COMBINATION OF MULTIVARIATE METHODS AND GRAPHICAL DATABASE MANAGEMENT IN SERVICE OF ECOLOGICAL MONITORING

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1 COMBINATION OF MULTIVARIATE METHODS AND GRAPHICAL DATABASE MANAGEMENT IN SERVICE OF ECOLOGICAL MONITORING M. GAÁL Department of Mathematics and Informatics of the Szent István University, Budapest, Hungary L. HUFNAGEL Systematic Zoology Research Group of the Hungarian Academy of Sciences, Budapest, Hungary ABSTRACT The monitoring of living being assemblies is a typical multivariate problem, which does not have yet a satisfactorily elaborated methodology. The aim of our study is to develop a methodological-computational framework that makes easily to survey the states and changes of insect communities and the related background data and helps their objective comparisons. Our approach to do this is the combination of multivariate methods and the possibilities of GIS. INTRODUCTION The states of the ambient animal and plant assemblies basically determine our everyday life and also the human activities have a great influence on them. The efficiency of the agriculture depends considerably on the activity and changes of states of agroecosystems. The modern pest management and integrated plant production should deal in a great part with the fine (environmentally sound and economical) regulation of these assemblies. The nature protection deals also with the states of these assemblies, with their changes and with the conservation possibilities. At the same time these ecological changes indicate the changes of the abiotic factors (climate, pollution etc.) too, so their monitoring has a very great scientific and social importance, first of all regarding the environment and nature protection, public sanitation, agriculture and water quality control. We would like to contribute to the methodological development of the plant protection prognosis (Gaál & Hufnagel 2000), environment and nature protection monitoring and synbiological qualification of the habitats. Additionally with our new approaches we would like to go ahead in the evaluation of states of complex systems. 285

2 MATERIALS AND METHODS Basic data For the purpose of the methodological development and for the testing of the elaborated technologies and ideas we need real monitoring data. Up to now we have been dealing with two different data sources, one about bugs (Gaál & Hufnagel 1999) and another about moths (Hufnagel et al. 1999b, Nyilas et al. 2000). To illustrate our results in this paper we use the Lepidoptera data obtained from the national plant protection and forestry light trap networks. The Hungarian light trap networks have the same type of traps, they are continuously operated and the caught material is managed taxonomically in the same way. The database contains data from 40 years and from more than 40 light traps (many of them are operated at the same place and in the same manner from the beginning to end), which cover roughly uniformly the whole area of Hungary. All these offer unique opportunities to eliminate some problems of sampling, which usually make difficult the statistical analysis of the results of light trappings. From the original database we use only the data of the regularly identified species. The aim of the filtering of data is to ensure the same potential variety of species in every sample, which is very important regarding the comparability. Usually it is very difficult to describe the changes of an insect ecosystem, but all mentioned above allow reveal the cenological phenomena and long-term events hidden in our database and serve as basic for the extensive monitoring. Multivariate methods The similarity patterns hidden in our data series were analysed using multivariate exploratory data analysis. To reveal the data structure many types of analyses were carried out: metrical and non-metrical ordinations (PCA, PCoA, NMDS, CCA), hierarchical classifications (cluster-analysis with distance- and homogeneity optimization) and table rearrangements (seriation, block clustering). We can use these analyses both with presence-absence data and with quantitative ones. For the multivariate analyses we used the SYNTAX 5.1 program package, which offer a wide range of methods (Podani 1993/a, b). The detailed description of the applied methods can be found in the books of Podani (1994, 1997). Database management and graphical presentation In the course of the development we use the ARC/Info 7.0 GIS program on a Silicon Graphics Iris Indigo computer (with Irix 5.2 operation system). Using its own AML (ARC Macro Language) programming language we develop a system that makes possible the representation and further analysis of the objects (places/dates) and other related information in many point of views. In this case the GIS handles data not from a geographical space but data of an abstract cenological space. If we assume continuity between the values of the different objects such as diversity, density, elevation, water quality etc. the TIN (Triangulated Irregular Network) module can be used to make 3D surface models or represent isolines. We use point coverages when the information belongs only to certain points to represent point features such as identifiers, habitat type and presence of insects' species. 286

3 RESULTS Our monitoring system is the combination of multivariate methods and GIS. The first conception was published in Hufnagel et al. (1999a). To realize our aims we developed the following new methodological framework: The first thing is to define a reference database usually with presence-absence data in which the records are the indicator species and the columns are the samples (objects) from different places and/or times. It must contain so much samples that represent well most of the possible assemblies. The data structure must be revealed warily using various multivariate methods in order to recognize the similarity patterns and relationships between the variables and objects. Based on these, an ordination of species must be chosen, that is rather informative for us. Hereafter we consider the coordinates of the species in this ordination as peculiarities of them (we can call them as cenological indicator coordinates). Using these coordinates, we can introduce the indirect ordination for the objects. The indirect ordination for the objects means the averaging of the coordinates of the indicator species found in the given samples. The result is that we define as a stable plane of states (a "map" of the possible states of communities) and we mark every state with a point, the changes with a vector on it. The objects in the indirect ordination can be all kind of samples, independently from the reference database. All these results can be represented and analysed using GIS methods. The system makes possible to analyse the data and the similarity patterns of the objects and values in many point of views. Among the advantages of the indirect ordination (our new system) are that it allows to handle a great amount of data and the addition of new objects does not change the similarity pattern of the other objects. In the followings we demonstrate our system using a simple case study connected with plant protection prognostic. Let us see how can we relate the patterns manifested in the plane of states with the appearance of certain pests and with the risk of injury. To illustrate it we chose an important, widespread pest, the cabbage moth (Mamestra brassicae L.). The cabbage moth is a dangerous pest, which has two generations per year. The feeding larvae produce the injury, then overwinter as older larva or pupa. The adults fly well to light traps, so it is easy to give a short-term prognostic for the two dangerous periods. We know from the specific literature that the density of the pests, the ecological conditions during the period over the dangerous density, their joint situation in time and coincidence with the sensitive phenological state of the plants determine the risk of injury. The actual states of the ecological and cenological circumstances, which vary in time and space, are indicated in our plane of states with the position of their representative points, so we must project the appearance of a pest and its quantitative proportions to this plane. In the Figure 1. we represent data of a light trap in different years (for the better visibility we do not show all points, only monthly sums) in the place called "Julianna-major". The first we can see is that the circle symbols, which indicate the presence of the cabbage moth, have a determined alignment and designate a limited area of the plane of states that means the period of summer and beginning of autumn. The injury is produced by the larvae, which can be expected 2-3 weeks after the first appearance of the circle symbols. Using former data series 287

