Ecological Research Research Articles

Forest Community Spatial Modeling Using Machine Learning and Remote Sensing Data

This study examines the application of unsupervised classification techniques in the mapping of forest vegetation, aiming to align vegetation cover with the Braun-Blanquet classification system through remote sensing. By leveraging Landsat 8 and 9 satellite imagery and advanced clustering algorithms, specifically the Weka X-Means, this research addresses the challenge of minimizing researcher subjectivity in vegetation mapping. The methodology incorporates a two-step clustering approach to accurately classify forest communities, utilizing a comprehensive set of vegetation indices to distinguish between different types of forest ecosystems. The validation of the classification model relied on a detailed analysis of over 17,000 relevés from the “Flora” database, ensuring a high degree of accuracy in matching satellite-derived vegetation classes with field observations. The study’s findings reveal the successful identification of 44 forest community types that was aggregated into seven classes of Braun-Blanquet classification system, demonstrating the efficacy of unsupervised classification in generating reliable vegetation maps. This work not only contributes to the advancement of remote sensing applications in ecological research, but also provides a valuable tool for natural resource management and conservation planning. The integration of unsupervised classification with the Braun-Blanquet system presents a novel approach to vegetation mapping, offering insights into ecological characteristics, and can be good starter point for sequestration potential of forest communities’ assessment in the Republic of Tatarstan.

Ecological city concept: Challenge and future research agenda in urban ecology perspective

Cities are no longer viewed as creatures with a linear-climax-established cycle but as ecosystems with dynamic and complicated processes, with people as the primary component. Thus, we must understand urban ecology’s structure and function to create urban planning and appreciate the mechanisms, dynamics, and evolution that connect human and ecological processes. The ecological city (ecocity) is one of the city conceptions that has evolved with the perspective of urban ecology history. The concept of ecocity development within urban ecology systems pertains to recognizing cities as complex ecosystems primarily influenced by human activities. In this context, individuals actively engage in dynamic problem-solving approaches to address environmental challenges to ensure a sustainable and satisfactory quality of life for future generations. Therefore, it is necessary to study how ecocity has developed since it was initiated today and how it relates to the urban ecology perspective. This study aims to investigate the progression of scholarly publications on ecocity research from 1980 to 2023. Additionally, it intends to ascertain the trajectory of ecological city research trends, establish connections between scientific concepts, and construct an ecological city science network using keyword co-occurrence analysis from the urban ecology perspective. The present study used a descriptive bibliometric analysis and literature review methodology. The data was obtained by utilizing the Lens.org database, was conducted using the VOS (Visualization of Similarities) viewer software for data analysis. The urban ecology research area ecology of cities can be studied further from density visualization of ecosystem services and life cycle assessment. Finally, the challenges and future agenda of ecocity research include addressing humans by modeling functions or processes that connect humans with ecosystems (ecology of cities), urban design, ecological imperatives, integration research, and improving the contribution to environmental goals, spatial distribution, agriculture, natural resources, policy, economic development, and public health.

Predator–Prey Interaction with Fear Effects: Stability, Bifurcation and Two-Parameter Analysis Incorporating Complex and Fractal Behavior

This study investigates the dynamics of predator–prey interactions with non-overlapping generations under the influence of fear effects, a crucial factor in ecological research. We propose a novel discrete-time model that addresses limitations of previous models by explicitly incorporating fear. Our primary question is: How does fear influence the stability of predator–prey populations and the potential for chaotic dynamics? We analyze the model to identify biologically relevant equilibria (fixed points) and determine the conditions for their stability. Bifurcation analysis reveals how changes in fear levels and predation rates can lead to population crashes (transcritical bifurcation) and complex population fluctuations (period-doubling and Neimark–Sacker bifurcations). Furthermore, we explore the potential for controlling chaotic behavior using established methods. Finally, two-parameter analysis employing Lyapunov exponents, spectrum, and Kaplan–Yorke dimension quantifies the chaotic dynamics of the proposed system across a range of fear and predation levels. Numerical simulations support the theoretical findings. This study offers valuable insights into the impact of fear on predator–prey dynamics and paves the way for further exploration of chaos control in ecological models.

