This article was downloaded by: [University of Auckland Library] On: 22 April 2015, At: 14:25 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
New Zealand Journal of Marine and Freshwater Research Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tnzm20
Volunteer and professional macroinvertebrate monitoring provide concordant assessments of stream health a
ab
ER Moffett & MW Neale a
Research, Investigations and Monitoring Unit, Auckland Council, Auckland, New Zealand b
Click for updates
School of Biological Sciences, University of Auckland, Auckland, New Zealand Published online: 21 Apr 2015.
To cite this article: ER Moffett & MW Neale (2015): Volunteer and professional macroinvertebrate monitoring provide concordant assessments of stream health, New Zealand Journal of Marine and Freshwater Research, DOI: 10.1080/00288330.2015.1018913 To link to this article: http://dx.doi.org/10.1080/00288330.2015.1018913
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
New Zealand Journal of Marine and Freshwater Research, 2015 http://dx.doi.org/10.1080/00288330.2015.1018913
RESEARCH ARTICLE Volunteer and professional macroinvertebrate monitoring provide concordant assessments of stream health ER Moffetta** and MW Nealea,b*
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
a Research, Investigations and Monitoring Unit, Auckland Council, Auckland, New Zealand; bSchool of Biological Sciences, University of Auckland, Auckland, New Zealand
(Received 29 October 2014; accepted 10 February 2015) Whilst volunteer monitoring has many benefits for both volunteers and professionals, volunteer data must be validated to understand the value and potential applications of information from volunteer monitoring programmes. Our study aimed to assess the concordance between volunteer and professional data, including state and trend assessments. We compared macroinvertebrate data collected by volunteers using a simplified identification protocol to data collected by professionals following standard national protocols for collection and identification. We found that volunteer and professional macroinvertebrate data expressed as summary indices of ecological health were significantly correlated. However, the coarser level of taxonomic identification in the volunteer dataset limited the use of taxon richness as a biodiversity measure. We also demonstrated that the ability of volunteer data to detect long-term trends in ecological health is comparable to professional data. Overall, stream monitoring data collected by volunteers provided an assessment of stream health that was concordant with assessments based on data collected by professionals, indicating that volunteer data could be used to support professional monitoring programmes. Keywords: macroinvertebrates; MCI; monitoring; New Zealand; streams; temporal trends; volunteer; Wai Care; WIMP
Introduction Environmental monitoring is increasingly being carried out by volunteer community groups as public interest in environmental issues increases (Riesch & Potter 2014). Volunteer monitoring has many perceived benefits for volunteers and professionals (Tulloch et al. 2013). For volunteers, benefits include increased environmental literacy and raised awareness about environmental change (Dickinson et al. 2012; Reynolds & Lowman 2013; Donnelly et al. 2014). Volunteer monitoring may
produce large volumes of data (Foster-Smith & Evans 2003) that may not be financially or logistically possible for professionals to collect (Dickinson et al. 2010; Catlin-Groves 2012). Therefore, volunteer monitoring data may also inform us about long-term environmental change, contribute to regional monitoring programmes and be used to support management decisions (Tulloch et al. 2013; Latimore & Steen 2014). Despite the potential benefits of volunteer moni‐ toring programmes, data collected by volunteers are
*Corresponding author. Current address: Golder Associates (NZ) Ltd., Takapuna, Auckland, New Zealand. Email:
[email protected] **Current address: School of Environment, University of Auckland, Auckland, New Zealand. Supplementary data available online at www.tandfonline.com/10.1080/00288330.2015.1018913 Supplementary file: Summary of sites used to assess the concordance between volunteer and professional data, including substrate type as defined by Auckland Council and distances in space and time between sampling events which were compared. © 2015 The Royal Society of New Zealand
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
2
ER Moffett and MW Neale
