Biology subjects

Baer, B.

Publications and source records attributed to Baer, B..

5 recordsLinked to original sources

bioRxiv2026

Atta Leafcutter Ants are Fine-Scale Bioindicators of Geographic and Seasonal Climate Changes Across the Americas

AimWe develop Atta leafcutter-ants as bioindicators that respond at fine scales to geographic and seasonal climate changes in the Americas, thereby addressing the paucity of versatile insect bioindicator systems capable of monitoring climate in both Southern and Northern Hemispheres. LocationAmerican tropics and sub-tropics from latitudes S33.6{degrees} to N33.2{degrees}, with case studies from Colombia, Mexico, and southern USA. Time Period2012-2024. Taxon StudiedAtta leafcutter-ants. MethodsWe elucidate biogeographic patterns of mating-flight phenology of Atta leafcutter-ants across the entire Atta range from Uruguay/Argentina to the USA, using 2335 records of Atta reproductives from the community database iNaturalist, then ground-truth these patterns by comparison with (i) mating-flight records (n=806) from the Atta literature; and (ii) mating-flight observations (n=836) accumulated by a consortium of experts who have researched Atta for a combined 1000+ work-years. Onset of mating flights can be timed with great precision in Atta populations because mass-mating flights are synchronized and triggered by the first major rainfall of a rainy season. ResultsBiogeographic patterns in climate-dependent mating-flight phenologies recorded at iNaturalist are corroborated by observations accumulated in the literature and by Atta experts. Analyses reveal so-far unknown gradients in mating-flight phenology (e.g., Colombia to USA) that are correlated to geographic climate gradients, and season switches of mating flights from early to late in the year between proximate Atta populations (e.g., 200 kilometers apart), for example in the climatically complex Andean regions of Colombia. Regional differences in Atta mating-flight phenology correspond to temporal differences in rainfall between ecoregions of Colombia. Main ConclusionsAtta ants are tractable insect bioindicators to monitor climate impacts with detailed spatial and temporal resolution across a 9200-kilometer trans-equatorial transect in the Americas. We outline future research directions to explore climate-dependent biology using the continuously growing, and now ground-truthed, information at iNaturalist on Atta mating behavior.

ecology↗

bioRxiv2025

Advanced Age in Mice Exacerbates Sepsis-Induced Inflammation, Vascular Permeability, and Multi-Organ Dysfunction

Sepsis is a life-threatening syndrome marked by a dysregulated immune response to an infection and significant endothelial vascular permeability, often leading to multi-organ failure. Elderly patients are particularly vulnerable to sepsis, with higher morbidity and mortality rates. We hypothesized that advanced age exacerbates sepsis-induced inflammation and endothelial vascular permeability, resulting in a delayed recovery, persistent inflammation, and sustained organ injury. Using a polymicrobial sepsis model in young (3-month-old) and aged (18-month-old) C57BL/6 mice, sepsis was induced via intraperitoneal cecal slurry (CS) injection. Outcomes were assessed during the acute (24-hour; 1.6mg/g CS) and recovery (8-day; 1.0 mg/g CS) phases. During the acute phase, aged mice exhibited worse physiologic dysfunction, higher systemic (plasma TNF-a: young septic 202.1 pg/mL [17.44, 398.9] vs. aged septic 482.6 pg/mL [279.8, 711.7]; p = 0.0352 Mann-Whitney) and organ-specific inflammation, increased endothelial injury and vascular permeability, as well as greater kidney and liver dysfunction compared to young mice. During recovery, aged mice had sustained physiologic dysfunction, prolonged systemic and organ-specific inflammation, and sustained organ injury (kidney tissue NGAL: young septic 291.5 RE [203.7, 373.2] vs. aged septic 821 RE [456, 1258] protein normalized to beta actin; p = 0.0008 Mann-Whitney) compared to young mice. These results support the hypothesis that advanced age worsens sepsis severity and outcomes and delays recovery, emphasizing the need for aged models and multi-organ evaluations to develop effective therapies for this vulnerable population.

physiology↗

bioRxiv2025

A Multi-Model Ensemble Reveals Soil Carbon Gains from Regenerative Practices in the U.S. Midwest Cropland

