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Lichen Elements As Pollution Indicators: Evaluation of Methods for Large Monitoring Programmes

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Lichen Elements As Pollution Indicators: Evaluation of Methods for Large Monitoring Programmes The Lichenologist 49(4): 415–424 (2017) © British Lichen Society, 2017 doi:10.1017/S0024282917000299 Lichen elements as pollution indicators: evaluation of methods for large monitoring programmes Susan WILL-WOLF, Sarah JOVAN and Michael C. AMACHER Abstract: Lichen element content is a reliable indicator for relative air pollution load in research and monitoring programmes requiring both efficiency and representation of many sites. We tested the value of costly rigorous field and handling protocols for sample element analysis using five lichen species. No relaxation of rigour was supported; four relaxed protocols generated data significantly different from rigorous protocols for many of the 20 validated elements. Minimally restrictive site selection criteria gave quality data from 86% of 81 permanent plots in northern Midwest USA; more restrictive criteria would likely reduce indicator reliability. Use of trained non-specialist field collectors was supported when target species choice considers the lichen community context. Evernia mesomorpha, Flavoparmelia caperata and Physcia aipolia/stellaris were successful target species. Non-specialists were less successful at distinguishing Parmelia sulcata and Punctelia rudecta from lookalikes, leading to few samples and some poor quality data. Key words: Evernia mesomorpha, Flavoparmelia caperata, metals, nitrogen, Parmelia sulcata, Physcia aipolia/stellaris, Punctelia rudecta, sulphur Accepted for publication 26 January 2017 Introduction (Markert et al. 1999; Bargagli & Mikhailova 2002; Forbes et al. 2015). Protocol variations Measurement of heavy metals, nitrogen and that decrease per-sample costs while main- sulphur in lichens is a well-established techni- taining data quality facilitate cost-effectiveness, que for indicating air pollution load (e.g. Ferry important for both large-scale monitoring et al. 1973; Martin & Coughtrey 1982) that programmes and research studies. remains in active use today (e.g. McMurray We evaluated the impact of variations in et al. 2013; Paoli et al. 2014; Yemets et al. 2014; lichen collecting and handling protocols on Will-Wolf et al.2017a). This technique the quality of element data in a study that requires less lichenological expertise and field included non-specialist field collectors. time than methods based on community Recent tests of methods, for example Loppi surveys (e.g. van Haluwyn & van Herk 2002; et al. (2014), involved only expert researchers. Will-Wolf et al. 2015), making it a potentially Our research hypothesis was that less rigorous cost-effective option for high-volume, large- protocols could generate data that would be scale inventories. It is generally assumed that useful at least in large monitoring pro- rigorous protocols for collecting and preparing grammes. The a priori assumption was that lichens are necessary to ensure accurate data, lower protocol rigour always degrades data given the known limitations of the technique quality to some extent but that this is not significant compared with field sample variability. Using five target lichen species, S. Will-Wolf: Department of Botany, University of Wisconsin-Madison, 430 Lincoln Drive, Madison, element data for which had been previously Wisconsin 53706-1381, USA. Email: [email protected] validated for bioindication (Will-Wolf et al. S. Jovan: USDA Forest Service, Portland Forestry Sci- 2017a), we tested the effects on data quality of ences Laboratory, 620 SW Main, Suite 400, Portland, varying rigour in sample collection, handling Oregon 97205-1381, USA. and preparation protocols. Higher rigour M. C. Amacher: USDA Forest Service, Rocky Moun- tain Research Station, Logan Forestry Sciences Labora- generally involves additional time and tory, 860 N 1200 E, Logan, Utah 84321, USA. expense, for example having experts collect Downloaded from https://www.cambridge.org/core. UW-Madison Libraries Wisconsin Historical Society, on 31 Jul 2017 at 18:17:44, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S0024282917000299 416 THE LICHENOLOGIST Vol. 49 samples and using extra care and supplies to distinguishable in the field), and Punctelia rudecta (Ach.) avoid chemical contamination of samples. We Krog. For brevity, these species are often referred to by also reviewed the potential impact of element genus only. Each species is relatively common in part of our study area (Will-Wolf et al. 2017b). Project vouchers for measurement with different methods. each species are deposited in the Oregon State University Herbarium (OSU). Methods Field sample and laboratory handling The study was conducted in the state of Wisconsin, USA and adjacent