Evaluating Water Management Scenarios To Support 

Habitat Management for the Cape Sable Seaside Sparrow 

By James M. Beerens, Stephanie S. Romañach, and Mark McKelvy

Open-File Report 2016–1107

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia: 2016

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Suggested citation:

Beerens, J.M., Romañach, S.S., and McKelvy, Mark, 2016, Evaluating water management scenarios to support habitat 

management for the Cape Sable seaside sparrow: U.S. Geological Survey Open-File Report 2016-1107, 52 p., 

http://dx.doi.org/10.3133/ofr20161107. 

ISSN 2331-1258 (online)
iii

Acknowledgments 

This work was supported by the U.S. Geological Survey (USGS) and by the National Park Service, IAA # 

P15PG00322. We thank Kevin Suir for hydrologic output conversion assistance. Many thanks to the five USGS 

research scientists and Victor Engel who provided helpful comments during their review of this document. 
iv

Contents

Abstract ......................................................................................................................................................... 1

Introduction.................................................................................................................................................... 1

Methods......................................................................................................................................................... 2

Results........................................................................................................................................................... 3

Summary and Conclusions............................................................................................................................ 5

References Cited........................................................................................................................................... 5

Figures

Figure 1. Map of south Florida study area displaying Cape Sable seaside sparrow (CSSS) critical habitat 

subpopulations and survey sites.................................................................................................................................... 7

Figure 2. Map showing locations of water management structures, Cape Sable seaside sparrow (CSSS) critical 

habitat subpopulations, and newly defined boundaries for the subpopulation A (A1 and A2). ....................................... 8

Figure 3. Graph comparing mean 4-year hydroperiod to bird count at study sites. For Cape Sable Seaside 

sparrow counts of 1-6, 50 percent of sparrow observations (25–75-percent quantile [Q25–Q75]) are located at sites 

having a 4-year mean hydroperiod (prior to detection) between the black and the grey bars. Everglades Depth 

Estimation Network (EDEN) hydroperiods of less than 90 days support many (4-6) birds. The area between the 

dashed red line and solid blue line represents the 90- to 210-day range....................................................................... 9

Figure 4. Graph comparing mean 4-year hydroperiod variability (HP SD) to bird count at study sites. Fifty percent 

of the Cape Sable Seaside sparrows observed (25–75-percent quantile) are located at sites with 4-year hydroperiod 

variability between the black and the grey bars. The area between the dashed red line and solid blue line represents 

the 17- to 43-day range. .............................................................................................................................................. 10

Figure 5. Graph showing the sum of Cape Sable seaside sparrow observations over the period of record (2000–

15) at a given site compared to hydroperiod interannual variability (HP SD); declining sparrow abundance is 

associated with higher interannual variability............................................................................................................... 11

Figure 6. Graph showing the sum of Cape Sable seaside sparrow observations over the period of record (2000–

15) at a given site compared to mean subpopulation water depth. Sparrow survey data were used to identify the 

number of sparrows detected in a given Everglades Depth Estimation Network (EDEN) cell on a given day. The mean 
v

water depths for the subpopulation area containing this cell were averaged over all detections, under the assumption 

that mean depth would converge on suitable values the more a cell was frequented. ................................................ 12

Figure 7. Maps showing, A, mean 4-year hydroperiod of baseline (1968–2004) condition, B, R2F scenario, C, 

difference between baseline and scenario, D, 4-year hydroperiod variability of baseline condition, and E, 4-year 

hydroperiod variability of scenario condition for Everglades National Park. Boundaries of Cape Sable seaside sparrow 

(CSSS) subpopulations A-F and populations A1 and A2 are denoted on the map...................................................... 13

Figure 8. Maps showing, A, mean 4-year hydroperiod of baseline (1968–2004) condition, B, R2G2 scenario, C, 

difference between baseline and scenario, D, 4-year hydroperiod variability of baseline condition, and E, 4-year 

hydroperiod variability of scenario condition for Everglades National Park. Boundaries of Cape Sable seaside sparrow 

(CSSS) subpopulations A-F and populations A1 and A2 are denoted on the map...................................................... 14

Figure 9. Maps showing, A, mean 4-year hydroperiod of baseline (1968–2004) condition, B, R2H scenario, C, 

difference between baseline and scenario, D, 4-year hydroperiod variability of baseline condition, and E, 4-year 

hydroperiod variability of scenario condition for Everglades National Park. Boundaries of Cape Sable seaside sparrow 

(CSSS) subpopulations A-F and populations A1 and A2 are denoted on the map...................................................... 15

Figure 10. Maps showing, A, mean 4-year hydroperiod of baseline (1968–2004) condition, B, R1E scenario, C, 

difference between baseline and scenario, D, 4-year hydroperiod variability of baseline condition, and E, 4-year 

hydroperiod variability of scenario condition for Everglades National Park. Boundaries of Cape Sable seaside sparrow 

(CSSS) subpopulations A-F and populations A1 and A2 are denoted on the map...................................................... 16

Figure 11. Graph showing mean water depth (1965–2005) for Everglades National Park (ENP) existing conditions 

baseline (ECB) run produced by the South Florida Water Management District (SFWMD) Regional Simulation Model 

(RSM). [cm, centimeter]............................................................................................................................................... 17

