From 7e5187d7a94267fe51ed9a16f56143e7778aaec3 Mon Sep 17 00:00:00 2001
From: Steffen <steffen.oppel@gmail.com>
Date: Fri, 17 Jul 2020 11:09:54 +0100
Subject: [PATCH] upload code and data to supplement manuscript

manuscript to be submitted to Journal of Applied Ecology must be accompanied by reproducible code and data
---
 Balkan_EGVU_PVA_DATA.RData           | Bin 0 -> 17192 bytes
 EGVU_IPM_Balkans.jags                | 348 ++++++++++++++++++++
 Oppel_etal_JApplEcol_PublishedCode.r | 465 +++++++++++++++++++++++++++
 3 files changed, 813 insertions(+)
 create mode 100644 Balkan_EGVU_PVA_DATA.RData
 create mode 100644 EGVU_IPM_Balkans.jags
 create mode 100644 Oppel_etal_JApplEcol_PublishedCode.r

diff --git a/Balkan_EGVU_PVA_DATA.RData b/Balkan_EGVU_PVA_DATA.RData
new file mode 100644
index 0000000000000000000000000000000000000000..af3692fd70c7845ebc0abd79a022f8ed514432f1
GIT binary patch
literal 17192
zcmeIZXF!wPvNkNDMn#B<fV4zJL8;PP2vtFl-n)Wy=`~al5dk40y@T{Fy%Xsoy+(o%
zX$cT|2p#gqXP^D-ea`lL?f1uzA@{7AS=Y?0A#2^U07qOt_t(#*brXOwwK6sQ>gCJt
z!&~;Fa@h4Z)>;DguHMy^zpy<ONtp8GJy>h;dOY;z>YH%)3pYxBJbJLVcQK}^<&{1U
zORpA40)ZAYvcgF1qZfWWRV_1$n3bKLt%ZQf&kemvfa`tLoLDpv+H9wLe-V=YI6#Lt
z!>;>D?s{rdgD;R?05vr<=JvkGX=q_-Uq_`}yCyhxVw1<jr|_tTcqK#akk7*Zt*DP`
z&zIWVt5zkDG>jg`eCzf@sd0Y60?&!UH&^u9!%Xu1*-fFhm$wiVWbud8UQpV!r7*RG
z#;UXDbXYf;&%c6rd1B}G>y7P@DHjj*{1aX66mt7+NP?z5)I(WabK#YTzxb5B*<+;4
zyq`CHnE_3^tyrgDXq|x_TjR$DeEbT2ZR+fz#yM%xd2wFF7BzcEN9|H&l45~YTX2Lh
zsA>2BpE{;BZF~@5<(It|udw;{me1~y+@=Nf#<5YXON8yI%j$=nDvPV-mGzrgpQ+$v
zx0en-G8eQk(3z?7)}fC*_jNaLT8V0&E72#mt((>5A=W>t;lATreP1GLeOE!gG&o_a
zXDhp`5|R?nRs)^i6#we{T0!t?KExRm4+Jyi&3Zm#9*1H2UJR~d!Deoa+DdQiIY-jn
zjO?-YT<($H%??JISvvk`Fehw1r859sXg=|-+2Og-!I&H|Kt*e#*-nccvs`yS+*&!x
z*aXEn;13@d+xnH_ILMNPoHr}~5ZF+q725h(X^*RJ<4JiqE`Mzw7<f9l(Kt{N8d3Mw
z?7>v*#~pE?vK@5Yzi`6|sp1(6h=uyuo`7bH3eq`|oSTCgRW`cvaK6U~C1Wq92s@#Y
zjVMz($9F&8cW)~=9_%91*)<9+^Yj89O&6I)n)Y3)=3=dO(p_{63+1HYt@UKkf3&qY
zyEoLhZ{IL#{}N$-=9!idx3)%W{$j;=Uio?38HHZ|%CuEQpmRPW4?6{@C{BI6@#nBm
zNc!aMgJ$EUDr<A*ZlJ$My?*Q48(n&>T(Og{MnjkA&2I5ZUFAf>S~3e??d=MX9Y5i5
z;{d&)cK60KUd)eTu2GFTb^mM!hw`RGsnBnZ9_KyIZw1q)c^EP=`S?7BKb@!R$p3N1
z`_azOVSY70#snzH^o!5!>DeK9{6^J=(5mP}&KN$Kx^~TUoY!g-Syjw2Z5YV3;9ZnM
zh}?BDiO{7%?GyZoKe(5tgR%mBH<cNNx2irL-_Hn*p)LJnCNb>tqANbcZq3!mwR>p}
zE*@Qoy|oe(+{_VaxV2<{SWCq+Voj%#-t226f!&b=KD)~0T5s^M!Mf#QQy(|f7>PVu
zibM*GYS}-b)Ad}fgjx*EJ~i7DS<w*flsM5a4&4;M(sG*i&vZEsG-<BOUV?`~nU^N_
zm0pD`P1gBz;on_jT=`k19E@iNdmT18`$X&9Z>??iUfDXHt7;^swh#hskK;pbtt`wu
zST7$1x2$f!vlbbxLT5LL{Bxa|rNOwcQmj%3$HVyiLE_PV0#diMojQ-Mye@Mz|AWbC
z4(HD_s`@Yog=vk1(og=7b%2v=Vgq(EGn$zJ&M0hYg>QTl>o*Gx8`(Fy=26I~S3Q#Z
zbQ5B3{BCfSQS_I7`Gy*us-12o3=zmim2lAz#YCB5oVE0v*aX*9E-9mrAAv2`EtJN$
znzVAM^C8Pro?LXwhjqL(M;b0zc_i)3zebKlLyK3#rah~+z=_S~-=toMIK~~Z+nHu0
zS){8eQYtC>6AJ9?vS~Jd`sHtj9{;!%sK1`HL^G94ZT3CZaf5a?veNDz`bC|c6EZU?
zsxQ<enR=^br0SHakEh?)c*#7%8P_$LLMv3>P-2`^&aV5c&L_7%M(SIy!}B1+F_f2=
z$<9#mR#W(fsqE@XnD71A$GpNhpE$)QD4XhQ{9Jnl!gQ_z%S~|&V?IqwG4X<SpQ5*g
zCsBql*TqO$b_y;Wv#uDzo)E!rNtk#{P6t1pUkt>5rRga~&VbT3_;SOIo*Z=d(&{|X
z`B8=a4)2OM>dpZSmQ<_$I?feaQScC-8ws#a!JiEb!c@;zynS7EIFa`|nwnYybax54
z--+XlfvjBBU+>h-H6cv4(;9Ph&(?9k4?h-0maEt5o$6vcc+@8E=@r8Q!b+qyJ6n1w
z)Og(_SdK6v__LyqZ+cQ%z^!mB|HGP*xkVb26D6)`&ocC$aN&}}y70G+`;AwansOxz
z@^3fn8tmULa<-fC6F`(~+0Nvy?b#7tRuUGtXeCPYJzsuFv5@R}9LmAtrEFihF-JJ?
z@!h*sazog&@5l*oN*xu6+E<(!$g)=-A>V6Z4|Pae)Y@K-9yLK-%F8)j4%m`AiDPLh
z(oLS};2kzEP@^l3It786h5fzdQsB%O0MFs1^-OYI^Aw%8+$1+c9icJUqbdtIc<Oxs
zZMG`djWel`Ee26uiPB%$`%${R*u~rp2`jlJ(&k}x+<2(DKt6~_ZrU-=F4)a##Yb)K
zAKuh$O?KGk<(DxzyQE|w<%cbe54x|GTZ$4Y|K6V~aLJfqY}cLDwf_0ksR6k7RXJp~
zad!N?XMU6j+PzYz;d8N^8OL{?Wo*uSAG1D|?f3Ry*6a?Z-##^U5m=Z1&a9y8-Jh{M
zY5(h#>cmU(Q=iFd-TK#^0N?M8>^w)N-Mr3zKdI2!<7lOXS_#87l|*Edp-Z~`nu0wT
z5iiByK=8-x*3J)b`umnE>Uv&F9?gq3srOqW)^=sAYU5zrex6>1Z-OFDMlaG>5n2Ut
z=5GtsV<_8t5T;^Yu7*GOUnpx(=0Em~Rs(z7q)_!OQ#`W%Y!r$~ki8`Q;UrlX%D=WG
zy4kwVh`=>h%)A-3l(iSrqx45T$nZr33na@ERv!(VW*nWeoqB&W0w3}3wv|?kz2T*C
zl1hN7UC$YxZS}D<^j@GIRiP_?P$bs5a~G_gexHHp9^$^TP@xREC3Rys!UkqzWjs-@
z3%0tDLsxLvV$?TN4ggZMx{IBtDt^UU<@QZ5w>)^E<xTjOLgfoochKXia7i3(Y#%%x
zd|}B?<-oJXUt}eXjW-c44@{bT=ZX?_nCaTIK5#R=-eZ9g@^D|EqhD$CAFoT27!2f{
zES+iKZR+R&s%&Pwx=*h&Exfulcr`tqwYl5-=fjHpmn|E*yrE{aj(!5qJ&Ip1IW=+9
zEAD-|=^Hp+tm%mjeXGYeAQE7(#ah<3#pkkflX=f0JpYNK;beDDfc=zSU>wL_x?+bg
zkmI6}vENh+^L-?2tDhvKkn4hd7nZEbJhGM+Vy&D%!s%Zd$s~B>K2Y>f;E8@&+O&(Y
zyZVry+$`|m%x8GVUux)bb3~c#4;E)%TbSBVEB&U`o?($T&&sX3Gx-Hp3N;9?<dS+1
z<FTWBH?+X6%11uD(<)h^w8}KjGspN#@!6hJAA>H`^V0|w5c^ngt;OB^uv#X>(Nn!v
zk=a1nXWzaPUtFH%9}Ep_#WAJ~BWmX567G)9Z`)I?ZDeeFYxJj{qA$&ey~JHVsXqLY
zB`}LNJFIDk?^h{bkl#vqtDU@ERy)wsI>w;sxJjF9Vy2O2yZJuXNlz`ZT#0gT!=jCd
znOquKZlsS;<|%_fV6p4#5i(68+$HP7LjhhH8f5}tvov*oPvrNIrqsx4o(lGChi#OA
z*I<(7j?eBuY>Q5sU25|{x#!v8$nq-16E(eS8>Xx43%#E@nYGFT9M@jLa`b{71}hW#
z*36ot!B?Jo!$K}AGtnFW^ql|I#+ccXZaJ-L;^VI8uoYe?zNCYav2+drTnQVlzOM9x
z)mOqWd!C-d8KMn(a1v-#@7#dFhkwOgFtfhL>&rqIFQ|9+i$%2JzuaaB<>aADuV|-1
z|2zmx{>U)kL1ZN`DGX%0t8QOQQwVEy*=><ZQLdj)VI-<i-(1D{5`zN+KhL)tq6wi`
zjqP26!j-Fl8s52fp#<l-WJamFD_3j{>&uMFJlI(zcp!2Mu5+1N^;-mHg{c)rU^=#P
z0o9W=@7(n`tTo^r%4I5rBfVjx&e)}4Rqz&+1C$_nRnH7^3!1Qu3vC}N9JDp&k@7Y*
z^Nj^7?M8OQg|>Py>oup&<8bgJ$@VySD*~Wi{|?pGPBWzLXMrE}`ghR-<8Adl<6}ya
