Modeling cotton (Gossypium spp.) leaves and canopy using computer aided geometric design (CAGD)

The research presented here develops a geometrically accurate model of cotton crop canopies that can be used to explore changes in canopy microenvironment and physiological function with leaf structure. We develop an accurate representation of the leaves, including changes in three-dimensional folding and orientation with age and cultivar. Photogrammetrical analysis of leaf surfaces is used to generate measured points at known positions. Interpolation of points located on the surface of the cotton leaves is then performed with a tensor product interpolants model that generates a generic leaf shape. Dynamic changes in leaf shape and canopy position over the growing season are based on measurements of cotton canopies in the field, and are used to modulate the generic leaf shape. The simulated leaves populate a canopy element based on statistical distributions from measured crop canopies. The simulation is found to give a good representation of cotton canopy leaves, adequately capturing the three-dimensional structure of the leaves and changes in leaf shape and size over the growing season. The simulated canopy accurately estimates leaf area index, except for the earliest measurement period prior to canopy closure. The application of the CAGD algorithm for representing cotton leaf and canopy geometry, and the technique for changing the leaves’ spatial position, size and shape through time of four representative cotton canopies is found to be a useful tool for developing a realistic crop canopy. We use leaf area index (LAI) as a measure of the accuracy of model-predicted LAI values in comparison to LAI in crop canopies in situ, obtaining r² values ranging from 0.82 to 0.92. The level of detail captured in the model could contribute greatly to future studies of physiological function and biophysical dynamics within a crop canopy.     

Computer simulation applied to the biological control of the insect Aphis gossypii for the parasitoid Lysiphlebus testaceipes

In integrated pest management (IPM), biological control is one of the possible options for the prevention or remediation of an unacceptable pest activity or damage. The success of forecast models in IPM depends, among other factors, on the knowledge of temperature effect over pests and its natural enemies. In this work, we simulated the effects of parasitism of Lysiphlebus testaceipes (Cresson, 1880) (Hymenoptera: Aphidiidae) on Aphis gossypii (Glover, 1877) (Hemiptera: Aphididae), a pest that is associated to crops of great economic importance in several parts of the world. We made use of experimental data relative to the host and its parasitoid at different temperatures. Age structure was incorporated into the dynamics through the Penna model. The results obtained showed that simulation, as a forecast model, can be a useful tool for biological control programs.     

Hierarchical Bayesian Modelling of plant colonisation by winged aphids: Inferring dispersal processes by linking aerial and field count data

Understanding and modelling insect pest dispersal is an important prerequisite for designing integrated pest management programs. Nevertheless, studies investigating the dispersal of small insects in natural conditions remain scarce mainly because of the difficulty of tracking the movements of these organisms. Here we propose to use Hierarchical Bayesian Modelling (HBM) framework to gain knowledge on hidden processes that cannot be observed directly in natura, such as insect landing and insect mortality, through the definition of latent variables. An HBM describing crop colonization by winged aphids was fitted to a large dataset of field observations issued from a long term survey at a wide scale of both aerial and field densities of the bird cherry-oat aphid Rhopalosiphum padi. This study provides the first evidence that suction trap data are reliable proxies of aphid colonizing rates in cereal fields in autumn and can be a nice alternative to the very time-consuming crop sampling. The proportion of winged aphids landing in cereal fields is shown to vary between regions according to the degree of investment of local R. padi population in sexual reproduction. Results also indicate that under autumnal field conditions, less than 5% of winged aphids survive more than 10 days after landing. This HBM provides the basis of a predictive model for aphid crop colonization that fully accounts for all sources of uncertainty. It should be of great value to improve the trust of users in any decision making systems.     

