Models Failing to Converge

Emma Wilson

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Jun 26, 2025, 4:18:00 PMJun 26

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Hello,

I am using "unmarked" to build occupancy models that includes data for 34 sites, some of which were surveyed a different number of times (1-4, 1-5, and 1-10). 0s represent non-detection, while NAs represent no survey for that particular survey occasion. Prior to running the models, all continuous variables were standardized and checked for collinearity.

My models are failing to converge unless specific covariates are removed (that is, average depth in m, the presence of Trachemys species, and the presence of Chrysemys species). Currently, I am using intercept-only for detection.

I receive the following errors when all site-level covariates are run:

Warning in sqrt(diag(vcov(model, ...))) : NaNs produced

Warning: Hessian is singular. Try providing starting values or using fewer covariates.
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'diag': Lapack routine dgesv: system is exactly singular: U[3,3] = 0

Is there anything about the code structure or data that may be preventing model convergence when including variables for avg_depth_m, Trachemys_present, and Chrysemys_present?

Please let me know if you have any thoughts as to how to best approach this issue. Thank you for your time and assistance!

All the best,

Emma

This is the script used:

> # Data Format:
> dput(obs_covs_list) # Observation covariates
list(max_air_temp_C = structure(list(max_air_temp_C.1 = c(-0.459496711703865,
-0.580604373234465, 0.424589209185752, 0.424589209185752, -0.738044331480295,
-2.04600706554707, 1.5630012201616, 0.642582998196882, -0.277835221587903,
1.67199811466717, 1.5630012201616, -1.88856710730124, 0.24292771906979,
0.24292771906979, 0.860576787208012, 0.30348154983509, -0.435275179833732,
-1.04081348312686, 1.12701364213934, 0.824244490492783, 0.872687554233047,
-0.302056753458035, 0.800022958622649, -1.25880727213799, -1.73112714905541,
-1.19825344137269, -0.338389052353203, -0.641158203999765, -0.665379735869898,
1.23601053664491, 0.279260017964958, 1.01801674763378, 0.0733769937989247,
-0.568493606209429), max_air_temp_C.2 = c(-0.303742800714979,
-1.41065499263084, 0.979804314811402, 0.979804314811402, -2.48224020109637,
-0.657012650055633, 0.43812388141666, 0.67363711360389, -1.04560948284662,
1.73344665950623, 0.803169391200885, -0.715890958632345, -0.551031695465399,
-0.551031695465399, -0.56280735590897, -0.680563973062394, -1.36355234661732,
-0.84542323622934, -0.162434862674413, 0.968028654367831, 1.00335563781816,
0.508777850436943, 1.38017681016558, 0.461675204423421, -0.185986185681174,
0.143732340652719, -0.386172432298452, 1.02690696294454, 0.838496376770836,
1.40372813317234, 0.449899541860231, 0.744291082624172, -1.45775764076398,
-1.17514176044361), max_air_temp_C.3 = c(-1.84373413837006, 0.111392955936023,
1.23978059325572, 1.23978059325572, -2.6593014416465, -1.01699468138367,
0.904615947770596, 0.926960257201473, -0.447214785868854, 1.27329705840753,
-0.301976773562663, -1.49739733917499, 0.10022080021509, 0.10022080021509,
-0.301976773562663, -0.301976773562663, -0.726518656771293, -0.570108488744169,
-0.927617443660169, 0.290147433394023, 0.223114503090405, 0.658828542019967,
1.50791230843723, 0.81523871004709, -0.670657882188607, 0.334836052255776,
-1.37450363629968, 0.189598039949584, 0.558279148575528, 1.78721617933966,
0.144909419076843, 0.837583019477967, -0.659485728478663, 0.0555321813533371
), max_air_temp_C.4 = c(0.917750799315473, -0.496410528188183,
-0.0825096516437463, -0.0825096516437463, -0.542399514011008,
-2.27848374641345, -0.254968351583598, -0.266465597004552, -0.634377487726163,
-0.151493132447488, 0.699303115622301, -2.41645070595143, -1.16325082882766,
-1.16325082882766, 0.641816882309017, 0.699303115622301, -0.105504144555159,
-0.795338940175556, -0.645874733147118, 0.239413251185535, 0.308396731989277,
0.80277833475841, 1.63058008784728, 0.503849922771037, -1.40469300750175,
0.170429772451297, -0.726355459371813, 1.21667921130285, 2.18244792186019,
0.676308620641384, 0.446363689457753, 0.710800361043255, 0.308396731989277,
1.05571775885345), max_air_temp_C.5 = c(-1.67138854913074, -1.45389713786756,
0.126540458126549, 0.126540458126549, 0.489026145305106, 1.24299637432332,
0.0975416034654518, 0.0975416034654518, -0.699926908849354, -0.0184538177888326,
1.66347977082407, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), max_air_temp_C.6 = c(NA,
-0.671913331308388, 0.363696206895718, 0.363696206895718, NA,
-0.597941222070713, NA, -1.1034173074094, NA, 1.64587944699707,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA), max_air_temp_C.7 = c(NA, -0.316357548422689,
-0.346016069199023, -0.346016069199023, NA, -0.0395446943684453,
NA, -0.90952795149279, NA, 1.95746233268197, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), max_air_temp_C.8 = c(NA, 0.923164964753146,
-1.20236607565684, -1.20236607565684, NA, 0.364762741324585,
NA, 0.0585421691212836, NA, 1.05826227611467, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), max_air_temp_C.9 = c(NA, 1.25268445710534, 0.0347967907651219,
0.0347967907651219, NA, NA, NA, -1.54265771222867, NA, 0.220379673593086,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA), max_air_temp_C.10 = c(NA, 1.7863752785884,
-0.499222721070786, -0.499222721070786, NA, NA, NA, -0.393964918223415,
NA, -0.393964918223415, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), class = "data.frame", row.names = c(NA,
-34L)), max_percent_RH = structure(list(max_percent_RH.1 = c(0.0596455250653726,
1.05892417688524, 0.19190299368859, 0.19190299368859, 0.60337067384971,
-1.7331779384938, 0.618065948141179, -1.14536696683505, 0.985447805427894,
0.14046953366845, -0.263650509346937, -1.43927245266443, -1.40988190408149,
-1.40988190408149, 1.05892417688524, -1.08658586966918, 0.985447805427894,
1.05892417688524, -0.880852029588619, 0.941361982553489, -0.212217049326797,
-2.2695554501324, -0.33712688080428, 1.00014307971936, 0.471113205226493,
1.05892417688524, 0.765018691055865, 1.05892417688524, 1.05892417688524,
-1.05719532108624, -1.10862878110638, -0.102002492140782, 0.772366328201599,
0.375593922331947), max_percent_RH.2 = c(0.60296343327545, 0.60296343327545,
0.366051760725003, 0.366051760725003, 0.60296343327545, -1.13438883209449,
-3.09285865851152, -1.76615329222902, 0.60296343327545, 0.60296343327545,
0.60296343327545, 0.445022318241819, 0.460816429745181, 0.460816429745181,
0.60296343327545, 0.60296343327545, 0.287081203208188, 0.60296343327545,
-2.96650576648461, -1.48185928516848, -1.03962416307431, -0.186742141892706,
0.129140088174556, 0.60296343327545, -0.0288010268590746, 0.60296343327545,
-0.0288010268590746, 0.60296343327545, 0.60296343327545, 0.60296343327545,
-0.139359807382617, -0.297300922416248, 0.60296343327545, 0.60296343327545
), max_percent_RH.3 = c(0.671864840101553, 0.671864840101553,
0.671864840101553, 0.671864840101553, 0.671864840101553, 0.525620252627463,
-1.65342410073647, -0.746707658397117, 0.671864840101553, 0.671864840101553,
0.671864840101553, 0.525620252627463, 0.671864840101553, 0.671864840101553,
-1.34631046704088, -1.15619250332457, -1.17081696207198, -2.58938946057065,
-2.76488296553955, 0.101510948952603, 0.525620252627463, -1.33168600829348,
-0.790581034639343, 0.0868864902051944, 0.0868864902051944, 0.613367005111916,
0.0868864902051944, 0.671864840101553, 0.525620252627463, 0.671864840101553,
0.671864840101553, 0.394000123900782, 0.671864840101553, 0.671864840101553
), max_percent_RH.4 = c(0.663915891045687, 0.663915891045687,
0.154473713499614, 0.154473713499614, 0.663915891045687, -2.48977377947762,
0.142344137843755, 0.663915891045687, -0.512652947572625, 0.663915891045687,
0.663915891045687, -3.46013983194633, -0.3428388883906, -0.3428388883906,
-0.0881177996175639, 0.0938258352203193, 0.639656739733969, 0.663915891045687,
0.603268012766393, -0.0638586483058458, -0.330709312734742, -0.209413556176153,
-0.427745917981612, 0.0453075325968848, 0.178732864811332, 0.251510318746484,
-2.7323652925948, 0.663915891045687, 0.542620134487098, 0.663915891045687,
0.324287772681639, 0.566879285798816, 0.663915891045687, 0.663915891045687
), max_percent_RH.5 = c(-1.73053052256212, 0.581107398283796,
0.581107398283796, 0.581107398283796, -1.30840533701635, 0.581107398283796,
0.581107398283796, 0.581107398283796, -1.6099233266919, 0.581107398283796,
0.581107398283796, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), max_percent_RH.6 = c(NA,
0.566556887333738, 0.149227037288797, 0.149227037288797, NA,
-1.99812473657881, NA, 0.566556887333738, NA, 0.566556887333738,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA), max_percent_RH.7 = c(NA, 0.793878974139019,
-1.22058892273874, -1.22058892273874, NA, -0.0198469743534753,
NA, 0.83357292284597, NA, 0.83357292284597, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA), max_percent_RH.8 = c(NA, 0.701221775882323, -1.28570561172535,
-1.28570561172535, NA, 0.467745895803724, NA, 0.701221775882323,
NA, 0.701221775882323, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), max_percent_RH.9 = c(NA,
0.897941653226101, -1.066305713206, -1.066305713206, NA, NA,
NA, 0.336728119959786, NA, 0.897941653226101, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), max_percent_RH.10 = c(NA, 0.896258159530275,
-0.128036879932895, -0.128036879932895, NA, NA, NA, 0.896258159530275,
NA, -1.53644255919475, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), class = "data.frame", row.names = c(NA,
-34L)), min_percent_RH = structure(list(min_percent_RH.1 = c(0.280939125470438,
-0.135483760354185, -1.40317820923622, -1.40317820923622, -0.581387912431879,
-1.17101323749329, -0.212872084268495, 0.56101115487461, -0.76564582651357,
-0.839348992146247, -0.41924094803999, -1.13416165467695, -1.15258744608512,
-1.15258744608512, 0.0229780457560698, 0.505733780650102, 1.26119122838504,
1.69972506389946, -0.555591804460442, 0.325161024850044, -0.113372810664382,
-0.931477949187093, -1.06045848904428, 1.63707737311169, 0.450456406425595,
1.7034102221811, 0.303050075160241, 1.7034102221811, 1.7034102221811,
-0.618239495248217, 0.616288529099117, -0.699312977444162, 1.06956299774008,
0.505733780650102), min_percent_RH.2 = c(0.0676741919390416,
1.95165102524873, -2.06852904801593, -2.06852904801593, 2.3222694186867,
-0.817691970162779, -0.0816026609734749, -0.354418978365315,
0.690518992022298, -0.220584558512714, 0.566979527542975, -0.148519870899775,
