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At least 19 recordsLinked to original sources
USGS2020

Scenarios for valuing sample information in natural resources

Uncertainty is ubiquitous in natural resource systems, science and management. Sample data are obtained in order to reduce uncertainty, thereby increasing knowledge and improving resource management, but sampling always comes at a cost of some sort. Is that cost worthwhile? Analysis of the value of sample information ( VSI ) addresses this question. In this paper we develop the valuation of sample information in terms of five elements: (a) a system whose attributes are the focus of analysis; (b) a range of management actions that affect the system's status; (c) uncertainty about system status or structure, as characterized by initial (prior) probabilities of possible system states or structures; (d) an experiment or other information source that produces new data points and updated (posterior) probabilities; and (e) a value measure that is a function of the management action taken, conditional on either the system state or structure. We describe five scenarios for analysing the VSI under uncertainty about system structure and state. Scenarios 1–3 comprise analyses of conditional, expected and optimal expected values of sample information. They focus primarily on choice of management adaptations with new information. Scenarios 4 and 5 involve pre-selected management actions, and are useful for comparing designs of data collection rather than for choosing a management action. These last scenarios expand the framework for VSI to include actions that have been selected independently of the updating of uncertainty. We discuss other extensions of VSI analysis, which include spatial applications, hybrid scenarios, applications involving dynamic systems, and a focus on costs rather than net benefits. Value of sample information analysis holds promise in emerging areas of ecology such as ecological forecasting and the use of remote sensing in conservation, where potential new data from models and satellites can be evaluated in advance, thereby allowing more efficient prioritization of scientific efforts. More generally, VSI can contribute to better ecological understanding and more effective management in a wide range of ecological situations.

Methods in Ecology and Evolution↗
USGS2022

Automating sandhill crane counts from nocturnal thermal aerial imagery using deep learning

Population monitoring is essential to management and conservation efforts for migratory birds, but traditional low-altitude aerial surveys with human observers are plagued by individual observer bias and risk to flight crews. Aerial surveys that use remote sensing can reduce bias and risk, but manual counting of wildlife in imagery is laborious and may be cost-prohibitive. Therefore, automated methods for counting are critical to cost-efficient application of remote sensing for wildlife surveys covering large areas. We conducted nocturnal surveys of sandhill cranes ( Antigone canadensis ) during spring migration in the Central Platte River Valley of Nebraska, USA, using midwave thermal infrared sensors. We developed a framework for automated counting of sandhill cranes from thermal imagery using deep learning, assessed and compared the performance of two automated counting models, and quantified the effect of spatial resolution on counting accuracy. Aerial thermal imagery data were collected in March 2018 and 2021; 40 images were analyzed. We applied two deep learning models: an object detection approach, Faster R-CNN and a recently developed pixel-density estimation approach, ASPDNet. Model performance was determined using data independent of the training imagery. The effect of spatial resolution was quantified with a beta regression on relative error. Our results showed model accuracy of 9% mean percent error for ASPDNet and 18% for Faster R-CNN. Most error was related to the undercounting of sandhill cranes. ASPDNet had < 50% of the error of Faster R-CNN as measured by mean percent error, root-mean-squared error and mean absolute error. Spatial resolution affected accuracy of both models, with error rate increasing with coarser resolution, particularly with Faster R-CNN. Deep learning models, particularly pixel-density estimators, can accurately automate counting of migratory birds in a dense, aggregate setting such as nocturnal roosting sites.

