Script Prob d’occu et de détection et modèle dynamique
Mathis Damestoy
2026-08-20
R Markdown
Modèle d’occupation Calcul des probabilité d’occupation et de détection pour chaque campagne annuelle de suivi
Pour 2022
library(readr)
library(unmarked)
traitement_R_2022 <- read_delim(
"traitement R 2022.txt",
delim = "\t",
escape_double = FALSE,
na = "NA",
trim_ws = TRUE
)## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 25 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (5): vent_P1, vent_P2, vent_P3, vent_P4, vent_P5
## dbl (15): Placettes, P1, P2, P3, P4, P5, Terriers, Patch_vegetation, Souches...
## num (5): temp_P1, temp_P2, temp_P3, temp_P4, temp_P5
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data <- traitement_R_2022
y <- data[, c("P1", "P2", "P3", "P4", "P5")]
# Covariables de site = variables qui ne changent pas entre passages
siteCovs <- data[, c(
"Terriers",
"Souches_bois_mort",
"Patch_vegetation",
"Gites_anthropiques")]
# Covariables d'observation = variables qui changent à chaque passage
obsCovs <- list(
temperature = data[, c("temp_P1", "temp_P2", "temp_P3", "temp_P4", "temp_P5")],
vent = data[, c("vent_P1", "vent_P2", "vent_P3", "vent_P4", "vent_P5")],
nebulosite = data[, c("neb_P1", "neb_P2", "neb_P3", "neb_P4", "neb_P5")])cor(siteCovs, method = "spearman")## Terriers Souches_bois_mort Patch_vegetation
## Terriers 1.00000000 -0.04682929 0.1847166
## Souches_bois_mort -0.04682929 1.00000000 0.3429972
## Patch_vegetation 0.18471661 0.34299717 1.0000000
## Gites_anthropiques -0.04682929 0.00000000 0.1286239
## Gites_anthropiques
## Terriers -0.04682929
## Souches_bois_mort 0.00000000
## Patch_vegetation 0.12862394
## Gites_anthropiques 1.00000000Création de l’objet pour unmarked
y <- ifelse(y > 0, 1, 0)
nSites <- nrow(y)
nVisits <- ncol(y)
visit <- matrix(
rep(paste0("V", 1:nVisits), each = nSites),
nrow = nSites,
ncol = nVisits)
visit <- as.data.frame(visit)
visit[] <- lapply(visit, factor)
umf <- unmarkedFrameOccu(
y = y,
siteCovs = siteCovs,
obsCovs = c(obsCovs, list(visit = visit)))## Warning: obsCovs contains characters. Converting them to factors.summary(umf)## unmarkedFrame Object
##
## 25 sites
## Maximum number of observations per site: 5
## Mean number of observations per site: 5
## Sites with at least one detection: 7
##
## Tabulation of y observations:
## 0 1
## 100 25
##
## Site-level covariates:
## Terriers Souches_bois_mort Patch_vegetation Gites_anthropiques
## Min. :0.00 Min. :0.0 Min. :0.00 Min. :0.0
## 1st Qu.:0.00 1st Qu.:0.0 1st Qu.:0.00 1st Qu.:0.0
## Median :0.00 Median :0.0 Median :1.00 Median :0.0
## Mean :0.24 Mean :0.2 Mean :0.68 Mean :0.2
## 3rd Qu.:0.00 3rd Qu.:0.0 3rd Qu.:1.00 3rd Qu.:0.0
## Max. :1.00 Max. :1.0 Max. :1.00 Max. :1.0
##
## Observation-level covariates:
## temperature vent nebulosite visit
## Min. : 16.0 : 1 Min. :1.000 V1:25
## 1st Qu.:191.0 faible:63 1st Qu.:1.000 V2:25
## Median :215.5 fort : 7 Median :1.000 V3:25
## Mean :188.5 moyen :36 Mean :1.792 V4:25
## 3rd Qu.:234.2 nul :18 3rd Qu.:2.000 V5:25
## Max. :274.0 Max. :5.000
## NA's :1Création des modèles :
# Modèle nul : occupation constante, détection constante
m0 <- occu(~ 1 ~ 1, data = umf)
#Modèle détection variable entre visites
m1 <- occu(~ visit ~ 1, data = umf)
#Différents modèle avec l'effet des covariables de site
m2 <- occu(~ 1 ~ Terriers, data = umf)
m3 <- occu(~ 1 ~ Souches_bois_mort, data = umf)
m4 <- occu(~ 1 ~ Patch_vegetation, data = umf)
m5 <- occu(~ 1 ~ Gites_anthropiques, data = umf)
m6 <- occu(~ 1 ~ Gites_anthropiques + Terriers, data = umf)
m7 <- occu(~ 1 ~ Gites_anthropiques + Patch_vegetation, data = umf)
m8 <- occu(~ 1 ~ Terriers + Patch_vegetation, data = umf)
m9 <- occu(~ 1 ~ Gites_anthropiques + Terriers + Patch_vegetation, data = umf)
m10 <- occu(~ 1 ~ Gites_anthropiques + Souches_bois_mort, data = umf)
#Différents modèles pour tester les covariables d’échantillonnage sur la détection
m_p_temp <- occu(~ temperature ~ 1, data = umf)
m_p_vent <- occu(~ vent ~ 1, data = umf)
m_p_neb <- occu(~ nebulosite ~ 1, data = umf)
m_p_temp_neb <- occu(~ temperature + nebulosite ~ 1, data = umf)
m_p_vent_neb <- occu(~ vent + nebulosite ~ 1, data = umf)Comparaison des modèles
#Comparaison des modèles
models <- fitList(
"psi(.) p(.)" = m0,
"psi(.) p(visit)" = m1,
"psi(Terriers) p(.)" = m2,
"psi(Souches_bois_mort) p(.)" = m3,
"psi(Patch_vegetation) p(.)" = m4,
"psi(Gites_anthropiques) p(.)" = m5,
"psi(GA + T) p(.)" = m6,
"psi(GA + VEG) p(.)" = m7,
"psi(T + VEG) p(.)" = m8,
"psi(GA + T + VEG) p(.)" = m9,
"psi(GA + Souches_bois_mort) p(.)" = m10)
modSel(models) ## nPars AIC delta AICwt cumltvWt
## psi(GA + T) p(.) 4 61.09 0.00 6.3e-01 0.63
## psi(GA + T + VEG) p(.) 5 62.73 1.65 2.8e-01 0.90
## psi(GA + Souches_bois_mort) p(.) 4 65.31 4.23 7.6e-02 0.98
## psi(Gites_anthropiques) p(.) 3 69.76 8.68 8.2e-03 0.99
## psi(GA + VEG) p(.) 4 70.65 9.57 5.3e-03 0.99
## psi(Terriers) p(.) 3 72.06 10.98 2.6e-03 1.00
## psi(T + VEG) p(.) 4 73.25 12.17 1.4e-03 1.00
## psi(Souches_bois_mort) p(.) 3 73.75 12.67 1.1e-03 1.00
## psi(.) p(.) 2 75.50 14.41 4.7e-04 1.00
## psi(Patch_vegetation) p(.) 3 75.95 14.87 3.7e-04 1.00
## psi(.) p(visit) 6 82.06 20.97 1.8e-05 1.00Le meilleur modèle est m6 et la prob de détection semble constante
#prob d'occupation pour chaque placette
predict(m6,type="state") ## Predicted SE lower upper
## 1 6.011574e-01 0.2195220741 2.003383e-01 0.9006763
## 2 7.514116e-01 0.2169504406 2.368095e-01 0.9671551
## 3 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 4 7.514116e-01 0.2169504406 2.368095e-01 0.9671551
## 5 6.011574e-01 0.2195220741 2.003383e-01 0.9006763
## 6 9.999962e-01 0.0002345713 9.122428e-48 1.0000000
## 7 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 8 7.514116e-01 0.2169504406 2.368095e-01 0.9671551
## 9 7.514116e-01 0.2169504406 2.368095e-01 0.9671551
## 10 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 11 6.011574e-01 0.2195220741 2.003383e-01 0.9006763
## 12 6.011574e-01 0.2195220741 2.003383e-01 0.9006763
## 13 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 14 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 15 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 16 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 17 6.011574e-01 0.2195220741 2.003383e-01 0.9006763
## 18 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 19 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 20 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 21 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 22 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 23 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 24 1.734058e-05 0.0010683793 6.227860e-58 1.0000000
## 25 1.734058e-05 0.0010683793 6.227860e-58 1.0000000#probabilité de détection
predict(m6,type="det")## Predicted SE lower upper
## 1 0.7130968 0.07719677 0.5426286 0.8388931
## 2 0.7130968 0.07719677 0.5426286 0.8388931
## 3 0.7130968 0.07719677 0.5426286 0.8388931
## 4 0.7130968 0.07719677 0.5426286 0.8388931
## 5 0.7130968 0.07719677 0.5426286 0.8388931
## 6 0.7130968 0.07719677 0.5426286 0.8388931
## 7 0.7130968 0.07719677 0.5426286 0.8388931
## 8 0.7130968 0.07719677 0.5426286 0.8388931
## 9 0.7130968 0.07719677 0.5426286 0.8388931
## 10 0.7130968 0.07719677 0.5426286 0.8388931
## 11 0.7130968 0.07719677 0.5426286 0.8388931
## 12 0.7130968 0.07719677 0.5426286 0.8388931
## 13 0.7130968 0.07719677 0.5426286 0.8388931
## 14 0.7130968 0.07719677 0.5426286 0.8388931
## 15 0.7130968 0.07719677 0.5426286 0.8388931
## 16 0.7130968 0.07719677 0.5426286 0.8388931
## 17 0.7130968 0.07719677 0.5426286 0.8388931
## 18 0.7130968 0.07719677 0.5426286 0.8388931
## 19 0.7130968 0.07719677 0.5426286 0.8388931
## 20 0.7130968 0.07719677 0.5426286 0.8388931
## 21 0.7130968 0.07719677 0.5426286 0.8388931
## 22 0.7130968 0.07719677 0.5426286 0.8388931
## 23 0.7130968 0.07719677 0.5426286 0.8388931
## 24 0.7130968 0.07719677 0.5426286 0.8388931
## 25 0.7130968 0.07719677 0.5426286 0.8388931
## 26 0.7130968 0.07719677 0.5426286 0.8388931
## 27 0.7130968 0.07719677 0.5426286 0.8388931
## 28 0.7130968 0.07719677 0.5426286 0.8388931
## 29 0.7130968 0.07719677 0.5426286 0.8388931
## 30 0.7130968 0.07719677 0.5426286 0.8388931
## 31 0.7130968 0.07719677 0.5426286 0.8388931
## 32 0.7130968 0.07719677 0.5426286 0.8388931
## 33 0.7130968 0.07719677 0.5426286 0.8388931
## 34 0.7130968 0.07719677 0.5426286 0.8388931
## 35 0.7130968 0.07719677 0.5426286 0.8388931
## 36 0.7130968 0.07719677 0.5426286 0.8388931
## 37 0.7130968 0.07719677 0.5426286 0.8388931
## 38 0.7130968 0.07719677 0.5426286 0.8388931
## 39 0.7130968 0.07719677 0.5426286 0.8388931
## 40 0.7130968 0.07719677 0.5426286 0.8388931
## 41 0.7130968 0.07719677 0.5426286 0.8388931
## 42 0.7130968 0.07719677 0.5426286 0.8388931
## 43 0.7130968 0.07719677 0.5426286 0.8388931
## 44 0.7130968 0.07719677 0.5426286 0.8388931
## 45 0.7130968 0.07719677 0.5426286 0.8388931
## 46 0.7130968 0.07719677 0.5426286 0.8388931
## 47 0.7130968 0.07719677 0.5426286 0.8388931
## 48 0.7130968 0.07719677 0.5426286 0.8388931
## 49 0.7130968 0.07719677 0.5426286 0.8388931
## 50 0.7130968 0.07719677 0.5426286 0.8388931
## 51 0.7130968 0.07719677 0.5426286 0.8388931
## 52 0.7130968 0.07719677 0.5426286 0.8388931
## 53 0.7130968 0.07719677 0.5426286 0.8388931
## 54 0.7130968 0.07719677 0.5426286 0.8388931
## 55 0.7130968 0.07719677 0.5426286 0.8388931
## 56 0.7130968 0.07719677 0.5426286 0.8388931
## 57 0.7130968 0.07719677 0.5426286 0.8388931
## 58 0.7130968 0.07719677 0.5426286 0.8388931
## 59 0.7130968 0.07719677 0.5426286 0.8388931
## 60 0.7130968 0.07719677 0.5426286 0.8388931
## 61 0.7130968 0.07719677 0.5426286 0.8388931
## 62 0.7130968 0.07719677 0.5426286 0.8388931
## 63 0.7130968 0.07719677 0.5426286 0.8388931
## 64 0.7130968 0.07719677 0.5426286 0.8388931
## 65 0.7130968 0.07719677 0.5426286 0.8388931
## 66 0.7130968 0.07719677 0.5426286 0.8388931
## 67 0.7130968 0.07719677 0.5426286 0.8388931
## 68 0.7130968 0.07719677 0.5426286 0.8388931
## 69 0.7130968 0.07719677 0.5426286 0.8388931
## 70 0.7130968 0.07719677 0.5426286 0.8388931
## 71 0.7130968 0.07719677 0.5426286 0.8388931
## 72 0.7130968 0.07719677 0.5426286 0.8388931
## 73 0.7130968 0.07719677 0.5426286 0.8388931
## 74 0.7130968 0.07719677 0.5426286 0.8388931
## 75 0.7130968 0.07719677 0.5426286 0.8388931
## 76 0.7130968 0.07719677 0.5426286 0.8388931
## 77 0.7130968 0.07719677 0.5426286 0.8388931
## 78 0.7130968 0.07719677 0.5426286 0.8388931
## 79 0.7130968 0.07719677 0.5426286 0.8388931
## 80 0.7130968 0.07719677 0.5426286 0.8388931
## 81 0.7130968 0.07719677 0.5426286 0.8388931
## 82 0.7130968 0.07719677 0.5426286 0.8388931
## 83 0.7130968 0.07719677 0.5426286 0.8388931
## 84 0.7130968 0.07719677 0.5426286 0.8388931
## 85 0.7130968 0.07719677 0.5426286 0.8388931
## 86 0.7130968 0.07719677 0.5426286 0.8388931
## 87 0.7130968 0.07719677 0.5426286 0.8388931
## 88 0.7130968 0.07719677 0.5426286 0.8388931
## 89 0.7130968 0.07719677 0.5426286 0.8388931
## 90 0.7130968 0.07719677 0.5426286 0.8388931
## 91 0.7130968 0.07719677 0.5426286 0.8388931
## 92 0.7130968 0.07719677 0.5426286 0.8388931
## 93 0.7130968 0.07719677 0.5426286 0.8388931
## 94 0.7130968 0.07719677 0.5426286 0.8388931
## 95 0.7130968 0.07719677 0.5426286 0.8388931
## 96 0.7130968 0.07719677 0.5426286 0.8388931
## 97 0.7130968 0.07719677 0.5426286 0.8388931
## 98 0.7130968 0.07719677 0.5426286 0.8388931
## 99 0.7130968 0.07719677 0.5426286 0.8388931
## 100 0.7130968 0.07719677 0.5426286 0.8388931
## 101 0.7130968 0.07719677 0.5426286 0.8388931
## 102 0.7130968 0.07719677 0.5426286 0.8388931
## 103 0.7130968 0.07719677 0.5426286 0.8388931
## 104 0.7130968 0.07719677 0.5426286 0.8388931
## 105 0.7130968 0.07719677 0.5426286 0.8388931
## 106 0.7130968 0.07719677 0.5426286 0.8388931
## 107 0.7130968 0.07719677 0.5426286 0.8388931
## 108 0.7130968 0.07719677 0.5426286 0.8388931
## 109 0.7130968 0.07719677 0.5426286 0.8388931
## 110 0.7130968 0.07719677 0.5426286 0.8388931
## 111 0.7130968 0.07719677 0.5426286 0.8388931
## 112 0.7130968 0.07719677 0.5426286 0.8388931
## 113 0.7130968 0.07719677 0.5426286 0.8388931
## 114 0.7130968 0.07719677 0.5426286 0.8388931
## 115 0.7130968 0.07719677 0.5426286 0.8388931
## 116 0.7130968 0.07719677 0.5426286 0.8388931
## 117 0.7130968 0.07719677 0.5426286 0.8388931
## 118 0.7130968 0.07719677 0.5426286 0.8388931
## 119 0.7130968 0.07719677 0.5426286 0.8388931
## 120 0.7130968 0.07719677 0.5426286 0.8388931
## 121 0.7130968 0.07719677 0.5426286 0.8388931
## 122 0.7130968 0.07719677 0.5426286 0.8388931
## 123 0.7130968 0.07719677 0.5426286 0.8388931
## 124 0.7130968 0.07719677 0.5426286 0.8388931
## 125 0.7130968 0.07719677 0.5426286 0.8388931Résultat du modèle constant :
#prob d'occupation globale
predict(m0,type="state")## Predicted SE lower upper
## 1 0.2805471 0.08997834 0.139977 0.483002
## 2 0.2805471 0.08997834 0.139977 0.483002
## 3 0.2805471 0.08997834 0.139977 0.483002
## 4 0.2805471 0.08997834 0.139977 0.483002
## 5 0.2805471 0.08997834 0.139977 0.483002
## 6 0.2805471 0.08997834 0.139977 0.483002
## 7 0.2805471 0.08997834 0.139977 0.483002
## 8 0.2805471 0.08997834 0.139977 0.483002
## 9 0.2805471 0.08997834 0.139977 0.483002
## 10 0.2805471 0.08997834 0.139977 0.483002
## 11 0.2805471 0.08997834 0.139977 0.483002
## 12 0.2805471 0.08997834 0.139977 0.483002
## 13 0.2805471 0.08997834 0.139977 0.483002
## 14 0.2805471 0.08997834 0.139977 0.483002
## 15 0.2805471 0.08997834 0.139977 0.483002
## 16 0.2805471 0.08997834 0.139977 0.483002
## 17 0.2805471 0.08997834 0.139977 0.483002
## 18 0.2805471 0.08997834 0.139977 0.483002
## 19 0.2805471 0.08997834 0.139977 0.483002
## 20 0.2805471 0.08997834 0.139977 0.483002
## 21 0.2805471 0.08997834 0.139977 0.483002
## 22 0.2805471 0.08997834 0.139977 0.483002
## 23 0.2805471 0.08997834 0.139977 0.483002
## 24 0.2805471 0.08997834 0.139977 0.483002
## 25 0.2805471 0.08997834 0.139977 0.483002#Probabilité de détection
predict(m0,type="det")## Predicted SE lower upper
