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1,271 results for “tropical forest”
Data from: Occupancy winners in tropical protected forests: a pantropical analysis
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High-resolution tropical rain-forest canopy climate data
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Data from: Shifting agriculture supports more tropical forest birds than oil palm or teak plantations in Mizoram, northeast India
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Data: Avian cultural services peak in tropical wet forests
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Riparian forests and macroinvertebrates support multiple ecosystem processes across temperate and tropical streams
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Active restoration fosters better recovery of tropical rainforest birds than natural regeneration in degraded forest fragments
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Data from: Seasonal plasticity in sympatric <em>Bicyclus</em> butterflies in a tropical forest where temperature does not predict rainfall
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Ant-scale mutualism increases scale infestation, decreases folivory, and disrupts biological control in restored tropical forests
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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.
Soil and litter chemistry, soil microbial communities and litter decomposition from tropical forest and oil palm
<b>Description: </b><p>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.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/124"><b>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Quantifying biogeochemistry across forest disturbance gradients in Sabah</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant, NE/K016377/1)</li></ul><p>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.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><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><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3929632">here</a></p><p><b>Files: </b>This consists of 1 file: SAFE_Dataset.xlsx</p><p><b>SAFE_Dataset.xlsx</b></p><p>This file contains dataset metadata and 5 data tables:</p><ol><li><p><b>Soil_Properties</b> (described in worksheet Soil_Properties)</p><p>Description: Basic measured soil properties</p><p>Number of fields: 9</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: 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><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>gravimetric moisture content</b>: Soil moisture content at the time of sample collection (Field type: numeric)</li><li><b>soil_pH</b>: Soil pH measured on fresh soils (Field type: numeric)</li><li><b>soil_N</b>: Total soil Nitrogen (Field type: numeric)</li><li><b>soil_C</b>: Total soil Carbon (Field type: numeric)</li><li><b>soil_C.N</b>: Soil carbon to nitrogen ratio (Field type: numeric)</li><li><b>soil_P</b>: soil inorganic phosphorus (Field type: numeric)</li></ul></li><li><p><b>Litter_Chemistry</b> (described in worksheet Litter_Chemistry)</p><p>Description: Litter chemistry data of mixed forest floor litter, collected, sorted to remove humified material, woody debris and dried</p><p>Number of fields: 20</p><p>Number of data rows: 40</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: 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><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Pretreatment</b>: Whether the litter sample was sterilised by autoclaving or not (Field type: categorical)</li><li><b>leaf_K</b>: leaf potassium concentration (Field type: numeric)</li><li><b>leaf_Ca</b>: leaf Calcium concentration (Field type: numeric)</li><li><b>leaf_Mg</b>: leaf Magnesium concentration (Field type: numeric)</li><li><b>leaf_Al</b>: leaf aluminium concentration (Field type: numeric)</li><li><b>leaf_P</b>: leaf phosphorus concentrations (Field type: numeric)</li><li><b>solubles</b>: leaf soluble cell content (Field type: numeric)</li><li><b>hem_pro_cel_lig_rec</b>: leaf hemicellulose, proteins, cellulose, lignin and recalcitrant fibres (Field type: numeric)</li><li><b>hem_pro</b>: leaf hemicellulose and proteins (Field type: numeric)</li><li><b>cel_lig_rec</b>: leaf cellulose, lignin and recalcitrant fibres (Field type: numeric)</li><li><b>cel</b>: leaf cellulose (Field type: numeric)</li><li><b>lig_rec</b>: leaf lignin and recalcitrants (Field type: numeric)</li><li><b>leaf_N</b>: leaf nitrogen concentration (Field type: numeric)</li><li><b>leaf_C</b>: leaf carbon concentration (Field type: numeric)</li><li><b>c.n</b>: leaf carbon to nitrogen ration (Field type: numeric)</li><li><b>d13c</b>: leaf carbon stable isotope ratio (Field type: numeric)</li><li><b>d15n</b>: leaf nitrogen stable isotope ratio (Field type: numeric)</li></ul></li><li><p><b>PLFA_Concentrations</b> (described in worksheet PLFA_Concentrations)</p><p>Description: Phospolipid Fatty Acid (PLFA) concentrations as biomarkers of soil bacteria and fungi. Extracted from freeze dried soils prior to the microcosm experiment</p><p>Number of fields: 10</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: 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><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Total_PLFA</b>: Total PLFA concentrations extracted from soil samples (Field type: numeric)</li><li><b>Fungal_PLFA</b>: Fungal PLFA biomarker concentrations extracted from soils (Field type: numeric)</li><li><b>Bacteria_PLFA</b>: Bacteria PLFA biomarkers extracted from soils (Field type: numeric)</li><li><b>Fungal:Bacteria</b>: Ratio of fungal to bacteria PLFAs (Field type: numeric)</li><li><b>Gram_Pos_PLFA</b>: Gram Positive PLFA Biomarker concentrations extracted from soil (Field type: numeric)</li><li><b>Gram_Neg_PLFA</b>: Gram Negative PLFA Biomarker concentrations extracted from soil (Field type: numeric)</li><li><b>GramPos:GramNeg</b>: Gram positive to Gram negative PLFA ratios (Field type: numeric)</li></ul></li><li><p><b>Soil_Microbial_Communities</b> (described in worksheet Soil_Microbial_Communities)</p><p>Description: Summary diversity statistics from bacterial 16S and fungal ITS biomarker microbial sequencing. DNA extracted from soils prior to microcosm experiment</p><p>Number of fields: 9</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Plot</b>: Plot name corresponding to the GEM Carbon plot where soils were sampled (Field type: id)</li><li><b>Plot_ID</b>: 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><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Bacteria_Richness</b>: Number of observed bacterial taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li><b>Bacteria_Shannon</b>: Bacterial Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li><li><b>Fungal_Richness</b>: Number of observed fungal taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li><b>Fungal_Shannon</b>: Fungal Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li><li><b>Saprotrophic_Fungal_Richness</b>: Number of observed saprotrophic fungal taxa from sequencing of 16S marker genes from soil samples (Field type: numeric)</li><li><b>Saprotrophic_Fungal_Shannon</b>: Saprotrophic Fungal Shannon diversity from 16S Marker gene sequencing (Field type: numeric)</li></ul></li><li><p><b>Ex_Situ_Litter_Decomposition</b> (described in worksheet Ex_Situ_Litter_Decomposition)</p><p>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.</p><p>Number of fields: 8</p><p>Number of data rows: 240</p><p>Fields: </p><ul><li><b>location_name</b>: Name of subplot where soils were collected (Field type: location)</li><li><b>Soil_ID</b>: Soil ID indicating which land use soil was collected from (Field type: categorical)</li><li><b>Litter_Location</b>: 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><b>Litter_ID</b>: Litter ID indicating which land use litter was collected from (Field type: categorical)</li><li><b>Experimental_Block</b>: Which experimental block the microcosm was assigned to. N= 5 (Field type: replicate)</li><li><b>Timepoint</b>: At what timepoint the litter was harvested from each microcosm (Field type: categorical)</li><li><b>Mass_Loss</b>: The mass loss of litter relative to the starting mass of 1g (Field type: numeric)</li><li><b>home_away</b>: Descriptor for whether the soil and litter combination in microcosm (Field type: categorical)</li></ul></li></ol><p><b>Date range: </b>2014-10-01 to 2018-09-01</p><p><b>Latitudinal extent: </b>4.6402 to 4.9539</p><p><b>Longitudinal extent: </b>117.4518 to 117.7942</p>
Data from: Intra-specific variation in tree growth responses to neighborhood composition and seasonal drought in a tropical forest
<p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p>
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.