0.0728216696256798, 0.0728216696256798, -0.98241125613521, -0.400746277545061,
-0.508843308964469, -1.58981362315855, -0.570613041204131, 0.191213656418365,
0.114001491118788, -0.169109781646329, -0.766217193296394, -0.534580697397662,
0.366227897764074, 0.031641848132572, 1.292773881359, 0.75228872426196,
0.947892876354223, -0.658120161876985, 0.0573792365657644, -0.498548353591192,
1.67883470785689, 1.26188901523917), min_percent_RH.3 = c(1.86051740977938,
-0.689592949926057, -1.52312416782331, -1.52312416782331, 1.86051740977938,
0.45754407272461, -0.924797303491122, -0.846395852302767, 0.428659327549953,
-0.0376229874123686, 1.83163266460473, 0.416280151046529, -1.48186024614522,
-1.48186024614522, 0.0572840324472189, 0.441038504053377, -1.14762248055276,
-1.30855177509728, -1.07747381370002, 0.0944215619574926, 0.0325256794403702,
0.185202189649272, -0.367734360837021, -0.466767772864417, 1.03523897621775,
-0.334723223494556, -0.32647043915894, 0.812413799156112, 0.622599759436937,
0.160443836642423, 0.977469485868438, 0.0118937186013294, 1.59230191887185,
0.65973728894721), min_percent_RH.4 = c(-0.615237620863046, -0.566146952809827,
-0.593419546172726, -0.593419546172726, -1.20432563750166, -0.527965322101769,
0.699301379228687, 1.02657249958348, 0.508393225688394, 2.90838144162351,
1.69747829631079, -0.309784575198576, -1.34068860431616, -1.34068860431616,
0.961118275512517, 0.595665524449671, -0.631601176880785, -0.680691844934003,
-1.14978045077587, -0.522510803429189, -0.358875243251795, 0.388393814891638,
-0.20069420174698, -0.0534221975873256, 0.0174865451562117, 0.644756192502889,
-1.45523349644034, 1.00475442489316, 0.835664346043182, -0.0588767162599055,
-0.413420429977593, -1.07887170803233, 1.70293281498337, 0.704755897901267
), min_percent_RH.5 = c(-0.253861459079237, 1.19713099820699,
-0.801405782583473, -0.801405782583473, -1.44933323206349, -1.0249863813477,
0.311934341875141, 0.339311558050353, 0.485323377651482, 1.89981288003743,
0.0974794818359813, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), min_percent_RH.6 = c(NA,
-1.02039676293566, -0.747424380494274, -0.747424380494274, NA,
0.572870612130394, NA, 0.411315528644675, NA, 1.53105938314914,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA), min_percent_RH.7 = c(NA, -0.791790120060484,
-0.583652520044585, -0.583652520044585, NA, -0.214208280016363,
NA, 0.285321960021796, NA, 1.88798148014422, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), min_percent_RH.8 = c(NA, -0.823388941971144,
-0.484437479016857, -0.484437479016857, NA, -0.252278942746798,
NA, 0.100602032383692, NA, 1.94394081036796, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), min_percent_RH.9 = c(NA, -0.806517055346018,
-0.672450353232506, -0.672450353232506, NA, NA, NA, 0.819923725557369,
NA, 1.33149403625366, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), min_percent_RH.10 = c(NA,
-0.973458214944405, -0.575093618054954, -0.575093618054954, NA,
NA, NA, 0.801074989381333, NA, 1.32257046167298, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA)), class = "data.frame", row.names = c(NA,
-34L)), doy = structure(list(doy.1 = c(-1.49457334197023, -1.45698791768991,
-1.1938899477277, -1.1938899477277, -0.667694007803267, -1.56974419053086,
0.121599902083376, 0.121599902083376, 0.384697872045591, 0.722966690568438,
0.760552114848755, -1.56974419053086, -0.818035704924533, -0.818035704924533,
-0.517352310682002, -0.517352310682002, -0.254254340719787, -0.254254340719787,
0.00884362924242735, 0.271941599204642, 0.271941599204642, 0.535039569166856,
0.535039569166856, 0.798137539129071, -1.15630452344738, 0.798137539129071,
-1.11871909916706, 1.06123550909129, 1.06123550909129, 1.3243334790535,
1.3243334790535, 1.3243334790535, 1.58743144901571, 1.58743144901571
), doy.2 = c(-1.49457334197023, -1.45698791768991, -1.1938899477277,
-1.1938899477277, -0.667694007803267, -1.56974419053086, 0.121599902083376,
0.121599902083376, 0.384697872045591, 0.722966690568438, 0.760552114848755,
-1.56974419053086, -0.818035704924533, -0.818035704924533, -0.517352310682002,
-0.517352310682002, -0.254254340719787, -0.254254340719787, 0.00884362924242735,
0.271941599204642, 0.271941599204642, 0.535039569166856, 0.535039569166856,
0.798137539129071, -1.15630452344738, 0.798137539129071, -1.11871909916706,
1.06123550909129, 1.06123550909129, 1.3243334790535, 1.3243334790535,
1.3243334790535, 1.58743144901571, 1.58743144901571), doy.3 = c(-1.49457334197023,
-1.45698791768991, -1.1938899477277, -1.1938899477277, -0.667694007803267,
-1.56974419053086, 0.121599902083376, 0.121599902083376, 0.384697872045591,
0.722966690568438, 0.760552114848755, -1.56974419053086, -0.818035704924533,
-0.818035704924533, -0.517352310682002, -0.517352310682002, -0.254254340719787,
-0.254254340719787, 0.00884362924242735, 0.271941599204642, 0.271941599204642,
0.535039569166856, 0.535039569166856, 0.798137539129071, -1.15630452344738,
0.798137539129071, -1.11871909916706, 1.06123550909129, 1.06123550909129,
1.3243334790535, 1.3243334790535, 1.3243334790535, 1.58743144901571,
1.58743144901571), doy.4 = c(-1.49457334197023, -1.45698791768991,
-1.1938899477277, -1.1938899477277, -0.667694007803267, -1.56974419053086,
0.121599902083376, 0.121599902083376, 0.384697872045591, 0.722966690568438,
0.760552114848755, -1.56974419053086, -0.818035704924533, -0.818035704924533,
-0.517352310682002, -0.517352310682002, -0.254254340719787, -0.254254340719787,
0.00884362924242735, 0.271941599204642, 0.271941599204642, 0.535039569166856,
0.535039569166856, 0.798137539129071, -1.15630452344738, 0.798137539129071,
-1.11871909916706, 1.06123550909129, 1.06123550909129, 1.3243334790535,
1.3243334790535, 1.3243334790535, 1.58743144901571, 1.58743144901571
), doy.5 = c(-1.30459446461163, -1.26110798245791, -0.956702607381863,
-0.956702607381863, -0.347891857229768, 0.260918892922326, 0.565324267998374,
0.565324267998374, 0.869729643074421, 1.26110798245791, 1.30459446461163,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA), doy.6 = c(NA, -1.05873648876414,
-0.760300699984984, -0.760300699984984, NA, 0.433442455131627,
NA, 0.731878243910779, NA, 1.4140171896917, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA), doy.7 = c(NA, -1.05873648876414, -0.760300699984984,
-0.760300699984984, NA, 0.433442455131627, NA, 0.731878243910779,
NA, 1.4140171896917, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), doy.8 = c(NA,
-1.05873648876414, -0.760300699984984, -0.760300699984984, NA,
0.433442455131627, NA, 0.731878243910779, NA, 1.4140171896917,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA), doy.9 = c(NA, -0.889715873362349,
-0.616557491189698, -0.616557491189698, NA, NA, NA, 0.749234419673558,
NA, 1.37359643606819, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), doy.10 = c(NA,
-0.889715873362349, -0.616557491189698, -0.616557491189698, NA,
NA, NA, 0.749234419673558, NA, 1.37359643606819, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA)), class = "data.frame", row.names = c(NA,
-34L)))
> dput(site_covs) # Site covariates
structure(list(avg_depth_m = c(-0.396852937869769, -0.143546538414424,
-0.330422948982401, -0.741865130232904, 0.686928042442984, -0.0101036528084492,
0.166867219195688, -0.109197376901493, -0.509447409526513, -0.407372059216273,
0.1391236624447, 0.990275045031617, -0.442967565991126, -0.244051498719891,
0.0825896222728747, -1.26323461181751, 0.414988839949808, 0.168961072535385,
4.65870609618114, 0.617569150565513, -0.717262353491461, -0.688471870070625,
-1.18052740489947, -0.50421277617727, 0.232300136061226, -0.787406440371318,
0.0818044272704883, 0.25952022947729, 0.179430339233871, 0.185711899252963,
-0.253997302083453, -1.23863183507607, 1.01121357842859, 0.093582352306285
), avg_veg_percent = c(-0.981252911640175, -0.932578362902605,
-0.363792708244395, 0.62952136981813, -0.444164594228708, -0.191952226559776,
0.0731006206959681, -1.11471538417126, -0.813050942720807, -0.289603275028107,
0.608913193924716, 2.04324223610629, -0.837780753792903, -0.685280252181644,
-1.13041685147937, -0.236022017705233, 1.56925419055778, -0.738861509504519,
-0.165954219667627, 0.163776594626987, 0.29154728516615, -0.153589314131579,
1.7794575846706, -0.314333086100203, -0.982905697733348, -0.359671073065713,
-1.06859232379913, 0.547088666244476, -0.520414845034337, 1.40438878341047,
-0.458590317354097, 1.32195607983682, 2.82223128487731, -0.470955222890145
), Trachemys_present = structure(c(1L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L), levels = c("0",
"1"), class = "factor"), Chrysemys_present = structure(c(1L,
1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
2L), levels = c("0", "1"), class = "factor"), urban_percent_2023_500m = c(-0.969742561719032,
-0.614368896225784, -0.561960459836506, -1.08241897426301, -0.958305252669325,
-0.882140705481457, -0.398730625289142, -0.412171084097736, -0.521958411757194,
1.13904289232575, -0.923435599096305, 0.88617603233421, -0.0529401814644404,
-0.303923239336219, 1.15264545027027, -1.0168973175224, 0.981094915798906,
1.94259731493199, -0.465912355297317, 0.884019313111931, -1.13846503050023,
-0.501578027387552, 0.761456544435323, 2.28219290951455, -0.689483768090551,
2.03404429383584, -0.464171141440133, 0.218341471324137, -0.953800010013493,
0.136445123262079, -1.22508166293566, 1.31882364835239, -0.192327342690211,
0.592932737616315), forest_percent_2023_500m = c(1.89411988963412,
-0.786652867191391, 0.562610558234537, 1.1783690039086, -0.0226232839207373,
-0.00607577651606223, 0.737291780476629, -0.893251563484275,
-0.436829851717775, -0.890481381943574, 0.587444687013665, -1.04719621818673,
0.424620997830237, 0.0897363697825507, -0.0494370648196247, 0.413919534898465,
-0.92039247204009, -0.909702597986621, 1.14347273198409, -0.898988205948345,
-0.541518666619878, 0.460502845513551, -0.869008512376215, -1.29844209144976,
0.233211682890451, -0.600962224939457, -0.43929957466133, -0.526918387485172,
2.54805625793507, -0.822396406897255, 2.43419507895804, -0.0611648985577873,
0.657477617957097, -1.34368699027501), min_dist_at_site_m = c(-0.295579037282997,
-0.406168740319293, -0.343210284897073, -0.253317324110564, 0.641800055792079,
1.51151668973138, -0.443887345591638, -0.409370993417729, -0.443887345591638,
-0.229694856031288, -0.360065411646804, 3.12993883706238, -0.443887345591638,
-0.443887345591638, -0.20316525015156, -0.366504625495844, 0.217965039546236,
-0.390635541727046, -0.0332391639833308, 4.09085344956443, -0.443887345591638,
-0.419384931106703, -0.431353535904455, -0.408872255087999, -0.391298796897592,
