52 resultados para EXTENDED EXPONENTIAL DISTRIBUTION
Resumo:
Soil biogeochemical cycles are largely mediated by microorganisms, while fire significantly modifies biogeochemical cycles mainly via altering microbial community and substrate availability. Majority of studies on fire effects have focused on the surface soil; therefore, our understanding of the vertical distribution of microbial communities and the impacts of fire on nitrogen (N) dynamics in the soil profile is limited. Here, we examined the changes of soil denitrification capacity (DNC) and denitrifying communities with depth under different burning regimes, and their interaction with environmental gradients along the soil profile. Results showed that soil depth had a more pronounced impact than the burning treatment on the bacterial community size. The abundance of 16S rRNA and denitrification genes (narG, nirK, and nirS) declined exponentially with soil depth. Surprisingly, the nosZ-harboring denitrifiers were enriched in the deeper soil layers, which was likely to indicate that the nosZ-harboring denitrifiers could better adapt to the stress conditions (i.e., oxygen deficiency, nutrient limitation, etc.) than other denitrifiers. Soil nutrients, including dissolved organic carbon (DOC), total soluble N (TSN), ammonium (NH4 +), and nitrate (NO3 −), declined significantly with soil depth, which probably contributed to the vertical distribution of denitrifying communities. Soil DNC decreased significantly with soil depth, which was negligible in the depths below 20 cm. These findings have provided new insights into niche separation of the N-cycling functional guilds along the soil profile, under a varied fire disturbance regime.
Resumo:
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.
Resumo:
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.
Resumo:
Banana bunchy top virus (BBTV; family Nanoviridae, genus Babuvirus) is a multi-component single-stranded DNA virus, which infects banana plants in many regions of the world, often resulting in large-scale crop losses. Weanalyzed 171 banana leaf samples from fourteen countries and recovered, cloned, and sequenced 855 complete BBTV components including ninety-four full genomes. Importantly, full genomes were determined from eight countries, where previously no full genomes were available (Samoa, Burundi, Republic of Congo, Democratic Republic of Congo, Egypt, Indonesia, the Philippines, and the USA [HI]). Accounting for recombination and genome component reassortment, we examined the geographic structuring of global BBTV populations to reveal that BBTV likely originated in Southeast Asia, that the current global hotspots of BBTV diversity are Southeast Asia/Far East and India, and that BBTV populations circulating elsewhere in the world have all potentially originated from infrequent introductions. Most importantly, we find that rather than the current global BBTV distribution being due to increases in human-mediated movements of bananas over the past few decades, it is more consistent with a pattern of infrequent introductions of the virus to different parts of the world over the past 1,000 years.
Resumo:
During the past 15 years, surveys to identify virus diseases affecting cool-season food legume crops in Australia and 11 CWANA countries (Algeria, China, Egypt, Ethiopia, Lebanon, Morocco, Sudan, Syria, Tunisia, Uzbekistan and Yemen) were conducted. More than 20,000 samples were collected and tested for the presence of 14 legume viruses by the tissue-blot immunoassay (TBIA) using a battery of antibodies, including the following Luteovirus monoclonal antibodies (McAbs): a broad-spectrum legume Luteovirus (5G4), BLRV, BWYV, SbDV and CpCSV. A total of 195 Luteovirus samples were selected for further testing by RT-PCR using 7 primers (one is degenerate, and can detect a wide range of Luteoviridae virus species and the other six are species-specific primers) at the Virology Laboratory, QDAF, Australia, during 2014. A total of 145 DNA fragments (represented 105 isolates) were sequenced. The following viruses were characterized based on molecular analysis: BLRV from Lebanon, Morocco, Tunisia and Uzbekistan; SbDV from Australia, Syria and Uzbekistan; BWYV from Algeria, China, Ethiopia, Lebanon, Morocco, Sudan, Tunisia and Uzbekistan; CABYV from Algeria, Lebanon, Syria, Sudan and Uzbekistan; CpCSV from Algeria, Ethiopia, Lebanon, Morocco, Syria and Tunisia, and unknown Luteoviridae species from Algeria, Ethiopia, Morocco, Sudan, Uzbekistan and Yemen. This study has clearly shown that there are a number of Polerovirus species, in addition to BWYV, all can produce yellowing/stunting symptoms in pulses (e.g. CABYV, CpCSV, and other unknown Polerovirus species). Based on our knowledge this is the first report of CABYV affecting food legumes. Moreover, there was about 95% agreement between results obtained from serological analysis (TBIA) and molecular analysis for the detection of BLRV and SbDV. Whereas, TBIA results were not accurate when using CpCSV and BWYV McAbs . It seems that the McAbs for CpCSV and BWYV used in this study and those available worldwide, are not virus species specific. Both antibodies, reacted with other Polerovirus species (e.g. CABYV, and unknown Polerovirus). This highlights the need for more accurate characterization of existing antibodies and where necessary the development of better, virus-specific antibodies to enable their use for accurate diagnosis of Poleroviruses.
