368 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 employs a mathematical model to demonstrate that optimal within-plant volatile-mediated signaling against herbivory, involving the co-evolution of signal emission and perception, is favored only under intermediate herbivory pressure, while the inclusion of perception-independent functions can decouple these traits and enable emission-only strategies.
This study proposes a novel, climate-aware conservation planning framework using reinforcement learning to optimize the protection of European crop wild relatives, demonstrating that accounting for niche coverage and range shifts significantly improves conservation outcomes compared to traditional methods.
This study presents a scalable, error-adjusted deep learning framework for monitoring California's urban tree canopy, revealing a statistically indistinguishable-from-zero statewide decline from 2016 to 2022 and highlighting that over half of urban canopy resides on private residential land, which underscores the necessity of rigorous uncertainty estimation for accurate policy tracking.
By fusing Landsat fire and canopy data with forest inventory records, this study reveals that Western U.S. forest live biomass, after increasing from 2005 to 2015, experienced a net 5% decline through 2022 driven primarily by wildfire-induced tree mortality—a trend reversal missed by official reporting and Earth system models.
This paper presents a prioritization framework that identifies 1,667 critical unprotected sites (Conservation Imperatives) across 20 countries, which, despite covering only 0.37% of Earth's terrestrial surface, offer high irreplaceability and connectivity to existing protected areas to effectively prevent species extinctions and support the global 30x30 conservation target.
By applying machine learning to DNA metabarcoding time series, this study reveals a globally consistent, semi-annual cycle in temperate aquatic biotic communities driven by insolation patterns and centered on photoautotrophs, offering a new framework for anticipating ecological events like phytoplankton blooms.
This study reveals that rodent activity enriches burrow soils with nitrate, which restructures amoeba-bacteria interactions to favor pathogenic bacterial taxa and enhance infectious disease-related functional potential, thereby linking rodent-driven soil heterogeneity to zoonotic pathogen emergence.
This study characterizes the diverse and variable seed microbiota of four major crops across different genotypes and production modes, revealing distinct ecological drivers for bacteria and fungi while establishing a comprehensive culture collection that highlights the complementary value of combining culturomics and sequencing for discovering potential bioinoculants.
This study of Nathusius pipistrelles in the Netherlands reveals that autumn migration stopover departure decisions and diverse movement patterns are driven by a complex interaction between intrinsic factors like reproductive status and environmental conditions such as wind and weather, highlighting the need to account for this behavioral variability in conservation planning.
The paper introduces SWARM, an AI agent-based modeling framework that integrates Large Language Models with biophysical simulations to overcome the limitations of static dispersal models by enabling Red Snapper larvae to exhibit adaptive behaviors, thereby improving the prediction of marine connectivity and informing ecosystem restoration strategies.