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430 results for “Forest Structure”
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>
Figure 1 in Structure of summer bat assemblages in forests in European Russia
Figure 1. Location of the reserves in European Russia (B: Bryansky, O: Oksky, V: Voronezhsky).
Figure 3 in Structure of summer bat assemblages in forests in European Russia
Figure 3. Location of main mist-netting site in "Bryansky Les" State Nature Biosphere Reserve.
Figure 4 in Structure of summer bat assemblages in forests in European Russia
Figure 4. Location of main mist-netting site in Oksky State Nature Biosphere Reserve.
Complex trait‒environment relationships underlie the structure of forest plant communities
<p>Traits differentially adapt plant species to particular conditions generating compositional shifts along environmental gradients. As a result, community-scale trait values show concomitant shifts, termed trait‒environment relationships. Trait‒environment relationships are often assessed by evaluating community-weighted mean (CWM) traits observed along environmental gradients. Regression-based approaches (CWMr) assume that local communities exhibit traits centered at a single optimum value and that traits do not covary meaningfully. Evidence suggests that the shape of trait‒abundance relationships can vary widely along environmental gradients—reflecting complex interactions—and traits are usually interrelated. We used a model that accounts for these factors to explore trait‒environment relationships in herbaceous forest plant communities in Wisconsin (USA). We built a generalized linear mixed model (GLMM) to analyze how abundances of 185 species distributed among 189 forested sites vary in response to four functional traits (vegetative height-VH, leaf size-LS, leaf mass per area-LMA, and leaf carbon content), six environmental variables describing overstory, soil, and climate conditions, and their interactions. The GLMM allowed us to assess the nature and relative strength of the resulting 24 trait‒environment relationships. We also compared results between GLMM and CWMr to explore how conclusions differ between approaches. The GLMM identified five significant trait‒environment relationships that together explain ~40% of variation in species abundances across sites. Temperature appeared as a key environmental driver, with warmer and more seasonal sites favoring taller plants. Soil texture and temperature seasonality affected LS and LMA; seasonality effects on LS and LMA were nonlinear, declining at more seasonal sites. Though often assumed for CWMr, only some traits under certain conditions had centered optimum trait‒abundance relationships. CWMr more liberally identified (13) trait‒environment relationships as significant but failed to detect the temperature-seasonality‒LMA relationship identified by the GLMM. Synthesis. Although GLMM represents a more methodologically complex approach than CWMr, it identified a reduced set of trait‒environment relationships still capable of accounting for the responses of forest understory herbs to environmental gradients. It also identified separate effects of mean and seasonal temperature on LMA that appear important in these forests, generating useful insights and supporting broader application of GLMM approach to understand trait‒environment relationships.</p>
Figure 3 in Distribution of gall-inducing arthropods in areas of deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil): effects of anthropization, vegetation structure and seasonality
Figure 3. Morphological characterization of the galls induced by arthropods in areas of deciduous seasonal forest of Parque da Sapucaia, Montes Claros, MG, Brazil: (A, B, C) Fabaceae – Dalbergia sp.; (D) undetermined; (E, F, G) Loganiaceae – Sthrychnos sp.; (H) Opiliaceae – Agonandra brasiliensis; (I) Sapindaceae – Serjania sp.; (J) Vitaceae – Cissus sp.; (K) Undetermined family 1; and (L) Undetermined family 2.
Figure 2 in Distribution of gall-inducing arthropods in areas of deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil): effects of anthropization, vegetation structure and seasonality
Figure 2. Morphological characterization of the galls induced by arthropods in areas of deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil): (A) Anacardiaceae – Myracrodruon urundeuva; (B) Schinopsis brasiliensis; (C, D) Asteraceae – Vernonanthura brasiliana; (E) Bignoniaceae undetermined; (F, G) Cannabaceae – Celtis brasiliensis; (H) Combretaceae – Combretum leprosum; (I) Combretum duarteanum; (J) Terminalia phaeocarpa; (K) Cucurbitaceae undetermined; (L) Fabaceae – Anadenanthera colubrine; (M, N) Apuleia leiocarpa; (O) Bauhinia pulchela; and (P) Bauhinia rufa.
Figure 1 in Distribution of gall-inducing arthropods in areas of deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil): effects of anthropization, vegetation structure and seasonality
Figure 1. Location and characterization of the study area. (A) Location of Parque da Sapucaia (Montes Claros, MG, Brazil), between the urban area of Montes Claros and Parque Estadual da Lapa Grande. Source: Google Earth.(B) Characterization of the vegetation in the rainy season.(C) Characterization of the vegetation in the dry season.
