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USGS2011

Social network models predict movement and connectivity in ecological landscapes

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

Proceedings of the National Academy of Sciences of↗

USGS2018

Wildland–urban interface residents’ relationships with wildfire: Variation within and across communities

Social science offers rich descriptions of relationships between wildland–urban interface residents and wildfire, but syntheses across different contexts might gloss over important differences. We investigate the potential extent of such differences using data collected consistently in sixty-eight Colorado communities and hierarchical modeling. We find substantial variation across responses for all considered measures, much of which occurs at the community-level. Our results show that many aspects of relationships with wildfire meaningfully differ both within and across communities. Our analysis suggests that some wildfire social science results will be relatively consistent across communities, whereas others will not, and this study contributes evidence to broader efforts for understanding which is which. As such, it provides important guidance for transferring the lessons of wildfire social science studies across contexts, and for practitioners who seek to understand the breadth of viewpoints within the communities with which they work.

Colorado↗

USGS2010

The influence of nutrients and physical habitat in regulating algal biomass in agricultural streams

This study examined the relative influence of nutrients (nitrogen and phosphorus) and habitat on algal biomass in five agricultural regions of the United States. Sites were selected to capture a range of nutrient conditions, with 136 sites distributed over five study areas. Samples were collected in either 2003 or 2004, and analyzed for nutrients (nitrogen and phosphorous) and algal biomass (chlorophyll a ). Chlorophyll a was measured in three types of samples, fine-grained benthic material (CHL FG ), coarse-grained stable substrate as in rock or wood (CHL CG ), and water column (CHL S ). Stream and riparian habitat were characterized at each site. TP ranged from 0.004–2.69 mg/l and TN from 0.15–21.5 mg/l, with TN concentrations highest in Nebraska and Indiana streams and TP highest in Nebraska. Benthic algal biomass ranged from 0.47–615 mg/m 2 , with higher values generally associated with coarse-grained substrate. Seston chlorophyll ranged from 0.2–73.1 μg/l, with highest concentrations in Nebraska. Regression models were developed to predict algal biomass as a function of TP and/or TN. Seven models were statistically significant, six for TP and one for TN; r 2 values ranged from 0.03 to 0.44. No significant regression models could be developed for the two study areas in the Midwest. Model performance increased when stream habitat variables were incorporated, with 12 significant models and an increase in the r 2 values (0.16–0.54). Water temperature and percent riparian canopy cover were the most important physical variables in the models. While models that predict algal chlorophyll a as a function of nutrients can be useful, model strength is commonly low due to the overriding influence of stream habitat. Results from our study are presented in context of a nutrient-algal biomass conceptual model.

Environmental Management↗

USGS2021

Contrasting patterns of demography and population viability among gopher tortoise (Gopherus polyphemus) populations at the species’ northern range edge

Population viability analyses are useful tools to predict abundance and extinction risk for imperiled species. In southeastern North America, the federally threatened gopher tortoise ( Gopherus polyphemus ) is a keystone species in the diverse and imperiled longleaf pine ( Pinus palustris ) ecosystem, and researchers have suggested that tortoise populations are declining and characterized by high extinction risk. We report results from a 30-year demographic study of gopher tortoises in southern Alabama (1991–2020), where 3 populations have been stable and 3 others have declined. To better understand the demographic vital rates associated with stable and declining tortoise populations, we used a multi-state hierarchical mark-recapture model to estimate sex- and stage-specific patterns of demographic vital rates at each population. We then built a predictive population model to project population dynamics and evaluate extinction risk in a population viability context. Population structure did not change significantly in stable populations, but juveniles became less abundant in declining populations over 30 years. Apparent survival varied by age, sex, and site; adults had higher survival than juveniles, but female survival was substantially lower in declining populations than in stable ones. Using simulations, we predicted that stable populations with high female survival would persist over the next 100 years but sites with lower female survival would decline, become male-biased, and be at high risk of extirpation. Stable populations were most sensitive to changes in apparent survival of adult females. Because local populations varied greatly in vital rates, our analysis improves upon previous demographic models for northern populations of gopher tortoises by accounting for population-level variation in demographic patterns and, counter to previous model predictions, suggests that small tortoise populations can persist when habitat is managed effectively. © 2021 The Wildlife Society.

