computing facilities were provided by the Penn State School of Forest Resources .... of potentially high geological and biological diversity, providing habitat for ...... University Park, the Dauphin County Agriculture & Natural Resources Center,.
Stream Plethodontid Assemblage Response (SPAR) Index: Development, Application, and Verification in the MAHA FINAL REPORT
Gian L. Rocco, Robert P. Brooks, and Jeremy T. Hite Report No. 2004-1 of the Penn State Cooperative Wetlands Center Penn State Institutes of the Environment/ Department of Geography Pennsylvania State University University Park, PA Prepared for U.S. Environmental Protection Agency, Star Grant Program April 2004
EXECUTIVE SUMMARY Part I - Stream Plethodontid Assemblage Response (SPAR) Index: Development, Application, and Verification in the MAHA Small headwater streams are vital components of rivers. They comprise 60-75% of the total stream length and watershed area in the Mid-Atlantic Highlands. The Appalachian region supports a diverse assemblage of Plethodontid (lungless) salamanders. Amphibians, in general, are considered to be valuable response indicators. Several efforts are underway to develop and test indices of biotic integrity based partly or wholly on stream salamanders. The objectives of this research were: 1) To describe the range and variability of stream plethodontid assemblage responses (SPAR) across commonly encountered gradients of anthropogenic degradation (stream acidification, forest and riparian corridor fragmentation and degradation, pollution, etc.) in the Mid-Atlantic Highlands Area (MAHA). 2) To develop a SPAR-based index for use in MAHA headwaters. 3) To evaluate the reliability and resolution of SPAR by application and testing. In 2000-2002, we sampled 138 EMAP stream sites (3 km2) covering a wide range of ecological and human disturbance gradients. The EMAP Mid-Atlantic Highlands wadeable stream sites were originally selected by a randomized, probability-based design to allow inference on environmental condition for 184,600 km streams in the region. Streams sites cover a broad range of ecological conditions, were situated in six EPA Level III ecoregions, and a large body of ecological information exists. Selected stream sites were sampled once and only at stream locations approximating EMAP stream site coordinates. Sampling along approximately 100 m of the stream channel entailed the measurement of climatic and water chemistry variables, stream channel physical characterization, and sampling for stream salamanders. Salamanders of all lifestages were captured from terrestrial and aquatic portions of three, 4 m2 rectangular plots (2 m x 2 m). Plots were always positioned to include dry and wetted portions of the stream channel. In developing IBIs we used the criteria described by Waite et al. (2000) to a priori classify each of the 138 stream sites into reference, non-reference (minimally degraded), and degraded (severely degraded). These criteria are based on the measurement of 9 variables related to water chemistry, stream physical habitat, and a total macroinvertebrate count. Several other IBIs have been developed using this criteria, including fish and macroinvertebrates. We initially screened 33 metrics. Based on this initial screening, we identified 11 metrics of potential value. Few of these metrics were linearly correlated to measures of degradation, but showed considerable association with benthic macroinvertebrate communities, an indication of an indirect response to degradation. Geographic and stream physical habitat was found to affect several metrics. Natural variability was partitioned by a 3-step process that consisted of initial classification of 34 ii
reference and near reference sites by salamander assemblage type, subsequent development of discriminant functions from environmental measurements to allow classification of new sites, and lastly, classification of new sites (non-reference and degraded) into classes identified in the first step by application of the discriminant functions. Initial classification of reference and near reference sites was performed by ordination (DCA) and reciprocal averaging (TWINSPAN). Discriminant functions were only developed from variables related to natural gradients. Multiple discriminant analysis (MDA) was used to develop the discriminat functions. The classification of reference and near reference sites resulted in 3 classes or groupings, each consisting of 9-16 sites. The mountain dusky and northern spring salamander were indicators for Group 1, the Appalachian seal was the indicator for Group 2, and the northern dusky and twolined were indicators for Group 3. DCA and TWINSPAN classifications were largely in agreement. Preliminary univariate analysis of the 9 environmental variables revealed the 3 groupings to be related to geographic and stream physical habitat, variables previously shown to be associated with salamander metrics. Subsequent MDA produced two significant discriminant functions which were linear combinations of 4 variables: latitude, stream temperature, cobble cover, and stream gradient. The first and second discriminant functions accounted for 86% and 14% of the total variance, respectively. The first discriminant function was most highly correlated to latitude (r = 0.793). The second canonical function was most highly correlated to water temperature ( r = -0.659), boulder cover ( r = 0.539), and slope (0.413). The second canonical discriminant function describes an environment where stream gradient becomes steeper, the abundance of boulders increases, and stream temperature decreases, as values along the this axis increase. In this respect, stream sites in Group 3 are relatively warmer (or less cold), less steep, and have fewer boulders relative to sites in Group 1 and Group 2. The latter groups are similar in terms of stream habitat. The overall number of sites correctly classified by cross-validation by this model, with equal prior probabilities for all groups (3 groups = 33%), was 88.2 %. The proportion of correctly classified sites by cross-validation by group was 89 %, 94 %, and 78 % for groups 1 - 3, respectively. Validation of the predictive model with a holdout sample was not possible because all reference / near reference sites were required for model development. Application of the predictive model to the 53 non-reference and 48 degraded EMAP sites resulted in the classification of 44 (43.6 %), 15 (14.9 %), and 42 (41.6 %) stream sites in Group 1, Group 2, and Group 3, respectively. Metrics were subsequently evaluated within each of the three groups and for the MAHA, all sites combined independent of group membership, the latter serving as a reference point to assess improvement. Secondary examination of metrics revealed that classification was effective in removing the strong latitudinal gradient in all groups, and with respect to Group 1 and Group 3, had accounted for most of the other gradients as well. In Group 2, however, new gradients surfaced that were not visible in the larger data set, including a longitudinal gradient that was stronger than before. These gradients are believed to have surfaced as a result of not having iii
classified sites in Group 2 further. Doing so, however, would have created one more category, and reduced the smallest sample size to 8. In spite of lower sample size, a 4-group, rather than a 3-group classification may have been more effective to removed “noise” observed among Group 2 sites. In Group 1, values varied significantly between reference and degraded sites for 9 of the 11 metrics. Correct classification for these metrics ranged from 53% - 81%. In Group 2, only 4 metrics were significant. The number of correctly classified sites by these metrics was 55% 59%. There were 7 significant metrics in Group 3; correct classification varied from 21% - 86%. For the MAHA, all groups combined, 10 of the 11 metrics were significant; correct classification ranged from 55% - 78%. These results indicate that metric performance varied widely within and among groups. The average group classification efficiency ranged from 56% - 66%. Group 2 had the lowest. Average classification efficiency for 8 IBIs by group ranged 62% - 68%, suggesting that collectively, classification efficiency was mediocre at best. Individually, however, IBI performance ranged considerably within and among groups i.e. the same IBI that performed reasonably well in one group (> 70% correctly classified), fared very poorly in another (e.g. IBIs 1- 4). Classification efficiency for Group 2 was consistently poor, and never exceeded 70% for the combined classification efficiency. Based on the results with the MAHA data set, benefits to assigning test sites to a group before applying an IBI will vary depending on group membership. Poor classification efficiency experienced with Group 2 may be resolved by further classification of this group, an approach that will require a larger data sets. Regardless of the approach taken, considerable evidence presented here suggests that strong natural gradients are affecting potentially useful metrics. Unless poor separation of reference and degraded sites is corrected by other means, future improvement of a regional index based on stream salamanders may most likely depend on subdivision of the MAHA into more homogenous geographic regions. Part II - Volunteer Study There is a widespread network of citizens volunteer monitoring groups throughout the US. Equally impressive is the number of organizations dedicated to the support and education of volunteer groups. Streams and rivers are by far the most intensively monitored aquatic habitat. Recently there has been substantial interest in developing and testing indices of biotic integrity (IBIs) based on stream salamanders, a relatively ubiquitous, abundant and fairly easy to sample taxa in the northern Appalachians. In consideration of the above, the prospect of involving volunteers for sampling salamanders appears very attractive. The objectives of the study was to evaluate the level of proficiency attained by volunteers to sample, process, and most importantly, identify Pennsylvania stream salamanders after exposure to training.
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The study entailed training and testing of volunteers. Volunteers were tested in the classroom and in the field. The classroom provided a controlled environment for testing volunteer identification skills with all 7 species and their lifestages. The field task required sampling by the plot method. It was intended to evaluate volunteer identification skills in the field and plot sampling proficiency. Vouchers were used to confirm the identity of a portion of the salamanders sampled by volunteers. Efforts to recruit individuals to assist with the volunteer phase of the SPAR project (a component of Phase II) took place in March - May, 2002. Recruitment efforts included public speaking, electronic publications, creation of web pages on a PSU web site, and phone and e-mail correspondence. To register as volunteers, respondents interested in participating were required to complete a volunteer application form linked to the web site. Respondents had to complete 15 questions, 9 of which were aimed at gauging their pre-training exposure and knowledge of stream salamanders. Volunteer training locations were established at University Park and in the vicinity of Harrisburg. The locations were selected to facilitate travel for volunteers. A total of five, 8-hr training sessions were offered on the last week of June, 2002. The syllabus consisted of the following components: a pre-training test (30 min), an introductory lecture on headwater assessments, salamander identification training aided by 94 slides, a “practicum" – a period in which volunteers examined live Pennsylvania stream Plethodontids, a post-training test (30 min), and discussion of the training manual and field sampling techniques. The pre-training and post-training tests were identical. The use of these tests was intended to measure how much the volunteers learned during the training. It required volunteers to identify all PA stream salamanders and their lifestages. This information also identified training weaknesses. Volunteers were trained and tested only with live specimens. The tests required volunteer groups to identify specimens to species when one or more live animals were presented. Volunteers were asked to sample a stream location of their choice. This post-training activity was designed to evaluate successful completion of SPAR sampling by volunteer groups in the field. Volunteers were equipped with a sampling kit to assist with sampling, processing, and shipping of vouchers and completed field data forms. By June 15, 82 individuals representing 56 volunteer groups applied to participate. Training was attended by 65 individuals representing 41 volunteer groups. Of the 64 trainees, 70% were biologists; 84% had searched for amphibians in the past. All applicants were entered in the study regardless of their level of experience with amphibians. Pre-training scores for 38 of the 41 volunteer groups attending the training averaged 49%, and ranged from 8% to 83%, a range of 76%. Volunteers with prior amphibian knowledge scored higher on average on the pre-training test than volunteers that did not.
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Post-training tests for 39 volunteer groups averaged 81% and ranged from 54% - 100%, range 46%. This outcome can be interpreted as an improvement in the average test scores of 32% and a reduction in the range of scores of 29%. The training thus had the dual effect of increasing test scores on average and reducing their variability. Both of these effects were significant. Test scores did not vary significantly among training sessions before training, but varied significantly following the training. As might be expected, some salamander species and lifestages were more difficult to identify than others. Volunteer proficiency varied by salamander species and lifestage even after the training. Identification of Desmognathus to species was the most challenging, especially when the northern dusky (Desmognathus f. fuscus) and the mountain dusky (Desmognathus ochrophaeus) were in the same set. It should be noted, however, that while many groups failed to make the correct identification to species, most had correctly identified Desmognathus specimens to genus. The field sampling task was completed by 23 (56%) volunteer groups. Sampling by volunteers occurred from July 6 - October 6, 2002 and took place in 15 PA counties. Two sites were located in MD. A total of 52 individuals, which included non SPAR trainees, participated in the sampling effort. Based on the completed field forms, sampling by volunteers resulted in the capture and processing of 612 salamanders, of which 461 (75%) were recorded as larval or transforming (gill stubs), and 151 (25%) as terrestrial. The median salamander abundance at volunteer sites (number of salamanders in 3 plots) was 13 and ranged from 1-105. Of the 612 salamanders recorded, 126 (21%) were processed and returned as vouchers. Examination of vouchers revealed 89 (71%) to be larvae or metamorphs. The remaining 37 (29%) were non-larval. The proportion of larval to non-larval for the vouchers was very close to that reported for animals processed by volunteers in the field. Laboratory examination of vouchers confirmed the presence of 7 species. Of the 126 vouchers examined, 28 (22%) were incorrectly identified. Most of these misidentifications were at genus (n = 24) rather than at species (n = 4) level. All of the former were larvae, whereas incorrectly identified species were non-larvae. Results show that larval E. bislineata were commonly confused with larval D. fuscus, a recurring error that accounted for 16 (67%) of the 24 misidentifications. Lifestage was incorrectly identified for 16 (13%) specimens. These results indicate that volunteer training was beneficial, but the level of proficiency attained under these favorable testing conditions varied among volunteer groups tested and depended on the salamander species presented. Future volunteer training efforts may benefit from further instruction and greater focus on the more difficult to identify species and lifestages identified in this study. Better methods or tools for discrimination are needed. Not all trainees completed the sampling task. Those that did, appeared to do so satisfactorily, albeit with an “effectiveness” that did not appear comparable to SPAR project crews. vi
The results suggest that collection of stream salamander data by minimally trained volunteer crews at this level of detail may be most fruitful and reliable when such efforts are conducted in concert with appropriately designed QA/QC programs that allow confirmation of species identity by whatever methods available. But much of the tasks and expertise demanded in this study may not be necessary depending on the goals and methods of a future proposed sampling/ monitoring program.
