Geology topics

Kevin Doherty

Publications and source records attributed to Kevin Doherty.

7 recordsLinked to original sources

USGS2023

Combining resilience and resistance with threat-based approaches for prioritizing management actions in sagebrush ecosystems

The sagebrush biome is a dryland region in the western United States experiencing rapid transformations to novel ecological states. Threat-based approaches for managing anthropogenic and ecosystem threats have recently become prominent, but successfully mitigating threats depends on the ecological resilience of ecosystems. We used a spatially explicit approach for prioritizing management actions that combined a threat-based model with models of resilience to disturbance and resistance to annual grass invasion. The threat-based model assessed geographic patterns in sagebrush ecological integrity (SEI) to identify core sagebrush, growth opportunity, and other rangeland areas. The resilience and resistance model identified ecologically relevant climate and soil water availability indicators from process-based ecohydrological models. The SEI areas and resilience and resistance indicators were consistent—the resilience and resistance indicators showed generally positive relationships with the SEI areas. They also were complementary—SEI areas provided information on intact sagebrush areas and threats, while resilience and resistance provided information on responses to disturbances and management actions. The SEI index and resilience and resistance indicators provide the basis for prioritizing conservation and restoration actions and determining appropriate strategies. The difficulty and time required to conserve or restore SEI areas increase as threats increases and resilience and resistance decrease.

Conservation Science and Practice↗

USGS2022

A sagebrush conservation design to proactively restore America’s sagebrush biome

A working group of experts with diverse professional backgrounds and disciplinary expertise was assembled to conceptualize a spatially explicit conservation design to support and inform the Sagebrush Conservation Strategy Part 2. The goal was to leverage recent advancements in remotely sensed landcover products to develop spatially and temporally explicit maps of sagebrush rangeland condition and landscape threats. In addition, the group sought to provide a common basis for understanding the state of sagebrush rangelands through time. First, the study team developed a spatially explicit model to assess geographic patterns in sagebrush ecological integrity and used this model to identify core sagebrush areas (CSAs), growth opportunity areas (GOAs), and other rangeland areas (ORAs) across the biome. Among the identified rangelands, 33.4 million acres were classified as CSAs; 84.3 million acres as GOAs; and 127.2 million acres as ORAs as of 2020. Second, the team sought to demonstrate the ecological relevance of the identified CSAs and GOAs by comparing these data with independent datasets for sagebrush obligate species of conservation concern. Geographical patterns in sagebrush ecological integrity were strongly associated with the occurrence of high-priority species and also displayed clear links to population performance for greater sage-grouse. Third, the team parsed out the type, location, and acres of primary threats within the different categories (CSAs, GOAs, and ORAs) to help focus active management by identifying places where multiagency and organization efforts can protect CSAs and GOAs that have higher levels of integrity with lower cumulative threats. The assessment of the condition of the sagebrush biome (that is, the location, amount, and conservation status) indicated that complex ecosystem function problems are driving ~73 percent of the demonstrated threats within the CSAs and GOAs (rather than point-source problems, such as human development). Fourth, the team developed trend estimates for the identified CSAs and GOAs and three selected primary threats (invasive annual grasses, conifer encroachment, and human modification) to the sagebrush biome from 2001 to 2020. Results showed that an average of 1.3 million acres per year have transitioned to ORAs at an annual rate of −1.34 percent. Fifth, the team developed an approach to integrate climate change effects into the threat-based landscape conservation design and conducted an initial assessment on the magnitude of near-term climate effects in the context of observed historical trends. The team’s analysis suggests that climate change alone is unlikely to be the dominant threat to sagebrush ecological integrity in the next few decades, although interactions of climate with wildfire and invasive annual grasses may be an important threat, especially in the longer term. A spatial overlap analysis was performed and highlighted 45.8 million acres of shared priorities among existing conservation frameworks to help anchor and guide collaborative landscape-scale conservation of areas that still have no to low threats. This information is critical to provide context for decisions about the volume and nature of conservation actions and funding requirements.

