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411 results for “Tropical rainforests”
Laying low: Rugged lowland rainforest preferred by feral cats in the Australian wet tropics
<p>Invasive mesopredators are responsible for the decline of many species of native mammals worldwide. Feral cats have been causally linked to multiple extinctions of Australian mammals since European colonisation. While feral cats are found throughout Australia, most research has been undertaken in arid habitats, thus there is a limited understanding of feral cat distribution, abundance, and ecology in Australian tropical rainforests. We carried out camera-trapping surveys at 108 locations across seven study sites, spanning 200 km in the Australian Wet Tropics. Single-species occupancy analysis was implemented to investigate how environmental factors influence feral cat distribution. Feral cats were detected at a rate of 5.09 photographs/100 days, 11 times higher than previously recorded in the Australian Wet Tropics. The main environmental factors influencing feral cat occupancy were a positive association with terrain ruggedness, a negative association with elevation, and a higher affinity for rainforest than eucalypt forest. These findings were consistent with other studies on feral cat ecology but differed from similar surveys in Australia. Increasingly harsh and consistently wet weather conditions at higher elevations, and improved shelter in topographically complex habitats may drive cat preference for lowland rainforest. Feral cats were positively associated with roads, supporting the theory that roads facilitate access and colonisation of feral cats within more remote parts of the rainforest. Higher elevation rainforests with no roads could act as refugia for native prey species within the critical weight range. Regular monitoring of existing roads should be implemented to monitor feral cats, and new linear infrastructure should be limited to prevent encroachment into these areas. This is pertinent as climate change modelling suggests that habitats at higher elevations will become similar to lower elevations, potentially making the environment more suitable for feral cat populations.</p>
Data from: fire in the rainforest: a 3,200-year history of fire in a West Kalimantan, Indonesia tropical rainforest
<p>Despite its perceived historical rarity, fire is an important disturbance in tropical rainforests. Very large rainforest fires have been observed multiple times in recent decades, often during years of strong El Niño-Southern Oscillation droughts. Fire in rainforest has major short-term consequences for humans and wildlife by converting forest to fire-prone fern, shrub, and grass, but the long-term effects remain to be seen. Borneo's indigenous groups have been using fire to clear land for centuries, yet the prevalence and spatial patterns of pre-modern fire across forest types in Borneo is not well understood. This research set out to reconstruct fire in a 1500-ha primary rainforest spanning 800 m of elevation in Indonesian Borneo with the goal of elucidating the role humans have played in rainforest fire. We found that humans played an important role in the occurrence of fire in recent centuries. Evidence of fire—charcoal >2 mm—is more abundant in forest types where humans would be more likely to live and/or practice swidden agriculture. However, pyrogenic material is ubiquitous across the study area, showing that all forest types have experienced fire. A set of 50 radiocarbon dates showed that in lowland areas—where human-caused fire is most likely—fire occurred throughout the last 3,200 years, peaking 1300-1600 CE. The upland areas lacked evidence of fire before 1250 CE but otherwise had a similar pattern to the lowlands. The period of high fire coincides with regional demographic changes as well as regional droughts documented elsewhere in Southeast Asia. In upland areas, fires likely burned only under regional drought when fires could more easily spread upslope. Although forest plot studies at this site show little structural evidence of past fires, tree diversity is lower than expected in the most burned areas (alluvial benches). Thus, our results suggest that land clearance was a major source of fire, but the current intact state of these rainforests indicates that they were largely resilient to fires and land use hundreds of years ago. Recent fires mirror patterns of fire spread that occurred hundreds of years ago, though their severity and extent is likely much greater.</p>
Microhabitat selection of the big-headed turtle Platysternon megacephalum in the Hainan Tropical Rainforest National Park, China
<p>Understanding species habitat requirements is vital for ensuring the success of targeted conservation and habitat restoration measures. The big-headed turtle (<em>Platysternon megacephalum</em>)<em> </em>is a freshwater species which is distributed across Southeast Asia. Due to the human threats posed by illegal pet trade and overharvesting for food and medicinal purposes, the species has undergone rapid decline. Furthermore, limited research has been conducted on this species, particularly regarding habitat preferences on Hainan Island, China. Therefore, this study examined the microhabitat selection of <em>P. megacephalum</em> using cage and sample plot methods in the Diaoluo Mountain area of the Hainan Tropical Rainforest National Park. Our results indicated that big-headed turtles select stream microhabitats at higher altitudes, in proximity to rocky substrates, several caves, and high diversity of food sources. Microhabitat utilization did not differ significantly between adults and juveniles. This suggests that protecting microhabitats and main food sources is important for the conservation of <em>P. megacephalum</em>. Our findings provide a reference for the protection of this species in Jianfeng, Yingge Ridge, and other areas in the Hainan Tropical Rainforest National Park.</p>
Do logging roads impede small mammal movement in Borneo's tropical rainforests?