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ACKNOWLEDGMENTS This research was supported by a Science to Achieve Results (STAR) grant from the U.S. Environmental Protection Agency. We extend thanks to Barbara Levinson for her assistance and patience in administering this project. Many individuals contributed to this study. Stimulating conversations, suggestions, and comments came from Bob Carline, Bob Davic, Sam Droege, David DeWalle, Ron Landy, James Lynch, Robin Jung, Wayne Myers, Chuck Tallie, Alan Taylor, Mark Southerland, and Walter Tzilkowski. Access to information on EMAP data sets was facilitated or made possible thanks to Jim Greene, Sandra Bryce, and Marlys Cappaert, and others to name a few at EPA that assisted us over the years. A special thanks is extended to Alan Herlihy (EPA) for his interest in the project, timely and insightful comments and suggestions. Many individuals of the Penn State Cooperative Wetlands Center, both past and present contributed their thoughts and ideas. Joe Bishop assisted with GIS-related mapping. Office and computing facilities were provided by the Penn State School of Forest Resources and Department of Geography. We are particularly grateful to the energy and efforts of Ryan Zerbe, Michael Osbourne, Sam Peleski, and Brian Armstrong for assisting with sampling activities conducted in 2000-2002. We relied on the kindness, trust, and hospitality of many private, state, and federal landowners. Without their consent this study would not have been possible. The following individuals are gratefully acknowledged for participating in the volunteer training and sampling effort: Beck, T., Bergmann, J., Blanchet, H., Bolitho, Z., Bolze, K., Brookens, A.,Carolyn, D., Caruso,N., Cohen, M., Colt, T., Cromer, S., Crouch, W., Davis, K., DeGregorio, A., Devine, A., Fava, J., Felix, D., Fitch, L., Fiegel, B.S., Frederick, J., French, J., Grosholz, J., Haibach, M., Johnston, G., Kafer, C., Kelly, K., Kibbe, D., Koch, R., Koscsis, S., Lees, B., Lethaby, M., Maltese, D., McGuire, T., Muraco, D., Nelson, K., Nightingale, B., Palmer, E., Parker, A., Patnode, K., Pernick, S., Price, D., Royer, M., Rudy, K., Rybka, S., Sabo, M., Sabo, L., Salonish, A., Sewak, K., Shervinske, T., Snyder, C., Spayd, P., Sucke, A., Terwilliger, S., Travers, K., Trybula, E., Turnquist, A., Walbeck, E., Weinrich, R., Wentzel, D., Wilson, D., Winner, J., Wood, R., Yeager, T., and Young, J. Special thanks is extended to S. Brockman, A. Devine, C. Snyder, and A. Walbeck for assisting with volunteer recruitment. Much gratitude is extended to W. Tzilkowski for assisting with design of the volunteer study. J.T. Hite, S. Pelesky, and B. Armstrong, Penn State Cooperative Wetland Research Technicians, assisted with the capture of specimens used in the training.
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TABLE OF CONTENTS Executive summary Acknowledgments Table of Contents List of Figures List of Tables Part I: Stream Plethodontid Assemblage Response (SPAR) Index:: Development, Application, and Testing in the MAHA Introduction and Objectives Methods Survey Design Measurement of Climatic Variables and Water Chemistry Physical Habitat Characterization Salamander Sampling Analysis Identification of Reference Conditions Preliminary Metric Screening Partitioning Natural Variability Classification of Reference Sites by Assemblage Attributes Development of a Predictive Model Secondary Metric Screening IBI Construction and Testing Results Overview - Site Conditions Hydrology Stream Salamander Total Abundance Preliminary Metric Screening Partitioning Natural Variability Classification with Presence-Absence data DCA TWINSPAN Classification with Abundance Data DCA TWINSPAN Summary of Classification Development of the Predictive Model for New Stream Sites Summary of MDA Analysis Classification of New Sites by the Predictive Model Secondary Metric Screening IBI Construction and Testing Discussion and Conclusion Part II: Volunteer Study ix
ii viii ix xi xi 1 2 3 3 3 4 4 4 6 6 7 7 8 8 9 10 11 11 12 12 14 14 16 16 17 18 19
Introduction and Objectives Methods Volunteer Recruitment Volunteer Training Training Locations and Dates Training Syllabus 1. Introductory Presentation 2. Identification of PA salamanders 3. Examination of live specimens 4. Stream Sampling Overview Volunteer Testing Classroom testing Field testing Results Volunteer Profile Geographic Distribution Volunteer past Amphibian Experience Volunteer Proficiency Classroom Evaluation Field Evaluation Discussion and Conclusion Literature Cited
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23 24 24 24 24 25 25 25 25 26 26 26 27 28 30 33
LIST OF FIGURES AND TABLES (follow consecutively after references) FIGURES Figure 1. Map of Mid-Atlantic Highlands Area (MAHA) showing location of 138 study sites. Figure 2. Boxplots showing stream and watershed characteristics of study sites. Figure 3. Interpolation of species richness by GIS for reference sites in the MAHA. Figure 4. DCA biplot for the 34 sites and 6 stream salamander species with presence-absence data. Figure 5. DCA biplot for the 34 sites and 6 stream salamander species with abundance data. Figure 6. Plot of canonical discriminant scores for reference sites by groups. Figure 7. Location of reference and predicted test sites in the MAHA by group. Figure 8. Boxplots for 4 IBIs by group. Figure 9. County map of PA showing distribution of volunteer applicants and trainees. Figure 10. Relationship of pre-training test scores to volunteer past amphibian experience. Figure 11. Box-plots summarizing volunteer test scores. Figure 12. Bar graph for pre-training and post training performance by test question. Figure 13. Bar graph for post training performance by degree of correctness for each question. Figure 14.County map of PA showing location of volunteer sampling sites. Figure 15. Boxplots showing salamander abundance at EMAP and volunteer sites. Figure 16. Relative abundance by species for vouchers collected by volunteers. TABLES Table 1. Names for the 138 EMAP stream sites. Table 2. Criteria for the classification of stream sites. Table 3. Channel and watershed characteristics of streams found interrupted on day of survey. Table 4. Stream and woodland salamanders captured at the 138 EMAP sites. Table 5. Description of the 11 stream salamander metrics tested beyond initial screening. Table 6. Correlation coefficients between salamander metrics and variables related to sressors. Table 7. Correlation coefficients between salamander metrics and natural gradients. Table 8. Results from DCA with presence-absence data. Table 9. Correlation coefficients between DCA axis 1 and 2 and variables related to natural gradients. Table 10. Two-way ordered table from TWINSPAN classification with presence-absence data. Table 11.Comparison of environmental variables by TWINSPAN groups with presence-absence data. Table 12. Results from DCA with abundance data. Table 13. Correlation coefficients between DCA axis 1-3 and variables related to natural gradients. Table 14. Two-way ordered table from TWINSPAN classification with abundance data. Table 15. Comparison of environmental variables by TWINSPAN groups with abundance data. Table 16. Ecoregion setting for the 34 reference and near reference sites by TWINSPAN groups. Table 17. Summary of MDA on the 3 TWINSPAN groups with four environmental variables. xi
Table 18. Descriptive statistics for the 4 variables in the MDA for training set and new test sites.
Table 19. Metric responsiveness b groups and for the MAHA. Table 20. Classification efficiency for 8 IBIs by groups and for the MAHA. Table 21. Summary of test scores by training date. Table 22. Salamander abundance at EMAP/SPAR sampled sites vs. volunteer sampled sites. Table 23. Salamander abundance at EMAP/ SPAR sampled sites and volunteer sampled sites. Table 24. Summary of incorrect taxonomic identification for 28 vouchers. Table 25. Summary of incorrect life stage identification for 16 vouchers.
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PART I - STREAM PLETHODONTID ASSEMBLAGE RESPONSE (SPAR) INDEX: DEVELOPMENT, APPLICATION, AND TESTING IN THE MAHA INTRODUCTION Small headwater streams are vital components of rivers. This is where water initially leaves the soil, enters the channel, and begins its journey downstream. As a result of this close association with the surrounding landscape, headwaters are a critical source of water, food, natural sediments, and nutrients for lower stream reaches (Gomi et al., 2002). They also represent areas of potentially high geological and biological diversity, providing habitat for numerous species, including amphibians, and natural communities (Meyer and Wallace, 2001). Headwater streams comprise 60-75% of the total stream length and watershed area in the MidAtlantic states. In the Appalachian Region, a diverse assemblage of Plethodontid (lungless) salamanders thrive and reproduce in seeps, brooks, and small streams, sometimes occurring in extremely high densities. Life histories within this group are highly variable and consist of aquatic and terrestrial egg-laying species with variable aquatic larval periods (8 months - 4.5 years). Unlike vernal pool breeding species, populations of most stream dwelling salamanders tend to be remarkably stable over time. Amphibians, in general, are considered to be valuable response indicators. By virtue of their diverse and complex life histories, and abundant, stable, and geographically widespread populations in the Northeast, stream plethodontids offer the opportunity of providing another biological tool to assess headwater impairment and degradation, especially where traditional species assemblages (macroinvertebrates, fishes) are poorly developed or absent. A pilot project conducted in 1997-98 in 14 headwaters in the Allegheny Plateau, Pennsylvania, (Rocco and Brooks, 2000) showed significant responses to various forms of stream impairment, specifically acid mine drainage, episodic acidification, and other non-acidification related watershed degradation. Most recently, the Stream Salamander Index of Biotic Integrity (SS IBI), developed for Maryland, shows a remarkable ability to distinguish degraded from non-degraded streams (Southerland et al., in prep.). Most notable is Ohio EPA Division of Surface Water’s (OH EPA, 2001) primary headwater initiative program. Since 1999, it has been developing the use of standardized techniques for the biological assessment of headwater streams generally too small ( 2 km2 matched this criteria on the day they were sampled, respectively. Yet, differences in basin size (T = 1.09; P > 0.05, DF = 44) were not significant across these two broad categories (Table 3). Differences related to the timing of the visit were not found to be significant (T = 0.45; P > 0.05, DF = 136), suggesting that drier sites were not sampled later in the summer when headwaters are more likely to run intermittently. Stream temperature did not differ significantly (T = 0.45; P > 0.05, DF = 136). Examination of water temperatures by month for sites sampled in June, July, and August also failed to reveal any significant differences (Kruskal Wallis H = 3.14, P = 0.679, DF = 5). The latter comparisons excluded a site fed by a warm spring. Greater disparity may have been detected in stream temperature had sampling continued through the fall. Dissolved oxygen concentration was significantly different between interstitial sites and continuous sites (T = 3.16; P > 0.004, DF = 29). This is not surprising; flow in many of the former sites was not measured because of the reduced depth and/or flow. Bankfull width, maximum bankfull depth, and maximum pool depth are channel measurements intimately related to stream hydrology. A multivariate comparison using the above three measurements as response variables revealed significant differences between interstitial and continuous flowing sites (Hotelling’s T2 = 15.48, P = 0.002, DF = 3;131). Thus, the interstitial condition was associated with at least several stream habitat variables. In Ohio, the presence of stream salamanders requiring an extended larval period (> 1 year) is considered an indicator of Class 3 primary streams (Ohio EPA 2002). In this study, Class 3 salamanders were found in 70.6% of interstitial streams and in 97% of continuously flowing stream sites. Salamanders with a larval period < 1 year, considered Class 2 primary headwater indicators, were observed in 64.7% of interstitial streams and in 83.5% of stream sites with 8
continuous surface flow. The distribution of Class 2 and Class 3 salamanders did not vary significantly between interstitial and continuously flowing stream sites (χ2 = 0.03, p = 0.862). To minimize the effect of stressors, the latter percentages and test were computed only from nondegraded and reference sites (n = 90). Salamanders were not observed at 8 sites. These consisted of 1 reference (VA508), 3 nonreference, and 4 degraded sites. Only 1 degraded site was interstitial. Interstitial flow was observed in 3 of the remaining sites, including VA508. Thus, 50% of sites without salamanders were interstitial, and 75% of non-degraded, interstitial sites did not have salamanders. In terms of the Ohio EPA primary headwater classification scheme, the latter may correspond to Class 1 primary headwaters. In this classification, Class 1 primary headwaters exist as dry swales or channels for most of the year, support “little biological potential”, and are rarely occupied by stream salamanders (Ohio EPA 2002). VA508 is an excellent case in point: the site was sampled at its EMAP coordinates in the summer of 2000 by the SPAR team. No salamanders were observed along the mostly dry stream bed. In 2002, the site was sampled again about 400 m downstream of the EMAP coordinates. This time a complete plethodontid assemblage comprising of Class 2 and Class 3 salamanders was documented along the continuously flowing stream reach (Kane, pers. comm. 2002). The latter underscores the importance of sampling hydrologically comparable stream sites if the objective is to study the effect of non hydrologically-related stressors on stream salamanders. In summary, the above coarse analysis suggests that most stream sites sampled in this study probably corresponded to Class 2 or Class 3 Ohio EPA primary headwaters. Exceptions might be VA 508, PA 550, VA 522 and maybe PA060, the only degraded, interstitial stream site sampled. Stream Salamander Total Abundance: A total of 6,466 salamanders were captured at the 138 EMAP sites. Total abundance ranged from 0 - 311 individuals per study site; the median was 34 individuals. No salamanders were found in 8 (5.7%) study sites. Total plot abundance ranged from 0 - 166 individuals; the median was 10 individuals. No salamanders were found in 51 of the 414 plots (12.3%) sampled. Plot salamander density (number of individuals/m2) ranged from 0 - 41.5 individuals/m2. The median was 2.5 individuals/m2 for all sites. There was no correlation between total salamander abundance and time of year (r = -0.059; P = 0.493) or longitude (r = 0.106; P = 0.217). Total abundance was inversely and significantly correlated to latitude (r = -0.203; P = 0.017). However, removal of 5 outliers, sites with 168 salamanders and above, rendered this relationship insignificant (r = -0.113; P = 0.196). All five outlying sites were at or below the 39.6th parallel, near the Mason-Dixon line; 3 were in VA, 1 in WV, and 1 in MD. None were classified as “reference” sites. In this study, individuals of the northern two-lined (Eurycea bislineata) and the southern twolined (Eurycea cirrigera) were identified as “two-lined”. Furthermore, because of the difficulty of distinguishing larval E. bislineata or E. cirrigera from larval E. longicauda, and the latter are very infrequently encountered, all larval Eurycea were identified as Eurycea spp in the field, and 9
for the purpose of metric development, were enumerated as “two-lined”. Based on this level of taxonomic resolution, a total of 10 “species” were captured during the study (Table 4). Preliminary Metric Screening Mann-Whitney U tests confirmed the poor performance of 19 salamander metrics; 3 other metrics were rejected because of redundancy. Other highly redundant metrics were flagged ( r > 0.75), but not rejected in anticipation of further evaluation. The remaining, better performing metrics are identified in Table 5. These metrics showed significant correlation with variables indicative of stream degradation, as measured by water chemistry, macroinvertebrate communities, riparian/ channel disturbance, and watershed land cover (Table 6). The metric, number of species, was the most responsive showing significant correlation to 14 of the 20 variables tested. There was an appreciable correlation between several salamanders metrics and measures related to benthic communities, most notable the positive associations with Ephemeroptera, Plecoptera and Trichoptera (EPT), taxa generally intolerant to various forms of impairment. The Hilsenhoff Biotic Index was inversely related to 4 salamander metrics, 3 of which represented species rarely encountered in nutrient rich streams and most typical of cooler forested headwaters. Only salamander richness showed significant association with riparian and watershed land cover metrics. Metric response to natural gradients were investigated with reference and near reference sites (n = 34). Stream gradient, boulder cover, latitude, and water temperature were significantly correlated with 3 or 4 salamander metrics (Table 7). Other variables correlated with at least one salamander metric were basin size, longitude, elevation, and Julian day (1-360). None of the salamander metrics were highly correlated to bankfull, a finding that could be viewed as contradictory in light of their significant association to other stream habitat variables, as well as its importance in predicting primary headwater classifications in Ohio. Discrepancies with the Ohio findings may be due to the narrower range of conditions sampled in this study, i.e. sampling may be best viewed as a subset of the hydrologic range sampled in Ohio. The negative correlation with Julian day and species richness indicates the latter decreased as the sampling season progressed. We purposely sampled southern sites first, then sampled more northern sites. Salamander diversity in general increases southwards consequently this association is not surprising. Strong association with geographical and stream physical habitat were anticipated and confirmed by these results. Attempts to partition these geographic and stream physical habitat gradients led us to several approaches, including the RIVPACS-based approach we describe in this report. Partitioning Natural Variability: Classification with Presence/Absence Data by DCA Ordination of species by DCA from the presence-absence matrix is summarized in Table 8. The total variance in the species data, or inertia, was 0.761. Gradient length for the first four ordination axes ranged from 2.49 - 1.26; cumulatively they accounted for almost 67% of the 10
