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Do diets vary over large spatial or temporal ranges? A test using inter-annual and inter-population data on
Diamondback Terrapins (Malaclemys terrapin) diets
Journal: Canadian Journal of Zoology
Manuscript ID cjz-2018-0211.R2
Manuscript Type: Article
Date Submitted by the Author: 13-Sep-2018
Complete List of Authors: Erazmus, Kayleigh; Hofstra University, Biology; Sacred Heart University College of Arts and Sciences, BiologyFigueras, Miranda; Hofstra University, BiologyLuiselli, Luca; Environmental Studies Centre DemetraBurke, Russell; Hofstra University, Biology
Is your manuscript invited for consideration in a Special
Issue?:Not applicable (regular submission)
Keyword: FEEDING < Discipline, COMPARATIVE < Discipline, REPTILIA < Taxon, FORAGING < Discipline, TESTUDINES < Taxon, Habitat
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Do diets vary over large spatial or temporal ranges?
A test using inter-annual and inter-population data
on Diamondback Terrapins (Malaclemys terrapin) diets
KAYLEIGH ROSE ERAZMUS1,2, MIRANDA FIGUERAS1, LUCA LUISELLI3,4,5, AND RUSSELL L. BURKE1,6
1: Department of Biology, Hofstra University, Hempstead, New York, USA
2: Biology Department, Sacred Heart University, Fairfield, Connecticut, USA
3. Institute for Development, Ecology, Conservation and Cooperation, via G. Tomasi di
Lampedusa 33, I-00144 Rome, Italy
4. Department of Applied and Environmental Biology, Rivers State University of Science and
Technology, P.M.B. 5080, Port Harcourt, Nigeria
5. Department of Zoology, University of Lomé, Lomé, Togo. Email: [email protected]
6: Corresponding author: [email protected]
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1 ABSTRACT.— Animal diets may vary spatially or temporally as resource availability vary. Diets of
2 species with extensive geographic ranges often span multiple habitats, thus their diets may vary
3 accordingly. Temporal diet variation is rarely explored because most diet studies are short-
4 term; this is problematic for long-lived species where individuals may persist as prey availability
5 changes. We analyzed diet variation in Malaclemys terrapin (Diamondback Terrapins Schoepf
6 1793), which inhabits nearly 70,000 km of United States Atlantic coastline, spanning 16.5o
7 latitude, 27.4o longitude, and 4 Köppen climatic zones, and Bermuda. We explored spatially or
8 temporally terrapin diet variation; including populations from Atlantic salt marshes, an
9 Everglades mangrove swamp, the Texas Gulf Coast, and a Caribbean golf course pond. We
10 found remarkably high levels of similarity indicating that although diets vary according to local
11 prey availability, at lower taxonomic resolution they are broadly similar. Even short-term
12 studies may be sufficient to accurately characterize diets of Diamondback Terrapins. These
13 results are surprising considering the geographic range sampled in this study, and indicate that
14 diamondback terrapin diets are conservative, reflecting local prey availability. Such diets
15 apparently allow terrapins to exploit their extensive range and may allow terrapin populations
16 to persist as local prey species wax and wane.
17 Keywords: Malaclemys terrapin, Diamondback Terrapins, feeding, comparative, Reptilia,
18 foraging, testudines, habitat.
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19 Diets of members of a population may vary temporally or spatially due to different
20 nutritional needs or availability of resources. Species with large geographic ranges typically
21 have different diets in different parts of their ranges, if only because prey availability varies
22 within the range (e.g., Iriarte et al. 1990; Simpfendorfer et al. 2001; Lozano et al. 2006; Rulison
23 et al. 2012). If the diet of a predator species is inflexible, its diet may not differ even between
24 spatially distant populations, and prey availability may limit its distribution. Inflexible diets may
25 also lead to local population reductions if the required prey species availability declines.
26 Conversely, flexible diets can allow predators to exploit more diverse habitats over wide ranges,
27 and also allow specific populations to persist as local prey species wax and wane.
