Other Forestry and Forest Sciences Commons^™

Effects Of Warming On Growth And Leaf Colonization By Litter Mat-Forming Fungi In A Wet Tropical Forest In Puerto Rico, Ari E. Puentes, D. Jean Lodge, Deyaneira A. Ortiz-Iglesias, Tatiana Barreto-Vélez, Laura C. Rubio-Lebrón, Hieu Chu, Christine S. O'Connell, Sasha C. Reed, Tana E. Wood

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wet tropical forests are experiencing rising temperatures and increased frequency and intensity of extreme climatic events, such as cyclones, which can increase rates of soil erosion and surface runoff. Fungal litter mats, formed by agaric decomposer fungi, play a crucial role in stabilizing slopes, preventing erosion, and aiding nutrient cycling; however, little is known about how warming affects litter mat growth and function. We investigated two litter mat-forming fungi, Gymnopus johnstonii and Marasmius aff. crinis-equi, in warmed (+4°C above ambient) and control plots in the Luquillo Experimental Forest, Puerto Rico. Growth and time-to-leaf colonization were monitored over 6 weeks …

Limited Soil Water Recharge In Summer Affects Seasonal Isotopic Signatures Of Tree Xylem Water, Marius G. Floriancic, Gregory R. Goldsmith, Harsh Beria, Scott T. Allen, James W. Kirchner

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Recent studies have reported that xylem waters in forest trees during the summer growing season are often isotopically similar to winter, rather than summer, precipitation. Several hypotheses have been proposed to explain this observation, which can be viewed as a form of ecohydrological separation, but empirical tests spanning space and time remain limited. We hypothesise that variations in the seasonal origin of precipitation found in tree xylem waters can be explained by seasonal differences in soil water recharge. We test this hypothesis by evaluating to what extent climatic aridity can explain seasonal signals in extensive surveys of the isotope ratios …

Integrating Belowground Recovery Into Tropical Forest Restoration Design And Monitoring, Lauren Toro, Leland K. Werden, Shalom D. Addo-Danso, Kelly M. Andersen, Sarah Batterman, Matilde M. Bragadini, Pooja Choksi, Rebecca J. Cole, Liza S. Comita, Daniela Cusack, Daisy H. Dent, Lee H. Dietterich, Joshua B. Fisher, Katrin Fleischer, Lucia Fuchslueger, Nohemi Huanca-Nunez, Janey R. Lienau, Lindsay A. Mcculloch, Ember M. Morrissey, Jennifer S. Powers, Mareli Sánchez-Juliá, Oscar Valverde-Barrantes, Anita Weissflog, Michelle Y. Wong

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

There is growing recognition that tropical forest restoration is key for sequestering carbon and enhancing ecosystem resilience. Soils, roots, and soil biota are central to ecosystem function and services, but belowground recovery is largely overlooked in restoration monitoring frameworks. Here, we outline current understanding of the links between above- and belowground recovery in tropical forests by examining how belowground properties before and after intervention influence recovery; by evaluating whether aboveground recovery can serve as a proxy for belowground dynamics; and by proposing a blueprint for monitoring dynamic soil physical (bulk density, aggregate stability), chemical (organic matter or carbon, pH), and …

Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wildfire prediction models that can be applied across diverse regions at fine scales (<  100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km² from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R² = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) …

Climate Constrains The Enhancement Of Co2 Fertilization On Forest Gross Primary Productivity, Xinyuan Wei, Daniel J. Hayes, Christopher R. Schwalm, Joshua B. Fisher, Deborah N. Huntzinger, Lei Ma, Rodrigo Vargas, Nathaniel A. Brunsell

Mathematics, Physics, and Computer Science Faculty Articles and Research

Forest gross primary production (GPP) is influenced by the interplay between climate conditions and atmospheric CO₂ levels, which interact in complex ways, generating both compensating and amplifying effects. In this study, eddy covariance flux measurements from 50 forest ecosystems were integrated with simulations from 14 terrestrial biosphere models to investigate how climate conditions and atmospheric CO₂ concentrations regulate forest GPP. This approach bridges site-level observations with biome-scale model estimates to develop a global understanding. Our findings suggest that in boreal and cold temperate regions, temperature primarily constrains the enhancement of the CO₂ fertilization on forest GPP; however, …

