2020 South Environmental Report – Volume I Chapter 6

1 Chapter 6: 2 Research and Evaluation

3 Edited by Fred Sklar

4 SUMMARY 5 This chapter summarizes Water Year 2019 (WY2019; May 1, 2018–April 30, 2019) hydrology in the 6 Everglades Protection Area (EPA), followed by an overview of key Everglades studies on wildlife, plants, 7 the ecosystem, and landscapes (Table 6-1). Programs of study are based on the short-term operational needs 8 and long-term restoration goals of the Water Management District (SFWMD or District), 9 including large-scale and regional hydrologic needs in relation to regulation schedules, permitting, 10 Everglades Forever Act (Section 373.4592, Florida Statutes [F.S.]) mandates, and the Comprehensive 11 Everglades Restoration Plan (CERP). In addition, the Decomp Physical Model (DPM) research is discussed 12 in Appendix 6-1 of this volume.

13 Table 6-1. WY2019 Everglades research findings in relation to operational mandates.

Hydrology Projects Findings Mandates a Across most of the Everglades, water depths began WY2019 just below average historic stages. Well above average late dry season rainfall raised stages quickly and depths peaked earlier than normal. Most areas within the Everglades Water Conservation Areas (WCAs) fell below average depths by the ROS Hydrologic Patterns for WY2019 end of the wet season. Dry season depths and recession rates MFL in the WCAs were largely not conducive for optimal wading bird foraging. The stage and recession rate in (ENP) were closer to optimal, and higher numbers of nesting wading birds were noted there. After beginning WY2019 with above average flows and below average salinities, low rainfall through the WY2019 wet season CERP led to salinity increases that continued through the dry season. MFL Florida Bay Hydrology Eastern and Bay ended WY2019 with above average salinities. Western Florida Bay salinities did not rise as ROS quickly in the dry season and ended WY2019 slightly below COP average. 14

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15 Table 6-1. Continued.

Wildlife Ecology Projects Findings Mandates a Wading bird nesting effort and success was relatively reduced in in the Everglades during WY2019. An estimated 32,165 wading bird nests were initiated. This effort represents three-quarters of CERP the decadal average nest count and only a quarter of the CEPP number recorded last year. All indicator species, with the Wading Bird Monitoring COP exception of the snowy egret, exhibited decreased nesting effort during 2019. Of note was the complete failure of nesting by the EFA federally threatened wood stork (no chicks fledged from ROS 970 nests). Low stages at the start of the breeding season followed by rain-driven reversals led to poor prey availability Excess salt in marine invertebrates impose a physiologic burden on developing white ibis chicks, which helps to explain some of the reduced breeding activity in the coastal mangroves. CERP Irruption of Ibis Nesting in However, the Calendar Year 2018 (CY2018) ibis nesting activity CEPP Association with Crayfish Foraging in the coastal colonies was more than 25-fold higher than in COP in the Southern Everglades CY2017 and was the highest reported for the region in at least EFA 80 years. An analysis of the gut content of the chicks indicated ROS that freshwater prey, especially crayfish provided the key trigger for high nesting activity in the coastal colonies of ENP. Plant Ecology Projects Findings Mandates a Growth Response of Florida Native The preservation of the plant community dynamics on tree CERP Trees to Hydrology on Two islands, including the natural shifts in species composition, forest CEPP Constructed Everglades’ Tree structure, aboveground production and tree growth are directly COP Islands in the Loxahatchee dependent on the existence of a mosaic of hydrological Impoundment Landscape conditions, and the promotion of a strong wet and dry cycle EFA Assessment (LILA) throughout the Everglades ecosystem. ROS Continued recovery from the 2015 seagrass die-off event has CERP been occurring, but recent water quality conditions as a result of MFL Florida Bay Benthic Vegetation disturbances, such as storms, may be slowing the recovery of ROS the seagrass community. COP Ecosystem Ecology Projects Findings Mandates a The AMI approach changed the physical characteristics of the landscape and created sloughs with connectivity similar to that observed historically. As of CY2019, 2 years after treatment, the CERP Active Marsh Improvement (AMI) in AMI sloughs are still open and slough vegetation species CEPP the Decomp Physical Model (DPM) (Utricularia sp., periphyton, water lilies) are expanding within the EFA treatment area. The success of this landscape level adaptive ROS management suggests that we have the potential to restore flow direction and velocities in degraded areas of the Everglades. Tropical Storm Gordon and impacted the water quality in Florida Bay. With the exception of chlorophyll a (Chla) and total phosphorus (TP) in the eastern region, WY2019 CERP results were elevated above the Florida Administrative Code Florida Bay Water Quality CEPP (F.A.C.) basin standards. Chla and TP in all three regions of the and Status bay elevated in WY2018, decreased back to WY2017 levels, but MFL were still elevated above the period of record (POR) averages in ROS the central and western regions. Total nitrogen (TN) has been slightly elevated in all three regions of the bay since WY2016. 16

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17 Table 6-1. Continued.

Ecosystem Ecology (continued) Projects Findings Mandates a Because we are documenting trends over time, it is important to document and understand the causes for any abrupt and/or unexpected changes in the long-term POR. An approximately CERP Total Nitrogen in the Florida Bay 30% increase in TN in Florida Bay was observed in CY2009, the MFL Water Quality Monitoring Network time of an analytical lab change. Results could not conclusively demonstrate that the CY2009 unexpected change in TN was ROS due to a difference in analytical methodology, however evidence against this is presented. The Upper Taylor Slough area is a unique short hydroperiod marsh that may experience shifts in community composition with CERP Upper Taylor Slough increased westward flow through the S-332s and S-200 MFL Adaptive Management structures. Community compositions have been documented as a baseline from which to compare as increased water deliveries ROS continue to Taylor Slough in future years. Landscape Ecology Projects Findings Mandates a Despite large-scale disturbance, Florida Bay exhibited resilience. Algal blooms that began in October 2017 (WY2018) subsided across the bay by June 2018 (WY2019). Elevated Chla CERP Synoptic Ecological returned in October following Tropical Storm Gordon and MFL Mapping in Florida Bay Hurricane Michael, forming algal bloom Hot Spots; however, ROS Chla concentrations were lower than those following and dissipated within 4 months. Decomp Physical Model Research (Appendix 6-1) Projects Findings Mandates a Despite inflow TP concentrations at or below 10 parts per billion CERP (ppb), accelerated flows at sites closest to the S-152/Decomp CEPP Evaluating Food Web and Sediment Physical Model (DPM) structure increased local TP loads. Local EFA Nutrient Responses to Flow: TP loading rates of approximately 10 to 25 grams per meter per Periphytic Community Response day downstream of the DPM structure, are associated with floc FEIM and periphyton biomass with TP content above 500 milligrams ROS per kilogram; a concentration indicative of nutrient enrichment. The enrichment of sediments downstream of the L-67C levee gap, particularly the unfilled canal treatment, suggests that sediment dynamics in canals that are not backfilled may impact CERP the process of sediment phosphorus (P) enrichment. The CEPP Evaluating Food Web and Sediment highest loading rates observed in this study (25 g/m/d) are within EFA Nutrient Responses to Flow: the range of moderately enriched (11.3 g/m/d) and highly Sediment Chemistry Changes enriched (27.9 g/m/d) cattail habitats, in which high P loads FEIM reflected high water column TP rather than high velocity. Flow ROS restoration targets should consider an envelope of flow and loading conditions that maximize ecosystem benefits and minimize ecological costs. 18

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19 Table 6-1. Continued.

Decomp Physical Model Research (continued) Projects Findings Mandates a An important objective of DPM is to test how levee gap design and the degree of backfill (no backfill, partial backfill, and complete backfill) influence sheetflow and conveyance of sediment and P. Backfill treatments differed in the degree to CERP which they passively conveyed constituents; however, the causal factors for differential “processing” of constituents is CEPP Water and Constituent Fluxes unfortunately confounded with the uneven distribution of fluxes EFA through the L-67C Levee Gap entering the three treatment areas and the non-uniform FEIM contributions from canal inflows and wetland sheetflow. ROS Nonetheless, there was clear evidence that the unblocked

extensions of L67-C canal dominated water and constituent throughput in the levee gap that disrupted and diminished wetland-to-wetland connectivity by sheetflow that is the desired outcome of removing levees. a. Mandates CEPP Central Everglades Planning Project CERP Comprehensive Everglades Restoration Plan COP Combined Operations Plan EFA Everglades Forever Act, Section 373.4592, F.S. FEIM Florida Everglades Improvement and Management LTP Long-Term Plan for Achieving Water Quality Goals in the Everglades Protection Area MFL Minimum Flows and Minimum Water Levels, Section 373.042, F.S., and Chapter 40E-8, F.A.C. ROS Regulation and Operational Schedules RS Restoration Strategies

20

21 HYDROLOGY 22 WY2019 water depths in the Everglades began the season very near average historic stages. Well above 23 average rainfall in late WY2018 raised stages quickly and depths remained elevated but, unlike the previous 24 year, did not last through the entirety of the wet season. The Everglades Water Conservation Areas (WCAs) 25 received nearly 300% of the historical rainfall amount in May 2018, with ENP receiving 208% according 26 to SFWMD’s Raindar estimates. This early influx of rain caused a rapid rise to above average stages in the 27 WCAs. Unlike the previous year, wet season rainfall amounts were below average, so stages peaked earlier 28 than is typical and fell to below average for the rest of the water year. Depths ended the water year at or 29 just below the historical average, and a series of reversals ended a below average wading bird nesting season 30 in the WCAs. On-the-other-hand, Everglades National Park’s (ENP’s) Shark River Slough experienced a 31 hydropattern well suited for wading bird foraging, with depths peaking around October and falling to typical 32 depths during the wading bird nesting season. Despite a reversal late in the season, that region produced 33 conditions that sustained wading bird foraging throughout most of the dry season. 34 In the southern part of the system, Hurricane Irma brought a lot of freshwater to the southern part of 35 the system in WY2018 helping to maintain low salinity conditions through the first four months of 36 WY2019. Starting in July 2018, rainfall fell below average and the creek flow followed suit. As a result, 37 salinity began rising during what would typically be the lowest salinity period of the year (September and 38 October). Western Florida Bay, furthest from the freshwater deliveries of Taylor Slough would typically 39 be the last region to show a salinity decrease, was the first region to have salinity begin to decrease while 40 eastern and central Florida Bay salinities continued to increase through the WY2019 dry season.

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41 WILDLIFE 42 This section summarizes wading bird nesting effort and success in the Everglades and Florida Bay 43 during the WY2019 breeding season. This part of the section will be added for the final version of 44 this chapter. 45 This section also summarizes the results of a two-year study on the diets of white ibis (Eudocimus 46 albus) nestlings in the coastal Everglades. Excess salt in marine invertebrates may impose a physiologic 47 burden on developing chicks. The irruption of ibis nesting in Calendar Year 2018 (CY2018) and the prey 48 composition shift suggests that freshwater prey, especially crayfish, availability provides an important 49 trigger for high nesting activity in the coastal colonies of ENP. The increased use of fish and Procambarus 50 fallax (a species of crayfish) as the nesting season progressed can be interpreted as ibis progressively 51 increasing use of Shark River Slough to the east and southeast of the Broad River colony as the marl prairies 52 dried. Shark River Slough, with its relatively long hydroperiods, is one of the few areas in the southern 53 Everglades where consistently more P. fallax can be found and where fish should be more abundant. The 54 results of this two-year comparison provide real hope that the overdrained southern Everglades is resilient 55 and can still support large nesting colonies of ibis if wetlands in the coastal Everglades National Park and 56 Big Cypress National Preserve can be rehydrated with restoration.

57 PLANT ECOLOGY 58 A study to evaluate the growth patterns of three native tree species in relation to changes in water depth 59 and hydroperiod was conducted on two types of constructed tree islands in the Loxahatchee Impoundment 60 Landscape Assessment (LILA) project, an experimental wetland facility at the Arthur R. Marshall. 61 Loxahatchee National Wildlife Refuge (LNWR or WCA-1). There were significant differences between 62 peat-based and limestone-core tree islands. The analysis indicates that trees growing on the limestone-core 63 tree island had, on average, higher growth rates than individuals growing on the peat-based tree island. 64 Overall, there were no statistically significant differences in growth rate among the three species; however, 65 there was a significant island type species interaction, which indicated that growth for at least red maple 66 (Acer rubrum) was affected by island type. Differences may be a function of the fact that peat-based tree 67 islands are characterized by low soil bulk density and high soil water content, while the limestone-core tree 68 islands are characterized by high soil bulk density and low soil water content. 69 The South Florida Fish Habitat Assessment Program (FHAP) provides estimates of submerged aquatic 70 vegetation (SAV) cover using the Braun-Blanquet Cover Abundance (BBCA) Index at 30 random sites 71 within each of 16 basins throughout Florida Bay and along the southwestern coast every May. The seagrass 72 communities in central and western Florida Bay had been slowly recovering from the seagrass die-off event 73 that began in summer 2015 with Halodule wrightii (shoal grass) initially colonizing the areas where 74 Thalassia testudinum (turtle grass) had died followed by some expansion of Thalassia beginning to appear 75 in Rankin Lake and Johnson Key Basin. However, between May 2017 and May 2018, additional losses of 76 Thalassia were detected in Johnson Key Basin, Whipray Basin, and Manatee Bay. For the stations in the 77 western and central areas of Florida Bay, these changes are likely the result of increased chlorophyll and 78 turbidity after the Hurricane Irma in September 2017. The decline in Thalassia in Manatee Bay to the east 79 occurred near the shoreline where S-197 flows would enter the bay. This suggests that the decline may have 80 been related to the large releases through the S-197 structure for flood control in southern -Dade 81 County after the passage of Hurricane Irma.

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82 ECOSYSTEM ECOLOGY 83 To explore the possibility that large-scale removal of sawgrass (Cladium jamaicense) could improve 84 flow throughout the entire DPM footprint and restore landscape pattern and direction, we first modeled 85 flow direction and speed using Environmental Fluid Dynamic Code (EFDC) Explorer (Version 8.0, 86 Dynamic Solutions International LLC, Edmonds, Washington); we then sprayed approximately 14 hectares 87 (ha) with herbicide to create continuous sloughs over 2 kilometers (km) in length. After 6 months, the 88 standing dead biomass was smashed down with airboats. These reconnected sloughs had greater flow rates; 89 slough velocities were enhanced by approximately 2 times up to 1,200 meters (m) from the structure, as 90 opposed to 500 meters prior to implementation of active marsh improvement (AMI). We were also able to 91 redirect a significant amount of flow to the south, along the historic flow path. As of 2019, 2 years after 92 treatment, the AMI sloughs are still open and slough vegetation species including bladderworts (Utricularia 93 sp.), periphyton, and water lilies [Nymphaea spp.]) are expanding within the treatment area. The success of 94 this landscape-level treatment suggests we have the potential to increase the spatial extent of enhanced 95 flow, as well as restoring flow direction and velocities, in degraded areas of the Everglades. 96 Water quality in Florida Bay has been monitored since 1991 (WY1992) to ensure that District 97 operations and projects protect and restore the coastal ecosystem to the extent possible. In the 2019 South 98 Florida Environmental Report (SFER) – Volume I; Sklar 2019), we reported on the impacts of Hurricane 99 Irma and how these impacts were likely exacerbated by the previous SAV die-off event in the central region 100 of the bay. In WY2019, Florida was impacted by both Tropical Storm Gordon (September 2018) and 101 Hurricane Michael (October 2018). Results presented show immediate impacts of these storms in most 102 constituents and some or all three regions of the bay, especially the central region of the bay, site of the 103 SAV die-off in 2015 (WY2016). This region has been more reactive to storm disturbance. Tropical Storm 104 Gordon likely set up conditions such as wind induced sediment resuspension exacerbating the impacts from 105 Hurricane Michael a month later. In addition to storm disturbance, impacts from more local events included 106 peaks in turbidity in January and February 2019 in the eastern region due to wind events, and peaks in 107 dissolved inorganic nitrogen (DIN) in all three regions of the bay in June 2018 and February 2019 108 (WY2019) due to higher precipitation. Both wind and precipitation effects may be due to the shallow nature 109 of the bay and the presence of numerous mud banks that restrict circulation. 110 A more in-depth examination of Florida Bay water quality was conducted because a discrepancy in the 111 long-term period of record (POR) was reported in the 2017 SFER, Volume 1, Chapter 6 (Sklar and Dreschel 112 2017). The approximately 30% increase in total nitrogen (TN) in Florida Bay in 2009 was evaluated to 113 assess whether it could be attributed to an analytical lab change. The causes for abrupt water quality changes 114 may be due to a number of factors including (1) cold fronts, hurricanes and tropical storms, which can alter 115 atmospheric deposition of nutrients, increase freshwater inputs to the bay, and cause sediment resuspension; 116 (2) changing precipitation patterns, which can also alter atmospheric deposition and freshwater inputs of 117 nutrients to the bay; (3) changes in the Gulf of circulation patterns, which is one of the largest 118 sources of TN to the bay; and (4) water management changes to the headwaters of Florida Bay. Results of 119 this investigation do not conclusively reveal that a difference in analytical methodology caused the abrupt 120 increase in TN seen between CY2008 and CY2009. Given the complex interactions between water quality 121 and environmental conditions, it is essential that changes to any analytical method, including lab changes, 122 should include an intercalibration that documents any potential significant differences due to these changes. 123 A multi-agency effort to facilitate increasing freshwater flows towards Florida Bay through Taylor 124 Slough began in 2017 despite concerns that the increased flows would increase nutrient loading that could 125 impact the communities within the Everglades National Park (ENP) boundary. To address the concerns, an 126 adaptive monitoring and management plan was initiated to document potential changes and to facilitate 127 discussion of potential improvements to the management of the system to minimize any potential damage 128 to the ecological community. The plan includes monitoring of periphyton, macrophytes, fish, and 129 invertebrates immediately surrounding and downstream of the S-332D-associated retention area. The initial 130 two years of data have characterized the study area as short to very short hydroperiod marsh with fish

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131 communities dominated by the eastern mosquitofish (Gambusia holbrooki), likely a result of its ability to 132 rapidly recolonize after the marsh is rehydrated. As the project continues, these data will be used to provide 133 insight into the potentially shifting zone of influence of water management within eastern ENP and provide 134 feedback for water management decision making.

135 LANDSCAPE ECOLOGY 136 Through spatial data interpolations from quarterly and event-driven high resolution sampling, the 137 Dataflow program allows the District to quantify and monitor Florida Bay water quality conditions, 138 examine changes to the ecosystem, and evaluate restoration efforts, which benefit projects such as the C-111 139 Spreader Canal Western Features Project Phase I, Florida Bay Minimum Flows and Minimum Water Levels 140 (MFL), and the Upper Taylor Slough Project. Throughout WY2018–WY2020, the salinities in the basins 141 across the Florida Bay study region differed spatially in a given survey by as much as 40, consistently 142 exhibiting the distinct compartmentalization of this estuary. Chlorophyll a (Chla) has been consistently 143 elevated in the western mangrove lakes up to 27 micrograms per liter (µg/L) compared to the open bay 144 where concentrations typically remain < 10 µg/L. Between WY2018 and WY2020, three named storms 145 impacted Florida Bay. After Hurricane Irma, phycocyanin fluorescence increased up to two orders of 146 magnitude, indicative of blue-green algae (cyanophyte) presence. Despite the large-scale disturbance of 147 Hurricane Irma, Florida Bay exhibited resilience. Within five months, much of the eastern bay Chla 148 returned to approximately 1 µg/L (February 2018) and 90% of the surveyed area was < 20 µg/L. One year 149 after Hurricane Irma, Tropical Storm Gordon and Hurricane Michael created Chla hot spots in the mangrove 150 lakes, Rankin Lake, and Whipray Basin. However, the more muted response in Chla following the 151 succession of these two storms suggests that localized seagrass die-off was likely lessened by Hurricane 152 Irma through the removal of biomass from the system.

153 DECOMP PHYSICAL MODEL RESEARCH 154 The Decomp Physical Model research is discussed in Appendix 6-1 of this volume. Highlights are 155 provided here. 156 Hydrological flow alters food webs through impacts on biogeochemical cycling. Flow restoration 157 needed to restore sediment erosion and redistribution aims to increase flow by 10 to 20 times above current 158 flow velocities; therefore, flow restoration has the potential to increase the loading of nutrients, specifically 159 phosphorus (P), even when low nutrient surface waters are present. In general, we found that accelerated 160 flows at sites closest to the S-152 structure increased local total phosphorus (TP) loads; however, in terms 161 of negative impacts to periphyton, the geometric TP concentrations remain important. TP loading rates of 162 approximately 10 to 25 grams per meter per day) are associated with floc and periphyton biomass with TP 163 content above 500 milligrams per kilogram. There was a lag in epiphytic TP concentration and 164 accumulation in response to higher loads, whether this is attributed to different periphyton species with 165 different TP optima and tolerances growing at different times of year, or the loss of the slough periphyton 166 mat in response to higher flows is still uncertain. Over the period of study, the TP content of horizontally 167 advected sediments varied approximately an order of magnitude (~135 to 4,100 milligrams per kilogram), 168 with most (> 95%) values typically ranging from 291 to 2,386 mg kg-1. In general, soil TP values values 169 were higher (~1,000–2000 mg kg-1) at sites within 1 km of the S-152 structure and decreased with distance 170 from the structure. During the baseline period, in CY2011 and CY2012, canal accumulation of sediment 171 averaged less than 5 grams per square meter per day at all the canal sites however, during high flow, 172 sediment accumulation in the unfilled canal site was more variable and higher than during no flow 173 conditions. There was a steady decrease in sediment density within the partial and complete backfill sites 174 suggesting that the SAV there produces more organic sediments. Based on the mass balance for the entire 175 canal backfill area, during high flow, large portions of the partial backfill and complete backfill sites 176 function as significant particulate P sinks (see Appendix 6-1 of this volume). The observed increase in 177 sediment TP in those sites could reflect increasing accumulation of high TP sediments transported into and

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178 settling within the sites. The enrichment of sediments downstream of the L67-C levee gap, particularly the 179 unfilled canal treatment, suggests that sediment dynamics in canals that are not backfilled may impact the 180 process of sediment P enrichment. While the enrichment of floc TP observed at sites immediately 181 downstream (< 100 m) is evident in the unfilled canal treatment, it is not clear yet that enrichment is 182 occurring beyond > 300 m from the levee gap.

183 HYDROLOGIC PATTERNS FOR WATER YEAR 2019

184 WATER CONSERVATION AREAS AND 185 NORTHEAST SHARK RIVER SLOUGH HYDROLOGY

186 Eric A. Cline and Fred Sklar

187 Annual rainfall totals and annual mean water depth stages in the Everglades during WY2019 were very 188 near average historical conditions: rainfall for the year was slightly above historical averages in WCA-1, 189 WCA-2, just below average in WCA-3, and slightly above average in ENP. Departures from annual average 190 stage were highest in WCA-1 (+0.61 feet [ft]) then trending towards the historical average moving south 191 with WCA-2 (+0.20 ft) and WCA-3 just below average (-0.02 ft) (Table 6-2). Despite average annual 192 rainfall totals in WY2019, above average late WY2018 dry season rainfall amounts led to elevated stages 193 in the Everglades early in the water year; unlike WY2018, those higher stages lasted only until the fall 194 season. Comparing the hydrologic conditions between WY2018 and WY2019 and the resultant differences 195 in ecological response could provide insight into the restoration potential for the Everglades.

