Bavarian Forest National Park Research Articles

Determining Statistically Robust Changes in Ungulate Browsing Pressure as a Basis for Adaptive Wildlife Management

Ungulate browsing has a major impact on the composition and structure of forests. Repeatedly conducted, large-scale regeneration inventories can monitor the extent of browsing pressure and its impacts on forest regeneration development. Based on the respective results, the necessity and extent of wildlife management activities such as hunting, fencing, etc., can be identified at a landscape scale. However, such inventories have rarely been integrated into wildlife management decision making. In this article, we evaluate a regeneration inventory method which was carried out in the Bavarian Forest National Park between 2007 and 2018. We predict the browsing impact by calculating browsing probabilities using a logistic mixed effect model. To provide wildlife managers with feedback on their activities, we developed a test which can assess significant changes in browsing probability between different inventory periods. To find the minimum observable browsing probability change, we simulated ungulate browsing based on the data of a potential browsing indicator species (Sorbus aucuparia) in the National Park. Sorbus aucuparia is evenly distributed, commonly found, selectively browsed and meets the ecosystem development objectives in our study area. We were able to verify a browsing probability change down to ±5 percentage points with a sample size of about 1,000 observations per inventory run. In view of the size of the National Park and the annual fluctuations in browsing pressure, this estimation accuracy seems sufficient. In seeking the maximal cost-efficiency, we were able to reduce this sample size in a sensitivity analysis by about two thirds without severe loss of information for wildlife management. Based on our findings, the presented inventory method combined with our evaluation tool has the potential to be a robust and efficient instrument to assess the impact of herbivores that are in the National Park and other regions.

Delineating Source Contributions to Stream Dissolved Organic Matter Composition Under Baseflow Conditions in Forested Headwater Catchments

AbstractDissolved organic matter (DOM) composition in streams reflects the dynamic interplay between DOM sources, mobilization mechanisms, and biogeochemical transformations within soils and receiving water bodies. The information regarding DOM sources being mobilized during baseflow can improve our ability to predict hydrological and biogeochemical responses to environmental changes, with implications for catchment management strategies. The objective of this study was to characterize the spatial changes in DOM composition along a headwater stream in a small mountainous catchment during baseflow and to link the findings in‐stream with possible DOM sources in the catchment. DOM was monitored over 1.5 years at three sites in the Große Ohe catchment (Bavarian Forest National Park, Southeast Germany) which strongly varied in slope between upper and lower part of the catchment, using UV‐Vis absorption indicators of aromaticity (SUVA) and molecular weight (E2:E3) from high‐frequency probe measurements. Additionally, discrete samples were collected and analyzed by ultrahigh‐resolution Fourier transform ion cyclotron resonance mass spectrometry (FT‐ICR‐MS). At baseflow conditions, dissolved organic carbon (DOC) concentrations, a proxy of DOM amount, ranged from 1.5 to 4.7 mg L−1, but were similar along the stream. However, DOM quality exhibited clear spatial patterns, with overall high aromatic and low molecular weight DOM in the lower part of the catchment, which has a higher proportion of hydromorphic soils. Moreover, molecular data revealed that oxygen‐rich, aromatic compounds increased in their abundance at high DOC concentrations in both the steep upper and the flatter lower part of the catchment, with also additional input of oxygen‐depleted aromatic compounds identified in the lower part. In contrast, nitrogen‐rich aliphatic compounds were negatively correlated with DOC concentration, indicating a higher contribution of deep groundwater at low DOC concentrations.

Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors

Over the last several years, semantic image segmentation based on deep neural networks has been greatly advanced. On the other hand, single-instance segmentation still remains a challenging problem. In this paper, we introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images obtained through fully convolutional networks. The contour evolution is cast as an energy minimization problem, where the aggregate energy functional incorporates a data fit term, an explicit shape model, and accounts for object overlap. Efficient solution neighborhood operators are proposed, enabling optimization through metaheuristics such as simulated annealing. We instantiate the proposed framework in the context of segmenting individual fallen stems from high-resolution aerial multispectral imagery, providing problem-specific energy potentials. We validated our approach on 3 real-world scenes of varying complexity, using 730 manually labeled polygon outlines as ground truth. The test plots were situated in regions of the Bavarian Forest National Park, Germany, which sustained a heavy bark beetle infestation. Evaluations were performed on both the polygon and line segment level, showing that the multi-contour segmentation can achieve up to 0.93 precision and 0.82 recall. An improvement of up to 7 percentage points (pp) in recall and 6 in precision compared to an iterative sample consensus line segment detection baseline was achieved. Despite the simplicity of the applied shape parametrization, an explicit shape model incorporated into the energy function improved the results by up to 4 pp of recall. Finally, we show the importance of using a high-quality semantic segmentation method (e.g. U-net) as the basis for individual stem detection, as the quality of the results degraded dramatically in our baseline experiment utilizing a simpler method. Our method is a step towards increased accessibility of automatic fallen tree mapping in forests, due to higher cost efficiency of aerial imagery acquisition compared to laser scanning. The precise fallen tree maps could be further used as a basis for plant and animal habitat modeling, studies on carbon sequestration as well as soil quality in forest ecosystems.

Seven Micarea (Pilocarpaceae) species new to Germany and notes on deficiently known species in the Bavarian Forest

Weber, L., Printzen, Ch., Bässler, C. & Kantelinen, A. 2021. Seven Micarea (Pilocarpaceae) species new to Germany and notes on deficiently known species in the Bavarian Forest. – Herzogia 34: 5 –17.We report new records of 19, predominantly rare, Micarea species, mostly from dead wood in mixed montane forests characterized mainly by Norway spruce, European beech and silver fir in the Bavarian Forest National Park on the German-Czech border. Their ecology and key morphological features are discussed. Micarea contexta, M. fallax, M. melanobola, M. pseudomicrococca, M. pusilla, M. soralifera and M. tomentosa are reported for the first time from Germany. Micarea anterior, M. byssacea, M. elachista, M. laeta, M. micrococca and M. nowakii, in addition to the aforementioned, are reported as new for the Bavarian Forest National Park.

Carcasses at Fixed Locations Host a Higher Diversity of Necrophilous Beetles.

Simple SummaryWhereas vertebrate scavengers have a higher diversity reported at randomly placed carcasses, the drivers of insect diversity on carrion, such as the exposure type (fixed versus random) or the carrion species, are still incompletely understood. We analyzed beetle diversity at differently exposed carcasses in the low-range mountain forest of the Bavarian Forest National Park in Germany. We tested if scavenging beetles, similarly to vertebrate scavengers, show a higher diversity at randomly placed carcasses compared to easily manageable fixed places. Ninety-two beetle species at 29 exposed wildlife carcasses (roe, red deer, and red foxes) were detected. Beetle diversity was higher at fixed locations possessing extended highly nutrient-rich cadaver decomposition islands as important refuges for threatened red-listed species, such as Necrobia violacea (Coleoptera: Cleridae). Particularly noticeable in our insect traps were the following two rare species, the “primitive” carrion beetle Necrophilus subterraneus (Coleoptera: Agyrtidae) and the false clown beetle Sphaerites glabratus (Coleoptera: Sphaeritidae). In Europe, only the species S. glabratus out of the genus Sphaerites is present. This emphasizes the importance of carrion for biodiversity conservation. We clearly show the relevance of leaving and additional providing wildlife carcasses in a dedicated place in protected forests for preserving very rare and threatened beetle species as essential members of the decomposing community.In contrast to other necromass, such as leaves, deadwood, or dung, the drivers of insect biodiversity on carcasses are still incompletely understood. For vertebrate scavengers, a richer community was shown for randomly placed carcasses, due to lower competition. Here we tested if scavenging beetles similarly show a higher diversity at randomly placed carcasses compared to easily manageable fixed places. We sampled 12,879 individuals and 92 species of scavenging beetles attracted to 17 randomly and 12 at fixed places exposed and decomposing carcasses of red deer, roe deer, and red foxes compared to control sites in a low range mountain forest. We used rarefaction-extrapolation curves along the Hill-series to weight diversity from rare to dominant species and indicator species analysis to identify differences between placement types, the decay stage, and carrion species. Beetle diversity decreased from fixed to random locations, becoming increasingly pronounced with weighting of dominant species. In addition, we found only two indicator species for exposure location type, both representative of fixed placement locations and both red listed species, namely Omosita depressa and Necrobia violacea. Furthermore, we identified three indicator species of Staphylinidae (Philonthus marginatus and Oxytelus laqueatus) and Scarabaeidae (Melinopterus prodromus) for larger carrion and one geotrupid species Anoplotrupes stercorosus for advanced decomposition stages. Our study shows that necrophilous insect diversity patterns on carcasses over decomposition follow different mechanisms than those of vertebrate scavengers with permanently established carrion islands as important habitats for a diverse and threatened insect fauna.

