Scholarly Works, Forest Resources and Environmental Conservation

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  • A Comparison of Forest Biomass and Conventional Harvesting Effects on Estimated Erosion, Best Management Practice Implementation, Ground Cover, and Residual Woody Debris in Virginia
    Garren, Austin M.; Bolding, Michael Chad; Barrett, Scott M.; Hawks, Eric M.; Aust, Wallace Michael; Coates, Thomas Adam (MDPI, 2023-11-17)
    Expanding markets for renewable energy feedstocks have increased demand for woody biomass. Concerns associated with forest biomass harvesting include increased erosion, the applicability of conventional forestry Best Management Practices (BMPs) for protecting water quality, and reduced woody debris retention for soil nutrients and cover. We regionally compared the data and results from three prior independent studies that estimated erosion, BMP implementation, and residual woody debris following biomass and conventional forest harvests in the Mountains, Piedmont, and Coastal Plain of Virginia. Estimated erosion was higher in the Mountains due to steep slopes and operational challenges. Mountain skid trails were particularly concerning, comprising only 8.47% of the total area but from 37.9 to 81.1% of the total site-wide estimated erosion. BMP implementation varied by region and harvest type, with biomass sites having better implementation than conventional sites, and conventional Mountain sites having lower implementation than other regions. Sufficient woody debris remained for BMPs on both harvest types in all regions, with conventional Mountain sites retaining twice that of Coastal Plain sites. BMPs reduced the estimated erosion on both site types suggesting increased implementation could reduce potential erosion in problematic areas. Therefore, proper BMP implementation should be ensured, particularly in Mountainous terrain, regardless of harvest type.
  • Near-term investments in forest management support long-term carbon sequestration capacity in forests of the United States
    Coulston, John W.; Domke, Grant M.; Walker, David M.; Brooks, Evan B.; O'Dea, Claire B. (Oxford University Press, 2023-11-21)
    The forest carbon sink of the United States offsets emissions in other sectors. Recently passed US laws include important climate legislation for wildfire reduction, forest restoration, and forest planting. In this study, we examine how wildfire reduction strategies and planting might alter the forest carbon sink. Our results suggest that wildfire reduction strategies reduce carbon sequestration potential in the near term but provide a longer term benefit. Planting initiatives increase carbon sequestration but at levels that do not offset lost sequestration from wildfire reduction strategies. We conclude that recent legislation may increase near-term carbon emissions due to fuel treatments and reduced wildfire frequency and intensity, and expand long-term US carbon sink strength.
  • Uncertainty in projections of future lake thermal dynamics is differentially driven by lake and global climate models
    Wynne, Jacob H.; Woelmer, Whitney; Moore, Tadhg N.; Thomas, R. Quinn; Weathers, Kathleen C.; Carey, Cayelan C. (PeerJ, 2023-06-02)
    Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics.
  • The NEON Ecological Forecasting Challenge
    Thomas, R. Quinn; Boettiger, Carl; Carey, Cayelan C.; Dietze, Michael C.; Johnson, Leah R.; Kenney, Melissa A.; McLachlan, Jason S.; Peters, Jody A.; Sokol, Eric R.; Weltzin, Jake F.; Willson, Alyssa; Woelmer, Whitney M. (Wiley, 2023-04)
  • Progress and opportunities in advancing near-term forecasting of freshwater quality
    Lofton, Mary E.; Howard, Dexter W.; Thomas, R. Quinn; Carey, Cayelan C. (Wiley, 2023-04)
    Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water-related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in the early stages of development (i.e., non-operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end-user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.
