Short,medium and long range transported airborne particles in viability and antigenicity analyses

Summary It is no longer enough that aerobiologists identify pollen grains and fugal spores under the microscope. They are expected to serve as well allergologists, who want to known about the concentrations of various allergens in the air samples, as meteorologists, who need to know about the frequency of primary biological aerosols, which may act in radiative forcing of the atmosphere. Methods utilized by the aerobiologists vary from immunochemical analyses, culture and germination to infecting host plants with certain pathogens. Difficulties to separate between short, medium and long range transport of airborne particles on the basis of their viability are discussed.     

An aerobiological study of pollen grains and fungal spores of Barcelona (Spain)

Summary A study of concentration of airborne pollen grains and fungal spores has been carried out in Barcelona (Spain) during 1989–90. The volumetric method of filtration, previously described for airborne pollen analysis (Suarez-Cervera and Seoane-Camba, 1983) has been used. In this case, the filters have also been cultivated in Czapecdox-agar, Sabouraud-agar and Sabouraud-agar with streptomycin for the identification of the fungal colonies. Analysis of the number of fungal spores growing on the filter shows that the maxima of colonies of spores developed in culture per m³ of air filtered, correspond to September–December. Pollen and spore concentrations start from November–December, reach a maximum in March–April and decline progressively until September–October. Therefore, in the city of Barcelona, the greatest concentration occurs in spring and the lowest in autumn.     

Detecting shrub encroachment in seminatural grasslands using UAS LiDAR

1. Shrub encroachment in seminatural grasslands threatens local biodiversity unless management is applied to reduce shrub density. Dense vegetation of Cytisus scoparius homogenizes the landscape negatively affecting local plant diversity. Detecting structural change (e.g., biomass) is essential for assessing negative impacts of encroachment. Hence, exploring new monitoring tools to achieve this task is important for effectively capturing change and evaluating management activities.
2. This study combines traditional field‐based measurements with novel Light Detection and Ranging (LiDAR) observations from an Unmanned Aircraft System (UAS). We investigate the accuracy of mapping C. scoparius in three dimensions (3D) and of structural change metrics (i.e., biomass) derived from ultrahigh‐density point cloud data (>1,000 pts/m²). Presence–absence of 12 shrub or tree genera was recorded across a 6.7 ha seminatural grassland area in Denmark. Furthermore, 10 individuals of C. scoparius were harvested for biomass measurements. With a UAS LiDAR system, we collected ultrahigh‐density spatial data across the area in October 2017 (leaf‐on) and April 2018 (leaf‐off). We utilized a 3D point‐based classification to distinguish shrub genera based on their structural appearance (i.e., density, light penetration, and surface roughness).
3. From the identified C. scoparius individuals, we related different volume metrics (mean, max, and range) to measured biomass and quantified spatial variation in biomass change from 2017 to 2018. We obtained overall classification accuracies above 86% from point clouds of both seasons. Maximum volume explained 77.4% of the variation in biomass.
4. The spatial patterns revealed landscape‐scale variation in biomass change between autumn 2017 and spring 2018, with a notable decrease in some areas. Further studies are needed to disentangle the causes of the observed decrease, for example, recent winter grazing and/or frost events.
5. Synthesis and applications: We present a workflow for processing ultrahigh‐density spatial data obtained from a UAS LiDAR system to detect change in C. scoparius. We demonstrate that UAS LiDAR is a promising tool to map and monitor grassland shrub dynamics at the landscape scale with the accuracy needed for effective nature management. It is a new tool for standardized and nonbiased evaluation of management activities initiated to prevent shrub encroachment.

