A Biological Mechanism for Enhanced Wading Bird Foraging

A BIOLOGICAL MECHANISM FOR ENHANCED WADING BIRD FORAGING

PATCHES IN SEASONALLY-PULSED WETLANDS

Thomas J. Faughnan

A Thesis Submitted to the Faculty of

The Charles E. Schmidt College of Science

In Partial Fulfillment of the Requirements for the Degree of

Master of Science

Florida Atlantic University

Boca Raton, FL

August 2018

ACKNOWLEDGEMENTS

First, I would like to thank my advisor, Dr. Dale Gawlik, for working with me to turn an idea into a successful research project. I would also like to thank my thesis committee members Dr. John Baldwin, Dr. Laura Brandt, and Dr. Erik Noonburg for editing my drafts and pushing me to think critically. I would like to thank my lab mates of the Avian Ecology Lab for helping me get through the analysis and editing process. I wish to thank my long-term volunteers: Heather Douglas, Travis DuBridge, Monica

Garcia, Anne Mauro, Jessica Novobilsky, Liz Scarlett, and Carl Underwood. They spent countless hours on the airboat and without them I could not have collected any data.

Lastly, I would like to thank the U.S. Army Corps of Engineers for financial support during this research.

iv ABSTRACT

Author: Thomas J. Faughnan

Title: A biological mechanism for enhanced wading bird foraging patches in seasonally-pulsed wetlands

Institution: Florida Atlantic University

Thesis Advisors: Dr. Dale E. Gawlik

Degree: Master of Science

Year: 2018

In tropical wetlands, breeding wading birds rely on concentrations of aquatic fauna during the dry season to meet increased energetic demands. Wetland microtopography increases aquatic fauna concentration levels. Crocodilians modify the landscape creating deep-water refugia but their role as a mechanism for aquatic fauna concentration is unknown. I sampled alligator (Alligator mississippiensis) abundance and slough microtopography to examine correlation between the two measures. Despite increased microtopography in high alligator use sloughs, the differences were not significant. Using an in situ experimental approach, I quantified the magnitude, timing, and spatial extent of aquatic fauna concentrations within simulated alligator depressions and the surrounding marsh. Aquatic fauna density and biomass were greater within simulated depressions, thus enhancing wading bird foraging habitat. Further understanding the mechanisms creating microtopography, thus enhancing wading bird

v habitat, is critical to facilitate restoration and prevent declines of wading bird populations in seasonally pulsed wetlands worldwide.

vi A BIOLOGICAL MECHANISM FOR ENHANCED WADING BIRD FORAGING

PATCHES IN SEASONALLY-PULSED WETLANDS

LIST OF TABLES ...... ix

LIST OF FIGURES ...... xii

1 INTRODUCTION ...... 1

2 CROCODILIAN USE AS A MECHANISM FOR INCREASED WETLAND

MICROTOPOGRAPHY...... 6

2.1 INTRODUCTION ...... 6

2.1.1 Crocodilians as Ecosystem Engineers ...... 6

2.1.2 The American Alligator and Ecosystem Function ...... 7

2.1.3 Microtopography...... 9

2.2 METHODS ...... 10

2.2.1 Study Site Selection ...... 10

2.2.2 Alligator Surveys ...... 10

2.2.3 Microtopography Transect ...... 12

2.2.4 Analysis...... 14

2.3 RESULTS ...... 15

2.3.1 Alligator Surveys ...... 15

2.3.2 Microtopography Transects ...... 16

2.4 DISCUSSION ...... 16

2.4.1 Effects of Alligator Presence on Microtopography ...... 16

vii 2.4.2 Conclusion ...... 18

3 THE EFFECTS OF DEPRESSIONS ON AQUATIC FAUNA

CONCENTRATION IN A SEASONALLY-PULSED WETLAND ...... Error!

Bookmark not defined.

3.1 INTRODUCTION ...... 33

3.2 METHODS ...... 35

3.2.1 Study Site Selection ...... 35

3.2.2 Aquatic Fauna Sampling ...... 36

3.2.3 Experimental Design ...... 37

3.2.4 Statistical Methods ...... 40

3.3 RESULTS ...... 42

3.3.1 Aquatic Fauna Biomass ...... 42

3.3.2 Aquatic Fauna Density ...... 43

3.3.3 Large Aquatic Fauna Biomass ...... 44

3.3.4 Large Aquatic Fauna Density ...... 44

3.3.5 Aquatic Fauna Species Composition ...... 45

3.4 DISCUSSION ...... 45

4 SYNTHESIS ...... 70

viii LIST OF TABLES

Table 2.1. Total alligators observed during four nocturnal airboat surveys within

Water Conservation Area 2A. Alligators fell within 100-meter detection zone

along a 40.7 km survey route. Individuals were grouped by size class based on

total length (TL)...... 20

Table 2.2. Relative alligator abundance at selected microtopography transect sites.

Relative abundance was calculated by creating a kernel density map based on

all individual sightings. Hatchlings were excluded based on the assumption that

they are not influencing microtopography. Standard deviation (SD) given for

group means...... 21

Table 2.3. Microtopography indices for high and low alligator density sites and

results of comparisons. P-values were calculated from t-test comparison of high

and low alligator abundance sloughs. R2 is the coefficient of determination and

represents the proportion of microtopography index variance explained by the

alligator density index...... 22

Table 2.4. Examples of important* seasonally-pulsed tropical wetlands around the

world. These wetlands share similar characteristics which link aquatic fauna,

crocodilians, and wading birds...... 23

Table 3.1. Descriptive statistics for all fauna. N=number of samples...... 51

Table 3.2. Summary of optimal models for aquatic fauna density and biomass.

Parameter estimates (β), standard error (SE), and lower and upper 95%

ix confidence limits (LCL, UCL) shown. The biomass response variable was cube

root transformed and is shown in this format. ForagingDays is the total number

of wading bird foraging days under 40 cm of water preceding sample date.

MeanDepth is the mean water depth at control trap locations...... 52

Table 3.3. Effects of treatment on aquatic fauna density and biomass indices.

Standard Deviation (SD), Standard Error (SE), and lower and upper 95%

confidence limits (LCL, UCL) are reported. Adjusted means control for

unbalanced sampling of water depths and foraging days. Predicted biomass and

density values were calculated throughout wading bird foraging depths using a

linear mixed model. Confidence limits were calculated using parametric

bootstrapping (r=1000). Predicted values for Near Control treatment were

omitted because lack of data at low water depths caused incorrect model fit...... 53

Table 3.4. Descriptive statistics for large fauna (> 5 cm) density and Biomass. N is

the sample size for respective treatments. i is the number of fish greater than 5

cm in total length...... 54

Table 3.5 List of species captured ordered by the number of traps which contained

respective taxa (N). Sunfish group includes: Redear sunfish (Lepomis

microlophus), Bluespotted sunfish (Enneacanthus gloriosus), Everglades

pygmy sunfish (Elassoma evergladei), and unidentified sunfish. Cichlid group

contains Mayan cichlids (Cichlasoma urophthalmus), and unknown cichlids.

Treatments are Near Control (NC), Depression (D), and Distant Control (DC)...... 55

Table 3.6. Species Richness by treatment type...... 56

x Table 3.7. Totals for large fish (Total Length (TL) ≥ 5 cm) within depression

treatments...... 57

xi LIST OF FIGURES

Figure 2.1. World map showing the distribution of the American alligator, the order

crocodilia (IUCN, 2016) and tropical climates (Kottek et al. 2006, Rubel and

Kottek 2010). Selected wetlands were chosen from the Ramsar list of Wetlands

of International Importance. Inset shows Florida Everglades, USA study site.

These wetlands share similar characteristics which link aquatic fauna,

crocodilians, and wading birds (Table 2.4). Map produced with Esri ArcMap

10.4...... 24

Figure 2.2. Aerial image showing great egrets (Ardea alba) foraging within disturbed

areas in an Everglades slough. Disturbances may be caused by alligators...... 25

Figure 2.3. Conceptual model showing hypothesized effects of ecosystem

engineering by American alligators within sloughs during the dry season in the

Everglades.1 (Craighead, 1968), 2 (Wade, et al., 1980), 3 (Davis, et al., 1994), 4

(Sklar, et al., 2002), 5(Kahl 1964, Kushlan et al. 1975, Carlson and Duever

1979, Alho 2005), 6(Herring and Gawlik 2012, Botson et al. 2016, Gawlik

2002), 7(Botson et al. 2016, Dvorac and Best 1982), 8(Botson et al. 2016, Loftus

and Eklund 1994), 9(Kahl 1964, Powell 1983, Powell & Powell 1986, Powell et

al. 1989, Frederick and Spalding 1994, Bancroft et al. 1994, Hoffman et al.

1994, Herring et al. 2010)...... 26

Figure 2.4. Map showing alligator survey area within Water Conservation Area 2A

(WCA2A). Relative non-hatchling alligator density (I/km2) is shown within

xii detection zone along transect route. High (H) and low (L) alligator abundance

microtopography transect locations are shown along route. Map produced with

Esri ArcMap 10.4 using kernel density...... 27

Figure 2.5. Water depth at sampling area in Water Conservation Area 2A (WCA2A)

during the 2017 dry season. Water depths, represented by the solid line, are

from the Everglades Depth Estimation Network (EDEN) (US Army Corps of

Engineers, US Geological Survey, 2017). The Site_17 gage was used because of

its proximity to the sampling sites. Dashed vertical lines represent dates of

alligator surveys. Shaded area represents microtopography sampling window.

Solid horizontal line represents mean ground elevation at gage. Dotted

horizontal line represents 10-year mean minimum water depth...... 28

Figure 2.6. Map showing total water depth microtopographic relief (cm) throughout

the Everglades. Data from 341 depth transects taken in companion study

between 2010-2014 (D.E. Gawlik, unpubl. data). Map produced with Esri

ArcMap 10.4 using Inverse Distance Weighted (IDW) interpolation...... 29

Figure 2.7. Comparison of total water depth microtopography indices and their

relationship with alligator density. Transects are grouped by high (H) and low

(L) density locations. Regression lines are fitted showing the slope of the

relationships...... 30

Figure 2.8. Cross-section elevation profiles from total water depth microtopography

transects. Rows represent paired surveys, with high alligator abundance sloughs

in the left column, and low alligator abundance sloughs on the right column.

xiii Horizontal dashed line represents mean water depth for each transect. Water

surface is defined as 0 cm...... 31

Figure 2.9. Maps showing the extent of the dry-down (surface water ≤ 10 cm) in

Water Conservation Area 2A (WCA2A) during the past two seasons. Alligator

survey route represented by black line on maps. Yellow dots represent transect

locations. Extent Everglades are shaded black in inset map of Florida. Maps

produced with Esri ArcMap 10.4 and data from Everglades Depth Estimation

Network (EDEN) (US Army Corps of Engineers, US Geological Survey 2017). ... 32

Figure 3.1. Great egrets foraging within disturbed areas in an Everglades slough.

Disturbances may be caused by alligators...... 58

Figure 3.2. Hypothesized biomass/density curves for 3 treatments as slough dries

down over the course of the dry season. Solid line represents depression

sampling unit, dotted line represents distant control (100 m) sampling unit,

dashed line represents near (5 m) control sampling unit...... 59

Figure 3.3. Map of aquatic fauna sampling sites within Water Conservation Areas

(WCA) 2A and 3A in the central Everglades. WCA2A was sampled early in the

2017 dry season, followed by sites in WCA3A...... 60

Figure 3.4. A Breder trap modified from the original design to capture prey from the

entire water column in water depths up to 45 cm...... 61

Figure 3.5. Mean water levels at sampling sites during the 2016 dry season. Blue line

represents water levels during the 2016 dry season. Shaded areas represent

timeframe outside of sampling window. Dotted line represents mean ground

xiv elevation. Water levels were based on nearest EDEN (Everglades Depth

Estimation Network) water gage to respective sites (EDEN 2017)...... 62

Figure 3.6. Distribution of microtopography index within the Everglades. Black

dashed line represents chosen microtopography index (90th percentile, 20 cm)

to manipulate in treatment areas (n=192) (D.E. Gawlik, unpubl. data)...... 63

Figure 3.7. Distribution of depression widths within the Everglades for depressions ≥

20 cm. Black dashed line represents the mode and chosen treatment depression

width (n=79) (D.E. Gawlik, unpubl. data)...... 64

Figure 3.8. Aerial view showing trap placement in a hypothetical slough. Inset shows

vertical slough cross section...... 65

Figure 3.9. Model predictions for fauna biomass index plotted against mean water

depth for distant controls (C) and depressions (D). Mean water depth was taken

at near and distant control sampling locations on the respective sampling day.

The term for foraging days was removed from the model to plot in two

dimensions...... 66

Figure 3.10. Relationship between actual and predicted values of optimal formula for

fauna biomass index. Dashed line represents a one-one relationship, solid line

represents fitted line. The relationship is weak (r2 = 0.25). Two outliers are not

shown...... 67

Figure 3.11. Model predictions for fauna density index plotted against mean water

depth for distant controls (C) and depressions (D). Mean water depth has been

defined as the mean water depth at control sampling locations on the respective

xv sampling day. The term for foraging days was removed from the model to plot

in two dimensions...... 68

Figure 3.12. Relationship between actual and predicted values of optimal formula for

fauna density index. Dashed line represents a one-one relationship, solid line

represents fitted line. The relationship is moderate (r2 = 0.35). Two outliers are

not shown...... 69

xvi 1 INTRODUCTION

Large tropical wetland systems have two pronounced seasons, flooding during the rainy season, and drying when the rains cease (Kushlan 1976, Fox 1976, Kushlan et al.

1985, Finlayson 1990, Gonzalez 1996, Gonzalez 1997, Maheswaran and Rahmani 2001,

Junk et al. 2006, Alho 2008, Van Zalinge and Evans 2008). While there is minimal elevation change within the wetlands, their surrounding watersheds are diverse, with inflow provided by direct rainfall, riparian flooding, and riparian flow.

As the dry season progresses, floodwaters recede diminishing the inundated area and aquatic fauna become concentrated in lower elevation pools (Kahl 1964, Kushlan et al. 1975, Kushlan 1976, Carlson and Duever 1979, Kushlan 1980, Loftus and Eklund

1994, Gonzalez 1996, Jordan et al. 1998, Lorenz 2000, Alho 2005). These pools provide temporary refuge from desiccation for fish, amphibians, and aquatic invertebrates, and consequently serve as high quality foraging sites for a variety of predators (Magoulick and Kobza 2003). This is imperative to top wetland predators such as wading birds

(Ciconiiformes and Pelecaniformes), which are often prey-limited (Butler 1994, Hafner

1997, Steinmetz et al. 2009).