Structural shifts in plant functional diversity during biogeomorphic succession: Moving beyond taxonomic investigations in an alpine glacier foreland

AbstractThe complex interrelation between plants and geomorphic processes is described in the concept of biogeomorphic succession. While ecological research on succession and community assembly has transitioned towards functional approaches, studies on functional diversity in biogeomorphic settings, particularly in glacier forelands, remain limited. In this study, we investigated abundance of vascular plant species and functional traits in an alpine glacier foreland using data from 199 plots. Our objective was to unravel the development of functional diversity during biogeomorphic succession. Specifically, the study determined whether structural shifts in functional diversity are associated with stability thresholds related to plant cover, geomorphic influence, and examined trait spectra for stages of biogeomorphic succession. Our findings revealed a nonlinear trajectory of functional diversity along the plant cover gradient, marked by two distinct structural shifts at 30% and 74% cover, corresponding to established stability thresholds. Along the gradient of geomorphic influence, we observed an increase in functional diversity until 54% of the plot area was affected, beyond which functional diversity declined below the initial level. The analysis of community‐weighted means of traits across four stages of biogeomorphic succession determined by plant cover and absence and presence of geomorphic influence revealed significant differences in trait values. In the transition to the biogeomorphic stage, associated with the identified initial structural shift, there is a shift from a prevalence of above‐ground adaptation and reproductive traits, such as leaf longevity, structure, growth form and mixed reproductive strategies, to an increased dominance of competitor species and traits related to below‐ground structures, including root type and structures, as well as vegetative reproduction. Our results contribute to understanding the relationship between vegetation succession and geomorphic influence by linking them to plant functional traits. This study advances beyond traditional taxonomic investigations by emphasizing functional approaches to biogeomorphic succession. Moreover, the functional trait data used in this study, easily downloadable from a public repository, can serve as a valuable template for future research in (bio)geomorphology, along with the employed methodologies.

Quantifying the mitigating effect of organic matter on heavy metal availability in soils with different manure applications: A geochemical modelling study

Manure is one of the main sources of heavy metal (HM) pollution on farmlands. It has become the focus of global ecological research because of its potential threat to human health and the sustainability of food systems. Soil pH and organic matter are improved by manure and play pivotal roles in determining soil HM behavior. Geochemical modeling has been widely used to assess and predict the behavior of soil HMs; however, there remains a research gap in manure applications. In this study, a geochemical model (LeachXS) coupled with a pH-dependent leaching test with continuously simulations over a broad pH range was used to determine the effects and pollution risks of pig or cattle manure separate application on soil HMs distribution. Both pig and cattle manure applications led to soil pH reduction in alkaline soils and increased organic matter content. Pig manure application resulted in a potential 90.5–156.0 % increase in soil HM content. Cattle manure did not cause significant HM contamination. The leaching trend of soil HMs across treatments exhibited a V-shaped change, with the lowest concentration at pH = 7, gradually increasing toward strong acids and bases. The dissolved organic matter-bound HM content directly increased the HM availability, especially for Cu (up to 8.4 %) after pig manure application. However, more HMs (Cr, Cu, Zn, Ni) were in the particulate organic matter-bound state than in other solid phases (e.g., Fe-Al(hydr) oxides, clay minerals), which inhibited the HMs leaching by more than 19.3 % after cattle manure application. Despite these variations, high HM concentrations introduced by pig manure raised the soil contamination risk, potentially exceeding 40 times at pH ±1. When manure is returned to the field, reducing its HM content and mitigating possible pollution is necessary to realize the healthy and sustainable development of circular agriculture.

