globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2457
论文题名:
Physiological plasticity increases resilience of ectothermic animals to climate change
作者: Frank Seebacher
刊名: Nature Climate Change
ISSN: 1758-1085X
EISSN: 1758-7205
出版年: 2014-12-08
卷: Volume:5, 页码:Pages:61;66 (2015)
语种: 英语
英文关键词: Ecophysiology
英文摘要:

Understanding how climate change affects natural populations remains one of the greatest challenges for ecology and management of natural resources. Animals can remodel their physiology to compensate for the effects of temperature variation, and this physiological plasticity, or acclimation, can confer resilience to climate change1, 2. The current lack of a comprehensive analysis of the capacity for physiological plasticity across taxonomic groups and geographic regions, however, constrains predictions of the impacts of climate change. Here, we assembled the largest database to date to establish the current state of knowledge of physiological plasticity in ectothermic animals. We show that acclimation decreases the sensitivity to temperature and climate change of freshwater and marine animals, but less so in terrestrial animals. Animals from more stable environments have greater capacity for acclimation, and there is a significant trend showing that the capacity for thermal acclimation increases with decreasing latitude. Despite the capacity for acclimation, climate change over the past 20 years has already resulted in increased physiological rates of up to 20%, and we predict further future increases under climate change. The generality of these predictions is limited, however, because much of the world is drastically undersampled in the literature, and these undersampled regions are the areas of greatest need for future research efforts.

In theory, environmental variability represents a selection pressure that results either in thermal adaptation or in the evolution of phenotypic plasticity1, 2. The efficacy of genetic adaptation depends on the relationship between generation time and rate of climate change. Under rapid human-induced climate change, short-lived animals may adapt successfully if the change in climate is relatively slow and the direction of change is constant to permit directional selection3, 4. In most cases, however, climate change is rapid and can occur across few generations or even within generations5. Furthermore, fluctuating climates do not provide a clear signal to drive directional selection, and selection in one generation may be maladaptive in subsequent generations4. Temperature fluctuations are predicted to increase under climate change, and plastic phenotypes should therefore be favoured4.

Many individual ectotherms can remodel their physiology to reduce the extent to which physiological rates change in response to a chronic, recurring, or extemporaneous change in temperature (that is, thermal compensation via the process of thermal acclimation (in response to a single environmental variable) or acclimatization (in response to multiple environmental variables under field conditions)6). If thermal compensation were perfect, physiological rates would remain constant across environmental conditions, so that animals could maintain fitness across a broader temperature range compared to animals that show little or no plasticity7.

We collated data from the literature (1968–2012) for ectothermic animals (n = 637 measurements of 202 species) that were chronically exposed (acclimated or acclimatized) to at least two temperatures and in which physiological rates (metabolic rates, heart rates, enzyme activities and locomotor performance) were measured acutely at these two acclimation temperatures (see Supplementary Methods and Data). These data allowed us, first, to determine by how much physiological rates changed in response to an acute change in temperature (Fig. 1). ‘Acute thermal sensitivity’ was defined as the change in a physiological rate function in response to a rapid change in environmental temperature in the absence of thermal acclimation—that is, within the acclimation set temperatures (see Fig. 1 for details). Second, we calculated by how much a physiological rate changes when an animal was allowed to acclimate to different thermal conditions—that is, across chronic acclimation conditions (Fig. 1; see ref. 8). This ‘post-acclimation thermal sensitivity’ thus provides the most physiologically realistic estimate of how sensitive ectothermic animals are to a temperature change that lasts longer than several days to weeks.

Figure 1: Generalized thermal responses of physiological rates to a temperature change.
Generalized thermal responses of physiological rates to a temperature change.

The schematic shows that acclimation to chronic warm conditions (red line) causes a shift to the right in reaction norm from cold conditions (blue line). The change in physiological rates with an increase in temperature is described by the Q10 effect, which is derived from van’t Hoff’s equation. Q10 values of 1 indicate no change in rate following a temperature change, Q10 < 1 indicates a decrease in rates with an increase in temperature, and Q10 > 1 shows an increase in rates with increasing temperature. We calculated Q10 values for acute thermal sensitivities from data for animals kept at single constant long-term (acclimation) conditions in which responses were measured at two different acute temperatures. Q10 values for post-acclimation thermal sensitivities were calculated across acclimation conditions; that is, as the change in rate of a physiological process between a cold acclimated animal measured at the same cold test temperature as acclimation temperature, and a warm acclimated animal measured at the same warm test temperature (broken line).

We performed a Web of Science (Thompson Reuters) search in January 2013 and from the resulting 4,000+ papers we analysed those that actually performed thermal acclimation treatments, or investigated acclimatization in ectotherms. We extracted data from those studies where ectothermic animals were exposed to at least two temperatures for at least one week. We included only those studies that, following acclimation, measured physiological rates at acute test temperatures that at least matched the acclimation temperatures. Hence, we extracted data from 205 publications (1968–2012), which yielded 637 measurements from 202 species. The physiological rates measured were burst and sustained locomotion, metabolic rates (standard, resting, routine and maximal), heart rates and enzyme activities. We recorded rates from each acclimation group at each test temperature, as well as taxonomic information, the geographical origin of the study species, and whether the animals were terrestrial or inhabited freshwater or marine environments (see Supplementary Methods for more detail).