Factors that determine the level of aquatic fauna concentration include dry season water recession rate (Herring and Gawlik 2012, Botson et al. 2016), wet season biomass production, and both landscape-level and local microtopography (Gawlik 2002, Garrett

2007, Botson et al. 2016, Klassen et al. 2016). When conditions permit, fauna

1 concentrations provide high quality foraging patches for top-predators such as wading birds.

Wading birds feed their chicks a variety of aquatic fauna and are dependent on prey concentration and availability to meet their energetic needs (Strong et al. 1997,

Boyle et al. 2012). Tactile foragers (i.e., storks (Ciconiidae) and ibises

(Threskiornithidae)) feed by moving their bills through the water, quickly clamping down when prey is detected. This strategy requires high prey density for success (Kushlan et al.

1985). Although visual foragers (i.e., herons and egrets (Ardeidae)) can forage effectively at lower prey densities, these species still benefit from highly concentrated prey (Gawlik

2002).

Many quantifiable aspects of wading bird nesting (i.e. timing, nest attempts, eggs per nest, hatch rate, chick condition, and fledge rate) are linked with dry-season prey density and availability levels (Kahl 1964, Powell 1983, Powell and Powell 1986, Powell et al. 1989, Frederick and Spalding 1994, Bancroft et al. 1994, Hoffman et al. 1994,

Ogden 1994, Gawlik 2002, Herring et al. 2010). If prey availability becomes prohibitively low, wading bird nest abandonment occurs (Frederick and Collopy 1989,

Gawlik et al. 2005, Cook and Call 2005, Lorenz 2014).

Increased microtopography allows prey to concentrate at higher densities, but the spatial scale and magnitude of the effect are unknown. Crocodilians are a biotic mechanism which increase heterogeneity throughout tropical wetlands. However, links between crocodilians, aquatic fauna, and wading birds are not known. I hypothesized that crocodilians increase microtopography within the drying marsh and that greater

2 microtopography results in widespread enhanced foraging patches for wading birds during the dry season.

Twenty-three extant species of crocodilians inhabit subtropical and tropical wetlands worldwide. In much of their range, adult crocodilians have been shown to be allogenic ecosystem engineers (Naiman 1988, Jones et al. 1994, Jones et al. 2010), modifying their physical environment by nesting activities and movement (Craighead

1968, Kushlan 1974, Kushlan and Kushlan 1980, Magnusson 1982, Gereta and Wolanski

1998).

Much less is known about the ecology of juvenile crocodilians. Juvenile

American alligators disperse from their natal area one to two years after hatching (Deitz

1979). At this vulnerable life stage, they avoid contact with larger alligators to decrease the risk of predation (Delany and Abercrombie 1986, Magnusson 1986, Rootes et al.

1991, Rootes and Chabreck 1993, Mazzotti and Brandt 1994). Juvenile alligators are often observed in pairs inhabiting smaller satellite holes (Campbell and Mazzotti 2004), or solitarily in sparsely vegetated sloughs (T. Faughnan, pers. obs.). The extent to which these juvenile alligators increase microtopography has yet to be studied.

The Everglades is a vast wetland occupying the southern end of the subtropical

Florida peninsula. A majority of the interior habitat is tropical savanna, while coastal areas have a tropical monsoon climate (Kottek et al. 2006, Rubel and Kottek 2010). Most rainfall (82%) occurs during the summer months while the remainder of the year is generally dry. These precipitation extremes result in a hyperseasonal wetland, with flooding in the rainy season and drought in the dry season (Kushlan 1987). The

Everglades has research infrastructure, such as the modeled water depths provided by a

3 network of water depth gauges. These resources are not available in most seasonally- pulsed tropical wetlands, making the Everglades an ideal study site to better understand the ecological processes in these ecosystems.

Within the Everglades landscape, the slightly higher elevation ridges are dominated by sawgrass (Cladium jamaicense) while the lower elevation sloughs have a longer hydroperiod and are characterized by aquatic plants such as water lilies

(Nymphaea spp.), spike-rushes (Eleocharis spp.), and bladderwort (Utricularia spp.)

(Loveless 1959). The remnant Everglades has 365,000 hectares of ridge and slough habitat, a 37% loss since pre-drainage times (Davis et al. 1994). The ridges and sloughs are maintained by water flow (Sklar et al. 2002), fire (Wade et al. 1980, Davis et al.

1994), and other natural processes. As the surrounding marsh dries, the heterogeneity of the ridge and slough landscape provides depressions for aquatic fauna concentration to occur, consequently providing a critical food source during wading bird reproduction

(Botson et al. 2016).

Because of the importance of prey concentrations to wading birds during their breeding season, understanding the mechanisms which increase prey availability during this time is critical to making informed management decisions to maintain and restore wading bird populations worldwide. First, I tested correlations between crocodilian presence and microtopography by conducting alligator surveys and sampling microtopography within high and low alligator abundance sloughs. Second, I investigated whether shallow depressions like those that may be created by crocodilians, concentrate aquatic fauna thus creating high-quality foraging patches for wading birds. I simulated

4 alligator depressions within Everglades sloughs and monitored relative fish density and biomass within depressions and at control sitess throughout the dry season.

2 CROCODILIAN USE AS A MECHANISM FOR INCREASED WETLAND

MICROTOPOGRAPHY

2.1 INTRODUCTION

2.1.1 Crocodilians as Ecosystem Engineers

Crocodilians are found throughout tropical wetlands worldwide (Figure 2.1) and have a complex ecological relationship with wading birds. When crocodilians are small they may be wading bird prey, but as they grow, a shift occurs in their relationship with wading birds from prey to protector or predator (Deitz 1979, Barr 1997, Subalusky et al.

2009). For example, crocodilians act as nest protectors for multiple avian species

(Robinson 1985, Hudgens 1997, Burtner 2011, Nell et al. 2016) and in return, benefit by consuming fallen nestlings, chick regurgitant (boluses), and bird predators (Burtner 2011,

Saalfield et al. 2011, Nell and Frederick 2015, Nell et al. 2016, Burtner and Frederick

2017). Crocodilians provide additional benefits to wading birds through modification of the landscape.

In much of their range, adult crocodilians have been shown to be allogenic ecosystem engineers (Naiman 1988, Jones et al. 1994, Jones et al. 2010), modifying their physical environment (Gereta and Wolanski 1998). Several species create burrows or small ponds during drought to protect themselves from extreme temperatures. These species include the saltwater crocodile (Crocodylus porosus) in northern Australia

(Magnusson, 1982), the Nile crocodile (Crocodylus niloticus) of Africa (Pooley 1969), the common caiman (Caiman crocodilus) of Central and South America (Staton and

Dixon 1975, Gorzula 1978), and the American alligator (Alligator Mississippiensis; hereafter alligator) in the Florida Everglades (Craighead 1968, Kushlan 1974, Kushlan and Kushlan 1980). As water levels recede, aquatic fauna survival, and subsequent population reestablishment is dependent on deep (> 1 m) water refugia, much of which is created by crocodilians (Craighead 1968, Pooley 1969, Kushlan 1974, Kushlan and

Kushlan 1980, Magnusson 1982).

Crocodilians, including the Nile crocodile in the Okavango Delta of central Africa

(Fox 1976), and the alligator in the Everglades (McIlhenny 1935, Craighead 1968) create depressed trails within wetland substrate while moving through their habitat. Trails created by crocodilians facilitate fish movement (T. Faughnan, pers. obs.) and colonization of isolated pools during the dry season (Mosepele et al. 2009).

Another example of crocodilian ecosystem engineering is bioturbation (Moore

2006), where substrate matter is mixed into the water column. Through excavation and movement, crocodilians, such as the common caiman in the South American Pantanal, and the Nile crocodile in the Serengeti plains of Africa, bioturbate wetland ecosystems

(Gereta & Wolanski 1998, Alho 2005). Bioturbation is important in tropical wetlands because of the circadian cycle of hypoxia often present (Pearson et al. 2003). The resulting increase in dissolved oxygen allows for increased survival of aquatic fauna

(Gereta and Wolanski 1998, Alho 2005).

2.1.2 The American Alligator and Ecosystem Function

The American alligators is found only in the southeast United States, with its range encompassing the Florida Everglades (Figure 2.1). Its distribution is the most northern of the 23 extent crocodilians. Adult alligators are both apex predators and

ecosystem engineers in the Everglades (Craighead 1968, Kushlan 1974, Kushlan and

Kushlan 1980), making them a keystone species (Mazzotti and Brandt 1994).

In the Florida Everglades, adult alligators create small ponds known as alligator holes (these can also be created by other processes), averaging 9 m across and 67 cm deep (Palmer and Mazzotti, 2004). These ponds are created to ensure the alligator’s consistent refuge during the dry season for thermoregulation and mating. Alligators excavate dens along banks and ridges within the marsh for refuge during extreme drought and cold (McIlhenny 1935, Hines 1968), and create nest mounds to protect their eggs from inundation using excavated material (Loveless 1959, Craighead 1968, Kushlan

1974, Mazzotti and Brandt 1994, Palmer and Mazzotti 2004, McIlhenny 1935). Increased landscape heterogeneity results in increased biodiversity of flora (Palmer and Mazzotti

2004) and fauna (Craighead 1968, Kushlan 1974) including higher diversity and density of fish (Loftus and Eklund 1994, Kushlan 1974), birds (Gawlik and Rocque 1998) amphibians (Ligas 1960), mammals (Loveless 1959), and reptiles (Campbell and

Mazzotti 2004). The deep water provides refuge for aquatic fauna, allowing survival during the dry season, and subsequent recolonization of the landscape during the wet season (Kushlan 1974). Upland areas on the edges of alligator holes where excavated soil is deposited are colonized with woody vegetation that provides wading birds nesting and roosting habitat (Palmer and Mazzotti 2004). The increase in aquatic fauna within alligator ponds consequently attracts animals such as raccoons (Procyon lotor) upon which the alligators prey (Subalusky et al. 2009).

Almost all previous research on the ecological role of crocodilians has focused on nesting adults as ecosystem engineers or on the predatory habits of crocodilians

improving nest survival of birds. The role of crocodilians in shaping microtopography outside of alligator holes is poorly understood and is the focus of this study.

Aerial observation suggests that during the dry season, alligators, and possibly other large animals (e.g. raccoons and white-tailed deer (Odocoileus virginianus), create disturbances within the marsh which are used by foraging wading birds (Figure 2.2) (T.

Faughnan, pers. obs). After dispersal from the natal area, juvenile alligators are often observed in pairs inhabiting smaller satellite holes (Campbell and Mazzotti 2004), or solitarily in sparsely vegetated sloughs (T. Faughnan, pers. obs.). At this vulnerable life stage, juvenile alligators often select shallow marsh habitat rather than prominent alligator holes (Throm 2013) to avoid contact with larger alligators thus decreasing the risk of antagonistic interactions such as competition, fighting, and cannibalism (Delany and Abercrombie 1986, Magnusson 1986, Mazzotti and Brandt 1994, Rootes et al. 1991,

Rootes and Chabreck 1993). While it is known that alligators create trails in the marsh through repeated movement (Craighead 1968), this has yet to be quantified, and little is known about the effect of these juveniles on the ecosystem during this prolonged and understudied life stage. I hypothesized that alligators interact with the marsh substrate and create microtopography (Figure 2.3). I tested this by conducting an observational study relating relative alligator abundance with slough microtopography.

2.1.3 Microtopography

Microtopography is the fine scale topography of a surface. In wetlands, increased microtopographic heterogeneity has been shown to increase biodiversity (Moser et al.

2007) and aquatic fauna concentration during the dry season (Garrett 2007, Botson et al.

2016, Klassen et al. 2016). Highly concentrated aquatic fauna is critical to wading birds

which are prey-limited and rely on high-quality food patches during breeding (Butler

1994, Frederick and Spalding 1994, Herring et al. 2010). In the Everglades, ridges and sloughs are maintained by water flow (Sklar et al. 2002), fire (Wade et al. 1980, Davis et al. 1994), and vegetation (Lewis 2005), and reduced by oxidation. Upland areas in the

Everglades and Okavango Delta are shaped by alluvial sediment deposits (Gleason and

Stone 1994, Gumbricht et al. 2005). Beyond alligator holes, the microtopography created by alligators has not been quantified.

2.2 METHODS

2.2.1 Study Site Selection

My research was conducted in Water Conservation Area 2A (WCA2A), a 544- km2 impounded wetland located in the Florida Everglades (Figure 2.4). This area contains ridge and slough habitat typical of the pre-drainage Everglades. The slightly higher elevation ridges are dominated by sawgrass (Cladium jamaicense) while the lower elevation sloughs have a longer hydroperiod and are characterized by aquatic plants such as water lilies (Nymphaea spp.), spike-rushes (Eleocharis spp.), and bladderworts

(Utricularia spp.) (Loveless 1959).

2.2.2 Alligator Surveys

I conducted four alligator surveys during January and February 2017, a time when water levels had receded to just below the mid-point for the dry season (Figure 2.5).

Surveys were completed at night using an airboat following protocol outlined in detail by

Fujisaki et al. (2011). I followed a previously used 40.7 km alligator survey route (Figure

2.4, Fujisaki et al. 2011). Fish (Rehage and Trexler 2006) and alligator behavior (Morea

et al. 2000) is influenced by canals within 1000 m. Thus, my survey route was limited to areas greater than 1000 m from the nearest canal (Figure 2.4).

I traveled at approximately 25 km/hr using a spotlight to detect eyeshine from alligators within 50 m of the survey route. Each alligator that was spotted was approached by airboat and total length (TL) was estimated. Alligators were classified as hatchlings (TL < 0.5 m), juveniles (0.5 m ≤ TL < 1.25 m), subadults (1.25 m ≤ TL < 1.75 m), and adults (TL ≥ 1.75 m) (Fujisaki et al. 2011). Due to their small size, hatchlings were removed from the dataset because they likely have minimal impact on their physical surroundings. A precise location (±2.5 m) for each alligator was collected using a Juniper

Systems Archer 2 handheld field PC. This method resulted in a simple count of alligators and did not incorporate detection probability. However, because I was interested only in identifying areas of high and low relative alligator abundance, this limitation should not affect my results.