Forest conversion to oil palm compresses food chain length in tropical streams
<b>Description: </b><p>Fish stable isotope data associated with the paper in Ecology. This includes bulk and compound specific stable isotope raw data for fish at 17 streams over 2 sampling years, across a land use gradient</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/57"><b>Composition and abundance of tropical freshwater vertebrate communities across a land use gradient</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Sime Darby Foundation (NA, NA)</li><li>Royal Geographic Society (RGS-IBG , PRA 01/16)</li><li>Royal Society (NA, NAF\R2\180791)</li><li>Singapore Ministry of Education (AcRF Tier 1 grant, R-154-000-A32-114)</li><li>Landmark Trust (Landmark Trust Futures Scheme , NA)</li></ul><p>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.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3974971">here</a></p><p><b>Files: </b>This consists of 1 file: Fish_SIA_data2.xlsx</p><p><b>Fish_SIA_data2.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>SIA_data</b> (described in worksheet Data)</p><p>Description: raw stable isotope fish data </p><p>Number of fields: 13</p><p>Number of data rows: 202</p><p>Fields: </p><ul><li><b>Date</b>: Date of sampling at each stream (Field type: date)</li><li><b>Stream</b>: Which stream was sampled (Field type: location)</li><li><b>Landuse</b>: Landuse of the stream sampled (Field type: categorical)</li><li><b>Species</b>: Species sampled (Field type: taxa)</li><li><b>SIA</b>: Was bulk or compound specific SIA used (Field type: categorical)</li><li><b>d15N_GLU</b>: d15N value of glutamic acid (Field type: numeric)</li><li><b>d15N_PHE</b>: d15N value of phenylalanine (Field type: numeric)</li><li><b>d13C_ILE</b>: d13C value of Isoleucine (Field type: numeric)</li><li><b>d13C_LEU</b>: d13C value of Leucine (Field type: numeric)</li><li><b>d13C_PHE</b>: d13C value of phenylalanine (Field type: numeric)</li><li><b>d13C_VAL</b>: d13C value of Valine (Field type: numeric)</li><li><b>D15N</b>: D15N (Field type: numeric)</li><li><b>D13C</b>: D13C (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2015-03-01 to 2017-05-30</p><p><b>Latitudinal extent: </b>4.5770 to 4.9613</p><p><b>Longitudinal extent: </b>117.4441 to 117.8053</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Actinopterygii <br> -  -  -  -  Cypriniformes <br> -  -  -  -  -  Cyprinidae <br> -  -  -  -  -  -  <i>Luciosoma</i> <br> -  -  -  -  -  -  -  <i>Luciosoma pelligrinii</i> <br> -  -  -  -  -  -  <i>Rasbora</i> <br> -  -  -  -  -  -  -  <i>Rasbora pycnopeza</i> <br> -  -  -  -  -  -  <i>Nematabramis</i> <br> -  -  -  -  -  -  -  <i>Nematabramis everetti</i> <br> -  -  -  -  -  -  <i>Hampala</i> <br> -  -  -  -  -  -  -  <i>Hampala sabana</i> <br> -  -  -  -  -  -  <i>Puntius</i> <br> -  -  -  -  -  -  -  <i>Puntius sealei</i> (as synonym: <i>Barbodes sealei</i>)<br> -  -  -  -  Siluriformes <br> -  -  -  -  -  Clariidae <br> -  -  -  -  -  -  <i>Clarias</i> <br> -  -  -  -  -  -  -  <i>Clarias anfractus</i> <br> -  -  -  -  -  Bagridae <br> -  -  -  -  -  -  <i>Hemibagrus</i> <br> -  -  -  -  -  -  -  <i>Hemibagrus baramensis</i> <br> -  -  -  -  Perciformes <br> -  -  -  -  -  Channidae <br> -  -  -  -  -  -  <i>Channa</i> <br> -  -  -  -  -  -  -  <i>Channa striata</i> <br></div><p></p>
Species Abundance Distributions (SADs) for local tree communities in 1-ha forest plots on 20 tropical islands in the Indo-Pacific region
<p>Species abundance distributions (SADs) characterise the distribution of individuals among species. This dataset was used to investigate the relative importance of disturbance regime (tropical cyclone regime) and island geography (the area and isolation of islands) on the shape of SADs.</p>
Data from: Tropical forest type influences community assembly processes in arbuscular mycorrhizal fungi
Aim: Plant community assembly in tropical rainforest has been shown to be largely governed by stochastic processes, but as arbuscular mycorrhizal (AM) fungi display limited host preference, they may not follow the same stochastic assembly pattern. Here, we determined the relative importance of environmental and spatial drivers responsible for the community assembly process of AM fungi in two types of tropical rainforest (semideciduous rainforest and dense ombrophilous forests). Location: Atlantic rainforest in northeastern Brazil, South America. Taxon: Arbuscular mycorrhizal fungi (Glomeromycota). Methods: We collected root samples from eight protected areas of Atlantic forest along a 700 km transect in northeastern Brazil. We measured the relative importance of deterministic and stochastic processes by redundancy analysis (RDA) and variation partitioning in comparison with null expectations using ad hoc generated neutral communities. Furthermore, we accessed species associations from co-occurrence data, at different scales using a Bayesian approach of Hierarchical Modelling of Species Communities (HMSC). Results: Overall, the extent to which stochastic and deterministic processes affected community assembly depended on the forest type and the spatial scale. Specifically, we found that abiotic and biotic predictors of AM fungal community assemblages are related to environmental homogeneity in tropical rainforests. Main conclusions: The results of the study show that dynamics in community assembly was clearly different between the two forest types, and that the difference most likely is due to differences in responses to environmental variables.
