Suchergebnisse

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

Abstract: Carrion plays an essential role in shaping the structure and functioning of ecosystems and has far‐reaching implications for biodiversity conservation. The change in availability and type of carcasses throughout ecosystems can involve negative effects for scavenging communities. To address this issue, there have been recent conservation management measures of carrion provision in natural systems. However, the optimal conditions under which exposing carcasses to optimize conservation outcomes are still limited. Here, we used camera traps throughout elevational and vegetational gradients to monitor the consumption of 48 deer carcasses over a study period of six years by evaluating 270,279 photographs resulting out of 15,373 trap nights. We detected 17 species visiting carcass deployments, including five endangered species. Our results show that large carcasses, the winter season, and a heterogeneous surrounding habitat enhanced the frequency of carcass visits and the species richness of scavenger assemblages. Contrary to our expectations, carcass species, condition (fresh/frozen), and provision schedule (continuous vs single exposure) did not influence scavenging frequency or diversity. The carcass visitation frequency increased with carcass mass and lower temperatures. The effect of large carcasses was especially pronounced for mesopredators and the Eurasian lynx (Lynx lynx). Lynx were not too influenced in its carrion acquisition by the season, but exclusively preferred remote habitats containing higher forest cover. Birds of prey, mesopredators, and top predators were also positively influenced by the visiting rate of ravens (Corvus corax), whereas no biotic or abiotic preferences were found for wild boars (Sus scrofa). This study provides evidence that any ungulate species of carrion, either in a fresh or in previously frozen condition, attracts a high diversity of scavengers especially during winter, thereby supporting earlier work that carcass provisions may support scavenger communities and endangered species

AbstractConservation grazing uses semi-feral or domesticated herbivores to limit encroachment in open areas and to promote biodiversity. However, we are still unaware of its effects on wild herbivores. This study investigates the influence of herded sheep and goats on red deer (Cervus elaphus) spatial behavior by testing three a-priori hypotheses: (i) red deer are expected to avoid areas used by livestock, as well as adjacent areas, when livestock are present, albeit (ii) red deer increase the use of these areas when sheep and goats are temporarily absent and (iii) there is a time-lagged disruption in red deer spatial behavior when conservation grazing practice ends. Using GPS-telemetry data on red deer from a German heathland area, we modelled their use of areas grazed by sheep and goats, using mixed-effect logistic regression. Additionally, we developed seasonal resource selection functions (use-availability design) to depict habitat selection by red deer before, during, and after conservation grazing. Red deer used areas less during conservation grazing throughout all times of the day and there was no compensatory use during nighttime. This effect mostly persisted within 21 days after conservation grazing. Effects on habitat selection of red deer were detectable up to 3000 meters away from the conservation grazing sites, with no signs of either habituation or adaption. For the first time, we demonstrate that conservation grazing can affect the spatio-temporal behavior of wild herbivores. Our findings are relevant for optimizing landscape and wildlife management when conservation grazing is used in areas where wild herbivores are present.

Abstract: 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

Abstract: Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for 'forest' and PROSAIL for 'short vegetation' ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future

Context Large carnivores are increasingly threatened by anthropogenic activities, and their protection is among the main goals of biodiversity conservation. The snow leopard (Panthera uncia) inhabits high-mountain landscapes where livestock depredation drives it into conflicts with local people and poses an obstacle for its conservation. Aims The aim of this study was to identify the livestock groups most vulnerable to depredation, target them in implementation of practical interventions, and assess the effectiveness of intervention strategies for conflict mitigation. We present a novel attempt to evaluate intervention strategies for particularly vulnerable species, age groups, time, and seasons. Methods In 2020, we conducted questionnaire surveys in two regions of the Annapurna Conservation Area, Nepal (Manang, n = 146 respondents and Upper Mustang, n = 183). We applied sample comparison testing, Jacobs' selectivity index, and generalised linear models (GLMs) to assess rates and spatio-temporal heterogeneity of depredation, reveal vulnerable livestock groups, analyse potential effects of applied intervention strategies, and identify husbandry factors relevant to depredation. Key results Snow leopard predation was a major cause of livestock mortality in both regions (25.4–39.8%), resulting in an estimated annual loss of 3.2–3.6% of all livestock. The main intervention strategies (e.g. corrals during night-time and herding during daytime) were applied inconsistently and not associated with decreases in reported livestock losses. In contrast, we found some evidence that dogs, deterrents (light, music playing, flapping tape, and dung burning), and the use of multiple interventions were associated with a reduction in reported night-time depredation of yaks. Conclusions and implications We suggest conducting controlled randomised experiments for quantitative assessment of the effectiveness of dogs, deterrents, and the use of multiple interventions, and widely applying the most effective ones in local communities. This would benefit the long-term co-existence of snow leopards and humans in the Annapurna region and beyond.

