Understanding invasion history: genetic structure and diversity of two globally invasive plants and implications for their management

Aim: Resolving the origin of invasive plant species is important for understanding the introduction histories of successful invaders and aiding strategies aimed at their management. This study aimed to infer the number and origin(s) of introduction for the globally invasive species, Macfadyena unguis-cati and Jatropha gossypiifolia using molecular data. Location: Native range: Neotropics; Invaded range: North America, Africa, Europe, Asia, Pacific Islands and Australia. Methods: We used chloroplast microsatellites (cpSSRs) to elucidate the origin(s) of introduced populations and calculated the genetic diversity in native and introduced regions. Results: Strong genetic structure was found within the native range of M. unguis-cati, but no genetic structuring was evident in the native range of J. gossypiifolia. Overall, 27 haplotypes were found in the native range of M. unguis-cati. Only four haplotypes were found in the introduced range, with more than 96% of introduced specimens matching a haplotype from Paraguay. In contrast, 15 haplotypes were found in the introduced range of J. gossypiifolia, with all invasive populations, except New Caledonia, comprising multiple haplotypes. Main conclusions: These data show that two invasive plant species from the same native range have had vastly different introduction histories in their non-native ranges. Invasive populations of M. unguis-cati probably came from a single or few independent introductions, whereas most invasive J. gossypiifolia populations arose from multiple introductions or alternatively from a representative sample of genetic diversity from a panmictic native range. As introduced M. unguis-cati populations are dominated by a single haplotype, locally adapted natural enemies should make the best control agents. However, invasive populations of J. gossypiifolia are genetically diverse and the selection of bio-control agents will be considerably more complex.

Net radiation dynamics: performance of 20 daily net radiation models as related to model structure and intricacy in two climates.

We compared daily net radiation (Rn) estimates from 19 methods with the ASCE-EWRI Rn estimates in two climates: Clay Center, Nebraska (sub-humid) and Davis, California (semi-arid) for the calendar year. The performances of all 20 methods, including the ASCE-EWRI Rn method, were then evaluated against Rn data measured over a non-stressed maize canopy during two growing seasons in 2005 and 2006 at Clay Center. Methods differ in terms of inputs, structure, and equation intricacy. Most methods differ in estimating the cloudiness factor, emissivity (e), and calculating net longwave radiation (Rnl). All methods use albedo (a) of 0.23 for a reference grass/alfalfa surface. When comparing the performance of all 20 Rn methods with measured Rn, we hypothesized that the a values for grass/alfalfa and non-stressed maize canopy were similar enough to only cause minor differences in Rn and grass- and alfalfa-reference evapotranspiration (ETo and ETr) estimates. The measured seasonal average a for the maize canopy was 0.19 in both years. Using a = 0.19 instead of a = 0.23 resulted in 6% overestimation of Rn. Using a = 0.19 instead of a = 0.23 for ETo and ETr estimations, the 6% difference in Rn translated to only 4% and 3% differences in ETo and ETr, respectively, supporting the validity of our hypothesis. Most methods had good correlations with the ASCE-EWRI Rn (r2 > 0.95). The root mean square difference (RMSD) was less than 2 MJ m-2 d-1 between 12 methods and the ASCE-EWRI Rn at Clay Center and between 14 methods and the ASCE-EWRI Rn at Davis. The performance of some methods showed variations between the two climates. In general, r2 values were higher for the semi-arid climate than for the sub-humid climate. Methods that use dynamic e as a function of mean air temperature performed better in both climates than those that calculate e using actual vapor pressure. The ASCE-EWRI-estimated Rn values had one of the best agreements with the measured Rn (r2 = 0.93, RMSD = 1.44 MJ m-2 d-1), and estimates were within 7% of the measured Rn. The Rn estimates from six methods, including the ASCE-EWRI, were not significantly different from measured Rn. Most methods underestimated measured Rn by 6% to 23%. Some of the differences between measured and estimated Rn were attributed to the poor estimation of Rnl. We conducted sensitivity analyses to evaluate the effect of Rnl on Rn, ETo, and ETr. The Rnl effect on Rn was linear and strong, but its effect on ETo and ETr was subsidiary. Results suggest that the Rn data measured over green vegetation (e.g., irrigated maize canopy) can be an alternative Rn data source for ET estimations when measured Rn data over the reference surface are not available. In the absence of measured Rn, another alternative would be using one of the Rn models that we analyzed when all the input variables are not available to solve the ASCE-EWRI Rn equation. Our results can be used to provide practical information on which method to select based on data availability for reliable estimates of daily Rn in climates similar to Clay Center and Davis.