Data From: Contrasting physiological traits of shade tolerance in Pinus and Podocarpaceae native to a tropical Vietnamese forest: Insight from an aberrant flat-leaved pine
<p>The absence of pines from tropical forests is a puzzling biogeographical oddity potentially explained by traits of shade intolerance. <i>Pinus krempfii</i>, a flat-leaved pine endemic to the Central Highlands of Vietnam, provides a notable exception as it seems to successfully compete with shade-tolerant tropical species. Here, we test the hypothesis that successful conifer performance at the juvenile stage depends on physiological traits of shade tolerance by comparing the physiological characteristics of <i>P. krempfii </i>to coexisting species from the genus <i>Pinus</i> and from the Podocarpaceae, a relatively abundant and shade tolerant conifer family found in pantropical forests. We examined leaf photosynthetic, respiratory and biochemical traits. Additionally, we compiled attainable maximum photosynthesis, maximum RuBP carboxylation (<i>Vc</i><sub>max</sub>) and maximum electron transport (<i>J</i><sub>max</sub>) values for <i>Pinus</i> and Podocarpaceae species from the literature. In our literature compilation, <i>P. krempfii </i>was intermediate between <i>Pinus</i> and Podocarpaceae in its maximum photosynthesis and its <i>Vc</i><sub>max</sub>. <i>Pinus</i> exhibited a higher <i>Vc</i><sub>max</sub> than Podocarpaceae, resulting in a less steep slope in the linear relationship between <i>J</i><sub>max</sub> and <i>Vc</i><sub>max</sub>. These results suggest that <i>Pinus </i>may be more shade intolerant than Podocarpaceae with <i>P. krempfii </i>falling between the two groups. However, in contrast, Vietnamese conifers' leaf mass per areas and biochemical traits did not highlight the same intermediate nature of <i>P. krempfii</i>. Furthermore, regardless of leaf shape or family assignation, all species demonstrated a common carbon gain efficiency. Overall, our findings highlight the importance of shade tolerance for conifer survival in tropical forests. However, they also demonstrate a diversity of shade tolerance strategies, all of which lead to the persistence of Vietnamese juvenile conifers in low-light tropical understories.</p>
Trade-offs in above and belowground biomass allocation influencing seedling growth in a tropical forest
<p>1. Plants allocate biomass to different organs in response to resource variation for maximizing performance, yet we lack a framework that adequately integrates plant responses to the simultaneous variation in above and belowground resources. Although traditionally, the optimal partition theory (OPT) has explained patterns of biomass allocation in response to a single limiting resource, it is well known that in natural communities multiple resources limit growth. We study trade-offs involved in plant biomass allocation patterns and their effects on plant growth under variable below and aboveground resources –light, soil N, and P– for seedling communities.</p> <p>2. We collected information on leaf, stem, and root mass fractions for more than 1,900 seedlings of 97 species paired with growth data and local-scale variation in abiotic resources from a tropical forest in China.</p> <p>3. We identified two trade-off axes that define the mass allocation strategies for seedlings – allocation to photosynthetic vs. non-photosynthetic tissues and allocation to roots over stems – that responded to the variation in soil P and N and light. Yet, the allocation patterns did not always follow predictions of OPT in which plants should allocate biomass to the organ that acquires the most limiting resource. Limited soil N resulted in high allocation to leaves at expense of non-photosynthetic tissues, while the opposite trend was found in response to limited soil P. Also, co-limitation in above and belowground resources (light and soil P) led to mass allocation to stems at expense of roots. Finally, we found that growth increased under high light availability and soil P for seedlings that either invested more in photosynthetic over non-photosynthetic tissues or/and that allocated mass to roots at expense of stem.</p> <p>4. Synthesis: Biomass allocation patterns to above and belowground tissues are described by two independent trade-offs that allow plants to have divergent allocation strategies (e.g., high root allocation at expense of stem or high leaf allocation at expense of allocation to non-photosynthetic tissues) and enhance growth under variable resources. Identifying the trade-offs driving biomass allocation is important to disentangle plant responses to the simultaneous variation in resources in diverse forest communities.</p>