196 Table 6-2. Average, minimum, and maximum stage and total annual 197 rainfall for WY2019 in comparison to historic stage and rainfall. a, b

Rainfall Mean (Minimum; Maximum) Stage c Elevation Area (inches) (ft NGVD29) (ft NGVD29) c WY2019 Historic d WY2019 Historic d WCA-1 53.97 51.96 16.37 (15.87; 16.96) 15.70 (10.0; 18.16) 15.1 WCA-2 53.97 51.96 12.71 (11.68; 14.03) 12.51 (9.33; 15.64) 11.2 WCA-3 49.07 51.24 9.62 (4.78; 12.79) 9.60 (4.78; 12.79) 8.2 ENP 55.30 54.55 6.46 (5.55; 7.44) 6.05 (2.01; 8.08) 5.1 Slough at P33 198 a. Historic averages are based upon varying lengths of records at gauges. See Chapter 2 of this volume for a more detailed 199 description of rain, stage, inflows, outflows, and historic databases. 200 b. Average depths are calculated by subtracting elevation from stage. 201 c. NGVD29 – National Geodetic Vertical Datum of 1929. 202 d. Historic period is WY1941–WY2017. 203 204 Despite near average amounts of rainfall for the water year (Table 6-2) water depths were elevated in 205 WCA-1 and WCA-2, and just below average in WCA-3A and ENP. The above average rainfall in May 206 2018 technically fell late in theWY2019 dry season, but the effects to water depths were realized in 207 WY2019. Wet season (from June 2, 2018, to November 1, 2018) rainfall amounts in WCA-1 and WCA-2 208 were well below average at 75%, WCA-3 at 69%, and ENP at 65% of the historic average. These rainfall 209 patterns resulted in high peak water depths early in the water year and lower than average depths at the 210 beginning of the wading bird nesting season. A shorter hydroperiod and a smaller spatial extent of flooded 211 marsh (compared to the record wading bird nesting of WY2018) could have limited wading bird prey

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212 availability because those key components are generally associated with decreased fish production (Trexler 213 et al. 2005). Early reports are indicating that wading bird nesting in the WCAs was minimal while birds 214 nested in much higher numbers within ENP’s coastal colonies. 215 Due to the unusually high rainfall and subsequent rapid rise in stages early in WY2019, a temporary 216 deviation in operations was issued by the Army Corp of Engineers (USACE) to address high 217 water concerns in the Everglades that allowed for higher water levels to be maintained in WCA-2A. An 218 executive order restricting public access to WCA-2A and WCA-3A was also issued by the Florida Fish and 219 Wildlife Conservation Commission (FWC) in June 2018. 220 The figures of hydropatterns at select sites (gauges) that are provided in the following subsections 221 highlight the average stage changes in each of the WCAs for the last 2.5 years. The figures relate stages 222 relative to historic averages, flooding tolerances for tree islands, drought tolerances for wetland peat, and 223 recession rates and depths that support foraging and nesting needs of wading birds during the breeding 224 season. The District uses these indices as part of the ecological recommendations to water managers at 225 weekly interagency water operations meetings. Tree island inundation tolerances are considered exceeded 226 when depths on the islands are above 2.0 or 2.5 ft, depending on the height of the tree islands, for longer 227 than 120 days (Wu et al. 2002). Lower islands are inundated at lower high water levels (e.g., 2 ft versus 228 2.5 ft). The ground elevations in these figures, which are in ft National Geodetic Vertical Datum of 1929 229 (NGVD29) are used to indicate the threshold for peat conservation. When water levels are more than one 230 foot below ground for more than 30 days, the drought tolerance of peat is considered exceeded according 231 to the criterion set forth in the Florida Administrative Code for Everglades Minimum Flows and Minimum 232 Water Levels (MFL) (SFWMD 2014). However, it should be noted that peat soils may be harmed at 233 shallower water levels than stipulated in this MFL rule. 234 The wading bird nesting period ranges from January through May each year. The suitability of habitat 235 is determined by water depths and recession rates and is divided into three categories (poor, moderate, and 236 good) according to recent research on foraging requirements of wading birds in the Everglades (Gawlik 237 2002b, Beerens et al. 2011, 2015, Cook 2014). A green arrow on the hydropattern figures indicates the time 238 period with good recession rates and depths for wading birds. A yellow arrow indicates water levels that 239 are too shallow or too deep and/or recession rates that are slightly too rapid or too slow. A red arrow 240 indicates poor conditions resulting from poor depths (high or low) and/or unsuitable recession rates (rising 241 or falling too rapidly). For the Cape Sable seaside sparrow (CSSS; Ammodramus maritimus mirabilis) 242 habitat suitability presented for the Marl Prairie in ENP, threshold water depths and the maintenance of 243 target depths are used to categorize if the nesting requirements of the sparrow are being met (USFWS 2009).

244 Water Conservation Area 1 245 The water level in WCA-1 (also called the Arthur R. Marshall Loxahatchee National Wildlife Refuge) 246 at the start of WY2019 at Site 9 was significantly elevated compared to the 25-year daily median 247 (Figure 6-1). Wet season depths rose quickly, the stage peaked in June well above the average depth for 248 that month, then remained below average from September to February. Stages moved to above average 249 during the spring and finished the water year above average. Below average depth conditions during most 250 of the wet season mean below average prey production for wading birds, and depths were too low at the 251 beginning of the nesting season for the drawdown to extend long enough for suitable foraging. The yellow 252 arrows in Figure 6-1 are an indication that depths during this time can support wading bird foraging but 253 are not optimal. The red arrow is indicating stage or stage change conditions that do not support wading 254 bird foraging, in this case a significant reversal in falling stage. The final yellow arrow indicates a slight 255 improvement in foraging conditions. Early reports indicated that wading bird foraging and nesting was 256 minimal, however nesting colonies of small herons was noted along the eastern boundary of WCA-1. The 257 upper tolerance level or depth for tree islands, above which indicates flooding stress, was not exceeded. 258 Water depths never fell below the lower tolerance band, below which is an indication of potential peat soil 259 loss due to oxidation.

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260 261 Figure 6-1. Hydrology in WCA-1 in relation to the 25-year median daily 262 stage, as well as indices for tree island flooding and wading bird foraging. 263 (Note: USGS – United States Geological Survey.)

264 Water Conservation Areas 2A and 2B 265 Similar to the hydropattern in WCA-1, stages in WCA-2A during the WY2019 wet season began close 266 to the long-term average (Figure 6-2). In June 2018, water levels rose rapidly with the maximum stage at 267 Site 17 for the water year reached on June 6. Depths then fell to the average by October and remained near 268 the average until a reversal in February 2019 reset foraging conditions there. WCA-2A had much better 269 hydrologic conditions (along with wet antecedent conditions) and this explains the much larger number of 270 foraging birds noted in this area. Recession rates were good for wading birds starting in January but depths 271 were generally low for foraging (yellow arrow in Figure 6-2), then a reversal seemed to rehydrate portions 272 of the basin that, following the reversal, served as near optimal foraging for the remainder of the wading 273 bird nesting season. Stages exceeded the upper flood tolerance for tree islands from June through December. 274 WCA-2A stages did not drop below the lower tolerance level for peat conservation. 275 Unlike the rest of the EPA, WCA-2B tends to be wet and deep most of the year (Figure 6-3). During 276 WY2019, water depths at Site 99 began near the historical average, rose rapidly, and peaked in late 277 July 2018, then generally following just above the 24-year average; the red arrow in Figure 6-3 signifies 278 water levels too deep to support wading bird foraging.

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279 280 Figure 6-2. Hydrology in WCA-2A in relation to the recent 25-year average, 281 with indices for tree islands, peat conservation, and wading bird foraging.

282 283 Figure 6-3. Hydrology in WCA-2B (Site 99) in relation to 284 the recent 24-year average with an index for wading bird foraging.

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285 Water Conservation Area 3A 286 Water levels in northeastern WCA-3A at Site 63 during WY2019 began below the historical average 287 (Figure 6-4), and below the lower tolerance for peat conservation. As in the rest of the Everglades, the 288 stage rose quickly in the early wet season peaking mid-June 2018 at near 10.4 ft NGVD29, more than 2.5 289 ft lower than the peak stage in WY2018. Although there are few tree islands left in northern WCA-3A, 290 some like Alley North, are critical for wading birds and water levels exceeded the upper tolerance for tree 291 islands from June into October 2018. Prior to nesting season, foraging conditions were optimal and large 292 flocks of birds were noted in this region, but depths fell well below average going into the nesting season 293 meaning that that foraging could not be sustained. During the early part of the wading bird nesting season, 294 depths were too low to support wading bird foraging (the red arrow in Figure 6-4). As in the rest of the 295 WCAs, a reversal in Febuary reset depths raising them to more ideal depths for wading bird foraging (the 296 green arrow in Figure 6-4), but likely led to nest abandonment. The hydrologic conditions present within 297 WCA-3A during wading bird nesting season for WY2019 were generally fair to poor caused by low 298 seasonal depths that could not support sustained foraging and all early indications are that wading bird 299 nesting was very limited at the Alley North colony, which produced a record number of nests in WY2018.

300 301 Figure 6-4. Hydrology in northeastern WCA-3A (Site 63) in relation to the recent 302 24-year average with indices for tree islands, peat conservation, and wading bird foraging.

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303 The hydrologic pattern in central WCA-3A at Site 64 (Figure 6-5) was very like that at Site 63. Water 304 levels peaked in July 2018, went below average in September and remained so until the reversal in February 305 2019 pushed depths up to near average. Depths and recession rates during the dry season were fair (the 306 yellow arrow in Figure 6-5) for wading bird foraging, and wading birds responded feeding along the drying 307 front in WCA-3A and nesting at the nearby Colony 66 was below average with no wood storks (Mycteria 308 americana) nesting there. A minor late season reversal ended all foraging in the area.

309 310 Figure 6-5. Hydrology in central WCA-3A (Site 64) in relation to the recent 26-year average 311 with indices for tree islands, peat conservation, and wading bird foraging.

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312 Water Conservation Area 3B 313 Water levels at Site 71 in WCA-3B began WY2019 at near the historic average (Figure 6-6). Stages 314 climbed quickly but instead of peaking early like the other WCAs, the water level continued to climb until 315 peaking for the water year in September 2018. Stage fell to the average in October, meaning that depths at 316 Site 71 exceeded the upper tolerance for tree islands only during September. Conditions were poor in 317 WCA-3B for wading bird foraging for most of the wading bird nesting season due to repeated reversals. 318 The multi-colored arrow in Figure 6-6 indicates poor foraging conditions as depths in early nesting season 319 were too deep for wading bird foraging, reversals also prevented suitable foraging conditions in the later 320 dry season. When depths dropped low enough, the reversals reset foraging habitat creating fair conditions.

321 + 322 Figure 6-6. Hydrology in central WCA-3B (Site 71) in relation to the recent 25-year 323 average and indices for tree islands, peat conservation, and wading bird foraging.

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324 Northeast Shark River Slough 325 At the beginning of WY2019, water levels in Northeast Shark River Slough were just below the historic 326 average (Figure 6-7) and near the lower tolerance for peat conservation. Water levels rose dramatically to 327 peak in September 2018 at a stage above 8.0 feet NGVD29, the third year in a row and only the fourth time 328 in the last twenty years that stage at this location has exceeded that mark (the other three water years were 329 WY1999, WY2018, and WY2017). Depths remained above the historic average until December 2018. The 330 multi-colored arrow at the beginning of the nesting season in Figure 6-7 indicates fair depth conditions as 331 recession rates were high, transitioning to optimal until a reversal in February 2019 that caused large-scale 332 abandonment of the early nesting wood storks and great egrets (Ardea alba). Post-reversal recession rates 333 and depths were near optimal for wading bird foraging at this location beginning in March 2019 and 334 continuing through the remainder of the water year. Initial reports indicate average to above average 335 numbers of nesting wading birds at the coastal colonies within ENP, much better than the other WCAs 336 during WY2019.

337 338 Figure 6-7. Hydrology in Northeast Shark River Slough in relation to the recent 339 34-year average with indices for peat conservation and wading bird foraging.

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340 EVERGLADES NATIONAL PARK MARL PRAIRIES 341 The marl prairies of ENP differ greatly from the peatlands of the WCAs. These are shorter hydroperiod 342 wetlands supporting mixed graminoid and short hydroperiod wet prairie vegetation and are particularly 343 important for their support of the endangered CSSS. Gauge NP-CR3 (Figure 6-8) is typical of this habitat.

344 345 Figure 6-8. Gauge NP-CR3 Hydrology in the Marl Prairies in ENP from January 1, 2017, through 346 July 1, 2019. The gray horizontal line represents ground level. Water levels above four inches are 347 harmful to the CSSS nests. (Note: NAVD88 − North American Vertical Datum of 1988.)

348 Preferred habitat for the endangered CSSS is this short hydroperiod (180–240 days) wet prairie 349 vegetation. They breed from early March through at least the middle of July so long as conditions remain 350 favorable. Water levels need to remain below ground with enough damp areas to provide aquatic insects 351 for their feeding. CSSSs nest close to the ground in clumps of bunch grasses. Dry conditions extending 60 352 to 90 days or more are needed to provide adequate time to nest and fledge chicks and when conditions 353 remain favorable, CSSSs may produce two to three nests in a year. 354 CSSS nesting season in WY2019 began with dry conditions as water levels within the subpopulations 355 on the eastern side of Shark River Slough were below ground, and levels at the western Subpopulations A 356 and Ax were below 10 centimeters (cm). Stages then dropped below ground and conditions remained 357 favorable for nesting until mid-June 2019, while not all the metrics used to assess the favorable or 358 unfavorable hydrologic conditions were met, habitat conditions were generally good (green arrow in Figure 359 6-8) and the bird response was favorable. The water level at Gauge CR3 (located in the CSSS-E 360 Subpopulation) was below ground until mid-June, at which time the water depths within the historical 361 eastern CSSS subpopulations and the western Subpopulation Ax continued to be favorable for successful 362 nesting, only Subpopulation A average water depth was unfavorable. Initial reports (Virzi and Davis 2019) 363 from monitoring efforts indicate an active breeding season continuing the promising trend observed during 364 the annual surveys conducted in WY2017 and WY2018.

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365 FLORIDA BAY HYDROLOGY

366 Amanda McDonald

367 Introduction 368 At the southern tip of the Florida Peninsula is Florida Bay, a triangular estuary bounded by the Florida 369 mainland and the . The District is part of a multi-agency partnership with ENP and the United 370 States Geological Survey (USGS) that maintains a long-term monitoring network throughout the southern 371 Everglades and Florida Bay to provide a continuous record of salinity, rainfall, temperature, water 372 elevation, and flow for the Florida Bay watershed (Figure 6-9). These data are used in support of District 373 operations, in developing and monitoring Comprehensive Everglades Restoration Plan (CERP) 374 performance measures, assessing effectiveness of MFLs, and supporting calibration and verification of 375 hydrologic models.

376 Methods 377 Salinity, rainfall, temperature, and flow are monitored at instrumented platforms with salinity, rainfall 378 and temperature reported every hour and flows measured every 15 minutes. Rainfall and most salinity and 379 temperature platforms are maintained by ENP and are located to allow determination of upstream to 380 downstream effects (Figure 6-10). Flow has been measured by USGS at five creeks discharging into 381 Florida Bay since WY1997. Rainfall was quantified by averaging daily data across 11 ENP platforms (CP, 382 DK, EPSW, JK, LM, P37, RPL, TB, TC, TR, and WB) and then summing to produce monthly totals for 383 the period of WY1996–WY2017, which is the period for which all 11 platforms existed. Average daily 384 water temperatures across 12 ENP platforms (BA, BK, BN, DK, GB, JK, LM, LR, MK, TB, TC, and WB) 385 within Florida Bay were also summarized for the corresponding period of WY1996–WY2017. For the 386 purposes of this assessment, salinity data are truncated to the period of WY2001–WY2016 to coincide with 387 the installation and operations of S-332D, which was a significant change to the management of the South 388 Dade Conveyance System and water movement towards Florida Bay.

389 Results 390 After Hurricane Irma in WY2018 brought a lot of fresh water through rainfall and subsequent creek 391 flow (Figure 6-11) reducing the high salinity from earlier in WY2018, the low salinity condition persisted 392 through the first four months of WY2019. Starting in July 2018, rainfall fell below average and the creek 393 flow followed suit. As a result, salinity began rising during what would typically be the lowest salinity 394 period of the year (September–October). Western Florida Bay, which is the furthest from freshwater 395 deliveries and would typically be the last region to show decreases, was the first region to have salinity 396 begin to decrease while eastern and central Florida Bay salinities continued to increase through the WY2019 397 dry season. 398 Relevance to Water Management 399 This pattern of elevated salinity at the end of the wet season that carries through the dry season and into 400 the following wet season is reminiscent of WY2015 and WY2016, which led to the seagrass die-off. 401 However, the central and western area salinities have begun small decreases as the potentially wettest part 402 of the wet season arrives due to tropical systems. Elevated temperatures and increased evaporation may be 403 preventing fresh water from reaching Florida Bay.

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404 405 Figure 6-9. Depiction of structure locations and monitoring stations for 406 southeastern ENP, the South Dade Conveyance System, and Florida Bay.

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407 408 Figure 6-10. Averaged rainfall data from 11 locations in southern ENP and northern Florida Bay along 409 with the total flow from the five gauged creeks feeding Florida Bay by month for the WY2017–WY2019 410 (WY17-19) period compared to the median and middle 50 percentile (interquartile range from the 25th 411 percentile to the 75th percentile) over the WY2001–WY2016 period. Rainfall data are from ENP and 412 creek flow data are from USGS. Note that the WY2019 creek flow data are considered provisional. 413 (Note: Flow units are in thousand acre-feet per month or 103 ac-ft/month.)

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414 415 Figure 6-11. Mean monthly salinity values in eastern Florida Bay (top), central Florida Bay 416 (middle), and western Florida Bay (bottom) in WY2017 through the first three months of 417 WY2020 (WY 17-20 in the legend), compared to monthly means (WY01-16 mean) and 418 standard deviation [WY 01-16 s.d.] for the WY2001–WY2016 period. Salinity values are 419 averaged between two continuous monitoring platforms within each region: LM and DK for 420 eastern, TB and WB for central, and GB and JK for western Florida Bay.

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421 WILDLIFE ECOLOGY 422 The District currently focuses its wildlife research towards gaining a better understanding of the links 423 among hydrology, aquatic prey availability, and wading bird foraging and reproduction. These relationships 424 have been formalized in the Trophic Hypothesis, a conceptual ecological model that provides the primary 425 scientific framework for District wildlife research, which is available online at 426 https://evergladesrestoration.gov/ssr/2012/ssr_full_2012.pdf (RECOVER 2012). This research has 427 improved the District’s capacity to effectively manage the system and its ability to predict future scenarios. 428 The utility of the research stems not only from an improved knowledge of how key ecological drivers affect 429 wading bird reproduction, but also from the recasting of these data into practical spatially explicit tools to 430 predict foraging and nesting responses to physical and biological processes in real time and space. This 431 section summarizes wading bird nesting effort and success in the Everglades and Florida Bay during the 432 WY2019 breeding season. The section also summarizes the results of a two-year study on the diets of white 433 ibis (Eudocimus albus) nestlings in the coastal Everglades. The coastal region was historically very 434 important for nesting white ibis, supporting about 90% of all their nests in the Greater Everglades, but over 435 the past decade it has supported between 10 and 20% (see the Spatial Distribution subsection below). 436 Understanding the diet of this indicator species in relation to hydrologic conditions is a key first step to 437 restore coastal nesting populations. 438

439 WADING BIRD MONITORING

440 Mark I. Cook

441 Introduction 442 Large populations of colonially nesting wading birds (order Pelecaniformes: egrets, ibises, herons, 443 spoonbills; and order Ciconiiformes: storks) were a common and defining feature of the pre-drainage 444 Everglades. Long-term records of their nesting stretch back to the early part of the last century, and some 445 clear reproductive responses to anthropogenic alterations have been established: 446 • A marked decline in the nesting populations of several species, particularly the tactile 447 foraging species (i.e. storks, spoonbills, and ibises) 448 • A movement of colonies from the overdrained estuarine region to the more ponded interior 449 marshes of the WCAs 450 • A marked decrease in the frequency of exceptionally large aggregations of nesting white 451 ibises (Eudocimus albus) 452 • Delayed nest initiations of wood storks (Mycteria Americana) by a few months (from 453 November–December to February–March), resulting in reduced nestling survival 454 These responses appear to be consistent with mechanisms that involve foraging and specifically the 455 role that hydrology plays on the production and vulnerability to predation of aquatic prey animals (see 456 Frederick et al. 2009 and references within). 457 Wading birds are excellent indicators of wetland ecosystem health and have a central role in evaluations 458 of CERP performance. Nesting figures for CERP performance measures are restricted to colonies in the 459 Greater Everglades Region, i.e., the WCAs and ENP, for the following five species: 460 • Great egret (Casmerodius albus) 461 • Snowy egret (Egretta thula) 462 • Tricolored heron (Egretta tricolor)

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463 • White ibis 464 • Wood stork 465 The timing of breeding, number of nests, and location of nesting colonies within the Everglades are 466 used as CERP targets to evaluate the progress of the Everglades restoration effort. In addition to CERP, 467 wading birds are of special interest to the public and play a prominent role in adaptive protocols, MFLs, 468 and day-to-day operations of the District. 469 The utility of this research stems not only from an improved knowledge of how key ecological drivers 470 affect wading bird reproduction, but also from the recasting of these data into practical spatially explicit 471 tools to predict foraging and nesting responses to physical and biological processes at appropriate temporal 472 and spatial scales. These tools are used in conjunction with real-time observations of foraging, nesting, and 473 hydrologic patterns to develop management recommendations that are communicated regularly to water 474 managers. This has improved both the District’s capacity to effectively manage the system and ability to 475 predict future scenarios, with tangible benefits for reproducing wading bird populations. 476 Recovery of pre-drainage (1930–1940) wading bird nesting patterns is evaluated using two independent 477 sets of measures. CERP’s Restoration Coordination and Verification program (RECOVER) established a 478 performance measure (RECOVER 2006b). The performance measure includes three metrics: the three-year 479 running average number of nesting pairs of the five key wading bird species, timing of wood stork nesting, 480 and proportion of the wading bird population that nests in the marsh-coastal ecotone (Ogden 1994). In 481 addition, the South Florida Ecosystem Restoration Task Force developed two additional measures for 482 annual stoplight reports: the ratio of visual to tactile wading bird species breeding in the Everglades, and 483 the frequency of exceptionally large white ibis nesting events (Frederick et al. 2009). The RECOVER and 484 South Florida Ecosystem Restoration Task Force metrics evaluated here include the following: 485 • Increase and maintain the total number of pairs of nesting birds in mainland colonies to a 486 minimum of 4,000 pairs of great egrets, 10,000–20,000 combined pairs of snowy egrets 487 and tricolored herons, 10,000–25,000 pairs of white ibises, and 1,500–2,500 pairs of 488 wood storks. 489 • Shift in timing of nesting in mainland colonies to more closely match 490 pre-drainage conditions. Specific recovery objectives would be for wood storks to initiate 491 nesting no later than January in most years and for ibises, egrets, and herons to initiate 492 nesting in February–March in most years. 493 • Return of major wood stork, great egret, white ibis and, Egretta nesting colonies from the 494 central Everglades to the coastal areas and the freshwater ecotone of the mangrove estuary 495 of Florida Bay and the . Reestablish historical distribution of wood stork 496 nesting colonies in the Big Cypress Basin and in the region of mainland mangrove forests 497 downstream from the Shark Slough and Taylor Slough basins. Increase the proportion of 498 birds that nest in the southern ridge and slough marsh-mangrove ecotone to greater than 499 50% of the total for the entire Greater Everglades region. 500 • For wood storks, restore productivity for all colonies combined to greater than 1.5 chicks 501 per nest. 502 • Return to an interval between exceptional white ibis nesting events, defined as 503 greater than 70th percentile of annual nest numbers for the period of record (16,977 nests). 504

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505 Summary of Nesting in Water Year 2019 506 The information reported in this section represents a compilation of data collected by the District, 507 University of Florida, ENP, and Audubon of Florida. The population counts include all wading bird species 508 (except cattle egret, Bubulcus ibis) nesting throughout the Greater Everglades region (i.e., WCAs and ENP) 509 and roseate spoonbills in Florida Bay, during the nesting season from November 2018 through June 2019. 510 Note that counts do not include estimates of precision due to the challenges of estimating detection bias at 511 the landscape scale, although progress is being made in this respect (e.g., Fredrick et al. 2003, Williams et 512 al. 2011). For details on independent sampling methods see Cook and Baranski (2019). Note that the nest 513 count estimates provided here are subject to minor changes as new or amended data become available; the 514 final count estimates will be published in the next South Florida Wading Bird Report (Volume 25; Cook 515 and Baranski in prep) in early 2020.

516 Nesting Effort 517 The Greater Everglades typically accounts for between 75 and 95% of the total annual nest count in 518 South Florida and is critically important to wading bird reproduction from a regional perspective. Overall, 519 CY2019 was a relatively poor nesting season for wading birds. An estimated 32,165 nests were initiated in 520 the WCAs and ENP during 2019, which is 78% of the decadal average (41,290.9 nests), 68% of the five- 521 year average (46,976.6 nests), and only a quarter (26 percent) of last year’s nesting effort when a record 522 122,571 nests were produced. 523 Several CERP indicator species exhibited relatively limited nesting effort during WY2019 524 (Figure 6-12). White ibis nesting effort (21,050 nests) was reduced by 22 and 37% respectively compared 525 to the ten- and five-year averages, and it was almost 5 times lower than the 2018 count (95,728 nests). 526 Given that the white ibis is the most numerous nesting species in the Everglades (typically between 45 and 527 78% of all wading bird nests) this decrease accounted for much of the reduction in the total nest count in 528 WY2019. Great egrets produced only 2,778 nests during WY2019, the lowest count since 2008 and a 64 529 and 68% decrease, respectively, compared to the ten- and five-year averages. The federally threatened wood 530 stork has exhibited a marked increase in nesting effort during the past two years, but produced only 531 970 nests in WY2019, which is fewer than half the ten-year (1,896.1 nests) and five-year (1,768.0 nests) 532 averages. In Florida Bay, roseate spoonbills (Platalea ajaja) produced 282 nests; while this nesting effort 533 is similar to the decadal average (272.6 nests) it represents a significant decline compared to the 30-year 534 mean (459 nests). 535 The smaller Egretta heron species have exhibited consistent and steep declines in nest numbers over 536 recent years, such that very few of these birds now nest in the Everglades. This year only 121 tricolored 537 heron and 169 little blue heron (Egretta caerulea) nests were counted, representing a 58 and 47% reduction, 538 respectively, in nesting effort for these species relative to their decadal averages. On the other hand, snowy 539 egret nesting effort (2,856 nests) was more than twice the ten-year average (1,349.2 nests) and almost three 540 times the five-year average (1,067.2 nests). Nonetheless, this snowy egret total remains considerably lower 541 than the ten thousand or so nests that historically graced the Everglades. It should be noted that many nests 542 of the white small heron species (snowy egret and cattle egret) or those with white nestlings (little blue 543 herons) could not be identified to species this year (2,732 nests), which suggests that the estimated counts 544 for snowy egrets or little blue herons or both were relatively conservative this year. The decline in nesting 545 by the three Egretta species remains a concern, and the causes for the sharp declines have yet to 546 be determined.