Nebraska↗
USGS2019

Invasive buffelgrass detection using high-resolution satellite and UAV imagery on Google Earth Engine

Methods to detect and monitor the spread of invasive grasses are critical to avoid ecosystem transformations and large economic costs. The rapid spread of non‐native buffelgrass (Pennisetum ciliare) has intensified fire risk and is replacing fire intolerant native vegetation in the Sonoran Desert of the southwestern US. Coarse‐resolution satellite imagery has had limited success in detecting small patches of buffelgrass, whereas ground‐based and aerial survey methods are often cost prohibitive. To improve detection, we trained 2 m resolution DigitalGlobe WorldView‐2 satellite imagery with 12 cm resolution unmanned aerial vehicle (UAV) imagery and classified buffelgrass on Google Earth Engine, a cloud computing platform, using Random Forest (RF) models in Saguaro National Park, Arizona, USA. Our classification models had an average overall accuracy of 93% and producer's accuracies of 94–96% for buffelgrass, although user's accuracies were low. We detected a 2.92 km 2 area of buffelgrass in the eastern Rincon Mountain District (1.07% of the total area) and a 0.46 km 2 area (0.46% of the total area) in the western Tucson Mountain District of Saguaro National Park. Buffelgrass cover was significantly greater in the Sonoran Paloverde‐Mixed Cacti Desert Scrub vegetation type, on poorly developed Entisols and Inceptisol soils and on south‐facing topographic aspects compared to other areas. Our results demonstrate that high‐resolution imagery improve on previous attempts to detect and classify buffelgrass and indicate potential areas where the invasive grass might spread. The methods demonstrated in this study could be employed by land managers as a low‐cost strategy to identify priority areas for control efforts and continued monitoring.

Arizona↗
USGS2019

UAV-derived estimates of forest structure to inform ponderosa pine forest restoration

Restoring forest ecosystems has become an increasingly high priority for land managers across the American West. Millions of hectares of forest are in need of drastic yet strategic reductions in density (e.g., basal area). Meeting the restoration and management goals requires quantifying metrics of vertical and horizontal forest structure, which has relied upon field‐based measurements, manned airborne or satellite remote sensing datasets. We used unmanned aerial vehicle (UAV ) image‐derived Structure‐from‐Motion (SfM) models and high‐resolution multispectral orthoimagery in this study to quantify vertical and horizontal forest structure at both the fine‐ (<4 ha) and mid‐scales (4–400 ha) across a forest density gradient. We then used these forest structure estimates to assess specific objectives of a forest restoration treatment. At the fine‐scale, we found that estimates of individual tree height and canopy diameter were most accurate in low‐density conditions, with accuracies degrading significantly in high‐density conditions. Mid‐scale estimates of canopy cover and forest density followed a similar pattern across the density gradient, demonstrating the effectiveness of UAV image‐derived estimates in low‐ to medium‐density conditions as well as the challenges associated with high‐density conditions. We found that post‐treatment conditions met a majority of the prescription objectives and demonstrate the UAV image application in quantifying changes from a mechanical thinning treatment. We provide a novel approach to forest restoration monitoring using UAV ‐derived data, one that considers varying density conditions and spatial scales. Future research should consider a more spatially extensive sampling design, including different restoration treatments, as well as experimenting with different combinations of equipment, flight parameters, and data processing workflows.

Western United States↗
USGS2020

Estimating inundation of small waterbodies with sub-pixel analysis of Landsat imagery: Long-term trends in surface water area and evaluation of common drought indices

Small waterbodies are numerically dominant in many landscapes and provide several important ecosystem services, but automated measurement of waterbodies smaller than a standard Landsat pixel (0.09 ha) remains challenging. To further evaluate sub‐Landsat pixel techniques for estimating inundation extent of small waterbodies (basin area: 0.06–1.79 ha), we used a partial spectral unmixing method with matched filtering applied to September 1985–2018 Landsat 5 and eight imagery from southern Arizona, USA. We estimated trends in modeled surface water area each September and evaluated the ability of several common drought indices to explain variation in mean water area. Our methods accurately classified waterbodies as dry or inundated (Landsat 5: 91.3%; Landsat 8: 98.9%) and modeled and digitized surface water areas were strongly correlated ( R 2 = 0.70–0.92; bias = −0.024 to −0.015 ha). Estimated surface water area was best explained by the 3‐month seasonal standardized precipitation index (SPI03; July‒September). We found a wide range of estimated relationships between drought indices (e.g. SPI vs. Palmer Drought Severity Index) and estimated water area, even for different durations of the same drought index (e.g. SPI01 vs. SPI12). Mean waterbody surface area decreased by ~14% from September 1985 to September 2018, which matches declines in local annual precipitation and regional trends of reduced inundation extent of larger waterbodies. These results emphasize the importance of understanding local systems when relying on drought indices to infer variation in past or future surface water dynamics. Several challenges remain before widespread application of sub‐pixel methods is feasible, but our results provide further evidence that partial spectral unmixing with matched filtering provides reliable measures of inundation extent of small waterbodies.

