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7,883 results for “Tropic”
An interpolated biogeographic framework for tropical Africa using plant species distributions and the physical environment
<p><strong>Aim:</strong> Existing phytogeographic frameworks for tropical Africa lack either spatial completeness, unit definitions smaller than the regional scale, or a quantitative approach. We investigate whether physical environmental variables can be used to interpolate floristically defined vegetation units, presenting an interpolated, hierarchical, quantitative phytogeographic framework for tropical Africa, which is compared to previously defined regions.</p> <p><strong>Location: </strong>Tropical mainland Africa 24°N to 24°S.</p> <p><strong>Taxon: </strong>31,046 vascular plant species and infraspecific taxa.</p> <p><strong>Methods: </strong>We calculate a betasim dissimilarity matrix from a comprehensive whole-flora database of plant species distributions. We investigate environmental correlates of floristic turnover with local non-metric multidimensional scaling. We derive a hierarchical biogeographic framework by clustering the dissimilarity matrix. The framework is modelled using a classification decision tree method and 12 physical environmental variables to interpolate and downscale the framework across the study region.</p> <p><strong>Results: </strong>Floristic turnover is related strongly to water availability and temperature, with smaller contributions from land cover, topographic ruggedness and lithology. Region can be predicted with 90% accuracy by the model. We define 19 regions and 99 districts. We find a novel arrangement of the arid regions. Regional subdivision within the savanna biome is supported with minor variation to borders. Within the forests of west and central Africa, our whole-flora gridded regionalisation supports the divisions identified by a previous analysis of trees only.</p> <p><strong>Main conclusions:</strong> Physical environmental variables can be used to predict floristically defined vegetation units with very high accuracy, and the approach could be pursued for other inc ompletely sampled taxa and areas outside of tropical Africa. Geographic coherence is higher than in previous quantitative phytoregional definitions. For most tropical African vascular plant species, we provide predictions of which species will occur within each mapped district and region of tropical Africa. The framework should be useful for future studies in ecology, evolution and conservation.</p>
Large contribution of recent photosynthate to soil respiration in tropical dipterocarp forest revealed by girdling
<b>Description: </b><p>The research site is one of the existing intensive carbon plots (Tower Plot) at the SAFE Project Experimental area. The area where the plot is located will be converted into oil palm plantation during 2015-2017 (for commercial purposes, not for research). The overarching aim of the project is to assess how the termination of the transport of sugars and defoliation alter forest ecosystem functioning and structure.The aim of the project is:<br>1. To quantify the contribution of photosynthate supply to soil respiration: via the contribution of roots and soil microbial communities utilising root-derived carbon.<br>2. To assess whether there is a relationship between root respiration and tree species.<br>To address these aims, we girdled trees in one half of the plot (0.5 ha), leaving the other half (0.5 ha) as a control. In girdling, a strip of bark (including cambium and phloem) was removed from around the trunk, with the aim of stopping the transport of sugars from the foliage into the roots and soil. The transport of sugars stop immediately, allowing us to quantify their role in the root and soil processes. The girdled trees will gradually defoliate and die due to the carbon starvation of the roots. We wish to emphasise that these trees would have been felled anyway during the conversion to oil palm - this project is not causing any additional deforestation.<br>The processes measured are:<br>- CO2 fluxes from soil measured with portable chambers from which a gas sample is drawn and analysed in the field with a portable instrument (CO2) <br>- Changes in tree circumference monitored with automatic dendrometer bands.<br>- Terrestrial laser scanning (T-lidar), non-destructive method to quantify the 3D structure of the forest stand.Pre-girdling data of all processes will be collected, starting at least two months before the girdling. The girdling took place in early 2016, and the monitoring continued for twelve months afterwards.