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371 results for “forest ecosystem”
Figure 3. Cluster Dendrogram indicating 11 in Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan
Figure 3. Cluster Dendrogram indicating 11 plant association types in the Lalkoo Valley.
Figure 1 in Composition and structure of plant communities in the Moist Temperate Forest Ecosystem of the Hindukush Mountains, Pakistan
Figure 1. Samping sites in Lalkoo valley.
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>
Occurrence of blood feeding terrestrial leeches in a degraded forest ecosystem
<b>Description: </b><p>This dataset includes the abundance of two species of terrestrial leech collected at multiple sites at the SAFE project in Sabah, Malaysia. Leech collections took place over two seasons, one in the dry season of 2015 and one in the wet season of 2016. For each of the sites, four repeated visits took place and 20 minute searches were conducted within the boundaries of 25 m2 vegetation plots. As these sites have been subjected to differennt degrees of current and historic degradation, the vegetation structure data is also included for each site. For a subset of the leech sites there is corresponding mammal detection data from camera traps across the landscape, which is also included in this dataset.</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/10"><b>The effects of rainforest fragmentation on mammal community assemblages using leech blood-meal analysis</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant , NE/K016148/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3476542">here</a></p><p><b>Files: </b>This consists of 1 file: Drinkwater2019_leech_occurrence.v2.xlsx</p><p><b>Drinkwater2019_leech_occurrence.v2.xlsx</b></p><p>This file contains dataset metadata and 4 data tables:</p><ol><li><p><b>Leech abundance and survey-covariates 2015</b> (described in worksheet abundance2015)</p><p>Description: This dataset has the abundance of all the leech individuals of both species collected during surveys in 2015 between February and June. The number of leech collected is split by species of leech and each of the four visits per site. For each survey at a site the associated survey-specific covariates are included. These are the associated effort (number of people collecting the leeches) and the date the visits happened (julian day since the beginning of the year). </p><p>Number of fields: 17</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point (Field type: location)</li><li><b>visit_B1</b>: Number of brown leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_B2</b>: Number of brown leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_B3</b>: Number of brown leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_B4</b>: Number of brown leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>visit_T1</b>: Number of tiger leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_T2</b>: Number of tiger leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_T3</b>: Number of tiger leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_T4</b>: Number of tiger leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>eff_1</b>: Number of people collecting leeches per survey as a measure of survey effort for the first visit to each site (Field type: abundance)</li><li><b>eff_2</b>: Number of people collecting leeches per survey as a measure of survey effort for the second visit to each site (Field type: abundance)</li><li><b>eff_3</b>: Number of people collecting leeches per survey as a measure of survey effort for the third visit to each site (Field type: abundance)</li><li><b>eff_4</b>: Number of people collecting leeches per survey as a measure of survey effort for the fourth visit to each site (Field type: abundance)</li><li><b>date.1</b>: Julian date of visit 1 (Field type: numeric)</li><li><b>date.2</b>: Julian date of visit 2 (Field type: numeric)</li><li><b>date.3</b>: Julian date of visit 3 (Field type: numeric)</li><li><b>date.4</b>: Julian date of visit 4 (Field type: numeric)</li></ul></li><li><p><b>Leech abundance and survey-covariates 2016</b> (described in worksheet abundance2016)</p><p>Description: This dataset has the abundance of all the leech individuals of both species collected during surveys in 2016 between September and December. The number of leech collected is split by species of leech and each of the four visits per site. For each survey at a