The role of abiotic and biotic factors in population dynamics of lepidopteran miners

Field studies were carried out during the April-November period of 2018-2019 in order to determine the phytophagous and predatory insect species living on ornamental trees and shrubs in Burdur city center parks. 15 parks located in the city center were chosen as the study area. The parks were visited three-four times throughout the year and specimens of insects and host plants were collected and brought to the laboratory for preparation and diagnosis. A total of 34 plant taxa were examined throughout the study. As a result, totally 55 phytophagous insect species were identified from three orders and 19 families [Pseudococcidae 1, Diaspididae 4, Marchalinidae 1, Tingidae 3, Psyllidae 2, Cicadellidae 3, Miridae 7, Lygaeidae 6, Coreidae 2, Rhapolidae 2, Pentatomidae 4, Cixidae 1 (Hemiptera), Apionidae 3, Chrysomelidae 4, Curculionidae 8, Malachiidae 1, Elateridae 1 (Coleoptera), Tortricidae 1, and Notodontidae 1 (Lepidoptera)]. Among them, the most common and abundant species was Marchalina hellenica (Gennadius, 1883) from the Marchalinidae family. We report Anthocomus equestris (Fabricius, 1781) from the Malachiidae family as a new record from Turkey. Totally 18 predator species were identified from seven families [Nabidae 1, Miridae 1, Anthocoridae 2, Coccinellidae 10, Cantharidae 2, Srypidae 1, and Forficulidae 1]. We identified 83 interactions between phytophagous insects and host plants, and 26 interactions among phytophagous insects, host plants, and predator species.

Comparing ensemble and cascaded neural networks that combine biotic and abiotic variables to predict insect species distribution

Test the relative importance of biotic and abiotic factors on species distribution – A case study in the Yellow River Delta

In phytophagous insects, oviposition strategy is determined by complex trade‐offs among many factors. The winter cherry bug, Acanthocoris sordidus Thunberg (Heteroptera: Coreidae), deposits its eggs not only on host plants, but also on non‐host plants. Oviposition on non‐host plants may cause higher nymph mortality, as nymphs may be unable to find their host plants during the second instar when the first feeding occurs. Therefore, we hypothesised that this uncommon oviposition strategy would be maintained by a trade‐off between egg and nymphal mortality risks. To test this hypothesis, we conducted field surveys to compare egg mortality due to biotic (predation and parasitism) and abiotic factors on host plants vs. non‐host plants. We also conducted a semi‐field experiment to quantify the risk of nymphal mortality caused by oviposition on non‐hosts during the second instar. In terms of mortality risk during the egg stage, a 2‐year field survey revealed that oviposition on non‐host plants resulted in higher survival of eggs than on host plants; this strategy was also particularly effective for avoiding attacks by ground‐living arthropod predators. In terms of mortality risk during the nymphal stage, the semi‐field experiment demonstrated that nymphs placed off host plants do risk being unable to reach host plants. However, the presence of conspecific adults on host plants increased the probability that nymphs reached host plants. Therefore, second instars may have evolved the ability to utilise chemical cues emitted by adults on host plants or by host plants fed by adults to reduce their risk of not reaching host plants. We concluded that oviposition on non‐host plants by A. sordidus females could be maintained by the balance between the benefit of lower egg mortality and the cost of higher nymph mortality compared to oviposition on host plants.

Biotic and abiotic factors are increasingly acknowledged to synergistically shape broad-scale species distributions. However, the relative importance of biotic and abiotic factors in predicting species distributions is unclear. In particular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic land-use change, are underrepresented in species distribution modeling, but could improve model predictions. Using generalized linear models and model selection techniques, we used 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents to evaluate the relative importance, magnitude, and direction of biotic and abiotic factors in predicting population density of an invasive large mammal with a global distribution. Incorporating diverse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into species distribution modeling substantially improved model fit and predictions. Abiotic factors, including precipitation and potential evapotranspiration, were also important predictors. The predictive map of population density revealed wide-ranging potential for an invasive large mammal to expand its distribution globally. This information can be used to proactively create conservation/management plans to control future invasions. Our study demonstrates that the ongoing paradigm shift, which recognizes that both biotic and abiotic factors shape species distributions across broad scales, can be advanced by incorporating diverse biotic factors.

