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1,271 results for “tropical forest”
Landslide age, elevation and residual vegetation determine tropical montane forest canopy recovery and biomass accumulation after landslide disturbances in the Peruvian Andes
<p>Landslides are common natural disturbances in tropical montane forests. While the geomorphic drivers of landslides in the Andes have been studied, factors controlling post-landslide forest recovery across the steep climatic and topographic gradients characteristic of tropical mountains are poorly understood.</p> <p>Here we use a LiDAR-derived canopy height map coupled with a 25-year landslide time series map to examine how landslide, topographic, and biophysical factors, along with residual vegetation, affect canopy height and heterogeneity in regenerating landslides. We also calculate aboveground biomass accumulation rates and estimate the time for landslides to recover to mature forest biomass levels.</p> <p>We find that age and elevation are the biggest determinants of forest recovery, and that the jump-start in regeneration that residual vegetation provides lasts for at least 18 years. Our estimates of time to biomass recovery (31.6-37.1 years) are surprisingly rapid, and as a result we recommend that future research pair LiDAR with hyperspectral imagery to estimate forest aboveground biomass in frequently disturbed landscapes.</p> <p>Synthesis: Using a high-resolution LiDAR dataset and a time-series inventory of 608 landslides distributed across a wide elevational gradient in Andean montane forest, we show that age and elevation are the most influential predictors of forest canopy height and canopy variability. Other features of landslides, in particular the presence of residual vegetation, shape post-landslide regeneration trajectories. LiDAR allows for a detailed analysis of forest structural recovery across large landscapes and numbers of disturbances, and provides a reasonable upper bound on aboveground biomass accumulation rates. However, because this method does not capture the effect of compositional change through succession on aboveground biomass, wherein high-wood density species gradually replace light-wooded pioneer species, it overestimates aboveground biomass. Given previously estimated stem turnover rates along this elevational gradient, we posit that aboveground biomass recovery takes at least three times as long as our recovery time estimates based on LiDAR-derived structure alone.</p>
Data from: Trait‐based signatures of cloud base height in a tropical cloud forest
<p>Clouds have profound consequences for ecosystem structure and function. Yet, the direct monitoring of clouds and their effects on biota is challenging especially in remote and topographically complex tropical cloud forests. We argue that known relationships between climate and the taxonomic and functional composition of plant communities may provide a fingerprint of cloud base height, thus providing a rapid and cost-effective assessment in remote tropical cloud forests. To detect cloud base height, we compared species turnover and functional trait values among herbaceous and woody plant communities in an ecosystem dominated by cloud formation. We measured soil and air temperature, soil nutrient concentrations, and extracellular enzyme activity. We hypothesized that woody and herbaceous plants would provide signatures of cloud base height, as evidenced by abrupt shifts in both taxonomic composition and plant function. We demonstrated abrupt changes in taxonomic composition and the community-weighted mean of a key functional trait, specific leaf area, across elevation for both woody and herbaceous species, consistent with our predictions. However, abrupt taxonomic and functional changes occurred 100 m higher in elevation for herbaceous plants compared to woody ones. Soil temperature abruptly decreased where herbaceous taxonomic and functional turnover was high. Other environmental variables including soil biogeochemistry did not explain the abrupt change observed for woody plant communities. We provide evidence that a trait-based approach can be used to estimate cloud base height. We outline how rises in cloud base height and differential environmental requirements between growth forms can be distinguished using this approach.</p>
Autogenic regulation and resilience in tropical dry forest
<p>1. Engineering resilience, a forest's ability to maintain its properties in the event of disturbance, comprises two components: resistance and recovery. In human-dominated landscapes, forest resilience depends mostly on recovery. Forest recovery largely depends on autogenic regulation, which entails a negative feedback loop between rates of change of forest state variables and state variables themselves. Hence community dynamics changes in response to deviations from forest equilibrium state. Based on the premise that autogenic regulation is a key aspect of the recovery process, here we tested the hypothesis that combined old-growth forest (OGF) and secondary forest (SF) dynamics should show autogenic regulation in state variables, and thus convergence towards OGF-based reference points, indicating forest resilience.</p> <p>2. We integrated dynamic data for OGF (11-year monitoring) and SF (16-year monitoring) to analyse three key state variables (basal area, tree density, species richness), their annual rates of change, and their underlying demographic processes (recruitment, growth, mortality). We examined autogenic regulation through generalized linear mixed-effects models (GLMMs) to quantify functional relationships between rates of change of state variables (and underlying demographic processes), and their respective state variables.