10.5061/DRYAD.5QK4S
Ma, Chun-Sen
Rice University
Wang, Lin
Chinese Academy of Agricultural Sciences
Zhang, Wei
Chinese Academy of Agricultural Sciences
Rudolf, Volker H. W.
Rice University
Data from: Resolving biological impacts of multiple heat waves:
interaction of hot and recovery days
Dryad
dataset
2017
Sitobion avenae
aphid
2017-11-29T14:39:25Z
2017-11-29T14:39:25Z
en
https://doi.org/10.1111/oik.04699
288407 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Heat waves are increasing with global warming and have more dramatic
biological effects on organisms in natural and agricultural ecosystems
than mean temperature increase. However, predicting the impact of future
heat waves on organisms and ecosystems is challenging because we still
have a limited understanding of how the different components that
characterize heat waves interact. Here we take an experimental approach to
examine the individual and combine consequences of two important features
that characterize heat waves: duration of successive hot days and recovery
days between two hot spells. Specifically we exposed individuals of a
global agricultural pest, the aphid Sitobion avenae to different heat wave
scenarios by factorially manipulating the number of extreme hot days vs.
normal days and altered which period individuals experienced first in
their life cycle. We found that effects of heat waves were driven by a
delicate balance of damage during hot periods vs. repair during normal
periods. Increasing the duration of hot days in heat waves had a negative
effect on various demographic rates and life-time fitness of individuals,
but magnitude of this effect was typically contingent on the temporal
clustering of hot periods. Importantly, this interaction effect indicates
that changes in the temporal distribution of extreme hot vs. normal days
can strongly alter the performance of organisms and dynamics of
populations even when the total number of hot days during a given period
remains unchanged. Together, these results emphasize the importance of
accounting for the temporal distribution and quantitative patterns of
extreme temperature events for predicting their consequences of natural
and agricultural ecosystems.
Development traitDevelopment.csvReproduction traitReproduction.csvother
traitsTraits.csv