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"Seifert, Thomas"
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Lipocalin-2 as an Infection-Related Biomarker to Predict Clinical Outcome in Ischemic Stroke
From previous data in animal models of cerebral ischemia, lipocalin-2 (LCN2), a protein related to neutrophil function and cellular iron homeostasis, is supposed to have a value as a biomarker in ischemic stroke patients. Therefore, we examined LCN2 expression in the ischemic brain in an animal model and measured plasma levels of LCN2 in ischemic stroke patients.
In the mouse model of transient middle cerebral artery occlusion (tMCAO), LCN2 expression in the brain was analyzed by immunohistochemistry and correlated to cellular nonheme iron deposition up to 42 days after tMCAO. In human stroke patients, plasma levels of LCN2 were determined one week after ischemic stroke. In addition to established predictive parameters such as age, National Institutes of Health Stroke Scale and thrombolytic therapy, LCN2 was included into linear logistic regression modeling to predict clinical outcome at 90 days after stroke.
Immunohistochemistry revealed expression of LCN2 in the mouse brain already at one day following tMCAO, and the amount of LCN2 subsequently increased with a maximum at 2 weeks after tMCAO. Accumulation of cellular nonheme iron was detectable one week post tMCAO and continued to increase. In ischemic stroke patients, higher plasma levels of LCN2 were associated with a worse clinical outcome at 90 days and with the occurrence of post-stroke infections.
LCN2 is expressed in the ischemic brain after temporary experimental ischemia and paralleled by the accumulation of cellular nonheme iron. Plasma levels of LCN2 measured in patients one week after ischemic stroke contribute to the prediction of clinical outcome at 90 days and reflect the systemic response to post-stroke infections.
Journal Article
Diversity—biomass relationship across forest layers
Forest stratification plays a crucial role in light interception and plant photosynthetic activities. However, despite the increased number of studies on biodiversity–ecosystem function, we still lack information on how stratification in tropical forests modulates biodiversity effects. Moreover, there is less investigation and argument on the role of species and functional traits in forest layers. Here, we analysed from a perspective of forest layer (sub-canopy, canopy and emergent species layers), the relationship between diversity and aboveground biomass (AGB), focusing on functional diversity and dominance, and underlying mechanisms such as niche complementarity and selection. The sub-canopy layer had the highest species richness and diversity, while the emergent layer had the highest AGB. Species richness–AGB relationship was positive for each forest layer, but stronger for sub-canopy layer than for canopy and emergent layers. Total AGB was strongly correlated with functional diversity, leaf and wood traits of species in the sub-canopy and canopy layers. This suggests that sub-canopy and canopy species are major drivers of stand diversity–AGB relationship, and that resource filtering by canopy or emergent trees may not reduce the strength of diversity–AGB relationship in the sub-canopy layer. We argue that complementary resource use by sub-canopy species that supports niche complementarity, is a key mechanism driving AGB in natural forests. Selection effects are most evident in emergent species and niche complementarity effects for sub-canopy and canopy species, supporting arguments that AGB is affected by sub-canopy species’ efficient use of limited resources despite competition from emergent species.
Journal Article
UAV-Based Forest Health Monitoring: A Systematic Review
In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.
Journal Article
Influence of drone altitude, image overlap, and optical sensor resolution on multi-view reconstruction of forest images
Recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging behind the technical developments. Our study aims to address this gap, exploring the effects of drone flight parameters (altitude, image overlap, and sensor resolution) on image reconstruction and successful 3D point extraction. This study was conducted using video footage obtained from flights at several altitudes, sampled for images at varying frequencies to obtain forward overlap ratios ranging between 91 and 99%. Artificial reduction of image resolution was used to simulate sensor resolutions between 0.3 and 8.3 Megapixels (Mpx). The resulting data matrix was analysed using commercial multi-view reconstruction (MVG) software to understand the effects of drone variables on (1) reconstruction detail and precision, (2) flight times of the drone, and (3) reconstruction times during data processing. The correlations between variables were statistically analysed with a multivariate generalised additive model (GAM), based on a tensor spline smoother to construct response surfaces. Flight time was linearly related to altitude, while processing time was mainly influenced by altitude and forward overlap, which in turn changed the number of images processed. Low flight altitudes yielded the highest reconstruction details and best precision, particularly in combination with high image overlaps. Interestingly, this effect was nonlinear and not directly related to increased sensor resolution at higher altitudes. We suggest that image geometry and high image frequency enable the MVG algorithm to identify more points on the silhouettes of tree crowns. Our results are some of the first estimates of reasonable value ranges for flight parameter selection for forestry applications.