site the associated survey-specific covariates are included. These are the associated effort (number of people collecting the leeches) and the date the visits happened (julian day since the beginning of the year). </p><p>Number of fields: 17</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point (Field type: location)</li><li><b>visit_B1</b>: Number of brown leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_B2</b>: Number of brown leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_B3</b>: Number of brown leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_B4</b>: Number of brown leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>visit_T1</b>: Number of tiger leeches collected during first visit to each site (Field type: abundance)</li><li><b>visit_T2</b>: Number of tiger leeches collected during second visit to each site (Field type: abundance)</li><li><b>visit_T3</b>: Number of tiger leeches collected during third visit to each site (Field type: abundance)</li><li><b>visit_T4</b>: Number of tiger leeches collected during fourth visit to each site (Field type: abundance)</li><li><b>eff_1</b>: Number of people collecting leeches per survey as a measure of survey effort for the first visit to each site (Field type: abundance)</li><li><b>eff_2</b>: Number of people collecting leeches per survey as a measure of survey effort for the second visit to each site (Field type: abundance)</li><li><b>eff_3</b>: Number of people collecting leeches per survey as a measure of survey effort for the third visit to each site (Field type: abundance)</li><li><b>eff_4</b>: Number of people collecting leeches per survey as a measure of survey effort for the fourth visit to each site (Field type: abundance)</li><li><b>date.1</b>: Julian date of visit 1 (Field type: numeric)</li><li><b>date.2</b>: Julian date of visit 2 (Field type: numeric)</li><li><b>date.3</b>: Julian date of visit 3 (Field type: numeric)</li><li><b>date.4</b>: Julian date of visit 4 (Field type: numeric)</li></ul></li><li><p><b>Site specific covariates</b> (described in worksheet covariates)</p><p>Description: Vegetation structure data associated with each site for which leech surveys were conducted. The metrics include canopy height, moran's I and plant-area-index. These data were extracted from LiDAR data with a 50 m2 buffer around the centroid for each site.</p><p>Number of fields: 6</p><p>Number of data rows: 169</p><p>Fields: </p><ul><li><b>site</b>: SAFE second order point code (Field type: location)</li><li><b>tch</b>: Top of canopy height per site (Field type: numeric)</li><li><b>canopy_height_moran</b>: Habitat heterogeneity - Morans I - per site (Field type: numeric)</li><li><b>canopy_height_sd</b>: Standard deviation of canopy height (Field type: numeric)</li><li><b>pai_mean</b>: Mean plant area index at site (Field type: numeric)</li><li><b>pai_sd</b>: Plant area index standard deviation (Field type: numeric)</li></ul></li><li><p><b>Mammal detections </b> (described in worksheet mammals)</p><p>Description: This dataset contains the mammal detections recorded from camera traps at a subset of the leech survey locations. Sampling effort is also included as a measure of survey effort. </p><p>Number of fields: 27</p><p>Number of data rows: 83</p><p>Fields: </p><ul><li><b>Camera</b>: Name of camera (Field type: location)</li><li><b>CTNs</b>: Measure of trapping effort - number of nights the cameras were operational (Field type: numeric)</li><li><b>Asian Elephant</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Banded Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Banteng</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Bearded Pig</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Bornean Yellow Muntjac</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Common Palm Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Greater Mouse-deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Leopard Cat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Lesser Mouse-deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Long-tailed Macaque</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Long-tailed Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Malay Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Malay Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Marbled Cat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Masked Palm Civet</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Moonrat</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Mousedeer sp.