The alternative Pharaoh approach: stingless bees mummify beetle parasites alive

Workers from social insect colonies use different defence strategies to combat invaders. Nevertheless, some parasitic species are able to bypass colony defences. In particular, some beetle nest invaders cannot be killed or removed by workers of social bees, thus creating the need for alternative social defence strategies to ensure colony survival. Here we show, using diagnostic radioentomology, that stingless bee workers (Trigona carbonaria) immediately mummify invading adult small hive beetles (Aethina tumida) alive by coating them with a mixture of resin, wax and mud, thereby preventing severe damage to the colony. In sharp contrast to the responses of honeybee and bumblebee colonies, the rapid live mummification strategy of T. carbonaria effectively prevents beetle advancements and removes their ability to reproduce. The convergent evolution of mummification in stingless bees and encapsulation in honeybees is another striking example of co-evolution between insect societies and their parasites.     

Modification of nitrifying biofilm into nitritating one by combination of increased free ammonia concentrations, lowered HRT and dissolved oxygen concentration

Nitrifying biomass on ring-shaped carriers was modified to nitritating one in a relatively short period of time (37 days) by limiting the air supply, changing the aeration regime, shortening the hydraulic retention time and increasing free ammonia (FA) concentration in the moving-bed biofilm reactor (MBBR). The most efficient strategy for the development and maintenance of nitritating biofilm was found to be the inhibition of nitrifying activity by higher FA concentrations (up to 6.5 mg/L) in the process. Reject water from sludge treatment from the Tallinn Wastewater Treatment Plant was used as substrate in the MBBR. The performance of high-surfaced biocarriers taken from the nitritating activity MBBR was further studied in batch tests to investigate nitritation and nitrification kinetics with various FA concentrations and temperatures. The maximum nitrite accumulation ratio (96.6%) expressed as the percentage of NO2??-N/NOx??-N was achieved for FA concentration of 70 mg/L at 36°C. Under the same conditions the specific nitrite oxidation rate achieved was 30 times lower than the specific nitrite formation rate. It was demonstrated that in the biofilm system, inhibition by FA combined with the optimization of the main control parameters is a good strategy to achieve nitritating activity and suppress nitrification.     

Testing the effectiveness of capture mark recapture population estimation techniques using a computer simulation with known population size

Estimation of small mammal population sizes is important for monitoring ecosystem condition and for conservation. Here, we test the accuracy of standard methods of population size estimation using Capture-Mark-Recapture (CMR) on a simulated population of agents. The use of a computer simulation allows complete control of population sizes and behaviors, thereby avoiding assumptions that may be violated in real populations. We find that the recommended protocol for CMR sampling, using uniformly distributed traps, consistently overestimates population sizes by as much as 100% when studies are conducted over only two trapping periods. More than 20 trapping periods are required before this method, or that of placing traps randomly, gives an accurate estimation of population size (i.e., within a 95% confidence limit of the actual value). Non-random sampling, by placing traps on runways used by small mammals, produces the most accurate, and least variable, estimates of population. However, we show that around 10 trapping periods are still required to produce an accurate population estimate using this method. Given that most real populations do not comply with the ‘ideal’ assumptions made by CMR, we suggest that population estimates based on CMR may be fundamentally flawed, and recommend that protocols for CMR population estimation methods may need revising.     

A novel statistical method for classifying habitat generalists and specialists

We develop a novel statistical approach for classifying generalists and specialists in two distinct habitats. Using a multinomial model based on estimated species relative abundance in two habitats, our method minimizes bias due to differences in sampling intensities between two habitat types as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori. Based on a user-defined specialization threshold, the model classifies species into one of four groups: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance in second-growth (SG) and old-growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest class (40.6%), followed by generalist tree species (36.7%) and SG specialists (22.7%). The multinomial model was more sensitive than indicator value analysis or abundance-based phi coefficient indices in detecting habitat specialists and also detects generalists statistically. Classification of specialists and generalists based on rarefied subsamples was highly consistent with classification based on the full sample, even for sampling percentages as low as 20%. Major advantages of the new method are (1) its ability to distinguish habitat generalists (species with no significant habitat affinity) from species that are simply too rare to classify and (2) applicability to a single representative sample or a single pooled set of representative samples from each of two habitat types. The method as currently developed can be applied to no more than two habitats at a time.     

The importance of quantifying propagule pressure to understand invasion: an examination of riparian forest invasibility

The widely held belief that riparian communities are highly invasible to exotic plants is based primarily on comparisons of the extent of invasion in riparian and upland communities. However, because differences in the extent of invasion may simply result from variation in propagule supply among recipient environments, true comparisons of invasibility require that both invasion success and propagule pressure are quantified. In this study, we quantified propagule pressure in order to compare the invasibility of riparian and upland forests and assess the accuracy of using a community's level of invasion as a surrogate for its invasibility. We found the extent of invasion to be a poor proxy for invasibility. The higher level of invasion in the studied riparian forests resulted from greater propagule availability rather than higher invasibility. Furthermore, failure to account for propagule pressure may confound our understanding of general invasion theories. Ecological theory suggests that species-rich communities should be less invasible. However, we found significant relationships between species diversity and invasion extent, but no diversity-invasibility relationship was detected for any species. Our results demonstrate that using a community's level of invasion as a surrogate for its invasibility can confound our understanding of invasibility and its determinants.     

Dependability assessment of network-based safety-related system

Advances in electronic integration and radio communication have led to the emergence of a new kind of safety systems, i.e. Wireless Sensor Network (WSN). This network-based safety-related system is becoming more and more present in the domain of safety due to its easy deployment. It does not need a wire infrastructure and its range of applications is wide. Usually, such a system is composed of various nodes (sensors) collaborating to monitor a targeted phenomenon. In most cases, nodes are battery powered and this is the weakness of the system makes it necessary to design an energy saving policy. The present paper gives a dependability viewpoint of such a system. A modeling framework is suggested integrating the interdependency of the components. Stochastic Petri nets are used to implement this model and two heuristics to schedule component activity. The first is based on the proposed importance measures, the second on a multi-objective genetic algorithm. The aim is to conserve energy and so to extend WSN dependability.