Bias in vegetation databases? A comparison of stratified‐random and preferential sampling

Aim: Vegetation plots collected since the early 20th century and stored in large vegetation databases are an important source of ecological information. These databases are used for analyses of vegetation diversity and estimation of vegetation parameters, however such analyses can be biased due to preferential sampling of the original data. In contrast, modern vegetation survey increasingly uses stratified‐random instead of preferential sampling. To explore how these two sampling schemes affect vegetation analyses, we compare parameters of vegetation diversity based on preferentially sampled plots from a large vegetation database with those based on stratified‐random sampling. Location: Moravian Karst and Silesia, Czech Republic. Methods: We compared two parallel analyses of forest vegetation, one based on preferentially sampled plots taken from a national vegetation database and the other on plots sampled in the field according to a stratified‐random design. We repeated this comparison for two different regions in the Czech Republic. We focussed on vegetation properties commonly analysed using data from large vegetation databases, including alpha (within‐plot) diversity, cover and participation of different species groups, such as endangered and alien species within plots, total species richness of data sets, beta diversity and ordination patterns. Results: The preferentially sampled data sets obtained from the database contained more endangered species and had higher beta diversity, whereas estimates of alpha diversity and representation of alien species were not consistently different between preferentially and stratified‐randomly sampled data sets. In ordinations, plots from the preferential samples tended to be more common at margins of plot scatters. Conclusions: Vegetation data stored in large databases are influenced by researcher subjectivity in plot positioning, but we demonstrated that not all of their properties necessarily differ from data sets obtained by stratified‐random sampling. This indicates the value of vegetation databases for use in biodiversity studies; however, some analyses based on these databases are clearly biased and their results must be interpreted with caution.     

Heterogeneity‐constrained random resampling of phytosociological databases

Aim: Phytosociological databases often contain unbalanced samples of real vegetation, which should be carefully resampled before any analyses. We propose a new resampling method based on species composition, called heterogeneity‐constrained random (HCR) resampling. Method: Many subsets of the source vegetation database are selected randomly. These subsets are sorted by decreasing mean dissimilarity between pairs of the vegetation plots, and then sorted again by increasing variance of these dissimilarities. Ranks from both sortings are summed for each subset, and the subset with the lowest summed rank is considered as the most representative. The performance of this method was tested using simulated point patterns that represented different levels of aggregation of vegetation plots within a database. The distributions of points in the subsets resulting from different resampling methods, both with and without database stratification, were compared using Ripley's K function. The mean of random selections from an unbiased sample was used as a reference in these comparisons. The efficiency of the method was also demonstrated with real phytosociological data. Results: Both stratified and HCR resampling yielded selection patterns more similar to the reference than resampling without these tools. Outcomes from the resampling that combined these two methods were the most similar to the reference. The efficiency of the HCR resampling method varied with different levels of aggregation in the database. Conclusions: This new method is efficient for resampling phytosociological databases. As it only uses information on species occurrences/abundances, it does not require the definition of strata, thereby avoiding the effect of subjective decisions on the selection outcome. Nevertheless, this method can also be applied to stratified databases.     

Phytosociological data give biased estimates of species richness

Abstract. Large phytosociological data sets of three types of grassland and three types of forest vegetation from the Czech Republic were analysed with a focus on plot size used in phytosociological sampling and on the species‐area relationship. The data sets included 12975 relevés, sampled by different authors in different parts of the country between 1922 and 1999. It was shown that in the grassland data sets, the relevés sampled before the 1960s tended to have a larger plot size than the relevés made later on. No temporal variation in plot sizes used was detected in forest relevés. Species‐area curves fitted to the data showed unnatural shapes, with levelling‐off or even decrease in plot sizes higher than average. This distortion is explained by the subjective, preferential method of field sampling used in phytosociology. When making relevés in species‐poor vegetation, researchers probably tend to use larger plots in order to include more species. The reason for this may be that a higher number of species gives a higher probability of including presumed diagnostic species, so that the relevé can be more easily classified in the Braun‐Blanquet classification system. This attitude of phytosociologists has at least two consequences: (1) in phytosociological data bases species‐poor vegetation types are underrepresented or relevés are artificially biased towards higher species richness; (2) the suitability of phytosociological data for species richness estimation is severely limited.     

