Ecological Applications Research Articles

First report of microbial symbionts in the digestive system of shipworms; wood boring mollusks

Shipworms, historically known for their destruction of wooden ships and coastal structures, play a key role in carbon and nutrient cycling in aquatic ecosystems. Despite extensive research, the exact mechanisms underlying their ability to digest wood have remained elusive, particularly regarding the degradation of lignin, a major component of wood that resists breakdown. Here we employ a combination of microbial culture, metagenomics, and FISH-probe microscopy analyses to explore previously overlooked aspects of their digestive system. Specifically, we identify the presence of bacterial symbionts within the typhlosole, a specialized structure within the main wood digestive organ (cecum), challenging the long-held belief that shipworm foreguts are nearly sterile environments. The discovery of Alteromonas species in bacterial clusters within the typhlosole suggests a symbiotic relationship that may play a crucial role in the digestion of woody substrates, offering a potential explanation for the shipworm's ability to degrade lignocellulose without ligninases from the host or gill symbionts. These findings provide insight into the digestive physiology and host defense of shipworms, but also suggests that the typhlosole in other related organisms might also harbor symbionts. This study also opens new insights into research into the ecological and biotechnological applications of shipworms and their gut symbionts, enhancing our understanding of lignocellulose degradation in marine environments.

Microorganism-mediated denitrogenation of aquaculture systems provoked by poly-β-hydroxybutyrate (PHB)

The biodegradable polymer poly-β-hydroxybutyrate (PHB) is a promising carbon source for biological mitigation of nitrogen pollution, a significant problem in aquaculture that physical and chemical methods have not provided a comprehensive solution. Here we investigated the impact of PHB on the zero-water-change largemouth bass culture by 30- and 40-day experiments. PHB loaded into the filter circulation pump at 4 g L−1, optimum value determined by the first experiment, significantly reduced the levels of nitrate by 99.65%, nitrite by 95.96%, and total nitrogen by 85.22% compared to the control without PHB. PHB also significantly increased denitrifying bacteria (e.g., Proteobacteria and Fusobacteria) and expression of denitrification genes (e.g., nirK and nirS) in the microbial community, improving growth and health parameters of largemouth bass. While the impact may vary in other culture systems, PHB thus demonstrated its remarkable utility in aquaculture, highlighting ecological assessment and application to larger aquaculture operations as future considerations.

Liquid dosimeter with sensitivity in low-kGy range for the characterization of a new module for EB wastewater treatment

Abstract In recent years, the use of low-energy electrons in various ecological and biotechnological applications has become increasingly relevant. One important application is the treatment of wastewater, wherein highly reactive species produced by water irradiation are used to oxidize pollutants. Low-energy electron irradiation has several advantages, such as minimal demands on radiation protection and electron beam (EB) source dimensions. However, to play into the main advantages of this technology and keep it economically viable, it is necessary to keep the absorbed dose as low as possible. This calls for a liquid dosimeter with sensitivity in the single digit kGy range. An extract from natural Hibiscus sabdariffa (Roselle) has been reported to show a radiochromic effect in this dose range. In the present work, Roselle dosimeter solutions were closely investigated and optimized to characterize a new module for EB wastewater treatment. Upon EB irradiation, the dosimeter solution demonstrated a dose-dependent fading in color, making it useful in the 0.3–7.5 kGy dose range.

Revisiting the Landscape Mosaic model.

The landscape mosaic model quantifies and maps the spatial juxtaposition of different land uses. It provides a landscape perspective of anthropic threats posed by agriculture and urban development, and the spatial-temporal shifting of the landscape mosaic indicates landscapes where anthropic intensity has changed. We use the U.S. Geological Survey provided National Land Cover Database (NLCD) for the years 2001 and 2021 to derive the landscape mosaic at five analysis scales. To improve earlier implementations of the model, we introduce the heatmap, a flexible scheme providing more thematic reporting opportunities and allowing for better quantitative summary reporting across analysis scales as well as for temporal trends. The results are exemplified at regional scale for the Atlanta metropolitan area. We use the improved model to investigate the land cover context over time and at different analysis scales and show how custom color tables detail different thematic features of the landscape mosaic, including the degree and change of anthropic intensity. We conclude with a discussion of potential applications in ecology, landscape planning, and restoration to illustrate the benefits of the revised landscape mosaic model. All assessment tools are now available in open-source software packages.

