Fish Community Structure, Substrate Particle Size, And

FISH COMMUNITY STRUCTURE, SUBSTRATE PARTICLE SIZE, AND

PHYSICAL HABITAT: AN ANALYSIS OF REFERENCE STREAMS IN THE

WESTERN ALLEGHENY PLATEAU ECOREGION OF SOUTHEAST OHIO

A thesis presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Ian M. Hughes

June 2006

This thesis entitled

FISH COMMUNITY STRUCTURE, SUBSTRATE PARTICLE SIZE, AND

PHYSICAL HABITAT: AN ANALYSIS OF REFERENCE STREAMS IN THE

WESTERN ALLEGHENY PLATEAU ECOREGION OF SOUTHEAST OHIO

by

IAN M. HUGHES

has been approved for

the Program of Environmental Studies

and the College of Arts and Sciences by

Matthew M. White

Associate Professor of Biological Sciences

Benjamin M. Ogles

Dean, College of Arts and Sciences

Abstract

HUGHES, IAN M., M.S., June 2006, Environmental Studies

FISH COMMUNITY STRUCTURE, SUBSTRATE PARTICLE SIZE, AND

PHYSICAL HABITAT: AN ANALYSIS OF REFERENCE STREAMS IN THE

WESTERN ALLEGHENY PLATEAU ECOREGION OF SOUTHEAST OHIO (94 pp.)

Director of Thesis: Matthew M. White

Correlations between fish community structure, substrate particle size

distributions, and physical habitat quality were investigated in wadeable and headwater

reference streams within the Western Allegheny Plateau ecoregion (WAP) of southeast

Ohio. An historic dataset was also utilized to determine fish-habitat correlations, as well

as habitat and fish community stability. The reference sites were found to display

considerable fish community persistence and physical habitat stability over time. In the absence of sediment impairments, the fish communities showed minimal correlations with substrate variables at headwater and wadeable sites. An overall lack of correlations

between fish and physical habitat data were displayed at wadeable sites, while several

fish variables were correlated with drainage area and pool quality at headwater sites.

These results suggest that fish communities at wadeable sites might be shaped by other factors, such as biotic interactions, and that the main factor influencing headwater communities may be water and/or habitat availability.

Approved:

Matthew M. White

Professor of Biological Sciences

Acknowledgments

I would like to thank M. White for his guidance, advice, and support throughout this project as well as D. Sack, G. Springer, and D. Kidder, for their professional insight, and assistance as committee members and mentors. The guidance and helpfulness provided by G. Mapes and C. Hanzel was greatly appreciated. A special thank you to E.

Rankin is also in order for organizing and providing a majority of the data used in this project. G. Svendsen assisted with statistical analysis and was a great help in that aspect of the study. The work done by C. Meyer and C. Larkins is also greatly appreciated and was essential for the completion of this study. To my wonderful parents, thank you so much for your continuous support and encouragement over the past six years.

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Table of Contents Page

Abstract...... 3 Acknowledgments...... 4 List of Tables ...... 7 List of Figures...... 8 Chapter 1: Sedimentation, Physical Habitat, and Fish Communities ...... 9 1.1 Sediment in streams ...... 11 1.1.1 Types of sediment...... 11 1.1.2 Sediment and connectivity in the fluvial system ...... 12 1.1.3 Land use...... 13 1.1.4 Riparian ecotones...... 13 1.1.5 Fluvial geomorphic response ...... 14 1.1.6 Gradient...... 15 1.2 The impacts of excessive sedimentation...... 15 1.2.1 Physical habitat impacts...... 15 1.2.2 Trophic disturbances...... 16 1.2.3 Impacts on fish...... 17 1.3 Using fish as bioindicators...... 18 1.3.1 Why use fish?...... 18 1.3.2 Single species vs. community approach ...... 19 1.4 Study area: The Western Allegheny Plateau ecoregion of Ohio ...... 20 1.4.1 Characterizing the ecoregion ...... 20 1.4.2 Previous assessments of the WAP ...... 22 1.5 Reference sites ...... 24 1.5.1 Selection and description of reference sites...... 24 1.5.2 Biological criteria for the WAP ...... 25 1.6 Literature review...... 25

6

Chapter 2: Western Allegheny Plateau Reference Site Analysis...... 31 2.1 Introduction...... 31 2.2 Methods...... 32 2.2.1 Study area...... 32 2.2.2 Study datasets...... 33 2.2.3 Fish sampling...... 35 2.2.4 Qualitative habitat evaluation index ...... 36 2.2.5 Substrate particle size distributions ...... 37 2.2.6 Statistical analysis...... 38 2.3 Results...... 40 2.3.1 Recent data...... 40 2.3.2 Recent statistical analysis ...... 43 2.3.3 Historic data...... 46 2.3.4 Historic statistical analysis...... 49 2.3.5 Reference site stability...... 50 2.4 Discussion...... 54 2.4.1 Wadeable vs. headwater habitat...... 54 2.4.2 Recent fish-habitat correlations ...... 55 2.4.3 Historic fish-habitat correlations...... 60 2.4.4 Reference site stability...... 61 2.5 Conclusions...... 63 References...... 66 Appendix A- Site list ...... 74 Appendix B- QHEI field sheet...... 76 Appendix C- Species lists ...... 78 Appendix D- Jaccard’s similarity coefficients...... 89 Appendix E- Figures displaying drought conditions for 2005 sampling...... 91

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List of Tables

Table Page

1: The pros and cons of using various methods to study fish ...... 20

2: Biological criteria for the WAP...... 25

3: Fish community indices and metrics ...... 36

4: Physical habitat metrics from the QHEI...... 37

5: Substrate variables obtained for this study ...... 38

6: Descriptive values for recently collected fish variables ...... 42

7: Descriptive values for recently collected habitat variables ...... 42

8: Descriptive values for recently collected substrate variables ...... 43

9: Spearman’s fish-habitat correlations at recent wadeable sites...... 44

10: Spearman’s fish-habitat correlations at recent headwater sites ...... 45

11: Spearman’s correlations between wadeable habitat and substrate variables...... 46

12: Spearman’s correlations between headwater habitat and substrate variables...... 46

13: Descriptive values for historic fish variables...... 48

14: Descriptive values for historic habitat variables...... 49

15: Spearman’s fish-habitat correlations at historic wadeable sites ...... 49

16: Spearman’s fish-habitat correlations at historic headwater sites...... 50

17: Wilcoxon significant differences between historic and recent fish data ...... 51

18: Wilcoxon significant differences between historic and recent habitat data...... 52

8

List of Figures

Figure Page

1: Map of the ecoregions of Indiana and Ohio ...... 22

2: Stressor rankings by ecoregion from the MAHSA study ...... 23

3: Diagram of substrate embeddedness characteristics...... 27

4: Study area location and WAP Level IV ecoregions ...... 33

5: Map of Ohio counties and reference site locations...... 34

6: Historic vs. recent cyprinid and lithophilic spawning species values...... 52

7: Historic vs. recent MIWB scores at headwater sites ...... 53

8: Historic vs. recent cover metric scores at wadeable sites...... 53

9: Wadeable vs. headwater gradient and drainage areas...... 55

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Chapter 1: Sedimentation, Physical Habitat, and Fish Communities

This thesis is comprised of two chapters. The first chapter is an

introduction/literature review covering the sources of excessive sediment and the impacts

that they can have on the physical habitat and fish communities of streams. The second

chapter focuses on the specific research methods, results, and conclusions used in this

study. The objectives of this study were to provide a comprehensive literature review on

sediment in streams and to utilize an existing dataset to investigate the correlations

between fish community structure, substrate particle distributions, and physical habitat

variables at reference sites of the Western Allegheny Plateau ecoregion of southeastern

Ohio. This study also investigates the stability of fish community and physical habitat

variables over time to further assist in developing a reference site model.

Introduction

The mandate of the Clean Water Act (CWA) of 1972 for fishable and swimmable streams has encouraged a wide array of physical, chemical, and biological research to assess the health of streams and determine attainment of the CWA objectives. A majority of stream quality assessments have focused on overall stream health in terms of chemical standards and sport fisheries impacts (USEPA 2000a). Progress has been made in alleviating the effects of chemical stressors and there has been a shift from focusing solely on chemical standards and sport fisheries to utilizing entire fish communities in determining the health

of streams and rivers (Yoder and Rankin 1998). Traditionally, point sources of pollution

have been the center of research attention. However, the detrimental effects of non-point

sources are proving to be just as significant as humans continue to alter the landscape. 10

One concern is the impact of excessive inputs of fine inorganic sediments into streams. Fine sediments are responsible for a majority of the impaired stream miles in the U.S. (USEPA 2000b). Natural levels of stream sediment normally fluctuate, but human activities can cause large amounts of sediments to enter streams and impact aquatic organisms, impair physical habitat, and interrupt normal biological productivity

(DFO 2000). Imbalances in sediment regimes typically point to larger problems within a basin, such as unstable streambeds, loss of riparian buffers, or increases in human- induced landscape alterations (Moore et al. 2001). In some cases, addressing sediment problems in streams experiencing more than one cause of impairment can potentially alleviate several other issues such as metals, pesticides, and nutrients, as they are commonly associated with sediment pollution and runoff (Henley et al. 2000).

Clarifying the terms sediment, sediments, siltation, sedimentation, and fines that will be used throughout this paper is important because there are a number of differing definitions in the literature (Wood and Armitage 1997). The terms above refer to uncontaminated, inorganic particles that are < 2mm in diameter. This will include sand

(< 2.0 to >0.063 mm), silt (<0.063 to >0.004 mm), and clay (<0.004 mm) particles.

Determining the impacts of excess sediment loads on aquatic ecosystems has become an increasingly important topic as watershed coordinators and state agencies attempt to add sediment criteria to total maximum daily loads (TMDLs) and create

TMDLs based solely on sediment variables (USEPA 1999). The extent to which increased sediment loads impact fish communities has been investigated in many studies focusing on coldwater streams, single species of interest, salmonids or other sport 11 fisheries (Waters 1995). Additional research is needed to further understand these impairments so that data from warmwater stream studies that show whole fish community responses to sedimentation can be used to support proper management decisions (Castro and Reckendorf 1995; Rabeni and Smale 1995; Moore et al. 2001).

Utilizing a fish community approach as a measure of stream health and an indicator of human stressors can be a much more robust method than relying on a single species. In collaboration with physical and chemical analyses it is an essential tool in the decision-making process (Hocutt 1981; Angermeier and Karr 1986). Biological indicators of stream health are not a new concept, but they have become much more widely used as evidenced by the development of several indices such as the Index of

Biotic Integrity (IBI) and the Modified Index of Well-Being (MIWB), used in the state of

Ohio (Ohio EPA 1991). Fish are ideal bioindicators because the diversity of species represents various trophic levels, they are sensitive to watershed degradation, they are relatively easy to identify, they hold economic and recreational value, and impacts on fish are more understandable to the general public (Fausch et al. 1984; Wood and Armitage

1997; USEPA 2000a).

1.1 Sediment in streams

1.1.1 Types of sediment

As sediment enters a stream, the particles sort themselves out according to their varying

sizes and the ability of the stream to transport them. This process has led to the

classification of two types of sediment in streams-- the bedload and the suspended load.

The bedload consists of the larger/heavier particles (such as sand and fine gravel) that are 12 transported downstream by rolling, sliding, and bouncing along the bed of the stream

(USEPA 1999). Bedloads are closely related to the slope, bed composition, and flow characteristics of streams (Syvitski et al. 2000). The suspended sediment load consists of the smaller/lighter particles that are suspended in the water column for long periods of time (USEPA 1999). Suspended loads are largely dependant upon several upstream features ranging from vegetation cover to rainfall intensity (Syvitski et al. 2000).

High suspended loads lead to increased turbidity in streams (Castro and Reckendorf

1995). The magnitude and particle sizes of both bed and suspended sediment loads play a critical role in shaping the physical habitat of streams.

1.1.2 Sediment and connectivity in the fluvial system

Sediment is a natural component of all fluvial systems as natural sources of sediment enter streams from the surrounding basin and the stream channel itself through erosional processes. Streams are in a state of dynamic equilibrium with their surrounding landforms, riparian ecotones, and fluvial-geomorphic processes as they work to achieve a balance in the entrainment, transport, and storage of sediment (Hupp and Osterkamp

1996; Harbor 1999; Clarke et al. 2003). The amount of sediment delivered to a stream and fluctuations in its sediment loads is strongly dependant upon the interconnectedness of these stream features (Hupp and Osterkamp 1996). Human events can disrupt this equilibrium and throw off the interconnectedness of these three stream features, which in turn hinders the ability of a stream to respond and recover from disturbances.

