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Pink Shrimp Farfantepenaeus duorarum Spatiotemporal Abundance Trends Along an Urban, Subtropical Shoreline Slated for Restoration

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The Biscayne Bay Coastal Wetlands (BBCW) project of the Comprehensive Everglades Restoration Plan (CERP) aims to reduce point-source freshwater discharges and spread freshwater flow along the mainland shoreline of southern Biscayne Bay to approximate conditions in the coastal wetlands and bay that existed prior to construction of canals and water control structures. An increase in pink shrimp (Farfantepenaeus duorarum) density to ≥ 2 individuals m-2 during the wet season (i.e., August-October) along the mainland shoreline was previously proposed as an indicator of BBCW success. This study examined pre-BBCW baseline densities and compared them with the proposed target. Densities were monitored by seasonal (wet, dry) throw-trapping (1 m2 replicated in triplicate) at 47 sites along ~22 km of the southwestern Biscayne Bay coastline over 10 years (2007-2016). Densities varied across years and were most often higher in dry seasons. Quantile regression revealed density limitation by four habitat attributes: water temperature (°C), depth (m), salinity (ppt), and submerged aquatic vegetation (SAV: % cover). Procrustean analyses that tested for congruence between shrimp densities and habitat metrics found that water temperature, water depth, and salinity explained ~ 28%, 28%, and 22% of density variability, respectively. No significant relationship with SAV was observed. Hierarchical clustering was used to identify spatially and temporally similar groupings of pink shrimp densities by sites or season-years. Significant groupings were later investigated with respect to potentially limiting habitat attributes. Six site and four year-season clusters were identified. Although habitat attributes significantly differed among spatial clusters, within-cluster median pink shrimp densities did not correlate with within-cluster minima, maxima, medians, or standard deviations of habitat attributes. Pink shrimp densities corresponded significantly with salinity and appeared limited by it. Salinity is an environmental attribute that will be directly influenced by CERP implementation.
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1
1Title
2Pink Shrimp Farfantepenaeus duorarum Spatiotemporal Abundance Trends Along an Urban,
3Subtropical Shoreline Slated for Restoration
4Short Title
5Pink Shrimp Spatiotemporal Abundance Along Shoreline Prior to Restoration
6Authors
7Ian C. Zink1,2, Joan A. Browder2, Diego Lirman3, Joseph E. Serafy2,3
81Cooperative Institute for Marine and Atmospheric Science, Rosenstiel School of Marine and
9Atmospheric Science, University of Miami, Miami, FL, USA
10
11 2Protected Resources Division, Southeast Fisheries Science Center, National Marine Fisheries
12 Service, National Oceanic and Atmospheric Administration, Miami, FL, USA
13
14 3Marine Biology and Ecology Department, Rosenstiel School of Marine and Atmospheric
15 Science, University of Miami, Miami, FL, USA
16
17 Corresponding Author
18 Ian C. Zink: ian.zink@noaa.gov; Office: 305-361-4297
19 Key Words
20 Penaeid, Biscayne Bay, CERP, Everglades, quantile regression, density limitation
21
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22 Abstract
23 The Biscayne Bay Coastal Wetlands (BBCW) project of the Comprehensive Everglades
24 Restoration Plan (CERP) aims to reduce point-source freshwater discharges and spread
25 freshwater flow along the mainland shoreline of southern Biscayne Bay to approximate
26 conditions in the coastal wetlands and bay that existed prior to construction of canals and water
27 control structures. An increase in pink shrimp (Farfantepenaeus duorarum) density to 2
28 individuals m-2 during the wet season (i.e., August-October) along the mainland shoreline was
29 previously proposed as an indicator of BBCW success. This study examined pre-BBCW baseline
30 densities and compared them with the proposed target. Densities were monitored by seasonal
31 (wet, dry) throw-trapping (1 m2 replicated in triplicate) at 47 sites along ~22 km of the
32 southwestern Biscayne Bay coastline over 10 years (2007-2016). Densities varied across years
33 and were most often higher in dry seasons. Quantile regression revealed density limitation by
34 four habitat attributes: water temperature (°C), depth (m), salinity (ppt), and submerged aquatic
35 vegetation (SAV: % cover). Procrustean analyses that tested for congruence between shrimp
36 densities and habitat metrics found that water temperature, water depth, and salinity explained ~
37 28%, 28%, and 22% of density variability, respectively. No significant relationship with SAV
38 was observed. Hierarchical clustering was used to identify spatially and temporally similar
39 groupings of pink shrimp densities by sites or season-years. Significant groupings were later
40 investigated with respect to potentially limiting habitat attributes. Six site and four year-season
41 clusters were identified. Although habitat attributes significantly differed among spatial clusters,
42 within-cluster median pink shrimp densities did not correlate with within-cluster minima,
43 maxima, medians, or standard deviations of habitat attributes. Pink shrimp densities
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44 corresponded significantly with salinity and appeared limited by it. Salinity is an environmental
45 attribute that will be directly influenced by CERP implementation.
46
47 Introduction
48 Biscayne Bay is a coastal lagoon located adjacent to the city of Miami, Florida, USA. Its
49 watershed was heavily modified during the 20th century and is currently highly managed to
50 prevent urban, suburban, and agricultural flooding while also meeting agricultural, commercial,
51 and residential freshwater demands. The Comprehensive Everglades Restoration Plan (CERP)
52 seeks to restore the quality, quantity, timing, and distribution of freshwater deliveries to southern
53 Florida nearshore areas [1], including Biscayne Bay. The Biscayne Bay Coastal Wetlands
54 (BBCW) project, a CERP component, aims to restore a more natural hydrology and salinity
55 regime along the western bay’s southwestern shoreline[2,3]. Three actions are needed to make
56 this improvement: (1) increasing the total volume of freshwater deliveries; (2) diverting part of
57 point-source freshwater discharge (i.e., canal discharges) to reestablish water delivery as
58 overland sheet flow; and (3) altering the present timing of deliveries by lengthening discharges
59 through the wet season (May-October) and into the dry season (November-April) [3,4]. The
60 REstoration COordination and VERification (RECOVER) team established Interim Goals (IGs)
61 to link ecological indicator metrics to CERP activities and thus evaluate restoration performance
62 and realization of post-implementation ecological benefits at 5-yr intervals [1].
63 The pink shrimp Farfantepenaeus duorarum is one of many ecological indicators
64 selected to assess ecological impacts of CERP implementation [1,5]. Pink shrimp was selected
65 to assess estuarine ecosystems due to previously suggested abundance linkages to salinity
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66 condition [1,5]. As reviewed by Zink et al. [6], salinities within polyhaline (18 – 30 ppt: [7]) and
67 euhaline (30 - 40 ppt) ranges would directly improve pink shrimp productivity. Expansion of
68 southwestern Biscayne Bay estuarine habitat was anticipated to benefit pink shrimp residing
69 there [8]. Indirectly, reduced stress on seagrass communities from extreme salinity fluctuations
70 would yield increased pink shrimp abundance due to increased seagrass cover and spatial extent
71 [5,8]. Higher abundance of pink shrimp has been reported in areas exhibiting higher and more
72 stable salinities [8-10]; these same coastline stretches exhibit more continuous seagrass cover
73 [11]. Areal expansion of shoal grass (Halodule wrightii) cover could further amplify pink
74 shrimp abundance due to an apparent affinity for this seagrass species [12]. The stated pink
75 shrimp IG for southwestern Biscayne Bay is “2 shrimp m-2 in nearshore optimal habitat (i.e.,
76 seagrasses)” during August-October peak abundance periods [1]. This IG was based upon a peak
77 density of ~1.8 shrimp m-2 observed in September during a 2 yr pilot study [8].
78 Historically, most freshwater delivery to Biscayne Bay was through transverse glades,
79 broad natural channels through the Miami Coastal Ridge that allowed Everglades Basin surface
80 water drainage [13,14] and groundwater seepage [15-20]. These natural drainage features fed
81 fresh water from the Everglades through the coastal ridge via transverse glades into creek
82 networks that spread surface water flows along the bay’s shoreline. Canalization converted the
83 freshwater delivery system to one dominated by pulsed point-source (i.e., canal mouth)
84 discharges that altered benthic submerged aquatic vegetation (SAV), infaunal, epifaunal, and
85 nekton communities [11,21-26] and lowered the water table, reducing groundwater seepage
86 [19,20].