</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/28"><b>Tree girdling - BALI project</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC, the Ministry of Education, Youth and Sports of the Czech Republic (Grant, NE/K01627X/1, NE/G018278/1, INTER-TRANSFER LTT19018)</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 Council (Research licence JKM/MBS.1000-2/2 JLD.4 (3))</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=5519572">here</a></p><p><b>Files: </b>This consists of 1 file: BALI_Nottingham_Girdling_Data_2021_rev.xlsx</p><p><b>BALI_Nottingham_Girdling_Data_2021_rev.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>CO2 and H2O data</b> (described in worksheet CO2_H2O_data)</p><p>Description: Tree identity and mortality collected taken January 2016- January 2017; Soil respiration, soil temperature and January moisture measurements taken January- March 2016 in a girdled tropical forest using a LiCor 8100a </p><p>Number of fields: 12</p><p>Number of data rows: 12548</p><p>Fields: </p><ul><li><b>PlotName</b>: reference to the experiment location within the SAFE plot network (experiment took place in the 'Tower plot / SAF-05'') (Field type: location)</li><li><b>daynight</b>: defined by 6pm to 6am (Field type: categorical)</li><li><b>date</b>: date of measurement (Field type: date)</li><li><b>plot</b>: subplot' in manuscript (Field type: id)</li><li><b>Rday</b>: relative data to the start of girdling (girdling day = 0) (Field type: id)</li><li><b>CO2</b>: soil CO2 efflux (Field type: numeric)</li><li><b>H2O</b>: soil volumetric moisture (Field type: numeric)</li><li><b>T</b>: soil temperature (Field type: numeric)</li><li><b>port</b>: refers to soil collar location (we allocated chamber port to soil collar location) (Field type: id)</li><li><b>portplot</b>: soil collar location nested within plot (Field type: id)</li><li><b>time</b>: time of measurement (24h) (Field type: numeric)</li><li><b>phase</b>: measurement period (see manuscript for phase definitions) (Field type: categorical)</li></ul></li><li><p><b>Tree mortality data</b> (described in worksheet Mortality_data)</p><p>Description: Tree census of trees surroudings the points where Licor 8100a measurements were taken</p><p>Number of fields: 20</p><p>Number of data rows: 259</p><p>Fields: </p><ul><li><b>PlotName</b>: reference to the experiment location within the SAFE plot network (experiment took place in the 'Tower plot / SAF-05'') (Field type: location)</li><li><b>ForestPlotsCode</b>: reference to the experiment location within the SAFE plot network (experiment took place in the 'Tower plot / SAF-05'') (Field type: id)</li><li><b>Subplot</b>: subplots 1-12 included in the manuscript (Field type: id)</li><li><b>CensusDate</b>: date when trees were originally measured (Field type: date)</li><li><b>TagNumber</b>: tree tag identity (Field type: id)</li><li><b>Height_m</b>: tree height (Field type: numeric)</li><li><b>Comments</b>: comments about the tree (Field type: comments)</li><li><b>Family</b>: tree family (Field type: taxa)</li><li><b>Genus</b>: tree genus (Field type: taxa)</li><li><b>SpeciesName</b>: tree species (Field type: comments)</li><li><b>WoodDensity</b>: wood density (Field type: numeric)</li><li><b>CrownProjection_Area_m2_in2016</b>: Crown Projection Area in 2016 (Field type: numeric)</li><li><b>X_m</b>: coordinates (Latitude) (Field type: numeric)</li><li><b>Y_m</b>: coordinates (Longitude) (Field type: numeric)</li><li><b>GirdlingDeathDate</b>: girdling tree death date (Field type: date)</li><li><b>Biomass_kgPerStem</b>: Biomass_kgPerStem (Field type: numeric)</li><li><b>Carbon_kgCperStem</b>: Carbon_kgCperStem (Field type: numeric)</li><li><b>mortality</b>: mortality (Field type: categorical)</li><li><b>DBHgrowth_cm_year</b>: DBHgrowth_cm_year (Field type: numeric)</li><li><b>DBHAnnualGrowthRate</b>: DBHAnnualGrowthRate (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2015-08-04 to 2017-02-07</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</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> -  Plantae <br> -  -  Tracheophyta <br> -  -  -  Magnoliopsida <br> -  -  -  -  Lamiales <br> -  -  -  -  -  Lamiaceae <br> -  -  -  -  -  -  <i>Callicarpa</i> <br> -  -  -  -  Rosales <br> -  -  -  -  -  Urticaceae <br> -  -  -  -  -  -  <i>Pipturus</i> <br> -  -  -  -  -  -  <i>Dendrocnide</i> <br> -  -  -  -  -  -  <i>Oreocnide</i> <br> -  -  -  -  -  Moraceae <br> -  -  -  -  -  -  <i>Ficus</i> <br> -  -  -  -  Malpighiales <br> -  -  -  -  -  Achariaceae <br> -  -  -  -  -  -  <i>Hydnocarpus</i> <br> -  -  -  -  -  Euphorbiaceae <br> -  -  -  -  -  -  <i>Macaranga</i> <br> -  -  -  -  -  -  <i>Cephalomappa</i> <br> -  -  -  -  -  -  <i>Mallotus</i> <br> -  -  -  -  -  Phyllanthaceae <br> -  -  -  -  -  -  <i>Aporosa</i> <br> -  -  -  -  -  Ixonanthaceae <br> -  -  -  -  -  -  <i>Ixonanthes</i> <br> -  -  -  -  -  Calophyllaceae <br> -  -  -  -  -  -  <i>Calophyllum</i> <br> -  -  -  -  -  Violaceae <br> -  -  -  -  -  -  <i>Rinorea</i> <br> -  -  -  -  Ericales <br> -  -  -  -  -  Pentaphylacaceae <br> -  -  -  -  -  -  <i>Adinandra</i> <br> -  -  -  -  -  Sapotaceae <br> -  -  -  -  -  -  <i>Palaquium</i> <br> -  -  -  -  -  Symplocaceae <br> -  -  -  -  -  -  <i>Symplocos</i> <br> -  -  -  -  -  Ebenaceae <br> -  -  -  -  -  -  <i>Diospyros</i> <br> -  -  -  -  Malvales <br> -  -  -  -  -  Dipterocarpaceae <br> -  -  -  -  -  -  <i>Shorea</i> <br> -  -  -  -  -  -  <i>Dipterocarpus</i> <br> -  -  -  -  -  -  <i>Dryobalanops</i> <br> -  -  -  -  -  -  <i>Parashorea</i> <br> -  -  -  -  -  Malvaceae <br> -  -  -  -  -  -  <i>Pterospermum</i> <br> -  -  -  -  -  -  <i>Scaphium</i> <br> -  -  -  -  -  -  <i>Brownlowia</i> <br> -  -  -  -  -  -  <i>Sterculia</i> <br> -  -  -  -  -  -  <i>Microcos</i> <br> -  -  -  -  -  -  <i>Neesia</i> <br> -  -  -  -  -  -  <i>Diplodiscus</i> <br> -  -  -  -  Laurales <br> -  -  -  -  -  Lauraceae <br> -  -  -  -  -  -  <i>Actinodaphne</i> <br> -  -  -  -  -  -  <i>Litsea</i> <br> -  -  -  -  -  -  <i>Eusideroxylon</i> <br> -  -  -  -  Celastrales <br> -  -  -  -  -  Celastraceae <br> -  -  -  -  -  -  <i>Lophopetalum</i> <br> -  -  -  -  Magnoliales <br> -  -  -  -  -  Myristicaceae <br> -  -  -  -  -  -  <i>Knema</i> <br> -  -  -  -  -  Annonaceae <br> -  -  -  -  -  -  <i>Goniothalamus</i> <br> -  -  -  -  -  -  <i>Polyalthia</i> <br> -  -  -  -  -  -  <i>Maasia</i> <br> -  -  -  -  Vitales <br> -  -  -  -  -  Vitaceae <br> -  -  -  -  -  -  <i>Leea</i> <br> -  -  -  -  Myrtales <br> -  -  -  -  -  Myrtaceae <br> -  -  -  -  -  -  <i>Syzygium</i> <br> -  -  -  -  -  Lythraceae <br> -  -  -  -  -  -  <i>Duabanga</i> <br> -  -  -  -  Cornales <br> -  -  -  -  -  Cornaceae <br> -  -  -  -  -  -  <i>Alangium</i> <br> -  -  -  -  Gentianales <br> -  -  -  -  -  Rubiaceae <br> -  -  -  -  -  -  <i>Neolamarckia</i> <br> -  -  -  -  -  -  <i>Neonauclea</i> <br> -  -  -  -  -  -  <i>Urophyllum</i> <br> -  -  -  -  -  -  <i>Pleiocarpidia</i> <br> -  -  -  -  Fabales <br> -  -  -  -  -  Fabaceae <br> -  -  -  -  -  -  <i>Saraca</i> <br> -  -  -  -  -  Polygalaceae <br> -  -  -  -  -  -  <i>Xanthophyllum</i> <br> -  -  -  -  Cucurbitales <br> -  -  -  -  -  Tetramelaceae <br> -  -  -  -  -  -  <i>Octomeles</i> <br> -  -  -  -  Fagales <br> -  -  -  -  -  Fagaceae <br> -  -  -  -  -  -  <i>Lithocarpus</i> <br> -  -  -  -  -  -  <i>Castanopsis</i> <br> -  -  -  -  Sapindales <br> -  -  -  -  -  Sapindaceae <br> -  -  -  -  -  -  <i>Nephelium</i> <br> -  -  -  -  -  -  <i>Dimocarpus</i> <br> -  -  -  -  -  -  <i>Pometia</i> <br> -  -  -  -  -  Meliaceae <br> -  -  -  -  -  -  <i>Dysoxylum</i> <br> -  -  -  -  -  -  <i>Aglaia</i> <br> -  -  -  -  -  Burseraceae <br> -  -  -  -  -  -  <i>Canarium</i> <br> -  -  -  -  -  Anacardiaceae <br> -  -  -  -  -  -  <i>Buchanania</i> <br></div><p></p>
Tropical forests are home to over half of the world's vertebrate species
<p>The four Excel workbooks contain processed data on Mammal, Bird, Reptile and Amphibian species that occur in tropical forests and other terrestrial biomes on Earth. The associated README text file contains metadata to describe the data in the xlsx workbooks. Python code to replicate the analyses is provided in the script: Vertebrates.py - ArcGIS Pro is required to be installed prior to running this script.</p>
Variability and drivers of the Oxygen Minimum Zone in the Tropical Pacific during 1981-2018
<p>This dataset contains model outputs simulated by a basin-scale model (OGCM-DEEC V1.4). It is supplementary of the paper "Variability and drivers of the Oxygen Minimum Zone in the Tropical Pacific during 1981-2018". Please let me know if you need any more information</p>
Storm-scale and fine-scale boundary layer structures of tropical cyclones simulated with the WRF-LES framework
<p>The numerical simulations were carried out on the Tianhe Supercomputer, China. The TCBL data used in this study are uploaded here. Due to the large number, the original simulation data are available on request (liuqy@cma.gov.cn or liguangwu@fudan.edu.cn).</p>
Data from: Spatiotemporal variation in the role of floral traits in shaping tropical plant-pollinator interactions
<p>Supplementary datasets for <strong>Klomberg <em>et al.</em> 2021 Spatiotemporal variation in the role of floral traits in shaping tropical plant-pollinator interactions</strong><strong>.</strong> <em>Ecology Letters.</em></p> <p>All related information can be found in the cited paper.</p> <p>When using the dataset for anything, cite the Klomberg <em>et al. </em>paper.</p> <p>For additional information, refer to the paper or write to robert.tropek@gmail.com</p>
Large wild herbivores slow down the rapid decline of plant diversity in a tropical forest biodiversity hotspot