Modelling coupled peach tree-aphid population dynamics and their control by winter pruning and nitrogen fertilization

Nitrogen fertilization and winter pruning are commonly used to control crop production in peach [Prunus persica (L.) Batsch] orchards. They are also known to affect the dynamics of Myzus persicae (Sulzer) (Homoptera: Aphididae) aphid populations via bottom-up regulation processes. Interactions between crops and pests can cause complex system behaviour in response to management practices. An integrated approach will therefore improve the understanding of the effects of these two cultural practices on aphid and peach performances.We developed a simulation model that describes the cultural control of interacting peach tree and aphid population dynamics. It uses the principles of common trophic models while gathering available knowledge and explicit assumptions on peach and aphid functioning and the effects of cultural practices.The model was able to qualitatively reproduce the system behaviour observed in the field. It accounted for actions and feedback such as stimulation of foliar growth by winter pruning, consecutive aphid population increase, subsequent damage to foliage, and partial compensatory growth of foliage. The model also reproduced low losses in fruit production due to aphid infestations. However, it called for further integration of ‘long-term’ effects. Analysis of the model showed the complexity of peach tree and aphid responses to leaf N × winter pruning interactions. Simulations indicated that fruit production losses remained low within a range of realistic values of leaf N and pruning intensity, whereas manipulating peach and aphid dynamics, their interactions and their relationships to practices could result in higher losses.The model is useful to evaluate the relevance of cultural practices for a bottom-up regulation of aphid dynamics in crop-pest management. After considering other control methods and fruit quality, it can be used to find a combination of practices that optimises trade-offs between fruit production and environmental conservation goals. A modelling approach that links crop growth and pest population dynamics and integrates management practice effects has strong potential for improving crop-pest management in an integrated crop production context.     

Simulation and analysis of outbreaks of bark beetle infestations and their management at the stand level

Outbreaks of bark beetles in forests can result in substantial economic losses. Understanding the factors that influence the development and spread of bark beetle outbreaks is crucial for forest management and for predicting outbreak risks, especially with the expected global warming. Although much research has been done on the ecology and phenology of bark beetles, the complex interplay between beetles, host trees, beetle antagonists and forest management makes predicting beetle population development especially difficult. Using the recent infestations of the European Spruce Bark Beetle (Ips typographus L. Col. Scol.) in the Bavarian Forest National Park (Germany) as a case study, we developed a spatially explicit agent-based simulation model (SAMBIA) that takes into account individual trees and beetles. This model primarily provides a tool for analysing and understanding the spatial and temporal aspects of bark beetles outbreaks at the stand scale. Furthermore, the model should allow an estimation of the effectiveness of concurrent impacts of both antagonists and management to confine outbreak dynamics in practice. We also used the model to predict outbreak probabilities in various settings. The simulation results indicated a distinct threshold behaviour of the system in response to pressure by antagonists or management of the bark beetle population. Despite the different scenarios considered, we were able to extract from the simulations a simple rule of thumb for the successful control of an outbreak: if roughly 80% of individual beetles are killed by antagonists or foresters, outbreaks will rarely take place. Our model allows the core dynamics of this complex system to be reduced to this inherent common denominator.     

Short run aspects of a genetic control program for Heliothis virescens (Lepidoptera: Noctuidae)

The possibility that suppressing the tobacco budworm, Heliothis virescens (F.), by means of genetic control will lead to short run crop yield sacrifices is examined. A simulation model of budworm/backcross population dynamics is used to investigate the effects of backcross releases which are too small or improperly timed. Apart from its potential for ultimate long-term suppression of the tobacco budworm, adverse short-term effects on crop yields are not likely to be a serious problem with a genetic control program. Furthermore, backcross release appears to have some potential as a substitute for chemical pesticides during the period prior to budworm elimination by genetic control.     

A population model for the peach fruit moth, Carposina sasakii Matsumura (Lepidoptera: Carposinidae), in a Korean orchard system

A population model for the peach fruit moth, Carposina sasakii Matsumura, was constructed to understand the population dynamics of this pest species and to develop an effective management strategy for various orchard (apple, peach, apple + peach) systems. The model was structured by the five developmental stages of C. sasakii: egg, larva, pupa, larval-cocoon (overwintering larva), and adult. The model consisted of a series of component models: (1) a bimodal spring adult emergence model, (2) an adult oviposition model, (3) stage emergence models of eggs, larvae, and pupae, (4) a larval survival rate model in fruits, (5) a larval-cocoon formation model, and (6) an insecticide effect model. Simulations using the model described the typical patterns of C. sasakii adult abundance in various orchard systems well, and was specific to the composition of host plants: three adult abundance peaks (first peak, mid-season peak, and last peak) a year with decreased peaks after the first peak in monoculture orchards of late apple, two adult peaks a year with a much higher last peak in monoculture orchards of early peach, and three adult peaks a year with much higher later peaks in mixed orchards of late apple and early peach. The average deviation between model outputs and actual records for first and second adult peak dates was 2.8 and 3.9 d, respectively, in simulations without an insecticide effect. The deviation decreased when insecticide effects were incorporated into the model. We also performed a sensitivity analysis of our model, and suggest possible applications of the model.     

