This document contains 5 separate datasets used in various statistical analyses described in the associated article called "An Experimental Test of Defenses Against Avian Brood Parasitism in a Recent Host" by Abernathy et al. 2021, doi: 10.3389/fevo.2021.651733. Each dataset is in its own sheet within the excel workbook. Please refer to the methods in the manuscript associated with this dataset for the full description of how these datasets were used and what statistical tests were done. 1. The first dataset is called "Vocalization_REMLs" and includes the number of each type of vocalizations given by Red Wattlebird nesting pairs that were exposed to one of three types of taxidermic mounts during mobbing experiments: a harmless Crimson Rosella (R), a nest predator, Pied Currawong (C) or a female Pacific Koel (K). We ran separate restricted naximum likelihood models (REMLs) for each vocalization type to determine if any of the vocalizations could predict which mount type was used in that trial. 2. The second dataset is called "Aggressive response_REML". We obtained a single aggressive response score for each Red Wattlebird breeding pair during mobbing experiments, which was determined by combining three variables (the time the pair spent less than 2 meters from the mount during the trial, the attack rate of the pair during the trial and the alarm call rate during the trial) using a PCA and using the first principle component as our aggressive response score. This score was our response variable in a REML and we used the following variables to determine which might predict aggressive response score: mount type (C, K, or R), Site (ACT = Canberra, SYD = Sydney), Julian calendar date of the mobbing trial, cage type (was it hanging from a branch or attached at the top of a ladder). 3. The third dataset is called "Pair_attack_GLMM". We performed a generalized linear mixed model (GLMM) to determine if the following variables could predict whether a Red Wattlebird breeding pair decided to attack the mount (1) or not (0): mount type (C, K, or R), Site (ACT = Canberra, SYD = Sydney), Julian calendar date of the mobbing trial, cage type (was it hanging from a branch or attached at the top of a ladder). 4. The fourth dataset is called "Time_F_Sat_GLMM". To determine if female Red Wattlebirds were using passive nest defense during the koel mount mobbing trial (remaining on the nest longer in the presence of a brood parasite), we took the total time a female sat during each trial ("Time_F_sat") out of the total time she was present during the trial ("Total_F_time") and calcualted the proportion of time a female sat during each trial. We used this "Proportion_Time_F_Sat" as our response variable in a GLMM to determine if the following variables could predict the amount of time that females sat on the nest: mount type (C, K, or R), Site (ACT = Canberra, SYD = Sydney), Julian calendar date of the mobbing trial, cage type (was it hanging from a branch or attached at the top of a ladder), or whether the male attacked the mount or not ("M_attack", with Y = attack and N = no attack). 5. The fifth dataset is called "Egg_ejection_GLM". We ran a generalized linear model (GLM) to determine if the following variables could predict whether a model egg was ejected (1) from the nest or not (0): Host (MPL = Magpie-lark, NFB = Noisy Friarbird, RWB = Red Wattlebird), Egg_type (Blue = blue non-mimetic egg, Spotted = egg made to appear similar to host's own eggs), Site (ACT = Canberra, SYD = Sydney), Days_till_clutch_complete (please see description in methods of paper for this variable), and breeding season year when experiment was conducted (F = first year, S = second year).