ACDC 2019 Naturalist |
Analysis 49 : Details |
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 | NTot Obs | Max Dist | Analyse | Mod Key Fn | Mod Adj Ser | Left Trunc Dist | Right Trunc Dist | Fit Dist Cuts | ExCod | Effort | NObs | Obs Rate | NumPars AdjSer | Delta AIC | Chi2 P | KS P | CvM Uw P | CvM Cw P | CoefVar Density | Qual Bal 3 | Qual Bal 2 | Qual Bal 1 | Qual Chi2+ | Qual KS+ | Qual DCv+ | Density | Min Density | Max Density | Number | Min Number | Max Number | EDR/ESW | Min EDR/ESW | Max EDR/ESW | PDetec | Min PDetec | Max PDetec | RunFolder | |
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49 | 1 | Sylvia atricapilla | a+b | m | 10mn | 403 | 511.41 | 43 | HAZARD | POLY | 13 | 1 | 190 | 403 | 100 | 0 | 0 | 0.16 | 0.52 | 0.6 | 0.6 | 9.9 | 0.6 | 0.59 | 0.6 | 0.52 | 0.59 | 0.63 | 36.95 | 30.45 | 44.85 | 887 | 731 | 1076 | 135.2 | 126 | 144.9 | 0.07 | 0.061 | 0.08 | SylvAtri-ab-10mn-m-haz-pol-ma-w3hq64b3 |
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)
Analyse | Echant | Espèce | Passage | Adulte | Durée | FonctionClé | SérieAjust | TrGche | TrDrte | NbTrchMod | Abrev. Analyse | OptimTrunc | NTot Obs | Min Dist | Max Dist | Mod Key Fn | Mod Adj Ser | Mod Chc Crit | Conf Interv | Left Trunc Dist | Right Trunc Dist | Fit Dist Cuts | 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) | EstA(3) | 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+ | Group Left Trunc | Group Right Trunc | Order Same Trunc AIC | Order Close Trunc Chi2 KS DCv | Order Close Trunc DCv | Order Close Trunc Bal 1 Qual | Order Close Trunc Bal 2 Qual | Order Close Trunc Bal 3 Qual | Order Close Trunc Bal Chi2+ Qual | Order Close Trunc Bal KS+ Qual | Order Close Trunc Bal DCv+ Qual | Order Global Chi2 KS DCv | Order Global Bal 1 Qual | Order Global Bal 2 Qual | Order Global Bal 3 Qual | Order Global Bal Chi2+ Qual | Order Global Bal KS+ Qual | Order Global Bal DCv+ Qual | Order Global DeltaAIC Chi2 KS DCv | |
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49 | 43 | 1 | Sylvia atricapilla | a+b | m | 10mn | HAZARD | POLY | nan | nan | 13.000000 | SylvAtri-ab-10mn-m-haz-pol-ma | 1 | 403 | 1.212094 | 511.409745 | HAZARD | POLY | AIC | 95 | nan | nan | 13.000000 | 1 | 2023-04-30 15:55:03.259000 | 0.854000 | SylvAtri-ab-10mn-m-haz-pol-ma-w3hq64b3 | 403 | 96 | 190 | 2.121053 | 0.068384 | 1.852082 | 2.429085 | 95 | 0.000000 | 511.409800 | 100.000000 | 2.000000 | 0.000000 | 4526.764000 | 0.163355 | 0.163355 | nan | nan | 0.000109 | 0.071058 | 0.000095 | 0.000126 | 401.000000 | 0.069854 | 0.071058 | 0.060758 | 0.080313 | 401.000000 | 135.165300 | 0.035529 | 126.049500 | 144.940400 | 401.000000 | 4526.793000 | 4534.762000 | -2261.382000 | 0.517396 | 0.600000 | 0.600000 | HAZARD | POLY | 2.000000 | 0.000000 | 0.000000 | 100.793500 | 3.829198 | nan | 36.954800 | 0.098619 | 30.451850 | 44.846450 | 321.971000 | 36.954800 | 0.016817 | 0.098619 | 30.451850 | 44.846450 | 321.971000 | 887 | 0.098619 | 731 | 1076 | 321.971000 | 0.597147 | 0.591720 | 0.599049 | 0.518512 | 0.589375 | 0.627415 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
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=anlys\230430-153402\SylvAtri-ab-10mn-m-haz-pol-ma-w3hq64b3\data.txt /NoEcho; Data will be input from file - [...]M-HAZ-POL-MA-W3HQ64B3\DATA.TXT End; Dataset has been stored. Estimate; Distance; Density=All; Encounter=All; Detection=All; Size=All; Estimator /Key=HAZARD /Adjust=POLY /Criterion=AIC; Monotone=Strict; Pick=AIC; GOF /NClass=13; Cluster /Bias=GXLOG; VarN=Empirical; End;
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: Hazard Rate Adjustments - Function : Simple polynomials - 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: ---------------- Based on user defined cut points 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
