**1.** Seed dispersal is a key biological process that remains poorly documented because dispersing seeds are notoriously hard to track. While long-distance dispersal is thought to be particularly important, seed-tracking studies typically yield incomplete data sets that are biased against long-distance movements.

**2.** We evaluate an analytical procedure developed by Jansen, Bongers & Hemerik (2004) to infer the tail of a seed dispersal kernel from incomplete frequency distributions of dispersal distances obtained by tracking seeds. This ‘censored tail reconstruction’ (CTR) method treats dispersal distances as waiting times in a survival analysis and censors nonretrieved seeds according to how far they can reliably be tracked. We tested whether CTR can provide unbiased estimates of long-distance movements which typically cannot be tracked with traditional field methods.

**3.** We used a complete frequency distribution of primary seed dispersal distances of the palm *Astrocaryum standleyanum*, obtained with telemetric thread tags that allow tracking seeds regardless of the distance moved. We truncated and resampled the data set at various distances, fitted kernel functions on CTR estimates of dispersal distance and determined how well this function approximated the true dispersal kernel.

**4.** Censored tail reconstruction with truncated data approximated the true dispersal kernel remarkably well but only when the best-fitting function (lognormal) was used. We were able to select the correct function and derive an accurate estimate of the seed dispersal kernel even after censoring 50–60% of the dispersal events. However, CTR results were substantially biased if 5% or more of seeds within the search radius were overlooked by field observers and erroneously censored. Similar results were obtained using additional simulated dispersal kernels.

**5.** Our study suggests that the CTR method can accurately estimate the dispersal kernel from truncated seed-tracking data if the kernel is a simple decay function. This method will improve our understanding of the spatial patterns of seed movement and should replace the usual practice of omitting nonretrieved seeds from analyses in seed-tracking studies.

**1.** Abundance estimation is a pervasive goal in ecology. The rate of detection by motion-sensitive camera traps can, in principle, provide information on the abundance of many species of terrestrial vertebrates that are otherwise difficult to survey. The random encounter model (REM, Rowcliffe *et al.* 2008) provides a means estimating abundance from camera trap rate but requires camera sensitivity to be quantified.

**2. **Here, we develop a method to estimate the area effectively monitored by cameras, which is one of the most important codeterminants of detection rate. Our method borrows from distance sampling theory, applying detection function models to data on the position (distance and angle relative to the camera) where the animals are first detected. Testing the reliability of this approach through simulation, we find that bias depends on the effective detection angle assumed but was generally low at less than 5% for realistic angles typical of camera traps.

**3. **We adapted standard detection functions to allow for the possibility of smaller animals passing beneath the field of view close to the camera, resulting in reduced detection probability within that zone. Using a further simulation to test this approach, we find that detection distance can be estimated with little or no bias if detection probability is certain for at least some distance from the camera.

**4. **Applying this method to a 1-year camera trapping data set from Barro Colorado Island, Panama, we show that effective detection distance is related strongly positively to species body mass and weakly negatively to species average speed of movement. There was also a strong seasonal effect, with shorter detection distance during the wet season. Effective detection angle is related more weakly to species body mass, and again strongly to season, with a wider angle in the wet season.

**5. **This method represents an important step towards practical application of the REM, including abundance estimation for relatively small (<1 kg) species.