Parasites as indicators of movement and population connectivity of a non-diadromous, tropical estuarine teleost: king threadfin Polydactylus macrochir

Temporal and spatial patterns in parasite assemblages were examined to evaluate the degree of movement and connectivity of post-recruitment life-history stages of a large, non-diadromous tropical estuarine teleost, king threadfin Polydactylus macrochir, collected from 18 locations across northern Australia. Ten parasites types (juvenile stages of two nematodes and seven cestodes, and adults of an acanthocephalan) were deemed to be suitable for use as biological tags, in that they were considered to have a long residence time in the fish, were relatively easy to find and were morphologically very different to each other which aided discrimination. Univariate and discriminant function analysis of these parasites revealed little difference in temporal replicates collected from five locations, suggesting that the parasite communities were stable over the timeframes explored. Univariate, discriminant function, and BrayCurtis similarity analyses indicated significant spatial heterogeneity, with BrayCurtis classification accuracies ranging from 55 to 100% for locations in north-western and northern Australia, 24 to 88% in the Gulf of Carpentaria, and 39 to 88% on the east coast of Queensland. Few differences were observed among locations separated by <200 km. The observed patterns of parasite infection are in agreement with concurrent studies of movement and connectivity of P. macrochir in that they indicate a complex population structure across northern Australia. These results should be considered when reviewing the management arrangements for this species.

Fungal community structure in disease suppressive soils assessed by 28S LSU gene sequencing

Natural biological suppression of soil-borne diseases is a function of the activity and composition of soil microbial communities. Soil microbe and phytopathogen interactions can occur prior to crop sowing and/or in the rhizosphere, subsequently influencing both plant growth and productivity. Research on suppressive microbial communities has concentrated on bacteria although fungi can also influence soil-borne disease. Fungi were analyzed in co-located soils 'suppressive' or 'non-suppressive' for disease caused by Rhizoctonia solani AG 8 at two sites in South Australia using 454 pyrosequencing targeting the fungal 28S LSU rRNA gene. DNA was extracted from a minimum of 125 g of soil per replicate to reduce the micro-scale community variability, and from soil samples taken at sowing and from the rhizosphere at 7 weeks to cover the peak Rhizoctonia infection period. A total of ∼994,000 reads were classified into 917 genera covering 54% of the RDP Fungal Classifier database, a high diversity for an alkaline, low organic matter soil. Statistical analyses and community ordinations revealed significant differences in fungal community composition between suppressive and non-suppressive soil and between soil type/location. The majority of differences associated with suppressive soils were attributed to less than 40 genera including a number of endophytic species with plant pathogen suppression potentials and mycoparasites such as Xylaria spp. Non-suppressive soils were dominated by Alternaria , Gibberella and Penicillum. Pyrosequencing generated a detailed description of fungal community structure and identified candidate taxa that may influence pathogen-plant interactions in stable disease suppression. © 2014 Penton et al.

Non-cellulosic cell wall polysaccharides are subject to genotype × environment effects in sorghum (Sorghum bicolor) grain

Sorghum is a staple food for half a billion people and, through growth on marginal land with minimal inputs, is an important source of feed, forage and increasingly, biofuel feedstock. Here we present information about non-cellulosic cell wall polysaccharides in a diverse set of cultivated and wild Sorghum bicolor grains. Sorghum grain contains predominantly starch (64–76) but is relatively deficient in other polysaccharides present in wheat, oats and barley. Despite overall low quantities, sorghum germplasm exhibited a remarkable range in polysaccharide amount and structure. Total (1,3;1,4)-β-glucan ranged from 0.06 to 0.43 (w/w) whilst internal cellotriose:cellotetraose ratios ranged from 1.8 to 2.9:1. Arabinoxylan amounts fell between 1.5 and 3.6 (w/w) and the arabinose:xylose ratio, denoting arabinoxylan structure, ranged from 0.95 to 1.35. The distribution of these and other cell wall polysaccharides varied across grain tissues as assessed by electron microscopy. When ten genotypes were tested across five environmental sites, genotype (G) was the dominant source of variation for both (1,3;1,4)-β-glucan and arabinoxylan content (69–74), with environment (E) responsible for 5–14. There was a small G × E effect for both polysaccharides. This study defines the amount and spatial distribution of polysaccharides and reveals a significant genetic influence on cell wall composition in sorghum grain.