often perceived as unreliable (Nerbonne & Vondracek 2003; Bonney et al. 2009; Catlin-Groves 2012). Scientists may be uncertain about volunteer data quality due to variation or errors in sampling protocols and taxonomic identification, or spatial and temporal inconsistencies in sample collection (Foster-Smith & Evans 2003; Snäll et al. 2014; Ward 2014). As a result, volunteer data is often not perceived as fit-for-use in monitoring program‐ mes or seen as publishable by scientists (CatlinGroves 2012). Given the issues with volunteer data, there remains uncertainty around the validity of using such data in professional monitoring programmes and scientist-led research projects. However, there have been some assessments of the validity of volunteer data, including direct comparisons between volunteer and professional data (Fore et al. 2001; Bonney et al. 2009; Latimore & Steen 2014). Such assessments have generally con‐ cluded that, when given the appropriate resources, volunteer data may be comparable to professional data. Globally, macroinvertebrates are widely used as an indicator of freshwater health. These biological communities are sensitive to changes in various aspects of environmental quality over their life spans (weeks to months); thus, species presence reflects overall site condition over a period of time (Roux et al. 1993; Karr 1999). In addition, macroinvertebrates are easy to sample using basic equipment (hand net), have keys readily available for identification and are ubiquitous (Metcalfe 1989). As a consequence, macroinvertebrates also make ideal indicator species for volunteer monitoring programmes. Wai Care is a volunteer monitoring programme in New Zealand’s largest city, Auckland (4894 km2), which advocates for community en‐ gagement in waterways (Auckland Council 2011; Wai Care 2013). Trained Wai Care volunteers independently sample and process macroinvertebrates; this programme therefore provides a viable example of data collection that could be achieved from volunteer monitoring programmes with limited professional involvement. Wai Care was launched in 2000 and has been successful in both its educational and data collection goals, including frequent visits to schools and a comprehensive database of stream
chemical and biological data. Data are currently used to monitor changes in stream health; however, there have been no assessments of how macroinvertebrate data collected by this programme relate to professional data. Furthermore, there are no other published comparisons of volunteer and professional monitoring data in New Zealand (but see Coates 2013). Our study tests the concordance between volunteer data from Wai Care and professional data from Auckland Council staff (local government) in assessing stream health and long-term trends. We hypothesised that data collected by professionals and Wai Care volunteers would be concordant, as volunteers were trained and supported with effective resources. Consequently, we predicted that both datasets would show a predictable response to in‐ creasing land use intensity and that temporal pat‐ terns in measures of stream health would be similar in both datasets where monitoring remained consistent through time. Methods Site selection Auckland Council has monitored a network of 52 streams consistently across the Auckland region from 2003 to 2013 (Moore & Neale 2008). Sites have been visited annually for macroinvertebrate collection between January and March. Wai Care volunteers have recorded data from 458 sites in Auckland since the programme’s formation in 2000; however, not all Wai Care site visits have included macroinvertebrate sampling. We selected data for our study from rivers that were common to both the Auckland Council and Wai Care programmes between 2003 and 2013. Data were not collected with the intention of comparing results; therefore, differences in sampling dates and locations exist in our dataset. For example, Auckland Council monitors its network of 52 streams annually in summer; in contrast, Wai Care volunteers sampled their sites throughout the year (SF Table). Volunteer sampling was therefore often more intensive than professional sampling. In order to reduce these spatial and temporal confounding effects, we further
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