Quantifying secondary pest outbreaks in cotton and their monetary cost with causal-inference statistics

Secondary pest outbreaks occur when the use of a pesticide to reduce densities of an unwanted target pest species triggers subsequent outbreaks of other pest species. Although secondary pest outbreaks are thought to be familiar in agriculture, their rigorous documentation is made difficult by the challenges of performing randomized experiments at suitable scales. Here, we quantify the frequency and monetary cost of secondary pest outbreaks elicited by early-season applications of broad-spectrum insecticides to control the plant bug Lygus spp. (primarily L. hesperus) in cotton grown in the San Joaquin Valley, California, USA. We do so by analyzing pest-control management practices for 969 cotton fields spanning nine years and 11 private ranches. Our analysis uses statistical methods to draw formal causal inferences from nonexperimental data that have become popular in public health and economics, but that are not yet widely known in ecology or agriculture. We find that, in fields that received an early-season broad-spectrum insecticide treatment for Lygus, 20.2% +/- 4.4% (mean +/- SE) of late-season pesticide costs were attributable to secondary pest outbreaks elicited by the early-season insecticide application for Lygus. In 2010 U.S. dollars, this equates to an additional $6.00 +/- $1.30 (mean +/- SE) per acre in management costs. To the extent that secondary pest outbreaks may be driven by eliminating pests' natural enemies, these figures place a lower bound on the monetary value of ecosystem services provided by native communities of arthropod predators and parasitoids in this agricultural system.     

Incorporating natural enemies in an economic threshold for dynamically optimal pest management

The control of pests by their natural enemies represents an important regulating ecosystem service that helps maintain the stability of crop ecosystems. These services, however, are often ignored in pest management decision making. In addition, the use of broad-spectrum insecticides can damage the populations of natural enemies, reducing the cost-effectiveness of insecticide investment if unaccounted for in treatment decisions.The existing literature on modeling of biological control of insect pests has generally focused on simulations of the population dynamics of pest and natural enemy species and the processes underlying pest control. But agriculture is a managed ecosystem where predator–prey relationships are heavily influenced by human managers. In modeling managerial choices, this study develops an intra-seasonal dynamic bioeconomic optimization model for insecticide-based pest management that explicitly takes into account both the biological control effect of natural enemies on pest density and the nontarget mortality effect of insecticides on the level of natural pest control supplied. The model captures predator–prey interactions, linking them to crop growth and yield damage functions, which in turn are evaluated in a dynamic optimization framework. We introduce a new decision rule for judicious insecticide decisions using a natural enemy-adjusted economic threshold. This threshold represents the pest population density at which insecticide control becomes optimal in spite of the opportunity cost of injury to natural enemies of the target pest. Using field data from Michigan, the model is applied to the case of soybean aphid (Aphis glycines, Matsumura), a recent invasive pest of soybean (Glycine max), whose management is of both economic and environmental importance to the North Central region of the United States. As illustrated by the numerical examples, such natural enemy-adjusted threshold is likely to lead to fewer recommendations for insecticide use than naïve models that ignore natural enemies, resulting in less insecticide use, while maintaining profitability for farmers that rely on chemical pest control methods.The bioeconomic model developed in this study can be used to conduct a wide variety of analyses such as identifying dynamically optimal spray strategies and estimating the implied economic value of natural control services. Furthermore, with the incorporation of inter-year carry-over factors, such as overwintering of pests and natural enemies, the current model can contribute to building multi-year models for studying long-term pest management.     

