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Information Flows in Community-Based Monitoring Exercises in the Ecuadorian Amazon

Information Flows in Community-Based Monitoring Exercises in the Ecuadorian Amazon Hindawi Publishing Corporation International Journal of Zoology Volume 2012, Article ID 980520, 4 pages doi:10.1155/2012/980520 Research Article Information Flows in Community-Based Monitoring Exercises in the Ecuadorian Amazon 1, 2 1 Johan A. Oldekop, Nathan K. Truelove, 3 1 Santiago Villamarın, ´ and Richard F. Preziosi Faculty of Life Sciences, The University of Manchester, Manchester M13 9PT, UK School of the Environment, Washington State University, Vancouver, WA 98686, USA Museo Ecuatoriano de Ciencias Naturales, Quito 17078976, Ecuador Correspondence should be addressed to Johan A. Oldekop, j.oldekop@gmx.net Received 3 February 2012; Accepted 24 May 2012 Academic Editor: Simon Morgan Copyright © 2012 Johan A. Oldekop et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Community-based monitoring schemes provide alternatives to costly scientific monitoring projects. While evidence shows that local community inhabitants can consistently measure environmental changes, few studies have examined how learned monitoring skills get passed on within communities. Here, we trained members of indigenous Kichwa communities in the Ecuadorian Amazon to measure fern and dung beetle species richness and examined how well they could pass on the information they had learned to other members of their community. We subsequently compared locally gathered species richness data to estimates gathered by trained biologists. Our results provide further evidence that devolved monitoring protocols can provide similar data to that gathered by scientists. In addition, our results show that local inhabitants can effectively pass on learned information to other community members, which is particularly important for the longevity of community-based monitoring initiatives. 1. Introduction we do not know whether trained community members can train other people within their communities. This Community-based monitoring schemes (CBMS) combine information is important for the creation of long-term and local traditional knowledge with existing organizational decentralized CBMS, where the majority of the collection systems to measure ecological changes [1, 2]. Because CBMS and interpretation of data is directly managed by local communities and stakeholders [1, 2]. can increase local understanding of environmental issues [3], they are considered capacity building exercises that provide Here, we use a CBMS exercise with indigenous Kichwa communities in the Ecuadorian Amazon to assess the evidence for local management decisions [4]. ability of locally trained community members to train other Evidence shows that, with appropriate training, CBMS residents within their communities. Specifically, we compare can provide precise data on environmental processes. species richness estimates of two biodiversity indicators, Danielsen et al. [5] show that trained community members ferns [7] and dung beetles [8] (henceforth beetles), gathered are able to accurately monitor biomass and logging activities by scientists, community members trained by scientists, and in India, Tanzania, and Madagascar. Similarly, Oldekop et community members trained by the community members al. [6] show that community inhabitants in Ecuador can originally trained by scientists. use simple and cost-effective methodologies to provide fern species richness estimates that accurately reflect biodiversity patterns observed by scientists. 2. Methods What has not yet been addressed, however, is how information gained by those attending training schemes is Exercises took place in the communities of San Josed ´ e passed on to other community members. In other words, Payamino (henceforth Payamino) and Chontococha in 2 International Journal of Zoology August and November 2008. The communities are located Table 1: Matched-pairs analysis results showing differences in species richness estimates between scientist, expert-trained, and within the Sumaco Biosphere Reserve and are classified as community-trained groups. areas of tropical forest [9]. Four menfromeachcommunitytookpartinthe Indicator Community Richness estimates exercises and received a typical regional day wage each Scientist: 5.8 ($10 per day) for the duration of each exercise (four days). Payamino Expert trained: 7.6 Participants in each community were divided into two Community trained: 5.9 groups: an expert-trained group and a community-trained Fern richness Scientist: 5.6 group. The expert-trained group received fern and dung Chontacocha Expert trained: 5.4 beetle identification training from the scientist group, which was composed of two