Canopy chlorophyll content retrieved from time series remote sensing data as a proxy for detecting bark beetle infestation

The European spruce bark beetle (Ips typographus, L.) is an invasive species resulting in a high degree of fragmentation, forest productivity, and phenology. Understanding its biology and its early detection based on its behaviour is essential for its successful management and eradication. This study demonstrates the potential of the canopy chlorophyll content (CCC) product retrieved from remote sensing datasets to detect early bark beetle infestations in the Bavarian Forest National Park. Time series CCC maps were generated through radiative transfer model inversion of images from RapidEye and Sentinel-2 (2011–2018). The CCC products were then classified into stressed and healthy classes using calculated CCC mean and variance values obtained for infected and healthy Norway spruce trees in 2016. The location of infected plots obtained from the interoperation of resolution (0.1 m) aerial photographs was used as reference data to validate the accuracy of the infestation maps generated from CCC. Validation of the infestation maps indicated a classification accuracy of up to 78%. Our results demonstrated that CCC products derived from satellite remote sensing data were a rigorous proxy for early detection of bark beetle infestation. Hence, CCC products may play a significant role in understanding the dynamics of the Infestation and improving the management of bark beetle outbreaks in forest ecosystems. Inclusion of other remotely sensed plant traits as additional parameters in the model, such as dry matter and nitrogen, may further improve the accuracy of early detection of bark beetle infestation using satellite remote sensing.

Impacts of mountain forest dieback on aquatic insect communities: A DNA metabarcoding analysis of samples from the Bavarian Forest

Mountain forests are increasingly affected by changes in rainfall and pest outbreaks, and the way forests are managed can have direct consequences for the streams flowing through forests. Aquatic macroinvertebrate communities are great bioindicators and changes to their ecosystem likely translates to changes in their overall composition and abundance. The Bavarian Forest National Park (SE Germany) is dominated by the Norway spruce (Picea abies) which, weakened by storms and other stressors, is susceptible to infestation by the European spruce bark beetle (Ips typographus). The result is large scale forest dieback in some areas, and forest management practices that lead to a predominance of three different forest types (hereafter habitats): Intact forest that is healthy and not impacted by Ips typographus; Disturbed forest that was impacted by Ips typographus, left to regenerate naturally, and from which deadwood was not removed; and Salvaged forest that was heavily impacted by Ips typographus with the same consequences, but from which deadwood was removed, creating a treeless forest meadow. Intact forest that is healthy and not impacted by Ips typographus; Disturbed forest that was impacted by Ips typographus, left to regenerate naturally, and from which deadwood was not removed; and Salvaged forest that was heavily impacted by Ips typographus with the same consequences, but from which deadwood was removed, creating a treeless forest meadow. To analyze the impacts these different forest management strategies have on the aquatic insect communities, 30 samples from 11 different streams were taken using kick-sampling. Operational taxonomic units (OTUs) were identified by bulk metabarcoding of dried, ground samples. A mock community was used to verify the setup and a DNA spike-in with three foreign OTUs was added to each sample to measure the biases introduced by PCR amplification and sequencing. Biases varied across samples, but spike-in OTUs produced a pattern indicating predictable biases which could lead to quantifiable metabarcoding results in the future. In total, 260 macroinvertebrate OTUs were identified. In comparison, a morphological study by Bojková et al. (2018) in the same region with twice the number of sampling sites collected 194 taxa in the same month as our samples. This underlines the potential for metabarcoding in evaluating species richness. Species richness was high across all habitats. A significant difference between the forest conditions was detected: The number of detected Diptera OTUs was lowest in disturbed habitats (55) and highest in salvaged areas (73). A permutational multivariate analysis of variance (PERMANOVA) indicated that habitat (i.e., intact, disturbed, salvaged) had an effect on the observed OTU distribution (9.2%), but that the stream catchment had a much larger effect (39.3%) regardless of the habitat. Our findings indicate that forest management can affect stream macroinvertebrate communities, and that this was most pronounced for the Diptera, a group for which DNA metabarcoding is particularly well suited because of their small size and high diversity.

