WHEN IS ECOLOGICAL RESTORATION A SUCCESS? A COMPARISON OF
MACROINVERTEBRATE DIVERSITY AND ABUNDANCE IN IMPAIRED,
UNIMPAIRED, AND RESTORED STREAMS IN SOUTHEAST OHIO
______
A Thesis Presented to
Honors Tutorial College
Ohio University
______
In Partial Fulfillment
of the Requirements for Graduation
from the Honors Tutorial College
with the degree of
Bachelor of Science in Biological Sciences
______
By
Austin R. Miles
April 2016
This thesis has been approved by
The Honors Tutorial College and the Department of Biological Sciences
______
Dr. Kelly Johnson
Professor, Biological Sciences
Thesis Advisor
______
Dr. Soichi Tanda
Honors Tutorial College, Director of Studies
Biological Sciences
______
Jeremy Webster
Dean, Honors Tutorial College
1
Table of Contents
List of Figures……………………………………………………………………...... 3
List of Tables…………………………………………………………………………...3
Acknowledgements…………………………………………………………………….4
Abstract………………………………………………………………………………...5
1. Introduction……………………………………………………………………...... 6
1.1 Origins of Acid Mine Drainage…………………………………………….6
1.2 Biological Effects of AMD………………………………………………...8
1.3 Restoration Background and Principles…………………………………..10
1.4 Efforts to Restore AMD Impaired Streams……………………………….12
1.5 Problems with Stream Restoration Efforts………………………………..15
1.6 Background on Biodiversity and Ecological Function………………...... 16
1.7 Food Webs and Ecosystem Functioning………………………………….20
1.8 Acid Mine Restoration Practices………………………………………….21
1.9 Post-Remediation Management and Efficacy of Remediation…………...23
1.10 Background on the Study Watersheds………………………………..27
i. Sunday Creek……………………………………………………..27
ii. Monday Creek…………………………………………………….30
iii. Raccoon Creek…………………………………………………....32
1.11 Objectives……………………………………………………………..34
1.12 Significance…………………………………………………………...35
2. Methods…………………………………………………………………………...35
2.1 Study Sites……………………………………………………………...... 35 2
2.2 Field Work……………………………………………………………….36
2.3 Statistical Analyses………………………………………………………39
3. Results…………………………………………………………………………….40
3.1 Structural Metrics and Community Data………………………………...40
3.2 Functional Metrics……………………………………………………….46
4. Discussion………………………………………………………………………...51
4.1 Structural Metrics………………………………………………………..52
4.2 Functional Metrics……………………………………………………….59
5. Conclusions……………………………………………………………………….62
Bibliography…………………………………………………………………………..64
Appendix……………………………………………………………………………...69
3
List of Figures
Figure 1. An example of a stream impaired by acid mine drainage……………………...7
Figure 2. Examples of EPT taxa……………………………………………………………..9
Figure 3. Map of each study watershed’s location in Ohio……………………………...27
Figure 4. Structural metric boxplots……………………………………………………….42
Figure 5. NMDS ordination made using structural metrics……………………………...43
Figure 6. NMDS ordination made using raw community data………………………….44
Figure 7. Functional metric boxplots………………………………………………………48
Figure 8. NMDS ordination made using functional metrics…………………………….50
List of Tables
Table 1. Descriptive statistics of structural metrics………………………………………41
Table 2. ANOVA output of structural metrics……………………………………………41
Table 3. Strength of correlations for structural metric ordination………………………44
Table 4. MRPP analysis of structural metrics…………………………………………….45
Table 5. MRPP analysis of raw community data………………………………………...45
Table 6. Descriptive statistics of functional metrics……………………………………...46
Table 7. Descriptive statistics of functional metrics……………………………………...47
Table 8. ANOVA output of functional metrics…………………………………………...49
Table 9. Strength of correlations for functional metric ordination……………………...50
Table 10. MRPP analysis of functional metrics…………………………………………..51
4
Acknowledgements
This study would not be possible without the guidance and encouragement of
Dr. Kelly Johnson, and I would like to express my sincere gratitude for her assistance.
I would also like to thank Dr. Soichi Tanda for his continued support. I am appreciative to the Ohio University Honors Tutorial College for providing me with numerous academic and research opportunities during my undergraduate career.
This study would also not be possible without the MAIS sampling data collected during the summer of 2014. I would like to thank Rural Action and the
Monday Creek Restoration Project and the Sunday Creek Restoration Project, as well as the Raccoon Creek Partnership for carrying out this work. I would like to thank in particular the various coordinators and members of these organizations, including
Michelle Shively, Nate Schlater, Tim Ferrell, Amy Mackey, and Sarah Landers.
Without their work and the help of numerous volunteers each summer the MAIS sampling would not be possible. I would also like to thank Jen Bowman for her work creating and maintaining watersheddata.com, which has been an incredibly helpful resource throughout my time working on this thesis.
I am also grateful for funding provided by the Jeanette G. Grasselli Brown
Undergraduate Research Award, without which I would not be able to have the opportunity to present my research at the International Society for Ecological
Monitoring conference this May.
5
Abstract
In streams impaired by acid mine drainage (AMD), restoration usually improves water quality more rapidly than stream biota. Biological recovery of fish and macroinvertebrates may lag behind, sometimes taking up to five years. Our goal in this study was to compare several measures of diversity in streams categorized in different stages of macroinvertebrate recovery to assess the extent to which what we call restored sites actually are restored. Twenty-four sites in 4 watersheds in southeast
Ohio were categorized as impaired, improved, restored, or unimpaired based on historical water chemistry and macroinvertebrate multimetric index scores. We calculated 7 structural metrics including macroinvertebrate abundance, taxa richness, percent composition of Ephemeroptera, Plecoptera, and Trichoptera (% EPT), and the
Simpson, Shannon-Weiner, Margalef, and Brillouin diversity indices for each site.
Using the classification of each macroinvertebrate family into one of five functional feeding groups, we also calculated functional metrics including the number of taxa belonging to each functional group at each site, and the abundance of individuals belonging to each functional group at each site. ANOVAs were used to compare the calculated scores of each metric among site categories, as well as non-metric multidimensional scaling (NMDS) ordinations and multi-response permutation procedure (MRPP) analyses. Amongst all metrics analyzed, three patterns emerged—a gradient of improvement from impaired sites on up to unimpaired sites, increased variability of impaired and improved sites relative to unimpaired and restored sites, and consistent similarity between unimpaired and restored sites. Overall these patterns 6 suggest that restoration efforts in streams within these watersheds are successful in recovering degraded ecosystems.
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1. Introduction
1.1 Origins of Acid Mine Drainage
Rivers and streams are an integral component of the world’s environment.
They play an important role in the water cycle and in the flux of minerals and nutrients down from the mountains and eventually to the ocean. They also contain a considerable number of species and habitat, which include some of the most threatened on Earth. Moreover, they provide humans with clean drinking water, harvestable plants and animals, navigable routes, waste removal, and renewable energy. Unfortunately, they are threatened by a number of disturbances, many anthropogenic in nature (Allan and Flecker 1993).
Acid mine drainage, or AMD, is such an anthropogenic disturbance. It is a widespread and tenacious problem for streams in areas around the world that have been subject to mineral extraction. Following the end of mining operations AMD may continue to leach out for thousands of years. In Europe, for instance, AMD still occurs due to mines in dug by ancient Romans before 476 BCE (Edmonds and Peplow 2000).
In the United States alone it is estimated that 500,000 inactive or abandoned mines exist in 32 states (Edmonds and Peplow 2000). These mines are all a potential source for AMD, and have impacted over 16,900 km of streams in the United States (Herlihy et al. 1990).
In Appalachia abandoned coal mines are widespread, a pattern that may be traced back to the United State’s dependency on coal. In the U.S. coal has been the primary source of electricity for 60 years, and much of American coal production has come from Appalachia, including parts of Ohio (EIA 2013). During the economic 8 downturn following World War I, Ohio’s coal production declined because of a lack of demand. As a result, many mines in the Southeastern Ohio region closed down during the 1920’s. By the 1940’s most mines in the area had been abandoned. These abandoned mines have in turn contaminated a number of streams with AMD. The number of abandoned mines in Appalachia is estimated to range from 3000-5000.
Because of AMD leaching out from these abandoned mines in the region, it is estimated that AMD has impacted 7000 to 13000 km of streams in the Appalachian region (USDA Forest Service 1993; Hill et al.; Herlihy et al 1990).
Figure 1. A stream reach impacted by AMD. Metal hydroxide precipitates give the stream its orange color.
9
AMD occurs when pyrite-rich rock or coal is exposed to rain and air as a result of mining operations (Hoffert 1946). Exposure of pyrite to the elements results in oxidation of pyrite, which in turn produces the acidic, sulfate and heavy metal-rich water characteristic of AMD. Pyrite oxidation involves four steps: pyritic sulfur oxidation, oxidation of ferrous to ferric iron, iron hydrolysis, and further pyrite oxidation by ferric iron. Oxidation of pyrite produces sulfuric acid. The sulfuric acid then mixes with stream water, and this acidified water then leaches metals out of rocks surrounding the stream, increasing heavy metal concentrations in impaired streams.
(Singer and Stumm, 1970).
1.2 Biological Effects of AMD
Widespread impairment due to AMD is a serious problem for these streams, and causes significant loss of biodiversity, simplified food webs, and altered key ecosystem processes such as primary production and organic matter breakdown
(Hogsden 2012, Bott et al. 2012). These effects are caused by either the direct toxicity of the acidity and dissolved metals or the physical effects due to metal and sulfur hydroxide precipitates on the substrata in less acidic reaches of impaired streams (Dsa et al. 2008; Johnson et al. 2014). AMD generally reduces macroinvertebrate diversity and abundance, and especially impacts acid-sensitive taxa within the orders
Ephemeroptera, Plecoptera, and Trichoptera (EPT) (Johnson et al. 2014).
10
A
B
C
Figure 2: Photos of insects from the orders Ephemeroptera (A), Plecoptera (B), and Trichoptera (C). Photos taken from Discoverlife.org, Wikipedia.org and the Royal British Colombia Museum website.
A 11
1.3 Restoration Background and Principles
Degradation of streams such as that caused by AMD requires restoration efforts informed by principles from ecology in order to return the degraded ecosystem to a desirable state. The field that tackles this issue, called restoration ecology, involves management practices that aid in the recovery of a degraded ecosystem with respect to its health, stability, and sustainability (Wright et al. 2009). Restoration has recently come into the spotlight of international policy and has a growing role in environmental policy, in part because the growing recognition that sustaining healthy ecosystems is crucial for the socio-economic stability of human populations across the globe.
In September 2014 governments rallied around an international agreement called the New York Declaration of Forests, which emphasized the importance of the restoration of degraded ecosystems as part of the solution to climate change. Multiple countries, including Ethiopia, Uganda, Guatemala, and Columbia pledged to restore large areas of forest within their borders. In total, all parties pledged to restore a total of 135,000 square miles of forest by 2030. Similar goals were made at the 2010 Aichi
Convention on Biological Diversity, in which parties involved pledged to restore at least 15% of degraded ecosystems around the globe, and at the 2011 Bonn Challenge, which involves the restoration of 580,000 square miles of degraded ecosystem.
(Suding et al. 2015). These pledges demonstrate the importance of restoration biology in the realm of international environmental policy.
Suding et al. 2015 recommends the consideration of four principles when planning restoration. First, restoration must increase ecological integrity. Integrity in 12 this case refers to the complexity of biological assemblages, including species composition and the representation of all functional groups, as well as the habitat and conditions necessary to support these species and the function they provide. Second, restoration must be sustainable in the long term. Therefore, in order for restoration to be successful it must result in an ecosystem that is self-sustaining, resilient, and needs minimal human intervention. Suding et al. 2015 acknowledge, however, that some intervention may be necessary to imitate natural processes that the environment no longer provides, such as fires or invasive species removal or to support the traditional practices of local communities, which may also be an important part of the ecosystem in question. Third, restoration practices must be informed by both knowledge gained from past experiments and restoration efforts, and the changes that happen in the present that may further affect an ecosystem in the future. Fourth, restoration must benefit and engage society. While restoration of an ecosystem focuses on recovering biodiversity and ecological function for the sake of the inherent value of nature, restoration also provides multiple ecosystem services such as improved water quality, fertile soils, and drought and flood buffering, which all enhance human quality of life.
Therefore, it is important to consider how restoration will affect society and how one may engage people in the restoration process through direct participation.