Alabama↗

USGS2024

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High-resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double-counted plants) error in counts from high-resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post-wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high-resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting ( p ) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Methods in Ecology and Evolution↗

USGS2016

Interactions of landscape disturbances and climate change dictate ecological pattern and process: spatial modeling of wildfire, insect, and disease dynamics under future climates

Context Interactions among disturbances, climate, and vegetation influence landscape patterns and ecosystem processes. Climate changes, exotic invasions, beetle outbreaks, altered fire regimes, and human activities may interact to produce landscapes that appear and function beyond historical analogs. Objectives We used the mechanistic ecosystem-fire process model FireBGCv2 to model interactions of wildland fire, mountain pine beetle ( Dendroctonus ponderosae ), and white pine blister rust ( Cronartium ribicola ) under current and future climates, across three diverse study areas. Methods We assessed changes in tree basal area as a measure of landscape response over a 300-year simulation period for the Crown of the Continent in north-central Montana, East Fork of the Bitterroot River in western Montana, and Yellowstone Central Plateau in western Wyoming, USA. Results Interacting disturbances reduced overall basal area via increased tree mortality of host species. Wildfire decreased basal area more than beetles or rust, and disturbance interactions modeled under future climate significantly altered landscape basal area as compared with no-disturbance and current climate scenarios. Responses varied among landscapes depending on species composition, sensitivity to fire, and pathogen and beetle suitability and susceptibility. Conclusions Understanding disturbance interactions is critical for managing landscapes because forest responses to wildfires, pathogens, and beetle attacks may offset or exacerbate climate influences, with consequences for wildlife, carbon, and biodiversity.

Landscape Ecology↗

USGS2016

The Pliocene Model Intercomparison Project (PlioMIP) Phase 2: Scientific objectives and experimental design

The Pliocene Model Intercomparison Project (PlioMIP) is a co-ordinated international climate modelling initiative to study and understand climate and environments of the Late Pliocene, as well as their potential relevance in the context of future climate change. PlioMIP examines the consistency of model predictions in simulating Pliocene climate and their ability to reproduce climate signals preserved by geological climate archives. Here we provide a description of the aim and objectives of the next phase of the model intercomparison project (PlioMIP Phase 2), and we present the experimental design and boundary conditions that will be utilized for climate model experiments in Phase 2. Following on from PlioMIP Phase 1, Phase 2 will continue to be a mechanism for sampling structural uncertainty within climate models. However, Phase 1 demonstrated the requirement to better understand boundary condition uncertainties as well as uncertainty in the methodologies used for data–model comparison. Therefore, our strategy for Phase 2 is to utilize state-of-the-art boundary conditions that have emerged over the last 5 years. These include a new palaeogeographic reconstruction, detailing ocean bathymetry and land–ice surface topography. The ice surface topography is built upon the lessons learned from offline ice sheet modelling studies. Land surface cover has been enhanced by recent additions of Pliocene soils and lakes. Atmospheric reconstructions of palaeo-CO 2 are emerging on orbital timescales, and these are also incorporated into PlioMIP Phase 2. New records of surface and sea surface temperature change are being produced that will be more temporally consistent with the boundary conditions and forcings used within models. Finally we have designed a suite of prioritized experiments that tackle issues surrounding the basic understanding of the Pliocene and its relevance in the context of future climate change in a discrete way.

