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Endemic moth species of the genus Wiseana spp. (Hepialidae) have become serious pests of introduced pasture plants in New Zealand. The original native host plants of these moths have not been confirmed. This study investigated the performance (survival, development time, weight gain) of three Wiseana species on seven putative host plants: five native and two exotic species. The aim was to identify native hosts for the three Wiseana species and to compare their performance on native plants and exotic pasture plants. The chemical composition of the seven putative host plants was investigated to compare native and exotic plant chemistries, and to test for associations between plant characteristics and performance of selected Wiseana species. Carbon, nitrogen, silica and fibre contents were measured for each plant species; primary metabolite composition was determined by gas chromatography-mass spectrometry. For the three moth species, increased survival and weight gain were significantly associated with high nitrogen and low fibre contents in one exotic host plant, white clover (Trifolium repens), although one species, W. umbraculata, did not complete development to adult on any of the plants tested, including clover. Two exotic plants (T. repens, Lolium perenne × Lolium multiflorum), and two native plants (Aciphylla squarrosa and Festuca actae) supported W. copularis development to the adult stage, but only one exotic (T. repens) and one native (F. actae) species supported complete development of W. cervinata. Exotic and native plant species had distinct metabolite profiles, but there was no significant association between metabolite composition and Wiseana performance. We conclude that W. copularis and W. cervinata, but not W. umbraculata, have expanded their host range, because of their ability to use both native and new hosts. No evidence was found for a host shift, i.e., a loss of performance on the ancestral host compared with the new host.

Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty., Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in writing of manuscript, or in the decision to publish the results.

Natural and human-induced events are continuously altering the structure of our landscapes and as a result impacting the spatial relationships between individual landscape elements and the species living in the area. Yet, only recently has the influence of the surrounding landscape on invasive species spread started to be considered. The scientific community increasingly recognizes the need for broader modeling framework that focuses on cross-study comparisons at different spatiotemporal scales. Using two illustrative examples, we introduce a general modeling framework that allows for a systematic investigation of the effect of habitat change on invasive species establishment and spread. The essential parts of the framework are (i) a mechanistic spatially explicit model (a modular dispersal framework-MDIG) that allows population dynamics and dispersal to be modeled in a geographical information system (GIS), (ii) a landscape generator that allows replicated landscape patterns with partially controllable spatial properties to be generated, and (iii) landscape metrics that depict the essential aspects of landscape with which dispersal and demographic processes interact. The modeling framework provides functionality for a wide variety of applications ranging from predictions of the spatiotemporal spread of real species and comparison of potential management strategies, to theoretical investigation of the effect of habitat change on population dynamics. Such a framework allows to quantify how small-grain landscape characteristics, such as habitat size and habitat connectivity, interact with life-history traits to determine the dynamics of invasive species spread in fragmented landscape. As such, it will give deeper insights into species traits and landscape features that lead to establishment and spread success and may be key to preventing new incursions and the development of efficient monitoring, surveillance, control or eradication programs.

Invasive species present significant threats to global agriculture, although how the magnitude and distribution of the threats vary between countries and regions remains unclear. Here, we present an analysis of almost 1,300 known invasive insect pests and pathogens, calculating the total potential cost of these species invading each of 124 countries of the world, as well as determining which countries present the greatest threat to the rest of the world given their trading partners and incumbent pool of invasive species. We find that countries vary in terms of potential threat from invasive species and also their role as potential sources, with apparently similar countries sometimes varying markedly depending on specifics of agricultural commodities and trade patterns. Overall, the biggest agricultural producers (China and the United States) could experience the greatest absolute cost from further species invasions. However, developing countries, in particular, Sub-Saharan African countries, appear most vulnerable in relative terms. Furthermore, China and the United States represent the greatest potential sources of invasive species for the rest of the world. The analysis reveals considerable scope for ongoing redistribution of known invasive pests and highlights the need for international cooperation to slow their spread.

Widespread replacement of native ecosystems by productive land sometimes results in the outbreak of a native species. In New Zealand, the introduction of exotic pastoral plants has resulted in diet alteration of the native coleopteran species, Costelytra zealandica (White) (Scarabaeidae) such that this insect has reached the status of pest. In contrast, C. brunneum (Broun), a congeneric species, has not developed such a relationship with these 'novel' host plants. This study investigated the feeding preferences and fitness performance of these two closely related scarab beetles to increase fundamental knowledge about the mechanisms responsible for the development of invasive characteristics in native insects. To this end, the feeding preference of third instar larvae of both Costelytra species was investigated using an olfactometer device, and the survival and larval growth of the invasive species C. zealandica were compared on native and exotic host plants. Costelytra zealandica, when sampled from exotic pastures, was unable to fully utilise its ancestral native host and showed higher feeding preference and performance on exotic plants. In contrast, C. zealandica sampled from native grasslands did not perform significantly better on either host and showed similar feeding preferences to C. brunneum, which exhibited no feeding preference. This study suggests the possibility of strong intraspecific variation in the ability of C. zealandica to exploit native or exotic plants, supporting the hypothesis that such ability underpins the existence of distinct host-races in this species.

