Not just about the trees: Key role of mosaic-meadows in restoration of ponderosa pine ecosystems

Matonis, Megan S.; Binkley, Dan

doi:10.1016/j.foreco.2018.01.019

Cited by 21 publications

(24 citation statements)

References 46 publications

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“…The importance and decline of small meadows in ponderosa pine has been documented in Colorado [102]. Between 1938 and 2015 fire-excluded areas with a historic low-severity fire regime in the lower montane of the CFR experienced an almost 16% net increase in forest cover and fire-excluded mixed-severity stands showed an almost 12% net increase [58].…”

Section: Predictability Of Conifer Refugia Using Landscape Variables:mentioning

confidence: 99%

“…Forest management may prioritize prescribed burning of existing savannas and meadows as a crucial first step in potentially increasing tree survivorship in savannas and in the surrounding denser forests [106]. A better understanding of drivers of more persistent meadows and associated soil properties in the CFR could also help guide management in locating areas for meadow restoration and may drive more innovative management strategies for fuel treatments, such as small meadow restoration for conifer refugia [102].…”

Section: Predictability Of Conifer Refugia Using Landscape Variables:mentioning

confidence: 99%

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Still standing: Recent patterns of post-fire conifer refugia in ponderosa pine-dominated forests of the Colorado Front Range

et al. 2020

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Forested fire refugia (trees that survive fires) are important disturbance legacies that provide seed sources for post-fire regeneration. Conifer regeneration has been limited following some recent western fires, particularly in ponderosa pine (Pinus ponderosa) forests. However, the extent, characteristics, and predictability of ponderosa pine fire refugia are largely unknown. Within 23 fires in ponderosa pine-dominated forests of the Colorado Front Range (1996-2013), we evaluated the spatial characteristics and predictability of refugia: first using Monitoring Trends in Burn Severity (MTBS) burn severity metrics, then using landscape variables (topography, weather, anthropogenic factors, and pre-fire forest cover). Using 1-m resolution aerial imagery, we created a binary variable of post-fire conifer presence ('Conifer Refugia') and absence ('Conifer Absence') within 30-m grid cells. We found that maximum patch size of Conifer Absence was positively correlated with fire size, and 38% of the burned area was � 50m from a conifer seed source, revealing a management challenge as fire sizes increase with warming further limiting conifer recovery. In predicting Conifer Refugia with two MTBS-produced databases, thematic burn severity classes (TBSC) and continuous Relative differenced Normalized Burn Ratio (RdNBR) values, Conifer Absence was high in previously forested areas of Low and Moderate burn severity classes in TBSC. RdNBR more accurately identified post-fire conifer survivorship. In predicting Conifer Refugia with landscape variables, Conifer Refugia were less likely during burn days with high maximum temperatures: while Conifer Refugia were more likely on moister soils and closer to higher order streams, homes, and roads; and on less rugged, valley topography. Importantly, pre-fire forest canopy cover was not strongly associated with Conifer Refugia. This study further informs forest management by mapping post-fire patches lacking conifer seed sources, validating the use of RdNBR for fire refugia, and detecting abiotic and topographic variables that may promote conifer refugia.

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Section: Predictability Of Conifer Refugia Using Landscape Variables:mentioning

confidence: 99%

Section: Predictability Of Conifer Refugia Using Landscape Variables:mentioning

confidence: 99%

Still standing: Recent patterns of post-fire conifer refugia in ponderosa pine-dominated forests of the Colorado Front Range

et al. 2020

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“…; Kalies and Rosenstock ; Bottero et al. ; Matonis and Binkley ). Evaluating individual treatments with accurate, quantitative datasets helps determine their contribution to the larger restoration plan and associated changes to the plan.…”

Section: Introductionmentioning

confidence: 99%

UAV‐derived estimates of forest structure to inform ponderosa pine forest restoration

Belmonte

Sankey

Bradford

et al. 2019

Remote Sens Ecol Conserv

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Restoring forest ecosystems has become an increasingly high priority for land managers across the American West. Millions of hectares of forest are in need of drastic yet strategic reductions in density (e.g., basal area). Meeting the restoration and management goals requires quantifying metrics of vertical and horizontal forest structure, which has relied upon field‐based measurements, manned airborne or satellite remote sensing datasets. We used unmanned aerial vehicle (UAV) image‐derived Structure‐from‐Motion (SfM) models and high‐resolution multispectral orthoimagery in this study to quantify vertical and horizontal forest structure at both the fine‐ (<4 ha) and mid‐scales (4–400 ha) across a forest density gradient. We then used these forest structure estimates to assess specific objectives of a forest restoration treatment. At the fine‐scale, we found that estimates of individual tree height and canopy diameter were most accurate in low‐density conditions, with accuracies degrading significantly in high‐density conditions. Mid‐scale estimates of canopy cover and forest density followed a similar pattern across the density gradient, demonstrating the effectiveness of UAV image‐derived estimates in low‐ to medium‐density conditions as well as the challenges associated with high‐density conditions. We found that post‐treatment conditions met a majority of the prescription objectives and demonstrate the UAV image application in quantifying changes from a mechanical thinning treatment. We provide a novel approach to forest restoration monitoring using UAV‐derived data, one that considers varying density conditions and spatial scales. Future research should consider a more spatially extensive sampling design, including different restoration treatments, as well as experimenting with different combinations of equipment, flight parameters, and data processing workflows.

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“…Second, Tinkham [15] used a subset of this dataset to produce visualizations to aid forest managers planning to implement restoration cuttings. Last, Matonis et al [16] supplemented the stem-map dataset to investigate relationships between overstory stocking and understory plant community composition. The potential reuse of this dataset has not yet been exhausted.…”

Section: Discussionmentioning

confidence: 99%

Stem-Maps of Forest Restoration Cuttings in Pinus ponderosa-Dominated Forests in the Interior West, USA

Ziegler

Hoffman

Battaglia

et al. 2019

Data

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Stem-maps, maps of tree locations with optional associated measurements, are increasingly being used for ecological study in forest and plant sciences. Analyses of stem-map data have led to greater scientific understanding and improved forest management. However, availability of these data for reuse remains limited. We present a description of eight 4-ha stem-maps used in four prior research studies. These stem-maps contain locations and associated measurements of residual trees and stumps measured after forest restoration cuttings in Colorado, Arizona, and New Mexico. Data are published in two file formats to facilitate reuse.

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Not just about the trees: Key role of mosaic-meadows in restoration of ponderosa pine ecosystems

Cited by 21 publications

References 46 publications

Still standing: Recent patterns of post-fire conifer refugia in ponderosa pine-dominated forests of the Colorado Front Range

Still standing: Recent patterns of post-fire conifer refugia in ponderosa pine-dominated forests of the Colorado Front Range

UAV‐derived estimates of forest structure to inform ponderosa pine forest restoration

Stem-Maps of Forest Restoration Cuttings in Pinus ponderosa-Dominated Forests in the Interior West, USA

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