RELATIONSHIP BETWEEN LAND USE, HABITAT, AND AQUATIC BENTHIC MACROINVERTEBRATE COMMUNITIES IN TROPICAL MONTANE FORESTS
A thesis submitted to the Kent State University Honors College in partial fulfillment of the requirements for General Honors
by
Savannah Justus
May, 2017
Thesis written by
Savannah Justus
Approved by
______, Advisor
______, Chair, Department of Biological Sciences
Accepted by
______, Dean, Honors College
ii
TABLE OF CONTENTS
LIST OF FIGURES…..…………………………………………………………..……....v
LIST OF TABLES………..…………………………………………………………..…vi
ACKNOWLEDGMENT………………………………………………………………..vii
CHAPTER
I. ABSTRACT…………………………………………………………….1
II. INTRODUCTION………………………………………………………2
III. METHODS……………………………………………………………...6
Study Site Description…………………………………………..6
Qualitative Habitat Evaluation Index…………………………...7
Macroinvertebrate Sampling……………………………………8
Water Quality Scoring ………………………………………….9
Data Analysis……………….…………………………………10
IV. RESULTS…………………………………………………………...….12
Physical Characteristics………………………………………...12
Diversity and Richness…………………………………………13
Bray-Curtis Dissimilarity ……………………………………...16
Community Composition………………………………………19
Functional Feeding Groups…………………………………….20
iii V. DISCUSSION………………………………………………………….22
Physical Characteristics ……………………………………….22
Diversity and Richness………………………………………...24
Indicator Score…………………………………………………25
Bray-Curtis Dissimilarity ……………………………………...26
Community Composition………………………………………27
Functional Feeding Groups…………………………………….28
Riparian Zone and Community Composition………………….30
Conclusion………….………………………………………….31
REFERENCES…………………………………………………………………………32
APPENDIX
1. Entire list of families found in agricultural and forested pools and riffles, organized by order………………………………………………………38
2. QHEI score ranges from Ohio Environmental Protection Agency…….41
3. Entire list of BMWP Indicator scores correlating with each family……41
4. Entire list of FBI scores correlating with each family based on Hilsenhoff
1988 …………………………………………………………………….43
5. List of families within each FFG assignment and amount of each in pools and riffles of agricultural and forested streams…………………………44
iv
LIST OF FIGURES
Figure 1. Location of the Alberto Manuel Brenes Biological Reserve in Costa Rica (Taken from Homier et al. 2006)……………….....………………….…..7
Figure 2. Interaction between macrohabitat and microhabitat based on total species. a) Richness. b) Shannon’s diversity. c) Fisher’s diversity. d) Simpson’s diversity. Values are rarified transfomations of indices…………………………………..…………….15
Figure 3. NMDS comparing Bray-Curtis dissimilarity between pool and riffle communities. Stress=0.2072911. Sites: P-Pools; R-Riffles…………...…17
Figure 4. NMDS comparing Bray-Curtis dissimilarity between pools and riffles of forested and agricultural streams. Stress=0.2061708. Sites: FR-Forested Riffles; FP-Forested Pools; AR-Agricultural Riffles; AP-Agricultural Pools…………………………………………………………………..….17
Figure 5. Individual Order contributions to total community based on abundance between pools and riffles of each macrohabitat…………..……………...20
Figure 6. Individual FFG contributions to total community based on abundance between pools and riffles of each macrohabitat ………………….……...21
v
LIST OF TABLES
Table 1. Biological Monitoring Working Party (BMWP) indicator score categories according to Springer et al. 2007.……………………………………..…..9
Table 2. Family Biotic Index (FBI) indicator score categories according to Hilsenhoff 1988……………………………………………………..…...10
Table 3. Qualitative Habitat Environmental Index (QHEI) scores for agricultural and forested streams. A one-way ANOVA of QHEI scores for each category and total score……………………………………………….....12
Table 4. A. Average richness, Shannon, Simpson, and Fisher diversity indices, BMWP and FBI indicators for macrohabitat type and microhabitat type within each habitat. Values are rarified transformations of each index. B Level of significance for the two main affects based on a two-way ANOVA with combined macrohabitat and microhabitat totals for each index……………………………………………….……………………..14
Table 5. List of taxa making the largest contributions to Bray-Curtis dissimilarity between pools and riffles. Based on SIMPER analysis in order of decreasing importance…………………………………………………...18
Table 6. List of taxa making the largest contributions to Bray-Curtis dissimilarity between agricultural and forested streams. Riffles includes riffle samples only while overall includes riffle and pool samples. Based on SIMPER analysis in order of decreasing importance…………………….………...19
vi
ACKNOWLEDGMENTS
I would like to thank Dr. Oscar Rocha for serving as my advisor for this thesis and being patient with me throughout this process. I would also like to thank Dr. Ferenc de Szelay for his assistance. I appreciate Dr. Ferenc de Szalay, Dr. Linda Spurlock, and
Dr. Alison Smith for taking the time to serve on my committee. I would like to acknowledge Cory James for assisting me with proofreading and providing support.
Lastly, and perhaps most importantly, a very special thank you goes out to Emma Given, who I would not have been able to complete this thesis without. Thank you Emma for sharing ideas, assisting with fieldwork, providing guidance with statistical analysis, and being there to support me.
vii 1
Chapter 1: Abstract
Research shows that changes in surrounding land use may have negative impacts on freshwater benthic systems through changes in surrounding physical habitat, increased nutrient inputs, or non-point pollution (Neumann & Dudgeon 2002). Riparian zone condition can alter erosion and sediment input, temperature, and food availability.
Benthic macroinvertebrates play a key role in ecosystem processing in freshwater systems and are indicators of environmental stress. Although the effects of agricultural land use has been studied in temperate regions, little research has been done in Costa
Rica, where high deforestation rates are threatening tropical montane forests (Foster
2001). This study compares invertebrate communities between protected forested streams and streams surrounded by agricultural land to understand how macrohabitat and microhabitat features affect richness, diversity, and community composition. Forested streams had significantly higher richness, diversity, habitat indicator scores, and QHEI scores. Channel morphology and riparian zone condition scores were significantly higher in forested streams. Riffles had more similar communities than pools based on Bray-
Curtis dissimilarity. Overall, agricultural streams are a less suitable habitat for benthic macroinvertebrates but it is still unclear if microhabitat or macrohabitat differences have a stronger effect on community structure. This study reflects the importance of understanding how natural variation compares to large-scale land use. As agricultural expansion continues, we must understand how this will affect stream systems so we are able to mitigate any negative effects. 2
Chapter 2: Introduction
It is widely accepted that changes in land use, such as deforestation, agriculture, and urbanization, are a global problem that negatively affects freshwater systems in a number of ways. For instance, deforestation and agricultural land use decreases the amount of wooded riparian zone along stream banks, increases erosion and sediment input into streams, increases non-point pollution of pesticides and fertilizer, and increases discharge volume while reducing organic matter inputs (Kobayashi et al. 2010; Neumann
& Dudgeon 2002). These subsequent changes in the physical and chemical characteristics of aquatic systems can decrease diversity and abundance of macroinvertebrate communities, often shifting community composition toward more tolerant invertebrates rather than sensitive taxa.
