Organic Geochemistry 66 (2014) 25–37

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Organic Geochemistry

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Effects of temperature, pH and nutrient concentration on branchedGDGT distributions in East African lakes: Implicationsfor paleoenvironmental reconstruction

0146-6380/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.orggeochem.2013.10.012

⇑ Corresponding author. Tel.: +1 401 863 2810.E-mail address: Shannon_Loomis@brown.edu (S.E. Loomis).

Shannon E. Loomis a,⇑, James M. Russell a, Hilde Eggermont b,c, Dirk Verschuren c,Jaap S. Sinninghe Damsté d

a Brown University, Department of Geological Sciences, 324 Brook St., Box 1846, Providence, RI 02912, USAb Royal Belgian Institute for Natural Sciences, Belgian Biodiversity Platform, Vautierstraat 29, 1000 Brussels, Belgiumc Ghent University, Limnology Unit, Ledeganckstraat 35, 9000 Ghent, Belgiumd NIOZ Royal Netherlands Institute for Sea Research, Department of Marine Organic Biogeochemistry, PO Box 59, 1790 AB Den Burg, The Netherlands

a r t i c l e i n f o a b s t r a c t

Article history:Received 19 June 2013Received in revised form 24 September 2013Accepted 17 October 2013Available online 28 October 2013

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are membrane lipids found in soils and sedi-ments and their relative abundance correlates with temperature and pH, enabling them to be used asproxies in reconstructing past climatic and environmental conditions. However, the potential for otherenvironmental variables, such as nutrient concentration, to affect brGDGT distributions remains largelyunexplored. We have examined the influence of a suite of environmental factors, including temperature,lake water and sediment chemistry, and lake morphometry on brGDGT concentration and distributions inthe surface sediments of 111 lakes in East Africa. We found that temperature was the major control onthe distributions, while the influence of pH was relatively minor. Water depth also had a minor but sta-tistically significant influence, perhaps due to the relationship between lake depth and deep wateranoxia. Water column nutrient concentration did not have a significant effect on the distributions or con-centration. We further explored the potential for these variables to affect brGDGT temperature recon-struction by examining the correlation between them and the residuals of our brGDGT temperaturecalibration. We found that, while the distribution of some cyclized brGDGTs may be influenced by pHand other environmental variables, they are necessary in brGDGT calibration equations in order to accu-rately reconstruct temperature, likely due to covariation between temperature and other environmentalvariables. While surface water pH correlated with the relative abundance of certain brGDGTs, cautionshould be exhibited when using brGDGTs as a pH proxy because of systematic calibration errors.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Branched glycerol dialkyl glycerol tetraethers (brGDGTs; Fig. 1)are bacterial membrane lipids found in peat (Sinninghe Damstéet al., 2000; Weijers et al., 2006a; Huguet et al., 2010), soil (Weijerset al., 2007a; Peterse et al., 2012), nearshore marine sediments(Weijers et al., 2007b) and lake sediments (Blaga et al., 2009,2010; Tierney et al., 2010; Loomis et al., 2012). Their relative abun-dance has been shown to correlate with temperature and pH (Weij-ers et al., 2007a) in a variety of environments, permitting theirapplication in reconstructing paleotemperature from nearshoreocean sediments (Weijers et al., 2007b), loess (Peterse et al.,2011b) and lake sediments (Fawcett et al., 2011; Loomis et al.,2012), as well as reconstructing past pH conditions using lake sed-iments (Tyler et al., 2010). Despite these advances, little is known

about the full range of environmental factors that affect brGDGT dis-tributions, particularly in aquatic environments. As paleoenviron-mental change through time typically involves simultaneousvariation in multiple environmental parameters, it is important tounderstand the relative importance of different environmental con-trols on brGDGT distributions to ensure robust temperature recon-struction and to adequately assess the potential for brGDGTs inreconstructing other environmental variables.

Weijers et al. (2007a) were the first to examine environmentalcontrols on the distributions of brGDGTs in a set of global soils andfound temperature to be one of the dominant controls. They cre-ated indices to quantify the degree of methylation (MBT) and cycli-zation (CBT) of these compounds and calculated a transfer functionto reconstruct mean annual air temperature (MAAT) using theseindices. Although the distributions of brGDGTs in lake sedimentsand surrounding soils are different, indicating that the majorityof brGDGTs in lake sediments are produced within the lake itself(Tierney and Russell, 2009; Loomis et al., 2011), temperature also

m/z

IIIa 1050HO

O

OO

OOH

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OOH

Ia 1022HO

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O

O

OOH

Ib 1020

HOO

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OOH

Ic 1018HO O

OO

O

OH

IS 744

HOO

OO

OOH

Fig. 1. Structures of brGDGTs and synthetic C46 internal standard (IS).

26 S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37

appears to have a major influence on brGDGT distributions in lakesediments (Blaga et al., 2010; Tierney et al., 2010; Pearson et al.,2011; Sun et al., 2011). This relationship has stimulated globaland regional lacustrine brGDGT temperature calibrations employ-ing different statistical methods, from recalibration of the MBT/CBT regressions (Tierney et al., 2010; Sun et al., 2011) to best-sub-set and stepwise forward selection methods that calculate regres-sions using the brGDGT compounds which explain most of thevariance in temperature (Pearson et al., 2011; Loomis et al.,2012). The development of these new lacustrine calibrations andtheir downcore applications, demonstrate current ability forbrGDGTs to be used as a temperature proxy.

However, there is evidence that other environmental factorsmay also affect brGDGT distributions in both soil and lacustrineenvironments, potentially complicating temperature reconstruc-tion. In their global soils dataset, Weijers et al. (2007a) found thatpH was a stronger control on brGDGT distributions than tempera-ture and created a linear transfer function relating CBT to soil pH.Many studies of lake sediments have also found a significant

influence of pH on lacustrine brGDGTs (Blaga et al., 2010; Tierneyet al., 2010; Pearson et al., 2011; Sun et al., 2011), although therelationships differed quantitatively from the CBT–pH relationshipdefined by Weijers et al. (2007a) for soils. Some studies have alsosuggested the possible influence of lake water conductivity (Tier-ney et al., 2010) or water column oxygenation (Tierney et al.,2012) on brGDGT distributions. Tierney et al. (2010) further notedthat water depth might influence brGDGT distributions, perhapsdue to the involvement of brGDGT producers in N cycling at theoxycline of deeper lakes. To date, there has been relatively littleexamination of the effects of lake chemistry and other variableson brGDGT distributions, due both to the difficulty in acquiringcomprehensive environmental datasets and to the size of the cali-bration datasets needed to detect the influence of secondary envi-ronmental variables.

Here we have explored the environmental controls on brGDGTdistributions in the surface sediments of 111 East African lakes.We have examined the influence of temperature, water chemistry,lake morphometry and sedimentary organic carbon (OC) contenton the concentration and distribution of brGDGTs. In addition totesting the direct effect of these variables on the distributions,we have investigated the influence that these environmental vari-ables have on temperature calibrations and the potential forbrGDGTs to be used as a paleo-pH proxy for lakes.

2. Methods

2.1. Sampling and laboratory analysis

Surface sediments and environmental data were collected from111 lakes in East Africa (Fig. 2; Supplementary material, Figs. S1–S4), with sites chosen to maximize the variance and minimizethe covariance of a suite of 16 environmental parameters, includ-ing water depth, surface area, MAAT, and the temperature, pHand dissolved O (DO) of surface and bottom waters, conductivity,total P (TP), total N (TN), dissolved OC (DOC) and chlorophyll a insurface water, as well as total OC (TOC) and total organic N(TON) in recently deposited bottom sediments (Supplementarymaterial, Table S1). The majority of the samples and data were col-lected during fieldwork by Brown and Ghent Universities in thecontext of diverse projects over the past decade. For specific infor-mation on the context and purpose of the collections, we refer toTalling and Talling (1965) for large lakes, Eggermont et al. (2007,2010) for high elevation lakes in Uganda (Rwenzori Mountains),Rumes et al. (2011) and Verschuren et al. (2011) for low elevationcrater lakes in western Uganda and Eggermont et al. (2006) andMergeay et al. (2006) for low elevation lakes and ponds in Kenya.Eggermont and Verschuren (2007) have provided information onsome of the high elevation lakes on Mt. Kenya represented in thedataset, but the environmental data linked to most of the lake sed-iment samples from Mt. Kenya (Supplementary material, Table S1)have not been published.

