Geology topics

Trevor Caughlin

Publications and source records attributed to Trevor Caughlin.

11 recordsLinked to original sources

USGS2025

Applications of unoccupied aerial systems (UAS) in landscape ecology: A review of recent research, challenges and emerging opportunities

Context Unoccupied aerial systems/vehicles (UAS/UAV, a.k.a. drones) have become an increasingly popular tool for ecological research. But much of the recent research is concerned with developing mapping and detection approaches, with few studies attempting to link UAS data to ecosystem processes and function. Landscape ecologists have long used high resolution imagery and spatial analyses to address ecological questions and are therefore uniquely positioned to advance UAS research for ecological applications. Objectives The review objectives are to: (1) provide background on how UAS are used in landscape ecological studies, (2) identify major advancements and research gaps, and (3) discuss ways to better facilitate the use of UAS in landscape ecology research. Methods We conducted a systematic review based on PRISMA guidelines using key search terms that are unique to landscape ecology research. We reviewed only papers that applied UAS data to investigate questions about ecological patterns, processes, or function. Results We summarize metadata from 161 papers that fit our review criteria. We highlight and discuss major research themes and applications, sensors and data collection techniques, image processing, feature extraction and spatial analysis, image fusion and satellite scaling, and open data and software. Conclusion We observed a diversity of UAS methods, applications, and creative spatial modeling and analysis approaches. Key aspects of UAS research in landscape ecology include modeling wildlife micro-habitats, scaling of ecosystem functions, landscape and geomorphic change detection, integrating UAS with historical aerial and satellite imagery, and novel applications of spatial statistics.

Landscape Ecology↗

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↗

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↗

USGS2024

Demography with drones: Detecting growth and survival of shrubs with unoccupied aerial systems

Large-scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush ( Artemisia tridentata ) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian-optimized segmentation of individual shrub canopies was high (73–95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground-truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field-based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management-relevant extents.

Restoration Ecology↗

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↗

USGS2023

Forecasting natural regeneration of sagebrush after wildfires using population models and spatial matching

Context Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct resource investment will improve scalability of restoration actions. Objectives An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where post-wildfire regeneration of sagebrush cover ( Artemisia spp.) is likely to occur within the burnt areas across the region (> 900,000 km 2 ). Methods First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfires not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy. Results Regional-level models revealed that sagebrush post-fire recovery is slow, estimating > 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfire and within-wildfire spatial heterogeneity improved out-of-sample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover. Conclusions We demonstrate that combining population models and non-parametric spatial matching provides a flexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration.

Great Basin↗

USGS2022

Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation

Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m −2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a ‘latent’ variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95–250 seeds per m 2 that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes.

Idaho↗

USGS2021

Intraspecific variation mediates density dependence in a genetically diverse plant species

Interactions between neighboring plants are critical for biodiversity maintenance in plant populations and communities. Intraspecific trait variation and genome duplication are common in plant species and can drive eco-evolutionary dynamics through genotype-mediated plant–plant interactions. However, few studies have examined how species-wide intraspecific variation may alter interactions between neighboring plants. We investigate how subspecies and ploidy variation in a genetically diverse species, big sagebrush ( Artemisia tridentata ), can alter the demographic outcomes of plant interactions. Using a replicated, long-term common garden experiment that represents range-wide diversity of A. tridentata , we ask how intraspecific variation, environment, and stand age mediate neighbor effects on plant growth and survival. Spatially explicit models revealed that ploidy variation and subspecies identity can mediate plant–plant interactions but that the effect size varied in time and across experimental sites. We found that demographic impacts of neighbor effects were strongest during early stages of stand development and in sites with greater growth rates. Within subspecies, tetraploid populations showed greater tolerance to neighbor crowding compared to their diploid variants. Our findings provide evidence that intraspecific variation related to genome size and subspecies identity impacts spatial demography in a genetically diverse plant species. Accounting for intraspecific variation in studies of conspecific density dependence will improve our understanding of how local populations will respond to novel genotypes and biotic interaction regimes. As introduction of novel genotypes into local populations becomes more common, quantifying demographic processes in genetically diverse populations will help predict long-term consequences of plant–plant interactions.

