Metadata

Title

Global dataset for carbon and nitrogen stable isotope ratios of lotic periphyton

Authors

Naoto F. Ishikawa1, †, *, Hideyuki Doi2, *, Jacques C. Finlay3

1Department of Earth Sciences, ETH Zürich, Sonneggstrasse 5 8092 Zürich, Switzerland, naoto.f.ishikawa@gmail.com

2Graduate School of Simulation Studies, University of Hyogo, 7-1-28 Minatojima-minamimachi, Chuo-ku, Kobe, 650-0047, Japan, hideyuki.doi@icloud.com

3Department of Ecology, Evolution and Behavior, University of Minnesota, 140 Gortner Lab, 1479 Gortner Ave., St. Paul, MN 55108 USA, jfinlay@umn.edu

Corresponding author, present address: Department of Biogeochemistry, Japan Agency for Marine-Earth Science and Technology, Yokosuka 237-0061 Japan

*Equal contribution

Abstract

Carbon and nitrogen stable isotope ratios (δ13C and δ15 N) have been widely employed in food web analysis. In lotic environments, periphyton is a major primary producer that makes a large contribution to food web production as well as carbon and nitrogen cycling. While the δ13C and δ15N values have many advantages as a natural tracer, the controls over their high spatial and temporal variability in stream periphyton are not well known. Here, we present the global dataset of δ13C and δ15N values of lotic periphyton from 54 published and two unpublished sources, including 978 observations from 148 streams/rivers in 38 regions around the world, from arctic to tropical sites. The 54 published sources were articles recorded during the period of 1994–2016 in 25 academic journals. The two unpublished sources were from the authors’ own data. The dataset showed that δ13C and δ15N values of periphyton ranged from −47.3 to −9.3‰ and from −5.6 to +22.6‰, respectively. The dataset also includes physicochemical factors (altitude, coordinates, catchment area, width, depth, geology, vegetation, canopy coverage, biome, season, presence of anadromous salmon, temperature, pH, current velocity, and discharge), nutrient data (nitrate and ammonium concentrations), and algal attributes (chlorophyll a concentration, algal species compositions, and carbonates removal) in streams/rivers studied, all of which may help interpret the δ13C and δ15N values of periphyton. The metadata file outlines structure of all the data and with references for data sources, providing a resource for future food web studies in stream and river ecosystems.

Keywords

  • δ15N
  • δ13C
  • algae
  • food web
  • stream
  • river
  • source
  • fractionation
  • environmental factors

Introduction

Periphytic algae attached to the surface of submerged substrates (hereafter, periphyton) play an important role as an energy base for lotic invertebrate and fish species. Carbon and nitrogen stable isotope ratios (δ 13C and δ15N) are often useful for distinguishing periphyton from other food sources such as terrestrial organic matters (Finlay 2001). However, considerable variation in both the δ13 C and δ15N values of periphyton (e.g., McCutchan and Lewis 2001; Chessman et al. 2009; Ishikawa et al. 2012) may complicate use of δ13C and δ15N in stream food web studies.

The δ13C and δ15N values of periphyton are largely dependent of fractionation (i.e., isotopic discrimination during uptake of inorganic carbon and nitrogen) and the δ13C and δ15N values of their resources (i.e., dissolved inorganic carbon and nitrogen). It has been suggested that environmental factors such as catchment area, canopy openness, and water current velocity strongly affect carbon isotopic fractionation during algal photosynthesis (Finlay et al. 1999; Doi et al. 2007; Ishikawa et al. 2012). On the other hand, nutrients discharged into streams from terrestrial and marine environments may affect δ15N values of inorganic nitrogen such as nitrate and ammonium (Harding et al. 2014; Pastor et al. 2014). Therefore, the controlling pathways on both the δ13C and δ15N values of periphyton are highly complicated, and no general model to predict their variability is currently available.

