Apple Snail Dynamics Model

1 1Modeling apple snail population dynamics on the Everglades Landscape

3Philip C. Darby, Donald L. DeAngelis, Stephanie Romañach, Kevin Suir, and Joshua

4Bridevaux

5 Supplementary Material

6Appendix 1. Detailed description of EverSnail model parameterization, submodels, and

7description of their derivation

9The estimation of parameters for the EverSnail model is described below, following a

10description of pertinent apple snail life history and ecology details. Additional details and

11references on apple snail life history and ecology are available in a literature review by the

12Pomacea Project (2013).

14Summary of Apple Snail Life History and Ecology

15Estimates for apple snail life span were reported from several lab and field studies, and based

16on different types of observations. Hanning (1979) concluded that Florida apple snails had a

171-1.5 year life span based on a shift in the size-frequency distribution (with a disappearance

18of larger size classes) as well as finding an increase in the number of dead adult-sized snails

19in a concentrated time period in June. Ferrer et al. (1990) reported a significant increase in

20mortality in 15-20 month old P. paludosa directly observed in situ in wetlands in Cuba.

21Darby et al. (2003) observed a steep decline in survival in May-June for both snails in the

22field (bearing transmitters) and in the lab, regardless of water level. Additional study showed

23the steep decline in survival immediately followed peak egg cluster production (i.e., a post-

24reproductive die-off) (Darby et al. 2008). The concentration of egg production in spring the

25previous year (see details below), and the increased mortality observed in summer the

2 1 3 26following year (~12-15 months later), are consistent with a 1-1.5 year life span for P.

27paludosa.

28 As with all Pomacea snails studied to date, the Florida apple snail can survive

29periodic dry downs typical of the tropical and subtropical wetlands they inhabit (Cowie 2002,

30Darby et al. 2008). Water depths recede, with possible temporary reversals, during the dry

31season, which generally occurs from January-June. Dry marsh conditions, in years they

32occur at all, are most often found in April-June (Kushlan 1990, Darby et al. 2008). Dry down

33survival rates depend on reproductive status of adults-sized snails, and snail size in general.

34Reproductive and post-reproductive adults die regardless of water depths (including dry

35downs) as a result of a life cycle that culminates in a post-reproductive die-off (Darby et al

362003). Adult-sized snails that had not reproduced (average shell width 31 ± 4.2 mm) survive

374 weeks in dry conditions with survival at 98%; this declines to 71% at 12 weeks and 27% at

3818 weeks (Darby et al. 2008). Juvenile-sized snails exhibited significantly lower survival

39rates, where 10-15 mm and 6-9 mm size class survival was ~80% after four weeks in dry

40conditions. After 8 weeks 10-15 mm snail survival was 43% and 6-9 mm snail survival was

4113%. Newly hatched snail survival (<6 mm) was more variable, but still significantly lower

42than the larger juvenile size classes (50% or fewer of the newly hatched snails survived after

434 weeks and all were dead at 8 weeks) (Darby et al. 2008).

44 All Pomacea species are dioecious (Cowie 2002), and Hanning (1979) estimated a 1:1

45sex ratio for the majority of samples he collected on Lake Okeechobee, which we apply to

46our population model. Hanning (1979) found that snails were sexually mature when shells

47reached approximately 35 mm in size. Darby et al. (2008) reported egg-laying females and

48mating males of somewhat smaller size (~25-30 mm) in the Upper St. Johns basin. Females

49deposit clusters of cleidoic eggs above the water line on emergent vegetation; each cluster

50typically consists of 20-40 eggs (Hanning 1979, Turner 1996). Hanning (1979) reported an

4 2 51average of one cluster produced per female per week, and Hurdle (1974) recorded 21 and 25

52total clusters produced by two Florida apple snails (total fecundity, and what influences it,

53requires further study). Egg clusters can be found year round; however, from November

54through January or early February few or no eggs are found (Hanning 1979, Darby et al.

552008). Darby et al. (2008) reported ~80% of annual egg cluster production in April-June

56(greatest annual egg counts also occurred in April, May, or June); in the seven transects

57reported, none experienced a drying event. This peak period corresponds to the late dry

58season and typically receding water levels (see Darby et al. 2008). In a drying event, snails

59do not move, mate or lay eggs (Darby et al. 2008). Spring dry downs can skew peak egg

60production toward the summer (i.e., wet) season (O’Hare 2010, Darby 2012 unpublished

61data). Hanning (1979) reported the incubation period for egg clusters as approximately 16-22

62days.

63 Although P. paludosa typically deposit their eggs 10-20 cm above the water line

64(Turner 1996, Hanning 1979, O’Hare 2010), they can be vulnerable to rising water that

65results in egg submersion. [Oviposition height can be higher, depending on the vegetation

66type available (Hanning 1979, Darby unpublished data).] Turner (1998) found that 1- to 8-

67day old eggs failed to hatch when submerged. Sixty-five percent of 12-day-old (fully

68hardened and calcified eggs) hatched despite being submerged. Based on egg height data

69from the Everglades (Darby unpublished data), a water level rise of more than 18 cm would

70be required to submerge most unhatched egg clusters.

71 Although a water level rise may not be problematic for snail eggs already deposited,

72there appears to be a negative effect of relatively deep water on egg cluster production.