</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Muntjac sp.</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Orangutan</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Pig-tailed Macaque</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Red Muntjac</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sambar Deer</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sun Bear</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Sunda Pangolin</b>: Count of detections for this taxon (Field type: abundance)</li><li><b>Thick-spined Porcupine</b>: Count of detections for this taxon (Field type: abundance)</li></ul></li></ol><p><b>Date range: </b>2015-02-01 to 2016-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Hystricidae <br> -  -  -  -  -  -  <i>Hystrix</i> <br> -  -  -  -  -  -  -  <i>Hystrix brachyura</i> <br> -  -  -  -  -  -  -  <i>Hystrix crassispinis</i> <br> -  -  -  -  -  -  <i>Trichys</i> <br> -  -  -  -  -  -  -  <i>Trichys fasciculata</i> <br> -  -  -  -  Proboscidea <br> -  -  -  -  -  Elephantidae <br> -  -  -  -  -  -  <i>Elephas</i> <br> -  -  -  -  -  -  -  <i>Elephas maximus</i> <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Viverridae <br> -  -  -  -  -  -  <i>Viverra</i> <br> -  -  -  -  -  -  -  <i>Viverra tangalunga</i> <br> -  -  -  -  -  -  <i>Paguma</i> <br> -  -  -  -  -  -  -  <i>Paguma larvata</i> <br> -  -  -  -  -  -  <i>Paradoxurus</i> <br> -  -  -  -  -  -  -  <i>Paradoxurus hermaphroditus</i> <br> -  -  -  -  -  -  <i>Hemigalus</i> <br> -  -  -  -  -  -  -  <i>Hemigalus derbyanus</i> <br> -  -  -  -  -  Felidae <br> -  -  -  -  -  -  <i>Pardofelis</i> <br> -  -  -  -  -  -  -  <i>Pardofelis marmorata</i> <br> -  -  -  -  -  -  <i>Prionailurus</i> <br> -  -  -  -  -  -  -  <i>Prionailurus bengalensis</i> <br> -  -  -  -  -  Ursidae <br> -  -  -  -  -  -  <i>Helarctos</i> <br> -  -  -  -  -  -  -  <i>Helarctos malayanus</i> <br> -  -  -  -  Primates <br> -  -  -  -  -  Cercopithecidae <br> -  -  -  -  -  -  <i>Macaca</i> <br> -  -  -  -  -  -  -  <i>Macaca fascicularis</i> <br> -  -  -  -  -  -  -  <i>Macaca nemestrina</i> <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br> -  -  -  -  -  -  <i>Homo</i> <br> -  -  -  -  -  -  -  <i>Homo sapiens</i> <br> -  -  -  -  Pholidota <br> -  -  -  -  -  Manidae <br> -  -  -  -  -  -  <i>Manis</i> <br> -  -  -  -  -  -  -  <i>Manis javanica</i> <br> -  -  -  -  Erinaceomorpha <br> -  -  -  -  -  Erinaceidae <br> -  -  -  -  -  -  <i>Echinosorex</i> <br> -  -  -  -  -  -  -  <i>Echinosorex gymnura</i> <br> -  -  -  -  Artiodactyla <br> -  -  -  -  -  Suidae <br> -  -  -  -  -  -  <i>Sus</i> <br> -  -  -  -  -  -  -  <i>Sus barbatus</i> <br> -  -  -  -  -  Bovidae <br> -  -  -  -  -  -  <i>Bos</i> <br> -  -  -  -  -  -  -  <i>Bos javanicus</i> <br> -  -  -  -  -  Tragulidae <br> -  -  -  -  -  -  <i>Tragulus</i> <br> -  -  -  -  -  -  -  <i>Tragulus napu</i> <br> -  -  -  -  -  -  -  <i>Tragulus kanchil</i> <br> -  -  -  -  -  Cervidae <br> -  -  -  -  -  -  <i>Muntiacus</i> <br> -  -  -  -  -  -  -  <i>Muntiacus atherodes</i> <br> -  -  -  -  -  -  -  <i>Muntiacus muntjak</i> <br> -  -  -  -  -  -  <i>Rusa</i> <br> -  -  -  -  -  -  -  <i>Rusa unicolor</i> <br> -  -  Annelida <br> -  -  -  Clitellata <br> -  -  -  -  Arhynchobdellida <br> -  -  -  -  -  Haemadipsidae <br> -  -  -  -  -  -  <i>Haemadipsa</i> <br> -  -  -  -  -  -  <i>Haemadipsa</i> <br> -  -  -  -  -  -  -  <i>Haemadipsa picta</i> <br></div><p></p>
Fig. 5 in Herpetofauna diversity in Zamrud National Park, Indonesia: baseline checklist for a Sumatra peat swamp forest ecosystem
Fig. 5. Principal Component Analysis (PCA) of the distribution of herpetofauna species.
Fig. 2 in Herpetofauna diversity in Zamrud National Park, Indonesia: baseline checklist for a Sumatra peat swamp forest ecosystem
Fig. 2. Locations of the survey sites in Zamrud National Park.
Fig. 1 in Herpetofauna diversity in Zamrud National Park, Indonesia: baseline checklist for a Sumatra peat swamp forest ecosystem
Fig. 1. Peat swamp forest in Zamrud National Park.
Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS
<p><span>This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.). <br></span></p>
Agreement of multiple night- and daytime filtering approaches of eddy covariance-derived net ecosystem CO2 exchange over a mountain forest. Reproducible workflow.
