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Keywords:

  • canopy photosynthesis;
  • carbon balance of leaves;
  • global change research;
  • leaf senescence;
  • optimisation models

Models of carbon gain

  1. Top of page
  2. Models of carbon gain
  3. Optimization models: a short history
  4. When do plants drop their leaves?
  5. Biological mechanisms
  6. Future prospects
  7. References

Canopy photosynthesis of terrestrial vegetation constitutes a major part of total global carbon uptake, and modelling this flux becomes increasingly important in global change research (Dewar et al., 2009). The C gain of vegetation is determined by its structural and physiological characteristics and includes, in essence, two features: the size of the canopy in terms of leaf area per unit soil area (LAI); and the photosynthetic rates of different canopy layers (Saeki, 1960). The latter is a declining function of canopy depth because leaves shade each other and because nitrogen (N), a key component of the photosynthetic machinery, is re-allocated to developing leaves higher up in the canopy. The C balance of leaves, shortly before being dropped from plants, provides an interesting test of vegetation C-gain models that combine both characteristics. In this issue of New Phytologist, Reich et al. (pp. 153–166) elegantly quantify the effects of leaf age and shading on leaf photosynthesis for 10 evergreen shrubs, and propose that plants drop their leaves when the daily net C gain (CLEAF(24)) of these leaves is no longer sufficient to provide the additional energy for the costs of the stem and root tissue that supports them.

‘To make optimality models applicable to global change research, critical tests are required regarding their ability to predict vegetation processes and the realism underlying their optimization criterion.’

So far, the application of vegetation models in global change research has been hampered by their heavy reliance on a wide range of parameters (Dewar et al., 2009). Optimality models (OMs) avoid this problem. They define a measure of plant performance, typically photosynthesis or growth, that needs to be maximized with respect to one or more traits (e.g. LAI, leaf longevity, leaf N distribution) under given constraints (e.g. light or N availability). To make OMs applicable to global change research, critical tests are required regarding their ability to predict vegetation processes and the realism underlying their optimization criterion.

Optimization models: a short history

  1. Top of page
  2. Models of carbon gain
  3. Optimization models: a short history
  4. When do plants drop their leaves?
  5. Biological mechanisms
  6. Future prospects
  7. References

Here, we review briefly various OMs and discuss their predictions of the C balance of leaves when dropped from the plant (CLEAF(24), Table 1). Saeki (1960) derived that if only light is limiting canopy growth, the LAI of a vegetation stand is optimized for maximum canopy photosynthesis if the lowest leaf has a CLEAF(24) value of zero. Interestingly, Ackerly (1999), using a different optimization criterion where leaf production, rather than C gain, is maximized, obtained the same result. Both studies considered photosynthesis to be only light-limited. However, in many ecosystems, leaf area growth and canopy photosynthesis are strongly limited by nutrients or water (Anten et al., 1995). Several studies have analysed optimal LAI, N distribution and leaf longevity considering direct restrictions imposed by limited N (see Hikosaka, 2005). But there are important deviations between predictions by these models and measured values, with LAI and leaf longevity consistently being underestimated.

Table 1.  An overview of different optimization models, including their optimization criterion (simple: the trait is optimal when the whole-stand performance is maximized; and game: the trait is optimal when the performance of individuals in the presence of neighbors is maximized), static or dynamic (referring to whether or not leaf turnover is dynamically incorporated), the constraint, performance measure, the trait to be optimized and, finally, the prediction of the model regarding the daily (24 h) carbon (C) balance of leaves that are about to be dropped from the plant (CLEAF(24))
ModelCriterionStatic or dynamicConstraintPerformance measureTrait to be optimizedPredicted CLEAF(24) value
  1. LAI, leaf area per unit soil area; N-, decreasing function of N availability.

Saeki (1960)SimpleStaticLightPhotosynthesisLAICLEAF(24) = 0
Kikuzawa (1991)SimpleStaticLeaf numberPhotosynthesisLeaf longevityCLEAF(24) > 0
Anten et al. (1995)SimpleStaticNitrogenPhotosynthesisLAICLEAF(24) </= 0; N-
Ackerly (1999)SimpleStaticLightLeaf productionLeaf numberCLEAF(24) = 0
Anten (2002)GameStaticNitrogenPhotosynthesisLAICLEAF(24) </= 0; N-
Hikosaka (2003)SimpleDynamicNitrogenPhotosynthesisLAICLEAF(24) </= 0; N-

Where do these inconsistencies come from? First, the current OMs consider plant canopies to be static, ignoring the fact that a given LAI arises from the integrated balance between leaf production and leaf loss. When a leaf is dropped, around half of the resources cannot be resorbed and are lost (see Hikosaka, 2005). Second, OMs in canopy research assume a trait to be optimal if photosynthesis at the vegetation level is maximized. Implicitly, these models assume that all individuals in a vegetation stand behave similarly. However, game theory shows that this is not necessarily the case. Models that took into account the first (Hikosaka, 2003) or the second (Anten, 2002) consideration predicted the LAI to be larger and the N-distribution less skewed than the current OMs, more closely resembling real canopies.

