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Reconstructions of prehistoric vegetation composition help establish natural baselines, variability, and trajectories of forest dynamics before and during the emergence of intensive anthropogenic land use. Pollen–vegetation models (PVMs) enable such reconstructions from fossil pollen assemblages using process-based representations of taxon-specific pollen production and dispersal. However, several PVMs and variants now exist, and the sensitivity of vegetation inferences to PVM selection, variant, and calibration domain is poorly understood. Here, we compare the reconstructions, parameter estimates, and structure of a Bayesian hierarchical PVM, STEPPS, both to observations and to REVEALS, a widely used PVM, for the pre–Euro-American settlement-era vegetation in the northeastern United States (NEUS). We also compare NEUS-based STEPPS parameter estimates to those for the upper midwestern United States (UMW). Both PVMs predict the observed macroscale patterns of vegetation composition in the NEUS; however, reconstructions of minor taxa are less accurate and predictions for some taxa differ between PVMs. These differences can be attributed to intermodel differences in structure and parameter estimates. Estimates of pollen productivity from STEPPS broadly agree with estimates produced for use in REVEALS, while comparison between pollen dispersal parameter estimates shows no significant relationship. STEPPS parameter estimates are similar between the UMW and NEUS, suggesting that STEPPS parameter estimates are transferable between floristically similar regions and scales.
Residual herbicides applied to summer cash crops have the potential to injure subsequent winter annual cover crops, yet little information is available to guide growers’ choices. Field studies were conducted in 2016 and 2017 in Blacksburg and Suffolk, Virginia, to determine carryover of 30 herbicides commonly used in corn, soybean, or cotton on wheat, barley, cereal rye, oats, annual ryegrass, forage radish, Austrian winter pea, crimson clover, hairy vetch, and rapeseed cover crops. Herbicides were applied to bare ground either 14 wk before cover crop planting for a PRE timing or 10 wk for a POST timing. Visible injury was recorded 3 and 6 wk after planting (WAP), and cover crop biomass was collected 6 WAP. There were no differences observed in cover crop biomass among herbicide treatments, despite visible injury that suggested some residual herbicides have the potential to effect cover crop establishment. Visible injury on grass cover crop species did not exceed 20% from any herbicide. Fomesafen resulted in the greatest injury recorded on forage radish, with greater than 50% injury in 1 site-year. Trifloxysulfuron and atrazine resulted in greater than 20% visible injury on forage radish. Trifloxysulfuron resulted in the greatest injury (30%) observed on crimson clover in 1 site-year. Prosulfuron and isoxaflutole significantly injured rapeseed (17% to 21%). Results indicate that commonly used residual herbicides applied in the previous cash crop growing season result in little injury on grass cover crop species, and only a few residual herbicides could potentially affect the establishment of a forage radish, crimson clover, or rapeseed cover crop.
We investigate the strict-exogeneity assumption, a necessary condition for estimator consistency in many finance panel-data applications. We outline tests for strict exogeneity in both traditional (non–instrumental variable (IV)) and IV settings. When we apply these tests in common traditional finance panel regressions, we find that the strict-exogeneity assumption is often strongly rejected, suggesting large inference errors. We test for strict exogeneity in specific finance panel-data IV settings and illustrate the potential for these tests to help confirm, or rule out, the validity of common panel-data IV estimators. We offer recommendations to address the strict-exogeneity issue in finance research.
The Neotoma Paleoecology Database is a community-curated data resource that supports interdisciplinary global change research by enabling broad-scale studies of taxon and community diversity, distributions, and dynamics during the large environmental changes of the past. By consolidating many kinds of data into a common repository, Neotoma lowers costs of paleodata management, makes paleoecological data openly available, and offers a high-quality, curated resource. Neotoma’s distributed scientific governance model is flexible and scalable, with many open pathways for participation by new members, data contributors, stewards, and research communities. The Neotoma data model supports, or can be extended to support, any kind of paleoecological or paleoenvironmental data from sedimentary archives. Data additions to Neotoma are growing and now include >3.8 million observations, >17,000 datasets, and >9200 sites. Dataset types currently include fossil pollen, vertebrates, diatoms, ostracodes, macroinvertebrates, plant macrofossils, insects, testate amoebae, geochronological data, and the recently added organic biomarkers, stable isotopes, and specimen-level data. Multiple avenues exist to obtain Neotoma data, including the Explorer map-based interface, an application programming interface, the neotoma R package, and digital object identifiers. As the volume and variety of scientific data grow, community-curated data resources such as Neotoma have become foundational infrastructure for big data science.