Volunteer stream monitoring limited our selection to those sites that were in both programmes, were located within 1 km of each other on the same stream and were sampled within 3 months of each other. This selection process yielded 21 sites consisting of 57 paired samples that were within our spatial and temporal criteria. That is, there were 21 unique stream sites sampled by both Wai Care and Auckland Council located within 1 km of each other. Five of these sites were hard-bottomed while the other 16 were soft-bottomed, according to Auckland Council habitat data. Habitat information was not available for Wai Care data; thus, habitat may vary between volunteer and professional sites. Sample collection and analysis Professional data were collected by Auckland Council staff following standard national protocols in summer (January to March) (Stark et al. 2001). Briefly, hard-bottom streams were sampled using protocol C1, in which a fixed area (0.2 m2) of stream bed is disturbed upstream of a D-net (0.5 mm mesh) at five locations within a reach (total area 1 m2). Soft-bottom streams were sampled using protocol C2, in which a fixed area of stable substrate (woody debris, macrophyte and bank margin) is sampled by dislodging organisms into a hand held D-net. Samples were collected from a fixed area of 3 m2; this included 10 sampling efforts (0.3 m2 each) across different substrates which were sampled in proportion to their abundance. Samples were preserved in ethanol immediately following collection and later identified in a laboratory to species, genus or family level following quality control and identification protocols (Stark & Maxted 2007). Data are summarised as MCI (Macroinvertebrate Community Index), % EPT (Ephemeroptera, Plecoptera, Trichopera) richness and taxon richness. Wai Care volunteers were accompanied during their first sampling event by a Wai Care coordinator who provided volunteers with technical training and photographic identification guides. At both hard- and soft-bottom streams, Wai Care volunteers used kicknet (0.8 mm mesh) sampling and targeted a range of habitats depending on the proportion of habitat at each site. Targeted areas included stream beds at riffles,
3
bank margins, woody debris, macrophytes and other substrates (such as gravel). Each sample consisted of 10 sampling efforts within a stream’s reach. Where all habitats were present, half of the sample was from riffles, a quarter from vegetated banks, and the last quarter from woody debris and macrophytes. Samples were identified and scored onsite using a simplified scoring system, the Wai Care Invertebrate Monitoring Protocol (WIMP; Jones et al. 2012). This index was created to allow an assessment of stream health based on simplified identification, but also to ensure that important indicator taxa are recorded. The protocol includes 40 potential scoring categories of invertebrate with scores dependent on pollution tolerance (e.g. a low score indicates high pollution tolerance). WIMP scores may range from 0 to 300; scores ranging from 0 to 290 have been noted at Wai Care sites. Invertebrates are identified to differing taxonomic levels, depending on ease of field identification. For example, mayflies are identified to three groups—swimming mayfly, flat mayfly or spiny gill mayfly—where the swimming group includes numerous taxa, but the spiny gill group includes only one species (Coloburiscus humeralis Walker). Wai Care data were expressed as WIMP score (based on average score per taxon) and WIMP taxon richness. Wai Care data were uploaded to an open access online database following sample col‐ lection (see http://www.waicare.org.nz).
Data analysis Concordance between volunteer and professional data Professional (MCI, % EPT, taxon richness) and volunteer (WIMP score and WIMP taxon richness) data were compared using Spearman’s rank correlation coefficients to determine the strength (rs) and significance of the relationships. Differences were considered significant where P < 0.05. Data were plotted and correlations were run using SigmaPlot® v12.0. Land use relationships were investigated using a land use stress score (LUSS), where a higher value is indicative of greater land use intensity (range 0 to 300). LUSS was determined using the weighted
4
ER Moffett and MW Neale
sum of catchment percentage land use data (Collier 2008): LUSS ¼ ð0 % nativeÞ þ ð1 % exoticÞ þ ð2 % ruralÞ þ ð3 % urbanÞ
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
Land use percentage data were from the Land Cover Database (LCDB3) (Snelder et al. 2010). Relationships between LUSS stream ecological condition as MCI or WIMP were described using Spearman’s rank correlation coefficients.
Trend analysis Trend analysis was carried out on the volunteer and professional datasets for sites with ≥10 data points; seven paired sites met this criterion. Stream health indicators MCI and WIMP score were analysed using the Mann Kendall test in Time Trends Software v3.31 (Time Trends, NIWA, New Zealand). Trends are described using Sen slope values to indicate the strength and direction of the relationship and p-values to indicate significance (P < 0.05).