28 We explored temporal and spatial diet variation in Diamondback terrapins (Malaclemys
29 terrapin Schoepf 1793), which comprise a monotypic emydid turtle genus within the relatively
30 speciose (approximately 41 species) Deirochelyinae subfamily (TTWG 2017). Most members of
31 the subfamily inhabit primarily freshwater. While at least 8 species are known to inhabit
32 brackish water regularly (Ernst and Lovich 2009), Malaclemys is a habitat specialist and the only
33 member of Deirochelyinae that specializes in brackish-water environments. Adult diets vary
34 both between and within Deirochelyinae, ranging from nearly strictly carnivorous (e.g.,
35 Deirochelys reticularia Latreille in Sonnini and Latreille 1801) to nearly herbivorous (e.g.,
36 Pseudemys rubiventris Le Conte 1830). Many species in genera Trachemys (sliders) and
37 Pseudemys (cooters) tend to undergo an ontogenetic shift from carnivory to herbivory as they
38 mature (Hart 1983; Parmenter and Avery 1990; Bouchard and Bjorndal 2006) associated with a
39 habitat shift (Hart 1983; Congdon et al. 1992) and changes in digestive physiology (Whelan et
40 al. 2000; Bouchard and Bjorndal 2006). Therefore, there is reason to hypothesize that a
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41 Deirochelyinae species with a large range, such as Diamondback terrapins, (nearly 70,000 km of
42 United States Atlantic coastline, spanning 16.5 latitude degrees, 27.4 longitude degrees, and 4
43 Köppen climatic zones, Roosenburg and Kennedy 2018) will exhibit wide diet diversity. Even
44 their sister taxon (Graptemys) includes species with diets ranging from primarily sponges and
45 bryozoans (e.g., G. flavimaculata Cagle 1954 and G. nigrinoda Cagle 1954) to primarily molluscs
46 (e.g., female G. barbouri Carr and Marchand 1942 and G. pulchra Baur, 1893), Lindeman 2013),
47 and no Graptemys species distribution matches the latitudinal or longitudinal range of
48 Malaclemys.
49 Diamondback terrapin diets have recently been studied in many parts of their range,
50 including the westernmost populations (Alleman and Guillen 2017), the southernmost
51 population (Denton et al. 2016), northern populations (Erazmus 2012), the Bermuda population
52 (Outerbridge et al. 2017) as well as several other locations (reviewed by Tucker et al. 2018).
53 Most diet studies show that despite variation in the availability of prey species, Diamondback
54 terrapins exhibit moderate to high levels of molluscivory (Tucker et al. 2018), much like many
55 Graptemys. Studies based on observations of feeding in the wild, fecal samples, and stomach
56 dissections have found that Diamondback terrapins consume primarily eastern mudsnails
57 (Nassarius obsoletus Say 1822) in New York (Petrochic 2009), soft-shelled clams (Mya arenaria
58 Linnaeus 1758) in Maryland (Roosenburg et al. 1999), Atlantic blue crabs (Callinectes sapidus M.
59 J. Rathbun 1896) in North Carolina (Spivey 1998), marsh periwinkles (Littoraria irrorate Say
60 1822) in South Carolina (Tucker et al. 1995), and dwarf surfclams (Mulinia lateralis Say 1822) in
61 northern Florida (Butler et al. 2012). Studies of captive Diamondback terrapins of all ages found
62 they willingly ate blue crabs, fish, oysters, clams, fiddler crabs, canned fish, liver, beef and small
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63 mollusks (Hildebrand 1928; Allen and Littleford 1955). Beyond these general observations,
64 there has been no overall quantitative analysis of Malaclemys diets (Tucker et al. 2018).
65 Our study addressed 2 general concerns. First, although there have been more than 20
66 Diamondback terrapin diet studies (Tucker et al. 2018), these were mostly short-term studies
67 and/or involved small numbers of turtles. The diet study with the largest sample size (N = 294)
68 was limited to a single nesting season (Tucker et al. 1995); in fact, very few studies have
69 included data from more than 1 year, thus the possibility of inter-annual variation has not been
70 explored. These studies have been insufficient to test for temporal variation over timescales
71 relevant to a species where individuals are typically long-lived. Second, while several studies
72 have explicitly compared their results to the results of other studies (e.g., Denton et al. 2016;
73 Tucker et al. 2018), thus far no inter-populational studies have used quantitative diet overlap
74 methods, and previous comparisons were strictly qualitative. Thus, previous authors have been
75 tested for spatial variation using statistically robust methods.
76 Our goals were to address these concerns by collecting multiple years of diet data from
77 a substantial number of Diamondback terrapins from a single population and measure inter-
78 annual variability using standard quantitative diet overlap measures. Second, using the same
79 methods, we compared the diets from our well-studied population to the results from all other
80 substantial Diamondback terrapin diet studies and thus assess intra-specific variability. We
81 limited our comparison to the 6 other studies that had data similar to ours: fecal samples
82 collected from adult females primarily in the middle of the activity season.
83 METHODS
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84 Jamaica Bay Study Site.— Jamaica Bay (JB) is a polyhaline embayment at the extreme
85 southwestern end of Long Island NY, part of the boroughs of Brooklyn and Queens NY, and
86 connected to the Atlantic Ocean through Rockaway Inlet. The salinity varies from 28-32 parts
87 per thousand near the Rockaway Inlet to 16-27 ppt near the Bergen Basin outfall (Ringenary,
88 unpub. data). Northern quahogs (Mercenaria mercenaria Linnaeus 1758), soft-shelled clams,
89 and blue mussels (Mytilus edulis Linnaeus 1758) are abundant in JB mud flats and deep waters.