Randomvs non-random sampling: Effects on patterns of species abundance, species richness and vegetation-environment relationships

From a strictly statistical perspective, most of the commonly used statistical tests cannot be performed on vegetation data obtained using a non-random sampling design. Despite this, non-randomly sampled plots such as phytosociological relevés still make sense: because they may focus on objectives not appropriately addressed by random sampling, such as the study of rare plant communities or species; and because random sampling is often more time-demanding and expensive. Considering the huge body of phytosociological data available, an interesting question arises: if we compare randomly and non-randomly sampled data sets, to what extent do the results of our analyses differ with respect to various species and vegetation parameters? We present an attempt to tackle this question by comparing two data sets collected in a 25 km² area close to the city of Bremen, northwestern Germany: the first data set consisted of 30 subjectively (non-randomly) placed, homogeneous plots across different plant communities, each of which was laid out in a nested design including 9 sizes from 0.5 m² to 1,000 m². The second data set consisted of 30 (again nested) plots randomly selected and located with a GPS device; plots were rejected only if they for some reason were inaccessible. The data collection was the same for both data sets: presence-absence of all vascular plants was recorded for the different plot sizes, and soil samples were collected for the determination of the values of some important environmental variables. For the comparison of the two data sets, we used either the complete data sets or sub-sets of those plots located in woodlands. The main results included the following: (1) Species abundance patterns: Random sampling resulted in a larger number of common and a smaller number of rare species than non-random sampling. (2) Species richness at different spatial scales: For the small plot sizes, the number of species in the non-randomly placed plots was higher than in the randomly placed plots, while the differences were less pronounced at larger spatial scales. As a consequence, also the parameters of species-area curves differed between the data sets, especially in the sub-set including woodland plots. (3) Vegetation differentiation: In random sampling, there was considerable redundancy, i.e., there were several plots with high floristic similarity. (4) Vegetation-environment relationships: The ordination scores of the non-randomly placed plots showed a larger number of significant correlations to soil parameters than the scores of randomly placed plots. The results suggest that conclusions drawn from the analysis of non-randomly placed plots such as phytosociological relevés may be biased, especially regarding estimates of species abundance and species richness patterns.     

To sample or not to sample? That is the question ... for the vegetation scientist

Lájer (2007) raised the problem of using a non-random sample for statistical testing of plant community data. He argued that this violates basic assumptions of the tests, resulting thus in non-significant results. However, a huge part of present-day knowledge of vegetation science is still based on non-random, preferentially collected data of plant communities. I argue that, given the inherent limits of preferential sampling, a change of approach is now necessary, with the adoption of sampling based on random principles seeming the obvious choice. However, a complete transition to random-based sampling designs in vegetation science is limited by the yet undefined nature of plant communities and by the still diffused opinion that plant communities have a discrete nature. Randomly searching for such entities is almost impossible, given their dependence on scale of observation, plot size and shape, and the need for finding well-defined types. I conclude that the only way to solve this conundrum is to consider and study plant communities as operational units. If the limits of the plant communities are defined operationally, they can be investigated using proper sampling techniques and the collected data analyzed using adequate statistical tools.     

Statistical and biological consequences of preferential sampling in phytosociology: Theoretical considerations and a case study

Due to the long tradition of the Braun-Blanquet approach, many relevés using this approach have been made. Recent developments in vegetation-plot databases provide an opportunity to effectively use these relevés to study ecological problems as well. Opinions differ, however, concerning the applicability of these datasets, often with their use being restricted to exploration and hypothesis generation only. We assert that preferential sampling, which is characteristic of the Braun-Blanquet approach, means using a special definition of statistical population rather than non-random sampling. We present a case study, where consequences of using a preferential and non-preferential definition of statistical population are studied. Although the traits of stands that are preferred or avoided by the phytosociologist during preferential sampling can be identified, there are no general rules that could predict the difference between the preferential and non-preferential datasets obtained for the same object.     

