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MEAN VS. VARIANCE: HYDROLOGIC CONTROLS ON WETLAND STRUCTURE AND FUNCTION

By

JOSEPH M. DELESANTRO

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2013

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© 2013 Joseph M. Delesantro

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To my friends and family

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ACKNOWLEDGMENTS

I would like to thank my advisor, Matthew Cohen, and committee members,

Katherine Ewel and Mark Brown for all their guidance and support. I would also like to

thank Lawrence Korhnak who spent untold hours knee deep in muck with me along the

Silver River. Without Larry’s experience and patience this project would not have been

possible. I would also like to thank Daniel Mclaughlin and David Kaplan who were

always there when I needed a sounding bored or advice in my work.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS .................................................................................................. 4

LIST OF TABLES ............................................................................................................ 7

LIST OF FIGURES .......................................................................................................... 8

LIST OF ABBREVIATIONS ............................................................................................. 9

ABSTRACT ................................................................................................................... 10

CHAPTER

1 INTRODUCTION .................................................................................................... 12

2 METHODS .............................................................................................................. 19

Study Area .............................................................................................................. 19 Hydrologic Evaluation ............................................................................................. 20 Productivity ............................................................................................................. 23 Forest Community Structure ................................................................................... 24 Organic Matter Accumulation .................................................................................. 24 Microtopography ..................................................................................................... 26 Statistical Analysis .................................................................................................. 26

3 RESULTS ............................................................................................................... 31

Sit Hydrology and Orthogonal Gradients ................................................................ 31 Forest Community Structure ................................................................................... 31 Productivity ............................................................................................................. 32 Morphological Adaptations ...................................................................................... 34 Organic Matter Accumulation .................................................................................. 34 Microtopography ..................................................................................................... 35

4 DISCUSSION ......................................................................................................... 47

Comparing Silver River Floodplain to Other Floodplains ........................................ 48 Dual Control of Ecosystem Metrics ......................................................................... 49 Cypress Knees ....................................................................................................... 52 Contingent Effects of Hydrology ............................................................................. 53 Soil Organic Matter Recalcitrance Peak ................................................................. 54

Management Implications ...................................................................................... 55

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APPENDIX

A ADDITIONAL SITE CHARACTERISTICS ............................................................... 57

B BASAL AREA AND COVER BY SPECIES ............................................................. 58

C ANNUAL LITTERFALL ........................................................................................... 60

D SOIL CORE DATA .................................................................................................. 61

LIST OF REFERENCES ............................................................................................... 64

BIOGRAPHICAL SKETCH ............................................................................................ 71

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LIST OF TABLES

Table page 3-1 Hydrologic characteristics of study sites along the Silver River .......................... 37

3-2 Characteristics of study sites along the Silver River ........................................... 38

3-3 Litterfall of study sites along the Silver River ...................................................... 39

A-1 Leaf litterfall indexed to species basal area ........................................................ 57

A-2 Additional Site Characteristics ............................................................................ 57

B-1 Long hydroperiod site percent cover by species ................................................ 58

B-2 Short hydroperiod site percent cover by species ................................................ 58

B-3 Basal area by species ........................................................................................ 59

D-1 Soil core data...................................................................................................... 61

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LIST OF FIGURES

Figure page 2- 1 Land surface elevation mapping of the Silver River, showing study sites for

the comparison of ecological responses to hydrologic mean and variation. . .... 28

2- 2 Gradient in water level variation along the length of the Silver River. ................. 29

2- 3 Schematic of a 30 m by 30 m study site and the sampling frames for forest community structure (8 m by 8 m), understory vegetation (0.25 m2), bathymetry (on N-S, E-W 4 m axis) and litterfall collection (0.5 m2). .................. 30

3-1 Independence of hydrologic metrics. .................................................................. 36

3-2 Multivariate regression of the response of non-Taxodium leaf litterfall to water level STD and hydroperiod by class. .................................................................. 40

3-3 Leaf area index. .................................................................................................. 41

3-4 Cypress knee structure compared to hydrologic metrics. ................................... 42

3-5 Soil organic matter content depth profile from three sites which demonstrate trends in soil organic matter quantity and quality with depth.. ............................ 43

3-6 Soil organic matter (SOM) content response to hydrologic metrics... ................. 44

3-7 Histograms of relative elevation measurements at sites with low, moderate, and high microtopographic relief.. ....................................................................... 45

3-8 The STD of soil elevation measurements modeled by multivariate regression to hydroperiod and water level STD... ................................................................ 46

C-1 Annual litterfall for four study sites in the Silver River floodplain......................... 60

D-1 Soil core profiles for long hydroperiod sites... ..................................................... 62

D-2 Soil core profiles for short hydroperiod sites... .................................................... 63

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LIST OF ABBREVIATIONS

HP Hydroperiod

MWL Mean water level

OM Organic matter

SOM Soil organic matter

STD Standard deviation

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

MEAN VS. VARIANCE: HYDROLOGIC CONTROLS ON WETLAND STRUCTURE

AND FUNCTION

By

Joseph M. Delesantro

May 2013

Chair: Matthew J. Cohen Major: Forest Resources and Conservation

The relative importance of mean vs. variance of exogenous drivers to the

organization of ecosystem structure and function remains an important open question in

ecosystem science. Wetlands provide a useful setting for examining this question,

because hydrology acts as the dominant ecosystem driver. However, previous studies

have considered variation in hydrology in terms of flood intensity or frequency, which

are not independent of the mean. Floodplain wetlands along the Silver River in Florida,

USA, provided two natural and orthogonal gradients in the mean and variance, allowing

their relative controls on ecological structure and function to be evaluated

independently.

All ecosystem attributes evaluated (forest community structure, aboveground net

primary productivity, organic matter accumulation, cypress knee morphology, and

microtopography) were controlled by both the mean and variance of hydrologic forcing.

Hydroperiod (a measure of hydrologic mean) and water level variation were significantly

correlated to soil organic matter content, species specific leaf litterfall (indexed to basal

area), and microtopographic variation in multivariate regressions. Structural attributes of

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cypress knees and the prevalence of wetland taxa were also correlated to hydroperiod

and water level variation. However, the magnitude of response to the mean was

consistently larger, suggesting that widespread use of hydroperiod as a predictor of

wetland organization is tenable. However, hydrologic variation was an important control,

with effects often contingent upon the mean, such that water level variation had a

greater ecological impact under short hydroperiod conditions. This study underscores

the need to consider variation in exogenous drivers when evaluating, managing, and

restoring ecosystems.

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CHAPTER 1 INTRODUCTION

Ecosystems organize in response to exogenous drivers, such as sunlight,

hydrology and nutrient availability. Natural gradients in these drivers have provided an

opportunity to understand how ecosystems work and thus predict how they change as

drivers are intentionally and unintentionally altered. While these exogenous drivers vary

in magnitude, duration, frequency, predictability, and timing, most studies are designed

to evaluate gradients in the mean (e.g., mean annual precipitation or hydroperiod),

implicitly overlooking effects of variation around the mean (Benedetti-Cecchi 2003). This

is an important shortcoming because where variation has been studied, its effects on

ecosystem structure and function is significant (Poff and Ward 1989, Odum et al. 1995,

Landres et al. 1999, Porporato et al. 2002 and 2004, Heffernan 2008). Moreover, most

studies that consider variation quantify variance in terms of event frequency or intensity,

which is generally not independent from the mean (Benedetti-Cecchi 2003). This

confounds inferences about the role of variation as a predictor of ecosystem function.

To adequately disentangle the independent effects of mean and variance, studies must

be conducted along orthogonal gradients in the mean and variance.

The floodplain wetlands adjacent to spring fed rivers in North Florida provide

model systems wherein mean and variance can be disentangled. Steady discharge

from the spring vent results in extremely low water level variation at the upstream

boundary (Jawitz and Mitchell 2011). Event-driven variation in downstream receiving

water bodies extends up the river channel resulting in bottom-up flooding dynamics, and

establishing a natural gradient in water level variance along the river from low at

upstream locations to high downstream. Variation in soil elevation across the floodplain

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creates a second natural gradient, in the water level mean. These gradients are relevant

because hydrology is the dominant exogenous driver of wetland ecosystems (Gosselink

et al. 1978). Hydrology exerts control over any system by providing water to organisms,

but in wetland ecosystems, standing water commonly controls the transport of gasses to

and from the soil column and rooting zone. Prolonged inundation results in anoxia and

the buildup of toxic chemicals, which in turn results in inundation stress. Because

hydrology is the dominant exogenous driver of wetland ecosystem organization, these

two orthogonal hydrologic gradients provide an appropriate setting for measuring the

relative effects of mean and variance on ecosystem structure and function.