90 Atlantic ribbed mussel (Geukensia demissa Dillwyn 1817) and northern rock barnacles
91 (Semibalanus balanoides Linnaeus 1767) are common in the upper half of the inter-tidal zone,
92 and both eastern mudsnails and common Atlantic slippersnails (Crepidula fornicate Linnaeus
93 1758) dominate tidal creeks. Additionally, sea lettuce (Ulva latuca Linnaeus) and smooth
94 cordgrass (Spartina alterniflora Muhl. ex Elliott) are abundant, especially in the summer.
95 Although late in the 19th century Eastern oysters (Crassostrea virginica (Gmelin 1791) were
96 abundant in JB, currently there are few or no oysters in JB due to overharvesting and pollution.
97 The loss of oyster populations can result in hypoxic conditions and greatly reduced water
98 filtration rates (Rothschild et al. 1994; Kemp and Boynton 1984), causing a shift in dominant
99 macrofaunal species in deep muddy sediments from larger, older bivalves to short-lived
100 opportunistic species (Holland et al. 1987). A similar shift may have occurred in Jamaica Bay,
101 thus altering the prey species available to Diamondback terrapins, but historical records are
102 insufficient to test this.
103 Fecal Sample Collections. — In June and July of 2008-2014 we collected adult female
104 Diamondback terrapins that had nested on the western half of Ruler’s Bar, an island centrally
105 located within JB. We induced defecation by soaking them in fresh water (Tucker et al. 1995) in
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106 individual containers with approximately 23 liters of freshwater each, allowing for full coverage
107 of their carapaces. We soaked each Diamondback terrapin for 2 days in 2008, for 5 days in 2009
108 and 2010, and 3 days in subsequent years. We released the Diamondback terrapins back into JB
109 after fecal collection. Feces were condensed by pouring the water into a sieve (1 mm standard
110 size mesh) and preserving the solid material in formalin or 70% ethanol. Prey pieces were
111 separated using forceps and a dissecting microscope, then air-dried in a hood and identified to
112 the highest taxonomic level possible. Molluscan and crustacean pieces were compared to live
113 specimens collected from JB and to Gosner (1978). Items we could not identify to species were
114 placed into more general categories.
115 Fecal Sample Analyses.— We classified prey items into the following groups: Plantae
116 (leaves/stems, seeds, grass); Algae (sea lettuce and algae other than sea lettuce); Bivalvia (soft-
117 shelled clams, Atlantic ribbed mussels, and amethyst gem clams (Gemma gemma Totten
118 1834)); Crustacea (crabs and ostracods); Gastopoda (eastern mudsnails, rough periwinkles,
119 convex slippersnails (Crepidula convexa Say 1822)); Bryozoa; Insecta; Annelids (polychaete
120 tubes); invertebrate eggs (Atlantic horseshoe crab (Limulus Polyphemus Linnaeus 1758) eggs,
121 and Invertebrate egg masses); Unidentifiable animal tissue/detritus and Sand (Table 1). We
122 calculated the percent frequency of occurrence (PFO) by summing the number of times each
123 prey species was identified in any fecal sample in a particular year and dividing that number by
124 the total number of individuals whose feces were collected that year. PFOs were calculated for
125 each year and for all 7 years combined.
126 We did not attempt to count the number of individuals of each prey species or to
127 estimate the volume eaten of any prey species because in most cases we could not confidently
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128 estimate these parameters. For example, clam shell fragments could have been the remains of
129 1 or multiple clams. Estimating the number of individuals or volume eaten would have been
130 especially problematic for Ulva or plant remains. Thus, we consider our estimates of prey
131 consumption to be appropriately conservative.
132 Comparisons with Published Diet Data. — We searched the available literature for
133 published Malaclemys diet data from other populations that met the following criteria: 1) Fecal
134 analysis was the primary method of sample collection, 2) During fecal collection, individuals
135 were housed in separate containers so PFO could be calculated for each individual’s prey, and
136 3) Diet data were obtained primarily from adult females collected in the summer. We included
137 data from Denton et al. (2016) even though 11 of the 30 adult females they sampled were
138 collected in either January or September. We re-categorized samples into major taxonomic
139 groups (Plantae, Bivalvia, Gastropoda, Crustacea, Algae, Insecta, Gnathostomata, Annelida,
140 Bryozoa/Porifera, and Invertebrate eggs) as necessary to facilitate inter-populational
141 comparisons. For example, 4 studies (Butler et al. 2012; Tulipani 2013; Denton et al. 2016;
142 Herrel et al. 2018) lumped Plants and Algae into a single category, and only 1 other
143 (Outerbridge et al. 2017) reported sediment, as we did. We assume that sediment and sand
144 were ignored in the other studies.
