Journal of Applied Science and Agriculture. Describing Morphological Integration and Modularity in the Shell of Strombus canarium

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AENSI Journals Journal of Applied Science and Agriculture ISSN 1816-9112 Journal home page: www.aensiweb.

Gorospe 1 Integrated Developmental School, MSU-IIT, Iligan City MSU-Iligan Institute of Technology, Iligan City, Philippines.

1 AENSI Journals Journal of Applied Science and Agriculture ISSN Journal home page: Describing Morphological Integration and Modularity in the Shell of Strombus canarium 1 Joy B. Bagaloyos, 2 Cesar G. Demayo, 2 Muhmin Michael Manting, 2 Mark Antony J. Torres, 3 Jessie G. Gorospe 1 Integrated Developmental School, MSU-IIT, Iligan City MSU-Iligan Institute of Technology, Iligan City, Philippines. 2 Department of Biological Sciences, College of Science and Mathematics, MSU-Iligan Institute of Technology, Iligan City, Philippines. 3 School of Graduate Studies, MSU Naawan, Naawan, Misamis Oriental, Philippines. A R T I C L E I N F O Article history: Received 25 June 2014 Received in revised form 8 July 2014 Accepted 10 August May 2014 Available online 30 August 2014 Keywords: Modularity, Integration Monte Carlo A B S T R A C T This study was conducted to determine modularity in the shell of S. canarium using the Modularity and Integration Analysis Tool (MINT) software for geometric morphometric data. The results of the goodness of fit test made use of the γ* statistic, and subsequent Monte Carlo randomization test and jackknife support. Among the five a priori models generated, the best-fit model of the shell of S. canarium is model 5 hypothesizing that the shell is partitioned into three distinct modules namely: a) the spire; b) the body whorl; and the c) apertural region. It is argued that different gene nets may control each module and subsequently affect the developmental and genetic modularity of the shell. While there are other models as shown in this that may also explain modularity and integration in the shell shape of S. canarium, these morphological variations may be explained by the interactions and influences among developmental modules that may affect functional and genetic interactions translated into phenotypic variations AENSI Publisher All rights reserved. To Cite This Article: Joy B. Bagaloyos, Mark Antony J. Torres, Muhmin Michael Manting, Jessie G. Gorospe, Ruben F. Amparado Jr.2 and Cesar G. Demayo. Describing morphological integration and modularity in the shell of Strombus canarium. J. Appl. Sci. & Agric., 9(11): 22-27, 2014 INTRODUCTION The conch Strombus canarium Linnaeus, 1758 was formally described by the Swedish naturalist and taxonomist Carl Linnaeus in S. canarium is native to the coastal waters of the Indo-Pacific region. The flesh of this dog conch is edible and it is a staple food for locals living along the seashore, and is fished in many parts of Southeast Asia. The species is usually abundant wherever it occurs, and is normally associated with sandy mud bottoms and seagrass beds (Abbott 1960, Amini and Pralampita 1987, Erlambang and Siregar 1995, Cob et al. 2005). It is the most abundant herbivorous mollusc and possibly contributes to the maintenance and well-being of the seagrass bed ecosystem (Cob et al. 2005). S. canarium populations is generally found to have wide variations in shell size and shape especially on the shell length (variations in anterior siphonal canal development), shell depth, shell width (variations in lateral flaring of the shell-lip), aperture length (variations in posterior flaring of the shell-lip), and lip thickness. Since the phenotype of an organism is determined by the genes, the environment, and developmental events (Vogt et al., 2008), it is important to understand how a phenotype is defined. The shell of S. canarium for example, is a good material in understanding how the different body parts are divided into modules and how these are integrated. The shell of this conch is generally divided into three clearly-defined parts- spire, body and aperture thus to theorize how these shell is compartmentalized is the major objective of this study. The idea that biological systems are fundamentally modular has been evoked to explain a variety of observations such as population level patterns of covariation among traits, mosaic patterns of evolution, and the compartmentalized effects of environmental, developmental and genetic perturbations (e.g., Olson and Miller, 1958; Kirschner and Gerhart, 1998; Bolker, 2000). How modular systems evolve and how modularity affects selection are currently the focus of a great deal of conceptual research, mathematical modeling, and experimental and observational inquiry thus is explored in this study applying the tools of geometric morphometrics and the modularity and integration analysis tool (Marquez and Knowles 2008). Modularity, which is the property of discreteness among parts and integration within parts, is a fundamental aspect of phenotypic complexity (Raff 1996; Wagner 1996; West-Eberhard 2003; Schlosser & Wagner 2005; Scholes 2008). It is hypothesized to facilitate the evolution of phenotypic disparity among evolutionary lineages by increasing evolvability and promoting mosaic evolution Corresponding Author: Cesar G. Demayo, Department of Biological Sciences, College of Science and Mathematics, MSU-Iligan Institute of Technology, Iligan City, Philippines. cgdemayo@gmail.com.