<p>Datasets and python scripts to reproduce results from the publication <em>Agreement of multiple night- and daytime filtering approaches of eddy covariance-derived net ecosystem CO2 exchange over a mountain forest.</em></p> <p>See README.txt for a description of the single files.</p>
Indicative distribution map for Ecosystem Functional Group T1.2 Tropical/Subtropical dry forests and thickets
<p>This archive contains indicative distribution maps and profiles for <strong>T1.2 Tropical/Subtropical dry forests and thickets</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Dataset (n=1671) of the publication "Small-scale private forest owners and the European Natura 2000 conservation network: Perceived ecosystem services, management practices, and nature conservation attitudes"
<p>The excel file contains the replies of small-scale private forest owners in Lower Saxony, Germany, to certain questions of a survey analyzed within the following publication: "Small-scale private forest owners and the European Natura 2000 conservation network: Perceived ecosystem services, management practices, and nature conservation attitudes”.<br> <br> The questions included in this dataset cover their objectives, management activities, attitudes and forest stand structures. Further details can be found in the second sheet of the file called “Code explanation”.<br> <br> More information about the methods of data collection can be found in the before-mentioned publication.</p>
Fire recurrence and time since last fire interact to determine the supply of multiple ecosystem services by Mediterranean forests
<p>Wildfires shape the composition and functioning of Mediterranean ecosystems, but we do not know how these ecosystems respond to both the higher fire recurrence and shorter recovery times expected for future climatic scenarios. We sampled 29 plots with different fire recurrences (from 0 to 4 fires over the past decades) and time since the last fire (up to 35 years; hereafter TSLF) in Southeast Spain, to assess the effect of fire recurrence and TSLF on 25 ecosystem attributes, five related ecosystem services (biodiversity conservation, carbon sequestration, disturbance regulation, food production, and supporting services), plus the synergies and trade-offs between them. High fire recurrence (number of fires) and TSLF interacted to determine ecosystem services but did not affect the synergies and trade-offs between them. Fire recurrence reduced many ecosystem functions and ecosystem multifunctionality. However, this effect dampened, and even became positive, for biodiversity conservation and food production services provided enough (> 20 years) time to recover. The combined effects of fire recurrence and TSLF, however, reduced carbon sequestration and had no overall effects on supporting services. Disturbance regulation, in turn, diminished drastically with the first fire, with no effect of further fires or their interaction with TSLF. Our results show which ecosystem services will suffer more from an increase in fire recurrence, and where restoration and management efforts should focus to maximize the provision of those services more demanded by stakeholders.</p>
The diversity of mycorrhiza-associated fungi and trees shape subtropical mountain forest ecosystem functioning
<p><span><strong>Aim</strong>: </span><span>Mycorrhiza play key roles in ecosystem structure and functioning in forests. However, how different mycorrhizal types influence mountain forest biodiversity-ecosystem functioning relationships is largely unknown. We evaluate how the diversity of distinct mycorrhiza-associated fungi and trees shape forest carbon storage along elevational gradients.</span></p> <p><span><strong>Location</strong>:</span><span> Gaoligong Mountains within Hengduan Mountains, Southwest China. </span></p> <p><span><strong>Taxon</strong>: </span><span>Seed plants and mycorrhizal fungi.</span></p> <p><span><strong>Methods</strong>: </span><span>We used the data from 31 subtropical forest plots along elevational gradients on two aspects (east and west) of the mountain. We quantified species richness of trees and symbiotic fungi and assigned both to their mycorrhizal type (arbuscular mycorrhiza (AM), ectomycorrhiza (EcM) and ericoid mycorrhiza (ErM)). We then examined the diversity effects of mycorrhiza-associated fungi and trees on above-ground carbon stored in trees and organic carbon stored in soils. </span></p> <p><span><strong>Results</strong>:</span><span> Species richness was highest for AM trees (79.5%), followed by ErM trees (13.4%) and then EcM trees (7.1%). Species richness of AM-associated trees and fungi decreased with increasing elevation, while ErM-associated trees and fungi showed an opposite trend. EcM-associated diversity followed a hump-shaped relationship with elevation. Positive relationships between diversity and above-ground carbon were detected in all three mycorrhizal associations, but despite low species number, canopy-dominating EcM trees comprised 64.4% of the amount of above-ground carbon. Furthermore, community-weighted means of height exhibited positive correlations with forest above-ground carbon, indicating that positive selection effects occur. Soil organic carbon was positively related to EcM-associated fungi diversity, above-ground carbon mass and soil nitrogen availability, with the latter having the strongest direct effects. </span></p> <p><span><strong>Main</strong> <strong>conclusions</strong>: </span><span>The distributions of forest biodiversity and carbon storage can be modulated by distinct mycorrhizal fungi and trees. Moreover, future global changes (e.g., climate warming, intensifying nitrogen deposition) could alter the mycorrhizal-mediated biodiversity-ecosystem functioning relationships in mountain forests.</span></p>
Future supply of boreal forest ecosystem services is driven by management rather than by climate change