145 Statistical Analyses. — Percent Frequency of Occurrence of prey items from our inter-
146 annual data and from studies meeting all criteria were compared to our JB results using 2
147 quantitative methods for measuring niche overlap: Pianka’s (1973) symmetric equation and the
148 Morisita-Horn equation (Morisita 1959; Horn 1966). These 2 methods for measuring niche
149 overlap have been well explored elsewhere (i.e., Smith and Zaret 1982; Winemiller and Pianka
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150 1990; Cortés 1997; Barwell et al. 2015) and have been used successfully in studies of a wide
151 variety of taxa (i.e., Pianka’s equation: Driscoll and Miranda 1999; Bulté et al. 2008; Crawford et
152 al. 2009; Durso et al. 2013; Barwell et al. 2015; Bonato et al. 2018; Morisita-Horn: Bergman and
153 Krebs 1993; Young and Winn 2003; Jaquemet et al. 2011; Mablouké et al. 2013; Estupiñán-
154 Montaño et al. 2018). Although the overlap formula has been traditionally used for assessing
155 similarity in resource use by sympatric species, it is just a similarity index and therefore can also
156 be confidently used for assessing similarities in resource use of distinct species or populations
157 inhabiting different localities. Both approaches perform well with PFO data (references above),
158 and both are easily employed using modern software that performs randomized bootstrapping
159 techniques to produce artificial datasets that would be expected in the absence of underlying
160 structure, which facilitates statistical comparisons. The Morisita-Horn test is particularly suited
161 to small data sets, because rare species (whether missing or present) have a negligible effect on
162 test results (Jost et al. 2011; Barwell et al. 2015), whereas the Pianka equation with a
163 scrambled-zero algorithm is particularly successful at revealing underlying prey use patterns
164 (Winemiller and Pianka 1990). Therefore, we considered the use of the 2 methods as a
165 potential test of whether our sample sizes were adequate.
166 We used Pianka’s (1973) symmetric equation to calculate the overall similarity of the
167 prey consumption patterns between years and between our combined (all years) data and data
168 from other studies. Its formula is Ojk = ∑ni pijpik /sqrt(∑n
i pij2∑n
i pik2) where n is the number of prey
169 categories, pij is the proportion of prey item i in 1 year (or in 1 study when comparing 1 study to
170 another) and pik is the proportion of prey item i in another year (or in another study when
171 comparing 1 study to another). O values are non-parametric and vary between 0.0 (no overlap)
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172 and 1.0 (total overlap).
173 To test whether the different Pianka O values of dietary similarity were significantly
174 different, first we used the R package ecosimR to calculate Pianka’s overlap (O) value between
175 data from each year of the JB study, then for all years combined against each of the datasets
176 from the intra-specific studies we compared. We next calculated P values associated with each
177 pairwise comparison by creating 100,000 Monte Carlo simulations with a scrambled-zero
178 algorithm of each diet matrix with Lawlor’s (1980) random RA3 algorithms. The RA3 procedure
179 conserves niche breadth for each seasonal sample at each simulation but destroys the resource
180 use matrix’s zero structure (Gotelli and Graves 1996). A value of Pianka’s equation was
181 calculated for each of these simulated data matrices, and the observed value was compared to
182 this population of simulated overlap values with appropriate correction for multiple
183 comparisons. For each pair of populations compared, if the observed O value was greater than
184 95% of the population of O values from the Monte Carlo simulations, we concluded that the 2
185 populations were more similar in overall diet composition than expected by chance. If the
186 observed O value was less than 95% of the population of O values from the Monte Carlo
187 simulations, we concluded that the populations were more different in overall diet composition
188 than expected by chance. We used Bonferroni corrections to adjust for multiple comparisons.
189 We also calculated the Morisita-Horn’s Index (Morisita 1959; Horn 1966) for the
190 comparisons of our inter-annual data and our combined data to data from studies meeting our
191 criteria, as described above. Its formula is Cλ = 2(∑ni pxipyi /(∑n
i pxi2 + ∑n
i pyi2)) where Cλ is the
192 Morisita-Horn index between species x and y, Pxi is the proportion of prey i relative to the total
193 prey consumed by predator x, Pyi is the proportion of prey i relative to the total prey consumed
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194 by y, and n is the total number of prey. M-H values range from 0 to 1, as with Pianka’s equation
195 above: M-H values close to zero indicate large dietary differences while values close to 1
196 indicate complete diet overlap. We calculated M-H values using the mh function in the ‘divo’
197 package of R, calculating 100,000 bootstrap values. We interpreted the significance of output
198 as described above, except this routine generated confidence intervals, for which we used
199 Bonferroni corrections to adjust for multiple comparisons.
200 We compared the resulting Pianka O and M-H values indicating the degree of diet
201 overlap between the overall diet data from JB and the data from each of the other 6 published
202 Malaclemys diet studies data using 2 measures of distance from our Ruler’s Bar study site, to
203 measure whether dissimilarity increased with increasing geographic distance. Our first distance
204 measure was the straight-line distance between each site and Ruler’s Bar, in Jamaica Bay, New
205 York; our second measure was the distance as traced along the shore line from Jamaica Bay,
206 New York. We used standard linear regression to test for relationships between the diet
207 overlap measures and the geographic distance measures.
