Spatial and Temporal Variability in Aquatic-Terrestrial Trophic Linkages in a Subtropical Estuary

Home , Aquatic insect, Nephilinae, Nocturnality, Tetragnatha

Spatial and temporal variability in aquatic-terrestrial trophic linkages in a subtropical estuary

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Martha Jeannette Zapata, B.S.

Graduate Program in Environment and Natural Resources

The Ohio State University

2018

Thesis Committee:

Dr. S. Mažeika P. Sullivan, Advisor

Dr. Suzanne M. Gray

Dr. Lauren M. Pintor

Martha Jeannette Zapata

2018

Abstract

Estuaries and coastal wetlands are highly productive ecosystems that support unique biodiversity and complex food webs. The movement of organisms among habitats is a critical facilitator of biological connectivity in estuaries, however, little attention has been directed towards biological connectivity between aquatic and terrestrial zones. Although a substantial body of research has documented aquatic insect emergence as a critical nutritional subsidy between rivers and their adjacent riparian zones that is essential to the functioning of both ecosystems, our understanding of this food-web linkage in estuaries remains unresolved.

My research aimed to characterize emergent (i.e., adult) aquatic insects and insect- facilitated subsidies to terrestrial consumers (i.e., orb-web spiders) across the Fakahatchee Strand and Ten Thousand Islands Estuary of southwestern Florida. Emergent aquatic insects and nearshore orb-weaving spiders were surveyed during the summer and winter seasons of 2015 and

2016 at nine study reaches (i.e., sites) representing upper, mid, and lower segments of the estuary, generally corresponding with freshwater, mesohaline, and polyhaline habitats, respectively. Salinity, as well as a suite of additional physicochemical parameters including dissolved oxygen, temperature, total dissolved solids, pH; nutrients (total nitrogen, nitrate, total phosphorus, phosphate); and shoreline habitat were also measured. These data were used to address the following questions: (1) How do the density, community composition, and individual traits of emergent aquatic insects vary seasonally across an estuarine salinity gradient, and how

ii are these characteristics related to the distribution and condition of nearshore orb-weaving spiders? (2) How are trophic dynamics (aquatically-derived nutritional subsidies, trophic position) of riparian spiders associated with spatial and temporal variability in estuaries as mediated by emergent insect subsidies?

Abnormally-high rainfall induced by El Niño-Southern Oscillation (ENSO) led to salinity levels that deviated from typical winter months. Emergent insect density varied by season and estuary position (linear mixed model [LMM]: F1,37 = 8.57, P = 0.006 and F2,5.8 = 8.75, P = 0.018, respectively), but was generally higher during the winter. Overall, emergence rates were highest at mid-estuary reaches during the winter (x̅ ± SE, 17.9 ± 5.8) and lowest at lower-estuary reaches during both the summer (x̅ ± SE, 1.89 ± 2.81) and winter (x̅ ± SE, 4.22 ± 1.98). Season was a strong predictor of Shannon diversity (LMM: F1,36 = 15.645, P < 0.001), Pielou’s evenness

(LMM: F1,31.8 = 5.316, P = 0.028), and richness (families) of emergent insects (LMM: F1,36 =

26.353, P < 0.001) most notably at lower-estuary sites where measures were higher during the winter. Water temperature, and phosphate and total nitrogen concentrations also received support as predictors of emergence rate (R2 = 0.19, F = 5.77, P = 0.024). Community composition varied spatially across the estuarine gradient (ANOSIM: R = 0.158, P = 0.001), and showed a marginal seasonal difference only at lower-estuary habitats (ANOSIM: R= 0.1023, P = 0.061). Insect communities were largely dominated by Chironomidae, which accounted for 53-56% of variation in community composition across the estuarine gradient. Spatial and seasonal patterns in density of Dolichopodidae also contributed to the observed community differences. Mean body size of Chironomidae and Dolichopodidae were generally 180 and 400% larger, respectively, at lower-estuary habitats compared to upper-estuary counterparts. At lower-estuary

iii reaches, emergent aquatic insects exhibited lower dispersal ability and higher prevalence of univoltinism than upper- and mid-estuary assemblages.

Orb-weaving spider density tracked emergent insects, with densities at mid-estuary reaches exceeding those of the upper- and lower-estuary, There was also a seasonal effect on orb-web density (LMM: F1,84.8 = 16.692, P < 0.0001), particularly at mid-estuary reaches where densities were higher during the winter than the summer (P < 0.0001). Estuary position strongly influenced body condition of Tetragnatha, which was 96% higher in the lower-estuary than in the upper-estuary (LMM: F2,19.50 = 11.254, P = 0.0006). Leucage body condition was strongly influenced by a season x position interaction (LMM: F2,348.31 = 2.506, P = 0.083), whereby individuals exhibited higher body condition during the winter at upper- (P = 0.046) and mid- estuary (P < 0.0001) habitats.

Bayesian mixing models using δ13C and δ15N signatures of primary producer sources and consumers showed that aquatically-derived energy (i.e., nutritional subsidies originating from epiphyton and phytoplankton) represented 0.79 to 0.99 of the diet of nearshore spiders across all study reaches and seasons. Reliance on aquatically-derived energy varied spatially (LMM: F1,174

= 358.57, P < 0.0001) and was higher overall at mid- and lower-estuary reaches than in the upper-estuary (Tukey HSD, P < 0.05). For the most common family of spiders, Tetragnathidae, reliance on aquatically-derived energy was slightly greater in winter (0.89) than summer (0.79) at FW and PH reaches (0.99 v. 0.94, respectively). Estuary position (LMM: F2,.6.31 = 57.420, P <

0.0001) and season (LMM: F1,172.94 = 128.947, P < 0.0001) also exerted strong effects on spider trophic position. Spiders occupied higher trophic positions during the summer at upper- and mid- estuary reaches (Tukey HSD, P < 0.0001), whereas trophic positions were consistently lower at

iv lower-estuary reaches. δ13C of orb-weaving spiders trended with δ13C of Chironomidae during

2 the summer (R = 0.52, F1,4 = 6.401, P = 0.065), but not during the winter (P > 0.05), suggesting that Chironomidae is an important dietary component and vector of aquatically-derived energy at least during some time periods.

Together, these findings contribute to our understanding of aquatic insect community structure and function in estuarine ecosystems. Wet-dry seasonal hydrology and ENSO events appear to drive emergent insect communities, largely via effects on salinity concentrations but also through effects on nutrients and water temperature. In addition, these results have important implications for subsidy dynamics in estuaries. In this study, spatial and temporal variability in the density and traits of emergent insects were related to nearshore spiders, and thus are likely to also mediate the distribution and trophic characteristics of a suite of other terrestrial insectivores including bats, birds, lizards and influence trophic-mediated ecosystem processes including nutrient cycling, biomagnification of contaminants, and maintenance of biodiversity. An improved understanding of seasonal subsidy dynamics in estuaries may help forecast and manage functional ecosystem responses to environmental disturbances (e.g., sea level rise). For example, artificial lighting at night (ALAN) is projected to increase in intensity alongside human population density in coastal areas. ALAN has been shown to affect emergent aquatic insect communities as well as riparian orb-weaving spiders. I conclude the thesis with a review of potential impacts that ALAN may pose in estuaries, from individual- to ecosystem-scale effects.

Acknowledgments

I would like to extend my wholehearted thanks to Scott Glassmeyer, Jessica Espinoza, Reina

Tyl, Edna Fernandez, S.J. Kwiatkowski, David O’Neill, and Ben Rubinoff for their assistance and support with field sampling and lab processing. Thank you all for your hard work and good spirit in the field. To Patrick O’Donnell of the Rookery Bay National Estuarine Research

Reserve – thank you for collaborating with us on this research, sharing extensive knowledge of the Ten Thousand Islands region, and teaching me how to safely navigate through the maze of mangroves. Thanks to all the folks at Rookery Bay NERR and Fakahatchee Strand Preserve

State Park, including Bob Sebold, Julie Drevenkar, Kevin Cunniff, Britta Johnson, Mike Owens,

Karen Schanzle. for moral and logistical support. Thank you to Lars Meyer, Kristen Diesburg, and David Manning of STRIVE Lab for gracious assistance with logistical planning, insect identification, and statistical analysis. To my committee members Dr. Suzanne Gray and Dr.

Lauren Pintor – your insights and expertise were helpful in the development of my project from beginning to end. To my family Ana, Rodrigo, and Rodannie Zapata— thank you for always encouraging me to follow my passions and supporting me whenever I was stuck in the mangroves without a paddle. Finally, I extend deep gratitude to my advisor Dr. Mažeika

Sullivan. Thank you for your patient encouragement and guidance during the past 3.5 years of this collaboration. Thank you for believing in me as a student and offering me the opportunity to grow personally and as a scientist.

Vita

2008…………………………… Barbara Goleman Senior High School

2013…………………………… B.S. Marine Biology, Florida International University 2014…………………………… Graduate Fellow, The Ohio State University 2017…………………………… Directorate Fellow, U.S. Fish and Wildlife Service 2016 to present………………… Graduate Teaching Associate, The Ohio State University

Publications

Zapata, MJ, Yeager, LA, and Layman, CA. 2014. Day-night patterns in natural and artificial patch reef fish assemblages of The Bahamas. Caribbean Naturalist 18:1-15 Yeager, LA, Stoner, EW, Zapata, MJ, and Layman, CA. 2014. Does landscape context mediate density dependence in a coral reef fish? Ecological Applications 24(7): 1833-1841. Zapata, MJ. 2013. Diel patterns in patch reef community dynamics in a Caribbean back-reef system. Department of Biological Sciences - Undergraduate Honors Theses. Paper 49.

Fields of Study

Major Field: Environment and Natural Resources

Specialization: Ecosystem Science

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Table of Contents

Abstract ...... ii Acknowledgments...... vi Vita ...... vii List of Tables ...... x List of Figures ...... xiv Chapter 1. Introduction ...... 1 Chapter 2. Spatial and seasonal variability of emergent aquatic insects and nearshore spiders in a subtropical estuary ...... 13 Abstract ...... 14 Introduction ...... 15 Materials and methods ...... 18 Results ...... 24 Discussion ...... 29 Conclusion ...... 36 Chapter 3: Aquatic primary production linked to nearshore spider trophic dynamics ...... 55 Abstract ...... 56 Introduction ...... 57 Methods...... 60 Results ...... 67 Discussion ...... 70 Conclusion ...... 75 Chapter 4: Artificial lighting at night in estuaries—ecological implications from individuals to ecosystems ...... 86 Abstract ...... 87 Introduction ...... 88 Individual-level responses to ALAN ...... 90 Community and ecosystem responses to ALAN ...... 100 viii

Conclusions ...... 110 References ...... 125 Appendix A. Chapter 2: Supplementary Material ...... 158 Appendix B. Chapter 3: Supplementary Information ...... 172 Appendix C. Supplementary Data ...... 180 Appendix D. Permit ...... 241

List of Tables

Table 2.1 Water-chemistry and nutrient parameters (x̅ ± SE) by estuary position and season for study sites in Fakahatchee Strand of the Ten Thousand Islands (TTI) Estuary...... 39

Table 2.2 Emergent aquatic insect families observed along with trait characteristics including functional feeding group, dispersal distance, body size, and voltinism, as well as relative occurrence in all samples (n = 97). Occurrence of taxa across all emergence samples collected at upper- (FW), mid- (MH), and lower-estuary (PH) reaches are denoted as: - = absent, 1 = rare (<

3%), 2 = uncommon (< 10%), 3 = fairly common (< 50%), 4 = common (< 70%), 5 = abundant

(> 70%)...... 40

Table 2.3 Analysis of variance (ANOVA) results (type III sum of squares with Satterthwaite approximation for degrees of freedom) from linear mixed-effects models testing the effects of season, estuary position, season x position, and for insect body size, family (fixed effects) and reach nested within transect (random effects) on emergent insect and orb-weaving spider response variables...... 41

Table 2.4 Eigenvalues and the percent variance captured by the principal components

(eigenvalues > 1), along with each principal component’s loadings and the proportion of the variance (r2) each variable shared with the PCA axes. Bold-faced axis (PC4) was the only significant predictor of emergence rate in subsequent linear regressions...... 43

Table 2.5 Orb-weaving spiders observed along with trait characteristics including body size and orb-web traits, and relative occurrence in species surveys. Occurrence of taxa in upper- (FW),

x mid- (MH), and lower-estuary (PH) reaches are denoted as: - = absent, 1 = rare (< 3%), 2 = uncommon (< 20%), 3 = fairly common (< 50%), 4 = common (< 70%), 5 = abundant (> 70%).

...... 44

Table 3.1. Orb-weaving spiders observed along with trait characteristics including body size and orb-web traits, and relative occurrence in species surveys. Occurrence of taxa in upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches are denoted as:

– = absent, 1 = rare (< 3%), 2 = uncommon (< 20%), 3 = fairly common (< 50%), 4 = common

(< 70%), 5 = abundant (> 70%)...... 77

Table 3.2 Analysis of variance (ANOVA) results (type III sum of squares with Satterthwaite approximation for degrees of freedom) from linear mixed-effects models testing the effects of season, estuary position, season x position (fixed effects), as well as family and reach (random effects) on spider trophic characteristics...... 78

Table 4.1 Published studies on community- (C) and ecosystem-level (E) effects of ALAN research on aquatic-associated organisms, excluding reviews by Gaston et al. (2012b, 2013),

Longcore and Rich (2004), etc...... 112

Table A.1 Fish species observed at upper- (FW: freshwater), mid- (MH: mesohaline), and lower- estuary (PH: polyhaline) reaches during the study period, along with foraging guild based on

FishBase (2017)...... 160

Table A.2 SIMPER results for emergent aquatic insect taxa contributing to dissimilarity (up to

97%) among upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) assemblages. Mean emergence rates (no. ind m-2 d-2) for each salinity level are also shown for each salinity level...... 161

Table B.1 δ13C and δ15N (mean ± SD) values of aquatic and terrestrial primary producers, emergent aquatic insects, and spiders...... 174

Table B.2 Mean proportional contributions and 95% credible intervals of aquatic (epiphyton, phytoplankton) and terrestrial vegetation to emergent aquatic insects and riparian orb-weaving spiders. Estimates of means and variability of nutritional contributions were derived from the

Bayesian stable isotope mixing model MixSIAR (Stock and Semmens 2013) in R (R Core Team

2017)...... 175

Table C.1 Location of study reaches in the Fakahatchee Strand Preserve State Park and Rookery

Bay National Estuarine Research Reserve in southwest Florida, U.S.A...... 181

Table C.2 Aquatic insect emergence rate and diversity metrics observed across spatial gradients of the Fakahatchee Strand and Ten Thousand Islands Estuary during the summer and winter summarized by transect...... 182

Table C.3 Mean body size of emergent insects across spatial gradients of the Fakahatchee Strand and Ten Thousand Islands Estuary, Florida...... 184

Table C.4 Densities of orb-weaving spiders (Tetragnathidae, Araneidae, and subfamily

Nephilinae) observed across upper- (FW: freshwater), mid- (MH: mesohaline), and lower- estuary (PH: polyhaline) reaches of the Fakahatchee Strand and Ten Thousand Islands Estuary,

Florida, U.S.A...... 185

Table C.5 Morphological measurements – abdominal length, width, and volume, and dry mass— and estimated measures of body condition of Tetragnathidae spiders (Leucage and Tetragnatha spp.) in the Fakahatchee Strand and Ten Thousand Islands Estuary, Florida, U.S.A...... 188

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Table C.6 Carbon (ẟ13C) and nitrogen (ẟ15N) isotopic signatures for aquatic and terrestrial primary producers and consumers in the Fakahatchee Strand and Ten Thousand Island Estuary,

Florida U.S.A...... 214

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List of Figures

Figure 1.1 Variation in salinity concentration typical of a range of aquatic ecosystems along a salinity gradient, from freshwater to hyperhaline habitats (Source: Basset et al. 2013)...... 3

Figure 1.2 Conceptual model of biodiversity (species) suggesting the relative proportion of freshwater, euryhaline, and marine species expected across an estuarine salinity gradient based on patterns observed in various taxa, including benthic invertebrates, fishes, and plants (Source:

Whitfield et al. 2012)...... 4

Figure 1.3 Conceptual model of reciprocal aquatic-terrestrial energy flows (e.g., insect emergence, detrital inputs) in a mangrove ecosystem...... 6

Figure 1.4 Physical dynamics of light have implications for photosensitive aquatic organisms. As light passes through water, its path is slowed and refracted creating a visual transformation for an organism perceiving the same image underwater. Of more consequence for visually-oriented organisms, light reflected and refracted through the air-water surface is polarized linearly and its electromagnetic field becomes planar (Cronin and Marshall 2011), and in some cases, provides visual cues of habitat quality for aquatic organisms. Furthermore, water molecules and other particles selectively absorb and scatter light of different wavelengths, affecting the spectral irradiance (i.e., power density of radiative flux at a particular wavelength) that reaches different depths (Lythgoe 1972, Erren et al. 2008)...... 9

Figure 2.1 Study reaches representing upper- (top), mid- (center), and lower-estuary (bottom) habitats in the Fakahatchee Strand (26°00′00″N 81°25′01″W) and Ten Thousand Islands Estuary

xiv of southwest Florida. These divisions generally represent freshwater, mesohaline, and polyhaline habitats, respectively...... 45

(D) Pielou’s evenness (P = 0.028). Differences in letters a, b indicate significant pairwise differences across estuarine gradient and season (Tukey HSD: P < 0.05). Emergence-rate data were log10-transformed for analysis; raw data are displayed in Appendix A: Supplementary

Material (Fig. A.4)...... 46

Figure 2.3 Relationship between PC4 and emergence rate/density (R2 = 0.19, P = 0.024; y = -

-3 0.1949x + 0.7199). From -2 to +2.5, PC4 represents increasing PO4 and temperature, and decreasing total N...... 48

Figure 2.4 NMDS plot depicting differences in community composition (ANOSIM: R = 0.158, P

= 0.001 and by trophic group, R = 0.168, P = 0.001) among emergent aquatic insect assemblages for upper- (denoted by the solid polygon), mid- (dashed), and lower-estuary (dotted) reaches by season (summer, winter) for (A) the entire emergent insect community and (B) excluding rare taxa...... 49

Figure 2.5 Mean body size of two most common families (Chironomidae and Dolichopodidae) observed at upper- (FW), mid- (MH), lower-estuary (PH) reaches. Body-size data were log10-

xv transformed for analysis; raw data are displayed in Appendix A: Supplementary Material (Fig.

A.5). Error bars are + 1SE...... 50

Figure 2.6 Densities of orb-weaving spiders (Araneidae, Nephilinae, and Tetragnathidae) by season (summer, winter) at upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary

(PH: polyhaline) reaches. Log10-transformed data are shown; raw data are displayed in Appendix

A, Supplementary Material (Fig. A.6). Different letters (a,b,c) indicate all pairwise differences between seasons and estuary position (Tukey’s HSD, P < 0.05). Error bars are + 1SE...... 51

Figure 2.7 Mean body condition of (A) Tetragnatha and (B) Leucage spp. from upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches by season

(summer, winter). Estimates based on residual values of the linear relationship between log10- transformed body mass and abdomen volume following Moya-Laraño et al. (2008). Different letters (a, b) indicate pairwise differences across season and estuary position (Tukey’s HSD, P <

0.05). Error bars are ± 1SE...... 52

Figure 2.8 Relationship of log10-transformed aquatic insect emergence rates/densities and orb- weaving spider densities in the Fakahatchee Strand and Ten Thousand Islands Estuary during

(A) summer (2016; R2 = 0.02, P > 0.05, y = 0.05x – 0.77), winter (2015 and 2016; R2 = 0.19, P =

0.081, y = 0.43x – 0.86), and all seasons combined (R2 = 0.14, P = 0.063, y = 0.31x – 0.83) and

(B) coded by estuary position...... 54

Figure 3.1 Stable isotope biplot with seasonal (summer, winter) means and standard deviations for δ13C and δ15N of aquatic and terrestrial primary producers, emergent aquatic insects (i.e.,

Chironomidae and Dolichopodidae), and orb-weaving spiders at (A) upper-, (B) mid-, and (C)

xvi lower-estuary reaches. Upper-, mid-, and lower-estuary correspond generally to freshwater, mesohaline, and polyhaline habitats, respectively...... 79

MixSIAR (Stock and Semmens 2013). Different letters (a,b,c,d) indicate all pairwise differences by season and estuary position (Tukey’s HSD, P < 0.05)...... 83

Figure 3.3 Trophic positions of orb-weaving spiders (Araneidae, Nephilinae, and Tetragnathidae) by season (summer, winter) at upper- (FW: freshwater), mid- (MH: mesohaline), and lower-

(PH: polyhaline) estuary reaches. Different letters (a,b,c) indicate all pairwise differences by season and estuary position (Tukey’s HSD, P < 0.05). Error bars are + 1SE...... 84

Figure 3.4 Relationships between δ13C signatures of Chironomidae and δ13C signatures of orb- weaving spiders. Upper-, mid-, and lower-estuary correspond generally to FW = freshwater, MH

= mesohaline, PH = polyhaline habitats, respectively...... 85

Figure 4.1 Image of southeastern United States at night taken by the Expedition 30 crew

(Astronaut photograph ISS030-E-55569; Source: earthobservatory.nasa.gov)...... 115

Figure 4.2 Conceptual map of individual- to ecosystem-level responses to ALAN in estuarine systems ...... 116

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(Toyama et al. 2008). Mixed photopigment systems that express A1 and A2 photopigments are common in freshwater, diadromous, and certain coastal-marine fishes that adapt to varying light environments throughout their life history. In these species, ratios of porphyropsin and rhodopsin are generally dependent on ambient light and spawning habitat (Toyama et al. 2008). Euryhaline fishes like (A) the Gray Snapper (Lutjanus griseus) and (B) Common Snook (Centropomus undecimalis) exhibit greater sensitivity toward longer or shorter wavelengths along the freshwater-marine gradient of an estuary. The changing proportion of these photoreceptors allows diadromous fishes to adapt to their environment during ontogenetic changes between marine and freshwater habitats (Allen & McFarland 1973; Robinson et al. 2011)...... 117

Figure 4.4 Emission spectra of four artificial lighting types including metal halide, light-emitting diode, halogen, and high-pressure sodium lamps (spectra from Lamp Spectral Power Distribution

Database 2017)...... 120

Figure 4.5 Emission spectra of sun (Source: LSPDD) and lunar light (Source: Moon-Olino.org).

...... 121

Figure 4.6 [Textbox 2] Wading birds (i.e., shorebirds and long-legged waders) are both permanent and transitory residents in temperate and tropical estuaries. Waders are highly- effective visual and tactile foragers (with color vision mediated by four cone classes) and often exhibit sensitivity to UV light (Hart 2001a). There are important differences in the visual morphology of wading birds associated with foraging tactic and time of day (McNeil, Drapeau &

Gosscustard 1992; Thomas et al. 2006). Visual foragers that feed both day and night (e.g., plovers and stilts) have a higher density of retinal receptors compared to tactile-feeding sandpipers (Rojas de Azuaje et al. 1993, 1999). More specifically, those that forage at

xviii crespuscular or nocturnal periods have greater rod densities and rod:cone ratios (de Azuaje, Tai

& McNeil 1993; Rojas et al. 1999a; Rojas et al. 1999b; McNeil et al. 2004). A visual system most sensitive to wavelengths in which upwelling light from the water is rich and surface reflectance relatively poor (425 to 500 nm for clear blue oceanic water) is best suited for seeing through the water surface (Lythgoe 1968) but that is rarely observed. Birds that look through an aquatic surface to locate prey tend to have relatively high proportion of long-wavelength- sensitive cones and yellow-red ocular filters (Hart 2001a; Hart 2001b)...... 122

Figure A.1 (A) Salinity trends over the course of this study at upper- (FW: freshwater), mid-

(MH: mesohaline), and lower-estuary (PH: polyhaline) reaches and (B) salinity levels observed at one of our lower-estuary reaches during the summer 2016 (Source: USGS 255327081275900).

...... 162

Figure A.2 Recent trends of drought intensity in Florida (D0 = Abnormally dry; D1 = Moderate drought; D2 = Severe drought; D3 = Extreme drought; D4 = Exceptional drought), including drought conditions experienced during the summer (Jun- Jul 2015) and winter (Dec 2016- Jan

2017) sampling periods of this study denoted by black dashed lines. (Source: NIDIS) ...... 163

Figure A.3 Species of fish representing carnivore, insectivore, omnivore, benthic invertivore, and planktivore foraging guilds detected at upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches of the Fakahatchee Strand and Ten Thousand Islands

Estuary...... 164

Figure A.4 Aquatic insect emergence rates observed at upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches of the Fakahatchee Strand- Ten

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Thousand Islands Estuary (Florida) during the winter and summer seasons of 2015-2017, plotted as raw data. Error bars represent ±1SE...... 165

Figure A.5 Mean body size of the two most common families of emergent aquatic insects

(Chironomidae and Dolichopodidae) observed at upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches of the Fakahatchee Strand- Ten

Thousand Islands Estuary (Florida), plotted as raw data. Error bars represent ±1SE...... 166

Figure A.6 Densities of orb-weaving spiders (Araneidae, Nephilinae, and Tetragnathidae) by season (S= summer, W= winter) and estuary position (upper-, mid-, lower-estuary), plotted as raw data...... 167

Figure A.7 (A) Estimated overhanging vegetation (as proxy for orb-weaving spider habitat) at upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches and (B) the relationship between canopy structure and spider density (R2 = 0.06, P = 0.125; y =

0.003x – 1.298)...... 168

Figure A.8 The relationship between body condition of Tetragnatha and body size of

Chironomidae: R2 = 0.16, P = 0.037; y = 0.15x + 0.15...... 169

Figure A.9 Spatial and seasonal patterns of (A) chlorophyll-a, (B) total nitrogen, (C) phosphate, and (D) aquatic insect emergence rate in the Fakahatchee Strand and Ten Thousand Islands

Estuary, plotted as raw data...... 170

Figure B.1 (A) Measurement of abdominal length (AL) and width (AW) used in estimation of abdomen volume in order to estimate body condition of shoreline spiders (B) Leucage and (C)

Tetragnatha following Moya-Laraño et al. (2008)...... 177

Figure B.2 Reliance on aquatically- and terrestrially-derived energy by emergent aquatic insects

(Chironomidae and Dolichopodidae) and orb-weaving spiders (Tetragnathidae, Nephilinae,

Araneidae) at (A) upper-estuary, (B) mid-estuary, and (C) lower-estuary reaches of Fakahatchee

Strand- Ten Thousand Islands Estuary during summer and winter seasons. Means are based on a

Bayesian mixing model developed using MixSIAR (Stock and Semmens 2013)...... 178

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Chapter 1. Introduction

Subtropical and tropical estuaries are highly productive ecosystems that support unique biodiversity and complex food webs (Barbier et al. 2011; Sheaves et al. 2015). Along the freshwater-marine ecotone of an estuary, fluvial and tidal flows create dynamic physicochemical conditions (e.g., salinity) and structure a remarkable diversity of biological communities.

Estuarine ecosystems have exhibited resilience to an array of natural and anthropogenic stressors

(Lotze et al. 2006; Duarte et al. 2015). For instance, the estuaries of south Florida are strongly influenced by decades of hydrological alteration yet ecosystem functioning has in part responded to hydrological restoration. Compounded by stressors associated with climatic change (e.g., rising sea level, increased storm intensity and frequency), it is unlikely that subtropical estuaries will fully return to historical conditions. However, long-term ecological research has characterized the influence of seasonal ecological processes that occur in subtropical estuaries in shaping the structure and function of communities along coastal gradients. Nevertheless, questions remain related to how seasonal processes affect ecosystem functioning (Wingard &

Lorenz 2014), including trophic-mediated connectivity. Understanding the spatial and temporal

(seasonality) dimensions of trophic dynamics across the estuarine gradient may help in forecasting ecosystem functional responses to a changing coastal landscape, hydrological regime, and environmental stressors.

Here, I briefly review existing paradigms in the study of coastal and estuarine food-web dynamics and ecosystem functioning. I also discuss the extent of artificial lighting at night as an ecological stressor in urbanizing coastal areas, and outline its potential impacts on estuarine food webs.

Estuarine food webs

Coastal ecosystems are intricately-linked by physical abiotic forces including winds and tidal influences as well as a complex network of trophic interactions (e.g., coastal ecosystem mosaic, sensu Sheaves 2009) Estuarine organisms must contend with highly variable hydrological and physicochemical conditions (Fig. 1.1, Basset et al. 2013). Salinity plays a key role in determining aquatic community composition across an estuary (Fig. 1.2). In subtropical and tropical estuaries, halophytic mangroves and saltmarsh plants are competitively-excluded from upland habitats by freshwater marsh plants, which in turn are limited by physical intolerance to saline soils (Crain et al. 2004). By influencing the distribution of macrophytes and filter-feeding macroinvertebrates, salinity may also influence water clarity and light availability to aquatic primary producers (Scheffer 1999), thus moderating the relative importance of aquatic energy to higher trophic levels.

Figure 1.1 Variation in salinity concentration typical of a range of aquatic ecosystems along a salinity gradient, from freshwater to hyperhaline habitats (Source: Basset et al. 2013).

Whitfield et al. 2012).

Terrestrial-aquatic connectivity in estuaries is driven by reciprocal resource fluxes mediated by physical and biotic vectors (e.g., organisms, detritus, nutrients) across the water- land interface. Research on energy flow across the estuarine ecotone has emphasized the role of streams as conduits of organic matter that affect the productivity of coastal marine ecosystems

(Odum, Fisher & Pickral 1979; Savage et al. 2012a). Marine nutrients are transferred to estuarine and terrestrial food webs via shore drift, tidal fluxes, and movement of mobile consumers that utilize both aquatic and terrestrial habitats. In estuarine ecosystems, subsidy dynamics may be of quantitative importance given their close physical and chemical integration with the adjacent

4 terrestrial landscape (Belicka et al. 2012), but are not widely examined (but see Loftus, Trexler

& Jones 1998; McCutchan Jr. et al. 2003). The magnitude and importance of aquatic-to- terrestrial subsidies vary with physical factors that determine hydrological connectivity (e.g., tidal range, coastal topography) and biological factors, such as aquatic productivity, resource-use efficiency, and mobility of consumers (Polis, Power & Huxel 2004).

Seasonal variation in freshwater and tidal fluxes affect how nutrients and energy are transferred throughout an estuary via abiotic vectors (i.e., water movement) and trophic interactions. For example, inland habitats may be more affected by seasonal flood and dry-down patterns (Childers et al. 2006; Green et al. 2006; Liston 2006) that determine boundary-volume ratios and passive resource exchange whereas more coastal habitats are flushed with tidal inputs of water and nutrients in addition to downstream freshwater flows. In coastal wetlands of the southern Everglades, levels of salinity and total phosphorus (P) increase with tidal influence during seasonal dry-downs (Boucek & Rehage 2013) with implications for basal-resource production (Williams & Trexler 2006; Belicka et al. 2012). During this period, spatial subsidies

(Polis, Anderson & Holt 1997) may promote algal production (Liston 2006) while a reduction in terrestrial productivity can alter influx of terrestrial detritus. As the dry season continues, mid- and lower reaches of an estuary are likely to receive stronger tidal influence (e.g., nitrogen and phosphorus influx), as freshwater flows from headwaters decrease during the dry season.

Seasonal shifts in nutrient and salinity gradients are likely to influence cross-habitat linkages (Fig. 1.3) mediated by emergent aquatic insects that cross the aquatic-terrestrial boundary as part of their life history. Aquatic insects are critical in mediating aquatic-terrestrial

Figure 1.3 Conceptual model of reciprocal aquatic-terrestrial energy flows (e.g., insect emergence, detrital inputs) in a mangrove ecosystem.

nutritional subsidies in riparian ecosystems as important prey resources for both aquatic and terrestrial consumers (Baxter, Fausch & Carl Saunders 2005; Sullivan & Rodewald 2012).

Our current understanding of their distribution and function in estuaries is relatively sparse.

Density of periphyton infauna can vary as a function of hydroperiod, P availability, as well as the presence of fish predators (Liston 2006), whereas benthic assemblages seem to be driven by taxon-specific limitations. Because diversity of aquatic insects is limited in marine environments, few studies have quantified emergence in estuaries (Ramirez 2008) despite their apparent presence and potential importance to estuarine consumers. In Chapter 2, I characterize emergent

(i.e., adult) aquatic insect communities and distribution of a terrestrial consumer (orb-weaving

6 spiders) at upper-, mid-, and lower-estuary habitats in the Fakahatchee Strand-Ten Thousand

Islands Estuary of southwestern Florida. Our findings contribute to our understanding of aquatic insect community structure and function in estuarine ecosystems. They further provide us with a framework through which to investigate their importance to nearshore terrestrial consumers.

Stable-isotope analysis

Stable-isotope analysis is widely used to study food-web structure by modeling energy sources and trophic linkages among organisms. Quantitative applications for stable-isotope data include identifying trophic position of an organism, resource pools, and food-chain length

(Layman et al. 2012). The ratio of nitrogen isotopes (15N to 14N relative to a standard, expressed as δ15N) increases with trophic transfers, providing an estimation of relative trophic position. The ratio of carbon isotopes (13C to 12C relative to a standard, expressed as δ13C) is commonly used to estimate the contribution of dietary sources to a consumer, as it varies among primary producers of different photosynthetic pathways but is relatively consistent through trophic transfers (Layman et al. 2012).

Stable-isotope research based in the southern Everglades has identified flocculent detritus and periphyton as primary basal resources for aquatic and terrestrial primary and secondary consumers (Bemis & Kendall 2004). Energy derived from flocculent detritus was shown to support chironomids and amphipods in freshwater marshes, whereas periphyton was important to ephemeropteran larvae and small omnivorous fish (Belicka et al. 2012). A persisting caveat in characterizing coastal wetland food webs is the challenge of distinguishing algal and detrital energy pathways in tidal waters where suspended and benthic sediment may exhibit similar isotopic compositions (Schwamborn & Giarrizzo 2015). Nevertheless, the application of stable

7 isotopes has allowed the study of food-web dynamics in estuaries. In Chapter 3, I monitored orb-weaving spiders in the Fakahatchee-TTI Estuary to understand their reliance on aquatically- derived nutritional subsidies. I explore the seasonal and spatial variability in the role of emergent aquatic insect prey as energetic vectors mediating this food web linkage.

Artificial Lighting at Night

Artificial light is a pervasive component of Earth’s night sky, spanning broad spatial, spectral, and temporal ranges globally. Artificial light at night (hereafter ALAN) exceeds and often masks nocturnal light generated by celestial bodies. Despite ubiquitous presence in human- colonized areas, only recently has it received attention as a broad-scale environmental shift.

More than a decade after Longcore and Rich’s (2004) seminal review of ecological consequences of ALAN, governing policies that regulate artificial light emissions are rare. For example, the Dark Sky Initiative (International Dark Sky 2003) are now setting the stage for management in many protected areas in the United States. Meanwhile, an understanding of its effects on the physiology and behavior of animals (Navara & Nelson 2007; Holker et al. 2010b), community structure and ecosystem functioning is needed to shape management priorities and strategies.

Artificial lighting is expected to elicit physiological and behavioral responses that vary by species, habitat type, and geographical region (Nightingale, Longcore & Simenstad 2006; Davies et al. 2013). Few comparisons of physiological, community-, and ecosystem-level responses to natural or artificial light have been drawn for estuarine and marine taxa (but see Nightingale,

Longcore & Simenstad 2006; Toyama et al. 2008; Becker & Suthers 2014). However, research

8 on stream-riparian connectivity has suggested that ALAN may have broad implications on ecosystem function in the form of cross-habitat food web linkages (Meyer & Sullivan 2013). In freshwater ecosystems, aquatic insects have been widely recognized for their role as energetic links between aquatic and riparian habitats (Sullivan & Rodewald 2012), often depending on aquatic habitats as larvae and as adults for oviposition. Direct and polarized light is an indicator of habitat quality for aquatic insects, affecting dispersal flight (Robertson et al. 2010; Boda et al.

2014) and in-stream drift behaviors (Henn et al. 2014).

Figure 1.4 Physical dynamics of light have implications for photosensitive aquatic organisms. As light passes through water, its path is slowed and refracted creating a visual transformation for an organism perceiving the same image underwater. Of more consequence for visually-oriented organisms, light reflected and refracted through the air-water surface is polarized linearly and its 9 electromagnetic field becomes planar (Cronin & Marshall 2011), and in some cases, provides visual cues of habitat quality for aquatic organisms. Furthermore, water molecules and other particles selectively absorb and scatter light of different wavelengths, affecting the spectral irradiance (i.e., power density of radiative flux at a particular wavelength) that reaches different depths (Lythgoe 1972; Erren et al. 2008).

Additionally, many terrestrial consumers that rely heavily on aquatic prey exploit foraging opportunities associated with ALAN (Rydell 1992; Santos et al. 2010; Dwyer et al.

2013). Phototaxic foraging behaviors are associated with increased fitness in certain orb-weaving spiders that have a genetic predisposition to construct webs in artificially illuminated areas where insect prey concentrate (Heiling 1999). For example, the Golden Silk Orbweaver (Nephila clavipes) modifies spectral properties of its web-building silk based on the intensity and spectral composition of ambient light to reflect color cues that attract insect prey (Craig, Bernard &

Coddington 1994). Individuals of a congener (N. plumipes) were found to exhibit larger body size and reproductive capacity within urban landscapes, which may be associated with lower vegetation cover (Lowe, Wilder & Hochuli 2014). An accompanying increase in ALAN in urban habitats may increase insect-mediated subsidies to spiders.

As the mangrove fringe retreats in response to changing salinity regime, coastal wetlands face compounding stressors including ALAN. In the United States, coastal shoreline counties represent less than 10% of total land area but support nearly 30% of the nation’s total population

(U.S. Census Bureau 2011). Estuaries are often highly populated areas with high levels of ALAN

(e.g., urban housing, roadway, and security lighting). Indeed, a recent global risk assessment of light pollution impacts reported that 16.8% of protected lands, including mangrove forests, in the 10

United States are subjected to ALAN (Aubrecht, Jaiteh & de Sherbinin 2010). Use of ALAN has appreciably increased over recent decades (Smith 2009) – which is likely to be a continuing trend as the urban population of Collier County increases more than 30% by 2030 (U.S. Census

Bureau 2011, NOAA 2012, and Woods & Poole Economics, Inc. 2011). Ecological implications of ALAN for the estuaries of southwest Florida depend on how natural patterns of light and dark structure communities and influence the drivers of ecosystem functioning.

Management of artificial light pollution (outlined in Gaston et al. 2012) will be necessary to reduce unintentional light ‘trespass’, to control the intensity, duration and spectral composition of artificial light as is currently done for sea turtle nesting beaches. To refine management priorities that include broader ecological impacts, we must work to understand its effects at the community and ecosystem level. In Chapter 4, I address these gaps and review current literature to support a cohesive theoretical framework of ecological effects of ALAN in estuaries, from individuals to ecosystems.

Summary of objectives

The overarching goal of my thesis research was to characterize emergent aquatic insect communities and aquatic-terrestrial subsidy dynamics spatially (upper-to-lower estuary) and seasonally in the Fakahatchee Strand- Ten Thousand Islands Estuary located in the southwestern coast of Florida. A secondary objective was to examine potential effects of ALAN in the context of estuaries to provide a framework for future investigations on ecological impacts. My specific research objectives are addressed in the three subsequent chapters:

Chapter 2: Spatial and seasonal variability of emergent aquatic insects and nearshore spiders in a subtropical estuary

Chapter 3: Aquatic primary production linked to nearshore spider trophic dynamics

Chapter 4: Artificial lighting at night in estuaries—ecological implications from individuals to ecosystems

Chapter 2. Spatial and seasonal variability of emergent aquatic insects and nearshore spiders in a

subtropical estuary

Abstract

Although variability in the quantity and distribution of adult aquatic insects is an important factor mediating aquatic-to-terrestrial nutritional subsidies in freshwater ecosystems, less is understood about insect-facilitated subsidy dynamics in estuaries. We surveyed emergent

(i.e., adult) aquatic insects and riparian orb-weaving spiders of the families Tetragnathidae and

Araneidae along a salinity gradient in Fakahatchee Strand and the Ten Thousand Islands Estuary of southwestern Florida in winter and summer 2015-2017. Emergence rates were lower yet taxonomic evenness was higher at polyhaline, lower-estuary sites compared to freshwater, upper- estuary sites. Emergent insect community composition also varied across season and salinity level, driven largely by relative densities of Chironomidae and Dolichopodidae. At lower-estuary reaches, emergent aquatic insects exhibited lower dispersal ability and higher prevalence of univoltinism than upper- and mid-estuary assemblages. Furthermore, the body size of

Chironomidae and Dolichopodidae – the predominant families observed – were 180 and 400% larger at lower-estuary reaches. Orb-weaving spider density tracked emergent insects, and was greater at mid-estuary than at upper or lower-estuary reaches, and higher in winter than summer at mid-estuary reaches. Body condition of Tetragnatha was 96% higher at lower-estuary than upper-estuary reaches. Our findings contribute to our understanding of aquatic insect communities in estuarine ecosystems and indicate that aquatic insects may provide important nutritional subsidies to riparian consumers despite their depressed abundance and diversity compared to freshwater ecosystems.

Introduction

Larval aquatic insects are vital components of freshwater ecosystems as primary consumers (Cummins 1973; Lamberti & Moore 1984) and as a prey base within aquatic food webs (Merritt & Cummins 1996; Trexler & Loftus 2016). In their adult form, many aquatic insect taxa emerge from the water as winged adults (hereafter, “emergent insects”) and become an important prey subsidy for terrestrial riparian consumers, such as orb-weaving spiders

(Collier, Bury & Gibbs 2002; Baxter, Fausch & Carl Saunders 2005; Sullivan & Rodewald

2012).

Far less is known about the distribution and function of aquatic insects in estuaries

(Williams & Williams 1998a; Bradley 2008). Although <0.1% of extant insect species inhabit marine habitats (Cheng 1976; Williams & Hamm 2002), many species of Diptera, Coleoptera, and Hemiptera can tolerate a wide range of salinities, inhabiting freshwater-to-hyperhaline wetlands despite the physiological constraints and energetic costs (Foster & Treherne 1976; Boix et al. 2007). Multiple evolutionary adaptations enable aquatic insects to inhabit estuarine environments, including mechanisms of osmoregulation that allow for exposure to high and widely-fluctuating salinity levels (Foster & Treherne 1976; Herbst 2001; Bradley et al. 2009).

However, maintaining osmotic balance in estuarine and marine environments comes at an energetic cost that may hinder the timing and success of larval development, pupation, and emergence (Nayar 1967). Estuarine and marine insects also exhibit adaptive behaviors, such as drifting with the incoming (i.e., reverse drift) and outgoing tide to more suitable habitat

(Williams & Williams 1998b). For example, in the mangroves of Sierra Leone, the dispersal of

Anopheles eggs and larvae is enhanced by tidal movement (Thomson 1946).

The distribution and abundance of aquatic insects in subtropical and tropical estuaries have been shown to vary spatially and seasonally with implications for higher trophic-level consumers and ecosystem functioning (Odum & Heald 1972; Williams & Williams 1998a;

Bierschenk 2015). In detrital-based estuarine food webs, larval aquatic insects contribute to the breakdown of terrestrial inputs to more labile organic matter (Odum & Heald 1972; Odum,

Fisher & Pickral 1979). Furthermore, larval aquatic insects can represent key dietary components of mangrove-dwelling fishes (e.g., 54%; Odum and Heald 1995) as well as other consumers throughout the estuary-river continuum (Williams & Williams 1998b; Ramirez 2008; Carassou et al. 2017). Benthic insect community composition in estuaries may depend on the degree of tidal influence (i.e., saltwater inundation), presence of stable substratum (e.g., cobbles, coarse sediment), and estuary size (Williams & Hamm 2002). In a study based in North Atlantic estuaries characterized by course-grained substrata, species richness of benthic insects was found to gradually decrease downstream, yet larval insects (particularly Chironomidae) were still abundant in lower-estuary sites (Williams & Hamm 2002). Chironomidae are present year-round in many subtropical estuaries although their abundance and diversity tends to be lower during drier periods (Odum and Heald 1972).

Emergent insects are an understudied component of estuarine food webs despite their ecological roles and potential application as bioindicators (Trexler & Loftus 2016). In freshwater ecosystems, emergent insects can represent both pulsed and consistent nutritional subsidies for myriad terrestrial consumers (Muehlbauer et al. 2014; Kautza and Sullivan 2016; Schindler and

Smits 2017). For instance, riparian spiders of the families Tetragnathidae and Araneidae build their webs over or adjacent to aquatic habitats and prey on mixture of emergent aquatic insects

16 and flying terrestrial insects (Collier, Bury & Gibbs 2002; Kato et al. 2003). However, the potential importance of emergent insects to nearshore spiders in estuaries remains unresolved.

MacKenzie (2005) observed that that insect emergence in a North Atlantic salt marsh was found to increase during June and September, coinciding with critical periods of breeding and migration by terrestrial and aquatic consumers. Yet many questions remain as to whether emergence biomass of saline-tolerant insect taxa is sufficient to function as important prey resources to terrestrial consumers in coastal and estuarine habitats (Williams and Williams

1998). The relative importance of emergent insect prey is expected to depend on seasonal abiotic factors (i.e., fluvial and tidal) as a driver of primary and secondary production in estuaries.

Functional traits of emergent insects (i.e., dispersal mode, voltinism, body size) have also been shown to be important in mediating insect-facilitated nutritional subsidies to riparian spiders and other consumers adjacent to freshwater systems (e.g, Stenroth et al. 2015; Tagwireyi & Sullivan

2016a) and may provide insight to their role in estuaries.

The primary objective of this study was to document the associations between spatial and seasonal variability in physicochemical estuarine gradients and the density, community composition, and functional traits of emergent insects in the Fakahatchee Strand of the Ten

Thousand Islands (TTI) Estuary in southwest Florida (USA). A secondary objective was to test whether the density and body condition of nearshore orb-weaving spiders was related to aquatic emergent insects along the estuarine gradient. To do this, we evaluated the following hypotheses:

(1) Emergent insect density and diversity would decrease from upper-to-lower estuary habitats

(i.e., freshwater to polyhaline), primarily as a response to water salinity concentrations (Williams

& Williams 1998a; MacKenzie 2005). (2) The density and diversity of emergent insects would

17 increase during the summer as a function of allochthonous energy inputs from freshwater flows

(MacKenzie 2005; Ramirez 2008). (3) The prevalence of functional traits (i.e., body size, voltinism, and aerial dispersal ability) would shift in accordance with salinity levels. For instance, we expected that within-family body size would be smaller at mid- and lower-estuary habitats due to the energetic cost of osmoregulation (Williams 2009). We anticipated that emergent insect families would have lower aerial dispersal capabilities (i.e., shorter dispersal distance from water) at mid- and lower-estuary habitats where stronger tidal influence may facilitate dispersal (Thomson 1946). (4) Lastly, we anticipated that the densities and body condition of nearshore orb-weaving spiders would track emergent insect density and body size.

Materials and methods

Study system and experimental design

The Ten Thousand Islands (TTI) Estuary is located at the northwest edge of the Florida

Everglades (Fig. 2.1). Numerous tributaries from slow-moving cypress strands flow into the TTI

Estuary, which exhibits low-wave energy (x̅ ± SD, 0.032 ± 0.11 kW m-1, Yeager et al. 2017) and tidal range (varies between 0.2- 1.8 m; Rookery Bay NERR 2012). Forested swamp and graminoid marshes dominate the inland coastal areas of the TTI Estuary, forming the Picayune and Fakahatchee Strands. In areas that experience tidal-influence, vegetation communities transition to mangrove forests interspersed with vegetated marsh, pools, and mudflats.

This study was conducted in the Fakahatchee Strand Preserve State Park in southwest

Florida. Here, the East River (~9.7 km) flows southwesterly from US-41 to Fakahatchee Bay.

Freshwater runoff and tidal fluxes lead to salinity ranges from 0.8 (freshwater) to 41.2 psu

(hyperhaline) (Booth & Soderqvist 2016). The East River is an important contributor of freshwater to the TTI Estuary, with flows tracking annual rainfall patterns (averaging 138.5 cm for 1981-2010) in the region (South Florida Water Management District 2015; Booth and

Soderqvist 2016). Highest average monthly rainfall (20-23 cm per month) typically occurs from

June to September, whereas November through March receives the lowest average monthly rainfall (2.5-5 cm per month; Rookery Bay NERR 2012).

Nine river-estuarine reaches/sites (200 m, upstream to downstream) representing upper-

(FW), mid- (MH), and lower-estuary (PH) habitats of the TTI Estuary were sampled for an array of physiochemical and biotic parameters at three time intervals over two years: Dec 2015- Jan

2016, Jun-Jul 2016, and Dec 2016- Jan 2017 (Fig. 2.1). Upper-estuary reaches (i.e., freshwater:

0.2-0.3 psu) were characterized by flood-tolerant trees – primarily bald cypress (Taxodinium distichum [L.] Rich), coastal plain willow (Salix caroliniana Michx), and pond apple (Annona glabra L.) – and emergent macrophytes including creeping primrose-willow (Ludwigia repens

J.R. Forst), pickerelweed (Pontederia cordata L.), and broadleaf arrowhead (Sagittaria lancifolia

L.). Mid-estuary reaches (i.e., mesohaline: 0.6-18.4 psu) represented transitional marsh- mangrove zones where red (Rhizophora mangle L.), white (Laguncularia racemose [L.] C.F.

Gaertn.), and black (Avicennia germinans L.) mangroves weave through marshes vegetated by needlegrass rush (Juncus roemerianus Scheele), cordgrasses (Spartina spp.), and sawgrass

(Cladium jamaicense [L.] Crantz). Lower-estuary reaches (primarily polyhaline, with some seasonal variability: 11.2-33.7 psu) were also vegetated by L. racemosa sparsely distributed among mature stands of R. mangle.

Biotic sampling

Emergent insects were sampled for 7-day intervals during both summer and winter sampling periods using floating pyramidal traps (0.4 m2) placed at upstream, mid, and downstream segments of each study reach (Alberts, Sullivan & Kautza 2013; Cadmus, Pomeranz

& Kraus 2016). Mesh size was 0.25 mm to ensure effective capture of small-bodied taxa.

