Geology topics

Richard D. Inman

Publications and source records attributed to Richard D. Inman.

At least 19 recordsLinked to original sources

USGS2026

Future aquatic invaders of the Northeast U.S.: How climate change, human vectors, and natural history could bring southern and western species north

As environmental conditions change, land managers are increasingly concerned about the potential for new aquatic invasive species to move into their jurisdictions. Because managers may have limited resources, detecting invasive species early is important as prevention is more effective and less costly than ongoing mitigation of established populations. Tools built to assist early detection efforts often use information on pathways of spread (how species move through a landscape) and maps of suitability (where habitat allows a species to live and reproduce). While each is useful, information on pathways or suitability alone provides only a part of the story of invasion risk. To better anticipate the risk of invasive species expanding their ranges into the Northeast U.S., there is a need to improve the way we combine and use pathways and suitability information, especially across large areas (e.g., states, regions). To fill this need, we took a new approach that combines estimates of current and future suitability with a diverse variety of pathways that gives us invasion risk scores for more than 100 freshwater invaders (fishes, plants, and invertebrates) across the Northeast U.S. In this report, we provide an overview of our methodology, results, and a description of the ongoing work to make the data publicly available. This work can be used to aid early detection efforts and associated management activities at state and local levels, including the identification of invasion risk hotspots and ranking of individual species risk to help anticipate and prevent invader establishment.

northeast United States↗

USGS2026

Evaluating evidence of changing regional occupancy of four bat species in response to forest management practices

Coordinated, regional strategies to guide effective management and conservation of forests can be used to balance conservation with management for other objectives such as timber, scenic viewsheds, and fire. A key part of these regional strategies is incorporating knowledge of how management actions may affect certain species, especially those that are sensitive or are of concern. However, knowledge of how management actions may affect species is inferred from studies conducted across small areas where the species’ behavior and forest conditions are easily assessed. Here, we examine how occupancy of four bat species responds to forest management across the eastern United States at regional scales. We used range-wide capture and stationary acoustic surveys from the North American Bat Monitoring Program from 2010 to 2020 to estimate yearly summer occupancy for four bat species of conservation concern identified in the U.S. Department of Agriculture Forest Service (USFS) Southern and Eastern Regions Bat Conservation Strategy: little brown bat ( Myotis lucifugus ), northern long-eared bat ( Myotis septentrionalis ), Indiana bat ( Myotis sodalis ), and tricolored bat ( Perimyotis subflavus ), and assessed the degree to which occupancy of each species changed after different vegetation management actions were implemented on USFS lands. We identified 78 different management actions that were hypothesized to influence summer bat occupancy at two spatial scales (5-km and 10-km) across the eastern United States from the Forest Service Activity Tracking System and grouped these management actions into four vegetation management types: clear-cutting, fire, thinning, and ground vegetation management. To evaluate potential effects of these vegetation management types on bat occupancy, we created a yearly management metric representing the average number of years that had passed since any one of the included management actions in each management type had been implemented in each 5-km or 10-km grid cell, weighted by the proportion of the grid cell covered by the management treatment history. We chose these metrics to ask if more management or management done recently had a larger effect on bat occupancy than less management or management done long-ago. We then fit Bayesian hierarchical multi-scale occupancy models for each species to assess how occupancy changed in response to the amount and time since implementation of each vegetation management type. Using the estimated relationships between the yearly metrics of management and bat occupancy, we created predictions for how bat occupancy responded at 1- and 5- years after implementation. We found substantial differences in the response of the four species to the four vegetation management types. Ground vegetation management provided the greatest increase in expected occupancy at 1 year after implementation for little brown bat, long-eared bat, and tricolored bat, while fire provided the greatest increase in expected occupancy for Indiana bat. Thinning provided increases for all species at 1 year after implementation, but even greater increases at 5 years after implementation. Clear-cutting, on the other hand, tended to result in decreased occupancy at both 1- and 5-years after implementation for each species and had the greatest effect on tricolored bat at 1 year after implementation. Clear evidence for how management types like these may be affecting bat populations can be used at regional scales to help private and public forest managers achieve their strategic goals.

eastern United States↗

USGS2025

Multi-scale predictors of Northern Long-eared Bat (Myotis septentrionalis) occupancy in the United States

Historically, Myotis septentrionalis (Northern Long eared Bat) was among the most common forest-interior species in North America. Largely due to high mortality from white-nose syndrome, this species has experienced severe population declines across its range. To create an updated species distribution map representing summer occupancy probabilities from 2017 to 2022, we integrated stationary acoustic data with live-capture data from the database of the North American Bat Monitoring Program into a multi-scale, multi-method occupancy modeling framework. Our results provide data-driven predictions with quantified uncertainty for summer occupancy probabilities for Northern Long-eared Bats at 2 spatial scales across the range of the species, while also accounting for inherent observation biases (e.g., imperfect detection).