<p><span>Forests provide a wide variety of ecosystem services (ES) to society. The boreal biome is experiencing the highest rates of warming on the planet and increasing demand for forest products. To foresee how to maximize the adaptation of boreal forests to future warmer conditions and growing demands of forest products, we need a better understanding of the relative importance of forest management and climate change on the supply of ecosystem services. Here, using Finland as a boreal forest case study, we assessed the potential supply of a wide range of ES (timber, bilberry, cowberry, mushrooms, carbon storage, scenic beauty, species habitat availability and deadwood) given seven management regimes and four climate change scenarios. We used the forest simulator SIMO to project forest dynamics for 100 years into the future (2016–2116) and estimate the potential supply of each service using published models. Then, we tested the relative importance of management and climate change as drivers of the future supply of these services using generalized linear mixed models. Our results show that the effects of management on the future supply of these ES were, on average, eleven times higher than the effects of climate change across all services but greatly differed among them (from 0.53 to 24 times higher for timber and cowberry, respectively). Notably, the importance of these drivers substantially differed among biogeographical zones within the boreal biome. The effects of climate change were 1.6 times higher in northern Finland than in southern Finland, whereas the effects of management were the opposite – they were three times higher in the south compared to the north. We conclude that new guidelines for adapting forests to global change should account for regional differences and the variation in the effects of climate change and management on different forest ES.</span></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>
Wildfire disturbance reveals evidence of ecosystem resilience and precariousness in a forest-grassland mosaic
<p>Forest and grassland ecosystems are sometimes located adjacently within the same climate. In interior British Columbia, Canada, there are complex forest-grassland mosaics within the Interior Douglas-fir biogeoclimatic zone. Historically, both grassland and forest ecosystems experienced high-frequency, low-severity fire regimes. Since European settlement and introduction of livestock grazing and fire exclusion, trees have encroached on grasslands, and tree densities in forests have increased. In this study, we characterize plant communities and near-surface soil moisture in forest and grassland sites, and in historical grassland sites affected by tree encroachment. We hypothesized that spatial and temporal patterns of near-surface soil moisture are reflected in above-ground plant community composition and structure. After initial sampling of soil moisture and plant communities, the study area was burned in a wildfire. Applying a multifactorial approach to comparing adjacent grassland and forest sites, we treated the wildfire event as a natural experiment, sampling post-wildfire plant species composition and soil moisture, and measuring the severity and spatial heterogeneity of surface burn conditions. Evidence supports the concept of mutually exclusive fire-reinforced bi-stable grassland and forest states, with greater spatial heterogeneity of soil moisture and burn severity in forests, and highly uniform patterns of soil moisture, vegetation, and burn severity in grasslands. Areas of forest encroachment on grasslands had understory plant communities dominated by exotic species, while restored grasslands had native bunchgrass cover like typical grasslands of the region. Additionally, there was post-wildfire divergence of forest- and grassland- associated plant communities. Viewed through a resilience theory conceptual framework, we suggest that ecosystem legacies are reinforcing post-wildfire ecosystem identity and associated native plant communities. External factors - particularly past heavy livestock grazing and fire suppression - have caused ecosystem precariousness that can be addressed with management actions.</p>
Data from: More future synergies and less trade‐offs between forest ecosystem services with natural climate solutions instead of bioeconomy solutions
<p>To reach the Paris Agreement, societies need to increase the global terrestrial carbon sink. There are many climate change mitigation solutions (CCMS) for forests, including increasing bioenergy, bioeconomy and protection. Bioenergy and bioeconomy solutions use climate-smart, intensive management to generate high quantities of bioenergy and bioproducts. Protection of (semi-)natural forests is a major component of 'natural climate solution' (NCS) since forests store carbon in standing biomass and soil. Furthermore, protected forests provide more habitat for biodiversity and non-wood ecosystem services (ES). We investigated the impacts of different CCMS and climate scenarios, jointly or in isolation, on future wood ES, non-wood ES, and regulating ES for a major wood provider for the international market. Specifically, we projected future ES given by three CCMS scenarios for Sweden 2020-2100. In the long term, fulfilling the increasing wood demand through bioenergy and bioeconomy solutions will decrease ES multifunctionality, but the increased stand age and wood stocks induced by rising greenhouse gas (GHG) concentrations will partially offset these negative effects. Adopting bioenergy and bioeconomy solutions will have a greater negative impact on ES supply than adopting NCS. Bioenergy or bioeconomy solutions, as well as increasing GHG emissions, will reduce synergies and increase trade-offs in ES. NCS, by contrast, increases the supply of multiple ES in synergy, even transforming current ES trade-offs into future synergies. Moreover, NCS can be considered an adaptation measure to offset negative climate change effects on the future supplies of non-wood ES. In boreal countries around the world, forestry strategies that integrate NCS more deeply are crucial to ensure a synergistic supply of multiple ES.</p>
