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Prevalence of pathogens important to human and companion animal health in an urban unowned cat population
Get PDFContext
The deleterious impacts of cat predation on wildlife have been well documented. Additionally, unowned cats may act as reservoirs of disease important to public and companion animal health and their proclivity for roaming and fighting enables effective disease transmission. Urban environments support the highest human populations and companion animal densities, increasing the potential for disease transmission from unowned cats to people and pets. However, there is little data on the prevalence of pathogens in unowned cat populations.
Aims
This aim of this research was to establish baseline prevalence data for priority pathogens in an urban population of unowned cats.
Methods
One hundred unowned cat cadavers were collected from the Brisbane City Council region, Queensland, Australia. Blood and additional organ or tissue samples were collected post-mortem. Diagnostic methods for pathogen detection included use of real-time polymerase-chain reaction, commercially available rapid enzyme-linked-immunosorbent assay, lavage and faecal flotation.
Key results
Pathogen carriage was found in 79% (95% CI 71, 87%) of sampled cats. In total, 62% (95% CI 52, 72%) of cats showed evidence of co-carriage of two or more pathogenic organisms. The overall prevalence found for pathogens and parasites investigated were: Toxoplasma gondii, 7% (95% CI 2, 12%); Coxiella burnetii, 0.0% (95% CI 0, 0%); feline immunodeficiency virus, 12% (95% CI 6, 18%); feline leukaemia virus, 0.0% (95% CI 0, 0%); and gastrointestinal parasites, 76.8% (95% CI 68, 85%).
Conclusions
This study reports contemporary prevalence data for these pathogens that have not previously been available for unowned cats of south-east Queensland. High rates of gastrointestinal parasitism observed throughout the study population prompt concerns of a general increase in pathogenic prevalence, especially in comparison with that of owned domestic cats, as per previously published literature. The presence of signs of fighting is an important risk factor for increased likelihood of infection.
Implications
Data produced from this study contribute to informing cat management efforts throughout urban regions. Continued and expanded investigations, considering prevalence and risk factors of pathogens important to human and companion animal health, are recommended for the south-east Queensland area and beyond
Pigeon pea crop stage strongly influences plant susceptibility to Helicoverpa armigera (Lepidoptera: Noctuidae)
Get PDFHelicoverpa armigera Hübner (Lepidoptera: Noctuidae; Hübner) is the major insect pest of pigeon pea [Cajanus cajan; Fabales: Fabaceae; (L.) Millspaugh] worldwide. Research to develop pest management strategies for H. armigera in pigeon pea has focused heavily on developing less susceptible cultivars, with limited practical success. We examined how pigeon pea crop stage influences plant susceptibility to H. armigera using a combination of glasshouse and laboratory experiments. Plant phenology significantly affected oviposition with moths laying more eggs on flowering and podding plants but only a few on vegetative plants. Larval survival was greatest on flowering and vegetative plants, wherein larvae mostly chose to feed inside flowers on flowering plants and on the adaxial surface of expanding leaves on vegetative plants. Larval survival was poor on podding plants despite moths laying many eggs on plants of this stage. When left to feed without restriction on plants for 7 days, larvae feeding on flowering plants were >10 times the weight of larvae feeding on plants of other phenological stages. On whole plants, unrestricted larvae preferred to feed on pigeon pea flowers and on expanding leaves, but in no-choice Petri dish assays H. armigera larvae could feed and survive on all pigeon pea reproductive structures. Our results show that crop stage and the availability of flowers strongly influence pigeon pea susceptibility to H. armigera. An increased understanding of H. armigera-pigeon pea ecology will be useful in guiding the development of resistant varieties and other management tactics
