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121 results for “terrestrial ecosystems”
Estimation results of carbon storage in terrestrial ecosystems in Xinjiang from 2000 to 2020
<p><span><span>基于美国国家航空航天局(NASA)2010年发布的碳密度数据以及2000—2020年气温、降水、NDVI等多种影响因素,采用多模式耦合法与inVEST模式相结合,估算新疆陆地生态系统碳储量。</span></span> <span><span>数据的空间分辨率为1KM,碳储量单位为Tg<strong><span><span>(T</span><span>=10<sup>12</sup></span><span>)</span> </span></strong></span></span></p>
Fig. 2 in Formation Dynamics Of Herpetocomplexes On Sections Of Secondary Succession In Terrestrial Ecosystems Of Belarus
Fig. 2. Dynamics of number the herpetoсomplexes on the plots of secondary succession.
Global heat map of probable importance of terrestrial ecosystems on meeting local demand of freshwater services
<p>This map (raster dataset, single layer) uses existing datasets to map globally “How important point x is likely to be for meeting the demand of a reliable & useable source of water on a scale of 0 to 1?” This relatively simple approach uses estimated water demand in a given basin as weight to identify pressure for flow regulation and water provisioning services. Precipitation and land cover estimates are then combined with it to give some insight into the hydrologic attributes of “location” and “timing” of flow that the ecosystems may influence. The underlying assumption here is that undisturbed ecosystems everywhere are performing the ecohydrological functions leading to freshwater services. The question is more (at the global scale): how dependent are the populations in the basin on the continued functioning of these services.</p> <p><strong>Input datasets:</strong></p> <ol> <li>Annual surface & groundwater (“blue”) water consumption estimates. URL: <a href="http://waterfootprint.org/en/resources/water-footprint-statistics/">http://waterfootprint.org/en/resources/water-footprint-statistics/</a></li> <li>HydroBasins watershed outline.</li> <li>European Space Agency (ESA) global land cover 2015.</li> <li>WorldClim annual average precipitation (Version 2.0).</li> </ol> <p><strong>Process:</strong></p> <p>Step 1: Calculate average annual water consumption estimates over HydroBasin outlines. This step spreads the demand laterally (in case of small basins) and upstream to the headwaters from (typically) downstream consumer concentration.</p> <p>Step 2: Normalize the demand globally and map the normalized values on to “natural” land cover classes from the land cover dataset [forests, grasslands, etc].</p> <p>Step 3: Normalize annual precipitation layer within basins on the scale 0-1 where 1 is the maximum annual precipitation in that basin. This is also mapped on the “natural” land cover. Precipitation is thus acting as ‘weight’ for importance within the basin. Example, upland headwaters will typically receive more rainfall and can be argued to be important for the flow regulation in the basin.</p> <p>Step 4: Combine the layers from 2 and 3.</p> <p><strong>Caveats:</strong></p> <ol> <li>Identification of what constitutes a “natural” land cover is not trivial, especially from global land cover maps. Example: Forests and plantations are hard to distinguish from these products.</li> <li>Improvement of quality of water is assumed to be implicit for functioning ecosystems.</li> </ol>
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>
Soil dissolved organic carbon in terrestrial ecosystems: global budget, spatial distribution and controls
<p><strong>Aims: </strong>Soil dissolved organic carbon (DOC) is a primary form of labile carbon in terrestrial ecosystems and therefore plays a vital role in soil carbon cycling. This study aims to quantify the budgets of soil DOC at biome- and global levels and to examine the variations in soil DOC and their environmental controls. Location: Global Time period: 1981 - 2019 Method: We compiled a global dataset and analyzed the concentration and distribution of DOC across 10 biomes.</p> <p><strong>Results: </strong>Large variations in DOC are found among biomes across space and the soil DOC concentration declines exponentially along soil depths. Tundra has the highest soil DOC concentration in 0 - 30 cm soils (453.75 (95% confidence interval: 324.95 – 633.5) mg·kg-1); whereas tropical and temperate forests have relatively lower DOC concentrations, ranging from 30.20 (24.78 - 36.80) mg·kg-1 to 54.54 (49.77 – 59.77) mg·kg-1. DOC generally accounts for < 1% of total organic carbon in soils, and DOC in 0 - 30 cm contributes more than half of total DOC in 0 - 100 cm soil profile. Furthermore, variations in DOC are primarily controlled by soil texture, moisture, and total organic carbon.</p> <p><strong>Main conclusion: </strong>A global synthesis is combined with an empirical model to extrapolate the DOC concentration along soil profiles across the globe, and global budgets of DOC are estimated as 7.20 Pg C in top 0 - 30 cm and 12.97 Pg C in 0 - 100 cm, respectively, with a considerable variation among biomes. The strong soil texture control but weak TOC control on DOC variations suggest that the investigation of physical protection of soil organic carbon might need to expand to consider the labile C in soils. The global maps of DOC concentration serve as a benchmark for validating land surface models in estimating carbon storage in soils.</p>