Survey methods eliminated the chance of counting the same alligator multiple times within a survey, but I may have encountered individuals multiple times among surveys. Because I was interested in relative alligator abundance and not density, whether individuals were repeat, or novel was unimportant.

I pooled observations from all surveys to produce a kernel density map using Esri

ArcMap 10.4. I used a spatial resolution of 10 m and a search radius of 476.4 m, calculated based on Everglades marsh alligators mean home range of 71.31 hectares

(Morea et al. 2000) under the assumption that individuals would impact microtopography within this area.

2.2.3 Microtopography Transect

To determine the number of transects necessary to detect a difference in microtopography should one exist, I performed a power analysis using the pwr.t.test function within the pwr package (Champely 2018) in R. I used the microtopographic index of relief outlined below. I examined a subset of microtopography transects taken during a long-term companion study (D.E. Gawlik, unpubl. data, Botson et al. 2016,

Pierce et al. 2010). Between 2010 and 2014, 341 microtopography transects were collected within sloughs across the Everglades, 43 of which were located within

WCA2A. Transects were conducted perpendicular to the direction of water flow, which was roughly north to south. Total depth from water surface to hard substrate, was measured to the nearest cm at 1-m intervals. Transects began at a randomly selected location within each slough and extended a maximum of 100 m. Transects were stopped a maximum of 5 m into upland areas, or 15 m into a sawgrass ridge. To quantify the spatial variation in microtopography across the landscape, I mapped microtopography using Esri ArcMap 10.4. I used Inverse Distance Weighted (IDW) interpolation in the 3D

Analyst toolbox with a default cell size of 360 m by 360 m based on the input point resolution.

I hypothesized that high and low relative alligator abundance sloughs would be at the top and bottom quartiles of microtopographic relief respectively. I determined that a sample size of 10 high alligator abundance and 10 low alligator abundance slough transects would have a 99% probability of detecting a difference if one existed.

I performed a matched observational study (Rosenbaum 2005, Rosenbaum 2010) by selecting the ten highest alligator abundance locations from the kernel density map

and pairing each with a nearby (< 1 km) low (at or near 0 individuals/km2) alligator abundance site. Pairing nearby locations served to account for the known spatial heterogeneity in microtopography throughout WCA2A (see 2.4 RESULTS, Figure 2.6), along with any other random variation within the study area. I navigated to the selected locations in the field to ensure each was suitable for wading bird foraging, determined to be a sparsely vegetated or open slough. Three of the site pairs were removed due to lack of slough habitat (Alligator sightings were all within the airboat trail). Additional locations were not selected due to lack of alternative high alligator abundance sites

(Figure 2.4).

I collected one microtopography transects at 7 high, and 7 low alligator abundance sloughs using methods similar to those outlined above. Transects were completed between March 13th, 2017 and March 22nd, 2017. Each transect began 2 m outside of the survey route to exclude the depressed airboat trail. Initial direction for each transect was determined by selecting an area with minimal thick emergent vegetation to survey potential wading bird foraging habitat. Transects were confined to a maximum distance of 50 m from the alligator survey detection zone. When a sawgrass ridge was encountered, the area was passed over and I continued the transect on the opposite side.

Each transect had a total of 100 measurements, done in parallel lines 10 m apart. I measured total water depth (water surface to hard substrate), and open water depth (water surface to soft substrate such as periphyton, muck, etc.) to the nearest 1 cm at 1-m intervals because each influences aquatic fauna habitat and may be impacted by alligators

(Kushlan 1976, Botson et al. 2016, Klassen 2016).

2.2.4 Analysis

I selected four metrics to characterize microtopography relevant to alligators, aquatic fauna, and wading birds:

1. I used a fine scale (spatial extent of tens of meters) microtopography index calculated

as the difference between the maximum and mean depth of a transect (Garrett 2007,

Botson et al. 2016, Klassen et al. 2016).

Relief(MI1) = DepthMAX - DepthMEAN

The index is positively correlated with aquatic fauna density (Garrett 2007, Botson et

al. 2016, Klassen et al. 2016).

2. A second measure of microtopography is dispersion (e.g. Klickenberg 1992,

Gumbricht et al. 2005), which I calculated using the standard deviation of water

depths.

Dispersion(MI2) = Standard Deviation (DepthALL)

In the absence of any mechanisms that produce microtopography, there would be a

dispersion of zero. Contrary to the measure of relief (MI1), this metric quantifies

roughness across the entire transect which would be particularly important if aquatic

fauna concentrations are localized within sloughs.

3. Tortuosity is a measure of the curviness between two points (Kamphorst et al. 2000).

This metric has been used in the calculation of wetland bathymetry (e.g. Moser 2007,

Moser 2009, Ahn 2010). I calculated tortuosity by finding the ratio of the length (L)

along the contour or the slough, to a straight line (C) between the two end points of

the transect.

퐿 Tortuosity(MI3) = 퐶 14

Tortuosity is a unitless index of slough substrate roughness that is based less on

depression depths than the previous indices.

4. The fourth microtopography index that I used quantified the number of depressions

within each transect (Nash et al. 2003). I examined four alternative hypotheses of

depression depth definition (5cm, 10 cm, 15 cm, 20 cm). I quantified depressions by

counting the instances of depression depths greater than the mean elevation by these

alternative depths. Adjacent high depths were grouped into single depressions. This

method of calculation was particularly relevant because it relates directly to the

experimental manipulation of depressions (see Chapter 3) and quantifies depressions

similar to those I have hypothesized that alligators make.

I used a paired one-tailed t-test to compare each measure of microtopography between high and low alligator abundance sloughs (Snedecor and Cochran 1989). All figures were created using the ggplot2 package in R (Wickham 2009).

2.3 RESULTS

2.3.1 Alligator Surveys

Over the course of 4 surveys, I encountered alligators a total of 191 times, with 86 encounters of non-hatchlings (Table 2.1). Relative alligator abundance (calculated using kernel density) ranged from 0 to 4.8 individuals per square kilometer with a mean relative abundance of 1.2 individuals/km2. I identified the top 7 highest alligator abundance locations within the detection zone along the transect route (Figure 2.4). The high alligator relative abundance sloughs ranged from 2.2 – 4.7 individuals/km2, with the mean (푥̅ = 3.3) almost 8 times higher (t = -9.4, df = 6, p ≤ 0.01) than the mean of low

alligator relative abundance sloughs (푥̅ = 0.4), which ranged from 0.0 - 1.3 individuals/km2 (Table 2.2).

2.3.2 Microtopography Transects

High alligator abundance sloughs had higher mean total water depth, microtopography indices of relief, dispersion, and tortuosity than low alligator abundance sloughs (Table 2.3). However, the differences were not significant (Table 2.3). Similarly, three out of four alternative depression definitions resulted in higher microtopography indices for high alligator abundance sloughs, but the differences were again not significant (Table 2.3).

Open water depths, dispersion and tortuosity were greater for high alligator abundance sloughs, and relief was lower, but again the differences were not significant

(Table 2.3). There were no significant differences between depression counts within the open water depths (Table 2.3).

Alligator relative abundance was not a discrete categorical classification of high and low, thus I regressed relative alligator abundance and each metric of microtopography and calculated the coefficients of determination (Figure 2.7, Table 2.3).

All measures of microtopography had weak relationships with relative alligator abundance (Figure 2.7, Table 2.3).

2.4 DISCUSSION

2.4.1 Effects of Alligator Presence on Microtopography

I was able to identify areas that differed greatly (8-fold difference in abundance) in the abundance of alligators, thus meeting the condition for a test of the hypothesis of alligators increasing microtopography. Correspondingly, measures of microtopographic

relief, dispersion, and tortuosity were all higher within high (relative to the survey route) alligator abundance sloughs and consistent with predictions in support of the hypothesis that alligators increase microtopography. For example, a depression depth definition of

10 cm produced a microtopography index 38% higher in high alligator abundance sloughs compared with low alligator abundance sloughs. A hole definition of 20 cm (the

90th percentile of depression depths within the Everglades) produced the largest difference in depression counts between high and low alligator abundance sloughs with an increase of 150%.

The collective visual profiles of microtopography (Figure 2.8), along with higher microtopography values for almost every metric, suggest a consistent overall increase in microtopography within the high alligator abundance sloughs. Despite all these apparent differences, I was unable to detect a significant difference in any microtopography parameter. This may have been due to the small sample size, shortcomings in my sampling methods and metrics, or the possibility that alligators do not significantly increase microtopography relative to what is naturally occurring in Everglades sloughs.

I may have sampled microtopography too early in the season to capture the effects of alligator abundance. Crocodilians are known to create depressions when faced with extreme drought (Craighead 1968, Pooley 1969, Staton and Dixon 1975, Gorzula 1978,

Magnusson 1982), and water levels had not yet reached extreme lows when I collected microtopographical data. At the time of the alligator surveys (late January - February), water levels were receding across the marsh, reducing the area of open water. Sawgrass ridges had become dry, and sloughs had become isolated. Water levels continued to fall after the completion of the alligator surveys, dropping an additional 23 cm to a typical

minimum level for this location based on the past 10 dry-season. Like other crocodilians,

Alligators may create microtopography when water levels are at extreme lows, forcing them to interact with the substrate. Shallow depressions in the Everglades fill in quickly with sediment making current use by alligators critical (Kushlan 1974, T. Faughnan, pers. obs). To examine if there may be increased alligator-induced microtopography from previous dry seasons, I mapped the spatial extent of the dry-down over the past two seasons (Figure 2.9). During the 2015 and 2016 dry seasons, the northern half of the survey route dried down more extensively than the southern half. However, there was no clear relationship between the dry-down extent and microtopography. To better understand the temporal scale at which changes in microtopography occur, regular collection of alligator presence and microtopography data within and across seasons is needed.

I assumed that alligators were creating microtopography across their home range while it may be more localized. Transects within high alligator abundance sloughs sampled microtopography amongst clusters of alligator sightings but did not necessarily pass directly over individual alligator sighting locations. These transects may have been at an incorrect spatial scale to detect changes in microtopography. Therefore, it may be more appropriate to sample microtopography in the immediate area of alligator sightings.

2.4.2 Conclusion

The vulnerability of crocodilian and wading bird populations has been witnessed over the past century. In the Everglades, populations of both were decimated by habitat loss and hunting during the 20th century. Today, they are used as metrics of ecosystem responses to restoration by the ongoing Comprehensive Everglades Restoration Plan

(CERP) which has the objective of partial restoration of the remnant Everglades (Crozier and Gawlik 2003, Mazzotti et al. 2009).

My research indicates a potential relationship between alligators and microtopography in the Florida Everglades, possibly leading to enhanced wading bird foraging habitat. In one of the flattest places on earth, understanding this link may be critical to the recovery of wading birds and other top predators.

The Everglades is a well-studied ecosystem with many similarities to seasonally- pulsed tropical wetlands around the world (Table 2.4). Tropical wetlands are disproportionately located in developing countries that lack the resources for research and protection (Junk 2002, Junk et al. 2006). For example, despite the wide distribution and ecological importance of crocodilians, tropical reptiles are relatively understudied

(Miranda 2017). Crocodilians have long been known to be ecosystem engineers and keystone species throughout their range. If crocodilians increase microtopography across wetland floodplains, wading bird foraging habitat would be enhanced system-wide.

Encouraging this invaluable service by protecting crocodilians could prove to be an effective way to protect and conserve tropical wetlands. Future research utilizing a larger sample may detect a significant correlation between crocodilians and microtopography. A better understanding of the mechanisms responsible for microtopography in seasonally- pulsed tropical wetlands will allow managers to facilitate a more heterogeneous landscape, and consequently, system-wide enhancement of wading bird foraging habitat.

This could prevent precipitous declines in wading birds such as those experienced in the

Everglades, from happening elsewhere.

Table 2.1. Total alligators observed during four nocturnal airboat surveys within Water Conservation Area 2A. Alligators fell within 100-meter detection zone along a 40.7 km survey route. Individuals were grouped by size class based on total length (TL). Class Size (m) Count hatchlings TL < 0.5 105 juveniles 0.5 ≤ TL < 1.25 32 subadults 1.25 m ≤ TL < 1.75 m 26 adults TL ≥ 1.75 m 11 Unknown (non-hatchling) - 17 Total non-hatchlings - 86 Total - 191

Table 2.2. Relative alligator abundance at selected microtopography transect sites. Relative abundance was calculated by creating a kernel density map based on all individual sightings. Hatchlings were excluded based on the assumption that they are not influencing microtopography. Standard deviation (SD) given for group means. Relative Alligator Abundance (I/km2) Transect ID High Low 1 4.7 0.7 2 4.4 1.3 4 3.2 0.1 5 2.9 0.3 7 3.3 0.0 8 2.3 0.0 9 2.2 0.7 Mean (±SD) 3.3 (1.0) 0.4 (0.5)

Table 2.3. Microtopography indices for high and low alligator density sites and results of comparisons. P-values were calculated from t-test comparison of high and low alligator abundance sloughs. R2 is the coefficient of determination and represents the proportion of microtopography index variance explained by the alligator density index. Mean High Mean Low Microtopography Index Density MI (SD) Density MI (SD) P-value R2 Total Water Depth MI1: Relief 20.0 (8.2) 18.1 (5.0) 0.31 <0.01 MI2: Dispersion 7.4 (3.0) 6.3 (1.4) 0.15 0.05 MI3: Tortuosity 3.3 (0.6) 3.2 (0.3) 0.29 0.02 MI4: Depression Count 5 cm depression 4.7 (1.4) 5.6 (1.3) 0.88 0.08 10 cm depression 2.6 (1.5) 1.9 (0.7) 0.07 0.03 15 cm depression 1.1 (1.1) 1.0 (0.8) 0.37 <0.01 20 cm depression 0.7 (1.1) 0.3 (0.5) 0.41 0.03 Mean Depth 24.3 (10.4) 16.1 (7.2) <0.01 0.14

Open Water Depth MI1: Relief 13.4 (3.4) 15.1 (7.4) 0.70 0.01 MI2: Dispersion 5.6 (1.6) 5.5 (2.2) 0.47 <0.01 MI3: Tortuosity 2.2 (1.1) 1.7 (0.6) 0.08 0.02 MI4: Depression Count 5 cm depression 3.6 (2.2) 3.6 (1.8) 0.50 <0.01 10 cm depression 1.6 (1.0) 1.3 (1.1) 0.30 <0.01 15 cm depression 0.1 (0.4) 0.4 (0.5) 0.91 0.05 20 cm depression 0.0 (0.0) 0.1 (0.4) 0.82 0.04

Table 2.4. Examples of important* seasonally-pulsed tropical wetlands around the world. These wetlands share similar characteristics which link aquatic fauna, crocodilians, and wading birds. Wetland Seasonally Aquatic fauna Bird foraging (continent) flooded concentration aggregations Crocodilian species1 Everglades* Alligator mississippiensis, ✓3,13,14 ✓3 ✓3 (North America) Crocodylus acutus Kakadu* Crocodylus johnsoni, ✓6,11,12,13,14,15 ✓15,12 ✓6,12 (Australia) Crocodylus porosus Okavango Delta* ✓2 ✓2 ✓2 Crocodylus niloticus (Africa) Caiman crocodilus, Crocodylus acutus, Llanos ✓3,8,13,14 ✓3,8 ✓7,8 Paleosuchus palpebrosus, (South America) Paleosuchus trigonatus, Crocodylus intermedius Caiman latirostris, Pantanal* ✓5,13,14 ✓5 ✓5 Caiman yacare, (South America) Paleosuchus palpebrosus Tonle Sap* Crocodylus porosus, ✓10,13,14 ✓10 ✓10 (Asia) Crocodylus siamensis Terai-Duar Crocodylus palustris, ✓9 ✓9 ✓9 (Asia) Gavialis gangeticus

* Sites recognized by the Ramsar convention as having international importance, 1IUCN 2016, 2Fox 1976, 3Kushlan 1976, 4Kushlan et al 1985, 5Alho 2008, 6Finlayson et al. 2006, 7Gonzalez 1996, 8Gonzalez 1997, 9Maheswaran and Rahmani 2001, 10Van Zalinge et al. 2008, 11Junk et al. 2006, 12Warfe et al. 2011, 13Kottek et al. 2006, 14Rubel and Kottek 2010, 15Finlayson 1990.

