Effects of release from suppression on hydraulic architecture, photosynthetic capacity and functional wood characteristics in Douglas-fir and western hemlock

Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), a shade intolerant species, and western hemlock (Tsuga heterophylla (Raf.) Sarg.), a shade tolerant species, were compared to learn more about the temporal pattern of release from suppression in both species, whether hydraulic architecture or photosynthetic capacity constrain release and how wood functional properties change after release from suppression. The study was conducted in 10-20 year old Douglas-fir and western hemlocks, either in a site that had been thinned to release suppressed trees or in a site that remained unthinned. Douglas-fir had lower height growth (from 1998-2003) and lower relative height growth (height growth from 1998-2003/height in 1998) than western hemlock. However, the relative height growth of released vs. suppressed trees was much higher in Douglas-fir (130%) than western hemlock (65%), suggesting that although height growth was lower, Douglas-fir did indeed release from suppression. Release seems to be constrained initially by photosynthetic capacity in Douglas-fir and western hemlock. In Douglas-fir released trees had 14 times the leaf area and 1.5 times the nitrogen per unit leaf area (Narea) as suppressed trees. Needles on released western hemlock trees had approximately twice the maximum assimilation rate (Amax) at ambient [CO2] as suppressed trees and did not exhibit photoinhibition at the highest light levels. Hydraulic architecture appears to constrain further release from suppression in Douglas-fir more so than western hemlock after the increase in leaf area, leaf N content and overall photosynthetic capacity. Released trees had significantly less negative foliar 13C values and there was also a positive relationship between leaf area:sapwood area ratios and 13C suggesting that trees with more leaf area for a given sapwood area experienced a stomatal limitation on carbon gain. Growth of released trees, thus, may have been limited by stomatal constraints on carbon gain despite a doubling of Amax after release. Nonetheless, trees exhibited no significant differences between the leaf-specific conductivities (KL) of suppressed vs. released trees of either species. However, leaf-specific root conductance (kRL) was significantly greater in suppressed Douglas-fir compared to released trees. Functional wood characteristics were also much different in trees released from suppression and those that remained suppressed. Growth ring widths in released trees increased by 370% for Douglas-fir and 300% for western hemlock, while specific conductivity (Ks) increased by 182% for Douglas-fir and 42% for western hemlock compared to suppressed trees. Earlywood width was approximately four times greater in released than suppressed trees of both species, whereas the relative increase in latewood width between suppressed and released trees was much greater in Douglas-fir than in western hemlock. Latewood proportion decreased by 21% in released Douglas-fir and by 47% released western hemlock compared to suppressed trees. Tracheids were 25% wider and 11% longer in released Douglas-fir saplings than suppressed saplings, whereas in western hemlock released saplings had 19% wider tracheids that were approximately the same length as suppressed saplings. Wood moisture content was 66% higher in released Douglas-fir compared to suppressed Douglas-fir and 41% higher in released western hemlock compared to suppressed western hemlock. Wood density decreased from 0.57 to 0.47 g cm-3 in Douglas-fir trees released from suppression and from 0.50 to 0.45 g cm-3 in western hemlock trees released from suppression. Therefore, it appears that as management patterns switch from even-age systems to uneven-age systems, both Douglas-fir and western hemlock will be able to release from suppression and the wood of released trees will be of good quality for most applications.Keywords: Photosynthesis, Advance regeneration, Xylem anatomy, Hydraulic architecture, Wood density, Stable isotope analysi

Comparison of Tissue Heat Balance- and Thermal Dissipation-Derived Sap Flow Measurements in Ring-Porous Oaks and a Pine