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547 548 Figure 6-12. Historical wading bird nesting numbers in 549 the Everglades for individual species from 1990 to 2019.

550 Spatial Distribution 551 The estuarine region of ENP historically supported about 90% of all nesting wading birds in the Greater 552 Everglades, probably because it was the most productive area of the Everglades ecosystem. During the past 553 half century, that productivity has declined due to reduced freshwater flows, and the location of nesting has 554 shifted to inland colonies in the WCAs. An important goal of restoration is to restore the hydrologic 555 conditions that will reestablish prey availability across the southern Everglades landscape that, in turn, will 556 support the return of large successful wading bird colonies to the traditional estuarine rookeries (RECOVER 557 2006a). The restoration target for the proportion of birds nesting in the coastal colonies is at least 50% of 558 the total nests for the entire Greater Everglades. This proportion has increased considerably since the lows 559 of the 1990s and early 2000s (2 to 10%) and over the last decade has tended to range between 20 to 25%. 560 In WY2018, the proportion of nests in the estuarine region increased further, to 32.8%, and in WY2019 it 561 was 41.7%, one of the highest recorded in recent decades. Moreover, the absolute nesting effort in the 562 coastal zone (13,342 nests) in WY2019 was relatively high. These patterns suggest the coastal region has 563 become more attractive to nesting birds relative to other regions of the Everglades, and it retains the capacity 564 to support significant populations of nesting birds given suitable hydrologic conditions. The uptick in 565 nesting during the past two years is almost certainly due to recent increased hydroperiods in the coastal 566 ecotone resulting in increased prey productivity and foraging opportunities in the area. Systemwide, ENP 567 supported 42.4% of the nests, WCA-3A supported 39.5%, while WCA-1 supported only 18%.

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568 The location of roseate spoonbill nesting colonies within the Florida Bay area has shifted in recent 569 years. Whereas most nesting historically occurred on small keys within the bay itself, many birds have 570 moved over the past five years to mainland colonies adjacent to the coast. During WY2019, Paurotis Pond 571 and other mainland coastal colonies supported 132 nests, 43% of all nests in the coastal region (282 nests). 572 Other individuals have deserted the coastal region entirely. During the last ten years, an average of 573 217.2 pairs nested at colonies in the central freshwater Everglades, and in WY2018, a record 380 nests were 574 recorded there. During WY2019, inland colonies supported only 92 roseate spoonbill nests.

575 Timing of Nesting 576 Wood stork nesting success is highly dependent on the availability of their aquatic prey (fish), which 577 are easy to find and feed upon when concentrated at high densities in shallow water during the dry season 578 (winter), but are not available in the wet season (summer) when they move back into deeper waters and 579 disperse throughout the landscape. To successfully fledge their young, wood storks require a continuous 580 supply of fish at high densities throughout the reproductive period. Wood storks have a relatively long 581 reproductive period (about four months), so it is critical they start nesting early in the dry season to ensure 582 nestlings have time to fledge and gain independence prior to the onset of the rainy season when water levels 583 rise and food availability declines. Wood stork nesting historically started in November or December, but 584 in recent decades nesting initiation has shifted to between January and March (Ogden 1994). This delay is 585 thought to occur because of a reduction in the amount and quality of the short hydroperiod wetlands that 586 provide foraging habitat early in the nesting season and it is often associated with reduced nesting success 587 (Frederick et al. 2009). However, the past two years have supported some of the earliest nesting dates in 588 decades, with nesting starting in late December (within the CERP target timeframe) in both 2017 and 2018. 589 In 2019, wood storks continued this recent trend of nesting relatively early in the season, with incubation 590 first observed on January 18, and egg laying starting up to a week or two earlier than that. Recent increases 591 in hydroperiods on higher elevation marshes that provide foraging habitat early in the nesting season might 592 account for these recent changes. 593 Roseate spoonbills in Florida Bay exhibited a shift towards later nesting in recent years. For at least 70 594 years (1936 to 2009) spoonbill nest initiations consistently fell between October 1 and December 31. 595 However, beginning in 2010, nesting began to start increasingly later in the season: from 2010 to 2014 596 nesting started between January 1 and 10, in 2015 it was January 24, and in 2016 it was February 5, the 597 latest start date ever recorded. Moreover, the timing of laying appears to be getting considerably more 598 asynchronous both within and among colonies. Whereas nest initiations within the bay would historically 599 span a few weeks, lay dates in the past two years have extended from January through April. These changes 600 in the phenology and synchrony of nesting might suggest that the timing of optimal foraging conditions for 601 roseate spoonbills is changing both temporally and spatially within and surrounding Florida Bay. However, 602 2017 was notable for a complete reversal of this trend, with most nest initiations starting in November. In 603 2018, nest initiations also were relatively early, ranging from early December to early January. In 2019, 604 nesting started early but was highly asynchronous (ranging from November to March) with an average lay 605 date of January 4, 2019. The reasons for these patterns are unclear.

606 Nesting Success 607 Nest success data are provided by the University of Florida but are not yet available for this report. 608 These data will be published in the next South Florida Wading Bird Report (Volume 25; Cook and Baranski 609 in prep). Observational evidence from District and ENP surveys of nesting colonies suggest that a large 610 proportion of nests of most species (wood stork, white ibis, great egret, and roseate spoonbill) failed to 611 produced fledglings. Of note was the low productivity of the wood stork such that all nests failed to 612 produce offspring.

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613 Role of Hydrology on Nesting Patterns 614 The most important process affecting wading bird nesting effort and success is the availability of prey 615 (fishes and aquatic invertebrates). Prey availability is a function of prey production (the amount and size of 616 prey animals) and how vulnerable they are to capture by birds, with both components strongly affected by 617 hydrologic conditions (Frederick and Ogden 2001, Herring et al. 2011). In a hydrologically fluctuating 618 wetland such as the Everglades, prey production is influenced largely by the duration and frequency of 619 wetland flooding and drying, with optimal conditions for population growth varying by species. Most fish 620 populations peak after extended periods (multiple years) of relatively deep, flooded conditions over 621 extensive areas of wetland (Trexler et al. 2005), while some invertebrate populations grow best during 622 moderate hydroperiods punctuated by periodic dry conditions (Dorn and Cook 2015). 623 Another important prey group in the Everglades are the crayfish (Procambarus spp.), which are critical 624 for fueling white ibis nesting colonies (Boyle et al. 2014). Crayfish populations are strongly limited by 625 predatory sunfishes (Lepomis spp.) such as warmouth (Lepomis gulosus) that eat small (young-of-the-year) 626 juveniles. Once crayfish grow beyond a certain size threshold, they are less sensitive to fish predation. 627 During periodic dry conditions, predatory fish populations decline but crayfish can survive in burrows until 628 the rains return and water levels rise again in the wet season. At this point, adult crayfish emerge and release 629 their young into a marsh habitat that is largely free of fish predators, allowing for a temporary (1 to 2 year) 630 boost in the crayfish population (Dorn and Cook 2015). 631 Prey vulnerability to capture is determined largely by water depths and whether water levels are rising 632 or falling. Prey become easiest to capture during drying conditions when water levels decline to depths at 633 which the birds can forage effectively (5 to 30 cm) and the areal coverage of water shrinks such that prey 634 become concentrated at relatively high densities (Gawlik 2002b, Cook et al. 2014). Conversely, prey 635 vulnerability declines when water levels rise, and concentrated prey can disperse into larger areas of the 636 marsh. Prey availability, therefore, is naturally variable among years depending on antecedent conditions 637 (affects prey production) and current water conditions (affects prey vulnerability), and birds require 638 appropriate conditions at both temporal scales for a successful nesting season. As such, wading bird nesting 639 effort and success fluctuate considerably from year to year. 640 From a prey production perspective, this year’s nesting season was preceded by generally wet 641 conditions (above average stages from July 2017 to July 2018) over relatively large areas of the ecosystem, 642 and there was minimal drying of the marsh at the peak of the dry season in May 2018 (see the Hydrologic 643 Patterns for Water Year 2019 section above for more details). These conditions likely led to increased 644 small- and large-bodied fish production but probably limited juvenile crayfish survival and recruitment 645 because of increased populations of predatory fishes. While prey (fish) production was generally high, the 646 limited nesting effort and poor nesting success during 2019 were largely a result of unsuitable hydrologic 647 conditions during the nesting season that led to reduced prey vulnerability. The long period of wet 648 conditions was followed by relatively dry conditions at the tail end of the wet season (August–September 649 2018) which led to below average stages in many regions of the Everglades by the start of the 2019 breeding 650 season (November 2018). WCA-3B and ENP were the exception to this pattern and retained relatively high 651 stages throughout the wet season and early dry season. November 2018 to early January 2019 was 652 characterized by a continuous drop in water level across the Everglades landscape that led to generally good 653 foraging conditions throughout. The relatively high stages in ENP coupled with rapid recession rates likely 654 allowed for the early wood stork nesting in this area. However, a systemwide rain-driven reversal event at 655 the end of January 2019, when water levels increased by over a foot in some regions, led to the almost 656 complete abandonment of the early nesting species (storks, great egrets, and roseate spoonbills) at colonies 657 throughout the Everglades. While some of these birds did renest, a subsequent reversal in March led to 658 further abandonments. A relatively consistent yet short dry down pattern through April and May did allow 659 most of the later nesting small herons and some white ibis to nest successfully and fledge chicks as late as 660 mid-June.

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661 Restoration Targets 662 White ibises, wood storks, and great egrets met the numeric CERP targets for number of nesting pairs 663 (based on the three-year running average) but snowy egrets did not (Table 6-3). For the third successive 664 year in decades, wood stork started egg laying relatively early in the breeding season (early January) but 665 just failed to meet the CERP target date (November through December). The third CERP target, an increase 666 in the proportion of nesting wading birds in the coastal Everglades, did not meet the necessary target (50% 667 of nests in the estuarine region) but continued the trend of recent improvements and was considerably higher 668 (41.7%) than earlier in the decade (10 to 20%). With respect to the annual stoplight targets, the 2019 white 669 ibis nesting effort (21,050 nests) surpassed the target number of 16,977 nests required for an exceptional 670 nesting event, and the interval between such events averaged over the past five years is 1.4 years, which 671 exceeds the restoration target of 1.6 years recorded from the 1930s. The ratio of tactile (white ibis and wood 672 storks) to visual foragers (great egrets, snowy egrets, and tricolored herons) was 6, which is considerably 673 lower than the restoration target of 32.

674 Table 6-3. Numbers of nesting pairs (3-year running average) for four indicator species in 675 the Everglades relative to CERP targets. Target numbers are based on known numbers of 676 nests for each species during the pre-drainage period 1930–1940 (Ogden 1994).

2008- 2009- 2010- 2011- 2012- 2013- 2014- 2015- 2016- 2017- Species Target 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Great 6,715 8,270 6,296 7,490 7,041 6,300 4,000 Egret 5,328 5,655 8,803 7,966

Snowy Egret 1723 1,947 1,599 1,299 1,017 710 837 639 1,224 1,868 10,000

10,000- White Ibis 21,415 22,020 11,889 16,282 17,194 21,272 17,379 17,974 41,465 45,485 25,000

Wood 1,500- 1,736 2,263 1,182 1,686 1,696 1,639 995 1,195 2,152 2,282 Stork 2,500

677

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678 IRRUPTION OF IBIS NESTING WAS ASSOCIATED WITH 679 CRAYFISH FORAGING IN THE SOUTHERN EVERGLADES

680 Nate Dorn1, Tasso Cocoves, and Mark I. Cook

681 Introduction 682 As noted in the previous section, wildlife-related goals of CERP include the maintenance of large 683 numbers of nesting wading birds, the frequent return of exceptionally large white ibis nesting years, and a 684 regional redistribution of ibis colonies toward the historically productive freshwater-marine ecotone in the 685 southern Everglades (Frederick et al. 2009). Over the past two decades, the mechanisms driving white ibis 686 nesting have become somewhat clearer. Examining the historical nesting patterns, Frederick and Ogden 687 (2001) argued that wading bird supercolonies typically followed 1 to 2 years after strong regional droughts. 688 While they were uncertain how prey might be enhanced by drying, Ogden et al. (2003) later suggested that 689 crayfish might be dynamically linked to drought and formation of exceptionally large nesting events. 690 Studies of ibis diets in the central and northern Everglades indicate crayfish (Procambarus fallax) are 691 important prey to white ibis throughout the breeding season (Boyle et al. 2014), suggesting that hydrologic 692 patterns that promote crayfish recruitment could influence white ibis breeding activity. Dorn and Cook 693 (2015) used a large experiment and an observational data set to demonstrate that crayfish densities pulse 694 following droughts that reduced large-bodied fish predators. Collectively, these projects have provided a 695 mechanistic understanding of how hydrological variation can produce pulsed prey (i.e., crayfish) production 696 in sloughs that may generate pulsed ibis breeding. In contrast, prey types that might fuel good nesting 697 success in the coastal colonies/freshwater-estuarine ecotone of the southern Everglades within ENP are not 698 well understood. 699 The last study of ibis diets in the coastal colonies of ENP was conducted in 1972 (Kushlan and Kushlan 700 1975). In that study, diets of nesting white ibis included both freshwater and marine prey (i.e., crayfish, 701 crabs, fish, and insects). Excess salt in marine invertebrates may impose a physiologic burden on developing 702 white ibis chicks, and reliance on this prey type might explain some of the reduced breeding activity in the 703 region. A second species of crayfish (Procambarus alleni) is found throughout the southern Everglades, 704 but its populations thrive in shorter-hydroperiod wetlands than those of P. fallax (Dorn and Trexler 2007). 705 While P. alleni have not been observed in white ibis diets, both crayfish species are potential prey for white 706 ibis breeding in the southern Everglades. We conducted a two-year study (2017–2018) of nesting ibis prey 707 use in coastal colonies of ENP (Figure 6-13). In 2017, the ibis nesting numbers in the coastal region of 708 ENP was rather normal when compared with the prior 15 years. 2018 nesting followed higher water levels 709 late in 2017, partly driven by Hurricane Irma. The 2018 ibis nesting activity in the coastal colonies was 710 more than 25-fold higher than in 2017 and was the highest reported for the region in at least 80 years (Ogden 711 1994; see last two decades in Figure 6-13). In this report we compared prey use between the years and also 712 examined intra-annual prey use variation in 2018 to discern which prey dominated the composition when 713 the high nesting activity was initiated. More information about the ibis diet results from 2017 and the 714 wading bird nesting of 2018 can be found in Chapter 6 of the 2019 South Florida Environmental Report 715 (SFER) – Volume I (Sklar 2019).

1 Florida Atlantic University, Davie, Florida.

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716 717 Figure 6-13. Peak numbers of nesting white ibis in the coastal colonies (headwaters region) of ENP 718 for each year from 1999 through 2018 based on aerial surveys. The numbers are the totals for Broad 719 River, Cabbage Bay, Otter, Lostmans, Rookery Branch, Rodgers River, and East River colonies. Not 720 every colony is active every year Source of the data are South Florida Wading Bird Reports (Gawlik 721 1999, 2000, 2001, 2002a, Crozier and Gawlik 2003, Crozier and Cook 2004, Cook and Call 2005, 722 2006, Cook and Kerring 2007, Cook and Kobza 2008, 2009, 2010, 2011, 2012, Cook, 2013,2014, 723 2016, Cook and Barnaski 2017, 2018, 2019) available at https://www.sfwmd.gov/documents-by- 724 tag/wadingbirdreport?sort_by=title&sort_order=DESC).

725 Methods

726 Field Collections 727 In the 2017 and 2018 dry seasons, we collected 160 boluses from white ibis nestlings at colonies within 728 the freshwater-marine ecotone of ENP. In 2017, nesting effort in the ecotone was typical of the past several 729 decades, and we collected 40 boluses on five days from April 27 to June 1 from two small colonies: Cabbage 730 Bay (350 nests) and Otter Creek (325 nests) separated by 13 km (Figure 6-14). The large colony that 731 developed at Broad River (11,000 nests) in 2018 allowed us to collect 120 boluses on four dates (over 732 5 weeks), but changes in the progression and location of nesting made it impossible to collect from the 733 Cabbage Bay (19,320 nests) or Otter Creek colonies (100 nests).

734 Lab Analyses 735 All boluses, hereafter samples, were placed on ice in the field and were placed into a freezer prior to 736 processing. Each sample was thawed and poured through a mesh net and washed. The remaining contents 737 were then searched for identifiable prey item parts with a low magnification microscope. Each sample was 738 picked through twice for parts of prey items, and all parts were recorded, counted, and placed in vials of 739 70% ethanol per prey type similar to methods in earlier studies (Dorn et al. 2011, Boyle et al. 2014).

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740 741 Figure 6-14. Map of sampled white ibis nesting colonies within ENP. Otter Creek 742 (OC) and Cabbage Bay (CB) colonies (~ 300 nests) were sampled in 2017, and 743 the large Broad River (BR) colony (11,000 nests) was sampled in 2018.

744 Prey parts picked from samples were counted to establish the number of individuals of a certain prey 745 type and measured to quantify the biomass (dry milligrams [mg]) of those individuals within each sample. 746 In this analysis, counts of crayfish and insects were established using counts of rostrums (beaklike 747 projections of the head) and heads, respectively. Reproductive organs of crayfish, pleopods (forked 748 swimming limbs of a crustacean that are typically attached to the abdomen), and annuli (ring-shaped 749 structures of an insect or crustacean), were also picked from samples and used to identify species. For crabs 750 and fish, counts were established using counts of chelipeds (pairs of legs that bears a pair of hinged pincer- 751 like claws terminating the anterior limbs) and otoliths (small oval bodies in the inner ear), respectively. For 752 prey parts that occur in pairs (i.e., chelipeds, otoliths), pairing of these parts was required prior to counting 753 individuals. Crab chelipeds were paired considering species, handedness, dominance, and size, while fish 754 otoliths were paired by species and size. Chelipeds and otoliths varying in dimension greater than 5% were 755 considered independent, representing multiple individuals except for Uca spp. (fiddler crab) chelipeds, 756 where male dominant chelipeds are ornamentally large. 757 Biomasses for all prey item were calculated with a series of regressions flowing from dimension of a 758 prey part to individual dry mass (in mg) (Boyle et al. 2014 plus new unpublished regressions). Biomasses 759 for prey items found in samples that could not be calculated with regressions were measured directly after 760 drying specimens at 70 degrees Celsius (ºC) for 24 hours or until reaching a consistent weight. Biomass 761 composition for each sample was reconstructed by summing calculated or measured biomasses of every 762 prey item found.

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763 Statistical Analyses 764 We compared total prey bolus size (prey mass/bolus) between years with a t-test. We compared 765 composition between years after collapsing prey biomasses into six groups (crayfish, crabs, fish, terrestrial 766 invertebrates, aquatic insects, and shrimp) and we summarized the dietary components in summary tables. 767 We conducted an ordination with PRIMER Version 7 (by PRIMER-e, Massey University, Albany, 768 Auckland, New Zealand) based on a bolus-bolus resemblance matrix of Bray-Curtis similarity to explore 769 the characteristics of the bolus composition by year. All biomass data were square-root transformed to 770 down-weight the relative contribution of the heaviest items in the ordination and statistical tests. 771 Multivariate analyses were conducted with a permutational multivariate analysis of variance 772 (PERMANOVA) to test for differences in prey composition (Anderson 2017). We followed that analysis 773 with an evaluation of the primary contributors to bolus-bolus compositional dissimilarity between years 774 (SIMPER procedure). We also examined the progression of prey use in the extended 2018 season to look 775 for intra-annual prey use shifts (Dorn et al. 2011) and to identify the prey targeted at the initiation of nesting 776 in 2018.

777 Results, Discussion, and Management Implications 778 We identified 1,909 prey animals within 160 boluses from 2017 and 2018. Prey animals included 779 freshwater crayfish, several genera of estuarine crabs (Uca ssp., Menippe sp., Aratus sp., and Sesarma sp.); 780 an assemblage of freshwater and estuarine fishes (Poeciliidae, Fundulidae, and Cyprinidae); insects 781 including aquatic hemipterans, aquatic and terrestrial coleopterans, larval odonates (Anisoptera), and 782 dipterans; arachnids; and shrimp (Palaemonetes sp.). Both crayfish species (P. alleni and P. fallax) were 783 present in boluses in both years. 784 Boluses contained almost twice the total prey biomass in 2018 than in 2017 (probability factor (p) < 785 0.001). Prey composition also varied between years (p < 0.001). Prey composition shifted from high crab 786 use in 2017 to greater crayfish and fish use in 2018 (Tables 6-4 and 6-5).

787 Table 6-4. Frequency of occurrence of six prey categories in white 788 ibis chick boluses from coastal colonies in 2017 (Otter Creek and 789 Cabbage Bay) and 2018 (Broad River) breeding seasons.

Frequency of Occurrence Prey Item 2017 2018 Crayfish 0.20 0.67 Crabs 0.63 0.13 Terrestrial Invertebrates 0.25 0.15 Aquatic Insects 0.43 0.42 Fish 0.38 0.58 Shrimp 0.00 0.12 Sample size (n) 40 120 790

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791 Table 6-4. Mean proportional biomasses (dry mass) of five prey types in 792 white ibis chick boluses from the coastal ENP colonies in the 2017 (Otter Creek 793 and Cabbage Bay) and 2018 (Broad River) breeding seasons.

Mean Proportional Biomass (grams) Prey Item 2017 2018 Crayfish 0.13 0.47 Crabs 0.56 0.11 Terrestrial Invertebrates 0.03 0.01 Aquatic Insects 0.16 0.14 Fish 0.13 0.36 Shrimp 0.00 0.01 Average Total Dry Biomass (in grams) per Bolus ( g/bolus) 2.14 4.25

Standard Error (SE) 𝑥𝑥̅ 0.31 0.39 Sample Size (n) 40 120 794 795 In 2018, the quantity and composition of prey also varied as the nesting season progressed and the 796 landscape dried. The average prey biomass per bolus varied among collection dates (p = 0.02). Average 797 prey biomass per bolus was highest on the first date (April 14) than the following two weeks (April 27 and 798 May 4; pairwise p < 0.05; Figure 6-15A) but was intermediate on the final date of collections (May 11) 799 (all pairwise p > 0.37). Prey composition also differed among collection dates (centroid [arithmetic mean 800 position of all the prey data]) p = 0.0001). The general trend from the beginning to the end of the nesting 801 season was declining crayfish biomass (80% of biomass on April 14 to 23% on May 11) and increasing 802 fish use (16% to 63%; Figure 6-15B). Use of other prey groups varied between dates to a lesser degree and 803 without obvious directional changes (Figure 6-15B). Two-thirds of crayfish identified in the first collection 804 were P. alleni, but as crayfish use declined over the nesting season, the proportional use of P. fallax 805 increased to 85% (on May 11). 806 The irruption of ibis nesting in 2018 and the prey composition shift suggests that freshwater prey, 807 especially crayfish, availability provides an important trigger for high nesting activity in the coastal colonies 808 of ENP. In 2017, ibis were clearly using the estuarine/mangrove habitats for foraging and we suspect that 809 the dominance of crabs in the boluses, with their associated high salt content, may indicate a poor quality 810 diet. Previous studies indicate that white ibis chicks struggle to shed salt; experimental diets of brackish 811 water crabs (i.e., Uca spp., fiddler crabs) or salt-loaded crayfish fed to chicks caused ibis chicks to lose 812 weight (Johnston and Bilstein 1990). Johnston and Bildstein (1990) argued that sizes of coastal nesting 813 colonies of ibis (in South Carolina) fluctuated in response to rainfall and the availability of crayfish in 814 inland wetlands. The crayfish use by ibis in 2018, was comparable to prey composition for large nesting 815 events at inland colonies in the central Everglades (Boyle et al. 2014) with the exception that P. alleni was 816 used heavily in ENP and P. alleni are not available for most central and northern Everglades colonies. The 817 intra-annual shift in 2018 was similar to other shifts observed in the central Everglades (i.e., crayfish to 818 fish; Dorn et al. 2011). 819 P. alleni is the dominant crayfish found in seasonally drying wetlands (Hendrix and Loftus 2000, Dorn 820 and Trexler 2007). The heavy boluses (Figure 6-15A) and use of P. alleni at the start of nesting in 2018 821 suggests that P. alleni production in marl prairies and other seasonally dried wetlands was the habitat used 822 when the high nesting activity was triggered in March 2018. The relatively high water following Hurricane 823 Irma late in 2017 increased the hydroperiods of seasonally dried wetlands in southern Big Cypress National 824 Preserve (BCNP) and western ENP. Lengthened hydroperiods in seasonal wetlands should make for better 825 P. alleni production (Acosta and Perry 2000, 2002) and simultaneously make the same wetlands available

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826 for foraging when ibis are staging and nesting in the late winter and early spring (i.e., still flooded in 827 February, March, or April). P. alleni survival and production requires annual drying to reduce predatory 828 fishes (Dorn and Trexler 2007, Dorn 2008, Kellogg and Dorn 2012). 2018 was preceded by a relatively 829 strong dry disturbance but then water levels were also relatively high in the wet season. Beyond the 830 importance of annual drying, the limitations of P. alleni in marl prairies and other seasonal wetlands are 831 not well studied (see Acosta and Perry 2000, Beerens et al. 2017). A recent paper projected that hydro- 832 restoration of ENP will cause crayfish densities, particularly P. alleni, to decline even as ibis use of ENP 833 should increase (Beerens et al. 2017). Our results suggested exactly the opposite. The relationship between 834 P. alleni populations and hydrologic variation of the southern Everglades needs to be further investigated.