-0.382538497711036, -0.00435689255584056, -0.180163140796787,
-0.428768504246683, -0.419274900127415, -0.443887345591638, -0.172814345147779,
-0.42123687211955, 0.0222639026186883), Open_to_Public = structure(c(2L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L), levels = c("0", "1"), class = "factor")), class = "data.frame", row.names = c(NA,
-34L))
> dput(umf) # For unmarked
new("unmarkedFrameOccu", y = structure(c(1, 0, 1, 0, 0, 1, 0,
1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0, 0, 0, NA, 0, NA, 0, NA, 1, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 0, 0, 0, NA, 0, NA, 1, NA, 1, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 1, 0, 0, NA, 0, NA, 0, NA, 1, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 0, 0, 0, NA, NA, NA, 0, NA, 0, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0, 0, 1, NA, NA, NA, 0, NA, 0, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA), dim = c(34L, 10L), dimnames = list(
NULL, c("so.1", "so.2", "so.3", "so.4", "so.5", "so.6", "so.7",
"so.8", "so.9", "so.10"))), obsCovs = structure(list(max_air_temp_C = c(-0.459496711703865,
-0.303742800714979, -1.84373413837006, 0.917750799315473, -1.67138854913074,
NA, NA, NA, NA, NA, -0.580604373234465, -1.41065499263084, 0.111392955936023,
-0.496410528188183, -1.45389713786756, -0.671913331308388, -0.316357548422689,
0.923164964753146, 1.25268445710534, 1.7863752785884, 0.424589209185752,
0.979804314811402, 1.23978059325572, -0.0825096516437463, 0.126540458126549,
0.363696206895718, -0.346016069199023, -1.20236607565684, 0.0347967907651219,
-0.499222721070786, 0.424589209185752, 0.979804314811402, 1.23978059325572,
-0.0825096516437463, 0.126540458126549, 0.363696206895718, -0.346016069199023,
-1.20236607565684, 0.0347967907651219, -0.499222721070786, -0.738044331480295,
-2.48224020109637, -2.6593014416465, -0.542399514011008, 0.489026145305106,
NA, NA, NA, NA, NA, -2.04600706554707, -0.657012650055633, -1.01699468138367,
-2.27848374641345, 1.24299637432332, -0.597941222070713, -0.0395446943684453,
0.364762741324585, NA, NA, 1.5630012201616, 0.43812388141666,
0.904615947770596, -0.254968351583598, 0.0975416034654518, NA,
NA, NA, NA, NA, 0.642582998196882, 0.67363711360389, 0.926960257201473,
-0.266465597004552, 0.0975416034654518, -1.1034173074094, -0.90952795149279,
0.0585421691212836, -1.54265771222867, -0.393964918223415, -0.277835221587903,
-1.04560948284662, -0.447214785868854, -0.634377487726163, -0.699926908849354,
NA, NA, NA, NA, NA, 1.67199811466717, 1.73344665950623, 1.27329705840753,
-0.151493132447488, -0.0184538177888326, 1.64587944699707, 1.95746233268197,
1.05826227611467, 0.220379673593086, -0.393964918223415, 1.5630012201616,
0.803169391200885, -0.301976773562663, 0.699303115622301, 1.66347977082407,
NA, NA, NA, NA, NA, -1.88856710730124, -0.715890958632345, -1.49739733917499,
-2.41645070595143, NA, NA, NA, NA, NA, NA, 0.24292771906979,
-0.551031695465399, 0.10022080021509, -1.16325082882766, NA,
NA, NA, NA, NA, NA, 0.24292771906979, -0.551031695465399, 0.10022080021509,
-1.16325082882766, NA, NA, NA, NA, NA, NA, 0.860576787208012,
-0.56280735590897, -0.301976773562663, 0.641816882309017, NA,
NA, NA, NA, NA, NA, 0.30348154983509, -0.680563973062394, -0.301976773562663,
0.699303115622301, NA, NA, NA, NA, NA, NA, -0.435275179833732,
-1.36355234661732, -0.726518656771293, -0.105504144555159, NA,
NA, NA, NA, NA, NA, -1.04081348312686, -0.84542323622934, -0.570108488744169,
-0.795338940175556, NA, NA, NA, NA, NA, NA, 1.12701364213934,
-0.162434862674413, -0.927617443660169, -0.645874733147118, NA,
NA, NA, NA, NA, NA, 0.824244490492783, 0.968028654367831, 0.290147433394023,
0.239413251185535, NA, NA, NA, NA, NA, NA, 0.872687554233047,
1.00335563781816, 0.223114503090405, 0.308396731989277, NA, NA,
NA, NA, NA, NA, -0.302056753458035, 0.508777850436943, 0.658828542019967,
0.80277833475841, NA, NA, NA, NA, NA, NA, 0.800022958622649,
1.38017681016558, 1.50791230843723, 1.63058008784728, NA, NA,
NA, NA, NA, NA, -1.25880727213799, 0.461675204423421, 0.81523871004709,
0.503849922771037, NA, NA, NA, NA, NA, NA, -1.73112714905541,
-0.185986185681174, -0.670657882188607, -1.40469300750175, NA,
NA, NA, NA, NA, NA, -1.19825344137269, 0.143732340652719, 0.334836052255776,
0.170429772451297, NA, NA, NA, NA, NA, NA, -0.338389052353203,
-0.386172432298452, -1.37450363629968, -0.726355459371813, NA,
NA, NA, NA, NA, NA, -0.641158203999765, 1.02690696294454, 0.189598039949584,
1.21667921130285, NA, NA, NA, NA, NA, NA, -0.665379735869898,
0.838496376770836, 0.558279148575528, 2.18244792186019, NA, NA,
NA, NA, NA, NA, 1.23601053664491, 1.40372813317234, 1.78721617933966,
0.676308620641384, NA, NA, NA, NA, NA, NA, 0.279260017964958,
0.449899541860231, 0.144909419076843, 0.446363689457753, NA,
NA, NA, NA, NA, NA, 1.01801674763378, 0.744291082624172, 0.837583019477967,
0.710800361043255, NA, NA, NA, NA, NA, NA, 0.0733769937989247,
-1.45775764076398, -0.659485728478663, 0.308396731989277, NA,
NA, NA, NA, NA, NA, -0.568493606209429, -1.17514176044361, 0.0555321813533371,
1.05571775885345, NA, NA, NA, NA, NA, NA), max_percent_RH = c(0.0596455250653726,
0.60296343327545, 0.671864840101553, 0.663915891045687, -1.73053052256212,
NA, NA, NA, NA, NA, 1.05892417688524, 0.60296343327545, 0.671864840101553,
0.663915891045687, 0.581107398283796, 0.566556887333738, 0.793878974139019,
0.701221775882323, 0.897941653226101, 0.896258159530275, 0.19190299368859,
0.366051760725003, 0.671864840101553, 0.154473713499614, 0.581107398283796,
0.149227037288797, -1.22058892273874, -1.28570561172535, -1.066305713206,
-0.128036879932895, 0.19190299368859, 0.366051760725003, 0.671864840101553,
0.154473713499614, 0.581107398283796, 0.149227037288797, -1.22058892273874,
-1.28570561172535, -1.066305713206, -0.128036879932895, 0.60337067384971,
0.60296343327545, 0.671864840101553, 0.663915891045687, -1.30840533701635,
NA, NA, NA, NA, NA, -1.7331779384938, -1.13438883209449, 0.525620252627463,
-2.48977377947762, 0.581107398283796, -1.99812473657881, -0.0198469743534753,
0.467745895803724, NA, NA, 0.618065948141179, -3.09285865851152,
-1.65342410073647, 0.142344137843755, 0.581107398283796, NA,
NA, NA, NA, NA, -1.14536696683505, -1.76615329222902, -0.746707658397117,
0.663915891045687, 0.581107398283796, 0.566556887333738, 0.83357292284597,
0.701221775882323, 0.336728119959786, 0.896258159530275, 0.985447805427894,
0.60296343327545, 0.671864840101553, -0.512652947572625, -1.6099233266919,
NA, NA, NA, NA, NA, 0.14046953366845, 0.60296343327545, 0.671864840101553,
0.663915891045687, 0.581107398283796, 0.566556887333738, 0.83357292284597,
0.701221775882323, 0.897941653226101, -1.53644255919475, -0.263650509346937,
0.60296343327545, 0.671864840101553, 0.663915891045687, 0.581107398283796,
NA, NA, NA, NA, NA, -1.43927245266443, 0.445022318241819, 0.525620252627463,
-3.46013983194633, NA, NA, NA, NA, NA, NA, -1.40988190408149,
0.460816429745181, 0.671864840101553, -0.3428388883906, NA, NA,
NA, NA, NA, NA, -1.40988190408149, 0.460816429745181, 0.671864840101553,
-0.3428388883906, NA, NA, NA, NA, NA, NA, 1.05892417688524, 0.60296343327545,
-1.34631046704088, -0.0881177996175639, NA, NA, NA, NA, NA, NA,
-1.08658586966918, 0.60296343327545, -1.15619250332457, 0.0938258352203193,
NA, NA, NA, NA, NA, NA, 0.985447805427894, 0.287081203208188,
-1.17081696207198, 0.639656739733969, NA, NA, NA, NA, NA, NA,
1.05892417688524, 0.60296343327545, -2.58938946057065, 0.663915891045687,
NA, NA, NA, NA, NA, NA, -0.880852029588619, -2.96650576648461,
-2.76488296553955, 0.603268012766393, NA, NA, NA, NA, NA, NA,
0.941361982553489, -1.48185928516848, 0.101510948952603, -0.0638586483058458,
NA, NA, NA, NA, NA, NA, -0.212217049326797, -1.03962416307431,
0.525620252627463, -0.330709312734742, NA, NA, NA, NA, NA, NA,
-2.2695554501324, -0.186742141892706, -1.33168600829348, -0.209413556176153,
NA, NA, NA, NA, NA, NA, -0.33712688080428, 0.129140088174556,
-0.790581034639343, -0.427745917981612, NA, NA, NA, NA, NA, NA,
1.00014307971936, 0.60296343327545, 0.0868864902051944, 0.0453075325968848,
NA, NA, NA, NA, NA, NA, 0.471113205226493, -0.0288010268590746,
0.0868864902051944, 0.178732864811332, NA, NA, NA, NA, NA, NA,
1.05892417688524, 0.60296343327545, 0.613367005111916, 0.251510318746484,
NA, NA, NA, NA, NA, NA, 0.765018691055865, -0.0288010268590746,
0.0868864902051944, -2.7323652925948, NA, NA, NA, NA, NA, NA,
1.05892417688524, 0.60296343327545, 0.671864840101553, 0.663915891045687,
NA, NA, NA, NA, NA, NA, 1.05892417688524, 0.60296343327545, 0.525620252627463,
0.542620134487098, NA, NA, NA, NA, NA, NA, -1.05719532108624,
0.60296343327545, 0.671864840101553, 0.663915891045687, NA, NA,
NA, NA, NA, NA, -1.10862878110638, -0.139359807382617, 0.671864840101553,
0.324287772681639, NA, NA, NA, NA, NA, NA, -0.102002492140782,
-0.297300922416248, 0.394000123900782, 0.566879285798816, NA,
NA, NA, NA, NA, NA, 0.772366328201599, 0.60296343327545, 0.671864840101553,
0.663915891045687, NA, NA, NA, NA, NA, NA, 0.375593922331947,
0.60296343327545, 0.671864840101553, 0.663915891045687, NA, NA,
NA, NA, NA, NA), min_percent_RH = c(0.280939125470438, 0.0676741919390416,
1.86051740977938, -0.615237620863046, -0.253861459079237, NA,
NA, NA, NA, NA, -0.135483760354185, 1.95165102524873, -0.689592949926057,
-0.566146952809827, 1.19713099820699, -1.02039676293566, -0.791790120060484,
-0.823388941971144, -0.806517055346018, -0.973458214944405, -1.40317820923622,
-2.06852904801593, -1.52312416782331, -0.593419546172726, -0.801405782583473,
-0.747424380494274, -0.583652520044585, -0.484437479016857, -0.672450353232506,
-0.575093618054954, -1.40317820923622, -2.06852904801593, -1.52312416782331,
-0.593419546172726, -0.801405782583473, -0.747424380494274, -0.583652520044585,
-0.484437479016857, -0.672450353232506, -0.575093618054954, -0.581387912431879,
2.3222694186867, 1.86051740977938, -1.20432563750166, -1.44933323206349,
NA, NA, NA, NA, NA, -1.17101323749329, -0.817691970162779, 0.45754407272461,
-0.527965322101769, -1.0249863813477, 0.572870612130394, -0.214208280016363,
-0.252278942746798, NA, NA, -0.212872084268495, -0.0816026609734749,
-0.924797303491122, 0.699301379228687, 0.311934341875141, NA,
NA, NA, NA, NA, 0.56101115487461, -0.354418978365315, -0.846395852302767,
1.02657249958348, 0.339311558050353, 0.411315528644675, 0.285321960021796,
0.100602032383692, 0.819923725557369, 0.801074989381333, -0.76564582651357,
0.690518992022298, 0.428659327549953, 0.508393225688394, 0.485323377651482,
NA, NA, NA, NA, NA, -0.839348992146247, -0.220584558512714, -0.0376229874123686,
