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  • 1
    Online Resource
    Online Resource
    Structural diversity and tree density drives variation in the biodiversity–ecosystem function relationship of woodlands and savannas
    Wiley ; 2021
    In:  New Phytologist Vol. 232, No. 2 ( 2021-10), p. 579-594
    Details
    In: New Phytologist, Wiley, Vol. 232, No. 2 ( 2021-10), p. 579-594
    Abstract: Positive biodiversity–ecosystem function relationships (BEFRs) have been widely documented, but it is unclear if BEFRs should be expected in disturbance‐driven systems. Disturbance may limit competition and niche differentiation, which are frequently posited to underlie BEFRs. We provide the first exploration of the relationship between tree species diversity and biomass, one measure of ecosystem function, across southern African woodlands and savannas, an ecological system rife with disturbance from fire, herbivores and humans. We used 〉 1000 vegetation plots distributed across 10 southern African countries and structural equation modelling to determine the relationship between tree species diversity and above‐ground woody biomass, accounting for interacting effects of resource availability, disturbance by fire, tree stem density and vegetation type. We found positive effects of tree species diversity on above‐ground biomass, operating via increased structural diversity. The observed BEFR was highly dependent on organismal density, with a minimum threshold of c . 180 mature stems ha −1 . We found that water availability mainly affects biomass indirectly, via increasing species diversity. The study underlines the close association between tree diversity, ecosystem structure, environment and function in highly disturbed savannas and woodlands. We suggest that tree diversity is an under‐appreciated determinant of wooded ecosystem structure and function.
    Type of Medium: Online Resource
    ISSN: 0028-646X , 1469-8137
    URL: Issue
    URL: Article
    DOI: 10.1111/nph.v232.2
    DOI: 10.1111/nph.17639
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 208885-X
    detail.hit.zdb_id: 1472194-6
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  • 2
    Online Resource
    Online Resource
    Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests
    IOP Publishing ; 2022
    In:  Environmental Research Letters Vol. 17, No. 1 ( 2022-01-01), p. 014047-
    Details
    In: Environmental Research Letters, IOP Publishing, Vol. 17, No. 1 ( 2022-01-01), p. 014047-
    Abstract: For monitoring and reporting forest carbon stocks and fluxes, many countries in the tropics and subtropics rely on default values of forest aboveground biomass (AGB) from the Intergovernmental Panel on Climate Change (IPCC) guidelines for National Greenhouse Gas (GHG) Inventories. Default IPCC forest AGB values originated from 2006, and are relatively crude estimates of average values per continent and ecological zone. The 2006 default values were based on limited plot data available at the time, methods for their derivation were not fully clear, and no distinction between successional stages was made. As part of the 2019 Refinement to the 2006 IPCC Guidelines for GHG Inventories, we updated the default AGB values for tropical and subtropical forests based on AGB data from 〉 25 000 plots in natural forests and a global AGB map where no plot data were available. We calculated refined AGB default values per continent, ecological zone, and successional stage, and provided a measure of uncertainty. AGB in tropical and subtropical forests varies by an order of magnitude across continents, ecological zones, and successional stage. Our refined default values generally reflect the climatic gradients in the tropics, with more AGB in wetter areas. AGB is generally higher in old-growth than in secondary forests, and higher in older secondary (regrowth 〉 20 years old and degraded/logged forests) than in young secondary forests (⩽20 years old). While refined default values for tropical old-growth forest are largely similar to the previous 2006 default values, the new default values are 4.0–7.7-fold lower for young secondary forests. Thus, the refined values will strongly alter estimated carbon stocks and fluxes, and emphasize the critical importance of old-growth forest conservation. We provide a reproducible approach to facilitate future refinements and encourage targeted efforts to establish permanent plots in areas with data gaps.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    URL: Article
    DOI: 10.1088/1748-9326/ac45b3
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2022
    detail.hit.zdb_id: 2255379-4
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  • 3
    Online Resource
    Online Resource
    Precipitation gradients drive high tree species turnover in the woodlands of eastern and southern Africa
    Wiley ; 2023
    In:  Ecography
    Details
    In: Ecography, Wiley
    Abstract: Savannas cover one‐fifth of the Earth's surface, harbour substantial biodiversity, and provide a broad range of ecosystem services to hundreds of millions of people. The community composition of trees in tropical moist forests varies with climate, but whether the same processes structure communities in disturbance‐driven savannas remains relatively unknown. We investigate how biodiversity is structured over large environmental and disturbance gradients in woodlands of eastern and southern Africa. We use tree inventory data from the Socio‐Ecological Observatory for Studying African Woodlands (SEOSAW) network, covering 755 ha in a total of 6780 plots across nine countries of eastern and southern Africa, to investigate how alpha, beta, and phylogenetic diversity varies across environmental and disturbance gradients. We find strong climate‐richness patterns, with precipitation playing a primary role in determining patterns of tree richness and high turnover across these savannas. Savannas with greater rainfall contain more tree species, suggesting that low water availability places distributional limits on species, creating the observed climate‐richness patterns. Both fire and herbivory have minimal effects on tree diversity, despite their role in determining savanna distribution and structure. High turnover of tree species, genera, and families is similar to turnover in seasonally dry tropical forests of the Americas, suggesting this is a feature of semiarid tree floras. The greater richness and phylogenetic diversity of wetter plots shows that broad‐scale ecological patterns apply to disturbance‐driven savanna systems. High taxonomic turnover suggests that savannas from across the regional rainfall gradient should be protected if we are to maximise the conservation of unique tree communities.