To evaluate the role of these orthogonal hydrologic gradients on ecosystem

organization, I chose a suite of ecological metrics responsive to inundation and

integrative over time so as not to be confounded by event scale variation. Gradient

studies often focus on variation in ecosystem productivity, and vegetation structure and

composition (Connell 1976, Brown 1981, Megonigal et al. 1997). These metrics, general

to ecosystems, evaluate the stress imposed along a gradient and the adaptations of the

system in response to the stress. In wetlands specifically, soil organic matter (SOM)

processes are also affected by inundation and the formation of elevated microsites that

result from the interaction of productivity and decomposition are an important

morphological adaptation of wetland ecosystems (Beatty 1984, Titus 1990, Scarano et

al. 1997, Vivian-Smith 1997, Pollock et al. 1998, Simmons et al. 2011). As such, I

evaluated the impacts of mean and variance in hydrology on surface morphology and

SOM accumulation in addition to ecosystem productivity, and vegetation structure and

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composition. These ecosystem attributes were expected to be controlled by both the

mean and variance.

Primary production is among the most important ecosystem functions and

responds strongly to environmental gradients (Schuur and Matson 2001). Wetland

primary productivity is sensitive to gradients in hydrology (Conner and Day 1976, Mitsch

and Ewel 1979, Brinson et al. 1981, Brown 1981, Conner and Day 1992, Megonigal et

al. 1997). Flooding frequency has been used to define hydrologic gradients of many

studies on wetland productivity (Johnson and Bell 1976, Keeley 1979, Gosselink et al.

1981, Taylor et al. 1990, Conner and Day 1992, Odum et al. 1995). While it has been

demonstrated that flood frequency strongly influences wetland ecosystems, it is not a

priori independent of mean water level, making it unclear whether mean or variation

exerts greater control. The mean water level may positively (via moisture or oxygen

availability) or negatively (via prolonged inundation or desiccation stress) influence

productivity. Similarly, variation in water level may regulate wetland function via multiple

pathways, including promoting productivity when high water events deliver moisture to

elevated microsites or when low water events oxygenate sites that are otherwise

anoxic. Variation may also limit productivity when changes in water level occur too

rapidly or frequently for plants to adjust, or where extreme water levels cause hypoxia or

desiccation. In light of the multiple pathways by which the water level regime can affect

primary production, and reasoning that water availability would not be a limiting factor in

this floodplain ecosystem, I hypothesized that higher mean water level and greater

water level variation would lower productivity.

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Wetland forest communities respond to multiple scales of hydrologic forcing by

adjusting community composition (Cronk and Fennessey 2001). Wetland species have

developed adaptations that allow them to survive under stressful hydrologic regimes.

Some (e.g. Taxodium distuchum) can tolerate a range of hydroperiods, while others are

confined to areas of long (e.g. Nyssa aquatica) or short hydroperiod (e.g. Acer rubrum).

I hypothesized that hydrology exerts strong control on forest composition, with greater

hydroperiod and water level variation exerting control via selection for inundation

tolerant species. In accordance with the intermediate disturbance hypothesis, which

predicts diversity patterns along gradients in tidal magnitude (Sousa 1979, Hacker

1999), I also predicted higher diversity at intermediate levels of hydrologic variation.

Notably, however, tidal wetlands are subject to frequent and highly predictable flooding,

not observed in non-tidal floodplain ecosystems where flooding is stochastic. As such,

the applicability of the intermediate disturbance hypothesis to non-tidal wetlands is

unknown.

Wetlands accumulate large quantities of organic matter (OM), making them

important settings for carbon storage and associated biogeochemical functions. OM

accumulation in an ecosystem represents the balance between production and

decomposition and is under strong hydrologic control (Sahrawat 2003). Moreover,

decomposition is responsible for the release and transformation of nutrients from

detritus, which may greatly affect ecosystem productivity (Lockaby et al. 1996).

Hydrology exerts control on the rate of decomposition in multiple ways. Standing water

impedes oxygen transport into the soil, which limits decomposition. In contrast, the rate

of decomposition may be enhanced by water availability when water is limiting, or when

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flooding distributes exoenzymes (Reddy and DeLaune 2008). Evidence that water

level variation, and specifically more frequent wetting and drying cycles, increases the

rate of decomposition comes from both laboratory and field studies (Reddy and Patrick

1975, Baker III et al. 2001, Battle and Golladay 2001). Spatial variation in

decomposition rates due to microtopography, uneven distributions of organic matter

from roots and litterfall and strong impacts of temporal variation in temperature and

water level make direct measurements of decomposition complicated and often poorly

representative of long term conditions. However, the quantity and quality of

accumulated organic matter can be used to measure integrated hydrologic controls on

organic matter dynamics. Organic matter quality refers to how easily organic matter can

be mineralized. Generally, high quality organic matter is mineralized before low quality

organic matter. Therefore, the quality of soil organic matter may respond to hydrology

when inundation affects the rate of decomposition. I hypothesized that the quantity and

quality of SOM would increase with longer hydroperiod and that greater variation in

water level would decrease SOM quantity and quality.

Wetland taxa can withstand varying levels of inundation stress, with a variety of

morphological adaptations. Despite a rich literature on these adaptations (Coutts and

Armstrong 1976, Kozlowski 1984, 1997, Jackson and Colmer 2005,), it remains unclear

which aspects of hydrology control their expression. Among these morphological

adaptation, cypress knees are particularly charismatic, and a ubiquitous feature of these

floodplain forests. Cypress knee variation has been attributed to hydrology (Brown and

Montz 1986, Kernell and Levy 1990, Kummer et al. 1991, Briand 2000), but the role of

knees in tree metabolism remain unclear, as do the controls on knee morphology and

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frequency. Because it has been suggested that cypress knees play an important role in

gas exchange (Kernell and Levy 1990), I predicted that cypress knees will increase in

frequency with longer hydroperiod (i.e., conditions requiring greater gas exchange), and

increase in height with both greater mean water level and water level variation (i.e., to

ensure atmospheric exchange under all inundation conditions). Furthermore, I

predicted that variation in cypress knee height will correspond to variation in water level

(i.e., to provide a range of knees that balance the cost of taller knees with the benefit of

gas exchange during frequent high water events).

Microtopographic variation is a critically important feature of many wetland

ecosystems, contributing to productivity and biodiversity (Beatty 1984, Titus 1990,

Scarano et al. 1997, Vivian-Smith 1997, Pollock et al. 1998, Simmons et al. 2011,

Washuta 2011). The mechanisms controlling formation of high and low areas, often

referred to as hummocks and hollows respectively, are thought to be self-reinforcing

feedbacks between soil elevation and below-ground root production. In brief, plant

growth is enhanced on hummocks because of reduced inundation stress. This

enhanced productivity on hummocks yields more detritus and below-ground biomass.

The rate of OM decomposition is also enhanced on hummocks, constraining hummock

growth (Hilbert et al. 2000, Eppinga et al. 2009, Belyea and Clymo 2001). Soil

elevations are stable where the rate of litter deposits and belowground biomass growth

equal the rate of decomposition, which can happen in deeper water (hollows) where

production and respiration are low, and at higher elevation (hummocks) where both OM

fluxes are high. I hypothesized that the prevalence of hummocks will increase with

longer hydroperiod. To that end, I predicted that soil elevations will be bimodal and that

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the elevation between modes will be positively correlated to hydroperiod. I also

predicted that variation in microtopographic relief around a mode would increase with

greater water level variation.

These predictions span a wide array of ecosystem attributes, from organismal

responses to community composition to ecosystem processes. Together, they help

isolate effects due to water level mean and variation, and provide a system-specific test

of the hypothesis that both mean conditions and the variation around the mean exert

control on ecosystem organization.

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CHAPTER 2 METHODS

Study Area

The Silver River is a minimally disturbed spring fed river that flows into the

Ocklawaha River, a major tributary of the St. Johns River (Figure 2-1). Because the

source of the spring flow is the Floridan Aquifer, underlying several southeastern U.S.

states, water level variation near the spring head is extremely low, both for rivers

broadly, and also relative to water levels in the Ocklawaha River, which is subject to

significant event-driven variation. This bottom up flooding regime creates a marked

gradient in water level variation along the river (Figure 2-2). Orthogonal to river flow,

variation in soil elevation creates a second axis of hydrologic variation in mean depth.

