Bavarian Forest National Park Research Articles

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

Hyperspectral remote sensing serves as an effective tool for estimating foliar nitrogen using a variety of techniques. Vegetation indices (VIs) are a simple means of retrieving foliar nitrogen. Despite their popularity, few studies have been conducted to examine the utility of VIs for mapping canopy foliar nitrogen in a mixed forest context. In this study, we assessed the performance of 32 vegetation indices derived from HySpex airborne hyperspectral images for estimating canopy mass-based foliar nitrogen concentration (%N) in the Bavarian Forest National Park. The partial least squares regression (PLSR) was performed for comparison. These vegetation indices were classified into three categories that are mostly correlated to nitrogen, chlorophyll, and structural properties such as leaf area index (LAI). %N was destructively measured in 26 broadleaf, needle leaf, and mixed stand plots to represent the different species and canopy structure. The canopy foliar %N is defined as the plot-level mean foliar %N of all species weighted by species canopy foliar mass fraction. Our results showed that the variance of canopy foliar %N is mainly explained by functional type and species composition. The normalized difference nitrogen index (NDNI) produced the most accurate estimation of %N (R2CV = 0.79, RMSECV = 0.26). A comparable estimation of %N was obtained by the chlorophyll index Boochs2 (R2CV = 0.76, RMSECV = 0.27). In addition, the mean NIR reflectance (800–850 nm), representing canopy structural properties, also achieved a good accuracy in %N estimation (R2CV = 0.73, RMSECV = 0.30). The PLSR model provided a less accurate estimation of %N (R2CV = 0.69, RMSECV = 0.32). We argue that the good performance of all three categories of vegetation indices in %N estimation can be attributed to the synergy among plant traits (i.e., canopy structure, leaf chemical and optical properties) while these traits may converge across plant species for evolutionary reasons. Our findings demonstrated the feasibility of using hyperspectral vegetation indices to estimate %N in a mixed temperate forest which may relate to the effect of the physical basis of nitrogen absorption features on canopy reflectance, or the biological links between nitrogen, chlorophyll, and canopy structure.

Detection of SNPs based on transcriptome sequencing in Norway spruce (Picea abies (L.) Karst)

A novel set of SNPs was derived from transcriptome data of ten Norway spruce (Picea abies) trees from the Bavarian Forest National Park in Germany (BaFoNP). SNPs were identified by mapping against a de-novo transcriptome assembly and against pre-mRNAs of predicted genes of the reference genome assembly. This resulted in 111,849 and 366,577 SNPs, respectively. Out of these, 311 were either randomly selected or chosen because of their pronounced divergence between sampling sites and genotyped in 218 trees with an Illumina Infinium HD iSelect BeadChip.

Mean reproductive traits of fungal assemblages are correlated with resource availability.

Organisms have evolved a fascinating variety of strategies and organs for successful reproduction. Fruit bodies are the reproductive organ of fungi and vary considerably in size and shape among species. Our understanding of the mechanisms underlying the differences in fruit body size among species is still limited. Fruit bodies of saprotrophic fungi are smaller than those of mutualistic ectomycorrhizal fungi. If differences in fruit body size are determined by carbon acquisition, then mean reproductive traits of saprotrophic and ectomycorrhizal fungi assemblages should vary differently along gradients of resource availability as carbon acquisition seems more unpredictable and costly for saprotrophs than for ectomycorrhizal fungi. Here, we used 48 local inventories of fungal fruit bodies (plot size: 0.02ha each) sampled along a gradient of resource availability (growing stock) across 3years in the Bavarian Forest National Park in Germany to investigate regional and local factors that might influence the distribution of species with different reproductive traits, particularly fruit body size. As predicted, mean fruit body size of local assemblages of saprotrophic fungi was smaller than expected from the distribution of traits of the regional species pool across central and northern Europe, whereas that of ectomycorrhizal fungi did not differ from random expectation. Furthermore and also as expected, mean fruit body size of assemblages of saprotrophic fungi was significantly smaller than for assemblages of ectomycorrhizal species. However, mean fruit body sizes of not only saprotrophic species but also ectomycorrhizal species increased with resource availability, and the mean number of fruit bodies of both assemblages decreased. Our results indicate that the differences in carbon acquisition between saprotrophs and ectomycorrhizal species lead to differences in basic reproductive strategies, with implications for the breadth of their distribution. However, the differences in resource acquisition cannot explain detailed species distribution patterns at a finer, local scale based on their reproductive traits.

