Bettina Nygaard

Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe

Recent studies from mountainous areas of small spatial extent (<2500 km(2) ) suggest that fine-grained thermal variability over tens or hundreds of metres exceeds much of the climate warming expected for the coming decades. Such variability in temperature provides buffering to mitigate climate-change impacts. Is this local spatial buffering restricted to topographically complex terrains? To answer this, we here study fine-grained thermal variability across a 2500-km wide latitudinal gradient in Northern Europe encompassing a large array of topographic complexities. We first combined plant community data, Ellenberg temperature indicator values, locally measured temperatures (LmT) and globally interpolated temperatures (GiT) in a modelling framework to infer biologically relevant temperature conditions from plant assemblages within <1000-m(2) units (community-inferred temperatures: CiT). We then assessed: (1) CiT range (thermal variability) within 1-km(2) units; (2) the relationship between CiT range and topographically and geographically derived predictors at 1-km resolution; and (3) whether spatial turnover in CiT is greater than spatial turnover in GiT within 100-km(2) units. Ellenberg temperature indicator values in combination with plant assemblages explained 46-72% of variation in LmT and 92-96% of variation in GiT during the growing season (June, July, August). Growing-season CiT range within 1-km(2) units peaked at 60-65°N and increased with terrain roughness, averaging 1.97 °C (SD = 0.84 °C) and 2.68 °C (SD = 1.26 °C) within the flattest and roughest units respectively. Complex interactions between topography-related variables and latitude explained 35% of variation in growing-season CiT range when accounting for sampling effort and residual spatial autocorrelation. Spatial turnover in growing-season CiT within 100-km(2) units was, on average, 1.8 times greater (0.32 °C km(-1) ) than spatial turnover in growing-season GiT (0.18 °C km(-1) ). We conclude that thermal variability within 1-km(2) units strongly increases local spatial buffering of future climate warming across Northern Europe, even in the flattest terrains.

PREDICTION OF HABITAT QUALITY USING ORDINATION AND NEURAL NETWORKS

The development of an automatic classification model for prediction of con- servation value is described. The classifier combines ordination and neural network (NN). The classifier was trained to predict the probability of a sample being of potential conser- vation interest. The neural network was trained on a priori classified data and used sample scores derived from ordination for prediction. The complexity of the NN classifier and the selection of the optimal ordination method were guided by cross-validation of a series of candidate models. The conservation value of a test data set was predicted by the NN classifier, and this classification was evaluated in terms of species richness, nativeness, rarity, and P diversity. Finally, we evaluated the capability of the approach to handle new samples not included in the ordination. These samples were derived from habitats of threatened vascular plants, and they were all successfully predicted to be valuable. It is shown that the combination of ordination and neural networks successfully repro- duces the a priori classification. It is further demonstrated on a test data set which the classifier discriminates with respect to traditional measures of conservation interest such as rarity, nativeness, and diversity. The developed method may be seen as a promising approach to assessment of biological integrity at the scale of plant communities, and further opportunities for its application are suggested.

Topographically controlled soil moisture is the primary driver of local vegetation patterns across a lowland region

Topography is recognized as an important factor in controlling plant distribution and diversity patterns, but its scale dependence and the underlying mechanisms by which it operates are not well understood. Here, we used novel high-resolution (2-m scale) topographic data from more than 30500 vegetation plots to assess the importance of topography for local plant diversity and distribution patterns across Denmark, a 43000 km2 lowland region. The vegetation data came from 901 nature conservation sites (mean size = 0.16 km2) distributed throughout Denmark, each having an average of 34 plots (five-meter radius) per site. We employed a variety of statistical measures and techniques to investigate scale dependence and mechanistic drivers operating within the study region. Ordinary Least Squares (OLS) multiple regression modeling scaled at different spatial resolutions (2 × 2, 10 × 10, 50 × 50, 100 × 100 and 250 × 250 m) was used to identify the horizontal resolution yielding the strongest vegetation–topography ...

Geographically Comprehensive Assessment of Salt-Meadow Vegetation-Elevation Relations Using LiDAR

Salt meadows are thought to be vulnerable to habitat loss under future sea-level rise (SLR) due to inundation and compression of coastal environments (coastal squeezing). The extent of this threat is poorly understood due to the lack of geographically comprehensive impact assessments. Here, we linked vegetation data for Danish salt meadows to novel very fine-resolution digital elevation models. We developed statistical models relating plant species richness and average salt tolerance to elevation at different spatial scales. The best models were used to quantify potential impacts of SLR on Danish salt-meadow vegetation under five potential 21st-century scenarios. Overall, species richness increased with elevation (average r2 = 0.21), while average salt tolerance decreased (average r2 = 0.45). Fine resolution (≤10-m) topography was required to fully represent vegetation-elevation relationships. At >50-m resolutions only feeble links were found. Under the worst scenarios 67–74% of the Danish salt-meadow area was projected to be lost. Notably, the relatively species-rich upper meadows were predicted to shrink drastically. If realized, these impacts may have severe consequences for salt-meadow biodiversity. We note that sedimentation, not accounted for here, may allow some salt meadows to partly keep up with SLR but the extent to which this will occur and where is uncertain.

A new approach to functional interpretation of vegetation data

Abstract In this paper we present a new approach to the simultaneous analysis of a species composition data set, an environmental gradient data set and a functional attribute data set. We demonstrate its advantages in terms of statistical modelling including model development and assessment as well as subsequent prediction. Our method is applied to a set of case data deriving from experimental wetland microcosms including 20 species, 12 treatment combinations and a classification of species into functional groups. Acknowledging that lack of independence between samples and over-interpretation of data may lead to overly optimistic assessment of model performance, we use cross-validation with different subsets of data to obtain realistic model performance measures. It is shown that although the outcome of the wetland experiment is predictable in terms of experimental treatments and taxonomic species, the functional groups cannot be used to explain the variation in species frequencies in the experiment. We compare the method with recently published approaches to the functional analysis of vegetation data, and discuss its applied perspectives. Nomenclature: Tutin et al. (1964–1980).