<p>1. The UN declaration of the Decade of Ecosystem Restoration 2021-2030 emphasizes the need for effective measures to restore ecosystems and safeguard biodiversity. Large herbivores regulate many ecosystem processes and functions, yet their potential as a nature-based solution to buffer against long-term temporal declines in biodiversity associated to global change and restore diversity in secondary forests remains unknown.</p> <p>2. By means of an exclusion experiment, we tested experimentally the buffering effects of large wild herbivores to avert against long-term biodiversity collapse in old-growth and secondary tropical forests in the Atlantic Forest of Brazil where sapling abundance and species richness declined circa 20% over the course of 10 years. The experiment comprised 50 large herbivore exclosure-open control plot pairs (25 at the old-growth forest and 25 at the secondary forest), where 2m2 were monitored in every plot during a 10-year period.</p> <p>3. Large herbivores were able to decelerate diversity declines and compositional change in the species-rich old-growth forest, but only decelerated compositional change in the secondary forest. In contrast, declines in species richness and abundance were unaffected by large herbivores on either forest.</p> <p>4. The buffering effects of large herbivores were strongly non-linear and contingent on the initial level of diversity at the patch scale: highly diverse communities suffered the strongest collapse in the absence of large herbivores. Thus, larger buffering effects of large herbivores on the old growth forest are the logical consequence of large herbivores buffering the many high diversity plant communities found in this forest. Conversely, as the secondary forest held fewer high diversity patches, buffering effects on the secondary forest were weak.</p> <p>5. Synthesis and applications: Our study indicates that large herbivores have moderate yet critical effects on slowing down community change and diversity loss of highly diverse plant communities, thus suggesting that the conservation of (and potentially trophic rewilding with) large herbivores is a fundamental nature-based solution for averting the global collapse of the strongholds of biodiversity. Its buffering effects on biodiversity loss operate at very small spatial scales, are likely contingent on successional stage, and most effective in old-growth or high diversity secondary forests.</p>
Pollinator foraging tactics have divergent consequences for the mating system of a tropical plant
<p>Resolving the consequences of pollinator foraging behaviour for plant mating systems is a fundamental challenge in evolutionary ecology. Pollinators may adopt particular foraging tactics: complete trapline foraging (repeated movements along a fixed route), sample-and-shift trapline foraging (a variable route that incorporates information from previous experiences), and territorial foraging (stochastic movements within a restricted area). Studies that integrate these pollinator foraging tactics with plant mating systems are generally lacking.</p> <p>We investigate the consequences of particular pollinator foraging tactics for <em>Heliconia</em> <em>tortuosa</em>. We combine parentage and sibship inference analysis with simulation modeling to: (1) estimate mating system parameters; (2) infer the foraging tactic adopted by the pollinators; and (3) quantify the impact of pollinator foraging tactics on mating system parameters.</p> <p>We found high outcrossing rates, ubiquitous multiple paternity, and a pronounced departure from near-neighbour mating. We also found that plants repeatedly receive pollen from a series of particular donors. We infer that the pollinators primarily adopt complete trapline foraging and occasionally engage in sample-and-shift trapline foraging. This enhances multiple paternity without a substantial increase in near-neighbour mating.</p> <p>The particular pollinator foraging tactics have divergent consequences for multiple paternity and near-neighbour mating. Thus, pollinator foraging behaviour is an important driver of the ecology and evolution of plant mating systems.</p>
Local timing of rainfall predicts the timing of moult within a single locality and the progress of moult among localities that vary in the onset of the wet season in a year-round breeding tropical songbird