species variance. Axis 1 and 2 combined, explained almost 58% of the species variance. These values, whilst slightly lower, are comparable to those reported in Rocco and Brooks (2000) for ordinations using abundance matrices. The ordination biplot for the first and second axis revealed familiar species gradients (Figure 4). Relative to the origin, all sites and species, with exception of two sites and one species, lie within the upper right quadrant. The northern red occupies the extreme end of axis 1 and axis 2. Somewhat centered, at least along the first axis, lie the northern dusky, the Appalachian seal, and the two-lined. The northern dusky, in contrast to the latter two, lies at the extreme end of axis 2. Centered along axis 2, but occupying the extreme end of axis 1 are the northern spring and mountain dusky. The 34 reference sites whilst poorly differentiated along axis 2, show a weak sloping trend from left to right. Drawing a perpendicular line from axis 1 at value 1.4 divides the 34 sites into two broad “groups”, each enclosing several smaller clusters. Sites contained in the right group are associated with the northern spring and the mountain dusky; sites in the left group lie close to the two-lined, the Appalachian seal, and to a lesser degree, to the northern dusky and northern red. Latitude was the only environmental variable significantly correlated to axis 1 (Table 9). Axis 2 was not significantly correlated to any of the 9 environmental variables. Latitude, slope, elevation, and boulder abundance were significantly correlated to axis 3, however, this axis only accounted for approx. 6.2% of the total species variance. Classification With Presence Absence Species Data by TWINSPAN In the first division, TWINSPAN classified the 34 sites in two groups, where among other characteristics, the mountain dusky was either present or absent (Table 10). Specifically, the northern spring and the mountain dusky were classified in one group, “1", whilst the remaining 4 species were assigned to the second, “0". Further division of the negative group resulted in the separation of the Appalachian seal and two-lined from the northern red, and the northern dusky. However, this classification, like the second division of the positive group, resulted in very unevenly-sized groups that for the purpose of this classification effort and data set were of limited utility. Inspection of means and two-sample T-tests (Table 11) suggest sites in group “1" are on average cooler and located in more northern latitudes than sites in the second group, “0". Differences in other environmental measurements were not significant (alpha = 0.05). Classification With Abundance Data: DCA The first and second ordination axes combined explained almost 60% of the total variation, with the subsequent third and fourth axes accounting a total cumulative of 74.6% (Table 12). Gradient lengths for the first and second axes were 2.36 and 1.72, respectively. The total inertia was 0.923, a value comparable to that reported in Rocco and Brooks (2000) for abundance data. The mountain dusky and spring salamander occupied the opposite end of the first ordination axis relative to all the other species, with the two-lined and northern dusky somewhat centrally located (Figure 5). Along the second axis, the Appalachian seal, mountain dusky, and spring 11
salamander occupied the upper one third; the two-lined the middle third, and northern dusky and northern red the lower third. Inspection of the biplot reveals 3 clusters: one near the mountain dusky and spring salamander (Group 1), a second near the Appalachian seal (Group 2), and a third associated with the two-line, northern dusky, and northern red (Group 3). There appears to be considerable overlap among all three groups depending on the ordination axis of reference. The most dissimilar pair appear to be groups 1 and 3. Group 2 clusters in a direction perpendicular to the other two. Furthermore, site scores for this group overlap substantially with axis scores from Group 3 along axis 1, whilst overlapping with Group 1 along axis 2. Ordination axes 1 and 2 were significantly correlated to latitude, longitude, water temperature, slope, and boulder cover, variables describing geographic gradients and stream physical environment (Table 13). Slope was also significantly correlated to ordination axis 3. From the associations described above, we can infer that Group 1 and Group 2 are similar with respect to stream physical environment, but differ geographically. Group 2 and Group 3 are similar geographically, but differ in stream physical environment. Lastly, Group 1 and Group 3 are on opposite ends of both axes - they appear to differ geographically and in their stream physical environment. Classification With Abundance Data: TWINSPAN The results from TWINSPAN were similar to DCA and to the analysis of the presence-absence matrix by both methods (Table 14). As in preceding analyses, sites featuring the mountain dusky and northern spring salamander as important assemblage components were split from sites lacking this attribute in the first division. This group, consisting of 9 sites and identified by a “1", corresponded to DCA Group 1, sites that clustered close to the two above species in ordination space, as well as group “1" from the first TWINSPAN classification. Small sample size precluded further subdivision of this group. The second group, identified by “0" in the first division and consisting of 25 sites, was subsequently divided in groups of 9, “00" and 16, “01" sites. High density of the two-lined salamander and northern dusky, and complete absence of the mountain dusky, characterized the “00" group. The presence of the Appalachian seal, as well as the occurrence of many of the other species in varying amounts, were assemblage characteristics of the “01" group. Most sites grouped by TWINSPAN in “01" and “00" groups were also members of the DCA-derived Group 2 and Group 3, respectively. The correspondence between the TWINSPAN grouping and DCA-derived grouping was surprisingly close; only 7 of the 34 reference sites (21 %) were assigned to non-corresponding groups (Figure 5). As might be expected, most were sites lying close to the edge of the manually delimited DCA clusters. Of these 7 “discrepant” sites, 6 were occupied by the Appalachian seal salamander, an indicator species for one of the three TWINSPAN generated groups, but a characteristic not always revealed by the location of some sites in the DCA biplot. Except for these discrepancies, both classification methods grouped sites similarly and along similar species gradients. 12
One-way ANOVA for the three TWINSPAN groups on each of the 9 environmental variables revealed significant differences for latitude (F = 41.76, P< 0.0001), longitude (F = 7.596, P < 0.002), water temperature (F = 5.31, P< 0.01), boulder cover (F= 4.25, P < 0.023), slope (F = 3.62, P < 0.039), and basin size (F = 3.47, P< 0.043). Further examination of these significantly different variables by Tukey’s pairwise comparisons identified latitude as the only variable capable of separating all three groups individually (Table 15). Insignificant differences existed among two of the three groups for all other variables, indicating similarity among some the groups in some aspects of stream physical environment, a situation also revealed in the DCA biplot. Small sample size precludes the use of all 9 variables in a multivariate comparison. However, a multivariate analysis of variance (MANOVA) for the 3 groups with latitude, water temperature, and boulder cover, variables that were the most dissimilar, from a univariate approach and were uncorrelated to each other, revealed significant differences among the three variable centroids (Wilks = 0.1394, F (6,58) = 16.227, P < 0.0001). Eight (89%) of the nine sites in Group “1" , were located in the “plateau”, a category used in McCormick et al. (2000) for the northern Appalachians, the central Appalachians and the western Allegheny plateaus (Table 16). By contrast, only 2 (22%) of the 9 sites in Group “00" fell in this category, with the remainder corresponding to “ridge” (ridges in the Ridge and Valley ecoregion and the Blue Ridge ecoregion). Group “01" showed the least affinity to any single ecoregion type with 9 (56.3%), 6 (37.5%), and 1 (6 %) sites corresponding to “ridge”, “plateau”, and “valley” (valleys in the Ridge and Valley ecoregion), respectively. These observations, coupled with correlates pertinent to geographic location, suggest that Group “1" assemblages may be more typical of ecoregions in the plateau, whereas Group “00" assemblages may be more typical of ridges. This is not surprising in consideration of the geographic means associated with these two groups. Group “01" was the least particular with respect to broad ecoregion setting, suggesting perhaps, among other possibilities, that variables that covary with broad ecoregion settings are less important to this assemblage type. ANC did not differ significantly among the groups indicating the presence of acid buffering lithology in most if not all 34 reference/near reference sites in spite of differences in broader ecoregion settings. On the other hand, ecoregion classifications of such a broad scale, as presented above, are unlikely to explain patterns in ANC observed among the 34 sites. The use of “near reference” sites to increase sample size came with the risk of influencing the classification with human disturbance gradients rather than strictly natural ones. The distribution of near reference sites among the three groups suggests this effect occurred to some degree, but was probably unavoidable because of human development patterns in the MAHA. Near reference sites exist in similar proportions in Group 1 (44%) and Group 2 (44% ), but account for 78% of sites in Group 3. In some respects this is not surprising. Relative to the other groups, these are stream environments that in the MAHA are more likely to occur in human-modified landscapes. Such environments exist naturally, but are rarely in reference condition.
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Summary of Classification Efforts to classify stream salamander assemblages from minimally degraded sites into discrete biological classes resulted in the identification of three groups. The small number of observations generally prevented classification at lower levels (i.e., subsequent division of any one of the 3 groups). On the basis of these classifications, the following was concluded: 1. The classification is tied to geographic gradients, latitudinal primarily. This is consistent with the associations described initially for several metrics as well as the distribution pattern for the Appalachian seal, a species common in minimally disturbed sites in the lower two-thirds of the study area. Ordination of sites with a data set that did not include the Appalachian seal (matrix consisting 5 species) largely preserved the relative arrangement of species and sites in ordination space described earlier, with the first axis continuing to be associated with latitude. This implies that assemblage variability across the MAHA is tied to geographical gradients independent of the Appalachian seal. If it were not, this classification would merely have duplicated a range map for one of the species in the assemblage. 2. The classification for the most part reflects natural gradients in stream physical habitat, with Group 1 and Group 2 representing opposite ends of these gradients relative to Group 3. 3. Reference sites where the mountain dusky and northern spring are prominent assemblage components are flagged as “distinct” communities from those that do not share this characteristic. This pattern persists through examination of two data sets (presence/absence, abundance) and by two analytical methods. These assemblages types are associated with sites in the northern region of the study area where stream salamander species diversity is not lower relative to stream salamander assemblages further south. Development of the Predictive Model for the Classification of New Stream Sites The MDA resulted in two significant canonical discriminant functions which were linear combinations of four environmental variables (Table 17). Both discriminant functions were significant (Chi- square test, P < 0.002). The first and second discriminant functions accounted for 86% and 14% of the total variance, respectively. The predictive model was based on 4 environmental variables. In order of entry into the model by the forward stepwise procedure, these were: latitude, boulder cover, water temperature, and stream gradient. The F to enter for the variable stream gradient at step 3 was not significant at P = 0.05, however, inclusion of this variable in the model improved the classification of sites in group “00" from 56% to 78%, for cross-validated (leave-one-out method) sites correctly classified. Inspection of the correlations between variables in the model and the standardized canonical discriminant functions (structure matrix) revealed latitude as the most highly correlated to the first function (r = 0.793). The second canonical function was most highly correlated to stream temperature (r = -0.659), boulder cover ( r = 0.539), and stream gradient (r = 0.413). The first canonical function can be viewed as a geographic or latitudinal gradient, where sites in 14
TWINSPAN groups “01" (Group 2), “00" (Group 3), and “1" (Group 1) occupy, respectively, southern, central, and northern portions of the latitudinal range (Figure 6). The second canonical discriminant function describes an environment where stream gradient becomes steeper, the abundance of boulders increases, and stream temperature decreases, as values along the this axis increase. In this respect, stream sites in Group 3 are warmer, less steep, and have fewer boulders relative to sites in Group 1 and Group 2. The latter groups are similar in terms of stream habitat. The data set used to develop the predictive model consisted of 34 observations, with 9 (9/34*100 = 26.5 %), 16 (16/34*100 = 47.0 %) , and 9 (9/34*100 = 26.5 %) observations for Groups 1 Group 3, respectively. The maximum chance criterion, or the percent of sites correctly classified if all observations were merely assigned to the group with the largest number of sites, is therefore 47%. Similarly, the proportional chance criterion, calculated from the sum of the squared proportions of each group, is 36 %. The overall number of sites correctly classified by cross-validation for this model, with equal prior probabilities for all groups (3 groups = 33%), was 88.2 %, a correct classification superior to the maximum chance criterion by 41%, and 52 % above the proportional chance criterion. The proportion of correctly classified sites by crossvalidation by group was 89 %, 94 %, and 78 % for groups 1 - 3, respectively. Validation of the predictive model with a holdout sample was not possible because all reference / near reference sites were required for model development. MDA assumes multivariate normality and equality among covariance matrices (Stevens, 2002). A test for the equality of the three population covariance matrices revealed no significant differences (Box’s M test = 27.09, F (df 1 = 20, df2 = 2108.15) = 1.065, P = 0.380). Box’s M test is also very sensitive to multivariate nonnormality so its insignificance suggests no severe departures existed in this respect as well (Stevens, 2002). There are concerns, however, that this test may be insignificant because the small data set provided minimal power for rejection. Furthermore, as visible in Figure 6, the 3 clusters differ in length and orientation in discriminant space, a characteristic of uneven covariance matrices. We proceeded with the predictive phase in spite of these concerns because of the demonstrative value of the approach and because preliminary multinomial logistic regression on the same data set confirmed the patterns revealed in the MDA. Summary of MDA Analysis MDA was used with relative success to develop a predictive model for the classification of new sites into one of the three groups identified in the preceding section. The MDA largely confirmed what was already emerging in the classification process: the 3 groups differed with respect to geographic location and stream physical habitat. More importantly, prediction of new sites to one of the 3 groups was possible from the linear discriminating functions. For convenience the groups may be described as one of two generalized small stream environments: high gradient vs. low gradient, where the former are steeper, have more boulder cover and are cooler. From this perspective, and in combination with each group’s geographic affinities, stream sites in Group 2 (“01"), hereafter called “southern Appalachian high gradient streams”, may be viewed as southern counterparts of Group 1 (“1") sites, the “northern 15
Appalachian high gradient streams”. Group 3 (“00") consists of low gradient sites that are geographically centered and more widespread relative to the other groups. As such they are best considered as “low gradient streams”. The four measurements required for site classification are easy to obtain and routinely recorded during wadeable stream assessments. Geographic coordinates for stream sites can be determined from a map or by GPS on site. Only latitude, in decimal degrees, is required for classification. Boulder cover in this study was measured by the zig-zag cobble count procedure (Ohio EPA 2001), a commonly used method to evaluate substrates in sampling transects. Stream gradient was determined from 7.5 minute USGS topographic quadrangles. Water temperature was measured at each sampling plot with a thermometer while measuring other water quality attributes. The coefficients and formula needed to predict group membership for new sites can be provided to anyone interested. Classification of New Sites by the Predictive Model Application of the predictive model to the 53 non-reference and 48 degraded EMAP sites resulted in the classification of 44 (43.6 %), 15 (14.9 %), and 42 (41.6 %) stream sites in Group 1, Group 2, and Group 3, respectively (Figure 7). Incomplete water chemistry data for 3 sites prevented their use in this portion of the analysis. Descriptive statistics for variables used in the prediction of group membership for “new” sites are compared to values for reference/ near reference stream sites (the training set) in Table 18. Minimum and maximum values for boulder cover and stream gradient appear beyond the range of conditions of the training data set. The unusually high maximum value reported for water temperature corresponded to a site fed by a warm spring, otherwise, the range of values for this variable at new sites appeared comparable. Out of range values for new sites are almost inevitable when minimally disturbed sites occupy different terrain and landscapes compared to degraded sites. A procedure that uses the square distance (D2 or Mahalanobis distance) of a site to the nearest group centroid to evaluate if site conditions have exceeded the range of conditions relevant to the model is described in Clarke et al. (2003) and Hawkins et al. (2000). Because of the small number of degraded streams per group available for metric development, we chose to flag these suspect sites by this method (38.6% of all the 101 predicted sites, at p < 0.05) but continue metric development with all of them. The proportion of suspect sites by group, ranging from 14 (33%) in Group 3, to 7 (47%) in Group 2, did not vary significantly among groups (Chi2 = 1.002; df = 2, p = 0.606). New sites were not assigned to the three groups in proportions comparable to the training set, an expected outcome considering human development patterns in the study area. Agriculture and other activities typically occur in the more fertile and lower-gradient terrain of the MAHA. Consequently it was not surprising to see a larger number of degraded sites assigned to the low stream gradient group. By comparison, only 9 of 34 (33%) reference/near reference sites fell in this category.