13

1.1.3 Land use

Land use changes can result in complete transformation of the fluvial system because it is strongly tied to the geomorphology of its surrounding basin (Hardy 2005). As a result of various land use practices, large amounts of fine inorganic sediment entering streams cause an imbalance in natural sediment levels and result in numerous detrimental effects on resident fish communities (Waters 1995; Harbor 1999). Fine sediments are a natural component of all stream systems, but as suspended and bed load sediment levels increase the aquatic ecosystem can become impaired (Castro and Reckendorf 1995; Wood and

Armitage 1997; DFO 2000; Moore et al. 2001; De Boer et al. 2005).

In some cases, streams can experience decreased sediment levels from land use changes (i.e., urbanization covers many erodible hillslopes with nonerodible surfaces, such as cement), which leads to channel incision and severe erosion upstream (Pizzuto et al. 2000). This occurs because the changes in land cover have significant impacts on the magnitude and frequency of runoff events and erosion that subsequently alter stream sedimentation dynamics, stream morphology, and flow regimes (Harbor 1999). Streams have geomorphic thresholds that determine their morphology and physical habitat, and human influence within basins can result in dramatic changes in the characteristics of streams by altering these thresholds (Church 2002).

1.1.4 Riparian ecotones

The riparian ecotone (the transition zone from the stream to the land adjacent to the stream channel) is of particular importance in mitigating the impacts of land use disturbances, sedimentation, and providing other morphological benefits. Riparian 14 vegetation benefits streams by acting as a natural filter for runoff and for trapping sediments (Nerbonne and Vondracek 2001), minimizing stream bank erosion (Harbor

1999), and strengthening channel and stream bank stability (Church 2002). Alteration or removal of streamside vegetation has direct consequences for sediment regimes and can also lead to changes in channel morphology (Davies-Colley 1997). This invokes a recovery stage in which erosional or depositional events will typically occur as the stream attempts to restore its sediment balance and channel stability (Hupp and Osterkamp

1996).

1.1.5 Fluvial geomorphic response

When an imbalance in sediment load occurs, the stream responds with natural geomorphic processes by either aggrading or degrading. Aggradation is a response to an upstream increase in sediment load to the point that the sediment carrying capacity of the stream is exceeded. This excess sediment is deposited within the stream channel or in its floodplain and can cause streams to experience a decrease in depth (WARSSS 2006).

Degradation occurs when there is a deficit in the sediment balance and the stream becomes sediment-starved. Causes of degradation are very complex, but the resulting changes in stream morphology include channel incision and stream bank instability

(WARSSS 2006). The geomorphology of the surrounding basin, extent and type of human land use, and presence or absence of a vegetated riparian zone all play an intricate part in these processes.

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1.1.6 Gradient

The geomorphic setting of a basin also plays an important role in determining the slope of the stream channel. Gradient (or stream slope) is one of the dominant factors in determining the physical habitat of streams, including sediment transport, deposition, and subsequently substrate particle size (Walters et al. 2003). This is because gradient is strongly related to the ability of a stream to carry sediment (Church 2002). Investigating the differences in bed texture between high-slope headwater streams and low-slope lowland streams demonstrates this occurrence. Headwater streams exhibit higher amounts of coarse bed material while lowland streams are typified by finer, sandy substrates (Coulombe-Pontbriand and Lapointe 2004). Because basin geomorphology plays such an important role (Kondolf 2000), sediment data should be considered with regards to the gradient of the reach being studied (Walters et al. 2003).

The responses and interactions among these processes underscore the importance of the interconnectedness of the stream system and its surrounding landforms, riparian ecotones, and fluvial-geomorphic processes (Hupp and Osterkamp 1996). These features of the fluvial system work in collaboration to strike a balance in the sedimentation process and determine the magnitude and composition of the bed and suspended sediment loads of streams.

1.2 The impacts of excessive sedimentation

1.2.1 Physical habitat impacts

As sedimentation is exacerbated by human activities, the receiving streams experience a

number of significant changes in physical habitat quality. Increased turbidity and 16 decreased light penetration can reduce visibility for predators and have a cascade effect on trophic structure (Ellis 1936; Richardson and Jowett 2002). Sedimentation decreases stream depth as it deposits in pools and clogs riffles (Shields et al. 1994) and interstitial spaces in stream substrates are filled, decreasing microhabitat availability (Angermeier and Schlosser 1989; Mecklenburg 1998; Richardson and Jowett 2002; USEPA 2003).

Another impact of interstitial space reduction and substrate blanketing by sediment is the loss of vital spawning habitat (Wood and Armitage 1997; DFO 2000; Henley et al. 2000).

Substrates dominated by shifting sand and finer particles have diminished habitat complexity and are very unstable (Schlosser 1982; Berkman et al. 1986; Powers et al.

2003). Physical habitat characteristics such as clean, complex, and diverse substrates are vital for the various stages of fish life, as well as other organisms that fish depend on for food sources (Richardson and Jowett 2002).

1.2.2 Trophic disturbances

When the environment is altered by excessive sedimentation, all levels of the food chain from primary producers to top carnivores are affected. Primary production (bacteria, algae, and protozoa) declines due to physical smothering and a decrease in incident light due to turbidity increases (Ellis 1936; Cordone and Kelley 1961). The resulting reductions in primary productivity have a ripple effect on primary consumers and organisms farther up the food chain (Crowe and Hay 2004).

Macroinvertebrates inhabiting interstitial spaces and other substrate habitats are also directly impacted. In instances of increased sediment loads and sediment deposition, macroinvertebrates may experience decreased feeding ability and growth, habitat 17 alteration, increased drift, community structure changes, and physical scouring and/or abrasion (Cordone and Kelley 1961; Crowe and Hay 2004). As these components of the aquatic food web are disrupted, organisms that rely on macroinvertebrates as a food source, such as insectivorous fishes, are also affected.

1.2.3 Impacts on fish

Much of the available research on fish-sediment relationships has focused on coldwater or special interest fish species. Because different species of fish will likely respond differently to increases in sediment, it is very important to understand the general impacts of sedimentation on fish. Sediments can adversely impact fish directly by gill abrasion/clogging, slowing growth rates, or reducing disease tolerance (Rabeni and

Smale 1995; Wood and Armitage 1997; Henley et al. 2000). Fish can also experience a decrease in spawning habitat viability (DFO 2000) and a reduction in egg, larvae, and juvenile development (Frissell et al. 1986; Wood and Armitage 1997). Fishes in early life history stages are adversely impacted by sediments as interstitial spaces that provide cover and protection from predation are lost or as the young are simply smothered by sediments (Cordone and Kelley 1961). Patterns of natural migration may be disrupted and food supplies reduced as primary production slows and macroinvertebrates decline

(Richardson and Jowett 2002). Many of these impacts occur simultaneously and the result can be devastating to fish communities.

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1.3 Using fish as bioindicators

1.3.1 Why use fish?

Fish communities are influenced by the diversity and complexity of available habitats in the aquatic system. Due to the diversity of fishes and their physical habitat needs, various complex habitat niches are required to support them. This variety of physical habitat variables is critical in shaping community structure, species richness and abundance, and presence/absence of certain species (Gorman and Karr 1978). Thus fish communities are excellent indicators for the aquatic ecosystem of direct and indirect environmental stress causing physical habitat impairments (Karr 1981; Fausch et al.

1990).

Fish are sensitive to multiple different stressors and they reflect the health of many aspects of the aquatic ecosystem (and the surrounding watershed) due to their dependence on it for reproductive, maturation, and survival purposes (Fausch et al. 1990).

Additionally, fish have longer life spans in comparison to other organisms in aquatic ecosystems allowing them to exhibit responses to disturbance events temporally (Barbour et al. 1999). Using macroinvertebrates, periphyton, or other bioindicators may not be as easily communicated to the general public. The economic and aesthetic value associated with fish provides a much more effective and clear method for using biology as an indicator of water quality or stream health (Wood and Armitage 1997). Extensive life history information is readily available for many fish species (e.g., Trautman 1981).

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1.3.2 Single species vs. community approach

Employing a single species approach to determining impairment impacts on fish has been used in previous studies (Castro and Reckendorf 1995), however utilizing whole community data, along with habitat quality data, can be an invaluable tool for making resource management decisions (Angermeier and Karr 1986). Incorporating community structure and function into an index is an increasingly popular and useful method for assessing the biotic integrity of streams. Many regions are creating variations of these indices, such as the Index of Biotic Integrity (IBI) (introduced by Karr 1981), for fish communities. Variation in IBI scores across temporal scales have shown to be indicative of many environmental disturbances (Paller 2002) and investigating this variation allows for the differentiation of anthropogenic changes from natural community variation. The advantages of using fish as bioindicators outweigh the disadvantages (Table 1) and utilizing fish communities is an accepted method to assess the biotic integrity of aquatic ecosystems and the stream impacts associated with human activities (Karr 1981).

Natural and anthropogenic disturbances work together to control the variability in fish communities. Changes in community composition are detected by the IBI and it is a useful measure of biotic integrity that is sensitive to a wide range of environmental perturbations (Karr 1987). The structure and function of fish communities provide direct and indirect evidence of the quality of streams and the biotic integrity of aquatic ecosystems (Hocutt 1981). Using the IBI allows for robust conclusions as it reflects not only on the community structure, but also on the functional attributes of the fish community (Berkman et al. 1986). 20

Table 1 The pros and cons of using various methods to study fish (modified from Fausch et al. 1990).

Method/Index PROS CONS Simple, easily applied and Taxa may be absent for other allows specific stressors to reasons, taxa sensitivity be targeted varies by season/region, may Indicator Taxa/Guilds depend on other species present, sometimes conveys too little information. Simple and easily applied Depends on sample size, Species Richness/Diversity varies by region Quantitative, compares all Sometimes complex and samples simultaneously hard to understand, results Multivariate Statistics depend on reference samples used. Broad ecol. Index, Requires moderate species biologically meaningful, richness and background IBI flexible, reproducible, each ecological data, statistical metric sensitive to properties not well studied. degradation types.

1.4 Study area: The Western Allegheny Plateau ecoregion of Ohio

1.4.1 Characterizing the ecoregion

Ecoregions are defined as areas with similar environmental resources, ecosystems, and

human influences. Sediment impairments, soil types, and the ability of streams to

transport sediment will vary between ecoregions (as well as within), so it is important to

make comparisons within ecoregions (Kuhnle et al. 2001). The various metrics used for

calculating the IBI are ecoregion specific (Angermeier and Karr 1986) and physical

habitat differs from region to region (Fausch et al. 1984) making an ecoregional approach

essential. 21

The Western Allegheny Plateau (WAP) ecoregion follows the Ohio River valley from western Pennsylvania, through parts of Ohio and West Virginia, and ends in central

Kentucky (Figure 1). This study will focus only on the portions of the WAP that are within the boundary of the state of Ohio. Located in the foothills of the Appalachian

Mountains, the WAP of southeastern Ohio is marked by moderately wooded, hilly, previously unglaciated terrain. In many areas it was formerly mined for coal and some mining continues to this day. The landscape is presently dominated by timber harvesting and agricultural and dairy establishments (Woods et al. 1998). The most common stressor in WAP streams is excessive sedimentation (USEPA 2000a; OCAFS 2001).

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Figure 1 Map of the ecoregions of Indiana and Ohio. The WAP is the green section (70) in southeastern Ohio (from Woods et al. 1998).

1.4.2 Previous assessments of the WAP

The Mid-Atlantic Highlands Streams Assessment (MAHSA) found that 30% of the

stream miles studied in the entire WAP had poor fish biotic integrity as defined by low

IBI scores, 32% of stream miles were fair, and only 3% of stream miles showed good fish

biotic integrity (35% of stream miles studied yielded no fish due to small stream sizes). 23

MAHSA noted that the main source of stream impairment for the WAP is sedimentation.

Over 38% of stream miles in their study area showed excessive fine sediments (Figure 2).

Additional sources of impairment included poor riparian habitat or missing riparian buffer (28%), mine drainage (24%), and total phosphorus (20%). These last three stressors are closely related to the first because excessive fine sediments in streams are often the result of riparian buffer removal, pre-law mining practices, and agricultural land use (USEPA 2000a). The MAHSA study found that fish tissue contamination was very low and acidic deposition and nitrogen runoff were absent in the stream miles studied.

Figure 2 Stressor rankings by ecoregion from the MAHSA study (from USEPA 2000a). The WAP is the dark green graph in the lower right.

MAHSA noted that mining discharge not only has chemical impacts on streams, but also an even larger impact on the physical habitat within the streams. Mining practices (prior to current restoration laws) can increase sediment runoff into streams, 24 smothering substrates and decreasing habitat diversity. This study found that the most detrimental stressor in the WAP is the destruction of physical habitat through excessive sedimentation (USEPA 2000a).

