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1,271 results for “tropical forest”
Ants may buffer the Janzen–Connell effect in a tropical forest in Southwest China
<p>These files contain the datasets and R codes used in the data analyses in the paper " Ants may buffer the Janzen–Connell effect in a tropical forest in Southwest China" that will be published in Ecology</p>
Acoustic phenology of tropical resident birds differs between native forest species and parkland colonizer species
<p>Most birds are characterized by a seasonal phenology closely adapted to local climatic conditions, even in tropical habitats where climatic seasonality is slight. In order to better understand the phenologies of resident tropical birds, and how phenology may differ among species at the same site, we used ~70,000 hours of audio recordings collected continuously for two years at four recording stations in Singapore and nine custom-made machine learning classifiers to determine the vocal phenology of a panel of nine resident bird species. We detected distinct seasonality in vocal activity in some species but not others. Native forest species sang seasonally. In contrast, species which have had breeding populations in Singapore only for the last few decades exhibited seemingly aseasonal or unpredictable song activity throughout the year. Urbanization and habitat modification over the last 100 years have altered the composition of species in Singapore, which appears to have influenced phenological dynamics in the avian community. It is unclear what is driving the differences in phenology between these two groups of species, but it may be due to either differences in seasonal availability of preferred foods, or newly established populations may require decades to adjust to local environmental conditions. Our results highlight the ways that anthropogenic habitat modification may disrupt phenological cycles in tropical regions in addition to altering the species community.</p>
Data from: Environmental conditions differently shape leaf, seed and seedling trait composition between and within elevations of tropical montane forests
<p>The composition of plant functional traits varies in response to environmental conditions due to processes of community assembly and species sorting. However, there is a lack of understanding of how plant trait composition responds to environmental conditions at different spatial scales and across the plant life cycle. We investigated the trait composition of leaves (specific leaf area), seeds (seed mass) and seedlings (initial seedling height) across elevations and within elevations in relation to soil and light conditions in a tropical montane forest in southern Ecuador. We surveyed traits and communities of adult trees, seeds and seedlings on nine plots at three elevations (1000-3000 m a.s.l.) and calculated community-weighted mean trait values to analyse trait variation across and within elevations. In addition, we measured two environmental factors (soil C/N ratio and canopy openness) to quantify local-scale variation in environmental conditions within elevations. We found that community-weighted means of specific leaf area, seed mass and initial seedling height decreased consistently with increasing elevation. Within elevations, mean trait values of trees, seeds and seedlings responded differently to local-scale environmental conditions. Specific leaf area decreased with increasing soil C/N ratio, and initial seedling height decreased with increasing canopy openness. Seed mass was associated neither with soil nor with light conditions. Our findings show that broad-scale and local-scale processes differently shape the composition of leaf, seed and seedling traits in tropical forests, indicating a scale-dependence in trait-environment associations. Furthermore, plant traits corresponding to different life stages were related differently to environmental conditions within elevations. Community assembly processes may therefore lead to differences in species sorting at early and late plant life stages.</p>
Data from: Tracking shifts in forest structural complexity through space and time in human-modified tropical landscapes
<p>Habitat structural complexity is an emergent property of ecosystems that directly shapes their biodiversity, functioning and resilience to disturbance. Yet despite its importance, we continue to lack consensus on how best to define structural complexity, nor do we have a generalised approach to measure habitat complexity across ecosystems. To bridge this gap, here we adapt a geometric framework developed to quantify the surface complexity of coral reefs and apply it to the canopies of tropical rainforests. Using high-resolution, repeat-acquisition airborne laser scanning data collected over 450 km2 of human-modified tropical landscapes in Borneo, we generated 3D canopy height models of forests at varying stages of recovery from logging. We then tested whether the geometric framework of habitat complexity – which characterises 3D surfaces according to their height range, rugosity and fractal dimension – was able to detect how both human and natural disturbances drive variation in canopy structure through space and time across these landscapes. We found that together, these three metrics of surface complexity captured major differences in canopy 3D structure between highly-degraded, selectively logged and old-growth forests. Moreover, the three metrics were able to track distinct temporal patterns of structural recovery following logging and wind disturbance. However, in the process we also uncovered several important conceptual and methodological limitations with the geometric framework of habitat complexity. We found that fractal dimension was highly sensitive to small variations in data inputs and was ecologically counteractive (e.g., higher fractal dimension in oil palms than old-growth forests), while rugosity and height range were tightly correlated (r=0.75) due to their strong dependency on maximum tree height. Our results suggest that forest structural complexity cannot be summarised using these three descriptors alone, as they overlook key features of canopy vertical and horizontal structure that arise from the way trees fill 3D space.</p> <p> </p> <p> </p>
Large trees in tropical dry forest facilitate the presence of stingless bee nests (Apidae: Meliponini): the case of Ficus crocata
<p>[ESP]</p> <p>Este repositorio contiene archivos .csv y .r, de los datos se utilizaron para el análisis estadístico del artículo de Manzanarez-Villasana, Briseño-Sánchez, Lobo y Quesada </p> <p>[ENG]</p> <p>This repository contains .csv and .r files of the data used for the statistical analysis of the article by Manzanarez-Villasana, Briseño-Sánchez, Lobo y Quesada </p>
Data from: Spatial variation of soil CO2, CH4 and N2O fluxes across topographical positions in tropical forests of the Guiana Shield in Ecosystems
<p>Data from: Spatial variation of soil CO2, CH4 and N2O fluxes across topographical positions in tropical forests of the Guiana Shield in Ecosystems</p>
Ant and termite assemblages along a tropical forest disturbance gradient in Sabah, Malaysia: A study of co-variation and trophic interactions
<b>Description: </b><p>Termite community composition from soil pits and deadwood</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/103"><b>Ant and termite assemblages along a tropical forest disturbance gradient in Sabah, Malaysia: A study of co-variation and trophic interactions</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=38">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Functional traits</b> (Worksheet Function)</p><p>Dimensions: 36 rows by 3 columns</p><p>Description: Functional traits associated with each genus</p><p>Fields: </p><ul><li><b>Genus</b>: Genus ID (Field type: Taxa)</li><li><b>Functional.group</b>: Humification gradient (Field type: Categorical Trait)</li></ul><br></li><li><p><b>Soil pit data</b> (Worksheet SoilPits)</p><p>Dimensions: 954 rows by 35 columns</p><p>Description: Termite community composition from soil pits</p><p>Fields: </p><ul><li><b>2nd.order.point</b>: SAFE Project sample site (Field type: Location)</li><li><b>Quadrat.number.(in.my.study)</b>: Quadrat number (Field type: ID)</li><li><b>Date</b>: Date of sample collection (Field type: Date)</li><li><b>Pit.number</b>: Pit number within the plot (Field type: Replicate)</li><li><b>Time</b>: Time samples were collected (Field type: Time)</li><li><b>No..of.termites</b>: Number adult termites (Field type: Abundance)</li><li><b>No.juvenile.termites</b>: Number juvenile termites (Field type: Abundance)</li><li><b>Unknown</b>: Number of damaged individuals or individuals that can't definitively be assigned to genera (Field type: Abundance)</li><li><b>Dicuspiditermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Schedorhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Prohamitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Malaysiotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Mirocapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Hypotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Procapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Homatermes.undescribed.genus</b>: Number of individuals (Field type: Abundance)</li><li><b>Termes</b>: Number of individuals (Field type: Abundance)</li><li><b>Syncapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Pericapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Microcerotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Macrotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Globitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Lacessititermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Pseudocapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Homallotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Oriencapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Oriensublitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Labritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Euramitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Rhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Nasutitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Bulbitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Odontotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Heterotermes</b>: Number of individuals (Field type: Abundance)</li></ul><br></li><li><p><b>Deadwood data</b> (Worksheet Deadwood)</p><p>Dimensions: 138 rows by 23 columns</p><p>Description: Termite community composition from deadwood</p><p>Fields: </p><ul><li><b>2nd.order.point</b>: SAFE Project sample site (Field type: Location)</li><li><b>Quadrat.number.(in.my.study)</b>: Quadrat number (Field type: ID)</li><li><b>Date</b>: Date of sample collection (Field type: Date)</li><li><b>Wood.sample</b>: Wood piece within Quadrat (Field type: Replicate)</li><li><b>Time</b>: Time samples were collected (Field type: Time)</li><li><b>No..of.termites</b>: Number adult termites (Field type: Abundance)</li><li><b>No.juvenile.termites</b>: Number juvenile termites (Field type: Abundance)</li><li><b>Unknown</b>: Number of damaged individuals or individuals that can't definitively be assigned to genera (Field type: Abundance)</li><li><b>Dicuspiditermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Schedorhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Homallotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Bulbitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Macrotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Globitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Nasutitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Heterotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Syncapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Malaysiotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Parrhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Pericapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Rhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Aciculitermes</b>: Number of individuals (Field type: Abundance)</li></ul><br></li></ol><p><b>Date range: </b>2010-04-21 to 2010-05-25</p><p><b>Latitudinal extent: </b>4.6353 to 4.7520</p><p><b>Longitudinal extent: </b>116.9542 to 117.6288</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> -  -  - Isoptera<br> -  -  -  -  - <i>Aciculitermes</i><br> -  -  -  -  - [<i>Euramitermes</i>]<br> -  -  -  -  - <i>Homallotermes</i><br> -  -  -  -  - <i>Hypotermes</i><br> -  -  -  -  - <i>Mirocapritermes</i><br> -  -  -  -  - <i>Procapritermes</i><br> -  -  -  -  - <i>Prohamitermes</i><br> -  -  -  -  - <i>Pseudocapritermes</i><br> -  -  -  - Rhinotermitidae<br> -  -  -  -  - <i>Heterotermes</i><br> -  -  -  -  - <i>Parrhinotermes</i><br> -  -  -  -  - <i>Rhinotermes</i><br> -  -  -  -  - <i>Schedorhinotermes</i><br> -  -  -  - Termitidae<br> -  -  -  -  - <i>Bulbitermes</i><br> -  -  -  -  - <i>Dicuspiditermes</i><br> -  -  -  -  - <i>Globitermes</i><br> -  -  -  -  - <i>Labritermes</i><br> -  -  -  -  - [<i>Lacessititermes</i>]<br> -  -  -  -  - <i>Macrotermes</i><br> -  -  -  -  - <i>Malaysiotermes</i><br> -  -  -  -  - <i>Microcerotermes</i><br> -  -  -  -  - <i>Nasutitermes</i><br> -  -  -  -  - <i>Odontotermes</i><br> -  -  -  -  - <i>Oriencapritermes</i><br> -  -  -  -  - <i>Oriensubulitermes</i><br> -  -  -  -  - <i>Pericapritermes</i><br> -  -  -  -  - <i>Syncapritermes</i><br> -  -  -  -  - <i>Termes</i><br> -  -  -  -  - <i>Hodotermes</i><br> -  - [Homatermes.undescribed.genus]<br></div><p></p>
The role of competition in structuring ant community composition across a tropical forest disturbance gradient
<b>Description: </b><p>Leaf litter ant community composition and competition</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/34"><b>The role of competition in structuring ant community composition across a tropical forest disturbance gradient.</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=1">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Ant community composition</b> (Worksheet Composition)</p><p>Dimensions: 430 rows by 69 columns</p><p>Description: Site x species matrix of ant community composition</p><p>Fields: </p><ul><li><b>Forest Type</b>: Shows the two forest types used in the study (Field type: Categorical)</li><li><b>Site</b>: Represents the site/day sampled. 10 Sampling sites were used in each forest type (Field type: Location)</li><li><b>Time</b>: Represents the time in the day points were sampled (Field type: Time)</li><li><b>Point</b>: Represents the column of 3 sampling points for each time of day (Field type: Replicate)</li><li><b>ID</b>: Represents individual sampling point. Order is: Site(Day)/Time/Type/Sampling point no. Logged ID's also have LF at the start (Field type: ID)</li><li><b>Method</b>: Method used to record community (Field type: Categorical)</li><li><b>Diacamma</b>: Number of individuals (Field type: Abundance)</li><li><b>Odontoponera</b>: Number of individuals (Field type: Abundance)</li><li><b>Pheidole</b>: Number of individuals (Field type: Abundance)</li><li><b>Leptogenys</b>: Number of individuals (Field type: Abundance)</li><li><b>Pheidologeton</b>: Number of individuals (Field type: Abundance)</li><li><b>Crematogaster</b>: Number of individuals (Field type: Abundance)</li><li><b>Odontomachus</b>: Number of individuals (Field type: Abundance)</li><li><b>Aphaenogaster</b>: Number of individuals (Field type: Abundance)</li><li><b>Acanthomyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Nylanderia</b>: Number of individuals (Field type: Abundance)</li><li><b>Camponotus</b>: Number of individuals (Field type: Abundance)</li><li><b>Cardiocondyla</b>: Number of individuals (Field type: Abundance)</li><li><b>Anochetus</b>: Number of individuals (Field type: Abundance)</li><li><b>Technomyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Monomorium</b>: Number of individuals (Field type: Abundance)</li><li><b>Recurvidris</b>: Number of individuals (Field type: Abundance)</li><li><b>Polyrhachis</b>: Number of individuals (Field type: Abundance)</li><li><b>Cladomyrma</b>: Number of individuals (Field type: Abundance)</li><li><b>Lophomyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Harpegnathos</b>: Number of individuals (Field type: Abundance)</li><li><b>Carebara</b>: Number of individuals (Field type: Abundance)</li><li><b>Cataulacus</b>: Number of individuals (Field type: Abundance)</li><li><b>Pachycondyla</b>: Number of individuals (Field type: Abundance)</li><li><b>Lordomyrma</b>: Number of individuals (Field type: Abundance)</li><li><b>Myrmecina</b>: Number of individuals (Field type: Abundance)</li><li><b>Proatta</b>: Number of individuals (Field type: Abundance)</li><li><b>Euprenolepis</b>: Number of individuals (Field type: Abundance)</li><li><b>Rhytidoponera</b>: Number of individuals (Field type: Abundance)</li><li><b>Paratrechina</b>: Number of individuals (Field type: Abundance)</li><li><b>Paraparatrechina</b>: Number of individuals (Field type: Abundance)</li><li><b>Tetramorium</b>: Number of individuals (Field type: Abundance)</li><li><b>Paratopula</b>: Number of individuals (Field type: Abundance)</li><li><b>Strumigenys</b>: Number of individuals (Field type: Abundance)</li><li><b>Pyramica</b>: Number of individuals (Field type: Abundance)</li><li><b>Ponera</b>: Number of individuals (Field type: Abundance)</li><li><b>Hypoponera</b>: Number of individuals (Field type: Abundance)</li><li><b>Tetraponera</b>: Number of individuals (Field type: Abundance)</li><li><b>Emeryopone</b>: Number of individuals (Field type: Abundance)</li><li><b>Centromyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Tapinoma</b>: Number of individuals (Field type: Abundance)</li><li><b>Myrmicaria</b>: Number of individuals (Field type: Abundance)</li><li><b>Rotrastruma</b>: Number of individuals (Field type: Abundance)</li><li><b>Prionopelta</b>: Number of individuals (Field type: Abundance)</li><li><b>Gnamptogenys</b>: Number of individuals (Field type: Abundance)</li><li><b>Eurhopalothrix</b>: Number of individuals (Field type: Abundance)</li><li><b>Myrmoteras</b>: Number of individuals (Field type: Abundance)</li><li><b>Oecophylla</b>: Number of individuals (Field type: Abundance)</li><li><b>Myopias</b>: Number of individuals (Field type: Abundance)</li><li><b>Pseudolasius</b>: Number of individuals (Field type: Abundance)</li><li><b>Plagiolepis</b>: Number of individuals (Field type: Abundance)</li><li><b>Dacetinops</b>: Number of individuals (Field type: Abundance)</li><li><b>Mystrium</b>: Number of individuals (Field type: Abundance)</li><li><b>Echinopla</b>: Number of individuals (Field type: Abundance)</li><li><b>Philidris</b>: Number of individuals (Field type: Abundance)</li><li><b>Vollenhovia</b>: Number of individuals (Field type: Abundance)</li><li><b>Rhoptromyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Anillomyrma</b>: Number of individuals (Field type: Abundance)</li><li><b>Cryptopone</b>: Number of individuals (Field type: Abundance)</li><li><b>Aenictus</b>: Number of individuals (Field type: Abundance)</li><li><b>Calyptomyrmex</b>: Number of individuals (Field type: Abundance)</li><li><b>Amblyopone</b>: Number of individuals (Field type: Abundance)</li><li><b>Prenolepis</b>: Number of individuals (Field type: Abundance)</li></ul><br></li><li><p><b>Morphometrics</b> (Worksheet Morpho)</p><p>Dimensions: 72 rows by 4 columns</p><p>Description: Size classes for the genera</p><p>Fields: </p><ul><li><b>Genera</b>: Genus ID (Field type: Taxa)</li><li><b>Size.Min</b>: Minimum body size (Field type: Categorical Trait)</li><li><b>Size.Max</b>: Maximum body size (Field type: Categorical Trait)</li></ul><br></li><li><p><b>Competition</b> (Worksheet Competition)</p><p>Dimensions: 866 rows by 15 columns</p><p>Description: Outcome of competitive interactions among individuals of different genera</p><p>Fields: </p><ul><li><b>Forest Type</b>: Shows the two forest types used in the study (Field type: Categorical)</li><li><b>Site</b>: Represents the site/day sampled. 10 Sampling sites were used in each forest type (Field type: Location)</li><li><b>Time</b>: Represents the time in the day points were sampled (Field type: Time)</li><li><b>ID</b>: Represents individual sampling point. Order is: Site(Day)/Time/Type/Sampling point no. Logged ID's also have LF at the start (Field type: ID)</li><li><b>Method</b>: Method used to record interaction (Field type: Categorical)</li><li><b>Genera1</b>: Genus ID of the first interacting individual (Field type: Taxa)</li><li><b>Genera2</b>: Genus ID of the second interacting individual (Field type: Taxa)</li><li><b>TimeG1</b>: The arrival time of the first genus in the interaction to the bait card in seconds (Field type: Numeric)</li><li><b>TimeG2</b>: The arrival time of the second genus in the interaction to the bait card in seconds (Field type: Numeric)</li><li><b>IntG1</b>: The competitive status of the first genus in the interaction (Field type: Categorical Interaction)</li><li><b>IntG2</b>: The competitive status of the second genus in the interaction (Field type: Categorical Interaction)</li><li><b>Interaction</b>: The type of interaction occuring between the two genera (Field type: Categorical Interaction)</li><li><b>GroupG1</b>: Whether or not the first genus was part of a group of individuals when interacting on the bait card (Field type: Categorical)</li><li><b>GroupG2</b>: Whether or not the second genus was part of a group of individuals when interacting on the bait card (Field type: Categorical)</li></ul><br></li></ol><p><b>Date range: </b>2016-02-02 to 2016-06-05</p><p><b>Latitudinal extent: </b>4.7273 to 4.7463</p><p><b>Longitudinal extent: </b>116.9669 to 117.5969</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Hymenoptera<br> -  -  -  - Formicidae<br> -  -  -  -  - <i>Acanthomyrmex</i><br> -  -  -  -  - <i>Aenictus</i><br> -  -  -  -  - <i>Amblyopone</i><br> -  -  -  -  - <i>Anillomyrma</i><br> -  -  -  -  - <i>Anochetus</i><br> -  -  -  -  - <i>Aphaenogaster</i><br> -  -  -  -  - <i>Calyptomyrmex</i><br> -  -  -  -  - <i>Camponotus</i><br> -  -  -  -  - <i>Cardiocondyla</i><br> -  -  -  -  - <i>Carebara</i><br> -  -  -  -  - <i>Cataulacus</i><br> -  -  -  -  - <i>Centromyrmex</i><br> -  -  -  -  - <i>Cladomyrma</i><br> -  -  -  -  - <i>Crematogaster</i><br> -  -  -  -  - <i>Cryptopone</i><br> -  -  -  -  - <i>Dacetinops</i><br> -  -  -  -  - <i>Diacamma</i><br> -  -  -  -  - <i>Echinopla</i><br> -  -  -  -  - <i>Emeryopone</i><br> -  -  -  -  - <i>Euprenolepis</i><br> -  -  -  -  - <i>Eurhopalothrix</i><br> -  -  -  -  - <i>Gnamptogenys</i><br> -  -  -  -  - <i>Harpegnathos</i><br> -  -  -  -  - <i>Hypoponera</i><br> -  -  -  -  - <i>Leptogenys</i><br> -  -  -  -  - <i>Lophomyrmex</i><br> -  -  -  -  - <i>Lordomyrma</i><br> -  -  -  -  - <i>Monomorium</i><br> -  -  -  -  - <i>Myopias</i><br> -  -  -  -  - <i>Myrmecina</i><br> -  -  -  -  - <i>Myrmicaria</i><br> -  -  -  -  - <i>Myrmoteras</i><br> -  -  -  -  - <i>Mystrium</i><br> -  -  -  -  - <i>Nylanderia</i><br> -  -  -  -  - <i>Odontomachus</i><br> -  -  -  -  - <i>Odontoponera</i><br> -  -  -  -  - <i>Oecophylla</i><br> -  -  -  -  - <i>Pachycondyla</i><br> -  -  -  -  - <i>Paraparatrechina</i><br> -  -  -  -  - <i>Paratopula</i><br> -  -  -  -  - <i>Paratrechina</i><br> -  -  -  -  - <i>Pheidole</i><br> -  -  -  -  - <i>Pheidologeton</i><br> -  -  -  -  - <i>Philidris</i><br> -  -  -  -  - <i>Plagiolepis</i><br> -  -  -  -  - <i>Polyrhachis</i><br> -  -  -  -  - <i>Ponera</i><br> -  -  -  -  - <i>Prenolepis</i><br> -  -  -  -  - <i>Prionopelta</i><br> -  -  -  -  - <i>Proatta</i><br> -  -  -  -  - <i>Pseudolasius</i><br> -  -  -  -  - <i>Pyramica</i><br> -  -  -  -  - <i>Recurvidris</i><br> -  -  -  -  - <i>Rhoptromyrmex</i><br> -  -  -  -  - <i>Rhytidoponera</i><br> -  -  -  -  - [<i>Rotrastruma</i>]<br> -  -  -  -  - <i>Strumigenys</i><br> -  -  -  -  - <i>Tapinoma</i><br> -  -  -  -  - <i>Technomyrmex</i><br> -  -  -  -  - <i>Tetramorium</i><br> -  -  -  -  - <i>Tetraponera</i><br> -  -  -  -  - <i>Vollenhovia</i><br></div><p></p>
Investigating small mammal microhabitat selection in logged and unlogged tropical forests
<b>Description: </b><p>Spool and line tracking data on small mammals</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/87"><b>Investigating small mammal microhabitat selection in logged and unlogged tropical forests</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=60">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Tracking data</b> (Worksheet Tracking)</p><p>Dimensions: 1396 rows by 15 columns</p><p>Description: Individual trap segment details for all spool-and-line tracked individuals</p><p>Fields: </p><ul><li><b>block</b>: SAFE Project Block in which trapping grid was located (Field type: Location)</li><li><b>Date</b>: Date individual was spooled (Field type: Date)</li><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>Trap</b>: Trap number on the grid where individual was captured (Field type: ID)</li><li><b>Respool</b>: Has this individual been tracked previously? (Field type: Categorical)</li><li><b>track</b>: Track ID for that day (Field type: Replicate)</li><li><b>Pit.tag.no.</b>: PIT tag number of the individual being tracked (Field type: ID)</li><li><b>Sex</b>: Sex oFxthe individual (Field type: Categorical)</li><li><b>Weight</b>: Body mass of the individual (Field type: Numeric)</li><li><b>Distance</b>: Length of straight line portion of track (Field type: Numeric)</li><li><b>Bearing</b>: Compass bearing of track portion (Field type: Numeric)</li><li><b>Dominant.habitat.feature</b>: Habitat taype on the observed route (Field type: Categorical)</li><li><b>feature.control</b>: Habitat type on the control route (Field type: Categorical)</li><li><b>height</b>: Height above ground (Field type: Ordered Categorical)</li></ul><br></li><li><p><b>microhabitat data</b> (Worksheet microhabitat)</p><p>Dimensions: 597 rows by 15 columns</p><p>Description: Microhabitat details recorded along spool tracks</p><p>Fields: </p><ul><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>individual</b>: PIT tag number of the individual being tracked (Field type: ID)</li><li><b>Control</b>: Is the record for the observed track of the control track? (Field type: Categorical)</li><li><b>ground</b>: Leaf density <0.5m above ground (Field type: Ordered Categorical)</li><li><b>understorey</b>: Leaf density 0.5m to 3m above ground (Field type: Ordered Categorical)</li><li><b>midstorey</b>: Leaf density 3m to 20m above ground (Field type: Ordered Categorical)</li><li><b>canopy</b>: Leaf density >20m above ground (Field type: Ordered Categorical)</li><li><b>forest.quality</b>: Forest quality (Field type: Ordered Categorical)</li><li><b>Predator</b>: Predation risk (Field type: Ordered Categorical)</li><li><b>Densiometer</b>: Canopy cover (Field type: Numeric)</li><li><b>Relascope</b>: Tree volume (Field type: Numeric)</li><li><b>height</b>: Height above ground (Field type: Ordered Categorical)</li><li><b>rain</b>: Rainfall (Field type: Ordered Categorical)</li><li><b>moon.phase</b>: Lunar phase (Field type: Ordered Categorical)</li></ul><br></li><li><p><b>environmental data</b> (Worksheet trap_covariates)</p><p>Dimensions: 295 rows by 14 columns</p><p>Description: Environmental quality metrics at trap locations</p><p>Fields: </p><ul><li><b>Site</b>: Site locality in camera trap grid (Field type: Location)</li><li><b>trap</b>: Trap identity within block (Field type: ID)</li><li><b>ground</b>: Leaf density <0.5m above ground (Field type: Ordered Categorical)</li><li><b>understorey</b>: Leaf density 0.5m to 3m above ground (Field type: Ordered Categorical)</li><li><b>midstorey</b>: Leaf density 3m to 20m above ground (Field type: Ordered Categorical)</li><li><b>canopy</b>: Leaf density >20m above ground (Field type: Ordered Categorical)</li><li><b>forest quality (O)</b>: Forest quality (Field type: Ordered Categorical)</li><li><b>Predator</b>: Predation risk (Field type: Ordered Categorical)</li><li><b>Densiometer 1</b>: Canopy cover - 1 of 4 measurements (Field type: Numeric)</li><li><b>Densiometer 2</b>: Canopy cover - 1 of 4 measurements (Field type: Numeric)</li><li><b>Densiometer 3</b>: Canopy cover - 1 of 4 measurements (Field type: Numeric)</li><li><b>Densiometer 4</b>: Canopy cover - 1 of 4 measurements (Field type: Numeric)</li><li><b>Relascope</b>: Tree volume (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2012-06-01 to 2012-07-21</p><p><b>Latitudinal extent: </b>4.6931 to 4.7166</p><p><b>Longitudinal extent: </b>117.5304 to 117.5975</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> (as <i>Maxomys whiteheadi</i>)<br> -  -  -  -  - <i>Leopoldamys</i><br> -  -  -  -  -  - <i>Leopoldamys sabanus</i><br> -  -  -  -  - <i>Maxomys</i><br> -  -  -  -  -  - <i>Maxomys surifer</i><br> -  -  -  -  - <i>Rattus</i><br> -  -  -  -  -  - <i>Rattus rattus</i><br></div><p></p>
Myrmecophilous Pselaphine beetles in tropical forests