Arizona↗
USGS2020

Ultra‐high‐resolution mapping of biocrusts with Unmanned Aerial Systems

Biological soil crusts (biocrusts) occur in drylands globally where they support ecosystem functioning by increasing soil stability, reducing dust emissions and modifying soil resource availability (e.g. water, nutrients). Determining biocrust condition and extent across landscapes continues to present considerable challenges to scientists and land managers. Biocrusts grow in patches, cover vast expanses of rugged terrain and are vulnerable to physical disturbance associated with ground‐based mapping techniques. As such, remote sensing offers promising opportunities to map and monitor biocrusts. While satellite‐based remote sensing has been used to detect biocrusts at relatively large spatial scales, few studies have used high‐resolution imagery from Unmanned Aerial Systems (UAS) to map fine‐scale patterns of biocrusts. We collected sub‐centimeter, true color 3‐band imagery at 10 plots in sagebrush and pinyon‐juniper woodland communities in a semiarid ecosystem in the southwestern US and used object‐based image analysis (OBIA) to segment and classify the imagery into maps of light and dark biocrusts, bare soil, rock and various vegetation covers. We used field data to validate the classifications and assessed the spatial distribution and configuration of different classes using fragmentation metrics. Map accuracies ranged from 46 to 77% (average 65%) and were higher in pinyon‐juniper (average 70%) versus sagebrush (average 60%) plots. Biocrust classes showed generally high accuracies at both pinyon‐juniper plots (average dark crust = 70%; light crust = 80%) and sagebrush plots (average dark crust = 69%; light crust = 77%). Point cloud density, sun elevation and spectral confusion between vegetation cover explained some differences in accuracy across plots. Spatial analyses of classified maps showed that biocrust patches in pinyon‐juniper plots were generally larger, more aggregated and contiguous than in sagebrush plots. Pinyon‐juniper plots also had greater patch richness and a lower Shannon evenness index than sagebrush plots, suggesting greater soil cover heterogeneity in this plant community type.

Utah↗
USGS2021

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non-forest ecosystems

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R 2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha −1 . Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.

Remote Sensing in Ecology and Conservation↗
USGS2021

Improved fire severity mapping in the North American boreal forest using a hybrid composite method

Fire severity is a key driver shaping the ecological structure and function of North American boreal ecosystems, a biome dominated by large, high-intensity wildfires. Satellite-derived burn severity maps have been an important tool in these remote landscapes for both fire and resource management. The conventional methodology to produce satellite-inferred fire severity maps generally involves comparing imagery from 1 year before and 1 year after a fire, yet environmental conditions unique to the boreal have limited the accuracy of resulting products. We introduce an alternative method – the ‘hybrid composite’ – based on deriving mean severity over time on a per-pixel basis within the cloud-computing environment of Google Earth Engine. It constructs the post-fire image from satellite data composited from all valid images (i.e., clear-sky and snow-free) acquired in the time period immediately after fire through the early growing season of the following year. We compare this approach to paired-scene and composite approaches where the post-fire time period is from the growing season 1 year after fire. Validation statistics based on field-derived data for 52 fires across Alaska and Canada indicate that the hybrid composite method outperforms the other approaches. This approach presents an efficient and cost-effective means to monitor and explore trends and patterns across broad spatial domains, and could be applied to fires in other regions, especially those with frequent cloud cover or rapid vegetation recovery.

