Biological Shape Analysis - Proceedings Of The 2nd International Symposium 9789814518413, 9789814518406

Citation preview

BIOLOGICAL SHAPE ANALYSIS Proceedings of the 2nd International Symposium

8852hc_9789814518406_tp.indd 1

3/5/13 2:24 PM

This page intentionally left blank

BIOLOGICAL SHAPE ANALYSIS Proceedings of the 2nd International Symposium Naha, Okinawa, Japan

7 – 9 September 2011

Pete E Lestrel

University of California, Los Angeles, USA editor

World Scientific NEW JERSEY

•

LONDON

8852hc_9789814518406_tp.indd 2

•

SINGAPORE

•

BEIJING

•

SHANGHAI

•

HONG KONG

•

TA I P E I

•

CHENNAI

3/5/13 2:24 PM

Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE

Library of Congress Cataloging-in-Publication Data Biological shape anaysis : proceedings of the 1st International Symposium Tsukuba, Japan, 3–6 June 2009 / Pete E Lestrel, editor. International Symposium of Biological Shape Analysis (1st : 2009 : Tsukuba, Japan) p. cm. ISBN 978-981-4355-23-0 QH83 .I53 2011 2012538663

British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library.

Biological Shape Analysis : Proceedings of the 2nd International Symposium Japan, 7–9 September 2011/ Pete E Lestrel, editor. International Symposium of Biological Shape Analysis (2nd : 2011 : Naha, Okinawa, Japan) ISBN 978-981-4518-40-6

Copyright © 2013 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.

For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher.

Printed in Singapore

The Second International Symposium of Biological Shape Analysis September 7-9, 2011 Okinawa, Japan Organizers: H. Iwata, S. Ninomiya, H. Tatsuta and P.E. Lestrel

Symposium Participants [1] Wei Guo

[7] Yoshinari Yonehara [13] Kyoko Yamaguchi

[2] Masanori Mashiko [8] Osamu Kondo

[14] Kazuo Takahashi

[3] Fusia Ishida

[9] Norikuni Kumano

[15] Rempei Suwa

[4] Hiroyoshi Iwata

[10] Ryosuke Kimura

[16] Seishi Ninomiya

[5] Reena Khullar

[11] Pete Lestrel

[17] Franck Guy

[6] Haruki Tatsuta

[12] Dagmar Lestrel

[18] Dimitri Neaux

This page intentionally left blank

vii

Preface With the success engendered by the First International Symposium of Biological Shape Analysis (ISBSA) held in Tsukuba, Japan at the National Agricultural Research Center (NARC), it was decided to try to continue to meet every two years. Plans were well underway to meet again in Tsukuba in June of 2011, when northern Japan experienced the Great Tōhoku or East Japan earthquake and tsunami on March 11, 2011. With a magnitude of 9.0, this was the most powerful earthquake to hit Japan. It was the fifth most powerful earthquake in modern times since modern records began in 1900. This tragic event left 15,878 dead, 6,126 injured and 2,713 missing as of September 12, 2012 [1]. Although Tsukuba is located considerably south from the Fukushima nuclear reactor (approximately 180 km), the ISBSA organizers felt that a different venue was indicated. Therefore, instead of meeting in Tsukuba as planned, we met in Naha, Okinawa on September 4-7, 2011. The choice of Okinawa was welcomed, as it is a beautiful tropical locale. Nevertheless, it was suspected that the change of venue resulted in the loss of some of the potential participants, which was considered unfortunate. The proceedings of the first ISBSA displayed a surprising diversity in the disciplines focused on biological shape analysis with the second ISBSA continuing this trend. In general, the biological form is composed of attributes such as size, shape, color, structure, etc., where shape continues to play a significant role in the biological sciences [2]. As argued in the preface of the first ISBSA proceedings [3], and worth re-emphasizing here, biological shape analysis remains a crucial aspect in the elucidation of those primary biological processes, growth and evolution, that affect all fauna and flora. Also worth again highlighting, is that the universally used conventional metrical approach, consisting of angles, distances and ratios, while simple and convenient in application, remains an inefficient measurement system when dealing with the irregularity of the kinds of shapes that are always encountered in the biological sciences. Thus, alternative methods are needed and these form part of the framework of biological shape analysis as advocated in these proceedings [2, 3, 4]. Moreover, the genetic basis that underlies the morphology of all organisms is reflected in the phenotype, which is generally a visible outcome and as such lends itself to measurement. Thus, these biological processes, whether viewed in terms of development or evolution, consist of three broad issues: (1) the transmission of the genetic material from the parental to the offspring generation, well-known now since Gregor Mendel’s seminal work in 1865; (2) the biochemical composition of the material that makes up the genotype, namely, the DNA, mRNA, etc., is now also well-established with the discovery of the Watson and Crick model in 1953; and (3)

viii

the complex pathways by which the genotype becomes expressed in the phenotype. The issues dealing with (1) and (2) have been largely resolved, even if not all the details have been clarified; however, issue (3) continues to remain a challenge. As mentioned in the first ISBSA proceedings, the genetic pathway(s) → to the initiation of the developmental process → final shape (the phenotype) also involves spatial interactions at the cellular level during morphogenesis that determine the ultimate shape of organisms [5]. This has stimulated innovative research involving new experimental models as well new quantitative approaches such as simulation [6, 7]. While this research is only in its infancy, it may well represent the future of biology. Consequently, focusing on questions dealing with process also leads one back to the phenotype and on how to quantify its shape, a major and continuing focus of this volume. Turning now to the contributions of the second ISBSA proceedings, the diversity in discipline and subject matter is readily evident. As mentioned earlier, the complex pathways that allow the genotype to be expressed in the phenotype was investigated by Takahashi who looked at the genetic regulation of developmental buffering in Drosophila wing shape. In a similar vein, Yamaguchi, et al., explored some of the common genetic variants associated with dental characteristics to assess their contributions to crown shape variation in the human dentition. In a continuation of the themes initiated in the first ISBSA, a number of papers focused on the application of numerical methods to characterize the shape of various diverse organismal structures. These included Tatsuta et al., who focused on the shape of mandibles in the male stag beetle using elliptical Fourier analysis (EFA). They found that the similarity in shape of the F1 hybrids resembled the female parental line more closely suggesting the presence of genetic maternal effects. Khullar, et al., also using elliptical Fourier functions (EFFs) proposed a threedimensional (3-D) model to characterize numerically the shape of the human mandible in an effort to extract more information than is currently available for the diagnosis and treatment planning in orthodontics. Lestrel, et al., using EFFs documented the presence of evolutionary changes in hominin mandibles from 2.0my years to the present. The mandibular results largely mirrored the evolutionary results documented earlier for the cranial vault [8, 9]. Also utilizing EFFs, Miyake, et al., considered the total human body outline in an attempt to establish reference standards useful for studies of body shape and its perception. Kondo focused on the application of the Fourier transform as a method for distinguishing the structural characteristics implicit in the trabecular network found in the thoracic to lumbar vertebrae in normal individuals compared to those exhibiting vertebral tuberculosis. Kumano et al., investigated the the shape of the genetic spines found in the copulatory organ of male sweet potato weevils in order to assess the effect of mating on sexual selection. Two studies looked at shape in an agricultural context. Iwata, et al., investigated the integration of genomic selection with morphological approaches in an effort to improve the shape of cultivars. Using rice as an exemplar, they were able to predict

ix

the shape of genotypes utilizing a multiple approach consisting of: (1) genome-wide association study, (2) genomic selection, and (3) elliptical Fourier analysis. Ninomiya explored the question of whether machine vision can substitute for the judgments of an expert breeder in the assessment of crop shape selection. Utilizing a variety of novel methods, including discriminant functions, multilayer perceptron (led to neural networks), fuzzy logic, and decision trees, found that plant images could be successfully differentiated and that such an approach could be useful for shape classification. The models proposed could augment the plant shape selection of the breeder by minimizing the subjectivity involved in the selection process. Last, to be mentioned, but certainly not least, is the research of Neaux, et al., who looked at the craniofacial variation in the great apes. The results displayed clear covariation between facial shape, facial orientation, and basicranial flexion, a pattern generally similar in the great apes and humans. Modern humans, however, were found to differ primarily in their basicranial flexion. Special gratitude goes to the co-organizers, Seishi Ninomiya, Hiro Iwata, and Haruki Tatsuta, without their help, this symposium would not have been as successful as it was. Thanks also go to Charles Wolfe and Al Bodt for their editing assistance. Credit needs to be given to Brian Bodt for masterfully utilizing Photoshop to improve the frontispiece photo of the participants. The programming expertise of Charles Wolfe is gratefully appreciated given his many years of assistance with the programming tasks involved in the development of the EFF and MLmetrics software packages. Special thanks are again in order for Sook Lim, Scientific Editor at World Scientific, Singapore, for agreeing to publish the second Proceedings of the ISBSA. It is hoped that these Proceedings, as the one from the first ISBSA, will be useful to those interested in the quantitative analysis of the biological form and especially its shape. Pete E. Lestrel Editor Van Nuys, California, USA, February, 2013

REFERENCES [1]

The 2011 Tōhoku Earthquake and Tsunami. Retrieved from Wikipedia on February 3, 2013. www.wikipedia.org.

[2]

Lestrel PE. (1997) Fourier Descriptors and their Applications in Biology. Cambridge University Press, New York.

x

[3]

Lestrel PE. (2011) Preface In: PE Lestrel (Ed.) Proceedings of the First International Symposium of Biological Shape Analysis. World Scientific, Singapore, pp. vi-xi.

[4]

Lestrel PE (2000) Morphometrics for the Life Sciences. World Scientific Publications, Singapore.

[5]

Edelman GM (1988) Topobiology. Basic Books, New York.

[6]

Keller R. (2012) Physical Biology Returns to Morphogenesis. Science 338: 201-203.

[7]

Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival Jr B, Assad-Garcia N, Glass JI, Covert MW. (2012) A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell 150: 389-401.

[8]

Lestrel PE, Ohtsuki F, Wolfe CA. (2010) Cranial vault shape in fossil hominids: Fourier descriptors in Norma lateralis. J. Comp. Hum. Biol. (HOMO) 61: 287-313.

[9]

Lestrel PE, Cesar RM Jr, Wolfe CA, Ohtsuki F. (2011) Computational Shape Analysis: Based on a Fourier-wavelet representation of the fossil human cranial vault. In: PE Lestrel (Ed.) Proceedings of the 1st International Symposium of Biological Shape Analysis. pp. 191-220. World Scientific, Singapore.

xi

Contents Preface

vii

List of Symposium Participants

xiii

1. Agricultural Crop Selection 1. SEISHI NINOMIYA: Can Machine Vision Substitute for Plant Breeders’ Eye? A Case of Whole Crop Shape Selection in Soybean Breeding ............................................................................

1

2. Entomological Studies 2. K. H. TAKAHASHI: Genetic Architecture of the Developmental Buffering Machinery for Wing Shape in Fruit Flies ......................... 3. N. KUMANO, T. KURIWADA, K. SHIROMOTO AND H. TATSUTA: Effect of Male Genital Spines on Female Remating Propensity in the West Indian Sweet Potato Weevil, Euscepes postfasciatus .... 4. H. TATSUTA, H. IWATA AND K. GOKA: Morphometric Studies on the Variation of Male Lucanid Beetle Mandibles ........................