Figure 6 in Distribution of gall-inducing arthropods in areas of deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil): effects of anthropization, vegetation structure and seasonality
Figure 6. Comparison of the richness of gall morphotypes between different sampling seasons in the deciduous seasonal forest of Parque da Sapucaia (Montes Claros, MG, Brazil). (A) Comparison of the richness of gall morphotypes between the rainy and dry seasons. (B) Richness of gall morphotypes in the preserved and anthropized plots during the rainy and dry seasons.
Connectivity and succession of open structures as a key to sustaining light-demanding biodiversity in deciduous forests
<p>1. European forests are facing a rapid decline of light-demanding biota. This has prompted active interventions to re-establish and maintain partial habitat openness in protected areas. Managers of protected areas, however, need substantially more scientific evidence to support their decisions on where, when, and how to intervene.</p> <p>2. We investigated the importance of spatial continuity of open forest habitats in different years of succession, using six pairs of experimental clearings established in the formerly open, oak-dominated forests of the Podyji National Park (Czech Republic). In each pair, one clearing was connected to the forest edge, while the other was isolated in closed forest. We sampled butterflies (74 spp.), moths (435 spp.), saproxylic beetles (465 spp.), and vascular plants (567 spp.) on the 12 clearings during the first five years of succession. We then compared species richness, abundance, and composition of the four taxa between the two clearing types and along the succession.</p> <p>3. All studied insect groups were substantially more species-rich and more abundant in connected than in isolated clearings. Species composition of plants, moths, and butterflies differed between the clearing types.</p> <p>4. The number of species of all studied taxa generally increased from the first to the second or third year after cutting; species composition of all taxa differed among years. This suggests rapid changes in habitat quality and thus limited time for colonisation by light-demanding organisms.</p> <p>5. <i>Synthesis and applications</i>: Our results offer an evidence that spatial connectivity and rapid temporal dynamics are important habitat features for light-demanding insects. Attempts to create or restore habitats for light-demanding forest biota should take into account that: (i) Insects benefit from direct connection of new open patches to open habitats or flight corridors such as forest edges. (ii) Considering plants, the optimal solution is to connect newly created open forest habitats to existing habitats with established biota of high conservation value. (iii) Interventions should be carried out within short time intervals, i.e. within years rather than decades. (iv) A fine mosaic of interconnected<span>, open woodland patches in various successional stages is more beneficial than a single large patch with a single successional stage.</span></p>
Feedbacks between forest structure and an opportunistic fungal pathogen
<p>Abiotic stresses, physiological dysfunction, forest stand dynamics, and primary tree attackers (native and non-native) are all recognized as important contributors to both anomalous tree mortality and background tree mortality, and thus as important influences on biogeochemical cycling and habitat for associated terrestrial organisms. Opportunistic and latent tree pathogens and insect pests have largely been left out of this discussion, probably because they are difficult to monitor and their effects sometimes more diffuse, yet they play important roles in tree mortality scenarios.</p> <p>We know very little about the influence of biotic gradients, e.g., forest dynamics and forest structure, on the occurrence and effects of these opportunistic biotic enemies. To better understand the influence of an important biotic gradient—forest structure as represented by tree density and competition—on the occurrence and distribution of a typical opportunistic pathogen, we analyzed the relationship between tree neighborhood competition and soil-dwelling rhizomorphs of the fungus Armillaria gallica in regenerating (20-yr-old) and mature (80-100-yr-old) hardwood-dominated forest stands in central Missouri following a severe drought.</p> <p>We detected A. gallica, rhizomorphs in twice as many sampling sites in young (dense) plots as in mature (open) plots, and by contrast, found a twofold occurrence of other saprophytic fungi in the soil of mature plots compared to young plots. Mean tree density and competition index were significantly higher in subplots where A. gallica was found than in those with no A. gallica detections, and the pattern of A. gallica occurrence tracked closely with the occurrence of dense "hotspots" of competition in the plots.</p> <p>Synthesis. Early stages of forest structure and succession—potentially through initial inputs of disturbance-produced woody biomass, increased host connectivity and competition-influenced host tree susceptibility to fungal colonization—provide important feedbacks to A. gallica foraging, which in turn influences forest development. The recognition that tree density and competition influence A. gallica foraging suggests that in order to achieve a more comprehensive understanding of forest growth, development, and biogeochemical cycling, it may be necessary to increase life-history studies and basic occurrence surveys for opportunistic tree pathogens and insect pests.</p>
