Dear Murray,
I hope this finds you well.
My name is Paolo, and I am a PhD student attempting to estimate African lion density with camera trap data from a number of purpose-built grids in Tanzania. I posted here once in the past, but with a different account. I’m writing as I’ve run into an issue with the mark-resight models.
I initially employed classic secr models, with no issue. However, due to the relatively high number of individuals which could not be IDd, I decided to fit mark-resight models, as per Rich et al. 2014 & 2019. Based on the mark-resight guide, and other threads on here, I treated it as a sighting-only dataset, with unknown pre-marking (knownmarks=FALSE), and without specifying pID (so model 3A in Table 1 in the guide). I treated all unmarked as marked but unknown (nonID, so Tm), with no unmarked (Tu) file specified.
I ran the models both with and without the ‘knownmarks’ parameter (as I forgot to include this the first time). However, both of these versions seem to have an issue. The density estimate from the model without specifying that knownmarks=FALSE is realistic (based on the results from the secr model), but the SE is 0. The results from the run with knownmarks=FALSE are even more strange, with parameter estimates being NA.
I re-ran the analysis with a bigger buffer, in case that could be a cause of the issue, but this did not make a difference. I am running secr 3.2.1.
Any idea why this could be happening? Even though I’ve played around with it for a while I’m a bit stumped – so would be extremely grateful for any help you could provide on this! The data is 'normal', with a five '1s' and two '2s' in the Tm file, so nothing that should be too strange. I’ve copied the outputs from both runs below, and would of course be happy to send over the input files if that might be of help.
Thanks so much and all the best,
Paolo
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secr.0 (without knownmarks=FALSE)
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secr.fit(capthist = Rungwa.lion.SMR.v1.capthist, model = list(g0 ~
1, sigma ~ 1, D ~ 1), buffer = buffer, detectfn = "HN",
binomN = 1, hcov = "V5", details = list(minprob = 1e-200),
trace = FALSE)
secr 3.2.1, 12:40:37 28 Jan 2020
Detector type proximity (101)
Detector number 40
Average spacing 3457.8 m
x-range 630379 660060 m
y-range 9228623 9258932 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
min 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
max 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
min 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
max 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101
min 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0
max 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Marking occasions
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N animals : 24
N detections : 70
N nonID sghting : 19 (c-hat 1)
N occasions : 101
Mask area : 480519.8 ha
Model : D~1 g0~1 sigma~1 pID~1 pmix~h2
Mixture (hcov) : V5
Fixed (real) : none
Detection fn : halfnormal
Distribution : poisson
N parameters : 5
Log likelihood : -553.658
AIC : 1117.316
AICc : 1120.649
Beta parameters (coefficients)
beta SE.beta lcl ucl
D -8.299959 0.0000000 -8.299959 -8.299959
g0 -5.463857 0.2398960 -5.934044 -4.993669
sigma 9.540185 0.1359532 9.273721 9.806648
pID 6.740249 0.2585893 6.233424 7.247075
pmix.h2M 1.500000 0.0000000 1.500000 1.500000
Variance-covariance matrix of beta parameters
D g0 sigma pID pmix.h2M
D 0 0.000000e+00 0.000000e+00 0.000000e+00 0
g0 0 5.755007e-02 -1.925953e-02 -3.806325e-05 0
sigma 0 -1.925953e-02 1.848327e-02 1.070616e-05 0
pID 0 -3.806325e-05 1.070616e-05 6.686842e-02 0
pmix.h2M 0 0.000000e+00 0.000000e+00 0.000000e+00 0
Fitted (real) parameters evaluated at base levels of covariates
session = 1, h2 = F
link estimate SE.estimate lcl ucl
D log 2.485270e-04 0.000000e+00 2.485270e-04 2.485270e-04
g0 logit 4.219305e-03 1.007923e-03 2.640760e-03 6.735070e-03
sigma log 1.390752e+04 1.899542e+03 1.065433e+04 1.815404e+04
pID logit 9.988190e-01 3.050220e-04 9.980411e-01 9.992883e-01
pmix logit 1.824255e-01 0.000000e+00 1.824255e-01 1.824255e-01
session = 1, h2 = M
link estimate SE.estimate lcl ucl
D log 2.485270e-04 0.000000e+00 2.485270e-04 2.485270e-04
g0 logit 4.219305e-03 1.007923e-03 2.640760e-03 6.735070e-03
sigma log 1.390752e+04 1.899542e+03 1.065433e+04 1.815404e+04
pID logit 9.988190e-01 3.050220e-04 9.980411e-01 9.992883e-01
pmix logit 8.175745e-01 0.000000e+00 8.175745e-01 8.175745e-01
-------------------------------------------------------------------
secr.0 (with knownmarks=FALSE)
-------------------------------------------------------------------
secr.fit(capthist = Rungwa.lion.SMR.v1.capthist, model = list(g0 ~
1, sigma ~ 1, D ~ 1), buffer = buffer, detectfn = "HN",
binomN = 1, hcov = "V5", details = list(minprob = 1e-200,
knownmarks = FALSE), trace = FALSE)
secr 3.2.1, 13:20:07 28 Jan 2020
Detector type proximity (101)
Detector number 40
Average spacing 3457.8 m
x-range 630379 660060 m
y-range 9228623 9258932 m
Usage range by occasion
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
min 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1
max 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
min 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
max 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101
min 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0
max 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Marking occasions
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N animals : 24
N detections : 70
N nonID sghting : 19 (c-hat 1)
N occasions : 101
Mask area : 480519.8 ha
Model : D~1 g0~1 sigma~1 pID~1 pmix~h2
Mixture (hcov) : V5
Fixed (real) : none
Detection fn : halfnormal
Distribution : poisson
N parameters : 5
Log likelihood : -1e+10
AIC : 2e+10
AICc : 2e+10
Beta parameters (coefficients)
beta SE.beta lcl ucl
D NA NA NA NA
g0 NA NA NA NA
sigma NA NA NA NA
pID NA NA NA NA
pmix.h2M NA NA NA NA
Variance-covariance matrix of beta parameters
NULL
Fitted (real) parameters evaluated at base levels of covariates
session = 1, h2 = F
link estimate SE.estimate lcl ucl
D log NA NA NA NA
g0 logit NA NA NA NA
sigma log NA NA NA NA
pID logit NA NA NA NA
pmix logit NA NA NA NA
session = 1, h2 = M
link estimate SE.estimate lcl ucl
D log NA NA NA NA
g0 logit NA NA NA NA
sigma log NA NA NA NA
pID logit NA NA NA NA
pmix logit NA NA NA NA