## 1 0.7128862 0.07734176 0.5421091 0.8388979
## 2 0.7128862 0.07734176 0.5421091 0.8388979
## 3 0.7128862 0.07734176 0.5421091 0.8388979
## 4 0.7128862 0.07734176 0.5421091 0.8388979
## 5 0.7128862 0.07734176 0.5421091 0.8388979
## 6 0.7128862 0.07734176 0.5421091 0.8388979
## 7 0.7128862 0.07734176 0.5421091 0.8388979
## 8 0.7128862 0.07734176 0.5421091 0.8388979
## 9 0.7128862 0.07734176 0.5421091 0.8388979
## 10 0.7128862 0.07734176 0.5421091 0.8388979
## 11 0.7128862 0.07734176 0.5421091 0.8388979
## 12 0.7128862 0.07734176 0.5421091 0.8388979
## 13 0.7128862 0.07734176 0.5421091 0.8388979
## 14 0.7128862 0.07734176 0.5421091 0.8388979
## 15 0.7128862 0.07734176 0.5421091 0.8388979
## 16 0.7128862 0.07734176 0.5421091 0.8388979
## 17 0.7128862 0.07734176 0.5421091 0.8388979
## 18 0.7128862 0.07734176 0.5421091 0.8388979
## 19 0.7128862 0.07734176 0.5421091 0.8388979
## 20 0.7128862 0.07734176 0.5421091 0.8388979
## 21 0.7128862 0.07734176 0.5421091 0.8388979
## 22 0.7128862 0.07734176 0.5421091 0.8388979
## 23 0.7128862 0.07734176 0.5421091 0.8388979
## 24 0.7128862 0.07734176 0.5421091 0.8388979
## 25 0.7128862 0.07734176 0.5421091 0.8388979
## 26 0.7128862 0.07734176 0.5421091 0.8388979
## 27 0.7128862 0.07734176 0.5421091 0.8388979
## 28 0.7128862 0.07734176 0.5421091 0.8388979
## 29 0.7128862 0.07734176 0.5421091 0.8388979
## 30 0.7128862 0.07734176 0.5421091 0.8388979
## 31 0.7128862 0.07734176 0.5421091 0.8388979
## 32 0.7128862 0.07734176 0.5421091 0.8388979
## 33 0.7128862 0.07734176 0.5421091 0.8388979
## 34 0.7128862 0.07734176 0.5421091 0.8388979
## 35 0.7128862 0.07734176 0.5421091 0.8388979
## 36 0.7128862 0.07734176 0.5421091 0.8388979
## 37 0.7128862 0.07734176 0.5421091 0.8388979
## 38 0.7128862 0.07734176 0.5421091 0.8388979
## 39 0.7128862 0.07734176 0.5421091 0.8388979
## 40 0.7128862 0.07734176 0.5421091 0.8388979
## 41 0.7128862 0.07734176 0.5421091 0.8388979
## 42 0.7128862 0.07734176 0.5421091 0.8388979
## 43 0.7128862 0.07734176 0.5421091 0.8388979
## 44 0.7128862 0.07734176 0.5421091 0.8388979
## 45 0.7128862 0.07734176 0.5421091 0.8388979
## 46 0.7128862 0.07734176 0.5421091 0.8388979
## 47 0.7128862 0.07734176 0.5421091 0.8388979
## 48 0.7128862 0.07734176 0.5421091 0.8388979
## 49 0.7128862 0.07734176 0.5421091 0.8388979
## 50 0.7128862 0.07734176 0.5421091 0.8388979
## 51 0.7128862 0.07734176 0.5421091 0.8388979
## 52 0.7128862 0.07734176 0.5421091 0.8388979
## 53 0.7128862 0.07734176 0.5421091 0.8388979
## 54 0.7128862 0.07734176 0.5421091 0.8388979
## 55 0.7128862 0.07734176 0.5421091 0.8388979
## 56 0.7128862 0.07734176 0.5421091 0.8388979
## 57 0.7128862 0.07734176 0.5421091 0.8388979
## 58 0.7128862 0.07734176 0.5421091 0.8388979
## 59 0.7128862 0.07734176 0.5421091 0.8388979
## 60 0.7128862 0.07734176 0.5421091 0.8388979
## 61 0.7128862 0.07734176 0.5421091 0.8388979
## 62 0.7128862 0.07734176 0.5421091 0.8388979
## 63 0.7128862 0.07734176 0.5421091 0.8388979
## 64 0.7128862 0.07734176 0.5421091 0.8388979
## 65 0.7128862 0.07734176 0.5421091 0.8388979
## 66 0.7128862 0.07734176 0.5421091 0.8388979
## 67 0.7128862 0.07734176 0.5421091 0.8388979
## 68 0.7128862 0.07734176 0.5421091 0.8388979
## 69 0.7128862 0.07734176 0.5421091 0.8388979
## 70 0.7128862 0.07734176 0.5421091 0.8388979
## 71 0.7128862 0.07734176 0.5421091 0.8388979
## 72 0.7128862 0.07734176 0.5421091 0.8388979
## 73 0.7128862 0.07734176 0.5421091 0.8388979
## 74 0.7128862 0.07734176 0.5421091 0.8388979
## 75 0.7128862 0.07734176 0.5421091 0.8388979
## 76 0.7128862 0.07734176 0.5421091 0.8388979
## 77 0.7128862 0.07734176 0.5421091 0.8388979
## 78 0.7128862 0.07734176 0.5421091 0.8388979
## 79 0.7128862 0.07734176 0.5421091 0.8388979
## 80 0.7128862 0.07734176 0.5421091 0.8388979
## 81 0.7128862 0.07734176 0.5421091 0.8388979
## 82 0.7128862 0.07734176 0.5421091 0.8388979
## 83 0.7128862 0.07734176 0.5421091 0.8388979
## 84 0.7128862 0.07734176 0.5421091 0.8388979
## 85 0.7128862 0.07734176 0.5421091 0.8388979
## 86 0.7128862 0.07734176 0.5421091 0.8388979
## 87 0.7128862 0.07734176 0.5421091 0.8388979
## 88 0.7128862 0.07734176 0.5421091 0.8388979
## 89 0.7128862 0.07734176 0.5421091 0.8388979
## 90 0.7128862 0.07734176 0.5421091 0.8388979
## 91 0.7128862 0.07734176 0.5421091 0.8388979
## 92 0.7128862 0.07734176 0.5421091 0.8388979
## 93 0.7128862 0.07734176 0.5421091 0.8388979
## 94 0.7128862 0.07734176 0.5421091 0.8388979
## 95 0.7128862 0.07734176 0.5421091 0.8388979
## 96 0.7128862 0.07734176 0.5421091 0.8388979
## 97 0.7128862 0.07734176 0.5421091 0.8388979
## 98 0.7128862 0.07734176 0.5421091 0.8388979
## 99 0.7128862 0.07734176 0.5421091 0.8388979
## 100 0.7128862 0.07734176 0.5421091 0.8388979
## 101 0.7128862 0.07734176 0.5421091 0.8388979
## 102 0.7128862 0.07734176 0.5421091 0.8388979
## 103 0.7128862 0.07734176 0.5421091 0.8388979
## 104 0.7128862 0.07734176 0.5421091 0.8388979
## 105 0.7128862 0.07734176 0.5421091 0.8388979
## 106 0.7128862 0.07734176 0.5421091 0.8388979
## 107 0.7128862 0.07734176 0.5421091 0.8388979
## 108 0.7128862 0.07734176 0.5421091 0.8388979
## 109 0.7128862 0.07734176 0.5421091 0.8388979
## 110 0.7128862 0.07734176 0.5421091 0.8388979
## 111 0.7128862 0.07734176 0.5421091 0.8388979
## 112 0.7128862 0.07734176 0.5421091 0.8388979
## 113 0.7128862 0.07734176 0.5421091 0.8388979
## 114 0.7128862 0.07734176 0.5421091 0.8388979
## 115 0.7128862 0.07734176 0.5421091 0.8388979
## 116 0.7128862 0.07734176 0.5421091 0.8388979
## 117 0.7128862 0.07734176 0.5421091 0.8388979
## 118 0.7128862 0.07734176 0.5421091 0.8388979
## 119 0.7128862 0.07734176 0.5421091 0.8388979
## 120 0.7128862 0.07734176 0.5421091 0.8388979
## 121 0.7128862 0.07734176 0.5421091 0.8388979
## 122 0.7128862 0.07734176 0.5421091 0.8388979
## 123 0.7128862 0.07734176 0.5421091 0.8388979
## 124 0.7128862 0.07734176 0.5421091 0.8388979
## 125 0.7128862 0.07734176 0.5421091 0.8388979Calcul prob de dét variable
predict(m1,type="det") #donne la prob de détection pour chaque visite## Predicted SE lower upper
## 1 0.5705954 0.1871380 0.2292244 0.8558521
## 2 0.8558927 0.1332306 0.4168933 0.9801347
## 3 0.7132441 0.1711253 0.3254830 0.9276455
## 4 0.7132441 0.1711253 0.3254830 0.9276455
## 5 0.7132441 0.1711253 0.3254830 0.9276455
## 6 0.5705954 0.1871380 0.2292244 0.8558521
## 7 0.8558927 0.1332306 0.4168933 0.9801347
## 8 0.7132441 0.1711253 0.3254830 0.9276455
## 9 0.7132441 0.1711253 0.3254830 0.9276455
## 10 0.7132441 0.1711253 0.3254830 0.9276455
## 11 0.5705954 0.1871380 0.2292244 0.8558521
## 12 0.8558927 0.1332306 0.4168933 0.9801347
## 13 0.7132441 0.1711253 0.3254830 0.9276455
## 14 0.7132441 0.1711253 0.3254830 0.9276455
## 15 0.7132441 0.1711253 0.3254830 0.9276455
## 16 0.5705954 0.1871380 0.2292244 0.8558521
## 17 0.8558927 0.1332306 0.4168933 0.9801347
## 18 0.7132441 0.1711253 0.3254830 0.9276455
## 19 0.7132441 0.1711253 0.3254830 0.9276455
## 20 0.7132441 0.1711253 0.3254830 0.9276455
## 21 0.5705954 0.1871380 0.2292244 0.8558521
## 22 0.8558927 0.1332306 0.4168933 0.9801347
## 23 0.7132441 0.1711253 0.3254830 0.9276455
## 24 0.7132441 0.1711253 0.3254830 0.9276455
## 25 0.7132441 0.1711253 0.3254830 0.9276455
## 26 0.5705954 0.1871380 0.2292244 0.8558521
## 27 0.8558927 0.1332306 0.4168933 0.9801347
## 28 0.7132441 0.1711253 0.3254830 0.9276455
## 29 0.7132441 0.1711253 0.3254830 0.9276455
## 30 0.7132441 0.1711253 0.3254830 0.9276455
## 31 0.5705954 0.1871380 0.2292244 0.8558521
## 32 0.8558927 0.1332306 0.4168933 0.9801347
## 33 0.7132441 0.1711253 0.3254830 0.9276455
## 34 0.7132441 0.1711253 0.3254830 0.9276455
## 35 0.7132441 0.1711253 0.3254830 0.9276455
## 36 0.5705954 0.1871380 0.2292244 0.8558521
## 37 0.8558927 0.1332306 0.4168933 0.9801347
## 38 0.7132441 0.1711253 0.3254830 0.9276455
## 39 0.7132441 0.1711253 0.3254830 0.9276455
## 40 0.7132441 0.1711253 0.3254830 0.9276455
## 41 0.5705954 0.1871380 0.2292244 0.8558521
## 42 0.8558927 0.1332306 0.4168933 0.9801347
## 43 0.7132441 0.1711253 0.3254830 0.9276455
## 44 0.7132441 0.1711253 0.3254830 0.9276455
## 45 0.7132441 0.1711253 0.3254830 0.9276455
## 46 0.5705954 0.1871380 0.2292244 0.8558521
## 47 0.8558927 0.1332306 0.4168933 0.9801347
## 48 0.7132441 0.1711253 0.3254830 0.9276455
## 49 0.7132441 0.1711253 0.3254830 0.9276455
## 50 0.7132441 0.1711253 0.3254830 0.9276455
## 51 0.5705954 0.1871380 0.2292244 0.8558521
## 52 0.8558927 0.1332306 0.4168933 0.9801347
## 53 0.7132441 0.1711253 0.3254830 0.9276455
## 54 0.7132441 0.1711253 0.3254830 0.9276455
## 55 0.7132441 0.1711253 0.3254830 0.9276455
## 56 0.5705954 0.1871380 0.2292244 0.8558521
## 57 0.8558927 0.1332306 0.4168933 0.9801347
## 58 0.7132441 0.1711253 0.3254830 0.9276455
## 59 0.7132441 0.1711253 0.3254830 0.9276455
## 60 0.7132441 0.1711253 0.3254830 0.9276455
## 61 0.5705954 0.1871380 0.2292244 0.8558521
## 62 0.8558927 0.1332306 0.4168933 0.9801347
## 63 0.7132441 0.1711253 0.3254830 0.9276455
## 64 0.7132441 0.1711253 0.3254830 0.9276455
## 65 0.7132441 0.1711253 0.3254830 0.9276455
## 66 0.5705954 0.1871380 0.2292244 0.8558521
## 67 0.8558927 0.1332306 0.4168933 0.9801347
## 68 0.7132441 0.1711253 0.3254830 0.9276455
## 69 0.7132441 0.1711253 0.3254830 0.9276455
## 70 0.7132441 0.1711253 0.3254830 0.9276455
## 71 0.5705954 0.1871380 0.2292244 0.8558521
## 72 0.8558927 0.1332306 0.4168933 0.9801347
## 73 0.7132441 0.1711253 0.3254830 0.9276455
## 74 0.7132441 0.1711253 0.3254830 0.9276455
## 75 0.7132441 0.1711253 0.3254830 0.9276455
## 76 0.5705954 0.1871380 0.2292244 0.8558521
## 77 0.8558927 0.1332306 0.4168933 0.9801347
## 78 0.7132441 0.1711253 0.3254830 0.9276455
## 79 0.7132441 0.1711253 0.3254830 0.9276455
## 80 0.7132441 0.1711253 0.3254830 0.9276455
## 81 0.5705954 0.1871380 0.2292244 0.8558521
## 82 0.8558927 0.1332306 0.4168933 0.9801347
## 83 0.7132441 0.1711253 0.3254830 0.9276455
## 84 0.7132441 0.1711253 0.3254830 0.9276455
## 85 0.7132441 0.1711253 0.3254830 0.9276455
## 86 0.5705954 0.1871380 0.2292244 0.8558521
## 87 0.8558927 0.1332306 0.4168933 0.9801347
## 88 0.7132441 0.1711253 0.3254830 0.9276455
## 89 0.7132441 0.1711253 0.3254830 0.9276455
## 90 0.7132441 0.1711253 0.3254830 0.9276455
## 91 0.5705954 0.1871380 0.2292244 0.8558521
## 92 0.8558927 0.1332306 0.4168933 0.9801347
## 93 0.7132441 0.1711253 0.3254830 0.9276455
## 94 0.7132441 0.1711253 0.3254830 0.9276455
## 95 0.7132441 0.1711253 0.3254830 0.9276455
## 96 0.5705954 0.1871380 0.2292244 0.8558521
## 97 0.8558927 0.1332306 0.4168933 0.9801347
## 98 0.7132441 0.1711253 0.3254830 0.9276455
## 99 0.7132441 0.1711253 0.3254830 0.9276455
## 100 0.7132441 0.1711253 0.3254830 0.9276455
## 101 0.5705954 0.1871380 0.2292244 0.8558521
## 102 0.8558927 0.1332306 0.4168933 0.9801347
## 103 0.7132441 0.1711253 0.3254830 0.9276455
## 104 0.7132441 0.1711253 0.3254830 0.9276455
## 105 0.7132441 0.1711253 0.3254830 0.9276455
## 106 0.5705954 0.1871380 0.2292244 0.8558521
## 107 0.8558927 0.1332306 0.4168933 0.9801347
## 108 0.7132441 0.1711253 0.3254830 0.9276455
## 109 0.7132441 0.1711253 0.3254830 0.9276455
## 110 0.7132441 0.1711253 0.3254830 0.9276455
## 111 0.5705954 0.1871380 0.2292244 0.8558521
## 112 0.8558927 0.1332306 0.4168933 0.9801347
## 113 0.7132441 0.1711253 0.3254830 0.9276455
## 114 0.7132441 0.1711253 0.3254830 0.9276455
## 115 0.7132441 0.1711253 0.3254830 0.9276455
## 116 0.5705954 0.1871380 0.2292244 0.8558521
## 117 0.8558927 0.1332306 0.4168933 0.9801347
## 118 0.7132441 0.1711253 0.3254830 0.9276455
## 119 0.7132441 0.1711253 0.3254830 0.9276455
## 120 0.7132441 0.1711253 0.3254830 0.9276455
## 121 0.5705954 0.1871380 0.2292244 0.8558521
## 122 0.8558927 0.1332306 0.4168933 0.9801347
## 123 0.7132441 0.1711253 0.3254830 0.9276455
## 124 0.7132441 0.1711253 0.3254830 0.9276455
## 125 0.7132441 0.1711253 0.3254830 0.9276455Test Ajustement des modèles
obs.boot.m0 <- AICcmodavg::mb.gof.test(m0, nsim = 100, lot.hist=F)
obs.boot.m0##
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
##
## Pearson chi-square table:
##
## Cohort Observed Expected Chi-square
## 00000 0 18 18.00 0.00
## 00001 0 1 0.03 27.47
## 01011 0 1 0.21 2.98
## 01111 0 1 0.52 0.44
## 11100 0 1 0.21 2.98
## 11110 0 1 0.52 0.44
## 11111 0 2 1.29 0.39
##
## Chi-square statistic = 38.9229
## Number of bootstrap samples = 100
## P-value = 0.11
##
## Quantiles of bootstrapped statistics:
## 0% 25% 50% 75% 100%
## 11 22 26 32 52
##
## Estimate of c-hat = 1.44Comme la p-value > 0,05 alors on peut conclure (au risque α = 0.05) qu’il n’y a pas un manque d’ajustement. Le fait que 𝑐̂= 1.46 (cette valeur n’est pas la même pour tous évidemment puisqu’elle est obtenue par simulation numérique) ; comme elle est supérieure à 1 (𝑐̂> 1) -> cela indique aussi qu’il y a surdispersion.
obs.boot.m1 <- AICcmodavg::mb.gof.test(m1, nsim = 100, lot.hist=F)
obs.boot.m1##
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
##
## Pearson chi-square table:
##
## Cohort Observed Expected Chi-square
## 00000 0 18 18.00 0.00
## 00001 0 1 0.03 37.33
## 01011 0 1 0.38 1.04
## 01111 0 1 0.93 0.00
## 11100 0 1 0.20 3.18
## 11110 0 1 0.50 0.50
## 11111 0 2 1.24 0.46
##
## Chi-square statistic = 46.2384
## Number of bootstrap samples = 100
## P-value = 0.08
##
## Quantiles of bootstrapped statistics:
## 0% 25% 50% 75% 100%
## 8.3e-04 1.0e+01 1.7e+01 2.7e+01 2.0e+02
##
## Estimate of c-hat = 2.13obs.boot.m6 <- AICcmodavg::mb.gof.test(m6, nsim = 100, lot.hist=F)
obs.boot.m6##
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
##
## Pearson chi-square table:
##
## Cohort Observed Expected Chi-square
## 00000 0 18 18.00 0.00
## 00001 0 1 0.03 27.55
## 01011 0 1 0.21 2.99
## 01111 0 1 0.52 0.44
## 11100 0 1 0.21 2.99
## 11110 0 1 0.52 0.44
## 11111 0 2 1.29 0.39
##
## Chi-square statistic = 39.0111
## Number of bootstrap samples = 100
## P-value = 0.07
##
## Quantiles of bootstrapped statistics:
## 0% 25% 50% 75% 100%
## 12 21 26 30 70
##
## Estimate of c-hat = 1.45Calcul des probabilités d’occupation et de détection en réserve et hors réserve :
data## # A tibble: 25 × 25
## Placettes P1 P2 P3 P4 P5 Terriers Patch_vegetation
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1 1 1 1 1 1
## 2 2 1 1 1 1 1 0 0
## 3 3 0 0 0 0 0 0 0
## 4 4 0 1 1 1 1 0 1
## 5 5 0 0 0 0 1 1 1
## 6 6 1 1 1 0 0 1 1
## 7 7 0 0 0 0 0 0 1
## 8 8 0 0 0 0 0 0 1
## 9 9 1 1 1 1 0 0 1
## 10 10 0 0 0 0 0 0 1
## # ℹ 15 more rows
## # ℹ 17 more variables: Souches_bois_mort <dbl>, Gites_anthropiques <dbl>,
## # temp_P1 <dbl>, temp_P2 <dbl>, temp_P3 <dbl>, temp_P4 <dbl>, temp_P5 <dbl>,
## # vent_P1 <chr>, vent_P2 <chr>, vent_P3 <chr>, vent_P4 <chr>, vent_P5 <chr>,
## # neb_P1 <dbl>, neb_P2 <dbl>, neb_P3 <dbl>, neb_P4 <dbl>, neb_P5 <dbl>umf## Data frame representation of unmarkedFrame object.
## y.1 y.2 y.3 y.4 y.5 Terriers Souches_bois_mort Patch_vegetation
## 1 1 1 1 1 1 1 0 1
## 2 1 1 1 1 1 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 1 1 1 1 0 0 1
## 5 0 0 0 0 1 1 0 1
## 6 1 1 1 0 0 1 0 1
## 7 0 0 0 0 0 0 0 1
## 8 0 0 0 0 0 0 1 1
## 9 1 1 1 1 0 0 0 1
## 10 0 0 0 0 0 0 0 1
## 11 0 1 0 1 1 1 0 1
## 12 0 0 0 0 0 1 0 0
## 13 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 1
## 15 0 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 1
## 17 0 0 0 0 0 1 1 1
## 18 0 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 1
## 20 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 1 1
## 22 0 0 0 0 0 0 0 1
## 23 0 0 0 0 0 0 1 1
## 24 0 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 1 1
## Gites_anthropiques temperature.1 temperature.2 temperature.3 temperature.4
## 1 0 164 195 206 247
## 2 1 165 22 194 233
## 3 0 159 217 196 246
## 4 1 166 232 218 248
## 5 0 172 257 201 23
## 6 1 171 235 216 23
## 7 0 195 256 214 22
## 8 1 179 216 209 217
## 9 1 264 213 21 211
## 10 0 265 225 216 213
## 11 0 272 192 233 217
## 12 0 261 211 24 221
## 13 0 255 212 246 222
## 14 0 259 205 246 234
## 15 0 242 219 255 233
## 16 0 249 186 274 24
## 17 0 236 192 226 24
## 18 0 235 205 226 23
## 19 0 227 213 23 22
## 20 0 222 202 237 22
## 21 0 218 201 248 22
## 22 0 213 191 243 21
## 23 0 202 188 24 20
## 24 0 203 188 231 19
## 25 0 16 208 231 238
## temperature.5 vent.1 vent.2 vent.3 vent.4 vent.5 nebulosite.1 nebulosite.2
## 1 201 moyen nul nul nul faible 1 1
## 2 22 moyen faible nul nul faible 1 1
## 3 207 fort faible faible nul faible 1 1
## 4 206 moyen faible faible nul faible 1 1
## 5 22 moyen faible faible nul faible 1 1
## 6 NA moyen faible faible nul 1 1
## 7 217 moyen faible faible nul nul 1 1
## 8 243 moyen faible moyen nul faible 1 1
## 9 265 faible faible nul faible moyen 1 2
## 10 245 faible faible faible faible moyen 1 2
## 11 256 moyen fort nul faible moyen 1 2
## 12 232 moyen moyen faible faible moyen 1 2
## 13 234 moyen fort faible faible moyen 1 2
## 14 228 fort moyen faible faible moyen 1 4
## 15 215 faible moyen moyen faible moyen 1 3
## 16 237 fort moyen faible moyen moyen 1 5
## 17 229 moyen nul faible faible faible 1 5
## 18 223 moyen faible faible moyen moyen 1 5
## 19 241 fort moyen faible moyen faible 1 5
## 20 224 moyen faible faible faible faible 1 4
## 21 219 faible faible faible nul faible 1 5
## 22 196 moyen faible faible faible faible 1 5
## 23 192 faible faible moyen nul nul 1 5
## 24 191 fort faible moyen faible faible 1 5
## 25 238 faible faible faible faible moyen 1 1
## nebulosite.3 nebulosite.4 nebulosite.5 visit.1 visit.2 visit.3 visit.4
## 1 1 2 3 V1 V2 V3 V4
## 2 1 5 2 V1 V2 V3 V4
## 3 1 5 1 V1 V2 V3 V4
## 4 1 5 1 V1 V2 V3 V4
## 5 1 3 1 V1 V2 V3 V4
## 6 2 2 1 V1 V2 V3 V4
## 7 2 2 1 V1 V2 V3 V4
## 8 1 1 1 V1 V2 V3 V4
## 9 1 3 1 V1 V2 V3 V4
## 10 1 4 1 V1 V2 V3 V4
## 11 1 4 1 V1 V2 V3 V4
## 12 1 3 1 V1 V2 V3 V4
## 13 1 3 1 V1 V2 V3 V4
## 14 1 4 1 V1 V2 V3 V4
## 15 1 3 1 V1 V2 V3 V4
## 16 1 1 1 V1 V2 V3 V4
## 17 1 1 1 V1 V2 V3 V4
## 18 1 1 1 V1 V2 V3 V4
## 19 1 1 1 V1 V2 V3 V4
## 20 1 1 1 V1 V2 V3 V4
## 21 1 1 4 V1 V2 V3 V4
## 22 1 1 5 V1 V2 V3 V4
## 23 1 1 5 V1 V2 V3 V4
## 24 1 1 5 V1 V2 V3 V4
## 25 1 1 1 V1 V2 V3 V4
## visit.5
## 1 V5
## 2 V5
## 3 V5
## 4 V5
## 5 V5
## 6 V5
## 7 V5
## 8 V5
## 9 V5
## 10 V5
## 11 V5
## 12 V5
## 13 V5
## 14 V5
## 15 V5
## 16 V5
## 17 V5
## 18 V5
## 19 V5
## 20 V5
## 21 V5
## 22 V5
## 23 V5
## 24 V5
## 25 V5umf_hors <- umf[1:8, ]
umf_hors## Data frame representation of unmarkedFrame object.