Interestingly, all the models based on N limitation tend to predict the CLEAF(24) > 0 at relatively low N availability, but the CLEAF(24) approaching zero if N availability is high (Table 1), although exact predictions differ between models. The rationale for this is that plants which drop their lowest most-shaded leaves can re-allocate the N that becomes available to produce leaves in higher, more illuminated parts of the canopy.

When do plants drop their leaves?

  1. Top of page
  2. Models of carbon gain
  3. Optimization models: a short history
  4. When do plants drop their leaves?
  5. Biological mechanisms
  6. Future prospects
  7. References

As noted, Reich et al. found (for 10 evergreen shrubs) that leaves about to be dropped from plants had a positive CLEAF(24) and suggested that this value should equal the C cost of stem and root tissue needed to support a leaf. How generally applicable are these results? In a deciduous temperate forest, the most-shaded leaves of a range of tree species also have clear positive CLEAF(24) values (Poorter et al., 2006). By contrast, Oikawa et al. (2006) found, in experimental stands of the annual Xanthium canadense, positive CLEAF(24) at low N availability but CLEAF(24) = 0 at high N availability, a result that seems consistent with predictions from N-based OMs. More vegetation types need to be studied in this respect. There seem to be two basic arguments in favour of plants dropping leaves with a positive CLEAF(24):

  • • 
    they can no longer ‘pay’ for their share of the total respiratory costs of stems and roots (as suggested by Reich et al.); and/or
  • • 
    the leaf contains resources (e.g. N) that plants can employ more efficiently in the more illuminated parts of the canopy (or in other functions, e.g. reproduction).

It should be noted that these two explanations are not mutually exclusive.

Biological mechanisms

  1. Top of page
  2. Models of carbon gain
  3. Optimization models: a short history
  4. When do plants drop their leaves?
  5. Biological mechanisms
  6. Future prospects
  7. References

Any proposed optimal regulation of leaf dynamics in plants is only possible if biological mechanisms exist by which this can be achieved. This raises the following question: ‘by what mechanism(s) is leaf senescence regulated?’ An interesting finding in this respect is that shading of whole plants tends to delay leaf senescence, whereas shading of some leaves, but not of others, accelerates this process (Hikosaka, 2005). If the C balance of the leaf were the only factor determining leaf death, senescence should occur equally as fast under both types of shading. This assertion also implies that the physiological mechanism regulating leaf senescence should be associated with the local sugar balance of a leaf. Indeed, sugar starvation has been proposed as a mechanism inducing senescence, but no clear evidence for this exists (Boonman et al., 2009). Most other proposed mechanisms appear to operate, at least indirectly, in a systemic manner. For example, there is strong evidence for a role of cytokinins transported through the transpiration stream (Pons & Bergkotte, 1996), which is induced by differences in transpiration of different leaves, rather than by the local C balance of a leaf. Interestingly, a number of mutant and transgenic Arabidopsis thaliana plants, disturbed in their signal transduction mediated by cytokinins, sugars or other mechanisms, were subjected to a light gradient across their leaves and exhibited senescence patterns similar to those of wild-type plants (Boonman et al., 2009). This indicates that there are several redundant pathways, acting both locally and systemically, which regulate leaf senescence in plant canopies, and supports the idea that both factors – local C balance of leaves and source–sink dynamics between leaves – may determine leaf death.

Future prospects

  1. Top of page
  2. Models of carbon gain
  3. Optimization models: a short history
  4. When do plants drop their leaves?
  5. Biological mechanisms
  6. Future prospects
  7. References

Optimality theory as a scaling principle in fundamental and global change ecology has a great potential. To exploit its possibilities fully, more comprehensive models are required that combine, for example, game theory with dynamic leaf turnover, and the assumed optimization criteria need to be tested experimentally (e.g. Boonman et al., 2006). The C gain of leaves, just before being dropped, provides an integrated test of how such models would predict canopy size and the distribution of photosynthesis within the canopy. The work of Reich et al. is one of the necessary steps that can help us in building these models.

References

  1. Top of page
  2. Models of carbon gain
  3. Optimization models: a short history
  4. When do plants drop their leaves?
  5. Biological mechanisms
  6. Future prospects
  7. References