Mapping individual silver fir trees using hyperspectral and LiDAR data in a Central European mixed forest

Mapping a specific tree species at individual tree level across landscapes using remote sensing is challenging, especially in forests where co-occurring tree species exhibit similar characteristics. In Central European mixed forests, silver fir and Norway spruce have been identified as a pair of coniferous tree species with similar spectral and structural characteristics, typically leading to a major misclassification error in mapping studies. Here, we aimed to accurately map individual silver fir trees in a spruce-dominated natural forest in the Bavarian Forest National Park using integrated airborne hyperspectral and LiDAR data. To accomplish this goal, we extracted a set of relevant spectral and structural features from the hyperspectral and LiDAR data and used them to build machine learning classification models. Specifically, we compared the performance of three one-class classification algorithms (i.e. one-class support vector machine, biased support vector machine, and maximum entropy) for mapping individual silver fir trees. Our results showed that the biased support vector machine classifier yielded the highest mapping accuracy, with the area under the curve for positive and unlabeled samples (puAUC) achieving 0.95 (kappa 0.90). We found that the intensity value of 95th percentile of normalized tree height and the percentage of first returns above 2 m high were the most influential structural features, capturing the main morphological difference between silver fir and Norway spruce at the top tree crown. We also found that the wavebands at 700.1 nm, 714.5 nm, and 1201.6 nm were the most robust spectral bands, which are strongly affected by chlorophyll and foliar water content. Our study suggests that discovering links between spectral and structural features captured by different remotely sensed data and species-specific traits can significantly improve the mapping accuracy of a focal species at the individual tree level.

Silvi-Net – A dual-CNN approach for combined classification of tree species and standing dead trees from remote sensing data

Forest managers and nature conservationists rely on precise mapping of single trees from remote sensing data for efficient estimation of forest attributes. In recent years, additional quantification of dead wood in particular has garnered interest. However, tree-level approaches utilizing segmented single trees are still limited in accuracy and their application is therefore mostly restricted to research studies. Furthermore, the combined classification of presegmented single trees with respect to tree species and health status is important for practical use but has been insufficiently investigated so far. Therefore, we introduce Silvi-Net, an approach based on convolutional neural networks (CNNs) fusing airborne lidar data and multispectral (MS) images for 3D object classification. First, we segment single 3D trees from the lidar point cloud, render multiple silhouette-like side-view images, and enrich them with calibrated laser echo characteristics. Second, projected outlines of the segmented trees are used to crop and mask the MS orthomosaic and to generate MS image patches for each tree. Third, we independently train two ResNet-18 networks to learn meaningful features from both datasets. This optimization process is based on pretrained CNN weights and recursive retraining of model parameters. Finally, the extracted features are fused for a final classification step based on a standard multi-layer perceptron and majority voting. We analyzed the network’s performance on data captured in two study areas, the Chernobyl Exclusion Zone (ChEZ) and the Bavarian Forest National Park (BFNP). For both study areas, the lidar point density was approximately 55 points/m2 and the ground sampling distance values of the true orthophotos were 10 cm (ChEZ) and 20 cm (BFNP). In general, the trained models showed high generalization capacity on independent test data, achieving an overall accuracy (OA) of 96.1% for the classification of pines, birches, alders, and dead trees (ChEZ) - and 91.5% for coniferous, deciduous, snags, and dead trees (BFNP). Interestingly, lidar-based imagery increased the OA by 2.5% (ChEZ) and 5.9% (BFNP) compared to experiments only utilizing MS imagery. Moreover, Silvi-Net also demonstrated superior OA compared to the baseline method PointNet++ by 11.3% (ChEZ) and 2.2% (BFNP). Overall, the effectiveness of our approach was proven using 2D and 3D datasets from two natural forest areas (400–530 trees/ha), acquired with different sensor models, and varying geometric and spectral resolution. Using the technique of transfer learning, Silvi-Net facilitates fast model convergence, even for datasets with a reduced number of samples. Consequently, operators can generate reliable maps that are of major importance in applications such as automated inventory and monitoring projects.