87 Post-BBCW salinity goals for southwestern Biscayne Bay (Shoal Point to Turkey Point:
88 Fig. 1) provide oligohaline (0.5-5 ppt) and mesohaline (5-18 ppt) regimes at the shoreline
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89 trending towards 20 ppt (polyhaline, 18-30 ppt) 500 m from the coast [4]. These conditions are
90 anticipated to enrich estuarine faunal assemblages as well as increase estuarine species
91 distributions and abundances [4,27,28]. Expansion of continuous submerged aquatic vegetation
92 (SAV) habitats dominated by Halodule wrightii, a species commonly associated with low and
93 variable salinity, is foreseen [11,24-26,29,30]. BBCW implementation goals for benthic habitat
94 include increased spatial extent of nearshore seagrass beds, especially seaward expansion of H.
95 wrightii [30]. Increased overlap of optimal salinity conditions with preferred benthic SAV
96 habitats would yield indirect, synergistic benefits to estuarine fauna such as pink shrimp
97 [4,5,27,31].
98
99 Fig. 1: Map of study area, including referenced geographical features, and location of survey
100 sites. The second panel depicts the same sites color-coded to match significant site clusters (Fig.
101 3.3B and 3.4).
102
103 The purpose of this study was to investigate spatiotemporal trends in pink shrimp density
104 along the southwestern Biscayne Bay shoreline. We investigated the plausibility of the post-
105 CERP establishment of ≥2 shrimp m-2 IG. Further, we address presumptions that (1) pink shrimp
106 peak abundance occurs during the wet season; and (2) nearshore mesohaline salinity goals would
107 yield increased pink shrimp abundance in the nearshore zone. Pink shrimp density relationships
108 to species-specific and total benthic SAV % cover, as well as SAV canopy height, were also
109 investigated. Our focus was on evaluating temporal (i.e., seasonal and inter-annual) and spatial
110 pink shrimp density trends relative to habitat attributes. This was achieved by (1) using quantile
111 regression to identify habitat attributes that potentially limit pink shrimp density, (2) organizing
112 pink shrimp density and habitat observations via heatmaps to visually assess spatiotemporal
113 variability and trends, (3) using Procrustean analysis to measure concordance between density
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114 and habitat attribute matrices, (4) employing hierarchical clustering analysis to identify
115 spatiotemporal density clusters, and (5) investigating distributional aspects (median, minimum,
116 maximum, and standard deviation) of habitat attribute values (temperature, salinity, water depth,
117 and SAV % cover) within density clusters to link density patterns to the environment. These
118 analyses employ data from wet and dry seasons of 10 years of epifaunal community monitoring
119 data from 47 sites within 50 m of shore spanning ~22 km of shoreline.
120
121 Materials and methods
122 Study area
123 Biscayne Bay is a large (1,110 km2), shallow (depths generally < 3 m), subtropical
124 lagoon system extending approximately 56 km north to south along the southeast coast of
125 Florida, USA (Fig 1). Where coastal urban development is low, the bay’s mainland and
126 shoreline consists of mangrove-seagrass ecotone punctuated by natural tidal creeks, constructed
127 channels, and freshwater canals [32]. Overland freshwater discharges and groundwater seepage,
128 create a salinity gradient perpendicular to the shoreline with three salinity zones: (1) western
129 nearshore areas usually affording the lowest salinities; (2) the bay central axis marked by near
130 oceanic salinities; and (3) oceanic salinities near the eastern passes through barrier islands to the
131 open ocean [24,29,33]. Tidal ranges are generally 0.5 to 1 m [34,35].
132
133 Field surveys
134 Epifaunal communities and SAV habitats were surveyed seasonally at fixed sampling
135 sites (n = 47) along the southwestern Biscayne Bay nearshore zone (0-50 m) from Shoal Point to
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136 Turkey Point (Fig. 1). Surveys were conducted in public waters under authority of Biscayne
137 National Park (Study #: BISC 06016, Permit #: BISC-2017-SCI0022). Surveys were conducted
138 in dry (January-March sampling) and wet (July-September sampling) seasons that characterize
139 south Florida’s climate. The primary sampling unit was the 20 m buffer around GPS coordinates
140 that identified permanent sampling sites for continuous (15-min) recording of salinity,
141 conductivity, and temperature. Sites were located in the shallow, open water along the western
142 shoreline mangrove-seagrass ecotone, an area likely to be directly affected by CERP
143 implementation. During each survey, the 47 fixed sampling sites were visited within 3 hr of high
144 tide over 5 to 7 days within a couple of weeks’ time or less. Water quality and habitat
145 parameters, including water temperature (˚C), salinity (ppt), pH, dissolved oxygen saturation
146 (%), dissolved oxygen concentration (mg L-1), water depth (m), and sediment depth (m), were
147 recorded at each site. Benthic habitats were assessed for species-specific SAV % cover by visual
148 assessment of 10 replicate 0.5 m2 quadrats per site [24,25]. In addition, canopy height
149 (maximum seagrass blade length) was measured to provide a topography metric. Species-
150 specific and total SAV % cover data following the methods of Lirman et al. [25] were obtained
151 for the period 2008 to 2016.
152 Epifaunal communities were sub-sampled at each site (n = 3) using an open-ended, rigid-
153 sided aluminum box (i.e., throw trap) measuring 45 cm by 1 m2 [36,37]. Two 3-mm stretch-
154 mesh cover nets affixed to opposite sides of the throw-trap upper surface prevented epifauna
155 escape during deployment. Once deployed, the throw-trap was cleared of trapped epifauna by
156 sweeping (n = 4) its interior from alternating directions with a metal-framed seine fitted with 3
157 mm stretch-mesh, while gently tapping the substrate with the seine frame. Organisms collected
158 from each sub-sample throw-trap deployment were bagged and numbered separately for storing
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159 and processing. Samples were frozen during storage until processing. No protected species were
160 sampled.
161
162 Epifauna identification and measurement
163 Taxonomic identifications and size measurements were conducted in the laboratory.
164 Organisms collected from each replicate throw-trap deployment at a site were maintained and
165 processed independently of each other. Where possible, carapace length (CL, mm) and total
166 length (TL, mm) were recorded for each farfantepenaeid shrimp. Shrimps >8.0 mm CL were
167 identified to species primarily using petasma and thelycum (i.e., sexual) morphology, although
168 other characteristics were also used [38-41]. Shrimps <8.0 mm CL were identified to genus due
169 to low degree of sexual morphological development [39].
170
171 Statistical analysis
172 All statistical analyses were performed using the R statistical package (The R
173 Foundation, https://www.r-project.org/). Statistical analyses were performed with a Type 1 error
174 criterion of α = 0.10 to reduce potential Type 2 errors. Combining the data for all three trap
175 samples for each site, density was calculated as the sum of observed shrimps per 3 m2 per site.
176 Density data were natural logarithm (x + 1) transformed before analysis to reduce influence of
177 outlying observations.
178
179 Potential habitat limitations on pink shrimp density
180 As a statistical interpretation of the ecological concept of Leibig’s Law of the Minimum
181 [42,43], quantile regression (QR) has been presented as a method to identify species distribution
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182 or abundance limitation by specific habitat attributes by focusing specifically on the upper bound
183 of the abundance vs. habitat attribute relationship [44-47]. Pink shrimp density was first plotted
184 against individual habitat factors to graphically assess potential limiting factors. QRs (function
185 ‘rq’ of package ‘quantreg’) fit to the 0.5 and 0.9 density percentiles were used to statistically
186 identify a subset of habitat attributes that suggested limitation at the median and upper edge of
187 the density distribution. Analyses considered water temperature (˚C), salinity (ppt), pH,
188 dissolved oxygen saturation (%), dissolved oxygen concentration (mg/L), water depth (m),
189 sediment depth (m), and the following SAV metrics: Thalassia testudinum % cover, H. wrightii
190 % cover, total seagrass % cover, total SAV % cover, and total SAV canopy height. As in
191 previous studies in the same region [24-26], Syringodium filiforme was rarely encountered (n =
192 10, 1.6% of total samples) and thus was not further considered.
