<p>AIC of tested models (<i>coh</i> = cohort, <i>D</i> = density of fish in the first year of life). For both marble trout populations of Zakojska and Gacnik, the best model had cohort as predictor for both and <i>k</i> in addition to individual random effects. No pred = model with no predictors for either parameter. In parentheses are reported the number of parameters of the model and the ΔAIC with respect to the best model.</p><p>AIC of tested models.</p
<p>Concordance = 0.724 (s.e. = 0.026). R-square = 0.331 (max possible = 0.991).</p><p>The effects on...
<p>Predictors of YOY, juvenile and adult brown trout density used in Random Forest (RF) regression m...
<p>Models investigated direct trophic interactions using every possible covariate combination as wel...
<p>Only interpretable predictor variables in at least one site are provided (see <a href="http://www...
<p>AICc = Akaike's information criterion with a correction for small sample size, K = number of mo...
<p>Plots of YOY brook trout density time series, the highest Akaike weighted (<i>w<sub>i</sub></i>) ...
1<p>Models are ranked by their AICc differences (Δ<i><sub>i</sub></i>) relative to the best model in...
<p>The model with smallest AIC value estimating the morbidity risk of the host (rainbow trout or zeb...
<p>Bold = model of best fit. CP = critical period, where Y = yes, N = no,? = could not be determi...
<p>K is the number of parameters in the models, and AIC<sub>c</sub> is the small sample size correct...
<p>All models contained fishing effort as an offset variable. AIC = Akaike Information Criterion, ΔA...
<p>Plots of adult brook trout density time series, the highest Akaike weighted (<i>w<sub>i</sub></i>...
a<p>All models contained the probability of detection function p(W, LW).</p>b<p>Variables subscripte...
Statistical aspects of the population regulation of a migratory brown trout population are investiga...
<p>Results for models describing variation in juvenile fish mass (g) after initial marine residence ...
<p>Concordance = 0.724 (s.e. = 0.026). R-square = 0.331 (max possible = 0.991).</p><p>The effects on...
<p>Predictors of YOY, juvenile and adult brown trout density used in Random Forest (RF) regression m...
<p>Models investigated direct trophic interactions using every possible covariate combination as wel...
<p>Only interpretable predictor variables in at least one site are provided (see <a href="http://www...
<p>AICc = Akaike's information criterion with a correction for small sample size, K = number of mo...
<p>Plots of YOY brook trout density time series, the highest Akaike weighted (<i>w<sub>i</sub></i>) ...
1<p>Models are ranked by their AICc differences (Δ<i><sub>i</sub></i>) relative to the best model in...
<p>The model with smallest AIC value estimating the morbidity risk of the host (rainbow trout or zeb...
<p>Bold = model of best fit. CP = critical period, where Y = yes, N = no,? = could not be determi...
<p>K is the number of parameters in the models, and AIC<sub>c</sub> is the small sample size correct...
<p>All models contained fishing effort as an offset variable. AIC = Akaike Information Criterion, ΔA...
<p>Plots of adult brook trout density time series, the highest Akaike weighted (<i>w<sub>i</sub></i>...
a<p>All models contained the probability of detection function p(W, LW).</p>b<p>Variables subscripte...
Statistical aspects of the population regulation of a migratory brown trout population are investiga...
<p>Results for models describing variation in juvenile fish mass (g) after initial marine residence ...
<p>Concordance = 0.724 (s.e. = 0.026). R-square = 0.331 (max possible = 0.991).</p><p>The effects on...
<p>Predictors of YOY, juvenile and adult brown trout density used in Random Forest (RF) regression m...
<p>Models investigated direct trophic interactions using every possible covariate combination as wel...