Data analyses were conducted in R (ref. 21) using lme4 v0.999999-0 (ref. 22), with linear mixed models fit using maximum likelihood. The non-independence of data for related species was incorporated by including nested random effects for the taxonomic levels of phylum, class, family and genus. The non-independence of data representing multiple measurements of the same species was accounted for by including species as a random effect (nested within the taxonomic levels). Outliers were excluded before analysis, and were defined as chronic slopes greater than 0.2 (Q10 = 7.4) and acute slopes less than −0.05 (Q10 = 0.007); this resulted in the exclusion of six and seven data points, respectively. Note also that in >90% of studies in our data set (Supplementary Data) physiological rates of animals from each acclimation treatment were measured either at a single acute test temperature which coincided with acclimation temperature, or at two acute test temperatures per treatment which coincided with the two acclimation conditions. In the former cases, it was not possible to calculate acute responses within acclimation treatments.

We further subdivided the data by trait for post-acclimation responses. Of the 32 different traits in the full data set (Supplementary Data) only 12 traits were represented by six or more measurements across all species; of these 12, only 10 traits were represented by six or more measurements for either freshwater, terrestrial or marine species. Four of these (metabolic rate, lactate dehydrogenase activity, citrate synthase activity, cytochrome c oxidase activity) are represented across species for at least two habitat types. Examination of the post-acclimation thermal sensitivity of these traits revealed that the enzyme activities all have broadly similar thermal sensitivities, which are lower than that of metabolic rate, so we have pooled their data. To estimate the relationship between climate variables and the post-acclimation thermal sensitivities of metabolic rate and enzyme activities, we constructed candidate model sets to explain variation in the post-acclimation thermal sensitivities (see Supplementary Method and Results).

Climate data for species from terrestrial and freshwater habitats were extracted in R (ref. 22) from WorldClim climatologies23 and long-term monthly climate grids24 using raster v2.1-16 (ref. 25) and rgdal v0.8-6 (ref. 26). Note that we used air temperature variation for predictions of freshwater environments as well because mean water temperatures track mean air temperatures, and variations in temperatures are proportional in air and water27. For marine environments, data were extracted from long-term monthly sea surface temperature grids28. To avoid the problems associated with data dredging and model overfitting (see, for example, ref. 29), we selected, a priori, a set of three predictor variables that captured spatial and temporal variation in biologically important abiotic variables at the scales relevant to acclimation (weeks to years). From the long-term records, we calculated the following three climate variables: mean trend in ambient temperature at monthly resolution from 1960 to 1990; temperature standard deviation of monthly mean temperature from 1900 to 1990; 12-month autocorrelation of temperature from 1900 to 1990; higher values mean that variation in temperature is predictable from year to year—that is, climate is predictable—and lower values indicate a less predictable climate. We verified whether temporal resolution of the data and acclimation duration influenced our conclusions, which was not the case (see Supplementary Methods and Results for details).

  1. Chevin, L-M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: Towards a predictive theory. PLoS Biol. 8, e1000357 (2010).
  2. Hoffmann, A. A. Sgrò C. M. Climate change and evolutionary adaptation. Nature 470, 479485 (2011).
  3. Lande, R. Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation. J. Evol. Biol. 22, 14351446 (2009).
  4. Kawecki, T. J. The evolution of genetic canalization under fluctuating selection. Evolution 54, 112 (2000).
  5. Alley, R. B. et al. Abrupt climate change. Science 299, 20052010 (2003).
  6. Guderley, H. Functional significance of metabolic responses to thermal acclimation in fish muscle. Am. J. Physiol. 259, R245R252 (1990).
  7. Wilson, R. & Franklin, C. E. Testing the beneficial acclimation hypothesis. Trends Ecol. Evol. 17, 6670 (2002).
  8. Kingsolver, J. & Huey, R. Evolutionary analyses of morphological and physiological plasticity in thermally variable environments. Am. Zool. 38, 545560 (1998).
  9. White, C. R., Frappell, P. B. & Chown, S. L. An information-theoretic approach to evaluating the size and temperature dependence of metabolic rate. Proc. R. Soc. B 279, 36163621 (2012).
http://www.nature.com/nclimate/journal/v5/n1/full/nclimate2457.html
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/4913
Appears in Collections:气候变化事实与影响
科学计划与规划
气候变化与战略

Files in This Item:
File Name/ File Size Content Type Version Access License
nclimate2457.pdf(891KB)期刊论文作者接受稿开放获取View Download

Recommended Citation:
Frank Seebacher. Physiological plasticity increases resilience of ectothermic animals to climate change[J]. Nature Climate Change,2014-12-08,Volume:5:Pages:61;66 (2015).
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Frank Seebacher]'s Articles
百度学术
Similar articles in Baidu Scholar
[Frank Seebacher]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Frank Seebacher]‘s Articles
Related Copyright Policies
Null
收藏/分享
文件名: nclimate2457.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.