Allochthonous detritus is the trophic base for macroinvertebrates in streams due to a lack of primary productivity that is caused by heavy shading from riparian vegetation. The majority of this allochthonous detritus comes from leaf litter derived from terrestrial vegetation (Wallace 1997), providing food and habitat for invertebrates and other microorganisms within streams. Therefore, by changing land use and the subsequent riparian zone, significant effects may be observed within the nearby aquatic macroinvertebrate community (Johnson & Vaughn 1995, Wallace et al. 1997, 1999).
While this topic has been widely studied in temperate regions, tropical streams have been relatively ignored in regards to how changes in surrounding land use impacts aquatic invertebrate communities. 3
Benthic macroinvertebrates play a key role in these ecosystems by processing litter inputs, acting as a food source for larger predators, and creating links between terrestrial and aquatic habitats. Invertebrates occupy an intermediate trophic level in aquatic food webs and are affected by both top down and bottom up changes (Wallace
1996). Invertebrate taxa abundance and diversity provide a way to monitor stream communities due to their unique place in the food web and sensitivity to changes in water quality (Masese et al. 2014). Exclusion of leaf litter, for instance, may have a strong bottom up effect on benthic macroinvertebrate communities that in turn affects the entire aquatic ecosystem due to their dependence on leaf litter inputs as food sources (Hall et al.
2001; Wallace 1996).
In addition to changes in carbon inputs, it has been shown that anthropogenic impacts have also led to decreased overall secondary and primary production and changes in ecosystem integrity and functioning (Rapport & Whitford 1999; Mases et al. 2014;
Buss et al. 2002; Quinn et al. 1997). In several studies, it has been shown that pasture streams have elevated turbidity, ion levels, dissolved nitrogen, and temperature due to low canopy cover (Masese et al. 2014; Lemly 1982; Quinn et al. 1997; Kobayashi et al.
2010). Furthermore, riparian tree cover reduces temperature fluctuations and prevents erosion, therefore obstructing excess sediment, nutrient, and chemical inputs from agricultural land (Encalada et al. 2010; Gomi et al. 2006; Kobayashi et al. 2010).
Although land use surrounding streams has a large impact on macroinvertebrate communities, it is important to recognize that microhabitat characteristics also affect macroinvertebrate community composition. By identifying how microhabitat 4
characteristics, like water flow and substrate composition, affect community structure, it is possible to assess which impacts are due to microhabitat and which are due to land use differences. The River Continuum Concept shows that food availability and physical factors determine community dynamics based on food transport (Vannote et al. 1980).
Apart from this, substrate and water flow affect communities as well. Pools are marked by: having slower current, deeper water, and an abundance of leaves. Riffles are shallow, fast moving, and typically have rocky substrate (Brown 1991; Logan & Brooker 1983;
Ohio EPA 2006). It is common that most taxa have higher abundance and biomass in riffles than pools, except chironomidae (Brown 1991; Logan & Brooker 1983; Ramirez
1998; Buss et al 2004; Encalada et al. 2010). This is due to fast moving water and riffle substrate being more suitable habitat for many invertebrates. It has also been suggested that microhabitat choice may be based on niche availability or interspecific facilitation
(Johnson & Vaughn 1995). Overall, invertebrate distribution is also affected by substrate type, but is more heavily affected by environmental integrity, water quality, and sampling period (Buss et al. 2004).
The biodiversity of tropical montane cloud forests in Costa Rica is extremely threatened due to their sensitivity and high deforestation rates in Costa Rica (Foster 2001;
Encalada et al 2010). Cloud forests are unique ecosystems and have high levels of biodiversity and endemism. Climate change has altered the water cycle, which increases flooding in the wet season and decreases stream base flow in the dry season. This change will likely result in a loss of biodiversity and altitude shifts of species. Land use changes have reduced vegetation cover which in turn reduces cloud cover and increase the 5
duration of the dry season, intensifying the negative effects of climate change (Foster
2001; Lawton et al. 2001). Due to the importance of freshwater systems within these forests, it is critical to understand how agricultural land use impacts freshwater streams so that steps can be taken to mitigate negative impacts on streams as agricultural expansion increases.
In this experiment, I compare benthic macroinvertebrate abundance, and composition of macroinvertebrate communities among streams within a protected forest and streams within an agricultural setting in a tropical montane cloud forest in Costa
Rica. In order to fully understand factors affecting these community differences, Quality
Habitat Environmental Index measurements were taken to assess the physical characteristics of the streams, and pools and riffles were compared among and across streams to account for microhabitat differences. Invertebrates were also organized into
Functional Feeding Groups (FFG) and given water quality indicator scores to measure the ecological functioning of the stream and the amount of sensitive taxa present. My hypotheses were:
H1 – Forested streams will have higher diversity than agricultural streams.
H2 – Microhabitat characteristics affect richness and composition of
macroinvertebrate communities, where riffles will have higher diversity than
pools.
H3 – Macrohabitat characteristics, such as land use and riparian zone, will have a
stronger effect on community composition than microhabitat factors such as pools
and riffles. 6
Chapter 3: Methods
Study Site Description
This study was conducted at the Alberto Manuel Brenes Biological
Reserve (ReBAMB) in Alajuela, Costa Rica (Figure 1). This 7,800 hectares reserve varies in altitude between 550-1,650 meters and has an annual precipitation between 4,000 -5,000 mm and a mean annual temperature of 21° C
(Avalos et al. 2005). The rainy season at ReBAMB typically occurs from May to
November, while the dry season is from December to April (Römich et al. 1996).
In late July to August, there is usually a brief (1-2 week) dry period called El
Veranillo (“little summer”); caused by a northward deflection of the Intertropical
Convergence Zone (ITCZ) (Coen 1983). Vegetation at ReBAMB is classified as a Tropical Premontane Rain Forest (Holdridge et al. 1971). This is an ideal location for this study due to stream diversity found within and outside of the reserve.
Although the aquatic systems within the reserve are protected, outside of the reserve there is heavy use of agricultural and plantation crop practices. Much of the land neighboring the southeastern limit of the reserve is used to grow “female dragon”, an ornamental plant in the genus Dracaena. This plant is a common houseplant exported in large quantities to the United States. This location provides the perfect opportunity to study the effects that agricultural practices may have on tropical premontane cloud forest stream communities. 7
Figure 1. Location of the Alberto Manuel Brenes Biological Reserve in Costa
Rica (Taken from Homier et al. 2006).
Qualitative Habitat Evaluation Index
Habitat quality was accessed in each stream using the Qualitative Habitat
Evaluation Index (QHEI) developed by the Ohio Environmental Protection Agency
(2006). This index was chosen for this study due to its universal application and a lack of a specific technique for tropical streams. The QHEI measures stream quality by quantifying physical habitat characteristics that are important to fish communities; using this metric, we make the assumption that characteristics important to fish communities are also important to benthic macroinvertebrates, as they are a food source to fish. The 8
QHEI measures six habitat characteristics to create a total possible score of 100 and each category score is summed to give the total QHEI score. The QHEI characteristics measured are substrate, instream cover, channel morphology, riparian zone and bank erosion, pool/run quality, and map gradient. A QHEI score greater than 70 is an excellent habitat quality, 55-69 is good, 43-54 is fair, 30-42 is poor, and less than 30 is very poor
(see Appendix 2).