21 35 55

3. Human Morphological Studies 3.1. Skull and Cranium 5. T. YAMAGUCHI, R. KIMURA, A. KAWAGUCHI, Y. TOMOYASU AND K. MAKI: Craniofacial Morphology in Human Genetics ..................

73

3.2. Vertebral Morphology 6. O. KONDO: An Application of Fourier Transform of Two-dimensional Images: A Case Study of Human Vertebral Tuberculosis of Hokkaido Ainu ................................................................................................. 92 3.3. Mandibular Studies 7. R. KHULLAR, P. E. LESTREL, W. MOON AND C. A. WOLFE: Representation of the Mandible as a Curve in 3-space: A Preliminary Study using Fourier Descriptors ...................................................... 107

xii

8. P. E. LESTREL, C. A. WOLFE AND A. BODT: Mandibular Shape Analysis of Plio-Pleistocene Hominins: Fourier Descriptors in Norma lateralis ............................................................................

140

3.4. Whole Body Studies 9. P. E. LESTREL, N. MIYAKE, M. ISHIHARA AND C. A. WOLFE: Assessment of Body Image Perception: A Preliminary Study using Elliptic Fourier Descriptors ....................................................

168

4. Primate Studies 10. D. NEAUX, F. GUY, E. GILISSEN, W. COUDYZER, P. VIGNAUD AND S. DUCROCQ: Craniofacial Covariation in Extant Great Apes: A Geometric Morphometric Study ..................................................

Index

193 207

xiii

List of Symposium Participants FUKASE, H. Hokkaido University Graduate School of Medicine, School of Medicine, Kita-ku, Sapporo, 060-8638, Japan, [email protected], *

GUO, W. Institute for Sustainable Agro-ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan, [email protected]

*

GUY, F. Université de Poitiers - Institut International de Paléoprimatologie, Paléontologie Humaine: Evolution et Paléoenvironnements CNRS UMR 7262, F-86022 Poitiers, France, [email protected]

*

IWATA, H. Graduate School of Agricultural Life Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-8657 Japan, [email protected]

*

KHULLAR, R. Section of Orthodontics, UCLA School of Dentistry, Los Angeles, California, 90095-1668, USA. Now in private practice. [email protected]

*

KIMURA, R. Transdisciplinary Research Organization for Subtropics and Island Studies, University of the Ryukyus, Okinawa, Japan, [email protected]

*

KONDO, O. Department of Biological Sciences (Anthropology), Graduate School of Science, The University of Tokyo, Japan, [email protected]

*

KUMANO, N. Okinawa Prefectural Plant Protection Center, Naha, Okinawa 902-0072, Japan, [email protected]

*

LESTREL, P. E. Formerly, School of Dentistry, University of California at Los Angeles (UCLA), USA, [email protected] or [email protected] MASANORI, M. Medical Corporation Sanshinkai Hikari Dental Clinic, [email protected] MISE, K. Sakura Dental Clinic MDS, [email protected] MITANI, Y. Medical [email protected]

*

Corporation

Sanshinkai

Hikari

Dental

Clinic,

MIYAKE, N. Department of Sport Psychology, Tokyo International University, Kawagoe-shi, Saitama 350-1198 Japan, [email protected]

xiv

*

NEAUX, D. Université de Poitiers - Institut International de Paléoprimatologie, Paléontologie Humaine: Evolution et Paléoenvironnements CNRS UMR 7262, F-86022 Poitiers, France, [email protected]

*

NINOMIYA, S. Institute of Sustainable Agro-ecosystem Services, Graduate School of Agriculture and Life Sciences, The University of Tokyo, Tokyo 1880002, Japan, [email protected] SATO, T. University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan, [email protected]

*

SUWA, R. FFPRI, Forestry and Forest Products Research Institute, Tsukuba, Ibaraki 305-8687, Japan, [email protected]

*

TAKAHASHI, K. Research Core for Interdisciplinary Sciences, Okayama University, [email protected]

*

TATSUTA, H. Faculty of Agriculture, University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan, [email protected] YAMAGUCHI, K. University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan, [email protected]

*

YAMAGUCHI, T. Department of Orthodontics, School of Dentistry, Showa University, Ohta-ku, Tokyo, 145-8515 Japan, [email protected]

*

YONEHARA, Y. University of Tsukuba, Faculty of Life Science, Department of Biology, Japan, [email protected]

* Active symposium participants

1. Agricultural Crop Selection

This page intentionally left blank

1

Can Machine Vision Substitute for Plant Breeders’ Eye? A Case of Whole Crop Shape Selection in Soybean Breeding SEISHI NINOMIYA1 Abstract Crop shape is often a target of selection in breeding programs, assuming that crop shape directly reflects their productivity, determines light intercepting efficiency, lodging resistance, machine harvest efficiency, etc. Because the functional relationship between shape and productivity has not yet been quantified, such shape selection is still empirical or based on the visual judgments of skilled breeders. This paper describes several discriminant models that have been examined to simulate such human visual judgments in the case of soybean plant shape. First, shape features were defined for whole plant images and then the shapes were differentiated into classes using linear discriminant functions, multilayer perceptron, fuzzy logic and decision trees with the shape features. The intent of these models was to act as substitutes for human judgments in the process of classifying soybean shape into classes. Second, models were also developed where images were directly used as input data without any shape feature extraction. Multilayer perceptron, simple perceptron and Hopfield models were adopted as models with direct image input. The discriminant models with shape features performed well indicating the possibility of substitution for human visual judgments. In particular, the decision tree achieved a practical level of discrimination with a low error rate. These models, however, required the definition of shape features, raising the issue of generalization to other crops. On the other hand, the models with direct image input performed as well as the fuzzy logic, multilayer perceptron models with shape features. Though their performance was still worse than that of the decision trees with shape features, it was concluded that the direct image input approach was useful and could be generalized to apply to other crops.

INTRODUCTION Whole crop shape is often an important factor for determining light intercepting efficiency, lodging resistance, machine harvesting adaptability, etc., which directly reflect crop productivity [1]. Soybean is one such example and the shape is a target of selection in breeding programs [2]. Because the functional relationship between shape and productivity has not yet been quantified, such shape selection is still empirically based on visual judgments of skilled breeders. Such an evaluation process, however, requires long experience of the breeders and evaluation results can be inconsistent even by a same breeder, because of the dependence on human subjectivity. 1

Institute of Sustainable Agro-ecosystem Services, Graduate School of Agriculture and Life Sciences, the University of Tokyo, Tokyo 188-0002, Japan [email protected]

2

To avoid such subjectivity, a series of studies to substitute for human visual judgments in soybean breeding programs have been conducted. These studies utilized biological image analysis and computational statistics [2, 3, 4, 5, 6, 7, 8]. The purpose of this paper is to summarize those studies, by comparing the capabilities and features of a number of different models, which simulate human visual evaluation in the selection of whole soybean crop shape. Because crossvalidation was not conducted in some of the original studies, a part of the results here were recalculated using cross-validation. The procedure in this paper utilizes the same discriminant models as in the original studies for the independent comparison among the different models.

MATERIALS AND SHAPE FEATURES One hundred and seventy six soybean cultivars were grown in the summer season of 1989 at the Nagano Prefecture Chushin Agricultural Experimental Station under the ordinal cultivation management of the station. A total of 875 individual plants of the cultivars were sampled by dissecting them at the nodes of cotyledons in the beginning of the pod growing stage. This is when the upper-most leaves were fully expanded and the shapes of the plants were almost fixed except in the cases of a few indeterminate cultivars. Then, each sampled plant was placed on a white board and photographed (Fig. 1).

Fig. 1. Definition of the shape features (see also Table 1).

3

Because a soybean plant grows in a mostly two-dimensional fashion, expanding its branches and petioles roughly every 180 degrees, an image of each plant taken in this way can represent its shape [2]. Some examples are shown in Fig. 2. An expert soybean breeder categorized the 875 shapes of the sampled soybean plants (Fig. 3) into three categories: GOOD, FAIR and POOR. GOOD ones represent those specimens that the breeder intended to leave for the further breeding program while POOR ones are discarded. The breeder gave a FAIR rating to intermediate shapes. This shape evaluation data set of 875 plants was used for the first step of the study. For the second step of the study, a data cleansing was conducted in order to make the discriminant models more general (see the Data cleansing and decision tree section for the details). The soybean plant part of each image was extracted by binarization [9] and 18 shape features of the binarized crop shape were defined ([2], Table 1) for the further analyses.

Fig. 2. Shown here are some examples of binarized shape images. All the shapes were normalized to have a unit height.

Discriminant models with shape features As an initial study, the discriminant power of fuzzy logic, linear discriminant function and neural network were examined in order to simulate human visual judgment on soybean plant shape, using the original 875 image data and the shape features defined for them (Table 1).

4

First, the discriminant power of a fuzzy logic model was examined. Fuzzy logic is an idea to simulate human ambiguous control quantitatively and was originally proposed by [10] and the idea was utilized to simulate human judgment of the soybean shape [5]. To develop such a fuzzy logic model was a rather empirical process, which involved three steps. These were: (1) the selection of proper shape features, (2) the determination of fuzzy ranges and (3) the adjustment of fuzzy rule sets. All the steps were repeated to achieve a set of satisfactory fuzzy rules to evaluate the plant shape (Fig. 3).

Good

Fair

Poor

Fig. 3. Examples of soybean shape as classified into three categories by a breeder. Table 1. Shape features of binarized soybean crop shape defined by [2]. (See also Fig. 1). WDT: D: WDT*: XD1: XD2: YM: XSD: XCV: XSK: XKU:* XFT: YFT:

Width of plant HGT: Height of plant Occupation of plant projection in the rectangular Area: Area of plant projection Normalized width against a unit height Degree of plant bending Discrepancy between mid range of X-distribution and the main axis Mean of Y-distribution SD of X-distribution YSD: SD of Y-distribution Coefficient of variation of X-distribution YCV: Coefficient of variation of Y-distribution Skewness of X-distribution YSK: Skewness of Y-distribution Kurtosis of X-distribution YKU: Kurtosis of Y-distribution Discrepancy of X-distribution from uniform distribution Discrepancy of Y-distribution from uniform distribution

After a manually time-consuming iteration process, a set of seven fuzzy ranges was finally developed. This set contained the same triangle membership functions (Fig. 4) and was designed with the rule set as given in Table 2.

5

Fig. 4. Fuzzy ranges and the membership functions used in this study. VS, S, MS, M, ML, L and VL stand for very small, small, medium small, medium, medium large, large and very large respectively. The range between the maximum and minimum values was equally separated for each class range. Table 2. Fuzzy rules and output values.

The shape features selected were WDT*, D, XD1 and XSK and were arranged in the manner shown in Fig. 5. As shown, two combinations of two shape features were used to generate new shapes variables, LI and WS. These were arrived at using

6

the rule sets (Table 2). The combination of LI and WS generates the final variable to evaluate the shape using the rule set for the combination (Table 2). The values of these newly defined shape variables were determined based on the fuzzy membership of the input shape variables [5].

