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Distribution, Abundance, and Habitat Characteristics of Fundulus jenkinsi (Evermann) (Saltmarsh Topminnow) in Coastal Mississippi Watersheds, with Comments on Range-wide Occurrences Based on Non-vouchered and Museum Records
Mark S. Peterson, William T. Slack, and Erik T. Lang

Southeastern Naturalist, Volume 15, Issue 3 (2016): 415–430

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Southeastern Naturalist 415 M.S. Peterson, W.T. Slack, and E.T. Lang 22001166 SOUTHEASTERN NATURALIST 1V5o(3l.) :1451,5 N–4o3. 03 Distribution, Abundance, and Habitat Characteristics of Fundulus jenkinsi (Evermann) (Saltmarsh Topminnow) in Coastal Mississippi Watersheds, with Comments on Range-wide Occurrences Based on Non-vouchered and Museum Records Mark S. Peterson1,*, William T. Slack2, 3, and Erik T. Lang4 Abstract - Fundulus jenkinsi (Saltmarsh Topminnow) is listed as “at risk” by the USFWS and as a Tier 2 conservation priority in Mississippi, in part, because of marsh-habitat loss due to storms, urbanization, and its specialized habitat requirements and limited geographic distribution. To provide additional quantitative data for conservation planning, our objectives were to (1) determine the distribution and abundance of Saltmarsh Topminnow within coastal Mississippi, (2) characterize its habitat requirements, and (3) organize and present all Saltmarsh Topminnow data records (non-vouchered and museum records and those from this study) for use in the development of management/conservation plans. We collected 497 fish and associated habitat data from 27 February to 1 August 2009. PCA produced 3 meaningful components: (1) a landscape-position axis (32.40% of the total variance), (2) a seasonal/spatial axis of species occurrence (18.99%), and (3) a geomorphic bank-slope axis (18.78%). Ninety-six percent of all fish (representing 78.8% of collection effort) were captured in water with salinity less than 13 psu. We compiled 831 geo-referenced occurrences with collection dates ranging from 1891 to 2015. To better quantify and conserve the closelylinked habitat requirements of this species within a reduced salinity range, additional sampling should be focused in undersampled areas between Lake Borgne, LA, to west of Galveston Bay, TX. Introduction Fundulus jenkinsi (Evermann) (Saltmarsh Topminnow) occurs sporadically from Galveston Bay, TX to Escambia Bay, FL, although Peterson et al. (2003) and Lopez et al. (2011) suggested that this species might be more widely distributed and abundant than previously believed. Two recent studies confirmed that initial hypothesis. First, Martin et al. (2012) reported new records of Saltmarsh Topminnow in Texas that documented a western range extension (the Tres Palacios River). Second, Guillen et al. (2015) reported new localities in the Galveston-Trinity Bay system as well as Sabine Lake, TX, and also documented Saltmarsh Topminnow 1Department of Coastal Sciences, The University of Southern Mississippi, 703 East Beach Drive, Ocean Springs, MS 39564. 2Mississippi Department of Wildlife, Fisheries and Parks, Mississippi Museum of Natural Science, 2148 Riverside Drive, Jackson, MS 39202-1353. 3Current address - US Army Engineer Research and Development Center, Waterways Experiment Station EE-A, 3909 Halls Ferry Road, Vicksburg, MS 39180-6199. 4Fisheries Management, Louisiana Department of Wildlife and Fisheries, 2000 Quail Drive, Baton Rouge, LA 70817. *Corresponding author - ecofishconsulting@gmail.com. Manuscript Editor: Carol Johnston Southeastern Naturalist M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 416 in the type locality watershed; no specimens had been found at the type locality (Dickinson Bayou; Evermann 1892) in Galveston-Trinity Bay since 1951 despite considerable collecting efforts Historically, 2 environmental factors have been suggested as influencing this species’ distribution and abundance. Saltmarsh Topminnow uses Spartina alterniflora Loiseleur (Smooth Cordgrass) marsh (Peterson and Turner 