areas of Illinois, Iowa and Minnesota (Fig. 1). protocol description and code Variations in protocols were evaluated for five macrolichen Lichens were collected from forest openings or edges species: Evernia mesomorpha Nyl., Flavoparmelia caperata within c.0·4km (c.0·25 mile) of a United States Forest (L.) Hale, Parmelia sulcata Taylor, Physcia aipolia (Ehrh. ex Service Forest Inventory and Analysis (FIA) permanent Humb.) Fürnr. var.aipoliaand P. stellaris (L.) Nyl. together plot, and from similar temporary sites (numbered in (widespread but small and the two species not 100% Fig. 1). Samples were collected within a c. 500 m2 area 0 50 100 200 Km FIG. 1. Map of lichen collection and instrument monitor sites. = lichen collection site (site 1 was the training site; site 13 had samples collected with Protocol 4 (lowest rigour) only); = instrument monitoring site (2–8); or = major city. Monitor sites with “ab” indicate two instrument monitor stations at that site. Intensity of background shading indicates ecoregion province: dark grey = Laurentian Mixed Forest; mid-grey = Eastern Broadleaf Forest; light grey = Prairie Parkland. Detailed data sets are presented for four sites: Baxter's Hollow (near site 6), Jerry Lake (near site 3), Gilbert Lake (site 13) and Madison (two sub-sites near site 7). Downloaded from https://www.cambridge.org/core. UW-Madison Libraries Wisconsin Historical Society, on 31 Jul 2017 at 18:17:44, subject to the Cambridge Core terms of use, available at https://www.cambridge.org/core/terms. https://doi.org/10.1017/S0024282917000299 2017 Lichen element analysis—Will-Wolf et al. 417 having 20–50% tree canopy, with no recent chemical dis- Utah, USA (Will-Wolf et al. 2017a, b). Combustion turbances nearby (no roads, lawns, ploughed fields, etc.). analysis was used for C, N, and Hg. Chemical digestion Four experienced FIA employees who each routinely sur- followed by inductively-coupled plasma optical emission vey field plots in their own geographical subregion (the spectroscopy (ICP-OES) was used for all other elements “field staff”) received one day of training from a profes- (Gatziolis et al. 2016). sional lichenologist (SWW; the “expert”)toidentifytarget lichen species and collect samples. The minimum goal for Data analysis field collection was two samples (any target species) from 90% of FIA plots: each single-species composite sample Effects on element data quality of sample quality, from six or more standing woody substrata, yielding ≥1g collection and treatment protocol variation, and differ- (air-dry) biomass, with field notes on sample quality (Will- ent collection expertise (field staff compared to the Wolf et al. 2017a, b). When material was abundant, field expert) were evaluated in several ways. Individual staff collected two replicates of every target species seen. observer effect was not tested since field staff did not Collections by the expert at supplemental sites, mostly near overlap. Field sample quality issues included the sample scattered suitable monitor sites linked with various national being too small, from too few substrata (field staff notes networks (Fig. 1: see Will-Wolf et al. 2017a), included or examination), arriving damp to the laboratory and many replicates of target species. Four collection and contaminated with dust (Ca and/or Sr high and pollution handling protocols varying in chemical cleanliness were elements low, Will-Wolf et al. 2017a). The effect of used. Field staff used Protocol 1 only; the expert licheno- sample quality on element data quality was estimated by logist collected using all four protocols. comparing the proportion of ‘odd’ data values from Protocol 1. Rigorous: fresh nitrile gloves worn during samples with or without observed quality issues. ‘Odd’ sample collection, collection tools alcohol-wiped. values included very high outlier values (>5 × the average Chemically clean single-species collection bags placed for remaining values) routinely excluded before data into clean plastic bags with desiccant packs, sealed just analysis, moderate outlier values (2–5 × the average for before cooling. Samples air-dried and kept cool, re-dried remaining values) that might be excluded and locally odd and stored in an air-conditioned laboratory after mailing. values (1·5–2 × higher or 25–50% lower than other Samples cleaned in the laboratory using fresh gloves with samples at that or nearby sites) that are often retained. alcohol-wiped implements on glass plates. Variation between replicates was calculated as the rela- Protocol 2. Mostly Rigorous: same as Protocol 1 in tive standard deviation (rSD = 100 × SD/|mean|;Haller- both field and laboratory except gloves not worn in the acker et al. (1998)); equivalent to the coefficient of variation field; hands wiped with alcohol instead. (Gailey & Lloyd 1986) when the mean is positive. The Protocol 3. Moderate
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