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216 results for “land-use”
Supporting data for "Mammalian species abundance across a gradient of tropical land-use intensity: A hierarchical multi-species modelling approach"
<p>Combined camera trap and live trap dataset underlying the analyses in a Biological Conservation paper (https://doi.org/10.1016/j.biocon.2017.05.007), provided in .csv format. This spatially- and temporally-replicated dataset is suitable for occupancy modelling.</p> <p>The first 3 columns in the dataset are:</p> <p>1) Trap location name – old-growth forest, logged forest and oil palm plantation locations have the prefixes "Old", "Log" and "Palm", respectively</p> <p>2) Sampling occasion number – camera trap and live trap occasions have the prefixes “Lvtrap” and “Ctrap”, respectively, and are defined in the paper</p> <p>3) Calendar year in which sampling took place (most locations were sampled in > 1 calendar years)</p> <p>Following these 3 columns, there are 66 columns for each of the mammal species detected during the study (species common names are used). The values for each species represent the number of independent captures, as defined in the paper. This can be reduced to detection/non-detection data (zeroes and ones), if needed, for occupancy modelling.</p>
LuccME/INLAND land-use scenarios for Brazil 2050
<p>Land use and land cover change models and scenarios are essential to understand the interconnections between global and regional factors influencing land use and demand changes, especially if we consider population growth and food demand projections in 2050.</p> <p>Understanding the future of changes in land use and land cover in Brazil is fundamental for the future of global climate and biodiversity, given the richness of its five biomes. Thus, the new spatially explicit regional scenarios were developed for Brazil by 2050. Those scenarios are aligned with the Shared Socio-Economic Pathways (SSPs) and Representative Concentration Pathway (RCPs). Aim to detail global models regionally and can be used both regionally to support decision-making and enrich the overall analysis.</p> <p>For the development of these new scenarios, the LuccME spatially explicit land change allocation modeling framework and the INLAND surface model were combined to incorporate climatic variables in water deficit and biophysical, socioeconomic, and institutional factors for Brazil. The scenarios were developed for land use and land cover classes: forest vegetation, grassland vegetation, planted pasture, agriculture, mosaic of occupations, and forestry.</p> <p>The dataset comes in NetCDF format and includes the following products:</p> <p> </p> <p><strong>LUCCMEBR_land_cover_type_100km2_2000.nc</strong>: Percentage of land use and land cover for the year 2000 (Observed data).</p> <p><strong>LUCCMEBR_land_cover_type_100km2_2010.nc</strong>: Percentage of land use and land cover for the year 2010 (Observed data).</p> <p><strong>LUCCMEBR_land_cover_type_100km2_2012.nc</strong>: Percentage of land use and land cover for the year 2012 (Observed data).</p> <p><strong>LUCCMEBR_land_cover_type_100km2_2014.nc</strong>: Percentage of land use and land cover for the year 2014 (Observed data).</p> <p><strong>LUCCMEBR_SSP1_RCP19_land_cover_type_100km2_2015_2050.nc</strong>: Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP1 and RCP1.9.</p> <p><strong>LUCCMEBR_SSP2_RCP45_land_cover_type_100km2_2015_2050.nc:</strong> Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP2 and RCP4.5.</p> <p><strong>LUCCMEBR_SSP3_RCP70_land_cover_type_100km2_2015_2050.nc</strong>: Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP3 and RCP7.0.</p> <p> </p> <p><strong>Data</strong></p> <p>Percentage of land use and land cover classes: Forest vegetation (veg), Grassland vegetation (gveg), Planted pasture (pastp), Agriculture (agric), Mosaic of occupation (mosc), Forestry (fores) and Others (others).</p> <p> </p> <p><strong>Spatial resolution</strong></p> <p>The scenarios are available in a spatial resolution of 0.083º x 0.083º (~100 km²) and cover the entire Brazilian territory.</p> <p> </p> <p><strong>Temporal resolution </strong></p> <p>Period of observed data: 2000, 2010, 2012 e 2014</p> <p>Scenario Period: 2015 – 2050 (each five-year)</p> <p> </p> <p><strong>Coordinate reference system</strong> </p> <p>Geographic Coordinate System with Datum WGS84 (EPSG4326)</p> <p> </p> <p><strong>Data format</strong></p> <p>Data is provided as NetCDF.</p> <p> </p> <p><strong>Dataset usage</strong> </p> <p>It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.</p> <p> </p> <p><strong>Publication & further information</strong></p> <p>For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The authors thank the project “MSA / BNDES (Environmental Monitoring by Satellite in the Amazon biome)” for financing the development of LuccMEBR.</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 2