Figure 12. Graph showing area within subpopulations A, A1, and A2 (1968–2005) meeting FWS target 4-year 

hydroperiod of 90–210 days for 4 scenarios (R2F, R2G2, R2H, R1E) and the baseline condition (ECB). .................. 18
vi

Figure 13. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference for R2F scenario and baseline 

condition for the R2F scenario, in subpopulation A. [cm, centimeter; CSSS, Cape Sable seaside sparrow]............... 19

Figure 14. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2G2 scenario and baseline condition (ECB), in subpopulation A. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow] 20

Figure 15. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2H 

scenario and baseline condition (ECB), in subpopulation A. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 21

Figure 16. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R1E 

scenario and baseline condition (ECB), in subpopulation A. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 22

Figure 17. Graphs showing area within subpopulations A, A1, and A2 (1965–2005) meeting FWS target of more 

than 90 consecutive dry days within the breeding season (Mar 1–Jul 15) for four scenarios (R2F, R2G2, R2H, R1E) 

and the baseline condition (ECB). ............................................................................................................................... 23

Figure 18. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2F 

scenario and baseline condition (ECB), in subpopulation E. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 24

Figure 19. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2G2 scenario and baseline condition (ECB), in subpopulation E. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow] 25
vii

Figure 20. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2H 

scenario and baseline condition (ECB), in subpopulation E. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 26

Figure 21. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R1E 

scenario and baseline condition (ECB), in subpopulation E. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 27

Figure 22. Graphs showing proportion of total area of subpopulation A (A.), B (B.), C (C.), D (D.), E (E.), and F (F.) 

meeting U.S. Fish and Wildlife Service target 4-year hydroperiod of 90–210 days for 4 scenarios (R2F, R2G2, R2H, 

R1E) and the baseline condition (ECB; 1968–2005). .................................................................................................. 30

Figure 23. Graphs showing proportion of total area of subpopulation A (A.), B (B.), C (C.), D (D.), E (E.), and F (F.) 

meeting U.S. Fish and Wildlife Service target of > 90 consecutive dry days within the breeding season (Mar 1–Jul 15; 

1965 –2005) for 4 scenarios (R2F, R2G2, R2H, R1E) and the baseline condition (ECB). .......................................... 33

Figure 24. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2F 

scenario and baseline condition (ECB), in subpopulation B. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 34

Figure 25. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2G2 scenario and baseline condition (ECB), in subpopulation B. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow] 35

Figure 26. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2H 

scenario and baseline condition (ECB), in subpopulation B. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 36
viii

Figure 27. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R1E 

scenario and baseline condition (ECB), in subpopulation B. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 37

Figure 28. Graph showing mean water depth difference (centimeters; cm) relative to the existing conditions 

baseline (ECB) across all breeding seasons (Mar–July 15, 1965–2005) for each scenario within each subpopulation.

38

Figure 29. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2F 

scenario and baseline condition (ECB), in subpopulation F. [cm, centimeter; CSSS, Cape Sable seaside sparrow] .. 39

Figure 30. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2G2 scenario and baseline condition (ECB), in subpopulation F. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow] 40

Figure 31. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2H 

scenario and baseline condition (ECB), in subpopulation F. [cm, centimeter; CSSS, Cape Sable seaside sparrow] .. 41

Figure 32. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R1E 

scenario and baseline condition (ECB), in subpopulation F. [cm, centimeter; CSSS, Cape Sable seaside sparrow] .. 42

Figure 33. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2F 

scenario and baseline condition (ECB), in subpopulation C. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 43

Figure 34. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 
ix

R2G2 scenario and baseline condition (ECB), in subpopulation C. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow] 44

Figure 35. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2H 

scenario and baseline condition (ECB), in subpopulation C. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 45

Figure 36. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R1E 

scenario and baseline condition (ECB), in subpopulation C. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 46

Figure 37. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2F 

scenario and baseline condition (ECB), in subpopulation D. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 47

Figure 38. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2G2 scenario and baseline condition (ECB), in subpopulation D. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow] 48

Figure 39. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R2H 

scenario and baseline condition (ECB), in subpopulation D. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 49

Figure 40. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, A, 

early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the R1E 

scenario and baseline condition (ECB), in subpopulation D. [cm, centimeter; CSSS, Cape Sable seaside sparrow].. 50

Tables

Table 1. Description of water management scenarios modeled by the South Florida Water Management District.

51
x

Table 2. Timespan and description of Cape Sable seaside sparrow variables processed to evaluate scenarios.52
1

Evaluating Water Management Scenarios To Support 

Habitat Management for the Cape Sable Seaside Sparrow 

By James M. Beerens, Stephanie S. Romañach, and Mark McKelvy 

Abstract

The endangered Cape Sable seaside sparrow (Ammodramus maritimus mirabilis) is endemic to 

south Florida and a key indicator species of marl prairie, a highly diverse freshwater community in the 

Florida Everglades. Maintenance and creation of suitable habitat is seen as the most important pathway 

to the persistence of the six existing sparrow subpopulations; however, major uncertainties remain in 

how to increase suitable habitat within and surrounding these subpopulations, which are vulnerable to 

environmental stochasticity. Currently, consistently suitable conditions for the Cape Sable seaside 

sparrow are only present in two of these subpopulations (B and E). The water management scenarios 

evaluated herein were intended to lower water levels and improve habitat conditions in subpopulation A 

and D, raise water levels to improve habitat conditions in subpopulations C and F, and minimize 

impacts to subpopulations B and E. Our objective in this analysis was to compare these scenarios 

utilizing a set of metrics (short- to long-time scales) that relate habitat suitability to hydrologic 

conditions. Although hydrologic outputs are similar across scenarios in subpopulation A, scenario R2H 

reaches the hydroperiod and depth suitability targets more than the other scenarios relative to ECB, 

while minimizing negative consequences to subpopulation E. However, although R2H hydroperiods are 

longer than those for ECB during the wet season in subpopulations C and F, depths during the breeding 

season are predicted to decrease in suitability (less than -50 cm) relative to existing conditions. 