Bayesian Hierarchical Modeling of Nitrate Concentration in a Forest Stream Affected by Large‐Scale Forest Dieback

AbstractThe ecosystem function of vegetation to attenuate export of nutrients is of substantial importance for securing water quality. This ecosystem function is at risk of deterioration due to an increasing risk of large‐scale forest dieback under climate change. The present study explores the response of the nitrogen (N) cycle of a forest catchment in the Bavarian Forest National Park, Germany, in the face of a severe bark beetle (Ips typographusLinnaeus) outbreak and resulting large‐scale forest dieback using top‐down statistical‐mechanistic modeling. Outbreaks of bark beetle killed the dominant tree species Norway spruce (Picea abies(L.) H.Karst.) in stands accounting for 55% of the catchment area. A Bayesian hierarchical model that predicts daily stream NO3concentration (C) over three decades with discharge (Q) and temperature (T) (C‐Q‐T relationship) outperformed alternative statistical models. A catchment model was subsequently developed to explain the C‐Q‐T relationship in top‐down fashion. Annually varying parameter estimates provide mechanistic interpretations of the catchment processes. Release of NO3from decaying litter after the dieback was tracked by an increase of the nutrient input parameter cs0. The slope of C‐T relation was near zero during this period, suggesting that the nutrient release was beyond the regulating capacity of the vegetation and soils. Within a decade after the dieback, the released N was flushed out and nutrient retention capacity was restored with the regrowth of the vegetation.

Informed conservation management of rare tree species needs knowledge of species composition, their genetic characteristics and ecological niche

Woodland nature reserves must be scientifically assessed so that subsequent management leads to optimal conservation of biodiversity. This entails knowledge of the species composition, the genetics of the local populations and their ecology. Here we assess Tilia species in the Bavarian Forest National Park (BFNP), a large mixed coniferous and deciduous forest in South-Eastern Germany. Tilia occurs here at low density, as in many other mixed forests in Central and Northern Europe. Therefore, results are not only relevant to BFNP but also to other areas.Exhaustive sampling resulted in the collection of 113 mature trees that were genotyped using 20 microsatellite markers, derived from both T. cordata and T. platyphyllos. For the first time, size and aspect of trees, and their community association were contrasted between the species. Genotyping confirmed that T. platyphyllos, T. cordata and their hybrid (T. x europaea) were present in the BFNP and both species deserve conservation. T. platyphyllos has a higher genetic diversity for both sets of markers than T. cordata, confirming earlier work. Both species showed genetic diversity comparable to other populations in Central Europe, which is likely to be sufficient for the maintenance of the species in the short term. However, increasing the number of trees, ensuring local sources are used, and gene flow from surrounding forests over the next decennia may be crucial for long-term survival.Further, within the T. platyphyllos group there was a set of 11 trees that were distinct from the others: they had a lower genetic diversity and were shorter. We hypothesise that these were planted and should not be used for propagation and augmentation. Most saplings analysed appeared to derive from asexual propagation (36 out of 41), although a few (five out of 41) were novel genotypes. This means that, currently, there is some, but rather limited, regeneration.T. cordata was found at a lower altitude and less steep terrain than T. platyphyllos and the hybrid. The hybrid was taller than the two species, while the diameter at breast height was smallest in T. cordata. T. cordata shows a preference for mixed and coniferous forests, while T. platyphyllos occurs mostly in deciduous forests.Our results indicate that biodiversity at the species and genetic level as well as species’ ecology have to be considered in order to guide informed conservation management. These results form the basis to recommend conservation management improving the long-term viability of Tilia in the BFNP and other mixed forests.