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. </p> <p>This Zenodo repository provides data on following land-use classes: grazing land characterized by open wooded lands (GL-owl)</p>
Coding frame and dataset for the study: Land-use governance: The interplay of social, market, and policy drivers – A global systematic review
<p>This file entails the coding frame and dataset used to conduct the systematic literature review "Land-use governance: The interplay of social, market, and policy drivers – A global systematic review".</p> <p>This study was first published as Chapter 2 of the PhD Dissertation "From soil to society - Rethinking governance for multifunctional land use and management" (Elsa L. Dingkuhn, 2025), and in a modified form in the journal Earth System Governance (Dingkuhn et al. 2025).</p> <p>The file consists of three sheets:<br>- Coding frame: Includes coding instructions and definitions used to extract and categorize the data.<br>- Variables: A list of dataset variables with explanations.<br>- List of included studies: The 81 studies from which the data was sourced.<br>- Data_List of observations: The dataset itself, consisting of 718 observations extracted from the included studies.</p>
Rapid ant community re-assembly in a Neotropical forest: recovery dynamics and land-use legacy
<p>Regrowing secondary forests dominate tropical regions today, and a mechanistic understanding of their recovery dynamics provides important insights for conservation. In particular, land-use legacy effects on the fauna have rarely been investigated. One of the most ecologically dominant and functionally important animal groups in tropical forests are ants. Here, we investigated the recovery of ant communities in a forest – agricultural habitat mosaic in the Ecuadorian Chocó region. We used a replicated chronosequence of previously used cacao plantations and pastures with 1 – 34 years of regeneration time to study the recovery dynamics of species communities and functional diversity across the two land use legacies. We compared two independent components of responses on these community properties: resistance, which is measured as the proportion of an initial property that remains following the disturbance; and resilience, which is the rate of recovery relative to its loss. We found that compositional and trait structure similarity to old-growth forest communities increased with regeneration age, whereas ant species richness remained always at a high level along the chronosequence. Land-use legacies influenced species composition, with former cacao plantations showing higher resemblance to old-growth forests than former pastures along the chronosequence. While resistance was low for species composition and high for species richness and traits, all community properties had similarly high resilience. In essence, our results show that ant communities of the Chocó recovery rapidly, with former cacao reaching predicted old-growth forest community levels after 21 years and pastures after 29 years. Recovery in this community was faster than reported from other ecosystems and was likely facilitated by the low-intensity farming in agricultural sites and their proximity to old-growth forest remnants. Our study indicates the great recovery potential for this otherwise highly threatened biodiversity hotspot.</p>
Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use
<p>This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication<br> <br><strong>Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use</strong></p><p>Julia Schroeder1, Tino Peplau1, Edward Gregorich2, Christoph C. Tebbe3, Christopher Poeplau1</p><p>1 Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, 38116 Braunschweig, Germany<br>2 Research and Development Centre, Central Experimental Farm, Agriculture and Agri-Food Canada, Ottawa, Canada<br>3 Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany</p><p>DOI: https://doi.org/10.1007/s10533-022-00943-7 </p><p>This study investigated how subarctic soils under different land use will respond to warming and increasing N availability to allow for better predictions of C cycling under global change. The short-term temperature sensitivity as well as N-input effects on microbial CUE, respiration, growth and turnover were assessed in a one-day incubation experiment according to the 18O-CUE approach. The warming and N response of SOM decomposition were assessed in a 50-days incubation experiment via measurement of cumulative respiration. Both experiments were conducted with the following three treatments: incubation at 10 °C, incubation at 20 °C, and incubation at 20 °C plus N-fertiliser addition at an amendment rate of 100 kg N ha-1. The response to warming or N addition were expressed as response ratios RRT = 20°C/10°C and RRN = 20°C+N/20°C for warming and N response, respectively.