zr^i21_ZsHuBZ{3MJbqfIKk3DX;(%OVz)I@%TM*>w!~BeFdV8R~{_d%9JSSKiJ?tNn
zC3@7o(2f*Qv)HabdJsqgP(Od0VG~VzbJW-q(=Mdl;iB#@=Mq^nh6Gy<O@!8D?Kl0r
zo0E<|LM16ZH#WrX?(-Fu^YU;+wR-0<h};6umK~;EwKFs4VG+Z)H3A>$D!vuoZ4}z`
zRnj_5b&kwlt6ytyR(E;EEmrF0dUX7{wr>TSV0<j@zumBPEiH7Y=f}{gI{tw~Y)0$0
zs~zxo<kJ{du73XWJkHl7bMmQK(87v@+TH3b^W}Z^ck`q8cLC^v@^_~i+t<2+9;3Y<
z%q5HbG{t3AoR@p@0dsm3NY>mxXIj)j#$TbE<lO|{+tsRxuw4r%pz1NJwPU0RiimhS
z5nqYo*EOO-`O)d74B1iWJFpaUo7Z%e2vZ~swJ%jUAG)dx?jZ)1{nOp2gP`hlO5<=*
zB^*vc>a)Uqwz@zkEtZhU&QQQW1b*7od>1Z6EtNnI4NgMeJb`WYa?*I5q?ait)f%DF
zH)8e%jbvK^#`%uWt7{TRS0A6O?YsFMm(hPo%Szs3|J0&dR(9aZ?+ThM)G<@3u5d`x
z`k87s%Cj55G9>Q#4Sxtq=Q}*yJx}yB4NT5MhE?kdn`~Ciy&fbi9w5H50dB$t0-h2}
zR=}x(>60(BZjueY08VN|OQxuD#A&34zo+sy3Df~{R4c#@SbK!--4z!_t8$ry*1uXZ
zlXTH~*j+CD&5xJY+G!pa^&wQt2YCO@t9t>C`?@jBwD~kUuX*La)zhS>ft=Xp;}Nba
z)8EtgSU-y!U@{P8prmrETw&Z<qvFq}QDCDrbw*L%?lX(;C3*G^MjBV1hGY+UL|Jxc
zz)UtOyRqk@4FlKq;Nel)!`<aN%8?GzCIP4lRm*aYPD-@&neLFB`1Ys-GpVMJC2plx
zBzs3AaAS@s!O@;=l?u6PdIpv}CsHcZ;Mk^lKx(5jZIof9gB`b%)tPC7ONTXwYV_mJ
zpDF6$iaAk(5~Doh1lEN*x7nE_nxVA2QKgLI_CH*VR%P0F$QeJFJhh*nO6_QehZIq>
zpv9edwIyEHM{DMtRD^ODZc!^NRS4~9?SDr$`f^kY-r(jjw2;=M=-xcWiSg&_Z7Md=
zoAPxWT@qL@!#2ATU5{_uI<0fG{2~A=>aD}haw87->yg5t`<b(AcPV~)xU8`!KSSMu
z?E4T#<VAVeW<!51Yzm~>Q;SID>16~~0|d@$iLb@6vc$>dsZLD1GEuMgF(XIO(Qp!{
zw{7QXX(1(VfMgl9S`7H=kiN>bbn&*ppq?8!GfQwv@n<Yf$rMDju^bcX;?*FNm`nm%
zp=727(bXbWm`o6rpDA1rKT^J}U;gp2Bp}W0wp<9oUSM~2Sx4JER3goh%```7`^@1-
z&PLpxhMTYN`fUBTjQ7B}Qp7|cpNm(!CDJ_tF27btsi#zpB0r7}G1+@RB)O@~bl7v0
z)^USR09;WU;-F~5zx~pu*8~JsyP=TAtkqP~-1qDL`pL-F<ih65q+y+ez_N^Io%Q;V
zAFG0(C}5YbYBR{n0==@$#HM=GK9YviV-swoiP_gW4rlaY`*2rN>aF*6BzQG-v(Tr7
zINR7Lesf4sC8veTn_7GR!{9;RPR*i`lE^*BPRZ5lik&IHBha}{KOoC$T4QnK0$yGB
z=i6r=BW|$z&$>)&+~s(7XsIMcX9cmC@yR!EtJUn9oSZr>b>bo{KFdMB{w!pxf`Sj;
zHkz^o)GTP*PKVaozkjr{$gs)3dcE!V>UfpchK$K<_0WBTon)o0y3^J`vn$<Fd734b
zNP6<H<Im+<PG$|taOw&VP`c!Gui$=OW?uu^*|%4L(~kQUUaz}bfzhtTUx}vGDCM1;
zr`$?AP|fHC-XGfdBWV*owh2#WcNsuvlrMj#W9X^VNf;cdl_snzr3O)@#6eUOGe`7n
zndawPKBmplHQiJ~*3$IEuV+)UTlT$C%J88wKice@;aQR_l6sZDps<jM5pgP&YC6np
z>FlG~;|5DfmIx60Jx42s5sLFqs1J(@kJ0=KybO3l^hWvfNmk<LnC6I9k;043Jz9P5
z2IQw!io-T7`=(mZ0zqS{LkEIpoxv!cQ2F~;S=M7XbdQ<5Mlu>rmOZ=P@G4NNG4^;F
zI|qq5tp;@9%s5e4_6VwK*WzV#QloH>?P2RNtH+ig?x`fV*7sFYHQy%+Cg^0%ifxHi
zS)2mytn%ckJad`)o$7sw`L;X>iZ@<}oX_Hd5FuAv07tIO5=B~pYBf0eCnmnq{p{T8
zd|!yJ$xA{(PRQe8|4Mr4(#xxr+}X-kx9@o?zPXnLav@Hg87V}RvU~>k@gTnh@Ncud
z*ej8Agv_5+vZ=67@+d#{N|`CQ$=B-4p9ba+EsBkKJ_`_ZY)?zmvt%cKkJa5wsCD}x
zdm#|&lhuDH!yx$~9MxJkRU3wUAN#cKh%MbwiL3JP*hMBO^@m>W{$O`*BOeS<7co__
zS;*^Bh5UjvgQRHqmV>7cvf3G~&XfooZ9%Mf+*W56JwjqqODQ#7^l8rqCIwohtfG1^
zwujAl`N)mW<)rse7rilb|5J9<hiXpD6Kt{AkcvCgyniNP=VF4?BYz>MxWtq49>r01
zReQz#70BhKya9hB{bm+Z+7!mRfbH9tfb$6z$o{eIL>L1AI`l)U%6Ky4EU&>4`W36J
zTg}Nz;fj`#G_6HAj~3P`&dOp+KA}Q&GR>V%QbBroq5{N^`vJxsv)P3yZar&LlbMnp
z9+CFBSb>hh0xRDc>dRgnnyUf@J>ksSixJb~g*=pn-xk#RuDMcvzWd~;(Nn6p5!@|;
z8nxQs?oQw4WSIHR9>7aefqmY4aQ|Y`_T{dv(0#Po=euW6v)bV>sP<ylt<7~8-+~m&
zo{8C+SCTYh{T#1QcmwUu$yVSVdOPnfEmgFxK|{>dNYQD3bwjV!lMm6>roz)@5p{R7
zPh9us$C;P%t1BmzG;gfHQI5xlap5I2$$Po=;F!Q$n<!`V=!;uTLhLKnaPN<1<L~Cm
zxkRpie|r)Z9v~66sn(4+OTKeI!d=pvl~_WI_LO{uyRf^dIvt~GnLmM_ee|}P^5%MY
zRDzZlHC0Qb#8kr7a^bu6pH$9SSWnrttu?5D)uT0B?&Bm5Z*E&VJxfcR)ZB+OX53$=
zHPZCL1wgCs)jk>y<#lSAJ-E~%_j1n1<H(O*Iz-Kuud*xu4o9<c`RE&ZKaR>5E4`)`
zapNZGGp`>8=c%*kA&HXszyNJ;?Z-_whSCKVynH238*0XcSDz5MbMr#I`9!@Y^=ybT
zv4e|l`L_5LFQa#S&7=N4>D|_ALTAorTd>cQ8lmu2ox7l?cAC*Gfc^5-3CfbOY_{!E
z>)D*UvbCb?AkR++*QYJ>UDqJ1rrs}Wt^#1=EeOP3(~0vHnoSQ`?Lw3xN5J=mWWClK
z+CPyj6jaxX8cW(I$Z0}D$$TTD>{0dF?VotH`>oODI~m}_ZGH5C)>u;`-|4{wAzV2O
z-x;F@>hKL<RG6`!ajtkd$#~t`h&T-p&b?_}Hf!qpc}|n#m3ZK2_DYTWmLTl<3g^(8
z)9u;VaLE?8ZiykR_AC4-mB&yy`L?-JCtb@S{V%6=%?C67=f~hHbYHf7rZ=?xLN}N<
z4(nJbW^gX+D+{A9H5AIL;N1aivn?%&U##@?R?|Da3uwjbu}0wG1jVww#LGai&?crx
zPkZ;nr#Br#2o1gg-%IXQAF>y2Iu!$*4rZ$^dGHbw`}Df6v9@B33U|#8^|6ro&{wYO
z?riTBo5XyHN{>Z29D}sNOW<@g61(?2O523zO+Rg!qKp+7GQv%!T|3&eaADOJkvzJu
zZpn2eP%@;XPsWA&J~Xr!DB-2CZ|G*r-nr7vQ}g5`<a*O%?Vk<IL2;iX3%0x%sUUW=
zUB5clZlhc5Dsa1pTF7F`wK*`eN@^NTJ=3~eqZ22BZc-`Ty|^`H9i@Z49GD08cdt7Y
z&+r<jmD;b%l@k_l$->{`H_VuJ-U<|ON8XsU#OA%-c%al}u$S)5>6yx7c4{4?CGnsT
zFmQ<i8I|@-Qa{b}2)Co7tyv{LK2^nDkYmJZL}$mBq4YU#aQy@ll!O4Ai&amy8$JV1
zZWq^X%O>aYofhD2-CD1COjJY^`LkuVR7u9Y+`gCB-?P&1J)f58=zGgLjMu#GtoKuq
z{FBx?e9x0!j}z+Cn*zpw5BDfi4V#<LgRbBFZoV55-8<~ia1Iy(xrfib=-aihqcV1R
zbV^3E`EgFSX^VgKsdIo|XS=fe&YBD#wzlzlVO8V6nx7vqZd}|MS#X!o>$hV&|1!a}
zbAeMJ;hl)q^*Yv^aPkbW`u?Ty+rFMF?BYDOLSM5pR2<#`M(13T$P!}vJcfdubO?{}
z(({KK5hg&LKDD<c;05s=ErHe9XbbRn@I%?=fKTSW1Va&0M%`9#ZLd4K%@;0&l{)IB
zYg~~^zXe(3j~)zd%$U>c;%K=|Bf^!HFr7yB)^%d8Qqp=NYrUXgtEnjhCX%}`RT4O3
z@$BY8!Z7D<54EN8-Jcq_H0NY>I6SKl@@$^HkQL(>YE3qSeNEV`?4G33ON2Vmoz$`i
zPA9cL0#>uNLj1R7c5``M(-Lug7Qe1ggx=v09-Qu|o#HfUeGvLuCr12c@umAyvCYqA
z?(Y+PQl&jlwFGY%6gR1Dckp$>H~n9=IQ1)_6jT-IUBb-JHb3ic@Bi`(Fzda$Ui!0K
zhgpUe;>Mq;({-_v(e_F9S2y;sSX<T8{RDb7Lt1cc`?kD{EbK+^jx$`XlVv4H$zxUI
zJ}B~@2@VMvTFjVlQc;yOmDzt<41z-(%X~v2=<=>(^qSNvnQc|!tE}MG+3}Y0wwXR2
zF&V#>ssdyfHlkqBN8(5nh<G-WA?t69^1b0=Kfe7LT^^OW(c|IaZR#u){mA?4iCwlZ
zpUcWNb-e!!EAX-UK3*qD7$c)L-;2*4O#7*`pS77ULR-N9*4usE5!t}EUvSu3d6Bm<
zF8A}i!Yj5<CW+0m_NMTi?Tsh*y&P_w?7V=k22I3D%1}c2MYH1ZoX<=KSVwhrZIcWe
zFT{zCZY=ljaS->)B~dL0gPn&XN}rqq<N+}z>|rSq<Dlh?BPV-)^zb;s*vdqHfZB)?
