429 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 proposes an ecosystem-based approach using production-to-biomass (P/B) ratios to establish protective harvest limits for native "rough" fishes, demonstrating that many long-lived nongame species have turnover rates similar to popular game fish and therefore require similarly conservative management to ensure sustainability.
This paper presents a reproducible methodological roadmap with standardized metrics and case studies to integrate extreme heat dynamics into ecological research, demonstrating how accounting for heat extremes—rather than just mean temperatures—improves predictions of species distributions, physiological stress, and population viability.
This study reveals that while urbanization generally drives a decline in avian biodiversity and a shift toward generalist dietary traits, the presence of diverse vegetation and canopy cover can buffer these losses and facilitate active prey selection, ultimately sustaining insectivorous bird communities and their ecological functions in cities.
Using a spatially explicit model for European lobster, this study demonstrates that while no-take zones generally increase abundance and size, their effectiveness in linear coastal systems with asymmetric larval dispersal critically depends on placement, with downstream reserves outperforming upstream ones due to the combined benefits of larval import and local retention.
Using a Bayesian Jolly-Seber mark-resight model, this study estimates that the 2025 Red Knot stopover population at Delaware Bay reached approximately 54,000 individuals with stable migration timing, providing critical data to support the Adaptive Resource Management framework for balancing horseshoe crab harvest and shorebird conservation.
Although managed bumble bees (Bombus impatiens) forage on strawberry flowers, their visitation rates are too low to alleviate pollen limitation, and their foraging is heavily influenced by surrounding landscape composition, often leading them to alternative floral resources in diverse agricultural settings where adding colonies may not be economically beneficial.
This study utilizes photogrammetry to quantify the morphological development of wild stony corals across six metrics, revealing that while massive and non-massive colonies follow distinct growth trajectories, traditional categorical growth forms often obscure significant early-life developmental variation.
This study reveals that the Eurasian spruce bark beetle (*Ips typographus*) and related species possess previously undetected mandibular mycetangia in both sexes, which serve as specialized structures for transmitting mutualistic fungi to new host trees.
This study demonstrates that the binary mixture of imidacloprid and epoxiconazole exhibits synergistic toxicity on earthworm life-history traits, particularly reproduction, suggesting that current environmental risk assessments based on single substances may significantly underestimate the ecological risks posed by pesticide mixtures in agricultural soils.
This study demonstrates that process-guided neural networks outperform both standalone process models and data-driven neural networks in predicting forest carbon fluxes, particularly offering superior robustness and generalizability in data-sparse settings and under unseen climatic conditions.