Germination timing has a strong influence on direct seeding efforts, and therefore is a closely tracked demographic stage in a wide variety of wildland and agricultural settings. Predictive seed germination models, based on soil moisture and temperature data in the seed zone are an efficient method of estimating germination timing. We utilized Visual Basic for Applications (VBA) to create Auto‐Germ, which is an Excel workbook that allows a user to estimate field germination timing based on wet‐thermal accumulation models and field temperature and soil moisture data. To demonstrate the capabilities of Auto‐Germ, we calculated various germination indices and modeled germination timing for 11 different species, across 6 years, and 10 Artemisia‐steppe sites in the Great Basin of North America to identify the planting date required for 50% or more of the simulated population to germinate in spring (1 March or later), which is when conditions are predicted to be more conducive for plant establishment. Both between and within the species, germination models indicated that there was high temporal and spatial variability in the planting date required for spring germination to occur. However, some general trends were identified, with species falling roughly into three categories, where seeds could be planted on average in either fall (Artemisia tridentata ssp. wyomingensis and Leymus cinereus), early winter (Festuca idahoensis, Poa secunda, Elymus lanceolatus, Elymus elymoides, and Linum lewisii), or mid‐winter (Achillea millefolium, Elymus wawawaiensis, and Pseudoroegneria spicata) and still not run the risk of germination during winter. These predictions made through Auto‐Germ demonstrate that fall may not be an optimal time period for sowing seeds for most non‐dormant species if the desired goal is to have seeds germinate in spring.
Wildfires can create or intensify water repellency in soil, limiting the soil's capacity to wet and retain water. The objective of this research was to quantify soil water repellency characteristics within burned piñon–juniper woodlands and relate this information to ecological site characteristics. We sampled soil water repellency across forty‐one 1,000 m2 study plots within three major wildfires that burned in piñon–juniper woodlands. Water repellency was found to be extensive—present at 37% of the total points sampled—and strongly related to piñon–juniper canopy cover. Models developed for predicting SWR extent and severity had R 2 adj values of 0.67 and 0.61, respectively; both models included piñon–juniper canopy cover and relative humidity the month before the fire as coefficient terms. These results are important as they suggest that postfire water repellency will increase in the coming years as infilling processes enhance piñon–juniper canopy cover. Furthermore, reductions in relative humidity brought about by a changing climate have the potential to link additively with infilling processes to increase the frequency and intensity of wildfires and produce stronger water repellency over a greater spatial extent. In working through these challenges, land managers can apply the predictive models developed in this study to prioritize fuel control and postfire restoration treatments.
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