XD1 & XSK

Fig. 5. Fuzzy rule sets developed for the soybean shape evaluation. The model runs using the rules shown in Table 2.

First, the classification results for the fuzzy logic model are shown in Table 3. Table 3. Classification result by the fuzzy logic model. The model was examined as a three-category model with the results for POOR and FAIR combined. Fuzzy

Model POOR+FAIR

Breeder

POOR+FAIR GOOD

Overall error rate

GOOD

523

161

0.765

0.186

45

146

0.236

0.764 0.235

Second, the discriminant power of a linear discriminant function was examined using the R-library, MASS [11, 12] and the same features selected in the fuzzy logic

7

model (WDT*, D, XD1 and XSK) were used as the input variables. The results under the 10-foldout cross-validation are shown in Table 4. Table 4. Classification result by the linear discriminant function under the 10-foldout cross-validation. The model was examined as a three-category model with the results for POOR and FAIR combined. Linear

Breeder

Model

POOR+FAIR GOOD

POOR+FAIR

GOOD

529

155

0.773

0.186

54

137

0.283

0.717

Overall error rate

0.239

Third, the discriminant power of a three-layer perceptron was examined using the R-library, nnet [11, 12] and the same features selected in the fuzzy logic model (WDT*, D, XD1 and XSK) as the input variables. The employed structure of the network and the results given by the 10-foldout cross-validation and are shown in Fig. 6 and Table 5, respectively. The classification performance with the three-layer perceptron2 was better than that of the fuzzy logic model and the linear discriminant function models, although, the performance of those two models were quite similar. Table 5. Classification result by the neural network under the 10-foldout cross-validation. The model was examined as a three-category model with the results for POOR and FAIR combined. Neural Network

Model POOR+FAIR

Breeder

POOR+FAIR GOOD

Overall error rate

GOOD

564

120

0.825

0.186

38

153

0.199

0.801 0.181

Data cleansing and the decision tree Originally, the shape evaluation of the 875 soybean plants was done by a single breeder as mentioned above and used for the previous analyses. However, it was recognized that the judgment by the same breeder was not always consistent even 2 Perceptron is a learning algorithm for the classification of an input into one of several possible outputs. It is a type of linear classifier, it makes predictions based on a linear predictor function. The learning algorithm processes elements in the training set one at a time. Led to neural networks.

8

with respect to the same image. This was particularly the case between the POOR and FAIR categories and that such inconsistency might affect the discriminant power. Moreover, a model may lose its generality by basing it on the judgment of a single breeder because of subjectivity. Therefore, data cleansing was conducted by requesting three breeders to categorize the same 875 soybean shapes into the three shape classes. These generated 326 shapes of the three classes by the three breeders for the further analyses. The 326 shape data consisted of 66 GOOD, 93 FAIR and 166 POOR shapes.

XD1

Fig. 6. Three layer perceptron neural network adopted in this study. The target outputs were defined as 3 three-dimensional vectors.

Using the new data set, the discriminant power of the decision tree [6] was examined using the CART algorithm [13, 14] available in the R-library, mvpart [11, 12]. This led to an optimum combination of variables as D, WDT, XSD, XD1 and XD2 (Table 1) and yielded two trees based on the 10-foldout cross-validation [12] (Fig. 7 and Fig. 8). The overall error rates given by the cross validations for two- and three-category classifications were 0.18 and 0.19 respectively (Table 6), showing the best performance among the models used in the series of the studies. Particularly, the error rate for POOR+FAIR in the two-class classification was as low as 0.02, indicating that the model simulated human eyes fairly well in terms of correctly discarding useless materials.

9

Discriminant models without shape feature extraction The decision tree demonstrated good discriminant power in simulating human visual judgments on soybean plant shape. However, because it still required shape feature definitions to characterize for soybean plant shape, one could not expect the generality of the model to apply to other crops. Moreover, feature selection for the best discriminant performance is usually troublesome. This led to the idea of developing a discriminant model, which directly received shape images themselves, as input data so that no shape feature definitions would be necessary. Because the number of input variables is in a sense equivalent to the number of pixels of the input images in such an approach, one can apply artificial neural networks [15] as the base models, because of their flexible structure in general. In this section, the same data set was used as in the decision tree case. The binarized images were normalized to have a unit height as input data (Fig. 2).

Fig. 7. Decision tree for the two-class classification where 1 and 2 indicate POOR+FAIR and GOOD respectively.

10

Fig. 8. Decision tree for the three-class classification where 1, 2 and 3 indicate POOR, FAIR and GOOD respectively.

Multilayer perceptron First, the multilayer perceptron (MLP) models were examined [7, 16]. Figure 9 shows the schematic diagram of the models. In an MLP with M layers, the first layer and the M-th layer are the input layer and the output layer respectively while the in-between layers are hidden layers. The output from the j-th unit of the m-th layer,
 , is defined as follows:
  1   0,  1,  ,   1,


     1,  ,  ,  1

and

  
  ∑ 
  for j  1,  ,  ,  2,  , ,



(1) (2) (3)

11 Table 6. The classification results by the decision tree under 10 foldout cross validations. (a) Classification results of the two-category model Tree

Model POOR+FAIR

GOOD

253

120

0.977

0.023

POOR+FAIR

Breeder

GOOD

10

56

0.152

0.848

Overall error rate

0.181

(b) Classification results of the three-category model Tree

Model

POOR Breeder

FAIR GOOD

POOR

FAIR

150

16

0

0.904

0.096

0.000

27

55

11

0.290

0.591

0.118

2

7

57

0.030

0.106

0.864

Overall error rate

GOOD

0.194

where  is the number of the units of the m-th layer,  is the intensity of the j-th  pixel of an image,  is the connection weight from the i-th unit of the (m-1)-th layer to the j-th unit of the m-th layer and σ$·& is a sigmoid function defined as  σ$'&  tanh '. The MLP parameters,  s were adjusted to minimize the error between the MLP outputs and the target outputs corresponding to the supervisor data. The error of the λth supervisor data is defined as: 0 , λ $-&  ∑ 
  /   /   1  1,  , 2,  .

.

(4)

where P is the number of supervisor (training) data,  / is the λth-supervisor vector  (image) and W is the connection weight matrix of  . Note that the output of the  / each unit
 is a function of  . The learning process was done based on the backpropagation [17]. In this study, we examined 5 types of images as input data; 4 x 4, 8 x 8, 16 x 16, 32 x 32 and 64 x 64 images, which had 16, 64, 256, 1024, and 4096 units on the first layer respectively. The output layer was set to have either three or six units. In the three unit case, the expected value was either (1, -1, -1), (-1, 1, -1) or (-1, -1, 1) corresponding to GOOD, FAIR or POOR shapes. We also examined the discriminant power of the number of hidden layers as well as the number of the units of the hidden layers. Combining those conditions on the network structure, a total of 175 different multilayer perceptron structures were examined.

12

Fig. 9. Schematic diagram of multilayer perceptron model with direct image input.

The supervisor data set, 26 images (7 GOOD, 9 FAIR and 10 POOR) was selected based on the typicality of the shapes and the rest (300) were used as the test data set. In the learning process, one of the supervisor data was randomly selected at each weight update and the process was repeated, randomly setting new initial connection weights until 100 convergences were attained for each network structure. The time to convergence varied from 1 to 60 minutes depending on the network structure using a workstation (HP9000/750, Hewlett-Packard Co. Ltd., Palo Alto, CA). Some of the networks structures failed to converge. The converged networks were then evaluated using the test data set of 300 images, which were not used as the supervisor data. The discriminant power differed depending on the network structures and Table 7 shows the result that achieved the best performance among them. The result was worse than that of the decision tree but similar to the performance of the other models that used the shape features. Table 7. Classification result of MLP with an input layer of 16 x 16 units, a hidden layer of 16 units and an output layer of 3 units. The results for POOR and FAIR were combined. The test data was given to each of the 100 converged structure to evaluate the discriminant performance and the table shows the mean of the 100 cases. MLP

Breeder

Model

POOR+FAIR GOOD

Overall error rate

POOR+FAIR

GOOD

206

54

0.792

0.208

24

42

0.364

0.636 0.239

Simple perceptron and Hopfield models Multilayer perceptron showed comparatively good performance for soybean shape discrimination al although though it did not require any shape feature definition. It was,

13

however, rather troublesome to find the best structure of the MLP as the MLPs could have rather flexible structures having several structure parameters, such as the number of hidden layers, the number of the units in each layer, and the shape of the non-linear function of these units. Moreover, the convergence process was sometimes extremely time-consuming. To solve these issues, the simple perceptron (SP) [18] and the Hopfield model (HM) [19] were adopted. These models are composed of linear units with much simpler structures than the MLP. SP is composed of only an input layer and an output layer where the unit number of the input layer is the same as the input image and the unit number of the output layer was set to be the same as the number of the supervisor images in this study. The dynamics of the SP (Fig. 10) are expressed as: '  / ,  3  1,  , 4, 1  1,  , 2

(5)

 '.  ∑6  5 ' ,   1,  , 2

(6)

Input 64 X 64

Output

Good +1 -1 -1

Fair -1 +1 -1

Poor -1 -1 +1

Fig. 10. Schematic diagram of simple perceptron, the case for the 64 X 64 input image is illustrated here.

where N is the number of pixels of an input image, P is the number of supervisor images, ' is an output of the i-th unit of the input layer, / is the i-th pixel value of the 1-th supervisor image, '. is an output of the j-th unit of the output layer, and 5 is the connection strength from the i-th input unit to j-th output unit. The SP was trained to map supervisory images onto target output values. Assuming that 7/ is the j-th target output value when the 1-th supervisor image is input, we set the output target values to be: 7/  8

91, 3  1 ,  , 1  1,;  , 2, 1, 3 : 1

(7)

14

and the relationship between the target value and the 1-th supervisor image is defined as: / 7/  ∑6  5  ,  ,  , 2, 1  1,  , 2,

(8)

> 5  ∑= = ,  3  1,  , 2,  1,  , 4,

(9)

whose solution for 5 is given by: 

6

where Q is an P A P overlap matrix defined as:

> = ?BC  ∑6  7  ,  ', D  1,  , 2. 

6

(10)

In the SP, the network structure is simply determined by the input image size and the number of the supervisor images. The number of the supervisor image needs to be to the number of the categories to be classified by the SP. Namely, we need to have just one representing supervisor image of each category even though we have several supervisor images in the classes. In this study, the mean image of the same supervisor images of each category (GOOD, FAIR or POOR shape) was used for representing the supervisor image. The i-th pixel value of the mean image is defined as: /  ∑E  F ,  3,

(11)

$1, 1, 1&, if λ  1 $GOOD&, 7/ , 7./ , 7G/   H$1, 1, 1&, if λ  2 $FAIR&, ; $1, 1, 1&, if λ  3 $POOR&,

(12)





E ,

where  F is the i-th pixel value of the k-th supervisor image of the 1-th category. The target values 7/ are set as: E ,

for the three category case and,

7/ , 7./   8

$1, 1&, if λ  1 $GOOD&, ; $1, 1&, if λ  2 $FAIR&2SST&,

(13)

for the two-category case. After the learning process, each input test image was expected to produce the 7/ pattern of one of the categories as output values. The output values, however, did not always coincide with the expected target values and the category for each test image was determined to be the Λ-th category when Λ satisfied UΛ  max U ,  3  1,  , 2,

where U is the i-th output value from the test image.