Individuals trapped in collection bottles were preserved in 70% ethanol and identified to family using Merritt and Cummins (1996) and Triplehorn, Borror, and Johnson (2005). Families were assigned to individual traits including functional feeding groups, voltinism, dispersal distance, and size at maturity following the classifications described by Merritt and Cummins (1996), Poff et al. (2006), and Kovalenko et al. (2014). Families that exhibited various traits and functional feeding groups were classified based on the based on the trait designation of the most common taxon (genus or species, depending on available information) in the study area (Trexler & Loftus

2016). Mean body size was calculated for the two most common families, Chironomidae and

Dolichopodidae (pooled by reach), by dividing the total number of individuals by total biomass.

To estimate density (no. ind m-2) of orb-weaving spiders of the families Tetragnathidae,

Araneidae, and its subfamily Nephilinae, we conducted morning surveys (700 to 1100 h EST) during which two surveyors walked or kayaked along 50-m transects along the left and right shorelines/mangrove fringes (~15 minutes per transect) counting intact orb webs within 1-m of the channel margin and 2-m above water level (Williams et al. 1995; Tagwireyi and Sullivan,

2016). From these surveys, the relative density of each family was calculated. Five to ten individuals from each family were collected along each transect. All spiders were measured for mass (g), abdomen length (mm; AL), and width (mm; AW). We derived a body condition index

20 for two genera of Tetragnathidae, Leucage (n = 358) and Tetragnatha (n = 358), based on body mass controlled for abdomen volume (Vabdm) following Moya-Laraño et al. (2008):

4휋 퐴푊 퐴푊 퐴퐿 푉 = × × × (1) 푎푏푑푚 3 2 2 2 where AW = abdomen width (mm) and AL = abdomen length (mm). As measures of body condition, we used residuals from linear relationships derived for each genus between log10- transformed body mass with log10-transformed Vabdm. Positive residuals indicate above-average body condition and negative residuals indicate poor condition.

As part of a companion study, fish assemblages were sampled using a combination of minnow trap, trawl, and cast-net methods (Appendix A: Supplementary Material, Additional

Methods). All fish were identified to species and assigned to a foraging group following

FishBase (2017); we report presence-absence of insectivorous fishes detected across salinity levels (Appendix A: Supplementary Material, Table A.1).

Physicochemical sampling

Salinity (psu), dissolved oxygen concentration (DO; mg L-1), temperature (ºC), and pH were measured using a Hach HQ40d meter (Loveland, Colorado, USA) at upstream, mid, and downstream segments of each study reach at each sampling period, corresponding to emergent insect trap locations. Qualitative estimates of in-vivo chlorophyll a were obtained using the

AquaFluor Fluorometer (Turner Designs, Sunnyvale, California, USA) during the summer and winter seasons of 2016. Bulk water samples were collected from mid-channel of each reach during each sampling periods and analyzed for concentrations in mg L-1 of total nitrogen (TN),

- 3- nitrate (NO3 ), total phosphorous (TP), phosphate (PO4 ), and total dissolved solids (TDS) at The

Ohio State University, Ohio Agricultural Research and Development Center’s Service Testing and Research Lab (Wooster, Ohio, USA).

As a proxy for spider habitat structure, which can also be driver of riparian spider density

(Tagwireyi and Sullivan 2016), we estimated vegetation cover over the water based on photos taken with an iPhone 6 fixed with a fish-eye lens. Photographs were taken at water level at left and right shorelines of upstream, mid, and downstream segments of all reaches (n = 6 photos per reach) during the late afternoon or on overcast days to avoid overexposure from direct sunlight.

Photographs were then processed using the Auto Threshold plug-in on ImageJ software

(Schneider, Rasband & Eliceiri 2012) to classify pixels as sky or vegetation. We applied the minimum thresholding algorithm (Prewitt & Mendelsohn 1966), which has been shown to deliver high accuracy independent of auto/manual exposure settings (Glatthorn & Beckschäfer

2014). The minimum algorithm establishes a threshold derived from repeated application of a moving average over three neighboring gray values; values below or equal to this threshold were classified as vegetation.

Data analyses

Aquatic insect density (expressed as emergence rate [no. ind m-2 day-1]), family richness

(Nf), Shannon-Wiener (H’) diversity index, and Pielou's evenness (J) were calculated for all study reaches:

′ 푅 퐻 = Σ푖=1 푝푖ln 푝푖 (2)

퐽 = 퐻′⁄ log (퐹) (3) 10 where 푝푖 is the proportional abundance of families i.

Individuals of unidentified (n = 41) and non-emergent taxa (n = 34; e.g., Coleoptera,

Hemiptera, and Collembola) were excluded from reported means and statistical analyses.

Emergence rates, spider densities, and functional traits were log10(x+0.1) transformed to meet assumptions of normality and homoscedasticity. To test for differences in the density (i.e., emergence rate), diversity, body size, and functional traits (expressed as a proportion of the total community) of emergent insects as well as density and body size of spiders, we used general linear mixed-effects models (LMM) fitted with season (summer, winter), estuary position (upper, mid, lower), and family (Chironomidae, Dolichopodidae) as fixed effects, and transect nested within reach as a random effect. Season x position was included as an interaction term. We report test statistics for the significance of fixed effects derived using type III Wald F tests with

Satterthwaite approximation for degrees of freedom for all LMMs (Luke 2017). To examine how emergent insect and spider density, community composition, and traits varied by estuary position/salinity level and by season, we made post-hoc pairwise comparisons for LMMs with

Tukey’s HSD (Tukey 1977).

Community structure of emergent insects (organized by family) was analyzed based on

Bray-Curtis similarity matrices of square-root transformed emergence rates (no. ind m-2 day-1) using non-metric multidimensional scaling (NMDS; Clarke 1993) and analyses of similarity

(ANOSIM). NMDS uses rank orders to evaluate how taxonomic composition compares across treatments. Similarity percentage (SIMPER; Clarke 1993) analysis was used to identify dominant taxa and trophic groups driving community structure.

Principal component analysis (PCA) was performed on physicochemical variables (e.g., total nitrogen, chl-a) that were selected a priori as additional predictors of insect emergence rates

23 and diversity metrics. PCA axes with eigenvalues > 1 were retained for use in subsequent post- hoc simple linear regressions (Rencher 2003). Simple linear regressions were used to examine potential influences of (1) emergent insects (i.e., emergence rates, body size) on spider density and body condition.

Given the potential lack of independence among the study reaches, we tested for spatial autocorrelation using Moran’s I (1950) among response variables. All statistical analyses were performed using R statistical software v 3.3.3 (R Core Team 2017) with α = 0.05 indicating statistical significance. We used the lme4 (Bates et al. 2015), lmerTest (Kuznetsova, Brockhoff

& Christensen 2015), and lsmeans (Lenth 2016) packages for LMMs and pairwise comparisons.

Emergent insect community structure was analyzed using the vegan package (Oksanen et al.

2017). Spatial autocorrelation tests were performed using the ape package (Paradis et al. 2004).

Results

Water-chemistry parameters varied seasonally and spatially in the Fakahatchee Strand and TTI Estuary (Table 2.1). Interannual variation in rainfall related to El Niño-Southern

Oscillation (ENSO) patterns led to seasonal salinity trends that deviated from typical wet and dry periods. ENSO rainfall during December 2015 (55.9 mm) and January 2016 (272 mm) in the Big

Cypress basin exceeded the normal average by over 500% (Rookery Bay NERR 2016). As a result, salinities were lower (x̅ ± SE, 13.95 ± 2.18 psu at lower-estuary reaches) compared to typical dry season conditions (Appendix A: Supplementary Material, Fig. A.1). Summer rainfall

(251 mm in June and 217.2 mm in July 2016) also exceeded normal averages (Rookery Bay

NERR 2016). During our second winter sampling period, in December 2016, Big Cypress basin

24 experienced abnormally dry conditions (Appendix A: Supplementary Material, Fig. A.2), receiving lower than average rainfall with most stations recording < 25 mm of rain.

Emergent insects

Emergent insects (n = 2,926) representing 20 families from 6 orders were collected throughout the study period. Insect communities were numerically dominated by true flies

(Diptera); nonbiting midges (Chironomidae) were most abundant and present in 81% of the samples. Long-legged flies (Dolichopodidae), biting midges (Ceratopogonidae), and mosquitoes

(Culicidae) were also prevalent, observed in 16-28% of samples. Of the emergent insect families observed, 16 occurred in the upper-estuary, 10 in mid-estuary, and 10 in lower-estuary reaches

(Table 2.2). Fishes of 17 species were collected throughout the study period. Carnivores, omnivores, and benthic invertivores were present at all our study sites (Appendix A:

Supplementary Material, Table A.1 and Fig. A.2). The surface insectivore Gambusia holbrooki was only detected at upper-estuary sites. Planktivorous Anchoa spp. were collected only in the lower-estuary sites.

Emergence rates (i.e., density) ranged from 0 to 146.5 n m-2 day-1 across all samples.

Rates were generally highest at mid-estuary reaches in the winter (x̅ ± SE, 17.9 ± 5.8) and lowest at lower-estuary reaches in both summer (x̅ ± SE, 1.89 ± 2.81) and winter (x̅ ± SE, 4.22 ± 1.98;

Fig. 2.2A and Appendix A: Supplementary Material, Fig. A.4). This variability led to significance differences in emergence rates by season and estuary position, and there was a marginal effect of season x position (Table 2.3). Rates in the lower-estuary reaches were marginally higher in the winter than summer (P = 0.069). Although seasonal differences in mean

25 emergence rates were evident at mid-estuary reaches (Appendix A: Supplementary Material, Fig.

A.4) this was not significant (P > 0.05).

Season was strong associated with Shannon-Wiener diversity (Fig. 2.2B), Pielou’s evenness (Fig. 2.2C), and richness (Fig. 2.2D) of emergent insects, most significantly at the lower-estuary sites (Table 2.3). Season x position within the estuary thus emerged as a significant effect on diversity, evenness, and richness (Table 2.3).

Principal component analysis of ten physicochemical measures identified five axes with eigenvalues > 1 (Table 2.4). The first principal component (PC1) described 24% of the variance and was influenced by canopy cover (r2 = 0.31), chl-a (r2 = 0.22), and ammonia (r2 = 0.19). PC2 described 19.7% of the variance and was influenced by temperature (r2 = 0.25), phosphate (r2 =

0.20), and DO (r2 = 0.18). PC3 captured 18.1% of the variance; important loadings were total P

(r2 = 0.43), DO (r2 = 0.19), and pH (r2 = 0.18). PC4 captured 14.5% of the variance and was driven by phosphate (r2 = 0.33), temperature (r2 = 0.26), and total N (r2 = 0.20). Lastly, PC5 accounted for 10.9% of the variance and was influenced primarily by nitrate (r2 = 0.58). Of the

PC axes, PC4 was the only significant predictor of emergence rates (P = 0.024; Fig. 2.3) but was not associated with family richness, Shannon diversity, or Pielou’s evenness indices (P > 0.05 for all).

Emergent insect community composition varied across the estuarine gradient by taxa (P

= 0.001) and by trophic group (P = 0.001) but were similar across seasons (P > 0.05; Fig. 2.4).

Relative densities of Chironomidae (collector-gatherers) accounted for a large proportion of this variation among upper-, mid-, and lower-estuary assemblages (SIMPER: 53-56%; Appendix A:

Supplementary Material, Table A.2). Higher densities of Dolichopodidae (predators) in the

26 lower-estuary, compared to mid- and upper-estuary reaches, accounted for an additional 11-12% of the differences observed. Lower-estuary assemblages showed a marginal seasonal difference in family composition (ANOSIM: R= 0.1023, P = 0.061), driven by variability in Chironomidae,

Dolichopodidae, and Culicidae densities. We observed shifts in mean body size (mg) of dominant taxa (Chironomidae and Dolichopodidae). On average, Chironomidae were about

400% larger at lower-estuary reaches (x̅ ± SE, 0.26 ± 0.13 mg) compared to upper-estuary counterparts (x̅ ± SE, 0.05 ± 0.01 mg). Dolichopodidae were generally 160% larger at lower- estuary reaches (x̅ ± SE, 0.65 ± 0.31 mg) compared to upper-estuary counterparts (x̅ ± SE, 0.25 ±

0.17 mg). However, size differences across the estuarine gradient were not significant (Table 2.3;

Fig. 2.5 and Appendix A: Supplementary Material, Fig. A.5). Aerial dispersal strength (i.e., proportion of community with dispersal ability of <1 km) was related to estuary position (Table

2.3); a greater proportion of lower-estuary assemblages typically exhibited weak aerial dispersal ability compared to upper- (summer) and mid-estuary assemblages (both seasons; P < 0.05 in all cases). Voltinism (i.e., proportion of community that have a single reproductive event per year) also partially depended on estuary position (Table 2.3), with lower-estuary assemblages comprising of more univoltine taxa than upper-estuary assemblages in the summer (P < 0.05 in all cases).

Overall, there was no evidence for non-random spatial patterns in insect emergence responses including emergence rate, as well as, family richness, diversity, and evenness (P >

0.05 in all cases), although emergence rates were spatially correlated when analyzed by season

(P = 0.030 for summer; P = 0.001 for winter).

Orb-weaving spiders

Nearshore orb-weaving spiders representing five genera of two families were collected (n

= 819) throughout the study period. Long-jawed horizontal orb-weavers Tetragnatha and

Leucage spp. were ubiquitous across our study area (Table 2.5). On average, tetragnathid spiders represented >54% of the orb-weaving spider assemblage. Vertical orb web-builders including

Araneidae (Neoscona, and Gasteracantha spp.) and Nephilinae (Nephila) and were less- abundant (on average, occupying 8 and 14% of detected webs, respectively). Nephilinae (both seasons) and Araneidae (summer) were not observed at upper-estuary sites.

Orb-web densities ranged from 0 to 2.72 no ind. m-2 across 231 surveys (Fig. 2.6 and

Appendix A: Supplemental Material, Fig. A.6). One extreme outlier (5.7 ind. m-2) was excluded from statistical analyses. Estuary position was a strong predictor of orb-web density (Table 2.3) with densities at mid-estuary reaches exceeding those of upper- and lower-estuary for both seasons (Fig. 2.6). There was also a seasonal effect on orb-web density, particularly at mid- estuary reaches where densities were higher in the winter than the summer (Table 2.3).

Estuary position also exerted a strong effect on body condition of Tetragnatha (Table

2.3), which was higher in the lower-estuary than upper-estuary reaches. However, Leucage body condition was not associated with estuary position (P > 0.05), although we found some initial evidence that season x position may be influential (Table 2.3). Specifically, Leucage individuals exhibited higher body condition in the winter compared to summer at upper- and mid-estuary reaches (Table 2.3; Fig. 2.7).

Spider density was not associated with overhanging vegetation (P > 0.05; Appendix A:

Supplemental Material, Fig. A.7) but positively trended with insect emergence rate (R2 = 0.14, P

= 0.063, y = 0.31x – 0.83), most notably in the winter (Fig. 2.8), but was unrelated to emergent insect family richness diversity, or evenness (P > 0.05 in all cases). The body condition of

Tetragnatha was positively correlated with body size of chironomids (R2 = 0.16; Appendix A:

Supplemental Material, Fig. A.8). We also found evidence for positive spatial autocorrelation in orb-web density (P = 0.02) and body condition of Tetragnatha (P = 0.004 for summer).

Discussion

Estuaries are highly complex both spatially and temporally, where the interaction of fluvial and tidal flows drive dynamic physicochemical gradients and create unpredictable ecosystems (Day 1989; Basset et al. 2013). In the subtropical Fakahatchee-TTI Estuary of the southwestern coast of Florida, we found that spatial and seasonal physicochemical variability was related to the density, diversity, and functional traits of emergent insects as well as the density and body condition of orb-weaving spiders, and that patterns in insect emergence and spider body size and distribution were linked. Although aquatic insects are monitored as bioindicators of water quality and ecosystem functioning in freshwater systems, many questions related to the composition and function of aquatic insects in estuaries remain. These findings contribute to our current understanding of the ecology of aquatic insects and orb-weaving spiders in estuaries and have implications for aquatic-to-terrestrial subsidy dynamics.

Emergence rate and diversity

Aquatic insect emergence rates exhibited distinct seasonal and spatial variability across the Fakahatchee-TTI study system. Emergent insect communities were largely dominated by

Chironomidae, aligning our findings with other studies that have found chironomids to be the 29 most abundant and diverse insect taxon in estuarine habitats (MacKenzie 2005; Ramirez 2008;

Kranzfelder & Ferrington 2016). For instance, MacKenzie (2005) observed that Chimnamus sp. represented 87% of all insects from brackish pools and Tanytantis spp. represented 50-80% of all insects surveyed from saltwater pools and vegetated marshes. Kranzfelder and Ferrington (2016) detected 98 species of Chironomidae present in just one estuary of Costa Rica and 228 species in nine tropical estuaries.

Our prediction that aquatic insect emergence rates (i.e., density) and diversity would generally decrease from the upper-to-lower estuary was accurate for the summer (Fig. 2.2A), likely related to physiological constraints experienced by aquatic insects in saline environments

(Cheng 1976). However, a distinct pattern emerged in the winter, when mean emergence rates were generally >180% higher at mid-estuary reaches and by two orders of magnitude at lower- estuary reaches. In addition to salinity, a suite of biological and physicochemical factors can also drive biotic assemblages in estuaries (Boyer 2006), and may have contributed to spatial and temporal variability in the Fakahatchee-TTI Estuary. Other investigators have also reported the role of nutrients in driving invertebrate communities in coastal environments. For instance,

Liston (2006) showed that the density and diversity of aquatic invertebrates can vary as a function of hydroperiod and P availability (as well as the presence of fish predators). In our

3- study, PO4 , temperature, and total N emerged as additional predictors of aquatic insect emergence.

During the wet season (May to October), flood pulses from heavy precipitation events increase downstream transport of freshwater, particulate organic matter, and nutrients (Davis et al. 2005). In oligohaline and mid-estuary habitats of the Everglades, this can flush nutrients

30 derived from wading bird colonies (Cook & Kobza 2011; Irick et al. 2015), such as a rookery island upstream of our mid-estuary sites that remained active throughout the study period.

During our first winter sampling period (Dec 2015- Jan 2016), delivery of allochthonous nutrients and freshwater was further intensified by ENSO-induced rainfalls (Childers et al.

2006), which may have stimulated primary and secondary production. However, this was not captured with measures of chl-a, and subsequent estimates of aquatic insect emergence were not positively associated with chl-a (Appendix A: Supplementary Material, Fig. A.9). Further, the

3 negative relationship we observed between PO4 and emergence is puzzling. It is possible that phosphate may be getting locked in sediment or another compartment, and therefore not available to primary producers and aquatic insects.

3- In the lower-estuary, PO4 concentrations slightly exceeded NO3 concentrations during the winter, lending evidence of N limitation associated with phytoplankton growth (Ryther &

Dunstan 1971; Meeuwig, Rasmussen & Peters 1998). The relative increase in emergence rates and diversity observed at mid- and lower-estuary habitats in the winter may have been associated with an ENSO-induced resource pulse (sensu Yang et al. 2008). Even under typical conditions, freshwater flows from the preceding wet season may continue to strongly influence downstream communities early into the dry season. Overall, seasonal differences observed in emergent insect communities point to the importance of temporal dynamics.

Predation regime (Paetzold & Tockner 2005; Dorn, Trexler & Gaiser 2006) across the estuary may also exert strong controls on aquatic insects. Thus, the composition of fish trophic guilds is known to vary across estuarine gradients (Winemiller & Leslie 1992; Ley, Montague &

McLvor 1994; Ley, McIvor & Montague 1999; Garcia et al. 2007) with implications on

31 spatiotemporal distributions of emergent insects (Wesner 2012). The presence of fish may hinder insect production via direct consumption (Wesner 2010) or lead to increased insect abundance via release from predation. For example, the presence of larger predators released primary consumers (e.g., small snails, amphipods, midges) inhabiting periphyton from high predation pressure by intermediate consumers (Dorn, Trexler & Gaiser 2006; Liston 2006). Based on our coarse-level qualitative surveys, we did not observe insectivores at mid- or lower-estuary reaches

(Appendix A: Supplemental Material, Fig. A.3) which may in part reduce predation pressure for aquatic insects at these sites. Although we only conducted preliminary fish surveys, our results align with trends in southern Everglades estuaries showing that the presence and relative abundance of insectivores decline with increasing salinity (Green et al. 2006). However, other monitoring efforts have detected insectivores, including G. holbrooki and Fundulus spp., in the coastal marshes of nearby Big Cypress National Preserve (Loftus 2004) and Rookery Bay

National Estuarine Research Reserve (Carter et al. 1973; United States. National Marine

Fisheries & Lindall 1973; Colby et al. 1985; Browder et al. 1986; Yokel 2006).

Orb-weaving spider distribution

Consistent with our hypotheses, we observed spatial variability in spider densities (which were highest at mid-estuary reaches) analogous to patterns of aquatic insect emergence (Figs.

2.6, 2.8). This variability may reflect a response in spider distribution to higher prey availability in the form of emergent insects during winter (Fig. 2.8A) and at the upper- and mid-estuary reaches, in general (Fig. 2.8B). Researchers have also reported a strong correlation between habitat complexity and riparian orb-weaving spiders (Lubin 1978; Chan, Zhang & Dudgeon

2009; Tagwireyi & Sullivan 2016b), yet vegetation cover was not strongly associated with spider density in this study (Appendix A: Supplemental Material, Fig. A.7). Nevertheless, increased variability in web-building substrate through habitat simplification (e.g., defoliation, mangrove forest destruction) will be an important mechanism driving spiders and other nearshore consumer communities. Indeed, severe disturbances such as Hurricane Irma – which made landfall in southwest Florida following this study (September 2017) – may cause severe structural effects to mangrove forests that might be expected to strongly alter habitat structure and spider distribution. In fact, we suspect this to be a hallmark of the cross-boundary effects of disturbances that drive temporal or physical decoupling of aquatic subsidies and adjacent terrestrial communities (Spiller & Schoener 2007; Greenwood & McIntosh 2008).

We found evidence indicating a positive spatial autocorrelation for orb-web density

(Moran’s I, P = 0.02) and body condition of Tetragnatha (Moran’s I, P = 0.004 for summer).

Despite a spatial autocorrelation in spider density and body condition responses, we have confidence that our data were collected with sufficient distance among sampling sites that distinct spider populations were sampled. Although orb-weaving spiders (e.g., Tetragnathidae) are able of dispersal as adults (Bell et al. 2005), the probability of changing sites is strongly reduced once webs are constructed (Gillespie 1987). Surveys were based on intact orb-webs and most individuals were collected directly from webs.

Functional traits of emergent insects and spiders

Rich taxonomic diversity of aquatic insects can have a wide variety of consequences for aquatic-terrestrial connectivity (Merritt & Cummins 1996). We expected to find that functional

33 traits associated with spatial and temporal patterns of emergence (e.g, voltinism, aerial dispersal) shift across the estuarine gradient. Although trait assignments were based on family-level identifications and thus provide a coarse assessment, it is a useful step in understanding the role of insect communities across time and space (Poff et al. 2006).

The annual frequency and timing of insect emergence (i.e., voltinism) vary among the families observed. Given the breadth of within-season variability in salinity found in our study system (Appendix A: Supplemental Material, Fig. A.1), we suspect that relative conditions for larval development and emergence were also highly variable. For instance, reduced salinities observed during the summer (Table 2.1) may have created conditions that allow development and emergence of univoltine taxa – e.g., Ceratopogonidae and larger-bodied Dolichopodidae – whose relative densities accounted for 14-16% of the seasonal variability, especially in the lower-estuary. Emergence by univoltine taxa may not provide a consistent energetic subsidy provisioned to riparian consumers but has the potential to be important during certain times of the year.

The variability in the body size of the families Chironomidae and Dolichopodidae across the estuarine gradient was unexpected as other studies have found evidence of suboptimal condition facing salinity stress (Williams 2009). Future efforts to measure oviposition, larval development, and drift would help address uncertainty as to whether individuals emerging at mid- and lower-estuary reaches are fully developing at these sites or drifting from upstream sites.

The availability of larger-bodied insect prey could have strong implications for aquatic-to- terrestrial nutritional subsidies to riparian and terrestrial consumers. For instance, larger body sizes of emergent insects may partially offset lower densities to contribute to the magnitude of

34 nutritional subsidies provisioned to terrestrial consumers. However, limited dispersal ability at lower-estuary sites suggests that emergent insects will likely benefit nearshore consumers such as shoreline spiders (e.g., Tetragnathidae, Araneidae) known in many riparian systems to be highly reliant on aquatic insect prey (Kato et al. 2003; Tagwireyi & Sullivan 2015; Yuen &

Dudgeon 2016). Indeed, we observed that body size of spiders and emergent insects was generally larger at lower-estuary sites (Figs. 2.5, 2.7) and that body condition of Tetragnatha positively tracked body size of Chironomidae (Appendix A: Supplemental Material, Fig. A.8).

Generic differences in body-condition patterns across the estuarine gradient among

Tetragnathidae (Fig. 2.7) may be evidence of differences in foraging effort, prey availability, composition or assimilation (Bucher & Entling 2011; Wilder 2013). Tetragnatha and Leucage both construct horizontally oriented orb-webs closer to the water. However, some noticeable differences in behavior may contribute to differences in foraging effort, prey captured, and capture rate. For instance, we made anecdotal observations consistent with other studies that

Leucage occupy orb webs within a larger, integrated network of support lines (Salomon et al.

2010) and these were typically intact throughout the day. While this behavior may serve as protection from predation and allow for extended foraging intervals, it also may lead to density- dependent limitation in prey intake (Bucher & Entling 2011). Tetragnatha tend to exhibit a quicker flight-response to disturbance (Aiken & Coyle 2000), deconstructing their ephemeral webs during the day, yet individuals exhibited higher body condition than Leucage at the lower- estuary reaches. Studies have yet to examine whether certain foraging tactics may be more or less energetically-profitable across an estuarine gradient. Studies on spider subsidies have rarely investigated the potential for species-specific foraging behavior and ontogenetic status to

35 mediate aquatic-to-terrestrial subsidy fluxes (but see Akamatsu, Toda & Okino 2007; Akamatsu

& Toda 2011b), although these traits may provide important insight to spatial and seasonal variability. For instance, reproductive dynamics of spiders may have played a role in driving seasonal patterns in orb-web density (LaSalle & de la Cruz 1985), foraging effort (Akamatsu,

Toda & Okino 2007), and body condition. Tetragnatha are known to reproduce in the fall months while Leucage, Nephilinae, and Neoscona usually reproduce between spring to summer, and have a ~1-year lifespan. Therefore, some degree of seasonal fluctuation in density and body condition is to be expected throughout the year, with influences on aquatic-to-terrestrial subsidy dynamics. For instance, Nephilinae studied in riparian habitats of Japan were found to rely strongly on aquatic insect as juveniles but shift to feeding on different or additional prey as they grew in size (Akamatsu, Toda & Okino 2007).

Conclusion

Emergent insects are critical prey resources for many terrestrial consumers (e.g., orb- weaving spiders, birds, bats) in freshwater habitats yet little is known about these insect- mediated nutritional subsidies in estuarine environments. In a subtropical estuary, we found that both spatial and temporal variability in emergent insect communities, largely via effects on salinity concentrations as well as nutrients and water temperature. Variability in the body size of emergent insects (Chironomidae) was linked to body condition of Tetragnatha spiders, suggesting that body size – among other insect traits – may have important implications for aquatic-to-terrestrial subsidies. Changes that affect key functional traits, such as phenology, emergence, and dispersal could alter the spatial and temporal variation in aquatic-to-terrestrial

36 energy flows (Fuller & Peckarsky 2011). For example, because wing and body size are important predictors of dispersal ability (Malmqvist 2000; Hoffsten 2004), losses of larger-bodied species may change the average dispersal distance of an emerging insect community. Such changes in movement can directly affect the spatial extent of nutritional subsidies to terrestrial consumers and the magnitude of aquatic-terrestrial connectivity. Similarly, changes in phenology resulting from species loss can alter the time period over which emergence and nutritional subsidies are available to riparian predators (Schindler & Smits 2017). Thus, our findings provide a foundational basis for investigations on the ecological role of emergent insects in mediating cross-boundary nutritional subsidies in estuaries. Improved understanding of aquatic insect communities along estuarine habitat gradients could help forecast ecological shifts in response to changing salinity and nutrient regime in coastal areas (Little et al. 2016).

Acknowledgments

This project was funded by support from The Ohio Agricultural Research and Development

Center and The Ohio State University. We are grateful for the support and collaboration with

Rookery Bay National Estuarine Research Reserve and Fakahatchee Strand Preserve State Park.

We thank Dr. Suzanne Gray and Dr. Lauren Pintor for their constructive comments during the preparation of this manuscript. Thank you to R. Zapata, S. Glassmeyer, J. Espinoza, R. Tyl, E.

Fernandez, L. Meyers, K. Diesburg, and B. Rubinoff for field and lab assistance.

Table 2.1 Water-chemistry and nutrient parameters (x̅ ± SE) by estuary position and season for study sites in Fakahatchee Strand of the Ten Thousand Islands (TTI) Estuary.

Upper-estuary (FW) Mid-estuary (MH) Lower-estuary (PH) Parameter Summer Winter Summer Winter Summer Winter Salinity 0.220 ± 0.012 0.258 ± 0.007 7.553 ± 1.622 5.368 ± 0.822 22.335 ± 1.656 26.572 ± 1.164 (psu)

pH 7.50 ± 0.035 7.62 ± 0.051 7.37 ± 0.047 7.49 ± 0.141 7.38 ± 0.041 7.48 ± 0.071

DO (mg L-1) 1.832 ± 0.306 1.540 ± 0.209 0.733 ± 0.432 3.050 ± 0.237 2.245 ± 0.521 4.628 ± 0.612

Temperature (°C) 27.09 ± 0.303 21.60 ± 0.322 28.51 ± 0.139 22.20 ± 0.614 29.28 ± 0.295 22.57 ± 0.677

Chl-a 13.69 ± 0.80 14.16 ± 0.71 28.39 ± 0.71 23.39 ± 0.81 29.84 ± 1.35 21.85 ± 2.11

Total P 0.051 ± 0.004 0.037 ± 0.001 0.833 ± 0.137 0.037 ± 0.001 0.822 ± 0.135 0.047 ± 0.008 (mg L-1) Total N 0.510 ± 0.088 0.628 ± 0.072 1.040 ± 0.139 0.930 ± 0.084 -0.168 ± 0.161 0.536 ± 0.036 (mg L-1) 3- PO4 0.006 ± 0.001 0.016 ± 0.001 0.043 ± 0.009 0.019 ± 0.002 0.081 ± 0.012 0.062 ± 0.006 (mg L-1)

NO3 0.078 ± 0.036 0.017 ± 0.005 0.044 ± 0.020 0.025 ± 0.004 0.077 ± 0.027 0.037 ± 0.003 (mg L-1)

NH4 < 0.010 < 0.010 0.056 ± 0.006 0.126 ± 0.020 0.057 ± 0.007 0.123 ± 0.026 (mg L-1)

Dispersal Size at Upper Mid Lower Order Family Voltinism Functional feeding group distance maturity (FW) (MH) (PH) Ceratopogonidae univoltine <1 km small predator 3 3 2 Chaoroboridae bi/multivoltine <1 km small predator 1 - - Chironomidae bi/multivoltine >1 km small collector-gatherer 5 5 4 Culicidae bi/multivoltine >1 km small collector-filterer 2 3 3 Dolichopodidae univoltine <1 km medium predator 3 3 3 Empididae univoltine >1 km medium predator - - 2 Diptera Ephydridae bi/multivoltine <1 km small predator-gatherer 1 1 2 Phoridae bi/multivoltine <1 km small collector-gatherer 1 1 1 Sarcophagidae bi/multivoltine >1 km medium collector-gatherer 3 - - Simuliidae bi/multivoltine <1 km small collector-filterer 1 - 2 Syrphidae bi/multivoltine >1 km small collector-gatherer - 1 - Tabanidae bi/multivoltine >1 km medium predator 2 - - Baetidae bi/multivoltine <1 km small collector-gatherer 2 - - Ephemeroptera Ephemerellidae univoltine <1 km small collector-gatherer 1 - - Hymenoptera Mymaridae bi/multivoltine <1 km small parasite-carnivore - 1 1 Lepidoptera univoltine <1 km medium shredder-herbivore 1 - - Hydroptilidae univoltine >1 km small piercer-herbivore 2 2 - Trichoptera Hydropsychidae univoltine < 1 km small filterer - - 1 Coenagrionidae univoltine <1 km large predator 1 - - Odonata Libellulidae semivoltine >1 km large predator 1 - - 40

(fixed effects) and reach nested within transect (random effects) on emergent insect and orb-weaving spider response variables.

Response variable / Sum of Mean Num. Den. Fixed effect Square Square DF DF F P Random effect Variance Std.Dev. log10 (emergence rate) Season 1.895 1.895 1 37.1 8.57 0.006 Transect:Reach 0.000 0.000 Position 3.871 1.935 2 5.8 8.75 0.018 Reach 0.039 0.197 Season:Position 1.286 0.643 2 37.1 2.91 0.067 Residual 0.221 0.470

--- Family richness Season 9.397 9.397 1 36.3 26.36 <0.001 Transect:Reach 0.000 0.000 Position 0.550 0.275 2 5.8 0.77 0.504 Reach 0.305 0.552 Season:Position 4.400 2.200 2 36.3 6.17 0.005 Residual 0.356 0.597

--- Shannon diversity Season 0.626 0.626 1 36.4 15.83 0.000 Transect:Reach 0.000 0.000 Position 0.067 0.034 2 5.6 0.85 0.477 Reach 0.015 0.123 Season:Position 0.479 0.240 2 36.4 6.06 0.005 Residual 0.040 0.199

---

Pielou's evenness Season 0.290 0.290 1 31.8 5.32 0.028 Transect:Reach 0.000 0.000 Position 0.005 0.003 2 5.9 0.05 0.954 Reach 0.007 0.086 Season:Position 1.224 0.612 2 31.7 11.21 <0.001 Residual 0.055 0.234

--- log10 (% weak dispersal) Season 0.130 0.130 1 84 1.42 0.237 Transect:Reach 0.000 0.000 Position 3.221 1.610 2 84 17.51 <0.001 Reach 0.000 0.000 Season:Position 0.203 0.102 2 84 1.10 0.336 Residual 0.092 0.303

--- 41 log10 (% univoltine) Season 0.010 0.010 1 82.3 0.10 0.751 Transect:Reach 0.001 0.024 Position 1.539 0.769 2 4.9 7.76 0.031 Reach 0.003 0.052 Season:Position 0.337 0.168 2 75.7 1.70 0.190 Residual 0.099 0.315

--- log10 (insect body size) Position 0.883 0.441 2 6.2 3.17 0.113 Transect:Reach 0.035 0.186 Reach 0.009 0.095 Family 0.149 0.387 Residual 0.139 0.373 --- log10 (orb-web density) Season 0.762 0.762 1 84.8 16.69 <0.001 Transect:Reach 0.000 0.000 Position 7.844 3.922 2 7.3 85.93 <0.001 Reach 0.002 0.040 Season:Position 1.250 0.625 2 84.7 13.69 <0.001 Residual 0.046 0.214 --- Tetragnatha body condition Season 0.002 0.002 1 266.6 0.03 0.860 Transect:Reach 0.000 0.014 Position 1.699 0.850 2 19.5 11.25 0.001 Reach 0.000 0.000 Season:Position 0.022 0.011 2 277.8 0.15 0.863 Residual 0.075 0.275 --- Leucage body condition Season 0.021 0.021 1 349.1 0.33 0.569 Transect:Reach 0.000 0.000 Position 0.043 0.021 2 7.8 0.33 0.727 Reach 0.006 0.078 Season:Position 0.321 0.160 2 348.3 2.51 0.083 Residual 0.064 0.253 ---

Table 2.4 Eigenvalues and the percent variance captured by the principal components (eigenvalues > 1), along with each principal component’s loadings and the proportion of the variance (r2) each variable shared with the PCA axes. Bold-faced axis (PC4) was the only significant predictor of emergence rate in subsequent linear regressions.

PC1 PC2 PC3 PC4 PC5 Parameter Loading r2 Loading r2 Loading r2 Loading r2 Loading r2 Temperature -0.09 0.01 0.50 0.25 -0.01 0.00 0.51 0.26 -0.06 0.00 pH -0.30 0.09 -0.24 0.06 0.43 0.18 0.27 0.07 -0.28 0.08 DO 0.12 0.01 -0.42 0.18 -0.43 0.19 0.02 0.00 -0.23 0.05 Canopy 0.56 0.31 0.01 0.00 0.18 0.03 0.14 0.02 0.21 0.04 Chl-a 0.47 0.22 0.23 0.05 0.12 0.02 0.30 0.09 0.25 0.06 Total P 0.12 0.01 0.09 0.01 -0.65 0.43 0.16 0.02 -0.16 0.03 Total N 0.35 0.12 0.02 0.00 0.35 0.12 -0.45 0.20 -0.35 0.12

PO4 0.01 0.00 -0.45 0.20 0.17 0.03 0.57 0.33 -0.13 0.02

NO3 0.17 0.03 0.33 0.11 0.02 0.00 0.06 0.00 -0.76 0.58

NH4 0.44 0.19 -0.38 0.14 -0.05 0.00 0.05 0.00 -0.06 0.00 Eigenvalue 2.400 1.969 1.810 1.445 1.091 % variance 24.01 19.69 18.09 14.45 10.90

Table 2.5 Orb-weaving spiders observed along with trait characteristics including body size and orb-web traits, and relative occurrence in species surveys. Occurrence of taxa in upper- (FW), mid- (MH), and lower-estuary (PH) reaches are denoted as: - = absent, 1 = rare (< 3%), 2 = uncommon (< 20%), 3 = fairly common (< 50%), 4 = common (< 70%), 5 = abundant (> 70%).

Size at Orb Upper Mid Lower Family Subfamily Species Common Name maturity type (FW) (MH) (PH) – Gasteracantha cancriformis Spiny-backed orbweaver small V 1 – 1 Araneidae – Neoscona crucifera Spotted orbweaver medium V 3 2 2 Nephilinae Nephila clavipes Golden silk orbweaver large V – 2 2 – Tetragnatha spp. Long-jawed orbweaver small H 5 5 5 Tetragnathidae Leucage spp. incl. L. – Orchard orbweaver small H 4 5 4 venusta and L. argyra

Figure 2.1 Study reaches representing upper- (top), mid- (center), and lower-estuary (bottom) habitats in the Fakahatchee Strand

(26°00′00″N 81°25′01″W) and Ten Thousand Islands Estuary of southwest Florida. These divisions generally represent freshwater, mesohaline, and polyhaline habitats, respectively.

Figure 2.2 Variability (x̅ ± SE) in emergent aquatic insect responses among upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches in the summer and winter periods; based on linear mixed-effects models and pairwise comparisons. For (A) emergence rate, both season and salinity were significant effects (P < 0.05 for both). Season was a significant effect for (B) family richness (P < 0.001), (C) Shannon diversity (P < 0.001) and (D) Pielou’s evenness (P = 0.028). Differences in letters a, b indicate significant pairwise differences across estuarine gradient and season (Tukey HSD: P < 0.05). Emergence-rate data were log10-transformed for analysis; raw data are displayed in Appendix A: Supplementary Material (Fig. A.4).

a a (A) a (B) a ab winter ab ab ab ab

winter ab b b

summer summer

a (C) (D) a winter

ab ab winter ab ab ab b ab ab b b

summer summer

Estuary position Estuary position (upper  lower) (upper  lower)

temperature

3- PO4

total N

Figure 2.3 Relationship between PC4 and emergence rate/density (R2 = 0.19, P = 0.024; y = -

-3 0.1949x + 0.7199). From -2 to +2.5, PC4 represents increasing PO4 and temperature, and decreasing total N.

(A) Stress = 0.1883 Estuary position Upper (FW) Mid (MH) Lower (PH)

(B) Stress = 0.1695 Estuary position Upper (FW) Mid (MH) Lower (PH)

Figure 2.4 NMDS plot depicting differences in community composition (ANOSIM: R = 0.158, P

Estuary position (upper  lower)

Figure 2.5 Mean body size of two most common families (Chironomidae and Dolichopodidae) observed at upper- (FW), mid- (MH), lower-estuary (PH) reaches. Body-size data were log10- transformed for analysis; raw data are displayed in Appendix A: Supplementary Material (Fig.

A.5). Error bars are + 1SE.

a a a a winter summer

Estuary position (upper  lower)

Figure 2.6 Densities of orb-weaving spiders (Araneidae, Nephilinae, and Tetragnathidae) by season (summer, winter) at upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary

(PH: polyhaline) reaches. Log10-transformed data are shown; raw data are displayed in Appendix

A, Supplementary Material (Fig. A.6). Different letters (a,b,c) indicate all pairwise differences between seasons and estuary position (Tukey’s HSD, P < 0.05). Error bars are + 1SE.

Figure 2.7 Mean body condition of (A) Tetragnatha and (B) Leucage spp. from upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches by season

0.05). Error bars are ± 1SE.

b (A) ab

ab a

a a

Tetragnatha

a a (B) ac

bc b

Leucage

Estuary position

(upper  lower)

(A)

winter

summer

(B)

mid-estuary

lower-estuary upper-estuary

Figure 2.8 Relationship of log10-transformed aquatic insect emergence rates/densities and orb- weaving spider densities in the Fakahatchee Strand and Ten Thousand Islands Estuary during

(A) summer (2016; R2 = 0.02, P > 0.05, y = 0.05x – 0.77), winter (2015 and 2016; R2 = 0.19, P =

0.081, y = 0.43x – 0.86), and all seasons combined (R2 = 0.14, P = 0.063, y = 0.31x – 0.83) and

(B) coded by estuary position.

Chapter 3: Aquatic primary production linked to nearshore spider trophic dynamics

Abstract

Although the importance of aquatic-to-terrestrial subsidies is widely recognized in freshwater ecosystems, their potential influence on terrestrial consumers and aquatic-terrestrial food-web connectivity remains relatively unexplored in estuaries. Here, we investigated the trophic characteristics of nearshore orb-weaving spiders (Tetragnathidae, Araneidae, and

Nephilinae) across a subtropical estuarine gradient (salinity range: 0 to 32 psu). Bayesian mixing models showed that aquatically-derived energy (i.e., nutritional subsidies originating from epiphyton and phytoplankton) represented 0.79 to 0.99 of the diet of nearshore spiders across all study reaches during summer and winter seasons. Overall, spider reliance on aquatically-derived energy was 12.8% higher at mid- and lower-estuary reaches than at upper-estuary reaches.

Spider trophic position ranged from 0.32 to 2.38, and was highest during the summer at upper- and mid-estuary reaches. δ13C of orb-weaving spiders trended with δ13C of the most common emergent insect family (Chironomidae) during the summer, suggesting that chironomids may be an important dietary component for this terrestrial consumer and a vector of aquatically-derived energy at least during certain times of year. Our findings suggest that spider trophic dynamics are highly variable across estuarine habitats and seasonally, and that aquatic primary production via emergent insects is an important energetic pathway for riparian spiders in estuaries. These results have strong implications for the trophic dynamics of other insectivorous terrestrial consumers in estuaries.

Introduction

Cross-habitat movement of nutrients, organisms, and organic matter can strongly influence food webs and productivity of recipient habitats (i.e., spatial subsidies, sensu Polis,

Anderson & Holt 1997; Riley et al. 2004). Research on subsidies in estuaries has emphasized the input of organic matter directly from terrestrial vegetation (Nelson, Deegan & Garritt 2015; Dias et al. 2016), as well as downstream transport via rivers (Odum, Fisher & Pickral 1979; Riley et al. 2004; Savage et al. 2012b). In coastal areas, marine nutrients are also transferred to estuarine and upstream riparian food webs via shore drift, tidal fluxes, and movement of mobile consumers

(Polis, Power & Huxel 2004; Valiela & Bartholomew 2015). However, whereas estuarine research has widely recognized multiple spatial dimensions of habitat connectivity (upstream-to- downstream, marine-to-river and marine-to-land, and land-to-water; Sheaves 2009), estuary-to- land connectivity has not been fully addressed. In this regard, estuarine science lags behind current paradigms in freshwater riverine science (but see Loftus, Trexler & Jones 1998), where a substantial body of research has documented that energetic transfers between rivers and their adjacent riparian zones are essential for the functioning of both ecosystems (Polis, Anderson &

Holt 1997; Richardson, Zhang & Marczak 2010; Bartels et al. 2012).

Stream ecosystem paradigms such as the River Continuum Concept (Vannote et al. 1980) highlight the unidirectional pathways of energy between terrestrial and aquatic ecosystems by which terrestrially-derived OM, nutrients, and biota provide energy to aquatic consumers. More recent findings illustrate that reverse energy exchanges (i.e., aquatic-to-terrestrial) can also provide important nutritional subsidies to riparian terrestrial food webs (Power & Rainey 2000;

Henschel, Mahsberg & Stumpf 2001; Baxter, Fausch & Saunders 2005). Prey items, such as

57 aquatic insects that emerge from the water as adults (hereafter, “emergent insects”), provide nutritional subsidies to a suite of terrestrial consumers including arthropods, birds, mammals, and reptiles (Ormerod & Tyler 1991; Murakami & Nakano 2002; Kautza & Sullivan 2016). Not only do emerging insects affect nutrition and energetic status, but behavioral studies also suggest that the timing and location of emergent insects influence the movement and distribution of terrestrial consumers, such as spiders and birds (Gillespie 1987; Orians & Wittenberger 1991).

Along the freshwater-marine continuum of subtropical estuaries, variability in freshwater and tidal influences structure physicochemical and biological gradients that govern ecosystem processes and food-web dynamics (Basset et al. 2013). Subtropical estuaries also receive climate-driven resource pulses (Yang et al. 2008; Garcia et al. 2017) in the form of nutrients and organic matter related to seasonal variability in tidal energy and rainfall (Childers et al. 2006;

Botson, Gawlik & Trexler 2016) and El Niño Southern Oscillation (ENSO) patterns (e.g., Stapp

& Polis 2003). Seasonal flood and drought conditions shift the spatial distribution of aquatic habitats (Boucek & Rehage 2013) and freshwater discharge, which has been shown to influence aquatic food-web properties. Variability in discharge has also been suggested as a mechanism linking ecosystem size with food-chain length (FCL) – the maximum trophic position occupied by a consumer and a key measure of vertical food-web structure – in riverine food webs whereby

FCL decreases with increasing flow variability and intermittency (Sabo et al. 2010). Likewise, trophic position of nearshore riparian spiders of the family Tetragnathidae, which build webs over the water to intercept emergent insects (Gillespie 1987; Zschokke et al. 2006), was observed to be related to both channel size and drainage area via variability in local flooding regimes in the central Sierra Nevada Range of California (Jackson and Sullivan 2017). Thus,

58 hydrologic regimes can exert strong controls on trophic dynamics in systems with intermittent precipitation patterns, such as most subtropical estuaries.

The hydrodynamic ecotones that characterize estuarine gradients also might be expected to influence the energetic contribution (i.e., nutritional subsidies) of aquatic and terrestrial basal resources to aquatic food webs. For example, in habitats (e.g., higher-gradient estuaries) or periods (e.g., following intense precipitation events) of high fluvial influence, terrigenous material can be flushed out quickly, limiting nutrient-processing time and rendering terrestrially- derived allochthonous energy less available to localized aquatic and riparian consumers (Howe

& Simenstad 2015). Dry-season or low-flow conditions might extend nutrient-processing times

(Childers et al. 2006) during which terrestrial detritus may become a more important as a basal resource to aquatic consumers via export by mobile consumers and trophic transfers.

Insect-facilitated aquatic-to-terrestrial nutritional subsidies in estuaries are also likely to vary both spatially and seasonally. For instance, in a southwest Florida estuary, the density and diversity of emergent aquatic insects was found to gradually decrease from upper-to-lower- estuary habitats, and these patterns generally tracked density of orb-weaving riparian spiders

(Chapter 2). Furthermore, emergent insect density and diversity were overall higher during the winter (December to January) compared to the summer (June to August) at lower-estuary habitats related to seasonal changes in salinity and nutrients (Chapter 2).

Here, we sought to quantify trophic position and the energetic contribution of aquatic primary production to terrestrial food webs in a subtropical estuary, using shoreline spiders as a focal consumer. Shoreline spiders are an excellent model organism and have been widely used to represent aquatic-to-terrestrial energy flows (Marczak & Richardson 2007; Meyer & Sullivan

2013; Tagwireyi & Sullivan 2015; O'Gorman 2016). Orb-weaving spiders of subtropical and tropical environments often exhibit bi- or multi-voltinism (Higgins 2006), a diversity of selective prey-capture strategies (Zschokke et al. 2006), and may serve as indicators of spatial and seasonal variability (e.g., complexity of habitat structure, density of aquatic insect prey) in estuaries.

Our overarching hypothesis was that trophic characteristics (i.e., reliance on aquatically- derived energy and trophic position) of nearshore orb-weaving spiders (Tetragnathidae,

Araneidae and the subfamily Nephilinae) would vary based on both relative position in the estuary (upper, mid, lower) and season (summer, winter). Specifically, we predicted that reliance on aquatically-derived energy (i.e., originating from aquatic primary producers) by spiders – via emergent insects – would increase from upper-to-lower-estuary habitats as the combination of epiphyton and higher phytoplankton biomass contributes to in-situ aquatic primary and secondary productivity (Winemiller et al. 2011; Cloern, Foster & Kleckner 2014). We also anticipated variable contributions of aquatically-derived energy during summer and winter given the seasonality of downstream flows of freshwater and organic matter. Furthermore, we anticipated that trophic position of spiders would be highest in the upper-estuary, freshwater habitats where hydrologic variation occurs at more-intermittent intervals (wet and dry seasons) compared to tidal habitats of the lower-estuary and where emergent insect communities might be expected to be more diverse and trophically complex (Chapter 2).