Ph.D. students (JAO, NKT) from The Community trained: 4.6 University of Manchester with several months experience Scientist: 11.2 conducting biodiversity monitoring of ferns and beetles in a Payamino Expert trained: 8.2 the region. The community-trained group received fern and Community trained: 8.6 Beetle richness dung beetle identification training from the expert-trained Scientist: 10.7 group. Information was therefore passed from the scientist Chontacocha Expert trained: 8.8 group to the expert-trained group to the community-trained Community trained: 13.2 group. Expert-trained group participants were chosen on a Values that are not connected with the same letter differ significantly from volunteer basis, whereas the expert-trained group recruited each other. the community-trained group participants. Preliminary results were presented during community meetings on subsequent visits in 2009. Beetles were sampled using dung-baited pit-fall traps placed in each quadrat. Traps were collected after 24 hrs, and beetles were stored in 95% ethanol. Expert-trained and 3. Training community-trained groups were then asked to determine the Evidence shows that training schemes increase the accuracy species richness of each trap. An expert taxonomist (SV) of CBMS [6]. Our study, therefore, only focused on com- confirmed beetle species richness after the exercise. parisons between participants who had received training. Expert-trained groups received fern identification training 5. Analysis for several weeks while working as field assistants with JAO and NKT, who conducted a larger regional biodiversity Correlations were analyzed separately for each community assessment. Expert-trained groups were taught how to dif- and each indicator. The accuracy, the amount by which ferentiate beetle species during a single 30 min session. In the groups over or underestimated species richness, was analyzed case of both ferns and beetles, the expert-trained groups were using paired t-tests. All analyses were performed in JMP8 given information on the key physical characteristics of each (SAS Institute Inc.). taxonomic group but were not given specific information to differentiate between specific species. Once trained, the expert-trained groups were asked to recruit and train the 6. Results community-trained groups using their choice of methods. While community-trained groups received training on fern With the exception of beetle richness in Chontacocha identification several days after the expert-trained group had (Figure 1(d)) all richness estimates between scientist and finished working with JAO and NKT, dung beetle identi- expert-trained groups correlated significantly and positively fication training for both expert- and community-trained (Figure 1(a)–(c)). All correlations between expert-trained groups occurred during the same day. Despite having no time and community-trained groups were positive and significant limit, training for each indicator lasted approximately 15 min (Figure 1(e)–(h)). Only beetle species richness estimates in both communities and consisted of field visits to review in Payamino (Figure 1(j)) and fern species richness esti- ferns and sessions examining beetle specimens. mates in Chontacocha (Figure 1(k)) were significantly and positively correlated between the scientist and community- trained groups. Both expert- and community-trained groups 4. Sampling over and under estimated species richness but there is no discernable pattern (Table 1). In each community, the different groups (scientist, expert- trained and community-trained) sampled ferns and beetles along three 500 m transects situated in primary forest. Ferns 7. Discussion were sampled along each transect in 10 equally spaced 5 × 5 m quadrats; groups were specifically asked not to remove Results show substantial positive and significant correlations ferns or break off samples for comparisons. Groups sampled between the different groups, suggesting that information transects in random order and were not allowed to sample was passed successfully between the groups. These results transects before previous groups had finished. confirm previous findings that CBMS can show similar International Journal of Zoology 3 San Jose de P ´ ayamino Chontacocha Fern richness Beetle richness Fern richness Beetle richness 12 14 8 6 4 6 ∗∗∗ ∗∗∗ ∗∗ r = 0.6 r = 0.66 r = 0.53 r = 0.32 26 10 816 8 16 2 610 Scientist Scientist Scientist Scientist (a) (c) (d) (b) 6 8 ∗∗ ∗∗∗ ∗ ∗∗∗ r = 0.53 r = 0.61 r = 0.5 r = 0.69 48 12 48 12 26 10 610 14 Expert trained Expert trained Expert trained Expert trained (e) (f ) (g) (h) 2 5 ∗∗∗ ∗ r = 0.19 r = 0.67 r = 0.5 r = 0.22 26 10 2 610 816 Scientist Scientist Scientist Scientist (j) (k) (l) (i) ∗ ∗∗ ∗∗∗ Figure 1: Correlations between scientist, expert-trained, and community-trained