<b>Description: </b><p>Data from small mammal trap grid adjacent to roads and an associated movement/translocation experiment</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/37"><b>Do logging roads impede small mammal movement in Borneo's tropical rainforests?</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=113">here</a></p><p><b>Files: </b>This consists of 1 file: template_Heon.xlsx</p><p><b>template_Heon.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Road crossing field data</b> (described in worksheet RoadCrossing)</p><p>Description: Data from trap grid plus translocation experiment; NB: Paper accompanying this dataset used data from Site1-7 only; no translocations were conducted at Site8 so this was not used in the paper.</p><p>Number of fields: 23</p><p>Number of data rows: 3456</p><p>Fields: </p><ul><li><b>Site</b>: Site code (sites not georeferenced) (Field type: Location)</li><li><b>Treatment</b>: Experimental treatment trap was assigned to (Field type: Categorical)</li><li><b>Road.width</b>: Width of the road adjacent to trap grid (Field type: Numeric)</li><li><b>Date</b>: Date trap was set (Field type: Date)</li><li><b>Trap.Night</b>: Number of nights trap had been set at this site for (Field type: Numeric)</li><li><b>Trap.Number</b>: For field reference on distance and location of each trap. (Field type: ID)</li><li><b>Set.distance</b>: Distance from the road's edge or the start point in control sites. (Field type: Numeric)</li><li><b>Rat.Present</b>: Trap status (Field type: Categorical)</li><li><b>Distance.fromroad</b>: distance of the trap site from a road (Field type: Numeric)</li><li><b>Trap.Status</b>: Basic information about captured individuals (Field type: Categorical)</li><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>Weight</b>: Body mass (Field type: Numeric)</li><li><b>Hind.foot</b>: Length of hind foot (Field type: Numeric)</li><li><b>Ear</b>: Length of ear (Field type: Numeric)</li><li><b>AGD</b>: Anal-genital distance (Field type: Numeric)</li><li><b>Sex</b>: Sex of the individual (Field type: Categorical)</li><li><b>Age</b>: Age of the individual (Field type: Categorical)</li><li><b>Release.distance</b>: Distance on the trap grid that the rat was released from (Field type: Numeric)</li><li><b>Returned.translocation</b>: Was the rat recaptured after translocation? (Field type: Categorical)</li><li><b>Recaptured.trap</b>: Trap ID code (Field type: ID)</li><li><b>Recaptured.Date</b>: Date rat was recaptured on trap grid after translocation (Field type: Date)</li><li><b>Recaptured.Distance</b>: Distance on the trap grid where the rat was re-captured (returned) from translocation (Field type: Numeric)</li><li><b>Recaptured.Day</b>: Day the rat is re-captured /return from translocation to trap grid (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2016-04-19 to 2016-07-20</p><p><b>Latitudinal extent: </b>4.6893 to 4.7472</p><p><b>Longitudinal extent: </b>117.5817 to 117.6158</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> -  -  - Rodentia<br> -  -  -  - Muridae<br> -  -  -  -  - <i>Chrotomys</i><br> -  -  -  -  -  - <i>Chrotomys whiteheadi</i><br> -  -  -  -  - <i>Leopoldamys</i><br> -  -  -  -  -  - <i>Leopoldamys sabanus</i><br> -  -  -  -  - <i>Maxomys</i><br> -  -  -  -  -  - <i>Maxomys baeodon</i><br> -  -  -  -  -  - <i>Maxomys rajah</i><br> -  -  -  -  -  - <i>Maxomys surifer</i><br> -  -  -  -  - <i>Niviventer</i><br> -  -  -  -  -  - <i>Niviventer cremoriventer</i><br> -  -  -  -  - <i>Rattus</i><br> -  -  -  -  -  - <i>Rattus exulans</i><br> -  -  -  -  - <i>Sundamys</i><br> -  -  -  -  -  - <i>Sundamys muelleri</i><br></div><p></p>
Movement patterns of invertebrates in tropical rainforest
<b>Description: </b><p>Community Data for directional malaise trapping across and along rivers from around the SAFE landscape</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/20"><b>Movement patterns of invertebrates in tropical rainforest</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=266">here</a></p><p><b>Files: </b>This consists of 1 file: Invert_Movement_Dataset_JW.xlsx</p><p><b>Invert_Movement_Dataset_JW.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Community Data for directional malaise trapping</b> (described in worksheet DF)</p><p>Description: Community Data for directional malaise trapping above rivers</p><p>Number of fields: 60</p><p>Number of data rows: 82</p><p>Fields: </p><ul><li><b>SAFESiteCode</b>: location code (Field type: Location)</li><li><b>Orientation</b>: trap orientation (Field type: Categorical)</li><li><b>Date_Out</b>: trap set date (Field type: Date)</li><li><b>Date_In</b>: trap collection date (Field type: Date)</li><li><b>Width_River_Along</b>: width of river at along trap (Field type: Numeric)</li><li><b>Width_River_Across</b>: width of river at across trap (Field type: Numeric)</li><li><b>Width_River_Middle</b>: width of river at the midpoint between the traps (Field type: Numeric)</li><li><b>Width_River_Average</b>: average river width (of three previous columns) (Field type: Numeric)</li><li><b>Height_Along</b>: height of across trap above the river (Field type: Numeric)</li><li><b>Height_Across</b>: height of along trap above the river (Field type: Numeric)</li><li><b>Rainfall</b>: rainfall collected (Field type: Numeric)</li><li><b>Forest_Type</b>: Surrounding forest type (Field type: Categorical)</li><li><b>Canopy_Across_L</b>: canopy cover (% from densiometer) on the left side (facing upstream) of the across trap (Field type: Numeric)</li><li><b>Canopy_Across_R</b>: canopy cover (% from densiometer) on the right side (facing upstream) of the across trap (Field type: Numeric)</li><li><b>Canopy_Across_LR</b>: average canopy cover (% from densiometer) on the left and right sides (facing upstream) of the across trap (Field type: Numeric)</li><li><b>Canopy_Across_River</b>: canopy cover (% from densiometer) above the river at the across trap (Field type: Numeric)</li><li><b>Canopy_Along_L</b>: canopy cover (% from densiometer) on the left side (facing upstream) of the along trap (Field type: Numeric)</li><li><b>Canopy_Along_R</b>: canopy cover (% from densiometer) on the right side (facing upstream) of the along trap (Field type: Numeric)</li><li><b>Canopy_Along_LR</b>: average canopy cover (% from densiometer) on the left and right sides (facing upstream) of the along trap (Field type: Numeric)</li><li><b>Canopy_Along_River</b>: canopy cover (% from densiometer) above the river at the along trap (Field type: Numeric)</li><li><b>Canopy_Middle_L</b>: canopy cover (% from densiometer) on the left side (facing upstream) of the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Middle_R</b>: canopy cover (% from densiometer) on the right side (facing upstream) of the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Middle_LR</b>: average canopy cover (% from densiometer) on the left and right sides (facing upstream) of the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Middle_River</b>: canopy cover (% from densiometer) above the river at the midpoint between traps (Field type: Numeric)</li><li><b>Canopy_Average_L</b>: left bank canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Canopy_Average_R</b>: right bank canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Canopy_Average_LR</b>: both banks canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Canopy_Average_River</b>: river canopycover average (% from densiometer) (Field type: Numeric)</li><li><b>Basal_Area_Across_L</b>: across trap, left bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Across_R</b>: across trap, right bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Across</b>: across trap, average of both banks, tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Along_L</b>: along trap, left bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Along_R</b>: along trap, right bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Along</b>: along trap, average of both banks, tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Middle_L</b>: midpoint, left bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Middle_R</b>: midpoint, right bank (looking upriver), tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area_Middle</b>: midpoint,both banks, tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Basal_Area</b>: average tree basal area (relascope and angle point method) (Field type: Numeric)</li><li><b>Eph_Ab</b>: Ephemeroptera (Field type: Abundance)</li><li><b>Odo_Ab</b>: Odonata (Field type: Abundance)</li><li><b>Ple_Ab</b>: Plecoptera (Field type: Abundance)</li><li><b>Ort_Ab</b>: Orthoptera (Field type: Abundance)</li><li><b>Der_Ab</b>: Dermaptera (Field type: Abundance)</li><li><b>Dic_Ab</b>: Blattodea (Field type: Abundance)</li><li><b>Iso_Ab</b>: Isoptera (Field type: Abundance)</li><li><b>Hem_Ab</b>: Hemiptera (Field type: Abundance)</li><li><b>Thy_Ab</b>: Thysanoptera (Field type: Abundance)</li><li><b>Col_Ab</b>: Coleoptera (Field type: Abundance)</li><li><b>Mec_Ab</b>: Mecoptera (Field type: Abundance)</li><li><b>Dip_Ab</b>: Diptera (Field type: Abundance)</li><li><b>Lep_Ab</b>: Lepidoptera (Field type: Abundance)</li><li><b>Tri_Ab</b>: Trichoptera (Field type: Abundance)</li><li><b>Hym_Ab</b>: Hymenoptera (Field type: Abundance)</li><li><b>Uni_Ab</b>: Unidentified individuals (Field type: Numeric)</li><li><b>Abundance</b>: Total Abundance (Field type: Numeric)</li><li><b>AbundanceUni</b>: Total Abundance without unidentified individuals (Field type: Numeric)</li><li><b>Staph_Ab</b>: subset of coleoptera that were staphylinids (Field type: Abundance)</li><li><b>Richness</b>: species richness (Field type: Numeric)</li><li><b>Mass</b>: total mass (Field type: Numeric)</li><li><b>Mass_Outlier</b>: total mass without individuals weighing over 0.25g (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2015-05-18 to 2015-07-14</p><p><b>Latitudinal extent: </b>4.6358 to 4.7342</p><p><b>Longitudinal extent: </b>117.4576 to 117.6411</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> -  -  - Blattodea<br> -  -  - Coleoptera<br> -  -  -  - Staphylinidae<br> -  -  - Dermaptera<br> -  -  - Diptera<br> -  -  - Ephemeroptera<br> -  -  - Hemiptera<br> -  -  - Hymenoptera<br> -  -  - Isoptera<br> -  -  - Lepidoptera<br> -  -  - Mecoptera<br> -  -  - Odonata<br> -  -  - Orthoptera<br> -  -  - Plecoptera<br> -  -  - Thysanoptera<br> -  -  - Trichoptera<br></div><p></p>
