There has been a steady rise in the use of dormant propagules to study biotic responses to environmental change over time. This is particularly important for organisms that strongly mediate ecosystem processes, as changes in their traits over time can provide a unique snapshot into the structure and function of ecosystems from decades to millennia in the past. Understanding sources of bias and variation is a challenge in the field of resurrection ecology, including those that arise because often-used measurements like seed germination success are imperfect indicators of propagule viability. Using a Bayesian statistical framework, we evaluated sources of variability and tested for zero-inflation and overdispersion in data from 13 germination trials of soil-stored seeds of Schoenoplectus americanus, an ecosystem engineer in coastal salt marshes in the Chesapeake Bay. We hypothesized that these two model structures align with an ecological understanding of dormancy and revival: zero-inflation could arise due to failed germinations resulting from inviability or failed attempts to break dormancy, and overdispersion could arise by failing to measure important seed traits.A model that accounted for overdispersion, but not zero-inflation, was the best fit to our data. Tetrazolium viability tests corroborated this result: most seeds that failed to germinate did so because they were inviable, not because experimental methods failed to break their dormancy. Seed viability declined exponentially with seed age and was mediated by seed provenance and experimental conditions. Our results provide a framework for accounting for and explaining variability when estimating propagule viability from soil-stored natural archives which is a key aspect of using dormant propagules in eco-evolutionary studies.

Elk (Cervus canadensis) were reintroduced to Tennessee, USA in the early 2000s, with limited reproductive monitoring since initial release. We assessed the efficacy of noninvasive sampling for determining pregnancy using invasive (capture) and noninvasive (fecal collection in the field) techniques at the North Cumberland Wildlife Management Area (NCWMA), Tennessee. We captured 20 female elk 2019–2020, used pregnancy-specific protein B (PSPB) in blood to determine pregnancy and compared results to fecal progesterone metabolite (FPM) concentrations using two commercially available enzyme immunoassay (EIA) kits. Based on PSPB concentrations, 8/11 and 3/4 of captured adult elk (≥2.5 yr of age) were pregnant in 2019 and 2020, respectively; no 1.5-yr-old elk were pregnant (n=5). Using the progesterone EIA kit, FPM concentrations were x̄=192.84±38.63 ng/g (95% CI, 96.48–289.20) for nonpregnant and x̄=536.17±74.98 ng/g (95% CI, 375.97–696.36) for pregnant captured females. For the progesterone metabolite kit, FPM concentrations were x̄=188.16±43.39 ng/g (95% confidence interval [CI], 76.63–299.69) for nonpregnant and x̄=693.52±126.52 ng/g (95% CI, 407.31–979.72) for pregnant captured females. From February to May 2019, we collected 357 fecal samples in 65 areas across 489.62 km2 of the NCWMA. Using extracted DNA and analysis of 15 microsatellites, we identified 62 unique individuals from 128 female fecal samples collected on the landscape. We categorized females from landscape-collected feces as nonpregnant (35.5–40.3%; Metabolite-EIA kits), undetermined (1.6–6.5%; Metabolite-EIA kits), or pregnant (62.9–53.2%; Metabolite-EIA kits) based on a 95% CI of captured female FPM concentrations, giving an overall pregnancy rate of 53.2% using the recommended EIA kit. The pregnancy rate in sexually mature females may be higher, as it was not possible to distinguish age classes of landscape-collected fecal samples; therefore, some may have been from younger age classes not expected to be pregnant. Analysis of FPM may be useful at a population level to detect pregnancy.

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