193 Multiple QR functional response shapes were investigated including linear, quadratic,
194 cubic, log-linear, natural cubic splines (function ‘ns’ of package ‘splines’) [48], and additive
195 quantile smoothing spline (AQSS) response curves (functions ‘rqss’ and ‘qss’ of package
196 ‘quantreg’ [49,50]). Natural cubic splines were constructed with 3 (0.25, 0.50, and 0.75
197 quantiles of the predictor), 2 (0.33 and 0.66 quantiles of the predictor) and 1 (0.5 quantile of the
198 predictor) internal knots [48]. QR coefficient confidence intervals were constructed and tested
199 for significance by xy-pair bootstrapping (function ‘summary.rq’ of package ‘quantreg’).
200 Natural cubic spline QRs were modeled without intercepts; these QRs were considered
201 significant if each individual spline describing sub-ranges of the data was significant.
202
203 Spatiotemporal relationships
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204 Heatmaps were generated to visualize spatiotemporal trends in pink shrimp density and
205 the habitat attributes found by QR to potentially limit pink shrimp density. Observation data
206 were converted to 47 row by 20 column matrices to display their spatial (47 sampling sites) and
207 temporal (10 yr by 2 seasons) patterns, and color gradients were used to represent the magnitude
208 of density.
209 Procrustean analyses allowed direct testing of statistical concordance between matrices of
210 shrimp density and habitat attributes [51-53]. Procrustean analysis minimizes the residual sum
211 of squares between a target matrix (X: here, shrimp density) and a second, fitted matrix (Y: here,
212 habitat attributes), superimposed on it by scaling, rotating, and dilating [51,52]. The Procrustean
213 Sum of Squares (PSS, also known as Gower’s Statistic: m2X,Y) represents the minimized residual
214 sum of squares from the fitting procedure and is used to assess Procrustean fit ranging from 0 to
215 1, with higher values presenting poorer fit [51-53]. The PSS metric is equivalent to 1 –r2, where
216 r is a Pearson correlation coefficient [52]. Because the method hinges on one-to-one
217 relationships between the matrices being compared, Procrustean analysis [54,55] cannot handle
218 missing values. Following Adams et al. [54] and Arbour and Brown [55], missing habitat
219 attribute values were imputed with linear regressions that included site, season, and year as
220 potential factors. PROTEST (function ‘protest,’ package ‘vegan’, permutation n = 9999)
221 provided statistical significance of Procrustean fits between density and habitat attribute matrices
222 [51].
223 Hierarchical clustering procedures were used to identify groups with similar density
224 among sites or year-seasons [56,57]. Bray-Curtis dissimilarity matrices were constructed
225 (‘vegdist’ function, ‘vegan’ package) with respect to site (i.e., spatial) and year-season (i.e.,
226 temporal) density observations. Hierarchical agglomerative clustering (function ‘hclust’) using
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227 the “Ward.D2” agglomeration method identified spatially and temporally similar density
228 groupings. A prioiri statistical significance of clusters was tested via similarity profiling
229 (function ‘simprof’ of package ‘clustsig’) (permutations = 999, number of expected groups =
230 1000) of identified density cluster memberships [57]. Permutational multivariate ANOVA
231 (PERMANOVA: function ‘adonis2’ of package ‘vegan’) testing provided a posteriori cluster
232 significance [58,59]. PERMANOVA was also used to investigate inter-annual and seasonal
233 density differences using year-season cluster membership as a categorical nesting factor. To
234 investigate potential dispersion influences on PERMANOVA significance, multivariate
235 homogeneity of dispersions analysis (function ‘betadisper’ of package ‘vegan’) was used to test
236 for inter-cluster differences in dispersion (i.e., distance to centroid) [60]. The density heat map
237 was rearranged to visualize site and year-season cluster memberships.
238
239 Pink shrimp density and habitat attributes among density clusters
240 Pink shrimp density and habitat attributes previously detected as potentially limiting to
241 pink shrimp density via QR were investigated among site and year-season clusters. First,
242 medians (± CI) of density and habitat attributes were computed for each site and year-season
243 cluster. Confidence intervals (CIs) about median values were computed as:
244 1.58*IQR/sqrt(n) (1)
245 where IQR = interquantile ranges and n = sample size, as described in McGill et al. [61] and
246 Chambers et al. [62]. Plots of density and habitat attributes’ median, CIs, minimum, and
247 maximum values were used to visualize their distributions within site and year-season clusters.
248 Density and habitat attributes were analyzed with respect to site or year-season clusters.
249 Nonparametric tests were used because parametric normality and equality of variance
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250 assumptions were usually violated. Kruskal-Wallis tests were used to investigate differences in
251 distribution shape and range (i.e., location: [63]) of density and habitat attributes among site or
252 year-season clusters. Post-hoc Tukey-type nonparametric Conover multiple comparison tests
253 (function ‘posthoc.tukey.conover.test’ of package ‘PMCMR’) were used to test for significant
254 pairwise differences. These tests were implemented as χ2 distributions to correct for data ties,
255 and p-values were Bonferroni-corrected [63,64]. A series of correlation analyses was used to
256 identify habitat attribute distribution characteristics that associated with site or year-season
257 cluster median densities. Pearson correlation analyses were applied to median, minimum,
258 maximum, and standard deviation of habitat attributes within site or year-season clusters.
259
260 Results
261 A total of 3,179 penaeid shrimp specimens were collected. The distribution of shrimp
262 sizes suggested a gear capture inefficiency for individuals <5mm CL; therefore, data only for
263 shrimps ≥5 mm CL (2,417 shrimps) were retained for further analysis (Fig. S1). Of the retained
264 shrimp, 1,573 (65.1%) were identified as F. duorarum and the remaining 844 (34.9%) were
265 identified as farfantepenaeids due to difficulties with species identification of individuals <8 mm
266 CL. Of the 1,937 individuals with measured CL, 1,931 individuals (79.9%) were considered
267 juveniles (≤17.5 mm CL) and the remaining 36 individuals were subadults.
268 Pink shrimp density observations ranged from 0 to 13.0 shrimp m-2; 105 instances
269 (11.2%, N = 940 samples) of densities ≥2 shrimp m-2 were observed, while no penaeid shrimps
270 were observed in 377 samples (40.1%). Overall, shrimp density averaged 0.86 (SD = 1.32)
271 shrimp m-2, and was significantly lower (t(α=0.10,2),939 = -26.53, P <0.0001) than the 2 shrimp m-2
272 CERP Interim Goal threshold. Average density in any year-season was always < 2.0 shrimp m-2
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273 (Table 1), although the highest year-season density (2014 Dry: 1.62 ± 2.02 shrimp m-2; Table 1)
274 was the only case that did not significantly differ from 2 shrimp m-2 (t(α=0.10,2),46 = -1.30, p >
275 0.10). Averaged over all sites, mean dry season shrimp densities were higher than those of the
276 subsequent wet season 50% of the time. Averaged over year-seasons, the highest mean site
277 density was 2.15 (±1.95) shrimp m-2 at site 33 (Table 2). Seven sites (7, 10, 12, 33, 34, 43, and
278 44: 14.9%) exhibited temporally averaged densities that did not significantly differ from 2.0
279 shrimp m-2 (t(α=0.10,2),19 = -0.080, -1.69, -0.30, 0.34, -1.63, -1.57, -0.32, respectively; p > 0.10).
