GEOG 566

         Advanced spatial statistics and GIScience

April 6, 2018

How does photosynthetic pathway of a grassland affect seasonality and drought response of productivity?

Filed under: 2018,My Spatial Problem @ 3:24 pm
  1.       A description of the research question that you are exploring.

Grasslands are key social, economic, ecological components of US landscapes, and globally, ecosystems containing abundant grassy cover are estimated to compose ~30 percent of non-glacial land cover (Still et al., 2003; Asner et al., 2004). Yet, compared to forests, we know relatively little about how the productivity of grassy landscapes will respond to future, more-intense droughts induced by climate change. The community composition of a grassland mediates its response to drought, and is critical to consider in forecasting the climate change impacts (Knapp et al., 2015). Photosynthetic pathway (C3 or C4) used by grass species is a first-order factor of community composition that strongly affects resource-use efficiencies. Grasses with the C4 photosynthetic pathway, in contrast to the ancestral C3 pathway, have comparatively higher light-use and photosynthetic efficiencies, especially under high temperatures, as well as higher water use efficiency. As a result, C3 or C4 grasses will have distinct responses to warming climate and rising CO2 (Collatz et al., 1992, 1998; Lloyd & Farquhar, 1994; Suits et al., 2005). Thus, the photosynthetic pathway composition (C3 or C4) of grass communities is a fundamental aspect of grassland and savanna function, ecology, and biogeography.

The light use efficiency (LUE) of photosynthesis is one metric that we can use to track the growth of natural grasslands. LUE is calculated as gross primary productivity (GPP) divided by absorbed photosynthetically active radiation (APAR), and can be obtained from eddy covariance (EC) flux tower measurements of ecosystem productivity and environmental conditions. Though the comparative water-use efficiency (WUE) of C3 and C4 grasslands has been well-studied, LUE has received less attention. Importantly, LUE is also correlated with sun-induced chlorophyll fluorescence (SIF), a new remote sensing index that captures intra-annual variation in production better than NDVI and EVI (Rossini et al., 2010; Guanter et al., 2014), and should be particularly useful across systems with distinct resource-use efficiencies.

Guided by these knowledge gaps, I am interested in a) comparing the seasonal dynamics of LUE between C3 and C4 grassland sites, and b) quantifying the impacts of a 2012 drought on the LUE of C3 and C4 grasslands.

Specific questions include:

  • How does the timing of annual spring greenup / increase in LUE differ between the C3 and C4 site?
  • How does the slope of the annual cycle of increase in LUE differ between the C3 and C4 site?
  • How much do these parameters vary from year to year?
  • What climatic factors (e.g., degree days, temperature, precipitation, drought severity, previous year production) are correlated, autocorrelated, or temporally cross-correlated with this variation?
  • What anomalies are associated with the timing, slope, and magnitude of LUE during a known drought year, and how do these anomalies differ between a C3 and C4 site?
  • How is coarse-scale SIF correlated with EC flux tower-scale measurements of GPP and LUE, and how does this relationship differ between the C3 and C4 sites?
  1.     A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

My study sites are two eddy covariance (EC) flux tower locations in natural grassland areas located ~90 miles apart in eastern Kansas. The sites experience nearly identical climates, but the first is a natural tallgrass prairie composed of 99% C4 grass at Konza Prairie Biological Station outside Manhattan, KS, while the second is a replanted agricultural field composed of 75% C3 grass at the University of Kansas Field Station (Fig. 1). Because the two sites experience a very similar climate, I hypothesize that photosynthetic type strongly controls differences in LUE at each site.

LUE can be calculated from ecophysiological equations that use ground-based measurements of atmospheric gas concentrations and meteorological data. The eddy covariance (EC) flux approach uses tower-mounted instruments to measure atmospheric concentrations of water and CO2, as well as air temperature, solar radiation, and other environmental data. All measurements are taken continuously every 30 minutes. EC flux data reflect the “footprint,” or area upwind of the tower where the instruments are mounted. The footprint varies with wind speed and direction, but averages about ~250m2. EC flux data span from 2008-2015.