Climate of the Past↗

USGS2024

Accounting for the fraction of carcasses outside the searched area in the estimation of bird and bat fatalities at wind energy facilities

Accurate estimation of bird and bat mortality at wind energy facilities requires accounting for carcasses that lie outside the search plots because they lie beyond the search radius or in areas within the search radius that remain unsearched due to sub-optimal search conditions such as thick vegetation, rough or dangerous ground, water, or restricted access to the land. However, carcass density is not constant around a turbine and the fraction of carcasses within the unsearched area can vary greatly depending on where the area lies relative to the turbine. The density-weighted proportion approach takes into account the changing density of carcasses around turbines to estimate the fraction of carcasses lying in unsearched areas (dwp). It involves tallying the carcasses found in concentric rings centered at the turbine, fitting a curve to the carcass densities in the rings, and dividing the integral of the curve over the area searched by the integral over the total area. Accounting for unsearched area presents special difficulties such as extrapolation beyond the search radius, spatial prediction, and model selection, which are frequently ignored or under-appreciated, potentially resulting in substantial estimation errors. A powerful new R software package (dwp) is available to perform the calculations, given the distances at which carcasses were found from turbines and a map of the searched area used to discern the fraction of the ground searched at each distance. If all ground within a given search radius has been searched, the map is simply the search radius. For more complicated search plots, other kinds of maps may be used: R polygons for plots that can be readily delineated into searched and not-searched areas (for example, searches restricted to access roads and turbine pads), GIS shape files for complicated search patterns (for example, non-uniform vegetation or ground texture resulting in spatially varying search conditions), or raster files for complicated search patterns coupled with carcass spatial distribution that depends on both distance and direction from turbines. This study discusses estimation and interpretation of dwp in the context of several realistic examples; provides guidance for use of the dwp software for doing the analyses; and addresses questions of extrapolation, spatial prediction, and model selection.

Techniques and Methods↗

USGS2013

Quantifying climate change mitigation potential in Great Plains wetlands for three greenhouse gas emission scenarios

We examined opportunities for avoided loss of wetland carbon stocks in the Great Plains of the United States in the context of future agricultural expansion through analysis of land-use land-cover (LULC) change scenarios, baseline carbon datasets and biogeochemical model outputs. A wetland map that classifies wetlands according to carbon pools was created to describe future patterns of carbon loss and potential carbon savings. Wetland avoided loss scenarios, superimposed upon LULC change scenarios, quantified carbon stocks preserved under criteria of carbon densities or land value plus cropland suitability. Up to 3420 km 2 of wetlands may be lost in the region by 2050, mainly due to conversion of herbaceous wetlands in the Temperate Prairies where soil organic carbon (SOC) is highest. SOC loss would be approximately 0.20 ± 0.15 megagrams of carbon per hectare per year (MgC ha −1 yr −1 ), depending upon tillage practices on converted wetlands, and total ecosystem carbon loss in woody wetlands would be approximately 0.81 ± 0.41 MgC ha −1 yr −1 , based on biogeochemical model results. Among wetlands vulnerable to conversion, wetlands in the Northern Glaciated Plains and Lake Agassiz Plains ecoregions exhibit very high mean SOC and on average, relatively low land values, potentially creating economically competitive opportunities for avoided carbon loss. This mitigation scenarios approach may be adapted by managers using their own preferred criteria to select sites that best meet their objectives. Results can help prioritize field-based assessments, where site-level investigations of carbon stocks, land value, and consideration of local priorities for climate change mitigation programs are needed.

Great Plains↗

USGS2023

Ediacaran-Ordovician magmatism and REE mineralization in the Wet Mountains, Colorado, USA: Implications for failed continental rifting