(14)

15

The same image data set as the MLP case was used to examine the performance of the SP. The cross validation was conducted by one-leave-out. Namely, one image was left for the test while the rest of the images were used to generate mean supervisor images of three categories and the process was repeated until every image was used as the test image. The discriminant power differed depending on the size of the input images and the structure with 64 X 64 images achieved the best performance among different image sizes (Table 8). The result was again worse than that of the decision tree but similar to the performance of the other models that used the shape features. Table 8. Classification results of the SP under cross-validation (see the text for further details). 64 X 64 image data were used. SP

Model POOR+FAIR POOR+FAIR

Breeder

GOOD

GOOD

218

42

0.838

0.162

27

39

0.409

0.591

Overall error rate

0.212

HM is composed of the same number of linear units as the input image in this study. The dynamics of the HM (Fig. 11) are expressed as ' $X&  / ,  3  1,  , 4, 1  1,  , 2, X  0

(15)

' $X 9 1&  ∑6  5 ' $X&,  3  1,  , 4, 1  1,  , 2, X  0, 1, 

(16)

/ /  ∑6  5  ,   1,  , 4, 1  1,  , 2,

(17)

> 5  ∑= = ,  3  1,  , 2,  1,  , 4,

(18)

> 6 ?>C  ∑  = ,  ', D  1,  , 2.

(19)

where N was the number of pixels of an image, P is the number of supervisor images, ' $X& is an output of the i-th unit at time t, / is the i-th pixel value of the 1-th supervisor image, and 5 is the connection strength from the j-th unit to i-th unit. All the units are updated synchronously with t. Note that the number of the units is the same as the number of the pixels. In the HM, the target output is equivalent to the supervisor image and the relationship is defined as where 5 is given by 

6

where Q is an P A P overlap matrix defined as 

6

16 Table 9. Classification results of the HM under cross-validation (see the text for further details). 64 X 64 image data were used. HM

Model POOR+FAIR

Breeder

POOR+FAIR GOOD

Overall error rate

GOOD

206

54

0.792

0.208

23

43

0.348

0.652 0.236

In the HM, the network structure is simply determined by the input image size. The MH is trained to have 5 s so as to produce the same output in its equilibrium as an associated image. Namely, a supervisor image converges to the same image. Following the case of the SP, we used the mean image of the supervisor images of each category as the representing supervisor image of the category. Input 64 X 64

Output 64 X 64

Fig. 11. Schematic diagram of Hopfield model, the case for the 64 X 64 input image is shown here.

After the learning process with the supervisor images, the HP generated an associated image using the test image when equilibrium was established. The associated image was not necessarily the same as any of the supervisor images but is instead one of the linear combinations of the supervisor images, so that the categories for test images could not be simply determined. In such cases, the categories were determined to be the Λ-th category when the correlation coefficient between the Λ-th supervisor image and the associated image from the test image was maximum among the correlation coefficients between the associated image and the supervisor images.

17

The same image data set as the MLP case was again used to examine the HM. The discriminant power differed depending on the size of the input images and the structure with the 64 X 64 image input again showed the best performance under cross validation among different image sizes (Table 9). The cross validation was conducted in the same manner as in the SP case. The result was worse than that of the decision tree while showing a similar discriminant power to other methods, which used the shape features. The result was almost the same as that of the MLP and was slightly worse that of the SP.

DISCUSSION AND CONCLUSIONS Discriminant models with shape features were first examined. They performed well indicating the possibility of substitution of human visual judgments by machine vision (Table 10). In general, the accuracy rate for the combined class of FAIR and POOR was equal or better than that of GOOD. Among the models examined, the decision trees showed good performance and its overall accuracy rate was as high as 95%. Particularly, the accuracy rate in discriminating the combined class of FAIR + POOR soybean plants was as high as 98%. The result indicated that the model could be practical and applicable for discarding plants which were not selected for further breeding programs in terms of their shapes. The models, however, require the definition of shape features of soybean plant. This means that the models are not likely to be directly applicable to other crops at least not until useful shape features for shape classification for those crops are defined, which raises the issue of model generalization. In addition, models with shape features always require shape feature selection, which is often troublesome particularly when there are many features. Table 10. Discriminant performance of the models employed in this study. The data shows the results of two-class cases. Note that the comparisons of the performance among the models can only be done approximately, because the background of the computing approaches sometimes differed. For example, there were two kinds of data sets, before and after the data cleansing, was conducted and the accuracy rates for the two-class classification were sometimes obtained by combining the results of the three -class cases. Models

Input varibales

Class

Poor Fair Good Overall Poor+Fair

Fuzzy Logic

4

875

Linear Discriminant Function

4

875

Three Layer Pecptron

4

875

5 out of 18

325

Three Layer Pecptron

64X64 IMG

326

Simple Percptron

64X64 IMG

326

Hopfield Network

64X64 IMG

326

Decision Tree

Accuracy Rate

Data

●＋● ●＋● ●＋● ● ●＋● ● ●

● ● ● ● ● ● ●

Good

Evaluation

0.76

0.76

0.76

0.76

0.77

0.72 10 foldout

0.82

0.82

0.80 10 foldout

0.95

0.98

0.85 10 foldout

0.76

0.79

0.64

0.79

0.84

0.59 one-leaveout

0.76

0.79

0.79 one-leaveout

supervisor=26 test=300

18

In order to solve such shape feature issues, it is proposed that models, which directly receive image data as input data for shape classification where shape feature definition was not necessary, be used. Thus, multilayer perceptron (MLP), simple perceptron (SP) and Hopfield models (HP) were adopted and examined for their discriminant power on soybean plant shape, using image data as input data. The performance of the three models was similar (Table 10). The MLP model required the most computational resources and time to discover the best structure with the most optimal number of hidden layers, number of nodes in each layer, and input image size. In contrast, it was rather easy to run the SP and HP models. The overall discrimination power of the MLP, SP, and HP was almost the same as that of fuzzy logic, linear discriminant function and multilayer perceptron with shape features. Although, the performance was still worse than that of the decision trees with shape features. One could, therefore, conclude that the approach using the models with direct image input was useful for shape classification. In fact, a succeeding study showed that exactly the same SP and HP models were generally applicable for discriminating the shapes in several different targets such as soybean leaf shape, weed shape, maple leaf shape, etc. [20]. In the above models with direct image input, the images were normalized so that the targets had a unit height. This means that the classifications were size-independent. Another study showed that the classification performance of the direct image input model was improved when the size factor such as the projection area of the target was combined in the model [21]. That study did not include classification of soybean plant shape but such an approach might be useful in the soybean plant shape case as well. Human visual judgments still play important roles not only in plant shape selection but also in other situations of dealing with agriculture and agricultural research, although such judgments are often subjective. The models introduced in this study will support such judgments in terms of both compensating for the lack of breeding experience of experts and for stabilizing judgments by minimizing the subjectivity that is always present.

ACKNOWLEDGEMENTS The series of the studies on soybean shape evaluation were conducted with Mr. Nobuo Takashi and Dr. Isao Shigemori of Nagano Prefecture Agricultural Experimental Station, Dr. Jack Ambule of Iowa State University, Dr. Vu Nguyen-Cong and Dr. Mari Oides of National Agricultural Research Center.

REFERENCES [1]

Johnson BJ, Harris HB. (1964) Influence of plant population on yield and other characteristics of soybeans, Agron J 59: 447-449.

19

[2]

Ninomiya S, Shigemori I. (1991) Quantitative evaluation of soybean (Glycine max L. MERRILL) plant shape by image analysis. Japan J Breed 41: 485-497.

[3]

Oide M, Ninomiya S, Takahashi N. (1995) Perceptron neural network to evaluate soybean plant shape. IEEE Intl. Conf. Neural Net. Proc. IEEE: 560-563.

[4]

Oide M, Morinaga S, Ninomiya S. (1996) Selection method of supervisor data set to develop neural network evaluator on soybean plant shape. J. JASS 12 (1): 13-20, in Japanese with English summary.

[5]

Ambuel J, Ninomiya S, Takahashi N. (1997) Fuzzy logic soybean plant shape evaluation. Breed. Sci. 47: 253-257.

[6]

Ninomiya, S, Nguyen-Cong V. (1998) Evaluation of soybean plant shape based on tree-based model. Breed. Sci. 48: 251-256.

[7]

Oide M, Ninomiya S. (1998) Evaluation of soybean plant shape by multi-layer perceptron with direct image input. Breed. Sci. 48: 257-262

[8]

Oide M, Ninomiya S. (2000) Discrimination of soybean plant shape by linear neural network with image input. Agr. Info. Res. 9: 91-102., in Japanese with an English summary.

[9]

Ninomiya S. (1990). Utilization of image data for breeding. 3. Analysis of binary image and its application. In “Ikushugaku Saikinno Sinpo 31”, Japanese Society of Breeding (ed.), Yokendo Publisher Ltd., Tokyo 143-152, (in Japanese).

[10]

Zadeh LA. (1965) Fuzzy Sets. Info. Control. 8: 338-353.

[11]

R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/

[12]

Venables WN, Ripley BD. (1994) Modern Applied Statistics with S-Plus. New York: Springer-Verlag.

[13]

Clark LA, Pregibon D. (1992) Tree-based models. In: JM Chambers & TJ Hastie (eds), Statistical Models in S, pp. 377-419 Chapman & Hall, New York.

[14]

Breiman LJ, Friedman H, Olshen RA, Stone CJ. (1984) Classification and Regression Trees. Wadsworth, Belmont, CA.

20

[15]

Hert J, Krogh A, Palmer RG. (1991) Introduction to the theory of neural computation, Addison-Wesley Pub., Richwood City.

[16]

Lippmann RP. (1987) An introduction to computing with neural nets. IEEE ASSP Magazine 4: 4-23.

[17]

Rumelhart DE, Hinton GE, Williams RJ. (1986) Learning representations by back-propagating errors. Nature 323: 533-536.

[18]

Rosenblatt F. (1961) Principles of neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Spartan Books, New York.

[19]

Hopfield JJ. (1982) Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. 79: 2554-2558.

[20]

Oide M, Ninomiya S. (2000) Plant shape discrimination of several taxa without shape features extraction using neural networks with image input. Breed. Sci. 50: 189-196.

[21]

Ninomiya S, Oide M. (2008) Enhancement of Plant Shape Classification Power by Combining Size-Independent Shape Analyses with Size Factors, Proc. of Joint Conf. IAALD, AFITA & WCCA 2008: 17-24.

