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Lake Naivasha, Kenya, is one of a number of freshwater lakes in the East African Rift System. Since the beginning of the twentieth century, it has experienced greater anthropogenic influence as a result of increasingly intensive farming of coffee, tea, flowers, and other horticultural crops within its catchment. The water-level history of Lake Naivasha over the past 200 years was derived from a combination of instrumental records and sediment data. In this study, we analysed diatoms in a lake sediment core to infer past lacustrine conductivity and total phosphorus concentrations. We also measured total nitrogen and carbon concentrations in the sediments. Core chronology was established by (210)Pb dating and covered a similar to 186-year history of natural (climatic) and human-induced environmental changes. Three stratigraphic zones in the core were identified using diatom assemblages. There was a change from littoral/epiphytic diatoms such as Gomphonema gracile and Cymbella muelleri, which occurred during a prolonged dry period from ca. 1820 to 1896 AD, through a transition period, to the present planktonic Aulacoseira sp. that favors nutrient-rich waters. This marked change in the diatom assemblage was caused by climate change, and later a strong anthropogenic overprint on the lake system. Increases in sediment accumulation rates since 1928, from 0.01 to 0.08 g cm(-2) year(-1) correlate with an increase in diatom-inferred total phosphorus concentrations since the beginning of the twentieth century. The increase in phosphorus accumulation suggests increasing eutrophication of freshwater Lake Naivasha. This study identified two major periods in the lake's history: (1) the period from 1820 to 1950 AD, during which the lake was affected mainly by natural climate variations, and (2) the period since 1950, during which the effects of anthropogenic activity overprinted those of natural climate variation.
Here we present a protocol to genetically detect diatoms in sediments of the Kenyan tropical Lake Naivasha, based on taxon-specific PCR amplification of short fragments (approximately 100 bp) of the small subunit ribosomal (SSU) gene and subsequent separation of species-specific PCR products by PCR-based denaturing high-performance liquid chromatography (DHPLC). An evaluation of amplicons differing in primer specificity to diatoms and length of the fragments amplified demonstrated that the number of different diatom sequence types detected after cloning of the PCR products critically depended on the specificity of the primers to diatoms and the length of the amplified fragments whereby shorter fragments yielded more species of diatoms. The DHPLC was able to discriminate between very short amplicons based on the sequence difference, even if the fragments were of identical length and if the amplicons differed only in a small number of nucleotides. Generally, the method identified the dominant sequence types from mixed amplifications. A comparison with microscopic analysis of the sediment samples revealed that the sequence types identified in the molecular assessment corresponded well with the most dominant species. In summary, the PCR-based DHPLC protocol offers a fast, reliable and cost-efficient possibility to study DNA from sediments and other environmental samples with unknown organismic content, even for very short DNA fragments.
Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution
(2011)
Potential paleoclimatic driving mechanisms acting on human evolution present an open problem of cross-disciplinary scientific interest. The analysis of paleoclimate archives encoding the environmental variability in East Africa during the past 5 Ma has triggered an ongoing debate about possible candidate processes and evolutionary mechanisms. In this work, we apply a nonlinear statistical technique, recurrence network analysis, to three distinct marine records of terrigenous dust flux. Our method enables us to identify three epochs with transitions between qualitatively different types of environmental variability in North and East Africa during the (i) Middle Pliocene (3.35-3.15 Ma B. P.), (ii) Early Pleistocene (2.25-1.6 Ma B. P.), and (iii) Middle Pleistocene (1.1-0.7 Ma B. P.). A deeper examination of these transition periods reveals potential climatic drivers, including (i) large-scale changes in ocean currents due to a spatial shift of the Indonesian throughflow in combination with an intensification of Northern Hemisphere glaciation, (ii) a global reorganization of the atmospheric Walker circulation induced in the tropical Pacific and Indian Ocean, and (iii) shifts in the dominating temporal variability pattern of glacial activity during the Middle Pleistocene, respectively. A reexamination of the available fossil record demonstrates statistically significant coincidences between the detected transition periods and major steps in hominin evolution. This result suggests that the observed shifts between more regular and more erratic environmental variability may have acted as a trigger for rapid change in the development of humankind in Africa.
The analysis of palaeoclimate time series is usually affected by severe methodological problems, resulting primarily from non-equidistant sampling and uncertain age models. As an alternative to existing methods of time series analysis, in this paper we argue that the statistical properties of recurrence networks - a recently developed approach - are promising candidates for characterising the system's nonlinear dynamics and quantifying structural changes in its reconstructed phase space as time evolves. In a first order approximation, the results of recurrence network analysis are invariant to changes in the age model and are not directly affected by non-equidistant sampling of the data. Specifically, we investigate the behaviour of recurrence network measures for both paradigmatic model systems with non-stationary parameters and four marine records of long-term palaeoclimate variations. We show that the obtained results are qualitatively robust under changes of the relevant parameters of our method, including detrending, size of the running window used for analysis, and embedding delay. We demonstrate that recurrence network analysis is able to detect relevant regime shifts in synthetic data as well as in problematic geoscientific time series. This suggests its application as a general exploratory tool of time series analysis complementing existing methods.