</p> <p>3. State variables in OGF decreased moderately over time, against our prediction of OGF constancy. In turn, the three state variables analysed showed negative relationships with their respective rates of change, which allows the return of SF to OGF values after disturbance. In all cases, recruitment decreased with increasing values in state variables, while mortality increased.</p> <p>4. The observed negative relationships between state variables, their rates of change and their underlying demographic processes support our hypothesis of integrated OGF and SF dynamics showing autogenic regulation for state variables. Competition seems to be a major driver of autogenic regulation given its dependence on a resource availability that declines as forest structure develops.</p> <p>5. Synthesis. Based on a straightforward and comprehensive approach to quantify the extent to which tropical forest dynamics is self-regulated, this study highlights the role of autogenic regulation in tropical dry forest as a basic component of its resilience. This approach is potentially valuable for a generalised assessment of engineering resilience of forests worldwide.</p>
The effect of tropical forest modification on primate population density and diversity
<b>Description: </b><p>This data represents orang-utan nest survey data, collected to investigate the effects of habitat disturbance on orang-utan populations. Our surveys were conducted in and around the SAFE project site, including areas of Ulu Segama forest reserves and surrounding oil palm estates, covering a total study area of ca. 13,000ha. We conducted nest surveys between April and August 2017, using the standing crop method. Transects were surveyed once by teams of two to four people walking at a steady pace of roughly 0.5km/hr. The data primarily contains perpendicular distances from directly under each nest we encountered to the transect line. We assigned a decay category to each nest, ranging from 1 (new nest) to 5 (heavily degraded). Additionally we recoded the location of each nest, the height of the nest and DBH of the host tree. </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/30"><b>The effect of tropical forest modification on primate population density and diversity.</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC - Human Modified Tropical Forest (HMTF) (Standard grant, NE/K016407/1, <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li><li>Primate Society of Great Britain (Conservation grant, NA, <a href="http://www.psgb.org/conservation_grants.php">http://www.psgb.org/conservation_grants.php</a>)</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.4(104))</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=5109892">here</a></p><p><b>Files: </b>This consists of 1 file: Orangutan_Transect_Data.xlsx</p><p><b>Orangutan_Transect_Data.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Orang-utan nest survey data</b> (described in worksheet Distance_data)</p><p>Description: Data from orang-utan nest surveys, using the standing crop method to collect perpendicular distances of orang-utan nests from line transects.</p><p>Number of fields: 13</p><p>Number of data rows: 677</p><p>Fields: </p><ul><li><b>Region</b>: Area in which the survey took place (Field type: id)</li><li><b>Habitat_Type</b>: Habitat type of the transect location (Field type: id)</li><li><b>Transect</b>: Transect ID (Field type: location)</li><li><b>TranLenght</b>: Transect length (Field type: numeric)</li><li><b>Side</b>: Side of the transect nest was located (L = left, R= Right and OT= On transect) (Field type: categorical)</li><li><b>Dist</b>: Perpendicular distance from direct under nest to the transect line (Field type: numeric)</li><li><b>Slope</b>: Angle of slope from transect line (measured to compensate for measuring perpendicular distance of steel slopes) (Field type: numeric)</li><li><b>Class</b>: Age class of east nest (From 1 = new nest to 5 = very old nest) (Field type: numeric)</li><li><b>Nest_Height</b>: Height of each nest in host tree (Field type: numeric trait)</li><li><b>DBH</b>: DBH of host tree (Field type: numeric)</li><li><b>Lat_Y</b>: Nest location latitude (Field type: latitude)</li><li><b>Long_X</b>: Nest location longitude (Field type: longitude)</li><li><b>Elevation</b>: Height above sea leave at each nest location (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2017-04-01 to 2017-08-28</p><p><b>Latitudinal extent: </b>4.5607 to 4.7828</p><p><b>Longitudinal extent: </b>117.4636 to 117.7008</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> -  -  -  Mammalia <br> -  -  -  -  Primates <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br></div><p></p>
Appendix 3 of "Analyses of three-dimensional species associations reveal departures from neutrality in a tropical forest"
<p>Temporal changes in three-dimensional structure of crowns in Luquillo, Puerto Rico. Each axis represents the overlap of one species over the other, measured as standardized effect sizes (SES). That is, deviations from a null model that randomizes crown three-dimensional positions. A high value of "SES of <em>Guarea guidonia</em> over <em>Psychotria brachiata</em>" means that <em>Guarea guidonia</em> shades <em>Psychotria brachiata</em> more than expected by chance, and so on.<br> <br> The coloured dots in the background of the figure represent all the observed relationships between species. Gray dots represent random relationships between pairs of species (i.e. similar to the expected by the null model). Black dots represent horizontal segregation between species. Blue dots represent horizontal aggregation but vertical segregation between species. Red dots represent three-dimensional aggregation between species.<br> <br> The larger green or red dots connected by lines represent the temporal changes observed for the relationship between the two target species in each figure. There are four censuses represented. Larger dots represent the more recent censuses. The green dots reflect the relationship between the two target species in the low-disturbance area within the Luquillo Forest Dynamics Plot. The red dots reflect the relationship between the two target species in the high-disturbance area within the Luquillo Forest Dynamics Plot.</p>
Role of species richness and human-impacts in resisting invasive species in tropical forests
<p>The biotic resistance hypothesis suggests that biodiversity rich areas should be resistant to biological invasions. Globally, conservationists use this hypothesis to protect diverse ecosystems. However, supporting data are often contradictory, possibly due to several confounding factors. Complexity in inferences increase in the tropics, which are sparsely studied.</p> <p>We hypothesize that human impacts, forest type and climate would modulate the relationship between native and invasive plant richness. To understand these interacting and varying effects of native richness and human disturbance on plant invasions, we sampled 354 grids of 25 km<sup>2</sup> with equal representation of protected areas and multi-use areas to record abundance of native and non-native plants from 34 protected areas across five forest types in tropical India. We used linear mixed effect models to investigate occurrence and abundance of invasive plants with respect to varying native richness, human impacts, forest types and climate.</p> <p>Human use of forests increased richness and abundance of invasive plants across all forest types. After accounting for human-use, native species richness of tropical wet forests had a negative relationship with invasive plants richness and abundance, while the relationship reversed with increasing aridity and temperature. Human infrastructure facilitated invasions within protected areas.</p> <p><em>Synthesis</em>. The biotic resistance hypothesis explained a lower number of invasions within protected tropical wet forests but not within dry forests. Human-free protected areas had lower richness and abundance of invasive plants across all systems, especially in wet tropical forests. Our results support the contextual importance of the biotic resistance hypothesis, while stressing the importance of protected areas, insulated from human impacts, to preserve the integrity of vulnerable natural systems. </p>
Figure 3 in Variation in dung beetle (Coleoptera: Scarabaeidae: Scarabaeinae) assemblages in a tropical forest remnant from a Mexican National Park
Figure 3. Non-Metric Multidimensional Scaling (NMDS) constructed from the Bray-Curtis index to establish the similarities between (A) seasons, (B) baits, and (C) daily activity of the dung beetle assemblages in the Cañón del Sumidero National Park, Chiapas.
Figure 1 in Variation in dung beetle (Coleoptera: Scarabaeidae: Scarabaeinae) assemblages in a tropical forest remnant from a Mexican National Park
Figure 1. Location of the two sampling areas in a tropical forest remnant in the core zone of the Cañón del Sumidero National Park, Chiapas.
Figure 4 in Variation in dung beetle (Coleoptera: Scarabaeidae: Scarabaeinae) assemblages in a tropical forest remnant from a Mexican National Park
Figure 4. Rank-abundance curves of the dung beetle assemblages collected with four different baits, pointing out the dominant species in each. U.mic = Uroxys microcularis; C.lae = Copris laeviceps; A.rod = Ateuchus rodriguezi; C.vaz. = Canthon vazquezae; P.end = Phanaeus endymion.
Traits data of exotic species in four tropical botanic gardens and adjacent natural forests
<p>The establishment of new botanic gardens in tropical regions highlights a need for weed risk assessment tools suitable for tropical ecosystems. The relevance of plant traits for invasion into tropical rainforests has not been well studied. </p> <p>Working in and around four botanic gardens in Indonesia where 600 exotic species have been planted, we estimated the effect of four plant traits and time since species were introduced on: a) naturalization probability of exotic species; b) the abundance (density) of naturalized species in adjacent native tropical rainforests; and c) the distance that naturalized exotics have spread from the botanic gardens. </p> <p>We found that specific leaf area (SLA) strongly differentiated 23 naturalized from 78 non-naturalized exotic species (randomly selected from 577 non-naturalized species) in our study. These trends may indicate that exotics with high SLA benefit from at least two factors when establishing in tropical forests: high growth rates and occupation of forest gaps. Exotic species that were present in the gardens for over 30 years and those with small seeds also had higher probabilities of becoming naturalized, indicating that plants can invade the understorey of closed canopy tropical rainforests, especially when invading species are shade-tolerant and have sufficient time to establish. On average, exotic species that were not animal dispersed spread 78 m further into the forests than animal-dispersed species. We did not detect relationships between the measured traits and estimated density of naturalized exotics in the adjacent forests.</p> <p><i><span>Synthesis</span></i>: Traits were able to differentiate exotic species that naturalized from botanic gardens from those that did not; this is promising for developing trait-based risk assessment in the tropics. We suggest tropical botanic gardens avoid planting exotic species with fast carbon capture strategies and those that are shade tolerant, to limit the risk of invasion and spread into adjacent native forests.</p>
Figure 1 in Muscidae (Diptera) of medico-legal importance associated with ephemeral organic substrates in seasonally dry tropical forests
Figure 1. Location of sample sites along the seasonally dry tropical forest (Caatinga) in Northeastern Brazil. (A) Petrolina; (B) Betânia; (C) Afogados da Ingazeira; (D) Buíque; (E) Boqueirão.