The deeper areas of the floodplain are dominated by bald cypress (Taxodium distichum)

and water tupelo (Nyssa aquatica) with an understory comprised of deep water habitat

emergent macropytes like pickerel weed (Pontedaria cordata). Higher elevation areas

are populated with green ash (Fraxinas caroliniana), red maple (Acer rubrum), sabal

palmetto (Sabal palmetto) and numerous understory plants, including maindencane

(Panicum hemitomon), lizard’s tail (Saururus cernuus), and marigold (Bidens laevis).

Because river and floodplain water levels are regulated by the downstream Oklawaha

River (i.e., backwater flooding), flow velocities throughout the floodplain are very low

regardless of flooding depth.

Five transects were established perpendicular to the river (Figure 2-1), spaced

evenly along the river starting 1.5 km from the spring head and extending 6.7 km

downstream to a location 0.65 km upstream of the confluence with the Ocklawaha

River. Stage data from Jan-1-1970 to March-1-2011 were obtained from two gauging

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stations, one 1200 m from the spring head (USGS ID #02239500) and the other 300

meters downstream of the Silver River on the Ocklawaha (USGS ID #02240000).

Continuous stage records between these end-points were interpolated to estimate daily

stage at each of the five transects. Four years of overlapping river stage data at each

transect location allowed us to refine the end-point interpolation (R2=0.96, p<0.001),

yielding modeled daily stage data since 1970 for any point along the river between the

two long-term gauges. Water elevation exceedance probabilities at each location were

developed from this 41 year interpolated record, and two target elevations were

obtained corresponding to long (40-60% exceedance probability) and short

hydroperiods (10-20% exceedance probability). Using a high resolution Light Detection

and Ranging (LiDAR) derived elevation map with a resolution of 5 ft by 5 ft, we identified

long and short hydroperiod locations in the floodplain along each transect. Two study

sites, each 30 m by 30 m, were selected along each transect for a total of 10 study

sites, 5 of which were long hydroperiod, and 5 short hydroperiod, spanning a

longitudinal gradient in water level variation. The corners and centers of each study site

were marked, and a benchmark was placed in each plot, to which hydrologic,

bathymetric, and soil core elevation measurements could be compared. At each site,

forest community structure, aboveground net primary productivity (ANNP), soil

bathymetry and soil cores for organic matter accumulation measurements were taken

(Figure 2-3).

Hydrologic Evaluation

A broad array of hydrologic metrics might be used to represent the variance and

mean of site hydrology. Critically, however, any metrics for the mean must be

orthogonal to metrics for variation to be useful in decomposing their relative causal

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effects. Many metrics examined were highly correlated to other metrics, providing little

additional value. After evaluating over 30 possible metrics, mean water level (MWL) and

hydroperiod (HP) were chosen to represent the mean hydrologic conditions, and the

standard deviation (STD) of water level was chosen to represent water level variance.

While these metrics are intuitive and common, they may be quantified at many temporal

scales (e.g., period of record, annual, monthly, weekly, 5 day moving average).

Evaluation at many scales yielded highly correlated results and little evidence to support

one time-window over another. As such, all hydrologic metrics were calculated for the

entire 41 year period of record as it is both simple and most integrative.

Mean water level and hydroperiod both describe the mean hydrologic condition

but in different ways. Hydroperiod describes how often flooding occurs regardless of

depth, whereas mean water level can be strongly influenced by extreme water levels.

Because many ecosystem processes may exhibit no additional response to waters level

above or below certain elevations, mean water level may be less informative than

hydroperiod. Therefore, hydroperiod is the most appropriate metric of mean. I retained

both hydrologic metrics, however, because mean water level is important when an

ecological response is driven by a distinct level (e.g., cypress knee height, depth to soil

horizon) rather than just the fraction of time a site is inundated.

To test the assumption that river and floodplain stage are directly connected, we

installed high precision pressure transducers (Solinst Level Loggers) in 5 cm diameter

shallow wells at least 1 m below ground elevation at each of the 10 sites. The water

depths reported by the transducers were linked to surveyed site benchmarks using

standing water elevation as the datum, yielding water surface elevations with the same

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datum as river stage measurements. Stage data from site wells were compared to river

stage data at each transect to test the concordance of floodplain and river stage, and to

identify conditions during which the records diverge. Long term stage data from the two

permanent gages indicate that average river stage for the one year period of site data

collection was fairly representative at the 54 percentiles and 64 percentile for the long

term stage gauges (USGS ID #02239500 and USGS ID #02240000 respectively).

During the period of site stage measurements, there was a period (February to May)

during which river and site stage diverged; this corresponded to the end of the dry

season and the beginning of the growing season, and indicates that there are regular

periods during which rainfall and evapotranspiration control site water level. This period

of disconnection between the river and floodplain was removed when assessing the

relationship used for predicting site stage with river stage. When the floodplain operates

independently of the river, the water level under the floodplain varies more than the river

and is generally lower. Therefore predicting site stage with river stage will

underestimate water level variation and overestimate stage for the period of

disconnection. The stage dynamics during this period are expected to have lower

impact on ecosystem structure and function relative to the rest of the year because the

water level is generally below the soil and not impeding gas transfer.

To estimate site mean water level and hydroperiod, I used the short term

relationship between site water level and the interpolated river water level to construct a

relationship that was then used to back-cast conditions over the last 40 years. Note that

both hydrologic metrics (mean water level and hydroperiod) vary with surface elevation

within sites, so all hydrologic metrics are reported for the median elevation, obtained

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from site bathymetry surveys. Measured site hydroperiods were all within the intended

hydroperiod ranges except for the two T1 sites (upper river), which both had a shorter

hydroperiod than the targets, though both show characteristics consistent with their

original long and short hydroperiod classifications, principally in SOM content.

Productivity

To compare productivity between study sites, I used litterfall traps to estimate

minimum aboveground net annual production. Litterfall measurements are a common

and proven method for estimating minimum aboveground net primary production

(ANPP) (Bray and Gorham 1964). Six litter traps (0.5 m2) elevated 1 m off the wetland

surface were sampled monthly July 2011 to June 2012. Litter trap locations within sites

were random (Figure 2-3), but were preferentially placed away from Sabal palmetto

because the large palm fronds may intercept and collect litter. Litterfall mass was dried

at 80 degrees C for 72 hours before being separated into leafy, woody, reproductive,

Taxodium reproductive and miscellaneous components. Leaf litter was further

subdivided into Taxodium and “other” (consisting mainly of Nyssa aquatica, Acer

rubrum and Fraxinas caroliniana). Each category of litter was weighed and reported as

grams per square meter per year. Litter and leaf litterfall per unit basal area was also

calculated to provide a measure of productivity that accounts for site forest community

structure and age variation. The productivity of Sabal palmetto was assessed separately

from canopy litterfall. At the onset of the project, a randomly placed 8 m by 8 m plot was

marked off and cleared of all existing Sabal palmetto litter at each short hydroperiod

site. The Sabal palmetto litter was collected monthly from the ground of plot. Litter was

not collected for two months when substantial flooding was known to have occurred on

the sites. As a result, the annual total Sabal palmetto litterfall was calculated as the 10

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month average multiplied by 12 months. Sabal palmetto litterfall was not assessed at

long hydroperiod sites because of the species very low occurrence at these sites.

Projected leaf area index (LAI) was also measured as a proxy for site productivity

(Brown 1981, Gholz 1982). Leaf area index was measured by ceptometer (AccuPar

LP-80), which evaluates the light passing through the canopy. Measurements, each of

which integrates 80 point readings of incident light, were taken every 5 m along seven

transects spanning the study site.

Forest Community Structure

Forest community structure was surveyed in 8 m by 8 m subplots randomly

placed within each of four study site quadrants at each site. Diameter at breast height

and species of each tree within the subplots was recorded. Ground cover was surveyed

and recorded by percent cover within 3 randomly selected 0.5 m2 areas within each

subplot (Figure 2-3).The dominance of facultative and obligate wetland species (as

defined by the 2012 National Wetlands Plant List; Lichvar et al. 2012) within each study

site was evaluated by the proportion of basal area, plant density, and ground cover,

contributed by wetland taxa. Cypress knee density was measured in each forest

structure subplot, and height was measured using a laser level elevation relative to the

site bench mark. Cypress knees were characterized by their density, site average

height, and the variance in heights at each site.