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Secondary Metric Screening: The objective of classifying salamander assemblages was to partition natural variability with the hope of amplifying metric response to stressors. Previously high correlations between metrics and variables related to natural gradients, such as latitude, were also expected to be reduced appreciably after classification. Inspection of correlation coefficients and p-values between metrics and natural gradient variables revealed that the classification was effective in partitioning the strong latitudinal gradient for all groups, and with respect to Group 1 and Group3, had accounted for most of the other gradients as well (Table 19). In Group 2, however, new gradients surfaced that were not visible in the larger data set, including a longitudinal gradient that was stronger than before. Thus, differences in environment within Group 1 and Group 3 were relatively minor compared to the more variable environment within Group 2, at least in terms of the variables considered. The TWINSPAN two-way ordered table for the abundance data (Table 14) provides insight on why such strong gradients might exist at this level of classification within Group 2. TWINSPAN grouped the 16 sites in Group 2 in the second level. The mountain dusky (“ochrp”) was found in only 8 of them, or half. The spring salamander (“Gyrin”) occurs more consistently in these sites as well. In subsequent divisions, not shown in the table, TWINSPAN splits Group 2 into sites with the mountain dusky (n = 8) and sites without (n = 8). The classification, however, was left at 3 groups instead of 4 to avoid reducing group size. The consequence of using a 3-group classification rather than a 4-group appears to have resulted in considerable diametric variability within Group 2 both in terms of assemblage types and environment. In other words, the level of classification chosen was suitable for removing gradients in Group 1 and Group 3, but apparently was insufficient for Group 2. Examination of physiographic affinities within Group 2 (Table 16) identifies the probable cause for the strong longitudinal gradient: about half of the sites in Group 2 are in the plateau (west), and the other half occur in the ridge or valley (east). In spite of the above situation, predictive model development, secondary testing of metrics, and IBI development proceeded with the 3-group classification. Results of Mann Whitney testing for the 11 better performing metrics by group and for the MAHA are summarized in Table 20. Also reported in the table are the classification efficiencies for each metric, after scoring of metric values. In Group 1, 9 of the 11 metrics revealed significant differences (P < 0.05) between reference/near reference sites and degraded sites. Correct classification of stream condition by these metrics ranged from 53% - 81%. In Group 2, only 4 metrics were significant and the number of correctly classified sites was 55% - 59%. Poor performance of many metrics in this group may be attributable to the very small number of degraded sites (n = 5). The relatively variable assemblage and stream environment in this group probably did not help either. There were 7 significant metrics in Group 3, but correct classification varied from 21% - 86%. For the MAHA, 10 of the 11 metrics showed significant differences between reference and degraded conditions and sites were correctly classified 55% 78%. These results indicate that metric performance varied widely from metric to metric and by group. Group average for classification efficiency ranged 56% - 66%. Group 2 was the lowest.
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IBI Construction and Testing Classification efficiency for 8 IBIs, including the SS IBI is shown in Table 21. Boxplots for a few of these IBIs are shown in Figure 8. The percent of correctly classified sites was computed for all categories combined (reference, non-reference, and degraded = Cmb), for degraded sites (Deg.), and for non-reference sites (Nrf). Classification efficiencies for the latter 2 categories are shown to reveal which sites were most likely to be incorrectly classified e.g. low percentages in the “degraded” column indicates degradation was not detected, a rare occurrence for most of the IBIs in this study; low values in the non-reference column indicates many non-degraded sites were identified as degraded. The best IBIs appear to be those that have values in all three columns >70%. Average classification efficiency by group, for the combined column, ranged 62% - 68%, an unremarkable yet not surprising outcome considering the classification efficiency of individual metrics. The performance of IBIs differed considerably within a group (e.g., for Group 1, the range for all stream types combined ranged 60% - 81%). Furthermore, the same IBI that performed reasonably well in one group (> 70% correctly classified), fared very poorly in another (e.g. IBIs 1- 4). Classification efficiency for Group 2 was particularly low, never exceeding 70%, for all stream types combined (Cmb.), probably the single best indicator of classification potential. In general, most IBIs performed better at correctly identifying degraded sites than at identifying non-reference sites as not degraded. Comparison of individual percentages in the column for classification of degraded sites (Deg.) and non-reference sites (Nrf.) largely confirms this, as do the means for each column. As presently scored and tested, many streams of intermediate quality are identified incorrectly as degraded. The rationale for establishing the 25th percentile of reference sites as a threshold for degraded sites, rather than the 10th percentile is discussed elsewhere. A larger data set consisting of truly reference sites, not “near reference” sites as required for this study, may allow the threshold for degraded sites to be lowered. In doing so, more “room” would be left for non-reference sites in the intermediate or non-reference category. Of some interest is IBI 7. It was based on only 2 metrics, yet performed relatively well compared to many of the other IBIs with 3-4 metrics. IBI 7 was the average of the metrics “species richness” and “No. LS class 3". The first metric is self-explanatory, the latter is the total number of individuals identified by Ohio EPA as Class 3 primary headwater indicator salamander species - aquatic nesting salamanders with a larval period exceeding 1 year (e.g., Eurycea, Gyrinophilus, and Pseudotriton). IBI 7 performed relatively well in all except Group 2. Performance of the SS IBI in this study was comparable to the other IBIs. The classification efficiency of sites by group was lower for Group 2, and slightly higher for Group 1 and Group 3, relative to the MAHA. This suggests there may be marginal to zero benefits to assigning sites to a group before applying an IBI.
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DISCUSSION AND CONCLUSION The EMAP stream site locations provided a unique opportunity to sample the region’s headwaters for stream salamanders within the confines of a probabilistic sample and for sites draining basins < 3 km2. Sampling was very productive. Stream salamanders were documented in all but 8 sites. Nearly 6,500 specimens were observed during the 3-year period. At one location, the density of adult and mostly larval animals was nearly 42 salamanders/m2, a further confirmation of the spectacular densities these organisms can achieve in relatively small headwater streams under certain conditions. The impact of hydrology on stream salamander assemblage could not be investigated in great depth in this study because of the sampling frame chosen, i.e. sampling was limited to wetted stream reaches. On the other hand, Ohio EPA’s primary headwater initiative has provided invaluable insight on the impact of hydrology on stream plethodontids, as well as other amphibians (Ohio EPA, 2002). The coarse analyses presented earlier suggest that all but 3 or 4 stream sites sampled in this study met the hydrologic thresholds to support salamanders with extended larval periods and certainly species with larval periods < 1 year. Class 2 primary headwaters are considered “transitional streams with permanent pools”. Class 3, are “perennial headwater streams” supporting “cool water macro-invertebrates, fish, and obligate salamander species” (Ohio EPA, 2001). In Ohio, Class 3 primary headwaters comprise streams with “constant surface flow” and streams with “isolated pools of water connected by subsurface or interstitial flow”. It may be reasonable to assume that salamander assemblage response described in this study is unlikely to be tied to a lack of hydrology. EPA’s recent and ongoing effort to study headwaters in several US regions is likely to provide a wealth of information on the ordering of headwater aquatic communities along hydrologic gradients comparable to those investigated by Ohio EPA (Fritz, 2003). The two-lined was by far the most numerically abundant and geographically widespread stream salamander. It accounted for > 60 % of all salamanders observed and was found in 76% of all sites sampled. These results are consistent with literature accounts for this species and headwater surveys in the Mid-Atlantic region (Pfingsten and Downs, 1989, Petranka, 1998, Southerland et al., in prep, Jung et al. 2000, Rocco and Brooks, 2000, Ohio EPA, 2002). By comparison, the next most commonly encountered species were the mountain dusky (49.5%), northern spring (45.7%), and northern dusky (39.9%). Metric C, the number of two-lined individuals, whilst performing very poorly in Group 1 and Group 2, correctly classified sites in Group 3 and the entire MAHA relatively well. There appears to be some interest in developing measures related to this species for headwater assessment purposes (Davic, pers. comm.). This study suggests that while the two-lined may be viewed as a tolerant species, opportunities to use their presence and related measures for monitoring and assessment of headwaters cannot be ruled out especially if the intent is to gather information about target areas rapidly and coarsely or to identify hydrologic status. The existence of what we call ecoregions and their effect on biota and ecological processes was probably recognized by humans before written history. Today, defining ecoregions and 19
delineating their boundaries has become the focus of many ecologists, geographers, and climatologists for purposes ranging from warfare to natural resource management (Omernik, 1995). Defining ecoregions or ecological strata is a particularly important task to the specialist engaged in developing ecological indicators. Freshwater biologists and water quality managers in the late 1970s, spurned in large part by the 1972 Water Pollution Control Act amendment (CWA), recognized rather quickly that biological measures seemed best developed within the framework of reference standards that were ecoregion specific (Omernick 1995). The river continuum concept (Vannote et al., 1980) provides a very broad conceptual framework for understanding and predicting the biology of riverine systems at various points along their trajectory. The metric adjustment implemented by McCormick et al. (2001) is a good case in point: a normalization is applied to offset the effect of watershed size on several fish metrics. The adjustment is the same regardless of ecoregion. This suggests that at some level of comparison, two stream reaches in different ecoregions will be more similar to each other than other streams in their own ecoregions. The fact that the adjustement is the same for all ecoregions is further proof of the strength of the patterns the adjustment is intended to correct. Ohio EPA’s primary headwater classification, likewise, provides a conceptual framework for biologists developing biocriteria in very small streams. US EPA’s intermittent stream study will undoubtedly strengthen that framework. Based on patterns emerging from these initiatives, an ephemeral reach in Pennsylvania should support the same biology as an ephemeral reach in Ohio or elsewhere. The ephemeral condition precludes the establishment of biological communities adapted to more permanent aquatic conditions found further downstream. In this case, the presence of water represents the lowest step on the “pyramid of needs”. Certainly most will agree that such streams occur throughout the landscape and are not unique to one ecoregion. These examples, although simplistic and extreme, are useful for making the point that developing and calibrating biocriteria (IBI) inside closed boxes (ecoregions) may not yield the most accurate assessments and require finer classification and/or adjustments. In concept, RIVPACS seems ideal for developing biocriteria because the initial classification step has the opportunity to identify natural strata without necessarily tying them to geographic boundaries, a desirable outcome if one ponders the ephemeral vs. perennial community example. In other words, the boundaries for development, calibration, and validation of biocriteria exist within synthetic boundaries that reflect the attributes of natural communities and all the biotic and abiotic variables that shape them. By this approach, the “boxes” are mere constructs and biological commonalities have the opportunity to group themselves quite independently of physical space. Most importantly, this should result in apples only being compared to apples. The approach, in theory, seems ideal. How more accurate could one get? The singular most important step in the multimetric approach is to identify biological measures that resolve stressor signals or respond to human-derived gradients (Karr and Wu, 1998). Poorly resolving metrics are typically eliminated. Identifying relevant strata helps to eliminate background noise. This initial classification aims to reduce variance inside groups whilst maximizing variance between groups (Barbour et al., 1999). Operationally, this translates in 20
developing and calibrating IBIs by ecoregion or other attributes. The classification of primary headwaters developed by Ohio EPA is an example of stratifying according to hydrologic factors. Where classification may not be practical, individual metrics are adjusted accordingly e.g. the normalization employed by McCormick et al. (2001). In this study, latitude was shown to have significant biogeographic effects. Classification by ecoregion seemed to provide a poor solution and normalization of affected metrics by regression was not very effective as judged from the pattern of the resulting residuals (Neter et al., 1989). Metrics were evidently responding to other attributes. Predictive modeling offered an alternative approach (Herlihy, pers. comm., 2003). Description of the steps and discussion of the results of this process form the core of the report and will not be repeated here. Of greater interest at this stage is a discussion of what may have been learned from this study by this approach and how it may contribute to future efforts. Independent of the magnitude of the gains or losses in the number of correctly classified sites achieved by grouping relative to non-grouping, the fact that metric and IBIs behavior varied across groups and against a regionally calibrated IBI, suggests there are benefits to stratifying. A single yardstick is unlikely to serve the entire region. Biogeographic patterns and stream habitat appear to be important attributes affecting assemblage response. The methods employed to assign sites to these attributes, whilst conceptually sound, may not have been the most suited for the sample size available. There will be future opportunities to test this possibility. Combining the SPAR data with highlands amphibian data sets from WV, VA, and MD may provide such an opportunity. There is an interest in working towards such a collaborative effort (Southerland, Jung, and Pauley, pers. comm). There may also be more effective methods for classifying, discriminating, and assigning sites to classes. Metric performance may improve by alternative scoring methods e.g. dose-response curves or 95th percentile value rather than by the trisection method used in this study. The alternative is to ignore the natural gradients described herein and proceed with an IBI for the entire region. If this is the desired approach, an IBI based on two metrics (A and K) seems to yield the highest combined classification efficiency (76%). Larger data sets using more restrictive reference sites may improve this and other future regional IBIs tested. Other adjustment techniques may exist to counter the effect of longitude. In this report, we computed and evaluated the performance of 7 potential IBIs, including the SS IBI. The latter performed exceedingly well in Maryland. With this data set, however, its performance was generally comparable to most of the other IBIs tested. There could be a number of reasons for this outcome, including among others, differences in sampling techniques, how some metrics were calculated, initial calibration and scoring vs. the calibration used in this study for the SS IBI, or lack of robustness of the SS IBI when applied outside its intended geographical boundaries. When evaluating the percentages of correctly classified sites for the entire MAHA relative to grouped sites (Table 20), it is important to realize that these percentages do not persist if scoring criteria developed from the entire MAHA are applied to grouped sites. In fact, preliminary 21