Recovery from air pollution and subsequent acidification masks the effects of climate change on a freshwater macroinvertebrate community

Freshwater ecosystems are dynamic, complex systems with a multitude of physical and ecological processes and stressors which drive fluctuations on the community-level. Disentangling the effects of different processes and stressors is challenging due to their interconnected nature. However, as protected areas (i.e. national parks) are less anthropogenically impacted, they are ideal for investigating single stressors. We focus on the Bavarian Forest National Park, a Long-Term Ecological Research (LTER) site in Germany, where the major stressors are climate warming, air pollution (i.e. acidification) and bark beetle infestations. We investigated the effects of these stressors on freshwater macroinvertebrates using comprehensive long-term (1983–2014) datasets comprising high-resolution macroinvertebrate and physico-chemical data from a near-natural stream. Macroinvertebrate communities have undergone substantial changes over the past 32 years, highlighted by increases in overall community abundance (+173%) and richness (+51.6%) as well as taxonomic restructuring driven by a disproportional increase of dipterans. Prior to the year 2000, regression analyses revealed a decline in sulphate deposition and subsequent recovery from historical acidification as potential drivers of the increases in abundance and richness rather than to increases in water temperature (1.5 °C overall increase). Post 2000, however, alterations to nutrient cycling caused by bark beetle infestations coupled with warming temperatures were correlated to taxonomic restructuring and disproportional increases of dipterans at the expense of sensitive taxa such as plecopterans and trichopterans. Our results highlight the challenges when investigating the effects of climate change within a multi-stressor context. Even in conservation areas, recovery from previous disturbance might mask the effects of ongoing disturbances like climate change. Overall, we observed strong community restructuring, demonstrating that stenothermal headwater communities face additional stress due to emerging competition with tolerant taxa. Conservation efforts should consider the temporal variability of communities and their recovery from disturbances to adequately identify species vulnerable to local or widespread extinction.

Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data

Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.

Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression

Machine learning algorithms, in particular, kernel-based machine learning methods such as Gaussian processes regression (GPR) have shown to be promising alternatives to traditional empirical methods for retrieving vegetation parameters from remotely sensed data. However, the performance of GPR in predicting forest biophysical parameters has hardly been examined using full-spectrum airborne hyperspectral data. The main objective of this study was to evaluate the potential of GPR to estimate forest leaf area index (LAI) using airborne hyperspectral data. To achieve this, field measurements of LAI were collected in the Bavarian Forest National Park (BFNP), Germany, concurrent with the acquisition of the Fenix airborne hyperspectral images (400−2500 nm) in July 2017. The performance of GPR was further compared with three commonly used empirical methods (i.e., narrowband vegetation indices (VIs), partial least square regression (PLSR), and artificial neural network (ANN)). The cross-validated coefficient of determination (Rcv2) and root mean square error (RMSEcv) between the retrieved and field-measured LAI were used to examine the accuracy of the respective methods. Our results showed that using the entire spectral data (400−2500 nm), GPR yielded the most accurate LAI estimation (Rcv2 = 0.67, RMSEcv = 0.53 m2 m−2) compared to the best performing narrowband VIs SAVI2 (Rcv2 = 0.54, RMSEcv = 0.63 m2 m−2), PLSR (Rcv2 = 0.74, RMSEcv = 0.73 m2 m−2) and ANN (Rcv2 = 0.68, RMSEcv = 0.54 m2 m−2). Consequently, when a spectral subset obtained from the analysis of VIs was used as model input, the predictive accuracies were generally improved (GPR RMSEcv = 0.52 m2 m−2; ANN RMSEcv = 0.55 m2 m−2; PLSR RMSEcv = 0.69 m2 m−2), indicating that extracting the most useful information from vast hyperspectral bands is crucial for improving model performance. In general, there was an agreement between measured and estimated LAI using different approaches (p > 0.05). The generated LAI map for BFNP using GPR and the spectral subset endorsed the LAI spatial distribution across the dominant forest classes (e.g., deciduous stands were generally associated with higher LAI values). The accompanying LAI uncertainty map generated by GPR shows that higher uncertainties were observed mainly in the regions with low LAI values (low vegetation cover) and forest areas which were not well represented in the collected sample plots. This study demonstrated the potential of GPR for estimating LAI in forest stands using airborne hyperspectral data. Owing to its capability to generate accurate predictions and associated uncertainty estimates, GPR is evaluated as a promising candidate for operational retrieval applications of vegetation traits.