Modelling the spatial population dynamics of the green oak leaf roller (Tortrix viridana) using density dependent competitive interactions: Effects of herbivore mortality and varying host-plant quality

Cyclic population dynamics of forest insects with periods of more than two generations have been discussed in relation to a variety of extrinsic and intrinsic forces. In the present study, we employed the selection pressure of density dependent competitive interactions according to Witting's equations (Witting, 2000) as driver for a discrete spatiotemporal model of the green oak leaf roller (Tortrix viridana). The model was successfully parameterised to rebuild the cyclic population dynamics of an empirical data set of a 30-year leaf roller monitoring in Russia. Our analysis focussed on the role of herbivore mortality and host plant food quality, which have a significant effect on T. viridana population dynamics. An additional egg or larvae mortality lowers population density and can lead to selection pressures that favour individuals with higher growth rate. This increased population growth rate can not only compensate the additional mortality, but also can lead to higher average moth abundances in subsequent generations. Furthermore, we analysed the effect of inter- and intraspecific variation in host plant quality on herbivore population dynamics and the spatial distribution of abundance and defoliation patterns. We found significant effects of the qualitative composition of a trees neighbourhood on the herbivore population of the respective tree. Also, the patchy damage patterns observable in reality have been reproduced by the present model. The applicability of the model approach and the putative genetic processes underlying Witting's model are discussed.     

The boll weevil (Coleoptera: Curculionidae) feeding process: A simulation model

A model is presented for predicting boll weevil, Anthonomus grandis Boheman, feeding damage to cotton. The model uses appropriate probability theory based on behavioral components of male and nonreproducing female boll weevils and includes the effects of (1) differential feeding site preferences, (2) previous damage to the sites, and (3) individual insect behavior extended to feeding damage caused by a population of insects. The model is sensitive to both crop and insect parameters. An example of how this model can be used in an insect-crop ecosystem simulation is offered.     

Agent-based simulation of effects of stress on forest tent caterpillar (Malacosoma disstria Hübner) population dynamics

The forest tent caterpillar (Malacosoma disstria Hübner) (FTC) has an outbreak cycle of approximately 10 years; however, smaller spatial scale analyses show some regions have longer or more frequent periods of high defoliation. This may be a result of local forest fragmentation, pollution or other sources of stress that may affect FTC directly or indirectly through stress on their hosts or parasitoids. Population dynamics of FTC were examined to investigate how stress may alter the severity and frequency of defoliation. We developed a spatially explicit agent-based model to simulate the host-parasitoid dynamics of FTC. Theoretical and empirically derived parameters were established using past literature and over 50 years of population data of FTC from Ontario, Canada. We find that increasing FTC fecundity, FTC dispersal or parasitoid mortality resulted in more severe outbreaks while a decrease in parasitoid fecundity or searching efficiency resulted in an overall elevation of defoliation. Parasitoid efficiency was the most effective parameter for altering the FTC defoliation. Since plant stress has been shown to alter several of these parameters in nature due to changes in food quality, habitat suitability, and chemical cue interference, our results suggest that forests affected by stressors such as climate change and pollution will have more severe and frequent defoliation from these insects than surrounding unaffected forests. As stressors such as drought and pollution emissions are predicted to increase in frequency or intensity over the next few decades, understanding how they may affect the outbreak cycle of a forest defoliator can aid in planning strategies to reduce the detrimental effects of this insect.     

Effects of temperature variation on TSD in turtle (C. picta) populations

We formulate a two-sex model of temperature-dependent sex determination (TSD) for a freshwater turtle (C. picta) population. The aim is to understand how environmental temperature variations and nest heat conduction properties affect the long term dynamics of the population. This is a key to understanding how global temperature changes may affect their survival. With stochastic inputs of ambient temperature and solar radiation, the model uses the heat equation to determine the temperature in the egg layer in the nest; in turn, this determines the sex ratio in the egg clutch using a variable degree-day model. Finally, a nonlinear Leslie type, stage-based, two-sex model, is used to determine the long term male and female populations. A two-sex model is required because of different development rates for males and females. The model is flexible enough to enable other researchers to examine the effects of temperature variation variations on other species with TSD, e.g., crocodilians, reptilians, as well as other turtle species. It can be adapted to study effects of nest location, soil type, rain events, different incubation periods, and density effects, for example, the dependence of the mating function on the ratio of males to females and each’s contribution to the sex of hatchlings. Modifications can be easily made to fit a specific life history traits. The model is a beginning step in understanding the long term, high fitness shown by many reptile species with TSD, and it may suggest to experimentalists what data may be relevant to these issues; it can also be useful to wildlife managers in developing strategies for intervention if needed. Among the principal findings are that temperature variability and detailed nest heat conduction properties may buffer projected negative effects on a population.     