Estimating leaf functional traits by inversion of PROSPECT: Assessing leaf dry matter content and specific leaf area in mixed mountainous forest

Assessments of ecosystem functioning rely heavily on quantification of vegetation properties. The search is on for methods that produce reliable and accurate baseline information on plant functional traits. In this study, the inversion of the PROSPECT radiative transfer model was used to estimate two functional leaf traits: leaf dry matter content (LDMC) and specific leaf area (SLA). Inversion of PROSPECT usually aims at quantifying its direct input parameters. This is the first time the technique has been used to indirectly model LDMC and SLA. Biophysical parameters of 137 leaf samples were measured in July 2013 in the Bavarian Forest National Park, Germany. Spectra of the leaf samples were measured using an ASD FieldSpec3 equipped with an integrating sphere. PROSPECT was inverted using a look-up table (LUT) approach. The LUTs were generated with and without using prior information. The effect of incorporating prior information on the retrieval accuracy was studied before and after stratifying the samples into broadleaf and conifer categories. The estimated values were evaluated using R2 and normalized root mean square error (nRMSE).Among the retrieved variables the lowest nRMSE (0.0899) was observed for LDMC. For both traits higher R2 values (0.83 for LDMC and 0.89 for SLA) were discovered in the pooled samples. The use of prior information improved accuracy of the retrieved traits. The strong correlation between the estimated traits and the NIR/SWIR region of the electromagnetic spectrum suggests that these leaf traits could be assessed at canopy level by using remotely sensed data.

A Comparative Study of Predicting DBH and Stem Volume of Individual Trees in a Temperate Forest Using Airborne Waveform LiDAR

Using airborne full-waveform LiDAR metrics derived by 3-D tree segmentation, this study estimated single tree's diameter at breast height (DBH) and stem volume (STV). Four regression models were used, including multilinear regression and three up-to-date regression models (i.e., least square boosting trees regression, random forest, and $\varepsilon$ -support vector regression) from the machine learning field. This study aimed to comparatively evaluate these regression models in predicting DBH and STV at single-tree level and find some clues to regression model's selection. The study sites were located in the Bavarian Forest National Park, Germany, a mixed temperate mountain forest. Our comparisons were performed across different tree species types (coniferous and deciduous) and foliage conditions (leaf-on/leaf-off seasons). The importance of predictor variables was also examined. Experimental results revealed that the best accuracy from machine learning methods outperformed the multilinear model by 1.5 cm for DBH and 0.18 m 3 for STV in terms of rmse. Through comparative analysis, our work provided some clues to the performance variation of regression models for extracting 3-D tree parameters.

Changes in runoff in two neighbouring catchments in the Bohemian Forest related to climate and land cover changes