Resumo:
This study aimed to define the frequency of resistance to critically important antimicrobials (CIAs) [i.e. extended-spectrum cephalosporins (ESCs), fluoroquinolones (FQs) and carbapenems] among Escherichia coli isolates causing clinical disease in Australian food-producing animals. Clinical E. coli isolates (n = 324) from Australian food-producing animals [cattle (n = 169), porcine (n = 114), poultry (n = 32) and sheep (n = 9)] were compiled from all veterinary diagnostic laboratories across Australia over a 1-year period. Isolates underwent antimicrobial susceptibility testing to 18 antimicrobials using the Clinical and Laboratory Standards Institute disc diffusion method. Isolates resistant to CIAs underwent minimum inhibitory concentration determination, multilocus sequence typing (MLST), phylogenetic analysis, plasmid replicon typing, plasmid identification, and virulence and antimicrobial resistance gene typing. The 324 E. coli isolates from different sources exhibited a variable frequency of resistance to tetracycline (29.0–88.6%), ampicillin (9.4–71.1%), trimethoprim/sulfamethoxazole (11.1–67.5%) and streptomycin (21.9–69.3%), whereas none were resistant to imipenem or amikacin. Resistance was detected, albeit at low frequency, to ESCs (bovine isolates, 1%; porcine isolates, 3%) and FQs (porcine isolates, 1%). Most ESC- and FQ-resistant isolates represented globally disseminated E. coli lineages (ST117, ST744, ST10 and ST1). Only a single porcine E. coli isolate (ST100) was identified as a classic porcine enterotoxigenic E. coli strain (non-zoonotic animal pathogen) that exhibited ESC resistance via acquisition of blaCMY-2. This study uniquely establishes the presence of resistance to CIAs among clinical E. coli isolates from Australian food-producing animals, largely attributed to globally disseminated FQ- and ESC-resistant E. coli lineages.
Resumo:
Some of the most productive taxa for forestry are interspecific F1 hybrids grown as exotics in the tropics and subtropics. Attributes of resilience, adaptability and vigour which engender the hybrids for wood production, may also exacerbate the risk they present from gene flow to native species gene pools or to local ecologies as weeds. To determine the biological and genetic factors that influence the extent of hybridisation, we examine the distribution and genealogy of wildlings surrounding plantings of locally-exotic Corymbia torelliana (Section Cadageria) near native C. henryi (Section Maculatae) in northern New South Wales. Our study showed pre-mating and pre- and post-zygotic barriers were incomplete, with in situ generation and natural establishment of both F1 hybrids (n = 3) and advanced generation hybrids under the disturbed conditions bordering native forest. As hybrids were located on alluvial flats exposed to frost, they also likely have an extended ecological range relative to native C. henryi. Despite the likely generation of large viable seed crops on F1 trees at the site over many years, establishment success and survival of advanced generation hybrids may be low, as only 5 immature and no mature advanced generation hybrids were identified. Propagation and genetic analysis of a seed crop from one F1 wildling showed early survival and vigour of seedlings in cultivation was high, and that at least for some F1 in some seasons, backcrossing to the recurrent native C. henryi parent is favoured (60%), whereas selfing (10%) and crossing with other F1 (30%) was less frequent. Transport of seed by stingless bees probably accounted for long distance dispersal from C. torelliana, but this mechanism does not appear to supplement gravity-dispersal of seed from the F1. Coupled with other evidence from studies of bee behaviour, controlled pollination in Corymbia sp., and long-term fitness in second generation eucalypt hybrids, we anticipate gene flow via pollen rather than seed will be the greater challenge for managing the risk of introgression of C. torelliana ancestry into native species from the planted F1 hybrid. If large sources of F1 pollen become available to compete with native pollen, gene flow will probably be frequent and hybrids may establish in disturbed conditions and in habitats beyond the ecological range of their native parent. Further study is needed to determine the degree to which outbreeding depression and poor survival inhibits on-going gene flow.