Alaska↗
USGS2023

Challenges and solutions for automated avian recognition in aerial imagery

Remote aerial sensing provides a non-invasive, large geographical-scale technology for avian monitoring, but the manual processing of images limits its development and applications. Artificial Intelligence (AI) methods can be used to mitigate this manual image processing requirement. The implementation of AI methods, however, has several challenges: (1) imbalanced (i.e., long-tailed) data distribution, (2) annotation uncertainty in categorization, and (3) dataset discrepancies across different study sites. Here we use aerial imagery data of waterbirds around Cape Cod and Lake Michigan in the United States to examine how these challenges limit avian recognition performance. We review existing solutions and demonstrate as use cases how methods like Label Distribution Aware Marginal Loss with Deferred Re-Weighting, hierarchical classification, and FixMatch address the three challenges. We also present a new approach to tackle the annotation uncertainty challenge using a Soft-fine Pseudo-Label methodology. Finally, we aim with this paper to increase awareness in the ecological remote sensing community of these challenges and bridge the gap between ecological applications and state-of-the-art computer science, thereby opening new doors to future research.

Remote Sensing in Ecology and Conservation↗
USGS2023

Beyond presence mapping: Predicting fractional cover of non-native vegetation in Sentinel-2 imagery using an ensemble of MaxEnt models

Non-native species maps are important tools for understanding and managing biological invasions. We demonstrate a novel approach to extend presence modeling to map fractional cover (FC) of non-native yellow sweet clover Melilotus officinalis in the Northern Great Plains, USA. We used ensembles of MaxEnt models to map FC across landscapes from satellite imagery trained from regional aerial imagery that was trained by local unmanned aerial vehicle (UAV) imagery. Clover cover from field surveys and classified UAV imagery were nearly identical ( n = 22, R 2 = 0.99). Two classified UAV images provided training data to map clover presence with MaxEnt and National Agricultural Imagery Program (NAIP) aerial imagery. We binned cover predictions from NAIP imagery within each Sentinel-2 pixel into eight cover classes to create pure (100%) and FC (20%–95%) training data and modeled each class separately using MaxEnt and Sentinel-2 imagery. We mapped pure clover with one classification threshold and compared its performance to 15 candidate maps that included FC predictions outside pure predictions. Each FC map represented alternative combinations of five MaxEnt thresholds and three approaches to assign cover to pixels with multiple predictions from the FC ensemble. Evaluations of performance with independent datasets revealed maps including FC corresponded to field ( n = 32, R 2 range: 0.39–0.68) and UAV ( n = 20, R 2 range: 0.61–0.84) data better than pure clover maps ( R 2 = 0.15 and 0.31, respectively). Overall, the pure clover map predicted 3.2% cover, whereas the three best performing FC maps predicted 6.6%–8.0% cover. Including FC predictions increased accuracy and cover predictions which can improve ecological understanding of invasions. Our method allows efficient FC mapping for vegetative species discernible in UAV imagery and may be especially useful for mapping rare, irruptive or patchily distributed species with poor representation in field data, which challenges landscape-level mapping.

Montana, South Dakota↗
USGS2023

Both Landsat- and LiDAR-derived measures predict forest bee response to large-scale wildfire

Large-scale disturbances such as wildfire can have profound impacts on the composition, structure, and functioning of ecosystems. Bees are critical pollinators in natural settings and often respond positively to wildfires, particularly in forests where wildfire leads to more open conditions and increased floral resources. The use of Light Detection and Ranging (LiDAR) provides opportunities for quantifying habitat features across large spatial scales and is increasingly available to scientists and land managers for post-fire habitat assessment. We evaluated the extent to which LiDAR-derived forest structure measurements can predict forest bee communities after a large, mixed-severity fire. We hypothesized that LiDAR measurements linked to post-fire forest structure would improve our ability to predict bee abundance and species richness when compared to satellite-based maps of burn severity. To test this hypothesis, we sampled wild bee communities within the Douglas Fire Complex in southwestern Oregon, USA. We then used LiDAR and Landsat data to quantify forest structure and burn severity, respectively, across bee sampling locations. We found that the LiDAR forest structure model was the best predictor of abundance, whereas the Landsat burn severity model had better predictive ability for species richness. Furthermore, the Landsat burn severity model was better at predicting the presence and species richness of bumble bees (Bombus spp.), an ecologically distinct and economically important group within the Pacific Northwest. We posit that the divergent responses of the two modeling approaches are due to distinct responses by bee taxa to variation in forest structure as mediated by wildfire, with bumble bees in particular depending on closed-canopy forest for some portions of their life cycle. Our study demonstrates that LiDAR data can provide information regarding the drivers of bee abundance in post-wildfire conifer forest, and that both remote sensing approaches are useful for predicting components of wild bee diversity after large-scale wildfire.