The restoration of acid-mine impaired streams sometimes successfully follows these principles, sometimes not. Though restoration efforts have successfully increased biotic integrity of some streams according to biological indices informed by ecological theory, functional measures such as photosynthetic rate, biomass accumulation, nutrient cycling or organic matter breakdown are not as commonly 13 measured when perhaps they ought to be considered when evaluating restoration.
Some authors have called for integration of more measures of function to assess stream impairment and recovery (Ruiz-Jaen and Aide 2005), but they have been slow to be incorporated. Though the streams seem to benefit from restoration efforts, how the human communities near the streams benefit is unclear, and may need evaluation.
The sustainability of some restoration practices are questionable as well because they sometimes require far more than 'minimal' human intervention in addition to lots of funding. For example, dosers, a type of installation that remediates AMD-impaired streams by constantly releases an alkalinizing substance into the streams, require constant maintenance and monitoring. When they shut off, either due to equipment failures, lack of maintenance, or human error, the consequences for the ecosystems downstream can be devastating, and affected streams may take years to recover (Kruse et al. 2011).
1.4 Efforts to Restore AMD Impaired Streams
The Ohio Environmental Protection Agency uses five categories of
“Designated Aquatic Life Uses,” to describe the biological integrity of a stream
(Sunday Creek AMDAT 2003). These categories include Exceptional Warmwater
Habitat (EWH), Warm Water Habitat (WWH), Modified Warmwater Habitat (MWH),
Limited Resource Water (LRW), and Coldwater Habitat (CWH). EWH have the highest biological integrity. These streams are typically characterized by high biodiversity, including some rare, endangered, or sensitive organisms not usually found in other habitats. WWH is described as the typical warm water assemblage of 14 aquatic organisms in Ohio Streams, and is the principal restoration target for most remediation efforts in Southeast Ohio. MWH describes streams with extensive and irreversible physical modifications for which the criteria for WWH are not attainable.
LRW describes small streams and streams that have been impacted in such a way that the ecosystem cannot support a healthy biological assemblage. Many AMD impaired streams received such a designation in the past because it was thought that they were beyond restoration. CWH is specialized designation that describes waters that support assemblages of cold water organisms. However, this classification is not applicable to streams in Sunday, Monday, or Raccoon Creek, as they all contain mostly warm water habitat (Sunday Creek AMDAT 2003).
To determine a stream use designation, biological and water quality sampling is conducted to generate numerical scores for various multimetric indices such as the qualitative habitat evaluation index (QHEI), the index of biologic integrity (IBI), which evaluates fish, and the macroinvertebrate aggregate index for streams (MAIS).
The IBI and MAIS in particular quantify biological integrity. These indices are generated from 8-10 metrics, or measures of the biological community, including some species diversity and richness measures as well as tolerance measures and some functional feeding group measures (Smith and Voshell 1997). Restoration goals are in turn based on these scores. For instance, a MAIS score of 12 or above corresponds to a
WWH designation, while scores below 12 may correspond to MWH or LRW designation (Kinney 2007, Johnson 2009).
The Surface Mining Control and Reclamation Act of 1977, which established national standards to regulate the mining industry, has encouraged reclamation of 15 habitat impaired by acid mine drainage resulting from abandoned mines (Monday
Creek AMDAT). This act has resulted in many remediation efforts in streams impaired by AMD, however the efficacy of these restoration efforts is questionable.
For instance, in their study of AMD impacted streams in Pennsylvania, Bott et al.
(2012) found that remediation efforts did not increase macroinvertebrate abundance or diversity, except in the case of a few families of stoneflies, despite an apparent improvement in water quality. This result must signify a failure on the part of remediation efforts to improve biological quality of the impacted streams, yet the
MAIS score calculated from the macroinvertebrate data from these streams contradicted this conclusion (Bott et al. 2012). This contradiction between the density and metric-based assessments strongly suggests that some multimeric indices do not fully account for macroinvertebrate community structure and function, and overestimate stream conditions (Bott et al. 2012).
In the Sunday, Monday, and Raccoon Creek watersheds in southeast Ohio, similar concerns have been raised about the efficacy of restoration efforts because acid mine-impaired streams do not consistently respond to restoration efforts. While some streams have responded positively, with MAIS scores indicative of a healthy stream, others show no signs of improved biology despite improved chemistry. Given the streams’ inconsistent responses, it may be possible that current restoration methods are insufficient for complete restoration of the streams, or that current metrics used to evaluate restoration are insufficient to accurately characterize biological integrity.
While chemistry may be restored to a healthy state, and biology may appear restored as well as defined by an increase in biodiversity or multimetric index of biotic 16 integrity, it is possible that some functional components (e.g. food web characteristics and energy flows within the stream) are not similar to an unimpaired stream. This means that the stream should not be called completely restored, and that more accurate criteria are needed to gauge success of restoration efforts. My project seeks to characterize the level of restoration using indices aside from the MAIS score. By doing so we hope to find how similar or dissimilar the recovering sites are to unimpaired sites in terms of indices such as the Simpson or Shannon-Weiner diversity indices, and compare these scores to the MAIS scores of each site. If it is true that restoration efforts are insufficient or measures of restoration are inaccurate, then including more criteria for restoration may be useful to more accurately characterize and restore the impaired streams. Example of such recommendations to this end might include adding more measures of biodiversity along with MAIS as restoration targets, or raising the MAIS target itself to 14 or 15.
1.5 Problems with Stream Restoration Efforts
Watershed managers and restoration ecologists frequently argue that current restoration efforts are not sufficient to promote complete biological recovery. In a study of 15 restored urban streams in the Eastern Piedmont Ecoregion, in Maryland specifically, Stranko et al. (2011) found that restoration efforts have not resulted in an increase of fish or macroinvertebrate diversity to conditions that would be similar to an unimpaired stream, which suggests the need for a substantial change in restoration approach. In her review of watershed restoration, Palmer (2009) also concluded that restoration efforts have largely failed to improve biological conditions in impaired 17 ecosystems. She contends that restoration efforts have not been effective because ecological theory has had little influence in shaping these efforts. This disconnect between ecological principles and ecological restoration has often resulted in over- engineered solutions that usually fail. She stresses the need to incorporate these neglected ecological principles into planning of restoration efforts, and the need to further test certain concepts within the restoration ecology field that have little empirical support.
1.6 Background on Biodiversity and Ecological Function
Loss of biodiversity has implications for overall ecosystem functioning and ecosystem stability. Ecosystem stability refers to an ecosystems resistance to disturbance, resilience after disturbance, and its range of variability through time.
Resilience refers to the ability of an ecosystem to return to its original state after a disturbance, such as a storm event or a wild fire, while resistance is the ability of an ecosystem to remain unchanged when subjected to a disturbance (Gamfeldt et al.
2008).
The idea that ecosystem stability and biodiversity are intimately linked is expressed in the diversity-stability hypothesis, which states that as biodiversity increases, ecosystem stability increases as well. Many studies have supported this hypothesis (Wright et al. 2009, Tilman 1996, McGrady-Steed et al. 1997, Naeem and
Li 1997). Tilman (1996) in particular found in grassland ecosystems that increased plant diversity resulted in more stable communities not only during major disturbances such as extended drought, but also during year-to-year variations in climate. His study 18 also found that species rich communities returned to pre-disturbance conditions more quickly than species-poor communities. These results suggest a correlation between stability, resistance and resilience.
Resistance and resilience themselves are also considered important considerations for restoration because once an ecosystem that was previously impaired is resistant and resilient, it is self-perpetuating and needs no more interventions in order to be considered sustainable. Unfortunately this is not always the case, as some
AMD remediation solutions require active maintenance (e.g. constant alkaline addition through added limestone). Additionally, degraded ecosystems have their own negative resistance and resilience, and shifting them to the desired state may be difficult because tolerant taxa have already become established and returning taxa may have difficulty competing with them. Such patterns have been documented in the case of eutrophied lakes, or in the case of acid mine impaired streams (Lake 2013).
Peterson et al. (1998) cogently organized current hypotheses regarding the relationship between biodiversity, ecological stability, and resilience. They summarized four models that attempt to explain the relationship between biodiversity and stability: the species diversity model, the idiosyncratic model, the rivet model, and the drivers and passengers model. The species diversity model simply states that an ecosystem with higher biodiversity is more stable than an ecosystem with lower biodiversity. This model is the most simplistic of the four, and so fails to completely explain the relationship between biodiversity and stability.
The more complex idiosyncratic model states that the stability of an ecosystem depends idiosyncratically on which species are present. This model asserts that 19 ecosystem function is dependent on the ecological history of the region and the evolutionary history of interacting species, which results in ecosystems that are extremely variable. According to this model, the same species may have variable roles in different ecosystems because the distinct ecosystems may have evolved in different ways. However, this model is insufficient to explain why ecosystems with dissimilar species compositions can have similar ecological function. Ecological convergence in structure and function despite distinct species composition suggests that species are organized in functional groups, which the idiosyncratic model does not fully take into account. Both the rivets model and the drivers and passengers model do.
The rivets model compares the ecological function of a species to the rivets that attach a wing to a plane. If you lose some rivets, you will not lose the wing. This model proposes that the ecological functions of different species may overlap, creating redundancy, so that even if a species is removed, the function will persist because other species compensate for the loss of that species. In this model, ecological function will not be lost until all species are lost. This redundancy enables an ecosystem to persist in the face of disturbance, lending it resistance.
The drivers and passengers model incorporates the idea of species complementarity and ecological redundancy and expands upon it by proposing that ecological function is primarily driven by “driver” species or functional groups of such species. According to this model, a driver is a species that has a strong influence on ecological function, while a passenger species has relatively minor impact on ecological function. An example of a driver species would be ecological engineers such as beavers, or keystone species such as sea otters, that have strong interactions 20 with other species and play an important role in maintaining the structure of an ecosystem. Because the driver species have a stronger influence on ecological function, it is their presence or absence that determines an ecosystem’s stability.
Several studies have demonstrated the importance of diversity for key functions in stream ecosystems as well. Jonsson et al. (2001) found that leaf litter breakdown rates is strongly correlated with the diversity of shredder species, which are macroinvertebrates that feed by cutting and tearing organic matter. The importance of shredder diversity for leaf litter breakdown rates implies an importance for the resilience of these streams. Their findings support the species diversity model in particular. Cardinale et al. (2002) and Mykra et al. (2011) also found that biodiversity is important for stability in stream ecosystems. Cardinale et al. (2002) observed that an increase in diversity of Trichoptera (caddisflies) resulted in facilitative interactions, which enhanced individual feeding success. Mykra et al. (2011) found that compositional stability, which refers to the structural stability of the macroinvertebrate communities they analyzed, is positively correlated with species richness.
Gamfeldt et al. (2008) found that biodiversity is important for overall ecosystem functioning, and therefore stability, despite previous findings that species loss may not affect single functions. This finding means that while single functions, such as primary productivity or nutrient uptake may be unaffected by species loss because many species perform the same function (known as ecological redundancy), overall ecosystem function will be impaired because those same species may perform multiple functions or species complementarity encourages enhancement of certain functions, and their loss will impair those functions. Therefore, Gamfeldt et al. 21 asserted that previous papers have missed the point because they failed to take into account the possibility that one species may be important for many functions, and have used a single function as a proxy for overall ecosystem functioning.
The models and studies discussed above all demonstrate the importance of biodiversity to ecosystems such as the streams in southeast Ohio. Loss of biodiversity due to AMD may impact functional processes, such as nutrient processing, which would in turn impact overall stability of communities. Therefore, if restoration efforts fail to improve the biology of a stream and recover lost biodiversity, then the ecosystem may not be considered fully restored.
1.7 Food Webs and Ecosystem Functioning
AMD impaired streams typically have shorter, simplified food webs that can be traced to reduced biodiversity due to the differing stress tolerances of individual taxa. Specifically, loss of sensitive species, declines in community diversity, or loss of entire trophic levels may result in simplified food webs. Limiting the amount and efficiency of energy transfer to higher trophic levels restricts food chain length within the food webs, and fewer links connect species because fewer species are present. This combination of reduced species diversity and fewer connections among species, combined with the limited energy flow to higher trophic levels as a result of loss of species due to AMD, results in the shorter simplified food webs (Hogsden 2012).
Food web structure, like biodiversity, is also intimately linked with ecosystem stability. Food web properties such as network complexity, as well as the strength of trophic interactions both influence stability (Layer et al. 2011). Network complexity 22 refers to the number of interactions in a food web, while the strength of trophic interactions refers to the strength of the interactions between different trophic levels such as producers and consumers.