Introduction

The Cape Sable seaside sparrow (Ammodramus maritimus mirabilis; hereafter “sparrow” or 

“CSSS”) is endemic to south Florida and a key indicator species of marl prairie, a highly diverse 

freshwater community in the Florida Everglades. Marl prairie habitat is shaped by distinct flooding, 

drying, and fire intervals, which maintain periphyton production (Gaiser and others, 2011), vegetation 

composition (Sah and others, 2011), and habitat structure for wildlife (Lockwood and others, 2003). 

Historically, the location of marl prairie patches in the Everglades landscape shifted in response to 

changing hydroclimatic conditions; however, habitat loss and hydrologic alteration now restrict the 

range of this habitat, thereby narrowing the sparrow’s range and increasing their sensitivity to changing 

hydropatterns. As a result, sparrow numbers have declined as much as 60 percent range-wide since 1992 

(Curnutt and others, 1998, Nott and others, 1998). Currently, the sparrow is restricted to the marl 

prairies of Everglades National Park (ENP) and Big Cypress National Preserve (Lockwood and others, 

1997). Because this nonmigratory bird is restricted in its range, it was among the first species to be 

listed as endangered by the U.S. Fish and Wildlife Service (FWS) on March 11, 1967 (Pimm and Bass, 

2002), and the marl prairies that it resides in are listed as critical habitat.

Maintenance and creation of suitable habitat is seen as the most important pathway to the 

persistence of the six existing sparrow subpopulations (fig. 1; Sustainable Ecosystems Institute, 2007); 

however, major uncertainties remain in how to increase suitable habitat within and surrounding these 
2

subpopulations, which are vulnerable to environmental stochasticity. An improved understanding of the 

relationships between environmental factors and sparrow habitat suitability is needed to guide 

restoration efforts.

The 2014 range-wide surveys conducted by ENP indicate the CSSS population (2,720 

individuals) fell below a threshold level (2,915), thereby requiring the U.S. Army Corps of Engineers 

(USACE) to reinitiate consultation in November 2014 on the Everglades Restoration Transition Plan 

(ERTP), a regional operation plan for water management. The criterion for reinitiating consultation is 

called the Incidental Take Reinitiation Trigger, which states, “If the annual CSSS population estimate 

falls below 2,915 sparrows [mean population estimate 2001 to 2009 = 3,145 ± 230]), reinitiation of 

consultation must occur.” 

As part of the ERTP consultation process, the South Florida Water Management District 

(SFWMD) produced a set of hydrologic management scenarios (table 1) representing simulations of 

various operational actions that could be taken at water management structures (fig. 2) to improve 

conditions for the CSSS. Four scenarios (R2F, R2G2, R2H, R1E) and an existing condition baseline 

(ECB) were generated using 1965–2005 climatological data. Currently, consistently suitable conditions 

for the sparrow are only present in the most productive subpopulations B and E (Slater and others, 

2009). The proposed water management scenarios are designed to lower water levels and improve 

habitat conditions in subpopulations A and D, raise water levels to improve habitat conditions in 

subpopulations C and F, and minimize impacts to subpopulations B and E. Our objective in this analysis 

was to compare these scenarios utilizing a set of metrics (short- to long-time scales) that relate habitat 

suitability to hydrologic conditions. The subset of metrics we present here were formulated through 

discussions with an interagency CSSS group (including representatives from FWS, ENP, and USACE) 

during a series of meetings in February 2016. 

Methods 

Sparrow surveys have been conducted by helicopter visits to sites located on a 1-kilometer (km) 

grid that encompassed any potential sparrow habitat (Kushlan and Bass, 1983). Observers recorded all 

sparrows detected over a 7-minute interval within about a 200-meter (m) radius of their set-down 

location. We used sparrow observations (from helicopter surveys from Mar–Jun, 1992–2015; n = 

13,404) to estimate the spatial distribution of sparrow counts within ENP.

Using ArcMap (v. 10.3; ESRI, 2011), survey data from 2000–15 were overlain on water-depth 

data collected from the Everglades Depth Estimation Network (EDEN) on the same dates. EDEN is a 

nearly real-time integrated network that consists of over 240 water-level gages and provides daily waterdepth data (within ±5 cm) in 400×400-m grid cells (Liu and others, 2009) and empirically accounts for 

evapotranspiration, rainfall, and sheetflow (Telis, 2006). From the EDEN data, a set of hydrologic 

variables were calculated over multiple temporal scales as proxies for landscape processes that may 

influence habitat suitability (table 2) and were used to evaluate the scenarios. 

Defining Suitable Habitats—The current FWS target for a discontinuous, 1-year hydroperiod 

suitable for the sparrow is 90–210 days in order to maintain and promote formation of the marl prairie 

habitat upon which it relies (U.S. Fish and Wildlife Service, 2010). However, a running average of the 

prior 4-year hydroperiod (HP) may better reflect the temporal scale at which marl prairie habitat is 

created and sustained (Ross and others, 2006). Interannual variability in hydroperiod at a site (defined as 

one standard deviation around the mean 4-year hydroperiod [HP SD]) reveals additional information 

about hydrologic conditions potentially affecting sparrow habitat. We calculated quantiles of sparrow 

observations over the entire period of record for HP and HP SD (table 2) and defined suitable ranges as 

between the 25–75-percent quantile (middle 50 percent of sparrow observations). From these ranges, we 
3

mapped hydroperiod duration and variability across the landscape for each SFWMD Regional 

Simulation Model (RSM) scenario (mean; 1968–2005). Here we assumed that relationships defined 

using EDEN data can be transferred to the RSM. Although both methods have been independently 

validated, no crosswalk currently exists to explicitly link them. We also mapped the mean change in 

hydroperiod of each scenario relative to the ECB. In addition, we calculated consecutive dry days 

(CDD) within the breeding season, a metric the FWS uses to indicate habitat conditions that allow for 

multiple breeding attempts in one season (greater than 90 days; U.S. Fish and Wildlife Service, in 

press).

We examined the geographic proportion of each subpopulation that met the FWS targets for HP

and CDD (table 2) for each scenario year 1968–2005 and 1965–2005, respectively. Across this time 

span, we then identified the frequency of years for each scenario in which 40 percent of each 

subpopulation met the targets (a defined FWS goal; U.S. Fish and Wildlife Service, in press). 

Defining New Subpopulation Boundaries—The historic location boundaries of the six CSSS 

subpopulations (fig. 1) were delineated by the U.S. Fish and Wildlife Service (2007).We mapped the 

frequency (from 2000–15) that each EDEN cell met both the HP and HP SD criteria (see Results) to 

identify “hydroperiod suitable” areas that may lie outside of these sparrow subpopulation boundaries. 

We classified cells around subpopulation A that met both hydroperiod criteria greater than 25 percent of 

years as additional hydroperiod suitable areas and included these cells in newly defined boundaries for 

the subpopulation (A1 and A2, fig. 2). The FWS has identified a target size of 24,000 acres of habitat 

that should meet their hydrologic metrics (greater than 90 consecutive dry days and 90–210 days of 

discontinuous hydroperiod) within the expanded boundary (U.S. Fish and Wildlife Service, in press). 

Using our newly defined boundaries, we calculated the area of subpopulation A (1968–2005 for HP, 

1965-2005 for CDD) meeting the targets to determine the number of years for each scenario in which

the area exceeded 24,000 acres. 

Associating Mean Subpopulation Water Depth with Sparrow Abundance—Sparrow survey data 

from 2000–15 were used to identify the number of sparrows detected in a given EDEN cell on a given 

day. The mean water depths for the subpopulation area containing this cell were then averaged over all 

detections. We then plotted the average water depth value for the subpopulation area against the sum of 

bird occurrences over the 16-year time period to determine mean water depths associated with increased 

sparrow abundance in the subpopulation area. The mean depths corresponding to highest abundance 

(2000–15) were defined as the most suitable. These depth values were used to evaluate the shifts in 

depth distribution observed between the ECB and each scenario. To compare the baseline to each 

scenario, kernel probability distribution curves were computed that represent the water-depth 

histograms. The kernel distribution curve is computed by summing the component smoothing functions 

for each data value to produce a smooth, continuous probability curve (SAS Institute, 2010). A scenario 

was considered more suitable than the ECB if there was an increased frequency of “suitable” breeding 

season water depths.

Results

Across all subpopulations, where 1–3 sparrows were observed, the 25–75-percent quantile range 

contained sites having an approximate 104- to 203-day HP; where 4–5 sparrows were observed, the 

middle 50 percent was within an approximate 21- to 218-day HP, and where 6 sparrows were observed, 

the middle 50 percent was within an approximate 25- to 90-day HP (fig. 3). Subsequent analyses use the 

FWS-defined metric of 90–210 days to be consistent with the 2010 Biological Opinion for the CSSS 

(U.S. Fish and Wildlife Service, 2010). Across all bird counts, the middle 50 percent of sparrow 

observations were located in sites having an HP SD range of about 17–43 days (fig. 4). Based on these 
4

assessments, the following analyses code a 17–43 day SD as “suitable,” with higher variability 

decreasing suitability (associated with declining sparrow use; fig. 5). The highest count of sparrows 

observed at a site (2000–15) occurred at a mean subpopulation water depth of -20 to -50 centimeters 

(cm) (fig. 6).

Under all scenarios (R2F, R2G2, R2H, R1E) HP decreased near subpopulation A and increased 

in subpopulation F compared to that of the ECB. The interannual variability in hydroperiod (HP SD)

increased the most under R1E and the least under R2H relative to the ECB (figs. 7–10). 

The FWS target of 24,000 acres of suitable hydroperiod (90–210 days) in subpopulation A was 

only met during periods having below-average rainfall (fig. 11): 1973–79 and 1990–92 (fig. 12; all 

scenarios). For the R2H scenario, the target was met during an additional 5 years (1980, 1981, 1982, 

1989, 2004). Correspondingly, for all scenarios, daily mean water depths decreased relative to the ECB 

in subpopulation A during both the early (Mar–Apr) and late (May–July 15) breeding season (figs. 13–

16A, B). Where a scenario has kernel values that exceed the baseline, there is an increased frequency of 

depths across that range; higher values from -20 to -50 cm show increased “suitability” for the sparrow. 