Abstract There is public concern that large-scale disturbances to forest cover caused by insects and storm winds in the Bohemian Forest could intensify high water flows and enhance the expected flooding risks predicted in current regional climate change scenarios. We analysed stream discharge in Upper Vydra and Große Ohe, neighbouring catchments in the Bohemian Forest, the largest contiguous forested area in Central Europe. Upper Vydra, in the Šumava National Park, and Große Ohe (including the Upper Große Ohe headwater catchment in the Bavarian Forest National Park) are similar in size, but differ in land use cover and the extent of disturbed Norway spruce stands. Publicly available runoff and meteorological data (1978–2011) were examined using non-parametric trend and breakpoint analysis. Together with mapped vegetation cover changes, the results were used to address the following questions: 1) are there significant changes in the hydrological cycle and, if so, do these changes relate to 2) the extent and expansion of disturbance in forests stands and/or 3) altered precipitation dynamics and thermal conditions? We found no marked overall change in annual runoff or in annual or seasonal precipitation, but a significant increase in high flows in March. This overall trend related to the marked warming in late winter and early spring (+~4 K in April, p < 0.01), irrespective of altitude and slope position. It significantly shifted the end of the snow cover period by more than three weeks to the beginning/middle of April depending on altitude, and intensified snow melt. In the Upper Große Ohe catchment, a significant decrease in catchment balance, the difference between the long term precipitation and runoff (–72 mm, 11%) was found when the loss of tree cover reached 30% of catchment area. Diminished evapotranspiration losses from severely disturbed stands increased groundwater recharge during summer and caused a significant rise in low flows in autumn. However, observed increases in late winter high flows were due to warming only. They could be further intensified by the increasing winter precipitation predicted under present climate change scenarios, and would therefore increase the risk of flooding at lower elevations.

Small beetle, large-scale drivers: how regional and landscape factors affect outbreaks of the European spruce bark beetle.

1. Unprecedented bark beetle outbreaks have been observed for a variety of forest ecosystems recently, and damage is expected to further intensify as a consequence of climate change. In Central Europe, the response of ecosystem management to increasing infestation risk has hitherto focused largely on the stand level, while the contingency of outbreak dynamics on large-scale drivers remains poorly understood. 2. To investigate how factors beyond the local scale contribute to the infestation risk from Ips typographus (Col., Scol.), we analysed drivers across seven orders of magnitude in scale (from 103 to 1010 m2) over a 23-year period, focusing on the Bavarian Forest National Park. Time-discrete hazard modelling was used to account for local factors and temporal dependencies. Subsequently, beta regression was applied to determine the influence of regional and landscape factors, the latter characterized by means of graph theory. 3. We found that in addition to stand variables, large-scale drivers also strongly influenced bark beetle infestation risk. Outbreak waves were closely related to landscape-scale connectedness of both host and beetle populations as well as to regional bark beetle infestation levels. Furthermore, regional summer drought was identified as an important trigger for infestation pulses. Large-scale synchrony and connectivity are thus key drivers of the recently observed bark beetle outbreak in the area. 4.Synthesis and applications. Our multiscale analysis provides evidence that the risk for biotic disturbances is highly dependent on drivers beyond the control of traditional stand-scale management. This finding highlights the importance of fostering the ability to cope with and recover from disturbance. It furthermore suggests that a stronger consideration of landscape and regional processes is needed to address changing disturbance regimes in ecosystem management.

Landscape configuration is a major determinant of home range size variation

Most animals restrain their movement activities to familiar areas which leads to home ranges. Although understanding both establishment and shifts of home ranges is highly relevant for basic science and conservation, pinpointing the factors that shape the dynamics of home ranges remains a challenge. Evidently home ranges are influenced by the underlying landscape. Landscape composition, i.e., the fraction of different land cover types, has recently been shown to affect home range size. Yet, the explicit spatial configuration of the landscape, a factor which is known to be of central importance in spatial ecology, is not taken into account by most studies. We quantify the effect of landscape configuration on summer home range sizes across multiple spatio‐temporal scales using GPS data from two behaviorally distinct ungulate species, red (Cervus elaphus) and roe deer (Capreolus capreolus), in the Bavarian Forest National Park, Germany. We show that the spatial configuration of the landscape is the dominant factor explaining home range size. Furthermore, we find that the shape of the relationship between home range size and landscape configuration depends on a species' habitat requirements: while roe deer decrease their home range size with increasing landscape patchiness, the relationship is hump‐shaped for red deer. Our results are robust at all tested spatio‐temporal scales.