Journal of North American Bat Research↗

USGS2025

Summer roost site suitability analyses for 4 special status bat species in the Eastern United States

Data describing habitat suitability are crucial for implementing effective conservation planning but are often lacking at regional and continental scales. We address this gap for 4 bat species that are listed, proposed for listing, or under Endangered Species Act listing review by highlighting a framework for estimating summer roost suitability with a presence-background approach to aid development of conservation policy and management plans. The 4 species of concern are the Little Brown Bat ( Myotis lucifugus ), the Northern Long-eared Bat ( M. septentrionalis ), the Indiana Bat ( M. sodalis ), and the Tricolored Bat ( Perimyotis subflavus ). Our estimates of summer roost suitability were developed for the eastern United States at a fine spatial resolution (250 m pixels) suitable for conservation planning across multiple scales. Summer roost habitat suitability was higher in areas with higher tree canopy cover for each of these species, though subtle differences were observed between the species that often use buildings (e.g., Little Brown Bat), tree crevices, cavities, and elements of dead or dying trees (e.g., Northern Long-eared Bat and Indiana Bat), and foliage (e.g., Tricolored Bat). To this end, roost suitability was not identical among species, and each showed subtly different relationships to the environmental covariates considered here. We also use a novel approach, gradient surface metrics, to quantify differences in the spatial pattern of summer roost suitability among the 4 species and found that tricolored bats and northern long-eared bats showed the most homogeneous and spatially smooth habitat suitability surfaces. Estimates of summer roost suitability developed here were also used to identify areas of good summer habitat where our models showed the least uncertainty that may be beneficial for targeted conservation, such as limiting disturbance to potential roost habitat. We also identified areas where additional data would benefit future summer roost modeling efforts. This work provides a first step toward developing multistate inventories of summer roost habitat suitable for implementing effective conservation planning at multiple scales.

Journal of Mammalogy↗

USGS2025

Greater sage-grouse seasonal habitat associations: A review and considerations for interpretation and management applications

Habitat features needed by wildlife can change in composition throughout the year, particularly in temperate ecosystems, leading to distinct seasonal spatial-use patterns. Studies of species-habitat associations therefore often focus on understanding relationships within discrete seasonal periods with common goals of prediction (e.g., habitat mapping) and inference (e.g., interpreting model coefficients). Across the range of the greater sage-grouse ( Centrocercus urophasianus ) of western North America, the increasing use of high-frequency tracking devices has led to a surge in habitat association studies covering multiple temporal periods and spatial extents. We reviewed the literature for seasonal habitat association studies corresponding to the second and third orders of selection (Johnson 1980). Our objectives were to summarize the methodological approaches used to estimate habitat associations to aid understanding in cross-study comparisons and identify common habitat features reported as selected or avoided within different seasonal periods. We reviewed 71 second- and third-order studies published from 2007–2023 that evaluated covariates collected in a geographic information system (GIS) and modeled probability of selection or intensity of use. The most common study design evaluated a single level of selection (third-order) and was multi-scale (i.e., covariates were measured at varying spatial grains). The most common model approach estimated habitat associations using resource selection functions (RSFs) fit with logistic regression. Studies mostly focused on the breeding periods and winter, but all seasons throughout the annual cycle were covered. There was clear support for selection of sagebrush and avoidance of trees and rugged terrain across seasons, and strong selection of mesic conditions in summer. However, habitat associations for most covariates were mixed, with proportionally equivalent selection and avoidance reported, even within the same seasons. Different factors hampered cross-study comparisons, including variation in study design, but additional contributors likely included important context-dependent habitat associations, such as functional responses to changing habitat availability. We suggest collaborative studies leveraging multiple datasets can help improve seasonal habitat inference by removing the effects of variable study designs.

