Biology subjects

Whittington, J. D.

Publications and source records attributed to Whittington, J. D..

3 recordsLinked to original sources

bioRxiv2026

Rodent-driven NO3--N enrichment reshapes amoeba--bacteria co-occurrence and bacterial functional potential in burrow soils

Interactions between amoebae and bacteria are increasingly viewed as key drivers of zoonotic pathogen emergence in rodent-dwelling burrows, yet the environmental factors shaping these interactions remain poorly understood. Here, we analyzed soil characteristics and used absolute quantitative high-throughput sequencing to assess microbial communities in active burrow, inactive burrow, and off-burrow soils across four rodent species (marmot, squirrel, gerbil, and vole) in the Hulunbuir grassland of Inner Mongolia, China. This study demonstrates that rodent activity creates chemically distinct soil microhabitats, with nitrate (NO --N) enrichment in active burrow soils consistently observed across rodent species. Elevated soil NO3--N was associated with reduced microbial phylogenetic diversity and reorganization of amoeba-co-occurring bacterial assemblages. Both absolute abundance-based correlations and functional prediction of co-occurring bacteria indicated that amoebae were primarily associated with nitrogen-cycling bacteria in off-burrow soils. In burrow soils, amoebae increasingly interacted with bacterial taxa associated with pathogenicity while retaining ties to nitrogen-cycling taxa. Structural equation modeling and mediation analysis revealed that NO3--N enrichment indirectly linked to increased infectious disease-related functional potential by amoeba-associated bacterial restructuring and coordinated shifts in nitrogen cycling, independent of changes in bacterial abundance. Together, our findings highlight the importance of rodent-driven soil heterogeneity in shaping amoeba-bacteria interactions and suggest that rodent-mediated NO --N enrichment may promote the emergence and persistence of potentially pathogenic bacteria, with broader implications for soil ecosystem functioning and disease-related processes in terrestrial ecosystems.

ecology↗

bioRxiv2025

Deep learning-based photo-identification for non-invasive monitoring of animal populations: Application to penguins and tortoises

We describe DL-ID, a deep learning-based framework for the individual identification of animals from images with higher accuracy and characteristics that make it more robust in challenging conditions, such as field studies, than widely used algorithms. This is demonstrated through testing with small datasets involving two species that exhibit distinctive morphological features: Humboldt penguins (Spheniscus humboldti Meyen, 1834) and Hermanns tortoises (Testudo hermanni Gmelin, 1789). We define a confidence index based on entropy within the model that significantly improves handling of the open set recognition problem, offering a new way to discriminate unknown individuals. DL-IDs accuracy of 87%, even with as few as four images per class, contrasts with the usual practice in deep learning of building large datasets to assess performance. The model outperforms traditional photo-identification methods like Wild-ID and I3S Pattern, offering a significant advancement for research and conservation efforts. Its efficiency and adaptability indicate its potential in real-time monitoring, opening new possibilities for wildlife conservation. Instead of designing a complex new deep learning model, we focused on adapting existing methods to address a relevant ecological problem. Our approach effectively tackles issues like limited training data and recognizing new individuals in field studies. Notably, it performs well even with small datasets, making it particularly useful for data-limited ecological research. This makes our approach a valuable step forward in applying deep learning to ecological studies.

ecology↗

bioRxiv2018

When is a fish stock collapsed?

Marine fish stock collapses are a major concern for scientists and society due to the potentially severe impacts on ecosystem resilience, food security and livelihoods. Yet the general state of harvested fish populations has proven difficult to summarize, and the actual occurrence rate of stock collapses remains unclear. We have carried out a literature review and multi-stock analysis to show that numerous definitions exist for classifying stocks as collapsed, and that the classification of a stocks status is sensitive to changes in the collapse definitions formulation. We suggest that the lack of a unified definition has contributed to contrasting perceptions on the state of fish stocks. Therefore, we comprehensively define what constitutes a fish stock collapse and provide a time-series based method for collapse detection. Unlike existing definitions, our definition is process-based, because it links together three important phases of collapse events: the abrupt decline, an ensuing period of prolonged depletion, and potential recovery. Furthermore, these phases are specified in terms of population turnover. Through systematic evaluation, our definition can accurately distinguish collapses from less severe depletions or natural fluctuations for stocks with diverse life histories, helping identify the stocks in greatest need of reparatory measures. Our study advocates the consistent use of definitions to limit both alarmist and conservative narratives on the state of fish stocks, and to promote cooperation between conservation and fisheries scientists. This will facilitate clear and accurate communication of science to both the public and to policy-makers to ensure healthy fish stocks in the future.

ecology↗