A better understanding of population density (i.e. the number of individuals per unit area) is essential for wildlife conservation and management. Despite the fact that a wide variety of methods with which to estimate population density have already been described and broadly used, there are still relevant gaps. In the last few decades, the use of remotely activated cameras (camera traps) has been established as an effective sampling tool when compared with alternative methods. Camera trapping could, therefore, be considered a reliable tool with which to monitor those situations in which classical methods have relevant limitations. It could, for example, be used with species whose behaviour is elusive and which have low detectability (as is the case of most mammals), or populations in which the animals can be identified individually by the spot patterns on their bodies. However, there is lack of information regarding those species for which it is not possible to identify individual animals (i.e. unmarked species). Some authors that have applied camera trapping originally considered relative abundance indexes in order to monitor unmarked populations. These indices were based on encounter rates (i.e. the number of animals detected per sampling unit) observed in camera trapping studies. Methods with which to estimate the population density of unmarked populations were later described, the first of which was the random encounter model (REM). The REM models the random encounters between moving animals and static cameras in order to estimate population density. The REM does this by employing three basic parameters: i) encounter rate, ii) detection zone (area in which the cameras effectively detect animals), and iii) day range (average daily distance travelled by each individual in the population). When this thesis was first started, it was broadly discussed that the application of the REM was limited by the difficulties involved in estimating the parameters required, especially the day range. In this context, the aim of this thesis was to develop and harmonise camera trapping methodologies so as to estimate the population density and movement parameters of unmarked populations, working principally in the REM framework.
The first research carried out for this thesis comprised a review of published studies concerning REM, which found that i) wrong practices in the estimation of REM parameters were frequent, and ii) the REM has rarely been compared with reference densities in empirical studies. We, therefore, then went on to evaluate the main factors that affect the probability of detection and the trigger speed of camera traps, which are relevant for encounter rate and detection zone estimation. This is shown in Chapter 1. We subsequently evaluated and described new methodologies that use camera traps to estimate the movement parameters of unmarked populations. We also evaluated the seasonal and spatial variation in these parameters. The information regarding this is provided in Chapter 2. Finally, we assessed the performance of the REM in a wide range of scenarios, and we compared it with other recently described camera trapping methods used to estimate the population density of unmarked species, as detailed in Chapter 3.
The results reported in Chapter 1 show that camera trap performance as regards trigger speed and detection probability are highly influenced by different factors, such as the period of the day, the camera trap model, deployment height or sensitivity, among others. We monitored the community of birds and mammals in the study area, and we discovered that a relevant proportion of the animals that entered the theoretical detection zone were not usually recorded. These missed detections introduce bias into the encounter rate, and consequently into density. However, several camera trapping methods with which to estimate effective detection zone have been described, and they should be applied to all the populations monitored. With regard to the day range, we considered the wild boar as a model species and showed that assuming straight-line distances between consecutive locations obtained by telemetry devices underestimates this parameter, while movement behaviours should be accounted when using camera traps to estimate day range, as shown in Chapter 2.1. We then explored the use of camera traps to monitor movement parameters in greater depth, and showed that they are a reliable method. We described a new procedure with which to estimate the day range that accounts for movement behaviour, and for the ratio between fast and slow speeds. The new procedure performed well in the wide range of scenarios that we simulated, and was also tested with populations of mammals around the world. In this respect, we also described a machine learning protocol with which to identify movement behaviour obtained from camera trap records. All of this is described in Chapter 2.2. We subsequently showed that geographical (e.g. altitude), environmental (e.g. habitat fragmentation), biological (e.g. species) and management (e.g. hunting) factors affect the day range, and we reported variable day ranges in ungulates and carnivores across Europe, as shown in Chapter 2.3. We use the combination of a literature review and an empirical study to compare REM densities with those obtained using reference methods. The results showed a strong correspondence between the REM and reference densities, especially when REM parameters are estimated accurately for the target population. We also showed that the precision of the REM is lower than that of the reference methods, and provided further insights into the survey design in order to increase precision. This information is provided in Chapter 3.1. Finally, and as shown in Chapter 3.2, we used ungulates and carnivores as a target in order to compare the REM, random encounter and staying time (REST), and camera trap distance sampling (CT-DS). The REST and CTDS are two recently described methods with which to estimate the population density of unmarked species using camera traps. The results showed that the performance of the three methods is similar in terms of accuracy and precision. We recommend a survey design that will make it possible to apply all the methods, as the final selection of one of them will be mediated by the number of animals recorded and the camera trap performance.
In conclusion, the results of this thesis show the usefulness of camera trapping to monitor the movement parameters and population density of wildlife and contributes with a methodological practical step forwards. In summary, the REM approach, which was tuned in this thesis, proved to be a reliable method in a wide range of environmental scenarios. The REM can be firmly established as a reference method to be implemented in multispecies monitoring programmes in the coming years, considering that it does not need to identify individual animals or spatial autocorrelation in captures. However, future developments of the REM in particular, and camera trapping unmarked methods in general, should be focused on optimising surveys designs in order to increase precision. Before this thesis was begun, the main limitations of applying the REM were the estimation of REM parameters, along with its reliability. This has, however, already been dealt with, and the main gap now concerns the low precisions obtained.