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257 results for “forest landscapes”

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zenodo36/100

Dataset: Substantial organic and particulate nitrogen and phosphorus export from geomorphologically stable African tropical forest landscapes

Raw chemical and stream data from the publication 'Substantial organic and particulate nitrogen and phosphorus export from geomophologically stable African tropical forest landscapes'.  Data shows dissolved and particulate nitrogen and phosphorus concentrations of stream waters of two forested first order streams withing the Congo Basin.

opencc-by-4.0May 2022View details →

dryad36/100

Size-dependent intraspecific variation in wood traits has little impact on aboveground carbon estimates in a tropical forest landscape

There is increasing evidence that intraspecific trait variation plays a role in governing rates of ecosystem functioning. While wood traits such as wood specific gravity (WSG) and wood carbon concentration (WCC) are key drivers of forest aboveground carbon (AGC) stocks, the sources of intraspecific variation in these wood traits and the consequences of this variation on AGC are poorly known, especially in the tropics. Here, we investigated intraspecific variation in wood specific gravity (WSG) and wood carbon concentration (WCC) from 556 individual trees belonging to 15 species that well characterize different successional stages of seasonal evergreen forests in Southeast Asia. Specifically, we tested the contribution of individual or species characteristics (tree size, growth rate and regeneration guilds) and local environmental conditions (topographic wetness index and successional stages) to intraspecific variation in WSG and WCC, and assessed the consequences of intraspecific variation in these wood traits on AGC estimates in 14 permanent forest plots established along a successional gradient in Khao Yai National park, Thailand. We found that tree size was the main driver of intraspecific variation in WSG and WCC as tree sizes increased from 10−100 cm in diameter, WSG increased by 7.3%, while WCC increased by 2.4% in heartwood, 1.6% and 2.7% in sapwood without and with volatile carbon included. There was no effect of the topographic wetness and other local environment condition in wood traits led to a slight overestimation of AGC in young secondary forests (+0.09 to +1.29%) and a small underestimation in older forests (-0.86 to -2.87%), but overall AGC estimates (13 of 14 forest plots) remained within error margins (the 95% interval). Our study provides evidence that tree size variation translates into intraspecific variability in wood traits, whereas local environmental conditions related to topography successional stages had no effect on wood trait variability. While size-dependent variation in WSG and WCC have largely been undocumented and thus ignored in forest carbon assessment approaches, we highlight that it has a limited impact on AGC estimates, indicating that it does not invalidate current forest carbon stock estimation approaches.

opencc-zeroJun 2022View details →

dryad36/100

Landscape conservation as a strategy for recovering biodiversity: lessons from a long-term program of pasture restoration in the southern Atlantic Forest

Although ecological restoration has entered the global agenda to reverse different anthropogenic disturbances, we still know little about how this solution interacts with other conservation strategies, to avoid the progressive loss of species and ecosystem services. Here we evaluate one of the pioneering restoration programs in the southern Brazilian Atlantic Forest, where the combination of conservation and restoration efforts have been carried out for 20 years. Specifically, we tested how landscape characteristics, restoration strategies and environmental characteristics affect the results of the restoration of pastures. We established 65 circular plots (total 4.0 ha) along restoration areas (3-10 years) and sampled trees and shrubs composing the canopy (DBH > 5cm) and understory (DBH< 5cm, height > 1.3m). We analyzed the landscape metrics (proportion of old-growth forests in 200 m, 500m and 1000m buffers around each plot; and area and distance of the nearest-neighboring old-growth forests). We explored the multiple effects of landscape, restoration strategy (reforestation, natural regeneration) and environmental variables (soil, pasture grass types) on the species composition and multiple diversity metrics of restoration areas. The species composition was very similar among restoration ages and restoration strategies. We found positive and strong effects of old-growth forest (200 m buffer) proportion on the species richness and Shannon diversity (canopy and understory), aboveground biomass (canopy) and functional diversity (understory) of restoration areas. The restoration strategies affected forest structure, and, in general, the reforestation strategy increased aboveground biomass, Shannon, functional and phylogenetic diversities (in canopy), and percentage of endemic species and biomass (understory), when compared to natural regeneration. The 20 year-experience in the southern Atlantic Forest showed that programs focused on landscape conservation associated with a mixture of restoration strategies (i.e. natural regeneration in larger areas and active restoration in more disturbed sites) , can be an efficient strategy to ensure biodiversity and ecosystem services in tropical landscapes. Synthesis and applications: To manage degraded tropical lands and achieve global targets for biodiversity and ecosystem services, it is necessary to first ensure the conservation of natural remnants and then use multiple restoration strategies in less resilient areas.

