Models for species-detection data collected along transects in the presence of abundance-induced heterogeneity and clustering in the detection process

1. Models have been devised previously that allow the estimation of abundance from detection data of unmarked individuals while accounting for imperfect detection, but these are restricted to models for discrete sampling protocols, i.e. replicated detection/non-detection or count data. Furthermore, these models assume that the detections from each individual are independent; however, there are cases in which this assumption is likely to be violated. For example, in surveys along transects, clustering in the signs left by each individual could be expected. 2. Here, we propose models to estimate abundance from species-detection data collected continuously along transects considering two cases: (i) independent detections and (ii) clustering within the detections of each individual. We account for clustering by describing the detection process as a Markov-modulated Poisson process. We study the properties of the estimators via simulation, assessing the impact of unmodelled detection clustering. 3. We show that bias may be induced in the estimator of abundance if clustering in individual detections is not accounted for and how an estimator with better coverage properties is obtained if clustering is modelled. We demonstrate that both abundance and the clustering pattern can be well estimated simultaneously, given enough data. 4. To illustrate our approach, we fit the models to tiger pugmark detection data from transect surveys in Kerinci Seblat National Park in Sumatra. The analysis suggested strong abundance-induced heterogeneity in detections when clustering was disregarded, but the evidence reduced drastically when clustering was accounted for. This example illustrates how unmodelled clustering can affect the estimation of abundance. 5. Estimates of abundance need to be reliable to ensure that conservation and management interventions are not misguided. Provided certain model assumptions are met, abundance can be estimated from detection data of unmarked individuals. This requires an adequate description of the detection process, or otherwise, bias may be induced in the abundance estimator. The models and discussion provided here deal with the issue of clustering within the detections of individuals and are of relevance for ecologists interested in methodological developments for the estimation of animal abundance.