evaluation reveals that most IBIs applied to group 2, when scored with MAHA scoring criteria, rather than scores developed from group 2 sites, classified sites very poorly. The percent of sites correctly classified in the combined column (Cmb) by this method only exceeded 60% once and the IBI scoring above that (AK), correctly identified degraded streams (Deg) less than 60% of the time. This indicates that application of the regionally-based SPAR index scores by land managers in VA and eastern WV, the area roughly corresponding to sites in group 2, is likely to perform very poorly even for degraded sites. Similar results, however not nearly as dramatic, were observed with group 1 sites. On the other hand, application of MAHA scores to group 3 sites, surprisingly, increased the number of correctly classified sites for most IBIs. The SS-IBI responded positively (69%, 79%, and 65%) as well as IBI 3 (EFIH; 71%, 68%, and 78%) for combined, degraded, and non-reference stream site categories, respectively. Degraded sites were identified correctly more often than non-reference (minimally degraded) sites, with the latter commonly identified as degraded, at least by plethodontid assemblage standards. It is not uncommon for IBIs to perform best at the extreme ends of the disturbance gradient continuum (Barbour, 2004). It also suggests that stream plethodontids may undergo assemblage changes in the presence of relatively minor degradation, a desirable indicator property. The possibility remains that stream salamander-based indices may be best developed and applied within smaller regional units. Such an approach would minimize geographic variation to stressor response. Future analysis of this and other data sets may provide insight on more effective approaches. In spite of the shortcomings described and seemingly poor performance of most IBIs tested, we believe there are opportunities for future use and improvement.
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PART II - VOLUNTEER STUDY INTRODUCTION There is a well-developed network of citizens volunteer monitoring groups throughout the US. In Pennsylvania alone 11,000 individuals from more than 140 groups participated in volunteer monitoring activities (River Network/PADEP, 2001). Equally impressive is the number and kinds of organizations dedicated to the support and education of volunteer groups. The Environmental Protection Agency (EPA) has developed numerous “guidance manuals” and facilitated or promoted this activity at various levels in their quest to encourage and support volunteer monitoring programs. There are 14 such organizations dedicated to volunteer programs in Pennsylvania alone. Undoubtedly, volunteer groups are playing an ever increasing role in environmental monitoring and assessment that are likely to become even more integrated in state, local, and national efforts. Streams and rivers are by far the most intensively monitored aquatic habitat. In Pennsylvania, 93% of volunteer groups monitor streams (River Network/PADEP, 2001). Volunteer monitoring activities typically focus on measuring water chemistry, stream habitat, and macroinvertebrates. Recently there has been substantial interest in developing indices of biotic integrity (IBIs) and biological criteria based on stream salamanders for the assessment or regulation of small headwaters (Ohio EPA, 2001, Rocco and Brooks, 2000, Rocco and Brooks, 2004 (part 1 of this report), Southerland et al. in prep.). Stream salamanders in the Appalachians are ubiquitous, abundant and fairly easy to sample. Typical assemblage diversity averages 3-4 species and rarely reaches 7, the maximum for Pennsylvania and much of the northern Appalachians. In consideration of the above, the prospect of involving volunteers for sampling salamanders appears very attractive. After all, salamanders are much less diverse than macroinvertebrates and require less time for processing. The level of volunteer training needed to achieve an acceptable level of “taxonomic” prowess is unknown for stream salamanders. In this report, we describe the recruitment, training, and testing of volunteers to address the above issues. Objectives The objectives of this study were to evaluate the level of proficiency attained by volunteers to sample, process, and most importantly, identify Pennsylvania stream salamanders after exposure to training. METHODS The study entailed training and testing. We tested volunteers in the classroom and in the field. The classroom provided a controlled environment for testing volunteer identification skills with all Pennsylvania species and their lifestages. The field task required sampling by the 4 m2 plot method (Rocco and Brooks, 2000) to test their identification skills in the field and volunteer sampling proficiency. Vouchers were returned to confirm the identity of a portion of the salamanders captured.
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Volunteer Recruitment Efforts to recruit individuals to assist with the volunteer phase of the SPAR project took place in March - May, 2002. Recruitment efforts consisted of announcements for volunteers in printed and electronic publications and through e-mail lists, a presentation to board members of the Pennsylvania Citizens Volunteer Monitoring Program, and creation of web pages on the PSU Cooperative Wetland Center web site. Contents of the web pages included a project abstract, FAQs related to the proposed study, and links to other pages of similar content e.g. NAAMP, Ohio EPA primary headwater initiative, etc. To register as volunteers, respondents were required to complete a volunteer application form linked to the web site. The form requested the name, address, contact information and required answering 15 questions, 9 of which were aimed at gauging volunteer pre-training exposure and knowledge of stream salamanders. Some of the questions served to indicate their willingness to conduct an activity which was likely to be arduous and expose them to cold water. Respondents were also informed they needed to purchase a PA fishing license ($17.00) and would be asked to euthanize and preserve a portion of the specimens collected. The license was needed for the legal collection of voucher specimens. Respondents were encouraged to recruit other individuals to assist with the stream salamander sampling effort. Incoming applicants formed their own volunteer groups. Training Training Locations and Dates Initially, volunteer training locations were established at three locations in PA: Forest Resources Laboratory, PSU, University Park, the Dauphin County Agriculture & Natural Resources Center, Dauphin, and the Penn State Pymatuning Laboratory, Linesville. These locations were selected to facilitate travel for volunteers. Training at the Linesville location was canceled because of poor applicant response from northwestern PA. A total of five, 8-hrs (9:00 am - 5:00 pm) training sessions were offered. Training was held at University Park on June 19-20, and at Dauphin on June 21-23, 2002. Training Syllabus All training activities took place indoors. The training syllabus consisted of the following components: 1. Introductory presentation on headwater assessment. 2. Identification of Pennsylvania stream salamanders. Lunch break 3. Examination of live specimens 4. Stream sampling overview 1.Introductory Presentation on Headwater Assessment Volunteers were informed on current approaches to assess headwaters and the potential role of stream salamanders for this purpose. The objective was to provide a context for the training and the rationale behind the use of this novel and experimental bioindicator.
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2. Identification of Pennsylvania Stream Salamanders. Discussion of taxonomy, natural history, breeding ecology, and identification of larval, juvenile, and adult Pennsylvania stream plethodontids was presented during the 90 min lecture. Discussion of distinguishing characteristics was aided by the presentation of 94, 35 mm slides and a brief overview of the Pfingsten and Downs (1989) keys to adult and larval stream salamanders, along with selected material from other sources. Towards the end of this period, another set of 15 slides was shown to test the class as a group. 3. Examination of Live Specimens The second half of the training, following the lunch break, gave volunteers the opportunity to apply what they had learned in the morning by examination of live specimens. This collection of animals included adult, juvenile, and larval individuals of all PA stream plethodontids, except for the mud salamander (P. montanus) and larvae of the longtail salamander (E. longicauda). An adult Jefferson salamander (Ambystoma jeffersonianum) was used to test the ability of volunteers to distinguish plethodontids from non-plethodontids. Unfortunately, woodland salamanders (Plethodont cinereus, P. glutinosus) sometimes encountered in headwater sampling were not made available. 4. Stream Sampling Overview Volunteers were trained to perform stream salamander sampling by the 4 m2 plot method, a technique also implemented by the PSU SPAR technicians (Rocco and Brooks, 2000). Field techniques were discussed indoor during the last 90 minutes of the training. Detailed descriptions and diagrams of each sampling activity were included in the stream sampling training manual, a document detailing all facets of stream site selection, measurement, stream salamander sampling, and specimen and voucher processing. The manual was packaged with other printed material given to each individual volunteer at the beginning of the training (Appendix 2). An overview of the training manual was presented whilst discussing field related techniques. Testing Classroom Testing Volunteers were tested with identical tests at the beginning and end of the training. The use of pre-training and post-training tests was intended to measure how much volunteers learned during the training. Testing also served to identify training weaknesses e.g difficulties volunteers experienced that could be addressed to make future training programs more effective. Most importantly, they tested the ability of volunteers to distinguish among all species, lifestages, and most variants of stream plethodontids in PA. Volunteers were trained and tested with live specimens. Both tests were “open book” and each volunteer group completed only one test regardless of the number of individuals in the group.Volunteers were not always tested with the same specimens. Thus, salamanders of the same species examined in the pre-training test may not have been the same examined in the posttraining test or by volunteer groups in different training sessions. On the other hand, since only a few individuals of several species and lifestages were available, some of the specimens examined in the training session were also the same used in all the tests. 25
The tests required volunteer groups to identify specimens to species when one or more live salamanders were presented. The tests consisted of 15 questions, requiring volunteers to identify 15 sets of specimens. Each set consisted of one or more containers with one or more specimens. Field Testing Volunteers were asked to sample a stream site of their choice. Sampling sites had to be forested and not degraded. This post-training activity was designed to evaluate sampling proficiency and identification of PA salamanders in the field. Initial plans to have volunteers sample EMAP stream sites previously sampled by PSU project technicians were subsequently abandoned. This approach, while preferable from the standpoint of evaluating volunteer sampling proficiency, was not practical. Volunteers were equipped with a sampling kit to assist with sampling, processing, and shipping of vouchers and completed field data forms. Sampling kits consisted of two dip nets, four plots flags, one plastic leak-proof container, large and small zip-lock bags, specimen labels, 200 ml of 10% formalin, a small quantity of tranquilizer (MS-222), and a shipping box with an affixed self-addressed, pre-paid shipping label for the return of vouchers and completed field forms. The collection and return of vouchers (4 specimens per species) allowed the identification of at least a portion of the specimens identified by volunteer groups in the field. RESULTS Volunteer Profile Geographic Distribution By June 15, 82 individuals representing 56 volunteer groups applied to participate in t he study. Training was attended by 65 individuals representing 41 volunteer groups. The geographical distribution of applicants and trainees was strongly skewed towards the southeastern quadrant of the state, the area also occupied by a large proportion of the state’s population (Figure 9). Volunteer Past Amphibian Experience Of the 64 trainees, 70% were biologists; 84% had searched for amphibians in the past, indicating a strong personal interest in this class of organisms. Half of the volunteers(50%) indicated they could distinguish adult Ambystoma from adult Desmognathus, suggesting that these individuals could tell apart a Plethodontid from an Ambystomid. About the same proportion (55%) indicated their ability to distinguish terrestrial forms of Desmognathus from Eurycea. A much smaller proportion (17%) of the volunteers indicated their ability to distinguish between various larval forms of Plethodontids, specifically larval Eurycea from Desmognathus, and larval Eurycea from Gyrinophilus. These observations provided some indication of volunteer pre-training experience. All applicants were entered in the study regardless of their level of experience with amphibians. The level of education among volunteers was not examined in this study, but most had earned a BS degree and few possessed advanced degrees (MS, PhD). Volunteer Proficiency - Classroom Evaluation A portion of the volunteers were initially tested with 15 questions. During the administration of the test to these volunteers it was recognized that one of the questions was ambiguous and should 26
