Innovative problem solving by wild falcons

Abstract

Innovation (i.e., a new solution to a familiar problem, or applying an existing behavior to a novel problem) plays a fundamental role in species’ ecology and evolution. It can be a useful measure for cross-group comparisons of behavioral and cognitive flexibility and a proxy for general intelligence. Among birds, experimental studies of innovation (and cognition more generally) are largely from captive corvids and parrots, though we lack serious models for avian technical intelligence outside these taxa. Striated caracaras (Phalcoboenus australis) are Falconiformes, sister clade to parrots and passerines, and those endemic to the Falkland Islands (Malvinas) show curiosity and neophilia similar to notoriously neophilic kea parrots, and face similar socio-ecological pressures to corvids and parrots. We tested wild striated caracaras as a new avian model for technical cognition and innovation using a field-applicable 8-task comparative paradigm. The setup allowed us to assess behavior, rate, and flexibility of problem solving over repeated exposure in a natural setting. Like other generalist species with low neophobia, we predicted caracaras to demonstrate a haptic approach to solving tasks, flexibly switching to new, unsolved problems and improving performance over time. Striated caracaras performed comparably to tool-using parrots, nearly reaching ceiling levels of innovation in few trials, repeatedly and flexibly solving tasks and rapidly learning. We attribute our findings to the birds’ ecology, including geographic restriction, resource unpredictability and opportunistic generalism, and encourage future work investigating their cognitive abilities in the wild.

Full text

Report
Innovative problem solving by wild falcons
Highlights
dAvian technical intelligence studies are mostly limited to
corvids and parrots
dWe introduce striated caracaras as a new avian model for
technical cognition
dWe use an 8-task comparative paradigm to assess problem
solving in the wild
dStriated caracaras show rapid, flexible innovation on par with
tool-using parrots
Authors
Katie J. Harrington,
Remco Folkertsma,
Alice M.I. Auersperg, Laura Biondi,
Megan L. Lambert
Correspondence
katherine.harrington@vetmeduni.ac.at
In brief
Harrington et al. present wild striated
caracaras with eight concurrent problem-
solving tasks adapted from an innovation
paradigm designed for comparative
purposes. Striated caracaras show rapid,
flexible innovation on par with tool-using
parrots, highlighting this species as a
promising new model for studies of avian
cognition in natural settings.
Harrington et al., 2024, Current Biology 34, 1–6
January 8, 2024 ª2023 The Author(s). Published by Elsevier Inc.
https://doi.org/10.1016/j.cub.2023.10.061 ll

Report
Innovative problem solving by wild falcons
Katie J. Harrington,
1,3,
*Remco Folkertsma,
1
Alice M.I. Auersperg,
1
Laura Biondi,
2
and Megan L. Lambert
1
1
Comparative Cognition Unit, Messerli Research Institute, University of Veterinary Medicine Vienna, Veterin€
arplatz 1, 1210 Vienna, Austria
2
Instituto de Investigaciones Marinas y Costeras (IIMyC), UNMdP - CONICET, Juan B. Justo 2550, Mar del Plata B7602GSD, Argentina
3
Lead contact
*Correspondence: katherine.harrington@vetmeduni.ac.at
https://doi.org/10.1016/j.cub.2023.10.061
SUMMARY
Innovation (i.e., a new solution to a familiar problem, or applying an existing behavior to a novel prob-
lem
1,2
) plays a fundamental role in species’ ecology and evolution. It can be a useful measure for
cross-group comparisons of behavioral and cognitive flexibility and a proxy for general intelligence.
3–5
Among birds, experimental studies of innovation (and cognition more generally) are largely from captive
corvids and parrots,
6–12
though we lack serious models for avian technical intelligence outside these
taxa. Striated caracaras (Phalcoboenus australis)areFalconiformes, sister clade to parrots and passer-
ines,
13–15
and those endemic to the Falkland Islands (Malvinas) show curiosity and neophilia similar to
notoriously neophilic kea parrots
16,17
and face similar socio-ecological pressures to corvids and par-
rots.
18,19
We tested wild striated caracaras as a new avian model for technical cognition and innovation
using a field-applicable 8-task comparative paradigm (adapted from Ro
¨ssler et al.
20
and Auersperg
et al.
21
). The setup allowed us to assess behavior, rate, and flexibility of problem solving over repeated
exposure in a natural setting. Like other generalist species with low neophobia,
21,22
we predicted cara-
caras to demonstrate a haptic approach to solving tasks, flexibly switching to new, unsolved problems
and improving their performance over time. Striated caracaras performed comparably to tool-using par-
rots,
20
nearly reaching ceiling levels of innovation in few trials, repeatedly and flexibly solving tasks, and
rapidly learning. We attribute our findings to the birds’ ecology, including geographic restriction, resource
unpredictability, and opportunistic generalism,
23–25
and encourage future work investigating their cogni-
tive abilities in the wild.
RESULTS AND DISCUSSION
Caracaras repeatedly innovated new solutions when concur-
rently faced with a subset of eight extractive foraging tasks
(Figure 1) selected from a task battery presented previously
to Goffin’s cockatoos (Tanimbar corella), another opportunist
generalist island endemic species.
20
We analyzed 51 trials (trial
length x ± s: 13.2 ± 5.4 min, range: 3.5–27.8 min) comprising
15 caracaras who participated in a maximum of five trials across
17 days (Table 1). Innovation rates during caracaras’ first trial
were as rapid as one solution per 1.6 min (i.e., 0.6 solutions
per min, 0.3 ± 0.15 solutions per min, Figure 2).
Performance rate continued to improve across trials, despite
temporary interruptions due to working within a natural setting
(see STAR Methods; effect of trial: b= 0.170, SE = 0.06,
t
(9.86)
= 3.05, p = 0.013; effect of interruptions: b=0.031,
SE = 0.04, t
(16.68)
=0.75, p = 0.46; Figure 2). As a group, perfor-
mance rate more than doubled from the first to fifth trials (0.3 ±
0.15 to 0.8 ± 0.6 solutions per min). In addition to performing
faster, as trials progressed, caracaras also found an increasing
proportion of solutions (trial 1–5: 0.64, 0.74, 0.82, 0.88, 0.92; ef-
fect of trial: b= 0.681, SE = 0.19, c
2(1)
= 11.39, p = 0.0007; effect
of interruptions: b= 0.174, SE = 0.23, c
2(1)
= 0.55, p = 0.46). Both
adult participants approached the box and performed on par
with younger birds, suggesting neophilia may not be restricted
to young age as in some other species (see Greenberg
26
for
review). On average, trial durations and solution latencies
decreased in later trials (trial 1 duration: 904 ± 414 s, trial 5:
678 ± 352 s; trial 1 solution latency: 39.5 ± 43.4 s, trial 5:
12.9 ± 23.3 s).