Plant communities,diversity and endemism of the Kula Volcano,Manisa, Turkey

Volcanoes often harbour specialized plant communities and shelter endemic plant species. Kula Volcano is one of 14 volcanoes in Turkey. Although this volcano is clearly a landmark of the Aegean region, only few botanical studies analysed the vegetation pattern at the Kula Volcano. None performed a phytosociological classification to delimit different plant communities. We applied a stratified random sampling design according to altitude and aspect and sampled 112 vegetation plots. We classified plant community types using a modified TWINSPAN analysis followed by the determination of diagnostic species based on φ coefficient fidelity values. Floristic relationships between plant community types were interpreted by ordination and ANOSIM analyses. Further, we used partial correlations of the ordination axes and environmental parameters in order to identify relationships between vegetation zonation and environment. We identified five major plant community types based on 85 diagnostic species. These plant community types were significantly correlated with altitude and aspect. Further, 13 endemic plant species were found from which one was endangered and one was classified as vulnerable according to International Union for Conservation of Nature (IUCN).     

Pattern analysis in Belgian limestone grasslands

Pattern analyses were made in several limestone grasslands of Belgium. Vegetation has been sampled in continuous transects. Monospecific patterns are defined by means of two non-parametric indices; multivariate analysis (reciprocal averaging) completed the analysis. The following kinds of pattern are described and explained: random, aggregate, clump, gap, stationary density variation, non-stationary density variation, gradient and many complex patterns. The multivariate analysis leads to a careful phytosociological study of the transects and to the definition of floristically homogeneous regions. Some particular points of pattern analysis, the problem of vegetation sampling and some perspectives are discussed.     

Random sampling does not exclude spatial dependence: The importance of neutral models for ecological hypothesis testing

Lájer (2007) notes that, to investigate phytosociological and ecological relationships, many authors apply traditional inferential tests to sets of relevés obtained by non-random methods. Unfortunately, this procedure does not provide reliable support for hypothesis testing because non-random sampling violates the assumptions of independence required by many parametric inferential tests. Instead, a random sampling scheme is recommended. Nonetheless, random sampling will not eliminate spatial autocorrelation. For instance, a classical law of geography holds that everything in a piece of (biotic) space is interrelated, but near objects are more related than distant ones. Because most ecological processes that shape community structure and species coexistence are spatially explicit, spatial autocorrelation is a vital part of almost all ecological data. This means that, independently from the underlying sampling design, ecological data are generally spatially autocorrelated, violating the assumption of independence that is generally required by traditional inferential tests. To overcome this drawback, randomization tests may be used. Such tests evaluate statistical significance based on empirical distributions generated from the sample and do not necessarily require data independence. However, as concerns hypothesis testing, randomization tests are not the universal remedy for ecologists, because the choice of inadequate null models can have significant effects on the ecological hypotheses tested. In this paper, I emphasize the need of developing null models for which the statistical assumptions match the underlying biological mechanisms.     

A climatic stratification of the environment of Europe

Aim To produce a statistical stratification of the European environment, suitable for stratified random sampling of ecological resources, the selection of sites for representative studies across the continent, and to provide strata for modelling exercises and reporting. Location A ‘Greater European Window’ with the following boundaries: 11° W, 32° E, 34° N, 72° N. Methods Twenty of the most relevant available environmental variables were selected, based on experience from previous studies. Principal components analysis (PCA) was used to explain 88% of the variation into three dimensions, which were subsequently clustered using an ISODATA clustering routine. The mean first principal component values of the classification variables were used to aggregate the strata into Environmental Zones and to provide a basis for consistent nomenclature. Results The Environmental Stratification of Europe (EnS) consists of 84 strata, which have been aggregated into 13 Environmental Zones. The stratification has a 1 km² resolution. Aggregations of the strata have been compared to other European classifications using the Kappa statistic, and show ‘good’ comparisons. The individual strata have been described using data from available environmental databases. The EnS is available for noncommercial use by applying to the corresponding author. Main conclusions The Environmental Stratification of Europe has been constructed using tried and tested statistical procedures. It forms an appropriate stratification for stratified random sampling of ecological resources, the selection of sites for representative studies across the continent and for the provision of strata for modelling exercises and reporting at the European scale.     

