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At least 1,675 records · Page 93Linked to original sources
USGS2016

Amphibian dynamics in constructed ponds on a wildlife refuge: developing expected responses to hydrological restoration

Management actions are based upon predictable responses. To form expected responses to restoration actions, I estimated habitat relationships and trends (2002–2015) for four pond-breeding amphibians on a wildlife refuge (Montana, USA) where changes to restore historical hydrology to the system greatly expanded (≥8 times) the flooded area of the primary breeding site for western toads ( Anaxyrus boreas ). Additional restoration actions are planned for the near future, including removing ponds that provide amphibian habitat. Multi-season occupancy models based on data from 15 ponds sampled during 7 years revealed that the number of breeding subpopulations increased modestly for Columbia spotted frogs ( Rana luteiventris ) and was stationary for long-toed salamanders ( Ambystoma macrodactylum ) and Pacific treefrogs ( Pseudacris regilla ). For these three species, pond depth was the characteristic that was associated most frequently with occupancy or changes in colonization and extinction. In contrast, a large decrease in colonization by western toads explained the decline from eight occupied ponds in 2002 to two ponds in 2015. This decline occurred despite an increase in wetland area and the colonization of a newly created pond. These changes highlight the challenges of managing for multiple species and how management responses can be unpredictable, possibly reducing the efficacy of targeted actions.

Hydrobiologia↗
USGS2026

Understanding co-invading species’ ecological strategies and interactions with causal inference and simple dynamic models: Bighead (Hypophthalmichthys nobilis) and Silver carp (H. molitrix) in the Illinois River, USA

Species interactions among multiple invasive species can present hidden challenges for management and eradication efforts. Bighead and Silver carp ( Hypophthalmichthys nobilis and H. molitrix , respectively) are closely related species that have co-invaded the Mississippi River and many of its tributaries, including the Illinois River. We used a combination of causal inference and dynamic models to test for interspecific interactions in a 10-year data set of hydroacoustic density data from the Illinois River. The results suggest that Bighead and Silver carp are exploiting different ecological strategies; where Bighead carp shows fast initial growth but low equilibrium density and Silver carp had a lower low-density growth rate but larger equilibrium population density. Further, we found evidence for a positive interspecific effect of Silver carp on Bighead carp. The mechanism leading to the observed positive interactions is unknown, but could be an effect of ecological facilitation, differential responses to harvest of both species, or hybridization and introgression. Finally, while these results provide valuable information in the ecological strategies and relationships between these species, simulations of the resulting models suggest that the magnitude of the positive interaction is likely too small to be exploited for innovative multispecies management interventions.

Illinois↗
USGS2022

A review of the microtremor horizontal-to-vertical spectral ratio (MHVSR) method

The single-station microtremor horizontal-to-vertical spectral ratio (MHVSR) method was initially proposed to retrieve the site amplification function and its resonance frequencies produced by unconsolidated sediments overlying high-velocity bedrock. Presently, MHVSR measurements are predominantly conducted to obtain an estimate of the fundamental site frequency at sites where a strong subsurface impedance contrast exists. Of the earthquake site characterization methods presented in this special issue, the MHVSR method is the furthest behind in terms of consensus towards standardized guidelines and commercial use. The greatest challenges to an international standardization of MHVSR acquisition and analysis are (1) the what — the underlying composition of the microtremor wavefield is site-dependent, and thus, the appropriate theoretical (forward) model for inversion is still debated; and (2) the how — many factors and options are involved in the data acquisition, processing, and interpretation stages. This paper reviews briefly a historical development of the MHVSR technique and the physical basis of an MHVSR (the what ). We then summarize recommendations for MHVSR acquisition and analysis (the how ). Specific sections address MHVSR interpretation and uncertainty assessment.