Figure 2.1. World map showing the distribution of the American alligator, the order crocodilia (IUCN, 2016) and tropical climates (Kottek et al. 2006, Rubel and Kottek 2010). Selected wetlands were chosen from the Ramsar list of Wetlands of International Importance. Inset shows Florida Everglades, USA study site. These wetlands share similar characteristics which link aquatic fauna, crocodilians, and wading birds (Table 2.4). Map produced with Esri ArcMap 10.4.

Figure 2.2. Aerial image showing great egrets (Ardea alba) foraging within disturbed areas in an Everglades slough. Disturbances may be caused by alligators.

Figure 2.3. Conceptual model showing hypothesized effects of ecosystem engineering by American alligators within sloughs during the dry season in the Everglades.1 (Craighead, 1968), 2 (Wade, et al., 1980), 3 (Davis, et al., 1994), 4 (Sklar, et al., 2002), 5(Kahl 1964, Kushlan et al. 1975, Carlson and Duever 1979, Alho 2005), 6(Herring and Gawlik 2012, Botson et al. 2016, Gawlik 2002), 7(Botson et al. 2016, Dvorac and Best 1982), 8(Botson et al. 2016, Loftus and Eklund 1994), 9(Kahl 1964, Powell 1983, Powell & Powell 1986, Powell et al. 1989, Frederick and Spalding 1994, Bancroft et al. 1994, Hoffman et al. 1994, Herring et al. 2010).

Figure 2.4. Map showing alligator survey area within Water Conservation Area 2A (WCA2A). Relative non-hatchling alligator density (I/km2) is shown within detection zone along transect route. High (H) and low (L) alligator abundance microtopography transect locations are shown along route. Map produced with Esri ArcMap 10.4 using kernel density.

Figure 2.5. Water depth at sampling area in Water Conservation Area 2A (WCA2A) during the 2017 dry season. Water depths, represented by the solid line, are from the Everglades Depth Estimation Network (EDEN) (US Army Corps of Engineers, US Geological Survey, 2017). The Site_17 gage was used because of its proximity to the sampling sites. Dashed vertical lines represent dates of alligator surveys. Shaded area represents microtopography sampling window. Solid horizontal line represents mean ground elevation at gage. Dotted horizontal line represents 10-year mean minimum water depth.

Figure 2.6. Map showing total water depth microtopographic relief (cm) throughout the Everglades. Data from 341 depth transects taken in companion study between 2010-2014 (D.E. Gawlik, unpubl. data). Map produced with Esri ArcMap 10.4 using Inverse Distance Weighted (IDW) interpolation.

Figure 2.7. Comparison of total water depth microtopography indices and their relationship with alligator density. Transects are grouped by high (H) and low (L) density locations. Regression lines are fitted showing the slope of the relationships.

Figure 2.8. Cross-section elevation profiles from total water depth microtopography transects. Rows represent paired surveys, with high alligator abundance sloughs in the left column, and low alligator abundance sloughs on the right column. Horizontal dashed line represents mean water depth for each transect. Water surface is defined as 0 cm.

Figure 2.9. Maps showing the extent of the dry-down (surface water ≤ 10 cm) in Water Conservation Area 2A (WCA2A) during the past two seasons. Alligator survey route represented by black line on maps. Yellow dots represent transect locations. Extent Everglades are shaded black in inset map of Florida. Maps produced with Esri ArcMap 10.4 and data from Everglades Depth Estimation Network (EDEN) (US Army Corps of Engineers, US Geological Survey 2017).

3 THE EFFECTS OF DEPRESSIONS ON AQUATIC FAUNA CONCENTRATION

IN A SEASONALLY-PULSED WETLAND

3.1 INTRODUCTION

Wading bird populations in tropical wetlands are frequently prey-limited during the breeding season (Butler 1994, Hafner 1997). They rely on high quality foraging patches with a high density of accessible aquatic fauna (hereafter prey) to meet their increased energetic needs (Butler 1994, Hafner 1997, Steinmetz et al. 2009). Seasonal flood pulses provide aquatic fauna increased access to resources across newly inundated floodplains, resulting in a biomass increase (Welcomme 1979). Dry-season water levels fluctuate and concentrate prey differently among systems. Aquatic fauna are forced from the floodplain to the main river channels of many tropical river-floodplain systems including the

Okavango Delta in Botswana where prey concentrate in the Okavango River (Merron

1993), and in northern Australia, where prey concentrate within riverine depressions and billabongs (oxbows) (Douglas et al. 2005, Welcomme 1979). Similarly, the Tonlé Sap

Lake in Cambodia fluctuates in size, with prey becoming caught in isolated ponds during the dry season (Van Zalinge & Evans 2008). In the Venezuelan Llanos, prey concentrate in lagoons, préstamos (borrow pits), and esteros (deep marshes) (Kushlan et al. 1985,

Gonzalez 1997). In the Florida Everglades, prey concentrate within sloughs (drying pools) in lower-elevation marshes (Kushlan 1976, Kushlan et al. 1985). One of the variables that greatly affects the maximum density of prey in these drying pools is fine scale topography, or microtopography (DeAngelis et al. 1997, Garrett 2007, Herring and

Gawlik 2012, Botson et al. 2016, Gawlik 2002, Klassen et al. 2016). Known mechanisms which create microtopography in the Everglades include water flow (Sklar et al. 2002), fire (Wade et al. 1980, Davis et al. 1994), and the American Alligator (Alligator mississippiensis; hereafter alligator) (Craighead 1968). Alligators create microtopography, scouring out soft substrate and vegetation to create trails, holes for refugia, and nest mounds (Loveless 1959, Craighead 1968, Kushlan 1974, Mazzotti and

Brandt 1994, Palmer and Mazzotti 2004). The increased heterogeneity provided by these often-perennial features of the Everglade’s landscape results in increased biodiversity of flora (Palmer and Mazzotti 2004) and fauna (Craighead 1968, Kushlan 1974) including fish (Kushlan 1974, Loftus and Eklund 1994), birds (Gawlik and Rocque 1998), amphibians, mammals, and reptiles (Campbell and Mazzotti 2004). The deep water of alligator holes, which average 67 cm deep (Palmer and Mazzotti 2004), provides refuge for aquatic fauna, allowing survival and recolonization of the landscape during the following wet season (Kushlan 1974). Upland areas created by the excavation of alligator holes support woody vegetation, providing wading birds nesting and roosting habitat

(Palmer and Mazzotti 2004). However, little is known about the ecological role of smaller depressions and microtopographic variations caused by alligators. Aerial photographs of wading birds in the Everglades show that birds sometimes forage selectively in areas that have been disturbed by alligators or other large animals (Figure

3.1). If aquatic animals that are prey for wading birds showed a similar attraction to these disturbed areas, alligators could be an important but overlooked mechanism for increasing the quality of wading bird foraging habitat.

To understand the microtopography that alligators create, I quantified the physical characteristics of depressions across the Everglades marsh. I examined microtopographic transect data from a companion study under the assumption that in an otherwise homogeneous landscape, slough microtopography is caused by alligators, the largest and most ubiquitous animal to use this habitat. However, because the origin of the depressions was not known, it is possible that some depressions in the sample were created by other mechanisms. I assume these were relatively uncommon. Based on these data, I determined specifications of simulated alligator depressions, which differ from perennial alligator holes by their significantly smaller size. I tested the concentration effects experimentally by excavating simulated alligator depressions and comparing them to unmanipulated sites at distances of 5 m and 100 m. I hypothesized a priori that aquatic fauna would concentrate within depressions at greater biomass and density than within control sites. Furthermore, I expected that movement of fauna toward depressions would deplete surrounding areas such that there would be a lower density of fauna at unmanipulated sites near depressions than at unmanipulated sites far from depressions

(Figure 3.1). I also hypothesized that the concentrating effects of depressions would increase with size of aquatic fauna because large animals require deeper water to survive.

3.2 METHODS

3.2.1 Study Site Selection

I conducted my experiment within Water Conservation Area 2A (WCA2A) and

Water Conservation Area 3A (WCA3A). WCA2A is a 544-km2 impounded wetland located in the northern remnant Everglades of South Florida (Figure 3.2). WCA3A is a

2036-km2 impounded wetland located adjacent and to the southeast of WCA2A. These

areas were chosen because they contain ridge and slough habitat typical of the pre- drainage Everglades and are accessible via airboat.

Study sites were chosen based on slough water depth, location, and accessibility.

Chosen sloughs were outside a 1000-m canal buffer, minimizing the known effects of canals on aquatic faunal communities (Davis et al. 1994, Newman et al. 1998, Trexler et al. 2000, Figure 3.2). To ensure access via airboat during the sampling period as water levels decreased across the marsh, I chose sloughs close to existing airboat trails. Chosen sampling sites were greater than 100 m from airboat trails to minimize potential bias of the depressed trail on aquatic fauna behavior. Over the course of the season, the experiment was conducted within 11 sloughs as they dried down, moving along the drying front beginning in WCA2A and then moving to WCA3A.

3.2.2 Aquatic Fauna Sampling

I sampled aquatic fauna (fish and invertebrates) using passive minnow traps known as Breder traps (Breder 1960, Figure 3.3). This trap type was selected because compared with other trapping methods, such as a throw trap (Kushlan 1981), Breder traps have significantly less impact on vegetation and substrate (Sargent and Carlson 1987,

Loftus and Eklund 1994) which was important because sampling was repeated weekly within each study slough. Additionally, Breder traps can be quickly deployed and retrieved, which allowed for a larger sample size than would have been possible with other more time intensive trapping methods. I modified the trap design to facilitate the capture of aquatic fauna from the entire water column (Breder 1960, Sargent and Carlson

1987).

I constructed Breder traps using 6.4 mm (¼”) clear acrylic sheeting. The trap consists of a box with 5 closed sides, and one side with wings which funnel the prey through a gap (Figure 3.2). The wings were removable to facilitate prey collection. Trap pieces were secured using acrylic cement. I modified the original trap design described by Breder (1960) to 45 cm tall to sample prey in water depths of up to 40 cm while leaving breathing room for incidentally caught amphibians and snakes. Two vertical pieces of 19.0 mm (¾”) PVC pipe were driven into the muck on either side of the trap and a horizontal cross piece kept the trap in place.

Diet studies in the Everglades have shown that wading birds select larger prey than what is available (Ogden et al. 1976, Klassen and Gawlik 2014). For example, 95% of biomass consumed by small herons (Egretta spp.) came from fish > 1.9 cm in standard length (Klassen et al. 2016). To target the appropriate size classes, I conducted a pilot study during the 2016 dry season using trap openings ranging from 0.25 cm to 2.50 cm. I collected a total of 52 samples and analyzed the relationship between trap opening and aquatic fauna size distribution. Traps with smaller openings retained smaller fauna at higher numbers but excluded larger fauna when compared to traps with larger openings.

A trap opening of 2.0 cm best targeted wading bird prey based on distributions found within diet studies (Ogden et al. 1976, Klassen and Gawlik 2014).

3.2.3 Experimental Design

To simulate alligator-created depressions found in the everglades landscape, I quantified depressions along 192 randomly-placed microtopography transects in the

Everglades from 2007-2014 (Gawlik et al. unpubl. data, Figure 3.5) under the assumption that those depressions were mostly created by alligators. Ten percent of these transects

had depressions greater or equal to 20 cm in depth. I chose a depth of 20 cm because it was feasible based on alligator size and based on what exists within Everglades sloughs.

Sixty-two percent of measured depressions had a width between 0 and 2 m. Based on these widths and observations of elongated disturbances, I chose horizontal dimensions of

2 m by 1 m.

Within each of the 11 selected sloughs, I selected 4 trapping locations with similar

(± 5 cm) water depths (Figure 3.7). Traps were placed at a standard distance of 1.5 m from, and facing perpendicular to, the slough edge (defined as having greater than 50% emergent vegetation) to control for the edge effect of increased aquatic fauna (Dvorac and Best 1982, Rozas and Odum 1988). One location was selected at random to be the depression treatment (TD) and an unmanipulated control site (TC5) 5 m (± 1 m) from the depression was chosen. Two additional unmanipulated control sites (TC100A, TC100B) 5 m

(± 1 m) apart were selected 100 m (± 10 m) away. Treatment locations remained constant throughout the sampling period. I used a randomized complete block design with each slough as a block to account for variation among sloughs.