## y.1 y.2 y.3 y.4 y.5 Terriers Souches_bois_mort Patch_vegetation
## 1 1 1 1 1 1 1 0 1
## 2 1 1 1 1 1 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 1 1 1 1 0 0 1
## 5 0 0 0 0 1 1 0 1
## 6 1 1 1 0 0 1 0 1
## 7 0 0 0 0 0 0 0 1
## 8 0 0 0 0 0 0 1 1
## Gites_anthropiques temperature.1 temperature.2 temperature.3 temperature.4
## 1 0 164 195 206 247
## 2 1 165 22 194 233
## 3 0 159 217 196 246
## 4 1 166 232 218 248
## 5 0 172 257 201 23
## 6 1 171 235 216 23
## 7 0 195 256 214 22
## 8 1 179 216 209 217
## temperature.5 vent.1 vent.2 vent.3 vent.4 vent.5 nebulosite.1 nebulosite.2
## 1 201 moyen nul nul nul faible 1 1
## 2 22 moyen faible nul nul faible 1 1
## 3 207 fort faible faible nul faible 1 1
## 4 206 moyen faible faible nul faible 1 1
## 5 22 moyen faible faible nul faible 1 1
## 6 NA moyen faible faible nul 1 1
## 7 217 moyen faible faible nul nul 1 1
## 8 243 moyen faible moyen nul faible 1 1
## nebulosite.3 nebulosite.4 nebulosite.5 visit.1 visit.2 visit.3 visit.4
## 1 1 2 3 V1 V2 V3 V4
## 2 1 5 2 V1 V2 V3 V4
## 3 1 5 1 V1 V2 V3 V4
## 4 1 5 1 V1 V2 V3 V4
## 5 1 3 1 V1 V2 V3 V4
## 6 2 2 1 V1 V2 V3 V4
## 7 2 2 1 V1 V2 V3 V4
## 8 1 1 1 V1 V2 V3 V4
## visit.5
## 1 V5
## 2 V5
## 3 V5
## 4 V5
## 5 V5
## 6 V5
## 7 V5
## 8 V5umf_reserve <- umf[9:25, ]#detection constante
# Hors réserve
m0_hors <- occu(~1 ~1, data = umf_hors)
# Réserve
m0_reserve <- occu(~1 ~1, data = umf_reserve)
m0_hors##
## Call:
## occu(formula = ~1 ~ 1, data = umf_hors)
##
## Occupancy (logit-scale):
## Estimate SE z P(>|z|)
## 0.516 0.732 0.704 0.482
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## 0.938 0.45 2.09 0.0369
##
## AIC: 44.21526
## Number of sites: 8m0_reserve##
## Call:
## occu(formula = ~1 ~ 1, data = umf_reserve)
##
## Occupancy (logit-scale):
## Estimate SE z P(>|z|)
## -2.01 0.753 -2.67 0.00754
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## 0.839 0.698 1.2 0.23
##
## AIC: 28.52257
## Number of sites: 17#Test Ajustement
obs.boot.m0_reserve <- AICcmodavg::mb.gof.test(m0_reserve, nsim = 100, lot.hist=F)
obs.boot.m0_reserve##
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
##
## Pearson chi-square table:
##
## Cohort Observed Expected Chi-square
## 00000 0 15 15.00 0.00
## 01011 0 1 0.06 14.15
## 11110 0 1 0.14 5.10
##
## Chi-square statistic = 21.0479
## Number of bootstrap samples = 100
## P-value = 0.44
##
## Quantiles of bootstrapped statistics:
## 0% 25% 50% 75% 100%
## 0.0 6.5 18.7 26.7 103.5
##
## Estimate of c-hat = 1.21obs.boot.m0_hors <- AICcmodavg::mb.gof.test(m0_hors, nsim = 100, lot.hist=F)
obs.boot.m0_hors##
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
##
## Pearson chi-square table:
##
## Cohort Observed Expected Chi-square
## 00000 0 3 3.00 0.00
## 00001 0 1 0.02 42.41
## 01111 0 1 0.38 1.04
## 11100 0 1 0.15 4.94
## 11111 0 2 0.96 1.12
##
## Chi-square statistic = 53.0043
## Number of bootstrap samples = 100
## P-value = 0.04
##
## Quantiles of bootstrapped statistics:
## 0% 25% 50% 75% 100%
## 0 19 25 30 95
##
## Estimate of c-hat = 1.98Calcul de la probabilité d’occupation et de détection en réserve et hors réserve
predict(m0_hors, type = "state")## Predicted SE lower upper
## 1 0.6261009 0.1714745 0.2849323 0.8755749
## 2 0.6261009 0.1714745 0.2849323 0.8755749
## 3 0.6261009 0.1714745 0.2849323 0.8755749
## 4 0.6261009 0.1714745 0.2849323 0.8755749
## 5 0.6261009 0.1714745 0.2849323 0.8755749
## 6 0.6261009 0.1714745 0.2849323 0.8755749
## 7 0.6261009 0.1714745 0.2849323 0.8755749
## 8 0.6261009 0.1714745 0.2849323 0.8755749predict(m0_reserve, type = "state")## Predicted SE lower upper
## 1 0.1179075 0.07833195 0.02963977 0.369062
## 2 0.1179075 0.07833195 0.02963977 0.369062
## 3 0.1179075 0.07833195 0.02963977 0.369062
## 4 0.1179075 0.07833195 0.02963977 0.369062
## 5 0.1179075 0.07833195 0.02963977 0.369062
## 6 0.1179075 0.07833195 0.02963977 0.369062
## 7 0.1179075 0.07833195 0.02963977 0.369062
## 8 0.1179075 0.07833195 0.02963977 0.369062
## 9 0.1179075 0.07833195 0.02963977 0.369062
## 10 0.1179075 0.07833195 0.02963977 0.369062
## 11 0.1179075 0.07833195 0.02963977 0.369062
## 12 0.1179075 0.07833195 0.02963977 0.369062
## 13 0.1179075 0.07833195 0.02963977 0.369062
## 14 0.1179075 0.07833195 0.02963977 0.369062
## 15 0.1179075 0.07833195 0.02963977 0.369062
## 16 0.1179075 0.07833195 0.02963977 0.369062
## 17 0.1179075 0.07833195 0.02963977 0.369062predict(m0_hors, type = "det")## Predicted SE lower upper
## 1 0.7187326 0.09087392 0.5142795 0.8604742
## 2 0.7187326 0.09087392 0.5142795 0.8604742
## 3 0.7187326 0.09087392 0.5142795 0.8604742
## 4 0.7187326 0.09087392 0.5142795 0.8604742
## 5 0.7187326 0.09087392 0.5142795 0.8604742
## 6 0.7187326 0.09087392 0.5142795 0.8604742
## 7 0.7187326 0.09087392 0.5142795 0.8604742
## 8 0.7187326 0.09087392 0.5142795 0.8604742
## 9 0.7187326 0.09087392 0.5142795 0.8604742
## 10 0.7187326 0.09087392 0.5142795 0.8604742
## 11 0.7187326 0.09087392 0.5142795 0.8604742
## 12 0.7187326 0.09087392 0.5142795 0.8604742
## 13 0.7187326 0.09087392 0.5142795 0.8604742
## 14 0.7187326 0.09087392 0.5142795 0.8604742
## 15 0.7187326 0.09087392 0.5142795 0.8604742
## 16 0.7187326 0.09087392 0.5142795 0.8604742
## 17 0.7187326 0.09087392 0.5142795 0.8604742
## 18 0.7187326 0.09087392 0.5142795 0.8604742
## 19 0.7187326 0.09087392 0.5142795 0.8604742
## 20 0.7187326 0.09087392 0.5142795 0.8604742
## 21 0.7187326 0.09087392 0.5142795 0.8604742
## 22 0.7187326 0.09087392 0.5142795 0.8604742
## 23 0.7187326 0.09087392 0.5142795 0.8604742
## 24 0.7187326 0.09087392 0.5142795 0.8604742
## 25 0.7187326 0.09087392 0.5142795 0.8604742
## 26 0.7187326 0.09087392 0.5142795 0.8604742
## 27 0.7187326 0.09087392 0.5142795 0.8604742
## 28 0.7187326 0.09087392 0.5142795 0.8604742
## 29 0.7187326 0.09087392 0.5142795 0.8604742
## 30 0.7187326 0.09087392 0.5142795 0.8604742
## 31 0.7187326 0.09087392 0.5142795 0.8604742
## 32 0.7187326 0.09087392 0.5142795 0.8604742
## 33 0.7187326 0.09087392 0.5142795 0.8604742
## 34 0.7187326 0.09087392 0.5142795 0.8604742
## 35 0.7187326 0.09087392 0.5142795 0.8604742
## 36 0.7187326 0.09087392 0.5142795 0.8604742
## 37 0.7187326 0.09087392 0.5142795 0.8604742
## 38 0.7187326 0.09087392 0.5142795 0.8604742
## 39 0.7187326 0.09087392 0.5142795 0.8604742
## 40 0.7187326 0.09087392 0.5142795 0.8604742predict(m0_reserve, type = "det")## Predicted SE lower upper
## 1 0.6981744 0.1471342 0.3705471 0.9008861
## 2 0.6981744 0.1471342 0.3705471 0.9008861
## 3 0.6981744 0.1471342 0.3705471 0.9008861
## 4 0.6981744 0.1471342 0.3705471 0.9008861
## 5 0.6981744 0.1471342 0.3705471 0.9008861
## 6 0.6981744 0.1471342 0.3705471 0.9008861
## 7 0.6981744 0.1471342 0.3705471 0.9008861
## 8 0.6981744 0.1471342 0.3705471 0.9008861
## 9 0.6981744 0.1471342 0.3705471 0.9008861
## 10 0.6981744 0.1471342 0.3705471 0.9008861
## 11 0.6981744 0.1471342 0.3705471 0.9008861
## 12 0.6981744 0.1471342 0.3705471 0.9008861
## 13 0.6981744 0.1471342 0.3705471 0.9008861
## 14 0.6981744 0.1471342 0.3705471 0.9008861
## 15 0.6981744 0.1471342 0.3705471 0.9008861
## 16 0.6981744 0.1471342 0.3705471 0.9008861
## 17 0.6981744 0.1471342 0.3705471 0.9008861
## 18 0.6981744 0.1471342 0.3705471 0.9008861
## 19 0.6981744 0.1471342 0.3705471 0.9008861
## 20 0.6981744 0.1471342 0.3705471 0.9008861
## 21 0.6981744 0.1471342 0.3705471 0.9008861
## 22 0.6981744 0.1471342 0.3705471 0.9008861
## 23 0.6981744 0.1471342 0.3705471 0.9008861
## 24 0.6981744 0.1471342 0.3705471 0.9008861
## 25 0.6981744 0.1471342 0.3705471 0.9008861
## 26 0.6981744 0.1471342 0.3705471 0.9008861
## 27 0.6981744 0.1471342 0.3705471 0.9008861
## 28 0.6981744 0.1471342 0.3705471 0.9008861
## 29 0.6981744 0.1471342 0.3705471 0.9008861
## 30 0.6981744 0.1471342 0.3705471 0.9008861
## 31 0.6981744 0.1471342 0.3705471 0.9008861
## 32 0.6981744 0.1471342 0.3705471 0.9008861
## 33 0.6981744 0.1471342 0.3705471 0.9008861
## 34 0.6981744 0.1471342 0.3705471 0.9008861
## 35 0.6981744 0.1471342 0.3705471 0.9008861
## 36 0.6981744 0.1471342 0.3705471 0.9008861
## 37 0.6981744 0.1471342 0.3705471 0.9008861
## 38 0.6981744 0.1471342 0.3705471 0.9008861
## 39 0.6981744 0.1471342 0.3705471 0.9008861
## 40 0.6981744 0.1471342 0.3705471 0.9008861
## 41 0.6981744 0.1471342 0.3705471 0.9008861
## 42 0.6981744 0.1471342 0.3705471 0.9008861
## 43 0.6981744 0.1471342 0.3705471 0.9008861
## 44 0.6981744 0.1471342 0.3705471 0.9008861
## 45 0.6981744 0.1471342 0.3705471 0.9008861
## 46 0.6981744 0.1471342 0.3705471 0.9008861
## 47 0.6981744 0.1471342 0.3705471 0.9008861
## 48 0.6981744 0.1471342 0.3705471 0.9008861
## 49 0.6981744 0.1471342 0.3705471 0.9008861
## 50 0.6981744 0.1471342 0.3705471 0.9008861
## 51 0.6981744 0.1471342 0.3705471 0.9008861
## 52 0.6981744 0.1471342 0.3705471 0.9008861
## 53 0.6981744 0.1471342 0.3705471 0.9008861
## 54 0.6981744 0.1471342 0.3705471 0.9008861
## 55 0.6981744 0.1471342 0.3705471 0.9008861
## 56 0.6981744 0.1471342 0.3705471 0.9008861
## 57 0.6981744 0.1471342 0.3705471 0.9008861
## 58 0.6981744 0.1471342 0.3705471 0.9008861
## 59 0.6981744 0.1471342 0.3705471 0.9008861
## 60 0.6981744 0.1471342 0.3705471 0.9008861
## 61 0.6981744 0.1471342 0.3705471 0.9008861
## 62 0.6981744 0.1471342 0.3705471 0.9008861
## 63 0.6981744 0.1471342 0.3705471 0.9008861
## 64 0.6981744 0.1471342 0.3705471 0.9008861
## 65 0.6981744 0.1471342 0.3705471 0.9008861
## 66 0.6981744 0.1471342 0.3705471 0.9008861
## 67 0.6981744 0.1471342 0.3705471 0.9008861
## 68 0.6981744 0.1471342 0.3705471 0.9008861
## 69 0.6981744 0.1471342 0.3705471 0.9008861
## 70 0.6981744 0.1471342 0.3705471 0.9008861
## 71 0.6981744 0.1471342 0.3705471 0.9008861
## 72 0.6981744 0.1471342 0.3705471 0.9008861
## 73 0.6981744 0.1471342 0.3705471 0.9008861
## 74 0.6981744 0.1471342 0.3705471 0.9008861
## 75 0.6981744 0.1471342 0.3705471 0.9008861
## 76 0.6981744 0.1471342 0.3705471 0.9008861
## 77 0.6981744 0.1471342 0.3705471 0.9008861
## 78 0.6981744 0.1471342 0.3705471 0.9008861
## 79 0.6981744 0.1471342 0.3705471 0.9008861
## 80 0.6981744 0.1471342 0.3705471 0.9008861
## 81 0.6981744 0.1471342 0.3705471 0.9008861
## 82 0.6981744 0.1471342 0.3705471 0.9008861
## 83 0.6981744 0.1471342 0.3705471 0.9008861
## 84 0.6981744 0.1471342 0.3705471 0.9008861
## 85 0.6981744 0.1471342 0.3705471 0.9008861#Modèle détection variable entre visites
m1_hors <- occu(~ visit ~ 1, data = umf_hors)
m1_reserve <- occu(~ visit ~ 1, data = umf_reserve)
predict(m1_hors, type = "det")## Predicted SE lower upper
## 1 0.5992162 0.2192354 0.1998832 0.8994769
## 2 0.7989556 0.1795916 0.3075478 0.9726463
## 3 0.7989556 0.1795916 0.3075478 0.9726463
## 4 0.5992156 0.2192354 0.1998828 0.8994766
## 5 0.7989556 0.1795916 0.3075478 0.9726463
## 6 0.5992162 0.2192354 0.1998832 0.8994769
## 7 0.7989556 0.1795916 0.3075478 0.9726463
## 8 0.7989556 0.1795916 0.3075478 0.9726463
## 9 0.5992156 0.2192354 0.1998828 0.8994766
## 10 0.7989556 0.1795916 0.3075478 0.9726463
## 11 0.5992162 0.2192354 0.1998832 0.8994769
## 12 0.7989556 0.1795916 0.3075478 0.9726463
## 13 0.7989556 0.1795916 0.3075478 0.9726463
## 14 0.5992156 0.2192354 0.1998828 0.8994766
## 15 0.7989556 0.1795916 0.3075478 0.9726463
## 16 0.5992162 0.2192354 0.1998832 0.8994769
## 17 0.7989556 0.1795916 0.3075478 0.9726463
## 18 0.7989556 0.1795916 0.3075478 0.9726463
## 19 0.5992156 0.2192354 0.1998828 0.8994766
## 20 0.7989556 0.1795916 0.3075478 0.9726463
## 21 0.5992162 0.2192354 0.1998832 0.8994769
## 22 0.7989556 0.1795916 0.3075478 0.9726463
## 23 0.7989556 0.1795916 0.3075478 0.9726463
## 24 0.5992156 0.2192354 0.1998828 0.8994766
## 25 0.7989556 0.1795916 0.3075478 0.9726463
## 26 0.5992162 0.2192354 0.1998832 0.8994769
## 27 0.7989556 0.1795916 0.3075478 0.9726463
## 28 0.7989556 0.1795916 0.3075478 0.9726463
## 29 0.5992156 0.2192354 0.1998828 0.8994766
## 30 0.7989556 0.1795916 0.3075478 0.9726463
## 31 0.5992162 0.2192354 0.1998832 0.8994769
## 32 0.7989556 0.1795916 0.3075478 0.9726463
## 33 0.7989556 0.1795916 0.3075478 0.9726463
## 34 0.5992156 0.2192354 0.1998828 0.8994766
## 35 0.7989556 0.1795916 0.3075478 0.9726463
## 36 0.5992162 0.2192354 0.1998832 0.8994769
## 37 0.7989556 0.1795916 0.3075478 0.9726463
## 38 0.7989556 0.1795916 0.3075478 0.9726463
## 39 0.5992156 0.2192354 0.1998828 0.8994766
## 40 0.7989556 0.1795916 0.3075478 0.9726463predict(m1_reserve, type = "det")## Predicted SE lower upper
## 1 0.5001208 0.353553414 5.889355e-02 0.9411600
## 2 0.9999524 0.004878698 1.203336e-83 1.0000000
## 3 0.4999999 0.353553420 5.886676e-02 0.9411332
## 4 0.9999524 0.004878698 1.203336e-83 1.0000000
## 5 0.4999999 0.353553420 5.886676e-02 0.9411332
## 6 0.5001208 0.353553414 5.889355e-02 0.9411600
## 7 0.9999524 0.004878698 1.203336e-83 1.0000000
## 8 0.4999999 0.353553420 5.886676e-02 0.9411332
## 9 0.9999524 0.004878698 1.203336e-83 1.0000000
## 10 0.4999999 0.353553420 5.886676e-02 0.9411332
## 11 0.5001208 0.353553414 5.889355e-02 0.9411600
## 12 0.9999524 0.004878698 1.203336e-83 1.0000000
## 13 0.4999999 0.353553420 5.886676e-02 0.9411332
## 14 0.9999524 0.004878698 1.203336e-83 1.0000000
## 15 0.4999999 0.353553420 5.886676e-02 0.9411332
## 16 0.5001208 0.353553414 5.889355e-02 0.9411600
## 17 0.9999524 0.004878698 1.203336e-83 1.0000000
## 18 0.4999999 0.353553420 5.886676e-02 0.9411332
## 19 0.9999524 0.004878698 1.203336e-83 1.0000000
## 20 0.4999999 0.353553420 5.886676e-02 0.9411332
## 21 0.5001208 0.353553414 5.889355e-02 0.9411600
## 22 0.9999524 0.004878698 1.203336e-83 1.0000000
## 23 0.4999999 0.353553420 5.886676e-02 0.9411332
## 24 0.9999524 0.004878698 1.203336e-83 1.0000000
## 25 0.4999999 0.353553420 5.886676e-02 0.9411332
## 26 0.5001208 0.353553414 5.889355e-02 0.9411600
## 27 0.9999524 0.004878698 1.203336e-83 1.0000000
## 28 0.4999999 0.353553420 5.886676e-02 0.9411332
## 29 0.9999524 0.004878698 1.203336e-83 1.0000000
## 30 0.4999999 0.353553420 5.886676e-02 0.9411332
## 31 0.5001208 0.353553414 5.889355e-02 0.9411600
## 32 0.9999524 0.004878698 1.203336e-83 1.0000000
## 33 0.4999999 0.353553420 5.886676e-02 0.9411332
## 34 0.9999524 0.004878698 1.203336e-83 1.0000000
## 35 0.4999999 0.353553420 5.886676e-02 0.9411332
## 36 0.5001208 0.353553414 5.889355e-02 0.9411600
## 37 0.9999524 0.004878698 1.203336e-83 1.0000000
## 38 0.4999999 0.353553420 5.886676e-02 0.9411332
## 39 0.9999524 0.004878698 1.203336e-83 1.0000000
## 40 0.4999999 0.353553420 5.886676e-02 0.9411332
## 41 0.5001208 0.353553414 5.889355e-02 0.9411600
## 42 0.9999524 0.004878698 1.203336e-83 1.0000000
## 43 0.4999999 0.353553420 5.886676e-02 0.9411332
## 44 0.9999524 0.004878698 1.203336e-83 1.0000000
## 45 0.4999999 0.353553420 5.886676e-02 0.9411332
## 46 0.5001208 0.353553414 5.889355e-02 0.9411600
## 47 0.9999524 0.004878698 1.203336e-83 1.0000000
## 48 0.4999999 0.353553420 5.886676e-02 0.9411332
## 49 0.9999524 0.004878698 1.203336e-83 1.0000000
## 50 0.4999999 0.353553420 5.886676e-02 0.9411332
## 51 0.5001208 0.353553414 5.889355e-02 0.9411600
## 52 0.9999524 0.004878698 1.203336e-83 1.0000000
## 53 0.4999999 0.353553420 5.886676e-02 0.9411332
## 54 0.9999524 0.004878698 1.203336e-83 1.0000000
## 55 0.4999999 0.353553420 5.886676e-02 0.9411332
## 56 0.5001208 0.353553414 5.889355e-02 0.9411600
## 57 0.9999524 0.004878698 1.203336e-83 1.0000000
## 58 0.4999999 0.353553420 5.886676e-02 0.9411332
## 59 0.9999524 0.004878698 1.203336e-83 1.0000000
## 60 0.4999999 0.353553420 5.886676e-02 0.9411332
## 61 0.5001208 0.353553414 5.889355e-02 0.9411600
## 62 0.9999524 0.004878698 1.203336e-83 1.0000000
## 63 0.4999999 0.353553420 5.886676e-02 0.9411332
## 64 0.9999524 0.004878698 1.203336e-83 1.0000000
## 65 0.4999999 0.353553420 5.886676e-02 0.9411332
## 66 0.5001208 0.353553414 5.889355e-02 0.9411600
## 67 0.9999524 0.004878698 1.203336e-83 1.0000000
## 68 0.4999999 0.353553420 5.886676e-02 0.9411332
## 69 0.9999524 0.004878698 1.203336e-83 1.0000000
## 70 0.4999999 0.353553420 5.886676e-02 0.9411332
## 71 0.5001208 0.353553414 5.889355e-02 0.9411600
## 72 0.9999524 0.004878698 1.203336e-83 1.0000000
## 73 0.4999999 0.353553420 5.886676e-02 0.9411332
## 74 0.9999524 0.004878698 1.203336e-83 1.0000000
## 75 0.4999999 0.353553420 5.886676e-02 0.9411332
## 76 0.5001208 0.353553414 5.889355e-02 0.9411600
## 77 0.9999524 0.004878698 1.203336e-83 1.0000000
## 78 0.4999999 0.353553420 5.886676e-02 0.9411332
## 79 0.9999524 0.004878698 1.203336e-83 1.0000000
## 80 0.4999999 0.353553420 5.886676e-02 0.9411332
## 81 0.5001208 0.353553414 5.889355e-02 0.9411600
## 82 0.9999524 0.004878698 1.203336e-83 1.0000000
## 83 0.4999999 0.353553420 5.886676e-02 0.9411332
## 84 0.9999524 0.004878698 1.203336e-83 1.0000000
## 85 0.4999999 0.353553420 5.886676e-02 0.9411332m1_hors##
## Call:
## occu(formula = ~visit ~ 1, data = umf_hors)
##
## Occupancy (logit-scale):
## Estimate SE z P(>|z|)
## 0.514 0.732 0.703 0.482
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) 4.02e-01 0.913 4.41e-01 0.660