Abstract: The Sentinel-2 Multi-Spectral Imager (MSI) has three spectral bands centered at 705, 740, and 783 nm wavelengths that exploit the red-edge information useful for quantifying plant biochemical traits. This sensor configuration is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving canopy chlorophyll content (CCC) from Sentinel-2 in a heterogeneous mixed mountain forest. Amongst the algorithms presented in the literature, 13 different vegetation indices (VIs), a non-parametric statistical approach, and two radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field campaign was conducted in July 2017 to collect in situ measurements of CCC in Bavarian forest national park, and the cloud-free Sentinel-2 image was acquired on 13 July 2017. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches, such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. As such the modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m2 (R2 = 0.75). The PLSR resulted in the highest R2 of 0.78, and slightly higher RMSE =0.22 g/m2 than mSR3. The physical-based approach-INFORM inversion using look-up table resulted in an RMSE =0.31 g/m2, and R2 = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for long-term terrestrial ecosystems monitoring across the globe

Cover -- Titelseite -- Inhalt -- Vorwort -- Einleitung -- Teil 1 Biologie, Verhalten und Ökologie -- 1 Der Wolf -- 1.1 Systematik und Evolution -- 1.2 Morphologie -- 1.3 Physische Leistungen und Sinnes­leistungen -- 1.4 Populationsdynamik -- 1.5 Sozialverhalten -- 1.6 Raum-Zeit-Verhalten -- 1.7 Wölfe auf der Jagd -- 1.8 Lebensraum und Verbreitung -- 2 Der Eurasische Luchs -- Marco Heurich -- 2.1 Systematik und Evolution -- 2.2 Morphologie -- 2.3 Sinnesleistungen -- 2.4 Fortpflanzung, Entwicklung, ­Sterblichkeit -- 2.5 Sozialverhalten -- 2.6 Lebensraum und Streifgebiet -- 2.7 Luchse als Raubtiere -- 2.8 Verbreitung der Luchse -- 3 Der Braunbär -- Michaela Skuban -- 3.1 Systematik und Evolution -- 3.2 Morphologie -- 3.3 Sinnesleistungen -- 3.4 Fortpflanzung, Entwicklung, ­Sterblichkeit -- 3.5 Sozialverhalten -- 3.6 Raum-Zeit-Verhalten - Lebens­raum, Streif­gebiet und Dispersal -- 3.7 Ernährung -- 3.8 Verbreitung im deutsch­sprachigen Raum -- 4 Die Rolle der großen Beute­greifer im Ökosystem -- 4.1 Einleitung -- 4.2 Theoretische Grundlagen -- 4.3 Empirische Belege für die Effekte von ­großen Beute­greifern -- 4.4 Welche Effekte haben ­Wölfe in der mitteleuropäischen Kulturlandschaft? -- Teil 2 Management von großen ­Beute­greifern -- 5 Was ist Wildtiermanagement? -- 6 Strukturen des Managements -- 6.1 Managementpläne -- 6.2 Organisationsstrukturen und Verfahrensweisen -- 6.3 Internationale Zusammenarbeit -- 7 Wildtiermanagement für ­Menschen -- 7.1 Der sozio-kulturelle Rucksack der ­Arten -- 7.2 Einstellungen gegenüber ­großen Beute­greifern -- 7.3 Große Beute­greifer als Symbol -- 7.4 Wildtiermanagement als Manage­ment von Konflikten -- 8 Rechtlicher Schutz von Wolf, Luchs und Bär -- 8.1 Berner Konvention -- 8.2 Fauna-Flora-Habitat-(FFH)-­Richtlinie -- 8.3 Deutschland -- 8.4 Österreich -- 8.5 Schweiz -- 9 Monitoring von großen Beute­greifern.

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support. ; Peer Reviewed

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.