Results Concordance between volunteer and professional data Macroinvertebrate data collected by volunteers, expressed as WIMP scores, were positively correlated with data collected by professionals, expressed as % EPT richness (rs = 0.579, P < 0.0001) and MCI (rs = 0.540, P < 0.0001) (Fig. 1A– B). There was no correlation between professional taxon richness and WIMP taxon richness (Fig. 1C) (rs = 0.116, P = 0.375). MCI scores from professional data and WIMP scores from volunteer data both showed a negative response to increasing land use stress (Fig. 2). However, the correlation with land use was stronger for MCI (rs = −0.757, P < 0.0001) than for WIMP scores (rs = −0.424, P = 0.0015), which showed greater variation observed at a given LUSS.
Figure 1 Correlation between stream ecological health data collected by volunteers and data collected by professionals. A, % EPT richness as WIMP score; B, MCI vs. WIMP score; C, professional taxon richness (MCI taxon richness) data vs. volunteer taxon richness (WIMP taxon richness).
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
Volunteer stream monitoring
5
Figure 2 Relationship between land use stress scores and stream health data. A, Collected by professionals (MCI); B, collected by volunteers (WIMP score). Relationships are described using Spearman’s rank correlation coefficients (rs).
Concordance in temporal trends The direction and statistical significance of trends were generally concordant between datasets (Table 1). Positive (Oakley 1 and 2) and negative (Cascades and Konini) trends identified in the professional MCI data were matched by trends in the same direction in the volunteer WIMP score data (Fig. 3). Neither dataset identified a significant trend at the Oteha site. However, for two sites (Opanuku and Puhinui), significant trends were identified in the volunteer dataset, but not the professional dataset. Trend slopes and the level of statistical significance were generally greater for the volunteer data compared to the professional data (Table 1). Discussion Volunteer and professional data provided concordant assessments of stream health as measured by a biotic index and an index of composition, including generally consistent identification of trends. Our findings are particularly notable given the spatial and temporal differences between the two datasets. However, volunteer data were considered limited for assessments of taxonomic richness due to the level of taxonomic identification used.
Concordance between volunteer and professional data We found that volunteer data provided a concordant assessment of stream health (Fig. 1). While the use of volunteer monitoring data for detecting environmental trends has been validated in some studies (Fore et al. 2001; O’Leary et al. 2004; Lovell et al. 2009; Kremen et al. 2011), others have questioned its accuracy (Penrose & Call 1995; Nerbonne & Nelson 2004; Latimore & Steen 2014). Variation in the accuracy of volunteer monitoring programmes may be attributed to protocols, training or resources provided (Nerbonne & Vondracek 2003). Training is particularly important for macroinvertebrate identification to ensure key features are recognisable (Kremen et al. 2011). For example, when given no training and only scientific identification tools, volunteers could not accurately identify stream invertebrates (Nerbonne & Vondracek 2003), but when trained and given simplified identification guides volunteers were accurate in their identification (Fore et al. 2001). In our study, Wai Care volunteers used a simplified protocol, were provided with picture identification guides, and given onsite training on at least one occasion, which likely
n, sample size. Data were analysed using the Mann Kendall trend test, where Sen slope values indicate the strength and direction of the relationship. *P < 0.05.
Yes Yes Yes Yes No No No 0.028* 0.035* 0.036* 0.036* 0.150 0.127 0.242 −8.54 −0.95 1.36 1.36 1.39 −1.14 0.41 Feb 2004–Feb 2013 Mar 2004–Feb 2013 Feb 2004–Feb 2013 Feb 2004–Feb 2013 Mar 2004–Feb 2013 Mar 2004–Feb 2013 Feb 2004–Feb 2013 0.010* 0.033* 0.002* 0.001* 0.500 0.042* 0.040* 12 18 23 33 10 50 10 Cascades River Konini Stream Oakley Creek 1 Oakley Creek 2 Oteha Stream Opanuku Stream Puhinui Stream
Apr 2006–Sept 2009 Nov 2009–Sept 2012 Apr 2005–Sept 2010 Feb 2010–Oct 2012 Dec 2006–Mar 2010 Feb 2005–Nov 2010 Oct 2005–Dec 2012
−18.41 −22.15 4.348 13.84 −1.39 −9.91 30.65
10 10 10 10 10 10 10
P Sen slope Sample period n P Sen slope Sample period n Site
Volunteer (WIMP score)
Professional (MCI)
Trend concordance
ER Moffett and MW Neale
Table 1 Comparison of temporal trends in stream health between samples collected by volunteers (WIMP score) and professionals (MCI).