## visitV2 9.78e-01 1.441 6.78e-01 0.498
## visitV3 9.78e-01 1.441 6.78e-01 0.498
## visitV4 -2.65e-06 1.290 -2.06e-06 1.000
## visitV5 9.78e-01 1.441 6.78e-01 0.498
##
## AIC: 51.04438
## Number of sites: 8m1_reserve##
## Call:
## occu(formula = ~visit ~ 1, data = umf_reserve)
##
## Occupancy (logit-scale):
## Estimate SE z P(>|z|)
## -2.01 0.753 -2.68 0.00744
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) 0.000483 1.41 0.000342 1.000
## visitV2 9.952090 102.50 0.097091 0.923
## visitV3 -0.000484 2.00 -0.000242 1.000
## visitV4 9.952090 102.50 0.097091 0.923
## visitV5 -0.000484 2.00 -0.000242 1.000
##
## AIC: 32.63331
## Number of sites: 17## Warning: Large or missing SE values. Be very cautious using these results.La même chose pour l’année 2024
traitement_R_2024 <-read_delim(
"traitement R 2024.txt",
delim = "\t",
escape_double = FALSE,
na = "NA",
trim_ws = TRUE
)## Rows: 25 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (5): vent_P1, vent_P2, vent_P3, vent_P4, vent_P5
## dbl (20): Placettes, P1, P2, P3, P4, P5, Terriers, Patch_vegetation, Souches...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.data1 <- traitement_R_2024
y <- data1[, c("P1", "P2", "P3", "P4", "P5")]# Covariables de site = variables qui ne changent pas entre passages
siteCovs <- data1[, c(
"Terriers",
"Souches_bois_mort",
"Patch_vegetation",
"Gites_anthropiques")]
# Covariables d'observation = variables qui changent à chaque passage
obsCovs <- list(
temperature = data1[, c("temp_P1", "temp_P2", "temp_P3", "temp_P4", "temp_P5")],
vent = data1[, c("vent_P1", "vent_P2", "vent_P3", "vent_P4", "vent_P5")],
nebulosite = data1[, c("neb_P1", "neb_P2", "neb_P3", "neb_P4", "neb_P5")])y <- ifelse(y > 0, 1, 0)
nSites <- nrow(y)
nVisits <- ncol(y)
visit <- matrix(
rep(paste0("V", 1:nVisits), each = nSites),
nrow = nSites,
ncol = nVisits)
visit <- as.data.frame(visit)
visit[] <- lapply(visit, factor)
umf <- unmarkedFrameOccu(
y = y,
siteCovs = siteCovs,
obsCovs = c(obsCovs, list(visit = visit)))## Warning: obsCovs contains characters. Converting them to factors.# Modèle nul : occupation constante, détection constante
m0 <- occu(~ 1 ~ 1, data = umf)
#Modèle détection variable entre visites
m1 <- occu(~ visit ~ 1, data = umf)
#Différents modèle avec l'effet des covariables de site
m2 <- occu(~ 1 ~ Terriers, data = umf)
m3 <- occu(~ 1 ~ Souches_bois_mort, data = umf)
m4 <- occu(~ 1 ~ Patch_vegetation, data = umf)
m5 <- occu(~ 1 ~ Gites_anthropiques, data = umf)
m6 <- occu(~ 1 ~ Gites_anthropiques + Terriers, data = umf)
m7 <- occu(~ 1 ~ Gites_anthropiques + Patch_vegetation, data = umf)
m8 <- occu(~ 1 ~ Terriers + Patch_vegetation, data = umf)
m9 <- occu(~ 1 ~ Gites_anthropiques + Terriers + Patch_vegetation, data = umf)
m10 <- occu(~ 1 ~ Gites_anthropiques + Souches_bois_mort, data = umf)
m11 <- occu(~ visit ~ Gites_anthropiques, data = umf)
#Différents modèles pour tester les covariables d’échantillonnage sur la détection
m_p_temp <- occu(~ temperature ~ 1, data = umf)
m_p_vent <- occu(~ vent ~ 1, data = umf)
m_p_neb <- occu(~ nebulosite ~ 1, data = umf)
m_p_temp_neb <- occu(~ temperature + nebulosite ~ 1, data = umf)
m_p_vent_neb <- occu(~ vent + nebulosite ~ 1, data = umf)#Comparaison des modèles
models <- fitList(
"psi(.) p(.)" = m0,
"psi(.) p(visit)" = m1,
"psi(Terriers) p(.)" = m2,
"psi(Souches_bois_mort) p(.)" = m3,
"psi(Patch_vegetation) p(.)" = m4,
"psi(Gites_anthropiques) p(.)" = m5,
"psi(GA + T) p(.)" = m6,
"psi(GA + VEG) p(.)" = m7,
"psi(T + VEG) p(.)" = m8,
"psi(GA + T + VEG) p(.)" = m9,
"psi(GA + Souches_bois_mort) p(.)" = m10,
"psi(Gites_anthropiques) p(visit)" = m11)
modSel(models) ## nPars AIC delta AICwt cumltvWt
## psi(Gites_anthropiques) p(visit) 7 79.39 0.00 0.196 0.20
## psi(Gites_anthropiques) p(.) 3 80.24 0.86 0.128 0.32
## psi(.) p(visit) 6 80.29 0.90 0.125 0.45
## psi(GA + T) p(.) 4 80.74 1.35 0.100 0.55
## psi(GA + VEG) p(.) 4 80.97 1.58 0.089 0.64
## psi(.) p(.) 2 81.15 1.76 0.082 0.72
## psi(Patch_vegetation) p(.) 3 81.60 2.21 0.065 0.79
## psi(GA + Souches_bois_mort) p(.) 4 81.92 2.53 0.055 0.84
## psi(Souches_bois_mort) p(.) 3 82.18 2.79 0.049 0.89
## psi(GA + T + VEG) p(.) 5 82.20 2.81 0.048 0.94
## psi(Terriers) p(.) 3 82.72 3.33 0.037 0.98
## psi(T + VEG) p(.) 4 83.53 4.14 0.025 1.00le meilleur modèle est m5 et la prob de détection semble constante le delta Aic entre detenction constante et variable est de 1.78 donc pas de preuve de différence mais seule année ou detection variable est meilleur
predict(m5,type="state")## Predicted SE lower upper
## 1 0.2128628 0.09584876 0.08099102 0.4534969
## 2 0.6385819 0.23557690 0.19288716 0.9288916
## 3 0.2128628 0.09584876 0.08099102 0.4534969
## 4 0.6385819 0.23557690 0.19288716 0.9288916
## 5 0.2128628 0.09584876 0.08099102 0.4534969
## 6 0.6385819 0.23557690 0.19288716 0.9288916
## 7 0.2128628 0.09584876 0.08099102 0.4534969
## 8 0.6385819 0.23557690 0.19288716 0.9288916
## 9 0.6385819 0.23557690 0.19288716 0.9288916
## 10 0.2128628 0.09584876 0.08099102 0.4534969
## 11 0.2128628 0.09584876 0.08099102 0.4534969
## 12 0.2128628 0.09584876 0.08099102 0.4534969
## 13 0.2128628 0.09584876 0.08099102 0.4534969
## 14 0.2128628 0.09584876 0.08099102 0.4534969
## 15 0.2128628 0.09584876 0.08099102 0.4534969
## 16 0.2128628 0.09584876 0.08099102 0.4534969
## 17 0.2128628 0.09584876 0.08099102 0.4534969
## 18 0.2128628 0.09584876 0.08099102 0.4534969
## 19 0.2128628 0.09584876 0.08099102 0.4534969
## 20 0.2128628 0.09584876 0.08099102 0.4534969
## 21 0.2128628 0.09584876 0.08099102 0.4534969
## 22 0.2128628 0.09584876 0.08099102 0.4534969
## 23 0.2128628 0.09584876 0.08099102 0.4534969
## 24 0.2128628 0.09584876 0.08099102 0.4534969
## 25 0.2128628 0.09584876 0.08099102 0.4534969predict(m5,type="det")## Predicted SE lower upper
## 1 0.4295209 0.0931614 0.2632811 0.6133396
## 2 0.4295209 0.0931614 0.2632811 0.6133396
## 3 0.4295209 0.0931614 0.2632811 0.6133396
## 4 0.4295209 0.0931614 0.2632811 0.6133396
## 5 0.4295209 0.0931614 0.2632811 0.6133396
## 6 0.4295209 0.0931614 0.2632811 0.6133396
## 7 0.4295209 0.0931614 0.2632811 0.6133396
## 8 0.4295209 0.0931614 0.2632811 0.6133396
## 9 0.4295209 0.0931614 0.2632811 0.6133396
## 10 0.4295209 0.0931614 0.2632811 0.6133396
## 11 0.4295209 0.0931614 0.2632811 0.6133396
## 12 0.4295209 0.0931614 0.2632811 0.6133396
## 13 0.4295209 0.0931614 0.2632811 0.6133396
## 14 0.4295209 0.0931614 0.2632811 0.6133396
## 15 0.4295209 0.0931614 0.2632811 0.6133396
## 16 0.4295209 0.0931614 0.2632811 0.6133396
## 17 0.4295209 0.0931614 0.2632811 0.6133396
## 18 0.4295209 0.0931614 0.2632811 0.6133396
## 19 0.4295209 0.0931614 0.2632811 0.6133396
## 20 0.4295209 0.0931614 0.2632811 0.6133396
## 21 0.4295209 0.0931614 0.2632811 0.6133396
## 22 0.4295209 0.0931614 0.2632811 0.6133396
## 23 0.4295209 0.0931614 0.2632811 0.6133396
## 24 0.4295209 0.0931614 0.2632811 0.6133396
## 25 0.4295209 0.0931614 0.2632811 0.6133396
## 26 0.4295209 0.0931614 0.2632811 0.6133396
## 27 0.4295209 0.0931614 0.2632811 0.6133396
## 28 0.4295209 0.0931614 0.2632811 0.6133396
## 29 0.4295209 0.0931614 0.2632811 0.6133396
## 30 0.4295209 0.0931614 0.2632811 0.6133396
## 31 0.4295209 0.0931614 0.2632811 0.6133396
## 32 0.4295209 0.0931614 0.2632811 0.6133396
## 33 0.4295209 0.0931614 0.2632811 0.6133396
## 34 0.4295209 0.0931614 0.2632811 0.6133396
## 35 0.4295209 0.0931614 0.2632811 0.6133396
## 36 0.4295209 0.0931614 0.2632811 0.6133396
## 37 0.4295209 0.0931614 0.2632811 0.6133396
## 38 0.4295209 0.0931614 0.2632811 0.6133396
## 39 0.4295209 0.0931614 0.2632811 0.6133396
## 40 0.4295209 0.0931614 0.2632811 0.6133396
## 41 0.4295209 0.0931614 0.2632811 0.6133396
## 42 0.4295209 0.0931614 0.2632811 0.6133396
## 43 0.4295209 0.0931614 0.2632811 0.6133396
## 44 0.4295209 0.0931614 0.2632811 0.6133396
## 45 0.4295209 0.0931614 0.2632811 0.6133396
## 46 0.4295209 0.0931614 0.2632811 0.6133396
## 47 0.4295209 0.0931614 0.2632811 0.6133396
## 48 0.4295209 0.0931614 0.2632811 0.6133396
## 49 0.4295209 0.0931614 0.2632811 0.6133396
## 50 0.4295209 0.0931614 0.2632811 0.6133396
## 51 0.4295209 0.0931614 0.2632811 0.6133396
## 52 0.4295209 0.0931614 0.2632811 0.6133396
## 53 0.4295209 0.0931614 0.2632811 0.6133396
## 54 0.4295209 0.0931614 0.2632811 0.6133396
## 55 0.4295209 0.0931614 0.2632811 0.6133396
## 56 0.4295209 0.0931614 0.2632811 0.6133396
## 57 0.4295209 0.0931614 0.2632811 0.6133396
## 58 0.4295209 0.0931614 0.2632811 0.6133396
## 59 0.4295209 0.0931614 0.2632811 0.6133396
## 60 0.4295209 0.0931614 0.2632811 0.6133396
## 61 0.4295209 0.0931614 0.2632811 0.6133396
## 62 0.4295209 0.0931614 0.2632811 0.6133396
## 63 0.4295209 0.0931614 0.2632811 0.6133396
## 64 0.4295209 0.0931614 0.2632811 0.6133396
## 65 0.4295209 0.0931614 0.2632811 0.6133396
## 66 0.4295209 0.0931614 0.2632811 0.6133396
## 67 0.4295209 0.0931614 0.2632811 0.6133396
## 68 0.4295209 0.0931614 0.2632811 0.6133396
## 69 0.4295209 0.0931614 0.2632811 0.6133396
## 70 0.4295209 0.0931614 0.2632811 0.6133396
## 71 0.4295209 0.0931614 0.2632811 0.6133396
## 72 0.4295209 0.0931614 0.2632811 0.6133396
## 73 0.4295209 0.0931614 0.2632811 0.6133396
## 74 0.4295209 0.0931614 0.2632811 0.6133396
## 75 0.4295209 0.0931614 0.2632811 0.6133396
## 76 0.4295209 0.0931614 0.2632811 0.6133396
## 77 0.4295209 0.0931614 0.2632811 0.6133396
## 78 0.4295209 0.0931614 0.2632811 0.6133396
## 79 0.4295209 0.0931614 0.2632811 0.6133396
## 80 0.4295209 0.0931614 0.2632811 0.6133396
## 81 0.4295209 0.0931614 0.2632811 0.6133396
## 82 0.4295209 0.0931614 0.2632811 0.6133396
## 83 0.4295209 0.0931614 0.2632811 0.6133396
## 84 0.4295209 0.0931614 0.2632811 0.6133396
## 85 0.4295209 0.0931614 0.2632811 0.6133396
## 86 0.4295209 0.0931614 0.2632811 0.6133396
## 87 0.4295209 0.0931614 0.2632811 0.6133396
## 88 0.4295209 0.0931614 0.2632811 0.6133396
## 89 0.4295209 0.0931614 0.2632811 0.6133396
## 90 0.4295209 0.0931614 0.2632811 0.6133396
## 91 0.4295209 0.0931614 0.2632811 0.6133396
## 92 0.4295209 0.0931614 0.2632811 0.6133396
## 93 0.4295209 0.0931614 0.2632811 0.6133396
## 94 0.4295209 0.0931614 0.2632811 0.6133396
## 95 0.4295209 0.0931614 0.2632811 0.6133396
## 96 0.4295209 0.0931614 0.2632811 0.6133396
## 97 0.4295209 0.0931614 0.2632811 0.6133396
## 98 0.4295209 0.0931614 0.2632811 0.6133396
## 99 0.4295209 0.0931614 0.2632811 0.6133396
## 100 0.4295209 0.0931614 0.2632811 0.6133396
## 101 0.4295209 0.0931614 0.2632811 0.6133396
## 102 0.4295209 0.0931614 0.2632811 0.6133396
## 103 0.4295209 0.0931614 0.2632811 0.6133396
## 104 0.4295209 0.0931614 0.2632811 0.6133396
## 105 0.4295209 0.0931614 0.2632811 0.6133396
## 106 0.4295209 0.0931614 0.2632811 0.6133396
## 107 0.4295209 0.0931614 0.2632811 0.6133396
## 108 0.4295209 0.0931614 0.2632811 0.6133396
## 109 0.4295209 0.0931614 0.2632811 0.6133396
## 110 0.4295209 0.0931614 0.2632811 0.6133396
## 111 0.4295209 0.0931614 0.2632811 0.6133396
## 112 0.4295209 0.0931614 0.2632811 0.6133396
## 113 0.4295209 0.0931614 0.2632811 0.6133396
## 114 0.4295209 0.0931614 0.2632811 0.6133396
## 115 0.4295209 0.0931614 0.2632811 0.6133396
## 116 0.4295209 0.0931614 0.2632811 0.6133396
## 117 0.4295209 0.0931614 0.2632811 0.6133396
## 118 0.4295209 0.0931614 0.2632811 0.6133396
## 119 0.4295209 0.0931614 0.2632811 0.6133396
## 120 0.4295209 0.0931614 0.2632811 0.6133396
## 121 0.4295209 0.0931614 0.2632811 0.6133396
## 122 0.4295209 0.0931614 0.2632811 0.6133396
## 123 0.4295209 0.0931614 0.2632811 0.6133396
## 124 0.4295209 0.0931614 0.2632811 0.6133396
## 125 0.4295209 0.0931614 0.2632811 0.6133396#modèle constant
predict(m0,type="state")## Predicted SE lower upper