opencc-zeroJun 2022View details →

dryad36/100

Highly-replicated soil, topography and vegetation sampling across an old-growth tropical rain forest landscape

Here we present data from highly-replicated sampling of soil, topography, and vegetation across an old-growth tropical rainforest landscape at the La Selva Biological Station, Costa Rica. Samples were taken at 100 x 50 m spacing using an existing surveyed grid system. The 573-ha sample area spanned a variety of soil, topographic and vegetation conditions, including flat terraces on old alluvial soil, ridge tops and steep slopes on residual soil, riparian habitats and fresh-water swamps. At each of 1170 grid points we established a circular 0.01 ha quadrat (radius = 5.64 m). We sampled soil at 30-50 cm depth with a soil augur and collected a sample for subsequent analysis. We measured slope angle with a clinometer and slope direction with a compass. We measured stem diameter to +1 mm with a synthetic fabric diameter tape for all stems >10 cm diameter (N= 5236) at 1.3 m from the ground or to ~ 6 m height if there were basal irregularities. We classified stems to life form (tree, palm, liana), and identified all trees and palms to species or morphospecies (N=266; lianas were not identified to species). We collected vouchers from all trees that we could not positively identify in the field (N=920). As a whole the data set presents an integrated view of soil, topography and vegetation across a mesoscale old-growth tropical rain forest landscape. The data have been used to refine a reserve-wide soils map for La Selva, and for a variety of papers analyzing the interactions of soil, topography and species distributions at landscape scales (see the 10 papers listed in the Related Works section below). There are no restrictions at all on the use of these data, and we think they will be useful for teaching and analysis projects as well as further original research applications. The data also provide a detailed benchmark of the status of old-growth vegetation in 1993-95 for one of the most intensively studied tropical rain forest landscapes in the world. Because the data are accurately georeferenced and detailed metadata on all methods are provided, this study could be repeated at any time to assess the trajectory of vegetation changes at La Selva, particularly in relation to local disturbances and changing regional and global climates.

opencc-zeroJun 2022View details →

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Highland forest's environmental complexity drives landscape genomics and connectivity of the rodent Peromyscus melanotis

We evaluate how the environmental complexity of La Malinche volcano influences patterns of genomic variation in Peromyscus melanotis, across two mountain slopes and global and regional geographic scales.Using reduced representation genomic sequencing we estimated population genetic diversity, subdivisions and migration rates. Remote sensing data and drone image processing were used to characterize landscape variables. We evaluated their effect on connectivity, based on a landscape analyses framework, using resistance surfaces, circuit theory and omnidirectional connectivity. Our findings showed how the forest environmental complexity across different geographic scales drives dispersal, genomic structure and connectivity patterns in this rodent, where dirt roads and disturbed areas limit its connectivity, while exhibiting higher connectivity at the highest elevations where the forest is less disturbed. Notably, that our 3D variable of tree height was significant demonstrates the utility in incorporating 3D vegetation structure variables into landscape genetic analyses.

opencc-by-4.0Jun 2022View details →

zenodo36/100

Data from: Filtering ground noise from LiDAR returns produces inferior models of forest aboveground biomass in heterogenous landscapes

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering -- that is, excluding returns from LiDAR point clouds based on simple height thresholds -- is a common practice meant to improve the 'signal' content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types.     In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman's $\rho$) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha^-1^ statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful 'signal' than extraneous 'noise' in these cases.

opencc-by-4.0Mar 2022View details →

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Data from: Patterns and drivers of recent land cover change on two trailing-edge forest landscapes

Climate change is altering the distribution of woody plants by influencing demographic processes and modifying disturbance regimes. Trailing-edge forests may be particularly vulnerable to these effects because they exist at warm, dry margins of tree distributions. To better understand recent climate-driven changes in trailing-edge forests, we used Landsat time series and 1,558 field reference plots to develop annual land cover maps from 1985 to 2020 in two large, biodiverse landscapes in central Arizona, USA. We then combined annual land cover maps with tree ring records and spatial data describing interannual climate, terrain, bark beetle (Curculionidae: Scolytinae) activity, wildfire, and harvest to quantify drivers of forest change. Throughout the two landscapes, forest extent declined by 0.3% and 0.8% from 1985 to 2020. However, considerable variation occurred within the study period, with abrupt (ca. 1–2 years) declines in forest extent followed by gradual (ca. 10 years) recovery on each landscape. Pinyon-juniper (Pinus edulis, Pinus monophylla, and/or Juniperus spp.) cover increased from 1985 to ca. 2000 but declined after 2000, a period of extreme drought and regional tree die-off. In contrast, pine-oak (Pinus ponderosa and Quercus spp.) cover increased from 2000 to 2020, primarily due to declines in ponderosa pine and mixed conifer cover over the same period. Wildfire was a key driver of transitions from forest to non-forest cover in our study area, with the occurrence of multiple compounded drought years playing an important role in unburned areas. By driving transitions to alternative forest types or non-forest cover, disturbance and drought will increasingly shape forest dynamics and ecosystem transformations throughout the southwestern US.