1994, Suttkus et al. 1999, Thompson 1980), and has been found mainly where salinity ranges between 1 and 4 psu (Bailey et al. 1954, Boschung and Mayden 2004, Gilbert and Relyea 1992, Thompson 1980). The most comprehensive study (Lopez et al. 2011) collected 661 Saltmarsh Topminnows from the Barataria-Terrebonne basin, in LA, to as far east as Escambia Bay, FL, and used principal component analysis (PCA) to ordinate physical–chemical data into a geomorphic axis (water depth, bank slope, and plant-stem density) and a seasonal/spatial axis of species occurrence (water temperature, salinity, and turbidity). PCA illustrated a higher mean CPUE in habitats comprised of low to moderate stem density (<25 stems/0.25 m2), depth (<25 cm), bank slope (<15°), turbidity (<30 NTU), and salinity (<16 psu) coupled with spring and early summer water temperatures (>15 °C). Saltmarsh Topminnow CPUE was significantly higher in Spartina cynosuroides (L.) Roth (Big Cordgrass) marsh edge compared to 5 other habitat types, even though it was one of the leastsampled habitats. Given its apparent low relative abundance and patchy distribution, there is a real need to obtain distribution-wide data on habitat characteristics for Saltmarsh Topminnow to better manage and conserve this species and its habitat (Guillen et al. 2015, Lopez et al. 2011). In fact, little is known about the distribution and habitat characteristics of Saltmarsh Topminnow throughout its entire range in coastal Mississippi (MMNS 2015). This situation is particularly important for 2 reasons. First, the extensive development of the dock-side gaming industry and associated activities in coastal Mississippi has added further concern about the status of Saltmarsh Topminnow in coastal wetlands which are being urbanized at a faster rate than estimated in the past (Meyer-Arendt et al. 1998, MMNS 2015) following shifts in general population demographics (e.g., Crossett et al. 2004, European Environmental Agency 2006). Second, the “at risk” designation of Saltmarsh Topminnow on the USFWS website (http://ecos.fws.gov/speciesProfile/profile/speciesProfile. action?spcode=E0BO) indicates considerable coastal-marsh habitat loss in the north-central Gulf of Mexico (GOM) attributable, in part, to multiple hurricanes (George [1998], Ivan [2004], Dennis, Katrina, and Rita [2005]), and continued coastal urbanization (Bulleri and Chapman 2010; Chapman and Underwood 2011; Lowe and Peterson 2014, 2015; Peterson and Lowe 2009). The Mississippi State Wildlife Plan (MMNS 2015) considers this species as a species of greatest conservation need and ranks it as a Tier 2 conservation-priority because of “… specialized habitat needs or habitat vulnerability.” Furthermore, the 2010 Deepwater Horizon oil spill (Alford et al. 2014) and projected sea-level rise (Fulford et al. 2014) threaten further potentially deleterious consequences for these communities. In addition to these impacts, dredging of waterways will have cumulative effects on hydrology, Southeastern Naturalist 417 M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 water quality, and habitat. Specifically, dredging allows salt-water intrusion further up bayous and tidal rivers, and shifts optimal salinity to locations with different habitat structure (Peterson 2003, Peterson et al. 2007, Guillen et al. 2015), potentially changing distribution patterns and reducing estuarine production. Saltmarsh Topminnow is also listed as a species of concern in Mississippi (Ross 2001), threatened in Florida (Gilbert and Relyea 1992), endangered in Alabama (Boschung and Mayden 2004), a species of concern in Louisiana (R. DeMay, Barataria-Terrebonne National Estuary Program, Thibodaux, LA; pers. comm.), and a species of greatest conservation need for the Gulf Coast Prairies and Marshes ecoregion of Texas (TPWD 2005, 2011, 2012). The specific objectives of this study were to (1) determine the distribution and abundance of Saltmarsh Topminnow within coastal Mississippi, (2) characterize its habitat requirements, and (3) organize