Evaluating Temporal Differences in Land Cover: Implications for Managing Bobwhite at the Landscape Scale in Virginia

Northern bobwhite (Colinus virginianus) populations have declined substantially across large portions of their range. A number of factors may be contributing to that decline including disease, predation, pesticides, and habitat loss. Of these, habitat loss has emerged as the primary factor. Habitat loss has occurred at large and small scales. It is relatively easy to evaluate bobwhite habitat at the micro scale, but evaluating habitat change at the landscape scale is difficult. The goal of this pilot study was to evaluate a novel technique using aerial imagery and line transects to evaluate both contemporary and historic landscapes effectively, quantifying the differences observed to describe what changes, if any, occurred through time. Contemporary photos were available through the 2013 Virginia Base Mapping Program. Historic photos were obtained via United States Geological Survey (1967 – 1969). Two Virginia Quail recovery Initiative focal counties were chosen for the study, Halifax (south central Piedmont) and Sussex (southeastern Coastal Plain). A 12-class habitat categorization system was developed to use in analysis. We developed a technique that allowed photo interpreters to identify and delineate features at a large scale (\u3e 1:6000) over a wide geographic area. Thirty-five to forty transects were evaluated for each site (n = 7). Favorable habitat decline observed ranged from -2% to -49%. Favorable edge decreased through time in four of five sites in Halifax County. Favorable edge increased dramatically within both Sussex County sites, particularly in the bobwhite focal area. Overall, habitat appears to have improved in Sussex County, and declined significantly in Halifax County. Habitats differed both through time and across the landscape. The largest habitat change noted was conversion from field to forest, predominantly pine. We feel this landscape scale habitat analysis technique holds much promise across the bobwhite’s range

ZNF410 represses fetal globin by devoted control of CHD4/NuRD [preprint]

Major effectors of adult-stage fetal globin silencing include the transcription factors (TFs) BCL11A and ZBTB7A/LRF and the NuRD chromatin complex, although each has potential on-target liabilities for rational β-hemoglobinopathy therapeutic inhibition. Here through CRISPR screening we discover ZNF410 to be a novel fetal hemoglobin (HbF) repressing TF. ZNF410 does not bind directly to the γ-globin genes but rather its chromatin occupancy is solely concentrated at CHD4, encoding the NuRD nucleosome remodeler, itself required for HbF repression. CHD4 has two ZNF410-bound regulatory elements with 27 combined ZNF410 binding motifs constituting unparalleled genomic clusters. These elements completely account for ZNF410’s effects on γ-globin repression. Knockout of ZNF410 reduces CHD4 by 60%, enough to substantially de-repress HbF while avoiding the cellular toxicity of complete CHD4 loss. Mice with constitutive deficiency of the homolog Zfp410 are born at expected Mendelian ratios with unremarkable hematology. ZNF410 is dispensable for human hematopoietic engraftment potential and erythroid maturation unlike known HbF repressors. These studies identify a new rational target for HbF induction for the β-hemoglobin disorders with a wide therapeutic index. More broadly, ZNF410 represents a special class of gene regulator, a conserved transcription factor with singular devotion to regulation of a chromatin subcomplex

Delaware Wetlands: Status and Changes from 1992 to 2007

A report which summarizes the status of the wetlands in Delaware in 2007 as well as the changes that have taken place since 1992

Computers and Productivity: Are Aggregation Effects Important?

This paper examines the empirical implications of aggregation bias when measuring the productive impact of computers. To isolate two specific aggregation problems relating to "aggregation in variables" and "aggregation in relations," we compare various production function estimates across a range of specifications, econometric estimators, and data levels. The results show that both sources of bias are important, especially as one moves from the sector to the economy level, and when the elasticity of all types of non-computer capital are incorrectly restricted to be equal. In terms of computers, however, the estimated elasticity is surprisingly stable between industry and sector regressions and does not appear to be biased by the incorporation of a restrictive measure of non-computer capital. The data consistently show that computers have a large impact on output.

Do Computers Make Output Harder to Measure?

In recent years, U.S. productivity growth accelerated sharply in manufacturing, but has remained sluggish in the most computer-intensive service industries. This paper explores the possibility that information technology is generating output that is increasingly hard to measure in non-manufacturing industries, which contributes to the divergence in industry productivity growth rates. Our results suggest that measurement error in 13 computer-intensive, non-manufacturing industries increased between 0.74 and 1.57 percentage points per year in the 1990s, which understates annual aggregate productivity growth by 0.10 to 0.20 percentage points in the 1990s. This adds to an estimated 0.22 to 0.30 percentage point error from the increasing share of aggregate output in these hard-to-measure industries. Thus, increasing measurement problems may understate aggregate productivity growth by an additional 0.32 to 0.50 percentage points per year in the 1990s and play an important role in understanding recent productivity trends at the industry level.

The Impact of Vintage and Survival on Productivity: Evidence from Cohorts of U.S. Manufacturing Plants

This paper examines the evolution of productivity in U.S. manufacturing plants from 1963 to 1992. We define a "vintage effect" as the change in productivity of recent cohorts of new plants relative to earlier cohorts of new plants, and a "survival effect" as the change in productivity of a particular cohort of surviving plants as it ages. The data show that both factors contribute to industry productivity growth, but play offsetting roles in determining a cohort's relative position in the productivity distribution. Recent cohorts enter with significantly higher productivity than earlier entrants did, while surviving cohorts show significant increases in productivity as they age. These two effects roughly offset each other, however, so there is a rough convergence in productivity across cohorts in 1992 and 1987.

The Impact of Vintage and Survival on Productivity: Evidence from Cohorts of U.S. Manufacturing Plants

This paper examines the evolution of productivity in U.S. manufacturing plants from 1963 to 1992. We define a “vintage effect” as the change in productivity of recent cohorts of new plants relative to earlier cohorts of new plants, and a “survival effect” as the change in productivity of a particular cohort of surviving plants as it ages. The data show that both factors contribute to industry productivity growth, but play offsetting roles in determining a cohort’s relative position in the productivity distribution. Recent cohorts enter with significantly higher productivity than earlier entrants did, while surviving cohorts show significant increases in productivity as they age. These two effects roughly offset each other, however, so there is a rough convergence in productivity across cohorts in 1992 and 1987. (JEL Code: D24, L6)CES,economic,research,micro,data,microdata,chief,economist

The Impact Of Vintage And Survival On Productivity: Evidence From Cohorts Of U.S. Manufacturing Plants

This paper examines the evolution of productivity in U.S. manufacturing plants from 1963 to 1992. We define a vintage effect as the change in productivity of recent cohorts of new plants relative to earlier cohorts of new plants, and a survival effect as the change in productivity of a particular cohort of surviving plants as it ages. Both factors contribute to industry productivity growth, but play offsetting roles in determining a cohort's relative position in the productivity distribution. Recent cohorts enter with higher productivity than earlier entrants did, whereas surviving cohorts show productivity increases as they age. These two effects roughly offset each other, however, so there is a rough convergence in productivity across cohorts in 1992 and 1987. © 2001 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology