TY - JOUR
T1 - Inferring a single variable from an assemblage with multiple controls: getting into deep water with cladoceran lake-depth transfer functions
AU - Davidson, Thomas A.
AU - Amsinck, Susanne Lildal
AU - Bennike, Ole
AU - Christoffersen, Kirsten S.
AU - Landkildehus, Frank
AU - Lauridsen, Torben L.
AU - Jeppesen, Erik
N1 - Funding Information:
Acknowledgments The authors thank Anne Mette Poulsen, Kathe Møgelvang and Tinna Christensen for manuscript assistance and layout, and the technical staff at NERI for valuable support, and Karina Jensen for identifying and counting the cladoceran remains. The authors moreover wish to thank the Former Danish Polar Centre for valuable logistic support during our stay at Zackenberg. This study was supported by the ‘Global Climate Change Programme’ (no. 9700195); the Commission for Scientific Research in Greenland; The North Atlantic Research Programme, 1998–2000; the Arctic Programme, 1998–2002; the Nordic Council of Ministers; the Danish project CRES; The Greenland Climate Research Centre (GCRC); the EU project REFRESH; Marie Curie Intra European Fellowship no. 255180 (PRECISE); and an Aarhus University Guest researcher award to TD. This is a Galathea 3 expedition article. The authors are grateful to Hilde Eggermont and two anonymous reviewers for their helpful comments on earlier versions of this manuscript.
PY - 2011/11
Y1 - 2011/11
N2 - Transfer functions have proved very useful for quantitative reconstruction of past environments. Inferring values of a single parameter based on changes in a community with multiple controls may result in unreliable inferences. To assess this unreliability cladoceran surface sediment assemblages from 53 lakes in Greenland, which have substantial variations in lake depth and fish abundance, both of which shape cladoceran communities, were analysed in this study. Redundancy analysis (RDA) revealed that maximum lake depth and either fish abundance or fish presence/absence exerted substantial and significant control on the cladoceran assemblage. Partial RDA showed that maximum lake depth and fish abundance uniquely explained 7.9 and 5.1%, respectively, with 5.3% variance being shared. A transfer function to infer lake depth from cladoceran sub-fossils was constructed and performed moderately well [coefficient of determination (r
2) = 0.65; root mean square error of prediction (RMSEP) = 0.32 log maximum depth] on the full dataset. When outliers, defined by a bootstrapped prediction error greater than 25% of the total depth gradient, were excluded, the model performed well (r
2 = 0.74, RMSEP = 0.25 log maximum depth). The improved transfer function was then applied to sedimentary assemblage from a sediment core from Lake Boresø, in North-eastern Greenland, covering 9,000 years. A large increase in lake depth was inferred around 6250 bp. Whilst the climate was wetter at that time, the inferred changes in depth likely reflect the alteration of the food web, which resulted from the arrival of fish in the lake. This highlights the risks of using single-variable inference models for hindcasting change in lake physical and/or food web structure when there are other important co-variables.
AB - Transfer functions have proved very useful for quantitative reconstruction of past environments. Inferring values of a single parameter based on changes in a community with multiple controls may result in unreliable inferences. To assess this unreliability cladoceran surface sediment assemblages from 53 lakes in Greenland, which have substantial variations in lake depth and fish abundance, both of which shape cladoceran communities, were analysed in this study. Redundancy analysis (RDA) revealed that maximum lake depth and either fish abundance or fish presence/absence exerted substantial and significant control on the cladoceran assemblage. Partial RDA showed that maximum lake depth and fish abundance uniquely explained 7.9 and 5.1%, respectively, with 5.3% variance being shared. A transfer function to infer lake depth from cladoceran sub-fossils was constructed and performed moderately well [coefficient of determination (r
2) = 0.65; root mean square error of prediction (RMSEP) = 0.32 log maximum depth] on the full dataset. When outliers, defined by a bootstrapped prediction error greater than 25% of the total depth gradient, were excluded, the model performed well (r
2 = 0.74, RMSEP = 0.25 log maximum depth). The improved transfer function was then applied to sedimentary assemblage from a sediment core from Lake Boresø, in North-eastern Greenland, covering 9,000 years. A large increase in lake depth was inferred around 6250 bp. Whilst the climate was wetter at that time, the inferred changes in depth likely reflect the alteration of the food web, which resulted from the arrival of fish in the lake. This highlights the risks of using single-variable inference models for hindcasting change in lake physical and/or food web structure when there are other important co-variables.
KW - Arctic lakes
KW - Cladocerans
KW - Fish
KW - Lake depth
KW - Transfer functions
KW - Trophic structure
KW - Zooplankton
UR - http://www.scopus.com/inward/record.url?scp=80255138634&partnerID=8YFLogxK
U2 - 10.1007/s10750-011-0901-3
DO - 10.1007/s10750-011-0901-3
M3 - Article
SN - 0018-8158
VL - 676
SP - 129
EP - 142
JO - Hydrobiologia
JF - Hydrobiologia
IS - 1
ER -