Diseases of humans and wildlife are typically tracked and studied through incidence, the number of new infections per time unit. Estimating incidence is not without difficulties, as asymptomatic infections, low sampling intervals and low sample sizes can introduce large estimation errors. After infection, biomarkers such as antibodies or pathogens often change predictably over time, and this temporal pattern can contain information about the time since infection that could improve incidence estimation. Antibody level and avidity have been used to estimate time since infection and to recreate incidence, but the errors on these estimates using currently existing methods are generally large. Using a semi-parametric model in a Bayesian framework, we introduce a method that allows the use of multiple sources of information (such as antibody level, pathogen presence in different organs, individual age, season) for estimating individual time since infection. When sufficient background data are available, this method can greatly improve incidence estimation, which we show using arenavirus infection in multimammate mice as a test case. The method performs well, especially compared to the situation in which seroconversion events between sampling sessions are the main data source. The possibility to implement several sources of information allows the use of data that are in many cases already available, which means that existing incidence data can be improved without the need for additional sampling efforts or laboratory assays.

Camera traps have proven very useful in ecological, conservation and behavioral research. Camera traps non-invasively record presence and behavior of animals in their natural environment. Since the introduction of digital cameras, large amounts of data can be stored. Unfortunately, processing protocols did not evolve as fast as the technical capabilities of the cameras. We used camera traps to record videos of Eurasian beavers (Castor fiber). However, a large number of recordings did not contain the target species, but instead empty recordings or other species (together non-target recordings), making the removal of these recordings unacceptably time consuming. In this paper we propose a method to partially eliminate non-target recordings without having to watch the recordings, in order to reduce workload. Discrimination between recordings of target species and non-target recordings was based on detecting variation (changes in pixel values from frame to frame) in the recordings. Because of the size of the target species, we supposed that recordings with the target species contain on average much more movements than non-target recordings. Two different filter methods were tested and compared. We show that a partial discrimination can be made between target and non-target recordings based on variation in pixel values and that environmental conditions and filter methods influence the amount of non-target recordings that can be identified and discarded. By allowing a loss of 5% to 20% of recordings containing the target species, in ideal circumstances, 53% to 76% of non-target recordings can be identified and discarded. We conclude that adding an extra processing step in the camera trap protocol can result in large time savings. Since we are convinced that the use of camera traps will become increasingly important in the future, this filter method can benefit many researchers, using it in different contexts across the globe, on both videos and photographs.

The availability of resources, their effect on population density and territoriality, and the ways in which these factors are interwoven with mating systems are important determinants of small mammal space use. It is often difficult to study these patterns in an integrated way, however, especially because long‐term data are needed but not readily available. In this paper, we investigate effects of population density, season and breeding status on home range patterns of the promiscuous rodent Mastomys natalensis using monthly capture‐mark‐recapture data collected over 17 years in a 3‐ha grid. Home ranges were estimated using minimum convex polygons bounded by trap locations, and home range overlap and visitation rates were calculated as a measure of territoriality. As higher population densities coincide with increased resource availability, we predicted that home range sizes would correlate negatively with density. Furthermore, as M. natalensis is promiscuous and population densities are generally high, we predicted that territoriality would be low, and home range overlap would therefore be high. Contrary to expectations the home ranges of female adults increased with population density, although those of male adults and subadults followed the expected decrease. Home range overlap and visitation rates were generally high, and increased significantly with population density. More importantly, they were never lower than those of simulated datasets consisting of randomly moved home ranges. These results therefore suggest that M. natalensis displays a complete lack of territoriality that is rarely seen in small mammals but still meets predictions based on knowledge of density and mating system.

In recent years, a great deal of research within the field of sound localization has been aimed at finding the acoustic cues that human listeners use to localize sounds and understanding the mechanisms by which they process these cues. In this paper, we propose a complementary approach by constructing an ideal-observer model, by which we mean a model that performs optimal information processing within a Bayesian context. The model considers all available spatial information contained within the acoustic signals encoded by each ear. Parameters for the optimal Bayesian model are determined based on psychoacoustic discrimination experiments on interaural time difference and sound intensity. Without regard as to how the human auditory system actually processes information, we examine the best possible localization performance that could be achieved based only on analysis of the input information, given the constraints of the normal auditory system. We show that the model performance is generally in good agreement with the actual human localization performance, as assessed in a meta-analysis of many localization experiments (Best et al. in Principles and applications of spatial hearing, pp 14-23. World Scientific Publishing, Singapore, 2011). We believe this approach can shed new light on the optimality (or otherwise) of human sound localization, especially with regard to the level of uncertainty in the input information. Moreover, the proposed model allows one to study the relative importance of various (combinations of) acoustic cues for spatial localization and enables a prediction of which cues are most informative and therefore likely to be used by humans in various circumstances.

Region of highest sensitivity, HRTF patterns, and IID patterns are shown to be in good agreement with earlier experimental measurements on other specimens of the same bat species, i.e., the differences are within the interspecies variability range. Next, it is argued that the proposed simulation method offers distinct advantages over acoustic measurements on real bat specimens. To illustrate this, it is shown how computer manipulation of the virtual morphology model allows a more detailed comprehension of bat spatial hearing by investigating the effects of different head parts on the HRTF. From this analysis it is concluded that for this species the pinna has a significantly larger effect on the HRTF and IID patterns than the head itself. This conclusion argues in favor of a series of recent simulation studies based on pinna morphology only [R. Muller, J. Acoust. Soc. Am. 116, 3701-3712 (2004); Muller et al., ibid 119, 4083-4092 (2006)].