Process-based cropping systems models (CSMs) are key components of measurement, monitoring, reporting, and verification (MMRV) frameworks of carbon markets, but their application suffers from model-specific differences that keep any one model from working well across all combinations of soils, climates, crops, and agronomic practices at varying scales. Multi-model ensemble (MME), successfully used to quantify soil, management and climate impact on crop productivity, provide an opportunity to better estimate changes in soil organic carbon (SOC) outcomes for agronomic practices that have the potential to mitigate SOC loss at scale. We used an MME across 46 million hectares of US Midwest cropland at a resolution of 4- km2 to assess the aggregate ability of different regenerative practices to sequester SOC at this scale compared to their dynamic baselines. MME was validated with long-term experimental data and compared to its constituent CSMs, showing greater accuracy and lower uncertainty. The results show that adopting no-till combined with cover crops increased SOC stocks by 0.36 {+/-} 0.12 Mg ha-1 yr-1 aggregated across the entire U.S. Midwest cropland. At the regional scale, this corresponds to a net SOC gain of 16.4 Tg C yr-1 compared to business-as-usual baselines. These benefits are approximately halved when each management change is practiced individually, and the modest gains are only fully realized when continued over the long-term in soils with low initial carbon stock. Results demonstrate the power of MMEs run at high resolution for providing robust estimates of environmental outcomes following agricultural practice change, and for pinpointing locations for most effective intervention. This approach can alleviate many producer carbon market participation barriers and help address market issues while ultimately supporting large-scale regenerative agriculture initiatives.

ecology↗

bioRxiv2024

Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field

Preventing beneficial insects like honey bees (Apis mellifera) from contacting pesticides on crops using odorants could counter current pollinator declines. However, the discovery of behaviorally aversive odorants is impeded by the complexity of the honey bee olfactory system where >180 olfactory receptors detect volatiles and generate valence. To solve this systems-level challenge we generated a machine-learning model to predict aversive valence from chemical structure using published olfactory behavior data in honey bees. We refine the predictive model by generating species level behavioral data for honey bees and Drosophila on an initial set of novel predicted repellents. The improved second computational model was then used to screen a chemical space of >50 million compounds and identify >130 repellent candidates. Behavioral validation using honey bees in the laboratory show a high predictive success. Additional testing of the top seven candidates using freely foraging honey bees in a field assay confirmed strong repellency, thus predicting a high probability to repel foraging bees from pesticide-treated crops. Machine learning, with iterative testing and modeling therefore provides a powerful approach for rational discovery of aversive volatiles for control of insects for which limited data is available. SIGNIFICANCE STATEMENTWith honey bee populations declining partly due to pesticide exposure, we aimed to find smells that could keep bees away from pesticide-treated crops. We overcome challenges studying the complex bee olfactory system by developing an AI model trained on existing bee behavior data to predict chemicals bees would find aversive. The predictive model screened millions of compounds, identifying more than 130 potential repellents. Behavior testing in the lab and in field tests confirmed the effectiveness of the bee repellents. This method could lead to bee-safe pesticide formulations, potentially protecting pollinator populations while maintaining crop protection.

animal behavior and cognition↗

bioRxiv2024

Population origin, body mass, and viral infections influence drone honey bee (Apis mellifera) heat tolerance

Extreme temperatures associated with climate change are expected to impact the physiology and fertility of a variety of insects, including honey bees. Most previous work has focused on female honey bees, and comparatively little research has investigated how heat exposure affects males (drones). To address this gap, we tested how body mass, viral infections, Africanization, and geographic origin (including stocks from Australia, California, and Ukraine as well as diverse locations within British Columbia, Canada) influenced drone and sperm heat tolerance. We found that individual body size was highly influential, with heavier drones being more likely to survive a heat challenge than smaller drones. Drones originating from feral colonies in Southern California (which are enriched for African genetics) were also more likely to survive a heat challenge than drones originating from commercially-supplied Californian stock. We found no association between drone mass and thermal tolerance of sperm over time in an in vitro challenge assay, but experimental viral infection decreased the heat tolerance of sperm. Overall, there is ample variation in sperm heat tolerance, with sperm from some groups displaying remarkable heat resilience and sperm from others being highly sensitive, with additional factors influencing heat tolerance of the drones themselves.

physiology↗