groups ( P< 0.05, P< 0.005, and P< 0.0005). trends to those found by scientists [4–6]. Beetle rich- explained by a poor information flow between the scientist ness estimates between scientist and expert-trained groups and the expert-trained group. The nonsignificant correlation showed no significant correlation in Chontacocha. Expert- of fern richness estimates between the scientist and the trained groups, however, only received dung beetle identi- community-trained group in Payamino, however, suggest fication training for 30 min. While Payamino’s significant that there is a significant loss of information passed from the correlations between the scientist and expert-trained group expert-trained to the community-trained groups. Despite suggest that community members have the ability to learn the potential for information loss at these two points of complex taxonomic information in a short time, a single communication, our data suggest that there is a large 30 min session might not always be sufficient. Conversely, potential for community members to train other members fern species identification training of the expert-trained within their communities, but that the way that information group took place over several weeks, and the correlations is transmitted to those individuals trained by professional between the scientist and the expert-trained group were scientists is critical. positive and significant in both communities. While long Significant over- and underestimations of fern and dung training schemes are not always feasible, there is evidence beetle richness suggest significant errors in the accuracy that CBMS participants can learn taxonomic identification of species richness estimates. Non-experts taking part in skills in substantially shorter periods of time [6]. monitoring exercises commonly fail to recognize certain Correlations between expert- and community-trained species as being either the same or different [10]. Differences groups suggest that the information flow is particu- between groups in our data are likely due to similar species larly strong between community members. Poor beetle “lumping” and “splitting” events. richness estimate correlations between the scientist and The success of monitoring schemes relies on adapting the community-trained groups in Chontacocha might be methodologies to specific needs. While CBMS might not Community trained Community trained Expert trained 4 International Journal of Zoology necessarily provide as detailed information as monitoring [10] I. Oliver and A. J. Beattie, “A possible method for the rapid assessment of biodiversity,” Conservation Biology, vol. 7, no. 3, exercise performed by trained scientists [11, 12], they can pp. 562–568, 1993. lead to quicker decision making [5, 13]. An important [11] P. Z. Goldstein, “How many things are there? A reply to Oliver factor influencing the longevity of CBMS is their ability to and Beattie, Beattie and Oliver, Oliver and Beattie, and Oliver become less dependent on external expertise and resources. and Beattie,” Conservation Biology, vol. 11, no. 2, pp. 571–574, Although only based on a few comparisons, our data suggest that participants are remarkably good at passing on learnt [12] F. T. Krell, “Parataxonomy vs. taxonomy in biodiversity information. Of key importance, however, is how initial studies—pitfalls and applicability of “morphospecies” sort- information gets passed on from scientific experts to local ing,” Biodiversity and Conservation, vol. 13, no. 4, pp. 795–812, practitioners and community-based monitoring initiatives. If we, the scientific community, can devise simple and [13] J. van Rijsoort and Z. Jinfeng, “Participatory resource mon- accurate training methodologies that can be easily taught, itoring as a means for promoting social change in Yunnan, China,” Biodiversity and Conservation, vol. 14, no. 11, pp. learned, and implemented, then CBMS can provide a 2543–2573, 2005. powerful and locally relevant tool to measure changes in biodiversity, natural resources, and ecosystem services. Acknowledgments The authors thank the participants and the communities of Payamino and Chontacocha for allowing them to work on their lands. Ecuadorian Ministry of the Environment research permit no. 005-08 IC-FAU-DNBAPVS/MA. References [1] F. Danielsen, M. M. Mendoza, P. Alviola et al., “Biodiversity monitoring in developing countries: what are we trying to achieve?” Oryx, vol. 37, no. 4, pp. 407–409, 2003. [2] F. Danielsen, N. D. Burgess, A. Balmford et al., “Local partic- ipation in natural resource monitoring: a characterization of approaches,” Conservation Biology, vol. 23, no. 1, pp. 31–42, [3] H. T. Andrianandrasana, J. Randriamahefasoa, J. Durbin, R. E. Lewis, and J. H. Ratsimbazafy, “Participatory ecological moni- toring of