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Background: Accurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data., Methods: We derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound., Results: Samples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh)., Conclusions: Algorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Hawken et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Ungulate populations face declines across the globe, and populations are commonly conserved by using protected areas. However, assessing the effectiveness of protected areas in conserving ungulate populations has remained difficult. Using herd size data from four years of line transect surveys and distance sampling models, we modeled population densities of four important herbivore species across a gradient of protection on the edge of Zambia's South Luangwa National Park (SLNP) while accounting for the role of various ecological and anthropogenic variables. Our goal was to test whether protection was responsible for density dynamics in this protection gradient, and whether a hunting moratorium impacted herbivore densities during the studies. For all four species, we estimated lower densities in partially protected buffer areas adjacent to SLNP (ranging from 4.5-fold to 13.2-fold lower) compared to protected parklands. Density trends through the study period were species-specific, with some species increasing, decreasing, or remaining stable in all or some regions of the protection gradient. Surprisingly, when controlling for other covariates, we found that these observed differences were not always detectably related to the level of protection or year. Our findings highlight the importance of accounting for variables beyond strata of interest in evaluating the effectiveness of a protected area. This study highlights the importance of comprehensively modeling ungulate population density across protection gradients, identifies lands within an important protection gradient for targeted conservation and monitoring, documents prey depletion and expands our understanding on the drivers in a critical buffer area in Zambia., Competing Interests: The authors have declared that no competing interests exist.

Factors that limit African lion populations are manifold and well-recognized, but their relative demographic effects remain poorly understood, particularly trophy hunting near protected areas. We identified and monitored 386 individual lions within and around South Luangwa National Park, Zambia, for five years (2008-2012) with trophy hunting and for three additional years (2013-2015) during a hunting moratorium. We used these data with mark-resight models to estimate the effects of hunting on lion survival, recruitment, and abundance. The best survival models, accounting for imperfect detection, revealed strong positive effects of the moratorium, with survival increasing by 17.1 and 14.0 percentage points in subadult and adult males, respectively. Smaller effects on adult female survival and positive effects on cub survival were also detected. The sex-ratio of cubs shifted from unbiased during trophy-hunting to female-biased during the moratorium. Closed mark-recapture models revealed a large increase in lion abundance during the hunting moratorium, from 116 lions in 2012 immediately preceding the moratorium to 209 lions in the last year of the moratorium. More cubs were produced each year of the moratorium than in any year with trophy hunting. Lion demographics shifted from a male-depleted population consisting mostly of adult (≥4 years) females to a younger population with more (>29%) adult males. These data show that the three-year moratorium was effective at growing the Luangwa lion population and increasing the number of adult males. The results suggest that moratoria may be an effective tool for improving the sustainability of lion trophy hunting, particularly where systematic monitoring, conservative quotas, and age-based harvesting are difficult to enforce., Competing Interests: Though we received funding from commercial sources namely Painted Dog Conservation Inc, Mfuwe Lodge/Bushcamp Company, and Vulcan Inc, this does not alter our adherence to PLOS ONE policies on sharing data and materials.