Methods

Study system and experimental design

This study was conducted in the Fakahatchee Strand Preserve State Park and Rookery

Bay National Estuarine Research Reserve, both located within The Ten Thousand Islands (TTI)

Estuary of southwestern Florida (see Chapter 2, Figure 2.1 for map of study area). In the TTI

Estuary, freshwater runoff and tidal fluxes (tidal range in Fakahatchee Bay varies between 0.2 and 1.8 m) create salinity concentrations ranging from 0.8 (freshwater) to 41.2 psu (hyperhaline)

(Booth & Soderqvist 2016). The East River (~9.7 km) is an important contributor of freshwater to the TTI Estuary, with flows tracking annual rainfall patterns (averaging 138.5 cm for 1981-

2010) in the region (Booth & Soderqvist 2016; South Florida Water Management District 2015).

The area typically receives the highest average monthly rainfall (20-23 cm per month) from June to September, and lower amounts from November to March (2.5-5 cm per month; Rookery Bay

National Estuarine Research Reserve 2012); however, El Nino Southern Oscillation (ENSO) patterns are known to reverse this trend. During this study, ENSO-related rainfall during

December 2015 (55.9 mm) and January 2016 (272 mm) exceeded the regional average by 500%

(Rookery Bay NERR 2016), resulting in higher fluvial flows and lower salinities (x̅ ± SE, 13.95

± 2.18 psu at lower-estuary reaches) compared to typical dry-season conditions (Appendix A:

Supplementary Material, Fig. A.1).

Nine river-estuarine reaches/sites (200 m, upstream to downstream) representing upper-, mid-, and lower-estuary sections – and generally corresponding to freshwater (FW), mesohaline

(MH), and polyhaline (PH) habitats – of the Fakahatchee-TTI Estuary were monitored at four time periods over two years: Jul-Aug 2015, Dec 2015- Jan 2016, Jun-Jul 2016, and Dec 2016-

Jan 2017.

Primary producers, emergent insects, and spiders

Emergent aquatic insects were collected for 5- to 10-day periods during each summer and winter sampling excursion using three floating pyramidal traps (0.4 m2) placed at upstream, mid, and downstream segments of study reaches (Alberts, Sullivan & Kautza 2013; Cadmus,

Pomeranz & Kraus 2016). All emergent insects collected in bottle traps were preserved in 70% ethanol and identified to family following Merritt & Cummins (1996) and Triplehorn et al.

(2005). Individuals of the two numerically dominant families (of total emergent insect community, 70% were Chironomidae and 11% Dolichopodidae; see Chapter 2 for details) were pooled by reach, dried at 40 to 45°C for 48 h, and pulverized for stable-isotope analysis.

Morning spider surveys (700 to 1100 h EST) were conducted during which two surveyors walked or kayaked along 50-m transects along the left and right shorelines/mangrove fringes

(~15 minutes per transect) and collected 5-10 individuals of the families Tetragnathidae,

Araneidae, and subfamily Nephilinae within 1-m of the channel margin and 2-m above water level. Whole-specimen tissues were dried at 60°C for 48 h, ground to a fine powder, and stored in combusted glass vials.

We collected samples of phytoplankton (winter only), epiphyton, and terrestrial vegetation at each study reach, which are important basal resources for food webs throughout estuaries of South Florida (Fry & Smith 2002). Phytoplankton was collected as a bulk water sample in 1-L opaque containers and stored on ice for transport to the lab for further processing.

Water was passively-filtered through a 149-μm sieve to remove zooplankton and any larger particulates and then vacuum-filtered through pre-combusted GF/F filters. Small sample quantities precluded acid treatment with HCl, which is often performed to remove carbonates

62 that may affect readings of 13C and 14C (Harris, Horwáth & van Kessel 2001). Additional subsamples are required as acid treatment alters δ15N signatures (Carabel et al. 2006).

Acidification has been shown to decrease δ13C signatures of oven-dried marine plankton (100-

300 μm) and macroinvertebrates by <1 ‰ (Carabel et al. 2006; Mateo et al. 2008; Schlacher &

Connolly 2014). However, our approach is consistent with multiple studies that have not treated samples (e.g., Ng, Wai & Williams 2007; Gritcan et al. 2016). Filters were rinsed with DI water then dried at 40-45°C for 48 h and desiccated tissue was extracted for subsequent stable-isotope analysis.

At each reach, vegetation falling into the water (predominantly leaves, but also small wood pieces) was captured using three floating pan traps (0.4 m2) and arranged at upstream, mid, and downstream transects of each reach (details on predominant species can be found in

Appendix B: Supplementary Material, Additional Methods). We used this material for stable- isotope analysis as conditioned detritus includes heterotrophic bacteria, fungi, and other organisms that can alter isotope ratios (Finlay 2001). Epiphyton was collected from submerged roots and/or logs and preserved in 70% ethanol. Once in the lab, plant and algal tissues were gently scrubbed of epibiota and sediment and rinsed with distilled water before drying at 40 to

45°C on aluminum foil dishes for 48 h and pulverized for stable-isotope analysis.

Stable-isotope analysis, trophic position, and reliance on aquatically-derived energy

Stable-isotope analysis is commonly used to model energy sources and trophic linkages among organisms. Quantitative applications include identifying the trophic position of an organism as well as partitioning its nutritional resources (Belicka et al. 2012; Layman et al.

2012; Kautza & Sullivan 2016). The ratio of nitrogen isotopes (15N to 14N relative to a standard, 63 expressed as δ15N) increases with trophic transfers, providing an estimation of relative trophic position. The ratio of carbon isotopes (13C to 12C relative to a standard, expressed as δ13C) is commonly used to distinguish among basal resources, as it varies among primary producers of different photosynthetic pathways but is relatively consistent through trophic transfers (Post

2002; Layman et al. 2012).

Desiccated tissues of basal resources, emergent insects (Chironomidae, Dolichopodidae) and spiders (Tetragnathidae, Araneidae, and Nephilinae) were sorted by reach and sampling period and transferred into pre-weighed tin capsules to generate at least three replicates per sample type, reach, and season. All samples were analyzed for 13C and 15N by continuous flow elemental analysis isotope-ratio mass spectrometry (Delta PlusXP, Thermofinnigan, Bremen,

Germany) at the Washington State University Stable Isotope Core (Pullman, Washington, USA).

Stable-isotope ratios are reported in δ notation as parts per mille (‰) deviation from an established standard (e.g., Pee Dee Belemnite limestone for δ13C; atmospheric N for δ15N):

푅 13 15 13 15 푠푎푚푝푙푒 퐶 푁 훿 퐶 표푟 훿 푁 = ( − 1) × 1000, 푤ℎ푒푟푒 푅 = 12 표푟 14 (1) 푅푠푡푎푛푑푎푟푑 퐶 푁

Typical analytical precision is 0.08‰ for δ15N and 0.19‰ for δ13C determination.

To characterize the relative contribution of aquatically- and terrestrially-derived energy

(via phytoplankton and epiphyton, and terrestrial vegetation, respectively) to orb-weaving spiders and emergent insects, we used a Bayesian mixing model approach implemented in stable- isotope analysis with MixSIAR v3.1 (Stock & Semmens 2013) in R (R Core Team 2017).

MixSIAR uses Markov chain Monte Carlo (MCMC) methods to calculate posterior distributions of variables of interest that are specified in the model (e.g., proportion of aquatic C from epiphyton in spider tissues). Based on previous studies using similar methodology, we were 64 confident that epiphyton and phytoplankton samples are essentially “pure” or uncontaminated

(see Kautza & Sullivan 2016), nonetheless, we recognize there was potential towards a terrestrial bias if bulk samples contained particulate matter of terrestrial origin that we did not remove. We used C and N mixing models incorporating means and standard deviations of season-specific isotopic signatures of aquatic (phytoplankton and epiphyton) and terrestrial (vegetation falling into water) sources collected during the study. We incorporated widely-used values for trophic discrimination factors as outlined by Post (2002). Thus, in our models, we used discrimination values of 3.4 + 0.98‰ for δ15N and 0.39 + 1.3‰ for δ13C to estimate the contribution of different basal energy sources to shoreline spiders (i.e., Tetragnathidae, Araneidae, and Nephilinae) that we expected to be feeding mainly on invertebrate primary consumers, whether aquatic or terrestrial in origin. As a reference, we also estimated the contribution of different basal energy sources to aquatic primary consumers (i.e., Chironomidae, and Dolichopodidae). Three models were fitted separately for estuary position (upper-, mid-, and lower-estuary reaches) with season and taxa as fixed effects.

In addition to visual interpretations of trace plots, we used MCMC diagnostics including

Gelman-Rubin and Geweke tests to assess model convergence (Stock & Semmens 2013). For the

Gelman-Rubin test, values <~1.1 are acceptable and indicate model convergence (Gelman et al.

2004). The Geweke test uses a two-sided z test to compare the means of the first and second part of each chain. Means that are approximately the same suggests model convergence; a large z score indicates model rejection.

We used δ13C or δ15N values to calculate trophic position (TP) of emergent insects and spiders (Cabana & Rasmussen 1996; Vander Zanden, Casselman & Rasmussen 1999; Anderson

& Cabana 2007). Trophic fractionation factors for spiders and emergent insects were estimated using the per trophic-step fractionation in Post (2002) – 3.4 + 0.98‰ for δ15N and 0.39 + 1.3‰ for δ13C:

15 15 15 15 훿 푁푝푟푒푑 − (훿 푁푏푎푠푒1 × 훼1 + 훿 푁푏푎푠푒2 × 훼2 + 훿 푁푏푎푠푒3 × 훼3) 푇푃푝푟푒푑 = + 휌 (2) Δ푛 where α1 = proportion of N acquired from baseline 1 (terrestrial vegetation); α2 = proportion of N acquired from baseline 2 (phytoplankton); α3 = proportion of N acquired from baseline 3

(epiphyton); Δn = fractionation rate of N; and ρ = TP of the baseline (TP = 1 for the primary producers used as the baselines). However, it is important to recognize that some investigations have questioned the use of a single, fixed enrichment factor (Caut, Angulo & Courchamp 2009;

Hussey et al. 2014) or argued that 3.4‰ may overestimate the per trophic level 15N enrichment for certain consumers (Vanderklift & Ponsard 2003). 0.4‰ is the commonly used enrichment value for C.

Statistical analyses

We used linear mixed-effects models (LMM) to evaluate potential fixed effects of estuary postion (upper-, mid-, and lower-estuary) and season (summer and winter) on the reliance on aquatically-derived energy and trophic position of orb-weaving spiders. Reach and family were included as random effects. We report test statistics for the significance of fixed effects derived using type III Wald F tests with Satterthwaite approximation for degrees of freedom for all linear mixed-effects regressions (Luke 2017). Two-way ANOVA and post-hoc Tukey HSD (Tukey

1977) were used to evaluate seasonal and spatial (upper-, mid-, and lower-estuary) differences in

δ13C signatures of basal resources, emergent aquatic insects, and spiders. Simple linear 66 regressions were used to examine potential influences of (1) emergent aquatic insects (i.e., emergence rates, body size; data from Chapter 2) on spider trophic position and reliance on aquatically-derived energy and (2) δ13C values of emergent aquatic insects on δ13C values of spiders, in order to confirm spider dietary reliance on emergent insects.

All analyses were performed in the R language and environment with α = 0.05 indicating statistical significance (R Core Team 2017). We used the lme4 (Bates et al. 2015) and lmerTest

(Kuznetsova, Brockhoff & Christensen 2015) packages for linear mixed-effects models.

Results

13 Terrestrial vegetation δ C signatures varied across the estuarine gradient (ANOVA: F2,82

= 14.961, P < 0.0001) but were similar between summer and winter (P > 0.05 for both). δ13C signatures of epiphyton varied seasonally (ANOVA: F1,70= 16.27, P = 0.0001) and spatially

13 (ANOVA: F2,70= 15.25, P < 0.0001). We used time-averaged δ C signatures (due to minimal seasonal variability) of terrestrial vegetation and season-specific epiphyton δ13C signatures in

MixSIAR models. Epiphyton was generally more 15N-enriched than terrestrial vegetation and phytoplankton (Fig. 3.1A-C). While we are cognizant of the challenge in obtaining pure algal samples (benthic or suspended; e.g., Zeug & Winemiller 2008), epiphyton (x̅ ± SD; upper- estuary: -30.74 ± 3.59, mid-estuary: -26.74 ± 3.26, lower-estuary: -28.65 ± 5.92), and phytoplankton (x̅ ± SD; upper-estuary: -33.41 ± 2.63, mid-estuary: -34.23 ± 1.11, lower-estuary:

-20.74 ± 1.19) signatures obtained in this study are generally consistent (but note higher epiphyton values in our study) with other reported values for estuarine ecosystems in the region

(e.g., range: -31 to -26‰ for epiphyton and -37 to -27‰ for phytoplankton in freshwater

67 habitats; Bemis and Kendall 2004; x̅ ± SD, -18.9 ± 5.15‰ for epiphyton and -18.4 ± 0.06‰ for phytoplankton in mangrove habitats; Kieckbusch et al. 2004).

13 Spider δ C signatures varied spatially (upper-to-lower estuary) (ANOVA: F2,178 = 18.46,

P < 0.0001) and seasonally (F1,178 = 217.87, P < 0.0001). A season x position interaction (F2,178 =

31.69, P < 0.0001) was driven by 13C-enrichment of spider tissues in the summer (P < 0.0001);

Tetragnathidae were more 13C-enriched at lower-estuary compared to mid- (P = 0.003) and upper-estuary (P < 0.0001) reaches (Fig. 3.1A-C). Nephilinae were more 13C-enriched at mid-

15 estuary reaches (P = 0.0008) (Fig. 3.1B). Spider δ N signatures varied spatially (ANOVA: F2,178

= 17.232, P < 0.0001) exhibiting more 15N-enriched tissues at mid-estuary compared to upper-

(P < 0.0001) and lower-estuary reaches (P = 0.050; Fig. 3.1A-C). δ13C signatures of emergent aquatic insects also varied spatially (ANOVA: F2,55 = 9.170, P = 0.0004) and, like tetragnathids, were more 13C-enriched at lower-estuary reaches (Fig. 3.1C).

One caveat in characterizing coastal wetland food webs is the potential difficulty of distinguishing algal and detrital energy pathways in tidal waters where suspended and benthic sediment can exhibit similar isotopic compositions (Schwamborn & Giarrizzo 2015). However, adequate separation of δ13C and δ15N signatures of primary-producer sources allowed for reasonable confidence in our mixing models (Fig. 3.1A-C). On average, isotopic signatures of terrestrial vegetation were separated from epiphyton by 3.46‰ (δ13C) and 2.66‰ (δ15N) and from phytoplankton by 5.44‰ (δ13C) and 0.80‰ (δ15N) (see Fig. 3.1A-C and Appendix B:

Supplementary Information, Table B.1 for δ13C and δ15N signatures by season, estuary position, and taxon). Furthermore, mixing models met requirements of convergence based on examination of the trace plots and diagnostic tests.

Reliance on aquatically-derived energy and trophic position

Aquatically-derived energy (from both epiphyton and phytoplankton) emerged as an important nutritional contribution to orb-weaving spiders, ranging from 0.79 to 0.99 across all study reaches and seasons. For Tetragnathidae, aquatically-derived energy was generally greater in the winter than summer at upper- (0.89 v. 0.79) and lower-estuary (0.99 v. 0.94) reaches. For

Araneidae and Nephilinae, reliance on aquatically-derived energy was > 0.95 at the mid- and lower-estuary reaches, although nephilids exhibited a lower reliance (0.83) in the lower-estuary reaches in the summer. Spider reliance on aquatically-derived energy (all families combined) varied spatially, and was highest at mid- and lower-estuary reaches (Table 3.2, Fig. 3.2).

Epiphyton-derived energy contributed a greater proportion than phytoplankton-derived energy to spider subsidies across the estuary: lower- (0.90 v. 0.04), mid- (0.94 v. 0.05), and upper-estuary

(0.49 v. 0.34) reaches (Appendix B: Supplementary Information, Fig. B.2). We also found that reliance on aquatically-derived energy varied seasonally, with an effect of salinity x estuary position (Table 3.2, Fig. 3.3). Aquatically-derived energy to spiders was greater during the winter at upper- (Tukey HSD, P < 0.0001) and lower-estuary reaches (Tukey HSD, P < 0.0001), yet were consistent across seasons at mid-estuary reaches (Tukey HSD, P > 0.05; Fig. 3.3).

Signatures of spider δ13C trended with δ13C of emergent insects (Chironomidae) during

2 the summer (Fig. 3.4; R = 0.52, F1,4 = 6.401, P = 0.065). However, we did not find a strong relationship between the δ13C signatures (based on reach averages) of Dolichopodidae and orb- weaving spiders (P > 0.05; data not shown). Both Chironomidae and Dolichopodidae (x̅ = 0.83 for both) were highly reliant on phytoplankton pathways in the summer yet more so on epiphyton pathways in the winter at upper-estuary reaches (0.88-0.90).

Trophic position of spiders ranged from 0.32 to 2.38; Tetragnathidae generally occupied the highest trophic positions (range: 0.77-2.38). Estuary position and season had strong effects on spider trophic position (Table 3.2, Fig. 3.4). For example, trophic position was consistently lowest at lower-estuary reaches (1.11 in summer and 1.12 in winter; Fig. 3.3). The interaction of season x position (Table 3.2) stemmed from seasonal variability in trophic positions of spiders at mid-estuary reaches, where trophic positions were lower during winter (Tukey HSD, P < 0.0001;

Fig. 3.4). Seasonal differences in trophic position were consistent among spider families (Table

3.2).

Discussion

Estuarine research has widely recognized multiple spatial dimensions of habitat connectivity (e.g., the Coastal Ecosytem Mosaic, sensu Sheaves 2009), yet water-to-land connectivity has not been fully addressed (but see Cederholm et al. 1999) and may be of quantitative importance to estuarine ecosystem processes. We observed spatial and seasonal variability in both reliance on aquatically-derived energy and trophic position of nearshore orb- weaving spiders in the Fakahatchee Strand-TTI Estuary. These findings suggest that variability in aquatic insect-mediated subsidies may be an important mechanism driving trophic dynamics of these and other insectivorous terrestrial consumers across estuaries.

Orb-weaving spiders as nearshore consumers

Shoreline orb-weaving spiders can be highly reliant on emergent insects (Kato et al.

2003; Akamatsu & Toda 2011a; Yuen & Dudgeon 2016), and we observed a positive trend

70 between the δ13C signatures of orb-weaving spiders and δ13C signatures of Chironomidae (Fig.

3.4). Although we had anticipated that this relationship would be stronger, it is important to note that we only measured isotopic signatures in Chironomidae and Dolichopodidae, and spiders likely feed on a broader diet of emergent insects (Blamires et al. 2011). However, we also found that spider density tracked aquatic insect emergence rate across the Fakahatchee-TTI Estuary, where this relationship varies seasonally and spatially (Chapter 2). Numerical responses of shoreline spiders to aquatic insect emergence have been documented by others as well (reviewed in Marczak, Thompson & Richardson 2007), and provide an additional line of evidence for spider dietary reliance on aquatic insects. For example, experimental manipulation of emergent insects in a coastal rainforest of the Pacific Northwest found that spider abundance was reduced by 57% in response to a 62.9% reduction in emergent aquatic insect abundance (Marczak &

Richardson 2007). Collectively, these findings document spider dietary reliance on emergent insects in the Fakahatchee Strand-TTI Estuary.

Aquatically-derived energy

The energetic contribution of aquatic primary producers (epiphyton and phytoplankton) to orb-weaving spiders was >0.75 across all reaches, approaching 1.0 for spiders at mid-estuary reaches during winter and summer (Fig. 3.2). These estimates exceed the 0.40-0.69 range reported by Kautza and Sullivan (2016) for tetragnathids in a temperate river during late spring and summer. Spider reliance on aquatically-derived energy was lowest at upper-estuary reaches

(0.79 summer, 0.89 winter) where, consistent with findings by Kautza and Sullivan (2016), phytoplankton-based pathways represented the main contribution of aquatic energy to shoreline

71 tetragnathid spiders in the summer (0.62; Appendix B: Supplementary Information, Fig. B.2).

Epiphyton-based pathways contributed more to spiders at mid- and lower-estuary reaches.

However, we recognize that bias may have been introduced due to our inability to acid-wash epiphyton and phytoplankton samples, which could possibly have shifted signatures to be slightly more 13C-depleted. At the lower-estuary reaches, this may have led to a higher estimated contribution of phytoplankton-derived energy to terrestrial consumers (Fig. 3.1C).

The fate and role of terrestrially-derived energy is different in upper-estuary habitats compared to lower in the estuary, where higher flows may flush out terrestrial detritus more rapidly (Howe & Simenstad 2015). For example, longer nutrient-processing times at upper- estuary reaches may support a spider aquatic prey base composed insect taxa that primarily consume terrestrially-derived detritus, leading to relatively lower contribution of aquatically- derived energy. Estuarine and coastal research widely recognizes the importance of detritus to aquatic food webs as well as the increasing importance of in-stream productivity along up-to- downstream gradients (Vannote et al. 1980; Winemiller et al. 2011). Many questions remain in understanding how aquatic energy pathways support terrestrial consumers along estuarine gradients. Although qualitative estimates of chorophyll-a did not suggest spatial differences in aquatic primary production (Chapter 2), we found that reliance on aquatically-derived energy was overall higher at mid- and lower-estuary reaches.

Seasonal differences in aquatically-derived energy to spiders were likely related to hydrological patterns. Wet-dry seasonality is known to affect aquatic production (e.g., phytoplankton, Burford et al. 2012) as well as subsidy dynamics (Boucek & Rehage 2013).

Aquatically-derived energy was an important subsidy to spiders at mid-estuary reaches, perhaps related to a higher density of emergent insect prey observed in the winter (Chapter 2).

Although statistically significant differences in aquatically-derived energy to spiders were observed both spatially and temporally, the mean magnitude of the differences across the estuarine gradient was 0.10, and among season was 0.06. However, even minor differences are likely to be ecological relevant. For example, aquatically-derived nutritional subsidies in the form of emergent insects represent high-quality food resources (Martin-Creuzburg, Kowarik &

Straile 2017; Twining et al. 2017) for riparian consumers with implications on their physiological functioning. The provisioning of aquatically-derived long-chain polyunsaturated fatty acids (PUFAs) were shown to enhance immune function of wolf spiders inhabiting wetland habitats compared to upland spiders (Fritz et al. 2017).

Trophic position

Consistent with our hypothesis, tetragnathid spiders occupied higher trophic positions at upper-estuary reaches (2.09 for summer, 1.49 for winter); yet these estimates are much lower in comparison to riparian tetragnathids in other aquatic-riparian systems such as the Merced River of the Sierra Nevada (range: 1.75-3.99; Jackson and Sullivan 2018) and the Scioto River of Ohio

(range: 2.35–3.98; Tagwireyi & Sullivan 2016a). We suspect that hydrological variability in upper-estuary reaches may be at least in part driving spider trophic responses. Flood magnitude

(Jackson) and flow variability (Sabo) have been implicated as mechanisms linking ecosystem size with food-chain length, and maximum trophic position. Although upper-estuary (i.e., freshwater) habitats in the estuaries of South Florida exhibit pronounced flood and dry-down

73 patterns (Carter et al. 1973) with impacts on trophic dynamics (e.g., Boucek & Rehage 2013), these disturbances occur across longer time intervals (i.e., wet and dry seasons) compared to tidal fluctuations. Therefore, estuarine hydrology does not conform to the relationship observed between ecosystem size and FCL in freshwater riverine systems (sensu Sabo et al. 2010). Higher diversity of aquatic and terrestrial prey at the upper-estuary reaches may have contributed to higher trophic position of orb-weaving spiders. Trophic position estimates for emergent insects–

2.25 (Chironomidae) and 2.33 (Dolichopidadae)– at upper-estuary reaches compared to means of

1.25 (Chironomidae) and 1.30 (Dolichopidadae) at mid- and lower-estuary reaches from this study also support this line of evidence (unpublished data, Zapata and Sullivan). Higher trophic positions of emergent aquatic insects in upper-estuary reaches is indicative of increased trophic complexity in the aquatic compartment of spider diet, and implicates variability in aquatic food- web structure as a driver of spider trophic position.

Spiders at mid- and lower-estuary reaches occupied relatively lower trophic positions and exhibited higher reliance on aquatic resources (Fig. 3.3). Higher reliance on aquatically-derived energy during the winter with lower trophic position of spiders was observed at upper-estuary reaches during this period (Figs. 3.2 and 3.3). Further investigation is needed to understand this potential correlation, as estuary position-level estimates of aquatically-derived energy contribution showed little variability across time and space. For example, one question to address is how the strength of this correlation may vary with prey community structure (e.g., emergent insect diversity).

Conclusion

Coastal wetland ecosystems of southwest Florida have been intensely altered by changes in freshwater budgets via wetland drainage and deforestation (Donders et al. 2008).

Spatiotemporal variability in hydrology and salinity likely influences food-web interactions between aquatic and terrestrial consumers of the coastal Everglades. Further, seasonal flood and dry-down patterns and fluctuations in fresh-to-salt water gradients regulate aquatic community composition across trophic levels (Childers et al. 2006; Green et al. 2006; Liston 2006). Within this context, our study offers important evidence related to the strength and importance of insect- facilitated connectivity between aquatic and terrestrial habitats in estuaries, with implications for estuarine biodiversity, ecosystem function, and management. In particular, our findings show that the energetic contribution of aquatic primary producers can be quantitatively important to to nearshore orb-weaving spiders, a model riparian consumer. Studies have shown that the influence of these nutritional subsidies can extend much farther than the water’s edge, for example, when provisioning larger transient or resident consumers (Rosenblatt & Heithaus 2011;

Kautza & Sullivan 2016). Given the inherent complexity of estuaries, coupled with large-scale perturbations, further research into the spatial and temporal variability on aquatic-to-terrestrial flow pathways and their relative magnitude must be identified to better understand subsidy influences on communities, ecosystems, and their conservation (Polis, Power & Huxel 2004) in estuarine ecosystems.

Acknowledgments

This project was funded by support from The Ohio Agricultural Research and Development

Center and The Ohio State University. We are grateful for the support and collaboration with

Rookery Bay National Estuarine Research Reserve and Fakahatchee Strand Preserve State Park.

We thank Dr. Suzanne Gray and Dr. Lauren Pintor for their constructive comments during the preparation of this manuscript. Thank you to R. Zapata, S. Glassmeyer, J. Espinoza, R. Tyl, E.

Fernandez, L. Meyers, K. Diesburg, and B. Rubinoff for field and lab assistance.

70%), 5 = abundant (> 70%).

Size at Orb Family Subfamily Species Common Name FW MH PH maturity type – Gasteracantha cancriformis Spiny-backed orbweaver small V 1 – 1 Araneidae – Neoscona crucifera Spotted orbweaver medium V 3 2 2 Nephilinae Nephila clavipes Golden silk orbweaver large V – 2 2 – Tetragnatha spp. Long-jawed orbweaver small H 5 5 5 Tetragnathidae Leucage spp. incl. L. – Orchard orbweaver small H 4 5 4 venusta and L. argyra

Response variable / Sum of Mean Num. Den. Fixed effect Square Square DF DF F P Random effect Variance Std.Dev. Trophic position Season 7.194 7.194 1 172.9 128.95 <0.001 Reach 0.001 0.038 Position 6.407 3.203 2 6.3 57.42 <0.001 Family 0.004 0.067 Season:Position 6.515 3.257 2 174.7 58.39 <0.001 Residual 0.056 0.236

--- Reliance on aquatically- derived energy Season 0.111 0.111 1 174.0 359.57 <0.001 Reach 0.000 0.004 Position 0.274 0.137 2 7.5 444.67 <0.001 Residual 0.000 0.018 Season:Position 0.063 0.032 2 174.1 102.57 <0.001

---

Figure 3.1 Stable isotope biplot with seasonal (summer, winter) means and standard deviations for δ13C and δ15N of aquatic and terrestrial primary producers, emergent aquatic insects (i.e., Chironomidae and Dolichopodidae), and orb-weaving spiders at (A) upper-, (B) mid-, and (C) lower-estuary reaches. Upper-, mid-, and lower-estuary correspond generally to freshwater, mesohaline, and polyhaline habitats, respectively.

(A)

Group

(B)

Group

(C)

Group

a a a b c d

Estuary position (upper  lower)

Figure 3.2 Reliance on aquatically-derived energy (i.e., derived from epiphyton and phytoplankton) by estuary position (upper-, mid-, and lower-estuary— corresponding generally to FW = freshwater, MH = mesohaline, PH = polyhaline habitats, respectively)— estimated using MixSIAR (Stock and Semmens 2013). Different letters (a,b,c,d) indicate all pairwise differences by season and estuary position (Tukey’s HSD, P < 0.05).

c c c

Estuary position (upper  lower)

Figure 3.3 Trophic positions of orb-weaving spiders (Araneidae, Nephilinae, and Tetragnathidae) by season (summer, winter) at upper- (FW: freshwater), mid- (MH: mesohaline), and lower- (PH: polyhaline) estuary reaches. Different letters (a,b,c) indicate all pairwise differences by season and estuary position (Tukey’s HSD, P < 0.05). Error bars are + 1SE.

Estuary position Upper-estuary (FW) Mid-estuary (MH) Lower-estuary (PH)

Figure 3.4 Relationships between δ13C signatures of Chironomidae and δ13C signatures of orb-weaving spiders. Upper-, mid-, and lower-estuary correspond generally to FW = freshwater, MH = mesohaline, PH = polyhaline habitats, respectively. 85

Chapter 4: Artificial lighting at night in estuaries—ecological implications from individuals to

ecosystems

Abstract

Artificial lighting at night (ALAN) produced by urban, industrial, roadway lighting, and other sources, has dramatically increased in recent decades, especially in coastal environments that support dense human populations. Artificial “lightscapes” are characterized by distinct spatial, temporal, and spectral patterns that can alter natural patterns of light and dark with consequences expected across levels of biological organization. At the individual level, ALAN can elicit a suite of physiological and behavioral responses associated with light-mediated processes such as diel activity patterns and predator-prey interactions. ALAN has also been shown to alter community diversity, composition, and trophic structure, with implications for ecosystem-level processes including productivity, nutrient cycling, and the energetic linkages between aquatic and terrestrial systems. Here, we review the state of the science relative to the impacts of ALAN on estuaries, which is an important step in the long-term sustainability of coastal regions. First, we consider how multiple properties of ALAN (e.g., intensity and spectral content) influence the interaction between visual physiology and behavior of estuarine biota (drawing from studies on invertebrates, fish, and birds). Second, we link individual- to community-level responses, with a focus on the impacts of ALAN on ecological trophic networks and implications for estuarine ecosystem functions. Coastal aquatic communities and ecosystems have been identified as a key priority for ALAN research, and a cohesive research framework will be critical for understanding and mitigating ecological consequences.

Introduction

Artificial lighting at night (hereafter, ALAN) alters natural patterns of light and dark, including the intensity, and spatial, temporal, spectral composition of light (Gaston et al. 2013).

Use of and technological advances in electric lighting have rapidly expanded since the early incandescendent lightbulbs of the early 1900s to levels of intensity visible in satellite imagery of the Earth’s surface at night (Fig. 4.1). A growing body of research has documented the ecological impacts of downwelling ALAN from urban, commercial, and industrial sources, increasing the recognition of light pollution as a global environmental concern (Longcore & Rich

2004; 2006; 2009).

Research based in both aquatic and terrestrial ecosystems has shown that ALAN can profoundly influence biological systems that are structured around day-night (diel) light cycles.

For example, light acts as a vital component of aquatic and terrestrial ecosystems by driving primary productivity, informing visual perception, and maintaining circadian rhythms across taxa

(Navara & Nelson 2007; Kronfeld-Schor et al. 2013). Ecological impacts of ALAN include altered daily activity patterns (e.g., foraging, Bird, Branch & Miller 2004; Dwyer et al. 2013;

Dominoni et al. 2014), habitat use (Henn et al. 2014), community organization (Davies, Bennie

& Gaston 2012; Becker & Suthers 2014), and the provisioning of ecosystem services (Lyytimäki

2013; Lewanzik & Voigt 2014). ALAN may cross terrestrial-aquatic ecosystem boundaries when light permeates through riparian habitats (Perkin et al. 2011). For example, Meyer and Sullivan

(2013) observed cross-habitat impacts of ALAN, in which elevated ALAN levels were associated with reduced diversity, body size, and biomass of emerging aquatic insects, with implications for terrestrial consumers that rely on aquatic prey subsidies (e.g., spiders, birds,

88 bats; reviewed in Baxter et al. 2005). For example, Jung and Kalko (2010) found that insectivorous bat activity was higher at street lamps than forested sites, corresponding with higher insect density during the rainy season. However, few studies have explicitly tested for potential direct and indirect consequences of ALAN across aquatic-terrestrial boundaries.

Estuaries and coastal wetlands, ecosystems disproportionately affected by ALAN (Aubrecht et al. 2010; Davies et al. 2016), might therefore offer insights into linkages from individual- to ecosystem-level effects of ALAN.

Estuaries and coastal wetlands provide vital ecosystem services by contributing to fisheries, water quality, carbon sequestration, coastal protection, and pollution control (Levin et al. 2001; Barbier et al. 2011). Along estuarine gradients, freshwater and marine ecosystems are intricately linked by abiotic forces and complex biotic interactions. ALAN poses a growing threat to estuarine biodiversity and ecosystem services in densely-populated coastal habitats

(Davies et al. 2014; Stanley et al. 2015). Assessments of ALAN impacts in coastal environments are limited (Depledge, Godard-Codding & Bowen 2010), yet can be replete with lighting sources including urban centers, off-shore oil and fisheries operations, commercial and recreational vessels, and the scattering of light in the atmosphere and subsequent downcasting by clouds

(Kyba et al. 2011). In subtropical and tropical regions, light pollution driven by ALAN can infiltrate protected mangrove forests (Aubrecht, Jaiteh & de Sherbinin 2010; Bennie et al. 2015).

Here, we synthesize known and potential impacts of ALAN on estuarine organisms, communities, and ecosystem functioning (Fig. 4.2). We draw from studies that focus on visual physiology and ecology of aquatic-associated animals and on the ecological effects of natural and artificial light in terrestrial, freshwater, estuarine, and marine systems. First, we consider

89 how the intensity, spectral composition, and duration of ALAN can influence the interaction between the visual physiology and behavior of estuarine biota. We then link individual-level responses to community and ecosystem responses, with a focus on the impacts of ALAN on ecological trophic networks (i.e., food webs). Finally, we discuss implications for estuarine ecosystem functioning. Given that global emissions of ALAN are projected to increase by 6% each year (Holker et al. 2010a), ecological impacts to coastal marine and estuarine ecosystems can also be expected to intensify. Thus, this review offers a framework to inform future research and conservation efforts.

Individual-level responses to ALAN

Physiological and behavioral responses to the visual environment inherently depend on the amount and spectral composition of environmental light and the visual sensitivity of an individual organism (Fig. 4.3). Growing evidence indicates that disruption to the visual environment through human-induced changes leads to loss of biodiversity and altered communities (e.g., Seehausen, Van Alphen & Witte 1997; Seehausen et al. 2008). Our knowledge of natural nighttime light environments and communities also continues to expand

(e.g., Veilleux & Cummings 2012), providing a more complete understanding of vision-mediated community dynamics; an understanding as to how ALAN might shift those dynamics is lacking.

In this section, we provide an overview of (a) the underwater visual environment and how it shapes visual systems, (b) the physiology of vision as it pertains to estuarine communities (e.g. visual sensitivity and spectral tuning, visual orientation with an emphasis on polarization

90 sensitivity, and circadian activity rhythms), and (c) the interaction of altered environments and individual responses.

a) The visual environment

Underwater visual environments are determined by downwelling light permeating through the water surface and light reflecting from benthic habitat. Selective absorption of wavelengths by suspended particulates influences underwater irradiance (i.e., radiant flux received by a surface per unit area) at different water depths (Loew & McFarland 1990). In shallow, clear coastal waters, the photic environment is dominated by medium to short wavelengths producing the blue-green color typical of these zones. In organic-rich brackish and freshwaters, dissolved organic carbon (DOC) creates a red-shifted (dominated by longer wavelengths) and less-intense

(darker) photic environment (Lythgoe 1968; Lythgoe & C. 1991). Thus, the freshwater- estuarine-coastal continuum encompasses extremely complex visual habitats.

Turbidity derived from suspended silt, phytoplankton, detritus, and other particulate and dissolved materials (e.g., colored dissolved organic matter, or CDOM) determines the spectral absorption and scattering properties of coastal surface waters (Cannizzaro et al. 2013). Spectral absorption by phytoplankton and detritus can alter the light field of aquatic habitats just as a forest canopy filters light that permeates into the understory (Endler 1990). For example, in the subtropical Pearl River estuary of China, spectral absorbance by non-algal particulates was more important than within the inner river plume, where terrestrial detritus from runoff dominates the visual scene (Cao et al. 2003); algal particulate absorption was found to be more important in more saline coastal habitats. Similarly, in a subtropical Florida estuary, shorter wavelengths of

91 ultraviolet (UV) light are more readily absorbed by CDOM in the upper estuary compared to downstream (Chen et al. 2015), contributing to a red-shifted light environment in the former.

Light attenuation related to this turbidity “filter” can further drive the distribution and productivity of phytoplankton, benthic algae, macrophytes (Burford et al. 2012; Cloern, Foster &

Kleckner 2014; Radabaugh et al. 2014), and higher trophic-level consumers. Organismal and community responses to light pollution are thus expected to vary throughout the freshwater- marine ecotone. Depth, turbidity, benthic substrate and other habitat variables that define estuarine gradients may serve as useful indicators in forecasting ecological effects of ALAN.

b) Animal visual systems

Visual systems are composed of light-sensitive progenitor cells and photoreceptors that evolved in response to the spectral qualities of the photic environment (Lythgoe 1972; Erren et al. 2008; Robinson et al. 2011). As such, the ability to detect different light intensities and wavelengths varies among individuals and species that inhabit distinct light environments

(Cronin et al. 2014). For example, freshwater Threespine Stickleback (Gasterosteus aculeatus) in clear versus tannin-stained lakes exhibited differences in visual sensitivity that is important for mate selection (Boughman 2001). Studying the spectral qualities of terrestrial light environments has informed our understanding of diurnal visual ecology, and specifically how color vision is integral in detection of food and prey resources, mates and competitors, and potential threats

(Endler 1993). Spatial and temporal niche partitioning have led to the evolution of visual sensory systems in invertebrates, birds, and fishes specialized for performance in different light environments (e.g., habitat or time of day; Cronin et al. 2014). For example, the eyes of

92 nocturnal fish species are generally characterized by a larger lens diameter and rounded pupils that enhance light sensitivity, dim-light image formation, and spatial resolution, but decreased depth of focus and accommodative lens movement (Schmitz & Wainwright 2011). Similar optical traits are observed in nocturnal shorebirds (Rojas et al. 1999a; Thomas et al. 2006). In addition to temporal partitioning of resources, the variables that influence underwater visual environments described above, such as turbidity or dissolved organic matter, can be strong drivers of the evolution of visual systems. Some estuarine fish species, such as the Flathead Grey

Mullet (Mugil cephalus), living in waters with high suspended-sediment loads or lower ambient light levels also exhibit morphological traits (e.g., high rod density in the retina) that support scotopic (dim-light) vision. Euryhaline and diadromous fishes tend to have mixed photopigment systems that allow them to adapt to varying light environments encountered throughout their life histories (Toyama et al. 2008). These evolutionary adaptations in visual physiology are examples of how ambient light can function as a selective pressure for aquatic animals and a few of the basic mechanisms—spectral sensitivity, visual orientation and circadian functioning— through which ALAN is expected to alter natural ecological functioning in estuaries.

Visual Sensitivity

The sensitivity of animal visual systems to the amount and color of light in an environment depends on the type, number, spectral characteristics, and distribution of photoreceptors in the retina (Lamb, Collin & Pugh 2007; Lamb 2013; Le Duc & Schöneberg

2016). Visual photopigments bind with photons via opsin proteins (G-protein coupled receptors) bound to an inactive form of photosensitive vitamin A-based chromophores (Wald 1935). Cone

93 opsins differ in wavelength sensitivity, and multiple classes of cone opsins are required for color vision. The photopigment rhodopsin evolved in a common metazoan ancestor, and photoreceptors have evolved independently along multiple invertebrate and vertebrate lineages

(Yokoyama 2008). Aquatic invertebrates exhibit a remarkable diversity of photoreceptors, deriving from ciliary (e.g., polychaete tubeworms), cnidarian (e.g., cephalopods, corals), rhabdomeric (e.g., arthropods, molluscs), and Go/RGR (e.g., scallops) opsins that vary in function (Shichida & Matsuyama 2009). Molluscs, arthropods, and vertebrates possess rhabdomeric melanopsins, which support circadian rhythms, pupillary reflex, and other non- image forming functions (Shichida & Matsuyama 2009). Rhabdomeric Gq opsins allow for color vision in arthropods (Koyanagi et al. 2008). Vertebrates possess two kinds of ciliary photoreceptors: rods (Rh1) are responsible for scotopic vision, and cones (long wavelength sensitive, LWS; short wavelength sensitive, SWS1, SWS2; and, Rhodopsin, Rh2) provide for color discrimination and visual acuity, or photopic vision. Other visual opsins in vertebrates include the melanopsins and opsins of the pineal subfamily (governed by PARA, PARE, and PIN genes). While humans are limited to three cone receptors for color vision (e.g. red (LWS), green

(Rh2), and blue (SWS), birds and shallow-water fishes have retained all four classes of cone visual pigments for tetrachromacy. Sabbah et al. (2013) suggest that this allows fishes to efficiently process signals in the higher spectral complexity of underwater light environments.

Nocturnality in birds is associated with rod-dominated (80-90%) retinas compared to diurnal species (20-30%; Le Duc & Schöneberg 2016). A similar retinal composition is present in waterbirds such as Adelie (Pygoscelis adeliae) and Emperor (Aptenodytes forsteri) penguins that have adapted to seasonal light cycles (Le Duc & Schöneberg 2016).

The ability to tune photoreceptors either by altering the molecular configuration of opsins or expression of different opsin genes allows aquatic animals to be more sensitive to specific wavelengths of light. Spectral tuning specifically refers to plastic or evolutionary change in peak sensitivities of visual pigments in response to the photic environment (Carleton 2009). Plasticity in opsin gene expression allows for tuning of visual pigments to different light environments

(Viets, Eldred & Johnston 2016). Evolutionary shifts in mammals and birds from nocturnal toward diurnal habits led to a loss of sensitivity to UV irradiation (Hunt et al. 2009) to longer wavelengths that correspond with crepuscular light in forests (Endler 1993). These shifts were likely adaptive in preventing retinal damage from UV exposure. In contrast, the SWS1 in gulls

(Laridae) are tuned to UV (Hastad, Partridge & Odeen 2009). Fluidity in opsin gene expression has led to a range of sensitivities across taxa, yet certain patterns have emerged for organisms that live in aquatic environments.

Blue light (~480 nm) plays a primary role in non-image-forming photoreception in marine and terrestrial vertebrates as the dominant light available in deeper water (λmax, 470-490 nm; Lythgoe 1979) and in terrestrial environments at crepuscular intervals (λmax, 450-500 nm;

Munz and McFarland 1973; Endler 1993). Circadian rhythms in aquatic organisms are thought to have coevolved with blue-light photoreception (Erren et al. 2008). For example, the blue- sensitive pigment PIN expressed in the pineal gland of birds has a role in controlling avian biorhythms. Other cases of spectral tuning have been linked to aquatic environments. For instance, expression of the RH1 opsin has undergone spectral tuning related to water turbidity in marine mammals (Borges et al. 2015). Visual specializations (i.e., opsin gene expression and spectral tuning) associated with habitat and temporal niches are expected to result in species-

95 specific responses to common artificial light spectra (Fig. 4.4). However, blue emissions, which are included in some broad-spectrum artificial lights, may influence circadian functioning in many aquatic and terrestrial organisms.

Artificial lights can emit light in narrow or broad-spectrum bands, the latter more closely mimicking natural light. Davies et al. (2013) compared low- and high-pressure sodium, light- emitting diode, and metal halide lamps to determine potential sensitivity of arachnids, birds, insects, mammals, and reptiles to these different light spectra. Broader-spectrum emissions (e.g., high-pressure sodium, light-emitting diode, and metal halide lamps) are expected to have profound ecological effects because more species can perceive this spectral range. Few comparisons of spectral perception to natural or artificial light have been drawn among riparian, freshwater, and marine taxa (Nightingale, Longcore & Simenstad 2006; Toyama et al. 2008).

Building upon this approach would support theoretical understanding of physiological and behavioral responses to artificial lightscapes.

Visual orientation: polarotaxis

Polarotaxis is the ability in some animals to orient based on the angle of the sun’s rays.

Polarization sensitivity or adaptive camouflage in polarized habitats are understudied organismal responses that can have consequences for individual fitness and predator-prey interactions. For instance, polarized light can provide cues for spatial navigation and habitat selection important to aquatic and terrestrial insects (Boda et al. 2014; Perkin, Holker & Tockner 2014), fishes

(Hawryshyn 2010; Kamermans & Hawryshyn 2011; Pignatelli et al. 2011), birds (Muheim

2011), and bats (Greif et al. 2014) in those animals sensitive to polarized light. Polarization

96 detection depends on the presence of a pigment that is sensitive to either UV or short wavelengths (e.g. less than 400nm). The freshwater crustacean Daphnia pulex exhibits sensitivity to polarized light although this ability is lacking in its congeners (Flamarique &

Browman 2000), indicating that polarotaxis can be species-specific in aquatic macroinvertebrates. Polarization sensitivity can be especially important for aquatic insects active during crepuscular light intervals (Bernath, Gal & Horvath 2004) or inhabiting environments characterized by high UV (<360 nm) such as clear, oceanic waters or yellow-green (550 nm) light (Schwind 1995), such as waters with dense phytoplankton growth. During oviposition, adult aquatic insects returning to water for oviposition detect the horizontal polarization of water-reflected light but instead are often attracted to urban light sources and horizontally- polarizing non-water surfaces (Robertson et al. 2010). Polarized light created by reflective non- water surfaces (e.g., asphalt, glass) can create ‘ecological traps’ for polarotactic insects (Horváth et al. 2009; Boda et al. 2014).

Polarization sensitivity can allow predators to perceive prey in higher contrast in polarized habitats (Shashar et al. 2000). Some open-ocean fishes have modified reflective platelets that support polarocrypsis from predators (Brady et al. 2015). Polarization sensitivity has also been studied relative to habitat orientation by estuarine fishes – primarily salmonids and cyprinids. Species with specialized ultraviolet-sensitive SWS1 cone photoreceptors can detect cues primarily from celestial polarization that help in mapping natal stream location (Hawryshyn

2010). As migratory Pacific salmonids adapt from living in freshwater to saltwater (i.e., during smoltification), UV-sensitive cones undergo apoptosis to prevent retinal damage in clearer coastal environments, but are later regenerated as mature adults migrate back upstream to natal

97 habitat (Quinn 2004). Unmanaged exposure to ALAN along precarious migratory routes through estuaries (Stich et al. 2015) may potentially impede navigation that leads to successful recruitment of salmonids to natal upstream habitats (Mueller & Simmons 2008).

Circadian activity levels

Diel and seasonal patterns of natural light structure the internal circadian rhythms that drive many physiological processes and behaviors of visual organisms. For example, polarotactic detectability (governed by solar elevation) of bright and dark waters during mid-morning, early afternoon, and dusk make these optimal periods for dispersal by polarotactic aquatic insects

(Csabai et al. 2006). Duration of these optimal dispersal periods varies latitudinally. For instance, changes to the ‘polarization sun dial’ (sensu Csabai et al. 2006) for aquatic insects may have a stronger impact in tropical systems where morning and evening periods are shorter.

Natural light cycles also regulate physiological signals (e.g., melatonin levels) that affect migratory and foraging behavior (McFarland, Ogden & Lythgoe 1979; Riley et al. 2012). A breakdown of natural light cycles disrupted by ALAN may lead to shifts in temporal niche partitioning, stemming from changes in foraging and predator-avoidance behaviors (McCauley et al. 2012). Variability in light intensity throughout the lunar cycle directly influences foraging by seabirds. During a full moon (Fig. 4.5), some nocturnal species forage for shorter intervals whereas activity is extended for some diurnal species (Tarlow et al. 2003; Navara & Nelson

2007).

Shorebirds and wading birds are important tertiary consumers in estuaries. Common species – such as herons, egrets, sandpipers, and plovers – vary in their visual capabilities,

98 feeding strategies (visual or tactile), and diel niches. Wading birds have scotopic vision capabilities but those with weaker photopic acuity (e.g., night herons and spoonbills) more often forage at night. Among the shorebirds, plovers (Pluvialis and Charadrius) and sandpipers

(Scolopacidae) are cathemeral, active for periods during both day and night. Aerial insectivores such as pratincoles (Glareola) and swallows (Hirundinidae) are particularly active during crespuscular periods. Painted-snipes (Rostratulidae), Stone-curlews (Burhinidae), and the Crab

Plover (Dromas ardeola) regularly forage at night. Night-feeding can help meet energetic requirements and minimize encounters with diurnal predators (reviewed in McNeil & Rodríguez

1996). ALAN may directly impact visual-feeding shorebirds by enabling longer foraging intervals. Coastal and estuarine birds must also contend with the tide and its effects on foraging habitat and prey availability. For instance, foraging activity of the tactile-feeding shorebirds

(e.g., Scolopacidae) follows the tides (which correlates to foraging habitat and prey availability) regardless of time of day (Le Duc & Schöneberg 2016). Therefore, ALAN may also indirectly impact nocturnal-feeding shorebirds by augmenting competition with diurnal species. Although here we outline potential effects of ALAN on diel activity patterns, light also plays a key role in physiological and molecular pathways that influence seasonal breeding, migratory instincts, and orientation (reviewed in Dominoni 2015).

c) Scaling up from the Individual

From a visual perspective, ALAN increases the intensity (measured in lux or irradiance) of nocturnal light environments and shifts the spectral distribution to longer wavelengths

(Johnsen et al. 2006; Cronin & Marshall 2011). ALAN can illuminate the nocturnal environment

99 to intensities ranging between that of nautical twilight to a full moon on clear nights (Kyba et al.