Enhancing biodegradable polymer surface wettability properties through atmospheric plasma treatmsurfaent and nanocellulose incorporation

This study investigates the effects of atmospheric plasma treatment on the surface properties of polylactic acid (PLA)/nanocrystalline cellulose (CNC) composites, aiming to improve their wettability and mechanical properties. The research utilizes a twin-screw extrusion process for fabricating PLA/CNC biocomposites, followed by surface modification using a custom-built, with a tenfold high-voltage atmospheric plasma system. The motivation of this treatment was to improved surface wettability and potential for enhanced adhesive bonding in ecological applications. These findings contribute to developing more sustainable composite materials by providing a method to improve the functionality of biodegradable polymers without compromising their environmental benefits.

Molecular differentiation analysis of ten putative species of Fannia (Diptera: Fanniidae) collected in carrion-baited traps from Colombia.

The genus Fannia is the most representative of the Fannidae family of true flies with worldwide distribution. Some species are attracted to decomposing materials and live vertebrate animals, which makes them important in forensics, medical and veterinary fields. However, identifying Fannia species can be difficult due to the high similarity in the external morphology of females and limited descriptions and morphological keys. Herein, molecular markers could provide a complementary tool for species identification. However, molecular identification has still limited application since databases contain few data for neotropical species of Fannia. This study assessed the potential of two molecular markers, the COI-3' region and internal transcribed spacer 2 (ITS2), to differentiate 10 putative species of the genus Fannia from Colombia using distance-based and tree-based approaches. The partial ITS2 and/or COI-3' regions allowed molecular diagnosis of six species, while pairs of species Fannia colazorrensis + F. dodgei and F. laclara + F. aburrae are conflicting. Although these results might suggest that conflicting pair species are conspecific, consistent morphological differences between males do not support this hypothesis. The lack of differentiation at the nuclear and mitochondrial molecular markers for the conflicting species may be due to incomplete evolutionary lineage separation, hybridization, or introgression events. In addition, sexual selection on male morphological traits before species-specific differences in molecular markers emerge may partially explain the results. Our study provides a valuable dataset to identify and confirm some Fannia species molecularly. Further, they could be used to associate females and immature stages with their conspecifics as a baseline to deep into their biology, ecology, distribution and potential applications in forensic and medico-veterinary entomology.

Discovery of Epichloë as novel endophytes of Psathyrostachys lanuginosa in China and their alkaloid profiling

The Epichloë genus represents a significant group of above-ground endophytes extensively researched for their potential applications in agriculture and ecology. Additionally, Epichloë species synthesize bioactive alkaloids, which generally cause health problems in livestock and have detrimental effects on the performance of insect herbivores. Psathyrostachys lanuginosa serves as a valuable forage grass for livestock owing to its high nutritional value and resilience in adverse environmental conditions. Nevertheless, to date, no reports have documented Epichloë as endophytes of P. lanuginosa. In this study, four strains (PF5, PF9, QG2, and QG4) were isolated and identified through morphological, molecular, and phylogenetic analyses as endophytes of P. lanuginosa. Morphological analysis indicated colony characteristics and conidia features consistent with symbiotic Epichloë, with no significant differences observed in growth rates or conidia dimensions among the four strains. Phylogenetic analysis confirmed all strains as E. bromicola. Additionally, alkaloid biosynthetic genes were detected, revealing differences in the potential synthesis of peramine and indole diterpenoid alkaloids among strains from different geographic origins. However, all four E. bromicola strains exhibited similar potential for synthesizing ergot alkaloids, but not loline alkaloids. Overall, this study identified P. lanuginosa as a novel host for E. bromicola and provided insights into the alkaloid profiles of these strains, laying a solid foundation for the scientific and rational utilization of Epichloë resources.