<b>Description: </b><p>This data set includes taxanomic and abunance data for Pselaphinae beetles and ants collected in the leaf litter across Primary and logged forests sites. Selected environemntal variables (soil temperature, soil moisture and canopy cover) were also recorded at each sample site. </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/46"><b>The Maliau Quantitative Inventory</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=180">here</a></p><p><b>Files: </b>This consists of 1 file: Psomas_Ant_Pselaphine_SAFE_dataset.xlsx</p><p><b>Psomas_Ant_Pselaphine_SAFE_dataset.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>EnvironVariables</b> (described in worksheet EnvironVariables)</p><p>Description: Environmental variables</p><p>Number of fields: 4</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Site</b>: Site of measurements (Field type: Location)</li><li><b>Temp</b>: Soil temperature (Field type: Numeric)</li><li><b>Moisture</b>: Soil moisture (Field type: Numeric)</li><li><b>Cover</b>: Canopy cover (Field type: Numeric)</li></ul></li><li><p><b>Ant-Psel</b> (described in worksheet Ant-Psel)</p><p>Description: Ant-Pselaphine data</p><p>Number of fields: 3</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Site</b>: Site where sample was collected (Field type: Location)</li><li><b>ant species richness</b>: Number of ant species in sample (Field type: Numeric)</li><li><b>ant abundance</b>: Total number of ants in sample (Field type: Abundance)</li></ul></li><li><p><b>MorphAbundance</b> (described in worksheet MorphAbundance)</p><p>Description: Morphospeices abundance</p><p>Number of fields: 43</p><p>Number of data rows: 20</p><p>Fields: </p><ul><li><b>Site</b>: Site where specimens were collected (Field type: Location)</li><li><b>Psel1</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel2</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel3</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel4</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel5</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel6</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel7</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel8</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel9</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel10</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel11</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel12</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel13</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel14</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel15</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel16</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel17</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel18</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel19</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel20</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel21</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel22</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel23</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel24</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel25</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel26</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel27</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel28</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel29</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel30</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel31</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel32</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel33</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel34</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel35</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel36</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel37</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel38</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel39</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel40</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel41</b>: Number of individuals collected (Field type: Abundance)</li><li><b>Psel42</b>: Number of individuals collected (Field type: Abundance)</li></ul></li><li><p><b>MorphFunctTraits</b> (described in worksheet MorphFunctTraits)</p><p>Description: Morphospecies_Functional Traits</p><p>Number of fields: 17</p><p>Number of data rows: 42</p><p>Fields: </p><ul><li><b>Morphospecies</b>: Morphospecies identity (Field type: Taxa)</li><li><b>AL</b>: Antennae length (Field type: Numeric Trait)</li><li><b>TAL</b>: Termianl antennomere length (Field type: Numeric Trait)</li><li><b>TAW</b>: Termianl antennomere width (Field type: Numeric Trait)</li><li><b>AN</b>: Antennomere number (Field type: Numeric Trait)</li><li><b>HCA</b>: Hollow cavity absent in terminal antennomere (Field type: Categorical Trait)</li><li><b>HCP</b>: Hollow cavity present in terminal antennomere (Field type: Categorical Trait)</li><li><b>TA</b>: Trichomes absent (Field type: Categorical Trait)</li><li><b>TP</b>: Trichomes present (Field type: Categorical Trait)</li><li><b>FP</b>: Foveae present (Field type: Categorical Trait)</li><li><b>FA</b>: Foveae absent (Field type: Categorical Trait)</li><li><b>Bef2</b>: Basal elytral foveae, two set (Field type: Categorical Trait)</li><li><b>Bef3</b>: Basal elytral fovea, thee set (Field type: Categorical Trait)</li><li><b>Bef1</b>: Basal elytral foveae, one set (Field type: Categorical Trait)</li><li><b>Bef0</b>: Basal elytral foveae, no set (Field type: Categorical Trait)</li><li><b>Bef4</b>: Basal elyral fovea, four set (Field type: Categorical Trait)</li><li><b>Myrmycophile</b>: Myrmecophily status (Field type: Categorical Trait)</li></ul></li></ol><p><b>Date range: </b>2012-09-01 to 2012-10-31</p><p><b>Latitudinal extent: </b>4.6922 to 4.9702</p><p><b>Longitudinal extent: </b>116.9669 to 117.7981</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> -  -  - Coleoptera<br> -  -  -  - Pselaphidae<br> -  -  -  -  - [Psel1]<br> -  -  -  -  - [Psel10]<br> -  -  -  -  - [Psel15]<br> -  -  -  -  - [Psel17]<br> -  -  -  -  - [Psel28]<br> -  -  -  -  - [Psel29]<br> -  -  -  -  - [Psel30]<br> -  -  -  -  - [Psel33]<br> -  -  -  -  - [Psel35]<br> -  -  -  -  - [Psel41]<br> -  -  -  -  - [Psel9]<br> -  -  -  - Staphylinidae<br> -  -  -  -  - <i>Apharinodes</i><br> -  -  -  -  -  - [Psel13]<br> -  -  -  -  -  - [Psel37]<br> -  -  -  -  - <i>Aphilia</i><br> -  -  -  -  -  - [Psel12]<br> -  -  -  -  -  - [Psel38]<br> -  -  -  -  - <i>Batraxis</i><br> -  -  -  -  -  - [Psel31]<br> -  -  -  -  -  - [Psel39]<br> -  -  -  -  - <i>Bibloporus</i><br> -  -  -  -  -  - [Psel2]<br> -  -  -  -  - <i>Cerylambus</i><br> -  -  -  -  -  - [Psel27]<br> -  -  -  -  - <i>Cratna</i><br> -  -  -  -  -  - [Psel16]<br> -  -  -  -  -  - [Psel19]<br> -  -  -  -  -  - [Psel32]<br> -  -  -  -  - <i>Curculionellus</i><br> -  -  -  -  -  - [Psel26]<br> -  -  -  -  - <i>Diaugis</i><br> -  -  -  -  -  - [Psel42]<br> -  -  -  -  - <i>Enantius</i><br> -  -  -  -  -  - [Psel36]<br> -  -  -  -  - <i>Harmophorus</i><br> -  -  -  -  -  - [Psel4]<br> -  -  -  -  - <i>Mechanicus</i><br> -  -  -  -  -  - [Psel14]<br> -  -  -  -  -  - [Psel34]<br> -  -  -  -  -  - [Psel40]<br> -  -  -  -  -  - [Psel7]<br> -  -  -  -  - <i>Mnia</i><br> -  -  -  -  -  - [Psel11]<br> -  -  -  -  -  - [Psel18]<br> -  -  -  -  - <i>Plagiophorus</i><br> -  -  -  -  -  - [Psel20]<br> -  -  -  -  -  - [Psel22]<br> -  -  -  -  -  - [Psel3]<br> -  -  -  -  -  - [Psel6]<br> -  -  -  -  -  - [Psel8]<br> -  -  -  -  - <i>Pselaphodes</i><br> -  -  -  -  -  - [Psel21]<br> -  -  -  -  -  - [Psel25]<br> -  -  -  -  - <i>Pseudacerus</i><br> -  -  -  -  -  - [Psel23]<br> -  -  -  -  - <i>Pseudophanias</i><br> -  -  -  -  -  - [Psel5]<br> -  -  -  -  - <i>Sathytes</i><br> -  -  -  -  -  - [Psel24]<br> -  -  - Hymenoptera<br> -  -  -  - Formicidae<br></div><p></p>