23 Cesar G. Demayo et al, 2014 where components are reorganized independently of each other through development and evolution (Raff, 1996; West-Eberhard, 2003; cited by Scholes, 2008).

2 23 Cesar G. Demayo et al, 2014 where components are reorganized independently of each other through development and evolution (Raff, 1996; West-Eberhard, 2003; cited by Scholes, 2008). Although modularity is expected to contribute only to statistical associations within modules, processes can mutually influence each other and therefore achieve a coordinated development of tissues, organs, and the whole organism. In addition, interactions between developmental modules can mediate the expression of genetic and environmental variation by transmitting their effects across different traits. Evolutionary interactions must also be inferred from covariation of morphological traits in experimental or comparative analyses. Developmental modularity therefore contributes to the patterning of all components of phenotypic covariation among traits (Klingenberg 2008). This study examines whether there are common patterns of modularity and integration in S. canarium and whether these patterns are compatible with hypotheses based on shared development and function. These patterns of variational modularity and integration are assessed by testing alternative a priori models, each of which hypothesizes a distinct modular structure caused by specific functional or developmental mechanisms (Marquez 2008). Methodology: A total of 30 specimens of S. canarium were collected from Mukas, Lanao del Norte, Philippines (Fig.1). Fig. 1: Map showing the study area, Mukas, Lanao del Norte (Source: google map.com). Specimens were placed in standard orientation and digital images were acquired of the apertural views of each shell using an 8.2 megapixel Kodak digital camera. Attention was given in orienting all specimens in the same manner to avoid errors in the digitization process. Outlines of curves were digitized using TpsDig2 (Rohlf 2006). A total of 100 points were digitized around the generated outline of the aperture. All points were digitized in the same order for consistency. X and Y coordinates of the outlined data were generated using tpsutilities and tpsrelw (Rohlf, 2004). Sample of the digital image of S. canarium shell (Fig. 2a) and an illustration of the outlined shell showing the 100 points that were digitized around the generated outline of the aperture (Fig. 2b) is shown in Figure 3. The data were then loaded to the MINT (Modularity and Integration Analysis Tool) software version 1.0b (Marquez 2008), which assumes that the data themselves have a modular structure. Covariance matrices expected under models of modularity were computed based on the modified data resulting from partitioning the entire data space into orthogonal subspaces or modules. A priori models were constructed using the model building option of the software. A total of 5 models of variational modularity in the shape data were generated, including the null model which assumes the absence of modularity and integration (Fig. 2c). Each model hypothesizes a distinct modular structure caused by specific functional or developmental mechanisms (Marquez 2008). RESULTS AND DISCUSSION From the shells of S. canarium, five models were set as shown in Figure 4. Model 1 represented the null model; the second, third, fourth and fifth models were a priori models set by the researcher. After the individual modules defined within loaded models (models 2, 3, 4 and 5) were mixed by MINT new models were generated (models 6-17) for analysis (Fig. 4). The criteria for the choice of landmarks included in the modules within every model of S. canarium are shown in Table 1. Every model was considered as a hypothesis.