z>3B3H{9bY6({RO3`{*~(i-EQ4sGJSmm?cZFkXO1>ZcS$v-87Xrou9(XYRS|eu5&Nu
zSJIxpfD5V0uYP$0b$Zu-AFADS3Tv-Blt0S6yLh&A_mkdWrT4_+!zAzg-ZRRpj@pl0
zA{s%GTp5@k2ztCoC}F=|Gd&z-ba485M4{lvU>%&Q-ZO5bx+PByGWf&jvyz8iH_<=i
zl0Wd)W|77IMrEbnh%PYEMHDIt^0z8PPryy)T<$J*epU!>S_)m@$68LmewEbf%R#g0
zk5f<cR=$Tw*n0;Gh<-Hj5;@XB0NQV?vSrI&Qn7P&QzPSWG5#=tNht0wecFVLLMw~7
zC=5`)m=(GzC@C3TjcZ-#F353FZ2HD%OBT2p^+?CG^O#ox#d{T`%7pm&(!vBWCck<p
zR0<=jD=4lbuZ1dks~CDzQ~xZ``*_faW;&a9burX(p&LUniN}2HZ1~wG@zS_U_1Vev
zchD7up13FTdEjL|$I<E8@g#dP=2vF^nF}{EIOCfI-M4i(P~CQ9LUmVZPG$HCU(`9z
zueWi}=S6iTZ8u#s^NGdPMuipkCWOOwEjw4j#}YmAj`{j-ViOEgq<dJIxs8M*M4h;4
ze7%gpZN*30?dVfw-NLO{r9_D3x44btf^S>UlU(astsCIID25-24XWQ{Jvxr)3cmz}
zyYn;>yNm6$PlDzu#oaKt)Qn2EXtK1E>68s|*EAC!B2OTPr)gZ9Rv}2+=04)s0`0Bw
zwEg@=uWreljiDL7R;&(i6;rzrho+>;zlnhMd28aX#kp61pRh|GD>(jC+_$HG{Ll_=
zIWphE9Hv!zbj?_IgI@qX+}Y8)!l~Bjl(XTB0gG1*XmYd<bn|+f(XCF-RC9zH`e=xh
zp>6T%Ka#3fO)CY&0x!osc+hktk5hh@D&R5O4?HpmRZRX_?KQ&DSNJx=H!qJiUFN<x
zbMp<pTpA073^Pr-jAew^ZocCN#m~d}9lyJl6DwKMtH@mQK6#K&X~~o6n8)!KZYcaL
zd73WQaKPF-b7ct=XjOMu8LI>t{Zf3VNXkH7dlpOhrk3+~4O1?1Rg#69$wx2Zj6J?l
z%4d-xV}?77^#*5$9W(gSfx&gJyp+w0^RQfl5qNWvdRP=Sw)1VvOK(w~Oz)WG<cYBA
zgBf3nR}p#URw@j_2!bL<q5HR&99tYTc(o#L?GXjiR%8`_#Otfpg~Ff-TFz1RMkX%;
z4+kD_2=7G2DOXr%KZ}N2AOP%q^>f>MF~@#edGmqBJi9MYcQ{y_zkNN*T`0s|aomos
z5GeR@$t7d{NQym*Yu)epZr4d?yyt#rv2TvL3qdI|3}hA-=GKSC^7_uH>PWWOcl-*%
z7xe8nsrc6QzPSpl#K4ov8`H~SyPJ5Mb2O%#!xYntBHAD70uNvNN{|u3aIi1b<&J@~
zs?S^l8F@n)#VW{5oAAAGV7%o}iw?-^Aa_4~3S^?NeEFbx7~0(4>ZI*=%&r585Z5q5
zz1k_F`SPLBFsI|RE5%ns16VfTq%QU`yo6<0@ct6S!izi0x<!dng735#otmNz<4+53
z=91KPU$El%>4Z%gI$`ODlw=ExzNH5F+~_eGG=)iagftpW5i@Q49GIKzDakLyai+3>
z5*%2sy|>E|055KPnY1>E%pq&$%`%L<wY>UFxNjKtCxrW$VzpbXc|I`vgT4@<PjlDb
z@X?Rgz}=!sjp`#n?4`UD8x7g9jN4`B__7w76wUQI?#CcEKA2<DbT4c`H)G{#sp)tG
zXY((uc)OY$1T~pzr+#HyvO$rpZY1ZE=OwVvr>=Wk?z`%5uO*Xv>4L*8G;5U8Dd+ZH
zTur}UXHfga;LR4c&z#fyoONY)Y1{+RKiE4`)Z085uLQ~;XdolfVkOvUvm9HU#M~N&
z9&ns|TZto5&#*Zf!?yJz5L|@?ul>>#($MlDs`Z2{?d;QMKexi~AM>&TG>RqSDPrU|
zv&;>Dx+nc|OKz0d+<Rx`lvXfpFS!sNvcc9q4S6-steMtWt%Wjh<&SE7Rac^+d`8JO
z2s>um@AkTu6@SmzbdLr4KAdUq1!(FBN+UTL<74!h%0py_nz!}DarINjLQ*Zcnnp!L
zox6iXo>afc_N%GLYe`E_7CmHe9~-WOo*et<4H_f1+!hxT9!L=IUKazbPL0#MhJKzh
zrB7Cc>;-@}Ow;9zB_S<6u%jaI`vtM<<#9dfk`ET2>Ea}pe@?$GdwIC6b*YtmRgU&%
z!$9~eOF3~*4z`SYNou-ge8iW2rICxTr)20Sjl4c>93vgUt@l#b28ciXN9q$^4s|lL
z(q~6}T5r|m6<*cXtL$yNyG`nOKmGiq73epelC$V0UpSUh{54P!9)RLY;7frC*$@fV
zlud|RDm=YL`0l{zQ>~o(l{8$-x?0R&aNP;b8Q+Hz=Ou%M<?psHANGCvq*BQ9GWcDP
zLijz<9pb8=PkX=PtrfG+MdKp^AxuzZRZ;JIF>(OoXUwabsZ9D#!_`!_aBYv-T`Jz$
z4|kdbeN6IoyJTJ}^sK~9n$c$u_O4oQENXcy5((}$yyIGF4+aSC557nJ_4Xfzxs@>q
zkz9WkV!Z*OBnSwWWg$beaQ#|D+TIgNO?8hS+v94IV+sNBwV~ZVLUXNBp<F_t{<Okh
z%4vWnY*nK_XvV8cg&cfJGP@@=7nJw8^((jxP&XO+R-X?U$XV2uoN#5i_<Vq3&kw^Y
z4m72DOE;6h8lvr`(cVOwG`%6l$?jR!Yh8RgrelGTbU&?ZKn*4fI9={{rgdU1rmlS+
z$N5CaFB0R(6ntb_R{-;yNq+KMpa2(aDi!;rtB(`8zS-DHSlU)29t!9ZA0oNe0B9_s
zi_iV1wkL7Y<4w6y&50hVEpf8tVQ+fsv@nO86!a8Z>Qav+jV#9F;&I|_$WqwWZlwK#
z^oqkITw}<3zsJmKN=8GOk=BBnt=~-1{d^?1-r1LKq{E&Iq3g7++{iz${KN>!jr1Ux
zAskga{E20_Jv1ue7jE(k{wva95`WOBb9`Eci;n<~i_;f1?8pvuV7q8~wEgJADTWvs
z5|mSp8-)Z-RgqKbzPm9$lZN6kLrgJ+QJPmLq;|7zvg=OYNOl`Ppwnee(@~(C7}0dI
zQ_)eNo|r2VzgHxe6P9tvRjfberNJzuqaZY(A>$US$($?HC~rjtAymBy5e%(i^Q2bk
z?>{&{p`qq6KEhO!X{=ln#c$yJF$wWlSgp#)CcT6E$GH8Mrv1xaN5Bzb{5}I_qi?s=
z@Yu)4$J@>)L?UvVS)^H0!3#LVV!@wQO`PY%>zO`H5*MqCej+kvb92P02+CL)mWRH*
zlaF12GNa=ZA{Y|ho)`|FF{nuUqgq+i7&G>eo$O2MW-)LK%!y)t^la9ey8H2su*bgp
z>9NM4^*a{on%a0zdHKTO0742tZpb?2)A6$tzZ_P0oZ5@9iv8A~d6B0xc(aj2KtDN#
z4F7s`cQXu~*0VYJPX_G3X-E2jN{W}`QrkGp0`v&@_sCbs$%c1gXmb~NmLOcxp~q5f
zr~}5BOcxH2!#@%NI)mQrJYORO!M;LncaZmXK%W1*@;7?m7v}G9dnPEaH&bqd_b?q&
zAGB)%)P|ta-AAJU8SEddxqt;YD*Q)+D24?G>kqD#!jOfNFA?&0UVjIW*oWqz%Gw@?
z4IJwMvVru=P2v*)4#ehe@=`va!LbD>Q&inF5pe3PG7#K)kQlm#ZzIWCfT~>{0S%@F
zwqgH}@xcVahDl}$gfKEBMY~9tx_y7Jp#^B10iQ_<LrwR88*;;+n<f@QoFgqjf-WBx
zS@I9RV#8qHe3;TnNdB5stg{Q^yci5q?`eY+EIbE|ljd6$O$2x_^#_YnVPu#A_{^w3
z45vz^0>r^`u-B(&4xs0O+R$n+hUM7BaO_On<&N<!pfePa0?;<r+Ro(N2uuU$TYxHi
zy8fDT{EP%s3L~=)hpCewjGr~60obTg0XZ7=;(nt6h$O&0kRZ_vD}#l2qZsfXYY>Ir
z!@VRJlYyeZ>TbxL`OFhBXFlK*$&+CllJi7mh;w&P4@)F$n1uA#d%%iRB%o#C>jK2k
zeHLwE;PGJyb2_siR30*j*7{og_L*{Lb>}nX_UfEx%H7qA$+|)5essf&!FlHdLoy$p
zSK+f!lGeJK%s+g==KO26bAKeVuh}lx{E6Is&34}APsIE++a;Snk(;mC$ZY;ZG+(n_
zwD}X^f6Yd2^C#l^nhjv{pCo^TOIvD&r$jhog7ynaR@bQ0F2ca!N-?)(jpe`EcwENN
ze|Gf#>!Z$$*|?XI??9S+=Bl$6Gpmk*?SzJjN4>qEq@JOGl%asBiGXhMM1BwdLhVE~
zK-R^ksbBw4pKsZ?8HU(cLqxf%^Xz(Cy#lMGMD}f1gUnTadX^lD7j>9<9N4`=*}7I}
zGY=L8mp9HxY<Y*=-_7TNJ?_X5UeZz7c?3jwf9ZyNk@7g5ft@(2=v_fbz|p0>nu(!i
zkk}5$$$ZvL%p4t1=eJ<NJJ>;&9A$q8FUW356?8e*7SD!TR0S!fhrtRGlwprtB&Ef%
znjj{X{$ORErM6DOx!$2%NMb$c&c%jU05y_4=TX9rdT%G>z3W3DTNyyn<sVWHd;cMo
z`(IN1U%cOrLiVbe6C@>pNb+}>+>T&l#tcqSufR1mGhb9xUwTFGDw!O9Gz?c1Ig}I{
zbnN55ZY#m#+f7vj_)seH?H%eM6(-8?EjWE|Ym7V9ljl0{5+;|rE?O9B4Z5^}Bh*-@
z^Ur^=Ud#0P`okyqRmmT%u>H1jyS^NWOs`$2{HO4}>TNN6!OJ^9AJmwq@!kh!ZQyMq
zr;j;*o6cXaU!#{O06nUmz4J7L_MbEUJ3Ga0!431`S1YN9{!ec#aFmsWvJ@EtK=OUU
z(SI{N@O#GLHQbflTIQdj>vJxs^vP;wCa^7gsP}Ny?CS+G&@BT@(4tILIXg_9$#gQH
zVDqAl?-OLOAOtwwAba35$><ERYKBe1?!ZWP=1xWIq@s>Y&&~FbUeg^YHod#vnohV>
z6mTbnv$CJOH#nku@J9GTVhwoJmn<!R$>a{@P={ya9ly(fB;YMLy;HOYND(A>?Bh+0
