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7 December 2016

Revisiting the fear of snakes in children: the role of aposematic signalling [Scientific Reports]

Keywords : evolution, human behaviour

Why humans fear snakes is an old, yet unresolved debate. Its innate origin from evolutionary causes is debated against the powerful influence early experience, culture, media and religion may have on people’s aversion to snakes. Here we show that the aversion to snakes in human beings may have been mistaken for an aversion to aposematic signals that are commonly displayed by snakes. A total of 635 children were asked to rate single item images as “nice” or “mean”. Snakes, pets and smiley emoticon items were not rated as “mean” unless they displayed subtle aposematic signals in the form of triangular (rather than round) shapes.(...)

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6 December 2016

Learning by Association in Plants [Scientific Reports]

Keywords : behavioural ecology, evolutionary ecology

In complex and ever-changing environments, resources such as food are often scarce and unevenly distributed in space and time. Therefore, utilizing external cues to locate and remember high-quality sources allows more efficient foraging, thus increasing chances for survival. Associations between environmental cues and food are readily formed because of the tangible benefits they confer. While examples of the key role they play in shaping foraging behaviours are widespread in the animal world, the possibility that plants are also able to acquire learned associations to guide their foraging behaviour has never been demonstrated.(...)

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5 December 2016

A Novel Large-Scale Temperature Dominated Model for Predicting the End of the Growing Season [Plos One]

Subject Areas : leaves, Northern Hemisphere, forests, grasslands, biosphere, shrubs, trees, seasons

Vegetation phenology regulates many ecosystem processes and is an indicator of the biological responses to climate change. It is important to model the timing of leaf senescence accurately, since the canopy duration and carbon assimilation are strongly determined by the timings of leaf senescence. However, the existing phenology models are unlikely to accurately predict the end of the growing season (EGS) on large scales, resulting in the misrepresentation of the seasonality and interannual variability of biosphere–atmosphere feedbacks and interactions in coupled global climate models. In this paper, we presented a novel large-scale temperature dominated model integrated with the physiological adaptation of plants to the local temperature to assess the spatial pattern and interannual variability of the EGS.(...)

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5 December 2016

Explaining global-scale diversification patterns in actinopterygian fishes [Journal of Biogeography]

Pablo A. Tedesco

Factors that isolate populations and reduce gene flow are considered key drivers of speciation and possibly diversification. Here we analyse the diversification rates of nearly 80% of the actinopterygian fish families in relation to biological traits and habitat factors associated with isolation and fragmentation levels.(...)

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4 December 2016

Land-use intensification causes multitrophic homogenization of grassland communities [Nature]

Land-use intensification is a major driver of biodiversity loss1, 2. Alongside reductions in local species diversity, biotic homogenization at larger spatial scales is of great concern for conservation. Biotic homogenization means a decrease in β-diversity (the compositional dissimilarity between sites). Most studies have investigated losses in local (α)-diversity1, 3 and neglected biodiversity loss at larger spatial scales. Studies addressing β-diversity have focused on single or a few organism groups (for example, ref. 4), and it is thus unknown whether land-use intensification homogenizes communities at different trophic levels, above- and belowground. Here we show that even moderate increases in local land-use intensity (LUI) cause biotic homogenization across microbial, plant and animal groups, both above- and belowground, and that this is largely independent of changes in α-diversity. We analysed a unique grassland biodiversity dataset, with abundances of more than 4,000 species belonging to 12 trophic groups. (...)

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