The aim of this data paper is to provide global dataset of the δ13C and δ15N values of periphyton as well as putative controlling variables, using worldwide data collected from a variety of sources including our own unpublished data. This dataset has fundamental information for aquatic ecologists who work on food web science.

Metadata

1. TITLE

Global dataset for carbon and nitrogen stable isotope ratios of lotic periphyton

2. IDENTIFIER

ERDP-2018-04

3. CONTRIBUTORS

A. Dataset Owner

Naoto F. Ishikawa
Department of Biogeochemistry, Japan Agency for Marine-Earth Science and Technology, 2-15 Natsushima-cho Yokosuka 237-0061 Japan
+81 46 867 9812
naoto.f.ishikawa@gmail.com; ishikawan@jamstec.go.jp

B. Contact Person

Naoto F. Ishikawa
Department of Biogeochemistry, Japan Agency for Marine-Earth Science and Technology, 2-15 Natsushima-cho Yokosuka 237-0061 Japan
+81 46 867 9812
naoto.f.ishikawa@gmail.com; ishikawan@jamstec.go.jp

4. GEOGRAPHIC COVERAGE

68°37’N–25°52.5’S, 151.5561°W–143°20.4’E

5. TEMPORAL COVERAGE

1989–2015 (calendar years when data were collected in the original papers)

6. METHODS

Data gathering

We searched for data from published sources using ISI Web of Science (http://www.isiknowledge.com). Search terms included ‘periphyt*’, ‘algae’, ‘isotope’, and ‘stream’, in accordance with our previous paper (Ishikawa et al. 2012). The search was conducted on 11 January 2016 and returned 256 studies. We also added studies found in the journals Limnology and Oceanography, Freshwater Biology, Journal of the North American Benthological Society, Canadian Journal of Aquatic and Fisheries Science, Hydrobiologia, and Ecology that were not detected in our Web of Science search. Also, we included unpublished data from our own studies.

We selected the studies that provided carbon and nitrogen stable isotope values of stream periphyton. Benthic algae, epilithic algae, filamentous algae, littoral algae, micro algae, epilithon, epiphyton, periphytic biofilms, phototrophic biofilms, and phytomicrobenthos were regarded as periphyton in this study.

Studies were screened according to the following criteria:

  1. The study was conducted in a field setting.
  2. The study provided periphyton δ13C and/or δ15N.
  3. The study did not use 13C and 15N tracer additions.

After this screening, we ultimately selected 54 papers and used 978 data points (including unpublished 260 data points from Hideyuki Doi and Jacques C. Finlay). Materials and methods for the unpublished data are available in Finlay (2001) and Doi et al. (2007). When the papers presented their data in figures, we extracted the data using graph digitizing software PlotDigitizer X ver. 2.0.1 (http://www.surf.nuqe.nagoya-u.ac.jp/~nakahara/Software/PlotDigitizerX/index-e.html).

Putative control variables

Putative control variables for carbon and nitrogen isotopes of periphyton were extracted from individual studies or, for a small number of cases, provided directly by authors. We categorized the biome (alpine; arctic; boreal; subtropical; temperate; tropical) of the study sites from the location of rivers studied. For temperate regions, sampling periods were categorized into groups approximating four seasons (Spring: March-May; Summer: June-August; Autumn: September-November Winter: December-February for Northern hemisphere, Spring: September-November; Summer: December-February; Autumn: March-May; Winter: June-August for Southern hemisphere). Physicochemical factors (altitude, coordinates, catchment area, width, depth, geology, vegetation, canopy coverage, biome, season, presence of anadromous salmon, temperature, pH, current velocity, and discharge), nutrient data (nitrate and ammonium concentrations), and algal attributes (chlorophyll a concentration, algal species compositions, and carbonates removal) in streams/rivers studied, were also obtained.

