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opencc-by-4.0Jan 2020View details →

Data of the paper: Atmospheric energy budget response to idealized aerosol perturbation in tropical cloud systems

Here you can find the data presented in the paper: Atmospheric energy budget response to idealized aerosol perturbation in tropical cloud systems The data include all variables included in the paper for the shallow-cloud and the deep-cloud dominated cases. The variable names are as in the paper (beside T_tot which is the 2m temperature). The numbers in the names of the variables represent the CDNC case. The time series variables are as a function of t. The vertical profiles are as a function of the pressure p. The maps are as a function of latitude and longitude.

Fig. 30 in Oosagitta gen. nov. from tropical Africa, with revision of two species and description of four new species (Coleoptera: Chrysomelidae, Galerucinae)

Fig. 30. Holotype of O. melanopicta sp. nov. A. Overview with labels. B. Close-up of specimen.

Fig. 18 in Oosagitta gen. nov. from tropical Africa, with revision of two species and description of four new species (Coleoptera: Chrysomelidae, Galerucinae)

Fig. 18. Holotype of O. anningae sp. nov. A. Overview with labels. B. Close-up of specimen.

Fig. 22 in Oosagitta gen. nov. from tropical Africa, with revision of two species and description of four new species (Coleoptera: Chrysomelidae, Galerucinae)

Fig. 22. Holotype of O. geescheae sp. nov. A. Overview with labels. B. Close-up of specimen.

Fig. 13 in Oosagitta gen. nov. from tropical Africa, with revision of two species and description of four new species (Coleoptera: Chrysomelidae, Galerucinae)

Fig. 13. Distribution of O. minuta comb. nov., O. anningae sp. nov. and O. geescheae sp. nov.

Fig. 8 in Oosagitta gen. nov. from tropical Africa, with revision of two species and description of four new species (Coleoptera: Chrysomelidae, Galerucinae)

Fig. 8. Distribution of O. angolensis comb. nov., O. melanopicta sp. nov. and O. thomasi sp. nov.

Fig. 14 in Oosagitta gen. nov. from tropical Africa, with revision of two species and description of four new species (Coleoptera: Chrysomelidae, Galerucinae)

Fig. 14. Lectotype of Oosagitta minuta comb. nov. A. Overview with labels. B. Close-up of specimen.

Fig. 26 in Oosagitta gen. nov. from tropical Africa, with revision of two species and description of four new species (Coleoptera: Chrysomelidae, Galerucinae)

Fig. 26. Holotype of O. thomasi sp. nov. A. Overview with labels. B. Close-up of specimen.

opencc-zeroDec 2019View details →

Data from: Can light-saturated photosynthesis in lowland tropical forests be estimated by one light level?

Leaf-level net photosynthesis (An) estimates and associated photosynthetic parameters are crucial for accurately parameterizing photosynthesis models. For tropical forests such data are poorly available and collected at variable light conditions. To avoid over- or underestimation of modelled photosynthesis, it is critical to know at which photosynthetic photon flux density (PPFD) photosynthesis becomes light saturated. We studied the dependence of An on PPFD in two tropical forests in French Guiana. We estimated the light saturation range, including the lowest PPFD level at which Asat (An at light saturation) is reached, as well as the PPFD range at which Asat remained unaltered. The light saturation range was derived from photosynthetic light-response curves, and within-canopy and interspecific differences were studied. We observed wide light saturation ranges of An. Light saturation ranges differed among canopy heights, but a PPFD level of 1000 µmol/m²/s was common across all heights, except for pioneer trees species that did not reach light saturation below 2000 µmol/m²/s. A light intensity of 1000 µmol/m²/s sufficed for measuring Asat of climax species at our study sites, independent of the species or the canopy height. Because of the wide light saturation ranges, results from studies measuring Asat at higher PPFD levels (for upper canopy leaves up to 1600 µmol/m²/s) are comparable with studies measuring at 1000 µmol/m²/s.

opencc-by-4.0Jul 2020View details →

Soil and litter chemistry, soil microbial communities and litter decomposition from tropical forest and oil palm