Do bark beetle outbreaks amplify or dampen future bark beetle disturbances in Central Europe?

Bark beetle outbreaks have intensified in many forests around the globe in recent years. Yet, the legacy of these disturbances for future forest development remains unclear. Bark beetle disturbances are expected to increase further because of climate change. Consequently, feedbacks within the disturbance regime are of growing interest, for example, whether bark beetle outbreaks are amplifying future bark beetle activity (through the initiation of an even‐aged cohort of trees) or dampening it (through increased structural and compositional diversity).We studied bark beetle–vegetation–climate interactions in the Bavarian Forest National Park (Germany), an area characterised by unprecedented bark beetle activity in the recent past. We simulated the effect of future bark beetle outbreaks on forest structure and composition and analysed how disturbance‐mediated forest dynamics influence future bark beetle activity under different scenarios of climate change. We used process‐based simulation modelling in combination with machine learning to disentangle the long‐term interactions between vegetation, climate and bark beetles at the landscape scale.Disturbances by the European spruce bark beetle were strongly amplified by climate change, increasing between 59% and 221% compared to reference climate. Bark beetle outbreaks reduced the dominance of Norway spruce (Picea abies (L.) Karst.) on the landscape, increasing compositional diversity. Disturbances decreased structural diversity within stands (α diversity) and increased structural diversity between stands (β diversity). Overall, disturbance‐mediated changes in forest structure and composition dampened future disturbance activity (a reduction of up to −67%), but were not able to fully compensate for the amplifying effect of climate change. Synthesis. Our findings indicate that the recent disturbance episode at the Bavarian Forest National Park was caused by a convergence of highly susceptible forest structures with climatic conditions favourable for bark beetle outbreaks. While future climate is increasingly conducive to massive outbreaks, the emerging landscape structure is less and less likely to support them. This study improves our understanding of the long‐term legacies of ongoing bark beetle disturbances in Central Europe. It indicates that increased diversity provides an important dampening feedback, and suggests that preventing disturbances or homogenizing post‐disturbance forests could elevate the future susceptibility to large‐scale bark beetle outbreaks.

Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest

ABSTRACT Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies. Here, we compared a radiative transfer model (RTM) inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and leaf area index (LAI), in a mixed temperate forest. The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park, Germany, was evaluated using in situ measurements collected contemporaneously. The RTM inversion using merit function resulted in estimations of LCC (R 2 = 0.26, RMSE = 3.9 µg/cm2), CCC (R 2 = 0.65, RMSE = 0.33 g/m2), and LAI (R 2 = 0.47, RMSE = 0.73 m2/m2), comparable to the estimations based on the machine learning method Random forest regression of LCC (R 2 = 0.34, RMSE = 4.06 µg/cm2), CCC (R 2 = 0.65, RMSE = 0.34 g/m2), and LAI (R 2 = 0.47, RMSE = 0.75 m2/m2). Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function. The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.

DNA metabarcoding for biodiversity monitoring in a national park: Screening for invasive and pest species.

DNA metabarcoding was utilized for a large-scale, multiyear assessment of biodiversity in Malaise trap collections from the Bavarian Forest National Park (Germany, Bavaria). Principal component analysis of read count-based biodiversities revealed clustering in concordance with whether collection sites were located inside or outside of the National Park. Jaccard distance matrices of the presences of barcode index numbers (BINs) at collection sites in the two survey years (2016 and 2018) were significantly correlated. Overall similar patterns in the presence of total arthropod BINs, as well as BINs belonging to four major arthropod orders across the study area, were observed in both survey years, and are also comparable with results of a previous study based on DNA barcoding of Sanger-sequenced specimens. A custom reference sequence library was assembled from publicly available data to screen for pest or invasive arthropods among the specimens or from the preservative ethanol. A single 98.6% match to the invasive bark beetle Ips duplicatus was detected in an ethanol sample. This species has not previously been detected in the National Park.

