Climate Overrides Effects of Fertilizer and Straw Management as Controls of Nitrous Oxide Emissions After Oilseed Rape Harvest

Oilseed rape (Brassica napus L.) is an important bioenergy crop that contributes to the diversification of renewable energy supply and mitigation of fossil fuel CO2 emissions. Typical oilseed rape crop management includes the use of nitrogen (N) fertilizer and the incorporation of oilseed rape straw into soil after harvest. However, both management options risk increasing soil emissions of nitrous oxide (N2O). The aim of this 2-years field experiment was to identify the regulating factors of N cycling with emphasis on N2O emissions during the post-harvest period. As well as the N2O emission rates, soil ammonia (NH4+) and nitrate (NO3−) contents, crop residue and seed yield were also measured. Treatments included variation of fertilizer (non-fertilized, 90 and 180 kg N ha−1) and residue management (straw remaining, straw removal). Measured N2O emission data showed large intra- and inter-annual variations ranging from 0.5 (No-fert + str) to 1.0 kg N2O-N ha−1 (Fert-180 + str) in 2013 and from 4.1 (Fert-90 + str) to 7.3 kg N2O-N ha−1 (No-fert + str) in 2014. Cumulative N2O emissions showed that straw incorporation led to no difference or slightly reduced N2O emissions compared with treatments with straw removal, while N fertilization has no effect on post-harvest N2O emissions. A process-based model, CoupModel, was used to explain the large annual variation of N2O after calibration with measured environmental data. Both modeled and measured data suggest that soil water-filled pore space and temperature were the key factors controlling post-harvest N2O emissions, even though the model seemed to show a higher N2O response to the N fertilizer levels than our measured data. We conclude that straw incorporation in oilseed rape cropping is environmentally beneficial for mitigating N2O losses. The revealed importance of climate in regulating the emissions implies the value of multi-year measurements. Future studies should focus on new management practices to mitigate detrimental effects caused by global warming, for example by using cover crops.

Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment

Recently, various indoor based sensors that were formerly separated from the digital world, are now intertwined with it. The data visualization may aid in the comprehension of large amounts of information. Building on current server-based models, this study intends to display real environmental data acquired by IoT agents in the interior environment. Sensors attached to Arduino microcontrollers are used to collect environmental data for the smart campus environment, including air temperature, light intensity, and humidity. This proposed framework uses the system's server and stores sensor readings, which are subsequently shown in real time on the server platform and in the environment application. However, most current IoT installations do not make use of the enhanced digital representations of the server and its graphical display capabilities in order to improve interior safety and comfort conditions. The storage of such real-time data in a standard and organized way is still being examined even though sensor data integration with storing capacity server-based models has been studied in academics.

Environmental regionalization and endemic plant distribution in the Maghreb

Clustering methods based on environmental variables are useful in the planning of conservation strategies for species and ecosystems. However, there is a lack of work on the regionalization of the vast space of North Africa and the distribution of plant species. The current lists of endemic plants are focused mostly on an occurrence at the country level and not on regions with different conditions. The aim of this work was to lay out an environmental scheme for northwest Africa and to collect data about the occurrence of endemic plants in this area. Clustering with 12 of 33 tested environmental rasters was performed to divide the Maghreb into environmental clusters. Then, a list of 1618 endemic plant taxa (1243 species and 375 subspecies) was prepared and their distribution in estimated environmental clusters was examined. Eleven clusters with different conditions were estimated. The main drivers of regionalization were temperature amplitude, precipitation seasonality, and precipitation of the warmest quarter. According to the occurrence of endemic plants, northwest Africa may be divided into three zones: Atlas, Mediterranean (two environmental clusters), and southern zone (eight environmental clusters). The presented results provide a good basis for understanding the spatial patterns of the Maghreb, including its environment and species diversity. A designed list of endemic plant species together with environmental data may facilitate the planning of future research in north Africa and arranging methods of biodiversity protection.

Development of a Seafloor Community Classification for the New Zealand Region Using a Gradient Forest Approach