The main metric I am interested in is daily total LUE, which is calculated as the daily sum of gross primary productivity (GPP) divided by the daily sum of the amount of incoming radiation, or photosynthetic photon flux density (PPFD). Daily LUE is converted to units of gC·MJ-1·day-1·m-2 from units of µmol CO2· µmol photon-1·day-1· m-2   using the molecular weight of carbon and Planck’s equation. Example time series of GPP, PPFD, and LUE appear in Fig. 2.

SIF is available from the NASA GOME-2 satellite at 0.5 degree spatial resolution, at 14- and 30-day temporal resolution. Because of the coarse spatial resolution of GOME-2 data (0.5 degree), SIF from GOME-2 will be weighted by MODIS Land Cover Type quantify the amount and type of land cover within the GOME-2 grid cell associated with each flux tower site, sensu Wagle et al. (2016).

Fig. 1: Study sites and ecoregions considered in the analysis. Study sites are the EC flux tower site at Konza Prairie Biological Station (US-Kon), in Manhattan KS, and the EC flux tower site at the University of Kansas Biological Field Station (US-KFS), outside Lawrence, KS.
Fig. 2: Time series of: GPP, photosynthetic photon flux density (PPFD), and LUE from EC flux data, as well as SIF from GOME-2 for for the C3 (orange) and C4 (blue) Kansas flux tower sites. The 2012 drought appears between the gray lines.
  1.     Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

My hypotheses are driven by the seasonality and comparative resource-use efficiencies of C3 and C4 photosynthesis (Fig. 3). I hypothesize that, when examining multi-year trends, the C4 site will have a later greenup, but higher maximum GPP and LUE than the C3 site. I also hypothesize that the average slope of annual increase in GPP will be statistically significantly different between the C3 and C4 sites.

I hypothesize that precipitation and growing degree days will be strongly correlated with parameters describing the timing and seasonality of GPP and LUE.

I hypothesize that C4 sites, compared to C3 sites, will show more stable GPP and LUE under 2012 drought conditions, due to the higher WUE of the C4 pathway and higher rates of photosynthesis under high temperatures.

I also hypothesize that there will be distinct relationships between SIF and GPP, and between SIF and LUE, when compared between C3 and C4 sites, driven by the distinct resource use efficiencies of the distinct functional types. I hypothesize that the slope of the relationship between SIF and GPP and SIF and LUE will be statistically significantly higher at the C4 site than at the C3 site.

Fig. 3: Comparison of the simulated responses of C3 (solid line) and C4 (dashed line) photosynthesis. Response of net photosynthesis (a) to quantum flux, at 25 degC, and intercellular C02 partial pressure (pi) of 25 and 15 Pa for Cg and C4 respectively; and (c) to leaf temperature at pi of 25 and 15 Pa for C3 and C4 respectively and quantum flux of 1500 kmol m-2 s-‘. From Collatz et al. 1992.
  1.    Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

I am interested in learning about harmonic curve fitting this term. I expect that harmonic curve fitting will allow me to quantify and investigate interannual patterns in production and extract coefficients, minima, maxima, and timing of production dynamics. better than simple linear regression or generalized linear models.

I am also curious about exploring wavelet analysis with my EC flux data to investigate the degree to which annual patterns of production mimic daily patterns of production.

  1.     Expected outcome: what do you want to produce — maps? statistical relationships? other?

I want to produce statistical models that describe interannual patterns of seasonality at the C3 and C4 grassland sites. Further, I also I want to produce statistical relationships between metrics average annual seasonality and environmental conditions. I also want to produce statistical relationships between drought year anomalies in production indices and environmental conditions.

