A spatially explicit multi-competitor coexistence model of penaeid (shrimp) distribution on the Australian Great Barrier Reef

A spatially explicit multi-competitor coexistence model was developed for meta-populations of prawns (shrimp) occupying habitat patches across the Great Barrier Reef, where dispersal was localised and dispersal rates varied between species. Prawns were modelled as individuals moving to and from patches or cells according to pre-set decision rules. The landscape was simulated as a matrix of cells with each cell having a spatially explicit survival index for each species. Mixed species prawn assemblages moved over this simplified spatially explicit landscape. A low level of chronic random environmental disturbance was assumed (cyclone and tropical storm damage) with additional acute spatially confined disturbance due to commercial trawling, modelled as an increase in mortality affecting inter-specific competition. The general form of the results was for increased disturbance to favour good-colonising "generalist" species at the expense of good-competitor "specialists". Increasing fishing mortality (local patch extinctions) combined with poor colonising ability resulted in low equilibrium abundance for even the best competitor, while in the same circumstances the poorest competitor but best coloniser could have the highest equilibrium abundance. This mimics the switch from high-value prawn species to lower-value prawn species as trawl effort increases, reflected in historic catch and effort logbook data and reported anecdotaly from the north Queensland trawl fleet. To match the observed distribution and behaviour of prawn assemblages, a combination inter-species competition, a spatially explicit landscape, and a defined pattern of disturbance (trawling) was required. Modelling this combination could simulate not only general trends in spatial distribution of each of prawn species but also localised concentrations observed in the survey data

Predicting wood value in Queensland Caribbean pine plantations using a decision support system

A decision support system has been developed in Queensland to evaluate how changes in silvicultural regimes affect wood quality, and specifically the graded recovery of structural timber. Models of tree growth, branch architecture and wood properties were developed from data collected in routine Caribbean pine plantations and specific silvicultural experiments. These models were incorporated in software that simulates the conversion of standing trees into logs, and the logs into boards, and generates detailed data on knot location and basic density distribution. The structural grade of each board was determined by simulating the machine stress-grading process, and the predicted graded recovery provided an indicator of wood value. The decision support system improves the basis of decision-making by simulating the performance of elite genetic material under specified silvicultural regimes and by predicting links between wood quality and general stand attributes such as stocking and length of rotation.

Genetic erosion and changes in distribution of sorghum (Sorghum bicolor L. (Moench)) landraces in north-eastern Ethiopia

Ethiopia is believed to be the centre of origin and domestication for sorghum, where sorghum remains one of the main staple crops. Loss of biodiversity is occurring at an alarming rate in Ethiopia and crops, including sorghum, have long been recognized as vulnerable to genetic erosion. A major collection of sorghum germplasm was made in 1973 by Gebrekidan and Ejeta from north-eastern Ethiopia. A new collection of landraces was made in 2003, and these were field evaluated at Sirinka in 2004 along with representative samples from the 1973 collection. Farmer surveys and soil and climate surveys were also performed. Preliminary analysis demonstrated that some important landraces have disappeared either locally or regionally in the past 30 years and many other landraces have become marginalized. Landraces which are less preferred in terms of agronomic value and end use, and introductions, have become increasingly important. Late maturing landraces were found to be particularly vulnerable, with a number disappearing altogether. Farmers have become more risk averse, and factors such as declining soil fertility, more frequent drought and unreliable rainfall, and increased pest infestation have contributed to a change in farmer landrace selection. Data are presented on the variability and unique characters of some of the Ethiopian landraces, and implications for conservation are discussed.

A method for mapping the distribution and density of rabbits and other vertebrate pests in Australia