Journal Article
Climate and soil effects on tree species diversity and aboveground carbon patterns in semi-arid tree savannas
Climatic and edaphic effects are increasingly being discussed in the context of biodiversity-ecosystem functioning. Here we use data from West African semi-arid tree savannas and contrasting climatic conditions (lower
vs
. higher mean annual precipitation-MAP and mean annual temperature-MAT) to (1) determine how climate modulates the effects of species richness on aboveground carbon (AGC); (2) explore how species richness and AGC relate with soil variables in these contrasting climatic conditions; and (3) assess how climate and soil influence directly, and/or indirectly AGC through species richness and stand structural attributes such as tree density and size variation. We find that greater species richness is generally associated with higher AGC, but more strongly in areas with higher MAP, which also have greater stem density
.
There is a climate-related influence of soils on AGC, which decreases from lower to higher MAP conditions. Variance partitioning analyses and structural equation modelling show that, across all sites, MAP, relative to soils, has smaller effect on AGC, mediated by stand structural attributes whereas soil texture and fertility explain 14% of variations in AGC and influence AGC directly and indirectly via species richness and stand structural attributes. Our results highlight coordinated effects of climate and soils on AGC, which operated primarily via the mediation role of species diversity and stand structures.
Journal Article
Allometric models for above-ground biomass, carbon and nutrient content of wild cherry (Prunus avium L.) trees in agroforestry systems
Key messageWe provide a set of allometric models for wild cherry trees (Prunus avium L.) established in agroforestry systems. A total of 70 trees in southwestern Germany were surveyed using terrestrial laser scanning and analysed using quantitative structure models. The derived allometric models provide a stable base for biomass estimation in comparable agroforestry systems. Our biomass model, based on volume estimates converted to biomass, shows no significant differences to a previous study in the same region on the same species, although it was conducted on agroforestry trees under a different management regime.ContextWild cherry (Prunus avium L.) is a common tree species in agroforestry systems (AFS). Utilised for either fruit production or for high-value timber production, it is a highly relevant species, yet even basic allometric models are lacking.AimsThe aim of this study was to develop a set of allometric models for wild cherry trees in AFS. Within this context, we present an innovative non-destructive approach to estimate bark and wood volume separately by applying bark thickness models to 3D models of trees. To assess model applicability to different AFS, we compared our allometric model for above-ground biomass with a previous biomass model for wild cherry trees under different management in the same region.MethodsWild cherry trees (n = 70) located within AFS in southern Germany were scanned with a terrestrial laser scanner. Quantitative structure models were used to derive tree dimensions and above-ground volume per tree. Using additional auxiliary data, the target variables were derived, and corresponding allometric models were fitted.ResultsThe allometric models estimating above-ground volume, oven-dry biomass, carbon content and nutrient content based on diameter at breast height (DBH) showed excellent fits (R2adj ≥ 0.97). The comparisons with a similar study conducted in the same region suggested that management practices such as pruning have only a minor influence on the relationship between DBH and above-ground tree biomass. The nutrient content in the trees decreased in the order Ca > N > K > Mg > P.ConclusionsThe derived allometric models provide valuable information on this important agroforestry tree species. Our findings can both inform management practices in AFS and advance ecological understanding of these systems. Future research should focus on developing allometric models for other tree species relevant to AFS.
Journal Article
Windbreaks as part of climate-smart landscapes reduce evapotranspiration in vineyards, Western Cape Province, South Africa
Under the conditions of climate change in South Africa, ecological and technical measures are needed to reduce the water consumption of irrigated crops. Windbreak hedges are long-rated systems in agriculture that significantly reduce wind speed. Their possibilities to reduce evapotranspiration and water demand are being investigated at a vineyard in the Western Cape Province, South Africa. Detailed measurements of meteorological parameters relevant for the computation of reference and crop-specific evapotranspiration following the FAO 56 approaches within a vineyard in the Western Cape Province of South Africa have shown the beneficial effect of an existing hedgerow consisting of 6 m high poplars (Populus simonii (Carrière) Wesm.). With reference to a control station in the open field, the mean wind speed in a position about 18 m from the hedgerow at canopy level (2 m) was reduced by 27.6% over the entire year and by 39.2% over the summer growing season. This effect leads to a parallel reduction of reference evapotranspiration of 15.5% during the whole year and of 18.4% over the growing season. When applying empirical crop-specific Kc values for well-irrigated grapes, the reduction of evapotranspiration is 18.8% over the summer growth period. The introduced tree shelterbelts are a suitable eco-engineering approach to reduce water consumption and to enhance water saving in vineyards.