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
6
Figure 3 Comparison of temporal trends in professional macroinvertebrate data as MCI (—) and volunteer macroinvertebrate data as WIMP score (– – –). A, Oakley Creek B, Cascades Stream.
contributed to the good quality of the volunteer dataset. The level of taxonomic identification required from volunteers may also influence the ability of volunteer data to distinguish levels of impact. Volunteer samples are often limited to higher levels of identification due to resource or training constraints, whereas professional samples are typically identified to species or genus levels (Fore et al. 2001; O’Leary et al. 2004). Within a family, or other higher groups, there are multiple species with potentially different sensitivities to environmental stressors. Therefore, by generalising to family level, information from sites with many genera may not be accurate (Chessman et al. 2007). To overcome this problem macroinvertebrate identification protocols may be modified (Penrose & Call 1995). For example, the US Environmental Protection Agency
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
Volunteer stream monitoring (USEPA) has a Volunteer Stream Monitoring Methods Manual (Walk 1997); however, use of its sampling protocol requires scientific involvement and it has been unsuccessful when given to untrained volunteers (Nerbonne & Nelson 2004; Nerbonne et al. 2008). In our study, volunteers used a tiered approach to identification, where indicator taxa were identified at species or genus level but other taxa were identified to family level. This modified protocol was successful at detecting trends in stream health whilst remaining simple enough to be done onsite and without requiring indepth taxonomic knowledge. Due to the coarse level of identification used by volunteers, volunteer monitoring often does not effectively capture taxonomic richness and hence has limited ability to detect biodiversity trends (Koss et al. 2009; Kremen et al. 2011). Simplifications to macroinvertebrate identification, such as in this study, reduce the possible number of taxa that volunteers may describe, whereas volunteer identification at finer taxonomic scales may lower data quality by increasing misidentifications or missed taxa (Nerbonne & Vondracek 2003). There is clearly a trade-off between identification accuracy and taxonomic resolution. Our data suggest that the use of a simplified protocol and identification at coarser levels permits a concordant assessment of stream health, but at the cost of limiting the use of taxon richness as a biodiversity measure. Other studies have shown that where volunteers are trained and given laboratory access, estimates of taxon richness have been improved due to the use of microscopes during identification (Fore et al. 2001). Finer-scale data collection may also allow for more sensitive detection of land use impacts (Hilsenhoff 1988; Lenat & Resh 2001); however, some studies have found only minor differences between data identified at family vs. genus levels in the ability to detect land use impacts (Fore et al. 2001; O’Leary et al. 2004; Chessman et al. 2007). Stream ecological monitoring is often carried out to detect land use impacts (Nerbonne & Nelson 2004). Both professional and volunteer monitoring data in our study were able to detect differences in land use intensity; however, this relationship was weaker for volunteer monitoring data (Fig. 2).