Caracaras showed inter- and intra-individual differences in so-
lution sequence across trials (Figures 3 and S1). Previous studies
suggest that inter-individual variation may be characteristic of
other neophilic, explorative species,
20,21,27,28
whereas more
neophobic species with less haptic exploration techniques
tend to pursue more ecologically valid and obvious solutions
(i.e., less inter-individual differences).
21,22
Latencies to absolute first solution tended to be shortest for
the seesaw, swish, and plank (65 ± 60 s, 70 ± 41, and 75 ± 55,
respectively) and longest for the tear and twig tasks (137 ±
84 s and 175 ± 118, respectively; Figure 3). For tasks with longer
absolute solution latencies, many individuals employed a strat-
egy of peering at the tasks from multiple angles, including
bending at a panel to look from below at a protrusion or jumping
atop the box to peer from above. When comparing the cara-
caras’ success at each task with Goffin’s cockatoos, we found
a positive correlation between species for the proportion of trials
in which a task was solved, but only when we exclude the wire
Current Biology 34, 1–6, January 8, 2024 ª2023 The Author(s). Published by Elsevier Inc. 1
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
ll
OPEN ACCESS
Please cite this article in press as: Harrington et al., Innovative problem solving by wild falcons, Current Biology (2023), https://doi.org/10.1016/
j.cub.2023.10.061

task, which the caracaras solved in proportionally more trials
(Spearman’s rho = 0.83, p = 0.02; Ro
¨ssler et al.
20
). The cara-
caras’ high success with the wire task could be due to the
frequent pulling and tearing motions that caracaras exhibit
when feeding.
18
Caracaras more quickly approached and contacted the box
as trials progressed. Contact latency decreased from 108 ±
124 s in trial 1 to 16 ± 12 s in trial 5 (effect of trial: b=
-0.477, SE = 0.12, t
(15.94)
=3.84, p = 0.001). In later trials,
some caracaras immediately started running from outside
the arena toward the box once it became available, with one
hatch-year (H17, Table 1) solving all tasks within 5 min 14 s
(including post-solution exploration time). Temporarily captive
wild-caught chimango caracaras (Milvago chimango), the only
other species within the caracara subfamily to receive atten-
tion in cognitive studies,
27,29
also show decreased approach
latencies and increased problem-solving success across trials
when faced with a 4-task problem-solving box.
27,28,30
Howev-
er, chimangos first had a habituation phase, during which they
could gather information prior to their initial problem-solving
trial
28,29
, whereas the striated caracaras did not. This lack of
neophobia in the striated caracaras may be due to the envi-
ronmental context in which the striated caracaras evolved
(i.e., almost risk-free habitat).
31
Qualitatively, caracaras’ contact strategies appeared to be
governed by morphological capabilities and maintained across
individuals. Notably, caracaras used their foot in a raking or kick-
ing (i.e., a quick, sharp punch) motion, and used their beak to
peck, bite, and pull. At a group level, most tasks were initially
solved with either the beak or the foot, whereas in later trials,
some tasks became exclusively solved with the beak (i.e., twig
and wire) or the foot (i.e., plank and seesaw; Figure S2). Addition-
ally, we found that as tasks were haptically explored less prior to
being solved, they were also being solved faster in subsequent
trials (Spearman’s rho = 0.90, p = 0.005). It is possible that—as
Figure 1. Eight-task innovation box and
movements required to solve each task
panel
Octagonal transparent Perspex innovation box
with a transparent lid, eight removable transparent
Perspex task panels (17 324 cm), and an opaque
wooden base, custom-built by Markus Fitzka
(Messerli Research Institute, University of Veteri-
nary Medicine, Vienna). Bottom left: movement
required to solve each task panel. Bottom right: a
juvenile striated caracara uses a foot to solve the
plank task.
See also Figures S2,S3, and Table S1.
with other birds that frequently manipu-
late items with their feet (e.g., barn owls
Tyto alba,
32
see also Guti
errez-Iba
´n
˜ez
et al.
33
)—caracaras may have specialized
sensory receptors in their foot pads that
aid in haptic exploration.
Individuals continued to spend time
with tasks and interact with task protru-
sions after having retrieved the reward
(post-solution duration within one body
length of tasks: 283 ± 144 s, 0–580 s; for post-solution contact
frequencies see Figure S3). Tasks that were contacted more
post-solution were solved faster in subsequent trials relative
to less explored panels (Spearman’s rho = 0.83, p =
0.015). Moreover, at the individual level, increased exploration
led to increased solution speeds in subsequent trials, though
this effect loses significance when including the effect of trial
(effect of contact frequency: b=0.583, SE = 0.244, t
(6.76)
=
2.39, p = 0.0496; with trial included: b=0.321, SE =
0.273, t
(5.32)
=1.17, p = 0.29). When analyzing the panels
separately, we found this effect differed depending on the
task. In general, increased exploration led to faster solution
times for all task panels except for the twig (see Table S1
for model results). Continued interactions with tasks after
the reward was taken may reflect exploration or play ten-
dencies modulated by stimulus complexity (e.g., size and
number of distinct elements), which affects exploratory
response in many species.
34–37
The behavior was most pro-
nounced on tasks with protrusions: Caracaras repeatedly
kicked the unbaited swish, plank, and seesaw features, and
prolongedly bit and pulled the wire feature, sometimes with
force to the point of jumping with it in their beak. Tactile explo-
ration can increase information gain, which for opportunist
extractive foragers could be especially important in revealing
novel and hidden food sources or increasing task efficiency
during future confrontations.
38–40
Task features may have
also induced a playful circular reaction,
41,42
in which the
interaction itself had a rewarding effect, e.g., by producing
sound or movement, which encouraged repeated interaction.
Furthermore, innovative problem solving shares important
overlaps with curiosity, a topic that is gaining renewed interest
in comparative cognition
43
due to its links with learning,
attention, memory, and decision making. Species that are
likely to exhibit more frequent and persistent interest in novel
items or structures they encounter may be more likely to glean
ll
OPEN ACCESS
2Current Biology 34, 1–6, January 8, 2024
Please cite this article in press as: Harrington et al., Innovative problem solving by wild falcons, Current Biology (2023), https://doi.org/10.1016/
j.cub.2023.10.061
Report

information that may be used later or discover new opportu-
nities or solutions to problems.
44,45
Overall, the caracaras performed in line with tool-using Gof-
fin’s cockatoos on the same tasks. Although the Goffin’s were
presented with a greater number of tasks, the caracaras
achieved an innovation rate of 0.3 ± 0.2 solutions per min, while
the Goffin’s innovated 0.5 ± 0.3 solutions per min. Notably,
nearly all caracaras solved all tasks, including more difficult
tasks such as the wire or tear tasks, which less than 50% of
Goffin’s solved over repeated trials. Similar to the Goffin’s, ca-
racaras were also slower to solve tasks in which rewards were
visually separated from the tasks’ functional mechanisms, and
instead tended to direct their initial interactions on these panels
toward the food.