280 Most sites (n =33, 70.2%) exhibited average densities below 1 shrimp m-2 (Table 2).
281
282 Table 1: Number of pink shrimp collected, average pink shrimp density (± SD), and average (±
283 SD) of water quality and habitat attributes for survey year-seasons.
284
285
Year
Season
#
Shrimp
Density
(# m-2)
Temp (°C)
Sal (ppt)
Depth (m)
2007
Dry
131
0.93 ± 1.11
23.90 ± 1.99
26.11 ± 5.62
0.67 ± 0.18
Wet
63
0.45 ± 0.9
30.81 ± 1.07
20.36 ± 5.98
0.72 ± 0.17
2008
Dry
165
1.17 ± 1.95
22.29 ± 1.44
25.28 ± 4.12
0.73 ± 0.17
Wet
104
0.74 ± 1.09
29.84 ± 0.99
23.57 ± 5.32
0.73 ± 0.17
2009
Dry
198
1.4 ± 1.4
21.61 ± 1.53
25.95 ± 5.61
0.63 ± 0.15
Wet
56
0.4 ± 0.58
31.25 ± 1.85
22.93 ± 6.47
0.65 ± 0.15
2010
Dry
98
0.7 ± 0.94
19.28 ± 2.89
25.50 ± 2.90
0.59 ± 0.14
Wet
123
0.87 ± 1.34
31.62 ± 1.17
24.18 ± 6.92
0.71 ± 0.20
2011
Dry
98
0.7 ± 0.9
21.31 ± 1.83
27.09 ± 3.02
0.60 ± 0.18
Wet
112
0.79 ± 0.99
31.72 ± 1.56
31.86 ± 3.68
0.73 ± 0.22
2012
Dry
182
1.29 ± 1.73
22.52 ± 1.00
24.47 ± 3.45
0.71 ± 0.16
Wet
182
1.29 ± 1.65
31.10 ± 1.85
15.30 ± 5.64
0.73 ± 0.15
2013
Dry
82
0.58 ± 0.68
21.45 ± 1.07
28.85 ± 3.41
0.86 ± 0.18
Wet
29
0.21 ± 0.43
29.30 ± 0.87
15.98 ± 7.24
0.79 ± 0.18
2014
Dry
228
1.62 ± 2.02
22.89 ± 1.39
23.33 ± 5.71
0.69 ± 0.15
Wet
15
0.11 ± 0.22
29.44 ± 1.12
29.30 ± 4.84
1.02 ± 0.20
2015
Dry
92
0.65 ± 0.81
26.06 ± 1.48
28.85 ± 3.42
0.74 ± 0.19
Wet
204
1.45 ± 2.25
30.87 ± 1.45
23.11 ± 6.19
0.80 ± 0.17
2016
Dry
119
0.84 ± 1.18
26.03 ± 2.06
18.60 ± 6.08
0.77 ± 0.20
Wet
136
0.96 ± 0.93
30.28 ± 1.22
12.22 ± 2.94
0.83 ± 0.17
Overall
Dry
1393
0.99 ± 1.38
22.73 ± 2.65
25.40 ± 5.27
0.70 ± 0.19
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Wet
1024
0.73± 1.25
30.65 ± 1.53
21.87 ± 8.10
0.77 ± 0.20
286
287
288
289 Table 2: Number of pink shrimp collected, average pink shrimp density (± SD), and average (±
290 SD) of water quality and habitat attributes for survey sites.
291
Site
# Shrimp
Density (# m-2)
Temp (°C)
Sal (ppt)
Depth (m)
SAV (% Cover)
1
73
1.22 ± 1.65
27.42 ± 4.04
29.03 ± 5.09
0.84 ± 0.14
71.44 ± 20.10
2
57
0.95 ± 1.54
26.90 ± 4.23
26.75 ± 4.92
0.88 ± 0.17
70.37 ± 19.51
4
35
0.58 ± 1.49
26.68 ± 4.06
27.78 ± 4.94
0.79 ± 0.2
74.40 ± 16.07
5
66
1.10 ± 1.93
26.78 ± 4.06
25.67 ± 5.09
0.76 ± 0.19
67.94 ± 21.58
6
19
0.32 ± 0.69
26.58 ± 3.89
25.10 ± 5.55
0.65 ± 0.14
79.31 ± 14.20
7
117
1.95 ± 2.80
26.72 ± 4.20
27.05 ± 5.10
0.63 ± 0.15
72.07 ± 15.29
8
27
0.45 ± 0.64
26.68 ± 4.23
26.05 ± 5.63
0.5 ± 0.12
67.45 ± 18.53
9
85
1.42 ± 1.10
26.58 ± 4.00
25.23 ± 5.82
0.59 ± 0.15
51.46 ± 19.84
10
94
1.57 ± 1.15
26.14 ± 3.87
24.97 ± 5.91
0.73 ± 0.18
77.71 ± 18.04
11
80
1.33 ± 1.36
26.31 ± 3.65
24.91 ± 5.99
0.66 ± 0.15
68.94 ± 17.80
12
111
1.85 ± 2.27
26.34 ± 3.64
24.46 ± 6.42
0.86 ± 0.17
73.90 ± 19.70
13
51
0.85 ± 1.11
26.41 ± 3.77
24.53 ± 6.17
0.8 ± 0.15
80.85 ± 10.49
14
45
0.75 ± 1.65
26.70 ± 3.75
23.75 ± 6.17
0.8 ± 0.15
82.69 ± 14.27
15
13
0.22 ± 0.36
26.36 ± 4.10
23.10 ± 5.46
0.75 ± 0.18
71.29 ± 18.34
16
36
0.60 ± 0.88
26.67 ± 3.96
22.78 ± 5.20
0.77 ± 0.21
72.08 ± 22.62
17
57
0.95 ± 1.77
26.58 ± 4.00
23.07 ± 5.26
0.86 ± 0.15
75.96 ± 17.11
18
9
0.15 ± 0.23
26.75 ± 4.13
17.10 ± 9.03
0.64 ± 0.17
70.33 ± 17.95
19
36
0.60 ± 0.65
26.77 ± 4.05
17.71 ± 7.82
0.7 ± 0.17
66.30 ± 21.90
20
38
0.63 ± 0.71
26.76 ± 3.99
17.72 ± 7.59
0.73 ± 0.22
54.80 ± 22.54
21
29
0.48 ± 0.64
26.68 ± 4.37
18.35 ± 6.88
0.68 ± 0.22
59.85 ± 26.64
22
33
0.55 ± 0.60
26.82 ± 4.64
18.93 ± 6.82
0.7 ± 0.2
57.64 ± 23.73
23
30
0.50 ± 0.72
26.84 ± 4.77
17.98 ± 7.25
0.67 ± 0.2
62.61 ± 19.56
24
12
0.20 ± 0.23
27.25 ± 5.18
18.29 ± 7.34
0.64 ± 0.19
66.70 ± 17.74
25
12
0.20 ± 0.35
27.05 ± 4.83
18.20 ± 6.45
0.65 ± 0.15
54.74 ± 23.68
26
27
0.45 ± 0.74
27.08 ± 5.07
20.53 ± 5.51
0.7 ± 0.14
53.51 ± 20.39
27
41
0.68 ± 0.96
26.73 ± 4.74
20.60 ± 5.79
0.67 ± 0.17
66.51 ± 19.81
28
29
0.48 ± 0.51
26.82 ± 4.38
20.51 ± 5.86
0.85 ± 0.19
38.07 ± 21.13
29
49
0.82 ± 1.02
26.82 ± 4.43
20.69 ± 5.78
0.79 ± 0.2
49.53 ± 23.86
30
33
0.55 ± 0.55
26.76 ± 4.09
22.13 ± 5.32
0.74 ± 0.23
53.08 ± 19.99
31
38
0.63 ± 0.69
27.27 ± 4.59
21.92 ± 6.26
0.77 ± 0.19
55.20 ± 23.45
32
43
0.72 ± 0.78
27.49 ± 4.91
22.06 ± 5.89
0.78 ± 0.23
55.56 ± 18.24
33
129
2.15 ± 1.95
26.80 ± 4.61
22.00 ± 6.02
0.82 ± 0.2
64.78 ± 15.60
34
88
1.47 ± 1.46
26.79 ± 4.79
22.50 ± 5.80
0.76 ± 0.22
63.24 ± 18.32
35
52
0.87 ± 0.84
26.90 ± 4.79
22.43 ± 6.96
0.74 ± 0.22
56.12 ± 23.22
36
52
0.87 ± 0.98
26.64 ± 5.11
22.04 ± 6.57
0.73 ± 0.18
53.76 ± 21.81
37
52
0.87 ± 1.13
26.43 ± 4.97
22.18 ± 6.88
0.81 ± 0.2
54.10 ± 22.31
38
33
0.55 ± 0.60
27.00 ± 5.28
21.01 ± 7.61
0.77 ± 0.18
51.91 ± 22.86
39
63
1.05 ± 2.89
27.24 ± 5.93
24.51 ± 6.65
0.84 ± 0.17
41.68 ± 22.78
40
76
1.27 ± 1.28
26.48 ± 5.57
27.16 ± 5.94
0.82 ± 0.17
79.73 ± 12.79
41
74
1.23 ± 1.24
26.31 ± 5.42
27.53 ± 5.76
0.69 ± 0.17
73.72 ± 18.74
42
42
0.70 ± 1.27
26.51 ± 5.34
28.47 ± 5.85
0.85 ± 0.22
66.68 ± 21.59
43
92
1.53 ± 1.33
26.17 ± 5.17
28.51 ± 6.09
0.68 ± 0.17
72.63 ± 17.57
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44
113
1.88 ± 1.65
25.98 ± 5.06
28.77 ± 6.18
0.54 ± 0.16
59.35 ± 15.19
45
12
0.20 ± 0.31
26.15 ± 5.71
29.83 ± 5.78
0.72 ± 0.17
89.86 ± 4.83
46
45
0.75 ± 1.00
25.87 ± 5.74
29.57 ± 6.03
0.58 ± 0.19
63.17 ± 17.5
47
10
0.17 ± 0.35
25.82 ± 5.69
30.02 ± 5.87
0.81 ± 0.18
89.86 ± 6.12
Overall
2417
0.86 ± 1.32
26.68 ± 4.52
23.64 ± 7.05
0.74 ± 0.20
65.57 ± 21.97
292 Temperatures ranged from 12.49 to 36.06 °C. Average temperatures demonstrated a
293 clear pattern of cooler (22.73 ± 2.65 °C) and warmer (30.65 ± 1.53 °C) values for dry and wet
294 seasons, respectively (Table 1, Fig. 3C). The dry season record was punctuated by an extreme