2.90838144162351, 1.89981288003743, 1.53105938314914, 1.88798148014422,
1.94394081036796, 1.33149403625366, 1.32257046167298, -0.41924094803999,
0.566979527542975, 1.83163266460473, 1.69747829631079, 0.0974794818359813,
NA, NA, NA, NA, NA, -1.13416165467695, -0.148519870899775, 0.416280151046529,
-0.309784575198576, NA, NA, NA, NA, NA, NA, -1.15258744608512,
0.0728216696256798, -1.48186024614522, -1.34068860431616, NA,
NA, NA, NA, NA, NA, -1.15258744608512, 0.0728216696256798, -1.48186024614522,
-1.34068860431616, NA, NA, NA, NA, NA, NA, 0.0229780457560698,
-0.98241125613521, 0.0572840324472189, 0.961118275512517, NA,
NA, NA, NA, NA, NA, 0.505733780650102, -0.400746277545061, 0.441038504053377,
0.595665524449671, NA, NA, NA, NA, NA, NA, 1.26119122838504,
-0.508843308964469, -1.14762248055276, -0.631601176880785, NA,
NA, NA, NA, NA, NA, 1.69972506389946, -1.58981362315855, -1.30855177509728,
-0.680691844934003, NA, NA, NA, NA, NA, NA, -0.555591804460442,
-0.570613041204131, -1.07747381370002, -1.14978045077587, NA,
NA, NA, NA, NA, NA, 0.325161024850044, 0.191213656418365, 0.0944215619574926,
-0.522510803429189, NA, NA, NA, NA, NA, NA, -0.113372810664382,
0.114001491118788, 0.0325256794403702, -0.358875243251795, NA,
NA, NA, NA, NA, NA, -0.931477949187093, -0.169109781646329, 0.185202189649272,
0.388393814891638, NA, NA, NA, NA, NA, NA, -1.06045848904428,
-0.766217193296394, -0.367734360837021, -0.20069420174698, NA,
NA, NA, NA, NA, NA, 1.63707737311169, -0.534580697397662, -0.466767772864417,
-0.0534221975873256, NA, NA, NA, NA, NA, NA, 0.450456406425595,
0.366227897764074, 1.03523897621775, 0.0174865451562117, NA,
NA, NA, NA, NA, NA, 1.7034102221811, 0.031641848132572, -0.334723223494556,
0.644756192502889, NA, NA, NA, NA, NA, NA, 0.303050075160241,
1.292773881359, -0.32647043915894, -1.45523349644034, NA, NA,
NA, NA, NA, NA, 1.7034102221811, 0.75228872426196, 0.812413799156112,
1.00475442489316, NA, NA, NA, NA, NA, NA, 1.7034102221811, 0.947892876354223,
0.622599759436937, 0.835664346043182, NA, NA, NA, NA, NA, NA,
-0.618239495248217, -0.658120161876985, 0.160443836642423, -0.0588767162599055,
NA, NA, NA, NA, NA, NA, 0.616288529099117, 0.0573792365657644,
0.977469485868438, -0.413420429977593, NA, NA, NA, NA, NA, NA,
-0.699312977444162, -0.498548353591192, 0.0118937186013294, -1.07887170803233,
NA, NA, NA, NA, NA, NA, 1.06956299774008, 1.67883470785689, 1.59230191887185,
1.70293281498337, NA, NA, NA, NA, NA, NA, 0.505733780650102,
1.26188901523917, 0.65973728894721, 0.704755897901267, NA, NA,
NA, NA, NA, NA), doy = c(-1.49457334197023, -1.49457334197023,
-1.49457334197023, -1.49457334197023, -1.30459446461163, NA,
NA, NA, NA, NA, -1.45698791768991, -1.45698791768991, -1.45698791768991,
-1.45698791768991, -1.26110798245791, -1.05873648876414, -1.05873648876414,
-1.05873648876414, -0.889715873362349, -0.889715873362349, -1.1938899477277,
-1.1938899477277, -1.1938899477277, -1.1938899477277, -0.956702607381863,
-0.760300699984984, -0.760300699984984, -0.760300699984984, -0.616557491189698,
-0.616557491189698, -1.1938899477277, -1.1938899477277, -1.1938899477277,
-1.1938899477277, -0.956702607381863, -0.760300699984984, -0.760300699984984,
-0.760300699984984, -0.616557491189698, -0.616557491189698, -0.667694007803267,
-0.667694007803267, -0.667694007803267, -0.667694007803267, -0.347891857229768,
NA, NA, NA, NA, NA, -1.56974419053086, -1.56974419053086, -1.56974419053086,
-1.56974419053086, 0.260918892922326, 0.433442455131627, 0.433442455131627,
0.433442455131627, NA, NA, 0.121599902083376, 0.121599902083376,
0.121599902083376, 0.121599902083376, 0.565324267998374, NA,
NA, NA, NA, NA, 0.121599902083376, 0.121599902083376, 0.121599902083376,
0.121599902083376, 0.565324267998374, 0.731878243910779, 0.731878243910779,
0.731878243910779, 0.749234419673558, 0.749234419673558, 0.384697872045591,
0.384697872045591, 0.384697872045591, 0.384697872045591, 0.869729643074421,
NA, NA, NA, NA, NA, 0.722966690568438, 0.722966690568438, 0.722966690568438,
0.722966690568438, 1.26110798245791, 1.4140171896917, 1.4140171896917,
1.4140171896917, 1.37359643606819, 1.37359643606819, 0.760552114848755,
0.760552114848755, 0.760552114848755, 0.760552114848755, 1.30459446461163,
NA, NA, NA, NA, NA, -1.56974419053086, -1.56974419053086, -1.56974419053086,
-1.56974419053086, NA, NA, NA, NA, NA, NA, -0.818035704924533,
-0.818035704924533, -0.818035704924533, -0.818035704924533, NA,
NA, NA, NA, NA, NA, -0.818035704924533, -0.818035704924533, -0.818035704924533,
-0.818035704924533, NA, NA, NA, NA, NA, NA, -0.517352310682002,
-0.517352310682002, -0.517352310682002, -0.517352310682002, NA,
NA, NA, NA, NA, NA, -0.517352310682002, -0.517352310682002, -0.517352310682002,
-0.517352310682002, NA, NA, NA, NA, NA, NA, -0.254254340719787,
-0.254254340719787, -0.254254340719787, -0.254254340719787, NA,
NA, NA, NA, NA, NA, -0.254254340719787, -0.254254340719787, -0.254254340719787,
-0.254254340719787, NA, NA, NA, NA, NA, NA, 0.00884362924242735,
0.00884362924242735, 0.00884362924242735, 0.00884362924242735,
NA, NA, NA, NA, NA, NA, 0.271941599204642, 0.271941599204642,
0.271941599204642, 0.271941599204642, NA, NA, NA, NA, NA, NA,
0.271941599204642, 0.271941599204642, 0.271941599204642, 0.271941599204642,
NA, NA, NA, NA, NA, NA, 0.535039569166856, 0.535039569166856,
0.535039569166856, 0.535039569166856, NA, NA, NA, NA, NA, NA,
0.535039569166856, 0.535039569166856, 0.535039569166856, 0.535039569166856,
NA, NA, NA, NA, NA, NA, 0.798137539129071, 0.798137539129071,
0.798137539129071, 0.798137539129071, NA, NA, NA, NA, NA, NA,
-1.15630452344738, -1.15630452344738, -1.15630452344738, -1.15630452344738,
NA, NA, NA, NA, NA, NA, 0.798137539129071, 0.798137539129071,
0.798137539129071, 0.798137539129071, NA, NA, NA, NA, NA, NA,
-1.11871909916706, -1.11871909916706, -1.11871909916706, -1.11871909916706,
NA, NA, NA, NA, NA, NA, 1.06123550909129, 1.06123550909129, 1.06123550909129,
1.06123550909129, NA, NA, NA, NA, NA, NA, 1.06123550909129, 1.06123550909129,
1.06123550909129, 1.06123550909129, NA, NA, NA, NA, NA, NA, 1.3243334790535,
1.3243334790535, 1.3243334790535, 1.3243334790535, NA, NA, NA,
NA, NA, NA, 1.3243334790535, 1.3243334790535, 1.3243334790535,
1.3243334790535, NA, NA, NA, NA, NA, NA, 1.3243334790535, 1.3243334790535,
1.3243334790535, 1.3243334790535, NA, NA, NA, NA, NA, NA, 1.58743144901571,
1.58743144901571, 1.58743144901571, 1.58743144901571, NA, NA,
NA, NA, NA, NA, 1.58743144901571, 1.58743144901571, 1.58743144901571,
1.58743144901571, NA, NA, NA, NA, NA, NA)), row.names = c(NA,
-340L), class = "data.frame"), siteCovs = structure(list(avg_depth_m = c(-0.396852937869769,
-0.143546538414424, -0.330422948982401, -0.741865130232904, 0.686928042442984,
-0.0101036528084492, 0.166867219195688, -0.109197376901493, -0.509447409526513,
-0.407372059216273, 0.1391236624447, 0.990275045031617, -0.442967565991126,
-0.244051498719891, 0.0825896222728747, -1.26323461181751, 0.414988839949808,
0.168961072535385, 4.65870609618114, 0.617569150565513, -0.717262353491461,
-0.688471870070625, -1.18052740489947, -0.50421277617727, 0.232300136061226,
-0.787406440371318, 0.0818044272704883, 0.25952022947729, 0.179430339233871,
0.185711899252963, -0.253997302083453, -1.23863183507607, 1.01121357842859,
0.093582352306285), avg_veg_percent = c(-0.981252911640175, -0.932578362902605,
-0.363792708244395, 0.62952136981813, -0.444164594228708, -0.191952226559776,
0.0731006206959681, -1.11471538417126, -0.813050942720807, -0.289603275028107,
0.608913193924716, 2.04324223610629, -0.837780753792903, -0.685280252181644,
-1.13041685147937, -0.236022017705233, 1.56925419055778, -0.738861509504519,
-0.165954219667627, 0.163776594626987, 0.29154728516615, -0.153589314131579,
1.7794575846706, -0.314333086100203, -0.982905697733348, -0.359671073065713,
-1.06859232379913, 0.547088666244476, -0.520414845034337, 1.40438878341047,
-0.458590317354097, 1.32195607983682, 2.82223128487731, -0.470955222890145
), Trachemys_present = structure(c(1L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L), levels = c("0",
"1"), class = "factor"), Chrysemys_present = structure(c(1L,
1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
2L), levels = c("0", "1"), class = "factor"), urban_percent_2023_500m = c(-0.969742561719032,
-0.614368896225784, -0.561960459836506, -1.08241897426301, -0.958305252669325,
-0.882140705481457, -0.398730625289142, -0.412171084097736, -0.521958411757194,
1.13904289232575, -0.923435599096305, 0.88617603233421, -0.0529401814644404,
-0.303923239336219, 1.15264545027027, -1.0168973175224, 0.981094915798906,
1.94259731493199, -0.465912355297317, 0.884019313111931, -1.13846503050023,
-0.501578027387552, 0.761456544435323, 2.28219290951455, -0.689483768090551,
2.03404429383584, -0.464171141440133, 0.218341471324137, -0.953800010013493,
0.136445123262079, -1.22508166293566, 1.31882364835239, -0.192327342690211,
0.592932737616315), forest_percent_2023_500m = c(1.89411988963412,
-0.786652867191391, 0.562610558234537, 1.1783690039086, -0.0226232839207373,
-0.00607577651606223, 0.737291780476629, -0.893251563484275,
-0.436829851717775, -0.890481381943574, 0.587444687013665, -1.04719621818673,
0.424620997830237, 0.0897363697825507, -0.0494370648196247, 0.413919534898465,
-0.92039247204009, -0.909702597986621, 1.14347273198409, -0.898988205948345,
-0.541518666619878, 0.460502845513551, -0.869008512376215, -1.29844209144976,
0.233211682890451, -0.600962224939457, -0.43929957466133, -0.526918387485172,
2.54805625793507, -0.822396406897255, 2.43419507895804, -0.0611648985577873,
0.657477617957097, -1.34368699027501), min_dist_at_site_m = c(-0.295579037282997,
-0.406168740319293, -0.343210284897073, -0.253317324110564, 0.641800055792079,
1.51151668973138, -0.443887345591638, -0.409370993417729, -0.443887345591638,
-0.229694856031288, -0.360065411646804, 3.12993883706238, -0.443887345591638,
-0.443887345591638, -0.20316525015156, -0.366504625495844, 0.217965039546236,
-0.390635541727046, -0.0332391639833308, 4.09085344956443, -0.443887345591638,
-0.419384931106703, -0.431353535904455, -0.408872255087999, -0.391298796897592,
-0.382538497711036, -0.00435689255584056, -0.180163140796787,
-0.428768504246683, -0.419274900127415, -0.443887345591638, -0.172814345147779,
-0.42123687211955, 0.0222639026186883), Open_to_Public = structure(c(2L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L), levels = c("0", "1"), class = "factor")), row.names = c(NA,
-34L), class = "data.frame"), obsToY = structure(c(1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), dim = c(10L, 10L)))
> summary(umf)
unmarkedFrame Object

