Appendix A: Calculation of trapping effort

Each trap station consisted of a Tomahawk and a Sherman trap, operated for 5 consecutive trapping occasions. Occasions 1 through 4 consisted of a day and night trapping event (traps checked in the afternoon and morning, respectively), and occasion 5 only had a night trapping event. This, in combination with occasional failure of one or both traps during a trapping event, led to variation in trapping effort per occasion. Because this translates into heterogeneity in capture probability among trap stations and occasions, we constructed a measure of trap effort to account for this variation.

Northern flying squirrels, according to raw capture data, were much more readily captured in Tomahawk traps (83% of captures) and during the night trapping events (99% of captures). We calculated the percentage of captures of Northern flying squirrels in the morning versus the evening, for both Tomahawk and Sherman traps (four values that sum to 1, Table A1). For each trap station and occasion we summed percentages of those trap type/trap event combinations that were operational to obtain a measure of relative effort. For example, if station X on occasion Y had both a day and night trapping event, and the Tomahawk trap was functional during both events, whereas the Sherman trap was only functional for the day trapping event, the effort measure would be 0.002 + 0.012 + 0.819 = 0.833.

We calculated effort for each trap station on each occasion and incorporated it into the SCR models using the usage() attribute for the trap object in secr. The effort is used in secr to scale baseline detection probability and incorporating it does not add parameters to the model.

Table A.1: Percentage of northern flying squirrel captures in Sherman and Tomahawk traps during day and night trapping events, used to construct trap station and occasion specific effort.

Day NightSherman 0.002 0.167Tomahawk 0.012 0.819

Appendix B: Sample size

Table B.1: Number of flying squirrels captured in the Stanislaus-Tuolumne Experimental Forest, California, within three blocks (Control, Central treatment and Western treatment) across the study landscape.

2009 2010 2013 2014 2015Control 46 12 9 23 10Central 93 36 39 34 19Western 93 51 29 50 17

Appendix C: Results of the spatial capture-recapture models

Table C.1: Model selection results for spatial capture-recapture models fit to live trapping data of Northern flying squirrels in the Stanislaus-Tuolumne Experimental Forest, over three blocks (control = no fuel reduction treatment, central and western = with fuel reduction treatments). Npar = number of parameters, AICc = Akaike Information Criterion adjusted for small sample size, dAICc = difference in AICc to top model, AICcwt = AICc weight.

Year Block Model npar AICc dAICc AICcwt2009 Control canopy 4 1094.237 0.000 0.744

Control canopy + behavior 5 1096.373 2.136 0.2562010 Control canopy 4 360.181 0.000 0.956

Control canopy + behavior 5 366.333 6.152 0.0442013 Control canopy 4 205.388 0.000 1.000

Control canopy + behavior --- --- --- ---2014 Control canopy 4 398.902 0.000 0.834

Control canopy + behavior 5 402.124 3.222 0.1662015 Control canopy 4 195.498 0.000 1.000

Control canopy + behavior --- --- --- ---

2009 Central canopy 4 3225.559 0.000 1.000Central canopy + behavior --- --- --- ---

2010 Central canopy + behavior 5 1268.830 0.000 1.000Central canopy --- --- --- ---

2013 Central canopy 4 1057.500 0.000 0.746Central canopy + behavior 5 1059.650 2.150 0.255

20141 Central Null 3 583.115 0.000 1.00020151 Central Null 3 324.789 0.000 1.000

2009 Western canopy 4 2710.927 0.000 0.786Western canopy + behavior 5 2713.29 2.598 0.214

2010 Western canopy 4 2163.326 0.000 1.000Western canopy + behavior --- --- --- ---

2013 Western canopy 4 717.932 0.000 0.800Western canopy + behavior 5 720.703 2.771 0.200

2014 Western canopy 4 1004.334 0 0.759Western burn 4 1006.628 2.294 0.241Western burn + behavior --- --- --- ---Western canopy + behavior --- --- --- ---

2015 Western canopy 4 290.556 0.000 0.697Western burn 4 293.168 2.612 0.189Western canopy + behavior 5 294.541 3.985 0.095Western burn + behavior 5 297.764 7.208 0.019

1: Covariate models did not converge

Table C.2: Parameter estimates with standard error (SE) and confidence intervals (lower limit = LC, upper limit = UC) from spatial capture-recapture model fit to live trapping data of Northern flying squirrels in the Stanislaus-Tuolumne Experimental Forest, Control block (no fuel reduction treatments) over five years; results of best model by AICc. D = density, g0 = baseline capture probability, σ = scale parameter of the half normal detection function, β(canopy) = effect of canopy cover on log(density).