295 cold front event that occurred during the 2010 field sampling. No pattern of variation in average
296 temperatures among sites was readily discernable (Table 2, Fig. 3C). Salinities ranged from 2.48
297 to 39.71 ppt; overall average salinity was 23.64 (± 7.05) ppt (Table 2). Spatially averaged wet
298 season salinities were generally lower than those of dry seasons, although 2011, 2014, and 2015
299 wet seasons were notable exceptions with higher average salinity than both the preceding and
300 following dry seasons (Table 1). Sampling sites’ mean salinity and standard deviation of salinity
301 were negatively correlated (Pearson r = -0.63, t = -5.49, d.f. = 45, p < 0.0001; Fig. S2). Wet
302 2011 and 2015 were considered ‘hypersaline’ due to duration of hypersaline (>40 ppt) conditions
303 observed in these year-seasons [65]. Only four temporally averaged site salinities were
304 mesohaline, most (n = 42) were polyhaline, and one was euhaline (Table 2). Water depths
305 ranged from 0.19 to 1.5 m and averaged 0.74 (± 0.20) overall (Table 2) with no appreciable
306 trends among year-seasons or among sites (Fig. 3E). Total SAV % cover ranged from 4.57 to
307 100% and averaged 66.57% (± 21.97) with no clear year-season variation patterns (Table 1, 2;
308 Fig. 3F). A planktonic microalgal bloom event was observed in parts of the Biscayne Bay
309 coastal area during the 2013 wet season [65,66].
310
311 Fig 3: Heatmaps depicting spatial (i.e., site) and temporal (i.e., year-season) trends in A) pink
312 shrimp density (shrimp m-2: LN[x+1]), C) temperature (°C), D) salinity (ppt), E) depth (m), and
313 F) SAV (% cover). Shrimp densities are also depicted as organized (B) by site and year-season
314 clusters. Color bars along the left and top margins of B) reflect significant sites and year-season
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315 clusters as denoted in the legend. Black cells in A) and B) highlight 0 shrimp m-2 observations
316 while in C) through F) black bars represent missing values. Year-season label colors depict
317 ecological perturbations: red = hypersalinity event, blue = cold snap, green = algal bloom.
318 Labels on the left margin of (A) refer to canal outlets (blue) and coastline features (black)
319 depicted in Fig. 1.
320
321 Habitat limitations on pink shrimp density
322 Of the multiple habitat attributes investigated, significant QR analysis results revealed
323 that temperature (°C), salinity (ppt), water depth (m), and SAV (% cover) potentially limited
324 pink shrimp density (Table 3, Fig 2). QR of density vs. temperature yielded a single-knot natural
325 cubic spline relationship. This relationship was roughly dome-shaped and maximized at 26.6 °C,
326 with tails that tapered off at higher and lower temperatures (Table 3, Fig 2A). Temperatures
327 between 21.08 and 31.33 °C did not appear to limit pink shrimp densities to <2 shrimp m-2
328 (Fig2A). Although a series of functional shapes was considered for the QR density vs. salinity
329 response curve, only the linear and log-linear responses were found to be both significant and
330 ecologically plausible [67]. The log-linear response, which suggested severe density limitation
331 below10 ppt and asymptotic at salinities above 10 ppt (Fig. 2B), seemed more plausible than the
332 linear response. Salinities < ~18 ppt limited shrimp density to <2 shrimp m-2 (Fig. 2B). QR of
333 pink shrimp density against water depth (m) yielded a 3 knot (0.25, 0.5, 0.75 quantile) splined
334 relationship with steep increases in limitation below ~0.6 m and above ~ 1.0 m and a bimodal
335 midsection (Fig. 2C). Apparent limitation of density to <2 shrimp m-2 occurred at water depths
336 less than 0.43 m and greater than 1.05 m (Fig. 2C). Shrimp density had a logarithmic linear
337 relationship with SAV % cover (Fig. 2D). SAV cover less than 45% limited density to <2
338 shrimp m-2 (Fig. 2D). For the four habitat attributes, significant QRs were observed at the 0.9
339 quantile, but not at the 0.5 quantile (Table 3).
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340 Table 3: Statistical significance of 0.5 and 0.9 quantile regressions of pink shrimp density
341 (shrimp m-2: LN([x+1]) against temperature (°C), salinity (ppt), water depth (m), and submerged
342 aquatic vegetation (SAV: % cover). LN = natural logarithm.
343
344
Quantile
Predictors
Coefficients (± SE)
t value
p value
0.5
Spline1(Temperature)
-0.59 ± 0.057
10.39
0.5551
Spline2(Temperature)
-0.33 ± 0.16
-2.08
0.0375
0.9
Spline1(Temperature)
2.24 ± 0.094
23.80
< 0.0001
Spline2(Temperature)
-0.49 ± 0.22
-2.26
0.0242
0.5
LN(Salinity)
0.00 ± 0.044
0.00
1.0000
Intercept
0.29 ± 0.12
2.44
0.0147
0.9
LN(Salinity)
0.26 ± 0.091
3.17
0.0016
Intercept
0.34 ± 0.28
1.22
0.2213
0.5
Spline1(Water Depth)
0.27 ± 0.053
5.04
< 0.0001
Spline2(Water Depth)
0.15 ± 0.068
2.26
0.0243
Spline3(Water Depth)
0.57 ± 0.14
4.07
0.0005
Spline4(Water Depth)
-0.22 ± 0.16
-1.43
0.1544
0.9
Spline1(Water Depth)
1.07 ± 0.11
10.04
< 0.0001
Spline2(Water Depth)
0.75 ± 0.23
3.29
0.0011
Spline3(Water Depth)
2.15 ± 0.18
11.70
< 0.0001
Spline4(Water Depth)
-0.83 ± 0.32
-2.62
0.0089
0.5
LN(SAV)
0.00 ± 0.036
0.00
1.0000
Intercept
0.29 ± 0.13
2.18
0.0298
0.9
LN(SAV)
0.21 ± 0.075
2.75
0.0061
Intercept
0.33 ± 0.30
1.10
0.2733
345
346 Fig. 2: Pink shrimp density (shrimp m-2) and back-transformed 0.50 and 0.90 quantile
347 regressions lines of predicted density (LN x+1) plotted against A) temperature (°C), B) salinity
348 (ppt), C) water depth (m), and D) submerged aquatic vegetation (SAV: % cover). Predicted
349 regression lines depict relationships reported in Table 2.