The European wild rabbit has been considered Australia’s worst vertebrate pest and yet little effort appears to have gone into producing maps of rabbit distribution and density. Mapping the distribution and density of pests is an important step in effective management. A map is essential for estimating the extent of damage caused and for efficiently planning and monitoring the success of pest control operations. This paper describes the use of soil type and point data to prepare a map showing the distribution and density of rabbits in Australia. The potential for the method to be used for mapping other vertebrate pests is explored. The approach used to prepare the map is based on that used for rabbits in Queensland (Berman et al. 1998). An index of rabbit density was determined using the number of Spanish rabbit fleas released per square kilometre for each Soil Map Unit (Atlas of Australian Soils). Spanish rabbit fleas were released into active rabbit warrens at 1606 sites in the early 1990s as an additional vector for myxoma virus and the locations of the releases were recorded using a Global Positioning System (GPS). Releases were predominantly in arid areas but some fleas were released in south east Queensland and the New England Tablelands of New South Wales. The map produced appears to reflect well the distribution and density of rabbits, at least in the areas where Spanish fleas were released. Rabbit pellet counts conducted in 2007 at 54 sites across an area of south east South Australia, south eastern Queensland, and parts of New South Wales (New England Tablelands and south west) in soil Map Units where Spanish fleas were released, provided a preliminary means to ground truth the map. There was a good relationship between mean pellet count score and the index of abundance for soil Map Units. Rabbit pellet counts may allow extension of the map into other parts of Australia where there were no Spanish rabbit fleas released and where there may be no other consistent information on rabbit location and density. The recent Equine Influenza outbreak provided a further test of the value of this mapping method. The distribution and density of domestic horses were mapped to provide estimates of the number of horses in various regions. These estimates were close to the actual numbers of horses subsequently determined from vaccination records and registrations. The soil Map Units are not simply soil types they contain information on landuse and vegetation and the soil classification is relatively localised. These properties make this mapping method useful, not only for rabbits, but also for other species that are not so dependent on soil type for survival.

Thermal tolerance and potential distribution of Carvalhotingis visenda (Hemiptera: Tingidae), a biological control agent for cat's claw creeper, Macfadyena unguis-cati (Bignoniaceae)

The specialist tingid, Carvalhotingis visenda, is a biological control agent for cat's claw creeper, Macfadyena unguis-cati (Bignoniaceae). Cat's claw creeper is an invasive liana with a wide climatic tolerance, and for biological control to be effective the tingid must survive and develop over a range of temperatures. We evaluated the effect of constant temperatures (0-45°C) on the survival and development of C. visenda. Adults showed tolerance for wider temperature ranges (0-45°C), but oviposition, egg hatching and nymphal development were all affected by both high (>30°C) and low (<20°C) temperatures. Temperatures between 20°C and 30°C are the most favourable for adult survival, oviposition, egg hatching and nymphal development. The ability of adults and nymphs to survive for a few days at high (40°C and 45°C) and low (0°C and 5°C) temperatures suggest that extreme temperature events, which usually occur for short durations (hours) in cat's claw creeper infested regions in Queensland and New South Wales states are not likely to affect the tingid population. The potential number of generations (egg to adult) the tingid can complete in a year in Australia ranged from three to eight, with more generations in Queensland than in New South Wales.

Determining changes in the distribution and abundance of a Rhyzopertha dominica phosphine resistance allele in farm grain storages using a DNA marker

BACKGROUND: The lesser grain borer, Rhyzopertha dominica (F.), is a highly destructive pest of stored grain that is strongly resistant to the fumigant phosphine (PH3). Phosphine resistance is due to genetic variants at the rph2 locus that alter the function of the dihydrolipoamide dehydrogenase (DLD) gene. This discovery now enables direct detection of resistance variants at the rph2 locus in field populations. RESULTS: A genotype assay was developed for direct detection of changes in distribution and frequency of a phosphine resistance allele in field populations of R. dominica. Beetles were collected from ten farms in south-east Queensland in 2006 and resampled in 2011. Resistance allele frequency increased in the period from 2006 to 2011 on organic farms with no history of phosphine use, implying that migration of phosphine-resistant R. dominica had occurred from nearby storages. CONCLUSION: Increasing resistance allele frequencies on organic farms suggest local movement of beetles and dispersal of insects from areas where phosphine has been used. This research also highlighted for the first time the utility of a genetic DNA marker in accurate and rapid determination of the distribution of phosphine-resistant insects in the grain value chain. Extending this research over larger landscapes would help in identifying resistance problems and enable timely pest management decisions. © 2013 Society of Chemical Industry © 2013 Society of Chemical Industry 69 6 June 2013 10.1002/ps.3514 Rapid Report Rapid Report © 2013 Society of Chemical Industry.

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.