<p>Rainfall seasonality is likely an important cue for timing key annual cycle events like moult in birds living in seasonally arid environments, but its precise effect is difficult to establish because seasonal rainfall may affect other covarying annual events such as breeding in the same way. In central Nigeria, however, Common Bulbuls <em>Pycnonotus barbatus</em> moult in the wet season but only show weak breeding seasonality. This suggests that moult is more sensitive to rainfall than breeding, but a similar outcome is possible if moult is simply periodic. We tested the relationship between rainfall and moult in Common Bulbuls at a single location over 18 years: on average moult started 5th May (± 41 days: 25th March–15th June), being on average later than the onset of the rains which is usually mid-April. The likelihood of finding a moulting Common bulbul was best predicted by rainfall 9–15 weeks before moult was scored. We then tested the generality of this across populations: the progress of moult should, therefore, correlate with the average timing of the wet season along a spatial environmental gradient where the rains start at different times each year south-to-north of Nigeria. To test this, we modelled moult progress just before the rains across 15 localities 6°–13° N as a function of the onset of the wet season among localities. As predicted, moult progressed further in localities with earlier wet seasons, confirming that the onset of moult is timed to the onset of the wet season in each locality despite weak breeding seasonality in the Common Bulbul. This strategy may evolve to maintain optimal annual cycle routine in seasonal environments where breeding is prone to unpredictable local perturbations like nest predation. It may, however, be less obvious in temperate systems where all annual cycle stages are seasonally constrained, but it may help with explaining the high frequency of breeding–moult overlaps in tropical birds.</p>
Data from: Simulating climate change in situ in a tropical rainforest understorey using active air warming and CO2 addition
<p><b>Background: </b>Future climate-change effects on plant growth are most effectively studied using microclimate-manipulation experiments, the design of which has seen much advance in recent years. For tropical forests, however, such experiments are particularly hard to install and have hence not been widely used. We present a system of active heating and CO<sub>2</sub> fertilisation for use in tropical forest understoreys, where passive heating is not possible. The system was run for two years to study climate-change effects on epiphytic bryophytes, but is also deemed suitable to study other understorey plants.</p> <p><b>Methods:</b> Warm-air and CO<sub>2</sub> addition were applied in 1.6-m tall, 1.2-m diameter hexagonal open-top chambers and the microclimate in the chambers compared to outside air. Warming was regulated with a feedback system while CO<sub>2</sub> addition was fixed.</p> <p><b>Results:</b> The setup successfully heated the air by 2.8K and increased CO<sub>2</sub> by 250 ppm, on average, with +3K and +300 ppm as the targets. Variation was high, especially due to technical break-downs, but not biased to times of the day or year. In the warming treatment, absolute humidity slightly increased but relative humidity dropped by between 6 to 15% (and the vapour-pressure deficit increased) compared to ambient, depending on the level of warming achieved in each chamber.</p> <p><strong>Conclusions:</strong> <span>Compared to other heating systems, the chambers provide a realistic warming and CO<sub>2</sub> treatment, but moistening the incoming air would be needed to avoid drying as a confounding factor. The method is preferable over infrared heating in the radiation-poor forest understory, particularly when combined with CO<sub>2</sub> fertilisation. It is suitable for plant-level studies, but ecosystem-level studies in forests may require chamber-less approaches like infrared heating and free-air CO<sub>2</sub> enrichment. By presenting the advantages and limitations of our approach we aim to facilitate further climate-change experiments in tropical forests, which are urgently needed to understand the processes determining future element fluxes and biodiversity changes in these ecosystems.</span></p>
Data from: Biotic homogenization and differentiation of plant communities in tropical and subtropical forests
<h4><strong>The article is published in Conservation Biology and available at: <a href="https://doi.org/10.1111/cobi.14025">https://doi.org/10.1111/cobi.14025</a></strong></h4> <h4>Here we developed a framework to assess the current 'state of the art' of biotic homogenization and differentiation processes in tropical and subtropical plant communities globally, using a systematic literature review.</h4> <h4>Please find below the analysis code and data used in the study.</h4> <h2>Descriptions for the files and scripts</h2> <h3>The `R` folder contains.</h3> <p><strong>1. *analysis_homogenization-differentiation.R*</strong> - script to generate the frequency and explanatory plots used in the manuscript.</p> <h3><br>The `output` folder contains:</h3> <p><strong>1. figures</strong><br><strong> 1. *Fig.1.tiff*</strong> - Figure 1 main text.