The roles of non-structural carbohydrates in fruiting: a review focusing on mango (Mangifera indica)
Get PDFReproductive development of fruiting trees, including mango (Mangifera indica L.), is limited by non-structural carbohydrates. Competition for sugars increases with cropping, and consequently, vegetative growth and replenishment of starch reserves may reduce with high yields, resulting in interannual production variability. While the effect of crop load on photosynthesis and the distribution of starch within the mango tree has been studied, the contribution of starch and sugars to different phases of reproductive development requires attention. This review focuses on mango and examines the roles of non-structural carbohydrates in fruiting trees to clarify the repercussions of crop load on reproductive development. Starch buffers the plant’s carbon availability to regulate supply with demand, while sugars provide a direct resource for carbon translocation. Sugar signalling and interactions with phytohormones play a crucial role in flowering, fruit set, growth, ripening and retention, as well as regulating starch, sugar and secondary metabolites in fruit. The balance between the leaf and fruit biomass affects the availability and contributions of starch and sugars to fruiting. Crop load impacts photosynthesis and interactions between sources and sinks. As a result, the onset and rate of reproductive processes are affected, with repercussions for fruit size, composition, and the inter-annual bearing pattern
Hyperspectral imaging predicts free fatty acid levels, peroxide values, and linoleic acid and oleic acid concentrations in tree nut kernels
No full textImaging technologies are advancing rapidly in food processing systems to reduce food waste. However, modelling techniques, sample sizes and dataset proportioning methods significantly affect the performance of models in predicting food quality variables. This study examined the potential of hyperspectral imaging to predict peroxide values (PV), free fatty acid (FFA) levels and fatty acid concentrations, including oleic and linoleic acid, in two tree nuts, canarium and macadamia. The effectiveness of artificial neural network (ANN) regression and partial least squares regression (PLSR) techniques to make these predictions was examined. Additionally, the importance of the dataset size on prediction accuracy and the dataset-proportioning method for developing predictive models were assessed. Both ANN and PLSR models predicted FFA levels and oleic acid concentrations with high accuracy, but PV and linoleic acid concentrations were predicted poorly. Changing the test-dataset proportioning method for the small dataset led to comparable R2test values by both ANN and PLSR in predicting FFA levels. Successful prediction of FFA levels could be explained partly by high variability, even in the dataset with the small number of samples. The study highlights the significance of factors like dataset size, test-dataset proportioning method, and model selection when predicting the quality attributes
Long-term evaluation of pasture production, seasonality, and variability: An application of the DairyMod pasture model for three tropical species
No full textAdoption of improved pastures coupled with intensified management provide quality pastures in adequate quantities and thus improve livestock productivity. While pasture modelling is imperative for exploring the performance of newer pastures, models are little used for long-term simulations of multiple tropical pastures (genotype), under varying soil, climate (environment) and pasture production systems (management). We applied the DairyMod, a biophysical model to simulate the long-term pasture production of Brachiaria ruziziensis x B. decumbens x B. brizantha ‘Brachiaria Mulato II’ (BM), Megathyrsus maximus ‘Gatton Panic’ (GP), and Chloris gayana ‘Rhodes grass cv. Reclaimer’ (RR) across major dairying regions of Sri Lanka under different management scenarios and characterize the long-term pasture growth, seasonality and spatial variability, and possible implications for dairying in Sri Lanka. Simulations of three pasture species were carried out for 16 locations (8 dry (DZ), 5 intermediate (IZ), and 3 wet zone (WZ)) over 30 years (1980–2010). Three pasture management