Linking longitudinal and cross-sectional biomarker data to understand host-pathogen dynamics: Leptospira in California sea lions (Zalophus californianus) as a case study.

Confronted with the challenge of understanding population-level processes, disease ecologists and epidemiologists often simplify quantitative data into distinct physiological states (e.g. susceptible, exposed, infected, recovered). However, data defining these states often fall along a spectrum rather than into clear categories. Hence, the host-pathogen relationship is more accurately defined using quantitative data, often integrating multiple diagnostic measures, just as clinicians do to assess their patients. We use quantitative data on a major neglected tropical disease (Leptospira interrogans) in California sea lions (Zalophus californianus) to improve individual-level and population-level understanding of this Leptospira reservoir system. We create a "host-pathogen space" by mapping multiple biomarkers of infection (e.g. serum antibodies, pathogen DNA) and disease state (e.g. serum chemistry values) from 13 longitudinally sampled, severely ill individuals to characterize changes in these values through time. Data from these individuals describe a clear, unidirectional trajectory of disease and recovery within this host-pathogen space. Remarkably, this trajectory also captures the broad patterns in larger cross-sectional datasets of 1456 wild sea lions in all states of health but sampled only once. Our framework enables us to determine an individual's location in their time-course since initial infection, and to visualize the full range of clinical states and antibody responses induced by pathogen exposure. We identify predictive relationships between biomarkers and outcomes such as survival and pathogen shedding, and use these to impute values for missing data, thus increasing the size of the useable dataset. Mapping the host-pathogen space using quantitative biomarker data enables more nuanced understanding of an individual's time course of infection, duration of immunity, and probability of being infectious. Such maps also make efficient use of limited data for rare or poorly understood diseases, by providing a means to rapidly assess the range and extent of potential clinical and immunological profiles. These approaches yield benefits for clinicians needing to triage patients, prevent transmission, and assess immunity, and for disease ecologists or epidemiologists working to develop appropriate risk management strategies to reduce transmission risk on a population scale (e.g. model parameterization using more accurate estimates of duration of immunity and infectiousness) and to assess health impacts on a population scale.

Determinants of genetic variation across eco-evolutionary scales in pinnipeds.

The effective size of a population (Ne), which determines its level of neutral variability, is a key evolutionary parameter. Ne can substantially depart from census sizes of present-day breeding populations (NC) as a result of past demographic changes, variation in life-history traits and selection at linked sites. Using genome-wide data we estimated the long-term coalescent Ne for 17 pinniped species represented by 36 population samples (total n = 458 individuals). Ne estimates ranged from 8,936 to 91,178, were highly consistent within (sub)species and showed a strong positive correlation with NC ([Formula: see text] = 0.59; P = 0.0002). Ne/NC ratios were low (mean, 0.31; median, 0.13) and co-varied strongly with demographic history and, to a lesser degree, with species' ecological and life-history variables such as breeding habitat. Residual variation in Ne/NC, after controlling for past demographic fluctuations, contained information about recent population size changes during the Anthropocene. Specifically, species of conservation concern typically had positive residuals indicative of a smaller contemporary NC than would be expected from their long-term Ne. This study highlights the value of comparative population genomic analyses for gauging the evolutionary processes governing genetic variation in natural populations, and provides a framework for identifying populations deserving closer conservation attention.