Description: A study examining the interactions between soil chemistry, litter chemistry and soil microbial decomposers as controls on rates of litter decomposition across a tropical land use disturbance gradient. Co-located soil and litter samples were collected from old growth forest, moderately logged forest, heavily logged forest and oil palm plantations. Soil and litter were chemically characterised and soil bacterial and fungal community composition and abundance were measured. These were then combined in fully factorial ex-situ microcosms and measured litter decomposition rates at 3 time points during different stages of decomposition.Project: This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/124">Biodiversity and land-use impacts on tropical ecosystem function (BALI): Quantifying biogeochemistry across forest disturbance gradients in Sabah</a>Funding: These data were collected as part of research funded by: <ul><li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant, NE/K016377/1)</li></ul>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.Permits: These data were collected under permit from the following authorities:<ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (20))</li><li>Sabah Biodiversity Centre (Export licence JKM/MBS.1000-2/3 JLD.2 (70))</li></ul>XML metadata: GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3929632">here</a>Files: This consists of 1 file: SAFE_Dataset.xlsxSAFE_Dataset.xlsxThis file contains dataset metadata and 5 data tables:<ol><li>Soil_Properties (described in worksheet Soil_Properties)Description: Basic measured soil propertiesNumber of fields: 9Number of data rows: 20Fields: <ul><li>Plot: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li>Plot_ID: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li>location_name: Name of subplot where soils were collected (Field type: location)</li><li>gravimetric moisture content: Soil moisture content at the time of sample collection (Field type: numeric)</li><li>soil_pH: Soil pH measured on fresh soils (Field type: numeric)</li><li>soil_N: Total soil Nitrogen (Field type: numeric)</li><li>soil_C: Total soil Carbon (Field type: numeric)</li><li>soil_C.N: Soil carbon to nitrogen ratio (Field type: numeric)</li><li>soil_P: soil inorganic phosphorus (Field type: numeric)</li></ul></li><li>Litter_Chemistry (described in worksheet Litter_Chemistry)Description: Litter chemistry data of mixed forest floor litter, collected, sorted to remove humified material, woody debris and driedNumber of fields: 20Number of data rows: 40Fields: <ul><li>Plot: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li>Plot_ID: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li>location_name: Name of subplot where soils were collected (Field type: location)</li><li>Pretreatment: Whether the litter sample was sterilised by autoclaving or not (Field type: categorical)</li><li>leaf_K: leaf potassium concentration (Field type: numeric)</li><li>leaf_Ca: leaf Calcium concentration (Field type: numeric)</li><li>leaf_Mg: leaf Magnesium concentration (Field type: numeric)</li><li>leaf_Al: leaf aluminium concentration (Field type: numeric)</li><li>leaf_P: leaf phosphorus concentrations (Field type: numeric)</li><li>solubles: leaf soluble cell content (Field type: numeric)</li><li>hem_pro_cel_lig_rec: leaf hemicellulose, proteins, cellulose, lignin and recalcitrant fibres (Field type: numeric)</li><li>hem_pro: leaf hemicellulose and proteins (Field type: numeric)</li><li>cel_lig_rec: leaf cellulose, lignin and recalcitrant fibres (Field type: numeric)</li><li>cel: leaf cellulose (Field type: numeric)</li><li>lig_rec: leaf lignin and recalcitrants (Field type: numeric)</li><li>leaf_N: leaf nitrogen concentration (Field type: numeric)</li><li>leaf_C: leaf carbon concentration (Field type: numeric)</li><li>c.n: leaf carbon to nitrogen ration (Field type: numeric)</li><li>d13c: leaf carbon stable isotope ratio (Field type: numeric)</li><li>d15n: leaf nitrogen stable isotope ratio (Field type: numeric)</li></ul></li><li>PLFA_Concentrations (described in worksheet PLFA_Concentrations)Description: Phospolipid Fatty Acid (PLFA) concentrations as biomarkers of soil bacteria and fungi. Extracted from freeze dried soils prior to the microcosm experimentNumber of fields: 10Number of data rows: 20Fields: <ul><li>Plot: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li>Plot_ID: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li>location_name: Name of subplot where soils were collected (Field type: location)</li><li>Total_PLFA: Total PLFA concentrations extracted from soil samples (Field type: numeric)</li><li>Fungal_PLFA: Fungal PLFA biomarker concentrations extracted from soils (Field type: numeric)</li><li>Bacteria_PLFA: Bacteria PLFA biomarkers extracted from soils (Field type: numeric)</li><li>Fungal:Bacteria: Ratio of fungal to bacteria PLFAs (Field type: numeric)</li><li>Gram_Pos_PLFA: Gram Positive PLFA Biomarker concentrations extracted from soil (Field type: numeric)</li><li>Gram_Neg_PLFA: Gram Negative PLFA Biomarker concentrations extracted from soil (Field type: numeric)</li><li>GramPos:GramNeg: Gram positive to Gram negative PLFA ratios (Field type: numeric)</li></ul></li><li>Soil_Microbial_Communities (described in worksheet Soil_Microbial_Communities)Description: Summary diversity statistics from bacterial 16S and fungal ITS biomarker microbial sequencing. DNA extracted from soils prior to microcosm experimentNumber of fields: 9Number of data rows: 20Fields: <ul><li>Plot: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li>Plot_ID: Plot ID indicating land use as referenced in the Frontiers in forests and global change publication "Soil microbial community and litter quality controls on decomposition across a tropical forest disturbance gradient" (Field type: categorical)</li><li>location_name: Name of subplot where soils were collected (Field type: location)</li><li>Bacteria_Richness: Number of observed bacterial taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li>Bacteria_Shannon: Bacterial Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li><li>Fungal_Richness: Number of observed fungal taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li>Fungal_Shannon: Fungal Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li><li>Saprotrophic_Fungal_Richness: Number of observed saprotrophic fungal taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li>Saprotrophic_Fungal_Shannon: Saprotrophic Fungal Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li></ul></li><li>Ex_Situ_Litter_Decomposition (described in worksheet Ex_Situ_Litter_Decomposition)Description: Fully factorial litter decomposition experiment. 16 unique soil and litter combinations (4x4) were incubated in petri dishes at constant temperature and moisture and mass loss measured after 31, 105 and 398 days.Number of fields: 8Number of data rows: 240Fields: <ul><li>location_name: Name of subplot where soils were collected (Field type: location)</li><li>Soil_ID: Soil ID indicating which land use soil was collected from (Field type: categorical)</li><li>Litter_Location: Location of which GEM carbon plot the litter was collected from. Litter was collected from the 5 carbon subplots as per soil collection and homogenised into one composite sample per carbon plot (Field type: location)</li><li>Litter_ID: Litter ID indicating which land use litter was collected from (Field type: categorical)</li><li>Experimental_Block: Which experimental block the microcosm was assigned to. N= 5 (Field type: replicate)</li><li>Timepoint: At what timepoint the litter was harvested from each microcosm (Field type: categorical)</li><li>Mass_Loss: The mass loss of litter relative to the starting mass of 1g (Field type: numeric)</li><li>home_away: Descriptor for whether the soil and litter combination in microcosm (Field type: categorical)</li></ul></li></ol>Date range: 2014-10-01 to 2018-09-01Latitudinal extent: 4.6402 to 4.9539Longitudinal extent: 117.4518 to 117.7942

