DOI: 10.5061/dryad.dfn2z351b
title: An experimental test of defenses in a recent host

description: Theoretical studies predict that hosts of avian brood parasites should
 evolve defenses against parasitism in a matter of decades. However,
 opportunities to test these predictions are limited because brood
 parasites rarely switch to naïve hosts. Here, we capitalize on a recent
 host switch by the brood-parasitic Pacific Koel (Eudynamys orientalis) in
 eastern Australia, to investigate how quickly the Red Wattlebird
 (Anthochaera carunculate), a recent host that has been annexed by the koel
 within the last 90 years, can learn to recognize and mob adult cuckoos and
 evolve the ability to eject parasite eggs. Pacific Koel nestlings kill all
 host young, so there should be strong selection for hosts to evolve
 defenses. However, low parasitism rates and high egg recognition costs
 might slow the spread of egg ejection in our study populations, while
 adult parasite recognition should be able to spread more rapidly, as this
 defense has been shown to be a learned trait rather than a genetically
 inherited defense. We tested Red Wattlebirds at two sites where parasitism
 rate differed. As predicted, we found that the Red Wattlebird showed
 little or no ability to eject foreign model eggs at either site, whereas
 two historical hosts showed high levels of egg ejection at both sites.
 However, Red Wattlebirds responded significantly more aggressively to a
 koel mount than to mounts of a harmless control and nest predator at the
 site with the higher parasitism rate and gave significantly more alarm
 calls overall towards the koel mount. Our results support previous
 evidence that recognition and mobbing of a brood parasite are learned
 traits and may be especially beneficial to naïve hosts that have not had
 enough time or a high enough selection pressure to evolve egg rejection.
description: 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.
 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).
description: 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. Please refer to the README file for more
 information on each of the five datasets contained in this excel
 workbook.