208 RESULTS
209 Fecal Analysis.— We collected 354 usable fecal samples from JB Diamondback terrapins
210 (Table 1). We assumed that all white shell fragments were soft-shelled clams even though
211 Baltic clams (Macoma baltica Linnaeus 1758) occur in JB and appear similar to soft-shelled
212 clams. However, all complete clam shells with the diagnostic hinge structures were identifiable
213 as soft-shelled clams, so we assumed that similar shells lacking a hinge fragment were soft-
214 shelled clams as well. We also lumped Crabs together because some fragments could not be
215 identified to species. Those that could be identified were flatback mud crabs (Eurypanopeus
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216 depressus S. I. Smith 1869), lady crabs (Ovalipes ocellatus J. F. W. Herbst 1799), Atlantic sand
217 fiddler crabs (Uca pugilator Bosc 1802), and European green crabs (Carcinus maenas Linnaeus
218 1758). We lumped insects into a single category Insecta, as many could not be identified to
219 species. Those that could be identified were from orders Coleoptera (family Dytiscidae) and
220 Hemiptera Heteroptera (family Saldidae). We found Atlantic horseshoe crab eggs and algae
221 containing invertebrate egg masses; these have not been reported in previous Diamondback
222 terrapin diet studies.
223 In 2008 Atlantic ribbed mussels and amethyst gem clams had the highest PFOs, but in
224 2009 and 2010, sea lettuce and soft-shelled clams occurred most frequently (Table 1). Plant
225 leaf/stems was the highest PFO category in the remaining years (2011-14). When all fecal
226 sample data were combined over the 7-year study, the prey items with the highest PFO were
227 Plant leaf/stems (57.6%) and grass (38.1%), and soft-shelled clams (44.19%) (Table 1). Thus, the
228 diets of these terrapins appear to be composed largely of plants and algae. Crabs and Bivalves
229 (Atlantic ribbed mussels, amethyst gem clams, and especially soft-shelled clams) are important
230 in some years, but generally animal prey did not have high PFO.
231 Our analyses using Pianka’s O values to test for inter-annual diet overlap showed that
232 overall diet composition was significantly more similar than expected by chance in 5 of the 21
233 year-by-year comparisons (2009 vs. 2010, 2009 vs. 2014, 2010 vs. 2014, 2011 vs.2014, and 2013
234 vs. 2014, Table 2). The other 16 comparisons were not distinguishable from random levels of
235 similarity, and no pair-wise comparisons were significantly different by this measure. Our
236 analyses using the Morisita-Horn’s Index to test for inter-annual diet overlap showed that
237 overall diet compositions were not distinguishable from random levels of similarity in any of the
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238 21 year-to-year comparisons (Table 2).
239 Comparisons with Published Malaclemys Diet Data.— We located 6 studies that met our criteria
240 (see above) for comparison: Butler et al. (2012); Tulipani (2013); Denton et al. (2016); Alleman
241 and Guillen (2017); Outerbridge et al. (2017); and Herrel et al. (2018). Our analyses using
242 Pianka’s O values to test for inter-population diet overlap between JB and other Malaclemys
243 studies showed that diet composition was significantly more similar than expected by chance in
244 the 2 studies (Herrel et al. (2018): Long Island Sound, NY and Tulipani (2013): Chesapeake Bay,
245 VA) that were geographically closest to our site in Jamaica Bay. The comparisons of JB diet data
246 with similar data from 4 other sites were not significantly different from chance (Table 3). Our
247 analyses using the Morisita-Horn’s Index to test for inter-population diet overlap showed that
248 overall diet compositions were not different from values than expected by chance in any of the
249 6 site-by-site comparisons using this measure (Table 3).
250 We found no significant relationships between the diet overlap measures and the
251 geographic distance measures. Straight-line distance between each site and Ruler’s Bar (NY)
252 explained only 15.6% of the variance (R2 = 0.156, F(1,4) = 0.740, P = 0.438) in Pianka O values,
253 and only 12.4% of the variance (R2 = 0.124, F(1,4) = 0.566, P = 0.494) in M-H values. Shoreline
254 distance between each site and Ruler’s Bar (NY) explained only 0.9% of the variance (R2 = 0.009,
255 F(1,4) = 0.038, P = 0.856) in Pianka O values, and only 1.1% of the variance (R2 = 0.011, F(1,4) =
256 0.044, P = 0.844) in M-H values.