Examining stream quality by ecoregion is a useful management perspective because ecoregions are areas of comparable soils, vegetation, climate, physical geography, and biota. Because streams within an ecoregion fall within a range of similar characteristics, general management decisions should be applicable to various areas within them (USEPA 2000a).

1.5 Reference sites

1.5.1 Selection and description of reference sites

Within these ecoregions, reference sites are determined. For the state of Ohio, reference

sites are selected by the Ohio EPA to represent relatively undisturbed sites that have

minimal impairments/human influence. These minimally impacted sites are evaluated

with several biological (fish, macroinvertebrates, etc) and physical habitat (QHEI)

surveys to determine the biotic integrity/diversity of the aquatic communities and the

physical habitat. This enables reference conditions to be determined for an ecoregion and

the subsequent implementation of biological criteria. Biological criteria are simply

numerical values that express the reference conditions for a given designated aquatic life

use. Reference sites can then be compared to disturbed sites within the same ecoregion to

determine the impacts that certain impairments have on the various aspects of aquatic

ecology (Davis and Simon 1995).

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1.5.2 Biological criteria for the WAP

The Ohio EPA recognizes seven aquatic life uses: warmwater habitat, limited warmwater habitat, exceptional warmwater habitat, modified warmwater habitat, seasonal salmonid habitat, coldwater habitat, and limited resource water (Table 2). The Ohio EPA uses biological criteria, aquatic life uses, and reference site conditions for evaluating water quality in impacted streams through comparative studies (USEPA 1990).

Table 2 Biological criteria for the WAP (Ohio EPA 1987). IBI and MIWB criteria values are listed for headwater, wading, and boat sites

1.6 Literature review

As early as 1936, scientists were beginning to note the impacts of sedimentation on

streams (Ellis 1936). Most of the studies that followed focused on single fish species or

sedimentation in coldwater streams, but a recent shift has caused warmwater stream

studies to utilize more of a community approach. Many of these studies call for

additional research so that management decisions can be further supported by studies on

siltation impacts on the fish communities of warmwater streams (Berkman and Rabeni

1987; Lobb and Orth 1991; USEPA 2003).

Lobb and Orth (1991) examined habitat use patterns of fish assemblages in a

warmwater stream in West Virginia. They noted that wadeable warmwater streams lack 26 data on habitat suitability and that previous studies focused primarily on a single species.

That study investigated the correlations between broad habitat guilds (edge pool, middle pool, edge channel, riffle, and generalist) and fish communities of warmwater habitat streams. Substrate composition has been found to be one of the major influences on species composition and diversity.

In seeking to understand the sources of sediment, several studies in southern

Appalachian streams focused on the benthic habitat variables of embeddedness (Figure

3), substrate composition, and coverage of fines as well as sediment transport data for the suspended load and bedload in relation to land use/land cover (LU/LC). They found that

LU/LC disturbances in the surrounding watershed created increased sediment loads to those streams. Compared to reference sites, the benthic crevice and gravel spawning guilds (strongly associated with stream substrates) were negatively correlated with increasing sedimentation and basin disturbances. Sediment particle sizes were also found to be a strong predictor of fish occurrence (Dalejones et al. 1999; Sutherland et al. 2002).

Increasing human alterations to the natural landscapes within watersheds continue to be an area of concern as land use decisions, without consideration for the local stream communities, typically result in stream impairments.

27

Figure 3 Diagram of substrate embeddedness characteristics (from Yoder 1995).

In addition to general fish-habitat and LU/LC studies, other studies have shown

the importance of substrate diversity and the impacts of sedimentation. Sand slugs (a

large release of sediments into a stream channel) caused channel geometry modification,

habitat diversity loss, channel volume reduction, decrease in water depth, and a loss in

channel complexity (Bond and Lake 2003). Higher fish abundances were associated with

unimpacted sites. Streams with shifting, unstable, sandy substrates (caused by channel 28 incision) had smaller fish and lower species richness than comparable reference streams with stable and diverse substrates (Shields et al. 1994). The sandy substrates smothered stream habitat and filled in the deeper pools that are critical for larger warmwater fish species. Woody debris can be the only source of habitat diversity/complexity in streams impacted by excessive sediment and Shields et al. (1994) also noted the impacts of woody debris burial by sedimentation.

According to Mecklenburg (1998), the main impact sediment has on stream substrates is filling the interstitial spaces creating an embedded condition (Figure 3).

Microhabitat that is essential for fish and aquatic insect survival, reproduction, and development is lost. This study measured sediment pollution differences between anthropogenically influenced streams and streams that were developing natural meanders.

The results showed that human land use decisions directly impacting stream channels and other in-stream processes can lead to sedimentation increases. The natural stream segments had higher quality riffles with fewer fines (fine sediments) and pools that were

1.5 times deeper and relatively free of fines.

Utilizing guilds to measure stressor impacts, in combination with the power of statistical analysis, can offer a more robust analysis than comparing habitat variables to species richness and abundance (Fausch et al. 1990). Combining guilds and other community metrics into an index (such as the IBI) provides further insight into the functional aspect of the fish community. In a study investigating sedimentation impacts on IBI and guild scores, reproductive guilds were found to decline with decreasing substrate quality. Many species depend on clean gravel substrates for spawning sites and 29 increasing sediment levels impair their ability to successfully spawn (Berkman and

Rabeni 1987). The reproductive and feeding guilds that were most impacted by siltation were herbivores, benthic insectivores, and simple lithophilous spawners (Berkman and

Rabeni 1987). A similar study found benthic crevice spawners and gravel spawning guilds to be most sensitive to excessive sediment (Sutherland et al. 2002). Simple lithophilous and gravel spawners, who need clean, coarse substrates for nesting, and crevice spawners are particularly sensitive.

Henley et al. (2000) explain the impacts of siltation in terms of the ripple effect felt in the aquatic food chain. Fish remaining at a site after an excessive sedimentation event will experience reductions in dissolved oxygen, reduced respiratory function, increased mortality, loss of spawning habitat, increases in egg and larvae mortality, reduced feeding success, and reduced visibility. Sedimentation results in alterations at the individual, population, and community levels. Size classification of sediment covering the substrata of streams is a good measure of sedimentation; however turbidity should not be used as a surrogate for those measurements due to its natural variability

(Henley et al. 2000).

Human activities such as agriculture, urbanization, construction, forestry, and road construction have greatly increased the rates of sedimentation (Wood and Armitage

1997). However, natural sources of sediment from within the stream channel and from the stream banks can make these sediment sources difficult to quantify. As stream velocity decreases, sediments fall out of suspension from the water column. The coarser sand material fills interstitial spaces, followed by smaller silt particles. Then the smallest 30 particles fill in the remaining gaps to create a practically impermeable layer that greatly reduces habitat availability. To counter the impacts of human-induced sedimentation in streams, Wood and Armitage (1997) suggest that riparian vegetation and stream morphology require restoration and natural flood defense properties need to be maintained in streams.

31

Chapter 2: Western Allegheny Plateau Reference Site Analysis

2.1 Introduction

This study examines fish-habitat relationships at reference streams in the Western

Allegheny Plateau (WAP) ecoregion of southeastern Ohio to determine correlations in the absence of sediment impairments. All recent data were obtained from a STAR grant project carried out in the summer of 2005. An historic dataset was also obtained from the

Ohio EPA. Wadeable and headwater streams were investigated separately to provide insight as to their characteristic fish community and physical habitat correlations.

First, least impaired WAP reference site Qualitative Habitat Evaluation Index

(QHEI) and individual metric scores, Wolman pebble count, and sieve analysis variables were analyzed to determine correlations with several structural and functional aspects of the fish communities present. Next, the reference site IBI and QHEI data were analyzed for temporal variation and stability through similar comparisons with an historic data set for the same reference sites. The following hypotheses were formulated for the reference site analyses:

-The various structural and functional aspects of the reference site fish

communities will show significant correlations with the substrate and physical

habitat variables.

-Similar fish community/physical habitat correlations will be found between the

historic and recent dataset.

-The fish communities and physical habitat variables will show

persistence/stability over time. 32

2.2 Methods

2.2.1 Study area

This study was carried out in the Western Allegheny Plateau (WAP) ecoregion of

southeastern Ohio (Figure 4) using previously collected fish, substrate, and physical

habitat data. The WAP is situated in the foothills of the Appalachian Mountains and is

marked by hilly, unglaciated terrain. In many areas the underlying sedimentary bedrock

was previously mined for coal. Some mining persists, but today the landscape is dotted

with timber harvesting and agriculture (Woods et al. 1998). The most common impairment affecting streams in the WAP is excessive sedimentation (USEPA 2000b;

OCAFS 2001).

33

Figure 4 Study area location and WAP Level IV ecoregions. The area shaded in green represents the WAP of southeastern Ohio where the sample sites are located.

2.2.2 Study datasets

Fish and habitat sampling took place at 53 reference sites (Figure 5) during the summer

of 2005. These sites were selected by the Ohio EPA for the 2005 STAR grant project and

all sampling was performed by those involved with the project. That reference site data

was obtained and utilized in this study to determine correlations between the fish

communities, substrate particle size, and physical habitat variables under minimally

impacted conditions. All sites were evaluated to ensure that they met reference

conditions through an investigation of field notes, site photographs, and the biological 34 data. This screening process found eight sites with biological, chemical, or other impairments that were indicative of some degradation. These sites were not included in the analysis, bringing the total number to 45.

Figure 5 Map of Ohio counties and reference site locations.

Of the 45 sites retained for analysis, 26 were headwater (drainage areas < 52 km2)

and 19 were wadeable streams (drainage areas > 52 km2, Ohio EPA 1987). All sites but

one (H26) have historic fish, IBI, and QHEI data from previous intermittent sampling

since 1983, and those data were obtained from a large database through personal

communication with an Ohio EPA employee (Appendix A). The historic sites were 35 subjected to the same screening process as the recent sites. A total of 33 wadeable and 30 headwater historic samples (multiple historical records were obtained for some sites) were retained and utilized for data analysis.

2.2.3 Fish sampling

Fish sampling was performed by the Ohio EPA or Midwest Biodiversity Institute (MBI) following Ohio EPA sampling guidelines (Ohio EPA 1987). Samples were obtained by pulsed D.C. electrofishing (USEPA 1993). Fish sampling distances for 2005 sites ranged from 0.15-0.2 km and historic distances from 0.04-0.51 km. Fish collected were identified, recorded, and released (or preserved and saved as voucher specimens). The richness and abundance data for each site was used to calculate IBI scores. All abundance data for individual species were converted to relative abundance (number of individuals multiplied by sampling distance/0.30km) to control for variable fishing effort.

Individual species abundance and twenty fish variable values were obtained at each site, including the total IBI and MIWB index scores, and the individual metric scores used in the IBI calculation (Table 3).

Index of Biotic Integrity

Due to the insight each metric provides, both headwater and wadeable metrics used in calculating the IBI will be included in all analyses. The raw data used to tabulate the metric scores were used in analyses (e.g., number of sunfish species or percent lithophilic spawners), as opposed to using the actual metric scores (1, 3, or 5). All percentage metrics were arcsine transformed.

36

Table 3 Fish community indices and metrics. IBI and MIWB index values were obtained for this study. Individual metrics representing various species, trophic, and tolerance guilds were also used. Each of the fish metrics are grouped by general community categories. Category Stream Type Metric Community Indices -- Index of Biotic Integrity -- Modified Index of Well-Being Species Richness and Abundance W/H Total # of all species -- Relative number of fish collected General Community W # of darter species Composition H # of sculpin/darter species W # of sunfish species H # of headwater species W # of sucker species H # of minnow species Trophic Composition W/H % omnivores W/H % insectivores W % top carnivores H % pioneering species Tolerance Guild Composition W/H % tolerant species W # of intolerant species H # of sensitive species W % simple lithophilic spawners H # of simple lithophilic species -- # of declining species a “W” indicates this metric is used for wadeable sites IBI calculation, an “H” indicates used for headwater sites IBI calculation, and a “W/H” indicates the metric is used for both. Note: Two metrics were not used. Total number of indigenous fish (very few hybrids caught) and percent individuals with DELT anomalies (minimal occurrence) metrics not used for analysis.

2.2.4 Qualitative habitat evaluation index

The QHEI (Rankin 1989) is a comprehensive method of assessing and scoring the various physical habitat components within streams that are essential to aquatic communities. The index consists of seven metrics (Appendix B) that are scored and added together to give a score ranging from 0-100. QHEIs were completed for all sites sampled in 2005 as well as historically. The QHEI metric values for total score and the individual components were used for data analysis (Table 4).

37

Table 4 Physical habitat metrics from the QHEI. The QHEI total score is determined by the individual metric scores listed below. QHEI Metric Maximum Score Total QHEI 100 Substrate metric 20 Cover metric 20 Channel metric 20 Riparian metric 10 Pool/current metric 12 Riffle/run metric 8 Gradienta 10 aActual gradient values (m/km) used in analyses as opposed to metric scores.

2.2.5 Substrate particle size distributions

Wolman pebble counts (WPC) were performed (following Wolman 1954, with slight modification) at each of the 45 WAP reference sites in the summer of 2005. This method

is used to evaluate the composition of the streambed through measuring a set number of

particles from a representative riffle. A minimum of one hundred particles were obtained

from each riffle and measured along their intermediate axis. All particles determined to

be < 5 mm were recorded as 5 mm.

The samples obtained for sieve analysis were taken adjacent to a representative

riffle at each of the 45 WAP reference sites. An area of approximately 0.3 m2 was

selected (on dry land just adjacent to the edge of the riffle to avoid fine particle loss) and

the top layer of armoring along with 7-13 cm of material below it was excavated from the

sample area. The samples were returned to the lab for drying. Hydrogen peroxide was

used to rid the samples of organic material. The samples were oven dried, weighed, and

put into a dry sieve shaker with various diameter screens (16mm to 63µm). The particles

were collected from each screen and weighed. 38

To analyze the sieve samples, the GRADISTAT program (Blott and Pye 2001) was used to calculate several substrate variables including percent gravel, sand, and very fine sand, as well as a range of cumulative percentile values for each sample (Table 5).

All percentage data for substrate parameters were arcsine transformed. Wolman pebble counts and sieve analyses were not performed historically, and are thus only used in the initial reference site investigation.

Table 5 Substrate variables obtained for this study. The variables included below are from Wolman pebble counts and sieve analyses. Substrate Variable Wolman Pebble Count: % finer than 5mm % finer than 15mm % finer than 30mm % finer than 65mm % finer than 130mm Wolman D25 (mm) Wolman D50 (mm) Wolman D84 (mm) Wolman Average particle size (mm)

Sieve Analysis: % gravel % sand % very fine sand Sieve D10 (µm) Sieve D50 (µm) Sieve D90 (µm) Sieve mean particle size (µm)

2.2.6 Statistical analysis

Recent data

Because they exhibit distinctive fish communities and physical habitat characteristics

(Belliard et al. 1999; Taylor et al. 2006), the wadeable (n = 19) and headwater sites (n = 39

26) were analyzed separately. Statistical analyses were performed using SPSS 14.0 and

Excel. A Bonferroni correction was used to adjust for multiple comparisons (Rice 1989).

Significance and direction of relationships between habitat quality and fish communities were determined using correlation analyses. Spearman’s rank-order correlation coefficients were calculated between all raw fish community and individual species abundance values, all substrate measurements, and QHEI total and metric scores

(Dalejones et al. 1999). This nonparametric test was chosen due to issues with data normality and the ordinal nature of the metric scores for the QHEI and its individual components. Spearman’s coefficients (Spearman’s rho) are values that measure linear relationships and are similar to Pearson’s r. Correlations between QHEI and substrate data were determined to investigate relationships between substrate composition and other physical habitat characteristics.

Historic data

As with the recent reference site data, all historic data were analyzed in two groups based on drainage area. The historic headwater (n = 30) and wadeable (n = 33) data for the same reference sites were utilized in similar analyses to investigate the similarity in historic and recent physical habitat correlations. The stability of the reference site fish communities and physical habitat was also explored. Historic sites with more than one set of biological data for a single sampling year were averaged prior to analysis.

To determine if similar fish-habitat relationships existed historically, Spearman’s correlation coefficients were determined between fish variables and the QHEI component scores. Reference site fish assemblage stability over time was investigated utilizing 40

Jaccard’s coefficient of similarity (JC) (Brown et al. 2006). This coefficient is a measure of the similarity between two selected samples based on the presence or absence of species collected at each site. JC values were calculated for each pair of historic/recent sample data. JC values range from 0 to 1. A score of zero shows low persistence/no similarity between sample communities. A value of one is indicative of identical assemblages with very high persistence (Cao et al. 2002). Wilcoxon signed ranks tests were used to determine statistically significant differences between the historic and recent

QHEI and fish data.

2.3 Results

2.3.1 Recent data

Fish community composition

At all wadeable sites sampled in 2005, a total of 72 fish species were collected representing 15 families (Appendix C). The individual sites ranged from 20-37 species

collected resulting in IBI scores of 42-56 (Table 6). A total of 12 declining species were

collected at wadeable sites and site W8 had the maximum at which 6 declining species

were obtained. Eastern sand darters (Ammocrypta pellucida), noted as a declining

species, were collected at site W12. The declining bigeye chub (Hybopsis amblops) was also collected at recent wadeable sites, but was not found in the historical dataset.

The headwater sites had a total of 52 fish species collected from nine families, with a range of nine to 29 for species richness per site (Appendix C). IBI scores ranged from 36-56 and headwater sites had greater average percentage of tolerant fish and pioneering species than wadeable sites. A total of 10 declining species were collected at 41 all headwater sites with the maximum of 4 at site H23. The declining rosyside dace

(Clinostomus funduloides) was collected recently at sites H5 and H9, but not historically.

Physical habitat quality

The total QHEI scores for wadeable sites averaged 73.0 (57.0-87.5) points (Table 7). Out of a maximum of 20 possible points each, the mean substrate metric value was 13.6 and the mean channel metric score was 14.3. Pool and riffle metrics had average values of

10.4 and 3.9, respectively. Headwater sites had QHEI scores ranging 50.5-80.0 with a mean of 68.1 points. Mean substrate metric scores at headwater sites averaged 14.8 and channel metrics averaged 15.0 points. The pool and riffle mean metric values were 7.7 and 3.0, respectively.

Substrate particle size distributions

The mean Wolman average particle size at wadeable sites was 38.4 mm, and the mean value at headwater sites was 48.4 mm (Table 8). The mean Wolman D50 and D84 values also show larger values at headwater sites. All Wolman percent finer (e.g., % finer than

15mm) values displayed an increase from headwater to wadeable sites. Wadeable sites had lower sieve percent gravel and greater accumulations of both sand and very fine sand. Median sieve particle size (D50) for wadeable sites was also lower than the headwater sites mean value.

42

Table 6 Descriptive values for recently collected fish variables. Wadeable Sites Headwater Sites Fish Variable Mean (Range) Mean (Range) IBI 49.7 (42-56) 50.0 (36-56) MIWB 9.1 (7.7-9.9) 7.7 (5.6-10.3) Number of Species 27.2 (20-37) 16.7 (9-29) Relative Number of Fish 1274.9 (421.5-2695.5) 1439 (266-3774) Darter Species 6.1 (4-9) 4.2 (1-7) Sculpin/Darter Species 6.4 (4-9) 4.4 (1-8) Sunfish 2.9 (2-4) 1.5 (0-4) Headwater Species 2.0 (1-4) 3.1 (1-5) Sucker Species 3.9 (3-5) 2.2 (0-4) Cyprinid Species 9.5 (5-15) 7.4 (4-13) Percent Omnivores 15.3 (3.1-36.8) 10.9 (0-31.41) Percent Insectivores 60.2 (32.6-81.6) 33.8 (7.9-75.2) Percent Top Carnivores 2.3 (0.2-6.7) 1.1 (0-5.9) Percent Pioneering 24.9 (3.3-45.7) 34.4 (6.7-53.7) Percent Tolerant 12.8 (2.4-22.6) 40.9 (7.2-67.8) Intolerant Species 3.7 (0-8) 1.4 (0-7) Sensitive Species 11.1(5-16) 4.5 (0-14) Percent Lithophilic Spawners 42.9 (29.0-68.7) 36.4 (2.1-84.2) Lithophilic Spawning Species 11.5 (7-15) 7.5 (1-13) Declining Species 2.7 (0-6) 1. 5 (0-4)

Table 7 Descriptive values for recently collected habitat variables. Wadeable Sites Headwater Sites Habitat Variable Mean (Range) Mean (Range) QHEI 73.0 (57-87.5) 68.1 (50.5-80) Substrate metric 13.6 (6.5-18) 14.8 (10.5-18) Cover metric 15.8 (11-21) 12.4 (7-18) Channel metric 14.3 (11-17.5) 15.0 (6-19) Riparian metric 6.7 (3-9.5) 7.4 (3-10) Pool metric 10.4 (8-12) 7.7 (1-12) Riffle metric 3.9 (0-7.5) 3.0 (0-6) Gradient 2.4 (0.5-5.0) 8.4 (3.0-18.5) Drainage Area 194.5 (53.6-398.9) 18.1 (3.4-51.0) 43

Table 8 Descriptive values for recently collected substrate variables. Data shown are the non-transformed values and are in mm (aside from percentage values). Wadeable Sites Headwater Sites Substrate Variable Mean (Range) Mean (Range) Wolman Pebble Count: Wolman Average Particle Size 38.4 (11.6-81.5) 48.4 (18.1-95.3) Wolman D25 20.5 (5-40) 20.9 (5-50) Wolman D50 32.1 (10-65) 40.1 (15-100) Wolman D84 60.7 (15-135.8) 80.8 (30-150.8) % finer than 5mm 9.3 (2-28) 8.7 (1-30) % finer than 15mm 32.2 (7-88) 25.6 (4-56) % finer than 30mm 56.2 (19-99) 46.5 (14-92) % finer than 65mm 84.9 (51-100) 76.3 (37-100) % finer than 130mm 97.4 (82-100) 94.5 (75-100)

Sieve Analysis: % gravel 73.5 (53.9-96.3) 75.7 (63.4-86.1) % sand 26.5 (3.7-46.1) 24.3 (13.9-36.6) % very fine sand 1.1 (0-6) 0.9 (0.3-2.1) Sieve D10 0.8 (0.2-5.8) 0.6 (0.2-1.5) Sieve D50 7.5 (2.7-14.2) 7.9 (3.9-17.0) Sieve D90 18.6 (10.7-20.7) 19.0 (15.5-21.2) Sieve mean particle size 4.9 (2.2-12.7) 5.1 (2.9-9.0)

2.3.2 Recent statistical analysis

Spearman correlation analyses were utilized for the investigation of significant

relationships between the substrate, habitat, and fish variables. The wadeable sites

analysis (Table 9) only showed a total of seven significant correlations between the

sunfish species, headwater species, and intolerant species metrics and one individual species significantly correlated with six of the WPC variables. Sunfish species were correlated to five WPC variables, headwater species were negatively associated with drainage area, and intolerant species showed a positive correlation to the channel metric.

There were no significant correlations between the fish variables and the sieve

measurements. 44

Table 9 Spearman’s fish-habitat correlations at recent wadeable sites. Spearman’s Fish Variable Independent Variable rho p IBI Metrics: Sunfish Species Wolman average particle size -.698 .001 Wolman D50 -.759 .000 Wolman D84 -.653 .002 % finer than 15mm .683 .001 % finer than 30mm .662 .002 Headwater Species Drainage area -.715 .001 Intolerant Species Channel metric .679 .001

Individual Species: Dusky Darter Wolman average particle size -.759 .000 Wolman D50 -.772 .000 Wolman D84 -.762 .000 % finer than 15mm .760 .000 % finer than 30mm .766 .000 % finer than 65mm .726 .000

The headwater sites analysis (Table 10) showed almost three times as many

significant correlations (19) than the wadeable sites for the IBI metrics and several more

individual species responses to the habitat variables. Ten of the fish variables were found

to correlate significantly with five independent variables. No significant relationships

were found with WPC or sieve data at headwater sites. Drainage area and pool metric scores were the most predominant independent variables to show significant associations with the fish measures. Gradient, riffle metric, and total QHEI score were found in one significant relationship each. Six individual species were correlated with drainage area, one with gradient and another with the pool metric.

45

Table 10 Spearman’s fish-habitat correlations at recent headwater sites. Spearman’s Fish Variable Independent Variable rho p IBI Metrics: MIWB Pool metric .609 .001 Drainage area .656 .000 Number of Species Pool metric .647 .000 Drainage area .766 .000 Sucker Species Drainage area .632 .001 Cyprinid Species QHEI .630 .001 Pool metric .667 .000 Percent Insectivores Gradient -574 .002 Drainage area .773 .000 Percent Top Carnivores Drainage area .641 .000 Percent Tolerant Drainage area -.633 .001 Intolerant Species Pool metric .608 .001 Drainage area .583 .002 Sensitive Species Pool metric .739 .000 Riffle metric .572 .002 Drainage area .771 .000 Lithophilic Spawning Species Pool metric .663 .000 Riffle metric .578 .002 Drainage area .618 .001

Individual Species: Creek Chub Drainage area -.646 .000 Northern Hog Sucker Pool metric .784 .000 Drainage area .637 .000 Rock Bass Drainage area .637 .000 Sand Shiner Drainage area .672 .000 Smallmouth Bass Gradient .692 .000 Southern Redbelly Dace Drainage area -.657 .000 Western Blacknose Dace Drainage area -.669 .000

Only one QHEI metric was found to significantly correlate with the substrate data at wadeable sites (Table 11). Six significant Spearman’s rho values were determined between the substrate metric and WPC data. Wolman average particle size, Wolman D25,

D50, and D84 were all found to have positive relationships with substrate quality. Percent finer than 15mm and % finer than 30mm showed negative correlations with substrate quality.

