**2.6 Pigments and light harvesting by red algae endosymbiont and cryptophytes**

Like all phytoplankton, cryptophytes have chlorophyll *a* as the primary lightharvesting pigment [57], and other accessory pigments ∝-carotene, alloxanthin, chlorophyll c2, and the PBPs for the capture of low light intensity in wavelengths not well absorbed by chlorophyll *a* (500–650 nm) [17, 58, 59].

The red algae and cyanobacteria have a color that depends on the predominance of PBPs, the orange-red PER or the blue PCY, which are in several hundred and are highly organized in supramolecular complexes, the phycobilisomes (PBS) (**Figure 2**). The PBS are their main light-harvesting antennae and cover the stromal surface of thylakoids [60]; the PBSs have mobility that lets them distribute the absorbed energy between photosystems (PSI and PSII) [61]. PBPs are composed of two kinds of α and β protein subunits and are more stable in trimer (3α + 3β) or hexamer (6α + 6β). The protein part of apoprotein is covalently bonded to a chromophore or phycobilin [32, 60, 62], these chomoproteins are united to colorless linker polypeptides [63], and constitute the light-harvesting antenna for transferring energy to chlorophyll *a* to PSII and possibly to PSI [32, 60, 62].

Like red algae, cryptophytes have PBPs pigments, but they do not have PBSs, and, they only produce one kind of PBP pigment per cell (Cr-PE or Cr-PC), packed into the thylakoid lumen [17, 64] without any arrangement [32, 65, 66], it gives the cell a red or blue color [9, 18, 19], but cells possess other accessory pigments, allowing them to display a great diversity of colors [67]. The ratio of Chlorophyll a: PBPs of cryptophytes can be several times higher than that of non-PBPs pigments [18]. The endosymbiosis provided the cryptophytes with new machinery that allowed diversification of light capture [65]; the PBPs are an auxiliary or second light-harvesting system, allowing them to occupy light spectra niches for more efficient light capture [65].

The PBPs of the cryptophytes are composed of two α and two β protein subunits and four linear tetrapyrrole chromophores or phycobilins covalently bonded by one or two thioether bonds to specific cysteine residues on the protein [68]; these PBPs provide unique spectral properties of absorption and emission fluorescence (**Table 1**).

The number and location of phycobilins within the protein are the primary factors that determine the visible absorption, the fluorescence spectrum, and the energy transfer pathway for any given PBP [66]. The complex of chromophores and protein subunits is a complete light-capturing unit; the α subunits of PBPs are encoded in the nuclear genome (derived from the ancestral host), whereas the β subunits are encoded in the plastid as in red algae, so the PBPs are unique chromoprotein

*Adapted from table VI [67, 68]. The PBPs are named, including the wavelengths of maximum absorption [9, 17].N.R. = no reported.*

#### **Table 1.**

*Different classes of phycobiliprotein (PBPs) in cryptophytes, spectral ranges of the main absorption and fluorescence emission maxima at visible wavelengths (nm) (Cr, cryptophytes; PE, phycoerythrin; PC, phycocyanin); and the actual Cryptomonad genera.*

complexes that originated from secondary endosymbiosis [17, 65]. Another function of PBPs in cryptophytes is to help them with photoacclimation; this process involves changes in PBPs concentrations and shifts in the PBPs absorbance peaks when they are grown under red, blue, or green light [58]. The anterior means the photosynthetic system of cryptophytes is very different from other algae [75, 76], the location of PBPs in thylakoid lumen, the presence of dimeric PBPs forms, and the principal connection to PSII, but the precise mechanism remains to be discovered.

Another structure in the chloroplast is the pyrenoid, where the enzyme RUBISCO responsibly for CO2 fixing is located, there is one pyrenoid per plastid, and its position is an identifying characteristic of each species (**Figure 1**) [31].


7. Conclusions

Willemet) and buffelgrass (Pennisetum ciliare (L.) Link) in Oklahoma, Texas, and Mexico [18]. To evaluate the ability of the model to simulate introduced or improved grasses, we tested coastal bermudagrass (Cynodon dactylon (L.) Pers.) and bahiagrass (Paspalum notatum Flügge var. saurae Parodi) at several sites in Texas [19]. Western grasses in low-rainfall sites in Montana were simulated using parameters derived for some common native grasses there [20]. The cool-season forage "tall fescue" was simulated at several sites in several states where this grass is commonly grown [14]. In addition, creosote bush (Larrea tridentata [DC.] Cov.) parameters were derived the and model testing for its ability to describe competition of this woody species

Overall, the ALMANAC model predicted forage yields with reasonable accuracy, and hence when fully calibrated, the model can be used as an effective management tool to evaluate management practices that maximize forage yields, optimize inputs, and minimize negative

The ALMANAC model uses the best plant growth modeling functions currently developed. Often, knowledge gaps force model developers to use placeholder functions with the hope that future research will enable development of improved, more realistic functions. Some areas for beneficial future research include nutrient and carbohydrate cycling, forage regrowth follow-

The simulated cycling of nutrients in the soil and between the roots to the shoots for perennials needs to be critically investigated for this model. As forages mature and leaves senesce during the fall and winter, often nutrients and carbohydrates are translocated back into the root system, to be used for regrowth the following spring. Grazing may also trigger plants to allocate more carbohydrate storage in roots to survive grazing pressures. Functions describing these processes need to be better developed and incorporated into the ALMANAC model in

Likewise, the regrowth of forages following hay cutting or grazing within the growing season, needs to be more extensively tested. The functions currently in ALMANAC appear to function reasonably. However, as more extensive data are available for testing the model, improve-

The response of forages to applied nutrients often is highly dependent on what is already in the soil. This includes nutrients readily available and those coming from transformations within the soil during the growing season. Very often publications report a nutrient response of a forage without adequately describing initial soil conditions. If adequate nutrients are already present in the soil, the response of the forage to applied nutrients can be much dampened. Likewise, if the soil is initially very nutrient poor, the forage may show a large response to applied nutrients. An extensive testing of the model with data having good values

6. Knowledge gaps and areas for future improvement as a guide for

with forages in arid sites in western Texas [34].

ing haying, nutrient response functions, and legacy effects.

environmental outcomes.

48 Forage Groups

additional research

the future.

ments likely will be made.

for initial soil nutrients will be valuable.

In this chapter, we described the ALMANAC model, including the process simulated, how to derive plant parameters for additional forage species, and how to validate using measured field data. Because of its accurate simulation of plant production, the water balance, and the nutrient balance, the model is capable of simulating a wide variety of environmental and management impacts on forage production, soil health, and conservation concerns, including nutrient and sediment losses. The model will be a useful and valuable tool for forage management in pastures and rangelands in a wide range of conditions.