Upon arrival at Brown University, sediments were freeze driedand homogenized. They were extracted with accelerated solventextraction (ASE) using 9:1 dichloromethane (DCM):MeOH (v:v). AC46 internal standard (IS; Huguet et al., 2006) was added to the ex-tract and the mixture was separated using an Al2O3 column for iso-lation of the polar fraction containing brGDGTs. The polar fractionwas dried under N2, dissolved in 99:1 hexane:isopropanol (v:v)and filtered through a 0.45 lm glass fiber filter. BrGDGTs wereanalyzed using high performance liquid chromatography–atmo-spheric pressure chemical ionization mass spectrometry (HPLC–APCI–MS) at the Royal Netherlands Institute for Sea Research(NIOZ) and were quantified according to the methods of Weijerset al. (2007a).

a b c

0.8°N

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0.4°S

37°E 37.2°E 37.4°E 37.6°E

1000 2000 3000 4000 2000 3000 4000

Fig. 2. (a) Sample locations, with enlarged digital elevation models (DEMs) of regions with a high density of locations: (b) southwestern Uganda including the RwenzoriMountains and (c) Mt. Kenya. DEM data were obtained through DEM Explorer (Han et al., 2012) using the GTOPO30 dataset and are plotted with contours of 250 m.

S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37 27

Environmental data, including temperature, pH, morphometricvariables, conductivity, DO and dissolved nutrients are derivedfrom the original literature and from this study (see Supplemen-tary material, Table S1 for site specific references). Replicate datapoints, based on analysis of multiple samples available from thesame site, were averaged. For the data presented here, surfaceand bottom water temperature, pH, and DO and surface conduc-tivity were measured in the field, at the time of sediment collec-tion, using a Hydrolab Quanta CTD profiler. TP and TN sampleswere unfiltered and fixed with H2SO4. TP was measured usingwet oxidation with an acid persulfate solution and TN usingwet oxidation with an alkaline persulfate solution (Eggermontet al., 2007; Verschuren et al., 2011). DOC was measured usinginfrared (IR) spectrometry (TOC-200A, Shimadzu). For chlorophylla measurements, ca. 500 ml water were filtered through a com-busted 45 lm glass fiber filter. The filter was extracted with Me2-

CO and chlorophyll a was quantified using HPLC (Eggermontet al., 2007; Verschuren et al., 2011). MAAT was calculated fromlapse rate values derived from a combination of in situ tempera-ture loggers, combined with data from the National Centers forEnvironmental Prediction (NCEP)/National Center for AtmosphericResearch (NCAR) Reanalysis and Global Summary of the Day(GSOD; Eggermont et al., 2007; Tierney et al., 2010). SedimentaryTOC concentration and TON concentration were measured with aCE Instruments NC2100 elemental analyzer.

2.2. Statistical analysis

Fractional abundance data for brGDGTs were obtained fromTierney et al. (2010) and Loomis et al. (2012; Supplementary mate-rial, Table S2); references to individual brGDGTs (e.g. IIIa, IIIb, etc.)refer to fractional abundance unless otherwise stated. Concentra-tion data for 41 of the lakes are from Tierney et al. (2010), whiledata from the other 70 lakes are presented here (Supplementarymaterial, Table S2). Concentration was calculated according toHuguet et al. (2006) and normalized to both sediment weight([brGDGT]sed) and TOC ([brGDGT]TOC). BrGDGTs were also statisti-cally analyzed in structural groupings, including main structure(Group I, Group II, and Group III) and by number of rings (ring0,ring1, ring2). The data were derived by addition of the fractionalabundance of each group, such that

Group III ¼ IIIaþ IIIbþ IIIc ð1Þ

ring1 ¼ Ibþ IIbþ IIIb ð2Þ

etc., for all of the combinations of ring and group structures. CBTwas calculated using the following equation (Weijers et al., 2007a):

CBT ¼ �logð½Ibþ IIb�=½Iaþ IIa�Þ ð3Þ

Environmental and concentration data were log transformed orsquare root transformed as needed to obtain the most Gaussiandistribution possible. This resulted in square root transformationof surface water DO, bottom water DO, TOC and TON, and logtransformation of depth, surface area, conductivity, TP, TN, DOC,chlorophyll a and brGDGT concentration (normalized to both sed-iment wt. and TOC).

Pearson correlation coefficients (r values) and p values were cal-culated for all combinations of normalized environmental andbrGDGT data (Table 1). Samples with missing environmental datawere excluded from correlation, so we report the number of sam-ples (n) used to calculate each correlation. We consider any corre-lation with p 6 0.01 to be significant.

We performed detrended correspondence analysis (DCA; Lepšand Šmilauer, 2003) to determine whether variability in thebrGDGT data was linear or unimodal and found that the variabilitywas linear. We performed linear indirect gradient analysis usingprincipal component analysis (PCA) to determine the major modesof variability in the brGDGT data, assigning individual brGDGTs as‘‘species’’. Environmental scores were calculated from the brGDGTPCA and were used to help interpret the PCA axes; however, thesedata only provide information on the major modes of variability inthe brGDGT data and their correlation with environmental data,but do not directly elucidate environmental controls on brGDGTs.In order to determine the variability in the brGDGT data that couldbe explained by the environmental variables, we also performed adirect gradient analysis using redundancy analysis (RDA). It is aform of regression analysis and ordination axes comprise linearcombinations of environmental variables, hence providing infor-mation on the amount of brGDGT variance that can be explainedby these variables (ter Braak, 1994). DCA analysis and gradientanalysis were performed using CANOCO (ter Braak and Šmilauer,2002). Missing environmental values were replaced with averagesof the normalized data (Lepš and Šmilauer, 2003).

Additional statistical tests were performed as needed and de-scribed below.

Table 1Pairwise correlations of transformed environmental and brGDGT data, including concentration, structural groups, and fractional abundance. Log transformations were carried outon lake depth (m), surface area (SA, m2), surface conductivity (cond, lS/cm), total P in surface waters (TP, lg/l), total N in surface waters (TN, lg/l), dissolved organic carbon insurface waters (DOC, mg/l), chlorophyll a in surface waters (Chl a, lg/l) and concentration of brGDGTs normalized to sediment weight ([brGDGT]sed, lg/g sediment) and TOC([brGDGT]TOC, lg/g TOC). Square root transformations were carried out on surface water dissolved oxygen (SW DO, mg/l), bottom water dissolved oxygen (BW DO, mg/l),sedimentary total organic carbon (TOC), and sedimentary total organic nitrogen (TON). No transformations were performed on mean annual air temperature (MAAT, �C), surfacewater temperature (SW T, �C), bottom water temperature (BW T, �C), surface water pH (SW pH), bottom water pH (BW pH) or the distributions of brGDGTs. Significantcorrelations are bold and italicized.