46

Table 11 Spearman’s correlations between wadeable habitat and substrate variables. Spearman’s Habitat Variable Substrate Variable rho p Substrate metric Wolman average particle size .664 .002 Wolman D25 .654 .002 Wolman D50 .705 .001 Wolman D84 .638 .003 % finer than 15mm -.625 .004 % finer than 30mm -.654 .002

Headwater sites also showed only one metric that responded to substrate data

(Table 12). Riffle quality was found to be negatively correlated to sieve percent gravel,

and positively correlated with sieve percent sand. A significant positive correlation was

found between drainage area, and pool metric scores at headwater sites.

Table 12 Spearman’s correlations between headwater habitat and substrate variables. Spearman’s Habitat Variable Independent Variable rho p Riffle metric % gravel -.544 .004 % sand .544 .004 Pool metric Drainage area .626 .001

2.3.3 Historic data

Fish community composition

The historic dataset also had nine families represented at headwater sites and 15 families

at wadeable sites, but there were differences in the specific families that were collected

(Appendix C). The family Clupeidae was collected historically at headwater sites, but

not during 2005 sampling, and species from the family Poecillidae were collected

recently but not historically. Wadeable sites only had one difference as well where

Umbridae were only collected in the past and Lepisosteidae were only obtained in 2005. 47

Wadeable sites were found to have a total of 84 different species collected (12 more than recent wadeable sampling) with values between 17-35 species per site. IBI scores ranged from 34 to 56 with metric data values that were very similar to 2005 (Table

13). Twelve declining species were found including the hornyhead chub (Nocomis bigutatus). Eastern sand darters were identified at site W12 (1988 and 1994), where they were also found in 2005, and additionally at site W16 (1998) where they were not found recently.

A total of 60 species were collected at headwater sites, eight more than the 2005 sampling, with a range of nine to 31 species collected per site. Headwater IBI values were slightly higher than historic wadeable. Perfect IBI scores were observed at sites

H20 (2000) and H22 (1999). The IBI metric values at historic headwater sites were similar to those in 2005. Ten sensitive species were collected with one that was not recorded recently, the scarlet shiner (Lythrurus fasciolaris), at site H10 (1992).

48

Table 13 Descriptive values for historic fish variables. Wadeable Sites Headwater Sites Fish Variable Mean (Range) Mean (Range) IBI 47.1 (34-56) 50.1 (40-60) MIWB 9.0 (4.9-10.4) 6.4 (4.1-9.9) Number of Species 25.5 (17-35) 17.4 (9-31) Relative Number of Fish 1363.6 (239.3-5077.1) 1360.8 (267.0-3182.0) Darter Species 5.9 (4-9) 4.0 (2-7) Sculpin/Darter Species 4.9 (0-9) 4.3 (2-8) Sunfish Species 3.0 (1-5) 1.7 (0-4) Headwater Species 1.8 (1-4) 3.4 (1-6) Sucker Species 3.5 (2-5) 2.1 (1-4) Cyprinid Species 6.8 (0-13) 7.9 (5-13) Percent Omnivores 15.4 (2.6-44.0) 10.6 (0-32.8) Percent Insectivores 58.0 (33.2-86.3) 36.8 (6.7-85.4) Percent Top Carnivores 4.1 (0.2-13.6) 0.9 (0-5.4) Percent Pioneering 18.9 (3.4-45.8) 35.2 (5.1-61.9) Percent Tolerant 19.7 (3.0-49.7) 38.4 (6.8-64.9) Intolerant Species 3.9 (0-8) 1.4 (0-8) Sensitive Species 10.8 (5-16) 4.8 (0-15) Percent Lithophilic Spawners 37.4 (9.7-65.4) 30.8 (1.3-51.6) Lithophilic Spawning Species 8.3 (0-15) 7.4 (2-14) Declining Species 2.1 (0-5) 1.7 (0-6)

Physical habitat quality

Substrate measurements (WPC and sieve analyses) were not performed historically, therefore the QHEI and its metric values were used to determine the physical habitat associated with historic biological sampling (Table 14). Historic wadeable sites had total

QHEI scores that averaged 74.3 (55.0-90.0). The wadeable substrate metric scores historic mean was 15.4 and the cover metric averaged 14.1 points. Both riffle and pool metrics had scores that ranged from their minimum allowable to the maximum allowable points (0-8 and 0-12, respectively). QHEI total scores ranged from 50.0-91.5, averaging

68.9 points at headwater sites. The pool metric score mean was 7.3 points, riffle metrics averaged 2.8 points, and substrate metric scores had a mean value of 14.8 points.

49

Table 14 Descriptive values for historic habitat variables. Wadeable Sites Headwater Sites Habitat Variable Mean (Range) Mean (Range) QHEI 74.3 (55-90) 68.9 (50-91.5) Substrate metric 15.4 (9-22) 14.8 (11-19) Cover metric 14.1 (7-20) 12.4 (6-18) Channel metric 16.1 (9.5-20) 16.0 (9-20) Riparian metric 7.0 (3.5-10) 7.6 (3-10) Pool metric 9.4 (0-12) 7.3 (3.5-12) Riffle metric 4.1 (0-8) 2.8 (0-7) Gradient 2.4 (0.5-5.0) 8.4 (3.0-18.5) Drainage Area 194.5 (53.6-398.9) 18.1 (3.4-51.0)

2.3.4 Historic statistical analysis

Spearman’s correlation coefficients calculated between the fish variables and the QHEI components found several significant relationships for historic sites, but only at headwater sites. Wadeable sites did not show any significant correlations between IBI metrics and the habitat variables. However, six individual species did show several correlations (Table 15). A majority of those relationships were with gradient, while two species were correlated to riparian quality and one to drainage area.

Table 15 Spearman’s fish-habitat correlations at historic wadeable sites. Spearman’s Fish Variable Independent Variable rho p Individual Species: Creek Chub Drainage area -.605 .000 Central Stoneroller Riparian metric .688 .000 Gradient .597 .000 Smallmouth Bass Gradient .696 .000 Dusky Darter Gradient -.601 .000 Blackside Darter Gradient -.600 .000 Rainbow Darter Riparian metric .682 .000 Gradient .699 .000

Headwater sites showed a total of 14 significant correlations between the IBI metrics and

habitat variables (Table 16). Drainage area and pool metric scores were the only 50 independent variables found to be associated with a total of nine fish measurements. All response variables were correlated to drainage area, and five were correlated to pool metric scores. Percent tolerant species was the only fish variable showing a negative relationship with pool metrics and drainage area. Four individual species were significantly correlated to drainage area.

Table 16 Spearman’s fish-habitat correlations at historic headwater sites. Spearman’s Fish Variable Independent Variable rho p IBI Metrics: MIWB Drainage area .667 .000 Number of Species Pool metric .693 .000 Drainage area .727 .000 Sunfish Species Pool metric .681 .000 Drainage area .582 .001 Sucker Species Pool metric .573 .001 Drainage area .701 .000 Percent Insectivores Drainage area .691 .000 Percent Top Carnivores Pool metric .710 .000 Drainage area .757 .000 Percent Tolerant Pool metric -.550 .002 Drainage area -.704 .000 Intolerant Species Drainage area .660 .000 Sensitive Species Drainage area .650 .000

Individual Species: Black Redhorse Drainage area .600 .000 Western Blacknose Dace Drainage area -.651 .000 Rock Bass Drainage area .698 .000 Smallmouth Bass Drainage area .673 .000

2.3.5 Reference site stability

Fish community stability was estimated using Jaccard’s coefficient (JC). Forty-

two wadeable site comparisons provided JC values with an average of 0.713 (Appendix

D). The greatest value, 0.973 (showing high species persistence), was obtained from site

W4 from 1983-1991. The least stable fish community was found at site W9 from 1996-

2005 with a JC value of 0.514. Headwater sites (35 comparisons) obtained a mean JC 51 value of 0.650 overall. The maximum score, 0.905, was found at site H13 from 2000-

2005 and a minimum score of 0.400 was calculated for site H3 from 2002-2005.

All recent fish variables were compared to historic values to determine significant differences using Wilcoxon signed ranks tests (Table 17). At wadeable sites, two fish variables were determined to be significantly different. These included number of cyprinid and lithophilic spawning species (Figure 6). Headwater sites analysis only showed one significant difference between the recent and historic MIWB scores (Figure

7).

Table 17 Wilcoxon significant differences between historic and recent fish data. Wadeable Sites Headwater Sites Comparison Z p Z p IBI -2.761 .006 -.702 .483 MIWB -1.439 .150 -3.836 .000* Number of Species -2.827 .005 -1.250 .211 Relative Number of Fish -.045 .964 -.195 .845 Darter Species -.246 .806 -.558 .577 Sculpin/Darter Species -2.454 .014 -.809 .419 Sunfish Species -1.323 .186 -.216 .829 Headwater Species -.630 .529 -2.285 .022 Sucker Species -2.351 .019 -.565 .572 Cyprinid Species -3.287 .001* -1.679 .093 Percent Omnivores -.563 .574 -1.327 .185 Percent Insectivores -1.144 .253 -.165 .869 Percent Top Carnivores -2.385 .017 -1.627 .104 Percent Pioneering -1.313 .189 -.216 .829 Percent Tolerant -0.080 .936 -.998 .318 Intolerant Species -1.590 .112 -.656 .512 Sensitive Species -1.933 .053 -.901 .368 Percent Lith. Spawners -2.153 .031 -1.450 .147 Lith. Spawning Species -3.304 .001* -.276 .783 Declining Species -1.923 .054 -2.066 .039 * Wilcoxon signed ranks statistic showing a significant difference.

Wilcoxon tests between recent and historic QHEI data at the reference sites

showed that there were no significant differences between the scores at headwater sites

(Table 18). The wadeable sites did not have significantly different scores for the QHEI 52 total, but the historic cover metric scores were determined to be significantly different from recent scores (Figure 8).

Table 18 Wilcoxon significant differences between historic and recent habitat data. Wadeable Sites Headwater Sites Comparison Z p Z p QHEI -.365 .715 -.216 .829 Substrate metric -1.421 .155 -.119 .905 Cover metric -3.376 .001* -.092 .927 Channel metric -1.884 .060 -1.096 .273 Riparian metric -1.379 .168 -.738 .460 Pool metric -2.394 .017 -1.144 .253 Riffle metric -1.307 .191 -.654 .513

Figure 6 Historic vs. recent cyprinid species and lithophilic spawning species values at wadeable sites. Boxplots show variables that were found to be significantly different using Wilcoxon tests.

Cyprinid Species Historic vs. Recent Values Lithophilic Spawning Species Historic vs. Recent Values

14 14

12 12

10 10

8 8

6 6 Number of Species Number of Species 4 4

2 2

0 0

Historic Recent Historic Recent

53

Figure 7 Historic vs. recent MIWB scores at headwater sites. Boxplots show variables that were found to be significantly different using Wilcoxon tests.

MIWB Score Historic vs. Recent Values

11

10

9

8

7 MIWB Score

6

5

4

Historic Recent

Figure 8 Historic vs. recent cover metric scores at wadeable sites. Boxplots show variables that were found to be significantly different using Wilcoxon tests.

Cover Metric Historic vs. Recent Values

20.0

17.5

15.0

12.5 Metric Score

10.0

7.5

Historic Recent

54

2.4 Discussion

2.4.1 Wadeable vs. headwater habitat

The headwater and wadeable streams of this study show clearly different basin

characteristics (drainage area and gradient, Figure 9) and physical habitat features.

Substrate and physical habitat analysis at recent sites showed a noticeable transition from

high-gradient, smaller drainage area headwater streams to lower-gradient, larger drainage

area wadeable streams (Walters et al. 2003). Increasing substrate particle sizes and decreasing percent fines as noted by the WPC measurements were significantly correlated to increasing substrate metric scores at wadeable sites, showing that WPC data are a strong indicator of substrate quality (Wolman 1954; Mebane 2001). Average substrate scores were higher at headwater sites and displayed a transition from diverse

and complex substrates relatively low in finer particles, to slightly more uniform

substrates at wadeable sites. Previous studies have found significant relationships

between degraded riparian vegetation quality, decreasing average particle size, and

increasing percent fines in riffles (Dalejones III et al. 1999; Wooster and DeBano 2006),

however, this study did not find any significant correlations between RIP quality and the

substrate variables.