Food web structure may also be linked with ecosystem functioning. Hogsden et al. (2011) found that acidified streams in New Zealand tended to have simpler food webs, with fewer species and links than that of a healthy food web. These simplified food webs had fewer species to process leaf litter and transfer detritus, thus AMD had disrupted the mechanisms responsible for breakdown as well as the links for energy flow between trophic levels. In contrast, faster breakdown rates were maintained in ecosystems with larger, more complex food webs.
1.8 Acid Mine Restoration Practices
In streams threatened by AMD, management options may involve prevention or remediation. In the case of prevention, the goal of techniques are to reduce or eliminate the reactions that generate the AMD in the first place. Prevention measures may include flooding abandoned mines or storing mine tailings underwater, which prevents oxygen from reacting from pyrite in the rock and forming sulfuric acid, which in turn results in AMD. Unfortunately, prevention measures are not always pragmatic. For instance, the flooding of an abandoned mine in order to prevent AMD is only effective when the location of all shafts and entrances to the mine are known and when the influx of oxygen-containing water does not occur. (Johnson and
Hallberg 2005). 23
Many of the streams in Sunday Creek, Monday Creek and Raccoon Creek have been impaired by AMD for over 50 years, sometimes severely. In order to attempt to restore these impaired ecosystems, these watersheds have all been the focus of remediation efforts since the 1990s. Remediation techniques aim to restore a stream to unimpaired conditions after impairement by AMD. These techniques may be further classified as active or passive strategies. Active remediation techniques require constant upkeep in order to function while passive remediation techniques only require installation and function without further upkeep thereafter (Johnson and Hallberg
2005).
Passive remediation techniques include successive alkalinating ponds, limestone channels, steel slag beds, while active techniques include lime dosers. Once installed these remediation projects release sodium bicarbonate, calcium carbonate or calcium oxides, to add alkalinity to acid-impaired streams. Doing so raises pH, which in turn precipitate metals out of the water as hydroxides or carbonates (Johnson and
Hallberg 2005). These precipitates may form a sludge that has some toxic effects for the stream biota and also may ruin stream habitat. Often precipitated metals form a
‘sacrificial zone’ in the stream reach downstream of the site of alkaline addition. In this zone, it is recognized and accepted that biological communities may be impaired despite acceptable water quality (Johnson and Hallberg 2005, Johnson et al 2014). In addition to the installations that add alkalinity to the streams, pyritic mine waste piles
–or gob piles– in Sunday, Monday and Raccoon Creek were capped, stream flow was diverted away from the mines, and seeps releasing acid from the mines were plugged 24 in order to prevent further input of AMD from the streams (Bowman and Johnson,
2014 NPS Report, Johnson et al. 2014).
Examples of two common types of remediation techniques. Above: a lime doser. Below: a steel slag bed. (Photos taken from the 2014 NPS report)
25
Images of a gob pile capping project. The photo on top shows the gob pile pre- construction and the photo below shows the end result of the project. (Photos taken from the 2014 NPS report)
1.9 Post-Remediation Management and Efficacy of Remediation
Extensive chemical and biological monitoring has accompanied remediation projects installed since 2000 in the Sunday, Monday, and Raccoon Creek watersheds.
Approximately 40 sites in these three watersheds have been monitored with quarterly water chemistry samples for pH, alkalinity, acidity, conductivity, iron, sulfur, aluminum, and other metals. A certified environmental lab conducts these chemical 26 analyses. In the summers, watershed restoration groups, such as the Raccoon Creek
Restoration Project, sample macroinvertebrates in order to generate a MAIS score, which is a broad aggregate multimeric index for assessing community health that meets credible data standards (Johnson et al. 2009). The Ohio Department of Natural resources or Ohio EPA also samples fish, however, fish sampling takes place in fewer streams than macroinvertebrate sampling because it only takes place in larger streams, and it is not done on a yearly basis. All data from sampling is entered into www.watersheddata.com.
Effects of remediation projects in these watersheds has varied. The orange precipitate created by increased pH downstream of alkalinity-releasing abiotic installations in particular has been a topic of concern. However, it has been found that when sediment from an AMD-impaired stream is transplanted to a healthy stream, macroinvertebrates still colonize the sediment, despite its retention of heavy metals, suggesting that the water, not the sediment, is toxic in AMD (D’sa et al. 2008;
Battaglia et al. 2005). This finding may suggest that impairments other than AMD itself may have contributed to the decreased biological quality in the sacrificial zones downstream of remediation projects, such as lack of nutrient (phosphate) availability, which can be an indirect effect of AMD (Bott et al. 2012), and of addition of alkalinity as well (Johnson et al. 2014). It is also possible that episodic pulses of AMD may be limiting recovery in impaired streams, rather than the orange precipitate generated by alkaline input (Johnson et al. 2014; MacCauseland and McTammany 2007).
In contrast, it has also in some cases been found that in AMD-impaired streams other metal-hydroxide precipitates have a more profound effect than water quality on 27 stream biota aside from macroinvertebrates, such as fish and periphyton (Cannon and
Kimmel, 1992; Kruse et al. 2013). Based on these findings, one may argue that that
AMD remediation must expand beyond its focus on increasing alkalinity and pH because ecological recovery of a stream relies on more than just the alkalinity and acidity of the stream in question (Cannon and Kimmel, 1992; Bott et al. 2012).
Remediation efforts should consider the watershed as a whole (Kruse et al. 2013;
Mclurg et al. 2007). Doing so may increase ecosystem connectivity, reduce sizes of the mixing zones, and promote recolonization of remediated streams (Mclurg et al
2007). Remediation efforts must also take into account physical parameters such as areas of deposition and erosion along the stream, which play a role in metal precipitation in these streams. Without consideration of these factors, a stream cannot be completely recovered biologically (Kruse et al. 2013).
Indeed, the addition of some alkaline neutralizing compound such as lime into the stream has yielded questionable results (Keener and Sharpe 2005, LeFevre and
Sharpe 2002, McClurg et al. 2007, and Simmons 2005, Johnson et al. 2014). Although these alkaline inputs improve water quality, macroinvertebrate diversity does not increase with treatment (Keener and Sharpe 2002, LeFevre and Sharpe 2002, Mclurg et al. 2007)
Simmons (2005) examined ecosystem function as well as diversity, and found that in terms of leaf decomposition and macroinvertebrate diversity and density, treated streams were no different from impaired streams. These results further strengthen the assertion that current restoration practices are insufficient for complete recovery of the streams. Johnson et al. (2014) found similar patterns of incomplete 28 recovery downstream of an alkaline doser. They found possible indication of limiting factors to recovery that would explain this problem, namely additional episodes of
AMD. Such acidification events may be common in AMD impaired streams, especially in late summer when stream flow and rainfall are lowest, when a treatment system fails, or when AMD discharge occurs simultaneously with rain events
(Johnson et al. 2014).
Many studies emphasize the need to wait in order to find out if remediation efforts are a success, as complete restoration may take years (Chadwick et al. 1986,
Clayton and Menendez 1996, Herricks 1977). Clayton and Menendez (1996), for instance, found that after four years of treatment the overall density of macroinvertebrate did not increase in treated streams relative to untreated control streams, though some acid-sensitive taxa were able to recolonize the treated reach due to improved water quality. This result mirrors that of the papers discussed above, which all indicate incomplete recovery. However, their data does suggest that recovery may take place with continued treatment in the future.
Herrick (1977) supports this conjecture, asserting that chemical conditions in an impaired stream may rebound quickly following remediation efforts, however the recovery of the ecology of the stream may take decades. The results of Chadwick et al.
(1986) further support this conjecture. They monitored a moderately sized, third to fourth order stream in Montana impacted by AMD discharged from an abandoned mine. The study began in 1972, after the installation of a new wastewater treatment system, and ended in 1983. They did not find any macroinvertebrates in any part of the stream until 1975, and in some parts of the stream they did not find macroinvertebrates 29 until 1981, nearly ten years after remediation efforts had begun. In 1982, after ten years of good water quality, the macroinvertebrate communities still showed indications of stress. The authors attributed this delayed recovery to the lack of a reliable upstream source of colonizers, implying a need for connectivity in these streams when dealing with AMD, which also supports the assertion of Kruse et al.
(2013) who emphasized the need to deal with the watershed as a whole rather than focusing on individual stream reaches. These results also demonstrate that recovery of an impaired stream may take up to a decade, rather than a few years, something that is important to consider when discussing the recovery of the streams in the Sunday,
Monday, and Raccoon Creek watersheds.
Figure 3. A map of the location of the Sunday, Monday, and Raccoon Creek watersheds in Ohio. Map reprinted from the Voinovich School of Leadership and Public Affairs, OU.
30
In the case of streams that seem to show improved water quality but impaired biology it is also possible that though periodic monitoring indicates healthy water quality, acute disturbances that may not be detected by current monitoring methods may still limit biological recovery. Johnson et al. (2014) hypothesized that episodic pulses of AMD may have been limiting biological recovery in their test stream.
Cravotta et al. (2010) also thought these acute disturbances may be an undetected problem in recovering streams, and advocated for continuous monitoring rather than quarterly or monthly monitoring. They argued that periodic monitoring is good for characterizing long-term trends in the streams, however, is not useful for characterizing acute variations in water quality, such as an episodic pulse. To this end, they argue for automated samplers or continuous water quality and stream flow monitoring methods.
1.10 Background on the Study Watersheds
i. Sunday Creek
The Sunday Creek Watershed originates in the southern portion of Perry
County, and The Sunday Creek Watershed originates in the southern portion of Perry
County, and drains into the Hocking River in Athens County. There are eight main tributaries of Sunday Creek: West Branch, East Branch, Big Bailey Run, Jackson Run,
Greens Run, Congress Run, Dotson Creek, and Eighteen Run. Both the East and West
Branch have named tributaries within each sub-watershed. There are a total of 14 named tributaries in the watershed and the entire watershed drains 139 square miles
(Sunday Creek AMDAT 2003). 31
Much of Sunday Creek has been mined extensively. Seventy-eight percent of the watershed is mixed temperate forest, which consists of a diverse assemblage of tree species. This tree assemblage includes such species as beech (Fagus grandifolia), silver and red maple (Acer spp.), white, red, and chestnut oaks (Quercus spp.), and tulip poplar (Liriodendron tulipifera). Land cover in the watershed is also composed of 17 % agriculture, 2.4 % brush, 1 % urban, 0.3 % barren (e.g. mines, quarries, sand/gravel pits), and 0.2 % non-forested wetland. Burr Oak Lake and other open waters or reservoirs make up 1% of the total watershed area. Coal mining, both surface and subsurface, has occurred in approximately 39% of the Sunday Creek basin. Today, small strip mines and deep mines continue to operate. For example, in
Glouster, the Buckingham Coal Company began operation in May 2000, with a contract to mine 5.1 million tons over five years. Current mining practices are not seriously impairing water quality. AMD is primarily a legacy effect from older mining practices (Sunday Creek AMDAT 2003).
An Ohio EPA report in 2000 designated the mainstem of Sunday Creek from
Glouster to the mouth was as Limited Resource Water (LRW), which is the designation for water with the lowest biological integrity. Coal mining was indicated as an impact to water quality in Sunday Creek. Similarly, the 1997 Ohio EPA Non- point Source Hydrologic Unit Water Quality Report estimated that the entire mainstem of Sunday Creek and the West Branch should have a water quality designation of
LRW. Again, coal mining was listed as a known or suspected impact. The East Branch of Sunday Creek was the only sub-watershed to be recommended as Exceptional
Warmwater Habitat (EWH). The mouth of Sunday Creek, as well as the mainstem, 32
Pine Run, Congo Run, Mud Fork, and Big Bailey creek have all been impacted by
AMD, sometimes severely, according to historical EPA and USGS reports. Cedar
Run, San Toy Creek, East Branch, Johnson Run, Indian Run, Hemlock Run, Eighteen
Run, and Dotson Creek have had no impairment from coal mining. The ultimate goal of remediation efforts within the Sunday Creek Watershed is to restore the AMD impacted areas throughout the watershed to the range of conditions that meets the requirements for designation as WWH wherever possible, or at the very least to bring the watershed to meet its potential aquatic designated life use (Sunday Creek AMDAT
2003).