Macroinvertebarate Sampling
In order to determine the abundance and composition of macroinverterbrates in streams, six forested streams within the reserve and three agricultural streams outside of the reserve were sampled at three different dates in August of 2016. During each sampling date, three replicates were taken at each pool and riffle site. Pools were identified as having slow moving water and deeper depth. Riffles were identified as having rapid flow of water and more shallow, rocky substrate. Riffle samples were taken using a Surber sampler (mesh size 125 µm, 0.09 m2 area) to dislodge and collect benthic macroinvertebrates within the substrate. Pool samples were taken by using a strainer to disrupt the substrate, then dipping the strainer in the water column to capture dislodged invertebrates. Samples were kept in bags and live sorted at the reserve. Samples were then stored in 80% ethanol and identified to family. Functional Feeding Group (FFG) was then assigned to each family using a collection of FFG data provided by Ramirez et al.
(2014). Grouping the taxa into FFG was challenging due to a lack of research of all taxa morphology and behavior in the tropics as well as many families fitting into more than 9
one FFG category. Due to this, the first or most common FFG of each family was used for this analysis and families were not separated further by species into multiple FFG
(Appendix 5). Samples taken from riffles and pools were maintained and separated by date and location.
Water Quality Scoring
Each stream was then scored based on the community of invertebrate samples and scored according to two methods. Streams were scored based on the presence of known sensitive species for both methods (Appendix 3 & 4). The Biological Monitoring
Working Party (BMWP) method is specific to Costa Rica, but does not account for amount of each sensitive taxon collected or total invertebrates collected (Springer et al.
2007). Due to this, Family Biotic Index (FBI) Assessment of Pollution, proposed by
Hilsenhoff (1988), was additionally used because it accounts for the amount of individuals per family.
Table 1. Biological Monitoring Working Party (BMWP) indicator score categories according to Springer et al. 2007.
BMWP Category >120 Excellent 101-120 Good 61-100 Regular 36-60 Bad 16-35 Poor <15 Very Poor
10
Table 2. Family Biotic Index (FBI) indicator score categories according to Hilsenhoff
1988.
Family Biotic Index Water Quality Degree of Organic Pollution 0.00-3.75 Excellent Organic pollution unlikely 3.76-4.25 Very good Possible slight organic pollution 4.26-5.00 Good Some organic pollution probable 5.01-5.75 Fair Fairly substantial pollution likely 5.76-6.50 Fairly poor Substantial pollution likely 6.51-7.25 Poor Very substantial pollution likely 7.26-10.00 Very poor Severe organic pollution likely
Data Analysis
QHEI scores were compared for each individual category as well as total score between streams using a one-way ANOVA. Two-way ANOVAs were also used to compare Species Richness, Shannon’s Diversity, Fisher’s Diversity, and Simpson
Diversity between sites and to test the interaction between microhabitat (pool versus riffle) and macrohabitat (forested versus agricultural). Shannon and Simpson Diversity both account for abundance and species evenness, but Simpson puts more weight on dominant species than Shannon’s. Fisher’s Alpha Diversity is not affected by samples size, but only the number of species in a habitat. Both biological indicator methods
(BMWP and FBI) were also compared between sites using a two-way ANOVA.
Macroinvertebrate community structure among samples was analyzed using a
Bray-Curtis based Nonmetric Multi-dimensional Scaling (NMDS) ordination. NMDS ordinations are used to visually display how independent samples from different communities group together in a multi-dimensional space. NMDS was chosen because it 11
does not make assumptions about the distribution of sample units and species in environmental space (Clark 1993).
Single-factor permutational multivariate analysis of variance (PERMANOVA) was used to investigate differences in assemblages between sample categories.
PERMANOVA post-hoc tests presented here were based on 10,000 permutations, using type III sums of squares and permutation of residuals under a reduced model (Anderson et al. 2008). SIMilarity PERcentages analysis was used as a post hoc test of between- group difference in multivariate abundance. This procedure was used to determine which taxa contributed most to between-group differences. The SIMPER procedure, based on the Bray–Curtis distance, measures the contribution of each taxon to the between-group effect using the between-group pairwise average.
12
Chapter 4: Results
Physical Characteristics
Significant differences were revealed for physical stream characteristics within each habitat type. Channel morphology, riparian zone and bank erosion, and total QHEI score were all significantly different between agricultural and forested streams (Table 4).
Agricultural streams had an average overall score of 62.5 (good quality), but poor averaged scores of 12 and 4.2 for channel morphology and riparian zone respectively.
Forested streams had a higher average overall score of 71.9 (excellent), and channel morphology and riparian zone ratings of 18.1 and 6.9 (Table 3).
Table 3. Qualitative Habitat Environmental Index (QHEI) scores for agricultural and forested streams. A one-way ANOVA of QHEI scores for each category and total score.
QHEI Category Agricultural Forested F(1,50) p Substrate 16+2.08 16.6+0.96 0.058 0.818
Instream Cover 12.3+0.58 11.6+1.0 1.565 0.258
Channel Morphology 12+1.73 18.4+0.58 64.000 >0.0001
Bank Erosion and 4.2+0.76 6.9+0.85 24.965 0.0020 Riparian Zone
Pool/Riffle Quality 7.6+1.53 8.6+1.0 0.769 0.414
Total QHEI Score 62.5+3.28 71.9+1.11 32.379 0.0100
13
Diversity and Richness
A total of 1,239 specimens in 52 families was collected in forested habitat at
ReBABM while 542 specimens in 44 families was collected in the nearby agricultural area (Appendix 1). All organisms were identified to family level. Samples collected at
ReBAMB were dominated by Leptoceridae. The next most abundant taxa were Velidae and Chironomidae. Agricultural samples were dominated by Velidae with the next most abundant taxa being Elmidae and Heptagenidae (Appendix 1).
A two-way ANOVA was conducted to compare diversity, richness, and indicator score between pools and riffles as well as forested and agricultural streams. Overall, species richness, Shannon’s diversity, and Simpson’s diversity were significantly greater in forested streams (Table 5). This pattern is similar when isolating riffle samples of each macrohabitat, but is not reflected when isolating pool samples. Fisher’s diversity is higher in forested streams, but is not significant based on macrohabitat. BMWP indicator score was significantly higher for forested streams while FBI was significantly lower (Table 5).
Both FBI and BMWP indicator scores suggest that forested streams had better water quality, with forested having a very good overall FBI and agricultural having a good overall FBI. Despite this, both overall BMWP scores are categorized as bad (Table 1).
Species richness, Shannon’s diversity, Fisher’s diversity, and Simpson’s diversity are significantly higher in riffles compared to pools (Table 5). In general, diversity was higher in riffles than in pools regardless of the index; however, different sampling techniques were used for pools and riffles. The richness, Simpson’s and Fisher’s diversity 14
indices are more sensitive to differences in the number of species, while the Shannon
diversity index considers both and the number of species and their relative contribution
(evenness). These findings indicated that there are differences in species richness as well
as in relative abundance between these microhabitats.