Un uso diferente de los productos del bosque tropical: la talla y pintura de semillas de palma por parte de los emberá panameños - A different use of tropical forest products: Carving and painting of palm seeds by the Panamanian Emberá
<p><strong>Resumen</strong></p> <p>Los emberá son una etnia panameña cuya producción incluye semillas talladas de palma tagua (<em>Phytelephas seemannii</em>). No se ha publicado ninguna descripción detallada de estas tallas, por lo que aquí las describo con base en 826 tallas a la venta en internet. La mayoría son de tipo realista y representan ranas, seguidas numéricamente de colibríes, jaguares, "geckos" y pericos. Los invertebrados, las flores y los humanos son escasos. Normalmente cada semilla representa un solo individuo y muy pocas muestran algún comportamiento propio de la especie. Los colores dominantes son café y amarillo; seguidos de verde y el color natural de la semilla. Los precios van desde $25 a $45 pero las esculturas complejas pueden superar los $100. Por su representación realista de animales individuales en las semillas, los emberá panameños han desarrollado un aporte cultural que difiere de las tallas grandes y estilizadas que se hacen en otros lugares del mundo, con potencial para uso sostenible del bosque lluvioso.</p> <p><strong>Abstract</strong></p> <p>The Emberá are a Panamanian ethnic group whose products include carved and painted Tagua palm seeds (<em>Phytelephas seemannii</em>). There are no detailed descriptions of these carved seeds, so here I describe the carvings, based on 826 carved seeds offered for sale on-line. Most are realistic and represent frogs, followed by hummingbirds, jaguars, geckos and parakeets. Invertebrates, flowers and humans are rare. Usually each seed represents a single individual; few represent the animal’s behavior. The dominant colors are brown and yellow, followed by green and the natural color of the seed. Prices range from $ 25 to $ 45 but complex sculptures can exceed $ 100. With their realistic representation of individual animals in the seeds, Panamanian emberá have developed a cultural product that differs from large and stylized Tagua sculptures made elsewhere and that has potential for a sustainable use of the rainforest.</p> <p> </p>
Dataset from: Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest Biogeosciences 2019
<p>Dataset used for the manuscript <strong>Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest</strong> in Biogesciences, 2019</p>
Drier tropical forests are susceptible to functional changes in response to a long-term drought
<p>Maps created and resulting data from analysis in changes in community weighted mean of traits. The raw trait data and forest census data used are available from their sources in www.<a href="http://gem.tropicalforests.ox.ac.uk/">gem.tropicalforests.ox.ac.uk</a> and ForestPlots.net.</p>
Bioacoustic monitoring reveals shifts in breeding songbird populations and singing behaviour with selective logging in tropical forests
<b>Description: </b><p>Counts of individual male songbirds, males and females, songs and duets and original WAV audio recordings used to generate them</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/131"><b>Population and behavioral responses of songbirds to logging and rain forest fragmentation</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=3366104">here</a></p><p><b>Files: </b>This dataset consists of 13 files: Pillay_et_al_Songbirds_Acoustic_Counts_Vegetation_Cover.xlsx, 2013_B.zip, 2013_D.zip, 2013_E.zip, 2013_F.zip, 2013_OG1.zip, 2013_OG2.zip, 2014_B.zip, 2014_D.zip, 2014_E.zip, 2014_F.zip, 2014_OG1.zip, 2014_OG2.zip</p><p><b>Pillay_et_al_Songbirds_Acoustic_Counts_Vegetation_Cover.xlsx</b></p><p>This file contains dataset metadata and 5 data tables:</p><ol><li><p><b>CountsMale</b> (described in worksheet CountsMale)</p><p>Description: Counts of male individuals of songbird species</p><p>Number of fields: 12</p><p>Number of data rows: 5700</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day</b>: Days 1 to 2 of sampling in each plot (Field type: numeric)</li><li><b>date</b>: Date of Sampling (Field type: date)</li><li><b>jul.date</b>: Julian Date of Sampling (Field type: numeric)</li><li><b>time1-6AM</b>: Counts of male individuals for 6:00-6:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time2-7AM</b>: Counts of male individuals for 7:00-7:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time3-8AM</b>: Counts of male individuals for 8:00-8:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li></ul></li><li><p><b>CountsMaleFemale</b> (described in worksheet CountsMaleFemale)</p><p>Description: Counts of male plus female individuals of songbird species</p><p>Number of fields: 12</p><p>Number of data rows: 1000</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day</b>: Days 1 to 2 of sampling in each plot (Field type: numeric)</li><li><b>date</b>: Date of Sampling (Field type: date)</li><li><b>jul.date</b>: Julian Date of Sampling (Field type: numeric)</li><li><b>time1-6AM</b>: Counts of male and female individuals for 6:00-6:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time2-7AM</b>: Counts of male and female individuals for 7:00-7:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time3-8AM</b>: Counts of male and female individuals for 8:00-8:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li></ul></li><li><p><b>CountsSong</b> (described in worksheet CountsSong)</p><p>Description: Counts of songs</p><p>Number of fields: 9</p><p>Number of data rows: 2850</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day3-6AM</b>: Counts of songs for 6:00-6:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-7AM</b>: Counts of songs for 7:00-7:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-8AM</b>: Counts of songs for 8:00-8:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li></ul></li><li><p><b>CountsDuet</b> (described in worksheet CountsDuet)</p><p>Description: Counts of duets</p><p>Number of fields: 9</p><p>Number of data rows: 500</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day3-6AM</b>: Counts of duets for 6:00-6:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-7AM</b>: Counts of duets for 7:00-7:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-8AM</b>: Counts of duets for 8:00-8:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li></ul></li><li><p><b>VegetationCover</b> (described in worksheet VegetationCover)</p><p>Description: Vegetation cover data</p><p>Number of fields: 6</p><p>Number of data rows: 50</p><p>Fields: </p><ul><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>forest.type</b>: Forest Type (Field type: categorical)</li><li><b>udens</b>: Proportion understory cover (Field type: numeric)</li><li><b>cc</b>: Proportion canopy cover (Field type: numeric)</li><li><b>can.ht</b>: Average canopy height (Field type: numeric)</li><li><b>max.canopy</b>: Maximum height of standing vegetation (Field type: numeric)</li></ul></li></ol><p><b>2013_B.zip</b></p><p>Description: WAV files from 2013 for site B</p><p><b>2013_D.zip</b></p><p>Description: WAV files from 2013 for site D</p><p><b>2013_E.zip</b></p><p>Description: WAV files from 2013 for site E</p><p><b>2013_F.zip</b></p><p>Description: WAV files from 2013 for site F</p><p><b>2013_OG1.zip</b></p><p>Description: WAV files from 2013 for site OG1</p><p><b>2013_OG2.zip</b></p><p>Description: WAV files from 2013 for site OG2</p><p><b>2014_B.zip</b></p><p>Description: WAV files from 2014 for site B</p><p><b>2014_D.zip</b></p><p>Description: WAV files from 2014 for site D</p><p><b>2014_E.zip</b></p><p>Description: WAV files from 2014 for site E</p><p><b>2014_F.zip</b></p><p>Description: WAV files from 2014 for site F</p><p><b>2014_OG1.zip</b></p><p>Description: WAV files from 2014 for site OG1</p><p><b>2014_OG2.zip</b></p><p>Description: WAV files from 2014 for site OG2</p><p><b>Date range: </b>2013-04-09 to 2014-07-26</p><p><b>Latitudinal extent: </b>4.6881 to 4.7530</p><p><b>Longitudinal extent: </b>116.9477 to 117.6249</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> -  -  -  Aves <br> -  -  -  -  Passeriformes <br> -  -  -  -  -  Timaliidae <br> -  -  -  -  -  -  <i>Stachyris</i> <br> -  -  -  -  -  -  -  <i>Stachyris maculata</i> <br> -  -  -  -  -  -  -  <i>Stachyris erythroptera</i> <br> -  -  -  -  -  -  -  <i>Stachyris poliocephala</i> <br> -  -  -  -  -  -  <i>Macronus</i> <br> -  -  -  -  -  -  -  <i>Macronus bornensis</i> <br> -  -  -  -  -  -  -  <i>Macronus ptilosus</i> (as synonym: <i>Macronous ptilosus</i>)<br> -  -  -  -  -  -  <i>Stachyridopsis</i> <br> -  -  -  -  -  -  -  <i>Stachyridopsis rufifrons</i> (as synonym: <i>Stachyris rufifrons</i>)<br> -  -  -  -  -  -  <i>Pomatorhinus</i> <br> -  -  -  -  -  -  -  <i>Pomatorhinus montanus</i> <br> -  -  -  -  -  Pellorneidae <br> -  -  -  -  -  -  <i>Trichastoma</i> <br> -  -  -  -  -  -  -  <i>Trichastoma bicolor</i> <br> -  -  -  -  -  -  <i>Alcippe</i> <br> -  -  -  -  -  -  -  <i>Alcippe brunneicauda</i> <br> -  -  -  -  -  -  <i>Pellorneum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum capistratum</i> <br> -  -  -  -  -  -  <i>Malacocincla</i> <br> -  -  -  -  -  -  -  <i>Malacocincla malaccensis</i> <br> -  -  -  -  -  -  <i>Malacopteron</i> <br> -  -  -  -  -  -  -  <i>Malacopteron magnirostre</i> <br> -  -  -  -  -  -  -  <i>Malacopteron magnum</i> <br> -  -  -  -  -  -  -  <i>Malacopteron cinereum</i> <br> -  -  -  -  -  -  -  <i>Malacopteron affine</i> <br> -  -  -  -  -  Pycnonotidae <br> -  -  -  -  -  -  <i>Alophoixus</i> <br> -  -  -  -  -  -  -  <i>Alophoixus bres</i> <br> -  -  -  -  -  -  -  <i>Alophoixus phaeocephalus</i> <br> -  -  -  -  -  -  <i>Tricholestes</i> <br> -  -  -  -  -  -  -  <i>Tricholestes criniger</i> <br> -  -  -  -  -  -  <i>Iole</i> <br> -  -  -  -  -  -  -  <i>Iole olivacea</i> <br> -  -  -  -  -  -  <i>Pycnonotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus atriceps</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus simplex</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus eutilotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus brunneus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus erythropthalmos</i> <br> -  -  -  -  -  Stenostiridae <br> -  -  -  -  -  -  <i>Culicicapa</i> <br> -  -  -  -  -  -  -  <i>Culicicapa ceylonensis</i> <br> -  -  -  -  -  Muscicapidae <br> -  -  -  -  -  -  <i>Cyornis</i> <br> -  -  -  -  -  -  -  <i>Cyornis superbus</i> <br> -  -  -  -  -  -  -  <i>Cyornis unicolor</i> <br> -  -  -  -  -  -  <i>Rhinomyias</i> <br> -  -  -  -  -  -  -  <i>Rhinomyias umbratilis</i> <br> -  -  -  -  -  -  <i>Trichixos</i> <br> -  -  -  -  -  -  -  <i>Trichixos pyrropygus</i> <br> -  -  -  -  -  -  <i>Copsychus</i> <br> -  -  -  -  -  -  -  <i>Copsychus stricklandii</i> <br> -  -  -  -  -  Monarchidae <br> -  -  -  -  -  -  <i>Terpsiphone</i> <br> -  -  -  -  -  -  -  <i>Terpsiphone paradisi</i> <br> -  -  -  -  -  -  <i>Hypothymis</i> <br> -  -  -  -  -  -  -  <i>Hypothymis azurea</i> <br></div><p></p>
Data from: Tropical forest soundscapes as testimonies of past land use
<p><span>Habitat loss is considered one of the factors that causes a decrease in biodiversity in the tropics. Many efforts have been made to protect and restore tropical forests, but it is difficult to quantify biodiversity and assess restoration areas. Studies have used soundscape analyses to gain information about the landscape, using acoustic indices as indicators of the health of faunal communities. We aimed to assess the changes in acoustic indices in habitats with different types of human exploitation and to evaluate the variation in acoustic indices during the hours of the day among these different habitats. The recordings were performed using passive acoustic monitoring (PAM), deriving 15 acoustic indices to assess the characteristics of each environment. The results suggest that in rubber plantations (RP) there was less acoustic activity, followed by rubber-forest plantations (RFP) and light selectively logged areas (LSL), while in habitats of young secondary forests (YSF), mature secondary forests (MSF) and intensive selectively logged forests (ISL) there was more acoustic activity, which indicates greater faunal activity.<span> </span>This study demonstrates that across the various indices tested, the plantation areas (RP and RFP) presented lower values, which indicate reduced acoustic activity compared to forested areas, with the exception of the area lightly selective logged (LSL) which showed lower values of the indices that measure the activity of sonoriferous specie. Therefore, assessing landscape use by monitoring the soundscape can be useful to timely evaluate the ecological dynamics of areas with high species richness, such as the Atlantic Forest.<span> </span></span></p>
Tracing diurnal variations of atmospheric CO2, O2 and δ13CO2 over a tropical and a temperate forest
<p>These are the datasets of the campaigns used in the paper: <em>Tracing diurnal variations of atmospheric CO2, O2 and δ13CO2 over a tropical and a temperate forest. </em>Two campaigns are included: <em>CloudRoots </em>and <em>Loobos</em>. Have a look at the README files on the specifics of what is inside the files and how to cite these datasets. </p> <p> </p>
Coping with branch excision when measuring leaf net photosynthetic rates in a lowland tropical forest