2011; Gaston et al. 2013). Without moonlight, ALAN can exhibit peak fluxes at 560, 590, 630, and 685 nm (Johnsen et al. 2006). Given trends of intensifying artificial lightscapes in coastal regions, future research should continue to quantify ALAN effects on individuals and species interactions in estuaries. Individual and species-specific responses to light regimes at night can vary widely. While certain nocturnal species are less active during bright nights in response to elevated perceived predation risk (e.g., estuarine fishes observed by Becker et al. 2013) and lower prey availability (e.g., seabirds observed by Mougeot & Bretagnolle 2000), others including bats (Rydell 1992) and wading birds (Rojas et al. 1999a; Santos et al. 2010) have been observed to increase foraging activity under artificial lighting. Interspecific comparisons provide valuable insight on foraging and risk trade-offs related to nocturnal activity under varying lightscapes (Polak et al. 2011; Rotics, Dayan & Kronfeld-Schor 2011; Azam et al. 2015).

Community and ecosystem responses to ALAN

ALAN has the potential to influence evolutionary and population trajectories and behavioral patterns mediated by visual sensitivity, habitat selection and orientation, and circadian activity rhythms of aquatic organisms. Much of the current literature on ALAN provides insight into individual and species-level responses. However, because, natural light drives trophic dynamics (e.g., primary production) and food-web interactions in terrestrial and aquatic ecosystems, ALAN has the potential to alter community functioning via its influence on trophic interactions. These shifts have implications for estuarine food webs and ecosystem processes.

100

Estuaries represent vital habitat for aquatic and semi-aquatic communities of organisms by providing structurally-complex habitat, refuge, and food resources. Gradients in salinity, turbidity (Benfield & Minello 1996), and habitat structure (Alvarez et al. 2013) shape estuarine community assembly and functioning. ALAN is expected to alter community dynamics in estuaries where organisms face high levels of competition and predation for resources. ALAN- altered communities may have implications for food web structure and ecosystem functioning.

For instance, Bolton et al. (2017) observed increased predatory fish behavior under ALAN in a

Sydney, Australia harbor, which in turn was associated with shifts in sessile invertebrate assemblage structure. To address potential impacts of ALAN on estuarine community dynamics and ecosystem functioning, we provide examples of how individual- and population-level responses to ALAN may mediate community- and ecosystem-level effects. Specifically, we discuss how day-night communities, dispersal processes, and food-web dynamics are dependent on spatial and temporal patterns of light.

a) Mechanisms reorganizing community structure

Temporal niche partitioning

Natural light cycles structure temporal niche partitioning in ecological communities, driven by energetic and risk trade-offs at different times of day. The temporal predictability of resource distribution and predation risk has led to ecomorphological adaptations for diurnal, nocturnal, or cathemeral activity. Nearly 15% of all described fishes feed, spawn, or migrate nocturnally during at least one life-history stage (Holker et al. 2010c). Diurnal disposition has led to the evolution of greater morphological, optical, and trophic diversity. Although ocular

101 adaptations allow for optimal visual performance at certain diel intervals, the strength of competitive interactions also drives optimal foraging strategies (Brown, Laundre & Gurung

1999). The constriction or expansion of diel niches can largely depend on shifts in predator communities (McCauley et al. 2012) and contribute to the avoidance of competitive exclusion and may mediate coexistence among predators, prey, and competitor species (Kronfeld-Schor &

Dayan 2003). Diel niche partitioning based on differences in photopic vision capabilities has been suggested as a mechanism that reduces competition among diurnal (e.g., Great Egret, Ardea alba), cathemeral (e.g., Roseate Spoonbill, Platalea ajaja), and nocturnal (e.g., Black-crown

Night Heron, Nycticorax nycticorax) wading birds (Britto & Bugoni 2015).

ALAN might be expected to alter competitive interactions via changes in day-night activity of predators, prey, and competitors in estuarine communities (Fig. 4.6). For example, it is known that ALAN disrupts orientation of sea turtle hatchlings, yet it may also increase hatchling predation by birds, reptiles, and mammals as they disperse from nest to sea. Sea-turtle nesting beaches are among the few, if not the only, nearshore aquatic habitats widely managed for ALAN intensity and spectra (Butler 1998). However, early studies on light pollution gleaned some understanding of its effects on community interactions on coastal beach habitats.

Researchers observed a limited foraging (i.e., number of seed harvested) and reduced patch preference for ALAN-treated (incandescent and LPS lighting) habitat by a nocturnal Beach

Mouse (Peromyscus polionotus leucocephalus) (Bird, Branch & Miller 2004). Later studies attributed this response to a heightened perceived risk of predation (Falcy & Danielson 2013;

Wilkinson, Branch & Miller 2013). By influencing risk trade-offs for individuals, ALAN may transform intraguild competitive interactions. Diurnal mice congeners in this habitat were not

102 more active under ALAN conditions (Rotics, Dayan & Kronfeld-Schor 2011), implying that both species may have faced increased daytime competition for resources. However, facilitative nocturnal foraging by diurnal consumers has been observed, as with wading birds in estuarine wintering habitat (Santos et al. 2010; Dwyer et al. 2013). In some scenarios, inducing these behavioral shifts can represent a management approach. For example, exposing the nocturnal

Signal Crayfish (Pacifastacus leniusculus) to high-pressure sodium lighting at night inhibited activity and competitive interactions with native crayfish species (Thomas et al. 2016). However, chronic exposure to ALAN may lead to habituation and thresholds (intensity or duration) at which behavioral responses depend on physiological sensitivity and predation risk regime (e.g.,

McCauley et al. 2012).

Natural lighting regimes have been shown to drive activity and distribution of different size and trophic classes in estuarine fish communities, with implications for ALAN effects. In estuarine mangroves, for instance, nocturnal fish assemblages are often composed of planktivores and piscivores, whereas detritivores have been shown to dominate diurnal communities (Ley & Halliday 2007). In an estuary of South Africa, Becker et al. (2013) used acoustical survey methods to record the effect of a sodium-vapor floodlight on the abundance and behavior of different size-classes in the fish community. Observations included a shift from exploratory behavior or foraging activity to more vigilant behavior by smaller fishes when exposed to nocturnal lighting. Change in species-specific predatory behavior altered community dynamics by reorganizing size structure of the nocturnal fish assemblage.

Terrestrial and aquatic species that rely heavily on prey with phototaxic behaviors (e.g., aquatic insects) often demonstrate facultative nocturnal activity to exploit ALAN-related

103 foraging opportunities. An increase in predator-prey interactions mediated by ALAN could potentially alter optimal foraging strategies. Because many swimming prey and invertebrates in muddy estuarine habitats are closer to water and sediment surfaces at dusk and night, visual predators may experience more efficient foraging in estuarine habitats under artificial lightscapes. Visually-feeding wading birds that forage opportunistically under artificial light are thought to exert low predation pressure (i.e., lower catch success) compared to daytime foraging

(Santos et al. 2010; Dwyer et al. 2013), yet nocturnal activity may have non-consumptive indirect effects. Yeager et al. (2016) observed that the simulated presence of a wading bird tripled foraging interactions between mangrove crabs (Aratus pisonii) and a euryhaline mesopredator, the Gray Snapper (Lutjanus griseus; λmax = 513 and 560 nm, McComb et al.

2013). As crabs move to lower root habitat structure to avoid detection from wading birds, they may become prey to visual fish predators. The strength of this and similar interactions should vary depending on the spatial and temporal distribution of prey and predators (Alvarez et al.

2013) throughout an estuary within implications on top-down trophic effects.

Dispersal of estuarine animals

Dispersal of riverine and marine invertebrates is a vital mechanism driving population dynamics and habitat connectivity in estuaries (Chew et al. 2015). By affecting aerial dispersal of adult aquatic insects (Horváth et al. 2009; Boda et al. 2014), ALAN can alter insect recruitment and prey availability for higher consumers (Horváth et al. 2009; Robertson et al.

2010). The potential consequences of ALAN on aquatic dispersal dynamics are less clear (i.e., organisms moving/dispersing through the water). Dispersal of planktonic invertebrates and

104 larvae are passive processes driven by the physical movement of water (i.e., downstream, tidal, and surface current flow; reviewed in Norcross & Shaw 1984). In estuaries, these processes are mediated by tidal dynamics (i.e., timing, stratification, and mixing). Passive dispersal and active migrations by zooplankton and mobile consumers are also strongly linked with seasonal and diel light cycles (Brittain & Eikeland 1988; Palmer, Allan & Butman 1996). For example, larval invertebrate drift in freshwater systems has been shown to peak during nocturnal periods, which aligns with lower predation risk (Flecker 1992; Miyasaka & Nakano 2001; Hernandez &

Peckarsky 2014). If predator activity increases under ALAN, nocturnal drift communities may face elevated perceived risk or predation. Shifts in nocturnal foraging and drift behaviors vary based on the type of predator (Peckarsky & McIntosh 1998). Although nocturnal activity generally persists even if predators are experimentally or naturally excluded (Flecker 1992;

Hampton & Duggan 2003), foraging and drift behaviors may vary based on the type of predator

(Peckarsky & McIntosh 1998). Nocturnal drift activity by stream invertebrates reduced following the addition of ALAN (Henn et al. 2014; Manfrin 2017), suggesting that ALAN can enhance, or exert a stronger effect than, predation risk on nighttime foraging and dispersal.

Zooplankton exhibit passive and active migration behavior that varies across tidal, diel, and lunar cycles. For example, in tropical estuaries, diel vertical migrations are induced by light and tidal patterns; these dynamics can differ among species based on salinity tolerance (Chew et al. 2015). Species adapted to lower salinity conditions ascended for nocturnal flood tides and descended for diurnal ebb tides whereas euryhaline and stenohaline species did just the opposite to maintain an optimal position in the estuary. In a North Wales estuary, drift macroinvertebrates of a tidal freshwater area were also spatially distributed based on their salinity tolerance.

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Freshwater chironomids, caddisflies, stoneflies, and mayflies drifted downstream whereas marine copepods and oligochaetes often exhibited ‘reverse’ drift as the flood tide moved them upstream (Williams & Williams 1998b). If the onset of drift behaviors is altered, estuarine invertebrates could experience physiological stressors that influence fitness. Light is often the primary factor structuring migrations for nocturnal and euryhaline species (Flecker 1992).

Notably, the synchronization of vertical positioning within the water column and tidal regime determines upstream-downstream movement and retention within the estuary (Chew et al. 2015).

Even minor changes in light can influence migration patterns (Haney 1993; Ringelberg 1999).

Spill-over of ALAN into estuarine habitats may desynchronize this process with consequences for recruitment, community composition, and adjacent trophic levels. For example, if zooplankton are largely confined by ALAN to deeper depths than under natural lighting conditions, prey availability to surface planktivores (e.g., nocturnally-adapted juvenile and other planktivorous fishes) is likely to be reduced.

Few studies have addressed how an ALAN-shifted nocturnal plankton community may affect higher trophic-level consumers. Artificial light-addition treatments have demonstrated potential shifts in the abundance and community structure of drift invertebrates (Henn et al.

2014). In freshwater streams, experimental light treatments (high-pressure sodium lamps) reduced the density of night-time drift by about 50% but did not affect drift-foraging Cutthroat

Trout (Oncorhynchus clarkii) growth rates (Perkin et al. 2014b). Despite impacts on invertebrate prey during this short-term study, predator responses may take longer to detect than those of primary consumers. In the short-term, ALAN can extend foraging conditions for visual predators, allowing them to compensate with alternative resources.

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Natural light regulates dispersal, diel migrations, and circadian behaviors of many coastal and estuarine fishes throughout their ontogeny (Bradbury et al. 2006; Naylor 2006; Epifanio &

Cohen 2016). For example, nocturnal movements by adult and juvenile grunts (Haemulidae) to foraging habitats are synchronized by size group and cued by changes in underwater light level

(McFarland, Ogden & Lythgoe 1979). Similarly, juvenile sockeye salmon (Onchorhyncus nerka) exhibit diel vertical migration to maintain position in optimal light environment that minimizes their exposure to predation (Scheuerell & Schindler 2003). Artificial light has the potential to mask circadian light cues and thus disrupt the adaptive significance of these behaviors (Kurvers

& Holker 2015). For example, elevated ALAN intensities in mesocosm trials led to a delay and desynchronization of fry dispersal of Atlantic Salmon (Salmo salar) (Riley et al. 2012; Riley et al. 2015). In natural systems, a disturbance of temporal movement patterns of fry would have implications for larval and juvenile survivorship due to increased risk of predation (Stich et al.

2015).

Seasonal and diel migrations by consumers enhance estuarine connectivity (Nagelkerken

2009; Rosenblatt et al. 2013; Sheaves et al. 2015). Although there is inter- and intraspecific variability in diel foraging among estuarine fishes (Ramirez-Martinez, Castellanos-Galindo &

Krumme 2016), many species (e.g., Lutjanidae) seek refuge in tidal mangroves during the day and migrate at night to feed in soft-bottom habitats. Nocturnal foraging by reef-dwelling grunts

(Haemulidae) is temporally-partitioned; migration from resting to foraging habitats occurs chronologically for groups at different stages of ocular development (McFarland, Ogden &

Lythgoe 1979; Robinson et al. 2011). The spectral composition of light changes rapidly during the dusk period, requiring the eye to adjust to a blue-green dominated environment. During dusk,

107 retinomotor movement (specifically the shift from stimulation of yellow-orange cones to predominantly blue-green cones in the retina) affects the timing and duration of foraging migrations by grunts traveling among coastal reef, mangrove, and seagrass habitats (McFarland,

Ogden & Lythgoe 1979; McFarland & Wahl 1996). Disruption or delay of visual adjustment under ALAN conditions may inhibit foraging activity and increase predation risk at diel or ontogenetic intervals of poor visual acuity.

b) Linking community responses to trophic networks and ecosystem processes

ALAN-induced impacts on individuals-to-communities will play out at the ecosystem scale, affecting critical functions (Fig. 4.7). Energy flow describes an ecosystem process by which nutrient are transferred within and across ecosystem boundaries, mediated by abiotic

(fluvial and tidal flow) and biotic (movement of consumers) vectors. Food webs, which reflect energy pathways, integrate individual, species, and community responses to environmental change (Thompson et al. 2012) and provide a framework to evaluate the effects of ALAN on ecosystem function.

Quantitative measures of food-web structure—such as food chain length (FCL), interaction strengths, and connectance— describe the topology of trophic networks and functional ecosystem properties. For example, the number of transfers of energy from basal organisms to apex predator (i.e., FCL) can relate with biodiversity to affect secondary production and biomass accumulation. For instance, in the seagrass communities of the York River estuary, predators regulate the grazer community and thus promote algal production (Emmett Duffy, Paul

Richardson & France 2005).

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In estuaries, we expect that increasing levels of ALAN will alter how energy is transferred through aquatic and terrestrial food webs. For example, deciduous trees under ALAN can exhibit a delay in leaf fall (Bennie et al. 2016) thus altering the magnitude and timing of nutrient inputs in the form of leaf detritus into aquatic habitats. This will also affect the relative openness of the canopy that determines phytoplankton production (via light intensity). Under greater light exposure, opportunistic phytoplankton may initially become more important as a basal resource to aquatic primary consumers. While ALAN might facilitate enhanced aquatic production in the short-term, long-term exposure may have different consequences.

Aquatic-terrestrial trophic linkages

Food-web interactions across the aquatic-terrestrial boundary represent a high level of system integration that reflects multiple structural and functional properties of both ecosystems.

In freshwater systems, emergent aquatic insects have been shown to be a critical nutritional subsidy for a suite of riparian consumers including arthropods, birds, mammals, and reptiles

(Baxter, Fausch & Carl Saunders 2005; Paetzold, Schubert & Tockner 2005; Fukui et al. 2006).

For example, emerging aquatic insects represented 79% of the diet of riparian horizontal web- building spiders (Tetragnathidae) and 33% of the diet of vertical web-building spiders (Araneus and Argiope) (Akamatsu, Toda & Okino 2004). The importance of aquatic primary production to riparian spiders and other terrestrial consumers via emerging insects in estuaries has been relatively underestimated (but see Chapter 3). Riparian birds also can be highly dependent on emerging insects, either directly as a food source (Gray 1993) or indirectly through other prey that feed on emerging insects (e.g., spiders). Thus, ALAN impacts on organismal behavior and

109 physiology, or on key ecosystem processes such as aquatic (or terrestrial) primary productivity is expected to propagate through linked aquatic-terrestrial food webs with cascading implications.

Conclusions

ALAN is prevalent throughout urban and developing coastal areas. Marine protected areas (MPAs) of the Gulf of Mexico and Caribbean, Atlantic Mediterranean, eastern coast of

South America, and Australia receive extensive amounts of ALAN (Davies et al. 2016). Trends of increasing light intensity have been linked with coastal development (Davies et al. 2014), where there are opportunities to implement policy and technologies that minimize ecological impacts (Gaston et al. 2012). Ecologically- and economically-important estuarine habitats will receive more light pollution in the coming decades. Mangrove forests are already highly- impacted biomes in subtropical and tropical protected areas (Aubrecht, Jaiteh & de Sherbinin

2010). When compounded by stressors related to climate change (e.g., rising sea level, changing salinity regime, freshwater inputs), we must anticipate changes to estuarine ecosystem functioning by understanding how ALAN alters ecosystem processes. Despite the presence of

ALAN in MPAs since the early 1990’s (Davies et al. 2016), research is only beginning to hone understanding of the scale of this environmental shift and its consequences on ecological processes in coastal ecosystems.

Based on our review and synthesis of documented ALAN impacts, we propose the following key research avenues:

1. Characterizing natural diel and seasonal light changes in estuarine habitats (sensu

Veilleux & Cummings 2012) would help predict ALAN impacts and inform management

of artificial light use (intensity, spectral composition, and timing). 110

2. Biological responses may vary with the magnitude, duration, and constancy of exposure

to ALAN. Future research should work to understand these across aquatic and terrestrial

systems with long-term controlled studies in aquatic, terrestrial, and coupled systems.

3. Future research should draw connections among known effects of natural and artificial

light on animal physiology and community ecology to evaluate potential effects on

estuarine processes and functional responses such as the multiple dimensions of estuarine

connectivity and productivity.

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Table 4.1 Published studies on community- (C) and ecosystem-level (E) effects of ALAN research on aquatic-associated organisms, excluding reviews by Gaston et al. (2012b, 2013), Longcore and Rich (2004), etc.

Latitudinal Publication Ecosystem Level Geographic region Key findings zone Underwater ALAN-addition treatments led to reduced exploratory activity and increased Becker et al. (2013) Estuary C South Africa Subtropical shoaling by fish of smaller size and increased abundance of both large and small fish LPS and incandescent light-addition treatments led to a reduction in use of ALAN habitat patches and Bird et al. (2004) Beach C Florida, U.S.A. Subtropical reduced foraging effort (number of seeds harvested) by nocturnal beach mice ALAN-addition treatments led to higher fish Bolton et al. (2017) Marine C Sydney, Australia Subtropical predation and altered invertebrate sessile assemblage structure. HPS light-addition treatments correlated with Davies et al. (2012) Grassland C Cornwall, U.K. Temperate increased abundances of predator and scavenging taxa A common shorebird (Tringa totanus) shifted from tactile- to sight-based foraging tactics under high- Dwyer et al. (2013) Estuary C Scotland, U.K. Temperate illumination (natural and ALAN combined) with implication on foraging effort and predator-prey interactions. ALAN-addition limited the vertical distribution of a benthic-pelagic crustaceans (Mysidaceae) during Freshwater Gal et al. (1999) C New York, U.S.A. Temperate day and night to varying degrees that depended on lake the season; Mysid distribution in was more constrained in August and October than in May. ALAN-addition treatments altered periphyton communities during early developmental stages, Freshwater Trentino Province, Grubisic et al. (2017) C Temperate by decreasing overall biomass by 43 to 57% stream Italy (season-dependent) and altering the assemblage structure. 112

ALAN-addition treatments led to 37% decrease in Freshwater Henn et al. (2014) C Texas, U.S.A. Subtropical aquatic invertebrate nocturnal drift abundance stream (most notably for Baetidae and Simuliidae) Short-term ALAN-addition treatments disrupted seasonal changes in microbial community Holker et al. (2015) Freshwater C/E Germany Temperate composition and nocturnal community respiration. Long-term ALAN-addition treatments resulted in positive net ecosystem production. This research group established a large-scale experimental setup to examine short- and long- term effects of ALAN on behavior, species Holzhauer et al. (2015) Grassland C Germany Temperate interactions, physiology, and species composition of grassland communities. One short-term study showed dramatic increase in insect and spider abundances under ALAN. Insect abundance and insectivorous bat activity was higher at streetlights than forested habitats Jung & Kalko (2010) Forest/Urban C Panama Tropical during the rainy season. Species were partitioned in microhabitat use of different streetlight types (bluish, yellow, and orange).

Freshwater Friesland province, ALAN-addition treatments altered flight pattern and Kuijper et al. (2008) C Temperate pond Netherlands reduced foraging effort in illuminated patches although insect abundance increased In a controlled feeding study, bats chose to feed in darker compartments compared to dimly- Lewanzik & Voight Terrestrial E Costa Rica Tropical illuminated habitats. Infructescences were less (2014) likely to be harvested by frugivorous bats when illuminated by a street lamp. ALAN-addition treatments led to reductions in aquatic insect emergence, abundance of flying Freshwater insects and nocturnal ground beetles as well as Manfrin et al. (2017) C/E Germany Temperate stream increases in activity by ground-dwelling predators with implications for aquatic-to-terrestrial subsidy flux.

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ALAN-addition treatments were correlated to reduced densities of riparian spiders, as well as Meyer & Sullivan Freshwater C Ohio, U.S.A. Temperate family richness and body size of emerging aquatic (2013) insects, and larger body size of terrestrial arthropods. LED and halogen light traps attracted higher amphipod abundance for all species. LED traps Navarro-Barranco & Marine C Great Barrier Reef Subtropical had a stronger effect on emergent fauna, Hughes (2015) increasing abundances of Guernea spp. by up to 80 orders of magnitude.

HPS light-addition attracted more insects Perkin et al. (2014) Freshwater C Germany Temperate (especially aquatic insects) by at least 27 orders of magnitude Short-term light-addition treatments in forested headwater streams led to reduction in invertebrate Perkin et al. (2014a) Freshwater C British Columbia Temperate drift abundance by 50% but did not affect other trophic levels. A reflective building created a novel predator-prey interaction in which birds consistently fed on Robertson et al. (2010) Freshwater C Hungary Temperate polarotactic caddisflies that were attracted to the polarized surfaces.

Rydell (1992) Forest/Urban C Sweden Temperate Bats that fed at streetlights to which insects were attracted exhbited higher energy intake.

Santos et al. (2010) Estuary C Portugal Temperate ALAN led to higher prey intake for wading birds. ALAN-addition treatments led to reduced activity and interactions for an invasive, non-native Thomas et al. (2016) Freshwater C South Wales, U.K. Temperate crayfish and thus presents a tool for managing negative effects of this invasive species.

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Figure 4.1 Image of southeastern United States at night taken by the Expedition 30 crew (Astronaut photograph ISS030-E-55569;

Source: earthobservatory.nasa.gov).

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Figure 4.2 Conceptual map of individual- to ecosystem-level responses to ALAN in estuarine systems 116

Figure 4.3 [Textbox 1] Estuarine fishes are known to have rhodopsin and porphyropsin photopigments, which vary in their spectral absorption. Light-sensitive photopigments are composed of opsin bound to an A1 chromophore to make rhodopsin (λmax = 500 nm) in marine fish, or bound to an A2 chromophore to make porphyropsin (λmax = 525 nm) in freshwater fish (Toyama et al. 2008). Mixed photopigment systems that express A1 and A2 photopigments are common in freshwater, diadromous, and certain coastal-marine fishes that adapt to varying light environments throughout their life history. In these species, ratios of porphyropsin and rhodopsin are generally dependent on ambient light and spawning habitat (Toyama et al. 2008). Euryhaline fishes like (A) the Gray Snapper

(Lutjanus griseus) and (B) Common Snook (Centropomus undecimalis) exhibit greater sensitivity toward longer or shorter wavelengths along the freshwater-marine gradient of an estuary. The changing proportion of these photoreceptors allows diadromous fishes to adapt to their environment during ontogenetic changes between marine and freshwater habitats (Allen & McFarland 1973;

Robinson et al. 2011).

ALAN implications: Artificial light spectra will theoretically stimulate the photopigments of freshwater, euryhaline, and marine fishes to varying degrees. Marine fishes are expected to be especially sensitive to light-emitting diodes (LEDs; see Figure 4.4 for details on intensity and wavelength), which emit high-irradiance spectra that can be readily absorbed by rhodopsin. Although low-pressure sodium (LPS) lamps are commonly used to minimize ecological impacts (e.g., disorientation of sea turtle hatchlings), certain freshwater and euryhaline fishes would still perceive this narrow-spectrum lighting (Bird, Branch & Miller 2004; Davies, Bennie &

117

Gaston 2012). Thus, LEDs might be expected to elicit behaviors typical during daylight in some fishes. For example, Becker and colleagues (2013) observed that large, predatory fish were more abundant on nights when the light was turned on, mimicking diurnal predatory activity. Whether such short-term individual behavioral responses would be favored over time would likely depend on the consistency of exposure to ALAN and the concomitant behavioral responses of their prey.

118

119

Figure 4.4 Emission spectra of four artificial lighting types including metal halide, light-emitting diode, halogen, and high-pressure sodium lamps (spectra from Lamp Spectral Power Distribution Database 2017). 120

Figure 4.5 Emission spectra of sun (Source: LSPDD) and lunar light (Source: Moon-Olino.org).

121

Figure 4.6 [Textbox 2] Wading birds (i.e., shorebirds and long-legged waders) are both permanent and transitory residents in temperate and tropical estuaries. Waders are highly-effective visual and tactile foragers (with color vision mediated by four cone classes) and often exhibit sensitivity to UV light (Hart 2001a). There are important differences in the visual morphology of wading birds associated with foraging tactic and time of day (McNeil, Drapeau & Gosscustard 1992; Thomas et al. 2006). Visual foragers that feed both day and night (e.g., plovers and stilts) have a higher density of retinal receptors compared to tactile-feeding sandpipers

(Rojas de Azuaje et al. 1993, 1999). More specifically, those that forage at crespuscular or nocturnal periods have greater rod densities and rod:cone ratios (de Azuaje, Tai & McNeil 1993; Rojas et al. 1999a; Rojas et al. 1999b; McNeil et al. 2004). A visual system most sensitive to wavelengths in which upwelling light from the water is rich and surface reflectance relatively poor (425 to 500 nm for clear blue oceanic water) is best suited for seeing through the water surface (Lythgoe 1968) but that is rarely observed. Birds that look through an aquatic surface to locate prey tend to have relatively high proportion of long-wavelength-sensitive cones and yellow-red ocular filters (Hart 2001a; Hart 2001b).

ALAN implications: Collisions with lighted structures over land (e.g., buildings with reflective surfaces) and at sea (e.g., vessels) represent the most direct impact of ALAN on aquatic birds. These events are especially common in urban areas, posing an additional threat to nocturnal migrants including shorebirds and wading birds that tend to fly at lower elevations (Evans Ogden 1996; Loss et al.

2014; Rodriguez et al. 2017). Indirect effects of ALAN on aquatic birds are less understood but understanding their visual physiology

122 may help glean insight on potential responses. Analysis of spectral sensitivity of 16 avian species to artificial light spectra suggests that, like other visual organisms, they would be most affected by LED lighting and less by LPS and other long-wavelength spectra

(Davies et al. 2013). This analysis was largely based on songbirds and future research should address whether visual sensitivities of aquatic birds may lead to similar or distinct response. One of the most likely responses of wading birds may be an increase in activity throughout the night under ALAN (Gaston et al. 2013), which may exert additional top-down pressures on fish.

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Threat to nocturnal species

Extends foraging intervals Alters predator-prey for diurnal visual predators interactions

Shifts foraging behavior in nocturnal prey species Alters emergent insect communities and subsidies

124

References

(2006) Ecological Consequences of Artificial Night Lighting. Island Press, Washington, D.C.

(2009) Artificial Light in the Environment. The Royal Commission on Environmental Pollution.

(2012) Rookery Bay National Estuarine Research Reserve Management Plan: January 2012- December 2017.

(2017) Fish Base (Internet Web Page. Accessed at: http://www.fishbase.org). (eds R. Froese & D. Pauly).

Aiken, M. & Coyle, F.A. (2000) Habitat distribution, life history and behavior of Tetragnatha spider species in the Great Smoky Mountains National Park. Journal of Arachnology, 28, 97-106.

Akamatsu, F. & Toda, H. (2011a) Aquatic subsidies transport anthropogenic nitrogen to riparian spiders. Environ Pollut, 159, 1390-1397.

Akamatsu, F. & Toda, H. (2011b) Flow regime alters body size but not the use of aquatic subsidies in a riparian predatory arthropod. Ecological Research, 26, 801-808.

Akamatsu, F., Toda, H. & Okino, T. (2004) Food source of riparian spiders analyzed by using stable isotope ratios. Ecological Research, 19, 655-662.

Akamatsu, F., Toda, H. & Okino, T. (2007) Relating body size to the role of aquatic subsidies for the riparian spider Nephila clavata. Ecological research, 22, 831-836.

Alberts, J.M., Sullivan, S.M. & Kautza, A. (2013) Riparian swallows as integrators of landscape change in a multiuse river system: implications for aquatic-to-terrestrial transfers of contaminants. Sci Total Environ, 463-464, 42-50.

Allen, D.M. & McFarland, W.N. (1973) Effect of temperature on rhodopsin/porphyropsin ratios in a fish. Vision Research, 13, 1303-1309.

125

Alvarez, M.F., Montemayor, D.I., Bazterrica, M.C., Addino, M., Fanjul, E., Iribarne, O. & Botto, F. (2013) Interaction strength varies in relation to tidal gradient and spatial heterogeneity in an intertidal Southwest Atlantic estuarine food web. Journal of Experimental Marine Biology and Ecology, 449, 154-164.

Anderson, C. & Cabana, G. (2007) Estimating the trophic position of aquatic consumers in river food webs using stable nitrogen isotopes. Journal of the North American Benthological Society, 26, 273-285.

Aubrecht, C., Elvidge, C.D., Ziskin, D., Rodrigues, P. & Gil, A. (2010) Observing stress of artificial night lighting on marine ecosystems- a remote sensing application study. ISPRS TC VII Symposium (eds W. Wagner & B. Szekely), pp. 41-46. IAPRS, Vienna, Austria.

Aubrecht, C., Jaiteh, M. & de Sherbinin, A. (2010) Global assessment of light pollution impact on protected areas. CIESIN. AIT Working Paper. Palisades, NY, USA: CIESIN and NASA SEDAC, The Earth Institute at Columbia University.

Azam, C., Kerbiriou, C., Vernet, A., Julien, J.F., Bas, Y., Plichard, L., Maratrat, J. & Le Viol, I. (2015) Is part-night lighting an effective measure to limit the impacts of artificial lighting on bats? Global Change Biology, 21, 4333-4341.

Barbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C. & Silliman, B.R. (2011) The value of estuarine and coastal ecosystem services. Ecological Monographs, 81, 169-193.

Bartels, P., Cucherousset, J., Steger, K., Eklov, P., Tranvik, L.J. & Hillebrand, H. (2012) Reciprocal subsidies between freshwater and terrestrial ecosystems structure consumer resource dynamics. Ecology, 93, 1173-1182.

Basset, A., Barbone, E., Elliott, M., Li, B.-L., Jorgensen, S.E., Lucena-Moya, P., Pardo, I. & Mouillot, D. (2013) A unifying approach to understanding transitional waters: Fundamental properties emerging from ecotone ecosystems. Estuarine, Coastal and Shelf Science, 132, 5-16.

Bates, D., Maechler, M., Bolker, B., Walker, S., Christensen, R.H.B. & Singmann, H. (2015) lme4: Linear mixed-effects models using Eigen and S4, 2014. R package version, 1.

Baxter, C.V., Fausch, K.D. & Carl Saunders, W. (2005) Tangled webs: reciprocal flows of invertebrate prey link streams and riparian zones. Freshwater Biology, 50, 201-220. 126

Baxter, C.V., Fausch, K.D. & Saunders, W.C. (2005) Tangled webs: reciprocal flows of invertebrate prey link streams and riparian zones. Freshwater Biology, 50.

Becker, A. & Suthers, I.M. (2014) Predator driven diel variation in abundance and behaviour of fish in deep and shallow habitats of an estuary. Estuarine, Coastal and Shelf Science, 144, 82-88.

Becker, A., Whitfield, A.K., Cowley, P.D., Jarnegren, J. & Naesje, T.F. (2013) Potential effects of artificial light associated with anthropogenic infrastructure on the abundance and foraging behaviour of estuary-associated fishes. Journal of Applied Ecology, 50, 43-50.

Belicka, L.L., Sokol, E.R., Hoch, J.M., Jaffe, R. & Trexler, J.C. (2012) A Molecular and Stable Isotopic Approach to Investigate Algal and Detrital Energy Pathways in a Freshwater Marsh. Wetlands, 32, 531-542.

Bell, J.R., Bohan, D.A., Shaw, E.M. & Weyman, G.S. (2005) Ballooning dispersal using silk: world fauna, phylogenies, genetics and models. Bulletin of entomological research, 95, 69-114.

Bemis, B.E. & Kendall, C. (2004) Isotopic views of food web structure in the Florida Everglades. U.S. Geological Service.

Benfield, M.C. & Minello, T.J. (1996) Relative effects of turbidity and light intensity on reactive distance and feeding of an estuarine fish. Environmental Biology of Fishes, 46, 211-216.

Bennie, J., Davies, T.W., Cruse, D. & Gaston, K.J. (2016) Ecological effects of artificial light at night on wild plants. Journal of Ecology, 104, 611-620.

Bennie, J., Duffy, J.P., Davies, T.W., Correa-Cano, M.E. & Gaston, K.J. (2015) Global Trends in Exposure to Light Pollution in Natural Terrestrial Ecosystems. Remote Sensing, 7, 2715- 2730.

Bernath, B., Gal, J. & Horvath, G. (2004) Why is it worth flying at dusk for aquatic insects? Polarotactic water detection is easiest at low solar elevations. Journal of Experimental Biology, 207, 755-765.

127

Bierschenk, A.M. (2015) Influence of catchment land-use intensity on macroinvertebrates and ecosystem function along a freshwater-marine continuum.

Bird, B.L., Branch, L.C. & Miller, D.L. (2004) Effects of coastal lighting on foraging behaviorof beach mice. Conservation Biology, 18, 1435-1439.

Blamires, S.J., Chao, Y.C., Liao, C.P. & Tso, I.M. (2011) Multiple prey cues induce foraging flexibility in a trap-building predator. Animal Behaviour, 81, 955-961.

Boda, P., Horvath, G., Kriska, G., Blaho, M. & Csabai, Z. (2014) Phototaxis and polarotaxis hand in hand: night dispersal flight of aquatic insects distracted synergistically by light intensity and reflection polarization. Naturwissenschaften, 101, 385-395.

Boix, D., Gascón, S., Sala, J., Badosa, A., Brucet, S., López-Flores, R., Martinoy, M., Gifre, J. & Quintana, X.D. (2007) Patterns of composition and species richness of crustaceans and aquatic insects along environmental gradients in Mediterranean water bodies. Hydrobiologia, 597, 53-69.

Bolton, D., Mayer-Pinto, M., Clark, G.F., Dafforn, K.A., Brassil, W.A., Becker, A. & Johnston, E.L. (2017) Coastal urban lighting has ecological consequences for multiple trophic levels under the sea. Science of the Total Environment, 576, 1-9.

Booth, A.C. & Soderqvist, L.E. (2016) Flow characteristics and salinity patterns of tidal rivers within the northern Ten Thousand Islands, southwest Florida, water years 2007–14. US Geological Survey.

Borges, R., Khan, I., Johnson, W.E., Gilbert, M.T.P., Zhang, G.J., Jarvis, E.D., O'Brien, S.J. & Antunes, A. (2015) Gene loss, adaptive evolution and the co-evolution of plumage coloration genes with opsins in birds. Bmc Genomics, 16.

Botson, B.A., Gawlik, D.E. & Trexler, J.C. (2016) Mechanisms that generate resource pulses in a fluctuating wetland. PloS one, 11, e0158864.

Boucek, R.E. & Rehage, J.S. (2013) No free lunch: displaced marsh consumers regulate a prey subsidy to an estuarine consumer. Oikos, no-no.

128

Boughman, J.W. (2001) Divergent sexual selection enhances reproductive isolation in sticklebacks. Nature, 411, 944.

Boyer, J.N. (2006) Shifting N and P limitation along a north-south gradient of mangrove estuaries in south Florida. Hydrobiologia, 569, 167-177.

Bradbury, I.R., Gardiner, K., Snelgrove, P.V.R., Campana, S.E., Bentzen, P. & Guan, L. (2006) Larval transport, vertical distributions and localized recruitment in anadromous rainbow smelt (Osmerus mordax). Canadian Journal of Fisheries and Aquatic Sciences, 63, 2822- 2836.

Bradley, T.J. (2008) Saline-water insects: ecology, physiology and evolution. Aquatic insects: challenges to populations. UK: CAB International, 20-35.

Bradley, T.J., Briscoe, A.D., Brady, S.G., Contreras, H.L., Danforth, B.N., Dudley, R., Grimaldi, D., Harrison, J.F., Kaiser, J.A. & Merlin, C. (2009) Episodes in insect evolution. Integrative and Comparative Biology, 49, 590-606.

Brady, P.C., Gilerson, A.A., Kattawar, G.W., Sullivan, J.M., Twardowski, M.S., Dierssen, H.M., Gao, M., Travis, K., Etheredge, R.I., Tonizzo, A., Ibrahim, A., Carrizo, C., Gu, Y.L., Russell, B.J., Mislinski, K., Zhao, S.L. & Cummings, M.E. (2015) Open-ocean fish reveal an omnidirectional solution to camouflage in polarized environments. Science, 350, 965-969.

Brittain, J.E. & Eikeland, T.J. (1988) Invertebrate drift - a review. Hydrobiologia, 166, 77-93.

Britto, V.O. & Bugoni, L. (2015) The contrasting feeding ecology of great egrets and roseate spoonbills in limnetic and estuarine colonies. Hydrobiologia, 744, 187-210.

Browder, J., Dragovich, A., Tashiro, J.E., Coleman-Duffie, E., Foltz, C. & Zweifel, J. (1986) A comparison of biological abundances in three adjacent bay systems downstream from the Golden Gate Estates canal system. US Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southeast Fisheries Center, Miami Laboratory.

Brown, J.S., Laundre, J.W. & Gurung, M. (1999) The ecology of fear: optimal foraging, game theory, and trophic interactions. Journal of Mammalogy, 80, 385-399.

129

Bucher, R. & Entling, M.H. (2011) Contrasting effects of habitat fragmentation, population density, and prey availability on body condition of two orb‐weaving spiders. Ecological entomology, 36, 680-685.

Burford, M.A., Webster, I.T., Revill, A.T., Kenyon, R.A., Whittle, M. & Curwen, G. (2012) Controls on phytoplankton productivity in a wet-dry tropical estuary. Estuarine Coastal and Shelf Science, 113, 141-151.

Butler, K.R. (1998) Coastal protection of sea turtles in Florida. Journal of Land Use & Environmental Law, 13, 399-441.

Cabana, G. & Rasmussen, J.B. (1996) Comparison of aquatic food chains using nitrogen isotopes. Proceedings of the National Academy of Sciences, 93, 10844-10847.

Cadmus, P., Pomeranz, J.P.F. & Kraus, J.M. (2016) Low-cost floating emergence net and bottle trap: comparison of two designs. Journal of Freshwater Ecology, 31, 653-658.

Cannizzaro, J.P., Carlson, P.R., Yarbro, L.A. & Hu, C.M. (2013) Optical variability along a river plume gradient: Implications for management and remote sensing. Estuarine Coastal and Shelf Science, 131, 149-161.

Cao, W.X., Yang, Y.Z., Xu, X.Q., Huang, L.M. & Zhang, J.L. (2003) Regional patterns of particulate spectral absorption in the Pearl River estuary. Chinese Science Bulletin, 48, 2344-2351.

Carabel, S., Godínez-Domínguez, E., Verísimo, P., Fernández, L. & Freire, J. (2006) An assessment of sample processing methods for stable isotope analyses of marine food webs. Journal of Experimental Marine Biology and Ecology, 336, 254-261.

Carassou, L., Whitfield, A.K., Moyo, S. & Richoux, N.B. (2017) Dietary tracers and stomach contents reveal pronounced alimentary flexibility in the freshwater mullet (Myxus capensis, Mugilidae) concomitant with ontogenetic shifts in habitat use and seasonal food availability. Hydrobiologia, 799, 327-348.

Carleton, K. (2009) Cichlid fish visual systems: mechanisms of spectral tuning. Integrative Zoology, 4, 75-86.

130

Carter, H.R., Burns, L.A., Cavinder, T.R., Dugger, R.R. & Fore, P.L. (1973) Ecosystem analysis of the Big Cypress Swamp and Estuaries. United States Environmental Protection Agency (Region IV): June 1973 South Florida Ecological Report.

Caut, S., Angulo, E. & Courchamp, F. (2009) Variation in discrimination factors (Δ15N and Δ13C): the effect of diet isotopic values and applications for diet reconstruction. Journal of Applied Ecology, 46, 443-453.

Cederholm, C.J., Kunze, M.D., Murota, T. & Sibatani, A. (1999) Pacific salmon carcasses: essential contributions of nutrients and energy for aquatic and terrestrial ecosystems. Fisheries, 24, 6-15.

Chan, E.K.W., Zhang, Y.X. & Dudgeon, D. (2009) Substrate Availability May Be More Important than Aquatic Insect Abundance in the Distribution of Riparian Orb-web Spiders in the Tropics. Biotropica, 41, 196-201.

Chen, Z.Q., Doering, P.H., Ashton, M. & Orlando, B.A. (2015) Mixing Behavior of Colored Dissolved Organic Matter and Its Potential Ecological Implication in the Caloosahatchee River Estuary, Florida. Estuaries and Coasts, 38, 1706-1718.

Cheng, L. (1976) Marine Insects. Scripps Institution of Oceanography, UC San Diego, San Diego, California, U.S.A.

Chew, L.L., Chong, V.C., Ooi, A.L. & Sasekumar, A. (2015) Vertical migration and positioning behavior of copepods in a mangrove estuary: Interactions between tidal, diel light and lunar cycles. Estuarine Coastal and Shelf Science, 152, 142-152.

Childers, D.L., Boyer, J.N., Davis, S.E., Madden, C.J., Rudnick, D.T. & Sklar, F.H. (2006) Relating precipitation and water management to nutrient concentrations in the oligotrophic “upside‐down” estuaries of the Florida Everglades. Limnology and Oceanography, 51, 602-616.

Cloern, J.E., Foster, S.Q. & Kleckner, A.E. (2014) Phytoplankton primary production in the world's estuarine-coastal ecosystems. Biogeosciences, 11, 2477-2501.

131

Colby, D.R., Thayer, G.W., Hettler, W.F. & Peters, D.S. (1985) A comparison of forage fish communities in relation to habitat parameters in Faka Union Bay, Florida and eight collateral bays during the wet season.

Collier, K.J., Bury, S. & Gibbs, M. (2002) A stable isotope study of linkages between stream and terrestrial food webs through spider predation. Freshwater biology, 47, 1651-1659.

Cook, M.I. & Kobza, M. (2011) South Florida Wading Bird Report (2012 South Florida Environmental Report). South Florida Water Management District. West Palm Beach, FL, 17.

Craig, C.L., Bernard, G.D. & Coddington, J.A. (1994) Evolutionary shifts in the spectral properties of spider silks. Evolution, 48, 287-296.

Crain, C.M., Silliman, B.R., Bertness, S.L. & Bertness, M.D. (2004) Physical and Biotic Drivers of Plant Distribution across Estuarine Salinity Gradients. Ecology, 85, 2539-2549.

Cronin, T.W., Johnsen, S., Marshall, N.J. & Warrant, E.J. (2014) Visual ecology. Princeton University Press.

Cronin, T.W. & Marshall, J. (2011) Patterns and properties of polarized light in air and water. Philos Trans R Soc Lond B Biol Sci, 366, 619-626.

Csabai, Z., Boda, P., Bernath, B., Kriska, G. & Horvath, G. (2006) A 'polarisation sun-dial' dictates the optimal time of day for dispersal by flying aquatic insects. Freshwater Biology, 51, 1341-1350.

Cummins, K.W. (1973) Trophic relations of aquatic insects. Annual review of entomology, 18, 183-206.

Davies, T.W., Bennie, J. & Gaston, K.J. (2012) Street lighting changes the composition of invertebrate communities. Biology Letters, 8, 764-767.

Davies, T.W., Bennie, J., Inger, R., De Ibarra, N.H. & Gaston, K.J. (2013) Artificial light pollution: are shifting spectral signatures changing the balance of species interactions? Global Change Biology, 19, 1417-1423.

132

Davies, T.W., Duffy, J.P., Bennie, J. & Gaston, K.J. (2014) The nature, extent, and ecological implications of marine light pollution. Frontiers in Ecology and the Environment, 12, 347-355.

Davies, T.W., Duffy, J.P., Bennie, J. & Gaston, K.J. (2016) Stemming the Tide of Light Pollution Encroaching into Marine Protected Areas. Conservation Letters, 9, 164-171.

Davis, S.M., Childers, D.L., Lorenz, J.J., Wanless, H.R. & Hopkins, T.E. (2005) A conceptual model of ecological interactions in the mangrove estuaries of the Florida Everglades. Wetlands, 25, 832-842.

Day, J.W. (1989) Estuarine ecology. John Wiley & Sons. de Azuaje, L.M.R., Tai, S. & McNeil, R. (1993) Comparison of rod/cone ratio in three species of shorebirds having different nocturnal foraging strategies. The Auk, 141-145.

Depledge, M.H., Godard-Codding, C.A.J. & Bowen, R.E. (2010) Light pollution in the sea. Marine Pollution Bulletin, 60, 1383-1385.

Dias, E., Morais, P., Cotter, A.M., Antunes, C. & Hoffman, J.C. (2016) Estuarine consumers utilize marine, estuarine, and terrestrial organic matter and provide connectivity among these food webs. Marine Ecology Progress Series, 554, 21-34.

Dominoni, D.M. (2015) The effects of light pollution on biological rhythms of birds: an integrated, mechanistic perspective. Journal of Ornithology, 156, S409-S418.

Dominoni, D.M., Carmona-Wagner, E.O., Hofmann, M., Kranstauber, B. & Partecke, J. (2014) Individual-based measurements of light intensity provide new insights into the effects of artificial light at night on daily rhythms of urban-dwelling songbirds. Journal of Animal Ecology, 83, 681-692.

Donders, T.H., Gorissen, P.M., Sangiorgi, F., Cremer, H., Wagner‐Cremer, F. & McGee, V. (2008) Three‐hundred‐year hydrological changes in a subtropical estuary, Rookery Bay (Florida): Human impact versus natural variability. Geochemistry, Geophysics, Geosystems, 9.

133

Dorn, N.J., Trexler, J.C. & Gaiser, E.E. (2006) Exploring the role of large predators in marsh food webs: evidence for a behaviorally-mediated trophic cascade. Hydrobiologia, 569, 375-386.

Duarte, C.M., Borja, A., Carstensen, J., Elliott, M., Krause-Jensen, D. & Marba, N. (2015) Paradigms in the Recovery of Estuarine and Coastal Ecosystems. Estuaries and Coasts, 38, 1202-1212.

Dwyer, R.G., Bearhop, S., Campbell, H.A. & Bryant, D.M. (2013) Shedding light on light: benefits of anthropogenic illumination to a nocturnally foraging shorebird. Journal of Animal Ecology, 82, 478-485.

Emmett Duffy, J., Paul Richardson, J. & France, K.E. (2005) Ecosystem consequences of diversity depend on food chain length in estuarine vegetation. Ecology Letters, 8, 301- 309.

Endler, J.A. (1990) On the measurement and classification of colour in studies of animal colour patterns. Biological Journal of the Linnean Society, 41, 315-352.

Endler, J.A. (1993) The color of light in forests and its implications. Ecological Monographs, 63, 1-27.

Epifanio, C.E. & Cohen, J.H. (2016) Behavioral adaptations in larvae of brachyuran crabs: A review. Journal of Experimental Marine Biology and Ecology, 482, 85-105.

Erren, T.C., Erren, M., Lerchl, A. & Meyer-Rochow, V.B. (2008) Clockwork blue: on the evolution of non-image-forming retinal photoreceptors in marine and terrestrial vertebrates. Naturwissenschaften, 95, 273-279.

Evans Ogden, L.J. (1996) Collision course: the hazards of lighted structures and windows to migrating birds.

Falcy, M.R. & Danielson, B.J. (2013) A complex relationship between moonlight and temperature on the foraging behavior of the Alabama beach mouse. Ecology, 94, 2632- 2637.

134

Finlay, J.C. (2001) Stable-carbon-isotope ratios of river biota: Implication for energy flow in lotic food webs. Ecology, 82, 1052-1064.

Flamarique, I.N. & Browman, H.I. (2000) Wavelength-dependent polarization orientation in Daphnia. Journal of Comparative Physiology a-Neuroethology Sensory Neural and Behavioral Physiology, 186, 1073-1087.

Flecker, A.S. (1992) Fish predation and the evolution of invertebrate drift periodicity - Evidence from neotropical streams. Ecology, 73, 438-448.

Foster, W.A. & Treherne, J.E. (1976) Insects of marine saltmarshes: problems and adaptations. Marine insects.

Fritz, K.A., Kirschman, L.J., McCay, S.D., Trushenski, J.T., Warne, R.W. & Whiles, M.R. (2017) Subsidies of essential nutrients from aquatic environments correlate with immune function in terrestrial consumers. Freshwater Science, 36, 893-900.

Fry, B. & Smith, T.J. (2002) Stable isotope studies of red mangroves and filter feeders from the Shark River Estuary, Florida. Bulletin of Marine Science, 70, 871-890.

Fukui, D.A.I., Murakami, M., Nakano, S. & Aoi, T. (2006) Effect of emergent aquatic insects on bat foraging in a riparian forest. Journal of Animal Ecology, 75, 1252-1258.

Fuller, M.R. & Peckarsky, B.L. (2011) Ecosystem engineering by beavers affects mayfly life histories. Freshwater Biology, 56, 969-979.