7. LITERATURE CITED

Chessman BC, Westhorpe DP, Mitrovic SM, Hardwick L (2009) Trophic linkages between periphyton and grazing macroinvertebrates in rivers with different levels of catchment development. Hydrobiologia 625:135–150. doi: 10.1007/s10750-009-9702-3

Doi H, Takemon Y, Ohta T, Ishida Y, Kikuchi E (2007) Effects of reach-scale canopy cover on trophic pathways of caddisfly larvae in a Japanese mountain stream. Mar Freshw Res 58:811–817. doi:10.1071/MF07067

Finlay JC, Power ME, Cabana G (1999) Effects of water velocity on algal carbon isotope ratios: Implications for river food web studies. Limnol Oceanogr 44:1198–1203. doi:10.4319/lo.1999.44.5.1198

Finlay JC (2001) Stable-carbon-isotope ratios of river biota: implications for energy flow in lotic food webs. Ecology 82:1052–1064. doi:10.1890/0012-9658(2001)082[1052:SCIROR]2.0.CO;2

Harding JN, Harding JMS, Reynolds JD (2014) Movers and shakers: nutrient subsidies and benthic disturbance predict biofilm biomass and stable isotope signatures in coastal streams. Freshw Biol 59:1361–1377. doi:10.1111/fwb.12351

Ishikawa NF, Doi H, Finlay JC (2012) Global meta-analysis for controlling factors on carbon stable isotope ratios of lotic periphyton. Oecologia 170:541–549. doi:10.1007/s00442-012-2308-x

McCutchan JH, Lewis WM (2001) Seasonal variation in stable isotope ratios of stream algae. Verh Internat Verein Limnol 27:3304–3307. doi:10.1080/03680770.1998.11902437

Pastor A, Riera JL, Peipoch M, Cañas L, Ribot M, Gacia E, Martí E, Sabater F (2014) Temporal variability of nitrogen stable isotopes in primary uptake compartments in four streams differing in human impacts. Environ Sci Technol 48:6612–6619. doi:10.1021/es405493k

8. DATA STRUCTURE

Variable name Variable definition Storage type Range numeric type
reference_code Reference source. Numbers correspond to the reference list Numeric 1–56
location Country or region of studied rivers/streams Character N/A
river_name Name of studied rivers/streams Character N/A
collection_year_started Year AD when data collecttion started. Blank if not specified in reference. Numeric 1989–2015
collection_year_ended Year AD when data collecttion ended. Blank if not specified in reference Numeric 1989–2015
altitude_masl Altitude (m a.s.l.) of studied rivers/streams Numeric 3–2425
latitude Latitude of studied rivers/streams Character N/A
longitude Longitude of studied rivers/streams Character N/A
catchment_km2 Catchment area (km2) of studied rivers/streams Numeric 0.04–22060
width_m Stream width (m) of studied rivers/streams Numeric 0.55–1094
depth_cm Water depth (cm) of studied rivers/streams Numeric 0.046–91
geology Description of geological feature underlying studied rivers/streams Character N/A
vegetation Despription of vegetation surrounding studied rivers/streams Character N/A
canopy_percent Percentage (%) of canopy cover above studied rivers/streams Numeric 0–100
biome Despription for biome of studied rivers/streams Character N/A
season Sampling season Character N/A
salmon_spawning Observed salmon spawning in studied rivers/streams Character N/A
temperature_Cdegree Water tempearture (°C) of studied rivers/streams Numeric 2.75–25
pH Water pH of studied rivers/streams Numeric 5.9–8.7
NO3_uM Water NO3 concentration (µmol L−1) of studied rivers/streams Numeric 0.0015–5.9
NH4_uM Water NH4+ concentration (µmol L-1) of studied rivers/streams Numeric 0–3.6
velocity_cm_s-1 Current velocity (cm s-1) of studied rivers/streams Numeric 0–132
discharge_m3 Water discharge (m-3) of studied rivers/streams Numeric 0.0004–7787
chla_mg_m-2 Chlorophyll a cncentration in periphyton (mg m-2) of studied rivers/streams Numeric 0.9–348.5
acid_treatment Whether or not samples were acidified to remove carbonates Character N/A
dominant_ taxa Name of doninamit algal taxa included in periphyton Character N/A
d15N_permil δ15N values (‰) of periphyton Numeric −5.6–+22.6
d13C_permil δ13C values (‰) of periphyton Numeric −47.3–−9.3
d15N_SD Standard deviation of δ15N values (‰) of periphyton Numeric 0.02–5.2
d13C_SD Standard deviation of δ13C values (‰) of periphyton Numeric 0.003–6.48
d15N_sample_size Sample size (N) of δ15N values (‰) of periphyton Numeric 1–45
d13C_sample_size Sample size (N) of δ13C values (‰) of periphyton Numeric 1–50
notes Special description of data Character N/A