  1.     Significance. How is your spatial problem important to science? to resource managers?

LUE is a relatively under-utilized metric of tracking plant production, but will be increasingly valuable for its relationship to new remote sensing indices. Quantifying seasonal differences in LUE and other production indices and drought response at closely-located C3 and C4 grassland sites will a) clarify how LUE differs between C3 and C4 grasslands, and b) describe the drought response of LUE and how it differs between C3 and C4 grasslands. Exploring LUE dynamics facilitates using LUE-correlated satellite indices to track and predict variation in plant production. Exploring initial correlations between LUE and SIF at these sites will facilitate using SIF to track variation in production across functional types at larger spatial scales. Ultimately, I am interested in investigating how seasonal dynamics of LUE differ across plant communities of varying C4%; developing statistical relationships between the C4% of a site, seasonality, and drought response; and using SIF to track the drought response of plant communities of varying functional types.

  1.     Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R?

  • a)  Significant experience with Arc Softwares and GUI-based image processing and analysis in Arc.
  • b)  Some exposure to ModelBuilder and Python in Arc. Some exposure to coding in Python outside Arc.
  • c)  Proficient and comfortable in statistical and spatial analysis and data visualization using R.

Works Cited

Asner GP, Elmore AJ, Olander LP, Martin RE, Harris AT (2004) Grazing Systems, Ecosystem Responses, and Global Change. Annual Review of Environment and Resources, 29, 261–299.

Collatz G, Ribas-Carbo M, Berry J (1992) Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants. Australian Journal of Plant Physiology, 19, 519.

Collatz GJ, Berry JA, Clark JS (1998) Effects of climate and atmospheric CO2 partial pressure on the global distribution of C4 grasses: Present, past, and future. Oecologia, 114, 441–454.

Guanter L, Zhang Y, Jung M et al. (2014) Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences, 111, E1327–E1333.

Knapp AK, Carroll CJW, Denton EM, La Pierre KJ, Collins SL, Smith MD (2015) Differential sensitivity to regional-scale drought in six central US grasslands. Oecologia, 177, 949–957.

Lloyd J, Farquhar GD (1994) 13C Discrimination during CO₂ Assimilation by the Terrestrial Biosphere. Source: Oecologia, 994, 201–215.

Rossini M, Meroni M, Migliavacca M et al. (2010) High resolution field spectroscopy measurements for estimating gross ecosystem production in a rice field. Agricultural and Forest Meteorology, 150, 1283–1296.

Still CJ, Berry JA, Collatz GJ, DeFries RS (2003) Global distribution of C3 and C4 vegetation: Carbon cycle implications. Global Biogeochemical Cycles, 17, 6-1-6–14.

Suits NS, Denning AS, Berry JA, Still CJ, Kaduk J, Miller JB, Baker IT (2005) Simulation of carbon isotope discrimination of the terrestrial biosphere. Global Biogeochemical Cycles, 19, 1–15.

Wagle P, Zhang Y, Jin C, Xiao X (2016) Comparison of solar-­induced chlorophyll fluorescence , light-use efficiency , and process-based GPP models in maize. Ecological Applications, 26, 1211–1222.


Print Friendly, PDF & Email

1 Comment

  1.   jonesju — April 8, 2018 @ 10:51 pm    

    hi Lila,
    Thanks for this helpful description of your spatial problem. If I understand correctly, you have only two point locations in space, but fairly detailed temporal data for each of the two points. So is it fair to say that you have a “temporal problem” i.e. that you are principally interested in comparing/contrasting the shapes of the sets of curves that you showed in Fig. 2? Or is there a spatial element that I am missing?

    regarding what to get started on: If you want to start with analysis of a time series, pick one site (c3 or c4). Then perhaps it might be useful (if you have multiple years of data) to overlay the annual (seasonal) curves for that site for all years on one graph.

RSS feed for comments on this post.

Sorry, the comment form is closed at this time.

© 2019 GEOG 566   Powered by WordPress MU    Hosted by