55

Figure 9 Wadeable vs. headwater gradient and drainage areas.

Gradient Wadeable vs. Headwater Values Drainage Area Wadeable vs. Headwater Values

60

150

50 125

40 100

30 75

20 Gradient (ft/mile) Gradient 50

10 miles) (square Area Drainage 25

0 0

Wadeable Headwater Wadeable Headwater

QHEI total, pool, and riffle mean metric scores were slightly lower at headwater

sights, reflective of the possible effects of limited water availability on the physical

habitat of headwater sites (Lamouroux et al. 1999) during a dry sampling summer

(Appendix E). The pool metric was found to be positively correlated to drainage area at headwater sites and that suggests an increase in the variability of pool quality moving from larger to smaller headwater basins. This is consistent with Thompson and Hoffman

(2001) who found that both pool depth and pool length were significantly influenced by drainage area. Alongside the fish-habitat correlations at headwater sites, that relationship reveals the important role that physical habitat permanence can play at headwater sites

(Coulombe-Pontbriand and Lapointe 2004).

2.4.2 Recent fish-habitat correlations

Wadeable sites

Fish communities at wadeable sites did not show any total community index, species richness and abundance, or trophic composition correlations with the substrate or 56 physical habitat variables. One general community composition metric, sunfish species, displayed several significant correlations with WPC variables. Sunfish are typically deep water, pool dwelling species (Ohio EPA 1987) and these correlations suggest that they are tolerant of sedimentation. Dusky darters (Percina sciera) showed similar correlations with the WPC data. These insectivorous darters utilize riffle and pool habitat and have been found to be associated with woody debris in deeper, slower moving water

(Trautman 1981). In the absence of diverse and complex substrates caused by excess sediment, especially in low gradient larger streams, woody debris can act as a critical component in providing physical habitat structure (Johnson et al. 2003). No other significant correlations were determined between the fish variables and the WPC or sieve measurements indicating the complex nature of the direct and indirect sedimentation influences on fish (Berkman et al. 1986).

Headwater species were found to decrease with increasing drainage area, as expected. However, when the IBI metrics for Ohio were derived, no correlation was found between headwater species and drainage area (Ohio EPA 1987). While the total

QHEI score was not found to significantly correlate with overall IBI score as found in other studies ( e.g., Sullivan et al. 2004), channel metric scores were positively correlated with intolerant species suggesting that wadeable streams with high sinuosity, channel development, and stability having minimal human modifications support more intolerant fishes (Rankin 1989).

The minimal number of significant correlations determined between the substrate/physical habitat and fish variables suggests that some other factor could be 57 influencing fish community structure at wadeable sites. Biotic interactions might have a significant effect on shaping community structure at wadeable sites.

Headwater sites

Fish variables showed many more significant correlations at headwater sites than at wadeable sites. These results suggest that headwater sites fish communities show much clearer links to in-stream physical habitat features (Li and Gelwick 2005). However, none of these correlations involved the WPC or sieve data. Overall community diversity and biomass (as noted by the MIWB) and species richness were positively correlated with both pool metric scores and drainage area. This relationship has been observed in previous studies (e.g., Walters et al. 2003; Pont et al. 2005). Two general community composition metrics, sucker species and cyprinid species, were significantly correlated to physical habitat variables. Sucker species were positively correlated with DRNAR

(Shields et al. 2000; Ohio EPA 1987). Cyprinid species were found to be indicative of good overall habitat quality as they positively correlated to total QHEI scores. This is the only fish variable that was found to be significantly correlated with total QHEI score.

The cyprinid species metric was designed to include both tolerant and intolerant species and has been found to be mainly influenced by drainage area (Ohio EPA 1987), but this study did not show that relationship.

The trophic composition of headwater sites was significantly correlated with drainage area, as both insectivores and top carnivores were found to be positively associated with drainage area. Streams with smaller drainage areas typically have less available deep pool habitat (Shields et al. 1994), and because top carnivores are typically 58 larger-bodied fishes, they rely on the availability of this habitat and have been shown to decrease in abundance with decreasing drainage area (Ohio EPA 1987).

Fish tolerance guilds were also significantly related to drainage area, as well as pool and riffle metric scores, while percent tolerant fishes were negatively associated with drainage area. This suggests that higher percentages of tolerant species are predominant in headwater streams with smaller drainage areas. These streams are most likely limited by intermittent flows creating conditions that intolerant fishes may not be able to survive (Taylor et al. 2006). The number of sensitive species (including all intolerant and moderately intolerant species) were positively correlated to drainage area and pool metric scores. This suggests increased stress in headwater streams with smaller drainage areas is likely related to the availability of permanent pool refuge in times of stream dissection (Jowett et al. 2005). During extreme low flow periods in headwater streams, available habitat often becomes reduced and often forces fish into a series of pools (Hakala and Hartman 2004).

Sensitive species were positively associated with both pool and riffle metrics.

High quality pool and riffle sequences typically indicate sites with flowing water. The lithophilic spawning species metric also showed positive correlations with drainage area, pool, and riffle metric scores. Water availability and habitat connectivity are important to these species as well because lithophilic spawners broadcast their eggs across stream substrates and rely on streamflow for dispersal (Ohio EPA 1987; Henley et al. 2000).

Creek chub (Semotilus atromaculatus), Southern Redbelly dace (Phoxinus erythrogaster), and Blacknose dace (Rhynichthys atratulus) were all negatively correlated 59 with drainage area. Creek chub and western blacknose dace are noted as tolerant species and both the dace species are common headwater fishes, so these relationships are to be expected (Trautman 1981). These species do indicate some degree of habitat permanence, but also suggest that higher stress levels exist at headwater sites because of their tolerance guild membership (Ohio EPA 1987). Several other species showed the opposite relationship with drainage area. Rock bass (Ambloplites rupestris), sand shiners

(Notropis ludibundus), and Northern hog suckers (Hypentelium nigricans) were positively related to drainage area. A positive correlation between drainage area and pool metric scores noted at headwater sites offers further insight as to these relationships because these fishes are all dependent upon the availability of pool habitat (Trautman

1981). Smallmouth bass (Mocropterus dolomieu) were positively correlated with gradient though they are not typically found in high gradient streams (Trautman 1981).

No substrate measurements significantly correlated with the fish community variables (as opposed to Walters et al. 2003) suggesting that other factors (water availability and pool persistence in particular) are more influential on community structure at headwater sites. Drainage area was the most frequent independent variable to significantly correlate with the headwater fish variables, additionally indicating the importance of pool refuge and the implications of low flow events. As pool quality/depth and drainage area increase, an increase in the number and biomass of fish, have been shown to occur in response to an increased availability of habitat (Butler and Fairchild

2005).

60

2.4.3 Historic fish-habitat correlations

Wadeable sites

As with the recent dataset, the fish communities at wadeable sites did not display as many significant relationships with the habitat variables. In the case of the historic data, no significant correlations were found between fish indices or metrics and the habitat variables. However, six individual species did show significant correlations at historic wadeable sites, including dusky darters which were found to be negatively associated with gradient. Similar to the recent headwater correlations, creek chubs were negatively associated with drainage area and smallmouth bass were positively related to gradient at historic wadeable sites. Central stonerollers (Campostoma anomalum) and rainbow darters (Etheostoma caeruleum) were positively correlated to both RIP quality and gradient, consistent with species accounts linking these species to high/moderate gradient streams (Trautman 1981). The significant relationship shown between central stonerollers and rainbow darters and riparian habitat suggests that these fish are indicative of high quality riparian buffers (Norton et al. 2000). The blackside darter (Percina maculata) was negatively correlated to gradient at wadeable sites and they have been found to prefer slower currents and moderate gradients (Trautman 1981).

Headwater sites

All significant correlations at the headwater sites involved drainage area. Some fish variables were also correlated with pool metric scores. Cyprinid and lithophilic spawning species did not show any significant correlations historically, however sunfish species did as they were found to positively correlate with pool metric scores and drainage area at 61 historic headwater sites (Shields et al. 2000). The community sensitivity metrics showed the same correlations, the only difference being percent tolerant species showing an increase with decreasing pool metric score. Tolerant species are persistent after disturbance events and are typically among the first to return to a degraded site.

Increases in the commonly found percent tolerant species can be indicative of an increase in environmental stress (Ohio EPA 1987).

Three of the seven individual fish species with significant correlations in the recent dataset were also found to have significant correlations historically and two of these relationships were the same. Black redhorse (Moxostoma duquesnei) and smallmouth bass were positively correlated with drainage area, due to their dependence on deep pools for habitat (Trautman 1981).

These results show that the relationships between fish and habitat quality at headwater sites have remained relatively similar from historic to recent sampling.

Headwater fishes show a strong association with pool metric scores, which is strongly linked to drainage area (Pont et al. 2005), and they depend on some degree of pool permanence and geomorphic diversity to retain their biomass/community composition over time (Rhoads et al. 2003). The relationships shown in analyzing the fish-habitat data overwhelmingly agree, with both historic and recent correlation analyses.

2.4.4 Reference site stability

Investigating the JC values for fish species stability between sampling dates revealed that the repeated samples were obtaining similar species sets. Based on similarity categories from Phillips and Johnston (2004), there were no JC scores noted as having low 62 similarity (JC = < 0.4, or 40% similarity) at wadeable sites. Fifty-two percent of JC values showed moderate similarity (JC between 0.4 and 0.7) and 48% showed high similarity (JC = >0.7). Headwater sites had one JC value showing low similarity, 22 fell within the moderately similar range, and 12 exhibited high similarity.

These fish communities appear to be very stable. Phillips and Johnston (2004) observed the impact that impoundments have on species persistence over time, and the JC range for all sites compared was 0.07-0.62, with a mean JC value of 0.28. Ganasan and

Hughes (1998) sampled fish at different distances from industrial effluents and found 18-

90% similarity furthest downstream, and 11-24% similarity at the most disturbed sites.

Wadeable sites had a mean of 0.713 with a minimum value of 52.4% and headwater sites showed an average JC of 0.650 with a minimum of 40% similarity. Thus there were minor shifts in species composition between sampling years at the sites displaying moderate similarity JC values. The single headwater site exhibiting a low similarity value experienced a more considerable shift in species composition between sampling years.

Wilcoxon signed ranks tests were run to determine statistically significant differences in the fish community and physical habitat variables from their historic to recent sample values. Two IBI metrics (cyprinid species and lithophilic spawning species) and one habitat variable (cover metric) were found to have significantly different historic and recent scores at wadeable sites and MIWB scores at headwater sites were found to be significantly different as well. All of the significant differences exhibited positive trends from historic to recent sampling dates, indicating an increase in these 63 scores over time. This trend shows wadeable sites increasing in cover quality, minnow species, and lithophilic spawning species and headwater sites increasing in overall diversity and biomass as indicated by the positive trend with MIWB scores. This suggests that the increases in the quality of stream cover at wadeable sites could be related to the increasing trend in the above mentioned fish metric scores. However, no significant Spearman’s correlation coefficients between these fish and habitat variables were shown in the previous analyses. The overall trend further displays reference site stability, and even shows slight improvements over time.

Headwater sites should exhibit more variability in physical habitat due to their variable hydrologic and geomorphic nature (Taylor et al. 2006) and susceptibility to human influence and land cover disturbances (Smiley et al. 2005). Despite the dynamic nature of headwater streams, the reference sites of this study show high fish species (and community) persistence as well as physical habitat stability.

2.5 Conclusions

The results of this study indicate that reference sites in the WAP of southeastern Ohio

show both habitat and fish community persistence between historic and recent sampling

dates spanning over 20 years. Wolman pebble count and sieve analysis data did not

reveal many significant correlations with fish variables; however several individual

species and fish metrics were significantly correlated with drainage area, pool and riffle

quality, and gradient. This suggests that the fish communities were more significantly

associated with broader scale habitat features influenced by basin size, as opposed to site-

specific microhabitat scale variables. Characteristics closely tied to drainage area and 64 geomorphic processes, such as pool depth/quality (stream size), habitat connectivity and permanence, and water availability, were suggested as limiting factors for headwater fish communities.

Fish-habitat correlations were minimal at recent wadeable sites, and not evident historically. This suggests that some other influence, possibly biotic, could be responsible for shaping community structure at wadeable sites. Headwater site fish variables showed many more significant correlations, several of which were strikingly similar between historic to recent sampling dates. While there were few correlations between the fish and habitat variables, those that were significant did provide insight as to the nature of the fish-habitat relationships and community composition at the reference sites.