Lack of social data hinders integrative urban ecology research: obstacles and opportunities

Lack of social data hinders integrative urban ecology research: obstacles and opportunities

Seeding the Pantanal: orchestrating research to overcome restoration barriers in the world's largest tropical wetland

Knowledge on seed ecology is foundational for effective seed‐based restoration including seedling production, direct seed sowing, topsoil transplant, and natural regeneration. Consequently, quantifying available knowledge and biases in seed‐based research allows practitioners to better plan and implement seed‐based restoration programs and identify research priorities. Using a systematic review comprising 45 papers, 401 species, and 2,415 case studies, we provide an overview of seed ecology research in the Pantanal, the world's largest tropical wetland. We identified geographic, taxonomic, and ecological biases, as well as knowledge gaps in seed functions including production, dispersal, dormancy, germination, predation, and stress tolerance. Studies concentrated near large cities resulting in extensive unexplored sites in central Pantanal, in Paraguay, and in Bolivia. Unexpectedly, the most studied species were conducted with dry‐fruited, terrestrial forbs, or shrubs with autochoric or zoochoric dispersal syndromes. Seed banks, stress tolerance, and dispersal were the most studied topics, whereas studies on dormancy and seedling establishment remain rare. We also found disproportionate research interest across the angiosperm tree of life with many examples of underrepresented and overrepresented families. Altogether, persistent knowledge gaps in seed‐based research in the Pantanal hinder upscaling restoration programs and consequently the likelihood of achieving the targets of UN Decade of Ecosystem Restoration. We propose an integrated agenda consisting of a series of orchestrated actions to overcome such barriers, close biodiversity knowledge shortfalls, and promote successful large‐scale restoration programs in the Pantanal.

Ecological validity of common behavioral decision making tasks: evidence across two samples

ABSTRACT Background Clinicians and scholars routinely use behavioral decision tasks to assess real-world decision making capabilities. However, many common behavioral decision making tasks lack data on the extent to which they predict real-world risky behaviors. Across two pre-registered studies, and two timepoints, we assessed decision making abilities using common behavioral tasks and predicted participants’ real-world risky decision making from task performance. Method In Study 1, 918 Mechanical Turk (MTurk) workers completed three decision making tasks in addition to assessments of real-world risk behavior: preventive health behaviors, COVID-19 vaccination status, and virtual social distancing task performance. In Study 2, 221 college student participants completed the Study 1 tasks plus additional assessments of decision making and real world risk and protective behaviors. Results Across both studies, the selected behavioral decision tasks rarely predicted real world behavior and, when they did, the relationship was weak at best. Conclusions These data suggest that these behavioral decision making tasks may not be good predictors of real world risky behavior at present, with some evidence that the specificity of the behavior being assessed matters (i.e. the closer the task was to the specific behavior being predicted), calling for additional ecological validity research, with a greater variety of tasks in the future.

Inferring the allometric growth coefficient of juvenile African mud catfish, <i>Clarias gariepinus</i> (Burchell, 1822), using Bayesian and Frequentist regression models

Statistics is essential in biological and ecological scientific research. However, the default Frequentist statistics based on p-value and null hypothesis testing is often misused and misinterpreted, hence causing reproducible crises. The p-value concept deserved further examination because it has been irretrievably lost. Therefore, there is dire need for reform in the default Frequentist statistics as practiced by researchers because of the perils of p-values. Bayesian statistics, using the tools of Bayes Factors and posterior distributions derived from priors and likelihood function; rooted in Bayes’ Theorem is one of the suggested alternatives. Frequentist (least square) and Bayesian (specifying uniform Jeffreys-Zellner-Siow prior, r-scale =0.35) regression models, a standard statistical protocol in fisheries were applied to determine the allometric growth coefficient based on length (mm) and weight (g) measurements of juvenile African mud catfish, Clarias gariepinus from Epe Lagoon. The growth coefficient, b=3.20, 95% Confidence Interval [3.07, 3.34], t(96)=47.55, p<0.001 was significant with 96% explanatory power (R2=0.96).While Bayesian method, with 96% explanatory power (R2=0.96), also estimated, b=3.20, (with Credible Interval between 3.06 and 3.32). The Bayes Factor (>100) suggested the data is more plausible under the alternative model than the null model, but p-value cannot quantify evidence in support of alternative hypothesis, since p-value can only reject or fail to reject a null hypothesis. These findings suggested that juvenile C. gariepinus thrived in Epe Lagoon. Therefore, Bayesian inference is a robust substitute for Frequentist regression model in fisheries.