Journal of Wildlife Management↗

USGS2024

Remote sensing for monitoring mine lands and recovery efforts

Under the Bipartisan Infrastructure Law Ecosystem Restoration Program, the U.S. Department of the Interior has invested in assessing and recovering degraded ecosystems to promote healthy human communities and wildlife habitats. One priority established by the program is the need to address degraded ecosystems associated with mine lands, including active, inactive, and abandoned mines. Mine lands occur in every State of the United States and present a range of environmental hazards and safety risks to human communities and wildlife habitats. However, limited information compiled across the United States exists on the whereabouts of mining activities and their potential environmental effects on landscapes. Remote sensing is the process of acquiring information about the landscape from ground platforms, aircraft, or satellites to assess surface characteristics such as topography, vegetation, and soil properties and can therefore provide important cost-effective methods for identifying mining sites and assessing their environmental effects. Based on a literature review of remote sensing applications that assessed land health conditions and monitoring of mine lands, this report highlights important approaches, capabilities, considerations, and case studies using a breadth of techniques. The report identifies considerations for setting appropriate vegetation recovery targets and demonstrates how remote sensing can inform the prioritization, recovery design, and long-term assessments of recovery to help support decision makers and land managers. Applications of remote sensing to mine recovery will be most effective when recovery targets are clearly defined and quantifiable from data collected before mining activity, per the Surface Mining Control and Reclamation Act of 1977 and similar laws. Additionally, the collected data would need to be accessible and maintained in databases for baseline references used to establish these recovery targets.

continental United States↗

USGS2024

Identifying transportation data and system needs for a Federal lands transportation data platform

Executive Summary Modern transportation and land-use planning efforts include information from many sources to address topics such as safety, efficiency, commercial, and social needs. This wide breadth of topics provides opportunities for collaboration and development of common tools for diverse users. In many cases, different information systems provide the spatial data and geographic content necessary for transportation and land-use planners to consider multiple lines of evidence. The Federal Highway Administration Office of Federal Lands Highway (FLH) and Federal Land Management Agency partners use detailed spatial and quantitative data to inform transportation decisions. However, logistic challenges to data sharing exist because data are often managed by separate agencies; data-exchange frameworks and interagency data agreements are insufficient; and consistency from aggregated data requires maintenance, coordination, and supporting infrastructure. The FLH and U.S. Geological Survey collaboratively examined (1) use and availability of spatial data for transportation planning and (2) a possible mechanism to use more shared and consistent data in a common planning environment. The goals of this collaborative effort were to describe data needs from the perspective of planners and to identify opportunities for shared data resources. Results presented here focus on two workshops and a subsequent investigation of data and tools available from partner agencies. The objectives of this report are to (1) describe information used in transportation planning with geographic data; (2) identify spatially explicit data that inform transportation plans and could be shared among all partners; and (3) describe current platforms, planning and administrative opportunities, and potential barriers to developing an integrated planning tool. Key information and data needs were identified in three major classes: system, user, and influential factors. System data are parts of the transportation network and information about the condition of individual segments and the network. User data provide details about the function of the system and insights into potential needs; for example, user trips between source and destinations inform road and network demands that can lead to congestion and safety issues (in the future, user data might also include scenarios and projections based on land-use plans). Influential data represent social and environmental factors that influence transit demands and network conditions. These factors could be popular locations or seasonal events that influence demand and congestion; wildlife habitat or migration intersections that affect safety and management priorities; or geologic features that influence hazards, maintenance, and safety. Responses described here provide specific information for web-tool design and give a framework for interagency communication and cooperation to address specific information needs for integrated planning. Existing web-mapping and web-services, and the data that inform them, are also described. Commonly, these data are created and published by one agency, and the core users are outside of that agency; for example, threatened species distributions are published by the U.S. Fish and Wildlife Service for consideration by planners in advance of National Environmental Policy Act (42 U.S.C. 4321 et seq.) evaluation. This report is provided to inform FLH leaders and Federal Land Management Agency partners by articulating user needs and requirements for integrated planning tool(s). Programmers creating a secure web-based data-sharing platform (with data-viewing, -analysis and -download functions) can use the information presented here to organize data and user interfaces. This integrated perspective can help FLH and Federal Land Management Agency partners develop transportation networks that better serve the needs of people in local communities and across States and the Nation.