There is a positive linear relationship between Breder trap soak time and the total prey caught (Sargent and Carlson 1987). Thus, I set each trap for approximately 3 hours, and calculated density or biomass on a per time basis. I set traps during morning daylight hours to control for time of day and to target aquatic fauna present during wading bird foraging.

I measured total water depth using a meter stick each time fauna was sampled. I defined total depth as the distance between the water surface and the firm slough substrate.

After retrieving the traps, I filtered the contents using a mesh bag and sorted aquatic fauna from other matter. I counted individual fauna items and placed them into a jar of water of known weight. The initial weight was subtracted from the final weight to determine total trap biomass. For each trap, species present were recorded. Within initial data collection in WCA2A I observed that large fish were more common within depressions. Subsequently within WCA3A, I separated fauna items into two size classes by total length (small fish < 5 cm and large fish ≥ 5 cm) by total length to investigate this observation. Total length was used rather than standard length to minimize the time aquatic fauna were out of water. I weighed individuals larger than 5 cm in total length.

After processing the samples, I removed accumulated matter from depressions to maintain aquatic habitat, as alligator use likely would (Kushlan 1974). The aquatic fauna items were then returned alive to their respective trapping locations.

I sampled 4 units within 11 selected sloughs weekly from 17 February - 31 May

2017, for a total 213 samples (104 distant controls (TC100), 61 depressions (TD), and 48 near control units (TC5)). Two sloughs in WCA2A were sampled from February 17th to

April 17th for a total of 22 samples, and nine sloughs within WCA3A from April 8th through May 31st for a total of 191 samples (Figure 3.4).

At the onset of sampling, all locations had open water, and fish had yet to begin concentrating within deeper refugia. As water depths declined, fish concentrated within depressions and large flocks of wading birds were observed foraging within sampling sloughs. As sloughs became dry, manipulated sampling locations became isolated, with the surrounding water column and control locations being made up of flocculent

(suspended sediment) and periphyton. By the end of the sampling period, the lack of open

water excluded aquatic fauna from control areas, and fish density and biomass were reduced significantly from their respective maximums within depressions. (Figure 3.8,

Figure 3.10). Sample collection ended abruptly with a large rain event in early June that ended the 2017 dry season (Figure 3.4).

3.2.4 Statistical Methods

I examined the within-slough difference in aquatic fauna concentration among the three treatment types (depression, near control, distant control) using linear mixed models. Response variables were aquatic fauna biomass index (grams/hour) and aquatic fauna density index (individuals/hour). The experimental units were defined as the biomass and density index of each sample. Terms tested for inclusion within the models were derived from a priori hypotheses. To account for the effects of wading bird predation, I calculated the number of days within the current dry season before the trapping date that slough water depths were within the wading birds foraging window (0

– 40 cm) (Powell 1987). Fixed effects included a polynomial term for water depth, foraging days, and their interactions. A random intercept for each slough was included to account for variation in aquatic fauna communities among sloughs. A random slope with respect for treatment type was included to account for variation in concentration effects of treatments among sloughs, caused for example by differing microtopography. I examined a series of possible transformations on the aquatic fauna biomass and density response variables to meet the assumptions of normality and found this was best accomplished using a cube-root transform. I fit models using the lme4 package (Bates et al. 2015) in R (R Core Team 2013) and used the likelihood ratio test to determine individual variable inclusion (Neyman & Pearson 1933, Burnham and Anderson 2002,

Posada and Buckley 2004, Lewis et al. 2011). Model likelihood was carried out using the anova function in the R stats package (R Core Team 2017).

After developing the initial models, I used the likelihood ratio test to compare goodness-of-fit with and without a term for treatment type. I reported χ2, degrees of freedom (df), and p-values (p). P-values less than 0.05 were considered significant. I calculated model fit using the MuMIn package (Barton 2017) in R, which uses the methods developed by Nakagawa and Schielzeth (2013). These methods provide an outline for calculating goodness-of-fit and variance explained for linear mixed models, avoiding theoretical problems of previous methods. I reported global marginal (fixed effects only) and conditional (combined fixed and random effects) R2 values for each model. I evaluated the models by regressing observed against predicted values (Gervasio et al. 2008). I evaluated effect size of each treatment by excluding the term for foraging days. Biologically, this was more suitable than holding it constant because of its implicit relationship with water depth. Treatments were considered significant if confidence intervals did not overlap alternative treatment means (Gardner and Altman 1986).

I back-transformed biomass and density indices to aid in interpretation of the effect sizes. Using the models, I calculated peak values for each treatment type for both biomass and density. I utilized parametric bootstrapping, using the bootpredictlme4 package (Duursma 2016) in R to calculate standard errors and 95% confidence intervals around model predictions (r=1000). Results were visualized using the ggplot2 package in

R (Wickham 2009). Treatments were compared by examining whether confidence intervals overlapped alternative treatment predictions (Gardner and Altman 1986).

Aquatic fauna items were broken into two size classes based on total length, with small items less than 5 cm, and large items greater than or equal to 5 cm. I was unable to transform the large fauna biomass and density data to conform to the assumption of normality, required of linear modeling. Thus, I compared treatments using the non- parametric Kruskal-Wallis test (Kruskal and Wallis 1952) using kruskal.test in the R stats package (R Core Team 2013). I did a pairwise comparison of treatments with the

Conover-Iman test (Conover and Iman 1979) using conover.test in the conover.test package in R (Dinno 2017). I adjusted p-values for multiple comparisons using the Holm method (Holm 1979).

I compared species composition among treatments using an analysis of similarity

(ANOSIM) in PRIMER v7 (Clarke 1993, Clarke and Gorley 2015). This produced an R- value and p-value representing similarity among treatments. I compared mean and maximum species richness among treatments using the Kruskal-Wallis test.

3.3 RESULTS

3.3.1 Aquatic Fauna Biomass

Mean biomass of fauna samples (n=213) was 4.7 times higher within depressions than within the distant control treatment, and 1.8 times higher within depressions than within the near control treatment. (Table 3.1). A model that included treatment type, had improved model fit over the null model (χ2(1) = 7.46, p = 0.02), suggesting a significant difference among treatment types. A total of 32% of the variation in aquatic fauna biomass index was explained by the model (marginal R2 = 0.32). The random effects explained an additional 4% of the variation (conditional R2 = 0.36). There was a weak correlation (r2 = 0.25) between observed and predicted values (Figure 3.9). Predicted

maximum aquatic fauna biomass index was significantly greater within depressions than distant controls, by a magnitude of 22.4 (Table 3.3, Figure 3.8). Aquatic fauna biomass within depressions peaked as surrounding water depths receded to 21 cm and then began to decline (Figure 3.8). The predicted mean biomass index across all sampled depths was significantly greater (t = -7.15, df = 33, p < 0.01) within depressions than distant controls, by a magnitude of 21.3 (Table 3.3, Figure 3.8).

3.3.2 Aquatic Fauna Density

I captured a total of 1,287 aquatic fauna individuals (Table 3.1). There were 1.73 times more individuals within depressions than distant control treatments (Table 3.1).

Near controls and distant controls had similar mean individuals per trap (Table 3.1).

As with biomass, a model for number of individuals that included a term for treatment type improved model fit over the null model (χ2(1) = 26.35, p < 0.01), indicating support for my hypothesis that treatments would differ. A total of 35% of the variation in aquatic fauna density index was explained by the model (marginal R2 = 0.35).

The random effects explained an additional 6% of the variation (conditional R2 = 0.41).

Without inclusion of treatment type, the explanatory power of the model drops to 18%

(marginal R2 = 0.18). There was a moderately strong relationship between observed and predicted values (r2 = 0.35, Figure 3.11). The predicted maximum aquatic fauna density index was higher within depressions than distant control sites, by a magnitude of 10.1

(Table 3.3, Figure 3.10). Aquatic fauna density within depressions peaked as surrounding water depths reached 16 cm and then began to decline (Figure 3.10). The predicted mean density index across all sampled depths was significantly greater (t = -5.29, df = 33, p <

0.01) within depressions than distant controls, by a magnitude of 6.2 (Table 3.3, Figure

3.10).

3.3.3 Large Aquatic Fauna Biomass

All aquatic fauna items within the large size class were fish. The larger fish represented only 5.9% of the total individuals, but 71.0% (739 g) of the total biomass.

There were differences in mean large fish biomass between treatment groups (χ2 = 18.80, df = 2, p < 0.01). Depressions had an 11.1 times greater mean biomass index than distant controls (t = -4.54, p < 0.01) (Table 3.4). Depressions also had a mean biomass index 1.8 times greater than near controls (t = 2.63, p = 0.02) (Table 3.4). Near control locations did not differ statistically from distant control locations (t = -1.53, p = 0.13) (Table 3.4).

There were differences in predicted maximum large fish biomass between treatment groups (χ2 = 10.51, df = 2, p = 0.01). Mean maximum large fish biomass index was 7.2 times greater in depression locations than distant control locations (t = -3.78, p < 0.01)

(Table 3.4). There was not a significant difference in biomass index between depression locations and near controls (t = 2.22, p = 0.06), or near and distant controls (t = -1.30, p =

0.20).

3.3.4 Large Aquatic Fauna Density

Within the 36 sampling locations, and 191 samples where fish were divided into size classes, 56 out of 955 individuals were greater or equal to 5 cm in total length. There were differences in mean large fish density between treatment groups (χ2 = 20.57, df = 2, p < 0.01). Depressions had 7.7 times greater mean density index than distant controls (t =

-4.78, p < 0.01) and 2.5 times greater than near controls (t = 2.76, p = 0.01) (Table 3.4).

Near control locations did not differ statistically from distant controls (t = -1.61, p = 0.11)

(Table 3.4).

There were differences in maximum large fish density between treatment groups

(χ2 = 14.35, df = 2, p < 0.01). Maximum large fish density index was 4.8 times greater in depression locations than distant control locations (t = -4.79, p < 0.01) (Table 3.4).

Maximum large fish density index was 2.8 times greater in depression locations than near control locations (t = 2.84, p = 0.02) (Table 3.4). There was not a significant difference in density index between near and distant controls (t = -1.51, p = 0.14).

3.3.5 Aquatic Fauna Species Composition

There were 25 taxonomic groups sampled within all traps (Table 3.5). Bluefin killifish (Lucania goodie) was the most commonly present species, found in almost half of all traps (Table 3.5). The species composition did not vary significantly among treatment groups (R-value = 0.03, p > 0.05). Mean species richness was also not significantly different among treatment groups (χ2 = 1.77, df = 2, p = 0.41) (Table 3.6).

Likewise, treatment groups did not differ statistically in maximum species richness (χ2 =

2.59, df = 2, p = 0.27) (Table 3.6). Sunfish (Centrarchidae) made up the majority of large fish within depressions comprising 79% of the identified large individuals, and 65% of the biomass of large fish (Table 3.7).

3.4 DISCUSSION

My results show that water depth and wading bird foraging were important mechanisms controlling aquatic fauna biomass and density within Everglades’ sloughs

(sensu Botson et al. 2016; Figure 3.1). The polynomial relationship between aquatic fauna and water depth indicated that there was an optimal water depth at which fauna

peaked, supporting previous research. As the dry season progresses in the Everglades, aquatic fauna from the surrounding wetlands concentrates in deeper ponds (Kushlan

1976). As water depths fall, these ponds become accessible to foraging wading birds

(Kushlan 1976). A combination of predation and water quality degradation due to overcrowding subsequently outpace the concentration effects of recession and reduces the aquatic fauna abundance (Kushlan 1976). Understanding the relationship between aquatic fauna concentration and environmental conditions throughout the dry season allowed me to control for these factors and examine the differences among treatments.

Depressions play a key role in the concentration of aquatic fauna, with density and biomass peaking substantially higher within depressions than within random locations throughout the marsh. My results support previous studies that found a positive relationship between microtopography and wading bird prey (Garrett 2007, Botson et al.

2016, Klassen et al. 2016). However, previous research sampled new locations at each time period and were therefore unable to control for differences in the physical characteristics surrounding sampling sites. This is the first study that sampled sloughs repeatedly throughout the dry-down, controlling for physical characteristics of sites and their position in the landscape, and determined precise estimates of effect size of depressions on peak aquatic fauna concentration.

My findings add support to the local concentration hypothesis proposed by

Trexler et al. (2002) which suggested that fish concentrate in shallow local depressions rather than swimming long distances to deeper water. This strategy is more energetically efficient for aquatic fauna and reduces the predation risk presented by aggressive exotic fish found in deeper water (Kobza et al. 2004). Trexler et al. (2002) based this conclusion

on the absence of an increase in fish density in long-hydroperiod marshes during the early dry-season, whereas I quantified aquatic fauna concentration within shallow depressions.

Additionally, while previous research on the link between microtopography and wading bird prey was observational (Garrett 2007, Botson et al. 2016, Klassen et al. 2016), taking an in situ experimental approach allowed me to control for extraneous factors, while approximating natural conditions.

Aquatic fauna selected depressions at all sampled water depths indicating that depressions provide enhanced foraging patches at all depths accessible to foraging wading birds (Bancroft et al. 2002, Petersen 2017; Figure 3.8, Figure 3.10). Biomass and density peaked at water depths of 31 cm and 19 cm respectively using mean water depth modeled by the Everglades Depth Estimation Network (EDEN). This increase in aquatic fauna biomass in advance of aquatic fauna density suggests that larger bodied fauna may be seeking refuge within depressions sooner than smaller fish as the marsh dries. Like fish, wading bird density at foraging patches tends to peak at shallow depths then drop just before the marsh dries. Wood stork (Mycteria americana) density peaked at 0 cm (all water depths given in comparable EDEN depths), white ibis (Eudocimus albus) at 10 cm, and great egret (Ardea alba) at 20 cm (Petersen 2017). Because EDEN depths are calculated as a mean of a 400 m x 400 m cell, deeper water is present within cells.

Wading birds typically forage in water so when an EDEN water depth is at or near 0 cm it is likely that birds are actually foraging in the remaining surface water found in depressions, also the places where fish concentrate based on this study. My findings support Petersen’s (2017) hypothesis that there is a greater cost of water depth for tactile foragers (e.g. wood storks and white ibis) than visual foragers (e.g. great egret),

explaining why the peak selection of these species is at lower water depths than peak prey abundance.