Dynamics and management of infectious disease in colonizing populations

The introduction of chronic, infectious diseases by colonizing populations (invasive or reintroduced) is a serious hazard in conservation biology, threatening the original host and other spillover species. Most research on spatial invasion of diseases has pertained to established host populations, either at steady state or fluctuating through time. Within a colonizing population, however, the spread of disease may be influenced by the expansion process of the population itself. Here we explore the simultaneous expansion of a colonizing population and a chronic, nonlethal disease introduced with it, describing basic patterns in homogeneous and structured landscapes and discussing implications for disease management. We describe expected outcomes of such introductions for three qualitatively distinct cases, depending on the relative velocities at which the population and epidemic expand. (1) If transmissibility is low the disease cannot be sustained, although it may first expand its range somewhat around the point of introduction. (2) If transmissibility is moderate but the wave-front velocity for the population, vp, is higher than that for the disease, vd, the disease wave front lags behind that of the population. (3) A highly transmissible disease, with vd > vp, will invade sufficiently rapidly to track the spread of the host. To test these elementary theoretical predictions, we simulated disease outbreaks in a spatially structured host population occupying a real landscape. We used a spatially explicit, individual-based model of Persian fallow deer (Dama mesopotamica) reintroduced in northern Israel, considering a hypothetical introduction of bovine tuberculosis. Basic patterns of disease expansion in this realistic setting were similar to our conceptual predictions for homogeneous landscapes. Landscape heterogeneity, however, induced the establishment of population activity centers and disease foci within them, leading to jagged wave fronts and causing local variation in the relative velocities at which the population and epidemic expanded. Based on predictions from simple theory and simulations of managed outbreaks, we suggest that the relative velocities at which the population and epidemic expand have important implications for the impact of different management strategies. Recognizing which of our three general cases best describes a particular outbreak will aid in planning an efficient strategy to contain the disease.     

Identifying key life-traits for the dynamics and gene flow in a weedy crop relative: Sensitivity analysis of the GeneSys simulation model for weed beet (Beta vulgaris ssp. vulgaris)

The benefits of genetically modified herbicide-tolerant (GMHT) sugar beet (Beta vulgaris) varieties stem from their presumed ability to improve weed control and reduce its cost, particularly targeting weed beet, a harmful annual weedy form of the genus Beta (i.e. B. vulgaris ssp. vulgaris) frequent in sugar beet fields. As weed beet is totally interfertile with sugar beet, it is thus likely to inherit the herbicide-tolerance transgene through pollen-mediated gene flow. Hence, the foreseeable advent of HT weed beet populations is a serious threat to the sustainability of GM sugar beet cropping systems. For studying and quantifying the long-term effects of cropping system components (crop succession and cultivation techniques) on weed beet population dynamics and gene flow, we developed a biophysical process-based model called GeneSys-Beet in a previous study. In the present paper, the model was employed to identify and rank the weed life-traits as function of their effect on weed beet densities and genotypes, using a global sensitivity analysis to model parameters. Monte Carlo simulations with simultaneous randomization of all life-trait parameters were carried out in three cropping systems contrasting for their risk for infestation by HT weed beets. Simulated weed plants and bolters (i.e. beet plants with flowering and seed-producing stems) were then analysed with regression models as a function of model parameters to rank processes and life-traits and quantify their effects. Key parameters were those determining the timing and success of growth, development, seed maturation and the physiological end of seed production. Timing parameters were usually more important than success parameters, showing for instance that optimal timing of weed management operations is more important than its exact efficacy. The ranking of life-traits though depended on the cropping system and, to a lesser extent, on the target variable (i.e. GM weeds vs. total weed population). For instance, post-emergence parameters were crucial in rotations with frequent sugar beet crops whereas pre-emergence parameters were most important when sugar beet was rare. In the rotations with frequent sugar beet and insufficient weed control, interactions between traits were small, indicating diverse populations with contrasted traits could prosper. Conversely, when sugar beet was rare and weed control optimal, traits had little impact individually, indicating that a small number of optimal combinations of traits would be successful. Based on the analysis of sugar beet parameters and genetic traits, advice for the future selection of sugar beet varieties was also given. In climatic conditions similar to those used here, the priority should be given to limiting the presence of hybrid seeds in seed lots rather than decreasing varietal sensitivity to vernalization.     