73Gleason et al. (1975) described an inverse relationship between a hydrologic index (average

74depth*hydroperiod) and snail eggs counted, although snail density estimates, which might

75have explained at least part of the variation in egg production, were not available. Darby

5 3 76(unpublished) calculated a per capita egg cluster count (PCE) (number of egg clusters divided

77by snail densities) and found higher PCE associated with lower depths (as long as depths

78were greater than approximately 10 cm). Egg cluster production dropped by an order of

79magnitude, despite relatively abundant snails, in a relatively wet year in WCA3A (2003,

80where water depths for the year never fell below 40 cm) compared to the previous year

81(2002) in which the study sites had depths of 10-40 cm during the spring reproductive season

82(Darby et al. 2005). In addition, the PCE dropped by an order of magnitude from 2002 to

832003 (Darby, unpublished calculations). The following year (2004) the snail population in

84this part of WCA3A dropped by over 80% (Darby et al. 2005). Although the depth range

85best suited for apple snail recruitment requires further study, especially for the upper depth

86limits (see Pomacea Project 2013), there is sufficient evidence to suggest that a negative

87influence of some upper depth limit on reproduction should be incorporated into the apple

88snail population model.

89 Apple snails hatch out at three to six millimeters in diameter (Hanning 1979, Darby et

90al. 2008). They grow approximately 10-13 mm per month during the first two months

91(Hanning 1979, Glass and Darby 2009); therefore, snails that hatch in spring can reach adult

92size (and sizes consumed by kites) by early to mid-summer. Growth rates slow down

93considerably once the snails reach ~25 mm (Hanning 1979). Although numerous

94environmental factors likely influence growth rates, they have not been sufficiently quantified

95to be parameterized in our model.

96 The sub-tropical climate of southern Florida results in sufficient temperature

97fluctuations to significantly influence apple snail activity in general. Stevens et al. (2002)

98found no activity in apple snails exposed to water temperatures <13°C, and below 10°C 92%

99of snails burrowed into the sediments. These observations of snails were consistent with

100Cary (1985), having found no snail captures by kites attempting to forage when air

6 4 101temperatures fell below 10°C. From 11°C up to 30°C, prey capture rates by kites increased

102linearly, most likely a function of increased rates of general activity and surfacing by snails

103(Cary 1985). McClary (1964) reported a 2.7-fold increase in the number of surface

104inspirations by Pomacea snails as temperatures increased from 12°C to 26°C. Stevens et al.

105(2002) observed that the greatest increase (nearly exponential) in general activity occurred

106between 20°C and 22°C degrees. Given the over-riding effect of low temperatures on

107activity, relatively low dry-season temperatures would be expected to reduce rates of egg

108cluster production (see Hanning 1979), and this effect may be amplified by a depth-

109temperature interaction (Pomacea Project 2013).

110 Several studies have made associations between snail density or relative abundance

111and attributes of plant community structure (Bryan 1990, Turner 1996, Darby et al. 2004,

112Karunarante et al. 2006); however, the current version of our apple snail model does not

113parameterize the complex influence of habitat components (other than water depth) on snail

114demographic metrics. In laboratory studies, diet (Sharfstein and Steinman 2001, Shuford et

115al. 2005), and calcium and pH (Glass and Darby 2009) have also been shown to influence P.

116paludosa growth and survival; however, these parameters have not yet been incorporated into

117the model.

119EverSnail Model Description Following Grimm et al. (2006, 2010) ODD protocol

120Purpose and Parameterization

121The purpose of EverSnail is to describe the dynamics of the apple snail population on the

122Everglades landscape over a period of years, in order to interpret effects of year-to-year

123variability in landscape hydrology on apple snail abundance. In particular, the numbers and

124size distributions of the snails are simulated by following cohorts of snails through their life

125cycles, which allows total numbers within any given size range to be calculated for any day

7 5 126within a year. The effects of scenarios based on historical environmental data, as well as

127scenarios based on artificially varied sequences of environmental conditions, can be

128employed in the model to study the effects on apple snail population distributions across the

129landscape.

131State variables and scales

132The age- and size-structured apple snail population is represented as a vector consisting of

133500 daily age-classes, with 500 d representing the approximate life span of Florida apple

134snails;

135 N = [N1(t), N2(t), …, Ni(t), … , N500(t)] (A.1)

136where Ni(t) is the number of apple snails in age class i in a given spatial cell at time t. The

137snails in each age class have an attribute of size (linear dimension in mm), which increases

138deterministically. Space is represented by 253125 spatial cells, each 400 x 400 m, which are

139contiguous and represent the Water Conservation Areas and ENP (see Figure 1, main text)

140and some surrounding wetlands. The temporal scale of resolution is one day, and simulations

141can be run for any period beginning January 1st, 1991 (details on water depth input,

142Appendix 3). The individual spatial cells are assumed independent (no movement of snails

143between cells is assumed), so simulations can be performed either on the whole spatial extent,

144or on individual cells or subsets of cells, such as those for which empirical data on snail

145densities are available. The analyses presented here emphasized WCA3A (see Figure 1),

146where the majority of Everglades snail density data has been collected (Pomacea Project

1472013).

