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Papers
20 resultsShowing papers similar to Ridge regression-based modeling for assessing the extent of response to biological invasions
ClearBiosecurity of agroecosystems under technogenic and environmental risks
Ukrainian researchers developed an interdisciplinary monitoring framework combining ecotoxicological analysis, biogeochemical modeling, and digital technologies to assess how technogenic and environmental risks interact to threaten agroecosystem biosecurity.
Biological invasions alter environmental microbiomes: a meta-analysis
This meta-analysis of publicly available data found that biological invasions consistently reduce microbial diversity and shift the structure of environmental microbial communities. The findings suggest that invasive species' ecological damage extends beyond visible plant and animal communities to the microbial level, making their impact more pervasive than previously recognized.
Development of a Classification Model for Physiological Parameters in Relation to Ecological Aspects Based on Cohort Data
Researchers developed a classification model linking physiological parameters to ecological and environmental factors using cohort data, aiming to understand how environmental variables, socioeconomic conditions, and demographic parameters influence human health outcomes in the context of ecosystem modelling.
Development of Cohort-Based Prediction Model for Human Health in Relation to Ecological Aspects
Researchers developed a cohort-based prediction model linking ecological factors including environmental conditions, socioeconomic constraints, and demographic parameters to human health outcomes. The model was designed to serve as a reference tool for ecosystem modeling and to assess health vulnerability to infectious diseases and environmental stressors across populations.
Analysis of Potential Supply of Ecosystem Services in Forest Remnants through Neural Networks
Researchers applied an artificial neural network to geospatial indicators to assess the potential supply of regulating ecosystem services from forest remnants in Campinas, Brazil. The study analyzed landscape configuration factors and evaluated how both the supply of and societal demand for ecosystem services influence the actual benefits provided by fragmented forest patches.
A trait‐based framework for assessing the vulnerability of marine species to human impacts
Researchers developed a trait-based framework to assess the vulnerability of over 44,000 marine species across 12 taxonomic groups to 22 anthropogenic stressors including pollution and climate change. They found that mollusks, corals, and echinoderms had the highest overall vulnerability, while biomass removal from fishing posed the greatest threat across species. The framework provides a systematic approach for predicting how marine biodiversity will respond to human pressures, which can help guide conservation priorities.
Common issues of data science on the eco-environmental risks of emerging contaminants.
This review examines common methodological pitfalls in data science approaches to emerging contaminants research, highlighting issues such as data leakage, inadequate ecological complexity, and over-reliance on laboratory data. Researchers argue that future work should integrate ensemble models, spatiotemporal causal frameworks, and field-based validation to close gaps between data-driven predictions and real-world environmental outcomes.
Prioritization of absent quarantine pests in Brazil through the Analytical Hierarchy Process
Researchers used the Analytical Hierarchy Process (AHP) to prioritize absent quarantine pests — invasive species not yet established in Brazil — based on their potential economic and environmental damage. The method helps authorities focus biosecurity resources on the highest-risk biological threats.
Applying Artificial Neural Networks to Oxidative Stress Biomarkers in Forager Honey Bees (Apis mellifera) for Ecological Assessment
Researchers applied artificial neural networks to analyze oxidative stress biomarkers in forager honey bees across urban, forested, and agricultural areas in Italy, identifying environmental matrix-specific patterns useful for ecological health assessment.
Threshold Response Identification to Multi-Stressors Using Fish- and Macroinvertebrate-Based Diagnostic Tools in the Large River with Weir-Regulated Flow
Researchers applied Gradient Forest models to 12 years of biological assemblage and environmental data from a large weir-regulated river, identifying the multi-stressor thresholds driving community turnover in both fish and macroinvertebrate assemblages across 66 environmental parameters. They found that threshold responses differed between biological assemblage types under identical environmental conditions, and that weir operation increased the dominance of non-native species, with results proposed as the first reference thresholds for similar regulated river environments.
Integrated Ecological Risk Assessment of the Agricultural Area under a High Anthropopressure Based on Chemical, Ecotoxicological and Ecological Indicators
Researchers conducted an integrated ecological risk assessment of agricultural land using chemical, ecotoxicological, and ecological indicators, finding that while chemical analysis overestimated risk, the combined approach revealed most of the area had acceptable risk levels despite over a century of anthropogenic pressure.
Predicting effects of multiple interacting global change drivers across trophic levels
Researchers proposed a framework using reaction norms to predict how multiple interacting global change drivers simultaneously affect vital rates and population dynamics across trophic levels, addressing a key challenge in ecology and conservation.
Progress of Ecological Restoration Research Based on Bibliometric Analysis
This bibliometric analysis of over 23,000 publications from 1990 to 2022 mapped global ecological restoration research trends, identified key hotspots around topics like soil and vegetation recovery, and projected future directions for the field.
Exploring multiple stressor effects with Ecopath, Ecosim, and Ecospace: Research designs, modeling techniques, and future directions
This review examined how the popular Ecopath with Ecosim modeling platform has been used to study multiple environmental stressors in ecosystems, including pollution, climate change, and invasive species. Researchers found that most studies focused on single stressors and rarely addressed microplastic pollution or combined effects of multiple threats. The paper calls for more integrated modeling approaches that capture how different stressors interact in real ecosystems.
Computing Invasive Species Population Based on a Generalized Random Walk Process: Application to Blue Crab (Callinectes sapidus)
Researchers developed a simulation model combining a generalized random walk process with field measurements to estimate the population of the invasive blue crab (Callinectes sapidus) in a Mediterranean coastal lagoon, providing the first stage of a methodology for modeling and predicting invasive species population dynamics in marine environments.
Soil-dwelling species-based biomarker as a sensitivity-risk measure of terrestrial ecosystems response to microplastics: A dose–response modeling approach
A dose-response modeling approach was applied to data from soil-dwelling organisms to assess the relative sensitivity of terrestrial ecosystems to microplastic contamination, producing species sensitivity distributions as a risk metric. The analysis revealed that certain soil invertebrates are particularly vulnerable even at relatively low microplastic concentrations.
A Fuzzy Ballast Water Risk Assessment Model in Maritime Transport
Researchers developed a fuzzy logic-based risk assessment model for evaluating the environmental hazards of ballast water discharge from maritime transport, including the spread of invasive species and pollutants. The model addresses the complex uncertainties that traditional assessment methods often fail to capture. The study suggests this approach can help port authorities and shipping companies better manage ballast water risks to marine ecosystems.
Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models
Researchers used machine learning models to predict the current and future geographic range of Oncomelania hupensis — a freshwater snail that carries the parasite causing schistosomiasis in humans — and found climate change is likely to push snail populations northward and westward in China's Yunnan Province. These projections can help public health agencies target snail control efforts in areas that may become newly suitable habitats.
The need for a sentinel species: considerations towards regional bioindicators
Researchers examined the need for sentinel bioindicator species to generate robust monitoring data for marine litter and microplastics, evaluating candidate species including fish and shellfish based on criteria such as geographic range, abundance, and economic importance. The study proposed a framework for selecting effective regional bioindicators to inform ecotoxicological models and support emerging plastic pollution policy.
Genomic Prediction of (Mal)Adaptation Across Current and Future Climatic Landscapes
This study developed genomic prediction models to forecast how organisms adapted to current climate conditions might (mal)adapt as climate change shifts selective pressures, offering a tool for conservation planning under future climate scenarios.