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.
By employing a global, species-resolved machine-learning approach, this study reveals that the widely studied *Gephyrocapsa huxleyi* contributes only about 7% of global coccolithophore calcium carbonate stocks, demonstrating that a diverse assemblage of other species, rather than this single species, underpins marine CaCO3 cycling.
Contrary to the prevailing assumption that zoonotic pathogens have minimal impact on their wildlife reservoirs, a three-year field experiment revealed that Puumala hantavirus infection significantly reduces the survival of young bank voles in natural settings.
This study reveals that the maintenance cost of photosynthesis, driven by accelerated PSII turnover at high irradiance, imposes a key energetic constraint that defines an optimal depth range for zooxanthellate corals by balancing photosynthetic capacity against repair costs, thereby providing a mechanistic framework to predict their depth distributions under environmental change.
This study demonstrates that uncontrolled goat overgrazing in Ecuador's Tumbesian dry forest drives a directional loss of ant taxonomic and functional diversity, causing community homogenization through habitat filtering that disproportionately eliminates specialist and soil-dwelling guilds while favoring opportunistic species.
This study demonstrates that high-intensity sheep grazing significantly impoverishes soil seed bank density and diversity in sand grasslands compared to cattle grazing, indicating that livestock type is a critical factor in conservation management.
This study introduces a framework to quantify climate change exposure for 60 Fraser River salmon and steelhead Conservation Units, revealing that lake-type sockeye and Chinook salmon face the highest risks due to elevated stream temperatures and specific life-stage thermal sensitivities.
By analyzing long-term river data from New Zealand, this study demonstrates that fast, slow, and seasonal flow fluctuations differentially impact macroinvertebrate communities, with slow variations showing the strongest trait-mediated responses and flood events causing pervasive negative effects, thereby highlighting the necessity of considering multiple temporal scales and functional traits to predict community dynamics.
Contrary to the expectation that plant presence would enhance decomposition through exudates, this study demonstrates that winter wheat actually slows decomposition rates due to resource competition with soil microbes, a process driven more by fungal activity and soil humidity than by bacteria or temperature.
By employing an efficient sequential sampling algorithm to apply a niche-neutral model to bird occurrence patterns in the Riau archipelago, the study demonstrates that while the model's baseline assumptions fail to explain observed segregation and nestedness, incorporating inter-island heterogeneity in niche diversity and immigration rates successfully diagnoses the underlying mechanisms structuring these communities.
This study demonstrates that increasing spatial approximation complexity in distribution models does not necessarily improve prediction quality and can actually degrade predictive performance by producing poorly calibrated uncertainty estimates, thereby highlighting the need for practitioners to carefully select appropriate mesh resolution rather than assuming higher complexity always yields better results.