A study of habitat conditions of the macrophytic vegetation in selected river systems in western Lower Saxony (Federal Republic of Germany)

In connection with a phytosociological survey of running water macrophytes in Lower Saxony, ecological investigations were carried out in selected river systems. Within these systems, 43 sampling sites were studied. The vegetation of the sampling sites was classified by means of cluster analysis into 7 groups, 3 of which occurred on the diluvial plains and 2 in the coastal marsh area only. Forty-one parameters were measured 3–7 times covering 2 vegetation periods. In the first instance, the structure of the data was carefully studied by bivariate correlation analysis and factor analysis. A high number of significant correlations was detected, which indicates difficulties in ecological interpretation. Temporal variation of the parameters measured was also studied, and they were classified into 3 groups according to stability. For a study of the relationships between the vegetation and the ecological parameters, the data set was split into 5 subsets (physical data, water chemical data, interstitial water chemical data, sediment characteristics, and a mixed set of simple field data). The relationships of each subset to the vegetation was studied separately using cluster analysis. The mixed data set FIELD showed the highest degree of similarity to the vegetation clustering. Analysis of variance was carried out in order to find out which variables differ most among the vegetation types. The best differentiation qualities were shown by some physical and water chemical parameters (oxygen content, turbitity, current velocity, acidity, calcium). This result can only be interpreted ecologically in connection with the intercorrelations observed. The ecological behaviour of some species of medium frequency was also studied in detail by means of analysis of variance. The means of all parameters for occurrence and non-occurrence were compared. In the case of Ranunculus peltatus Schrank, Myriophyllum alterniflorum DC and Elodea canadensis Michx., several differentiation variables could be detected. Finally, the zonation of two rivers was studied in detail by comparing the vegetation sequence with important physical and chemical parameters. The interaction between these parameter groups is clearly shown. Physical parameters like current velocity are responsible for the basic zonation, whilst chemical parameters can modify the zones to a large extent. The necessity for a comprehensive approach to such types of data sets, including profound structural data analysis, is stressed in the discussion. The special problem of relating phytosociological and ecological data is discussed. The methods used are explained and possible objections are noted. The difficulties of using the habitat ecological results for bioindication purposes are outlined. Spatial autocorrelation, vegetation dynamics, interactive processes between the system parameters and the genetic variability of species have to be considered as the main problems in this special application. Nevertheless, the study produced some results which indicated the significance of physical, chemical and sediment parameters for macrophyte growth in the type of waters under investigation, and suggested subject areas for future research.     

A survey of plant associations and alliances from Svalbard

Abstract. Most vegetation classification studies on Svalbard have followed the phytosociological tradition. Exposed ridges and accumulation sea shores are the habitats investigated best, whereas the open vegetation of screes, active sedimentation plains, erosion plains, young moraines, polar desert and saxicolous vegetation range from almost unknown to poorly known. All published associations and non-ranked plant communities based on relevé data are reviewed and discussed in an ecological framework with 19 major habitat types. Most of them are arranged in a system of 17 alliances. Most of the alliances are expected to occur elsewhere in the Arctic, but this remains to be documented. The majority of the Svalbard studies are local and have obvious shortcomings. Nevertheless, vegetation types are probably better known on Svalbard than elsewhere in the northern parts of the Arctic.     

