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Abstract 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 <  < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 <  < 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. 

Abstract ContextUnoccupied 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. ObjectivesThe 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. MethodsWe 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. ResultsWe 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. ConclusionWe 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. 

Abstract 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 m2 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. 

Biodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline‐specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross‐disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape‐level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross‐scale studies that promote a holistic approach to detect, monitor and manage biodiversity. 

Abstract The structure and composition of plant communities in drylands are highly variable across scales, from microsites to landscapes. Fine spatial resolution field surveys of dryland plants are essential to unravel the impact of climate change; however, traditional field data collection is challenging considering sampling efforts and costs. Unoccupied aerial systems (UAS) can alleviate this challenge by providing standardized measurements of plant community attributes with high resolution. However, given widespread heterogeneity in plant communities in drylands, and especially across environmental gradients, the transferability of UAS imagery protocols is unclear. Plant functional types (PFTs) are a classification scheme that aggregates the diversity of plant structure and function. We mapped and modeled PFTs and fractional photosynthetic cover using the same UAS imagery protocol across three dryland communities, differentiated by a landscape‐scale gradient of elevation and precipitation. We compared the accuracy of the UAS products between the three dryland sites. PFT classifications and modeled photosynthetic cover had highest accuracies at higher elevations (2241 m) with denser vegetation. The lowest site (1101 m), with more bare ground, had the least agreement with the field data. Notably, shrub cover was well predicted across the gradient of elevation and precipitation (~230–1100 mm/year). UAS surveys captured the heterogeneity of plant cover across sites and presented options to measure leaf‐level composition and structure at landscape levels. Our results demonstrate that some PFTs (i.e., shrubs) can readily be detected across sites using the same UAS imagery protocols, while others (i.e., grasses) may require site‐specific flight protocols for best accuracy. As UAS are increasingly used to monitor dryland vegetation, developing protocols that maximize information and efficiency is a research and management priority. 

Abstract 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 ofA. 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.