A recent approach to detecting genetic polymorphism involves the amplification of genomic DNA using single primers of arbitrary sequence. When separated electrophoretically in agarose gels, the amplification products give banding patterns that can be scored for genetic variation. The objective of this research was to apply these techniques to cultivated peanut (Arachis hypogaea L.) and related wild species to determine whether such an approach would be feasible for the construction of a genetic linkage map in peanut or for systematic studies of the genus. Two peanut cultivars, 25 unadapted germplasm lines of A. hypogaea, the wild allotetraploid progenitor of cultivated peanut (A. monticola), A. glabrata (a tetraploid species from section Rhizomatosae), and 29 diploid wild species of Arachis were evaluated for variability using primers of arbitrary sequence to amplify segments of genomic DNA. No variation in banding pattern was observed among the cultivars and germplasm lines of A. hypogaea, whereas the wild Arachis species were uniquely identified with most primers tested. Bands were scored (+/-) in the wild species and the PAUP computer program for phylogenetic analysis and the HyperRFLP program for genetic distance analysis were used to generate dendrograms showing genetic relationships among the diploid Arachis species evaluated. The two analyses produced nearly identical dendrograms of species relationships. In addition, approximately 100 F2 progeny from each of two interspecific crosses were evaluated for segregation of banding patterns. Although normal segregation was observed among the F2 progeny from both crosses, banding patterns were quite complex and undesirable for use in genetic mapping. The dominant behavior of the markers prevented the differentiation of heterozygotes from homozygotes with certainty, limiting the usefulness of arbitrary primer amplification products as markers in the construction of a genetic linkage map in peanut.

Peanut germ plasm consists of the cultivated allotetraploid species Arachis hypogaea L. and a large number of wild species, which are nearly all diploids. Our previous work indicated a very low level of genetic variability in American cultivars, as assayed by restriction fragment length polymorphism (RFLP) analysis. Since American cultivars might represent a narrow genetic base, we expanded our study to include unadapted germ-plasm lines from the various South American centers of origin, Africa, and China, where considerable morphological and physiological variability has been reported to exist. Wild species of section Arachis were included in the evaluations since they show a high degree of variation when assayed by RFLPs. Three methods were used to assay for RFLP variation: (i) conventional RFLP analysis using random genomic clones from peanut and cDNA clones from peanut and alfalfa (Medicago sativa); (ii) polymerase chain reaction (PCR) amplification of random primer sequences; (iii) four-cutter analysis of PCR-amplified fragments. In all cases a very low level of variability was found in cultivated peanut, while abundant variability was present among wild diploid species. The results are discussed in terms of peanut evolution and significance to peanut breeding.Key words: polymerase chain reaction, Arachis hypogaea, restriction fragment length polymorphism.

The study of vegetation community and structural change has been central to ecology for over a century, yet the ways in which disturbances reshape the physical structure of forest canopies remain relatively unknown. Moderate severity disturbances affect different canopy strata and plant species, resulting in variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits (CSTs) with known links to ecosystem function. We used lidar-derived CSTs within a machine learning framework to detect and describe the structural changes that result from various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, senescence, pathogen, and insect infestation altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, counter to common assumptions regarding many moderate severity disturbances. While findings are limited in their generalizability due to lack of replication among disturbances, they do suggest that the current limitations of standard disturbance detection methods-such as optical-based remote sensing platforms, which are often above-canopy perspectives-limit our ability to understand the full ecological and structural impacts of disturbance, and to evaluate the consistency of structural patterns within and among disturbance agents. A more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structural change may potentially improve the modeling, detection, and prediction of functional implications of moderate severity disturbance as well as broaden our understanding of the ecological impacts of disturbance.

Biodiversity is believed to be closely related to ecosystem functions. However, the ability of existing biodiversity measures, such as species richness and phylogenetic diversity, to predict ecosystem functions remains elusive. Here, we propose a new vector of diversity metrics, structural diversity, which directly incorporates niche space in measuring ecosystem structure. We hypothesize that structural diversity will provide better predictive ability of key ecosystem functions than traditional biodiversity measures. Using the new lidar-derived canopy structural diversity metrics on 19 National Ecological Observation Network forested sites across the USA, we show that structural diversity is a better predictor of key ecosystem functions, such as productivity, energy, and nutrient dynamics than existing biodiversity measures (i.e. species richness and phylogenetic diversity). Similar to existing biodiversity measures, we found that the relationships between structural diversity and ecosystem functions are sensitive to environmental context. Our study indicates that structural diversity may be as good or a better predictor of ecosystem functions than species richness and phylogenetic diversity.