7
These results indicate that volunteer monitoring data can detect gross differences in land use, but may lack the sensitivity to detect smaller impacts. Small differences in land use intensity are thought to be apparent at invertebrate species and genus levels, which were not described by the WIMP protocol (Fore et al. 2001). Despite this, our data show that volunteer monitoring across large areas, such as in this study, can identify gross human impacts. Such broad-scale monitoring may be difficult for professionals due to resourcing constraints (Dickinson et al. 2010). However, data from suitably trained and equipped volunteer monitoring programmes, such as Wai Care, may be a valuable resource for scientists and managers, providing spatial detail in land use impact assessments. Concordance in temporal trends Where volunteer monitoring data spans long periods of time, changes to stream health associated with development or restoration may be monitored. Such information may extend the monitoring capacity of local councils and allow citizens to play a more active role in stream management, and may be used as legal testimony (Penrose & Call 1995; Conrad & Hilchey 2011; Tulloch et al. 2013). Globally, there have been no published investigations of volunteer stream ecological monitoring data over long periods; however, volunteers from other fields, particularly ornithology, have successfully collected longterm datasets (Bonney et al. 2009; Wilson et al. 2013). We found concordant long-term trends in the direction of relationships between volunteer and professional datasets (Table 1). Moreover, volunteer data identified trends that were not detected in the professional dataset. Generally, p-values were lower in the volunteer dataset compared to the professional dataset. In some cases this may be explained by the greater number of data points in the volunteer dataset. In others this is likely explained by the greater slope values over the monitoring period. Our volunteer dataset often covered a shorter monitoring period than the professional dataset. Duration of temporal trends is an important
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
8
ER Moffett and MW Neale
consideration for trend analysis as this influences slope values. Volunteer monitoring duration may be influenced by changes in members or dissatisfaction with programme design, thus influencing the ability of volunteer groups to effectively collect long-term datasets (Koss et al. 2009). However, where volunteer monitoring continued long-term in our study, volunteer data described ecological trends consistent with the professional dataset. For example, Oakley Creek has had a number of community groups monitoring stream health; where some groups have only made a few visits, possibly for educational purposes, others have demonstrated long-term commitment to monitoring the health of this stream (Fig. 3a). The success of long-term volunteer monitoring at sites such as Oakley Creek may be due to the broader restoration aims of community groups or due to wide community support (e.g. Friends of Oakley Creek). Where communities aim to monitor sites to detect changes in stream health, long-term regular monitoring is required to create a sufficient dataset to inform stream management decisions (Catlin-Groves 2012). Temporal trends may also be influenced by natural variation (e.g. climatic effects), changes in land use (e.g. % urban) or changes in land use intensity. We make no attempt to explain these trends; however, to our knowledge, land use distribution has remained consistent over the duration of our dataset. Therefore, while we should exercise caution in the trend analysis because temporal variation in the WIMP score is poorly understood, we consider it is a positive finding that both datasets showed similar patterns of trends. As this is the first comparison of temporal changes in the relationship between professional and volunteer data we recommend further targeted studies of this relationship.
spatial (local habitat) and temporal (seasonal and climatic) differences. These spatial and temporal differences between the volunteer and professional samples in our dataset could be considered a limitation, but this variation is representative of the real-world differences between professional and volunteer monitoring programmes. Despite these differences, the data from the two monitoring programmes demonstrated concordant assessments of stream health and consistency in trend identification. We hypothesise that these relationships may be stronger if streams were sampled concurrently in both space and time. Our findings support the growing body of literature which indicates that data collected by volunteers are comparable at detecting land use and temporal trends to that of data collected by professionals (Fore et al. 2001; O’Leary et al. 2004; Chessman et al. 2007; Koss et al. 2009; Lovell et al. 2009; Kremen et al. 2011). Our study did, however, highlight that the coarser level of macroinvertebrate identification used in the volunteer protocol restricted the use of volunteer richness data as a biodiversity measure. We conclude that volunteer monitoring data provides an invaluable source of information which may be used to monitor trends in stream health over large areas. Acknowledgements We would like to thank the many volunteers who have contributed to the Wai Care programme and the Wai Care coordinators who have trained and managed these volunteers. In particular we thank Sophie Tweddle, Hazel Meadows, Marnie Prickett, Rachel Griffiths and Kate Mcintosh. In addition, we thank those involved in macroinvertebrate sample collection and identification for Auckland Council. We thank the two anonymous reviewers for their constructive comments. Associate Editor: Dr Joanne Clapcott.