## 1 0.2980086 0.09684705 0.1462717 0.5126346
## 2 0.2980086 0.09684705 0.1462717 0.5126346
## 3 0.2980086 0.09684705 0.1462717 0.5126346
## 4 0.2980086 0.09684705 0.1462717 0.5126346
## 5 0.2980086 0.09684705 0.1462717 0.5126346
## 6 0.2980086 0.09684705 0.1462717 0.5126346
## 7 0.2980086 0.09684705 0.1462717 0.5126346
## 8 0.2980086 0.09684705 0.1462717 0.5126346
## 9 0.2980086 0.09684705 0.1462717 0.5126346
## 10 0.2980086 0.09684705 0.1462717 0.5126346
## 11 0.2980086 0.09684705 0.1462717 0.5126346
## 12 0.2980086 0.09684705 0.1462717 0.5126346
## 13 0.2980086 0.09684705 0.1462717 0.5126346
## 14 0.2980086 0.09684705 0.1462717 0.5126346
## 15 0.2980086 0.09684705 0.1462717 0.5126346
## 16 0.2980086 0.09684705 0.1462717 0.5126346
## 17 0.2980086 0.09684705 0.1462717 0.5126346
## 18 0.2980086 0.09684705 0.1462717 0.5126346
## 19 0.2980086 0.09684705 0.1462717 0.5126346
## 20 0.2980086 0.09684705 0.1462717 0.5126346
## 21 0.2980086 0.09684705 0.1462717 0.5126346
## 22 0.2980086 0.09684705 0.1462717 0.5126346
## 23 0.2980086 0.09684705 0.1462717 0.5126346
## 24 0.2980086 0.09684705 0.1462717 0.5126346
## 25 0.2980086 0.09684705 0.1462717 0.5126346predict(m0,type="det")## Predicted SE lower upper
## 1 0.4295277 0.09316144 0.263287 0.6133455
## 2 0.4295277 0.09316144 0.263287 0.6133455
## 3 0.4295277 0.09316144 0.263287 0.6133455
## 4 0.4295277 0.09316144 0.263287 0.6133455
## 5 0.4295277 0.09316144 0.263287 0.6133455
## 6 0.4295277 0.09316144 0.263287 0.6133455
## 7 0.4295277 0.09316144 0.263287 0.6133455
## 8 0.4295277 0.09316144 0.263287 0.6133455
## 9 0.4295277 0.09316144 0.263287 0.6133455
## 10 0.4295277 0.09316144 0.263287 0.6133455
## 11 0.4295277 0.09316144 0.263287 0.6133455
## 12 0.4295277 0.09316144 0.263287 0.6133455
## 13 0.4295277 0.09316144 0.263287 0.6133455
## 14 0.4295277 0.09316144 0.263287 0.6133455
## 15 0.4295277 0.09316144 0.263287 0.6133455
## 16 0.4295277 0.09316144 0.263287 0.6133455
## 17 0.4295277 0.09316144 0.263287 0.6133455
## 18 0.4295277 0.09316144 0.263287 0.6133455
## 19 0.4295277 0.09316144 0.263287 0.6133455
## 20 0.4295277 0.09316144 0.263287 0.6133455
## 21 0.4295277 0.09316144 0.263287 0.6133455
## 22 0.4295277 0.09316144 0.263287 0.6133455
## 23 0.4295277 0.09316144 0.263287 0.6133455
## 24 0.4295277 0.09316144 0.263287 0.6133455
## 25 0.4295277 0.09316144 0.263287 0.6133455
## 26 0.4295277 0.09316144 0.263287 0.6133455
## 27 0.4295277 0.09316144 0.263287 0.6133455
## 28 0.4295277 0.09316144 0.263287 0.6133455
## 29 0.4295277 0.09316144 0.263287 0.6133455
## 30 0.4295277 0.09316144 0.263287 0.6133455
## 31 0.4295277 0.09316144 0.263287 0.6133455
## 32 0.4295277 0.09316144 0.263287 0.6133455
## 33 0.4295277 0.09316144 0.263287 0.6133455
## 34 0.4295277 0.09316144 0.263287 0.6133455
## 35 0.4295277 0.09316144 0.263287 0.6133455
## 36 0.4295277 0.09316144 0.263287 0.6133455
## 37 0.4295277 0.09316144 0.263287 0.6133455
## 38 0.4295277 0.09316144 0.263287 0.6133455
## 39 0.4295277 0.09316144 0.263287 0.6133455
## 40 0.4295277 0.09316144 0.263287 0.6133455
## 41 0.4295277 0.09316144 0.263287 0.6133455
## 42 0.4295277 0.09316144 0.263287 0.6133455
## 43 0.4295277 0.09316144 0.263287 0.6133455
## 44 0.4295277 0.09316144 0.263287 0.6133455
## 45 0.4295277 0.09316144 0.263287 0.6133455
## 46 0.4295277 0.09316144 0.263287 0.6133455
## 47 0.4295277 0.09316144 0.263287 0.6133455
## 48 0.4295277 0.09316144 0.263287 0.6133455
## 49 0.4295277 0.09316144 0.263287 0.6133455
## 50 0.4295277 0.09316144 0.263287 0.6133455
## 51 0.4295277 0.09316144 0.263287 0.6133455
## 52 0.4295277 0.09316144 0.263287 0.6133455
## 53 0.4295277 0.09316144 0.263287 0.6133455
## 54 0.4295277 0.09316144 0.263287 0.6133455
## 55 0.4295277 0.09316144 0.263287 0.6133455
## 56 0.4295277 0.09316144 0.263287 0.6133455
## 57 0.4295277 0.09316144 0.263287 0.6133455
## 58 0.4295277 0.09316144 0.263287 0.6133455
## 59 0.4295277 0.09316144 0.263287 0.6133455
## 60 0.4295277 0.09316144 0.263287 0.6133455
## 61 0.4295277 0.09316144 0.263287 0.6133455
## 62 0.4295277 0.09316144 0.263287 0.6133455
## 63 0.4295277 0.09316144 0.263287 0.6133455
## 64 0.4295277 0.09316144 0.263287 0.6133455
## 65 0.4295277 0.09316144 0.263287 0.6133455
## 66 0.4295277 0.09316144 0.263287 0.6133455
## 67 0.4295277 0.09316144 0.263287 0.6133455
## 68 0.4295277 0.09316144 0.263287 0.6133455
## 69 0.4295277 0.09316144 0.263287 0.6133455
## 70 0.4295277 0.09316144 0.263287 0.6133455
## 71 0.4295277 0.09316144 0.263287 0.6133455
## 72 0.4295277 0.09316144 0.263287 0.6133455
## 73 0.4295277 0.09316144 0.263287 0.6133455
## 74 0.4295277 0.09316144 0.263287 0.6133455
## 75 0.4295277 0.09316144 0.263287 0.6133455
## 76 0.4295277 0.09316144 0.263287 0.6133455
## 77 0.4295277 0.09316144 0.263287 0.6133455
## 78 0.4295277 0.09316144 0.263287 0.6133455
## 79 0.4295277 0.09316144 0.263287 0.6133455
## 80 0.4295277 0.09316144 0.263287 0.6133455
## 81 0.4295277 0.09316144 0.263287 0.6133455
## 82 0.4295277 0.09316144 0.263287 0.6133455
## 83 0.4295277 0.09316144 0.263287 0.6133455
## 84 0.4295277 0.09316144 0.263287 0.6133455
## 85 0.4295277 0.09316144 0.263287 0.6133455
## 86 0.4295277 0.09316144 0.263287 0.6133455
## 87 0.4295277 0.09316144 0.263287 0.6133455
## 88 0.4295277 0.09316144 0.263287 0.6133455
## 89 0.4295277 0.09316144 0.263287 0.6133455
## 90 0.4295277 0.09316144 0.263287 0.6133455
## 91 0.4295277 0.09316144 0.263287 0.6133455
## 92 0.4295277 0.09316144 0.263287 0.6133455
## 93 0.4295277 0.09316144 0.263287 0.6133455
## 94 0.4295277 0.09316144 0.263287 0.6133455
## 95 0.4295277 0.09316144 0.263287 0.6133455
## 96 0.4295277 0.09316144 0.263287 0.6133455
## 97 0.4295277 0.09316144 0.263287 0.6133455
## 98 0.4295277 0.09316144 0.263287 0.6133455
## 99 0.4295277 0.09316144 0.263287 0.6133455
## 100 0.4295277 0.09316144 0.263287 0.6133455
## 101 0.4295277 0.09316144 0.263287 0.6133455
## 102 0.4295277 0.09316144 0.263287 0.6133455
## 103 0.4295277 0.09316144 0.263287 0.6133455
## 104 0.4295277 0.09316144 0.263287 0.6133455
## 105 0.4295277 0.09316144 0.263287 0.6133455
## 106 0.4295277 0.09316144 0.263287 0.6133455
## 107 0.4295277 0.09316144 0.263287 0.6133455
## 108 0.4295277 0.09316144 0.263287 0.6133455
## 109 0.4295277 0.09316144 0.263287 0.6133455
## 110 0.4295277 0.09316144 0.263287 0.6133455
## 111 0.4295277 0.09316144 0.263287 0.6133455
## 112 0.4295277 0.09316144 0.263287 0.6133455
## 113 0.4295277 0.09316144 0.263287 0.6133455
## 114 0.4295277 0.09316144 0.263287 0.6133455
## 115 0.4295277 0.09316144 0.263287 0.6133455
## 116 0.4295277 0.09316144 0.263287 0.6133455
## 117 0.4295277 0.09316144 0.263287 0.6133455
## 118 0.4295277 0.09316144 0.263287 0.6133455
## 119 0.4295277 0.09316144 0.263287 0.6133455
## 120 0.4295277 0.09316144 0.263287 0.6133455
## 121 0.4295277 0.09316144 0.263287 0.6133455
## 122 0.4295277 0.09316144 0.263287 0.6133455
## 123 0.4295277 0.09316144 0.263287 0.6133455
## 124 0.4295277 0.09316144 0.263287 0.6133455
## 125 0.4295277 0.09316144 0.263287 0.6133455#calcul de la prob de dét variable entre les visites
predict(m1,type="state") ## Predicted SE lower upper
## 1 0.2876617 0.09270058 0.1426517 0.4949758
## 2 0.2876617 0.09270058 0.1426517 0.4949758
## 3 0.2876617 0.09270058 0.1426517 0.4949758
## 4 0.2876617 0.09270058 0.1426517 0.4949758
## 5 0.2876617 0.09270058 0.1426517 0.4949758
## 6 0.2876617 0.09270058 0.1426517 0.4949758
## 7 0.2876617 0.09270058 0.1426517 0.4949758
## 8 0.2876617 0.09270058 0.1426517 0.4949758
## 9 0.2876617 0.09270058 0.1426517 0.4949758
## 10 0.2876617 0.09270058 0.1426517 0.4949758
## 11 0.2876617 0.09270058 0.1426517 0.4949758
## 12 0.2876617 0.09270058 0.1426517 0.4949758
## 13 0.2876617 0.09270058 0.1426517 0.4949758
## 14 0.2876617 0.09270058 0.1426517 0.4949758
## 15 0.2876617 0.09270058 0.1426517 0.4949758
## 16 0.2876617 0.09270058 0.1426517 0.4949758
## 17 0.2876617 0.09270058 0.1426517 0.4949758
## 18 0.2876617 0.09270058 0.1426517 0.4949758
## 19 0.2876617 0.09270058 0.1426517 0.4949758
## 20 0.2876617 0.09270058 0.1426517 0.4949758
## 21 0.2876617 0.09270058 0.1426517 0.4949758
## 22 0.2876617 0.09270058 0.1426517 0.4949758
## 23 0.2876617 0.09270058 0.1426517 0.4949758
## 24 0.2876617 0.09270058 0.1426517 0.4949758
## 25 0.2876617 0.09270058 0.1426517 0.4949758#Predicted SE lower upper
#1 0.2876617 0.09270058 0.1426517 0.4949758
predict(m1,type="det")## Predicted SE lower upper
## 1 0.5562036 0.1892288 0.21810513 0.8491923
## 2 0.4171531 0.1861269 0.13765887 0.7624085
## 3 0.2781059 0.1681887 0.06940716 0.6655393
## 4 0.1390525 0.1293816 0.01905153 0.5732221
## 5 0.8343112 0.1502019 0.37446098 0.9769353
## 6 0.5562036 0.1892288 0.21810513 0.8491923
## 7 0.4171531 0.1861269 0.13765887 0.7624085
## 8 0.2781059 0.1681887 0.06940716 0.6655393
## 9 0.1390525 0.1293816 0.01905153 0.5732221
## 10 0.8343112 0.1502019 0.37446098 0.9769353
## 11 0.5562036 0.1892288 0.21810513 0.8491923
## 12 0.4171531 0.1861269 0.13765887 0.7624085
## 13 0.2781059 0.1681887 0.06940716 0.6655393
## 14 0.1390525 0.1293816 0.01905153 0.5732221
## 15 0.8343112 0.1502019 0.37446098 0.9769353
## 16 0.5562036 0.1892288 0.21810513 0.8491923
## 17 0.4171531 0.1861269 0.13765887 0.7624085
## 18 0.2781059 0.1681887 0.06940716 0.6655393
## 19 0.1390525 0.1293816 0.01905153 0.5732221
## 20 0.8343112 0.1502019 0.37446098 0.9769353
## 21 0.5562036 0.1892288 0.21810513 0.8491923
## 22 0.4171531 0.1861269 0.13765887 0.7624085
## 23 0.2781059 0.1681887 0.06940716 0.6655393
## 24 0.1390525 0.1293816 0.01905153 0.5732221
## 25 0.8343112 0.1502019 0.37446098 0.9769353
## 26 0.5562036 0.1892288 0.21810513 0.8491923
## 27 0.4171531 0.1861269 0.13765887 0.7624085
## 28 0.2781059 0.1681887 0.06940716 0.6655393
## 29 0.1390525 0.1293816 0.01905153 0.5732221
## 30 0.8343112 0.1502019 0.37446098 0.9769353
## 31 0.5562036 0.1892288 0.21810513 0.8491923
## 32 0.4171531 0.1861269 0.13765887 0.7624085
## 33 0.2781059 0.1681887 0.06940716 0.6655393
## 34 0.1390525 0.1293816 0.01905153 0.5732221
## 35 0.8343112 0.1502019 0.37446098 0.9769353
## 36 0.5562036 0.1892288 0.21810513 0.8491923
## 37 0.4171531 0.1861269 0.13765887 0.7624085
## 38 0.2781059 0.1681887 0.06940716 0.6655393
## 39 0.1390525 0.1293816 0.01905153 0.5732221
## 40 0.8343112 0.1502019 0.37446098 0.9769353
## 41 0.5562036 0.1892288 0.21810513 0.8491923
## 42 0.4171531 0.1861269 0.13765887 0.7624085
## 43 0.2781059 0.1681887 0.06940716 0.6655393
## 44 0.1390525 0.1293816 0.01905153 0.5732221
## 45 0.8343112 0.1502019 0.37446098 0.9769353
## 46 0.5562036 0.1892288 0.21810513 0.8491923
## 47 0.4171531 0.1861269 0.13765887 0.7624085
## 48 0.2781059 0.1681887 0.06940716 0.6655393
## 49 0.1390525 0.1293816 0.01905153 0.5732221
## 50 0.8343112 0.1502019 0.37446098 0.9769353
## 51 0.5562036 0.1892288 0.21810513 0.8491923
## 52 0.4171531 0.1861269 0.13765887 0.7624085
## 53 0.2781059 0.1681887 0.06940716 0.6655393
## 54 0.1390525 0.1293816 0.01905153 0.5732221
## 55 0.8343112 0.1502019 0.37446098 0.9769353
## 56 0.5562036 0.1892288 0.21810513 0.8491923
## 57 0.4171531 0.1861269 0.13765887 0.7624085
## 58 0.2781059 0.1681887 0.06940716 0.6655393
## 59 0.1390525 0.1293816 0.01905153 0.5732221
## 60 0.8343112 0.1502019 0.37446098 0.9769353
## 61 0.5562036 0.1892288 0.21810513 0.8491923
## 62 0.4171531 0.1861269 0.13765887 0.7624085
## 63 0.2781059 0.1681887 0.06940716 0.6655393
## 64 0.1390525 0.1293816 0.01905153 0.5732221
## 65 0.8343112 0.1502019 0.37446098 0.9769353
## 66 0.5562036 0.1892288 0.21810513 0.8491923
## 67 0.4171531 0.1861269 0.13765887 0.7624085
## 68 0.2781059 0.1681887 0.06940716 0.6655393
## 69 0.1390525 0.1293816 0.01905153 0.5732221
## 70 0.8343112 0.1502019 0.37446098 0.9769353
## 71 0.5562036 0.1892288 0.21810513 0.8491923
## 72 0.4171531 0.1861269 0.13765887 0.7624085
## 73 0.2781059 0.1681887 0.06940716 0.6655393
## 74 0.1390525 0.1293816 0.01905153 0.5732221
## 75 0.8343112 0.1502019 0.37446098 0.9769353
## 76 0.5562036 0.1892288 0.21810513 0.8491923
## 77 0.4171531 0.1861269 0.13765887 0.7624085
## 78 0.2781059 0.1681887 0.06940716 0.6655393
## 79 0.1390525 0.1293816 0.01905153 0.5732221
## 80 0.8343112 0.1502019 0.37446098 0.9769353
## 81 0.5562036 0.1892288 0.21810513 0.8491923
## 82 0.4171531 0.1861269 0.13765887 0.7624085
## 83 0.2781059 0.1681887 0.06940716 0.6655393
## 84 0.1390525 0.1293816 0.01905153 0.5732221
## 85 0.8343112 0.1502019 0.37446098 0.9769353
## 86 0.5562036 0.1892288 0.21810513 0.8491923
## 87 0.4171531 0.1861269 0.13765887 0.7624085
## 88 0.2781059 0.1681887 0.06940716 0.6655393
## 89 0.1390525 0.1293816 0.01905153 0.5732221
## 90 0.8343112 0.1502019 0.37446098 0.9769353
## 91 0.5562036 0.1892288 0.21810513 0.8491923
## 92 0.4171531 0.1861269 0.13765887 0.7624085
## 93 0.2781059 0.1681887 0.06940716 0.6655393
## 94 0.1390525 0.1293816 0.01905153 0.5732221
## 95 0.8343112 0.1502019 0.37446098 0.9769353
## 96 0.5562036 0.1892288 0.21810513 0.8491923
## 97 0.4171531 0.1861269 0.13765887 0.7624085
## 98 0.2781059 0.1681887 0.06940716 0.6655393
## 99 0.1390525 0.1293816 0.01905153 0.5732221
## 100 0.8343112 0.1502019 0.37446098 0.9769353
## 101 0.5562036 0.1892288 0.21810513 0.8491923
## 102 0.4171531 0.1861269 0.13765887 0.7624085
## 103 0.2781059 0.1681887 0.06940716 0.6655393
## 104 0.1390525 0.1293816 0.01905153 0.5732221
## 105 0.8343112 0.1502019 0.37446098 0.9769353
## 106 0.5562036 0.1892288 0.21810513 0.8491923
## 107 0.4171531 0.1861269 0.13765887 0.7624085
## 108 0.2781059 0.1681887 0.06940716 0.6655393
## 109 0.1390525 0.1293816 0.01905153 0.5732221
## 110 0.8343112 0.1502019 0.37446098 0.9769353
## 111 0.5562036 0.1892288 0.21810513 0.8491923
## 112 0.4171531 0.1861269 0.13765887 0.7624085
## 113 0.2781059 0.1681887 0.06940716 0.6655393
## 114 0.1390525 0.1293816 0.01905153 0.5732221
## 115 0.8343112 0.1502019 0.37446098 0.9769353
## 116 0.5562036 0.1892288 0.21810513 0.8491923
## 117 0.4171531 0.1861269 0.13765887 0.7624085
## 118 0.2781059 0.1681887 0.06940716 0.6655393
## 119 0.1390525 0.1293816 0.01905153 0.5732221
## 120 0.8343112 0.1502019 0.37446098 0.9769353
## 121 0.5562036 0.1892288 0.21810513 0.8491923
## 122 0.4171531 0.1861269 0.13765887 0.7624085
## 123 0.2781059 0.1681887 0.06940716 0.6655393
## 124 0.1390525 0.1293816 0.01905153 0.5732221
## 125 0.8343112 0.1502019 0.37446098 0.9769353#Test Ajustement des modèle
obs.boot.m0 <- AICcmodavg::mb.gof.test(m0, nsim = 100, lot.hist=F)
obs.boot.m0##
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
##
## Pearson chi-square table:
##
## Cohort Observed Expected Chi-square
## 00000 0 18 18.00 0.00
## 00001 0 1 0.34 1.29
## 01000 0 1 0.34 1.29
## 01101 0 1 0.19 3.40
## 10001 0 2 0.26 11.93
## 10011 0 1 0.19 3.40
## 11101 0 1 0.14 5.06
##
## Chi-square statistic = 31.8978
## Number of bootstrap samples = 100
## P-value = 0.25
##
## Quantiles of bootstrapped statistics:
## 0% 25% 50% 75% 100%
## 19 24 27 32 64
##
## Estimate of c-hat = 1.09obs.boot.m1 <- AICcmodavg::mb.gof.test(m1, nsim = 100, lot.hist=F)
obs.boot.m1##
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
##
## Pearson chi-square table:
##
## Cohort Observed Expected Chi-square
## 00000 0 18 18.00 0.00
## 00001 0 1 0.96 0.00
## 01000 0 1 0.14 5.43
## 01101 0 1 0.27 2.03
## 10001 0 2 1.21 0.52
## 10011 0 1 0.20 3.32
## 11101 0 1 0.33 1.33
##
## Chi-square statistic = 16.521
## Number of bootstrap samples = 100
## P-value = 0.4
##
## Quantiles of bootstrapped statistics:
## 0% 25% 50% 75% 100%
## 1.4e-04 7.2e+00 1.3e+01 2.3e+01 1.0e+02
##
## Estimate of c-hat = 0.94obs.boot.m5 <- AICcmodavg::mb.gof.test(m5, nsim = 100, lot.hist=F)
obs.boot.m5##
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
##
## Pearson chi-square table:
##
## Cohort Observed Expected Chi-square
## 00000 0 18 18.00 0.00
## 00001 0 1 0.34 1.29
## 01000 0 1 0.34 1.29
## 01101 0 1 0.19 3.40
## 10001 0 2 0.26 11.93
## 10011 0 1 0.19 3.40
## 11101 0 1 0.14 5.06
##
## Chi-square statistic = 31.8983
## Number of bootstrap samples = 100
## P-value = 0.33
##
## Quantiles of bootstrapped statistics:
## 0% 25% 50% 75% 100%
## 14 24 28 34 104
##
## Estimate of c-hat = 1.04Calcul de la prob d’occupation et de détection en réserve et hors réserve
data1## # A tibble: 25 × 25
## Placettes P1 P2 P3 P4 P5 Terriers Patch_vegetation
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 0 1 1 0 1 1 1
## 2 2 1 1 1 0 1 0 0
## 3 3 0 0 0 0 0 0 0
## 4 4 0 0 0 0 0 0 1
## 5 5 0 0 0 0 0 0 1
## 6 6 1 0 0 1 1 0 1
## 7 7 0 0 0 0 0 0 1
## 8 8 0 0 0 0 0 0 1
## 9 9 1 0 0 0 1 0 1
## 10 10 0 0 0 0 1 0 1
## # ℹ 15 more rows
## # ℹ 17 more variables: Souches_bois_mort <dbl>, Gites_anthropiques <dbl>,
## # temp_P1 <dbl>, temp_P2 <dbl>, temp_P3 <dbl>, temp_P4 <dbl>, temp_P5 <dbl>,
## # vent_P1 <chr>, vent_P2 <chr>, vent_P3 <chr>, vent_P4 <chr>, vent_P5 <chr>,
## # neb_P1 <dbl>, neb_P2 <dbl>, neb_P3 <dbl>, neb_P4 <dbl>, neb_P5 <dbl>umf## Data frame representation of unmarkedFrame object.