Idaho, Utah↗

USGS2021

Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites

Altered climate, including weather extremes, can cause major shifts in vegetative recovery after disturbances. Predictive models that can identify the separate and combined temporal effects of disturbance and weather on plant communities and that are transferable among sites are needed to guide vulnerability assessments and management interventions. We asked how functional group abundance responded to time since fire and antecedent weather, if long‐term vegetation trajectories were better explained by initial post‐fire weather conditions or by general five‐year antecedent weather, and if weather effects helped predict post‐fire vegetation abundances at a new site. We parameterized models using a 30‐yr vegetation monitoring dataset from burned and unburned areas of the Orchard Training Area (OCTC) of southern Idaho, USA, and monthly PRISM data, and assessed model transferability on an independent dataset from the well‐sampled Soda wildfire area along the Idaho/Oregon border. Sagebrush density increased with lower mean air temperature of the coldest month and slightly increased with higher mean air temperature of the hottest month, and with higher maximum January–June precipitation. Perennial grass cover increased in relation to higher precipitation, measured annually in the first four years after fire and/or in September–November the year of fire. Annual grass increased in relation to higher March–May precipitation in the year after fire, but not with September–November precipitation in the year of fire. Initial post‐fire weather conditions explained 1% more variation in sagebrush density than recent antecedent 5‐yr weather did but did not explain additional variation in perennial or annual grass cover. Inclusion of weather variables increased transferability of models for predicting perennial and annual grass cover from the OCTC to the Soda wildfire regardless of the time period in which weather was considered. In contrast, inclusion of weather variables did not affect transferability of the forecasts of post‐fire sagebrush density from the OCTC to the Soda site. Although model transferability may be improved by including weather covariates when predicting post‐fire vegetation recovery, predictions may be surprisingly unaffected by the temporal windows in which coarse‐scale gridded weather data are considered.

Idaho↗

USGS2020

Shared functional traits explain synchronous changes in long‐term count trends of migratory raptors

Aim Assessing long‐term shifts in faunal assemblages is important to understand the consequences of ongoing global environmental change. One approach to assess drivers of assemblage changes is to identify the traits associated with synchronous shifts in count trends among species. Our research identified traits influencing trends in 73 years of count data on migrating raptors recorded in the north‐eastern USA. Location Pennsylvania, USA. Time period 1946–2018. Major taxa studied Birds of prey/raptors. Methods Migrating raptors were counted during autumn, following a standardized protocol. We used a hierarchical breakpoint model to identify when count trends shifted and to assess the role of traits in driving these trends before and after the breakpoint. Specifically, we quantified the probability of the direction (PD) of an effect of body mass, habitat or dietary specialization, migratory behaviour and susceptibility to dichlorodiphenyltrichloroethane (DDT) on count trends. Results We documented an assemblage‐wide mean shift in count trends of migrating raptors in 1974. In general, species that exhibited negative count trends before the breakpoint exhibited positive count trends afterwards. We found that traits associated with resource use (diet and habitat specialization) had high probabilities of affecting count trends, pre‐ and post‐breakpoint (> 90%). Moreover, the direction of their effects differed during both periods. Unexpectedly, other traits we evaluated, including DDT susceptibility, had relatively weaker associations with count trends. Main conclusions Trait‐based frameworks have promise for testing generalized assumptions about drivers of population trajectories. Historically, DDT was considered a key driver of changes in raptor population trends. However, our analysis suggests that other factors were also relevant. Moreover, the positive association between count trends and generalist behaviour depended on the temporal context. This result has implications for other settings where demographic trends can be linked to traits and help to identify drivers of biodiversity change.

Pennsylvania↗

USGS2020

Impact of unburned remnant sagebrush versus outplants on post-fire landscape rehabilitation

Nearly half of the vast sagebrush steppe in the western United states has lost many or nearly all native plant species, largely due to the interaction of invasive species and increased wildfire. Re-establishing sagebrush, a keystone component of these ecosystems, has become a management focus in recent decades using aerial broadcast seeding or limited plantings. One promising avenue for improving the planning and assessment of post-fire seedings involves the spatial patchiness of burn patterns and in the recovery of sagebrush after fire. Unburned remnant or post-fire planted islands (or patches) of sagebrush could be valuable seed sources for species recovery in surrounding burned areas. Information on how much spatial expansion of unburned remnant patches is expected over time could help in the planning of post-fire treatments. However, previous research has indicated that sagebrush seeds do not disperse far, which would imply that unburned or created patches do not contribute much to sagebrush reestablishment effects. Our objective was to determine whether remnant/unburned sagebrush patches contribute to sagebrush recovery in the surrounding burned areas. We quantified seed rain and seedling establishment in relation to patches of sagebrush that were either unburned remnant or had been planted in the first year or so after wildfire. We conducted a seed trapping experiment across 6 different wildfires during two winters to determine seed transport distances. We paired this with a seedling recruitment study on the Soda wildfire where we mapped distances between remnants and seedlings. We found that although a few seeds did travel much farther than previously recorded (maximum of 26 m), seed dispersal was highly variable across sites and patches, and only a small portion of seeds dispersed farther than a few meters from sagebrush patches. Our seedling recruitment assessment confirmed a limited contribution of remnants to seedling recruitment. Specifically, a microsite was only marginally more likely to have a sagebrush seedling even if there was >50 neighbors within a 40 m radius. There were no differences in the quantity of seeds dispersed from remnant versus actively managed patches. Overall, we found that isolated sagebrush patches are unlikely to significantly contribute to landscape regeneration of sagebrush on large fires and that aerial seeding is likely needed to overcome seed limitations. We did detect substantial variation in site-level sagebrush seed production among years, including one site that did not produce any seed in one year. Variability in seed production in space and time appeared to be a potentially more important variable potentially affecting sagebrush seed availability than dispersal distances and is a topic that merits more investigation.

Idaho, Oregon↗