Conclusions Our study demonstrated that volunteers are capable of collecting meaningful data regarding stream health, including identifying land use impacts and temporal trends. There are numerous environmental factors that could affect the relationship between our volunteer and professional data points, including
Supplementary data Supplementary file: Summary of sites used to assess the concordance between volunteer and professional data, including substrate type as defined by Auckland Council and distances in space and time between sampling events which were compared.
Volunteer stream monitoring
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
References Auckland Council 2011. Auckland council annual report – 1 November 2010 to 30 June 2012. Auckland, Auckland Council. 242 p. Bonney R, Cooper CB, Dickinson J, Kelling S, Phillips T, Rosenberg KV et al. 2009. Citizen science: a developing tool for expanding science knowledge and scientific literacy. BioScience 59: 977–984. Catlin-Groves CL 2012. The citizen science landscape: from volunteers to citizen sensors and beyond. International Journal of Zoology 2012: 1–14. Chessman B, Williams S, Besley C 2007. Bioassessment of streams with macroinvertebrates: effect of sampled habitat and taxonomic resolution. Journal of the North American Benthological Society 26: 546–565. Coates A 2013. Volunteer monitoring of water quality in New Zealand: where does the value lie? Unpublished MSc thesis. Christchurch, University of Canterbury. 116 p. Collier KJ 2008. Temporal patterns in the stability, persistence and condition of stream macroinvertebrate communities: relationships with catchment land-use and regional climate. Freshwater Biology 53: 603–616. Conrad CC, Hilchey KG 2011. A review of citizen science and community-based environmental monitoring: issues and opportunities. Environmental Monitoring and Assessment 176: 273–291. Dickinson JL, Zuckerberg B, Bonter DN 2010. Citizen science as an ecological research tool: challenges and benefits. Annual Review of Ecology, Evolution, and Systematics 41: 149–172. Dickinson JL, Shirk J, Bonter D, Bonney R, Crain RL, Martin J et al. 2012. The current state of citizen science as a tool for ecological research and public engagement. Frontiers in Ecology and the Environment 10: 291–297. Donnelly A, Crowe O, Regan E, Begley S, Caffarra A 2014. The role of citizen science in monitoring biodiversity in Ireland. International Journal of Biometeorology 58: 1237–1249. Fore LS, Paulsen K, O’Laughlin K 2001. Assessing the performance of volunteers in monitoring streams. Freshwater Biology 46: 109–123. Foster-Smith J, Evans SM 2003. The value of marine ecological data collected by volunteers. Biological Conservation 113: 199–213. Hilsenhoff WL 1988. Rapid field assessment of organic pollution with a family-level biotic index. Journal of the North American Benthological Society 7: 65–68. Jones R, Moore S, Jenks A, Henley C 2012. Wai Care invertebrate field guide: Wai Care Invertebrate
9
Monitoring Protocol (WIMP). Auckland, Auckland Council. p. 15. Karr JR 1999. Defining and measuring river health. Freshwater Biology 41: 221–234. Koss RS, Miller K, Wescott G, Bellgrove S, Boxshall A, McBurnie J et al. 2009. An evaluation of sea search as a citizen science programme in marine protected areas. Pacific Conservation Biology 15: 116–127. Kremen C, Ullmann KS, Thorp RW 2011. Evaluating the quality of citizen-scientist data on pollinator communities. Conservation Biology 25: 607–617. Latimore JA, Steen PJ 2014. Integrating freshwater science and local management through volunteer monitoring partnerships: the Michigan Clean Water Corps. Freshwater Science 33: 686–692. Lenat DR, Resh VH 2001. Taxonomy and stream ecology – the benefits of genus- and species-level identifications. Journal of the North American Benthological Society 20: 287–298. Lovell S, Hamer M, Slotow R, Herbert D 2009. An assessment of the use of volunteers for terrestrial invertebrate biodiversity surveys. Biodiversity and Conservation 18: 3295–3307. Metcalfe JL 1989. Biological water quality assessment of running waters based on macroinvertebrate communities: history and present status in Europe. Environmental Pollution 60: 101–139. Moore S, Neale MW 2008. Freshwater invertebrate monitoring 2003–2007 analysis and evaluation. Auckland Regional Council Technical Report 2008/010. Auckland, Auckland Regional Council. 