Multiscale modeling of longleaf pine (Pinus palustris)

2007 ◽  
Vol 37 (11) ◽  
pp. 2080-2089 ◽  
Author(s):  
E. Louise Loudermilk ◽  
Wendell P. Cropper
Keyword(s):  
United States ◽  
Matrix Model ◽  
Fire Regime ◽  
Landscape Connectivity ◽  
Longleaf Pine ◽  
Pinus Palustris ◽  
The United States ◽  
Sensitivity Analyses ◽  
Modeling Framework ◽  
Landis Model

There are few remaining longleaf pine ( Pinus palustris Mill.) ecosystems left in the southeastern coastal plain of the United States. Restoration and maintenance of these remaining habitats requires an understanding of ecosystem processes at multiple scales. The focus of this study was to develop and evaluate a modeling framework for analyzing longleaf pine dynamics at the spatially explicit landscape scale and at the spatially implicit population scale. The landscape disturbance and succession (LANDIS) model was used to simulate landscape fire dynamics in a managed forest in north-central Florida. We constructed a density-dependent longleaf pine population matrix model using data from a variety of studies across the southeastern United States to extend an existing model. Sensitivity analyses showed that the most sensitive parameters were those from the original pine model, which was based on extensive observations of individual trees. A hybrid approach integrated the two models: the fire frequencies output from the LANDIS model were input to the matrix model for specific longleaf pine populations. These simulations indicated that small isolated longleaf pine populations are more vulnerable to fire suppression and that landscape connectivity is a critical concern. A frequent prescribed fire regime is nonetheless necessary to maintain even large longleaf pine sandhill communities that have better landscape connectivity.

scholarly journals Seasonality of Biennial Burning Has No Adverse Effects on Mature Longleaf Pine Survival or Productivity

2021 ◽  
Vol 4 ◽  
Author(s):  
John L. Willis ◽  
Ajay Sharma ◽  
John S. Kush
Keyword(s):  
United States ◽  
Prescribed Burning ◽  
Fire Regime ◽  
Management Practice ◽  
Longleaf Pine ◽  
Volume Growth ◽  
Pinus Palustris ◽  
Size Class ◽  
Dormant Season

Emulating natural disturbance has become an increasingly important restoration strategy. In the fire-maintained woodlands of the southeastern United States, contemporary restoration efforts have focused on approximating the historical fire regime by burning at short intervals. Due to concerns over escape and damage to mature trees, most prescribed burning has occurred in the dormant season, which is inconsistent with the historical prevalence of lightning-initiated fire in the region. This discordance between contemporary prescribed burning and what is thought to be the historical fire regime has led some to question whether dormant season burning should remain the most common management practice; however, little is known about the long-term effects of repeated growing season burning on the health and productivity of desirable tree species. To address this question, we report on a long-term experiment comparing the effects of seasonal biennial burning (winter, spring, and summer) and no burning on the final survival status, height, diameter, and volume growth of 892 mature longleaf pine (Pinus palustris) over 23 years in three mature even-aged stands in southern Alabama, United States. Overall, longleaf pine survival across all treatments averaged 81 ± 2% [s.e]. Among seasonal burn treatments, survival was highest in the spring burns (82 ± 4%) but did not vary significantly from any other treatment (summer – 79 ± 4%, winter – 81 ± 4%, unburned – 84 ± 4%). However, survival was statistically influenced by initial diameter at breast height, as survival of trees in the largest size class (30 cm) was 40% higher than trees in the smallest size class (5 cm). Productivity of longleaf pine was not significantly different among treatment averages in terms of volume (38.9–44.1 ± 6.0 m3 ha–1), diameter (6.0–6.7 ± 0.3 cm), and height (2.5–3.4 ± 0.4 m) growth. Collectively, our results demonstrate that burning outside the dormant season will have little impact on mature longleaf pine survival and growth. This finding has important implications for the maintenance of restored southeastern woodlands, as interest in burning outside the dormant season continues to grow.

scholarly journals Power Laws in Cone Production of Longleaf Pine across Its Native Range in the United States

2017 ◽  
Vol 6 (4) ◽  
pp. 64 ◽  
Author(s):  
Xiongwen Chen ◽  
Qinfeng Guo ◽  
Dale G. Brockway
Keyword(s):  
United States ◽  
Longleaf Pine ◽  
Pinus Palustris ◽  
Power Laws ◽  
The United States ◽  
Native Range ◽  
Scaling Exponents ◽  
Ecosystem Monitoring ◽  
Scaling Properties ◽  
Cone Production