Open-File Report↗

USGS2024

Estimating traffic volume and road age in Wyoming to inform resource management planning: An application with wildlife-vehicle collisions

Road networks and their associated vehicular traffic disturb many terrestrial systems, but inventories of roads used to assess these effects often focus on the ‘where’ (e.g., local road type and density) and neglect the ‘when’ (e.g., temporal disturbance) or ‘how much’ (e.g., traffic volume disturbance). We developed annual estimates of the ‘when’ (road age) and ‘how much’ (vehicular traffic volume) across 148,172 km of highways, arterials, collectors, local, and gravel/graded roads within the state of Wyoming for the years 1986 to 2020 to provide a comprehensive dataset for future ecological investigations. We leveraged a suite of ancillary data on surface disturbances (e.g., oil & gas drilling operations, wind turbines, and open pit mines) with known establishment dates and combined them using graph theory and centrality metrics to estimate the age of each road. We then predicted traffic volume obtained from the Wyoming Department of Transportation for each year across Wyoming using a machine learning method, XGBoost, and a separate set of spatial covariates hypothesized to explain traffic patterns across large regions. We found that 132,476 km of these roads likely existed before 1986, but that 16,693 km (10.7 %) of roads have been built since 1986. Overall, our estimates of road age were 89 % accurate when assessed on a subset of 1,330 roads with high-resolution aerial imagery. Mean absolute error for predicting traffic volume ranged from 35.2 to 77.9 annual average daily traffic (aadt) for trucks and 269.2 to 516.7 aadt for all-vehicles across the 35 years. We found that mean traffic volume across the state increased by 23 % for both truck-only traffic and all vehicular traffic from 1986 to 2020. However, changes in traffic volume have varied substantially across the state (e.g., 100 % increases in volume in some areas, while other areas experienced declines of up to 1,786 %). We also illustrate a novel application of these data by predicting rates of reported wildlife-vehicle collisions (WVCs) along a subset of roads. We found evidence of a non-linear relationship that supported a threshold hypothesis for WVCs, wherein increases in traffic volume equate to increases in WVCs up to a threshold, above which increases in traffic volume result in declines in WVCs. The data provided here will enable better-informed studies of road ecology to address how roads may affect wildlife populations and key ecosystems across Wyoming.

Wyoming↗

USGS2022

Dispersal limitations increase vulnerability under climate change for reptiles and amphibians in the southwestern United States

Species conservation plans frequently rely on information that spans political and administrative boundaries, especially when predictions are needed of future habitat under climate change; however, most species conservation plans and their requisite predictions of future habitat are often limited in geographical scope. Moreover, dispersal constraints for species of concern are not often incorporated into distribution models, which can result in overly optimistic predictions of future habitat. We used a standard modeling approach across a suite of 23 taxa of amphibians and reptiles in the North American deserts (560,024 km 2 across 13 ecoregions) to assess impacts of climate change on habitat and combined landscape population dispersal simulations with species distribution modeling to reduce the risk of predicting future habitat in areas that are not available to species given their dispersal abilities. We used 3 general circulation models and 2 representative concentration pathways (RCPs) to represent multiple scenarios of future habitat potential and assess which study species may be most vulnerable to changes forecasted under each climate scenario. Amphibians were the most vulnerable taxa, but the most vulnerable species tended to be those with the lowest dispersal ability rather than those with the most specialized niches. Under the most optimistic climate scenario considered (RCP 2.6; a stringent scenario requiring declining emissions from 2020 to near zero emissions by 2100), 76% of the study area may experience a loss of >20% of the species examined, while up to 87% of the species currently present may be lost in some areas under the most pessimistic climate scenario (RCP 8.5; a scenario wherein greenhouse gases continue to increase through 2100 based on trajectories from the mid-century). Most areas with high losses were concentrated in the Arizona and New Mexico Plateau ecoregion, the Edwards Plateau in Texas, and the Southwestern Tablelands in New Mexico and Texas, USA. Under the most pessimistic climate scenario, all species are predicted to lose some existing habitat, with an average of 34% loss of extant habitat across all species. Even under the most optimistic scenario, we detected an average loss of 24% of extant habitat across all species, suggesting that changing climates may influence the ranges of reptiles and amphibians in the Southwest.