     

Palynological characterization of honeys from the Andean Patagonia (Chubut,Argentina)

The pollen content of 58 honey samples of Apis mellifera L. from the Andean region of Chubut (Argentinian Patagonia) was analyzed. The samples were provided by beekeepers between 1999 and 2004. Eighty‐eight pollen types were identified, 30 of them are not reported in Argentinian honeys. Identified types belong to 47 families, of which the most diverse were Asteraceae (15 pollen types), Fabaceae (13 pollen types), and Rosaceae (four pollen types). From the samples analyzed, 47% were monofloral and corresponded to the following taxa: Trifolium spp. (16%), Rosaceae (10%), Aristotelia chilensis (Molina) Stuntz (7%), Discaria‐Colletia (5%), Escallonia spp. (3%), Schinus patagonica (Phil.) I. M. Johnst. (2%), Phacelia secunda J. F. Gmel. (2%), Myrtaceae (2%). The results of this study show a high level of utilization of native flora by Apis mellifera. Four new types of monofloral honey were recognized: Aristotelia chilensis, Discaria‐Colletia, Phacelia secunda, and Schinus patagonica. The high representation of native flora distinguishes these honeys geographically.     

Estimation of carbon storage based on individual tree detection in <Emphasis Type="Italic">Pinus densiflora</Emphasis> stands using a fusion of aerial photography and LiDAR data

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.     

Habitat suitability modelling of an invasive plant with advanced remote sensing data

Aim  Lepidium latifolium (Brassicaceae; perennial pepperweed) is a noxious Eurasian weed invading riparian and wetland areas of the western USA. Understanding which sites are most susceptible to invasion by L. latifolium will allow more efficient management of this weed. We assessed the ability of advanced remote sensing techniques to develop habitat suitability models for L. latifolium .
Location  San Francisco Bay/Sacramento-San Joaquin River Delta, California, USA.
Methods  Lepidium latifolium distribution was mapped with hyperspectral image data of Rush Ranch Open Space Preserve, providing presence/absence data to train and validate habitat models. A high-resolution light detection and ranging digital elevation model was used to derive predictor environmental variables (distance to channel, distance to upland, elevation, slope, aspect and convexity). Aggregate decision tree models were used to predict the potential distribution of this species.
Results  Lepidium latifolium infested two zones: near the marshland–upland margin and along channels within the marsh. Topographical data, which are typically strongly correlated with wetland species distributions, were relatively unimportant to L. latifolium occurrence, although relevant microtopography information, particularly relative elevation, was subsumed in the distance to channel variable. The map of potential L. latifolium distribution reveals that Rush Ranch contains considerable habitat that it is susceptible to continued invasion.
Main conclusions  Lepidium latifolium invades relatively less stressful sites along the inundation and salinity gradients. Advanced remote sensing datasets were shown to be sufficient for species distribution modelling. Remote sensing offers powerful tools that deserve wider use in ecological research and management.     

Remotely sensed between‐individual functional trait variation in a temperate forest

1. Trait‐based ecology holds the promise to explain how plant communities work, for example, how functional diversity may support community productivity. However, so far it has been difficult to combine field‐based approaches assessing traits at the level of plant individuals with limited spatial coverage and approaches using remote sensing (RS) with complete spatial coverage but assessing traits at the level of vegetation pixels rather than individuals. By delineating all individual‐tree crowns within a temperate forest site and then assigning RS‐derived trait measures to these trees, we combine the two approaches, allowing us to use general linear models to estimate the influence of taxonomic or environmental variation on between‐ and within‐species variation across contiguous space.
2. We used airborne imaging spectroscopy and laser scanning to collect individual‐tree RS data from a mixed conifer‐angiosperm forest on a mountain slope extending over 5.5 ha and covering large environmental gradients in elevation as well as light and soil conditions. We derived three biochemical (leaf chlorophyll, carotenoids, and water content) and three architectural traits (plant area index, foliage‐height diversity, and canopy height), which had previously been used to characterize plant function, from the RS data. We then quantified the contributions of taxonomic and environmental variation and their interaction to trait variation and partitioned the remaining within‐species trait variation into smaller‐scale spatial and residual variation. We also investigated the correlation between functional trait and phylogenetic distances at the between‐species level. The forest consisted of 13 tree species of which eight occurred in sufficient abundance for quantitative analysis.
3. On average, taxonomic variation between species accounted for more than 15% of trait variation in biochemical traits but only around 5% (still highly significant) in architectural traits. Biochemical trait distances among species also showed a stronger correlation with phylogenetic distances than did architectural trait distances. Light and soil conditions together with elevation explained slightly more variation than taxonomy across all traits, but in particular increased plant area index (light) and reduced canopy height (elevation). Except for foliage‐height diversity, all traits were affected by significant interactions between taxonomic and environmental variation, the different responses of the eight species to the within‐site environmental gradients potentially contributing to the coexistence of the eight abundant species.
4. We conclude that with high‐resolution RS data it is possible to delineate individual‐tree crowns within a forest and thus assess functional traits derived from RS data at individual level. With this precondition fulfilled, it is then possible to apply tools commonly used in field‐based trait ecology to partition trait variation among individuals into taxonomic and potentially even genetic variation, environmental variation, and interactions between the two. The method proposed here presents a promising way of assessing individual‐based trait information with complete spatial coverage and thus allowing analysis of functional diversity at different scales. This information can help to better understand processes shaping community structure, productivity, and stability of forests.

