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  • 1
    Publication Date: 2022-01-04
    Description: Abstract
    Description: Existing methodologies for estimating woody aboveground biomass and carbon stored therein have been developed for forests but are not tailored to the vast dryland ecosystems where vegetation is heterogenous and highly disturbed. Still, those methods are widely applied with questionable results and possible problematic implications, not only for biomass quantification but also for disturbance ecology, biodiversity research, and ecosystem service assessments. We hereby propose a new methodology especially designed to encompass small, disturbed, and irregular woody growth while keeping sampling effort within reasonable limits. Meaningful demographic growth classes are deployed which enable a stratified sampling design and structure a practicable workflow for integration of different allometric models. To account for the high natural and anthropogenic disturbance levels typically shaping dryland vegetation, our method incorporates a detailed damage assessment by harnessing the ecological archive contained in trees. This allows for quantification of biomass losses to certain disturbance agents, uncovers interactive effects between disturbance agents, and enables assessing the impact of disturbance regime shifts. Extrapolation of biomass losses to stand or landscape level also greatly improves the usual reference state comparison approach. Here, we review the problems of conventional methodologies being applied to drylands, develop and present the improved method proposed by us, and perform a formal method comparison between the two. Results indicate that the conventional allometric method is systematically underestimating biomass and carbon storage in disturbed dryland ecosystems. The bias is highest where general biomass density is lowest and disturbance impacts are severest. Damage assessment demonstrates a dependency between main disturbance agents (elephants and fire) while generally biomass is decreased by increasing elephant densities. The method proposed by us is more time consuming than a conventional allometric approach, yet it can cover sufficient areas within reasonable timespans. Consequent higher data accuracy with concomitant applicability to a wider range of research questions are worth the effort. The proposed method can easily be attuned to other ecosystems or research questions, and elements of it may be adapted to fit alternative sampling schemes.
    Description: Other
    Description: This article is a preprint and has not been certified by peer review. The finally published paper can be accessed at: https://doi.org/10.1016/j.ecolind.2021.108466
    Keywords: Ecology ; Biota ; Biomass ; Carbon ; Carbon Storage Dynamics ; Conservation Areas ; Ecology ; Ecosystem ; National Park ; Vegetation ; Vegetation Structure ; Wildlife
    Type: Text , Text
    Format: PDF
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  • 2
    Publication Date: 2022-03-09
    Description: Abstract
    Description: Nature conservation and restoration in terrestrial ecosystems is often focused on increasing the numbers of megafauna, expecting them to have positive impacts on ecological self-regulation processes and biodiversity. In sub-Saharan Africa, conservation efforts also aspire to protect and enhance biodiversity with particular focus on elephants. However, elephant browsing carries the risk of woody biomass losses. In this context, little is known about how increasing elephant numbers affects carbon stocks in soils, including the subsoils. We hypothesized that (1) increasing numbers of elephants reduce tree biomass, and thus the amount of C stored therein, resulting (2) in a loss of soil organic carbon (SOC). If true, a negative carbon footprint could limit the sustainability of elephant conservation from a global carbon perspective. To test these hypotheses, we selected plots of low, medium, and high elephant densities in two national parks and adjacent conservancies in the Namibian component of the Kavango Zambezi Transfrontier Area (KAZA), and quantified carbon storage in both woody vegetation and soils (1 m). Analyses were supplemented by the assessment of soil carbon isotopic composition. We found that increasing elephant densities resulted in a loss of tree carbon storage by 6.4 t ha−1. However, and in contrast to our second hypothesis, SOC stocks increased by 4.7 t ha−1 with increasing elephant densities. These higher SOC stocks were mainly found in the topsoil (0–30 cm) and were largely due to the formation of SOC from woody biomass. A second carbon input source into the soils was megaherbivore dung, which contributed with 0.02–0.323 t C ha−1 year−1 to ecosystem carbon storage in the low and high elephant density plots, respectively. Consequently, increasing elephant density does not necessarily lead to a negative C footprint, as soil carbon sequestration and transient C storage in dung almost compensate for losses in tree biomass.The dataset contains the raw data of soil analyses up to 1 m soil depth and vegetation data on plot level. A third sheet of the excelfile contains necessary information about abbreviations used within the dataset.
