df2<-subset(df, !is.na(Estimate)&r_sq >= 0.5 & !is.na(Nat_change)&length_time >=10 & System!="Marine" &Specific_location == 1 & !is.na(Bodymass) )
nrow(df2)
## [1] 850
species<-"Capra_ibex"
prob_scenario<-c(0.5,0.5) #need to check this
noise<-0.95 #need to check this
stages<-comadre$matrixClass[keep][[1]]$MatrixClassAuthor
stagesf<-stages[1:3]
list_names_matrices<-colnames(matrices)
sumweight<-rep(1, length(stages)) #weight of stages - should be equal for all mine just in plants seed not included in calculating population sizes - or if you wanted to just calculate the female population it would be c(1,1,1,0,0,0)
#sumweightf<-c(1,1,1)
transition_affected_niche<-"all" #which parts of the matrix are affected by the niche values
transition_affected_env <- "all"
transition_affected_demogr <- "all"
env_stochas_type<-"normal" #can also be lognormal
matrices_var <- matrix(0.01, ncol = 1, nrow = nrow(matrices), dimnames = list(NULL, "sd")) #standard deviation of matrices
proportion_initial<- rep(1/length(stages), length(stages)) #proportion of population in each stage - no idea what this should be and will likely have a big impact on results! - just doing eqaul splits for now
#proportion_initialf<- c(1/3,1/3,1/3)
density_individuals <- 1 #also compulsory not sure what best value would be
K<-NULL
K_weight<-FALSE
fraction_SDD <- 0.05 #short distance dispersal
#fraction_LDD_mine <- 0.05 #long distance dispersal
Minimal Setup
demoniche_setup(modelname = "RPyran",Populations = Populations, matrices_var = matrices_var,matrices = matrices,
stages = stages, proportion_initial = proportion_initial,density_individuals = density_individuals,
no_yrs = 80, sumweight =sumweight) #important to include sumweight, I think the default is FALSE but that causes the population to be 0 in all years
RPyran_min_run <- demoniche_model(modelname = "RPyran", Niche = FALSE, Dispersal = FALSE, repetitions = 1,foldername = "RPyran_minimal")
## [1] Starting projections for repetition: 1
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Calculating summary values
## [1] All repetitions completed!
RPyran_min_run[,"Meanpop","Reference_matrix"]
## year1 year2 year3 year4 year5 year6 year7 year8 year9
## 1971 2151 2340 2529 2736 2961 3213 3492 3798
## year10 year11 year12 year13 year14 year15 year16 year17 year18
## 4149 4527 4950 5391 5895 6435 7047 7695 8433
## year19 year20 year21 year22 year23 year24 year25 year26 year27
## 9216 10098 11061 12132 13302 14589 16011 17568 19269
## year28 year29 year30 year31 year32 year33 year34 year35 year36
## 21150 23211 25488 27990 30726 33750 37071 40734 44748
## year37 year38 year39 year40 year41 year42 year43 year44 year45
## 49158 54009 59346 65205 71658 78741 86535 95121 104526
## year46 year47 year48 year49 year50 year51 year52 year53 year54
## 114894 126288 138807 152568 167715 184356 202662 222786 244908
## year55 year56 year57 year58 year59 year60 year61 year62 year63
## 269226 295974 325377 357705 393255 432324 475281 522531 574461
## year64 year65 year66 year67 year68 year69 year70 year71 year72
## 631566 694341 763353 839241 922689 1014426 1115280 1226178 1348101
## year73 year74 year75 year76 year77 year78 year79 year80
## 1482138 1629522 1791549 1969713 2165580 2380923 2617686 2878002
Maximal set up
demoniche_setup(modelname = "RPyran_max",Populations = Populations, Nichemap = nichemap,matrices = matrices,
matrices_var = matrices_var, noise = 0.1, prob_scenario = prob_scenario,stages = stages,
proportion_initial = proportion_initial, density_individuals = density_individuals, fraction_LDD = 0.05, fraction_SDD = 0.05, dispersal_constants = FALSE, transition_affected_niche = "all",
transition_affected_demogr = "all", transition_affected_env = "all", env_stochas_type = "normal", no_yrs = 9, K = 10000, Kweight = FALSE, sumweight = sumweight)
RPyran_max_run <- demoniche_model(modelname = "RPyran_max", Niche = TRUE, Dispersal = FALSE, repetitions = 10,foldername = "RPyran_minimal")
## [1] Starting projections for repetition: 1
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Starting projections for repetition: 2
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Starting projections for repetition: 3
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Starting projections for repetition: 4
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Starting projections for repetition: 5
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Starting projections for repetition: 6
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Starting projections for repetition: 7
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Starting projections for repetition: 8
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Starting projections for repetition: 9
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Starting projections for repetition: 10
