Diversity of weed vegetation on arable land in Slovenia

Transcription

1 Journal of Plant Diseases and Protection, Special Issue XXI, , 2008, ISSN Eugen Ulmer KG, Stuttgart Diversity of weed vegetation on arable land in Slovenia Diversität der Unkrautvegetation auf dem Ackerland in Slowenien U. Šilc* Institute of Biology, Scientific Research Centre of Slovenian Academy of Sciences and Arts, Novi trg 2, SI Ljubljana, Slovenia * Corresponding author, urban@zrc-sazu.si Summary On the basis of 482 geographically stratified relevés sampled between 1937 and 2004 the diversity of weed vegetation on arable land in Slovenia was analysed. Partial Canonical Correspondence Analysis and several factors were used as explanatory variables (altitude, crop type, year of the record, phytogeographical region, climatic district, temperature and precipitation) and the species composition was related to major gradients. Residuals from the regression of the number of species on a log plot size were used to eliminate variance due to species-area relationship. Then they were used as species richness. Mean Sørensen dissimilarity was used as β-diversity for all pairs of relevés to detect changes in a partitioned data-set along the gradients. Ecological interpretations of the gradients associated were done using Ellenberg indicator values for relevés. Crop is the main factor in determining the species composition of the weed vegetation, the second is the climate. Crop related differences contradict the findings in Central Europe and show a transitional position of the studied area. Differences in β-diversity are attributable to altitude, climate and phytogeography. Key words: β-diversity, multivariate analysis, plant community Zusammenfassung Auf der Grundlage der 482 zwischen 1937 und 2004 entnommenen geographisch geschichteten Aufnahmen wurde die Diversität der Unkrautvegetation auf dem Ackerland in Slowenien analysiert. Partielle kanonische Korrespondenzanalyse und einzelne Faktoren wurden als erklärende Variablen (Höhenlage, Kulturarten, Jahr der Aufnahme, phytogeographische Region, klimatisches Gebiet, Temperatur und Niederschlag) verwendet, die Zusammensetzung der Arten bezog sich auf Hauptgradienten. Die Residuen aus der Regression der Anzahl der Arten auf der logarithmierten Aufnahmefläche wurden verwendet, um die Abweichung infolge des Verhältnisses zwischen dem Grundstück und der Anzahl der Arten zu beseitigen. Sie werden dann als Artenreichtum verwendet. Die durchschnittliche Sørensen Verschiedenheit wurde als Beta-Diversität für sämtliche Aufnahmepaare verwendet, um Veränderungen in einer partitionierten Datenmenge entlang der Gradienten festzustellen. Ökologische Interpretationen dieser Gradiente wurden durch Ellenberg- Zeigerwerte der Aufnahmen vorgenommen. Kulturart ist der Hauptfaktor bei der Feststellung der Zusammenstellung der Arten für die Unkrautvegetation, das Klima spielt die zweitwichtigste Rolle. Die mit der Kultur verbundenen Differenzen widersprechen den Ergebnissen in Zentraleuropa und weisen eine Übergangslage des betreffenden Gebiets auf. Unterschiede in der Beta-Diversität sind der Höhenlage, dem Klima und der Phytogeographie zugeordnet. Stichwörter: Beta-Diversität, multivariate Analyse, Pflanzengesellschaft 1 Introduction Diversity of weed vegetation is the subject of many studies employing multivariate analyses of large datasets (HALLGREN et al. 1999, LOSOSOVÁ et al. 2004). These large datasets enable the verification of classifications of higher phytosociological units of weed vegetation that were proposed in recent years. Division of higher units at the first level was changed from crop type to soil properties (which were treated as the most important factor) (HÜPPE and HOFMEISTER 1990, JAROLÍMEK et al. 1997). Climate also is reported as being an environmental factor that influences the species composition and diversity of weed vegetation more than the human impact (PYŠEK et al. 2005). Weed vegetation changes its characteristics in the gradient from the south to the north, which is the direction of expansion of agriculture into Europe. The long journey of the species that originate in southwest Asia changed their phytosociological behaviour. The farther the species are from their centre of areal the more specialised they become and are more able to survive only in extreme habitats (acid, moist etc.) (OBERDORFER 1954, HOLZNER 1978, GLEMNITZ et al. 2000). The differences in weed vegetation from different parts of Europe in the north-south direction are therefore the result of environmental variables that have a different impact and intensity on weed species. Also diverse is their reaction along this gradient. The aim of our paper is: (1) to detect the diversity pattern of weed vegetation in Slovenia, (2) to find relationships between the factors affecting the species composition and vegetation diversity and (3) to compare the results obtained to Central Europe. 