Pseudo-absence selection for spatial distribution models (SDMs) is the subject of ongoing investigation. Numerous techniques continue to be developed, and reports of their effectiveness vary. Because the quality of presence and absence data is key for acceptable accuracy of correlative SDM predictions, determining an appropriate method to characterise pseudo-absences for SDM's is vital. The main methods that are currently used to generate pseudo-absence points are: 1) randomly generated pseudo-absence locations from background data; 2) pseudo-absence locations generated within a delimited geographical distance from recorded presence points; and 3) pseudo-absence locations selected in areas that are environmentally dissimilar from presence points. There is a need for a method that considers both geographical extent and environmental requirements to produce pseudo-absence points that are spatially and ecologically balanced. We use a novel three-step approach that satisfies both spatial and ecological reasons why the target species is likely to find a particular geo-location unsuitable. Step 1 comprises establishing a geographical extent around species presence points from which pseudo-absence points are selected based on analyses of environmental variable importance at different distances. This step gives an ecologically meaningful explanation to the spatial range of background data, as opposed to using an arbitrary radius. Step 2 determines locations that are environmentally dissimilar to the presence points within the distance specified in step one. Step 3 performs K-means clustering to reduce the number of potential pseudo-absences to the desired set by taking the centroids of clusters in the most environmentally dissimilar class identified in step 2. By considering spatial, ecological and environmental aspects, the three-step method identifies appropriate pseudo-absence points for correlative SDMs. We illustrate this method by predicting the New Zealand potential distribution of the Asian tiger mosquito (Aedes albopictus) and the Western corn rootworm (Diabrotica virgifera virgifera).

Pesticide resistance monitoring includes resistance detection and subsequent documentation/ measurement. Resistance detection would require at least one (≥1) resistant individual(s) to be present in a sample to initiate management strategies. Resistance documentation, on the other hand, would attempt to get an estimate of the entire population (≥90%) of the resistant individuals. A computer simulation model was used to compare the efficiency of simple random and systematic sampling plans to detect resistant individuals and to document their frequencies when the resistant individuals were randomly or patchily distributed. A patchy dispersion pattern of resistant individuals influenced the sampling efficiency of systematic sampling plans while the efficiency of random sampling was independent of such patchiness. When resistant individuals were randomly distributed, sample sizes required to detect at least one resistant individual (resistance detection) with a probability of 0.95 were 300 (1%) and 50 (10% and 20%); whereas, when resistant individuals were patchily distributed, using systematic sampling, sample sizes required for such detection were 6000 (1%), 600 (10%) and 300 (20%). Sample sizes of 900 and 400 would be required to detect ≥90% of resistant individuals (resistance documentation) with a probability of 0.95 when resistant individuals were randomly dispersed and present at a frequency of 10% and 20%, respectively; whereas, when resistant individuals were patchily distributed, using systematic sampling, a sample size of 3000 and 1500, respectively, was necessary. Small sample sizes either underestimated or overestimated the resistance frequency. A simple random sampling plan is, therefore, recommended for insecticide resistance detection and subsequent documentation.

The temperature-dependent development of tomato psyllid (also called potato psyllid), Bactericera cockerelli (Sulc), was studied in the laboratory at seven constant temperatures (8, 10, 15, 20, 23, 27, and 31°C), 50-60% RH, and a photoperiod of 16:8 (L:D) h on leaves of whole potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum L.) plants. Developmental time in days for immature stages and total development (egg to adult) on both host species were inversely proportional to temperature between 8 and 27°C but increased at 31°C. One linear and two nonlinear models were fitted to the data. The lower developmental thresholds, calculated using the linear model for egg, total nymph, and total development (from oviposition to adult emergence) were 7.9, 4.2, and 7.1°C (reared on potato) and 7.2, 5.3, and 7.5°C (reared on tomato), respectively. The thermal constant (K) for total development was 358 (reared on potato), and 368 (reared on tomato) degree-days (DD). Two nonlinear models, Briere and Lactin, fit the data well as measured by goodness-of-fit criteria, the residual sum of square (RSS) and Akaike information criterion (AIC). Temperature threshold parameters for these nonlinear models (T(0), T(opt), and T(max)) were estimated for eggs, total nymphal stages, and total development time (egg to adult). The Briere model is highly recommended for the description of temperature-dependent development of tomato psyllid. Results from this study will provide basic information on the biology of tomato psyllid and have potential for the development of predictive models of the seasonal progress of this invasive pest.

