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Journal ArticleDOI

The feeding behavior of the weevil, Exophthalmus jekelianus, with respect to the nutrients and allelochemicals in host plant leaves

01 Jan 2003-Oikos (Munksgaard International Publishers)-Vol. 100, Iss: 1, pp 172-184
TL;DR: The model is shown to provide an effective framework for understanding the complex interactions among the chemical constituents of plants in a living host plant and predicts the phagostimulatory power of foods in the context of the regulation of multiple nutrients.
Abstract: The aim of this study was to relate nutritional and allelochemical variables in a living host plant to the feeding behavior of herbivorous insects in a field setting. We chose to study the foraging behavior of individual folivorous weevils (Exophthalmus jekelianus) while they were feeding on Central American mahogany (Cedrela odorata) in plantation in Costa Rica. All leaves contacted by the weevils during each observation were subjected to chemical analysis, and the weevils’ choice of leaves and their meal durations on those leaves were examined with respect to leaf chemical composition. Leaves that contained limonoids (allelochemicals present in the leaves) had fewer meals taken on them than did leaves without limonoids. Regression analysis and factor analysis were employed to investigate associations between leaf chemistry and meal duration. Univariate regressions indicated significant associations between meal duration and sucrose concentration, and between meal duration and nitrogen concentration. Factor analysis indicated that soluble sugars, nitrogen and limonoids were important variables that accounted for variation in meal duration. Sucrose and nitrogen concentrations were incorporated into a mathematical model that predicts the phagostimulatory power of foods in the context of the regulation of multiple nutrients. The model is shown to provide an effective framework for understanding the complex interactions among the chemical constituents of plants in
Citations
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Journal ArticleDOI
TL;DR: Comparative studies of nutrient regulation suggest coexisting generalist herbivores occupy unique nutritional feeding niches, and work with pathogens and parasitoids has revealed the manner in which top-down pressures influence patterns of nutrient intake.
Abstract: The primary reason animals, including insect herbivores, eat is to acquire a mix of nutrients needed to fuel the processes of growth, development, and reproduction. Most insect herbivores strongly regulate their nutrient intake when given the opportunity. When they are restricted to imbalanced diets, they employ regulatory rules that govern the extent to which nutrients occurring in excess or deficit are eaten. Insect herbivores also regularly encounter allelochemicals as they eat, and recent work indicates the effect an allelochemical has on nutrient regulation, and insect herbivore performance, is modified depending on a food's nutrient composition. Comparative studies of nutrient regulation suggest coexisting generalist herbivores occupy unique nutritional feeding niches, and work with pathogens and parasitoids has revealed the manner in which top-down pressures influence patterns of nutrient intake. Insect herbivores regulate their nutrient intake using pre- and postingestive mechanisms, plus learning, and there is evidence that some of these mechanisms are shaped by natural selection.

656 citations

Journal ArticleDOI
01 May 2006-Ecology
TL;DR: It is suggested that animals most vulnerable to effects of high food nutrient content are those that normally feed on low- quality (low-nutrient: C) food, and have a relatively low body nutrient content themselves, such as herbivores and detritivores.
Abstract: Nutritional imbalances are of great interest in the ecological stoichiometry literature, in which researchers have focused almost exclusively on cases where nutrients are available in low amounts relative to energy (carbon), and animal growth is impaired due to insufficient nutrient intake. Little attention has been given to situations where food elemental content is higher than the level that satisfies animal requirements. However, most animals are strongly homeostatic with respect to the elemental composition of their body; hence they must excrete the excess of elements that are not in short supply. To date, stoichiometric theory has assumed that excretion of superfluous elements does not come with a cost and, thus, that consumption of food with surplus nutrients does not impair performance. Here we challenge this assumption, based on a compilation of several examples involving food phosphorus content that show that the performance of a wide variety of animals decreases when supplied with food containing high concentrations of (potentially) limiting nutrients. We discuss possible mechanisms for this phenomenon, and suggest that animals most vulnerable to effects of high food nutrient content are those that normally feed on low- quality (low-nutrient: C) food, and have a relatively low body nutrient content themselves, such as herbivores and detritivores.