835 836 Figure 6-15. (A) Average (± standard error) total prey biomass per bolus and (B) proportional 837 composition of the average bolus for four collection dates in 2018 at the Broad River colony in ENP.

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838 The increased use of fish and P. fallax as the nesting season progressed can be interpreted as ibis 839 progressively increasing use of Shark River Slough to the east and southeast of the Broad River colony as 840 the marl prairies dried. Shark River Slough, with its relatively long hydroperiods, is one of the few areas in 841 the southern Everglades where consistently more P. fallax can be found (Hendrix and Loftus 2000) and 842 where fish should be more abundant (Trexler et al. 2005). The high nesting activity of 2018 seems to have 843 been triggered by availability of crayfish in higher elevation wetlands. Shark River Slough became 844 important for foraging later in the nesting cycle. 845 The results of this two-year comparison provide real hope that the overdrained southern Everglades is 846 resilient and can still support large nesting colonies of ibis if wetlands in coastal ENP and BCNP can be 847 rehydrated with restoration. However, observations of prey use are currently limited to a few colonies and 848 for one pair of years. Furthermore, the relationship between hydrologic variation and population dynamics 849 of P. alleni remain to be clarified.

850 PLANT ECOLOGY

851 GROWTH RESPONSE OF FLORIDA NATIVE TREES TO HYDROLOGY 852 ON TWO CONSTRUCTED EVERGLADES’ TREE ISLANDS IN 853 LOXAHATCHEE IMPOUNDMENT LANDSCAPE ASSESSMENT 854 Carlos Coronado, Fabiola Santamaria, 855 Kelsey Pollack2, and Darlene Marley

856 Introduction 857 Tree Islands are a characteristic feature of the Everglades ridge and slough landscape and critical habitat 858 for many native flora and fauna (Givnish et al. 2008). Sklar and Van der Valk (2002) described two basic 859 types of tree islands in the Everglades: fixed (teardrop) and floating (pop-up). Fixed tree islands usually 860 become established at high points in the bedrock topography where peat has accumulated and they form in 861 a teardrop shape aligned with flow direction with an upstream head, which is the highest and widest part of 862 the island, and a tapering, downstream tail that is characterized by relatively low elevation. Floating tree 863 islands are formed when large pieces of peat “popup” from the substrate and float downstream incorporating 864 wetland vegetation to form local high points favorable to be colonized by shrubs and woody vegetation. 865 Tree growth, measured by diameter at breast height increments over time, is a biological process 866 influenced by light, nutrient inputs, air temperature, soil moisture, and hydrology (Keeland and Sharitz 867 1997). Wetland tree species are particularly affected by changes in hydrology (Conner and Day 1976). 868 Megonigal et al. (1997) showed that forested wetlands subjected to seasonal flooding are more productive 869 than forested wetlands exposed to continuous flooded conditions. The higher productivity associated with 870 seasonal flooding was attributed to increased soil moisture and nutrient inputs by flowing water (Wetzel et 871 al. 2002, Conner and Day 1992). In contrast, more permanently flooded conditions are less productive and 872 may not be as healthy due to lower levels of dissolved oxygen, high levels of sulfide in the soil, and low 873 water and nutrient uptake (Megonigal et al. 1997). 874 The abundance, distribution, and growth of wetlands tree species are related to water level, and the 875 frequency and duration of flooding (Keeland et al. 1997, Armentano et al. 2002, Stoffella et al. 2010). 876 These studies indicate that each wetland tree species has an optimum distribution along a hydrologic 877 gradient. Thus, species located outside of its optimum hydrological zone will have lower production and 878 growth rates relative to species located within its hydrological zone (Jones et al. 2006). Moreover, extreme

2 Greenman-Pederson, Inc., Orlando, Florida.

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879 hydrological conditions, either too dry or too wet, can limit plant community structure and function 880 (Givnish et al. 2008). For instance, long-term soil saturation induces soil anoxia, which limits nutrient 881 availability and gas exchange, while short hydroperiods promote muck fires that decrease soil elevation 882 (Givnish et al. 2008). Yet, even though extreme hydroperiods have negative effects on individual woody 883 species, their overall effects on tree growth are not well understood because the net integrative effects of 884 flooding are masked by plants compensatory responses across the hydrological gradient that include shifts 885 between above- and belowground biomass and growth allocations (Klimensová and Klimeŝ 2007). 886 This study evaluates the growth of native tree species in relation to changes in water depth and 887 hydroperiod on two types of constructed tree islands in the Loxahatchee Impoundment Landscape 888 Assessment (LILA), an 80-acre experimental wetland facility on the grounds of the Arthur R. Marshall 889 Loxahatchee National Wildlife Refuge in Boynton Beach constructed by SFWMD in 2003 to research and 890 apply restoration techniques on a small, controlled scale before taking them into the 1.7 million-acre 891 Everglades ecosystem (Figure 6-16). Water levels and flows in LILA are controlled independently by 892 automated pump operation to create hydrologic regimens similar to that of the Everglades. The specific 893 objective of this LILA study was to characterize the spatial and temporal pattern of tree growth and 894 determine the effect of water depth, hydroperiod, and island type on growth pattern.

895 896 Figure 6-16. Map and location of the study conducted in LILA. There are two type of 897 tree islands, peat-based (P) and limestone-core (L) lined-up in a west-east direction.

898 Study Site and Methods 899 LILA consists of four replicated 20-acre macrocosms (M1 through M4), each containing ridge and 900 slough habitats and two tree islands, each representing the two major types of islands found in the 901 Everglades (peat-based and a peat-limestone core). More than 700 native trees of eight common Everglades 902 species were planted on each of the 8 tree island in LILA (a total of 5,736 trees) between May 2006 and 903 March 2007 (Stoffella et al. 2010).

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904 This study was conducted on two of the eight man-made islands in LILA. A total of 53 native trees 905 from 3 different species—Annona glabra (pond apple), Acer rubrum (red maple), and Morella cerifera 906 (wax myrtle)—with a diameter of at least 5 cm were randomly selected for tree band installation. In July 907 2016, 24 trees were chosen in a limestone-core island (M1E) and 29 trees in peat-based island (M1W). 908 Dendrometer bands are a widely used method (Keeland and Young 2004) to study plant growth patterns 909 of the tree species planted in LILA (Figure 6-17). Trees were randomly selected and banded at breast height 910 with a D1 Dendrometer, which provides a permanent manual reading of the diameter of trees based on the 911 circumference (Anemaet and Middleton 2013). This kind of band is made of Astralon material and produces 912 minimal friction as the tree grows. The scale division is 0.05 cm and the vernier reading is 0.01 cm, where 913 the measured value indicates the diameter of the trunk.

914 915 Figure 6-17. A D1 Dendrometer made 916 by UMS, designed for permanent 917 manual readings of tree diameter, used 918 to measure spatial and temporal 919 growth patterns at LILA.

920 In July 2018, an initial diameter at breast height was measured with a regular diameter tape and exact 921 location of every tree were registered using a Trimble RS1 and Collector (Aurora, new version) – ArcGIS 922 software connected to an Apple iPad. Additionally, the local habitat was described, tree height was 923 measured with a telescoping measuring rod and a Laser Rangefinder TruPulse 200 made by Laser 924 Technology, Inc., and water levels next to each tree were measured with a metric ruler monthly. 925 Surface water stage data were obtained for the study period (CY2016–CY2019) from stage recorders 926 at the east and west ends of each macrocosm and retrieved from the DBHYDRO database maintained by 927 SFWMD. Stages for each tree island were estimated from linear interpolation between water levels at the 928 western (input) and eastern (output) ends. Precipitation data were obtained from the Loxahatchee weather 929 station located about 1.1 km northeast of LILA.

930 Data Analysis 931 Tree growth rates for each of the three woody species, expressed as millimeters per day (mm/d or 932 mm day-1), were used for testing significant differences (p < 0.05) between peat-based and limestone-core 933 tree islands using a split-plot repeated measure analysis (JMP® 13.1.0). The initial diameter was used as a 934 covariate to consider initial tree size variability along with environment (tree island type) and tree species 935 (A. glabra, A. rubrum, and M. cerifera). In the subplot, the factor time and the interaction of tree species x 936 time and tree island type x time were tested. In this analysis, time is the growth measured monthly.

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937 Violations of the assumptions for the method, normality, and homeostasis, were checked by residual 938 analysis. Statistical analyses were performed using Mixed Model procedures of JMP® Version13.1.0.

939 Results

940 Hydrology 941 During the study period, water levels were usually belowground during the dry season (January–June) 942 and 5 cm to 20 cm above the ground level during the wet season (July–December), which matched the 943 seasonal pattern observed in the Everglades (Figure 6-18). Accordingly, precipitation was higher during 944 the summer months (July–October) and lower during the dry months (February–May).

945 946 Figure 6-18. Average monthly surface water levels and rainfall pattern 947 on the most elevated regions of all tree islands located at LILA.

948 Tree Growth 949 Overall, there were significant differences between peat-based and limestone-core tree islands. The 950 analysis indicates that trees growing on the limestone-core tree island had, on average, higher growth rates 951 than individuals growing on the peat-based tree island (p = 0.001). Overall, there were not statistically 952 significant differences in growth rate among the three species (p = 0.054); however, there was a significant 953 island type x species interaction (Table 6-5), which indicated that growth for at least one species was 954 affected by island type (p = 0.023). Indeed, A. rubrum growing in the limestone-core tree island had a higher 955 growth rate than A. rubrum individuals growing on the peat-based tree island, as shown in the contrast 956 analysis (p = 0.0001) (Table 6-5). 957

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958 Table 6-5. Repeated measured analysis and p-values of growth rate (mm/d). The analysis tests the 959 significant differences between peat and limestone tree islands and among the species over time.

Fisher’s Ratio Degrees of Source p > F (F) Freedom (DF) Island Type 18.03 1 0.001 Tree Species 3.11 2 0.054 Island Type x Species 4.06 2 0.023 Time 20.26 26 <0.0001 Island Type x Time 16.88 26 <0.0001 Species x Time 3.34 52 <0.0001 Island Type x Time x Species 2.62 52 0.0007 Species Source F DF p > F A. glabra 1.55 1 0.231 Island Type sample size (n) = 18 A. rubrum 33.80 1 0.0001 Island Type n = 25 M. cerifera Island` Type 2.93 1 0.125 n=10 960 961 On the peat-based tree island, all three woody species showed little growth during the first seven 962 months of the study (September 2016 to April 2017). However, all three species showed relatively improved 963 growth starting in May 2017. All three species showed similar temporal patterns of growth in relation to 964 precipitation and water level patterns (Figure 6-19) with greatest growth rates occurring at the start of the 965 rainy season when water levels on the tree islands were low (Figure 6-20a, b, and c). Growth rate for 966 A. rubrum was greatest during the summer months (May–September) with a maximum during June 2018 967 (0.032 mm/d) and lowest during the dry season (December–May) (Figure 6-20a). The temporal growth 968 pattern for A. glabra was similar to that of A. rubrum with high growth rates during the summer months 969 (May–October) and low rates during the dry months (December–May). Maximum growth rate 970 (0.045 mm/d) occurred at the onset of the wet season (Figure 20b). M. cerifera had the lowest growth rate 971 of the three study species with its maximum growth rate (0.026 mm/d) occurring during July 2018 972 (Figure 6-20c). There were no significant differences in growth rate among the three woody species on the 973 peat-based tree island, but they grew significantly faster during the wet season than the dry season 974 (Table 6-5). 975 On the limestone-core tree island all three species showed a clear growth temporal pattern in which 976 the timing and duration of growth rates coincided with the precipitation and water level patterns, with high 977 growth rates occurring during the rainy season and when water levels on the tree island were low 978 (Figure 6-21a, b, and c). In particular, the growth rate pattern for A. rubrum shows a clear seasonality with 979 high growth rates during June–July and low growth rates during December–May. Maximum growth rate 980 (0.082 mm/d) occurred on June 2018 (Figure 6-21a). A. glabra also had a clear seasonal growth pattern, 981 with higher growth rates during June–July and low during December–May. Maximum growth rate occurred 982 during July 2017 (Figure 6-21b). M. cerifera had the lowest growth rate among the three woody species 983 under study with the maximum growth rate (0.035 mm/d) occurring in August 2018 (Figure 6-21c). Among 984 the three woody species, A. rubrum had a significantly higher growth rate than A. glabra and M. cerifera 985 (Table 6-5). 986

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987

988

989 990 Figure 6-19. Growth rate temporal pattern on the peat-based tree island for 991 (a) A. rubrum (n = 15), (b) A. glabra (n = 9), and (c) M. cerifera (n = 5).

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992

993

994 995 Figure 6-20. Growth rate temporal pattern on the peat-based tree island for 996 (a) A. rubrum (n = 15), (b) A. glabra (n = 9), and (c) M. cerifera (n = 5).

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997

998

999 1000 Figure 6-21. Growth rate temporal pattern on the limestone-core tree island 1001 for (a) A. rubrum (n = 10), (b) A. glabra (n = 9), and c) M. cerifera (n = 5).

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1002 Discussion 1003 The hydrological and soil characteristics observed on study tree islands are related to tree growth rates. 1004 More specifically, the peat-based tree islands are characterized by low soil bulk density, high soil water 1005 content, and high water levels while the limestone-core tree islands are characterized by high soil bulk 1006 density, low soil water content, and relatively low water levels (Stoffella et al. 2010). Tree growth rate 1007 patterns showed a clear seasonality, particularly on the deciduous species (A. glabra and A. rubrum). 1008 However, rainfall and water levels on the tree islands seem also to play an important role on the observed 1009 growth pattern. For instance, tree growth rates started decreasing at the time when water levels were 1010 increasing and trees starting to increase growth rates when water levels were decreasing. This growth 1011 pattern associated with water depth requires a long monitoring to clearly discern the effect of hydrology on 1012 tree growth. Nonetheless, these results are consistent with previous findings (Keeland et al. 1997, Jones et 1013 al. 2006, Coronado et al. 2015) and suggest that hydrology impacts tree growth patterns on tree islands 1014 regardless of the island type. 1015 In the Everglades, earlier studies (Givnish et al. 2008, Stoffella et al. 2010) that have attempted to 1016 understand the relationship between tree island vegetation and hydrology were limited by sample sizes that 1017 were relatively small given the variability among tree islands and regions, or because hydrologic data was 1018 not available. Here our results showed that trees respond to the hydrological conditions they are exposed to 1019 and that tree species respond to changes in hydrology depending on whether the tree species are located 1020 within or outside of their optimum hydrological zone as it has been observed in other studies 1021 (Jones et al. 2006). 1022 This study presented tree growth rates for a period of 24 months. Growth rates of individual tree species 1023 were generally conservative and linear over time; however, environmental differences in hydrology, soil 1024 characteristics, and light can produce significant differences in seasonal growth rates and morphology. Our 1025 results indicate that tree growth is an ecological process that is strongly influenced by environmental 1026 parameters, including hydrology, soil nutrients, air temperature, and rainfall operating over large temporal 1027 scales. Thus, assessing the effect of hydrology and soil nutrient conditions on tree islands requires long- 1028 term monitoring programs.

1029 Relevance to Water Management 1030 Our understanding of tree island resilience and restoration is not yet complete. It is complicated by the 1031 fact that there are spatial and temporal differences in tree growth patterns along a hydrologic gradient that 1032 are also indicative of a lag in response to changes in hydrology and the result of interactions between 1033 hydrology and regional factors such as air temperature, relative humidity, and natural disturbances. This 1034 understanding is critical to setting water management targets, interpreting the results of monitoring 1035 activities, and implementing adaptive management strategies. The preservation of the plant community 1036 dynamics on tree islands, including the natural shifts in species composition, forest structure, aboveground 1037 production, and tree growth are directly dependent on the existence of a mosaic of hydrological conditions, 1038 which requires water management policies that promote natural wet and dry cycles through the 1039 Everglades ecosystem.

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1040 FLORIDA BAY BENTHIC VEGETATION

1041 Amanda McDonald, Margaret O. Hall3, and Brad Furman

1042 Introduction 1043 Benthic vegetation, composed of seagrass and benthic macroalgae, provides habitat structure in Florida 1044 Bay and its associated creeks, ponds, swamp forests, and marshes of the mangrove transition zone. 1045 Monitoring and research of benthic vegetation is critical to understanding the effects of water management 1046 and restoration on wetland and estuarine ecosystems. These surveys are used to provide ecosystem status 1047 updates for RECOVER, conduct assessments of District operations, calibrate and verify the Florida Bay 1048 Seagrass Community Model (SEACOM), and calculate status indicators for submersed aquatic vegetation 1049 (SAV) in Florida Bay.

1050 Methods 1051 The South Florida Fish Habitat Assessment Program (FHAP) provides estimates of SAV cover using 1052 the Braun-Blanquet Cover Abundance (BBCA) Index at 30 random sites within each of 16 basins 1053 throughout Florida Bay and along the southwestern coast every May (Figure 6-22). The BBCA Index is a 1054 semi-quantitative metric with a range of 0 to 5 where 0 means that there is no presence and 5 represents a 1055 coverage of greater than 75% (Fourqurean et al. 2002). At each random site, 8 0.25-square meter (m2) 1056 quadrats are sampled. In addition to the mapping effort, fixed transects are sampled twice a year (May and 1057 October) for finer scale temporal changes and to assess physiological characteristics such as shoot density 1058 and biomass. 1059 The frequency of occurrence for each of the 3 main seagrass species—Thalassia testudinum 1060 (turtlegrass), Halodule wrightii (shoal grass), and Syringodium filiforme (manatee grass)—and the average 1061 BBCA score are calculated from the 8 quadrats per site. The individual site averages (30 per basin) are used 1062 to produce spatially interpolated maps within each basin to evaluate spatial change within each basin over 1063 time. Basin averages of the frequency of occurrence and the average BBCA are analyzed for interannual 1064 variability and patterns.

1065 Results 1066 The seagrass communities in central and western Florida Bay had been slowly recovering from the 1067 seagrass die-off event that began in summer 2015 with Halodule initially colonizing the areas where 1068 Thalassia had died followed by some expansion of Thalassia beginning to appear in Rankin Lake and 1069 Johnson Key Basin. However, between May 2017 and May 2018, additional losses of Thalassia were 1070 detected in Johnson Key Basin, Whipray Basin, and Manatee Bay (Figure 6-23). For the stations in the 1071 western and central areas of Florida Bay, these changes are likely the result of increased chlorophyll and 1072 turbidity after Hurricane Irma passed over the area in September 2017 (see the Florida Bay Water Quality 1073 Conditions and Status section later in this chapter). The decline in Thalassia in Manatee Bay to the east 1074 occurred near the shoreline where flows from the S-197 structure enter the bay. This suggests that the 1075 decline may have been related to the large releases through S-197 for flood control in South Miami-Dade 1076 County after the passage of Hurricane Irma. 1077 Similarly, the permanent transect data for Garfield Bight and Rankin Lake (Figure 6-24) reveal a nearly 1078 complete loss of Thalassia in fall 2015 that has persisted through May 2018. Terrapin Bay, further to the 1079 east, was not impacted during the seagrass die-off event in 2015, but showed a decline starting in October 1080 2017, roughly one month after the passage of Hurricane Irma. The initial decline could have been due to

3 Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission, St. Petersburg, Florida.

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1081 physical scouring during the passage of the storm. The continued decline would likely be due to degraded 1082 water quality conditions resulting after the storm.

1083 1084 Figure 6-22. The South Florida FHAP monitors 16 basins throughout Florida Bay and 1085 Whitewater Bay annually to document the seagrass and macroalgae species within 1086 each basin. There are also 15 permanent transects established that are sampled twice 1087 a year for finer scale physiologic characteristics such as shoot density and biomass.

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1088 1089 Figure 6-23. Spatial interpolation of mean BBCA scores for Thalassia from May 2017 and May 2018. The darker the 1090 green, the greater the density of Thalassia. Between the sampling events in 2017 and 2018, losses and thinning of 1091 Thalassia were detected in Johnson Key Basin, Whipray Basin, and Manatee Bay likely as a result of physical impacts 1092 of Hurricane Irma, flood control freshwater discharges, and post-storm degraded water quality.

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1093 1094 Figure 6-24. In examining the twice-a-year transect sampling, the 1095 seagrass die-off can be clearly seen at the Garfield Bight and Rankin Lake 1096 stations starting in October 2015 with an almost complete loss of Thalassia 1097 cover that has persisted through October 2018. However, further east in 1098 Terrapin Bay, a decrease and eventual loss of Thalassia started in fall 2017, 1099 roughly one month after the passage of Hurricane Irma.

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1100 Discussion 1101 Increases in the distribution of Halodule within Rankin Lake and Johnson Key Basin during 2017 and 1102 2018 (data not shown but can be found in Hall and Durako [2019]) indicate that seagrass community 1103 recovery is progressing after the 2015 die-off event. However, additional declines in Thalassia in Johnson 1104 Key Basin and Whipray Basin indicate that water clarity issues may be slowing the recovery of Thalassia 1105 in the central and western Florida Bay area. Initial results from the May 2019 sampling event suggest that 1106 further loss of Thalassia has occurred in Johnson Key Basin bringing the Thalassia cover to below 2016 1107 levels. Halodule does not appear to be decreasing in these areas suggesting that water quality conditions 1108 are sufficient for this more opportunistic species that has lower light requirements. 1109 Relevance to Water Management 1110 Continued instability in water quality can lead to increased seagrass loss and a reversal of the seagrass 1111 community recovery that is currently underway. Unfortunately, the perturbations of the past few years were 1112 outside the control of water managers. As the successional recovery continues in the benthic community, 1113 the community will become less vulnerable to storms and other perturbations.

1114 ECOSYSTEM ECOLOGY

1115 ACTIVE MARSH IMPROVEMENT IN THE DECOMP PHYSICAL MODEL

1116 Christa Zweig, Sue Newman, Colin Saunders, Erik Tate-Boldt, 1117 Michael Manna, Lisa Jackson, Chris Hansen, and Allyson Genson

1118 Introduction 1119 The Decomp Physical Model (DPM) was constructed (Figure 6-25) within the historic ridge and slough 1120 landscape in an area isolated from the surrounding marshes by levees. Decades of decreased hydroperiods 1121 and water levels caused sloughs to fill in with sawgrass and the landscape lost its characteristic linear 1122 pattern. When DPM flow experiments began in 2013 (Coronado-Molina et al. 2015), slough flow velocities 1123 were enhanced up to 500 m from the flow structure, but the lack of continuous, open water sloughs affected 1124 the ability of flow to penetrate further into the environment. Also, flow direction did not follow the historical 1125 path, flowing east versus southeast (Coronado-Molina et al. 2015). 1126 To increase the spatial extent and connectivity of open water habitats, we used active management 1127 methods, called active marsh improvement (AMI), that have been successfully employed in other areas of 1128 the Everglades (Newman et al. 2017). We monitored conditions created within and adjacent to the actively 1129 managed footprint to answer the following questions: (1) Can we create faster flows and propagate flow 1130 further into the DPM footprint?, (2) Can we restore slough and ridge patterns and flow direction on a 1131 landscape scale?, and (3) How does an AMI approach impact sediment creation and movement?

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1132 1133 Figure 6-25. DPM footprint within the Everglades.

1134 Methods 1135 In a previous small-scale experiment, we documented increased flow speeds within the marsh with the 1136 removal of vegetative resistance to flow (C. Zweig, SFWMD, unpublished data). To explore the possibility 1137 that large-scale removal of sawgrass could improve flow throughout the entire DPM footprint and restore 1138 landscape pattern and direction, we modeled flow direction and speed using Environmental Fluid Dynamic 1139 Code (EFDC) Explorer (Version 8.0, Dynamic Solutions International LLC, Edmonds, Washington, United 1140 States of America). EFDC Explorer is a graphical user interface developed by the United States 1141 Environmental Protection Agency to model hydrodynamics and contaminant/sediment transport in flowing 1142 systems (Tetra Tech, Inc. 2002). For details on EFDC’s underlying hydrodynamic model, please see Tetra 1143 Tech, Inc. (2007).