Gal, G., Loew, E.R., Rudstam, L.G. & Mohammadian, A.M. (1999) Light and diel vertical migration: spectral sensitivity and light avoidance by Mysis relicta. Canadian Journal of Fisheries and Aquatic Sciences, 56, 311-322.

Garcia, A.M., Hoeinghaus, D.J., Vieira, J.P. & Winemiller, K.O. (2007) Isotopic variation of fishes in freshwater and estuarine zones of a large subtropical coastal lagoon. Estuarine Coastal and Shelf Science, 73, 399-408.

Garcia, A.M., Winemiller, K.O., Hoeinghaus, D.J., Claudino, M.C., Bastos, R., Correa, F., Huckembeck, S., Vieira, J., Loebmann, D. & Abreu, P. (2017) Hydrologic pulsing

135

promotes spatial connectivity and food web subsidies in a subtropical coastal ecosystem. Marine Ecology Progress Series, 567, 17-28.

Gaston, K.J., Bennie, J., Davies, T.W. & Hopkins, J. (2013) The ecological impacts of nighttime light pollution: a mechanistic appraisal. Biological Reviews, 88, 912-927.

Gaston, K.J., Davies, T.W., Bennie, J. & Hopkins, J. (2012) Reducing the ecological consequences of night-time light pollution: options and developments. J Appl Ecol, 49, 1256-1266.

Gelman, A., Carlin, J.B., Stern, H.S. & Rubin, D.B. (2004) Bayesian data analysis. Texts in statistical science series. Chapman & Hall/CRC, Boca Raton, FL.

Gillespie, R.G. (1987) The mechanism of habitat selection in the long-jawed orb-weaving spider Tetragnatha elongata (Araneae, Tetragnathidae). Journal of Arachnology, 81-90.

Glatthorn, J. & Beckschäfer, P. (2014) Standardizing the protocol for hemispherical photographs: accuracy assessment of binarization algorithms. PloS one, 9, e111924.

Gray, L.J. (1993) Response of insectivorous birds to emerging aquatic insects in riparian habitats of a tallgrass prairie stream. American Midland Naturalist, 288-300.

Green, D.P.J., Trexler, J.C., Lorenz, J.J., McIvor, C.C. & Philippi, T. (2006) Spatial patterns of fish communities along two estuarine gradients in southern Florida. Hydrobiologia, 569, 387-399.

Greenwood, M.J. & McIntosh, A.R. (2008) Flooding impacts on responses of a riparian consumer to cross-ecosystem subsidies. Ecology, 89, 1489-1496.

Greif, S., Borissov, I., Yovel, Y. & Holland, R.A. (2014) A functional role of the sky's polarization pattern for orientation in the greater mouse-eared bat. Nature Communications, 5, 4.

Gritcan, I., Duxbury, M., Leuzinger, S. & Alfaro, A.C. (2016) Leaf Stable Isotope and Nutrient Status of Temperate Mangroves As Ecological Indicators to Assess Anthropogenic Activity and Recovery from Eutrophication. Frontiers in Plant Science, 7, 1922.

136

Grubisic, M., Singer, G., Bruno, M.C., van Grunsven, R.H.A., Manfrin, A., Monaghan, M.T. & Hölker, F. (2017) Artificial light at night decreases biomass and alters community composition of benthic primary producers in a sub‐alpine stream. Limnology and Oceanography, 62, 2799-2810.

Hampton, S.E. & Duggan, I.C. (2003) Diel habitat shifts of macrofauna in a fishless pond. Marine and Freshwater Research, 54, 797-805.

Haney, J.F. (1993) Environmental control of diel vertical migration behaviour. Ergebnisse der Limnologie ERLIA 6, 39.

Harris, D., Horwáth, W.R. & van Kessel, C. (2001) Acid fumigation of soils to remove carbonates prior to total organic carbon or carbon-13 isotopic analysis. Soil Science Society of America Journal, 65, 1853-1856.

Hart, N.S. (2001a) The visual ecology of avian photoreceptors. Progress in Retinal and Eye Research, 20, 675-703.

Hart, N.S. (2001b) Variations in cone photoreceptor abundance and the visual ecology of birds. Journal of Comparative Physiology a-Neuroethology Sensory Neural and Behavioral Physiology, 187, 685-697.

Hastad, O., Partridge, J.C. & Odeen, A. (2009) Ultraviolet photopigment sensitivity and ocular media transmittance in gulls, with an evolutionary perspective. Journal of Comparative Physiology a-Neuroethology Sensory Neural and Behavioral Physiology, 195, 585-590.

Hawryshyn, C.W. (2010) Ultraviolet polarization vision and visually guided behavior in fishes. Brain Behav Evol, 75, 186-194.

Heiling, A.M. (1999) Why do nocturnal orb-web spiders (Araneidae) search for light? Behav Ecol Sociobiol, 46, 43-49.

Henn, M., Nichols, H., Zhang, Y.X. & Bonner, T.H. (2014) Effect of artificial light on the drift of aquatic insects in urban central Texas streams. Journal of Freshwater Ecology, 29, 307-318.

137

Henschel, J.R., Mahsberg, D. & Stumpf, H. (2001) Allochthonous aquatic insects increase predation and decrease herbivory in river shore food webs. Oikos, 93, 429-438.

Herbst, D.B. (2001) Gradients of salinity stress, environmental stability and water chemistry as a templet for defining habitat types and physiological strategies in inland salt waters. Saline Lakes, pp. 209-219. Springer.

Hernandez, S.A. & Peckarsky, B.L. (2014) Do stream mayflies exhibit trade-offs between food acquisition and predator avoidance behaviors? Freshwater Science, 33, 124-133.

Higgins, L. (2006) Quantitative shifts in orb-web investment during development in Nephila clavipes (Araneae, Nephilidae). Journal of Arachnology, 34, 374-386.

Hoffsten, P.O. (2004) Site‐occupancy in relation to flight‐morphology in caddisflies. Freshwater Biology, 49, 810-817.

Holker, F., Moss, T., Griefahn, B., Kloas, W., Voigt, C.C., Henckel, D., Hanel, A., Kappeler, P.M., Volker, S., Schwope, A., Franke, S., Uhrlandt, D., Fischer, J., Klenke, R., Wolter, C. & Tockner, K. (2010a) The Dark Side of Light: A Transdisciplinary Research Agenda for Light Pollution Policy. Ecology and Society, 15, 11.

Holker, F., Wolter, C., Perkin, E.K. & Tockner, K. (2010b) Light pollution as a biodiversity threat. Trends in Ecology & Evolution, 25, 681-682.

Holker, F., Wolter, C., Perkin, E.K. & Tockner, K. (2010c) Light pollution as a biodiversity threat. Trends in Ecology & Evolution, 25, 681-682.

Holker, F., Wurzbacher, C., Weissenborn, C., Monaghan, M.T., Holzhauer, S.I.J. & Premke, K. (2015) Microbial diversity and community respiration in freshwater sediments influenced by artificial light at night. Philosophical Transactions of the Royal Society B-Biological Sciences, 370, 10.

Holzhauer, S.I.J., Franke, S., Kyba, C.C.M., Manfrin, A., Klenke, R., Voigt, C.C., Lewanzik, D., Oehlert, M., Monaghan, M.T., Schneider, S., Heller, S., Kuechly, H., Bruning, A., Honnen, A.C. & Holker, F. (2015) Out of the Dark: Establishing a Large-Scale Field Experiment to Assess the Effects of Artificial Light at Night on Species and Food Webs. Sustainability, 7, 15593-15616.

138

Horváth, G., Kriska, G., Malik, P. & Robertson, B. (2009) Polarized light pollution: a new kind of ecological photopollution. Frontiers in Ecology and the Environment, 7, 317-325.

Howe, E.R. & Simenstad, C.A. (2015) Using stable isotopes to discern mechanisms of connectivity in estuarine detritus-based food webs. Marine Ecology Progress Series, 518, 13-29.

Hunt, D.M., Carvalho, L.S., Cowing, J.A. & Davies, W.L. (2009) Evolution and spectral tuning of visual pigments in birds and mammals. Philosophical Transactions of the Royal Society B-Biological Sciences, 364, 2941-2955.

Hussey, N.E., MacNeil, M.A., McMeans, B.C., Olin, J.A., Dudley, S.F.J., Cliff, G., Wintner, S.P., Fennessy, S.T. & Fisk, A.T. (2014) Rescaling the trophic structure of marine food webs. Ecology letters, 17, 239-250.

International Dark Sky, A. (2003) International Dark Sky Association. Internet Web Page. http://www. darksky. org.

Irick, D.L., Gu, B.H., Li, Y.C.C., Inglett, P.W., Frederick, P.C., Ross, M.S., Wright, A.L. & Ewe, S.M.L. (2015) Wading bird guano enrichment of soil nutrients in tree islands of the Florida Everglades. Science of the Total Environment, 532, 40-47.

Johnsen, S., Kelber, A., Warrant, E., Sweeney, A.M., Widder, E.A., Lee, R.L. & Hernandez- Andres, J. (2006) Crepuscular and nocturnal illumination and its effects on color perception by the nocturnal hawkmoth Deilephila elpenor. Journal of Experimental Biology, 209, 789-800.

Jung, K. & Kalko, E.K.V. (2010) Where forest meets urbanization: foraging plasticity of aerial insectivorous bats in an anthropogenically altered environment. Journal of Mammalogy, 91, 144-153.

Kamermans, M. & Hawryshyn, C. (2011) Teleost polarization vision: how it might work and what it might be good for. Philos Trans R Soc Lond B Biol Sci, 366, 742-756.

Kato, C., Iwata, T., Nakano, S. & Kishi, D. (2003) Dynamics of aquatic insect flux affects distribution of riparian web‐building spiders. Oikos, 103, 113-120.

139

Kautza, A. & Sullivan, S.M.P. (2016) The energetic contributions of aquatic primary producers to terrestrial food webs in a mid‐size river system. Ecology, 97, 694-705.

Kieckbusch, D.K., Koch, M.S., Serafy, J.E. & Anderson, W.T. (2004) Trophic linkages among primary producers and consumers in fringing mangroves of subtropical lagoons. Bulletin of Marine Science, 74, 271-285.

Koyanagi, M., Nagata, T., Katoh, K., Yamashita, S. & Tokunaga, F. (2008) Molecular evolution of arthropod color vision deduced from multiple opsin genes of jumping spiders. Journal of Molecular Evolution, 66, 130-137.

Kranzfelder, P. & Ferrington, L.C. (2016) Temporal and spatial variability of Chironomidae (Diptera) species emergence in a Neotropical estuary. Freshwater Science, 35, 631-643.

Kronfeld-Schor, N. & Dayan, T. (2003) Partitioning of time as an ecological resource. Annual Review of Ecology Evolution and Systematics, 34, 153-181.

Kronfeld-Schor, N., Dominoni, D., de la Iglesia, H., Levy, O., Herzog, E.D., Dayan, T. & Helfrich-Forster, C. (2013) Chronobiology by moonlight. Proceedings of the Royal Society B-Biological Sciences, 280, 11.

Kuijper, D.P.J., Schut, J., van Dullemen, D., Toorman, H., Goossens, N., Ouwehand, J. & Limpens, H. (2008) Experimental evidence of light disturbance along the commuting routes of pond bats (Myotis dasycneme). Lutra, 51, 37.

Kurvers, R. & Holker, F. (2015) Bright nights and social interactions: a neglected issue. Behavioral Ecology, 26, 334-339.

Kuznetsova, A., Brockhoff, P.B. & Christensen, R.H.B. (2015) Package ‘lmerTest’. R package version, 2.

Kyba, C.C.M., Ruhtz, T., Fischer, J. & Holker, F. (2011) Cloud coverage acts as an amplifier for ecological light pollution in urban ecosystems. Plos One, 6, 9.

Lamb, T.D. (2013) Evolution of phototransduction, vertebrate photoreceptors and retina. Progress in Retinal and Eye Research, 36, 52-119.

140

Lamb, T.D., Collin, S.P. & Pugh, E.N. (2007) Evolution of the vertebrate eye: opsins, photoreceptors, retina and eye cup. Nature Reviews Neuroscience, 8, 960-975.

Lamberti, G.A. & Moore, J.W. (1984) Aquatic insects as primary consumers.

LaSalle, M.W. & de la Cruz, A.A. (1985) Seasonal Abundance and Diversity of Spiders in Two Intertidal Marsh Plant Communities. Estuaries, 8, 381-393.

Layman, C.A., Araujo, M.S., Boucek, R., Hammerschlag-Peyer, C.M., Harrison, E., Jud, Z.R., Matich, P., Rosenblatt, A.E., Vaudo, J.J., Yeager, L.A., Post, D.M. & Bearhop, S. (2012) Applying stable isotopes to examine food-web structure: an overview of analytical tools. Biol Rev Camb Philos Soc, 87, 545-562.

Le Duc, D. & Schöneberg, T. (2016) Adaptation to nocturnality–learning from avian genomes. BioEssays, 38, 694-703.

Lenth, R.V. (2016) Least-squares means: the R package lsmeans. J Stat Softw, 69, 1-33.

Levin, L.A., Boesch, D.F., Covich, A., Dahm, C., Erseus, C., Ewel, K.C., Kneib, R.T., Moldenke, A., Palmer, M.A., Snelgrove, P., Strayer, D. & Weslawski, J.M. (2001) The function of marine critical transition zones and the importance of sediment biodiversity. Ecosystems, 4, 430-451.

Lewanzik, D. & Voigt, C.C. (2014) Artificial light puts ecosystem services of frugivorous bats at risk. Journal of Applied Ecology, 51, 388-394.

Ley, J.A. & Halliday, I.A. (2007) Diel variation in mangrove fish abundances and trophic guilds of northeastern Australian estuaries with a proposed trophodynamic model. Bulletin of Marine Science, 80, 681-720.

Ley, J.A., McIvor, C.C. & Montague, C.L. (1999) Fishes in Mangrove Prop-root Habitats of Northeastern Florida Bay: Distinct Assemblages across an Estuarine Gradient. Estuarine, Coastal and Shelf Science, 48, 701-723.

Ley, J.A., Montague, C.L. & McLvor, C.C. (1994) Food habits of mangrove fishes: a comparison along estuarine gradients in northeastern Florida Bay. Bulletin of Marine Science, 54, 881-899. 141

Liston, S.E. (2006) Interactions between nutrient availability and hydroperiod shape macroinvertebrate communities in Florida Everglades marshes. Hydrobiologia, 569, 343- 357.

Loew, E.R. & McFarland, W.N. (1990) The underwater visual environment. The visual system of fish, 1-43.

Loftus, W.F. (2004) Inventory of freshwater fish species within Big Cypress National Preserve: the basis for a long-term sampling program.

Loftus, W.F., Trexler, J.C. & Jones, R.D. (1998) Mercury transport through an Everglades food web. Final Report. Contract SP-329. Florida Department of Environemental Protection, Homestead, FL.

Longcore, T. & Rich, C. (2004) Ecological light pollution. Frontiers in Ecology and the Environment, 2, 191-198.

Loss, S.R., Will, T., Loss, S.S. & Marra, P.P. (2014) Bird–building collisions in the United States: Estimates of annual mortality and species vulnerability. The Condor, 116, 8-23.

Lotze, H.K., Lenihan, H.S., Bourque, B.J., Bradbury, R.H., Cooke, R.G., Kay, M.C., Kidwell, S.M., Kirby, M.X., Peterson, C.H. & Jackson, J.B.C. (2006) Depletion, degradation, and recovery potential of estuaries and coastal seas. Science, 312, 1806-1809.

Lowe, E.C., Wilder, S.M. & Hochuli, D.F. (2014) Urbanisation at multiple scales is associated with larger size and higher fecundity of an orb-weaving spider. PLoS One, 9, e105480.

Lubin, Y.D. (1978) Seasonal abundance and diversity of web-building spiders in relation to habitat structure on Barro-Colorado-Island, Panama. Journal of Arachnology, 6, 31-51.

Luke, S.G. (2017) Evaluating significance in linear mixed-effects models in R. Behavior Research Methods, 49, 1494-1502.

Lythgoe, J.N. (1968) Visual pigments and visual range underwater. Vision Res, 8, 997-1011.

142

Lythgoe, J.N. (1972) The adaptations of visual pigments to the photic environment. Handbook of Sensory Physiology (eds H. Autrum, R. Jung, W.R. Loewenstein, D.M. MacKay & H.L. Teuber), pp. 567-600. Springer, Berlin.

Lythgoe, J.N. & C., P.J. (1991) The modelling of optimal visual pigments of dichromatic teleosts in green coastal waters. Vision Res, 31, 361-371.

Lyytimäki, J. (2013) Nature’s nocturnal services: Light pollution as a non-recognised challenge for ecosystem services research and management. Ecosystem Services, 3, e44-e48.

MacKenzie, R.A. (2005) Spatial and temporal patterns in insect emergence from a southern Maine salt marsh. American Midland Naturalist, 153, 257-269.

Malmqvist, B. (2000) How does wing length relate to distribution patterns of stoneflies (Plecoptera) and mayflies (Ephemeroptera)? Biological Conservation, 93, 271-276.

Manfrin, A. (2017) Effects of artificial light at night (ALAN) on interactions between aquatic and terrestrial ecosystems.

Marczak, L.B. & Richardson, J.S. (2007) Spiders and subsidies: results from the riparian zone of a coastal temperate rainforest. J Anim Ecol, 76, 687-694.

Marczak, L.B., Thompson, R.M. & Richardson, J.S. (2007) Meta-Analysis: Trophic Level, Habitat, and Productivity Shape the Food Web Effects of Resource Subsidies. Ecology, 88, 140-148.

Martin-Creuzburg, D., Kowarik, C. & Straile, D. (2017) Cross-ecosystem fluxes: Export of polyunsaturated fatty acids from aquatic to terrestrial ecosystems via emerging insects. Science of The Total Environment, 577, 174-182.

Mateo, M.A., Serrano, O., Serrano, L. & Michener, R.H. (2008) Effects of Sample Preparation on Stable Isotope Ratios of Carbon and Nitrogen in Marine Invertebrates: Implications for Food Web Studies Using Stable Isotopes. Oecologia, 157, 105-115.

McCauley, D.J., Hoffmann, E., Young, H.S. & Micheli, F. (2012) Night shift: expansion of temporal niche use following reductions in predator density. PLoS One, 7, e38871.

143

McComb, D.M., Kajiura, S.M., Horodysky, A.Z. & Frank, T.M. (2013) Comparative visual function in predatory fishes from the Indian River Lagoon. Physiol Biochem Zool, 86, 285-297.

McCutchan Jr., J.H., Lewis Jr., W.M., Kendall, C. & McGrath, C.C. (2003) Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos, 102, 378-390.

McFarland, W.N., Ogden, J.C. & Lythgoe, J.N. (1979) The influence of light on the twilight migrations of grunts. Environmental Biology of Fishes, 4, 9-22.

McFarland, W.N. & Wahl, C.M. (1996) Visual constraints on migration behavior of juvenile French grunts. Environmental Biology of Fishes, 46, 109-122.

McNeil, R., Drapeau, P. & Gosscustard, J.D. (1992) The occurrence and adaptive significance of nocturnal habits in waterfowl. Biological Reviews of the Cambridge Philosophical Society, 67, 381-419.

McNeil, R. & Rodríguez, J.R. (1996) Nocturnal foraging in shorebirds. International Wader Studies, 8, 114-121.

McNeil, R., Rojas, L.M., Marín, G. & Figueroa, Y.M.R. (2004) Actividad nocturna y visión en aves playeras de la región Neotropical. Ornotologia Neotropical, 15, 223-232.

Meeuwig, J.J., Rasmussen, J.B. & Peters, R.H. (1998) Turbid waters and clarifying mussels: their moderation of empirical chl: nutrient relations in estuaries in Prince Edward Island, Canada. Marine Ecology Progress Series, 139-150.

Merritt, R.W. & Cummins, K.W. (1996) An introduction to the aquatic insects of North America. Kendall Hunt.

Meyer, L.A. & Sullivan, S.M.P. (2013) Bright lights, big city: influences of ecological light pollution on reciprocal stream-riparian invertebrate fluxes. Ecological Applications, 23, 1322-1330.

Miyasaka, H. & Nakano, S. (2001) Drift dispersal of mayfly nymphs in the presence of chemical and visual cues from diurnal drift- and nocturnal benthic-foraging fishes. Freshwater Biology, 46, 1229-1237. 144

Moran, P.A.P. (1950) Notes on continuous stochastic phenomena. Biometrika, 37, 17-23.

Mougeot, F. & Bretagnolle, V. (2000) Predation risk and moonlight avoidance in nocturnal seabirds. Journal of Avian Biology, 31, 376-386.

Moya‐Laraño, J., Macías‐Ordóñez, R., Blanckenhorn, W.U. & Fernández‐Montraveta, C. (2008) Analysing body condition: mass, volume or density? Journal of Animal Ecology, 77, 1099-1108.

Mueller, R.P. & Simmons, M.A. (2008) Characterization of gatewell orifice lighting at the Bonneville Dam second powerhouse and compendium of research on light guidance with juvenile salmonids. pp. 1-55. Pacific Northwest National Laboratory, Richland, Washington.

Muheim, R. (2011) Behavioural and physiological mechanisms of polarized light sensitivity in birds. Philos Trans R Soc Lond B Biol Sci, 366, 763-771.

Munz, F.W. & McFarland, W.N. (1973) The significance of spectral position in the rhodopsins of tropical marine fishes. Vision research, 13, 1829-IN1821.

Murakami, M. & Nakano, S. (2002) Indirect effect of aquatic insect emergence on a terrestrial insect population through bird predation Ecology Letters, 5, 333.

Nagelkerken, I. (2009) Ecological Connectivity among Tropical Coastal Ecosystems Introduction. Ecological Connectivity among Tropical Coastal Ecosystems, 1-6.

Navara, K.J. & Nelson, R.J. (2007) The dark side of light at night: physiological, epidemiological, and ecological consequences. Journal of Pineal Research, 43, 215-224.

Navarro-Barranco, C. & Hughes, L.E. (2015) Effects of light pollution on the emergent fauna of shallow marine ecosystems: Amphipods as a case study. Marine Pollution Bulletin, 94, 235-240.

Nayar, J.K. (1967) The pupation rhythm in Aedes taeniorhynchus (Diptera: Culicidae). II. Ontogenetic timing, rate of development, and endogenous diurnal rhythm of pupation. Annals of the Entomological Society of America, 60, 946-971.

145

Naylor, E. (2006) Orientation and navigation in coastal and estuarine zooplankton. Marine and Freshwater Behaviour and Physiology, 39, 13-24.

Nelson, J.A., Deegan, L. & Garritt, R. (2015) Drivers of spatial and temporal variability in estuarine food webs. Marine Ecology Progress Series, 533, 67-77.

Ng, J.S.S., Wai, T.-C. & Williams, G.A. (2007) The effects of acidification on the stable isotope signatures of marine algae and molluscs. Marine chemistry, 103, 97-102.

Nightingale, B., Longcore, T. & Simenstad, C.A. (2006) Artificial night lighting and fishes. Ecological consequences of artificial night lighting (eds C. Rich & T. Longcore), pp. 257-276. Island Press, Washington, DC, USA.

Norcross, B.L. & Shaw, R.F. (1984) Oceanic and estuarine transport of fish eggs and larvae: a review. Transactions of the American Fisheries Society, 113, 153-165.

O'Gorman, E.J. (2016) It's only a matter of time: the altered role of subsidies in a warming world. Journal of Animal Ecology, 85, 1133-1135.

Odum, W.E., Fisher, J.S. & Pickral, J.C. (1979) Factors controlling the flux of particulate organic carbon from estuarine wetlands. Ecological processes in coastal and marine systems, pp. 69-80.

Odum, W.E. & Heald, E.J. (1972) Trophic analyses of an estuarine mangrove community. Bulletin of Marine Science, 22, 671-738.

Orians, G.H. & Wittenberger, J.F. (1991) Spatial and temporal scales in habitat selection. The American Naturalist, 137, S29-S49.

Ormerod, S.J. & Tyler, S.J. (1991) Exploitation of prey by a river bird, the dipper Cinclus cinclus (L.), along acidic and circumneutral streams in upland Wales. Freshwater Biology, 25, 105-116.

Paetzold, A., Schubert, C.J. & Tockner, K. (2005) Aquatic terrestrial linkages along a braided- river: riparian arthropods feeding on aquatic insects. Ecosystems, 8, 748-759.

146

Paetzold, A. & Tockner, K. (2005) Effects of riparian arthropod predation on the biomass and abundance of aquatic insect emergence. Journal of the North American Benthological Society, 24, 395-402.

Palmer, M.A., Allan, J.D. & Butman, C.A. (1996) Dispersal as a regional process affecting the local dynamics of marine and stream benthic invertebrates. Trends in Ecology & Evolution, 11, 322-326.

Peckarsky, B.L. & McIntosh, A.R. (1998) Fitness and community consequences of avoiding multiple predators. Oecologia, 113, 565-576.

Perkin, E.K., Holker, F., Richardson, J.S., Sadler, J.P., Wolter, C. & Tockner, K. (2011) The influence of artificial light on stream and riparian ecosystems: questions, challenges, and perspectives. Ecosphere, 2, 122.

Perkin, E.K., Holker, F. & Tockner, K. (2014) The effects of artificial lighting on adult aquatic and terrestrial insects. Freshwater Biology, 59, 368-377.

Perkin, E.K., Holker, F., Tockner, K. & Richardson, J.S. (2014a) Artificial light as a disturbance to light-naive streams. Freshwater Biology, 59, 2235-2244.

Perkin, E.K., Hölker, F., Tockner, K. & Richardson, J.S. (2014b) Artificial light as a disturbance to light-naïve streams. Freshwater Biology, 59, 2235-2244.

Pignatelli, V., Temple, S.E., Chiou, T.H., Roberts, N.W., Collin, S.P. & Marshall, N.J. (2011) Behavioural relevance of polarization sensitivity as a target detection mechanism in cephalopods and fishes. Philos Trans R Soc Lond B Biol Sci, 366, 734-741.

Poff, N.L., Olden, J.D., Vieira, N.K.M., Finn, D.S., Simmons, M.P. & Kondratieff, B.C. (2006) Functional trait niches of North American lotic insects: traits-based ecological applications in light of phylogenetic relationships. Journal of the North American Benthological Society, 25, 730-755.

Polak, T., Korine, C., Yair, S. & Holderied, M.W. (2011) Differential effects of artificial lighting on flight and foraging behaviour of two sympatric bat species in a desert. Journal of Zoology, 285, 21-27.

147

Polis, G.A., Anderson, W.B. & Holt, R.D. (1997) Toward an integration of landscape and food web ecology: the dynamics of spatially subsidized food webs. Annual Review of Ecology and Systematics, 28, 289-316.

Polis, G.A., Power, M.E. & Huxel, G.R. (2004) Food webs at the landscape level. University of Chicago Press, Chicago, Illinois.

Post, D.M. (2002) Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology, 83, 703-718.

Power, M.E. & Rainey, W.E. (2000) Food webs and resource sheds: towards spatially delimiting trophic interactions. The ecological consequences of environmental heterogeneity (eds M.J. Hutchings, E.A. John & A.J. Stewart), pp. 291-314. Blackwell Science, London, England.

Prewitt, J. & Mendelsohn, M.L. (1966) The analysis of cell images. Annals of the New York Academy of Sciences, 128, 1035-1053.

Quinn, T.P. (2004) The behavior and ecology of Pacific Salmon and Trout. University of Washington Press, Seattle, WA, USA.

Radabaugh, K.R., Malkin, E.M., Hollander, D.J. & Peebles, E.B. (2014) Evidence for light- environment control of carbon isotope fractionation by benthic microalgal communities. Marine Ecology Progress Series, 495, 77-90.

Ramirez, M.F. (2008) Emergent Aquatic Insects: Assemblage Structure and Patterns of Availability in Freshwater Wetlands of the Lower Colombia River Estuary.

Ramirez-Martinez, G.A., Castellanos-Galindo, G.A. & Krumme, U. (2016) Tidal and Diel Patterns in Abundance and Feeding of a Marine-Estuarine-Dependent Fish from Macrotidal Mangrove Creeks in the Tropical Eastern Pacific (Colombia). Estuaries and Coasts, 39, 1249-1261.

Rencher, A.C. (2003) Methods of multivariate analysis. John Wiley & Sons.

148

Richardson, J.S., Zhang, Y.X. & Marczak, L.B. (2010) Resource subsidies across the land- freshwater interface and responses in recipient communities. River Research and Applications, 26, 55-66.

Riley, R.H., Townsend, C.R., Raffaelli, D.A. & Flecker, A.S. (2004) Sources and effects of subsidies along the stream-estuary continuum. pp. 241-260. The University of Chicago Press, Chicago, IL.

Riley, W.D., Bendall, B., Ives, M.J., Edmonds, N.J. & Maxwell, D.L. (2012) Street lighting disrupts the diel migratory pattern of wild Atlantic salmon, Salmo salar L., smolts leaving their natal stream. Aquaculture, 330-333, 74-81.

Riley, W.D., Davison, P.I., Maxwell, D.L., Newman, R.C. & Ives, M.J. (2015) A laboratory experiment to determine the dispersal response of Atlantic salmon (Salmo salar) fry to street light intensity. Freshwater Biology, 60, 1016-1028.

Ringelberg, J. (1999) The photobehaviour of Daphnia spp. as a model to explain diel vertical migration in zooplankton. Biological Reviews, 74, 397-423.

Robertson, B., Kriska, G., Horvath, V. & Horvath, G. (2010) Glass buildings as bird feeders: urban birds exploit insects trapped by polarized light pollution. Acta Zoologica Academiae Scientarum Hungaricae, 56, 283-293.

Robinson, E., Jerrett, A.R., Black, S.E. & Davison, W. (2011) Visual acuity of snapper Pagrus auratus: effect of size and spectral composition. J Fish Biol, 79, 1883-1894.

Rodriguez, A., Holmes, N.D., Ryan, P.G., Wilson, K.J., Faulquier, L., Murillo, Y., Raine, A.F., Penniman, J.F., Neves, V., Rodriguez, B., Negro, J.J., Chiaradia, A., Dann, P., Anderson, T., Metzger, B., Shirai, M., Deppe, L., Wheeler, J., Hodum, P., Gouveia, C., Carmo, V., Carreira, G.P., Delgado-Alburqueque, L., Guerra-Correa, C., Couzi, F.X., Travers, M. & Le Corre, M. (2017) Seabird mortality induced by land-based artificial lights. Conservation Biology, 31, 986-1001.

Rojas, L.M., McNeil, R., Cabana, T. & Lachapelle, P. (1999a) Behavioral, morphological and physiological correlates of diurnal and nocturnal vision in selected wading bird species. Brain Behavior and Evolution, 53, 227-242.

149

Rojas, L.M., McNeil, R., Cabana, T. & Lachapelle, P. (1999b) Diurnal and nocturnal visual capabilities in shorebirds as a function of their feeding strategies. Brain Behavior and Evolution, 53, 29-43.

Rosenblatt, A.E. & Heithaus, M.R. (2011) Does variation in movement tactics and trophic interactions among American alligators create habitat linkages? Journal of Animal Ecology, 80, 786-798.

Rosenblatt, A.E., Heithaus, M.R., Mazzotti, F.J., Cherkiss, M. & Jeffery, B.M. (2013) Intra- population variation in activity ranges, diel patterns, movement rates, and habitat use of American alligators in a subtropical estuary. Estuarine Coastal and Shelf Science, 135, 182-190.

Rotics, S., Dayan, T. & Kronfeld-Schor, N. (2011) Effect of artificial night lighting on temporally partitioned spiny mice. Journal of Mammalogy, 92, 159-168.

Rydell, J. (1992) Exploitation of insects around streetlamps by bats in Sweden. Functional Ecology, 6, 744-750.

Ryther, J.H. & Dunstan, W.M. (1971) Nitrogen, phosphorus, and eutrophication in the coastal marine environment. Science, 171, 1008-1013.

Sabbah, S., Habib-Nayany, M.F., Dargaei, Z., Hauser, F.E., Kamermans, M. & Hawryshyn, C.W. (2013) Retinal region of polarization sensitivity switches during ontogeny of rainbow trout. J Neurosci, 33, 7428-7438.

Sabo, J.L., Finlay, J.C., Kennedy, T. & Post, D.M. (2010) The Role of Discharge Variation in Scaling of Drainage Area and Food Chain Length in Rivers. Science, 330, 965-967.

Salomon, M., Sponarski, C., Larocque, A., Avil, xe & s, L. (2010) Social organization of the colonial spider Leucauge sp. in the Neotropics: vertical stratification within colonies. The Journal of Arachnology, 38, 446-451.

Santos, C.D., Miranda, A.C., Granadeiro, J.P., Lourenco, P.M., Saraiva, S. & Palmeirim, J.M. (2010) Effects of artificial illumination on the nocturnal foraging of waders. Acta Oecologica-International Journal of Ecology, 36, 166-172.

150

Savage, C., Thrush, S.F., Lohrer, A.M. & Hewitt, J.E. (2012a) Ecosystem services transcend boundaries: estuaries provide resource subsidies and influence functional diversity in coastal benthic communities. PLoS One, 7, e42708.

Savage, C., Thrush, S.F., Lohrer, A.M. & Hewitt, J.E. (2012b) Ecosystem Services Transcend Boundaries: Estuaries Provide Resource Subsidies and Influence Functional Diversity in Coastal Benthic Communities. Plos One, 7.

Scheffer, M. (1999) The effect of aquatic vegetation on turbidity; how important are the filter feeders? Hydrobiologia, 408/409, 307-316.

Scheuerell, M.D. & Schindler, D.E. (2003) Diel vertical migration by juvenile sockeye salmon: empirical evidence for the antipredation window. Ecology, 84, 1713-1720.

Schindler, D.E. & Smits, A.P. (2017) Subsidies of aquatic resources in terrestrial ecosystems. Ecosystems, 20, 78-93.

Schlacher, T.A. & Connolly, R.M. (2014) Effects of acid treatment on carbon and nitrogen stable isotope ratios in ecological samples: a review and synthesis. Methods in Ecology and Evolution, 5, 541-550.

Schmitz, L. & Wainwright, P.C. (2011) Nocturnality constrains morphological and functional diversity in the eyes of reef fishes. BMC Evol Biol, 11, 338.

Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. (2012) NIH Image to ImageJ: 25 years of image analysis. Nature methods, 9, 671-675.

Schwamborn, R. & Giarrizzo, T. (2015) Stable Isotope Discrimination by Consumers in a Tropical Mangrove Food Web: How Important Are Variations in C/N Ratio? Estuaries and coasts, 38, 813-825.

Schwind, R. (1995) Spectral regions in which aquatic insects see reflected polarized light. Journal of Comparative Physiology a-Sensory Neural and Behavioral Physiology, 177, 439-448.

151

Seehausen, O., Terai, Y., Magalhaes, I.S., Carleton, K.L., Mrosso, H.D.J., Miyagi, R., van der Sluijs, I., Schneider, M.V., Maan, M.E. & Tachida, H. (2008) Speciation through sensory drive in cichlid fish. Nature, 455, 620-626.

Seehausen, O., Van Alphen, J.J.M. & Witte, F. (1997) Cichlid fish diversity threatened by eutrophication that curbs sexual selection. Science, 277, 1808-1811.

Shashar, N., Hagan, R., Boal, J.G. & Hanlon, R.T. (2000) Cuttlefish use polarization sensitivity in predation on silvery fish. Vision research, 40, 71-75.

Sheaves, M. (2009) Consequences of ecological connectivity: the coastal ecosystem mosaic. Marine Ecology Progress Series, 391, 107-115.

Sheaves, M., Baker, R., Nagelkerken, I. & Connolly, R.M. (2015) True Value of Estuarine and Coastal Nurseries for Fish: Incorporating Complexity and Dynamics. Estuaries and Coasts, 38, 401-414.

Shichida, Y. & Matsuyama, T. (2009) Evolution of opsins and phototransduction. Philosophical Transactions of the Royal Society B-Biological Sciences, 364, 2881-2895.

Smith, M. (2009) Time to turn off the lights. Nature, 457, 27-27.

Spiller, D.A. & Schoener, T.W. (2007) Alteration of island food-web dynamics following major disturbance by hurricanes. Ecology, 88, 37-41.

Stanley, M.C., Beggs, J.R., Bassett, I.E., Burns, B.R., Dirks, K.N., Jones, D.N., Linklater, W.L., Macinnis-Ng, C., Simcock, R., Souter-Brown, G., Trowsdale, S.A. & Gaston, K.J. (2015) Emerging threats in urban ecosystems: a horizon scanning exercise. Frontiers in Ecology and the Environment, 13, 553-560.

Stapp, P. & Polis, G.A. (2003) Influence of pulsed resources and marine subsidies on insular rodent populations. Oikos, 102, 111-123.

Stenroth, K., Polvi, L.E., Fältström, E. & Jonsson, M. (2015) Land‐use effects on terrestrial consumers through changed size structure of aquatic insects. Freshwater Biology, 60, 136-149.

152

Stich, D.S., Zydlewski, G.B., Kocik, J.F. & Zydlewski, J.D. (2015) Linking Behavior, Physiology, and Survival of Atlantic Salmon Smolts During Estuary Migration. Marine and Coastal Fisheries, 7, 68-86.

Stock, B.C. & Semmens, B.X. (2013) MixSIAR User Manual, Version 3.1.

Sullivan, S.M. & Rodewald, A.D. (2012) In a state of flux: the energetic pathways that move contaminants from aquatic to terrestrial environments. Environ Toxicol Chem, 31, 1175- 1183.

Tagwireyi, P. & Sullivan, S.M.P. (2015) Riverine landscape patch heterogeneity drives riparian ant assemblages in the Scioto River Basin, USA. PloS one, 10, e0124807.

Tagwireyi, P. & Sullivan, S.M.P. (2016a) Distribution and trophic dynamics of riparian tetragnathid spiders in a large river system. Marine and Freshwater Research, 67, 309- 318.

Tagwireyi, P. & Sullivan, S.M.P. (2016b) Riverine landscape patches influence trophic dynamics of riparian ants. River Research and Applications, 32, 1721-1729.

Tarlow, E.M., Hau, M., Anderson, D.J. & Wikelski, M. (2003) Diel changes in plasma melatonin and corticosterone concentrations in tropical Nazca boobies (Sula granti) in relation to moon phase and age. General and Comparative Endocrinology, 133, 297-304.

Team, R.C. (2017) R: A language and environment for statistical computing [Internet]. Vienna, Austria; 2014.

Thomas, J.R., James, J., Newman, R.C., Riley, W.D., Griffiths, S.W. & Cable, J. (2016) The impact of streetlights on an aquatic invasive species: Artificial light at night alters signal crayfish behaviour. Applied Animal Behaviour Science, 176, 143-149.

Thomas, R.J., Szekely, T., Powell, R.F. & Cuthill, I.C. (2006) Eye size, foraging methods and the timing of foraging in shorebirds. Functional Ecology, 20, 157-165.

Thompson, R.M., Brose, U., Dunne, J.A., Hall, R.O., Hladyz, S., Kitching, R.L., Martinez, N.D., Rantala, H., Romanuk, T.N. & Stouffer, D.B. (2012) Food webs: reconciling the structure and function of biodiversity. Trends in ecology & evolution, 27, 689-697. 153

Thomson, R.C.M. (1946) Studies on the breeding places and control of Anopheles gambiae and A. gambiae var. melas in coastal districts of Sierra Leone. Bulletin of Entomological Research, 36, 185-252.

Toyama, M., Hironaka, M., Yamahama, Y., Horiguchi, H., Tsukada, O., Uto, N., Ueno, Y., Tokunaga, F., Seno, K. & Hariyama, T. (2008) Presence of Rhodopsin and Porphyropsin in the Eyes of 164 Fishes, Representing Marine, Diadromous, Coastal and Freshwater Species—A Qualitative and Comparative Study. Photochemistry and Photobiology, 84, 996-1002.

Trexler, J.C. & Loftus, W.F. (2016) Invertebrates of the Florida everglades. Invertebrates in Freshwater Wetlands, pp. 321-356. Springer.

Triplehorn, C.A.J., Borror, N.F., Triplehorn, D.J.C.A. & Johnson, N.F. (2005) Borror and DeLong's Introduction to the Study of Insects.

Tukey, J.W. (1977) Exploratory data analysis.

Twining, C.W., Lawrence, P., Winkler, D.W., Flecker, A.S. & Brenna, J.T. (2017) Conversion efficiency of alpha linolenic acid to omega-3 highly unsaturated fatty acids in aerial insectivore chicks. The Journal of Experimental Biology.

United States. National Marine Fisheries, S. & Lindall, W.N. (1973) A survey of fishes and commercial invertebrates of the nearshore and estuarine zone between Cape Romano and Cape Sable, Florida. National Marine Fisheries Service.

Valiela, I. & Bartholomew, M. (2015) Land-Sea Coupling and Global-Driven Forcing: Following Some of Scott Nixon's Challenges. Estuaries and Coasts, 38, 1189-1201.

Vander Zanden, M.J., Casselman, J.M. & Rasmussen, J.B. (1999) Stable isotope evidence for the food web consequences of species invasions in lakes. Nature, 401, 464-467.

Vanderklift, M.A. & Ponsard, S. (2003) Sources of variation in consumer-diet delta(15)N enrichment: a meta-analysis. Oecologia, 136, 169-182.

Vannote, R.L., Minshall, G.W., Cummins, K.W., Sedell, J.R. & Cushing, C.E. (1980) The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences, 37, 130-137. 154

Veilleux, C.C. & Cummings, M.E. (2012) Nocturnal light environments and species ecology: implications for nocturnal color vision in forests. Journal of Experimental Biology, 215, 4085-4096.

Viets, K., Eldred, K.C. & Johnston, R.J. (2016) Mechanisms of Photoreceptor Patterning in Vertebrates and Invertebrates. Trends in Genetics, 32, 638-659.

Wald, G. (1935) Pigments of the bull frog retina. Nature, 136, 382.

Wesner, J.S. (2010) Aquatic predation alters a terrestrial prey subsidy. Ecology, 91, 1435-1444.

Wesner, J.S. (2012) Predator diversity effects cascade across an ecosystem boundary. Oikos, 121, 53-60.

Whitfield, A.K., Elliott, M., Basset, A., Blaber, S.J.M. & West, R.J. (2012) Paradigms in estuarine ecology – A review of the Remane diagram with a suggested revised model for estuaries. Estuarine, Coastal and Shelf Science, 97, 78-90.

Wilder, S.M. (2013) Variation among clutches in the response of spiders to prey nutrient content. The Journal of Arachnology, 41, 53-58.

Wilkinson, E.B., Branch, L.C. & Miller, D.L. (2013) Functional habitat connectivity for beach mice depends on perceived predation risk. Landscape Ecology, 28, 547-558.

Williams, A.J. & Trexler, J.C. (2006) A preliminary analysis of the correlation of food-web characteristics with hydrology and nutrient gradients in the southern Everglades. Hydrobiologia, 569, 493-504.

Williams, D.D. (2009) Coping with Saltwater: The Condition of Aquatic Insects in Estuaries as Determined by Gut Content Analysis. Open Marine Biology Journal, 3, 21-27.

Williams, D.D. & Hamm, T. (2002) Insect community organisation in estuaries: the role of the physical environment. Ecography, 25, 372-384.

Williams, D.D. & Williams, N.E. (1998a) Aquatic insects in an estuarine environment: densities, distribution and salinity tolerance. Freshwater Biology, 39, 411-421.

155

Williams, D.D. & Williams, N.E. (1998b) Seasonal variation, export dynamics and consumption of freshwater invertebrates in an estuarine environment. Estuarine, Coastal and Shelf Science, 46, 393-410.

Winemiller, K.O., Hoeinghaus, D.J., Pease, A.A., Esselman, P.C., Honeycutt, R.L., Gbanaador, D., Carrera, E. & Payne, J. (2011) Stable isotope analysis reveals food web structure and watershed impacts along the fluvial gradient of a Mesoamerican coastal river. River Research and Applications, 27, 791-803.

Winemiller, K.O. & Leslie, M.A. (1992) Fish assemblages across a complex, tropical freshwater/marine ecotone. Environmental Biology of fishes, 34, 29-50.

Wingard, G.L. & Lorenz, J.J. (2014) Integrated conceptual ecological model and habitat indices for the southwest Florida coastal wetlands. Ecological Indicators, 44, 92-107.

Yang, L.H., Bastow, J.L., Spence, K.O. & Wright, A.N. (2008) What can we learn from resource pulses? Ecology, 89, 621-634.

Yeager, L.A., Marchand, P., Gill, D.A., Baum, J.K. & McPherson, J.M. (2017) MSEC: Queryable global layers of environmental and anthropogenic variables for marine ecosystem studies. Ecology.

Yeager, L.A., Stoner, E.W., Peters, J.R. & Layman, C.A. (2016) A terrestrial-aquatic food web subsidy is potentially mediated by multiple predator effects on an arboreal crab. Journal of Experimental Marine Biology and Ecology, 475, 73-79.

Yokel, B.J. (2006) A comparison of animal abundance and distribution in similar habitats in Rookery Bay, Marco Island and Fakahatchee on the southwest coast of Florida, 1971- 1972. NOAA Technical Memorandum NOS NCCOS, 35.

Yokoyama, S. (2008) Evolution of dim-light and color vision pigments. Annu. Rev. Genomics Hum. Genet., 9, 259-282.

Yuen, E.Y.L. & Dudgeon, D. (2016) Dietary Dependence of Predatory Arthropods on Volant Aquatic Insects in Tropical Stream Riparia. Biotropica, 48, 218-228.

156

Zeug, S.C. & Winemiller, K.O. (2008) Evidence supporting the importance of terrestrial carbon in a large-river food web. Ecology, 89, 1733-1743.

Zschokke, S., Henaut, Y., Benjamin, S.P. & Garcia-Ballinas, J.A. (2006) Prey-capture strategies in sympatric web-building spiders. Canadian Journal of Zoology-Revue Canadienne De Zoologie, 84, 964-973.

157

Appendix A. Chapter 2: Supplementary Material

158

Additional Methods

Riparian Vegetation Collections

Dominant vegetation at upper-estuary sites included bald cypress (Taxodinium distichum [L.]

Rich), coastal plain willow (Salix caroliniana Michx), pond apple (Annona glabra L.), creeping primrose-willow (Ludwigia repens J.R. Forst), pickerelweed (Pontederia cordata L.), and broadleaf arrowhead (Sagittaria lancifolia L.). Red (Rhizophora mangle L.), white

(Laguncularia racemose [L.] C.F. Gaertn.), and black (Avicennia germinans L.) mangroves were dominant at mid- and lower-estuary sites. Plant specimens were collected, wrapped in aluminum foil, then placed in plastic bags and stored on ice for transport back to the lab. Leaves were gently scrubbed of epibiota, rinsed with DI water, then dried at 40-45°C for 48 h, crushed, and packaged for stable isotope analysis.

Fish Surveys

Fish surveys were used to provide a preliminary description of assemblages across the study system, but were not meant to be quantitative. During each sampling period, galvanized steel

Gee minnow traps (3.175-mm) were placed at upstream, mid, and downstream segments, submerged just below the low-tide line for 48-h. We used a fine-mesh cast-net to sample shallow areas at freshwater reaches. Due to limitations of large boat access, trawling surveys were only conducted at the lower-estuary reaches (closer to the mouth of the East River) once per field season during diurnal mid- to high-tide level.

159

Table A.1 Fish species observed at upper- (FW: freshwater), mid- (MH: mesohaline), and lower- estuary (PH: polyhaline) reaches during the study period, along with foraging guild based on

FishBase (2017).

Foraging Estuary Position Species Common Name group Belonesox belizanus Pike Killifish Carn Gambusia holbrooki Eastern Mosquitofish Insect Upper-estuary Hemichromis bimaculatus African Jewelfish Omni FW Lepomis punctatus Spotted Sunfish Invert Lepomis sp. Sunfish Invert Belonesox belizanus Pike Killifish Carn Mid-estuary Cichlasoma urophthalmus Mayan Cichlid Carn MH Fundulus luciae Spotfin Killifish Omni Menidia sp. Silverside Invert Achirus lineatus Lined Sole Carn Anchoa mitchilli Bay Anchovy Plank Anchoa sp. Anchovy Plank Archosagrgus probatocephalus Sheepshead Invert Ariopsis felis Hardhead Catfish Omni Lower-estuary Bairdiella chrysoura Silver Perch Omni PH Eucinostomus argenteus Spotfin Mojarra Invert Eucinostomus gula Silver Jenny Invert Eucinostomus sp. Mojarra Invert Lutjanus synagris Lane Snapper Carn Synodus foetens Lizard Fish Carn

160

Table A.2 SIMPER results for emergent aquatic insect taxa contributing to dissimilarity (up to

97%) among upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) assemblages. Mean emergence rates (no. ind m-2 d-1) for each salinity level are also shown for each salinity level.

% Contribution Proportion of total Family FW MH to difference variation explained Chironomidae 12.25 15.79 53.13 0.82 Dolichopodidae 0.25 0.15 2.76 0.86 Upper (FW)- Culicidae 0.05 0.16 1.99 0.89 Mid (MH) Hydroptilidae 0.05 0.17 1.88 0.92 Ceratopogonidae 0.14 0.14 1.80 0.95 Sarcophagidae 0.06 0.00 0.69 0.96 Tabanidae 0.03 0.00 0.60 0.97 Baetidae 0.14 0.00 0.41 0.97 % Contribution Proportion of total Family FW PH to difference variation explained Chironomidae 12.25 1.03 54.93 0.65 Dolichopodidae 0.25 1.48 12.00 0.80 Upper (FW)- Culicidae 0.05 0.77 5.31 0.86 Lower (PH) Ceratopogonidae 0.14 0.42 4.70 0.92 Sarcophagidae 0.06 0.00 1.43 0.93 Tabanidae 0.03 0.00 1.40 0.95 Ephydridae 0.01 0.07 0.78 0.96 Simuliidae 0.01 0.02 0.74 0.97 % Contribution Proportion of total Family MH PH to difference variation explained Chironomidae 15.79 1.03 56.44 0.68 Mid (MH)- Dolichopodidae 0.15 1.48 11.18 0.81 Lower (PH) Culicidae 0.16 0.77 6.59 0.89 Ceratopogonidae 0.14 0.42 3.67 0.94 Hydroptilidae 0.17 0.00 2.68 0.97

161

S15 (A) W16

S16 W15

Estuary position (B) (upper  lower)

Figure A.1 (A) Salinity trends over the course of this study at upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches and (B) salinity levels observed at one of our lower-estuary reaches during the summer 2016 (Source:

USGS 255327081275900). 162

Extent of drought cover of Florida cover (%) ExtentFlorida of drought of

Figure A.2 Recent trends of drought intensity in Florida (D0 = Abnormally dry; D1 =

Moderate drought; D2 = Severe drought; D3 = Extreme drought; D4 = Exceptional drought), including drought conditions experienced during the summer (Jun- Jul 2015) and winter (Dec 2016- Jan 2017) sampling periods of this study denoted by black dashed lines. (Source: NIDIS)

163

Estuary position (upper  lower)

Figure A.3 Species of fish representing carnivore, insectivore, omnivore, benthic invertivore, and planktivore foraging guilds detected at upper- (FW: freshwater), mid-

(MH: mesohaline), and lower-estuary (PH: polyhaline) reaches of the Fakahatchee Strand and Ten Thousand Islands Estuary.