232 citations

Journal ArticleDOI
TL;DR: To determine the capacity of black soldier fly larvae (BSFL) to convert fresh human faeces into larval biomass under different feeding regimes, and to determine how effective BSFL are as a means ofhuman faecal waste management.
Abstract: Objectives To determine the capacity of black soldier fly larvae (BSFL) (Hermetia illucens) to convert fresh human faeces into larval biomass under different feeding regimes, and to determine how effective BSFL are as a means of human faecal waste management. Methods Black soldier fly larvae were fed fresh human faeces. The frequency of feeding, number of larvae and feeding ratio were altered to determine their effects on larval growth, prepupal weight, waste reduction, bioconversion and feed conversion rate (FCR). Results The larvae that were fed a single lump amount of faeces developed into significantly larger larvae and prepupae than those fed incrementally every 2 days; however, the development into pre-pupae took longer. The highest waste reduction was found in the group containing the most larvae, with no difference between feeding regimes. At an estimated 90% pupation rate, the highest bioconversion (16–22%) and lowest, most efficient FCR (2.0–3.3) occurred in groups that contained 10 and 100 larvae, when fed both the lump amount and incremental regime. Conclusion The prepupal weight, bioconversion and FCR results surpass those from previous studies into BSFL management of swine, chicken manure and municipal organic waste. This suggests that the use of BSFL could provide a solution to the health problems associated with poor sanitation and inadequate human waste management in developing countries.

185 citations


Cites background from "The feeding behavior of the weevil,..."

  • ...Based on the slower development and larger prepupae of the larvae fed once, it is theorised that there was a nutritional imbalance in the lump amount diet that led to an increase in consumption to compensate for deficient nutrients (Raubenheimer & Simpson 1997; Bennett 2000; Wright et al. 2003)....

    [...]

  • ...Growth rate plasticity (Metcalfe & Monaghan 2001; Tu & Tatar 2003; Wright et al. 2003; Dmitriew & Rowe 2005; Dmitriew 2011) means that larvae are capable of successfully developing on a range of resources that may be transient in nature....

    [...]

  • ...Growth rate plasticity (Metcalfe & Monaghan 2001; Tu & Tatar 2003; Wright et al. 2003; Dmitriew & Rowe 2005; Dmitriew 2011) means that larvae are capable of successfully developing on a range of resources that may be transient in nature....

    [...]

Journal ArticleDOI
TL;DR: The findings show, for the first time, the effect of food p/c content over an insect herbivore’s entire life, and indicate that there is a narrow range of p/ c ratios that maximize lifetime performance, and for H. virescens, this range coincides with its self-selected p/C ratio.
Abstract: Summary 1. Food protein–carbohydrate content has significant consequences for animal survival, growth and reproduction. Rarely, though, is its effect examined over an animal’s entire lifetime. 2. In this study, we reared newly hatched caterpillars [Heliothis virescens Fabricus (Lepidoptera: Noctuidae)] on diets containing different protein/carbohydrate (p/c) ratios. We recorded larval survival, time to pupation, pupal mass, eclosion success, time to eclosion and pupal body lipid content. Additionally, for each treatment, we mated eclosed males and females and measured egg production and egg viability. 3. Larval performance (survival to pupation and time to pupation) was similar across all except the two most extreme treatments. In contrast, pupal performance (mass, eclosion success and time to eclosion) was best on diets that were balanced or slightly protein-biased. However, eclosion success differed between sexes. For males, it was best on diets with balanced p/c ratios, while female eclosion was strong across all but the most carbohydrate-biased diet. Pupal body lipid content in both males and females increased as the food p/c ratio decreased. 4. Egg production was best on diets with balanced or slightly protein-biased p/c ratios. 5. We also estimated the effect of food p/c ratio at the population level, using the data generated in this study. Population size was largest on diets with a balanced p/c ratio and declined steadily and strongly as the food p/c ratio became increasingly more imbalanced. 6. Our findings show, for the first time, the effect of food p/c content over an insect herbivore’s entire life. Our data indicate that there is a narrow range of p/c ratios that maximize lifetime performance, and for H. virescens, this range coincides with its self-selected p/c ratio.

102 citations


Cites background from "The feeding behavior of the weevil,..."

  • ...…by eating a range of different plants, or by feeding on different vegetative tissues within a plant (Chambers et al. 1996; Singer, Bernays & Carriere 2002; Villalba, Provenza & Bryant 2002; Wright et al. 2003; Villalba & Provenza 2005; Clements, Raubenheimer & Choat 2009; Felton et al. 2009)....

    [...]