Organic Matter Accumulation

Soil organic matter accumulation between sites was compared by evaluating the

quantity and quality of organic matter in soil cores. Three 5 cm diameter soil cores, 50

cm long, were taken from random locations within each study site; I avoided taking

cores from hummocks. Because cores were collected when sites were dry in winter,

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the elevation of the top of each soil core was measured by laser level relative to the

benchmark. Sharpened 5 cm diameter PVC pipe was driven into the ground to a depth

of 50 cm. An airtight seal was placed on the top of the pipe and the core was carefully

removed. Cores that compacted more than 10 cm or that lost material from the bottom

during extraction were rejected and the sites were resampled. Each core was separated

in 2 cm segments. Live root mass was removed and samples were dried at 105 degrees

C for 72 hours. Samples were then ground and passed through a #10 sieve. A two-step

combustion process was utilized to provide an index of recalcitrance (Schnitzer and

Hoffman 1966, Kristensesn and Andersen 1987, Angehrn-Bettinazzi et al. 1988,

Sharma 1989, Kristensen 1990, Lopez-Capel et al. 2005). Pilot testing indicated that a

1 g sample of soil was sufficient to represent each core segment and would undergo

complete combustion of organic matter in 4 h. The exact weight of each sample plus the

weight of the aluminum trays were recorded. The samples were placed into a preheated

muffle furnace at 350 degrees C for 2 hours, then removed and placed in a desiccation

chamber to cool for 15 min before being weighed again. Samples were then placed into

the preheated muffle furnace at 550 degrees C for an additional 2 hours, after which

samples were once again removed and placed in a desiccation chamber to cool for 15

min before being weighed a final time. The total mass of organic matter was measured

by the total fractional loss from the combined two step combustion and the recalcitrance

index was reported as the mass lost in the second combustion step over the total mass

lost. The average % SOM was well correlated (R2 = 0.84, p < 0.001) to the % SOM of

the top 10 cm. Therefore, the whole core average % SOM was used to analyze OM

accumulation in response to hydroperiod and water level STD. Likewise, the mean

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recalcitrance index for the whole core was used to analyze OM quality in response to

hydroperiod and water level STD. Hydroperiod and mean water level were calculated

relative to the ground elevation at which each core was collected. However, because

water level STD is a site level attribute, % SOM and recalcitrance index was averaged

at the site level to prevent pseudoreplication.

In order to systematically identify peaks in SOM recalcitrance, a piece-wise linear

model was used. The peak in SOM recalcitrance is identified as the depth at which the

model predicting SOM recalcitrance switches from one linear regression to the next.

The depth to peaks were also averaged by site for comparison to water level STD.

Microtopography

Site microtopographic relief was obtained using bathymetric surveys in two

randomly selected, non-overlapping, sampling stations in each quadrant of the study

sites (n = 8 stations per site, Figure 2-3). Soil elevation measurements were taken

relative to the site benchmark by measuring water depth in areas with standing water

and using a laser level everywhere else. Within each sampling station a total of 17

elevation measurements were taken, starting at the sampling station center with four

more at random distances along each cardinal direction to a maximum distance of 4

meters from the center (Fig. 2-3). This resulted in a total of 136 soil elevation

measurements at each study site. Soil elevation bimodality, variance, and skewness

were evaluated for each site. The number of modes in the elevation distribution was

analyzed using a BIC analysis in the MCLUST package in R.

Statistical Analysis

The statistical significance of all results was analyzed by t-tests and regression

(F-test) with a primary and secondary significance level (p⁰<0.05, p’<0.10). The

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secondary significance level was selected to reduce the probability of type II errors

because of the study’s low statistical power (n=10), and to allow us to investigate trends

among the long and short hydroperiod sites independently (n=5). The following methods

were utilized in the analysis of each hypothesis on ecosystem metric response.

Method 1: The influence of hydroperiod or mean water level, and water level STD on

each metric of structure and function was analyzed in a multivariate regression.

Method 2: Hydroperiod is discretized into short and long hydroperiod classes and

hypotheses are tested again by multivariate regression.

Method 3: In order to identify countervailing effects in each hydroperiod class, water

level STD is compared to metrics of ecosystem structure and function in a single

variable regression for each hydroperiod class.

Method 4: All hypotheses are tested for long and short hydroperiod sites separately in

order to identify sensitivity of ecological metric to hydrologic metric dependent upon

hydroperiod class.

28

Figure 2-1. Land surface elevation mapping of the Silver River, showing study sites for the comparison of ecological responses to hydrologic mean and variation. Transect along which sites where selected are numbered 1-5 from upriver to downriver. The L and S denote long and short hydroperiod respectively. The callout demonstrates the selection of study sites based on elevation denoted by grayscale. Elevation and modeled mean water level are used to identify study sites with hydroperiod in the target ranges (40% - 60 % inundation for long hydroperiod sites and 10% - 20% for short hydroperiod sites).Study sites are chosen based on modeled mean water level. Stage is recorded hourly where transects cross the river and at each study site.

29

Figure 2-2. Gradient in water level variation along the length of the Silver River. A) Hydrograph for 2010 showing river stage for the upper (T1), middle (T3), and lower (T5) portions of the Silver River. B) Box and whisker plots of 40 yrs. of back casted stage data for 5 locations along the Silver River. C) Autocorrelation function (ACF) data for 40 yrs of back casted data stage data at three locations along the upper, middle, and lower portions of the Silver River.

30

Figure 2- 3. Schematic of a 30 m by 30 m study site and the sampling frames for forest community structure (8 m by 8 m), understory vegetation (0.25 m2), bathymetry (on N-S, E-W 4 m axis) and litterfall collection (0.5 m2).

31

CHAPTER 3 RESULTS

Site Hydrology and Orthogonal Gradients of Mean and Variance

The observed gradients in mean and variance varied independently along the

length of the river (Fig 3-1). Flooding frequency, a metric used commonly to describe

hydrologic variation, was much better correlated to mean water level than was water

level STD (R2=0.22 vs. R2=0.03). When flooding frequency was defined by the number

of floods of X duration, the correlation between mean water level and flood frequency

increased with X (e.g., When X=60 days, R2=0.96). Original estimates of site elevation

and hydroperiod were made from LIDAR data. Later estimates of hydroperiod were

obtained by comparing the recorded water level in the floodplain and river at high stage.

The original hydroperiod predictions were generally accurate and within the defined

hydroperiod classes (Table 3-1), however the post-hoc estimate of hydroperiod was

much lower than expected at both T1 sites. Despite this the T1 sites share many

attributes with their original classification of hydroperiod, especially %SOM, and as such

are kept in the original classification.

While mean water level and hydroperiod are very well correlated (R2=0.96), both

metrics were still used to accommodate the evaluation of processes where actual

depths were relevant (mean water level) as well as processes where the wet/dry

fractions were more relevant (hydroperiod).

Forest Community Structure

Basal areas ranged from 31 to 132 m2/ha, and were dominated by Taxodium

distichum at all sites except T3L and T5S where a handful of large Fraxinas

carolinanand Acer rubrum dominated the basal area (Table 3-2). Basal area was

32

negatively correlated to the variation in hydroperiod among sites with in the short

hydroperiod class (R2=0.59, p=0.08). Understory percent cover was generally larger at

long hydroperiod sites, but was a function of LAI (R2=0.54) and not hydrologic metrics.

The prevalence of obligate wetland (OBL) and facultative wetlands (FACW)

plants, as defined by the national wetland plant list (Lichvar 2012), was generally higher

(Table 3-2) and was moderately significantly greater at long hydroperiod sites than at

short hydroperiod sites (p=0.07). However, variation in hydroperiod among sites was

only a significant predictor of the prevalence of OBL and FACW plant species at long

hydroperiod sites (R2=0.68, p=0.05). The prevalence of OBL and FACW plant species

was negatively correlated to water level STD at short hydroperiod sites (R2=0.92,

p<0.001) but that strong correlation was absent at long hydroperiod sites. I observed no

significant correlation between species richness and any hydrologic metrics.

Productivity

Litterfall (Table 3-3) was strongly concentrated in the autumn, with 79% of total

annual litterfall occurring between September and December and 91% of leaf litterfall

occurring during this period. Litterfall at all sites was dominated by leaf litter; non-leaf

litter (branches, reproductive material) comprised a small fraction (23% ± 7%; mean ± 1

STD) of the total mass, and was pooled for all subsequent analyses. Taxodium

accounted for the majority of leaf litterfall throughout the floodplain (56%), but across

sites there was a large range in the proportion of Taxodium leaf litterfall (28% - 88%).