20
The caracaras’ eagerness to participate in the tasks, coupled
with their speed and flexibility in innovation, highlight them as a
promising and relevant new model for examining avian technical
intelligence in the wild. Caracaras achieved rapid success and
showed signs of learning through improved performance and
speed as trials progressed (Figure 2). These results support the
feasibility of further research into the contexts of curiosity, explo-
ration, and play in a falcon species. We emphasize the rare op-
portunity to expand comparative research to include a readily
participatory wild falcon to investigate the processes underpin-
ning innovative problem solving.
46
There is a growing body of evidence that falcons in general
represent an important taxon for broadening our understanding
of the diversity and evolution of avian cognition
27–29
; however,
they remain surprisingly understudied. We encourage future
research with striated caracaras and more broadly within the
caracara subfamily to examine: (1) What features and task func-
tional mechanisms are most attractive, (2) how does interest in
novel, unbaited structures differ from goal-directed exploration
of baited apparatuses, (3) how do caracaras structure their
play and object exploration, and (4) how does interest in novel
structures vary within the population (e.g., inter-individual differ-
ences, age and sex effects, and effects of social dynamics). We
also have the possibility to track their neophilic, exploratory, and
problem-solving behaviors over time, as well as to evaluate the
fitness-related value of these behaviors for those individuals
who enter the breeding population.
Figure 2. Rate of solutions per minute over
trials
Innovation rates during individuals’ first trial, when
subjects were required to apply for the first-time
existing behaviors to a novel situation, and sub-
sequent increasing performance rates (i.e., solu-
tions per minute) across trials 2–5.
Gray lines represent individuals and the black line
represents the mean.
Table 1. Individuals’ success (total solutions found out of eight
tasks) per trial grouped by age
Trial
Age ID 1 2 3 4 5
HY A17 5
y
7–––
E18* 3 8 7 7 5
G18* 3 –– ––
H17* 8 8 7 6 8
JUV E17 6 6 8 6 –
K15 5 3 –––
M15 1 8 6 8 7
M17 0 7 6 8 6
M18* 4 8 –––
P16 6
y
88 76
y
V14 3 8 7 ––
SA C16 5 4 8 8 8
V19 6 –– ––
AD P19 1 –– ––
X37 3 6 4 8 5
Min 0 3 4 6 5
Max 8 8 8 8 8
Mean 4 7 7 7 6
N 15129 87
Trial duration spanned 3.5 to 27.8 min (mean ± SD: 13.2 ± 5.4 min). Aster-
isks denote males (inferred by pretrial weight). Daggers represent trials
when only 7 of 8 solutions were possible (e.g., experimenter or transport
error). En dashes denote no trial. Sample size with mean and range of so-
lutions included at the bottom of the table. HY, hatch-year; JUV, juvenile;
SA, sub-adult; AD, adult.
ll
OPEN ACCESS
Current Biology 34, 1–6, January 8, 2024 3
Please cite this article in press as: Harrington et al., Innovative problem solving by wild falcons, Current Biology (2023), https://doi.org/10.1016/
j.cub.2023.10.061
Report

STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:
dKEY RESOURCES TABLE
dRESOURCE AVAILABILITY
BLead contact
BMaterials availability
BData and code availability
dEXPERIMENTAL MODEL AND STUDY PARTICIPANT DE-
TAILS
BSubjects and study site
BEthics statement
dMETHOD DETAILS
BApparatus
BProcedure and data collection
dQUANTIFICATION AND STATISTICAL ANALYSIS
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.
cub.2023.10.061.
ACKNOWLEDGMENTS
This work was funded by the Austrian Science Fund (FWF Stand-Alone grant
P-34533 to M.L.L.) and Hawk Mountain Sanctuary. We thank Markus Fitzka for
constructing the apparatus; the Pole-Evans family for graciously hosting us
and providing logistical support during our fieldwork on Saunders Island;
and the Falkland Islands Government for permitting our continued research
in the Falklands. We thank the reviewers for their helpful comments on a pre-
vious version of this manuscript.
AUTHOR CONTRIBUTIONS
Conceptualization, K.J.H. and M.L.L.; methodology, K.J.H., M.L.L., and
A.M.I.A.; investigation, K.J.H. and M.L.L.; formal analysis, R.F. and K.J.H.;
writing – original draft, K.J.H.; writing – review & editing, K.J.H., M.L.L.,
A.M.I.A., R.F., and L.B.; supervision, M.L.L.; funding acquisition, M.L.L. and
K.J.H.; resources, K.J.H., A.M.I.A., and M.L.L.
DECLARATION OF INTERESTS
The authors declare no competing interests.
INCLUSION AND DIVERSITY
We support inclusive, diverse, and equitable conduct of research.
Received: June 30, 2023
Revised: October 6, 2023
Accepted: October 26, 2023
Published: November 20, 2023
REFERENCES
1. Kummer, H., and Goodall, J. (1985). Conditions of innovative behaviour in
primates. Philos. Trans. R. Soc. Lond. B Biol. Sci. 308, 203–214. https://
doi.org/10.1098/rstb.1985.0020.
2. Reader, S.M., and Laland, K.N. (2003). Animal innovation (Oxford
University Press).
3. Griffin, A.S., and Guez, D. (2014). Innovation and problem solving: A review
of common mechanisms. Behav Processes 109, 121–134. https://doi.org/
10.1016/j.beproc.2014.08.027.
4. Goodman, M., Hayward, T., and Hunt, G.R. (2018). Habitual tool use inno-
vated by free-living New Zealand kea. Sci. Rep. 8, 13935. https://doi.org/
10.1038/s41598-018-32363-9.
5. Lefebvre, L., Reader, S.M., and Sol, D. (2004). Brains, Innovations and
Evolution in Birds and Primates. Brain Behav. Evol. 63, 233–246. https://
doi.org/10.1159/000076784.
6. Emery, N.J. (2006). Cognitive ornithology: The evolution of avian intelli-
gence. Philos Trans R Soc Lond B Biol Sci 361, 23–43. https://doi.org/
10.1098/rstb.2005.1736.
7. Emery, N.J., and Clayton, N.S. (2004). The Mentality of Crows: Convergent
Evolution of Intelligence in Corvids and Apes. Science 306, 1903–1907.
https://doi.org/10.1126/science.1098410.
8. Pepperberg, I.M. (2000). The Alex studies: cognitive and communicative
abilities of grey parrots (Harvard University Press).