<br><strong> 2. *Fig.2.tiff*</strong> - Figure 2 main text.<br><strong> 3. *Fig.3.tiff*</strong> - Figure 3 main text.<br><strong> 4. *Fig.4.tiff*</strong> - Figure 4 main text.<br><strong> 5. *Fig.5.tiff*</strong> - Figure 5 main text.<br><strong> 6. *Fig.6.tiff*</strong> - Figure 6 main text.<br><strong> 7. *Fig.7.tiff*</strong> - Figure 7 main text.</p> <p><strong>2. supp</strong><br><strong> 1. *appendix_S1-homogenization-differentiation.xlsx*</strong> - Supplementary material of the article. The file contains 4 sheets (*data*, *legend*, *scales_size* and *search_keywords*)</p> <h3><br>The `data` folder contains:</h3> <p><strong>1. processed</strong><br><strong> 1. *data_homogenization_refined.xlsx*</strong> - the data used in the analysis. Each analyzed variable was separated by a different sheet.</p> <p><br><strong>1. raw</strong><br><strong> 1. *data_homogenization.xlsx*</strong> - the raw data used in the analysis.</p> <p> </p> <h2>Acknowledgments</h2> <p>This work was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (Finance code 001), through Portal de Periódicos to articles access and scholarship granted to J.M.F.K.</p>
Data from: Heat tolerance variation reveals vulnerability of tropical herbivore-parasitoid interactions to climate change
<p>Assessing the heat tolerance (CTmax) of organisms is central to understand the impact of climate change on biodiversity. While both environment and evolutionary history affect CTmax, it is unclear how these factors and their interplay influence ecological interactions, communities, and ecosystems under climate change. We collected and reared caterpillars and parasitoids from canopy and ground layers in different seasons in a tropical rainforest. We tested the CTmax and Thermal Safety Margins (TSM) of these food webs with implications for how species interactions could shift under climate change. We identified strong influence of phylogeny in herbivore-parasitoid community heat tolerance. The TSM of all insects were narrower in the canopy and parasitoids had lower heat tolerance compared to their hosts. Our CTmax-based simulation showed higher herbivore-parasitoid food web instability under climate change than previously assumed, highlighting the vulnerability of parasitoids and related herbivore control in tropical rainforests, particularly in the forest canopy.</p>
Habitat loss shapes the structure and species roles in tropical plant-frugivore networks
<p>Habitat loss is a global threat to biodiversity with pervasive effects on species and populations. These impacts may generate cascading effects on ecological processes propagating across ecological networks. Thus, understanding how habitat loss affects ecological networks is fundamental for conservation. We used a database of 25 plant-frugivore networks distributed across the whole Brazilian Atlantic Forest to understand how landscape-scale habitat loss shapes network structure, robustness, species role and traits related to seed dispersal. We compared whether these network properties have linear or non-linear relationships and used centrality metrics and indirect effects to evaluate if habitat loss change the role of species in plant-frugivore networks. We found linear and non-linear relationships with negative effects of habitat loss on the network structure. As a consequence of shifts in species richness and number of links, the number of interactions and the proportion of possible interactions observed (connectance) were negatively associated with habitat loss. In contrast, nestedness increased with habitat loss. Network robustness, mean bill width and mean seed size were not significantly related to habitat loss. In addition to changes in interaction patterns at network level, habitat loss also favors changes in interaction among species, shifting the species playing central roles in network organization or contributing to indirect effects in the networks. In forested landscapes, obligate frugivores are the main central species in the network, and the ones potentially contributing to indirect effects, while in deforested landscapes these roles are fulfilled by occasional frugivores. Thus, our results emphasize the widespread effect of habitat loss on plant-frugivore systems, adding evidence that its pervasive effects on biodiversity also proliferate on mutualistic interactions with negative consequences for seed dispersal that potentially go beyond the direct pairs of interacting species. </p>
Data for: Phosphorus limitation of early growth differs between nitrogen-fixing and non-fixing dry tropical forest tree species