zM8UIL>0a+|KQn#+9WCQ~TW;MFR0RG;8_tE7-pQOob{sp62QU#@&NaiW=ViB}Jyq`b
zN#ZEAPo-=7#ew&L$^Mef#3$=L8fc2I$KGr_x4^av(!T*090k29{Ko&ki2UW#Z;`nF
z1&XQ`Fw^<t;hj}LW!&99$VZ6H9%1W@u?DQ*&wFljA*o*7FULgdve}RixsKGw(yBp{
z+g4`4O@zj=%lQ8JEogceMVA7~c9rZF6+c*>1XuTE65tbHpcOP5n7y|^itsOg2sK?e
zyxRWK9L~TglQfgczXzBKN%lq3nD;Bm=Yd<O117?yt9qe;2PB7fu_s#rARUGJyWMj>
z?KGoXkXJ~ic5H!&dc8PAnv|nVDQxIIzCipli#bynJA*7kdg!_!;oMbH_&*0c_kO)2
zy(1ksxOu+Ir56P(BY*B&Z~A(Hb=^?<PqPR<`Ora-w@~cBe@V*&NzdKE%ZvlB0o6$<
zTpl`{JB1WQ*}S<OiVI`=_zwG`DgHWCp(Ut&Vvg)Pc`x~?m{fCy!o-aWzkyu-lMTg4
z=D#HF{gzPtEn&iW8Qd;|2`&KN24zBqArHnq|8iH_<J>DClERr3c+T~D<KOW}0IZWz
zGn|3%IEHC6kKE#;LQ<=+{9bLPEPhcw<RQf!<gtKcKnRLzb|`!Gask!r^*JB1C{oZ%
znG=d<rRP?6GSf(DIdTi|mVy_tCbN)2`k6jw4)&vr^6ij@Q>L@W?|9)e)u-Ej&yiJu
zqz}bi5&yl=@;ics|4QyT?_pB@|8{oZ#)7lt2-EMp0PtV`(+1hZ-<cJM*ev`N(AWRn
zlk>kl;r^>`9s&POy&&*v%|D{0&U|jVgK(Zy!~Yuy?!Q3X{Qm|bMxChuIF0)dgK|my
zz0#Tc$^TC3K(H*e0@)l#<!&(U+z(J0-LC^h31-l4KR`q39BCu;Z~0*O?M3Nd`S|Z%
zs4!i8Tl{VG-`1YH_pkt|Oh}RJmf1PC-jU=&%IBru`VOQhu&$|#e)GFd2&RJGbut0n
zx?XlyS&9^mQqe}pgUZQ|%vt^7PA{SAkZjoN-wXeI7rC+8SJ+q59URabfHw5~5epXb
z;EwU*_y8NwEkhS~nEK1o7ZUdCQ0w1gJ&>=za~m{IR>uAd@(UK5#`=Gu#s0r(nLNAz
zEFhtj8-%G#*N1bRlGbkZ{PIrbH^UCkrRw8U(w~!1Cj7ZgtcJ&ha6o<fYy4}hhG*P$
zNahZyro^x^e}z>JpE(?!-4}e<o^o1eWW()uCAjvJ|5YUqK)-y>50a-VOzDjM*T`V-
z`F5R`E1?*fEE<Ibz~f1V+TyGk?C*3eCIX^PTUr;M;0(q30G1i{u!8KFp(@Du*ap@B
zlv;BPiwDq=zK;ixzn?FlP9c54>P;Eeu<N$_crKv%dTSv}`b>(dOmvI5`A7tKv%u^=
zE+7gfeflq+W`P;^Z(b&jPVVsSN&jH*wP+Cqs1IIUIa}=DUhLFyQW`*PygX#N0~yu|
z875Gi5WSvDnC=BwVcTd{kutk;IC(SB@=-2rXp6^hZWhaUdFbELtHjM=6rZnD6@K{g
zss!uNmd9saAP+<25mBPr^ZV@38bt8j5zl+5U)pjXxd|fkj7U|gY`JOe2}+jshkgc{
zu^;V7BYAn4_LeI51tfqM#_8@Hsgv-Lm<2?H98T#kgg1kRqT#c@;r*VFVKzfL0E3l@
z7J~kEg(NDN@%}V%@a)1~@BzOVP$jGN_DCpRrj)g0<tPM0T>&1O%J>>^-7N1f9X)7)
z$6Fy2#IWDpJqz(;>L0-QzD3B_A(b`{Iu7u-c)MZVh+xLn;V5aI-&*2?_amfr+>QMX
zsEPWLhoehY>kN2i9eDn@&2Rey#7V#7U#iJvW;W0p$9!q0SJnV{ONY;RYj5?Jk)%s<
zcFZqS{vGI{tKd%a3rZ=Gi%63KKXu$?`^JE*!?!~TlUu+2IDCV<kxi<DKdN$AF2Xt(
z0vxZFHK`wFPTrVwKb6IY{eZ8msXdoU<gEwsC6iX#f$_h_Q1G_jj$(>GMPn)L4;miU
zZ^$kM8!G6>FsR_e{y7?954L{<3@_Pk*pVsCrUE7(2xb(V-for(I(^<+zgO|j?q8aU
zcyHQ6So3))*s2$_7=@T+mo$}hj21I}CXjx(Vc%osw(4b$-F*1mP&V(rxuJR9{TGIs
zdG{?0f0ZuzZB4pDz3s)o%{8gOS;Ikq6E`-vE*{@z)OYyLVQK1ff@FUt=LK1$ssBXC
z1aC-F|A~+b@<~(wfyA5>ydwQaa$fMEbj+U!nIOG%%%2FkpqO;bf09V6tfLjr7d3|^
zwXY{=D^YiKjr#4B^&K7-b6eCH)ceu$WQ6ot&+Yurk7i{JTH-HfpRN=o)7Hap#-_EN
zb%$rc+^}(GT?#LrMp3taDqcc2I#xVydvlS3mP`N3^R^xAl`_Ei8<;u;MuwYoKqQ?+
z|K~$rU>xbC0L}~lyv)y5!XFXJPX~O5dBNl7Hie}non9~EGigc3!AC&5|MtKq1VrE(
zxZHaBR`B*1S_}M}{cYo+>7-Sds1}kvZ@x|Y46`>T0>ZFZZ}R~$+ah(O%axCSVi9oI
zBRHbrbT={-_RYYrKNxm0u%7QkI=T<eTY&l(oE)Y67hZr|X~FpCpg^}{($!Y{$Et%w
z0RA9lS`F=u!L;c^EdExP8$96y=Cuh-l7<pPQ@eT~i~S@`hW}fUk1eMN!eQipVG87N
z#gKqerdI?Ub$37(xu7K6q!abnFM8bH;Qrr>v^ID@5~NB<^z}cphh$b~Z4<RY^^0mt
zZQ21xN~%NPJq1E!jmV^$SuZ!lxr<Nt5fDX+k%7J3sfNGBIw8`c<T<FC6phNscSqSr
z-6$MmkA814>Fehk$=v*syHuJU?3XyOKiKw!5QiQh{|=rInKVU<c3;KFNRUEr?@%CB
z<W$nq>hgi6KiIRG2nGJ5M0F^AC$jAD9K})Ft1c1!+vdsJ{a&ULXUSR!3W-29vay@|
z+&9K<3UFT;`^3*pKX%@UlT?p!__pcqhrb+1<yuQBS0hrnmXgX<msGC*7$!?TmwDyf
zAIbI1D`d%kB9}A6&L#he+|CRmOa2qNkoojn@}J19%%^0@e<Ig1d(S2Ri7;jMk|qBq
zu@$74P^+UT9<Z*tSW^3Og0=!>u4~k8r)=OrUp(;F_1ynF*OM;o>g>}I()jU?$?*Kv
zlu?HXlQ?Xqc+s|?`6}9w$TBkDtGxqWl!(dqdr4h@zO+iQ>=@LK#_%M&Sl#LavwEAH
zJ!`}5Sbs0~kkPRho6!G2TUA0~YMp&wuS6MSj5Wi_+IcqTjs6e5HKB1_-RoIy#cggY
z1G?tHC(32|36T21Rgc2ljYK62KEwV>9`Cfsc;gBlm~+|$PE%oqFRt6XI?>W(OPrCr
z!Q5o$mv}KUH@(q<BeKheudnf!hp?e*SL0%vhi-X9)t}jU{}fI%#LD#2;LPpPDCAps
zCRd{h1!=RpxNvWfQ#K_2Cjio*g(vFIY}G%rbH|yrY`LC97))L)uNwKpIe8ygR^?;z
zy-?y$-fL_A+56A`;k{Fu0I{(^2V+L(v1oZF$v=P|a-;A3on&D=R)G6}ixJxQ4)}`e
zutYkEnXIXUf1zsN;FUnB&H37-*A8sYQ?5)|$<`))bYL@23HxDnw>Bx!fz2u<%;41*
z&8p{?R&7fCZ{iXza&lAX>e80vaOsN~e9^3YZuzoJ>D!w$_*+ZX7gLJ*!kY1sv6ifs
zQ{VK3`Qsx$Sh8AAeKQc=d6r_PcbgZik{p`Ql#zjP%?3Dx*2CSrK&v<?ikiP}n;D-_
zRc8koTSB6{bPCQ65Aj%s*>c3j0j_+RW{EFQr6<{J;qa}E?22f;h5Qjz*1&G5{%iEu
z70cYrNC~h9du+|DnE?tRoYgNFaU}C#sYNF~`fcTkpY1Wu_p;@*8+li!w(JyPDfOy#
z;|qt>OE`0}*6N+&RCQGA%{;%`6qEVhKfe^0gKm7UOtVT=<ffP`e2425dKz)Xa)#CG
zEDCXHQn|9>>wD*iS+BH9{LsUE&u0uz*HFu@SF_fBNfn)ij3SPkiSCq>A*O(2uVm|F
zq!cT<`7@<i!n3Lt^yS>m8*xiKvSP&r`=~CH%a#}iClsA*gXCmw#Sm3V<k)kQ<zqU3
z^k-yftJ=suyHDEku?FuxcL<#K0!|1u!LgGu^43)r6S+aAU)Lp7tCH%Gq+aHGW6bH(
zmRH7pMp;|qFisk;ihqVbvR+Kl6V#{kAwEVUmQQI~pfQ?;VAtep8+rhK5ohWvmKfKw
zP3o*>bY_2iMBt6@{SHQ@Kz+vif&JqpdCZeWYW=!`%}v#spHCiS)ED0MJea|r=G%Ya
z*%cpd9)pZEpV1#;XdDhU>3q@zk^YT3LKDnu;JcD;1_<lxD@9E+IkCbdYa~F&k4PBG
zfi39I`v$w>BgaK@dMVmWx=ba=7MH!I-V3sYvtNQdIFDPpn%-2scMsSN(xOlQ_b5^h
zT`LcK(h7SV?2#o!{2YiFJLWw)qltf5mw5c#Cq#gwFZ@orTHc)gU4Q?)S6@-0r0AcW
zM3jM72w9tEcz#Q$K6}wodtJ*zXHsN~zwxq+;wTMW*4_NSI0hq1ZLiPE<fz%3?QD;%
z{QI<jC$Fus0MXQbG&Brp<_XNf%y}HL3hkEqY78W#T|6A*DJhQ@GC6AT(nU0yPv$62
z>*hFcj#kFmOe<A8+&8S{cQ8t-Eq!&pxaVlrbS4|iCE(l7d0Y*qIahvVK=80DL}=jR
z4(AI-%s|0a&@tn4ZnnNLGe$Lc5OxJ85%4y{>g(*ZQ>Um}nhY3yBRfk*Ib&`kAU#=?
zWhhH73x>;L^*im3q`!7FSVWarmvlkwR_gK^UpLy9X=c4_qGk<4I$Q;BR9kqg1tcA+
zy6<f>FUSG(;E`K7!F1mt8oWaz=^S!CHU%3W69zxF96ut>0|0tuxHRTvD_k2BB7o*3
z<*lph>+rXMKrh4%i{?}JD)lanw7y5yG=gqX9MU%ZwaRsa>xROcv$byQ1By4KfND-y
zvxdVW^EXQsL?J%&YIs!!V$<96*|*{3iWg<{7$r`@u(*f3h}Cxn`uwLz(#Fd{AIq(>
zGrrS=KbT>_zb184mhEK2NWcDWr_3P+gA4K9|6-8(IIx2m(r$v(ocTuHD*%^F1N?r!