Overall, total IBI scores were not found to be significantly associated with substrate particle size distribution measurements, total QHEI score, or its metric scores.

The IBI metrics showed numerous significant correlations (as did several individual fish species) with the QHEI metric scores. This is to be expected as the total IBI is sensitive to a host of various chemical, physical, and biological influences. Using the individual metrics and species in analysis allows for a more specific and refined analysis of fish- habitat associations. The habitat and fish community stability shown in this analysis further supports the utility of incorporating reference sites into comparative studies while displaying that these WAP reference sites are in fact credible as “minimally influenced” sites. These results also promote the use of multimetric indices and their components to 65 reflect biologic integrity, as well as the QHEI in tandem with quantitative habitat measurements to provide robust insight on the quality and quantity of lotic habitat.

66

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Appendix A- Site list

All wadeable reference sites and sampling dates.

Site ID Stream Name River Mile Drainage Area Historic Sample Years W1 North Fork Yellow Creek 6.2 41.0 1991 W2 Storms Creek 3.3 32.1 1990 W3 Witten Fork 0.9 42.1 1984, 1991, 2000 W4 Elkhorn Creek 0.5 34.4 1983, 1991 W5 Little Scioto River 27.2 43.0 1996 W6 Camp Creek 1.9 28.7 1997 W7 Cranenest Fork 4.0 20.7 2000 W8 Sunfish Creek 8.0 132.0 1983, 1997 W9 Captina Creek 14.5 134.0 1983, 1996 W10 West Branch Wolf Creek 3.5 140.0 1984 W11 Sunfish Creek 7.1 99.0 1983, 1991, 1996, 2000 W12 Wakatomika Creek 12.4 154.0 1988, 1994 W13 Jonathan Creek 12.3 105.0 1984, 1999 W14 Indian Guyan Creek 5.8 67.0 1990 W15 Middle Fork Little Beaver Creek 9.0 114.0 1985, 1998, 1999 W16 White Eyes Creek 0.6 53.0 1983, 1998 W17 Rocky Fork Little Scioto 0.6 68.4 1990 W18 Middle Branch Shade Creek 8.1 60.0 1996 W19 Middle Fork Salt Creek 4.7 58.0 1997

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All headwater reference sites and sampling dates.

Site ID Stream Name River Mile Drainage Area Historic Sample Years H1 Patty Creek 0.1 2.3 2000 H2 Trib. to Sunday Creek 0.4 2.0 2001 H3 N. Fork Brush Creek 0.6 2.3 2002 H4 Millers Fork 3.4 3.7 2000 H5 Oldcamp Run 0.4 4.2 2000 H6 Ellis Run 0.5 1.3 1995 H7 Trail Run 0.3 3.3 1983, 1999 H8 Nelots Creek 1.1 1.8 2000 H9 Jordan Run 1.1 6.7 1995 H10 Middle Fork Laurel Run 0.15 11.3 1992 H11 Dismal Creek 1.7 5.7 2000 H12 Trail Run 0.9 4.8 2000 H13 Pawpaw Creek 8.2 9.0 2000 H14 Wingett Run 0.1 5.3 2000 H15 Leith Run 0.8 9.9 1983, 1991 H16 Nancy Run 1.0 7.5 1983, 1991 H17 Black Fork 3.5 8.4 1987, 1998 H18 Witten Run 2.7 6.5 1991 H19 Archers Fork 2.2 14.5 1983, 1996 H20 Lower Twin Creek 2.2 15.0 2000 H21 Winding Fork 1.8 19.1 1994 H22 Queer Creek 4.4 11.7 1999 H23 Wakatomika Creek 32.0 19.7 1994 H24 Trib. to Pawpaw Creek 0.1 1.4 2000 H25 Creighton Run 0.8 2.2 2000 H26 Oldcamp Run 1.1 3.3 Not Sampled Historically

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Appendix B- QHEI field sheet

77

78

Appendix C- Species lists

Fish collected at wadeable sites during the summer of 2005. Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat BANDED DARTER Etheostoma zonale I D I S RF D N R BIGEYE CHUB Hybopsis amblops I M I S P N Y R BLACK REDHORSE Moxostoma duquesnei I R I S RN R N P BLACKSIDE DARTER Percina maculata - D I S P D N B BLACKSTRIPE TOPMINNOW Fundulus notatus - T I M P - N P BLUEGILL SUNFISH Lepomis macrochirus P S I C P S N P BLUNTNOSE MINNOW Pimephales notatus T M O C P N N B BRINDLED MADTOM Noturus miurus I O I C RF - Y B BROOK SILVERSIDE Labidesthes sicculus M O I M P - N P CENTRAL STONEROLLER Campostoma anomalum - M H N RF N N B CENTRARCHIDAE SP. Centrarchidae sp. ------CHANNEL CATFISH Ictalurus punctatus - F - C P F N P COMMON CARP Cyprinus carpio T G O M P G N P COMMON SHINER Luxilus cornutus - N I S RN N N B CREEK CHUB Semotilus atromaculatus T M G N G N N B DUSKY DARTER Percina sciera M D I S P D N B EASTERN SAND DARTER Ammocrypta pellucida R D I S P D Y R EMERALD SHINER Notropis atherinoides - N I S P N N P FANTAIL DARTER Etheostoma flabellare - D I C RF D N R FATHEAD MINNOW Pimephales promelas T M O C P N N B FRESHWATER DRUM Aplodinotus grunniens P F - M P - N P GIZZARD SHAD Dorosoma cepedianum - GS O M P - N P GOLDEN REDHORSE Moxostoma erythrurum M R I S RN R N P Notemigonus GOLDEN SHINER crysoleucas T N I M P N N P 79

Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat GRAVEL CHUB Erimystax x-punctata M M I S RN N N R GREEN SF X BLUEGILL SF HYBRID - S ------GREEN SUNFISH Lepomis cyanellus T S I C P S N P GREENSIDE DARTER Etheostoma blennioides M D I S RF D N R JOHNNY DARTER Etheostoma nigrum - D I C P D N B LARGEMOUTH BASS Micropterus salmoides - B C C P F N P LEAST BROOK LAMPREY Lampetra aepyptera - O F N SB - Y P LOGPERCH Percina caprodes M D I S P D N B LONGEAR SUNFISH Lepomis megalotis M S I C P S N P LONGNOSE GAR Lepisosteus osseus - L P M P - N P MIMIC SHINER Notropis volucellus I N I M P N Y B MOTTLED SCULPIN Cottus bairdi - SC I C RF - N R NORTHERN HOG SUCKER Hypentelium nigricans M R I S RF R N R ORANGETHROAT DARTER Etheostoma spectabile - D I S RF D N B QUILLBACK CARPSUCKER Carpiodes cyprinus - C O M P C N P RAINBOW DARTER Etheostoma caeruleum M D I S RF D N R REDEAR SUNFISH Lepomis microlophus - S I C P E N P REDFIN SHINER Lythrurus umbratilis - N I N P N N P REDSIDE DACE Clinostomus elongates I M I S RF N Y P RIVER CHUB Nocomis micropogon I M I N RN N Y B RIVER REDHORSE Moxostoma carinatum I R I S RN R N P ROCK BASS Ambloplites rupestris - B C C P S N P ROSYFACE SHINER Notropis rubellus I N I S RN N Y R SAND SHINER Notropis ludibundus M N I M P N N B SAUGER Stizostedion canadense - V P S P F N P SAUGER X WALLEYE HYBRID - V P - - E N - SCARLET SHINER Lythrurus fasciolaris M N I S RN N Y P 80

Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat SILVER LAMPREY Ichthyomyzon unicuspis - O P N SB - Y B SILVER REDHORSE Moxostoma anisurum M R I S RN R N P SILVER SHINER Notropis photogenis I N I S RN N N P SILVERJAW MINNOW Notropis buccatus - M I M P N N B SMALLMOUTH BASS Micropterus dolomieui M B C C P F N P SMALLMOUTH REDHORSE Moxostoma breviceps M R I S RN R N P SOUTH. REDBELLY DACE Phoxinus erythrogaster - M H S P N Y B SPOTFIN SHINER Cyprinella spiloptera - N I M P N N B SPOTTED BASS Micropterus punctulatus - B C C P F N P SPOTTED SUCKER Minytrema melanops - R I S P R N P STEELCOLOR SHINER Cyprinella whipplei P N I M P N N P STONECAT MADTOM Noturus flavus I O I C RF - N R STRIPED SHINER Luxilus chrysocephalus - N I S RN N N B SUCKERMOUTH MINNOW Phenacobius mirabilis - M I S RN N N R Percopsis TROUT-PERCH omiscomaycus - O I M P - N P VARIEGATE DARTER Etheostoma variatum I D I S RF D Y R BLACKNOSE DACE Rhinichthys atratulus T M G S RF N N R WHITE BASS Morone chrysops - W P M P F N P WHITE SUCKER Catostomus commersoni T R O S RN W N B YELLOW BULLHEAD Ameiurus natalis T F I C P - N P

81

Fish collected at headwater sites during the summer of 2005. Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat BANDED DARTER Etheostoma zonale I D I S RF D N R BIGEYE CHUB Hybopsis amblops I M I S P N Y R Pomoxis BLACK CRAPPIE nigromaculatus - B I C P S N P BLACK REDHORSE Moxostoma duquesnei I R I S RN R N P BLACKSIDE DARTER Percina maculate - D I S P D N B BLUEGILL SUNFISH Lepomis macrochirus P S I C P S N P BLUNTNOSE MINNOW Pimephales notatus T M O C P N N B BRINDLED MADTOM Noturus miurus I O I C RF - Y B BULLHEAD MINNOW Pimephales vigilax - N O C P N N P Campostoma CENTRAL STONEROLLER anomalum - M H N RF N N B COMMON SHINER Luxilus cornutus - N I S RN N N B Semotilus CREEK CHUB atromaculatus T M G N G N N B FANTAIL DARTER Etheostoma flabellare - D I C RF D N R GOLDEN REDHORSE Moxostoma erythrurum M R I S RN R N P Notemigonus GOLDEN SHINER crysoleucas T N I M P N N P GREEN SF X BLUEGILL SF HYBRID - S - - - - N - GREEN SUNFISH Lepomis cyanellus T S I C P S N P Etheostoma GREENSIDE DARTER blennioides M D I S RF D N R HYBRID X MINNOW HYBRID - M - - - - N - JOHNNY DARTER Etheostoma nigrum - D I C P D N B LARGEMOUTH BASS Micropterus salmoides - B C C P F N P LEAST BROOK LAMPREY Lampetra aepyptera - O F N SB - D P LOGPERCH Percina caprodes M D I S P D N B LONGEAR SUNFISH Lepomis megalotis M S I C P S N P 82

Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat MIMIC SHINER Notropis volucellus I N I M P N Y B MOTTLED SCULPIN Cottus bairdi - SC I C RF - N R NORTHERN HOG SUCKER Hypentelium nigricans M R I S RF R N R ORANGETHROAT DARTER Etheostoma spectabile - D I S RF D N B PUMPKINSEED SUNFISH Lepomis gibbosus P S I C P S N P RAINBOW DARTER Etheostoma caeruleum M D I S RF D N R REDFIN SHINER Lythrurus umbratilis - N I N P N N P REDSIDE DACE Clinostomus elongates I M I S RF N Y P RIVER CHUB Nocomis micropogon I M I N RN N Y B RIVER REDHORSE Moxostoma carinatum I R I S RN R N P ROCK BASS Ambloplites rupestris - B C C P S N P ROSYFACE SHINER Notropis rubellus I N I S RN N Y R Clinostomus ROSYSIDE DACE funduloides S M I S RF N Y P SAND SHINER Notropis ludibundus M N I M P N N B SILVER SHINER Notropis photogenis I N I S RN N N P SILVERJAW MINNOW Notropis buccatus - M I M P N N B SMALLMOUTH BASS Micropterus dolomieui M B C C P F N P SOUTH. REDBELLY DACE Phoxinus erythrogaster - M H S P N Y B SPOTFIN SHINER Cyprinella spiloptera - N I M P N N B STEELCOLOR SHINER Cyprinella whipplei P N I M P N N P STONECAT MADTOM Noturus flavus I O I C RF - N R STRIPED SHINER Luxilus chrysocephalus - N I S RN N N B Percopsis TROUT-PERCH omiscomaycus - O I M P - N P VARIEGATE DARTER Etheostoma variatum I D I S RF D Y R BLACKNOSE DACE Rhinichthys atratulus T M G S RF N N R WESTERN MOSQUITOFISH Gambusia affinis - O I N P E N P 83

Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat Catostomus WHITE SUCKER commersoni T R O S RN W N B YELLOW BULLHEAD Ameiurus natalis T F I C P - N P

