Results: main figures
Study type: Point transect, Radial distance, No clustering.
Units used: Meter for distances, Sq. Kilometer for areas.
Note: Most figures have been rounded for readability, but 'CoefVar Density' have been further modified : converted to %
| Echant | Espèce | Passage | Adulte | Durée | Mod Key Fn | Mod Adj Ser | ExCod | NObs | Max Dist | Effort | AIC | Chi2 P | KS P | CoefVar Density | Density | Min Density | Max Density | Number | Min Number | Max Number | EDR/ESW | Min EDR/ESW | Max EDR/ESW | PDetec | Min PDetec | Max PDetec | RunFolder | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 04 | 3 | Prunella modularis | a+b | m | 10mn | UNIFORM | COSINE | 2 | 47 | 271.221 | 190 | 494.6 | 0.21 | 0.47 | 24.2 | 6.24 | 3.89 | 9.99 | 150 | 93 | 240 | 112.4 | 95.3 | 132.5 | 0.172 | 0.124 | 0.238 | PrunModu-ab-10mn-m-uni-cos-f3smf1_a |
Results: all details
Study type: Point transect, Radial distance, No clustering.
Units used: Meter for distances, Sq. Kilometer for areas.
Note: All values have been left untouched, as output by MCDS (no rounding, no conversion)
| Echant | Espèce | Passage | Adulte | Durée | Abrev. Echant | NTot Obs | Min Dist | Max Dist | Mod Key Fn | Mod Adj Ser | Mod Chc Crit | Conf Interv | ExCod | StartTime | ElapsedTime | RunFolder | NObs | NSamp | Effort | EncRate | CoefVar EncRate | Min EncRate | Max EncRate | DoF EncRate | Left Trunc | Right Trunc | Obs Rate | TotNum Pars | Delta AIC | AIC | Chi2 P | Chi2 P 1 | Chi2 P 2 | Chi2 P 3 | f/h(0) | CoefVar f/h(0) | Min f/h(0) | Max f/h(0) | DoF f/h(0) | PDetec | CoefVar PDetec | Min PDetec | Max PDetec | DoF PDetec | EDR/ESW | CoefVar EDR/ESW | Min EDR/ESW | Max EDR/ESW | DoF EDR/ESW | AICc | BIC | LogLhood | KS P | CvM Uw P | CvM Cw P | Key Fn | Adj Ser | NumPars KeyFn | NumPars AdjSer | Num Covars | EstA(1) | EstA(2) | DensClu | CoefVar DensClu | Min DensClu | Max DensClu | DoF DensClu | Density | Delta CoefVar Density | CoefVar Density | Min Density | Max Density | DoF Density | Number | CoefVar Number | Min Number | Max Number | DoF Number | Qual Bal 1 | Qual Bal 2 | Qual Bal 3 | Qual Chi2+ | Qual KS+ | Qual DCv+ | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 04 | 3 | Prunella modularis | a+b | m | 10mn | PrunModu-ab-10mn-m | 47.000000 | 10.176839 | 271.221090 | UNIFORM | COSINE | AIC | 95 | 2 | 2023-04-16 18:55:40.254027 | 0.523999 | PrunModu-ab-10mn-m-uni-cos-f3smf1_a | 47.000000 | 96.000000 | 190.000000 | 0.247368 | 0.177070 | 0.174524 | 0.350618 | 95.000000 | 0.000000 | 271.221100 | 100.000000 | 2.000000 | 0.000000 | 494.608800 | 0.212556 | 0.365375 | 0.041433 | 0.212556 | 0.000158 | 0.164232 | 0.000114 | 0.000220 | 45.000000 | 0.171655 | 0.164232 | 0.123582 | 0.238429 | 45.000000 | 112.370500 | 0.082116 | 95.267530 | 132.543800 | 45.000000 | 494.881600 | 498.309100 | -245.304400 | 0.473960 | 0.600000 | 0.600000 | UNIFORM | COSINE | 0.000000 | 2.000000 | 0.000000 | 1.331354 | 0.351025 | 6.235769 | 0.241508 | 3.893096 | 9.988147 | 128.303600 | 6.235769 | 0.000000 | 0.241508 | 3.893096 | 9.988147 | 128.303600 | 150.000000 | 0.241508 | 93.000000 | 240.000000 | 128.303600 | 0.563369 | 0.522783 | 0.516768 | 0.468195 | 0.511827 | 0.487815 |
Summary computation log
This is mcds.exe version 6.2.0
Options;
Type=Point;
Distance=Radial /Measure='Meter';
Area /Units='Sq. Kilometer';
Object=Single;
SF=1;
Selection=Sequential;
Lookahead=1;
Maxterms=5;
Confidence=95;
print=Selection;
End;
Data /Structure=Flat;
Fields=STR_LABEL,STR_AREA,SMP_LABEL,SMP_EFFORT,DISTANCE;
Infile=pranlys\230416-180310\PrunModu-ab-10mn-m-uni-cos-f3smf1_a\data.txt /NoEcho;
Data will be input from file - [...]MN-M-UNI-COS-F3SMF1_A\DATA.TXT
End;
Dataset has been stored.