Small changes in species composition despite stand-replacing bark beetle outbreak in Picea abies mountain forests

In the mid-1990s, a Spruce Bark Beetle (Ips typographus L.) outbreak affected the Norway spruce (Picea abies (L.) Karst.) forests in the higher elevations of the Bavarian Forest National Park in Germany. To monitor the effect of this large-scale disturbance on the understory vegetation, a series of permanent plots was established in 1998. Until 2000, most of the trees of the spruce-dominated forests at elevations > 1.100 m a.s.l. died. To explore the long-term vegetation development after the disturbance, we resurveyed these plots in 2010. We hypothesised that as a consequence of the stand-replacing disturbance, (i) species composition would change substantially, (ii) shade-tolerant forest species would be replaced by light-demanding species, and (iii) the expansion of the light-demanding species would homogenize species composition. Comparing the 1998 and 2010 situations, we found that species composition did not change substantially after the dieback of the tree layer, shade-tolerant species survived, light-demanding species increased only marginally, and vegetation heterogeneity did not change. The main reason is the missing forest floor disruption: there is no open space emerging for species to establish when trees die standing. As a consequence, a dramatic change in ecosystem structure (loss of the whole canopy layer) is not necessarily connected with a general change in species composition.

Functional response of lignicolous fungal guilds to bark beetle deforestation

Conifer-dominated forests in the northern hemisphere are prone to large-scale natural disturbances, yet our understanding of their effects beyond changes in species diversity is limited. Bark beetle disturbances provide dead wood for lignicolous fungal guilds and increase insolation but also desiccation. We investigated whether species richness of these guilds increases and functional diversity decreases after bark beetle disturbance, which would promote through habitat filtering the coexistence of species adapted to harsh conditions, i.e. light stress for lichens and substrate desiccation for wood-inhabiting fungi.We sampled epixylic and epiphytic lichens (primary producers) and wood-inhabiting fungi (mainly wood decomposers, some form ectomycorrhizas) in the Bohemian Forest (Long Term Ecological Research – LTER – Site Bavarian Forest National Park), an area in Central Europe most heavily affected by the bark beetle Ips typographus, on undisturbed plots and disturbed plots with spruce (Picea abies) dieback 8 years ago. We analysed species diversity, functional diversity (optimized by phylogeny), and functional compositions.Species richness of lichens but not that of wood-inhabiting fungi was higher on disturbed plots than on undisturbed plots. Community compositions of both guilds differed considerably on disturbed and undisturbed plots. On both types of plots, lichen communities were clustered according to functional diversity, which indicated habitat filtering, and fungal communities were overdispersed, which indicated competition. Disturbance increased the strength of these two patterns only slightly and was significant only for fungi. Single-trait analysis revealed changes in the functional composition; on disturbed plots, lichenous species with larger and more complex growth forms and fungi with large, perennial fruit bodies were favoured. Although the forest canopy changed tremendously because of the bark beetle disturbance, the most important driver of lichen and fungal diversity and mean trait assemblages seemed to be the enrichment of dead wood. The changes in insolation and moisture did not act as habitat filters for either guild. This indicated that the assembly patterns of lichen and fungal communities in coniferous forests are not affected by stand-replacing disturbances in contrast to the predictions for other disturbance regimes.