Arizona, California, Nevada, New Mexico, Texas, Ut↗

USGS2022

Demographic and environmental correlates of home ranges and long-distance movements of Mohave ground squirrels

Space use by mammals can differ among age-classes, sexes, or seasons, and these processes are recognized as adaptive behavioral strategies. Semi-fossorial ground squirrels, in particular, have shown age- and sex-specific patterns in their aboveground movement behaviors. We studied space use of Mohave ground squirrels ( Xerospermophilus mohavensis ) at the Freeman Gulch study site in the central region of their range in the Mojave Desert, California. We documented the timing of their full annual cycle, investigated correlates of size of home ranges of adults and distance of long-distance movements by juveniles, and evaluated whether juvenile body masses and movements were related to interannual climatic variation. Adult males emerged from burrows and entered hibernation sooner than did adult females. Home ranges were larger for males ( ⁠ x ¯ "> x ¯ x¯ ± SD = 0.50 ± 0.40 km 2 ) than females (0.05 ± 0.03 km 2 ), especially during the mating season. Maximum distances moved by juveniles did not differ significantly between males (1.6 ± 2.3 km) and females (1.0 ± 1.8 km), and both sexes were equally likely to travel long distances from natal sites. The longest-distance movement we documented was 7.7 km by a juvenile male. Juveniles born in a year of low vegetation productivity gained significantly less mass (97 ± 7 g) and traveled shorter distances than those born in a year of higher vegetation productivity (177 ± 20 g). Our findings were similar to those documented in the Coso Range study site in the northern region of the geographic range of the species, indicating that movement patterns are similar range-wide. Mohave ground squirrels responded strongly to climate variation, and global climate change will likely result in a northward shift in suitable habitats. Informed conservation planning, therefore, will be essential for this rare, declining species.

California↗

USGS2021

Comparing sample bias correction methods for species distribution modeling using virtual species

A key assumption in species distribution modeling (SDM) with presence‐background (PB) methods is that sampling of occurrence localities is unbiased and that any sampling bias is proportional to the background distribution of environmental covariates. This assumption is rarely met when SDM practitioners rely on federated museum records from natural history collections for geo‐located occurrences due to inherent sampling bias found in these collections. We use a simulation approach to explore the effectiveness of three methods developed to account for sampling bias in SDM with PB frameworks. Two of the methods rely on careful filtering of observation data—geographic thinning (G‐Filter) and environmental thinning (E‐Filter)—while a third, FactorBiasOut, creates selection weights for background data to bias locations toward areas where the observation dataset was sampled. While these methods have been assessed previously, evaluation has emphasized spatial predictions of habitat potential. Here, we dig deeper into the effectiveness of these methods by exploring how sampling bias not only affects predictions of habitat potential, but also our understanding of niche characteristics such as which explanatory variables and response curves best represent species–environment relationships. We simulate 100 virtual species ranging from generalist to specialist in their habitat preferences and introduce geographic and environmental bias at three intensity levels to measure the effectiveness of each correction method to (1) predict true probability of occurrence across a study area, (2) recover true species–environment relationships, and (3) identify true explanatory variables. We find that the FactorBiasOut most often showed the greatest improvement in recreating known distributions but did no better at correctly identifying environmental covariates or recreating species–environment relationships than G‐Filter or E‐Filter methods. Narrow niche species are most problematic for biased calibration datasets, such that correction methods can, in some cases, make predictions worse.

Ecosphere↗

USGS2020

Quantifying development to inform management of Mojave and Sonoran desert tortoise habitat in the American southwest

Two tortoise species native to the American southwest have experienced significant habitat loss from development and are vulnerable to ongoing threats associated with continued development. Mojave desert tortoises Gopherus agassizii are listed as threatened under the US Endangered Species Act, and Sonoran desert tortoises G. morafkai are protected in Arizona (USA) and Mexico. Substantial habitat for both species occurs on multiple-use public lands, where development associated with traditional and renewable energy production, recreation, and other activities is likely to continue. Our goal was to quantify development to inform and evaluate actions implemented to protect and manage desert tortoise habitat. We quantified a landscape-level index of development across the Mojave and Sonoran desert tortoise ranges using models of potential habitat for each species (152485 total observations). We used 13 years of Mojave desert tortoise monitoring data (4732 observations) to inform the levels and spatial scales at which tortoises may be affected by development. Most (66–70%) desert tortoise habitat has some development within 1 km. Development levels on desert tortoise habitat are lower inside versus outside areas protected by actions at national, state, and local levels, suggesting that protection efforts may be having the desired effects and providing a needed baseline for future effectiveness evaluations. Of the relatively undeveloped desert tortoise habitat, 43% (74030 km2) occurs outside of existing protections. These lands are managed by multiple federal, state, and local entities and private landowners, and may provide opportunities for future land acquisition or protection, including as mitigation for energy development on public lands.