Since 2004 remediation efforts have attempted to improve stream conditions in
Sunday Creek by sealing off sources of mine drainage and installation of alkaline input systems such as limestone leach beds, steel slag beds, and a lime doser. Projects sealing off sources of mine drainage as well as stream channelization to divert water flow from sources of AMD have taken place in Congo Run, Pine Run, Rodger’s
Hollow, Little Hocking, and West Branch. Limestone channels have been added to
Rodger’s Hollow, West Branch, Pine Run, and in 2013 a doser was installed in Pine
Run. As a result of these remediation efforts, the MAIS scores of many sites have improved, some consistently scoring above 12 within the last four years. (Bowman and Johnson, 2014 NPS Report).
ii. Monday Creek
The Monday Creek Watershed’s northern most boundaries are located in southern Perry County, while the western portion is located in eastern Hocking 33
County. The watershed drains into the Hocking River just south of Nelsonville. The two major tributaries in the watershed are Little Monday Creek and Snow Fork.
Monday Creek drains 116-square mile area in total. The Wayne National Forest currently owns and manages approximately 42 % of the land in the watershed. Sunday
Creek Coal Company is the second largest landowner in the watershed, with approximately 8.5 % of the land (Monday Creek AMDAT 2005).
The vegetation of Monday Creek Watershed is similar to that of Sunday Creek.
The watershed’s forest assemblage includes tulip poplar, beech, silver and red maple, white, red and chestnut oaks, as well as white, pitch and Virginia pine. Primary land cover categories consist of 87% forest, 4% mining, 3% cropland, 1% pasture, 2% wetlands, 1% grazing, and 1% urban (Monday Creek AMDAT 2005).
Historically, the first reported coal mining operations in the area began in Perry
County in 1816, followed by Athens County in 1820 and Hocking County in 1840.
However, coal mining did not become a major industry in the Monday Creek
Watershed until the mid 1800s. followed by Athens County in 1820 and Hocking
County in 1840. However, coal mining did not become a major industry in the
Monday Creek Watershed until the mid 1800s. By the late 1960s, however, nearly all of the mining in the watershed had come to an end. Underground mining operations ceased in 1972 in the Athens and Hocking County portions of the watershed, however mining operations did not cease in the Perry County portion of the watershed until
1991. Because of mining operations in the area, there are approximately 14,797 acres of underground mines and 3,172 acres of surface mines within the Monday Creek
Watershed (Monday Creek AMDAT 2005). 34
In order to facilitate remediation of the watershed, the Monday Creek
Restoration Project was formed in 1994. The severe impairment of the watershed has been reported for a fairly long time. For instance, a 1985 USDA study stated ranked the Monday Creek Watershed as 11th in severity for environmental damage among the
30 most severely impacted watersheds in southeast Ohio, and 3rd in terms of impact due to AMD. The Monday Creek Restoration Project seeks to reverse this trend of impairment and restore the Monday Creek mainstem to Warmwater Habitat use designation by installing AMD remediation projects within the most severely impacted areas within the watershed (Monday Creek AMDAT 2005).
Remediation efforts in Monday Creek first began in 1999. In order to stop sources of AMD, gob piles have been capped in Rock Run and Grimmett Hollow.
Subsidence holes were closed in Snake Hollow and Lost Run, and limestone leaching beds were installed in Grimmett Hollow, Big Four Hollow, Snake Hollow, Rock Run,
Lost Run, Coe Hollow, and Big Four Hollow. Lime dosers were installed in Job’s
Hollow and Essex in 2004 and 2006, and a steel slag bed was installed in Shawnee
(Bowman and Johnson 2014 NPS Report). Because of these remediation efforts, the majority of monitored sites in the Monday Creek watershed have shown increased
MAIS scores, with some consistently earning scores above 12 within the last three to four years (Bowman and Johnson 2014 NPS Reports).
iii. Raccoon Creek
Raccoon Creek flows 108 miles from the confluence of the East and West
Branch Raccoon Creek flows 108 miles from the confluence of the East and West 35
Branch tributaries near the town of New Plymouth in Vinton County to the Ohio
River. The Watershed basin covers 200 square miles and drains 683.5 square miles, including area in Athens, Hocking, Vinton, Jackson, Meigs and Gallia counties.
Within the watershed, the two tributaries of particular interest for my study are Hewett
Fork, which is in the Raccoon Creek Headwaters and drains 40.5 square miles, and
Little Racoon Creek, which drains 155 square miles (Raccoon Creek Headwaters
AMDAT 2002, Little Raccoon Creek AMDAT 2000).
Around seventy five percent of the entire Raccoon Creek Watershed is forested. The remaining land use is comprised of 4% cropland, 15% pastureland, 4% urban, 10% active or abandoned mines, and 1% for various other uses. Coal mining has taken place in the Raccoon Creek watershed since the 1840s and continues today.
Much of the mining was concentrated in the headwaters or upper reaches of the watershed. Using GIS, the USGS found that approximately 25,610 acres of underground mines and 21,550 acres of surface mining are present in the watershed. In
Raccoon Creek, AMD from abandoned underground and surface coalmines has severely degraded water quality and reduced the diversity and abundance of fish and macroinvertebrate populations (Raccoon Creek Headwaters AMDAT 2002, Little
Raccoon Creek AMDAT 2000).
Historically, Hewett Fork and Little Raccoon Creek have been two of the most degraded sections of Raccoon Creek. An ODNR study in 1982 ranked Hewett Fork as the stream most impacted by AMD. Little Raccoon Creek was the third most degraded. Other historical data and a report from 1983 found that among the primary sources of AMD affecting Raccoon Creek are Hewett Fork and Little Raccoon Creek. 36
Hewett Fork continues to contribute acidity, however its water quality is not as severely degraded as it once was. Similarly, Little Raccoon Creek still shows indications of impairment due to AMD, and this is reflected in its macroinvertebrate and fish communities, though the watershed has improved over the past 15 years, and some sites have little or no impairment due to AMD (Raccoon Creek Headwaters
AMDAT 2002, Little Raccoon Creek AMDAT 2000).
In Raccoon creek, remediation efforts first began in 1999. In order to stop sources of AMD, gob piles were capped in Buckeye Furnace, East Branch, and
Orland. Steel slag beds were installed in Mulga Run, Middleton Run, Lake Milton,
East Branch, and Pierce Run. Limestone beds were installed near State Route 124, in
Hope Clay, Middleton Run, East Branch, Orland, and Harble Griffith, while a doser as installed in Carbondale in 2004. As a result of these projects, MAIS scores in many sites within the watershed have improved, and some have maintained MAIS scores above 12 for the past four years (Johnson and Bowman 2014 NPS Report).
1.11 Objectives
The goal of this study is to characterize the diversity and abundance of macroinvertebrate found in streams at different stages of restoration. We aim to compare ‘restored streams’, which have met the target criteria in terms of an aggregate biotic index score (MAIS>12), to ‘unimpaired’ and ‘impaired/unrestored’ streams. A body of literature that compares differences in biodiversity and food web structure between impaired and unimpaired sites in other regions already exists. For instance,
Hogsden (2012) reviewed the effects of AMD on food webs structure and biodiversity 37 and Hogsden (2013) investigated the relationship between leaf breakdown and food web structure in both impaired and unimpaired streams in New Zealand. However, we are unaware of any studies that provide detailed comparison of the macroinvertebrate communities of remediated streams to impaired, and unimpaired streams in eastern
North America. Therefore, this study will be the first that does so. Given the assertions that restoration efforts are often insufficient for complete restoration of a stream, we hypothesize that streams that meet chemical and multimetric (MAIS) targets may not actually be as similar to healthy streams in a biological sense, meaning that the macroinvertebrate communities in restored streams are not within the desirable range of conditions that one may find in unimpaired streams. Our analysis will provide a more detailed examination of the macroinvertebrate community structure in these three categories of streams.
1.12 Significance
By analyzing the macroinvertebrate sampling data collected in the summer of
2014, we hope to determine the extent to which what we call ‘restored’ streams are actually restored. Because streams may have improved water quality after remediation, but still have impacted macroinvertebrate communities, it is possible that remediation efforts in the watersheds in question may be in need of some reform.
Using indices aside from the MAIS score, such as the Shannon-Weiner, Margalef, and
Brillouin Diversity indices, may provide new perspectives on the similarity of macroinvertebrate biodiversity between restored and unimpaired streams, and allow us to determine how close restored streams are to a desirable range of conditions. Given 38 the reported limitations of the MAIS score in fully representing macroinvertebrate community structure and function, we expect to find that sites with a MAIS score that suggests they are recovered, may not actually be similar to unimpaired streams, and might better be described as ‘impaired’ or ‘improved’ or perhaps simply ‘recovering’.
We also expect to find that sensitive taxa, such mayflies, stoneflies and caddisflies, are the most difficult to restore after a stream is impaired, and that an abundant and diverse assemblage of macroinvertebrates from these taxa may be better indicators of restoration success.
2. Methods
2.1 Study Sites
We identified 32 study sites located in the Sunday Creek, Monday Creek, and
Raccoon Creek watersheds for which recent macroinvertebrate data are available from prior long-term monitoring efforts. Four sites are unimpaired by AMD, nine are impaired, seven are improved but not fully recovered, and six are recovered. Sites were placed into these categories based on their historical water chemistry and recent biological integrity (macroinvertebrate index— MAIS) scores from the past 5 years.
Unimpaired sites are defined as sites that have consistently exhibited MAIS scores well above 12, usually ranging from 14-16, and that have no history of AMD or other significant anthropogenic influence. The target score of 12 is used in our region as equivalent to attainment of Warm Water Habitat status (Johnson 2007). AMD impaired sites have a history of acidic and sulfur rich water chemistry and have MAIS scores are consistently below 12, with no detectable change in score for the past 5 39 years. Improved sites exhibit statistical improvement in MAIS scores when regressed against time (5-10 years) however, their scores usually are still consistently below 12.
Restored sites are similar to improved sites in that they have statistically improved over time, except they have consistently achieved MAIS scores that are as high as unimpaired sites (usually 14-16).
2.2 Field Work
Watershed groups and technical staff from Ohio University and various state agencies collected macroinvertebrates at the study sites from June to August in 2014 as part of their annual biomonitoring programs. Collection involved two methods: kick-net sampling and D-ring dip net sampling. The kick-net sampling technique is quantitative, and provides information on the diversity and relative abundance of macroinvertebrates in the riffle habitat. The dip net sampling technique is more qualitative, and provides data on many different habitats (e.g. root wads, leaf packs and pool bottoms). The dip net technique also yields a different assemblage of organisms than kick-net sampling, and better represents the full diversity of taxa at the site. Samples collected were preserved in 70% ethanol, identified to family level and enumerated (Johnson 2007). The MAIS scores used in this study, which are permanently documented at www.watersheddata.com, were generated using these samples.
The MAIS is a family-level bioassessment method useful for assessment of mine-impacted streams, such as Raccoon Creek, Monday Creek, and Sunday Creek.
MAIS scores are calculated based on nine metrics and can range from 0 to 18. Scores 40 below 12 are typical of impaired streams and scores around 14-16 are typical of unimpaired streams. The nine metrics used in calculation of MAIS scores include the
EPT index, the number of mayfly families present, the percent abundance of mayflies, the percent of the population made up by the five most dominant taxa combined, the
Simpson Diversity Index (which integrates richness and evenness), the Modified
Hilsenhoff Biotic Index (in which taxa are weighted by pollution tolerance), number of intolerant taxa, percent of the sample that are scrapers (macroinvertebrates that feed on periphyton), and percent of the sample that are haptobenthos (macroinvertebrates that require clean, coarse, firm substrates) (Smith and Voshell 1997, Johnson 2009). In addition to the overall MAIS scores, the community measures that were used in our analysis include taxa richness (family level) and abundance, the Shannon Weiner,
Simpson, Margalef, and Brillouin diversity indices, and family level diversity within the Ephemeroptera, Plecoptera and Trichoptera.
In addition to these taxonomic metrics, we also calculated functional metrics from the same data. In order to calculate these metrics we first classified the macroinvertebrates into five different functional feeding groups: grazers, shredders, predators, collector-gatherers, and collector-filterers. Descriptions of these feeding groups are found in Merritt and Cummins (1996). Macroinvertebrates that fall within the grazer functional group have morphological and behavioral adaptations for scraping off food, usually periphyton, that adheres to various surfaces. Periphyton refers to an algal and microbial community often associated with detritus attached to aquatic surfaces. Examples of dominant grazers in our study sites include riffle beetles
(Elmidae), flat-headed mayflies (Heptageniidae) and snails. 41
Shredders are those macroinvertebrates that are adapted to consume course particulate organic matter (CPOM), such as leaves or needles, that fall into streams from terrestrial trees and shrubs. As their name implies they have morphological features that allow them to shred CPOM, which they help convert to fine particulate organic matter (FPOM) after consumption and excretion in pellets. Examples of shredders common at our study sites include the Tipulidae (crane fly larvae), the
Leuctridae family of stoneflies and the Limnephilidae family of caddisflies.