and present all data records (non-vouchered and museum records and those from this study) of Saltmarsh Topminnow for use in the development of management/conservation plans. Methods We set and fished Breder traps that faced inshore at 8 general sites occurring within identified watersheds of the Pascagoula River, Old Ft. Bayou, Biloxi River, Tchoutacabouffa River, Bernard Bayou, Wolf River, Jourdan River, and Pearl River, which are all major waterways within the coastal Mississippi landscape. Based on previous research (Lang et al. 2012, Lopez et al. 2011, Peterson et al. 2003), we selected sites according to salinity regimes and vegetation present along the marsh edge within each watershed. We focused on sampling pure Juncus roemarianus Scheele (Black Needlerush), Smooth Cordgrass bordered by Black Needlerush, and pure Smooth Cordgrass habitats; other habitat types were sampled as encountered. Initially, we sampled at 3 sites using 5 Breder traps (Breeder 1960, Lang et al. 2012, Lopez et al. 2011, Peterson et al. 2003) and 5 Gee minnow traps on 27 February 2009 and again on 2 March 2009 (30 Breder traps and 30 Gee traps) in order to compare catch-per-unit-effort (CPUE; fishing from high to low tide) with time each trap fished and CPUE by gear type in order to choose gear type. There was no correlation between time fished and CPUE pooled by gear type (r = 0.180, P = 0.504), Breder trap samples only (r = 0.210, P = 0.369) or Gee minnow traps only (r = 0.230, P = 0.279). Also, there was no difference between Breder trap and Gee minnow trap CPUE (ANOVA: F1,17 = 0.320; P = 0.580); thus, we only used the Breder trap geartype collections for the entire study because that collection method is consistent with the one we used in earlier research (Lopez et al. 2010, 2011). We fished the falling tide from 27 February to 1 August 2009 (21 dates) in each habitat type with up to 30 Breder traps (Fig. 1) at a time (6 traps per habitat type). At each site, we placed the traps 1 m apart at 3–5 locations within a watershed across the entire study area. Each Breder traps was constructed out of 0.635-cm-thick Plexiglass to make a box that was 30 cm x 15 cm with two 15 cm x 15 cm wings protruding from each side. The wings formed a “V” with a 12-mm opening to catch anything that was moving with the outgoing tide. At each site, we measured water Southeastern Naturalist M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 418 temperature (°C), turbidity (NTU, nephlometric turbidity units), and salinity (psu) once for each 6-trap set. For each individual trap, we measured depth (cm) and bank slope (in degrees), and we used a 0.25-m2-PVC quadrat to determine plant-stem density (number of stems/0.25 m2) immediately in front of each trap. We used a Garmin GPS 76 unit (Garmin International, Inc., Olathe, KS) to geo-reference each trap location. We also measured dissolved oxygen (DO, mg/L) at each site, but did not include it in our analyses because DO was negatively correlated with water temperature (see Lopez et al. 2011). To examine the relationship between physical–chemical variables and Saltmarsh Topminnow CPUE, we used a 2-step multivariate procedure (Lopez et al. 2011, Peterson and Vanderkooy 1997). First, we ordinated the 7 physical–chemical variables (only 6 variables used in final analysis; see below) using PCA of the correlation matrix (Field 2013) with varimax rotation to maximally resolve loadings, and considered any variable that loaded on a component at an absolute value ≥0.50 as making a significant contribution to interpreting that component (Hair et al. 1984, Lopez et al. 2011). All variables used in the PCA were either the mean value of the 6 traps (water depth, bank slope and stem density) or the single value for the set of 6 traps as noted earlier. Thus, descriptive summary statistics are grand mean (± SEM) values for water depth, bank slope, and stem density, and standard mean (± SEM) values for the other variables. We plotted standardized factor-scores Figure 1. Image of a Breder trap (Breder 1960) used in this study. Southeastern Naturalist 419 M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 of the most meaningful components for each sample with mean CPUE as a third axis. We employed the Kaiser-Meyer-Olin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity to test the adequacy of our sample size for the PCA analysis. The KMO statistic ranges from 0 to 1, with 0 indicating diffusion in the pattern of correlations (hence, PCA is likely not appropriate for the data set) and values close to 1 indicating the pattern of correlation is compact and thus PCA is appropriate (Field 2013). We considered values 0.5–0.7 as mediocore, 0.7–0.8 as good, 0.8–0.9 as great, and >0.9 as superb. KMO values less than 0.5 indicate that data are not appropriate to use in PCA (Field 2013). Bartlett’s measure tests the null hypothesis that the original correlation matrix is an identity matrix; for PCA to work correctly, the measure must be significantly (P < 0.05) different from the null because the variables must have some degree of correlation. Finally, we used stepwise multiple-regression analysis to examine the relationship between the standardized factor-scores from the important components of the PCA analysis and mean CPUE of Saltmarsh Topminnow as a support tool for the results of the PCA analysis. We also conducted a one-way ANOVA to compare Saltmarsh Topminnow CPUE across habitat types and among locations sampled. When F-values were significant, we used either a Sidak (equal variances) or a Games-Howell (G-H) (heterogeneous variances) post-hoc test to separate mean responses. All statistical analyses were conducted in SPSS software (ver. 15.0; SPSS, Inc., Chicago, IL) and we set significance at P ≤ 0.05 (Field 2013). We used ArcMap 10 to plot occurrence records of Saltmarsh Topminnow based on our collections, non-vouchered records, and compiled museum records to depict the currently recognized range of the species and to identify potential regions for future studies (see Supplemental File 1, available online at http://www.eaglehill. us/SENAonline/suppl-files/s15-3-S2249-Peterson-s1, and, for BioOne subscribers, at http://dx.doi.org/10.1656/S2249.s1). We obtained data directly from partnering institutions (GCRL, Inland Fishes of Mississippi database [Ross, 2001], and MMNS), online data sources (FishNet2 [http://www.fishnet2.net/], FoTX [https:// sites.cns.utexas.edu/hendricksonlab/FoTX_Sandbox], Tulane Museum of Natural History, New Orleans, LA), and through personal communication (J. Knight, Florida FWCC, Holt, FL; G. Guillen, University of Houston-Clear Lake, TX). We obtained occurrence records from ANSP, AUM, CAS, CUMV, the current study, FMNH, FoTX, FSBC, FWC, GCRL, INHS, KU, LSUMZ, MCZ, MMNS, TCWC, TNHC, TU, UAIC, UF, UHCL, UMMZ, UNO, USM, USNM, UT and YPM. Source abbreviations follow Sabaj Pérez (2010) except for FoTX (Fishes of Texas database [Hendrickson and Cohen, 2015]), FWC (Florida Fish and Wildlife Conservation Commission) and UHCL (University of Houston - Clear Lake). We accessed Tulane Museum of Natural History collection records on 17 December 2009 through the GBIF data portal, Tulane University Museum of Natural History (http://data.gbif. org/datasets/resource/8077), and records from the Fishnet2 Portal (www.fishnet2. net) on 16 July 2015. We did not examine museum voucher material except where noted (current study, GCRL, and MMNS). Southeastern Naturalist M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 420 Results Between February and August 2009, we sampled 674 Breder traps and collected 497 Saltmarsh Topminnow at 8 sites across the state of Mississippi from the Pearl to the Pascagoula watersheds (Fig. 2), areas not previously systematically sampled. Table 1 shows CPUE, water quality and vegetation summary-statistics (mean ± 1 standard error of the mean [SEM]) for the 8 sites for all Breder trap sets. Collections from the Tchoutacoubouffa watershed had the highest CPUE (1.44 ± 0.49), followed by the Pascagoula (1.27 ± 0.44), and then the Pearl (0.77 ± 0.19); other watersheds had lower CPUE values (Table 1). However, Saltmarsh Topminnow CPUE (log10) did not differ significantly (ANOVA: F7,113 = 1.823, P = 0.090) among the 8 sites or among the 12 replicated vegetation types (within watersheds) at our sites (range = 2–18 replicate samples each) (ANOVA: F11,74 = 0.939, P = 0.510). Saltmarsh Topminnow CPUE (log10) also did not differ among the 3 main habitat types pooled across sites (Smooth Cordgrass, Black Needlerush, mixed Smooth Cordgrass–Black Needlerush); F1,25 = 0.255, P = 0.777). Finally, we collected 97.2% of all Saltmarsh Topminnow (representing 84.9% of our collection effort) at salinity levels of less than 16 psu and 95.8% (78.8% of our collection effort) when salinity was less than 13 psu. The highest salinity at any site where we collected Saltmarsh Topminnow was 19.8 psu. The KMO measure of sampling adequacy of 0.610 suggests a mediocre factorsolution of the data set (Field 2013) and that the PCA analysis produced distinct Figure 2. Overall project area in coastal Mississippi where sites were sampled with Breder traps during the 2008–2009 Saltmarsh Topminnow project (top left panel). Sites sampled west of Highway 49 (Panel A) and sites sampled east of Highway 49 (Panel B) during the project period noting samples where Saltmarsh Topminnow were present (solid circle) or absent (gray square). What may appear to be darker or black squares are actually overlapping gray squares due to close spatial positioning of some of the Breder traps. Southeastern Naturalist 421 M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 Table 1. Summary statistics for Saltmarsh Topminnow CPUE, water quality, and plant-stem density by watershed for all Breder traps set. Number of traps set is found parenthetically under each watershed name. Temperature, salinity, dissolved oxygen (DO) and turbidity values are presented as mean ± 1 SEM (standard error of the mean); bank slope, depth, and total stem-density are presented as grand mean ± 1 SEM. Stem density is expressed as number of stems/0.25m2. Watershed CPUE Temp. (°C) DO (mg/L) Salinity (psu) Turbidity (NTU) Depth (cm) Pascagoula (n = 120) 1.27 ± 0.44 25.01 ± 0.24 7.49 ± 0.30 1.31 ± 0.20 13.17 ± 0.99 22.97 ± 0.51 Ft. Bayou (n = 90) 0.03 ± 0.02 29.63 ± 1.68 6.67 ± 0.23 13.13 ± 2.59 8.00 ± 1.39 22.38 ± 0.97 Biloxi (n = 60) 0.58 ± 0.19 30.70 ± 0.30 5.77 ± 0.38 8.32 ± 0.63 2.03 ± 0.31 24.27 ± 0.65 Tchoutacabouffa (n = 90) 1.44 ± 0.49 26.67 ± 1.49 5.99 ± 0.40 5.89 ± 1.10 10.85 ± 2.65 19.68 ± 0.60 Bernard Bayou (n = 12) 0.33 ± 0.19 30.00 ± 1.20 5.35 ± 2.10 10.00 ± 0.10 2.12 ± 0.47 19.00 ± 1.57 Wolf (n = 120) 0.44 ± 0.12 29.65 ± 0.37 5.41 ± 0.47 7.15 ± 1.66 11.62 ± 2.72 16.50 ± 0.75 Jourdan (Diamondhead) (n = 90) 0.49 ± 0.12 28.36 ± 0.80 6.52 ± 0.37 11.20 ± 2.11 14.76 ± 2.96 20.64 ± 0.57 Pearl (n = 90) 0.77 ± 0.19 30.57 ± 1.97 7.41 ± 0.41 7.43 ± 1.89 19.95 ± 3.30 20.40 ± 0.46 Juncus roemerianus Spartina alterniflora Spartina cynosuroides Watershed Bank slope (°) density density density Total stem density Pascagoula (n = 120) 6.95 ± 0.44 4.60 ± 0.98 3.50 ± 0.63 1.05 ± 0.61 6.32 ± 0.81 Ft. Bayou (n = 90) 5.43 ± 0.49 2.69 ± 0.79 1.54 ± 0.56 - 3.73 ± 0.73 Biloxi (n = 60) 9.56 ± 0.75 5.87 ± 1.93 4.00 ± 1.06 - 4.98 ± 1.07 Tchoutacabouffa (n = 90) 5.70 ± 0.49 3.48 ± 1.19 - - 4.37 ± 0.97 Bernard Bayou (n = 12) 4.42 ± 0.89 - - - - Wolf (n = 120) 6.56 ± 0.41 2.98 ± 0.77 4.11 ±0.75 1.00 ± 0.72 7.27 ± 0.80 Jourdan (Diamondhead) (n = 90) 7.82 ± 0.63 1.93 ± 0.65 2.54 ± 0.57 0.17 ± 0.17 3.68 ± 0.61 Pearl (n = 90) 8.21 ± 0.67 0.83 ± 0.83 1.67 ± 1.00 0.42 ± 0.29 1.11 ± 0.29 Southeastern Naturalist M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 422 components based on sampling adequacy. The Bartlett’s test of sphericity was also significant (P < 0.001), supporting the KMO results. Results of the PCA indicated that the 6 original variables were reduced to 3 meaningful components (eigenvalues > 1.00) that explained 70.18% of the variation (Table 2). Component I explained 32.40% of the total variance and was