Arizona, California, Nevada, Utah↗

USGS2019

Spatially consistent high-resolution land surface temperature mosaics for thermophysical mapping of the Mojave Desert

Daytime and nighttime thermal infrared observations acquired by the ASTER and MODIS instruments onboard the NASA Terra spacecraft have produced a dataset that can be used to map thermophysical properties across large regions, which have implications on surface processes, thermal environments and habitat suitability for desert species. ASTER scenes acquired between 2004 and 2012 are combined using new mosaicking and data-fusion techniques to produce a map of daytime and nighttime land surface temperature with coverage exclusive of the effects of clouds and weather. These data are combined with Landsat 7 visible imagery to generate a consistent map of apparent thermal inertia (ATI), which is related to the presence of exposed bedrock, rocks, fine-grained sediments and water on the surface. The resulting datasets are compared to known geomorphic units and surface types to generate an interpreted mechanical composition map of the entire Mojave Desert at 100 m per pixel that is most sensitive to large clast size distinctions in grain size distribution.

ARizone, California, Nevada, Utah↗

USGS2019

Local niche differences predict genotype associations in sister taxa of desert tortoise

Aims To investigate spatial congruence between ecological niches and genotype in two allopatric species of desert tortoise that are species of conservation concern. Location Mojave and Sonoran Desert ecoregions; California, Nevada, Arizona, Utah, USA. Methods We compare ecological niches of Gopherus agassizii and Gopherus morafkai using species distribution modelling (SDM) and then calibrate a pooled‐taxa distribution model to explore local differences in species–environment relationships based on the spatial residuals of the pooled‐taxa model. We use multiscale geographically weighted regression (MGWR) applied to those residuals to estimate local species–environment relationships that can vary across the landscape. We identify multivariate clusters in these local species–environment relationships and compare them against models of (a) a geographically based taxonomic designation for two sister species and (b) an environmental ecoregion designation, with respect to their ability to predict a genotype association index for these two species. Results We find non‐identical niches for these species, with differences that span physiographic and vegetation niche dimensions. We find evidence for two distinct clusters of local species–environment relationships that when mapped, predict an index of genotype association for the two sister taxa better than did either the geographically based taxonomic designation or an environmental ecoregion designation. Main conclusions Exploring local species–environment relationships by coupling SDM and MGWR can benefit studies of biogeography and conservation. We find that niche separation in habitat selection conforms to genotypic differences between sister taxa of tortoise in a recent secondary contact zone. This result may inform decision making by agencies with regulatory or land management authority for the two sister taxa addressed here.

Arizona, California, Nevada, Utah↗

USGS2018

Spatial sampling bias in the Neotoma paleoecological archives affects species paleo-distribution models

The ability to infer paleo-distributions with limited knowledge of absence makes species distribution modeling (SDM) a useful tool for exploring paleobiogeographic questions. Spatial sampling bias is a known issue when modeling extant species. Here we quantify the spatial sampling bias in a North American packrat midden archive and explore its impact on estimating paleo-distributions. We test whether (1) spatial sampling bias inherent in this macrofossil record can influence estimates of paleo-distributions, (2) this bias can alter the ability to measure shifts in distributions and climatic niche breadth from the Northgrippian subdivision of the Holocene (8.3 ka – 4.2 ka) to present day (1950–2000 yr), and (3) bias correction methods can improve estimates of paleo-distributions and analyses of range shifts and niche breadth. We estimate spatial sampling bias for the mid-Holocene period with a three-stage statistical model, each representing a hypothesized source of bias: fossil site availability, preservation and accessibility. This approach enables the use of SDM to evaluate three separate paleo-distributions calibrated on the packrat midden archive: those without bias correction (σ-naïve), those created with a standard method (σ-standard), and those created with a novel alternative (σ-modeled) incorporating the three-stage model of bias. We find that paleo-distributions modeled for the mid-Holocene without bias correction (σ-naïve) provided poor estimates of hindcast paleo-distributions, and that the σ-modeled correction method improved paleo-distributions for our six species with, on average, 50% higher overlap to hindcast distributions than σ-naïve paleo-distributions (σ-standard results fell between σ-naïve and σ-modeled).