9. DATASET REFERENCES

Paper code Reference
1 Brito EF, Moulton TP, De Souza ML, Bunn SE (2006) Stable isotope analysis indicates microalgae as the predominant food source of fauna in a coastal forest stream, south-east Brazil. Austral Ecol 31:623–633. doi: 10.1111/j.1442-9993.2006.01610.x
2 Primavera J (1996) Stable carbon and nitrogen isotope ratios of penaeid juveniles and primary producers in a riverine mangrove in Guimaras, Philippines. Bull Mar Sci 58:675–683
3 Rasmussen JB, Trudeau V (2007) Influence of velocity and chlorophyll standing stock on periphyton δ13C and δ15N in the Ste. Marguerite River system, Quebec. Can J Fish Aquat Sci 64:1370–1381. doi:10.1139/f07-109
4 Finlay JC (2001) Stable-carbon-isotope ratios of river biota: implications for energy flow in lotic food webs. Ecology 82:1052–1064. doi:10.1890/0012-9658(2001)082[1052:SCIROR]2.0.CO;2
5 France R, Cattaneo A (1998) δ13C variability of benthic algae: effects of water colour via modulation by stream current. Freshw Biol 39:617–622. doi:10.1046/j.1365-2427.1998.00307.x
6 Godwin CM, Arthur MA, Carrick HJ (2009) Periphyton nutrient status in a temperate stream with mixed land- uses: implications for watershed nitrogen storage. Hydrobiologia 623:141–152. doi:10.1007/s10750-008-9654-z
7 Rasmussen JB, Trudeau V, Morinville G (2009) Estimating the scale of fish feeding movements in rivers using δ13C signature gradients. J Anim Ecol 78:674–685. doi:10.1111/j.1365-2656.2008.01511.x
8 Finlay JC (2004) Patterns and controls of lotic algal stable carbon isotope ratios. Limnol Oceanogr 49:850– 861. doi:10.4319/lo.2004.49.3.0850
9 Finlay JC, Power ME, Cabana G (1999) Effects of water velocity on algal carbon isotope ratios: Implications for river food web studies. Limnol Oceanogr 44:1198–1203. doi:10.4319/lo.1999.44.5.1198
10 Singer GA, Panzenböck M, Weigelhofer G, Marchesani C, Waringer J, Wanek W, and Battin TJ (2005) Flow history explains temporal and spatial variation of carbon fractionation in stream periphyton. Limnol Oceanogr 50:706–712. doi:10.4319/lo.2005.50.2.0706
11 Doi H, Takemon Y, Ohta T, Ishida Y, Kikuchi E (2007) Effects of reach-scale canopy cover on trophic pathways of caddisfly larvae in a Japanese mountain stream. Mar Freshw Res 58:811–817. doi:10.1071/MF07067
12 Thorp JH, Delong MD, Greenwood KS, Casper AF (1998) Isotopic analysis of three food web theories in constricted and floodplain regions of a larger river. Oecologia 117:551–563. doi:10.1007/s004420050692
13 Verburg P, Kilham SS, Pringle CM, Lips KR, Drake DL (2007) A stable isotope study of a neotropical stream food web prior to the extirpation of its large amphibian community. J Trop Ecol 23:643–651. doi:10.1017/ S0266467407004518
14 Zah, R, Burgherr P, Bernasconi SM, Uehlinger U (2001) Stable isotope analysis of macroinvertebrates and their food sources in a glacier stream. Freshw Biol 46:871–882. doi:10.1046/j.1365-2427.2001.00720.x
15 Evans-White M, Dodds W, Gray L, Fritz KM (2001) A comparison of the trophic ecology of the crayfishes (Orconectes nais (Faxon) and Orconectes neglectus (Faxon)) and the central stoneroller minnow (Campostoma anomalum (Rafinesque)): omnivory in a tallgrass prairie stream. Hydrobiologia 462:131–144. doi: 10.1023/A:1013182100150