Journal of Seismology↗
USGS2009

Conservation of northern bobwhite on private lands in Georgia, USA under uncertainty about landscape-level habitat effects

Large-scale habitat enhancement programs for birds are becoming more widespread, however, most lack monitoring to resolve uncertainties and enhance program impact over time. Georgia?s Bobwhite Quail Initiative (BQI) is a competitive, proposal-based system that provides incentives to landowners to establish habitat for northern bobwhites (Colinus virginianus). Using data from monitoring conducted in the program?s first years (1999?2001), we developed alternative hierarchical models to predict bobwhite abundance in response to program habitat modifications on local and regional scales. Effects of habitat and habitat management on bobwhite population response varied among geographical scales, but high measurement variability rendered the specific nature of these scaled effects equivocal. Under some models, BQI had positive impact at both local farm scales (1, 9 km2), particularly when practice acres were clustered, whereas other credible models indicated that bird response did not depend on spatial arrangement of practices. Thus, uncertainty about landscape-level effects of management presents a challenge to program managers who must decide which proposals to accept. We demonstrate that optimal selection decisions can be made despite this uncertainty and that uncertainty can be reduced over time, with consequent improvement in management efficacy. However, such an adaptive approach to BQI program implementation would require the reestablishment of monitoring of bobwhite abundance, an effort for which funding was discontinued in 2002. For landscape-level conservation programs generally, our approach demonstrates the value in assessing multiple scales of impact of habitat modification programs, and it reveals the utility of addressing management uncertainty through multiple decision models and system monitoring.

Landscape Ecology↗
USGS2009

Addressing foundational elements of regional land-use change forecasting

Regional land-use models must address several foundational elements, including understanding geographic setting, establishing regional land-use histories, modeling process and representing drivers of change, representing local land-use patterns, managing issues of scale and complexity, and development of scenarios. Key difficulties include managing an array of biophysical and socioeconomic processes across multiple spatial and temporal scales, and acquiring and utilizing empirical data to support the analysis of those processes. The Southeastern and Pacific Northwest regions of the United States, two heavily forested regions with significant forest industries, are examined in the context of these foundational elements. Geographic setting fundamentally affects both the primary land cover (forest) in the two regions, and the structure and form of land use (forestry). Land-use histories of the regions can be used to parameterize land-use models, validate model performance, and explore land-use scenarios. Drivers of change in the two regions are many and varied, with issues of scale and complexity posing significant challenges. Careful scenario development can be used to simplify process-based land-use models, and can improve our ability to address specific research questions. The successful modeling of land-use change in these two areas requires integration of both top-down and bottom-up drivers of change, using scenario frameworks to both guide and simplify the modeling process. Modular approaches, with utilization and integration of existing process models, allow regional land-use modelers the opportunity to better represent primary drivers of land-use change. However, availability of data to represent driving forces remains a primary obstacle.

Landscape Ecology↗
USGS2010

Predicting carnivore occurrence with noninvasive surveys and occupancy modeling

Terrestrial carnivores typically have large home ranges and exist at low population densities, thus presenting challenges to wildlife researchers. We employed multiple, noninvasive survey methods—scat detection dogs, remote cameras, and hair snares—to collect detection–nondetection data for elusive American black bears ( Ursus americanus ), fishers ( Martes pennanti) , and bobcats ( Lynx rufus ) throughout the rugged Vermont landscape. We analyzed these data using occupancy modeling that explicitly incorporated detectability as well as habitat and landscape variables. For black bears, percentage of forested land within 5 km of survey sites was an important positive predictor of occupancy, and percentage of human developed land within 5 km was a negative predictor. Although the relationship was less clear for bobcats, occupancy appeared positively related to the percentage of both mixed forest and forested wetland habitat within 1 km of survey sites. The relationship between specific covariates and fisher occupancy was unclear, with no specific habitat or landscape variables directly related to occupancy. For all species, we used model averaging to predict occurrence across the study area. Receiver operating characteristic (ROC) analyses of our black bear and fisher models suggested that occupancy modeling efforts with data from noninvasive surveys could be useful for carnivore conservation and management, as they provide insights into habitat use at the regional and landscape scale without requiring capture or direct observation of study species.

Vermont↗
USGS2012

Modeling habitat dynamics accounting for possible misclassification

Land cover data are widely used in ecology as land cover change is a major component of changes affecting ecological systems. Landscape change estimates are characterized by classification errors. Researchers have used error matrices to adjust estimates of areal extent, but estimation of land cover change is more difficult and more challenging, with error in classification being confused with change. We modeled land cover dynamics for a discrete set of habitat states. The approach accounts for state uncertainty to produce unbiased estimates of habitat transition probabilities using ground information to inform error rates. We consider the case when true and observed habitat states are available for the same geographic unit (pixel) and when true and observed states are obtained at one level of resolution, but transition probabilities estimated at a different level of resolution (aggregations of pixels). Simulation results showed a strong bias when estimating transition probabilities if misclassification was not accounted for. Scaling-up does not necessarily decrease the bias and can even increase it. Analyses of land cover data in the Southeast region of the USA showed that land change patterns appeared distorted if misclassification was not accounted for: rate of habitat turnover was artificially increased and habitat composition appeared more homogeneous. Not properly accounting for land cover misclassification can produce misleading inferences about habitat state and dynamics and also misleading predictions about species distributions based on habitat. Our models that explicitly account for state uncertainty should be useful in obtaining more accurate inferences about change from data that include errors.