Table 4. A. Average richness, Shannon, Simpson, and Fisher diversity indices,
BMWP and FBI indicators for macrohabitat type and microhabitat type within each
habitat. Values are rarified transformations of each index. B. Level of significance for the
two main affects based on two-way ANOVA with combined macrohabitat and
microhabitat totals for each index.
A.
Macrohabitat Microhabitat Richness Shannon’s Fisher’s Simpson’s BMWP FBI Forest 12.4±0.74 8.66±0.54 8.76±0.61 6.52±0.44 54.9±3.69 4.08±0.19
Riffles 16.2±0.53 11.5±0.39 11.2±0.51 8.71±0.37 72.7±2.73 4.01±0.08
Pools 8.61±0.53 5.75±0.35 6.35±0.53 4.33±0.29 37.1±2.84 4.16±0.26
Agricultural 9.44±0.48 6.57±0.32 7.91±0.83 5.10±0.28 39.3±2.37 4.95±0.23
Riffles 11.6±0.41 7.49±0.27 8.03±0.65 5.41±0.25 51.4±1.97 4.14±0.14
Pools 7.33±0.36 5.64±0.33 7.78±1.01 4.79±0.32 27.2±1.41 5.76±0.25
B. Habitat Microhabitat F(1,50) p F(1,50) p Richness 7.942 0.0069 42.799 <0.0001
Shannon’s 7.7807 0.0073 40.77 <0.0001
Fisher’s 0.38 0.5406 6.425 0.0144
Simpson’s 4.452 0.0399 24.29 <0.0001
BMWP 8.607 0.0050
FBI 4.353 0.0421 15
Figure 2. Interaction between macrohabitat and microhabitat based on total species. a) Richness. b) Shannon’s diversity. c) Fisher’s diversity. d) Simpson’s diversity.
Values are rarified transformations of indices.
The two-way ANOVA was conducted to examine the effect of macro and microhabitat as well as interaction based on each diversity indice. There is a significant effect of macrohabitat in three of the four indices and significant microhabitat effects for all indices. There was a significant interaction between macro and microhabitat effects for Simpson’s diversity (F(1, 50)=7.79 p=0.0074), indicating that the reduction in diversity in riffles in agricultural streams with respect to forested streams is larger that that of pools. No significant interaction was found for richness, and the Shannon’s and Fisher’s diversity indexes. 16
Bray-Curtis Dissimilarity
Further comparisons of macroinvertebrate community structure among macrohabitats and microhabitats were conducted using Non-Metric Multi-dimensional
Scaling (NMDS) based on Bray-Curtis dissimilarity. NMDS ordinations are used to visually display how independent samples from different communities group together in multi-dimensional space.
Overall, microhabitat composition showed differences in community between pools and riffles (F(1, 50)=8.7452 p=0.001), with each microhabitat type orienting towards opposite sides of the x-axis (Figure 3). When comparing pools and riffles of each macrohabitat type (F(1, 50)=4.6617, p=0.001), forested and agricultural riffles overlap, with agricultural riffles having a larger distribution. Pools appear to have less overlap and are more variable based on macrohabitat. Pools and riffles also orient on opposite sides of the x-axis (Figure 4).
17
Figure 3. NMDS comparing Bray-Curtis dissimilarity between pool and riffle communities. Stress=0.2072911. Sites: P-Pools; R-Riffles.
Figure 4. NMDS comparing Bray-Curtis dissimilarity between pools and riffles of forested and agricultural streams. Stress=0.2061708. Sites: FR-Forested Riffles; FP-
Forested Pools; AR-Agricultural Riffles; AP-Agricultural Pools. 18
Tables 6 and 7 provide a list of taxa making the largest contributions to Bray-
Curtis dissimilarity for microhabitat and macrohabitat. Veliidae, Elmidae,
Hydropsychidae, Ptilodactylidae, Baetidae, Heptageniidae, and Corydalidae all make significant contributions to the differences between pools and riffles (Table 6). Only
Heptageniidae and Baetidae make a significant contribution to the differences between forested and agricultural overall, while only Heptageniidae contributes to Bray-Curtis dissimilarity differences between macrohabitat based on riffle samples (Table 7).
Table 5. List of taxa making the largest contributions to Bray-Curtis dissimilarity between pools and riffles. Based on SIMPER analysis in order of decreasing importance.
Rank Riffles versus Pools p 1 Veliidae 0.006 2 Leptoceridae 0.999 3 Elmidae 0.001 4 Hydropsychidae 0.001 5 Chironomidae 0.900 6 Leptohyphidae 0.061 7 Ptilodactylidae 0.001 8 Baetidae 0.021 9 Heptageniidae 0.005 10 Corydalidae 0.001
19
Table 6. List of taxa making the largest contributions to Bray-Curtis dissimilarity between agricultural and forested streams. Riffles includes riffle samples only while overall includes riffle and pool samples. Based on SIMPER analysis in order of decreasing importance.
Forested versus Agricultural
Rank Riffles p Overall p 1 Elmidae 0.41 Veliidae 0.324 2 Hydropsychidae 0.123 Leptoceridae 0.289 3 Leptoceridae 0.848 Elmidae 0.122 4 Heptageniidae 0.013 Hydropsychidae 0.619 5 Corydalidae 0.12 Heptageniidae 0.001 6 Leptohyphidae 0.434 Leptohyphidae 0.432 7 Chironomidae 0.45 Chironomidae 0.899 8 Ptilodactylidae 0.157 Baetidae 0.010 9 Leptophlebiidae 0.332 Corydalidae 0.112 10 Baetidae 0.673 Leptophlebiidae 0.254
Community Composition
Figure 5 shows differences in composition of each habitat type based on order.
Hemiptera and Coleoptera appear to make up similar proportions of communities based on microhabitat while Trichoptera appear similar based on macrohabitat. Odonata and
Ephemeroptera similarity may be based on microhabitat, but Ephemeroptera contributes slightly more in agricultural communities (Figure 5). Diptera appears to be similar across all habitats except forested riffles. Very few Plecoptera were sampled and only appear in forested riffles. 20
Figure 5. Individual Order contributions to total community based on abundance between pools and riffles of each macrohabitat.
Functional Feeding Groups
There are also differences in the composition of macroinvertebrate communities based functional feeding groups (FFG). Figure 6 displays FFG differences between macrohabitat and microhabitat. Collector-Gatherer, Predators, and Filter Feeders appear to make similar contributions to community structure based on microhabitat, although
Filter Feeders make larger contributions in forested riffles than agricultural. Shredder composition is based on macrohabitat, with a similar shredder contribution in both pools and riffles of forested streams (Figure 6). Scrapers may also be affected by macrohabitat and have the largest presence in agricultural riffles (Figure 6). 21
Figure 6. Individual FFG contributions to total community based on abundance between pools and riffles of each macrohabitat.
22
Chapter 5: Discussion
Physical Characteristics
Differences in QHEI score between agricultural and forested streams indicate that changes in land use surrounding agricultural streams has a negative affect on the physical characteristics of the macrohabitat (see Table 3). While it is difficult to identify the reason behind these differences, large-scale landscape changes often alter stream communities by influencing local scale physical conditions (Allen 2004). The structure and function of stream communities along river systems are based on a gradient of physical factors and structures that affect energy input and organic matter transport
(Vannote et al. 1980), so it is important to carefully analyze differences in these physical factors and understand how they may affect invertebrate communities.