the Alaotra wetlands in Madagascar,” Biodiversity and Conservation, vol. 14, no. 11, pp. 2757–2774, 2005. [4] J. G. Mueller, I. H. B. Assanou, I. D. Guimbo, and A. M. Almedom, “Evaluating rapid participatory rural appraisal as an assessment of ethnoecological knowledge and local biodiversity patterns,” Conservation Biology,vol. 24, no.1,pp. 140–150, 2010. [5] F. Danielsen, N. D. Burgess, P. M. Jensen, and K. Pirhofer- Walzl, “Environmental monitoring: the scale and speed of implementation varies according to the degree of peoples involvement,” Journal of Applied Ecology, vol. 47, no. 6, pp. 1166–1168, 2010. [6] J.A.Oldekop,A.J.Bebbington, F. Berdel,N.K.Truelove, T. Wiersberg, and R. F. Preziosi, “Testing the accuracy of non-experts in biodiversity monitoring exercises using fern species richness in the Ecuadorian Amazon,” Biodiversity and Conservation, vol. 20, no. 12, pp. 2615–2626, 2011. [7] R. Pardini, D. Faria, G. M. Accacio et al., “The challenge of maintaining Atlantic forest biodiversity: a multi-taxa conservation assessment of specialist and generalist species in an agro-forestry mosaic in Southern Bahia,” Biological Conservation, vol. 142, no. 6, pp. 1178–1190, 2009. [8] T. A. Gardner, J. Barlow, I. S. Araujo et al., “The cost- effectiveness of biodiversity surveys in tropical forests,” Ecology Letters, vol. 11, no. 2, pp. 139–150, 2008. [9] H. Navarrete, Helechos comunes de la Amazon´ıa baja Ecuatori- ana, Simbioe, Quito, Ecuador, 2001. International Journal of Peptides Advances in International Journal of BioMed Stem Cells Virolog y Research International International Genomics Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Journal of Nucleic Acids International Journal of Zoology Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Submit your manuscripts at http://www.hindawi.com The Scientific Journal of Signal Transduction World Journal Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 International Journal of Advances in Genetics Anatomy Biochemistry Research International Research International Microbiology Research International Bioinformatics Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Enzyme Journal of International Journal of Molecular Biology Archaea Research Evolutionary Biology International Marine Biology Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Zoology Hindawi Publishing Corporation

Information Flows in Community-Based Monitoring Exercises in the Ecuadorian Amazon

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Hindawi Publishing Corporation International Journal of Zoology Volume 2012, Article ID 980520, 4 pages doi:10.1155/2012/980520 Research Article Information Flows in Community-Based Monitoring Exercises in the Ecuadorian Amazon 1, 2 1 Johan A. Oldekop, Nathan K. Truelove, 3 1 Santiago Villamarın, ´ and Richard F. Preziosi Faculty of Life Sciences, The University of Manchester, Manchester M13 9PT, UK School of the Environment, Washington State University, Vancouver, WA 98686, USA Museo Ecuatoriano de Ciencias Naturales, Quito 17078976, Ecuador Correspondence should be addressed to Johan A. Oldekop, j.oldekop@gmx.net Received 3 February 2012; Accepted 24 May 2012 Academic Editor: Simon Morgan Copyright © 2012 Johan A. Oldekop et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Community-based monitoring schemes provide alternatives to costly scientific monitoring projects. While evidence shows that local community inhabitants can consistently measure environmental changes, few studies have examined how learned monitoring skills get passed on within communities. Here, we trained members of indigenous Kichwa communities in the Ecuadorian Amazon to measure fern and dung beetle species richness and examined how well they could pass on the information they had learned to other members of their community. We subsequently compared locally gathered species richness data to estimates gathered by trained biologists. Our results provide further evidence that devolved monitoring protocols can provide similar data to that gathered by scientists. In addition, our results show that local inhabitants can effectively pass on learned information to other community members, which is particularly important for the longevity of community-based monitoring initiatives. 