Influence of sampling strategy on the relationships between fauna and vegetation structure,plant lifeform and floristics

The effect of sampling strategy on animal-habitat relationships was evaluated with data collected from a 50 ha area containing a sequence of tropical vegetation types. Sampling sites were located randomly within defined habitat types (i.e. stratified random sampling) and systematically irrespective of habitat type. At each site the fauna, comprising birds (63 species), reptiles (15 species), amphibia (13 species) and grasshoppers (32 species) were sampled for the abundance of species. Simultaneously, vegetation and related data, comprising vertical structure (52 attributes), ground surface condition (18 attributes), plant lifeform (18 attributes) and the abundance of plant species (200) were recorded. Random and systematic data matrices, comprising sites defined by animal or vegetation attributes, were reduced dimensionally by correspondence analysis. Animal first dimension vectors were then regressed on the first dimension vectors of vegetation structure, lifeform and floristics, respectively. With stratified random sampling, vegetation structure (comprising vertical and ground attributes) and lifeform explained most of the variation in the fauna; floristics were not a significant factor. On the other hand with the systematic data, fioristics explained almost all of the variation in animal abundance and distribution. By removing the ecotonal sites from the systematic data set and recalculating vectors, the animal—vegetation relationships became similar to those generated from the stratified random sampling data. Clearly, the sampling strategy employed in a faunal survey has a major influence on the inventory of species, and on the relative importance of vegetation structure, lifeform and floristics in explaining animal distribution. The presence of ecotones in the systematic data set was highlighted as the key to the difference between the sampling strategies.     

Use of Phytosociological Databases for Species Distribution Models

Species Distribution Models are key in modern ecological studies. They employ information about species locations and environmental factors to generate statistical functions that predict the potential distribution of species on the basis of landscape suitability. Although these models are powerful and useful tools, often the required information about species distribution is lacking, and the only resources are pre-collected museum data. Phytosociological databases contain a myriad of relevés with precious information, but are often considered to be the exclusive ownership of vegetation scientists. Our study tested the efficiency of a phytosociological database in the building of Species Distribution Models, including spatial autocorrelation (SAC) as a predictor to evaluate its effects on model performance. Spatial autocorrelation (SAC) is a natural characteristic of species distribution that depends on exogenous and endogenous processes. The latter??s effects could be overestimated by a subjective sample choice. We chose Festuca riccerii, an Italian endemic species. We split the whole dataset (671 relevés) into a calibration (443 relevés) and testing set (228 relevés) and performed a GLM on these data to identify the main ecological factors that lead distribution in order to build a Species Distribution Model. The dataset??s efficiency was assessed by testing the predicting power of the calibrated model on the testing subset. The phytosociological database proved to be good for building model (AUC?=?0.821), providing a useful basis for fast and low cost ecological analysis, and could be used subsequently for more detailed analyses.     

Plot sizes used for phytosociological sampling of European vegetation

Abstract. In European phytosociology, variable plot sizes are traditionally used for sampling different vegetation types. This practice may generate problems in current vegetation or habitat survey projects based on large data sets, which include relevés made by many authors at different times. In order to determine the extent of variation in plot sizes used in European phytosociology, we collected a data set of 41 174 relevés with an indication of plot size, published in six major European journals focusing on phytosociology from 1970 to 2000. As an additional data set, we took 27 365 relevés from the Czech National Phytosociological Database. From each data set, we calculated basic statistical figures for plot sizes used to sample vegetation of various phytosociological classes. The results show that in Europe the traditionally used size of vegetation plots is roughly proportional to vegetation height; however, there is a large variation in plot size, both within and among vegetation classes. The effect of variable plot sizes on vegetation analysis and classification is not sufficiently known, but use of standardized plot sizes would be desirable in future projects of vegetation or habitat survey. Based on our analysis, we suggest four plot sizes as possible standards. They are 4 m² for sampling aquatic vegetation and low‐grown herbaceous vegetation, 16 m² for most grassland, heathland and other herbaceous or low‐scrub vegetation types, 50 m² for scrub, and 200 m² for woodlands. It has been pointed out that in some situations, sampling in either small or large plots may result in assignment of relevés to different phytosociological classes or habitat types. Therefore defining vegetation and habitat types as scale‐dependent concepts is needed.     