24 Cesar G. Demayo et al, 2014 Fig. 2: (A) Digital image of S. canarium shell (B) Data processing using tpsdig2 produced outlined data at 100 landmark points (C) models of variational modularity. Fig. 3: Models of modularity as a result of combining the first 5 hypothetical models (1-5).

MODEL LANDMARK POINTS DESCRIPTION H 1 No modules Null model, predicting absence of modular structure; all covariances are hypothesized to be zero.

3 24 Cesar G. Demayo et al, 2014 Fig. 2: (A) Digital image of S. canarium shell (B) Data processing using tpsdig2 produced outlined data at 100 landmark points (C) models of variational modularity. Fig. 3: Models of modularity as a result of combining the first 5 hypothetical models (1-5). Table 1: A priori developmental and genetic models tested study of Strombus canariun. MODEL LANDMARK POINTS DESCRIPTION H 1 No modules Null model, predicting absence of modular structure; all covariances are hypothesized to be zero. H 2 2 modules The spire, body and aperture are considered as one module H 3 3 modules The spire on top represents one module; the body as the second module; and the apertural region as another module. Genes controlling each module are hypothesized to affect developmental and genetic modularity of the shell. H 4 2 modules The spire represents one module; the body and apertural region as the second module. H 5 2 modules The spire and body whorl represent one module; the aperture as a second module.

(Krzanowski 2000; cited by Marquez 2008), generating 1000 random covariance matrices from this distribution.

4 25 Cesar G. Demayo et al, 2014 Goodness of fit (GoF) between expected and observed covariance matrices was assessed using the gamma value (γ*). The computed γ* values were subjected to a Monte Carlo randomization test in which model covariance matrix and the original sample size of each model were used to parametize a Wishart distribution (Krzanowski 2000; cited by Marquez 2008), generating 1000 random covariance matrices from this distribution. γ* was computed between covariance matrix and random covariances, giving a probability value for the hypothesis that this value of γ* is no larger than that between two matrices from the same model. A low P-value (P<0.05) corresponds to large values of γ*, indicating a large difference between data and model and thus a poorly fitting model. Alternatively, a low γ* value indicates high degree of similarity between the observed data and the proposed model thus corresponding to the best supported models (Marquez 2008). Results of the tests for modularity and integration of the shell shape of S. canarium Linnaeus are illustrated in Table 2. Gamma (γ*) values computed for each of the seventeen combined models of variational modularity are summarized. As shown in Table 2, results for Model 5, ranked as #1 (=1), shows a P-value greater than α = 0.05; this accepts the null hypothesis that the proposed model and the observed data are not significantly different. Model 5 also acquired the lowest γ* value which qualifies it as the best-supported model among the seventeen combined models hypothesized for all the samples tested. Results further show that models 10 and 11 can also explain the modularity of the S. canarium shell albeit to a lesser extent than Model 5 as indicated by their low γ* values and P-values greater than Table 2: Gamma (γ*) and P values for each of the top 3 models of variational modularity. Model Rank γ*values Fig. 5: Top three models. The results revealed significant differences in integration between the body whorl and the spires of the shell and the aperture, which is suggestive of at least two different modules. The samples show top three of a total of