Potential Impact of Climate Change-Induced Alterations on Pyroptotic Cell Death in Animal Cells: A Review.

Climate change-induced alterations in temperature variation, ozone exposure, water salinity and acidification, and hypoxia might influence immunity and thus survival in diverse groups ofanimals from fish to mammals. Pyroptosis is a type of lytic pro-inflammatory programmed cell death, which participates in the innate immune response, and is involved in multiple diseases characterized by inflammation and cell death, mostly studied in human cells. Diverse extrinsic factors can induce pyroptosis, leading to the extracellular release of pro-inflammatory moleculessuch as IL-18. Climate change-related factors, either directly or indirectly, can also promote animal cell death via different regulated mechanisms, impacting organismal fitness. However, pyroptosis has been relatively less studied in this context compared to another cell death process, apoptosis. This review covers previous research pointing to the potential impact of climate change, through various abiotic stressors, on pyroptotic cell death in different animal cells in various contexts. It was proposed that temperature, ozone exposure, watersalinity, wateracidification and hypoxia have the potential to induce pyroptotic cell death in animal cells and promote inflammation, and that these pyroptotic events should be better understood to be able to mitigate the adverse effects of climate change on animal physiology and health. This is of high importance considering the increasing frequency, intensity and duration of climate-based changes in these environmental parameters, and the critical function of pyroptosis in immune responses of animals and in their predisposition to multiple diseasesincluding cancer. Furthermore, the need for further mechanistic studies showing the more direct impact of climate change-induced environmental alterations on pyroptotic cell death in animals at the organismal level was highlighted. A complete picture of the association between climate change and pyroptosis in animals will be also highly valuable in terms of ecological and clinical applications, and it requires an interdisciplinary approach. SIGNIFICANCE: Climate change-induced alterations might influence animal physiology. Pyroptosis is a form of cell death with pro-inflammatory characteristics. Previous research suggests that temperature variation, ozone exposure, water salinity and acidification, and hypoxia might have the potential to contribute to pyroptotic cell death in certain cell types and contexts. Climate change-induced pyroptotic cell death should be better understood to be able to mitigate the adverse effects of climate change on animal health.

Comparison of δ13C and δ15N of ecologically relevant amino acids among beluga whale tissues

Ecological applications of compound-specific stable isotope analysis (CSIA) of amino acids (AAs) include 1) tracking carbon pathways in food webs using essential AA (AAESS) δ13C values, and 2) estimating consumer trophic position (TP) by comparing relative differences of ‘trophic’ and ‘source’ AA δ15N values. Despite the significance of these applications, few studies have examined AA-specific SI patterns among tissues with different AA compositions and metabolism/turnover rates, which could cause differential drawdown of body AA pools and impart tissue-specific isotopic fractionation. To address this knowledge gap, especially in the absence of controlled diet studies examining this issue in captive marine mammals, we used a paired-sample design to compare δ13C and δ15N values of 11 AAs in commonly sampled tissues (skin, muscle, and dentine) from wild beluga whales (Delphinapterus leucas). δ13C of two AAs, glutamic acid/glutamine (Glx, a non-essential AA) and, notably, threonine (an essential AA), differed between skin and muscle. Furthermore, δ15N of three AAs (alanine, glycine, and proline) differed significantly among the three tissues, with glycine δ15N differences of approximately 10 ‰ among tissues supporting recent findings it is unsuitable as a source AA. Significant δ15N differences in AAs such as proline, a trophic AA used as an alternative to Glx in TP estimation, highlight tissue selection as a potential source of error in ecological applications of CSIA-AA. Amino acids that differed among tissues play key roles in metabolic pathways (e.g., ketogenic and gluconeogenic AAs), pointing to potential physiological applications of CSIA-AA in studies of free-ranging animals. These findings underscore the complexity of isotopic dynamics within tissues and emphasize the need for a nuanced approach when applying CSIA-AA in ecological research.