## y.1 y.2 y.3 y.4 y.5 Terriers Souches_bois_mort Patch_vegetation
## 1 0 1 1 0 1 1 0 1
## 2 1 1 1 0 1 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 1
## 5 0 0 0 0 0 0 0 1
## 6 1 0 0 1 1 0 0 1
## 7 0 0 0 0 0 0 0 1
## 8 0 0 0 0 0 0 1 1
## 9 1 0 0 0 1 0 0 1
## 10 0 0 0 0 1 0 0 1
## 11 1 0 0 0 1 1 1 1
## 12 0 0 0 0 0 1 1 1
## 13 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 1 1
## 16 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 1 1 1
## 18 0 0 0 0 0 0 1 0
## 19 0 0 0 0 0 0 1 1
## 20 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 1 1 1
## 22 0 0 0 0 0 0 1 1
## 23 0 0 0 0 0 0 1 1
## 24 0 0 0 0 0 0 0 0
## 25 0 1 0 0 0 0 1 1
## Gites_anthropiques temperature.1 temperature.2 temperature.3 temperature.4
## 1 0 15.0 25.6 19.8 21.8
## 2 1 16.0 23.8 19.1 20.1
## 3 0 16.0 23.4 20.4 20.3
## 4 1 16.0 24.4 20.5 20.7
## 5 0 16.0 25.8 19.1 19.5
## 6 1 16.0 NA 20.9 20.4
## 7 0 16.0 24.5 17.9 19.0
## 8 1 16.0 23.4 18.0 20.3
## 9 1 19.1 22.0 22.0 22.2
## 10 0 17.5 20.2 19.3 26.2
## 11 0 15.8 22.0 19.8 26.7
## 12 0 16.6 17.9 19.3 26.2
## 13 0 16.6 19.0 20.0 24.4
## 14 0 17.3 19.5 21.4 26.3
## 15 0 19.8 19.1 19.4 26.3
## 16 0 21.2 19.0 18.7 26.5
## 17 0 19.4 19.3 20.3 26.8
## 18 0 17.8 18.4 18.4 27.8
## 19 0 18.0 18.5 19.4 24.7
## 20 0 16.7 17.5 21.2 25.5
## 21 0 18.6 17.9 20.0 26.0
## 22 0 17.8 21.1 20.1 24.0
## 23 0 16.9 21.1 18.9 23.6
## 24 0 14.4 19.2 18.0 21.0
## 25 0 16.8 24.3 19.8 20.1
## temperature.5 vent.1 vent.2 vent.3 vent.4 vent.5 nebulosite.1 nebulosite.2
## 1 23.9 Faible Faible Faible Moyen Faible 1 1
## 2 23.2 Moyen Fort Fort Fort Moyen 1 1
## 3 23.5 Moyen Fort Fort Fort Moyen 1 1
## 4 22.9 Moyen Moyen Fort Fort Moyen 1 1
## 5 23.3 Moyen Faible Fort Fort Faible 1 1
## 6 23.7 Moyen <NA> Moyen Fort Faible 1 1
## 7 22.3 Moyen Moyen Moyen Fort Faible 1 1
## 8 21.4 Moyen Faible Moyen Fort Faible 1 1
## 9 21.0 Faible Nul Faible Faible Nul 1 2
## 10 22.2 Moyen Nul Moyen Faible Faible 1 2
## 11 23.3 Fort Faible Faible Nul Nul 1 2
## 12 23.2 Fort Faible Faible Nul Faible 1 2
## 13 23.1 Fort Fort Moyen Faible Faible 1 2
## 14 21.7 Fort Fort Fort Nul Moyen 1 4
## 15 22.2 Moyen Faible Faible Faible Faible 1 3
## 16 22.8 Faible Moyen Moyen Faible Faible 1 5
## 17 22.7 Faible Faible Faible Nul Nul 1 5
## 18 22.3 Faible Moyen Fort Faible Moyen 1 5
## 19 24.4 Moyen Faible Moyen Moyen Moyen 1 5
## 20 24.5 Faible Faible Faible Faible Faible 1 4
## 21 24.4 Faible Nul Faible Nul Moyen 1 5
## 22 25.4 Faible Nul Faible Faible Nul 1 5
## 23 25.5 Faible Nul Faible Faible Faible 1 5
## 24 23.4 Moyen Nul Moyen Faible Moyen 1 5
## 25 26.2 Faible Faible Faible Faible Faible 1 1
## nebulosite.3 nebulosite.4 nebulosite.5 visit.1 visit.2 visit.3 visit.4
## 1 1 2 3 V1 V2 V3 V4
## 2 1 5 2 V1 V2 V3 V4
## 3 1 5 1 V1 V2 V3 V4
## 4 1 5 1 V1 V2 V3 V4
## 5 1 3 1 V1 V2 V3 V4
## 6 2 2 1 V1 V2 V3 V4
## 7 2 2 1 V1 V2 V3 V4
## 8 1 1 1 V1 V2 V3 V4
## 9 1 3 1 V1 V2 V3 V4
## 10 1 4 1 V1 V2 V3 V4
## 11 1 4 1 V1 V2 V3 V4
## 12 1 3 1 V1 V2 V3 V4
## 13 1 3 1 V1 V2 V3 V4
## 14 1 4 1 V1 V2 V3 V4
## 15 1 3 1 V1 V2 V3 V4
## 16 1 1 1 V1 V2 V3 V4
## 17 1 1 1 V1 V2 V3 V4
## 18 1 1 1 V1 V2 V3 V4
## 19 1 1 1 V1 V2 V3 V4
## 20 1 1 1 V1 V2 V3 V4
## 21 1 1 4 V1 V2 V3 V4
## 22 1 1 5 V1 V2 V3 V4
## 23 1 1 5 V1 V2 V3 V4
## 24 1 1 5 V1 V2 V3 V4
## 25 1 1 1 V1 V2 V3 V4
## visit.5
## 1 V5
## 2 V5
## 3 V5
## 4 V5
## 5 V5
## 6 V5
## 7 V5
## 8 V5
## 9 V5
## 10 V5
## 11 V5
## 12 V5
## 13 V5
## 14 V5
## 15 V5
## 16 V5
## 17 V5
## 18 V5
## 19 V5
## 20 V5
## 21 V5
## 22 V5
## 23 V5
## 24 V5
## 25 V5umf_hors <- umf[1:8, ]
umf_hors## Data frame representation of unmarkedFrame object.
## y.1 y.2 y.3 y.4 y.5 Terriers Souches_bois_mort Patch_vegetation
## 1 0 1 1 0 1 1 0 1
## 2 1 1 1 0 1 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 1
## 5 0 0 0 0 0 0 0 1
## 6 1 0 0 1 1 0 0 1
## 7 0 0 0 0 0 0 0 1
## 8 0 0 0 0 0 0 1 1
## Gites_anthropiques temperature.1 temperature.2 temperature.3 temperature.4
## 1 0 15 25.6 19.8 21.8
## 2 1 16 23.8 19.1 20.1
## 3 0 16 23.4 20.4 20.3
## 4 1 16 24.4 20.5 20.7
## 5 0 16 25.8 19.1 19.5
## 6 1 16 NA 20.9 20.4
## 7 0 16 24.5 17.9 19.0
## 8 1 16 23.4 18.0 20.3
## temperature.5 vent.1 vent.2 vent.3 vent.4 vent.5 nebulosite.1 nebulosite.2
## 1 23.9 Faible Faible Faible Moyen Faible 1 1
## 2 23.2 Moyen Fort Fort Fort Moyen 1 1
## 3 23.5 Moyen Fort Fort Fort Moyen 1 1
## 4 22.9 Moyen Moyen Fort Fort Moyen 1 1
## 5 23.3 Moyen Faible Fort Fort Faible 1 1
## 6 23.7 Moyen <NA> Moyen Fort Faible 1 1
## 7 22.3 Moyen Moyen Moyen Fort Faible 1 1
## 8 21.4 Moyen Faible Moyen Fort Faible 1 1
## nebulosite.3 nebulosite.4 nebulosite.5 visit.1 visit.2 visit.3 visit.4
## 1 1 2 3 V1 V2 V3 V4
## 2 1 5 2 V1 V2 V3 V4
## 3 1 5 1 V1 V2 V3 V4
## 4 1 5 1 V1 V2 V3 V4
## 5 1 3 1 V1 V2 V3 V4
## 6 2 2 1 V1 V2 V3 V4
## 7 2 2 1 V1 V2 V3 V4
## 8 1 1 1 V1 V2 V3 V4
## visit.5
## 1 V5
## 2 V5
## 3 V5
## 4 V5
## 5 V5
## 6 V5
## 7 V5
## 8 V5umf_reserve <- umf[9:25, ]
umf_reserve## Data frame representation of unmarkedFrame object.
## y.1 y.2 y.3 y.4 y.5 Terriers Souches_bois_mort Patch_vegetation
## 1 1 0 0 0 1 0 0 1
## 2 0 0 0 0 1 0 0 1
## 3 1 0 0 0 1 1 1 1
## 4 0 0 0 0 0 1 1 1
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 1 1
## 8 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 1 1 1
## 10 0 0 0 0 0 0 1 0
## 11 0 0 0 0 0 0 1 1
## 12 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 1 1 1
## 14 0 0 0 0 0 0 1 1
## 15 0 0 0 0 0 0 1 1
## 16 0 0 0 0 0 0 0 0
## 17 0 1 0 0 0 0 1 1
## Gites_anthropiques temperature.1 temperature.2 temperature.3 temperature.4
## 1 1 19.1 22.0 22.0 22.2
## 2 0 17.5 20.2 19.3 26.2
## 3 0 15.8 22.0 19.8 26.7
## 4 0 16.6 17.9 19.3 26.2
## 5 0 16.6 19.0 20.0 24.4
## 6 0 17.3 19.5 21.4 26.3
## 7 0 19.8 19.1 19.4 26.3
## 8 0 21.2 19.0 18.7 26.5
## 9 0 19.4 19.3 20.3 26.8
## 10 0 17.8 18.4 18.4 27.8
## 11 0 18.0 18.5 19.4 24.7
## 12 0 16.7 17.5 21.2 25.5
## 13 0 18.6 17.9 20.0 26.0
## 14 0 17.8 21.1 20.1 24.0
## 15 0 16.9 21.1 18.9 23.6
## 16 0 14.4 19.2 18.0 21.0
## 17 0 16.8 24.3 19.8 20.1
## temperature.5 vent.1 vent.2 vent.3 vent.4 vent.5 nebulosite.1 nebulosite.2
## 1 21.0 Faible Nul Faible Faible Nul 1 2
## 2 22.2 Moyen Nul Moyen Faible Faible 1 2
## 3 23.3 Fort Faible Faible Nul Nul 1 2
## 4 23.2 Fort Faible Faible Nul Faible 1 2
## 5 23.1 Fort Fort Moyen Faible Faible 1 2
## 6 21.7 Fort Fort Fort Nul Moyen 1 4
## 7 22.2 Moyen Faible Faible Faible Faible 1 3
## 8 22.8 Faible Moyen Moyen Faible Faible 1 5
## 9 22.7 Faible Faible Faible Nul Nul 1 5
## 10 22.3 Faible Moyen Fort Faible Moyen 1 5
## 11 24.4 Moyen Faible Moyen Moyen Moyen 1 5
## 12 24.5 Faible Faible Faible Faible Faible 1 4
## 13 24.4 Faible Nul Faible Nul Moyen 1 5
## 14 25.4 Faible Nul Faible Faible Nul 1 5
## 15 25.5 Faible Nul Faible Faible Faible 1 5
## 16 23.4 Moyen Nul Moyen Faible Moyen 1 5
## 17 26.2 Faible Faible Faible Faible Faible 1 1
## nebulosite.3 nebulosite.4 nebulosite.5 visit.1 visit.2 visit.3 visit.4
## 1 1 3 1 V1 V2 V3 V4
## 2 1 4 1 V1 V2 V3 V4
## 3 1 4 1 V1 V2 V3 V4
## 4 1 3 1 V1 V2 V3 V4
## 5 1 3 1 V1 V2 V3 V4
## 6 1 4 1 V1 V2 V3 V4
## 7 1 3 1 V1 V2 V3 V4
## 8 1 1 1 V1 V2 V3 V4
## 9 1 1 1 V1 V2 V3 V4
## 10 1 1 1 V1 V2 V3 V4
## 11 1 1 1 V1 V2 V3 V4
## 12 1 1 1 V1 V2 V3 V4
## 13 1 1 4 V1 V2 V3 V4
## 14 1 1 5 V1 V2 V3 V4
## 15 1 1 5 V1 V2 V3 V4
## 16 1 1 5 V1 V2 V3 V4
## 17 1 1 1 V1 V2 V3 V4
## visit.5
## 1 V5
## 2 V5
## 3 V5
## 4 V5
## 5 V5
## 6 V5
## 7 V5
## 8 V5
## 9 V5
## 10 V5
## 11 V5
## 12 V5
## 13 V5
## 14 V5
## 15 V5
## 16 V5
## 17 V5#detection constante
# Hors réserve
m0_hors <- occu(~1 ~1, data = umf_hors)
# Réserve
m0_reserve <- occu(~1 ~1, data = umf_reserve)
m0_hors##
## Call:
## occu(formula = ~1 ~ 1, data = umf_hors)
##
## Occupancy (logit-scale):
## Estimate SE z P(>|z|)
## -0.504 0.732 -0.688 0.491
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## 0.68 0.557 1.22 0.222
##
## AIC: 33.65516
## Number of sites: 8m0_reserve##
## Call:
## occu(formula = ~1 ~ 1, data = umf_reserve)
##
## Occupancy (logit-scale):
## Estimate SE z P(>|z|)
## -0.639 0.853 -0.75 0.454
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## -1.36 0.725 -1.88 0.0607
##
## AIC: 44.93397
## Number of sites: 17predict(m0_reserve, type = "det")## Predicted SE lower upper
## 1 0.2043329 0.11783 0.05841966 0.5152569
## 2 0.2043329 0.11783 0.05841966 0.5152569
## 3 0.2043329 0.11783 0.05841966 0.5152569
## 4 0.2043329 0.11783 0.05841966 0.5152569
## 5 0.2043329 0.11783 0.05841966 0.5152569
## 6 0.2043329 0.11783 0.05841966 0.5152569
## 7 0.2043329 0.11783 0.05841966 0.5152569
## 8 0.2043329 0.11783 0.05841966 0.5152569
## 9 0.2043329 0.11783 0.05841966 0.5152569
## 10 0.2043329 0.11783 0.05841966 0.5152569
## 11 0.2043329 0.11783 0.05841966 0.5152569
## 12 0.2043329 0.11783 0.05841966 0.5152569
## 13 0.2043329 0.11783 0.05841966 0.5152569
## 14 0.2043329 0.11783 0.05841966 0.5152569
## 15 0.2043329 0.11783 0.05841966 0.5152569
## 16 0.2043329 0.11783 0.05841966 0.5152569
## 17 0.2043329 0.11783 0.05841966 0.5152569
## 18 0.2043329 0.11783 0.05841966 0.5152569
## 19 0.2043329 0.11783 0.05841966 0.5152569
## 20 0.2043329 0.11783 0.05841966 0.5152569
## 21 0.2043329 0.11783 0.05841966 0.5152569
## 22 0.2043329 0.11783 0.05841966 0.5152569
## 23 0.2043329 0.11783 0.05841966 0.5152569
## 24 0.2043329 0.11783 0.05841966 0.5152569
## 25 0.2043329 0.11783 0.05841966 0.5152569
## 26 0.2043329 0.11783 0.05841966 0.5152569
## 27 0.2043329 0.11783 0.05841966 0.5152569
## 28 0.2043329 0.11783 0.05841966 0.5152569
## 29 0.2043329 0.11783 0.05841966 0.5152569
## 30 0.2043329 0.11783 0.05841966 0.5152569
## 31 0.2043329 0.11783 0.05841966 0.5152569
## 32 0.2043329 0.11783 0.05841966 0.5152569
## 33 0.2043329 0.11783 0.05841966 0.5152569
## 34 0.2043329 0.11783 0.05841966 0.5152569
## 35 0.2043329 0.11783 0.05841966 0.5152569
## 36 0.2043329 0.11783 0.05841966 0.5152569
## 37 0.2043329 0.11783 0.05841966 0.5152569
## 38 0.2043329 0.11783 0.05841966 0.5152569
## 39 0.2043329 0.11783 0.05841966 0.5152569
## 40 0.2043329 0.11783 0.05841966 0.5152569
## 41 0.2043329 0.11783 0.05841966 0.5152569
## 42 0.2043329 0.11783 0.05841966 0.5152569
## 43 0.2043329 0.11783 0.05841966 0.5152569
## 44 0.2043329 0.11783 0.05841966 0.5152569
## 45 0.2043329 0.11783 0.05841966 0.5152569
## 46 0.2043329 0.11783 0.05841966 0.5152569
## 47 0.2043329 0.11783 0.05841966 0.5152569
## 48 0.2043329 0.11783 0.05841966 0.5152569
## 49 0.2043329 0.11783 0.05841966 0.5152569
## 50 0.2043329 0.11783 0.05841966 0.5152569
## 51 0.2043329 0.11783 0.05841966 0.5152569
## 52 0.2043329 0.11783 0.05841966 0.5152569
## 53 0.2043329 0.11783 0.05841966 0.5152569
## 54 0.2043329 0.11783 0.05841966 0.5152569
## 55 0.2043329 0.11783 0.05841966 0.5152569
## 56 0.2043329 0.11783 0.05841966 0.5152569
## 57 0.2043329 0.11783 0.05841966 0.5152569
## 58 0.2043329 0.11783 0.05841966 0.5152569
## 59 0.2043329 0.11783 0.05841966 0.5152569
## 60 0.2043329 0.11783 0.05841966 0.5152569
## 61 0.2043329 0.11783 0.05841966 0.5152569
## 62 0.2043329 0.11783 0.05841966 0.5152569
## 63 0.2043329 0.11783 0.05841966 0.5152569
## 64 0.2043329 0.11783 0.05841966 0.5152569
## 65 0.2043329 0.11783 0.05841966 0.5152569
## 66 0.2043329 0.11783 0.05841966 0.5152569
## 67 0.2043329 0.11783 0.05841966 0.5152569
## 68 0.2043329 0.11783 0.05841966 0.5152569
## 69 0.2043329 0.11783 0.05841966 0.5152569
## 70 0.2043329 0.11783 0.05841966 0.5152569
## 71 0.2043329 0.11783 0.05841966 0.5152569
## 72 0.2043329 0.11783 0.05841966 0.5152569
## 73 0.2043329 0.11783 0.05841966 0.5152569
## 74 0.2043329 0.11783 0.05841966 0.5152569
## 75 0.2043329 0.11783 0.05841966 0.5152569
## 76 0.2043329 0.11783 0.05841966 0.5152569
## 77 0.2043329 0.11783 0.05841966 0.5152569
## 78 0.2043329 0.11783 0.05841966 0.5152569
## 79 0.2043329 0.11783 0.05841966 0.5152569
## 80 0.2043329 0.11783 0.05841966 0.5152569
## 81 0.2043329 0.11783 0.05841966 0.5152569
## 82 0.2043329 0.11783 0.05841966 0.5152569
## 83 0.2043329 0.11783 0.05841966 0.5152569
## 84 0.2043329 0.11783 0.05841966 0.5152569
## 85 0.2043329 0.11783 0.05841966 0.5152569predict(m0_reserve, type = "state")## Predicted SE lower upper
## 1 0.3454687 0.1927732 0.09030331 0.7372846
## 2 0.3454687 0.1927732 0.09030331 0.7372846
## 3 0.3454687 0.1927732 0.09030331 0.7372846
## 4 0.3454687 0.1927732 0.09030331 0.7372846
## 5 0.3454687 0.1927732 0.09030331 0.7372846
## 6 0.3454687 0.1927732 0.09030331 0.7372846
## 7 0.3454687 0.1927732 0.09030331 0.7372846
## 8 0.3454687 0.1927732 0.09030331 0.7372846
## 9 0.3454687 0.1927732 0.09030331 0.7372846
## 10 0.3454687 0.1927732 0.09030331 0.7372846
## 11 0.3454687 0.1927732 0.09030331 0.7372846
## 12 0.3454687 0.1927732 0.09030331 0.7372846
## 13 0.3454687 0.1927732 0.09030331 0.7372846
## 14 0.3454687 0.1927732 0.09030331 0.7372846
## 15 0.3454687 0.1927732 0.09030331 0.7372846
## 16 0.3454687 0.1927732 0.09030331 0.7372846
## 17 0.3454687 0.1927732 0.09030331 0.7372846predict(m0_hors, type = "det")## Predicted SE lower upper
## 1 0.663812 0.1243886 0.3984036 0.8548028
## 2 0.663812 0.1243886 0.3984036 0.8548028
## 3 0.663812 0.1243886 0.3984036 0.8548028
## 4 0.663812 0.1243886 0.3984036 0.8548028
## 5 0.663812 0.1243886 0.3984036 0.8548028
## 6 0.663812 0.1243886 0.3984036 0.8548028
## 7 0.663812 0.1243886 0.3984036 0.8548028
## 8 0.663812 0.1243886 0.3984036 0.8548028
## 9 0.663812 0.1243886 0.3984036 0.8548028
## 10 0.663812 0.1243886 0.3984036 0.8548028
## 11 0.663812 0.1243886 0.3984036 0.8548028
## 12 0.663812 0.1243886 0.3984036 0.8548028
## 13 0.663812 0.1243886 0.3984036 0.8548028
## 14 0.663812 0.1243886 0.3984036 0.8548028
## 15 0.663812 0.1243886 0.3984036 0.8548028
## 16 0.663812 0.1243886 0.3984036 0.8548028
## 17 0.663812 0.1243886 0.3984036 0.8548028
## 18 0.663812 0.1243886 0.3984036 0.8548028
## 19 0.663812 0.1243886 0.3984036 0.8548028
## 20 0.663812 0.1243886 0.3984036 0.8548028
## 21 0.663812 0.1243886 0.3984036 0.8548028
## 22 0.663812 0.1243886 0.3984036 0.8548028
## 23 0.663812 0.1243886 0.3984036 0.8548028
## 24 0.663812 0.1243886 0.3984036 0.8548028
## 25 0.663812 0.1243886 0.3984036 0.8548028
## 26 0.663812 0.1243886 0.3984036 0.8548028
## 27 0.663812 0.1243886 0.3984036 0.8548028
## 28 0.663812 0.1243886 0.3984036 0.8548028
## 29 0.663812 0.1243886 0.3984036 0.8548028
## 30 0.663812 0.1243886 0.3984036 0.8548028
## 31 