Landscape Ecology↗
USGS2013

Spatial heterogeneity in statistical power to detect changes in lake area in Alaskan National Wildlife Refuges

Over the past 50 years, the number and size of high-latitude lakes have decreased throughout many regions; however, individual lake trends have been variable in direction and magnitude. This spatial heterogeneity in lake change makes statistical detection of temporal trends challenging, particularly in small analysis areas where weak trends are difficult to separate from inter- and intra-annual variability. Factors affecting trend detection include inherent variability, trend magnitude, and sample size. In this paper, we investigated how the statistical power to detect average linear trends in lake size of 0.5, 1.0 and 2.0 %/year was affected by the size of the analysis area and the number of years of monitoring in National Wildlife Refuges in Alaska. We estimated power for large (930–4,560 sq km) study areas within refuges and for 2.6, 12.9, and 25.9 sq km cells nested within study areas over temporal extents of 4–50 years. We found that: (1) trends in study areas could be detected within 5–15 years, (2) trends smaller than 2.0 %/year would take >50 years to detect in cells within study areas, and (3) there was substantial spatial variation in the time required to detect change among cells. Power was particularly low in the smallest cells which typically had the fewest lakes. Because small but ecologically meaningful trends may take decades to detect, early establishment of long-term monitoring will enhance power to detect change. Our results have broad applicability and our method is useful for any study involving change detection among variable spatial and temporal extents.

Alaska↗
USGS2013

Using landscape epidemiological models to understand the distribution of chronic wasting disease in the Midwestern USA

Animal movement across the landscape plays a critical role in the ecology of infectious wildlife diseases. Dispersing animals can spread pathogens between infected areas and naïve populations. While tracking free-ranging animals over the geographic scales relevant to landscape-level disease management is challenging, landscape features that influence gene flow among wildlife populations may also influence the contact rates and disease spread between populations. We used spatial diffusion and barriers to white-tailed deer gene flow, identified through landscape genetics, to model the distribution of chronic wasting disease (CWD) in the infected region of southern Wisconsin and northern Illinois, USA. Our generalized linear model showed that risk of CWD infection declined exponentially with distance from current outbreaks, and inclusion of gene flow barriers dramatically improved fit and predictive power of the model. Our results indicate that CWD is spreading across the Midwestern landscape from these two endemic foci, but spread is strongly influenced by highways and rivers that also reduce deer gene flow. We used our model to plot a risk map, providing important information for CWD management by identifying likely routes of disease spread and providing a tool for prioritizing disease monitoring and containment efforts. The current analysis may serve as a framework for modeling future disease risk drawing on genetic information to investigate barriers to spread and extending management and monitoring beyond currently affected regions.

Landscape Ecology↗
USGS2017

A Bayesian method for assessing multiscalespecies-habitat relationships

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large. Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance. Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA. Results Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%. Conclusions Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.

Nebraska↗
USGS2018

Landscape structure and temporal dynamic effects on Wintering Mallard abundance and distributions in the Mississippi alluvial valley

Context Management of wintering waterfowl in North America requires adaptability because constant landscape and environmental change challenges existing management strategies regarding waterfowl habitat use at large spatial scales. Migratory waterfowl including mallards (Anas platyrhynchos) use the lower Mississippi Alluvial Valley (MAV) for wintering habitat, making this an important area of emphasis for improving wetland conservation strategies, while enhancing the understanding of landscape-use patterns. Objectives We used aerial survey data collected in the Arkansas portion of the MAV (ARMAV) to explain the abundance and distribution of mallards in relation to variable landscape conditions. Methods We used two-stage, hierarchical spatio-temporal models with a random spatial effect to identify covariates related to changes in mallard abundance and distribution within and among years. Results We found distinct spatio-temporal patterns existed for mallard distributions across the ARMAV and these distributions are dependent on the surrounding landscape structure and changing environmental conditions. Models performing best indicated seasonal surface water extent, rice field, wetland and fallow (uncultivated) field abundance positively influenced mallard distribution. Rice fields, surface water and weather were found to influence mallard abundance. Additionally, are results suggest weather and changing surface water affects mallard presence and abundance throughout the winter, because the probability of mallard presence and abundance changed from the northern ARMAV in November to the southern ARMAV in January. Conclusions Using novel datasets to identify which environmental factors drive changes in regional wildlife distribution and abundance can improve management by providing managers additional information to manage land over landscapes spanning private and public lands. We suggest our analytical approach may be informative in other areas and for other wildlife species.