  • Eddy Covariance Data Reveal That a Small Freshwater Reservoir Emits a Substantial Amount of Carbon Dioxide and Methane
    Hounshell, Alexandria G.; D'Acunha, Brenda M.; Breef-Pilz, Adrienne; Johnson, Mark S.; Thomas, R. Quinn; Carey, Cayelan C. (American Geophysical Union, 2023-03-14)
    Small freshwater reservoirs are ubiquitous and likely play an important role in global greenhouse gas (GHG) budgets relative to their limited water surface area. However, constraining annual GHG fluxes in small freshwater reservoirs is challenging given their footprint area and spatially and temporally variable emissions. To quantify the GHG budget of a small (0.1 km2) reservoir, we deployed an Eddy covariance (EC) system in a small reservoir located in southwestern Virginia, USA over 2 years to measure carbon dioxide (CO2) and methane (CH4) fluxes near-continuously. Fluxes were coupled with in situ sensors measuring multiple environmental parameters. Over both years, we found the reservoir to be a large source of CO2 (633–731 g CO2-C m−2 yr−1) and CH4 (1.02–1.29 g CH4-C m−2 yr−1) to the atmosphere, with substantial sub-daily, daily, weekly, and seasonal timescales of variability. For example, fluxes were substantially greater during the summer thermally stratified season as compared to the winter. In addition, we observed significantly greater GHG fluxes during winter intermittent ice-on conditions as compared to continuous ice-on conditions, suggesting GHG emissions from lakes and reservoirs may increase with predicted decreases in winter ice-cover. Finally, we identified several key environmental variables that may be driving reservoir GHG fluxes at multiple timescales, including, surface water temperature and thermocline depth followed by fluorescent dissolved organic matter. Overall, our novel year-round EC data from a small reservoir indicate that these freshwater ecosystems likely contribute a substantial amount of CO2 and CH4 to global GHG budgets, relative to their surface area.
  • Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability
    Woelmer, Whitney M.; Thomas, R. Quinn; Lofton, Mary E.; McClure, Ryan P.; Wander, Heather L.; Carey, Cayelan C. (Wiley, 2022-10)
    As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1–14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.
  • Predicting Spring Phenology in Deciduous Broadleaf Forests: NEON Phenology Forecasting Community Challenge
    Wheeler, Kathryn I.; Dietze, Michael C.; LeBauer, David; Peters, Jody A.; Richardson, Andrew D.; Ross, Arun A.; Thomas, R. Quinn; Zhu, Kai; Bhat, Uttam; Munch, Stephan; Buzbee, Raphaela Floreani; Chen, Min; Goldstein, Benjamin; Guo, Jessica; Hao, Dalei; Jones, Chris; Kelly-Fair, Mira; Liu, Haoran; Malmborg, Charlotte; Neupane, Naresh; Pal, Debasmita; Shirey, Vaughn; Song, Yiluan; Steen, McKalee; Vance, Eric A.; Woelmer, Whitney M.; Wynne, Jacob H.; Zachmann, Luke (Elsevier, 2024-01-01)
    Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts.
  • Future climate change effects on US forest composition may offset benefits of reduced atmospheric deposition of N and S
    Clark, Christopher M.; Phelan, Jennifer; Ash, Jeremy; Buckley, John; Cajka, James; Horn, Kevin; Thomas, R. Quinn; Sabo, Robert D. (Wiley, 2023-09-01)
    Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) are important drivers of forest demography. Here we apply previously derived growth and survival responses for 94 tree species, representing >90% of the contiguous US forest basal area, to project how changes in mean annual temperature, precipitation, and N and S deposition from 20 different future scenarios may affect forest composition to 2100. We find that under the low climate change scenario (RCP 4.5), reductions in aboveground tree biomass from higher temperatures are roughly offset by increases in aboveground tree biomass from reductions in N and S deposition. However, under the higher climate change scenario (RCP 8.5) the decreases from climate change overwhelm increases from reductions in N and S deposition. These broad trends underlie wide variation among species. We found averaged across temperature scenarios the relative abundance of 60 species were projected to decrease more than 5% and 20 species were projected to increase more than 5%; and reductions of N and S deposition led to a decrease for 13 species and an increase for 40 species. This suggests large shifts in the composition of US forests in the future. Negative climate effects were mostly from elevated temperature and were not offset by scenarios with wetter conditions. We found that by 2100 an estimated 1 billion trees under the RCP 4.5 scenario and 20 billion trees under the RCP 8.5 scenario may be pushed outside the temperature record upon which these relationships were derived. These results may not fully capture future changes in forest composition as several other factors were not included. Overall efforts to reduce atmospheric deposition of N and S will likely be insufficient to overcome climate change impacts on forest demography across much of the United States unless we adhere to the low climate change scenario.