Persistent effects of fragmentation on tropical rainforest canopy structure after 20 years of isolation
<p>Data of Ecological Application paper "Persistent effects of fragmentation on tropical rainforest canopy structure after 20 years of isolation"</p>
Figure 2 in TEMPORAL PARTITIONING OF CHIRONOMIDAE EMERGENCE IN AN INSULAR, TROPICAL RAINFOREST STREAM Abstract
Figure 2. Emergence trap on Quebrada Prieta.
Data from "The relevance of flash coloration against avian predation in a Morpho butterfly: A field experiment in a tropical rainforest"
<p>This dataset contains all the data related to the manuscript "The relevance of flash coloration against avian predation in a <em>Morpho </em>butterfly: A field experiment in a tropical rainforest". </p>
Two decades of annual landscape-scale tree growth and dynamics in old-growth tropical rainforest in the CARBONO Project, La Selva Biological Station, 1997-2018
<p>Here we present the complete data series from a 21-yr study of the annual growth and dynamics of trees, palms and lianas in the old-growth tropical rainforest at the La Selva Biological Station in Costa Rica. These observations were part of the CARBONO Project, a multidisciplinary team study of forest carbon cycling. The project was designed to assess forest processes at the landscape scale by sampling with replication across the within-landscape edaphic heterogeneity typical of tropical forests. Through more than two decades, forest growth and dynamics were assessed annually. The annual time-step used in the CARBONO Project effectively captured forest responses to major disturbances and to interannual and climatic variation. Annual measurements also enhanced the accuracy and long-term consistency of the data. To our knowledge, the resulting records are unique for tropical forests, where the dominant approach to studying the dynamics of a given forest has been to use a single plot and multi-year inter-census intervals. To date these CARBONO Project data have revealed: multi-decadal forest stability in spite of the short-term changes in forest structure resulting from major natural disturbances (e.g., the 1997-1998 Strong El Niño, and the extreme windstorm of May 2018); the dynamics and importance of large trees; and the responses of a major component of ecosystem productivity, aboveground wood production, to interannual and long-term climatic and atmospheric change. These data have also contributed to many remote-sensing studies.</p> <p>The data set consists of annual observations through the period 1997–2018 of the floristics, survival, recruitment, and growth of all woody stems (diameter <u>></u> 10 cm) in a landscape-scale plot network. At completion of the study, the data spanned 6705 individuals and 21 years. The data set is complete and has been through extensive internal checks for quality assurance. Detailed data documentation and an emphasis on measurement repeatability were prioritized through the study. The metadata include an extensive README file describing the data files and the methods, a document detailing the data management and qa/qc, and the scanned original field data-sheets for the 22 annual censuses.</p> <p>We gratefully acknowledge the careful long-term field work and data entry and checking by paraforesters Leonel Campos Otoya and William Miranda Conejo. Logistical support and the long-term protection of the La Selva reserve were provided by the Organization for Tropical Studies. The Ministerio de Ambiente y Energía of Costa Rica granted permits to carry out this study through the years of the study (most recently: Resolución No. 037-2018-ACCVC-PI).</p>
Supplemental information for: Annual tropical-rainforest productivity through two decades: Complex responses to climatic factors, CO2 and storm damage
<p>The supplemental files in this deposition contribute additional information useful for understanding the analyses in the associated manuscript. Two files contain the annual (CARBONO Project measurement-year) data for productivity and for environmental conditions that were analyzed in the paper. The annual productivity metrics were derived from the CARBONO Project litterfall and tree-growth data that were provided and documented in prior Dryad data depositions. A third file in this deposition provides the daily data underlying the analyzed meteorological factors. The fourth file lists the standardized set of 66 test models that were used to test each of the five productivity metrics for environmental responses. The fifth file is an auxiliary table documenting tree mortality by year. The included README file documents the information in these files.</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: Season and herbivore defence trait mediate tri-trophic interactions in tropical rainforest