Large fish (total length ≥ 5 cm) showed an even stronger selection preference for depressions than small fish, with substantially higher mean and peak biomass and density. This supports previous research showing that the concentration effects of microtopography increase with fish size (Klassen 2016). The dramatic increase in large fish within depressions also complements the previous finding that larger fish need larger depressions and can swim longer distances to find them (Trexler et al. 2002).

Counter to my prediction, I did not detect a significant difference between the near and distant controls for either biomass or density indices. The greater number of small and large aquatic fauna at the near controls suggests that individuals may congregate near depressions (within 5 meters), thus the effects of depressions on aquatic fauna extend 5 m beyond the depression and likely farther. The fact that aquatic fauna biomass and density within distant controls have relatively little fluctuation during the dry-down suggests that the effects of depressions do not extend 100 m. However, it is unclear whether this is affected by aquatic fauna migrating from the nearby marsh. The low abundance of fish in non-depressed locations within sloughs throughout the wading bird foraging depth window suggests that these are not wading bird foraging habitat.

Whereas large fish made up a small proportion of the total number of fish, they accounted for greater than half of the total aquatic fauna biomass. Diet studies have found that wading birds select for larger fish, with larger fish being especially valuable to large wading birds such as the threatened Wood stork (Ogden et al. 1976, Klassen and Gawlik

2014). Additionally, a majority (65%) of the large fish within depressions were sunfish

(Centrarchidae) which make up a majority of the biomass in the diets of wood storks

(Ogden et al. 1976, Klassen and Gawlik 2014; Table 3.7) and great egrets (Frederick et al. 1999). Depressions concentrate prey for these species and may be an important source for their diet. Interestingly, exotic fishes, which are widespread in deep water refugia in the Everglades (Schofield et al. 2010), did not make up any of the large fish sampled within depressions. This may highlight a key difference between shallow depressions and deep water refugia such as alligator holes and canals; these shallow depressions provide temporary habitat for concentrations of aquatic prey, but do not provide aquatic fauna safe refuge from dry-down, or thermal refuge (necessary for many exotic species) during low winter temperatures (Schofield et al. 2010).

The higher mean aquatic fauna biomass and density combined with substantially higher peaks within depressions compared to controls for all fauna sizes, indicate that depressions are a mechanism for aquatic fauna concentration. Wading birds are prey limited and rely on concentrated prey during their breeding season. Depressions offer enhanced foraging opportunities throughout the range of water depths accessible to wading birds (Bancroft et al. 2002, Petersen 2017). In an otherwise flat landscape with low aquatic fauna density, depressions are integral to creating prey concentrations and thus critical for wading bird population success.

The aquatic fauna abundances that I measured were a response to simulated alligator depressions and not to actual depressions created by alligators. Although I did base the dimensions of the simulated depressions on the size and shapes of a large sample of observed depressions in the Everglades, it is likely that some of those depressions were created by mechanisms other than alligators. The degree to which actual alligator

depressions differ from those I measured is a limitation of this experiment. However, alligators are known to create depressions within the marsh, and this study showed that depressions concentrate aquatic fauna. If depressions created by alligators’ slough use are similar to those that I measured, as I believe they are, this would be the first documentation of crocodilians as a mechanism for creating wide-spread high-quality foraging patches for wading birds.

Table 3.1. Descriptive statistics for all fauna. N=number of samples. Total Mean sample Mean sample Total Biomass Biomass (g) number of Treatment N Individuals (g) (±SD) Individuals (±SD) 104 520 249 2.3 (5.4) 4.8 (5.8) Distant Control Depression 61 526 663 10.9 (16.96) 8.6 (12.8) Near Control 48 243 297 6.19 (16.89) 5.1 (5.7) All 213 1289 1209 5.64 (13.04) 6.0 (8.5)

Table 3.2. Summary of optimal models for aquatic fauna density and biomass. Parameter estimates (β), standard error (SE), and lower and upper 95% confidence limits (LCL, UCL) shown. The biomass response variable was cube root transformed and is shown in this format. ForagingDays is the total number of wading bird foraging days under 40 cm of water preceding sample date. MeanDepth is the mean water depth at control trap locations. Parameter β SE LCL UCL t value Biomass index(1/3) (grams/hour) Intercept -2.02 0.68 -3.35 -0.68 -2.95 ForagingDays -0.01 0.00 0.01 0.02 3.40 MeanDepth 0.10 0.05 0.01 0.19 2.26 MeanDepth2 0.00 0.00 -0.003 0.001 -1.09 Treatments Distant Control (C) 0 - - - - Depression (D) 2.16 1.05 0.10 4.23 2.05 Interactions C × MeanDepth 0 - - - - D × MeanDepth 0.10 0.07 -0.04 0.23 1.44 C × MeanDepth2 0 - - - - D × MeanDepth2 0.00 0.00 -0.01 -0.001 -2.97 C × DU40 0 - - - - D × DU40 -0.02 0.01 -0.03 -0.01 -3.06

Density index(1/3) (individuals/hour) Intercept -2.98 0.87 -4.68 -1.28 -3.44 DU40 0.01 0.003 0.005 0.02 3.46 MeanDepth 0.29 0.14 0.03 0.56 2.14 MeanDepth2 -0.008 0.007 -0.02 0.005 -1.26 MeanDepth3 0.00008 0.0001 -0.0001 0.0003 0.87 Treatments Distant Control 0.00 - - - - Depression -0.53 1.31 -3.09 2.03 -0.41 Interactions C × MeanDepth 0.00 - - - - D × MeanDepth 0.43 0.20 0.03 0.83 2.09 C × MeanDepth2 0.00 - - - - D × MeanDepth2 -0.02 0.01 -0.04 -0.003 -2.30 C × MeanDepth3 0.00 - - - - D × MeanDepth3 0.0003 0.0001 0.00003 0.001 2.16 C × DU40 0.00 - - - - D × DU40 -0.004 0.006 -0.02 0.01 -0.77

Table 3.3. Effects of treatment on aquatic fauna density and biomass indices. Standard Deviation (SD), Standard Error (SE), and lower and upper 95% confidence limits (LCL, UCL) are reported. Adjusted means control for unbalanced sampling of water depths and foraging days. Predicted biomass and density values were calculated throughout wading bird foraging depths using a linear mixed model. Confidence limits were calculated using parametric bootstrapping (r=1000). Predicted values for Near Control treatment were omitted because lack of data at low water depths caused incorrect model fit. Observed Adjusted Predicted Mean Mea LCL UC Mean Max Treatment (±SD) n L (±SD) (LCL,UCL) All Biomass Index (g/hr) 0.74 (1.72) 0.15 0.05 0.3 0.11 (0.09) 0.22 (0.04,0.62) Distant Control 4 3.59 (5.62) 1.21 0.69 1.9 2.34 (1.80) 4.93 (2.63,8.28) Depression 6 2.04 (5.42) 0.26 0.000 1.7 - -

53 Near Control 3 6

Density index (individuals/hr) 0.40 1.1 1.07 (0.50,1.97) Distant Control 1.56 (1.86) 0.73 9 0.68 (0.38) 0.58 0.2 10.76 (5.62,18.36) Depression 2.80 (4.27) 1.04 0 4.23 (3.87) -5.12 23. - Near Control 1.72 (1.89) 0.19 64 -

Table 3.4. Descriptive statistics for large fauna (total length ≥ 5 cm) density and Biomass. N is the sample size for respective treatments. i is the number of large fauna. Biomass Index (g/hr) Density Index (i/hr) Obs. Mean Obs. Mean Obs. Mean Obs. Mean Treatment N i g (±SD) Max. (±SD) (±SD) Max (±SD) Distant Control 95 10 68 0.23 (1.33) 1.05 (2.94) 0.03 (0.11) 0.14 (0.19) Depression 48 33 374 2.55 (5.69) 7.57 (8.34) 0.23 (0.41) 0.76 (0.67) Near Control 48 13 205 1.39 (5.20) 4.98 (10.28) 0.09 (0.20) 0.27 (0.28)

Table 3.5 List of species captured ordered by the number of traps which contained respective taxa (N). Sunfish group includes: Redear sunfish (Lepomis microlophus), Bluespotted sunfish (Enneacanthus gloriosus), Everglades pygmy sunfish (Elassoma evergladei), and unidentified sunfish. Cichlid group contains Mayan cichlids (Cichlasoma urophthalmus), and unknown cichlids. Treatments are Near Control (NC), Depression (D), and Distant Control (DC). Common name Scientific name All (%) DC (%) D (%) NC (%) Bluefin killifish Lucania goodei 105(48) 47 (44) 33 (54) 25 (52) Grass shrimp Palaemonetes paludosus 60 (28) 34 (13) 16 (26) 10 (21) Mosquito fish Gambusia holbrooki 60 (28) 39 (36) 13 (21) 8 (17) Sunfish Family: Centrarchidae 60 (28) 26 (24) 19 (31) 15 (31) Golden topminnow Fundulus chrysotus 45 (21) 22 (20) 16 (26) 6 (13) Sailfin molly Poecilia latipinna 17 (8) 8 (7) 7 (11) 2 (4) Flagfish Jordanellae floridae 16 (7) 5 (5) 10 (16) 1 (2) Least killifish Heterandria formosa 16 (7) 12 (11) 4 (7) 0 (0) Cichlid Family: Cichlidae 15 (7) 9 (8) 3 (5) 4 (8) Dragonfly larvae Order: Odonata 9 (4) 5 (5) 2 (3) 2 (4) Marsh killifish Fundulus confluentus 6 (3) 0 (0) 6 (10) 0 (0) Taillight shiner Notropis maculatus 6 (3) 1 (1) 2 (3) 3 (6) Swamp darter Etheostoma fusiforme 5 (2) 2 (2) 1 (2) 2 (4) Bass Micropterus spp. 4 (2) 1 (1) 1 (2) 2 (4) Tadpole Order: Anura 4 (2) 2 (2) 1 (2) 1 (2) Crayfish Procambarus spp. 3 (1) 1 (1) 2 (3) 0 (0) Apple snail Pomacea paludosa 1 (<1) 0 (0) 1 (2) 0 (0)

Table 3.6. Species Richness by treatment type. Treatment Mean (±SD) Mean Peak (±SD) Distant Control 2.01 (1.47) 3.78 (0.94) Depression 2.08 (1.86) 4.36 (1.69) Near Control 1.67 (1.37) 3.33 (1.22)

Table 3.7. Totals for large fish (Total Length (TL) ≥ 5 cm) within depression treatments. Taxonomic Group Total Individuals Total Biomass (g, %) Sunfish (Centrarchidae) 19 244.3 (47.5%) Cichlid (Cichlidae) 0 0 (0%) Other/unknown 3 66.5 (12.9%) Bass (Micropterus spp.) 2 63.1 (12.3%) Unknown 9 140.2 (27.3%) Total 33 514.1

Figure 3.1. Great egrets foraging within disturbed areas in an Everglades slough. Disturbances may be caused by alligators.

Figure 3.2. Hypothesized biomass/density curves for 3 treatments as slough dries down over the course of the dry season. Solid line represents depression sampling unit, dotted line represents distant control (100 m) sampling unit, dashed line represents near (5 m) control sampling unit.

Figure 3.3. Map of aquatic fauna sampling sites within Water Conservation Areas (WCA) 2A and 3A in the central Everglades. WCA2A was sampled early in the 2017 dry season, followed by sites in WCA3A.

Figure 3.4. A Breder trap modified from the original design to capture prey from the entire water column in water depths up to 45 cm.

WCA2A

WCA3A

Figure 3.5. Mean water levels at sampling sites during the 2016 dry season. Blue line represents water levels during the 2016 dry season. Shaded areas represent timeframe outside of sampling window. Dotted line represents mean ground elevation. Water levels were based on nearest EDEN (Everglades Depth Estimation Network) water gage to respective sites (EDEN 2017).

Figure 3.6. Distribution of microtopography index within the Everglades. Black dashed line represents chosen microtopography index (90th percentile, 20 cm) to manipulate in treatment areas (n=192) (D.E. Gawlik, unpubl. data).

Figure 3.7. Distribution of depression widths within the Everglades for depressions ≥ 20 cm. Black dashed line represents the mode and chosen treatment depression width (n=79) (D.E. Gawlik, unpubl. data).

Figure 3.8. Aerial view showing trap placement in a hypothetical slough. Inset shows vertical slough cross section.

Figure 3.9. Model predictions for fauna biomass index plotted against mean water depth for distant controls (C) and depressions (D). Mean water depth was taken at near and distant control sampling locations on the respective sampling day. The term for foraging days was removed from the model to plot in two dimensions.

Figure 3.10. Relationship between actual and predicted values of optimal formula for fauna biomass index. Dashed line represents a one-one relationship, solid line represents fitted line. The relationship is weak (r2 = 0.25). Two outliers are not shown.

Figure 3.11. Model predictions for fauna density index plotted against mean water depth for distant controls (C) and depressions (D). Mean water depth has been defined as the mean water depth at control sampling locations on the respective sampling day. The term for foraging days was removed from the model to plot in two dimensions.

Figure 3.12. Relationship between actual and predicted values of optimal formula for fauna density index. Dashed line represents a one-one relationship, solid line represents fitted line. The relationship is moderate (r2 = 0.35). Two outliers are not shown.

4 SYNTHESIS

A defining characteristic of seasonally-pulsed tropical wetlands is their fluctuating hydrology and resources. Top predators, such as wading birds, rely on high- quality foraging patches to feed their young during the breeding season (Butler 1994,

Hafner 1997, Steinmetz et al. 2009). In this case, high quality patches come in the form of highly concentrated aquatic fauna. This thesis quantifies the effects of crocodilian use of a shallow wetland, and the effects of depressions within the marsh on aquatic fauna concentration.

In Chapter 2, I examined the relationship between crocodilian use and microtopography within Everglades sloughs. Despite each of four microtopography metrics being higher in high alligator (Alligator mississippiensis) use sloughs, none differed significantly. The lack of significance may be due to timing and location of sampling and was most likely exacerbated by a small sample size. Crocodilians are ecosystem engineers, often modifying the marsh substrate and thus the habitat of aquatic fauna. Previous crocodilian research focused on their role in providing deep-water refugia for aquatic fauna, increasing survival (Craighead 1968, Pooley 1969, Kushlan 1974,

Kushlan and Kushlan 1980, Magnusson 1982). This research specifically focused on shallow depressions that crocodilians may be creating as the marsh dries and the effects they have on aquatic fauna.