[brGDGT]sed [brGDGT]TOC Group I Group II Group III Ring0 Ring1 Ring2 IIIa IIIb IIIc IIa IIb IIc Ia Ib Ic

Depth r 0.18 0.47 0.21 0.03 �0.24 �0.49 0.48 0.41 �0.25 0.24 0.32 �0.32 0.42 0.31 0.04 0.48 0.41p 0.070 0.000 0.031 0.747 0.012 0.000 0.000 0.000 0.009 0.011 0.001 0.001 0.000 0.001 0.674 0.000 0.000n 99 99 111 111 111 111 111 111 111 111 111 111 111 111 111 111 111

SA r 0.09 0.30 0.40 �0.05 �0.41 �0.43 0.43 0.37 �0.42 0.17 0.09 �0.33 0.33 0.22 0.29 0.47 0.43p 0.374 0.003 0.000 0.583 0.000 0.000 0.000 0.000 0.000 0.074 0.343 0.001 0.000 0.024 0.002 0.000 0.000n 94 94 106 106 106 106 106 106 106 106 106 106 106 106 106 106 106

MAAT r 0.13 0.22 0.89 �0.21 �0.89 �0.66 0.68 0.36 �0.89 0.08 0.21 �0.54 0.46 0.01 0.77 0.85 0.54p 0.212 0.030 0.000 0.029 0.000 0.000 0.000 0.000 0.000 0.393 0.024 0.000 0.000 0.928 0.000 0.000 0.000n 99 99 111 111 111 111 111 111 111 111 111 111 111 111 111 111 111

SW T r 0.10 0.18 0.88 �0.20 �0.87 �0.62 0.64 0.32 �0.87 0.04 0.19 �0.50 0.42 �0.03 0.76 0.82 0.50p 0.320 0.074 0.000 0.031 0.000 0.000 0.000 0.001 0.000 0.668 0.046 0.000 0.000 0.764 0.000 0.000 0.000n 99 99 111 111 111 111 111 111 111 111 111 111 111 111 111 111 111

BW T r 0.08 0.16 0.87 �0.18 �0.87 �0.60 0.63 0.31 �0.87 0.03 0.18 �0.47 0.41 �0.03 0.76 0.80 0.50p 0.445 0.121 0.000 0.059 0.000 0.000 0.000 0.001 0.000 0.750 0.066 0.000 0.000 0.753 0.000 0.000 0.000n 99 99 111 111 111 111 111 111 111 111 111 111 111 111 111 111 111

SW pH r 0.05 0.20 0.53 0.05 �0.60 �0.69 0.71 0.39 �0.61 0.36 0.19 �0.43 0.60 0.16 0.35 0.74 0.48p 0.590 0.050 0.000 0.631 0.000 0.000 0.000 0.000 0.000 0.000 0.050 0.000 0.000 0.087 0.000 0.000 0.000n 99 99 111 111 111 111 111 111 111 111 111 111 111 111 111 111 111

BW pH r �0.05 0.14 0.44 �0.01 �0.47 �0.48 0.49 0.30 �0.48 0.25 0.07 �0.31 0.38 0.11 0.32 0.53 0.40p 0.613 0.171 0.000 0.891 0.000 0.000 0.000 0.001 0.000 0.007 0.483 0.001 0.000 0.250 0.001 0.000 0.000n 99 99 111 111 111 111 111 111 111 111 111 111 111 111 111 111 111

SW DO r �0.22 �0.07 �0.12 �0.07 0.16 �0.04 0.05 �0.06 0.16 0.12 0.10 �0.10 0.06 �0.06 �0.16 0.02 �0.07p 0.034 0.526 0.217 0.466 0.098 0.723 0.629 0.546 0.107 0.230 0.333 0.299 0.559 0.581 0.101 0.828 0.499n 93 93 103 103 103 103 103 103 103 103 103 103 103 103 103 103 103

BW DO r �0.24 �0.30 �0.68 0.22 0.64 0.50 �0.52 �0.21 0.64 �0.03 �0.22 0.44 �0.33 0.04 �0.58 �0.67 �0.35p 0.020 0.003 0.000 0.024 0.000 0.000 0.000 0.029 0.000 0.752 0.022 0.000 0.001 0.657 0.000 0.000 0.000n 95 95 105 105 105 105 105 105 105 105 105 105 105 105 105 105 105

Conductivity r �0.06 0.14 0.71 �0.09 �0.74 �0.61 0.63 0.30 �0.74 0.24 0.22 �0.44 0.47 0.01 0.58 0.73 0.45p 0.555 0.152 0.000 0.360 0.000 0.000 0.000 0.001 0.000 0.010 0.018 0.000 0.000 0.901 0.000 0.000 0.000n 99 99 111 111 111 111 111 111 111 111 111 111 111 111 111 111 111

TP r 0.02 0.09 0.62 �0.26 �0.56 �0.26 0.30 �0.05 �0.55 �0.03 �0.02 �0.32 0.13 �0.28 0.64 0.45 0.12p 0.825 0.402 0.000 0.011 0.000 0.011 0.004 0.664 0.000 0.789 0.853 0.001 0.227 0.007 0.000 0.000 0.269n 83 83 93 93 93 93 93 93 93 93 93 93 93 93 93 93 93

TN r 0.12 �0.21 0.36 �0.30 �0.27 �0.07 0.10 �0.20 �0.26 �0.09 0.06 �0.23 �0.02 �0.32 0.40 0.23 �0.11p 0.264 0.056 0.000 0.004 0.010 0.525 0.322 0.052 0.010 0.397 0.565 0.027 0.857 0.002 0.000 0.026 0.304n 83 83 93 93 93 93 93 93 93 93 93 93 93 93 93 93 93

DOC r 0.16 0.17 0.40 �0.37 �0.27 0.00 0.02 �0.14 �0.27 �0.14 0.04 �0.24 �0.09 �0.20 0.48 0.16 �0.08p 0.215 0.182 0.001 0.002 0.023 0.981 0.896 0.263 0.026 0.255 0.744 0.041 0.440 0.103 0.000 0.196 0.491n 61 61 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70

Chl a r �0.26 �0.30 0.40 0.02 �0.38 �0.17 0.21 �0.08 �0.38 0.01 0.05 �0.09 0.14 �0.22 0.39 0.29 0.02p 0.066 0.032 0.002 0.869 0.003 0.183 0.104 0.550 0.003 0.929 0.698 0.510 0.279 0.099 0.002 0.023 0.866n 52 52 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60

TOC r 0.57 0.01 0.08 �0.20 0.00 �0.05 0.04 0.05 0.00 �0.08 0.26 �0.19 0.01 0.03 0.06 0.08 0.03p 0.000 0.936 0.427 0.038 0.971 0.638 0.654 0.613 0.966 0.421 0.006 0.051 0.918 0.718 0.514 0.393 0.751n 99 99 110 110 110 110 110 110 110 110 110 110 110 110 110 110 110

TON r 0.76 �0.19 0.19 �0.08 �0.17 �0.20 0.20 0.14 �0.17 �0.08 0.28 �0.20 0.16 0.09 0.15 0.23 0.13p 0.000 0.130 0.122 0.523 0.154 0.094 0.090 0.262 0.155 0.514 0.017 0.093 0.189 0.468 0.215 0.058 0.297n 63 63 70 70 70 70 70 70 70 70 70 70 70 70 70 70 70

28 S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37

3. Results

3.1. Distributions and correlation of environmental data

The depth of lake sampling sites ranged from 0.3 m to 180 m(mean 24 m). MAAT at the lake sites ranged from 1.5 �C to26.8 �C (mean 15.1 �C), on average 2.8 �C cooler than surface watertemperature and 1.4 �C cooler than bottom water temperature. ThepH in the surface water ranged from 3.8 to 9.8 (mean 7.7), on aver-age 0.7 higher than bottom water pH. Surface water DO ranged

from 2.2 mg/l to 12.7 mg/l (mean 6.3 mg/l), while bottom waterDO ranged from 0 mg/l to 8.4 mg/l (mean 2.7 mg/l).

Many of the environmental data correlated strongly with eachother (Supplementary material, Table S3), highlighted by correla-tion of MAAT with surface water temperature (r 0.98, p < 0.001)and bottom water temperature (r 0.97, p < 0.001), and correlationof surface water pH with bottom water pH (r 0.80, p < 0.001).Due to these strong correlations and similarity in ordinationtrends, we omitted surface water temperature, bottom water tem-perature, and bottom water pH from further analysis. MAAT and

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Fig. 3. Ordination biplots of indirect (PCA, left) and direct (RDA, right) gradient analysis of (a) environmental variables only, (b and c) environmental variables and brGDGTstructural groups, and (d and e) environmental variables and relative distributions of individual brGDGTs. Gray vectors ending in dots show scores of environmental variablesand black vectors ending in arrows show scores of brGDGT distribution data. Environmental data displayed include lake depth (m), surface area (SA, m2), mean annual airtemperature (MAAT, �C), surface water pH (SW pH), surface water dissolved oxygen (SW DO, mg/l), bottom water dissolved oxygen (BW DO, mg/l), conductivity (cond,lS/cm), total phosphorus in surface water (TP, lg/l), total nitrogen in surface water (TN, lg/l), dissolved organic carbon in surface water (DOC, mg/l), chlorophyll a in surfacewater (Chl a, lg/l), sedimentary total organic carbon (TOC) and sedimentary total organic nitrogen (TON).