western United States↗

USGS2021

Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients

Plant community response to climate change will be influenced by individual plant responses that emerge from competition for limiting resources that fluctuate through time and vary across space. Projecting these responses requires an approach that integrates environmental conditions and species interactions that result from future climatic variability. Dryland plant communities are being substantially affected by climate change because their structure and function are closely tied to precipitation and temperature, yet impacts vary substantially due to environmental heterogeneity, especially in topographically complex regions. Here, we quantified the effects of climate change on big sagebrush ( Artemisia tridentata Nutt.) plant communities that span 76 million ha in the western United States. We used an individual-based plant simulation model that represents intra- and inter-specific competition for water availability, which is represented by a process-based soil water balance model. For dominant plant functional types, we quantified changes in biomass and characterized agreement among 52 future climate scenarios. We then used a multivariate matching algorithm to generate fine-scale interpolated surfaces of functional type biomass for our study area. Results suggest geographically divergent responses of big sagebrush to climate change (changes in biomass of −20% to +27%), declines in perennial C 3 grass and perennial forb biomass in most sites, and widespread, consistent, and sometimes large increases in perennial C 4 grasses. The largest declines in big sagebrush, perennial C 3 grass and perennial forb biomass were simulated in warm, dry sites. In contrast, we simulated no change or increases in functional type biomass in cold, moist sites. There was high agreement among climate scenarios on climate change impacts to functional type biomass, except for big sagebrush. Collectively, these results suggest divergent responses to warming in moisture-limited versus temperature-limited sites and potential shifts in the relative importance of some of the dominant functional types that result from competition for limiting resources.

Global Change Biology↗

USGS2017

Hierarchical population monitoring of greater sage-grouse ( Centrocercus urophasianus ) in Nevada and California—Identifying populations for management at the appropriate spatial scale

Population ecologists have long recognized the importance of ecological scale in understanding processes that guide observed demographic patterns for wildlife species. However, directly incorporating spatial and temporal scale into monitoring strategies that detect whether trajectories are driven by local or regional factors is challenging and rarely implemented. Identifying the appropriate scale is critical to the development of management actions that can attenuate or reverse population declines. We describe a novel example of a monitoring framework for estimating annual rates of population change for greater sage-grouse ( Centrocercus urophasianus ) within a hierarchical and spatially nested structure. Specifically, we conducted Bayesian analyses on a 17-year dataset (2000–2016) of lek counts in Nevada and northeastern California to estimate annual rates of population change, and compared trends across nested spatial scales. We identified leks and larger scale populations in immediate need of management, based on the occurrence of two criteria: (1) crossing of a destabilizing threshold designed to identify significant rates of population decline at a particular nested scale; and (2) crossing of decoupling thresholds designed to identify rates of population decline at smaller scales that decouple from rates of population change at a larger spatial scale. This approach establishes how declines affected by local disturbances can be separated from those operating at larger scales (for example, broad-scale wildfire and region-wide drought). Given the threshold output from our analysis, this adaptive management framework can be implemented readily and annually to facilitate responsive and effective actions for sage-grouse populations in the Great Basin. The rules of the framework can also be modified to identify populations responding positively to management action or demonstrating strong resilience to disturbance. Similar hierarchical approaches might be beneficial for other species occupying landscapes with heterogeneous disturbance and climatic regimes.

California, Nevada↗

USGS2017

Science framework for conservation and restoration of the sagebrush biome: Linking the Department of the Interior’s Integrated Rangeland Fire Management Strategy to long-term strategic conservation actions, Part 1. Science basis and applications

The Science Framework is intended to link the Department of the Interior’s Integrated Rangeland Fire Management Strategy with long-term strategic conservation actions in the sagebrush biome. The Science Framework provides a multiscale approach for prioritizing areas for management and determining effective management strategies within the sagebrush biome. The emphasis is on sagebrush (Artemisia spp.) ecosystems and Greater sage-grouse (Centrocercus urophasianus). The approach provided in the Science Framework links sagebrush ecosystem resilience to disturbance and resistance to nonnative, invasive plant species to species habitat information based on the distribution and abundance of focal species. A geospatial process is presented that overlays information on ecosystem resilience and resistance, species habitats, and predominant threats and that can be used at the mid-scale to prioritize areas for management. A resilience and resistance habitat matrix is provided that can help decisionmakers evaluate risks and determine appropriate management strategies. Prioritized areas and management strategies can be refined by managers and stakeholders at the local scale based on higher resolution data and local knowledge. Decision tools are discussed for determining appropriate management actions for areas that are prioritized for management. Geospatial data, maps, and models are provided through the U.S. Geological Survey (USGS) ScienceBase and Bureau of Land Management (BLM) Landscape Approach Data Portal. The Science Framework is intended to be adaptive and will be updated as additional data become available on other values and species at risk. It is anticipated that the Science Framework will be widely used to: (1) inform emerging strategies to conserve sagebrush ecosystems, sagebrush dependent species, and human uses of the sagebrush system, and (2) assist managers in prioritizing and planning on-the-ground restoration and mitigation actions across the sagebrush biome.