Predators are the macroinvertebrates that are adapted for the capture of live prey. Examples include damselflies and dragonflies (Odonata), dobsonflies and alder flies (Megaloptera), and several beetle (Coleoptera) families. Some genera within the
Tipulidae (craneflies) are also predatory.
Collector-filterers and collector-gatherers, in contrast with shredders, are adapted to feed on FPOM (fine particulate matter). The difference between filtering and gathering collectors lies in the way they each acquire their food. Collector-filterers feed on FPOM suspended in the water, while collector-gatherers feed on deposited
FPOM. An example of a common collector-filterer is the Hydrosychidae, or net- spinning family of caddisfly, which constructs nets to filter food out of the stream current. Examples of collector-gatherers are the Caenidae and Isonychiidae families of mayflies.
The functional metrics we calculated included the number of grazer taxa found at each site, number of shredder taxa, number of predator taxa, number of collector filterer taxa, and the number of collector gatherer taxa, as well as the total abundance of each functional group at each site. We also used the raw community data to analyze 42 and compare the composition of the communities found at each site using calculations of similarity and ordination techniques (described below).
2.3 Statistical Analyses
Analysis of the raw community data and the taxonomic and functional metrics consisted of three primary approaches. The first was a series of Analysis of Variances
(ANOVAs) which were used to test for differences between the means of various metrics among the four different site categories: unimpaired, impaired, improved, and restored. Data that did not meet expectations of normality and homogeneity of variances were transformed to meet the assumptions of the ANOVA (see results for specific transformations). The second approach was the use of Nonmetric
Multidimensional Scaling (NMS) ordination of macroinvertebrate measures of diversity found at the five types of sites. Ordination is a technique that adapts a multi- dimensional mass of data points in such a way that when it is presented in a two- dimensional space any pattern within the data may become apparent. NMS ordination in particular is an ordination method without parameters. Ordination in itself does not test for significant differences, our objective was to use it to visualize differences in the macroinvertebrate communities found at the four different categories of sites.
After generating each ordination, we then calculated the strength of correlation of each metric for each axis. In addition, we used multiresolution permutation procedure (MRPP) to test for significant differences among the four site categories in terms of the taxonomic metrics and also in terms of the functional metrics. All statistical analyses were done using the programming language R (R core team 2013). 43
3. Results 3.1 Structural Metrics and Community Data
A total of 62 different macroinvertebrate taxa were collected at the study sites.
The means and standard errors of the MAIS scores separated our four site categories well, which suggests that our method of site categorization works. The first boxplot of column A in figure 4 compares MAIS scores between site categories. We also calculated macroinvertebrate abundance, taxa richness, % EPT, and the Simpson,
Shannon-Weiner, Margalef, and Brillouin indices at each site. Table 1 reports the means and standard errors calculated for each taxonomic metric within each site category.
Table 1. Mean and Standard Error of each taxonomic metric organized by site category.
Figure 4 contains the boxplots generated from the taxonomic metrics. The plots illustrate a general gradient of improvement in each metric from impaired to improved and finally to restored. Data met criteria for normality and equal variances and ANOVAs of taxonomic metrics revealed that MAIS scores, taxa richness, the
Shannon-Weiner Index, and the Brillouin Index were significantly different among site categories. 44
Table 2. ANOVA output comparing each taxonomic metric amongst site categories.
Metric df F value P value MAIS 3 23.9 <0.0001 Total abundance 3 1.36 0.284 Taxa Richness 3 5.96 0.00449 % EPT 3 2.31 0.107 Simpson 3 2.15 0.111 Shannon-Weiner 3 5.42 0.00681 Margalef 3 1.14 0.356 Brillouin 3 7.43 0.00156
From the Tukey’s post-hoc tests, we found that restored sites differed significantly from both impaired and improved sites in taxa richness. Restored sites and unimpaired sites both differed significantly from impaired sites in their Shannon-
Weiner and Brillouin Index scores and also in their MAIS scores. It is worth noting that all six measures of diversity indicated that restored sites are not different from unimpaired, though only three of those metrics are sensitive to impact from AMD
(taxa richness, Shannon-Weiner, and Brillouin). Table 2 contains the statistical output from the ANOVA’s of the taxonomic metrics, in which a P-value less than 0.05 is significant
45
A 20 a a b b B
900 15
600 MAIS 10 Abundances 300
5 0 Impaired Improved Restored Unimpaired Impaired Improved Restored Unimpaired
40 a a b ab
30 30 20
20 % EPT
Taxa Richness Taxa 10 10 0
Impaired Improved Restored Unimpaired Impaired Improved Restored Unimpaired
3.0 a ab b ab 0.90 0.85 2.5 0.80 Weiner Index Weiner
− 0.75 2.0 0.70 Shannon Simpson Diversity Index Simpson Diversity 1.5 0.65 Impaired Improved Restored Unimpaired Impaired Improved Restored Unimpaired
3.0 a ab b b 5
4 2.5
3 2.0 Brillouin Index Index Margalef 2 1.5 1 Impaired Improved Restored Unimpaired Impaired Improved Restored Unimpaired
Figure 4. Boxplots comparing the means and SE of each structural metric amongst each of the four site categories. In each plot, site categories with differing letters indicates significant difference. Absence of any letters indicates a lack of any difference.
46
Figure 5. NMDS ordination generated using structural metrics: MAIS, abundance, taxa richness, % EPT, and the Simpson, Shannon, Brillouin, and Margalef diversity indices. Ellipses represent 95% confidence. Stress of 0.0411, k = 3.
Chance-correlated within-group agreement (A), which measures between- group differences, was highest between unimpaired and impaired and improved sites as well. According to the MRPP analysis, unimpaired and restored sites were also significantly different, and the chance-correlated within-group agreement was nearly as high as it is between unimpaired and impaired and improved sites. The MRPP analysis of taxonomic metrics compared amongst site categories indicated that unimpaired and improved sites were the most different, as were unimpaired and improved sites (Table 4). 47
Figure 6. NMDS ordination of the presence/absence and abundance of each family of macroinvertebrates collected at each study site. Ellipses represent 95% confidence. Stress of 0.102, k = 2.
Table 3. Strength of correlations for the ordination generated using the taxonomic metrics. The columns MDS1 and MDS2, named after each axis of the ordination, contain the strength of correlation of each metric to its respective axis.
Metric MDS1 MDS2 MAIS -0.956 -0.294 Abundance -0.931 -0.365 Richness -0.958 -0.288 EPT -0.287 0.958 Simpson -0.441 0.897 Shannon -0.418 0.908 Margalef -0.0685 0.998 Brillouin -0.450 0.893 48
According to the MRPP analysis of community data, A was highest between unimpaired and impaired sites and restored and impaired sites. Unimpaired and impaired, unimpaired and improved, and restored and impaired were the only site categories significantly different.
Table 4. Output of a pairwise multi-resolution permutation procedure (MRPP) comparing the taxonomic metrics amongst the four site categories. A indicates the Agreement between categories, p the significance.
Categories A p Unimpaired vs. Impaired 0.219 0.0350 Unimpaired vs. Improved 0.275 0.0130 Unimpaired vs. Restored 0.199 0.0620 Impaired vs. Improved 0.0266 0.217 Impaired vs. Restored 0.180 0.0490 Improved vs. Restored 0.119 0.0330
Table 5. Output of a pairwise multi-resolution permutation procedure (MRPP) comparing the community data amongst the four site categories.
Categories A p Unimpaired vs. Impaired 0.125 0.00600 Unimpaired vs. Improved 0.0746 0.0280 Unimpaired vs. Restored 0.00991 0.284 Impaired vs. Improved -2.76*10-5 0.391 Impaired vs. Restored 0.106 0.0130 Improved vs. Restored 0.0328 0.0700
3.2 Functional Metrics
We calculated the number of grazer, shredder, predator, collector-filterer, and collector gatherer taxa found at each site, as well as the abundance of each functional group at each site. Tables 6 and 7 report the means and standard errors calculated for each functional metric within site categories. 49
Table 6. Means and standard errors of the functional metrics measuring the number of taxa within each functional group in the different site categories
Grazers Shredders Predators Collector-F. Collector- G. Impaired Mean 1.43 1.71 6.43 2.00 2.29 Std. Error 0.297 0.565 1.25 0.617 0.522 Improved Mean 1.43 2.57 8.00 3.00 3.00 Std. Error 0.369 0.369 1.25 0.756 0.488 Restored Mean 3.00 2.33 9.50 6.00 6.83 Std. Error 0.000 0.494 0.671 0.516 0.601 Unimpaired Mean 3.25 1.75 8.50 4.75 6.00 Std. Error 0.250 0.479 0.957 0.854 1.35
Table 7. Means and standard errors of the functional metrics measuring the abundances of individuals from each functional group in each site category. Grazers Shredders Predators Collector-F. Collector-G. Impaired Mean 15.9 7.14 25.4 124 43.4 Std. Error 6.74 3.92 5.99 58.1 16.4 Improved Mean 74.6 24.9 49.3 86.0 54.3 Std. Error 50.0 10.6 12.8 53.2 25.0 Restored Mean 98.5 8.50 64.7 103 94.8 Std. Error 12.2 3.66 4.69 30.7 30.5 Unimpaired Mean 140 40.7 67.7 208 85.0 Std. Error 20.2 31.3 13.0 40.0 17.5
Figure 7 contains the boxplots generated from the functional metrics. The plots generally illustrate a gradient of improvement in each metric from impaired to improved and finally to restored. ANOVAs of the functional metrics indicated strong effects of site category on most of the functional metrics calculated. Five did not differ 50 among site categories: the number of shredders and predators, and the abundances of shredders, collector-gatherers, and collector-filterers.
From the Tukey’s post-hoc tests, we found that the number of grazer taxa and the number of collector-gatherer taxa differed between recovered and unimpaired sites and impaired and improved sites. The number of shredder taxa and predators exhibited no difference between site category. The number of collector-filterer taxa differed between recovered sites and impaired sites as well as improved sites, but unimpaired sites did not differ significantly from any other site category. Both the abundance of grazers and the abundance of predators exhibited a difference between recovered and unimpaired sites and impaired sites. Table 8 contains the statistical output from the
ANOVA’s of the functional metrics, in which a P-value equal to or less than 0.05 is significant.
The NMS ordination generated using functional metrics, like the ordination made with taxonomic metrics, exhibited a gradient from impaired and improved sites, on to recovered and unimpaired sites, which occupied a somewhat similar space relative to the other two site categories (Figure 9). The ordination had a stress value of
0.0752, and was generated with 2 dimensions.
51
A a a b b B 4 6 3 4 2
2 # of Grazers 1
# of Shredders
0 0 Impaired Improved Recovered Unimpaired Impaired Improved Recovered Unimpaired
a a b ab 7.5 10 5.0
5 2.5 # of Predators
# of Collector Filterers 0.0 Impaired Improved Recovered Unimpaired Impaired Improved Recovered Unimpaired
10.0 a a b b 300 7.5 a ab b b 200 5.0 100 2.5 Grazer Abundances
# of Collector Gatherers 0 Impaired Improved Recovered Unimpaired Impaired Improved Recovered Unimpaired
150 a ab b b 100 100
50 50
Predator Abundances Abundances Shredder 0 0 Impaired Improved Recovered Unimpaired Impaired Improved Recovered Unimpaired
400 200 300 150 200 100 Filterer Abundances
Gatherer Abundances − 100 − 50 0 0 Impaired Improved Recovered Unimpaired Impaired Improved Recovered Unimpaired Collector Collector
Figure 7. Boxplots comparing means and SE’s of each functional metric amongst each of the four site categories. In each plot, site categories with differing letters indicates significant difference. Absence of any letters indicates a lack of any difference.
52
Table 8. The ANOVA output comparing each functional metric amongst site categories. Metrics include the number of taxa from each functional group in each site category and also the abundance of individuals from each functional group in each site category.