opencc-zeroAug 2022View details →

dryad36/100

Landscape-scale drivers of liana load across a Southeast Asian forest canopy differ to the Neotropics

Lianas (woody vines) are a key component of tropical forests, known to reduce forest carbon storage and sequestration and to be increasing in abundance. Analysing how and why lianas are distributed in forest canopies at landscape scales will help us determine the mechanisms driving changes in lianas over time. This will improve our understanding of liana ecology and projections of tropical forest carbon storage now and into the future. Despite competing hypotheses on the mechanisms driving spatial patterning of lianas, few studies have integrated multiple tree-level biotic and abiotic factors in an analytical framework. None have done so in the Palaeotropics, which are biogeographically and evolutionarily distinct from the Neotropics, where most research on lianas has been conducted. We used an unoccupied aerial system (UAS; drone) to assess liana load in 50-ha of Palaeotropical forest canopy in Southeast Asia. We obtained data on hypothesised drivers of liana spatial distribution in the forest canopy, including disturbance, tree characteristics, soil chemistry, and topography, from the UAS, from airborne LiDAR, and from ground surveys. We integrated these in a comprehensive analytical framework to extract variables at an individual-tree level and evaluated the relative strengths of the hypothesised drivers and their ability to predict liana distributions through boosted regression tree (BRT) modeling. Tree height and distance to canopy gaps were the two most important predictors of liana load, with relative contribution values in BRT models of 34.60% 45.39% and 7.93% - 10.19%, respectively. Our results suggest that taller trees were less often and less heavily infested by lianas than shorter trees, opposite to Neotropical findings. Lianas also occurred more often, and to a greater extent, in tree crowns close to canopy gaps and to neighbouring trees with lianas in their crown. Synthesis: Despite their known importance and prevalence in tropical forests, lianas are not well understood, particularly in the Palaeotropics. Examining 2,428 trees across 50-ha of Palaeotropical forest canopy in Southeast Asia, we find support for the hypothesis that canopy gaps promote liana infestation. Our finding that liana presence and load declined with tree height, opposite to well-established Neotropical findings, suggests a fundamental difference between Neotropical and Southeast Asian forests. Considering that most liana literature has focused on the Neotropics, this highlights the need for additional studies in other biogeographic regions to clarify potential differences and enable us to better understand liana impacts on tropical forest ecology, carbon storage and sequestration.

opencc-zeroOct 2022View details →

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Supplementary material: Mammal diversity responses to anthropic, environmental, and seasonal changes within Caatinga seasonal dry forest landscapes

Caatinga's conservation and biodiversity are threatened due to the intensification of anthropic activities and climate change. The mammals have different responses to seasonal and anthropic changes, however particularly in Caatinga, these effects are still poorly understood. We assessed the influence of anthropic (distance from urban areas and wind farms), environmental (distance from water), and seasonal (Normalized Difference Vegetation Index – NDVI and land surface temperature - LST) variables on the number of records and richness of medium and large-sized mammals in Brazilian Caatinga. We used camera traps in 2016/2017 and 2018/2019, estimated the variation (cv) of NDVI and LST, and generated Euclidean distance maps to anthropic and environmental variables at 250, 500, and 1000 m spatial scales. We performed Generalized Linear Models, used the Akaike information criterion, and calculated model averaging to assess the strength and direction of effect and the uncertainties of the winner models, respectively. The distance from wind farms and maximum LST had a noticeable effect on the number of records and total richness. The distance from wind farms had a negative effect on the records of Dicotyles tajacu and a positive effect on the records of Leopardus pardalis and richness. The maximum LST had a negative effect on the records of Leopardus pardalis and a positive effect on the records of Puma concolor and Cerdocyon thous. Our results emphasize that an unsustainable expansion of wind farms is likely to compromise mammal diversity. We found an opposite pattern for some species regarding LST. However, it is important to highlight that the conservation of vegetation areas on the top of mountains and springs, and the installation of artificial water sources are important strategies to mitigate the impacts of high temperatures on mammals' biodiversity in Caatinga.