Sap flow measurements have become integral in many physiological and ecological investigations. A number of methods are used to estimate sap flow rates in trees, but probably the most popular is the thermal dissipation (TD) method because of its affordability, relatively low power consumption, and ease of use. However, there have been questions about the use of this method in ring-porous species and whether individual species and site calibrations are needed. We made concurrent measurements of sap flow rates using TD sensors and the tissue heat balance (THB) method in two oak species (Quercus prinus Willd. and Quercus velutina Lam.) and one pine (Pinus echinata Mill.). We also made concurrent measurements of sap flow rates using both 1 and 2-cm long TD sensors in both oak species. We found that both the TD and THB systems tended to match well in the pine individual, but sap flow rates were underestimated by 2-cm long TD sensors in five individuals of the two ring-porous oak species. Underestimations of 20–35% occurred in Q. prinus even when a “Clearwater” correction was applied to account for the shallowness of the sapwood depth relative to the sensor length and flow rates were underestimated by up to 50% in Q. velutina. Two centimeter long TD sensors also underestimated flow rates compared with 1-cm long sensors in Q. prinus, but only at large flow rates. When 2-cm long sensor data in Q. prinus were scaled using the regression with 1-cm long data, daily flow rates matched well with the rates measured by the THB system. Daily plot level transpiration estimated using TD sap flow rates and scaled 1 cm sensor data averaged about 15% lower than those estimated by the THB method. Therefore, these results suggest that 1-cm long sensors are appropriate in species with shallow sapwood, however more corrections may be necessary in ring-porous species

A comparison of the hydraulic efficiency of a palm species (Iriartea deltoidea) with other wood types

Palms are an important component of tropical ecosystems, living alongside dicotyledonous trees, even though they have a very different growth pattern and vascular system. As monocots, vessels in palms are located within vascular bundles and, without a vascular cambium that many dicotyledonous trees possess, palms cannot add additional vessels to their vascular system as they get older and taller. This means that hydraulic architecture in palms is more predetermined, which may require a highly efficient hydraulic system. This preset nature, along with the decoupling of hydraulic and mechanical functioning to different cell types, may allow palms to have a more efficient hydraulic system than dicotyledonous trees. Therefore, this study seeks to determine the efficiency of the hydraulic system in the palm Iriartea deltoidea (Ruiz & Pav.) and compare this efficiency with other tree forms. We measured cross-sectional areas of roots, stems and fronds as well as leaf areas of I. deltoidea saplings. Likewise, cross-sections were made and vessel diameters and frequencies measured. This allowed for the calculation of theoretical specific-conductivity (KS, calc), theoretical leaf-specific conductivity (KL, calc), and vessel diameter and vessel number ratios between distal and proximal locations in the palms. I. deltoidea palms were found to have the largest, least frequent vessels that diverged most from the square packing limit (maximum number of vessels that fit into a given area) compared with other major tree forms, and they therefore invested the least space and carbon into water transport structures. Likewise, conduits tapered by approximately one third between ranks (root, bole, petiole), which represents an efficient ratio with regard to the trade-offs between safety and efficiency of the conducting system. Conduits also exhibited a high conservation of the sum of the conduit radii cubed (Σr³) across ranks, thereby approximating Murray’s Law patterning. Therefore, our results indicate that the palm, I. deltoidea, has a very efficient hydraulic system in terms of maintaining a large conducting capacity with a minimal vascular investment. This efficiency may allow palms to compete well with dicotyledonous trees in tropical and subtropical climates but other developmental factors largely restrict palms from regions that experience prolonged freezing temperatures.This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Oxford University Press and can be found at: http://treephys.oxfordjournals.org/.Keywords: Hydraulic architecture, Murray’s law, Palms, Conduit tapering, Vascular anatom

Climate Change and Fire Management in the Mid-Atlantic Region

In this review, we summarize the potential impacts of climate change on wildfire activity in the mid­-Atlantic region, and then consider how the beneficial uses of prescribed fire could conflict with mitigation needs for climate change, focusing on patters of carbon (C) sequestration by forests in the region. We use a synthesis of field studies, eddy flux tower measurements, and simulation studies to evaluate how the use of prescibed fire affects short-and long-term forest C dynamics. Climate change may create weather conditions more conducive to wildfire activity, but successional changes in forest composition, altered gap dynamics, reduced understory and forest floor fuels, and fire suppression will likely continue to limit wildfire occurrence and severity throughout the region. Prescribed burning ls the only major viable option that land managers have for redudng hazardous fuels in a cost-effective manner, or ensuring the regeneration and maintenance of fire-dependent species. Field measurements and model simulations indicate that consumption of fine fuels on the forest floor and understory vegetation during most prescribed burns is equivalent t