Figure 3. Latency to absolute first success
per task (trials pooled, i.e., 51 trials of 15 in-
dividuals)
Points represent the latencies (min) to absolute
first solution. Point shapes represent the trial in
which the solution was first achieved. Boxplot bars
show median and first and third quartiles, while
whiskers extend to the largest and smallest values
(at most 1.5 * IQR [inter-quartile range]).
See also Figures S1,S2, and S3.
ll
OPEN ACCESS
4Current Biology 34, 1–6, January 8, 2024
Please cite this article in press as: Harrington et al., Innovative problem solving by wild falcons, Current Biology (2023), https://doi.org/10.1016/
j.cub.2023.10.061
Report

9. Lambert, M., Farrar, B., Garcia-Pelegrin, E., Reber, S., and Miller, R.
(2022). ManyBirds: A multi-site collaborative Open Science approach to
avian cognition and behavior research. Anim. Behav. Cogn. 9, 133–152.
10. Lambert, M., Jacobs, I., Osvath, M., and von Bayern, A. (2019). Birds of a
feather? Parrot and corvid cognition compared. Behaviour. 156, 505–594.
https://doi.org/10.1163/1568539X-00003527.
11. Auersperg, A.M., and von Bayern, A.M. (2019). Who’s a clever bird —
now? A brief history of parrot cognition. Behaviour. 156, 391–407.
https://doi.org/10.1163/1568539X-00003550.
12. Ro
¨ssler, T., and Auersperg, A.M. (2023). Recent developments in parrot
cognition: a quadrennial update. Anim. Cognit. 26, 199–228. https://doi.
org/10.1007/s10071-022-01733-2.
13. Suh, A., Paus, M., Kiefmann, M., Churakov, G., Franke, F.A., Brosius, J.,
Kriegs, J.O., and Schmitz, J. (2011). Mesozoic retroposons reveal parrots
as the closest living relatives of passerine birds. Nat. Commun. 2, 443.
https://doi.org/10.1038/ncomms1448.
14. Jarvis, E.D., Mirarab, S., Aberer, A.J., Li, B., Houde, P., Li, C., Ho, S.Y.W.,
Faircloth, B.C., Nabholz, B., Howard, J.T., et al. (2014). Whole-genome an-
alyses resolve early branches in the tree of life of modern birds. Science
346, 1320–1331. https://doi.org/10.1126/science.1253451.
15. Prum, R.O., Berv, J.S., Dornburg, A., Field, D.J., Townsend, J.P.,
Lemmon, E.M., and Lemmon, A.R. (2015). A comprehensive phylogeny
of birds (Aves) using targeted next-generation DNA sequencing. Nature
526, 569–573. https://doi.org/10.1038/nature15697.
16. Diamond, J., and Bond, A.B. (1999). Kea, bird of paradox: the evolution
and behavior of a New Zealand Parrot (University of California Press).
17. O’Hara, M., Huber, L., and Gajdon, G.K. (2012). Chapter 2 - Kea Logics:
How These Birds Solve Difficult Problems and Outsmart Researchers. In
Logic and sensibility, S. Watanabe, ed. (Keio University Press), pp. 23–38.
18. Autilio, A.R., Bechard, M.J., and Bildstein, K.L. (2019). Social scavenging
by wintering striated caracaras (Phalcoboenus australis) in the Falkland
Islands. Behav. Ecol. Sociobiol. 73, 27. https://doi.org/10.1007/s00265-
019-2638-3.
19. Harrington, K.J., and Meiburg, J. (2021). Use of appeasement display and
recruitment by an adult Striated Caracara (Phalcoboenus australis) to
overcome territorial defense. Wilson J. Ornithol. 133, 127–131. https://
doi.org/10.1676/20-00060.
20. Ro
¨ssler, T., Mioduszewska, B., O’Hara, M., Huber, L., Prawiradilaga, D.M.,
and Auersperg, A.M.I. (2020). Using an Innovation Arena to compare wild-
caught and laboratory Goffin’s cockatoos. Sci. Rep. 10, 8681. https://doi.
org/10.1038/s41598-020-65223-6.
21. Auersperg, A.M.I., von Bayern, A.M.P., Gajdon, G.K., Huber, L., and
Kacelnik, A. (2011). Flexibility in Problem Solving and Tool Use of Kea
and New Caledonian Crows in a Multi Access Box Paradigm. PLoS One
6, e20231.
22. Auersperg, A.M.I. (2015). Chapter 3 - Exploration Technique and Technical
Innovations in Corvids and Parrots. In Animal Creativity and Innovation,
A.B. Kaufman, and J.C. Kaufman, eds. (Academic Press), pp. 45–72.
https://doi.org/10.1016/B978-0-12-800648-1.00003-6.
23. Melin, A.D., Young, H.C., Mosdossy, K.N., and Fedigan, L.M. (2014).
Seasonality, extractive foraging and the evolution of primate sensorimotor
intelligence. J. Hum. Evol. 71, 77–86. https://doi.org/10.1016/j.jhevol.
2014.02.009.
24. Pravosudov, V.V., Roth, T.C., II, LaDage, L.D., and Freas, C.A. (2015).
Environmental Influences on Spatial Memory and the Hippocampus in
Food-Caching Chickadees. Comp. Cogn. Behav. Rev. 10, 25–43.
25. Daniels, S.E., Fanelli, R.E., Gilbert, A., and Benson-Amram, S. (2019).
Behavioral flexibility of a generalist carnivore. Anim. Cognit. 22, 387–396.
https://doi.org/10.1007/s10071-019-01252-7.
26. Greenberg, R.S. (2003). Chapter 8 - The role of neophobia and
neophilia in the development of innovative behaviour of birds. In
Animalinnovation,S.M.Reader,andK.N.Laland,eds.(Oxford
University Press), pp. 175–196. https://doi.org/10.1093/acprof:oso/
9780198526223.003.0008.
27. Biondi, L.M., Bo
´, M.S., and Vassallo, A.I. (2010). Inter-individual and age
differences in exploration, neophobia and problem-solving ability in a
Neotropical raptor (Milvago chimango). Anim. Cognit. 13, 701–710.
https://doi.org/10.1007/s10071-010-0319-8.
28. Biondi, L.M., Fuentes, G., and Susana, M. (2022). Behavioural factors un-
derlying innovative problem-solving differences in an avian predator from
two contrasting habitats. Anim. Cognit. 25, 529–543. https://doi.org/10.
1007/s10071-021-01569-2.
29. Biondi, L.M., Bo
´, M.S., and Vassallo, A.I. (2008). Experimental assessment
of problem solving by Milvago chimango (Aves: Falconiformes). J. Ethol.
26, 113–118. https://doi.org/10.1007/s10164-007-0035-2.