Estimate;
Distance;
Density=All;
Encounter=All;
Detection=All;
Size=All;
Estimator /Key=UNIFORM /Adjust=COSINE /Criterion=AIC;
Monotone=Strict;
Pick=AIC;
GOF;
Cluster /Bias=GXLOG;
VarN=Empirical;
End;
** Warning: Parameters are being constrained to obtain monotonicity. **
** Warning: Parameters are being constrained to obtain monotonicity. **
Detailed computation log
Estimation Options Listing
Parameter Estimation Specification
----------------------------------
Encounter rate for all data combined
Detection probability for all data combined
Density for all data combined
Distances:
----------
Analysis based on exact distances
Width: use largest measurement/last interval endpoint
Estimators:
-----------
Estimator 1
Key: Uniform
Adjustments - Function : Cosines
- Term selection mode : Sequential
- Term selection criterion : Akaike Information Criterion (AIC)
- Distances scaled by : W (right truncation distance)
Estimator selection: Choose estimator with minimum AIC
Estimation functions: constrained to be nearly monotone non-increasing
Variances:
----------
Variance of n: Empirical estimate from sample
(design-derived estimator R2/P2)
Variance of f(0): MLE estimate
Goodness of fit:
----------------
Cut points chosen by program
Glossary of terms
-----------------
Data items:
n - number of observed objects (single or clusters of animals)
L - total length of transect line(s)
k - number of samples
K - point transect effort, typically K=k
T - length of time searched in cue counting
ER - encounter rate (n/L or n/K or n/T)
W - width of line transect or radius of point transect
x(i) - distance to i-th observation
s(i) - cluster size of i-th observation
r-p - probability for regression test
chi-p- probability for chi-square goodness-of-fit test
Parameters or functions of parameters:
m - number of parameters in the model
A(I) - i-th parameter in the estimated probability density function(pdf)
f(0) - 1/u = value of pdf at zero for line transects
u - W*p = ESW, effective detection area for line transects
h(0) - 2*PI/v
v - PI*W*W*p, is the effective detection area for point transects
p - probability of observing an object in defined area
ESW - for line transects, effective strip width = W*p
EDR - for point transects, effective detection radius = W*sqrt(p)
rho - for cue counts, the cue rate
DS - estimate of density of clusters
E(S) - estimate of expected value of cluster size
D - estimate of density of animals
N - estimate of number of animals in specified area
Detection Fct/Global/Model Fitting
Effort : 190.0000
# samples : 96
Width : 271.2211
# observations: 47
Model 1
Uniform key, k(y) = 1/W
Results:
Convergence was achieved with 1 function evaluations.
Final Ln(likelihood) value = -285.35577
Akaike information criterion = 570.71155
Bayesian information criterion = 570.71155
AICc = 570.71155
Final parameter values:
Model 2
Uniform key, k(y) = 1/W
Cosine adjustments of order(s) : 1
Results:
Convergence was achieved with 24 function evaluations.
Final Ln(likelihood) value = -252.28137
Akaike information criterion = 506.56274
Bayesian information criterion = 508.41290
AICc = 506.65164
Final parameter values: 0.97570701
Likelihood ratio test between models 1 and 2
Likelihood ratio test value = 66.1488
Probability of a greater value = 0.000000
*** Model 2 selected over model 1 based on minimum AIC
Model 3
Uniform key, k(y) = 1/W
Cosine adjustments of order(s) : 1, 2
Results:
Convergence was achieved with 33 function evaluations.