In Chapter 3, I found that aquatic fauna concentrate locally within small, shallow depressions during the dry season at orders of magnitude greater than non-depressed

areas within the marsh. This supports the local concentration hypothesis proposed by

Trexler et al. (2002). Aquatic fauna concentrations are elevated within microtopographic depressions throughout the range of water depths used by foraging wading birds. Aquatic fauna levels are also higher adjacent to depressions suggesting that the concentrating effects of depressions extend out at least 5 m. Large fish, which make up an important part of wading bird diets (Ogden et al. 1976, Klassen et al. 2016), selected for depressions at elevated rates over all aquatic fauna. Our current understanding of the relationship between aquatic fauna concentrations and microtopography is based on observational studies which have found a positive correlation (Botson et al. 2016,

Klassen et al. 2016, Klassen & Gawlik 2017). This chapter, however, experimentally demonstrates that aquatic fauna concentrate in microtopographical depressions and determines the magnitude, timing, and spatial extent of this concentration.

This research stresses the importance that crocodilians may play in enhancing the quality of wading bird foraging habitat. If crocodilians create shallow depressions like those created in this study, they are the first known biotic mechanism to enhance the quality of wading bird foraging habitat. Wading birds are declining worldwide and are food-limited throughout the tropics (Butler 1994, Hafner 1997, Herring et al. 2010).

Understanding the mechanisms which create high quality wading bird foraging patches is critical to facilitate restoration and prevent declines of wading bird populations in seasonally pulsed wetlands worldwide.

5 LITERATURE CITED

Ahn, C. 2010. Plant community development in the first growing season of a created

mitigation wetland bank as influenced by design elements. Journal of Ecology and

Environment 33:363-376.

Alho, C. J. 2005. The Pantanal. Pages 203-271 in L. H. Fraser & P. A. Keddy, editors.

The World’s Largest Wetlands - Ecology and Conservation. Cambridge

University Press, New York, New York.

Alho, C. J. 2008. Biodiversity of the Pantanal: response to seasonal flooding regime and

to environmental degradation. Brazilian Journal of Biology 68:957-966.

Bancroft, G. T. et al., 1994. Relationships among wading bird foraging patterns, colony

locations, and hydrology in the Everglades. Pages 615–657 in S. M. Davis and J.

C. Ogden, editors. Everglades: the ecosystem and its restoration. St. Lucie Press,

Delray Beach, Florida, USA.

Bancroft, G. T., D. E. Gawlik, and K. Rutchey. 2002. Distribution of wading birds

relative to vegetation and water depths in the northern Everglades of Florida.

Waterbirds 25:265-277.

Barr, B. 1997. Food habits of the American alligator, Alligator mississippiensis, in the

southern Everglades. PhD. dissertation, University of Miami, Miami, Florida.

Barton, K. 2017. MuMIn: Multi-Model Inference. R package version 1.40.0.

https://CRAN.R-project.org/package=MuMIn

Bates, D., M. Maechler, B. Bolker, and S. Walker. 2015. Fitting Linear Mixed-Effects

Models Using lme4. Journal of Statistical Software 67:1-48.

Botson, B. A., D. E. Gawlik, and J. C. Trexler. 2016. Mechanisms that generate resource

pulses in a fluctuating wetland. PLOS One 11:e0158864.

Boyle, R. A., N. J. Dorn, and M. I. Cook. 2012. Nestling diet of three sympatrically

nesting wading bird species in the Florida Everglades. Waterbirds 35:154-159.

Breder, C. M., 1960. Design for a fry trap. Zoologica 45:155-160.

Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodal inference: A

practical information-theoretic approach. 2nd ed. SpringerVerlag, New York,

New York.

Burtner, B. F. 2011. Symbiosis between long legged wading birds (Ciconiiformes) and

alligators (Alligator mississippiensis)? Testing the nest protector hypothesis. M.S.

Thesis. University of Florida. Gainesville, Florida.

Burtner, B. F. and P. C. Frederick. 2017. Attraction of Nesting Wading Birds to

Alligators. Wetlands 37:697-704.

Butler, R. W. 1994. Population regulation of wading ciconiiform birds. Colonial

Waterbirds 17:189-199.

Campbell, M. and F. Mazzotti. 2004. Characterization of natural and artificial alligator

holes. Southeastern Naturalist 3:583-594.

Carlson, J. E. and M. J. Duever. 1979. Seasonal fish population fluctuations in South

Florida swamp. Southeastern association of fish and wildlife agencies.

Champely, S. 2018. pwr: Basic Functions for Power Analysis. R package version 1.2-2.

http://CRAN.R-project.org/package=pwr

Clarke, K. R. 1993. Non-parametric multivariate analyses of changes in community

structure. Australian Journal of Ecology. 18:117-143.

Clarke, K. R. and R. N. Gorley. 2015. PRIMER v7: User Manual/Tutorial. PRIMER E.

Plymouth: Plymouth Marine Laboratory.

Conover, W. J. and R. L. Iman. 1979. On multiple-comparisons procedures. Technical

Report. LA-7677-MS. Los Alamos Scientific Laboratory, Los Alamos, New

Mexico.

Cook, M. I. and E. M. Call. 2005. South Florida wading bird report vol. 11. South Florida

Water Management District, West Palm Beach, FL.

Craighead, F. C. 1968. The role of the alligator in shaping plant communities and

maintaining wildlife in the southern Everglades. Florida Naturalist. Volume 41.

Crozier, G. E. and D. E. Gawlik. 2003. Wading Bird Nesting Effort as an Index to

Wetland Ecosystem Integrity. Waterbirds 26:303-324.

Davis, S. M., L. H. Gunderson, W. A. Park, J. R. Richardson, and J. E. Mattson. 1994.

Landscape dimension, composition, and function in a changing Everglades

ecosystem. Pages 419–444 in S. M. Davis and J. C. Ogden, editors. Everglades:

the ecosystem and its restoration. St. Lucie Press, Delray Beach, Florida, USA.

DeAngelis, D. L., W. F. Loftus, J. C. Trexler, and R. E. Ulanowicz. 1997. Modeling fish

dynamics and effects of stress in a hydrologically pulsed ecosystem. Journal of

Aquatic Ecosystem Stress and Recovery (Formerly Journal of Aquatic Ecosystem

Health) 6:1-13.

Delany, M. F. and C. L. Abercrombie. 1986. American alligator food habitats in

northcentral Florida. The Journal of Wildlife Management 50:348-353.

Deitz, D. C. 1979. Behavioral ecology of young American alligators. PhD. dissertation,

University of Florida, Gainesville, FL.

Dinno, A. 2017. conover.test: Conover-Iman Test of Multiple Comparisons Using Rank

Sums. R package version 1.1.5. https://CRAN.R-

project.org/package=conover.test

Douglas, M. M., S. E. Bunn, and P. M. Davies. 2005. River and wetland food webs in

Australia's wet-dry tropics: General principles and implications for management.

Marine and Freshwater Research 56: 329-342.

Duursma, R. 2016. bootpredictlme4. Predict Method For lme4 With Bootstrap.

https://github.com/RemkoDuursma/bootpredictlme4/blob/master/DESCRIPTION

Dvorac, J. and E. P. H. Best. 1982. Macroinvertebrate communities associated with the

macrophytes of Lake Vechten: structural and functional relationships.

Hydrobiologia 95:115-126.

Finlayson, C. M. 1990. Productivity and nutrient dynamics of seasonally inundated

floodplains in the Northern Territory. Pages 58-83 in D. Wade-Marshall and P.

Loveday editors. Northern Australia: Progress and prospects, vol. 2 Floodplain

research. ANU Press, Darwin, Australia.

Finlayson, C. M., J. Lowry, M. G. Bellio, S. Sou, R. Pidgeon, D. Walden, C. Humphrey,

and G. Fox. 2006. Biodiversity of the wetlands of the Kakadu Region, northern

Australia. Aquatic Sciences 68:374-399.

Fox, P. J. 1976. Preliminary observations on fish communities in the Okavango Delta.

Botswana Society 125-130.

Frederick, P. C. & Spalding, M. G., 1994. Factors affecting reproductive success of

wading birds (Ciconiiformes). Pages 659–691 in S. M. Davis and J. C. Ogden,

editors. Everglades: the ecosystem and its restoration. St. Lucie Press, Delray

Beach, Florida, USA.

Frederick, P. C. and M. W. Collopy. 1989. Nesting success of five Ciconiiform species in

relation to water conditions in the Florida Everglades. The Auk 106:625-634.

Frederick, P. C., M. G. Spalding, M. S. Sepalveda, G. E. Williams, L. Nico, and R.

Robins. Exposure of great egret (Ardea albus) nestlings to mercury through diet in

the Everglades ecosystem. Environmental Toxicology and Chemistry 18:1940-

1947.

Fujisaki, I., F. J. Mazzotti, R. M. Dorazio, K. G. Rice, M. Cherkiss, and B. Jeffery. 2011.

Estimating Trends in Alligator Populations from Nightlight Survey Data.

Wetlands 31:147-155.

Gardner, M. J. and D. G. Altman. 1986. Confidence intervals rather than P values:

estimation rather than hypothesis testing. British Medical Journal 292:746-750.

Gawlik, D. E. 2002. The effects of prey availability on the numerical response of wading

birds. Ecological monographs 72:329-346.

Gawlik, D. E. 2005. Aquatic fauna seasonal concentration final pilot project report.

Florida Atlantic University, Boca Raton, FL.

Gawlik, D. E. and D. A. Rocque. 1998. Avian communities in bayheads, willowheads,

and sawgrass marshes of the central Everglades. The Wilson Bulletin 110:45-55.

Garrett, P. B. 2007. Factors affecting wading bird prey concentrations in the Everglades

during the dry season. M.S. thesis, Florida Atlantic University, Boca Raton,

Florida.

Gereta, E. and E. Wolanski. 1998. Wildlife-water quality interactions in the Serengeti

National Park, Tanzania. African Journal of Ecology 36:1-14.

Gervasio, P., S. Perelman, J. P. Guerschman, and J. M. Paruelo. 2008. How to evaluate

models: Observed vs. predicted. Ecological Modeling 216:316-322.

Gleason, P. J. and P. Stone. 1994. Age, origin, and landscape evolution of the Everglades

peatland. Pages 149–197 in S. M. Davis and J. C. Ogden, editors. Everglades: the

ecosystem and its restoration. St. Lucie Press, Delray Beach, Florida, USA.

Gonzalez, J. 1996. Kleptoparasitism in mixed-species foraging flocks of wading birds

during the late dry season in the Llanos of Venezuela. Colonial Waterbirds

19:226-231.

Gonzalez, J. 1997. Seasonal variation in the foraging ecology of the wood stork in the

southern Llanos of Venezuela. The Condor 99:671-680.

Gorzula, S. J. 1978. An Ecological Study of Caiman crocodilus crocodilus Inhabiting

Savanna Lagoons in the Venezuelan Guayana. International Association for

Ecology 35:21-34.

Gumbricht, T., T. S. McCarthy, and P. Bauer. 2005. The micro-topography of the

wetlands of the Okavango Delta, Botswana. Earth Surface Processes and

Landforms 30:27-39.

Hafner, H. 1997. Ecology of wading birds. Colonial Waterbirds 1:115-120.

Herring, G., D. E. Gawlik, M. I. Cook, and J. M. Beerens. 2010. Sensitivity of nesting

great egrets (Ardea alba) and white ibises (Eudocimus albus) to reduced prey

availability. The Auk 127:660-670.

Herring, G. and D. E. Gawlik. 2012. Differential physiological responses to prey

availability by the great egret and white ibis. Journal of Wildlife Management

77:58-67.

Hines, T. C. 1968. Alligator research in Florida: A progress report. Game and Fresh

Water Fish Commission, West Palm Beach, Florida.

Hoffman, W., G. T. Bancroft, and R. J. Sawicki. 1994. Foraging habitat of wading birds

in the water conservation areas of the Everglades. Pages 585–614 in S. M. Davis

and J. C. Ogden, editors. Everglades: the ecosystem and its restoration. St. Lucie

Press, Delray Beach, Florida, USA.

Holm, S. 1979. A simple sequentially rejective multiple test procedure. Scandinavian

Journal of Statistics 6:65-70.

Hudgens, B. R. 1997. Nest predation avoidance by the blue-billed malimbe Malimbus

nitens (Ploceinae). Ibis 139:692-694.

IUCN, 2016. IUCN Red List of Threatened Species.

http://www.iucnredlist.org/technical-documents/spatial-data. Accessed 14 May

2016.

Jones, C. G., J. H. Lawton, and M. Shachak. 1994. Organisms as Ecosystem Engineers.

Oikos 69:373-386.

Jones, C. G., J. L. Gutierrez, J. E. Byers, J. A. Crooks, J. G. Lambrinos, and T. S. Talley.

2010. A framework for understanding physical ecosystem engineering by

organisms. Oikos 119:1862-1869.

Jordan, F., K. J. Babbit and C. C. McIvor. 1998. Seasonal variation in habitat use by

marsh fishes. Ecology of freshwater fish 7:159-166.

Junk, W.J. 2002. Long-term environmental trends and the future of tropical wetlands.

Environmental Conservation 29:414-435.

Junk, W. J., M. Brown, I. C. Campbell, M. Finlayson, B. Gopal, L. Ramberg, and B. G.

Warner. 2006. The comparative biodiversity of seven globally important

wetlands: a synthesis. Aquatic Sciences 68:400-418.

Kahl, M. P. 1964. Food Ecology of the Wood Stork. Ecological Monographs 34:97-117.

Kamphorst, E. C., V. Jetten, J. Guerif, B. V. Iversen, J. T. Douglas, and A. Paz. 2000.

Predicting depressional storage from soil surface roughness. Soil and Water

Management and Conservation 64:1749-1758.

Klassen, J. A. and D. E. Gawlik. 2014. South Florida Wading Bird Report, Vol 20: Wood

stork prey composition at a coastal and interior colony in Everglades National

Park. South Florida Water Management District, West Palm Beach, Florida.

Klassen, J. A. 2016. Wading bird prey production and prey selection in a dynamic

wetland. PhD dissertation, Florida Atlantic University, Boca Raton, Florida.

Klassen, J. A., D. E. Gawlik, and P. C. Frederick. 2016. Linking wading bird prey

selection to number of nests. Journal of Wildlife Management 80:1450-1460.

