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.
While wing biopsies from common vampire bats show a lower probability of detecting blood-borne pathogens compared to blood samples, they remain a viable, less invasive method for initial pathogen discovery and characterizing infections, though they are less ideal for accurately estimating infection prevalence.
This study demonstrates that herbarium specimens are reliable resources for tracking plant phenological responses to climate change in montane systems, showing consistent flowering time estimates and sensitivity to snow density with field observations, though they may slightly underestimate temperature sensitivity and benefit from focusing on early-flowering individuals to capture community-level trends.
This study analyzes over 42,000 forest plots across North America to quantify the risk of trait misalignment between current forest communities and projected late-century climates, revealing that high-latitude and montane conifer forests face the greatest functional incompatibility while high species richness serves as a critical buffer against these adverse outcomes.
This paper presents a transparent, manual-matching protocol that derives regional elevation correction factors from reference weather stations to standardize temperature data across heterogeneous study sites, thereby enabling robust cross-site ecological meta-analyses for taxa with documented altitudes.
This paper presents an automated, open-source deep learning framework using the Segment Anything Model (SAM) to predict the weight of juvenile Chinook Salmon from 2D images with high accuracy (R²=0.99), thereby minimizing the need for physical handling of threatened fish populations.
This paper introduces a novel LLM-based pipeline that rapidly extracts morphological trait data from fungal species descriptions to accelerate ecological research, demonstrating promising results despite observed variations in accuracy and systematic biases.
This study presents the first quantitative, multi-scale characterization of the previously undocumented Solito Seamount in the southeast Pacific, using an integrated framework of high-resolution geophysical data and ROV observations to map distinct megahabitats and reveal how geomorphology, substrate, and depth-dependent oceanographic conditions structure unique benthic megafaunal assemblages.
This experimental field study in Hungary demonstrates that while ground-based ULV deltamethrin spraying effectively reduces mosquito populations by approximately 45%, it simultaneously causes significant short-term declines of over 40% in non-target flying insects, highlighting a critical trade-off between vector control and insect conservation.
This study demonstrates that airborne environmental DNA is a highly sensitive and efficient complementary tool for rapidly assessing terrestrial vertebrate diversity in the Luangwa Valley, Zambia, successfully detecting the majority of species found by camera traps within a short sampling window.
An analysis of the Global Soil Virus Atlas reveals that while virus-encoded metabolic genes are globally rare in soils, they are non-randomly enriched for carbohydrate-active enzymes, particularly chitinases, suggesting that ignoring viral contributions may lead to underestimating the functional potential for specific carbon degradation traits in soil metagenomic studies.