural resource issues at WICA, as it can impact prairie dog colony and forest expansion, ungulate foraging behavior, invasive plant species, and hydrological processes. Although not capable of capturing all of these complexities, dynamic vegetation models do provide a means for quantitatively projecting vegetation futures in future climates under plausible fire and grazing regimes. Our work uses the DGVM MC1 to simulate the effects of future climate projections and management practices on the vegetation of WICA. MC1 is designed to project potential vegetation as influenced by natural processes and hence is appropriate for national parks, where conservation of native biota and ecosystems is of great importance. Since the initial application of MC1 to a small portion of WICA (Bachelet et al. 2000), the model has been altered to improve model performance with the inclusion of dynamic fire. Applying this improved version to WICA required substantial recalibration, during which we have made a number of improvements to MC1 that will be incorporated as permanent changes. In this report we document these changes and our calibration procedure following a brief overview of the model. We compare the projections of current vegetation to the current state of the park and present projections of vegetation dynamics under future climates downscaled from three GCMs selected to represent the existing range in available GCM projections. In doing so, we examine the consequences of different management options regarding fire and grazing, major aspects of biotic management at Wind Cave.

South Dakota↗
USGS2012

Groundwater flow, quality (2007-10), and mixing in the Wind Cave National Park area, South Dakota

A study of groundwater flow, quality, and mixing in relation to Wind Cave National Park in western South Dakota was conducted during 2007-11 by the U.S. Geological Survey in cooperation with the National Park Service because of water-quality concerns and to determine possible sources of groundwater contamination in the Wind Cave National Park area. A large area surrounding Wind Cave National Park was included in this study because to understand groundwater in the park, a general understanding of groundwater in the surrounding southern Black Hills is necessary. Three aquifers are of particular importance for this purpose: the Minnelusa, Madison, and Precambrian aquifers. Multivariate methods applied to hydrochemical data, consisting of principal component analysis (PCA), cluster analysis, and an end-member mixing model, were applied to characterize groundwater flow and mixing. This provided a way to assess characteristics important for groundwater quality, including the differentiation of hydrogeologic domains within the study area, sources of groundwater to these domains, and groundwater mixing within these domains. Groundwater and surface-water samples collected for this study were analyzed for common ions (calcium, magnesium, sodium, bicarbonate, chloride, silica, and sulfate), arsenic, stable isotopes of oxygen and hydrogen, specific conductance, and pH. These 12 variables were used in all multivariate methods. A total of 100 samples were collected from 60 sites from 2007 to 2010 and included stream sinks, cave drip, cave water bodies, springs, and wells. In previous approaches that combined PCA with end-member mixing, extreme-value samples identified by PCA typically were assumed to represent end members. In this study, end members were not assumed to have been sampled but rather were estimated and constrained by prior hydrologic knowledge. Also, the end-member mixing model was quantified in relation to hydrogeologic domains, which focuses model results on major hydrologic processes. Finally, conservative tracers were weighted preferentially in model calibration, which distributed model errors of optimized values, or residuals, more appropriately than would otherwise be the case The latter item also provides an estimate of the relative effect of geochemical evolution along flow paths in comparison to mixing. The end-member mixing model estimated that Wind Cave sites received 38 percent of their groundwater inflow from local surface recharge, 34 percent from the upgradient Precambrian aquifer, 26 percent from surface recharge to the west, and 2 percent from regional flow. Artesian springs primarily received water from end members assumed to represent regional groundwater flow. Groundwater samples were collected and analyzed for chlorofluorocarbons, dissolved gasses (argon, carbon dioxide, methane, nitrogen, and oxygen), and tritium at selected sites and used to estimate groundwater age. Apparent ages, or model ages, for the Madison aquifer in the study area indicate that groundwater closest to surface recharge areas is youngest, with increasing age in a downgradient direction toward deeper parts of the aquifer. Arsenic concentrations in samples collected for this study ranged from 0.28 to 37.1 micrograms per liter (μg/L) with a median value of 6.4 μg/L, and 32 percent of these exceeded 10 μg/L. The highest arsenic concentrations in and near the study area are approximately coincident with the outcrop of the Minnelusa Formation and likely originated from arsenic in shale layers in this formation. Sample concentrations of nitrate plus nitrite were less than 2 milligrams per liter for 92 percent of samples collected, which is not a concern for drinking-water quality. Water samples were collected in the park and analyzed for five trace metals (chromium, copper, lithium, vanadium, and zinc), the concentrations of which did not correlate with arsenic. Dye tracing indicated hydraulic connection between three water bodies in Wind Cave.

South Dakota↗