34 sites
Maximum number of observations per site: 10
Mean number of observations per site: 5.15
Sites with at least one detection: 16

Tabulation of y observations:
0 1 <NA>
138 37 165

Site-level covariates:
avg_depth_m avg_veg_percent Trachemys_present Chrysemys_present urban_percent_2023_500m
Min. :-1.26323 Min. :-1.1304 0:13 0:14 Min. :-1.2251
1st Qu.:-0.48890 1st Qu.:-0.7255 1:21 1:20 1st Qu.:-0.8340
Median :-0.05965 Median :-0.3020 Median :-0.4055
Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
3rd Qu.: 0.18414 3rd Qu.: 0.4832 3rd Qu.: 0.8534
Max. : 4.65871 Max. : 2.8222 Max. : 2.2822
forest_percent_2023_500m min_dist_at_site_m Open_to_Public
Min. :-1.3437 Min. :-0.4439 0:19
1st Qu.:-0.8574 1st Qu.:-0.4208 1:15
Median :-0.0553 Median :-0.3745
Mean : 0.0000 Mean : 0.0000
3rd Qu.: 0.5371 3rd Qu.:-0.1747
Max. : 2.5481 Max. : 4.0909

Observation-level covariates:
max_air_temp_C max_percent_RH min_percent_RH doy
Min. :-2.65930 Min. :-3.4601 Min. :-2.0685 Min. :-1.5697
1st Qu.:-0.64352 1st Qu.:-0.3339 1st Qu.:-0.6851 1st Qu.:-0.8180
Median : 0.07338 Median : 0.4677 Median :-0.1134 Median : 0.1216
Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
3rd Qu.: 0.72755 3rd Qu.: 0.6639 3rd Qu.: 0.6060 3rd Qu.: 0.7793
Max. : 2.18245 Max. : 1.0589 Max. : 2.9084 Max. : 1.5874
NA's :165 NA's :165 NA's :165 NA's :165
>
> covariates_site <- colnames(site_covs)
>
> # Create the formula for detection (constant) and occupancy (using covariates)
> formula <- as.formula(paste("~ 1 ~", paste(covariates_site, collapse = " + ")))
>
> # Print warnings
> options(warn = 1)
>
> # Fit the model using formula
> m_all <- occu(formula, data=umf)
> # m_all <- occu(formula, data=umf, control = list(trace = TRUE))
>
> # Model selection
> model_selection <- dredge(m_all, rank = "AICc")
Fixed terms are "psi(Int)" and "p(Int)"
Warning in sqrt(diag(vcov(model, ...))) : NaNs produced
Warning in sqrt(diag(vcov(model, ...))) : NaNs produced
Warning in sqrt(diag(vcov(model, ...))) : NaNs produced
Warning in sqrt(diag(vcov(model, ...))) : NaNs produced
Warning: Hessian is singular. Try providing starting values or using fewer covariates.
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'diag': Lapack routine dgesv: system is exactly singular: U[3,3] = 0

Ken Kellner

unread,

Jun 26, 2025, 4:24:44 PMJun 26

to 'Emma Wilson' via unmarked

Hi Emma,

Thanks for sending the data. It looks like the major issue is with the average depth covariate, as a model fit with only that covariate indicates a separation issue (i.e., the huge negative estimate and large SE). The average depth does seem to be higher at sites where the species was detected at least once, but it's not so different I'd expect this much of an issue. There's also a very large outlier in depth (z score > 4), but the issue persists even after removing it. So, I'm afraid I haven't been able to find a good reason for the issue. The small sample size may be part of it. Maybe someone else on the list will see something I missed.

More generally, I think you have too many site covariates (looks like the global model has 8) to realistically handle with only ~30 sites.

Ken

On Thu, Jun 26, 2025 at 09:42:30AM -0700, 'Emma Wilson' via unmarked wrote:
> Hello,
>
> I am using "unmarked" to build occupancy models that includes data for 34
> sites, some of which were surveyed a different number of times (1-4, 1-5,
> and 1-10). 0s represent non-detection, while NAs represent no survey for
> that particular survey occasion. Prior to running the models, all
> continuous variables were standardized and checked for collinearity.
>
> My models are failing to converge unless specific covariates are removed

> (that is, average depth in m, the presence of *Trachemys *species, and the
> presence of *Chrysemys *species). Currently, I am using intercept-only for

> detection.
>
> I receive the following errors when all site-level covariates are run:
>

> *Warning in sqrt(diag(vcov(model, ...))) : NaNs produced*
>
>
> *Warning: Hessian is singular. Try providing starting values or using fewer
> covariates.Error in h(simpleError(msg, call)) : error in evaluating the

> argument 'x' in selecting a method for function 'diag': Lapack routine

> dgesv: system is exactly singular: U[3,3] = 0*

>
> Is there anything about the code structure or data that may be preventing
> model convergence when including variables for avg_depth_m,
> Trachemys_present, and Chrysemys_present?
>
> Please let me know if you have any thoughts as to how to best approach this
> issue. Thank you for your time and assistance!
>
> All the best,
> Emma
>

> *This is the script used:*

> --
> *** Three hierarchical modeling email lists ***
> (1) unmarked (this list): for questions specific to the R package unmarked
> (2) SCR: for design and Bayesian or non-bayesian analysis of spatial capture-recapture
> (3) HMecology: for everything else, especially material covered in the books by Royle & Dorazio (2008), Kéry & Schaub (2012), Kéry & Royle (2016, 2021) and Schaub & Kéry (2022)
> ---
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> To view this discussion visit https://groups.google.com/d/msgid/unmarked/bd03cc64-098a-40b1-948f-94f167282794n%40googlegroups.com.

Luis Santiago

unread,

Jun 26, 2025, 4:31:31 PMJun 26

to unma...@googlegroups.com

Hi Ella,

I didn't run your code, or anything, but was just taking a quick look at your script.

Maybe someone else can confirm this, but I think you might be getting that error because your global model might be two complex (I. E. Have too many variables) for the number of sampling sites you have? There's an argument on dredge() to limit the number of variables allowed in each model, if you have 34 sites, I'd go with 3 max. Have a try and let us know if it works.

Luís

--

Emma Wilson

unread,

Jun 26, 2025, 4:52:11 PMJun 26

to unmarked

Hello,

Thank you both so much for taking the time to consider my post.

I removed both outliers for both depth and vegetation (missed data points when the data was originally cleaned). Then, if I remove my Trachemys_present variable, the models appear to converge (I no longer receive the error "Hessian is singular"). See the updated code below. However, I do receive the error if Trachemys_present is included with all other variables. When I fit the models, the model with the lowest AICc value gives the warning, "Large or missing SE values. Be very cautious using these results."

Is there a way to address the separation issue of depth, or do you suspect that the only way to solve these problems would be to run with fewer covariates?

Thanks again for all of your assistance!

All the best,

Emma

See code below:

# TEST:
> site_covs <- site_covs %>%
+ select(-Trachemys_present)
>
> # Data Format:
> # dput(obs_covs_list) # Observation covariates
> dput(site_covs) # Site covariates
structure(list(avg_depth_m = c(-0.389498387746285, 0.0409834313133644,
-0.276603874088152, -0.975829706148633, 1.45233429971534, 0.267763075808222,
0.568516406392073, 0.0993581554487836, -0.580847175061033, -0.407375117488776,
0.521367590744229, 1.96785767080828, -0.467867937565256, -0.129819825373165,
0.425290758858056, -1.86187160021001, 0.990186946336943, 0.572074807573043,
-1.4855706753225, 1.33446226059573, -0.934018492272243, -0.885090476033914,
-1.72131475356172, -0.57195117210861, 0.679716443297366, -1.05322493183472,
0.423956358415192, 0.725975658649968, 0.58986681347789, 0.600542017020798,
-0.14672223098277, -1.82006038633362, 2.00344168261797, 0.443972365058145
), avg_veg_percent = c(-1.01740136888724, -0.965785905513251,
-0.362634240763982, 0.690695840428582, -0.447862193391596, -0.18041116256214,
0.100656168391253, -1.15892763942882, -0.839036642631159, -0.283962284492339,
0.668842519242014, 2.18983367382713, -0.86526062805504, -0.703546051274439,
-1.1755777889043, -0.227143649407263, 1.68720728653607, -0.760364686359515,
-0.152842357372932, 0.196810781612151, 0.332301372968871, -0.139730364660992,
1.91011116263906, -0.31018626991622, -1.01915401620045, -0.358263576526669,
-1.1100178253446, 0.603282555682311, -0.528719481781897, 1.51238071704353,
-0.463159518222194, 1.42496743229726, 2.22916965196295, -0.476271510934135

), Chrysemys_present = structure(c(1L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L), levels = c("0",