Despite the negative impact that many scarab larvae have on agro-ecosystems, very little attention has been paid to their taxonomy. Their often extremely similar morphological characteristics have probably contributed to this impediment, which has also meant that they are very difficult to identify in the field. Molecular methods can overcome this challenge and are particularly useful for the identification of larvae to enable management of pest species occurring sympatrically with nonpest species. However, the invasive collection of DNA samples for such molecular methods is not compatible with subsequent behavioural, developmental or fitness studies. Two noninvasive DNA sampling and DNA analysis methods suitable for the identification of larvae from closely related scarab species were developed here. Using the frass and larval exuviae as sources of DNA, field-collected larvae of Costelytra zealandica (White) and Costelytra brunneum (Broun) (Scarabaeidae: Melolonthinae) were identified by multiplex PCR based on the difference in size of the resulting PCR products. This study also showed that small quantities of frass can be used reliably even 7 days after excretion. This stability of the DNA is of major importance in ecological studies where timeframes rarely allow daily monitoring. The approach developed here is readily transferable to the study of any holometabolous insect species for which morphological identification of larval stages is difficult., (© 2011 Blackwell Publishing Ltd.)

Because invasive species threaten the integrity of natural ecosystems, a major goal in ecology is to develop predictive models to determine which species may become widespread and where they may invade. Indeed, considerable progress has been made in understanding the factors that influence the local pattern of spread for specific invaders and the factors that are correlated with the number of introduced species that have become established in a given region. However, few studies have examined the relative importance of multiple drivers of invasion success for widespread species at global scales. Here, we use a dataset of >5,000 presence/absence records to examine the interplay between climatic suitability, biotic resistance by native taxa, human-aided dispersal, and human modification of habitats, in shaping the distribution of one of the world's most notorious invasive species, the Argentine ant (Linepithema humile). Climatic suitability and the extent of human modification of habitats are primarily responsible for the distribution of this global invader. However, we also found some evidence for biotic resistance by native communities. Somewhat surprisingly, and despite the often cited importance of propagule pressure as a crucial driver of invasions, metrics of the magnitude of international traded commodities among countries were not related to global distribution patterns. Together, our analyses on the global-scale distribution of this invasive species provide strong evidence for the interplay of biotic and abiotic determinants of spread and also highlight the challenges of limiting the spread and subsequent impact of highly invasive species.

Predicting and ranking potential invasive species present significant challenges to researchers and biosecurity agencies. Here we analyse a worldwide database of pest species assemblages to generate lists of the top 100 insect pests most likely to establish in the United States and each of its 48 contiguous states. For the United States as a whole, all of the top 100 pest species have already established. Individual states however tend to have many more 'gaps' with most states having at least 20 species absent from their top 100 list. For all but one state, every exotic pest species currently absent from a state's top 100 can be found elsewhere in the contiguous United States. We conclude that the immediate threat from known invasive insect pests is greater from within the United States than without. Our findings have potentially significant implications for biosecurity policy, emphasizing the need to consider biosecurity measures beyond established national border interventions.

The characteristics of spread for an invasive species should influence how environmental authorities or government agencies respond to an initial incursion. High-resolution predictions of how, where, and the speed at which a newly established invasive population will spread across the surrounding heterogeneous landscape can greatly assist appropriate and timely risk assessments and control decisions. The Argentine ant (Linepithema humile) is a worldwide invasive species that was inadvertently introduced to New Zealand in 1990. In this study, a spatially explicit stochastic simulation model of species dispersal, integrated with a geographic information system, was used to recreate the historical spread of L. humile in New Zealand. High-resolution probabilistic maps simulating local and human-assisted spread across large geographic regions were used to predict dispersal rates and pinpoint at-risk areas. The spatially explicit simulation model was compared with a uniform radial spread model with respect to predicting the observed spread of the Argentine ant. The uniform spread model was more effective predicting the observed populations early in the invasion process, but the simulation model was more successful later in the simulation. Comparison between the models highlighted that different search strategies may be needed at different stages in an invasion to optimize detection and indicates the influence that landscape suitability can have on the long-term spread of an invasive species. The modeling and predictive mapping methodology used can improve efforts to predict and evaluate species spread, not only in invasion biology, but also in conservation biology, diversity studies, and climate change studies.

