Large-Scale Variation in Photosynthesis

Learning Outcome

• Ability to describe variation in photosynthesis across very large spatial scales (global) and long temporal scales (seasonal to evolutionary).
• Specific foci:
  • Seasonal/global patterns (satellite NDVI & atmospheric $CO_2$ data).
  • Environmental controls on capacity ($V<em>{Cmax}$, $J</em>{max}$) vs actual rates.
  • Nutrient-driven variation (leaf $N$ & $P$; allocation to Rubisco).
  • Differences among plant functional/phylogenetic groups.
  • Genetic variation within a single species (crop breeding examples).

Global Seasonal Variation

NASA NDVI video ("Earth breathing" metaphor)

• Dataset: Normalized Difference Vegetation Index (NDVI) = relative greenness / photosynthetic activity of land.
• Visualization details:
  • Globe spins slowly while seasons advance rapidly.
  • Northern Hemisphere (N.H.): pronounced switch from white (snow, low NDVI) in winter ➜ green (high NDVI) in summer.
  • African continent: Latitudinal migration of peak NDVI follows Inter-Tropical Convergence Zone rains—green band shifts pole-ward in local summer and equator-ward in local winter.
  • Satellite map biased to N.H.; NZ hardly visible, Australia faint.

Combined NDVI + Atmospheric $CO_2$ video

• Display: NDVI (green/white) overlaid by semi-transparent orange/red clouds (= $CO_2$ column concentration).
• Flattened cylindrical projection shows Australia + NZ.
• Seasonal signal in $CO_2$:
  • Late N.H. winter / early spring (Jan–Mar): land warms, respiration releases $CO_2$; deciduous canopies not yet deployed ⇒ atmospheric build-up (deep orange clouds).
  • Spring➜Summer: Leaf-out / peak greenness; photosynthesis draws down $CO_2$, orange color fades.
  • Demonstrates tight land–atmosphere coupling; metaphorically like the planet breathing.

Global Photosynthetic Capacity (VCmax at $25^{\circ}\text{C}$)

• Map of $V_{Cmax,25}$ (blue-green = high; brown = low; white = no data/vegetation).
  • N. America temperate forests/crops: $\sim70–100\;\mu\text{mol}\,\text{m}^{-2}\,\text{s}^{-1}$.
  • Sub-Saharan Africa savannas: very high capacity (dark blue-green).
  • Europe: moderate ($\sim50–80$).
  • New Zealand: $60–70$; Australia shows high capacity though often unrealized due to water stress.
• Reminder: Capacity ≠ actual rate; soil moisture, nutrients, temp., etc. constrain realized photosynthesis.

Nutrient Controls on Actual Light-Saturated Photosynthesis ($A_{sat}$)

• Dataset: $A<em>{sat}$ (nmol $CO</em>2$ g$^{-1}$ s$^{-1}$) vs leaf [N] and leaf [P].
• Strong positive linear relationships:
  • Higher nitrogen ⇒ more Rubisco & light-harvesting proteins ⇒ higher $A_{sat}$.
  • Higher phosphorus ⇒ more ATP (contains phosphate), plus phosphorylated metabolic intermediates ⇒ supports Calvin-cycle flux ⇒ higher $A_{sat}$.

Allocation of Leaf N to Rubisco

• Global map of fraction of leaf N invested in Rubisco:
  • Dark blue-green = high allocation (>30 %).
  • Sub-Saharan Africa: >30 % (matches high $V_{Cmax}$).
  • New Zealand: ~20–30 %.
• Drivers of allocation (analysis across sites):
  • Climate dominant in Africa (temperature, precip.).
  • Leaf structural traits (e.g., leaf mass per area) dominate in Amazonia.
  • Soil fertility important elsewhere.
• Conclusion: $V_{Cmax}$ & Rubisco N are emergent from interaction of climate × soil × leaf economics.

Variation by Plant Functional / Phylogenetic Type

Multi-species study (recent paper)

• Measured $V<em>{Cmax}$ & $J</em>{max}$ under standard conditions across 12 taxa spanning major evolutionary lineages.

Species panel & approximate evolutionary order (ancient ➜ recent)

• Ferns: Adiantum, Polystichum.
• Cycad: Cycas sp.
• Gymnosperm: Ginkgo biloba.
• Basal Angiosperms: Trimenia, Podocarp (gymnosperm, but placed here in talk), Magnolia.
• Eudicots: Toona (Indian tree), Salix (willow).
• Monocots (grasses): Phragmites australis (wetland reed), Miscanthus (giant grass).

Findings

• Clear gradient: Recently evolved grasses >> eudicots > gymnosperms > ferns in both $V<em>{Cmax}$ & $J</em>{max}$.
• Evolutionary interpretation:
  • Early plants evolved when atmospheric $CO_2$ was high ⇒ could achieve carbon gain with lower enzyme investment.
  • As Earth’s $CO<em>2$ declined, selective pressure favored higher capacities; grasses (monocots) arose under low-$CO</em>2$, evolving very high $V<em>{Cmax}$ & $J</em>{max}$.

Intraspecific Genetic Variation & Crop Improvement

Wheat doubled-haploid mapping population (150 lines)

• Parents: ‘Halberd’ (higher $A<em>{max}$) × ‘Cranbrook’ (lower $A</em>{max}$).
• Measurement conditions: High light, non-limiting $CO_2$, ample water & nutrients.
• Histogram range: $A_{max}=22–35\;\mu\text{mol}\,\text{m}^{-2}\,\text{s}^{-1}$ (≈60 % spread!).
• Identified two quantitative trait loci (QTLs) on distinct chromosomes controlling $A_{max}$.

Breeding / biotech implications

• Traditional selection can pyramid alleles for higher photosynthesis.
• Modern tools (e.g. CRISPR-Cas9) could edit causal genes while retaining agronomic background.
• Aimed at boosting yield & resource-use efficiency in rain-fed Australian wheat.

Summary of Entire 4-Lecture Series (contextual linkage)

1 Electron Transport Chain

• Converts light → chemical energy.
• Outputs: $ATP$ & $NADPH$.

2 Calvin Cycle & Photorespiration

• Rubisco carboxylates $RuBP$ ⇒ triose/hexose-P, regenerates $RuBP$ using $ATP$ & $NADPH$.
• Rubisco also oxygenates ⇒ photorespiration; salvages $2\,PG$ back to Calvin cycle but costs carbon & energy.

3 Short-term Environmental Responses

• $\uparrow$ Light, $CO_2$, water availability ⇒ $\uparrow A$.
• $\uparrow O_2$ ⇒ $\downarrow A$ via photorespiration.
• Thermal optimum: bell-shaped $A(T)$ curve.

4 Large-scale Variation (current lecture)

• Seasonal land greening drives annual oscillation in atmospheric $CO_2$.
• Spatial heterogeneity in $V_{Cmax}$ linked to nutrients & climate.
• Functional groups differ: grasses highest capacity.
• Ample standing genetic variation exists within species for breeding gains.

Looking Ahead (next class)

• Hands-on modelling of $A$ vs environment using the two key parameters:
  • $V_{Cmax}$ (Rubisco capacity)
  • $J_{max}$ (electron transport capacity)
• Explore how altering these or environmental drivers predicts canopy and ecosystem photosynthesis.