Economic Effects of the 1988 Drought in North Dakota: A 1989 Update of the Financial Conditions of Farm and Ranch Operators

Agricultural Finance, Farm Management, Resource /Energy Economics and Policy,

Outlook of North Dakota Farm Households: Results of the 1988 Longitudinal Farm Survey

Agricultural Finance, Farm Management,

Economic Impact of the Conservation Reserve Program in North Dakota

Environmental Economics and Policy, Land Economics/Use,

Revitalizing the Retail Trade Sector in Rural Communities: Experiences of 13 North Dakota Towns

Community/Rural/Urban Development,

Temporal rarity is a better predictor of local extinction risk than spatial rarity

Spatial rarity is often used to predict extinction risk, but rarity can also occur temporally. Perhaps more relevant in the context of global change is whether a species is core to a community (persistent) or transient (intermittently present), with transient species often susceptible to human activities that reduce niche space. Using 5–12 yr of data on 1,447 plant species from 49 grasslands on five continents, we show that local abundance and species persistence under ambient conditions are both effective predictors of local extinction risk following experimental exclusion of grazers or addition of nutrients; persistence was a more powerful predictor than local abundance. While perturbations increased the risk of exclusion for low persistence and abundance species, transient but abundant species were also highly likely to be excluded from a perturbed plot relative to ambient conditions. Moreover, low persistence and low abundance species that were not excluded from perturbed plots tended to have a modest increase in abundance following perturbance. Last, even core species with high abundances had large decreases in persistence and increased losses in perturbed plots, threatening the long-term stability of these grasslands. Our results demonstrate that expanding the concept of rarity to include temporal dynamics, in addition to local abundance, more effectively predicts extinction risk in response to environmental change than either rarity axis predicts alone.Fil: Wilfahrt, Peter A.. University of Minnesota; Estados UnidosFil: Asmus, Ashley L.. University of Minnesota; Estados UnidosFil: Seabloom, Eric. University of Minnesota; Estados UnidosFil: Henning, Jeremiah A.. University of Minnesota; Estados UnidosFil: Adler, Peter. State University of Utah; Estados UnidosFil: Arnillas, Carlos A.. University of Toronto Scarborough; CanadáFil: Bakker, Jonathan. University of Washington; Estados UnidosFil: Biederman, Lori. University of Iowa; Estados UnidosFil: Brudvig, Lars A.. Michigan State University; Estados UnidosFil: Cadotte, Marc W.. University of Toronto Scarborough; CanadáFil: Daleo, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Eskelinen, Anu. German Centre for Integrative Biodiversity Research; AlemaniaFil: Firn, Jennifer. University of Queensland; AustraliaFil: Harpole, W. Stanley. German Centre for Integrative Biodiversity Research; Alemania. Helmholtz Centre for Environmental Research; Alemania. Martin Luther University Halle-Wittenberg; AlemaniaFil: Hautier, Yann. Utrecht University; Países BajosFil: Kirkman, Kevin P.. University of KwaZulu-Natal; SudáfricaFil: Komatsu, Kimberly J.. Smithsonian Environmental Research Center; Estados UnidosFil: Laungani, Ramesh. Doane University; Estados UnidosFil: MacDougall, Andrew. University of Guelph; CanadáFil: McCulley, Rebecca L.. University of Kentucky; Estados UnidosFil: Moore, Joslin L.. Monash University; AustraliaFil: Morgan, John W.. La Trobe University; AustraliaFil: Mortensen, Brent. Benedictine College; Estados UnidosFil: Ochoa Hueso, Raul. Universidad de Cádiz; EspañaFil: Ohlert, Timothy. University of New Mexico; Estados UnidosFil: Power, Sally A.. University of Western Sydney; AustraliaFil: Price, Jodi. Charles Sturt University; AustraliaFil: Risch, Anita C.. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Schuetz, Martin. Swiss Federal Institute for Forest, Snow and Landscape Research; SuizaFil: Shoemaker, Lauren. University of Wyoming; Estados UnidosFil: Stevens, Carly. Lancaster University; Reino UnidoFil: Strauss, Alexander T.. University of Minnesota; Estados Unidos. University of Georgia; Estados UnidosFil: Tognetti, Pedro Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Virtanen, Risto. University of Oulu; FinlandiaFil: Borer, Elizabeth. University of Minnesota; Estados Unido

Empirical Evidence on Inflation and Unemployment in the Long Run

We examine the relationship between inflation and unemployment in the long run, using quarterly US data from 1952 to 2010. Using a band-pass filter approach, we find strong evidence that a positive relationship exists, where inflation leads unemployment by some 3 to 3 1/2 years, in cycles that last from 8 to 25 or 50 years. Our statistical approach is atheoretical in nature, but provides evidence in accordance with the predictions of Friedman (1977) and the recent New Monetarist model of Berentsen, Menzio, and Wright (2011): the relationship between inflation and unemployment is positive in the long run

Rare coding variants in ten genes confer substantial risk for schizophrenia

Rare coding variation has historically provided the most direct connections between gene function and disease pathogenesis. By meta-analysing the whole exomes of 24,248 schizophrenia cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in 10 genes as conferring substantial risk for schizophrenia (odds ratios of 3-50, PPeer reviewe

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression

No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (β = 16.1, CI(β) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (β = 4.86,CI(β) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest

Age at first birth in women is genetically associated with increased risk of schizophrenia

Prof. Paunio on PGC:n jäsenPrevious studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.Peer reviewe