The influence of camera trap flash type on the behavioural reactions and trapping rates of red deer and roe deer

AbstractCamera traps have become an important tool in wildlife monitoring. However, an issue in interpreting their data in statistical analyses of population densities, demography or behaviour is that the probability of detecting the target animals and their behaviours may vary depending on environmental and methodological factors. A specific problem is the type of flash used in the camera trap, as animals may react differently to different flash types and change their avoidance or habituation behaviour accordingly over time. Here, we provide the first systematic test of the impact of flash type in studies of red deer (Cervus elaphus) and roe deer (Capreolus capreolus), based on an analysis of behavioural responses to white, standard infrared and black flashes during 900 camera trap deployments in the Bavarian Forest National Park and the Northern Black Forest. The results revealed that both deer species were more likely to react to standard infrared than to black flash, but trigger delays prevented comparisons to white flash. Red deer reacted more frequently to camera traps than did roe deer, and responses were more common in the Northern Black Forest than in the Bavarian Forest National Park, where hunting is severely restricted. Contrary to our expectations, camera trapping rates of free‐ranging deer did not significantly decline over time for any flash type or species. Despite the lack of evidence for avoidance behaviour, we recommend the use of black flash for behavioural studies of deer populations to minimize the risk of introducing a source of disturbance whereas infrared and white flash are equally suitable for determinations of demographic parameters.

Large-Scale Mapping of Tree Species and Dead Trees in Šumava National Park and Bavarian Forest National Park Using Lidar and Multispectral Imagery

Knowledge of forest structures—and of dead wood in particular—is fundamental to understanding, managing, and preserving the biodiversity of our forests. Lidar is a valuable technology for the area-wide mapping of trees in 3D because of its capability to penetrate vegetation. In essence, this technique enables the detection of single trees and their properties in all forest layers. This paper highlights a successful mapping of tree species—subdivided into conifers and broadleaf trees—and standing dead wood in a large forest 924 km2 in size. As a novelty, we calibrate the critical stopping criterion of the tree segmentation based on a normalized cut with regard to coniferous and broadleaf trees. The experiments were conducted in Šumava National Park and Bavarian Forest National Park. For both parks, lidar data were acquired at a point density of 55 points/m2. Aerial multispectral imagery was captured for Šumava National Park at a ground sample distance (GSD) of 17 cm and for Bavarian Forest National Park at 9.5 cm GSD. Classification of the two tree groups and standing dead wood—located in areas of pest infestation—is based on a diverse set of features (geometric, intensity-based, 3D shape contexts, multispectral-based) and well-known classifiers (Random forest and logistic regression). We show that the effect of under- and oversegmentation can be reduced by the modified normalized cut segmentation, thereby improving the precision by 13%. Conifers, broadleaf trees, and standing dead trees are classified with overall accuracies better than 90%. All in all, this experiment demonstrates the feasibility of large-scale and high-accuracy mapping of single conifers, broadleaf trees, and standing dead trees using lidar and aerial imagery.

Using the visitor-employed photography method to analyse deadwood perceptions of forest visitors: a case study from Bavarian Forest National Park, Germany

This study investigates visitors’ perceptions and preferences regarding the characteristics of deadwood in the Bavarian Forest National Park (SE-Germany). Visitor-employed photography has been used to collect information about visitors’ preferences. Deadwood is the second most photographed forest element which underlines its important role in forest perception. Even though deadwood photographs are evaluated significantly worse than most other forest elements, most visitors nevertheless associate deadwood photographs with a positively rated forest image. Based on the relationship between aesthetics and recreation revealed in the interviews, it can be concluded that the forest path during the hike was both pleasant and restful. This is confirmed by the post-trip interviews, showing that a higher proportion of deadwood is associated with more aesthetics or recreational value. Regarding deadwood structure results show that deadwood fragments and debris are perceived negatively, while root stocks were assessed more positively. We also found a gradient with positive evaluations increasing with progressing decomposition.