Oregon↗
USGS2023

Efficacy of machine learning image classification for automated occupancy-based monitoring

Remote cameras have become a widespread data-collection tool for terrestrial mammals, but classifying images can be labor intensive and limit the usefulness of cameras for broad-scale population monitoring. Machine learning algorithms for automated image classification can expedite data processing, but image misclassifications may influence inferences. Here, we used camera data for three sympatric species with disparate body sizes and life histories – black-tailed jackrabbits ( Lepus californicus ), kit foxes ( Vulpes macrotis ), and pronghorns ( Antilocapra americana ) – as a model system to evaluate the influence of competing image classification approaches on estimates of occupancy and inferences about space use. We classified images with: (i) single review (manual), (ii) double review (manual by two observers), (iii) an automated-manual review (machine learning to cull empty images and single review of remaining images), (iv) a pretrained machine-learning algorithm that classifies images to species (base model), (v) the base model accepting only classifications with ≥95% confidence, (vi) the base model trained with regional images (trained model), and (vii) the trained model accepting only classifications with ≥95% confidence. We compared species-specific results from alternative approaches to results from double review, which reduces the potential for misclassifications and was assumed to be the best approximation of truth. Despite high classification success, species-level misclassification rates for the base and trained models were sufficiently high to produce erroneous occupancy estimates and inferences related to space use across species. Increasing the confidence thresholds for image classification to 95% did not consistently improve performance. Classifying images as empty (or not) offered a reasonable approach to reduce effort (by 97.7%) and facilitated a semi-automated workflow that produced reliable estimates and inferences. Thus, camera-based monitoring combined with machine learning algorithms for image classification could facilitate monitoring with limited manual image classification.

Utah↗
USGS2023

High-resolution thermal imagery reveals how interactions between crown structure and genetics shape plant temperature

Understanding interactions between environmental stress and genetic variation is crucial to predict the adaptive capacity of species to climate change. Leaf temperature is both a driver and a responsive indicator of plant physiological response to thermal stress, and methods to monitor it are needed. Foliar temperatures vary across leaf to canopy scales and are influenced by genetic factors, challenging efforts to map and model this critical variable. Thermal imagery collected using unoccupied aerial systems (UAS) offers an innovative way to measure thermal variation in plants across landscapes at leaf-level resolutions. We used a UAS equipped with a thermal camera to assess temperature variation among genetically distinct populations of big sagebrush ( Artemisia tridentata ), a keystone plant species that is the focus of intensive restoration efforts throughout much of western North America. We completed flights across a growing season in a sagebrush common garden to map leaf temperature relative to subspecies and cytotype, physiological phenotypes of plants, and summer heat stress. Our objectives were to (1) determine whether leaf-level stomatal conductance corresponds with changes in crown temperature; (2) quantify genetic (i.e., subspecies and cytotype) contributions to variation in leaf and crown temperatures; and (3) identify how crown structure, solar radiation, and subspecies-cytotype relate to leaf-level temperature. When considered across the whole season, stomatal conductance was negatively, non-linearly correlated with crown-level temperature derived from UAS. Subspecies identity best explained crown-level temperature with no difference observed between cytotypes. However, structural phenotypes and microclimate best explained leaf-level temperature. These results show how fine-scale thermal mapping can decouple the contribution of genetic, phenotypic, and microclimate factors on leaf temperature dynamics. As climate-change-induced heat stress becomes prevalent, thermal UAS represents a promising way to track plant phenotypes that emerge from gene-by-environment interactions.

