First thing first: check out the model results and look for interesting species pairs
p_relimp_pois
# Relative importance
grps_pois_relimp
## # A tibble: 4 x 6
## species env_rel_imp anthro_rel_imp biotic_rel_imp env_bio_rel_imp
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Epinep~ 0.201 0.0108 0.426 0.0598
## 2 Epinep~ 0 0.731 0.152 0
## 3 Serran~ 0.933 0.0147 0.0262 0
## 4 Serran~ 0.879 0.104 0 0
## # ... with 1 more variable: mpa_bio_rel_imp <dbl>
# And coefficients
grps_coefs
## # A tibble: 16 x 4
## species coefficient_type value direction
## <chr> <chr> <dbl> <chr>
## 1 Epinephelus.costae bio 0.179 pos
## 2 Epinephelus.marginatus bio 0.0888 pos
## 3 Serranus.cabrilla bio 0.0906 pos
## 4 Epinephelus.costae bio_env 0.0475 pos
## 5 Epinephelus.costae bio_mpa -0.100 neg
## 6 Epinephelus.marginatus bio_mpa -0.0483 neg
## 7 Epinephelus.costae env 0.0871 pos
## 8 Serranus.cabrilla env -0.498 neg
## 9 Serranus.cabrilla env 0.277 pos
## 10 Serranus.scriba env -0.390 neg
## 11 Epinephelus.costae mpa -0.0202 neg
## 12 Epinephelus.marginatus mpa 0.195 pos
## 13 Serranus.cabrilla mpa -0.0679 neg
## 14 Serranus.scriba mpa 0.0998 pos
## 15 Serranus.cabrilla temp -0.498 neg
## 16 Serranus.scriba temp -0.260 neg
Find out which species pairs are effected by covarites interaction
Temperature
lapply(grps_pois$key_coefs, function(x) x %>%
filter(str_detect(string = Variable, pattern = "temp_")))
## $Epinephelus.costae
## Variable Rel_importance Standardised_coef Raw_coef
## 1 temp_Serranus.cabrilla 0.05981719 0.047545 0.047545
##
## $Epinephelus.marginatus
## [1] Variable Rel_importance Standardised_coef Raw_coef
## <0 rows> (or 0-length row.names)
##
## $Serranus.cabrilla
## [1] Variable Rel_importance Standardised_coef Raw_coef
## <0 rows> (or 0-length row.names)
##
## $Serranus.scriba
## [1] Variable Rel_importance Standardised_coef Raw_coef
## <0 rows> (or 0-length row.names)
Epinephelus.costae ~ Serranus.cabrilla x Temperature (RI = 0.08; Coef = 0.06) Serranus.cabrilla ~ Epinephelus.costae x Temperature (RI = 0.01; Coef = 0.06)
MPA
lapply(grps_pois$key_coefs, function(x) x %>%
filter(str_detect(string = Variable, pattern = "mpa_")))
## $Epinephelus.costae
## Variable Rel_importance Standardised_coef Raw_coef
## 1 mpa_Serranus.cabrilla 0.07097833 -0.05179105 -0.05179105
## 2 mpa_Epinephelus.marginatus 0.06184192 -0.04834296 -0.04834296
##
## $Epinephelus.marginatus
## Variable Rel_importance Standardised_coef Raw_coef
## 1 mpa_Epinephelus.costae 0.04490714 -0.04834296 -0.04834296
##
## $Serranus.cabrilla
## [1] Variable Rel_importance Standardised_coef Raw_coef
## <0 rows> (or 0-length row.names)
##
## $Serranus.scriba
## [1] Variable Rel_importance Standardised_coef Raw_coef
## <0 rows> (or 0-length row.names)
Epinephelus.costae ~ Serranus.cabrilla x MPA (RI = 0.07; Coef = -0.05) Epinephelus.costae ~ Epinephelus.marginatus x MPA (RI = 0.07; Coef = -0.05) Epinephelus.marginatus ~ Epinephelus.costae x MPA (RI = 0.06; Coef = -0.05)
MRF_predict
to predict the abundance of species i along temperature gradient / MPA status in two scenarios:Epinephelus.costae ~ Serranus.cabrilla x Temperature RI = 0.08 Coef = 0.06
Serranus.cabrilla ~ Epinephelus.costae x Temperature RI = 0.01 Coef = 0.06
Epinephelus.costae ~ Serranus.cabrilla x MPA RI = 0.07 Coef = -0.05
Epinephelus.costae ~ Epinephelus.marginatus x MPA RI = 0.07 Coef = -0.05
Epinephelus.marginatus ~ Epinephelus.costae x MPA RI = 0.06 Coef = -0.05
# Relative importance
dip_pois_relimp
## # A tibble: 4 x 6
## species env_rel_imp anthro_rel_imp biotic_rel_imp env_bio_rel_imp
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Diplod~ 0.818 0 0.0653 0.0714
## 2 Diplod~ 0.102 0.341 0.182 0.0110
## 3 Diplod~ 0.499 0 0.210 0.0399
## 4 Diplod~ 0.364 0 0.204 0.0551
## # ... with 1 more variable: mpa_bio_rel_imp <dbl>
# And coefficients
dip_coefs
