Literature DB >> 21466552

An empirical link between the spectral colour of climate and the spectral colour of field populations in the context of climate change.

Bernardo García-Carreras1, Daniel C Reuman.   

Abstract

1. The spectral colour of population dynamics and its causes have attracted much interest. The spectral colour of a time series can be determined from its power spectrum, which shows what proportion of the total variance in the time series occurs at each frequency. A time series with a red spectrum (a negative spectral exponent) is dominated by low-frequency oscillations, and a time series with a blue spectrum (a positive spectral exponent) is dominated by high-frequency oscillations. 2. Both climate variables and population time series are characterised by red spectra, suggesting that a population's environment might be partly responsible for its spectral colour. Laboratory experiments and models have been used to investigate this potential link. However, no study using field data has directly tested whether populations in redder environments are redder. 3. This study uses the Global Population Dynamics Database together with climate data to test for this effect. We found that the spectral exponent of mean summer temperatures correlates positively and significantly with population spectral exponent. 4. We also found that over the last century, temperature climate variables on most continents have become bluer. 5. Although population time series are not long or abundant enough to judge directly whether their spectral colours are changing, our two results taken together suggest that population spectral colour may be affected by the changing spectral colour of climate variables. Population spectral colour has been linked to extinction; we discuss the potential implications of our results for extinction probability.
© 2011 The Authors. Journal of Animal Ecology © 2011 British Ecological Society.

Mesh:

Year:  2011        PMID: 21466552     DOI: 10.1111/j.1365-2656.2011.01833.x

Source DB:  PubMed          Journal:  J Anim Ecol        ISSN: 0021-8790            Impact factor:   5.091


  11 in total

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Journal:  Proc Biol Sci       Date:  2015-08-07       Impact factor: 5.349

2.  The benefits of maternal effects in novel and in stable environments.

Authors:  Rebecca B Hoyle; Thomas H G Ezard
Journal:  J R Soc Interface       Date:  2012-05-09       Impact factor: 4.118

3.  Stochastic environmental fluctuations drive epidemiology in experimental host-parasite metapopulations.

Authors:  Alison B Duncan; Andrew Gonzalez; Oliver Kaltz
Journal:  Proc Biol Sci       Date:  2013-08-21       Impact factor: 5.349

4.  An updated perspective on the role of environmental autocorrelation in animal populations.

Authors:  Jake M Ferguson; Felipe Carvalho; Oscar Murillo-García; Mark L Taper; José M Ponciano
Journal:  Theor Ecol       Date:  2015-08-30       Impact factor: 1.432

5.  Increased spatial and temporal autocorrelation of temperature under climate change.

Authors:  Grace J Di Cecco; Tarik C Gouhier
Journal:  Sci Rep       Date:  2018-10-04       Impact factor: 4.379

6.  Phenotypic memory drives population growth and extinction risk in a noisy environment.

Authors:  Marie Rescan; Daphné Grulois; Enrique Ortega-Aboud; Luis-Miguel Chevin
Journal:  Nat Ecol Evol       Date:  2020-01-27       Impact factor: 15.460

7.  Adaptive and nonadaptive plasticity in changing environments: Implications for sexual species with different life history strategies.

Authors:  Daniel Romero-Mujalli; Markus Rochow; Sandra Kahl; Sofia Paraskevopoulou; Remco Folkertsma; Florian Jeltsch; Ralph Tiedemann
Journal:  Ecol Evol       Date:  2021-04-04       Impact factor: 2.912

8.  How does variation in the environment and individual cognition explain the existence of consistent behavioral differences?

Authors:  Petri T Niemelä; Anssi Vainikka; Jukka T Forsman; Olli J Loukola; Raine Kortet
Journal:  Ecol Evol       Date:  2012-12-21       Impact factor: 2.912

9.  Are changes in the mean or variability of climate signals more important for long-term stochastic growth rate?

Authors:  Bernardo García-Carreras; Daniel C Reuman
Journal:  PLoS One       Date:  2013-05-14       Impact factor: 3.240

10.  Trait-based predictions and responses from laboratory mite populations to harvesting in stochastic environments.

Authors:  Isabel M Smallegange; Hedwig M Ens
Journal:  J Anim Ecol       Date:  2018-07       Impact factor: 5.091

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