S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37 29

surface water pH were chosen over other temperature and pHmeasurements, as they had the strongest correlation with the rel-ative abundance of brGDGTs and the strongest loadings on ordina-tion axes.

PCA of the remaining variables showed that 94.5% of the vari-ance in the environmental data was explained by principal compo-nent (PC) 1, while PC2 and PC3 explained only 2.3% and 1.0%,respectively. MAAT had the strongest loading on PC1 (score�1.00; Fig. 3a). There were smaller but strong contributions fromconductivity (score �0.88), bottom water DO (score 0.70) and sur-face water pH (score �0.75). The result is not surprising given thecorrelation of conductivity, bottom water DO and surface water pHwith MAAT (r 0.88, �0.72, and 0.74, respectively; p < 0.001 for all;Supplementary material, Table S3). PC2 was primarily controlledby surface area and PC3 by surface water pH and TOC. However,

PC2 and PC3 only accounted for 3% of the combined variance inthe dataset, giving them little explanatory power. Thus, the mainsources of variance in the environmental data were MAAT, conduc-tivity, bottom water DO and surface water pH.

3.2. Absolute concentration and structural groupings of brGDGTs

The [brGDGT]sed ranged from 0.02 to 176 lg/g sediment (Sup-plementary material, Table S2) and correlated significantly withsedimentary TON (r 0.76, p < 0.001) and TOC (r 0.57, < 0.001).The [brGDGT]TOC, which ranged from 4.9 to 847 lg/g TOC, corre-lated significantly with depth (r 0.47, p < 0.001), surface area(r 0.30, p 0.003) and bottom water DO (r �0.30, p 0.003). Concen-trations of individual brGDGTs, normalized to both sediment wt.and TOC, were not significantly correlated with TN or TP (p > 0.05).

Table 2RDA results. (a) Variance (%) in brGDGT distributions that can be explained by allenvironmental variables (broken down by axis) and maximum amount of variancethat could be explained by individual environmental variables. (b) Variance (%) indistributions of individual brGDGTs and brGDGT structural groups that could beexplained by the environmental variation between lakes.

Table 2a

Individual brGDGTs Structural groupings

All variables 72.9 71.4Axis 1 66.2 63.3Axis 2 5.5 7.3

Individual effectsDepth 6.2 10.3SA 13.0 15.6MAAT 62.8 64.6SW pH 27.9 35.4SW DO 1.9 1.2BW DO 31.8 33.4Conductivity 41.8 44.9TP 25.2 22.1TN 7.9 6.5DOC 9.4 7.4Chl a 3.9 3.4TOC 0.5 0.5TON 1.9 1.9

Table 2b

BrGDGT Explained variance

IIIa 81.8IIIb 27.9IIIc 23.1IIa 37.7IIb 43.0IIc 27.3Ia 77.7Ib 80.1Ic 44.0Group 1 82.2Group 2 25.6Group 3 87.6Ring0 59.8Ring1 62.2Ring2 34.9

30 S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37

Group I ranged from 0.10 to 0.66 and Groups II and III from0.31–0.59 and 0.02–0.59, respectively (Supplementary material,Table S2). Groups I and III correlated significantly with all environ-mental variables except depth, surface water DO, TOC and TON(Table 1). Group I and Group III correlated most strongly withMAAT (p < 0.001 for both, r 0.89 and �0.89, respectively), withweaker correlation with bottom water DO (p < 0.001 for both, r�0.68 and 0.64, respectively) and conductivity (p < 0.001 for both,r 0.71 and �0.74, respectively). The relative abundance of Group IIcorrelated significantly with TN (r �0.30, p 0.004) and DOC(r �0.37, p 0.002). Ring0 ranged between 0.63 and 0.99, ring1

between 0.007 and 0.324, and ring2 from below detection limitto 0.0679. Ring0 and ring1 had the strongest correlation with sur-face water pH (r �0.69 for ring0, r 0.71 for ring1; p < 0.001 for both),while ring2 had the strongest correlation with depth (r 0.41,p < 0.001).

PC1 captured 75.4% of the variance in the brGDGT structuralgroupings, while PC2 captured 18.0%. The relative abundance ofGroups I and III had the highest loadings on PC1 (Fig. 3b), withscores of �0.94 and 0.96, respectively. Ring structures had thestrongest loading on PC2, with scores of 0.63 for ring0, �0.62 forring1 and �0.57 for ring2.

The first four RDA axes cumulatively explained 74.3% of the spe-cies–environment relationship of brGDGT structural groupings(Table 2a); 88.6% of the explained variance was captured by axis1 and 10.2% was captured by axis 2. As with the PCA, Groups I

and III had the strongest loading on axis 1 (score �0.91 and 0.90,respectively), while cyclized brGDGTs loaded onto axis 2 (score0.43 for ring0, �0.42 for ring1 and �0.36 for ring2). While bothMAAT and bottom water DO loaded predominantly onto axis 1,MAAT was the only variable significantly controlling axis 1(p < 0.001; for bottom water DO p 0.62). Axis 2 was significantlycontrolled by surface water pH (p < 0.001) and depth (p 0.009).Overall, the environmental data could explain 82.2% of the variancein Group I, 25.6% in Group II, 87.6% in Group III, 59.8% in ring0,62.2% in ring1, and 34.9% in ring2 (Table 2b).

3.3. Relative abundance of individual brGDGTs

The non-cyclized brGDGTs, Ia, IIa and IIIa, were the most abun-dant in the samples, with fractional abundance ranging from 0.10–0.61, 0.25–0.57 and 0.02–0.59, respectively (Supplementary mate-rial, Table S2). Fractional abundance of brGDGTs with one cyclo-pentyl moiety was less abundant, ranging from 0.003–0.168,0.003–0.243 and under detection limit to 0.021 for Ib, IIb and IIIb,respectively. Those with two cyclopentyl rings were less abundantstill, with fractional abundance ranging from under detection limitto 0.04.

The distributions of the three non-cyclized brGDGTs correlatedsignificantly with most of the environmental variables, but showedthe strongest correlation with MAAT (p < 0.001 for all; r 0.77, r�0.54 and r �0.89 for Ia, IIa and IIIa, respectively; Table 1); Ib,IIb and IIIb correlated significantly with surface water pH (r 0.74,r 0.60 and r 0.36, respectively; p < 0.001 for all) and all threebrGDGTs with two cyclopentyl rings correlated significantly withdepth (r 0.41, p 6 0.001 for Ic; r 0.31, p 0.001 for IIc; r 0.32, p0.001 for IIIc). IIIa (r �0.55, p < 0.001), IIa (r �0.32, p 0.001), IIc(r �0.28, p 0.007), Ia (r 0.64, p < 0.001), and Ib (r 0.45, p < 0.001)correlated significantly with TP, while IIc (r �0.32, p 0.002) andIa (r 0.40, p < 0.001) correlated significantly with TN.

PC1 captured 76.7% of the variability in the relative abundancesof brGDGTs, while PC2 captured 13.6%; IIIa, Ia, and Ib had thestrongest loadings on PC1 (score 0.97, �0.92 and �0.75, respec-tively; Fig. 3d), while IIIb, IIb, and IIc had the strongest loadingson PC2 (score 0.72, 0.87 and 0.67, respectively). RDA showed that72.9% of the variance in the distribution of individual brGDGTscould be explained by the environmental data and 90.8% of thiswas captured in axis 1 (Table 2a). Axis 2 captured an additional7.6%, and higher order axes described < 2% of the brGDGT/environ-mental relationship. Axis 1 correlated significantly with MAAT(p < 0.001; Fig. 3e) and axis 2 significantly with surface water pH(p < 0.001) and depth (p 0.010). As with PCA, IIIa, Ia, and Ib loadedmost strongly onto axis 1 (score�0.89, 0.84 and 0.79, respectively).IIIb and IIb had the highest loadings on axis 2 (score �0.44 and�0.54, respectively), while IIc and Ib had lower scores (�0.38and �0.40, respectively). These environmental variables could ex-plain 81.8% of the variance in IIIa, 77.7% in Ia and 80.1% in Ib; themeasured environmental variables explained < 50% of the variancein the other brGDGTs (Table 2b).

