Suslik Capture-Recapture Analysis Miroslav 2019
Fernando Mateos-Gonzalez (ALKA Wildlife)
This report analises data from capture-recapture sessions in Miroslav during 17-18-19 April, 24-25 April and 15-19, 24 July 2019, in order to calculate the density of the population.
Library
Analysis
We used the data below, which combines the information obtained from our captures and the trap locations. The analysis was performed using the package ‘secr’ version 3.2.1 and R 4.1.2.
## Session 2
## More than one detection per detector per occasion at binary detector(s)## $`1`
## Object class capthist
## Detector type proximity
## Detector number 20
## Average spacing 13.63913 m
## x-range -623778.5 -623653.2 m
## y-range -1187164 -1187091 m
##
## Counts by occasion
## 1 2 3 4 5 Total
## n 15 23 15 11 14 78
## u 15 22 11 7 11 66
## f 56 8 2 0 0 66
## M(t+1) 15 37 48 55 66 66
## losses 0 0 0 0 0 0
## detections 15 23 15 11 14 78
## detectors visited 8 13 9 10 10 50
## detectors used 20 20 20 20 20 100
##
## $`2`
## Object class capthist
## Detector type proximity
## Detector number 20
## Average spacing 13.63913 m
## x-range -623778.5 -623653.2 m
## y-range -1187164 -1187091 m
##
## Counts by occasion
## 1 2 3 4 5 6 Total
## n 23 27 16 19 8 7 100
## u 23 22 12 11 5 3 76
## f 59 11 5 1 0 0 76
## M(t+1) 23 45 57 68 73 76 76
## losses 0 0 0 0 0 0 0
## detections 24 27 17 21 8 7 104
## detectors visited 14 12 12 15 8 7 68
## detectors used 20 20 20 20 20 20 120n number of distinct individuals detected on each occasion t
u number of individuals detected for the first time on each occasion t
f number of individuals detected on exactly t occasions
M(t+1) cumulative number of detected individuals on each occasion t
The following plots show the trap array and the consecutive captures of each individual.
## 1 2
## 78 104The most important insight from these figures is that individuals tend to be recaptured near their site of first capture. This is expected when the individuals of a species occupy home ranges. In SECR models the tendency for detections to be localised is reflected in the spatial scale parameter σ.
Successive trap-revealed movements and distances shown below
SECR model fit
We will employ the estimate of the spatial scale σ from the July session (6.21) to fit the simplest possible SECR model with function secr.fit.
##
## secr.fit(capthist = multi, buffer = 4 * 6.21, verify = FALSE,
## biasLimit = NA, trace = FALSE)
## secr 4.5.1, 14:29:46 25 Jan 2022
##
## $`1`
## Detector type count
## Detector number 20
## Average spacing 13.63913 m
## x-range -623778.5 -623653.2 m
## y-range -1187164 -1187091 m
##
## Usage range by occasion
## 1
## min 5
## max 5
##
## $`2`
## Detector type count
## Detector number 20
## Average spacing 13.63913 m
## x-range -623778.5 -623653.2 m
## y-range -1187164 -1187091 m
##
## Usage range by occasion
## 1
## min 6
## max 6
##
##
## 1 2
## Occasions 1 1
## Detections 78 104
## Animals 66 76
## Detectors 20 20
## Count model : Binomial, size from usage
##
## Model : D~1 g0~1 sigma~1
## Fixed (real) : none
## Detection fn : halfnormal
## Distribution : poisson
## N parameters : 3
## Log likelihood : -181.5544
## AIC : 369.1088
## AICc : 369.2827
##
## Beta parameters (coefficients)
## beta SE.beta lcl ucl
## D 5.556471 0.15073115 5.261043 5.851899
## g0 -2.064790 0.22177395 -2.499459 -1.630121
## sigma 1.913138 0.08850139 1.739678 2.086597
##
## Variance-covariance matrix of beta parameters
## D g0 sigma
## D 0.022719881 -0.01284470 -0.004411263
## g0 -0.012844702 0.04918368 -0.012350711
## sigma -0.004411263 -0.01235071 0.007832497
##
## Fitted (real) parameters evaluated at base levels of covariates
##
## session = 1
## link estimate SE.estimate lcl ucl
## D log 258.9075396 39.24814835 192.68242635 347.8942803
## g0 logit 0.1125664 0.02215415 0.07589609 0.1638137
## sigma log 6.7743118 0.60071192 5.69551054 8.0574516
##
## session = 2
## link estimate SE.estimate lcl ucl
## D log 258.9075396 39.24814835 192.68242635 347.8942803
## g0 logit 0.1125664 0.02215415 0.07589609 0.1638137
## sigma log 6.7743118 0.60071192 5.69551054 8.0574516The report comprises:
• function call and time stamp
• summary of the data
• description of the model, including the maximized log likelihood, Akaike’s Information Criterion AIC
• estimates of model coefficients (beta parameters)
• estimates of variance-covariance matrix of the coefficients
• estimates of the ‘real’ parameters
. The estimated density is 266 susliks per hectare, 95% confidence interval 196-360 susliks per hectare
. The other two real parameters jointly determine the detection function, plotted below with 95% confidence limits
The theory of SECR tells us that buffer width is not critical as long as it is wide enough that animals at the edge have effectively zero chance of appearing in our sample.We check that for the present model with the function esa.plot.
The estimated density has easily reached a plateau at the chosen buffer width (dashed red line):
Homeranges
Mean distance in meters, between consecutive capture locations, pooled over individuals, per capture session (e.g. Efford 2004).
## [[1]]
## [1] 9.868384
##
## [[2]]
## [1] 6.047118MMDM (for ‘Mean Maximum Distance Moved’) is the average maximum distance (in m) between detections of each individual i.e. the observed range length averaged over individuals (Otis et al. 1978).
## [[1]]
## [1] 10.51957
##
## [[2]]
## [1] 8.131099RPSV (for ‘Root Pooled Spatial Variance’) is a measure of the 2-D dispersion of the locations at which individual animals are detected, pooled over individuals (cf Calhoun and Casby 1958, Slade and Swihart 1983).
## [[1]]
## [1] 10.05093
##
## [[2]]
## [1] 7.759548