"1"), class = "factor"), urban_percent_2023_500m = c(-0.96974256172531,
-0.61436889605347, -0.561960459872329, -1.08241897426264, -0.958305252675376,
-0.882140705482269, -0.398730625270325, -0.412171084168558, -0.521958411589929,
1.13904289233102, -0.923435599121015, 0.886176032335736, -0.052940181472824,
-0.303923239551309, 1.15264545018644, -1.01689731752145, 0.981094915577794,
1.94259731480216, -0.465912355322655, 0.884019313114863, -1.13846503052991,
-0.501578027492737, 0.761456544530391, 2.28219290963089, -0.689483768155142,
2.03404429384648, -0.464171141314351, 0.218341471443845, -0.953800010026294,
0.136445123393531, -1.22508166294296, 1.31882364836996, -0.192327342739003,
0.592932737726738), forest_percent_2023_500m = c(1.89411988961185,
-0.786652867337189, 0.562610558348043, 1.17836900367902, -0.0226232840212273,
-0.00607577639894511, 0.737291780284911, -0.89325156342949, -0.436829851740641,
-0.890481382120851, 0.587444687128572, -1.04719621800556, 0.424620998004217,
0.0897363698270903, -0.0494370648434187, 0.413919534833904, -0.920392472036028,
-0.909702597976999, 1.14347273211131, -0.898988205858196, -0.541518666443765,
0.460502845714727, -0.869008512525781, -1.29844209145382, 0.23321168269566,
-0.600962224882789, -0.43929957476116, -0.526918387305091, 2.54805625785236,
-0.822396406953772, 2.43419507910021, -0.0611648987063241, 0.65747761789821,
-1.34368699028903), min_dist_at_site_m = c(-0.295579037294054,
-0.406168740309109, -0.343210284897966, -0.253317324077313, 0.641800055519752,
1.51151668989067, -0.443887345580402, -0.409370993406144, -0.443887345580402,
-0.229694856011296, -0.360065411637357, 3.1299388369543, -0.443887345580402,
-0.443887345580402, -0.203165250136327, -0.36650462547338, 0.217965039406724,
-0.39063554174273, -0.0332391639966218, 4.09085344966048, -0.443887345580402,
-0.41938493111986, -0.431353535906906, -0.408872255073332, -0.391298796869729,
-0.382538497680391, -0.0043568925402347, -0.180163140778535,
-0.428768504221682, -0.419274900125707, -0.443887345580402, -0.172814345136862,
-0.421236872131327, 0.0222639026173606), Open_to_Public = structure(c(2L,

"so.8", "so.9", "so.10"))), obsCovs = structure(list(max_air_temp_C = c(-0.459496712299354,
-0.303742801197613, -1.84373413915882, 0.917750799740554, -1.67138854970143,
NA, NA, NA, NA, NA, -0.580604372905396, -1.41065499347536, 0.111392955475827,
-0.496410528378457, -1.45389713747688, -0.671913332174162, -0.316357548663697,
0.923164964422706, 1.25268445771418, 1.78637527859954, 0.42458921012467,
0.979804315379768, 1.23978059295068, -0.0825096518558178, 0.126540458021561,
0.36369620732363, -0.346016068850925, -1.20236607561396, 0.0347967904920631,
-0.499222720955428, 0.42458921012467, 0.979804315379768, 1.23978059295068,
-0.0825096518558178, 0.126540458021561, 0.36369620732363, -0.346016068850925,
-1.20236607561396, 0.0347967904920631, -0.499222720955428, -0.738044331693233,
-2.48224020089319, -2.65930144149214, -0.542399514658746, 0.489026145062489,
NA, NA, NA, NA, NA, -2.04600706623838, -0.657012649796902, -1.01699468199903,
-2.27848374673982, 1.24299637410761, -0.59794122221004, -0.0395446935829704,
0.364762742040183, NA, NA, 1.56300121982139, 0.43812388086088,
0.904615948156163, -0.254968350406931, 0.0975416030582898, NA,
NA, NA, NA, NA, 0.642582999215529, 0.673637113260399, 0.926960257809128,
-0.266465596976993, 0.0975416030582898, -1.10341730696493, -0.909527952408127,
0.0585421684755924, -1.5426577118147, -0.393964918344342, -0.277835221390312,
-1.04560948325611, -0.447214785848363, -0.634377487219344, -0.699926908431756,
NA, NA, NA, NA, NA, 1.67199811436682, 1.73344665905825, 1.27329705743014,
-0.151493131276271, -0.0184538167948205, 1.64587944670187, 1.95746233235664,
1.05826227628944, 0.220379673116389, -0.393964918344342, 1.56300121982139,
0.803169391080134, -0.301976773104079, 0.699303114909172, 1.66347977107508,
NA, NA, NA, NA, NA, -1.88856710745055, -0.715890957896787, -1.49739733953783,
-2.41645070558071, NA, NA, NA, NA, NA, NA, 0.242927719215629,
-0.551031695217117, 0.100220800649335, -1.16325082944271, NA,
NA, NA, NA, NA, NA, 0.242927719215629, -0.551031695217117, 0.100220800649335,
-1.16325082944271, NA, NA, NA, NA, NA, NA, 0.860576788306387,
-0.562807356837082, -0.301976773104079, 0.641816882058801, NA,
NA, NA, NA, NA, NA, 0.30348154951865, -0.680563973036852, -0.301976773104079,
0.699303114909172, NA, NA, NA, NA, NA, NA, -0.43527518017815,
-1.36355234699546, -0.726518656510459, -0.105504144995962, NA,
NA, NA, NA, NA, NA, -1.04081348320832, -0.845423235716522, -0.570108488939682,
-0.795338939200375, NA, NA, NA, NA, NA, NA, 1.12701364163968,
-0.162434861757914, -0.927617443387166, -0.645874733789406, NA,
NA, NA, NA, NA, NA, 0.824244490124592, 0.968028653759804, 0.290147432699569,
0.239413252106224, NA, NA, NA, NA, NA, NA, 0.872687554367, 1.00335563861972,
0.223114503740654, 0.308396731526677, NA, NA, NA, NA, NA, NA,
-0.302056753511517, 0.508777850580729, 0.658828541973526, 0.802778334039832,
NA, NA, NA, NA, NA, NA, 0.800022958003388, 1.38017681045896,
1.50791230878628, 1.63058008708511, NA, NA, NA, NA, NA, NA, -1.25880727229917,
0.46167520410083, 0.815238709544302, 0.503849923217915, NA, NA,
NA, NA, NA, NA, -1.73112714866271, -0.185986184997864, -0.670657882378036,
-1.40469300741425, NA, NA, NA, NA, NA, NA, -1.19825344199615,
0.143732340361476, 0.3348360520055, 0.170429772685791, NA, NA,
NA, NA, NA, NA, -0.338389051693333, -0.386172432537448, -1.37450363644651,
-0.726355459779921, NA, NA, NA, NA, NA, NA, -0.641158203208417,
1.02690696185969, 0.189598039261216, 1.21667921056247, NA, NA,
NA, NA, NA, NA, -0.665379735329621, 0.838496375940069, 0.558279148535172,
2.18244792244862, NA, NA, NA, NA, NA, NA, 1.2360105361851, 1.40372813369891,
1.78721617944838, 0.676308621769007, NA, NA, NA, NA, NA, NA,
0.279260017397446, 0.449899542480844, 0.144909419955265, 0.446363690367543,
NA, NA, NA, NA, NA, NA, 1.01801674709425, 0.744291082980249,
0.837583019197268, 0.710800361479234, NA, NA, NA, NA, NA, NA,
0.073376994367179, -1.45775763995528, -0.659485727551563, 0.308396731526677,
NA, NA, NA, NA, NA, NA, -0.568493606844783, -1.17514176107584,
0.0555321813434044, 1.05571775858144, NA, NA, NA, NA, NA, NA),

siteCovs = structure(list(avg_depth_m = c(-0.389498387746285,
0.0409834313133644, -0.276603874088152, -0.975829706148633,
1.45233429971534, 0.267763075808222, 0.568516406392073, 0.0993581554487836,
-0.580847175061033, -0.407375117488776, 0.521367590744229,
1.96785767080828, -0.467867937565256, -0.129819825373165,
0.425290758858056, -1.86187160021001, 0.990186946336943,
0.572074807573043, -1.4855706753225, 1.33446226059573, -0.934018492272243,
-0.885090476033914, -1.72131475356172, -0.57195117210861,
0.679716443297366, -1.05322493183472, 0.423956358415192,
0.725975658649968, 0.58986681347789, 0.600542017020798, -0.14672223098277,
-1.82006038633362, 2.00344168261797, 0.443972365058145),
avg_veg_percent = c(-1.01740136888724, -0.965785905513251,
-0.362634240763982, 0.690695840428582, -0.447862193391596,
-0.18041116256214, 0.100656168391253, -1.15892763942882,
-0.839036642631159, -0.283962284492339, 0.668842519242014,
2.18983367382713, -0.86526062805504, -0.703546051274439,
-1.1755777889043, -0.227143649407263, 1.68720728653607,
-0.760364686359515, -0.152842357372932, 0.196810781612151,
0.332301372968871, -0.139730364660992, 1.91011116263906,
-0.31018626991622, -1.01915401620045, -0.358263576526669,
-1.1100178253446, 0.603282555682311, -0.528719481781897,
1.51238071704353, -0.463159518222194, 1.42496743229726,
2.22916965196295, -0.476271510934135), Trachemys_present = structure(c(1L,

1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 1L, 2L, 2L), levels = c("0", "1"), class = "factor"),
Chrysemys_present = structure(c(1L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L

), levels = c("0", "1"), class = "factor"), urban_percent_2023_500m = c(-0.96974256172531,
-0.61436889605347, -0.561960459872329, -1.08241897426264,
-0.958305252675376, -0.882140705482269, -0.398730625270325,
-0.412171084168558, -0.521958411589929, 1.13904289233102,
-0.923435599121015, 0.886176032335736, -0.052940181472824,
-0.303923239551309, 1.15264545018644, -1.01689731752145,
0.981094915577794, 1.94259731480216, -0.465912355322655,
0.884019313114863, -1.13846503052991, -0.501578027492737,
0.761456544530391, 2.28219290963089, -0.689483768155142,
2.03404429384648, -0.464171141314351, 0.218341471443845,
-0.953800010026294, 0.136445123393531, -1.22508166294296,
1.31882364836996, -0.192327342739003, 0.592932737726738
), forest_percent_2023_500m = c(1.89411988961185, -0.786652867337189,
0.562610558348043, 1.17836900367902, -0.0226232840212273,
-0.00607577639894511, 0.737291780284911, -0.89325156342949,
-0.436829851740641, -0.890481382120851, 0.587444687128572,
-1.04719621800556, 0.424620998004217, 0.0897363698270903,
-0.0494370648434187, 0.413919534833904, -0.920392472036028,
-0.909702597976999, 1.14347273211131, -0.898988205858196,
-0.541518666443765, 0.460502845714727, -0.869008512525781,
-1.29844209145382, 0.23321168269566, -0.600962224882789,
-0.43929957476116, -0.526918387305091, 2.54805625785236,
-0.822396406953772, 2.43419507910021, -0.0611648987063241,
0.65747761789821, -1.34368699028903), min_dist_at_site_m = c(-0.295579037294054,
-0.406168740309109, -0.343210284897966, -0.253317324077313,
0.641800055519752, 1.51151668989067, -0.443887345580402,
-0.409370993406144, -0.443887345580402, -0.229694856011296,
-0.360065411637357, 3.1299388369543, -0.443887345580402,
-0.443887345580402, -0.203165250136327, -0.36650462547338,
0.217965039406724, -0.39063554174273, -0.0332391639966218,
4.09085344966048, -0.443887345580402, -0.41938493111986,
-0.431353535906906, -0.408872255073332, -0.391298796869729,
-0.382538497680391, -0.0043568925402347, -0.180163140778535,
-0.428768504221682, -0.419274900125707, -0.443887345580402,
-0.172814345136862, -0.421236872131327, 0.0222639026173606

), Open_to_Public = structure(c(2L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L

), levels = c("0", "1"), class = "factor")), row.names = c(NA,
-34L), class = "data.frame"), obsToY = structure(c(1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1), dim = c(10L, 10L)))
> summary(umf)
unmarkedFrame Object

34 sites
Maximum number of observations per site: 10
Mean number of observations per site: 5.15
Sites with at least one detection: 16

