We wanted to test whether arboreal mammals were using natural canopy bridges – connections between tree branches over a clearing – to travel over a natural gas pipeline in the Peruvian Amazon. The challenge was figuring out how to monitor branches 100 feet up in the tree tops. In this case, the clearing was a 30-foot-wide pipeline path, and we expected arboreal mammals – like monkeys, squirrels and porcupines – to prefer crossing on the branches rather than on the ground. The ground is an unfamiliar and often dangerous place for an animal that’s spent its life way up in the canopy.
The yellow arrow shows the path captured by the camera trap.
In fact, we wondered if without branches, would arboreal mammals cross at all? How could we find out if animals were using the branches? There were 13 canopy bridges and finding a person to sit and wait all day (and night) under each of them for animals to cross wasn’t an option. With our goal of a year’s worth of monitoring, we had a conundrum. We needed a more efficient way to gather the data and concluded that camera traps – motion sensitive cameras – could be an excellent way to monitor the bridges continuously and remotely.
But, we discovered that no one had ever really used camera traps in the high canopy before. How were we going to get them all the way up there? If we were able to get up to the canopy, how could we make sure they were taking photos of the correct points where animals would potentially cross? Continue reading →
We have two freely available articles this month: one Application and one Open Access Article.
– rSPACE: An open-source R package for implementing a spatially based power analysis for designing monitoring programs. This method incorporates information on species biology and habitat to parameterize a spatially explicit population simulation.
Tim Lucas et al. provide this month’s Open Access article: A generalised random encounter model for estimating animal density with remote sensor data. The authors have developed a Generalised Random Encounter Model (gREM) to estimate absolute animal density from count data from both camera traps and acoustic detectors. They show that gREM produces accurate estimates of absolute animal density for all combinations of sensor detection widths and animal signal widths. This model is applicable for count data obtained in both marine and terrestrial environments, visually or acoustically. It could be used for big cats, sharks, birds, echolocating bats, cetaceans and much more. Continue reading →
A round-up of methods papers published in the last month. If there are any papers that you think should be featured, email me or leave a comment and I will add them.
Liam Revell has a paper in Evolution on size correction and principal components analysis of phylogenetic comparative data. Olivier Gimenez and colleagues also have a paper in the same issue on generating fitness landscapes using mark-recapture data.
Systematic Biology has a number of papers with interesting methods: Campbell & Lapointe have a paper on the use and validity of composite taxa in phylogenetic analysis; Fitzjohn et al. have a nice paper on estimating trait-dependent speciation and extinction rates in phylogenies that are not complete; Bui Quang Minh and colleages present an algorithm for efficiently estimating phylogenetic diversity; Michael D. Pirie, Aelys M. Humphreys, Nigel P. Barker, and H. Peter Linder present an approach for dealing with implications of conflicting gene trees on inferences of evolutionary history above the species level.
In Ecological Applications, Cang Hui and colleagues compare approaches for extrapolating population sizes from abundance-occupancy relationships. Matthew Etterson et al. look at the problem of estimating population trends when there is detection heterogeneity and overdipsersion in the data. Paul Beier and co-workers use a case study to examine the use of least-cost modelling to design wildlife corridors.
Finally for this month in Animal Conservation, Heidy Kikillus et al. look at minimising false negatives in predicting distributions of invasive species. (Thanks to Andrew Tyre for pointing this one out).