opencc-zeroApr 2024View details →

dryad36/100

Data from: Changes of Chinese forest-grassland ecotone in geographical scope and landscape structure from 1990 to 2020

Forest-grassland ecotone (FGE) has essential ecological and economic value. Unfortunately, it is impacted greatly by environmental changes and anthropogenic disturbance, and is considered one of the most severely threatened biomes in China. To protect Chinese FGE, identifying its exact boundary and exploring its landscape structure dynamic are badly needed, especially on nationwide scale at one-year temporal resolution. Here, we mapped the annual FGE distribution of China from 1990 to 2020, investigated its changing trends of area, location and landscape patterns, and revealed the underlying driving factors. Our results showed that FGE area over the 31 years totaled 1,011,870 km2, covering about 10.54% of China's land. The FGE area first increased from 1990 and peaked in 1999, and then kept decreasing until 2020. The FGE gravity center has moved accumulatively 590.15 km over the 31 years, with the net moving distance of 228.76 km southwestward. From 1990 to 2020, forest area increased continuously while grassland and cropland area decreased, but these three landscape types had been dominating the FGE. The increase in forest area was largely converted from grassland. The decline in grassland mainly resulted from its conversion into cropland and forest. Meanwhile, the conversion of cropland to grassland supplemented grassland loss to a certain extent. At landscape level, the total area with decreased fragmentation is larger than that with increased fragmentation. Returning Farmland to Grassland Project and land reclamation were primary drivers for changes of fragmentation in the northern and middle part of the FGE, while temperature and precipitation were primary drivers in southern part. Our results will improve the understanding into the dynamic trends of distribution and pattern of FGE at nationwide scale, and thus help to optimize the designing of ecological projects and protective schemes for FGE as a unique and integral biome.

opencc-zeroMay 2024View details →

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Data from: Tracking shifts in forest structural complexity through space and time in human-modified tropical landscapes

Habitat structural complexity is an emergent property of ecosystems that directly shapes their biodiversity, functioning and resilience to disturbance. Yet despite its importance, we continue to lack consensus on how best to define structural complexity, nor do we have a generalised approach to measure habitat complexity across ecosystems. To bridge this gap, here we adapt a geometric framework developed to quantify the surface complexity of coral reefs and apply it to the canopies of tropical rainforests. Using high-resolution, repeat-acquisition airborne laser scanning data collected over 450 km2 of human-modified tropical landscapes in Borneo, we generated 3D canopy height models of forests at varying stages of recovery from logging. We then tested whether the geometric framework of habitat complexity – which characterises 3D surfaces according to their height range, rugosity and fractal dimension – was able to detect how both human and natural disturbances drive variation in canopy structure through space and time across these landscapes. We found that together, these three metrics of surface complexity captured major differences in canopy 3D structure between highly-degraded, selectively logged and old-growth forests. Moreover, the three metrics were able to track distinct temporal patterns of structural recovery following logging and wind disturbance. However, in the process we also uncovered several important conceptual and methodological limitations with the geometric framework of habitat complexity. We found that fractal dimension was highly sensitive to small variations in data inputs and was ecologically counteractive (e.g., higher fractal dimension in oil palms than old-growth forests), while rugosity and height range were tightly correlated (r=0.75) due to their strong dependency on maximum tree height. Our results suggest that forest structural complexity cannot be summarised using these three descriptors alone, as they overlook key features of canopy vertical and horizontal structure that arise from the way trees fill 3D space.

opencc-zeroJun 2024View details →

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Fig. 2 in Landscape Ecological Analysis Of Taurkalne Forest Tract Fragmentation

Fig. 2. Road network length (km) by category.

opencc-by-4.0Dec 2011View details →

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Spatial dataset of breeding bird territories in East-Estonian forested landscapes, 2020-2022

