Depth-stratified soil sampling for assessing nematode communities.

This study assessed the importance of stratified soil sampling on the detection (and therefore the distribution) of nematode communities and the differentiation of ecosystems by collecting stratified soil samples at intervals of 10 cm and non-stratified samples from 0 to 30 cm in two soil management systems (no-tillage and conventional tillage) and in a native forest fragment. The nematode frequency and prominence values were obtained after extraction by successive screening operations, sugar floatation clarification and the identification of nematodes to the genus level. The nematode communities were compared two-by-two based on Sorensen’s community coefficient (CC) and the percentage similarity (PS). Relative abundances of functional guilds were subjected to a principal component analysis (PCA) and classified in dendrograms. Thirty-two edaphic nematode genera were found, and the nematode communities sampled on a non-stratified basis in the soil profile exhibited a high level of similarity because they could not be accurately characterized. Genera with low abundances were not detected. In the stratified samples, we were able to classify and group the nematodes present at different depths, mainly from 0 to 10 cm. Stratified soil sampling allowed a more accurate characterization and greater differentiation of nematode communities, identifying taxa that occurred at lower abundance levels, irrespective of frequency

Suscetibilidade de milho, soja e capim amargoso ao nematoide das lesões radiculares.

Plantas daninhas são fatores limitantes à produtividade de soja e milho pela matointerferência, principalmente o capim amargoso (Digitaria insularis), cuja ocorrência aumenta anualmente devido à resistência ao herbicida glyphosate. Além da competição direta com a planta cultivada, a planta daninha pode atuar como hospedeiro alternativo de nematoides. Assim, foi avaliada a suscetibilidade do capim amargoso ao nematoide Pratylenchus brachyurus em relação a plantas de milho e soja. O experimento foi conduzido em casa de vegetação, comparando a reprodução do nematoide no capim amargoso, quatro genótipos de milho (DKB 285 VT PRO 2, AS 1555 PRO 2, P 30F53 YHR e DKB 330 VT PRO 2) e uma cultivar de soja (NA 5909 RR). Observou-se que a reprodução de P. brachyurus no capim amargoso foi tão eficiente quanto aquela observada nos genótipos de milho e soja. Além disto, o capim amargoso pode ser considerado uma planta suscetível ao nematoide por apresentar fator de reprodução igual a 4,2 e não diferir das demais espécies estudadas.Comunicação científica

Integrating field sampling, geostatistics and remote sensing to map wetland vegetation in the Pantanal, Brazil

Development of efficient methodologies for mapping wetland vegetation is of key importance to wetland conservation. Here we propose the integration of a number of statistical techniques, in particular cluster analysis, universal kriging and error propagation modelling, to integrate observations from remote sensing and field sampling for mapping vegetation communities and estimating uncertainty. The approach results in seven vegetation communities with a known floral composition that can be mapped over large areas using remotely sensed data. The relationship between remotely sensed data and vegetation patterns, captured in four factorial axes, were described using multiple linear regression models. There were then used in a universal kriging procedure to reduce the mapping uncertainty. Cross-validation procedures and Monte Carlo simulations were used to quantify the uncertainty in the resulting map. Cross-validation showed that accuracy in classification varies according with the community type, as a result of sampling density and configuration. A map of uncertainty derived from Monte Carlo simulations revealed significant spatial variation in classification, but this had little impact on the proportion and arrangement of the communities observed. These results suggested that mapping improvement could be achieved by increasing the number of field observations of those communities with a scattered and small patch size distribution; or by including a larger number of digital images as explanatory variables in the model. Comparison of the resulting plant community map with a flood duration map, revealed that flooding duration is an important driver of vegetation zonation. This mapping approach is able to integrate field point data and high-resolution remote-sensing images, providing a new basis to map wetland vegetation and allow its future application in habitat management, conservation assessment and long-term ecological monitoring in wetland landscapes