Structures associated with Ediacaran-Ordovician alkaline magmatism and the timing of rare earth element (REE) mineralization in the Wet Mountains, CO, were analyzed using field, geophysical, and U-Th-Pb isotope methods to interpret their tectonic setting in the context of previously proposed rift models. The Wet Mountains are known for thorium and REE mineralization associated with failed rift-related, Ediacaran-Ordovician alkaline intrusions and veins. Structural field data indicate that alkaline dikes and mineralized veins are controlled by a system of northwest-striking, high-angle faults and tension fractures formed in a 040°-directed extensional regime. Magnetic and surface expressions of Democrat Creek and McClure Mountain complexes show tectonic elongation toward ∼045°, consistent with NE-directed extension. Magnetic data also suggest the existence of a fourth, previously unrecognized mafic-ultramafic complex of inferred Cambrian age with a similar elongated orientation. Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) 208 Pb/ 232 Th analysis of low-uranium zircon from carbonatite dikes and in situ 206 Pb/ 238 U LA-ICP-MS analysis of monazite in mineralized dikes yielded 465 ± 18 Ma and 489 ± 33 Ma ages, respectively. These ages are consistent with the expected age based on slightly older, cross-cut syenite dikes and the hypothesized Ordovician end to failed rift-related magmatism. The Ediacaran-Ordovician age of alkaline magmatic rocks and the associated northeast-directed extension direction are similar to those of the along-strike, Ediacaran-Cambrian Southern Oklahoma Aulacogen. Therefore, the failed rift system in the Wet Mountains is interpreted to be a northwestern continuation of the Southern Oklahoma Aulacogen with carbonatite magmatism and thorium/REE mineralization representing late intrusive phases.

Colorado↗

USGS2012

Ecological context for the North Pacific Landscape Conservation Cooperative

The North Pacific Landscape Conservation Cooperative (NPLCC) encompasses the temperate coastal rainforest and extends from the coastal mountains to the near-shore from the Kenai Peninsula, Alaska to Bodega Bay, California. The area spans multiple agency, state, and international boundaries over more than 22 degrees of latitude, including a wide range of type and intensity of human land-use activities. Development of NPLCC goals and administrative structures will be facilitated by a shared ecological context for discussing this expansive, diverse, and complex landscape. In support of activities to organize the NPLCC, we provided conceptual models to describe the ecological structure of the NPLCC. Recognizing that the boundaries of LCCs were primarily based on Level 2 of the hierarchical ecoregional classification of Omernik (Comission for Environmental Cooperation 1997), we used nested Level 3 ecoregions to define subregions within the NPLCC. Rather than develop conceptual models for all nine constituent subregions, we opted to consider five groups: Puget-Georgia Basin Lowland and Willamette Valley, Alaska-British Columbia Coast, Alaska-British Columbia Mountains, Klamath-Olympic-Cascade Mountains, and Washington-Oregon-Northern California Coast. At the conclusion of the project, we felt that the close relationship between mountain and coastal areas support combining them to create three major subregions: Alaska-British Columbia coast and mountains, Washington-Oregon-Northern California coast and mountains, and the lowlands of the Georgia Basin and Willamette Valley. The following figures present the Omernik Level 3 ecoregions comprising the NPLCC; how the ecoregions were grouped to create conceptual models; and conceptual models for each group. The five models each consist of a table listing resources, stressors, potential climate change impacts; a landcover map; and a cartoon to summarize the table and evoke the landscape. A final figure summarizes resources, stressors, and climate change impacts that are common across the NPLCC.