Metric df F value P value
# of Grazers 3 10.8 0.000199
# of Shredder 3 0.775 0.521
# of Predators 3 1.36 0.285
# of Collector-F. 3 7.02 0.00208
# of Collector-G. 3 11.1 0.000167
Abundance of Grazers 3 5.50 0.00640
Abundance of Shredders 3 1.42 0.267
Abundance of Predators 3 4.08 0.0205
Abundance of Collector-F. 3 1.26 0.314
Abundance of Collector-G. 3 1.62 0.217
Figure 8. NMDS ordination generated using functional metrics: number of grazer taxa, shredder taxa, predator taxa, collector filterer taxa, and collector gatherer taxa, as well as the abundances of each taxon. Stress of 0.0752, k = 2. 53
Every functional metric was strongly correlated with at least one axis (Table
8). MRPP analysis, reported in table 9, found that the only comparisons that yielded no difference were those between unimpaired and restored sites and impaired and improved sites. The highest chance-correlated within-group agreements were found between impaired sites and unimpaired sites, improved and unimpaired sites, and also between impaired and restored sites. Notably, the functional metrics showed no significant differences between restored and unimpaired sites.
Table 9. Strength of correlations for the ordination generated using the taxonomic metrics. The columns MDS1 and MDS2, named after each axis of the ordination, contain the strength of correlation of each metric to its respective axis.
Metric MDS1 MDS2 # Grazer 0.494 0.869 # Shredder 0.911 0.412 # Predator 0.359 -0.933 # Collector F. 0.434 -0.901 # Collector G. 0.971 0.241 Grazer Ab. 0.444 0.896 Shredder Ab. 0.688 0.726 Predator Ab. 0.996 0.0866 Collector F. Ab. 0.687 0.726 Collector G. Ab. 0.623 -0.782 MAIS 0.613 0.790
Table 10. Output of a pairwise multi-resolution permutation procedure (MRPP) comparing the taxonomic metrics amongst the four site categories.
Categories A p Unimpaired vs. Impaired 0.140 0.0260 Unimpaired vs. Improved 0.143 0.0160 Unimpaired vs. Restored 0.0458 0.130 Impaired vs. Improved 0.00853 0.266 Impaired vs. Restored 0.149 0.008 Improved vs. Restored 0.0874 0.0170 54
4. Discussion
Many have suggested that current restoration efforts are not enough for complete restoration of stream ecosystems. Sometimes the failure of restoration efforts may be attributed to a disconnect between ecological theory and the design of management practices meant to recover degraded ecosystems (Palmer 2009). This disconnect in turn leads to over-engineered solutions that fail because they ignore ecology. Other times this failure may be connected to the lack of recognition of the connection between biological diversity and ecological stability. Ecosystem stability refers to an ecosystem’s ability to remain the same over the course of time, even in the face of disturbance, such as a storm event or a wild fire. It is composed of two elements: resistance and resilience. Resistance is the ability of an ecosystem to remain unchanged when subjected to a disturbance, while resilience refers to the ability of an ecosystem to return to its original state after a disturbance.
The diversity-stability hypothesis, which argues that as biodiversity increases so too does ecological stability, is supported by much empirical evidence, including some studies done on stream macroinvertebrates (Jonsson et al. 2001; Cardinale et al.
2002; Mykra et al. 2011). But restoration efforts sometimes ignore the importance of biodiversity for ecosystem stability in favor of other targets that focus on physical parameters such as flow regime or water chemistry, presumably with the assumption that reaching these targets inevitably will lead to the recovery of degraded ecosystems.
Unfortunately, as streams within our study watersheds show, this assumption is not always the case. Restoration efforts within these watersheds focus on improving water chemistry, but while some streams have responded positively to these efforts, others 55 show no signs of recovered biology despite improved chemistry. This incongruence between the recovery of biological and chemical parameters may suggest that restoration methods are insufficient for complete restoration of degraded streams, or that the metrics used to evaluate restoration are insufficient for accurate characterization of biological integrity. The primary aim of the current study was to evaluate the current biological target for recovery, the MAIS macroinvertebrate index, as a tool for characterizing biological integrity. Our results suggest that the metric does provide a reasonable measure of biological recovery, and at some sites the AMD remediation efforts in the Sunday, Monday, and Raccoon Creek Watersheds performed over the past 5-10 years have restored streams impaired by acid mine drainage to a desirable state.
4.1 Structural Metrics
Our analysis compared the congruence of a variety of diversity metrics to sites that had been ranked by the multimetric biotic index, the MAIS. The MAIS offered reasonably good separation of the four site categories according to the ANOVA and
Tukey’s post-hoc analysis, although resolution was less clear at intermediately impacted sites. This separation demonstrated that our method of site categorization was effective. Further comparisons of the additional measures of diversity amongst the four site categories, which included total abundances, taxa richness, % EPT, and the
Simpson, Shannon-Weiner, Margalef, and Brillouin diversity indices, exhibited three general patterns. First, overall all measures of taxonomic diversity reflected a gradient of AMD impairment, in which improved sites supported higher diversity than impaired sites, and restored and unimpaired sites more than improved sites. Second, 56 and most importantly, restored and unimpaired site categories were never significantly different. All analyses also demonstrated higher variability of the impaired and improved site categories as opposed to the restored and unimpaired sites.
The variability within these site categories suggests that they may be less stable than ‘restored’ and ‘unimpaired’ sites, possibly as a result of lower levels of biodiversity, as the diversity-stability hypothesis would postulate. Increased instability as a result of decreased biodiversity in ‘impaired’ and ‘improved’ sites may be a possibility given that it has been demonstrated previously that stability is related to species richness in stream macroinvertebrate communities (Jonsson et al. 2001;
Cardinale et al. 2002; Mykra et al. 2011). However, we cannot say for certain if this is the case because our analysis only includes data from one year. Future investigation of multiple years of data could measure how much macroinvertebrate communities change from year to year would and more directly address the stability issue.
Virtually all of the metrics showed trends that followed the AMD impairment gradient, but some metrics appeared better suited to detect differences between impaired sites to restored or restored and unimpaired sites because they were more sensitive to AMD impairment. For instance, the abundance metric did not show significant differences between site categories. This might be explained by the possibility that in an impaired site just as many individuals may be collected at an impaired site as an unimpaired or restored site because some macroinvertebrate species are AMD-tolerant. Tolerant taxa can flourish and be numerically abundant under stressful conditions that eliminate sensitive taxa. Thus, abundance is not always a useful measure for evaluating the recovery of a stream. 57
The lack of significance between site categories in terms of % EPT was somewhat surprising, because this metric, which measures the percentage of taxa at each site that belongs to the Ephemeroptera, Plecoptera, or Trichoptera orders, is commonly used as an indication of stream health. This odd result might be explained by a couple of possibilities. First, in our study area there are several common and abundant EPT taxa that are relatively tolerant of AMD conditions, their presence drives up the % EPT in impaired and improved sites. Hydropsychids, for instance, were common in both impaired and improved sites in our study. Though this family of caddisflies is part of the EPT taxa and therefore should be sensitive to AMD, the hydropsychids are relatively tolerant to low water pH and because of this are often found at impaired and improved sites (Johnson et al. 2014). Similarly, many stoneflies are more tolerant of low pH than other stressors, such as warm temperatures, lack of riffle habitat, or low oxygen (Ledger and Hildrew 2001). A second possible explanation for high %EPT we observed at poor quality sites is at some of the EPT taxa present at improved are impaired sites are represented by just one individual that has drifted in from higher quality habitat upstream. Given this possibility, had we calculated % EPT using abundance of EPT taxa at each site rather than richness
(presence/absence) of EPT taxa, or simply calculated the number of EPT taxa at each site, we may have seen differences between site categories according to this metric.
Taxa richness exhibited the three general patterns discussed above, but there was an additional trend for unimpaired sites to not be different from impaired or improved sites, while restored sites were. This was somewhat surprising, because we expected restored sites to be intermediate between unimpaired and improving. 58
However, it is important to recognize that total richness is simply a count of how many total taxa were present, and does not account for which taxa are present or the relative abundance of each taxon at a particular site. Therefore, while a site may have many taxa, it still could not be considered diverse because one particular taxa dominates, while other taxa are only represented by one or two individuals. Thus, richness may be informative about the types of species that make up a community, but is not so helpful for evaluating the health of the community, so an impaired site that has as many species as an unimpaired site may still have an impaired and more simplified community.
The pattern seen amongst the various diversity indices is expected based on the results of Gray and Delaney (2008). Their study analyzed the ability of various diversity indices to discriminate between various degrees of AMD impact in a riverine ecosystem in Ireland by running correlations between the scores of each metric at various sites along a gradient of AMD impairment and AMD indicator parameters such as pH, sulfate, and conductivity. They found that of the indices tested, the
Simpson index was not sensitive to AMD, while the Shannon-Weiner, Margalef, and
Brillouin indices were the most sensitive and precise (Gray and Delaney 2008). Given their results, we expected that the Simpson index would be insensitive to AMD impact while the Shannon-Weiner, Margalef, and Brillouin indices would be sensitive and thus exhibit differences amongst site categories. In any case, our results have implications for additional diversity indices that might be informative in the West
Allegheny Plateau ecoregion. 59
The results from data from our study demonstrated that the Simpson’s index is not as useful for evaluating the recovery of acid mine-impaired streams because according to this index none of the site categories were significantly different. The
Shannon-Weiner and Brillouin indices, in contrast were both sensitive to AMD impact. Though Gray and Delaney (2008) found that the Margalef index was sensitive to AMD impact, they also noted that no single index may be universally reliable due to the variability of impacts possible amongst different streams and rivers. Our data suggests that the Margalef index is no more reliable the Simpson index for evaluating the recovery of impaired streams in our ecoregion. It may be the case that the mathematical structure that underlies each diversity index affects its efficacy in different situations. The Simpson Index, for instance, is a dominance index, which means that it is weighted towards the abundance of the most common taxa, while both the Shannon-Weiner and Brillouin indices are information-statistic indices, which means that the way they are calculated based on the assumption that diversity in a natural system is similar to the way information contained in a code is measured and will reflect individual taxon abundance (Gray and Delaney 2008; Washington 1984).
Since the Brillouin and Shannon-Weiner indices were both sensitive to AMD in our study sites and are both information-statistic indices, it is possible that this sort of diversity index is especially useful for assessing stream macroinvertebrate communities in our ecoregion. The lack of sensitivity of the Simpson Index also may be explained by the dominance of hydropsychids (a tolerant taxa) across all of our study sites. Since the Simpson index is influenced mostly by the most common taxa, 60 the dominance of the hydropsychids may contribute to its insensitivity to AMD impairment.
Because the Brillouin and Shannon-Weiner indices are the most sensitive of the metrics measured, we recommend that they might be useful for detection of complete restoration of impaired streams. Taxa richness was the only metric that could detect a difference between restored and improved sites, which suggests that taxa richness may be used to distinguish between a site that is still recovering and a site that has crossed the threshold into complete recovery.
The NMDS ordination generated using the structural metrics also exhibited the three general patterns described above: there was a gradient from impaired to improved sites and on to unimpaired and restored sites, restored and unimpaired sites occupied the same space in the ordination, and the impaired and improved site categories were more variable than restored and unimpaired sites. The MRPP analysis generally agreed with the patterns shown in the ordination as well, suggesting together with its corresponding ordination that restoration efforts have succeeded in recovering degraded streams back to a desirable range of conditions.
The second NMDS ordination generated using the raw data collected during
MAIS sampling, which involved the presence of each macroinvertebrate family at each site and their abundances, displayed some differences from the first ordination.
This ordination contained the same gradient from impaired sites to improved sites, to recovered sites and finally to unimpaired sites, however in this ordination the various site categories separated out more. 61
Particularly important is the fact that in this ordination unimpaired sites and restored sites do not occupy the exact same space, suggesting that they are not in the same range of conditions, which would in turn imply that restoration is not a complete success. In addition, the impaired site category was the least variable according to this ordination, which suggests that impaired sites are more stable or predictable, perhaps because their communities are consistently dominated by the same tolerant taxa.
Additional taxa represented by just a few individuals would have little influence on this ordination because it takes into account abundances as well as presence or absence of taxa.
The MRPP analysis done using the abundance of each macroinvertebrate taxa at each site displayed nearly the same pattern as the first MRPP analysis. In this analysis three comparisons were not different: impaired vs. improved, unimpaired vs. restored, and restored vs. improved. It is important to highlight the lack of difference between unimpaired and restored sites given the ordination showed that they separated out more, suggesting they are not as similar as the analyses of the structural metrics illustrated. The statistical analysis shows that that separation is apparently unimportant and that restored and unimpaired sites are within a similar range of conditions, again suggesting that restoration is a success in that sense. It is also important to note the lack of difference between the improved and restored site categories. Given that they overlap in the ordination this lack of difference makes sense, and may be explained by the possibility that improved and restored sites share taxa that are dominant in AMD- impaired sites but rare or non-existent in unimpaired sites, and that are not extirpated by ecological recovery. Further analysis of the exact taxonomic composition of the site 62 categories was beyond the scope of our study, but could reveal which taxa persist along the AMD gradient and which are replaced.