Sabal palmetto accounted for a significant proportion of the ANPP estimate of the T2S,

T3S, T4S, and T5S sites (9 % - 20 %). Total litterfall (including Sabal palmetto litter)

was moderately correlated to hydroperiod (R2=0.26, p=0.076) and decreased with

33

longer hydroperiod. Litterfall excluding Sabal palmetto was not correlated to metrics of

hydrology. Leaf litterfall including Sabal palmetto was also well correlated to

hydroperiod (R2=0.59, p=0.006) exhibiting the same trend, however, canopy leaf litterfall

was only moderately correlated to hydroperiod (R2=0.28, p=0.067). There was no

significant response to water level STD in either case. Taxodium leaf litterfall was

positively correlated to water level STD, but only among long hydroperiod sites

(R2=0.78, p=0.03). Litterfall per unit basal area as a whole did not correlate to any

hydrologic metric. Similarly, Taxodium distchum leaf litterfall per unit basal area alone

was not correlated to any hydrologic metrics (Figure 3-2.a) However, litterfall per unit

basal area excluding Taxodium distichum was significantly negatively correlated to

water level STD and hydroperiod class in a multivariate model (Figure 3-2.b). The

slopes of independent fits of the data for each hydroperiod class are not significantly

different. The non-Taxodium leaf litterfall data for the short hydroperiod sites had a

better fit to water level STD than did the long hydroperiod sites (R2=0.53, p=0.09 vs.

R2=0.18, p=0.26).

Leaf area index varied significantly across sites, but was generally high (range

3.31 to 7.54 m2 m-2; Table 1). Replicate measurement of LAI in subsequent growing

seasons was highly correlated (R2 = 0.57) suggesting that site variation observed is

fairly robust to inter-annual variation. Leaf area index was significantly negatively

correlated to hydroperiod (Figure 5). Water level STD was not a significant predictor of

LAI and there was no correlation between LAI and water level STD within hydroperiod

classes. While both LAI and leaf litterfall declined with longer hydroperiod, they were

34

only significantly correlated when Sabal palmetto litter was included in leaf litterfall (R2

=0.57, p<0.001).

Morphological Adaptations

Cypress knees were present at all sites, and varied dramatically in their density,

mean height, and height variance (Table 3-2). Cypress knee density was moderately

well correlated to hydroperiod (R2=0.25, p=0.08), decreasing with longer hydroperiod.

Cypress knee height was strongly correlated to mean water level (Figure 3-4.a).

Variation in knee height was also positively correlated to water level STD, but only at

the long hydroperiod sites (Figure 3-4.b). The mean elevation of cypress knees was

inundated only 5.5% of the year at any site, suggesting that knees respond to high

water conditions. The mean elevation of cypress knees was between 50 and 80 cm

above the mean water level at all but one site, T1L, which had the shortest modeled

hydroperiod of any site (ca. 4%).

Organic Matter Accumulation

Soil organic matter content was high at all sites (Table 3-2), but histic epipedons

(i.e., > 20% OM) occurred only at the long hydroperiod sites. Soil organic matter content

was strongly depth dependent, but the pattern varied between short and long

hydroperiod sites. At short hydroperiod sites there was a consistent pattern of declining

SOM content with depth. In contrast, at long hydroperiod sites, there was an initial

decline in SOM with depth, but a consistent inflection below which SOM increased

again (Figure 3-5.a,b). Soil organic matter recalcitrance also varied with depth,

increasing to a threshold, below which it declined (Figure 3-5.a,b). Unlike SOM content,

35

this peak was observed at both long and short hydroperiod sites. The depth to this peak

was negatively correlated to mean water level (R2=0.16, p=0.018).

Depth averaged % SOM and recalcitrance index were strongly correlated to

hydroperiod (Figure 3-6.a). Average site %SOM was negatively correlated to water

level STD and positively correlated to hydroperiod in a multivariate regression (Figure 3-

6.b). While hydroperiod explains most of the variation in SOM content, the effects of

water level STD are significant and exert strong effects within hydroperiod classes,

especially for short hydroperiod sites (Figure 3-6.c). Soil organic matter recalcitrance

was not correlated to water level STD.

Microtopography

Although the distributions of soil elevation data were not bimodal (Fig 3-7),

elevation variation was positively correlated to hydroperiod class and water level STD in

a multivariate model (Fig. 3-8). The slopes of independent fits of soil elevation data for

each hydroperiod class were not significantly different. Soil elevation distributions for all

sites were skewed left (Table 3-2). However, the degree of skewness did not correlate

to hydrologic metrics.

36

Figure 3-1. Independence of hydrologic metrics. A) Hydroperiod and mean water level are strongly correlated and co-vary. B) Mean water level is not correlated to water level STD demonstrating that hydrologic metrics for mean (hydroperiod and mean water level) are orthogonal of metric of variance (water level STD).

37

Table 3-1. Hydrologic characteristics of study sites along the Silver River.

Study Site

Metric T1L T2L T3L T4L T5L T1S T2S T3S T4S T5S Hydroperiod (% inundation) 21.7 60.8 46.1 46.2 46.2 4.5 13.3 16.8 12.3 21.0 Mean Water Level (m) -0.22 0.06 -0.01 0.00 -0.01 -0.47 -0.34 -0.31 -0.40 -0.31 Water level STD (m) 0.27 0.28 0.31 0.33 0.38 0.27 0.28 0.31 0.33 0.38

38

Table 3-2. Characteristics of study sites along the Silver River.

Study Site

Metric T1L T2L T3L T4L T5L T1S T2S T3S T4S T5S Basal Area (m²·ha⁻¹) 63.6 89.4 31.1 57.0 132.5 70.4 55.7 51.7 77.0 41.1 Taxodium 25.7 57.0 19.8 14.6 74.3 54.0 42.1 48.2 46.8 13.3 Non-Taxodium 37.8 32.5 11.3 42.4 58.2 16.4 13.6 3.5 30.3 27.8

Projected Leaf Area Index 4.8 ± 0.08

3.3 ± 0.43

4.2 ± 0.09

4.2 ± 0.55

3.5 ± 0.05

4.9 ± 0.01

7.54 ± 0.15

5.9 ± 0.56

6.5 ± 1.11

5.1 ± 0.19

Stem Density (stems per ha) 703 625 625 508 664 508 2852 586 781 2344 Understory Cover (%) 79 87 116 14 108 43 4 18 0 0 Fraction Wetland Taxa 0.60 0.94 0.84 0.89 0.99 0.86 0.73 0.70 0.58 0.19 Cypress Knee Density (knees per 100 m²) 31.3 16.0 14.5 31.3 45.3 27.0 173.8 87.9 146.5 74.2

Cypress Knee Height (cm) 49 ± 15

70 ± 18

52 ± 23

71 ± 23

78 ± 33

41 ± 17

29 ± 15

26 ± 17

17 ± 17 20 ± 10

Soil Elevation Variance (cm) 6.38 8.70 9.00 8.43 10.22 5.03 4.95 3.73 6.45 6.62

Soil Organic Matter Content (%)

27.7 ± 1.7

56.8 ± 3.5

33.1 ± 1.7

43.9 ± 2.3

34.5 ± 6.6

13.4 ± 2.0

11.8 ± 2.3

10.7 ± 2.4

5.2 ± 0.8

6.3 ± 0.5

Soil Organic Matter Recalcitrance Index

0.39 ± 0.03

0.26 ± 0.02

0.26 ± 0.01

0.31 ± 0.00

0.31 ± 0.02

0.34 ± 0.01

0.38 ± 0.01

0.32 ± 0.03

0.47 ± 0.02

0.41 ± 0.01

39

Table 3-3. Litterfall of study sites along the Silver River.

Study Site

Litterfall (g·m-2·yr-1) T1L T2L T3L T4L T5L T1S T2S T3S T4S T5S Total Leaf 569 436 404 614 545 642 646 491 626 487 Taxodium 158 252 239 290 343 367 436 433 254 238 Other 411 184 166 324 201 275 210 58 372 249 Woody 137 85 41 161 91 124 87 85 109 24 Totoal Reproductive 102 99 23 94 81 92 73 62 100 19 Taxodium 72 74 19 53 66 83 67 54 28 2 Other 30 24 4 41 15 8 6 8 72 17 Sabal Palmetto - - - - - 24 73 132 71 80 Total 811 624 469 874 721 885 882 774 911 610

40

Figure 3-2. Multivariate regression of the response of non-Taxodium leaf litterfall to water level STD and hydroperiod by class. The regression shows that hydroperiod and water level STD influence the leaf litterfall of non-Taxodium species

41

Figure 3-3. Leaf Area Index. The leaf area index of all study sites decreases with hydroperiod even within classes of long and short hydroperiod.