16 Bunn SE, Davies PM, Winning M (2003) Sources of organic carbon supporting the food web of an arid zone floodplain river. Freshw Biol 48:619–635. doi:10.1046/j.1365-2427.2003.01031.x
17 Perry RW, Bradford MJ, Grout JA (2003) Effects of disturbance on contribution of energy sources to growth of juvenile chinook salmon (Oncorhynchus tshawytscha) in boreal streams. Can J Fish Aquat Sci 60:390–400. doi:10.1139/f03-035
18 England LE, Rosemond AD (2004) Small reductions in forest cover weaken terrestrial-aquatic linkages in headwater streams. Freshw Biol 49:721–734. doi:10.1111/j.1365-2427.2004.01219.x
19 Bergfur J, Johnson RK, Sandin L, Goedkoop W (2009) Effects of nutrient enrichment on C and N stable isotope ratios of invertebrates, fish and their food resources in boreal streams. Hydrobiologia 628:67–79. doi:10.1007/ s10750-009-9746-4
20 Füreder L, Welter C, Jackson JK (2003) Dietary and stable isotope (δ13C, δ15N) analyses in alpine stream insects. Int Rev Hydrobiol 88:314–331. doi:10.1002/iroh.200390028
21 Hamilton SK, Sippel SJ, Bunn SE (2005) Separation of algae from detritus for stable isotope or ecological stoichiometry studies using density fractionation in colloidal silica. Limnol Oceanogr Meth 3:149–157. doi:10.4319/ lom.2005.3.149
22 Huryn AD, Riley RH, Young RG, Arbuckle CJ, Peacock K (2002) Natural-abundance stable C and N isotopes indicate weak upstream-downstream linkage of food webs in a grassland river. Arch Hydrobiol 153:177–196. doi: 10.1127/archiv-hydrobiol/153/2002/177
23 McCutchan JH, Lewis WM (2001) Seasonal variation in stable isotope ratios of stream algae. Verh Internat Verein Limnol 27:3304–3307. doi:10.1080/03680770.1998.11902437
24 Parkyn SM, Collier KJ, Hicks BJ (2001) New Zealand stream crayfish: functional omnivores but trophic predators? Freshw Biol 46:641–652. doi:10.1046/j.1365-2427.2001.00702.x
25 Junger M, Planas D (1994) Quantitative use of stable carbon isotope anaysis to determine the trophic base of invertebrate communities in a boreal forest lotic system. Can J Fish Aquat Sci 51:52–61. doi:10.1139/f94-007
26 Pereira AL, Benedito E, Sakuragui CM (2007) Spatial variation in the stable isotopes of 13C and 15N and trophic position of Leporinus friderici (Characiformes, Anostomidae) in Corumba Reservoir, Brazil. An Acad Bra Cienc 79:41–49. doi:10.1590/S0001-37652007000100006
27 Manetta GI, Benedito-Cecilio E, Martinelli M (2003) Carbon sources and trophic position of the main species of fishes of Baía River, Paraná River floodplain, Brazil. Braz J Biol 63:283–290. doi:10.1590/ S1519-69842003000200013
28 Lau DCP, KMY Leung, Dudgeon D (2009a) Evidence of rapid shifts in the trophic base of lotic predators using experimental dietary manipulations and assimilation-based analyses. Oecologia 159:767–776. doi:10.1007/ s00442-008-1262-0