zign=Pebm|t`>lyN5o`bN8s9h}%P9a35EEcf4h_2nrQ?CXe>XOkNE8~_FSke1t6p!|
z;iDs+oJqTt1!vR5(2^bvQd6jMh=}MWw*%obNiB$PP$H27_~5t3p#^BSBZLHGs%Int
z;7{5NPGN&#!=O-nJLF{skhDqpA5x?ajXetVkCu;bB<vdqjQ{8lO$4mFi1Pln_Z(EY
z*F#bzH9Mq?Z}XP#d=+x!0)Fu~T_QDNGS^7<dpiDViCl{aCTY{*GtEfa4k|ml5@sZQ
z2K;k<D5(>){{KjjbQr~2*f9OrL}SSI6qZN^p@G_@|GoMe5O@_>P@gv+)JNK%3W5Hu
z@$#R27=OIn)YvJheIhrcfEIn=+eqrskT&E9QLzD}F=SDY>N{!AmiL_w|J?WtgONew
z1$9~z0Si0Ca{R|J(zdo_VVM!$m(Ceg0qZ#H?mjNxdS0T77;QL*4|kr>Aa!E&;6^W_
x=*B(}e-)b1s3Lg8|FDJoG`1<Y+1tEoJn{ecWd7Wf`3@!mqWFCnl@8CJ`#+bJoq+%V

literal 0
HcmV?d00001

diff --git a/EGVU_IPM_Balkans.jags b/EGVU_IPM_Balkans.jags
new file mode 100644
index 0000000..435d800
--- /dev/null
+++ b/EGVU_IPM_Balkans.jags
@@ -0,0 +1,348 @@
+
+    model {
+    #-------------------------------------------------
+    # integrated population model for the Balkan Egyptian Vulture population
+    # - age structured model with 6 age classes 
+    # - age-specific probability to recruit at ages 4 or 5
+    # - age-specific survival derived from tracking data for the first 3 years
+    # - adult survival based on territory occupancy
+    # - pre breeding census, female-based assuming equal sex ratio & survival
+    # - FUTURE PROJECTION COMBINES SCENARIOS: baseline scenario (=do nothing, no chick removal)
+    # - productivity supplemented by captive-bred juveniles (0-15 per year, for 10,20, or 30 years)
+    # - improvement of survival 0-10% with time lag for full improvement to occur after 10 years
+    #-------------------------------------------------
+    
+    
+    #-------------------------------------------------  
+    # 1. PRIORS FOR ALL DATA SETS
+    #-------------------------------------------------
+    
+    # Priors and constraints FOR FECUNDITY
+    mu.fec ~ dunif(0,2)           # Priors on fecundity can range from 0- 2 chicks per pair (uninformative)
+    
+    # Priors and constraints FOR POPULATION COUNTS OBSERVATION
+    for (s in 1:countries){			### start loop over every country
+    sigma.obs.count[s] ~ dunif(0.1,100)	#Prior for SD of observation process (variation in detectability)
+    tau.obs.count[s]<-pow(sigma.obs.count[s],-2)
+    }
+    
+    # Priors and constraints FOR JUVENILE SURVIVAL FROM TELEMETRY
+    #### MONTHLY SURVIVAL PROBABILITY
+    for (i in 1:nind.telemetry){
+    for (t in f.telemetry[i]:(n.occasions.telemetry)){
+    logit(phi.telemetry[i,t]) <- lp.mean.telemetry +      ### intercept for mean survival 
+    b.phi.capt*(capt.telemetry[i]) +     ### survival dependent on captive-release (captive-raised or other)
+    b.phi.mig*(mig.telemetry[i,t]) +     ### survival dependent on first migration across the sea
+    b.phi.age*(age.telemetry[i,t])     ### survival dependent on age (juvenile or other)
+    } #t
+    } #i
+    
+    #### BASELINE FOR SURVIVAL PROBABILITY (wild adult stationary from east)
+    mean.phi.telemetry ~ dunif(0.9, 1)   # uninformative prior for all MONTHLY survival probabilities
+    lp.mean.telemetry <- log(mean.phi.telemetry/(1 - mean.phi.telemetry))    # logit transformed survival intercept
+    
+    #### SLOPE PARAMETERS FOR SURVIVAL PROBABILITY
+    b.phi.age ~ dnorm(0, 0.001)           # Prior for changes in survival probability with age on logit scale
+    b.phi.capt ~ dnorm(0, 0.001)          # Prior for captive release on survival probability on logit scale
+    b.phi.mig ~ dunif(-2,0)               # Prior for  COST OF MIGRATION on survival probability on logit scale
+    
+    
+    #### TAG FAILURE AND LOSS PROBABILITY
+    for (i in 1:nind.telemetry){
+    for (t in f.telemetry[i]:(n.occasions.telemetry)){
+    logit(p.obs.telemetry[i,t]) <- base.obs.telemetry
+    logit(tag.fail.telemetry[i,t]) <- base.fail.telemetry
+    logit(p.found.dead.telemetry[i,t]) <- base.recover.telemetry
+    } #t
+    } #i
+    
+    
+    ##### SLOPE PARAMETERS FOR OBSERVATION PROBABILITY
+    base.obs.telemetry ~ dnorm(0, 0.001)                # Prior for intercept of observation probability on logit scale
+    base.fail.telemetry ~ dnorm(0, 0.001)               # Prior for intercept of tag failure probability on logit scale
+    base.recover.telemetry ~ dnorm(0, 0.001)            # Prior for intercept of recovery probability on logit scale
+    
+    
+    # Priors and constraints FOR ADULT SURVIVAL FROM TERRITORY MONITORING
+    ## Priors for detection probability
+    lmu.p.terrvis <- log(mean.p.terrvis/(1 - mean.p.terrvis))    # logit transformed survival intercept
+    mean.p.terrvis ~ dunif(0.5, 1)			
+    
+    # Det prob relationship with observation effort
+    beta.obs.eff.terrvis ~ dnorm(0, 0.0001)
+    
+    # Priors for survival
+    for (nypterr in 1:2){   ## only 2 survival 'phases' - good or bad years
+    mean.phi.terrvis[nypterr] ~ dunif(0.5, 1)   # informative prior for annual survival probability
+    mean.rec.terrvis[nypterr] ~ dunif(0, 1)     # informative prior for annual recruitment probability after territory abandonment
+    } 
+    
+    
+    ### RANDOM OBSERVATION AND SURVIVAL EFFECT FOR EACH TERRITORY AND YEAR
+    
+    for (nyRpterr in 1:nprim.terrvis){
+    rand.obs.terrvis[nyRpterr] ~ dnorm(0, tau.obs.terrvis)
+    }
+    
+    tau.obs.terrvis <- 1 / (sd.obs.terrvis * sd.obs.terrvis)
+    sd.obs.terrvis ~ dunif(0, 3)
+    
+    
+    # Priors for population process and immigration
+    
+    # Initial population sizes for first year of monitoring
+    nestlings[1] ~ dunif(50, 100)   ##changed from JUV
+    N1[1] ~ dunif(10, 35)
+    N2[1] ~ dunif(5, 20)
+    N3[1] ~ dunif(3, 15)
+    N4[1] ~ dunif(0, 10)
+    N5[1] ~ dunif(0, 5)
+    N6[1] ~ dunif(200, 250)
+    
+    
+    #-------------------------------------------------  
+    # 2. LIKELIHOODS AND ECOLOGICAL STATE MODEL
+    #-------------------------------------------------
+    
+    # -------------------------------------------------        
+    # 2.1. System process: female based matrix model
+    # -------------------------------------------------
+    
+    for (tt in 2:T.count){
+    
+    nestlings[tt] <- mu.fec * 0.5 * Nterr[tt]                                                              ### number of local recruits
+    N1[tt]  ~ dbin(ann.phi.juv.telemetry, round(nestlings[tt-1]))                                                    ### number of 1-year old survivors - add CAPT.ADD in here
+    N2[tt] ~ dbin(ann.phi.sec.telemetry, round(N1[tt-1]))                                                      ### number of 2-year old survivors
+    N3[tt] ~ dbin(ann.phi.third.telemetry, round(N2[tt-1]))                                                    ### number of 3-year old survivors
+    N4[tt] ~ dbin(mean.phi.terrvis[phase[tt]], round(N3[tt-1]))                                                       ### number of 4-year old survivors
+    N5[tt] ~ dbin(mean.phi.terrvis[phase[tt]], round(N4[tt-1]))                                                ### number of 5-year old survivors
+    N6[tt] ~ dbin(mean.phi.terrvis[phase[tt]], round((N5[tt-1]+N6[tt-1])))                                   ### number of 6-year or older (adult) birds
+    
+    } # tt
+    
+    
+    
+    # -------------------------------------------------        
+    # 2.2. Observation process for population counts: state-space model of annual counts
+    # -------------------------------------------------
+    
+    for (tlc in 1:T.count){
+    Nterr[tlc] <- N4[tlc] * 0.024 + N5[tlc] * 0.124 + N6[tlc]                                    ### number of observable territorial birds
+    for (s in 1:countries){			### start loop over every country
+    Nterr.country[tlc,s] ~ dbin(country.prop[s],round(Nterr[tlc]))
+    y.count[tlc,s] ~ dnorm(Nterr.country[tlc,s], tau.obs.count[s])								# Distribution for random error in observed numbers (counts)
+    }
+    
+    
+    # -------------------------------------------------        
+    # 2.3. Likelihood for fecundity: Poisson regression from the number of surveyed broods
+    # -------------------------------------------------
+    
+    J.fec[tlc] ~ dpois(rho.fec[tlc])
+    rho.fec[tlc] <- R.fec[tlc]*mu.fec
+    } #	close loop over every year in which we have count and fecundity data
+    
+    
+    
+    # -------------------------------------------------        
+    # 2.4. Likelihood for juvenile survival from telemetry
+    # -------------------------------------------------
+    
+    # -------------------------------------------------
+    # Define state-transition and observation matrices 
+    # -------------------------------------------------
+    
+    for (i in 1:nind.telemetry){
+    
+    for (t in f.telemetry[i]:(n.occasions.telemetry-1)){
+    
+    # Define probabilities of state S(t+1) [last dim] given S(t) [first dim]
+    
+    ps[1,i,t,1]<-1    ## dead birds stay dead
+    ps[1,i,t,2]<-0
+    ps[1,i,t,3]<-0
+    
+    ps[2,i,t,1]<-(1-phi.telemetry[i,t])
+    ps[2,i,t,2]<-phi.telemetry[i,t] * (1-tag.fail.telemetry[i,t])
+    ps[2,i,t,3]<-phi.telemetry[i,t] * tag.fail.telemetry[i,t]
+    
+    ps[3,i,t,1]<-(1-phi.telemetry[i,t])
+    ps[3,i,t,2]<-0
+    ps[3,i,t,3]<-phi.telemetry[i,t]
+    
+    # Define probabilities of O(t) [last dim] given S(t)  [first dim]
+    
+    po[1,i,t,1]<-0
+    po[1,i,t,2]<-p.obs.telemetry[i,t] * (1-tag.fail.telemetry[i,t]) * (1-p.found.dead.telemetry[i,t])
+    po[1,i,t,3]<-0
+    po[1,i,t,4]<-p.found.dead.telemetry[i,t]
+    po[1,i,t,5]<-(1-p.obs.telemetry[i,t]) * tag.fail.telemetry[i,t] * (1-p.found.dead.telemetry[i,t])
+    