Fish collected at historic wadeable sites. Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat AMER BROOK LAMPREY Lampetra appendix R O F N SB - N P BANDED DARTER Etheostoma zonale I D I S RF D N R BIGMOUTH SHINER Notropis dorsalis - N I M P N N B BLACK BULLHEAD Ameiurus melas P F I C P - N P BLACK REDHORSE Moxostoma duquesnei I R I S RN R N P BLACKSIDE DARTER Percina maculata - D I S P D N B BLACKSTRIPE TOPMINNOW Fundulus notatus - T I M P - N P BLUEGILL SUNFISH Lepomis macrochirus P S I C P S N P BLUEGILL X PUMPKINSEED HYBRID - S - - - - N - BLUNTNOSE MINNOW Pimephales notatus T M O C P N N B BRINDLED MADTOM Noturus miurus I O I C RF - Y B BROOK SILVERSIDE Labidesthes sicculus M O I M P - N P CENTRAL MUDMINNOW Umbra limi T T I C P - N P Campostoma CENTRAL STONEROLLER anomalum - M H N RF N N B CHANNEL CATFISH Ictalurus punctatus - F - C P F N P COMMON CARP Cyprinus carpio T G O M P G N P COMMON SHINER Luxilus cornutus - N I S RN N N B Semotilus CREEK CHUB atromaculatus T M G N G N N B DUSKY DARTER Percina sciera sciera M D I S P D N B EASTERN SAND DARTER Ammocrypta pellucida R D I S P D Y R 84

Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat EMERALD SHINER Notropis atherinoides - N I S P N N P FANTAIL DARTER Etheostoma flabellare - D I C RF D N R FLATHEAD CATFISH Pylodictis olivaris - F P C P F N B FRESHWATER DRUM Aplodinotus grunniens P F - M P - N P GIZZARD SHAD Dorosoma cepedianum - GS O M P - N P GOLDEN REDHORSE Moxostoma erythrurum M R I S RN R N P Esox americanus GRASS PICKEREL vermiculatus P P P M P - N P GREEN SF X BLUEGILL SF HYBRID - S - - - - N - GREEN SF X LONGEAR SF HYBRID - S - - - - N - GREEN SF X PUMPKINSEED HYBRID - S - - - - N - GREEN SUNFISH Lepomis cyanellus T S I C P S N P Etheostoma GREENSIDE DARTER blennioides M D I S RF D N R HORNYHEAD CHUB Nocomis biguttatus I M I N RN N Y B HYBRID X MINNOW HYBRID - M - - - - N - HYBRID X SUNFISH HYBRID - S - - - - N - JOHNNY DARTER Etheostoma nigrum - D I C P D N B LARGEMOUTH BASS Micropterus salmoides - B C C P F N P LEAST BROOK LAMPREY Lampetra aepyptera - O F N SB - Y P LOGPERCH Percina caprodes M D I S P D N B LONGEAR SF X BLUEGILL SF HYBRID - S - - N - LONGEAR SUNFISH Lepomis megalotis M S I C P S N P MIMIC SHINER Notropis volucellus I N I M P N Y B MOTTLED SCULPIN Cottus bairdi - SC I C RF - N R NORTHERN HOG SUCKER Hypentelium nigricans M R I S RF R N R NORTHERN PIKE Esox lucius - P P M P F N P 85

Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat OHIO LAMPREY Ichthyomyzon bdellium S O P N SB - N B ORANGETHROAT DARTER Etheostoma spectabile - D I S RF D N B PUMPKINSEED SUNFISH Lepomis gibbosus P S I C P S N P QUILLBACK CARPSUCKER Carpiodes cyprinus - C O M P C N P RAINBOW DARTER Etheostoma caeruleum M D I S RF D N R REDFIN SHINER Lythrurus umbratilis - N I N P N N P REDSIDE DACE Clinostomus elongatus I M I S RF N Y P RIVER CHUB Nocomis micropogon I M I N RN N Y B ROCK BASS Ambloplites rupestris - B C C P S N P ROSYFACE SH X SILVER SH HYBRID I N I - - - N - ROSYFACE SHINER Notropis rubellus I N I S RN N Y R SAND SHINER Notropis ludibundus M N I M P N N B SAUGER Stizostedion canadense - V P S P F N P SCARLET SHINER Lythrurus fasciolaris M N I S RN N Y P Moxostoma SHORTHEAD REDHORSE macrolepidotum M R I S RN R N P SILVER LAMPREY Ichthyomyzon unicuspis - O P N SB - Y B SILVER REDHORSE Moxostoma anisurum M R I S RN R N P SILVER SHINER Notropis photogenis I N I S RN N N P SILVERJAW MINNOW Notropis buccatus - M I M P N N B SMALLMOUTH BASS Micropterus dolomieui M B C C P F N P SOUTH. REDBELLY DACE Phoxinus erythrogaster - M H S P N Y B SPOTFIN SHINER Cyprinella spiloptera - N I M P N N B SPOTTED BASS Micropterus punctulatus - B C C P F N P SPOTTED SUCKER Minytrema melanops - R I S P R N P STEELCOLOR SHINER Cyprinella whipplei P N I M P N N P STONECAT MADTOM Noturus flavus I O I C RF - N R STRIPED SH X CREEK CHUB HYBRID - M I - - - N - 86

Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat STRIPED SH X REDFIN SH HYBRID - N I - - - N - STRIPED SH X ROSYFACE SH HYBRID - N I - - - N - STRIPED SHINER Luxilus chrysocephalus - N I S RN N N B SUCKERMOUTH MINNOW Phenacobius mirabilis - M I S RN N N R Percopsis TROUT-PERCH omiscomaycus - O I M P - N P VARIEGATE DARTER Etheostoma variatum I D I S RF D Y R WARMOUTH SUNFISH Lepomis gulosus - S C C P S N P BLACKNOSE DACE Rhinichthys atratulus T M G S RF N N R WHITE BASS Morone chrysops - W P M P F N P WHITE CRAPPIE Pomoxis annularis - B I C P S N P Catostomus WHITE SUCKER commersoni T R O S RN W N B YELLOW BULLHEAD Ameiurus natalis T F I C P - N P

Fish collected at historic headwater sites. Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat AMER BROOK LAMPREY Lampetra appendix R O F N SB - N P BANDED DARTER Etheostoma zonale I D I S RF D N R BIGEYE CHUB Hybopsis amblops I M I S P N Y R BLACK BULLHEAD Ameiurus melas P F I C P - N P BLACK REDHORSE Moxostoma duquesnei I R I S RN R N P BLACKSIDE DARTER Percina maculata - D I S P D N B BLUEGILL SUNFISH Lepomis macrochirus P S I C P S N P BLUNTNOSE MINNOW Pimephales notatus T M O C P N N B BRINDLED MADTOM Noturus miurus I O I C RF - Y B Campostoma CENTRAL STONEROLLER anomalum M H N RF N N B 87

Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat COMMON CARP Cyprinus carpio T G O M P G N P COMMON SH X STONEROLLER HYBRID - M - - - - N - COMMON SHINER Luxilus cornutus - N I S RN N N B CR CHUB X S. REDBELLY D HYBRID - - - B - - N - Semotilus CREEK CHUB atromaculatus T M G N G N N B EMERALD SHINER Notropis atherinoides - N I S P N N P FANTAIL DARTER Etheostoma flabellare - D I C RF D N R FATHEAD MINNOW Pimephales promelas T M O C P N N B GIZZARD SHAD Dorosoma cepedianum - GS O M P N P GOLDEN REDHORSE Moxostoma erythrurum M R I S RN R N P GREEN SF X BLUEGILL SF HYBRID - S - - - - N - GREEN SUNFISH Lepomis cyanellus T S I C P S N P Etheostoma GREENSIDE DARTER blennioides M D I S RF D N R HYBRID X MINNOW HYBRID - M - - - - N - JOHNNY DARTER Etheostoma nigrum - D I C P D N B LARGEMOUTH BASS Micropterus salmoides - B C C P F N P LEAST BROOK LAMPREY Lampetra aepyptera - O F N SB - Y P LOGPERCH Percina caprodes M D I S P D N B LONGEAR SF X BLUEGILL SF HYBRID - S - - - - N - LONGEAR SUNFISH Lepomis megalotis M S I C P S N P MIMIC SHINER Notropis volucellus I N I M P N Y B MOTTLED SCULPIN Cottus bairdi - SC I C RF - N R NORTHERN HOG SUCKER Hypentelium nigricans M R I S RF R N R ORANGETHROAT DARTER Etheostoma spectabile D I S RF D N B 88

Common Name Scientific Name Tolerance Species Feeding Breeding Hab Pref IBI Declining Habitat RAINBOW DARTER Etheostoma caeruleum M D I S RF D N R REDEAR SUNFISH Lepomis microlophus - S I C P E N P REDFIN SHINER Lythrurus umbratilis - N I N P N N P REDSIDE DACE Clinostomus elongatus I M I S RF N Y P RIVER CHUB Nocomis micropogon I M I N RN N Y B ROCK BASS Ambloplites rupestris - B C C P S N P ROSYFACE SHINER Notropis rubellus I N I S RN N Y R SAND SHINER Notropis ludibundus M N I M P N N B SCARLET SHINER Lythrurus fasciolaris M N I S RN N Y P SILVER SHINER Notropis photogenis I N I S RN N N P SILVERJAW MINNOW Notropis buccatus - M I M P N N B SMALLMOUTH BASS Micropterus dolomieui M B C C P F N P SOUTH. REDBELLY DACE Phoxinus erythrogaster - M H S P N Y B STONECAT MADTOM Noturus flavus I O I C RF - N R SPOTFIN SHINER Cyprinella spiloptera - N I M P N N B SPOTTED BASS Micropterus punctulatus - B C C P F N P SPOTTED SUCKER Minytrema melanops - R I S P R N P STRIPED SH X S REDBELLY D HYBRID - N - - - - N - STRIPED SHINER Luxilus chrysocephalus - N I S RN N N B SUCKERMOUTH MINNOW Phenacobius mirabilis - M I S RN N N R Percopsis TROUT-PERCH omiscomaycus - O I M P - N P VARIEGATE DARTER Etheostoma variatum I D I S RF D Y R BLACKNOSE DACE Rhinichthys atratulus T M G S RF N N R WHITE CRAPPIE Pomoxis annularis - B I C P S N P Catostomus WHITE SUCKER commersoni T R O S RN W N B YELLOW BULLHEAD Ameiurus natalis T F I C P - N P

89

Appendix D- Jaccard’s similarity coefficients

Jaccard’s similarity coefficients at headwater sites for each sample year comparison. Site Year Comparison JC W1 83-91 0.792 91-05 0.656 83-05 0.667 W2 90-05 0.780 W3 84-91 0.617 91-00 0.698 00-05 0.531 84-05 0.828 W4 83-91 0.973 91-05 0.700 83-05 0.677 W5 96-05 0.629 W6 97-05 0.571 W7 00-05 0.654 W8 83-97 0.867 97-05 0.633 83-05 0.667 W9 83-96 0.606 96-05 0.514 83-05 0.676 W10 84-05 0.932 W11 83-91 0.833 91-96 0.820 96-00 0.690 00-05 0.677 83-05 0.862 W12 88-94 0.838 94-05 0.734 88-05 0.718 W13 84-99 0.800 99-05 0.690 84-05 0.720 W14 90-05 0.679 W15 85-98 0.759 98-99 0.920 99-05 0.754 85-05 0.808 W16 83-98 0.750 98-05 0.516 83-05 0.643 W17 90-05 0.611 W18 96-05 0.542 W19 97-05 0.609

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Jaccard’s similarity coefficients at headwater sites for each sample year comparison. Site Year Comparison JC H1 00-05 0.650 H2 01-05 0.846 H3 02-05 0.400 H4 00-05 0.875 H5 00-05 0.611 H6 95-05 0.500 H7 83-99 0.583 99-05 0.607 83-05 0.524 H8 00-05 0.563 H9 95-05 0.682 H10 92-05 0.450 H11 00-05 0.759 H12 00-05 0.556 H13 00-05 0.905 H14 00-05 0.625 H15 83-91 0.700 91-05 0.732 83-05 0.800 H16 83-91 0.556 91-05 0.684 83-05 0.438 H17 87-98 0.636 98-05 0.545 87-05 0.524 H18 91-05 0.619 H19 83-96 0.810 96-05 0.750 83-05 0.682 H20 00-05 0.536 H21 94-05 0.500 H22 99-05 0.759 H23 94-05 0.781 H24 00-05 0.786 H25 00-05 0.786

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Appendix E- Figures displaying drought conditions for 2005 sampling

The following images display short-term drought conditions as noted by the Palmer Z index. Figures were obtained from: http://lwf.ncdc.noaa.gov/oa/climate/research/prelim/drought/zimage.html

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