Arkansas↗
USGS2019

Individual based modelling of fish migration in a 2-D river system: Model description and case study

Context: Diadromous fish populations in the Pacific Northwest face challenges along their migratory routes from declining habitat quality, harvest, and barriers to longitudinal connectivity. These stressors complicate the prioritization of proposed management actions intended to improve conditions for migratory fishes including anadromous salmon and trout. Objectives: We describe a multi-scale hybrid mechanistic–probabilistic simulation model linking migration corridor conditions to fish fitness outcomes. We demonstrate the model’s utility using a case study of salmon and steelhead adults in the Columbia River migration corridor exposed to spatially- and temporally-varying stressors. Methods: The migration corridor simulation model is based on a behavioral decision tree that governs individual interactions with the environment, and an energetic submodel that estimates the hourly costs of migration. Emergent properties of the migration corridor simulation model include passage time, energy use, and survival. Results: We observed that the simulated fish’s initial energy density, the migration corridor temperatures they experienced, and their history of behavioral thermoregulation were the primary determinants of their fitness outcomes. Insights gained from use of the model might be exploited to identify management interventions that increase successful migration outcomes. Conclusions: This paper describes new methods that extend the suite of tools available to aquatic biologists and conservation practitioners. We have developed a 2-dimensional spatially-explicit behavioral and physiological model and illustrated how it can be used to simulate fish migration within a river system. Our model can be used to evaluate trade-offs between behavioral thermoregulation and fish fitness at population scales.

Landscape Ecology↗
USGS2023

Exposure to risk factors experienced during migration is not associated with recent Vermivora warbler population trends

Context Understanding the factors limiting populations of animals is critical for effective conservation. Determining which factors limit populations of migratory species can be especially challenging because of their reliance on multiple, often geographically distant regions during their annual cycles. Objectives We investigated whether distribution-wide variation in recent breeding population trends was more strongly associated with exposure to risk factors experienced during migration (i.e., natural and anthropogenic threats often associated with increased mortality or carry-over effects) or factors associated with breeding and nonbreeding areas in golden-winged warblers ( Vermivora chrysoptera ) and blue-winged warblers ( V. cyanoptera ), two Nearctic-Neotropical migrants experiencing regionally variable population trends. Methods We used geolocator data from 85 Vermivora warblers ( n = 90 geolocator tracks) tracked from North American breeding locations and Central American nonbreeding locations from 2013 to 2017 to determine variation in space use among populations. We assessed whether differences in space use among populations of Vermivora warblers during migration were associated with exposure to migration risk-factors and whether increased relative exposure to migration risk factors was associated with population declines at regional and subregional scales. Results Regional and subregional populations of Vermivora warblers exhibited variation in space use and exposure to anthropogenic and natural risk-factors. However, we found no evidence that recent variation in population trends of Vermivora warblers was associated with risk-factors experienced by different populations during migration. Instead, factors associated with land cover-types in breeding and nonbreeding areas were more strongly associated with recent population trends. Conclusions Understanding how populations of migratory birds are affected by factors experienced during migration is critical for their conservation. We did not find evidence that variation in exposure to migration risk-factors is associated with recent regional or subregional variation in Vermivora warbler population trends. Consequently, our results suggest that efforts to reverse ongoing population declines of Vermivora warblers may be more effective if directed toward conservation actions targeting limiting factors within the breeding and nonbreeding periods versus those directed at conditions encountered during migration. We caution that geographic variation in projected land-use change may differentially affect areas used by different populations of Vermivora warblers during migration, posing a potential threat to these species in the future.