4.2 Functional Metrics
Functional Metrics, as opposed to the structural metrics, relate to the number and abundance of taxa that comprise the functional feeding groups, which perform specific ecological functions such as organic matter breakdown. Some authors (e.g.
Ruiz-Jaen and Aide 2005; Gessner and Chauvet 2002; Young et al. 2008; Dangles et al. 2004) have called for the integration of functional measures in addition to measures of diversity and species composition to assess ecological restoration. These metrics might be useful indicators that may be more sensitive to AMD, or may help discern patterns that the structural metrics could not.
Analyses comparing the functional metrics amongst the four site categories resulted in the same three patterns as discussed above: there was a general gradient of improvement from impaired sites to improved sites to restored and unimpaired sites, restored and unimpaired sites were never significantly different, and impaired and improved sites were more variable than unimpaired and restored sites. These results agreed with studies have found that AMD impairs important ecosystem processes such as leaf litter breakdown (Hogsden and Harding 2013; Simmons 2005).
Of the all the functional metrics calculated, half were sensitive to AMD: the number of grazers, the number of collector-filterers and collector-gatherers, and the grazer and predator abundances. The number of shredders may not have shown a difference because shredders emerge in the fall, while MAIS sampling is done in the 63 summer. The number of predators may not have exhibited any difference because many predator taxa found within our study watersheds are tolerant to AMD. For example, the dominant predators at our sites were alderflies, odonates and hellgrammites.
The number of collector-filterer taxa showed no difference between unimpaired and impaired and improved sites, possibly because while impaired and improved sites had many taxa present, they were still dominated by the tolerant hydropsychid caddisflies, while most taxa present were represented by relatively few individuals. Unimpaired and restored sites still had an arguably more diverse community of collector-filterers, containing an abundance of sensitive taxa such as
Isonychiidae, which is a family of mayfly, and Philopotamidae and Brachycentridae, both of which are families of caddisfly. The overall response of the collector-filterers seemed to suggest there is no limitation in the food resource for collector-filterers.
Neither the abundances nor the taxa richness of collector-filterers were influenced by
AMD impairment. This result contrasted with that of the grazers, in which there was a sharp decrease in total abundances at highly impaired sites. This contrast may make sense given that most of the particulate organic matter that serves as a food source for collector-filterers is coming from upstream and may be less influenced by chemical conditions at the site itself. In contrast, growth of periphyton, the food source for grazers, is very much dependent on the chemical conditions of the site itself, and so the food source of the grazers and thus the grazers themselves may be much more heavily affected by AMD. 64
As would be expected, the abundances of individuals belonging to most functional groups at each site did not differ significantly, except in the case of the grazers and predators, in which unimpaired and restored sites differed significantly from impaired sites. These results overall may be expected because the abundance of individuals belonging to a particular functional group at a site does not necessarily reflect the diversity of taxa at each site. An impaired site may have the same number of individuals that are collector-filterers as an unimpaired site, which may give the impaired site a guise of ecological integrity, but the unimpaired site may have more taxa belonging to the collector-filterer functional group, and so has more biodiversity and overall may be healthier as a result. The unimpaired site may be healthier because biodiversity has been demonstrated to be linked to ecological stability, as posited by the diversity-stability hypothesis (Wright et al. 2009, Tilman 1996, McGrady-Steed et al. 1997, Naeem and Li 1997; Jonsson et al. 2001; Cardinale et al. 2002; Mykra et al.
2011). Therefore, the abundance metrics tell little about the stability of a community at a site because they do not allow for interpretations on important characteristics of stability such as functional redundancy.
The exception in the case of grazer abundances is perhaps an indication of nutrient limitation at these degraded sites. The growth of periphyton, one of the grazers primary food sources, is impaired by AMD, which would cause this nutrient limitation (McKnight and Feder 1984; Niyogi et al. 2002a; Simmons 2005). The exception in the case of predator abundances suggests that while multiple taxa are tolerant of AMD, only a certain number can survive because their food base is 65 impaired. As a result, these streams can support the same predator taxa but in smaller abundances.
The NMDS ordination of functional metrics also showed a gradient from impaired sites to improved sites to unimpaired and recovered sites. Impaired and improved sites were also much more variable than recovered and unimpaired sites, however, unimpaired and recovered sites did not seem to occupy the same space, like the NMDS ordination generated using the raw community data collected during MAIS sampling, suggesting that restored sites do not fall within a desirable range of conditions. However, the MRPP analysis done using functional metrics reported no difference between unimpaired and restored sites, which suggests that the apparent separation between these two site categories in the ordination does not denote an important difference. Therefore, restored and unimpaired sites are similar, as seen in the first two ordinations and MRPP analyses, which suggests that restoration efforts succeed in recovering streams degraded by AMD.
5. Conclusions
This study assessed the effectiveness of restoration in streams impaired by
AMD by examining their macroinvertebrate community compositions using additional measures of taxonomic diversity and functional feeding groups. In general, this study found three patterns. The first was a gradient of improvement in macroinvertebrate communities from impaired sites to unimpaired and restored sites. The second was higher biological variability in the macroinvertebrate communities at impaired sites relative to restored and unimpaired sties, and the third was a strong similarity between unimpaired and restored sites. But most importantly, in none of the comparisons made were the macroinvertebrate communities in restored and unimpaired sites statistically 66 different from each other. This suggests that the MAIS index is an effective tool for assessing biological restoration, and that the MAIS score of “12” is producing macroinvertebrate communities that are statistically very similar to those found healthy, unimpaired streams, at least at the taxonomic resolution of macroinvertebrate family.
Of the 7 structural metrics evaluated (total abundances, taxa richness, % EPT, and the Simpson, Shannon-Weiner, Margalef, and Brillouin diversity indices), three were sensitive to AMD, and of the functional metrics, five of ten tested were sensitive.
Our study thus identified several additional biological signals that change across the acid mine drainage recovery gradient that can be useful in determining the status of recovering streams. Particularly of note is the patterns seen within the diversity indices: the Simpson diversity index was not sensitive to AMD, while the Shannon-
Weiner and Brillouin indices were. This result has implications for the sensitivity of the MAIS index, which includes the Simpson index as the primary measure of diversity. It is possible that substitution of a different, more sensitive diversity index might increase the accuracy of the MAIS score. Further investigation assessing the usefulness of each diversity index within streams in the area would be useful in this sense.
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Appendix A.1: The list of study sites and their categorizations, as well as their historical MAIS scores and regression results.
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A.2: Data on macroinvertebrate family presence/absence and abundances at each site collected during MAIS sampling of summer 2014. Each family also has its functional feeding group classification in parentheses below its name. SC = Grazers, SH = Shredders, PR = Predators, CG = Collector-Gatherers, CF = Collector-Filterers, GN = Generalist, MP = Macrophyte Piercer. GN and MP macroinvertebrates were not included in the study, nor were families without a functional feeding group designation.
Aeshnidae Ancylidae Asellidae Athericidae Baetidae Site ID (PR) (SC) (CG) (PR) (CG) SC RM 25.4 14 - - - 27 SC 080 - - - - - SC 076 1 - - - - WB 051 8 - - - 27 WB 003 8 - - - 20 SC 075 11 - - - 10 WBHW 003 - - 8 - - WB 004 - - - - - WB 002 18 - - - 28 MC00240 4 - - - 4 MC00900 14 - - 2 153 MC00180 7 - - - 10 MC00580 16 - 1 5 19 MC00510 11 - 2 - 40 MC00300 8 - - - 177 HF 190 - - - - - HF095 - - - - - HF 090 - - - - 5 HF 060 5 - 25 - 38 HF 010 13 - 4 - 32 HF 039 16 - 1 - 6 LRC0010 7 - - - 66 LRC0030 16 - - - 51 SC RM 10.2 8 1 - - 46 Baetiscidae Brachycentridae Caenidae Calopterygidae (CG) (CF) (CG) (PR) SC RM 25.4 - - 12 5 SC 080 - - - - SC 076 - - 6 - WB 051 - - 1 3 WB 003 - - 19 8 74