350
351
352 Spatiotemporal relationships
353 Heatmap visualization of pink shrimp spatiotemporal density trends revealed a general
354 absence of pink shrimp from sites 13 to 28 (approximately Black Point to Fender Point, Fig. 1)
355 and sites 45 to 47 (near Turkey Point, Fig. 1) across all year-seasons (Fig. 3A). Within these
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356 groups of sites, only 16 (4.4%, N = 360) and 4 (6.7%, N=60) instances of pink shrimp densities
357 >2 shrimp m-2 were observed respectively. Generally higher densities were observed in sites 31
358 through 44 and sites 1 to 12, where 42 (15%, N=280) and 40 (16.7%, N = 240) instances,
359 respectively, of densities >2 shrimp m-2 were observed across all year-seasons. Densities were
360 particularly low during 2007, 2009, 2013, and 2014 wet seasons (< 0.5 shrimp m-2: Table 1),
361 when shrimp were absent from a high proportion of samples (55.3, 51.1, 70.2, and 76.6%,
362 respectively: Fig 3A). Other year-seasons (2008 dry, 2009 dry, 2012 dry, 2012 wet, 2014 dry,
363 and 2015 wet: Fig. 3A) exhibited high average density (> 1 shrimp m-2) because of a
364 preponderance of higher density observations, which offset low and zero-catch observations
365 from Black Point to Fender Point (Fig. 1). Average densities in these year-seasons yielded the
366 highest year-season average densities, which were all >1 shrimp m-2 (Table 1).
367 Heatmaps were also developed to visualize spatiotemporal trends in temperature, salinity,
368 water depth, and SAV (Fig. 3.3C, D, E, F). Procrustean analyses revealed significant
369 concordance of the shrimp density matrix (Fig. 3A) to water depth, temperature, and salinity
370 habitat attribute matrices (Fig. 3 C, D, and E) but not to the SAV matrix (Fig. 3F, Table 4).
371 Water depth and temperature exhibited the highest correlations, followed by salinity (Table 4).
372 Each comparison yielded a high residual sum of squares (high m2X,Y values), indicating
373 relatively weak explanatory power of individual habitat attributes (Table 4). Procrustean fitting
374 procedures explained 28.3, 27.1, and 22.1% of the variability in density for water depth,
375 temperature, and salinity, respectively.
376
377
378
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379 Table 4: Results of Procrustean analysis of density (shrimp m-2: LN([x+1]) relative to
380 temperature (°C), salinity (ppt), water depth (m), and SAV (% cover) including goodness-of-fit
381 measure (m2), correlation of the Procrustean rotation (r), and p value of the fit.
382
m^2
r
p value
Temperature
0.7287
0.5209
<0.0001
Salinity
0.779
0.4701
<0.0001
Water Depth
0.7169
0.5321
<0.0001
SAV
0.7777
0.4715
0.1162
383
384 SIMPROF testing identified six significant site clusters and four significant year-season
385 clusters (Fig 3B). PERMANOVA testing of cluster membership confirmed SIMPROF site (F5,41
386 = 4.765, p = 0.001, R2 = 0.368) and year-season (F7316 = 3.727, p = 0.001, R2 = 0.411) clustering.
387 Two site clusters (i.e., 2 and 6: Fig. 3B) together included most (66%, n = 31) of the sampling
388 stations. One large year-season cluster included most year-seasons (75%, n = 15: Fig. 3B).
389 Smaller membership year-season clusters (80%, n = 4) were mostly comprised of wet seasons
390 representing pink shrimp densities (<0.5 shrimp m-2). Substantial differences in shrimp densities
391 of members of different clusters likely drove the significantly differing multivariate dispersion
392 among site (F5,41 = 14.886, p < 0.0001) and year-season (F3,16 = 22.987, p = <0.0001) clusters.
393 PERMANOVA testing detected significant season (F1,9 = 1.912, p = 0.0063), but not year (F9,9 =
394 1.020, p = 0.4279), categorical temporal effects. Multivariate dispersions differed significantly
395 between years (F9,10 = 5.56*1029, p < 0.0001) and seasons (F1,18 = 7.047, p = 0.0161), with
396 greater observed variability in the wet season than the dry season. PERMANOVA p values were
397 considered to be conservative because greater multivariate dispersion was observed in groups
398 with larger sample sizes.
399
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400 Pink shrimp density and habitat attributes among density clusters
401 Significant differences in density distributions were detected among both site and year-
402 season clusters (Table 5, Fig. 4A, Fig. 5A). Site clusters represented three relative median
403 density levels: high (~0.7 shrimp m-2: site cluster 6); intermediate (~0.3 shrimp m-2: site clusters
404 2, 3, and 5); and low density (0.0 and ~0.14 shrimp m-2: site clusters 1 and 4, respectively: Table
405 5A, Fig. 4A). Year-season clusters also exhibited three relative density levels: high (0.51 shrimp
406 m-2: year-season cluster 2), intermediate (0.29 shrimp m-2: year-season cluster 4) and low density
407 (0.0 shrimp m-2: year-season clusters 1 and 3) (Table 5B, Fig. 5A). Significant differences were
408 also observed for salinity, water depth, and SAV distributions among site clusters (Table 5A;
409 Fig. 4B, C, D), while temperature distributions did not differ among site clusters. All four
410 habitat attributes exhibited significant differences among year-season clusters (Table 5B, Fig.
411 5B, C, D, E). No significant correlations were found between site cluster median density and
412 median, minimum, maximum, or standard deviation of habitat attributes within clusters. Low
413 sample size prevented investigation of correlations of year-season clusters’ median density or
414 maximum density with clusters’ habitat attributes’ distribution characteristics.
415
416 Table 5: Median and ~95% CI of density (shrimp m-2: LN([x+1]) , temperature (°C), salinity
417 (ppt), water depth (m), and submerged aquatic vegetation (SAV: % cover) and the χ2, d.f., and p
418 values associated with Kruskal-Wallis testing of density clusters relative to A) site and B) year-
419 season. Median CI computed as described in the text. Values in A) and B) are depicted in Fig. 4
420 and 5, respectively.
421
422 A)
Site
Cluster
n
Density
Temperature
SAV
1
160
0.00 ± 0.036
27.73 ± 1.04
79.00 ± 4.00
2
320
0.29 ± 0.061
28.50 ± 0.71
56.50 ± 2.97
3
80
0.29 ± 0.090
28.05 ± 1.32
78.00 ± 5.65
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4
20
0.14 ± 0.20
28.70 ± 3.32
69.75 ± 10.05
5
60
0.29 ± 0.15
28.60 ± 1.58
77.00 ± 4.13
6
300
0.69 ± 0.074
27.80 ± 0.72
70.25 ± 2.86
χ2
NA
148.27
0.64
98.24
d.f.
NA
5
5
5
p value
NA
<0.0001
0.9861
<0.0001
423
424
425
426
427
428
429
430 B)
Year-
Season
Cluster
n
Density
Temperature
Salinity
1
47
0.00 ± 0.00
29.40 ± 0.25
28.12 ± 0.96
2
705
0.51 ± 0.075
25.30 ± 0.48
24.55 ± 0.59
3
47
0.00 ± 0.051
29.20 ± 0.28
18.26 ± 2.59
4
141
0.29 ± 0.18
30.01 ± 1.20
24.58 ± 1.25
χ2
NA
89.77
32.28
70.07
d.f.