Low-severity winds reduce tropical forest structural complexity regardless of climate, topography or forest age
<p>Forests are often exposed to regular, non-severe winds (chronic wind exposure), yet the effect of such winds on canopy structure in tropical forests remains understudied. The height and structural complexity of a forest canopy are strongly and positively correlated with biodiversity and carbon accumulation. Understanding the drivers of canopy structural complexity across broad environmental gradients can therefore improve the mapping and modeling of diversity and carbon dynamics. Here we predict the height and structural complexity of forests in the heterogeneous island of Puerto Rico, with a particular focus on the impacts of chronic wind exposure. To do so, we used remote sensing to randomly sample ~20,000, 0.28 ha forested sites stratified by forest age, and used airborne LiDAR data from 2016 to quantify canopy height and a key metric of structural complexity, rugosity – the standard deviation in canopy height. We then ran random forest models to predict canopy height and rugosity based on chronic wind exposure, forest age, mean annual precipitation, elevation, slope, soil type, soil available water storage, and exposure to two previous hurricanes (in 1989 and 1998). Canopy height was 4 m taller on average (41%) between forests aged 17-25 years and old-growth forests and by 4 m on average (41%) between 1,000 and 2,000 mm<sup>-yr</sup> precipitation, leveling off at 2,000 mm<sup>-yr</sup>. Height was 2.12 m (16%) shorter on average between sites exposed to chronic winds and protected sites after accounting for all other factors. Rugosity was 1 m (32%) greater between the tallest and shortest forests, by 0.5 m (15%) between 1,000 and 2,000 mm<sup>-yr</sup> precipitation, and smaller by 0.5 m (15%) between forests above and below 1,000 m elevation. Rugosity was highest in forests of intermediate age (25-40 years), and lowest in old-growth forests, possibly because of higher elevation and chronic wind exposure in old-growth forests. We found no effect of slope, soil characteristics or previous hurricane exposure on either height or rugosity. Our results suggest that alongside forest age and climate context, chronic wind exposure plays an integral role in shaping the structure and carbon cycle of tropical forests.</p>
Landscape structure, predictability of forest regeneration trajectories, and recovery rate on secondary forests
<p>Abandonment of agricultural lands promotes the global expansion of secondary forests, which are critical for preserving biodiversity and ecosystem functions and services. Such roles largely depend, however, on two essential successional attributes, trajectory and recovery rate, which are expected to depend on landscape-scale forest cover in non- linear ways. This dataset is the synthesis outcome of 22 independent databases from studies of woody plant species recovery as part of the research project entitled "Impacts of landscape structure on secondary tropical forest regeneration". This work aimed to understand the effect of landscape-level disturbance on forest regeneration, specifically through the predictability of trajectories and the recovery rate of these forests.</p> <p>Using a multiscale approach and a large vegetation dataset (843 plots, 3511 tree species) from 22 secondary forest chronosequences distributed across the Neotropics, we show that successional trajectories of woody plant species richness, stem density, and basal area are less predictable in landscapes (4-km radius) with intermediate (40-60%) forest cover than in landscapes with high (>60%) forest cover. This supports theory suggesting that high spatial and environmental heterogeneity in intermediately deforested landscapes can increase the variation in key ecological factors for forest recovery (e.g. seed dispersal, seedling recruitment), increasing the uncertainty of successional trajectories. Regarding the recovery rate, only the species richness is positively related to forest cover in relatively small (1-km radius) landscapes. These findings highlight the importance of using a spatially-explicit landscape approach in restoration initiatives and suggest that these initiatives can be more effective in more forested landscapes, especially if implemented across spatial extents of 1-4 km radius. </p>
Floristic composition, structure and diversity of riparian forests in southwestern Nigeria: Conservation is inevitable