Journal ArticleDOI
TL;DR: Testing whether predators could learn to use color signals to make strategic decisions about when to include prey that varied in their toxin content in their diets found that birds made state-dependent decisions based upon their knowledge of the amount of toxin prey contained and their current energetic need.
Abstract: Animals often eat foods containing toxins to benefit from the nutrients that they contain. Understanding how animals balance the costs of eating toxins with the benefits of gaining nutrients is important for understanding the evolution of antipredator defenses, particularly aposematism and mimicry. In this study, we tested whether predators could learn to use color signals to make strategic decisions about when to include prey that varied in their toxin content in their diets. We gave European starlings (Sturnus vulgaris) daily sessions of sequentially presented mealworms (Tenebrio molitor). There were 3 types of mealworm which were made discriminable using color signals: undefended mealworms injected with water, mildly defended mealworms injected with 1% quinine solution, and moderately defended mealworms injected with 3% quinine solution. Birds learned to eat more undefended than defended prey and more mildly than moderately defended prey. Crucially, when we manipulated the birds’ energetic states using food restriction, we found that they increased the number of defended prey that they ate but maintained their relative preferences. Birds made state-dependent decisions based upon their knowledge of the amount of toxin prey contained and their current energetic need. Our results provide novel insights into the evolution of aposematic signals and also demonstrate that we may need to develop new models of the evolution of mimicry based on the state-dependent behavior of predators. Our data also have broader implications for the study of nutrient‐toxin trade-offs across a range of different ecological

83 citations

References
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Book
01 Jan 1982
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Abstract: (NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED. 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Distance. Final Comments. 2. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A Square-Root Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts. 3. Sample Geometry and Random Sampling. The Geometry of the Sample. Random Samples and the Expected Values of the Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean, Covariance, and Correlation as Matrix Operations. Sample Values of Linear Combinations of Variables. 4. The Multivariate Normal Distribution. The Multivariate Normal Density and Its Properties. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation. The Sampling Distribution of 'X and S. Large-Sample Behavior of 'X and S. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality. II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS. 5. Inferences About a Mean Vector. The Plausibility of ...m0 as a Value for a Normal Population Mean. Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations. Supplement 5A Simultaneous Confidence Intervals and Ellipses as Shadows of the p-Dimensional Ellipsoids. 6. Comparisons of Several Multivariate Means. Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors from Two Populations. Comparison of Several Multivariate Population Means (One-Way MANOVA). Simultaneous Confidence Intervals for Treatment Effects. Two-Way Multivariate Analysis of Variance. Profile Analysis. Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models. 7. Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model. Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression. Comparing the Two Formulations of the Regression Model. Multiple Regression Models with Time Dependant Errors. Supplement 7A The Distribution of the Likelihood Ratio for the Multivariate Regression Model. III. ANALYSIS OF A COVARIANCE STRUCTURE. 8. Principal Components. Population Principal Components. Summarizing Sample Variation by Principal Components. Graphing the Principal Components. Large-Sample Inferences. Monitoring Quality with Principal Components. Supplement 8A The Geometry of the Sample Principal Component Approximation. 9. Factor Analysis and Inference for Structured Covariance Matrices. The Orthogonal Factor Model. Methods of Estimation. Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis. Structural Equation Models. Supplement 9A Some Computational Details for Maximum Likelihood Estimation. 10. Canonical Correlation Analysis Canonical Variates and Canonical Correlations. Interpreting the Population Canonical Variables. The Sample Canonical Variates and Sample Canonical Correlations. Additional Sample Descriptive Measures. Large Sample Inferences. IV. CLASSIFICATION AND GROUPING TECHNIQUES. 11. Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments. 12. Clustering, Distance Methods and Ordination. Similarity Measures. Hierarchical Clustering Methods. Nonhierarchical Clustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables. Procustes Analysis: A Method for Comparing Configurations. Appendix. Standard Normal Probabilities. Student's t-Distribution Percentage Points. ...c2 Distribution Percentage Points. F-Distribution Percentage Points. F-Distribution Percentage Points (...a = .10). F-Distribution Percentage Points (...a = .05). F-Distribution Percentage Points (...a = .01). Data Index. Subject Index.