Longleaf pine (Pinus palustris Mill.) forests in the southeastern United States are considered endangered ecosystems, because of their dramatic decrease in area since European colonization and poor rates of recovery related to episodic natural regeneration. Sporadic seed production constrains restoration efforts and complicates sustainable management of this species. Previous studies of other tree species found invariant scaling properties in seed output. Here, using long-term monitoring data for cone production at seven sites across the native range of longleaf pine, we tested the possible presence of two types of power laws. Findings indicate that (i) the frequency distribution of cone production at seven sites, from 1958 to 2014, follows power law relationships with high level of significance; (ii) although there is no general trend in the dynamics of scaling exponents among all sites, there are dynamics of scaling exponents at each site, with sudden changes in scaling exponents generally corresponding to the years of higher or lower cone production; and (iii) Taylor’s power laws explain cone production at different locations, but the scaling exponents vary among these. Results from this computational approach provide new insight into the irregular cone production of longleaf pine at spatial and temporal scales. Integrated ecosystem monitoring will be necessary to more fully understand future changes in cone production. 

scholarly journals 2158. Cost-Effectiveness and Budget Impact of a Point-of-Care Nucleic Acid Amplification Test for Diagnosis of Group A Streptococcal Pharyngitis in the United States

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S732-S732
Author(s):  
James Karichu ◽  
Mindy Cheng ◽  
Joanna Sickler ◽  
Julie Munakata ◽  
S Pinar Bilir ◽  
...  
Keyword(s):  
United States ◽  
Cost Effectiveness ◽  
Nucleic Acid ◽  
Budget Impact ◽  
Point Of Care ◽  
Cost Savings ◽  
The United States ◽  
Nucleic Acid Amplification ◽  
Sensitivity Analyses ◽  
Group A

Abstract Background Group A streptococcal (GAS) pharyngitis is common in the United States (US). Each year, approximately 12 million people seek medical care for pharyngitis, accounting for ~2% of ambulatory care visits. The gold standard method for diagnosing GAS is culture. However, because culture is time intensive, rapid antigen detection tests (RADTs), with or without culture confirmation, are commonly used. Although RADTs provide results quickly, test sensitivity has been shown to be sub-optimal, which can lead to inappropriate treatment decisions. Recently, highly sensitive point-of-care nucleic acid amplification tests (POC NAAT), such as the cobas® Liat® System, have emerged. The objective of this study was to evaluate the cost-effectiveness (CE) and budget impact (BI) of adopting POC NAAT compared with RADT+culture confirmation to diagnose GAS pharyngitis from the US third-party payer perspective. Methods A decision-tree economic model was developed in Microsoft Excel to quantify costs and clinical outcomes associated with POC NAAT and RADT+culture over a one-year period. All model inputs were derived from published literature and public databases. Model outputs included costs and clinical effects measured as quality-adjusted life days (QALDs) lost. One-way and probabilistic sensitivity analyses were performed to assess the impact of uncertainty on results. Results CE analysis showed that POC NAAT would cost $44 per patient compared with $78 with RADT+ culture. POC NAAT was associated with fewer QALDs lost relative to RADT+ culture. Therefore, POC NAAT may be considered the “dominant” strategy (i.e., lower costs and higher effectiveness). Findings were robust in sensitivity analyses. BI analysis showed that adopting POC NAAT for diagnosis of GAS could yield cost-savings of 0.3% vs. current budget over 3 years. This is due to savings associated with testing, GAS-related complications, antibiotic treatment and treatment-associated complication costs. Conclusion Results suggest that adopting POC NAAT to diagnose GAS would be considered cost-effective and yield cost-savings for US payers relative to RADT+culture. Access to POC NAAT would be important to optimize appropriate GAS diagnosis and treatment decisions. Disclosures All authors: No reported disclosures.

scholarly journals Dynamic Site Index Equation for Thinned Stands of Even-Aged Natural Longleaf Pine