1. Introduction we do not know whether trained community members can train other people within their communities. This Community-based monitoring schemes (CBMS) combine information is important for the creation of long-term and local traditional knowledge with existing organizational decentralized CBMS, where the majority of the collection systems to measure ecological changes [1, 2]. Because CBMS and interpretation of data is directly managed by local communities and stakeholders [1, 2]. can increase local understanding of environmental issues [3], they are considered capacity building exercises that provide Here, we use a CBMS exercise with indigenous Kichwa communities in the Ecuadorian Amazon to assess the evidence for local management decisions [4]. ability of locally trained community members to train other Evidence shows that, with appropriate training, CBMS residents within their communities. Specifically, we compare can provide precise data on environmental processes. species richness estimates of two biodiversity indicators, Danielsen et al. [5] show that trained community members ferns [7] and dung beetles [8] (henceforth beetles), gathered are able to accurately monitor biomass and logging activities by scientists, community members trained by scientists, and in India, Tanzania, and Madagascar. Similarly, Oldekop et community members trained by the community members al. [6] show that community inhabitants in Ecuador can originally trained by scientists. use simple and cost-effective methodologies to provide fern species richness estimates that accurately reflect biodiversity patterns observed by scientists. 2. Methods What has not yet been addressed, however, is how information gained by those attending training schemes is Exercises took place in the communities of San Josed ´ e passed on to other community members. In other words, Payamino (henceforth Payamino) and Chontococha in 2 International Journal of Zoology August and November 2008. The communities are located Table 1: Matched-pairs analysis results showing differences in species richness estimates between scientist, expert-trained, and within the Sumaco Biosphere Reserve and are classified as community-trained groups. areas of tropical forest [9]. Four menfromeachcommunitytookpartinthe Indicator Community Richness estimates exercises and received a typical regional day wage each Scientist: 5.8 ($10 per day) for the duration of each exercise (four days). Payamino Expert trained: 7.6 Participants in each community were divided into two Community trained: 5.9 groups: an expert-trained group and a community-trained Fern richness Scientist: 5.6 group. The expert-trained group received fern and dung Chontacocha Expert trained: 5.4 beetle identification training from the scientist group, which was composed of two Ph.D. students (JAO, NKT) from The Community trained: 4.6 University of Manchester with several months experience Scientist: 11.2 conducting biodiversity monitoring of ferns and beetles in a Payamino Expert trained: 8.2 the region. The community-trained group received fern and Community trained: 8.6 Beetle richness dung beetle identification training from the expert-trained Scientist: 10.7 group. Information was therefore passed from the scientist Chontacocha Expert trained: 8.8 group to the expert-trained group to the community-trained Community trained: 13.2 group. Expert-trained group participants were chosen on a Values that are not connected with the same letter differ significantly from volunteer basis, whereas the expert-trained group recruited each other. the community-trained group participants. Preliminary results were presented during community meetings on subsequent visits in 2009. Beetles were sampled using dung-baited pit-fall traps placed in each quadrat. Traps were collected after 24 hrs, and beetles were stored in 95% ethanol. Expert-trained and 3. Training community-trained groups were then asked to determine the Evidence shows that training schemes increase the accuracy species richness of each trap. An expert taxonomist (SV) of CBMS [6]. Our study, therefore, only focused on com- confirmed beetle species richness after the exercise. parisons between participants who had received training. Expert-trained groups received fern identification training 5. Analysis for several weeks while working as field assistants with JAO and NKT, who conducted a larger regional biodiversity Correlations were analyzed separately for each community assessment. Expert-trained groups were taught how to dif- and each indicator. The accuracy, the amount by which ferentiate beetle species during a single 30 min session. In the groups over or underestimated species richness, was analyzed case of both ferns and beetles, the expert-trained groups were using paired t-tests. All analyses were performed in JMP8 given information on the key physical characteristics of each (SAS Institute Inc.). taxonomic group but were not given specific information to differentiate between specific species. Once trained, the expert-trained groups were asked to recruit and train the 6. Results community-trained groups using their choice of methods. While community-trained groups received training on fern With the exception of beetle richness in Chontacocha identification several days after the expert-trained group had (Figure 