    Type of Medium: Online Resource
    ISSN: 0906-7590 , 1600-0587
    URL: Article
    DOI: 10.1111/ecog.06720
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2024917-2
    detail.hit.zdb_id: 1112659-0
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Combining Sentinel-1 and Landsat 8 Does Not Improve Classification Accuracy of Tropical Selective Logging
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 1 ( 2022-01-01), p. 179-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 1 ( 2022-01-01), p. 179-
    Abstract: Tropical forests play a key role in the global carbon and hydrological cycles, maintaining biological diversity, slowing climate change, and supporting the global economy and local livelihoods. Yet, rapidly growing populations are driving continued degradation of tropical forests to supply wood products. The United Nations (UN) has developed the Reducing Emissions from Deforestation and Forest Degradation (REDD+) programme to mitigate climate impacts and biodiversity losses through improved forest management. Consistent and reliable systems are still needed to monitor tropical forests at large scales, however, degradation has largely been left out of most REDD+ reporting given the lack of effective monitoring and countries mainly focus on deforestation. Recent advances in combining optical data and Synthetic Aperture Radar (SAR) data have shown promise for improved ability to monitor forest losses, but it remains unclear if similar improvements could be made in detecting and mapping forest degradation. We used detailed selective logging records from three lowland tropical forest regions in the Brazilian Amazon to test the effectiveness of combining Landsat 8 and Sentinel-1 for selective logging detection. We built Random Forest models to classify pixel-based differences in logged and unlogged regions to understand if combining optical and SAR improved the detection capabilities over optical data alone. We found that the classification accuracy of models utilizing optical data from Landsat 8 alone were slightly higher than models that combined Sentinel-1 and Landsat 8. In general, detection of selective logging was high with both optical only and optical-SAR combined models, but our results show that the optical data was dominating the predictive performance and adding SAR data introduced noise, lowering the detection of selective logging. While we have shown limited capabilities with C-band SAR, the anticipated opening of the ALOS-PALSAR archives and the anticipated launch of NISAR and BIOMASS in 2023 should stimulate research investigating similar methods to understand if longer wavelength SAR might improve classification of areas affected by selective logging when combined with optical data.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14010179
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 5
    Online Resource
    Online Resource
    Sentinel-1 observation frequency significantly increases burnt area detectability in tropical SE Asia
    IOP Publishing ; 2020
    In:  Environmental Research Letters Vol. 15, No. 5 ( 2020-05-01), p. 054008-
    Details
    In: Environmental Research Letters, IOP Publishing, Vol. 15, No. 5 ( 2020-05-01), p. 054008-
    Abstract: Frequent cloud cover in the tropics significantly affects the observation of the surface by satellites. This has enormous implications for current approaches that estimate greenhouse gas (GHG) emissions from fires or map fire scars. These mainly employ data acquired in the visible to middle infrared bands to map fire scars or thermal data to estimate fire radiative power and consequently derive emissions. The analysis here instead explores the use of microwave data from the operational Sentinel-1A (S-1A) in dual-polarisation mode (VV and VH) acquired over Central Kalimantan during the 2015 fire season. Burnt areas were mapped in three consecutive periods between August and October 2015 using the random forests machine learning algorithm. In each mapping period, the omission and commission errors of the unburnt class were always below 3%, while the omission and commission errors of the burnt class were below 20% and 5% respectively. Summing the detections from the three periods gave a total burnt area of ∼1.6 million ha, but this dropped to ∼1.2 million ha if using only a pair of pre- and post-fire season S-1A images. Hence the ability of Sentinel-1 to make frequent observations significantly increases fire scar detection. Comparison with burnt area estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product at 5 km scale showed poor agreement, with consistently much lower estimates produced by the MODIS data-on average 14%–51% of those obtained in this study. The method presented in this study offers a way to reduce the substantial errors likely to occur in optical-based estimates of GHG emissions from fires in tropical areas affected by substantial cloud cover.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    URL: Article
    DOI: 10.1088/1748-9326/ab7765
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2255379-4
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  • 6
    Online Resource
    Online Resource
    Pervasive gaps in Amazonian ecological research
    Elsevier BV ; 2023
    In:  Current Biology Vol. 33, No. 16 ( 2023-08), p. 3495-3504.e4
    Details
    In: Current Biology, Elsevier BV, Vol. 33, No. 16 ( 2023-08), p. 3495-3504.e4
    Type of Medium: Online Resource
    ISSN: 0960-9822
    URL: Article
    DOI: 10.1016/j.cub.2023.06.077
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 2019214-9
    SSG: 12
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