composed of positive correlations with salinity and mean water-depth, and a negative correlation with mean turbidity, which we interpret as a landscape position axis. The standardized factor-scores were scattered across the axis suggesting that we sampled a range of conditions. Component II explained an additional 18.99% of the total variance and was composed of water temperature and mean total plant-stem density (both positive correlations); however, the standardized factor-scores were somewhat narrowly distributed. This result suggests that we made our collections across a limited range of both water temperature and plant-stem density. We interpreted this component as a seasonal/ spatial axis of species occurrence (Table 2). Component III explained 18.78% of the total variance and was composed of a positive correlation with mean bankslope. The standardized factor-scores were distributed across a range of conditions (Table 2), and we interpreted Component III as a geomorphic bank- slope axis. Plots of combinations of Components I, II, and III arrayed the mean Saltmarsh Topminnow CPUE and suggested that these axes provide important information about the habitat characteristics of Saltmarsh Topminnow across all systems studied (Fig. 3). The stepwise linear regression supported Component I and II as significant but weak predictors of mean CPUE of Saltmarsh Topminnow across all systems (adjusted R2 = 0.063, n = 107, P = 0.013). We used ArcMap 10 to plot all occurrence records of Saltmarsh Topminnow based on our collections, non-vouchered specimens, and museum records (Fig. 4). We compiled 874 total occurrence records, which included 10 non-vouchered observations and 33 records lacking sufficient data for geo-referencing (see Supplemental File 1, available online at https://www.eaglehill.us/SENAonline/suppl-files/s15-3-S2249- Peterson-s1, and, for BioOne subscribers, at http://dx.doi.org/10.1656/S2249.s1). The range map includes 831 geo-referenced occurrences representing 27 institutions (museums, universities and agencies) and 5 states (FL: 79, AL: 172, MS: 355, LA: 216 and TX: 52). The occurrences within Alabama, Mississippi and Louisiana comprise nearly 85% of the total records compiled. Dates for collection range from 1891 through 2015, with 91% of the records dating since 1980. Table 2. Varimax-rotated component matrix of the PCA. * indicates loadings used to identify the components. Components (% explained = 70.18) Variables I (32.40%) II (18.99%) III (18.78%) Water temperature (log10) 0.377 0.618* -0.309 Turbidity (log10) -0.777* -0.090 -0.012 Salinity (log10) 0.831* 0.013 -0.121 Mean plant-stem density (log10) -0.150 0.861* 0.199 Mean bank slope (log10) 0.061 0.050 0.920* Mean water depth (log10) 0.694* -0.070 0.362 Southeastern Naturalist 423 M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 Figure 3. 3-D plots of (A) PC I vs. PC II, (B) PC I vs. PC III, and (C) PC II vs. Pc III versus the mean CPUE of Saltmarsh Topminnow (n = 490) based on collections with 674 Breder traps fished across coastal Mississippi environments. H = high loadings, L = low loadings, WD = mean water depth, BS = mean bank slope, Sal = salinity, Turb= turbidity, Temp = water temperature, and plant stem = mean plant-stem density. Southeastern Naturalist M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 424 Discussion Results of this study suggested a link between the landscape/geomorphic and seasonal/spatial environmental conditions (PCA axes) that characterized the habitat types with the highest Saltmarsh Topminnow CPUE, and also suggested that multiple environmental factors influence habitat use by this species. Water depth, bank slope and plant-stem density are inter-related within marsh environments and have been shown to influence marsh access to a number of nekton species (Johnston and Sheaves 2007, Lang et al. 2012, McIvor and Rozas 1996, McIvor et al. 1989). For example, a number of other studies have illustrated that water depth and marsh-edge type (i.e., depositional or erosional) influence