Alaska;British Columbia;California;Oregon;Washingt↗

USGS1993

Wetland modeling and information needs at Stillwater National Wildlife Refuge

The marshes in and around Stillwater National Wildlife Refuge (the Refuge) are extremely dynamic; expanding and contracting in size both seasonally, due to runoff and subsequent evapotranspiration, and over longer periods, due to climatic variation. The dynamic nature of these marshes results in a diversity of wetland habitats, which support a variety of migratory birds. To maintain this wetland diversity and control the loss of migratory bird habitat in the Lahontan Valley, the Refuge was established and currently manages a complex of marsh units. However, changes in the hydrology, and changes that will occur as a result of the Fallon Paiute-Shoshone and Truckee-Carson-Pyramid Lake Water Rights Settlement Act (Public Law 101-618, 104 Stat. 3389), greatly affect the Refuge's wetland management capability. In light of these changes, and the legal requirements associated with environmental impact assessments, the Refuge convened a workshop to discuss several aspects of wetland management in the Lahontan Valley. The workshop, described in this report, had three primary objectives: 1. discuss the types and relative proportions of primary wetland habitats that should be provided as described in the settlement act; 2. discuss wetland management models that might be developed to help manage these marshes under hydrologic regimes likely in the future; and 3. discuss future information and monitoring needs, including proposals for valley-wide biodiversity surveys, which would be helpful when considering withdrawn Bureau of Reclamation (BR) lands for possible incorporation into the Refuge. Several presentations at the beginning of the workshop provided a common basis for discussing these objectives. Refuge staff provided background on the history and past management. The Nature Conservatory discussed their role in the settlement act, proposals for valley-wide biodiversity surveys, and results of a literature review for Stillwater Marsh and the Lahontan Valley (Nachlinger 1993). Kay Fowler provided an historical context of changes in vegetation and waterbird use of the marshes based on her ethnography of the local Paiute Indians (Fowler 1993). Finally, Bob Elston discussed a model that predicts archaeological sites based on environmental variables (Raven and Elston 1989). The workshop was organized by staff from the Refuge and facilitated by the authors of this report. Participants included Ron Anglin, Bill Henry, Anne Janik, Cliff Creger, Fred Paveglio, and Mary Jo Elpers of the U.S. Fish and Wildlife Service (the Service); Jeff Baumgartner, Jan Nachlinger, Hope Humphries, and Graham Chisholm of The Nature Conservancy; David Yardas of the Environmental Defense Fund; David Robertson of Robertson Software, Inc.; Norm Saake, Terry Retterer, and Larry Neel of the Nevada Department of Wildlife; Lew Oring and Kay Fowler of the University of Nevada; and Robert Elston of Intermountain Research.

Report↗

USGS2021

A reassessment of Chao2 estimates for population monitoring of grizzly bears in the Greater Yellowstone Ecosystem