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1144 Model Calibration and Validation 1145 The DPM Active Marsh Improvement (AMI) model used a georeferenced polygon representing the 1146 footprint of the DPM project as the model domain and an existing digital elevation model provided by the 1147 USGS (http://sofia.usgs.gov/eden/models/groundelevmod.php) for the bathymetry layer. Boundary 1148 conditions were created to represent the S-152 inflow structure, output gap in the L-67C levee, and seepage 1149 through both bounding levees. Seepage rates were estimated by pre-flow FlowTracker measurements near 1150 the levees (C. Saunders, SFWMD, unpublished data). Flows from the input structure were downloaded 1151 from USGS website (https://waterdata.usgs.gov/) and output through the levee gap was estimated from 1152 FlowTracker measurements (C. Saunders, SFWMD unpublished data). The entire area was assumed to be 1153 covered in a constant density of sawgrass with a drag coefficient estimated in previous literature (Larsen et 1154 al. 2010). Initial water depths were set from average pre-flow field data. Overall landscape flow vectors 1155 were calibrated to field data (C. Saunders, SFWMD, and David. Ho, University of Hawaii, unpublished 1156 data). Grid size and time steps were estimated to satisfy the Courant-Friedrichs-Lewy condition: 1157 (U* ∆t)/∆x<1 1158 This condition states that the time step (t) should not be so large that a parcel of water will move through 1159 more than one grid cell (x) for the given velocity (U). 1160 We ran the model first with constant vegetation cover and then with landscape-level AMI, which used 1161 two linear lines, one pixel wide each, from the S-152 structure to the L-67C levee gap. The AMI consisted 1162 of changing the vegetation and corresponding drag coefficient from sawgrass to slough. The slough drag 1163 coefficient was calculated by dividing the sawgrass coefficient by 2. For model validation, flow direction, 1164 speed, and water depth were compared to field data at the end of the model run. Comparisons between 1165 depth, velocity, and angle were analyzed using a paired Wilcoxon rank sum test.

1166 Landscape-Level Slough Creation 1167 After the EFDC modeling and smaller active management experiments indicated AMI could be 1168 successful, we created two landscape-level sloughs in November 2016. We approximated original slough 1169 paths from 1940s aerial imagery (Figure 6-26) and sprayed ~14 ha with herbicide to create continuous 1170 sloughs over 2 km in length (Figure 6-27A). After 6 months, the standing dead biomass was smashed down 1171 with airboats (Figure 6-27B). We measured flow using a FlowTracker at sites we had observed to be in 1172 areas of preferred flow paths, sites E250 and Z5-1, and along the southern transect at sites RS1, Z5-3, and 1173 Z5-4, before and after active management.

1174 Slough Monitoring 1175 Sediment transport was measured using horizontal sediment traps adapted from Phillips et al. (2000) 1176 and deployed mid-depth in the water column parallel to flow. Traps were deployed for 6-week intervals 1177 and samples were immediately processed, dried, and weighed. Loading rates per frontal area were estimated 1178 from horizontal traps and converted to loading per ground area based on the inner diameter of the inlet and 1179 outlet tubes (6.4 millimeter) and water column depth (surface to floc layer).

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1180 1181 Figure 6-26. DPM footprint (A) pre-active management, (B) 1940s historical imagery 1182 of DPM footprint, and (C) historical sloughs superimposed on recent imagery.

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1183 1184 Figure 6-27. Actively managed (AMI) sloughs 1185 (A) post-spray and (B) post-smashing in the DPM footprint

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1186 Results and Discussion

1187 Slough Modeling 1188 To validate the EFDC model, we compared field and model velocities, flow direction, and rate of water 1189 depth increase. In the model, water flowed radially from the input structure and velocities dropped off 1190 within 500 m of the structure, mimicking field observations. Model velocities closely matched the pattern 1191 of flow but were slower than measured field velocities by 0.2 to 2.6 centimeters per second (cm/s). Modeled 1192 water depths were within 15% of measured depths and the slope of the lines at each time step were not 1193 significantly different (p > 0.05). 1194 Running the EFDC model with AMI significantly increased velocities (p < 0.0001) and changed 1195 direction of flow in the managed cells (p < 0.0001), as opposed to the cells adjacent (Figure 6-28). Velocity 1196 was faster in the AMI cells and the mean velocity difference between AMI cells and adjacent sawgrass cells 1197 was 0.4 cm/s. AMI also changed flow direction by a mean of 8.5 degrees with AMI cells flowing more to 1198 the south than adjacent, non-managed cells.

1199 1200 Figure 6-28. (A) Pre-AMI and (B) post-AMI models for the DPM footprint.

1201 Landscape-Level Slough Creation 1202 Flow measurements were taken across two flow events (regular flow in 2016 and emergency flow in 1203 June–July 2017) but discharge through the S-152 structure differed between measurements, so it is useful 1204 to look at the flow comparisons as ratios. All ratios are in reference to the site with the most flow in the 1205 original preferred flow path, E250, and are based on the fastest flow measured at each site (Figure 6-29 1206 and Table 6-6). RS1, our first study site along the actively managed flow path, increased its ratio from 0.37 1207 in 2016 to 1.38 in June 2017 and 0.97 in July 2017. Z5-1, the next further study site decreased from 1.14 to 1208 0.68 and 0.29, respectively, because the active management routed water around that site. Z5-3, which had 1209 once been too far away from the structure to receive experimental flow at 700 m, increased from 0.14 in

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1210 2016 to 0.26 in June 2017 and 0.22 in July 2017 (Table 6-6). Flows of 2 cm/s (near the critical entrainment 1211 threshold; Larsen et al. 2010) were measured 1,200 m from the structure (Figure 6-29). It is interesting to 1212 note that a site southeast of the S-152 structure, Z5-1, had its flows reduced by ~66% post-AMI. This AMI 1213 approach appears to have split the original radial flow from a point source structure into stronger east and 1214 south flow paths.

1215 1216 Figure 6-29. Flow speeds in cm/s pre-AMI and post-AMI within the DPM footprint.

1217 Table 6-6. Ratio of flow velocity before and after active management. 1218 Ratios were calculated from site velocity/velocity at first site in the preferred flow path, E250.

Before AMI After AMI Site November 2015 June 2017 July 2017 RS1 0.37 1.38 0.97 Z5-1 1.14 0.68 0.29 Z5-3 0.14 0.26 0.22 1219

1220 Slough Monitoring 1221 Higher flows progressed downstream over a period of 2 to 3 months as is evidenced by periphyton 1222 clearing (Figure 6-30). AMI also affected sediment transport (Figure 6-31), likely from decomposing 1223 sawgrass and the periphyton clearing associated with higher flows. Sediment transport was significantly 1224 greater in treated sections of the south transect, but not statistically different in flow paths without AMI, 1225 both east sites and non-AMI sites in the southern flow path (Figure 6-31).

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1226

1227 1228 Figure 6-30. Evidence of periphyton (white and tan areas) clearing to more open water (dark brown areas) from higher flow speeds 1229 in active management (AMI) sloughs within the DPM footprint. Flow and animals create small pathways (dark brown horizontal lines) 1230 through periphyton that are then enlarged by flow over a matter of months, propagating flow further into the flow path.

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1231 1232 Figure 6-31. Sediment transport (per frontal area) versus S-152 discharge for pre-AMI (pre-August 2017) and post- 1233 AMI samples. Stars indicate eastern flow path sites. Sediment transport was significantly greater in treated sections 1234 (AMI) of the southern transect, but not statistically different in high flow paths without AMI (eastern flow path).

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1235 Relevance to Water Management 1236 With active management, we were able to change the physical characteristics of the landscape and 1237 create sloughs with similar connectivity observed historically. These reconnected sloughs had greater flow 1238 rates, which extended further from the S-152 structure. Slough velocities were enhanced by ~2 times up to 1239 1,200 m from the structure, as opposed to 500 m previous to AMI. We were also able to redirect a significant 1240 amount of flow to the south, along the historic flow path. As of 2019, 2 years after treatment, the AMI 1241 sloughs are still open and slough vegetation species—bladderwort (Utricularia sp.), periphyton, and water 1242 lilies (Nymphaea spp.) are expanding within the treatment area. The success of this landscape-level 1243 treatment suggests we have the potential to increase the spatial extent of enhanced flow, as well as restoring 1244 flow direction and velocities, in degraded areas of the Everglades.

1245 FLORIDA BAY WATER QUALITY CONDITIONS AND STATUS

1246 Stephen P. Kelly

1247 Water quality in Florida Bay has been monitored since 1991 (WY1992) to ensure that District 1248 operations and projects protect and restore the coastal ecosystem to the extent possible. CERP performance 1249 measures for the bay focus on chlorophyll a (Chla) concentration, an indicator of algal biomass, as well as 1250 the nutrient inputs that initiate and sustain blooms. Operational changes to the South Dade Conveyance 1251 System, including implementation of the C-111 South Dade Project, Modified Water Deliveries to ENP 1252 (especially Tamiami Trail modifications), C-111 Spreader Canal Western Features Project, which became 1253 operational in July 2012, and the newly implemented Florida Bay Improvement Project in the upper Taylor 1254 Slough area, will further change freshwater flow patterns and may alter downstream water quality. 1255 There are many factors that influence the nutrient and Chla concentrations and variation in the bay 1256 including inflows, storm and wind events, circulation patterns, nutrient recycling, and even construction 1257 events. It is not possible to attribute any one of these factors as the most important driver of current 1258 conditions, but rather it is likely a synergistic effect of some or all of them (Chapter 12 [Abbott et al. 2007] 1259 and Appendix 12-3 [Rudnick et al. 2007] of the 2007 SFER – Volume I). In last year’s SFER, we reported 1260 on the impacts of Hurricane Irma and how these impacts were likely exacerbated by the previous SAV die- 1261 off event in the central region of the bay (Chapter 6 of the 2019 SFER – Volume I; Sklar 2019). Presented 1262 here is a brief update of ecologically important parameters and how they have changed in WY2019. 1263 In WY2019, Florida was impacted by both Tropical Storm Gordon (September 2018) and Hurricane 1264 Michael (October 2018). The potential effects on water quality in the bay from these storms could be due 1265 to increased precipitation, the resulting increases in freshwater flows, wind, or a combination of all. Tropical 1266 Storm Gordon passed over Key Largo and the bay on September 3, 2018, bringing easterly winds of 1267 25 meters per second (m/s; 50 miles per hour [mph]) and 10 to 15 cm (4 to 6 inches) of rain, with a 1268 maximum of 18 cm (7 inches) in Homestead, FL, approximately 50 km (30 miles) from Key Largo (Brown 1269 et al. 2019). Water quality sampling in the bay occurred 3 days later on September 6, 2018. Hurricane 1270 Michael came closest to the bay, approximately 400 km (250 miles) to the west on October 9, 2018, with 1271 sustained winds of 20 m/s (35 mph) and > 5 cm (2 inches) of rain reported at Key West (Beven et al. 2019). 1272 Water quality sampling in the bay occurred 2 weeks later on October 23, 2018.

1273 Methods 1274 Water samples and physical parameters (temperature, salinity, conductivity, pH, and dissolved oxygen 1275 [DO]) are collected every other month at some sites and monthly starting in April 2016 at a subset of sites. 1276 Samples are collected at 0.5 m below the surface and processed according to the SFWMD Field Sampling 1277 Quality Manual (SFWMD 2017) following Florida Department of Environmental Protection (FDEP) 1278 protocols. Physical parameters are collected with a calibrated multi-parameter water quality sonde

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1279 following SFWMD protocols. Samples are processed on site, stored on ice, and shipped overnight to the 1280 SFWMD Chemistry Laboratory in West Palm Beach for analysis according to the SFWMD Chemistry 1281 Laboratory Quality Manual (SFWMD 2019) and following FDEP protocols. Sample analysis includes 1282 Chla, total phosphorus (TP), total nitrogen (TN), nitrite + nitrate (NOx), ammonium (NH4), total organic 1283 carbon (TOC), and turbidity. All sample results are quality assured before being uploaded to the Districts’ 1284 corporate environmental database, DBHYDRO. For a complete description of water quality assessments in 1285 Florida Bay, refer to Chapter 6 of the 2015 SFER – Volume I (Sklar and Dreschel 2015). 1286 As first reported in Chapter 6 of the 2017 SFER – Volume I, a discrepancy was discovered in the long- 1287 term TN data set requiring further investigation and possibly an intercalibration (Sklar and Dreschel 2017). 1288 This investigation is now complete and while the results do not conclusively reveal that a difference in 1289 analytical methodology was the cause for an approximate 30% increase in TN in CY2009, evidence against 1290 this conclusion is presented in the Total Nitrogen in the Florida Bay Water Quality Monitoring Network 1291 section of this chapter.

1292 Results 1293 Data analysis included parsing the bay into three regions depending on site location based on zones of 1294 similar water quality in Florida Bay (Boyer and Briceno 2008): 4 sites in the eastern region, 4 sites in the 1295 central region, and 5 sites in the western region (See Figure 6-9 earlier in the chapter). Annual averages of 1296 all parameters in each region of the bay were examined for any trends and any statistically significant 1297 differences from the last three water year’s as well as the POR (WY1992–WY2018) averages (Tables 6-7 1298 and 6-8). The legal standards for each basin as set forth in Section 65-302.532, Florida Administrative Code 1299 (F.A.C.), are included in Table 6-7. WY2019 trends include the following: 1300 • With the exception of Chla and TP in the eastern region, WY2019 results were elevated 1301 above the F.A.C. basin standards (Table 6-7). 1302 • Chla and TP in all three regions of the bay, elevated in WY2018, decreased back to 1303 WY2017 levels (Figure 6-32) but were still elevated above the POR averages in the 1304 central and western regions (Table 6-8). 1305 • TN has been slightly elevated in all three regions of the bay since WY2016 1306 (Figure 6-32), the year of the SAV die-off, and were still elevated above the POR 1307 averages (Table 6-8). 1308 • Turbidity in all three regions of the bay, elevated in WY2018, decreased back to 1309 WY2017 levels (Figure 6-33) and were lower than the POR averages (Table 6-8). 1310 • DIN was elevated above the POR average in the western region (Table 6-8). 1311 • There were no statistically significant differences between WY2019 and WY2017– 1312 WY2018 except for TOC in the eastern region that was slightly elevated (Table 6-8). 1313

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1314 Table 6-7. Annual water year averages and standard deviations of WY2017, WY2018, 1315 and WY2019, as well as Section 65-302.532, F.A.C., basin standards. Annual means 1316 that are statistically different from each other (t-test, p < 0.05) are in bold red text.

Parameter (unit) a F.A.C. b,c WY2017 WY2018 WY2019 Eastern Region Total Nitrogen (mg/L) 0.65 0.74 + 0.12 0.82 + 0.17 0.85 + 0.09 Dissolved Inorganic Nitrogen (mg/L) 0.12 + 0.06 0.11 + 0.10 0.10 + 0.07 Total Phosphorus (µg/L) 7 3.45 + 1.09 5.83 + 4.28 3.15 + 1.25 Chlorophyll a (µg/L) 0.4 0.39 + 0.18 1.40 + 2.00 0.33 + 0.14

Total Organic Carbon (mg/L) 7.35 + 1.88 7.59 + 1.43 8.72 + 1.33 Turbidity (NTU) 4.39 + 3.49 12.41 + 20.00 5.53 + 6.87 Central Region Total Nitrogen (mg/L) 0.99 1.40 + 0.46 1.53 + 0.51 1.41 + 0.56 0.08 + 0.10 Dissolved Inorganic Nitrogen (mg/L) 0.06 + 0.06 0.11 + 0.13 Total Phosphorus (µg/L) 19 18.83 + 14.49 26.27 + 21.25 19.83 + 14.10 Chlorophyll a (µg/L) 2.2 6.01 + 7.73 10.70 + 12.60 5.88 + 6.48 11.92 + 3.62 Total Organic Carbon (mg/L) 11.86 + 3.43 12.30 + 3.41 Turbidity (NTU) 3.64 + 2.83 8.32 + 9.40 5.10 + 4.40 Western Region Total Nitrogen (mg/L) 0.37 0.56 + 0.41 0.69 + 0.36 0.61 + 0.50 Dissolved Inorganic Nitrogen (mg/L) 0.03 + 0.03 0.08 + 0.11 0.04 + 0.07 Total Phosphorus (µg/L) 15 16.70 + 10.91 21.50 + 14.97 18.36 + 13.10 Chlorophyll a (µg/L) 1.4 3.77 + 6.02 5.35 + 6.98 5.31 + 8.58 4.76 + 2.80 Total Organic Carbon (mg/L) 5.01 + 2.89 5.23 + 1.95 Turbidity (NTU) 4.73 + 5.05 9.06 + 13.04 5.63 + 6.64 1317 a. Key to units: µg/L – micrograms per liter, mg/L - milligrams per liter, and NTU – nephelometric turbidity units. 1318 b. Section 65-302.532, F.A.C. 1319 c. Concentrations are based on the annual geometric mean and are basin specific. 1320

1321 Table 6-8. Deviation of WY2019 from the POR (WY1992–WY2018) averages. 1322 Numbers in bold red italicized are statistically different (t-test, p < 0.05).

Region TN a DIN TP Chla TOC Turbidity Eastern + 21 % - 19 % - 105 % - 53 % + 5 % - 49 % Central + 26 % - 13 % + 11 % + 52 % - 8 % - 36 % Western + 31 % + 41 % + 13 % + 64 % - 7 % - 31 % 1323

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12 Chla FBE FBC FBW 10

8

6 µg/l 4

2

0 1990 1995 2000 2005 2010 2015 2020 WY 1324

35 TP FBE FBC FBW 30 25 20

µg/l 15 10 5 0 1990 1995 2000 2005 2010 2015 2020 WY 1325

2.0 TN FBE FBC FBW

1.5

1.0 mg/l

0.5

0.0 1990 1995 2000 2005 2010 2015 2020 WY 1326 1327 Figure 6-32. Annual water year average concentrations of Chla (top), TP (middle), 1328 and TN (bottom) in the three regions of the bay for the entire POR (WY1992–WY2019).

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20 Turbidity

FBE FBC FBW 15

10 NTU

5

0 1990 1995 2000 2005 2010 2015 2020 WY 1329 1330 Figure 6-33. Annual water year average concentrations of turbidity 1331 in the three regions of the bay for the entire POR (WY1992–WY2019).

1332 In the figures that follow, the monthly spatial averages for WY2017–WY2019 of each parameter in the 1333 three regions of the bay were compared to the interquartile range of the temporal median of the monthly 1334 spatial means for the entire POR (WY1992–WY2019). The median and interquartile range were used as a 1335 comparison to the last three water years to reduce the occurrence of outliers that may skew the results. 1336 The most striking result in WY2019 was the combined impacts from Tropical Storm Gordon and 1337 Hurricane Michael, which struck the area in September and October 2018, respectively. All three regions 1338 of the bay were affected but to different degrees. As mentioned earlier, the potential effects on water quality 1339 in the bay from these storms could be due to increased precipitation, the resulting increases in freshwater 1340 flows, wind, or a combination of all. In addition, the central region of the bay, site of the SAV die-off in 1341 2015 (WY2016), has been more reactive to storm disturbance, as was evident from the impacts due to 1342 Hurricane Irma in 2017 (WY2018). Although the impacts from Tropical Storm Gordon may not be evident, 1343 this storm likely set up conditions, such as wind induced sediment resuspension, exacerbating the impacts 1344 from Hurricane Michael a month later. The impacts from these storms include the following: 1345 • Chla was near or slightly above the long-term median in all three regions at the start of 1346 the water year but was elevated well above the 75th percent quartile in the central and 1347 western regions of the bay after the passage of Hurricane Michael. The western region 1348 returned to the long-term median concentration by the end of the water year, while the 1349 central region was variable (Figure 6-34). 1350 • TP in the eastern region was below the 25th percent quartile during most of the water 1351 year and showed no impacts of the storms. In the central and western regions, TP was 1352 within the interquartile range at the start of the water year but was elevated above the 1353 75th percent quartile after the passage of Hurricane Michael. The western region 1354 returned to the long-term median concentration by the end of the water year, while the 1355 central region was variable (Figure 6-35). 1356 • TN in the eastern region was at or above the 75th quartile for the entire water year. In 1357 the central and western regions, TN was within the interquartile range at the start of 1358 the water year but was elevated well above the 75th percent quartile after the passage 1359 of Hurricane Michael. Both regions were variable at the end of the water year 1360 (Figure 6-36).

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Chl a East 25 WY17 WY18 WY19 20

15

µg/l 10

5

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1361

Chl a Central 25 WY17 WY18 WY19 20

15

µg/l 10

5

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1362

Chl a West 25 WY17 WY18 WY19 20

15

µg/l 10

5

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1363 1364 Figure 6-34. Mean Chla concentrations in the three regions of the bay during WY2017– 1365 WY2019 (solid symbols) compared to the interquartile range of monthly, spatially- 1366 averaged data for the entire POR, WY1992–WY2016 (blue bands). The legal basin 1367 standards as set forth in Section 65-302.532, F.A.C., are also shown (black dashed 1368 line). Hurricanes Irma (September 2017) and Michael (October 2018) are indicated.

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TP East 60 WY17 WY18 WY19 50

40

30 µg/l 20

10

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1369

TP Central 60 WY17 WY18 WY19 50

40

30 µg/l 20

10

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1370

TP West 60 WY17 WY18 WY19 50

40

30 µg/l 20

10

0 M J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1371 1372 Figure 6-35. Mean TP concentrations in the three regions of the bay during WY2017– 1373 WY2019 (solid symbols) compared to the interquartile range of monthly, spatially- 1374 averaged data for the entire POR, WY1992–WY2016 (blue bands). The legal basin 1375 standards as set forth in Section 65-302.532, F.A.C., are also shown (black dashed 1376 line). Hurricanes Irma (September 2017) and Michael (October2018) are indicated.

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TN East 2.5 WY17 WY18 WY19 2.0

1.5

mg/l 1.0

0.5

0.0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1377

TN Central 2.5 WY17 WY18 WY19 2.0

1.5

mg/l 1.0

0.5

0.0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1378

TN West 2.5 WY17 WY18 WY19 2.0

1.5

mg/l 1.0

0.5

0.0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1379 1380 Figure 6-36. Mean TN concentrations in the three regions of the bay during WY2017– 1381 WY2019 (solid symbols) compared to the interquartile range of monthly, spatially- 1382 averaged data for the entire POR, WY1992–WY2016 (blue bands). The legal basin 1383 standards as set forth in Section 65-302.532, F.A.C., are also shown (black dashed 1384 line). Hurricanes Irma (September 2017) and Michael (October 2018) are indicated.

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1385 • Turbidity started above the 75th quartile in the eastern region and fell back to or below 1386 the 25th quartile for most of the rest of the water year, with a jump up in January and 1387 February 2019, possibly due to wind. North winds the day before sampling this region 1388 of 10 m/s (22 mph) in January 2019, and southeast winds of 4 m/s (10 mph) for 9 days 1389 prior to sampling in February 2019 (www.nhc.noaa.gov) could have caused sediment 1390 resuspension and an increase in turbidity. In the central and western regions, turbidity 1391 was within the interquartile range for most of the water year, even after the passage of 1392 Hurricane Michael (Figure 6-37). 1393 • DIN in all three regions of the bay had a peak in DIN in June 2018, likely due to 1394 precipitation in late May 2018. Bay-wide average precipitation was 21 cm (8.1 inches), 1395 with 20 cm (7.7 inches) in the last half of the month. Another peak in January 2019 in 1396 the eastern region could be due to 4 cm (1.5 inches) of precipitation in the seven days 1397 prior to sampling. A final peak in all three regions of the bay in February 2019 could 1398 also be due to the 3 cm (1.2 inches) of precipitation in the 10 days prior to sampling 1399 (Figure 6-38). The peaks are all attributable to increases in NH4. 1400 • TOC was within the interquartile range in the eastern region for much of the water 1401 year. In the central region, TOC started the water year at or below the 25th percentile 1402 but increased after Hurricane Michael and remained within the interquartile range. The 1403 western region had a peak in TOC after Hurricane Michael but was otherwise within 1404 the interquartile range (Figure 6-39). 1405

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Turbidity East 60 WY17 WY18 WY19 50

40

30 NTU 20

10

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1406

Turbidity Central 60 WY17 WY18 WY19 50

40

30 NTU 20

10

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1407

Turbidity West 60 WY17 WY18 WY19 50

40

30 NTU 20

10

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1408 1409 Figure 6-37. Mean turbidity concentrations in the three regions of the bay during 1410 WY2017–WY2019 (solid symbols) compared to the interquartile range of monthly, 1411 spatially-averaged data for the entire POR, WY1992–WY2016 (blue bands). Hurricanes 1412 Irma (September 2017) and Michael (October 2018) are indicated.