Monitoring Mangrove Dynamics And Evaluating Future Afforestation Potential In The Egyptian Red Sea, Rasha M. Abou Samra, Mansour Almazroui, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Mangrove forests are vital for ecosystem services and coastal management but face stressors from anthropogenic activities and climate change. This study estimates the extent and afforestation potential of mangroves along the Egyptian Red Sea coast from 1984 to 2022 using NDVI derived from Landsat-5 and Sentinel-2 data in Google Earth Engine. Aboveground biomass (AGB), belowground biomass (BGB), carbon (C) stock, and CO₂ sequestration potential were evaluated using Sentinel-2 and elevation data. Future afforestation suitability (2020–2050) under SSP2-4.5 and SSP5-8.5 scenarios was assessed with the MaxEnt model. Mangrove area increased from 0.95 km² in 1984 to 1.46 km^{2 …}

Scaling Individual Tree Transpiration With Thermal Cameras Reveals Interspecies Differences To Drought Vulnerability, Mostafa Javadian, Donald M. Aubrecht, Joshua B. Fisher, Russell L. Scott, Sean P. Burns, Jen L. Diehl, J. William Munger, Andrew D. Richardson

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Understanding tree transpiration variability is vital for assessing ecosystem water-use efficiency and forest health amid climate change, yet most landscape-level measurements do not differentiate individual trees. Using canopy temperature data from thermal cameras, we estimated the transpiration rates of individual trees at Harvard Forest and Niwot Ridge. PT-JPL model was used to derive latent heat flux from thermal images at the canopy-level, showing strong agreement with tower measurements (R2 = 0.70–0.96 at Niwot, 0.59–0.78 at Harvard at half-hourly to monthly scales) and daily RMSE of 33.5 W/m2 (Niwot) and 52.8 W/m2 (Harvard). Tree-level analysis revealed species-specific responses to …

Regional Forest Volume Estimation By Expanding Lidar Samples Using Multi-Sensor Satellite Data, Bo Xie, Chunxiang Cao, Min Xu, Barjeece Bashir, Ramesh P. Singh, Zhibin Huang, Xiaojuan Lin

Mathematics, Physics, and Computer Science Faculty Articles and Research

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini …

Remote Sensing Monitoring Of Vegetation Dynamic Changes After Fire In The Greater Hinggan Mountain Area: The Algorithm And Application For Eliminating Phenological Impacts, Zhibin Huang, Chunxiang Cao, Wei Chen, Min Xu, Yongfeng Dang, Ramesh P. Singh, Barjeece Bashir, Bo Xie, Xiaojuan Lin

Mathematics, Physics, and Computer Science Faculty Articles and Research

Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent …

Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to …

Using Multi-Indices Approach To Quantify Mangrove Changes Over The Western Arabian Gulf Along Saudi Arabia Coast, Wenzhao Li, Hesham El-Askary, Mohamed A. Qurban, Jingjing Li, K. P. Manikandan, Thomas Piechota

Mathematics, Physics, and Computer Science Faculty Articles and Research

Mangroves habitat present an important resource for large coastal communities benefiting from activities such as fisheries, forest products and clean water as well as protection against coastal erosion and climate related extreme events. Yet they are increasingly threatened by natural pressure and anthropogenic activities. We observed an inaccurate distribution of mangroves over the Western Arabian Gulf (WAG) which is a vital habitat and resource for the local ecosystem, according to the United Stated Geological Survey (USGS) mangrove database through spectral analysis. Change detection analysis is conducted on mangrove forests along the Saudi Arabian coast of the WAG for the years …

Phylogenetic And Biogeographic Controls Of Plant Nighttime Stomatal Conductance, Kailiang Yu, Gregory R. Goldsmith, Yujie Wang, William R. L. Anderegg

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The widely documented phenomenon of nighttime stomatal conductance (g_sn) could lead to substantial water loss with no carbon gain, and thus it remains unclear whether nighttime stomatal conductance confers a functional advantage. Given that studies of g_sn have focused on controlled environments or small numbers of species in natural environments, a broad phylogenetic and biogeographic context could provide insights into potential adaptive benefits of g_sn.