257 DISCUSSION
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258 We used our own long-term diet data from a single site in combination with diet studies
259 from 6 other widely-spaced populations to test for patterns of prey use in a turtle species with
260 an exceptionally large range. To our knowledge this is the first such tests for any turtle species.
261 Temporal variation in diet.— Our statistical tests found that despite some obvious inter-
262 annual fluctuations in consumption of particular prey species, Jamaica Bay Diamondback
263 terrapin diets were highly similar over 7 consecutive years. These findings indicate that single-
264 year studies may often be adequate to characterize Diamondback Terrapin diets, which is
265 reassuring given that so many diet studies are short-term.
266 Unfortunately, there are no data on prey species abundance during this time, so we
267 cannot speculate whether the minor fluctuations we observed were due to changes in prey
268 abundance or predator choice. However, JB may have undergone a recent ecosystem state
269 change influencing Diamondback terrapin prey abundance and distribution. Extirpation of
270 oysters in the past century, the shift from a hard-bottom substrate to a soft-bottom substrate,
271 and the dramatic loss of coastal and inner salt marshes (Hartig et al. 2002; Mackenzie Jr. 2005;
272 Campbell et al. 2017) likely caused changes to abundance and distribution of Diamondback
273 terrapin prey species that inhabited oyster beds. Studies from elsewhere in the Diamondback
274 terrapin range have shown that artificial or restored oyster reefs can be as successful as natural
275 reefs for providing habitats for high densities of Atlantic ribbed mussels, common mud crabs,
276 flatback mud crabs, soft-shelled clams, and amethyst gem clams (Meyer and Townsend 2000;
277 Rodney and Paynter 2006), all of which Diamondback terrapins consume in JB. Researchers are
278 investigating the potential for reintroduction of eastern oysters, bay scallop (Argopecten
279 irradians Lamarck 1819) and eelgrass (Zostera marina Linnaeus) beds to restore JB native plant
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280 and animal communities (Frame, pers. comm.); Diamondback terrapin diets should be
281 monitored during these restoration projects to explore whether diets change as available prey
282 diversity changes.
283 Diet analyses can be used to indicate the microhabitats in which Diamondback terrapins
284 feed, which are otherwise poorly known. JB Diamondback terrapins most commonly ate
285 Atlantic ribbed mussels, amethyst gem clams, soft-shelled clams and sea lettuce. Although
286 detailed invertebrate distributions have not been studied in JB, soft-shelled clams typically bury
287 themselves in soft coastal and marsh-edge sediments (Zwarts and Wanink 1989) and are found
288 throughout JB (Franz, pers. comm.). Atlantic ribbed mussels embedded themselves in and
289 amongst salt marsh sediments, often attached to smooth cordgrass stalks in the upper half of
290 the inter-tidal zone (Bertness 1980). Sea lettuce is abundant throughout JB (RLB, unpub. data).
291 Diamondback terrapins may feed on sea lettuce and soft-shelled clams throughout much of JB,
292 and may move into intertidal, smooth cordgrass-dominated habitats where Atlantic ribbed
293 mussels, amethyst gem clams, and crabs are found. Kumiga (2004) and Palmer and Cordes
294 (1988) also suggested that Diamondback terrapins move into intertidal areas to feed.
295 JB Diamondback terrapins eat far more plant material and algae than has generally been
296 reported for terrapins elsewhere; only Tulipani (2013) was comparable. This may be because JB
297 salt marshes, and therefore Diamondback terrapin prey availability, are affected by sea level
298 rise and pollution. Local sea level rise within JB is higher than the global average (Hartig et al.
299 2002) and urban pollution, specifically nitrogen loading, has also been linked to current salt
300 marsh loss and the growth of sea lettuce (Odum et al. 1984; Hanson and Lindh 1993; Mackenzie
301 2005; Ehrenfeld 2008). Areas of JB that once had dense stands of smooth cordgrass are now
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302 covered with sea lettuce (Hartig et al. 2002) and dense clusters of Atlantic ribbed mussel are
303 now common, attached to the bases of remaining smooth cordgrass stems (Hartig et al. 2002).