<p>Tropical forests are often characterized by low soil phosphorus (P) availability, suggesting that P limits plant performance. However, how seedlings from different functional types respond to soil P availability is poorly known but important for understanding and modeling forest dynamics under changing environmental conditions.</p> <p>We grew four nitrogen (N)-fixing Fabaceae and seven diverse non-N-fixing tropical dry forest tree species in a shade house under three P fertilization treatments, and evaluated carbon (C) allocation responses, P demand, P-use, investment in P acquisition traits, and correlations among P acquisition traits.</p> <p>N-fixers grew larger with increasing P addition in contrast to non-N-fixers, which showed fewer responses in C allocation and P-use. Foliar P increased with P addition for both functional types, while P acquisition strategies did not vary among treatments but differed between functional types, with N-fixers showing higher root phosphatase activity (RPA) than non-fixers.</p> <p>Growth responses suggest that N-fixers are limited by P, but non-fixers may be limited by other resources. However, regardless of limitation, P acquisition traits such as mycorrhizal colonization and RPA were non-plastic across a steep P gradient. Differential limitation among plant functional types has implications for forest succession and earth system models.</p>
Global distribution and climate sensitivity of the tropical montane forest nitrogen cycle
<p>Tropical forests are pivotal to global climate and biogeochemical cycles, yet the geographic distribution of nutrient limitation to plants and microbes across the biome is unresolved. One long-standing generalization is that tropical montane forests are nitrogen (N)-limited whereas lowland forests tend to be N-rich. However, empirical tests of this hypothesis have yielded equivocal results. Here we evaluate the topographic signature of the ecosystem-level tropical N cycle by examining climatic and geophysical controls of surface soil N content and stable isotopes (δ15N) from elevational gradients distributed across tropical mountains globally. We document steep increases in soil N concentration and declining δ15N with increasing elevation, consistent with decreased microbial N processing and lower gaseous N losses. Temperature explained much of the change in N, with an apparent temperature sensitivity (Q10) of ~1.9. Although montane forests make up 11% of forested tropical land area, we estimate they account for > 17% of the global tropical forest soil N pool. Our findings support the existence of widespread microbial N limitation across tropical montane forest ecosystems and high sensitivity to climate warming.</p>
Lightning activity and wind speed variations in Tropical Cyclones of the Southwest Pacific Region
<p>Lightning data used in 'Lightning activity and wind speed variations in Tropical Cyclones of the Southwest Pacific Region'.</p>
Invasion of the coccinellid community associated with a tropical annual agroecosystem
<p>To examine the potential effect of an invasive coccinellid (Harmonia axyridis) on the resident community of coccinellids, we sampled predators and prey associated with organic Brassica oleracea production on 6 farms during 2 years in the Distrito Federal, Brasil and conducted a food acceptability trial to estimate niche breadth and overlap among the coccinellids. The data are in two files. The first is a community sample data set, with columns describing the sample characteristics (year, farm, sample period) and the summed counts of all macroinvertebrates found on 30 plants at each sample period, farm and year. There are 120 samples and 36 populations of macroinvertebrates in the data set. The second data set is the acceptability of 4 aphid prey species to 3 stages of 4 species of coccinellids. There are 15 replicates of the 48 treatments. The data also include the biomass of the predators at the end of the experiment. Farms have been de-identified, and the data can be used by anyone without any legal or ethical considerations. The authors request that anyone who reuses the data provide and acknowledgement of this in any publication as well as a citation to the originating study.</p>
Low-severity winds reduce tropical forest structural complexity regardless of climate, topography or forest age