Please cite this dataset as: Lõhmus, A. 2024. A high-precision dataset of breeding bird distributions in forested landscapes in Estonia. Data in Brief, 57, 111012. https://doi.org/10.1016/j.dib.2024.111012 The dataset depicts the distribution of all breeding bird pairs across a 14.3 km2 area in East Estonia, along River Ahja. The area comprises three adjacent, mostly forested landscape plots (forest land 81%), of which one plot (A) was mapped in three years (2020-2022) and the others once (plot B in 2021; plot C in 2022). The bird data includes the most likely centroids of each nesting territory of each species (ideally, nest location) as interpreted from multiple records; all the field data have been collected and interpreted by the author. The fieldwork included standard multi-visit mapping of nesting territories (on average, 7–8 visits from April to July), and each bird data point (5398 in total) includes a spatial accuracy assessment. In total, 98 bird species were detected. The bird data are accompanied with map layers depicting the study area borders and forest stand descriptions to facilitate habitat and landscape analyses; the available formats are MapInfo 10.5, ESRI Shape File, and csv; the co-ordinates are WGS84. Detailed descriptions of the data are included in a separate uploaded text file. The data have been used for several publications as indicated in the Reference list, notably for habitat analyses of woodland birds (Certhia familiaris; Cuculus canorus; Lophophanes cristatus; Turdus viscivorus) and for assessing forest management impacts on bird assemblages.  NOTE 19.07.2024: The following corrections are to be made (v2 coming soon; thus far please consider). 1) Birddata – one TETURO record missing in Plot C. 2) Birddata – one BONBON record (Pair ID 4379) under the species code TETBON. 3) Birddata_explanations – codes for field Type missing; should read as follows: Pinus = Pinus sylvestris dominated; Picea = Picea abies dominated; Con = Mixed conifer forest (≥80% in total); Mix = Conifer-deciduous mixture (neither ≥80%); Bet = Betula sp. dominated; Ainc = Alnus incana dominated; Aglu = A. glutinosa dominated; Ptre = Populus tremula dominated; Dec = Other deciduous forest (≥80% in total).

opencc-by-4.0Jul 2024View details →

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Data from: A landscape approach for optimizing the cost-effectiveness of large-scale forest restoration

This is a complete dataset for achieving results of the manuscript "A landscape approach for optimizing the cost-effectiveness of large-scale forest restoration".    Abstract: <ol> <li>Achieving global targets for forest restoration will require cost-effective strategies to return agricultural land to forest, while minimizing implementation costs and negative outcomes for agricultural production.</li> <li>We present a landscape approach for optimizing the cost-effectiveness of large-scale forest restoration. Across three different landscapes within Brazil's Atlantic Forest biodiversity hotspot, we modelled landscape scenarios based on spatially-explicit data on the probability of natural regeneration, restoration costs, land opportunity costs, and forest restoration outcomes for increasing carbon stocking and landscape connectivity. We compare benefits of our cost-reduction approach to the legally mandated riparian restoration and randomly distributed approaches.</li> <li>Compared with riparian prioritization and considering both implementation and opportunity costs, our cost-reduction scenario produced the greatest savings (20.9%) in mechanized agricultural landscapes.</li> <li>When only considering implementation costs, our cost-reduction scenario led to the highest savings (38.4%) in the landscape with highest forest cover where natural regeneration potential is highest and enables cost-effective carbon stocking and connectivity.</li> </ol> Synthesis and applications. We present a guide for forest restoration planning that maximizes specific outcomes with minimal costs and reduction of agricultural production. Furthermore, we show how policies could encourage prioritization of low-cost restoration via natural regeneration, increasing cost-effectiveness. While our study focuses on Brazil’s Atlantic Forest, the approach can be parameterized for other regions. Resumo: <ol> <li>Atingir metas globais para a restauração florestal exigirá estratégias economicamente viáveis para transformar terras agrícolas em floresta, minimizando custos de implementação e os resultados negativos para a produção agrícola.</li> <li>Apresentamos uma abordagem de paisagem para otimizar a relação custo-eficácia da restauração florestal em larga escala. Em três diferentes paisagens, no Bioma da Mata Atlântica, modelamos cenários baseados em dados espacialmente explícitos sobre a probabilidade de regeneração natural, custos de restauração, custos de oportunidade da terra e resultados de restauração florestal com o objetivo de aumentar o estoque de carbono e a conectividade da paisagem. Por fim, comparamos os benefícios da nossa abordagem de redução de custos com a tradicional abordagem de restauração da paisagem em zonas ripárias (áreas de preservação permanente) e abordagens de espacialidade aleatoriamente distribuídas.</li> <li>Comparado com a priorização ripária e considerando os custos de implementação e de oportunidade, nosso cenário de redução de custos produziu as maiores economias (20,9%) em paisagens agrícolas mecanizadas.</li> <li>Ao considerar apenas os custos de implementação, nosso cenário de redução de custos levou à maior economia (38,4%) na paisagem com maior cobertura florestal, onde o potencial de regeneração natural é maior e permite uma melhor relação de custo-oportunidade no estoque de carbono e na conectividade da paisagem.</li> </ol> Síntese e aplicações. Apresentamos aqui um guia para o planejamento de restauração florestal que maximiza resultados específicos com redução de custos e mínima influência na produção agrícola. Além disso, mostramos como políticas públicas poderiam incentivar a priorização da restauração de baixo custo via regeneração natural, aumentando a relação custo-benefício. Enquanto nosso estudo se concentra na Mata Atlântica do Brasil, a abordagem pode ser parametrizada para outras regiões.