Idaho↗
USGS2017

Weather radar data correlate to hail-induced mortality in grassland birds

Small-bodied terrestrial animals such as songbirds (Order Passeriformes) are especially vulnerable to hail-induced mortality; yet, hail events are challenging to predict, and they often occur in locations where populations are not being studied. Focusing on nesting grassland songbirds, we demonstrate a novel approach to estimate hail-induced mortality. We quantify the relationship between the probability of nests destroyed by hail and measured Level-III Next Generation Radar (NEXRAD) data, including atmospheric base reflectivity, maximum estimated size of hail and maximum estimated azimuthal wind shear. On 22 June 2014, a hailstorm in northern Colorado destroyed 102 out of 203 known nests within our research site. Lark bunting ( Calamospiza melanocorys ) nests comprised most of the sample ( n = 186). Destroyed nests were more likely to be found in areas of higher storm intensity, and distributions of NEXRAD variables differed between failed and surviving nests. For 133 ground nests where nest-site vegetation was measured, we examined the ameliorative influence of woody vegetation, nest cover and vegetation density by comparing results for 13 different logistic regression models incorporating the independent and additive effects of weather and vegetation variables. The most parsimonious model used only the interactive effect of hail size and wind shear to predict the probability of nest survival, and the data provided no support for any of the models without this predictor. We conclude that vegetation structure may not mitigate mortality from severe hailstorms and that weather radar products can be used remotely to estimate potential for hail mortality of nesting grassland birds. These insights will improve the efficacy of grassland bird population models under predicted climate change scenarios.

Remote Sensing in Ecology and Conservation↗
USGS2025

Object detection-assisted workflow facilitates cryptic snake monitoring

Camera traps are an important tool used to study rare and cryptic animals, including snakes. Time-lapse photography can be particularly useful for studying snakes that often fail to trigger a camera's infrared motion sensor due to their ectothermic nature. However, the large datasets produced by time-lapse photography require labor-intensive classification, limiting their use in large-scale studies. While many artificial intelligence-based object detection models are effective at identifying mammals in images, their ability to detect snakes is unproven. Here, we used camera data to evaluate the efficacy of an object detection model to rapidly and accurately detect snakes. We classified images manually to the species level and compared this with a hybrid review workflow where the model removed blank images followed by a manual review. Using a ≥0.05 model confidence threshold, our hybrid review workflow correctly identified 94.5% of blank images, completed image classification 6× faster, and detected large (>66 cm) snakes as well as manual review. Conversely, the hybrid review method often failed to detect all instances of a snake in a string of images and detected fewer small (<66 cm) snakes than manual review. However, most relevant ecological information requires only a single detection in a sequence of images, and study design changes could likely improve the detection of smaller snakes. Our findings suggest that an object detection-assisted hybrid workflow can greatly reduce time spent manually classifying data-heavy time-lapse snake studies and facilitate ecological monitoring for large snakes.

Florida↗
USGS2018

Looking beyond wildlife: Using remote cameras to evaluate accuracy of gridded snow data

