A New Approach to Forest Site Quality Modeling

Multiple regression and discriminant analysis procedures are commonly used to develop forest site quality models. \u27When they contain many independent variables relative to sample size, these models may be subject to predicton bias. Fit statistics such as R2 in regression and classification tables in discriminant analysis show the apparent model accuracy but this may be a biased estimate of the model\u27s actual accuracy. Sample splitting methods such as cross-validation and the bootstrap can be used to get an unbiased actual accuracy estimate. A discriminant procedure called classification tree analysis uses cross-validation to build the classifier with the greatest estimated actual accuracy. Because cross-validation is used in model development, the model is less likely to be over-fit with insignificant variables when compared with stepwise linear discriminant analysis. Classification tree analysis and linear discriminant analysis were used to develop models that discriminate prime vs. nonprime ponderosa pine (Pinus ponderosa) sites. Prime sites are defined as having site index 25 greater than 7.6 meters; nonprime sites have site index 25 less than 7.6 meters. Forest habitat type, percent sand content, and soil pH were incorporated in both models. The cross-valiation estimate of classification tree actual accuracy was 88 percent. A random bootstrap estimate of the linear discriminant function actual accuracy was 80 percent. viii A multiple regression model developed with random plots revealed little useful information and was biased when applied to prime site plots. The conventional regression approach using random plots may be misleading if one is interested in identifying relatively rare prime sites. Forest habitat types within the ponderosa pine series in southern Utah were examined as site quality indicators. The site index range within any one habitat type was broad. However, the best ponderosa pine sites consistently occurred in only Pinus ponderosa/Quercus gambelii, and Pinus ponderosa/Symphoricarpos oreophilus habitat types; or in habitat types within the Pseudotsuga menziesii or Abies concolor series. Therefore forest habitat type when used with other site variables may be useful in predicting prime sites. The effect of aspect at the upper elevational limit of ponderosa pine was examined by comparing mean site index and mean initial 10 year diameter increment on southerly and northerly slopes from two cinder cones. Southerly aspects on both cinder cones had greater mean diameter increment. Southerly aspects on the highest elevation cinder cone had the greatest mean site index. There was no significant difference in mean site index on the lower elevation cinder cone. Optimal aspect for height and diameter growth may differ due to l) the effect of density on diameter increment; and/or 2) available soil water limiting height growth during the spring and ambient temperature/solar radiation limiting diameter growth in late summer. Optimal aspect for forest production is not constant but varies with tree species, elevation, latitude, and other factors affecting site microclimate

Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model

<p>Abstract</p> <p>Background</p> <p>Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line.</p> <p>Methods</p> <p>The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).</p> <p>The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV.</p> <p>Results</p> <p>Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.</p

Positive biodiversity-productivity relationship predominant in global forests

The biodiversity-productivity relationship (BPR) is foundational to our understanding of the global extinction crisis and its impacts on ecosystem functioning. Understanding BPR is critical for the accurate valuation and effective conservation of biodiversity. Using ground-sourced data from 777,126 permanent plots, spanning 44 countries and most terrestrial biomes, we reveal a globally consistent positive concave-down BPR, showing that continued biodiversity loss would result in an accelerating decline in forest productivity worldwide. The value of biodiversity in maintaining commercial forest productivity alone - US$166 billion to 490 billion per year according to our estimation - is more than twice what it would cost to implement effective global conservation. This highlights the need for a worldwide reassessment of biodiversity values, forest management strategies, and conservation priorities.Peer Reviewe

Use of haplotypes to identify regions harbouring lethal recessive variants in pigs

Additional file 2: Figure S1. Heatmap of the linkage disequilibrium r 2 values between each of the SNPs located within regions 1.1. and 1.2 on SSC1

Remote sensing of interannual boreal forest NDVI in relation to climatic conditions in interior Alaska

Climate has warmed substantially in interior Alaska and several remote sensing studies have documented a decadal-scale decline in the normalized difference vegetation index (NDVI) termed a ‘browning trend’. Reduced summer soil moisture due to changing climatic factors such as earlier springs, less snowpack, and summer drought may reduce boreal productivity and NDVI. However, the relative importance of these climatic factors is poorly understood in boreal interior Alaska. In this study, I used the remotely sensed peak summer NDVI as an index of boreal productivity at 250 m pixel size from 2000 to 2014. Maximum summer NDVI was related to last day of spring snow, early spring snow water equivalent (SWE), and a summer moisture index. There was no significant correlation between early spring SWE and peak summer NDVI. There was a significant correlation between the last day of spring snow and peak summer NDVI, but only for a few higher elevation stations. This was likely due to snowmelt occurring later at higher elevations, thus having a greater effect on summer soil moisture relative to lower elevation sites. For most of boreal interior Alaska, summer drought was likely the dominant control on peak summer NDVI and this effect may persist for several years. Peak summer NDVI declined at all 26 stations after the 2004 drought, and the decline persisted for 2 years at all stations. Due to the shallow rooting zone of most boreal plants, even cool and moist sites at lower elevations are likely vulnerable to drought. For example the peak summer NDVI response following the 2004 drought was similar for adjacent cold and warm watershed basins. Thus, if frequent and severe summer droughts continue, moisture stress effects are likely to be widespread and prolonged throughout most of interior boreal Alaska, including relatively cool, moist sites regardless of spring snowpack conditions or spring phenology

Pratical GIS Analysis

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The Browning of Alaska’s Boreal Forest

We used twelve Landsat scenes from the 1980s–2009 and regional 2000–2009 MODIS data to examine the long-term trend in the normalized difference vegetation index (NDVI) within unburned areas of the Alaskan boreal forest. Our analysis shows that there has been a declining trend in NDVI in this region, with the strongest “browning trend” occurring in eastern Alaska where the climate during the growing season is relatively dry and warm. Possible reasons for the "browning trend" are decreased vegetation due to temperature-induced drought stress and increased infestations of insect pests