opencc-by-nc-nd-4.0May 2018View details →

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Soil greenhouse gas fluxes and associated parameters from forest and oil palm in the SAFE landscape

Description: Greenhouse gas fluxes measured by the static chamber method including associated environmental parametersProject: This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126">Characterising soil microbial communities and measuring associated biogeochemical fluxes</a>Funding: These data were collected as part of research funded by: <ul><li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li></ul>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.Permits: These data were collected under permit from the following authorities:<ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (79))</li></ul>XML metadata: GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3258117">here</a>Files: This consists of 1 file: 3_GHG_jdrewer.xlsx3_GHG_jdrewer.xlsxThis file contains dataset metadata and 2 data tables:<ol><li>data one off field (described in worksheet Data_one_off)Description: Soil and litter parametersNumber of fields: 14Number of data rows: 56Fields: <ul><li>Location: Location measurement was taken (Field type: Location)</li><li>site: Location measurement was taken (Field type: ID)</li><li>chamber_id: Chamber ID (Field type: ID)</li><li>landuse: Land use of location (Field type: Categorical)</li><li>pH: Soil pH (Field type: Numeric)</li><li>bulk_density: dry weight of soil (Field type: Numeric)</li><li>soil_N%: Percentage of soil N (Field type: Numeric)</li><li>soil_C%: Percentage of soil C (Field type: Numeric)</li><li>litter_N%: Percentage of leaf Nitrogen (Field type: Numeric)</li><li>litter_C%: Percentage of leaf Carbon (Field type: Numeric)</li><li>C/N_soil: Ratio of soil Carbon: Nitrogen (Field type: Numeric)</li><li>Latitude: Latitude of sampling point (Field type: Latitude)</li><li>Longitude: Longitude of sampling point (Field type: Longitude)</li><li>Elevation: Elevation of sampling point (Field type: Numeric)</li></ul></li><li>data of repeated measures (described in worksheet Data_repeated_measures)Description: Soil greenhouse gas flux data and associated variablesNumber of fields: 14Number of data rows: 672Fields: <ul><li>Location: Location measurement was taken (Field type: Location)</li><li>site: Location measurement was taken (Field type: ID)</li><li>chamber_id: Chamber ID (Field type: ID)</li><li>landuse: Land use of location (Field type: Categorical)</li><li>date: Date the measurement was taken (Field type: Date)</li><li>time: Time the measurement was taken (Field type: Time)</li><li>flux_CH4: Soil CH4 flux (Field type: Numeric)</li><li>flux_CO2-C: Soil CO2 flux (Field type: Numeric)</li><li>flux_N2O-N: Soil N2O flux (Field type: Numeric)</li><li>NH4-N: Soil NH4 concentration (Field type: Numeric)</li><li>NO3-N: Soil NO3 concentration (Field type: Numeric)</li><li>air_temp: Air temperature around the flux chamber (Field type: Numeric)</li><li>soil_temp: Soil temperature around the flux chamber (Field type: Numeric)</li><li>soil_moisture: Soil moisture around the flux chamber (Field type: Numeric)</li></ul></li></ol>Date range: 2015-01-01 to 2016-12-31Latitudinal extent: 4.5000 to 5.0700Longitudinal extent: 116.7500 to 117.8200

opencc-by-4.0Dec 2018View details →

zenodo36/100

Soil greenhouse gas fluxes along transects from oil palm to riparian forests in the SAFE landscape