Substrate scores for both macrohabitats were relatively high, suggesting normal silt levels and low sedimentation. Both also had moderate amounts of instream cover
(i.e. overhanging vegetation), undercut banks, and woody debris within streams (Table
3). Loss of woody debris within a stream could reduce habitat for many species as well as alter flow and decrease bank stability (Allen 2004). Channel morphology was significantly different between forested and agricultural streams, with agricultural streams having a poor score of 12 and forested having a near-perfect score of 18.4 (Table
4). This is likely a result of changes in land use modifying the channel. Channel morphology refers to the meandering, development, channelization, and stability of the stream channel that maintains macrohabitat. 23
Riparian zone and bank erosion scores were also different between macrohabitats with the agricultural score being much lower at 4.2 than the forested score of 6.9 (Table
3). Riparian zone scoring is based on erosion, riparian width, and flood plain quality. In this study, the riparian zone surrounding agricultural streams consisted of short, shrubby vegetation, while the forested riparian zone was compromised of trees. In addition, deforestation for agricultural land use also reduced the riparian width of agricultural streams, and together with the changes in vegetation increases the potential for erosion.
Riparian zone presence is important to reduce temperature increases, nutrient and pollution runoff, as well as maintain proper levels of productivity (Neumann & Dudgeon
2002; Encalada et al. 2010; Buss et al. 2004; Allan 2004). A loss in riparian zone also affects the availability of detritus entering the food web, altering energy sources and FFG diversity (Quinn et al. 1997).
While it is difficult to determine the degree of impact of these physical characteristics on invertebrate communities, it is important to point out that in one study, differences in landscape were able to explain 65-84% of variation in nutrient and sediment levels (Jones et al. 2001). Invertebrate communities are often correlated with riparian land cover, but are more strongly related to local factors such as large-scale landscapes (Allen 2004).
24
Diversity and Richness
The data from this study demonstrate an overall reduction in diversity from forested to agricultural streams. Species richness, Shannon’s diversity, and Simpson’s diversity are all significantly higher in forested streams (Table 4). Fisher’s alpha diversity was higher in forested streams, but was not significant, likely due to Fisher’s diversity only focusing on taxa richness and not including abundance. Shannon’s and Simpson’s diversity are both based on richness of taxa as well as diversity, with Shannon’s diversity having a larger emphasis on common or dominant species. Conditional on the degree of degradation, it was expected for agricultural streams to have lower richness and diversity; it has been previously shown that heavily degraded streams have lower diversity as sensitive taxa become replaced by tolerant or generalist species (Rapport & Whitford
1999; Simpson et al. 2014); an idea supported by numerous studies (Quinn et al. 1997;
Lemly 1982; Buss et al. 2002). The present interaction between macrohabitat systems supports a reduction in the diversity of agricultural streams compared to forested streams
(Figure 2). In addition, the significant interaction between macrohabitat and microhabitat for Simpson’s diversity suggest that dominant invertebrates in agricultural systems are shifting from sensitive to tolerant taxa.
As seen in tables 4A and 4B, species richness and all diversity metrics are significantly higher in riffles than pools. As expected, differences between microhabitats were more pronounced in agricultural streams than forested. Typically, almost all taxa except Diptera are more abundant in riffles and diversity has been found to be higher in riffles regularly (Brown & Brussock 1991; Ramirez et al 1998; Logan & Brooker 1983). 25
This is typically due to resource availability and substrate suitability of riffles as well as high risk of fish predation in pools (Brown & Brussock 1991; Buss et al. 2004; Johnson
& Vaughn 1995). The results of this study showed that there is higher abundance and taxa diversity in riffle habitat in both forested and agricultural habitats.
Indicator Score
The BMWP indicator score was significantly higher for macrohabitat (Table 4).
This score was higher overall between macrohabitats and when isolating pools or riffles samples. FBI was significant for macrohabitat and was consistently lower overall as well as among riffles and pools of each habitat type (Table 4). While a BMWP score increases with water quality, a lower FBI score indicates higher water quality (Tables 1 & 2). FBI was categorized as very good for forested streams and good in agricultural streams while
BMWP scores for both streams were categorized as bad.
FBI scores may indicate greater pollution in clean streams by overestimating indicator values or indicate less pollution in polluted streams by underestimating these values (Hilsenhoff 1988). Due to this it is important to consider how much weight is put on FBI indicator scores, but despite BMWP and FBI indicators using different methods to assess score, both suggest that overall forested streams have somewhat higher water quality than agricultural streams. Because these scores are based on presence of sensitive or tolerant taxa, it is difficult to assess the cause for difference in water quality since other water quality measures were not taken. Water quality differences could be based on temperature, nutrient levels, non-point pollution, sedimentation, or resource availability. 26
Despite this, changes in chemical water quality are commonly associated with land use changes (Hoare and Rowe 1992), so it is possible that these differences related to physical characteristics of the surrounding habitat.
Bray-Curtis Dissimilarity
NMDS shows differences in community composition between macrohabitat and microhabitat. It is hard to determine if similarity is more closely related to macrohabitat or microhabitat as there are clear patterns for each present. Microhabitats seem to align on opposite sides of the x-axis when comparing pools and riffles alone as well as pools and riffles of each larger macrohabitat (Figure 3). When comparing pools and riffles of each, it appears that riffles are much more similar than pools. In ordination space, forested riffles are completely enclosed within the agricultural riffle community, suggesting that they have a similar community structure while agricultural communities have more variance (Figures 3 & 4). Brown & Brussock (1991) also found a similar pattern supporting that riffles of different streams are often more similar to each other than pools.
Veliidae, Elmidae, and Hydropsychidae contribute significantly to differences in
Bray-Curtis dissimilarity between pools and riffles (Tables 5 & 6). In contrast,
Heptageniidae and Baetidae, were responsible for differences within overall macrohabitat dissimilarity, with Heptageniidae specifically distinguishing between macrohabitat riffles. 27
Heptageniidae may be a large contributor to this difference since it was the third most abundant taxon found in agricultural streams. Elmidae, Hydropsychidae,
Calamoceratidae, and Ptilodactylidae have been found to be most dominant in forests
(Lorion & Kennedy 2009), but this pattern was not reflected in this study and Elmidae was a dominant species in agricultural samples.
Community Composition
The community composition between pools and riffles of each habitat also reflects the varied correlation based on microhabitat or macrohabitat. Hemiptera and
Coleoptera appear to be similar based on microhabitat. Coleoptera is higher in riffles than pools and very similar between macrohabitats, while Hemiptera is much higher within pools. Odonata is similar based on microhabitat but has a slightly higher composition in pools. Odonata has been found to be a fairly tolerant species, which may explain why little differences in composition were found between macrohabitats (Buss et al. 2002).
Diptera demonstrate a unique pattern in which the percent composition in both pool types is nearly identical while percent composition in forested riffles is much higher than agricultural riffles. This is an unexpected trend as Diptera typically make up a higher proportion of the community in pools (Logan & Brooker 1983). Diptera have also been typically more correlated with agricultural steams (Encalada 2010).