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DIN East 0.4 WY17 WY18 WY19

0.3

0.2 mg/l

0.1

0.0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1413

DIN Central 0.4 WY17 WY18 WY19

0.3

0.2 mg/l

0.1

0.0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1414

DIN West 0.4 WY17 WY18 WY19

0.3

0.2 mg/l

0.1

0.0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1415 1416 Figure 6-38. Mean DIN concentrations in the three regions of the bay during WY2017– 1417 WY2019 (solid symbols) compared to the interquartile range of monthly, spatially-averaged 1418 data for the entire POR, WY1992–WY2016 (blue bands). Hurricanes Irma (September 1419 2017) and Michael (October 2018) are indicated.

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TOC East 20 WY17 WY18 WY19

15

10 mg/l

5

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1420

TOC Central 20 WY17 WY18 WY19

15

10 mg/l

5

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1421

TOC West 20 WY17 WY18 WY19

15

10 mg/l

5

0 M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A 1422 1423 Figure 6-39. Mean TOC concentrations in the three regions of the bay during WY2017–WY2019 (solid 1424 symbols) compared to the interquartile range of monthly, spatially-averaged data for the entire POR, 1425 WY1992–WY2016 (blue bands). Hurricanes Irma (9/2017) and Michael (10/2018) are indicated.

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1426 Discussion 1427 As mentioned earlier, there are many factors that influence the nutrient and Chla concentrations and 1428 variation in Florida Bay. In the 2019 SFER, we reported on the impacts of Hurricane Irma in September 1429 2017 (Chapter 6 of the 2019 SFER – Volume I, Sklar 2019). These impacts included increases in Chla and 1430 most nutrients in all three regions of the bay. The latter half of WY2018 showed most of these constituents 1431 returning to their long-term median concentrations. This trend continued during the start of WY2019 until 1432 Tropical Storm Gordon and Hurricane Michael impacted the area in September and October 2018. The 1433 potential effects on water quality in the bay from these storms could be due to increased precipitation, the 1434 resulting increases in freshwater flows, wind, or a combination of all. In some instances, the impacts from 1435 Tropical Storm Gordon and Hurricane Michael were not as dramatic as Hurricane Irma a year earlier 1436 because it was a much more powerful storm with 33 m/s (75 mph) winds and 25 cm (10 inches) of 1437 precipitation in Key Largo, Florida (Cangialosi et al. 2018). 1438 Results presented above show immediate impacts of these storms in most constituents and some or all 1439 three regions of the bay, especially the central region of the bay, site of the SAV die-off in 2015 (WY2016). 1440 This region has been more reactive to storm disturbance, as is evident from the impacts due to Hurricane 1441 Irma in 2017 (WY2018). Although the impacts from Tropical Storm Gordon may not be evident, this storm 1442 likely set up conditions such as wind induced sediment resuspension exacerbating the impacts from 1443 Hurricane Michael a month later. In addition to storm disturbance, impacts from other events is further 1444 evidence that water quality in the bay is subject to environmental conditions occurring in the bay. For 1445 instance, the peaks in turbidity in January and February 2019 in the eastern region are likely due to wind 1446 events, while peaks in DIN in all three regions of the bay in June 2018 and February 2019 are likely due to 1447 higher precipitation during the days prior to sampling. Both wind and precipitation effects may be due to 1448 the shallow nature of the bay and the presence of numerous mud banks that restrict circulation.

1449 Relevance to Water Management 1450 Water management in the southern Everglades area that feeds Florida Bay is focused on operational 1451 changes to the South Dade Conveyance System. These changes are designed to increase freshwater flows 1452 to the bay and to lessen the frequency, duration, and extent of elevated salinity levels in the bay. The 1453 implementation of the C-111 South Dade Project, Modified Water Deliveries to ENP, C-111 Spreader 1454 Canal Western Project, and the newly implemented Florida Bay Improvement Project in the upper Taylor 1455 Slough area are expected to improve freshwater flow patterns in the southern Everglades and Florida Bay. 1456 However, water management in these areas are dependent on the availability of fresh water in the system 1457 and the ability to move that water south towards Florida Bay. The localized drought in the area during 2014 1458 and 2015 likely lead to the SAV die-off event in July 2015. The newly implemented Florida Bay 1459 Improvement Project was developed in direct response to this event and is intended to send additional fresh 1460 water into Taylor Slough and ultimately Florida Bay. 1461 During WY2019, Tropical Storm Gordon and Hurricane Michael impacted the water quality in Florida 1462 Bay. Storms and other smaller events can also impact the bay’s water quality. These events can result in 1463 nutrient inputs directly from the precipitation and indirectly from overland flows into the bay. These 1464 flows are restricted to a few creeks in the eastern and central region and are dependent on the amount of 1465 fresh water available in the coastal marsh and mangrove zones. The modifications to the South Dade 1466 Conveyance System are expected to increase freshwater flows to the bay with the goal of lowering the 1467 salinity in the bay, especially the nearshore coastal region.

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1468 TOTAL NITROGEN IN THE FLORIDA BAY 1469 WATER QUALITY MONITORING NETWORK

1470 Stephen P. Kelly

1471 Introduction 1472 The District has been collecting water quality samples in Florida Bay and the southwest coast since 1473 1991 as part of the Coastal Water Quality Monitoring Network (CWQMN). Analyses performed on the 1474 samples include physical parameters, Chla, and dissolved and total nutrients. Up through September 2008 1475 (WY2009), analyses were performed at the Southeast Environmental Research Center (SERC) at Florida 1476 International University. In January 2009, the SFWMD Chemistry Laboratory began conducting these 1477 analyses. During the transition, a new contract to perform the sampling had to be generated resulting in a 1478 3-month gap in data collection between October and December 2008. 1479 One of the analytes measured for the CWQMN is TN. A discrepancy in the long-term POR, an 1480 approximately 30% increase in TN in Florida Bay in 2009, was discovered when water quality was 1481 examined for influences on SAV communities in the bay for the Fisheries Habitat Assessment Program 1482 (M. Durako, University of at Wilmington, personal communication, December 2015). 1483 Because we are documenting trends over time, it is important to document and understand the causes for 1484 any abrupt and/or unexpected changes in the long-term POR. This increase coincided with the time of the 1485 analytical lab change. This had not immediately been noticed as the concentrations were within the long- 1486 term range of TN and our analysis of the annual trends for the SFER are based on water years, not calendar 1487 years. This section is intended to document our efforts to investigate this shift and potentially explain it.

1488 Methods 1489 We used three different approaches as part of our assessment: (1) analysis of the TN values for the 1490 entire POR and for all sites in Florida Bay (Figure 6-40), (2) analytical methodology comparison using 1491 standards and natural samples, and (3) an independent analysis using a different data set.

1492 Period of Record Evaluation 1493 For this evaluation, all Florida Bay sites were analyzed together. Additional analyses were conducted 1494 dividing the sites into four subregions based on zones of similar water quality: FB East, FB Central, FB 1495 West, and FB South as shown in Figure 6-40 (Boyer and Briceno 2008). In this investigation, as with the 1496 SFER analysis, results are based on water years (May 1 to April 30 of the subsequent year) that coincide 1497 with annual hydrologic cycles. Note that the WY2009 results were split into two parts, the first includes 1498 five months (May–September) of SERC results; the second includes four months (January–April) of 1499 SFWMD results.

1500 Analytical Methodology Comparisons 1501 The SFWMD Chemistry Laboratory uses a persulfate oxidation method that converts all forms of 1502 nitrogen to nitrate nitrogen (NO3-N), cadmium reduction to nitrite nitrogen (NO2-N), and detection of the 1503 resulting NO2-N using wet chemical techniques (Standard Method 4500-N C; Standard Methods Online 1504 2005). The SERC lab used a high temperature, dry combustion method that converts all nitrogen 1505 compounds to nitric oxide, which is then measured with chemiluminescent detection (Frankovich and Jones 1506 1998). Walsh (1989) found the methods were directly comparable so an intercalibration between the two 1507 labs and the two methods was not done. This section documents the overall findings to determine if the 1508 discrepancy is the result of natural variability or the result of any analytical methodological differences.

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1509 1510 Figure 6-40. CWQMN Florida Bay subregions. Sites with 1511 stars are the FHAP SAV sites used by Durako (personal communication).

1512 An intercalibration was undertaken in CY2016–CY2017 to determine if analytical differences did exist. 1513 To account for any differences in the digestion and/or analytical process caused by salinity interference, 1514 nitrogen standards were prepared using acetanilide (CH3CONHC6H5), a stable form of organic nitrogen, in 1515 water with two different salinities: (1) deionized water and (2) artificial seawater solution with a salinity of 1516 36. In addition, split natural samples collected during three separate mapping trips in Florida Bay during 1517 2016 and 2017 were analyzed by both labs to determine if there were differences between methods and labs.

1518 Independent Data Set Analysis 1519 As an independent check of the results, a different data set was analyzed for similar trends in TN values. 1520 This data set was produced from samples collected as part of the C-111 Spreader Canal Project from the 1521 creeks that feed into Florida Bay from the southern Everglades. The data set consisted of two subsets: (1) 1522 Trout Creek and Joe Bay in the east, and McCormick Creek in the central bay with TN from these three 1523 sites analyzed at SERC until 2015 when the samples the SFWMD Chemistry Laboratory, and (2) Taylor 1524 Creek and Argile Hendry, also collected as part of the C-111 Spreader Canal Project, but analyzed for TN 1525 at SERC during the entire POR.`

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1526 Results and Discussion

1527 Period of Record Evaluation 1528 TN annual water year averages of all sites in Florida Bay are shown in Figure 6-41. In this and 1529 subsequent figures, data are presented as box-and-whisker plots showing the median (line in center of the 1530 box), the mean (x in the box), the middle quartile (the ends of the box) and the upper and lower quartile 1531 (the whiskers). Figure 6-41 includes separating the WY2009 data discussed above. Within the data set, 1532 there are multiple instances of fairly large (> 20%) annual change in TN concentrations (Figure 6-41). 1533 Taking this into account, the change in annual averages during the period of interest for this investigation 1534 (WY2009-WY2010) was not unprecedented and continued to increase after the lab change (Figure 6-4).

1535 1536 Figure 6-41. TN annual water year averages for all sites in Florida Bay. Statistically significant 1537 differences are in boxes (t-test, p<0.05). Note that sampling was monthly until WY2012, bi-monthly 1538 from WY2012 to 2016, and back to monthly in WY2017 at a subset of sites.

1539 The causes for these abrupt changes could be due to natural variability or water management changes 1540 to the headwaters of Florida Bay including structural, operational, and restoration changes to the South 1541 Dade Conveyance System (Table 6-9). Precipitation over Florida Bay is a significant source of fresh water 1542 to the bay, averaging 98 centimeters per year (cm/yr) (Nuttle et al. 2000). Swart and Price (2002) report the 1543 proportion of fresh water from precipitation ranges from < 10% in the eastern region to > 80% in the FB 1544 Central and FB West regions. However, analysis of annual and seasonal (wet versus dry) precipitation 1545 collected by ENP at 17 permanent platforms in Florida Bay as part of their Marine Monitoring Network 1546 and TN concentration yielded no statistically significant correlations (Figure 6-42).

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1547 Table 6-9. Timeline of natural and management impacts affecting 1548 Florida Bay (bolded text is the WY2009 period of interest).

Timeframe Event October 1999 , high water 1999 S-332D operational April 2000 S-332B operational June 2002 S-332C operational 2005–2009 District-wide drought June 2008 25 cm precipitation in ENP July 2008 15 cm precipitation in ENP Tropical Storm Fay, 18 to 28 cm August 2008 precipitation in Monroe County Hurricane Gustov, Hurricane Hanna, September 2008 and October–December 2008 Sampling suspended Sampling restarted and SFWMD January 2009 starts performing analysis Drought in the southern Everglades, November 2008–May 2009 33-cm deficit Minimum flows and minimum water 2008–2009 levels (MFL) salinity threshold exceeded during the dry season June 2012 C-111 Spreader Canal operational 2011 S-199 and S-200 operational May 2014–August 2015 Drought in the southern Everglades July 2015 Florida Bay SAV die-off begins February–May 2016 High water emergency June 2017–March 2018 High water emergency 2017 Florida Bay Project completed June–November 2018 High water emergency

Florida Bay - All Sites 1.2

1.0

0.8

0.6

TN (mg/L) 0.4

0.2 y = 0.1791x + 0.6005 R² = 0.0303 0.0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Precipitation (cm) 1549 1550 Figure 6-42. Annual water year averages of TN versus precipitation for all sites in Florida Bay.

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1551 A largely uncertain source of TN to the bay is groundwater and it has been reported that groundwater 1552 may contribute 70% of TN inputs baywide (Rudnick 2010, FDEP, 2011). In addition, sea level rise may 1553 also alter the groundwater influence on surface water TN in the bay due to saltwater intrusion (Price et. al. 1554 2006, Wilson et. al. 2019). 1555 Results from the analyses by subregion are shown in Table 6-10 and Figure 6-43. Fairly large changes 1556 (> 20%) of TN between water years in the four subregions occur quite often but are variable (Table 6-10). 1557 The greatest number of significant changes were in FB West (10), followed by FB Central (7), FB South (6), 1558 and FB East (5) over the 27 water years of the POR. The higher variability of the western sites is not 1559 surprising as the Gulf of Mexico, one of the largest contributors of TN to the bay, has the most impacts on 1560 these sites (Rudnick et. al. 1999). Conversely, the lower variability of the eastern sites is likely due to a lack 1561 of connection to the Gulf of Mexico and is mostly impacted by freshwater flow and atmospheric inputs.

1562 Table 6-10. Percentage change (> 20%) of TN between water years in the four subregions of Florida 1563 Bay during the 27-year POR. Statistically significant differences are in bold italic text (t-test, 1564 p < 0.05). Elevated changes not significantly different are due to high variability in the data. Water Year FB East FB Central FB West FB South 1992 1993 1994 -21% 1995 +33% 1996 -26% 1997 +28% 1998 -29% 1999 2000 2001 -20% -25% 2002 -29% -32% -33% -35% 2003 +49% +40% +68% +63% 2004 2005 -31% -38% -37% 2006 2007 +25% 2008 2009 +28% -32% 2010 +22% +33% +51% 2011 2012 2013 2014 2015 +34% 2016 +31% +39% 2017 2018 +23% 1565

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1566

1567

1568

1569 1570 Figure 6-43. TN water year averages for the four subregions in Florida Bay. 1571 Note that sampling was monthly until WY2012, bimonthly from WY2012 to 1572 WY2016, and back to monthly in WY2017 at a subset of sites.

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1573 Curiously, all four subregions showed a drop in TN between WY2001 and WY2002, and an increase 1574 between WY2002 and WY2003 back to WY2001 levels (Figure 6-43). This may be due to operational 1575 changes to the South Dade Conveyance System as this included the time when the final of the three S-332 1576 (S-332D, S-332B, and S-332C) pump stations came online (Kotun and Renshaw 2014, Lorenz and Kotun 1577 2014). The S-332 pump stations were constructed and are operated to increase flows and hydroperiods and 1578 decrease seepage losses in upper Taylor Slough. In addition, the detention area system meant to receive the 1579 discharges from the S-332 pump stations was completed in early CY2009 (Lorenz and Kotun 2014) and 1580 would have been fully operational during the period of interest for this investigation (WY2009-WY2010). 1581 These changes in annual averages during this timeframe were not unprecedented and occurred in three of 1582 the four regions (Table 6-10). Analysis of annual and seasonal (wet versus dry) precipitation collected by 1583 ENP staff and TN concentration in all four subregions of the bay yielded no statistically significant 1584 correlations (data not shown). In addition, creek flows collected by USGS in the eastern and central regions 1585 (5 creeks) also yielded no significant correlations to TN (data not shown).

1586 Analytical Methodology Comparisons 1587 The SERC analysis of the District-prepared standards did not reveal any systematic bias or salinity 1588 effects in the SERC analytical method (Figure 6-44). The direct analytical comparison between the 1589 SFWMD and SERC labs used samples collected in Florida Bay during 2016 and 2017 that were split and 1590 analyzed at both labs. These samples were analyzed for both TN and total dissolved nitrogen (TDN) at the 1591 SFWMD lab but only for TDN at the SERC lab. The SFWMD results revealed that on average 84% of the 1592 TN is TDN (median of 90%, sample size [n] = 40). A more robust data set from Florida Bay covering 2007– 1593 2013 confirms that 91% (+10%, n = 203) of the TN is TDN. This is important as any differences could be 1594 due to the presence of particulate matter. Additionally, these results do not correlate with other analytes that 1595 include the particulate fraction: total suspended solids (TSS), total organic carbon (TOC), and Chla. There 1596 is a slight systematic error of approximately 10%, with the SERC results being lower (Figure 6-45), 1597 however, this does not account for the much larger errors of > 20% presented previously.

1598 1599 Figure 6-44. TN results using SFWMD standards analyzed at SERC.

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1600 1601 Figure 6-45. TDN results analyzed at both SERC and SFWMD labs. 1602 The black line is the 1:1 line.

1603 Independent Data Set Analysis 1604 All CWQMN TN results that were analyzed based on water years, were also analyzed based on calendar 1605 years to aid in comparisons and trend analysis with the data approach being used when the discrepancy was 1606 identified. The Florida Bay results plotted by calendar year are shown in Figure 6-46.

1607 1608 Figure 6-46. Average TN concentration plotted by calendar year from all 1609 Florida Bay monitoring sites. Note that sampling was monthly until 2012, bi- 1610 monthly from 2012 to 2016, and back to monthly in 2017 at a subset of sites.

1611 The results from the independent data set of the creek sites—Joe Bay, Trout Creek, and McCormick 1612 Creek—are shown in Figure 6-47 and Taylor Creek and Argile Hendry are in Figure 6-48. The analysis 1613 includes the original period of interest for this investigation during CY2008–CY2009 and an additional 1614 period of interest during CY2014–CY2015, which is the timeframe when analysis of samples collected at 1615 the creek sites changed from SERC to SFWMD.

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1616 1617 Figure 6-47. Average TN concentration plotted by calendar year from Joe Bay (top), 1618 Trout Creek (middle), and McCormick Creek (bottom). These analyses were done at 1619 the SERC lab until 2015 after which they were analyzed at the SFWMD lab.

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1620 1621 Figure 6-48. Average TN concentration plotted by calendar year from Argile Hendry (top) 1622 and Taylor Creek (bottom). These analyses were done at the SERC lab for the entire POR.

1623 Results from both the CWQMN and the C-111 Spreader Canal project, which were collected and 1624 analyzed independently, show similar trends during the CY2008–CY2009 period. Results during the 1625 CY2014–CY2015 period were variable between the Argile Hendry and Taylor Mouth sites and the 1626 differences between WY2014 and WY2015 were much lower than those between Joe Bay, Trout Creek, 1627 and McCormick Creek. This could indicate natural variability and effects of water management changes 1628 upstream of Florida Bay. The magnitude of these trends may differ because of location of the sites. Possible 1629 causes of the discrepancy are summarized in the following bulleted list. 1630 • Natural variability may include the following:

1631 - Cold fronts, hurricanes, and tropical storms can alter atmospheric deposition of TN, 1632 increase freshwater inputs to the bay, and cause sediment resuspension.

1633 - Changes in the Gulf of Mexico circulation patterns have been reported by Rudnick et 1634 al. (1999) as the largest source of TN to the bay

1635 - Changing rainfall patterns, including drought due to El Niño, La Niña, and climate 1636 change can alter atmospheric deposition and freshwater inputs of TN to the bay.

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1637 - Sea level rise may also alter the groundwater influence on surface water TN in the bay 1638 due to saltwater intrusion.

1639 - In addition, the SAV die-off in the central bay which occurred during WY2016, could 1640 have elevated the TN concentrations seen during that water year due to nutrient 1641 releases from decaying vegetation. 1642 • Water management changes to the headwaters of Florida Bay may include structural, 1643 operational and restoration changes to the South Dade Conveyance System and may 1644 include the following:

1645 - Construction of the S-332 pump stations and detention areas (1999–2002) 1646 - Modified water deliveries to ENP which began in 1983. 1647 - Construction of the S-199 and S-200 pump stations (2011). 1648 - Beginning operations of the C-111 Spreader Canal project (2012). 1649 - Completion of components of the Florida Bay Project (2017). 1650 - Changes to the operation of the system due to high water emergencies (February–May 1651 2016, June 2017–March 2018, and June–November 2018) 1652 Relevance to Water Management 1653 Results of this investigation do not conclusively reveal that a difference in analytical methodology 1654 caused the abrupt increase in TN seen between CY2008 and CY2009, but instead the results provide 1655 evidence against this conclusion. In addition, taking the entire 27-year POR into account, there were 1656 numerous abrupt changes in TN between water years despite no changes in methodology. There were both 1657 positive and negative changes to the TN concentrations between water years that are likely due to both 1658 natural variability and water management changes to the headwaters of Florida Bay. An additional 1659 component of the TN inputs to Florida Bay is groundwater. This is not well documented, but baywide 1660 estimates of 70% may have significant impacts on the TN concentrations (Rudnick 2010, FDEP 2011). 1661 Given the complex interactions between water quality and environmental conditions, reducing 1662 measurement uncertainty is key to assessing trends. It is essential that changes to any analytical method, 1663 including lab changes, should include an intercalibration that documents any potential significant 1664 differences due to these changes.

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1665 UPPER TAYLOR SLOUGH ADAPTIVE MANAGEMENT

1666 Amanda McDonald, Peter Flood4, Evelyn Gaiser3, Franco Tobias3, and Joel Trexler3

1667 Introduction 1668 In an effort to increase freshwater flows towards Florida Bay to enhance and accelerate restoration 1669 benefits after the seagrass die-off in 2015, alterations to the South Dade Conveyance System infrastructure 1670 and operations were initiated, some highlights of which are adding pumps to S-200 and S-199 to increase 1671 capacity, operating S-328 in the S-332D detention area, and building G-737 to connect the Frog Pond 1672 Detention Area westward to the L-31W canal (Sending Water South to Florida Bay, 1673 https://www.sfwmd.gov/our-work/florida-bay; see Figure 6-49 for structure locations). Increasing water 1674 flows into Taylor Slough in ENP was anticipated to cause a change in nutrient loading into the park and 1675 potentially impact the communities within the park boundary even while improving hydrologic conditions 1676 downstream. To address these concerns, an adaptive management plan was implemented in 2017 between 1677 the District and ENP to monitor potential changes and facilitate discussions about needed improvements to 1678 management of the system to enhance the benefits of southwestward water movement while minimizing 1679 potential damage to the ecological communities immediately southwest of the S-332D detention area. 1680 As part of the plan, scientists from multiple agencies (i.e., FDEP, National Park Service, SFWMD, and 1681 Florida Department of Agriculture and Consumer Services) and Florida International University have met 1682 after each of the last two water years (WY2018 and WY2019) to discuss the status of the ecosystem and 1683 any detectable changes. Data from existing monitoring within the area have been leveraged to provide 1684 information about the local system prior to these most recent changes in management. New water quality, 1685 periphyton, macrophyte, fish, and invertebrate sampling was initiated in 2017 immediately downstream of 1686 the water management structures in Upper Taylor Slough (UTS) as part of the adaptive management plan 1687 with ENP funding the fish and invertebrate monitoring and the District funding the water quality, 1688 periphyton, and macrophyte monitoring. 1689 This information is also being reported in support of FDEP Permit 0293559-012 for the C-111 Spreader 1690 Canal Western Project. The annual permit report for the project is Appendix 2-4 in Volume III of this report.

1691 Methods

1692 Study Area 1693 The study area was established immediately southwest of the S-332D detention area (Figure 6-49) and 1694 focused only on the area in the immediate vicinity of the water inflows and seepage barriers. A set of 1695 10 sampling locations were established (UTS 1 through 10) with an additional 2 stations outside the central 1696 flow region of Taylor Slough established as reference locations (UTS-S1 and UTS-S2). At each location, 1697 water quality, periphyton, macrophytes, fish, and macroinvertebrates are sampled when the sites are 1698 hydrated and have a depth of at least 5 cm. Hydroperiods were estimated using daily depth estimates for 1699 each site from the Everglades Depth Estimation Network (EDEN; http://sofia.usgs.gov/projects/eden/).

4 Florida International University, Miami, Florida.

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1700 1701 Figure 6-49. Locations of the newly established Upper Taylor Slough monitoring stations (yellow 1702 dots), the long-term ecological monitoring stations (blue dots), and the neighboring water 1703 management structures (white dots). There is another long-term ecological monitoring station 1704 (TS/PH-3) which is not shown but is located south of this region.

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1705 Water Quality 1706 Water quality grab samples were collected at each site during every site visit by the periphyton and 1707 vegetation team. These samples are analyzed for TN, TP, TOC, calcium, magnesium, potassium, sodium, 1708 chloride, sulfate, and alkalinity. In-situ water measurements include temperature, pH, specific conductance, 1709 salinity, total water depth, and sample collection depth. In addition to water samples from the marsh, water 1710 samples in the canal west of the S-328 and G-737 structures are collected monthly during the periphyton 1711 sampling trips and water quality samples are collected at each structure weekly by as part of the District’s 1712 water quality monitoring program.

