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

Zaiats, Andrii; Cattau, Megan E.; Pilliod, David S.; Liu, Rongsong; Requena‐Mullor, Juan M.; Caughlin, T. Trevor

doi:10.1007/s10980-023-01621-1

Cited by 2 publications

(4 citation statements)

References 104 publications

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“…We found that stand-level estimates of plant cover or counts were more accurate than products that required crown-to-crown matching. Additionally, cover estimates are now possible with satellite imagery leading to opportunities for long-term and historic estimates of population dynamics that are not possible with UAS imagery alone (Zaiats et al 2023). However, in other cases, estimates of individual demography may be required.…”

Section: Discussionmentioning

confidence: 99%

“…Hierarchical models that partition noisy data into measurement error and process variability are common in wildlife studies and have enabled inference on animal demography from camera trap data, which poses similar detectability challenges to UAS imagery. Adapting these hierarchical models for plant detection from UAS imagery is feasible, including simultaneously estimating the probability of false negatives (failure to detect a focal plant, when one is present) and false positives (incorrect detection due to species misclassification, split crowns, or error in the CHM) (Zaiats et al 2023), and explicitly modeling detection probabilities is likely to improve demographic estimates, particularly for smaller plants.…”

Section: Discussionmentioning

confidence: 99%

“…High variability in restoration outcomes is the norm across sagebrush steppe, motivating the need for scalable monitoring techniques (Germino et al 2022). While mediumresolution satellite products are increasingly used to forecast trajectories of sagebrush cover after disturbance (Simler-Williamson & Germino 2022;Zaiats et al 2023), the $30-m resolution of these products cannot represent individual sagebrush plants. For sagebrush, individual-level measurements are key, given high levels of between-plant genetic variation with relevance for local adaptation (Richardson & Chaney 2018;Davidson & Germino 2020), differences in recovery trajectories depending on individual plant sizes (Shriver et al 2019), and reliance on individual species, patch, and plant traits by vertebrate herbivore species of conservation concern (Frye et al 2013;Ulappa et al 2014;Fremgen-Tarantino et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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

Olsoy,

Zaiats,

Delparte

et al. 2024

Restoration Ecology

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Olsoy,

Zaiats,

Delparte

et al. 2024

Restoration Ecology

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“…(ii) What scales of structural heterogeneity are most sensitive to recovery, measured as the abundance of shrub recruits? As land management in the region increasingly relies on spatial decision-making (Meinke et al 2009;Pilliod et al 2021;Duchardt et al 2021;Zaiats et al 2023), we also determined how maps of structural heterogeneity can predict recovery by asking: (iii) Does a scale-explicit approach to analyzing structural heterogeneity enable out-of-site predictions of native shrub recruitment?…”

Section: Introductionmentioning

confidence: 99%

Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands

Zaiats,

Cattau,

Pilliod

et al. 2024

Landsc Ecol

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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 (R2 = 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.

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

Cited by 2 publications

References 104 publications

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

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

Structural heterogeneity predicts ecological resistance and resilience to wildfire in arid shrublands

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