+    po[2,i,t,1]<-p.obs.telemetry[i,t] * (1-tag.fail.telemetry[i,t])
+    po[2,i,t,2]<-0
+    po[2,i,t,3]<-0
+    po[2,i,t,4]<-0
+    po[2,i,t,5]<-(1-p.obs.telemetry[i,t]) * tag.fail.telemetry[i,t]
+    
+    po[3,i,t,1]<-0
+    po[3,i,t,2]<-0
+    po[3,i,t,3]<-0
+    po[3,i,t,4]<-0
+    po[3,i,t,5]<-1
+    
+    } #t
+    } #i
+    
+    # Likelihood 
+    for (i in 1:nind.telemetry){
+    # Define latent state at first capture
+    z.telemetry[i,f.telemetry[i]] <- 2 ## alive when first marked
+    for (t in (f.telemetry[i]+1):n.occasions.telemetry){
+    # State process: draw S(t) given S(t-1)
+    z.telemetry[i,t] ~ dcat(ps[z.telemetry[i,t-1], i, t-1,])
+    # Observation process: draw O(t) given S(t)
+    y.telemetry[i,t] ~ dcat(po[z.telemetry[i,t], i, t-1,])
+    } #t
+    } #i
+    
+    
+    # -------------------------------------------------        
+    # 2.5. Likelihood for adult survival from territory monitoring
+    # -------------------------------------------------
+    
+    ### ECOLOGICAL STATE MODEL WITH ESTIMATE OF SURVIVAL
+    
+    for (ilterr in 1:nsite.terrvis){
+    x.terrvis[ilterr,f.obsvis[ilterr]] <- 2 #firstobs[ilterr]
+    
+    for (klterr in (f.obsvis[ilterr]+1):nprim.terrvis){
+    z.terrvis[ilterr,klterr] ~ dbin(mean.phi.terrvis[phase[klterr]],x.terrvis[ilterr,klterr-1])
+    mu.x[ilterr,klterr] ~ dbin(mean.rec.terrvis[phase[klterr]],(2-z.terrvis[ilterr,klterr]))
+    x.terrvis[ilterr,klterr] <- mu.x[ilterr,klterr] + z.terrvis[ilterr,klterr]
+    
+    } 						# close klterr loop over primary period  - years
+    } 							# close ilterr loop over sites
+    
+    ### OBSERVATION MODEL WITH RANDOM EFFECT OF YEAR
+    
+    for (iobs in 1:nsite.terrvis){
+    for (kobs in f.obsvis[iobs]:nprim.terrvis){
+    linpred.terrvis[iobs,kobs] <- lmu.p.terrvis+beta.obs.eff.terrvis*eff.terrvis[iobs,kobs] + rand.obs.terrvis[kobs]
+    p.terrvis[iobs,kobs] <- 1 / (1 + exp(-linpred.terrvis[iobs,kobs]))
+    y.terrvis[iobs,kobs] ~ dbin(p.terrvis[iobs,kobs], x.terrvis[iobs,kobs])
+    
+    } 						 # close kobs loop over year
+    } 							# close iobs loop over sites
+    
+    
+    
+    
+    # -------------------------------------------------        
+    # 3. DERIVED PARAMETERS
+    # -------------------------------------------------
+    
+    ### 3.1 TELEMETRY DERIVED SURVIVAL ESTIMATES
+    
+    ## for WILD BIRDS
+    for (ageprog in 1:36){
+    logit(phi.wild.telemetry[ageprog]) <- lp.mean.telemetry +      ### intercept for mean survival 
+    b.phi.mig*(migprog.age[ageprog]) +     ### survival dependent on migration (first-time crossing of sea)
+    b.phi.age*ageprog     ### survival dependent on age (juvenile or other)
+    }
+    
+    ann.phi.juv.telemetry<-phi.wild.telemetry[1]*phi.wild.telemetry[2]*phi.wild.telemetry[3]*phi.wild.telemetry[4]*phi.wild.telemetry[5]*phi.wild.telemetry[6]*phi.wild.telemetry[7]*phi.wild.telemetry[8]*phi.wild.telemetry[9]*phi.wild.telemetry[10]*phi.wild.telemetry[11]*phi.wild.telemetry[12]					### multiply monthly survival from age 1-12
+    ann.phi.sec.telemetry<-phi.wild.telemetry[13]*phi.wild.telemetry[14]*phi.wild.telemetry[15]*phi.wild.telemetry[16]*phi.wild.telemetry[17]*phi.wild.telemetry[18]*phi.wild.telemetry[19]*phi.wild.telemetry[20]*phi.wild.telemetry[21]*phi.wild.telemetry[22]*phi.wild.telemetry[23]*phi.wild.telemetry[24]					### multiply monthly survival from age 13-24
+    ann.phi.third.telemetry<-phi.wild.telemetry[25]*phi.wild.telemetry[26]*phi.wild.telemetry[27]*phi.wild.telemetry[28]*phi.wild.telemetry[29]*phi.wild.telemetry[30]*phi.wild.telemetry[31]*phi.wild.telemetry[32]*phi.wild.telemetry[33]*phi.wild.telemetry[34]*phi.wild.telemetry[35]*phi.wild.telemetry[36]					### multiply monthly survival from age 25-36
+    
+    ## for CAPTIVE-REARED DELAYED RELEASE BIRDS
+    for (captageprog in 1:36){
+    logit(phi.capt.telemetry[captageprog]) <- lp.mean.telemetry +      ### intercept for mean survival
+    b.phi.capt +     ### survival dependent on captive-release (captive-raised or other)
+    b.phi.mig*(captprog.age[captageprog]) +     ### survival dependent on migration (first-time crossing of sea)
+    b.phi.age*captageprog     ### survival dependent on age (juvenile or other)
+    }
+    
+    ann.phi.capt.rel.first.year<-phi.capt.telemetry[8]*phi.capt.telemetry[9]*phi.capt.telemetry[10]*phi.capt.telemetry[11]*phi.capt.telemetry[12]*phi.capt.telemetry[13]*phi.capt.telemetry[14]*phi.capt.telemetry[15]*phi.capt.telemetry[16]*phi.capt.telemetry[17]*phi.capt.telemetry[18]*phi.capt.telemetry[19]*phi.capt.telemetry[20]*phi.capt.telemetry[21]*phi.capt.telemetry[22]*phi.capt.telemetry[23]*phi.capt.telemetry[24]					### first year for delayed-release bird is longer, from month 8 to 24 
+    
+    ### 3.2 POPULATION GROWTH DERIVED FROM COUNTS    
+    
+    # Annual population growth rate
+    for (ipop in 1:(T.count-1)){
+    lambda.t[ipop] <- Nterr[ipop+1] / max(Nterr[ipop],1)
+    loglambda.t[ipop]<-log(lambda.t[ipop])## for calculating geometric mean of overall population growth rate
+    }
+    
+    #### OVERALL POPULATION GROWTH RATE  #########
+    mean.lambda<-exp((1/(T.count-1))*sum(loglambda.t[1:(T.count-1)]))   # Geometric mean
+    
+    
+    
+    
+    # -------------------------------------------------        
+    # 4. PREDICTION INTO THE FUTURE
+    # -------------------------------------------------
+    ### INCLUDE SCENARIOS FOR N CAPTIVE BIRDS AND SURVIVAL IMPROVEMENT
+    
+    # CAPTIVE RELEASE OF JUVENILE BIRDS
+    for (ncr in 1:scen.capt.release){
+    
+    ### FUTURE FECUNDITY IS BASED ON mu.fec - removed the chick removal scenario
+    
+    for (fut in 1:PROJECTION){
+    fut.fec[fut,ncr] <-mu.fec  ## no chicks taken after 5 years anymore
+    }
+    
+    
+    
+    # SPECIFY IMPROVEMENT OF SURVIVAL
+    for (is in 1:scen.imp.surv){ 
+    
+    
+    ## POPULATION PROCESS
+    ### DOES NOT WORK WITH SAME ARRAY OF POP TRAJECTORY AS 3 dimensions required
+    ## need to copy previous array elements
+    nestlings.f[ncr,is,1] <- round(nestlings[T.count])   ##JUV[T.count]
+    N1.f[ncr,is,1] <- N1[T.count]
+    N2.f[ncr,is,1] <- N2[T.count]
+    N3.f[ncr,is,1] <- N3[T.count]
+    N4.f[ncr,is,1] <- N4[T.count]
+    N5.f[ncr,is,1] <- N5[T.count]
+    N6.f[ncr,is,1] <- N6[T.count]
+    Nterr.f[ncr,is,1] <- Nterr[T.count]
+    N2wild.f[ncr,is,1] <- 0
+    N2released.f[ncr,is,1] <- 0
+    
+    for (fut in 2:PROJECTION){
+    
+    fut.survival[ncr,is,fut] <-min(imp.surv[fut,is]*mean.phi.terrvis[2],1) ### invalid parent error if survival>1
+    
+    ### probabilistic formulation
+    nestlings.f[ncr,is,fut] <- (fut.fec[fut,ncr] * 0.5 * Nterr.f[ncr,is,fut])           ### number of local recruits calculated as fecundity times number of territorial pairs
+    N1.f[ncr,is,fut] ~ dbin(min((imp.surv[fut,is]*ann.phi.juv.telemetry),1),round(nestlings.f[ncr,is,fut-1]))             ### number of 1-year old survivors
+    N2wild.f[ncr,is,fut] ~ dbin(min((imp.surv[fut,is]*ann.phi.sec.telemetry),1),round(N1.f[ncr,is,fut-1]))                ### number of 2-year old wild survivors
+    N2released.f[ncr,is,fut] ~ dbin(min((imp.surv[fut,is]*ann.phi.capt.rel.first.year),1),round(capt.release[fut,ncr]))             ### number of 2-year old captive-released survivors 
+    N2.f[ncr,is,fut] <-  N2wild.f[ncr,is,fut] + N2released.f[ncr,is,fut]       ### sum of the N2 cohort derived from wild and captive-bred juveniles 
+    N3.f[ncr,is,fut] ~ dbin(min((imp.surv[fut,is]*ann.phi.third.telemetry),1),round(N2.f[ncr,is,fut-1]))                                                    ### number of 3-year old survivors
+    N4.f[ncr,is,fut] ~ dbin(fut.survival[ncr,is,fut],round(N3.f[ncr,is,fut-1]))                                                       ### number of 4-year old survivors
+    N5.f[ncr,is,fut] ~ dbin(fut.survival[ncr,is,fut],round(N4.f[ncr,is,fut-1]))                                                       ### number of 5-year old survivors
+    N6.f[ncr,is,fut] ~ dbin(fut.survival[ncr,is,fut],round((N5.f[ncr,is,fut-1]+N6.f[ncr,is,fut-1])))                                   ### number of 6-year or older (adult) birds
+    Nterr.f[ncr,is,fut] <- round((N4.f[ncr,is,fut] * 0.024) + (N5.f[ncr,is,fut] * 0.124) + (N6.f[ncr,is,fut]))
+    
+    
+    } # fut
+    
+    for (fut2 in 1:(PROJECTION-1)){
+    lambda.t.f[ncr,is,fut2] <- Nterr.f[ncr,is,fut2+1] / max(Nterr.f[ncr,is,fut2],1)
+    loglambda.t.f[ncr,is,fut2]<-log(lambda.t.f[ncr,is,fut2])## for calculating geometric mean of overall population growth rate
+    } # fut2
+    
+    
+    #### FUTURE POPULATION GROWTH RATE  #########
+    fut.lambda[ncr,is]<-exp((1/(PROJECTION-1))*sum(loglambda.t.f[ncr,is,1:(PROJECTION-1)]))   # Geometric mean
+    
+    } # end scenario of imp.surv
+    
+    } # end scenario of n capt released
+    
+    } # END of the JAGS model
+    
diff --git a/Oppel_etal_JApplEcol_PublishedCode.r b/Oppel_etal_JApplEcol_PublishedCode.r
new file mode 100644
index 0000000..07e842e
--- /dev/null
+++ b/Oppel_etal_JApplEcol_PublishedCode.r
@@ -0,0 +1,465 @@
+##########################################################################
+#
+# EGYPTIAN VULTURE POPULATION VIABILITY ANALYSIS ON BALKAN PENINSULA
+#
+##########################################################################
+# original model based on Lieury et al. 2015: Relative contribution of local demography and immigration in the recovery of a geographically-isolated population of the endangered Egyptian vulture. Biological Conservation 191: 349-356.