Measuring leaf gas exchange from canopy leaves is fundamental for our understanding of photosynthesis and for a realistic representation of carbon uptake in vegetation models. Since canopy leaves are often difficult to reach, especially in tropical forests with emergent trees up to 60 meters at remote places, canopy access techniques such as canopy cranes or towers have facilitated photosynthetic measurements. These structures are expensive and therefore not very common. As an alternative, branches are often cut to enable leaf gas exchange measurements. The effect of branch excision on leaf gas exchange rates should be minimised and quantified to evaluate possible bias. We compared light-saturated leaf net photosynthetic rates measured on excised and intact branches. We selected branches positioned at three canopy positions, estimated relative to the top of the canopy: upper sunlit foliage, middle canopy foliage, and lower canopy foliage. We studied the variation of the effects of branch excision and transport amongst branches at these different heights in the canopy. After excision and transport, light-saturated leaf net photosynthetic rates were close to zero for most leaves due to stomatal closure. However, when the branch had acclimated to its new environmental conditions – which took on average 20 minutes –light-saturated leaf net photosynthetic rates did not significantly differ between the excised and intact branches. We therefore conclude that branch excision does not affect the measurement of light-saturated leaf net photosynthesis, provided that the branch is recut under water and is allowed sufficient time to acclimate to its new environmental conditions.
Data from: Lianas abundance is positively related with the avian acoustic community in tropical dry forests
Dry forests are important sources of biodiversity where lianas are highly abundant given their ability to grow during times of drought and as a result of secondary growth processes. Lianas provide food and shelter for fauna such as birds, but there are no studies assessing the influence of liana abundance on birds in dry forests. Here we evaluate the influence of liana abundance on the avian acoustic community in the dry forests of Costa Rica at Santa Rosa National Park. We selected forest sites with different levels of liana abundance and set up automated sound recorders for data collection, analysis and estimation of the avian acoustic community. When the number of lianas increases, the avian acoustic community becomes more complex. Lianas could provide important direct and indirect resources for birds such as structure for shelter, protection, nesting and roosting, and food. The positive relationship that lianas have with birds is particularly important in dry forests where lianas are becoming highly abundant due to the level of forest disturbance and climate change, especially for some bird species that are restricted to this ecosystem. By validating the number of bird species detected in the recordings with the acoustic complexity index, we found that a higher acoustic complexity means higher species richness.
Thinner bark increases sensitivity of wetter Amazonian tropical forests to fire
<p>Understory fires represent an accelerating threat to Amazonian tropical forests and can, during drought, affect larger areas than deforestation itself. These fires kill trees at rates varying from < 10 to c. 90% depending on fire intensity, forest disturbance history and tree functional traits. Here, we examine variation in bark thickness across the Amazon. Bark can protect trees from fires, but it is often assumed to be consistently thin across tropical forests. Here, we show that investment in bark varies, with thicker bark in dry forests and thinner in wetter forests. We also show that thinner bark translated into higher fire‐driven tree mortality in wetter forests, with between 0.67 and 5.86 gigatonnes CO<sub>2</sub> lost in Amazon understory fires between 2001 and 2010. Trait‐enabled global vegetation models that explicitly include variation in bark thickness are likely to improve the predictions of fire effects on carbon cycling in tropical forests.</p>
A standardized assessment of forest mammal communities reveals consistent functional composition and vulnerability across the tropics
<p class="MsoPlainText">Understanding global diversity patterns has benefitted from a focus on functional traits and how they relate to variation in environmental conditions among assemblages. Distant communities in similar environments often share characteristics, and for tropical forest mammals, this functional trait convergence has been demonstrated at coarse scales (110-200 km resolution), but less is known about how these patterns manifest at fine scales, where local processes (e.g., habitat features and anthropogenic activities) and biotic interactions occur. Here, we used standardized camera trapping data and a novel analytical method that accounts for imperfect detection to assess how the functional composition of terrestrial mammal communities for two traits – trophic guild and body mass – varies across 16 protected areas in tropical forests and three continents, in relation to the extent of protected habitat and anthropogenic pressures. We found that despite their taxonomic differences, communities generally have a consistent trophic guild composition, and respond similarly to these factors. Insectivores were found to be sensitive to the size of protected habitat and surrounding human population density. Body mass distribution varied little among communities both in terms of central tendency and spread, and interestingly, community average body mass declined with proximity to human settlements. Results indicate predicted trait convergence among assemblages at the coarse scale reflects consistent functional composition among communities at the local scale, suggesting that broadly similar habitats and selective pressures shaped communities with similar trophic strategies and responses to drivers of change. These similarities provide a foundation for assessing assemblages under anthropogenic threats and sharing conservation measures.Understanding global diversity patterns has benefitted from a focus on functional traits and how they relate to variation in environmental conditions among assemblages. Distant communities in similar environments often share characteristics, and for tropical forest mammals, this functional trait convergence has been demonstrated at coarse scales (110-200 km resolution), but less is known about how these patterns manifest at fine scales, where local processes (e.g., habitat features and anthropogenic activities) and biotic interactions occur. Here, we used standardized camera trapping data and a novel analytical method that accounts for imperfect detection to assess how the functional composition of terrestrial mammal communities for two traits – trophic guild and body mass – varies across 16 protected areas in tropical forests and three continents, in relation to the extent of protected habitat and anthropogenic pressures. We found that despite their taxonomic differences, communities generally have a consistent trophic guild composition, and respond similarly to these factors. Insectivores were found to be sensitive to the size of protected habitat and surrounding human population density. Body mass distribution varied little among communities both in terms of central tendency and spread, and interestingly, community average body mass declined with proximity to human settlements. Results indicate predicted trait convergence among assemblages at the coarse scale reflects consistent functional composition among communities at the local scale, suggesting that broadly similar habitats and selective pressures shaped communities with similar trophic strategies and responses to drivers of change. These similarities provide a foundation for assessing assemblages under anthropogenic threats and sharing conservation measures.</p>
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.