2 Materials and methods 2.1 Materials A dataset of 792 relevés was compiled from Slovenia. After stratified resampling, 482 sampling plots were used for further analyses. The same dataset was already used in a formal classification of the weed vegetation of Slovenia (for the resampling strategy see ŠILC and ČARNI 2007). Coordinates were assigned for all relevés (plots without originally assigned coordinates were georeferenced posterior). Each plot was then assigned to a climatic district

2 350 Šilc: Diversity of weed vegetation on arable land in Slovenia -1 4 Tab. 1: Tab. 1: 0 5 Fig. 1: Detrended correspondence analysis (DCA) diagram of species and passively projected environmental variables. For simplicity only altitude, crop type, temperature and precipitation are shown. Species with low weight are not presented. Abb. 1: Darstellung der Detrended Correspondence Analysis (DCA) von Arten und passive Projektion der Umweltvariablen. Wegen der Einfachheit werden nur Höhenlage, Kulturtyp, Temperatur und Niederschlag dargestellt. Arten mit niedrigem Gewicht werden nicht aufgezeigt. Variable Environmental variables used in the analysis. Umweltvariablen verwendet in der Analyse. Range Year Altitude Crop 2 Climatic district 1 9 Phytogeographical region 1 6 Mean annual temperature ºC Annual percipitation Antharv Mentarv Galiapa Aphaarv Ceraglo Violarv Polyavi Myosarv Paparho Fallcon Veroarv Matrcha Cereal Temperature Convarv Ranurep Lamipur Elymrep Stelmed Cirsarv Veroper Capsbur Chenalb Traaoff Oxalstr Euphhel Calysep Polylap Altitude Year Root Crop Chenpol Polymac mm Digisan Echicru Galipar Setapum Amarret Precipitation Climatic district and Phytogeographical region were transformed into ordinal scale increasing from warm/dry to cold/wet. Temperature and precipitation were taken from Mekinda-Majaron (1995) and Zupančič (1995). Das klimatische Gebiet und die phytogeographische Region wurden in einer Ordinal-Skala ansteigend von warm/trocken bis kalt/feucht umgesetzt. Temperatur und Niederschlag wurden von Mekinda-Majaron (1995) und Zupančič (1995) entnommen. (OGRIN 1996) and phytogeographical region (WRABER 1969). Other variables used in the multivariate analysis are listed in Table 1. As the plot size varied within the dataset, the number of species was replaced by residuals from the regression of the number of species on the log plot size (ROSENZWEIG 1995, LOSOSOVÁ et al. 2004). These residuals were then used in the subsequent analyses as the species richness measure. The relevés are stored in Slovenian Phytosociological Database (ŠILC 2006). 2.2 Statistical analysis Detrended correspondence analysis (DCA) was used to calculate the overall variation of the species composition of weed vegetation. For a better interpretation explanatory variables were passively projected onto the ordination graph, but they do not contribute to the extraction of ordination axes. We employed partial ordination (CCA) to determine the gross and net effects of the variables on the species composition. Gross effect is the effect of a single explanatory variable on the species composition tested by the permutation test on the first canonical axis (999 permutations were used). Net effect is the effect of a particular variable after the effects shared with other variables were partialled out. Net effects were tested with a series of partial CCA s where one variable was used explanatory variable and others as covariables. The net effects for the species richness were calculated for each explanatory variable as a dependant variable. Net effects were calculated with standardized residuals from the general linear model (GLM). Species richness was than regressed on theses standardized residuals. To access β-diversity we followed the procedure of LOSOS- OVÁ et al. (2004) (, done) in Juice (TICHÝ 2002) program. In order to detect the patterns of β-diversity along the major gradients the dataset was partitioned within A particular factor, and for each part of the dataset β-diversity was calculated. β-diversity is calculated as mean Sørensen dissimilarity for all of the pairs of records. With 500 bootstrap samples taken from each partition Box-Whisker plots were made. Multivariate analyses were calculated with the CANOCO program (AND/ter BRAAK and ŠMILAUER 2002) and univariate with the STATISTICA program (ANON. 2001). 