Final Ln(likelihood) value = -245.30442
Akaike information criterion = 494.60883
Bayesian information criterion = 498.30914
AICc = 494.88156
Final parameter values: 1.3313540 0.35102447
** Warning: Parameters are being constrained to obtain monotonicity. **
Likelihood ratio test between models 2 and 3
Likelihood ratio test value = 13.9539
Probability of a greater value = 0.000187
*** Model 3 selected over model 2 based on minimum AIC
Model 4
Uniform key, k(y) = 1/W
Cosine adjustments of order(s) : 1, 2, 3
Results:
Convergence was achieved with 16 function evaluations.
Final Ln(likelihood) value = -527.06363
Akaike information criterion = 1060.1273
Bayesian information criterion = 1065.6777
AICc = 1060.6854
Final parameter values: 61575.195 -27718.076 -61572.895
** Warning: Parameters are being constrained to obtain monotonicity. **
Likelihood ratio test between models 3 and 4
Likelihood ratio test value = -563.5184
Probability of a greater value = 1.000000
*** Model 3 selected over model 4 based on minimum AIC
Detection Fct/Global/Parameter Estimates
Effort : 190.0000
# samples : 96
Width : 271.2211
# observations: 47
Model
Uniform key, k(y) = 1/W
Cosine adjustments of order(s) : 1, 2
Point Standard Percent Coef. 95 Percent
Parameter Estimate Error of Variation Confidence Interval
--------- ----------- ----------- -------------- ----------------------
A( 1) 1.331 0.1018
A( 2) 0.3510 0.1000
h(0) 0.15839E-03 0.26013E-04 16.42 0.11403E-03 0.22000E-03
p 0.17166 0.28191E-01 16.42 0.12358 0.23843
EDR 112.37 9.2274 8.21 95.268 132.54
--------- ----------- ----------- -------------- ----------------------
Sampling Correlation of Estimated Parameters
A( 1) A( 2)
A( 1) 1.000 0.981
A( 2) 0.981 1.000
Detection Fct/Global/Plot: Qq-plot
Detection Fct/Global/K-S GOF Test
Kolmogorov-Smirnov test
-----------------------
D_n = 0.1232 p = 0.4740
Cramer-von Mises family tests
-----------------------------
W-sq (uniform weighting) = 0.1046 0.500 < p <= 0.600
Relevant critical values:
W-sq crit(alpha=0.600) = 0.0974
W-sq crit(alpha=0.500) = 0.1193
C-sq (cosine weighting) = 0.0668 0.500 < p <= 0.600
Relevant critical values:
C-sq crit(alpha=0.600) = 0.0625
C-sq crit(alpha=0.500) = 0.0773
Detection Fct/Global/Plot: Detection Probability 1
Detection Fct/Global/Plot: Pdf 1
Detection Fct/Global/Chi-sq GOF Test 1
Cell Cut Observed Expected Chi-square
i Points Values Values Values
-----------------------------------------------------------------
1 0.000 67.8 16 14.65 0.125
2 67.8 136. 24 22.97 0.047
3 136. 203. 6 8.31 0.642
4 203. 271. 1 1.08 0.006
-----------------------------------------------------------------
Total Chi-square value = 0.8193 Degrees of Freedom = 1.00
Probability of a greater chi-square value, P = 0.36538
The program has limited capability for pooling. The user should
judge the necessity for pooling and if necessary, do pooling by hand.
Detection Fct/Global/Plot: Detection Probability 2
Detection Fct/Global/Plot: Pdf 2
Detection Fct/Global/Chi-sq GOF Test 2
Cell Cut Observed Expected Chi-square
i Points Values Values Values
-----------------------------------------------------------------
1 0.000 45.2 7 7.09 0.001
2 45.2 90.4 11 16.02 1.575
3 90.4 136. 22 14.49 3.890
4 136. 181. 6 7.09 0.168
5 181. 226. 0 1.71 1.706
6 226. 271. 1 0.59 0.284
-----------------------------------------------------------------
Total Chi-square value = 7.6250 Degrees of Freedom = 3.00
Probability of a greater chi-square value, P = 0.05443
The program has limited capability for pooling. The user should
judge the necessity for pooling and if necessary, do pooling by hand.
