Camera traps and activity signs to estimate density and population trends in wild pigs

Massei, G., Cowan, D., Lambert, M., Coats, J., Watola, G., Fox, S., Ward, A., Pietravalle, S

A method for the objective selection of landscape-scale study regions and sites at the national level

1. Ecological processes operating on large spatio-temporal scales are difficult to disentangle with traditional empirical approaches. Alternatively, researchers can take advantage of ‘natural’ experiments, where experimental control is exercised by careful site selection. Recent advances in developing protocols for designing these ‘pseudo-experiments’ commonly do not consider the selection of the focal region and predictor variables are usually restricted to two. Here, we advance this type of site selection protocol to study the impact of multiple landscape scale factors on pollinator abundance and diversity across multiple regions. 2. Using datasets of geographic and ecological variables with national coverage, we applied a novel hierarchical computation approach to select study sites that contrast as much as possible in four key variables, while attempting to maintain regional comparability and national representativeness. There were three main steps to the protocol: (i) selection of six 100 9 100 km2 regions that collectively provided land cover representative of the national land average, (ii) mapping of potential sites into a multivariate space with axes representing four key factors potentially influencing insect pollinator abundance, and (iii) applying a selection algorithm which maximized differences between the four key variables, while controlling for a set of external constraints. 3. Validation data for the site selection metrics were recorded alongside the collection of data on pollinator populations during two field campaigns. While the accuracy of the metric estimates varied, the site selection succeeded in objectively identifying field sites that differed significantly in values for each of the four key variables. Between-variable correlations were also reduced or eliminated, thus facilitating analysis of their separate effects. 4. This study has shown that national datasets can be used to select randomized and replicated field sites objectively within multiple regions and along multiple interacting gradients. Similar protocols could be used for studying a range of alternative research questions related to land use or other spatially explicit environmental variables, and to identify networks of field sites for other countries, regions, drivers and response taxa in a wide range of scenarios

The SECURE project – Stem canker of oilseed rape: : molecular methods and mathematical modelling to deploy durable resistance

N Evans et al, "The SECURE Project - Stem Canker of oilseed rape: Molecular methods and mathematical modeling to deploy durable resistance", in Vol 4 of the Proceedings of the 12th International Rapeseed Congress : Sustainable Development in Cruciferous Oilseed Crops Production, Wuhan, China, March 26 - 30, 2007. The proceedings are available online at: http://gcirc.org/intranet/irc-proceedings/12th-irc-wuhan-china-2007-vol-4.htmlModelling done during the SECURE project has demonstrated the dynamic nature of the interaction between phoma stem canker (Leptosphaeria maculans), the oilseed rape host (Brassica napus) and the environment. Experiments done with near-isogenic lines of L. maculans to investigate pathogen fitness support field data that suggest a positive effect of the avirulence allele AvrLm4 on pathogen fitness, and that the loss of this allele renders isolates less competitive under field conditions on cultivars without the resistance gene Rlm4. The highlight of molecular work was the cloning of AvrLm1 and AvrLm6. L. maculans is now one of the few fungal species for which two avirulence loci have been cloned. Subsequent research focused on understanding the function of AvrLm1 and AvrLm6 and on the analysis of sequences of virulent isolates to understand molecular evolution towards virulence. Isolates of L. maculans transformed with GFP and/or DsRed were used to follow growth of the fungus in B. napus near-isogenic-lines (NIL) with or without MX (Rlm6) resistance under different temperature and wetness conditions. The results greatly enhanced our knowledge of the infection process and the rate and extent of in planta growth on different cultivars. Conclusions from work to model durability of resistance have been tested under field conditions through a series of experiments to compare durability of resistance conferred by the major resistance gene Rlm6 alone in a susceptible background (EurolMX) or in a resistant background (DarmorMX) under recurrent selection over 4 growing seasons. A major priority of the project was knowledge transfer of results and recommendations to target audiences such as plant breeding companies and extension services. CETIOM developed a “diversification scheme” that encourages French growers to make an informed choice about the cultivars that are grown within the rotation based on the resistance genes carried by the individual cultivars. Use of such schemes, in association with survey data on the population structure of L. maculans at both national and European scales will provide opportunities for breeders and the industry to manage available B. napus resistance more effectively.Non peer reviewe

Elevated serum levels of soluble CD154 in children with juvenile idiopathic arthritis

