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Anhand eines Datensatzes von 1.708 Vegetationsaufnahmen aus 154 bayerischen Naturwaldreservaten wurde die realisierte ökologische Nische von 25 Baumarten hinsichtlich Lichtbedarf bzw. Schattentoleranz untersucht. Für jede Baumart wurde die Stetigkeit des Vorkommens in Baumschicht und Verjüngung berechnet. Für jede Aufnahme wurde die dem Bestandesunterwuchs zur Verfügung stehende Lichtmenge durch Berechnung des mittleren ungewichteten Licht-Zeigerwertes (mL) aller vorkommenden Arten (ohne Baumschicht) auf einer Relativskala geschätzt. Für jede 0,5-Einheiten-Stufe von mL wurde die Präferenz jeder Baumart, getrennt nach Baum- (> 5m) und Verjüngungsschicht (< 5m), als Differenz zwischen relativer Häufigkeit der jeweiligen Art und der relativen Häufigkeit aller Aufnahmen in der mL-Stufe im gesamten Datensatz berechnet. Die Präferenzprofile von Baumschicht und Verjüngungsschicht bildeten die Grundlage einer numerischen Klassifikation von 6 lichtökologischen Nischen typen. Diese Typen werden hinsichtlich ihrer Bindung an bestimmte Entwicklungsphasen und Strukturen der natürlichen Walddynamik diskutiert, mit geläufigen Einteilungen der Baumarten verglichen und im Hinblick auf eine Prognose des Verhaltens unter sich ändernden Umweltbedingungen ausgewertet. – Während sich Edellaubbäume des Tilio-Acerion in den Reservaten sehr ähnlich wie Fagus und Abies verhalten, bilden die Baumarten der Eichenmischwälder eine lichtökologische Gruppe mit rückläufiger Verjüngungstendenz. Unter den übrigen Halbschattbaumarten hebt sich eine Gruppe heraus, welche sich in geschlossenen Beständen vorausverjüngt und nach Störung in die Baumschicht vordringt. Pionierbaumarten bleiben in Naturwaldreservaten weitestgehend auf Sonderstandorte, wo ihre Verjüngung viel Licht vorfindet, beschränkt.
Good quality data on precipitation are a prerequisite for applications like short-term weather forecasts, medium-term humanitarian assistance, and long-term climate modelling. In Sub-Saharan Africa, however, the meteorological station networks are frequently insufficient, as in the Cuvelai-Basin in Namibia and Angola. This paper analyses six rainfall products (ARC2.0, CHIRPS2.0, CRU-TS3.23, GPCCv7, PERSIANN-CDR, and TAMSAT) with respect to their performance in a crop model (APSIM) to obtain nutritional scores of a household’s requirements for dietary energy and further macronutrients. All products were calibrated to an observed time series using Quantile Mapping. The crop model output was compared against official yield data. The results show that the products (i) reproduce well the Basin’s spatial patterns, and (ii) temporally agree to station records (r = 0.84). However, differences exist in absolute annual rainfall (range: 154 mm), rainfall intensities, dry spell duration, rainy day counts, and the rainy season onset. Though calibration aligns key characteristics, the remaining differences lead to varying crop model results. While the model well reproduces official yield data using the observed rainfall time series (r = 0.52), the products’ results are heterogeneous (e.g., CHIRPS: r = 0.18). Overall, 97% of a household’s dietary energy demand is met. The study emphasizes the importance of considering the differences among multiple rainfall products when ground measurements are scarce.
Wenn auch schon jede Gelegenheit, durch welche einsichtsvolle, die Wissenschaften liebende Männer sich einander nähern und kennen lernen, schätzbar bleibt: so muß die gegenwärtige Veranlassung dem Freunde des Vaterlandes zwiefach willkommen seyn.
Nützlicher Anbau des Landes, Verbesserung unserer Landwirthschaft in ihren vielen, so manchfaltigen Zweigen sind die Absicht, der Zweck dieser Versammlung. ...
Jede neue Bienenkolonie, die sich von dem alten Stocke trennt, heißt ein Schwarm und dieser sucht sich alsbald zu seinen neuen Arbeiern eine Wohnung, und findet sie, wenn Menschen sie ihm nicht darbieten, in hohlen Bäumen, Gebäudespalten und Steinhöhlen, worin sie ohne alle menschliche Hilfe wild leben. ...
We analyzed the possibility of introducing a single stochastic scaling parameter a to describe the spatial variability of soil hydraulic properties, using the soil hydraulic properties of the Hamra field (Russo and Bresler 1981) and the Panache field (Nielsen, Biggar, and Erh 1973). In the traditional approach (Peck, Luxmoore, and Stolzy 1977; Russo and Bresler 1980; Warrick, Mullen, and Nielsen 1977), sets of scaling factors are estimated from the h(s) and K(s) functions. For "perfectly similar media," the two sets of a should be identical. Even though the sets of a in these studies were found to be correlated (table 2), they possessed different statistical properties, and were not identical. Results of structural analyses of the sets of a from the two fields suggested that the spatial structures of the two a-sets are quite distinct, reflecting the different spadal behavior of the h(θ) and the K(θ) functions. Moreover, there was poor correlation between the uncorrelated residuals of the a-sets, indicating that part of the high correlation between the a-sets found in earlier work must stem from the presence of an undetected drift and from correlation between nearby measurements. Under field conditions, the saturated hydraulic conductivity is controlled by the flow of water through large structural voids (macropores), which drain at very small negative values of water pressure. Because of this, we tried eliminating Ks by using relative hydraulic properties instead of the hydraulic properties themselves to estimate the scaling factor sets. For the Hamra field, for which we assumed that the hydraulic properties could be described by the model of Brooks and Corey (1964), we found the resultant sets of scaling factors to be highly correlated (R2 = 0.996) with the same spatial structure, but with slightly different variance. By examining the relationships between the two a-sets implied by the Brooks and Corey (1964) model we saw that (1) in general, both sets will be functions of the range of water saturation values used to estimate them, (2) the correlation between the two sets can be improved for media with broad pore-size distributions, and (3) the two sets will be identical if and only if the relative hydraulic conductivity function K,.(hr) is described by the deterministic function Kr(hr) = hy -2 ("strictly similar media"). This analysis suggests that, for media that are not well described by Kr = hr -2, a scaling factor would be required in addition to a in order to achieve agreement between scaled values of hr(θ) and Kr(θ) at all points. A general model Kr = hr -η was proposed, with η as a second stochastic scaling factor for media that do not obey the restrictive assumptions of macroscopic Miller similitude. In the Hamra field, this modified scaling procedure produced perfect agreement between the scaling hydraulic properties. In the Panache field, with values of η determined from linear regression analysis of the logarithmic transformations of Kr and h,., agreement was improved considerably between the scaled hydraulic properties as compared to the more restrictive scaling procedure. In contrast to the Hamra field, however, there remained some significant differences between the scaled properties. These differences may have been artifacts of the different methods used to estimate the hIs) and the K(s) functions for the Panache field. The results of our analysis suggest that in any transient transport problem involving both K(s) and h(s), the description of their spatial variability requires the use of at least three stochastic variates-Ks , α, and η-not a alone.