The Yellowstone Ecosystem Subcommittee (YES) asked the Interagency Grizzly Bear Study Team (IGBST) to re-assess a technique used in annual population estimation and trend monitoring of grizzly bears in the Greater Yellowstone Ecosystem (GYE). This technique is referred to as the Chao2 approach and estimates the number of females with cubs-of-the-year (hereafter, females with cubs) and, in association with other demographic data, is used by the IGBST to produce annual population estimates. Females with cubs are an easily recognizable population segment, and trends for this reproductive segment of the population are assumed to be representative of trend for the entire population. The overarching objective of the analyses presented in this report was to provide a more accurate representation of the GYE grizzly bear population using the current methodologies in place. Specifically, we addressed two limitations of the current Chao2 approach: 1) underestimation bias associated with a distance criterion used to differentiate annual sightings of females with cubs into unique individuals and 2) limitations of the model-averaging approach to effectively distinguish among potential future population trajectories (decline, stability, and growth). The first issue addressed in this report is the underestimation bias associated with the rule set that Knight et al. (1995) developed to differentiate sightings of females with cubs into unique individuals (i.e., unique family groups). The rule set was originally designed to be conservative by reducing the risk of identifying more females with cubs than actually existed, primarily through use of a distance criterion of 30 km to separate sightings of unique females. This approach resulted in an underestimation bias, and previous research demonstrated that this bias increases with increasing number of females with cubs. Using location data from radio-marked females with cubs, we evaluated alternative distance criteria by simulating scenarios with varying numbers of true females with cubs and sightings. Findings from these analyses demonstrate that bias in estimates of females with cubs can be substantially reduced by changing the 30-km distance criterion in the rule set to 16 km, which produced relatively unbiased estimates. Findings also indicate, however, the importance of adaptability with regard to the distance criteria because of the complex relationships and biases among the various parameters involved in estimation of unique females with cubs. The total number of annual sightings and the true number of females with cubs play particularly important roles. Whereas these analyses remind us that there is no perfect approach to estimating the number of females with cubs from sightings under various scenarios, they provide us with new tools to determine when and how to adapt the monitoring program. The second issue we were tasked to investigate was the potential for improvement of the technique referred to as model-averaging, which serves to smooth relatively high variation in annual estimates. This technique was chosen by YES as the basis for monitoring the Yellowstone grizzly bear population, as described in the 2016 Conservation Strategy. This choice was made in part because the technique has been well documented and population estimates derived from counts of females with cubs are conservative. Using simulations of population trends, we demonstrate why the model-averaging technique currently used cannot distinguish between plausible future trend scenarios. As a suitable alternative to model averaging, we propose the use of generalized additive models (GAMs). Using a suite of simulated trend dynamics relevant to management, we demonstrate GAM performance for tracking trends in females with cubs within the context of the annual monitoring program. We demonstrate the ability to not only document directional changes in population trend but also patterns of stabilization or resiliency after such changes. Furthermore, the proposed monitoring framework provides objective measures useful for early detection of directional changes in trend. The new framework is flexible, allowing retrospective analysis of Chao2-based estimates and future applications to time series of other population metrics, such as vital rates. The aforementioned updates provide us with new tools to determine when and how to adapt the monitoring program. Within the context of current monitoring protocols and effort, and considering the full suite of simulations presented in this report and previous studies, the IGBST plans to incorporate the following changes to the population monitoring protocol: 1) modify the distance criterion, starting with 16 km under current sampling conditions and 2) revise the population monitoring framework using GAMs as the basis for smoothing of annual estimates and detecting trends and changes in trend. Implementation of the 16-km distance criterion combined with use of GAM techniques would affect some of the population metrics (e.g., annual population size and uncertainty, population trend, mortality rates) used to inform management responses. A primary consideration is that the 16-km distance criterion results in total population estimates derived from the Chao2 estimates that are greater than those we have reported in the past. This increase is due to a change in the implementation of the technique and more accurately represents the number of females with cubs in the GYE grizzly bear population. Additionally, interpretation of retrospective trend patterns may change due to the combination of a different distance criterion and enhanced trend monitoring based on the GAM approach we present here. Implementation will require relatively minor changes in the monitoring protocols described in Appendices B and C of the 2016 Conservation Strategy. Finally, we note that the IGBST has ongoing investigations into the merits of an Integrated Population Model (IPM), for which annual Chao2-based estimates are important input data. The IGBST plans to continue those investigations using the 16-km distance criterion to derive Chao2 estimates.

Idaho, Montana, Wyoming↗

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↗

USGS2016

Toward improved simulation of river operations through integration with a hydrologic model

Advanced modeling tools are needed for informed water resources planning and management. Two classes of modeling tools are often used to this end–(1) distributed-parameter hydrologic models for quantifying supply and (2) river-operation models for sorting out demands under rule-based systems such as the prior-appropriation doctrine. Within each of these two broad classes of models, there are many software tools that excel at simulating the processes specific to each discipline, but have historically over-simplified, or at worse completely neglected, aspects of the other. As a result, water managers reliant on river-operation models for administering water resources need improved tools for representing spatially and temporally varying groundwater resources in conjunctive-use systems. A new tool is described that improves the representation of groundwater/surface-water (GW-SW) interaction within a river-operations modeling context and, in so doing, advances evaluation of system-wide hydrologic consequences of new or altered management regimes.