BackgroundWhile the communities constituted by phytophageous insects and their parasites may represent half of all terrestrial animal species, understanding their diversification remains a major challenge. A neglected idea is that geographic phenotypic variation in a host plant may lead to heterogeneous evolutionary responses of the different members of the associated communities. This could result in diversification on a host plant by ecological speciation in some species, leading to geographic variation in community composition. In this study we investigated geographic variation of inflorescence receptacle size in a plant, Ficus hirta, and how the hymenopteran community feeding in the inflorescences has responded. Our predictions were:Inflorescence size variation affects wasp species differently depending on how they access oviposition sites.In some affected lineages of wasps, we may observe vicariant, parapatric species adapted to different inflorescence sizes.ResultsWe show that fig (the enclosed inflorescence of Ficus) wall thickness varies geographically. The fig-entering pollinating wasp was not affected, while the parasites ovipositing through the fig wall were. Two parapatric species of Philotrypesis, exhibiting strikingly different ovipositor lengths, were recorded. One species of Sycoscapter was also present, and it was restricted, like the shorter-ovipositor Philotrypesis, to the geographic zone where fig walls were thinner.ConclusionsPrevious work on fig wasps suggested that parapatric geographic ranges among congenerics were due to adaptation to variation in abiotic factors, complemented by interspecific competition. Our results show that parapatric ranges may also result from adaptation to variation in biotic factors. Within an insect community, differences among species in their response to geographic phenotypic variation of their host plant may result in geographically heterogeneous community structure. Such heterogeneity leads to heterogeneous interaction networks among sites. Our results support the hypothesis that plant geographic phenotypic variation can be a driver of diversification in associated insect communities, and can complement other diversification processes.

Research Article| May 01 2009 Building Ecological Complexity in the Classroom Using Pea Aphids & Components of Their Community Matthew L. Richardson, Matthew L. Richardson Search for other works by this author on: This Site PubMed Google Scholar Janice Hari Janice Hari Search for other works by this author on: This Site PubMed Google Scholar The American Biology Teacher (2009) 71 (5): 286–290. https://doi.org/10.2307/27669432 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Matthew L. Richardson, Janice Hari; Building Ecological Complexity in the Classroom Using Pea Aphids & Components of Their Community. The American Biology Teacher 1 May 2009; 71 (5): 286–290. doi: https://doi.org/10.2307/27669432 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentThe American Biology Teacher Search This content is only available via PDF. Copyright National Association of Biology Teachers Article PDF first page preview Close Modal You do not currently have access to this content.

Objective. To form a special collection of lines with marker genes of watermelon for use in hybrid breeding (heterosis) and for providing initial material to scientific and educational institutions. Object. 37 lines with marker genes of common watermelon (Citrullus lanatus (Thunb.) Matsum. et Nakai). Methods. General scientific, measurement and weight analysis, computational, statistical methods. Results. A screening of watermelon lines with marker genes (Citrullus lanatus (Thunb.) Matsum. et Nakai) was conducted based on phenological, morphological, economically valuable traits, and resistance to abiotic and biotic factors. Work was carried out to form a special watermelon collection, which includes 37 lines with marker genes, all developed at the Institute of Vegetable and Melon Growing of NAAS. The collection was created based on biological and morphological traits, resistance to biotic and abiotic factors, and the chemical composition of fruits – using 42 characteristics and 186 levels of expression, with standard reference samples identified for each trait. A working database of the collection was developed, containing information on 20 biological, morphological, economically valuable, and chemical traits, resistance to biotic and abiotic factors, and the presence of marker genes in each line of the collection. The samples in the collection are intended for use in breeding heterotic hybrids of watermelon as parental lines containing marker genes that determine specific signal traits. As a result of the analysis based on a complex of economically valuable traits, a number of lines were identified that can be recommended as parental components for heterosis breeding, serving as sources of individual or combined valuable traits. Most samples combine high yield, monoecy, earliness, a high content of biochemical compounds, adaptability to extreme climatic conditions, and resistance to diseases. Based on the evaluation of economically valuable traits, the most productive samples (approximately 35 t/ha) were identified for use as parental forms to increase yield in the development of competitive heterotic hybrids. Lines that serve as sources of earliness (vegetation period <70 days) were also identified. According to the assessment of resistance to biotic and abiotic factors, lines with significant value for use as parental forms were selected to enhance disease resistance, heat tolerance, and cold tolerance in the creation of competitive heterotic watermelon hybrids. Conclusions. A special collection of watermelon lines with marker traits has been formed. The collection comprises 37 samples with marker genes. It was developed based on biological and morphological traits, resistance to biotic and abiotic factors, and the chemical composition of fruits – covering 42 traits with 186 levels of expression. Reference samples have been identified for each trait. A working database of the collection was created, containing information on 20 biological, morphological, economically valuable, and chemical traits, as well as resistance to biotic and abiotic stress factors and the presence of marker genes in each line. The collection is intended for use as parental forms to enhance yield, earliness, disease resistance, heat tolerance, and cold tolerance in the development of competitive heterotic watermelon hybrids.

