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Abstract Active restoration often aims to accelerate ecosystem recovery. However, active restoration may not be worthwhile if its effects are overwhelmed by changes that occur passively. Moreover, it can be challenging to separate the effects of passive processes, such as dispersal and natural succession, from active restoration efforts.We assess the 24‐year impact of actively restoring a Minnesota old‐field grassland via seed addition of native tallgrass prairie species. We compared the abundance of four functional plant groups in actively restored plots against abundances in three reference classes: (1) unrestored plots undergoing passive recovery within the same old field, (2) passively recovering plots in two nearby old fields of similar age and (3) a chronosequence of 21 old fields within the same landscape.Active restoration led to a higher abundance of native grasses and forbs in the 36 m2treatment plots. Seed addition was more effective if the original vegetation was first removed using herbicide, burning and tilling. However, long‐term conclusions about the efficacy of active restoration varied widely depending on the choice of reference class.In our small‐scale restoration experiment, native abundance was similarly high in both the actively restored and reference plots after 24 years, suggesting either (1) passive recovery or (2) local dispersal of native species from nearby treatment plots (i.e. cross‐contamination). In contrast, a comparison with two nearby reference fields suggested active restoration resulted in much higher native abundance relative to passive recovery. A smaller, positive effect was detected when we compared actively restored plots to the chronosequence of old fields. In the chronosequence, many passively recovering old fields had transitioned to native grass dominance naturally, although active restoration appeared to increase native forb abundance.Synthesis and applications: Our findings highlight the importance of using scale‐appropriate references for assessing the efficacy and need for active restoration. Comparing actively restored plots with the surrounding landscape, we found that active restoration and passive recovery led to similar plant communities after 24 years. Because local dispersal from actively restored sites can nearby references, caution should be exercised when evaluating long‐term restoration projects using only small‐scale experiments.
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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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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/2041-210X.14421