4. Discussion

Weijers et al. (2007a) first demonstrated that the relative distri-butions of brGDGTs in soils correlated with, and were likely con-trolled by, temperature and pH. Many studies have sinceattempted to constrain the influence of temperature and pH onbrGDGTs in soils and lakes in regional and global datasets (Sinnin-ghe Damsté et al., 2008; Blaga et al., 2010; Tierney et al., 2010;Pearson et al., 2011; Sun et al., 2011; Loomis et al., 2012; Peterseet al., 2012). Understanding the environmental controls onbrGDGTs in lake sediments is complicated by the fact that the

S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37 31

habitat of brGDGT-producing bacteria (e.g. surface water, bottomwater or sediment) is unknown. We have examined the controlson lacustrine brGDGT concentration in order to better understandthe environmental factors controlling the growth of brGDGT-pro-ducing bacteria and whether and how other environmental vari-ables for the sediment and water column control brGDGTdistributions. We also explored the potential for these other envi-ronmental variables to affect the performance of lacustrinebrGDGT-based transfer functions for temperature and pHreconstruction.

4.1. Environmental controls on brGDGT concentration

The total concentration of sedimentary brGDGTs only corre-lated significantly with sedimentary TOC and TON, indicating thatbrGDGT production and/or preservation increased with increasingsedimentary organic matter (OM). It is unsurprising that TOC cor-related positively with [brGDGT]sed given that brGDGTs arethought to be produced by heterotrophic bacteria (Weijers et al.,2006a, 2010), likely acidobacteria (Weijers et al., 2009). However,after normalizing to TOC concentration, [brGDGT]TOC did not corre-late with sedimentary TOC or TON, indicating that increased sedi-mentary carbon leads to a proportional increase in brGDGTproduction and/or preservation but does not control all of thevariability.

Conversely, [brGDGT]TOC correlated significantly with depth,surface area and bottom water DO, indicating that one or moreof these variables influences brGDGT production and/or preserva-tion in sediments, independent of their influence on OM preserva-tion. Multiple studies of peat samples have shown that brGDGTconcentration is much higher in deeper, anoxic layers than in shal-lower, oxic layers (Weijers et al., 2006a; Huguet et al., 2010; Pe-terse et al., 2011a) and an increase in sedimentary brGDGTconcentration compared favorably with proxies for decreasedwater column oxygenation in a small pond from Rhode Island,USA (Tierney et al., 2012). Thus, the correlation of [brGDGT]TOC

with depth and surface area could be a byproduct of the significantcorrelation of bottom water DO with these environmental vari-ables (r �0.56, p < 0.001 for depth; r �0.31, p 0.002 for surfacearea), as the large deep lakes in the dataset tend to be highly pro-ductive, resulting in increased microbial respiration and depletionof DO in the hypolimnion. However, [brGDGT]TOC correlated morestrongly with depth (r 0.47) than bottom water DO (r �0.30), indi-cating that depth likely has an additional affect on brGDGT produc-tion, independent of bottom water DO. This trend has beenobserved for several lakes, as brGDGT concentration in suspendedparticulate matter increased with depth in Lakes Challa (Kenya/Tanzania; anoxic hypolimnion; Sinninghe Damsté et al., 2009),Superior (USA; oxic hypolimnion; Woltering et al., 2012) and Lug-ano (Switzerland; anoxic hypolimnion; Bechtel et al., 2010), irre-spective of DO concentration in the hypolimnion. Furthermore,the distribution of brGDGTs in suspended particulate matter variedgreatly with depth in Lake Challa (Sinninghe Damsté et al., 2009),indicating that brGDGTs are likely produced throughout the watercolumn. If brGDGTs are produced throughout the entire water col-umn, greater lake depth would expand the volume of habitableenvironments, increasing the overall production of brGDGTs andthus their concentration in the sediment.

Studies of global soils (Weijers et al., 2007a) and manipulatedsoil pH plots (Peterse et al., 2010) found that [brGDGT]sed increaseswith decreasing pH. In contrast, we observed no relationship be-tween [brGDGT]sed or [brGDGT]TOC and pH in our lake sediments.While the relationship between TOC and pH was not examinedin the global soils dataset (Weijers et al., 2007a), there was a signif-icant negative correlation (p < 0.0001) between pH and TOC inglobally distributed soils (ISRIC-WISE Global Soil Dataset; Batjes,

2008). This implies that the [brGDGT]sed/pH relationship in soilsmay be a byproduct of the strong TOC dependence of [brGDGT]sed

(Weijers et al., 2006b) or that there is a difference in the relation-ship between pH and brGDGT production in these two differentsettings, possibly related to H+ mobilization differences in subaer-ial vs. subaqueous environments.

4.2. Environmental controls on brGDGT distributions

The distributions of brGDGTs in East African lakes exhibited sig-nificant correlation with all of the environmental variables exceptTON and surface water DO. Correlation tests (Table 1) and RDA(Fig. 3c) show that Group I and Group III were most strongly con-trolled by temperature, consistent with several previous studies ofbrGDGTs in lake sediments (Tierney et al., 2010; Pearson et al.,2011; Sun et al., 2011). This holds true for the non-cyclizedbrGDGTs in these groups, as Ia and IIIa had the strongest correla-tion with MAAT and the highest loading on RDA axis 1, whichwas significantly controlled only by MAAT (Table 1). This differsfrom global soils, in which IIa has the highest correlation to MAAT(r �0.73; p < 0.0001) of any of the brGDGTs (Peterse et al., 2012).This difference could be a result of (i) differing microbial popula-tions between soils and lakes, which alter their membrane struc-tures to adjust membrane fluidity to changing temperature butdo so in different ways or (ii) water availability, which can altermembrane fluidity and permeability. Water availability is not achanging variable in perennial lakes, but it can be in soils. In theglobal soils dataset, MAAT and mean annual precipitation (MAP),which controls soil moisture, are highly correlated (p < 0.0001)and MAP is also the most strongly correlated to IIa (r 0.64;p < 0.0001; Peterse et al., 2012), potentially influencing theMAAT/IIa relationship. There may be further interaction amongthese processes, such that changes in water availability drive dif-ferent microbial populations among soils, influencing brGDGT dis-tributions through both directly via the effects water on membranestructure and indirectly via changes in microbial population.

While our results confirm the dominant control of MAAT onbrGDGT distributions in East African lake sediments, other envi-ronmental variables also influenced the degree of methylationand cyclization of brGDGTs. Ring1 showed the strongest correlationwith surface water pH, and IIb and IIIb had a stronger correlationwith surface water pH than any other environmental variable, con-firming results from previous lacustrine brGDGT studies of theinfluence of pH on brGDGTs with one cyclopentyl ring (Blagaet al., 2010; Tierney et al., 2010; Pearson et al., 2011; Sun et al.,2011). Furthermore, depth also appears to have some control onthe relative distributions of brGDGTs. Ring2 has the strongest cor-relation with depth and depth is the only environmental variablethat correlates significantly with all three brGDGTs with two cyclo-pentyl moieties (Ic, IIc, and IIIc). Tierney et al. (2010) also foundthis relationship in their study of 41 East African lakes (comprisinga subset of the 111 lakes). As crenarchaeol concentration was theonly other variable that correlated with depth in that study, andsince the thaumarchaeota that produce crenarchaeol are ammoniaoxidizers (Francis et al., 2005), Tierney et al. (2010) speculated thatthe production of brGDGTs with two cyclopentyl rings may also belinked to bacterial populations involved in N cycling. However, wedid not find any correlation between ring2, Ic, IIc, IIIc or[brGDGT]TOC with sedimentary or water column N concentration,nor did we find a strong relationship between these compoundsand water column oxygenation, which might be expected to influ-ence redox cycling of water column N. Thus, we do not believe thatbrGDGT-producing bacteria are tied to or affected by the N cycle orN concentration.