Population of arbuscular mycorrhizal fungi in semi-arid environment of Jordan as influenced by biotic and abiotic factors

Fungi are both agents of disease and mutualistic partners of plants. Previous studies have tested the effects of abiotic or biotic factors on plant‐associated fungal communities in isolation. However, to better understand patterns of plant–fungal associations, the combined effects of abiotic and biotic drivers across environmental gradients may be important. We investigated the effects of temperature, pH, soil moisture, vegetation cover and distance to host plant on the occurrence and abundance of fungi associated with Swiss stone pine (Pinus cembra). We did this by DNA metabarcoding 288 soil samples taken across and beyond the elevation range of P. cembra (i.e. 1,850–2,250 m a.s.l.) in two valleys in the Swiss Alps. We modelled the effects of abiotic and biotic factors on DNA read abundance of pathogenic and mutualistic fungal operational taxonomic units (OTUs) associated with P. cembra. We also tested whether abiotic and biotic factors differentially affected fungi of varying host specificity (i.e. host generalists, host specialists). We found that the occurrences of both host generalist and specialist fungi exceeded the current elevational range of their host plant. Abiotic factors had only minor effects on the abundances of all fungal OTUs. However, we found positive effects of the host plant on the abundance of a host specialist pathogenic fungus, providing support for a Janzen–Connell effect of high pathogen accumulation close to conspecific host plants. We also found a positive response to the host plant in a specialist ectomycorrhizal fungus, suggesting an “inverse” Janzen–Connell effect. Synthesis. Our findings imply that negative distance dependence shapes not only the distribution of host‐specific fungal pathogens, but also host‐specific fungal mutualists. We conclude that the occurrence of both pathogenic and mutualistic fungi beyond the current elevational range of host plants may determine their potential range shifts under projected climate warming.

A central theme of range‐limit theory (RLT) posits that abiotic factors form high‐latitude/altitude limits, whereas biotic interactions create lower limits. This hypothesis, often credited to Charles Darwin, is a pattern widely assumed to occur in nature. However, abiotic factors can impose constraints on both limits and there is scant evidence to support the latter prediction. Deviations from these predictions may arise from correlations between abiotic factors and biotic interactions, as a lack of data to evaluate the hypothesis, or be an artifact of scale. Combining two tenets of ecology—niche theory and predator–prey theory—provides an opportunity to understand how biotic interactions influence range limits and how this varies by trophic level.We propose an expansion of RLT, interactive RLT (iRLT), to understand the causes of range limits and predict range shifts. Incorporating the main predictions of Darwin's hypothesis, iRLT hypothesizes that abiotic and biotic factors can interact to impact both limits of a species’ range. We summarize current thinking on range limits and perform an integrative review to evaluate support for iRLT and trophic differences along range margins, surveying the mammal community along the boreal‐temperate and forest‐tundra ecotones of North America.Our review suggests that range‐limit dynamics are more nuanced and interactive than classically predicted by RLT. Many (57 of 70) studies indicate that biotic factors can ameliorate harsh climatic conditions along high‐latitude/altitude limits. Conversely, abiotic factors can also mediate biotic interactions along low‐latitude/altitude limits (44 of 68 studies). Both scenarios facilitate range expansion, contraction or stability depending on the strength and the direction of the abiotic or biotic factors. As predicted, biotic interactions most often occurred along lower limits, yet there were trophic differences. Carnivores were only limited by competitive interactions (n = 25), whereas herbivores were more influenced by predation and parasitism (77%; 55 of 71 studies). We highlight how these differences may create divergent range patterns along lower limits.We conclude by (a) summarizing iRLT; (b) contrasting how our model system and others fit this hypothesis and (c) suggesting future directions for evaluating iRLT.