4 (many years/places) we can draw the area of the appearance of a pest with arbitrary reliability and then we need to follow with attention only the changes of states in the year of our observation. From the position of our actual points in the plane we can see how far or near they are to the area of the probable appearance of a pest, whether they will have a bypass or they will cross it. So the planar situations of the points earlier than the appearance of the circle symbols have now a prognostic value. Let us see in our example how can we relate all mentioned above with the trajectories of the changes of states in different years and how they specify our results. Beyond the points we drew two trajectories in the picture, which came from very different cenodynamic years and we connected their points with spline method (polynomial fitting) to have more expressivity. In the picture we can see that one of the trajectories (in the year 1981) runs in the internal part of the plane of states and crosses the risk area of the cabbage moth for some length. But the line of the year 1976 runs in the external regions and bypasses the possible formation of the risk situation. The cenological states are determined by the joint behaviour of all species found in a sample, even so we can delimit some regions of the plane of states based on the appearance of properly selected species. For example in the figure the triangle symbols mean the presence of red chestnut (Cerastis rubricosa, Schiff.). We can observe that when the curve (trajectory) passes through this area it will pass later also through the risk area of the cabbage moth with a high probability. The relationships like this offer a new possibility for the plant protection prognostic. The purpose of the plant protection prognostic is to provide prediction not only for the probable dates of risk periods, but also for the extent (rate) of the injury. For this the analysis of the presence patterns is not enough, we need to examine also the density circumstances. We suggest to do it with 3D surface models or with isolines. In the Figure 2. we can observe that within the previously seen risk area, uniformly marked with circles, we can find different densities of the pest and the states with really high risk can be found in a definitely smaller area and form two peaks. The dotted line means the boundary of our present point-pattern. DISCUSSION The aim of our work is to develop a methodological-computational framework that makes easily to survey the states and changes of insect communities and the related background data and helps their objective comparisons. The idea for the development of the monitoring system is the combination of multivariate dimension-reducing methods (multidimensional scaling) and GIS. Using the indirect ordinations (we apply the dimension-reducing methods for the variables, then from this we calculate for the objects) we define a stable plane of states, a "map" of the possible states of communities. The objects in the indirect ordination can be from any kind of samples, independently from the original reference database. For the representation and further analysis of the objects and related data we use a GIS program. The system can handle theoretically indefinite number of objects and related variables. Based on our results (also with the bugs' data) it seems that this monitoring system reflects well the real cenological states and offers an easy way to examine the related data in many point of views. Of course we would like to go ahead in the development with more data, further testing and with new ideas. 288

5 FIGURE 1. The presence of the examined moths and trajectories from two different cenodynamic years ( external, internal line) FIGURE 2. The density pattern of the cabbage moth using isolines 289

6 REFERENCES Gaál, M., Hufnagel, L. (1999) Vízi élôhelyek állapotának monitorozása poloskaközösségek alapján [Monitoring the state of freshwater habitats based on aquatic and semiaquatic bug communities], Proceedings of the Agroinformatics 99 p , Debrecen, Hungary. Gaál, M., Hufnagel, L. (2000) New Methods in the Applied Entomological Monitoring, Proceedings of the Lippay János & Vas Károly Scientific Symposium, p , Budapest, Hungary Hufnagel, L., Gaál, M., Ferenczy, A., Ôszi, B. (1999a) Többváltozós módszerek alkalmazása rovaregyüttesek tér-idôbeli zoocönológiai monitorozásában [Application of multivariate methods in spatial-temporal zoocenological monitoring of insect communities], Proceedings of the Informatics in the Hungarian Higher Education 99 p , Debrecen, Hungary. Hufnagel, L., Mészáros, Z., Gaál, M., Ferenczy, A. (1999b) Temporal-Spatial Patterns of Noctuinae Communities (Lep. Noctuidae) in Hungarian Apple Orchards (Apple Ecosystem Research), Acta Phytopathologia et Entomologica Hungarica 34 (4) p Nyilas, L., Hufnagel, L., Gaál, M., Mészáros, Z. (2000) Seasonal and Successive Dynamics of Noctuidae Assemblies Based on Light Trap Collections in Julianna Major, Proceedings of the Lippay János & Vas Károly Scientific Symposium, p , Budapest, Hungary. Podani, J. (1993/ a) SYN-TAX Version 5.0 Users Guide, Scientia, Budapest, Hungary. Podani, J. (1993/b) SYN-TAX. Computer programs for multivariate data analysis in ecology and systematics, Abstracta Botanica 17(1-2): Podani, J. (1994) Multivariate analysis in Ecology and Systhematics, SPB Publishing, The Hague, the Netherlands. Podani, J. (1997) Bevezetés a többváltozós biológiai adatfeltárás rejtelmeibe. [Introduction to the multivariate biological data analysis], Scientia Kiadó, Budapest, Hungary. 290