Species‐level tree crown maps improve predictions of tree recruit abundance in a tropical landscape

Barber, Cristina; Graves, Sarah J.; Hall, Jefferson S.; Zuidema, Pieter A.; Brandt, Jodi S.; Bohlman, Stephanie A.; Asner, Gregory P.; Bailon, Mario; Caughlin, T. Trevor

doi:10.1002/eap.2585

Cited by 7 publications

(5 citation statements)

References 89 publications

(121 reference statements)

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“…However, these recent modeling efforts have relied on moderate resolution data such as 30 m burn severity maps from Monitoring Trends in Burn Severity (MTBS, https://www.mtbs.gov/), which may obscure fine-scale variation in seed availability. Fine scale maps of surviving trees, such as those created using fine scale imagery or lidar data, may more accurately reflect the variation in seed source availability on the landscape, and thus more closely represent true seed availability (Barber et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Spatially explicit models of seed availability improve predictions of conifer regeneration following the 2018 Carr Fire in northern California

Wright,

van Mantgem,

Buffington

et al. 2023

Front. Ecol. Evol.

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For many conifer species in dry conifer forests of North America, seeds must be present for postfire regeneration to occur, suggesting that seed dispersal from surviving trees plays a critical role in postfire forest recovery. However, the application of tree fecundity and spatial arrangement to postfire conifer recovery predictions have only recently become more common, and is often included at relatively coarse scales (i.e., 30 meters). In this study, we mapped surviving trees using lidar and created a spatially explicit estimate of seed density (seed shadows) with 10 m, 50 m, and 100 m median dispersal distances. We estimated the number of seeds produced by each tree using allometric relationships between tree size and fecundity. Along with the seed shadows, we used a suite of topographic variables as inputs to negative binomial hurdle models to predict conifer seedling abundance in 131 plots following the 2018 Carr Fire in northern California, USA. We compared models using each of the seed shadows to each other as well as to a model using the distance to the nearest surviving tree, which served as a baseline. All model formulations indicated that estimated seed availability was positively associated with conifer regeneration. Despite the importance of seed availability plays in regeneration and the substantial differences in seed availability represented by the different seed shadows in our analysis, we found surprisingly little difference in model performance regardless of which seed shadow was used. However, the models employing seed shadows outperformed the models with distance to the nearest live tree. Although we have demonstrated a modest improvement in predicting postfire conifer regeneration, the uncertainty in our results highlights the importance of tree detection and classification in future studies of this kind. Future studies may find it useful to consider other factors such as predation, site suitability, and seed mortality as potential drivers of discrepancies between total and realized dispersal kernels.

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Section: Introductionmentioning

confidence: 99%

Spatially explicit models of seed availability improve predictions of conifer regeneration following the 2018 Carr Fire in northern California

Wright,

van Mantgem,

Buffington

et al. 2023

Front. Ecol. Evol.

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“…Continued improvement of segmentation algorithms will potentially improve crownto-crown matching. However, in other cases, models that combine field measurements of mortality for a subsample of plants with large-scale remotely sensed estimates may be necessary for best performance (Barber 2021).…”

Section: Discussionmentioning

confidence: 99%

“…For a single time step, we found that segmentation performed well, including fairly accurate estimates of cover and plant abundance. Counts of individual plants and cover estimates are essential for many ecological applications, from predicting future population growth to measuring potential forage for threatened herbivores (Olsoy et al 2020; Barber et al 2022). Estimating plant population dynamics with remote sensing, including forecasting cover trajectories and population growth rate, will require integrating measurements across years.…”

Section: Discussionmentioning

confidence: 99%

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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“…Decomposing structural heterogeneity into specific scales boosts the ability of these metrics to predict ecologically relevant outcomes, particularly when multi-scale metrics are included in the same predictive model. Predictions of natural regeneration after disturbance, including recruitment, will aid decision-making on where to allocate limited resources for restoration (Barber et al 2022).…”

Section: Structural Heterogeneity As a Predictor Of Recruitmentmentioning

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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Species‐level tree crown maps improve predictions of tree recruit abundance in a tropical landscape

Cited by 7 publications

References 89 publications

Spatially explicit models of seed availability improve predictions of conifer regeneration following the 2018 Carr Fire in northern California

Spatially explicit models of seed availability improve predictions of conifer regeneration following the 2018 Carr Fire in northern California

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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