164

winter

summer

Estuary position (upper  lower)

Figure A.4 Aquatic insect emergence rates observed at upper- (FW: freshwater), mid-

(MH: mesohaline), and lower-estuary (PH: polyhaline) reaches of the Fakahatchee

Strand- Ten Thousand Islands Estuary (Florida) during the winter and summer seasons of

2015-2017, plotted as raw data. Error bars represent ±1SE.

165

Estuary position (upper  lower)

Figure A.5 Mean body size of the two most common families of emergent aquatic insects

(Chironomidae and Dolichopodidae) observed at upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches of the Fakahatchee Strand- Ten

Thousand Islands Estuary (Florida), plotted as raw data. Error bars represent ±1SE.

166

Estuary position (upper  lower)

Figure A.6 Densities of orb-weaving spiders (Araneidae, Nephilinae, and Tetragnathidae) by season (S= summer, W= winter) and estuary position (upper-, mid-, lower-estuary), plotted as raw data.

167

(A)

R2 = 0.06, P = 0.125; y = 0.003x – 1.298

(B)

(R2 = 0.06, P = 0.125; y = 0.003x – 1.298).

168

R2 = 0.16, P = 0.037; y = 0.15x + 0.15

Figure A.8 The relationship between body condition of Tetragnatha and body size of

Chironomidae: R2 = 0.16, P = 0.037; y = 0.15x + 0.15.

169

Figure A.9 Spatial and seasonal patterns of (A) chlorophyll-a, (B) total nitrogen, (C) phosphate, and (D) aquatic insect emergence rate in the Fakahatchee Strand and Ten Thousand Islands Estuary, plotted as raw data.

170

(A)

(B) Season/Year

TotalN

Chlorophyll

) 1

)

Phosphate(PO Emergencerate (no. ind m

Estuary position Estuary position (upper  lower) (upper  lower)

171

Appendix B. Chapter 3: Supplementary Information

172

Additional Methods

Fish Collections

Sampling of fish assemblages was modified for efficient capture in each environment and thus we are limited to qualitative measures of fish communities. At each study reach, small fishes were sampled using 1/8-inch metal-mesh minnow traps. Each trap was tied to a fixed structure on the bank and submerged just below the low-tide line. After 48 h, fishes were identified, measured for total length and a subset was collected for stable isotope analysis. Trawling surveys were conducted only at lower-estuary reaches (due to limitations of boat access to upper- and mid-estuary reaches) once per field season to characterize the benthic fish assemblage. Lastly, we used cast-netting techniques to sample shallow upper-estuary areas. At least one individual of each species per reach was frozen for processing in the lab. A fragment of dorsal white muscle tissue from each collected specimen was extracted, freeze-dried for 48 h, and pulverized for stable isotope analysis.

173

Table B.1 δ13C and δ15N (mean ± SD) values of aquatic and terrestrial primary producers, emergent aquatic insects, and spiders.

Estuary Summer Winter Summer Winter Category position 2015 2016 2015 2016 ẟ13C ẟ15N ẟ13C ẟ15N Upper – – – X – – -33.406 ± 2.626 -0.358 ± 1.615 Phytoplankton Mid – – – X – – -34.225 ± 1.108 1.178 ± 0.495 Lower – – – X – – -20.737 ± 1.192 0.673 ± 1.019 Upper – X X X -27.489 ± 2.164 0.880 ± 0.840 -33.064 ± 2.376 2.863 ± 1.131 Epiphyton Mid X X X X -28.409 ± 2.535 1.986 ± 1.562 -25.074 ± 3.111 4.876 ± 0.568 Lower – X X X -36.210 ± 2.521 4.737 ± 0.200 -25.121 ± 2.815 4.409 ± 1.269 Upper – X X X -31.893 ± 0.774 1.14 ± 1.176 -30.431 ± 1.628 0.221 ± 0.753 Mid X X X X -29.804 ± 1.186 -0.146 ± 2.177 -30.463 ± 1.413 -1.203 ± 2.050 Leaves Lower X X X X -28.361 ± 0.741 1.485 ± 1.412 -28.725 ± 1.037 2.317 ± 0.923

Upper – X X X -29.189 ± 0.812 0.291 ± 0.833 -29.205 ± 0.834 0.543 ± 0.903 Terrestrial Mid X X X X -28.617 ± 0.595 0.832 ± 1.364 -28.952 ± 0.980 0.217 ± 1.590 vegetation Lower X X X X -27.392 ± 0.482 1.927 ± 0.295 -27.392 ± 0.482 1.927 ± 0.295 Upper – X X X -36.139 ± 4.428 5.204 ± 0.601 -36.103 ± 2.161 5.669 ± 0.586 Chironomidae Mid – X X X -28.713 ± 1.785 5.303 ± 0.990 -32.445 ± 1.773 4.934 ± 0.549 Lower – – X X – – -28.102 ± 2.705 4.628 ± 1.177 Upper – X – X -23.675 ± 0.451 6.343 ± 2.570 -28.113 ± 3.369 5.045 ± 0.494 Dolichopodidae Mid – X – X – – -25.39 4.6 Lower – X X X -26.973 ± 0.546 6.608 ± 0.177 -27.487 ± 0.744 6.126 ± 0.503 Araneidae Mid X X X X -27.035 ± 2.019 5.247 ± 0.432 -29.377 ± 0.743 4.582 ± 0.470 Lower – – X X – – -27.927 ± 0.646 4.082 ± 1.591 Nephilinae Mid X X – – -25.493 ± 1.252 4.731 ± 0.789 – – Lower – X X X -26.455 ± 0.900 3.323 ± 1.221 -27.491 ± 0.910 4.96 ± 0.818 Tetragnathidae Upper – X X X -28.397 ± 1.933 3.711 ± 0.610 -29.837 ± 1.696 4.053 ± 0.747 Mid X X X X -25.465 ± 2.266 5.111 ± 0.691 -30.799 ± 0.989 5.045 ± 0.463 Lower X X X X -26.750 ± 0.795 4.924 ± 0.870 -28.615 ± 0.682 4.922 ± 0.761

174

Summer Winter Salinity Family Source Mean SD 95% CI Mean SD 95% CI Upper-estuary Chironomidae Epiphyton 0.134 0.066 0.037 - 0.257 0.902 0.154 0.181 - 0.976 (FW) Phytoplankton 0.829 0.073 0.661 - 0.933 0.081 0.152 0.011 - 0.156 Terrestrial 0.037 0.032 0.001 - 0.099 0.017 0.016 0 - 0.048

Dolichopodidae Epiphyton 0.137 0.117 0.015 - 0.359 0.888 0.144 0.429 - 0.984

Phytoplankton 0.831 0.127 0.519 - 0.967 0.093 0.141 0.008 - 0.233

Terrestrial 0.032 0.043 0 - 0.115 0.019 0.027 0 - 0.07 Tetragnathidae Epiphyton 0.163 0.145 0.016 - 0.486 0.825 0.107 0.616 - 0.989 Phytoplankton 0.623 0.177 0.324 - 0.947 0.063 0.077 0.004 - 0.21 Terrestrial 0.214 0.169 0.001 - 0.508 0.112 0.088 0 - 0.262 Mid-estuary Chironomidae Epiphyton 0.977 0.02 0.925 - 0.997 0.95 0.041 0.85 - 0.997 (MH) Phytoplankton 0.017 0.018 0 - 0.052 0.042 0.041 0 - 0.124 Terrestrial 0.006 0.008 0 - 0.021 0.008 0.011 0 - 0.029

Epiphyton ------0.899 0.081 0.711 - 0.994 Dolichopodidae Phytoplankton ------0.068 0.07 0 - 0.224 Terrestrial ------0.033 0.052 0 - 0.145 Araneidae Epiphyton 0.943 0.034 0.862 - 0.987 0.877 0.072 0.732 - 0.989 Phytoplankton 0.039 0.031 0.001 - 0.102 0.096 0.075 0.001 - 0.229 Terrestrial 0.018 0.019 0 - 0.056 0.026 0.034 0 - 0.099

Epiphyton 0.943 0.042 0.846 - 0.994 ------Nephilinae Phytoplankton 0.034 0.034 0.001 - 0.105 ------Terrestrial 0.023 0.029 0 - 0.089 ------Tetragnathidae Epiphyton 0.978 0.016 0.937 - 0.997 0.95 0.038 0.862 - 0.997 175

Phytoplankton 0.015 0.014 0 - 0.045 0.042 0.038 0 - 0.117 Terrestrial 0.006 0.008 0 - 0.022 0.008 0.011 0 - 0.031 Chironomidae Epiphyton ------0.979 0.022 0.92 - 0.998 Lower-estuary Phytoplankton ------0.007 0.01 0 - 0.026 (PH)

Terrestrial 0.013 0.019 0 - 0.051 Dolichopodidae Epiphyton 0.942 0.041 0.845 - 0.991 0.99 0.012 0.958 - 0.999 Phytoplankton 0.031 0.033 0 - 0.1 0.004 0.005 0 - 0.013 Terrestrial 0.026 0.028 0 - 0.084 0.006 0.01 0 - 0.026

Araneidae Epiphyton ------0.966 0.031 0.886 - 0.996

Phytoplankton ------0.011 0.012 0 - 0.036

Terrestrial ------0.023 0.028 0 - 0.084 Nephilinae Epiphyton 0.747 0.102 0.538 - 0.896 0.942 0.051 0.807 - 0.994 Phytoplankton 0.086 0.068 0.002 - 0.205 0.012 0.013 0 - 0.037 Terrestrial 0.167 0.141 0.001 - 0.413 0.046 0.052 0 - 0.153

Tetragnathidae Epiphyton 0.866 0.055 0.753 - 0.953 0.978 0.017 0.935 - 0.996

Phytoplankton 0.067 0.051 0.001 - 0.154 0.007 0.007 0 - 0.021

Terrestrial 0.067 0.064 0.001 - 0.198 0.014 0.017 0 - 0.049

176

(A)

(B) (C) R2 = 0.83, P < 0.001; y = 1.14x + 3.55 R2 = 0.70, P < 0.001; y = 0.92x + 2.96

Figure B.1 (A) Measurement of abdominal length (AL) and width (AW) used in estimation of abdomen volume in order to estimate body condition of shoreline spiders (B) Leucage and (C) Tetragnatha following Moya-Laraño et al. (2008).

177

Figure B.2 Reliance on aquatically- and terrestrially-derived energy by emergent aquatic insects

(Chironomidae and Dolichopodidae) and orb-weaving spiders (Tetragnathidae, Nephilinae,

Araneidae) at (A) upper-estuary, (B) mid-estuary, and (C) lower-estuary reaches of Fakahatchee

Strand- Ten Thousand Islands Estuary during summer and winter seasons. Means are based on a

Bayesian mixing model developed using MixSIAR (Stock and Semmens 2013).

178

(A)

(B)

(C)

179

Appendix C. Supplementary Data

180

Table C.1 Location of study reaches in the Fakahatchee Strand Preserve State Park and

Rookery Bay National Estuarine Research Reserve in southwest Florida, U.S.A.

Estuary position Reach Latitude Longitude CW 25.97995 -81.4007 upper CM 25.97998 -81.3894 CE 25.97998 -81.3833 Long 25.92737 -81.4435 mid Bryan 25.91818 -81.4422 Diagon 25.91212 -81.4436 USGS 25.89611 -81.4596 lower Fork 25.89253 -81.4604 Summit 25.89315 -81.4692

181

Table C.2 Aquatic insect emergence rate and diversity metrics observed across spatial gradients of the Fakahatchee Strand and Ten

Thousand Islands Estuary during the summer and winter summarized by transect.

Emergence rate Shannon diversity Pielou's evenness Richness Season Estuary position Reach Transect (no. ind m-2 d-1) (H') (J) (Nf) summer upper CW top 8.4 0.44 0.63 2.00 summer upper CW mid 8.2 0.39 0.59 2.25 summer upper CW btm 68.6 0.07 0.20 1.50 summer upper CM top 9.5 0.20 0.36 2.00 summer upper CM mid 3.9 0.34 0.63 2.00 summer upper CM btm 9.3 0.05 0.14 1.50 summer upper CE top 3.9 0.47 0.68 2.00 summer upper CE mid 19.9 0.15 0.32 1.67 summer upper CE btm 6.8 0.42 0.49 2.33 summer mid Long mid 7.0 0.47 0.86 2.00 summer mid Bryan top 0.6 0.00 -- 1.00 summer mid Bryan mid 1.7 0.56 0.81 2.00 summer mid Bryan btm 3.6 0.00 -- 1.00 summer mid Diagon top 4.0 0.13 0.37 1.50 summer mid Diagon mid 16.0 0.07 0.19 1.50 summer mid Diagon btm 3.9 0.00 -- 1.00 summer lower USGS top 0.0 0.00 0.00 0.00 summer lower USGS btm 0.5 0.00 -- 1.00 summer lower Fork top 3.7 0.53 0.81 2.25 summer lower Fork mid 0.0 0.00 0.00 0.00 summer lower Fork btm 7.7 0.21 0.30 2.50 summer lower Summit top 0.0 0.00 0.00 0.00 summer lower Summit mid 3.9 0.06 0.13 1.00 182 summer lower Summit btm 0.0 0.00 0.00 0.00 winter upper CW top 11.5 0.18 0.26 2.00 winter upper CW mid 5.5 0.64 0.58 3.00 winter upper CW btm 3.3 0.66 0.96 2.50 winter upper CM mid 22.5 0.29 0.26 3.00 winter upper CM btm 14.8 0.73 0.67 3.00 winter upper CE top 7.3 0.58 0.56 2.50 winter upper CE mid 4.3 0.29 0.41 2.00 winter upper CE btm 14.6 0.26 0.38 2.00 winter mid Long top 3.2 0.35 0.50 2.00 winter mid Long mid 3.1 0.40 0.76 1.75 winter mid Long btm 13.4 0.45 0.56 3.00 winter mid Bryan top 18.2 0.05 0.13 1.50 winter mid Bryan mid 15.4 0.04 0.11 1.50 winter mid Bryan btm 6.6 0.45 0.64 2.00 winter mid Diagon top 16.0 0.14 0.20 2.00 winter mid Diagon mid 60.1 0.07 0.24 1.40 winter mid Diagon btm 25.1 0.07 0.20 1.50 winter lower USGS top 2.4 0.68 0.98 2.50 winter lower USGS mid 0.5 0.00 -- 1.00 winter lower USGS btm 4.6 0.54 0.78 2.00 winter lower Fork mid 15.5 0.81 0.51 5.00 winter lower Fork btm 4.3 1.07 0.85 3.50 winter lower Summit mid 1.5 0.64 0.92 2.00 winter lower Summit btm 0.7 0.35 1.00 1.50

183

Table C.3 Mean body size of emergent insects across spatial gradients of the Fakahatchee

Strand and Ten Thousand Islands Estuary, Florida.

Estuary position Reach Transect Family Mean body size (mg) CW top Chironomidae 0.05 CW mid Chironomidae 0.09 CW btm Chironomidae 0.06 CM top Chironomidae 0.02 CM mid Chironomidae 0.02 CM btm Chironomidae 0.03 Upper-estuary CE top Chironomidae 0.11 (FW) CE mid Chironomidae 0.02 CE btm Chironomidae 0.09 CW mid Dolichopodidae 0.15 CW btm Dolichopodidae 0.75 CE mid Dolichopodidae 0.05 CE btm Dolichopodidae 0.03 Long top Chironomidae 0.15 Long mid Chironomidae 0.09 Long btm Chironomidae 0.11 Bryan top Chironomidae 0.05 Bryan mid Chironomidae 0.06 Mid-estuary Bryan btm Chironomidae 0.01 (MH) Diagon top Chironomidae 0.03 Diagon mid Chironomidae 0.06 Diagon btm Chironomidae 0.03 Long mid Dolichopodidae 0.30 Long btm Dolichopodidae 0.15 Diagon btm Dolichopodidae 0.30 USGS top Chironomidae 0.11 USGS mid Chironomidae 0.50 Fork mid Chironomidae 0.02 Fork btm Chironomidae 0.09 Lower-estuary Summit mid Chironomidae 0.02 (PH) Summit btm Chironomidae 0.80 USGS mid Dolichopodidae 0.40 Fork mid Dolichopodidae 0.53 Fork btm Dolichopodidae 0.58 Summit btm Dolichopodidae 1.10

184

Table C.4 Densities of orb-weaving spiders (Tetragnathidae, Araneidae, and subfamily

Nephilinae) observed across upper- (FW: freshwater), mid- (MH: mesohaline), and lower-estuary (PH: polyhaline) reaches of the Fakahatchee Strand and Ten Thousand

Islands Estuary, Florida, U.S.A.

Estuary Spider Density Season Season/Year Reach Transect position (no. ind m-2) summer S15 mid Long top 0.4 summer S15 mid Long mid 0.4 summer S15 mid Long btm 0.2 summer S15 mid Bryan top 0.4 summer S15 mid Bryan mid 0.5 summer S15 mid Bryan btm 0.3 summer S15 mid Diagon top 0.4 summer S15 mid Diagon mid 0.3 summer S15 mid Diagon btm 0.4 summer S15 lower USGS top 0.0 summer S15 lower USGS mid 0.1 summer S15 lower USGS btm 0.1 summer S15 lower Fork top 0.1 summer S15 lower Fork mid 0.1 summer S15 lower Fork btm 0.1 summer S15 lower Summit top 0.1 summer S15 lower Summit mid 0.1 summer S15 lower Summit btm 0.1 summer S16 upper CW top 0.2 summer S16 upper CW mid 0.2 summer S16 upper CW btm 0.2 summer S16 upper CM top 0.2 summer S16 upper CM mid 0.1 summer S16 upper CM btm 0.1 summer S16 upper CE top 0.1 summer S16 upper CE mid 0.1 summer S16 upper CE btm 0.1 summer S16 mid Long top 0.5 summer S16 mid Long mid 0.4 summer S16 mid Long btm 0.4 summer S16 mid Bryan top 0.3 summer S16 mid Bryan mid 0.3 summer S16 mid Bryan btm 0.2 summer S16 mid Diagon top 0.3 summer S16 mid Diagon mid 0.3 summer S16 mid Diagon btm 0.3 summer S16 lower USGS top 0.1 summer S16 lower USGS mid 0.2 summer S16 lower USGS btm 0.1 summer S16 lower Fork top 0.1 185 summer S16 lower Fork mid 0.1 summer S16 lower Fork btm 0.2 summer S16 lower Summit top 0.2 summer S16 lower Summit mid 0.2 summer S16 lower Summit btm 0.1 winter W15 upper CW top 0.2 winter W15 upper CW mid 0.1 winter W15 upper CW btm 0.1 winter W15 upper CE top 0.3 winter W15 upper CE mid 0.4 winter W15 upper CE btm 0.3 winter W15 mid Long top 1.7 winter W15 mid Long mid 2.2 winter W15 mid Long btm 2.4 winter W15 mid Bryan top 0.8 winter W15 mid Bryan mid 2.3 winter W15 mid Bryan btm 1.1 winter W15 mid Diagon top 2.1 winter W15 mid Diagon mid 1.1 winter W15 mid Diagon btm 1.3 winter W15 lower USGS top 0.2 winter W15 lower USGS mid 0.3 winter W15 lower USGS btm 0.3 winter W15 lower Fork top 0.2 winter W15 lower Fork mid 0.2 winter W15 lower Fork btm 0.1 winter W15 lower Summit top 0.2 winter W15 lower Summit mid 0.2 winter W15 lower Summit btm 0.1 winter W16 upper CW top 0.1 winter W16 upper CW mid 0.1 winter W16 upper CW btm 0.1 winter W16 upper CM top 0.1 winter W16 upper CM mid 0.1 winter W16 upper CM btm 0.1 winter W16 upper CE top 0.1 winter W16 upper CE mid 0.1 winter W16 upper CE btm 0.1 winter W16 mid Long top 1.2 winter W16 mid Long mid 1.5 winter W16 mid Long btm 1.6 winter W16 mid Bryan top 0.5 winter W16 mid Bryan mid 0.3 winter W16 mid Bryan btm 0.5 winter W16 mid Diagon top 1.1 winter W16 mid Diagon mid 0.6 winter W16 mid Diagon btm 0.7 winter W16 lower USGS top 0.1 winter W16 lower USGS mid 0.0 winter W16 lower USGS btm 0.1 winter W16 lower Fork top 0.1 186 winter W16 lower Fork mid 0.1 winter W16 lower Fork btm 0.2 winter W16 lower Summit top 0.2 winter W16 lower Summit mid 0.2 winter W16 lower Summit btm 0.1

187

Florida, U.S.A.

Abdom. Abdom. Dry Abdom. Season/ Estuary Body Season Reach Family Genus Length Width mass Volume Year position condition (mm) (mm) (mg) (mm3) summer S15 mid Long Tetragnathidae Leucage 4.5 2.5 0.01 14.72 -0.11 summer S15 mid Long Tetragnathidae Leucage 5.0 2.8 0.01 19.79 -0.08 summer S15 mid Long Tetragnathidae Leucage 5.5 3.0 0.01 25.91 -0.05 summer S15 mid Long Tetragnathidae Leucage 6.5 2.5 0.02 21.26 -0.29 summer S15 mid Long Tetragnathidae Leucage 5.0 2.0 0.01 10.47 -0.30 summer S15 mid Long Tetragnathidae Leucage 7.5 3.5 0.03 48.08 -0.14 summer S15 mid Long Tetragnathidae Leucage 6.0 2.0 0.01 12.56 -0.29 summer S15 mid Long Tetragnathidae Leucage 4.0 1.8 0.01 6.78 -0.19 summer S15 mid Long Tetragnathidae Leucage 6.0 2.5 0.01 19.63 -0.11 summer S15 mid Long Tetragnathidae Leucage 5.5 2.3 0.01 14.57 -0.14 summer S15 mid Long Tetragnathidae Leucage 6.0 3.5 0.03 38.47 -0.26 summer S15 mid Long Tetragnathidae Leucage 6.0 3.0 0.01 28.26 0.07 summer S15 mid Long Tetragnathidae Leucage 4.5 2.5 0.02 14.72 -0.54 summer S15 mid Long Tetragnathidae Tetragnatha 3.5 1.5 0.01 4.12 -0.36 summer S15 mid Bryan Tetragnathidae Leucage 2.5 1.3 0.00 2.04 0.18 summer S15 mid Bryan Tetragnathidae Leucage 4.3 3.0 0.01 20.02 0.20 summer S15 mid Bryan Tetragnathidae Leucage 6.3 3.5 0.01 40.07 0.26 summer S15 mid Bryan Tetragnathidae Leucage 5.5 2.0 0.01 11.51 -0.06