5 26 Cesar G. Demayo et al, models that were previously proposed (Figure 6). Model 5 shows that there are two modules, one module represent for the body whorl and spire and second module includes the aperture region. Model 11 shows three possible modules controlling the shell shape, one for the aperture, one for the body whorl and one for the spire. Some hypothetical models do not show clear demarcation between modules and that they show overlaps. The three top models hypothesizes that gene sets controlling each module affect developmental and genetic modularity of the shell. The results of the goodness of fit test using the γ* statistic, and subsequent Monte Carlo randomization test and jackknife support through the MINT software reveal that among the five a priori models generated, the best-fitting model S. canarium Linnaeus is Model 5, which obtained the lowest γ*value and P-values greater than 0.05 indicating the highest degree of similarity between model and observed data. Jackknife support values further indicate that Model 5 consistently ranked as the best-fit model for all the samples tested. This model hypothesized that the S. canarium shell is partitioned into two distinct modules namely: a) the spire; b) the body whorl including the apertural region. Different gene nets may, therefore, control each module and subsequently affect the developmental and genetic modularity of the shell. Analysis further show that Models 10 and 11 may also explain modularity and integration in the shell shape of S. canarium supported by the low γ*values these models obtained, which are still indicative of the high degree of similarity between the data and the proposed models. These morphological variations may be further explained by the interactions and influences among developmental modules that also affect functional and genetic interactions, which will translate to phenotypic variations (Klingenberg 2008). Gene nets, as modules are also called, represent a grouping of gene actions and their products into discrete units during the course of development. These modules not only help in the developmental process, but also allow genetic change to occur in one of these gene nets without influencing the others, thereby increasing its chance of being viable. The grouping leads to a limiting of pleiotropy and provides a way in which complex developing organisms can change in evolution (Bonner 1988; Wagner and Altenberg 1996). 5. Conclusion: This study was conducted to determine patterns of variational modularity in the shell shape of S. canarium, which in this study have shown that the shell is partitioned in two distinct modules indicating that different gene nets control each module affecting the developmental and genetic modularity of the shell. REFERENCES Abbot, R.T., The genus Strombus in the Indo-Pacific. Indo-Pacific Mollusca, 1(2): Amini, S. and W.P. Pralampita, Growth estimates and some biological parameters of gonggong (Strombus canarium) in Bintan Riau Waters. Jurnal Penelitian Perikanan Laut., 41: Bolker, J.A., Modularity in development and why it matters to evo-devo. American Zoologist, 40(5): Bonner, J.T., The evolution of complexity by means of Natural Selection. Princeton, N.J., Princeton University Press. Cob, Z.C., A. Arshad, J.S. Bujang, M. Graffar, Diversity and population structure characteristics of Strombus (Mesogastropod: Strombidae) in Johor Straits. Proceedings of the 2 nd Regional Symposium on Environmental and Natural Resources, Bangi University Kabangasan, Malaysia, 2: Erlambang, T. and Y.I. Sinegar, Ecological aspects and marketing of dog conch Strombus canarium Linne, 1758 at Bintan Island, Sumatra, Indonesia. Special Publication Phuket Marine Biological Center, 15: Klingenberg, C.P., Morphological integration and developmental modularity. Annual Review of Ecology, Evolution, and Systematics, 39: Marquez, E.J., A statistical framework for testing modularity in multidimensional data. Evolution, 62(10): Marquez, E.J., Mint: Modularity and Integration analysis tool for morphometric data. Version 1.0b (compiled 09/07/08). Mammals Division, University of Michigan Museum of Zoology. Olson, E.C. and R.I. Miller, Morphological Integration. Chicago University of Chicago Press. Raff, R.A., The shape like genes, development and evolution.of animal form, Chicago: University of Chicago Press. Rohlf, F.J., Tps Dig version 2.10 Department of Ecology and Evolution. State University of New York at Stony Brook. New York. Rohlf, F.J., TPS util version Department of Ecology and Evolution, State University of New York at Stony Brook. New York. Schlosser, G., 2004.The role of modules in development and evolution. In modularity in development and evolution. Chicago: University of Chicago Press.

6 27 Cesar G. Demayo et al, 2014 Vogt, G., M. Huber, M. Thiemann, G. van den Boogart, O.J. Schmitz and C.D. Schubart, Production of different phenotypes from the same genotype in the same environment by developmental variation. Journal of Experimental Biology, 211: Wagner, G.P., Homologues, natural kinds, and the evolution of modularity. American Zoologist, 36: Wagner, G.P. and L. Altenberg, Perspective: complex adaptations and the evolution of evolvability. Evolution., 50(3):

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