The interest in understanding ecosystem functioning has grown in recent years due to the effects of species loss on ecosystem processes. Even though biotic and abiotic factors control ecosystem processes, their relative influence may vary according to ecosystem dynamics. In flood and coastal plains, these dynamics are mainly represented by flood pulses and hydroregime, respectively. The objective of this study was to investigate the importance of abiotic and biotic factors for the ecosystem processes represented by zooplankton secondary production (SP), biomass (ZB), and resource use efficiency (RUE) in lentic waterbodies subjected to different hydrological regimes. We hypothesised that abiotic factors would more strongly determine the ecosystem processes in temporary waterbodies and floodplain lakes, given their greater susceptibility to environmental changes. Biotic factors would be more relevant in coastal lagoons due to their greater temporal stability. Sampling was undertaken quarterly over 1 year in eight coastal lagoons, 10 temporary ponds and five floodplain lakes. The environments were characterised in relation to limnological variables, and zooplankton functional divergence, functional dispersion (FDis), functional evenness, functional richness, and taxonomic richness were measured. Analysis of variance (ANOVA) was used to verify seasonal changes in SP, ZB, RUE, functional diversity, richness, and abiotic factors. Linear mixed models were used to determine which abiotic and biotic factors were the most important for ZB, SP, and RUE. In the coastal lagoons, RUE differed over time. In the temporary ponds and floodplain lakes, no seasonal significant differences were observed for any of the zooplankton production variables. The linear mixed model analyses showed that models composed mainly of biotic factors were better fitted to the production variables. For coastal lagoons, phytoplankton density affected ZB, SP, and RUE increasing them by 9.9 mg DW/m3, 12.4 mg DW/m3, and 1.23, respectively. For temporary ponds, FDis lowered ZB by 6.9 mg DW/m3 and taxonomic richness increased SP and RUE by 14.2 mg DW/m3 and 1.17, respectively. For floodplain lakes, FDis lowered ZB it by 9.9 mg DW/m3 and functional divergence lowered RUE by 0.81. The present study demonstrates that biotic factors are the main determinants of ecosystem processes in neotropical lentic waterbodies, irrespective of their annual hydrological regimes. Complementarity effects and high functional diversity are more important in more stable environments, whereas redundancy and low functional diversity prevail in environments subject to more frequent environmental changes. Biotic factors play a major role in ensuring the functioning of aquatic ecosystems and indicate the important role of biodiversity in enabling ecosystem states to be maintained after disturbances and to prevent changes in ecosystem processes.

This study explores the relationships of AM fungal abundance and diversity with biotic (host plant, ungulate grazing) and abiotic (soil properties, precipitation) factors in the Serengeti National Park, Tanzania. Soil and root samples were collected from grazed and ungrazed plots at seven sites across steep soil fertility and precipitation gradients. AM fungal abundance in the soil was estimated from the density of spores and the concentration of a fatty acid biomarker. Diversity of AM fungi in roots and soils was measured using DNA sequencing and spore identification. AM fungal abundance in soil decreased with grazing and precipitation and increased with soil phosphorus. The community composition of AM fungal DNA in roots and soils differed. Root samples had more AM fungal indicator species associated with biotic factors (host plant species and grazing), and soil samples had more indicator species associated with particular sample sites. These findings suggest that regional edaphic conditions shape the site-level species pool from which plant species actively select root-colonizing fungal assemblages modified by grazing. Combining multiple measurements of AM fungal abundance and community composition provides the most informed assessment of the structure of mycorrhizal fungal communities in natural ecosystems.