149Process overview and scheduling

8 6 150As there is no immigration to or emigration from a spatial cell, the dynamics of the age-

151structured subpopulation in each spatial cell is entirely governed by a Markov chain matrix

152model, A, shown below.

0 0 0 . fi f i1 f i2 . f 499 f500

a12 0 0 . 0 0 0 . 0 0

0 a23 0 . 0 0 0 . 0 0 ...... 0 0 0 . 0 0 0 . 0 0 153 A  (A.2) 0 0 0 . ai,i1 0 0 . 0 0

0 0 0 . 0 ai1,i2 0 . 0 0 ...... 0 0 0 . 0 0 0 . 0 0

0 0 0 . 0 0 0 . a499,500 0

155where ai,i+1 represents the fractional survival of a given one-day age class cohort from age

156class i to age class i+1, and fi is fecundity (per capita offspring) of age class i. The number of

157new 1-day old snails on each day is given by

158 N1 (t+20) = f iN i (t) + f i+1N i+1(t) … + … f 500 N500(t), (A.3)

159starting from the first age class capable of reproduction, where the ‘20’ represents a 20-day

160delay for eggs to hatch. Survival from one age class to the next is given by

161 Ni+1(t+1) = ai,i+1Ni(t) (i = 1, 2….., 500) (A.4)

163Initialization

164At the start of each simulation, an equal number of snails is assumed present in each age class

165and in all spatial cells. Simulations are run over preliminary periods of several years to allow

166transient effects of the initialization to disappear, which occurs after about three years.

168Environmental conditions

9 7 169We assume a grid of 400 x 400 meter cells covering the area of the simulation. For each cell,

170we assume that we can assign:

171  daily values of water depth from Everglades Depth Estimator Network (EDEN)

172  daily values of ambient air temperature.

174Daily water depth values for each spatial cell are input each day using data from the

175Everglades Depth Estimation Network (see Appendix 3 for details). Regional temperature

176values were linearly interpolated from existing data points across the whole set of spatial cells

177(Appendix 3). The specific spatial area to be simulated (the whole area or a subset of cells

178examined for sensitivity analyses) and the number of years simulated are prescribed as input.

180Submodels

181There are three basic submodels that determine the simulated snail population; growth,

182survival, and reproduction, each of which is based on empirical information. The matrix A

183(equation A.2) contains the elements ai,i+1(t) and fi(t), for age class survival and reproduction,

184respectively. Both of these elements are functions of sizes of the snails, so the relationship

185between age and size, or ‘growth’ is first described. Parameter values are given in Table A1-

188Growth. A logistic function is used to describe the mean individual size with age for every

189snail cohort;

kgrowth Age Sizemine 190 Sizei  , (A.5) kgrowth Age 1 ( Sizemin / Sizemax )( e 1)

191where Sizemin = linear size of newly hatched snail, Sizemax = maximum size of snail, and

192kgrowth = daily growth rate (see Figure A1-1). Growth is assumed to follow the pattern of

10 8 193Equation (A.5) regardless of changing environmental conditions, and thus every cohort

194follows the same growth pattern. The influence of environmental conditions such as

195temperature requires additional empirical data on P. paludosa to support further

196parameterization of the growth component of EverSnail.

199Survival. The values of day-to-day survival, ai,i+1, depend on snail size. Four survival

200categories were selected based on empirical data that show distinct survival rates between

201three small juvenile categories as well as a fourth category that includes juveniles >16 mm

202and reproductively mature adults. Survival can be divided into two conditions, wet (water

203depth > 0 cm) and dry (see Table A1-1 and Figure A1-2).

205 Size Wet conditions Dry conditions

206 size ≤ 6 mm Survwet1 Survdry1

207 6 mm ≤ size < 10 mm Survwet2 Survdry2

208 10 mm ≤ size < 16 mm Survwet3 Survdry3

209 16 mm ≤ size Survwet4 Survdry4

211The maximum lifespan of adults does not extend much beyond 500 days. In order to achieve

212rapid die-off around this age, we substitute the following expressions for mortality of snails

213of size > 16 mm under wet and dry conditions, respectively.

kage ( Agemortage) 214 Survadult,wet  Survwet 4 /1 e  (A.6)

kage ( Agemortage) 215 Survadult,dry  Survdry4 /1 e  (A.7)

11 9 217Reproduction. The values of fi(t), or the numbers of offspring produced per female snail per

218day, depend on a number of factors specific to the snails and the environmental conditions.

219We can write fi(t) as a product of factors:

220 fi(t) = (fraction of snails of age i that are female)

221 x (fraction of females of age i that are sexually mature) x (clutch size)

222 x (effects of time of year) x (effects of temperature) x (effects of water depth)

223 x (effects of egg submersion) (A.8)

224where ‘egg submersion’ means that the snail eggs are under water for some time, whereas

225‘effects of water depth’ refer to the effects of water depth independent of submersion of the

226eggs.

227We consider each of these reproduction factors in turn.