Life history of an evolutionary distinct aquatic caecilian amphibian (genus Potomotyphlus) in eastern Amazonia, Brazil

Abstract Basic ecological data for caecilians are relatively lacking. This study presents the first comprehensive ecological investigation of the Amazonian aquatic caecilian Potomotyphlus kaupii, utilizing a large sample size. We collected population data over a 14-month period, and also used data of museum specimens, both from Belém, State of Pará, Brazil. Our analysis focused on sex ratio, sexual size dimorphism (SSD), female fecundity, and ecological observations. No significant differences in adult sex ratio were found. SSD was found in some characters such as circumference at midbody and body mass with females being larger than males, while males had a wider and longer cloacal disc. We provide the first determination of the approximate size at which males diverge from females in the terminal portion of the body, demonstrating an allometric growth pattern of the cloacal region. Female size showed a positive correlation with the number of ovarian eggs, indicating an important reproductive strategy. Furthermore, a novel behaviour in P. kaupii was documented. We expand the understanding of Gymnophiona biology, in particular of a fully aquatic species, and provide support for future ecological and conservation research on caecilians.

Acoustic monitoring of anurans and birds in tropical biomes

AbstractPassive acoustic monitoring (PAM) is increasingly popular in ecological research, but recording and analyzing large amounts of data is still a critical bottleneck for the long‐term monitoring of multiple species. We evaluated how temporal and spatial sampling effort affects species diversity estimates using a set of 14,045 1‐min recordings from various neotropical birds and anuran communities. Our goals were to evaluate (i) the daily vocal activity cycle of birds and anurans, (ii) the effect of temporal structure (e.g., number of minutes listened each hour; continuous versus intermittent recordings) on determining the species composition, and (iii) the species–area relationship, and how the number of recorders affects species richness estimates. Based on sampling coverage and completeness, we (iv) evaluate manual inspection schedules for birds and anurans across four biomes of Brazil. We found marked diel variation in vocal activity between taxonomic groups, indicating that birds and anurans are more efficiently detected during early periods of the day and night, respectively. For proper diversity estimates, biomes with higher biodiversity required longer inspecting periods and a larger number of replicates, irrespective of taxa. Although fewer recordings per hour are less informative than full‐hour sampling, species diversity is better estimated when inspected minutes are interspersed over longer periods than inspecting minutes recorded over shorter timespans. Based on our findings, we recommend how to set PAM programs over highly diverse ecosystems.Abstract in Portuguese is available with online material.

Too much diversity—Multiple definitions of geodiversity hinder its potential in biodiversity research

AbstractGeodiversity—the diversity of abiotic features and processes of the Earth's surface and subsurface—is an increasingly used concept in ecological research. A growing body of scientific literature has provided evidence of positive links between geodiversity and biodiversity. These studies highlight the potential of geodiversity to improve our understanding of biodiversity patterns and to complement current biodiversity conservation practices and strategies. However, definitions of geodiversity in ecological research vary widely. This can hinder the progress of geodiversity–biodiversity research and make it difficult to synthesize findings across studies. We therefore call for greater awareness of how geodiversity is currently defined and for more consistent use of the term ‘geodiversity’ in biodiversity research.

Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning

Reliable sex identification in Varanus salvator traditionally relied on invasive methods like genetic analysis or dissection, as less invasive techniques such as hemipenes inversion are unreliable. Given the ecological importance of this species and skewed sex ratios in disturbed habitats, a dataset that allows ecologists or zoologists to study the sex determination of the lizard is crucial. We present a new dataset containing morphometric measurements of V. salvator individuals from the skin trade, with sex confirmed by dissection post- measurement. The dataset consists of a mixture of primary and secondary data such as weight, skull size, tail length, condition etc. and can be used in modelling studies for ecological and conservation research to monitor the sex ratio of this species. Validity was demonstrated by training and testing six machine learning models. This dataset has the potential to streamline sex determination, offering a non-invasive alternative to complement existing methods in V. salvator research, mitigating the need for invasive procedures.