304 These changes might be responsible for our finding that JB Diamondback terrapins consume
305 these 2 species far more heavily than do Diamondback terrapin populations elsewhere. The
306 expansion of sea lettuce also dramatically reduces the number of macroinvertebrates on
307 sediment surfaces in JB (Franz and Freidman 2002). Studies of nearby New Jersey estuaries
308 have found similar effects of sea lettuce mat expansion on populations of copepods,
309 polychaetes, amethyst gem clams, Eastern mudsnails, and soft-shelled clams (MacKenzie 2000;
310 MacKenzie and McLaughlin 2000), all of which are important prey items for Diamondback
311 terrapins. Because JB is rapidly losing salt marshes (Hartig et al. 2002; National Park Service
312 2007; Campbell et al. 2017), it is therefore likely that some Diamondback terrapin prey species
313 are decreasing in abundance. For example, rough periwinkles, which typically live in smooth
314 cordgrass marshes, and are consumed by Diamondback terrapins in nearby Oyster Bay NY
315 (Herrel et al. 2017) are not abundant in JB (G. Frame pers. comm., D. Franz pers. comm.).
316 Between population diet comparisons.— Although previous Diamondback terrapin diet
317 studies (e.g., Tucker et al. 1995; Spivey 1998; and Butler et al. 2006) provided valuable
318 information, they reported diet data from fecal and/or stomach content analyses as percent
319 volume and/or percent mass, which are highly variable according to prey type. This is especially
320 problematic with prey items such as sea lettuce, carrion, and hard-shelled clams and small
321 snails. Additionally, Tucker et al. (1995) and Spivey (1998) developed similarity indices with
322 arbitrary significance values which cannot be analyzed statistically. We were also unable to
323 directly compare our results to either Tucker et al. (1995) or Spivey (1998) because fecal
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324 collections from different individuals were pooled (Tucker, pers. comm.; Spivey, pers. comm.).
325 Using 2 traditional statistical techniques enhanced with bootstrapping to create null
326 models, we found a pattern of few significant comparisons among long-term inter-annual
327 (same population) and inter-populational diet comparisons. This is surprising because the
328 techniques we used have detected general inter-seasonal and intersexual diet differences
329 between and within other turtle species (Luiselli 2006a; 2008a; Del Vecchio et al. 2011; Luiselli
330 et al. 2011) as well as lizards (Lorenzo and Luiselli 2007; Luiselli 2008b) and snakes (Luiselli
331 2006b; Akani et al. 2008) and many other species (listed above). Although diamondback
332 terrapin diets vary at the specific levels according to local prey availability, we suspect our
333 findings of overall similarity are the result of the fact that at fairly high taxonomic levels (i.e.,
334 Gastropods), Diamondback Terrapin diets are conservative and reflect whatever prey is found
335 in their brackish-water habitats. This suggests that Diamondback Terrapins are diet generalists,
336 readily attempting novel prey (e.g., Bulte and Blouin-Demers 2008) and may be limited by
337 factors other than specific prey species availability. Future work exploring the factors that limit
338 their distribution into lower salinity environments, such as upstream into the many rivers
339 whose estuaries they inhabit, could indicate the factors that limit this wide-spread species.
340 Acknowledgements. —P. Lindeman was extraordinarily generous with his time and
341 advice. We thank D. Franz and W. Miller for help identifying prey species and C. Peterson, J.
342 Williams, D. Franz, and two anonomous reviewers for reviewing this manuscript. M. Denton,
343 and M. Outerbridge graciously answered important clarifying questions about their published
344 data. Special thanks go to National Park Service employees, especially J. Browning, G. Frame, J.
345 Gracey and P. Rafferty for supplying space and resources for this project. A. Kanonik, G.
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346 Mnkande, L. Peyer, and Y. Weiss provided valuable field and lab assistance. This work was
347 approved by Hofstra University’s Institutional Animal and Use Committee and conducted under
348 New York State License to Collect and Possess #383 and National Park Service permit #GATE-
349 0039.
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541 Table 1. Percent Frequency of Occurrence of Ruler’s Bar (NY) diamondback terrapin
542 (Malaclemys terrapin Schoepf 1793) prey species in 2008-2014.