<p>The Nigerian riparian forest ecosystems had declined in extent and distribution and this had been attributed mainly to land use change. This study intended to provide an understanding of the links between plant diversity, composition, structures, and disturbances both anthropogenic and natural processes inducing the vegetation dynamics. Nine study sites were used for this study, within each site, five (5) plots (0.25 ha in size) were marked out and placed systematically at an interval of 10 m along the transect. A complete enumeration of plant species was carried out and identified at the species level. Diversity indices and structural parameters were determined and anthropogenic activities were ranked. A total number of 233 plant species were identified, belonging to 80 families; out of which, Euphorbiaceae and Apocynaceae were dominant families The density and basal area ranged from 2,200-6,000 ha<sup>-1</sup> and 2.59-17.58 m<sup>2</sup> ha<sup>-1</sup> respectively across the study sites. <em>Pterocarpus santalinoides</em>, <em>Alchornea cordiflora</em>, <em>Chassalia kolly</em>, <em>Tetracera</em> spp,<em> Fimbristylis</em>, <em>Bambusa vulgaris</em> and <em>Cyrtosperma senegalense</em> were the dominant species. The Shannon diversity index ranged from (1.38-3.49), Simpson (0.66-0.97), and Evenness diversity (0.43-0.84). Fisher alpha (10.03-30.21) and Whittaker beta diversity (0.36-0.89) values were highest in Ipetumodu (site VIII) and lowest in Ilesha (site II). Seventy-three (73%) of the species in this study had a low important value index (IVI). The dominance of some lianas and herbaceous species in the riparian forest sites showed disturbances, stages of ecological succession, and regeneration of the vegetation. Conservation is inevitable towards maintaining and protecting species diversity, ecosystem roles, and services of these forests in Nigeria.</p>
Differential effects of ecosystem engineering by the superb lyrebird Menura novaehollandiae and herbivory by large mammals on floristic regeneration and structure in wet eucalypt forests
<p>Ecosystem engineers that modify soil and ground-layer properties exert a strong influence on vegetation communities in ecosystems worldwide. Understanding the interactions between animal engineers and vegetation is challenging when in the presence of large herbivores, as many vegetation communities are simultaneously affected by both engineering and herbivory. The superb lyrebird <em>Menura novaehollandiae</em>, an ecosystem engineer in wet forests of south-eastern Australia, extensively modifies litter and soil on the forest floor. The aim of this study was to disentangle the impacts of engineering by lyrebirds and herbivory by large mammals on the composition and structure of ground-layer vegetation. We carried out a two-year, manipulative exclusion experiment in the Central Highlands of Victoria, Australia. We compared three treatments: fenced plots with simulated lyrebird foraging; fenced plots excluding herbivores and lyrebirds; and open controls. This design allowed assessment of the relative impacts of engineering and herbivory on germination rates, seedling density, vegetation cover and structure, and community composition. Engineering by lyrebirds enhanced the germination of seeds in the litter layer. After two years, more than double the number of germinants were present in 'engineered' than 'non-engineered' plots. Engineering did not affect the density of seedlings, but herbivory had strong detrimental effects. Herbivory also reduced the floristic richness and structural complexity (< 0.5 m) of forest vegetation, including the cover of herbs. Neither process altered the floristic composition of the vegetation within the 2-year study period. Ecosystem engineering by lyrebirds and herbivory by large mammals both influence the structure of forest-floor vegetation. The two-fold increase in seeds stimulated to germinate by engineering may contribute to the evolutionary adaptation of plants by allowing greater phenotypic expression and selection than would otherwise occur. Over long timescales, engineering and herbivory likely combine to maintain a more-open forest floor conducive to ongoing ecosystem engineering by lyrebirds.</p>
Datasets used for the publication: UAV-Lidar reveals that canopy structure mediates the influence of edge effects on forest diversity, function and microclimate
<p>Datasets used for the publication: UAV-Lidar reveals that canopy structure mediates the influence of edge effects on forest diversity, function and microclimate.</p> <p>The file "Blanchard_et_al_JoE_2023_data_plot_trees.csv" contains individual tree indentification data for the 46 plots used in the study.</p> <p>The file "Blanchard_et_al_JoE_2023_data_plot_aggregated.csv" contains plot-level aggregated metrics used for the analyses:</p> <p>- distance to the forest edge</p> <p>- diversity indices : the 20-sp rarefied species richness "rar_sp_richness_20" and the 20-sp rarefied Beta diversity "Beta_div" which corresponds to the plos coordinates on the PCoA first axis.</p> <p>- functional indices: the community weighted mean trait values for the four traits used in this study : ,"WD","SLA","LA","LDMC"; the fonctional divergence index "FD_trans.FDiv"; and the synthetic community weigthed mean trait wich corresponds to the postion on plots on the principal component analysis of species trait values "Functional_composition_trans". Note that the SLA and LA values were log-transformed before computing "FD_trans.FDiv" and "Functional_composition_trans".</p> <p>- UAV-LiDAR-dervived metrics: canopy height, gap fraction ("gap_fraction2"), slope, curvature</p> <p>- The estimated plot above graound biomass "agb_plot", and the mean value of the vapor pressure deficit during the drisest month "max_monthly_VPD".</p> <p>- coordinates of the plots in UTM 58S (Coordinate reference system)</p> <p>Please read the material and method section of the article for more informations on this dataset.</p>