Globe-LFMC 2.0, an enhanced and updated dataset for live fuel moisture content research.

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

Fish Species Classification Using Deep Learning and Appearance-Based Feature Extraction

As aquatic ecosystems face increasing threats and challenges, the accurate and efficient classification of fish species has become a critical task for fisheries management, biodiversity conservation, and environmental monitoring. This research explores the application of deep learning techniques in conjunction with appearance-based feature extraction for the automated and precise identification of fish species from images. The study employs a comprehensive dataset of underwater fish images, encompassing various species, sizes, and environmental conditions. Convolutional Neural Networks (CNNs) are used for feature extraction and classification, allowing for the capture of intricate visual characteristics that distinguish different fish species. Moreover, appearance-based features, such as colour patterns, fin shapes, and other morphological attributes, are integrated into the model to enhance classification accuracy. Additionally, the study introduces Improved Sequential Forward Selection (ISFS) as a method to select relevant features for classification, further enhancing the efficiency and effectiveness of the model. The research evaluates the performance of the proposed methodology against existing classification methods, emphasizing the advantages of deep learning and appearance-based feature extraction in terms of classification accuracy, robustness, and efficiency. In addition, the study explores the potential for real-world applications in fisheries management, species monitoring, and ecological research. The findings of this research demonstrate that the combination of deep learning and appearance-based feature extraction provides a promising approach for fish species classification, offering a valuable tool for conservation efforts and ecological studies. This research contributes to the growing body of knowledge in the field of computer vision and biodiversity conservation and opens up new possibilities for automated fish species identification in aquatic environments.

The drivers of intraspecific trait variation and their implications for future tree productivity and survival.

Forests are facing unprecedented levels of stress from pest and disease outbreaks, disturbance, fragmentation, development, and a changing climate. These selective agents act to alter forest composition from regional to cellular levels. Thus, a central challenge for understanding how forests will be impacted by future change is how to integrate across scales of biology. Phenotype, or an observable trait, is the product of an individual's genes (G) and the environment in which an organism lives (E). To date, researchers have detailed how environment drives variation in tree phenotypes over long time periods (e.g., long-term ecological research sites [LTERs]) and across large spatial scales (e.g., flux network). In parallel, researchers have discovered the genes and pathways that govern phenotypes, finding high degrees of genetic control and signatures of local adaptation in many plant traits. However, the research in these two areas remain largely independent of each other, hindering our ability to generate accurate predictions of plant response to environment, an increasingly urgent need given threats to forest systems. I present the importance of both genes and environment in determining tree responses to climate stress. I highlight why the difference between G versus E in driving variation is critical for our understanding of climate responses, then propose means of accelerating research that examines G and E simultaneously by leveraging existing long-term, large-scale phenotypic data sets from ecological networks and adding newly affordable sequence (-omics) data to both drill down to find the genes and alleles influencing phenotypes and scale up to find how patterns of demography and local adaptation may influence future response to change.

A compendium of ruminant gastrointestinal phage genomes revealed a higher proportion of lytic phages than in any other environments

BackgroundRuminants are important livestock animals that have a unique digestive system comprising multiple stomach compartments. Despite significant progress in the study of microbiome in the gastrointestinal tract (GIT) sites of ruminants, we still lack an understanding of the viral community of ruminants. Here, we surveyed its viral ecology using 2333 samples from 10 sites along the GIT of 8 ruminant species.ResultsWe present the Unified Ruminant Phage Catalogue (URPC), a comprehensive survey of phages in the GITs of ruminants including 64,922 non-redundant phage genomes. We characterized the distributions of the phage genomes in different ruminants and GIT sites and found that most phages were organism-specific. We revealed that ~ 60% of the ruminant phages were lytic, which was the highest as compared with those in all other environments and certainly will facilitate their applications in microbial interventions. To further facilitate the future applications of the phages, we also constructed a comprehensive virus-bacteria/archaea interaction network and identified dozens of phages that may have lytic effects on methanogenic archaea.ConclusionsThe URPC dataset represents a useful resource for future microbial interventions to improve ruminant production and ecological environmental qualities. Phages have great potential for controlling pathogenic bacterial/archaeal species and reducing methane emissions. Our findings provide insights into the virome ecology research of the ruminant GIT and offer a starting point for future research on phage therapy in ruminants.CJAF8kS71Z3NBgTNJ_uZFeVideo