NA
3
3
3
p value
NA
<0.0001
<0.0001
<0.0001
431
432
433
434 Fig. 4: Median (± CI) and maximum, and minimum values of A) density (shrimp m-2: LN([x+1]),
435 B) salinity (ppt), C) water depth (m), and D) submerged aquatic vegetation (SAV: % cover) in
436 shrimp density site clusters. Point colors coincide with Fig. 1 and 3B. Letters denote statistically
437 similar groups. Horizontal line of A) depicts the 2 shrimp m-2 CERP Interim Goal.
438
439 Fig. 5: Median (± CI) and maximum, and minimum values of A) density (shrimp m-2: LN([x+1]),
440 B) temperature (°C), C) salinity (ppt), D) water depth (m), and E) submerged aquatic vegetation
441 (SAV: % cover) relative to shrimp density year-season clusters. Point colors coincide with Fig.
442 3B. Letters denote statistically similar groups.
443
444 Discussion
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445 The analysis of 10 years of monitoring data revealed few instances (11.2 %) of pink
446 shrimp densities > 2 shrimp m-2, the IG for Biscayne Bay pink shrimp populations [1]. All but
447 one spatially averaged year-season density and all but a few temporally averaged site densities in
448 the 2007-2016 database were significantly below 2 shrimp m-2. CERP implementation is
449 expected to result in more favorable salinity conditions for pink shrimp, leading to higher shrimp
450 densities [1,5,27]. Reductions in extreme salinity variability in the southern half of the study
451 area (i.e., Black Point to Convoy Point: Fig. 3.1) could lead to average densities > 2 shrimp m-2
452 across the entire study spatial domain. However, our results suggest that ~ 8ppt salinity (i.e., low
453 mesohaline to oligohaline: Fig. 2B) may have been a threshold below which pink shrimp
454 densities were severely limited. Above this threshold, the log-linear response continued to
455 increase in a more linear fashion. Limitation of pink shrimp densities at salinities <8 ppt does
456 not support coexistence of CERP post-restoration IGs of >2 shrimp m-2 and reduction of
457 Biscayne Bay nearshore salinity regimes to oligohaline and low mesohaline conditions.
458 Spatial pink shrimp density patterns from Black Point to Convoy Point (Fig. 1) were
459 dominated by membership within one low density site cluster (cluster 1) and one intermediate
460 density site cluster (cluster 2). This zone is strongly influenced by canal discharges [11,23-
461 26,33,68]. Both rapid (<60 min to 2 d) and extreme (~25 ppt) salinity reductions can occur along
462 this stretch of coastline [22,24,25,29,69,70]. Such salinity fluctuations can alter fish community
463 assemblages [23] and may affect foraging behavior and survival [22,23]. Pink shrimp may avoid
464 these conditions as they have been reported to migrate to avoid large-volume riverine inflows
465 [71]. Rapid salinity reductions of greater than 20 ppt cause near complete pink shrimp mortality
466 in laboratory settings [72-74]. The low and intermediate density site clusters 1 and 2 included
467 sites 45, 46, and 47 (Fig 3,4), which are located near Turkey Point (Fig 1), well south of the
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468 canal zone (Black Point to Convoy Point) and so were not impacted by low and variable salinity
469 conditions. These sites had moderate minimum salinities (≥11.06 ppt), the highest average
470 salinity across all sites (≥29.57 ppt: Table 2), and generally high SAV cover (Table 2, Fig. 3F).
471 The cause of their low shrimp density remains undetermined but does not seem to be related to
472 limitation due to salinity or SAV cover.
473 Site cluster 6 (Fig. 1), also comprised of sites not located in the canal-zone section of
474 shoreline, had the highest median density. This site cluster had the second highest minimum
475 salinity (9.62 ppt) and was comprised of sites located further from canal mouths (Fig. 1).
476 Previous field [9,77] and modeling [35,76] studies describe the area corresponding to the more
477 northern sites of this cluster as an area of relatively high shrimp abundance. These northern sites
478 were situated immediately across the bay from a large ocean inlet known as the Safety Valve,
479 considered a primary postlarval immigration pathway [35,76], which may have contributed to
480 their high densities [77,78]. Shrimp cumulative size frequency distributions differed between
481 these northern (sites 1 - 17) and southern (sites 18 – 47) sampling sites (Fig S1) with the
482 maximal difference at 7.54 mm CL. This suggests higher abundance of juvenile sized shrimps
483 occurred in the north, which may have been related to growth and/or mortality more so than
484 recruitment. Further, the inclusion of more southern sites (33, 34, 37, 40, 41, 43, and 44) within
485 site cluster 6 (Fig. 1,4) does not fully support the notion of recruitment limitation. These sites
486 are located near mangrove creeks that drain more natural watersheds, although their watersheds
487 are likely reduced due to inland canalization. Several sites, both northern and southern, are
488 located near, but not immediately adjacent to, canals that discharge relatively small volumes of
489 freshwater (Military Canal: 1994-2003 annual mean canal output = 21.9 cfs; Cutler Drain C-100:
490 1994-2003 annual mean output = 46.1 cfs; [33]). One might anticipate higher shrimp densities
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491 along shoreline segments that experience lower-volume freshwater discharges. However, sites
492 immediately adjacent to low-volume discharge canals (i.e., site 6 and site 32) clustered with
493 intermediate and low-density site clusters (cluster 1 and 2, respectively: Fig 3.1, 3.3B, 3.4A).
494 Most year-seasons (75%) were aggregated within one large cluster, indicating a general
495 lack of inter-annual and inter-season variability in Biscayne Bay juvenile pink shrimp
496 populations. However, the majority (60%) of year-season cluster 2 consisted of dry season
497 sampling events. Observation of higher densities in the dry season is at odds with the pink
498 shrimp IG, which focused on improvement of ‘peak’ fall (wet) season abundances [1]. The
499 small shrimp size associated with this significant difference (5.53 mm CL) suggests that
500 abundance of small juveniles – presumably due to recent postlarval recruitment – caused this
501 seasonal difference. A lack of understanding of Biscayne Bay pink shrimp recruitment
502 complicates study of their abundance patterns. The only study available on recruitment reported
503 a late fall through early winter peak (i.e., October through March: [79]), although the study’s
504 short duration (1 yr) limited consideration of inter-annual trends or consistency. This peak
505 agreed with juvenile abundance studies reporting a late fall/early winter peak [75,80]. Modeling
506 of pink shrimp postlarval recruitment found that oceanographic processes favored Florida Keys
507 potential recruitment during late wet season and early dry season months [81]. Presumably these
508 conditions also favor recruitment to Biscayne Bay: modeling of larval permit Trachinotus
509 falcatus originating from spawning grounds near those of pink shrimp also found similar
510 recruitment patterns for the Florida Keys and Biscayne Bay [82]. Oceanographic, coastal, and
511 climatic conditions affect pink shrimp adult reproductive activity [83,84], and larval abundances
512 [85] and interact with behavior to influence early life stage recruitment to nearshore areas
513 [81,86-93]. Use of pink shrimp and other offshore spawning species as indicators of ecological
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514 conditions in their nearshore nursery grounds is complicated by life cycles affected by unrelated,
515 prior, external and stochastic conditions [5,32].
516 Quantile regression found four habitat attributes (water temperature, salinity, water depth,
517 and SAV % cover) that exhibited strong limitation of pink shrimp densities. Two of these (i.e.,
518 salinity regime and SAV % cover) can be influenced by freshwater management. Procrustean
519 analysis confirmed the influence of all each habitat attribute except SAV % cover. It should be
520 noted that these habitat attributes vary at differing time scales; for example, water depth can
521 differ by as much as 1.3 m within 12 hr during extreme tidal cycles while SAV % cover may be
522 integrative of salinity, nutrient, water clarity, and other influential factors from 6 mo. to 1 yr or
523 more.