StoManager1: Automated, High-throughput Tool to Measure Leaf Stomata Using Convolutional Neural Networks

The characteristics of stomata on leaves are crucial for the performance of plants and their impact on global water and carbon cycling. However, manually counting stomata can be time-consuming, prone to bias, and limited to small scales and sample sizes. We have created StoManager1, a high-throughput tool that automates detecting, counting, and measuring stomata to address this issue. StoManager1 uses convolutional neural networks to estimate parameters such as stomatal density, area, orientation, and variance. Our results show that StoManager1 is highly precise and has an excellent recall for the stomatal characterizing leaves from various species. This tool can automate measuring leaf stomata, making it easier to explore how leaf stomata control and regulate plant growth and adaptation to environmental stress and climate change. An online demonstration of StoManager1 is available on GitHub at https://github.com/JiaxinWang123/StoManager.git. We have also developed a standalone, user-friendly Windows application for StoManager1 that does not require any programming or coding experience.Substantially improved group analysis speed. Added Toy dataset for users to play around. Updated line-edit default text

Modeling respiration from snags and coarse woody debris before and after an invasive gypsy moth disturbance

Although snags and coarse woody debris are a small component of ecosystem respiration, disturbances can significantly increase the mass and respiration from these carbon (C) pools. The objectives of this study were to (1) measure respiration rates of snags and coarse woody debris throughout the year in a forest previously defoliated by gypsy moths, (2) develop models for dead stem respiration rates, (3) model stand-level respiration rates of dead stems using forest inventory and analysis data sets and environmental variables predisturbance and postdisturbance, and (4) compare total dead stem respiration rates with total ecosystem respiration and net ecosystem exchange. Respiration rates were measured on selected Pinus and Quercus snags and coarse woody debris each month for 1 year in a northeastern U.S. temperate forest. Multiple linear regression using environmental and biometric variables including wood temperature, diameter, density, species, and decay class was used to model respiration rates of dead stems. The mass of snags and coarse woody debris increased more than fivefold after disturbance and respiration rates increased more than threefold. The contribution of dead stems to total ecosystem respiration more than tripled from 0.85% to almost 3% and respiration from dead stems alone was approximately equal to the net ecosystem exchange of the forest in 2011 (fourth year postdisturbance). This study highlights the importance of dead stem C pools and fluxes particularly during disturbance and recovery cycles. With climate change increasing the ranges of many forest pests and pathogens, these data become particularly important for accurately modeling future C cycling

StoManager1: Automated, High-throughput Tool to Measure Leaf Stomata Using Convolutional Neural Networks