scenarios simulated were; 1) potential pasture production system under non-limiting N and irrigation (Yp) 2) rainfed pasture production system under non-limiting N fertilizer (Yw), and 3) rainfed pasture production system under current nitrogen (N) fertilizer rate (Ya). Statistical techniques were used to identify the long-term growth rates, variability, and trends in pasture production. The long-term pasture production varied greatly among climate, species, and management scenarios. Overall, the Ya showed a seasonal cycle following the rainfall pattern, with a reduction in growth rates in dry seasons (May–September). Pasture growth rates were greater in GP at Ya, and BM at Yw and Yp while RR showed the lowest growth rate at all times. Variability of pasture growth was high in DZ (May–September) and RR has the lowest growth variability. The Yw increased the growth rate (doubled) while the Yp substantially increased (nearly tripled) the growth rate and growth pattern producing less variable pastures. Simulated growth rates suggest that GP in low-input and BM in high-input farming areas would be more suitable. Our study suggested that the BM, GP, and RR are edaphic-climatologically fit for major dairying regions in Sri Lanka and the appropriate fertilizer and irrigation management can greatly increase the herbage accumulation and availability of year-round pastures. While this study offers valuable insights, the species-specific growth pattern, growth variability, yield potential under different managements and the possible implications for herbage quality need to be sensibly considered when selecting the appropriate species
Management strategy evaluation of the Queensland east coast sea cucumber fishery, with data to June 2023
Get PDFThe Queensland Sea Cucumber Fishery is a recreational and commercial fishery comprised of twenty-one sea cucumber species. The fishery has a dynamic history of species catch composition whereby the main target species were black teatfish (Holothuria whitmaei), then white teatfish (Holothuria fuscogilva) and presently burrowing blackfish (Actinopyga spinea) with opportunistic harvest of herrmanni curryfish (Stichopus herrmanni) and prickly redfish (Thelonata ananas).
This is the second management strategy evaluation conducted on the Queensland sea cucumber fishery but the first by Fisheries Queensland. A management strategy evaluation of the Queensland sea cucumber fishery conducted by CSIRO in 2014 evaluated the benefits of the rotational harvest strategy (Skewes et al. 2014). While some specific results differ between the previous and current management strategy evaluation are difficult to compare as fishery reference points have been updated between reports, consistent conclusions were reached.
Management strategy evaluation is a simulation tool for comparing the effectiveness of different management procedures against fishery objectives. The simulations capture the growth, reproduction, movement and mortality of a fish population and potential management procedures which dictate the fishery
operating on the population. Uncertainty in these processes is characterised by running many simulations with slightly different biological specifications. The management procedures prescribe a mode of operation rather than a specific catch-limit or effort control. The performance of each management procedure is quantified to answer important management questions. Management procedures that perform well over a range of simulations are more likely to achieve the desired management goals. Well-performing management procedures become recommendations for the fishery.
This management strategy evaluation was undertaken using the openMSE package developed by Blue Matter Science.
The evaluation considered commercial catch and effort data spanning 1995 to 2023, biological data provided by Fishwell Consulting and Macquarie University and results from co-produced stock assessments.
The biology of many sea cucumber species is unknown or uncertain and often places this taxon in a data-limited space. This applies to many species in the Queensland sea cucumber fishery and the data-limited nature of the fishery has been captured in this management strategy evaluation through an increased level of uncertainty for species biology.
