Changes in food web dynamics of low Arctic ponds with varying content of dissolved organic carbon

Figures & data

Table 1. Summary of the range of phytoplankton end member data and its determination in previously published stable isotope mixing models

Habitat	n	δ13Cphyto (‰)	δ2Hphyto (‰)	δ13Cphyto Determination	δ2Hphyto Determination	Reference
Boreal lakes	4	−32 to −27	−200 to −180	δ^13Cphyto = δ^13CO2- εH	δ^2Hphyto = δ^2H2O- εH	Solomon et al. (2011)
Boreal river	1	−32 to −26	−250 to −200	Measured from isolated phytoplankton	Measured from isolated phytoplankton	Cole and Solomon (2012)
Boreal lakes	40	−40 to −20	−250 to −200	δ^13Cphyto = δ^13CO2- εH	δ^2Hphyto = δ^2H2O- εH	Wilkinson, Carpenter, and Cole (2013)
Boreal lakes	10	−34 to −19	−236 to −167	δ^13Cphyto = δ^13CO2- εH	δ^2Hphyto = δ^2H2O- εH	Yang et al. (2014)
Boreal lakes	18	−41 to −31	−254 to −228	FA method	δ^2Hphyto = δ^2H2O- εH	Berggren et al. (2014)
Boreal lake (three seasons)	3	−42 to −37	−250 to −230	FA method	δ^2Hphyto = δ^2H2O- εH	Grosbois, del Giorgio, and Rautio (2017)
Arctic ponds	9	−44 to −38	−248 to −225	δ^13Cphyto = δ^13CPOM- εH	δ^2Hphyto = δ^2H2O- εH	This article

Table 2. Location, physicochemical properties, productivity, and vegetation cover of the study sites. The ponds are sorted by increasing DOC content. Bold values are mean ± standard deviation (SD) of the ponds in each category

Pond	Lat.	Lon.	Altitude	Max depth	Mean depth	TP	TN	POM	DOC	CDOM	SUVA	Kd PAR	Light on bottom	PPpel	PPben	BPpel	AqP	CaP
										a440
	(°N)	(°W)	(m a.s.l)	(m)	(m)	(µg L^−1)	(mg L^−1)	(mg L^−1)	(mg L^−1)	(m^−1)		(m^−1)	(%)	(µg C L^−1 d^−1)	(mg C m^−2 d^−1)	(µg C L^−1 d^−1)	(%)	(%)
Low-DOC			439 ± 222	2.9 ± 0.6	1.5 ± 0.4	7.5 ± 1.3	0.7 ± 0.3	0.9 ± .2	10.9 ± 3.4	1.5 ± 0.5	2.2 ± 0.7	0.3 ± 0.0	51.6 ± 27.1	0.03 ± 0.02	101.5 ± 97.2	0.4 ± 0.2	39 ± 36	57 ± 30
																			9	66.943	50.681	607	3.5	1.5	6.2	0.4	1.10	6.6	2.03	2.9	0.7	10.3	0.03	50.4	0.44	25-50	50-75
8	66.952	50.719	634	3	1.5	6.2	0.4	0.80	7.3	2	2.7	0.3	47.2	0.012	51.2	0.3	5-25	50-75
3	67.149	50.08	437	3.5	2	7.8	0.8	0.90	11.8	1.56	2.2	0.2	49.7	0.034	59.1	0.42	75-95	50-75
4	67.147	50.081	444	2	1	9.4	1.1	1.20	13.8	0.9	1.4	0.1	81.9	0.07	274.7	0.22	25-50	50-75
2	67.024	50.669	75	2.5	1.5	7.8	0.7	0.80	15.3	1.16	1.6	0.2	68.7	0.014	72.1	0.72	0-5	0-5
High-DOC			213 ± 139	2.6 ± 1.1	1.7 ± 0.7	14.1 ± 4.6	1.7 ± 0.6	1.0 ± 0.5	39.4 ± 16.1	10.2 ± 7.6	2.9 ± 0.4	1.5 ± 1.1	6.9 ± 4.6	0.01 ± 0.0	0.2 ± 0.2	2.2 ± 1.5	64 ± 33	92 ± 8
																			7	67.025	50.726	421	4	2.5	14.1	1.3	1.00	23.3	5.51	2.8	0.7	6.1	0.006	0.2	1.32	25-50	75-95
6	67.057	50.452	137	3	2	9.4	1.3	0.70	30.7	5.49	2.7	0.7	12.2	0.01	0.04	1.68	75-95	95-100
1	67.002	50.661	165	2	1.5	12.5	1.7	0.70	43.4	8.44	2.7	1.5	8.2	0.001	0.5	4.44	75-95	75-95
5	67.008	50.643	129	1.5	0.75	20.3	2.7	1.80	60.1	21.38	3.6	3	1.1	0.004	0.1	1.45	5-25	75-95

Note. TP = total phosphorus; TN = total nitrogen; DOC = dissolved organic carbon; CDOM (a440 = absorption coefficient at 440 nm; SUVA = specific UV absorbance ; K_d PAR = diffuse attenuation coefficient for PAR; PP_pel = pelagic primary production; PP_ben = benthic primary production; BP_pel = pelagic bacterial production; AqP = plant cover in the pond; CaP = plant cover in the catchment.

