Implementing GitHub Actions Continuous Integration to Reduce Error Rates in Ecological Data Collection

Accurate field data are essential to understanding ecological systems and forecasting their responses to global change. Yet, data collection errors are common, and data analysis often lags far enough behind its collection that many errors can no longer be corrected, nor can anomalous observations be revisited. Needed is a system in which data quality assurance and control (QA/QC), along with the production of basic data summaries, can be automated immediately following data collection. Here, we implement and test a system to satisfy these needs. For two annual tree mortality censuses and a dendrometer band survey at two forest research sites, we used GitHub Actions continuous integration (CI) to automate data QA/QC and run routine data wrangling scripts to produce cleaned datasets ready for analysis. This system automation had numerous benefits, including (1) the production of near real-time information on data collection status and errors requiring correction, resulting in final datasets free of detectable errors, (2) an apparent learning effect among field technicians, wherein original error rates in field data collection declined significantly following implementation of the system, and (3) an assurance of computational reproducibility—that is, robustness of the system to changes in code, data and software. By implementing CI, researchers can ensure that datasets are free of any errors for which a test can be coded. The result is dramatically improved data quality, increased skill among field technicians, and reduced need for expert oversight. Furthermore, we view CI implementation as a first step towards a data collection and analysis pipeline that is also more responsive to rapidly changing ecological dynamics, making it better suited to study ecological systems in the current era of rapid environmental change

Life Stage and Neighborhood-Dependent Survival of Longleaf Pine after Prescribed Fire

Determining mechanisms of plant establishment in ecological communities can be particularly difficult in disturbance-dominated ecosystems. Longleaf pine (Pinus palustris Mill.) and its associated plant community exemplify systems that evolved with disturbances, where frequent, widespread fires alter the population dynamics of longleaf pine within distinct life stages. We identified the primary biotic and environmental conditions that influence the survival of longleaf pine in this disturbance-dominated ecosystem. We combined data from recruitment surveys, tree censuses, dense lidar point clouds, and a forest-wide prescribed fire to examine the response of longleaf pine individuals to fire and biotic neighborhoods. We found that fire temperatures increased with increasing longleaf pine neighborhood basal area and decreased with higher oak densities. There was considerable variation in longleaf pine survival across life stages, with lowest survival probabilities occurring during the bolt stage and not in the earlier, more fire-resistant grass stage. Survival of grass-stage, bolt-stage, and sapling longleaf pines was negatively associated with basal area of neighboring longleaf pine and positively related to neighboring heterospecific tree density, primarily oaks (Quercus spp.). Our findings highlight the vulnerability of longleaf pine across life stages, which suggests optimal fire management strategies for controlling longleaf pine density, and—more broadly—emphasize the importance of fire in mediating species interactions

Academic Aide — Free online math question database for academic improvement

Lack of funding is a common problem for many public schools and small private tutoring centers. Some schools have a policy that prevents students from taking textbooks home to study. Sometimes teachers will take money out of their own pocket to let students use existing online services to improve education quality. However, those internet services are not guaranteed to have materials that are best fit for individuals\u27 teaching style. In some cases, the best fit material simply does not exist on the internet, and creating it would take many hours. We have created Academic Aide to combat this exact problem. Academic Aide is a free online database that allow users to generate and share content in a well organized manner. We allow users to create problems that fit their needs and upload it to the website. People from around the world can access to these problems at no cost. If the type of problem is not available on the website, the user can simply create their own. We believe that Academic Aide will be a place for people to learn from each other, create their own content and share it with people around the world. Academic Aide is the result of a project-based learning approach for undergraduate computer science students

Seed dispersal and tree legacies influence spatial patterns of plant invasion dynamics

Invasive plant species alter community dynamics and ecosystem properties, potentially leading to regime shifts. Here, the invasion of a non-native tree species into a stand of native tree species is simulated using an agent-based model. The model describes an invasive tree with fast growth and high seed production that produces litter with a suppressive effect on native seedlings, based loosely on Melaleuca quinquenervia, invasive to southern Florida. The effect of a biocontrol agent, which reduces the invasive tree's growth and reproductive rates, is included to study how effective biocontrol is in facilitating the recovery of native trees. Even under biocontrol, the invader has some advantages over native tree species, such as the ability to tolerate higher stem densities than the invaded species and its litter's seedling suppression effect. We also include a standing dead component of both species, where light interception from dead canopy trees influences neighboring tree demographics. The model is applied to two questions. The first is how the mean seedling dispersal rate affects the spread of the invading species into a pure stand of natives, assuming the same mean dispersal distance for both species. For assumed litter seedling suppression that roughly balances the fitness levels of the two species, which species dominates depends on the mean dispersal distance. The invader dominates at both very high and very low mean seedling dispersal distances, while the native tree dominates for dispersal distances in the intermediate range. The second question is how standing dead trees affect either the rate of spread of the invader or the rate of recovery of the native species. The legacy of standing dead invasive trees may delay the recovery of native vegetation. The results here are novel and show that agent-based modeling is essential in illustrating how the fine-scale modeling of local interactions of trees leads to effects at the population level

Life Stage and Neighborhood-Dependent Survival of Longleaf Pine after Prescribed Fire