30. Biondi, L.M., Fuentes, G.M., Co
´rdoba, R.S., Bo
´, M.S., Cavalli, M.,
Paterlini, C.A., Castano, M.V., and G arcı
´a, G.O. (2020). Variation in bold-
ness and novelty response between rural and urban predatory birds: The
Chimango Caracara, Milvago chimango as study c ase. Behav. Process.
173, 104064. https://doi.org/10.1016/j.beproc.2020.104064.
31. Strange, I. (1996). The Striated Caracara Phalcoboenus australis in the
Falkland Islands (Philip Myers).
32. Manger, P.R., Elston, G.N., and Pettigrew, J.D. (2002). Multiple maps and
activity-dependent representational plasticity in the anterior Wulst of the
adult barn owl (Tyto alba). Eur. J. Neurosci. 16, 743–750. https://doi.org/
10.1046/j.1460-9568.2002.02119.x.
33. Guti
errez-Iba
´n
˜ez, C., Amaral-Pec¸ anha, C., Iwaniuk, A.N., Wylie, D.R., and
Baron, J. (2023). Online repositories of photographs and videos provide in-
sights into the evolution of skilled hindlimb movements in birds. Commun
Biol 6, 781. https://doi.org/10.1038/s42003-023-05151-z.
34. Berlyne, D.E. (1950). Novelty and curiosity as determinants of exploratory
behaviour. Br. J. Psychol. 41, 68–80.
35. Mettke-Hofmann, C., Rowe, K.C., Hayden, T.J., and Canoine, V. (2006).
Effects of experience and object complexity on exploration in garden war-
blers (Sylvia borin). J Zool 268, 405–413. https://doi.org/10.1111/j.1469-
7998.2005.00037.x.
36. Heyser, C.J., and Chemero, A. (2012). Novel object exploration in mice:
Not all objects are created equal. Behav. Process. 89, 232–238. https://
doi.org/10.1016/j.beproc.2011.12.004.
37. Biondi, L.M., Guido, J.M., Bo
´, M.S., Muzio, R.N., and Vassallo, A.I. (2015).
The role of stimulus complexity, age and experience in the expression of
exploratory behaviour in the Chimango Caracara, Milvago chimango.
Anim. Cognit. 18, 139–150. https://doi.org/10.1007/s10071-014-0785-5.
38. Biondi, L.M., Guido, J., Madrid, E., Bo
´, M.S., and Vassallo, A.I. (2013). The
Effect of Age and Sex on Object Exploration and Manipulative Behavior in
a Neotropical Raptor, the Chimango Caracara, Milvago chimango.
Ethology 119, 221–232. https://doi.org/10.1111/eth.12056.
39. Carducci, P., Schwing, R., Huber, L., and Truppa, V. (2018). Tactile infor-
mation improves visual object discrimination in kea, Nestor notabilis, and
capuchin monkeys, Sapajus spp. Anim. Behav. 135, 199–207. https://doi.
org/10.1016/j.anbehav.2017.11.018.
40. Overington, S.E., Cauchard, L., Co
ˆt
e, K.A., and Lefebvre, L. (2011).
Innovative foraging behaviour in bird s: What characterizes an innovator?
Behav. Process. 87, 274–285. https://doi.org/10.1016/j.beproc.2011.
06.002.
41. Piaget, J., Cook, M., and others. (1952). The origins of intelligence in chil-
dren (International Universities Press).
42. Smith, P.K. (1982). Does play matter? Functional and evolutionary aspects
of animal and human play. Behav. Brain Sci. 5, 139–155. https://doi.org/
10.1017/S0140525X0001092X.
43. Kidd, C., and Hayden, B.Y. (2015). The psychology and neuroscience of
curiosity. Neuron 88, 449–460.
44. Huber, L., Rechberger, S., and Taborsky, M. (2001). Social learning affects
object exploration and manipulation in keas, Nestor notabilis. Anim.
Behav. 62, 945–954. https://doi.org/10.1006/anbe.2001.1822.
45. Call, J. (2013). Chapter 1 - Three ingredients for becoming a creative tool
user. In Tool Use in Animals: Cognition and Ecology, C. Sanz, J. Call, and
ll
OPEN ACCESS
Current Biology 34, 1–6, January 8, 2024 5
Please cite this article in press as: Harrington et al., Innovative problem solving by wild falcons, Current Biology (2023), https://doi.org/10.1016/
j.cub.2023.10.061
Report

C. Boesch, eds. (Cambridge University Press), pp. 3–20. https://doi.org/
10.1017/CBO9780511894800.002.
46. Tebbich, S., Griffin, A.S., Peschl, M.F., and Sterelny, K. (2016). Fro m
mechanisms to function: an integrated framework of animal innovation.
Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 20150195. https://doi.org/
10.1098/rstb.2015.0195.
47. Friard, O., and Gamba, M. (2016). BORIS: a free, versatile open-source
event-logging software for video/audio coding and live observations.
Methods Ecol. Evol. 7, 1325–1330. https://doi.org/10.1111/2041-210X.
12584.
48. R Core Team (2022). R: A Language and Environment for Statistical
Computing.
49. Balza, U., Lois, N.A., and Raya Rey, A. (2017). Status and Reproductive
Outcome of the Breeding Population of Striated Caracaras (Phalcoboenus
australis) at Franklin Bay, Staten Island, Argentina. Wilson J. Ornithol. 129,
890–898. https://doi.org/10.1676/16-189.1.
50. Reeves, M., Bildstein, K.L., and Bildstein, K.L. (2018). Distribution and
Abundance of Breeding Striated Caracaras in the Falkland Islands
(Malvinas). J. Raptor Res. 52, 309–315. https://doi.org/10.3356/JRR-17-
31.1.
51. Herculano-Houzel, S. (2019). Longevity and sexual maturity vary across
species with number of cortical neurons, and humans are no exception.
J. Comp. Neurol. 527, 1689–1705. https://doi.org/10.1002/cne.24564.
52. Catry, P., Lecoq, M., and Strange, I.J. (2008). Population growth and den-
sity, diet and breeding success of striated caracaras Phalcoboenus aus-
tralis on New Island, Falkland Islands. Polar Biol. 31, 1167–1174. https://
doi.org/10.1007/s00300-008-0454-8.
53. Harrington, K.J., Meiburg, J., and Houtz, J.L. (2021). Winter bird gets the
worm: consumption of earthworms (Lumbricidae) by striated caracaras
(Phalcoboenus australis) in invasive Yorkshire fog (Holcus lanatus) in the
Falkland Islands (Malvinas). Polar Biol. 44, 1009–1013. https://doi.org/
10.1007/s00300-021-02845-0.
54. Harrington, K.J., Fahlbusch, J.A., Langrock, R., Therrien, J.-F., Houtz, J.L.,
and McDonald, B.I. (2020). Seasonal activity levels of a farm-island popu-
lation of striated caracaras (Phalcoboenus australis) in the Falkland
Islands. Anim. Biotelemetry 8, 27. https://doi.org/10.1186/s40317-020-
00214-y.