Goodness of Fit Testing with some Pooling
Cell Cut Observed Expected Chi-square
i Points Values Values Values
-----------------------------------------------------------------
1 0.000 45.2 7 7.09 0.001
2 45.2 90.4 11 16.02 1.575
3 90.4 136. 22 14.49 3.890
4 136. 181. 6 7.09 0.168
5 181. 271. 1 2.30 0.732
-----------------------------------------------------------------
Total Chi-square value = 6.3673 Degrees of Freedom = 2.00
Probability of a greater chi-square value, P = 0.04143
Detection Fct/Global/Plot: Detection Probability 3
Detection Fct/Global/Plot: Pdf 3
Detection Fct/Global/Chi-sq GOF Test 3
Cell Cut Observed Expected Chi-square
i Points Values Values Values
-----------------------------------------------------------------
1 0.000 27.1 2 2.67 0.168
2 27.1 54.2 9 7.24 0.428
3 54.2 81.4 6 9.80 1.475
4 81.4 108. 10 9.90 0.001
5 108. 136. 13 7.99 3.134
6 136. 163. 4 5.15 0.258
7 163. 190. 2 2.56 0.121
8 190. 217. 0 0.94 0.943
9 217. 244. 0 0.37 0.372
10 244. 271. 1 0.37 1.102
-----------------------------------------------------------------
Total Chi-square value = 8.0034 Degrees of Freedom = 7.00
Probability of a greater chi-square value, P = 0.33229
The program has limited capability for pooling. The user should
judge the necessity for pooling and if necessary, do pooling by hand.
Goodness of Fit Testing with some Pooling
Cell Cut Observed Expected Chi-square
i Points Values Values Values
-----------------------------------------------------------------
1 0.000 27.1 2 2.67 0.168
2 27.1 54.2 9 7.24 0.428
3 54.2 81.4 6 9.80 1.475
4 81.4 108. 10 9.90 0.001
5 108. 136. 13 7.99 3.134
6 136. 163. 4 5.15 0.258
7 163. 271. 3 4.24 0.361
-----------------------------------------------------------------
Total Chi-square value = 5.8256 Degrees of Freedom = 4.00
Probability of a greater chi-square value, P = 0.21256
Density Estimates/Global
Effort : 190.0000
# samples : 96
Width : 271.2211
# observations: 47
Model 3
Uniform key, k(y) = 1/W
Cosine adjustments of order(s) : 1, 2
Point Standard Percent Coef. 95% Percent
Parameter Estimate Error of Variation Confidence Interval
--------- ----------- ----------- -------------- ----------------------
D 6.2358 1.5060 24.15 3.8931 9.9881
N 150.00 36.226 24.15 93.000 240.00
--------- ----------- ----------- -------------- ----------------------
Measurement Units
---------------------------------
Density: Numbers/Sq. kilometers
EDR: meters
Component Percentages of Var(D)
-------------------------------
Detection probability : 46.2
Encounter rate : 53.8
Estimation Summary - Encounter rates
Estimate %CV df 95% Confidence Interval
------------------------------------------------------
n 47.000
k 96.000
K 190.00
n/K 0.24737 17.71 95.00 0.17452 0.35062
Left 0.0000
Width 271.22
Estimation Summary - Detection probability
Estimate %CV df 95% Confidence Interval
------------------------------------------------------
Uniform/Cosine
m 2.0000
LnL -245.30
AIC 494.61
AICc 494.88
BIC 498.31
Chi-p 0.21256
h(0) 0.15839E-03 16.42 45.00 0.11403E-03 0.22000E-03
p 0.17166 16.42 45.00 0.12358 0.23843
EDR 112.37 8.21 45.00 95.268 132.54
Estimation Summary - Density&Abundance
Estimate %CV df 95% Confidence Interval
------------------------------------------------------
Uniform/Cosine
D 6.2358 24.15 128.30 3.8931 9.9881
N 150.00 24.15 128.30 93.000 240.00