Actor-specific contributions to the deforestation slowdown in the Brazilian Amazon.

Annual deforestation rates in the Brazilian Amazon fell by 77% between 2004 and 2011, yet have stabilized since 2009 at 5,000-7,000 km(2). We provide the first submunicipality assessment, to our knowledge, of actor-specific contributions to the deforestation slowdown by linking agricultural census and remote-sensing data on deforestation and forest degradation. Almost half (36,158 km(2)) of the deforestation between 2004 and 2011 occurred in areas dominated by larger properties (>500 ha), whereas only 12% (9,720 km(2)) occurred in areas dominated by smallholder properties (<100 ha). In addition, forests in areas dominated by smallholders tend to be less fragmented and less degraded. However, although annual deforestation rates fell during this period by 68-85% for all actors, the contribution of the largest landholders (>2,500 ha) to annual deforestation decreased over time (63% decrease between 2005 and 2011), whereas that of smallholders went up by a similar amount (69%) during the same period. In addition, the deforestation share attributable to remote areas increased by 88% between 2009 and 2011. These observations are consistent across the Brazilian Amazon, regardless of geographical differences in actor dominance or socioenvironmental context. Our findings suggest that deforestation policies to date, which have been particularly focused on command and control measures on larger properties in deforestation hotspots, may be increasingly limited in their effectiveness and fail to address all actors equally. Further reductions in deforestation are likely to be increasingly costly and require actor-tailored approaches, including better monitoring to detect small-scale deforestation and a shift toward more incentive-based conservation policies.

Statistical Series: Opportunities and challenges of sperm motility subpopulation analysis.

Computer-assisted sperm analysis (CASA) allows assessing the motility of individual spermatozoa, generating huge datasets. These datasets can be analyzed using data mining techniques such as cluster analysis, to group the spermatozoa in subpopulations with biological meaning. This review considers the use of statistical techniques for clustering CASA data, their challenges and possibilities. There are many clustering approaches potentially useful for grouping sperm motility data, but some options may be more appropriate than others. Future development should focus not only in improvements of subpopulation analysis, but also in finding consistent biological meanings for these subpopulations.

The relationship between ram sperm head morphometry and fertility depends on the procedures of acquisition and analysis used.

Sperm head morphometry is a parameter in the evaluation of semen that has been associated with fertility in two ways: comparing morphometric measures between predefined groups of fertility; or analyzing morphometric data by multivariate techniques to identify cell populations. We analyzed the morphometry of ram sperm head by three procedures and checked its relationship with male fertility. A Computer-Aided Sperm Morphometric Assessment procedure (CASMA), an image analysis software (NIS-Elements) in combination with an optical microscope (MO-NIS) and this image analysis software in combination with a scanning electron microscope (SEM-NIS) were used. Eight morphometric parameters were assessed: length, width, area, perimeter, ellipticity, form factor, elongation and regularity. We observed significant differences between the morphometric data of sperm head obtained with three study procedures. The CASMA procedure shows the highest values for all parameters and the SEM-NIS procedure the lowest. The analysis of a semen sample, when only the mean of morphometric parameters is used to describe the cell population, is too limited to interpret their fertilizing capacity. It is essential to analyze the complex structure of the samples by defining subpopulations by multivariate methods. With few exceptions, the means of each morphometric parameter differ between the three subpopulations analyzed in each procedure. Only the subpopulations obtained with the MO-NIS procedure showed a significant correlation with male fertility. In short, it is necessary to establish an instrumental standard for the analysis of sperm morphometry to obtain reliable results and we believe that the MO-NIS system presents these basic requirements.