Daily differences in mean subpopulation water depth during the breeding season for the 1965 to 2005 

period are shown in figures 13C–16C. Negative values indicate a scenario has decreases in mean water 

depth relative to the baseline, and positive values indicate a scenario has increases in depth. Scenario 

R2H produced the greatest number of years in which scenario water depths were lower than ECB water 

depths in subpopulation A (figs. 13C–16C). The FWS target of consecutive dry days (CDD; > 90 days) 

was met in 8 years in all scenarios (fig. 17). 

Increases in HP affecting the eastern subpopulations were highest in scenario R1E followed by 

R2F and R2G2, and then R2H (figs. 7–10). The increase in water volume (relative to the ECB) tended 

to increase water depths in the reverse order of scenarios in subpopulation E, with the least disruption to 

sparrow breeding depths provided by the R2H scenario (figs. 18–21). The HP target was met by all 

scenarios during 23 of 38 years (61 percent) and during an additional 3 years under the ECB (fig. 22E), 

whereas the CDD target was only met during 10 of 40 years (25 percent; all scenarios) and during 1 

additional year under R2H (1980; fig. 23E). 

For subpopulation B, HP and mean water depths remained more stable, with depths varying ±2 

cm or less from the baseline (figs. 24C –27C). There were little differences in performance across 

scenarios in subpopulation B, meeting targets for HP (fig. 22B) and CDD (fig. 23B) during 32 of 38 

years (84 percent) and 18 of 40 years (45 percent), respectively. 

In subpopulation F, the lengthening of HP relative to ECB was most pronounced in R1E, 

followed by R2F; the top two performing scenarios during 20 of 38 (53 percent) years (fig. 22F). R2G2 

and R2H performed better than the baseline in approximately half of the scenario years (fig. 22F). 

Although there was little change in HP between R2H and ECB in subpopulation F, R2H was the only 

proposed scenario in which mean water depth decreased (mean –3.22 cm, range -21.29 to 5.56 cm; fig.

28) during the early- and late-season breeding period (figs. 29–32), leading to an increased frequency of 

unsuitable sparrow subpopulation water depths of less than -50 cm (fig. 6). This disparity of results 

between long- and short-term metrics suggests that despite an extension in hydroperiod, there was a 

decrease in water-depth suitability within the sparrow’s breeding season. The CDD target was met in 13 

of 40 years (33 percent) in all scenarios and an additional 1 year in ECB and R2H (1998; fig. 23F).

For subpopulation C, R1E and R2F provided the most benefit relative to ECB during periods 

considered excessively dry (for example, shorter HP) for the sparrow (for example, 1973–82; fig. 22C). 

During periods having above average rainfall, R2H provided the best scenario HP outcome during 17 of 

38 years (45 percent; fig. 22C) and provided a slight increase in the CDD metric (fig. 23C), exceeding 

all other scenarios during 25 of 40 years (63 percent). Similar to subpopulation F, water depths 
5

decreased during the breeding season relative to ECB under R2H only, contributing to a higher 

frequency of mean depths within an unsuitable depth range (less than -50 cm) in both the early- and 

late-breeding season (figs. 33–36). This again demonstrates the value of examining metrics across 

multiple time scales because increases in the HP metric under R2H would only be considered a positive 

outcome with corresponding increases in the frequency of breeding season water depths in the suitable

range (-20 to -50 cm). 

Farthest to the southeast of ENP in subpopulation D, the HP target was only met during the 12 

driest years (fig. 11) under all scenarios, during an additional 5 average years under ECB, and during an 

additional 2 dry years under R2H (fig. 22D). The CDD target was met during 4 years for ECB and an 

additional 2 years under R2H only (fig 24D). Further, the interannual variation in hydroperiod (HP SD) 

was substantially reduced with R2H (figs. 7–10C). In scenarios R1E, R2F, and R2G2, the frequency of 

unsuitable mean water depths (greater than -20 cm) increased in the early breeding season, whereas the 

frequency of greater depths decreased under R2H (figs. 37–40). In addition, R2H was the only scenario 

in which there was a consistent reduction in breeding season water depths in most years (fig. 39C).

Summary and Conclusions 

Currently, consistently suitable conditions for the Cape Sable seaside sparrow (Ammodramus 

maritimus mirabilis) are only present in subpopulations B and E. The water management scenarios 

evaluated herein were intended to lower water levels and improve habitat conditions in subpopulation A 

and D, raise water levels to improve habitat conditions in subpopulations C and F, and minimize 

impacts to subpopulations B and E. Although hydrologic outputs are similar across scenarios in 

subpopulation A, scenario R2H reaches the hydroperiod (HP) and depth suitability targets more than the 

other scenarios relative to ECB, while minimizing negative consequences to subpopulation E. However, 

although R2H hydroperiods are longer than those for ECB during the wet season in subpopulations C 

and F, depths during the breeding season are predicted to decrease in suitability (less than -50 cm) 

relative to existing conditions. 

References Cited

Curnutt, J.L., Mayer, A.L., Brooks, T.M., Manne, Lisa, Bass, O.L., Jr., Fleming, D.M., Nott, M.P., and 

S.L. Pimm, S.L., 1998, Population dynamics of the endangered Cape Sable seaside sparrow: Animal 

Conservation, v. 1, p. 11–20. 

Environmental Systems Research Institute (ESRI), 2011, ArcGIS Desktop—Release 10: Redlands, 

Calif., Environmental Systems Research Institute.

Gaiser, E.E., McCormick, P.V., Hagerthey, S.E., 2011, Landscape patterns of periphyton in the Florida 

Everglades: Critical Reviews in Environmental Science and Technology, v. 41, supplement 1, p. 92–

120. 

Kushlan, J.A., and Bass, O.L., Jr., 1983, Habitat use and the distribution of the Cape Sable seaside 

sparrow, in Quay, T.L., Funderburg, J.B., Jr., Lee, D.S., Potter, E.F., and Robbins , C.S., eds., The 

seaside sparrow—Its biology and management: North Carolina Biological Survey Occasional Paper 

1983–5, p. 139–146. 

Liu, Z., Volin, J.C., Owen, V.D., Pearlstine, L.G., Allen, J.R., Mazzotti, F.J., and Higer, A.L. 2009, 

Validation and ecosystem applications of the EDEN water-surface model for the Florida Everglades: 

Ecohydrology, v. 2, p. 182–194 

Lockwood, J.L., Fenn, K.H., Curnutt, J.L., Rosenthal, Deborah, Balent, K.L., and Mayer, A.L., 1997, 

Life history of the endangered Cape Sable seaside sparrow: Wilson Bulletin, v., 109, p. 720–731. 
6

Lockwood, J.L., Ross, M.S., and Sah, J.P., 2003, Smoke on the water: the interplay of fire and water 

flow on Everglades restoration: Frontiers in Ecology and the Environment, v. 1, no. 9, p. 462–468. 

Nott, M.P., Bass, O.L., Jr., Fleming, D.M., Killeffer, S.E., Fraley, Nancy, Manne, Lisa, Curnutt, J.L, 

Brooks, T.M., Powel, Robert, and Pimm, S.L., 1998, Water levels, rapid vegetational changes, and the 

endangered Cape Sable seaside sparrow: Animal Conservation, v. 1, p. 21–29. 

Pimm, S.L., and Bass, O.L., Jr., 2002, Range-wide risks to large populations—The Cape Sable sparrow 

as a case history, in Beissinger, S.R., and McCullough, D.L., eds., Population viability analysis: 

Chicago, Ill, University of Chicago Press, p. 406–424. 

 Ross, M.S., Sah, J.P., Ruiz, P.L., Jones, D.T., Cooley, Hillary, Travieso, Rafael, Tobias, Franco, 

Snyder, J.R., and Hagyari, David, 2006, Effect of hydrologic restoration on the habitat of the Cape 

Sable seaside sparrow—Annual report to Everglades National Park of 2004–2005, accessed June 17, 

2016, at http://digitalcommons.fiu.edu/sercrp/85/. 

Sah, J.P., Ross, M.S., Ruiz, P.L., Snyder, J.R., Rodriguez, Diana, and Hilton, W.T., 2011, Cape Sable 

seaside sparrow habitat monitoring and assessment—2010 Final Report: Southeast Environmental 

Research Center Research Reports, Paper 96, Miami, Fla., accessed June 17, 2016, at 

http://digitalcommons.fiu.edu/sercrp/96.

SAS Institute, 2010, SAS 9.3 help and documentation—SGPLOT procedure: Cary, N.C., SAS Institute. 

Slater, G.L., Boulton, R.L., Jenkins, C.N., Lockwood, J.L., and Pimm, S.L., 2009, Emergency 

management action plan for the endangered Cape Sable seaside sparrow—Report to the U. S. Fish 

and Wildlife Service: Mount Vernon, Wash., Ecostudies Institute, 112 p., accessed June 17, 2016, at 

http://www.ecoinst.org/wp-content/uploads/2014/03/Slater_etal_2009_CSSS_EMAP.pdf. 

Sustainable Ecosystems Institute, 2007, Everglades multi-species avian ecology and restoration review: 

accessed June 17, 2016, at 

http://www.fws.gov/verobeach/CERPPDFs/Everglades2007FinalReport.pdf.

Telis, P.A., 2006, The Everglades Depth Estimation Network (EDEN) for support of ecological and 

biological assessments: U.S. Geological Survey Fact Sheet 2006–3087, 4 p. 

U.S. Fish and Wildlife Service, 2007, Critical habitat revised designation for the Cape Sable seaside 

sparrow: Federal Register, v. 72, no. 214, p. 62735-62766. 

U.S. Fish and Wildlife Service, 2011, Biological opinion for Everglades Restoration Plan, Phase 1—

Submitted to US Army Corps of Engineers: Accessed June 17, 2016, at 

141.232.10.32/pm/pm_docs/ertp/022511_ertp_v3_app_f.pdf. 