188 summer S15 mid Bryan Tetragnathidae Leucage 6.0 2.0 0.01 12.56 -0.04 summer S15 mid Bryan Tetragnathidae Leucage 4.5 2.0 0.00 9.42 0.26 summer S15 mid Bryan Tetragnathidae Leucage 6.0 4.0 0.01 50.24 0.27 summer S15 mid Bryan Tetragnathidae Leucage 6.0 4.0 0.01 50.24 0.33 summer S15 mid Bryan Tetragnathidae Leucage 4.0 2.0 0.01 8.37 -0.20 summer S15 mid Bryan Tetragnathidae Leucage 4.5 1.5 0.00 5.30 0.20 summer S15 mid Bryan Tetragnathidae Leucage 3.5 2.0 0.00 7.33 0.07 summer S15 mid Bryan Tetragnathidae Leucage 6.0 2.5 0.01 19.63 -0.07 summer S15 mid Bryan Tetragnathidae Leucage 6.0 2.7 0.02 22.89 -0.12 summer S15 mid Bryan Tetragnathidae Leucage 4.5 2.5 0.01 14.72 -0.10 summer S15 mid Bryan Tetragnathidae Leucage 4.0 2.0 0.00 8.37 0.08 summer S15 mid Bryan Tetragnathidae Leucage 4.0 2.0 0.01 8.37 -0.14 summer S15 mid Bryan Tetragnathidae Leucage 6.0 3.0 0.01 28.26 0.10 summer S15 mid Bryan Tetragnathidae Leucage 6.3 4.0 0.01 52.33 0.36 summer S15 mid Bryan Tetragnathidae Leucage 4.3 2.5 0.01 13.90 -0.20 summer S15 mid Bryan Tetragnathidae Leucage 3.0 1.5 0.01 3.53 -0.62 summer S15 mid Bryan Tetragnathidae Leucage 3.0 2.0 0.01 6.28 -0.26 summer S15 mid Bryan Tetragnathidae Leucage 3.0 1.0 0.01 1.57 -0.93 summer S15 mid Bryan Tetragnathidae Leucage 4.3 2.0 0.01 8.90 -0.29 summer S15 mid Diagon Tetragnathidae Leucage 6.0 3.0 0.03 28.26 -0.37 summer S15 mid Diagon Tetragnathidae Leucage 6.5 3.0 0.01 30.62 0.21 summer S15 mid Diagon Tetragnathidae Leucage 2.5 1.3 0.02 2.04 -1.31 summer S15 mid Diagon Tetragnathidae Leucage 6.0 3.0 0.03 28.26 -0.37 summer S15 mid Diagon Tetragnathidae Leucage 6.5 3.0 0.01 30.62 0.21 summer S15 mid Diagon Tetragnathidae Leucage 5.0 2.5 0.01 16.35 -0.06 summer S15 mid Diagon Tetragnathidae Leucage 7.0 2.8 0.01 28.72 0.18 summer S15 mid Diagon Tetragnathidae Leucage 6.0 3.0 0.02 28.26 -0.17 summer S15 mid Diagon Tetragnathidae Leucage 3.5 2.0 0.01 7.33 -0.41 189 summer S15 mid Diagon Tetragnathidae Leucage 4.0 1.8 0.01 6.41 -0.47 summer S15 mid Diagon Tetragnathidae Leucage 4.5 2.5 0.01 14.72 -0.11 summer S15 mid Diagon Tetragnathidae Leucage 5.5 2.0 0.01 11.51 -0.21 summer S15 mid Diagon Tetragnathidae Leucage 6.0 2.5 0.01 19.63 0.02 summer S15 mid Diagon Tetragnathidae Leucage 4.5 2.0 0.01 9.42 -0.30 summer S15 mid Diagon Tetragnathidae Leucage 5.5 2.5 0.01 17.99 -0.02 summer S15 mid Diagon Tetragnathidae Leucage 5.8 2.5 0.01 18.97 0.00 summer S15 lower USGS Tetragnathidae Leucage 6.0 3.5 0.01 38.47 0.15 summer S15 lower USGS Tetragnathidae Tetragnatha 5.0 1.5 0.01 5.89 -0.17 summer S15 lower USGS Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 0.00 summer S15 lower USGS Tetragnathidae Tetragnatha 3.8 1.3 0.00 3.07 -0.19 summer S15 lower USGS Tetragnathidae Tetragnatha 6.0 2.0 0.01 12.56 0.03 summer S15 lower USGS Tetragnathidae Tetragnatha 6.0 1.5 0.01 7.07 -0.17 summer S15 lower USGS Tetragnathidae Tetragnatha 6.5 1.8 0.01 10.42 -0.08 summer S15 lower USGS Tetragnathidae Tetragnatha 4.5 1.8 0.00 7.21 0.06 summer S15 lower Fork Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 0.58 summer S15 lower Fork Tetragnathidae Tetragnatha 5.0 2.8 0.01 19.79 0.46 summer S15 lower Fork Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 0.16 summer S15 lower Fork Tetragnathidae Tetragnatha 4.3 1.5 0.00 5.00 0.23 summer S15 lower Fork Tetragnathidae Tetragnatha 7.0 3.0 0.02 32.97 0.22 summer S15 lower Fork Tetragnathidae Tetragnatha 1.3 1.0 0.00 0.65 -0.38 summer S15 lower Fork Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 0.00 summer S15 lower Fork Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.27 summer S15 lower Fork Tetragnathidae Tetragnatha 6.0 2.5 0.01 19.63 0.22 summer S15 lower Fork Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 0.20 summer S15 lower Fork Tetragnathidae Tetragnatha 4.5 2.0 0.01 9.42 0.14 summer S15 lower Fork Tetragnathidae Tetragnatha 4.0 1.3 0.00 3.27 0.32 summer S15 lower Fork Tetragnathidae Tetragnatha 5.0 2.0 0.00 10.47 0.39 190 summer S15 lower Fork Tetragnathidae Tetragnatha 3.3 1.0 0.00 1.70 0.04 summer S15 lower Fork Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 -0.02 summer S15 lower Summit Tetragnathidae Tetragnatha 5.5 1.5 0.01 6.48 -0.16 summer S15 lower Summit Tetragnathidae Tetragnatha 7.5 2.0 0.02 15.70 -0.09 summer S15 lower Summit Tetragnathidae Tetragnatha 1.5 1.0 0.00 0.79 0.35 summer S15 lower Summit Tetragnathidae Tetragnatha 4.0 1.5 0.01 4.71 -0.36 summer S15 lower Summit Tetragnathidae Tetragnatha 3.0 1.5 0.00 3.53 0.12 summer S15 lower Summit Tetragnathidae Tetragnatha 5.5 1.5 0.01 6.48 -0.15 summer S15 lower Summit Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.03 summer S15 lower Summit Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 -0.14 summer S15 lower Summit Tetragnathidae Tetragnatha 4.5 1.3 0.01 3.68 -0.27 summer S15 lower Summit Tetragnathidae Tetragnatha 7.0 2.0 0.01 14.65 -0.02 summer S15 lower Summit Tetragnathidae Tetragnatha 4.5 2.0 0.00 9.42 0.23 summer S15 lower Summit Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 0.11 summer S15 lower Summit Tetragnathidae Tetragnatha 5.0 1.8 0.00 8.01 0.16 summer S16 upper CW Tetragnathidae Leucage 3.5 2.5 0.00 11.45 0.21 summer S16 upper CW Tetragnathidae Leucage 2.5 1.5 0.00 2.94 -0.03 summer S16 upper CW Tetragnathidae Leucage 2.5 1.0 0.00 1.31 0.24 summer S16 upper CW Tetragnathidae Leucage 3.5 2.0 0.01 7.33 -0.09 summer S16 upper CW Tetragnathidae Leucage 1.0 1.0 0.00 0.52 -0.30 summer S16 upper CW Tetragnathidae Leucage 3.0 1.5 0.00 3.53 0.10 summer S16 upper CW Tetragnathidae Leucage 12.5 2.0 0.01 26.17 0.12 summer S16 upper CW Tetragnathidae Leucage 2.0 1.0 0.00 1.05 0.06 summer S16 upper CW Tetragnathidae Leucage 5.5 3.0 0.01 25.91 -0.03 summer S16 upper CW Tetragnathidae Leucage 2.8 1.5 0.00 3.24 -0.26 summer S16 upper CW Tetragnathidae Leucage 3.0 1.6 0.00 4.02 -0.04 summer S16 upper CW Tetragnathidae Leucage 2.0 1.5 0.00 2.20 0.32 summer S16 upper CW Tetragnathidae Leucage 3.5 2.3 0.00 9.27 0.05 191 summer S16 upper CW Tetragnathidae Leucage 3.0 2.0 0.00 6.28 0.25 summer S16 upper CW Tetragnathidae Leucage 3.0 1.8 0.00 4.81 0.08 summer S16 upper CW Tetragnathidae Leucage 3.5 2.5 0.01 11.45 0.06 summer S16 upper CW Tetragnathidae Leucage 2.5 0.8 0.00 0.74 -0.56 summer S16 upper CW Tetragnathidae Tetragnatha 2.0 1.5 0.00 2.36 0.83 summer S16 upper CW Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.03 summer S16 upper CW Tetragnathidae Tetragnatha 3.0 0.8 0.00 0.88 -0.24 summer S16 upper CW Tetragnathidae Tetragnatha 2.0 0.8 0.00 0.59 0.22 summer S16 upper CW Tetragnathidae Tetragnatha 4.0 1.3 0.00 3.27 -0.03 summer S16 upper CW Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.03 summer S16 upper CW Tetragnathidae Tetragnatha 4.0 1.3 0.00 3.27 0.03 summer S16 upper CW Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 0.05 summer S16 upper CW Tetragnathidae Tetragnatha 3.3 1.0 0.00 1.70 -0.10 summer S16 upper CW Tetragnathidae Tetragnatha 5.5 1.5 0.00 6.48 0.11 summer S16 upper CW Tetragnathidae Tetragnatha 2.5 0.6 0.00 0.47 -0.37 summer S16 upper CW Tetragnathidae Tetragnatha 4.0 0.8 0.00 1.18 -0.45 summer S16 upper CW Tetragnathidae Tetragnatha 2.5 1.0 0.00 1.31 -0.03 summer S16 upper CW Tetragnathidae Tetragnatha 2.3 1.0 0.00 1.18 0.53 summer S16 upper CW Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 0.00 summer S16 upper CW Tetragnathidae Tetragnatha 4.0 0.5 0.00 0.52 -1.08 summer S16 upper CW Tetragnathidae Tetragnatha 1.3 0.5 0.00 0.16 -0.70 summer S16 upper CW Tetragnathidae Tetragnatha 2.3 0.8 0.00 0.66 -0.41 summer S16 upper CW Tetragnathidae Tetragnatha 4.0 1.3 0.01 3.27 -0.39 summer S16 upper CW Tetragnathidae Tetragnatha 4.3 1.0 0.00 2.22 -0.10 summer S16 upper CW Tetragnathidae Tetragnatha 2.5 1.0 0.00 1.31 -0.11 summer S16 upper CW Tetragnathidae Tetragnatha 5.0 1.0 0.00 2.62 -0.35 summer S16 upper CW Tetragnathidae Tetragnatha 4.5 1.0 0.00 2.36 -0.05 summer S16 upper CM Tetragnathidae Leucage 2.0 1.5 0.00 2.36 0.07 192 summer S16 upper CM Tetragnathidae Leucage 2.5 1.5 0.00 2.94 -0.24 summer S16 upper CM Tetragnathidae Leucage 2.5 1.5 0.00 2.94 -0.39 summer S16 upper CM Tetragnathidae Leucage 4.0 2.0 0.01 8.37 -0.15 summer S16 upper CM Tetragnathidae Leucage 2.5 1.5 0.00 2.94 -0.40 summer S16 upper CM Tetragnathidae Leucage 2.0 1.0 0.00 1.05 -0.20 summer S16 upper CM Tetragnathidae Leucage 2.0 1.5 0.00 2.36 0.19 summer S16 upper CM Tetragnathidae Leucage 3.5 1.5 0.01 4.12 -0.32 summer S16 upper CM Tetragnathidae Leucage 3.5 2.0 0.00 7.33 0.11 summer S16 upper CM Tetragnathidae Leucage 2.5 1.5 0.00 2.94 -0.05 summer S16 upper CM Tetragnathidae Leucage 2.0 1.5 0.00 2.36 0.07 summer S16 upper CM Tetragnathidae Leucage 2.5 1.5 0.00 2.94 -0.01 summer S16 upper CM Tetragnathidae Leucage 4.5 2.5 0.01 14.72 0.04 summer S16 upper CM Tetragnathidae Leucage 2.5 1.5 0.00 2.94 -0.16 summer S16 upper CM Tetragnathidae Leucage 2.0 1.0 0.00 1.05 -0.40 summer S16 upper CM Tetragnathidae Leucage 2.5 1.0 0.00 1.31 -0.22 summer S16 upper CM Tetragnathidae Leucage 6.0 3.5 0.01 38.47 0.17 summer S16 upper CM Tetragnathidae Leucage 2.5 1.5 0.00 2.94 0.29 summer S16 upper CM Tetragnathidae Leucage 2.3 1.3 0.00 1.84 -0.07 summer S16 upper CM Tetragnathidae Leucage 2.0 1.5 0.00 2.36 0.15 summer S16 upper CM Tetragnathidae Leucage 3.0 1.5 0.00 3.53 0.37 summer S16 upper CM Tetragnathidae Leucage 2.8 1.3 0.00 2.25 -0.12 summer S16 upper CM Tetragnathidae Leucage 2.0 1.5 0.00 2.36 0.15 summer S16 upper CM Tetragnathidae Leucage 2.5 1.5 0.00 2.94 -0.34 summer S16 upper CM Tetragnathidae Leucage 2.5 1.3 0.00 2.04 0.05 summer S16 upper CM Tetragnathidae Leucage 1.5 1.5 0.00 1.77 -0.05 summer S16 upper CM Tetragnathidae Leucage 2.0 1.5 0.00 2.36 -0.05 summer S16 upper CM Tetragnathidae Tetragnatha 4.5 1.3 0.00 3.68 -0.19 summer S16 upper CM Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.13 193 summer S16 upper CM Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.16 summer S16 upper CM Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.35 summer S16 upper CM Tetragnathidae Tetragnatha 7.5 2.5 0.01 24.53 0.19 summer S16 upper CM Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 0.08 summer S16 upper CM Tetragnathidae Tetragnatha 4.5 1.0 0.00 2.36 -0.37 summer S16 upper CM Tetragnathidae Tetragnatha 2.8 0.5 0.00 0.36 -0.59 summer S16 upper CE Tetragnathidae Leucage 2.0 1.0 0.00 1.05 0.14 summer S16 upper CE Tetragnathidae Leucage 2.0 1.0 0.00 1.05 -0.16 summer S16 upper CE Tetragnathidae Leucage 1.8 1.0 0.00 0.92 -0.12 summer S16 upper CE Tetragnathidae Leucage 2.5 1.0 0.00 1.31 -0.47 summer S16 upper CE Tetragnathidae Leucage 3.5 1.8 0.00 5.61 0.01 summer S16 upper CE Tetragnathidae Leucage 1.0 0.8 0.00 0.29 -0.79 summer S16 upper CE Tetragnathidae Leucage 2.0 1.0 0.00 1.05 -0.24 summer S16 upper CE Tetragnathidae Leucage 3.0 1.5 0.00 3.53 -0.15 summer S16 upper CE Tetragnathidae Leucage 3.0 1.5 0.00 3.53 -0.02 summer S16 upper CE Tetragnathidae Leucage 2.3 1.5 0.00 2.65 -0.24 summer S16 upper CE Tetragnathidae Leucage 1.5 1.0 0.00 0.79 0.56 summer S16 upper CE Tetragnathidae Leucage 2.0 1.5 0.00 2.36 0.69 summer S16 upper CE Tetragnathidae Leucage 3.5 2.5 0.00 11.45 0.17 summer S16 upper CE Tetragnathidae Leucage 1.5 1.3 0.00 1.23 0.01 summer S16 upper CE Tetragnathidae Leucage 2.0 1.0 0.00 1.05 -0.43 summer S16 upper CE Tetragnathidae Leucage 3.5 1.5 0.00 4.12 0.22 summer S16 upper CE Tetragnathidae Leucage 4.0 1.5 0.00 4.71 -0.23 summer S16 upper CE Tetragnathidae Leucage 2.3 0.8 0.00 0.66 -0.40 summer S16 upper CE Tetragnathidae Leucage 1.0 0.5 0.00 0.13 -0.76 summer S16 upper CE Tetragnathidae Leucage 3.0 1.3 0.01 2.45 -0.55 summer S16 upper CE Tetragnathidae Leucage 4.0 2.5 0.01 13.08 0.16 summer S16 upper CE Tetragnathidae Leucage 3.0 1.5 0.00 3.53 -0.15 194 summer S16 upper CE Tetragnathidae Leucage 4.0 1.5 0.00 4.71 0.11 summer S16 upper CE Tetragnathidae Leucage 2.0 1.0 0.00 1.05 -0.20 summer S16 upper CE Tetragnathidae Leucage 2.5 1.5 0.00 2.94 -0.12 summer S16 upper CE Tetragnathidae Leucage 1.5 1.0 0.00 0.79 -0.24 summer S16 upper CE Tetragnathidae Tetragnatha 3.5 1.5 0.00 4.12 0.07 summer S16 upper CE Tetragnathidae Tetragnatha 5.0 1.0 0.00 2.62 -0.26 summer S16 upper CE Tetragnathidae Tetragnatha 4.0 1.3 0.00 3.27 0.11 summer S16 upper CE Tetragnathidae Tetragnatha 2.0 0.5 0.00 0.26 -0.41 summer S16 upper CE Tetragnathidae Tetragnatha 2.0 0.8 0.00 0.67 -0.09 summer S16 upper CE Tetragnathidae Tetragnatha 3.0 1.3 0.00 2.45 0.29 summer S16 mid Long Tetragnathidae Leucage 4.0 1.0 0.00 2.09 -0.13 summer S16 mid Long Tetragnathidae Leucage 5.5 3.8 0.01 40.48 0.16 summer S16 mid Long Tetragnathidae Leucage 7.5 4.0 0.02 62.80 0.14 summer S16 mid Long Tetragnathidae Leucage 4.5 2.0 0.01 9.42 -0.34 summer S16 mid Long Tetragnathidae Leucage 6.0 4.0 0.02 50.24 0.14 summer S16 mid Long Tetragnathidae Tetragnatha 8.0 2.0 0.01 16.75 -0.02 summer S16 mid Long Tetragnathidae Tetragnatha 7.5 2.0 0.01 15.70 0.03 summer S16 mid Long Tetragnathidae Tetragnatha 9.0 2.0 0.02 18.84 -0.02 summer S16 mid Long Tetragnathidae Tetragnatha 8.5 2.0 0.02 17.79 -0.03 summer S16 mid Long Tetragnathidae Tetragnatha 11.0 4.0 0.02 92.11 0.55 summer S16 mid Long Tetragnathidae Tetragnatha 8.0 2.0 0.02 16.75 -0.11 summer S16 mid Long Tetragnathidae Tetragnatha 5.5 1.0 0.00 2.88 -0.23 summer S16 mid Long Tetragnathidae Tetragnatha 7.0 2.8 0.01 27.70 0.20 summer S16 mid Long Tetragnathidae Tetragnatha 9.3 2.8 0.02 36.61 0.13 summer S16 mid Long Tetragnathidae Tetragnatha 9.5 2.5 0.02 31.07 0.11 summer S16 mid Long Tetragnathidae Tetragnatha 10.0 2.0 0.02 20.93 -0.05 summer S16 mid Long Tetragnathidae Tetragnatha 7.5 2.0 0.01 15.70 -0.08 summer S16 mid Long Tetragnathidae Tetragnatha 6.0 1.5 0.01 7.07 -0.22 195 summer S16 mid Long Tetragnathidae Tetragnatha 6.8 1.5 0.01 7.95 -0.18 summer S16 mid Long Tetragnathidae Tetragnatha 5.0 1.0 0.00 2.62 -0.19 summer S16 mid Long Tetragnathidae Tetragnatha 8.3 2.3 0.02 21.86 0.02 summer S16 mid Long Tetragnathidae Tetragnatha 7.5 1.8 0.01 12.02 -0.08 summer S16 mid Long Tetragnathidae Tetragnatha 9.0 2.5 0.02 29.44 0.10 summer S16 mid Long Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 -0.08 summer S16 mid Long Tetragnathidae Tetragnatha 7.3 2.0 0.01 15.18 0.01 summer S16 mid Long Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.23 summer S16 mid Long Tetragnathidae Tetragnatha 6.5 1.5 0.01 7.65 -0.38 summer S16 mid Long Tetragnathidae Tetragnatha 4.3 3.3 0.02 23.49 0.03 summer S16 mid Long Tetragnathidae Tetragnatha 7.0 1.8 0.01 11.22 -0.11 summer S16 mid Long Tetragnathidae Tetragnatha 8.3 2.0 0.02 17.27 -0.11 summer S16 mid Long Tetragnathidae Tetragnatha 9.0 2.5 0.02 29.44 0.09 summer S16 mid Long Tetragnathidae Tetragnatha 3.5 0.8 0.00 1.03 -0.14 summer S16 mid Long Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 0.00 summer S16 mid Long Tetragnathidae Tetragnatha 7.0 2.5 0.01 22.90 0.11 summer S16 mid Long Tetragnathidae Tetragnatha 8.0 2.0 0.01 16.75 0.00 summer S16 mid Long Tetragnathidae Tetragnatha 7.8 2.5 0.01 25.35 0.19 summer S16 mid Bryan Tetragnathidae Leucage 4.5 2.5 0.01 14.72 -0.10 summer S16 mid Bryan Tetragnathidae Leucage 2.5 1.0 0.00 1.31 -0.45 summer S16 mid Bryan Tetragnathidae Leucage 3.0 1.5 0.00 3.53 0.15 summer S16 mid Bryan Tetragnathidae Leucage 3.0 1.8 0.00 4.81 0.12 summer S16 mid Bryan Tetragnathidae Leucage 4.0 1.5 0.00 4.71 -0.18 summer S16 mid Bryan Tetragnathidae Leucage 5.0 4.0 0.01 41.87 0.24 summer S16 mid Bryan Tetragnathidae Leucage 3.5 1.5 0.01 4.12 -0.52 summer S16 mid Bryan Tetragnathidae Leucage 4.5 3.0 0.01 21.20 0.05 summer S16 mid Bryan Tetragnathidae Leucage 3.0 1.5 0.00 3.53 -0.02 summer S16 mid Bryan Tetragnathidae Leucage 3.0 1.5 0.01 3.53 -0.60 196 summer S16 mid Bryan Tetragnathidae Leucage 2.5 1.0 0.00 1.31 0.09 summer S16 mid Bryan Tetragnathidae Leucage 1.0 0.5 0.00 0.13 -0.67 summer S16 mid Bryan Tetragnathidae Leucage 5.0 3.0 0.01 23.55 -0.05 summer S16 mid Bryan Tetragnathidae Leucage 4.0 2.0 0.01 8.37 -0.08 summer S16 mid Bryan Tetragnathidae Leucage 2.0 1.5 0.00 2.36 0.35 summer S16 mid Bryan Tetragnathidae Leucage 1.8 0.8 0.00 0.52 0.38 summer S16 mid Bryan Tetragnathidae Tetragnatha 8.0 2.5 0.01 26.17 0.25 summer S16 mid Bryan Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 0.12 summer S16 mid Bryan Tetragnathidae Tetragnatha 5.5 2.3 0.00 14.57 0.37 summer S16 mid Bryan Tetragnathidae Tetragnatha 4.3 2.5 0.00 13.90 0.43 summer S16 mid Bryan Tetragnathidae Tetragnatha 5.5 1.0 0.00 2.88 -0.32 summer S16 mid Bryan Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 0.18 summer S16 mid Bryan Tetragnathidae Tetragnatha 10.5 1.5 0.00 12.36 0.28 summer S16 mid Bryan Tetragnathidae Tetragnatha 4.0 1.0 0.00 2.09 -0.13 summer S16 mid Bryan Tetragnathidae Tetragnatha 5.5 1.0 0.00 2.88 -0.13 summer S16 mid Bryan Tetragnathidae Tetragnatha 5.0 1.0 0.00 2.62 -0.19 summer S16 mid Bryan Tetragnathidae Tetragnatha 6.8 1.5 0.01 7.95 -0.21 summer S16 mid Bryan Tetragnathidae Tetragnatha 2.5 0.8 0.00 0.74 -0.49 summer S16 mid Bryan Tetragnathidae Tetragnatha 6.5 1.5 0.01 7.65 -0.19 summer S16 mid Bryan Tetragnathidae Tetragnatha 6.3 2.0 0.01 13.08 0.15 summer S16 mid Bryan Tetragnathidae Tetragnatha 8.0 1.8 0.02 12.82 -0.17 summer S16 mid Diagon Tetragnathidae Leucage 4.0 1.5 0.01 4.71 -0.37 summer S16 mid Diagon Tetragnathidae Leucage 2.8 1.5 0.00 3.24 -0.40 summer S16 mid Diagon Tetragnathidae Leucage 4.0 2.0 0.01 8.37 -0.07 summer S16 mid Diagon Tetragnathidae Leucage 4.5 2.0 0.01 9.42 -0.28 summer S16 mid Diagon Tetragnathidae Leucage 5.5 3.0 0.02 25.91 -0.06 summer S16 mid Diagon Tetragnathidae Leucage 2.0 1.5 0.00 2.36 -0.31 summer S16 mid Diagon Tetragnathidae Leucage 4.5 2.5 0.01 14.72 0.22 197 summer S16 mid Diagon Tetragnathidae Leucage 5.5 3.0 0.01 25.91 0.09 summer S16 mid Diagon Tetragnathidae Leucage 5.5 3.3 0.02 30.40 -0.01 summer S16 mid Diagon Tetragnathidae Leucage 6.0 3.5 0.01 38.47 0.14 summer S16 mid Diagon Tetragnathidae Leucage 2.8 1.5 0.00 3.24 -0.37 summer S16 mid Diagon Tetragnathidae Leucage 5.5 3.5 0.01 35.26 0.10 summer S16 mid Diagon Tetragnathidae Leucage 3.0 1.3 0.00 2.45 -0.49 summer S16 mid Diagon Tetragnathidae Leucage 6.5 4.0 0.02 54.43 0.16 summer S16 mid Diagon Tetragnathidae Leucage 3.5 2.0 0.00 7.33 0.07 summer S16 mid Diagon Tetragnathidae Leucage 4.5 2.0 0.01 9.42 -0.06 summer S16 mid Diagon Tetragnathidae Leucage 3.5 1.8 0.00 5.61 0.06 summer S16 mid Diagon Tetragnathidae Leucage 4.5 2.5 0.01 14.72 -0.13 summer S16 mid Diagon Tetragnathidae Tetragnatha 5.5 1.5 0.01 6.48 -0.10 summer S16 mid Diagon Tetragnathidae Tetragnatha 5.0 1.5 0.01 5.89 -0.10 summer S16 mid Diagon Tetragnathidae Tetragnatha 7.5 2.5 0.02 24.53 0.06 summer S16 mid Diagon Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 0.06 summer S16 mid Diagon Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 0.34 summer S16 mid Diagon Tetragnathidae Tetragnatha 4.0 1.0 0.00 2.09 -0.39 summer S16 mid Diagon Tetragnathidae Tetragnatha 5.3 1.5 0.00 6.18 0.07 summer S16 mid Diagon Tetragnathidae Tetragnatha 6.0 2.5 0.01 19.63 0.42 summer S16 mid Diagon Tetragnathidae Tetragnatha 5.0 1.6 0.01 6.70 -0.07 summer S16 mid Diagon Tetragnathidae Tetragnatha 5.8 1.5 0.01 6.77 -0.12 summer S16 mid Diagon Tetragnathidae Tetragnatha 9.0 3.0 0.02 42.39 0.31 summer S16 mid Diagon Tetragnathidae Tetragnatha 6.8 2.8 0.01 26.71 0.47 summer S16 mid Diagon Tetragnathidae Tetragnatha 7.8 2.0 0.01 16.22 -0.02 summer S16 mid Diagon Tetragnathidae Tetragnatha 5.5 1.5 0.00 6.48 0.03 summer S16 mid Diagon Tetragnathidae Tetragnatha 7.3 1.8 0.01 11.62 -0.13 summer S16 mid Diagon Tetragnathidae Tetragnatha 4.5 1.0 0.00 2.36 -0.23 summer S16 lower USGS Tetragnathidae Tetragnatha 5.5 2.0 0.00 11.51 0.24 198 summer S16 lower USGS Tetragnathidae Tetragnatha 6.0 2.5 0.01 19.63 0.32 summer S16 lower USGS Tetragnathidae Tetragnatha 7.0 2.5 0.01 22.90 0.40 summer S16 lower USGS Tetragnathidae Tetragnatha 10.0 1.5 0.01 11.78 0.23 summer S16 lower USGS Tetragnathidae Tetragnatha 7.0 2.0 0.01 14.65 0.16 summer S16 lower USGS Tetragnathidae Tetragnatha 6.0 2.0 0.01 12.56 0.21 summer S16 lower USGS Tetragnathidae Tetragnatha 7.3 2.3 0.01 19.21 0.13 summer S16 lower USGS Tetragnathidae Tetragnatha 6.0 1.5 0.01 7.07 -0.10 summer S16 lower USGS Tetragnathidae Tetragnatha 5.0 1.3 0.00 4.09 -0.05 summer S16 lower USGS Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 0.34 summer S16 lower USGS Tetragnathidae Tetragnatha 3.0 1.0 0.01 1.57 -0.65 summer S16 lower USGS Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 0.09 summer S16 lower USGS Tetragnathidae Tetragnatha 2.5 1.0 0.00 1.31 -0.43 summer S16 lower USGS Tetragnathidae Tetragnatha 6.0 2.0 0.01 12.56 0.19 summer S16 lower USGS Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 0.01 summer S16 lower USGS Tetragnathidae Tetragnatha 6.5 1.8 0.01 10.42 -0.06 summer S16 lower USGS Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 0.07 summer S16 lower USGS Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 0.27 summer S16 lower USGS Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 0.04 summer S16 lower USGS Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 0.15 summer S16 lower USGS Tetragnathidae Tetragnatha 7.5 2.5 0.01 24.53 0.28 summer S16 lower USGS Tetragnathidae Tetragnatha 7.0 3.3 0.01 38.69 0.52 summer S16 lower Fork Tetragnathidae Tetragnatha 7.0 2.0 0.01 14.65 0.12 summer S16 lower Fork Tetragnathidae Tetragnatha 6.0 1.5 0.00 7.07 0.05 summer S16 lower Fork Tetragnathidae Tetragnatha 6.0 2.0 0.01 12.56 0.11 summer S16 lower Fork Tetragnathidae Tetragnatha 9.0 2.5 0.02 29.44 0.13 summer S16 lower Fork Tetragnathidae Tetragnatha 6.0 1.5 0.00 7.07 0.02 summer S16 lower Fork Tetragnathidae Tetragnatha 2.5 1.0 0.00 1.31 0.29 summer S16 lower Fork Tetragnathidae Tetragnatha 5.0 2.5 0.01 16.35 0.34 199 summer S16 lower Fork Tetragnathidae Tetragnatha 7.5 2.8 0.01 29.68 0.37 summer S16 lower Fork Tetragnathidae Tetragnatha 8.0 2.5 0.01 26.17 0.24 summer S16 lower Fork Tetragnathidae Tetragnatha 4.0 1.0 0.00 2.09 -0.08 summer S16 lower Fork Tetragnathidae Tetragnatha 4.5 2.0 0.00 9.42 0.42 summer S16 lower Fork Tetragnathidae Tetragnatha 4.5 1.0 0.00 2.36 0.22 summer S16 lower Fork Tetragnathidae Tetragnatha 4.0 1.3 0.00 3.27 0.25 summer S16 lower Fork Tetragnathidae Tetragnatha 6.8 2.0 0.01 14.13 0.13 summer S16 lower Fork Tetragnathidae Tetragnatha 5.5 1.0 0.00 2.88 -0.11 summer S16 lower Fork Tetragnathidae Tetragnatha 6.0 2.3 0.01 15.90 0.29 summer S16 lower Fork Tetragnathidae Tetragnatha 6.3 1.5 0.01 7.36 -0.14 summer S16 lower Fork Tetragnathidae Tetragnatha 7.0 2.5 0.01 22.90 0.37 summer S16 lower Fork Tetragnathidae Tetragnatha 6.0 2.0 0.01 12.56 0.07 summer S16 lower Fork Tetragnathidae Tetragnatha 5.5 1.5 0.00 6.48 0.14 summer S16 lower Fork Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 0.15 summer S16 lower Summit Tetragnathidae Tetragnatha 6.0 1.5 0.01 7.07 -0.01 summer S16 lower Summit Tetragnathidae Tetragnatha 8.0 2.5 0.01 26.17 0.20 summer S16 lower Summit Tetragnathidae Tetragnatha 5.5 1.0 0.00 2.88 -0.34 summer S16 lower Summit Tetragnathidae Tetragnatha 5.5 1.0 0.00 2.88 -0.35 summer S16 lower Summit Tetragnathidae Tetragnatha 5.0 1.5 0.01 5.89 -0.10 summer S16 lower Summit Tetragnathidae Tetragnatha 6.5 2.5 0.01 21.26 0.26 summer S16 lower Summit Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 0.08 summer S16 lower Summit Tetragnathidae Tetragnatha 5.5 1.5 0.01 6.48 -0.23 summer S16 lower Summit Tetragnathidae Tetragnatha 7.3 1.8 0.01 11.62 0.03 summer S16 lower Summit Tetragnathidae Tetragnatha 7.0 2.5 0.01 22.90 0.26 summer S16 lower Summit Tetragnathidae Tetragnatha 5.8 1.5 0.01 6.77 -0.12 winter W15 upper CW Tetragnathidae Leucage 2.5 1.5 0.00 2.94 0.59 winter W15 upper CW Tetragnathidae Leucage 1.5 1.5 0.00 1.77 -0.15 winter W15 upper CW Tetragnathidae Leucage 1.5 1.0 0.00 0.79 -0.06 200 winter W15 upper CW Tetragnathidae Leucage 1.3 1.0 0.00 0.65 0.14 winter W15 upper CW Tetragnathidae Leucage 1.5 0.7 0.00 0.38 -0.44 winter W15 upper CW Tetragnathidae Leucage 1.8 0.8 0.00 0.59 -0.11 winter W15 upper CW Tetragnathidae Leucage 2.5 1.3 0.00 2.04 0.18 winter W15 upper CW Tetragnathidae Leucage 1.5 1.0 0.00 0.79 0.36 winter W15 upper CW Tetragnathidae Leucage 1.3 0.8 0.00 0.37 0.23 winter W15 upper CW Tetragnathidae Leucage 1.3 0.6 0.00 0.24 -0.15 winter W15 upper CW Tetragnathidae Leucage 1.5 1.0 0.00 0.79 0.01 winter W15 upper CW Tetragnathidae Leucage 2.0 1.5 0.00 2.36 0.58 winter W15 upper CW Tetragnathidae Leucage 2.0 1.5 0.00 2.36 0.15 winter W15 upper CW Tetragnathidae Leucage 4.5 2.5 0.00 14.72 0.40 winter W15 upper CW Tetragnathidae Leucage 3.5 2.0 0.00 7.33 0.32 winter W15 upper CW Tetragnathidae Leucage 3.0 1.8 0.00 4.81 0.08 winter W15 upper CE Tetragnathidae Leucage 3.0 1.0 0.00 1.57 0.11 winter W15 upper CE Tetragnathidae Leucage 7.0 3.0 0.01 32.97 0.31 winter W15 upper CE Tetragnathidae Leucage 3.0 2.0 0.00 6.28 0.72 winter W15 upper CE Tetragnathidae Leucage 2.0 1.5 0.00 2.36 0.11 winter W15 upper CE Tetragnathidae Leucage 3.0 1.5 0.00 3.53 0.18 winter W15 upper CE Tetragnathidae Leucage 1.0 1.0 0.00 0.52 0.73 winter W15 upper CE Tetragnathidae Leucage 2.5 2.0 0.00 5.23 0.22 winter W15 upper CE Tetragnathidae Leucage 3.0 2.0 0.00 6.28 0.23 winter W15 upper CE Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 0.25 winter W15 mid Long Tetragnathidae Leucage 4.0 2.0 0.01 8.37 -0.54 winter W15 mid Long Tetragnathidae Leucage 6.0 3.0 0.01 28.26 0.08 winter W15 mid Long Tetragnathidae Leucage 6.0 2.0 0.02 12.56 -0.44 winter W15 mid Long Tetragnathidae Leucage 6.0 4.0 0.03 50.24 -0.09 winter W15 mid Long Tetragnathidae Leucage 7.0 4.0 0.03 58.61 -0.03 winter W15 mid Long Tetragnathidae Leucage 6.0 3.5 0.02 38.47 0.01 201 winter W15 mid Long Tetragnathidae Leucage 6.0 4.0 0.02 50.24 0.07 winter W15 mid Long Tetragnathidae Leucage 5.5 2.5 0.01 17.99 -0.15 winter W15 mid Long Tetragnathidae Leucage 5.0 2.5 0.02 16.35 -0.36 winter W15 mid Long Tetragnathidae Leucage 6.5 2.0 0.02 13.61 -0.40 winter W15 mid Long Tetragnathidae Leucage 5.0 3.5 0.02 32.05 -0.07 winter W15 mid Long Tetragnathidae Leucage 7.0 3.5 0.02 44.88 -0.06 winter W15 mid Long Tetragnathidae Leucage 3.5 2.0 0.01 7.33 -0.17 winter W15 mid Long Tetragnathidae Leucage 7.0 4.0 0.02 58.61 0.07 winter W15 mid Long Tetragnathidae Leucage 4.5 2.0 0.01 9.42 -0.17 winter W15 mid Long Tetragnathidae Tetragnatha 7.0 2.0 0.01 14.65 0.12 winter W15 mid Long Tetragnathidae Tetragnatha 5.0 1.0 0.01 2.62 -0.56 winter W15 mid Long Tetragnathidae Tetragnatha 10.0 3.0 0.03 47.10 0.12 winter W15 mid Long Tetragnathidae Tetragnatha 6.0 2.0 0.01 12.56 -0.01 winter W15 mid Long Tetragnathidae Tetragnatha 8.0 2.5 0.02 26.17 -0.01 winter W15 mid Long Tetragnathidae Tetragnatha 5.0 1.5 0.01 5.89 -0.10 winter W15 mid Long Tetragnathidae Tetragnatha 9.0 2.5 0.02 29.44 0.05 winter W15 mid Long Tetragnathidae Tetragnatha 4.0 1.0 0.01 2.09 -0.70 winter W15 mid Long Tetragnathidae Tetragnatha 2.5 1.0 0.00 1.31 -0.49 winter W15 mid Long Tetragnathidae Tetragnatha 3.0 1.0 0.01 1.57 -0.71 winter W15 mid Bryan Tetragnathidae Leucage 9.0 4.0 0.02 75.36 0.28 winter W15 mid Bryan Tetragnathidae Leucage 5.0 3.0 0.01 23.55 0.35 winter W15 mid Bryan Tetragnathidae Leucage 7.0 4.0 0.02 58.61 0.19 winter W15 mid Bryan Tetragnathidae Leucage 2.0 4.0 0.00 16.75 0.30 winter W15 mid Bryan Tetragnathidae Leucage 6.0 4.0 0.01 50.24 0.32 winter W15 mid Bryan Tetragnathidae Leucage 9.5 4.0 0.02 79.55 0.19 winter W15 mid Bryan Tetragnathidae Leucage 10.0 5.0 0.02 130.83 0.56 winter W15 mid Bryan Tetragnathidae Leucage 5.0 2.0 0.01 10.47 -0.05 winter W15 mid Bryan Tetragnathidae Leucage 7.0 4.0 0.02 58.61 0.26 202 winter W15 mid Bryan Tetragnathidae Leucage 3.0 1.5 0.01 3.53 -0.40 winter W15 mid Bryan Tetragnathidae Leucage 10.0 5.0 0.03 130.83 0.33 winter W15 mid Bryan Tetragnathidae Leucage 7.5 4.5 0.02 79.48 0.20 winter W15 mid Bryan Tetragnathidae Leucage 12.0 7.0 0.04 307.72 0.50 winter W15 mid Bryan Tetragnathidae Leucage 8.0 5.0 0.02 104.67 0.42 winter W15 mid Bryan Tetragnathidae Leucage 8.0 5.0 0.02 104.67 0.38 winter W15 mid Bryan Tetragnathidae Leucage 7.0 5.0 0.03 91.58 0.16 winter W15 mid Bryan Tetragnathidae Leucage 9.0 6.0 0.02 169.56 0.57 winter W15 mid Bryan Tetragnathidae Leucage 4.0 2.5 0.01 13.08 0.10 winter W15 mid Bryan Tetragnathidae Leucage 6.0 3.5 0.01 38.47 0.19 winter W15 mid Bryan Tetragnathidae Leucage 9.0 5.0 0.02 117.75 0.43 winter W15 mid Bryan Tetragnathidae Leucage 8.0 5.0 0.02 104.67 0.52 winter W15 mid Bryan Tetragnathidae Leucage 4.5 3.0 0.01 21.20 0.13 winter W15 mid Bryan Tetragnathidae Leucage 6.0 3.5 0.01 38.47 0.34 winter W15 mid Bryan Tetragnathidae Leucage 4.0 2.0 0.01 8.37 -0.05 winter W15 mid Bryan Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 0.08 winter W15 mid Diagon Tetragnathidae Leucage 8.5 4.0 0.05 71.17 -0.23 winter W15 mid Diagon Tetragnathidae Leucage 7.0 3.0 0.03 32.97 -0.27 winter W15 mid Diagon Tetragnathidae Leucage 7.0 4.0 0.02 58.61 0.28 winter W15 mid Diagon Tetragnathidae Leucage 7.5 4.5 0.02 79.48 0.32 winter W15 mid Diagon Tetragnathidae Leucage 6.0 4.0 0.02 50.24 0.10 winter W15 mid Diagon Tetragnathidae Leucage 5.0 2.8 0.01 19.79 0.16 winter W15 mid Diagon Tetragnathidae Leucage 9.0 6.0 0.03 169.56 0.46 winter W15 mid Diagon Tetragnathidae Leucage 10.0 6.0 0.03 188.40 0.48 winter W15 mid Diagon Tetragnathidae Leucage 5.0 3.0 0.01 23.55 0.22 winter W15 mid Diagon Tetragnathidae Leucage 5.0 2.0 0.01 10.47 0.02 winter W15 mid Diagon Tetragnathidae Leucage 5.5 3.0 0.01 25.91 -0.03 winter W15 mid Diagon Tetragnathidae Leucage 8.0 5.0 0.02 104.67 0.37 203 winter W15 mid Diagon Tetragnathidae Leucage 9.0 5.0 0.03 117.75 0.31 winter W15 mid Diagon Tetragnathidae Leucage 9.0 5.0 0.02 117.75 0.49 winter W15 mid Diagon Tetragnathidae Leucage 9.0 5.5 0.03 142.48 0.33 winter W15 mid Diagon Tetragnathidae Leucage 8.0 4.5 0.02 84.78 0.24 winter W15 mid Diagon Tetragnathidae Leucage 8.5 5.0 0.02 111.21 0.37 winter W15 mid Diagon Tetragnathidae Leucage 8.0 4.0 0.03 66.99 0.07 winter W15 mid Diagon Tetragnathidae Leucage 8.0 4.5 0.02 84.78 0.23 winter W15 mid Diagon Tetragnathidae Leucage 11.0 6.0 0.04 207.24 0.31 winter W15 mid Diagon Tetragnathidae Leucage 9.5 5.0 0.03 124.29 0.25 winter W15 mid Diagon Tetragnathidae Tetragnatha 6.0 1.5 0.00 7.07 0.02 winter W15 mid Diagon Tetragnathidae Tetragnatha 7.0 2.0 0.01 14.65 0.12 winter W15 mid Diagon Tetragnathidae Tetragnatha 6.5 3.0 0.00 30.62 0.68 winter W15 lower USGS Tetragnathidae Leucage 8.0 4.0 0.04 66.99 -0.10 winter W15 lower USGS Tetragnathidae Leucage 4.5 2.3 0.01 11.92 -0.11 winter W15 lower USGS Tetragnathidae Leucage 4.8 2.5 0.01 15.54 -0.12 winter W15 lower USGS Tetragnathidae Leucage 4.5 2.0 0.01 9.42 0.00 winter W15 lower USGS Tetragnathidae Leucage 7.0 4.0 0.03 58.61 -0.02 winter W15 lower USGS Tetragnathidae Leucage 6.5 4.0 0.03 54.43 -0.02 winter W15 lower USGS Tetragnathidae Leucage 6.0 2.3 0.01 15.90 -0.10 winter W15 lower USGS Tetragnathidae Leucage 4.0 1.5 0.00 4.71 -0.25 winter W15 lower USGS Tetragnathidae Leucage 5.0 2.5 0.01 16.35 -0.24 winter W15 lower USGS Tetragnathidae Leucage 4.0 2.5 0.01 13.08 -0.30 winter W15 lower USGS Tetragnathidae Leucage 4.5 2.5 0.01 14.72 -0.09 winter W15 lower USGS Tetragnathidae Leucage 7.8 3.3 0.03 42.84 -0.12 winter W15 lower USGS Tetragnathidae Leucage 8.5 4.5 0.04 90.08 -0.06 winter W15 lower USGS Tetragnathidae Leucage 4.0 1.8 0.01 6.41 -0.43 winter W15 lower USGS Tetragnathidae Leucage 9.0 4.5 0.05 95.38 -0.04 winter W15 lower USGS Tetragnathidae Leucage 6.0 2.9 0.05 25.50 -0.68 204 winter W15 lower USGS Tetragnathidae Leucage 4.8 2.5 0.01 15.54 0.12 winter W15 lower USGS Tetragnathidae Leucage 7.5 4.0 0.03 62.80 0.06 winter W15 lower USGS Tetragnathidae Leucage 5.0 2.0 0.01 10.47 -0.18 winter W15 lower USGS Tetragnathidae Leucage 7.9 4.0 0.03 65.73 0.05 winter W15 lower USGS Tetragnathidae Leucage 6.0 3.0 0.02 28.26 -0.08 winter W15 lower USGS Tetragnathidae Tetragnatha 5.0 1.0 0.00 2.62 -0.39 winter W15 lower USGS Tetragnathidae Tetragnatha 7.5 3.0 0.01 35.33 0.28 winter W15 lower USGS Tetragnathidae Tetragnatha 7.0 2.5 0.01 22.90 0.26 winter W15 lower Fork Tetragnathidae Leucage 6.0 3.5 0.02 38.47 0.10 winter W15 lower Fork Tetragnathidae Leucage 7.0 4.0 0.03 58.61 0.01 winter W15 lower Fork Tetragnathidae Leucage 5.0 3.5 0.01 32.05 0.10 winter W15 lower Fork Tetragnathidae Leucage 4.3 2.0 0.01 8.90 -0.22 winter W15 lower Fork Tetragnathidae Leucage 4.0 2.5 0.01 13.08 0.13 winter W15 lower Fork Tetragnathidae Leucage 6.5 2.5 0.01 21.26 0.15 winter W15 lower Fork Tetragnathidae Leucage 6.0 3.3 0.02 33.17 -0.04 winter W15 lower Fork Tetragnathidae Leucage 8.0 4.3 0.04 75.62 -0.02 winter W15 lower Fork Tetragnathidae Leucage 4.5 2.3 0.01 11.92 -0.05 winter W15 lower Fork Tetragnathidae Tetragnatha 5.0 2.0 0.00 10.47 0.19 winter W15 lower Fork Tetragnathidae Tetragnatha 7.0 2.0 0.01 14.65 0.11 winter W15 lower Fork Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 -0.08 winter W15 lower Fork Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 0.03 winter W15 lower Fork Tetragnathidae Tetragnatha 7.0 2.5 0.01 22.90 0.34 winter W15 lower Fork Tetragnathidae Tetragnatha 5.0 1.9 0.01 8.96 0.04 winter W15 lower Fork Tetragnathidae Tetragnatha 5.8 1.9 0.01 10.30 0.08 winter W15 lower Fork Tetragnathidae Tetragnatha 5.5 2.0 0.01 11.51 0.15 winter W15 lower Summit Tetragnathidae Leucage 6.0 3.5 0.02 38.47 -0.04 winter W15 lower Summit Tetragnathidae Leucage 6.0 3.5 0.02 38.47 0.00 winter W15 lower Summit Tetragnathidae Leucage 6.0 2.5 0.01 19.63 -0.13 205 winter W15 lower Summit Tetragnathidae Leucage 9.0 5.0 0.04 117.75 0.10 winter W15 lower Summit Tetragnathidae Leucage 6.0 3.0 0.01 28.26 0.01 winter W15 lower Summit Tetragnathidae Leucage 6.0 3.0 0.01 28.26 0.16 winter W15 lower Summit Tetragnathidae Tetragnatha 5.0 1.2 0.00 3.46 -0.20 winter W15 lower Summit Tetragnathidae Tetragnatha 6.5 2.5 0.01 21.26 0.27 winter W15 lower Summit Tetragnathidae Tetragnatha 5.0 1.8 0.00 8.01 0.14 winter W15 lower Summit Tetragnathidae Tetragnatha 4.0 2.0 0.00 8.37 0.38 winter W15 lower Summit Tetragnathidae Tetragnatha 4.2 1.5 0.01 4.89 -0.46 winter W15 lower Summit Tetragnathidae Tetragnatha 8.5 3.0 0.01 40.04 0.39 winter W15 lower Summit Tetragnathidae Tetragnatha 4.5 2.0 0.00 9.42 0.18 winter W15 lower Summit Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 0.02 winter W15 lower Summit Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 0.00 winter W15 lower Summit Tetragnathidae Tetragnatha 6.3 2.0 0.01 13.08 0.12 winter W16 upper CW Tetragnathidae Leucage 1.0 0.5 0.00 0.13 -0.67 winter W16 upper CW Tetragnathidae Leucage 6.5 3.5 0.01 41.67 0.27 winter W16 upper CW Tetragnathidae Leucage 5.0 2.5 0.01 16.35 0.21 winter W16 upper CW Tetragnathidae Leucage 2.5 1.0 0.00 1.31 -0.31 winter W16 upper CW Tetragnathidae Leucage 5.0 3.0 0.01 23.55 0.03 winter W16 upper CW Tetragnathidae Leucage 6.0 4.0 0.02 50.24 0.18 winter W16 upper CW Tetragnathidae Leucage 3.0 1.8 0.00 4.81 0.04 winter W16 upper CW Tetragnathidae Leucage 4.5 2.5 0.01 14.72 0.22 winter W16 upper CW Tetragnathidae Leucage 2.3 1.8 0.00 3.61 0.22 winter W16 upper CW Tetragnathidae Leucage 1.5 1.3 0.00 1.23 -0.17 winter W16 upper CW Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.55 winter W16 upper CW Tetragnathidae Tetragnatha 4.5 1.0 0.00 2.36 -0.03 winter W16 upper CW Tetragnathidae Tetragnatha 2.8 1.0 0.00 1.44 -0.11 winter W16 upper CW Tetragnathidae Tetragnatha 4.5 1.3 0.00 3.68 -0.16 winter W16 upper CW Tetragnathidae Tetragnatha 5.0 2.0 0.01 10.47 0.14 206 winter W16 upper CW Tetragnathidae Tetragnatha 2.8 0.8 0.00 0.81 -0.45 winter W16 upper CW Tetragnathidae Tetragnatha 5.5 2.0 0.01 11.51 0.09 winter W16 upper CW Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 0.17 winter W16 upper CW Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.21 winter W16 upper CW Tetragnathidae Tetragnatha 4.0 1.0 0.00 2.09 0.02 winter W16 upper CW Tetragnathidae Tetragnatha 3.5 1.5 0.00 4.12 0.00 winter W16 upper CW Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.26 winter W16 upper CW Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.24 winter W16 upper CW Tetragnathidae Tetragnatha 3.0 2.5 0.00 9.81 0.26 winter W16 upper CM Tetragnathidae Leucage 1.0 0.8 0.00 0.29 -0.55 winter W16 upper CM Tetragnathidae Leucage 2.0 1.0 0.00 1.05 0.14 winter W16 upper CM Tetragnathidae Leucage 1.5 1.0 0.00 0.79 -0.13 winter W16 upper CM Tetragnathidae Tetragnatha 1.5 1.3 0.00 1.23 -0.31 winter W16 upper CM Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.46 winter W16 upper CM Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.16 winter W16 upper CM Tetragnathidae Tetragnatha 1.5 0.8 0.00 0.50 0.43 winter W16 upper CM Tetragnathidae Tetragnatha 1.5 0.5 0.00 0.20 0.02 winter W16 upper CM Tetragnathidae Tetragnatha 2.0 1.0 0.00 1.05 -0.03 winter W16 upper CM Tetragnathidae Tetragnatha 1.3 0.8 0.00 0.37 0.02 winter W16 upper CM Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.29 winter W16 upper CM Tetragnathidae Tetragnatha 2.5 1.0 0.00 1.31 -0.54 winter W16 upper CM Tetragnathidae Tetragnatha 2.0 1.0 0.00 1.05 0.75 winter W16 upper CM Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.46 winter W16 upper CM Tetragnathidae Tetragnatha 2.5 0.8 0.00 0.74 -0.18 winter W16 upper CM Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.12 winter W16 upper CM Tetragnathidae Tetragnatha 2.5 0.8 0.00 0.74 -0.05 winter W16 upper CM Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 -0.02 winter W16 upper CM Tetragnathidae Tetragnatha 3.5 1.5 0.00 4.12 0.11 207 winter W16 upper CM Tetragnathidae Tetragnatha 2.5 1.0 0.00 1.31 -0.18 winter W16 upper CM Tetragnathidae Tetragnatha 4.3 1.0 0.00 2.22 -0.35 winter W16 upper CM Tetragnathidae Tetragnatha 3.0 0.5 0.00 0.39 -0.70 winter W16 upper CM Tetragnathidae Tetragnatha 2.8 0.5 0.00 0.36 -0.63 winter W16 upper CM Tetragnathidae Tetragnatha 4.0 1.0 0.00 2.09 -0.24 winter W16 upper CM Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 0.06 winter W16 upper CM Tetragnathidae Tetragnatha 1.5 1.0 0.00 0.79 0.63 winter W16 upper CM Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.18 winter W16 upper CM Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.19 winter W16 upper CM Tetragnathidae Tetragnatha 5.5 1.5 0.01 6.48 -0.10 winter W16 upper CE Tetragnathidae Leucage 4.0 2.5 0.01 13.08 0.00 winter W16 upper CE Tetragnathidae Leucage 5.0 3.0 0.01 23.55 0.03 winter W16 upper CE Tetragnathidae Leucage 4.0 2.3 0.01 10.60 -0.06 winter W16 upper CE Tetragnathidae Leucage 2.5 1.5 0.00 2.94 0.13 winter W16 upper CE Tetragnathidae Leucage 3.5 1.8 0.00 5.61 0.04 winter W16 upper CE Tetragnathidae Leucage 4.5 2.5 0.01 14.72 0.02 winter W16 upper CE Tetragnathidae Leucage 3.0 1.5 0.00 3.53 -0.06 winter W16 upper CE Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.10 winter W16 mid Long Tetragnathidae Leucage 4.0 2.3 0.01 10.60 -0.14 winter W16 mid Long Tetragnathidae Leucage 7.0 4.5 0.02 74.18 0.18 winter W16 mid Long Tetragnathidae Leucage 6.5 3.5 0.01 41.67 0.22 winter W16 mid Long Tetragnathidae Leucage 7.0 4.5 0.02 74.18 0.21 winter W16 mid Long Tetragnathidae Leucage 9.0 4.5 0.03 95.38 0.24 winter W16 mid Long Tetragnathidae Leucage 7.5 4.0 0.03 62.80 0.01 winter W16 mid Long Tetragnathidae Leucage 6.5 4.0 0.02 54.43 0.14 winter W16 mid Long Tetragnathidae Leucage 7.0 3.5 0.02 44.88 0.03 winter W16 mid Long Tetragnathidae Leucage 6.5 4.0 0.02 54.43 0.19 winter W16 mid Long Tetragnathidae Leucage 7.5 4.5 0.03 79.48 0.12 208 winter W16 mid Long Tetragnathidae Leucage 7.0 4.5 0.02 74.18 0.22 winter W16 mid Long Tetragnathidae Tetragnatha 7.5 2.0 0.01 15.70 0.11 winter W16 mid Long Tetragnathidae Tetragnatha 8.5 3.0 0.01 40.04 0.35 winter W16 mid Long Tetragnathidae Tetragnatha 7.5 2.5 0.01 24.53 0.13 winter W16 mid Long Tetragnathidae Tetragnatha 7.0 1.5 0.01 8.24 -0.25 winter W16 mid Long Tetragnathidae Tetragnatha 9.5 3.0 0.02 44.75 0.27 winter W16 mid Long Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 0.11 winter W16 mid Long Tetragnathidae Tetragnatha 6.5 2.3 0.01 17.22 0.33 winter W16 mid Long Tetragnathidae Tetragnatha 6.5 1.5 0.01 7.65 -0.18 winter W16 mid Long Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 0.03 winter W16 mid Long Tetragnathidae Tetragnatha 5.5 1.5 0.01 6.48 -0.17 winter W16 mid Long Tetragnathidae Tetragnatha 7.5 3.0 0.01 35.33 0.32 winter W16 mid Long Tetragnathidae Tetragnatha 8.5 2.5 0.01 27.80 0.25 winter W16 mid Long Tetragnathidae Tetragnatha 6.0 2.0 0.01 12.56 0.11 winter W16 mid Long Tetragnathidae Tetragnatha 5.5 1.5 0.01 6.48 -0.12 winter W16 mid Long Tetragnathidae Tetragnatha 4.0 1.0 0.00 2.09 -0.45 winter W16 mid Long Tetragnathidae Tetragnatha 6.5 2.5 0.01 21.26 0.44 winter W16 mid Long Tetragnathidae Tetragnatha 5.5 1.5 0.00 6.48 -0.02 winter W16 mid Long Tetragnathidae Tetragnatha 6.5 2.5 0.01 21.26 0.26 winter W16 mid Bryan Tetragnathidae Leucage 7.0 4.0 0.03 58.61 0.00 winter W16 mid Bryan Tetragnathidae Leucage 6.5 3.5 0.03 41.67 -0.13 winter W16 mid Bryan Tetragnathidae Leucage 5.0 3.5 0.01 32.05 0.15 winter W16 mid Bryan Tetragnathidae Leucage 6.0 3.0 0.01 28.26 0.11 winter W16 mid Bryan Tetragnathidae Leucage 7.5 4.0 0.02 62.80 0.25 winter W16 mid Bryan Tetragnathidae Leucage 7.5 4.5 0.02 79.48 0.38 winter W16 mid Bryan Tetragnathidae Leucage 7.0 3.5 0.02 44.88 0.09 winter W16 mid Bryan Tetragnathidae Leucage 6.5 3.0 0.02 30.62 -0.18 winter W16 mid Bryan Tetragnathidae Leucage 6.0 3.5 0.02 38.47 0.03 209 winter W16 mid Bryan Tetragnathidae Leucage 6.5 3.5 0.01 41.67 0.22 winter W16 mid Bryan Tetragnathidae Tetragnatha 8.0 2.0 0.01 16.75 0.00 winter W16 mid Bryan Tetragnathidae Tetragnatha 7.5 1.5 0.00 8.83 0.18 winter W16 mid Bryan Tetragnathidae Tetragnatha 7.5 3.0 0.01 35.33 0.39 winter W16 mid Bryan Tetragnathidae Tetragnatha 8.0 2.8 0.01 31.66 0.30 winter W16 mid Bryan Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 0.27 winter W16 mid Bryan Tetragnathidae Tetragnatha 7.5 2.3 0.01 19.87 0.16 winter W16 mid Bryan Tetragnathidae Tetragnatha 3.3 1.3 0.01 2.66 -0.53 winter W16 mid Bryan Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.07 winter W16 mid Bryan Tetragnathidae Tetragnatha 6.5 2.3 0.01 17.22 0.11 winter W16 mid Bryan Tetragnathidae Tetragnatha 4.5 1.0 0.00 2.36 -0.43 winter W16 mid Bryan Tetragnathidae Tetragnatha 8.0 1.5 0.01 9.42 -0.17 winter W16 mid Bryan Tetragnathidae Tetragnatha 4.3 1.3 0.00 3.48 -0.29 winter W16 mid Bryan Tetragnathidae Tetragnatha 5.0 1.0 0.01 2.62 -0.48 winter W16 mid Bryan Tetragnathidae Tetragnatha 4.5 1.5 0.01 5.30 -0.25 winter W16 mid Bryan Tetragnathidae Tetragnatha 5.5 1.5 0.01 6.48 -0.26 winter W16 mid Diagon Tetragnathidae Leucage 7.0 3.0 0.02 32.97 -0.01 winter W16 mid Diagon Tetragnathidae Leucage 6.0 4.0 0.01 50.24 0.43 winter W16 mid Diagon Tetragnathidae Leucage 8.0 4.0 0.02 66.99 0.23 winter W16 mid Diagon Tetragnathidae Leucage 6.0 3.0 0.02 28.26 -0.15 winter W16 mid Diagon Tetragnathidae Leucage 5.5 3.0 0.01 25.91 0.06 winter W16 mid Diagon Tetragnathidae Leucage 6.0 3.5 0.02 38.47 0.04 winter W16 mid Diagon Tetragnathidae Leucage 6.5 2.5 0.01 21.26 0.01 winter W16 mid Diagon Tetragnathidae Leucage 7.0 4.0 0.03 58.61 -0.03 winter W16 mid Diagon Tetragnathidae Tetragnatha 8.0 2.0 0.02 16.75 -0.16 winter W16 mid Diagon Tetragnathidae Tetragnatha 6.5 2.0 0.01 13.61 0.09 winter W16 mid Diagon Tetragnathidae Tetragnatha 6.5 1.5 0.01 7.65 -0.17 winter W16 mid Diagon Tetragnathidae Tetragnatha 6.3 2.0 0.01 13.08 0.21 210 winter W16 mid Diagon Tetragnathidae Tetragnatha 7.0 1.8 0.02 11.22 -0.25 winter W16 mid Diagon Tetragnathidae Tetragnatha 9.5 3.0 0.02 44.75 0.30 winter W16 mid Diagon Tetragnathidae Tetragnatha 5.5 1.5 0.01 6.48 -0.07 winter W16 mid Diagon Tetragnathidae Tetragnatha 8.0 3.0 0.01 37.68 0.50 winter W16 mid Diagon Tetragnathidae Tetragnatha 7.0 2.0 0.01 14.65 -0.09 winter W16 mid Diagon Tetragnathidae Tetragnatha 4.0 2.0 0.01 8.37 0.04 winter W16 mid Diagon Tetragnathidae Tetragnatha 4.3 1.0 0.00 2.22 -0.35 winter W16 mid Diagon Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 -0.01 winter W16 mid Diagon Tetragnathidae Tetragnatha 3.5 1.0 0.01 1.83 -0.63 winter W16 mid Diagon Tetragnathidae Tetragnatha 7.0 2.0 0.01 14.65 0.09 winter W16 lower USGS Tetragnathidae Leucage 4.0 2.0 0.00 8.37 0.09 winter W16 lower USGS Tetragnathidae Leucage 6.5 3.0 0.03 30.62 -0.30 winter W16 lower USGS Tetragnathidae Leucage 7.0 3.0 0.02 32.97 -0.16 winter W16 lower USGS Tetragnathidae Leucage 4.0 2.0 0.01 8.37 -0.24 winter W16 lower USGS Tetragnathidae Leucage 6.0 3.0 0.02 28.26 -0.11 winter W16 lower USGS Tetragnathidae Leucage 6.5 3.0 0.02 30.62 -0.07 winter W16 lower USGS Tetragnathidae Leucage 5.0 2.5 0.01 16.35 0.02 winter W16 lower USGS Tetragnathidae Leucage 5.0 2.5 0.01 16.35 -0.17 winter W16 lower USGS Tetragnathidae Leucage 5.0 2.5 0.02 16.35 -0.31 winter W16 lower USGS Tetragnathidae Leucage 6.5 3.0 0.02 30.62 -0.19 winter W16 lower USGS Tetragnathidae Leucage 5.5 2.5 0.01 17.99 -0.18 winter W16 lower USGS Tetragnathidae Leucage 3.5 2.5 0.00 11.45 0.25 winter W16 lower USGS Tetragnathidae Leucage 3.0 1.5 0.00 3.53 -0.21 winter W16 lower USGS Tetragnathidae Tetragnatha 1.5 1.0 0.00 0.79 0.35 winter W16 lower USGS Tetragnathidae Tetragnatha 2.0 0.5 0.00 0.26 0.15 winter W16 lower USGS Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 0.15 winter W16 lower Fork Tetragnathidae Leucage 9.0 4.0 0.02 75.36 0.20 winter W16 lower Fork Tetragnathidae Leucage 6.0 3.0 0.02 28.26 -0.07 211 winter W16 lower Fork Tetragnathidae Leucage 5.0 2.5 0.01 16.35 -0.04 winter W16 lower Fork Tetragnathidae Leucage 6.0 3.5 0.02 38.47 0.05 winter W16 lower Fork Tetragnathidae Leucage 5.5 3.0 0.01 25.91 0.06 winter W16 lower Fork Tetragnathidae Leucage 5.0 3.0 0.01 23.55 0.08 winter W16 lower Fork Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 -0.09 winter W16 lower Fork Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.31 winter W16 lower Fork Tetragnathidae Tetragnatha 1.0 0.5 0.00 0.13 -0.71 winter W16 lower Fork Tetragnathidae Tetragnatha 2.5 0.8 0.00 0.74 -0.36 winter W16 lower Fork Tetragnathidae Tetragnatha 5.0 1.5 0.01 5.89 -0.10 winter W16 lower Fork Tetragnathidae Tetragnatha 7.5 3.0 0.01 35.33 0.27 winter W16 lower Fork Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 -0.07 winter W16 lower Fork Tetragnathidae Tetragnatha 3.0 1.0 0.00 1.57 -0.51 winter W16 lower Fork Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 0.07 winter W16 lower Fork Tetragnathidae Tetragnatha 3.8 1.0 0.00 1.96 -0.41 winter W16 lower Fork Tetragnathidae Tetragnatha 4.0 1.3 0.00 3.27 -0.27 winter W16 lower Fork Tetragnathidae Tetragnatha 6.5 2.5 0.01 21.26 0.30 winter W16 lower Fork Tetragnathidae Tetragnatha 5.0 2.0 0.00 10.47 0.21 winter W16 lower Fork Tetragnathidae Tetragnatha 5.0 1.5 0.01 5.89 -0.12 winter W16 lower Fork Tetragnathidae Tetragnatha 2.0 0.5 0.00 0.26 -0.41 winter W16 lower Fork Tetragnathidae Tetragnatha 2.5 0.8 0.00 0.74 0.16 winter W16 lower Summit Tetragnathidae Leucage 7.5 5.5 0.01 118.73 0.83 winter W16 lower Summit Tetragnathidae Leucage 3.5 2.0 0.00 7.33 0.13 winter W16 lower Summit Tetragnathidae Leucage 4.0 2.0 0.01 8.37 -0.04 winter W16 lower Summit Tetragnathidae Leucage 3.0 1.5 0.00 3.53 -0.04 winter W16 lower Summit Tetragnathidae Leucage 6.5 4.0 0.01 54.43 0.27 winter W16 lower Summit Tetragnathidae Leucage 5.0 2.5 0.01 16.35 -0.01 winter W16 lower Summit Tetragnathidae Leucage 4.5 3.0 0.01 21.20 0.11 winter W16 lower Summit Tetragnathidae Leucage 6.5 4.0 0.01 54.43 0.54 212 winter W16 lower Summit Tetragnathidae Tetragnatha 4.0 1.3 0.00 3.27 0.25 winter W16 lower Summit Tetragnathidae Tetragnatha 3.5 1.5 0.00 4.12 0.32 winter W16 lower Summit Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 0.22 winter W16 lower Summit Tetragnathidae Tetragnatha 5.5 1.5 0.00 6.48 0.02 winter W16 lower Summit Tetragnathidae Tetragnatha 7.0 2.5 0.01 22.90 0.27 winter W16 lower Summit Tetragnathidae Tetragnatha 2.0 1.0 0.00 1.05 0.11 winter W16 lower Summit Tetragnathidae Tetragnatha 3.5 1.3 0.00 2.86 0.08 winter W16 lower Summit Tetragnathidae Tetragnatha 7.5 2.5 0.01 24.53 0.32 winter W16 lower Summit Tetragnathidae Tetragnatha 6.5 2.5 0.01 21.26 0.33 winter W16 lower Summit Tetragnathidae Tetragnatha 5.5 2.5 0.01 18.28 0.43 winter W16 lower Summit Tetragnathidae Tetragnatha 1.5 0.5 0.00 0.20 -0.42 winter W16 lower Summit Tetragnathidae Tetragnatha 6.0 2.0 0.01 12.56 0.14 winter W16 lower Summit Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 1.22 winter W16 lower Summit Tetragnathidae Tetragnatha 4.0 2.0 0.00 8.37 0.28 winter W16 lower Summit Tetragnathidae Tetragnatha 4.0 1.0 0.00 2.09 -0.50 winter W16 lower Summit Tetragnathidae Tetragnatha 3.0 1.5 0.00 3.53 0.10 winter W16 lower Summit Tetragnathidae Tetragnatha 3.5 1.0 0.00 1.83 -0.19 winter W16 lower Summit Tetragnathidae Tetragnatha 4.5 2.0 0.00 9.42 0.17 winter W16 lower Summit Tetragnathidae Tetragnatha 4.5 2.0 0.00 9.42 0.22 winter W16 lower Summit Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 -0.15 winter W16 lower Summit Tetragnathidae Tetragnatha 5.0 1.5 0.00 5.89 0.05 winter W16 lower Summit Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 0.04 winter W16 lower Summit Tetragnathidae Tetragnatha 4.5 1.5 0.00 5.30 0.18 winter W16 lower Summit Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 0.08 winter W16 lower Summit Tetragnathidae Tetragnatha 2.0 1.0 0.00 1.05 0.11 winter W16 lower Summit Tetragnathidae Tetragnatha 4.0 1.5 0.00 4.71 0.07

213

Table C.6 Carbon (ẟ13C) and nitrogen (ẟ15N) isotopic signatures for aquatic and terrestrial primary producers and consumers in the

Fakahatchee Strand and Ten Thousand Island Estuary, Florida U.S.A.