Determining mechanisms of plant establishment in ecological communities can be particularly difficult in disturbance-dominated ecosystems. Longleaf pine (Pinus palustris Mill.) and its associated plant community exemplify systems that evolved with disturbances, where frequent, widespread fires alter the population dynamics of longleaf pine within distinct life stages. We identified the primary biotic and environmental conditions that influence the survival of longleaf pine in this disturbance-dominated ecosystem. We combined data from recruitment surveys, tree censuses, dense lidar point clouds, and a forest-wide prescribed fire to examine the response of longleaf pine individuals to fire and biotic neighborhoods. We found that fire temperatures increased with increasing longleaf pine neighborhood basal area and decreased with higher oak densities. There was considerable variation in longleaf pine survival across life stages, with lowest survival probabilities occurring during the bolt stage and not in the earlier, more fire-resistant grass stage. Survival of grass-stage, bolt-stage, and sapling longleaf pines was negatively associated with basal area of neighboring longleaf pine and positively related to neighboring heterospecific tree density, primarily oaks (Quercus spp.). Our findings highlight the vulnerability of longleaf pine across life stages, which suggests optimal fire management strategies for controlling longleaf pine density, and—more broadly—emphasize the importance of fire in mediating species interactions

NEON Tree Species Predictions

<h3>Individual Tree Predictions for 100 million trees in the National Ecological Observatory Network</h3><p>For site abbreviations see: https://www.neonscience.org/field-sites/explore-field-sites</p><p>For each site, there is a .zip and .csv. The .zip is a set 1km .shp tiles. The .csv is all trees in a single file.</p><p>Please see the manuscript for detailed methods.</p><h4>Summary</h4><p>We use the DeepForest python package to predict individual crown location in the RGB camera mosaic <a href="https://www.zotero.org/google-docs/?GvCOoU">(Weinstein et al. 2020a)</a>. Tree crowns with less than 3m maximum height in the LiDAR derived canopy height model are removed. At this stage in the workflow each individual tree has a unique ID, predicted crown location, crown area and confidence score from the DeepForest tree detection model. Following individual tree detection, we classify each individual as Alive or Dead based on the appearance in the RGB data. Since NEON captures airborne data during the leaf-on season, any standing tree with no leaf cover was annotated as 'dead'. During prediction, the location of each predicted crown is cropped and passed to the Alive-Dead model for labeling as each Alive (0) or Dead (1) with a confidence score for each class. To classify each tree crown to species we use the multi-temporal hierarchical model in Weinstein et al. 2023. Using the best trained model for each site we predict all available areas within the NEON AOP footprint that have overlapping RGB data for crown prediction and hyperspectral data for species prediction. The predicted species label confidence score, as well labels from the higher levels are included in the shapefile. </p><p>Column Name</p><p>Definition</p><p>Geometry</p><p>A four pointed bounding box location in utm coordinates.</p><p>indiv_id</p><p>A unique crown identifier that combines the year, site and geoindex of the NEON airborne tile (e.g. 732000_4707000) is the utm coordinate of the top left of the tile. </p><p>sci_name</p><p>The full latin name of predicted species aligned with NEON's taxonomic nomenclature. </p><p>ens_score</p><p>The confidence score of the species prediction. This score is the output of the multi-temporal model for the ensemble hierarchical model. </p><p>bleaf_taxa</p><p>Highest predicted category for the broadleaf model</p><p>bleaf_score</p><p>The confidence score for the broadleaf taxa submodel </p><p>oak_taxa</p><p>Highest predicted category for the oak model </p><p>dead_label</p><p>A two class alive/dead classification based on the RGB data. 0=Alive/1=Dead.</p><p>dead_score</p><p>The confidence score of the Alive/Dead prediction. </p><p>site_id</p><p>The four letter code for the NEON site. See <a href="https://www.neonscience.org/field-sites/explore-field-sites">https://www.neonscience.org/field-sites/explore-field-sites</a> for site locations.</p><p>conif_taxa</p><p>Highest predicted category for the conifer model</p><p>conif_score</p><p>The confidence score for the conifer taxa submodel</p><p>dom_taxa</p><p>Highest predicted category for the dominant taxa mode submodel</p><p>dom_score</p><p>The confidence score for the dominant taxa submodel</p&gt

Review article: nitric oxide from dysbiotic bacterial respiration of nitrate in the pathogenesis and as a target for therapy of ulcerative colitis

The definitive version may be found at www.wiley.comBackgroundFactors initiating human ulcerative colitis (UC) are unknown. Dysbiosis of bacteria has been hypothesized to initiate UC but, to date, neither the nature of the dysbiosis nor mucosal breakdown has been explained.AimTo assess whether a dysbiosis of anaerobic nitrate respiration could explain the microscopic, biochemical and functional changes observed in colonocytes of UC.MethodsPublished results in the gastroenterological, biochemical and microbiological literature were reviewed concerning colonocytes, nitrate respiration and nitric oxide in the colon in health and UC. A best-fit explanation of results was made regarding the pathogenesis and new treatments of UC.ResultsAnaerobic nitrate respiration yields nitrite, nitric oxide (NO) and nitrous oxide. Colonic bacteria produce NO and UC in remission has a higher lumenal NO level than control cases. NO with sulphide, but not NO alone, impairs beta-oxidation, lipid and protein synthesis explaining the membrane, tight junctional and ion channel changes observed in colonocytes of UC. The observations complement therapeutic mechanisms of those probiotics, prebiotics and antibiotics useful in treating UC.ConclusionsThe prolonged production of bacterial NO with sulphide can explain the initiation and barrier breakdown, which is central to the pathogenesis of UC. Therapies to alter bacterial nitrate respiration and NO production need to evolve. The production of NO by colonic bacteria and that of the mucosa need to be separated to pinpoint the sequential nature of NO damage in UC.W. E. W. Roedige