Insect behavioural ecology and other factors affecting the control efficacy of agro-ecosystem diversification strategies

In the last decade there is an increased interest in the design and use of diversified pest-suppressive agro-ecosystems. A diversification approach aims to manipulate the spatial dynamics of herbivores by adding a trap crop that attracts and retains herbivores in the non-crop area or by adding a disruptive crop that induces an emigration response. Empirical studies have shown that there is a wide variation in insect herbivore response to vegetation diversification. To increase the predictability and reliability of this approach it is necessary to understand the mechanisms underlying herbivore population response to diversified agro-ecosystems. We use a spatially explicit, individual-based, simulation framework, with a strong emphasis on the behavioural ecology of insects, to explore the factors that influence the population regulatory effect of agro-ecosystem diversification. The reported wide variation in population response of herbivores to diverse agro-ecosystems is replicated in this study. In our simulations we found that the population regulation effect of diversification can be positive, negative or negligible. Behavioural factors that influenced the spatial dynamics of herbivore populations were the colonisation pattern, movement speed and sensory mode of finding host plants. Simulations show that the strength of inhibition of flight by the trap crop (i.e. arrestment) is the most important parameter to manipulate the spatial dynamics of insects with post-alighting host recognition behaviour. For herbivore species that use olfactory or visual cues to find host plants, the mechanism of aggregation in the trap crop is a combination of attraction and arrestment and hence the population regulatory effect of the trap crop is higher compared to herbivores with post-alighting host recognition behaviour. An important factor that influences the efficacy of the disruptive cropping strategy is the strength of the emigration-inducing effect of the vegetation. The simulation framework is a valuable tool to test hypotheses on insect behaviour and dynamics in heterogeneous environments and can be used to determine optimal diversification systems and hence generate guidance for establishing environmentally benign pest control strategies.     

Impact of minimum winter temperatures on the population dynamics of Dendroctonus frontalis.

Predicting population dynamics is a fundamental problem in applied ecology. Temperature is a potential driver of short-term population dynamics, and temperature data are widely available, but we generally lack validated models to predict dynamics based upon temperatures. A generalized approach involves estimating the temperatures experienced by a population, characterizing the demographic consequences of physiological responses to temperature, and testing for predicted effects on abundance. We employed this approach to test whether minimum winter temperatures are a meaningful driver of pestilence from Dendroctonus frontalis (the southern pine beetle) across the southeastern United States. A distance-weighted interpolation model provided good, spatially explicit, predictions of minimum winter air temperatures (a putative driver of beetle survival). A Newtonian heat transfer model with empirical cooling constants indicated that beetles within host trees are buffered from the lowest air temperatures by approximately 1-4 degrees C (depending on tree diameter and duration of cold bout). The life stage structure of beetles in the most northerly outbreak in recent times (New Jersey) were dominated by prepupae, which were more cold tolerant (by >3 degrees C) than other life stages. Analyses of beetle abundance data from 1987 to 2005 showed that minimum winter air temperature only explained 1.5% of the variance in interannual growth rates of beetle populations, indicating that it is but a weak driver of population dynamics in the southeastern United States as a whole. However, average population growth rate matched theoretical predictions of a process-based model of winter mortality from low temperatures; apparently our knowledge of population effects from winter temperatures is satisfactory, and may help to predict dynamics of northern populations, even while adding little to population predictions in southern forests. Recent episodes of D. frontalis outbreaks in northern forests may have been allowed by a warming trend from 1960 to 2004 of 3.3 degrees C in minimum winter air temperatures in the southeastern United States. Studies that combine climatic analyses, physiological experiments, and spatially replicated time series of population abundance can improve population predictions, contribute to a synthesis of population and physiological ecology, and aid in assessing the ecological consequences of climatic trends.     