Living on the edge: Predicting invertebrate richness and rarity in disturbance-prone aquatic–terrestrial ecosystems
<p>1. Temporal fluctuations in water levels cause the spatial extent of wet and dry habitats to vary in aquatic–terrestrial riverine ecosystems, complicating their biomonitoring. As such, biomonitoring efforts may fail to characterise the species that inhabit such habitats, hampering assessments of their biodiversity and implementation of evidence-informed management strategies.</p> <p>2. Relationships between the dynamic characteristics of aquatic-terrestrial habitats and their communities are well known. Thus, habitat characteristics may enable estimation of faunal assemblage characteristics such as taxonomic richness, regardless of in-channel conditions.</p> <p>3. We investigated whether indicators summarising habitat survey data can predict two metrics representing terrestrial invertebrate assemblages (e.g. taxa richness) in two aquatic–terrestrial habitats: exposed riverine sediments and dry temporary streams. We also compared the performance of unimetric and multimetric habitat indicators in making predictions.</p> <p>4. In exposed riverine sediments, >88% of predictions were correlated with observed taxa richness and an index of conservation status. Values predicted by exposed riverine sediment samples were correlated with those observed in temporary stream channels with comparable riparian (i.e. largely agricultural) land use, but not those observed in channels with contrasting (i.e. more urban) land use.</p> <p>5. Unimetric habitat indicators performed similarly to more complex multimetric indicators, with each explaining ≤6% of the variability in taxa richness and the index of conservation status. The different spatial scales at which invertebrates respond to habitat conditions and at which indicators record habitat conditions, and a more comprehensive training dataset that incorporates a full range of habitat conditions (i.e. land use), may improve future predictions.</p> <p>6. We demonstrate that invertebrate assemblage characteristics can be predicted regardless of in-channel conditions. Agreement between exposed riverine sediment predictions and temporary stream observations suggests that these predictions are transferable among a range of aquatic–terrestrial habitat types, and could thus be widely applied to aid conservation of riverine biodiversity in dynamic aquatic–terrestrial ecosystems.</p>
Embracing fine-root system complexity in terrestrial ecosystem modelling
<p>Model outputs for manuscript " <strong>Embracing fine-root system complexity in terrestrial ecosystem modelling</strong>".</p>
Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM) simulations focused on Eight Mile Lake, Alaska and Imnavait Creek, Alaska [2000-2015]
<p>This set of files store model simulations using the biosphere model Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM), developed to simulate biophysical and biogeochemical interactions between the soil, vegetation and atmosphere. To improve predictions of net carbon releases from thawing permafrost, we tested the sensitivity of a suite of model parameters. We analyzed the responses of ecosystem carbon balances to permafrost thaw by running site-level simulations at two long-term tundra ecological monitoring sites in Alaska: Eight Mile Lake (EML) and Imnavait Creek watershed (IMN). These sites are characterized by similar tussock tundra vegetation but differing soil drainage conditions and climate, IMN consists of well-drained soils, and EML has historically well-drained soils, however permafrost thaw has altered drainage conditions to wetter soils. Simulations were conducted at a 1km resolution, over a 1,000 km2 area (10x10 km square) centered on two long term ecological research sites in Alaska: Eight Mile Lake located in Interior Alaska (63.8900° N, 149.2535° W), and Imnavait creek watershed located on the northern foothills of the Brooks range (68°37′ N, 149°18′ W).</p> <p>Historical simulations are spanning the 2000 to 2015, and forced using climate simulations from the Climate Research Unit, time series 4.0. We ran 1,000 site level simulations for each model variable. The variables that are produced are gross primary productivity (GPP, in gC.m-2.m-1), net ecosystem exchange (NEE, gC.m-2.m-1), ecosystem respiration (RECO, gC/m2/m-1), active layer thickness (ALT, m), soil temperature (TLAYER,°C) at 5, 10, 40 cm depths, soil moisture (LWCLAYER, m-3/m-3) at 5, 10 cm depths, and snow depth (SNOWDEPTH, m), evapotransipiration(EET, mm/m2/time), potential evapotransipiration (PET, mm/m2/time), leaf area index (LAI, m2/m2), organic layer thickness (OLT, m). The data are stored as compiled csv files, with time as the index, and each model sample output stored in the columns. In addition, there is a postprocessing python script to demonstrate the step and workflow used to generate the individual csv files post processed from the raw model outputs stored as netcdfs.</p>