To support ongoing marine spatial planning in New Zealand, a numerical environmental classification using Gradient Forest models was developed using a broad suite of biotic and high-resolution environmental predictor variables. Gradient Forest modeling uses species distribution data to control the selection, weighting and transformation of environmental predictors to maximise their correlation with species compositional turnover. A total of 630,997 records (39,766 unique locations) of 1,716 taxa living on or near the seafloor were used to inform the transformation of 20 gridded environmental variables to represent spatial patterns of compositional turnover in four biotic groups and the overall seafloor community. Compositional turnover of the overall community was classified using a hierarchical procedure to define groups at different levels of classification detail. The 75-group level classification was assessed as representing the highest number of groups that captured the majority of the variation across the New Zealand marine environment. We refer to this classification as the New Zealand “Seafloor Community Classification” (SCC). Associated uncertainty estimates of compositional turnover for each of the biotic groups and overall community were also produced, and an added measure of uncertainty – coverage of the environmental space – was developed to further highlight geographic areas where predictions may be less certain owing to low sampling effort. Environmental differences among the deep-water New Zealand SCC groups were relatively muted, but greater environmental differences were evident among groups at intermediate depths in line with well-defined oceanographic patterns observed in New Zealand’s oceans. Environmental differences became even more pronounced at shallow depths, where variation in more localised environmental conditions such as productivity, seafloor topography, seabed disturbance and tidal currents were important differentiating factors. Environmental similarities in New Zealand SCC groups were mirrored by their biological compositions. The New Zealand SCC is a significant advance on previous numerical classifications and includes a substantially wider range of biological and environmental data than has been attempted previously. The classification is critically appraised and considerations for use in spatial management are discussed.

Devil in the details: how can we avoid potential pitfalls of CATS regression when our data do not follow a Poisson distribution?

Background Community assembly by trait selection (CATS) allows for the detection of environmental filtering and estimation of the relative role of local and regional (meta-community-level) effects on community composition from trait and abundance data without using environmental data. It has been shown that Poisson regression of abundances against trait data results in the same parameter estimates. Abundance data do not necessarily follow a Poisson distribution, and in these cases, other generalized linear models should be fitted to obtain unbiased parameter estimates. Aims This paper discusses how the original algorithm for calculating the relative role of local and regional effects has to be modified if Poisson model is not appropriate. Results It can be shown that the use of the logarithm of regional relative abundances as an offset is appropriate only if a log-link function is applied. Otherwise, the link function should be applied to the product of local total abundance and regional relative abundances. Since this product may be outside the domain of the link function, the use of log-link is recommended, even if it is not the canonical link. An algorithm is also suggested for calculating the offset when data are zero-inflated. The relative role of local and regional effects is measured by Kullback-Leibler R2. The formula for this measure presented by Shipley (2014) is valid only if the abundances follow a Poisson distribution. Otherwise, slightly different formulas have to be applied. Beyond theoretical considerations, the proposed refinements are illustrated by numerical examples. CATS regression could be a useful tool for community ecologists, but it has to be slightly modified when abundance data do not follow a Poisson distribution. This paper gives detailed instructions on the necessary refinement.

Multi-year, spatially extensive, watershed-scale synoptic stream chemistry and water quality conditions for six permafrost-underlain Arctic watersheds

Repeated sampling of spatially distributed river chemistry can be used to assess the location, scale, and persistence of carbon and nutrient contributions to watershed exports. Here, we provide a comprehensive set of water chemistry measurements and ecohydrological metrics describing the biogeochemical conditions of permafrost-affected Arctic watersheds. These data were collected in watershed-wide synoptic campaigns in six stream networks across northern Alaska. Three watersheds are associated with the Arctic Long-Term Ecological Research site at Toolik Field Station (TFS), which were sampled seasonally each June and August from 2016 to 2018. Three watersheds were associated with the National Park Service (NPS) of Alaska and the U.S. Geological Survey (USGS) and were sampled annually from 2015 to 2019. Extensive water chemistry characterization included carbon species, dissolved nutrients, and major ions. The objective of the sampling designs and data acquisition was to characterize terrestrial–aquatic linkages and processing of material in stream networks. The data allow estimation of novel ecohydrological metrics that describe the dominant location, scale, and overall persistence of ecosystem processes in continuous permafrost. These metrics are (1) subcatchment leverage, (2) variance collapse, and (3) spatial persistence. Raw data are available at the National Park Service Integrated Resource Management Applications portal (O'Donnell et al., 2021, https://doi.org/10.5066/P9SBK2DZ) and within the Environmental Data Initiative (Abbott, 2021, https://doi.org/10.6073/pasta/258a44fb9055163dd4dd4371b9dce945).

Framing Cutting-Edge Integrative Deep-Sea Biodiversity Monitoring via Environmental DNA and Optoacoustic Augmented Infrastructures