Trichoptera is the only Order that varies largely based on macrohabitat. Both pools and riffles of forested habitat have similar Trichoptera compositions that are much higher than those of either agricultural microhabitat. Ephemeroptera appears to be mixed, 28
with higher composition of riffles than pools, but higher composition in agricultural streams than forested. This is an interesting pattern because Ephemeroptera are often more abundant in riffles, but are also a sensitive taxa that are more abundant in high quality streams (Logan & Brooker 1983). ETP (Ephemeroptera, Trichoptera, and
Plecoptera) are sensitive taxa that are associated with high water quality. ETP taxa have been found at much higher rates in forested streams than pasture (Lemly 1982; Quinn et al. 1997; Encalada et al. 2010; Buss et al. 2002). While Trichoptera seem to follow this pattern, Ephemeroptera do not. Too few Plecoptera were captured overall to assess composition patterns.
Functional Feeding Groups
Functional feeding group composition plays a key role in productivity and nutrient cycling of a stream system. A functional feeding group may be assigned to taxa based on gut content, mouth morphology or behavior and is meant to reflect how an organism will interact within their ecosystem. Ramirez and Gutierrez-Fonesca (2014) urge that more value be put on mouth morphology and behavior of invertebrates as using gut content alone can be misleading and make it seem like most invertebrates are generalists or occupy multiple FFGs (Tomanova et al. 2006; Boyero et al. 2009).
Functional Feeding Groups are not fully assigned in Costa Rica due to a lack of research of all taxa morphology. Due to this, the first or most common FFG of each family was used for this analysis and families were not separated further by species into multiple
FFG (Appendix 5). 29
As expected, Shredder percentages in both pools and riffles of forested streams were much greater than the Shredder proportions of pools or riffles within agricultural streams. Previous research suggests that the abundance of shredders may be higher in forested compared to pasture streams due to differences in water temperature and leaf litter presence (Masese et al. 2014). In contrast, Predator and Collector-Gatherer taxa appear to be dependent on microhabitat with Predators having a higher composition in pools and Collector-Gatherers having a higher composition in riffles. Collector-Gatherers have been found at higher compositions in pasture streams (Lorion & Kennedy 2009), but this pattern is only slightly visible in this study.
Scrapers had a larger presence in agricultural streams, particularly in riffles. This pattern has been observed before with pasture streams having a higher abundance of scrapers and lower abundance of Collector-Gatherers compared to forested streams
(Lorion & Kennedy 2009). Filter feeders also seemed to only make up a meaningful contribution in forested riffles. Filter feeders rely on water flow, so they are much more abundant in riffles (Ramirez and Gutierrez-Fonesca 2014; Logan & Brooker 1983).
Increase in sedimentation may disrupt filter feeding, which suggests that the lower
Filterer composition in agricultural riffles may be due to sedimentation (Buss et al. 2004;
Lemly 1982), although this contradicts the high substrate QHEI scores for both macrohabiats suggesting normal silt and low sedimentation.
30
Riparian Zone and Community Composition
Based on significant the reduction of Riparian Zone and Channel Morphology between the present agricultural and forested streams, it is likely that these two physical factors are greatly affecting the benthic macroinvertebrate communities in these systems by reducing overall ecosystem integrity. Riparian zones often have the strongest relationship with invertebrate communities than any other physical stream factor, and changes in land use are correlated closely with water quality (Buss et al. 2002; Hoare and
Rowe 1992). A change in the riparian zone is also correlated with an increase in scrapers due to amplified algae growth resulting from temperature and sunlight increases (Lorion
& Kennedy 2009), a pattern also observed in this study. The present decrease in the number of filter feeder within agricultural streams may also be related to riparian zone loss as riparian zones reduce erosion and sedimentation, which disrupts filter feeding
(Allen 2004).
Habitats under stress are characterized by shifts from sensitive taxa to tolerant species. This change often indicates nutrient enrichment, which may increase due to loss of the riparian zone (Allen 2004). Although trends in Trichoptera presence between macrohabitats match this prediction, the patterns for Ephemeroptera taxa do not. Overall, these changes in riparian zone condition and bank erosion rates result in lower richness, diversity, and organic matter retention. In addition, high community instability and variable composition have been reported (Simpson et al. 2014), which mirrors present trends in my Bray-Curtis dissimilarity of agricultural riffles compared to forested riffles.
31
Conclusion
The hypotheses that forested streams would have higher richness and diversity than agricultural streams and that that riffles would have higher richness and diversity than pools were both supported. However, the hypothesis of macrohabitat characteristics having a stronger effect on community composition than microhabitat is not supported, as these relationships are still unclear from this study.
Overall, it was shown that agricultural streams are a less suitable habitat for benthic macroinvertebrates but it is still unclear if microhabitat or macrohabitat differences have a stronger effect on community structure. This research reiterates the importance of fully understanding how important natural variation is to invertebrate composition compared to large-scale land use. As agricultural expansion continues, we must understand how this will affect stream systems so we are able to mitigate any negative effects. The riparian zone is of particular importance, as it has so many impacts on stream systems. The presence of a riparian zone may greatly reduce the effects of deforestation and agriculture by reducing temperature increase and habitat degradation and streams with riparian buffers of at least 15 meters have been found to have similar invertebrate community compositions to forested streams.
32
REFERENCES
Allan, D. J. 2004. Landscapes and riverscapes: the influence of land use on stream
ecosystems. Annual Review of Ecology and Evolution 35: 257-284.
Avalos, G., D. Salazar, and A. L. Araya. 2005. Stilt root structure in Neotropical Palms.
Biotropica 37(1): 44-53.
Anderson, M. J., R. N. Gorley, and K. R. Clarke. 2008. PERMANOVA+ for PRIMER:
Guid to software and statistical methods. PRIMER-E, Plymouth, UK.
Boyero, L. A. Ramirez, D. Dudgeon, R. G. Pearson. 2009. Are tropical streams really
different? Journal of the North American Benthological Society 28(2): 397-403.
Brown, A. V. and P. P. Brussock. 1991. Comparisons of benthic invertebrates
between riffles and pools. Hydrobiologia 220: 99-108.
Buss, D. F., D. F. Baptista, M. P. Silveira, J. L Nessimian, and L. F. M. Dorville. 2002.
Influence of water chemistry and environmental degredation on macroinvertebrate
assemblages in a river basin in south-east Brazil. Hydrobiologia 481: 125-136.
Buss, D. F., D. F. Baptista, J. L. Nessimian, and M. Egler. 2004. Substrate specificity,
environmental degredation and disturbance structuring macroinvertebrate
assemblages in neotropical streams. Hydrobiologia 518: 179-188.
Clark, K. R. 1993. Non-parametric multivariate analyses of changes in community
structure. Australian Journal of Ecology 18: 117-143
Coen, E. 1983. Climate: Costa Rican Natural History [D.H. Janzen (E.)]. University of
Chicago Press, Chicago, USA. 33
Colon-Guad, C., S. Peterson, M. R. Whiles, S. S. Kilham, K. R. Lips, C. M. Pringle.
2008. Allochthonous litter inputs, organic matter standing stocks, and organic
seston dynamics in upland Panamanian streams: potential effects of larval
amphibians on organic matter dynamics. Hydrobiologia 603: 301-312.