55. Harrington, K.J., Pole-Evans, S., Reeves, M., Bechard, M., Bobowski, M.,
Barber, D.R., Rexer-Huber, K., Lecomte, N., and Bildstein, K.L. (2018).
Seasonal micro-migration in a farm-island population of striated caracaras
(Phalcoboenus australis) in the Falkland Islands. Mov. Ecol. 6,4.https://
doi.org/10.1186/s40462-018-0122-8.
56. Harrington, K.J., and Bildstein, K.L. (2019). Predation of Southern Red
Octopus (Enteroctopus megalocyathus) by Striated Caracaras
(Phalcoboenus australis) in the Falkland Islands. J. Raptor Res. 53,
220–222. https://doi.org/10.3356/JRR-18-65.
57. Lefebvre, L. (2021). A global database of feeding innovations in birds.
Wilson J. Ornithol. 132, 803–809. https://doi.org/10.1676/20-00101.
58. Jacobson, S.L., Dechanupong, J., Horpiencharoen, W., Yindee, M., and
Plotnik, J.M. (2023). Innovating to solve a novel puzzle: wild Asian ele-
phants vary in their ability to problem solve. Anim. Behav. 205, 227–239.
https://doi.org/10.1016/j.anbehav.2023.08.019.
59. Thornton, A., and Samson, J. (2012). Innovative problem solving in wild
meerkats. Anim. Behav. 83, 1459–1468. https://doi.org/10.1016/j.anbe-
hav.2012.03.018.
60. Johnson-Ulrich, L., Holekamp, K.E., and Hambrick, D.Z. (2020). Innovat ive
problem-solving in wild hyenas is reliable across time and contexts. Sci.
Rep. 10, 13000. https://doi.org/10.1038/s41598-020-69953-5.
61. Byrne, R.W., and Bates, L.A. (2011). Cognition in the wild: explorin g animal
minds with observational evidence. Biol. Lett. 7, 619–622. https://doi.org/
10.1098/rsbl.2011.0352.
62. Szabo, B., Valencia-Aguilar, A., Damas-Moreira, I., and Ringler, E. (2022).
Wild cognition – linking form and function of cognitive abilities within a nat-
ural context. Curr. Opin. Behav. Sci. 44, 101115. https://doi.org/10.1016/j.
cobeha.2022.101115.
63. BirdLife International (2023). Important Bird Area factsheet: Saunders
Island.
64. Ro
¨ssler, T., Mioduszewska, B., O’Hara, M., Huber, L., Prawiradilaga, D.M.,
and Auersperg, A.M.I. (2022). The Innovation Arena: A Method for
Comparing Innovative Problem-Solving Across Groups. JoVE, e63026.
https://doi.org/10.3791/63026.
65. Griffin, A.S., and Guez, D. (2014). Innovation and problem solving: A review
of common mechanisms. Behav. Process. 109, 121–134. https://doi.org/
10.1016/j.beproc.2014.08.027.
66. Farrar, B.G., Voudouris, K., and Clayton, N.S. (2021). Replications,
Comparisons, Sampling and the Problem of Representativeness in
Animal Cognition Research. Anim. Behav. Cogn. 8, 273–295. https://doi.
org/10.26451/abc.08.02.14.2021.
67. Benson-Amram, S., and Holekamp, K.E. (2012). Innovative problem solv-
ing by wild spotted hyenas. Proc. Biol. Sci. 279, 4087–4095. https://doi.
org/10.1098/rspb.2012.1450.
68. Petelle, M.B., Jacobs, P.J., and le Roux, A. (2023). Innovative problem-
solving in a small, wild canid. Anim. Cognit. 26, 405–413. https://doi.org/
10.1007/s10071-022-01678-6.
69. Ford, S. (2010). Raptor Gastroenterology. J. Exot. Pet Med. 19, 140–150.
https://doi.org/10.1053/j.jepm.2010.05.004.
70. Webster, M.M., and Rutz, C. (2020). How STRANGE are your study ani-
mals? Nature 582, 337–340.
71. Gamer, M., Lemon, J., Fellows, I., and Singh, P. (2019). irr: Various
Coefficients of Interrater Reliability and Agreement.
72. Baayen, R.H., Davidson, D.J., and Bates, D.M. (2008). Mixed-effects
modeling with crossed random effects for subjects and items. J. Mem.
Lang. 59, 390–412. https://doi.org/10.1016/j.jml.2007.12.005.
73. McCullagh, P., and Nelder, J.A. (1989). Binary data. In Generalized linear
models (Springer), pp. 98–148.
74. Bates, D., Kliegl, R., Vasishth, S., and Baayen, H. (2015). Parsimonious
mixed models. Preprint at arXiv. https://doi.org/10.48550/arXiv.1506.
04967.
75. Schielzeth, H. (2010). Simple means to improve the interpretability of
regression coefficients. Methods Ecol. Evol. 1, 103–113. https://doi.org/
10.1111/j.2041-210X.2010.00012.x.
76. Schielzeth, H., and Forstmeier, W. (2009). Conclusions beyond support:
overconfident estimates in mixed models. Behav. Ecol. 20, 416–420.
https://doi.org/10.1093/beheco/arn145.
77. Barr, D.J. (2013). Random effects structure for testing interactions in linear
mixed-effects models. Front. Psychol. 4, 328.
78. Fox, J., and Weisberg, S. (2019). An R Companion to Applied Regression
Third (Sage).
79. Nieuwenhuis, R., Grotenhuis, M., and Pelzer, B. (2012). Influence.ME:
Tools for detecting influential data in mixed effects models. R J. 4,
38–47. https://doi.org/10.32614/rj-2012-011.
80. Luke, S.G. (2017). Evaluating significance in linear mixed-effects models in
R. Behav. Res. Methods 49, 1494–1502. https://doi.org/10.3758/s13428-
016-0809-y.
81. Kuznetsova, A., Brockhoff, P.B., and Christensen, R.H.B. (2017). lmerTest
Package: Tests in Linear Mixed Effects Models. J. Stat. Softw. 82, 1–26.
https://doi.org/10.18637/jss.v082.i13.
82. Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis
(Springer-Verlag).
83. Field, A. (2005). Discovering Statistics using SPSS (London: Sage
Publications).
ll
OPEN ACCESS
6Current Biology 34, 1–6, January 8, 2024
Please cite this article in press as: Harrington et al., Innovative problem solving by wild falcons, Current Biology (2023), https://doi.org/10.1016/
j.cub.2023.10.061
Report

STAR+METHODS
KEY RESOURCES TABLE
RESOURCE AVAILABILITY
Lead contact
Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Katie Harrington
(katherine.harrington@vetmeduni.ac.at).
