I read a study which had the objective of generating baseline datasets on vegetation and mammal density to investigate the ecology of mammalian hosts for trypanosomiasis in the remote Luambe National Park in Zambia. The study was formulated in recognition of the crucial link between wild mammals and perpetuation of human african trypanosomiasis. Distance sampling was used to estimate wild mammalian density through 40 transects perpendicular to the Luangwa river of 4.5Km in length and 250m in between parallel transects, providing a total of 180Km survey length.
The overall density estimate for wild mammals was found to be 17.32 animals/Km2. In terms of habitat type, more observations were made in aquatic grassland and combretum woodland than other habitats.
The study concluded that the generated datasets can indeed be used for modelling disease systems and improve understanding of effects of interventions on biodiversity ecosystems.
I looked at a study comparing methods for establishing density estimates for mountain hares. This would help to understand the impact of anthropogenic disturbance on this species over time, e.g. impact of roadkill or control efforts on grouse moorland, and to understand effectiveness of management interventions on this species. This particular study was carried out at Holme Moss, a large hill, situated in the north of England, UK.
The study uses distance sampling to estimate abundance. This species is cryptic; they hide by day in rough vegetation, lying flat and motionless in shallow depressions, burrows or rocks, and can flee unseen. They are nocturnal, emerging for feeding at night. This makes it challenging to monitor them and estimate density reliably.
Three different methods are compared and evaluated for estimating density, including daylight visual surveys, night-time thermal imaging and camera traps.
The study found that daylight visual surveys tended to underestimate density, failing to reflect nocturnal activity. Thermal imaging captured nocturnal activity, providing a higher detection rate, but required fine weather. Both of these approaches work well in applying the principles of distance sampling.
Camera traps captured nocturnal activity, and operated 24/7 throughout harsh weather, but needed careful consideration of empirical assumptions involved in extrapolating this data into density estimates.
Did they use distance sampling for their data collection/analysis?
I’m curious because there are methods to calculate density of non-identifiable individuals using camera traps and occupancy modelling[1] instead of distance sampling, but you might make different choices about how/where to position cameras
If you can identify individuals, you would usually use capture-mark-recapture ↩︎
Did they explain why they placed their transects perpendicular to the river? This relates to one of the elements of survey design in this second week of our course
They used distance sampling for both data collection and analysis(nMDS, non-metric dimensional scaling-a distance based ordination technique). they made different choices on placement of cameras, where there were lots of tracks and proximity to water sources and roads.
Thanks, Lorato. I’m not familiar with NMDS, so I did a little reading
NMDS is a statistical method to assess similarity between samples (=species) in multi-dimensional space (=covariates). It’s similar to Principle Component Analysis if anyone is familiar with that
The distances that they talk about in your paper are distances in multi-dimensional that signify ecological dissimilarities amongst the carnivore species, rather than distances from a transect line or point. They went on to estimate occupancy from their camera-trap data using occupancy modelling, but didn’t estimate density or population size directly, as far as I can tell
Thanks for the opportunity to learn about a new technique!