Cosgrove, C. 2015. Comparing aquatic invertebrate communities on pin oak,
cottonwood, and red maple leaf litter in vernal pools in northeastern Ohio.
Dudgeon, David. 1994. The influence of riparian vegetation on macroinvertebrate
community structure and functional organization in six new Guinea streams.
Hydrobiologia 294: 65-85.
Encalada, A. C., J. Calles, V. Ferreira, C. M. Canhoto, and M. A. S. Graca. 2010.
Riparian land use and the relationship between the benthos and litter
Decomposition in tropical montane streams. Freshwater Biology 55: 1719-1733.
Forio, M. A. E., W. V. Echelpoel, L. Domingues-Granda, S. T. Mereta, A. Ambelu, T. H.
Hoang, P. Boets, and P. L. M. Gethals. 2016. Analysing the effects of water
quality on the occurrence of freshwater macroinvertebrate taxa among
tropical river basins from different continents. SI Communications 29: 665
685.
Foster, P. 2001. The potential negative impacts of global climate change on tropical
montane cloud forests. Earth-Science Reviews 55: 73-106.
Gomi, T., R. C. Sidle, S. Noguchi, J. N. Negishi, A. R. Nik, S. Sasaki. 2006. Sediment
And wood accumulations in humid tropical headwater streams: effects of logging
and riparian buffers. Forest Ecology and Management 224: 166-175. 34
Hall, R. O., G. E. Likens, and H. M. Malcom. 2001. Tropic basis of invertebrate
production in 2 streams at the Hubbard Brook Experimental Forest. Journal
of the North American Benthological Society 20: 432-447.
Heino, J., D. Schmera, and T. Eros. 2013. A macroecological perspective of trait
patterns in stream communities. Freshwater Biology 58: 1539-1555.
Hilsenhoff, W. L. 1988. Rapid field assessment of organic pollution with a family
level biotic index. Journal of the North American Benthological Society 7(1):
65-68.
Hoare, R. A. and L. K. Rowe. 1992. Water quality in New Zealand. New Zealand
Hydrological Society: 207-228.
Holdridge, L. R., W. C. Grenke, W. H. Hatheway, T. Liang, and J. A. Tosi Jr. 1971.
Forest environment in tropical life zones: A pilot study. Pergamon Press, Oxford:
747.
Johnson, S. L. and C. C. Vaughn. 1995. A hierarchical study of macroinvertebrate
recolonization of disturbed patches along a longitudinal gradient in a prairie
river. Freshwater Biology 34: 531-540.
Kobayashi, S., T. Gomi, R. C. Sidle, and Y. Takemon. 2010. Disturbances structuring
macroinvertebrate communities in steep headwater streams: relative
importance of forest clearcutting and debris flow occurrence. Journal of Fisheries
and Aquatic Sciences 67: 427-444.
Laurance, W. F., J. Sayer, and K. G. Cassman. 2014. Agricultural expansion and its
impacts on tropical nature. Trends in Ecology and Evolution 29(2): 107-116. 35
Lawton, R. O., U. S. Nair, R. A. Pielke Sr., and R. M. Welch. 2001. Climatic impact of
tropical lowland on nearby montane cloud forests. Science 249:
584-586.
Lemly, A. D. 1982. Modification of benthic insect communities in polluted streams: ‘
Combined effects of sedimentation and nutrient enrichment. Hydrobiologia
87: 229-245.
Logan, P. and M. P. Brooker. 1983. The macroinvertebrate faunas of riffles and pools.
Water Res 17(3): 263-270.
Loke, L. H. L, E. Clews. E. Low, C. C. Belle, P. A. Todd, H. S. Eikaas, P. K. L. Ng.
2010. Methods for sampling benthic macroinvertebrates in tropical lentic systems.
Aquatic Biology 10: 1190130.
Lorion, C. M. and B. P Kennedy. 2009. Relationships between deforestation, riparian
forest buffers, and benthic macroinvertebrates in neotropical headwater
streams. Freshwater Biology 54: 165-180.
Masese, F. O., N. Kitaka, J. Kipkemboi, Gr. M. Gettel, K. Irvine, M. E. McClain. 2014.
Litter processing and shredder distribution as indicators of riparian and
catchment influences on ecological health of tropical streams. Ecological
Indicators 46: 23-37.
Neumann, M. and D. Dudgeon. 2002. The impact of agricultural runoff on stream
benthos in Hong Kong, China. Water Research 36: 3103-3109.
Ohio Environmental Protection Agency, Division of Surface Water. 2006. Methods for
assessing habitat in flowing waters: using the qualitative habitat evaluation index. 36
Available online at:
http://www.epa.state.oh.us/portals/35/documents/qheimanualjune2006.pdf
Pearson, R. G. 2014. Dynamics of invertebrate diversity in a tropical stream.
Diversity 6: 771-791.
Peckarsky, B. L., P. R. Fraissinet, M. A. Penton, and D. J. Conklin Jr. 1990. Freshwater
Macroinvertebrates of Northeastern North America.
Quinn, J. M., A. B. Cooper, R. J. Davies-Colley, J. C. Rutherford, and R. B. Williamson.
1997. Land use effects on habitat, water quality, periphyton, and benthic
invertebrates in Waikato, New Zealand, hill-country streams. New Zealand
Journal of Marine and Freshwater Research 31: 579-597.
Ramirez, A., P. Paaby, C. M. Pringle, and G. Aguero. 1998. Effect of habitat type on
benthic macroinvertebrates in two lowland tropical streams, Costa Rica.
Revista de Biologia Tropical 6: 201-213
Ramirez, A. and P. E. Gutierrez-Fonseca. 2014. Functional feeding groups of aquatic
insect families in Latin America: a critical analysis and review of existing
literature. Revista de Biologia Tropical 62: 155-167.
Rapport, D. J. and W. G. Whitford. 1999. How ecosystems respond to stress: common
properties of arid and aquatic systems. BioScience 49(3): 193-202.
Saunders, D. L., J. J. Meeuwig, and A. C. J. Vincent. 2002. Freshwater protected areas:
strategies for conservation . Conservation Biology 16(1): 30-41.
Simpson, A., I. Turner, E. Brantley, B. Helms. 2014. Bank erosion hazard index as an
indicator or near-bank aquatic habitat and community structure in a 37
southeastern Peidmont stream. Ecological Indicators 43: 19-28.
Springer, M., D. Vasquez, A. Castro, B. Kohlmann. 2007. Ioindicadores de la calidad del
agua. University of Costa Rica.
Springer, M., A. Ramirez, P. Hanson. 2010. Macroinvertebrados de agua dulce de
Costa Rica I. Revista de Biologia Tropical 58(4).
Tomanova, S., E. Goitia, and J. Helesic. 2006. Trophic levels and functional feeding
groups of macroinvertebrates in neotropical streams. Hydrobiologia 556:
251-264.
Vannote, R. L., G. W. Minshall, K. W. Cummins, J. R. Sedell, C. E. Cushing. 1980. The
river continuum concept. Canadian Journal of Fisheries and Aquatic Science
37(1): 130-137.
Wallace, B. J. 1996. The role of macroinvertebrates in stream ecosystem function.
Annual Review of Entomology 41: 115-139.