2. Entomological Studies

This page intentionally left blank

21

Genetic Architecture of the Developmental Buffering Machinery for Wing Shape in Fruit Flies K. H. TAKAHASHI1 Abstract The ability of organisms to buffer external and internal disturbances is collectively called developmental buffering. Developmental buffering has been quantified using morphological traits of various organisms, including a microbe, insects, plants, and a mammal. Among these, wings of Drosophila melanogaster have been used as a model system to study developmental buffering. With the recent development of various genetic tools, genetic regulation of developmental buffering of wing shape has been intensively studied in D. melanogaster using both candidate gene and genome-wide screening approaches. This article briefly introduces morphometric approaches for quantifying wing shape of D. melanogaster, and reviews recent progress in the examination of the genetic architecture underlying developmental buffering using those morphometric approaches. Detailed phenotyping using morphometrics combined with genetic analysis has proved to be a powerful approach when applied to a quantitative genetic phenomenon such as developmental buffering. Applying morphometric approaches to developing organs such as imaginal discs, and quantifying their shape throughout development would lead to a better understanding of the genetic regulation of developmental buffering.

INTRODUCTION It is important for organisms to be able to produce a highly fit and replicable phenotype in spite of environmental and genetic perturbations. Several concepts have been proposed to describe the ability of organisms to buffer against external and internal disturbances, and they are collectively known as developmental buffering. For example, Waddington [1] proposed the notion of ‘‘canalization,’’ the ability of a genotype to produce relatively consistent phenotypes across different environments and genetic backgrounds. Closely tied to the notion of canalization is the idea of developmental stability, the tendency of morphological traits to resist the effect of developmental noise [2, 3]. Corrective mechanisms that buffer against developmental noise and stabilize phenotypes have been theoretically demonstrated to evolve through stabilizing or fluctuating selection [4] because genotypes that produce a fit phenotype with high reproducibility would be superior to ones with low reproducibility. Artificial selection experiments on wing traits of Drosophila melanogaster has revealed that phenotypic variance is strongly increased under disruptive selection but decreased under stabilizing and fluctuating selection, supporting the above theory [5]. 1

Research Core for Interdisciplinary Sciences, Okayama University, Tsushima-naka 3-1-1, Kita-ku, Okayama 700-8530, Japan [email protected]

22

Both canalization and developmental stability have been characterized using quantitative trait variation. Canalization is often quantified as phenotypic variation among individuals that share genetic or environmental backgrounds [6, 7, 8], using measures of group-level trait variation (or among-individual variation) such as the coefficient of variation (CV). A common measure of developmental stability is fluctuating asymmetry (FA), defined as random deviations from perfect bilateral symmetry [3, 9, 10, 11, 12]. Developmental buffering has been quantified using morphological traits of various organisms, including a microbe, insects, plants, and a mammal [14, 15, 16, 17]. Among these, insect wings have several characteristics that make them particularly suitable for the study of developmental buffering. Insect wings in general have ecologically important functions in flight and communication, and in fact, various insects show intra-specific polymorphism in wing shape [17, 18, 19, 20], making them a target of natural selection. In addition, the two-dimensional structure of insect wings makes them relatively easy to digitize with a CCD camera connected to a microscope. In fruit flies particularly, the stability of wing shape has been suggested to be a sensitive indicator of environmental perturbations on developmental processes [21]. This sensitivity comes from the greater amount of information present in a flexible morphological transformation of wings compared with simple meristic traits such as body part size or bristle number [9]. Based on their ecological and functional importance, and the technical advantages of a morphometric approach, wings of D. melanogaster have become a model system for studying developmental buffering [9, 10, 16, 22, 23, 24, 25]. Due to the recent development of various genetic tools such as RNAi and isogenic deficiency strains [26, 27], D. melanogaster has emerged as an ideal study organism for investigating the genetic architecture underlying a quantitative and mostly polygenic trait such as developmental buffering. Taking advantage of the morphometric features described above, and the various genetic tools available, genetic regulation of the developmental buffering of wing shape has been intensively studied in D. melanogaster using both candidate gene and genome-wide screening approaches. The aim of this paper is to briefly introduce morphometric approaches in quantifying wing shape in D. melanogaster, and to review recent progress in the examination of the genetic architecture underlying developmental buffering using these morphometric approaches. Wing shape quantification When quantifying wing shape in fruit flies, landmarks are often placed on the junctions between longitudinal veins and cross veins or on wing margins. Major landmarks used to quantify the wing shape of Drosophila flies are shown in Fig. 1. These landmarks are suitable for a morphometric quantification of wing shape for two main reasons. First, the homology of each of these landmarks within and between species is obvious and shape scores for intra- and inter-specific samples are perfectly comparable. Second, these landmarks are identifiable with sufficiently high accuracy, allowing highly repeatable quantification.

23

A traditional morphometric index used to quantify wing shape is the elongation index, which is calculated based on wing length (measured as the linear distance between landmarks 2 and 13 or d (2, 13) and wing width between landmarks 12 and 15 (measured as d (12, 15) as shown in Fig. 2A, and defined as follows:

elongation index =

d [2,13] d [12,15]

(1)

This index is often used, even in recent studies, because it is relatively easy to measure [22, 27].

Fig. 1. Positions of the 16 landmarks commonly used in studies of fruit fly wings.

Another index is crossvein position, which represents the relative position of the posterior crossvein of the wing as seen in Fig. 2B [29]. A definition with slight modification is as follows:

d [3,10] d [3,11] + d [3,13] d [3,15] crossvein position = 2

(2)

The elongation index and crossvein position describe similar but slightly different shape characteristics. The former index roughly corresponds to proportions from the length to width of the contour of the wing, whereas the latter describes the internal venation structure of the wing. Another approach to describe the wing shape based on these landmarks is to evaluate the ratio of the area of a single component of the wing to the area of the entire wing. Such an approach can effectively take developmental constraints into

24

account. In fact, Drosophila wings can be subdivided into several compartments that are subject to different genetic controls [30, 31, 32]. Tsujino and Takahashi [24] measured the anterior and posterior compartment sizes (anterior compartment: the area surrounded by landmarks 6, 12, 13; posterior compartment: the area surrounded by landmarks 10, 11, 13, 15. See Fig. 2C. They then calculated the proportion of those compartments to the entire wing area (the area surrounded by landmarks 1, 12, 13, 14, 15, 16). These indices allow evaluation of the effect of the experimental treatment on different wing compartments.

Fig. 2. Wing shape indices and the landmarks used to calculate them. A. elongation index. B. crossvein position. C. anterior and posterior compartments. D. landmarks for Procrustes analysis.

The latest and most popular approach in quantifying wing shape is based on geometric morphometrics with Procrustes analysis. The eight landmarks that show a high degree of repeatability of coordinate acquisition (Fig. 2D) are commonly analyzed. One of the advantages of this approach is that it is unnecessary to make a prior determination of which partial shape component to focus on, one that allows evaluation of various morphological transformations. As reviewed above, there is a wide variety of approaches for quantifying wing shape. The best approach depends on the purpose of the study, what is known about the target morphology, and the effort available to digitize landmark coordinates. The next section reviews recent progress in investigating the genetic architecture of developmental buffering using the previously mentioned morphometric approaches. Natural genetic architecture and variation of developmental buffering Natural genetic variation in FA of wing shape has been investigated in a few studies but has not been confirmed [28, 33], perhaps because the sample collection

25

range was too limited to encompass regions with varying degrees of environmental stresses where local adaptation has occurred. The natural genetic variation in FA of wing shape using 20 wild strains of D. melanogaster originally collected from across the Japanese archipelago was investigated [23]. In that study, four indices (elongation index, crossvein position, proportion of anterior compartment size to whole wing size, and proportion of posterior compartment size to whole wing size) were used to quantify wing shape. In addition, they executed the Procrustes generalized least squares procedure to obtain Procrustes coordinates. They then performed principal component analysis (PCA), and used the first principal component as an index of whole wing shape. FA was evaluated as the absolute difference between index values of the left and right wings. Diversification among strains in wing shape FA was investigated using one-way ANOVA with strain as a random effect. Heritability of wing shape indices was also estimated using mass-bred populations of the wild strains. Additive genetic variance for wing shape was estimated using a three-generation design, and the animal model method [34] was adopted to estimate heritability in the narrow-sense (h2) using all known kin relationships among individuals. Therefore, significant diversification in FA among wild strains was only detected for crossvein position in females, and no significant heritability was estimated for any index (Table 1). This result suggests that natural genetic variation in FA was limited to individual wing components, and could not be detected by assessing FA of whole wing shape. Table 1. F values from the ANOVA on FA of wing shape indices and their heritability estimates from [23]. Index Elongation index Crossvein position Anterior compartment size Posterior compartment size Whole wing shape

F values for among-strain effect Female Male

1.166 2.210 * 0.787 1.081 1.451

0.999 1.427 1.083 1.074 1.713

Heritability estimates ± SE Female Male

0.000 ± 0.052 0.022 ± 0.086 0.000 ± 0.071 0.000 ± 0.089 0.183 ± 0.106

0.044 ± 0.095 0.000 ± 0.071 0.019 ± 0.067 0.000 ± 0.072 0.022 ± 0.083

* p < 0.05 after Bonferroni correction

Candidate genes with effects on developmental buffering The molecular mechanism of developmental buffering remained unknown until Rutherford and Lindquist [34] showed that both the genetic and pharmacological inactivation of HSP90 exposes hidden phenotypic variation in D. melanogaster. HSP90 is a molecular chaperone, and most of its identified cellular targets are involved in signal transduction and chromatin organization [36, 37, 38]. Given its function as a molecular chaperone, HSP90 may contribute to the stability of processes under environmental and genetic perturbations. However, the inhibition of HSP90 does not always influence canalization or developmental stability of traits, particularly quantitative traits in Drosophila [9, 10, 39]. Other molecular chaperones

26

such as Hsp22, Hsp67, Hsp68, Hsc70, and Hsp70 are also known to respond to environmental stresses [40, 41, 42, 43]. Although details of the chaperone activity and the molecular mechanism of the Hsp-mediated stress resistance are largely unknown, it is possible that these genes may affect developmental processes. To examine the developmental buffering effect of candidate Hsp genes, Takahashi et al. [22] performed RNA inhibition (RNAi) to suppress the Hsp gene expression and test its effect on developmental buffering. Similarly, investigators [23] studied the effect of genomic deficiencies encompassing Hsp70Ba on developmental buffering. Because the RNAi strains used in [21] and one of the deficiency strains used in [23] were constructed in an isogenic background (DSK001), both studies could perform an ideal comparison with a control genotype. Developmental buffering was evaluated as FA and among-individual variation in wing shape based on the eight landmarks shown in Fig. 2D. As a measure of wing shape FA, a univariate measure of FA devised by Klingenberg and Monteiro [43] was used. This measure is based on the idea of one-sample standard distance [44], and is equivalent to the one-sample version of the Mahalanobis distance [45]. As a measure of wing shape canalization, the trace of the total covariance matrix for among-individual variation from all the components of relative warps was used as an index of among-individual variation. The genotypes examined are listed in Table 2. Hsp67Ba-RNAi was found to have a significant effect on mean wing shape (Fig. 3), wing shape FA, and among-individual variation (Fig. 4), but the study did not detect an effect of other Hsp genes on developmental buffering of wing shape. Hsp67Ba showed significant effects on both FA and among-individual variation in wing shape, suggesting that some developmental buffering mechanisms affect both the within- and the among-individual phenotypic variation. Although Hsp67Ba has been implicated in developmental buffering, the manner in which it buffers developmental perturbations is unknown. Rutherford et al. [46] suggested a possible mechanism of developmental buffering by Hsp90, based on the idea of thresholds for the expression of phenotypes in response to continuously varying strengths of signaling mediated through the Hsp90-targeted pathways. Table 2. Target Hsp genes and the genotypes to examine their effects on developmental buffering in [22, 24]. Target Hsp gene