opencc-by-4.0Jan 2020View details →

Bangladesh - Tropical Cyclone Historical Catalogue

Gridded data for major historical tropical cyclones over Bangladesh. Each tropical cyclone has a 9 member ensemble, and comprises of time series and footprints at resolutions of 4.4km and 1.5km based on the Met Office Unified Model dynamically downscaling ECMWF ERA5 data. There are 12 available variables, including: air temperature, maximum wind gust speed, minimum air pressure at sea level and precipitation amounts, available at a range of temporal scales, including model instantaneous values, and hourly and daily aggregations. The catalogue contains the following tropical cyclones (landfall date): BOB01 (30/04/1991 00:00), BOB07 (25/11/1995 09:00), TC01B (19/05/1997 15:00), Akash (14/05/2007 18:00), Sidr (15/11/2007 18:00), Rashmi (26/10/2008 21:00), Aila (25/05/2009 06:00), Viyaru (16/05/2013 09:00), Roanu (21/05/2016 12:00), Mora (30/05/2017 03:00), Fani (04/05/2019 06:00), Bulbul (09/11/2019 18:00).. File Types <ul> <li>tsens.*.tar.gz Time series data for each named storm. Dimensions are typically: forecast_period, forecast_reference_time, latitude and longitude. Compressed tar archive containing multiple netCDF files.</li> <li>fpens.*.tar.gz Time-aggregated data for each ensemble member for each storm.  Variables: max gust speed (fg), minimum sea-level pressure (psl), instantaneous u-wind (ua) and v-wind (va) components.  Dimensions are typically: forecast_reference_time, latitude and longitude. Compressed tar archive containing multiple netCDF files.</li> <li>fp.fg.T1Hmax.tar.gz A single best estimate gust-speed (fg) footprint with lower, median and upper bounds accounting for ensemble variation, for each names storm.  Compressed tar archive containing multiple netCDF files.</li> <li>fp.Rmodels.fg.tar.gz  R GAM model data used to create best estimate netCDF footprints.  Compressed tar archive containing output from mgcv gam model saved as an Rdata file.</li> <li>storm_tracks.tar.gz Storm tracks for each ensemble member of each names storm from Tempest Extremes tracking algorithm.  Compressed tar archive containing multiple .DAT text files.</li> </ul>