not count towards the final score. Consequently, all scores were adjusted to reflect the number of correct answers out of the remaining 14 questions. This adjustment was performed on all pretraining and post-training tests. Final test scores were computed as percentages by dividing the number of correct answers by 14, then multiplying the quotient by 100. Pre-training scores for 38 of the 41 volunteer groups attending the training averaged 49%, and ranged from 8% to 83%, a range of 76%. Although 41 volunteer groups attended the training, only 38 arrived at the training in time to complete both tests. Volunteer groups with prior amphibian knowledge scored higher on average than groups without it (Figure 10). Groups with individuals capable of distinguishing among the following 4 pairs of salamanders were considered to have prior amphibian knowledge. Two-sample T-tests on each of the four pairs of comparisons detected significant differences at p < 0.05, indicating prior knowledge to be an important variable influencing volunteer classroom testing. Post-training tests for 39 volunteer groups averaged 81% and varied 54% - 100% (range 46%). This outcome was an improvement in the average test scores of 32% and a reduction in the range of scores of 29% (Figure 11a). The training had the dual effect of increasing test scores and reducing their variability. Both of these effects were significant. The variance (variability) of pre-training test scores differed significantly from the variance of post-training test scores (F = 3.883, p < 0.0001). A one-tailed, paired T-test indicated post-training test scores were significantly greater than pre-training test scores for the same group (T = 11.71, p < 0.0001). Test scores for only 36 volunteer groups were used in the comparison because groups that missed the pre-training test were not the same that did not complete the post-training test. Group size had no significant effect on pre-training (T = -0.81, df = 37, p = 0.421) or posttraining test scores (T = -0.44, df = 36, p = 0.662). In these comparisons, groups consisting of two or more volunteers were compared to groups consisting of only one volunteer. There were only 3 groups with three or more individuals per group (Figure 11b). Training had a significant effect on test scores even when compared within training session. Test scores and results of paired T-tests are summarized in Table 22 and depicted in Figure 11c by training date. Test scores did not vary significantly among training sessions (F = 1.39, p = 0.258) before training, but varied significantly following the training (F = 4.52, p = 0.005). Post-training test scores from volunteer groups in later training sessions seemed to score higher and with less variability than volunteer groups exposed to earlier training sessions, suggesting an “instructor” effect from the improvement of how the training material was presented over time. Individuals attending later training sessions may also have differed as a group in their ability to assimilate or respond to the presented material. As might be expected, some salamander species and lifestages were more difficult to identify than others. Volunteer proficiency also varied by salamander species and lifestage even after the training (Figure 12). As depicted in the bar graph, none of the volunteer groups had trouble 27
correctly identifying an adult longtailed salamander (Eurycea longicauda) and most (79.5%) correctly identified larval Eurycea to genus (question no. 5). On the other hand, identification of Desmognathus to species was the most challenging, especially when the northern dusky (Desmognathus f. fuscus) and the mountain dusky (Desmognathus ochrophaeus) were in the same set. Test question numbered10, 13, 11, and 4, which included one or both of the above Desmoganthus species were answered correctly by only 21%, 36%, 49%, and 51% of the volunteers, respectively. It should be noted, however, that while many groups failed to make the correct identification to species, most had correctly identified Desmognathus specimens to genus (Figure 13). Ultimately, and depending on the goals of a given monitoring program, such distinction may not be needed. Volunteer Proficiency - Field Evaluation The field sampling task was completed by 23 volunteer groups. This represented 56% of the 41 groups attending the training and 42% of the 56 groups that initially applied to participate in the study. This progressive loss of volunteers may be typical of such efforts. No effort was made to contact volunteers that did not complete the field sampling task. Sampling by volunteers occurred from July 6 - October 6, 2002 and took place in 15 PA counties (Figure 14). Two sites, not shown in the map, were located in MD. A total of 52 individuals, which included newly recruited individuals, participated in the sampling effort. The number of volunteers per group ranged from 1-4 individuals. Field forms were completed by 22 groups. Very detailed information, including maps, photos, and other site relevant information not requested was submitted by several groups. Based on the completed field forms, sampling by volunteers resulted in the capture and processing of 612 salamanders, of which 461 (75%) were recorded as larval or transforming (gill stubs), and 151 (25%) were terrestrial. The median salamander abundance at volunteer sites was 13 and ranged from 1-105. Plot medians were 4 - 6 salamanders for each of the three plots. Examination of these results relative to those obtained by the highly experienced SPAR technicians can serve as a coarse comparison of volunteer sampling proficiency. Table 23 summarizes the number of salamanders captured by volunteers and PSU SPAR technicians. Only sites sampled in PA by the latter are shown. Summary statistics are presented by 3 stream conditions: degraded, mildly degraded, and non-degraded (reference) condition (Figure 15). A one-way ANOVA on salamander abundance, square root transformed to achieve normality and variance homogeneity by the four groups revealed significant differences among these groups (F = 8.01, df 3, 85, p < 0.0001). Subsequent testing by Tukey’s a posteriori tests identified homogeneous means only for volunteer, impaired, and non-reference stream sites. In other words, volunteer results were not comparable to those obtained by SPAR technicians for reference sites, suggesting among other possibilities, that volunteers either sampled sites of poorer quality or were not as effective as the SPAR technicians. Volunteers were asked to sample relatively pristine sites in forested watersheds. No effort was made in this analysis to assess the actual condition of volunteer sites. 28
Volunteers reported capturing all PA stream plethodontids except for the longtail (E. longicauda). The slimy salamander (Plethodon glutinosus), a woodland species occasionally captured by the SPAR technicians, was reported by one group but was not collected as a voucher. Of the 612 salamanders observed, 126 (21%) were returned as vouchers. Examination of vouchers revealed 89 (71%) to be larvae or metamorphs, salamanders in the process of transforming or possessing gill-stubs. The remaining 37 (29%) were non-larval. The proportion of larval to non-larval for the vouchers was very close to that reported for all animals processed by volunteers. These proportions are also comparable to what we have observed over the years, and by others engaged in similar stream plethodontid work in the MAHA (Southerland, et al., in prep, Jung et al., 2000). Volunteers were very conservative with respect to vouchers. Based on training guidelines, 64 additional specimens should have been processed as vouchers, indicating a desire by volunteers to minimize this practice to the extent possible. Laboratory examination of vouchers confirmed the presence of 7 species (Figure 16). The most abundant was the northern two-lined (E. bislineata), followed by the mountain dusky (D. ochrophaeus). The northern red (Pseudotriton ruber) was as common as the northern dusky (D.fuscus) in this group of vouchers. This is not surprising because many volunteer sites were located in the Piedmont region of PA, an area where this species is commonly observed in small streams. Of the 126 vouchers examined, 28 (22%) were incorrectly identified (Table 24). Most of the vouchers were incorrectly identified to genus (n = 24) than to species (n = 4). All of the former were larvae, whereas incorrectly identified species were adults or juveniles (not gilled). Inspection of the table shows that larval E. bislineata were commonly confused with larval D. fuscus, a recurring error that accounted for 16 (67%) of the 24 genus misidentifications. Of the 79 E. bislineata vouchers, 18 (22%) were incorrectly identified. All were larvae and 16 (89%) of these were incorrectly identified as larval D. fuscus, as indicated above. Larvae of this species were also confused with larval Gyrinophilus, and Pseudotriton. All non-gilled or stubbed E. bislineata were correctly identified. The only D. fuscus larva among the 126 vouchers was identified correctly. The misidentification of E. bislineata occurred among 6 (26%) volunteer groups with 1 group identifying this species as two different species, and 1 group as 3 different species (e.g. larvae E. bislineata vouchers from the latter group were identified as D. fuscus, Gyrinophilus porphyriticus, and E. bislineata). Only 4 (10.5%) of the 38 non-gilled vouchers were incorrectly identified. Based on this limited number of observations, distinguishing between D. fuscus and D. ochrophaeus proved to be the most challenging for the 3 volunteer groups generating the errors. Classroom testing predicted these difficulties. The frequency of incorrect identification, however, was much lower than anticipated. It should be noted that most volunteer sampling occurred in counties outside the range of D. ochrophaeus. Maps showing the range of this and other species were provided to 29
volunteers to aid identification on the basis of geographic range. A very different outcome may have been observed if more volunteers had sampled in areas where D. ochrophaeus and D. fuscus are sympatric. A single redback (Plethodon cinereus), a common woodland species occasionally found near stream plots was identified as a young D. ochrophaeus. Several notable characteristics facilitate their discrimination e.g. lack of the white line from eye to corner of mouth, length and shape of the tail, and size of the hind limbs relative to the anterior limbs (Conant et al. 1998). Woodland species were not included in the training collection. Lifestage was incorrectly identified for 16 (13%) specimens (Table 25). The presence of gill stubs was either not noticed or recorded on all but one of these vouchers. Evidently this characteristic is easily overlooked. Sometimes careful examination of the underside of the lower mandible is needed to reveal remnants of the gills. It can easily go unnoticed if hastily processed, particularly on the relatively smaller metamorphic specimens of Eurycea and D. fuscus, two species that combined accounted for 15 of these 16 vouchers. DISCUSSION Most studies rely on a sample to draw inferences on a population of interest. In Pennsylvania, 11,000 individuals participated in volunteer citizens monitoring activities. The objectives of this investigation was to evaluate volunteer proficiency to identify and sample PA stream salamanders. Were the participants in this study a representative sample of the population of volunteers likely to assist with future stream salamander monitoring? We recruited directly through the PA Citizens Volunteer Monitoring Program so a fairly high number of current citizen monitors had the opportunity to enroll. Participation was restricted to individuals older than 16 yrs of age, possessed a PA fishing license, were able to conduct a physically demanding activity and willing to voucher a small number of salamanders. Future monitoring of stream salamanders are unlikely to require such intensive sampling and voucher collection. Consequently, future recruitment efforts may attract a much different group of volunteers. An unknown proportion had participated as volunteer citizen monitors in the past. Many of the participants were professional biologists (70%) seeking to further their education in a specialty likely to become integrated with existing headwater assessment methods. Hence, in many respects, the sample in this study may be marginally representative of volunteers likely to assist in future stream salamander monitoring efforts. Additionally, it may be fair to conclude that proficiency and motivation displayed by this group may rank fairly high and represent the upper bounds of performance for volunteers. The training had a significant impact on volunteers as a group, a very desirable outcome. This indicates that volunteers will respond positively to training and as a result of training, will identify stream salamanders in PA more effectively than without it. In other words, cost and time expended to train volunteers is worthwhile. The study indicates that volunteer training was beneficial, but the level of proficiency attained was highly variable among volunteer groups and varied by salamander species. Variability in volunteer proficiency is unlikely to be eliminated entirely. Testing of volunteers after the training, however, would identify deficient groups and help set desirable standards. Future 30
training programs may also benefit by focusing instruction on the more difficult to identify species and lifestages identified in this study. Devising better methods for discrimination may diminish identification errors as well. Case in point: description of a procedure facilitating discrimination of larval E. bislineata from larval D. fuscus is soon to be published (Davic pers. comm.). This identification aid may reduce the number of incorrectly identified larvae considerably. In this study, E. bislineata was incorrectly identified by 20% - 26% of volunteer groups even after training. Of greater relevance to Davic’s forthcoming technique was the finding that of the 28 incorrectly identified vouchers, 16 (57%) were larval E. bislineata incorrectly identified as larval D. fuscus. Implementation of this technique is anticipated to reduce misidentification of E. bislineata larvae appreciably. Since E. bislineata larvae are the most widespread and commonly encountered stream salamander, implementation of this technique alone is likely to have a tremendously positive effect. An easy, fail-proof method to distinguish among Desmognatus species has yet to be published. Until that time, future training will need to place greater emphasis on current techniques to distinguish Desmognatus species. Thus, there is considerable optimism that training limitations documented in this study can be addressed. “Certification” may help set desirable standards. Revising the training syllabus to incorporate techniques that aid identification will improve volunteer proficiency overall. Volunteers in this study were asked to sample by a rather time-consuming and arduous method. Not all trainees completed the sampling task. Those that did, appeared to do so satisfactorily, albeit with an “effectiveness” not comparable to SPAR project crews. The total number of salamanders captured was used as an indicator of sampling effectiveness. Both outcomes suggest that plot sampling may not be suited for volunteer sampling activities. Much of the tasks and expertise demanded in this study may not be necessary depending on the goals and methods of a future monitoring program. For example, in Maryland, the total number of salamanders, a stream salamander metric investigated in part 1 of this study, was found to be an effective predictor of stream condition (Southerland, in prep.). If volunteer effectiveness was calibrated accordingly, the only necessary skill required of a volunteer would be counting salamanders! Currently, efforts are under way in Ohio to develop a modified leaf-bag sampling technique (Pauley and Little, 1998) that someday may render plot and other intensive sampling methods unnecessary (Davic, pers. comm.). At that point, some of the concerns raised in this study may be irrelevant.