Control Hsp22 Hsp67Ba Hsp67Bb Hsp67Bc Hsp68 Hsp70Ba Hsp70Bb Hsp70Bbb Hsp70Bc

Genotype examined in Takahashi et al. (2011a)

DSK001/DSK001

Df(3R)ED5579 /DSK001, DSK001; Hsp70Ba

Genotype examined in Takahashi et al. (2012) Act5C -GAL4/DSK001

304

Act5C -GAL4/UAS-Hsp22 -RNAi(Strain ID: 43632) Act5C -GAL4/UAS-Hsp67Ba -RNAi(Strain ID: 21806) Act5C -GAL4/UAS-Hsp67Bb -RNAi(Strain ID: 49795, 49796) Act5C -GAL4/UAS-Hsp67Bc -RNAi(Strain ID: 26416) Act5C -GAL4/UAS-Hsp68 -RNAi(Strain ID: 47145) Act5C -GAL4/UAS-Hsp70Ba -RNAi(Strain ID: 33207, 36641) Act5C -GAL4/UAS-Hsp70Bb -RNAi(Strain ID: 33207, 36641) Act5C -GAL4/UAS-Hsp70Bbb -RNAi(Strain ID: 33207, 36641) Act5C -GAL4/UAS-Hsp70Bbc -RNAi(Strain ID: 33207, 36641)

27

Fig. 3. Thin-plate spline (×30) between mean shape of Act5C-GAL4/DSK001 (A) and mean shape of each of Act5C-GAL4/UAS-Hsp67Ba-RNAi (B). Modified from [22].

Fig. 4. Wing shape FA and among-individual variation of control (Act5C-GAL4/DSK001) and RNAi (Act5C-GAL4/UAS-Hsp67Ba-RNAi) genotypes. Modified from [22].

According to their hypothesis [47], when Hsp90 levels decrease, signal transduction clients begin to lose their activity, and the strength of the target pathways becomes severely reduced. In particular, genetic interactions between Hsp90 and the signaling pathways, leads to a reduction of signaling to the expression threshold of a mutant phenotype and reveals cryptic variation. Whether Hsp67Ba has similar interactions with the signaling pathways is still unknown. The expression of Hsp67Ba is regulated by the steroid molting hormone ecdysone and other enhancer elements [48], suggesting a possible interaction with a number of signaling pathways. Genome-wide deficiency screening for genomic regions with effects on developmental buffering As explored in the previous section, only Hsp67Ba has been demonstrated to be a candidate gene for developmental buffering of wing shape. However, it was

28

shown that genome deficiencies in D. melanogaster could potentially affect FA of wing shape; thus, suggesting the existence of a specific genetic architecture to control wing shape FA [16]. Unfortunately, although they assessed the effect of genetic factors on FA based on variation among deficiency strains using 115 deficiency lines, they did not evaluate the statistical significance of each deficiency effect. Further experimentation may locate genomic regions responsible for FA and shed light on how FA and developmental stability are regulated in organisms. To map genomic regions associated with effects on the mean and FA of wing shape, four-hundred and thirty-five genome deficiencies, approximately 64.9% of the entire genome of D. melanogaster were screened [49]. The RS element-FLP system used to construct those deficiency strains allows the deletion breakpoints to be determined with single-base-pair resolution [27]. The control strain DSK001 is isogenic for the X, second, and third chromosomes, and was used to create RS-element inserted strains; therefore, except for deletions, the controls and all the deficiency strains have an isogenic background, thus providing an ideal tool with which genome regions involved in quantitative polygenic traits such as developmental stability can be screened. Wing shape FA was evaluated in the same way as in [22] and [24], based on the eight landmarks in Fig. 2D. Therefore, 89 genomic deficiencies were shown to have significant effects on wing shape FA. These genomic regions were primarily distributed over the second and third chromosomes (Fig. 5). Some genomic regions associated with effects on wing shape FA also had a significant effect on mean wing shape, whereas some did not, thereby indicating that there are two types of genomic regions with an effect on FA: those involved in both morphogenesis and stabilization of the mean, and those involved only in stabilization of the developmental process. Because each deficiency encompasses an average of approximately 63 genes, it is unclear to date whether a single gene affects both FA and the trait mean.

Fig. 5. Distribution of deficiencies with significant effects on FA of wing shape in females and males. Gray bars indicate chromosome arms and the gray circles in the middle or at the tip indicate centromeres. Genome regions that were covered by deletions are black. Modified from [49].

29

Most of the genomic regions associated with effects on FA of wing shape encompassed genes hypothesized to be involved in wing morphogenesis or genes that show spatially and temporally wing-disc-specific expression patterns during development. These genes could be candidates for developmental buffering of wings [50, 51]. Further investigation for candidate genes within specific genomic regions could reveal novel genes for developmental stability and provide a comprehensive picture of how developmental stability is regulated in insects.

DISCUSSION AND CONCLUSIONS Integration of morphometric and genetic approaches has effectively characterized the genetic architecture underlying developmental buffering of wing shape. Detailed phenotyping using morphometrics combined with genetic analysis has proven to be a powerful approach when applied to a quantitative genetic phenomenon such as developmental buffering. As suggested in [23], the developmental buffering of wing shape may be under independent genetic regulation in different wing compartments or partial shape components. In fact, detailed genetic regulation of wing morphogenesis has been extensively studied in D. melanogaster, and it is known that a number of morphogenic genes are expressed at specific locations and times on wing discs during development [52]. However, how the strict temporal and spatial regulation of the expression pattern of these morphogenic genes will contribute to adult wing shape remains largely unknown. These genes may directly buffer developmental noise, or genes that are expected to play a more general role such as Hsp genes, may interact with these genes to stabilize development locally. To further understand how developmental buffering is regulated, the effect of the candidate gene and the genomic regions found in [22] and [49], should be re-examined using smaller wing segments during development. Applying morphometric approaches to developing organs such as imaginal discs and quantifying their shape throughout development would lead to a better understanding of the genetic regulation of developmental buffering.

REFERENCES [1]

Waddington CH. (1942) Canalization of development and the inheritance of acquired characters. Nature 150: 563-565.

[2]

Waddington CH. (1957) The strategy of the genes. George Allen & Unwin: London.

[3]

Debat V, David P. (2001) Mapping phenotypes: canalization, plasticity and developmental stability. Trends Ecol Evol 16: 555-561.

[4]

Kawecki TJ. (2000) The evolution of genetic canalization under fluctuating selection. Evolution 54: 1-12.

30

[5]

Pelabon C, Hansen TF, Carter AJ, Houle D. (2010) Evolution of variation and variability under fluctuating, stabilizing, and disruptive selection. Evolution 64: 1912-1925.

[6]

Stearns SC, Kaiser M, Kawecki TJ. (1995) The differential genetic and environmental canalization of fitness components in Drosophila melanogaster. J Evol Biol 8: 539-557.

[7]

Dworkin I. (2005) Evidence for canalization of Distal-less function in the leg of Drosophila melanogaster. Evol Dev 7: 89-100.

[8]

Flatt T. (2005) The evolutionary genetics of canalization. Q Rev Biol 80: 287-316.

[9]

Milton CC, Huynh B, Batterham P, Rutherford S, Hoffmann AA. (2003) Quantitative trait symmetry independent of Hsp90 buffering: Distinct modes of genetic canalization and developmental stability. Proc Natl Acad Sci USA 100: 13396-13401.

[10]

Debat V, Milton CC, Rutherford S, Klingenberg CP, Hoffmann AA. (2006) HSP90 and the quantitative variation of wing shape in Drosophila melanogaster. Evolution 60: 2529-2538.

[11]

Kellermann VM, Hoffmann AA, Sgro CM. (2007) Hsp90 inhibition and the expression of phenotypic variability in the rainforest species Drosophila birchii. Biol J Linn Soc 92: 457-465.

[12]

Palmer RA, Strobeck C. (1986) Fluctuating asymmetry: measurement, analysis, patterns. Annu Rev Ecol Syst 17: 391-421.

[13]

Leamy LJ, Routman EJ, Cheverud JM. (2002) An epistatic genetic basis for fluctuating asymmetry of mandible size in mice. Evolution 56: 642-653.

[14]

Levy SF, Siegal ML. (2008) Network hubs buffer environmental variation in Saccharomyces cerevisiae. PLoS Biol 6: e264.

[15]

Pelabon C, Carlson ML, Hansen TM, Yoccoz NG, Armbruster WS. (2004) Consequences of inter-population crosses on developmental stability and canalization of floral traits in Dalechampia scandens (Euphorbiaceae). J Evol Biol 17: 19-32.

[16]

Breuker CJ, Patterson JS, Klingenberg CP. (2006) A single basis for developmental buffering of Drosophila wing shape. PloS One 1: e7.

31

[17]

Baylac M, Villemant C, Simbolotti G. (2003) Combining geometric morphometrics with pattern recognition for the investigation of species complexes. Biol J Linn Soc 80: 89-98.

[18]

Soto IM, Hasson ER, Manfrin MH. (2008) Wing morphology is related to host plants in cactophilic Drosophila gouveai and Drosophila antonietae (Diptera, Drosophilidae). Biol J Linn Soc 95: 655-665.

[19]

Favret C. (2009) Wing morphometry helps diagnose cryptic species and resurrect Mindarus pinicolus (Hemiptera: Aphididae). Ann Entomol Soc Am 102: 970-981.

[20]

Johansson F, Soderquist M, Bokma F. (2009) Insect wing shape evolution: independent effects of migratory and mate guarding flight on dragonfly wings. Biol J Linn Soc 97: 362-372.

[21]

Debat V, Begin M, Legout H, David JR. (2003) Allometric and nonallometric components of Drosophila wing shape respond differently to developmental temperature. Evolution 57: 2773-2784.

[22]

Takahashi KH, Rako L, Takano-Shimizu T, Hoffmann AA, Lee SF. (2010) Effects of small Hsp genes on developmental stability and microenvironmental canalization. BMC Evol Biol 10: 284.

[23]

Tsujino M, Takahashi KH. (2012) Natural genetic variation in fluctuating asymmetry of wing shape in Drosophila melanogaster. Ecol Res 27: 133-143.

[24]

Takahashi KH, Daborn PJ, Hoffmann AA, Takano-Shimizu T. (2011) Environmental stress-dependent effects of deletions encompassing Hsp70Ba on canalization and quantitative trait asymmetry in Drosophila melanogaster. PLoS One 6: e17295.

[25]

Takahashi KH, Okada Y, Teramura K. (2011) Genome-wide deficiency mapping of the regions responsible for temporal canalization of the developmental processes of Drosophila melanogaster. J Hered 102: 448-457.

[26]

Dietzl G, et al. (2007) A genome-wide transgenic RNAi library for conditional gene inactivation in Drosophila. Nature 448: 151-157.

