Biology subjects

Packer, C.

Publications and source records attributed to Packer, C..

4 recordsLinked to original sources

bioRxiv2025

President Roosevelt's lions reveal a century of population fragmentation in Africa's largest carnivore

Over the past century, lion (Panthera leo) populations across Africa have experienced rapid and severe declines. Despite this, East Africa is considered a modern-day lion stronghold. Here, we use whole-genome sequencing of both recent and historical lion populations, primarily collected during the Smithsonians Roosevelt East African (1909-1911) and Rainey (1911-1912) Expeditions, to investigate changes in population structure, connectivity, and diversity over the last [~]100 years in East Africa. We find a clear signal of population fragmentation when comparing historical (1896-1946) and recent (1990-present) lion populations. Our analyses reveal genetic distinctions between remaining lion populations in Kenya and Tanzania and document loss of genetic diversity over time including reduced heterozygosity and the accumulation of runs of homozygosity. We detect a severe bottleneck in both Kenya and Tanzania approximately 25 generations ago, coinciding with a severe rinderpest outbreak in the region that is known to have decimated bovid and ultimately carnivore populations in the area. Nevertheless, modern lions in East Africa still exhibit overall high levels of diversity and low levels of inbreeding. Our results provide direct evidence of the effects of increasing habitat fragmentation and the significance of temporal data for contextualizing current patterns of population connectivity and diversity.

evolutionary biology↗

bioRxiv2020

Can citizen science analysis of camera trap data be used to study reproduction? Lessons from Snapshot Serengeti program

Ecologists increasingly rely on camera-trap data to estimate biological parameters such as population abundance. Because of the huge amount of data, the assistance of non-scientists is often sought after, but an assessment of the data quality is necessary. We tested whether volunteers data from one of the largest citizen science projects - Snapshot Serengeti - could be used to study breeding phenology. We tested whether the presence of juveniles (less than one or 12 months old) of species in the Serengeti: topi, kongoni, Grants gazelle, could be reliably detected by the "naive" volunteers vs. trained observers. We expected a positive correlation between the proportion of volunteers identifying juveniles and their effective presence within photographs, assessed by the trained observers. The agreement between the trained observers was good (Fleiss {kappa} > 0.61 for juveniles of less than one and 12 month(s) old), suggesting that morphological criteria can be used to determine age. The relationship between the proportion of volunteers detecting juveniles less than a month old and their actual presence plateaued at 0.45 for Grants gazelle, reached 0.70 for topi and 0.56 for kongoni. The same relationships were much stronger for juveniles younger than 12 months, reaching 1 for topi and kongoni. The absence of individuals < one month and the presence of juveniles < 12 months could be reliably assumed, respectively, when no volunteer and when all volunteers reported a presence of a young. In contrast, the presence of very young individuals and the absence of juveniles appeared more difficult to ascertain from volunteers classification, given how the classification task was presented to them. Volunteers classification allows a moderately accurate but quick sorting of photograph with/without juveniles. We discuss the limitations of using citizen science camera-traps data to study breeding phenology, and the options to improve the detection of juveniles.

ecology↗

bioRxiv2019

How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure

O_LIPredicting infectious disease dynamics is a central challenge in disease ecology. Models that can assess which individuals are most at risk of being exposed to a pathogen not only provide valuable insights into disease transmission and dynamics but can also guide management interventions. Constructing such models for wild animal populations, however, is particularly challenging; often only serological data is available on a subset of individuals and non-linear relationships between variables are common.\nC_LIO_LIHere we take advantage of the latest advances in statistical machine learning to construct pathogen-risk models that automatically incorporate complex non-linear relationships with minimal statistical assumptions from ecological data with missing values. Our approach compares multiple machine learning algorithms in a unified environment to find the model with the best predictive performance and uses game theory to better interpret results. We apply this framework on two major pathogens that infect African lions: canine distemper virus (CDV) and feline parvovirus.\nC_LIO_LIOur modelling approach provided enhanced predictive performance compared to more traditional approaches, as well as new insights into disease risks in a wild population. We were able to efficiently capture and visualise strong non-linear patterns, as well as model complex interactions between variables in shaping exposure risk from CDV and feline parvovirus. For example, we found that lions were more likely to be exposed to CDV at a young age but only in low rainfall years.\nC_LIO_LIWhen combined with our data calibration approach, our framework helped us to answer questions about risk of pathogen exposure which are difficult to address with previous methods. Our framework not only has the potential to aid in predicting disease risk in animal populations, but also can be used to build robust predictive models suitable for other ecological applications such as modelling species distribution or diversity patterns.\nC_LI

ecology↗

bioRxiv2018

Chronic infections can shape epidemic exposure: Pathogen co-occurrence networks in the Serengeti lions

Pathogens are embedded in a complex network of microparasites that can collectively or individually alter disease dynamics and outcomes. Chronic pathogens, for example, can either facilitate or compete with subsequent pathogens thereby exacerbating morbidity and mortality. Pathogen interactions are ubiquitous in nature, but poorly understood, particularly in wild populations. We report here on ten years of serological and molecular data in African lions, leveraging comprehensive demographic and behavioral data to utilize pathogen networks to test if chronic infections shape infection by acute pathogens. We combine network and community ecology approaches to assess broad network structure and characterize associations between pathogens across spatial and temporal scales. We found significant non-random structure in the lion-pathogen co-occurrence network and identified potential facilitative and competitive interactions between acute and chronic pathogens. Our results provide a novel insight for untangling the complex associations underlying pathogen co-occurrence networks.

ecology↗