The stereochemistry of the glycerol moieties in brGDGTs (Weij-ers et al., 2006a), combined with DNA evidence (Weijers et al.,

32 S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37

2009), indicates that brGDGTs are likely derived from acidobacte-ria. Although brGDGT-producing bacteria are likely heterotrophic(Weijers et al., 2010), there is little to no information on their pre-cise metabolism or ecological niche. The acidobacteria have a di-verse array of metabolic pathways, and unfortunately only twocultures have been documented to produce a single brGDGT struc-ture (Sinninghe Damsté et al., 2011). Bacteria will alter their lipidmembrane structures to optimize the permeability of smaller spe-cies, such as H+ and dissolved gas, but larger species, like nutrients,are transported through membrane channels, which are notdependent on lipid membrane structure (Becker et al., 1996). Thus,we would not expect brGDGT distributions to be affected by theconcentration of nutrients in the water column, regardless of therole that brGDGT-producing bacteria play in nutrient cycling.Interestingly, all studies that have investigated crenarchaeol con-centration in lake sediments found a significant, positive relation-ship between its concentration and depth (Blaga et al., 2010;Tierney et al., 2010; Pearson et al., 2011), but only the East Africanlakes (Tierney et al., 2010) showed a relationship betweenbrGDGTs with two cyclopentyl rings and depth. Therefore, we be-lieve that production of Ic, IIc, and IIIc is not tied to nutrient con-centration or the N cycle, but is likely tied to other deep waterprocesses which occur in tropical African lakes.

Hypolimnetic anoxia is more prevalent in tropical lakes thantemperate lakes (Lewis, 2010), and deep lakes in particular, andcould explain the correlation of brGDGTs with two cyclopentylrings with depth. Of the brGDGTs with two cyclopentyl moieties,only Ic correlated significantly with bottom water DO. However,in the RDA biplot, all three brGDGTs with two cyclopentyl ringsplotted near bottom water DO (Fig. 3e). Furthermore, ring2 corre-lated more strongly with bottom water DO than depth and alsoplotted near depth in the RDA biplot (Fig. 3c), demonstrating thatanoxic conditions caused an overall increase in the relative

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abundance of brGDGTs with two cyclopentyl rings, regardless ofthe methylation of the individual compound. Thus, brGDGTs withtwo cyclopentyl rings may be produced in response to low O2 con-ditions. It is also possible that brGDGTs with two cyclopentyl ringsare produced in response to other environmental variables thataccompany low O2 conditions but are not explored here. High res-piration rates that lead to hypolimnetic anoxia are associated withincreased levels of CO2, which is known to diffuse through cellmembranes (Becker et al., 1996), and in more extreme cases, arealso associated with H2S and CH4 produced by redox processes(Wetzel, 2001). It would therefore not be surprising that bacteriamight alter their membrane structure, and thus permeability, toregulate the diffusion of these biogenic gases. Unfortunately, datafor these gases were not available for comparison with the brGDGTdata. Further work should explore the correlation between hypo-limnetic water chemistry and brGDGT distributions and concentra-tion to better understand the ecological niche of brGDGT-producing bacteria and their response to changes in hypolimneticconditions.

4.3. Influence of environmental variability on brGDGT lacustrinepaleothermometer

Our data confirm that temperature is the dominant control onsurface sediment brGDGT distributions in lakes (Tierney et al.,2010; Pearson et al., 2011; Sun et al., 2011), making brGDGTs anappealing paleothermometer. However, as demonstrated above,the relative abundance of some brGDGTs also correlates with otherenvironmental variables, so changes in these variables could affectpaleotemperature reconstruction. Here we systematically test thiseffect on the East African stepwise forward selection (SFS) brGDGTtemperature calibration of Loomis et al. (2012).

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Table 3Correlation statistics between transformed environmental variables and residuals ofregressions calculated at every step of the MAAT SFS regression process (significantcorrelations are bold and italicized).

Step 1 Step 2 Step 3 Step 4

Depth r �0.29 �0.02 �0.14 �0.16p 0.003 0.871 0.164 0.097n 107 107 107 107

SA r �0.33 �0.24 �0.29 �0.29p 0.001 0.017 0.003 0.003n 102 102 102 102

SW pH r �0.42 �0.20 �0.21 �0.23p 0.000 0.035 0.033 0.015n 107 107 107 107

SW DO r �0.17 �0.07 0.01 0.02p 0.098 0.513 0.930 0.829n 99 99 99 99

BW DO r 0.34 0.20 0.09 0.08p 0.001 0.041 0.389 0.409n 101 101 101 101

Conductivity r �0.47 �0.39 �0.32 �0.33p 0.000 0.000 0.001 0.001n 107 107 107 107

TP r �0.29 �0.36 �0.15 �0.13p 0.005 0.001 0.152 0.216n 89 89 89 89

TN r �0.15 �0.16 0.05 0.08p 0.166 0.147 0.663 0.473n 89 89 89 89

DOC r �0.11 �0.19 �0.07 �0.01p 0.390 0.109 0.575 0.947n 69 69 69 69

Chl a r �0.14 �0.21 �0.02 �0.03p 0.293 0.107 0.889 0.839n 58 58 58 58

TOC r �0.13 �0.05 �0.07 �0.04p 0.187 0.631 0.488 0.690n 106 106 106 106

TON r �0.14 �0.08 �0.15 �0.14p 0.235 0.507 0.222 0.239n 69 69 69 69

S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37 33

SFS is used to guide the selection of the variables (in this caseindividual brGDGTs) that significantly explain the variation be-tween sites in the environmental variable of interest (in this caseMAAT; Hocking, 1976). The first brGDGT chosen was the one withthe strongest correlation with MAAT, which was then linearly re-gressed against MAAT. Subsequently, multivariate linear regres-sions were constructed with every possible combination ofbrGDGTs that includes brGDGTs chosen in the previous steps. Be-tween each step, an F-test was performed to measure the signifi-cance (net improvement of correlation) of adding additionalbrGDGTs to the regression. This process was repeated until theaddition of another brGDGT no longer explained a significant addi-tional amount of variance in the environmental variable. Loomiset al. (2012) created an SFS regression against MAAT beginningwith IIIa, as it had the strongest correlation with MAAT (Fig. 4a).Their hypothesis was that brGDGT IIIa was dominantly controlledby MAAT and addition of other brGDGTs to the calibration cor-rected for the confounding effects of other environmental variableson the brGDGT distributions among lakes. To test this hypothesis,we examined the correlation of environmental variables on theresiduals of the regressions at each step in the forward selectionprocess (Table 3; Fig. 4a–d). If correlations between residuals andenvironmental variables lose significance between two steps inthe selection process, the addition of that particular brGDGT likelycorrects for the influence of that environmental variable on thetemperature equation. Outliers in the SFS equation (Lakes Edward,

Nyungu, Tanganyika, and Albert; Loomis et al., 2012) were omittedfrom the test.

In Step 1 of the process (a regression utilizing only IIIa; Fig. 4a),half of the environmental variables correlated significantly withthe residuals (Table 3). Addition of Ib to the SFS calibration(Fig. 4b) increased the correlation of the residuals to TP, but thecorrelations with surface water pH, depth, surface area and bottomwater DO were rendered no longer significant. All four of theseenvironmental variables correlated significantly with each other(low elevation lakes in the dataset are highly productive and alka-line due to high evaporation, and were formed volcanically or tec-tonically, making them more anoxic, deeper and less acidic thanthe high elevation lakes; Supplementary material, Table S3), so itis difficult to determine which of these variables most directly con-trols Ib. While we cannot definitively rule out the influence of anyof these environmental variables, Ib correlated most significantlywith surface water pH (Table 1). The East African dataset is theonly lacustrine dataset to show significant correlation of Ib withdepth or surface area, while all lacustrine datasets demonstratethe influence of pH on brGDGTs with one cyclopentyl ring (Blagaet al., 2010; Tierney et al., 2010; Pearson et al., 2011; Sun et al.,2011). As such, we believe that the addition of Ib to the SFS tem-perature calibration corrects for the influence of pH on the distri-bution of brGDGTs. The correlation of depth and surface areawith the residuals in Step 1 of SFS is likely a byproduct of the cor-relation of surface water pH with these variables in the environ-mental dataset (Supplementary material, Table S3).