228 1. Fraction of snails of age i that are female; we assume Female fraction = 0.5

230 2. Fraction of females of age i that are sexually mature. Sexual maturity depends on snail

231 size, which is calculated as a function of age in equation (A.5). We assume there is a

232 mean size of arrival at maturity (MaturityThreshold), 27.5 mm, but that there is also

233 variation about this value, described by the equation

e krepr (Sizei MaturityThreshold ) 234 Fraction of mature females at agei  ’ (A.9) 1 ekrepr (Sizei MaturityThreshold )

235 where Sizei is the size of females in age class i (see Figure A1-3). A value of krepr =

236 1.0 will result in virtually all females being sexually mature by Size = 30 mm.

238 3. Clutch size, Clutch. A clutch size of Clutch = 30 eggs is assumed.

12 10 240 4. Effects of time of year, f(time of year). There is some seasonality in reproduction for

241 which we do not yet have a mechanistic explanation. To account for this seasonal

242 variation, we assume (see Figure A1-4)

243 f(time of year) = 1.0 Day 15 ≤ time of year < Day 189

244 f(time of year) = 0.3 Day 190 ≤ time of year < Day 328

245 f(time of year) = 0.0 otherwise

247 5. Effects of water depth, f(water depth). Apple snails do not reproduce when water

248 depth is too low, because of difficulty in moving. High water depths also inhibit

249 reproduction. We represent the effects of water depth by a parabolic function between

250 upper and lower bounds, Depthmin and Depthmax, respectively, with a midpoint

251 Depthmid (see Figure A1-5),

252 f(water depth) = 0.0 water depth < Depthmin

2 2 253 f(water depth) = 1.0 - (water depth –Depthmid ) /Wh Depthmin ≤ water depth

254 f(water depth) = 0.0 Depthmax ≤ water depth

256 6. Effects of temperature on reproduction. The probability of reproduction decreases

257 rapidly as temperature decreases below 17° C and reaches a maximum (plateau) at

258 21° C and above. We can represent this as, f(temperature) (see Figure A1-6)

1 259 f(temperature) = (A.10) 1 e ktemp ( TemperatureTempraturethreshold )

261 7. Effects of egg submersion, f(egg submersion). Note that this is different from #5

262 ‘Effects of water depth’, which does not take into account egg submersion. Eggs may

263 die if submersed for some period following oviposition during the time of 20 days that

264 they take to hatch. The egg clusters are oviposited about 18 cm above the water level.

13 11 265 Rule 1. If they are not submersed, they will hatch within 20 days, with the assumption

266 that 5% of the original number of eggs hatch each day, such that 100% hatch after 20

267 days. Rule 2. If the eggs are submersed for longer than 14 days, then only 5% of the

268 remaining unhatched eggs survive to hatching. It is assumed in the model that all

269 surviving eggs hatch in the model 20 days after the egg cluster is oviposited.

271Carrying capacity. A carrying capacity is imposed on the population of cells in any given

272cell by assuming there is an upper limit on the number of eggs that can exist on a given day,

273as reproductive sites are limited to emergent vegetation. This acts as the ‘density-dependent’

274regulation in the model. The upper limit on total eggs at a given time is assumed to be

-1 275 Egglimit = 35,000 ha

276If this limit is reached, no more eggs are assumed to be oviposited. It is possible that this

277limit will not be reached under realistic conditions.

279References

280Bryan DC (1990) Apple snail densities at Alexander Springs, Lake County, and observations

281 on snail ecology. Fla Sci 53:13

282Cary DM (1985) Climatological factors affecting the foraging behavior and ecology of snail

283 kites (Rostrhamus sociabilis plumbeus Ridgway) in Florida. Thesis. University of Miami,

284 Florida

285Cowie RH (2002) Apple snail (Ampullariidae) as agricultural pests: their biology, impacts,

286 and management In Barker GM (ed) Molluscs as Crop Pests. CABI Publishing,

287 Wallingford, UK, pp 145-192

288Darby PC, Valentine-Darby PL, Percival HF (2003) Dry season survival in a Florida apple

289 snail (Pomacea paludosa Say) population. Malacologia 45:179-184

14 12 290Darby PC, Valentine-Darby PL, Percival HF, Kitchens WM (2004) Florida apple snail

291 (Pomacea paludosa Say) responses to lake habitat restoration activity. Arch Hydrobiol

292 161:561-575

293Darby PC, Karunaratne LB, Bennetts RE (2005) The influence of hydrology and associated

294 habitat structure on spatial and temporal patterns of apple snail abundance and

295 recruitment. Final Report to U.S. Geological Survey. University of West Florida,

296 Pensacola

297Darby PC, Bennetts RE, Percival HF (2008) Dry down impacts on apple snail (Pomacea

298 paludosa) demography: Implications for wetland water management. Wetlands 28:204-

299 214

300Ferrer JR, Perera G, Yong M (1990) Life tables of Pomacea paludosa (say) in natural

301 conditions. Fla Sci 53 (supplement):15

302Glass NH, Darby PC (2009) The effect of calcium and pH on Florida apple snail Pomacea

303 paludosa (Gastropoda: Ampullariidae) shell growth and crush weight. Aquat Ecol

304 43:1085-1093

305Gleason PJ, Stone PA, Rhoads P, Davis SM, Zaffke M, Harris L (1975) The impact of

306 agricultural runoff on the everglades marsh located in the conservation areas of the

307 central and southern Florida flood control district. South Florida Water Management