42

Figure 3-4. Cypress knee structure compared to hydrologic metrics. A) The height of cypress knees is positively correlated to mean water level. B) The STD in cypress knee height is positively correlated to the water level STD at long HP sites. However, there is no correlation between the knee height STD and water level STD at short HP sites.

43

Figure 3-5. Soil organic matter content depth profile from three sites which demonstrate trends in soil organic matter quantity and quality with depth. A) Soil organic matter content depth profile of core T2L-1 demonstrating the trend amoung long hydroperiod sites. Notice the pronouced inflection at intermediate depth. B) Soil organic matter content depth profile of core T2S-1 that demonstrates the trend amoung short hydroperiod sites. C) Recalcitrance index depth profile of core T4L-3 that demonstrates the trend observed at long and short hydroperiod sites. Notice the inflection in recalcitrance at depth.

44

Figure 3-6. Soil organic matter (SOM) content response to hydrologic metrics. A) Percent soil organic matter is positively correlated to hydroperiod while the recalcitrance index is negatively correlated to hydroperiod. B) 3D mesh of the multivariate model describing the response of %SOM to hydroperiod and water level STD. The %SOM increases with hydroperiod and decreases with water level STD. C) The % SOM is positively correlated to water level STD for short hydroperiod sites, but not for long hydroperiod sites.

45

Figure 3-7. Histograms of relative elevation measurements at sites with low, moderate, and high microtopographic relief.

46

Water Level STD (m)

0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.40

Soil

Elev

atio

n ST

D (c

m)

3

4

5

6

7

8

9

10

11

Long HPShort HPMultivariate regression

R2=0.80, p=0.002

Figure 3-8. The STD of soil elevation measurements modeled by multivariate regression to hydroperiod and water level STD.

47

CHAPTER 4 DISCUSSION

The floodplain of the Silver River provides a model system for investigating

effects of mean environmental conditions independent from those of variation around

the mean. Several features are of particular note. First, the backwater flooding and

length of the river create a strong gradient in hydrologic variation between stable

conditions at the spring vent and marked event-driven variation at the downstream

confluence. This longitudinal gradient is orthogonal to the typical lateral gradients in

hydroperiod present in riparian swamps. Second, the absence of scouring floods

(floodwaters are typically the slowest velocity flows during these backwater flooding

rivers) means that flooding events are not characterized by variable flow throughout the

floodplain, and measured conditions are integrative over long time periods. Finally,

because the gradients occur over relatively short distances (ca. 8 km), concerns about

dispersal and climate variation confounding comparisons among more distant sites are

alleviated.

Despite site aspects that are well suited to addressing the core question of this

research, several important aspects of historical land use and floodplain morphology

merit consideration. First and foremost, this floodplain was subject to timber extraction

in the last 100 years, but the timing and spatial extent of this is largely unknown.

Second, water quality in the river has declined over the last 50 years, principally from

nitrate enrichment, which may have altered floodplain wetland function (though

presumably relatively uniformly across sites). Finally, there are several notable relict

channels and lateral inputs of unknown variation across the floodplain. However, sites

were located so as to minimize the possible influence of these inputs. While these

48

issues merit consideration in the interpretation of results, none seems to significantly

occlude the findings of the study.

There were a couple possible issues with the data analysis of the study. First, the

shorter than expected hydroperiod of the most upstream “long hydroperiod” site (T1L)

may merit its inclusion in the short hydroperiod class; however, it was kept in the long

hydroperiod class because it exhibited many of the characteristics of long hydroperiod

sites, in particular, the high SOM content. Second, Sabal Palmetto may have biased LAI

and total litterfall measurements as it was only prevalent at short hydroperiod sites.

Comparing Silver River Floodplain to Other Floodplains

Although the Silver River floodplain has features that distinguish it from other

floodplains in the region, I observed important similarities with other riparian and non-

riparian wetlands of the southeastern United States. While average leaf litterfall (545 ±

83 g/m2/yr) fell within the range reported for southeastern floodplain forests (405-837

g/m2/yr) (Brown 1981, Megonigal et al. 1997, Shure et al. 1985, Cuffney 1988, Conner

and Day 1992, Clawson et al. 1996), T3L was just below the range. Stem density is not

widely reported in the literature and available figures suggest a large range (530 – 2000

stems/ha; Dabel and Day 1977, Brown 1981, Megonigal et al. 1997), but mean stem

density in the Silver River was within this range (1010 stems/ha >2.5 cm DBH) despite

two study sites above (T2S and T5S) and two below (T4L and T1S) the range. Mean

basal area across sites (67 ± 27 m2/ha, 60 ± 17 m2/ha excluding T5L: 132 m2/ha) was

higher than values reported for other floodplains (22-47 m2/ha; Brown 1981, Megonigal

et. al. 1997, Jones 1981, Clawson et al. 1996), potentially as a result of the timing and

extent of historical logging, which may have been greater in the large southeastern

49

floodplain forests that have been the focus of most ecological research. This may also

be evident in observed Taxodium distchum dominance along the Silver River. Taxodium

distchum is a commercially attractive wetland tree species and is very slow to

reestablish after harvest (Conner et al. 1986, Dunn and Shartiz 1987). Taxodium

distchum accounts for 59% of basal area and 55% of leaf litterfall in the Silver River

floodplain. The large basal area and dominance of Taxodium compare more favorably

to alluvial swamps such as the Okefenokee Swamp and the Dismal Swamp (52 - 87

m2/ha of which 50 – 73 % is from Taxodium), which are less accessible to harvest

equipment and have a legacy of protection (Dabel and Day 1977, Schlesinger 1978).

The large range in SOM content throughout the Silver River floodplain was

consistent with other southeastern wetlands. The SOM content of the short hydroperiod

sites was within the range of reported SOM content for floodplains (Bruland and

Richardson 2006, Craft 2000). However, SOM content for the long hydroperiod sites

was outside this range, but well within the reported range for non-riverine swamps

(Bruland and Richardson 2006). While this is likely partially due to variation in

hydroperiod, this may also result from the lack of high flow velocities during large floods,

which limits flooding scour that may arise in other rivers. In either case, SOM in the

Silver River floodplain is typical of southeastern wetlands and spans the range of

reported values for both floodplain and non-riverine wetlands.

Dual Control of Ecosystem Metrics

Across all ecosystem attributes measured, my results lend strong support to the

primary hypothesis of this study, and suggest that both hydroperiod and water level

variation regulate the structure and function of floodplain wetlands. Perhaps more

50

importantly, the results also indicate that hydroperiod and water level variation exert

interdependent control on ecosystem structure and function.

Hydroperiod consistently exerted a stronger effect than water level STD,

supporting the use of hydroperiod as the primary metric of hydrology. However, water

level STD explained a significant portion of the variation in ecosystem metrics,

especially within hydroperiod classes and clearly merits consideration.

Both mean and variance appeared to be important regulators of primary

production, though the impact of water level variation was species dependent.

Aboveground net primary productivity estimates (from tree and palm litterfall) and LAI

both decreased with increased hydroperiod, but were not significantly influenced by

water level variation. Taxodium distichum leaf litterfall was positively correlated to water

level variation at long hydroperiod sites; however, Taxodium distichum leaf litterfall per

unit basal area was independent of hydrologic metrics. In contrast, leaf litterfall per unit

basal area of all other tree species decreased with both hydroperiod and water level

variation, as I hypothesized. This suggests that other variables contribute to the

regulation of Taxodium distichum productivity (e.g., reduced competitive stress from

other taxa) and highlights the species adaptability to a wide range of hydrologic

conditions.

Both mean and variance in water levels also regulated wetland organic matter

dynamics. While my results support the hypothesis that SOM quantity increases with

longer hydroperiod and decreases with greater water level variation, SOM quality only

responded to hydroperiod. Numerous studies have investigated the effects of flooding

and inundation on soil processes with varied findings (Brinson et. al. 1981, Day 1983,

51

Shure and Gottschalk 1986, Lockaby 1996). However, few studies have demonstrated a

role of water level variation (Reddy and Patrick 1975, Baker III et al. 2001, Battle and

Golladay 2001), increases in which led to decreased SOM content. While hydroperiod

effects were generally stronger, water level STD explained most of the remaining SOM

content variation. Water level variation may enhance decomposition, reducing SOM

content either by promoting frequent switching from anaerobic to aerobic decomposition

or by allowing more oxygen to enter the soil while still widely dispersing exoenzymes

(Reddy and Patrick 1975). Due to very low flow velocities within the floodplain, scour of

OM is unlikely to be significant.

