| Literature DB >> 21611168 |
Daniel Sol1, Andrea S Griffin, Ignasi Bartomeus, Hayley Boyce.
Abstract
For an animal invading a novel region, the ability to develop new behaviors should facilitate the use of novel food resources and hence increase its survival in the new environment. However, the need to explore new resources may entail costs such as exposing the animal to unfamiliar predators. These two opposing forces result in an exploration-avoidance conflict, which can be expected to interfere with the acquisition of new resources. However, its consequences should be less dramatic in highly urbanized environments where new food opportunities are common and predation risk is low. We tested this hypothesis experimentally by presenting three foraging tasks to introduced common mynas (Acridotheres tristis) from environments with low and high urbanization levels from Australia. Individuals from the highly urbanized environments, where mynas are both more opportunistic when foraging and less fearful to predators, resolved a technical task faster than those from less urbanized environments. These differences did not reflect innovative 'personalities' and were not confounded by sex, morphology or motivational state. Rather, the principal factors underlying differences in mynas' problem-solving ability were neophobic-neophilic responses, which varied across habitats. Thus, mynas seem to modulate their problem-solving ability according to the benefits and costs of innovating in their particular habitat, which may help us understand the great success of the species in highly urbanized environments.Entities:
Mesh:
Year: 2011 PMID: 21611168 PMCID: PMC3097186 DOI: 10.1371/journal.pone.0019535
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Figure 1Adaptation of the Two-Factor model proposed by Greenberg & Mettke-Hoffmann [ to describe the interplay between neophobia, exploration and innovation as a function of the ecological context.
Figure 2Difference in resource innovation, technical innovation, neophobia and exploration between mynas from the urban and suburbia environments.
In the survival curves, solid lines represent birds from the urban environment whereas the dashed lines represent birds from the suburbia.
Survival models relating problem-solving latency in technical innovation, consumer innovation and neophobia as a function of habitat and a set of confounding variables.
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| coefficient | exp(coef) | S.E. (coef) | z | P | |
| Habitat (suburbs) | 0.259 | 1.296 | 0.423 | 0.611 | 0.5411 |
| Population (Canberra) | 0.056 | 1.057 | 0.480 | 0.116 | 0.9070 |
| Sex (male) | 0.435 | 1.546 | 0.636 | 0.684 | 0.4940 |
| PC1 | 1.200 | 3.321 | 1.728 | 0.695 | 0.4871 |
| PC2 | 2.568 | 13.035 | 3.478 | 0.738 | 0.4610 |
| Food (green rice) | 0.299 | 1.348 | 0.372 | 0.803 | 0.4223 |
| Motivation | −0.163 | 0.849 | 0.157 | −1.043 | 0.2972 |
In categorical variables (habitat, population, sex, food color and type of object), the reference category was set to zero and compared with the category shown between brackets. Variables that were retained in the minimum adequate model are indicated with an asterisk.
Poisson GLM relating pecking frequency in technical innovation as a function of habitat and a set of confounding variables.
| coefficient | S.E. | z | P | |
| Habitat (urban) | 0.954 | 0.269 | 3.55 | 0.0004* |
| Population (Newcastle) | −0.315 | 0.310 | −1.01 | 0.3103 |
| Sex (male) | −0.061 | 0.291 | −0.21 | 0.8337 |
| PC1 | −3.907 | 1.025 | −3.81 | 0.0001* |
| PC2 | 1.240 | 1.779 | 0.70 | 0.4856 |
| Motivation | 5.620 | 6.883 | 0.82 | <0.0001* |
In categorical variables (habitat, population and sex), the reference category was set to zero and compared with the category shown between brackets. Variables that were retained in the minimum adequate model are indicated with an asterisk.
Figure 3Path models (A–C) deconstructing direct and indirect effects in the relationship between technical innovation propensity (INNOV) and habitat (HABITAT) as a function of neophobia (NEOPH) and exploration (EXPLOR).
Solid lines indicate the paths that are significant at P<0.05. All models fit well to the data, as indicated by the non-significance of the Chi-square tests, yet model A performs better than the others based on its lower values of AIC and BIC and the significance of all the paths. The terms e1–e4 refer to the error terms.