Honeydew: A keystone in insect–plant interactions, current insights and future perspectives

AbstractHoneydew, a sugary excretion produced by sap‐feeding insects, plays a pivotal role in shaping intricate interactions between insects and plants. This review explains the multifaceted aspects of honeydew, beginning with an overview of its production mechanisms and factors influencing its variability across insect species and plant hosts. Exploring its composition, we examine the chemical components and nutritional aspects, highlighting variations based on insect species and plant sources. The impact of honeydew on microbial communities is explored, shedding light on its role in influencing the diversity and abundance of microorganisms. Furthermore, the review investigates how honeydew affects pest behaviour and population dynamics, elucidating plant responses to honeydew deposition. In the realm of biocontrol, we examine the intricate relationships between honeydew and natural enemies, considering potential implications for integrated pest management. Beyond these individual components, the paper discusses the broader ecological consequences of honeydew in ecosystems, emphasizing the interconnected relationships between insects, plants and microorganisms. The review concludes by identifying research gaps and suggesting future directions, recognizing the need for further investigation into areas with potential applications for agriculture and ecology. This review provides a comprehensive synthesis of the current understanding of honeydew's significance in insect–plant interactions, offering insights for both researchers and practitioners in the field.

Understanding Urban Women's Interaction with Domestic Technologies in Malawi: Advancing Feminist Theories in HCI

In sub-Saharan Africa, women are caretakers of their homes. Despite the gendered nature of technology use, human-computer interaction research has rarely focused on women's interaction with domestic technologies in this context. We explore how urban women in Malawi use domestic technologies to support activities in their homes. We conducted semi-structured interviews, observations, and focus group discussions with 33 married women living with their husbands and other family members. Our findings suggest that women are the primary users of utilitarian domestic technologies; however, their spouses have control over how they use these technologies. Further, we found that purpose and time influence women's decision-making process for selecting domestic technologies to use, especially cooking appliances. The structure of some Malawian homes—which consist of a combination of modern and traditional structures—also influences women's decision-making process. Our findings contribute to feminist theories in human-computer interaction through the application of ecology, pluralism, and self-disclosure aspects.

OPTIMIZATION OF CNN + MOBILENETV3 FOR INSECT IDENTIFICATION: TOWARD HIGH ACCURACY

Developments in the field of artificial intelligence and deep learning, particularly Convolutional Neural Networks (CNN) techniques, have expanded the research potential and applications in ecology, including efficient and accurate insect classification. However, there are challenges in achieving high levels of accuracy with similar computational efficiency. In response, the efficient MobileNetV3 architecture was investigated to improve the insect pest classification process. Through an analytical descriptive quantitative approach and insect datasets from Kaggle, this study tested the effectiveness of CNN models optimized with MobileNetV3. The results indicated that the optimized model achieved classification accuracy of up to 90%, with consistent performance between training and validation data and significant loss reduction. With high precision and processing efficiency, this discovery makes a substantial contribution to deep learning applications in the field of intelligent agriculture, promising methodological improvements for other classification problems. Despite offering a promising solution, this study recognizes the limitation in dataset diversity and suggests further exploration with more varied datasets to strengthen the model's application in actual agricultural practices.

Enhancing soil organic carbon estimation accuracy: Integrating spatial vegetation dynamics and temporal analysis with Sentinel 2 imagery

This article introduces an improved method for estimating Soil Organic Carbon (SOC) using Sentinel 2 images, with a specific emphasis on the Dakshina Kannada area in India. By examining 364 soil samples, SOC estimation models were constructed using Random forests (RF) and Partial Least Squares Regression (PLSR), focusing on the impact of nearby vegetation pixels. The approach consisted of classifying soil samples by the presence of plant pixels at distances of 0, 10, and 20 m, and evaluating the influence of dry vegetation by the use of the Normalised Burn Ratio 2 (NBR2). The findings demonstrated a significant improvement in the precision of the model (by up to 20 %) when vegetation pixels within a 20-meter radius of the sample locations were omitted. The research also included a temporal analysis utilizing Sentinel-2 images from April 2017 to May 2023. This analysis showed strong relationships between the amount of exposed soil and the accuracy of predicting soil organic carbon (SOC) levels. These results emphasize the need to take into account both the spatial dynamics of vegetation and the temporal variations in bare soil covering to get an accurate estimate of soil organic carbon (SOC). This study improves the accuracy and dependability of SOC evaluations by including geographical and temporal aspects, providing useful insights for agricultural and ecological applications.