  • Embedding communication concepts in forecasting training increases students' understanding of ecological uncertainty
    Woelmer, Whitney M.; Moore, Tadhg N.; Lofton, Mary E.; Thomas, R. Quinn; Carey, Cayelan C. (Wiley, 2023-08)
    Communicating and interpreting uncertainty in ecological model predictions is notoriously challenging, motivating the need for new educational tools, which introduce ecology students to core concepts in uncertainty communication. Ecological forecasting, an emerging approach to estimate future states of ecological systems with uncertainty, provides a relevant and engaging framework for introducing uncertainty communication to undergraduate students, as forecasts can be used as decision support tools for addressing real-world ecological problems and are inherently uncertain. To provide critical training on uncertainty communication and introduce undergraduate students to the use of ecological forecasts for guiding decision-making, we developed a hands-on teaching module within the Macrosystems Environmental Data-Driven Inquiry and Exploration (EDDIE; MacrosystemsEDDIE.org) educational program. Our module used an active learning approach by embedding forecasting activities in an R Shiny application to engage ecology students in introductory data science, ecological modeling, and forecasting concepts without needing advanced computational or programming skills. Pre- and post-module assessment data from more than 250 undergraduate students enrolled in ecology, freshwater ecology, and zoology courses indicate that the module significantly increased students' ability to interpret forecast visualizations with uncertainty, identify different ways to communicate forecast uncertainty for diverse users, and correctly define ecological forecasting terms. Specifically, students were more likely to describe visual, numeric, and probabilistic methods of uncertainty communication following module completion. Students were also able to identify more benefits of ecological forecasting following module completion, with the key benefits of using forecasts for prediction and decision-making most commonly described. These results show promise for introducing ecological model uncertainty, data visualizations, and forecasting into undergraduate ecology curricula via software-based learning, which can increase students' ability to engage and understand complex ecological concepts.
  • Parameterizing Lognormal state space models using moment matching
    Smith, John W.; Thomas, R. Quinn; Johnson, Leah R. (Springer, 2023-09)
    In ecology, it is common for processes to be bounded based on physical constraints of the system. One common example is the positivity constraint, which applies to phenomena such as duration times, population sizes, and total stock of a system’s commodity. In this paper, we propose a novel method for parameterizing Lognormal state space models using an approach based on moment matching. Our method enforces the positivity constraint, allows for arbitrary mean evolution and variance structure, and has a closed-form Markov transition density which allows for more flexibility in fitting techniques. We discuss two existing Lognormal state space models and examine how they differ from the method presented here. We use 180 synthetic datasets to compare the forecasting performance under model misspecification and assess the estimation of precision parameters between our method and existing methods. We find that our models perform well under misspecification, and that fixing the observation variance both helps to improve estimation of the process variance and improves forecast performance. To test our method on a difficult problem, we compare the predictive performance of two Lognormal state space models in predicting the Leaf Area Index over a 151 day horizon by using a process-based ecosystem model to describe the temporal dynamics. We find that our moment matching model performs better than its competitor, and is better suited for intermediate predictive horizons. Overall, our study helps to inform practitioners about the importance of incorporating sensible dynamics when using models of complex systems to predict out-of-sample.