A process-based population dynamics model to explore target and non-target impacts of a biological control agent

The risks and benefits associated with efforts to control invasive alien species using classical biological control are being subjected to increasing scrutiny. A process-based population dynamics model was developed to explore the interactions between a folivorous biological control agent, Cleopus japonicus, and its plant host Buddleja davidii. The model revealed that climate could have a significant impact upon the interactions between B. davidii and C. japonicus. At the coolest sites, the impact of C. japonicus on B. davidii was slowed, but it was still eventually capable of controlling populations of B. davidii. At the warmer sites where both B. davidii and C. japonicus grew faster, B. davidii succumbed rapidly to weevil damage. We hypothesise that barring an encounter with a natural enemy, C. japonicus will eventually be able to provide sustained control B. davidii throughout the North Island of New Zealand. The model scenarios illustrate the potential for the C. japonicus population to attain high densities rapidly, and to defoliate patches of B. davidii, creating the potential for spill-over feeding on non-target plants. The potential magnitude of this threat will depend partly on the climate suitability for C. japonicus, the pattern by which it migrates in response to a reduction in the available leaf resource, and the suitability of non-target plants as hosts. In all migration scenarios considered, the pattern of population growth and resource consumption by C. japonicus was exponential, with a strong tendency toward complete utilisation of resource patches more quickly at the warmer compared to colder sites. In addition to providing some useful hypotheses about the effects of climate on the biological control system, and the non-target risks, it also provides some insight into the mechanisms by which climate affects the system.     

Spectral tuning and perceptual differences do not explain the rejection of brood parasitic eggs by American robins (Turdus migratorius)

By laying their eggs in the nests of other birds, avian brood parasites impose the cost of rearing young upon their hosts. The recognition and rejection of foreign eggs are primary host defenses against costly brood parasitism. Hosts of parasitic brown-headed cowbirds (Molothrus ater) challenge coevolutionary theory because most cowbird hosts accept parasitic eggs despite their drastically different appearance from the hosts’ own eggs. American robins (Turdus migratorius) are one of only 10 % of the over 200 potential cowbird host species to robustly reject parasitic eggs, but the mechanisms driving the sensory bases of foreign egg rejection remain elusive. Our research combined avian visual perceptual modeling and behavioral experimentation to investigate chromatic cues eliciting parasitic egg rejection in American robins. We assessed the effects of perceivable background color differences between real host and model parasite eggs, across all four avian photoreceptors, on rates of rejection of model eggs spanning in color across the entire avian spectral sensitivity range, and including immaculate model eggs matching the natural colors of robin and cowbird eggs. The results suggest that egg rejection in robins is driven by the overall perceivable difference in color between own and artificial eggs, and input from all four single-cone avian photoreceptors affects the rejection decision. The results, however, also reveal that when viewed by the avian eye, natural cowbird eggs appear more similar in background color to robin eggs than predicted by the high rejection rate of these parasitic eggs. This suggests that robins respond specifically to parasitism by cowbirds, despite an apparent lack of sensory tuning toward the detection of the background color of cowbird eggs.     

Influence of host migration between woodland and pasture on the population dynamics of the tick Ixodes ricinus: A modelling approach

Ticks act as vectors of pathogens that can be harmful to animals and/or humans. Epidemiological models can be useful tools to investigate the potential effects of control strategies on diseases such as tick-borne diseases. The modelling of tick population dynamics is a prerequisite to simulating tick-borne diseases and the corresponding spread of the pathogen. We have developed a dynamic model to simulate changes in tick density at different stages (egg, larva, nymph and adult) under the influence of temperature. We have focused on the tick Ixodes ricinus, which is widespread in Europe. The main processes governing the biological cycles of ticks were taken into account: egg laying, hatching, development, host (small, mainly rodents, or large, like deer and cattle, mammals) questing, feeding and mortality. This model was first applied to a homogeneous habitat, where simulations showed the ability of the model to reproduce the general patterns of tick population dynamics. We considered thereafter a multi-habitat model, where three different habitats (woodland, ecotone and meadow) were connected through host migration. Based on this second application, it appears that migration from woodland, via the ecotone, is necessary to sustain the presence of ticks in the meadow. Woodland can therefore be considered as a source of ticks for the meadow, which in turn can be regarded as a sink. The influence of woodland on surrounding tick densities increases in line with the area of this habitat before reaching a plateau. A sensitivity analysis to parameter values was carried out and demonstrated that demographic parameters (sex ratio, development, mortality during feeding and questing, host finding) played a crucial role in the determination of questing nymph densities. This type of modelling approach provides insight into the influence of spatial heterogeneity on tick population dynamics.