Hydrology appears to exert less control on SOM recalcitrance than SOM content.

Inferences about SOM recalcitrance are likely limited by low method resolution;

however, because labile OM is preferentially consumed, it seems unlikely that water

level variation would impact SOM quantity without also influencing SOM quality.

Soil elevation bimodality is a hallmark feature of patterned landscapes such as

boreal bogs (Foster et al. 1983, Charman 2002) and the ridge-slough mosaic of the

Everglades (Wu et al. 2006, Watts et al. 2010). However the absence of the predicted

bimodal elevations suggests that feedbacks between hummock elevation and

productivity are not as regular. Floodplain wetlands produce more woody debris than

the marsh and moss-dominated wetlands where bimodality is most often observed. It is

possible that woody debris such as large branches and fallen trees may be creating

elevated microsites stochastically at a rate greater than self-reinforcing processes (i.e.,

balance of SOM production and respiration) can regulate their abundance or height.

However, the increase in soil elevation variation in response to increasing hydroperiod

52

and water level variation as well as the skewness of the distributions towards higher

elevations may be diagnostic of low prevalence hummocks which form as a result of

feedbacks between organic matter accumulation, primary production and reduced

hydroperiod. While hydroperiod explains most of the total variation in soil elevation

variance, water level variation explained a significant portion of the variation in

topography within hydroperiod classes. Although I found no evidence of increased

species richness with water level variation or soil elevation variation, other studies have

observed that microtopography is an important factor in maintaining and enhancing

biodiversity (Beatty 1984, Titus 1990, Scarano et al. 1997, Vivian-Smith 1997, Pollock et

al. 1998, Simmons et al. 2011, Washuta 2011).

Cypress Knees

Despite insufficient evidence to demonstrate that cypress knees provide direct

benefits to the tree, my data clearly support the hypothesis that mean and variation in

cypress knee height is regulated by hydrology. This, in turn, strongly suggests a

dynamic feedback between tree investments in cypress knee development and

exogenous drivers. I note in particular that across sites, knees consistently grew to a

height not inundated more than 5% of the time. The ongoing debate about the role of

knees remains unresolved, but these data provide evidence that may help reject some

prevailing hypotheses. For example, Kummer et al. (1991) suggested that the primary

function of cypress knees was to extract nutrients from stumps. Brown and Montz

(1986) suggested that knees may store starches. Lamborn (1890) proposed that knees

helped to stabilize cypress trees by penetrating the subsurface root mat. Finally,

Kramer et al. (1952) suggested that cypress knees growth is triggered by inundation

53

stress. In this work, longer hydroperiod did not result in greater knee prevalence, but did

affect knee height, which would suggest a feedback between environmental controls on

inundation stress and tree investment in knee biomass. I posit that even modest gas

exchange is sufficient to alleviate inundation stress creating a feedback that regulates

cypress knee growth. Within the long hydroperiod sites, water level variation is strongly

correlated with variation in cypress knee heights, suggesting that cypress invests in a

range of knee heights that balance the increasing metabolic costs of knees as they get

taller with the relative frequency of high stage events; in short, cypress trees invest in a

portfolio of knees that is commensurate with their hydrologic regime. These results

support the contention in Kernell and Levy (1990) that cypress knees respond to

hydrology and that the height of the tallest knees correlates to high water events.

Contingent Effects of Hydrology

Many measured attributes of ecosystem structure and function exhibited greater

sensitivity to water level variation within a single hydroperiod class, suggesting strong

interaction effects wherein the influence of hydroperiod and water level variation are

interdependent. These interactions are difficult to identify due to the low power of the

study, however, several significant results support this supposition. I observed strong

effects of water level variation on leaf litterfall per unit basal area (decrease), wetland

taxa prevalence (decrease), and SOM content (decrease) at short hydroperiod sites.

Similarly I observed strong effects of hydroperiod variation on basal area, but principally

at short hydroperiod sites. This likely arises because all of these functions are controlled

by impacts of soil saturation on gas exchange with the atmosphere. At short

hydroperiod sites, mean water levels are typically below the soil surface. As a result,

54

variation in water level may result in greater change to the status of gas transfer.

Likewise, even small differences in mean water level or hydroperiod among short

hydroperiod sites may result in significantly greater gas exchange. In contrast, water

levels during the growing season in the long hydroperiod sites are generally above the

soil surface. Consequently, differences in mean water level are less likely to significantly

expose the soil column to allow gas exchange, muting the impacts of both variation in

mean water level across long hydroperiod sites and the effects of water level variation

within sites.

The mean and variation in the height of cypress knees was more responsive to

hydrology at the long hydroperiod sites. Cypress knees at the short hydroperiod site

may not have been particularly sensitive to hydrology because soil saturation, not

inundation depth, controls knee formation at short hydroperiod sites. Therefore knees

need only to break the soil surface at short hydroperiod sites to be useful to the tree.

Soil Organic Matter Recalcitrance Peak

I expected that SOM recalcitrance would increase with depth as decomposition

incrementally consumes labile SOM deposited at the soil surface. This prediction

assumes that the age of organic matter increases with depth in the soil column.

However, in every soil core, SOM recalcitrance increased to a peak and then decreased

indicating that labile OM is being introduced into the soil column throughout the soil

profile. This OM may be introduced by roots and root exudates. Furthermore, this

suggests that below the SOM recalcitrance peak (usually 20-30 cm below the soil

surface), the OM lability is conserved due to increased anoxia and thus reduced

decomposition. This provides evidence that a periodically aerobic soil layer (acrotelm)

55

overlays a permanently anoxic soil layer (catotelm) throughout the floodplain and that

the thickness and depth of the acrotelm is a function of mean water level.

Management Implications

Wetlands are conferred special protections because of their significant role in

delivery of ecosystem services. Among the most important services are biodiversity,

carbon storage, and nutrient retention and transformations (Gren et al. 1994), the loss

of which has implications at multiple scales. This study shows that carbon storage is

partially controlled by water level variation. The accumulation of organic matter also

stores and retains nutrients. This study has also shown that microtopography is also

influenced by water level variation and other studies have shown microtopography to

enhancing biodiversity (Beatty 1984, Titus 1990, Scarano et al. 1997, Pollock et al.

1998, Vivian-Smith 1997, Simmons et al. 2011, Washuta 2011).

While managing for water level variation is clearly important in maintaining

ecosystem services, current regulatory approaches to managing environmental flows

often fail to adequately consider hydrologic variation (though see Richter et al. 1997).

The majority of regulatory methods for determining the maximum allowable withdrawal

from a water body are based on maintaining flows and levels which prevent significant

harm to dependent water bodies and wetlands; such is the case with the Minimum

Flows and Levels (MFLs) mandate placed on Florida’s environmental regulators by the

1972 Florida Water Resources Act. Minimum flows and levels are often quantified and

represented largely by flow-duration curves which relate flow/stage to the percentage of

the time it is exceeded (Beecher 1990, Gillilan and Brown 1997, Tharme 2003,

Neubauer et al. 2008). While this method acknowledges that there is no meaningful

56

“minimum” but rather a suitable regime, it does not allow for management of the

duration and return intervals of flooding events, which are crucial components of the

hydrologic regime and characterize water level variation (Gordon et. al. 1991, Neubauer

et al. 2008). More robust methods, like those implemented by the St.Johns River Water

Management District (SJRWMD) in north east Florida, define the duration and

frequency of a handful of flooding events (Neubauer et al. 2008). While this practice

addresses the importance of variation, it only maintains a few hydrologic events rather

than protecting the water level variation regime. As we seek to conserve our valuable

ecosystems and the underlying drivers that regulate their structure and function, we

need to evaluate and manage for both aspects of the drivers, defining not only a

characteristic mean, but also a characteristic variance.

57

APPENDIX A ADDITIONAL SITE CHARACTERISTICS

Table A-1. Leaf litterfall indexed to species basal area.

Leaf Litterfall per unit Basal Area (g·m-2·yr-1)

Study Site T1L T2L T3L T4L T5L T1S T2S T3S T4S T5S

Total 8.9 4.9 13 11 4.1 9.1 12 9.5 8.1 12 Taxodium 6.1 4.4 12 20 4.6 6.8 10 9 5.4 18 Other 11 5.7 15 7.6 3.5 17 15 16 12 9

Table A-2. Additional site characteristics.