Materials availability
This study did not generate new unique reagents.
Data and code availability
dAll datasets used for analysis in this study have been deposited at Science DataBank (file: df_box.RData, ContactFreq.RData,
PropSolv_CaraGoffin.RData) and are publicly available as of the date of publication (ScienceDB: https://doi.org/10.57760/
sciencedb.13251).
dAll code generated during this study has been deposited at Science DataBank (files: CaracaraInnovation.R, CaracaraFunc-
tions.R,) and is publicly available as of the date of publication (ScienceDB: https://doi.org/10.57760/sciencedb.13251).
dAny additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS
Subjects and study site
Striated caracaras are opportunistic, generalist, mostly ground-dwelling falcons restricted to harsh coastal areas of southern South
America and the Falkland Islands (Malvinas).
49,50
Relative to many bird species, striated caracaras have a long adolescence,
31
a sug-
gested physiological correlate to behavioral and cognitive flexibility.
51
The caracara population on the Falklands (Malvinas) is genet-
ically and behaviorally distinct from the mainland (U. Balza and KH, unpublished data). Falklands striated caracaras’ ecology and
behavior overlaps extensively with an established parrot model for technical innovation: Like kea parrots (Nestor notabilis), Falklands
striated caracaras face seasonal resource unpredictability and are neophilic, exploratory, and playful.
16,19,31,52
Moreover, they are
behaviorally flexible, e.g., they associate with anthropogenic resources, seasonally alter activity levels, extractively forage, and albeit
historically persecuted, remain unwary of humans similar to how they were first described by sailors prior to human settlement in the
Falklands.
31,53–55
Furthermore, Falklands striated caracaras are known for foraging innovations, including predation of an
octopus
56,57
and unearthing invasive invertebrates in invasive grasslands.
53
Studies of innovative problem solving in the wild provide invaluable insight from the use of experimental protocols in natural set-
tings (e.g., Jacobson et al.,
58
Thornton and Samson,
59
and Johnson-Ulrich et al.
60
and reviewed in Byrne and Bates
61
and Szabo et
al.
62
). Our study occurred on Saunders Island, Falkland Islands (Malvinas) (51.37S 60.09W) approximately 400 km northeast of
Cape Horn. Saunders is a designated Important Bird and Biodiversity Area,
63
a privately-owned sheep farm (human pop. 6), and
the site of long-term monitoring where caracaras have been ringed biannually from 2010 to 2019 (see Harrington et al.
54
and Harring-
ton et al.
55
). Prior to testing, we ringed 83 potential subjects and inferred age by plumage
31
and estimated sex by mass (11 adults [5
females, 6 males], 9 sub-adults [4, 5], 28 juveniles [13, 17], and 35 hatch-years [12, 21) (see Harrington et al.
54
for protocol). Caracaras
are conspicuous and attracted to group trapping events that mimic naturally occurring ephemeral feeding events, thus we are confi-
dent that our effort represents the population on Saunders during our study.
REAGENT or RESOURCE SOURCE IDENTIFIER
Experimental models: Organisms/strains
Striated caracara (Phalcoboenus australis) Saunders Island, Falkland Islands N/A
Software and algorithms
BORIS v 6.0.574 Friard and Gamba
47
https://www.boris.unito.it/
R v.4.2.2 R Development Core Team
48
https://www.r-project.org/
Other
Panasonic HC-V180 Full HD Panasonic https://www.panasonic.com/de/consumer/
foto-video/camcorder/hc-v180.html
ll
OPEN ACCESS
Current Biology 34, 1–6.e1–e3, January 8, 2024 e1
Please cite this article in press as: Harrington et al., Innovative problem solving by wild falcons, Current Biology (2023), https://doi.org/10.1016/
j.cub.2023.10.061
Report

Ethics statement
All capture and handling methods complied with the Conservation of Wildlife and Nature Ordinance of 1999, Section 9, License to
carry out Scientific Research (Permit #R15/2022, Falkland Islands Government) and were approved by the University of Ottawa
Animal Ethics Committee (protocol no. BLf-3745).
METHOD DETAILS
Apparatus
We adopted a comparative paradigm that blends the designs by Ro
¨ssler et al.,
20
Ro
¨ssler et al.,
64
and Auersperg et al.,
21
adapting the
presentation to meet the requirements for testing in natural conditions and to overcome limitations of motivation, species-specific
motor capabilities, and sampling bias.
65,66
Our custom-built innovation box (see Figure 1,a–h) allows participants to shift among
and interact with eight food-rewarded tasks using distinct motor actions to solve and obtain as many rewards as possible during
a single trial. Unlike the MAB paradigm (i.e., one solution per session and previously solved tasks blocked in subsequent sessions,
e.g., Benson-Amram and Holekamp
67
and Petelle et al.
68
), task panel positions are randomized and panels are rebaited between
trials to allow for specific measures of innovation and performance rates, group specific strategies (e.g., order of approach, solution
order, and motor techniques), and problem-solving behavior (e.g., individual differences in approach and handling).
20
As sample
sizes in cognitive studies are generally small, the increased variation of treatments (i.e., number and diversity of tasks) in place of
a single or small set of problem-solving tasks improves replicability and generalizability of results.
66
Procedure and data collection
Trials were opportunistic from 18 August to 3 September 2022 (i.e., austral winter non-breeding season) between 0800 and 1800. For
each trial, the 8-task apparatus was presented fully baited in the same ground location centered in a 3-m radial trial arena. Prior to
presentation and out of sight, task panels were baited with a high-quality familiar food (i.e., 1 cm
2
mutton). Between trials, the box was
removed and rebaited out of sight, and remained unavailable. For experimenters to place the box in the trial arena, at least one po-
tential subject needed to be nearby the trial arena (i.e., a ringed bird, identified using the naked eye or binoculars). More than one bird
could be present in the vicinity (i.e., within 100 m) when a trial began.
A trial began when a potential subject entered the arena and ended after the subject retrieved all food rewards or 3 min lapsed with
no contact. Experimenters tried to prevent conspecific interruptions and mitigate social learning by concurrently providing an attrac-
tive food source outside the arena, such that only the active subject was likely to attend to the apparatus. While test subjects were not
drawn away from the apparatus by the alternative food source (perhaps as they had a monopoly on it), it is possible that the alter-
native food source was distracting at an imperceivable level for experimenters, which may have had a conservative effect on sub-
jects’ performance levels. Birds were permitted one trial per day. If a bird tried to participate in more than one trial within the
same day, an experimenter would intercept their approach before they reached the arena threshold and walk them off to allow
another subject to enter and participate. Caracaras most often approached on foot, making it possible for experimenters to prohibit
birds from entering the arena during another subject’s trial. Due to time constraints, birds were capped at five trials within the study
period to facilitate repeated measures across individuals. For each trial, we recorded the subject’s identity (i.e., alphanumeric ring)
and used a Likert scale to visually assess their crop (i.e., a distensible food storage area at the top of the digestive tract
69
as a proxy
for hunger driven motivation (i.e., crop not visible, partially visible, or fully visible). We videotaped trials 4 m from the apparatus using a
handheld Panasonic HC-V180 Full HD Camcorder.