Response of bird assemblages to windstorm and salvage logging — Insights from analyses of functional guild and indicator species

Natural disturbances, such as fire, windstorms and insect outbreaks, are important drivers of biodiversity in forest ecosystems, but at the same time cause large economic losses. Among the natural disturbances in Europe, windstorms cause the highest economic loss. After such storms, damaged forest stands are commonly salvage logged to restore economic value. However, such interventions could affect species assemblages of various taxonomic groups, including breeding birds. Despite these potential effects, investigations of the impacts of post-storm logging are largely lacking. We thus investigated assemblages of breeding birds in 21 logged and 21 unlogged windstorm-disturbed forest plots and 18 undisturbed, control forest plots using fixed-radius point-stop counts three, five and seven years after a windstorm within the Bavarian Forest National Park as part of the European Long-Term Ecosystem Research Network. We recorded 2100 bird individuals of 55 bird species. Bird assemblages were predominantly altered by the consequences of the windstorm and affected only to a minor degree by subsequent logging of storm-felled trees. Nevertheless, bird species richness was significantly reduced by post-storm logging within the first season. In general, the windstorm led to a shift in bird assemblage composition from typical forest species towards open- and shrub-land species. Assemblages of logged and unlogged disturbed plots consisted mainly of long-distance migrants and ground-foraging bird species, whereas assemblages of undisturbed control plots consisted of resident species that forage within vegetation. Both unlogged and logged storm-felled coniferous forest stands were inhabited by endangered or declining bird species, such as Water Pipit (Anthus spinoletta) on logged plots and Eurasian Redstart (Phoenicurus phoenicurus) on unlogged plots. Indicator species analyses suggested that species of unlogged storm disturbed plots depended on storm-created legacies, such as pits and snags, for foraging and nesting. Hence, we recommend reducing post-storm logging of these legacies to support species restricted to unlogged disturbed forest. To increase the diversity of breeding birds on the landscape scale, diverse logged and unlogged post-disturbance stands should be provided, which could be gained by a partial benign neglect strategy of storm disturbed forest stands.

Forest inventories by LiDAR data: A comparison of single tree segmentation and metric-based methods for inventories of a heterogeneous temperate forest

Inventories of temperate forests of Central Europe mainly rely on terrestrial measurements. Rapid alterations of forests by disturbances and multilayer silvicultural systems increasingly challenge the use of conventional plot based inventories, particularly in protected areas. Airborne LiDAR offers an alternative or supplement to conventional inventories, but despite the possibility of obtaining such remote sensing data, its operational use for broader areas in Central Europe remains experimental. We evaluated two methods of forest inventory that use LiDAR data at the landscape level: the single tree segment-based method and an area-based method. We compared a set of structural forest attributes modeled by these methods with a conventional forest inventory of the highly heterogeneous forest of the Bavarian Forest National Park (Germany), which partially includes stands affected by severe natural disturbances. Area-based models were accurate for all structural attributes, with cross-validated average root mean squared error ranging from ∼3.4 to ∼13.4 in the best modeling case. The coefficients of variation for the mapped area-based estimations were mostly minor. The area-based estimations were varied but highly correlated (Pearson’s correlations between ∼ 0.56 and 0.85) with single tree segmentation estimations; undetected trees in the single tree segmentat-based method were the main sources of inconsistency. The single tree segment-based method was highly correlated (∼ 0.54 to 0.90) with data from ground-based forest inventories. The single tree-based algorithm delivered highly reliable estimates for a set of forest structural attributes that are of interest in forest inventories at the landscape scale. We recommend LiDAR forest inventories at the landscape scale in both heterogeneous commercial forests and large protected areas in the central European temperate sites.

Prospect inversion for indirect estimation of leaf dry matter content and specific leaf area

