Change of Species and Habitat Diversity in the Pannonian Region of Eastern Lower Austria over 170 Years: Using Herbarium Records as a Witness

Alexandra Grass, Karin Tremetsberger, Reinhard Hössinger, Karl-Georg Bernhardt

Institute for Transport Studies, Department of Landscape, Spatial and Infrastructure Sciences, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria.
Institute of Botany, Department of Integrative Biology and Biodiversity Research, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria.

DOI: 10.4236/nr.2014.511051                

Abstract

By dint of historical herbarium specimens, we show how changes in species and habitat diversity can be reviewed and correlated with historical events. Our work is based on a digital database of specimens of the BOKU herbarium (WHB), which can be assigned to the Pannonian region of eastern Lower Austria. The complete dataset (n = 6655 specimens) was analyzed with the aid of statistical methods allowing computational elimination of collectors' effects (i.e. unbalanced collecting interests of collectors over time; multiple regression analysis, general linear model), from the first herbarium specimens (dating back to 1830) to the present. As a result, a significant decrease in the proportion of species of some habitats (above all water bodies and closely associated habitats, humid and wet meadows, fens and fen meadows, and nutrient poor grassland) was detected. For water-influenced habitats, this decrease correlates with the time of Danube regulation. Moreover, an increase in the proportion of species of ruderal sites was asserted during the observation period. The analysis procedure developed can be used for evaluation of major digitized herbaria in order to trace historical changes in species and habitat diversity.

Keywords

Danube Regulation, Fens, Grassland, Herbarium Analysis, Water Bodies

1. Introduction

With intensification of agriculture and forestry and increased settlement activities, the development of human civilization is heavily transforming landscapes and this transformation is considered to be one of the main drivers of changes in species composition of ecosystems [1] -[5] . Increased rates of species extinctions and species invasions are found at all scales, from local to global [6] . Half of all organisms—plants, animals and microorganisms—living on earth today might have disappeared until the end of the 21st century [7] . Ecosystem properties, however, depend greatly on biodiversity in terms of the functional characteristics of organisms present in the ecosystem [6] [8] -[10] . Through the irretrievable loss of species, the potential for further development of ecosystems gets lost. A high level of biodiversity is essential for the intact ecologic system of the earth. Our agricultural capacity and public health also depend on diverse natural biota and their interspecific relations and activities [7] [11] -[13] .

Empirical evidence for tracking historical changes in species and habitat diversity may be based on historical maps, aerial photographs and surveys (e.g. [14] -[20] ). However, adequate historical data for studying specific groups of organisms or regions may be very sparse or lacking. Another important source of historical information may stem from museum collections [21] . For example, herbarium specimens in conjunction with other data sources have allowed inferences to be made about drivers of floristic changes [22] , probabilities of extinction [23] , effects of urbanization on flowering phenology [24] , and plant responses to climate change [25] -[28] . Last but not least, historical herbarium records can be a valuable source of information for decision-making in nature conservation efforts. For example, the EU Habitats Directive and national Red Lists need to classify habitats and species of European importance into categories of endangerment. However, use of herbarium records for inference of historical changes may be hampered by misidentifications of specimens, uneven collecting intensity over time and, most seriously, unbalanced collecting interests or foci of collectors over time, the latter two points being especially severe in small herbaria.

Here, we investigate the potential of herbarium records from a single small herbarium for tracking historical changes in species and habitat diversity. Our area of interest is the Pannonian region of eastern Lower Austria, characterized by a warmer and drier climate than the more western parts of Austria. The data source for this analysis was provided by the digitized database of specimens of the herbarium of the University of Natural Resources and Life Sciences, Vienna (WHB), spanning the time period from 1830 to 2007. Specifically, we ask the following questions: 1) Can herbarium specimens be used for inference of changes in species and habitat diversity? If the answer to this question is in the affirmative: 2) How have species and habitat diversity in the Pannonian region changed over the last 170 years? Which specific landscape transformations can be assessed by the aid of the historical specimens? For example, we hypothesize that intensification of agriculture has negatively affected nutrient poor grassland and that settlement activities have led to an increase of ruderal sites. Likewise, we hypothesize that regulation of the Danube river from 1870-1875 has negatively affected habitats influenced by water.