What are infochemicals?Chemical compounds play an important role in interactions between organisms. Some of these chemicals are to the benefit (e.g. nutrients) or detriment (e.g. toxins) of an organism. Others are of benefit or detriment in an indirect way: through the behavioural response they elicit. The latter chemicals are termed infochemicals (chemicals that, in the natural context, convey information in an interaction between two individuals, evoking in the receiver a behavioural or physiological response that is adaptive to either one of the interactants or both; chapter 2). On an evolutionary time scale, the fate of an infochemical depends on selection pressures on each interactant. Selection pressure is determined by costs and benefits which result from all interactions of an organism in which the infochemical is involved. Yet, for pragmatic reasons, to analyse the function of an infochemical in the biology of an organism, a cost-benefit analysis is made for each interaction between two organisms separately. In this way the cost-benefit analysis is restricted to the smallest number of interactants possible, which ensures its simplicity. Consequently, for each interaction the infochemical is classified according to the corresponding costs and benefits for the two interactants (chapter 2; cf. Nordlund and Lewis, 1976). Moreover, classification also reflects whether the interaction under consideration is between conspecifics or between individuals of different species. This resulted in the terminology represented in Figure 1.1 and Table 1.1 (cf. chapter 2). Its structure and terms are based on those of semiochemicals. However, infochemical terminology differs from semiochemical terminology in two respects (chapter 2):(1) Infochemical terminology regards compounds that convey information, whereas semiochemical terminology in addition also includes toxins (Whittaker and Feeny, 1971; Nordlund and Lewis, 1976; Nordlund, 1981). In some instances toxins or nutrients may convey information. If that is the case, these toxins and nutrients are classified as infochemicals when their role as information carrier is considered. When poisonous or nutritious aspects are considered, they are not classified as infochemicals, but as toxins and nutrients respectively.(2) Semiochemical terminology is based on origin of the compounds, in addition to the cost-benefit analysis. Although knowledge of the origin is Important to understand the interaction between two organisms, it may be very difficult to elucidate the origin (e.g. Brand et al., 1975; chapter 4). Therefore, application of the origin criterion may lead to ambiguities. Because the cost- benefit criterion by itself is good and useful, infochemical terminology is based on that criterion alone.Infochemicals in tritrophic systems.Infochemicals play a role in interactions between consecutive trophic levels (e.g plant-herbivore, phytophagous insect- entomophagous insect; Figure 1.2) (e.g. Nordlund et al., 1981; Visser, 1986). Moreover, infochemicals may also mediate interactions between other trophic levels (e.g. plant-entomophagous insect; Figure 1.2) (Price, 1981). Therefore, to understand the selection pressure on an organism, as a result of an infochemical, all trophic levels involved should be regarded. As a consequence, investigations of infochemicals in interactions between herbivores and their predators should also regard involvement of at least the first trophic level, the plant.The tritrophic system of this study: predatory mites, phytophagous mites and their host plants.The herbivore-predator system investigated most extensively in this thesis consists of phytophagous mites and predatory mites that occur in Dutch orchards. Figure 1.3a,b depicts the two most abundant phytophagous mites that occur as pest organisms in Dutch apple orchards: the apple rust mite, Aculusschlechtendali (Nalepa), and the European red spider mite, Panonychusulmi (Koch) (Van de Vrie, 1973; Van Epenhuijsen, 1981; Gruys, 1982).Several species of predatory mites occur in Dutch orchards. The most abundant of these are Typhlodromuspyri Scheuten (Figure 1.3c), Amblyseiusfinlandicus (Oudemans) and A.potentillae (Garman) (McMurtry & Van de Vrie, 1973; Overmeer, 1981; Gruys, 1982). All three species feed on P.ulmi and A.schlechtendali , as well as on other food sources such as several pollens (Overmeer, 1981; Kropczynska, 1970; Overmeer, 1985).In this system consisting of two phytophagous prey species and three predator species (Figure 1.4a), prey preference of the predators was investigated. Optimal foraging theory predicts that natural selection favours predators preferring prey species that are most profitable in terms of reproductive success (Krebs, 1978). Reproductive success is determined, among others, by development time, oviposition rate, mortality during development and offspring quality. Each of these components can be affected by the prey species consumed. As a first step in analysing which selection pressures may have moulded prey preference of the predatory mites in the system outlined above, I have tested whether prey preference is matched by the associated reproductive success. If this most simple explanation for prey preference does not hold, other explanations should be considered (see below).Do infochemicals play a role in prey preference ?Kairomones (Table 1.1, Figure 1.1) may inform predators on presence and identity of prey (Greany and Hagen, 1981) and thereby affect foraging decisions, such as where to search, how long to search at a specific site, which prey to accept and when to disperse on air currents (chapter 3).Investigation of the response to kairomones may therefore yield information on prey preference. However, the conclusion on prey preference must be restricted to the foraging phase that was studied. Relative costs involved in finding individuals of each prey species might differ for different foraging phases. Therefore, to obtain a comprehensive view of prey preference, several foraging phases should be investigated. Such analyses should be carried out independently to obtain complementary conclusions. In this study, prey preference was determined in three independent analyses.Two laboratory analyses were carried out:- Analysis of response towards volatile kairomones. This investigation regards decisions of the predators when prey individuals are not contacted, as is the situation after termination of aerial dispersal or after eradication of a prey patch.- Analysis of predation rates at different prey supplies. This relates to acceptance/rejection decisions during contacts with prey items.To complement the prey preference analyses carried out in the laboratory, an investigation was made under field conditions: - This was done by determination of diet composition by means of electrophoretic analysis of gut contents of field-collected predators.Spider-mite kairomones in a tritrophic context.Predatory mites distinguish plants infested by spider mites from clean plants by a volatile kairomone (e.g. Sabelis & Van de Baan, 1983). This kairomone seems to be a product of the interaction between plant and spider mites: after removal of spider mites from an infested plant, the plant remains attractive to the predators during several hours, whereas the mites alone do not remain attractive (Sabelis & Van de Baan, 1983; Sabelis et al., 1984a). Current data on spider mite - predatory mite interactions do not explain the role of this infochemical in the biology of the spider mites (cf. chapter 3 for a review). It may, for instance, be an inevitable byproduct of damage inflicted on the plant by the spider mite, and/or have an indispensable function in the biology of the spider mite. Moreover, the plant may be involved in production of the infochemical. To elucidate the role of this volatile infochemical, its effects in interactions between plant and spider mite, between plant and predatory mite and between spider mites of one species should be investigated. Before this can be done, chemical identification of the infochemical is a necessary first step.These investigations were made for a tritrophic system consisting of Lima bean plants, the two-spotted spider mite, Tetranychusurticae Koch and the predatory mite Phytoseiuluspersimilis Athias-Henriot (Figure 1.4b). This system was chosen for practical reasons. The plant and phytophagous mite can be reared throughout the year and therefore, this system is much more suitable to develop a method for the chemical analysis of spider-mite kairomones than a system in which the plant is a perennial.Origin and function of T.urticae kairomone in a tritrophic system.Two-spotted spider mites distinguish between a clean plant and a plant that is infested by conspecifics on the basis of a volatile infochemical (chapter 4). The spider mites move away from heavily infested leaves. This response is advantageous to spider mites on the infested leaf as well as to spider mites that avoid settling on these leaves: increased competition for food is avoided, cf. Wrensch and Young (1978). In addition, the spider mite that disperses thus avoids settling on a spot that has an increased risk of being detected by predatory mites (Sabelis and Van de Baan, 1983). Therefore, the infochemical in this interaction between conspecific spider mites is called a (+,+)dispersing pheromone. Biological evidence suggests that this pheromone is (at least partly) identical to the volatile kairomone to which predatory mites respond (chapter 4).Volatiles emitted from plants infested by T.urticae were identified and subsequent behavioural analyses resulted in identification of four kairomone components that attract the predatory mite P.persimilis : linalool (3,7-dimethyl-1,6-octadiene- 3-ol), methyl salicylate, ( E )-β-ocimene (3,7-dimethyl-1,3( E ),6- octatriene) and 4,8-dimethyl-1,3( E ),7-nonatriene. The structure of these compounds is shown in Figure 1.5. At least two of these (linalool and methyl salicylate) are also components of a kairomone in the interaction between T.urticae and A.potentillae (when reared on V.faba pollen; see below) (chapter 4). Literature data on the behavioural response of T.urticae indicate that one of these kairomone components (linalool) is also a component of the (+,+)dispersing pheromone (Dabrowski and Rodriguez, 1971).All identified kairomone components are well-known in the plant kingdom. This suggests that the plant is involved in production of the infochemical, but it is no proof. It may for Instance be that spider-mite enzymes injected into the plant break down a plant compound. Investigation of e.g. site and moment of production and possible storage of precursors are needed as a next step to