308 District, West Palm Beach

309Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J, Grand T,

310 Heinz SK, Huse G, Huth A, Jepsen JU, Jørgensen C, Mooij WM, Müller B, Pe΄er G, Piou

311 C, Railsback SF, Robbins AM, Robbins MM, Rossmanith E, Rüger N, Strand E, Souissi

312 S, Stillman RA, Vabø R, Visser U, DeAngelis DL (2006) A standard protocol for

313 describing individual-based and agent-based models. Ecol Model 198:115-126

15 13 314Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF (2010) The ODD

315 protocol: A review and first update. Ecol Model 221:2760-2768

316Hanning GW (1979) Aspects of reproduction in Pomacea paludosa (Mesogastropods:

317 Pillidae). Thesis, University of Florida, Gainesville

318Hurdle MT (1974) Life history studies and habitat requirements of the apple snail at Lake

319 Woodruff National Wildlife Refuge. Proc Ann Conf Southeastern Assoc Game Fish

320 Commissioners 27:215-224

321Karunaratne LB, Darby PC, Bennetts RE (2006) The effects of wetland habitat structure on

322 Florida apple snail density. Wetlands 26:1143-1150

323Kushlan JA (1990) Freshwater marshes. In: Myers RL, Ewel JJ (eds) Ecosystems of Florida.

324 University of Central Florida Press, Orlando, pp 324-363

325McClary A (1964) Surface inspiration and ciliary feeding in Pomacea paludosa

326 (Prosobrachia: Mesogastropoda: Ampullariidae). Malacologia 2:87-104

327O’Hare NK (2010) Pomacea paludosa (Florida apple snail) reproduction in restored and

328 natural seasonal wetlands in the Everglades. Wetlands 30:1045-1052

329Pomacea Project, Inc. (2013) Literature review of Florida apple snails and snail kites, and

330 recommendations for their adaptive management. Final Report. Submitted to the

331 National Park Service, Everglades National Park. Available from

332 http://www.nps.gov/ever/naturescience/upload/ASS10-3FinalReportSecure.pdf. Accessed

333 22 June 2014.

334Sharfstein B, Steinman AD (2001) Growth and survival of the Florida apple snail (Pomacea

335 paludosa) fed 3 naturally occurring macrophyte assemblages. J North Am Benthol Soc

336 20:84-95

337Shuford III RBE, McCormick PV, Magson J (2005) Habitat related growth of juvenile

338 Florida applesnails (Pomacea Paludosa). Fla Sci 68:11-19

16 14 339Stevens AJ, Welch ZC, Darby PC, Percival HF (2002) Temperature effects on Florida

340 applesnail activity: Implications for snail kite foraging success and distribution. Wildl Soc

341 Bull 30:75-81

342Turner RL (1996) Use of stems of emergent plants for oviposition by the Florida applesnail,

343 Pomacea paludosa, and implications for marsh management. Fla Sci 59:34-49

344Turner RL (1998) Effects of submergence on embryonic survival and development rate of the

345 Florida applesnail, Pomacea paludosa: Implications for egg predation and marsh

346 management. Fla Sci 61:118-129

17 15 348Table A1-1. Life history parameters and their baseline values

349______

350Sizemin = 3 mm

351Sizemax = 50 mm

352kgrowth = 0.05

-1 353Survwet1 = 0.987 day

-1 354Survwet2 = 0.987 day

-1 355Survwet3 = 0.987 day

356Survwet4 = 0.99

-1 357Survdry1 = 0.976 day

-1 358Survdry2 = 0.984 day

-1 359Survdry3 = 0.989 day

-1 360Survdry4 = 0.99 day

361Agemort = 500. day

-1 362kage = 0.1 day

363MaturityThreshold = 27.5 mm

364krepr = 1.0

365Clutch = 30

366Depthmid = 50 cm

367Wh = 40 cm

368Depthmin = 10 cm

369Depthmax = 90. cm.

370ktemp = 1.0 per º C

371Temperaturethreshold = 17°C

-1 372Egglimit = 35,000 ha

18 16 373Appendix 1 Figure Captions

375Figure A1-1. Snail size versus age in days (Equation A1).

376Figure A1-2. Daily survival rate as a function of snail size for both wet and dry conditions.

377Figure A1-3. Cumulative fraction of sexually mature snails, which is used to determine the

378fraction of snails of a given age class that are mature.

379Figure A1-4. Maximum possible rate of reproduction as a function of time of year.

380Figure A1-5. Effect of water depth on reproduction.

381Figure A1-6. Affect of temperature on snail reproduction (Equation A6).

19 17 382

384 385Figure A1-1. Snail size versus age in days (Equation A1)

20 18 396

398 399

400Figure A1-2. Daily survival rate as a function of snail size for both wet and dry conditions.

21 19 412

413Figure A1-3. Cumulative fraction of sexually mature snails, which is used to determine the

414fraction of snails of a given age class that are mature.

22 20 426

427 428Figure A1-4. Maximum possible rate of reproduction as a function of time of year.

23 21 441

24 22 443Figure A1-5. Effect of water depth on reproduction.

25 23 457Figure A1-6. Affect of temperature on snail reproduction (Equation A6).

