Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings

To quantify the impact that planting indigenous trees and shrubs in mixed communities (environmental plantings) have on net sequestration of carbon and other environmental or commercial benefits, precise and non-biased estimates of biomass are required. Because these plantings consist of several species, estimation of their biomass through allometric relationships is a challenging task. We explored methods to accurately estimate biomass through harvesting 3139 trees and shrubs from 22 plantings, and collating similar datasets from earlier studies, in non-arid (>300mm rainfallyear-1) regions of southern and eastern Australia. Site-and-species specific allometric equations were developed, as were three types of generalised, multi-site, allometric equations based on categories of species and growth-habits: (i) species-specific, (ii) genus and growth-habit, and (iii) universal growth-habit irrespective of genus. Biomass was measured at plot level at eight contrasting sites to test the accuracy of prediction of tonnes dry matter of above-ground biomass per hectare using different classes of allometric equations. A finer-scale analysis tested performance of these at an individual-tree level across a wider range of sites. Although the percentage error in prediction could be high at a given site (up to 45%), it was relatively low (<11%) when generalised allometry-predictions of biomass was used to make regional- or estate-level estimates across a range of sites. Precision, and thus accuracy, increased slightly with the level of specificity of allometry. Inclusion of site-specific factors in generic equations increased efficiency of prediction of above-ground biomass by as much as 8%. Site-and-species-specific equations are the most accurate for site-based predictions. Generic allometric equations developed here, particularly the generic species-specific equations, can be confidently applied to provide regional- or estate-level estimates of above-ground biomass and carbon. © 2013 Elsevier B.V.

Germinating the seeds of three species of Pimelea sect. Epallage (Thymelaeaceae)1

Pimelea trichostachya Lindl., P. simplex F.Muell. and P. elongata Threlfall frequently cause pimelea poisoning of cattle. Fresh seeds of these species, belonging to sect. Epallage (Endl.) Benth. of Pimelea Gaertn. (Thymelaeaceae) are strongly dormant for years when in laboratory storage. Common methods of stimulating germination, such as scarification, dry heat and cold stratification, did not remove much of the dormancy. ‘Smoke water’ stimulated some germination but its effect was unpredictable and many seedlings then grew aberrantly. Exposure of imbibed seeds to gibberellic acid greatly and reliably improved the germination of all three species. However, the manner of application and the concentration of gibberellic acid used had to be appropriate or many young seedlings grew abnormally or died suddenly, limiting successful plant establishment rates. The dormancy type involved is non-deep Type 2 physiological. Ten days of good moisture, in addition to gibberellic acid exposure, is required before appreciable laboratory germination occurs at optimal temperatures. Thus, the mechanism by which gibberellic acid stimulates good germination does not appear to be the same as that which primes seeds for the rapid and prolific germination often seen under natural conditions in arid Australia. Seeds of P. simplex subsp. continua (J.M.Black) Threlfall proved most difficult to germinate and those of P. elongata the easiest.

Molecular diversity of Chickpea chlorotic dwarf virus in Sudan: High rates of intra-species recombination – a driving force in the emergence of new strains

In Sudan Chickpea chlorotic dwarf virus (CpCDV, genus Mastrevirus, family Geminiviridae) is an important pathogen of pulses that are grown both for local consumption, and for export. Although a few studies have characterised CpCDV genomes from countries in the Middle East, Africa and the Indian subcontinent, little is known about CpCDV diversity in any of the major chickpea production areas in these regions. Here we analyse the diversity of 146 CpCDV isolates characterised from pulses collected across the chickpea growing regions of Sudan. Although we find that seven of the twelve known CpCDV strains are present within the country, strain CpCDV-H alone accounted for ∼73% of the infections analysed. Additionally we identified four new strains (CpCDV-M, -N, -O and -P) and show that recombination has played a significant role in the diversification of CpCDV, at least in this region. Accounting for observed recombination events, we use the large amounts of data generated here to compare patterns of natural selection within protein coding regions of CpCDV and other dicot-infecting mastrevirus species.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.