63 p. Nerbonne JF, Nelson KC 2004. Volunteer macroinvertebrate monitoring in the United States: resource mobilization and comparative state structure. Society and Natural Resources 17: 817–839. Nerbonne, JF, Vondracek B 2003. Volunteer macroinvertebrate monitoring: assessing training needs through examining error and bias in untrained volunteers. Journal of the North American Benthological Society 22: 152–163. Nerbonne JF, Ward B, Ollila A, Williams M, Vondracek B 2008. Effect of sampling protocol and volunteer bias when sampling for macroinvertebrates. Journal of the North American Benthological Society 27: 640–646. O’Leary N, Vawter AT, Wagenet LP, Pfeffer M 2004. Assessing water quality using two taxonomic levels of benthic macroinvertebrate analysis: implications for volunteer monitors. Journal of Freshwater Ecology 19: 581–586. Penrose D, Call SM 1995. Volunteer monitoring of benthic macroinvertebrates: regulatory biologists’ perspectives. Journal of the North American Bentho‐ logical Society 14: 203–209.
Downloaded by [University of Auckland Library] at 14:25 22 April 2015
10
ER Moffett and MW Neale
Reynolds JA, Lowman MD 2013. Promoting ecoliteracy through research service-learning and citizen science. Frontiers in Ecology and the Environment 11: 565–566. Riesch H, Potter C 2014. Citizen science as seen by scientists: methodological, epistemological and ethical dimensions. Public Understanding of Science 23: 107–120. Roux DJ, Van Vilet HR, Van Veelen M 1993. Towards integrated water quality monitoring: assessment of ecosystem health. Water SA 4: 275–280. Snäll T, Forslund P, Jeppsson T, Lindhe A, O’Hara RB 2014. Evaluating temporal variation in citizen science data against temporal variation in the environment. Ecography 37: 293–300. Snelder T, Biggs BJ, Weatherhead M 2010. New Zealand river environment classification user guide. Wellington, New Zealand, Ministry for the Environment. 144 p. Stark JD, Boothroyd IKG, Harding JS, Maxted JR, Scarsbrook MR 2001. Protocols for sampling macroinvertebrates in wadeable streams. New Zealand Macroinvertebrate Working Group Report No. 1. Prepared for Ministry for the Environment. Sustainable Management Fund Project No. 5103. Wellington, Ministry for the Environment. 60 p.
Stark JD, Maxted JR 2007. A user guide for the macroinvertebrate community index. Prepared for the Ministry for the Environment, New Zealand. Cawthron Report No. 1166. Wellington, Ministry for the Environment. 58 p. Tulloch AIT, Possingham HP, Joseph LN, Szabo J, Martin TG 2013. Realising the full potential of citizen science monitoring programs. Biological Conservation 165: 128–138. Wai Care 2013. About Wai Care. https://www.waicare. org.nz/AboutWaiCare.aspx (accessed 2 October 2014). Walk SH 1997. Volunteer stream monitoring: a methods manual. Washington, DC, Office of Water, USA Environmental Protection Agency. http://water.epa. gov/type/rsl/monitoring/stream_index.cfm (accessed 5 October 2014). Ward DF 2014. Understanding sampling and taxonomic biases recorded by citizen scientists. Journal of Insect Conservation 18: 753–756. Wilson S, Anderson EM, Wilson ASG, Bertram DF, Arcese P 2013 Citizen science reveals an extensive shift in the winter distribution of migratory western grebes. PLoS ONE 8: 1–8.
