Biomass functions for the two alien tree species Prunus serotina Ehrh. and Robinia pseudoacacia L. in floodplain forests of Northern Italy

As one cause for biodiversity loss, invasive alien species are a worldwide threat. In forests, however, invasive tree species can also have an enormous biomass potential which can be harvested while taking measures against the species. Allometric equations help estimating the biomass but are often only available for the native range of the species. This lack on information complicates the management of invaded stands, and the equations presented here should help fill this gap. The above-ground biomass for single trees of black cherry (Prunus serotina Ehrh.) and black locust (Robinia pseudoacacia L.) in Ticino/Italy was estimated with differing explanatory variables as total, stem, crown, and leaf biomass. Regression equations of P. serotina were compared with equations from North America. The methods to derive biomass estimates from fresh weight and volumetric measurements in combination with wood densities were critically examined. The biomass could be estimated well by using "diameter" as explanatory variable. The productivity of P. serotina was lower here compared to its range of origin. Biomass estimates from volumetric measurements combined with the truncated cone formula have lead to systematic overestimations. Also the use of volumetric measurements combined with wood density measurements has overestimated comparable estimates from fresh weight measurements. peerReviewe

Different Effects of Regional Species Pool on Plant Diversity between Forest and Grassland Biomes in Arid Northwest China.

Species pool hypothesis is broadly known and frequently tested in various regions and vegetation types. However it has not been tested in the arid Xinjiang region of China due to lack of data. Here with systematic data from references and field survey, we comprehensively examined species pool hypothesis in this region. Took species richness in 0.1° × 0.1° grid cells as regional species richness (RSR) which were obtained from the distribution maps of vascular plant species, and took species diversity of 190 and 103 plots in forest and grassland biomes across Xinjiang as local species richness (LSR), together with the digitalized soil pH and climate data, we tested the species pool hypothesis in this region. We found that: (1) the average RSR was higher in mountains than that in basins and it was negatively correlated with soil pH in mountains while positively correlated with soil pH in basins in Xinjiang; (2) RSR showed a positive correlation with mean annual precipitation (MAP) while showed a hump-shaped pattern with mean annual temperature (MAT); and the changing patterns of LSR were different for forest and grassland along the geographical and climate gradients; (3) LSR of forest was more affected by RSR than by climate, while on the contrary, LSR of grassland was more affected by climate than by RSR. Our results validated the species pool hypothesis in revealing that RSR had a significant role in shaping LSR patterns in addition to climate. We concluded that the relative effects of climate vs. RSR on LSR differed markedly between the forest and grassland communities across Xinjiang. Our results also showed that RSR revealed a contrasting relationship with soil pH in mountains and in basins, which might reflect differences in evolutionary processes of various habitats. In summary, our research systematically analyzed the correlation of species richness in regional and local scales in Xinjiang which provides more insights into the understanding of species pool hypothesis

Patterns and Environmental Determinants of Medicinal Plant : Vascular Plant Ratios in Xinjiang, Northwest China

<div><p>With both a full collection of native vascular plant distributions and a full checklist of source plants of the Chinese Materia Medica (CMM), the Uygur Medicine (UM), and the Kazak Medicine (KM) for the Xinjiang region, we defined medicinal plant: vascular plant ratios (simplified as medicinal plant ratios hereafter) as the value of medicinal plant richness divided by vascular plant richness. We aimed to find whether the ratios are constant or change in different environments, which environmental variables determine medicinal plant ratios, and whether the ratios are more influenced by human or by natural environments. Finally, suggestions for medicinal plant conservation were addressed. We found that (1) medicinal plant ratios were not constant, and they were high in the Tarim Basin which was largely covered by desert, while they were relatively low in mountainous areas, especially in the Tianshan Mountains where the general species richness was high; (2) medicinal plant ratios were not significantly influenced by human activities, indicated by human population density distributions, but they were highly correlated with plant species richness and climate, i.e. ratios decreased with plant species richness and MAP, and were related quadratically with MAT; (3) CMM ratio and UM ratio were more influenced by plant richness than by climate, while KM ratio was more influenced by climate. We concluded that the percentages of plants used as medicines were not influenced by distances from human settlements, but were determined by species richness or climate. We suggest that (1), in general, the medicinal plant ratio could be a complementary indicator for medicinal plant conservation planning and (2), for the region of Xinjiang, not only high diversity areas, but also some extreme environments should be considered as compensation for a better protection of medicinal plants.</p></div

The effects of plant species richness, population, and climate on the medicinal plant ratios of CMM, UM, and KM as results from the General Linear Models (GLMs).

<p>The effects of plant species richness, population, and climate on the medicinal plant ratios of CMM, UM, and KM as results from the General Linear Models (GLMs).</p

The relationship of medicinal plant ratio and plant species richness.

<p>For (a) CMM ratio, (b) UM ratio, and (c) KM ratio.</p

The relationship of medicinal plant ratio with climatic variables.

<p>With MAP for CMM ratio (a), UM ratio (b), and KM ratio (c); with MAT for CMM ratio (d), UM ratio (e), and KM ratio (f).</p