## # A tibble: 24 x 4
## species coefficient_type value direction
## <chr> <chr> <dbl> <chr>
## 1 Diplodus.annularis bio -0.0315 neg
## 2 Diplodus.annularis bio 0.0521 pos
## 3 Diplodus.puntazzo bio 0.141 pos
## 4 Diplodus.sargus bio 0.349 pos
## 5 Diplodus.vulgaris bio 0.244 pos
## 6 Diplodus.annularis bio_env 0.0636 pos
## 7 Diplodus.puntazzo bio_env -0.0272 neg
## 8 Diplodus.sargus bio_env 0.116 pos
## 9 Diplodus.vulgaris bio_env 0.173 pos
## 10 Diplodus.annularis bio_mpa 0.0459 pos
## # ... with 14 more rows
Find out which species pairs are effected by covarites interaction
Temperature
lapply(dip_pois$key_coefs, function(x) x %>%
filter(str_detect(string = Variable, pattern = "temp_")))
## $Diplodus.annularis
## Variable Rel_importance Standardised_coef Raw_coef
## 1 temp_Diplodus.vulgaris 0.07138733 0.06358139 0.06358139
##
## $Diplodus.puntazzo
## Variable Rel_importance Standardised_coef Raw_coef
## 1 temp_Diplodus.vulgaris 0.01104622 -0.02722366 -0.02722366
##
## $Diplodus.sargus
## Variable Rel_importance Standardised_coef Raw_coef
## 1 temp_Diplodus.vulgaris 0.03994941 0.1158576 0.1158576
##
## $Diplodus.vulgaris
## Variable Rel_importance Standardised_coef Raw_coef
## 1 temp_Diplodus.sargus 0.04128987 0.10984683 0.11585760
## 2 temp_Diplodus.annularis 0.01383338 0.06358139 0.06358139
Diplodus.annularis ~ Diplodus.vulgaris x Temperature (RI = 0.1; Coef = 0.08) Diplodus.puntazzo ~ Diplodus.vulgaris x Temperature (RI = 0.01; Coef = -0.03) Diplodus.sargus ~ Diplodus.vulgaris x Temperature (RI = 0.05; Coef = 0.13) Diplodus.vulgaris ~ Diplodus.sargus x Temperature (RI = 0.05; Coef = 0.13) Diplodus.vulgaris ~ Diplodus.annularis x Temperature (RI = 0.02; Coef = 0.08)
MPA
lapply(dip_pois$key_coefs, function(x) x %>%
filter(str_detect(string = Variable, pattern = "mpa_")))
## $Diplodus.annularis
## Variable Rel_importance Standardised_coef Raw_coef
## 1 mpa_Diplodus.vulgaris 0.03718557 0.04588879 0.04588879
##
## $Diplodus.puntazzo
## Variable Rel_importance Standardised_coef Raw_coef
## 1 mpa_Diplodus.vulgaris 0.3320568 0.1492608 0.1492608
##
## $Diplodus.sargus
## Variable Rel_importance Standardised_coef Raw_coef
## 1 mpa_Diplodus.vulgaris 0.1918045 0.2538626 0.2538626
##
## $Diplodus.vulgaris
## Variable Rel_importance Standardised_coef Raw_coef
## 1 mpa_Diplodus.sargus 0.2205292 0.2538626 0.2538626
## 2 mpa_Diplodus.puntazzo 0.0762360 0.1492608 0.1492608
Diplodus.annularis ~ Diplodus.vulgaris x MPA (RI = 0.05; Coef = 0.06) Diplodus.puntazzo ~ Diplodus.vulgaris x MPA (RI = 0.34; Coef = 0.15) Diplodus.sargus ~ Diplodus.vulgaris x MPA (RI = 0.2; Coef = 0.26) Diplodus.vulgaris ~ Diplodus.sargus x MPA (RI = 0.21; Coef = 0.26) Diplodus.vulgaris ~ Diplodus.puntazzo x MPA (RI = 0.07; Coef = 0.15) Diplodus.vulgaris ~ Diplodus.annularis x MPA (RI = 0.01; Coef = 0.06)
MRF_predict
to predict the abundance of species i along temperature gradient / MPA status in two scenarios:Diplodus.annularis ~ Diplodus.vulgaris x Temperature RI = 0.1 Coef = 0.08 Diplodus.vulgaris ~ Diplodus.annularis x Temperature RI = 0.02 Coef = 0.08
Diplodus.puntazzo ~ Diplodus.vulgaris x Temperature RI = 0.01 Coef = -0.03
Diplodus.sargus ~ Diplodus.vulgaris x Temperature RI = 0.05 Coef = 0.13
Diplodus.vulgaris ~ Diplodus.sargus x Temperature RI = 0.05 Coef = 0.13
Diplodus.annularis ~ Diplodus.vulgaris x MPA RI = 0.05 Coef = 0.06
Diplodus.vulgaris ~ Diplodus.annularis x MPA RI = 0.01; Coef = 0.06
Diplodus.puntazzo ~ Diplodus.vulgaris x MPA RI = 0.34 Coef = 0.15
Diplodus.vulgaris ~ Diplodus.puntazzo x MPA RI = 0.07 Coef = 0.15
Diplodus.sargus ~ Diplodus.vulgaris x MPA RI = 0.2; Coef = 0.26
Diplodus.vulgaris ~ Diplodus.sargus x MPA RI = 0.21; Coef = 0.26