Chapter 9 Activity

Given that camera traps record the time of the photo, they represent a powerful tool to explore and contrast the acivity pattterns of the species they detect. Such analyses can give insight into ompetition, predation and coexistance.

Must read Frey, Sandra, et al. "Investigating animal activity patterns and temporal niche partitioning using camera‐trap data: Challenges and opportunities." Remote Sensing in Ecology and Conservation 3.3 (2017): 123-132.

Two key packages

9.1 Example

To demonstrate how we might investigate temporal niche partitioning, we will be working from the independent observations data frame.

# Import the data
dat <- read.csv("data/processed_data/Algar_30min_Independent.csv", header=T)

Which looks like this:

Project.ID Deployment.Location.ID Species Date_Time.Captured Age Sex Number.of.Animals Minimum.Group.Size Behaviour Blank Event.ID Event.Duration Event.Groupsize Event.Observations
Algar Algar08 Odocoileus virginianus 2018-12-18 20:54:56 Adult Male 1 1 Travelling FALSE E0 15 1 7
Algar Algar08 Alces alces 2019-04-20 14:37:51 Adult 1 2 Travelling FALSE E1 57 2 10
Algar Algar08 Alces alces 2019-04-21 19:58:59 Unidentified 2 2 FALSE E2 58 2 11
Algar Algar08 Odocoileus virginianus 2019-05-07 09:43:59 Adult 1 1 Travelling FALSE E3 5 1 4
Algar Algar08 Rangifer tarandus 2019-05-23 08:31:29 Unidentified 1 2 Travelling FALSE E4 34 2 9
Algar Algar08 Alces alces 2019-08-04 05:31:51 Unidentified 1 2 Travelling FALSE E5 4 2 4

Then load the activity package.

# Load the package

We first need to convert the "time" in our datasets into radian time (on the range [0, 2*pi]) and proportion time (on the range 0-1):

#Radian time
dat$rtime <- gettime(dat$Date_Time.Captured, "%Y-%m-%d %H:%M:%S")

Then fit the basic activity models available in the package. Note, more complex models are available, including accountting for the fact that the detection distance of camera traps varies from night to day. Here we will keep this simple!

White-tailed deer

m1 <- fitact(dat$rtime[dat$Species=="Odocoileus virginianus"])


m2 <- fitact(dat$rtime[dat$Species=="Rangifer tarandus"])

We can compare the activity plots of both species on the same axis visually:

# Plot both on the same axis

plot(m1, yunit="density", data="none", ylim=c(0,0.1), las=1, lwd=2)
plot(m2, yunit="density", data="none", add=TRUE, tline=list(col="red"))
legend("topleft", c("White tailed deer", "Caribou"), col=1:2, lty=1)

We can compare different activity patterns using coefficient of overlap (∆) - devleoped by Ridout and Linkie. The coefficient ranges from 0 (no overlap) to 1 (complete overlap). We can implement for a two species comparison as follows:

# Note reps reduced to speed up running time
compareCkern(m1, m2, reps = 250)
##        obs       null     seNull      pNull 
## 0.79074068 0.84491965 0.03787047 0.09600000

The output above represents: obs = observed overlap index; null = mean null overlap index; seNull = standard error of the null distribution; pNull = probability observed index arose by chance.

Which suggets that there is no significant different between our two species, perhaps not surprising given that they spatially degregate not temporally.

9.2 Further reading

Houngbégnon, Fructueux GA, et al. "Daily Activity Patterns and Co-Occurrence of Duikers Revealed by an Intensive Camera Trap Survey across Central African Rainforests." Animals 10.12 (2020): 2200.

Ross J, Hearn AJ, Johnson PJ, Macdonald DW (2013). patterns and temporal avoidance by prey in response to Sunda clouded leopard predation risk." Journal of Zoology, 290(2), 96{106.

Ramesh T, Kalle R, Sankar K, Qureshi Q (2012). -temporal partitioning among large carnivores in relation to major prey species in Western Ghats." Journal of Zoology, 287(4), 269{275.

Azevedo FC, Lemos FG, Freitas-Junior MC, Rocha DG, Azevedo FCC (2018). activity patterns and temporal overlap with prey in a human-modi ed landscape at Southeastern Brazil." Journal of Zoology,