Remote sensing of mountain snow from space: status and recommendations

The spatial and temporal variation of the seasonal snowpack in mountain regions is recognized as a clear knowledge gap for climate, ecology and water resources applications. Here, we identify three salient topics where recent developments in snow remote sensing and data assimilation can lead to significant progress: snow water equivalent, high resolution snow-covered area and long term snow cover observations including snow albedo. These topics can be addressed in the near future with institutional support.

Determining Riverine Surface Roughness at Fluvial Mesohabitat Level and Its Influence on UAV-Based Thermal Imaging Accuracy

Water surface roughness (SR) is a highly relevant parameter governing data reliability in remote sensing applications, yet lacking appropriate methodology in riverine habitats. In order to assess thermal accuracy linked to SR of thermal imaging derived from an unmanned aerial vehicle (UAV), we developed the SR Measurement Device (SRMD). The SRMD uses the concept of in situ quantification of wave frequency and wave amplitude. Data of nine installed SRMDs in four different fluvial mesohabitat classes presented a range of 0 to 47 waves per 30 s and an amplitude range of 0 to 6 cm. Even subtle differences between mesohabitat classes run, riffle, and no-/low-flow still and pool areas could be detected with the SRMD. However, SR revealed no significant influence on the accuracy of thermal infrared (TIR) imagery data in our study case. Overall, the presented device expands existing methods of riverine habitat assessments and has the potential to produce highly relevant data of SR for various ecological and technical applications, ranging from remote sensing of surface water and habitat quality characterizations to bank stability and erosion risk assessments.

Rotated Lorenz Curves of Biological Size Distributions Follow Two Performance Equations

The Lorenz curve is used to describe the relationship between the cumulative proportion of household income and the number of households of an economy. The extent to which the Lorenz curve deviates from the line of equality (i.e., y = x) is quantified by the Gini coefficient. Prior models are based on the simulated and empirical data of income distributions. In biology, the Lorenz curves of cell or organ size distributions tend to have similar shapes. When the Lorenz curve is rotated by 135 degrees counterclockwise and shifted to the right by a distance of 2, a three-parameter performance equation (PE), and its generalized version with five parameters (GPE), accurately describe this rotated and right-shifted curve. However, in prior studies, PE and GPE were not compared with the other Lorenz equations, and little is known about whether the skewness of the distribution could influence the validity of these equations. To address these two issues, simulation data from the beta distributions with different skewness values and six empirical datasets of plant (organ) size distributions were used to compare PE and GPE with three other Lorenz equations in describing the rotated and right-shifted plant (organ) size distributions. The root-mean-square error and Akaike information criterion were used to assess the validity of the two performance equations and the three other Lorenz equations. PE and GPE were both validated in describing the rotated and right-shifted simulation and empirical data of plant (organ) distributions. Nevertheless, GPE worked better than PE and the three other Lorenz equations from the perspectives of the goodness of fit, and the trade-off between the goodness of fit and the model structural complexity. Analyses indicate that GPE provides a powerful tool for quantifying size distributions across a broad spectrum of organic entities and can be used in a variety of ecological and evolutionary applications. Even for the simulation data from hypothetical extreme skewed distribution curves, GPE still worked well.

