Effects of different soil pH and nitrogen fertilizers on Bidens pilosa L. Cd accumulation

Environmental Science and Pollution Research - Bidens pilosa L. was a Cd hyperaccumulator. This experiment determined the effects of different soil pH (adjusted by weak acid and alkali at 4.83,...     

Tri-sector partnership for community development in mining: a case study of the SAPI Foundation and Target Fund in the Republic of Sakha (Yakutia)

This paper explores the nature of tri-sector partnerships applied to community development in the mining sector. The case study approach was used to examine an initiative for environmental improvement and socio-economic development in the diamond province of the Republic of Sakha (Yakutia), in the Russian Federation. The study follows the journey of this initiative from a potential tri-sector partnership model between the diamond mining company ALROSA, the Government of the Republic of Sakha (Yakutia) and the SAPI Foundation, to a bi-lateral partnership between ALROSA and the Government represented by the Target Fund. The study explores the community development in the diamond province in the light of economic and political transition within the mining sector of the Russian Federation.     

Beyond men and women: a critical perspective on gender and disaster

Consideration of gender in the disaster sphere has centred almost exclusively on the vulnerability and capacities of women. This trend stems from a polarised Western understanding of gender as a binary concept of man—woman. Such an approach also mirrors the dominant framing of disasters and disaster risk reduction (DRR), emphasising Western standards and practices to the detriment of local, non‐Western identities and experiences. This paper argues that the man—woman dichotomy is an insufficient construct with which to address the gendered dimensions of a disaster as it fails to capture the realities of diverse gender minorities in non‐Western contexts. The paper presents case studies from the Philippines, Indonesia, and Samoa, where gender minorities display specific patterns of vulnerability associated with their marginal positions in society, yet, importantly, also possess a wide array of endogenous capacities. Recognition of these differences, needs, skills, and unique resources is essential to moving towards inclusive and gender‐sensitive DRR.     

A novel statistical method for classifying habitat generalists and specialists

We develop a novel statistical approach for classifying generalists and specialists in two distinct habitats. Using a multinomial model based on estimated species relative abundance in two habitats, our method minimizes bias due to differences in sampling intensities between two habitat types as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori. Based on a user-defined specialization threshold, the model classifies species into one of four groups: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance in second-growth (SG) and old-growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest class (40.6%), followed by generalist tree species (36.7%) and SG specialists (22.7%). The multinomial model was more sensitive than indicator value analysis or abundance-based phi coefficient indices in detecting habitat specialists and also detects generalists statistically. Classification of specialists and generalists based on rarefied subsamples was highly consistent with classification based on the full sample, even for sampling percentages as low as 20%. Major advantages of the new method are (1) its ability to distinguish habitat generalists (species with no significant habitat affinity) from species that are simply too rare to classify and (2) applicability to a single representative sample or a single pooled set of representative samples from each of two habitat types. The method as currently developed can be applied to no more than two habitats at a time.