access and use of low, intermediate, and high marsh by fishes (Ennis and Peterson 2015, McIvor and Odum 1988, McIvor and Rozas 1996, McIvor et al. 1989, Meyer and Posey 2009). Peterson et al. (2003) reported that Saltmarsh Topminnow abundance was highest at water depths of ~50 cm (depositional marsh edges) in main-channel marsh-edge habitats but mainly in low-salinity areas (≤12 psu). Our data are similar to those presented in Lopez et al. (2011) in that it showed Saltmarsh Topminnow caught in Breder traps were very common in depths less than 25 cm, but this may be because of how the traps are placed within the shallow marsh. However, we also recognize that Saltmarsh Topminnow are collected with seines at low tide when Breder traps are not as efficient (Fulling et al. 1999, Peterson et al 2013). Finally, coupled with the Figure 4. Upper-left panel illustrates watersheds (8 digit HUC) where Saltmarsh Topminnow has been documented. Lower panel illustrates the regional distribution of Saltmarsh Topminnow based on results of the current project and compiled non-vouchered and museum records (see Supplemental File 1, available online at https://www.eaglehill.us/ SENAonline/suppl-files/s15-3-S2249-Peterson-s1, and, for BioOne subscribers, at http:// dx.doi.org/10.1656/S2249.s1). Southeastern Naturalist 425 M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 geomorphology relative to marsh access, habitats with high vegetation-biomass or stem-density provide greater food-availability (food attachment) and refuges from predation (Dibble et al. 2006, Warfe and Barmuta 2004) when compared to lessdense or less-complex habitats. Although we did not collect specifically at different marsh elevations, we hypothesize that Saltmarsh Topminnow use the intermediate and high marsh when they are flooded, and our samples were collected as these fish moved into the low marsh at low tide (sensu Ennis and Peterson 2015, Lopez et al. 2011). In Louisiana marshes (Rozas and Reed 1993), the euryhaline fundulids Fundulus grandis Baird and Girard (Gulf Killifish) and Adinia xenica (Jordan and Gilbert) (Diamond Killifish) were abundant in high-, intermediate-, and low-marsh elevations, but the Saltmarsh Topminnow was found in only high and intermediate marsh. Peterson and Turner (1994) found that Saltmarsh Topminnow were more abundant near the marsh-edge habitat (>3 m from the creek) in Louisiana; however, Ennis and Peterson (2015) found Saltmarsh Topminnows used micro-topography and especially rivulets to access shallow interior-marsh habitat in Mississippi. These data support our hypothesis that the small-bodied Saltmarsh Topminnow uses intermediate to high marsh and may favor shallow waters for refuge or foraging as do other fundulids, including Fundulus luciae (Baird) (Spotfin Killifish; Kneib 1984, Yozzo and Ottman 2003, Yozzo and Smith 1998). Shields and Mayes (1983) in North Carolina collected Spotfin Killifish most often in high-marsh habitat dominated by either Black Needlerush or Spartina patens (Ait.) Muhl. (Salt Meadow Cordgrass), whereas Able et al. (1983) collected Spotfin Killifish in New Jersey’s high marsh mainly from areas dominated by the short form of Smooth Cordgrass or Salt Meadow Cordgrass. In fact, Peterson et al. (2003), who only sampled in main-channel marsh-edge habitat, collected fewer Saltmarsh Topminnow than Lopez et al. (2011) and the present study, where the focus was collecting in small dendritic creeks off of main channels. Finally, Lang et al. (2012) noted that the oocyte composition of ovaries of Saltmarsh Topminnow suggested spawns occur over multiple days around the time of spring tides both within a population and on the individual level. Our findings contribute to a better understanding of the importance of linkages of intertidal-saltmarsh habitat to Saltmarsh Topminnow because spawning intensity appears to increase with tidal height and marsh inundation (Lang et al. 2012). Our work also suggests that small creeks are important vectors for marsh access by Saltmarsh Topminnow