Journal of Quaternary Science Reviews↗

USGS2017

Spatial demographic models to inform conservation planning of golden eagles in renewable energy landscapes

Spatial demographic models can help guide monitoring and management activities targeting at-risk species, even in cases where baseline data are lacking. Here, we provide an example of how site-specific changes in land use and anthropogenic stressors can be incorporated into a spatial demographic model to investigate effects on population dynamics of Golden Eagles ( Aquila chrysaetos ). Our study focused on a population of Golden Eagles exposed to risks associated with rapid increases in renewable energy development in southern California, U.S.A. We developed a spatially explicit, individual-based simulation model that integrated empirical data on demography of Golden Eagles with spatial data on the arrangement of nesting habitats, prey resources, and planned renewable energy development sites. Our model permitted simulated eagles of different stage-classes to disperse, establish home ranges, acquire prey resources, prospect for breeding sites, and reproduce. The distribution of nesting habitats, prey resources, and threats within each individual's home range influenced movement, reproduction, and survival. We used our model to explore potential effects of alternative disturbance scenarios, and proposed conservation strategies, on the future distribution and abundance of Golden Eagles in the study region. Results from our simulations suggest that probable increases in mortality associated with renewable energy infrastructure (e.g., collisions with wind turbines and vehicles, electrocution on power poles) could have negative consequences for population trajectories, but that site-specific conservation actions could reduce the magnitude of negative effects. Our study demonstrates the use of a flexible and expandable modeling framework to incorporate spatially dependent processes when determining relative effects of proposed management options to Golden Eagles and their habitats.

California↗

USGS2017

An assessment of food habits, prey availability, and nesting success of golden eagles within the Desert Renewable Energy Conservation Plan Area

Within the Desert Renewable Energy Conservation Plan area, which encompasses California’s Mojave Desert, development and operation of renewable energy facilities has the potential to impact golden eagle (Aquila chrysaetos) populations through loss of habitat and prey base. Developing an effective conservation strategy that aims to mitigate for such operations is necessary to lessen these impacts; however, this requires site-specific knowledge of how golden eagle productivity is influenced by variability in prey abundance. In this study, researchers studied the food habits, prey availability, and nesting success of golden eagles in the conservation plan area over two seasons (2014 and 2015). In addition, as part of a collaborative research effort funded by the U.S. Fish and Wildlife Service, the same research was conducted within the adjoining Mojave Desert ecoregion of southern Nevada; these research results are presented as well. To examine prey availability, researchers conducted nocturnal spotlight surveys along 140 fivekilometer transects. Diet selection was determined using motion-activated trail cameras and by collecting prey remains at 20 active nests. Nesting success was determined by conducting occupancy and reproductive assessment surveys within 50 historic breeding areas and evaluating camera data collected at active nests. Preliminary results indicate high spatial variability in prey species abundance and selection. Black-tailed jackrabbits (Lepus californicus) represented over half the available prey, as well as nearly half the prey species identified by nest cameras. Overall, nesting success was 47 percent. Productivity was 0.67 young per occupied breeding area, and mean brood size was 1.4 young per successful nest. No evidence was found indicating that camera installation caused nest failures or influenced eagle behavior for any sites. Results from this project are incorporated into a spatial demographic model linking prey availability and abundance to golden eagle productivity across a changing Mojave Desert landscape.

Report↗

USGS2016

Multi-scale connectivity and graph theory highlight critical areas for conservation under climate change

Conservation planning and biodiversity management require information on landscape connectivity across a range of spatial scales from individual home ranges to large regions. Reduction in landscape connectivity due changes in land-use or development is expected to act synergistically with alterations to habitat mosaic configuration arising from climate change. We illustrate a multi-scale connectivity framework to aid habitat conservation prioritization in the context of changing land use and climate. Our approach, which builds upon the strengths of multiple landscape connectivity methods including graph theory, circuit theory and least-cost path analysis, is here applied to the conservation planning requirements of the Mohave ground squirrel. The distribution of this California threatened species, as for numerous other desert species, overlaps with the proposed placement of several utility-scale renewable energy developments in the American Southwest. Our approach uses information derived at three spatial scales to forecast potential changes in habitat connectivity under various scenarios of energy development and climate change. By disentangling the potential effects of habitat loss and fragmentation across multiple scales, we identify priority conservation areas for both core habitat and critical corridor or stepping stone habitats. This approach is a first step toward applying graph theory to analyze habitat connectivity for species with continuously-distributed habitat, and should be applicable across a broad range of taxa.

Ecological Applications↗