    Keywords: Ecology ; Environment ; Carbon ; Soil Organic Carbon ; Conservation Areas ; Ecosystem ; Vegetation
    Type: Dataset , Dataset
    Format: MS Excel
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  • 3
    Publication Date: 2022-03-09
    Description: Abstract
    Description: 1.Nature conservation is fostered through the expansion of protected areas. This is particularly evident in Sub-Saharan Africa (SSA), where conservation is intended to simultaneously promote the recovery of megafauna like elephants. Rising numbers of megaherbivores induce woody biomass losses but restore soil organic carbon (SOC). We hypothesized that increases of SOC under conservation with wildlife in SSA go directly along with increases in the preservation of plant residues in soil organic matter (SOM), traceable by plant biomarkers such as lignin and n-alkane. In contrast, intensification with agriculture leads to a reduction of them. To test this, we sampled topsoil (0-10 cm) and corresponding plant samples along different intensities of conservation and intensification in the Zambezi Region of Namibia, comprising a) conservation sites with low, medium and high elephant densities and b) adjacent intensification sites with rangeland and cropland. We found that lignin and n-alkane patterns of the above-ground vegetation were preserved in the soil. Confirming our hypothesis, increasing SOC contents with rising elephant densities went along with increasing accumulation of lignin-derived phenols. Under conservation, lignin concentrations were influenced by the input of woody debris into the soil, traced by carbon isotopes, clay, and total woody biomass. This could not be proved for n-alkanes. Under intensification, lignin derived phenols were lower than under conservation, but again, there was no clear pattern for n-alkanes. We showed that conservation with wildlife leads to an increase of SOC, which was accompanied by an accumulation of lignin-derived phenols in the soil organic matter. Increased input of woody debris, clay content and total biomass were important parameters for this lignin accumulation. In contrast, intensification with agriculture leads to a loss of lignin. Contrary, n- alkanes were not sensitive to detect effects of conservation or intensification. We conclude that increasing incorporation of woody residues into soil is a key mechanism controlling SOC accrual and to offset losses of aboveground biomass on SOC in sites under conservation with wildlife. The dataset contains raw data of lignin and n-alkanes and related soil properties. A third sheet contains a legend with information on abbreviations.
    Keywords: Ecology ; Environment ; Conservation ; Intensification ; Soil Organic Carbon ; Carbon Storage Dynamics ; Carbon Sequestration ; Biomarker ; Lignin ; n-Alkanes
    Type: Dataset , Microsoft excel file
    Format: MS Excel
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  • 4
    Publication Date: 2024-04-26
    Description: Abstract
    Description: This dataset includes a shapefile representing the digitized historical road network of Kenya. It contains over 56,000 km of historical roads extracted from 449 historical topographic maps of 1:50,000 scale and 71 maps of 1:100,000 scale covering the time period from the 1950s to the 1980s. The topographic maps were obtained from various sources in Kenya and the UK. Most of maps were collected in Kenya provided by the Survey of Kenya and several local county governments’ survey and urban planning departments. Additionally, some maps were obtained from archives in Great Britain, namely the Bodleian Library of the University of Oxford and the Cambridge University Library. All the acquired maps were originally created and published by the Directorate of Overseas Surveys (DOS), the War Office, General Staff, Geographical Section and the Survey of Kenya. The road data was extracted from these maps using deep learning techniques, including a Python script and ArcGIS Pro “Multi-Task Road Extractor” tool.
    Keywords: Infrastructure ; Imagery/Base Maps/Earth Cover ; Road Network ; Roads ; Geodata ; Historical Data ; Vector Data ; GIS ; Africa ; African History ; Historical Maps ; Infrastructure ; Road ; Geographic information system ; Data ; road network ; road ; data ; infrastructure development
    Type: Dataset , Shapefile
    Format: ESRI Shapefile
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