Study Site

Metric T1L T2L T3L T4L T5L T1S T2S T3S T4S T5S Soil Elevation Skew 0.65 0.35 0.65 0.17 1.57 0.95 0.59 0.11 0.35 0.21 Species Richness 20 10 7 11 10 19 9 18 10 6 Recalcitrance Peak Depth (cm)

26 ± 5.8

16 ± 6.4

27 ± 5.2

24 ± 0.0

27 ± 4.8

38 ± 5.5

35 ± 1.6

21 ± 1.6

22 ± 2.1

30 ± 1.6

58

APPENDIX B BASAL AREA AND COVER BY SPECIES

Table B-1. Long hydroperiod site percent cover by species. A zero indicates species presence without contribution to overall cover. Basal area by species (m2/ha)

Table B-2. Short hydroperiod site percent cover by species. A zero indicates species presence without contribution to overall cover. Basal area by species (m2/ha)

Site Acer

rubr

um

Bide

ns sp

.

Boeh

mer

ia

cylin

dric

a

Carp

inus

ca

rolin

iana

Diod

ia v

irgin

iana

Hydr

ocot

yle

bow

lesio

ides

Hym

enoc

allis

sp.

Mik

ania

sc

ande

ns

Pani

cum

ab

sciss

um

Pers

icar

ia

amph

ibia

Phyl

a fr

utic

osa

Taxo

dium

di

chtu

m

Toxi

code

ndro

n ra

dica

ns

Ulm

us

amer

ican

a

Unk

now

n gr

ass

Vitis

rotu

ndifo

lia

T5S

T4S

0

T3S 17

0 5 2 40

6 5 0 32 30 T2S 5

0

1 0

T1S 26 8 55 0 105 11

35 10 3 4 7

Site Lem

na m

inor

Pani

cum

ab

sciss

um

Ulm

us

amer

ican

a

Acer

rubr

um

Pont

eder

ia

cord

ata

Boeh

mer

ia

cylin

dric

a Sa

urur

us

cern

uus

Rhyn

chos

pora

sp

. Lo

belia

ca

rdin

alis

Hydr

ocot

yle

amer

ican

a

Ludw

igia

sp.

Pers

icar

ia

amph

ibia

Frax

inus

pe

nnsy

lvan

ica

T5L 100 18 1 1 T4L

5

5 0 4

T3L 68

13

19 0 T2L 13

51 0

15

1 0

T1L

20

1

3 3 28

1

12 1

59

Table B-3. Basal are by species (m2/ha).

Site Ta

xodi

um

dist

ichu

m

Acer

rubr

um

Fras

inua

pe

nnsy

lvan

ica

Ulm

us

amer

ican

a

Nys

sa

aqua

tica

Saba

l pa

lmet

to

Pers

ea

palu

stris

Que

rcus

nig

ra

Frax

inus

am

eric

ana

Liqu

idam

bar

styr

acuf

lua

T1L 25.74 8.86 4.41 16.24 8.32 0.63 T2L 56.97 5.36 27.11

T3L 19.79 4.91 6.39 T4L 14.61 6.02 20.66

13.49

1.11 1.14 T5L 74.29

15.78 1.17 41.25

T1S 53.98 3.26 12.43 0.76

6.67 T2S 42.07 0.06 13.58

20.70

T3S 48.19

1.10

21.72 2.42 T4S 46.75 0.03 0.44 1.70

4.71

11.34 16.75

T5S 13.27 27.58 0.21

31.78

60

APPENDIX C ANNUAL LITTERFALL

Figure C-1. Annual litterfall for four study sites in the Silver River floodplain. A) The most upstream long hydroperiod site. B) The most upstream short hydroperiod site. C) The most downstream long hydroperiod site. D) The most downstream short hydroperiod site.

61

APPENDIX D SOIL CORE DATA

Table D-1. Soil core data. Mean water level and hydroperiod are calculated relative to the elevation of the core top.

Soil Core

Organic Matter

Fraction Recalcitrance

Index

Depth to Recalcitrance

Peak (cm)

Mean Water Level Hydroperiod

T1L-1 0.30 0.36 17 -22 0.21 T1L-2 0.26 0.42 30 -21 0.22 T1L-3 0.27 0.39 29 -20 0.24 T2L-1 0.53 0.29 25 7 0.59 T2L-2 0.56 0.25 11 10 0.64 T2L-3 0.61 0.25 11 13 0.67 T3L-1 0.36 0.27 24 4 0.51 T3L-2 0.32 0.26 34 -4 0.41 T3L-3 0.32 0.27 22 -5 0.40 T4L-1 0.42 0.32 24 0 0.45 T4L-2 0.46 0.31 24 16 0.66 T5L-1 0.40 0.31 21 8 0.53 T5L-2 0.38 0.29 30 11 0.57 T5L-3 0.25 0.33 32 10 0.56 T1S-1 0.11 0.35 36 -50 0.03 T1S-2 0.13 0.33 33 -47 0.04 T1S-3 0.16 0.36 45 -46 0.04 T2S-1 0.09 0.37 33 -33 0.13 T2S-2 0.15 0.37 37 -29 0.16 T2S-3 0.12 0.39 35 -30 0.15 T3S-1 0.07 0.35 21 -33 0.15 T3S-2 0.13 0.28 23 -24 0.21 T3S-3 0.12 0.31 19 -25 0.21 T4S-1 0.06 0.46 24 -38 0.14 T4S-2 0.04 0.49 20 -52 0.08 T5S-1 0.06 0.41 27 -28 0.22 T5S-2 0.07 0.40 31 -40 0.16 T5S-3 0.07 0.42 31 -36 0.18

62

Organic Matter Fraction0.0 0.1 0.2 0.3 0.4 0.5 0.6

Dep

th (c

m)

0

10

20

30

40

Recalcitrance Index0.0 0.1 0.2 0.3 0.4 0.5 0.6

Labile OM Recalcitrant OM Recalcitrance Index

T2L-1Organic Matter Fraction

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

10

20

30

40

Recalcitrance Index0.0 0.1 0.2 0.3 0.4 0.5

T3L-1Organic Matter Fraction

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

10

20

30

40

Recalcitrance Index0.0 0.1 0.1 0.2 0.2 0.3 0.3 0.4

T4L-3Organic Matter Fraction

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Dep

th (c

m)

0

10

20

30

40

Recalcitrance Index0.0 0.1 0.2 0.3 0.4 0.5

T5L-1Organic Matter Fraction

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

10

20

30

40

Recalcitrance Index0.0 0.1 0.2 0.3 0.4 0.5

T1L-1

Figure D-1. Soil core profiles for long hydroperiod sites. One soil core profile is shown for each site. Profiles show the organic matter fraction split between the labile and recalcitrant fractions as well as the recalcitrance index.

63

T1S-2Organic Matter Fraction

0.00 0.05 0.10 0.15 0.20 0.25

Dep

th (c

m)

0

10

20

30

40

Recalcitrance Index0.0 0.1 0.2 0.3 0.4 0.5

Labile OMRecalcitrant OMRecalcitrance Index

T2S-1Organic Matter Fraction

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.180

10

20

30

40

Recalcitrance Index0.0 0.1 0.2 0.3 0.4 0.5

T3S-2Organic Matter Fraction

0.00 0.05 0.10 0.15 0.20 0.25 0.300

10

20

30

40

Recalcitrance Index0.0 0.1 0.2 0.3 0.4

T4S-2Organic Matter Fraction

0.00 0.02 0.04 0.06 0.08

Dep

th (c

m)

0

10

20

30

40

Recalcitrance Index

0.0 0.1 0.2 0.3 0.4 0.5 0.6

T5S-1Organic Matter Fraction

0.00 0.02 0.04 0.06 0.08 0.10 0.120

10

20

30

40

Recalcitrance Index

0.0 0.1 0.2 0.3 0.4 0.5 0.6

Figure D-2. Soil core profiles for short hydroperiod sites. One soil core profile is shown for each site. Profiles show the organic matter fraction split between the labile and recalcitrant fractions as well as the recalcitrance index.

64

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71

BIOGRAPHICAL SKETCH

Joseph Delesantro received his bachelor’s degree in environmental engineering

from the University of Florida in 2010. He remained at the University of Florida, moving

over to the School of Forest Resources and Conservation to pursue his master’s

degree, completed in 2013. He plans to find employment in the field of environmental

hydrology and work towards the sustainable use of water resources.