Plant communities preferences of terrestrial crustaceans (Isopoda: Oniscidea) in a protected coastal area of southeastern Sicily (Italy)

We studied the plant communities preferences of oniscidean isopods in a protected coastal area in southeastern Sicily that is characterized by ponds, dunes and rocky coast. In total, five sites were sampled between March 2003 and February 2004 using pitfall traps. A total of 7,326 specimens were found, belonging to 23 species. The vegetation analysis, using the phytosociological method, recognized a total of 11 types of plant communities, with specific ecological features. Measuring the species richness, diversity and annual activity density (aAD) values of the terrestrial isopod communities from each sampling site revealed differences among the various plant communities. PCA analysis confirmed the correlation between isopods species and specific plant communities. Moreover our results show that the high species richness is associated with a high vegetation cover and number of plants.     

The application of multivariate land classification to vegetation survey in the Wicklow Mountains,Ireland

Multivariate land classification and land cover mapping by aerial photographic interpretation were used to model spatial variation of land cover in the Wicklow Mountains, Ireland and to structure a stratified random sampling programme of upland blanket bog vegetation. The total area of blanket bog with gully-erosion features was estimated as 33% of the area studied. Vegetation with hand peat-cutting patterns was estimated at 5%, and there was 35% undissected (intact) vegetation. There were differences between land classes in the estimated area of land cover with gully-erosion features or hand peat-cutting patterns.Sample vegetation quadrats, stratified by land class and aerial photographic land cover type, were grouped by their plant species composition. The groups represented ombrotrophic mire, soligenous mire and shrub heath vegetation. There was significant association between vegetation group and land class, related to variation in regional landscape type, but no significant association between vegetation group and the aerial photographic land cover types, undissected (intact) and dissected (gullied and cut-over) peats. It is proposed that the similarity of vegetation between undissected and dissected blanket bog is related to vegetation regeneration. The need to consider differences in vegetation distribution, composition and dynamics in ecological management strategies is emphasised. The study demonstrated the value of stratified random field sampling for cost-efficient regional ecological assessment in upland blanket bog landscapes typified by the Wicklow mountains, Ireland.     

Spontanvegetation Nordgriechischer Bergdörfer

The synanthropic vegetation in seven villages of Northern Greece (N Pindhos and Olympus massif)—situated either in theQuercion confertae or in theFagetalia zone—is analyzed on the basis of about 100 phytosociological relevés. The most important vascular plant species of ruderal sites have also been mapped floristically. Nineteen plant communities are discernible, some of which are mentioned for the first time here. Nevertheless, the author refrains from hastily describing new associations, unless a more detailed phytosociological data basis on ruderal vegetation in the Southern Balkans is available. A table refers to differences and similarities between the recognized plant communities in Greek villages and those which correspond to them in Central Europe. Non-industrial methods of agriculture and animal husbandry are among the most important conditions for the remarkable rich vegetation in Greek mountain villages.     

SaudiVeg ecoinformatics: Aims,current status and perspectives

During the last decade many electronic databases of vegetation plots were established in many countries around the world. These databases contain valuable phytosociological information assisting both governmental and NGO (Non-governmental organizations) agencies to formulate strategies and on-ground plans to manage and protect nature resources. This paper provides an account on aims, current status and perspectives of building of a vegetation database for the Central Region (Najd) of Saudi Arabia – the founding element of the Saudi Vegetation Database (SVD). The data stored by the database are sample plots (vegetation relevés) collected according to the field techniques of the Braun-Blanquet approach (lists of taxa accompanied by semi-quantitative cover assessment), and are accompanied by general vegetation characteristics such as vegetation layering and cover, information on life-form of the recorded species, geographical coordinates, altitude, soil typology, topography and many more. More than 2900 vegetation-plot records (relevés) have so far been collected in the Najd region; of these more than 2000 have already been stored using the Turboveg database platform. These field records cover many habitats such as depressions, wadis (dry river beds), agricultural lands, sand dunes, sabkhas, and ruderal habitats. The ecological information collected in the database is currently the largest set of vegetation data collated into a database in the Middle East. These data are of great importance for biodiversity studies in Saudi Arabia, since the region is recording a loss of biodiversity at a fast rate due to environmental problems such as global warming and land-use changes. We envisage that this database would catalyze further data collection on vegetation of the entire Arabian Peninsula, and shall serve as one of the most important datasets for classification and mapping of the vegetation of the Kingdom of Saudi Arabia.