Maize genetic diversity has been used to understand the molecular basis of phenotypic variation and to improve agricultural efficiency and sustainability. We crossed 25 diverse inbred maize lines to the B73 reference line, capturing a total of 136,000 recombination events. Variation for recombination frequencies was observed among families, influenced by local (cis) genetic variation. We identified evidence for numerous minor single-locus effects but little two-locus linkage disequilibrium or segregation distortion, which indicated a limited role for genes with large effects and epistatic interactions on fitness. We observed excess residual heterozygosity in pericentromeric regions, which suggested that selection in inbred lines has been less efficient in these regions because of reduced recombination frequency. This implies that pericentromeric regions may contribute disproportionally to heterosis.

In ecological analyses of species and community distributions there is interest in the nature of their responses to environmental gradients and in identifying the most important environmental variables, which may be used for predicting patterns of biodiversity. Methods such as random forests already exist to assess predictor importance for individual species and to indicate where along gradients abundance changes. However, there is a need to extend these methods to whole assemblages, to establish where along the range of these gradients the important compositional changes occur, and to identify any important thresholds or change points. We develop such a method, called "gradient forest," which is an extension of the random forest approach. By synthesizing the cross-validated R2 and accuracy importance measures from univariate random forest analyses across multiple species, sampling devices, and surveys, gradient forest obtains a monotonic function of each predictor that represents the compositional turnover along the gradient of the predictor. When applied to a synthetic data set, the method correctly identified the important predictors and delineated where the compositional change points occurred along these gradients. Application of gradient forest to a real data set from part of the Great Barrier Reef identified mud fraction of the sediment as the most important predictor, with highest compositional turnover occurring at mud fraction values around 25%, and provided similar information for other predictors. Such refined information allows for more accurate capturing of biodiversity patterns for the purposes of bioregionalization, delineation of protected areas, or designing of biodiversity surveys.

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We present an objective method for identifying associations between environmental (habitat) conditions and the distributions of marine fishes using survey data. The method tests the null hypothesis of a random association between fish distribution and habitat conditions. We apply this method to bottom depth, temperature, and salinity data and to the distributions of four groundfish species (yellowtail flounder (Pleuronectes ferruginens, previously Limanda ferruginea), haddock (Melanogrammus aeglefinus), silver hake (Merluccius bilinearis), and Atlantic cod (Gadus morhua)) from trawl surveys of the eastern Scotian Shelf (northwest Atlantic) conducted in winter/spring (March) and summer (July) 1979–84. Haddock and silver hake maintained similar temperatures in winter and summer by changing their depth distributions (temperature-keepers), with haddock generally at cooler temperatures than silver hake. Yellowtail flounder (a depth-keeper) maintained similar depths between seasons while tolerating a wide range of temperatures and salinities. Atlantic cod were not consistently associated with particular depths in either sprang or summer, and we were unable to distinguish between temperature or salinity as a single factor modifying their distributions, perhaps because of age-related effects. Identification of persistent habitat associations of marine fishes provides an opportunity to improve fisheries management procedures.

Acknowledgment and also a reference citing the precedent that inspired their use of backscattered-electron scanning electron microscopy (e.g., as illustrated in Figure 7).

Concurrent to yield, maize (Zea Mays L.) plant density has significantly increased over the years. Unlike yield, however, the rate of change in plant density and its contribution to maize yield gain are rarely reported. The main objectives of this study were to examine the trend in the agronomic optimum plant density (AOPD) and quantify the contribution of plant density to yield gain. Maize hybrid by seeding rate trials were conducted from 1987–2016 across North America (187,662 data points). Mixed model, response surface, and simple linear regression analyses were applied on the meta-data. New outcomes from this analysis are: (i) an increase in the AOPD at rate of 700 plant ha−1 yr−1, (ii) increase in the AOPD of 1386, 580 and 404 plants ha−1 yr−1 for very high yielding (VHY, > 13 Mg ha−1), high yielding (HY, 10–13 Mg ha−1) and medium yielding (MY, 7–10 Mg ha−1), respectively, with a lack of change for the low yielding (LY, < 7 Mg ha−1) environment; (iii) plant density contribution to maize yield gain ranged from 8.5% to 17%, and (iv) yield improvement was partially explained by changes in the AOPD but we also identified positive impacts on yield components as other sources for yield gain.

Maize inbred lines with expired Plant Variety Protection Act (PVPA) certificates are publicly available and potentially represent a new germplasm resource for many public and private breeding programs. However, accurate pedigree and genetic background information for ex‐PVPA maize inbreds is necessary if they are to be effectively utilized in breeding efforts. We have used single nucleotide polymorphism (SNP) markers to evaluate the relationships and population structure among 92 ex‐PVPA inbred lines in relation to 17 well‐known public inbreds. Based on unweighted pair group method with arithmetic mean clustering, principal components analysis, and model‐based clustering, we identified six primary genetic clusters represented by the prominent inbred lines B73, Mo17, PH207, A632, Oh43, and B37. We also determined the genetic background of ex‐PVPA inbreds with conflicting, ambiguous, or undisclosed pedigrees. We assessed genetic diversity across subsets of ex‐PVPA lines and concluded that the ex‐PVPA lines are no more diverse than the public set evaluated here. Additionally, all alleles present in the ex‐PVPA inbreds, for the 614 SNPs included in this study, are also found in public temperate maize germplasm.

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