Effects of environmental factors on community structure of Leptophlebiidae (Insecta, Ephemeroptera) in Cerrado streams, Brazil

We analyzed the effects of environmental factors on abundance, species richness, and functional group richness of Leptophlebiidae in 16 sampling points along four Cerrado streams. Across three periods of 2005, we collected 5,492 larvae from 14 species in stream bed substrate. These species belong to three functional feeding groups: scrapers, filtering collectors and shredders. The abundance and species richness were not affected by water quality, but habitat quality related to presence of riparian vegetation had positive effects on the abundance of shredders. Our results add important information on the natural history of the species and functional groups of aquatic insects and also provide relevant data for the monitoring and conservation of streams in the Brazilian Cerrado.

Bulletin of the Museum of Comparative Zoology at Harvard College.

v.93 (1943-1944)

Report of the Agricultural Research Institute and College, Pusa.

190712

Median stable matching for college admission

We give a simple and concise proof that so-called generalized median stable matchings are well-defined stable matchings for college admissions problems. Furthermore, we discuss the fairness properties of median stable matchings and conclude with two illustrative examples of college admissions markets, the lattices of stable matchings, and the corresponding generalized median stable matchings.

Os anofelinos do sub-gênero kerteszia em relação à distribuição das bromeliáceas em comunidades florestais do município de Brusque, Estado de Santa Catarina

The present work is part of the studies realized under the authority of the National Service of Malaria (Brazil), with the collaboration of scientists of the Oswaldo Cruz Institute, in some forests of the southern part of Brazil.This is the first of a series and its subject is the development of the Anopheles mosquitoes of the kerteszia in water collected in Bromeliaceae leaves. The ecology of Bromeliaceae was studied in a previous work. The botanical material was classified by specialists from several botanical institutions from Europe and the United States of America. The most important ecological relations of the “bromeliad-kerteszia” problem were presented through four indices: 1st Positivity index – Relative frequency of bromeliad with watery forms in the bromeliad examined. 2nd Larval index – Mean number of watery forms in the positive bromeliad. 3rd Ovoposition index – Product of the Positivy index by the Larval index. 4th MK index – Product of the Ovoposition index by the total number of bromeliad, positive or not, in a unity of area (1.000 m²). The capture of flying forms in relation to the relative humidity was also studied. From the several forests of the Brusque region we have selected one community of each type, which were the most representative forests in Southern Brazil. Conclusions on the “bromeliad-kerteszia” problem – From a general point of view only a few factors are really important and these are listed below: 1°) The volum of water on the bromeliad. 2°) The level where the bromeliad is fixed. 3°) The number of bromeliad in unity of area. The distribution of microclimas in the forest through the considered levels has a direct influence on the species of subgenus Kerteszia (qualitative influence) and an indirect influence through the ecological distribution of the more frequent bromeliad with best qualities as biotope for the watery forms (qualitative influence). The MK index is roughly proportional to the square of half the total number of Bromeliaceae in a certain type of forest. Then the MK index would be a certain function of the ecological type of the forest and of the total number of bromeliad in a unity of area. MK approximately α x (x/10)² . x = n° of bromeliad in a unity of área (1.000 m²); α = qualitative factor. It would be interesting to see if this proportion is maintained when we have examined a greater number of forests of different types.

Biomphalaria straminea and other planorbids in the Dominican Republic

In addition to previous records of Biomphalaria glabrata in the Dominican Republic, the southern central communities of Haina Arriba and Boca Chica, in the National District, are reported as new localities for that species; other species collected were Biomphalaria obstructa, B. helophila, Drepanotrema lucidum and Lymnaea viatrix. Biomphalaria straminea, a potential vector of Schistosoma mansoni, was found for the first time in the country, in the River Iguamo, just outside of the community of San Pedro de Macorís.

Spatial modelling of biodiversity at the community level

1. Statistical modelling is often used to relate sparse biological survey data to remotely derived environmental predictors, thereby providing a basis for predictively mapping biodiversity across an entire region of interest. The most popular strategy for such modelling has been to model distributions of individual species one at a time. Spatial modelling of biodiversity at the community level may, however, confer significant benefits for applications involving very large numbers of species, particularly if many of these species are recorded infrequently. 2. Community-level modelling combines data from multiple species and produces information on spatial pattern in the distribution of biodiversity at a collective community level instead of, or in addition to, the level of individual species. Spatial outputs from community-level modelling include predictive mapping of community types (groups of locations with similar species composition), species groups (groups of species with similar distributions), axes or gradients of compositional variation, levels of compositional dissimilarity between pairs of locations, and various macro-ecological properties (e.g. species richness). 3. Three broad modelling strategies can be used to generate these outputs: (i) 'assemble first, predict later', in which biological survey data are first classified, ordinated or aggregated to produce community-level entities or attributes that are then modelled in relation to environmental predictors; (ii) 'predict first, assemble later', in which individual species are modelled one at a time as a function of environmental variables, to produce a stack of species distribution maps that is then subjected to classification, ordination or aggregation; and (iii) 'assemble and predict together', in which all species are modelled simultaneously, within a single integrated modelling process. These strategies each have particular strengths and weaknesses, depending on the intended purpose of modelling and the type, quality and quantity of data involved. 4. Synthesis and applications. The potential benefits of modelling large multispecies data sets using community-level, as opposed to species-level, approaches include faster processing, increased power to detect shared patterns of environmental response across rarely recorded species, and enhanced capacity to synthesize complex data into a form more readily interpretable by scientists and decision-makers. Community-level modelling therefore deserves to be considered more often, and more widely, as a potential alternative or supplement to modelling individual species.

The Impact of Higher Education Institution-Firm Knowledge Links on Firm-level Productivity in Britain

This paper estimates whether both sourcing knowledge from and/or cooperating on innovation with HEIs (Higher Education Institutions)1 impacts on establishment-level total factor productivity (TFP) using a dataset created by merging the UK government’s Community Innovation Survey (CIS) with the Annual Respondents Database (ARD). It also considers whether higher graduate employment (as a measure of human capital) also impacts positively on TFP at the establishment-level. Many studies have investigated the relationship between university-firm knowledge links and innovation (see, for example, Mansfield, 1991; Becker, 2003; Thorn et al, 2007). Most of these studies find a positive impact. Fewer studies have investigated the impact of university-firm knowledge links on productivity. Belderbos et al. (2004), using the Dutch CIS, find that cooperation with universities has no statistically significant impact on the growth of labour productivity. Medda et al. (2005) find no statistically significant effect of collaborative research undertaken by Italian manufacturing firms and universities on the growth of TFP. Arvanitis et al. (2008), using Swiss data, show that university-firm knowledge and technology transfer has both a direct impact on labour productivity and an indirect impact through its positive impact on innovation. In sum, there is as yet no clear consensus as to the impact of university-firm knowledge links on productivity.