英文摘要: | One feature of global change is that biota must respond not to single, but to multiple environmental drivers. By growing a model photosynthetic microbe in environments containing between one and eight different drivers, including changes in CO2, temperature, and pH, in different combinations, we show that the number as well as the identities of drivers explain shifts in population growth rates. This is because the biotic response to multiple environmental drivers depends on the response to the single dominant driver, and the chance of a driver of large effect being present increases with the number of drivers. Interactions between drivers slightly counteract the expected drop in growth. Our results demonstrate that population growth declines in a predictable way with the number of environmental drivers, and provide an empirically supported model for scaling up from studies on organismal responses to single drivers to predict responses to large numbers of environmental drivers.
A major challenge facing freshwater and marine biologists is to quantify how aquatic biota will respond to our changing climate. One of the hallmarks of global change is that it is complex; changes in temperature, pH, light levels, carbon dioxide and oxygen concentrations, nutrient availability, salinity and other environmental variables can occur together1, 2. Predicting the action of multiple environmental drivers (MEDs) on population growth is required for understanding how aquatic biota, at all levels from individual genotypes to communities, respond to global change3. Studies in freshwater3, 4 and marine systems5, 6 have historically focused on understanding organismal responses to key environmental drivers alone, such as changing temperature, CO2 levels, or light levels, or investigated MEDs by using pairs or trios of drivers1. This has shown that interactions between the effects of environmental drivers vary with the drivers and the organisms being tested6, but use a small number of environmental drivers relative to the number of drivers in most natural environments2. This leaves open the possibility that when the number of environmental drivers is larger, the effects of interactions between individual drivers may become less important in determining overall organismal responses. The goal of our study is to determine if knowing the interactions between specific environmental drivers at the organismal level is necessary when the number of environmental drivers is large, or whether patterns emerge that allow us to predict organismal responses without knowing particular driver interactions. Studies on MEDs until now are mainly concerned with understanding interactions between the effects of individual drivers (see ref. 3 for definitions). Driver effects can either be additive, where the response to MEDs is equal to the sum of their individual effects, or multiplicative, where the response exceeds the sum of their individual effects. Interactions that are additive or multiplicative can be further synergistic (having a positive feedback) or antagonistic (having a negative feedback). Antagonistic interactions can thus lead to outcomes where responses to MEDs are less than the sum or product of their individual effects. These definitions must be contextualized in terms of the level of organization they affect, such as cellular processes or community composition. Driver interactions can be studied mechanistically, where the interactions are between drivers themselves (for example, the chemistry that links pH and CO2 levels), or be outcome-based and describe effects on organisms. Here, we use an outcome-based definition of drivers and driver interactions. We focus on the effects of drivers and interactions as population-level organismal responses. Building an outcome-based prediction of biotic responses to MEDs by understanding specific interactions between key drivers requires that key drivers be identified and the interactions between them be measured. This approach is fruitful when the number of drivers is small. For example, high CO2 and low pH enhance the detrimental effects of ultraviolet irradiation on a key pelagic calcifier, Emiliania huxleyi7, and although many diatom assemblages do not respond to CO2 enrichment alone, CO2 and high light levels interact synergistically to reduce their growth rates8. These experiments can investigate the interactions between drivers, but are difficult to scale up, because measuring interactions between all drivers becomes impossible as the number of drivers increases. This is problematic, because these and similar studies on natural phytoplankton assemblages4, E. huxleyi7, 9, 10, 11, Phaeodactylum tricornutum12, and the freshwater alga Chlamydomonas reinhardtii13, suggest that interactions among drivers are not easily predicted even if they can be explained once observed. If this is the case, then one cannot use studies of pairs or trios of drivers to predict responses to those same pairs when many other drivers are also present (for example, if pH, CO2 and ultraviolet levels change alongside temperature, oxygen levels, and micronutrient levels). One way to reduce the size of experiments is to measure responses to groups of MEDs using combinations of drivers that are likely to change in concert2. This requires knowing how drivers group, and how these groups change on relevant geographic and temporal scales. Alternatively, it may be possible to make reasonable outcome-based predictions of responses to MEDs based on the number of environmental drivers. Our general reasoning can be explained using environmental tolerance curves (Fig. 1), which usually show the relationship between some aspect of organismal function (for example, growth) and an environmental value (for example, temperature). Here, we consider a tolerance curve showing the relationship between organismal function and the total environment experienced by the organism in a multidriver environment. We assume that organismal function is initially somewhere on the plateau. Changes to one or more randomly chosen drivers will affect organismal function in some unknown way, either as a direct result of one driver, or as a result of interactions among drivers. If a subset of drivers or their interactions have large enough effects to push organismal function off the plateau, but the effects of most drivers are unlikely to be severe (as organisms are generally tolerant of some environmental variability), the chances of at least one of these large-effect drivers occurring grows as the number of drivers and interactions increases. This is analogous to altering organismal function through genetic mutations, where most mutations have small effects on organismal function, but mutations of large effect will eventually occur if enough mutations are sampled14. Here, we instead approach the idea that organismal phenotype is a result of interactions between genotype and environment using environmental ‘mutations’ rather than genetic ones.
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