<p>Abstract</p> <p>Objective</p> <p>Cytokines play important roles in mediating inflammation in autoimmunity. Several cytokines are elevated in serum and synovial fluid samples from children with Juvenile Idiopathic Arthritis (JIA). Soluble CD154 (sCD154) is elevated in other autoimmune disorders, but has not been characterized in JIA. Our objectives were to determine if sCD154 is elevated in JIA, and to examine correlations between sCD154 and other inflammatory cytokines.</p> <p>Methods</p> <p>Serum from 77 children with JIA and 81 pediatric controls was analyzed for interleukin (IL)1β, IL2, IL4, IL5, IL6, IL8, IL10, IL12, IL13, sCD154, interferon-γ (IFNγ), soluble IL2 receptor (sIL2R), and tumor necrosis factor-α (TNFα), using the Luminex Multi-Analyte Profiling system. Differences in levels of cytokines between cases and controls were analyzed. Logistic regression was also performed.</p> <p>Results</p> <p>sCD154 was significantly elevated in cases compared to controls (p < 0.0001). IL1β, IL5, IL6, IL8, IL13, IFNγ, sIL2R, and TNFα were also significantly elevated in JIA. Levels of sCD154 were highly correlated with IL1β, IL6, IL8, and TNFα (p < 0.0001). Logistic regression analysis suggested that IL6 (odds ratio (OR): 1.4, p < 0.0001), sCD154 (OR: 1.1, p < 0.0001), and TNFα (OR: 1.1, p < 0.005) were positively associated with JIA, while IL10 (OR: 0.5, p < 0.002) was protective. sCD154 was elevated in all JIA subtypes, with highest levels among more severe subtypes. IL1β, IL6, IL8, sIL2R and TNFα were also elevated in several JIA subtypes.</p> <p>Conclusion</p> <p>Serum levels of sCD154, IL1β, IL6, IL8, sIL2R and TNFα are elevated in most JIA subtypes, suggesting a major role for sCD154, and these cytokines and cytokine receptors in the pathogenesis of JIA.</p

Pervasiveness of Parasites in Pollinators

Many pollinator populations are declining, with large economic and ecological implications. Parasites are known to be an important factor in the some of the population declines of honey bees and bumblebees, but little is known about the parasites afflicting most other pollinators, or the extent of interspecific transmission or vectoring of parasites. Here we carry out a preliminary screening of pollinators (honey bees, five species of bumblebee, three species of wasp, four species of hoverfly and three genera of other bees) in the UK for parasites. We used molecular methods to screen for six honey bee viruses, Ascosphaera fungi, Microsporidia, and Wolbachia intracellular bacteria. We aimed simply to detect the presence of the parasites, encompassing vectoring as well as actual infections. Many pollinators of all types were positive for Ascosphaera fungi, while Microsporidia were rarer, being most frequently found in bumblebees. We also detected that most pollinators were positive for Wolbachia, most probably indicating infection with this intracellular symbiont, and raising the possibility that it may be an important factor in influencing host sex ratios or fitness in a diversity of pollinators. Importantly, we found that about a third of bumblebees (Bombus pascuorum and Bombus terrestris) and a third of wasps (Vespula vulgaris), as well as all honey bees, were positive for deformed wing virus, but that this virus was not present in other pollinators. Deformed wing virus therefore does not appear to be a general parasite of pollinators, but does interact significantly with at least three species of bumblebee and wasp. Further work is needed to establish the identity of some of the parasites, their spatiotemporal variation, and whether they are infecting the various pollinator species or being vectored. However, these results provide a first insight into the diversity, and potential exchange, of parasites in pollinator communities

Predicting the establishment and spread of plant disease from regulatory sampling

Invasive plant diseases can have devastating consequences on the local plant populations, in both agricultural and natural landscapes. Knowledge of the spatial patterns of pathogen spread can be used to guide more time- and cost-effective disease management strategies. Based on disease dispersal principles and consideration of host pattern, an improved plant disease epidemiological model was developed and tested for plant disease mapping. The model is able to characterize the disease dispersal gradient and predict infection risk, with indication of uncertainty, through heterogeneous environments without reference to the source of infection. As a result, sampling methods can be informed by the predicted prevalence map of the disease. In order to better describe the shapes of the dispersal gradients, three different dispersal functions (Exponential, Modified power law, and Cauchy distribution) were considered in the model. Two data sets of disease observations of Huanglongbing (HLB) of citrus in different landscapes (Southern Garden and Devils Garden plantation) in Florida were used to evaluate the performance of the improved method for disease mapping. The results showed that the improved model provided estimates of greater precision for unsampled hosts. With all different dispersal models compared, the exponential dispersal gradient gave the most satisfactory performance. All the determined information can help decision makers understand the spatial aspects of disease processes, and formulate decisions about disease control accordingly

Predicting the establishment and spread of plant disease from regulatory sampling

Invasive plant diseases can have devastating consequences on the local plant populations, in both agricultural and natural landscapes. Knowledge of the spatial patterns of pathogen spread can be used to guide more time- and cost-effective disease management strategies. Based on disease dispersal principles and consideration of host pattern, an improved plant disease epidemiological model was developed and tested for plant disease mapping. The model is able to characterize the disease dispersal gradient and predict infection risk, with indication of uncertainty, through heterogeneous environments without reference to the source of infection. As a result, sampling methods can be informed by the predicted prevalence map of the disease. In order to better describe the shapes of the dispersal gradients, three different dispersal functions (Exponential, Modified power law, and Cauchy distribution) were considered in the model. Two data sets of disease observations of Huanglongbing (HLB) of citrus in different landscapes (Southern Garden and Devils Garden plantation) in Florida were used to evaluate the performance of the improved method for disease mapping. The results showed that the improved model provided estimates of greater precision for unsampled hosts. With all different dispersal models compared, the exponential dispersal gradient gave the most satisfactory performance. All the determined information can help decision makers understand the spatial aspects of disease processes, and formulate decisions about disease control accordingly