26 24 461Appendix 2. EverSnail programming and file formats

463In order to facilitate parameterization and execution of simulations, a software program

464encapsulating the apple snail population model logic was developed in the Java™

465programming language. The software operates against water depth and temperature inputs

466that are stored in the Network Common Data Form (NetCDF) file format and conform to the

467Comprehensive Everglades Restoration Plan (CERP) NetCDF Metadata Conventions (JEM

4682011). Each input must be organized as a regular grid of cells in three dimensions: 2D

469coordinate space plus time. A graphical user interface allows users to supply the location of

470the inputs on the file system and the simulation time period, as well as adjust any of 25 model

471parameters to support model validation and sensitivity analysis. All selections made by the

472user are stored in an internal data structure which is accessed by the model logic during

473runtime. Output is a CERP conventions-compliant NetCDF file containing daily simulated

474population values per spatial cell for total snails (adults plus juveniles), adults only, juveniles

475only, and eggs. These data were then analyzed both graphically, by using the EverVIEW

476Data Viewer (Romañach et al. 2014), and in tabular format with Microsoft Excel.

478References

479Romañach SS, McKelvy M, Conzelmann C, Suir K (2014) EverVIEW Data Viewer: A

480 visualization framework and exploration tool for decision support of large-scale

481 biological planning. Environ Model Software 62:221-229

482 doi:10.1016/j.envsoft.2014.09.008

483JEM (Joint Ecosystem Modeling) (2011) CERP NetCDF standard. Available from

484 http://www.jem.gov/Standards/NetCDF. Accessed 30 June 2014

27 25 486Appendix 3. Detailed description of the air temperature and EDEN water depth inputs

487to EverSnail

489The two input variables to EverSnail are daily average air temperature (°C) and estimated

490daily water depths from the Everglades Depth Estimation Network (EDEN). The spatial

491extent of the EverSnail model is dictated by the EDEN coverage. The Everglades Depth

492Estimation Network (EDEN) is a system more than 250 real-time water level monitoring

493stations throughout the Greater Everglades landscape. Water level data are combined with a

494ground surface elevation model to create a water surface simulation model. Water surface

495data are available from 1991 to present for the entire freshwater Everglades region. Data can

496be downloaded from http://sofia.usgs.gov/eden/models/watersurfacemod_download.php.

497EDEN daily water depth estimates can be manipulated to create artificial sequences of

498hydrologic conditions using the Slice and Dice tool available at

499http://jem.gov/Modeling/SliceAndDice. The South Florida Water Management District,

500south Florida’s water governing authority, has been collecting surface water, ground water,

501and meteorological data for many decades. For this model, we used daily air temperature

502values from 16 stations located throughout the model domain (applicable temperature data

503are presented in the main manuscript) dating back to 1991. Data can be downloaded from

504http://www.sfwmd.gov/dbhydroplsql/show_dbkey_info.main_menu.

505 Intra- and inter-annual fluctuations in water depths drive the observed changes in the

506EverSnail population output. Temperature influences were also evident, in EverSnail they

507impact reproduction (especially at lower temperatures), with applicable details presented in

508the main manuscript. The primary areas of interest for examining hydrologic influences on

509apple snail populations, as presented in the manuscript, are Water Conservation Area 3A

510(WCA3A), WCA3B, and northern portions of Everglades National Park (ENP) (see Figure 1,

28 26 511main manuscript, and Figure A3-1, below). In order to characterize hydrologic impacts

512described in the manuscript, we obtained EDEN data for representative sites. WCA3A has a

513hydrologic gradient represented by Sites 13 (lower ground elevation, southern WCA3A) and

51418 (higher elevations further north), and one site each was selected for comparison in

515WCA3B and ENP (Figure A3-1).

516 For the Everglades region, the driest year for the period of interest (2000-2004

517hydrology, which influenced 2001-2005 snail abundance, see main manuscript) occurred in

5182001. In order to represent low water and high water extremes for the period of interest, we

519focused on WCA3A, but considered conditions for adjoining wetlands, WCA3B and ENP, as

520well (Figure A3-1). In 2001, portions of WCA3A, WCA3B and ENP experienced dry

521conditions (Figures A3-2, A3-3 and A3-4). For DrySim we copied the 2001 hydrologic data

522into simulation years 2002 and 2003 to artificially create 3 consecutive low water years. For

523WetSim we copied the 2003 hydrologic data into simulation years 2001 and 2002 to

524artificially create 3 consecutive high water years. Note that 2003 exhibited the highest dry

525season (winter-spring) water depths for the period of interest (Figures A3-2, A3-3, A3-4).

29 27 527Appendix 3 Figure Headings

529Figure A3-1. Everglades sites (*) for which daily water depths for 2000-2004 are presented

530in Figures A3-2, A3-3 and A3-4. Wetland units (as labeled in main text Figure 1) are

531L=WCA3A (showing sites 13 and 18), M=WCA3B (one site) and N=ENP (one site).

533Figure A3-2. WCA3A sites 13 (black diamonds) and 18 (white circles, higher ground

534elevation) water depths from 2000-2004. 2001 conditions are noted as being used to create 3

535consecutive low water years for DrySim and 2003 conditions are noted as being used to

536create 3 consecutive high water years for WetSim. For site locations see Figure A3-1.

537Dashed horizontal line represents the depth (10 cm) below which Florida apple snails stop

538moving, mating or laying eggs (see Appendix 1).

