Participatory injustice in Mexico’s Readiness process to Reduce Emissions from Deforestation and forest Degradation (REDD +)

Human Ecology - Since 2007 many developing countries have laid groundwork for Reducing Emissions from Deforestation and forest Degradation and sustainable management and conservation of forests...     

Alternative approaches of transforming bimodal into unimodal mutualistic networks. The usefulness of preserving weighted information

Pollination and seed dispersal networks are by definition bimodal, linking two sets of species, plants and animals. Bimodal networks are often analysed after being transformed into unimodal ones, since most attributes in network theory are defined for the latter. Such unimodal projections (e.g. of plants sharing flower visitors or seed dispersers) map potential inter-specific competition or facilitation, and can thus be useful for instance when identifying native species potentially sensitive to aliens in the communities. In this work, we introduce procedures to project unweighted and weighted bimodal networks into unimodals, for animals or plants, and calculate two centrality measures that inform us about the species’ role in the communities. By using 20 empirical weighted networks worldwide, we obtained 160 unimodal networks via four projection methods and evaluated correlations among centrality parameters across the different methodologies to assess how consistent the results are when including different link weights between species. Degree centralities obtained by projecting unweighted and weighted bimodal networks were not significantly correlated, suggesting that the role of the species differs when considering link weights in the original bimodal networks. By contrast, betweenness centralities were highly correlated, indicating the consistent importance of the species as connectors regardless of the projection method used. We conclude that preserving the weighted information when transforming bimodal into unimodal networks may allow us to make more realistic predictions on the potential competitive or facilitative interactions among species of one set (e.g. plants) that share species of the other (e.g. flower visitors or dispersers).     

Fertility prediction in dairy goats from Murciano-Granadina breed: The role of sperm evaluation and female traits

Fertility is one of the most economically important traits in farm animals, due to the direct and indirect costs associated to low pregnancy rates. Thus, one of the priority goals in animal reproduction is to predict the performance that the semen doses will have in vivo based on the quality values obtained in laboratory assays. Attempts have been made for getting a predictive model of fertility of frozen-thawed sperm in dairy goats, but similar studies have not been conducted for chilled goat buck sperm doses that are mostly used for artificial insemination in many countries including Spain. We study how parameters of in vitro sperm quality and characteristics of Murciano-Granadina dairy goats may affect the in vivo fertility obtained after artificial insemination with semen doses chilled at 4 °C. Moreover, this information was used for obtaining predictive models of the fertility. Sixty-three ejaculates from 13 males were used to prepare chilled doses for the insemination of 495 goats over 13 sessions. Fresh and chilled sperm were evaluated for motility and plasma membrane integrity with a computer-assisted sperm analysis system and flow cytometry, respectively. Fertility was determined at parturition, according to the kidding goats. Overall fertility was 59.6%. Pearson’s correlation coefficients between in vivo fertility and quality variables of fresh sperm were not significant and were low (below 0.34 in absolute value) for chilled sperm. Females’ characteristics had a low negative impact on fertility (correlation coefficients of ?0.19 with age, ?0.20 with parturitions and ?0.11 with total milk yield obtained in the best lactation). Fixed and mixed logistic regression procedures were used trying to explain the fertility results. None of the models accurately predicted fertility, but the best models included the percentage of total motile sperm or average path velocity from fresh semen, age of the females and the session effect (uncontrolled environmental effects). These analyses showed that primiparous goats were 2.42 times more likely to get pregnant than goats that had kidded four or more times. Our field assay data on fertility in Murciano-Granadina dairy goats highlighted the importance of making quality controls of sperm, of choosing the doses presenting high percentages of motile sperm exhibiting regular trajectories and of selecting the youngest goats for AI, after their first kidding. Efforts should continue to obtain better predictive models for improving fertility in goat dairy herds.