  • Development of a lateral topographic weathering gradient in temperate forested podzols
    Bower, Jennifer A.; Ross, Donald S.; Bailey, Scott W.; Pennino, Amanda M.; Jercinovic, Michael J.; McGuire, Kevin J.; Strahm, Brian D.; Schreiber, Madeline E. (Elsevier, 2023-11)
    Mineral weathering is an important soil-forming process driven by the interplay of water, organisms, solution chemistry, and mineralogy. The influence of hillslope-scale patterns of water flux on mineral weathering in soils is still not well understood, particularly in humid postglacial soils, which commonly harbor abundant weatherable primary minerals. Previous work in these settings showed the importance of lateral hydrologic patterns to hillslope-scale pedogenesis. In this study, we hypothesized that there is a corresponding relationship between hydrologically driven pedogenesis and chemical weathering in podzols in the White Mountains of New Hampshire, USA. We tested this hypothesis by quantifying the depletion of plagioclase in the fine fraction (≤2 mm) of closely spaced, similar-age podzols along a gradient in topography and depth to bedrock that controls lateral water flow. Along this gradient, laterally developed podzols formed through frequent, episodic flushing by upslope groundwater, and vertically developed podzols formed through characteristic vertical infiltration. We estimated the depletion of plagioclase-bound elements within the upper mineral horizons of podzols using mass transfer coefficients (τ) and quantified plagioclase losses directly through electron microscopy and microprobe analysis. Elemental depletion was significantly more pronounced in the upslope lateral eluvial (E horizon-dominant) podzols relative to lateral illuvial (B horizon-dominant) and vertical (containing both E and B horizons) podzols downslope, with median Na losses of ∼74 %, ∼56 %, and ∼40 %, respectively. When comparing genetic E horizons, Na and Al were significantly more depleted in laterally developed podzols relative to vertically developed podzols. Microprobe analysis revealed that ∼74 % of the plagioclase was weathered from the mineral pool of lateral eluvial podzols, compared to ∼39 % and ∼23 % for lateral illuvial podzols and vertically developed podzols, respectively. Despite this intense weathering, plagioclase remains the second most abundant mineral in soil thin sections. These findings confirm that the concept of soil development as occurring vertically does not accurately characterize soils in topographically complex regions. Our work improves the current understanding of pedogenesis by identifying distinct, short-scale gradients in mineral weathering shaped by local patterns of hydrology and topography.
  • Assessing Ecosystem State Space Models: Identifiability and Estimation
    Smith, John W.; Johnson, Leah R.; Thomas, R. Quinn (Springer, 2023-03)
    Hierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. Further, we show that data cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where (1) data are not available for all states and transfers at the operational time step for the ecosystem model and (2) process uncertainty estimation is desired.
  • Mammal and tree diversity accumulate different types of soil organic matter in the northern Amazon
    Losada, Maria; Martinez, Antonio M.; Silvius, Kirsten M.; Varela, Sara; Raab, Ted K.; Fragoso, Jose M. V.; Sobral, Mar (Cell Press, 2023-03)
    Diversity of plants and animals influence soil carbon through their contributions to soil organic matter (SOM). However, we do not know whether mammal and tree communities affect SOM composition in the same manner. This question is relevant because not all forms of carbon are equally resistant to mineralization by microbes and thus, relevant to carbon storage. We analyzed the elemental and molecular composition of 401 soil samples, with relation to the species richness of 83 mammal and tree communities at a landscape scale across 4.8 million hectares in the northern Amazon. We found opposite effects of mammal and tree richness over SOM composition. Mammal diversity is related to SOM rich in nitrogen, sulfur and iron whereas tree diversity is related to SOM rich in aliphatic and carbonyl compounds. These results help us to better understand the role of biodiversity in the carbon cycle and its implications for climate change mitigation.
  • Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the US
    Thomas, R. Quinn; McClure, Ryan P.; Moore, Tadhg N.; Woelmer, Whitney M.; Boettiger, Carl; Figueiredo, Renato J.; Hensley, Robert T.; Carey, Cayelan C. (Wiley, 2023-04)
    The US National Ecological Observatory Network's (NEON's) standardized monitoring program provides an unprecedented opportunity for comparing the predictability of ecosystems. To harness the power of NEON data for examining environmental predictability, we scaled a near-term, iterative, water temperature forecasting system to all six NEON lakes in the conterminous US. We generated 1-day-ahead to 35-days-ahead forecasts using a process-based hydrodynamic model that was updated with observations as they became available. Among lakes, forecasts were more accurate than a null model up to 35-days-ahead, with an aggregated 1-day-ahead root-mean square error (RMSE) of 0.61 degrees C and a 35-days-ahead RMSE of 2.17 degrees C. Water temperature forecast accuracy was positively associated with lake depth and water clarity, and negatively associated with fetch and catchment size. The results of our analysis suggest that lake characteristics interact with weather to control the predictability of thermal structure. Our work provides some of the first probabilistic forecasts of NEON sites and a framework for examining continental-scale predictability.