Tropical riparian forests in danger from large savanna wildfires
<p>1. Tropical savannas are known for the fire-prone ecosystems, yet, riparian evergreen forests are another important landscape feature. These forests usually remain safe from wildfires in the wet riparian zones. With global changes, large wildfires are now more frequent in savanna landscapes, exposing riparian forests to unprecedented impact.</p> <p>2. In 2017, a large wildfire spread across the Chapada dos Veadeiros National Park, an iconic UNESCO site in central Brazil, raising concerns about its impact on the fire-sensitive ecosystems. By combining remote sensing analysis of Google Earth images (2003-2019) with detailed field information from 36 sites, we assessed wildfire impacts on riparian forests. For this, we measured the structure of trees, saplings and herbaceous plants, as well as topsoil variables.</p> <p>3. Since 2003, all riparian forests had canopy cover above 90 %, but after 2017, canopy cover dropped to 20 % in some forests, indicating large variation in wildfire damage. A closer look in the field revealed that, on average, the wildfire killed 52 % of adult trees and 87 % of tree saplings in flooded forests. In non-flooded forests, impacts on adult trees were negligible, but fire killed 75 % of tree saplings. Opportunistic vines and the invasive grass Melinis minutiflora were already present in severely disturbed flooded forests. In all forests, impacts on many ecosystem variables were related to canopy damage, a variable measurable from satellite. Overall, seasonally flooded riparian forests were the most severely impacted, possibly due to the relatively thinner barks of their trees.</p> <p>4. Synthesis and applications. Our findings reveal how riparian forests embedded in tropical savanna landscapes are in danger from large wildfires. The destruction of some forests has opened space for new plant species that may propel a shift to an alternative ecosystem state. Riparian forests are habitat of large savanna animals and their loss could affect entire trophic networks. Managing wildfires and invasive grasses locally is probably the best strategy to maintain riparian forests resilient. As wildfire regimes intensify in tropical savanna landscapes, our findings stress the need for an integrated management that considers riparian forests as a vulnerable element of the system.</p>
The Resilience of Tropical Forest Invertebrates to Microclimate Change
<b>Description: </b><p>This dataset examines the thermal physiology of ants accross the SAFE project, with the goal of understanding how changing microclimates affect communities of invertebrates in disturbed landscapes. Tropical invertebrates are expected to already live close to their upper thermal tolerances, and so the rapid changes to microclimate brought about by logging may be a powerful determinant of the emergent communites in disturbed forests. The worksheet contains the upper critical temperature (CTmax) of individual ants identified to genus level. Ants were collected from the ground or soil layer unless specified as arboreal. CTmax was determined using a ramping procedure whereby temeprature was increased from 32 degrees upwards at a rate of 0.2 degrees per minuted until individuals lost motor control. Ants were found by searching opportunistically throughout entire blocks, therefore for locations we have simply inputted one large fractal order from each sampling block used.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/135"><b>The Resilience of Tropical Forest Invertebrates to Microclimate Change</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=4297673">here</a></p><p><b>Files: </b>This consists of 1 file: MJWB_SAFE_CTmax_Upload.xlsx</p><p><b>MJWB_SAFE_CTmax_Upload.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Ant.CTmax</b> (described in worksheet Ant.CTmax)</p><p>Description: The worksheet contains the upper critical temperature (CTmax in degrees centigrade) of individual ants identified to genus level. Ants were collected from the ground or soil layer unless specified as arboreal. CTmax was determined using a ramping procedure whereby temeprature was increased from 32 degrees upwards at a rate of 0.2 degrees per minuted until individuals lost motor control. Ants were found by searching opportunistically throughout entire blocks, therefore for locations we have simply inputted one large fractal order from each sampling block used.