3 Results In Fig. 1 the overall variation in weed vegetation as detected by DCA analysis is shown. The first ordination axis explained 2.8 % of the total variation and could be associated to the Crop type. The second axis corresponds more to the climatic gradients (temperature and precipitation) and altitude, which is associated with both temperature and precipitation. The gradient length of the first axis indicates large beta diversity, i.e. the change in the species composition along the environmental gradient. The gross and net effects calculated by CCA and partial CCA s are presented in Tab. 2. The most important explanatory variables are Crop type and Year of sampling. Relationships between the environmental variables and characteristics of weed vegetation are shown in Tab. 3, species richness and Ellenberg indicator values were used to interpret the gradients. Beta diversity (Fig. 2) is higher in cereals than in root crops, it decreases with high altitude, but shows no interpretable increase of β-diversity between 1981 and The highest diversity is in the Submediterranean phytogeographic region, but it decreases in climatic districts from the west towards the east.

3 Šilc: Diversity of weed vegetation on arable land in Slovenia 351 Tab. 2: Tab. 2: Gross and net effects of environmental variables on the species composition of weed vegetation in Slovenia. Effects were calculated by (partial) CCA and presented by percentage variation in the species composition. **=p<0.01; *=p<0.05; n.s.= non significant. Brutto- und Netto-Auswirkungen der Umweltvariablen auf die Zusammensetzung der Arten der Unkrautvegetation in Slowenien. Auswirkungen wurden durch (partielle) CCA ausgerechnet und durch prozentuale Variation in der Zusammensetzung der Arten dargestellt. **=p<0.01; *=p<0.05; n.s.= unbedeutend. Gross Net F-value p Climatic district ** Crop ** Phytogeography * Year ** Precipitation ** Temperature n.s. Altitude ** All Tab. 3: Tab. 3: Relationship between net effects of environmental variables and some vegetation characteristics. **=p<0.01; *=p<0.05; n.s.= non significant. Verhältnis zwischen Netto-Auswirkungen der Umweltvariablen und einigen Vegetationscharakteristiken. **=p<0.01; *=p<0.05; n.s.= unbedeutend. Altitude Year Phytogeography Climatic district Precipitation Temperature Crop Light n.s n.s n.s ** ** n.s ** Temperature ** ** ** ** ** ** ** Continentality n.s n.s n.s ** * ** ** Moisture ** ** ** ** ** ** ** Soil Reaction n.s ** n.s ** ** ** n.s. Nutrients ** ** ** ** ** ** ** Species richness n.s ** n.s ** ** n.s n.s. 4 Discussion 4.1 The importance of environmental variables According to Pcca, the most important factor influencing the weed species composition is the crop type. This contradicts the results of the study from Central Europe (LOSOSOVÁ et al. 2004), but is in accordance with HOLZNER S forecasts (1978) for southeastern Europe. Similar results were obtained through the analysis of a large dataset from the Balkan peninsula (ŠILC et al. subm.). The second most important factor is the climatic district. Climatic districts also correspond to the type of the cultural plants grown (OGRIN 1996). It is interesting that mean annual temperature is not significant, while the amount of precipitation is the fourth most important environmental variable. Some other temperatures (of the average for a single month) that were not tested in our dataset are probably more important for the weed vegetation diversity than mean annual temperature. The significance of temporal changes of weed flora and vegetation is well studied (ANDREASEN et al. 1996, LOSOSOVÁ et al. 2004, PYŠEK et al. 2005, ŠILC and ČARNI 2005), and the changes over time in the species composition are the third most important factor. Changes over time affected the species composition, but less SO the species richness (see discussion below). Ellenberg indicator values show the increase in nutrients with altitude and time. A decrease of soil reaction is associated with the use of fertilizers. A strong relationship of climatic districts with Ellenberg indicator values shows the great importance of climate on weed vegetation. A transitional position of Slovenia in Europe in the north-south gradient is indicated by the significance of climate and crop type on the species composition of weed vegetation. Crop is a factor that has far more influence in southern Europe than in Central and northern Europe (OBERDORFER 1954, 1993, HOLZNER 1978).