2010 ◽  
Vol 34 (1) ◽  
pp. 28-37 ◽  
Author(s):  
Dwight K. Lauer ◽  
John S. Kush
Keyword(s):  
Longleaf Pine ◽  
Pinus Palustris ◽  
Site Index ◽  
The United States ◽  
Permanent Plots ◽  
Gulf Region ◽  
Dummy Variable ◽  
Variable Approach ◽  
One Step ◽  
Generalized Algebraic Difference Approach

Abstract A dynamic site equation derived using the generalized algebraic difference approach was developed for thinned stands of natural longleaf pine (Pinus palustris Mill.) in the East Gulf region of the United States using 40 years of measurements on 285 permanent plots. The base model predicts height growth of trees once they reach 4.5 ft and was fit using a varying parameter for each tree and global parameters that are constant for all 3,267 trees. Parameters were estimated in one step using the dummy variable approach and a first-order autoregressive error term to account for serial correlation. The final base-age invariant equation allows the user to specify the number of years required for trees to reach 4.5 ft in height.

scholarly journals Using Forest Inventory Data with Landsat 8 Imagery to Map Longleaf Pine Forest Characteristics in Georgia, USA

2019 ◽  
Vol 11 (15) ◽  
pp. 1803 ◽  
Author(s):  
John Hogland ◽  
Nathaniel Anderson ◽  
David L. R. Affleck ◽  
Joseph St. Peter
Keyword(s):  
United States ◽  
Forest Inventory ◽  
Forest Type ◽  
Longleaf Pine ◽  
Basal Area ◽  
Pinus Palustris ◽  
Machine Learning Algorithms ◽  
Landsat 8 ◽  
Inventory Data ◽  
Forest Inventory Data

This study improved on previous efforts to map longleaf pine (Pinus palustris) over large areas in the southeastern United States of America by developing new methods that integrate forest inventory data, aerial photography and Landsat 8 imagery to model forest characteristics. Spatial, statistical and machine learning algorithms were used to relate United States Forest Service Forest Inventory and Analysis (FIA) field plot data to relatively normalized Landsat 8 imagery based texture. Modeling algorithms employed include softmax neural networks and multiple hurdle models that combine softmax neural network predictions with linear regression models to estimate key forest characteristics across 2.3 million ha in Georgia, USA. Forest metrics include forest type, basal area and stand density. Results show strong relationships between Landsat 8 imagery based texture and field data (map accuracy > 0.80; square root basal area per ha residual standard errors < 1; natural log transformed trees per ha < 1.081). Model estimates depicting spatially explicit, fine resolution raster surfaces of forest characteristics for multiple coniferous and deciduous species across the study area were created and made available to the public in an online raster database. These products can be integrated with existing tabular, vector and raster databases already being used to guide longleaf pine conservation and restoration in the region.

scholarly journals Fire Survival of Longleaf Pine (Pinus palustris) Grass Stage Seedlings: The Role of Seedling Size, Root Collar Position, and Resprouting

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1070
Author(s):  
Songheng Jin ◽  
Brett Moule ◽  
Dapao Yu ◽  
G. Geoff Wang
Keyword(s):  
South Carolina ◽  
Fire Regime ◽  
Longleaf Pine ◽  
Mineral Soil ◽  
Pinus Palustris ◽  
Current Frequency ◽  
Seedling Size ◽  
Grass Stage ◽  
Root Collar

Longleaf pine (Pinus palustris Mill.) forest is a well-known fire-dependent ecosystem. The historical dominance of longleaf pine in the southeast United States has been attributed to its adaptation known as the grass stage, which allows longleaf pine seedlings to survive under a frequent surface fire regime. However, factors affecting post-fire survival of grass stage seedlings are not well understood. In this study, we measured live and dead longleaf pine grass stage seedlings to quantify the role of seedling size, root collar position, and sprouting in seedling survival following a wildfire in the sandhills of South Carolina. We found that fire resulted in almost 50% mortality for longleaf pine grass stage seedlings. Fire survival rate increased with seedling size, but a size threshold for fire tolerance was not supported. Fire survival depended on the position of root collar relative to the mineral soil. Seedlings with protected root collars (i.e., buried in or at the level of mineral soil) experienced <21%, while seedlings with exposed root collars (i.e., elevated above mineral soil) suffered >90% post-fire mortality. Ability to resprout contributed to 45.6% of the total fire survival, with the small seedlings (root collar diameter (RCD) < 7.6 mm) almost exclusively depending on resprouting. Our findings had significant implications for fire management in longleaf pine ecosystems, and the current frequency of prescribed fire in sandhills might need to be lengthened to facilitate longleaf pine natural regeneration.