California, Colorado, Idaho, Montana, Nevada, Oreg↗

USGS2016

Importance of regional variation in conservation planning: A rangewide example of the Greater Sage-Grouse

We developed rangewide population and habitat models for Greater Sage-Grouse ( Centrocercus urophasianus ) that account for regional variation in habitat selection and relative densities of birds for use in conservation planning and risk assessments. We developed a probabilistic model of occupied breeding habitat by statistically linking habitat characteristics within 4 miles of an occupied lek using a nonlinear machine learning technique (Random Forests). Habitat characteristics used were quantified in GIS and represent standard abiotic and biotic variables related to sage-grouse biology. Statistical model fit was high (mean correctly classified = 82.0%, range = 75.4–88.0%) as were cross-validation statistics (mean = 80.9%, range = 75.1–85.8%). We also developed a spatially explicit model to quantify the relative density of breeding birds across each Greater Sage-Grouse management zone. The models demonstrate distinct clustering of relative abundance of sage-grouse populations across all management zones. On average, approximately half of the breeding population is predicted to be within 10% of the occupied range. We also found that 80% of sage-grouse populations were contained in 25–34% of the occupied range within each management zone. Our rangewide population and habitat models account for regional variation in habitat selection and the relative densities of birds, and thus, they can serve as a consistent and common currency to assess how sage-grouse habitat and populations overlap with conservation actions or threats over the entire sage-grouse range. We also quantified differences in functional habitat responses and disturbance thresholds across the Western Association of Fish and Wildlife Agencies (WAFWA) management zones using statistical relationships identified during habitat modeling. Even for a species as specialized as Greater Sage-Grouse, our results show that ecological context matters in both the strength of habitat selection (i.e., functional response curves) and response to disturbance.

Ecosphere↗

USGS2013

Fall survival of American woodcock in the western Great Lakes Region

We estimated fall (10 Sep–8 Nov) survival rates, cause-specific mortality rates, and determined the magnitude and sources of mortality of 1,035 radio-marked American woodcock ( Scolopax minor ) in Michigan, Minnesota, and Wisconsin during 2001–2004. In all 3 states, we radio-marked woodcock on paired study areas; 1 of which was open to hunting and expected to receive moderate to high hunter use and the other of which was either closed to hunting (Michigan and Minnesota) or was relatively inaccessible to hunters (Wisconsin). We used Program MARK to estimate fall survival rates, to evaluate a set of candidate models to examine the effects of hunting and several covariates (sex, age, year, state) on survival, and to examine the relationship between survival rates and kill rates due to hunting. Hunting accounted for 70% of the 86 woodcock deaths in the hunted areas, followed by predation (20%) and various other sources of mortality (10%). Woodcock deaths that occurred in the non-hunted and lightly hunted areas ( n  = 50) were caused by predators (46%), hunting (32%), and various other sources (22%). Based on small-sample corrected Akaike's Information Criterion values, variation in fall survival of woodcock was best explained by treatment (i.e., hunted vs. non-hunted), year, and period (pre-hunting season intervals vs. hunting season intervals). The average fall survival estimate from our best model for woodcock in the non-hunted areas (0.893, 95% CI = 0.864–0.923) was greater than the average for the hunted areas (0.820, 95% CI = 0.786–0.854 [this estimate includes data from the lightly hunted area in Wisconsin]), and the average treatment effect (i.e., greater survival rates in non-hunted areas) was 0.074 (95% CI = 0.018–0.129). The kill rate due to hunting was 0.120 (95% CI = 0.090–0.151) when data were pooled among states and years. We detected a negative relationship between hunting kill rates and survival in our hunted areas, which suggests that hunting mortality was at least partially additive during fall. Our results illustrate the influence of hunting relative to other sources of mortality in Michigan, Minnesota, and Wisconsin, and indicate that managers may be able to influence fall survival rates by manipulating hunting regulations or access on public land.

Michigan, Minnesota, Wisconsin↗