2. Research Methods

2.1. Study Area

The Pannonian region is mainly situated in the Hungarian lowlands, but it also covers parts of Romania, Serbia, Slovakia, the Czech Republic and Austria, where its westernmost part is localized in Lower Austria and Burgenland. In Lower Austria, the western border of the Pannonian region follows the eastern foothills of the Bohemian Massif (Manhartsberg), from Retz (48.756˚N, 15.952˚E) to Krems an der Donau (48.411˚N, 15.610˚E), from where it extends to the eastern part of Wachau and the Tullnerfeld. From Vienna southwards, the western border of the Pannonian region follows the eastern foothills of the Northern Limestone Alps (Thermenlinie), to Gloggnitz (47.676˚N, 15.938˚E), where it is limited by the eastern foothills of the Central Eastern Alps (Bucklige Welt and Rosaliengebirge) [29] [30] . The Pannonian region is characterized by a warm and dry climate, with an average annual temperature ranging from 8˚C - 10˚C (averaged over the years 1901-1950) [31] . Average temperature in July is 18˚C or higher and the difference between the average temperature in January and the average temperature in July is 20˚C - 22˚C [31] . The Pannonian region receives low average annual rainfall, with yearly precipitation ranging from 600 - 700 mm (averaged over the years 1901-1950) [32] . In addition to low precipitation, desiccating winds contribute to the dry climate [33] . In Lower Austria, the Pannonian region extends in the planar-colline altitudinal zone, with forest steppe, Quercus cerris-Q. petraea forests and Q. petraea-Q. roburCarpinus betulus forests as zonal vegetation and a broad spectrum of thermophile plant communities [30] . The Danube river has shaped the Tullnerfeld and parts of the Vienna Basin (Wiener Becken).

2.2. Specimens of the BOKU Herbarium

The data source for this work stems from the herbarium of the University of Natural Resources and Life Sciences, Vienna (WHB). It comprises approximately 59,000 specimens as of June 2013, covering ≈9000 species. The collection focuses mainly on Austria, the former Austro-Hungarian monarchy and the old regional flora of Vienna [34] . The specimens are digitized in an Access database. Query of the database for the keyword “Lower Austria” retrieved 10,419 specimens, which were screened for their belonging to the Pannonian region. 6655 such specimens were retrieved, which represent 1643 species or subspecies.

For further analyses, each specimen was given several attributes describing the affiliation of the species represented by the specimen to a given habitat. Information on the occurrence of species in different habitats was extracted from the habitat descriptions provided for each species in Fischer et al. [35] . Attributes are represented by dummy variables, which take the value of one, if the attribute applies (i.e. the species represented by the specimen occurs in that habitat), and zero otherwise. The primary list of habitats is very diverse (Table 1). In order to achieve a more holistic picture in the analysis, we assembled the primary habitats to aggregate habitats, each of which includes several primary habitats with similar characteristics of vegetation (Table 1).

2.3. Analysis Procedure

Prior to the analysis, double and multiple representations of the same species within the same year were removed. The in this way adjusted dataset includes 6,446 specimens. It was evaluated by means of a two step analysis procedure (Figure 1) using PASW Statistics ver. 18.0.0 (^© Polar Engineering and Consulting, 1993- 2007).

Table 1. Tested primary habitats and their combination into aggregate habitats.

Table2 Significant results of the multiple linear regression analyses. Each row corresponds to a separate model. The left column specifies the dependent variable in terms of an attribute, which either applies to the collected species or not. The two other columns show the size and significance of the linear effect of the year of collection on the dependent variable. The effects of the dummy variables of collectors, which served to control for the collectorsʼ bias, are left aside.

In the first step, we tested the attributes assigned to the specimens, i.e. the primary and aggregate habitats according to Table 1, for a linear time trend: Is there a linear increase or decrease in the proportion of species of certain habitats during the observation period from 1830 to 2007? This was done by means of linear regression using the attribute in question as dependent variable (e.g. the affiliation of the species represented by the specimen to the aggregate habitat “water bodies and closely associated habitats”; Figure 2) and the year of collection as independent variable.

A problem of this approach is that some collectors contributed a significant number of specimens to the herbarium. If they had specific interests, the result would be a systematic overor underrepresentation of certain species or habitats in a given period of time. We solved this problem by creating a dummy variable for each “significant collector” with a minimum of 15 sampled specimens. It takes the value of one if the collector sampled the specimen and zero otherwise. These dummies were included as independent variables in the regression analysis in addition to the year of collection, that way extending the simple linear regression to a multiple linear regression. This procedure computationally eliminates the “collectors bias” from the trend estimation. It can, however, not correct for an eventual “within collectors bias”, i.e. for a bias arising from changing interests of a particular collector during his or her collecting career. The independent variables entered in the multiple regres-

Figure 1. Two step analysis procedure.