4 352 Šilc: Diversity of weed vegetation on arable land in Slovenia Fig. 2: Beta diversity along the studied gradients. Abb. 2: Beta-Diversität entlang den Gradienten. 4.2 Beta diversity Changes of beta diversity along the temporal gradient show a pronounced peak in the 1980 s that has no clear ecological meaning. This is similar to the pattern in Czech dataset of weed vegetation and it could also be associated with a bias influenced by a lower intensity of sampling (LOSOSOVÁ et al. 2004). Beta diversity along the altitudinal gradient shows an obvious decrease in weed vegetation over 600 m a.s.l., while in Central Europe a decrease in β-diversity is more gradual. This could be explained by a lack of cereal fields above that altitude. This altitude is also a limit for vineyards in Slovenia (OGRIN 1996) and A point of transition between associations Echinochloo-Setarietum and Galeopsido-Galinsogetum (ŠILC & ČUŠIN 2005). Diversity in the species composition between the sites is the highest in the Sub Mediterranean region and the lowest in the Subpannonian region, where agriculture is the most intensified. In the Sub Mediterranean region there is also a high diversity of crops grown. Differences between climatic districts are similar to phytogeographical regions. There is an evident decrease in gradient from the west towards the east and could be associated with the index of the Mediterranean type of precipitation that was used to delimit an individual district (OGRIN 1996). 4.3 Species richness The number of species per plot was used as a measure of the species richness. The effect of altitude on the species richness is not significant in our dataset compared to the

5 Šilc: Diversity of weed vegetation on arable land in Slovenia 353 one from Central Europe. The species richness is affected by the climatic district and precipitation. The species richness increases with the southern climate (GLEMNITZ et al 2006) and the decreasing precipitation. A higher number of species per plot in mountain climate districts is probably the result of invading species from the neighbouring vegetation types. The species richness shows its increase over time. Several studies from different parts of Europe report on changes in the species composition and richness over time. Generally, it is reported that the species richness decreased in the last century (for the Czech Republic LOSOSOVÁ et al. 2004, PYŠEK et al. 2005) and that there has been a decline in rare species and an increase in herbicide tolerant species (HILBIG and BACHTHALER 1992a, 1992b, ŠILC and ČARNI 2005). In our dataset, although it consists of relevés from the largest time span found in literature ( ), a slight increase in the species richness is detected. This is opposite to many studies in Europe, but in a sense confirms observations of ANDREASEN et al. (1996) and HALLGREN et al. (1999) that temporal trends are more evident in the lost rare species than in the species number or abundance reduction. Mass use of herbicides started after WW II, but their effect on the richness is generally secondary to that on weed densities (LÉGÈRE et al. 2005). An increase in the number of species over time could also be a bias in methodology as plots were subjectively sampled. SUTCLIFFE and KAY (2000) mention the sampling methodology, annual climatic differences or a reflection of real long-term changes as reasons for the obvious increase or decrease of commonly distributed species. As indicated in several studies (LÉGÈRE et al. 2005, PYŠEK et al. 2005, GLEMNITZ et al 2006), the pattern of the species richness and the diversity of weed vegetation of Europe is complex. Factors that drive this diversity are partly attributable to management and partly to the environmental variables and as they are mutually correlated it is hard to partial out a separate influence. This is additionally aggravated by the changes in the strength of their influence depending on the geographical gradient and latitude (GLEMNITZ et al 2006). Acknowledgements I would like to thank Andraž Čarni for valuable comments on the previous versions of the paper. This work was supported by a grant from ARRS L Literature ANDREASEN, C., H. STRYHN, J.C. STREIBIG, 1996: Decline of the flora in Danish arable fields. Journal of Applied Ecology. 33, BRAUN-BLANQUET, J., 1964: Pflanzensoziologie. Grundzüge der Vegetationskunde. 3. Springer Verlag, Wien. GLEMNITZ, M., G. CZIMBER, L. RADICZ, J. 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