413 papers
MycorrhizaFinder is a user-friendly, semi-automated machine learning tool designed to efficiently quantify endomycorrhizal colonisation in diverse field roots, overcoming the time limitations of traditional microscopy to enable large-scale ecosystem monitoring.
This study demonstrates that photodegradation significantly accelerates litter decomposition and alters physical traits in South American monsoonal mountain grasslands, proving that this process is not limited to arid ecosystems and can enhance subsequent biotic decomposition through photofacilitation.
This study demonstrates that while negative density-dependence and Allee effects individually have limited long-term impacts on malaria mosquito populations, their interaction—particularly when sustained vector control interventions reduce population sizes to trigger Allee effects—significantly accelerates extinction and offers a strategic pathway for eliminating disease vectors.
This global review of 464 studies synthesizes the complex and predominantly negative impacts of invasion-fire interactions on ecosystems, highlighting that while fire often facilitates invasive species and prescribed fire is more effective for management, significant knowledge gaps remain regarding understudied taxa, geographic regions, and broad-scale ecological consequences.
This study demonstrates that while the marine bacterium *Alteromonas macleodii* ATCC 27126 can catabolize most amino acids, its actual growth success is strictly determined by whether the amino acid's degradation pathway feeds into pyruvate or acetyl-CoA, revealing a "TCA-centric" metabolic constraint that overrides simple genomic predictions.
This study demonstrates that Asian elephants in southern India drive fine-scale vegetation heterogeneity by depositing nutrient-rich dung, which creates localized growth hotspots and alters competitive dynamics between nitrogen-fixing and non-nitrogen-fixing plant species, thereby reshaping community assembly through significant bottom-up effects.
This paper introduces the `ramp.micro` R package and behavioral state microsimulation models to demonstrate that mosquito dispersal is an emergent property driven by resource-seeking behaviors and local context, revealing complex spatial population structures that traditional diffusion-based models fail to capture.
This paper presents the development and validation of a rapid, cost-effective, and accurate molecular toolkit using SNP-based qPCR and RFLP assays to specifically identify and differentiate African-derived honey bees and *Apis mellifera capensis* for regulatory purposes in the United States.
This study introduces FishTherm, a comprehensive dataset of thermal responses for 107 wild fish species, to demonstrate that thermal performance traits systematically vary with latitude, habitat, and biological organization, thereby providing a crucial resource for understanding fish vulnerability to climate change and validating key ecological theories.
This study reveals that the critically endangered largetooth sawfish hosts a distinct, resilient skin microbiome characterized by lower taxonomic diversity but higher functional redundancy and anaerobic metabolic adaptations compared to the surrounding water, suggesting a host-filtered assembly process that maintains metabolic potential during environmental stress.
This study demonstrates that invasion patterns in Long Island forests are governed by community assembly rules where a regional exotic species pool biased toward nutrient-rich traits fails to establish in nutrient-poor soils, explaining why some disturbed sites remain uninvaded despite high propagule pressure.
An analysis of 258 invasive plant species in North America reveals that the majority have not yet reached climatic stasis, challenging the common assumption of environmental equilibrium in species distribution models and highlighting the unrealized expansion potential of these invaders.
This study developed and validated random forest models using Sentinel-3, Landsat, and in situ data to accurately predict surface water temperatures across U.S. lakes, thereby providing a critical tool for improving cyanobacterial harmful algal bloom forecasting and water resource management.
This study demonstrates that interannual climate variability driven by ENSO, rather than just long-term mean temperature trends, acts as a primary mechanism that exponentially expands dengue transmission into high-altitude Andean cities, thereby exposing immunologically naive populations and necessitating a shift in how epidemic risks are projected and attributed to climate change.
This study uses mechanistic modeling to project that by 2085, climate change will render most of France climatically suitable for the establishment of *Aedes albopictus* and theoretically capable of supporting autochthonous dengue transmission, with the extent of risk varying significantly based on greenhouse gas emission scenarios and demographic trends.
By combining experiments with flour beetles and a modified stage-structured model, this study demonstrates that while transmission mode is a key determinant of population stability, facilitation between co-infecting parasites is a critical mechanism that rapidly drives host populations into oscillations and chaos.
This study demonstrates that machine learning models, particularly neural networks, can effectively predict life-history traits such as elytral length and adult lifespan in the azuki bean beetle *Callosobruchus chinensis* by analyzing biological and environmental variables, thereby offering a valuable tool for advancing insect ecology and pest management research.
By modeling vegetation-fire feedbacks in Boreal and Mediterranean ecosystems, this study reveals that both compositional and functional plant diversity generally peak at intermediate fire frequencies, underscoring the critical role of these feedbacks in predicting biodiversity responses to global change.
This study demonstrates that cross-kingdom biotic interactions, particularly direct interactions in low-diversity leaf habitats and diffuse interactions in high-diversity root habitats, are significant drivers of plant microbiome assembly comparable to environmental and spatial factors, thereby enhancing the understanding of microbial community structure for applications like synthetic community development.
This study demonstrates that while the widely distributed C4 grass *Themeda triandra* exhibits remarkable phenotypic flexibility in physiological traits across thermal regimes, its flowering time remains the most robust adaptive trait correlated with its climate of origin, underscoring the importance of reproductive phenology in its global distribution.