## [1] Projecting for scenario/matrix: Reference_matrix
## [1] Calculating summary values
## [1] All repetitions completed!
RPyran_max_run[,"Meanpop","Reference_matrix"]
## year1 year2 year3 year4 year5 year6 year7 year8 year9 year10
## 848.2 453.6 262.2 174.6 128.4 98.8 74.9 56.9 42.0 32.8
## year11 year12 year13 year14 year15 year16 year17 year18 year19 year20
## 25.5 19.2 13.2 7.2 2.7 1.3 0.3 0.0 0.0 0.0
## year21 year22 year23 year24 year25 year26 year27 year28 year29 year30
## 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## year31 year32 year33 year34 year35 year36 year37 year38 year39 year40
## 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## year41 year42 year43 year44 year45 year46 year47 year48 year49 year50
## 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## year51 year52 year53 year54 year55 year56 year57 year58 year59 year60
## 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## year61 year62 year63 year64 year65 year66 year67 year68 year69 year70
## 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
## year71 year72
## 0.0 0.0
which species are in both Comadre and LPI
## Common_name Comadre_Matrices
## 1 Northern goshawk 1
## 2 Cheetah 2
## 3 Impala / Black-faced impala 4
## 4 Common kingfisher 1
## 5 Mallard / Common mallard 2
## 6 Greylag goose 3
## 7 Gaur 2
## 8 Northern muriqui 26
## 9 Temminck's stint 2
## 10 Coyote 1
## 11 Grey wolf / Gray wolf 4
## 12 Alpine ibex 1
## 13 White-faced capuchin 23
## 14 Blue monkey 29
## 15 Red deer / Elk 4
## 16 Greater snow goose / Lesser snow goose 7
## 17 White stork 29
## 18 Black rhinoceros 1
## 19 Peregrine falcon 28
## 20 Mountain gorilla / Eastern lowland gorilla 42
## 21 Eurasian oystercatcher 2
## 22 White-tailed eagle 2
## 23 Black stilt 16
## 24 Waterbuck 1
## 25 Rock ptarmigan 8
## 26 Snowshoe hare 6
## 27 African elephant 1
## 28 African wild dog 3
## 29 Lynx 2
## 30 Yellow-bellied marmot 18
## 31 Black kite 1
## 32 Urial 7
## 33 Bighorn sheep / Mountain sheep 13
## 34 Leopard 3
## 35 North American deermouse / deer mouse 5
## 36 Desert warthog / Cape warthog 1
## 37 Koala 5
## 38 Raccoon 1
## 39 Reindeer / Caribou 2
## 40 Common tern 8
## 41 Red squirrel 1
## 42 Grizzly bear / Brown bear 2
## 43 Red fox 13
## LPI_Populations
## 1 1
## 2 3
## 3 7
## 4 1
## 5 5
## 6 5
## 7 1
## 8 1
## 9 1
## 10 5
## 11 3
## 12 10
## 13 1
## 14 1
## 15 9
## 16 1
## 17 1
## 18 10
## 19 2
## 20 2
## 21 1
## 22 2
## 23 1
## 24 9
## 25 1
## 26 5
## 27 12
## 28 3
## 29 1
## 30 1
## 31 1
## 32 3
## 33 1
## 34 1
## 35 3
## 36 2
## 37 4
## 38 1
## 39 2
## 40 1
## 41 1
## 42 3
## 43 2