0.663812 0.1243886 0.3984036 0.8548028
## 32 0.663812 0.1243886 0.3984036 0.8548028
## 33 0.663812 0.1243886 0.3984036 0.8548028
## 34 0.663812 0.1243886 0.3984036 0.8548028
## 35 0.663812 0.1243886 0.3984036 0.8548028
## 36 0.663812 0.1243886 0.3984036 0.8548028
## 37 0.663812 0.1243886 0.3984036 0.8548028
## 38 0.663812 0.1243886 0.3984036 0.8548028
## 39 0.663812 0.1243886 0.3984036 0.8548028
## 40 0.663812 0.1243886 0.3984036 0.8548028predict(m0_reserve, type = "det")## Predicted SE lower upper
## 1 0.2043329 0.11783 0.05841966 0.5152569
## 2 0.2043329 0.11783 0.05841966 0.5152569
## 3 0.2043329 0.11783 0.05841966 0.5152569
## 4 0.2043329 0.11783 0.05841966 0.5152569
## 5 0.2043329 0.11783 0.05841966 0.5152569
## 6 0.2043329 0.11783 0.05841966 0.5152569
## 7 0.2043329 0.11783 0.05841966 0.5152569
## 8 0.2043329 0.11783 0.05841966 0.5152569
## 9 0.2043329 0.11783 0.05841966 0.5152569
## 10 0.2043329 0.11783 0.05841966 0.5152569
## 11 0.2043329 0.11783 0.05841966 0.5152569
## 12 0.2043329 0.11783 0.05841966 0.5152569
## 13 0.2043329 0.11783 0.05841966 0.5152569
## 14 0.2043329 0.11783 0.05841966 0.5152569
## 15 0.2043329 0.11783 0.05841966 0.5152569
## 16 0.2043329 0.11783 0.05841966 0.5152569
## 17 0.2043329 0.11783 0.05841966 0.5152569
## 18 0.2043329 0.11783 0.05841966 0.5152569
## 19 0.2043329 0.11783 0.05841966 0.5152569
## 20 0.2043329 0.11783 0.05841966 0.5152569
## 21 0.2043329 0.11783 0.05841966 0.5152569
## 22 0.2043329 0.11783 0.05841966 0.5152569
## 23 0.2043329 0.11783 0.05841966 0.5152569
## 24 0.2043329 0.11783 0.05841966 0.5152569
## 25 0.2043329 0.11783 0.05841966 0.5152569
## 26 0.2043329 0.11783 0.05841966 0.5152569
## 27 0.2043329 0.11783 0.05841966 0.5152569
## 28 0.2043329 0.11783 0.05841966 0.5152569
## 29 0.2043329 0.11783 0.05841966 0.5152569
## 30 0.2043329 0.11783 0.05841966 0.5152569
## 31 0.2043329 0.11783 0.05841966 0.5152569
## 32 0.2043329 0.11783 0.05841966 0.5152569
## 33 0.2043329 0.11783 0.05841966 0.5152569
## 34 0.2043329 0.11783 0.05841966 0.5152569
## 35 0.2043329 0.11783 0.05841966 0.5152569
## 36 0.2043329 0.11783 0.05841966 0.5152569
## 37 0.2043329 0.11783 0.05841966 0.5152569
## 38 0.2043329 0.11783 0.05841966 0.5152569
## 39 0.2043329 0.11783 0.05841966 0.5152569
## 40 0.2043329 0.11783 0.05841966 0.5152569
## 41 0.2043329 0.11783 0.05841966 0.5152569
## 42 0.2043329 0.11783 0.05841966 0.5152569
## 43 0.2043329 0.11783 0.05841966 0.5152569
## 44 0.2043329 0.11783 0.05841966 0.5152569
## 45 0.2043329 0.11783 0.05841966 0.5152569
## 46 0.2043329 0.11783 0.05841966 0.5152569
## 47 0.2043329 0.11783 0.05841966 0.5152569
## 48 0.2043329 0.11783 0.05841966 0.5152569
## 49 0.2043329 0.11783 0.05841966 0.5152569
## 50 0.2043329 0.11783 0.05841966 0.5152569
## 51 0.2043329 0.11783 0.05841966 0.5152569
## 52 0.2043329 0.11783 0.05841966 0.5152569
## 53 0.2043329 0.11783 0.05841966 0.5152569
## 54 0.2043329 0.11783 0.05841966 0.5152569
## 55 0.2043329 0.11783 0.05841966 0.5152569
## 56 0.2043329 0.11783 0.05841966 0.5152569
## 57 0.2043329 0.11783 0.05841966 0.5152569
## 58 0.2043329 0.11783 0.05841966 0.5152569
## 59 0.2043329 0.11783 0.05841966 0.5152569
## 60 0.2043329 0.11783 0.05841966 0.5152569
## 61 0.2043329 0.11783 0.05841966 0.5152569
## 62 0.2043329 0.11783 0.05841966 0.5152569
## 63 0.2043329 0.11783 0.05841966 0.5152569
## 64 0.2043329 0.11783 0.05841966 0.5152569
## 65 0.2043329 0.11783 0.05841966 0.5152569
## 66 0.2043329 0.11783 0.05841966 0.5152569
## 67 0.2043329 0.11783 0.05841966 0.5152569
## 68 0.2043329 0.11783 0.05841966 0.5152569
## 69 0.2043329 0.11783 0.05841966 0.5152569
## 70 0.2043329 0.11783 0.05841966 0.5152569
## 71 0.2043329 0.11783 0.05841966 0.5152569
## 72 0.2043329 0.11783 0.05841966 0.5152569
## 73 0.2043329 0.11783 0.05841966 0.5152569
## 74 0.2043329 0.11783 0.05841966 0.5152569
## 75 0.2043329 0.11783 0.05841966 0.5152569
## 76 0.2043329 0.11783 0.05841966 0.5152569
## 77 0.2043329 0.11783 0.05841966 0.5152569
## 78 0.2043329 0.11783 0.05841966 0.5152569
## 79 0.2043329 0.11783 0.05841966 0.5152569
## 80 0.2043329 0.11783 0.05841966 0.5152569
## 81 0.2043329 0.11783 0.05841966 0.5152569
## 82 0.2043329 0.11783 0.05841966 0.5152569
## 83 0.2043329 0.11783 0.05841966 0.5152569
## 84 0.2043329 0.11783 0.05841966 0.5152569
## 85 0.2043329 0.11783 0.05841966 0.5152569#Test Ajustement
obs.boot.m0_reserve <- AICcmodavg::mb.gof.test(m0_reserve, nsim = 100, lot.hist=F)
obs.boot.m0_reserve##
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
##
## Pearson chi-square table:
##
## Cohort Observed Expected Chi-square
## 00000 0 13 13.00 0.00
## 00001 0 1 0.48 0.56
## 01000 0 1 0.48 0.56
## 10001 0 2 0.12 28.51
##
## Chi-square statistic = 32.5424
## Number of bootstrap samples = 100
## P-value = 0.08
##
## Quantiles of bootstrapped statistics:
## 0% 25% 50% 75% 100%
## 2.7e-08 1.4e+01 1.8e+01 2.6e+01 4.8e+01
##
## Estimate of c-hat = 1.69obs.boot.m0_hors <- AICcmodavg::mb.gof.test(m0_hors, nsim = 100, lot.hist=F)
obs.boot.m0_hors##
## MacKenzie and Bailey goodness-of-fit for single-season occupancy model
##
## Pearson chi-square table:
##
## Cohort Observed Expected Chi-square
## 00000 0 5 5.0 0.00
## 01101 0 1 0.1 8.14
## 10011 0 1 0.1 8.14
## 11101 0 1 0.2 3.28
##
## Chi-square statistic = 22.1662
## Number of bootstrap samples = 100
## P-value = 0.65
##
## Quantiles of bootstrapped statistics:
## 0% 25% 50% 75% 100%
## 1.2e-08 1.8e+01 2.7e+01 2.9e+01 5.1e+01
##
## Estimate of c-hat = 0.93Les mêmes script de modèle d’occupation ont été réalisés pour les autres années (2026, 2020, 2018, 2015)
Evolution de la probabilité d’occupation et de détection
Comparaison de la probabilité d’occupation du Lézard ocellé entre les placettes du secteurs «réserve» et «hors réserve» de 2015 à 2026
library(ggplot2)
occ <- data.frame(
Annee = c(2015,2015,
2018,2018,
2020,2020,
2022,2022,
2024,2024,
2026,2026),
Secteur = c("Réserve","Hors réserve",
"Réserve","Hors réserve",
"Réserve","Hors réserve",
"Réserve","Hors réserve",
"Réserve","Hors réserve",
"Réserve","Hors réserve"),
occu = c(0.125,0.75,
0.128,0.636,
0.188,0.625,
0.118,0.626,
0.287,0.375,
0.326,0.376),
SE = c(0.083,0.153,
0.085,0.175,
0.098,0.171,
0.078,0.171,
0.144,0.171,
0.264,0.171),
IC_inf = c(0.031,0.377,
0.032,0.285,
0.062,0.289,
0.030,0.285,
0.093,0.125,
0.044,0.126),
IC_sup = c(0.385,0.937,
0.394,0.884,
0.449,0.875,
0.369,0.876,
0.614,0.715,
0.836,0.716))#graphique
library(ggplot2)
#Comparaison de la probabilité d'occupation du Lézard ocellé entre les placettes du secteurs «réserve» et «hors réserve» de 2015 à 2026
ggplot(occ, aes(x = factor(Annee), y = occu, fill = Secteur)) +
geom_col(position = position_dodge(width = 0.7),width = 0.6,colour = "black",linewidth = 0.3) +
geom_errorbar(aes(ymin = occu - SE, ymax = occu + SE),position = position_dodge(width = 0.7),width = 0.2,linewidth = 0.5) +
scale_fill_manual(values = c("Réserve" = "darkgreen","Hors réserve" = "red3")) +
labs(x = "Année",y = "Probabilité d'occupation",fill = "") +
ylim(0, 1) +
theme_classic(base_size = 10) +
theme(panel.grid.major.y = element_line(colour = "grey80"),panel.grid.minor = element_blank(),panel.grid.major.x = element_blank(),legend.position = "top")
#Avec IC
ggplot(occ, aes(x = factor(Annee), y = occu, fill = Secteur)) +
geom_col(position = position_dodge(width = 0.7),width = 0.6,colour = "black",linewidth = 0.3) +
geom_errorbar(aes(ymin = IC_inf,ymax = IC_sup),position = position_dodge(width = 0.7),width = 0.2,linewidth = 0.5) +
scale_fill_manual(values = c("Réserve" = "darkgreen","Hors réserve" = "red3")) +
labs(x = "Année",y = "Probabilité d'occupation",fill = "") +
ylim(0, 1) +
theme_classic(base_size = 10) +
theme(panel.grid.major.y = element_line(colour = "grey80"),panel.grid.minor = element_blank(),panel.grid.major.x = element_blank(),legend.position = "top")
#Pareil mais pour la probilité de détection
det <- data.frame(
Annee = c(2015,2015,
2018,2018,
2020,2020,
2022,2022,
2024,2024,
2026,2026),
Secteur = c("Réserve","Hors réserve",
"Réserve","Hors réserve",
"Réserve","Hors réserve",
"Réserve","Hors réserve",
"Réserve","Hors réserve",
"Réserve","Hors réserve"),
detection = c(0.900,0.900,
0.611,0.639,
0.664,0.800,
0.698,0.719,
0.204,0.664,
0.144,0.732),
SE = c(0.095,0.055,
0.185,0.114,
0.124,0.080,
0.147,0.091,
0.118,0.124,
0.125,0.115),
IC_inf = c(0.533,0.732,
0.255,0.403,
0.398,0.600,
0.371,0.514,
0.058,0.398,
0.023,0.463),
IC_sup = c(0.986,0.967,
0.878,0.823,
0.855,0.914,
0.901,0.860,
0.515,0.855,
0.551,0.897))#avec SE
ggplot(det,aes(x = factor(Annee),y = detection,colour = Secteur)) +
geom_point(position = position_dodge(width = 0.35),size = 1.4) +
geom_errorbar(aes(ymin = detection - SE, ymax = detection + SE),width = 0.2,linewidth = 0.5,position = position_dodge(width = 0.35)) +
scale_colour_manual(values = c("Réserve" = "darkgreen","Hors réserve" = "red3")) +
labs(x = "Année",y = "Probabilité de détection",colour = "") +
ylim(0,1) +
theme_classic(base_size = 10) +
theme(panel.grid.major.y = element_line(colour = "grey80"),panel.grid.minor = element_blank(),panel.grid.major.x = element_blank())
#avec IC
ggplot(det,aes(x = factor(Annee),y = detection,colour = Secteur)) +
geom_point(position = position_dodge(width = 0.35),size = 1.4) +
geom_errorbar(aes(ymin = IC_inf,ymax = IC_sup),width = 0.2,linewidth = 0.5,position = position_dodge(width = 0.35)) +
scale_colour_manual(values = c("Réserve" = "darkgreen","Hors réserve" = "red3")) +
labs(x = "Année",y = "Probabilité de détection",colour = "") +
ylim(0,1) +
theme_classic(base_size = 10) +
theme(panel.grid.major.y = element_line(colour = "grey80"),panel.grid.minor = element_blank(),panel.grid.major.x = element_blank())
Pour le site entier
#occupation
occ_site <- data.frame(
Annee = c(2015, 2018, 2020, 2022, 2024, 2026),
occu = c(0.333,
0.297,
0.334,
0.281,
0.288,
0.254),
SE = c(0.096,
0.095,
0.096,
0.088,
0.093,
0.091),
IC_inf = c(0.176,
0.148,
0.176,
0.140,
0.143,
0.117),
IC_sup = c(0.539,
0.507,
0.539,
0.483,
0.495,
0.467))#avec SE
ggplot(occ_site,aes(x = factor(Annee),y = occu)) +
geom_col(width = 0.6,fill = "steelblue3",colour = "black",linewidth = 0.3) +
geom_errorbar(aes(ymin = occu - SE,ymax = occu + SE),width = 0.2,linewidth = 0.5) +
labs(x = "Année",y = "Probabilité d'occupation") +
theme_classic(base_size = 10) +
theme(
panel.grid.major.y = element_line(colour = "grey80"),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank())
#avec IC
ggplot(occ_site,aes(x = factor(Annee),y = occu)) +
geom_col(width = 0.6,fill = "steelblue3",colour = "black",linewidth = 0.3) +
geom_errorbar(aes(ymin = IC_inf,ymax = IC_sup),width = 0.2,linewidth = 0.5) +
labs(x = "Année",y = "Probabilité d'occupation") +
theme_classic(base_size = 10) +
theme(
panel.grid.major.y = element_line(colour = "grey80"),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank())
Détection
#Détection
det_site <- data.frame(
Annee = c(2015, 2018, 2020, 2022, 2024, 2026),
detection = c(0.900,
0.631,
0.749,
0.713,
0.430,
0.481),
SE = c(0.047,
0.097,
0.069,
0.077,
0.093,
0.099),
IC_inf = c(0.762,
0.431,
0.593,
0.542,
0.263,
0.299),
IC_sup = c(0.962,
0.794,
0.860,
0.839,
0.613,
0.668))
ggplot(det_site,aes(x = factor(Annee),y = detection)) +
geom_point(size = 2,colour = "red") +
geom_errorbar(aes(ymin = detection - SE,ymax = detection + SE),width = 0.2,linewidth = 0.5) +
geom_text(aes(label = round(detection, 3)), hjust = -0.2, size = 3) +
labs(x = "Année",y = "Probabilité de détection") +
ylim(0,1) +
theme_classic(base_size = 10) +
theme(
panel.grid.major.y = element_line(colour = "grey80"),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank())
Modèle d’occupation dynamique
library(readxl)
Mod_dyn <- read_excel("C:/Users/mathi/Downloads/Mod_dyn.xlsx")
Mod_dyn## # A tibble: 24 × 135
## Placettes `2015_V1` `2015_V2` `2015_V3` `2015_V4` `2015_V5` `2018_V1`
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1 1 1 1 1
## 2 2 1 1 1 1 1 1
## 3 3 0 0 0 0 0 0
## 4 4 1 1 0 1 0 1
## 5 5 0 0 0 0 0 0
## 6 6 1 1 1 1 1 1
## 7 7 1 1 1 0 1 0
## 8 8 1 1 1 1 1 0
## 9 9 1 1 1 1 1 1
## 10 10 0 0 0 0 0 0
## # ℹ 14 more rows
## # ℹ 128 more variables: `2018_V2` <dbl>, `2018_V3` <dbl>, `2018_V4` <dbl>,
## # `2020_V1` <dbl>, `2020_V2` <dbl>, `2020_V3` <dbl>, `2020_V4` <dbl>,
## # `2020_V5` <dbl>, `2022_V1` <dbl>, `2022_V2` <dbl>, `2022_V3` <dbl>,
## # `2022_V4` <dbl>, `2022_V5` <dbl>, `2024_V1` <dbl>, `2024_V2` <dbl>,
## # `2024_V3` <dbl>, `2024_V4` <dbl>, `2024_V5` <dbl>, `2026_V1` <dbl>,
## # `2026_V2` <dbl>, `2026_V3` <dbl>, `2026_V4` <dbl>, `2026_V5` <dbl>, …#on rajoute une visite en 2018 car il n'y avait eu que 4 visites
Mod_dyn$`2018_V5` <- NA
y <- as.matrix(Mod_dyn[, c(
paste0("2015_V", 1:5),
paste0("2018_V", 1:5),
paste0("2020_V", 1:5),
paste0("2022_V", 1:5),
paste0("2024_V", 1:5),
paste0("2026_V", 1:5))])
# Vérification
dim(y)## [1] 24 30head(y)## 2015_V1 2015_V2 2015_V3 2015_V4 2015_V5 2018_V1 2018_V2 2018_V3 2018_V4
## [1,] 1 1 1 1 1 1 1 1 0
## [2,] 1 1 1 1 1 1 1 1 1
## [3,] 0 0 0 0 0 0 0 0 0
## [4,] 1 1 0 1 0 1 0 0 0
## [5,] 0 0 0 0 0 0 0 0 0
## [6,] 1 1 1 1 1 1 1 1 0
## 2018_V5 2020_V1 2020_V2 2020_V3 2020_V4 2020_V5 2022_V1 2022_V2 2022_V3
## [1,] NA 1 1 1 1 1 1 1 1
## [2,] NA 1 1 1 1 1 1 1 1
## [3,] NA 0 0 0 0 0 0 0 0
## [4,] NA 0 0 1 0 0 0 1 1
## [5,] NA 0 0 0 0 0 0 0 0
## [6,] NA 1 1 1 1 1 1 1 1
## 2022_V4 2022_V5 2024_V1 2024_V2 2024_V3 2024_V4 2024_V5 2026_V1 2026_V2
## [1,] 1 1 0 1 1 0 1 1 0
## [2,] 1 1 1 1 1 0 1 1 1
## [3,] 0 0 0 0 0 0 0 0 0
## [4,] 1 1 0 0 0 0 0 0 0
## [5,] 0 1 0 0 0 0 0 0 0
## [6,] 0 0 1 0 0 1 1 1 1
## 2026_V3 2026_V4 2026_V5
## [1,] 0 0 0
## [2,] 1 1 1
## [3,] 0 0 0
## [4,] 0 0 0
## [5,] 0 0 0
## [6,] 1 1 1Ter <- as.matrix(Mod_dyn[, c(
"Ter_2015", "Ter_2018", "Ter_2020",
"Ter_2022", "Ter_2024", "Ter_2026")])
Veg <- as.matrix(Mod_dyn[, c(
"Veg_2015", "Veg_2018", "Veg_2020",
"Veg_2022", "Veg_2024", "Veg_2026")])
GA <- as.matrix(Mod_dyn[, c(
"G_A_2015", "G_A_2018", "G_A_2020",
"G_A_2022", "G_A_2024", "G_A_2026")])
#Création de l’objet dynamique
siteCovs_dyn <- data.frame(
Ter_2015 = Mod_dyn$Ter_2015,
Veg_2015 = Mod_dyn$Veg_2015,
GA_2015 = Mod_dyn$G_A_2015)
umf_dyn <- unmarkedMultFrame(
y = y,
siteCovs = siteCovs_dyn,
yearlySiteCovs = list(
Ter = Ter,
Veg = Veg,
GA = GA),
numPrimary = 6)
umf_dyn## Data frame representation of unmarkedFrame object.