540Figure A3-3. WCA3B site water depths from 2000-2004. 2001 conditions are noted as being

541used to create 3 consecutive low water years for DrySim and 2003 conditions are noted as

542being used to create 3 consecutive high water years for WetSim. For site locations see Figure

543A3-1. Dashed horizontal line represents the depth (10 cm) below which Florida apple snails

544stop moving, mating or laying eggs (see Appendix 1).

546Figure A3-4. ENP site water depths from 2000-2004. 2001 conditions are noted as being

547used to create 3 consecutive low water years for DrySim and 2003 conditions are noted as

548being used to create 3 consecutive high water years for WetSim. For site locations see Figure

549A3-1. Dashed horizontal line represents the depth (10 cm) below which Florida apple snails

550stop moving, mating or laying eggs (see Appendix 1).

30 28 551Figure A3-1.

31 29 554Fig A3-2.

120 2000 2001 2002 2003 2004 (used in DrySim) (used in WetSim) 100 ) 80 m c (

t 60 p e D

40 r e t a 20 W

0 Site 13 WCA3A Site 18 -20 556

32 30 557Fig A3-3.

2000 2001 2002 2003 2004 (used in DrySim) (used in WetSim) 80

-40 WCA3B

559 -60

33 31 560Fig A3-4.

80 2000 2001 2002 2003 2004 (used in DrySim) (used in WetSim)

m 40 c (

p 20 e D

r e t 0 a W

562 -40

34 32 563Appendix 4. Detailed results of EverSnail sensitivity analyses

-1 -1 565The model was very sensitive to changing Survwet4 from 0.99 d down to 0.988 d ; this

566resulted in the snail population quickly falling to zero (Table A4-1). Increasing Survwet4 to

5670.992 d-1 had no effect on the snail population. Sensitivity for all the remaining parameter

568changes ranged from −62.0% to +5.5%, and in some cases there were marked differences in

569sensitivity between sites (13 and 18) for the same parameter (Table A4-1). Sensitivity was

570less than ±10% of baseline snail abundance for kgrowth, ktemp, Survwet1, Survwet2, Survwet3. A 20%

571decrease in Tempthreshold (down to 13.6 °C) resulted in <6% increase in the population (Table

572A4-1).

573 A +20% increase in Tempthreshold (up to 20.4 °C) resulted in ~≈20-22% decrease, based

574on quarterly averages, in the snail population. This population decline resulted from the input

575temperatures having to rise to a higher level to stimulate reproduction. Very small declines in

576air temperature below a threshold set at 20.4 °C (+20% of baseline) resulted in substantial

577changes in sensitivity (Figure A4-1). Changing Depthmax for reproduction from 90 cm

578(baseline) down to 72 cm (−20% of baseline) resulted in significant changes to snail

579abundance, but varied with topography. Parameterizing the model to prevent snails from

580reproducing at depths >72 cm greatly constrained egg production in the breeding season. The

581relative impact of setting Depthmax to 72 cm across a depth gradient was quantified by

582comparing output from WCA3A Site 13 vs 18; for site 13 (lower ground elevation) the

583EDEN depth data more frequently exceeded 72 cm and resulted in a population decline of

584−62%, compared to Site 18 at −12% (Figure A4-2). Due to overall lower depths in Site 18,

585the negative influence of lowering Depthmax was much less, compared to Site 13, for the site-

586specific snail population. At only one point in time did mean quarterly depth in Site 18

587exceed 72 cm (in 2005), and this resulted in a significant negative effect on the snail

35 33 588population (−51% relative to the baseline) (Figure A4-2). The fact that raising Depthmax to

589108 cm resulted in no change in the population relative to baseline (Table A4-1) reflects the

590fact that quarterly mean depths in Sites 13 and 18 never exceeded 108 cm, and at this setting

591the simulated snail population continued to produce eggs. When EDEN depth inputs stayed

592between 10 and 90 cm under baseline conditions the population remained relatively stable

593and the annual cycle was driven by seasonal variation in egg cluster production (see main

594text).

595 Since a change in Depthmax for reproduction resulted in a significant impact in the

596sensitivity analyses, and since Everglades restoration and management focuses primarily on

597restoring certain hydrologic conditions, we will elaborate on the response of the population to

598changes in seasonal depths. The overall lowest water depths (falling below 10 cm in Site 18)

599were observed in 2001 during the dry season (Figure A4-2, also see Appendix 3). The

600highest dry-season water depths were observed in 2003; during that year depths stayed above

60125 cm in site 18 and in site 13 depths never fell below 59 cm. For any given year, the

602greatest depths occurred in the fall, and 2005 fall depths were the greatest overall (Figure A4-

6032). Generally lower Site 18 water depths reflect a higher ground elevation than Site 13 (for

60475% of the simulation days, depths were ~20-40 cm lower than site 13, see Appendix 3).

605These differences in water depth differentially impacted the sensitivity to lowering the

606Depthmax for reproduction down to 72 cm. From 2001-2005, Site 18 depths exceeded 72 cm

607only once in 20 quarters, so sensitivity was <20% the majority of this period. In contrast,

608Site 13 depths exceeded 72 cm one to two quarters each year, and sensitivity ranged from

609-29% to -86%. The relationship between egg cluster production and seasonal water depth,

610represented here as Depthmax, needs to be addressed with more empirical data (see Discussion,

611main text), since the value of this parameter in EverSnail has a substantial impact on

612predicted snail abundance over the Everglades landscape (Figure A4-3).