Effort : 190.0000 # samples : 96 Width : 511.4098 # observations: 403 Model 1 Hazard Rate key, k(y) = 1 - Exp(-(y/A(1))**-A(2)) Results: Convergence was achieved with 14 function evaluations. Final Ln(likelihood) value = -2261.3819 Akaike information criterion = 4526.7637 Bayesian information criterion = 4534.7617 AICc = 4526.7935 Final parameter values: 100.79350 3.8291985 Model 2 Hazard Rate key, k(y) = 1 - Exp(-(y/A(1))**-A(2)) Simple polynomial adjustments of order(s) : 4 Results: Convergence was achieved with 27 function evaluations. Final Ln(likelihood) value = -2261.0780 Akaike information criterion = 4528.1558 Bayesian information criterion = 4540.1528 AICc = 4528.2158 Final parameter values: 99.715858 3.7165854 -0.44561603 Likelihood ratio test between models 1 and 2 Likelihood ratio test value = 0.6079 Probability of a greater value = 0.435586 *** Model 1 selected over model 2 based on minimum AIC
Effort : 190.0000 # samples : 96 Width : 511.4098 # observations: 403 Model Hazard Rate key, k(y) = 1 - Exp(-(y/A(1))**-A(2)) Point Standard Percent Coef. 95 Percent Parameter Estimate Error of Variation Confidence Interval --------- ----------- ----------- -------------- ---------------------- A( 1) 100.8 5.191 A( 2) 3.829 0.2107 h(0) 0.10947E-03 0.77788E-05 7.11 0.95216E-04 0.12586E-03 p 0.69854E-01 0.49637E-02 7.11 0.60758E-01 0.80313E-01 EDR 135.17 4.8023 3.55 126.05 144.94 --------- ----------- ----------- -------------- ---------------------- Sampling Correlation of Estimated Parameters A( 1) A( 2) A( 1) 1.000 0.760 A( 2) 0.760 1.000
Kolmogorov-Smirnov test ----------------------- D_n = 0.0407 p = 0.5174 Cramer-von Mises family tests ----------------------------- W-sq (uniform weighting) = 0.1158 0.500 < p <= 0.600 Relevant critical values: W-sq crit(alpha=0.600) = 0.0968 W-sq crit(alpha=0.500) = 0.1187 C-sq (cosine weighting) = 0.0654 0.500 < p <= 0.600 Relevant critical values: C-sq crit(alpha=0.600) = 0.0622 C-sq crit(alpha=0.500) = 0.0769
Cell Cut Observed Expected Chi-square i Points Values Values Values ----------------------------------------------------------------- 1 0.000 39.3 39 34.14 0.692 2 39.3 78.7 93 101.02 0.636 3 78.7 118. 109 111.40 0.052 4 118. 157. 64 63.59 0.003 5 157. 197. 42 34.17 1.794 6 197. 236. 27 19.89 2.545 7 236. 275. 10 12.51 0.504 8 275. 315. 5 8.38 1.361 9 315. 354. 1 5.89 4.057 10 354. 393. 6 4.30 0.672 11 393. 433. 4 3.24 0.178 12 433. 472. 1 2.50 0.904 13 472. 511. 2 1.98 0.000 ----------------------------------------------------------------- Total Chi-square value = 13.3971 Degrees of Freedom = 10.00 Probability of a greater chi-square value, P = 0.20231 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 39.3 39 34.14 0.692 2 39.3 78.7 93 101.02 0.636 3 78.7 118. 109 111.40 0.052 4 118. 157. 64 63.59 0.003 5 157. 197. 42 34.17 1.794 6 197. 236. 27 19.89 2.545 7 236. 275. 10 12.51 0.504 8 275. 315. 5 8.38 1.361 9 315. 354. 1 5.89 4.057 10 354. 393. 6 4.30 0.672 11 393. 433. 4 3.24 0.178 12 433. 511. 3 4.48 0.491 ----------------------------------------------------------------- Total Chi-square value = 12.9835 Degrees of Freedom = 9.00 Probability of a greater chi-square value, P = 0.16335
Effort : 190.0000 # samples : 96 Width : 511.4098 # observations: 403 Model 1 Hazard Rate key, k(y) = 1 - Exp(-(y/A(1))**-A(2)) Point Standard Percent Coef. 95% Percent Parameter Estimate Error of Variation Confidence Interval --------- ----------- ----------- -------------- ---------------------- D 36.955 3.6444 9.86 30.452 44.846 N 887.00 87.475 9.86 731.00 1076.0 --------- ----------- ----------- -------------- ---------------------- Measurement Units --------------------------------- Density: Numbers/Sq. kilometers EDR: meters Component Percentages of Var(D) ------------------------------- Detection probability : 51.9 Encounter rate : 48.1
Estimate %CV df 95% Confidence Interval ------------------------------------------------------ n 403.00 k 96.000 K 190.00 n/K 2.1211 6.84 95.00 1.8521 2.4291 Left 0.0000 Width 511.41
Estimate %CV df 95% Confidence Interval ------------------------------------------------------ Hazard/Polynomial m 2.0000 LnL -2261.4 AIC 4526.8 AICc 4526.8 BIC 4534.8 Chi-p 0.16335 h(0) 0.10947E-03 7.11 401.00 0.95216E-04 0.12586E-03 p 0.69854E-01 7.11 401.00 0.60758E-01 0.80313E-01 EDR 135.17 3.55 401.00 126.05 144.94
Estimate %CV df 95% Confidence Interval ------------------------------------------------------ Hazard/Polynomial D 36.955 9.86 321.97 30.452 44.846 N 887.00 9.86 321.97 731.00 1076.0