Description: Riparian greenhouse gas fluxes measured by the static chamber method including associated environmental parameters and river water Project: This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126">Characterising soil microbial communities and measuring associated biogeochemical fluxes</a>Funding: These data were collected as part of research funded by: <ul><li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li></ul>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.Permits: These data were collected under permit from the following authorities:<ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (79))</li></ul>XML metadata: GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3258079">here</a>Files: This consists of 1 file: 1_HJ_river_water_riparian.xlsx1_HJ_river_water_riparian.xlsxThis file contains dataset metadata and 3 data tables:<ol><li>river_water (described in worksheet river_water)Description: river water measurmentsNumber of fields: 17Number of data rows: 63Fields: <ul><li>site: location sample was taken (Field type: Location)</li><li>location: habitat (Field type: Categorical)</li><li>replicate: water sample replicate number (Field type: Replicate)</li><li>sampling_occasion: date of sample collection (Field type: Date)</li><li>date: date of sample analysis (Field type: Date)</li><li>TDS: Total Desolved Solids (Field type: Numeric)</li><li>pH: water pH (Field type: Numeric)</li><li>conductivity: water conductivity (Field type: Numeric)</li><li>Temp: tempreture of river water (Field type: Numeric)</li><li>air_CH4: air concentration of CH4 (Field type: Numeric)</li><li>water_CH4: water concentration of CH4 (Field type: Numeric)</li><li>air_N2O: air concentration of N2O (Field type: Numeric)</li><li>water_N2O: water concentration of N2O (Field type: Numeric)</li><li>air_CO2: air concentration of CO2 (Field type: Numeric)</li><li>water_CO2: water concentration of CO2 (Field type: Numeric)</li><li>NH4-N: concentration of NH4-N in water (Field type: Numeric)</li><li>NO3-N: concentration of NO3-N in water (Field type: Numeric)</li></ul></li><li>data_one_off_field (described in worksheet data_one_off_field)Description: soil and littter property measurementsNumber of fields: 12Number of data rows: 48Fields: <ul><li>Location: location of chamber (Field type: Location)</li><li>chamber_id: chamber ID (Field type: ID)</li><li>site: Site ID (Field type: ID)</li><li>landuse: land use type (Field type: Categorical)</li><li>pH: soil pH (Field type: Numeric)</li><li>soil_N: soil nitrogen content (Field type: Numeric)</li><li>soil_C: soil carbon content (Field type: Numeric)</li><li>litter_N: litter nitrogen content (Field type: Numeric)</li><li>litter_C: litter carbon content (Field type: Numeric)</li><li>C_N: soil C:N ratio (Field type: Numeric)</li><li>Latitude: GPS co-ordinate that the sample was taken (Field type: Latitude)</li><li>Longitude: GPS co-ordinate that the sample was taken (Field type: Longitude)</li></ul></li><li>data_repeated_measures (described in worksheet data_repeated_measures)Description: repeated soil measuresNumber of fields: 16Number of data rows: 336Fields: <ul><li>chamber_id: Chamber ID (Field type: ID)</li><li>site: Site ID (Field type: ID)</li><li>landuse: land use type (Field type: Categorical)</li><li>sampling_occasion: date of sample collection (Field type: Date)</li><li>date: date of sample analysis (Field type: Date)</li><li>time: Time the measurement was taken (Field type: Time)</li><li>flux_CH4-C: Soil CH4 flux (Field type: Numeric)</li><li>flux_CO2-C: Soil CO2 flux (Field type: Numeric)</li><li>flux_N2O-N: Soil N2O flux (Field type: Numeric)</li><li>NH4-N_H2O: Soil NH4 concentration (Field type: Numeric)</li><li>NO3-N_H2O: Soil NO3 concentration (Field type: Numeric)</li><li>NH4-N_KCl: Soil NH4 concentration (Field type: Numeric)</li><li>NO3-N_KCl: Soil NO3 concentration (Field type: Numeric)</li><li>air_temp: Air temperature around the flux chamber (Field type: Numeric)</li><li>soil_temp: Soil temperature around the flux chamber (Field type: Numeric)</li><li>soil_moisture: Soil moisture around the flux chamber (Field type: Numeric)</li></ul></li></ol>Date range: 2016-11-01 to 2017-11-30Latitudinal extent: 4.3960 to 5.0700Longitudinal extent: 116.7500 to 117.8200

opencc-by-4.0Dec 2018View details →

dryad36/100

Data from: Influences of fire–vegetation feedbacks and post-fire recovery rates on forest landscape vulnerability to altered fire regimes