The use of remote cameras is widespread in wildlife ecology, yet few examples exist of their utility for collecting environmental data. We used a novel camera trap method to evaluate the accuracy of gridded snow data in a mountainous region of the northeastern US. We were specifically interested in assessing (1) how snow depth observations from remote cameras compare with gridded climate data, (2) the sources of error associated with the gridded data and (3) the influence of spatial sampling on bias. We compared daily observations recorded by remote cameras with Snow Data Assimilation System (SNODAS ) gridded predictions using data from three winters (2014–2016). Snow depth observations were correlated with SNODAS predictions for sites ( R 2 = 0.20) and regions ( R 2 = 0.16), yet we detected factors associated with SNODAS bias at both scales. Specifically, SNODAS underpredicted depths at high elevations, at sites with higher solar radiation, and within conifer‐dominated forest. Depths were most underpredicted at highest elevations, up to 44 and 26 cm on average at the site and region scales, respectively. Bias was greatest when predictions were lowest, occasionally predicting snow absence when depths were >100 cm at camera sites. We also detected breakdowns in accuracy when certain environmental conditions varied within the 1 km 2 SNODAS grid cells. For example, underprediction was greatest when the solar radiation values of camera stations increased relative to the mean of the SNODAS grid cells. This relationship was most prominent in mountainous regions, suggesting that factors which influence solar radiation (e.g. topographic complexity) contribute to SNODAS inaccuracy. We caution using gridded snow data for ecological studies when bias is unknown. We suggest increased sampling to adjust for errors associated with gridded data products that arise from factors, such as forest cover and topographic variability. Increasing resolution and accuracy of climate data will improve predictions of species’ responses to climate change.

New Hampshire, Vermont↗
USGS2019

Characteristics of feeding sites of California Condors (Gymnogyps californianus) in the human-dominated landscape of Southern California

Wildlife conservation is often improved by understanding the movement ecology of species and adapting management strategies to dynamic conditions associated with movement. Despite a remarkable recovery over the past 30 year, the establishment of self-sustaining populations of California Condors (Gymnogyps californianus) has been challenging in the human-dominated landscapes of southern California. Among these challenges are those imposed by condor ground-foraging behavior that exposes them to environmental contamination. These include lead poisoning from the ingestion of spent ammunition and micro-trash ingestion and, during takeoff and landing, collisions with human structures. We tracked 28 California Condors for 24 months with patagially mounted GPS telemetry units to investigate the characteristics of ground sites condors visited and to identify spatiotemporal trends that might aid in conservation of this critically endangered species. Ground sites occurred on a wide variety of land cover types, primarily on steep slopes, and those more frequently used were associated with open cover. Condors concentrated their visits to ground sites around a 3 h period near midday, and usage increased from winter to late summer. Our study is the first to use remotely sensed telemetry data to describe fine-scale ecological correlates of condor ground-foraging ecology and therefore has important relevance for ongoing conservation and management strategies for this species. The descriptions of ground sites we provide can be used to target conservation or management actions.

California↗
USGS2023

AIMS for wildlife: Developing an automated interactive monitoring system to integrate real-time movement and environmental data for true adaptive management

To effectively manage species and habitats at multiple scales, population and land managers require rapid information on wildlife use of managed areas and responses to landscape conditions and management actions. GPS tracking studies of wildlife are particularly informative to species ecology, habitat use, and conservation. Combining GPS data with administrative data and a diverse suite of remotely sensed, geo-referenced environmental (e.g., climatic) data, would more comprehensively inform how animals interact with and utilize habitats and ecosystems and our goal was to create a conceptual model for a system that would accomplish this – the ‘Automated Interactive Monitoring System (AIMS) for Wildlife’. Our objective for this study was to develop a Customized Wildlife Report (CWR) - the first AIMS for Wildlife deliverable product. CWRs collate and summarize our 8-year GPS tracking dataset of ∼11 million locations from 1338 individual (16 species) avifauna and make actionable, real-time data on animal movements and trends in a specific area of interest available to managers and stakeholders for rapid application in day-to-day management. The CWR exemplar presented in this paper was developed to address needs identified by habitat managers of Sacramento National Wildlife Refuge and illustrates the highly specific, information offered and how it contributes to assessing the efficacy of conservation actions while allowing for near real-time adaptive management. The report can be easily customized for any of the thousands of wildlife refuges or regional areas of interest in the United States, emphasizing the broad application of an animal movement data stream. Utilizing diverse, extensive telemetry data streams through scientific collaboration can aid managers and conservation stakeholders with short and long-term research and conservation planning and help address a cadre of issues from local-scale habitat management to improving the understanding of landscape level impacts like drought, wildfire, and climate change on wildlife populations.

Journal of Environmental Management↗