<p>The characteristics of stomata on leaves are crucial for the performance of plants and their impact on global water and carbon cycling. However, manually counting stomata can be time-consuming, prone to bias, and limited to small scales and sample sizes. We have created StoManager1, a high-throughput tool that automates detecting, counting, and measuring stomata to address this issue. StoManager1 uses convolutional neural networks to estimate parameters such as stomatal density, area, orientation, and variance. Our results show that StoManager1 is highly precise and has an excellent recall for the stomatal characterizing leaves from various species. This tool can automate measuring leaf stomata, making it easier to explore how leaf stomata control and regulate plant growth and adaptation to environmental stress and climate change. An online demonstration of StoManager1 is available on GitHub at <a href="https://github.com/JiaxinWang123/StoManager.git">https://github.com/JiaxinWang123/StoManager.git</a>. We have also developed a standalone, user-friendly Windows application for StoManager1 that does not require any programming or coding experience.</p><ul><li>Substantially improved group analysis speed. </li><li>Added Toy dataset for users to play around. </li><li>Updated line-edit default text. </li><li>Fine-tuned weights for Hardwoods. </li><li>Enhanced detection capacity for blurred images. </li><li>Enhanced version with more stomatal metrics measured with geometrical algorithms!! </li><li>Note: to use gpu version, you must have your cuda11.7 installed. </li><li>Bugs fixed. </li><li>Enhanced weights for non-nail polish images. </li><li>Added functions to convert the units of width and length from pixels to μm. </li><li>Added Stomata arrangement pattern indices, such as stomata evenness index, stomatal divergence index, and stomatal aggregation index. </li><li>Enhanced models trained with more species such as ginkgo, poplar, cuticle, and usnm images from: Fetter, Karl C. et al. (2019), Data from: StomataCounter: a neural network for automatic stomata identification and counting, Dryad, Dataset, https://doi.org/10.5061/dryad.kh2gv5f. </li><li>Bugs fixed (calculate stomata/guard cell area for image size over 1280*760). ---Fixed issues in generating stomatal indices.</li><li>Support more image formats such as .jpg, .png, .tif, .jpeg.</li><li>Fixed bugs for not continuing measurement when no stoma detected.</li><li>Integrated custom datatset model training module.</li><li>Fine-tunned weights for more species (over 100 species that were not in training dataset were tested).</li></ul&gt

StoManager1: Automated, High-throughput Tool to Measure Leaf Stomata Using Convolutional Neural Networks

<p>The characteristics of stomata on leaves are crucial for the performance of plants and their impact on global water and carbon cycling. However, manually counting stomata can be time-consuming, prone to bias, and limited to small scales and sample sizes. We have created StoManager1, a high-throughput tool that automates detecting, counting, and measuring stomata to address this issue. StoManager1 uses convolutional neural networks to estimate parameters such as stomatal density, area, orientation, and variance. Our results show that StoManager1 is highly precise and has an excellent recall for the stomatal characterizing leaves from various species. This tool can automate measuring leaf stomata, making it easier to explore how leaf stomata control and regulate plant growth and adaptation to environmental stress and climate change. An online demonstration of StoManager1 is available on GitHub at <a href="https://github.com/JiaxinWang123/StoManager.git">https://github.com/JiaxinWang123/StoManager.git</a>. We have also developed a standalone, user-friendly Windows application for StoManager1 that does not require any programming or coding experience.</p><ul><li>Substantially improved group analysis speed. </li><li>Added Toy dataset for users to play around. </li><li>Updated line-edit default text. </li><li>Fine-tuned weights for Hardwoods. </li><li>Enhanced detection capacity for blurred images. </li><li>Enhanced version with more stomatal metrics measured with geometrical algorithms!! </li><li>Note: to use gpu version, you must have your cuda11.7 installed. </li><li>Bugs fixed. </li><li>Enhanced weights for non-nail polish images. </li><li>Added functions to convert the units of width and length from pixels to μm. </li><li>Added Stomata arrangement pattern indices, such as stomata evenness index, stomatal divergence index, and stomatal aggregation index. </li><li>Enhanced models trained with more species such as ginkgo, poplar, cuticle, and usnm images from: Fetter, Karl C. et al. (2019), Data from: StomataCounter: a neural network for automatic stomata identification and counting, Dryad, Dataset, https://doi.org/10.5061/dryad.kh2gv5f. </li><li>Bugs fixed (calculate stomata/guard cell area for image size over 1280*760). ---Fixed issues in generating stomatal indices.</li><li>Support more image formats such as .jpg, .png, .tif, .jpeg.</li><li>Fixed bugs for not continuing measurement when no stoma detected.</li><li>Integrated custom datatset model training module.</li><li>Fine-tunned weights for more species (over 100 species that were not in training dataset were tested).</li></ul&gt