312 papers
Ecology explores the intricate relationships between living organisms and their environments, revealing how life adapts, interacts, and sustains itself across the planet. From microscopic soil communities to vast migratory patterns, this field examines the delicate balance that keeps ecosystems functioning and resilient. Understanding these connections is vital for addressing urgent challenges like climate change and biodiversity loss.
At Gist.Science, we make the latest research from bioRxiv accessible to everyone. We process every new preprint in this category as it appears, offering both clear plain-language explanations and detailed technical summaries to suit every reader. This approach ensures that groundbreaking findings in ecological science are not locked behind complex terminology but are available for immediate understanding and discussion.
Below are the latest papers in ecology, curated to help you stay informed about the evolving story of life on Earth.
This study demonstrates that the dispersal of released ring-necked pheasants is significantly further, more directional, and more persistent than current assumptions suggest, with a substantial proportion of birds traveling well beyond 500m and potentially encroaching on sensitive conservation sites.
This paper describes the development of the UKCEH AMI-system, an open-source, second-generation automated light trap that utilizes a single-board computer, USB camera, and AI to provide a scalable, non-lethal solution for long-term nocturnal insect monitoring.
A community-led vermicomposting initiative in Khayelitsha, South Africa, significantly increased Swiss chard yields by approximately 51–59% through the application of worm-based compost and liquid extracts, demonstrating the potential of low-cost, grassroots innovations to enhance urban food security and economic benefits in marginalized areas.
This study demonstrates that acoustic monitoring and individual tracking provide complementary, rather than contradictory, insights into offshore Nathusius pipistrelle migration patterns by detecting different subsets of the population based on wind conditions and flight capabilities, highlighting the need to integrate both methods for effective wind farm mitigation.
By integrating high-frequency multi-omics sampling during a host bloom in Japan, this study reveals that the *Heterosigma akashiwo* virus population undergoes rapid diversification driven by host-driven selection acting on standing genetic variation, characterized by fluctuating microdiversity and high expression of accessory genes involved in host interaction.
This study demonstrates that the impact of annual precipitation anomalies on plant biomass is significantly modulated by intra-annual precipitation evenness, with evenly distributed rainfall amplifying growth responses while infrequent large events mute them across diverse global grasslands.
This paper introduces and validates an elasticity-consistent aggregation method for Leslie matrix population models that overcomes the limitations of standard aggregators by preserving key demographic properties and enabling more accurate, fair comparisons across diverse life cycles in the era of big data ecology.
By integrating multidisciplinary surveillance with citizen science, researchers found that while Usutu virus has repeatedly and widely established itself in the UK, its significant impact on Eurasian Blackbird populations remains localized to urban areas likely due to heat island effects, suggesting that cities may serve as early warning systems for future mosquito-borne pathogen emergence in temperate zones.
This study evaluates the ecological knowledge of Gemini 1.5 Pro and GPT-4o using a new benchmark dataset, revealing that while these models outperform naive baselines in tasks like species presence prediction, their overall performance remains significantly below expert levels, indicating a critical need for domain-specific fine-tuning to effectively integrate LLMs into ecological science.
This paper presents a data-efficient framework that integrates indicator species analysis, ecological network theory, and machine learning to accurately infer the occurrence of non-indicator species across diverse abundance regimes by leveraging the structural properties of species co-occurrence networks.