29 Lau DCP, KMY Leung, Dudgeon D (2009b) What does stable isotope analysis reveal about trophic relationships and the relative importance of allochthonous and autochthonous resources in tropical streams? A synthetic study from Hong Kong. Freshw Biol 54:127–141. doi:10.1111/j.1365-2427.2008.02099.x
30 Watanabe K, Monaghan MT, Takemon Y, Omura T (2008) Biodilution of heavy metals in a stream macroinvertebrate food web: Evidence from stable isotope analysis. Sci Total Environ 394:57–67. doi:10.1016/ j.scitotenv.2008.01.006
31 Zeug SC, Winemiller KO (2008) Evidence supporting the importance of terrestrial carbon in a large-river food web. Ecology 89:1733–1743. doi:10.1890/07-1064.1
32 Walters AW, Barnes RT, Post DM (2009) Anadromous alewives (Alosa pseudoharengus) contribute marine- derived nutrients to coastal stream food webs. Can J Fish Aquat Sci 66:439–448. doi:10.1139/F09-008
33 Chessman BC, Westhorpe DP, Mitrovic SM, Hardwick L (2009) Trophic linkages between periphyton and grazing macroinvertebrates in rivers with different levels of catchment development. Hydrobiologia 625:135–150. doi: 10.1007/s10750-009-9702-3
34 Li AOY, Dudgeon D (2008) Food resources of shredders and other benthic macroinvertebrates in relation to shading conditions in tropical Hong Kong streams. Freshw Biol 53:2011–2025. doi:10.1111/j.1365-2427.2008.02022.x
35 Robinson CT, Schmid D, Svoboda M, Bernasconi SM (2008) Functional measures and food webs of high elevation springs in the Swiss alps. Aquat Sci 70:432–445. doi:10.1007/s00027-008-8125-y
36 Dekar MP, Magoulick DD, Huxel GR (2009) Shifts in the trophic base of intermittent stream food webs. Hydrobiologia 635:263–277. doi:10.1007/s10750-009-9919-1
37 Göthe E, Lepori F, Malmqvist B (2009) Forestry affects food webs in northern Swedish coastal streams. Fundam Appl Limnol / Arch Hydrobiol 175:281–294. doi:10.1127/1863-9135/2009/0175-0281
38 Rasmussen JB, Trudeau V (2010) How well are velocity effects on  ∂13C signatures transmitted up the food web from algae to fish? Freshw Biol 55:1303–1314. doi:10.1111/j.1365-2427.2009.02354.x
39 Winemiller KO, Hoeinghaus DJ, Pease AA, Esselman PC, Honeycutt RL, Gbanaador D, Carrera E, Payne J (2011) Stable isotope analysis reveals food web structure and watershed impacts along the fluvial gradient of a Mesoamerican coastal river. River Res Appl 27:791–803. doi:10.1002/rra.1396
40 Trimmer M, Hildrew AG, Jackson MC, Pretty JL, Grey J (2009) Evidence for the role of methane-derived carbon in a free-flowing, lowland river food web. Limnol Oceanogr 54:1541–1547. doi:10.4319/lo.2009.54.5.1541
41 Ishikawa NF, Togashi H, Kato K, Yoshimura M, Kohmatsu Y, Yoshimizu C, Ogawa NO, Ohte N, Tokuchi N, Ohkouchi N, Tayasu I (2016) Terrestrial–aquatic linkage in stream food webs along a forest chronosequence: multi-isotopic evidence. Ecology 97:1146–1158. doi:10.1890/15-1133.1
42 Rosewarne PJ, Mortimer RJG, Newton RJ, Grocock C, Wing CD, Dunn AM (2016) Feeding behaviour, predatory functional responses and trophic interactions of the invasive Chinese mitten crab (Eriocheir sinensis) and signal crayfish (Pacifastacus leniusculus). Freshw Biol 61:426–443. doi:10.1111/fwb.12717