     

北京市夏季空气微生物群落结构和生态分布

着重研究北京市夏季空气微生物的群落结构和生态分布特征。结果表明 :北京市夏季空气中革兰氏阳性菌明显多于革兰氏阴性菌 ,约占 70 %～ 85 % ,其中阳性球菌占总数的占 35 %～ 4 5 %。 3个功能区 (文教区、交通干线和公园绿地 )共发现 30属空气细菌 ,其中革兰氏阳性菌 2 0属 ,革兰氏阴性菌 10属。优势细菌属为微球菌属 (Micrococcus)、芽孢杆菌属 (Bacillus)、葡萄球菌属 (Staphylococcus)和假单胞菌属 (Pseudomonas)。 3个功能区共出现 10属空气真菌 ,优势菌属枝孢属 (Cladosporium)、链格孢属 (Alternaria)、无孢菌 (nonsporing)、青霉属 (Penicillium)和曲霉属 (Aspergillus) ,其中枝孢属是绝对优势菌属 ,占总数的4 8.2 %。空气细菌浓度交通干线和文教区明显高于公园绿地 ,而空气真菌浓度公园绿地和文教区明显高于交通干线。空气细菌浓度一日中 13:0 0时较低 ,0 9:0 0时和 17:0 0较高     

Study of airborne pollen composition and allergen spectrum of hay fever patients in South Hungary (1990–1999)

On the basis of their 10-year-longaeropalynological and allergological studies,the authors examined the changes in theallergen spectrum of hay fever patients inconnection with the seasonality of the symptomsand pollen count of this period of 10 years.The results of skin prick tests (SPT) and casehistories of patients examined in 1990/91 and1998/99 were compared. Pollen counting wasperformed with a Hirst-type pollen trap. By theend of the observed period, the ratio ofseverely polysensitized patients (4 or morepositive skin prick tests) increasedsignificantly according to the significantlyincreased amount of positive skin prick teststo trees, rye and plantain. As the seasonalityof the patients' symptoms did not change duringthis period, this increase in skin prick testpositivity means latent allergy. However, in afew years time, it may become manifest allergycausing symptoms in the early springtime.According to the results of pollen counting,the atmospheric pollen pollution of the regionwas markedly high during the possiblesensitization period of these patients.     

Automated pollen recognition using 3D volume images from fluorescence microscopy

Identifying and counting of pollen grains in ambient air samples is still a demanding and time-consuming task even for an experienced microscopist. This article describes a technique which may be employed to establish a fully automated system for this task. Based on a 3D volume fluorescence image of a pollen grain taken with a confocal laser scanning microscope, the described system is able to recognize the pollen taxa. The system autonomously extracts all required information for the recognition from a data base with reference objects (self-learning system) and only needs to calculate very general purpose features of the volumetric data sets (so-called gray scale invariants). This allows for easy adaptation of the system to other conditions (e.g., pollen of a special area) or even other objects than pollen (e.g., spores, bacteria etc.) just by exchanging the reference data base. When using a reference data base with the 26 most important German pollen taxa, the recognition rate is 92%. With a special database for allergic purposes recognizing only Corylus, Alnus, Betula, Poaceae, Secale, Artemisia and ``allergically non-relevant' the recognition rate is 97.4%.