scholarly journals Spatiotemporal Invasion Severity of Chinese Tallow (Triadica sebifera) and Invasibility of Forest Types in Southern US Forestlands

2021 ◽  
Author(s):  
Sunil Nepal ◽  
W Keith Moser ◽  
Zhaofei Fan
Keyword(s):  
United States ◽  
Forest Type ◽  
Coastal Region ◽  
Pinus Palustris ◽  
The United States ◽  
Slash Pine ◽  
Forest Types ◽  
Triadica Sebifera ◽  
Chinese Tallow ◽  
Severity Class

Abstract Quantifying invasion severity of nonnative invasive plant species is vital for the development of appropriate mitigation and control measures. We examined more than 23,250 Forest Inventory and Analysis (FIA) plots from the southern coastal states of the United States to develop an alternative method to classify and map the invasion severity of Chinese tallow (Triadica sebifera). Remeasured FIA plot-level data were used to examine the spatiotemporal changes in the presence probability and cover percentage of tallow. Four invasion severity classes were identified by using the product of presence probability and cover percentage. Chinese tallow invasion severity increased over time with 90 and 123 counties being classified into the highest severity class for the first and second measurement, respectively. Further, the invasibility of major forest-type groups by severity class was examined using the product of the county-level mean presence probability and mean cover percentage of Chinese tallow as a proxy of invasibility. Longleaf/slash pine (Pinus palustris/P. elliottii) forests were highly resilient to the Chinese tallow invasion. In contrast, elm/ash/cottonwood (Ulmus spp./Fraxinus spp./Populus deltoides) and oak/gum/cypress (Quercus spp./Nyssa spp./Taxodium spp.) forest-type groups were vulnerable to invasion. Study Implications: In the southern United States forestland, differences in invasion severity and vulnerability of forest types to Chinese tallow invasion have been observed across time and space. Our findings provide insight into spatial variations in the severity of Chinese tallow invasion and the relative susceptibility of different forest-type groups in the region to inform monitoring and management of this invasive species. High invasion severity occurs in the lower Gulf of Mexico coastal region of Texas, Louisiana, and Mississippi and the Atlantic coastal region of South Carolina and Georgia, with the longleaf/slash pine and oak/gum/cypress forest-type groups being most susceptible to Chinese tallow invasion. Based on these results, we recommend that management efforts be tailored to the different invasion severity classes. Forests in the high-severity class need a management program coordinated across different agencies and landowners to curb the increase of tallow populations to prevent stand replacing risks. The monitoring of Chinese tallow spread should focus on longleaf/slash pine, loblolly/shortleaf pine, and oak/gum/cypress groups, because the spread rate was higher in these forest-type groups. A better use of scarce resources could be to treat lands in the moderate- and low-severity classes to reduce the propagule pressure levels and post-invasion spread. For those counties with a minimal-severity condition, early detection and eradiction measures should be taken in a timely maner to prevent tallow from invading noninvaded neighboring counties. Managers may be able to treat a larger area of these lands for a given investment compared with lands already severely invaded.

scholarly journals Mask Wearing and Control of SARS-CoV-2 Transmission in the United States

2020 ◽  
Author(s):  
Benjamin Rader ◽  
Laura F White ◽  
Michael R Burns ◽  
Jack Chen ◽  
Joe Brilliant ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
Grocery Store ◽  
Sensitivity Analyses ◽  
Reproductive Number ◽  
Positive Trend ◽  
Respiratory Pathogens ◽  
Transmission Control ◽  
Cross Sectional ◽  
Social Distancing