1(d)) all richness estimates between scientist and finished working with JAO and NKT, dung beetle identi- expert-trained groups correlated significantly and positively fication training for both expert- and community-trained (Figure 1(a)–(c)). All correlations between expert-trained groups occurred during the same day. Despite having no time and community-trained groups were positive and significant limit, training for each indicator lasted approximately 15 min (Figure 1(e)–(h)). Only beetle species richness estimates in both communities and consisted of field visits to review in Payamino (Figure 1(j)) and fern species richness esti- ferns and sessions examining beetle specimens. mates in Chontacocha (Figure 1(k)) were significantly and positively correlated between the scientist and community- trained groups. Both expert- and community-trained groups 4. Sampling over and under estimated species richness but there is no discernable pattern (Table 1). In each community, the different groups (scientist, expert- trained and community-trained) sampled ferns and beetles along three 500 m transects situated in primary forest. Ferns 7. Discussion were sampled along each transect in 10 equally spaced 5 × 5 m quadrats; groups were specifically asked not to remove Results show substantial positive and significant correlations ferns or break off samples for comparisons. Groups sampled between the different groups, suggesting that information transects in random order and were not allowed to sample was passed successfully between the groups. These results transects before previous groups had finished. confirm previous findings that CBMS can show similar International Journal of Zoology 3 San Jose de P ´ ayamino Chontacocha Fern richness Beetle richness Fern richness Beetle richness 12 14 8 6 4 6 ∗∗∗ ∗∗∗ ∗∗ r = 0.6 r = 0.66 r = 0.53 r = 0.32 26 10 816 8 16 2 610 Scientist Scientist Scientist Scientist (a) (c) (d) (b) 6 8 ∗∗ ∗∗∗ ∗ ∗∗∗ r = 0.53 r = 0.61 r = 0.5 r = 0.69 48 12 48 12 26 10 610 14 Expert trained Expert trained Expert trained Expert trained (e) (f ) (g) (h) 2 5 ∗∗∗ ∗ r = 0.19 r = 0.67 r = 0.5 r = 0.22 26 10 2 610 816 Scientist Scientist Scientist Scientist (j) (k) (l) (i) ∗ ∗∗ ∗∗∗ Figure 1: Correlations between scientist, expert-trained, and community-trained groups ( P< 0.05, P< 0.005, and P< 0.0005). trends to those found by scientists [4–6]. Beetle rich- explained by a poor information flow between the scientist ness estimates between scientist and expert-trained groups and the expert-trained group. The nonsignificant correlation showed no significant correlation in Chontacocha. Expert- of fern richness estimates between the scientist and the trained groups, however, only received dung beetle identi- community-trained group in Payamino, however, suggest fication training for 30 min. While Payamino’s significant that there is a significant loss of information passed from the correlations between the scientist and expert-trained group expert-trained to the community-trained groups. Despite suggest that community members have the ability to learn the potential for information loss at these two points of complex taxonomic information in a short time, a single communication, our data suggest that there is a large 30 min session might not always be sufficient. Conversely, potential for community members to train other members fern species identification training of the expert-trained within their communities, but that the way that information group took place over several weeks, and the correlations is transmitted to those individuals trained by professional between the scientist and the expert-trained group were scientists is critical. positive and significant in both communities. While long Significant over- and underestimations of fern and dung training schemes are not always feasible, there is evidence beetle richness suggest significant errors in the accuracy that CBMS participants can learn taxonomic identification of species richness estimates. Non-experts taking part in skills in substantially shorter periods of time [6]. monitoring exercises commonly fail to recognize certain Correlations between expert- and community-trained species as being either the same or different [10]. Differences groups suggest that the information flow is particu- between groups in our data are likely due to similar species larly strong between community members. Poor beetle “lumping” and “splitting” events. richness estimate correlations between the scientist and The success of monitoring schemes relies on adapting the community-trained groups in Chontacocha might be methodologies to specific needs. While CBMS might not Community trained Community trained Expert trained 4 International Journal of Zoology necessarily provide as detailed information as