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 paper presents the AvianMetaNetwork, a comprehensive, open dataset documenting 25,907 empirically verified, directional interspecific interactions among 1,989 bird species across the continental United States and Canada, designed to fill critical knowledge gaps and enable macroecological and eco-evolutionary research at continental scales.
This study models the persistence of management effects in *Calluna vulgaris* communities on deep peat, revealing that a 50-year transition period to a natural age structure is required after banning rotational burning, thereby highlighting the urgent need for alternative strategies like rewetting to mitigate wildfire risks and avoid biased ecological assessments.
This paper introduces a novel explainable machine learning consensus framework that quantifies explanation consistency across multiple model architectures to reliably identify robust environmental drivers and flag areas of uncertainty in ecological population dynamics, demonstrated through synthetic coral cover data.
This study demonstrates that short, severe autumn droughts at moderate temperatures trigger phenotype-dependent deterministic filtering of bacterial communities and destabilize root microbiome networks in *Pinus halepensis* seedlings, ultimately compromising their recovery capacity and hindering Mediterranean forest restoration efforts under climate change.
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.