This management strategy evaluation found that the settings contained in the harvest strategy and other legislated and enforceable management arrangements are likely sufficient to meet the fishery’ objective of attaining maximum economic yield (defined in the harvest strategy as target biomass level of 60% of unfished biomass for stocks harvested in the fishery). The current management containing the rotational harvest strategy, catch limits and size limits management arrangements suggests the risk of depletion for most species was low
Molecular identification and characterisation of Mannheimia haemolytica
Get PDFMannheimia haemolytica is known as one of the major bacterial contributors to Bovine Respiratory Disease (BRD) syndrome. This study sought to establish a novel species-specific PCR to aid in identification of this key pathogen. As well, an existing multiplex PCR was used to determine the prevalence of serovars 1, 2 or 6 in Australia. Most of the 65 studied isolates originated from cattle with a total of 11 isolates from small ruminants. All problematic field isolates in the identification or serotyping PCRs were subjected to whole genome sequencing and bioinformatic analysis. The field isolates were also subjected to rep-PCR fingerprinting. A total of 59 out of the 65 tested isolates were conformed as M. haemolytica by the new species-specific PCR which is based on the rpoB gene. The confirmed M. haemolytica field isolates were assigned to serovars 1 (24 isolates), 2 (seven isolates) and 6 (26 isolates) while two of the isolates were negative in the serotyping PCR. The two non-typeable isolates were assigned to serovar 7 and 14 following whole genome sequencing and bioinformatic analysis. The rep-PCR typing resulted in five major clusters with serovars 1 and 6 often within the same cluster. The M. haemolytica-specific PCR developed in this work was species specific and should be a valuable support for frontline diagnostic laboratories. The serotyping results support the relative importance of serovars 1 and 6 in bovine respiratory disease
Development of the decision-support tool ‘Harvest Mate’: agronomic algorithms
No full textChanging cane harvester’s primary-extractor fan speed and flow rate impacts tonnes of cane and sugar delivered to the mill and the cost of harvesting. Although past research shows a negative impact of high harvester flow rates and fan speeds on delivered cane yield, adoption rates of harvesting best practice (HBP) remain low. This is despite the potential of HBP substantially increasing overall harvested sugarcane to the Australian industry without an increase in cane area. A key barrier to adoption is the challenge for growers and contractors to confidently determine the economic benefit or cost of adopting alternative harvesting practices over standard practices. One practical solution initiated and supported by the industry is the development of a decision-support tool to assist harvesting groups in estimating both grower revenue and harvesting cost impacts. However, estimates of both yield and CCS are required to determine revenue outputs. Given the extensive production data collected during the 2017/18 harvesting trials from the project ‘Adoption of practices to mitigate harvest losses’, various algorithms were developed for a harvesting decision-support tool. These included estimates of yield, extraneous matter (utilised in CCS calculations) and billet diameter. Algorithms were also required to estimate changes in harvesting costs due to differences in fuel utilisation and bulk density. This paper examines the development of these algorithms and the functionality of Harvest Mate, a new tool that incorporates agronomic and economic considerations to determine the most economically optimal harvester settings
Efficiently limiting yield loss from net-blotch in barley – a meta-analysis
Get PDFTake home message
• In marginal barley cropping regions where yield targets are below or equal to 3.4 tons per hectare, using foliar fungicides to control net-blotch might not be economically beneficial
• The most effective period for applying foliar fungicide to minimise yield loss is during and immediately after the emergence of the top three leaves (flag leaf, flag leaf -1 and flag leaf -2)
• Two fungicide applications timed between Z35 and Z60 provided the best yield protection
• Efficacy of spray programs to protect yield don’t tend to differ between the two net-blotch forms, SFNB (Spot Form Net Blotch) and NFNB (Net Form Net Blotch)
• Consult the ‘NetBlotchBM’ app when considering a disease management program
Tracking and modeling the movement of Queensland fruit flies, Bactrocera tryoni, using harmonic radar in papaya fields
Get PDFDetermining movement parameters for pest insects such as tephritid fruit flies is critical to developing models which can be used to increase the effectiveness of surveillance and control strategies. In this study, harmonic radar was used to track wild-caught male Queensland fruit flies (Qflies), Bactrocera tryoni, in papaya fields. Experiment 1 continuously tracked single flies which were prodded to induce movement. Qfly movements from this experiment showed greater mean squared displacement than predicted by both a simple random walk (RW) or a correlated random walk (CRW) model, suggesting that movement parameters derived from the entire data set do not adequately describe the movement of individual Qfly at all spatial scales or for all behavioral states. This conclusion is supported by both fractal and hidden Markov model (HMM) analysis. Lower fractal dimensions (straighter movement paths) were observed at larger spatial scales (> 2.5 m) suggesting that Qflies have qualitatively distinct movement at different scales. Further, a two-state HMM fit the observed movement data better than the CRW or RW models. Experiment 2 identified individual landing locations, twice a day, for groups of released Qflies, demonstrating that flies could be tracked over longer periods of time