Landmarks

23 landmarks of the dorsal view of the skull in 2D. 2 replicates per individua

A Novel Method to Reduce Time Investment When Processing Videos from Camera Trap Studies

<div><p>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 (<i>Castor fiber</i>). 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.</p></div

Temporal variation of antibody levels obtained from experimental data [23] for 15 different individuals (a) and for all individuals combined (red dots) with fitted function mean and standard deviation (blue lines) (b).

<p>Temporal variation of antibody levels obtained from experimental data [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004882#pcbi.1004882.ref023" target="_blank">23</a>] for 15 different individuals (a) and for all individuals combined (red dots) with fitted function mean and standard deviation (blue lines) (b).</p

A bootstrap analysis was performed for both filter methods (Filter 1 and Filter 2) on the complete dataset (n = 1991), on the videos recorded at dry locations (n = 933) and on the videos recorded at wet locations (n = 1058).

<p>A bootstrap analysis was performed for both filter methods (Filter 1 and Filter 2) on the complete dataset (n = 1991), on the videos recorded at dry locations (n = 933) and on the videos recorded at wet locations (n = 1058).</p

Possible gain (true positive rate, TP-rate) given an accepted loss (false positive rate, FP-rate).

<p>The FP-rate represents the proportion of target recordings (beavers) classified as non-target recordings. The TP-rate is the proportion of non-target recordings correctly classified as non-target. This is the proportion of non-target recordings that will be discarded correctly given a certain FP-rate. The best performing filter maximizes the TP-rate while minimizing the FP-rate. Filter 2 performs better in all environmental circumstances. The dashed diagonal represents the outcome of a random model which cannot discriminate between target and non-target recordings. The dashed vertical line represents a 5% threshold (FP-rate). Dry<10% water area in footage (5 locations, n = 933 recordings), Wet>10% water area (7 locations, n = 1058 recordings), Complete dataset is the combined Dry and Wet dataset (12 locations, n = 1991 recordings).</p

Estimating Time of Infection Using Prior Serological and Individual Information Can Greatly Improve Incidence Estimation of Human and Wildlife Infections

<div><p>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.</p></div

Classification matrix of the recordings.

<p>Classification matrix of the recordings.</p

Estimation error () on the time of infection for different encounter probabilities (p_trap) and for different levels of included prior information (ab: antibody, vb: virus in blood, ve: virus in excretions, age: individual age); (a) is based on antibody presence/absence, while (b) is based on antibody levels.

<p>The trapping interval was 14 days for all situations.</p

Probability of virus presence in blood (a) and excretions (b), estimated from experimental data [23].

<p>Detection probability is given by the proportion of tested individuals that was RNA-positive on a given sampling day.</p

Simulated (blue) and estimated (red) incidence using different sources of information; (a) and (c) are based on antibody presence/absence, while (b) and (d) are based on antibody levels; (a) and (b) only include antibody information, while (c) and (d) include all available information; (a) represents the situation that is mostly used in existing studies.

<p>Larger dots on <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004882#pcbi.1004882.g005" target="_blank">Fig 5</a> (28 day line) indicate the situation for which incidence plots are shown.</p