Data from: Intra-specific variation in tree growth responses to neighborhood composition and seasonal drought in a tropical forest

1. Functional traits are expected to provide insights into the abiotic and biotic drivers of plant demography. However, successfully linking traits to plant demographic performance likely requires the consideration of important contextual and individual-level information that is often ignored in trait-based ecology. 2. Here, we modeled 8 years of growth from 1,138 individual trees from 36 tropical rain forest species. We compared models of tree growth parameterized using individual-level versus species mean trait data. We also compared models that considered regional climatic, local biotic and whole-plant allocation contexts to those that do not. 3. Our analyses show that growth models parameterized using individual-level trait information outperformed those that used species mean trait information and that these models often contradicted one another indicating that the common practice of using species mean trait data requires more scrutiny. Additionally, we found that models including climatic, biotic and allocation contexts outperformed those that did not and provide nuanced insights into the drivers of tree growth in a tropical forest. 4. Synthesis. Here, we have shown that the development of models of tree demographic performance upon the basis of traits can be improved through a consideration of individual-level trait variation as well as phenotypic and climatic contexts. We highlight that our ability to understand the drivers of tree population and community structure and dynamics in current and in future climates will be limited if contextual and individual-level data remains understudied.

Data from: Bat ensembles differ in response to use zones in a tropical biosphere reserve

Biosphere reserves, designated under The United Nations Education, Scientific and Cultural Organization's (UNESCO) Man and Biosphere Programme, aim to sustainably integrate protected areas into the biological and economic landscape around them by buffering strictly protected habitats with zones of limited use. However, the effectiveness of biosphere reserves and the contribution of the different zones of use to protection is poorly known. We assessed the diversity and activity of bats in the Crocker Range Biosphere Reserve (CRBR) in Sabah, Malaysia, using harp traps, mist nets and acoustic surveys in each zone—core, buffer, transition and in agricultural plots outside of the reserve. We captured 30 species, bringing the known bat fauna of CRBR to 50 species, half of Borneo's bat species. Species composition and acoustic activity varied among zones and by foraging ensemble, with the core and buffer showing particular importance for conserving forest-dependent insectivorous bats. Frugivorous bats were found in all zones but were the most abundant and most species-rich ensemble within agricultural sites. Although sampling was limited, bat diversity and activity was low in the transition zone compared to other zones, indicating potential for management practices that increase food availability and enhance biodiversity value. We conclude that, collectively, the zones of the CRBR effectively protect diversity, but the value of the transition zone can be improved.

opencc-zeroJun 2020View details →

Species identity and diversity effects on invasion resistance of tropical freshwater plant communities

Biotic resistance mediated by native plant diversity has long been hypothesized to reduce the success of invading plant species in terrestrial systems in temperate regions. However, still little is known about the mechanisms driving invasion patterns in other biomes or latitudes. We help to fill this gap by investigating how native plant community presence and diversity, and the presence of native phylogenetically closely related species to an invader, would affect invader Hydrilla verticillata establishment success in tropical freshwater submerged plant communities. The presence of a native community suppressed the growth of H. verticillata, but did not prevent its colonisation. Invader growth was negatively affected by native plant productivity, but independent of native species richness and phylogenetic relatedness to the invader. Native plant production was not related to native species richness in our study. We show that resistance in these tropical aquatic submerged plant communities is mainly driven by the presence and biomass of a native community independent of native species diversity. Our study illustrates that resistance provided by these tropical freshwater submerged plant communities to invasive species contrasts to resistance described for other ecosystems. This emphasizes the need to include understudied systems when predicting patterns of species invasiveness and ecosystem invasibility across biomes.