A biotope landscape model for prediction of semi-natural vegetation in Denmark

Abstract The work presented is part of a research effort, addressing the development of biological concepts for assessing the quality of Danish terrestrial biotopes. The aim of the study has been to develop a spatial model describing impacts of agricultural land use on natural and semi-natural terrestrial biotopes. Approaches in other countries fall into two main categories, broad scale, nation-wide models and detailed models across minor study areas. In this paper, we present an operational model capable of working at sufficient detail to assess impacts in spatial detail while at the same time covering a broader region. Based upon a classification of plant communities found within natural and semi-natural areas in Denmark a Biotope Landscape Model was developed and implemented into a geographic information system (GIS). The work included compilation of an Ecotope Map as a basis for the prediction of spatial distribution of the vegetation at three aggregation levels — 10 main types, 31 sub types and 130 plant communities. For model implementation, a large project area was chosen covering a range of characteristic landscapes in Denmark. Testing against vegetation samples shows convincing predictions for main types (87% correct) and sub types (59%), while predictions at plant community level was found unreliable (28%). Evaluation results indicate the potentials of GIS-based ecological models as tools in landscape planning.

When Has an Abandoned Field Become a Semi-Natural Grassland or Heathland?

This study presents a meta-analysis of a collective dataset describing the succession from abandoned fields to semi-natural grassland and heathland vegetation over the past century. The study objectives were to develop a method for statistical discrimination between abandoned fields and semi-natural habitats and to analyze the probability that an abandoned field had developed into a semi-natural habitat. A statistical classification model was developed, based on lists of vascular plants from 2059 plots from Danish semi-natural grasslands and heathlands, and abandoned fields of varying age. This model was shown to discriminate effectively between abandoned fields and semi-natural habitats, and it was found to be potentially useful for the detection of abandoned fields approaching semi-natural vegetation. We suggest that the model may help clarify restoration targets and assess biological condition in formerly cultivated areas. Statistical modeling revealed that succession age, period of abandonment and succession trajectory had significant effects on the probability that abandoned fields reached the semi-natural phase. Our study indicates that restoration projects targeting grassland and heathland should take local species pools and soil fertility into account.

The impact of hydrology and nutrients on species composition and richness: evidence from a microcosm experiment.

Protection of biodiversity and restoration of species-rich plant communities rely on an adequate understanding of how diversity is regulated. We studied species diversity patterns and community assembly in a simulated three-year wetland succession using factorial combinations of two nutrient levels, two water levels, and three water level fluctuation regimes. A standard seed mixture of 23 wetland species representing a wide range of plant functional types was sown in each microcosm. We found strong and consistent effects of water depth and nutrient level on species composition, species richness, and biomass, but no clear effect of water level fluctuations. The relationship between biomass and species richness was positive in the infertile range (16 to 204 g m−2) but negative in the fertile range (372 to 1156 g m−2). This pattern is consistent with the “humped-back model”, with maximum species richness at an above ground biomass between 200 and 250 g m−2. Increasing species richness in the low fertility range could partly be explained by limited seedling establishment in the harsh environment of nutrient poor and water logged soils. We interpret the decreasing species richness at high fertility as an effect of increasing competitive asymmetry.

State-Space Modeling Indicates Rapid Invasion of an Alien Shrub in Coastal Dunes

Abstract Invasion by alien plants has negative effects on coastal dunes. Monitoring local spread of invasive species depends on long-term data with sufficient spatial resolution. Bayesian state-space models are a new method for monitoring invasive plants based on unbalanced permanent-plot data. The method allows separation of process and sampling variance, thus enabling ecological predictions with a known degree of uncertainty. The method is applied for the invasive shrub Rosa rugosa (Japanese rose) in Danish fixed dunes. The probability of observing R. rugosa increased significantly from 0.18 to 0.28 during the period 2004–2007. The species was found in all Danish coastal regions, albeit slightly less common in northern Denmark. We discuss the advantages and limitations of using Bayesian state-space models for monitoring and predicting plant invasions using presence–absence data.

Measuring Diversity in Plant Communities with Mosaic Spatial Patterns:Danish Coastal Dunes

Hierarchical pin-point data from 5316 plots from 73 Danish coastal dune sites were analysed in order to describe the species diversity in dune plant communities on a regional scale. Due to the mosaic spatial pattern of the dune communities, it was decided to describe the spatial structure of each plant species in each community using a vegetation type conditioned approach, where the hierarchical pin-point data were fitted to a zero-inflated generalised binomial distribution. Furthermore, summary statistics of the Lorenz curve of the regional estimates of species abundance are suggested in order to describe inequality of species abundance and to test for a possible log-normal species abundance distribution. The mean plant cover and the degree of spatial aggregation were estimated for all species found in six dune communities. Most plant species had a significant aggregated spatial distribution, and there was a significant positive correlation between the mean plant cover and the degree of aggregation. Species abundance did not depart from a log- normal species abundance distribution in any of the investigated dune community types. A vegetation type conditioned approach was found to be appropriate for analysing vegetation data of mosaic vegetation at a regional scale, and it is expected that the introduced method of measuring the direction of the deviation from a log-normal distribution will be important for interpreting the underlying cause of observed departures from log-normally distributed abundance curves.