A resource-based mechanistic framework for castration-resistant prostate cancer (CRPC)

Cancer therapy often leads to the selective elimination of drug-sensitive cells from the tumour. This can favour the growth of cells resistant to the therapeutic agent, ultimately causing a tumour relapse. Castration-resistant prostate cancer (CRPC) is a well-characterised instance of this phenomenon. In CRPC, after systemic androgen deprivation therapy (ADT), a subset of drug-resistant cancer cells autonomously produce testosterone, thus enabling tumour regrowth. A previous theoretical study has shown that such a tumour relapse can be delayed by inhibiting the growth of drug-resistant cells using biotic competition from drug-sensitive cells. In this context, the centrality of resource dynamics to intra-tumour competition in the CRPC system indicates clear scope for the construction of theoretical models that can explicitly incorporate the underlying mechanisms of tumour ecology. In the current study, we use a modified logistic framework to model cell–cell interactions in terms of the production and consumption of resources. Our results show that steady state composition of CRPC can be understood as a composite function of the availability and utilisation efficiency of two resources-oxygen and testosterone. In particular, we show that the effect of changing resource availability or use efficiency is conditioned by their general abundance regimes. Testosterone typically functions in trace amounts and thus affects steady state behaviour of the CRPC system differently from oxygen, which is usually available at higher levels. Our data thus indicate that explicit consideration of resource dynamics can produce novel and useful mechanistic understanding of CRPC. Furthermore, such a modelling approach also incorporates variables into the system’s description that can be directly measured in a clinical context. This is therefore a promising avenue of research in cancer ecology that could lead to therapeutic approaches that are more clearly rooted in the biology of CRPC.

Effects of the hydrologic process and geochemistry on dissolved carbon in shallow groundwater surrounding Qinghai Lake

A high concentration of dissolved carbon in groundwater increases drinking water health risks and carbon transport. Understanding the comprehensive impact of hydrologic processes and geochemistry on dissolved carbon in shallow groundwater also is a fundamental prerequisite for estimating the carbon budget of lakes in the Tibetan Plateau. This study investigated the spatial–temporal characteristics of the hydrologic process, geochemistry and dissolved carbon in shallow groundwater by the stable isotope tracer method, Piper diagram and Boomerang envelope model. The driving factors of dissolved carbon in shallow groundwater were explored by correlation analysis and redundancy analysis. The results showed low dissolved inorganic carbon (DIC) concentrations and high dissolved organic carbon (DOC) concentrations during the thawing period and rainy season and high DIC concentrations and low DOC concentrations during the freezing period. The seepage velocity, soil carbon dioxide dissolution and gypsum dissolution were the main factors influencing DIC concentrations during the thawing period. The mineralization and decomposition of DOC and dissolution of carbonate rocks were the main factors influencing DIC concentrations during the freezing period. The concentrations of DOC were mainly controlled by the adsorption of Ca2+ and Mg2+, microbial activity and pollutants produced by human activities. Our results are useful for ecological sustainable development, human health, and research on the carbon transport in groundwater in the Tibetan Plateau.

Celebrating A New Milestone for Environmental Research and Technology

Leading the launch of a new journal as Editors-in-Chief has been a captivating journey filled with triumphs and challenges. In the beginning of 2017, we commenced our efforts on the inaugural issue of the Environmental Research and Technology. Since our inception in 2018, our journal has been dedicated to fostering cutting-edge research in environmental science and technology, striving to address the critical challenges facing our planet. Today, as we stand on the threshold of another year of scholarly exploration and dissemination, it is an opportune moment to reflect on our accomplishments, express our appreciation, and outline our vision for the future. As we commence the new volume of Environmental Research and Technology Journal in the year 2024, it is with great pleasure and a sense of profound gratitude that we address you.