A decade of diversity and forest structure: Post-logging patterns across life stages in an Afrotropical forest
<div class="page"> <div class="layoutArea"> <div class="column"> <p>Tropical forests are under threat of increasing pressure from income-generating land uses. Selective logging is a compromise that allows the use of the land while leaving much of the forest canopy intact across a landscape. However, the ecological impacts of selective logging are unclear, with evidence of positive, negative, and negligible effects on forest structure and diversity. We examined the impact of selective logging on the structure and diversity of evergreen tropical forests in the Monts de Cristal region, a chain of mid-elevation hills in northwestern Gabon. For three size classes (seedling, sapling, and adult) of woody plant species, we tested whether forest structure (canopy openness, stem density, basal area, and relative liana abundances) and diversity were altered in forests that had been logged one year and ten years prior, compared to unlogged forest. In general, we found no large impact of selective logging treatment on the structure and diversity of adult woody plant communities, but the seedling and sapling communities were affected. Compared to unlogged forest, one-year post-logging forest had greater variation in canopy openness and lower sapling stem density. Ten-year post-logging forest had higher seedling and sapling species evenness, higher sapling species diversity, and higher relative abundance of sapling-sized lianas compared to unlogged forest. Our results show that key differences between intact and selectively logged forests persist in the understory at least a decade after logging. Overall, these results contribute an additional data point in the literature on selective logging, specifically representing the impacts of very low impact selective logging in Central African forests. Our study highlights the value of exploring selective logging impacts at multiple time periods of recovery, and makes an important contribution to the knowledge of Central African managed forests.</p> </div> </div> </div>
UAV-Based Height Measurement and Height-Diameter Model integrating Taxonomic Effects: Exploring Vertical Structure of Aboveground Biomass and Species Diversity in a Malaysian Tropical Forest
<p>These Excel files are the dataset used for the analysis in the submitted paper</p> <p>Dataset S1: Data for 6-ha pot in Pasoh Forest Researve</p> <p>Dataset S2: Data for height–diameter (HD) models</p>
Supporting dataset for "Surface energy dynamics and canopy structural properties in intact and disturbed forests in the Southern Amazon"
<p>Supporting dataset for the manuscript “Surface energy dynamics and canopy structural properties in intact and disturbed forests in the Southern Amazon", currently under review in the Journal of Geophysical Research: Biogeosciences.</p>
Avian species functional diversity and habitat use the role of forest structural attributes and tree diversity in the Midlands Mistbelt forests of KwaZulu-Natal, South Africa
<p><span>Forest transformation has major impacts on biodiversity and ecosystem functioning. Identifying the influence of forest habitat structure and composition on avian functional communities is important for conserving and managing forest systems. This study investigated the effect of forest structure and composition characteristics on bird species community structure, habitat use, and functional diversity in 14 Mistbelt forest patches of the Midlands of KwaZulu-Natal in South Africa. We surveyed bird communities using point counts. We quantified bird functional diversity for each forest patch using three diversity indices: functional richness, functional evenness, and functional divergence. We further assessed species-specific responses by focusing on three avian forest specialists, orange ground-thrush </span><span><em>Geokichla</em> <em>gurneyi</em></span><span>, forest canary </span><span><em>Crithagra</em> <em>scotops</em></span><span>, and Cape parrot </span><span><em>Poicephalus</em> <em>robustus</em></span><span>. We found that bird community and forest-specialist species responses to forest structure and tree species diversity differed. Also, forest structural complexity, canopy cover, and tree species richness were the main forest characteristics better at explaining microhabitat influence on bird functional diversity. Forest patches with relatively high structural complexity and tree species richness had higher functional richness. Different structural characteristics influenced habitat use by the three forest specialists. Tree species diversity influenced </span><em><span>C. scotops</span> </em><span>and</span><em><span> G. </span><span>gurneyi</span></em><span> positively, </span><span>while </span><em><span>P. robustus</span></em><span> responded negatively to forest patches with high tree species richness. </span><span>Our study showed that site-scale forest structure and composition characteristics are important for bird species richness and functional richness. Forest patches with high tree species diversity and structural complexity should be maintained to conserve forest specialists, bird species richness, and functional richness. </span></p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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.