  • Soil Respiration and Related Abiotic and Remotely Sensed Variables in Different Overstories and Understories in a High-Elevation Southern Appalachian Forest
    Hammer, Rachel L.; Seiler, John R.; Peterson, John A.; Thomas, Valerie A. (MDPI, 2023-08-15)
    Accurately predicting soil respiration (Rs) has received considerable attention recently due to its importance as a significant carbon flux back to the atmosphere. Even small changes in Rs can have a significant impact on the net ecosystem productivity of forests. Variations in Rs have been related to both spatial and temporal variation due to changes in both abiotic and biotic factors. This study focused on soil temperature and moisture and changes in the species composition of the overstory and understory and how these variables impact Rs. Sample plots consisted of four vegetation types: eastern hemlock (Tsuga canadensis L. Carriere)-dominated overstory, mountain laurel (Kalmia latifolia L.)-dominated understory, hardwood-dominated overstory, and cinnamon fern (Osmundastrum cinnamomeum (L.) C. Presl)-dominated understory, with four replications of each. Remotely sensed data collected for each plot, light detection and ranging, and hyperspectral data, were compiled from the National Ecological Observatory Network (NEON) to determine if they could improve predictions of Rs. Soil temperature and soil moisture explained 82% of the variation in Rs. There were no statistically significant differences between the average annual Rs rates among the vegetation types. However, when looking at monthly Rs, cinnamon fern plots had statistically higher rates in the summer when it was abundant and hemlock had significantly higher rates in the dormant months. At the same soil temperature, the vegetation types’ Rs rates were not statistically different. However, the cinnamon fern plots showed the most sensitivity to soil moisture changes and were the wettest sites. Normalized Difference Lignin Index (NDLI) was the only vegetation index (VI) to vary between the vegetation types. It also correlated with Rs for the months of August and September. Photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), and normalized difference nitrogen index (NDNI) also correlated with September’s Rs. In the future, further research into the accuracy and the spatial scale of VIs could provide us with more information on the capability of VIs to estimate Rs at these fine scales. The differences we found in monthly Rs rates among the vegetation types might have been driven by varying litter quality and quantity, litter decomposition rates, and root respiration rates. Future efforts to understand carbon dynamics on a broader scale should consider the temporal and finer-scale differences we observed.
  • Assessing opportunities and inequities in undergraduate ecological forecasting education
    Willson, Alyssa M.; Gallo, Hayden; Peters, Jody A.; Abeyta, Antoinette; Watts, Nievita Bueno; Carey, Cayelan C.; Moore, Tadhg N.; Smies, Georgia; Thomas, R. Quinn; Woelmer, Whitney M.; McLachlan, Jason S. (Wiley, 2023-05)
    Conducting ecological research in a way that addresses complex, real-world problems requires a diverse, interdisciplinary and quantitatively trained ecology and environmental science workforce. This begins with equitably training students in ecology, interdisciplinary science, and quantitative skills at the undergraduate level. Understanding the current undergraduate curriculum landscape in ecology and environmental sciences allows for targeted interventions to improve equitable educational opportunities. Ecological forecasting is a sub-discipline of ecology with roots in interdisciplinary and quantitative science. We use ecological forecasting to show how ecology and environmental science undergraduate curriculum could be evaluated and ultimately restructured to address the needs of the 21(st) century workforce. To characterize the current state of ecological forecasting education, we compiled existing resources for teaching and learning ecological forecasting at three curriculum levels: online resources; US university courses on ecological forecasting; and US university courses on topics related to ecological forecasting. We found persistent patterns (1) in what topics are taught to US undergraduate students at each of the curriculum levels; and (2) in the accessibility of resources, in terms of course availability at higher education institutions in the United States. We developed and implemented programs to increase the accessibility and comprehensiveness of ecological forecasting undergraduate education, including initiatives to engage specifically with Native American undergraduates and online resources for learning quantitative concepts at the undergraduate level. Such steps enhance the capacity of ecological forecasting to be more inclusive to undergraduate students from diverse backgrounds and expose more students to quantitative training.