elucidate the role of the plant in kairomone production. However, suppose that it is the spider mite who produces the infochemical to serve as a dispersing pheromone. Then, it is not clear why this pheromone should necessarily consist of volatiles. As a result of the production of volatiles the spider mites incur more risks of being detected by predators than by production of non-volatile chemicals. Detection by predators inevitably leads to local extermination of spider mites (Sabelis and Van der Meer, 1986). For this reason it seems more likely that the volatiles are plant produced and that the spider mite makes the best of a bad job by using them as information to decide where not to colonize. To understand the evolution of plant-produced volatiles after herbivore attack, it is crucial to assess how they are produced, how much it costs to produce them and what the benefits are in terms of a lowered probability of herbivore attack.Involvement of volatile kairomones in prey preference of predatory mites.The response of T.pyri and A.potentillae to volatile kairomones is dependent on the diet of the predators. When reared on a carotenoid-poor diet these predators respond to the kairomones of more prey species than when reared on a carotenoid-rich diet (chapters 6, 7 and 8). Carotenoids are indispensable to A.potentillae because of their function in diapause induction (Overmeer, 1985a). The function of these nutrients to T.pyri remains unknown (chapter 8). All prey species to whose kairomones carotenoid-deficient A.potentillae and T.pyri respond can relieve the lack of carotenoids. Carotenoid-containing A.potentillae and T. pyri only respond to the P.ulmi kairomone. The above observations were made for predators that were starved for 20 h. Longer starvation of predators reared on a carotenoid-rich diet also enlarges the number of prey species responded to. Investigations of the response to volatile kairomones indicates that A.potentillae and T.pyri (whether carotenoids are available or not) prefer P.ulmi to A.schlechtendali (chapters 6, 7 and 8) and that A.finlandicus has a reverse preference (chapter 11).This corresponds to conclusions from predation experiments performed at different composition of prey supply (chapters 9 and 11). The observed predation rates when mixed prey supplies were offered, were compared with a model provided with parameters estimated from experiments with each of both prey species alone. Amblyseiuspotentillae and T.pyri fed more on P.ulmi and A.finlandicus fed more on A.schlechtendali than was predicted by the model. This difference between observed and predicted predation rates cannot be explained by a change in behaviour of the prey species as a result of being together, nor by a change in walking behaviour of the predator. Therefore, these data indicate that A. potentillae and T.pyri prefer P.ulmi and that A.finlandicus prefers A.schlechtendali , in terms of a change in acceptance/rejection ratio ('success ratio').Analysis of prey preference under field conditions showed that most T.pyri collected from apple leaves that widely varied in P.ulmi : A.schlechtendali numbers contained P.ulmi esterase, whereas A.schlechtendali esterase was present in a minor fraction of predators (chapter 10). Rust-mite esterase and P.ulmi esterase were found equally frequent in A.finlandicus . The data for A.finlandicus , obtained over a narrower range of prey-number ratios than for T.pyri , do not allow a definite conclusion on prey preference. However, they certainly do not cause rejection of the conclusion on prey preference as obtained in the laboratory analyses (chapter 11). No field data are available for A.potentillae .Because the conclusions on prey preference as determined in these independent analyses are consistent for each predator species, the inference on prey preference is firmly established.Prey preference and reproductive success of predatory mites in an orchard system with two species of phytophagous prey mites.Analysis of reproductive success of these three predator species, when feeding on either P.ulmi or A.schlechtendali , indicates that A.finlandicus selects the best prey species in terms of reproductive success. This predator species suffers high larval mortality on P.ulmi , but not on A.schlechtendali . This results in a much higher intrinsic rate of population increase when feeding on apple rust mites (chapter 12).Amblyseiuspotentillae and T.pyri would also do better by feeding preferentially on A.schlechtendali : development times when feeding on this prey species are shorter than when feeding on P.ulmi , whereas these prey species do not differentially affect mortality or oviposition rate (chapter 12). For A.potentillae this may not be the case at the end of the season because P.ulmi is a better prey species in terms of diapause induction. Thus, on the basis of current data, optimal prey-choice theory cannot satisfactorily predict actual prey peference of A.potentillae and T.pyri . Future investigations should concentrate on e.g. (1) possible effect of competition between prey species on prey availability, (2) possible effect of competition between predator species on prey availability, and (3) possible shift in prey preference during the season.

Predicting potential winter wheat yield losses caused by multiple disease systems and climatic conditions