31
LITERATURE CITED Barbour, M.T., J. Gerritsen, B.D. Snyder, and J.B. Stribling. 1999. Rapid bioassessment protocols for use in streams and wadeable rivers: Periphyton, benthic macroinvertebrates and fish, Second Edition. EPA 841-B-99-002. U.S. Environmental Protection Agency; Office of Water, Washington, D.C. Barbour, M.T. 2004. Technical guidelines: Critical elements for an adequate bioassesment program. Oral presentation presented at the Mid-Atlantic Water Pollution Biology Workshop, March 25, 2004. Clarke, R.T., J.F.Wright, and M.T. Furse. 2003. RIVPACS models for predicting the expected macroinvertebrate fauna and assessing the ecological quality of rivers. Ecological Modelling 160:2003 pp 219-233. Conant, R, J.T. Collins, I.H., Conant, and T.R. Johnson. 1998. Reptiles and amphibians of Eastern and Central North America. 3rd Edition, The Peterson Field guide Series. pp. 616 Fritz, K.M. 2003. Development of biological indicators methods and assessment techniques for use in headwater intermittent streams. Powerpoint presentation found available at www.epa.gov/region5/water/wqb/pdf/fritz_presentation.pdf Gomi, T., R.C. Sidle, and J.S. Richardson. 2002.Understanding processes and downstream linkages of headwater systems. Bioscience 52:10 pp 905-916 Herlihy, A.T., Larsen, D.P., Paulsen, S.G., Urquhart, N.S., and Rosenbaum, B.J. 2000. Designing a spatially balanced, randomized site selection process for regional stream surveys: the EMAP Mid-Atlantic pilot study. Env. Monit. Assess. Hawkins, C.P., R.H. Norris, J.N. Hogue, and J.W. Feminella. 2000. Development and evaluating of predictive models for measuring the biological integrity of streams. Ecological Applications 10:5 pp 1456-1477. Jung,. R.E., S. Droege, J.R. Sauer, and R.B. Landy. 2000. Evaluation of terrestrial and streamside salamander monitoring techniques at Shenandoah National Park. Environmental Monitoring and Assessment 63:65-79 McCormick, F.H. R.M Hughes, P.R. Kaufmann, D.V Peck, J.L Stoddard, and A.T. Herlihy. 2001. Development of an index of biotic integrity for the Mid-Atlantic Highlands Region. Transactions of the American Fisheries Society, 130:857-877
32
Meyer, J.L.and J.B. Wallace. 2001. Lost linkages and lotic ecology: rediscovering small streams. pp 295-317 in M.C. Press, N.J. Huntly, and S.Levin, Eds. Ecology: Achievement and Challange. Symposium of British Ecological Society and Ecological Society of America, Orlando, Fl Neter J., M.H. Kutner, C.J. Nachtsheim, and W. Wasserman. 1989. Applied linear statistical models. Fourth Edition. Irwin, Chicago. pp. 1408 Ohio EPA. 2001. Field evaluation manual for Ohio’s primary headwater habitat streams. Ohio EPA, Division of Surface Water, Columbus, Ohio, USA Ohio EPA. 2002. Technical report: Ohio’s Primary Headwater Streams - Fish and Amphibian Assemblages. Ohio EPA, Division of Surface Water, Columbus, Ohio, USA Pauley, T.K., and M. Little. 1998. A new technique to monitor larval and juvenile salamanders in stream habitats. Benisteria 12:32-36 Petranka, J.W. 1998. Salamanders of the United states and Canada. The Smithsonian Institution Press, Washington and London. pp 587 Pfingsten, R.A. and F. Downs, eds. 1989. Salamanders of Ohio. Ohio Biol. Surv. Bull. New Series Vol 7, No. 2. pp. 1-315. River Network/PADEP. 2001. Designing your monitoring program: a technical handbook for community-based monitoring in Pennsylvania. Pennsylvania Citizens’ Volunteer Monitoring Program. Rocco, G.L., and Brooks, R.P. 2000. Abundance and distribution of a stream Plethodontid salamander assemblage in 14 ecologically dissimilar watersheds in the Pennsylvania Central Appalachians. Final Technical Report No. 2000-4 of the Penn State Cooperative Wetlands Center, PSU, University Park, PA. 70 pp. Southerland, M.T., R.E.Jung, D.P. Baxter, I.C. Chellman, G. Mercurio, and J.H. Volstad. in prep. Stream salamanders as indicators of stream quality in Maryland. Stevens, J.P. 2002. Applied multivariate statistics for the social sciences. Lawrence Erlbaum Associates, 4th ed.. Pp. 285-317. Thompson, W.L., G.C. White, and C. Gowan. 1998. Monitoring vertebrate populations. Academic Press, inc. San Diego pp. 233-260 Vannote, R.L., G.W. Minshall, K.W. Cummins, J.R. Sedell, and C.E. Cushing. 1980. The river continuum concept. Can J. Fish Aquatic Sci. 37:130-137.
33
Waite, I.R., A.T. Herlihy, D.P. Larsen, and D.J. Klemm. 2000. Comparing the strengths of geographic and nongeographic classifications of stream benthic macroinvertebrates in the mid-Atlantic Highlands, USA. Journal of the North American Benthological Society 19:429-441. Woods, A.J, Omernick, J.M., Brown, D.D., and Kiilsgaard, C.W.. 1996. Level III and IV Ecoregions of Pennsylvania and the Blue Ridge Mountains, the Ridge and Valley, and the Central Appalachians of Virginia, West Virginia, and Maryland. US Environmental Protection Agency, Corvallis, Oregon. EPA/600/R-96/077, 50 pp. Zug, G.R., L.J. Vitt, and J.P. Caldwell. 2001. Herpetology: An introductory biology of amphibians and reptiles. Second edition. Academic Press, San Diego. pp. 331-332
34
Figure 1. Map of Mid-Atlantic Highlands Area (MAHA) showing the approximate location of the 138 EMAP wadeable stream sites sampled in 2000 - 2002. Fine contours delineate Level III ecoregion boundaries (Woods et al., 1996)
1000
5000
750
3000
Total N
10000
Cl
2000
500
2000
250
1000
0
0
4000 8
2000
7
pH
ANC
1500 1000 500
6 5
Percent Watershed in Agriculture
0
Percent Watershed in Forest
100
100
80 60 40 20 0 3
80 60 40 20 0
1200 1000
Elevation (m)
Watershed Area (Km2)
4
2
1
800 600 400 200
0
p. p. p. ey ge ge all Ap Ap h Ap Rid rid l t V e s a t r e Blu entr W No C
0
MAHA Ecoregions
p. p. p. ey ge ge all Ap Ap h Ap Rid rid l t V e s a t r e Blu entr W No C
Figure 2. Boxplots showing stream and watershed characteristics of the 138 EMAP streams sampled in 2000-2002.
Figure 3. Interpolation of species richness values by GIS for 39 reference, near reference sites in the MAHA. Shading represents average species richness values ranging from 2 - 2.3 for northernmost band to 4.7 - 5.0 for southernmost band.
3 fuscus
2 montc 1
Axis 2
Eryc Gyrnp
ochrp
0
-1 Pseudt -2 -1
0
1
2
3
Axis 1 Figure 4. DCA ordination biplot showing the 34 reference/ near reference sites (circles) and the 6 stream salamander species (cross). Open and closed circles distinguish TWINSPAN sites in group “0" and “1", respectively. montc = Appalachian seal, Pseudt = northern red, fuscus = northern dusky, Eryc = two-lined, Gyrinp = northern spring, ochrp = mountain dusky
montc 2
Group 2
ochrp Gyrnp 1
Group 1
Eryc
0
Group 3 fuscus -1 Pseudt 0
1
2
3
4
Axis 1 Figure 5. DCA ordination biplot showing the 34 reference/ near reference sites (circles) and the 6 stream salamander species (cross). Dashed lines separate clusters identified as DCA groups 13. Arrows point to TWINSPAN grouping of the 7 “discrepant” sites. montc = Appalachian seal, Pseudt = northern red, fuscus = northern dusky, Eryc = two-lined, Gyrinp = northern spring, ochrp = mountain dusky
low
flat
stream temperature
stream gradient
boulder cover
high steep cold
warm
south
north
Figure 6 Plot of canonical discriminant function scores for Group 1 - Group 3. The small squares in each cluster represent the group centroids. Scores along the first canonical axis are positively and most highly correlated to latitude. The second axis is negatively correlated to stream temperature but positively correlated to boulder cover and slope.
Group 1
Group 2
Group 3 Figure 7. Location of reference and near reference sites (closed circles) and non-reference and degraded sites (open circles) in the Mid-Atlantic Highlands region by group. Classification of the latter to one of the three groups was achieved by application of the MDA predictive model. Discriminant functions were based on the variables latitude, stream temperature, boulder cover, and stream gradient.
10
GROUP 2 - IBI 3
GROUP 1 - IBI 3
10
5
0
0 Degraded
Non-reference
Reference
10
Degraded
Non-reference
Reference
Degraded
Non-reference
Reference
10
MAHA - SS IBI
GROUP 3 - IBI 7
5
5
0
5
0 Degraded
Non-reference
Reference
Figure 8. Boxplots showing 4 IBI scores for degraded, non-reference, and reference sites in Group 1- Group 3, and the MAHA. Percent correct classification for reference and impaired sites for these IBIs ranged 59% - 82%. The closed circles included in the lower left boxplot represent medians.
Applicants 82 individuals; 56 groups
Trainees 64 individuals; 41 groups
1
1 1
1
1 1 1
1 2
1
2
1
2
1 1
1
2
6
1
5
1
1
1
2 12
1
2
3
2
5
1
1
1
2 12
2
1
3
3
4 1
2
1
6
1
MD applicant not shown
2 1
11
1
1
2
1
9
MD Applicant not shown
Figure 9. County map of Pennsylvania showing the geographic distribution of SPAR volunteer applicants (left) and trainees (right).
100
Adult Ambystoma and Desmognathus
Adult Desmognathus and Eurycea
Pre-training Test Scores
80 60 40 20 0
P 5000
Mean RBP habitat score
> 15 (optimal)
< 10 (marginal)
pH
level not set
99
level not set
Chloride (ueq L-1 Sulfate (ueq L-1
) )
Total phosphorus (ueq L-1 Total nitrogen (ueq L-1
)
)
)
Degraded Criteria
Table 2. Criteria developed by Waite et al. (2000) for the classification of stream sites Reproduced from Klemm et al., 2002
Surface Flow on Sampling Day Interstitial Study Site Attributes
n
Basin Size (Km2)
34
Elevation (m)
33
Bankfull width (cm)
Mean 0.96
Results of Univariate t-tests * = unequal variances ns = p >0.05
Continuous SE
n
Mean
t
p
DF
0.08
1.09
ns
44*
0.13
104
365
47.40
100
403
26.10
-0.99
ns
131
34
235.3
17.20
102
328.1
14.0
-3.40
0.001
134
Max. Bankfull depth (cm)
34
19.2
1.42
102
26.9
1.30
-3.51
0.001
134
Max Pool Depth (cm)
34
25.9
2.40
102
31.8
1.54
-2.09
0.038
133
Julian Day
34
3.59
104
2.25
0.45
ns
136
Stream temperature (deg. C)
34
16.5
0.36
103
16.0
0.28
1.34
ns
81*
Dissolved Oxygen (mg/l)
28
6.2
0.42
94
7.7
0.13
-3.16
0.004
29*
15.48
0.002
3,131
184
1.43
SE
183
Hotelling’s T2 (Bankfull width, Max. Bankfull depth, and Max. Pool Depth)
Table 3. Study site characteristics of streams found interstitial and with dry channel on the day of the survey compared to stream sites found freely flowing (continuous). Results for univariate t-tests are provided for comparison of individual attributes only. Reported t-statistics are for logtransformed data. Variance estimates were calculated separately for samples with unequal variances. The multivariate t-test (Hotelling’s T2) included 3 variables: bankfull width, max. bankfull depth, and max. pool depth. These variables are related to stream site hydraulics and had equal variances. A site fed by a warm spring was not included in the above analysis.
Species Name
Abundance
Percent Relative Abundance
Percent of Streams Occupied
Mountain dusky (Desmognathus ochrophaeus)
613
9.5
39.9
Northern dusky (D. fuscus)
807
12.5
49.5
Appalachian seal (D. monticola)
499
7.7
25.4
Black belly (D. qudramaculatus)
136
2.1
5.8
3,929
60.1
76.1
5
0.1
1.4
50
0.8
14.5
415
6.4
45.7
Slimy (Plethodon glutinosus)
3
0.15, P < 0.05) between salamander metrics and variables related to natural gradients. (n = 34)
Axes
I
II
III
IV
Eigenvalue
0.286
0.153
0.047
0.021
Gradient Length
2.488
1.714
1.687
1.256
37.6
57.7
63.9
66.7
Cumulative Percentage Variance of Species Data
Total Inertia 0.761
Table 8. Results from detrended correspondence analysis (DCA) for stream salamander presence- absence data. Statistics are given for the first four ordination axes.
Environmental Variables
Axis 1
Axis 2
Axis 3
0.542**
-
-0.489*
Longitude
-
-
-
Elevation
-
-
0.350
Basin Size
-
-
-
Stream gradient
-
-
-0.352
Bankfull width
-
-
-
Boulder cover
-
-
0.382
Stream temperature
-
-
-
ANC
-
-
-
Latitude
Table 9. Pearson product-moment correlation coefficients between DCA ordination axis 1- axis 3 site scores (based on presence absence data) and nine geographic, watershed, and stream environmental variables measured at each of the 34 reference sites. Only coefficients with pvalue < 0.05 are shown. (** p-value < 0.000, * p-value = < 0.01)
2 2 1 1 1 2 2 2 2 3 1 2 1 2 3 3 1 1 1 1 1 2 2 3 3 9 2 0 4 6 7 8 2 3 4 5 2 1 8 1 1 3 7 4 7 0 1 5 6 9 0 2 3 5 9 6 8 3 4 Psdt
1 1 1 - - - - - - - - - - - - - - -
- - - - 1 - - - - - - - - - - - 00
fusc
-
1 1 1 1 - - 1 - 1 1 - 1 1 1 1 1 1 1 1 1 1 -
mont
-
- 1 1 1 1 1 1 1 1 1 1 - - - - - 1 - 1 1 1 1 - - - - - - 1 - 1 1 - 01
Eury
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 - 1 - 1 1 1 1 - 1 1 1 1 1 1 1 1 1 1 01
Gyrn
1 - - 1 - 1 1 1 - - - 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 - 1 1 1 10
ochr
-
- - - - - - - - - - - 00
- - - - - - - - - - - - - - - 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 - 11
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1
Table 10. Two-way ordered table from TWINSPAN analysis with presence-absence species data for the 34 reference/ near reference stream sites. mont = Appalachian seal, Psdt = northern red, fuscs = northern dusky, Euryc. = two-lined, Gyrin = northern spring, ochrp = mountain dusky
Group (“0") n = 16
Group (“1") n = 18
Univariate t-value, df (separate variance estimate)
Latitude
38.53 ± 0.252
39.67 ± 0.398
-2.417, 28.19b
Longitude
-79.043 ± 0.254
-79.316 ± 0.345
NS
Elevation a
294 ± 50.2
414 ± 57.1
NS
Basin size
1.24 ± 0.188
1.54 ± 0.194
NS
Stream gradient a
5.82 ± 0.727
7.25 ± 0.874
NS
Bankfull width
329.8 ± 24.1
363.6 ± 31.4
NS
Boulder cover
24.1 ± 3.06
28.9 ± 2.89
NS
Stream temperature
16.4 ± 0.50
14.8 ± 0.59
2.039, 31.65
ANC a
186.6 ± 41.5
249.2 ± 55.3
NS
Environmental Variables
a b
Mean ± SE
Variable was square-root transformed prior to all statistical tests Levene test < 0.001 (df =32)
Table 11. Comparison of environmental variables by TWINSPAN derived classification for presence absence matrix. Only univariate T-values of p-value < 0.05 are shown.
