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.     

A decade of monitoring at Swiss Long-Term Forest Ecosystem Research (LWF) sites: can we observe trends in atmospheric acid deposition and in soil solution acidity?

Trends in atmospheric acid deposition and in soil solution acidity from 1995 or later until 2007 were investigated at several forest sites throughout Switzerland to assess the effects of air pollution abatements on deposition and the response of the soil solution chemistry. Deposition of the major elements was estimated from throughfall and bulk deposition measurements at nine sites of the Swiss Long-Term Forest Ecosystem Research network (LWF) since 1995 or later. Soil solution was measured at seven plots at four soil depths since 1998 or later. Trends in the molar ratio of base cations to aluminum (BC/Al) in soil solutions and in concentrations and fluxes of inorganic N (NO(3)-N + NH(4)-N), sulfate (SO(4)-S), and base cations (BC) were used to detect changes in soil solution chemistry. Acid deposition significantly decreased at three out of the nine study sites due to a decrease in total N deposition. Total SO(4)-S deposition decreased at the nine sites, but due to the relatively low amount of SO(4)-S load compared to N deposition, it did not contribute to decrease acid deposition significantly. No trend in total BC deposition was detected. In the soil solution, no trend in concentrations and fluxes of BC, SO(4)-S, and inorganic N were found at most soil depths at five out of the seven sites. This suggests that the soil solution reacted very little to the changes in atmospheric deposition. A stronger reduction in base cations compared to aluminum was detected at two sites, which might indicate that acidification of the soil solution was proceeding faster at these sites.     

Rapid one-step assays for on-site monitoring of mouse and rat urinary allergens

Allergy to rodent proteins is common among laboratory animal workers. Sensitive methods to measure exposure to these allergens have been developed. These assays are, however, expensive, time-consuming, and require a laboratory facility and methodological expertise. A simple method to screen for allergen spread, or to test whether hygiene standards are maintained, would be useful. Lateral flow immunoassays (LFIAs) are especially suited for field settings; the tests are simple and results are visible within minutes. LFIAs were developed for detection of the rodent urinary allergens Mus m 1 and Rat n 1. Pilot studies were performed in animal facilities in three countries using both extracts from airborne dust samples and samples collected by wiping surfaces. For comparison and determination of sensitivity, the concentrations of rodent urinary allergens in the samples were also measured using enzyme immunoassays (EIAs). The LFIAs for rat and mouse urinary allergens had a detection limit of 31 pg allergen per mL in a buffer system with purified allergen standards. Results of environmental dust extracts tested in LFIAs correlated well with levels obtained using EIAs. Spread of rodent allergens, or non-adherence to hygiene around laboratory animal facilities, may aggravate rodent allergy. Using a simple, sensitive one-step assay, allergens can be detected to prevent allergen exposure. The results reveal that the rapid assays are suited for on-site demonstration of exposure to rodent allergens, and thus, useful in occupational hygiene practice.     

Behavioural Reactivation and Subjective Assessment of the State of Vigilance—Application to Simulated Car Driving

The frequency of some behaviour (such as self-centred gestures) increases during a task that leads to the occurrence of low-vigilance episodes. These gestures can be useful in stimulating oneself. A study carried out in 20 adults has enabled us to state that motor activity (recorded with an actimeter) increases with the duration of a monotonous driving task and sleep deprivation. The analysis of the scores recorded using the Karolinska Sleepiness Scale has shown that drivers can assess the deterioration of their state of vigilance according to the actual sleep preceding the driving test. Finally, the joint analysis of the subjective and objective data revealed a co-variation of these two types of indices. We discuss the stimulatory function of the motor activity in a task leading to the occurrence of low-vigilance episodes by investigating, among other things, the use, conscious or not, of this type of activity.