Landscape Ecology↗
USGS2024

Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts

Context Wildland-urban interface (WUI) areas are facing increased forest fire risks and extreme precipitation events due to climate change, which can lead to post-fire flood events. The city of Flagstaff in northern Arizona, USA experienced WUI forest thinning, fire, and record rainfall events, which collectively contributed to large floods and damages to the urban neighborhoods and city infrastructure. Objectives We demonstrate multi-temporal, high resolution image applications from an unoccupied aerial vehicle (UAV) and terrestrial lidar in estimating landscape disturbance impacts within the WUI. Changes in forest vegetation and bare ground cover in WUIs are particularly challenging to estimate with coarse-resolution satellite images due to fine-scale landscape processes and changes that often result in mixed pixels. Methods Using Sentinel-2 satellite images, we document forest fire impacts and burn severity. Using 2016 and 2021 UAV multispectral images and Structure-from-Motion data, we estimate post-thinning changes in forest canopy cover, patch sizes, canopy height distribution, and bare ground cover. Using repeat lidar data within a smaller area of the watershed, we quantify geomorphic effects in the WUI associated with the fire and subsequent flooding. Results We document that thinning significantly reduced forest canopy cover, patch size, tree density, and mean canopy height resulting in substantially reduced active crown fire risks in the future. However, the thinning equipment ignited a forest fire, which burned the WUI at varying severity at the top of the watershed that drains into the city. Moderate-high severity burns occurred within 3 km of downtown Flagstaff threatening the WUI neighborhoods and the city. The upstream burned area then experienced 100-year and 200–500-year rainfall events, which resulted in large runoff-driven floods and sedimentation in the city. Conclusion We demonstrate that UAV high resolution images and photogrammetry combined with terrestrial lidar data provide detailed and accurate estimates of forest thinning and post-fire flood impacts, which could not be estimated from coarser-resolution satellite images. Communities around the world may need to prepare their WUIs for catastrophic fires and increase capacity to manage sediment-laden stormwater since both fires and extreme weather events are projected to increase.

Arizona↗
USGS2024

Key breeding habitats of threatened golden eagles across Eastern Canada identified using a multi-level, multi-scale habitat selection approach

Context In a conservation context, identifying key habitats suitable for reproduction, foraging, or survival is a useful tool, yet challenging for species with large geographic distributions and/or living in remote regions. Objectives The objective of this study is to identify selected habitats at multiple levels and scales of the threatened eastern North American population of golden eagles ( Aquila chrysaetos ). We studied habitat selection at three levels: landscape (second order of selection), foraging (third order of selection), and nesting (fourth order of selection). Methods Using tracking data from 30 adults and 366 nest coordinates spanning over a 1.5 million km 2 area in remote boreal and Arctic regions, we modelled the three levels of habitat selection with resource selection functions using seven environmental features (aerial, topographical, and land cover). We then calculated the relative probability of selection in the study area to identify regions with higher probabilities of selection. Results Eagles selected more for terrain ruggedness index and relative elevation than land cover (i.e., forest cover, distance to water; mean difference in relative selection strength: 1.2 [0.71; 1.69], 95% CI) at all three levels. We also found that the relative probability of selection at all three levels was ~ 25% higher in the Arctic than in the boreal regions. Eagles breeding in the Arctic travelled shorter foraging distances with greater access to habitat with a high probability of selection than boreal eagles. Conclusion Here we found which aerial and topographical features were important for several of the eagles’ life cycle needs. We also identified important areas to monitor and preserve this threatened population. The next step is to quantify the quality of habitat by linking our multi-level, multi-scale approach to population demography and performance such as reproductive success.

Landscape Ecology↗
USGS2024

Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands

Context Dynamic feedbacks between physical structure and ecological function drive ecosystem productivity, resilience, and biodiversity maintenance. Detailed maps of canopy structure enable comprehensive evaluations of structure–function relationships. However, these relationships are scale-dependent, and identifying relevant spatial scales to link structure to function remains challenging. Objectives We identified optimal scales to relate structure heterogeneity to ecological resistance, measured as the impacts of wildfire on canopy structure, and ecological resilience, measured as native shrub recruitment. We further investigated whether structural heterogeneity can aid spatial predictions of shrub recruitment. Methods Using high-resolution imagery from unoccupied aerial systems (UAS), we mapped structural heterogeneity across ten semi-arid landscapes, undergoing a disturbance-mediated regime shift from native shrubland to dominance by invasive annual grasses. We then applied wavelet analysis to decompose structural heterogeneity into discrete scales and related these scales to ecological metrics of resilience and resistance. Results We found strong indicators of scale dependence in the tested relationships. Wildfire effects were most prominent at a single scale of structural heterogeneity (2.34 m), while the abundance of shrub recruits was sensitive to structural heterogeneity at a range of scales, from 0.07 – 2.34 m. Structural heterogeneity enabled out-of-site predictions of shrub recruitment (R 2 = 0.55). The best-performing predictive model included structural heterogeneity metrics across multiple scales. Conclusions Our results demonstrate that identifying structure–function relationships requires analyses that explicitly account for spatial scale. As high-resolution imagery enables spatially extensive maps of canopy heterogeneity, models for scale dependence will aid our understanding of resilience mechanisms in imperiled arid ecosystems.