1713 Periphyton 1714 Two types of periphyton sampling are conducted. Benthic periphyton mats and macrophyte samples 1715 are collected twice a year to assess the abundance, species composition, and nutrient composition. Benthic 1716 mats are collected at the beginning and end of the wet sampling season, while macrophytes are collected 1717 during September–October (growing season) and January–February (dormant season). To measure biomass 1718 accrual rates of periphyton, standardized substrates (periphytometers) are placed in the marsh. 1719 Periphytometers consist of 20 glass microscope slides suspended in floating Plexiglas boxes tethered to a 1720 PVC pipe driven into the soil at each site. Periphytometers were deployed every two months at the 12 marsh 1721 sites with 6 sites being visited every month (odd sites one month and then even sites the next month). 1722 Processing and analysis of the periphyton samples followed the protocols detailed in Gaiser et al. (2006, 1723 2015). The periphytometer collects only new accumulation while the benthic mat is also influenced by more 1724 historic conditions.

1725 Fish and Macroinvertebrates 1726 Fish and macroinvertebrates at the 12 sites were sampled bimonthly when water depths exceeded 5 cm. 1727 Based upon previous experience with sampling in ENP, seven throw-trap samples were collected per site 1728 during each sampling period for the sites that were accessible on foot: Sites 1, 2, 4, 6, 8, and 9. Sites 3, 5, 1729 7, 10, S1, and S2 are only accessible by helicopter, and to maximize resources, five throw-trap samples 1730 were collected at each site during a sampling period; this protocol is the same as those used by this group 1731 in similar monitoring for the Modified Water Deliveries to ENP project, which is a related restoration 1732 project in northeastern ENP, Fish and macroinvertebrate community structure (species relative abundance) 1733 and species richness were assessed for each site and the UTS sites were compared to long-term monitoring 1734 data in other areas of ENP including further downstream in Taylor Slough and the ENP Panhandle region, 1735 which is south of the west-to-east stretch of the C-111 canal just north of S-197 and east of Taylor Slough.

1736 Results

1737 Hydrology 1738 Average hydroperiod over the past 10 years for the UTS sites as estimated using EDEN data ranged 1739 from 129 days at UTS-1 to 228 days at site UTS-S1. This yields a sampling period roughly from July to 1740 January on average. WY2018 was a relatively wet year where water depths allowed sampling of periphyton 1741 from July and August 2017 through March and April 2018. WY2019 was drier and behaved more like an 1742 average year with sampling able to start in July and August 2018 and all sites were dry by the end of 1743 January 2019.

1744 Periphyton 1745 Periphyton mat ash-free dry biomass (AFDM) ranged from 50 to 400 grams per square meter (g/m2) 1746 across sites and sampling events and were highly calcareous, containing only 20 to 60% organic matter. 1747 Periphyton accumulation rates ranged from 0.01 to 0.6 grams per square meter per day (g/m2/d) and these 1748 communities were less calcareous than the mats, containing 30 to 100% organic matter (Figure 6-50). It is

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1749 notable that the 3 sites that are directly west and southwest of the old S-332 structure where water was 1750 released as a point source into ENP prior to 2000 (UTS-8,9, and 10) have the highest benthic mat TP and 1751 the lowest AFDM.

1752 1753 Figure 6-50. Dry weight, organic content and TP content of periphyton from the periphytometers (left 1754 side) and from the benthic mat samples (right side). Yellow bars are for the UTS sites 1 through 10. 1755 Red and dark red are the control sites of UTS-S1 and UTS-S1. Blue bars are for the long-term 1756 ecological sites where TS/Ph 1 is the closest to the S-332D detention area and TS/Ph 3 is further 1757 south than the area shown in Figure 6-49 and not visible on the map.

1758 Fish and Macroinvertebrates 1759 Average fish density at each of the sites from December 2017 through December 2018 ranged from 1760 1.05 fish per square meter (/m2) at UTS-7 to 4.32 fish/m2 at UTS-2 (Figure 6-51). The highest fish densities 1761 tended to be the sites closer to the canal. This pattern held true for the invertebrate density (data not shown). 1762 When compared to the neighboring regions, UTS had lower average density of fish (2.84 fish/m2) during 1763 this period than further down in Taylor Slough (7.56 fish/m2) but higher than the ENP panhandle area 1764 (1.17 fish/m2). When looking at invertebrate density, UTS (4.1 invertebrates/m2) was lower than both lower 1765 Taylor Slough (13.97 invertebrates/m2) and the ENP panhandle (4.75 invertebrates/m2).

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1766 1767 Figure 6-51. Average fish density by site over from December 2017 through December 2018. Sampling only 1768 occurred when water depths were greater than 5 cm. Circles on the map correspond to the colored density categories 1769 (number of fishes/m2) for the UTS sampling sites to illustrate the spatial locations of each density category.

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1770 Discussion 1771 The project has only been online for 2 years and during this time period, components of the water 1772 management infrastructure were being repaired, upgraded, or built. Each of the S-332s (B, C, and D) were 1773 taken offline during portions of the wet season in each of the last two years for construction activities. In 1774 addition, both years that this project was active saw emergency orders being issued due to high water levels 1775 in WCA-3A, which altered expected operations for the South-Dade Conveyance System. Hurricane Irma 1776 in 2017 delivered a large amount of fresh water that helped maintain elevated water levels into April 2018 1777 when UTS would typically dry out in January or February. 1778 Despite the disturbances, we were able to characterize the UTS area with regards to the vegetation and 1779 faunal communities that currently exist to create a baseline from which to gauge future changes. We can 1780 group the sites into four categories based on hydroperiods and vegetation: 1781 • Sites UTS 1, 2, 4 and 10: Short hydroperiod, shallow, sawgrass marshes that are highly 1782 oligotrophic and have thick, prolific periphyton mats. 1783 • Sites UTS 6, 9 and S1: Short hydroperiod, deeper water, spikerush (Eleocharis spp.) 1784 marshes with sparse periphyton mats with higher phosphorus concentrations. 1785 • Sites UTS 2, 7, and S2: Very short hydroperiod, muhly grass (Muhlenbergia spp.) 1786 marshes, with shallower water and oligotrophic periphyton mats. 1787 • Sites UTS 5 and 8: Short hydroperiod, beak-rush (Rhynchospora spp.) marshes with 1788 shallow water and oligotrophic periphyton mats. 1789 While other study areas in ENP (Shark River Slough, Lower Taylor Slough, and the ENP panhandle) 1790 have fish communities dominated by bluefin killifish (Lucania goodei), UTS is dominated by eastern 1791 mosquitofish (Gambusia holbrooki), most likely as a result of their rapid recolonization following rewetting 1792 of the marsh. It is possible that increases to the UTS hydroperiod will cause a shift in the community 1793 composition to be more similar to the other areas of ENP. However, increases to the hydroperiod on the 1794 marsh and increased connectivity with the canal system may also allow non-native fishes such as Mayan 1795 cichlids (Cichlasoma urophthalmus) to expand further into this region. Continued monitoring within this 1796 region will be needed to assess this. 1797 Relevance to Water Management 1798 The data gathered through this initiative will provide insight into the potentially shifting zone of influence 1799 of water management within eastern ENP as well as provide feedback for water management decision 1800 making. Already, we have been able to document dry season rises in TP concentrations (as high as 82 1801 µg/L) upstream of the culverts in the detention areas when conditions are dry and the subsequent decrease 1802 of those TP concentrations when water depths increase again after water deliveries begin through the S- 1803 332D and S-200 structures. We can use this knowledge to predict when the western-most structures can 1804 be opened to prevent high levels of TP (greater than 10 µg/L) from reaching ENP.

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1805 LANDSCAPE ECOLOGY

1806 SYNOPTIC ECOLOGICAL MAPPING IN FLORIDA BAY

1807 Theresa Strazisar, Christopher J. Madden, Stephen Kelly, and Michelle Blaha

1808 Introduction 1809 The District monitors systemwide conditions in Florida Bay to detect environmental trends and to 1810 document the effects of water management and restoration activities on bay ecology. Surface water quality 1811 mapping of salinity, Chla and other photosynthetic pigments, turbidity, colored dissolved organic matter 1812 (CDOM), temperature, and dissolved oxygen has been implemented in Florida Bay for over 20 years. 1813 Through spatial data interpolations from quarterly and event-driven high-resolution sampling, the Dataflow 1814 program has produced a long-term data set encompassing 1996 to June 2020. This program allows us to 1815 quantify and monitor Florida Bay water quality conditions, examine changes to the ecosystem, and evaluate 1816 restoration efforts, which benefit projects such as the C-111 Spreader Canal Western Features Project 1817 Phase I, the Florida Bay MFL, and the Upper Taylor Slough Project. 1818 In this section, we present findings from quarterly baywide surveys in WY2018 (August and October 1819 2017 and February 2018), WY2019 (June and October 2018 and February 2019), and the beginning of 1820 WY2020 (June 2019). These findings include overall trends and responses of conditions in Florida Bay to 1821 a series of disturbance events: Hurricane Irma (September 2017; WY2018), Tropical Storm Gordon 1822 (September 2018; WY2019), and Hurricane Michael (October 2018; WY2019). While effects of Hurricane 1823 Irma and their relationship with the previous SAV die-off event in the western Dataflow study area were 1824 already discussed in SFERs (Chapter 6 of the 2018 and 2019 SFERs – Volume I; Sklar and Dreschel 2018, 1825 Sklar 2019), impacts of the event are included for comparison to the storms in WY2019.

1826 Methods 1827 The Dataflow mapping system is a portable, self-contained flow-through instrument containing sensors 1828 for high speed real-time measurements of water quality. From the moving boat, a continuous stream of 1829 water is pumped from the water body though a fixed ram and passed to a series water quality probes. Probes 1830 measure salinity, temperature, specific conductivity, turbidity, pH, and DO, CDOM, Chla, and phycocyanin 1831 (blue photosynthetic) and phycoerythrin (red photosynthetic) pigment fluorescence. Data values are 1832 continuously logged every 5 seconds and georeferenced with a global positioning system (GPS) to enable 1833 two-dimensional spatial water quality pattern maps. Continuous map surfaces for each parameter are 1834 created using inverse path distance weighting interpolation from the collected data points (Madden and Day 1835 1992, Stachelek and Madden 2015). 1836 Chla concentrations are extracted from grab samples collected at locations in 16 basins across the bay 1837 (Figure 6-52). Baywide chlorophyll concentrations are then interpolated using regression relationships 1838 between extracted Chla values and recorded fluorescence parameters (Sklar 2019, Stachelek et al. in prep). 1839 Spatial patterns in Chla were further examined with a spatial pattern analysis that identified statistically 1840 significant clusters using the ArcMap 10.3 Hot Spot Analysis tool to calculate the Getis-Ord Gi* statistic 1841 with false discovery rate corrected p-values to adjust for spatial dependency. This analysis uses z-scores to 1842 characterize direction and magnitude of statistically significant clusters, with positive scores indicating 1843 elevated Chla and negative indicating lower concentrations and greater intensity with higher values.

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1844 1845 Figure 6-52. Major basins in Florida Bay where surface water quality conditions are measured 1846 (abbreviations at right, west to east). In basins, red circles indicate locations of 16 in-situ grab 1847 samples collected each survey. Also shown are the C-111 canal and location of the S-197 structure.

1848 Results 1849 Dataflow mapping is a key tool to examine spatial and temporal freshwater inputs, salinity distributions, 1850 and Chla concentrations, indicative of algal blooms, in the Florida Bay estuary. This tool captured periods 1851 of hypersalinity in WY2018 and WY2020, freshwater inputs from C-111 canal operations in WY2018, and 1852 effects from disturbances caused by Hurricane Irma, Tropical Storm Gordon, and Hurricane Michael. 1853 Patterns in salinity and Chla were evident throughout the regions of distinct basins created by the complex 1854 geography of shoals and banks.

1855 Salinity 1856 The salinities in the basins across the Florida Bay study region differed spatially in a given survey by 1857 as much as 40, consistently exhibiting the distinct compartmentalization of this estuary. Prior to Hurricane 1858 Irma in WY2018, hypersaline conditions existed across much of the bay (> 70%; Figure 6-53) and reached 1859 47 in Whipray Basin in August 2017 (Sklar and Dreschel 2018, Sklar 2019). Despite higher than historical 1860 average salinities (70th percentile; see the Florida Bay Hydrology section), influence of the rainy season 1861 that began in June 2017 was visible in the salinity gradient that developed in the northern transition zone 1862 and nearshore areas. The WY2018 rainy season began with total precipitation of approximately 8 inches 1863 throughout June 2017, necessitating emergency water releases from the C-111 canal through the S-197 1864 structure to provide upstream flood protection. By August 2017, salinity at the mouth of the C-111 canal in 1865 Manatee Bay was low (7) relative to the hypersaline conditions in the eastern and central zones of the main 1866 bay. Despite this notable decrease in salinity, most of Manatee Bay was between 10 and 25, within regional 1867 historical norms for the wet season.

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1868 1869 Figure 6-53. Surface salinities mapped from WY2018 to WY2020.

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1870 Areas away from nearshore Florida Bay saw a reprieve from hypersalinity with the passage of 1871 Hurricane Irma in September 2017, which brought approximately 7 inches of direct precipitation (see 1872 Ecological Monitoring of Storm Impacts on Florida Bay subsection below). Development of hypersaline 1873 conditions (salinity ≥ 35) did not occur throughout the remainder of WY2018 or WY2019 and a salinity 1874 gradient was maintained in the northern transition zone. Throughout WY2018–WY2019, salinities in the 1875 northwestern lakes (i.e., Seven Palm and Monroe lakes; Figure 6-52) and Joe Bay in the northeastern 1876 transition zone generally experienced notably lower salinity than the rest of the bay (Figure 6-53). Salinities 1877 ranged from 4 to only 18 in Joe Bay and remained < 20 in Seven Palm Lake, contrasting with other coastal 1878 bays, which maintain more polyhaline to marine conditions on average (14 to 38). The tempered salinity 1879 levels in Joe Bay may be related to operation of the S-199 and S-200 structures, which discharge water into 1880 the Aerojet Canal directly upstream as part of the CERP C-111 Spreader Canal Western Features Project 1881 Phase I. As WY2020 began, high temperatures (29 to 33oC; > 75th percentile) and low rainfall (< 75th 1882 percentile) caused lower than average freshwater inputs into the bay (see the Florida Bay Hydrology section 1883 earlier in this chapter) resulting in hypersaline conditions > 36 throughout 99% of the bay, including Joe 1884 Bay and most of the northwestern lakes.

1885 Chlorophyll 1886 Chlorophyll is indicative of algal blooms, which can occur with changes to water quality, such as 1887 increased water column nutrients. Chla has been consistently elevated in the western mangrove lakes (up 1888 to 27 µg/L; see Chapter 6 of the 2017, 2018, and 2019 SFERs; Sklar and Dreschel 2017, 2018, Sklar 2019) 1889 compared to the open bay where concentrations typically remain < 10 µg/L. Notably, in June 2019 at the 1890 beginning of WY2020, Chla was ≤ 10 µg/L across the entire bay including the mangrove lakes 1891 (Figure 6-54). In August 2017, prior to Hurricane Irma, algae were present in the lakes (15 to 27 µg/L) but 1892 nearly undetectable across 74% of the bay (< 1 µg/L; Figure 6-54). During this August survey, Chla also 1893 remained low in Manatee Bay and Barnes Sound (< 3 ug/L) despite several weeks of C-111 canal releases 1894 that ranged between 110 and 925 cubic feet per second per day (cfs/d) through the S-197 structure, 1895 indicating that releases were not facilitating algal growth through nutrient loading or causing benthic 1896 vegetation in Manatee Bay and Barnes Sound to degrade and release nutrients (Sklar and Dreschel 2018, 1897 Sklar 2019). During WY2018–WY2020, impacts of several storms were detected across Florida Bay and 1898 in the western study area. Notably elevated Chla was found baywide following Hurricane Irma 1899 (Figure 6-54; Sklar 2019).

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1900 1901 Figure 6-54. Surface Chla mapped from WY2018 to WY2020.

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1902 Ecological Monitoring of Storm Event Impacts on Florida Bay 1903 Between WY2018 and WY2020, three named storms impacted Florida Bay. Hurricane Irma was a 1904 direct systemwide disturbance that made in the Florida Keys on September 10, 2017 (WY2018). 1905 We completed a Dataflow survey in October, one month after the event, and detected effects of the storm 1906 on environmental conditions baywide (reported in detail in Chapter 6 of the 2018 and 2019 SFERs; Sklar 1907 and Dreschel 2018, Sklar 2019). Hypersalinity that was present across much of Florida Bay prior to the 1908 storm (May–August 2017) was reduced to nearly average conditions for September (20 to 30) as a result of 1909 direct precipitation, creek discharge, and sheetflow into the bay. Throughout the remainder of WY2018 and 1910 WY2019, hypersaline conditions did not develop, and lower salinities were maintained nearshore and in 1911 the transition zone (Figure 6-53). Hurricane Irma likely assisted in preventing hypersalinity development 1912 into WY2019 as salinities remained < 35 baywide (Sklar 2019). 1913 Following Hurricane Irma, Chla concentrations increased baywide from nearly undetectable levels one 1914 month prior (< 1 µg/L; Figure 6-54; Sklar 2019). However, the effect of the storm on algae in Florida Bay 1915 varied over space and time. While Chla increased throughout most of the bay, the algal bloom in the western 1916 Dataflow survey area developed into hot spots of elevated Chla at two statistically significant levels: zones 1917 ≥ 30 ug/L including Rankin Lake and Terrapin Bay and Monroe Lake in the mangrove lakes region (mean 1918 z-score = 3.9; p < 0.005) and 25 to 30 µg/L in Whipray Basin (mean z-score = 2.2; p < 0.05; Figure 6-55). 1919 The hot spots in Rankin Lake and Whipray Basin corresponded with the area of localized seagrass dieoff 1920 that occurred in 2015, which was followed by a subsequent algal bloom in 2016. It is likely that runoff and 1921 discharge from the land, especially in the lakes, and decaying vegetation from the previous seagrass dieoff 1922 in Rankin Lake and Whipray Basin introduced a large amount of nutrients into the water column that 1923 facilitated algal growth (Sklar and Dreschel 2018, Sklar 2019). The coincidence of these hot spots with the 1924 2015 seagrass dieoff epicenter suggests that the effects of Hurricane Irma were exacerbated by the 1925 mortality event. 1926 After Hurricane Irma, phycocyanin fluorescence increased up to two orders of magnitude, indicative of 1927 blue-green algae (cyanophyte) presence (Figure 6-5). Phycocyanin pigment levels were approximately 1928 3 times higher in the western and central regions compared to the eastern bay (approximately 145 relative 1929 fluorescence units [RFU] and < 50 RFU, respectively; Sklar and Dreschel 2018) and spatially coincided 1930 with elevated Chla (Figures 6-54 and 6-56). There was also an order-of-magnitude increase in 1931 phycoerythrin fluorescence one month after Hurricane Irma in the central and eastern bay in portions of 1932 Eagle Key, Nest Key, and Duck Key basins that surpassed previously recorded levels (Figure 6-56). Basins 1933 with high phycoerythrin spatially coincided with highest turbidity, which rose by up to 300% (1,000 RFU) 1934 to a maximum of 1,100 RFU in Nest Key basin (Figure 6-56) suggesting that algae in the water column 1935 increased turbidity shortly after this system-level disturbance (Sklar and Dreschel 2018, Sklar 2019). 1936 Despite the large-scale disturbance of Hurricane Irma, Florida Bay exhibited resilience. Within five 1937 months, much of the eastern bay returned to approximately 1 ug/L (February 2018) and 90% of the surveyed 1938 area was < 20 ug/L. As Chla returned to lower levels in the eastern and more central survey area, hot spots 1939 remained in Rankin Lake, Whipray Basin, and Madeira Bay where Chla concentrations did not rebound as 1940 quickly (Figure 6-55). Chla hot spots were 14 to 19 and 19 to 42 ug/L (mean z-scores = 4.1 and 2.2, 1941 respectively). At the beginning of WY2019, Chla and phycocyanin that persisted in the western region 1942 through the end of WY2018 decreased to ≤ 10 ug/L and < 55 RFU, respectively, by June 2018. In fact, no 1943 Chla hot spots remained and Chla levels in the mangrove lakes were consistent with pre-storm levels 1944 (Figure 6-54). The subsiding algal blooms coincided with a greater than 60% reduction in turbidity 1945 (Figure 6-56).

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1946 1947 Figure 6-55. Chla concentrations indicating presence of algae in the water (left), and statistically 1948 significant “hot spots” in Chla (right) one month after Hurricane Irma (October 2017; top panels), five 1949 months post-Irma (February 2018; center), and one month after Tropical Storm Gordon and 6 days 1950 after Hurricane Michael (October 2018; bottom panels). Significance of hot spots in the right column 1951 were determined by false discovery rate-adjusted p-values (ArcMap 10.3).

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1952 1953 Figure 6-5. (a) Turbidity in nephelometric turbidity units (NTU), (b) phycocyanin, and (c) phycoerythrin photosynthetic pigments indicating 1954 presence of algae in the water column one month after Hurricane Irma (October 2017; top panels), five months post-Irma (February 2018; 1955 center), and one month after Tropical Storm Gordon and 6 days after Hurricane Michael (October 2018; bottom panels).

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1956 One year after Hurricane Irma, Tropical Storm Gordon and Hurricane Michael impacted Florida Bay 1957 (September and October 2018, respectively). For details of these storms, see the Florida Bay Water Quality 1958 Conditions and Status section at the earlier in this chapter. After their passage, Chla hot spots were present 1959 in the mangrove lakes, Rankin Lake, and Whipray Basin. However, all Chla concentrations were 1960 diminished (10 to 18 µg/L; mean z-score = 2.9) compared to one- and five-months after Irma (≥ 25 and 1961 ≥ 14 µg/L, respectively; Figure 6-65). While runoff and discharge from the land coincided with the 1962 appearance of algal blooms, the more muted response in Chla following the succession of Tropical Storm 1963 Gordon and Hurricane Michael suggests that the long-term effects of the localized die-off was likely 1964 lessened by Hurricane Irma through the removal of biomass from the system. This is further supported by 1965 the most recent survey at the beginning of WY2020 (June 2019), in which Chla returned to the lowest levels 1966 of all the surveys discussed in this chapter, remaining ≤ 10 µg/L baywide (Figure 6-54).

1967 Relevance to Water Management 1968 Dataflow mapping is one of the key tools to examine how storms and other disturbance events, 1969 freshwater inputs, and water management can impact salinity distributions and the living resources of 1970 Florida Bay. Between WY2018 and the beginning of WY2020, this program determined the spatial extent 1971 and temporal timeframe for environmental responses of Florida Bay to water releases and large-scale storm 1972 events. In WY2018, mapping captured the spatial impact of C-111 canal emergency releases through the 1973 S-197 structure into Manatee Bay following 53 days of flows (110 to 925 cubic feet per second) after high 1974 June 2017 precipitation. In August 2017, algal blooms were not detected, and the survey determined that 1975 freshening of salinities (< 7) was highly localized at the S-197 structure discharge point in Manatee Bay, 1976 while more typical historical salinities were maintained across most of the basin (10 to 25). Effects of 1977 repeated storms in WY2018 and WY2019 were large in scale as salinities freshened and algal growth 1978 occurred nearly baywide following direct precipitation, creek discharge, and sheetflow into the bay. The 1979 highest magnitude algal blooms spatially coincided with the western survey region where seagrass die-off 1980 occurred in 2015 (Sklar and Dreschel 2018). Despite the development of algal blooms after these storms, 1981 Florida Bay showed resilience, as blooms subsided within a 4-month to one-year timeframe to pre-storm 1982 levels. Over time, the Dataflow mapping has shown that the spatial extent and temporal sequence of events 1983 are important in understanding Florida Bay environmental conditions in response to operations decisions 1984 and impacts of ongoing restoration efforts.

1985 LITERATURE CITED 1986 Abbott, L., T. Barnes, R. Bennett, R. Chamberlain, T. Coley, T. Conboy, C. Conrad, K. Cunniff, P. Doering, 1987 M. Gostel, D. Haunert, K. Haunert, M. Hedgepeth, G. Hu, M. Hunt, S. Kelly, C. Madden, A. McDonald, 1988 R. Robbins, D. Rudnick, E. Skornick, P. Walker, and Y. Wan. 2007. Chapter 12: Management and 1989 Restoration of Coastal Ecosystems. In: 2007 South Florida Environmental Report – Volume I, South 1990 Florida Water Management District, West Palm Beach, FL. 1991 Acosta, C.A., and S.A. Perry. 2000. Differential growth of crayfish Procambarus alleni in relation to 1992 hydrological conditions in marl prairie wetlands of Everglades National Park, USA. Aquatic Ecology 1993 34:389-395. 1994 Acosta, C.A., and S.A. Perry. 2002. Spatial and temporal variation in crayfish production in disturbed marl 1995 prairie marshes of the Florida Everglades. Journal of Freshwater Ecology 17:641-650. 1996 Anderson, M.J. 2017. Permutational multivariate analysis of variance (PERMANOVA). In Wiley StatsRef: 1997 Statistics Reference Online. doi: 10.1002/9781118445112.stat07841. 1998 Anemaet, E.R., and B.A. Middleton. 2013. Dedrometer bands made easy: Using modified cable ties to 1999 measure incremental growth of trees. Application in Plant Sciences 1(9).