Data from: Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India

This dataset includes bird community, habitat structure, and vegetation data from the following publication: Raman, T.R.S. 2006. Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India. Biodiversity and Conservation 15: 1577–1607. https://doi.org/10.1007/s10531-005-2352-5 Abstract: As large nature reserves occupy only a fraction of the earth's land surface, conservation biologists are critically examining the role of private lands, habitat fragments, and plantations for conservation. This study in a biodiversity hotspot and endemic bird area, the Western Ghats mountains of India, examined the effects of habitat structure, floristics, and adjacent habitats on bird communities in shade-coffee and cardamom plantations and tropical rainforest fragments. Habitat and birds were sampled in 13 sites: six fragments (three relatively isolated and three with canopy connectivity with adjoining shade-coffee plantations and forests), six plantations differing in canopy tree species composition (five coffee and one cardamom), and one undisturbed primary rainforest control site in the Anamalai hills. Around 3300 detections of 6000 individual birds belonging to 106 species were obtained. The coffee plantations were poorer than rainforest in rainforest bird species, particularly endemic species, but the rustic cardamom plantation with diverse, native rainforest shade trees, had bird species richness and abundance comparable to primary rainforest. Plantations and fragments that adjoined habitats providing greater tree canopy connectivity supported more rainforest and fewer open-forest bird species and individuals than sites that lacked such connectivity. These effects were mediated by strong positive effects of vegetation structure, particularly woody plant variables, cane, and bamboo, on bird community structure. Bird community composition was however positively correlated only to floristic (tree species) composition of sites. The maintenance or restoration of habitat structure and (shade) tree species composition in shade-coffee and cardamom plantations and rainforest fragments can aid in rainforest bird conservation in the regional landscape.

Life history data for longer-lived tropical songbirds reduce breeding activity as they buffer impacts of drought

Droughts are expected to increase in frequency and severity with climate change. Population impacts of such harsh environmental events are theorized to vary with life history strategies among species. However, existing demographic models generally do not consider behavioral plasticity that may modify the impact of harsh events. Here we show that tropical songbirds in the New and Old World reduced reproduction during drought, with greater reductions in species with higher average long-term survival. Large reductions in reproduction by longer-lived species were associated with higher survival during drought than pre-drought years in Malaysia, whereas shorter-lived species maintained reproduction and survival decreased. Behavioral strategies of longer-lived, but not shorter-lived, species mitigated the effect of increasing drought frequency on long-term population growth. Behavioral plasticity can buffer the impact of climate change on populations of some species, and differences in plasticity among species related to their life histories are critical for predicting population trajectories.

opencc-zeroAug 2020View details →

Data from: The weight of the crust: biomass of crustose lichens in tropical dry forest represents more than half of foliar biomass

In recent years, our ecological knowledge of tropical dry forests has increased dramatically. However, the functional contributions of whole ecosystem components, such as lichens, remain mostly unknown. In these forests, the abundance of epiphyte crustose lichens is responsible for the characteristic white bark on most woody plants, conspicuous during the dry season, but the amount of resources that the lichen component represents remains unexplored. We estimated lichen biomass in a Mexican tropical dry forest using the bark area of trees, the dry mass of lichens per unit area and the percentage of bark covered by lichens, together with previously known tree densities. The lowest 2.5 m of the forests main trunks contained 188 kg/ha of lichen biomass, with lichens covering 85% of the available bark for trees <12 cm DBH and 38% for trees >12 cm. Total epiphytic lichen biomass was 1.34–1.99 Mg/ha. Lichen biomass represented 61% of the foliar biomass in the forest. To our knowledge, this is the first time that a lichen biomass estimate is provided for an ecosystem in which crustose lichens are the dominant lichen growth form. Crustose lichens are typically considered to contribute little to the total lichen biomass and to be difficult to include in ecological analyses. The high lichen biomass in this ecosystem implies a significant ecological role which so far is unexplored. We suggest the crustose lichen component should not be underestimated a priori in ecological studies, especially in ecosystems with abundant lichen cover.