Abstract. Quantification of vegetation properties plays an indispensable role in assessments of ecosystem function with leaf dry mater content (LDMC) and specific leaf area (SLA) being two important vegetation properties. Methods for fast, reliable and accurate measurement of LDMC and SLA are still lacking. In this study, the inversion of the PROSPECT radiative transfer model was used to estimate these two leaf parameters. Inversion of PROSPECT traditionally aims at quantifying its direct input parameters rather than identifying the parameters which can be derived indirectly from the input parameters. The technique has been tested here to indirectly model these parameters for the first time. Biophysical parameters such as leaf area, as well as fresh and dry weights of 137 leaf samples were measured during a field campaign in July 2013 in the mixed mountain forests of the Bavarian Forest National Park, Germany. Reflectance and transmittance of the leaf samples were measured using an ASD field spec III equipped with an integrating sphere. The PROSPECT model was inverted using a look-up table (LUT) approach for the NIR/SWIR region of the spectrum. The retrieved parameters were evaluated using their calculated R2 and normalized root mean square error (nRMSE) values with the field measurements. Among the retrieved variables the lowest nRMSE (0.0899) was observed for LDMC. For both traits higher R2 values (0.83 for LDMC and 0.89 for SLA) were discovered. The results indicate that the leaf traits studied can be quantified as accurately as the direct input parameters of PROSPECT. The strong correlation between the estimated traits and the NIR/SWIR region of the electromagnetic spectrum suggests that these leaf traits could be assessed at canopy and in the landscape by using hyperspectral remote sensing data.

DETECTION OF SINGLE STANDING DEAD TREES FROM AERIAL COLOR INFRARED IMAGERY BY SEGMENTATION WITH SHAPE AND INTENSITY PRIORS

Abstract. Standing dead trees, known as snags, are an essential factor in maintaining biodiversity in forest ecosystems. Combined with their role as carbon sinks, this makes for a compelling reason to study their spatial distribution. This paper presents an integrated method to detect and delineate individual dead tree crowns from color infrared aerial imagery. Our approach consists of two steps which incorporate statistical information about prior distributions of both the image intensities and the shapes of the target objects. In the first step, we perform a Gaussian Mixture Model clustering in the pixel color space with priors on the cluster means, obtaining up to 3 components corresponding to dead trees, living trees, and shadows. We then refine the dead tree regions using a level set segmentation method enriched with a generative model of the dead trees’ shape distribution as well as a discriminative model of their pixel intensity distribution. The iterative application of the statistical shape template yields the set of delineated dead crowns. The prior information enforces the consistency of the template’s shape variation with the shape manifold defined by manually labeled training examples, which makes it possible to separate crowns located in close proximity and prevents the formation of large crown clusters. Also, the statistical information built into the segmentation gives rise to an implicit detection scheme, because the shape template evolves towards an empty contour if not enough evidence for the object is present in the image. We test our method on 3 sample plots from the Bavarian Forest National Park with reference data obtained by manually marking individual dead tree polygons in the images. Our results are scenario-dependent and range from a correctness/completeness of 0.71/0.81 up to 0.77/1, with an average center-of-gravity displacement of 3-5 pixels between the detected and reference polygons.

Detection of fallen trees in ALS point clouds using a Normalized Cut approach trained by simulation

Downed dead wood is regarded as an important part of forest ecosystems from an ecological perspective, which drives the need for investigating its spatial distribution. Based on several studies, Airborne Laser Scanning (ALS) has proven to be a valuable remote sensing technique for obtaining such information. This paper describes a unified approach to the detection of fallen trees from ALS point clouds based on merging short segments into whole stems using the Normalized Cut algorithm. We introduce a new method of defining the segment similarity function for the clustering procedure, where the attribute weights are learned from labeled data. Based on a relationship between Normalized Cut’s similarity function and a class of regression models, we show how to learn the similarity function by training a classifier. Furthermore, we propose using an appearance-based stopping criterion for the graph cut algorithm as an alternative to the standard Normalized Cut threshold approach. We set up a virtual fallen tree generation scheme to simulate complex forest scenarios with multiple overlapping fallen stems. This simulated data is then used as a basis to learn both the similarity function and the stopping criterion for Normalized Cut. We evaluate our approach on 5 plots from the strictly protected mixed mountain forest within the Bavarian Forest National Park using reference data obtained via a manual field inventory. The experimental results show that our method is able to detect up to 90% of fallen stems in plots having 30–40% overstory cover with a correctness exceeding 80%, even in quite complex forest scenes. Moreover, the performance for feature weights trained on simulated data is competitive with the case when the weights are calculated using a grid search on the test data, which indicates that the learned similarity function and stopping criterion can generalize well on new plots.