Year Model Parameter Estimate SE LC UC2009 canopy D 0.746 0.122 0.542 1.027

g0 0.022 0.004 0.016 0.031σ 101.407 4.939 92.179 111.557β(canopy) -0.012 0.232 -0.466 0.441

2010 canopy D 0.194 0.078 0.091 0.416g0 0.016 0.006 0.008 0.033σ 103.562 16.298 76.219 140.714β(canopy) 0.237 0.596 -0.931 1.405

2013 canopy D 0.168 0.086 0.066 0.431g0 0.016 0.000 0.015 0.016σ 68.379 0.739 66.946 69.843β(canopy) -0.386 0.092 -0.566 -0.205

2014 canopy D 0.719 0.227 0.393 1.315g0 0.015 0.006 0.007 0.031σ 68.305 12.878 47.355 98.521β(canopy) -0.099 0.349 -0.784 0.585

2015 canopy D 0.337 0.216 0.107 1.064g0 0.025 0.000 0.025 0.025σ 43.200 0.230 42.751 43.654β(canopy) 0.430 0.536 -0.621 1.481

Table C.3: Parameter estimates with standard error (SE) and confidence intervals (lower limit = LC, upper limit = UC) from spatial capture-recapture model fit to live trapping data of Northern flying squirrels in the Stanislaus-Tuolumne Experimental Forest, central block (with fuel reduction treatments) over five years; results of best model by AICc. D = density, g0 = baseline capture probability, σ = scale parameter of the half normal detection function, β(canopy) = effect of canopy cover on log(density), β(canopy) = effect of recapture on g0.

Year Model Parameter Estimate SE LC UC20091 canopy D 0.661 0.069 0.539 0.810

g0 0.023 0.000 0.023 0.023σ 134.661 0.426 133.829 135.499β(canopy) 0.078 0.334 -0.577 0.734

20101 canopy + b D 0.259 0.063 0.162 0.415

g0 0.011 0.000 0.011 0.012σ 139.624 0.751 138.160 141.104β(canopy) -0.227 0.157 -0.535 0.080β(behavior) 0.605 0.064 0.480 0.731

20132 canopy D 0.325 0.054 0.235 0.449g0 0.010 0.000 0.010 0.010σ 157.621 0.890 155.886 159.375β(canopy) 0.528 0.490 -0.433 1.488

20143 Null4 D 0.538 0.095 0.382 0.758g0 0.002 0.000 0.002 0.002σ 162.265 2.154 158.099 166.541

20153 Null4 D 0.373 0.098 0.225 0.621g0 0.002 0.000 0.001 0.003σ 154.691 2.133 150.566 158.928

1: pre fuel reduction treatment years; 2: post-thinning, pre burning; 3: post-thinning and burning; 4: models with covariates did not converge.

Table C.4: Parameter estimates with standard error (SE) and confidence intervals (lower limit = LC, upper limit = UC) from spatial capture-recapture model fit to live trapping data of Northern flying squirrels in the Stanislaus-Tuolumne Experimental Forest, Western block (with fuel reduction treatments) over five years; results of best model by AICc. D = density, g0 = baseline capture probability, σ = scale parameter of the half normal detection function, β(canopy) = effect of canopy cover on log(density).

Year Model Parameter Estimate SE LC UC20091 canopy D 0.808 0.094 0.644 1.013

g0 0.021 0.000 0.021 0.021σ 118.302 0.268 117.777 118.828β(canopy) -0.022 0.338 -0.683 0.640

20101 canopy D 0.334 0.074 0.217 0.514g0 0.027 0.001 0.026 0.029σ 125.872 0.484 124.927 126.824β(canopy) 0.365 0.254 -0.133 0.862

20132 canopy D 0.203 0.101 0.081 0.510g0 0.008 0.001 0.007 0.010σ 130.434 3.181 124.347 136.819β(canopy) 1.704 0.571 0.584 2.824

20143 canopy D 0.661 0.275 0.302 1.446g0 0.009 0.000 0.009 0.010σ 93.563 0.571 92.451 94.688β(canopy) 1.230 0.582 0.089 2.372

20153 canopy D 0.247 0.254 0.047 1.304

g0 0.003 0.000 0.003 0.004σ 130.491 8.723 114.484 148.737β(canopy) 1.468 1.179 -0.842 3.778

1: pre fuel reduction treatment years; 2: post-thinning, pre burning; 3: post-thinning and burning.

Figure C.1: Density of Northern flying squirrels in the Stanislaus-Tuolumne Experimental Forest, California, in different designated mechanical fuel reduction + prescribed burn treatment units before treatment was implemented (designated treatments: UTB = unthinned + burned, ETB = even thin + burned, VTB = variable thin + burned, ETUB = even thin + unburned, VTUB = variable thin + unburned, UTUB = unthinned + unburned), for the central and western block of the study landscape. Density was estimated by spatial capture-recapture models for 30x30-m pixels, and violin plots show spread of values across all pixels located in a given treatment type.