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Gerber, G.

Publications and source records attributed to Gerber, G..

4 recordsLinked to original sources

Why they move: breeding-site availability drives partial migration in a large terrestrial reptile

1. Migration is a widespread phenomenon across taxa, yet the ecological mechanisms underlying its evolution and maintenance, particularly whether migratory behaviors are primarily driven by access to spatially restricted breeding sites or by seasonal tracking of trophic resources, remain poorly documented outside birds and large mammals. Despite increasing evidence that reptiles perform seasonal migrations, the ecological mechanisms underlying these movements have rarely been formally tested. 2. The critically endangered Galapagos pink land iguana (Conolophus marthae), endemic to Wolf Volcano, Isabela Island, exhibits partial migration along a steep altitudinal gradient, providing an opportunity to disentangle the relative roles of breeding-site availability, trophic resource dynamics, and thermoregulatory conditions as drivers of migration. 3. We used GPS tracking data from 22 individuals (7 males, 15 females) monitored between 2019 and 2023, combined with high-resolution spatio-temporal models of vegetation productivity and air temperature across the species' altitudinal range, to characterize population-level movement patterns and evaluate competing hypotheses explaining the evolution of this migratory behavior. 4. Movement models revealed a clear pattern of partial migration: 16 out of 22 tracked individuals performed seasonal altitudinal movements between a restricted high-elevation mating area and a larger dispersal area at lower elevation, with males reaching the mating area approximately 48 days earlier than females. The dispersal area remained consistently more productive than the mating area throughout the year, rejecting the prediction that individuals should track the shifting trophic resource peaks. Instead, the mating season coincided with the local productivity peak within the mating area, whereas temperature differences between areas were small (ca. 2{degrees}C) and did not explain migration timing. 5. These results support a site-dependent hypothesis of partial migration over a resource-tracking hypothesis, indicating that access to spatially restricted breeding sites is the primary driver of migration in this species, with local trophic resource dynamics fine-tuning reproductive timing. Providing empirical evidence for the ecological mechanisms underlying migration in a large terrestrial reptile, our results extend site-dependent theories of migration beyond birds and mammals and identify breeding-site availability as a key ecological driver of migratory behaviors across taxa.

ecology↗

AI-driven Discovery of Morphomolecular Signatures in Toxicology

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which in vitro assays can be designed. However, existing works that aim to identify morphological correlates of expression changes rely on qualitative or semi-quantitative morphological characterization and remain limited in scale or morphological diversity. Artificial intelligence (AI) offers a promising approach for quantitatively modeling this relationship at an unprecedented scale. Here, we introduce GEESE, an AI model designed to impute morphomolecular signatures in toxicology data. Our model was trained to predict 1,536 gene targets on a cohort of 8,231 hematoxylin and eosin-stained liver sections from Rattus norvegicus across 127 preclinical toxicity studies. The model, evaluated on 2,002 tissue sections from 29 held-out studies, can yield pseudo-spatially resolved gene expression maps, which we correlate with six key drug-induced liver injuries (DILI). From the resulting 25 million lesion-expression pairs, we established quantitative relations between up and downregulated genes and lesions. Validation of these signatures against toxicogenomic databases, pathway enrichment analyses, and human hepatocyte cell lines asserted their relevance. Overall, our study introduces new methods for characterizing toxicity at an unprecedented scale and granularity, paving the way for AI-driven discovery of toxicity biomarkers. Live demo: https://mahmoodlab.github.io/tox-discovery-ui/

bioinformatics↗

Health assessment of the pink land iguana, Conolophus marthae.

The pink land iguana, Conolophus marthae, is one of four species of iguanas (three terrestrial and one marine) in the Galapagos Islands, and the only one listed as critically endangered by the IUCN. The species can only be found on the north-west slopes of the highest volcano on the island of Isabela and was first described to science in 2009. As part of a population telemetry study, a health assessment was authorized by the Galapagos National Park. Wild adult iguanas were captured on Wolf Volcano in September 2019 and April 2021 to record morphological and physiological parameters including body temperature, heart rate, body measurements, intraocular pressures, tear formation, and infrared iris images. Blood samples were also collected and analyzed. An i-STAT portable blood analyzer was used to obtain values for base excess in the extracellular fluid compartment (BEECF), glucose, hematocrit, hemoglobin, ionized calcium (iCa), partial pressure of carbon dioxide (pCO2), partial pressure of oxygen (pO2), percent oxygen saturation (sO2%), pH, potassium (K), and sodium (Na). Standard laboratory hematology techniques were employed for packed-cell-volume (PCV) determination. When possible, data were compared to previously published and available data for the other Galapagos iguanas. The results reported here provide baseline values that may be useful in detecting changes in health status among pink land iguanas affected by climate change, invasive species, anthropogenic threats, or natural disturbances.

zoology↗

MITRE: predicting host status from microbiota time-series data

Longitudinal studies are crucial for discovering casual relationships between the microbiome and human disease. We present Microbiome Interpretable Temporal Rule Engine (MITRE), the first machine learning method specifically designed for predicting host status from microbiome time-series data. Our method maintains interpretability by learning predictive rules over automatically inferred time-periods and phylogenetically related microbes. We validate MITREs performance on semi-synthetic data, and five real datasets measuring microbiome composition over time in infant and adult cohorts. Our results demonstrate that MITRE performs on par or outperforms \"black box\" machine learning approaches, providing a powerful new tool enabling discovery of biologically interpretable relationships between microbiome and human host.

bioinformatics↗