Fire decreases soil respiration and its components in terrestrial ecosystems
<ol> <li>The impact of fire on aboveground biomass has significant consequences on soil carbon (C) dynamics, which is essential in predicting the global C budget during the Anthropocene. However, there is considerable spatiotemporal variability in the directions and magnitudes of fire effects on soil respiration, and the drivers associated with these effects are not well understood.</li> <li>Here, we conducted a global meta-analysis of 1327 individual observations from 170 studies to determine the extent to which fire influenced soil total respiration (R<sub>s</sub>), heterotrophic respiration (R<sub>h</sub>), and autotrophic respiration (R<sub>a</sub>).</li> <li>We found fires reduced R<sub>s</sub>, R<sub>h</sub>, and Ra, with an average effect of -11.0%, -17.5%, and -40.6%, compared to unburnt sites. Specifically, wildfires significantly reduced R<sub>s</sub>and R<sub>h </sub>(-20.4% and -25.0%, respectively), and prescribed fire significantly decreased Ra (-74.8%). The influences of fire on R<sub>s </sub>and its components were moderated by fire severity, season, type, climate zones, and biomes. After several years from the time of the fire, the negative effects of fire on R<sub>s </sub>diminished and then recovered to a state not significantly different from unburnt sites; Rh exhibited a similar but decayed temporal response. Similarly, the negative effects on R<sub>a</sub> disappeared after 3 years following the latest fire. The magnitude of the effect on R<sub>s </sub>was strongly associated with soil temperature, cation exchange capacity, total nitrogen (N) content, and N-acquiring enzyme activity. In contrast, the magnitude of the effect on R<sub>h </sub>significantly changed with pH, bulk density, texture, soil C and nutrient contents, and C- acquiring enzyme activity.</li> <li>Our findings advance the understanding of the inhibition and associated mechanisms of fire on R<sub>s </sub>and its components, highlighting the need for new research efforts to predict the spatial-temporal shifts in underground C cycling induced by fire. </li> </ol>
Soil dissolved organic carbon in terrestrial ecosystems: global budget, spatial distribution and controls
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Data and code for: Terrestrial eDNA survey outperforms conventional approach for detecting an invasive pest insect within an agricultural ecosystem
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Continuous abrupt vegetation shifts in the global terrestrial ecosystem
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Predatory synapsid ecomorphology signals growing dynamism of late Palaeozoic terrestrial ecosystems
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Warming of aquatic ecosystems disrupts aquatic-terrestrial linkages in the tropics
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Data from: Large-scale variation in biodiversity–ecosystem functioning (BEF) relationships in aquatic metacommunities on terrestrial islands
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Fire decreases soil respiration and its components in terrestrial ecosystems
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Data from: Predator-driven behavioral shifts in a common lizard shape resource-flow from marine to terrestrial ecosystems
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Drying and fragmentation drive the dynamics of resources, consumers and ecosystem functions across aquatic-terrestrial habitats in a river network
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Living on the edge: Predicting invertebrate richness and rarity in disturbance-prone aquatic–terrestrial ecosystems
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Latitudinal gradient in the intensity of biotic interactions in terrestrial ecosystems: Sources of variation and differences from the diversity gradient revealed by meta-analysis
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.
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