Season/ Estuary Reach Group Taxa ẟ13C C% ẟ15N N% Year position W16 upper CW Phytoplankton - -31.03 47.53 0.21 3.69 W16 upper CW Phytoplankton - -32.28 5.98 1.81 0.69 W16 upper CW Phytoplankton - -30.17 4.36 -1.79 0.40 W16 upper CW Phytoplankton - -30.87 40.36 0.64 3.09 W16 upper CM Phytoplankton - -34.24 7.06 1.64 0.91 W16 upper CE Phytoplankton - -36.23 22.56 -1.80 2.51 W16 upper CE Phytoplankton - -36.21 22.06 -1.94 2.49 W16 upper CE Phytoplankton - -36.22 23.44 -1.63 2.65 W16 mid Long Phytoplankton - -32.99 14.61 1.58 1.91 W16 mid Long Phytoplankton - -32.65 14.34 1.56 1.85 W16 mid Bryan Phytoplankton - -34.83 6.55 1.42 0.92 W16 mid Bryan Phytoplankton - -34.78 6.58 1.42 0.94 W16 mid Diagon Phytoplankton - -35.27 5.28 0.56 0.75 W16 mid Diagon Phytoplankton - -34.83 5.19 0.53 0.73 W16 lower USGS Phytoplankton - -22.81 7.12 0.64 0.61 W16 lower USGS Phytoplankton - -21.53 8.15 1.48 0.60 W16 lower USGS Phytoplankton - -21.50 9.88 1.92 0.63 W16 lower Fork Phytoplankton - -20.40 6.83 -1.54 0.38 W16 lower Fork Phytoplankton - -20.61 9.77 1.00 0.63 W16 lower Fork Phytoplankton - -21.49 9.19 1.30 0.67 W16 lower Summit Phytoplankton - -19.51 7.32 0.87 0.48 214

W16 lower Summit Phytoplankton - -19.52 8.46 0.51 0.58 W16 lower Summit Phytoplankton - -19.26 6.55 -0.12 0.44 S15 mid Long Epiphyton - -28.03 28.20 0.82 3.89 S15 mid Long Epiphyton - -28.73 34.41 0.79 4.88 S15 mid Long Epiphyton - -28.60 30.17 0.88 4.22 S15 mid Bryan Epiphyton - -26.91 40.73 1.14 3.37 S15 mid Bryan Epiphyton - -26.88 41.75 1.16 3.40 S15 mid Bryan Epiphyton - -26.97 39.36 1.28 3.26 S16 upper CW Epiphyton - -26.37 25.61 1.24 1.34 S16 upper CW Epiphyton - -28.05 14.02 0.00 0.79 S16 upper CW Epiphyton - -28.13 41.25 -0.56 2.47 S16 upper CM Epiphyton - -28.22 30.88 1.13 1.61 S16 upper CM Epiphyton - -29.94 40.28 0.47 1.77 S16 upper CM Epiphyton - -26.86 33.08 0.39 1.19 S16 upper CE Epiphyton - -31.35 25.01 2.03 2.13 S16 upper CE Epiphyton - -26.97 22.70 0.66 1.61 S16 upper CE Epiphyton - -24.21 24.58 1.96 1.57 S16 upper CE Epiphyton - -24.79 26.85 1.48 1.30 S16 mid Long Epiphyton - -28.03 41.36 5.32 1.77 S16 mid Long Epiphyton - -23.06 29.46 4.20 1.54 S16 mid Long Epiphyton - -30.16 35.84 5.19 3.84 S16 mid Bryan Epiphyton - -28.17 35.26 1.32 4.86 S16 mid Bryan Epiphyton - -28.21 33.12 1.01 4.15 S16 mid Bryan Epiphyton - -29.74 21.98 1.30 2.61 S16 mid Diagon Epiphyton - -33.16 34.37 1.88 2.69 S16 mid Diagon Epiphyton - -33.15 34.29 1.69 2.75 S16 mid Diagon Epiphyton - -26.33 36.64 1.81 3.49 S16 lower USGS Epiphyton - -32.95 30.88 4.53 2.67 215

S16 lower USGS Epiphyton - -38.73 33.74 4.72 4.27 S16 lower USGS Epiphyton - -32.47 33.02 5.09 3.39 S16 lower Fork Epiphyton - -37.94 36.82 4.83 4.11 S16 lower Fork Epiphyton - -38.17 36.07 4.84 3.90 S16 lower Fork Epiphyton - -36.49 34.19 4.55 3.61 S16 lower Fork Epiphyton - -36.72 34.38 4.60 3.69 W15 upper CW Epiphyton - -31.82 37.45 3.41 2.40 W15 upper CW Epiphyton - -31.99 38.53 3.23 2.43 W15 upper CW Epiphyton - -31.87 38.15 3.32 2.43 W15 upper CE Epiphyton - -35.05 43.74 1.20 1.54 W15 upper CE Epiphyton - -37.42 28.75 1.54 2.68 W15 upper CE Epiphyton - -37.66 29.86 1.55 2.82 W15 mid Long Epiphyton - -26.27 29.12 5.06 1.64 W15 mid Long Epiphyton - -26.42 30.33 5.03 1.66 W15 mid Long Epiphyton - -26.30 29.01 4.98 1.63 W15 mid Bryan Epiphyton - -29.06 31.11 5.39 1.66 W15 mid Bryan Epiphyton - -28.95 36.34 5.53 1.92 W15 mid Diagon Epiphyton - -24.11 29.84 4.57 1.43 W15 mid Diagon Epiphyton - -24.05 31.54 4.68 1.52 W15 mid Diagon Epiphyton - -24.16 31.14 4.64 1.50 W15 lower USGS Epiphyton - -27.65 29.02 0.69 2.15 W15 lower USGS Epiphyton - -26.16 27.65 4.95 2.81 W15 lower USGS Epiphyton - -31.00 23.74 4.74 3.15 W15 lower Fork Epiphyton - -29.86 28.24 5.06 4.19 W15 lower Fork Epiphyton - -26.45 26.67 5.87 2.30 W15 lower Fork Epiphyton - -26.46 26.26 5.91 2.29 W16 upper CW Epiphyton - -33.01 28.88 4.67 3.17 W16 upper CW Epiphyton - -32.90 29.92 4.52 3.31 216

W16 upper CM Epiphyton - -33.73 32.41 2.38 3.47 W16 upper CM Epiphyton - -32.86 35.79 2.31 3.57 W16 upper CM Epiphyton - -32.76 36.31 2.17 3.59 W16 upper CE Epiphyton - -29.59 26.08 3.84 2.55 W16 upper CE Epiphyton - -29.54 27.73 3.90 2.71 W16 upper CE Epiphyton - -32.70 38.39 2.04 3.75 W16 mid Long Epiphyton - -23.74 28.09 6.16 4.16 W16 mid Long Epiphyton - -19.93 30.54 4.89 2.10 W16 mid Long Epiphyton - -30.54 36.79 4.22 5.47 W16 mid Long Epiphyton - -22.57 35.53 5.05 3.67 W16 mid Diagon Epiphyton - -21.23 30.34 3.75 3.42 W16 mid Diagon Epiphyton - -27.30 34.08 4.72 4.31 W16 mid Diagon Epiphyton - -21.48 40.62 4.47 3.56 W16 lower USGS Epiphyton - -24.83 21.84 4.91 3.27 W16 lower USGS Epiphyton - -24.87 22.07 4.94 3.29 W16 lower USGS Epiphyton - -24.67 19.77 4.85 2.90 W16 lower Fork Epiphyton - -21.87 34.62 5.08 2.75 W16 lower Fork Epiphyton - -22.68 26.26 4.40 2.30 W16 lower Fork Epiphyton - -22.26 19.91 4.06 1.98 W16 lower Summit Epiphyton - -22.59 26.17 3.65 1.82 W16 lower Summit Epiphyton - -22.60 24.24 3.52 1.68 W16 lower Summit Epiphyton - -22.87 23.99 3.50 1.69 Terrestrial S15 mid Long - -29.80 46.97 -2.17 0.83 vegetation Terrestrial S15 mid Long - -28.22 48.51 -3.52 0.84 vegetation Terrestrial S15 mid Long - -29.22 49.46 0.45 0.76 vegetation Terrestrial S15 mid Bryan - -29.03 46.10 0.09 0.63 vegetation 217

Terrestrial S15 mid Bryan - -28.40 48.85 1.03 0.77 vegetation Terrestrial S15 mid Bryan - -28.89 49.60 1.20 0.68 vegetation Terrestrial S15 mid Diagon - -28.29 40.47 0.95 0.61 vegetation Terrestrial S15 mid Diagon - -28.72 50.10 2.19 0.84 vegetation Terrestrial S15 mid Diagon - -28.34 51.32 1.42 1.16 vegetation Terrestrial S15 lower USGS - -27.98 47.62 1.50 0.84 vegetation Terrestrial S15 lower Fork - -27.28 45.24 1.67 0.56 vegetation Terrestrial S15 lower Fork - -27.23 42.97 1.92 0.82 vegetation Terrestrial S15 lower Fork - -26.67 39.46 2.07 0.67 vegetation Terrestrial S15 lower Summit - -27.30 44.79 2.29 0.60 vegetation Terrestrial S15 lower Summit - -27.89 42.77 2.11 0.62 vegetation Terrestrial S16 upper CW - -29.83 46.19 0.32 1.18 vegetation Terrestrial S16 upper CW - -29.98 48.92 -0.58 1.54 vegetation Terrestrial S16 upper CW - -28.95 46.09 -1.10 1.61 vegetation Terrestrial S16 upper CW - -28.55 41.96 0.95 1.37 vegetation Terrestrial S16 upper CW - -29.72 48.37 0.02 1.65 vegetation Terrestrial S16 upper CW - -29.41 49.17 -1.32 1.56 vegetation

218

Terrestrial S16 upper CM - -29.71 46.66 0.30 1.29 vegetation Terrestrial S16 upper CM - -28.69 47.48 1.09 1.43 vegetation Terrestrial S16 upper CM - -28.68 43.26 0.33 1.72 vegetation Terrestrial S16 upper CM - -28.53 46.05 -0.51 1.28 vegetation Terrestrial S16 upper CM - -30.72 43.21 0.66 1.15 vegetation Terrestrial S16 upper CM - -28.06 46.36 0.52 1.72 vegetation Terrestrial S16 upper CE - -30.65 47.05 -0.22 2.12 vegetation Terrestrial S16 upper CE - -27.82 42.53 2.12 2.17 vegetation Terrestrial S16 upper CE - -28.81 46.56 0.67 1.54 vegetation Terrestrial S16 upper CE - -28.85 44.23 1.31 1.44 vegetation Terrestrial S16 upper CE - -28.92 45.77 -0.10 0.95 vegetation Terrestrial S16 upper CE - -29.99 48.93 0.23 1.72 vegetation Terrestrial S16 upper CE - -28.73 44.29 0.83 1.40 vegetation Terrestrial S16 mid Long - -29.56 50.62 -1.50 0.62 vegetation Terrestrial S16 mid Long - -28.82 46.62 0.32 1.49 vegetation Terrestrial S16 mid Long - -29.70 50.73 -0.91 0.75 vegetation Terrestrial S16 mid Long - -28.13 50.34 0.63 0.89 vegetation

219

Terrestrial S16 mid Long - -28.71 52.77 0.41 0.65 vegetation Terrestrial S16 mid Bryan - -28.20 46.38 1.28 0.74 vegetation Terrestrial S16 mid Bryan - -29.07 49.83 1.20 0.69 vegetation Terrestrial S16 mid Bryan - -27.77 48.19 1.92 0.73 vegetation Terrestrial S16 mid Bryan - -28.71 46.93 2.46 0.71 vegetation Terrestrial S16 mid Bryan - -28.96 47.87 1.57 0.73 vegetation Terrestrial S16 mid Bryan - -27.70 51.03 1.25 0.86 vegetation Terrestrial S16 mid Bryan - -27.31 46.67 1.99 0.92 vegetation Terrestrial S16 mid Diagon - -28.79 46.98 2.15 0.81 vegetation Terrestrial S16 mid Diagon - -29.27 48.69 1.15 0.70 vegetation Terrestrial S16 mid Diagon - -28.79 50.85 2.14 0.64 vegetation Terrestrial S16 mid Diagon - -28.38 47.53 0.91 0.90 vegetation Terrestrial S16 mid Diagon - -28.80 46.65 1.54 1.02 vegetation Terrestrial S16 mid Diagon - -28.22 47.26 0.34 0.78 vegetation Terrestrial S16 mid Diagon - -28.14 51.18 1.15 0.74 vegetation Terrestrial S16 mid Diagon - -28.35 51.33 0.06 0.80 vegetation Terrestrial S16 mid Diagon - -29.11 47.38 1.14 0.67 vegetation

220

Terrestrial S16 mid Diagon - -27.72 45.44 2.94 0.76 vegetation Terrestrial W15 upper CW - -29.43 47.10 -0.16 1.37 vegetation Terrestrial W15 upper CW - -29.22 42.69 -0.37 1.14 vegetation Terrestrial W15 upper CW - -29.19 43.95 -1.22 0.95 vegetation Terrestrial W15 upper CE - -29.24 43.63 1.82 1.48 vegetation Terrestrial W15 upper CE - -29.59 47.06 0.68 0.95 vegetation Terrestrial W15 upper CE - -29.29 46.49 -0.38 0.95 vegetation Terrestrial W15 mid Long - -30.41 47.32 -3.10 0.95 vegetation Terrestrial W15 mid Long - -28.75 42.71 -0.57 1.15 vegetation Terrestrial W15 mid Long - -29.63 48.46 -0.02 0.97 vegetation Terrestrial W15 mid Bryan - -29.02 44.61 -1.91 0.81 vegetation Terrestrial W15 mid Bryan - -28.77 45.74 0.80 1.01 vegetation Terrestrial W15 mid Bryan - -28.44 48.03 -0.18 1.16 vegetation Terrestrial W15 mid Diagon - -28.99 47.21 1.76 0.99 vegetation Terrestrial W15 mid Diagon - -27.80 45.09 0.37 0.64 vegetation Terrestrial W15 mid Diagon - -28.40 45.01 1.09 0.67 vegetation Terrestrial W16 upper CW - -29.30 43.27 1.28 1.11 vegetation

221

Terrestrial W16 upper CW - -27.33 43.67 0.15 2.28 vegetation Terrestrial W16 upper CW - -28.47 48.73 -0.47 1.67 vegetation Terrestrial W16 upper CM - -28.95 50.03 0.79 0.99 vegetation Terrestrial W16 upper CM - -29.87 46.39 1.00 0.85 vegetation Terrestrial W16 upper CM - -29.71 45.89 0.68 1.12 vegetation Terrestrial W16 upper CE - -31.19 43.07 1.47 2.36 vegetation Terrestrial W16 upper CE - -28.39 45.68 1.44 0.69 vegetation Terrestrial W16 upper CE - -28.90 45.98 1.44 0.97 vegetation Terrestrial W16 mid Long - -30.39 51.95 -1.64 0.94 vegetation Terrestrial W16 mid Long - -27.54 45.55 0.22 0.30 vegetation Terrestrial W16 mid Bryan - -28.07 44.33 2.24 0.55 vegetation Terrestrial W16 mid Bryan - -30.16 52.02 1.06 0.96 vegetation Terrestrial W16 mid Bryan - -27.46 49.67 2.13 0.51 vegetation Terrestrial W16 mid Diagon - -29.39 46.06 -1.09 2.23 vegetation Terrestrial W16 mid Diagon - -30.01 42.99 2.31 0.66 vegetation W16 upper CW Riparian leaves Acer rubrum -27.92 50.10 -0.23 4.38 W16 upper CW Riparian leaves Annona glabra -33.11 46.17 -0.56 1.40 W16 upper CM Riparian leaves Annona glabra -31.55 47.51 0.06 1.50 W16 upper CE Riparian leaves Annona glabra -30.05 45.59 -0.50 3.91

222

W16 upper CM Riparian leaves Persea palustris -31.84 48.88 -0.16 1.85 W16 upper CE Riparian leaves Pleopeltis -27.80 51.40 2.47 0.73 W16 upper CW Riparian leaves Salix caroliniana -31.12 46.60 -0.60 3.16 W16 upper CM Riparian leaves Taxodium -29.66 46.94 -0.27 0.80 W16 upper CE Riparian leaves Taxodium -29.44 43.03 0.46 0.94 S15 upper CM Riparian leaves - -32.72 46.06 0.19 1.14 S15 mid Long Riparian leaves - -29.62 48.24 -1.02 1.56 S15 mid Long Riparian leaves - -29.32 47.42 -2.00 1.87 S15 mid Long Riparian leaves - -29.25 46.09 -0.69 1.62 S15 mid Bryan Riparian leaves - -30.82 44.50 -2.12 1.64 S15 mid Bryan Riparian leaves - -31.43 42.92 -2.30 1.54 S15 mid Bryan Riparian leaves - -30.84 43.80 -0.20 1.63 S15 mid Diagon Riparian leaves - -31.25 43.80 -0.08 1.46 S15 mid Diagon Riparian leaves - -30.39 43.10 2.64 1.69 S15 mid Diagon Riparian leaves - -30.99 42.41 0.47 1.43 S15 lower USGS Riparian leaves - -29.65 45.02 -3.08 1.16 S15 lower USGS Riparian leaves - -28.13 45.17 0.42 1.25 S15 lower USGS Riparian leaves - -28.11 44.85 1.62 1.12 S15 lower Fork Riparian leaves - -27.85 43.29 1.79 0.95 S15 lower Fork Riparian leaves - -27.42 42.57 1.14 0.91 S15 lower Fork Riparian leaves - -27.32 41.93 3.18 0.89 S15 lower Summit Riparian leaves - -28.34 43.00 2.50 0.83 S15 lower Summit Riparian leaves - -27.22 45.46 1.85 0.92 S15 lower Summit Riparian leaves - -28.75 45.18 2.62 0.90 S16 upper CW Riparian leaves - -31.82 45.04 1.90 1.82 S16 upper CW Riparian leaves - -31.24 46.84 0.44 1.80 S16 upper CW Riparian leaves - -33.08 43.05 0.62 2.14 S16 upper CM Riparian leaves - -32.05 44.26 1.83 1.76 223

S16 upper CM Riparian leaves - -31.80 45.79 0.99 1.67 S16 upper CM Riparian leaves - -32.15 46.07 -0.72 1.37 S16 upper CE Riparian leaves - -31.05 43.95 3.58 1.68 S16 upper CE Riparian leaves - -30.57 47.17 1.64 1.64 S16 upper CE Riparian leaves - -32.45 43.87 0.93 1.83 S16 mid Long Riparian leaves - -28.61 45.11 0.21 1.41 S16 mid Long Riparian leaves - -28.20 46.39 0.59 1.75 S16 mid Long Riparian leaves - -29.46 47.09 0.34 1.15 S16 mid Bryan Riparian leaves - -31.06 46.43 -1.05 1.23 S16 mid Bryan Riparian leaves - -29.84 44.52 -6.14 1.51 S16 mid Bryan Riparian leaves - -27.20 48.08 2.31 1.58 S16 mid Diagon Riparian leaves - -28.50 45.77 1.99 1.48 S16 mid Diagon Riparian leaves - -30.39 42.66 2.07 1.38 S16 mid Diagon Riparian leaves - -29.31 43.10 2.35 1.51 S16 lower USGS Riparian leaves - -28.29 46.86 0.72 1.41 S16 lower USGS Riparian leaves - -29.25 46.63 0.59 1.16 S16 lower USGS Riparian leaves - -27.46 44.26 0.81 1.25 S16 lower Fork Riparian leaves - -27.98 43.79 1.40 0.93 S16 lower Fork Riparian leaves - -28.80 45.20 2.12 0.82 S16 lower Fork Riparian leaves - -28.56 42.21 1.17 0.78 S16 lower Summit Riparian leaves - -29.18 47.18 2.69 1.00 S16 lower Summit Riparian leaves - -28.84 47.35 2.35 0.92 S16 lower Summit Riparian leaves - -29.35 42.15 2.84 0.90 W15 upper CW Riparian leaves - -29.40 46.31 0.44 1.68 W15 upper CW Riparian leaves - -29.24 44.86 0.33 1.60 W15 upper CW Riparian leaves - -29.30 45.53 0.43 1.70 W15 upper CE Riparian leaves - -32.04 47.61 0.74 1.57 W15 upper CE Riparian leaves - -31.90 44.58 0.39 1.47 224

W15 upper CE Riparian leaves - -32.09 47.97 0.31 1.55 W15 mid Long Riparian leaves - -28.97 45.40 -1.90 1.58 W15 mid Long Riparian leaves - -28.88 46.04 -1.83 1.61 W15 mid Long Riparian leaves - -28.98 52.94 -1.81 1.85 W15 mid Bryan Riparian leaves - -29.71 45.41 -0.81 1.68 W15 mid Bryan Riparian leaves - -29.73 46.59 -0.56 1.82 W15 mid Bryan Riparian leaves - -29.59 48.05 -0.56 1.91 W15 mid Diagon Riparian leaves - -31.53 42.68 0.30 1.78 W15 mid Diagon Riparian leaves - -31.57 43.79 0.59 1.81 W15 mid Diagon Riparian leaves - -31.46 43.61 0.58 1.80 W15 lower USGS Riparian leaves - -29.28 44.38 1.73 1.20 W15 lower USGS Riparian leaves - -29.23 41.60 1.57 1.08 W15 lower USGS Riparian leaves - -27.92 45.04 1.45 1.09 W15 lower USGS Riparian leaves - -28.26 46.44 3.97 1.46 W15 lower USGS Riparian leaves - -28.09 46.81 3.86 1.39 W15 lower Fork Riparian leaves - -27.16 46.08 1.59 0.61 W15 lower Fork Riparian leaves - -29.26 41.74 2.05 1.07 W15 lower Fork Riparian leaves - -27.16 45.77 1.43 1.14 W15 lower Fork Riparian leaves - -27.05 44.10 2.40 0.64 W15 lower Summit Riparian leaves - -29.75 44.93 3.34 1.12 W15 lower Summit Riparian leaves - -27.20 42.62 3.62 0.74 W15 lower Summit Riparian leaves - -27.13 41.75 3.43 0.76 W15 lower Summit Riparian leaves - -29.61 46.74 2.10 1.11 W16 mid Long Riparian leaves - -31.05 43.43 -1.12 1.05 W16 mid Long Riparian leaves - -31.06 46.96 -0.87 1.16 W16 mid Long Riparian leaves - -31.03 45.21 -1.51 1.11 W16 mid Bryan Riparian leaves - -32.62 42.04 -5.12 1.67 W16 mid Bryan Riparian leaves - -32.59 42.56 -5.02 1.68 225

W16 mid Bryan Riparian leaves - -32.66 44.90 -5.09 1.78 W16 mid Diagon Riparian leaves - -29.09 45.03 1.32 0.84 W16 mid Diagon Riparian leaves - -28.79 43.66 1.25 0.76 W16 mid Diagon Riparian leaves - -29.03 48.54 0.51 0.89 W16 lower USGS Riparian leaves - -28.85 48.57 1.81 1.38 W16 lower USGS Riparian leaves - -28.87 43.96 1.34 1.23 W16 lower USGS Riparian leaves - -28.84 44.33 1.64 1.24 W16 lower Fork Riparian leaves - -29.85 43.08 1.44 0.81 W16 lower Fork Riparian leaves - -29.84 42.05 1.63 0.79 W16 lower Fork Riparian leaves - -29.86 43.93 1.54 0.83 W16 lower Summit Riparian leaves - -29.58 43.96 3.62 0.76 W16 lower Summit Riparian leaves - -29.53 42.40 2.39 0.75 W16 lower Summit Riparian leaves - -29.64 45.22 3.02 0.83 Emergent W16 upper CW - -28.40 42.54 -4.21 1.62 vegetation Emergent W16 upper CW - -28.16 49.08 -4.22 1.68 vegetation Emergent W16 upper CW - -28.78 43.71 1.80 3.26 vegetation Emergent W16 upper CM - -31.93 45.77 1.02 3.84 vegetation Emergent W16 upper CM - -30.68 42.39 3.21 2.50 vegetation Emergent W16 upper CM - -29.44 42.18 2.19 2.43 vegetation Emergent W16 upper CM - -31.28 42.08 1.07 3.20 vegetation Emergent W16 upper CE - -32.61 43.53 -0.48 2.87 vegetation Emergent W16 upper CE - -29.94 41.42 4.63 3.19 vegetation

226

Emergent W16 upper CE - -32.40 44.86 2.20 1.14 vegetation Emergent W16 mid Long - -28.63 47.31 0.83 2.56 vegetation S16 upper CW Aquatic insect Chironomidae -38.67 42.01 6.22 11.78 S16 upper CW Aquatic insect Chironomidae -33.51 38.67 5.15 10.50 S16 upper CW Aquatic insect Chironomidae -44.99 49.08 5.28 13.35 S16 upper CM Aquatic insect Chironomidae -31.61 50.60 4.56 13.82 S16 upper CM Aquatic insect Chironomidae -31.78 39.69 4.53 10.19 S16 upper CM Aquatic insect Chironomidae -32.62 43.72 4.73 11.11 S16 upper CE Aquatic insect Chironomidae -38.33 49.94 5.63 13.12 S16 upper CE Aquatic insect Chironomidae -38.80 65.30 5.88 16.69 S16 upper CE Aquatic insect Chironomidae -34.94 51.76 4.86 13.74 mid Long Aquatic insect Chironomidae -27.40 30.38 5.37 8.18 mid Long Aquatic insect Chironomidae -26.29 9.25 3.40 2.27 mid Bryan Aquatic insect Chironomidae -31.54 40.91 5.24 11.81 mid Diagon Aquatic insect Chironomidae -28.83 31.21 6.12 8.54 mid Diagon Aquatic insect Chironomidae -29.00 55.21 5.77 15.13 mid Diagon Aquatic insect Chironomidae -29.22 51.63 5.92 13.96 upper CW Aquatic insect Chironomidae -33.95 58.16 5.99 16.12 upper CW Aquatic insect Chironomidae -36.84 50.31 6.80 13.60 upper CW Aquatic insect Chironomidae -37.41 60.23 6.20 16.30 upper CM Aquatic insect Chironomidae -33.82 28.52 5.33 7.53 upper CM Aquatic insect Chironomidae -33.69 82.91 5.40 22.31 upper CM Aquatic insect Chironomidae -34.12 50.62 4.77 12.12 upper CE Aquatic insect Chironomidae -38.66 48.61 5.52 13.81 upper CE Aquatic insect Chironomidae -38.28 59.72 5.43 16.68 upper CE Aquatic insect Chironomidae -38.16 57.72 5.58 15.78 mid Long Aquatic insect Chironomidae -29.95 61.25 4.55 15.01 227

mid Long Aquatic insect Chironomidae -30.06 49.79 5.00 12.88 mid Long Aquatic insect Chironomidae -29.89 39.21 4.61 9.94 mid Bryan Aquatic insect Chironomidae -33.43 40.14 4.90 10.82 mid Bryan Aquatic insect Chironomidae -33.01 42.57 4.69 11.74 mid Bryan Aquatic insect Chironomidae -34.33 57.72 4.96 16.14 mid Diagon Aquatic insect Chironomidae -33.47 83.99 4.17 23.43 mid Diagon Aquatic insect Chironomidae -34.25 55.57 4.72 11.96 mid Diagon Aquatic insect Chironomidae -33.02 34.87 5.94 9.76 mid Diagon Aquatic insect Chironomidae -33.04 40.20 5.80 11.00 lower USGS Aquatic insect Chironomidae -30.38 49.41 6.00 12.47 lower USGS Aquatic insect Chironomidae -30.17 40.02 5.45 9.75 lower Fork Aquatic insect Chironomidae -28.67 16.07 2.94 4.00 lower Fork Aquatic insect Chironomidae -27.57 39.00 4.35 10.82 lower Summit Aquatic insect Chironomidae -23.72 36.84 4.40 10.27 S16 upper CW Aquatic insect Dolichopodidae -23.30 33.82 7.72 9.07 S16 upper CW Aquatic insect Dolichopodidae -23.57 34.65 7.66 9.31 S16 upper CW Aquatic insect Dolichopodidae -23.50 41.77 7.50 11.06 S16 upper CE Aquatic insect Dolichopodidae -24.33 9.27 2.49 2.14 lower Fork Aquatic insect Dolichopodidae -27.64 55.44 6.65 14.86 lower Fork Aquatic insect Dolichopodidae -27.56 42.00 6.65 11.30 lower Fork Aquatic insect Dolichopodidae -27.14 38.24 6.92 10.64 lower Summit Aquatic insect Dolichopodidae -26.52 45.94 6.50 12.46 lower Summit Aquatic insect Dolichopodidae -26.49 50.26 6.43 13.59 lower Summit Aquatic insect Dolichopodidae -26.49 67.90 6.50 18.62 upper CW Aquatic insect Dolichopodidae -26.50 34.74 4.67 9.67 upper CW Aquatic insect Dolichopodidae -26.20 30.22 5.76 7.98 upper CW Aquatic insect Dolichopodidae -26.59 72.95 4.98 20.29 upper CE Aquatic insect Dolichopodidae -33.16 34.56 4.77 9.58 228

mid Long Aquatic insect Dolichopodidae -25.39 33.23 4.60 9.51 lower USGS Aquatic insect Dolichopodidae -28.19 53.12 5.72 14.92 lower USGS Aquatic insect Dolichopodidae -27.97 54.50 5.81 15.43 lower USGS Aquatic insect Dolichopodidae -28.09 41.25 5.74 11.49 lower Fork Aquatic insect Dolichopodidae -27.45 49.21 6.02 13.99 lower Fork Aquatic insect Dolichopodidae -27.55 61.81 6.03 17.41 lower Fork Aquatic insect Dolichopodidae -27.12 51.54 6.44 14.75 lower Summit Aquatic insect Dolichopodidae -26.04 32.42 7.12 9.07 W15 lower Summit Spider Gasteracantha cancriformis -29.18 33.88 3.50 10.15 W16 lower USGS Spider Gasteracantha cancriformis -27.94 39.73 6.23 10.57 W16 lower USGS Spider Gasteracantha cancriformis -27.93 40.29 6.35 10.66 W16 lower USGS Spider Gasteracantha cancriformis -28.02 40.94 6.41 10.80 W16 mid Diagon Spider Neoscona crucifera -29.21 32.66 4.52 10.62 W16 mid Diagon Spider Neoscona crucifera -28.50 44.08 5.05 12.87 W16 mid Diagon Spider Neoscona crucifera -28.51 51.98 5.17 15.02 W16 lower Fork Spider Neoscona crucifera -28.36 41.54 3.08 11.85 W16 lower Fork Spider Neoscona crucifera -28.08 38.45 2.73 11.64 W16 lower Fork Spider Neoscona crucifera -28.31 41.96 3.12 11.98 W16 lower Summit Spider Neoscona crucifera -27.21 53.22 3.08 16.70 W16 lower Summit Spider Neoscona crucifera -27.17 30.84 2.53 10.36 W16 lower Summit Spider Neoscona crucifera -27.07 32.04 3.79 10.23 S15 mid Diagon Spider Araneidae -25.18 55.41 5.45 12.58 S15 mid Diagon Spider Araneidae -25.17 47.99 5.55 10.95 S15 mid Diagon Spider Araneidae -25.23 42.00 5.38 9.53 S16 mid Diagon Spider Araneidae -29.02 60.22 5.63 14.03 S16 mid Diagon Spider Araneidae -28.83 48.27 4.97 10.32 S16 mid Diagon Spider Araneidae -28.78 48.56 4.50 9.46 W15 mid Diagon Spider Araneidae -30.01 38.32 4.14 9.04 229

W15 mid Diagon Spider Araneidae -30.01 48.99 4.00 11.49 W15 mid Diagon Spider Araneidae -30.02 48.48 4.61 11.92 S15 mid Long Spider Nephilinae -23.93 43.99 5.23 11.11 S15 mid Long Spider Nephilinae -24.14 55.94 6.15 15.53 S15 mid Bryan Spider Nephilinae -24.34 63.54 4.93 17.06 S15 mid Bryan Spider Nephilinae -24.43 42.83 4.97 11.46 S15 mid Bryan Spider Nephilinae -24.56 37.60 4.92 10.04 S15 mid Diagon Spider Nephilinae -25.41 39.91 5.36 9.38 S15 mid Diagon Spider Nephilinae -25.35 45.14 3.42 9.99 S15 mid Diagon Spider Nephilinae -25.32 39.75 3.04 8.70 S16 mid Long Spider Nephilinae -27.44 38.34 4.90 10.13 S16 mid Long Spider Nephilinae -27.47 44.94 5.26 11.97 S16 mid Long Spider Nephilinae -26.46 24.96 4.02 6.60 S16 mid Diagon Spider Nephilinae -26.47 26.24 4.21 6.53 S16 mid Diagon Spider Nephilinae -26.60 46.09 4.86 11.72 S16 mid Diagon Spider Nephilinae -26.53 41.70 4.52 10.57 S16 lower USGS Spider Nephilinae -27.34 43.64 2.66 9.48 S16 lower USGS Spider Nephilinae -27.28 55.04 2.51 11.86 S16 lower USGS Spider Nephilinae -27.20 42.67 1.62 8.32 S16 lower Fork Spider Nephilinae -25.55 44.76 4.22 11.83 S16 lower Fork Spider Nephilinae -25.61 38.40 4.33 10.16 S16 lower Fork Spider Nephilinae -25.75 42.11 4.60 11.01 W15 lower USGS Spider Nephilinae -29.17 37.59 5.87 9.56 W15 lower USGS Spider Nephilinae -28.70 19.21 4.81 4.47 W16 lower USGS Spider Nephilinae -27.21 40.69 5.52 11.84 W16 lower USGS Spider Nephilinae -27.13 45.20 5.78 13.25 W16 lower USGS Spider Nephilinae -27.09 42.92 5.54 12.62 W16 lower Fork Spider Nephilinae -26.87 53.59 4.17 15.53 230

W16 lower Fork Spider Nephilinae -26.88 57.15 4.11 16.78 W16 lower Fork Spider Nephilinae -26.88 41.08 3.88 11.58 S15 mid Long Spider Nephilinae -23.94 41.67 5.18 10.38 S16 upper CM Spider Dolomedes -23.20 57.64 5.99 15.09 S16 upper CM Spider Dolomedes -23.03 57.66 5.31 14.57 S16 upper CM Spider Dolomedes -23.07 61.96 5.45 15.80 S16 mid Diagon Spider Dolomedes -27.55 31.15 6.01 8.38 S16 mid Diagon Spider Dolomedes -26.79 31.43 6.27 7.10 S16 mid Diagon Spider Dolomedes -26.93 60.17 7.07 14.33 W16 mid Diagon Spider Dolomedes -29.14 56.89 5.48 15.88 W16 mid Diagon Spider Dolomedes -29.27 54.23 5.59 14.53 W16 mid Diagon Spider Dolomedes -29.18 56.59 5.54 15.31 S15 mid Long Spider Leucage -24.41 36.39 5.05 9.11 S15 mid Long Spider Leucage -23.41 46.90 4.76 12.48 S15 mid Long Spider Leucage -22.84 55.94 4.75 12.72 S15 mid Long Spider Leucage -23.13 51.50 4.88 12.43 S15 mid Bryan Spider Leucage -24.16 37.84 4.49 10.43 S15 mid Bryan Spider Leucage -24.49 65.30 4.59 17.99 S15 mid Bryan Spider Leucage -24.96 40.25 4.37 10.49 S15 mid Diagon Spider Leucage -24.35 49.76 4.34 12.77 S15 mid Diagon Spider Leucage -24.72 51.46 4.75 12.64 S15 mid Diagon Spider Leucage -24.73 50.95 4.37 12.70 S15 lower USGS Spider Leucage -26.74 55.18 3.55 13.17 S15 lower USGS Spider Leucage -26.71 49.70 3.73 11.86 S15 lower USGS Spider Leucage -26.62 50.17 3.58 11.96 W15 upper CW Spider Leucage -28.50 47.64 3.35 12.77 W15 upper CW Spider Leucage -28.30 29.45 2.84 7.40 W15 mid Long Spider Leucage -29.31 32.38 4.78 8.98 231

W15 mid Long Spider Leucage -29.86 39.94 5.39 11.20 W15 mid Long Spider Leucage -29.76 32.28 5.14 8.39 W15 mid Diagon Spider Leucage -31.91 47.88 5.01 12.21 W15 mid Diagon Spider Leucage -31.50 44.19 4.71 11.12 W15 lower USGS Spider Leucage -30.38 45.33 5.80 10.96 W15 lower USGS Spider Leucage -30.05 38.10 5.63 10.14 W16 upper CW Spider Leucage -29.71 48.94 4.66 12.49 W16 upper CW Spider Leucage -29.33 46.77 3.47 12.81 W16 upper CW Spider Leucage -30.19 46.19 3.90 11.99 W16 upper CM Spider Leucage -28.38 63.36 3.50 16.79 W16 upper CE Spider Leucage -28.39 43.61 4.30 11.04 W16 upper CE Spider Leucage -28.72 44.12 4.14 12.45 W16 upper CE Spider Leucage -28.94 45.94 3.81 11.92 W16 mid Long Spider Leucage -31.21 55.85 5.35 13.09 W16 mid Long Spider Leucage -30.65 57.49 5.07 13.49 W16 mid Long Spider Leucage -30.62 43.04 4.94 10.33 W16 mid Bryan Spider Leucage -29.40 37.85 4.67 9.81 W16 mid Bryan Spider Leucage -30.64 51.11 5.62 11.76 W16 mid Bryan Spider Leucage -30.13 41.75 5.69 11.13 W16 mid Diagon Spider Leucage -30.11 49.69 4.80 12.69 W16 mid Diagon Spider Leucage -31.86 52.91 5.87 13.71 W16 mid Diagon Spider Leucage -31.76 49.70 6.04 12.40 W16 mid Diagon Spider Leucage -30.56 49.61 5.46 12.86 W16 lower USGS Spider Leucage -28.53 42.57 4.17 11.05 W16 lower USGS Spider Leucage -28.59 45.07 4.42 11.68 W16 lower USGS Spider Leucage -28.59 44.37 4.41 11.55 W16 lower Fork Spider Leucage -28.46 31.88 3.55 8.76 W16 lower Fork Spider Leucage -28.19 48.46 4.71 11.70 232

W16 lower Fork Spider Leucage -28.09 48.06 4.03 13.15 W16 lower Summit Spider Leucage -28.38 50.52 6.74 12.59 W16 lower Summit Spider Leucage -28.95 47.90 4.06 13.93 W16 lower Summit Spider Leucage -28.13 40.90 4.82 11.27 W15 mid Long Spider Tetragnatha -30.44 26.85 4.66 7.34 W15 mid Long Spider Tetragnatha -29.59 38.46 5.13 10.12 W15 mid Long Spider Tetragnatha -29.20 35.80 4.96 8.86 W16 upper CW Spider Tetragnatha -34.89 58.18 5.36 17.15 W16 upper CW Spider Tetragnatha -32.13 43.81 5.08 11.73 W16 upper CW Spider Tetragnatha -32.24 49.08 5.45 12.82 W16 upper CM Spider Tetragnatha -30.54 48.30 4.63 12.84 W16 upper CM Spider Tetragnatha -30.89 42.07 4.49 11.60 W16 upper CM Spider Tetragnatha -30.36 43.71 4.55 12.08 W16 upper CM Spider Tetragnatha -30.40 50.38 4.45 13.99 W16 upper CM Spider Tetragnatha -31.75 53.46 4.29 14.67 W16 upper CE Spider Tetragnatha -28.09 74.20 3.23 19.35 W16 upper CE Spider Tetragnatha -27.96 60.27 3.52 17.56 W16 upper CE Spider Tetragnatha -28.08 56.25 3.83 15.56 W16 mid Long Spider Tetragnatha -31.42 51.59 4.49 13.02 W16 mid Long Spider Tetragnatha -30.78 46.37 4.52 12.40 W16 mid Long Spider Tetragnatha -30.23 45.18 5.44 11.88 W16 mid Bryan Spider Tetragnatha -31.52 36.54 4.40 10.24 W16 mid Bryan Spider Tetragnatha -31.32 26.79 4.60 7.04 W16 mid Bryan Spider Tetragnatha -31.05 53.55 5.78 14.28 W16 mid Bryan Spider Tetragnatha -32.72 45.23 5.42 11.54 W16 mid Diagon Spider Tetragnatha -29.93 39.16 5.45 11.00 W16 mid Diagon Spider Tetragnatha -32.26 61.89 4.69 17.14 W16 mid Diagon Spider Tetragnatha -32.76 46.85 5.37 12.84 233

W16 mid Diagon Spider Tetragnatha -32.21 40.02 4.84 10.51 W16 lower Fork Spider Tetragnatha -28.55 42.01 5.59 11.01 W16 lower Fork Spider Tetragnatha -28.64 25.77 4.60 7.66 W16 lower Fork Spider Tetragnatha -28.09 21.29 5.50 6.28 W16 lower Summit Spider Tetragnatha -27.92 40.08 4.86 11.82 W16 lower Summit Spider Tetragnatha -28.57 36.14 5.88 10.09 W16 lower Summit Spider Tetragnatha -28.76 34.39 4.40 10.35 S15 mid Long Spider Tetragnathidae -22.69 4.06 5.58 1.01 S15 mid Long Spider Tetragnathidae -22.63 43.97 5.53 11.02 S15 mid Long Spider Tetragnathidae -22.47 44.01 5.46 10.96 S15 lower USGS Spider Tetragnathidae -27.21 37.34 3.71 7.97 S15 lower USGS Spider Tetragnathidae -26.22 47.60 4.69 11.14 S15 lower USGS Spider Tetragnathidae -26.15 40.82 4.46 9.50 S15 lower Fork Spider Tetragnathidae -25.95 46.29 4.15 11.50 S15 lower Fork Spider Tetragnathidae -25.58 60.22 4.80 14.63 S15 lower Fork Spider Tetragnathidae -25.80 67.65 4.95 15.37 S15 lower Summit Spider Tetragnathidae -25.87 59.02 5.01 13.80 S15 lower Summit Spider Tetragnathidae -26.22 28.27 4.91 6.49 S15 lower Summit Spider Tetragnathidae -25.47 44.53 4.49 10.85 S16 upper CW Spider Tetragnathidae -30.84 39.61 3.86 8.76 S16 upper CW Spider Tetragnathidae -29.61 52.27 3.48 11.25 S16 upper CW Spider Tetragnathidae -31.25 38.00 4.48 8.62 S16 upper CW Spider Tetragnathidae -27.54 36.30 3.70 8.83 S16 upper CM Spider Tetragnathidae -29.28 40.27 4.48 10.02 S16 upper CM Spider Tetragnathidae -26.46 41.62 3.42 10.15 S16 upper CE Spider Tetragnathidae -25.77 48.58 4.13 11.34 S16 upper CE Spider Tetragnathidae -27.10 38.64 3.26 8.59 S16 upper CE Spider Tetragnathidae -27.72 35.89 2.59 7.40 234

S16 mid Long Spider Tetragnathidae -28.42 45.92 6.65 12.25 S16 mid Long Spider Tetragnathidae -27.87 33.73 6.17 8.39 S16 mid Long Spider Tetragnathidae -28.00 33.89 5.64 8.51 S16 mid Bryan Spider Tetragnathidae -27.20 36.85 4.55 9.12 S16 mid Bryan Spider Tetragnathidae -26.67 45.75 4.03 10.01 S16 mid Bryan Spider Tetragnathidae -28.91 40.61 5.82 10.74 S16 mid Diagon Spider Tetragnathidae -27.93 41.65 5.53 10.36 S16 mid Diagon Spider Tetragnathidae -26.94 46.04 5.15 12.11 S16 mid Diagon Spider Tetragnathidae -29.29 36.47 5.99 9.51 S16 lower USGS Spider Tetragnathidae -26.87 41.38 6.41 10.39 S16 lower USGS Spider Tetragnathidae -27.92 31.40 5.21 7.95 S16 lower USGS Spider Tetragnathidae -26.79 38.54 6.06 10.27 S16 lower Fork Spider Tetragnathidae -27.12 41.99 5.31 9.80 S16 lower Fork Spider Tetragnathidae -27.65 46.04 5.00 10.03 S16 lower Fork Spider Tetragnathidae -27.60 39.21 5.51 9.30 S16 lower Summit Spider Tetragnathidae -27.98 48.78 6.29 11.02 S16 lower Summit Spider Tetragnathidae -27.66 43.64 5.69 10.17 S16 lower Summit Spider Tetragnathidae -27.63 45.77 5.89 10.25 W15 upper CE Spider Tetragnathidae -29.50 26.25 4.30 7.56 W15 upper CE Spider Tetragnathidae -29.41 30.32 3.45 10.03 W15 upper CE Spider Tetragnathidae -29.56 32.50 2.63 9.92 W15 mid Bryan Spider Tetragnathidae -29.73 33.19 4.54 8.89 W15 mid Bryan Spider Tetragnathidae -30.11 43.53 4.55 11.09 W15 mid Bryan Spider Tetragnathidae -29.90 33.60 4.21 9.52 W15 mid Diagon Spider Tetragnathidae -31.38 48.58 5.11 12.45 W15 mid Diagon Spider Tetragnathidae -31.32 52.07 4.83 12.99 W15 lower USGS Spider Tetragnathidae -30.16 43.97 5.79 11.00 W15 lower Fork Spider Tetragnathidae -28.75 34.20 4.28 8.47 235

W15 lower Fork Spider Tetragnathidae -28.21 38.42 5.13 10.54 W15 lower Fork Spider Tetragnathidae -27.72 40.09 5.13 10.17 W15 lower Summit Spider Tetragnathidae -28.76 31.14 4.14 7.93 W15 lower Summit Spider Tetragnathidae -28.11 41.61 5.36 11.32 W15 lower Summit Spider Tetragnathidae -28.17 26.88 5.13 7.32 W15 lower Fork Fish Achirus lineatus -27.77 36.76 6.34 11.11 W16 lower USGS Fish Ariopsis felis -26.11 51.80 8.45 16.16 W16 lower USGS Fish Ariopsis felis -22.66 48.60 8.42 14.99 W15 mid Long Fish Menidia sp. -25.12 49.55 8.44 15.95 W15 mid Long Fish Menidia sp. -27.91 49.04 9.32 14.52 S16 upper CW Fish Lepomis sp. -27.36 37.75 7.57 11.65 W16 upper CW Fish Lepomis punctatus -29.58 43.52 7.05 13.45 W16 upper CM Fish Lepomis punctatus -30.49 48.13 6.78 14.47 W16 upper CM Fish Lepomis punctatus -31.59 42.60 7.26 12.89 W16 upper CM Fish Lepomis punctatus -27.26 48.36 7.35 14.75 W16 upper CM Fish Lepomis punctatus -22.00 53.11 6.39 16.16 W16 upper CM Fish Lepomis punctatus -27.97 48.75 7.29 15.00 W16 upper CM Fish Lepomis punctatus -28.20 49.40 7.46 15.22 S15 mid Long Fish Cichlasoma urophthalmus -28.62 40.75 7.82 11.50 S15 mid Long Fish Cichlasoma urophthalmus -28.48 53.05 7.80 15.19 S15 mid Long Fish Cichlasoma urophthalmus -29.40 30.42 7.86 8.48 S16 mid Long Fish Cichlasoma urophthalmus -32.02 44.88 9.16 12.58 S16 mid Long Fish Cichlasoma urophthalmus -33.13 44.89 10.01 12.57 S16 mid Bryan Fish Cichlasoma urophthalmus -24.98 41.79 6.66 11.96 S16 mid Bryan Fish Cichlasoma urophthalmus -28.71 54.46 8.28 14.99 S16 mid Bryan Fish Cichlasoma urophthalmus -29.10 39.19 6.39 11.98 S16 mid Bryan Fish Cichlasoma urophthalmus -28.79 35.57 6.94 9.85 S16 mid Bryan Fish Cichlasoma urophthalmus -25.12 44.56 6.42 14.16 236

W16 mid Bryan Fish Cichlasoma urophthalmus -28.29 50.39 7.14 15.35 W16 mid Bryan Fish Cichlasoma urophthalmus -30.75 53.36 5.93 16.20 W16 mid Bryan Fish Cichlasoma urophthalmus -31.08 60.36 6.59 18.56 W16 mid Bryan Fish Cichlasoma urophthalmus -26.74 46.90 6.99 14.65 W16 mid Bryan Fish Cichlasoma urophthalmus -28.99 55.48 6.81 17.35 W16 mid Bryan Fish Cichlasoma urophthalmus -25.97 38.05 6.40 11.95 W16 mid Bryan Fish Cichlasoma urophthalmus -25.92 54.24 6.84 17.07 S15 upper CE Fish Hemichromis bimaculatus -30.83 39.97 6.83 12.32 S15 upper CE Fish Hemichromis bimaculatus -28.81 47.79 6.36 14.86 S16 upper CW Fish Hemichromis bimaculatus -29.69 44.96 7.67 14.02 S16 upper CW Fish Hemichromis bimaculatus -32.34 42.10 7.72 12.93 S16 upper CW Fish Hemichromis bimaculatus -31.64 35.62 6.02 10.89 S16 upper CW Fish Hemichromis bimaculatus -26.87 60.06 7.47 17.94 S16 upper CW Fish Hemichromis bimaculatus -29.57 48.40 7.04 14.91 S16 upper CE Fish Hemichromis bimaculatus -29.11 46.63 7.22 14.55 W16 upper CW Fish Hemichromis bimaculatus -32.88 39.76 8.20 8.95 W16 upper CW Fish Hemichromis bimaculatus -34.88 44.07 8.09 13.40 W16 upper CW Fish Hemichromis bimaculatus -32.65 39.39 7.04 12.09 W16 upper CM Fish Hemichromis bimaculatus -26.28 49.85 7.46 15.44 W16 upper CM Fish Hemichromis bimaculatus -27.93 49.99 6.38 14.86 W16 upper CM Fish Hemichromis bimaculatus -29.98 36.79 7.25 11.04 W16 upper CE Fish Hemichromis bimaculatus -29.59 41.33 7.21 12.86 W16 upper CE Fish Hemichromis bimaculatus -32.27 46.80 8.41 14.24 S15 lower Fork Fish Anchoa mitchilli -23.89 38.90 8.24 11.62 S15 lower Summit Fish Anchoa mitchilli -24.20 43.91 7.81 14.28 S16 lower Summit Fish Anchoa mitchilli -26.67 73.07 9.84 23.30 W15 lower Summit Fish Anchoa mitchilli -27.06 45.52 9.34 13.79 W15 lower Summit Fish Anchoa mitchilli -26.80 55.23 9.59 17.73 237

W16 lower Summit Fish Anchoa mitchilli -23.64 50.20 10.30 14.15 W16 lower Summit Fish Anchoa mitchilli -27.39 46.21 9.66 14.80 W16 lower Summit Fish Anchoa mitchilli -25.65 48.49 10.18 15.58 S16 lower Fork Fish Eucinostomus sp. -27.80 40.90 7.12 12.20 S16 lower Fork Fish Eucinostomus sp. -27.76 51.00 7.58 15.17 S16 lower Fork Fish Eucinostomus sp. -26.97 53.76 7.70 16.50 S16 lower Fork Fish Eucinostomus sp. -27.34 54.14 7.24 16.51 S16 lower Fork Fish Eucinostomus sp. -26.08 42.21 8.11 12.78 S16 lower Fork Fish Eucinostomus sp. -28.25 37.35 7.50 11.52 S16 lower Fork Fish Eucinostomus sp. -27.44 37.43 7.73 11.44 S16 lower Fork Fish Eucinostomus sp. -26.89 45.43 7.87 14.07 S16 lower Fork Fish Eucinostomus sp. -24.07 40.26 8.80 12.10 S16 lower Fork Fish Eucinostomus sp. -27.73 30.24 7.76 9.10 S16 lower Fork Fish Eucinostomus sp. -27.92 37.41 7.54 11.56 S16 lower Summit Fish Eucinostomus sp. -26.92 46.92 7.31 14.97 S16 upper CM Fish Belonesox belizanus -23.14 62.64 8.73 19.44 S16 upper CM Fish Belonesox belizanus -28.59 35.53 8.91 10.93 S16 upper CM Fish Belonesox belizanus -28.82 51.65 8.52 15.89 S16 upper CM Fish Belonesox belizanus -25.75 50.31 9.18 15.72 S16 upper CM Fish Belonesox belizanus -27.46 49.11 9.54 15.09 W15 mid Long Fish Belonesox belizanus -26.19 40.22 7.23 12.40 S15 mid Long Fish Fundulus luciae -21.23 40.69 6.10 13.19 S15 mid Bryan Fish Fundulus luciae -20.47 53.61 6.36 16.27 S15 mid Bryan Fish Fundulus luciae -19.66 36.58 8.35 10.98 S15 mid Bryan Fish Fundulus luciae -20.91 47.49 6.49 15.35 S15 lower USGS Fish Eucinostomus argenteus -23.64 36.05 8.25 10.76 S15 lower USGS Fish Eucinostomus argenteus -28.17 57.45 7.18 18.05 S15 lower USGS Fish Eucinostomus argenteus -24.03 40.25 8.61 12.23 238

S15 lower USGS Fish Eucinostomus argenteus -27.18 52.30 7.86 16.26 S15 lower USGS Fish Eucinostomus argenteus -27.47 39.75 7.05 12.35 S15 lower Fork Fish Eucinostomus argenteus -27.82 40.34 7.46 12.71 S15 lower Fork Fish Eucinostomus argenteus -25.92 44.19 7.16 13.51 S15 lower Summit Fish Eucinostomus argenteus -22.95 37.64 7.35 11.79 S15 lower Summit Fish Eucinostomus argenteus -23.06 32.95 7.04 10.56 S15 lower Summit Fish Eucinostomus argenteus -24.22 43.88 7.27 13.73 S15 lower Summit Fish Eucinostomus argenteus -24.92 47.99 7.13 15.24 S15 lower Summit Fish Eucinostomus argenteus -24.76 44.89 7.38 14.25 S16 lower USGS Fish Eucinostomus argenteus -26.76 42.75 5.61 12.50 S16 lower USGS Fish Eucinostomus argenteus -27.77 52.46 7.42 16.22 S16 lower USGS Fish Eucinostomus argenteus -27.66 45.00 7.31 13.79 S16 lower USGS Fish Eucinostomus argenteus -27.66 34.68 7.47 10.88 S16 lower USGS Fish Eucinostomus argenteus -27.78 46.17 7.88 14.21 W16 lower USGS Fish Eucinostomus argenteus -28.55 43.96 7.49 13.39 W16 lower USGS Fish Eucinostomus argenteus -28.97 47.35 8.24 14.51 W16 lower USGS Fish Eucinostomus argenteus -28.51 57.44 8.62 17.71 W16 lower USGS Fish Eucinostomus argenteus -28.31 48.05 8.16 14.83 W16 lower USGS Fish Eucinostomus argenteus -28.36 42.84 7.99 13.10 S15 lower Fork Fish Eucinostomus gula -24.37 30.19 7.15 9.11 S15 lower Fork Fish Eucinostomus gula -20.70 33.92 8.68 10.46 W15 lower Fork Fish Eucinostomus gula -25.37 37.60 7.13 12.46 S15 lower Fork Fish Eucinostomus sp. -24.70 45.95 7.45 13.81 S15 lower Summit Fish Lutjanus synagris -23.22 37.06 7.90 11.35 S16 upper CM Fish Gambusia holbrooki -25.98 43.56 6.41 12.60 W16 upper CM Fish Gambusia holbrooki -26.43 42.06 5.72 12.30 W16 upper CM Fish Gambusia holbrooki -32.35 47.06 5.99 14.11 W16 upper CM Fish Gambusia holbrooki -27.46 38.82 6.77 11.36 239

W16 upper CM Fish Gambusia holbrooki -25.94 44.11 6.55 12.61 W16 upper CM Fish Gambusia holbrooki -26.05 37.69 6.46 11.25 W16 upper CM Fish Gambusia holbrooki -32.00 38.67 6.17 11.07 W16 upper CM Fish Gambusia holbrooki -29.72 49.46 6.83 14.45 W16 mid Long Fish Gambusia holbrooki -27.97 33.96 7.09 10.00 W16 lower USGS Fish Bairdiella chrysoura -24.71 52.36 10.01 16.36 Archosargus S16 upper CW Fish -29.79 49.65 9.82 15.36 probatocephalus S16 upper CW Anole Anolis sagrei -26.85 35.51 5.52 10.93 S16 upper CW Anole Anolis sagrei -26.78 32.15 5.76 10.04 S16 upper CW Anole Anolis sagrei -26.67 36.47 5.70 11.44 S16 lower fbay Fish Eucinostomus sp. -25.29 41.225 7.65 13.041

240

Appendix D. Permit

241

242