</p><p>Number of fields: 4</p><p>Number of data rows: 2359</p><p>Fields: </p><ul><li><b>CTmax</b>: Critical upper thermal tolerance in degrees centigrade (Field type: numeric)</li><li><b>Genus</b>: Genus name of ant (Field type: taxa)</li><li><b>Location</b>: SAFE Project sampling block (Field type: location)</li><li><b>Arboreal</b>: Comment on if the ant was sampled from the ground or arboreal layer (Field type: comments)</li></ul></li></ol><p><b>Date range: </b>2015-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.6380 to 4.7412</p><p><b>Longitudinal extent: </b>116.9568 to 117.6245</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Arthropoda <br> -  -  -  Insecta <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  -  <i>Acanthomyrmex</i> <br> -  -  -  -  -  -  <i>Aenictus</i> <br> -  -  -  -  -  -  <i>Bothriomyrmex</i> <br> -  -  -  -  -  -  <i>Camponotus</i> <br> -  -  -  -  -  -  <i>Cardiocondyla</i> <br> -  -  -  -  -  -  <i>Carebara</i> <br> -  -  -  -  -  -  <i>Cataulacus</i> <br> -  -  -  -  -  -  <i>Centromyrmex</i> <br> -  -  -  -  -  -  <i>Crematogaster</i> <br> -  -  -  -  -  -  <i>Cryptopone</i> <br> -  -  -  -  -  -  <i>Diacamma</i> <br> -  -  -  -  -  -  <i>Dolichoderus</i> <br> -  -  -  -  -  -  <i>Echinopla</i> <br> -  -  -  -  -  -  <i>Euprenolepis</i> <br> -  -  -  -  -  -  <i>Hypoponera</i> <br> -  -  -  -  -  -  <i>Iridomyrmex</i> <br> -  -  -  -  -  -  <i>Lepisiota</i> <br> -  -  -  -  -  -  <i>Leptogenys</i> <br> -  -  -  -  -  -  <i>Lophomyrmex</i> <br> -  -  -  -  -  -  <i>Lordomyrma</i> <br> -  -  -  -  -  -  <i>Monomorium</i> <br> -  -  -  -  -  -  <i>Myrmecina</i> <br> -  -  -  -  -  -  <i>Myrmicaria</i> <br> -  -  -  -  -  -  <i>Nylanderia</i> <br> -  -  -  -  -  -  <i>Ochetellus</i> <br> -  -  -  -  -  -  <i>Odontomachus</i> <br> -  -  -  -  -  -  <i>Odontoponera</i> <br> -  -  -  -  -  -  <i>Oecophylla</i> <br> -  -  -  -  -  -  <i>Pachycondyla</i> <br> -  -  -  -  -  -  <i>Paraparatrechina</i> <br> -  -  -  -  -  -  <i>Paratopula</i> <br> -  -  -  -  -  -  <i>Paratrechina</i> <br> -  -  -  -  -  -  <i>Pheidole</i> <br> -  -  -  -  -  -  <i>Pheidologeton</i> <br> -  -  -  -  -  -  <i>Philidris</i> <br> -  -  -  -  -  -  <i>Plagiolepis</i> <br> -  -  -  -  -  -  <i>Polyrhachis</i> <br> -  -  -  -  -  -  <i>Ponera</i> <br> -  -  -  -  -  -  <i>Prenolepis</i> <br> -  -  -  -  -  -  <i>Prionopelta</i> <br> -  -  -  -  -  -  <i>Pristomyrmex</i> <br> -  -  -  -  -  -  <i>Pseudolasius</i> <br> -  -  -  -  -  -  <i>Rhoptromyrmex</i> <br> -  -  -  -  -  -  <i>Rhytidoponera</i> <br> -  -  -  -  -  -  <i>Tapinoma</i> <br> -  -  -  -  -  -  <i>Technomyrmex</i> <br> -  -  -  -  -  -  <i>Tetramorium</i> <br> -  -  -  -  -  -  <i>Tetraponera</i> <br> -  -  -  -  -  -  <i>Vollenhovia</i> <br></div><p></p>
Data from: Leaf litter decomposition in tropical freshwater swamp forests is slower in swamp than non-swamp conditions
<p><span><span>Decomposition is a key ecosystem function, and the rate of decomposition in forests affects their carbon storage potentials. Processes and factors determining leaf litter decomposition rates in dry-land and temperate forests are well understood, but these are generally poorly studied in tropical wetland forests, especially freshwater swamp forests (FSF). The home-field advantage (HFA) hypothesis predicts that soil microbes specialize in decomposing leaf litter produced by the tree species in their immediate vicinity. However, empirical support for the HFA is equivocal, and the HFA has never been tested in the highly heterogeneous and biodiverse ecosystems of tropical FSFs. We collected leaf litter from swamp and non-swamp tree species in a tropical FSF in Singapore and monitored the decomposition rates of these in swamp and non-swamp plots for a period of eight months. Leaf litter decomposed 3.7 times more slowly in swamp plots. Leaf litter from swamp tree species were significantly poorer in quality (higher C:N ratio) than those of non-swamp FSF tree species, but this had only a weak effect on decomposition rates. There was also only weak evidence for the HFA and only in non-swamp conditions. Our results show that while the leaf litter of tropical FSF swamp and non-swamp tree species differ significantly in chemical traits, litter decomposition rate is ultimately determined by local abiotic conditions, such as hydrology. Additionally, the high FSF tree diversity may prevent decomposer communities from specializing on any group of leaf litter types and thus limit the extent of HFA observed in such heterogeneous forests.</span></span></p>
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