36 34 613Table A4-1. EverSnail sensitivity expressed as percent change in total snail density when

614comparing simulations set at baseline parameter values to those with -20% and +20%

615changes for four model parameters shown, and a change of ±0.002 for daily survival rate (for

616four size categories) in flooded conditions (equivalent to ±20% of the mortality rate). Sites

61713 and 18 are shown to illustrate differences as a function of location within WCA3A (i.e.,

618site 13 has a lower ground elevation). The mean snail density was calculated for each quarter

619of the simulation period (2001-2005), with the standard deviation (SD) (n= 20 total quarterly

620calculations).

621 622 Quarterly Quarterly

Baseline % change % change

Parameter Snail density Snail density

Parameter* Value Site w/-20% BPV w/+20% BPV

(submodel) (BPV) Mean ± SD (n=20) Mean ± SD (n=20)

kgrowth (size) 0.05 13 -4.2 ± 2.7 1.6 ± 1.5

18 -3.1 ± 2.1 0.6 ± 1.6

ktemp (reproduction) -1.0 / °C 13 1.1 ± 1.0 -0.2 ± 0.80

18 0.2 ± 1.0 -1.3 ± 1.0

Tempthreshold 17 °C 13 5.5 ± 7.6 -21.7 ± 15.7

(reproduction) 18 2.5 ± 3.4 -19.9 ± 12.1

Depthmax 90 cm 13 -62.0 ± 17.1 0.0 ± 0.0

(reproduction) 18 -12.1 ± 12.0 0.0 ± 0.0 w/-0.002 w/+0.002 (survival) (survival)

Survwet1 0.987 / d 13 -1.4 ± 1.0 1.8 ± 0.9

(survival) 18 -2.1 ± 1.3 1.3 ± 1.6

Survwet2 0.987 / d 13 -1.1 ± 0.6 1.7 ± 1.0

(survival) 18 -1.9 ± 1.4 0.9 ± 1.0

Survwet3 0.987 / d 13 -1.0 ± 0.6 1.6 ± 1.0

(survival) 18 -1.9 ± 1.4 0.9 ± 1.0

Survwet4 0.990 / d 13 -100 ± 0.0 0.0 ± 0.0

(survival) 18 -100 ± 0.1 0.0 ± 0.0 37 35 623*See Appendix 1 for description of parameters

38 36 637Appendix 4 Figure Headings

639Figure A4-1. Variation in model sensitivity through the simulation period when the

640Tempthreshold (for reproduction) was set at +20% of baseline, expressed on a quarterly basis for

641simulation years 2001-2005. Solid black line represents the average quarterly deviation in

642snail abundance from the baseline abundance after Tempthreshold was set at +20%. Dashed-gray

643horizontal line represents 20.4 °C (+20% of the baseline 17 °C) in order to reference when

644input data fall below this temperature threshold, which yielded the greatest percent deviation

645in snail abundance. Temperature data used as input for the model (solid gray line) are shown

646for Site 18 (sensitivity results were very similar for Site 13, not shown).

648Figure A4-2. Variation in model sensitivity to Depthmax (for reproduction) expressed on a

649quarterly basis for simulation years 2001-2005. Solid black lines represent the average

650quarterly deviation in snail abundance from the baseline abundance after Depthmax was set at

651minus 20%. Dashed-gray horizontal lines represent 72 cm (-20% of the baseline 90 cm) in

652order to reference when input data fall below this depth threshold. EDEN-estimated depths

653(solid gray line) shown for Site 18 and Site 13 (the latter having a lower ground elevation).

655Figure A4-3. Landscape level output of the simulated Everglades total snail population

656associated with sensitivity analyses that lowered Depth Max for reproduction from the

657baseline setting of 90 cm (panel a) down to 72 cm (-20% of baseline) (panel b). The output is

658for February 1st of 2004, which reflects a snail population exposed to relatively high water

659levels (Appendix 3) in the 2003 breeding season.

39 37 661Fig A4-1.

-60.0 30.0

-50.0 25.0 e c

n -40.0 a

d 20.0 ) n u C ( b

e A -30.0

r l i u t a a

n 15.0 r S e

i -20.0

m e e g T

n 10.0 a

h -10.0 C

% 5.0 0.0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 2001 2002 2003 2004 2005 10.0 0.0 662

40 38 666 fig A4-2.

-100.0 100

Site 18 90 -80.0

e 80 c n a

d 70 n -60.0 u b A

60 ) l i m a c n (

-40.0 50 h t n i p e e g D

n 40 a h -20.0 C 30 %

20 0.0 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 10 2001 2002 2003 2004 2005 20.0 0 667

668 -100.0 100

Site 13 90 -80.0 80

n 70 a

d -60.0 n u )

b 60 A m

l c i (

-40.0 50 h n t S p

40 D e g

n -20.0 a

h 30 c

% Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 20 0.0 2001 2002 2003 2004 2005 10

20.0 0 669

41 39 670FigA4-3

671 Panel a. (depthmax=90) Panel b. (depthmax=72)