Idaho↗
USGS2026

Advances and applications of Unoccupied Aerial Systems (UAS) research in landscape ecology

Landscape ecologists have long depended on satellite and aerial remote sensing to address questions about landscape pattern and process, structure, and change (Foody 2023 ). Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) technology is becoming an increasingly popular research tool in environmental sciences allowing scientists to generate low-cost, high-quality, and high-resolution imagery on demand that can be tailored to specific research questions. While satellite data are of a fixed resolution and temporal interval, UAS offer researchers control and flexibility to design studies and collect data at resolutions and scales that provide ecologically relevant information at finer spatial resolutions (e.g., < 30 cm) than what is currently available from satellite platforms (typically > 3m), thus helping capture objects such as individual plant canopies, micro-topography, and individual animals. Unlike satellites with fixed orbits, UAS can be deployed at more optimal temporal frequencies for ecological monitoring. We organized the special collection “Advances and Applications of Unoccupied Aerial Systems (UAS) Research in Landscape Ecology” to showcase the many ways that UAS tools and technologies are currently applied to advance landscape ecological research. When we announced the collection in 2023, only 11 papers published in the journal Landscape Ecology used UAS data, which was a notably small number compared to many other general ecology, environmental science and remote sensing journals. In an attempt to understand why UAS were not more widely used in landscape ecology and provide possible solutions, we published a review article (Villarreal et al. 2025) that identified the challenges, knowledge gaps, and obstacles for the adoption of UAS technologies in landscape ecology research. The main issues we identified include: (1) an abundance of UAS methods papers in the existing literature, with comparatively few studies demonstrating how UAS can be applied to address ecological questions; (2) a perceived scale mismatch between the geographic extent of UAS data collection (local) compared to larger study areas (landscapes) and a need to design robust scaling approaches to connect fine-scale UAS data with broader ecological patterns; and (3) a need for improved integration of UAS data with other commonly used remote sensing datasets including historical high resolution aerial imagery. Additionally, researchers new to UAS remote sensing may be discouraged or overwhelmed by the general lack of scientific consensus and standardized protocols for typical tasks such as data collection, vegetation classification, and change detection, as well as restrictive and/or confusing policy, regulatory, and legal issues surrounding UAS operations (Villarreal et al. 2025).

Landscape Ecology↗
USGS2013

Commercial possibilities for stranded conventional gas from Alaska's North Slope

Stranded gas resources are defined for this study as gas resources in discrete accumulations that are not currently commercially producible, or producible at full potential, for either physical or economic reasons. Approximately 35 trillion cubic feet (TCF) of stranded gas was identified on Alaska’s North Slope. The commercialization of this resource requires facilities to transport gas to markets where sales revenue will be sufficient to offset the cost of constructing and operating a gas delivery system. With the advent of the shale gas revolution, plans for a gas pipeline to the conterminous US have been shelved (at least temporarily) and the State and resource owners are considering a liquefied natural gas (LNG) export project that targets Asian markets. This paper focuses on competitive conditions for Asian gas import markets by estimating delivered costs of competing supplies from central Asia, Russia, Indonesia, Malaysia, and Australia in the context of a range of import gas demand projections for the period from 2020 to 2040. These suppliers’ costs are based on the cost of developing, producing, and delivering to markets tranches of the nearly 600 TCF of recoverable gas from their own conventional stranded gas fields. The results of these analyses imply that Alaska’s gas exports to Asia will likely encounter substantial competitive challenges. The sustainability of Asia’s oil-indexed LNG pricing is also discussed in light of a potentially intense level of competition.

Alaska↗