and supports the value of the dendritic nature of salt marshes to marsh residents (Ennis and Peterson 2015; Kneib 2000, 2003; Lopez et al. 2010, 2011; Meyer and Posey 2009). It has been shown that abiotic factors such as water temperature, salinity, and turbidity can initiate movements, drive distribution and abundance patterns, and affect the foraging ecology of a species (Fulford et al. 2011, Peterson et al. 2004). The environmental conditions where Saltmarsh Topminnow was most abundant represent a subset of available conditions across the geographic range we sampled. These findings support earlier work from other geographically smaller-scale Saltmarsh Topminnow collections in Mississippi and Alabama Southeastern Naturalist M.S. Peterson, W.T. Slack, and E.T. Lang 2016 Vol. 15, No. 3 426 (Ennis and Peterson 2015, Fulling et al. 1999, Peterson et al. 2003), other general sources across its range (Boschung and Mayden 2004, Gilbert and Relyea 1992, Guillen et al. 2015, Peterson and Ross 1991, Ross 2001) and the spatially wideranging study of Lopez et al. (2011). This more-limited range of environmental conditions described herein appears to be preferred by Saltmarsh Topminnow within systems as compared to other resident fundulids. Thus, these habitats must be protected if the Saltmarsh Topminnow is to remain extant. These saltmarshes are in coastal regions where increasing urbanization, land subsidence, and projected sea-level rise threaten this species and its restricted habitat. Our study documented the distribution and abundance of Saltmarsh Topminnow across the coastal systems of Mississippi in most systems sampled. Landscape/geomorphic and seasonal/spatial axes of physical–chemical variables showed a narrow distribution within these systems for Saltmarsh Topminnow as compared to other resident fundulids (sensu Lopez et al. 2011). We collected more individuals of Saltmarsh Topminnow during the spring and summer months with increased numbers of juveniles; it has been suggested that seasonal abiotic cues such as water temperature, salinity, and turbidity may be influencing life-history traits like reproduction and spawning (Lang et al. 2012, Lopez et al. 2011). Our results suggest other factors such as water depth, bank slope, and stem density (landscape/geomorphic characters) influence CPUE and distribution when nested within the seasonal/spatial axis. Our data present the current knowledge of the distribution of the rare Saltmarsh Topminnow across most of its known range. The species has a fairly contiguous distribution from eastern localities in Florida, through Alabama and Mississippi. However, Saltmarsh Topminnow is patchily distributed from Lake Pontchartrain to Galveston Bay. Four potential regions for future studies which should include systematic sampling are: (1) Lake Borgne to the Mississippi River Delta (Louisiana), (2) Atchafalaya/Vermillion bays to Sabine Lake (Louisiana), (3) Sabine Lake to Galveston Bay (Louisiana and Texas), and (4) west of Galveston Bay (Texas). Additional sampling should be focused in regions that have been minimally sampled within coastal habitats based on the Saltmarsh Topminnow’s closely linked habitat requirements within a reduced salinity range. Findings from these studies will also support state and federal conservation initiatives. Acknowledgments We thank J.D. Lopez, P. Grammer, M. Lowe, J.-M. Havrylkoff, M. Andres, and S. Manning for all their help on this project. Sara LeCroy provided records from the GCRL Museum, and S.T. Ross provided access to the compiled database from the Inland Fishes of Mississippi. George Guillen and John Knight provided non-vouchered collection records from their regions. This Project was funded by the US Fish and Wildlife Service through State Wildlife Grant T-7-1 funds administered through the Mississippi Department of Wildlife, Fisheries, and Parks. This research was approved and performed under the University of Southern Mississippi IACUC protocol # 10040802. 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