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The importance of effective moisture and landscape controls on diatom assemblages and primary production in Roche Lake, British Columbia, Canada over the past ca. 1800 years

Published online by Cambridge University Press:  20 July 2022

Graham R. Mushet*
Affiliation:
Paleoecological Environmental Assessment and Research Laboratory (PEARL), Department of Biology, Queen's University, Kingston, Ontario, K7L 3J9
Eduard G. Reinhardt
Affiliation:
McMaster University, School of Earth, Environment and Society, Hamilton, Ontario, L8S 4K1
Brian F. Cumming
Affiliation:
Paleoecological Environmental Assessment and Research Laboratory (PEARL), Department of Biology, Queen's University, Kingston, Ontario, K7L 3J9 School of Environmental Studies, Queen's University, Kingston, Ontario, K7L 3J9
*
*Corresponding author email address: 14grm@queensu.ca
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Abstract

Lakes in the semiarid Southern Interior Plateau of British Columbia, Canada are vulnerable to future climate-driven changes in water quantity and quality. However, few long-term monitoring records exist to assess the effects of varying hydroclimate conditions on lake function. We present a high-resolution multiproxy paleolimnological record containing subfossil diatoms, μXRF via ITRAX, and chlorophyll-a that spans the past 1800 years from Roche Lake in south-central British Columbia. Generalized additive models indicate that the diatom assemblages were associated with changes in lake level and the accompanying effects of atmospheric forcing on lake thermal structure, and that these forcing mechanisms had a weak relationship with primary production. We infer two pronounced arid periods between ca. AD 600–950 and ca. AD 1730–1950 that correspond to evidence of reduced lake mixing and enhanced anoxia, but variable patterns in primary production. Synthesis of regional proxy records suggests that inferences of moist but possibly warm conditions during the Medieval Climate Anomaly may have occurred because of a complex interaction between prolonged La Niña-like conditions and higher radiative forcing. Overall, lakes in this region are sensitive to changes in hydroclimate generated by interactions between external forcing and Pacific Ocean–atmosphere processes, and the resulting changes in aridity may have potentially negative consequences on salmonid habitat.

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Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © University of Washington. Published by Cambridge University Press, 2022
Figure 0

Figure 1. Maps of the study region. a) Star indicates location of Roche Lake within the context of Canada (dark gray shading) and much of the United States (light gray shading). b) Black circles indicate location of Roche Lake (RcL), and additional sites in western Canada and Washington State, United States (WA) from Cumming et al. (2002; BL = Big Lake) and Steinman et al. (2014; ML = Marcella Lake, PL = Paradise Lake, CL = Castor Lake, RnL = Renner Lake) that were used to generate a composite of sedimentary δ18O for the past 2000 years for comparison with Roche Lake climate proxy data. Tan shading indicates the Interior Douglas Fir (IDF) biogeoclimatic zone. NWT is Northwest Territories, Canada. c) Inset map of the bathymetry of Roche Lake with the coring location for the composite sedimentary core (RLDM) indicated by the white +.

Figure 1

Figure 2. Bayesian age-depth model for the Roche Lake composite core (RLDM) based on calibrated 14C ages. Top left panel: Markov Chain Monte Carlo iterations; top middle and right panels show prior (green lines) and posterior densities (gray fills) for mean accumulation rate and memory, respectively. Large bottom panel shows calibrated 14C ages and the age-depth model. The central red dotted line is the model based on the weighted mean age, the grayscale cloud represents age model probability, and is bounded by a 95% confidence interval (dashed gray line).

Figure 2

Table 1. Summary of the 14C dating results for the composite sediment core (RLDM) collected from Roche Lake. All 14C analyses were performed on pollen that was isolated from raw sediment at the LacCore Facility at the University of Minnesota. CAMS = Center for Accelerator Mass Spectrometry. * indicates date was obtained from 210Pb analyses of a gravity core taken from the same coring location in 2016, which was then correlated to the composite piston core using high-resolution diatom biostratigraphy (Mushet et al., 2022).

Figure 3

Figure 3. The diatom stratigraphy for RLDM from Roche Lake, plotted by composite core depth. Significant zonations based on stratigraphically constrained cluster analysis (CONISS) coupled with a broken-stick model are indicated by solid red lines. The zone above the dashed line represents twentieth-century eutrophication of Roche Lake and was removed from CONISS analysis. The MCA (Medieval Climate Anomaly; AD 900–1300) and LIA (Little Ice Age; AD 1450–1850) are shown for reference.

Figure 4

Figure 4. Principal components analysis (PCA) biplot showing diatom species scores for the high-resolution analysis of the composite core from Roche Lake (RLDM). Taxa are coded by habitat type and total phosphorus (TP) optima based on the 251-lake calibration set presented in Cumming et al. (2015). AC.zgl = Achnanthes ziegleri, AH.mnt = Achnanthidium minutissimum, AM.pdc = Amphora pediculus, AM.thmn = Amphora thumensis, AS.frm = Asterionella formosa, AU.amb = Aulacoseira ambigua, AU.grn = Aulacoseira. granulata, CC.plc = Cocconeis placentula sensu lato, CF.dlv = Cymbellafalsa diluviana, CM.1PIS = Cymbella sp. 1 PISCES, CM.3PIS = Cymbella sp. 3 PISCES, L.lem = Lindavia lemanensis, DN.ktz = Denticula kuetzingii, EY.cst = Encyonopsis cesatti, EY.mcph = E. microcephala, FR.2PRL = Fragilaria sp. 2 PIRLA, FR.crot = F. crotonensis, FR.nan = F. nanana, FR.ten = F. tenera, GO.ang = Gomphonema angustum, L.mch = Lindavia michiganiana, NA.cpt = Navicula cryptotenella, NA.psd = Na. pseudoventralis, NA.uter = Na. aff. utermoehlii, NA.vtb = N. vitabunda, NI.bclm = Nitzschia bacillum, PD.brv = Pseudostaurosira brevistriata, PS.ros = Psammothidium rosenstockii, SA.pnt = Staurosirella pinnata, SR.ctrn = Staurosira construens, SR.vent = Staurosira construens var. venter, ST.1PIS = Stephanodiscus sp. 1 PISCES, ST.hant = Steph. hantzschii, ST.hant.ten = Steph. hantzschii f. tenuis, ST.med = Steph. medius, ST.niag = Steph. niagarae, ST.parv = Steph. parvus.

Figure 5

Figure 5. Summary of proxy data from the composite core from Roche Lake (RLDM), plotted by age (Year AD). Bold lines through each time-series (except for Fragilaria crotonensis) are trends and associated 95% confidence intervals estimated using generalized additive models (GAMs). Zonations determined from the stratigraphically constrained cluster analysis (CONISS) performed on the diatom relative abundance data are indicated by vertical gray lines. The MCA (Medieval Climate Anomaly; AD 900–1300) and LIA (Little Ice Age; AD 1450–1850) are shown for reference. a) Titanium (Ti) normalized to the sum of coherent and incoherent scatter (tot.scat), b) potassium (K) normalized to the sum of coherent and incoherent scatter (tot.scat), c) log-ratio of calcium (Ca) to titanium (Ti), d) diatom principal component axis-2 scores (PC-axis 2), e) proportion of F. crotonensis relative to total plankton, f) log-ratio of manganese (Mn) to calcium (Ca), g) diatom principal component axis-1 scores (PC-axis 1), h) chlorophyll-a (Chl-a) normalized to the proportion of organic matter.

Figure 6

Table 2. Summaries for the generalized additive models (GAMs) fitted to the diatom PCA axis-1 and axis-2 scores, and chlorophyll-a. EDF = effective degrees of freedom; Ref. df = reference degrees of freedom, F = F-statistic, p = p-value of each model term.

Figure 7

Figure 6. Fitted smooth functions for log (Ca/Ti) from the three separate generalized additive models (GAMs) that used: a) diatom principal component axis-1 (PC axis-1), b) diatom principal component axis-2 (PC axis-2), and c) chlorophyll-a (Chl-a) as response variables. Pointwise confidence intervals (95%) for each model are indicated by gray bands.

Figure 8

Figure 7. The contribution of log (Ca/Ti) to each of the three generalized additive models (GAMs) and observed and GAM-fitted values using all model covariates for each of the three models. a) Contribution of log (Ca/Ti) to the fitted diatom principal component axis-1 (PC axis-1) scores, b) observed and final GAM-fitted values for diatom principal component axis-1 (PC axis-1) scores, c) the contribution of log (Ca/Ti) to the fitted diatom principal component axis-2 (PC axis-2) scores, d) observed and final GAM-fitted values for diatom principal component axis-2 (PC axis-2) scores, e) contribution of log (Ca/Ti) to the fitted chlorophyll-a (Chl-a) values, f) observed and final GAM fitted values for chlorophyll-a (Chl-a) values. Gray bands represent 95% pointwise confidence intervals. For panels a, c, and e, where the band encompasses the dashed zero line, the contribution of log (Ca/Ti) is not significantly different from the intercept.

Figure 9

Figure 8. Synthesis of climate proxy data over the past 2000 years from lakes from Western Canada and Washington State, USA. a) Diatom-inferred salinity from Big Lake in south-central BC (Fig. 1; Cumming et al., 2002). The smooth bold and light-colored lines are GAM estimated trends and 95% confidence intervals, respectively; b) Roche Lake composite core (RLDM) log-ratio of calcium to titanium. The smooth bold and light-colored lines are GAM estimated trends and 95% confidence intervals, respectively; c) composite of z-scored δ18O in sediment cores from several lakes throughout the Pacific Northwest (Fig. 1; Steinman et al., 2014). Light-colored lines represent the 95% confidence interval based on assessment of age model uncertainty; d) north Pacific Decadal Oscillation (PDO) region sea-surface temperature reconstruction of Mann et al. (2009), which is averaged over 22.5°N–57.5°N, 152.5°E–132.5°W; e) 25-year moving average of total radiative forcing anomalies presented in Steinman et al. (2014), based on data from Crowley (2000). Shading indicates the extent of the Medieval Climate Anomaly (MCA; AD 900–1300) and the Little Ice Age (LIA; AD 1450–1850).

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