Wallace, J. B., S. L. Eggert, J. L. Meyer, and J. R. Webster. 1997. Multiple trophic levels
of a forest stream linked to terrestrial litter inputs. Science 277: 102-104.
38
APPENDIX
1. Entire list of families found in agricultural and forested pools and riffles, organized by
order.
Agricultural Forested Pool Riffle Pool Riffle Ephemperoptera Leptophlebiidae 0 12 21 39 Baetidae 18 24 6 28 Leptohyphidae 20 11 6 77 Heptageniidae 8 44 2 10 Unknown 4 6 1 14
Trichoptera Leptoceridae 10 8 92 97 Hydropsychidae 1 19 5 90 Philoptamidae 0 6 0 7 Xiphocentronidae 2 2 1 7 Polycentropodidae 0 3 0 2 Glossosomatidae 0 1 2 1 Hydroptilidae 0 0 1 6 Limnephilidae 1 0 1 2 Ecnomidae 0 0 0 1 Calamoceratidae 0 0 6 1 Odontoceridae 0 0 5 4 Unknown 1 1 2 5
Plecoptera Perlidae 0 1 2 30
Continenticola Planariidae 10 11 9 6
Diptera Chironomidae 6 9 27 77 Tipulidae 0 1 8 36 Psychodidae 11 4 0 5 Simuliidae 0 2 3 11 Exuvia 1 11 5 22 39
Blephariceridae 2 0 0 0 Tabanidae 0 0 0 7 Dixidae 0 0 0 7
Coleoptera Elmidae 3 57 15 83 Ptilodactylidae 1 7 4 51 Psephenidae 0 1 0 4 Gyrinidae 0 0 2 0 Staphylinidae 0 0 0 1 Unknown 0 0 1 0
Hemiptera Veliidae 69 20 130 10 Naucoridae 6 3 5 1 Gerridae 10 0 3 2 Unknown 0 0 0 1
Odonata Calopterygidae 3 2 7 2 Libellulidae 10 12 21 20 Coenagrionidae 1 0 0 2 Polythoridae 0 0 0 1 Gomphidae 0 0 2 1 Platysticitidae 0 0 0 6 Synlestidae 2 0 0 3 Aeshnidae 1 0 0 0 Megapodagrionidae 0 0 3 1 Unknown 0 0 3 12
Megaloptera Corydalidae 0 39 1 19
Oligochaeta 0 2 1 0
Lepidoptera Pyralidae 1 4 2 1 Crambidae 0 1 0 0
Trombidiformes Hydrachnidia 0 3 0 0
40
Amphipoda Gammaridae 0 3 1 2
Isopoda 0 1 0 0
Decapoda Psuedothelphusidae 0 1 0 9
Blattodea Blaberidae 1 0 0 0
Collembola 0 0 1 0
Hirudinea Glossiphoniidae 1 0 0 0
Mollusca Planorbidae 1 0 1 0
Unknown 1 2 2 1
Total Abundance 208 333 414 825
Total Species 29 32 34 44
41
2. QHEI score ranges from Ohio Environmental Protection Agency
Table General narrative ranges assigned to QHEI scores. Ranges vary slightly in headwater (< 20 sq mi) vs. larger waters.
QHEI Range Narrative Rating Headwaters Larger Streams Excellent > 70 > 75
Good 55- to 69 60 to 74
Fair 43 to 54 45 to 59
Poor 30 to 42 30 to 44
Very Poor < 30 < 30
3. Entire list of BMWP Indicator scores correlating with each family
BMPW Score Ephemperoptera Leptophlebiidae 8 Baetidae 5 Leptohyphidae 5 Heptageniidae 10
Trichoptera Leptoceridae 8 Hydropsychidae 5 Philoptamidae 7 Xiphocentronidae 6 Polycentropodidae 6 Glossosomatidae 8 Hydroptilidae 6 Calamoceratidae 8
Plecoptera Perlidae 10
42
Continenticola Planariidae 5
Diptera Chironomidae 2 Tipulidae 4 Psychodidae 3 Simuliidae 4 Blephariceridae 10 Tabanidae 10
Coleoptera Elmidae 5 Ptilodactylidae 7 Psephenidae 7 Gyrinidae 4 Staphylinidae 4
Hemiptera Naucoridae 4
Odonata Calopterygidae 4 Libellulidae 6 Coenagrionidae 4 Polythoridae 10 Gomphidae 7 Platystictidae 7 Aeshnidae 8 Megapodagrionidae 7
Megaloptera Corydalidae 6
Oligochaeta 1
Lepidoptera Pyralidae 5
Blattodea Blaberidae 8
43
4. Entire list of FBI scores correlating with each family based on Hilsenhoff 1988.
FBI Score Ephemperoptera Leptophlebiidae 2 Baetidae 4 Heptageniidae 4
Trichoptera Leptoceridae 4 Hydropsychidae 3 Polycentropodidae 6 Glossosomatidae 0 Hydroptilidae 4 Limniphilidae 4 Odontoceratidae 0
Plecoptera Perlidae 1
Diptera Chironomidae 7 Tipulidae 3 Psychodidae 10 Simuliidae 6 Blephariceridae 0 Tabanidae 6
Coleoptera Elmidae 4 Psephenidae 4
Odonata Calopterygidae 5 Libellulidae 9 Coenagrionidae 9 Gomphidae 1 Aeshnidae 3
Megaloptera Corydalidae 0
44
Lepidoptera Pyralidae 5
Amphipoda Gammaridae 4
5. List of families within each FFG assignment and amount of each in pools and riffles of
agricultural and forested streams.
Agricultural Forested Pool Riffle Pool Riffle Collector-Gatherer Leptophlebiidae 0 12 21 39 Baetidae 18 24 6 28 Leptohyphidae 20 11 6 77 Xiphocentronidae 2 2 1 7 Chironomidae 6 9 27 77 Dixidae 0 0 0 7 Psychodidae 11 4 0 5 Elmidae 3 57 15 83
Scraper Heptageniidae 8 44 2 10 Hydroptilidae 0 0 1 6 Glossosomatidae 0 1 2 1 Blephariceridae 2 0 0 0 Psephenidae 0 1 0 4
45
Predator Perlidae 0 1 2 30 Tabanidae 0 0 0 7 Gyrinidae 0 0 2 0 Staphylinidae 0 0 0 1 Veliidae 69 20 130 10 Naucoridae 6 3 5 1 Gerridae 10 0 3 2 Coenagrionidae 1 0 0 2 Calopterygidae 3 2 7 2 Polythoridae 0 0 0 1 Gomphidae 0 0 2 1 Platystictidae 0 0 0 6 Synlestidae 2 0 0 3 Aeshnidae 1 0 0 0 Megapodagrionidae 0 0 3 1 Libellulidae 10 12 21 20 Corydalidae 0 39 1 19
Shredder Leptoceridae 10 8 92 97 Calamoceratidae 0 0 6 1 Odontoceridae 0 0 5 4 Limnephilidae 1 0 1 2 Tipulidae 0 1 8 36 Ptilodactylidae 1 7 4 51 Crambidae 0 1 0 0
Filter Hydropsychidae 1 19 5 90 Philopotamidae 0 6 0 7 Ecnomidae 0 0 0 1 Polycentropodidae 0 3 0 2 Simuliidae 0 2 3 11