Eriophyoid big bud mites are key pests of hazelnut throughout the world, but they are difficult to control with chemicals or other methods because they are protected inside the bud. The most effective time for control is during the relatively short emergence period which is difficult for growers to predict. The key objectives of this study were to monitor mite emergence from big buds in spring, determine the phenology of mites in relation to tree phenology and weather, and identify the optimum timing for control measures. Mite emergence was found to occur between early and late spring in Canterbury, New Zealand. Mite emergence and movement occurred when daily maximum temperatures were >15 degrees C and when mean temperatures were >9 degrees C, with mite emergence increasing with temperature. The developmental status of new buds during mite emergence was a crucial factor in the infestation of new buds. An accumulated heat sum model (DD), starting at Julian date 152 and using a lower threshold temperature of 6 degrees C, predicted the onset of emergence on two cultivars and at two sites at approximately 172 DD. A regression model based on leaf number, bud length, bud width, DD and Julian date provided a more satisfactory prediction of percent accumulated mite emergence. It is recommended both peak mite emergence and the developmental status of hazelnut buds be used to optimise the time to apply control measures. The optimum time to apply a control was predicted to be before buds measure 0.5 x 0.5 mm (width x length), are enclosed within the axil, and have a rounded tip, or, when 50% accumulated mite emergence has occurred, whichever occurs first.

The aim of this work was to predict the worldwide distribution of two pest species-Ceratitis capitata (Wiedemann), the Mediterranean fruit fly, and Lymantria dispar (L.), the gypsy moth-based on climatic factors. The distribution patterns of insect pests have most often been investigated using classical statistical models or ecoclimatic assessment models such as CLIMEX. In this study, we used an artificial neural network, the multilayer perceptron, trained using the backpropagation algorithm, to model the distribution of each species. The data matrix used to model the distribution of each species was divided into three data sets to (1) develop and train the model, (2) validate the model and prevent over-fitting, and (3) test each model on novel data. The percentage of correct predictions of the global distribution of each species was high for Mediterranean fruit fly for the three data sets giving 95.8, 81.5, and 80.6% correct predictions, respectively, and 96.8, 84.3, and 81.5 for the gypsy moth. Kappa statistics used to test the level of significance of the results were highly significant (in all cases P < 0.0001). A sensitivity analysis applied to each model based on the calculation of the derivatives of each of a large number of input variables showed that the variables that contributed most to explaining the distribution of C. capitata were annual average temperature and annual potential evapotranspiration. For L. dispar, the average minimum temperature and minimum daylength range were the main explanatory variables. The ANN models and methods developed in this study offer powerful additional predictive approaches in invasive species research.

The effect of residues of esfenvalerate on oviposition of the resistant strain of the predatory mite Typhlodromus pyri and its main prey, European red mite Panonychus ulmi and two-spotted spider mite Tetranychus urticae, were investigated. T. pyri showed a significant linear reduction in oviposition after 24 h in the presence of increasing levels of esfenvalerate residue applied at the field rate. Furthermore, when given a choice, T. pyri preferred to lay eggs on residue-free surfaces. Of the two prey species, only P. ulmi showed significant avoidance of increasing levels of residues of the field rate concentration of esfenvalerate, as measured by runoff mortality, however both P. ulmi and T urticae, when given a choice, showed a preference for esfenvalerate-free surfaces. As with the predatory mite T. pyri, both prey species showed a significant linear reduction of oviposition with increasing esfenvalerate residues and a preference to lay eggs on esfenvalerate-free surfaces. Esfenvalerate residues as high as 15X field rate were not repellent to pyrethroid-resistant T. pyri. The possible effects of these sublethal effects on predator-prey dynamics and implications for integrated mite control programmes in apple orchards are discussed.

Ethyl nicotinate was found to be a potent attractant forThrips obscuratus in peaches and apricots. The male-to-female ratio in traps was 1∶8. The chemical was more attractive than ripe fruit, with peak catches at harvest. Season-long trapping showed the efficiency of the chemical compared to unbaited traps. The chemical remained attractive for at least 2 weeks when 50 μl were placed in open 2-ml vial caps. Various pyralids, geometrids (Lepidoptera), and chironomids (Diptera) were also attracted in low numbers to ethyl nicotinate.