2008 2009 2010 2011 2012 2013 2014 combined
N 50 43 36 74 44 51 56 354
Plantae
Leaf/Stem 30.6 18.6 32.0 70.8 79.5 95.9 85.7 57.6
Seeds 8.3 18.6 7.0 9.7 4.5 14.9 3.6 8.3
Grass 11.1 34.9 36.0 43.1 27.3 51.4 66.1 38.1
Algae
Sea Lettuce (Ulva
lactuca)19.4 74.4 42.0 22.2 6.8 0.0 53.6 27.3
Algae (other than
sea lettuce)5.6 2.3 0.0 0.0 54.5 0.0 0.0 7.1
Bivalvia
Soft-Shelled Clam
(Mya arenaria)27.8 62.8 40.0 8.3 20.5 39.2 33.9 27.0
Atlantic Ribbed
Mussel (Geukensia
demissa)
33.3 2.3 6.0 16.7 18.2 2.7 12.5 9.0
Amethyst Gem
Clam (Gemma
gemma)
33.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0
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Crustacea
Crab sp. 30.6 7.0 10.0 18.1 13.6 12.2 0.0 9.1
Ostracods 5.6 2.3 0.0 0.0 0.0 0.0 0.0 0.3
Gastropoda
Eastern Mudsnail
(Ilyanassa
obsoleta)
0.0 4.7 2.0 2.8 2.3 0.0 0.0 1.6
Periwinkle
(Littorina sp.)0.0 4.7 0.0 0.0 0.0 0.0 0.0 0.6
Convex Slippersnail
(Crepidula convexa)0.0 0.0 2.0 4.2 0.0 0.0 16.1 3.6
Annelids
Polychaete tube 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.2
Invertebrate eggs
Horseshoe Crab
Eggs (Limulus
polyphemus)
2.8 4.7 0.0 0.0 0.0 0.0 17.9 3.4
Invertebrate egg
masses5.6 2.3 0.0 0.0 0.0 0.0 0.0 0.3
Bryozoan/Porifera
Bryozoans 2.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Insecta
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Insects 0.0 11.6 16.0 1.4 4.5 6.8 5.4 5.7
Unidentifiable
animal tissue13.9 20.9 8.0 2.8 2.3 74.3 17.9 17.8
Sand 16.7 47.9 7.0 9.7 4.5 48.6 46.4 23.5
543
544
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Table 2. All possible comparisons of Ruler’s Bar (NY) diamondback terrapin (Malaclemys terrapin Schoepf 1793) diet data 2008-2014
calculated using Pianka’s O and the Morisita-Horn Index. Significant findings (after Bonferroni correction for multiple comparisons)
are indicated with *.
Pianka’s O Morisita-Horn Index
O Value P (obs<null) P (obs>null) M-H Index Value 95% Confidence Interval
2008 vs. 2009 0.612 0.921 0.079 0.661 0.508-0.774
2008 vs. 2010 0.605 0.924 0.076 0.730 0.567-0.831
2008 vs. 2011 0.623 0.934 0.066 0.746 0.592-0.849
2008 vs. 2012 0.503 0.803 0.197 0.648 0.486-0.770
2008 vs. 2013 0.514 0.847 0.153 0.643 0.497-0.755
2008 vs. 2014 0.551 0.846 0.154 0.694 0.549-0.796
2009 vs. 2010 0.911 0.999 <0.001* 0.890 0.767-0.952
2009 vs. 2011 0.677 0.967 0.033 0.595 0.447-0.722
2009 vs. 2012 0.418 0.777 0.223 0.452 0.318-0.580
2009 vs. 2013 0.103 0.547 0.453 0.624 0.488-0.738
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2009 vs. 2014 0.881 0.999 <0.001* 0.801 0.667-0.889
2010 vs. 2011 0.799 0.997 0.003 0.794 0.629-0.897
2010 vs. 2012 0.499 0.849 0.151 0.656 0.489-0.784
2010 vs. 2013 0.582 0.934 0.061 0.705 0.550-0.815
2010 vs.2014 0.856 0.999 0.001* 0.884 0.751-0.942
2011 vs. 2012 0.547 0.917 0.083 0.823 0.680-0.907
2011 vs. 2013 0.572 0.923 0.077 0.771 0.642-0.856
2011 vs. 2014 0.325 0.999 <0.001* 0.870 0.746-0.934
2012 vs. 2013 0.377 0.757 0.243 0.715 0.581-0.811
2012 vs. 2014 0.454 0.813 0.187 0.738 0.602-0.831
2013 vs. 2014 0.700 0.986 0.014* 0.836 0.726-0.900
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Table 3. Comparisons of comparable diamondback terrapin (Malaclemys terrapin Schoepf 1793) diets elsewhere in the range with
Ruler’s Bar (NY) diet data, all years combined. Significant findings (after Bonferroni correction for multiple comparisons) are
indicated with *.
Pianka’s O Morisita-Horn Index
O value7 P (Obs ≤ null) P (Obs ≥ null) M-H Index Value 95% Confidence Interval
Long Island, NY1 0.737 0.989 0.011* 0.569 0.339-0.764
Chesapeake Bay, VA2 0.759 0.968 0.032* 0.643 0.523-0.746
North FL3 0.103 0.547 0.453 0.213 0.118-0.331
South FL4 0.095 0.283 0.719 0.287 0.173-0.423
Galveston Bay, TX5 0.733 0.922 0.078 0.589 0.450-0.707
Bermuda6 0.581 0.756 0.245 0.570 0.431-0.695
1: Herrel et al. 2018
2: Tulipani 2013
3: Butler et al. 2012
4: Denton et al. 2016
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5: Alleman and Guillen 2017
6: Outerbridge et al. 2017
7: O values indicate degree of similarity to Ruler’s Bar (NY) all years combined diet data
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