Deviations on a theme: peat patterning in sub-tropical wetlands

Transcription

1 Greater Everglades Ecosystem Restoration Conference April 23, 2015 Deviations on a theme: peat patterning in sub-tropical wetlands Christa Zweig Research Scientist Applied Sciences Bureau

2 Ridge and Slough Landscape Central WCA3A South Southern WCA3A South 1. How did the pattern develop? 2. Why is it changing?

3 Peat patterning mechanism How did the pattern develop? Peat accumulation mechanism Increased biomass production = higher elevations = more vascular plant biomass = increased peat production Evidence for peat accumulation mechanism sharp microtopographical differences

4 Differential peat accumulation Statistical evidence of sharp microtopographical differences: Bimodal frequency distribution of surface elements (vascular plant biomass or acrotelm thickness) (Eppinga et al 2008) Watts et al (2010) demonstrated elevation bimodality in conserved RSL Density Northeast conserved Histogram of Normal fit Elevation (cm) Density Southeast degraded Histogram of Normal fit Elevation (cm)

5 Differential peat accumulation Ridge Slough Elevation Biomass Bimodality? Yes (and no) No (and yes) 5

6 Differential peat accumulation No biomass bimodality Two reasons 1) Degradation Wet prairies artifact of disturbance? Milimeters/m2 per year of peat deposited Ridge Slough Prairie Community

7 Differential peat accumulation 2. Differences in sub-tropical climate/ species pool Disconnect between biomass production and peat accumulation (elevation) Decomposition is the key Serna et al. 2013(Everglades plant decomposition) species not habitat or water depth Different processes (species-based decomposition and temperature) are important for sub-tropical patterned peatlands than boreal peatlands

8 Why is it changing? Local vs. landscape

9 Transition probability model of local dynamics Multistate models are a specialized population model that calculates transition probabilities between states Biomass/density data for repeated samples ~ 10 years apart Clustered data into states (ridge, wet prairie, slough) not pure clusters Local elevations = tailored hydrology for each point; and global hydrology (Site 65) 5, 15 year means for hydrology (minimum, maximum, amplitude (max-min))

10 Transition probability model of local dynamics Best model: 15 year mean hydrologic amplitude 15 year average hydrologic maximum Elevation Peat depth Northing Output: 1. Decadal transition probabilities 2. Targets for management 3. Predictive model

11 Probability of stability amplitude 100.0% Probability of stability (staying in the same state) 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Mean 15 year amplitude (cm) Blue = slough Green = ridge 11

12 Probability of stability depth 120.0% Probability of stability (staying in the same state) 100.0% 80.0% 60.0% 40.0% 20.0% Blue = slough Green = ridge 0.0% Mean 15 year maximum depth (cm) 12

13 Dynamic stability based on transition probability model Digitized ridge/slough (1984) Loaded into CA software Kriged elevation map Applied MS model to pixels with custom hydrology transition probabilities 50 separate runs (decadal) Averaged runs for rate of change and landscape Compared to 1995 imagery

14 Landscape expression of dynamic stability Rate of change Y coordinate Importance Value Number of transitions X coordinate Importance value X coordinate Final importance value: (% ridge * 51) + (% slough * 21) Y coordinate

15 How and why Importance of species and decomposition in future RSL modeling Local elevation is more important than landscape Long-term hydrology factors for landscape stability 15 year average amplitude 15 year average maximum depth Now what? Improve upon existing peat patterning models Hydrologic targets for water management Model landscape changes over time: Restoration scenarios Water management scenarios

16 Questions? Co-authors: Sue Newman, Fred Sklar, Wiley Kitchens Acknowledgements: Colin Saunders, Joe Stachelek