Klinkenberg, B. 1992. Fractals and morphometric measures: is there a relationship?

Geomorphology 5:5-20.

Kobza, R. M., J. C. Trexler, W. S. Loftus, and S. A. Perry. 2004. Community structure of

fishes inhabiting aquatic refuges in a threatened Karst wetland and its

implications for ecosystem management. Biological Conservation 116:153-165.

Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel. 2006. World map of the

Koppen-Geiger climate classification updated. Meteorologische Zeitschrift

15:259-263.

Kruskal, W. H. and W. A. Wallis. 1952. Use of ranks in one-criterion variance analysis.

Journal of the American statistical Association 47:583-621.

Kushlan, J. A. 1974. Observations on the role of the American alligator (Alligator

mississippiensis) in the southern Florida wetlands. Copeia 1974:993-994.

Kushlan, J. A., J. C. Ogden, A. L. Higler. 1975. Relation of water level and fish

availability to wood stork reproduction in the southern Everglades. USGS,

Tallahassee, Florida.

Kushlan, J. A. 1976. Wading bird predation in a seasonally fluctuating pond. The Auk

93:464-476.

Kushlan, J. A. and M. S. Kushlan. 1980. Everglades alligator nests: Nesting sites for

marsh reptiles. Copeia 1980:930-932.

Kushlan, J. A. 1981. Sampling characteristics of enclosure fish traps. Trans American

Fisheries Society 110:557-562.

Kushlan, J. A., M. Gonzalo, and P. C. Frohring. 1985. Foraging Niche Relations of

Wading Birds in Tropical Wet Savannas. Ornithological Monographs 1985:663-

682.

Kushlan, J. A. 1987. External threats and internal management: The hydrologic

regulation of the Everglades, Florida, USA. Environmental Management 11:109-

119.

Lewis, C. G. 2005. Linkages among vegetative substrate quality, biomass production, and

decomposition in maintaining Everglades ridge and slough vegetative

communities. PhD dissertation, University of Florida, Gainesville, Florida.

Lewis, F., A. Butler, and L. Gilbert. 2011. A unified approach to model selection using

the likelihood ratio test. Methods in Ecology and Evolution 2:155-162.

Ligas, F. J. 1960. The Everglades bullfrog - life history and management. Florida Game

and Fresh Water Fish Commission.

Loftus, W. F. and A. M. Eklund. 1994. Long-term dynamics of an Everglades small-fish

assemblage. Pages 461–484 in S. M. Davis and J. C. Ogden, editors. Everglades:

the ecosystem and its restoration. St. Lucie Press, Delray Beach, Florida, USA.

Lorenz, J. 2000. The impact of water management on roseate spoonbills and their piscine

prey in the coastal wetlands of Florida Bay. PhD dissertation, University of

Miami, Miami, Florida.

Lorenz, J. 2014. The relationship between water level, prey availability and reproductive

success in roseate spoonbills foraging in a seasonally-flooded wetland while

nesting in Florida Bay. Wetlands 34:201-211.

Loveless, C. M. 1959. A study of vegetation of the Florida Everglades. Ecology 40:1-9.

Magnusson, W. E. 1982. Mortality of Eggs of the Crocodile Crocodylus porosus in

Northern Australia. Journal of Herpetology 16:121-130.

Magnusson, W. E. 1986. The peculiarities of crocodilian population dynamics and their

possible importance for management strategies. International Union for the

Conservation of Nature and Natural Resources, Gland, Switzerland.

Magoulick, D. D. and M. Kobza. 2003. The role of refugia for fishes during drought: a

review and synthesis. Freshwater Biology 48:1186-1198.

Maheswaran, G. and A. R. Rahmani. 2001. Effects of water level changes and wading

bird abundance on the foraging behaviour of blacknecked storks

Ephippiorhynchus asiaticus in Dudwa National Park, India. Journal of

biosciences 26:373-382.

Mazzotti, F. J. and L. A. Brandt. 1994. Ecology of the American alligator in a seasonally

fluctuating environment. Pages 485–505 in S. M. Davis and J. C. Ogden, editors.

Everglades: the ecosystem and its restoration. St. Lucie Press, Delray Beach,

Florida, USA.

Mazzotti, F.J., R. Best, L. A. Brandt, M. S. Cherkiss, B. M. Jeffery, and K.G. Rice. 2009.

Alligators and crocodiles as indicators for restoration of Everglades ecosystems.

Ecological Indicators 9:S137-S149.

Miranda, E.B.P. 2017. The Plight of Reptiles as Ecological Actors in the Tropics.

Frontiers in Ecology and Evolution 5:159.

McIlhenny, E. A. 1935. The alligator’s life history. Christopher, Boston, Massachusetts.

Merron, G. S. 1993. Pack-hunting in two species of catfish, Clarias gariepinus and C.

ngamensis, in the Okavango Delta, Botswana. Journal of Fish Biology, 43:575-

584.

Moore, J. W. 2006. Animal ecosystem engineers in streams. BioScience 56:237-246.

Morea, C. R., K. G. Rice, H. F. Percival, and S. R. Howarter. 2000. Home range and

movement of the American alligator in the Everglades. Crocodiles: Proceedings

of the 15th working meeting of the crocodile specialists group. The World

Conservation Union, Gland, Switzerland.

Mosepele, K., P. B. Moyle, G. S. Merron, D. R. Purkey, and B. Mosepele. 2009. Fish,

floods, and ecosystem engineers: Aquatic conservation in the Okavango Delta,

Botswana. BioScience 59:53-64.

Moser, K. F., C. Ahn, and G. B. Noe. 2007. Characterization of microtopography and its

influence on vegetation patterns in created wetlands. Wetlands 27:1081-1097.

Moser, K. F., C. Ahn and G. B. Noe. 2009. The Influence of Microtopography on Soil

Nutrients in created mitigation wetlands. Restoration Ecology 17:641-651.

Naiman, R. J. 1988. Animal influences on ecosystem dynamics. BioScience 38:750-752.

Nakagawa, S. and H. Schielzeth. 2013. A general and simple method for obtaining R2

from generalized linear mixed-effects models. British Ecological Society. 4:133-

142.

Nash, M. S., E. Jackson, and W. G. Whitford. 2003. Soil microtopography on grazing

gradients in Chihuahuan desert grasslands. Journal of Arid Environments 55:181-

192.

Nell, L. A. and P. C. Frederick. 2015. Fallen nestlings and regurgitant as mechanisms of

nutrient transfer from nesting wading birds to crocodilians. Wetlands 35: 723-732.

Nell, L. A., P. C. Frederick, F. J. Mazzotti, K. A. Vliet, and L. A. Brandt. 2016. Presence

of breeding birds improves body condition for a crocodilian nest protector. PLOS

One 11:e0149572.

Newman, S., J. Schuette, J. B. Grace, K. Rutchey, T. Fontaine, K. R. Reddy, and M.

Pietrucha. 1998. Factors influencing cattail abundance in the northern Everglades.

Aquatic Botany 60:265-280.

Neyman, J. and E. S. Pearson. 1933. The testing of statistical hypotheses in relation to

probabilities a priori. Mathematical Proceedings of the Cambridge Philosophical

Society 29:492-510.

Ogden, J. C., J. A. Kushlan, and J. T. Tilmant. 1976. Prey selectivity by the wood stork.

The Condor 78:324-330.

Ogden, J. C. 1994. A comparison of wading bird nesting colony dynamics (1931-1946

and 1974-1989) as an indication of ecosystem conditions in the southern

Everglades. Pages 533–570 in S. M. Davis and J. C. Ogden, editors. Everglades:

the ecosystem and its restoration. St. Lucie Press, Delray Beach, Florida, USA.

Palmer, M. L. and F. J. Mazzotti. 2004. Structure of Everglades alligator holes. Wetlands

24:115-122.

Pearson, R. G., B. Butler, and M. Crossland. 2003. Effects of cane-field drainage on the

ecology of tropical waterways. Australian Centre for Tropical Freshwater

Research, Townsville, Queensland, Australia.

Petersen, M. L. 2017. Quantifying wading bird resource selection and nesting effort: a

tool for the restoration of pulsed ecosystems. PhD dissertation, Florida Atlantic

University, Boca Raton, Florida.

Pierce, R. L. and D. E. Gawlik. 2010. Wading bird foraging habitat selection in the

Florida Everglades. Waterbirds 33:494-503.

Pooley, A. C. 1969. The burrowing behaviour of crocodiles. Lammergeyer 10:60-63.

Posada, D. and T. R. Buckley. 2004. Model selection and model averaging in

phylogenetics: advantages of Akaike information criterion and Bayesian

approaches over likelihood ratio tests. Pages 53, 793-808 in T. J. Near, editor.

Systematic Biology. Oxford University Press, Oxford, England.

Powell, G. N. 1983. Food availability and reproduction by great white herons Ardea

herodias, a food addition study. Colonial Waterbirds 6:139-147.

Powell, G. N. and A. H. Powell. 1986. Reproduction by great white herons Ardea

herodias in Florida Bay as an indicator of habitat quality. Biological Conservation

36:101-113.

Powell, G. V. 1987. Habitat use by wading birds in a subtropical estuary: Implications of

hydrography. The Auk 104:740-749.

Powell, G. N., R. D. Bjork, J. C. Ogden, R. T. Paul, A. H. Powell, and W. B. Robertson.

1989. Population trends in some South Florida wading birds. Wilson Bulletin

101:436-457.

Rehage, J. S. and J. C. Trexler. 2006. Assessing the net effect of anthropogenic

disturbance on aquatic communities. Hydrobiologia 569:359-373.

Robinson, S. K. 1985. Coloniality in the yellow-rumped cacique as a defense against nest

predators. Auk 102:506-519.

Rootes, W. L., R. H. Chabreck, V. L. Wright, B. W. Brown, T. J. Hess. 1991. Growth

rates of American alligators in estuarine and palustrine wetlands in Louisiana.

Estuaries 14:489-494.

Rootes, W. L. and R. H. Chabreck. 1993. Cannibalism in the American alligator.

Herpetologica 49:99-107.

Rosenbaum, P. R. 2005. Heterogeneity and causality. The American Statistician 59:147-

152.

Rosenbaum, P. R. 2010. Design of Observational Studies. Springer, New York, New

York.

Rozas, L. P. and W. E. Odum. 1988. Occupation of submerged aquatic vegetation by

fishes. Oecologia 77:101-106.

Rubel, F. and M. Kottek. 2010. Observed and projected climate shifts 1901-2100

depicted by world maps of the Koppen-Geiger climate classification.

Meteorologische Zeitschrift 19:135-141.

Saalfield, D. T., W. C. Conway, and G. E. Calkins. 2011. Food habits of American

alligators in East Texas. Southeastern Naturalist, 10:659-672.

Sargent, W. B. and P. R. Carlson. 1987. The utility of Breder traps for sampling

mangrove and high marsh fish assemblages. Proceedings of the Fourteenth

Annual conference on Wetlands Restoration and Creation, Tampa, Florida.

Schofield, P. J., W. F. Loftus, R. M. Kobza, M. I. Cook, and D. H. Slone. 2010.

Tolerance of nonindigenous cichlid fishes (Cichlasoma urophthalmus,

Hemichromis letourneuxi) to low temperature: laboratory and field experiments in

South Florida. Biological Invasions 12:2441-2457.

Sklar, F., C. McVoy, R. VanZee, D. E. Gawlik, K. Tarboton. D. Rudnick, S. Miao, and T.

Armentano. 2002. The effects of altered hydrology on the ecology of the

Everglades. Pages 63–106 in J. Porter and K. Porter, editors. The Everglades,

Florida Bay, and Coral Reefs of the Florida Keys: An Ecosystem Sourcebook.

CRC Press, Boca Raton, Florida, USA.

Snedecor, G. W. and W. G. Cochran. 1989. Statistical Methods. 8th Edition. Iowa State

University Press, Iowa City, Iowa.

Staton, M. A. and J. R. Dixon. 1975. Studies on the dry season biology of Caiman

crocodilus crocodilus from the Venezuelan Llanos. Memoria de la Sociedad de

Ciencias Naturales La Salle, 35:237-265.

Steinmetz, J., S. L. Kohler, and D. Soluk. 2009. Ontogenetic shifts in the American

Alligator establish functional connectivity between aquatic systems. Biological

Conservation 142:1507-1514.

Strong, A. M., G. T. Bancroft, and S. D. Jewell. 1997. Hydrological constraints on

tricolored heron and snowy egret resource use. The Condor 99:894-905.

Subalusky, A. L., L. L. Smith, and L. A. Fitzgerald. 2009. Detection of American

alligators in isolated, seasonal wetlands. Applied Herpetology 6:199-210.

Throm, R. 2013. Movement and habitat selection of immature alligators in a restored

wetland in Florida. M.S. thesis, University of Florida, Gainesville, Florida.

Trexler, J. C., W. F. Loftus, F. Jordan, J. H. Chick, K. L. Kandl, T. C. McElroy, and O. L.

Bass Jr. 2002. Ecological scale and its implications for freshwater fishes in the

Florida Everglades. Pages 153–181 in J. Porter and K. Porter, editors. The

Everglades, Florida Bay, and Coral Reefs of the Florida Keys: An Ecosystem

Sourcebook. CRC Press, Boca Raton, Florida, USA.

US Army Corps of Engineers; US Geological Survey (USGS), 2017. Everglades Depth

Estimation Network (EDEN). https://sofia.usgs.gov/eden/. Accessed 3 May 2017.

Van Zalinge, R. and T. Evans. 2008. A review of the status and distribution of large

waterbirds in the Tonle Sap biosphere reserve. Wildlife Conservation Society:

Cambodia Program.

Wade, D. D., J. J. Ewel, and R. H. Kadlec. 1980. Fire in South Florida ecosystems.

Technical report SE-17. U.S. Department of Agriculture, Ashville, North

Carolina.

Warfe, D.M., N. E. Pettit, P. M. Davies, B. J. Pusey, S. K. Hamilton, M. J. Kennard, S.

A. Townsend, P. Bayliss, D. P. Ward, M. M. Douglas, M. A. Burford, M. Finn, S.

E. Bunn, and I. A. Halliday. 2011. Freshwater Biology 56:2169-2195.

Welcomme, R. L. 1979. Fisheries ecology of floodplain rivers. Longman, London,

England.

Wickham, H., 2009. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New

York, New York.