[27]

Ryder E, Fiona B, Michael A, Rosa B-L, Darin C, Jenny D, Jane W, David G, Nicola G, Glynnis J, Cahir OKJ, David H, Punita S, Zoltan A, Heiko B, Janet S, Maria K, Kathrin K, Gunter R, Peter M, Janos S, Åsa R-L, Karin E, Barry D, Christoph H, Hugo S, Ernst H, Jean LA, Gert P, Martin H, Bernard M,

32

Florenci S, Montserrat C, Stephan S, Thomas P, John R, Steven R. (2004) The DrosDel collection: a set of P-element insertions for generating custom chromosomal aberrations in Drosophila melanogaster. Genetics 167: 797-813. [28]

Debat V, Cornette R, Korol AB, Nevo E, Soulet D, David JR. (2008) Multidimensional analysis of Drosophila wing variation in Evolution Canyon. J Genet 87: 407-419.

[29]

Pelabon C, Hansen TF, Houle D. (2006) Response of fluctuating and directional asymmetry to selection on wing shape in Drosophila melanogaster. J Evol Biol 19: 764-776.

[30]

Cavicchi S, Guerra D, Giorgi G, Pezzoli C. (1985) Temperature-Related Divergence in Experimental Populations of DROSOPHILA MELANOGASTER. I. Genetic and Developmental Basis of Wing Size and Shape Variation. Genetics 109: 665-689.

[31]

Garcia-Bellido A, Cortes F, Milan M. (1994) Cell interaction in the control of size in Drosophila wings. Proc Natl Acad Sci USA 91: 10222–10226.

[32]

Pezzoli MC, Guerra D, Giorgi G, Garoia F, Cavicchi S. (1997) Developmental constraints and wing shape variation in natural populations of Drosophila melanogaster. Heredity 79: 572-577.

[33]

Woods RE, Hercus MJ, Hoffmann AA. (1998) Estimating the heritability of fluctuating asymmetry in field Drosophila. Evolution 52: 816-824.

[34]

Kruuk LEB. (2004) Estimating genetic parameters in natural populations using the ‘animal model’. Philosophical Transactions of the Royal Society B: Biological Sciences 359: 873-890.

[35]

Rutherford S, Lindquist S. (1998) Hsp90 as a capacitor for morphological evolution. Nature 396: 336-342.

[36]

Wagner GP, Chiu CH, Hansen TF. (1999) Is Hsp90 a regulator of evolvability? J Exp Zool 285: 116-118.

[37]

Yahara I. (1999) The role of HSP90 in evolution. Genes Cells 4: 375-379.

[38]

Sollars V, Lu X, Xiao L, Wang X, Garfinkel MD, Ruden DM. (2003) Evidence for an epigenetic mechanism by which Hsp90 acts as a capacitor for morphological evolution. Nat Genet 33: 1-5.

33

[39]

Milton CC, Batterham P, McKenzie JA, Hoffmann AA. (2005) Effect of E (sev) and Su (Raf) Hsp83 mutants and Trans-heterozygotes on bristle trait means and variation in Drosophila melanogaster. Genetics 171: 119-130.

[40]

McColl G, Hoffmann AA, McKechnie SW. (1996) Response of two heat shock genes to selection for knockdown heat resistance in Drosophila melanogaster. Genetics 143: 1615-1627.

[41]

Bettencourt BR, Hogan CC, Nimali M, Drohan BW. (2008) Inducible and constitutive heat shock gene expression responds to modification of Hsp70 copy number in Drosophila melanogaster but does not compensate for loss of thermotolerance in Hsp70 null flies. BMC Biol 6: 5.

[42]

Calderwood SK. (2005) Evolving connections between molecular chaperones and neuronal function. Int J Hyperthermia 21: 375-378.

[43]

Bronk P, Wenniger JJ, Dawson-Scully K, Guo X, Hong S, Atwood HL, Zinsmaier KE. (2001) Drosophila Hsc70-4 is critical for neurotransmitter exocytosis in vivo. Neuron 30: 475-488.

[44]

Klingenberg CP, Monteiro LR. (2005) Distances and directions in multidimensional shape spaces: implications for morphometric applications. Syst Biol 54: 678-688.

[45]

Flury B, Riedwyl H. (1986) Standard distance in univariate and multivariate analysis. Am Stat 40: 249-251.

[46]

Mardia KV, Kent JT, Bibby JM. (1979) Multivariate analysis. Academic Press: London.

[47]

Rutherford S, Knapp JR, Csermely P (2007). Hsp90 and developmental networks Molecular aspect of the stress response: Chaperones, Membranes and Networks. Springer New York New York.

[48]

Morrow G, Tanguay RM. (2003) Heat shock proteins and aging in Drosophila melanogaster. Seminars in Cell & Developmental Biology 14: 291-299.

[49]

Takahashi KH, Okada Y, Teramura K, Tsujino M. (2011) Deficiency mapping of the genomic regions associated with effects on developmental stability in Drosophila melanogaster. Evolution 65: 3565-3577.

[50]

Butler MJ, Jacobsen TL, Cain DM, Jarman MG, Hubank M, Whittle JR, Phillips R, Simcox A. (2003) Discovery of genes with highly restricted

34

expression patterns in the Drosophila wing disc using DNA oligonucleotide microarrays. Development 130: 659-670. [51]

Ren N, Zhu C, Lee H, Adler PN. (2005) Gene expression during Drosophila wing morphogenesis and differentiation. Genetics 171: 625-638.

[52]

Held LI, Jr. (ed) (2002). Imaginal Discs. Cambridge University Press: Cambridge.

35

Effect of Male Genital Spines on Female Remating Propensity in the West Indian Sweet Potato Weevil, Euscepes postfasciatus NORIKUNI KUMANO1,2, TAKASHI KURIWADA3, KEIKO SHIROMOTO1, and HARUKI TATSUTA2 Abstract Male genitalia are considered one of the fastest evolving morphological characters of internal fertilization and often show a great diversity even in the same species. The evolution of harmful structures such as spines or hooks on the male genitalia is a controversial issue, the functional process of which is not fully understood. In order to clarify the reproductive function of genital spines, the effect of the length, number, and shape of genital spines on female remating propensity was investigated in the West Indian sweet potato weevil Euscepes postfasciatus. Both the shape and number but not the length of spines affected female remating propensity in this weevil. Males possessing an acute or large number of genital spines prevented their mating partner from remating. That is, because wounded females rejected additional mating opportunities more often than healthy ones. Thus, it is likely that a large numbers of genital spines can seriously damage the female copulatory tract. This is the first empirical study to demonstrate the relationship between male genital spines and female remating propensity.

INTRODUCTION The amount of diversity in the morphology of the male copulatory organ among animal species is extensive, and male genitalia are considered to be among internal fertilizations one of the fastest evolving morphological characters [17, 25]. Many authors have demonstrated that the driving force behind the extraordinary diversity in male genital morphology is post-mating sexual selection through either sperm competition or cryptic female choice [3, 5, 6, 18, 42]. Harmful structures, such as spines or hooks, can be found on male genitalia in many taxa, and a number of studies have shown that such structures can damage the female copulatory tract [7, 11, 12, 23, 52]. Many authors have difficulty explaining the adaptive significance of such harmful genital structures [7, 12]. Two adaptive hypotheses have been proposed to explain the evolution of this copulatory harm. 1

Okinawa Prefectural Plant Protection Center, Naha, Okinawa 902-0072, Japan [email protected] 2 Department of ecology and environmental science, Graduate school of agriculture, University of the Ryukyus, Sembaru, Nishihara, Okinawa 903-0213, Japan 3 National Agriculture and Food Research Organization, Kyushu Okinawa Agricultural Research Center, Makabe 820, Itoman, Okinawa 901-0336, Japan

36

The first hypothesis is the pleiotropic harm hypothesis [24, 29, 38, 41, 43], which suggests that harm caused during copulation is a side effect of adaptations that are beneficial in male-male competition [38, 42]. For example, a male mutation that gives bearers a reproductive advantage (e.g., in terms of sperm competition), but which simultaneously harms their mates, will be favored by selection provided the reproductive benefit to bearers of this mutation outweighs the reproductive cost they suffer owing to the reduced reproductive output of their mates. The second hypothesis is the adaptive harm hypothesis, which posits a direct gain from causing harm [29]. For example, the harm caused during copulation may directly confer a benefit on a male if the harm reduces female remating propensity and leads to lower sperm competition for that male. Furthermore, females might respond to harm by reallocating their resources from maintenance to current reproduction, because any injury will very likely decreases a female’s future reproductive value [40]. Therefore, female remating inhibition may offer numerous advantages to males. However, the limited support for these two hypotheses [22, 24, 27, 41, 55] makes them controversial explanations for the presence of the harmful structure of the male genitalia. In many insect species, females mate multiple times during their lifetimes [42, 44, 45]. In polyandrous species, males are likely to be favored by selection if they prevent or delay their sexual partners from remating, and/or increase the rate at which their partners produce and lay eggs [53]. Damage incurred by the female during copulation effectively reduces her remating propensity, thus the harmful genital spines are thought to be of direct benefit to the male [29, 39, 42]. The male bean beetle Callosobruchus maculatus has complex genitalia. Its internal sac (or endophallus) is covered with spines that puncture the female genital tract during copulation [12, 19, 27, 48]. Many authors have used this bean beetle species as a model organism for addressing the issue of the evolution of polyandry including female remating propensity [4, 16, 21, 20, 19]. The characteristics of the genital spines, such as number or shape, seem to be the most important factors in determining the degree of damage to the female. For example, during copulation the prongs and/or long spines on the male genitalia might protrude from the wall of the female copulatory tract but not the obtuse and/or short spines. The quantification of male genital spines (e.g., the length, number, and shape) is indispensable in trying to explain any male advantage conferred by the harm. However, none of the previous studies focused on testing the primary function of genital spines in C. maculatus [16, 19, 34, 41], have attempted to quantify the shape of male genital spines. As a result, precisely how the male genital structure of this bean beetle inhibits female remating is not fully understood. The complexity of C. maculatus male genital spines seems to restrict the quantification of the characteristic because of the non-uniform alignment of the spines on their intromittent organs (see [12]). As as consequence, one needs to address the function of the genital spines by using an alternative insect species that also possesses this unique genital structure. To evaluate the function of the male genital spines, the West Indian sweet potato weevil, Euscepes postfasciatus (Fairmaire) was chosen (Fig. 1).

37

Fig. 1. Mating pair of Euscepes postfasciatus weevils. Left: female, right: male.

Females are polyandrous and mated females remain unreceptive to males for approximately 7 days after copulation [35, 50]. This suggests that an evaluation of the male genital spines and the damage sustained by E. postfasciatus females should be the first steps in clarifying the presence of any male advantage(s) conferred by the evolution of such harmful genital spines. This weevil is a major pest of the sweet potato, Ipomoea batatas (L.) Lam., in tropical and subtropical regions including the southwestern islands of Japan [9, 28, 51, 58, 57]. Male weevils have genital spines on the surface of the internal sac (or endophallus) of the intromittent organ (Fig. 2a, 3a, 3b, 3c). These are everted in the bursa during copulation (Fig. 2b) as in C. maculatus. In E. postfasciatus, the size and shape of the spines as well as the degree of damage caused to the female copulatory tract can be evaluated after dissection (N. Kumano, personal observation). In the present study, whether or not the number, length, and shape of genital spines, have any influence on female remating propensity was investigated.