524 The previously discussed seasonal pattern was confirmed by Procrustean analysis, with
525 QR results indicating higher pink shrimp densities in the dry season and temperature yielding
526 one of the higher concordances of the habitat attributes tested. The pink shrimp IG was based
527 upon observation of a summer/fall (i.e., wet season) peak in abundance [8], which was consistent
528 with Diaz [9], although his investigation was limited to on the four summer/fall months June,
529 July, August, and September. Others reported peak juvenile abundances in late fall/early winter
530 (i.e., November/December) [12] or estimated maximal Biscayne Bay juvenile pink shrimp
531 populations occurring in November (i.e., late fall) [8,75]. Differences in sampling gear and
532 spatial domain and the short durations (2 yr) of the four reference studies [8,9,75,80]
533 complicate comparisons with the present study. Although of greater duration, the present study’s
534 bi-seasonal sampling effort may be of insufficient resolution to precisely identify the period of
535 peak pink shrimp density, especially if it changes from year to year.
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536 Procrustean analysis revealed water depth (R2 = 28.3) explained the most variability in
537 pink shrimp density of the four habitat attributes presently investigated. This was unexpected
538 given the narrow spatial sampling domain within the mangrove-seagrass ecotone. Associations
539 between nearshore pink shrimp abundance and depth have been previously reported
540 [75,80,94,95]. Other studies that focused on very nearshore areas (<100 m) also found higher
541 abundances there [8-10,12,96]. Recruiting postlarval pink shrimp often concentrate in SAV near
542 the low-tide mark along shorelines [9,12,97-102]. Fluctuating tidal depths and/or detection
543 probability at greater depths likely contributed to the domed shape of the QR relationship [103].
544 Procrustean analysis confirmed a direct relationship between salinity and density, but also
545 suggested that salinity was less influential than temperature (i.e., seasonality) or water depth on
546 spatiotemporal density patterns. Other pink shrimp habitat investigations [80,94] found multiple
547 habitat attributes (e.g., salinity, salinity standard deviation, standard deviation of turbidity,
548 temperature, median sediment size, dissolved oxygen concentration, water depth, and benthic
549 habitat characteristics) can influence pink shrimp abundance. As re-iterated by Zink et al. [6],
550 Costello et al. [12] stated that “…factors other than salinity per se control abundance of the
551 euryhaline juveniles…”
552 Re-analyses by Zink et al. [6] of data presented by Brusher and Ogren [104] and Minello
553 [105] found increasing abundance with increasing salinity and no statistical difference between
554 polyhaline and mesohaline pink shrimp abundances. It was unexpected to not find a limitation of
555 pink shrimp density at higher salinities, especially at conditions >35 ppt (e.g., a significant,
556 negative quadratic term or the significant cubic splines functions in Fig. A2, Table A1). The
557 range of salinity values observed in the study did not include hypersaline values (>40 ppt).
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558 Perhaps this range of salinity observations was not broad enough to characterize suspected
559 reduction of densities in extreme hypersaline conditions [5].
560 The Biscayne Bay pink shrimp IG suggested a pink shrimp preference for seagrasses, and
561 presumed that increased % cover of seagrasses would increase pink shrimp abundance [1,5,30]
562 and increase the seaward spatial extent of H. wrightii [30]. Presently, total SAV QRs yielded the
563 most plausible relationship between pink shrimp density and the benthic habitat metrics
564 investigated. Contrary to this relationship, associated Procrustean analysis test results were non-
565 significant. The seemingly weak statistical relationships with either total or species-specific
566 SAV metrics was unexpected. Pink shrimp associations with H. wrightii have been previously
567 reported [12,96,97,106], while other studies have reported maximal pink shrimp densities
568 relative to total SAV biomass or % cover [10,107,108]. Although one study reports negative
569 impacts of drift and attached algal biomass [109], the positive relationships reported by most
570 studies suggested a stronger relationship between pink shrimp density and either species-specific
571 or total SAV would be readily observed.
572 Several environmental perturbations occurred during this study. Variability in climatic
573 conditions led to both wetter and drier than normal wet seasons (Fig. 3D). However, alteration
574 of typical salinity regimes did not seem to influence temporal density patterns substantially. For
575 example, the second highest wet season pink shrimp density (1.29 ± 1.65 shrimp m-2: Table 1)
576 coincided with 2012 record rainfall that reduced salinities (3.34 to 22.08 ppt) across the spatial
577 domain (Fig. 3D). Conversely, the highest wet season pink shrimp average density (1.45 ± 2.25
578 shrimp m-2: Table 1) occurred during the 2015 wet season, a period that was previously denoted
579 as a ‘hypersaline’ period [65]. Record dry season rainfall during 2016 yielded the lowest
580 average dry season salinity (Table 1, Fig. 3D) while pink shrimp densities that dry season were
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581 moderate (0.84 ± 1.18 shrimp m-2: Table 1). Despite their differing salinity conditions, these
582 year-seasons were assigned to the same shrimp density cluster (Fig. 3B). The 2013 wet season
583 clustered separately from the others, suggesting a negative impact of microalgal bloom
584 conditions [65,66] on pink shrimp density. No discernable impact on pink shrimp densities was
585 observed due to passage of an extreme cold front in the 2010 dry season.
586 Due the field sampling design, the present results apply only to shallow nearshore areas
587 very near (mainly within ~50 meters of) the shoreline. Application of the present results to areas
588 further offshore should proceed with caution, if done at all, due to potential interaction with other
589 habitat attributes that influence trends in pink shrimp density. The study was also limited by
590 apparent low catchability of very recently settled pink shrimp by the throw trap gear manifested
591 as reduced numbers of pink shrimp from 3 to 5 mm CL (Fig. S1). Pink shrimp postlarvae are
592 generally considered settled in their nursery habitat by 3 mm CL [9,12,86-88]. Pink shrimp
593 postlarvae settle in the shallow (≤1 m), calm water areas along shorelines [12,106], which would
594 suggest they should be readily available to the present field sampling program that samples
595 nearshore waters generally < 1 m deep.
596 The RECOVER Biscayne Bay IG anticipates >2 shrimp m-2 as a target wet season pink
597 shrimp density to be achieved with CERP BBCW implementation. But achievement of BBCW
598 and CERP salinity IGs, which include low mesohaline (<10 ppt) and even oligohaline conditions
599 (<5 ppt), may negatively impact pink shrimp density. The Biscayne Bay pink shrimp IG may
600 need modification to clarify whether the 2 shrimp m-2 target refers to all monitoring
601 observations or a seasonal or annual average density across the entire shoreline and to further
602 consider spatial and seasonal abundance patterns.
603
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604 Acknowledgements
605 This study used monitoring data collected over a substantial period by many of
606 individuals; without their efforts, this study would not have been possible. This study was
607 conducted during pursuit of the PhD degree of I. Zink; he would like to thank committee
608 members D. Die and J. Luo for their comments and suggestions during review of earlier versions
609 of this manuscript. The authors would also like to thank two anonymous reviewers for their
610 suggestion prior to submission of the manuscript for publication.
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Article
This paper reviews the estuarine portion of the life histories of Penaeus setiferus, P. duorarum and P. aztecus in North Carolina. After larval migrations from spawning places at sea, the young enter estuaries as benthonic post-larvae. P. setiferus is judged to grow 36 mm, P. duorarum 52 mm, and P. aztecus 46 mm per month in brackish nursery areas during the warmer months. Juveniles gradually move toward the sea as they approach mature sizes. P. aztecus recruitment is greatest in May. The earliest recruits reach commercial size by July. No juveniles and few adults overwinter in N.C. P. duorarum recruitment extends from June to October. The earliest recruits reach commercial size in autumn, but the remainder overwinter and attain commercial size in spring. Mature adults occur in the littoral zone prior to the recruitment period. P. setiferus recruitment occurs chiefly in June. The young attain commercial size by late summer. A sparse population of adults overwinters in the littoral zone. These are sexually mature in spring. A number of ecological factors in the nursery areas are discussed. The role of interspecific competition for nursery areas is considered. P. aztecus and P. duorarum occupy these areas at different times. P. setiferus may compete with both of these species for nursery ground.
Florida Bay ecology project
  • T J Costello
  • D M Allen
Costello TJ, Allen DM, Florida Bay ecology project. In: Annual Report of the Bureau of