As caracaras self-selected to voluntarily participate in trials, our sample may reflect a STRANGE-related bias,
70
despite this, to the
best of our knowledge, our results are representative of juvenile Falkland Islands striated caracaras during austral winter (i.e., non-
breeding season).
QUANTIFICATION AND STATISTICAL ANALYSIS
We used Behavioral Observation Research Interactive Software (BORIS, version 6.0.574
47
) to behaviorally code all videotaped trials.
We developed an ethogram to code contact with each task panel modified by motor skill applied. We initially scored four behaviors
(bite, peck, kick, and grab), as these were the most discrete and reliable to identify. However, we observed bite and grab less
frequently than peck and kick, so we found that level of resolution did not enhance our analysis. We additionally coded when a
task was solved (i.e., an action that made the food reward accessible) and when a reward was retrieved. Because this study was
conducted in the wild and test birds could be interrupted by naturally occurring events, we also coded all trial interruptions, defined
as a subject moving more than three body lengths away from the apparatus seemingly in response to an external factor (e.g., an ex-
perimenter’s movement while deterring conspecifics from entering the trial arena or a curious farm animal displacing the subject at
the apparatus [e.g., domestic goose, horse, cat, or sheep]).
From the coded videos, for each trial, we recorded latency to contact the box (i.e., time from entering arena threshold until first
contact), latency to solution per task panel (i.e., total time within one body length of a task panel prior to solving), frequency of motor
actions applied to each task panel, duration within one body length at each task panel before and after solving (i.e., as a conservative
measure of visual exploration and interest), and total solutions found. We subtracted interruption durations from the individuals’ total
trial duration; we also noted the total number of interruptions per trial which we later used to control for possible effects on
ll
OPEN ACCESS
e2 Current Biology 34, 1–6.e1–e3, January 8, 2024
Please cite this article in press as: Harrington et al., Innovative problem solving by wild falcons, Current Biology (2023), https://doi.org/10.1016/
j.cub.2023.10.061
Report

performance. We calculated innovation rate as an individual’s total solutions per unit time during their first trial, when the subject was
required to apply for the first time existing behaviors to a novel situation. In subsequent trials, we consider the same measure as the
individual’s general performance rate.
Twenty percent of videos were pseudo-randomly selected and externally coded to assess inter-rater reliability. Agreement was
high for latencies [ICC
(1)
= 0.996, F
(6,6.8)
= 440, p < 000.1], durations [ICC
(1)
= 0.744, F
(93,94)
= 6.81, p < 0.0001], and innovation rates
[ICC
(1)
= 0.99, F
(8,8.07)
= 187, p < 0.0001] (R package ‘irr’
71
).
For our response variables in our box-level models, we use performance rate, proportion of solutions found (i.e., number of suc-
cesses and failures per trial), and latency to contact the box (log transformed to improve fit for linear model assumptions). To inves-
tigate performance rate and latency to contact across trials, we fitted linear mixed models (LMM
72
). To investigate proportion of tasks
solved across trials, we fitted a logistic generalized linear mixed model with binomial error structure and a logit link function, using a
two-columns matrix with the number of successes and failures per trial for each individual as the response.
72,73
For each model, we
use trial number as our test predictor and total number of interruptions as a control predictor. As random intercept effects, we
included subject and date to account for repeated observations of the same individual and day to day effects. We analyzed 51 trials
comprising 15 individuals who each participated in a maximum of five trials across a 17-day period.
To investigate whether increased contact with tasks resulted in faster solutions in subsequent trials, we first fitted a LMM including
all observations to assess an overall effect. To explore any panel specific effects, we further ran separate LMMs at the panel level. For
each model, we included contact frequency as a test predictor and individual as a random intercepts effect. As an alternative, we
fitted a model that included contact frequency as a test predictor together with trial number as a control predictor, to control for a
general increased exposure to the panels. These models had low sample size and high levels of collinearity between predictors
and are therefore presented as alternatives (Table S1).
We fitted the models in R (version 4.2.2; R Core Team 2022
48
) using the functions lmer and glmer, respectively, from the package
‘lme4’ (version 1.1–31
74
). All covariates were z-transformed to ease model convergence and interpretation of model estimates.
75
To
keep Type I error rate at the nominal level of 5%, we included all possible identifiable random slopes within the random intercepts
effects.
76,77
After fitting our models, we confirmed that (1) the model assumptions were not violated by visually inspecting QQ-plots (Field
83
), (2)
the ‘Best Linear Unbiased Predictors’ (BLUPS) were approximately normally distributed,
72
(3) overdispersion was not an issue for the
logistic model (dispersion parameter = 1.07), and (4) the absence of collinearity by calculating the ‘Variance Inflation Factor’ using the
R package ‘car’ version 3.0–12 (VIF = 1.001, 1.02, and 1.02, respectively for performance rate, proportion of tasks solved, and latency
to contact; for VIF values for solution latency models see Table S1; Fox and Weisberg
78
). We confirmed model stability by comparing
model estimates of the full model to estimates of models in which levels of random effects were excluded one at a time
79
using a
function written by Roger Mundry (Leibniz ScienceCampus Primate Cognition, Go
¨ttingen). We tested the significance of trial number
by means of the Satterthwaite approximation
80
using the function ‘lmer’ of the package ‘lmerTest’
81
and a model fitted with restricted
maximum likelihood.
We used a Spearman’s rank correlation to assess the relationship between contact frequency after the reward was taken
(i.e., post-solution) and latency to solution in subsequent trials averaged for each panel, and to assess the proportion of trials in which
a task was solved between the Goffin’s cockatoos in Ro
¨ssler et al.
20
and the striated caracaras in this study.
Due to the voluntary participation of caracaras, the unpredictable availability of potential participants, and the limited winter day-
length, we were unable to reach a sufficient sample spread of age, sex, or crop status to analyze these factors and these were thus
not included. We excluded from the analysis six trials from six individuals (i.e., one trial per individual) that contacted the box and did
not engage further (e.g., they were interrupted by a conspecific, heterospecific, farm event, etc., and did not return for the remainder
of testing).
We created plots using the R package ‘ggplot2’.
82
ll
OPEN ACCESS
Current Biology 34, 1–6.e1–e3, January 8, 2024 e3
Please cite this article in press as: Harrington et al., Innovative problem solving by wild falcons, Current Biology (2023), https://doi.org/10.1016/
j.cub.2023.10.061
Report