I am posting this question here after being advised to do so on StackOverflow. I am trying to use the rma.uni function from the metafor package to estimate the impact of fishing gears on my abundance data. Following the method published in Sciberas et al. 2018 (DOI: 10.1111/faf.12283), I think I used correctly the function, however, I am not sure how to interpret the output. In the function, c is the log response ratio and var_cis the associated variance. log2(t+1) represent times in days. In my data, gear is a factor with three levels: CD, QSD and KSD.

As I am not familiar with models in general and especially this type of model, I read online documentation including this : https://faculty.nps.edu/sebuttre/home/R/contrasts.html Thus, I understood that only two levels from my factor gear need to be display in the output.

Below is the output I have when I run the rma.uni function. My questions are:

• if gearCD is considered as a 'reference' in the model then it would mean that the effect of gearKSD is 0.14 more positive (I don't know how to word it) than gearCD and that on the opposite, gearQSD is 0.12 times more damaging ?
• How should I interpret the fact that the pvalues for gearKSD and gearQSD are not significant ? Does it mean that their intercept is not significantly different from the one of gearCD ? If so, is the intercept of gearCD the same thing than intercpt?
• Do you know how I could obtain one intercept value for each level of my factor gear ? I am aiming at distinguishing the intial impact of these three gears so it would be of interest to have one interpect per gear.
• Similarly, if I had interaction terms with log2(t+1) (for example gearKSD:log2(t+1)) the interpreation would be silimar to how we interpret intercept ?

I am sorry I know these are a lot of questions.. Also, isn't it weird to have a R² of 100% and all other values at 0 (tau, tau² and I²)?

Thank you all very much for your help !

rma.uni(c,var_c,mods=~gear+log2(t+1),data=data_AB,method="REML")

Mixed-Effects Model (k = 15; tau^2 estimator: REML)

tau^2 (estimated amount of residual heterogeneity):     0 (SE = 0.0097)
tau (square root of estimated tau^2 value):             0
I^2 (residual heterogeneity / unaccounted variability): 0.00%
H^2 (unaccounted variability / sampling variability):   1.00
R^2 (amount of heterogeneity accounted for):            100.00%

Test for Residual Heterogeneity:
QE(df = 11) = 7.5027, p-val = 0.7570

Test of Moderators (coefficients 2:4):
QM(df = 3) = 31.7446, p-val < .0001

Model Results:

             estimate      se     zval    pval    ci.lb    ci.ub 
intrcpt       -1.1145  0.1407  -7.9200  <.0001  -1.3904  -0.8387  *** 
gearKSD        0.1488  0.1025   1.4517  0.1466  -0.0521   0.3497      
gearQSD       -0.1274  0.0916  -1.3919  0.1640  -0.3069   0.0520      
log2(t + 1)    0.1007  0.0195   5.1626  <.0001   0.0625   0.1389  *** 

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Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1