<p>Forests are often exposed to regular, non-severe winds (chronic wind exposure), yet the effect of such winds on canopy structure in tropical forests remains understudied. The height and structural complexity of a forest canopy are strongly and positively correlated with biodiversity and carbon accumulation. Understanding the drivers of canopy structural complexity across broad environmental gradients can therefore improve the mapping and modeling of diversity and carbon dynamics. Here we predict the height and structural complexity of forests in the heterogeneous island of Puerto Rico, with a particular focus on the impacts of chronic wind exposure. To do so, we used remote sensing to randomly sample ~20,000, 0.28 ha forested sites stratified by forest age, and used airborne LiDAR data from 2016 to quantify canopy height and a key metric of structural complexity, rugosity – the standard deviation in canopy height. We then ran random forest models to predict canopy height and rugosity based on chronic wind exposure, forest age, mean annual precipitation, elevation, slope, soil type, soil available water storage, and exposure to two previous hurricanes (in 1989 and 1998). Canopy height was 4 m taller on average (41%) between forests aged 17-25 years and old-growth forests and by 4 m on average (41%) between 1,000 and 2,000 mm<sup>-yr</sup> precipitation, leveling off at 2,000 mm<sup>-yr</sup>. Height was 2.12 m (16%) shorter on average between sites exposed to chronic winds and protected sites after accounting for all other factors. Rugosity was 1 m (32%) greater between the tallest and shortest forests, by 0.5 m (15%) between 1,000 and 2,000 mm<sup>-yr</sup> precipitation, and smaller by 0.5 m (15%) between forests above and below 1,000 m elevation. Rugosity was highest in forests of intermediate age (25-40 years), and lowest in old-growth forests, possibly because of higher elevation and chronic wind exposure in old-growth forests. We found no effect of slope, soil characteristics or previous hurricane exposure on either height or rugosity. Our results suggest that alongside forest age and climate context, chronic wind exposure plays an integral role in shaping the structure and carbon cycle of tropical forests.</p>
Hydraulic traits are not robust predictors of tree species stem growth during a drought in a wet tropical forest
<p>Severe droughts have led to lower plant growth and high mortality in many ecosystems worldwide, including tropical forests. Drought vulnerability differs among species but there is limited consensus on the nature and degree of this variation in tropical forest communities. Understanding species-level vulnerability to drought requires examination of hydraulic traits since these reflect the different strategies species employ for surviving drought. Here we examined hydraulic traits and growth reductions during a severe drought for 12 common woody species in a wet tropical forest community in Puerto Rico to ask:</p> <p>Q1. To what extent can hydraulic traits predict growth declines during drought? We expected that species with more hydraulicly vulnerable xylem and narrower safety margins would grow less during drought.</p> <p>Q2. How do species successional association relate to levels of vulnerability to drought and hydraulic strategies? We predicted that early- and mid-successional species would exhibit more acquisitive strategies, making them more susceptible to drought than shade-tolerant species.</p> <p>Q3. What are the different hydraulic strategies employed by species and are there trade-offs between drought avoidance and drought tolerance?</p> <p>We anticipated that species with greater water storage capacity would have leaves that lose turgor at higher xylem water potential and be less resistant to embolism forming in their xylem (P50). We found a large range of variation in hydraulic traits across species; however, they did not closely capture the magnitude of growth declines during drought. Among larger trees (≥10 cm diameter at breast height—DBH), some tree species with high xylem embolism vulnerability and risk of hydraulic failure experienced substantial declines during drought but this pattern was consistent across species. We found a trade-off among species between drought avoidance (capacitance) and drought tolerating (P50) in this tropical forest community. Hydraulic strategies did not align with successional associations. Instead, some of the more drought-vulnerable species were shade-tolerant dominants in the community, suggesting that a drying climate could lead to shifts in long-term forest composition and function in Puerto Rico and the Caribbean.</p>
Data from: Mixed Signals from the Stable Isotope Composition of Precipitation and Plant Waxes in the Northern Tropical Andes
<p>These files are the supplementary information for Pérez‐Angel, L. C., Sepúlveda, J., Montes, C., Smith, J. J., Molnar, P., González-Arango, C., Snell, K., Dildar, N., Mixed Signals from the Stable Isotope Composition of Precipitation and Plant Waxes in the Northern Tropical Andes. Journal of Geophysical Research: Biogeosciences (In Revision). </p> <p> </p> <p>This work was funded by the National Science Foundation NSF | GEO | Division of Earth Sciences (EAR): 1929199</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
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