Deep-sea ecosystems are reservoirs of biodiversity that are largely unexplored, but their exploration and biodiscovery are becoming a reality thanks to biotechnological advances (e.g., omics technologies) and their integration in an expanding network of marine infrastructures for the exploration of the seas, such as cabled observatories. While still in its infancy, the application of environmental DNA (eDNA) metabarcoding approaches is revolutionizing marine biodiversity monitoring capability. Indeed, the analysis of eDNA in conjunction with the collection of multidisciplinary optoacoustic and environmental data, can provide a more comprehensive monitoring of deep-sea biodiversity. Here, we describe the potential for acquiring eDNA as a core component for the expanding ecological monitoring capabilities through cabled observatories and their docked Internet Operated Vehicles (IOVs), such as crawlers. Furthermore, we provide a critical overview of four areas of development: (i) Integrating eDNA with optoacoustic imaging; (ii) Development of eDNA repositories and cross-linking with other biodiversity databases; (iii) Artificial Intelligence for eDNA analyses and integration with imaging data; and (iv) Benefits of eDNA augmented observatories for the conservation and sustainable management of deep-sea biodiversity. Finally, we discuss the technical limitations and recommendations for future eDNA monitoring of the deep-sea. It is hoped that this review will frame the future direction of an exciting journey of biodiscovery in remote and yet vulnerable areas of our planet, with the overall aim to understand deep-sea biodiversity and hence manage and protect vital marine resources.

Automation of tree-ring detection and measurements using deep learning

We present an implementable neural network-based automated detection and measurement of tree-ring boundaries from coniferous species. We trained our Mask R-CNN extensively on over 8,000 manually annotated rings. We assessed the performance of the trained model from our core processing pipeline on real world data. The CNN performed well, recognizing over 99% of ring boundaries (precision) and a recall value of 95% when tested on real world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were a 99% match with human measurements of the same samples. Our CNN is readily deployable through a Docker container and requires only basic command line skills. Application outputs include editable annotations which facilitate the efficient generation of ring-width measurements from tree-ring samples, an important source of environmental data.

Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Mytilus galloprovincialis Using a LSTM Neural Network Model from Environmental Data

Paralytic shellfish toxins (PSTs) are produced mainly by Alexandrium catenella (formerly A. tamarense). Since 2000, the National Institute of Fisheries Science (NIFS) has been providing information on PST outbreaks in Korean coastal waters at one- or two-week intervals. However, a daily forecast is essential for immediate responses to PST outbreaks. This study aimed to predict the outbreak timing of PSTs in the mussel Mytilus galloprovincialis in Jinhae Bay and along the Geoje coast in the southern coast of the Korea Peninsula. We used a long-short-term memory (LSTM) neural network model for temporal prediction of PST outbreaks from environmental data, such as water temperature (WT), tidal height, and salinity, measured at the Geojedo, Gadeokdo, and Masan tidal stations from 2006 to 2020. We found that PST outbreaks is gradually accelerated during the three years from 2018 to 2020. Because the in-situ environmental measurements had many missing data throughout the time span, we applied LSTM for gap-filling of the environmental measurements. We trained and tested the LSTM models with different combinations of environmental factors and the ground truth timing data of PST outbreaks for 5479 days as input and output. The LSTM model trained from only WT had the highest accuracy (0.9) and lowest false-alarm rate. The LSTM-based temporal prediction model may be useful as a monitoring system of PSP outbreaks in the coastal waters of southern Korean.

A Structural Equation Modeling Approach to Disentangling Regional-Scale Landscape Dynamics in Ghana

Land cover (LC) change is an integrative indicator of changes in ecosystems due to anthropogenic or natural forcings. There is a significant interest in the investigation of spatio-temporal patterns of LC transitions, and the causes and consequences thereof. While the advent of satellite remote sensing techniques have enhanced our ability to track and measure LC changes across the globe, significant gaps remain in disentangling specific factors that influence, or in certain cases, are influenced by, LC change. This study aims to investigate the relative influence of regional-scale bioclimatology and local-scale anthropogenic factors in driving LC and environmental change in Ghana. This analysis builds upon previous research in the region that has highlighted multiple drivers of LC change in the region, especially via drivers such as deforestation, urbanization, and agricultural expansion. It used regional-scale remotely sensed, demographic, and environmental data for Ghana across 20 years and developed path models on causal factors influencing LC transitions in Ghana. A two-step process is utilized wherein causal linkages from an exploratory factor analysis (EFA) are constrained with literature-based theoretical constructs to implement a regional-scale partial least squares path model (PLSPM). The PLSPM reveals complex interrelationships among drivers of LC change that vary across the geography of Ghana. The model suggests strong effects of local urban expansion on deforestation and vegetation losses in urban and peri-urban areas. Losses of vegetation are in turn related to increases in local heating patterns indicative of urban heat island effects. Direct effects of heat islands are however masked by strong latitudinal gradients in climatological factors. The models confirm that decreases in vegetation cover results in increased land surface albedo that is indirectly related to urban and population expansion. These empirically-estimated causal linkages provide insights into complex spatio-temporal variations in potential drivers of LC change. We expect these models and spatial data products to form the basis for detailed investigations into the mechanistic underpinnings of land cover dynamics across Ghana. These analyses are aimed at building a template for methods that can be utilized to holistically design spatially-disaggregated strategies for sustainable development across Ghana.