Axes
I
II
III
IV
Eigenvalue
0.348
0.204
0.107
0.028
Gradient Length
2.358
1.716
1.766
1.527
37.8
59.9
71.5
74.6
Cumulative Percentage Variance of Species Data
Total Inertia 0.923
Table 12. Results from DCA for stream salamander abundance data. Statistics are given for the first four ordination axes.
Geographic,Watershed, & Stream Variables
Axis 1
Latitude
-0.696 **
-
-
Longitude
-0.365
-
-
Elevation
-
-
-
Basin size
-
-
-
Stream gradient
-
0.522 *
-0.450 *
Bankfull width
-
-
-
Boulder cover
-
0.425
-
0.413
-
-
-
-
-
Stream temperature ANC
Axis 2
Axis 3
Table 13. Pearson product-moment correlation coefficients for DCA ordination axis 1 - axis 3 site scores (based on abundance data) against nine geographic, watershed, and stream environmental variables measured at each of the 34 reference sites. Only coefficients with pvalue < 0.05 are shown. (** p-value < 0.000, * p-value = < 0.01)
1 2 2 3 1 2 1 1 2 2 2 2 3 1 2 2 3 3 3 1 1 1 1 2 1 1 2 8 1 1 9 4 6 4 4 7 8 0 2 3 5 2 5 7 9 7 8 0 1 3 9 0 2 3 5 6 3 6 4 monti
-
- - - - - - 2 2 5 4 3 4 4 5 4 4 3 3 5 2 3 5 2 5 - - - - - - - - - 00
Psdt
-
3 - - - 2 - - - - - - 2 - - - - 2 - - - - -
fuscs
4 4 5 2 2 - - 2 4 4 - - 3 5 - 2 - -
Euryc
5 5 5 5 5 5 5 5 5 4 4 4 5 5 4 2 5 3 4 4 5 5 5 2 5 4 4 5 2 4 5 - - - 01
Gyrin
2 - 2 2 4 2 2 - - 3 4 2 - 5 - - 5 2 2 2 2 3 2 2 2 3 4 4 2 4 - 2 3 4 10
ochrp
-
- - - - - - - - - - - 00
2 - 2 - 2 5 - - - - - - - 2 - 3 01
- - - - - - - - - - - - - - - - 3 4 3 3 4 2 5 3 5 2 3 5 5 2 2 5 5 11
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1
Table 14. Two-way ordered table from TWINSPAN analysis with abundance data for the 34 reference/ near reference stream sites. Cell values -, 2, 3, 4, and 5 correspond to 0, 1-4, 5-9, 1019, and > 20 individuals, respectively. Mont = Appalachian seal, Psdt = northern red, fuscs = northern dusky, Euryc. = two-lined, Gyrin = northern spring, ochrp = mountain dusky
Environmenta l Variables Latitude
Mean ± SE Group (“00") n=9 39.001 ± 0.293 a
F-value for One-way ANOVA
Group (“01") n = 16
Group (“1") n=9
38.073 ± 0.181 b
41.141 ± 0.292 c
41.76**
Longitude
-78.865 ± 0.452 ab
-79.904 ± 0.193 a
-78.236 ± 0.402 c
7.59*
Elevation a
284 ± 80
422 ± 61.7
316 ± 54.9
NS
Basin size
1.4 ± 0.31 ab
1.1 ± 0.15 a
1.9 ± 0.25 b
3.47
Slope a
4.6 ± 0.68 a
8.1 ± 0.95 b
5.8 ± 0.84 ab
3.62
Bankfull width
280.4 ± 19.2
372.6 ± 31.3
370.6 ± 43.2
NS
Boulder cover.
18.1 ± 3.32 a
31.8 ± 3.43 b
26.2 ± 2.11 ab
4.25
Stream temp.
17.3 ± 0.64 a
15.4 ± 0.43 ab
14 ± 0.97 b
5.31
185.2 ± 36
252.7 ± 67.3
195.8 ± 48.5
NS
ANC a
Table 15. Group means and standard errors for 9 geographic and stream habitat variables. Fvalues are based on one-way ANOVA. Pairwise comparisons were carried out a posteriori with a Tukey-Kramer test (P < 0.05). Only F-values of P-value < 0.05 are shown. ** = p-value < 0.000, * = p-value < 0.01). Within rows, means with different letters vary significantly. a Variable was square-root transformed prior to statistical testing.
Ecoregion
No. Of Sites
Geology/Lithology
EPA Ecoregion Level IV
Group “00" n=9
Plateau
2
Cumberland mts., harder sedimentary
69a, 69d
Ridge
7
igneous, sandstone, sed./metasedimentary, shale
66a, 66b, 67c, 67d
Group “01" n = 16
Plateau
6
soft and hard sedimentary
69a, 69b
Ridge
9
igneous, sandstone, sed./ metasedimentary, shale
66a, 66b, 66e, 67d
Valley
1
shale
67b
Plateau
8
hard sedimentary, Western Appalachian Plateau
62c, 62d, 70
Ridge
1
sandstone
67c
Group “1" n=9
Table 16. Ecoregion and geological setting for the 34 reference and near reference sites by the three TWINSPAN groups. Ecoregion is according to McCormick et al. (2001). Geology and EPA Ecoregion Level IV is according to Woods et al. (1996)
Discriminant Functions 1 Eigenvalue
2 4.136
0.682
% of Variance
85.8
14.2
Cumulative %
85.8
100.0
0.897
0.637
63.6
15.33
8
3
< 0.0001
< 0.002
Canonical Correlation Chi-square test df P-value
Structure Matrix (Correlations between variables and functions) Latitude
0.793
0.372
Longitude
0.529
0.202
Basin size
0.155
-0.039
Bankfull width
0.074
-0.052
Stream temp.
-0.106
-0.659
Boulder freq.
-0.135
0.539
Stream gradient a
-0.168
0.413
Elevation a
-0.106
0.155
ANC a
-0.049
0.111
Table 17. Summary of MDA on 3 groups with four environmental variables. Linear correlations (r) between variables and functions are shown for all nine variables. Variables names in bold identify variables included in the model. Variables are sorted by largest absolute size of correlation. a
Variable was square-root transformed prior to statistical testing.
Variables Latitude
Reference /Near reference (n = 34)
Non-reference / Impaired (n = 101)
39.132 ± 0.258
39.604 ± 0.165
36.800 Boulder cover
41.787
26.68 ± 2.12 6.73
Stream temperature
36.601
18.51 ± 1.53
56.25
15.54 ± 0.410 10.0
Stream gradient
20.2 6.58 ± 0.58
1.75
41.897
0
72.67
16.48 ± 0.0.316 9.5
31.3
4.44 ± 0.0.283 15.24
0.96
16.93
Table 18. Mean, standard error, and minimum and maximum values for the four environmental variables used in the predictive model for 34 reference/ near reference sites and 101 nonreference / degraded sites.
STREAM SALAMANDER METRICS A
B
C
D
E
F
G
H
I
J
K
Group 1 (n = 9) Julian day Elevation Basin size Latitude Longitude Stream gradient Bankfull Boulder Cover Stream temperature Group 2 (n = 16) Julian day Elevation Basin size Latitude Longitude Stream gradient Bankfull Boulder Cover Stream temperature Group 3 (n = 9) Julian day Elevation Basin size Latitude Longitude Stream gradient Bankfull Boulder Cover Stream temperature
0.731
0.722
0.763
0.729
-0.728
-0.51 -0.58
-0.743 -0.588
-0.623 -0.566
-0.594
-0.552
0.62 -0.549
-0.537 -0.514
-0.547
-0.675 0.792
Table 19. Pearson product-moment correlation coefficients (r > 0.15, P < 0.05) between salamander metrics and variables related to natural gradient by group.
-0.535
Group 1 (n = 32) Metric
P
Group 2 (n = 23)
% correct
P
Group 3 (n = 28)
% correct
P
% Correct
A
Species Richness
0.023
72
ns
B
No. mountain dusky
0.018
53
ns
C
No. two-lined
D
No. northern spring
0.008
69
E
No. salamanders
0.002
78
F
No. intolerants
0.008
78
G
No. nutrient tolerant
H
No. acid tolerant
0.004
81
0.038
59
I
No. terrestrial
0.012
53
0.027
55
J
No. larvae
0.045
63
ns
0.015
K
No. LS class 3
0.046
59
ns
0.017
Group mean
ns
0.003
86
P
% Correct
0.000
none
ns
78 ns
0.027
68
0.001
60
ns
0.003
21
0.000
65
0.035
55
0.046
61
0.000
62
0.011
55
ns
0.000
67
ns
0.002
56
0.001
55
0.000
63
71
0.000
59
75
0.000
63
ns
ns
66
MAHA (n = 82)
56
0.026
61 ns
63
Table 20. Metric responsiveness by group and for the MAHA (all sites combined). For each metric, the equality of medians for reference vs. degraded sites were tested by a Mann-Whitney test before scoring. Only p -values < 0.05 are shown. Classification efficiency for a metric is the proportion of sites correctly classified as reference or degraded (score > 50th percentile of reference for reference sites; < 25th percentile of reference for degraded sites). In this comparison non-reference sites were not included. Responsiveness of metric “B” for sites in Group 3 could not be calculated.
63
IBI Name
Identifier for Metrics in IBI
Percent of sites correctly classified Group 1 Cmb (53)
Deg (23)
Group 2 Nrf (21)
Cmb (31)
“SS IBI”
AEFI
62
81
1
DEFI
62
91
2
DEF
66
91
65
67
3
EFIH
64
87
65
83
4
EFI
68
83
65
67
5
ACG
81
83
81
6
ACKJ
70
61
86
7
AK
74
74
81
61
68
82
58
62
Mean
68
Deg (6) 67
Group 3 Nrf (9)
Deg (19)
78
63
74
67
65
68
71 67 78
Nrf (23)
Cmb (135)
Deg (48)
62
81
65
63
81
74
74
69
77
65
63
70
64
81
69
63
78
69
81
65
89
63
89
78
76
89
63
67
76
67
63
Cmb (51)
MAHA Nrf (53)
62
75 64
75
70
76
83
75
62
66
79
57
Table 21. Classification efficiency for 8 IBIs by group and for the MAHA (all sites combined). Classification efficiency was calculated as the proportion of sites correctly classified as reference, non-reference, and degraded (scores >50th percentile of reference for reference sites, > 25th percentile of reference for non-reference sites, < 25th percentile of reference for degraded sites). Within each group, the percent of correctly classified sites is given for all three stream types (Cmb.), degraded (Deg.), and non-reference (Nrf.). Number of observations per column are in brackets. Except for means, only percentages > 60 % are shown. Refer to Table 20 for key to metric identifier. Cells in bold identify the top 2 IBIs per group.
Test Scores (mean ± SE) Training Date
Pre-Training
Post-training
T-value, N
June 19
45.5% ± 10.2
77.6% ± 5.7
5.38* , 7
June 20
51.1% ± 5.56
78.9% ± 2.18
4.89** , 11
June 21
35.7% ± 5.11
75.0% ± 2.20
7.35** , 7
June 22
57.9% ± 6.91
87.1% ± 3.11
4.95** , 5
June 23
56.5% ± 8.3
91.1% ± 2.73
4.30 , 6
48.8% ± 3.34
81.0% ± 1.71
11.71** , 36
Total
Table 22. Summary of test scores by training date. T-value is for one-tailed, paired T-test. All test statistics have p-value < 0.05 or less. * = p < 0.001, ** = p-value < 0.0001.
Sampled by: Stream Type Mean ± SE n
SPAR Technicians Degraded
Non-Reference
Volunteers Reference
Not Degraded?
14.9 ± 2.28 A
43.9 ± 6.76 A B
56.3 ± 11.8 B
26.5 ± 6.13 A
28
28
10
23
Table 23. Salamander abundance at EMAP/ SPAR sampled sites and volunteer sampled sites. All except 2 volunteer sites are in PA. Homogeneous means were tested a posteriori with a Tukey-Kramer test (P < 0.05). Within rows, means with different letters vary significantly. Salamander abundance was square-root transformed prior to statistical testing.
Actual Identity of Vouchers
Volunteer Identification:
No. of Vouchers Not gilled
Stubbed
No. of Groups Gilled
Mountain dusky n = 15 [15 ng] Groups = 6
N. dusky
3
1
N. dusky n = 13 [12 ng, 1g] Groups = 10
Mountain dusky
1
1
Appalachian seal n = 3 [3 ng] Groups = 2
All Correctly identified
N. two-lined n = 79 [7 ng, 14s, 58 g] Groups = 22
N. dusky
16
5
N. spring
2
1
Total
18
6
N. spring n = 3 [3 g] Groups = 3
N. red
1
1
N. two-lined
1
1
Total
2
2
N. spring
2
2
N. two-lined
1
1
Total
3
3
N. red n = 12 [1 s, 11g] Groups = 8
redback n = 1[1 ng] Groups = 1 Total n = 126 [38 ng, 15 s, 73 g) groups = 23
mountain dusky
1
5
1
23
11 groups (48%); 3 (13%) groups twice
Table 24. Summary of incorrect taxonomic identification for 28 vouchers. A total of 126 vouchers submitted by 23 volunteer groups were examined. Columns from left to right identify: actual identity of vouchers, voucher identity by volunteer, the number of vouchers identified, and the number of volunteer groups involved. Actual lifestage of vouchers are noted as gilled (g), stubbed (s), and not gilled (ng). The total number of vouchers by lifestage is given in the first column. Vouchers were incorrectly identified by 11 groups. Of these groups, 3 incorrectly identified vouchers twice (different species).
Actually Present:
Identified as having:
Actual Species
Gill stubs
No gills
N. red
No gills
N. two-lined
No Gills
N. dusky
Gills Total
N observed 1 14 1 16
Table 25. Summary of incorrect life stage identification for 16 vouchers. The left column identifies the actual condition of vouchers. The left middle column is the condition identified by the volunteer. The right middle column is the actual species. Right column is number of vouchers matching the condition.