monitoring [10] I. Oliver and A. J. Beattie, “A possible method for the rapid assessment of biodiversity,” Conservation Biology, vol. 7, no. 3, exercise performed by trained scientists [11, 12], they can pp. 562–568, 1993. lead to quicker decision making [5, 13]. An important [11] P. Z. Goldstein, “How many things are there? A reply to Oliver factor influencing the longevity of CBMS is their ability to and Beattie, Beattie and Oliver, Oliver and Beattie, and Oliver become less dependent on external expertise and resources. and Beattie,” Conservation Biology, vol. 11, no. 2, pp. 571–574, Although only based on a few comparisons, our data suggest that participants are remarkably good at passing on learnt [12] F. T. Krell, “Parataxonomy vs. taxonomy in biodiversity information. Of key importance, however, is how initial studies—pitfalls and applicability of “morphospecies” sort- information gets passed on from scientific experts to local ing,” Biodiversity and Conservation, vol. 13, no. 4, pp. 795–812, practitioners and community-based monitoring initiatives. If we, the scientific community, can devise simple and [13] J. van Rijsoort and Z. Jinfeng, “Participatory resource mon- accurate training methodologies that can be easily taught, itoring as a means for promoting social change in Yunnan, China,” Biodiversity and Conservation, vol. 14, no. 11, pp. learned, and implemented, then CBMS can provide a 2543–2573, 2005. powerful and locally relevant tool to measure changes in biodiversity, natural resources, and ecosystem services. Acknowledgments The authors thank the participants and the communities of Payamino and Chontacocha for allowing them to work on their lands. Ecuadorian Ministry of the Environment research permit no. 005-08 IC-FAU-DNBAPVS/MA. References [1] F. Danielsen, M. M. Mendoza, P. Alviola et al., “Biodiversity monitoring in developing countries: what are we trying to achieve?” Oryx, vol. 37, no. 4, pp. 407–409, 2003. [2] F. Danielsen, N. D. Burgess, A. Balmford et al., “Local partic- ipation in natural resource monitoring: a characterization of approaches,” Conservation Biology, vol. 23, no. 1, pp. 31–42, [3] H. T. Andrianandrasana, J. Randriamahefasoa, J. Durbin, R. E. Lewis, and J. H. Ratsimbazafy, “Participatory ecological moni- toring of the Alaotra wetlands in Madagascar,” Biodiversity and Conservation, vol. 14, no. 11, pp. 2757–2774, 2005. [4] J. G. Mueller, I. H. B. Assanou, I. D. Guimbo, and A. M. Almedom, “Evaluating rapid participatory rural appraisal as an assessment of ethnoecological knowledge and local biodiversity patterns,” Conservation Biology,vol. 24, no.1,pp. 140–150, 2010. [5] F. Danielsen, N. D. Burgess, P. M. Jensen, and K. Pirhofer- Walzl, “Environmental monitoring: the scale and speed of implementation varies according to the degree of peoples involvement,” Journal of Applied Ecology, vol. 47, no. 6, pp. 1166–1168, 2010. [6] J.A.Oldekop,A.J.Bebbington, F. Berdel,N.K.Truelove, T. Wiersberg, and R. F. Preziosi, “Testing the accuracy of non-experts in biodiversity monitoring exercises using fern species richness in the Ecuadorian Amazon,” Biodiversity and Conservation, vol. 20, no. 12, pp. 2615–2626, 2011. [7] R. Pardini, D. Faria, G. M. Accacio et al., “The challenge of maintaining Atlantic forest biodiversity: a multi-taxa conservation assessment of specialist and generalist species in an agro-forestry mosaic in Southern Bahia,” Biological Conservation, vol. 142, no. 6, pp. 1178–1190, 2009. [8] T. A. Gardner, J. Barlow, I. S. Araujo et al., “The cost- effectiveness of biodiversity surveys in tropical forests,” Ecology Letters, vol. 11, no. 2, pp. 139–150, 2008. [9] H. Navarrete, Helechos comunes de la Amazon´ıa baja Ecuatori- ana, Simbioe, Quito, Ecuador, 2001. International Journal of Peptides Advances in International Journal of BioMed Stem Cells Virolog y Research International International Genomics Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Journal of Nucleic Acids International Journal of Zoology Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Submit your manuscripts at http://www.hindawi.com The Scientific Journal of Signal Transduction World Journal Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 International Journal of Advances in Genetics Anatomy Biochemistry Research International Research International Microbiology Research International Bioinformatics Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Enzyme Journal of International Journal of Molecular Biology Archaea Research Evolutionary Biology International Marine Biology Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014

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International Journal of ZoologyHindawi Publishing Corporation

Published: Jul 16, 2012

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