U.S. Fish and Wildlife Service, in press, Draft biological opinion for the U.S. Army Corps of Engineers, 

Everglades Restoration Transition Plan: Vero Beach, Fla., South Florida Ecological Services Office. 
7

Figure 1. Map of south Florida study area displaying Cape Sable seaside sparrow (CSSS) critical habitat 

subpopulations and survey sites.
8

Figure 2. Map showing locations of water management structures, Cape Sable seaside sparrow (CSSS) critical 

habitat subpopulations, and newly defined boundaries for the subpopulation A (A1 and A2). 
9

Figure 3. Graph comparing mean 4-year hydroperiod to bird count at study sites. For Cape Sable Seaside 

sparrow counts of 1-6, 50 percent of sparrow observations (25–75-percent quantile [Q25–Q75]) are located at sites 

having a 4-year mean hydroperiod (prior to detection) between the black and the grey bars. Everglades Depth 

Estimation Network (EDEN) hydroperiods of less than 90 days support many (4-6) birds. The area between the

dashed red line and solid blue line represents the 90- to 210-day range.
10

Figure 4. Graph comparing mean 4-year hydroperiod variability (HP SD) to bird count at study sites. Fifty percent 

of the Cape Sable Seaside sparrows observed (25–75-percent quantile) are located at sites with 4-year 

hydroperiod variability between the black and the grey bars. The area between the dashed red line and solid blue 

line represents the 17- to 43-day range. 
11

Figure 5. Graph showing the sum of Cape Sable seaside sparrow observations over the period of record (2000–

15) at a given site compared to hydroperiod interannual variability (HP SD); declining sparrow abundance is 

associated with higher interannual variability.
12

Figure 6. Graph showing the sum of Cape Sable seaside sparrow observations over the period of record (2000–

15) at a given site compared to mean subpopulation water depth. Sparrow survey data were used to identify the 

number of sparrows detected in a given Everglades Depth Estimation Network (EDEN) cell on a given day. The 

mean water depths for the subpopulation area containing this cell were averaged over all detections, under the 

assumption that mean depth would converge on suitable values the more a cell was frequented. 
13

 

 

A. B.

C.

D. E.

C.

Figure 7. Maps showing, A, mean 4-year hydroperiod of baseline (1968–2004) condition, B, R2F scenario, C, 

difference between baseline and scenario, D, 4-year hydroperiod variability of baseline condition, and E, 4-year 

hydroperiod variability of scenario condition for Everglades National Park. Boundaries of Cape Sable seaside 

sparrow (CSSS) subpopulations A-F and populations A1 and A2 are denoted on the map.
14

 

 

A. B.

C.

D. E.

Figure 8. Maps showing, A, mean 4-year hydroperiod of baseline (1968–2004) condition, B, R2G2 scenario, C, 

difference between baseline and scenario, D, 4-year hydroperiod variability of baseline condition, and E, 4-year 

hydroperiod variability of scenario condition for Everglades National Park. Boundaries of Cape Sable seaside 

sparrow (CSSS) subpopulations A-F and populations A1 and A2 are denoted on the map.
15

 

 

A. B.

C.

D. E.

Figure 9. Maps showing, A, mean 4-year hydroperiod of baseline (1968–2004) condition, B, R2H scenario, C, 

difference between baseline and scenario, D, 4-year hydroperiod variability of baseline condition, and E, 4-year 

hydroperiod variability of scenario condition for Everglades National Park. Boundaries of Cape Sable seaside 

sparrow (CSSS) subpopulations A-F and populations A1 and A2 are denoted on the map.
16

 

 

A. B.

C.

D. E.

Figure 10. Maps showing, A, mean 4-year hydroperiod of baseline (1968–2004) condition, B, R1E scenario, C, 

difference between baseline and scenario, D, 4-year hydroperiod variability of baseline condition, and E, 4-year 

hydroperiod variability of scenario condition for Everglades National Park. Boundaries of Cape Sable seaside 

sparrow (CSSS) subpopulations A-F and populations A1 and A2 are denoted on the map.
17

Figure 11. Graph showing mean water depth (1965–2005) for Everglades National Park (ENP) existing conditions 

baseline (ECB) run produced by the South Florida Water Management District (SFWMD) Regional Simulation 

Model (RSM). [cm, centimeter]
18

Figure 12. Graph showing area within subpopulations A, A1, and A2 (1968–2005) meeting FWS target 4-year 

hydroperiod of 90–210 days for 4 scenarios (R2F, R2G2, R2H, R1E) and the baseline condition (ECB). 
19

A. B.

C.

Figure 13. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference for R2F scenario and 

baseline condition for the R2F scenario, in subpopulation A. [cm, centimeter; CSSS, Cape Sable seaside sparrow]
20

A. B.

C.

Figure 14. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R2G2 scenario and baseline condition (ECB), in subpopulation A. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
21

A. B.

C.

Figure 15. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R2H scenario and baseline condition (ECB), in subpopulation A. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
22

A. B.

C.

Figure 16. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R1E scenario and baseline condition (ECB), in subpopulation A. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
23

Figure 17. Graphs showing area within subpopulations A, A1, and A2 (1965–2005) meeting FWS target of more 

than 90 consecutive dry days within the breeding season (Mar 1–Jul 15) for four scenarios (R2F, R2G2, R2H, R1E) 

and the baseline condition (ECB).
24

A. B.

C.

Figure 18. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R2F scenario and baseline condition (ECB), in subpopulation E. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
25

A. B.

C.

Figure 19. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R2G2 scenario and baseline condition (ECB), in subpopulation E. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
26

A. B.

C.

Figure 20. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R2H scenario and baseline condition (ECB), in subpopulation E. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
27

A. B.

C.

Figure 21. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R1E scenario and baseline condition (ECB), in subpopulation E. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
28

A.

B.
29

C.

D.
30

E.