<p>Bottom-up effects from host plants and top-down effects from predators on herbivore abundance and distribution vary with physical environment, plant chemistry, predator and herbivore trait and diversity. Tri-trophic interactions in tropical ecosystems may follow different patterns from temperate ecosystems due to differences in above abiotic and biotic conditions. We sampled leaf-chewing larvae of Lepidoptera (caterpillars) from a dominant host tree species in a seasonal rainforest in Southwest China. We reared out parasitoids and grouped herbivores based on their diet preferences, feeding habits, and defence mechanisms. We compared caterpillar abundance with leaf numbers ('bottom-up' effects) and parasitoid abundance ('top-down' effects) between different seasons and herbivore traits. We found bottom-up effects were stronger than top-down effects. Both bottom-up and top-down effects were stronger in the dry season than in the wet season, which were driven by polyphagous rare species and host plant phenology. Contrary to our predictions, herbivore traits did not influence differences in the bottom-up or top-down effects except for stronger top-down effects for shelter-builders. Our study shows season is the main predictor of the bottom-up and top-down effects in the tropics and highlights the complexity of these interactions. </p>
Fig. 110 in Comb-footed spiders (Araneae: Theridiidae) in the tropical rainforest of Xishuangbanna, Southwest China
Fig. 110. Locality of Xishuangbanna, from which the species of theridiid were collected.
Canopy height, rather than neighborhood effects, shapes leaf herbivory in a tropical rainforest
<p>These files contain the datasets and R codes used in the data analyses in the paper " Canopy height, rather than neighborhood effects, shapes leaf herbivory in a tropical rainforest " that will be published in Ecology.</p>
Phytochemical diversity impacts herbivory in a tropical rainforest tree community
<p class="p1">Metabolomics provides an unprecedented window <span class="s1">into </span>diverse plant secondary<span class="s2"> </span>metabolites that represent a potentially critical niche dimension in tropical forests<span class="s2"> </span>underlying <span class="s1">species </span>co-existence. Here, we used untargeted metabolomics to evaluate<span class="s2"> </span>chemical composition of 358 tree species and its relationship <span class="s1">with </span>phylogeny and<span class="s2"> </span>variation in light environment, soil nutrients, and insect-herbivore leaf damage in a<span class="s3"> </span><span class="s4">tropical rain forest plot. </span>We report no phylogenetic signal in most compound classes,<span class="s3"> </span>indicating rapid diversification in tree metabolomes. <span class="s4">We found that </span>locally <span class="s4">co-</span>occur<span class="s1">ring species were more </span>chemically <span class="s1">dis</span>similar than random, and that local<span class="s2"> </span>chemical dispersion and metabolite diversity <span class="s1">was associated with lower </span>herbivory,<span class="s2"> </span>especially that of specialist insect herbivores. <span class="s1">Our results highlight the role of secondary</span><span class="s3"> </span>metabolites in mediating plant-herbivore interactions and their potential to facilitate<span class="s3"> </span>niche differentiation in a manner that contributes to species coexistence. Furthermore,<span class="s3"> </span>our findings suggest that specialist herbivore pressure is an important mechanism<span class="s3"> </span>promoting phytochemical diversity in tropical forests.</p>
Rainfall seasonality shapes belowground root trait dynamics in an Amazonian tropical rainforest: A test of the stress-dominance hypothesis
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Laying low: Rugged lowland rainforest preferred by feral cats in the Australian wet tropics
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Data from: Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India
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Data from: Season and herbivore defence trait mediate tri-trophic interactions in tropical rainforest
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Two decades of annual landscape-scale tree growth and dynamics in old-growth tropical rainforest in the CARBONO Project, La Selva Biological Station, 1997-2018
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
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.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.