Journal Article
Crown–branching trade-offs mediate growth and resilience to drought, frost, and masting under moderate thinning in European Beech (Fagus sylvatica L.)
Key message
Thinning boosts radial growth in European beech (
Fagus sylvatica
L.), but more is not always better: moderate thinning offers the best vitality balance by keeping resilience comparable to heavily thinned trees, while reducing the sensitivity to late frost and the interannual growth variability and avoiding excessive branching in the crown.
Context
Forest management practices affect growth dynamics and crown architecture, yet their effects under increasing stress remain insufficiently understood.
Aims
This study compared the effects of thinning on basal area increment (BAI) of European beech at breast height, with a focus on growth sensitivity to fructification, drought, and late frost at the tree level. Specifically, we assessed whether moderate or heavy thinning improve drought resilience and we explored the role of crown architecture in growth responses.
Methods
The study was conducted in European beech-dominated stands in Rhineland-Palatinate, Germany, across three age classes (50, 70, 90 years) and three thinning treatments (control, moderate, heavy). In total, 125 trees were sampled. BAI was derived from increment cores, and crown structure from terrestrial laser scans processed into quantitative structure models. Linear mixed-effects modelling was used to analyse tree-level BAI and crown traits. We also assessed drought carry over effects and analysed the resilience index in relation to thinning.
Results
BAI, its relative and absolute interannual variation as well as crown volume increased with thinning intensity. Excessive branching promoted growth in small crowned trees, but limited it in large ones. Drought lag effects persisted for up to two years, particularly in unthinned plots, yet BAI resilience to early summer drought was equally high under both thinning treatments.
Conclusion
Thinning intensity strongly influenced crown architecture and stem growth, with heavier thinning promoting larger crowns and greater tree-level productivity, but at the cost of higher growth variability and frost sensitivity. Compared to moderate thinning, heavy thinning offers no clear vitality and growth resilience related advantages. Importantly, modelling radial growth as a function of crown volume and branching offers a more precise, tree-centred alternative to traditional thinning classifications.
Journal Article
From Dawn to Dusk: High-Resolution Tree Shading Model Based on Terrestrial LiDAR Data
Light availability and distribution play an important role in every ecosystem as these affect a variety of ecosystem processes and functions. To estimate light availability and distribution, light simulations can be used. Many previous models were based on highly simplified tree models and geometrical assumptions about tree form, or were sophisticated and computationally demanding models based on 3D data which had to be acquired in every season to be simulated. The aim of this study was to model the shadow cast by individual trees at high spatial and temporal resolution without the need for repeated data collection during multiple seasons. For our approach, we captured trees under leaf-off conditions using terrestrial laser scanning and simulated leaf-on conditions for individual trees over the remainder of the year. The model was validated against light measurements (n=20,436) collected using 60 quantum sensors underneath an apple tree (Malus domestica Borkh.) on a sunny and cloudless summer day. On this day, the leaves and the shadow were simulated with a high spatial (1 cm) and temporal resolution (1 min). The simulated values were highly correlated with the measured radiation at r=0.84. Additionally, we simulated the radiation for a whole year for the sample apple tree (tree height: 6.6 m, crown width: 7.6 m) with a resolution of 10 cm and a temporal resolution of 10 min. Below the tree, an area of 49.55 m² is exposed to a radiation reduction of at least 10%, 17.74 m² to at least 20% and only 0.12 m² to at least 30%. The model could be further improved by incorporating branch growth, curved leaf surfaces, and gravity to take the weight of the foliage into account. The presented approach offers a high potential for modelling the light availability in the surroundings of trees with an unprecedented spatial and temporal resolution.
Journal Article