Figure 3 in Muscidae (Diptera) of medico-legal importance associated with ephemeral organic substrates in seasonally dry tropical forests
Figure 3. Cluster analysis for the similarity between muscid assemblages, according to the type of bait.
Figure 2 in Muscidae (Diptera) of medico-legal importance associated with ephemeral organic substrates in seasonally dry tropical forests
Figure 2. Sex ratio (female/male) of muscids collected in SDTF's fragments in Brazil, according to the type of bait (A) and species (B).
Data for: The El Nino-Southern Oscillation dramatically reduces the frequency of reproduction and reproductive rate of a tropical forest bird
<p>Although climate change has been implicated in population declines of tropical forest birds, there is a critical lack of data on the mechanisms underlying these declines. Attempts to link climatic factors to variation in adult survival, fecundity, or nest success have been largely inconclusive. Recent community-scale analyses have suggested that tropical birds may be less likely to breed under adverse conditions, but long-term data on individual reproduction are needed to test this hypothesis. Here we leverage 12 years of data on a lowland forest bird, the greater ani (<i>Crotophaga major</i>), to investigate how demographic parameters vary with phase of the El Niño – Southern Oscillation (ENSO), a major driver of climatic conditions in tropical wet forest. The likelihood of breeding and annual reproductive rate both decreased dramatically in El Niño-like years, with only 37.5% of adults attempting breeding in 2015 (a strong El Niño year). For birds that did breed, however, clutch size and daily nest predation rate were unaffected by climate. Of the local climate variables investigated, dry season length and the frequency of high temperatures were most closely associated with reproductive failure. These results indicate that El Niño conditions alter the demography of greater anis by reducing the likelihood of reproduction, a response that may be more widespread than currently recognized. We suggest that reduced reproduction under adverse conditions represents an important and understudied aspect of the life histories of tropical forest birds.</p>
Aboveground net primary productivity in regenerating seasonally dry tropical forest: contributions of rainfall, forest age, and soil
<p>Identifying factors controlling forest productivity is critical to understanding forest-climate change feedbacks, modeling vegetation dynamics, and carbon finance schemes. However, little research has focused on productivity in regenerating tropical forest which are expanding in their fraction of global area have an order of magnitude larger carbon uptake rates relative to older forest.</p> <p>We examined aboveground net primary productivity (ANPP) and its components (wood production and litterfall) over ten years in forest plots that vary in successional age, soil characteristics, and species composition using band dendrometers and litterfall traps in regenerating seasonally dry tropical forests in northwestern Costa Rica.</p> <p>We show that the components of ANPP are differentially driven by age and annual rainfall and that local soil variation is important. Total ANPP was explained by a combination of age, annual rainfall, and soil variation. Wood production comprised 35% of ANPP on average across sites and years, and was explained by annual rainfall but not forest age. Conversely, litterfall increased with forest age and soil fertility yet was not affected by annual rainfall. In this region, edaphic variability is highly correlated with plant community composition. Thus, variation in ecosystem processes explained by soil may also be partially explained by species composition.</p> <p>These results suggest that future changes in annual rainfall can alter the secondary forest carbon sink, but that this effect will be buffered by the litterfall flux which varies little among years. In determining the long-term strength of the secondary forest carbon sink, both rainfall and forest age will be critical variables to track. We also conclude that a detailed understanding of local site variation in soils and plant communities may be required to accurately predict the impact of changing rainfall on forest carbon uptake.</p> <p>Synthesis We show that in seasonally dry tropical forests, annual rainfall has a positive relationship with the growth of aboveground woody tissues of trees and that droughts lead to significant reductions in aboveground productivity. These results provide evidence for climate change – carbon cycle feedbacks in the seasonal tropics and highlight the value of longitudinal data on forest regeneration.</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>
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>
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 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>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.