43 Kautza A, Sullivan M (2016) The energetic contributions of aquatic primary producers to terrestrial food webs in a mid size river system. Ecology 97:694–705. doi:10.1890/15-1095.1
44 Samways KM, Quiñones-Rivera ZJ, Leavitt PR, Cunjak RA (2015) Spatiotemporal responses of algal, fungal, and bacterial biofilm communities in Atlantic rivers receiving marine-derived nutrient inputs. Freshw Sci 34:881–896. doi:10.1086/681723
45 Ishikawa NF, Yamane M, Suga H, Ogawa NO, Yokoyama Y, Ohkouchi N (2015) Chlorophyll a-specific Δ14C, δ13C and δ15N values in stream periphyton: implications for aquatic food web studies. Biogeosciences 12:6781–6789. doi:10.5194/bg-12-6781-2015
46 Harding JN, Harding JMS, Reynolds JD (2014) Movers and shakers: nutrient subsidies and benthic disturbance predict biofilm biomass and stable isotope signatures in coastal streams. Freshw Biol 59:1361–1377. doi:10.1111/ fwb.12351
47 Reisinger AJ, Chaloner DT, Rüegg J, Tiegs SD, Lamberti GA (2013) Effects of spawning Pacific salmon on the isotopic composition of biota differ among southeast Alaska streams. Freshw Biol 58:938–950. doi:10.1111/fwb. 12098
48 Chang HY, Wu SH, Shao KT, Kao WY, Maa CJW, Jan RQ, Liu LL, Tzeng CS, Hwang JS, Hsieh HL, Kao SJ, Chen YK, Lin HJ (2012) Longitudinal variation in food sources and their use by aquatic fauna along a subtropical river in Taiwan. Freshw Biol 57:1839–1853. doi:10.1111/j.1365-2427.2012.02843.x
49 Dekar MP, King RS, Back JA, Whigham DF, Walker CM (2012) Allochthonous inputs from grass-dominated wetlands support juvenile salmonids in headwater streams: evidence from stable isotopes of carbon, hydrogen, and nitrogen. Freshw Sci 31:121–132. doi:10.1899/11-016.1
50 Hayden B, McWilliam-Hughes SM, Cunjak RA (2014) Evidence for limited trophic transfer of allochthonous energy in temperate river food webs. Freshw Sci 35:544–558. doi:10.1086/686001
51 Pastor A, Riera JL, Peipoch M, Cañas L, Ribot M, Gacia E, Martí E, Sabater F (2014) Temporal variability of nitrogen stable isotopes in primary uptake compartments in four streams differing in human impacts. Environ Sci Technol 48:6612–6619. doi:10.1021/es405493k
52 Peipoch M, Gacia E, Blesa A, Ribot M, Riera JL, Martí E (2014) Contrasts among macrophyte riparian species in their use of stream water nitrate and ammonium: Insights from 15N natural abundance. Aquat Sci 76:203–215. doi:10.1007/s00027-013-0330-7
53 Ishikawa NF, Uchida M, Shibata Y, Tayasu I (2012) Natural C-14 provides new data for stream food-web studies: a comparison with C-13 in multiple stream habitats. Mar Freshw Res 63:210–217. doi:10.1071/MF11141
54 Spencer CN, Gabel KO, Hauer FR (2003) Wildfire effects on stream food webs and nutrient dynamics in Glacier National Park, USA. For Ecol Manag 178:141–153. doi:10.1016/S0378-1127(03)00058-6
55 Doi H (unpublished data)
56 Finlay JC (unpublished data)

10. ACCESSIBILITY

This work is licensed under a Creative Commons Attribution 4.0 International License.

11. ACKNOWLEDGMENTS

N.F.I. was a Research Fellow (25-1021) and an Overseas Research Fellow (28-0214) ofthe Japan Society for the Promotion of Science.