11,697 citations

Journal ArticleDOI
TL;DR: In this article, the authors present an overview of the basic concepts of multivariate analysis, including matrix algebra and random vectors, as well as a strategy for analyzing multivariate models.
Abstract: (NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED. 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. Data Displays and Pictorial Representations. Distance. Final Comments. 2. Matrix Algebra and Random Vectors. Some Basics of Matrix and Vector Algebra. Positive Definite Matrices. A Square-Root Matrix. Random Vectors and Matrices. Mean Vectors and Covariance Matrices. Matrix Inequalities and Maximization. Supplement 2A Vectors and Matrices: Basic Concepts. 3. Sample Geometry and Random Sampling. The Geometry of the Sample. Random Samples and the Expected Values of the Sample Mean and Covariance Matrix. Generalized Variance. Sample Mean, Covariance, and Correlation as Matrix Operations. Sample Values of Linear Combinations of Variables. 4. The Multivariate Normal Distribution. The Multivariate Normal Density and Its Properties. Sampling from a Multivariate Normal Distribution and Maximum Likelihood Estimation. The Sampling Distribution of 'X and S. Large-Sample Behavior of 'X and S. Assessing the Assumption of Normality. Detecting Outliners and Data Cleaning. Transformations to Near Normality. II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS. 5. Inferences About a Mean Vector. The Plausibility of ...m0 as a Value for a Normal Population Mean. Hotelling's T 2 and Likelihood Ratio Tests. Confidence Regions and Simultaneous Comparisons of Component Means. Large Sample Inferences about a Population Mean Vector. Multivariate Quality Control Charts. Inferences about Mean Vectors When Some Observations Are Missing. Difficulties Due To Time Dependence in Multivariate Observations. Supplement 5A Simultaneous Confidence Intervals and Ellipses as Shadows of the p-Dimensional Ellipsoids. 6. Comparisons of Several Multivariate Means. Paired Comparisons and a Repeated Measures Design. Comparing Mean Vectors from Two Populations. Comparison of Several Multivariate Population Means (One-Way MANOVA). Simultaneous Confidence Intervals for Treatment Effects. Two-Way Multivariate Analysis of Variance. Profile Analysis. Repealed Measures, Designs, and Growth Curves. Perspectives and a Strategy for Analyzing Multivariate Models. 7. Multivariate Linear Regression Models. The Classical Linear Regression Model. Least Squares Estimation. Inferences About the Regression Model. Inferences from the Estimated Regression Function. Model Checking and Other Aspects of Regression. Multivariate Multiple Regression. The Concept of Linear Regression. Comparing the Two Formulations of the Regression Model. Multiple Regression Models with Time Dependant Errors. Supplement 7A The Distribution of the Likelihood Ratio for the Multivariate Regression Model. III. ANALYSIS OF A COVARIANCE STRUCTURE. 8. Principal Components. Population Principal Components. Summarizing Sample Variation by Principal Components. Graphing the Principal Components. Large-Sample Inferences. Monitoring Quality with Principal Components. Supplement 8A The Geometry of the Sample Principal Component Approximation. 9. Factor Analysis and Inference for Structured Covariance Matrices. The Orthogonal Factor Model. Methods of Estimation. Factor Rotation. Factor Scores. Perspectives and a Strategy for Factor Analysis. Structural Equation Models. Supplement 9A Some Computational Details for Maximum Likelihood Estimation. 10. Canonical Correlation Analysis Canonical Variates and Canonical Correlations. Interpreting the Population Canonical Variables. The Sample Canonical Variates and Sample Canonical Correlations. Additional Sample Descriptive Measures. Large Sample Inferences. IV. CLASSIFICATION AND GROUPING TECHNIQUES. 11. Discrimination and Classification. Separation and Classification for Two Populations. Classifications with Two Multivariate Normal Populations. Evaluating Classification Functions. Fisher's Discriminant Function...nSeparation of Populations. Classification with Several Populations. Fisher's Method for Discriminating among Several Populations. Final Comments. 12. Clustering, Distance Methods and Ordination. Similarity Measures. Hierarchical Clustering Methods. Nonhierarchical Clustering Methods. Multidimensional Scaling. Correspondence Analysis. Biplots for Viewing Sample Units and Variables. Procustes Analysis: A Method for Comparing Configurations. Appendix. Standard Normal Probabilities. Student's t-Distribution Percentage Points. ...c2 Distribution Percentage Points. F-Distribution Percentage Points. F-Distribution Percentage Points (...a = .10). F-Distribution Percentage Points (...a = .05). F-Distribution Percentage Points (...a = .01). Data Index. Subject Index.

10,148 citations

Book
01 Mar 1973
TL;DR: An ideal text for an upper-level undergraduate or first-year graduate course, Nonparametric Statistical Methods, Second Edition is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics.
Abstract: This Second Edition of Myles Hollander and Douglas A. Wolfe's successful Nonparametric Statistical Methods meets the needs of a new generation of users, with completely up-to-date coverage of this important statistical area. Like its predecessor, the revised edition, along with its companion ftp site, aims to equip readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for a given situation. An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. An ideal text for an upper-level undergraduate or first-year graduate course, Nonparametric Statistical Methods, Second Edition is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics.

7,240 citations


"The feeding behavior of the weevil,..." refers background in this paper

  • ...Given the conservative nature of the Bonferroni test for multiple comparisons (Hollander and Wolfe 1999), it is possible that lack of significance in this test represents type II error, and, therefore, a larger sample size would have revealed a significant result....

    [...]

Journal ArticleDOI

4,794 citations

Journal ArticleDOI
01 Mar 1974

3,841 citations