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2000 Armentano, T.V., D.T. Jones, M.S. Ross, and B.W. Gamble. 2002. Vegetation pattern and process in tree 2001 islands of the southern Everglades and adjacent areas. Pages 225–282. in: F.H. Sklar and A. van der 2002 Valk (eds.), Tree islands of the Everglades, Kluwer Academic Publishers, Dordrecht, The Netherlands. 2003 Beerens, J.M., D.E. Gawlik, G. Herring, and M.I. Cook. 2011. Dynamic habitat selection by two wading 2004 bird species with divergent foraging strategies in a seasonally fluctuating wetland. The Auk 128:651- 2005 662. 2006 Beerens, J.M., E.G. Noonburg, and D.E. Gawlik. 2015. Linking dynamic habitat selection with wading bird 2007 foraging distributions across resource gradients. PLoS ONE 10(6):e0128182. 2008 Beerens, J.M., J.C. Trexler, and C.P. Catano 2017. Predicting wading bird and aquatic faunal responses to 2009 ecosystem restoration scenarios. Restoration Ecology 25:S86-S98. 2010 Beven, J.L. II, R. Berg, and A. Hagen. 2019. Hurricane Michael (AL142018), 7–11 October 2018. Tropical 2011 Cyclone Report, National Hurricane Center, National Oceanic and Atmospheric Administration, 2012 Florida International University, Miami, FL. May 17, 2019. Available online at 2013 https://www.nhc.noaa.gov/data/tcr/AL142018_Michael.pdf. 2014 Boyer, J.N., and H.O. Briceno. 2008. 2007 Cumulative Annual Report for the Coastal Water Quality 2015 Monitoring Network. Southeast Environmental Research Center, Florida International University, 2016 Miami, FL. 2017 Boyle, R.A., N.J. Dorn, and M.I. Cook. 2014. Importance of crayfish prey to nesting white ibis (Eudocimus 2018 albus). Waterbirds 37:19–29. 2019 Brown, D.P., A. Latto, and R. Berg. 2019. Tropical Storm Gordon (AL072018), 3–6 September 2018. 2020 Report, National Hurricane Center, National Oceanic and Atmospheric 2021 Administration, Florida International University, Miami, FL. May 16, 2019. Available online at 2022 https://www.nhc.noaa.gov/data/tcr/AL072018_Gordon.pdf. 2023 Cangialosi, J.P., A.S. Latto, and R. Berg. 2018. Hurricane Irma (AL112017), 30 August–12 September 2024 2017. Tropical Cyclone Report, National Hurricane Center, National Oceanic and Atmospheric 2025 Administration, Florida International University, Miami, FL. June 30, 2018. Available online at 2026 https://www.nhc.noaa.gov/data/tcr/AL112017_Irma.pdf. 2027 Coronado-Molina, C., J. Harvey, J. Choi, B. Buskirk, J. Gomez-Velez, A. Schwartz, D. Ho, B. Hickman, 2028 L. Larsen, J. Trexler, M. Bush, M. Manna, S. Newman, C. Saunders, F. Sklar, C. Madden, 2029 F. Santamaria, and M. Blaha. 2015. Landscape Patterns and Ecology. In: Sklar, F.H., and T. Dreschel 2030 (eds.), Chapter 6: Everglades Research and Evaluation, In: 2015 South Florida Environmental Report 2031 – Volume I, South Florida Water Management District, West Palm Beach, FL. 2032 Conner, W.H., and J.W. Day Jr. 1992. Water level variability and litterfall productivity of forested 2033 freshwater wetlands of Louisiana. American Midland Naturalist 128:237-245 2034 Conner. W.H. and J.W. Day Jr. 1976. Productivity and composition of a baldcypress-water tupelo site and 2035 a bottomland hardwood site in a Louisiana swamp. American Journal of Botany 63:1354-1364 2036 Cook, M.I. (ed.). 2013. South Florida Wading Bird Report – Volume 19. South Florida Water Management 2037 District, West Palm Beach, FL. 2038 Cook, M.I. (ed.). 2014. South Florida Wading Bird Report – Volume 20. South Florida Water Management 2039 District, West Palm Beach, FL. 2040 Cook, M.I. (ed.). 2016. South Florida Wading Bird Report – Volume 21. South Florida Water Management 2041 District, West Palm Beach, FL. 2042 Cook, M.I., and M. Baranski. (eds.). 2017. South Florida Wading Bird Report – Volume 22. South Florida 2043 Water Management District, West Palm Beach, FL.

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2044 Cook, M.I., and M. Baranski. (eds.). 2018. South Florida Wading Bird Report – Volume 23. South Florida 2045 Water Management District, West Palm Beach, FL. 2046 Cook, M.I., and M. Baranski. (eds.). In prep. South Florida Wading Bird Report – Volume 24. South Florida 2047 Water Management District, West Palm Beach, FL. 2048 Cook, M.I., and E.M. Call (eds.). 2005. South Florida Wading Bird Report – Volume 11. South Florida 2049 Water Management District, West Palm Beach, FL. 2050 Cook, M.I., and E.M. Call (eds.). 2006. South Florida Wading Bird Report – Volume 12. South Florida 2051 Water Management District, West Palm Beach, FL. 2052 Cook, M.I., and H.K. Herring (eds.). 2007. South Florida Wading Bird Report – Volume 13. South Florida 2053 Water Management District, West Palm Beach, FL. 2054 Cook, M.I., and M. Kobza. 2008 (eds.). South Florida Wading Bird Report – Volume 14. South Florida 2055 Water Management District, West Palm Beach, FL. 2056 Cook, M.I., and M. Kobza. 2009 (eds.). South Florida Wading Bird Report – Volume 15. South Florida 2057 Water Management District, West Palm Beach, FL. 2058 Cook, M.I., and M. Kobza. 2010 (eds.). South Florida Wading Bird Report – Volume 16. South Florida 2059 Water Management District, West Palm Beach, FL. 2060 Cook, M.I., and M. Kobza. 2011 (eds.). South Florida Wading Bird Report – Volume 17. South Florida 2061 Water Management District, West Palm Beach, FL. 2062 Cook, M.I., and M. Kobza. 2012 (eds.). South Florida Wading Bird Report – Volume 18. South Florida 2063 Water Management District, West Palm Beach, FL. 2064 Cook, M.I., E.M. Call, R.M. Kobza, S.D. Hill and C.J. Saunders. 2014. Seasonal movements of crayfish in 2065 a fluctuating wetland: Implications for restoring wading bird populations. Freshwater 2066 Biology 59:1608-1621. 2067 Coronado-Molina, C., F. Santamaria, and M. Blaha. 2015. Litterfall and tree growth dynamics in a pristine 2068 tree island and a degraded tree island in Water Conservation Area 3A: The importance of ecological 2069 functions on tree islands. In: F. Sklar and T. Dreschel (ed.). Chapter 6: Everglades Research and 2070 Evaluation. In: 2015 South Florida Environmental Report, South Florida Water Management District, 2071 West Palm Beach, FL. 2072 Crozier, G.E., and M.I. Cook (eds.). 2004. South Florida Wading Bird Report – Volume 10. South Florida 2073 Water Management District, West Palm Beach, FL. 2074 Crozier, G.E., and D.E. Gawlik (eds.). 2003. South Florida Wading Bird Repor –, Volume 9. South Florida 2075 Water Management District, West Palm Beach, FL. 2076 Dorn, N.J., 2008. Colonization and reproduction of large macroinvertebrates are enhanced by drought- 2077 related fish reductions. Hydrobiologia 605:209-218. 2078 Dorn, N.J., and M.I. Cook. 2015. Hydrological disturbance diminishes predator control in wetlands. 2079 Ecology 96:2984-2993. 2080 Dorn, N.J., and J.C. Trexler. 2007. A shifting predator-permanence gradient promotes crayfish regional 2081 coexistence in an open wetland landscape. Freshwater Biology 52:2399-2411. 2082 Dorn, N.J., and M.I. Cook. 2015. Hydrological disturbance diminishes predator control in wetlands. 2083 Ecology 96:2984-2993.

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2084 Dorn, N.J., M.I. Cook, G. Herring, R.A. Boyle, J. Nelson, and D.E. Gawlik. 2011. Aquatic prey switching 2085 and urban foraging by the white ibis Eudocimus albus are determined by wetland hydrological 2086 conditions. Ibis 153:323–335. 2087 FDEP. 2011. Site-Specific Information in Support of Establishing Numeric Nutrient Criteria for Florida 2088 Bay. Florida Department of Environmental Protection, Tallahassee, FL. 2089 Fourqurean, J.W., M.J. Durako, M.O. Hll, and L.N. Hefty. 2002. Seagrass distribution in South Florida: A 2090 multi-agency coordinated monitoring program. Pages 497–522 in: J.W. Porter and K.G. Porter (eds.), 2091 The Everglades, Florida Bay, and Coral Reefs of the Florida Keys, An Ecosystem Sourcebook, CRC 2092 Press, Boca Raton, FL. 2093 Frankovich, T.A., and R.D. Jones. 1998. A rapid, precise and sensitive method for the determination of 2094 total nitrogen in natural waters. Marine Chemistry 60(3-4):227-234. 2095 Frederick P.C., B. Hylton, J.A. Heath, and M. Ruane. 2003. Accuracy and variation in estimates of large 2096 numbers of birds by individual observers using an aerial survey simulator. Journal of Field Ornithology 2097 74:281-287. 2098 Frederick P.C., D.E. Gawlik, J.C. Ogden, M.I. Cook, and M. Lusk. 2009 The white ibis and wood stork as 2099 indicators for restoration of the Everglades ecosystem. Ecological Indicators 9:S83-S95. 2100 Frederick, P.C., and J.C. Ogden. 2001. Pulsed breeding of long-legged wading birds and the importance of 2101 infrequent severe drought conditions in the Florida Everglades. Wetlands 21:484-491. 2102 Frederick, P., D.E. Gawlik, J.C. Ogden, M.I. Cook, and M. Lusk. 2009. The white ibis and wood stork as 2103 indicators for restoration of the Everglades ecosystem. Ecological Indicators 9:83-95. 2104 Frederick, P.C., and J.C. Ogden. 2001. Pulsed breeding of long-legged wading birds and the importance of 2105 infrequent severe drought conditions in the Florida Everglades. Wetlands 21:484-491. 2106 Gaiser, E., J. Richards, J. Trexler, R. Jones, and D. Childers. 2006. Periphyton responses to eutrophication 2107 in the Florida Everglades: Cross-system patterns of structural and compositional change. Limnology 2108 and Oceanography 51:617-630. 2109 Gaiser, E., A. Gottlieb, S. Lee, and J. Trexler. 2015. The importance of species-based microbial assessment 2110 of water quality in freshwater Everglades wetlands. Pages 115–130 in J. Entry, K. Jayachandrahan, 2111 A. Gottlieb, and A. Ogram (eds.), Microbiology of the Everglades Ecosystem, Science Publishers, CRC 2112 Press, Boca Raton, FL. 2113 Gawlik, D.E. (ed.). 1999. South Florida Wading Bird Report – Volume 5. South Florida Water Management 2114 District, West Palm Beach, FL. 2115 Gawlik, D.E. (ed.). 2000. South Florida Wading Bird Report – Volume 6. South Florida Water Management 2116 District, West Palm Beach, FL. 2117 Gawlik, D.E. (ed.). 2001. South Florida Wading Bird Report – Volume 7. South Florida Water Management 2118 District, West Palm Beach, FL. 2119 Gawlik, D.E. (ed.). 2002a. South Florida Wading Bird Report – Volume 8. South Florida Water 2120 Management District, West Palm Beach, FL. 2121 Gawlik, D.E. 2002b. The effects of prey availability on the numerical response of wading birds. Ecological 2122 Monographs 72:329-346. 2123 Givnish, T.J., J.C. Volin, V.D. Owen, J.D. Muss, and P.H. Glaser. 2008. Vegetation differentiation in the 2124 patterned landscape of the central Everglades: Importance of local and landscape drivers. Global 2125 Ecology and Biogeography 17:384-402

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2126 Hall, M.O., and M.J. Durako. 2019. South Florida Fisheries Habitat Assessment Program Annual Report. 2127 to South Florida Water Management District, West Palm Beach, FL. 2128 Hendrix, A.N., and W.F. Loftus. 2000. Distribution and relative abundance of the crayfishes Procambarus 2129 alleni (Faxon) and P. fallax (Hagen) in southern Florida. Wetlands 20:194-199. 2130 Herring, G., M.I. Cook, D.E. Gawlik, and E.M. Call. 2011. Physiological stress responses of nestling white 2131 ibis to variable food availability. Functional Ecology 25:682-690. 2132 Johnston, J.W., and K.L. Bildstein. 1990. Dietary salt as a physiological constraint in white ibis breeding 2133 in an estuary. Physiological Zoology 63:190-207. 2134 Jones D.T., J.P. Saha, M.S. Ross, S.F. Oberbauer, B. Hwang, and K. Jayachandran. 2006. Responses of 2135 twelve tree species common in Everglades tree islands to simulated hydrologic regimes. Wetlands 2136 26(3):830-844. 2137 Keeland B.D., and R.R. Sharitz. 1997. The effects of water-level fluctuations on weekly tree growth in 2138 southeastern USA swamp. American Journal of Botany 84(1):131-139. 2139 Keeland B.D., W.H. Conner, and R.R. Sharitz. 1997. A comparison of wetland tree growth response to 2140 hydrologic regime in Louisiana and South Carolina. Forest Ecology and Management 90:237-250 2141 Keeland, B.D., and P.J. Young. 2004. Construction and installation of dendrometer bands for periodic tree 2142 growth measurements. United States Geological Survey, Washington, D.C. Available online at 2143 https://www.nwrc.usgs.gov/topics/Dendrometer/. 2144 Kellogg, C.M., and N.J. Dorn. 2012. Consumptive effects of fish reduce wetland crayfish recruitment and 2145 drive species turnover. Oecologia 168:1111-1121. 2146 Klimensová, J. and L. Klimeŝ. 2007. Bud banks and their role in vegetative regeneration: A literature review 2147 and proposal for simple classification and assessment. Perspectives in Plant Ecology and Systematics 2148 8:115-129 2149 Kotun, K., and A. Renshaw. 2014. Taylor slough hydrology, fifty years of water management 1961–2010. 2150 Wetlands 34:9-22. 2151 Kushlan, J.A., and M.S. Kushlan. 1975. Food of the white ibis in southern Florida. Florida Field Naturalist 2152 3:31-38. 2153 Larsen, L.G., and J.W. Harvey. 2010. How vegetation and sediment transport feedbacks drive landscape 2154 change in the Everglades and wetlands worldwide. The American Naturalist 176(3):E66-E79. 2155 Lorenz, J.J., and K. Kotun. 2014. Chronology of Water Management Infrastructure and Operations that 2156 Altered Taylor Slough. Audubon Florida Tavernier Science Center, Tavernier, FL. 2157 Madden, C.J., and J.W. Day. 1992. An instrument system for high-speed mapping of chlorophyll a and 2158 physico-chemical variables in surface waters. Estuaries 15:421-427. 2159 Megonigal, J.P., W.H. Conner, S. Kroeger, and R.R. Sharitz. 1997. Aboveground production in 2160 southeastern floodplain forests: A test for the subsidy-stress hypothesis. Ecology 78(2):370-384. 2161 Newman, S., M. Manna, and M.I. Cook. 2017. Ecosystem Ecology. In: F.H. Sklar and T. Dreschel (eds.), 2162 Chapter 6: Everglades Research and Evaluation, in: 2017 South Florida Environmental Report – 2163 Volume I, South Florida Water Management District, West Palm Beach, FL. 2164 Nuttle, W.K., J.W. Fourqurean, B.J. Cosby, J.C. Zieman, and M.B. Robblee. 2000. Influence of net 2165 freshwater supply on salinity in Florida Bay. Water Resources Research 36(7):1805-1822. 2166 Ogden, J.C. 1994. A Comparison of Wading Bird Nesting Dynamics, 1931–1946 and 1974–1989 as an 2167 Indication of Changes in Ecosystem Conditions in the Southern Everglades. Pages 533–570 in: S. Davis

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2168 and J.C. Ogden (eds.), Everglades: The Ecosystem and Its Restoration. St. Lucie Press, Delray Beach, 2169 FL. 2170 Ogden, J.C., S.M. Davis, and L.A. Brandt. 2003. Science strategy for a regional ecosystem monitoring and 2171 assessment program: The Florida Everglades example. Pages 135–143 in: D.E. Busch and J.C. Trexler 2172 (eds.), Monitoring Ecosystems: Interdisciplinary Approaches for Evaluating Ecoregional Initiatives. 2173 Island Press, Washington, D.C. 2174 Phillips, J.M., M.A. Russell, and D.E. Walling. 2000. Time-integrated sampling of fluvial suspended 2175 sediment: A simple method for small catchments. Hydrological Processes 14:2589-2602. 2176 Price, R.M., P.K. Swart, and J.W. Fourqurean. 2006. Coastal groundwater discharge – an additional source 2177 of phosphorus in the oligotrophic wetlands of the Everglades. Hydrobiologia 569:23-36. 2178 RECOVER. 2006a. Wetland Trophic Relationships – Regional Populations of Fishes, Crayfish and 2179 Amphibians Performance Measure. Restoration Coordination and Verification program, c/o United 2180 States Army Corps of Engineers, Jacksonville, FL; and South Florida Water Management District, West 2181 Palm Beach, FL. December 2006. 2182 RECOVER. 2006b. Wetland Trophic Relationships – Wading Bird Nesting Patterns Performance Measure. 2183 Restoration Coordination and Verification program, c/o United States Army Corps of Engineers, 2184 Jacksonville, FL; and South Florida Water Management District, West Palm Beach, FL. December 2185 2006. 2186 RECOVER. 2012. Predator-Prey Relationships. In: Chapter 5: Greater Everglades Module, Pages 124–151 2187 in: Comprehensive Everglades Restoration Plan, 2012 Systems Status Report Interim Update , 2188 Restoration Coordination and Verification program, c/o United States Army Corps of Engineers, 2189 Jacksonville, FL, and South Florida Water Management District, West Palm Beach, FL. 2190 December 2012. 2191 Rudnick, D.T. 2010. Florida Bay Nutrient Loading Estimates: An Update. Coastal Nutrient Criteria 2192 Development Technical Workshop, Miami, FL. 2193 Rudnick, D.T., Z. Chen, D.L. Childers and T.D. Fontaine. 1999. Phosphorus and nitrogen inputs to Florida 2194 Bay: The importance of the Everglades Watershed. Estuaries 22(28):398-416. 2195 Rudnick, D., C. Madden, S. Kelly, R. Bennett, and K. Cunniff. Appendix 12-3: Report on Algae Blooms 2196 in Eastern Florida Bay and Southern Biscayne Bay. In: 2007 South Florida Environmental Report – 2197 Volume I, South Florida Water Management District, West Palm Beach, FL. 2198 SFWMD. 2014. Rules of the South Florida Water Management District, Minimum Flows and Levels. 2199 Chapter 40E-8, Florida Administrative Code, South Florida Water Management District, West Palm 2200 Beach, FL. 2201 SFWMD. 2017. Field Sampling Quality Manual. SFWMD-FIELD-QM-001-09, South Florida Water 2202 Management District, West Palm Beach, FL. Effective June 29, 2017. 2203 SFWMD. 2019. Chemistiry Laboratory Quality Manual. SFWMD-LAB-QM-2019-001, South Florida 2204 Water Management District, West Palm Beach, FL. Effective January 11, 2019. 2205 Sklar, F.H. (ed.). 2019. Chapter 6: Everglades Research and Evaluation. 2019 South Florida Environmental 2206 Report – Volume I, South Florida Water Management District, West Palm Beach, FL. 2207 Sklar, F.H., and T. Dreschel (eds.). 2014. Chapter 6: Everglades Research and Evaluation. 2014 South 2208 Florida Environmental Report – Volume I, South Florida Water Management District, West Palm 2209 Beach, FL.

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2210 Sklar, F.H., and T. Dreschel (eds.). 2015. Chapter 6: Everglades Research and Evaluation. 2015 South 2211 Florida Environmental Report – Volume I, South Florida Water Management District, West Palm 2212 Beach, FL. 2213 Sklar, F.H., and T. Dreschel (eds.). 2016. Chapter 6: Everglades Research and Evaluation. 2016 South 2214 Florida Environmental Report – Volume I, South Florida Water Management District, West Palm 2215 Beach, FL. 2216 Sklar, F.H., and T. Dreschel (eds.). 2017. Chapter 6: Everglades Research and Evaluation. 2017 South 2217 Florida Environmental Report – Volume I, South Florida Water Management District, West Palm 2218 Beach, FL. 2219 Sklar, F.H., and T. Dreschel (eds.). 2018. Chapter 6: Everglades Research and Evaluation. 2018 South 2220 Florida Environmental Report – Volume I, South Florida Water Management District, West Palm 2221 Beach, FL. 2222 Sklar F.H., and A.G. van der Valk). 2002. Tree Islands of the Everglades: An Overview. Pages 1–18 in: 2223 F.H. Sklar and A.G. van der Valk (eds.), Tree Islands of the Everglades, Kluwer, Dordecht, The 2224 Netherlands. 2225 Stacheleck, J., and C.J. Madden. 2015. Application of inverse path distance weighting for high-density 2226 spatial mapping of coastal water quality patterns. International Journal of Geographical Information 2227 Science 29:1240-1250. 2228 Stachelek, J., C.J. Madden, S.P. Kelly, and M. Blaha. In prep. Improved Estimation of Phytoplankton 2229 Abundance and Fine-Scale Water Quality Features via Simultaneous Discrete and Semi-Continuous 2230 Surveys. South Florida Water Management District, West Palm Beach, FL. 2231 Standard Methods Online. 2005. 4500-N C: Nitrogen Persulfate Method. In: Standard Methods for the 2232 Examination of Water and Wastewater, standardmethods.org. 2233 Stoffella S.L., M.S. Ross, J.P. Sah, M.P. Price, P.L. Sullivan, A.E. Cline, and L.J. Scinto. 2010. Survival 2234 and growth responses of eight Everglades tree species along an experimental hydrologic gradient on 2235 two tree island types. Applied Vegetation Science 13(4):439-449; doi:10.1111/j.1654- 2236 109X.2010.01081.x 2237 Swart, P.K., and R. Price. 2002. Origin of salinity variation in Florida Bay. Limnology & Oceanography 2238 47(4):1234-1241. 2239 Tetra Tech, Inc. 2002. Draft User’s Manual for Environmental Fluid Dynamics Code Hydro Version 2240 (EFDC-Hydro). Prepared for the United States Environmental Protection Agency, , GA. 2241 Tetra Tech, Inc., 2007. The Environmental Fluid Dynamics Code Theory and Computation, Volume 3: 2242 Water Quality Module. Prepared for the United States Environmental Protection Agency, Atlanta, GA. 2243 Trexler, J.C., W.F. Loftus, and S. Perry. 2005. Disturbance Frequency and Community Structure in a 2244 Twenty-Five Year Intervention Study. Oecologia 145:140-152. 2245 USFWS. 2009. Fish and Wildlife Coordination Act Report, C-111 Spreader Canal, Western Phase 1 2246 Project, Miami-Dade County, Florida. South Florida Ecological Services Office, United States Fish 2247 and Wildlife Service, Vero Beach, FL. July 30, 2009. 2248 Virzi, T., and M.J. Davis. 2019. C-111 Project and Cape Sable Seaside Sparrow Subpopulation D Annual 2249 Report 2019. Conservation Insight, Happy Valley, OR. 2250 Walsh, T.W. 1989. Total dissolved nitrogen in seawater: A new-high-temperature combustion method and 2251 a comparison with photo-oxidation. Marine Chemistry 26(4):295-311.

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2252 Wetzel, P.R. 2002. Tree Islands Ecosystems of the World. Pages 19–69 in: F.H. Sklar and A. van der Valk 2253 (eds.), Tree Islands of the Everglades, Kluwer Academic Publishers, Dordrecht, The Netherlands. 2254 Williams, K.A., P.C. Frederick, and J.D. Nichols. 2011. Use of the superpopulation approach to estimate 2255 breeding population size: An example in asynchronously breeding birds. Ecology 92:821-828. 2256 Wilson, B.J., S. Servais, S.P. Charles, V. Mazzei, E.E. Gaiser, J.S. Kominoski, J.H. Richards, and T.G. 2257 Troxler. 2019. Phosphorus alleviation of salinity stress: Effects of saltwater intrusion on an Everglades 2258 freshwater peat marsh. Ecology 100(5), doi 10.1002/ecy.2672. 2259 Wu, Y., K. Rutchey, W. Guan, L. Vilchek, and F.H. Sklar. 2002. Spatial Simulations of Tree Islands for 2260 Everglades Restoration. Pages 469–498 in: F. Sklar and A. van der Valk (eds.), Tree Islands of the 2261 Everglades, Kluwer Academic Publishers, Dordrecht, Netherlands.

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