## y.1 y.2 y.3 y.4 y.5 y.6 y.7 y.8 y.9 y.10 y.11 y.12 y.13 y.14 y.15 y.16 y.17
## 1 1 1 1 1 1 1 1 1 0 NA 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1 1 1 1 NA 1 1 1 1 1 1 1
## 3 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 4 1 1 0 1 0 1 0 0 0 NA 0 0 1 0 0 0 1
## 5 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 6 1 1 1 1 1 1 1 1 0 NA 1 1 1 1 1 1 1
## 7 1 1 1 0 1 0 0 0 0 NA 0 0 0 0 0 0 0
## 8 1 1 1 1 1 0 1 1 0 NA 0 1 1 1 1 0 0
## 9 1 1 1 1 1 1 1 0 0 NA 1 1 1 1 0 1 1
## 10 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 11 0 1 1 1 1 0 1 1 1 NA 1 1 0 1 1 0 1
## 12 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 0 NA 0 0 0 1 1 0 0
## 16 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 20 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0 0 NA 0 0 0 0 0 0 0
## y.18 y.19 y.20 y.21 y.22 y.23 y.24 y.25 y.26 y.27 y.28 y.29 y.30 Ter_2015
## 1 1 1 1 0 1 1 0 1 1 0 0 0 0 1
## 2 1 1 1 1 1 1 0 1 1 1 1 1 1 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 1 1 1 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 6 1 0 0 1 0 0 1 1 1 1 1 1 1 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 9 1 1 0 1 0 0 0 1 0 1 1 0 0 0
## 10 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## 11 0 1 1 1 0 0 0 1 0 1 0 0 0 1
## 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 16 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Veg_2015 GA_2015 Ter.1 Ter.2 Ter.3 Ter.4 Ter.5 Ter.6 Veg.1 Veg.2 Veg.3 Veg.4
## 1 1 0 1 0 1 1 1 1 1 1 1 1
## 2 0 1 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 1 1 0 0 0 0 0 1 1 1 1 1
## 5 1 0 0 0 0 1 0 0 1 1 1 1
## 6 1 1 0 0 0 1 0 1 1 1 1 1
## 7 1 0 0 0 0 0 0 0 1 1 1 1
## 8 1 1 1 0 1 0 0 0 1 1 1 1
## 9 0 1 0 0 0 0 0 0 0 0 1 1
## 10 1 0 0 0 0 0 0 0 1 1 1 1
## 11 0 0 1 1 1 1 1 1 0 1 1 1
## 12 1 0 0 0 1 1 1 1 1 1 1 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 1
## 15 1 0 0 0 0 0 0 0 1 1 1 0
## 16 0 0 0 0 0 0 0 0 0 0 0 1
## 17 0 0 1 0 1 1 1 0 0 1 1 1
## 18 0 0 0 0 0 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 0 0 1 1 1
## 20 0 0 0 0 0 0 0 0 0 0 0 0
## 21 1 0 0 0 0 0 1 0 1 1 1 1
## 22 1 0 1 0 1 0 0 0 1 1 1 1
## 23 1 0 1 1 1 0 0 0 1 1 1 1
## 24 0 0 0 0 0 0 0 0 0 0 0 0
## Veg.5 Veg.6 GA.1 GA.2 GA.3 GA.4 GA.5 GA.6
## 1 1 1 0 0 0 0 0 0
## 2 0 0 1 1 1 1 1 1
## 3 0 0 0 0 0 0 0 0
## 4 1 1 1 1 1 1 1 1
## 5 1 1 0 0 0 0 0 0
## 6 1 1 1 1 1 1 1 1
## 7 1 1 0 0 0 0 0 0
## 8 1 1 1 1 1 1 1 1
## 9 1 1 1 1 1 1 1 1
## 10 1 1 0 0 0 0 0 0
## 11 1 1 0 0 0 0 0 0
## 12 1 1 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0
## 15 1 1 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 0
## 17 1 1 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 0
## 19 1 1 0 0 0 0 0 0
## 20 0 0 0 0 0 0 0 0
## 21 1 1 0 0 0 0 0 0
## 22 1 1 0 0 0 0 0 0
## 23 1 1 0 0 0 0 0 1
## 24 0 0 0 0 0 0 0 0#Premier modèle dynamique nul
m_dyn0 <- colext(
psiformula = ~1,
gammaformula = ~1,
epsilonformula = ~1,
pformula = ~1,
data = umf_dyn)
m_dyn0##
## Call:
## colext(psiformula = ~1, gammaformula = ~1, epsilonformula = ~1,
## pformula = ~1, data = umf_dyn)
##
## Initial (logit-scale):
## Estimate SE z P(>|z|)
## -0.692 0.433 -1.6 0.11
##
## Colonization (logit-scale):
## Estimate SE z P(>|z|)
## -2.99 0.513 -5.84 5.17e-09
##
## Extinction (logit-scale):
## Estimate SE z P(>|z|)
## -1.61 0.448 -3.6 0.000323
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## 0.771 0.152 5.07 3.96e-07
##
## AIC: 356.0707
## Number of sites: 24#modèle avec Gites anthropique
m_dyn_GA <- colext(
psiformula = ~ GA_2015,
gammaformula = ~ GA,
epsilonformula = ~ GA,
pformula = ~1,
data = umf_dyn)
m_dyn_GA##
## Call:
## colext(psiformula = ~GA_2015, gammaformula = ~GA, epsilonformula = ~GA,
## pformula = ~1, data = umf_dyn)
##
## Initial (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) -1.67 0.629 -2.6584 0.00785
## GA_2015 13.72 184.304 0.0744 0.94068
##
## Colonization (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) -2.95 0.513 -5.7621 8.31e-09
## GA -7.89 131.257 -0.0601 9.52e-01
##
## Extinction (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) -0.92 0.593 -1.55 0.121
## GA -1.38 0.949 -1.46 0.145
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## 0.772 0.152 5.09 3.68e-07
##
## AIC: 345.5344
## Number of sites: 24## Warning: Large or missing SE values. Be very cautious using these results.#pour les terriers
m_dyn_Ter <- colext(
psiformula = ~ Ter_2015,
gammaformula = ~ Ter,
epsilonformula = ~ Ter,
pformula = ~ 1,
data = umf_dyn)
#pour la veg
m_dyn_Veg <- colext(
psiformula = ~ Veg_2015,
gammaformula = ~ Veg,
epsilonformula = ~ Veg,
pformula = ~ 1,
data = umf_dyn)
# GA + Terriers
m_dyn_GA_Ter <- colext(
psiformula = ~ GA_2015 + Ter_2015,
gammaformula = ~ GA + Ter,
epsilonformula = ~ GA + Ter,
pformula = ~ 1,
data = umf_dyn)
# GA + Végétation
m_dyn_GA_Veg <- colext(
psiformula = ~ GA_2015 + Veg_2015,
gammaformula = ~ GA + Veg,
epsilonformula = ~ GA + Veg,
pformula = ~ 1,
data = umf_dyn)
# Terriers + Végétation
m_dyn_Ter_Veg <- colext(
psiformula = ~ Ter_2015 + Veg_2015,
gammaformula = ~ Ter + Veg,
epsilonformula = ~ Ter + Veg,
pformula = ~ 1,
data = umf_dyn)
# GA + Terriers + Végétation
m_dyn_GA_Ter_Veg <- colext(
psiformula = ~ GA_2015 + Ter_2015 + Veg_2015,
gammaformula = ~ GA + Ter + Veg,
epsilonformula = ~ GA + Ter + Veg,
pformula = ~ 1,
data = umf_dyn)
# GA + Terriers
m_dyn_GA_Ter <- colext(
psiformula = ~ GA_2015 + Ter_2015,
gammaformula = ~ GA + Ter,
epsilonformula = ~ GA + Ter,
pformula = ~ 1,
data = umf_dyn)
# GA + Végétation
m_dyn_GA_Veg <- colext(
psiformula = ~ GA_2015 + Veg_2015,
gammaformula = ~ GA + Veg,
epsilonformula = ~ GA + Veg,
pformula = ~ 1,
data = umf_dyn)
# Terriers + Végétation
m_dyn_Ter_Veg <- colext(
psiformula = ~ Ter_2015 + Veg_2015,
gammaformula = ~ Ter + Veg,
epsilonformula = ~ Ter + Veg,
pformula = ~ 1,
data = umf_dyn)#Comparaison des modèles
models_dyn <- fitList(
"psi(.) gamma(.) epsilon(.) p(.)" = m_dyn0,
"psi(GA) gamma(GA) epsilon(GA) p(.)" = m_dyn_GA,
"psi(Ter) gamma(Ter) epsilon(Ter) p(.)" = m_dyn_Ter,
"psi(Veg) gamma(Veg) epsilon(Veg) p(.)" = m_dyn_Veg,
"psi(GA+Ter) gamma(GA+Ter) epsilon(GA+Ter) p(.)" = m_dyn_GA_Ter,
"psi(GA+Veg) gamma(GA+Veg) epsilon(GA+Veg) p(.)" = m_dyn_GA_Veg,
"psi(Ter+Veg) gamma(Ter+Veg) epsilon(Ter+Veg) p(.)" = m_dyn_Ter_Veg,
"psi(GA+Ter+Veg) gamma(GA+Ter+Veg) epsilon(GA+Ter+Veg) p(.)" = m_dyn_GA_Ter_Veg)
modSel(models_dyn)## nPars AIC delta
## psi(GA+Ter+Veg) gamma(GA+Ter+Veg) epsilon(GA+Ter+Veg) p(.) 13 343.17 0.00
## psi(GA+Veg) gamma(GA+Veg) epsilon(GA+Veg) p(.) 10 343.63 0.46
## psi(GA) gamma(GA) epsilon(GA) p(.) 7 345.53 2.37
## psi(GA+Ter) gamma(GA+Ter) epsilon(GA+Ter) p(.) 10 345.79 2.62
## psi(Veg) gamma(Veg) epsilon(Veg) p(.) 7 353.20 10.03
## psi(Ter+Veg) gamma(Ter+Veg) epsilon(Ter+Veg) p(.) 10 355.95 12.79
## psi(.) gamma(.) epsilon(.) p(.) 4 356.07 12.90
## psi(Ter) gamma(Ter) epsilon(Ter) p(.) 7 359.70 16.54
## AICwt cumltvWt
## psi(GA+Ter+Veg) gamma(GA+Ter+Veg) epsilon(GA+Ter+Veg) p(.) 0.42012 0.42
## psi(GA+Veg) gamma(GA+Veg) epsilon(GA+Veg) p(.) 0.33389 0.75
## psi(GA) gamma(GA) epsilon(GA) p(.) 0.12861 0.88
## psi(GA+Ter) gamma(GA+Ter) epsilon(GA+Ter) p(.) 0.11311 1.00
## psi(Veg) gamma(Veg) epsilon(Veg) p(.) 0.00279 1.00
## psi(Ter+Veg) gamma(Ter+Veg) epsilon(Ter+Veg) p(.) 0.00070 1.00
## psi(.) gamma(.) epsilon(.) p(.) 0.00066 1.00
## psi(Ter) gamma(Ter) epsilon(Ter) p(.) 0.00011 1.00Les deux premiers modèles ont ΔAIC<2, Ils sont donc considérés comme équivalents. Cela signifie que les données ne permettent pas de départager clairement : le modèle complet et le modèle sans les terriers. On remarque que : les gîtes anthropiques apparaissent dans tous les meilleurs modèles ; la végétation apparaît aussi systématiquement ; les terriers n’améliorent que très légèrement l’ajustement. Autrement dit, les gîtes anthropiques semblent être la covariable la plus importante dans la dynamique d’occupation.
Le modèle nul a : AIC = 356.07 Le meilleur : AIC = 343.17 soit ΔAIC=12.9 C’est énorme. On peut donc conclure que les covariables expliquent une part importante de la dynamique d’occupation.
#estimation du modèle nul
summary(m_dyn0)##
## Call:
## colext(psiformula = ~1, gammaformula = ~1, epsilonformula = ~1,
## pformula = ~1, data = umf_dyn)
##
## Initial (logit-scale):
## Estimate SE z P(>|z|)
## -0.692 0.433 -1.6 0.11
##
## Colonization (logit-scale):
## Estimate SE z P(>|z|)
## -2.99 0.513 -5.84 5.17e-09
##
## Extinction (logit-scale):
## Estimate SE z P(>|z|)
## -1.61 0.448 -3.6 0.000323
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## 0.771 0.152 5.07 3.96e-07
##
## AIC: 356.0707
## Number of sites: 24backTransform(m_dyn0, type = "psi")## Backtransformed linear combination(s) of Initial estimate(s)
##
## Estimate SE LinComb (Intercept)
## 0.334 0.0963 -0.692 1
##
## Transformation: logisticLe modèle estime qu’en 2015 environ 33 % des placettes étaient occupées.
backTransform(m_dyn0, type = "col")## Backtransformed linear combination(s) of Colonization estimate(s)
##
## Estimate SE LinComb (Intercept)
## 0.0477 0.0233 -2.99 1
##
## Transformation: logisticSeulement 4.8 % de probabilité qu’une placette vide soit colonisée entre deux campagnes. C’est très faible. Biologiquement cela signifie que les nouvelles installations sont rares.
backTransform(m_dyn0, type = "ext")## Backtransformed linear combination(s) of Extinction estimate(s)
##
## Estimate SE LinComb (Intercept)
## 0.166 0.0621 -1.61 1
##
## Transformation: logisticEnviron 17 % des placettes occupées disparaissent entre deux campagnes.
backTransform(m_dyn0, type = "det")## Backtransformed linear combination(s) of Detection estimate(s)
##
## Estimate SE LinComb (Intercept)
## 0.684 0.0329 0.771 1
##
## Transformation: logisticTrès cohérent avec les modèles annuels. Le protocole détecte un lézard environ 68 % du temps lorsqu’il est présent. Au cours de l’ensemble du suivi 2015–2026, lorsqu’une placette est occupée, la probabilité moyenne de détecter le Lézard ocellé lors d’une visite est de 68,4 %.
#Meilleur modèle
summary(m_dyn_GA_Ter_Veg)##
## Call:
## colext(psiformula = ~GA_2015 + Ter_2015 + Veg_2015, gammaformula = ~GA +
## Ter + Veg, epsilonformula = ~GA + Ter + Veg, pformula = ~1,
## data = umf_dyn)
##
## Initial (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) -2.932 1.33 -2.20000 0.0278
## GA_2015 23.794 19483.18 0.00122 0.9990
## Ter_2015 2.077 1.40 1.48642 0.1372
## Veg_2015 0.728 1.43 0.51072 0.6095
##
## Colonization (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) -15.4 375 -0.04109 0.967
## GA -12.6 862 -0.01465 0.988
## Ter -15.5 1931 -0.00805 0.994
## Veg 13.4 375 0.03582 0.971
##
## Extinction (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) -11.89 327.76 -0.0363 0.971
## GA -1.82 1.18 -1.5362 0.124
## Ter -1.48 1.21 -1.2245 0.221
## Veg 12.02 327.76 0.0367 0.971
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## 0.772 0.152 5.09 3.67e-07
##
## AIC: 343.1669
## Number of sites: 24## Warning: Large or missing SE values. Be very cautious using these results.ce modèle est probablement trop complexe pour le jeu de données. erreurs standards gigantesques sont un signal d’alarme. Elles signifient que le modèle n’arrive pas à estimer correctement ces coefficients. Pas assez de placettes AIC est meilleur. Mais les coefficients sont mauvais. L’AIC te dit ce modèle décrit mieux les données. Mais les coefficients disent : je n’arrive pas à identifier précisément les effets individuels. Donc juste dire que c’est le meileur modèle
Ce que je présente dans les résultats du modèle dynamique modèle avec des probabilités d’extinction, de colonisation et de détection dépendant de l’année.
#modèle avec des probabilités d'extinction, de colonisation et de détection dépendant de l'année.
year <- matrix(
rep(c("2015", "2018", "2020", "2022", "2024", "2026"),
each = 5),
nrow = nrow(y),
ncol = ncol(y),
byrow = TRUE)
year <- as.data.frame(year)
year[] <- lapply(year, factor)
# Année pour gamma et epsilon : 24 sites x 6 années
year_site <- matrix(
rep(c("2015", "2018", "2020", "2022", "2024", "2026"),
each = nrow(y)),
nrow = nrow(y),
ncol = 6)
year_site <- as.data.frame(year_site)
year_site[] <- lapply(year_site, factor)
# Année pour la détection : 24 sites x 30 visites
year_obs <- matrix(
rep(rep(c("2015", "2018", "2020", "2022", "2024", "2026"),
each = 5),
each = nrow(y)),
nrow = nrow(y),
ncol = ncol(y))
year_obs <- as.data.frame(year_obs)
year_obs[] <- lapply(year_obs, factor)
umf_dyn_year <- unmarkedMultFrame(
y = y,
siteCovs = siteCovs_dyn,
yearlySiteCovs = list(
Ter = Ter,
Veg = Veg,
GA = GA,
year_site = year_site),
obsCovs = list(
year_obs = year_obs),
numPrimary = 6)## Warning: obsCovs contains characters. Converting them to factors.## Warning: yearlySiteCovs contains characters. Converting them to factors.#mise en place des différents modèles
m_year <- colext(
psiformula = ~1,
gammaformula = ~ year_site,
epsilonformula = ~ year_site,
pformula = ~ year_obs,
data = umf_dyn_year,
method = "BFGS"
)
summary(m_year)##
## Call:
## colext(psiformula = ~1, gammaformula = ~year_site, epsilonformula = ~year_site,
## pformula = ~year_obs, data = umf_dyn_year, method = "BFGS")
##
## Initial (logit-scale):
## Estimate SE z P(>|z|)
## -0.693 0.433 -1.6 0.109
##
## Colonization (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) -16.2 812 -0.0199 0.984
## year_site2018 13.4 812 0.0165 0.987
## year_site2020 13.4 812 0.0166 0.987
## year_site2022 13.4 812 0.0165 0.987
## year_site2024 13.3 812 0.0164 0.987
##
## Extinction (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) -1.946 1.07 -1.82023 0.0687
## year_site2018 -15.225 2041.38 -0.00746 0.9940
## year_site2020 0.844 1.35 0.62652 0.5310
## year_site2022 1.010 1.37 0.73946 0.4596
## year_site2024 0.116 1.74 0.06673 0.9468
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) 2.20 0.527 4.17 3.06e-05
## year_obs2018 -1.61 0.658 -2.44 1.45e-02
## year_obs2020 -1.10 0.641 -1.71 8.66e-02
## year_obs2022 -1.28 0.648 -1.98 4.72e-02
## year_obs2024 -2.22 0.646 -3.44 5.92e-04
## year_obs2026 -2.27 0.659 -3.45 5.68e-04
##
## AIC: 355.7634
## Number of sites: 24## Warning: Large or missing SE values. Be very cautious using these results.m0_year <- colext(~1, ~1, ~1, ~1, data = umf_dyn_year, method = "BFGS")
m_gamma_year <- colext(~1, ~year_site, ~1, ~1, data = umf_dyn_year, method = "BFGS")
m_epsilon_year <- colext(~1, ~1, ~year_site, ~1, data = umf_dyn_year, method = "BFGS")
m_p_year <- colext(~1, ~1, ~1, ~year_obs, data = umf_dyn_year, method = "BFGS")#Comparaison
models_year <- fitList(
"constant" = m0_year,
"gamma(year)" = m_gamma_year,
"epsilon(year)" = m_epsilon_year,
"p(year)" = m_p_year,
"gamma(year) epsilon(year) p(year)" = m_year
)
modSel(models_year)## nPars AIC delta AICwt cumltvWt
## p(year) 9 345.05 0.00 0.99072 0.99
## gamma(year) epsilon(year) p(year) 17 355.76 10.72 0.00466 1.00
## constant 4 356.07 11.02 0.00400 1.00
## epsilon(year) 8 360.46 15.42 0.00044 1.00
## gamma(year) 8 362.35 17.30 0.00017 1.00La probabilité de détection varie fortement selon les années, alors que les modèles avec une colonisation ou une extinction dépendante de l’année ne sont pas soutenus par les données. Les coefficients de p(year) dans le modèle complet indiquent que 2015 est l’année de référence. Les coefficients négatifs pour 2018, 2022, 2024 et 2026 signifient que la détection est plus faible ces années-là qu’en 2015. Le meilleurs modèle est m_p_year
summary(m_p_year)##
## Call:
## colext(psiformula = ~1, gammaformula = ~1, epsilonformula = ~1,
## pformula = ~year_obs, data = umf_dyn_year, method = "BFGS")
##
## Initial (logit-scale):
## Estimate SE z P(>|z|)
## -0.693 0.433 -1.6 0.109
##
## Colonization (logit-scale):
## Estimate SE z P(>|z|)
## -2.98 0.513 -5.81 6.08e-09
##
## Extinction (logit-scale):
## Estimate SE z P(>|z|)
## -1.65 0.461 -3.58 0.000349
##
## Detection (logit-scale):
## Estimate SE z P(>|z|)
## (Intercept) 2.20 0.527 4.17 3.06e-05
## year_obs2018 -1.62 0.661 -2.45 1.42e-02
## year_obs2020 -1.10 0.641 -1.71 8.66e-02
## year_obs2022 -1.29 0.648 -1.98 4.72e-02
## year_obs2024 -2.23 0.649 -3.44 5.77e-04
## year_obs2026 -2.26 0.654 -3.46 5.39e-04
##
## AIC: 345.0462
## Number of sites: 24#occupation initiale en 2015
plogis(coef(m_p_year, type="psi")) ## psi(Int)
## 0.333334#Colonisation entre deux campagnes
plogis(coef(m_p_year, type="col"))## col(Int)
## 0.04811043#Extinction entre deux campagnes
plogis(coef(m_p_year, type="ext"))## ext(Int)
## 0.1612772#prob de détection par année
newdata_det <- data.frame(
year_obs = factor(
c("2015", "2018", "2020", "2022", "2024", "2026"),
levels = c("2015", "2018", "2020", "2022", "2024", "2026")))
predict(m_p_year, type = "det", newdata = newdata_det)## Predicted SE lower upper
## 1 0.8999976 0.04743662 0.7620905 0.9619559
## 2 0.6399902 0.09195939 0.4484469 0.7953680
## 3 0.7499047 0.06853486 0.5943268 0.8598840
## 4 0.7132195 0.07710630 0.5429457 0.8388825
## 5 0.4911281 0.09447021 0.3151055 0.6693784
## 6 0.4835868 0.09664092 0.3048845 0.6665876#En choisissant le modèle qui varie par année, on peut faire un bootstrap afin de calculer les prob d'occupation entre les années
m1 <- nonparboot(m_p_year,
B = 200)
predicted_occupancy <- data.frame(year = c(1:6),
smoothed_occ = smoothed(m_p_year)[2,],
SE = m1@smoothed.mean.bsse[2,])
predicted_occupancy## year smoothed_occ SE
## 1 1 0.3333340 0.09162878
## 2 2 0.2929731 0.08968852
## 3 3 0.3333756 0.09307825
## 4 4 0.2921026 0.09079357
## 5 5 0.2545160 0.08445046
## 6 6 0.2584852 0.08602306On calcule les probabilités d’occupation lissées (smoothed occupancy probabilities). Quelle est la probabilité qu’une placette soit réellement occupée cette année-là, en utilisant toutes les données du suivi (avant et après). On peut donc tracer exactement le même type de graphique que tu avais fait précédemment : année en abscisse ;probabilité d’occupation lissée en ordonnée ; barres d’erreur bootstrap.
#Graphique pour illustrer
ggplot(predicted_occupancy,
aes(x = year,
y = smoothed_occ)) +
geom_line(linewidth = 0.8, colour = "steelblue3") +
geom_point(size = 2, colour = "steelblue3") +
geom_errorbar(aes(ymin = smoothed_occ - SE,
ymax = smoothed_occ + SE),
width = 0.15,
linewidth = 0.5) +
geom_text(aes(label = round(smoothed_occ, 3)),
vjust = -0.8,
size = 3) +
scale_x_continuous(
breaks = 1:6,
labels = c("2015", "2018", "2020", "2022", "2024", "2026") ) +
labs(x = "Année",
y = "Probabilité d'occupation lissée") +
ylim(0, 0.5) +
theme_classic(base_size = 10) +
theme(
panel.grid.major.y = element_line(colour = "grey80"),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank())