Longleaf Pine Growth and Yield

Kush, John S.; Goelz, J.C.G.; Williams, Richard; Carter, Douglas R.; Linehan, Peter E.

doi:10.1007/978-0-387-30687-2_8

Cited by 7 publications

(7 citation statements)

References 17 publications

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“…In order to reintroduce fire to some of the more heavily damaged sites, low impact timber salvage will be necessary to remove dangerous fuel sources and open up the understory to promote fire contiguity while minimizing impact to the soil and understory (70,71). In sites where the mature trees are significantly reduced, such as Apalachee WMA, natural regeneration may no longer be possible and restoration should include planting of seedlings (22,60).…”

Section: Discussionmentioning

confidence: 99%

The impact of Hurricane Michael on longleaf pine habitats in Florida

Zampieri

Pau

Okamoto

2019

Preprint

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9The longleaf pine (Pinus palustris) ecosystem of the North American Coastal Plain (NACP) is a global 10 biodiversity hotspot. Disturbances such as tropical storms play an integral role in ecosystem maintenance in 11 these systems. However, altered disturbance regimes as a result of climate change may be outside the historical 12 threshold of tolerance. Hurricane Michael impacted the Florida panhandle as a Category 5 storm on October 13 10th, 2018. In this study, we estimate the extent of Florida longleaf habitat that was directly impacted by 14 Hurricane Michael. We then quantify the impact of Hurricane Michael on tree density and size structure using 15 a Before-After study design at four sites (two wet flatwood and two upland pine communities). Finally, we 16 identify the most common type of tree damage at each site and community type. We found that 39% of the 17 total remaining extent of longleaf pine habitat was affected by the storm in Florida alone. Tree mortality 18 ranged from 1.3% at the site furthest from the storm center to 88.7% at the site closest. Most of this mortality 19 was in mature sized trees (92% mortality), upon which much of the biodiversity in this habitat depends. As the 20 frequency and intensity of extreme events increases, management plans that mitigate for climate change 21 impacts need to account for large-scale stochastic mortality events in order to effectively preserve critical 22 habitats. 26 27 28 2 30 1. Introduction 31Disturbance plays an integral role in maintaining ecosystem structure and functioning (1,2). However, many 32 ecological disturbances are expected to change as the climate changes (3), altering the frequency, intensity, duration, 33 and timing of events (4). Shifting disturbance regimes due to climate change pose a threat to the conservation of 34 biodiversity as species experience conditions outside their historical norms (5-7). In forest and savanna ecosystems, 35disturbances can include fire, hurricanes, extreme wind events, insect outbreaks, exotic plant invasions, or drought, 36 among many others (1). 37Longleaf pine (Pinus palustris Mill.) forests and savannas provide critical habitat for numerous endangered 38 species of animals such as the red-cockaded woodpecker (Picoides borealis), gopher tortoise (Gopherus 39 polyphemus), and the indigo snake (Drymarchon corais couperi) as well as many endangered plants such as the 40 American chaffseed (Schwalbea americana), Florida skullcap (Scutellaria floridana), and Harper's beauty 41 (Harperocallis flava) (8)(9)(10)(11). About 30% of all plant species associated with longleaf pine habitat are endemic to the 42 region (12). Yet, the range of longleaf pine has been reduced to <3% of its historical extent (13). Florida, and more 43 specifically the Florida Panhandle, is one of the most important strongholds of endangered longleaf pine habitat 44 (14,15) containing 51% and 31%, respectively, of all the remaining longleaf pine ecosystem (16,17). 45Longleaf pine habitats in the Panhandle of Florida are located within...