F.

Figure 22. Graphs showing proportion of total area of subpopulation A (A.), B (B.), C (C.), D (D.), E (E.), and F 

(F.) meeting U.S. Fish and Wildlife Service target 4-year hydroperiod of 90–210 days for 4 scenarios (R2F, R2G2, 

R2H, R1E) and the baseline condition (ECB; 1968–2005).
31

A.

B.
32

C.

D.
33

E.

F.

Figure 23. Graphs showing proportion of total area of subpopulation A (A.), B (B.), C (C.), D (D.), E (E.), and F 

(F.) meeting U.S. Fish and Wildlife Service target of > 90 consecutive dry days within the breeding season (Mar 1–

Jul 15; 1965 –2005) for 4 scenarios (R2F, R2G2, R2H, R1E) and the baseline condition (ECB).
34

A. B.

C.

Figure 24. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R2F scenario and baseline condition (ECB), in subpopulation B. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
35

A. B.

C.

Figure 25. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2G2 scenario and baseline condition (ECB), in subpopulation B. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
36

A. B.

C.

Figure 26. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2H scenario and baseline condition (ECB), in subpopulation B. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
37

A. B.

C.

Figure 27. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R1E scenario and baseline condition (ECB), in subpopulation B. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
38

Figure 28. Graph showing mean water depth difference (centimeters; cm) relative to the existing conditions 

baseline (ECB) across all breeding seasons (Mar–July 15, 1965–2005) for each scenario within each 

subpopulation.
39

A. B.

C.

Figure 29. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2F scenario and baseline condition (ECB), in subpopulation F. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
40

A. B.

C.

Figure 30. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R2G2 scenario and baseline condition (ECB), in subpopulation F. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
41

A. B.

C.

Figure 31. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2H scenario and baseline condition (ECB), in subpopulation F. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
42

A. B.

C.

Figure 32. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R1E scenario and baseline condition (ECB), in subpopulation F. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
43

A. B.

C.

Figure 33. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R2F scenario and baseline condition (ECB), in subpopulation C. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
44

A. B.

C.

Figure 34. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2G2 scenario and baseline condition (ECB), in subpopulation C. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
45

A. B.

C.

Figure 35. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2H scenario and baseline condition (ECB), in subpopulation C. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
46

A. B.

C.

Figure 36. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R1E scenario and baseline condition (ECB), in subpopulation C. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
47

A. B.

C.

Figure 37. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2F scenario and baseline condition (ECB), in subpopulation D. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
48

A. B.

C.

Figure 38. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R2G2 scenario and baseline condition (ECB), in subpopulation D. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
49

A. B.

C.

Figure 39. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the 

R2H scenario and baseline condition (ECB), in subpopulation D. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
50

A. B.

C.

Figure 40. Graphs showing distribution and kernal probability curve of mean subpopulation water depths during, 

A, early (Mar–Apr) and, B, late (May–July 15) breeding seasons, and C, daily difference in water depth between the

R1E scenario and baseline condition (ECB), in subpopulation D. [cm, centimeter; CSSS, Cape Sable seaside 

sparrow]
51

Table 1. Description of water management scenarios modeled by the South Florida Water Management District. [WCA, water conservation area; ENP, Everglades National Park; SFWMD, South Florida Water Management District; 

CSSS, Cape Sable seaside sparrow] 

Scenario name Description of scenario

Existing conditions

baseline (ECB)

Update previous base condition runs developed for Central Everglades Planning 

Process (CEPP) and South Dade Investigation for current features and Everglades 

Restoration Transition Plan operations. Priority use of S–333 for WCA–3A Rainfall 

Plan deliveries, followed by S–12D, S–12C, S–12B, S–12A, L–28 Tie–Back Levee 

gaps and L–28 Canal Old Tamiami Trail Borrow Canal, with Tram Road east–west 

culvert S–12 gate overtopping if headwater stage exceeds 11.0 feet.

R2F January through December closure period for S–12A, S–343A, S–343B and S–344; 

closure of S–12B from October 1 through August 16.

R2G2 Conditional closures of S–12A, S–12B, S–343A, S–343B and S–344 based on 

antecedent conditions in WCA–3A and sparrow breeding opportunity. Structures

tend to open as stages increase at 3A–28 (should also help to avoid “overtopping” 

operations), tend to close during La Niña, neutral, and weak El Niño years.

R2H Early dry season operations (Sep-Dec) informed by SFWMD South Dade study to 

promote more flow toward ENP and extend hydroperiods; look for later dry season 

opportunity (Feb-May) to move water toward Biscayne National Park and away 

from CSSS populations; attempt to avoid water-level excursion above ground 

surface March 1 to July 15 because of operation of eastern infrastructure (S–332s, 

S–200s, S–199s).

R1E Closure of the ENP Tram Road borrow-canal connection, and January through 

December closure period for S–12A, S–12B, S–343A, S–343B, and S– 344.
52

Table 2. Timespan and description of Cape Sable seaside sparrow variables processed to evaluate scenarios.

[cm, centimeter; SD, standard deviation]

Variable Span Description

Depth mean of subpopulation 1 day Depth average of subpopulation

Consecutive dry days 1 breeding season Count of consecutive days with depth > 0 cm 

Mean hydroperiod 4 years Mean count of days in year with depth > 0 cm

Hydroperiod variability 4 years SD of days in year with depth > 0 cm