  • ArcGIS Pro, Python, and R-Bridge Support Small Area Estimation for Forests
    Bell, David M.; Blinn, Christine E.; Peery, Stephen S.; Wynne, Randolph H.; Radtke, Philip J.; Thomas, Valerie A.; Oswalt, Christopher M.; Wilson, B. Ty (2023-07-12)
  • Effects of Hemlock Woolly Adelgid Control Using Imidacloprid on Leaf-Level Physiology of Eastern Hemlock
    McDonald, Kelly M.; Seiler, John R.; Wang, Bingxue; Salom, Scott M.; Rhea, Rusty J. (MDPI, 2023-06-14)
    Widespread mortality of eastern hemlock (Tsuga canadensis [L.] Carr.) has been occurring due to the introduction of hemlock woolly adelgid (Adelges tsugae Annand) (HWA), threatening millions of hectares of hemlock-dominated forests in the eastern United States. HWA feeds at the base of needles and removes stored carbohydrates, which can impact leaf-level physiology, contributing to the decline of the tree. However, these physiological mechanisms in HWA-infested hemlocks are still not clearly understood. We investigated hemlock leaf physiology year-round at three forested sites with various degrees of infestation. At each site, half the trees were treated with imidacloprid (Merit® 2 F, Bayer, Kansas City, MO, USA) while the rest were left untreated. Imidacloprid is widely used to control HWA but can itself have phytotoxic effects. After one growing season, there was an increase in photosynthetic rates (7.5%, p = 0.0163) and stomatal conductance (7.1%, p = 0.0163) across sites in the trees treated with imidacloprid. After two years, the imidacloprid treatment also increased bud break from 22.5% to 88.7% at Fishburn (the most severely impacted site) and from 22.7% to 58.9% at Mountain Lake (the least impacted site), and slightly increased chlorophyll fluorescence for treated trees at Fishburn. Chemical treatment also slightly increased water use efficiency at Mountain Lake. These results suggest that HWA is causing tree mortality largely through a reduction in leaf area caused by decreasing bud break and also by a slight, but significant, reduction in leaf-level photosynthesis and stomatal conductance.
  • GWAS on the Attack by Aspen Borer Saperda calcarata on Black Cottonwood Trees Reveals a Response Mechanism Involving Secondary Metabolism and Independence of Tree Architecture
    Sepúlveda, Sebastián L.; Neale, David B.; Holliday, Jason A.; Famula, Randi; Fiehn, Oliver; Stanton, Brian J.; Guerra, Fernando P. (MDPI, 2023-05-30)
    Black cottonwood (Populus trichocarpa) is a species of economic interest and an outstanding study model. The aspen borer (Saperda calcarata) causes irreversible damage to poplars and other riparian species in North America. The insect can produce multiple effects ranging from the presence of some galleries in the stem to tree death. Despite the ecological and commercial importance of this tree–insect interaction, the genetic mechanisms underlying the response of P. trichocarpa to S. calcarata are scarcely understood. In this study, a common garden trial of P. trichocarpa provenances, established in Davis, California, was assessed at the second year of growth, regarding the infestation of S. calcarata from a natural outbreak. A genome-wide association study (GWAS) was conducted using 629k of exonic SNPs to assess the relationship between genomic variation and insect attack. Tree architecture, in terms of stem number per plant, and the wood metabolome were also included. Insect attack was independent of the number of stems per tree. The performed GWAS identified three significantly associated SNP markers (q-value < 0.2) belonging to the same number of gene models, encoding proteins involved in signal transduction mechanisms and secondary metabolite production, including that of R-mandelonitrile lyase, Chromodomain-helicase-DNA-binding family protein, and Leucine-rich repeat protein. These results are aligned with the current knowledge of defensive pathways in plants and trees, helping to expand the understanding of the defensive response mechanisms of black cottonwood against wood borer insects.