An experimental test of defenses in a recent host
Data files
Apr 26, 2021 version files 35.66 KB
Abstract
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.
Methods
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).
Usage notes
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.