We measured g_sn on a diverse suite of species (n = 73) across various functional groups and climates‐of‐origin in a common garden to study the phylogenetic and biogeographic/climatic controls …

What Controls Variation In Carbon Use Efficiency Among Amazonian Tropical Forests?, Christopher E. Doughty, Gregory R. Goldsmith, Nicolas Raab, Cecile A. J. Girardin, Filio Farfan-Amezquita, Walter Huaraca-Huasco, Javier E. Silva-Espejo, Alejandro Araujo-Murakami, Antonio C. L. Da Costa, Wanderley Rocha, David Galbraith, Patrick Meir, Dan B. Metcalfe, Yadvinder Malhi

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Why do some forests produce biomass more efficiently than others? Variations in Carbon Use Efficiency (CUE: total Net Primary Production (NPP)/ Gross Primary Production (GPP)) may be due to changes in wood residence time (Biomass/NPP_wood), temperature, or soil nutrient status. We tested these hypotheses in 14, one ha plots across Amazonian and Andean forests where we measured most key components of net primary production (NPP: wood, fine roots, and leaves) and autotrophic respiration (R_a; wood, rhizosphere, and leaf respiration). We found that lower fertility sites were less efficient at producing biomass and had higher rhizosphere respiration, …

The Variation Of Productivity And Its Allocation Along A Tropical Elevation Gradient: A Whole Carbon Budget Perspective, Yadvinder Malhi, Cécile A. J. Girardin, Gregory R. Goldsmith, Christopher E. Doughty, Norma Salinas, Daniel B. Metcalfe, Walter Huaraca Huasco, Javier E. Silva-Espejo, Jhon Del Aguilla-Pasquell, Filio Farfán Amézquita, Luiz E.O.C. Aragão, Rossella Guerrieri, Françoise Yoko Ishida, Nur Bahar, William Farfan-Rios, Oliver L. Phillips, Patrick Meir, Miles Silman

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

• Why do forest productivity and biomass decline with elevation? To address this question, research to date generally has focused on correlative approaches describing changes in woody growth and biomass with elevation.
  
  
• We present a novel, mechanistic approach to this question by quantifying the autotrophic carbon budget in 16 forest plots along a 3300 m elevation transect in Peru.
  
  
• Low growth rates at high elevations appear primarily driven by low gross primary productivity (GPP), with little shift in either carbon use efficiency (CUE) or allocation of net primary productivity (NPP) between wood, fine roots and canopy. The lack of trend in …

The Linkages Between Photosynthesis, Productivity, Growth And Biomass In Lowland Amazonian Forests, Yadvinder Malhi, Christopher E. Doughty, Gregory R. Goldsmith, Daniel B. Metcalfe, Cécile A.J. Girardin, Toby R. Marthews, Jhon Del Aguila-Pasquel, Luiz E.O.C. Aragão, Alejandro Araujo-Murakami, Paulo Brando, Antonino C.L. Da Costa, Javier E. Silva-Espejo, Filio Farfán Amézquita, David R. Galbraith, Carlos A. Quesada, Wanderley Rocha, Norma Salinas-Revilla, Divino Silvério, Patrick Meir, Oliver L. Phillips

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Understanding the relationship between photosynthesis, net primary productivity and growth in forest ecosystems is key to understanding how these ecosystems will respond to global anthropogenic change, yet the linkages among these components are rarely explored in detail. We provide the first comprehensive description of the productivity, respiration and carbon allocation of contrasting lowland Amazonian forests spanning gradients in seasonal water deficit and soil fertility. Using the largest data set assembled to date, ten sites in three countries all studied with a standardized methodology, we find that (i) gross primary productivity (GPP) has a simple relationship with seasonal water deficit, but …

Impact Of Vegetation On Land-Atmosphere Coupling Strength And Its Implication For Desertification Mitigation Over East Asia, Boksoon Myoung, Yong-Sang Choi, Suk-Jin Choi, Seon Ki Park

Mathematics, Physics, and Computer Science Faculty Articles and Research

Desertification of the East Asian drylands and the consequent dust transport have been serious concerns for adjacent Asian countries as well as the western United States. Tree planting has been considered one applicable strategy to mitigate the desertification. However, the desired effect of the tree planting would not be brought to fruition unless the newly planted trees change the coupling characteristics between the land and the atmosphere. Based on this perception, we attempt to clarify the effects of vegetation on the coupling strength between the atmosphere and land surface, and we suggest the most efficient areas of tree planting for …