Creating a landscape of management: Unintended effects on the variation of browsing pressure in a national park

The principle objective of management in strictly protected areas, such as national parks, is to reduce human intervention as much as possible to secure natural assemblages and processes. As wildlife management in many national parks has to deal with increased ungulate populations and a broad lack of predators, park managers need to know how their wildlife management, including feeding and hunting, disturbs ungulate behaviour, which in turn might affect natural processes. One measure for this effect is the spatial distribution of browsing pressure in the landscape. Here we measured the browsing activity of ungulates on 5841 vegetation plots in the montane Bavarian Forest National Park to test the hypothesis that browsing in the landscape is mostly influenced by environmental covariables not related to park management. The survey revealed a browsing intensity that allows regrowth of tree species most palatable for ungulates. A comparison of different predictor sets in our spatial additive logistic regression models for silver fir, common rowan and European beech revealed that management activities and space are most important in explaining the variation in browsing level. These quantitative results underline that management activities are of major relevance for the variation of browsing intensity. Thereby, these activities shape a landscape of management that strongly contrasts the aims of the national park to reduce anthropogenic influence on natural processes. We therefore urge all park managers to carefully reconsider the necessity and effect of their management activities, especially of winter feeding, deer control areas and hiking trails.

Object-based extraction of bark beetle (Ips typographus L.) infestations using multi-date LANDSAT and SPOT satellite imagery

As major agents of biological disturbances, bark beetle infestations have been reported to account for a large portion of damage that occur in European forest stands. As a result, accurate spatiotemporal characterization of the vulnerable areas is crucial for subsequent post-infestation management. Remote sensing-assisted mapping of bark beetle-induced forest mortality has been an important research focus during the last decade. Due to the occurrence of mostly small- to medium-scale infestation patches in European stands, high-resolution optical data is commonly applied for mapping mortality. Despite this, we hypothesize the widely available satellite products to be potentially advantageous due to their multitemporal availability and reasonable costs. Here, we combined multi-date LANDSAT and SPOT scenes across an 11-year time span in which various epidemic and non-epidemic infestations occurred within the Bavarian Forest National Park in Germany. The aim was to map temporally adjacent mortality classes. The spectral, geometric and textural metrics extracted from the segmented imagery were applied to perform a full object-based classification, for which a digital terrain model was additionally employed. A number of potentially influential factors were also explored, including the spatial aggregation of image segments and the spatial enhancement of the multispectral imagery. The analysis resulted in a nearly perfect separation of non-infested and dead trees, while different levels of confusion were observed when classifying the transitional mortality classes. While the pan-sharpening of selected image scenes contributed to the stability of mapping results for non-infested and dead trees, no explicit trend was observed when aggregating small image segments prior to classification. Furthermore, combining the metrics from image objects and the digital terrain model suggested an obviously improved classification compared to the previously achieved pixel-based results across the same study site. In this paper, we thoroughly discuss the practical aspects of applying object-based image processing for monitoring bark beetle-induced forest mortality.

Can divergent selection save the rare Senecio hercynicus from genetic swamping by its spreading congener S. ovatus (Compositae, Senecioneae)?