Introduction: Cloth face coverings and surgical masks have become commonplace across the United States in response to the SARS-CoV-2 epidemic. While evidence suggests masks help curb the spread of respiratory pathogens, population level, empirical research remains limited. Face masks have quickly become a topic of public debate as government mandates have started requiring their use. Here we investigate the association between self-reported mask wearing, social distancing and community SARS-CoV-2 transmission in the United States, as well as the effect of statewide mandates on mask uptake. Methods: Serial cross-sectional surveys were administered June 3 through July 27, 2020 via a web platform. Surveys queried individuals' likelihood to wear a face mask to the grocery store or with family and friends. Responses (N = 378,207) were aggregated by week and state and combined with measures of the instantaneous reproductive number (Rt), social distancing proxies, respondent demographics and other potential sources of confounding. We fit multivariate logistic regression models to estimate the association between mask wearing and community transmission control (Rt <1) for each state and week. Multiple sensitivity analyses were considered to corroborate findings across mask wearing definitions, Rt estimators and data sources. Additionally, mask wearing in 12 states was evaluated two weeks before and after statewide mandates. Results: We find an increasing trend in mask usage across the U.S., although uptake varies by geography and demographic groups. A multivariate logistic model controlling for social distancing and other variables found a 10% increase in mask wearing was associated with a 3.53 (95% CI: 2.03, 6.43) odds of transmission control (Rt <1). We also find that communities with high mask wearing and social distancing have the highest predicted probability of a controlled epidemic. These positive associations were maintained across sensitivity analyses. Following state mandates, mask wearing did not show significant statistical changes in uptake, however the positive trend of increased mask wearing over time was preserved. Conclusion: Widespread utilization of face masks combined with social distancing increases the odds of SARS-CoV-2 transmission control. Mask wearing rose separately from government mask mandates, suggesting supplemental public health interventions are needed to maximize mask adoption and disrupt the spread of SARS-CoV-2, especially as social distancing measures are relaxed.

scholarly journals Exposure to air pollution and COVID-19 mortality in the United States: A nationwide cross-sectional study

2020 ◽  
Author(s):  
Xiao Wu ◽  
Rachel C Nethery ◽  
M Benjamin Sabath ◽  
Danielle Braun ◽  
Francesca Dominici
Keyword(s):  
United States ◽  
Air Pollution ◽  
Death Rate ◽  
The United States ◽  
Sensitivity Analyses ◽  
List Type ◽  
County Level ◽  
Cross Sectional ◽  
Increased Risk

AbstractObjectivesUnited States government scientists estimate that COVID-19 may kill tens of thousands of Americans. Many of the pre-existing conditions that increase the risk of death in those with COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigated whether long-term average exposure to fine particulate matter (PM2.5) is associated with an increased risk of COVID-19 death in the United States.DesignA nationwide, cross-sectional study using county-level data.Data sourcesCOVID-19 death counts were collected for more than 3,000 counties in the United States (representing 98% of the population) up to April 22, 2020 from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center.Main outcome measuresWe fit negative binomial mixed models using county-level COVID-19 deaths as the outcome and county-level long-term average of PM2.5 as the exposure. In the main analysis, we adjusted by 20 potential confounding factors including population size, age distribution, population density, time since the beginning of the outbreak, time since state’s issuance of stay-at-home order, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables such as obesity and smoking. We included a random intercept by state to account for potential correlation in counties within the same state. We conducted more than 68 additional sensitivity analyses.ResultsWe found that an increase of only 1 μg/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate (95% confidence interval [CI]: 2%, 15%). The results were statistically significant and robust to secondary and sensitivity analyses.ConclusionsA small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate. Despite the inherent limitations of the ecological study design, our results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available so our analyses can be updated routinely.Summary BoxWhat is already known on this topicLong-term exposure to PM2.5 is linked to many of the comorbidities that have been associated with poor prognosis and death in COVID-19 patients, including cardiovascular and lung disease.PM2.5 exposure is associated with increased risk of severe outcomes in patients with certain infectious respiratory diseases, including influenza, pneumonia, and SARS.Air pollution exposure is known to cause inflammation and cellular damage, and evidence suggests that it may suppress early immune response to infection.What this study addsThis is the first nationwide study of the relationship between historical exposure to air pollution exposure and COVID-19 death rate, relying on data from more than 3,000 counties in the United States. The results suggest that long-term exposure to PM2.5 is associated with higher COVID-19 mortality rates, after adjustment for a wide range of socioeconomic, demographic, weather, behavioral, epidemic stage, and healthcare-related confounders.This study relies entirely on publicly available data and fully reproducible, public code to facilitate continued investigation of these relationships by the broader scientific community as the COVID-19 outbreak evolves and more data become available.A small increase in long-term PM2.5 exposure was associated with a substantial increase in the county’s COVID-19 mortality rate up to April 22, 2020.

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