+
+library(jagsUI)
+library(tidyverse)
+load("https:\\github.com\\steffenoppel\\vultures\\Balkan_EGVU_PVA_DATA.RData")
+# load("C:\\STEFFEN\\MANUSCRIPTS\\in_prep\\EGVU_papers\\PVA_CaptiveRelease\\EGVU_IPM2020_output_v4_FINAL.RData")
+# rm(list=setdiff(ls(), c("z.telemetry","INPUT")))
+# head(INPUT)
+# INPUT$nrep.terrvis<-NULL
+# INPUT$rand.phi.offset<-NULL
+# INPUT$J.fec.red<-NULL
+# save.image("Balkan_EGVU_PVA_DATA.RData")
+
+
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPLANATION OF INPUT DATA
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+### the object 'INPUT' is a list with the following elements:
+str(INPUT)
+# y.terrvis: matrix of territory occupancy for 87 territories (rows) over 14 years (columns) with maximum number of adult birds observed per territory and year
+# z.terrvis: matrix of initial values for territory occupancy for 87 territories (rows) over 14 years (columns) to initiate the estimation
+# eff.terrvis: matrix of territory monitoring effort for 87 territories (rows) over 14 years (columns) scaled to the range -2 to +2
+# f.obs: vector with one value per monitored territory specifying the first year of monitoring (1-13) 
+# nsite.terrvis: number of observed territories for territory monitoring  (87)
+# nprim.terrvis: number of observed primary occasions for territory monitoring (14)
+# phase: vector of indicator (1 or 2) whether survival in a particular year was 'good' or 'bad'
+# y.count: matrix with the number of adult breeding birds counted in each country (column, 4) in each year (row, 14)
+# T.count: number of years with count data (14)
+# countries: number of countries with count data (4)
+# country.prop: vector of length 'countries' with proportion of the Balkan population in each country
+# R.fec: vector of the number of monitored breeding pairs in each year for calculation of fecundity
+# J.fec: vector of the number of fledglings produced by monitored breeding pairs in each year for calculation of fecundity
+# y.telemetry: matrix of observed states (1-5) for 33 tracked juvenile birds (rows) over 118 months (columns):
+# Observation states are as follows:
+# 1 Tag ok, bird moving
+# 2 Tag ok, bird not moving (dead)
+# 3 Tag failed, bird observed alive
+# 4 Dead bird recovered
+# 5 No signal (=not seen)
+# nind.telemetry: number of individual juveniles tracked with satellite telemetry (33)
+# n.occasions.telemetry: number of months over which individuals were tracked (118)
+# f.telemetry: vector with one value per tracked individual specifying the first month of tracking
+# l.telemetry: vector with one value per tracked individual specifying the last month of tracking
+# age.telemetry: matrix of individual age per month for 33 tracked juvenile birds (rows) over 119 months (columns)
+# capt.telemetry: vector with one value per tracked individual specifying whether the bird was captive-bred (1) or wild (0)
+# mig.telemetry: matrix specifying whether an individual migrated (1) or not (0) in a given month for 33 tracked juvenile birds (rows) over 119 months (columns)
+# migprog.age: vector specifying at which age first autumn migration occurs in wild juveniles
+# captprog.age: vector specifying at which age first autumn migration occurs in captive-released juveniles
+# PROJECTION: number of years over which population is projected into the future (30)
+# scen.capt.release: number of scenarios of captive-released birds (48)
+# scen.imp.surv: number of scenarios of survival improvement in the wild (18)
+# capt.release: matrix of the number of captive birds released every year (rows, 50 - only 30 are used if PROJECTION=30) for each scenario of captive release strategies (columns, 48)
+# imp.surv: matrix of survival improvement (multiplier for estimated survival) for every year (rows, 30 - only 30 are used if PROJECTION=30) for each scenario of survival improvement (columns, 18)
+
+
+
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# SPECIFY INTEGRATED POPULATION MODEL FOR JAGS
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+sink("EGVU_IPM_Balkans.jags")
+cat("
+    model {
+    #-------------------------------------------------
+    # integrated population model for the Balkan Egyptian Vulture population
+    # - age structured model with 6 age classes 
+    # - age-specific probability to recruit at ages 4 or 5
+    # - age-specific survival derived from tracking data for the first 3 years
+    # - adult survival based on territory occupancy
+    # - pre breeding census, female-based assuming equal sex ratio & survival
+    # - FUTURE PROJECTION COMBINES SCENARIOS: baseline scenario (=do nothing, no chick removal)
+    # - productivity supplemented by captive-bred juveniles (0-15 per year, for 10,20, or 30 years)
+    # - improvement of survival 0-10% with time lag for full improvement to occur after 10 years
+    #-------------------------------------------------
+    
+    
+    #-------------------------------------------------  
+    # 1. PRIORS FOR ALL DATA SETS
+    #-------------------------------------------------
+    
+    # Priors and constraints FOR FECUNDITY
+    mu.fec ~ dunif(0,2)           # Priors on fecundity can range from 0- 2 chicks per pair (uninformative)
+    
+    # Priors and constraints FOR POPULATION COUNTS OBSERVATION
+    for (s in 1:countries){			### start loop over every country
+    sigma.obs.count[s] ~ dunif(0.1,100)	#Prior for SD of observation process (variation in detectability)
+    tau.obs.count[s]<-pow(sigma.obs.count[s],-2)
+    }
+    
+    # Priors and constraints FOR JUVENILE SURVIVAL FROM TELEMETRY
+    #### MONTHLY SURVIVAL PROBABILITY
+    for (i in 1:nind.telemetry){
+    for (t in f.telemetry[i]:(n.occasions.telemetry)){
+    logit(phi.telemetry[i,t]) <- lp.mean.telemetry +      ### intercept for mean survival 
+    b.phi.capt*(capt.telemetry[i]) +     ### survival dependent on captive-release (captive-raised or other)
+    b.phi.mig*(mig.telemetry[i,t]) +     ### survival dependent on first migration across the sea
+    b.phi.age*(age.telemetry[i,t])     ### survival dependent on age (juvenile or other)
+    } #t
+    } #i
+    
+    #### BASELINE FOR SURVIVAL PROBABILITY (wild adult stationary from east)
+    mean.phi.telemetry ~ dunif(0.9, 1)   # uninformative prior for all MONTHLY survival probabilities
+    lp.mean.telemetry <- log(mean.phi.telemetry/(1 - mean.phi.telemetry))    # logit transformed survival intercept
+    
+    #### SLOPE PARAMETERS FOR SURVIVAL PROBABILITY
+    b.phi.age ~ dnorm(0, 0.001)           # Prior for changes in survival probability with age on logit scale
+    b.phi.capt ~ dnorm(0, 0.001)          # Prior for captive release on survival probability on logit scale
+    b.phi.mig ~ dunif(-2,0)               # Prior for  COST OF MIGRATION on survival probability on logit scale
+    
+    
+    #### TAG FAILURE AND LOSS PROBABILITY
+    for (i in 1:nind.telemetry){
+    for (t in f.telemetry[i]:(n.occasions.telemetry)){
+    logit(p.obs.telemetry[i,t]) <- base.obs.telemetry
+    logit(tag.fail.telemetry[i,t]) <- base.fail.telemetry
+    logit(p.found.dead.telemetry[i,t]) <- base.recover.telemetry
+    } #t
+    } #i
+    
+    
+    ##### SLOPE PARAMETERS FOR OBSERVATION PROBABILITY
+    base.obs.telemetry ~ dnorm(0, 0.001)                # Prior for intercept of observation probability on logit scale
+    base.fail.telemetry ~ dnorm(0, 0.001)               # Prior for intercept of tag failure probability on logit scale
+    base.recover.telemetry ~ dnorm(0, 0.001)            # Prior for intercept of recovery probability on logit scale
+    
+    
+    # Priors and constraints FOR ADULT SURVIVAL FROM TERRITORY MONITORING
+    ## Priors for detection probability
+    lmu.p.terrvis <- log(mean.p.terrvis/(1 - mean.p.terrvis))    # logit transformed survival intercept
+    mean.p.terrvis ~ dunif(0.5, 1)			
+    
+    # Det prob relationship with observation effort
+    beta.obs.eff.terrvis ~ dnorm(0, 0.0001)
+    
+    # Priors for survival
+    for (nypterr in 1:2){   ## only 2 survival 'phases' - good or bad years
+    mean.phi.terrvis[nypterr] ~ dunif(0.5, 1)   # informative prior for annual survival probability
+    mean.rec.terrvis[nypterr] ~ dunif(0, 1)     # informative prior for annual recruitment probability after territory abandonment
+    } 
+    
+    
+    ### RANDOM OBSERVATION AND SURVIVAL EFFECT FOR EACH TERRITORY AND YEAR
+    
+    for (nyRpterr in 1:nprim.terrvis){
+    rand.obs.terrvis[nyRpterr] ~ dnorm(0, tau.obs.terrvis)
+    }
+    
+    tau.obs.terrvis <- 1 / (sd.obs.terrvis * sd.obs.terrvis)
+    sd.obs.terrvis ~ dunif(0, 3)
+    
+    
+    # Priors for population process and immigration
+    
+    # Initial population sizes for first year of monitoring
+    nestlings[1] ~ dunif(50, 100)   ##changed from JUV
+    N1[1] ~ dunif(10, 35)
+    N2[1] ~ dunif(5, 20)
+    N3[1] ~ dunif(3, 15)
+    N4[1] ~ dunif(0, 10)
+    N5[1] ~ dunif(0, 5)
+    N6[1] ~ dunif(200, 250)
+    
+    
+    #-------------------------------------------------  
+    # 2. LIKELIHOODS AND ECOLOGICAL STATE MODEL
+    #-------------------------------------------------
+    
+    # -------------------------------------------------        
+    # 2.1. System process: female based matrix model
+    # -------------------------------------------------
+    
+    for (tt in 2:T.count){
+    
+    nestlings[tt] <- mu.fec * 0.5 * Nterr[tt]                                                              ### number of local recruits
+    N1[tt]  ~ dbin(ann.phi.juv.telemetry, round(nestlings[tt-1]))                                                    ### number of 1-year old survivors - add CAPT.ADD in here
+    N2[tt] ~ dbin(ann.phi.sec.telemetry, round(N1[tt-1]))                                                      ### number of 2-year old survivors
+    N3[tt] ~ dbin(ann.phi.third.telemetry, round(N2[tt-1]))                                                    ### number of 3-year old survivors
+    N4[tt] ~ dbin(mean.phi.terrvis[phase[tt]], round(N3[tt-1]))                                                       ### number of 4-year old survivors
+    N5[tt] ~ dbin(mean.phi.terrvis[phase[tt]], round(N4[tt-1]))                                                ### number of 5-year old survivors
+    N6[tt] ~ dbin(mean.phi.terrvis[phase[tt]], round((N5[tt-1]+N6[tt-1])))                                   ### number of 6-year or older (adult) birds
+    
+    } # tt
+    
+    
+    
+    # -------------------------------------------------        
+    # 2.2. Observation process for population counts: state-space model of annual counts
+    # -------------------------------------------------
+    
+    for (tlc in 1:T.count){
+    Nterr[tlc] <- N4[tlc] * 0.024 + N5[tlc] * 0.124 + N6[tlc]                                    ### number of observable territorial birds
+    for (s in 1:countries){			### start loop over every country
+    Nterr.country[tlc,s] ~ dbin(country.prop[s],round(Nterr[tlc]))
+    y.count[tlc,s] ~ dnorm(Nterr.country[tlc,s], tau.obs.count[s])								# Distribution for random error in observed numbers (counts)
+    }
+    
+    
+    # -------------------------------------------------        
+    # 2.3. Likelihood for fecundity: Poisson regression from the number of surveyed broods
+    # -------------------------------------------------
+    
+    J.fec[tlc] ~ dpois(rho.fec[tlc])
+    rho.fec[tlc] <- R.fec[tlc]*mu.fec
+    } #	close loop over every year in which we have count and fecundity data
+    
+    
+    
+    # -------------------------------------------------        
+    # 2.4. Likelihood for juvenile survival from telemetry
+    # -------------------------------------------------