Hybridisation leading to genetic swamping of a rare species by its more common congener is considered to become an issue of growing importance in conservation biology due to the fact that an increasing human impact on natural habitats and changes in climatic conditions will lead to higher frequencies of hybrid formation by shifting distributions and/or phenologies. In order to judge the future trajectories of hybridising plant species, which could eventually lead to the loss of taxonomic biodiversity, it is necessary to discern scenarios that lead to a complete merging of two species from those where hybridisation is an intermediary stage towards the establishment of reproductively isolated entities. We present a population-genomic approach that may allow discriminating between these two scenarios by estimating the strength of divergent selection regimes in hybrid swarms along ecological gradients. Using AFLP fingerprinting, 196 individuals from 11 populations of Senecio hercynicus and S. ovatus along an elevational transect (750–1300m) in the Bavarian Forest National Park were genotyped for 634 anonymous loci. Based on Bayesian cluster algorithms, all populations were identified as being hybrid swarms with different admixture proportions of both parental genotypes, indicating the intense introgressive hybridisation realised in this hybrid zone. Searching for loci under selection by using either FST-based or logistic regression model-based techniques resulted in the identification of only few AFLP markers under selection, which showed a significant correlation with some climatic variables along the transect. The simultaneous finding, however, that species-specific markers were not among these selected markers support the interpretation that there is no climatically mediated divergent selection or reinforcement process going on that could (re-)establish genetically isolated (biological) species.

Identification of factors influencing the Puumala virus seroprevalence within its reservoir in aMontane Forest Environment.

Puumala virus (PUUV) is a major cause of mild to moderate haemorrhagic fever with renal syndrome and is transmitted by the bank vole (Myodes glareolus). There has been a high cumulative incidence of recorded human cases in South-eastern Germany since 2004 when the region was first recognized as being endemic for PUUV. As the area is well known for outdoor recreation and the Bavarian Forest National Park (BFNP) is located in the region, the increasing numbers of recorded cases are of concern. To understand the population and environmental effects on the seroprevalence of PUUV in bank voles we trapped small mammals at 23 sites along an elevation gradient from 317 to 1420m above sea level. Generalized linear mixed effects models(GLMEM) were used to explore associations between the seroprevalence of PUUV in bank voles and climate and biotic factors. We found that the seroprevalence of PUUV was low (6%–7%) in 2008 and 2009, and reached 29% in 2010. PUUV seroprevalence was positively associated with the local species diversity and deadwood layer, and negatively associated with mean annual temperature, mean annual solar radiation, and herb layer. Based on these findings, an illustrative risk map for PUUV seroprevalence prediction in bank voles was created for an area of the national park. The map will help when planning infrastructure in the national park (e.g., huts, shelters, and trails).

Comparison of airborne lidar, aerial photography, and field surveys to model the habitat suitability of a cryptic forest species – the hazel grouse

Light detection and ranging (lidar) is a useful tool for measuring three-dimensional habitat structure; hence, its use in habitat suitability models has been explored, both as a single resource and in combination with other remote-sensing techniques. Here, we evaluated the suitability of airborne lidar data in comparison with aerial photographs and field surveys for modelling the distribution of an endangered and cryptic forest species, the hazel grouse (Bonasa bonasia). The study was conducted in the Bavarian Forest National Park of southeast Germany. Subsequently, a prediction map for conservation planning was generated for a large area, which encompassed the National Park. We examined the utility of lidar data for generating a hazel grouse distribution model by using machine learning (boosted regression trees), and then compared the results to variables derived from field surveys and aerial photographs, both separately and in combination. The cross-validated discrimination ability of the model was slightly higher when using lidar data (area under the receiver operator characteristic curve (AUC), 0.79) compared to models using aerial photographs (AUC, 0.75) or field survey data (AUC, 0.78). The predictive performance consistently increased when combining the predictors from different sources, with an AUC of 0.86 being produced in the model combining all three data sources. The three data sources complemented one another, with each data source probably having an advantage at deriving one of three key aspects of the hazel grouse habitat, namely, vertically well-structured forest stands, horizontally mixed successional vegetation stages, and certain deciduous trees as food resources such as mountain ash (Sorbus aucuparia). In addition, the diverse lidar metrics might be applied to simultaneously characterize vertically and horizontally well-structured forest stands. We conclude that public available airborne lidar data are a viable source for creating habitat suitability maps for large areas and may have increased utility for detecting forest characteristics and valuable wildlife habitats.