Environmental Modelling and Software↗

USGS2014

Wave-driven sediment mobilization on a storm-controlled continental shelf (Northwest Iberia)

Seafloor sediment mobilization on the inner Northwest Iberian continental shelf is caused largely by ocean surface waves. The temporal and spatial variability in the wave height, wave period, and wave direction has a profound effect on local sediment mobilization, leading to distinct sediment mobilization scenarios. Six grain-size specific sediment mobilization scenarios, representing seasonal average and storm conditions, were simulated with a physics-based numerical model. Model inputs included meteorological and oceanographic data in conjunction with seafloor grain-size and the shelf bathymetric data. The results show distinct seasonal variations, most importantly in wave height, leading to sediment mobilization, specifically on the inner shelf shallower than 30 m water depth where up to 49% of the shelf area is mobilized. Medium to severe storm events are modeled to mobilize up to 89% of the shelf area above 150 m water depth. The frequency of each of these seasonal and storm-related sediment mobilization scenarios is addressed using a decade of meteorological and oceanographic data. The temporal and spatial patterns of the modeled sediment mobilization scenarios are discussed in the context of existing geological and environmental processes and conditions to assist scientific, industrial and environmental efforts that are directly affected by sediment mobilization. Examples, where sediment mobilization plays a vital role, include seafloor nutrient advection, recurrent arrival of oil from oil-spill-laden seafloor sediment, and bottom trawling impacts.

Journal of Marine Systems↗

USGS2019

Do observer fatigue and taxon-bias compromise visual encounter surveys for small vertebrates?

Context. Visual encounter surveying is a standard animal inventory method, modifications of which (e.g. distance sampling and repeated count surveys) are used for modelling population density. However, a variety of factors may bias visual survey counts. Aims. The aim of the present study was to evaluate three observer-related biases: (1) whether fatigue compromises detection rate as a survey occasion progresses; (2) whether long-term fatigue or boredom compromise detection rates over the course of a survey period; and (3) whether observers exhibit biases in detection rates of different animal taxa. Methods. We analysed >2.3 × 10 4 observations of lizards and small mammals from nocturnal pedestrian visual encounter surveys, each 4 h in duration, conducted by a pool of 29 observers, each of whom surveyed for up to 31 nights. Key results. Detections of sleeping (diurnal) emerald tree skinks ( Lamprolepis smaragdina ) exhibited a small but statistically verified decline as the evening progressed, whereas detections of sleeping (diurnal) green anoles ( Anolis carolinensis ) increased as the evening progressed. Detections of nocturnal geckos (several species pooled) showed a weak and non-significant declining trend. Small mammal sightings (rats, shrews and mice pooled) declined strongly over the course of an evening. The participants saw greater or equal numbers of animals the more nights they surveyed. Most participants exhibited statistically significant, and often strong, taxonomic detection bias compared with the pool of peer observers. The skills of some observers appeared to be consistently above average; others consistently below average. Conclusions. Data on sleeping lizards suggest that neither short-term nor long-term observer fatigue is of much concern for 4-h visual searches. On the contrary, differences among observers in taxonomic bias and overall detection skills pose a problem for data interpretation. Implications. By comparing temporal detection patterns of immobile (e.g. sleeping) with actively moving animal taxa, sampling biases attributable to searcher fatigue versus the animals’ circadian rhythm can be disentangled and, if need be, statistically corrected for. Observer skill differences and observer-specific taxonomic biases may hamper efforts to statistically evaluate survey results, unless explicitly included as covariates in population models.

Wildlife Research↗

USGS2011

Recovering from the ShakeOut earthquake

Recovery from an earthquake like the M7.8 ShakeOut Scenario will be a major endeavor taking many years to complete. Hundreds of Southern California municipalities will be affected; most lack recovery plans or previous disaster experience. To support recovery planning this paper 1) extends the regional ShakeOut Scenario analysis into the recovery period using a recovery model, 2) localizes analyses to identify longer-term impacts and issues in two communities, and 3) considers the regional context of local recovery.Key community insights about preparing for post-disaster recovery include the need to: geographically diversify city procurement; set earthquake mitigation priorities for critical infrastructure (e.g., airport), plan to replace mobile homes with earthquake safety measures, consider post-earthquake redevelopment opportunities ahead of time, and develop post-disaster recovery management and governance structures. This work also showed that communities with minor damages are still sensitive to regional infrastructure damages and their potential long-term impacts on community recovery. This highlights the importance of community and infrastructure resilience strategies as well.

Earthquake Spectra↗