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Section: Discussionmentioning

confidence: 99%

The impact of Hurricane Michael on longleaf pine habitats in Florida

Zampieri

Pau

Okamoto

2019

Preprint

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“…It could very well be that, when using younger data such as age 15 dominant heights, an anamorphic equation form is not flexible enough to account for reduced height growth near the ages of 50 years and beyond [31]. Farrar (1981, [19]) compared predictions from his equation (equation 5) with predictions from [20,28]-equation (3). He concluded for the RLGS dataset, based on his selected approach, that equation [5] produced slightly better estimates of site index (base age 50 years).…”

Section: Predictions Of Site Indexmentioning

confidence: 99%

“…In its native area, longleaf pine can be a viable alternative to other southern yellow pines because it is resistant to many pathogens and insects and has relatively low site requirements [2]. With proper management, more valuable poles can be produced [3][4][5]. Additionally, longleaf may incur less damage during hurricanes [6,7].…”

Section: Introductionmentioning

confidence: 99%

Currently Available Site Index Equations That Use On-Site Tree Measurements for Naturally Regenerated Longleaf Pine in Its Historical Range

VanderSchaaf

2024

Preprint

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The longleaf pine (Pinus palustris P. Mill.) forest type occupied a much greater area in the United States prior to the arrival of Europeans, estimated to be around 37.2 million hectares. This area has been greatly reduced, and these ecosystems now occupy only about 1.2 to 1.6 million hectares. However, there has been great interest in the restoration of this forest type, and existing stands are actively managed. Thus, managers need tools to estimate site quality and ultimately productivity. Here, 16 existing equations to estimate site index and dominant height in naturally regenerated longleaf pine stands are examined for their behavior across a range of site quality and age conditions. Others can use this information as a reference to determine which equation may be best for their situation.

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“…Samples used for this study were collected from 16 stands of planted, unthinned longleaf pine trees throughout southern Georgia (U.S.). Stands sampled were from either cutover forest sites (where the previous rotation was trees; most likely southern pine species) or old agricultural fields (the prior rotation was not a tree species) (Hainds 2004;Johnson and Gjerstad 2006;Kush et al 2006), with eight stands sampled per site type. Stands ages ranged from 12 to 25 yr.…”

Section: Tree Selection and Disk Extractionmentioning

confidence: 99%

Image Analysis To Assess Wood Variability In Longleaf Pine Cross-Sectional Disks

Raut,

Dahlen

2023

WFS

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Image analysis is an important method for rapidly measuring wood property variation, but it is infrequently applied to disks collected from forestry studies. The objective of this study was to compare image-estimated wood and bark volumes and diameters to reference measurements, and to extract more information from the images including the shape (out-of-round index, eccentric pith) and the amount and location of severe compression wood. A total of 1120 disks were cut from multiple height levels of 48 defect-free and 56 defect-containing (forking, excessive sweep, and ramicorn branching) longleaf pine (Pinus palustris) trees from 16 stands across Georgia (U.S.). Disks were machined on one transverse surface using a computer numeric controlled router to prepare a clean surface for imaging. Three images, one under white light, the second under blue light, and the third under blue light with a green longpass filter, were taken for each disk. Volumes and diameters estimated from images were in close agreement with reference methods. Linear models fitted as measured vs image volumes for wood and bark had coefficient of determination (R 2 ) values of .0.99 and 0.96. Linear models fitted as measured vs image diameters had R 2 values of .0.99. Out-of-round index and pith eccentricity values calculated from images showed a moderate positive correlation (R 5 0.43). Algorithms developed were able to correctly identify severe compression wood, but not mild-to-moderate compression wood. Severe compression wood was moderately correlated to out-of-round index (R 5 0.54) and pith eccentricity (R 5 0.48). More than 98% of the disks having severe compression wood came from defect-containing trees.

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Longleaf Pine Growth and Yield

Cited by 7 publications

References 17 publications

The impact of Hurricane Michael on longleaf pine habitats in Florida

The impact of Hurricane Michael on longleaf pine habitats in Florida

Currently Available Site Index Equations That Use On-Site Tree Measurements for Naturally Regenerated Longleaf Pine in Its Historical Range

Image Analysis To Assess Wood Variability In Longleaf Pine Cross-Sectional Disks

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