Tabulation of y observations:
0 1 <NA>
138 37 165

Site-level covariates:
avg_depth_m avg_veg_percent Trachemys_present Chrysemys_present urban_percent_2023_500m

Min. :-1.86187 Min. :-1.1756 0:13 0:14 Min. :-1.2251
1st Qu.:-0.57862 1st Qu.:-0.7462 1:21 1:20 1st Qu.:-0.8340
Median : 0.07017 Median :-0.2971 Median :-0.4055

Mean : 0.00000 Mean : 0.0000 Mean : 0.0000

3rd Qu.: 0.58542 3rd Qu.: 0.5355 3rd Qu.: 0.8534
Max. : 2.00344 Max. : 2.2292 Max. : 2.2822

forest_percent_2023_500m min_dist_at_site_m Open_to_Public
Min. :-1.3437 Min. :-0.4439 0:19
1st Qu.:-0.8574 1st Qu.:-0.4208 1:15
Median :-0.0553 Median :-0.3745
Mean : 0.0000 Mean : 0.0000
3rd Qu.: 0.5371 3rd Qu.:-0.1747
Max. : 2.5481 Max. : 4.0909

Observation-level covariates:
max_air_temp_C max_percent_RH min_percent_RH doy
Min. :-2.65930 Min. :-3.4601 Min. :-2.0685 Min. :-1.5697
1st Qu.:-0.64352 1st Qu.:-0.3339 1st Qu.:-0.6851 1st Qu.:-0.8180
Median : 0.07338 Median : 0.4677 Median :-0.1134 Median : 0.1216
Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
3rd Qu.: 0.72755 3rd Qu.: 0.6639 3rd Qu.: 0.6060 3rd Qu.: 0.7793
Max. : 2.18245 Max. : 1.0589 Max. : 2.9084 Max. : 1.5874
NA's :165 NA's :165 NA's :165 NA's :165
>
> covariates_site <- colnames(site_covs)

> boxplot(site_covs)

>
> # Create the formula for detection (constant) and occupancy (using covariates)
> formula <- as.formula(paste("~ 1 ~", paste(covariates_site, collapse = " + ")))
>
> # Print warnings
> options(warn = 1)
>
> # Fit the model using formula

> m_all <- occu(formula, data=umf, control = list(maxit = 1000))
> # m_all <- occu(formula, data=umf, control = list(trace = TRUE, maxit = 1000))

>
> # Model selection
> model_selection <- dredge(m_all, rank = "AICc")
Fixed terms are "psi(Int)" and "p(Int)"
Warning in sqrt(diag(vcov(model, ...))) : NaNs produced
>

> print(model_selection) # See models
Global model call: occu(formula = formula, data = umf, control = list(maxit = 1000))
---
Model selection table
psi(Int) psi(avg_dpt_m) psi(avg_veg_prc) psi(Chr_prs) psi(frs_prc_2023_500) psi(min_dst_at_sit_m)
98 34.1400 -63.710
42 36.2300 -68.670 -8.01800
36 49.1500 -93.160 -10.32000
10 101.3000 -255.000 -54.48000
34 13.9500 -24.110
8 -19.8000 -76.130 -39.57000 +
14 20.8800 -63.610 + -9.91000
66 41.4300 -79.560
70 13.5800 -63.880 +
40 13.6900 -51.040 -14.19000 +
58 68.9000 -122.900 -13.35000 9.8110
52 80.5600 -168.200 -19.39000 -19.5800
114 40.2900 -72.670 7.8340
46 48.6800 -97.360 + -11.10000
106 32.7800 -62.260 -3.34100
100 24.7600 -50.730 -5.65900
102 29.1300 -67.320 +
44 26.7600 -53.090 -5.45500 -5.01000
74 75.0500 -193.600 -39.57000
68 73.8400 -335.300 -143.10000
24 -68.0700 -176.800 -118.10000 + 8.4070
2 5.8830 -12.910
72 32.3900 -399.100 -84.14000 +
26 53.8400 -141.000 -29.91000 -7.0860
12 61.1100 -153.400 0.03821 -32.58000
38 8.3200 -19.750 +
50 13.7900 -24.510 -1.1950
4 10.6100 -39.110 -15.25000
6 3.8380 -15.870 +
18 5.0860 -11.000 0.9195
82 31.8900 -57.290 7.7730
16 0.6788 -54.890 -13.22000 + -3.25200
86 20.4600 -79.240 + 8.4740
30 19.0100 -61.810 + -9.46200 -3.7580
78 13.1800 -52.420 + -4.67400
84 -1.8900 -62.790 -40.63000 17.4000
90 68.1700 -180.400 -37.59000 -8.3700
76 74.1500 -190.700 2.14900 -37.62000
28 57.8500 -152.500 -0.61830 -32.43000 -7.6410
48 4.3200 -53.680 -20.45000 + 3.99700
108 25.9100 -58.110 -9.20700 -1.33600
56 10.4300 -44.050 -11.48000 + -1.6360
104 13.0700 -41.730 -8.57800 +
118 28.2800 -66.240 + 7.7800
116 24.7900 -51.690 -6.11900 -0.5752
60 27.4300 -55.910 -5.25200 -5.58600 -3.4430
122 29.3400 -55.220 -3.12400 2.1200
110 27.8200 -52.930 + -3.51300
62 35.9200 -65.600 + -8.68700 -5.0400
88 -14.5200 -153.100 -50.19000 + 13.9600
80 6.4300 -115.800 -19.94000 + 8.21500
54 8.2090 -20.110 + -0.6885
20 2.5490 -9.128 -3.13800 1.2250
22 3.9080 -16.350 + -0.1771
94 15.0300 -60.350 + -3.27900 6.9160
92 4.0470 -43.890 -25.47000 6.49400 12.1100
32 3.0210 -44.580 -8.03500 + -3.08300 -0.9544
112 7.0680 -39.380 -12.83000 + 3.71900
120 12.5600 -45.930 -11.09000 + -0.1932
124 28.5000 -54.340 -5.22600 -2.02800 -0.0682
96 -3.5920 -131.100 -33.70000 + 9.32300 3.9440
64 7.7530 -37.620 -12.52000 + 2.67700 3.8330
126 27.0300 -57.600 + -1.87100 4.5660
128 9.9870 -37.400 -10.88000 + 3.69500 4.5130
3 0.3715 -0.75670
1 0.3470
15 40.4900 -20.76000 + -15.76000
103 104.3000 -29.36000 +
17 0.3103 -0.7043
119 279.6000 -80.85000 + -26.3000
19 0.3465 -0.69740 -0.6135
psi(Opn_to_Pbl) psi(urb_prc_2023_500) p(Int) df logLik AICc delta weight
98 + 5.742000 -0.8081 5 -74.158 160.5 0.00 0.089
42 + -0.8081 5 -74.168 160.5 0.02 0.088
36 + -0.8080 5 -74.177 160.5 0.04 0.087
10 -0.8551 4 -75.595 160.6 0.11 0.084
34 + -0.8330 4 -76.012 161.4 0.94 0.055
8 -0.8163 5 -74.989 162.1 1.66 0.039
14 -0.8551 5 -75.587 163.3 2.86 0.021
66 6.094000 -0.9001 4 -76.983 163.3 2.89 0.021
70 6.048000 -0.8552 5 -75.608 163.4 2.90 0.021
40 + -0.8079 6 -74.133 163.4 2.92 0.021
58 + -0.8080 6 -74.134 163.4 2.92 0.021
52 + -0.8079 6 -74.134 163.4 2.92 0.021
114 + 6.093000 -0.8080 6 -74.139 163.4 2.93 0.021
46 + -0.8080 6 -74.140 163.4 2.93 0.021
106 + 4.966000 -0.8075 6 -74.140 163.4 2.93 0.021
100 + 3.225000 -0.8081 6 -74.141 163.4 2.93 0.021
102 + 6.824000 -0.8080 6 -74.147 163.4 2.94 0.020
44 + -0.8082 6 -74.147 163.4 2.95 0.020
74 1.812000 -0.8554 5 -75.635 163.4 2.95 0.020
68 6.710000 -0.8556 5 -75.657 163.5 3.00 0.020
24 -0.8085 6 -74.182 163.5 3.01 0.020
2 -0.8412 3 -78.342 163.5 3.02 0.020
72 25.690000 -0.8083 6 -74.189 163.5 3.03 0.020
26 -0.8568 5 -75.689 163.5 3.06 0.019
12 -0.8560 5 -75.708 163.6 3.10 0.019
38 + -0.8349 5 -75.873 163.9 3.43 0.016
50 + -0.8335 5 -76.003 164.1 3.69 0.014
4 -0.8747 4 -77.412 164.2 3.74 0.014
6 -0.8385 4 -77.537 164.5 3.99 0.012
18 -0.8394 4 -78.288 166.0 5.50 0.006
82 5.589000 -0.9001 5 -76.984 166.1 5.65 0.005
16 -0.8549 6 -75.580 166.3 5.81 0.005
86 6.679000 -0.8550 6 -75.580 166.3 5.81 0.005
30 -0.8551 6 -75.587 166.3 5.83 0.005
78 2.700000 -0.8552 6 -75.590 166.3 5.83 0.005
84 9.697000 -0.8551 6 -75.596 166.3 5.84 0.005
90 0.971200 -0.8550 6 -75.614 166.3 5.88 0.005
76 3.407000 -0.8552 6 -75.619 166.3 5.89 0.005
28 -0.8556 6 -75.654 166.4 5.96 0.005
48 + -0.8080 7 -74.132 166.6 6.11 0.004
108 + 3.453000 -0.8080 7 -74.134 166.6 6.12 0.004
56 + -0.8079 7 -74.135 166.6 6.12 0.004
104 + 1.524000 -0.8080 7 -74.136 166.6 6.12 0.004
118 + 6.200000 -0.8080 7 -74.138 166.6 6.12 0.004
116 + 3.108000 -0.8085 7 -74.140 166.6 6.13 0.004
60 + -0.8080 7 -74.142 166.6 6.13 0.004
122 + 3.847000 -0.8081 7 -74.144 166.6 6.14 0.004
110 + 3.152000 -0.8078 7 -74.148 166.6 6.14 0.004
62 + -0.8082 7 -74.153 166.6 6.15 0.004
88 15.290000 -0.8077 7 -74.153 166.6 6.15 0.004
80 11.110000 -0.8084 7 -74.162 166.6 6.17 0.004
54 + -0.8358 6 -75.869 166.8 6.39 0.004
20 -0.8540 5 -77.518 167.2 6.72 0.003
22 -0.8385 5 -77.536 167.2 6.75 0.003
94 4.474000 -0.8550 7 -75.581 169.5 9.01 0.001
92 9.665000 -0.8551 7 -75.586 169.5 9.02 0.001
32 -0.8550 7 -75.586 169.5 9.02 0.001
112 + 2.894000 -0.8080 8 -74.133 170.0 9.57 0.001
120 + 1.220000 -0.8080 8 -74.134 170.0 9.57 0.001
124 + 3.562000 -0.8083 8 -74.136 170.0 9.57 0.001
96 12.420000 -0.8081 8 -74.139 170.0 9.58 0.001
64 + -0.8087 8 -74.143 170.0 9.59 0.001
126 + 4.393000 -0.8078 8 -74.144 170.0 9.59 0.001
128 + 2.903000 -0.8082 9 -74.135 173.8 13.31 0.000
3 -0.7546 3 -84.787 176.4 15.91 0.000
1 -0.7504 2 -86.045 176.5 16.02 0.000
15 -1.0710 5 -82.357 176.9 16.40 0.000
103 + 9.888000 -1.0240 6 -80.906 176.9 16.46 0.000
17 -0.7482 3 -85.092 177.0 16.52 0.000
119 + 45.040000 -0.9843 7 -79.601 177.5 17.05 0.000
19 -0.7583 4 -84.169 177.7 17.26 0.000
[ reached 'max' / getOption("max.print") -- omitted 57 rows ]
Models ranked by AICc(x)
>
> # Extract all models from dredge output
> all_models <- get.models(model_selection, subset = TRUE) # list of models
>
> best_model <- all_models[[1]]
> summary(best_model)

Call:
occu(formula = ~1 ~ avg_depth_m + Open_to_Public + urban_percent_2023_500m +
1, data = umf, control = list(maxit = 1000))

Occupancy (logit-scale):
Estimate SE z P(>|z|)
(Intercept) 34.14 44.9 0.761 0.447
avg_depth_m -63.71 82.6 -0.771 0.441
Open_to_Public1 -19.13 32.1 -0.596 0.551
urban_percent_2023_500m 5.74 15.6 0.367 0.713

Detection (logit-scale):
Estimate SE z P(>|z|)
-0.808 0.198 -4.09 4.36e-05

AIC: 158.3169
Number of sites: 34Warning: Large or missing SE values. Be very cautious using these results.