SC 075 - - 33 2 WBHW 003 - - - - WB 004 - - - 2 WB 002 - - 11 41 MC00240 - - - 1 MC00900 - - 11 1 MC00180 - - - 19 MC00580 - - 6 1 MC00510 - - 1 - MC00300 - - 1 3 HF 190 - - - 5 HF095 - - - 2 HF 090 - - - 32 HF 060 - - 7 67 HF 010 - - 5 42 HF 039 - - 4 28 LRC0010 3 1 15 6 LRC0030 - 27 3 22 SC RM 10.2 - - 11 2 Cambaridae Ceratopogonidae Chironomidae Chloroperlidae (GN) (PR) (CG) (PR) SC RM 25.4 30 2 19 - SC 080 12 - 14 - SC 076 4 - 13 - WB 051 5 4 64 - WB 003 17 1 49 - SC 075 87 - 41 - WBHW 003 15 - 9 1 WB 004 8 - 2 - WB 002 25 1 17 - MC00240 16 1 19 - MC00900 18 1 27 - MC00180 17 1 10 - MC00580 14 - 23 - MC00510 11 3 32 - MC00300 30 4 51 - HF 190 2 - 1 - HF095 10 - 2 - HF 090 1 1 22 - HF 060 26 - 18 - 75
HF 010 9 - 13 - HF 039 21 - 9 - LRC0010 7 - 37 - LRC0030 5 1 8 - SC RM 10.2 13 - - -
Coenagrionidae Corbiculidae Corduligastridae Corduliidae (PR) (CF) (PR) (PR) SC RM 25.4 19 1 - - SC 080 - - - - SC 076 - - - - WB 051 1 - - - WB 003 - - 2 - SC 075 1 47 - - WBHW 003 - - - - WB 004 1 - - - WB 002 1 7 1 - MC00240 6 1 - 1 MC00900 1 - - - MC00180 4 - - 2 MC00580 4 - 2 1 MC00510 3 - 1 - MC00300 6 - - - HF 190 - - - - HF095 - - - - HF 090 - - - - HF 060 3 - - - HF 010 7 8 - - HF 039 6 5 - - LRC0010 9 36 - - LRC0030 9 19 - - SC RM 10.2 - 7 - - Corydalidae Culicidae Dixidae Dryopidae Dytiscidae (PR) (CF) (CG) (SC) (PR) SC RM 25.4 - - 8 19 25 SC 080 - - - - - SC 076 - - - - 1 WB 051 1 5 - 10 1 WB 003 1 10 - 16 - SC 075 11 1 - 3 1 76
WBHW 003 1 - - - 2 WB 004 - - - - 2 WB 002 4 - - 7 - MC00240 1 7 7 1 - MC00900 12 3 - - - MC00180 4 - - - - MC00580 22 1 - 13 - MC00510 13 2 - 11 2 MC00300 24 2 - 2 1 HF 190 - - - 4 - HF095 1 - - - 11 HF 090 - 1 - 9 - HF 060 2 11 - 6 1 HF 010 6 - - 25 - HF 039 8 - - 13 - LRC0010 2 - - 12 - LRC0030 - 1 - 7 - SC RM 10.2 24 - - 2 - Elmidae Empididae Gammaridae Gomphidae Gyrinidae (SC) (PR) (CG) (PR) (PR) SC RM 25.4 93 - - 4 2 SC 080 1 - - - 1 SC 076 - - - 1 2 WB 051 20 1 - 1 10 WB 003 31 - - - 3 SC 075 18 - - 2 1 WBHW 003 - - - - - WB 004 4 - - - - WB 002 117 - 1 3 - MC00240 50 - 1 1 5 MC00900 371 2 - 2 2 MC00180 28 - - 7 3 MC00580 94 1 - 5 - MC00510 33 - 2 7 3 MC00300 69 - 2 7 11 HF 190 7 - - - 1 HF095 1 - - - - HF 090 11 - - 1 10 HF 060 18 - - 10 3 HF 010 39 - - 3 1 77
HF 039 46 - - 4 1 LRC0010 81 - - 17 1 LRC0030 78 - 23 3 - SC RM 10.2 89 - - 1 11 Haliplidae Heptageniidae Hirudinea Hydrophilidae (MP) (SC) (PR) (PR) SC RM 25.4 - 4 - 5 SC 080 - - - - SC 076 - - - - WB 051 1 - - - WB 003 - - - - SC 075 - - - - WBHW 003 - - - 9 WB 004 - - - 1 WB 002 - 12 - - MC00240 2 - - - MC00900 - - - 1 MC00180 5 - - - MC00580 - 8 - - MC00510 1 7 - - MC00300 - 12 - - HF 190 - - - - HF095 - - 1 - HF 090 - - - - HF 060 - 24 - - HF 010 - 135 1 - HF 039 - 55 - - LRC0010 - 19 - - LRC0030 20 7 - 1 SC RM 10.2 - 41 - 1 Hydropsychidae Isonychiidae Leptoceridae Leptohyphidae (CF) (CF) (CG) (CG) SC RM 25.4 104 - - - SC 080 - - - - SC 076 4 - - - WB 051 275 - - - WB 003 218 - - - SC 075 276 - - - WBHW 003 - - - - WB 004 7 - - - 78
WB 002 16 6 2 - MC00240 24 - - - MC00900 393 - - - MC00180 4 - - - MC00580 206 - - - MC00510 51 - 1 - MC00300 14 3 - - HF 190 - - - - HF095 5 - - - HF 090 70 - 2 - HF 060 28 - - - HF 010 164 25 1 - HF 039 8 2 2 - LRC0010 40 15 - 6 LRC0030 1 4 1 11 SC RM 10.2 124 22 - - Leptophlebiidae Leuctridae Libellulidae Limnephilidae (CG) (SH) (PR) (SH) SC RM 25.4 1 88 5 2 SC 080 - - - - SC 076 - - 1 1 WB 051 - 23 1 1 WB 003 - 1 - 3 SC 075 - - - 1 WBHW 003 - 35 - 1 WB 004 - 30 - - WB 002 - 1 1 6 MC00240 - - - 1 MC00900 - 46 1 4 MC00180 - - - - MC00580 - 5 - 15 MC00510 - - - - MC00300 1 2 3 1 HF 190 - - - 5 HF095 - - 1 - HF 090 - 1 - 1 HF 060 - 3 2 2 HF 010 - - 2 1 HF 039 - - 4 6 LRC0010 - - - - 79
LRC0030 - - - 1 SC RM 10.2 - 7 - - Lumbriculidae Macromiidae Nemouridae Nemertinea (N/A) (PR) (SH) (N/A) SC RM 25.4 - - - 4 SC 080 6 - - - SC 076 3 - - - WB 051 - - 1 - WB 003 - - - - SC 075 13 - - - WBHW 003 1 - 5 - WB 004 - - - - WB 002 - - - - MC00240 - - - - MC00900 - - - - MC00180 - - - - MC00580 - - - - MC00510 - - 1 - MC00300 - - - - HF 190 - - - - HF095 - - - - HF 090 - - - - HF 060 - - - - HF 010 - - - - HF 039 - - - - LRC0010 - 2 - - LRC0030 - 2 - - SC RM 10.2 3 - - - Oligochaeta Perlidae Perlodidae Philopotamidae Physidae (CG) (PR) (PR) (CF) (CG) SC RM 25.4 - 9 - 23 8 SC 080 - - - - - SC 076 - - - - - WB 051 - - - - 3 WB 003 - - - - - SC 075 - - - 4 1 WBHW 003 - - - - 1 WB 004 - - - - - WB 002 - - - 3 2 MC00240 - - - 1 - 80
MC00900 - 1 6 - - MC00180 - - - - - MC00580 - 13 - 10 - MC00510 - 1 - 46 1 MC00300 - - - 1 4 HF 190 - - - 1 - HF095 - - - - - HF 090 - - - 1 - HF 060 - - - 2 - HF 010 - - - 13 2 HF 039 - - - - 1 LRC0010 7 - - 196 1 LRC0030 - - - 25 - SC RM 10.2 - - - - -
Planorbidae Polycentropodidae Pyralidae Rhyacophilidae (CG) (CF) (SH) (PR) SC RM 25.4 1 - - - SC 080 - - - - SC 076 - - - - WB 051 - 6 - - WB 003 - 9 - 1 SC 075 - - - - WBHW 003 - - - - WB 004 - 2 - - WB 002 5 15 - - MC00240 - 1 - - MC00900 - - - - MC00180 - 6 - - MC00580 - 1 1 - MC00510 10 1 - - MC00300 2 2 - - HF 190 - - - - HF095 - - - - HF 090 - 1 - - HF 060 1 2 - - HF 010 14 - - - HF 039 5 2 - - LRC0010 1 5 - - LRC0030 1 3 - - 81
SC RM 10.2 - - - - Saldidae Sialidae Simuliidae Sphaeriidae Staphylinidae (N/A) (PR) (CF) (CF) (PR) SC RM 25.4 - 7 - - - SC 080 - 4 - - - SC 076 - 8 - - - WB 051 - 5 - - - WB 003 - 10 9 - 2 SC 075 - - - - - WBHW 003 - 7 - - - WB 004 - 6 - - - WB 002 - 5 4 1 1 MC00240 - 1 - - - MC00900 - 13 - - - MC00180 - 6 - - - MC00580 - 1 21 - - MC00510 - 4 16 - - MC00300 6 - 86 - - HF 190 - 4 - - - HF095 - 31 - - - HF 090 - 32 21 - - HF 060 - 11 16 - - HF 010 - 7 31 - - HF 039 - 1 5 - - LRC0010 - - 12 - - LRC0030 1 - 1 - - SC RM 10.2 - 1 7 - - Tabanidae Tipulidae Turbellaria Scirtidae (PR) (SH) (CG) (N/A) SC RM 25.4 - 44 - - SC 080 - - - - SC 076 - 5 - - WB 051 - 5 - 2 WB 003 1 1 - - SC 075 - 3 - - WBHW 003 - 4 - - WB 004 - 5 - - WB 002 3 2 1 - MC00240 - 1 - - MC00900 - 25 - - 82
MC00180 - 1 - - MC00580 - 5 - - MC00510 - 4 - - MC00300 1 1 - - HF 190 - - - - HF095 - - - - HF 090 - 8 - - HF 060 - 1 - - HF 010 - - - - HF 039 - - - - LRC0010 - 11 - - LRC0030 - - - - SC RM 10.2 - 10 - -
83
A.3: Average values for various AMD chemical parameters at each site recorded at quarterly intervals over the course of 2013/14.
Conductivity Acidity Alkalinity sulfate Site ID Temp (°C) pH (uS/cm) (mg/L) (mg/L) (mg/L) SC RM 25.5 12.5 6.94 518 8.01 73.4 108 HF010 6.91 7.03 374 3.88 33.3 126 SC RM 10.2 15.3 7.28 838 4.87 80.7 243 LRC0010 12.1 7.40 736 3.73 87.4 246 SC 080 13.4 6.09 1160 71.6 74.6 447 SC 076 13.7 6.60 1080 19.7 65.0 403 WB 51 10.4 6.93 809 5.53 41.6 320 WB 003 15.6 6.95 845 6.44 49.7 340 HF190 12.1 7.22 608 3.05 46.7 254 HF095 9.84 6.52 583 5.55 21.9 247 SC 075 11.7 7.03 426 5.02 10.1 172 WBHW 03 11.0 7.03 978 9.11 63.2 378 WB 004 12.1 6.83 793 7.66 38.3 333 MC00900 8.36 8.26 618 1.99 26.5 250 MC00180 13.4 6.93 566 5.45 37.7 187 HF090 10.6 6.78 547 5.88 22.1 221 HF060 11.1 7.00 403 4.64 24.8 143 MC00240 14.2 6.89 574 5.63 36.8 189 WB 002 16.3 7.30 764 4.11 63.2 282 MC00580 13.5 6.93 720 4.81 30.7 258 MC00510 12.6 7.15 591 4.97 32.6 191 MC00300 14.1 6.99 573 4.82 45.4 172 HF039 9.62 6.95 421 3.96 35.8 148 LRC0030 13.1 7.23 785 4.60 81.4 272
Mn Al Fe total Fe dissolved Mn total dissolved Al total dissolved Site ID (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) SC RM 25.5 0.360 0.0500 0.612 1.51 0.497 0.0900 HF010 0.515 0.377 0.355 0.355 0.067 0.0500 SC RM 10.2 0.630 0.520 0.240 0.270 0.0800 0.0500 LRC0010 0.350 0.165 0.425 0.425 0.100 0.0800 SC 080 18.7 21.8 1.23 1.50 0.0850 0.0500 SC 076 5.16 2.03 1.08 1.20 0.0650 0.0500 WB 51 1.24 0.562 0.952 0.950 0.372 0.135 WB 003 0.920 0.472 0.827 0.820 0.160 0.0533 84
HF190 11.3 1.19 1.23 1.07 3.54 0.390 HF095 5.73 1.93 1.32 1.63 1.65 0.440 SC 075 0.170 0.0800 1.05 0.950 0.0500 0.0500 WBHW 03 4.57 3.29 1.03 1.04 0.563 0.213 WB 004 4.60 2.47 1.70 1.62 1.08 0.437 MC00900 0.822 0.137 0.717 0.652 0.500 0.0525 MC00180 0.357 0.135 0.545 0.542 0.492 0.0625 HF090 1.83 0.290 1.02 1.22 0.435 0.0500 HF060 1.16 -- 0.680 -- 0.180 -- MC00240 0.640 0.157 0.630 0.630 0.425 0.0500 WB 002 0.430 0.287 0.315 0.312 0.172 0.0533 MC00580 0.335 0.177 0.585 0.587 0.270 0.0500 MC00510 0.420 0.255 0.580 0.570 0.0550 0.0500 MC00300 0.477 0.335 0.510 0.507 0.132 0.0500 HF039 0.602 0.340 0.385 0.185 0.0775 0.0500 LRC0030 0.465 -- 0.515 -- 0.147 --
85
A.4: Strength of correlations for the ordination generatedusing the the presence/absence and abundance of each family of macroinvertebrates collected at each study site. The columns MDS1 and MDS2, named after each axis of the ordination, contain the strength of correlation of each metric to its respective axis.
Metric MDS1 MDS2 Aeshnidae -0.940 0.341 Asellidae 0.366 -0.931 Athericidae -0.625 -0.781 Baetidae -0.960 0.278 Baetiscidae -0.578 0.816 Brachycentridae -0.243 0.970 Caenidae -0.755 -0.655 Calopterygidae -0.352 0.936 Cambaridae -0.611 -0.791 Ceratopogonidae -0.999 -0.0263 Chironomidae -0.762 -0.647 Chloroperlidae 0.386 -0.922 Coenagrionidae -0.641 0.767 Corbiculidae -0.988 0.153 Corduligastridae -0.827 -0.561 Corduliidae 0.0770 0.997 Corydalidae -0.999 0.0359 Culicidae -0.781 -0.624 Dixidae -0.923 0.386 Dryopidae -0.980 0.201 Dytiscidae 0.233 -0.973 Elmidae -0.969 -0.245 Empididae -0.462 -0.887 Gammaridae -0.249 0.968 Gomphidae -0.586 0.810 Gyrinidae -0.983 -0.181 Haliplidae -0.196 0.981 Heptageniidae -0.756 0.655 Hirudinea 0.608 -0.794 Hydrophilidae 0.223 -0.975 Hydropsychidae -0.509 -0.861 Isonychiidae -0.788 0.616 Leptoceridae -0.347 0.938 Leptohyphidae -0.323 0.946 Leptophlebiidae -0.794 0.607 Leuctridae -0.117 -0.993 Libellulidae -0.728 0.686 86
Limnephilidae -0.745 0.667 Lumbriculidae 0.0683 -0.998 Macromiidae -0.369 0.929 Nemouridae 0.304 -0.953 Nemertinea -0.658 -0.753 Oligochaeta -0.578 0.816 Perlidae -0.708 -0.707 Perlodidae -0.486 -0.874 Philopotamidae -0.613 0.790 Physidae -0.954 -0.301 Planorbidae -0.665 0.747 Polycentropodidae -0.655 0.756 Pyralidae -0.743 -0.669 Rhyacophilidae -0.371 -0.929 Saldidae -0.403 0.915 Sialidae 0.472 -0.882 Simuliidae -0.708 0.707 Sphaeriidae -0.255 0.967 Staphylinidae -0.756 -0.654 Tabanidae -0.475 0.880 Tipulidae -0.511 -0.860 Turbellaria -0.255 0.967 Scirtidae -0.255 -0.967