Phylogenetic grouping, curvature and metabolic scaling in terrestrial invertebrates
J.F. Blumenbach Institute of Zoology and Anthropology, Berliner Str. 28, 37073 Göttingen, Germany. Ecology Letters
(Impact Factor: 10.69).
07/2011; 14(10):993-1000. DOI: 10.1111/j.1461-0248.2011.01660.x
Ecology Letters (2011) 14: 993–1000
For more than a century, the scaling of animal metabolic rates with individual body masses and environmental temperature has predominantly been described by power-law and exponential relationships respectively. Many theories have been proposed to explain these scaling relationships, but were challenged by empirically documented curvatures on double-logarithmic scales. In the present study, we present a novel data set comprising 3661 terrestrial (mainly soil) invertebrate respiration rates from 192 independent sources across a wide range in body masses, environmental temperatures and phylogenetic groups. Although our analyses documented power-law and exponential scaling with body masses and temperature, respectively, polynomial models identified curved deviations. Interestingly, complex scaling models accounting for phylogenetic groups were able to remove curvatures except for a negative curvature at the highest temperatures (>30 °C) indicating metabolic down regulation. This might indicate that the tremendous differences in invertebrate body architectures, ecology and physiology may cause severely different metabolic scaling processes.
Available from: Gregor Kalinkat
- "already thoroughly investigated (Brown et al. 2004, Savage et al. 2004, Ehnes et al. 2011, Englund et al. 2011, Dell et al. 2011, Rall et al. 2012, Fussmann et al. 2014). In the remainder of this chapter we will set our focus on the presumably most important of these rates, the feeding rate, and how the strength of feeding interactions depends on temperature (Brown et al. 2004, Englund et al. 2011, Rall et al. 2012) and structural habitat complexity (Vucic-Pestic et al. 2010a, Kalinkat et al. 2013a) and what this means for the specific interactions between insect pests and their natural enemies. "
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ABSTRACT: All biological rates depend on temperature, as they are based on biochemical reactions. Hence, the same holds for feeding rates and their functional components. From a ‘biologically relevant’ scope, feeding rates increase exponentially with temperature. Recent studies suggest that this increase of feeding rates with warming is shallower than the increase in metabolism. Theoretically, this mismatch should lead to a lower numerical response of biological control agents, presumably resulting in a higher probability of insect pest outbreaks. While depending on temperature, the more complex, non-linear nature of feeding rates further implies that they are also critically dependent on prey densities (i.e. the functional response). The fundamental elements of the functional response are the capture rate and the handling time. Basically, the capture rate determines the feeding success at low densities, whereas the handling time determines the maximum amount a predator is able to consume in a given time window. Moreover, capture rates themselves can also depend on prey density, turning a hyperbolic type II into a sigmoid type III functional response. This shift in the shape of the response is introduced by refuges for the prey, among other mechanisms. Contrasting the type II functional response, type III functional responses are well known to promote stable population dynamics and community structure. Therefore, changes in habitat complexity driven by climate change might also affect feeding interactions and insect pest control. Here, we review how climate change influences the functional responses of predator–prey and parasitoid–host pairs directly via increased temperature and indirectly via changes in habitat structure. We complement our review by exploring the potential consequences of feeding relations that are altered by climate change-induced mechanisms, through the application of model simulations of such consumer–resource population dynamics.
- "(Meehan, 2006)), growth (r in [1/s], (Savage et al., 2004)), metabolism (x in [1/s], (Ehnes et al., 2011)) attack rate (a in [m 2 /s]) and handling time (Th in [s], both calculated from (Rall et al., 2012)). The parameters scale with the body mass of the resource species (i) of the species pair considered, attack rate and handling time scale additionally with the body mass of the consumer species (j). "
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ABSTRACT: Warming and eutrophication are two of the most important global change stressors for natural ecosystems, but their interaction is poorly understood. We used a dynamic model of complex, size-structured food webs to assess interactive effects on diversity and network structure. We found antagonistic impacts: warming increases diversity in eutrophic systems and decreases it in oligotrophic systems. These effects interact with the community size structure: communities of similarly-sized species such as parasitoid-host systems are stabilized by warming and destabilized by eutrophication, whereas the diversity of size-structured predator-prey networks decreases strongly with warming, but decreases only weakly with eutrophication. Non-random extinction risks for generalists and specialists lead to higher connectance in networks without size structure and lower connectance in size-structured communities. Overall, our results unravel interactive impacts of warming and eutrophication and suggest that size structure may serve as an important proxy for predicting the community sensitivity to these global change stressors.This article is protected by copyright. All rights reserved.
Available from: Melanie Body
- "Metabolic rate has been studied across many organisms, during ontogeny within a species or over short periods of time related to behavioral activities of individuals (Nisbet et al., 2012; Maino and Kearney, 2014). Metabolic rate has been found to increase with body mass, a phenomenon named metabolic scaling that is widespread in nature (West et al., 1997; Ehnes et al., 2011). Metabolic rate is defined as the energy turnover of an organism and is usually quantified using either heat production with calorimetry or CO 2 production and O 2 consumption with respirometry (Sibly et al., 2012). "
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