>
> # Function to check convergence of a fitted unmarkedFitOccu model:
> check_convergence <- function(model) {
+ # 'model@opt$convergence' holds convergence info: 0 = converged, anything else = no
+ return(model@opt$convergence == 0)
+ }
>
> # Apply convergence check to all models
> convergence_status <- sapply(all_models, check_convergence)
>
> # Show which models converged
> converged_models <- which(convergence_status)
> non_converged_models <- which(!convergence_status)
>
> cat("Number of converged models:", length(converged_models), "\n")
Number of converged models: 128
> cat("Number of non-converged models:", length(non_converged_models), "\n")
Number of non-converged models: 0
>
> # Print the model formulas of non-converged models
> cat("\nNon-converged models formulas:\n")

Non-converged models formulas:
> lapply(all_models[non_converged_models], function(m) print(m@call$formula))
named list()
>

Luis Santiago

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Jun 26, 2025, 4:59:06 PMJun 26

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No worries Ella.

I don't know if Ken agrees, but yes, my take is that you should use fewer covariates. The occasions when I have encountered that error, myself, had to do with model overparameterisation.

Luís

Emma Wilson

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Jun 26, 2025, 5:06:09 PMJun 26

to unmarked

Hello,

I'll definitely give it a shot! Unfortunately, even when I use models that have fewer variables (for example, ~1 ~avg_depth_m + avg_veg_percent), I still get warning messages. I suspect that depth is a crucial factor in occupancy for the species, so hopefully someone might have some ideas on what about the depth covariate may be driving separation.

Thanks again!

All the best,

Emma

Luis Santiago

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Jun 26, 2025, 5:09:02 PMJun 26

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Can you post a screenshot of the variable distribution?

Emma Wilson

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Jun 26, 2025, 5:10:12 PMJun 26

to unmarked

Is this helpful?

Jim Baldwin

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Jun 27, 2025, 12:28:10 AMJun 27

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One can get your model to converge but doesn't really fix the underlying issue which is that the model is too complicated for the available data (as mentioned by others.)

First, to get occu to converge, pick "better" starting values (although it's difficult to give general advice about picking better starting values).

(fm2 <- occu(~ 1 ~ avg_depth_m + avg_veg_percent + Trachemys_present +
Chrysemys_present + urban_percent_2023_500m + forest_percent_2023_500m +
min_dist_at_site_m + Open_to_Public, umf,
starts=c(-39.8253, -182.467, -17.1697, -92.5466, 138.132, 4.59815,
-0.0776043, -51.6599, -15.3034, -0.70657)))

Call:
occu(formula = ~1 ~ avg_depth_m + avg_veg_percent + Trachemys_present +
Chrysemys_present + urban_percent_2023_500m + forest_percent_2023_500m +
min_dist_at_site_m + Open_to_Public, data = umf, starts = c(-39.8253,
-182.467, -17.1697, -92.5466, 138.132, 4.59815, -0.0776043,
-51.6599, -15.3034, -0.70657))

Occupancy (logit-scale):
Estimate SE z P(>|z|)

(Intercept) -39.8253 8028 -4.96e-03 0.996
avg_depth_m -182.4670 51861 -3.52e-03 0.997
avg_veg_percent -17.1697 18125 -9.47e-04 0.999
Trachemys_present1 -92.5466 4898 -1.89e-02 0.985
Chrysemys_present1 138.1320 19924 6.93e-03 0.994
urban_percent_2023_500m 4.5982 25739 1.79e-04 1.000
forest_percent_2023_500m -0.0776 19974 -3.89e-06 1.000
min_dist_at_site_m -51.6599 56799 -9.10e-04 0.999
Open_to_Public1 -15.3034 8402 -1.82e-03 0.999

Detection (logit-scale):
Estimate SE z P(>|z|)

-0.707 0.201 -3.52 0.000436

AIC: 162.1126
Number of sites: 34

Warning message:

Large or missing SE values. Be very cautious using these results.

I obtained "better" starting values by using a crude approximation of occu written for Mathematica (where I have more control over the precision of the calculations). Then I plugged those results into the code above. Again, "better" means that convergence is obtained rather than necessarily obtaining a desirable fit. The full model (and apparently several submodels) are overparameterized such that there are an infinite number of solutions that will get you the same predictions. Also note that one obtains NaN's for the standard errors when the estimated covariance matrix has negative variance (i.e., any negative values along the diagonal). The AIC value is just slightly smaller than the

The overparameterization can be detected (or at least, strongly indicated) by looking at the estimate of the parameter correlation matrix and observing that there are several off-diagonal entries very close to +1 and/or -1 (and because of internal round-off error there are a some entries that are a bit larger than +1 and some less than -1). Here is that correlation matrix rounded to 4 decimal places and only the 9 site covariates (8 + intercept). (All parameter estimators have essentially zero correlation with the detection probability in your model.)

covmat <- vcov(fm2)
cormat <- matrix(rep(1, 100), nrow=10, ncol=10)
for (i in 1:9) {
for (j in (i+1):10) {
cormat[i, j] <- covmat[i, j]/sqrt(covmat[i, i]*covmat[j, j])
cormat[j, i] <- cormat[i, j]
}}
cormat <- matrix(as.numeric(formatC(cormat, format = "f", digits = 4)), nrow = nrow(cormat))
print(cormat[1:9, 1:9])

[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] 1.0000 -0.7163 0.4810 -1.5772 0.9266 0.5976 0.6257 0.6907 -0.8580
[2,] -0.7163 1.0000 -0.9656 0.1729 -0.9863 -0.9997 -1.0111 -1.0242 0.5746
[3,] 0.4810 -0.9656 1.0000 -0.0878 0.9200 0.9788 0.9537 0.8977 -0.3529
[4,] -1.5772 0.1729 -0.0878 1.0000 -0.2425 -0.2317 -0.3235 -0.5367 0.9763
[5,] 0.9266 -0.9863 0.9200 -0.2425 1.0000 0.9903 1.0297 1.0935 -0.7568
[6,] 0.5976 -0.9997 0.9788 -0.2317 0.9903 1.0000 0.9926 0.9626 -0.4998
[7,] 0.6257 -1.0111 0.9537 -0.3235 1.0297 0.9926 1.0000 0.9816 -0.5828
[8,] 0.6907 -1.0242 0.8977 -0.5367 1.0935 0.9626 0.9816 1.0000 -0.6650
[9,] -0.8580 0.5746 -0.3529 0.9763 -0.7568 -0.4998 -0.5828 -0.6650 1.0000

I wish I had better news.

Jim

To view this discussion visit https://groups.google.com/d/msgid/unmarked/aF2sgjPHCOYlXoqH%40BADGER.

Marc Kery

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Jun 27, 2025, 4:33:27 AMJun 27

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Dear Emma,

funny in the other email thread active on this list right now, there is a discussion about how complex an occupancy model ought to be at the max. There, Rory reminds us of some rules of thumb in use in regression modeling, that for each estimated parameter one ought to have 10 or even 20 data points. These are only rules of thumb, but arguably we ought to keep them in mind when fitting models with unmarked. So, accordingly, with 34 sites, you ought to perhaps fit not more than 1-3 parameters.

Best regards --- Marc

From: 'Emma Wilson' via unmarked <unma...@googlegroups.com>
Sent: Thursday, June 26, 2025 23:10
To: unmarked <unma...@googlegroups.com>
Subject: Re: [unmarked] Models Failing to Converge

--

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(3) HMecology: for everything else, especially material covered in the books by Royle & Dorazio (2008), Kéry & Schaub (2012), Kéry & Royle (2016, 2021) and Schaub & Kéry (2022)
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Emma Wilson

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Jun 27, 2025, 11:43:52 AMJun 27

to unmarked

Hi, all!

Thanks so much for your detailed responses! I'm just getting my foot into the door with model building, so this has been a really valuable conversation!

When minimizing the covariates used to avoid overparameterization, can the global model still contain more covariates that are used build less complex submodels, or does the global model also need to be limited until we receive reasonable SE values? If the latter, is there a recommended approach to still incorporate all variables into multiple "global" models where the resulting submodels could then still be ranked via AICc?

Thanks for all of your feedback!

All the best,

Emma

Luis Santiago

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Jun 27, 2025, 7:00:30 PMJun 27

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If I understood you correctly, Emma, your global model can have all the variables you used. What you can do is limit the max number of variables used in each model combination when you use dredge(). In this case, on the follow-up of what was said before, given your data, I would go for 3 max.

Luís

Jim Baldwin

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Jun 30, 2025, 12:57:43 AMJun 30

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The suggestions from others concerned limiting the number of predictors in the model to 1 to 3 (which is really 2 to 4 if one counts the intercept).

The top 2-variable model with respect to AIC has average depth and Open to Public. Unfortunately, when one plots the 95% confidence intervals for the probability of occupancy, those essentially go from 0 to 1 which isn't much use. Below is a figure created from the attached code.The darker gray area shows the 95% confidence band.

In short while occu gives the warning "Large or missing SE values. Be very cautious using these results", the function is producing the correct results (meaning the maximum likelihood estimates are correctly found).

The data is certainly suggestive that average depth is the most important variable of the ones you've considered but 95% confidence intervals are just too wide to be useful to convince others.. Here's a jittered strip plot illustrating what Ken Kellner mentioned about the average depth covariate:

I'm not a wildlife biologist but I wonder if there might be some other function of average depth that is more predictive. I wonder if below some threshold depth the occupancy probability is constant (and well below 1) but above that threshold the occupancy probability declines rapidly. Such transformations would (or at least should) need to have some biological justification.

Jim

To view this discussion visit https://groups.google.com/d/msgid/unmarked/f710854e-b734-4bcc-9d1b-54e4f5173f1an%40googlegroups.com.

occu example with 2 predictors.r

Jim Baldwin

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Jun 30, 2025, 1:04:11 AMJun 30

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Sorry, don't know why the images don't appear. I will try this again...

The suggestions from others concerned limiting the number of predictors in the model to 1 to 3 (which is really 2 to 4 if one counts the intercept).

The top 2-variable model with respect to AIC has average depth and Open to Public. Unfortunately, when one plots the 95% confidence intervals for the probability of occupancy, those essentially go from 0 to 1 which isn't much use. Below is a figure created from the attached code.The darker gray area shows the 95% confidence band.

In short while occu gives the warning "Large or missing SE values. Be very cautious using these results", the function is producing the correct results (meaning the maximum likelihood estimates are correctly found).

The data is certainly suggestive that average depth is the most important variable of the ones you've considered but 95% confidence intervals are just too wide to be useful to convince others.. Here's a jittered strip plot illustrating what Ken Kellner mentioned about the average depth covariate:

I'm not a wildlife biologist but I wonder if there might be some other function of average depth that is more predictive. I wonder if below some threshold depth the occupancy probability is constant (and well below 1) but above that threshold the occupancy probability declines rapidly. Such transformations would (or at least should) need to have some biological justification.

Jim

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