The addition of IIc in Step 3 (IIIa + Ib + IIc; Fig. 4c) of the SFScaused residuals to no longer be significantly correlated with TP(Table 3), which could mean that the addition of IIc to the equationcorrects for some influence of TP on brGDGT distributions. How-ever, while IIIa, IIa, IIc, Ia, and Ib correlated with TP, and all exceptIa were included in the SFS regression, we believe that this corre-lation is a byproduct of the correlation between MAAT and TP(r 0.62; p < 0.0001) rather than a result of TP affecting brGDGTdistributions.

With the final addition of IIa to the regression equation (Step 3to Step 4; Fig. 4d), significance in the correlation of environmentalvariables to SFS residuals remained the same (Table 3). Thus, theaddition of IIa could correct for some unexplored environmentalvariable, or, potentially more revealing, the ratios of these particu-lar combinations of brGDGTs, as expressed by their coefficients inthe SFS equation, result in optimal bacterial membrane fluidity atthese temperatures.

It is interesting to note that the three large East African Riftlakes included in the dataset (Tanganyika, Albert, and Edward)are all outliers in the SFS calibration (Loomis et al., 2012). Whilering2 compounds are significantly correlated with depth, and riftlakes can be exceptionally deep, only Tanganyika is unusually deepcompared with other lakes in the dataset (Supplementary material,Table 1), so depth by itself is not the reason for these lakes’ outly-ing behavior. These are also three of the four largest lakes in thedataset, indicating that surface area may influence brGDGT distri-butions, possibly by limiting the potential for brGDGTs to bewashed from the catchment to the depocenter of the lake. How-ever, if this were the case, we would also expect that Lake Victoria,the largest of all the lakes, would be an outlier, especially since itreceives substantially more water through direct precipitationthan runoff/tributaries (Spigel and Coulter, 1996), even comparedwith other large lakes. This is not the case. At present, we haveno explanation for the systematic offsets in brGDGT distributionsin these large lakes. More investigation is needed to understandwhy the relative abundances of brGDGTs in rift lakes differ fromother lakes in the region.

In summary, IIIa was controlled most strongly by changing tem-perature, while changes in the methylation and cyclization of the

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34 S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37

membrane, as described by the addition of IIa and IIc to the SFSequation, likely optimize the permeability and fluidity of the lipidmembrane at the temperature in which the brGDGT-producingbacteria grow. The addition of Ib in the SFS calibration likely cor-rects for any influence that pH may have on the distributions ofbrGDGTs, but it is hard to definitively disentangle the unique influ-

ence of pH from that of depth and surface area, given thesignificant correlation of these variables with each other (Supple-mentary material, Table S3). Given the high r2 value (0.94) andthe low root mean square error (RMSE, 1.9 �C) of the SFScalibration (Loomis et al., 2012), combined with the weak correla-tion of environmental variables with the residuals of the final SFS

Table 4Correlation statistics between transformed environmental variables and residuals ofregressions calculated at every step of the surface water pH SFS regression process(significant correlations are bold and italicized).

Step 1 Step 2 Step 3 Step 4

Depth r 0.01 0.01 0.05 0.01p 0.938 0.894 0.600 0.921n 110 110 110 110

SA r �0.16 �0.16 �0.16 �0.19p 0.098 0.099 0.112 0.056n 105 105 105 105

MAAT r �0.17 �0.15 �0.21 �0.16p 0.083 0.107 0.029 0.099n 110 110 110 110

SW DO r 0.09 0.06 0.09 0.15p 0.389 0.527 0.355 0.143n 102 102 102 102

BW DO r �0.08 �0.09 �0.04 �0.10p 0.428 0.379 0.678 0.299n 104 104 104 104

Conductivity r �0.34 �0.33 �0.32 �0.24p 0.000 0.000 0.001 0.010n 110 110 110 110

TP r �0.13 �0.12 �0.17 �0.05p 0.221 0.244 0.099 0.626n 92 92 92 92

TN r 0.01 0.00 �0.05 0.07p 0.899 0.977 0.628 0.535n 92 92 92 92

DOC r 0.19 0.16 0.09 0.14p 0.124 0.201 0.476 0.242n 69 69 69 69

Chl a r �0.18 �0.16 �0.18 �0.07p 0.165 0.213 0.180 0.599n 59 59 59 59

TOC r 0.32 0.29 0.25 0.23p 0.001 0.002 0.009 0.017n 109 109 109 109

TON r 0.20 0.19 0.14 0.10p 0.100 0.114 0.252 0.401n 69 69 69 69

S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37 35

calibration (Table 3), brGDGTs in East African lake sedimentshave a strong potential to provide accurate paleotemperaturereconstruction.

4.4. BrGDGTs as a lacustrine pH proxy?

BrGDGT distributions correlate significantly with pH (e.g. Weij-ers et al., 2007a; Tierney et al., 2010; Peterse et al., 2012), implyingthat they may have the potential to be used as a paleo-pH proxy.Weijers et al. (2007a) created a transfer function linearly relatingCBT to the pH of the soils in their calibration dataset (r2 0.70).The calibration was successfully used to reconstruct pH in a Scot-tish lake, producing results similar to a diatom-based pH recon-struction from the same core samples (Tyler et al., 2010). Peterseet al. (2012) calculated a new pH calibration based upon an ex-panded soil dataset (r2 0.70, RMSE 0.80) and Tierney et al. (2010)calculated a CBT/surface water pH transfer function for their EastAfrican lakes dataset, with similar results (r2 0.83, RMSE 0.67).

Not surprisingly, we also found that brGDGT distributions weresignificantly correlated with pH. While we speculate that brGDGTsare produced throughout the entire water column (Section 4.1),surface water pH has a stronger correlation with the relative andgroup abundances of brGDGTs than bottom water pH (Table 1).Moreover, ring1 and surface water pH plotted near each other inboth the PCA and RDA biplots (Fig. 3b and c), indicating that sur-face water pH likely exhibits some control on ring addition inbrGDGTs. CBT was also strongly correlated with surface water pH(r �0.72, p < 0.0001), but there was a lot of scatter in the relation-ship. In more alkaline lakes (pH > 8.2), CBT values are exclu-sively < 1, while CBT values are exclusively > 1 in more acidiclakes (pH < 6.5; Fig. 5a). However, in circumneutral to mildly alka-line lakes (6.5 < pH < 8.2), a large range of CBT values (0.2–2.2) oc-curs, including the maximum and minimum CBT values of all 111study lakes.

To test the potential of brGDGT-based reconstruction of surfacewater pH from lake sediments, we also calculated a CBT/surfacewater pH regression:

pH ¼ 9:46� 2:03� CBT ð4Þ

which has r2 0.52 and RMS 0.87 (Fig. 6a). This calibration exhibits areasonable fit with surface water pH in our dataset, generally recon-structing patterns of surface water pH. However, residuals remainstrongly negatively correlated with observed pH (r �0.69,p < 0.001), with the largest misfit at circumneutral surface waterpH (Fig. 6b).

The misfit in the CBT/pH relationship under non-circumneutralconditions is also seen in suspended particulate matter from USMidwestern lakes (Schoon et al., 2013), Chinese soils (Xie et al.,2012; Fig. 5b) and manipulated pH soil plots in Scotland (Peterseet al., 2010; Fig. 5c), and has been attributed to a potential thresh-old effect, whereby higher (lower) pH no longer results in more(less) extensive cyclization of brGDGTs. This threshold responsecould result from a physiological response in brGDGT-producingbacteria or a change in microbial community, if brGDGTs are pro-duced by more than one species of bacteria. In their survey of aci-dobacteria in soils, Jones et al. (2009) found that acidobacterialdiversity was highest in soils with circumneutral pH values anddiversity drastically decreased with a departure from neutral pH.If brGDGTs are produced by multiple species, the apparent linearrelationship between CBT and pH at circumneutral conditionscould be a byproduct of mixing of two (or more) end member aci-dobacterial populations: one that thrives in more acidic conditionsand has an uncyclized membrane structure, and one that thrives inmore alkaline conditions and produces a highly cyclized mem-brane structure. To test these hypotheses, more work needs to bedone to elucidate the full suite of brGDGT-producing bacteria

and to test the ability of individual species to alter the cyclizationof the cell membrane under varying pH.