MATERIALS AND METHODS Insects E. postfasciatus is a major pest of sweet potato in tropical and subtropical regions. After infesting sweet potato roots, this weevil induces terpenoid production

38

that may make even slightly damaged roots unpalatable for humans and other animals [1, 49, 56]. Therefore, even low weevil densities may cause devastating crop losses. It is thought that this weevil was accidentally introduced into the southwestern islands of Japan from Hawaii or Saipan [32, 54]. The movement of weevils and their host plants, including the sweet potato, from infected to uninfected areas is strictly prohibited under the Japanese Plant Protection Law. In Okinawa Prefecture, a sterile insect technique (SIT) eradication program for this weevil is currently underway on Kume Island [34].

Fig. 2. Genital organs of male E. postfasciatus. (a) A photograph of the intromittent organ. The endophallus is usually placed in the aedeagus, and the arrow indicates the genital spines on the inner surface of the endophallus. (b) A diagrammatic representation of male and female genital tracts during copulation. Males evert their endophalluses inside female copulatory tracts during copulation.

SIT programs are often rapidly expanded and integrated into operational area-wide integrated pest management (AW-IPM) programs (reviewed by [15]). The SIT principle dictates that a large number of reproductively sterile male insects are released into a wild population of the same species so that they mate with them, and

39

thereby suppress the reproduction of wild females [30]. Since the efficiency of SIT eradication programs depends on the mating behavior of released sterile males, specific information on the mating behavior of the target pest species is indispensable.

Fig. 3. Genital spines on the surface of the endophallus. (a) Overall shot of the spiny area. The spiny area consists of two sets of areas. Each area is represented by either solid or dotted lines. (b) Large spiny area (inside the dotted line) and (c) Small spiny area (inside the solid line).

In the present study, Euscepes postfasciatus males, mass-reared for the SIT programs were used. The culture was developed from adults collected at Yaese Town, Okinawa Island, Japan (26°70N, 127°42E), in November 2004, and was

40

maintained on sweet potato roots at 25 ± 1°C and a photoperiod of L14:D10 (light between 0400 and 1800 hours) for 34-36 generations at the Okinawa Prefectural Plant Protection Center (OPPPC), Naha, Okinawa, Japan. To obtain virgin weevils, sweet potato roots were dissected approximately 7 weeks after inoculation with weevils, and newly emerged virgin adult weevils were extracted. Newly-collected adults designated 0 days old were sexed using stereomicroscopy as described by [33]. These were maintained in separate mesh-covered 250 mL plastic cups (diameter 8 cm, height 5 cm) with a piece of sweet potato root (approximately 100 g) as food under at 25 ± 1°C and a renewed photoperiod of L14:D10 (light between 0000 and 1400 hours) until the experiments were undertaken. General methods The male genital spines of E. postfasciatus were first evaluated. Then, the following two experiments were conducted to investigate the function of the spines on the endophallus of the intromittent organ. Experiments were conducted in an OPPPC laboratory (25 ± 1°C) from September to December 2010 and from March and April 2011. Mating trials We observed the mating behavior of E. postfasciatus in two equal-sex ratio (male: female = 1:1) experiments. For each observation, one randomly chose and placed a female onto a small plastic Petri dish (Easy Grip Petri Dish, Falcon, NJ, USA; 35 mm in diameter and 10 mm in height) together with a randomly chosen virgin male. To discriminate focal weevils, we marked the elytron of each male with a single spot of white paint (Sakura Mat Water Colors; Sakura Color Products, Osaka, Japan). As the mating behavior of E. postfasciatus occurs in the evening, the observation period was set approximately at the laboratory lights-out time. All trials began at 1330 hours, and mating was allowed for 120 min. The lights were turned off at 1400 hours, and weevils were observed at 5-min intervals using a flashlight equipped with a red-filter to confirm that the pair was copulating. A detailed explanation of the mating behavior observations is described below. Evaluating the damage to the female After the mating trial, mated females were individually placed into small plastic cups (ca. 90-mL) with a piece of sweet potato for 7 days to allow the melanization of the female copulatory tract. On the 7th day, the female copulatory tracts were dissected free under a stereomicroscope (Nikon SMZ645-1), placed into a drop of deionized water (~0.3 ml) on a glass slide and covered with a cover glass. Digital images of the female copulatory tracts were captured using a stereomicroscope (Nikon Eclipse 50, 10× magnification) coupled to a digital camera (Dino-Eye; AnMo Electronics Corporation, Taipei City, Taiwan). The digital images were used to calculate the total melanized area of each copulatory tract. Image analysis was conducted using ImageJ software (http://rsb.info.nih.gov/ij) for Mac OSX.

41

Evaluation of male genital spines After the mating trials, the mated male weevils were dissected. The endophallus of the intromittent organ was isolated under a stereomicroscope, and the spiny endophallic region removed with tweezers. Each specimen was placed into a drop of glycerin (~0.3 ml) on a glass slide, and covered with a cover glass in order to flatten it. Digital images of the male genital spines were then captured using the above-mentioned imaging system. In order to analyze the shape of male genital spines, the (x, y) coordinates of the three explicit landmarks: (#1) the tip of spine and (#2) and (#3) the base of the spine (Fig. 4a) were estimated for each spine from the digital images using tpsDIG2 and tpsUtil software programs [46, 47] with Windows7. The configurations of the spines can be summarized with a triangle (see, Fig. 4a). The Bookstein coordinates were then estimated. The spine tips were size-standardized by setting the two landmarks of the base to a fixed position with respective coordinates (0,0) and (1,0) for spinal shape comparison [8]. Of the two oblique lines drawn from the tip to the other two points, the longer line was considered as the length of the spine (Fig. 4b). Finally, the following three morphological features were estimated: (1) the average length of the five longest spines, (2) the average standardized tip coordinates (#1) for the five longest spines (x and y coordinates, respectively) and (3) the number of spines in the small spiny area (Fig. 3).

Fig. 4. Photographic and diagrammatic representation of the measured landmarks. (a) The three predefined spinal landmarks (the tip and the two base points), which were used for the analysis. (b) Definitions of the line segments. The longer oblique line (between landmark #1 and #2) was considered to represent the size of the genital spine.

42

Experiment 1: Effect of damage inflicted by male genital spines on female fitness In order to investigate the effect of the wounds sustained by the female copulatory tract during copulation on female fitness in E. postfasciatus, both the degree of female wounding and fecundity was evaluated. The mating behavior of this weevil consists of three independent phases (i.e., pre-copulatory guarding, copulation and post-copulatory guarding), and the duration of copulation in virgin females is about 23 minutes [35]. Males inflate the endophallus of their intromittent organs inside the female copulatory tract, and most sperm is transferred to the female storage organs within 5 minutes after initiating male genital intromission in this weevil [37]. Therefore, artificially terminating genital intromission after sperm transfer is likely to limit damage to the female copulatory tract without influencing reproduction in this weevil. Mated females for the mating trials were prepared. The time when copulation (genital intromission) started and when forcibly terminated was recorded. This was 10 minutes after the initiation of the genital intromission as the treatment (forced termination treatment) was ended. Normally copulated females were used as the control. Since the effect of the artificial termination treatment on the damage to the female in this weevil was unknown, it was first demonstrated that the influence of the forced termination treatment depended on the size of the melanized area in the female copulatory tract (the damage to the female, hereafter). Mated females were supplied with pieces of sweet potato for food and individually maintained in small plastic cups for 7 days to allow melanization and oviposition to occur. After 7 days, females were removed and the degree of wounding was evaluated (total dimensions of the melanized area(s) in the female copulatory tract). The degree of female damage among the three female groups was compared: (1) females with forced termination treatment, (2) without forced treatment (normal copulation), and (3) without copulation (virgin females). The inoculated sweet potato roots were maintained for about 70 days and the newly emerged weevils counted. Female fecundity between normally copulated females and females with forced termination treatment daily for seven subsequent days after the treatment, were compared. Experiment 2: Effect of genital spine shape on female damage and remating rate This experiment consisted of two sets of mating trials (looking at the 7-day interval between trials). After the first mating trial, mated weevils were removed and maintained individually. Then, the handling of weevils varied according to the sex. Mated male weevils were dissected within three days after the mating trial by the above-mentioned procedure to evaluate the genital spines. Mated females were removed just after the first mating trial and individually maintained in small plastic cups with pieces of sweet potato roots until the next mating trial. The already-mated females were randomly chosen, and then each one placed into small Petri dishes containing one 14-day-old virgin male (second mating trial). In this mating trial, whether the females would accept an additional mating

43

(remating) during the observation interval was recorded. After the second mating trial, all females were placed individually in 1.5 ml plastic tubes and maintained until dissection and their wounds were evaluated on the day of the second mating trial (within four hours after the end of the mating trial). This experimental setup enabled one to evaluate the damage inflicted on females during the first mating trial because any wounds the females incurred during the second mating trial would not have melanized by the time of dissection. Digital images of mated the males were divided into two groups depending on the fate of their mating partners - whether with or without an additional mating in the second mating trial. The three aspects of the genital spines (previously described) of the males mated in the first mating trial, and the wounds of females, were compared between the two male groups. Data analysis To compare the differences in the number of spines, the dimensions, and the length of spines between the spiny areas, Wilcoxon signed rank tests was used. The difference in the Bookstein coordinates of the genital spines between the two spiny areas was evaluated by multivariate analysis of variance (MANOVA). The standardized (x, y) coordinates of the genital spine tips were used as dependent variables. GLMs were used to analyze the differences in the damage inflicted on females during copulation, and the gamma distribution and inverse link function were used in this model. Then, Tukey all-pairwise comparisons were used to correct for multiple comparisons of the GLM model, as appropriate (R add-on package multcomp [26]). Female fitness (or the number of progeny) was analyzed with the GLM using the Poisson distribution in Experiment 1. The treatment (forced termination) and female body mass were used as independent variables, and the number of progeny as a dependent variable. Female remating propensity was analyzed by the GLM with the binomial distribution and with the MANOVA in Experiment 2. In the GLM, the degree of damage to the female (or the total dimensions of the melanized area) was used as an independent variable, and the acceptance of female remating as a dependent variable. In the MANOVA, the acceptance of female remating was used as an independent variable, and the standardized (x, y) coordinates of the tips of the five long spines, and the number and length of genital spines were used as dependent variables.

RESULTS Genital spines of E. postfasciatus Nineteen male weevils were dissected. E. postfasciatus males have many genital spines on the surface of the endophallus of the intromittent organ (Fig. 3). The spiny area of the endophallus consists of two explicit spiny areas (large and small), and the dimensions of these two areas differed significantly. The large area (mean ± sd) was 291,440.0 ± 66,297.7 in pixel (px) units, while the small area was 47,728.8 ± 8,271.2 px; (V = 0, P < 0.001, Wilcoxon signed rank test).

44

The shape, number, and length of spines also differed significantly between the two areas, respectively. The spines in the small area were shorter and fewer in number compared to those the large area. The length of the spines in the large area was 302.8 ± 13.8 px, while the length of the spines for the small area was 278.1 ± 12.5 px (V=0, P