The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem
<p>We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories – a long-term research platform for comparative and experimental biodiversity and ecosystem research. The dataset combines several disciplines, including computer science and robotics, biology, bio-geochemistry, and forestry science. We present results for common 3D perception tasks, including classification, depth estimation, localization, and path planning. We combine the full suite of modern perception sensors, including high-resolution fisheye cameras, 3D dense LiDAR, differential GPS, and an inertial measurement unit, with ecological metadata of the area, including stand age, diameter, exact 3D position, and species. The dataset consists of three hand-held measurement series taken from sensors mounted on a UAV during each of three seasons: winter, spring, and early summer. This enables new research opportunities for the forest ecosystem and paves the way for testing forest environment 3D perception tasks and mission set automation.</p>
Climate change accelerates ecosystem restoration in the mountain forests of Central Europe
<ol> <li>Restoring degraded forest ecosystems is an important element in the ongoing challenge to sustain the integrity and functioning of the biosphere. However, the evaluation of restoration success is hampered by long lead times of management measures in forests. Moreover, forest change is accelerating in the absence of management because of ongoing climate change. Yet, because a counterfactual is frequently missing, it remains unclear whether restoration measures are aided or impeded by climate change. </li> <li>Here, we analyzed the pace and success of forest restoration under climate change, combining field data and simulation modelling. We focused on the management zone of Berchtesgaden National Park (BGNP), Germany, where restoration aims to restore homogeneous Norway spruce (<em>Picea abies</em>) forests to structurally diverse mixed mountain forests. We evaluated three alternative restoration strategies: Two active strategies focused on planting the currently underrepresented silver fir (<em>Abies alba</em>) and European beech (<em>Fagus</em> <em>sylvatica</em>) but differing in the creation of gap-cuts, and a third passive restoration strategy without interventions. Strategies were simulated with the forest landscape model iLand from 2020 to 2100 under different climate scenarios (historic, RCP 2.6, 4.5, and 8.5).</li> <li>The forests of BGNP developed into structurally diverse and mixed forests under all evaluated management strategies, and differences between active and passive restoration were generally small. While restoration goals for forest structure were largely met by 2100, forest composition remained far from target in all strategies. Climate change aided restoration by significantly increasing the prevalence of silver fir and European beech (+104.2 % to +258.6 %). Field data on short-term restoration effects were in line with simulated long-term trajectories.</li> <li> <em>Synthesis and applications</em>. We here show that forest restoration efforts in Central European mountain forests will likely be accelerated by climate change. Nonetheless, the slow pace of restoration underscores the need for taking action. Our study highlights that active restoration measures such as tree planting can bring the system closer to restoration targets. However, it also demonstrates that passive restoration (no intervention) is a viable option for management, highlighting the need to evaluate restoration measures against the counterfactual of a no-intervention strategy.</li> </ol>
Environmental heterogeneity on landslide slopes affects the long-term recoveries of forest ecosystem components
<p>Landslides are a common disturbance in mountainous areas of the world. Transporting and accumulating landslide debris, i.e., disturbance legacies, such as coarse woody debris (CWD), vegetation patches, and surface soils, generate a heterogeneous environment along slopes (zones), which are suggested to affect forest recovery. However, the long-term changes in forest ecosystems after landslides remain unknown, particularly zone-dependent change patterns. We aimed to reveal the differences in the changes in live trees, understory vegetation, CWD, and soils among zones by surveying forests with landslide ages (years since the landslide) ranging from 3 to 74 years and reference stands. The increase in live tree aboveground biomass occurred at a faster rate at the lower part of the slopes where the disturbance legacies were rich and surface soils were stabilized due to the smaller slope angle. Chronological patterns of understory vegetation amounts were determined by the differences in disturbance legacy richness and the timing of subsequent canopy closure. The amounts and decay-class diversity of CWD were determined by the differences in legacy richness and mortality through stand development. These zone-dependent chronological changes influenced litter production and determined the recovery rates of surface soil carbon and nitrogen stocks. The increase in the dominance rates of forest herbaceous species was faster in the lower part of the slopes due to the faster surface soil development and canopy closure. Our results illustrate that long-term forest ecosystem succession and recovery after landslides occurs more rapidly at the lower parts than at the upper parts of slopes.</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.