Table 3. Summary of the averaged δ¹³C, δ¹⁵N, and δ²H values and standard deviation from the sources and aquatic consumers used for the mixing models. The δ²H values of consumers are adjusted accounting for dietary water in their body tissue

			δ^13C		δ^2H		δ^15N
	Abbreviation	n	(‰)	SD	(‰)	SD	(‰)	SD
High-DOC
Aquatic plants	aqp	8	−22.6	1.6	−137.5	7.1
Benthic algae	ben	11	−25.9	2.1	−137.7	17.3
Branchinecta paludosa	bra	6	−27.8	0.5	−136.7	4.5	3.1	0.6
Chironomidae	chr	24	−26.8	2.2	−132.3	7.6	3.0	0.7
Colymbetes dolabratus	col	9	−28.8	2.1	−134.2	5.9	3.2	0.8
Leptodiaptomus minutus	dia	9	−32.1	1.8	−134.2	9.1	5.7	0.8
Dissolved organic matter	dom	12	−22.8	3.5	−112.3	8.5
Daphnia pulex	dpu	12	−27.0	1.4	−123.1	5.7	1.9	0.7
Eurycercus glacialis	egl	12	−27.5	2.1	−139.5	6.5	3.1	1.5
Periphyton	peri	12	−23.5	4.2	−161.7	16.2
Phytoplankton	phyto	12	−41.8	1.5	−229.5	2.7
Particulate organic matter	pom	12	−27.0	1.3	−147.3	4.5
Terrestrial plants	terr	8	−28.1	0.4	−139.7	6.1
Bulk water	water	13			−99.1	2.4
Low-DOC
Aquatic plants	aqp	10	−20.4	4.9	−163.4	9.2
Benthic algae	ben	16	−21.5	2.7	−141.2	14.4
Branchinecta paludosa	bra	12	−24.3	3.0	−142.6	11.7	2.9	0.6
Chironomidae	chr	29	−21.5	3.8	−141.7	15.4	3.1	1.3
Colymbetes dolabratus	col	12	−24.7	2.5	−137.8	9.4	3.7	1.0
Leptodiaptomus minutus	dia	12	−28.3	3.7	−141.3	10.4	6.3	1.6
Dissolved organic matter	dom	15	−17.1	5.1	−119.5	9.8
Daphnia pulex	dpu	15	−24.9	3.5	−131.5	7.2	2.6	1.2
Eurycercus glacialis	egl	9	−21.7	1.9	−148.2	9.6	2.7	0.9
Periphyton	peri	14	−17.3	4.8	−164.0	12.1
Phytoplankton	phyto	15	−39.3	3.3	−240.9	4.3
Particulate organic matter	pom	15	−24.6	3.2	−147.4	6.2
Terrestrial plants	terr	10	−27.7	0.8	−144.5	5.1
Bulk water	water	14			−110.2	4.8

Figure 1. Primary production rates in water column (PP pelagic) and in benthos (PP benthic), as well as pelagic bacterial production rates (BP pelagic) along a DOC gradient. The dashed line discriminates between low-DOC (clear-water) ponds and high-DOC (brown-water) ponds

Figure 2. The distribution of δ²H and δ¹³C (‰) values by (A) mean of end to better show the separation by DOC concentration and (B) full spread of data with consumers and potential food sources. High-DOC ponds are marked with a dot, and low-DOC ponds are marked with a triangle. Aqp = aquatic plants, ben = benthic algae, dom = dissolved organic matter, peri = periphyton, phyto = phytoplankton, pom = particulate organic matter, terr = terrestrial, zoop = all aquatic invertebrates collected

Figure 3. The contributions of benthic algae, dissolved organic matter (DOM), and phytoplankton-based carbon sources for the diets of the six most common aquatic invertebrates in high- and low-DOC ponds using a Bayesian mixing model, using δ¹³C, δ¹⁵N, and δ²H values. Whisker plots indicate the 95 percent Bayesian probability intervals

Solomon, C. T., S. R. Carpenter, M. K. Clayton, J. J. Cole, J. J. Coloso, M. L. Pace, J. M. Vader Zaden, and B. C. Weidel. 2011. Terrestial, benthic, and pelagic resource use in lakes: Results form a three-isotope Bayesian mixing model. Ecology 92 (5):1115–25. doi:https://doi.org/10.1890/10-1185.1.

 PubMed Web of Science ®Google Scholar

Cole, J. J., and C. T. Solomon. 2012. Terrestrial support of zebra mussels and Hudson River food web: A multi-isotope Bayesian analysis. Limnology and Oceanography 55 (6):1802–15. doi:https://doi.org/10.4319/lo.2012.57.6.1802.

 Google Scholar

Wilkinson, G. M., S. R. Carpenter, and J. J. Cole. 2013. Terrestrial support of pelagic consumers: Patterns and variability revealed by a multilake study. Freshwater Biology 58:2037–49. doi:https://doi.org/10.1111/fwb.12189.

 Web of Science ®Google Scholar

Yang, C., G. Wilkinson, J. Cole, S. A. Macko, and M. L. Pace. 2014. Assigning hydrogen, carbon, and nitrogen isotope values for phytoplankton and terrestrial detritus in aquatic food web studies. Inland Waters 4:233–42. doi:https://doi.org/10.5268/IW.

 Web of Science ®Google Scholar

Berggren, M., S. E. Ziegler, N. F. St-Gelais, B. Beisner, and P. A. del Giorgio. 2014. Contrasting patterns of allochthony among three major groups of crustacean zooplankton in boreal and temperate lakes. Ecology 97(5): 1–16.

 Google Scholar

Grosbois, G., P. A. del Giorgio, and M. Rautio. 2017. Zooplankton allochthony is spatially heterogeneous in a small boreal lake. Freshwater Biology 62(3):474–90. doi:https://doi.org/10.1111/fwb.12879.

 Web of Science ®Google Scholar