+    
+    # -------------------------------------------------
+    # Define state-transition and observation matrices 
+    # -------------------------------------------------
+    
+    for (i in 1:nind.telemetry){
+    
+    for (t in f.telemetry[i]:(n.occasions.telemetry-1)){
+    
+    # Define probabilities of state S(t+1) [last dim] given S(t) [first dim]
+    
+    ps[1,i,t,1]<-1    ## dead birds stay dead
+    ps[1,i,t,2]<-0
+    ps[1,i,t,3]<-0
+    
+    ps[2,i,t,1]<-(1-phi.telemetry[i,t])
+    ps[2,i,t,2]<-phi.telemetry[i,t] * (1-tag.fail.telemetry[i,t])
+    ps[2,i,t,3]<-phi.telemetry[i,t] * tag.fail.telemetry[i,t]
+    
+    ps[3,i,t,1]<-(1-phi.telemetry[i,t])
+    ps[3,i,t,2]<-0
+    ps[3,i,t,3]<-phi.telemetry[i,t]
+    
+    # Define probabilities of O(t) [last dim] given S(t)  [first dim]
+    
+    po[1,i,t,1]<-0
+    po[1,i,t,2]<-p.obs.telemetry[i,t] * (1-tag.fail.telemetry[i,t]) * (1-p.found.dead.telemetry[i,t])
+    po[1,i,t,3]<-0
+    po[1,i,t,4]<-p.found.dead.telemetry[i,t]
+    po[1,i,t,5]<-(1-p.obs.telemetry[i,t]) * tag.fail.telemetry[i,t] * (1-p.found.dead.telemetry[i,t])
+    
+    po[2,i,t,1]<-p.obs.telemetry[i,t] * (1-tag.fail.telemetry[i,t])
+    po[2,i,t,2]<-0
+    po[2,i,t,3]<-0
+    po[2,i,t,4]<-0
+    po[2,i,t,5]<-(1-p.obs.telemetry[i,t]) * tag.fail.telemetry[i,t]
+    
+    po[3,i,t,1]<-0
+    po[3,i,t,2]<-0
+    po[3,i,t,3]<-0
+    po[3,i,t,4]<-0
+    po[3,i,t,5]<-1
+    
+    } #t
+    } #i
+    
+    # Likelihood 
+    for (i in 1:nind.telemetry){
+    # Define latent state at first capture
+    z.telemetry[i,f.telemetry[i]] <- 2 ## alive when first marked
+    for (t in (f.telemetry[i]+1):n.occasions.telemetry){
+    # State process: draw S(t) given S(t-1)
+    z.telemetry[i,t] ~ dcat(ps[z.telemetry[i,t-1], i, t-1,])
+    # Observation process: draw O(t) given S(t)
+    y.telemetry[i,t] ~ dcat(po[z.telemetry[i,t], i, t-1,])
+    } #t
+    } #i
+    
+    
+    # -------------------------------------------------        
+    # 2.5. Likelihood for adult survival from territory monitoring
+    # -------------------------------------------------
+    
+    ### ECOLOGICAL STATE MODEL WITH ESTIMATE OF SURVIVAL
+    
+    for (ilterr in 1:nsite.terrvis){
+    x.terrvis[ilterr,f.obsvis[ilterr]] <- 2 #firstobs[ilterr]
+    
+    for (klterr in (f.obsvis[ilterr]+1):nprim.terrvis){
+    z.terrvis[ilterr,klterr] ~ dbin(mean.phi.terrvis[phase[klterr]],x.terrvis[ilterr,klterr-1])
+    mu.x[ilterr,klterr] ~ dbin(mean.rec.terrvis[phase[klterr]],(2-z.terrvis[ilterr,klterr]))
+    x.terrvis[ilterr,klterr] <- mu.x[ilterr,klterr] + z.terrvis[ilterr,klterr]
+    
+    } 						# close klterr loop over primary period  - years
+    } 							# close ilterr loop over sites
+    
+    ### OBSERVATION MODEL WITH RANDOM EFFECT OF YEAR
+    
+    for (iobs in 1:nsite.terrvis){
+    for (kobs in f.obsvis[iobs]:nprim.terrvis){
+    linpred.terrvis[iobs,kobs] <- lmu.p.terrvis+beta.obs.eff.terrvis*eff.terrvis[iobs,kobs] + rand.obs.terrvis[kobs]
+    p.terrvis[iobs,kobs] <- 1 / (1 + exp(-linpred.terrvis[iobs,kobs]))
+    y.terrvis[iobs,kobs] ~ dbin(p.terrvis[iobs,kobs], x.terrvis[iobs,kobs])
+    
+    } 						 # close kobs loop over year
+    } 							# close iobs loop over sites
+    
+    
+    
+    
+    # -------------------------------------------------        
+    # 3. DERIVED PARAMETERS
+    # -------------------------------------------------
+    
+    ### 3.1 TELEMETRY DERIVED SURVIVAL ESTIMATES
+    
+    ## for WILD BIRDS
+    for (ageprog in 1:36){
+    logit(phi.wild.telemetry[ageprog]) <- lp.mean.telemetry +      ### intercept for mean survival 
+    b.phi.mig*(migprog.age[ageprog]) +     ### survival dependent on migration (first-time crossing of sea)
+    b.phi.age*ageprog     ### survival dependent on age (juvenile or other)
+    }
+    
+    ann.phi.juv.telemetry<-phi.wild.telemetry[1]*phi.wild.telemetry[2]*phi.wild.telemetry[3]*phi.wild.telemetry[4]*phi.wild.telemetry[5]*phi.wild.telemetry[6]*phi.wild.telemetry[7]*phi.wild.telemetry[8]*phi.wild.telemetry[9]*phi.wild.telemetry[10]*phi.wild.telemetry[11]*phi.wild.telemetry[12]					### multiply monthly survival from age 1-12
+    ann.phi.sec.telemetry<-phi.wild.telemetry[13]*phi.wild.telemetry[14]*phi.wild.telemetry[15]*phi.wild.telemetry[16]*phi.wild.telemetry[17]*phi.wild.telemetry[18]*phi.wild.telemetry[19]*phi.wild.telemetry[20]*phi.wild.telemetry[21]*phi.wild.telemetry[22]*phi.wild.telemetry[23]*phi.wild.telemetry[24]					### multiply monthly survival from age 13-24
+    ann.phi.third.telemetry<-phi.wild.telemetry[25]*phi.wild.telemetry[26]*phi.wild.telemetry[27]*phi.wild.telemetry[28]*phi.wild.telemetry[29]*phi.wild.telemetry[30]*phi.wild.telemetry[31]*phi.wild.telemetry[32]*phi.wild.telemetry[33]*phi.wild.telemetry[34]*phi.wild.telemetry[35]*phi.wild.telemetry[36]					### multiply monthly survival from age 25-36
+    
+    ## for CAPTIVE-REARED DELAYED RELEASE BIRDS
+    for (captageprog in 1:36){
+    logit(phi.capt.telemetry[captageprog]) <- lp.mean.telemetry +      ### intercept for mean survival
+    b.phi.capt +     ### survival dependent on captive-release (captive-raised or other)
+    b.phi.mig*(captprog.age[captageprog]) +     ### survival dependent on migration (first-time crossing of sea)
+    b.phi.age*captageprog     ### survival dependent on age (juvenile or other)
+    }
+    
+    ann.phi.capt.rel.first.year<-phi.capt.telemetry[8]*phi.capt.telemetry[9]*phi.capt.telemetry[10]*phi.capt.telemetry[11]*phi.capt.telemetry[12]*phi.capt.telemetry[13]*phi.capt.telemetry[14]*phi.capt.telemetry[15]*phi.capt.telemetry[16]*phi.capt.telemetry[17]*phi.capt.telemetry[18]*phi.capt.telemetry[19]*phi.capt.telemetry[20]*phi.capt.telemetry[21]*phi.capt.telemetry[22]*phi.capt.telemetry[23]*phi.capt.telemetry[24]					### first year for delayed-release bird is longer, from month 8 to 24 
+    
+    ### 3.2 POPULATION GROWTH DERIVED FROM COUNTS    
+    
+    # Annual population growth rate
+    for (ipop in 1:(T.count-1)){
+    lambda.t[ipop] <- Nterr[ipop+1] / max(Nterr[ipop],1)
+    loglambda.t[ipop]<-log(lambda.t[ipop])## for calculating geometric mean of overall population growth rate
+    }
+    
+    #### OVERALL POPULATION GROWTH RATE  #########
+    mean.lambda<-exp((1/(T.count-1))*sum(loglambda.t[1:(T.count-1)]))   # Geometric mean
+    
+    
+    
+    
+    # -------------------------------------------------        
+    # 4. PREDICTION INTO THE FUTURE
+    # -------------------------------------------------
+    ### INCLUDE SCENARIOS FOR N CAPTIVE BIRDS AND SURVIVAL IMPROVEMENT
+    
+    # CAPTIVE RELEASE OF JUVENILE BIRDS
+    for (ncr in 1:scen.capt.release){
+    
+    ### FUTURE FECUNDITY IS BASED ON mu.fec - removed the chick removal scenario
+    
+    for (fut in 1:PROJECTION){
+    fut.fec[fut,ncr] <-mu.fec  ## no chicks taken after 5 years anymore
+    }
+    
+    
+    
+    # SPECIFY IMPROVEMENT OF SURVIVAL
+    for (is in 1:scen.imp.surv){ 
+    
+    
+    ## POPULATION PROCESS
+    ### DOES NOT WORK WITH SAME ARRAY OF POP TRAJECTORY AS 3 dimensions required
+    ## need to copy previous array elements
+    nestlings.f[ncr,is,1] <- round(nestlings[T.count])   ##JUV[T.count]
+    N1.f[ncr,is,1] <- N1[T.count]
+    N2.f[ncr,is,1] <- N2[T.count]
+    N3.f[ncr,is,1] <- N3[T.count]
+    N4.f[ncr,is,1] <- N4[T.count]
+    N5.f[ncr,is,1] <- N5[T.count]
+    N6.f[ncr,is,1] <- N6[T.count]
+    Nterr.f[ncr,is,1] <- Nterr[T.count]
+    N2wild.f[ncr,is,1] <- 0
+    N2released.f[ncr,is,1] <- 0
+    
+    for (fut in 2:PROJECTION){
+    
+    fut.survival[ncr,is,fut] <-min(imp.surv[fut,is]*mean.phi.terrvis[2],1) ### invalid parent error if survival>1
+    
+    ### probabilistic formulation
+    nestlings.f[ncr,is,fut] <- (fut.fec[fut,ncr] * 0.5 * Nterr.f[ncr,is,fut])           ### number of local recruits calculated as fecundity times number of territorial pairs
+    N1.f[ncr,is,fut] ~ dbin(min((imp.surv[fut,is]*ann.phi.juv.telemetry),1),round(nestlings.f[ncr,is,fut-1]))             ### number of 1-year old survivors
+    N2wild.f[ncr,is,fut] ~ dbin(min((imp.surv[fut,is]*ann.phi.sec.telemetry),1),round(N1.f[ncr,is,fut-1]))                ### number of 2-year old wild survivors
+    N2released.f[ncr,is,fut] ~ dbin(min((imp.surv[fut,is]*ann.phi.capt.rel.first.year),1),round(capt.release[fut,ncr]))             ### number of 2-year old captive-released survivors 
+    N2.f[ncr,is,fut] <-  N2wild.f[ncr,is,fut] + N2released.f[ncr,is,fut]       ### sum of the N2 cohort derived from wild and captive-bred juveniles 
+    N3.f[ncr,is,fut] ~ dbin(min((imp.surv[fut,is]*ann.phi.third.telemetry),1),round(N2.f[ncr,is,fut-1]))                                                    ### number of 3-year old survivors
+    N4.f[ncr,is,fut] ~ dbin(fut.survival[ncr,is,fut],round(N3.f[ncr,is,fut-1]))                                                       ### number of 4-year old survivors
+    N5.f[ncr,is,fut] ~ dbin(fut.survival[ncr,is,fut],round(N4.f[ncr,is,fut-1]))                                                       ### number of 5-year old survivors
+    N6.f[ncr,is,fut] ~ dbin(fut.survival[ncr,is,fut],round((N5.f[ncr,is,fut-1]+N6.f[ncr,is,fut-1])))                                   ### number of 6-year or older (adult) birds
+    Nterr.f[ncr,is,fut] <- round((N4.f[ncr,is,fut] * 0.024) + (N5.f[ncr,is,fut] * 0.124) + (N6.f[ncr,is,fut]))
+    
+    
+    } # fut
+    
+    for (fut2 in 1:(PROJECTION-1)){
+    lambda.t.f[ncr,is,fut2] <- Nterr.f[ncr,is,fut2+1] / max(Nterr.f[ncr,is,fut2],1)
+    loglambda.t.f[ncr,is,fut2]<-log(lambda.t.f[ncr,is,fut2])## for calculating geometric mean of overall population growth rate
+    } # fut2
+    
+    
+    #### FUTURE POPULATION GROWTH RATE  #########
+    fut.lambda[ncr,is]<-exp((1/(PROJECTION-1))*sum(loglambda.t.f[ncr,is,1:(PROJECTION-1)]))   # Geometric mean
+    
+    } # end scenario of imp.surv
+    
+    } # end scenario of n capt released
+    
+    } # END of the JAGS model
+    ",fill = TRUE)
+sink()
+
+
+
+
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# SET UP MCMC PARAMETERS AND RUN MODEL
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+## Parameters to be estimated ('monitored') by JAGS
+paraIPM<-c("mu.fec","lambda.t","b.phi.age","b.phi.capt","b.phi.mig","ann.phi.capt.rel.first.year",
+           "ann.phi.juv.telemetry","ann.phi.sec.telemetry","ann.phi.third.telemetry", "ann.phi.terrvis",     #"breed.prop4","breed.prop5",
+           "mean.phi.terrvis","mean.lambda","fut.lambda","Nterr", "Nterr.f")
+
+
+# MCMC settings
+nc <- 3
+nt <- 4
+ni <- 50000
+nb <- 10000
+
+# specify initial values
+initIPM <- function(){list(mean.p.terrvis=runif(1,0.5,1),
+                           mean.phi.terrvis=runif(2,0.75, 1),
+                           sigma.obs.count=runif(4,0.1,100),
+                           mu.fec = runif(1,0,1),
+                           z.telemetry = z.telemetry,                 ### matrix with initial values for the true states of tracked individuals
+                           mean.phi.telemetry = runif(1, 0.9, 0.999),
+                           base.obs.telemetry = rnorm(1,0, 0.001),
+                           base.fail.telemetry = rnorm(1,0, 0.001),
+                           base.recover.telemetry = rnorm(1,0, 0.001))}   
+
+
+### RUN THE MODEL ###
+
+EGVU.IPM <- jags(data=INPUT,
+                       inits=initIPM,
+                       parameters.to.save=paraIPM,
+                       model.file="C:/STEFFEN/RSPB/Bulgaria/Analysis/PopulationModel/vultures/EGVU_IPM_Balkans.jags",    
+                       n.chains=nc, n.thin=nt, n.burnin=nb, n.iter=ni,parallel=T)
+
+
+
+
+
+
+
+  
\ No newline at end of file