Given the errors in the above CBT/pH calibration, we exploreddifferent ways of calibrating brGDGT distributions to surface waterpH, such as log transformation of ring1 and the fractional abun-dances of individual brGDGTs with one cyclopentyl ring, but thesemethods achieved similar results to the CBT calibration, with onlyslightly better error statistics. To try to correct for the thresholdbehavior and large misfit at circumneutral surface water pH values,we calculated an SFS regression of brGDGT distributions for surfacewater pH, resulting in the following equation:

pH ¼ 3:78þ 119:02� IIIbþ 5:71� IIa� 50:12� IIcþ 25:25

� Ib ð5Þ

This calibration has r2 0.65 and RMSE 0.71 (outlier, Bukurungu East;Fig. 6c) and resolves the misfit at circumneutral surface water pH(Fig. 6d). However, like the CBT-based calibration, the residualshave a significant negative correlation with surface water pH(r �0.59).

It is interesting that the brGDGTs featured in the SFS regressionequation for the surface water pH calibration are almost the sameas those in the SFS equation for the MAAT calibration (Loomis et al.,2012), except that IIIb is substituted for IIIa. This substitution likelytakes place because of the influence that surface water pH has on

36 S.E. Loomis et al. / Organic Geochemistry 66 (2014) 25–37

brGDGTs with one cyclopentyl moiety (Fig. 3b–e, Table 1). The factthat IIa and IIc are both in the SFS MAAT and SFS surface water pHcalibrations indicates that they likely correct for the influence ofenvironmental variables other than surface water pH on thebrGDGTs in the pH calibration. However, environmental correla-tion tests with SFS residuals, as explained in Section 4.2, do notshow significant change between steps (Table 4), although the cor-relation of pH residuals with conductivity and TOC consistently de-creased with the addition of each variable. It seems likely that thepH and the MAAT SFS equations are similar because pH and MAATare highly correlated in this dataset, making quantitative pH recon-struction using brGDGTs untenable.

Surface water pH exhibited the most significant control on RDAaxis 2, both for fractional abundances of individual brGDGTs andbrGDGTs grouped by structure (Fig. 3c and e), and the brGDGTswith one cyclopentyl moiety had the highest loadings on theseaxes. However, RDA axis 2 only explained 5.5% of the variance inthe fractional abundance of individual brGDGTs and 7.3% of thebrGDGTs grouped by structure (Table 2a). Moreover, Ib, whichhad the strongest correlation with surface water pH (Table 1),had a stronger loading on RDA axis 1 (Fig. 3e), which was con-trolled mainly by MAAT, than it did on RDA axis 2, which was con-trolled mainly by surface water pH. This correlation with surfacewater pH is much smaller than that for global soils (Weijerset al., 2007a; Peterse et al., 2012), but comparable with those ob-served in other lacustrine brGDGT studies (Blaga et al., 2010; Tier-ney et al., 2010; Pearson et al., 2011).

These correlation statistics and ordinations demonstrate thatsurface water pH does influence the distribution of brGDGTs inEast African lake sediments. However, the relationship betweenthem is different from soils (Fig. 5a–c), indicating that (East Afri-can) lacustrine brGDGT producers have a very different responseto pH. Moreover, there is evidence to suggest that distributionalvariation in brGDGTs has a threshold response to a change in pH(Fig. 5a–c, Fig. 6a and b; Peterse et al., 2012; Schoon et al., 2013),instead of a log linear response as previously suggested (Weijerset al., 2007a). Thus, brGDGTs have the potential to record changesin pH in lacustrine environments, but given the large, systematicerrors in the currently best-possible calibrations, caution is advisedwhen reconstructing paleo pH using brGDGTs. Semi-quantitativepH reconstructions may be more feasible than quantitative recon-structions given the threshold response of brGDGTs to pH, as CBTvalues above (below) 1 are found exclusively in lakes with pH val-ues under (above) 8.2 (6.5).

5. Conclusions

Our dataset includes 111 lakes from East Africa with a temper-ature gradient of over 25 �C, a pH gradient of 6.0 and lake depth <1 m to > 1 km. It covers lakes that are oligotrophic to eutrophic, an-oxic to supersaturated with O2, and fresh to saline. Despite this un-iquely wide variability, temperature explained more of thevariance in brGDGT distributions (62.8%) than any other environ-mental variable. Other environmental variables had a small butsignificant influence on brGDGT distributions: brGDGTs with onecyclopentyl ring correlated with pH, while brGDGTs with twocyclopentyl rings correlated with depth, or some associated envi-ronmental variable like DO or CO2.

While temperature was the major control on brGDGT variabil-ity, some brGDGTs were not significantly correlated with MAATand/or had a stronger correlation with other environmental vari-ables. Together, four brGDGTs (IIIa, IIa, IIc, and Ib) produced theoptimal temperature calibration for the dataset (Loomis et al.,2012); IIIa had the strongest correlation with temperature, makingit the backbone compound in the SFS calibration. The addition of Ib

to the SFS regression helped correct for the influence of pH onbrGDGT distributions. It is not clear for which environmental vari-ables the other two compounds incorporated in the SFS calibrationare correcting; it could be some unexplored environmental vari-able or simply that these specific ratios of brGDGT structures re-flected in the SFS calibration provided the optimal membranepermeability at a given temperature.

The cyclization of brGDGTs in soils, as quantified from CBT, wasstrongly correlated with environmental pH (Weijers et al., 2007a).However, we found that the CBT-based pH calibration in lacustrinesettings resulted in large errors, with a systematic offset at bothhigh and low pH values. Multivariate linear regression, constructedthrough SFS, did not correct for the systematic offset, leading us topostulate that the brGDGT response to pH variability resembles athreshold rather than a continuous response.

Acknowledgements

This work was conducted with funds from the American Chem-ical Society Petroleum Research Fund and the National ScienceFoundation awarded to J.M.R and also received funding from theEuropean Research Council under the European Union’s SeventhFramework Programme (FP7/2007–2013)/ERC Grant AgreementNo. [226600]. Fieldwork was sponsored by US National GeographicSociety Grants 7999-06 and 8938-11, the Research Foundation ofFlanders Grants G.0528.07N and G.0096.12N, and the Federal Sci-ence Policy of Belgium through project SD/BD/03 ‘CLANIMAE’ andan Action 1 grant to H.E.

Fieldwork in Kenya was conducted under permits from the Na-tional Council for Science and Technology (NCST/5/002/R/439/4),Kenya Wildlife Service (KWS/CL&P/029), and National Environ-mental Monitoring Authority (NEMA Access Permit AGR/7/2010),and samples from Mt. Kenya National Park were collected underMaterial Transfer Agreement A11/TT/1040 between the KenyaWildlife Service, the University of Nairobi and Ghent University.Fieldwork in the Rwenzori Mountains was conducted under Ugan-da NCST research clearance NS21 and Uganda Wildlife AuthorityPermit UWA/TBDP/RES50.

We thank J. Orchardo (Brown), D. Murray (Brown), K. Costa(Brown), J. Ossebaar (NIOZ), and E. Hopmans (NIOZ) for assistancein the laboratory, as well as the Rwenzori Mountaineering Services,Ngaara Mountaineering Services, Kenya Wildlife Services, the Na-tional Museums of Kenya (Dr. S.M. Rucina), and Nairobi University(Prof. D. Olago) for logistical support. We would also like to thankthree anonymous reviewers for their comments, which helped usto greatly improve this manuscript.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.orggeo-chem.2013.1 0.012.

Associate Editor—J.K. Volkman

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