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Abstract Ecological restoration often targets plant community recovery, but restoration success may depend on the recovery of a complex web of biotic interactions to maintain biodiversity and promote ecosystem services. Specifically, management that drives resource availability, such as seeding richness and provenance, may alter species interactions across multiple trophic levels. Using experimentally seeded prairies, we examine three key groups—plants, pollinators and goldenrod crab spiders (Misumena vatia, predators of pollinators)—to understand the effects of species richness and admixture seed sourcing of restoration seed mixtures on multitrophic interactions.Working with prairie plants, we experimentally manipulated seed mix richness and the number of seed source regions (single‐source region or admixture seed sourcing). In each experimental prairie, we surveyed floral abundance and richness, pollinator visitation and plant–M. vatiainteractions.A high richness seed mix increased floral abundance when seeds were sourced from a single geographic region, and floral abundance strongly increased pollinator visitation,M. vatiaabundance and prey capture. Seeding richness and admixture seed sourcing of the seed mixture did not affect floral species richness, but floral species richness increased pollinator visitation.Pollinators interacted with different floral communities across seeding treatments, indicating a shift in visited floral species with restoration practices.Synthesis and applications. Long‐term success in prairie restoration requires the restoration of plant–arthropod interactions. We provide evidence that seed mix richness and admixture seed sourcing affect arthropod floral associations, but effective restoration of plant–arthropod interactions should consider total floral resource availability. Incorporating a food web perspective in restoration will strengthen approaches to whole ecosystem restoration.
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This content will become publicly available on November 1, 2025
Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery
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.
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- Award ID(s):
- 2207158
- PAR ID:
- 10592213
- Publisher / Repository:
- Methods in Ecology and Evolution
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 15
- Issue:
- 11
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- 2074 to 2086
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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