Chapter 11 Occupancy

Occupancy modelling has been one of the mainstays of camera traps data analysis for many years, so learning how to wangle our data into occupancy-style formats is essential.

When we survey wild and free ranging populations using any sampling methodology, the probability of detecting a given individual or species if it is actually present on the landscape at the time of sampling is typically less than one. This is because wild animals are often hard to see! This issue is termed “imperfect detection”.

In order to deal with the imperfect detection issue - occupancy models separate our the counts of a given species at a site into two processes:

  1. occupancy (ψ) - which is the probability of a species occurring within a spatial unit (or “site”) during the sampling session
  2. detection probability (p) - the probability that the species will be detected given that it already occurs at a site

In order to separate out the occupancy process from the detection process, surveys need to occur at replicated ‘sites’ and we need repeated ‘visits’ to the same site. It is important to know that in camera trap studies, practitioners typically treat individual locations as sites and rather than repeated return to a location to survey it at different times, they divide the continuous camera activity data into block of time (e.g. 1 to 7 day windows).

Occupancy models were not developed specifically for camera traps - thus there are a suite of assumptions we need to make about the populations we survey when applying occupancy models. We do not adress these here. However, below we provide a list introductory resources for you to dig into the occupancy models to decide if they are appropriate for your situation:

Burton, A. Cole, et al. “Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes.” Journal of Applied Ecology 52.3 (2015): 675-685.

MacKenzie, Darryl I., et al. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Elsevier, 2017.

Let’s focus our time on getting our data into the right formt, and applying some occupancy models!

# Check you have them and load them
list.of.packages <- c("kableExtra", "tidyr", "ggplot2", "gridExtra", "dplyr", "unmarked", "lubridate", "tibble", "sf", "gfcanalysis", "MuMIn", "spOccupancy")

new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
lapply(list.of.packages, require, character.only = TRUE)

11.1 Single species occupancy model

In this example we will use the ...weekly_observations dataframe we created in the data creation section. We do this because 7 days is a time interval which occupancy models are often devided into for occupancy analyses.

# Import the weekly observations data set
week_obs <- read.csv("data/processed_data/AlgarRestorationProject_30min_independent_weekly_observations.csv", header=T)

Which, as a quick reminder, looks like this:

placename date days Alces.alces Canis.latrans Canis.lupus Cervus.canadensis Lepus.americanus Lontra.canadensis Lynx.canadensis Martes.americana Odocoileus.virginianus Oryctolagus.cuniculus Rangifer.tarandus Tamiasciurus.hudsonicus Ursus.americanus Vulpes.vulpes
ALG027 2018-W14 4 0 0 0 0 0 0 1 0 0 0 0 0 0 0
ALG027 2018-W15 7 0 0 1 0 0 0 1 0 0 0 0 0 0 0
ALG027 2018-W16 7 0 0 1 0 0 0 0 0 0 0 0 0 1 0
ALG027 2018-W17 7 0 0 1 0 0 0 0 0 0 0 0 0 1 0
ALG027 2018-W18 7 0 0 0 0 0 0 0 0 0 0 0 0 1 0
ALG027 2018-W19 7 0 0 0 0 0 0 0 0 0 0 0 0 1 0
ALG027 2018-W20 7 0 0 0 0 0 0 0 0 0 0 0 0 3 0
ALG027 2018-W21 7 0 0 1 0 0 0 0 0 0 0 0 0 8 0
ALG027 2018-W22 7 0 0 0 0 0 0 1 0 0 0 0 0 3 0
ALG027 2018-W23 7 0 0 0 0 0 0 0 0 0 0 0 0 2 0
ALG027 2018-W24 7 0 0 0 0 0 0 0 0 0 0 0 0 1 0
ALG027 2018-W25 7 1 0 0 0 0 0 1 0 2 0 0 0 1 0
ALG027 2018-W26 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W27 7 0 0 1 0 0 0 1 0 0 0 0 0 1 0
ALG027 2018-W28 7 0 0 0 0 0 0 0 0 0 0 0 0 2 0
ALG027 2018-W29 7 0 0 0 0 0 0 0 0 0 0 0 0 2 0
ALG027 2018-W30 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W31 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W32 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W33 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W34 7 0 0 1 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W35 7 0 0 0 0 0 0 0 0 0 0 0 0 1 0
ALG027 2018-W36 7 0 0 0 0 0 0 0 0 0 0 0 0 2 0
ALG027 2018-W37 7 0 0 0 0 0 0 1 0 0 0 0 0 0 0
ALG027 2018-W38 7 0 0 1 0 0 0 0 0 0 0 0 0 1 0
ALG027 2018-W39 7 1 0 0 0 0 0 0 0 0 0 0 0 1 0
ALG027 2018-W40 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W41 7 0 0 1 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W42 7 0 1 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W43 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W44 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W45 7 0 0 1 0 0 0 0 0 1 0 0 0 0 0
ALG027 2018-W46 7 0 0 0 1 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W47 7 0 1 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W49 7 0 0 2 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W00 5 0 1 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W01 7 0 1 1 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W04 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W06 7 0 0 1 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W07 7 0 0 0 0 0 0 1 0 0 0 0 0 0 0
ALG027 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG027 2019-W11 7 0 0 0 0 0 0 2 0 0 0 0 0 0 0
ALG027 2019-W12 7 0 0 0 0 0 0 1 0 0 0 0 0 0 0
ALG027 2019-W13 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W13 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W14 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W15 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W16 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W17 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W18 7 3 0 0 0 0 0 0 0 2 0 0 0 1 0
ALG029 2018-W19 7 2 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W20 7 2 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W21 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W22 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W23 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2018-W24 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W25 7 1 0 0 0 0 0 0 0 3 0 0 0 0 0
ALG029 2018-W26 7 1 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2018-W27 7 0 0 0 0 0 0 0 0 0 0 0 0 1 0
ALG029 2018-W28 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W29 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W30 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W31 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W32 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W33 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W34 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2018-W35 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W36 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W37 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2018-W38 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W39 7 0 0 1 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W40 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W41 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W42 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W43 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W44 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2018-W45 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2018-W46 7 0 0 0 0 0 0 0 0 2 0 0 0 0 0
ALG029 2018-W47 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2018-W48 7 0 0 0 0 0 0 1 0 1 0 0 0 0 0
ALG029 2018-W49 7 0 0 1 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W50 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W06 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W14 7 0 0 0 0 0 0 0 0 2 0 0 0 0 0
ALG029 2019-W15 7 1 0 0 0 0 0 0 0 0 0 0 0 1 0
ALG029 2019-W16 7 1 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2019-W17 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W18 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2019-W19 7 1 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2019-W20 7 0 0 0 0 0 0 0 0 4 0 0 0 0 0
ALG029 2019-W21 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W22 7 1 0 0 0 0 0 0 0 0 0 0 0 2 0
ALG029 2019-W23 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W24 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W25 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2019-W26 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W28 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W29 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W30 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W31 7 0 0 0 0 0 0 0 0 0 0 0 0 1 0
ALG029 2019-W32 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W33 7 0 0 0 0 0 0 0 0 2 0 0 0 0 0
ALG029 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W36 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 0 2 0
ALG029 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 0 1 0
ALG029 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W41 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG029 2019-W42 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2019-W43 7 0 0 0 0 0 0 0 0 3 0 0 0 0 0
ALG029 2019-W44 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2019-W45 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG029 2019-W46 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W14 7 0 0 0 0 0 1 0 0 0 0 0 0 0 0
ALG031 2018-W15 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W16 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W17 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W18 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W19 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W20 7 1 0 0 0 0 0 0 1 0 0 0 0 0 0
ALG031 2018-W21 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W22 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W23 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W24 7 1 0 0 0 0 0 0 1 0 0 2 0 0 0
ALG031 2018-W25 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W26 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W27 7 0 0 1 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W28 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W29 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W30 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W31 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W32 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W33 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W34 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W35 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W36 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W37 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG031 2018-W38 7 0 0 1 0 0 0 0 0 0 0 1 0 0 0
ALG031 2018-W39 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG031 2018-W40 7 0 0 0 0 0 0 0 0 0 0 0 0 0 1
ALG031 2018-W41 7 1 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG031 2018-W42 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W43 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W44 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W45 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W46 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W51 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W11 7 0 0 0 0 0 0 0 2 0 0 0 0 0 0
ALG031 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W13 7 1 0 2 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W15 7 0 0 1 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W16 7 0 0 1 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W17 7 0 0 1 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W18 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W20 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W21 7 0 0 0 0 0 0 0 1 0 0 0 0 0 0
ALG031 2019-W22 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W23 7 1 0 0 0 0 0 0 0 1 0 1 0 0 0
ALG031 2019-W24 7 0 0 1 0 0 0 0 0 0 1 1 0 0 0
ALG031 2019-W25 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG031 2019-W26 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG031 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W28 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG031 2019-W29 7 0 0 0 0 0 0 0 0 0 0 0 0 1 0
ALG031 2019-W30 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG031 2019-W31 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W32 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W33 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W36 7 0 0 0 0 0 0 0 0 0 0 2 0 0 0
ALG031 2019-W37 7 0 0 0 0 0 0 0 0 0 0 3 0 0 0
ALG031 2019-W38 7 0 0 0 0 0 0 0 0 0 0 3 0 0 0
ALG031 2019-W39 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W40 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W41 7 0 0 0 0 0 0 0 0 0 0 1 0 1 0
ALG031 2019-W42 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W43 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W44 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W45 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG031 2019-W46 5 0 0 0 0 1 0 0 1 0 0 0 0 0 0
ALG032 2018-W13 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W14 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W15 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W16 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W17 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W18 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W19 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W20 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W21 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W22 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG032 2018-W23 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W24 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W25 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W26 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W27 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W28 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG032 2018-W29 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG032 2018-W30 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W31 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W32 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W33 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W34 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W35 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W36 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W37 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W38 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W39 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W40 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W41 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W42 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W43 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W44 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG032 2018-W45 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W46 7 1 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W47 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W48 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W49 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W50 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W51 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2018-W52 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W00 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W01 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W02 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W03 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W04 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W05 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W06 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W07 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W08 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W09 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W10 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W11 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W12 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W13 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W14 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W15 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W16 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W17 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W18 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W19 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W20 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W21 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W22 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W23 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W24 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0
ALG032 2019-W25 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W26 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W27 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W28 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W29 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG032 2019-W30 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W31 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W32 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W33 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W34 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W35 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W36 7 0 0 0 0 0 0 0 0 0 0 1 0 0 0
ALG032 2019-W37 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ALG032 2019-W38 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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