1. In the context of on-going climatic warming, forest landscapes face increasing risk of conversion to non-forest vegetation through alteration of their fire regimes and their post-fire recovery dynamics. However, this pressure could be amplified or dampened, depending on how fire-driven changes to vegetation feed back to alter the extent or behavior of subsequent fires. 2. Here we develop a mathematical model to formalize understanding of how fire–vegetation feedbacks and the time to forest recovery following high-severity (i.e., stand-replacing) fire affect the extent and stability of forest cover across landscapes facing altered fire regimes. We evaluate responses to increasing burn rates while varying the direction (negative vs. positive) of fire–vegetation feedbacks under a continuum of values for feedback strength and post-fire recovery time to determine how interactions among these variables produce thresholds and tipping points in landscape responses to changing fire regimes. 3. Where the early-seral vegetation is less fire-prone than older forests, negative feedbacks limited the reductions in forest cover in response to increased fire frequency or slower forest recovery. By contrast, positive feedbacks (more flammable early-seral vegetation) produced a tipping point beyond which increased burn rates or slower forest recovery drove extensive forest loss. 4. With negative feedbacks, the rates of forest loss and expansion in response to variation in fire frequency were similar. However, where feedbacks are positive, the conversion from predominantly forested to non-forested conditions in response to increased fire frequency was faster than the re-expansion of forest cover following a return to the initial burn rate. Strengthening the positive feedbacks increased this asymmetry. 5. Synthesis. Our analyses elucidate how fire–vegetation feedbacks and post-fire recovery rates interact to affect the trajectories and rates of landscape response to altered fire regimes. We illustrate the vulnerability of ecosystems with positive fire–vegetation feedbacks to climate change-driven increases in fire activity, especially where post-fire recovery is slow. Although negative feedbacks initially provide resistance to forest loss with increasing burn rates, this resistance is eventually overwhelmed with sufficient increases to burn rates relative to recovery times.

opencc-zeroDec 2017View details →

dryad36/100

Immigration credit of temperate forest herbs in fragmented landscapes – implications for restoration of habitat connectivity

1. In many agricultural landscapes, it is important to restore networks of forests to provide habitat and stepping stones for forest specialist taxa. More knowledge is, however, needed on how to facilitate the immigration of such taxa in restored forest patches. Here, we present the first chronosequence study to quantify the dynamics of immigration credits of forest specialist plants in post-arable forest patches. 2. We studied the distribution of herbaceous forest specialist plant species in 54 post-arable broadleaved forest patches along gradients of age (20-140 years since forest establishment), distance from ancient forest (0-2600 m) and patch area (0.5-9.6 ha). With Linear Mixed Models we estimated the effects of these factors on species richness, patch means of four dispersal-related plant traits and with Generalized Linear Models on the occurrence of 20 individual species. 3. Post-arable forest patch age and spatial isolation from ancient forest, but not patch size, were important predictors for species richness of forest specialists, suggesting that also small patches are valuable for habitat connectivity. Compared to species richness in ancient forest stands, the immigration credit was reduced by more than 90% after 80 years in post-arable forest patches contiguous to ancient forest compared to 40% after 80 years and 60% after 140 years in isolated patches (at least 100 m to next forest). Tall-growing species with adaptations to long-distance dispersal were faster colonizers while species with heavy diaspores and clonal growth were slower to colonize. 4. Synthesis and applications: We show that post-arable oak plantations have a high potential for restoration of forest herb vegetation. Dispersal-related plant traits play a key role in explaining interspecific differences among forest specialists. To facilitate forest herb immigration across all functional groups in agricultural landscapes, we suggest to create clusters of relatively small new forest patches nearby older forest with source populations.

opencc-zeroJul 2021View details →

dryad36/100

Insect RTUs from the degraded forest fragments in the Attappady and Anaikatti landscapes.

Datasets were collected as part of the project titled "EVALUATING THE EFFICEINCY OF RESTORATION EFFORTS IN REVIVING TROPICAL FORESTS USING GROUND INSECTS AS INDICATORS."

opencc-zeroOct 2021View details →

dryad36/100

Occurrence patterns of crop-foraging sika deer distribution in an agriculture-forest landscape revealed by nitrogen stable isotopes

Conflicts arising from the consumption of anthropogenic foods by wildlife are increasing worldwide. Conventional tools for evaluating the spatial distribution pattern of large terrestrial mammals that consume anthropogenic foods have various limitations, despite their importance in management to mitigate conflicts. In this study, we examined the spatial distribution pattern of crop-foraging sika deer by performing nitrogen stable isotope analyses of bone collagen. We evaluated whether crop-foraging deer lived closer to agricultural crop fields during the winter and spring, when crop production decreases. We found that female deer in proximity to agricultural crop fields during the winter and spring were more likely to be crop-foraging individuals. Furthermore, the likelihood of crop consumption by females decreased by half as the distance to agricultural crop fields increased to 5-10 km. We did not detect a significant trend in the spatial distribution of crop-foraging male deer. The findings of spatial distribution patterns of crop-foraging female deer will be useful for the establishment of management areas, such as zonation, for efficient removal of them.

opencc-zeroOct 2022View details →

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Last verified 2026-04-30Open record

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Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

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electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

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behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record