Assessing the Interpretability–Performance Trade-Off of Artificial Neural Networks Using Sentinel Fish Health Data

A number of sentinel species are regularly sampled from the environment near the Oil Sands Region (OSR) in Alberta, Canada. In particular, trout-perch are sampled as a proxy for the health of the aquatic ecosystem. As the development of the OSR began before the environmental monitoring program was in place, there is currently no established measure for the baseline health of the local ecosystem. A common solution is to calculate normal ranges for fish endpoints. Observations found to be outside the normal range are then flagged, alerting researchers to the potential presence of stressors in the local environment. The quality of the normal ranges is dependent on the accuracy of the estimates used to calculate them. This paper explores the use of neural networks and regularized regression for improving the prediction accuracy of fish endpoints. We also consider the trade-off between the prediction accuracy and interpretability of each model. We find that neural networks can provide increased prediction accuracy, but this improvement in accuracy may not be worth the loss in interpretability in some ecological studies. The elastic net offers both good prediction accuracy and interpretability, making it a safe choice for many ecological applications. A hybridized method combining both the neural network and elastic net offers high prediction accuracy as well as some interpretability, and therefore it is the recommended method for this application.

Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3−), Magnesium (Mg2+), Sulphate (SO42−), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.

Preface

GEOSHANGHAI 2024 INTERNATIONAL CONFERENCE Volume 6: Geoenvironmental and Energy Geotehnics Shanghai, 26-29 May, 2024 GeoShanghai 2024 International ConferenceCollege of Civil Engineering, Tongji University, 2024All Rights ReservedGeoShanghai is a series of international conferences on geotechnical engineering held in Shanghai quadrennially. The conference was inaugurated in 2006 and was successfully held in 2010, 2014 and 2018, with more than 1600 participants in total. Since the last conference, the geotechnical community has witnessed many advances both in fundamental understanding and engineering practices. To demonstrate the latest developments and promote collaborations in geotechnical engineering and related fields, we launch the 5th GeoShanghai International Conference to be held in May 2024.This volume contains three themes: Environmental Geotechnics, Energy Geotechnicsand Geosynthetics. Environmental Geotechnics is at the forefront of our efforts to rehabilitate and repurpose land, transforming landfills and contaminated soils into safe and productive environments. Energy Geotechnics addresses the critical need for Economically Valuable strategies that harness the Earth’s energy resources responsibly. Geosynthetics provide us equitable access to advanced materials that reinforce, protect, and sustain our environment. These synthetic products are pivotal in our integrative approach, offering versatility and durability in ecological applications. In our pursuit to harmonize human development with the planet’s well-being, this conference unites thought leaders and practitioners in the fields of Environmental Geotechnics, Energy Geotechnics, and Geosynthetics to explore innovative solutions for the pressing environmental issues of our time.This volume received a total of 61 papers from countries such as China, United States, Thailand, and others, totaling 58 papers accepted. All papers were reviewed and the accepted papers will be submitted to IOP Conference Series: Earth and Environmental Science indexed by Scopus.List of Tracks, Conference Steering Committee, International Advisory Committee, Technical Committee, Organizing Committee are available in this pdf.

Beyond yield and toward sustainability: Using applied ecology to support biodiversity conservation and food production

Abstract Agroecological research has an important role in supporting evidence‐based land management to ensure food production is achieved whilst minimising the impacts on biodiversity and its associated ecosystem processes and services. This nexus between often competing priorities of optimal production and sustainability requires greater knowledge of management decisions to enact more sustainable and transformative practice change and to better conserve biodiversity. We highlight three key knowledge gaps in this area: (i) The use of appropriate indicators to measure dependencies and impacts on biodiversity; (ii) Methods to improve the management of resources to support the “health” of biodiversity within agroecosystems; (iii) Greater understanding of the ecosystem processes, and services generated and received across terrestrial and aquatic habitats. Synthesis and applications. Ecological research and robust science will play a vital role in supporting more sustainable food production. We see Journal of Applied Ecology playing a key role in promoting agricultural research that goes beyond a narrow focus on increasing output, and encourage more submissions at the interface of agriculture, ecology and biodiversity conservation, considering social and economic aspects for a sustainable future.