Figure 2. Demonstration of the first step of the analysis procedure of the attribute “water bodies and closely associated habitats” (aggregate habitat): simple linear regression. The X-axis shows the collection year as independent variable. The Y-axis shows the attribute value, i.e. the affiliation of the species represented by the specimen to the aggregate habitat “water bodies and closely associated habitats” according to Fischer et al. [35] , as dependent variable.

sion analysis by the stepwise method. If the year of collection fell out of the model by using the stepwise method, the analysis was repeated by using the enter method of the multiple regression. In the latter case, the entering independent variables were the year of collection and the dummies of those collectors, who had a significant influence when using the stepwise method of the multiple regression analysis. The main result is the estimated parameter of the year of collection, i.e. the regression coefficient describing the effect of year on the proportion of species affiliated to a particular habitat. It describes the average increase or decrease of the dependent variable (e.g. the proportion of species affiliated to the aggregate habitat “water bodies and closely associated habitats”; Figure 3), if the year of collection increases by one unit. The original attribute value is either one or zero. The computational elimination of the collectorsʼ effects causes that the expected values of those specimens, which were sampled by a collector with a specific interest in plants of water bodies and closely associated habitats, are reduced by a factor, which corresponds to the regression coefficient of the dummy variable specifying this collector (Figure 3). The attribute values therefore either fall below one (if the original value was one) or below zero (if the original value was zero). In contrast, it is also possible that the expected value of specimens, which were sampled by a collector, who avoided plants of a particular habitat, are enhanced by a factor corresponding to the regression coefficient of the dummy variable specifying this collector (this case does not apply to the example in Figure 3). In order to distinguish significant trends from barely random fluctuations, the parameter of the year of collection was subjected to a test of statistical significance yielding the error probability α of an erroneous rejection of the null hypothesis (0 ≤ α ≤ 1).

In the second step, we searched for evidence for a discrete change within the observation period. Plots with data aggregated into five year intervals served for a visual detection of discrete changes. These plots were obtained by plotting the time of collection in intervals of five years on the X-axis and the mean attribute value after elimination of collectorsʼ effects over this five years period on the Y-axis (Figure 4). The linear trend-line in Figure 4 corresponds to the result of the multiple regression analysis (same as in Figure 3). The low number of specimens at the beginning of the observation period (Figure 5) results in a strong fluctuation of the mean attribute values in the early time intervals. For example, the graphic in Figure 4 shows a sudden decline after 1875 that coincides notably with Danube regulation, which took place from 1870 to 1875. Such a discrete change was tested for by a general linear model using the attribute in question as dependent variable. The main independent variable was a discrete time variable in terms of a dummy. It takes the value of zero, if the specimen has been collected before a given event (e.g. before Danube regulation in 1875), and one otherwise. In order to ac-

Figure 3. Continuation of the demonstration of the first step of the analysis procedure of the attribute “water bodies and closely associated habitats” (aggregate habitat): computational elimination of collectorsʼ effects by means of multiple linear regression. The X-axis shows the collection year as independent variable. The Y-axis shows the attribute value after computational elimination of collectorsʼ effects as dependent variable. The graphic shows a decreasing time trend (α < 0.05), suggesting that the proportion of species belonging to the aggregate habitat “water bodies and closely associated habitats” has significantly decreased in the observation period.

Figure 4. Plot with data from Figure 3 aggregated into five year intervals showing the progress of the mean attribute value after computational elimination of collectorsʼ effects during the observation period. The multiple regression analysis reveals a significant linear decrease over time. The plot, however, shows a sudden decline after 1875 that coincides notably with the Danube regulation from 1870 to 1875.

Figure 5. Number of specimens belonging to the Pannonian region of eastern Lower Austria at WHB and number of species represented by these specimens.

count for a possible collectorsʼ bias, the dummies of those collectors with a significant effect in the linear regression were included as additional independent variables. If the effect of a collectorsʼ dummy was no longer significant in the general linear model, it was omitted, so that only significant collectors remained in the model. If the effect of the discrete time variable was not significant (α > 0.05), we rejected the hypothesis of a discrete change in the frequency of the attribute.

3. Results

Out of all habitats tested (Table 1), four aggregate and four primary habitats show a significant linear time trend in the multiple regression analyses (Table 2).

The aggregate habitat “water bodies and closely associated habitats” comprises all types of water bodies such as ponds, rivulets, rivers, and springs along with their banks, as well as alder carrs. The β-value of the multiple regression analysis is −0.00019 (α = 0.007), indicating a 1.9% reduction in the proportion of species of water bodies and closely associated habitats in 100 years. According to the analysis, the proportion of species of water bodies and closely associated habitats fell from 3.6% to 0.3%, i.e. to only 7.4% of the initial value, from the be

Conflicts of Interest

The authors declare no conflicts of interest.

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