</p><p>The R code was developed under R v3.6.3 and adapted to work under version R v.4.1.2.</p><p>The repository includes the following files:</p><ul><li>general_soil_parameters_per_sample.csv - general soil data for each field sample (n=27)</li><li>general_soil_parameters_per_plot.csv - general soil data assessed on pooled replicated field samples (n=9)</li><li>respiration_over_50d_incubation.csv - respiration rate and cumulative respiration for each time-point and laboratory sample over the 50-days incubation</li><li>sample_data.csv - data measured for each laboratory sample (n=81)</li></ul><p> </p><ul><li>Warming_and_nitrogen_response_of_CUE_in_subarctic_soils.Rproj - Rproject (load project to work on provided scripts and data)</li><li>load_data_script.R - loads required data</li><li>absolute_values_script.R - summary of absolute ranges of parameters per land-use type and site</li><li>absolute_linear_mixed_effects_model_script.R - run statistical analysis</li><li>correlograms_absolute_soil_params_script.R - correlation analysis to identify what drives absolute values</li><li>plot_correlations_absolute_soil_params_script.R - plot drivers of CUE and cumulative respiration</li><li>RRT_RRN_calculation_script.R - calculates response ratios</li><li>plot_RRT_RRN_script.R - plot response ratios</li><li>RRT_RRN_linear_mixed_effects_models_script.R - run statistical analysis</li><li>correlograms_RRT_RRN_soil_param_script.R - correlation analysis to identify drivers of response ratios</li><li>plot_correlations_RRT_RRN_soil_params_script.R - plot drivers of response ratios</li><li>RRT_RRN_resprate_cumulresp_over_time_50d_incubation_script.R - plot response ratios over time course</li></ul>
Supporting data: Land-use change alters the mechanisms assembling rainforest mammal communities in Borneo
<p>These supporting data files were used in the analyses for a forthcoming<em> </em>paper (DOI to be confirmed). The two files consist of: </p> <p>1. Combined camera trap and live trap species-abundance matrix. Each row corresponds to a separate location, with species in different columns. Old-growth forest, logged forest and oil palm plantation locations have the prefixes "Old", "Log" and "Palm", respectively. Values in each cell are the number of independent captures (as defined in the paper) per seven days summed over the camera- and live-trapping protocols.</p> <p>2. Covariate data for each location, covering habitat structure, topography and local landscape context (covariates as defined in the paper). </p>
Fig. 2 in Seasonal population abundance of the assembly of solitary wasps and bees (Hymenoptera) according to land-use in Maranhão state, Brazil
Fig. 2. Abundance of solitary bees according to land-use (a), month (b) and interactions between land-use and month (c). Repeated measures ANOVA followed by post hoc Fisher LSD tests (P <0.05). Means ± SE are given.
Fig. 1 in Seasonal population abundance of the assembly of solitary wasps and bees (Hymenoptera) according to land-use in Maranhão state, Brazil
Fig. 1. Abundance of solitary wasps according to land-use (a), month (b) and interaction between land-use and month (c). Repeated measures ANOVA followed by post hoc Fisher LSD tests (P <0.05). Means ± SE are given.
Figure S4 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure S4. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Gower distance measure indicating annual forb plant functional types (PFTs).
Figure S5 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure S5. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Gower distance measure indicating perennial forb plant functional types (PFTs).
Figure S3 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure S3. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Gower distance measure indicating perennial grass plant functional types (PFTs).
Figure S1 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure S1. Principal Co-ordinate Analysis (PCoA) scatter diagram of the species-trait matrix revealing a strong clustering based on life history.
Figure 3 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure 3. Herbaceous species (left) and trait (right) diversity measures benchmarked against the mean value calculated for the untransformed (protected) area (----) across transformed land-use types. Vertical bars denote 0.95 confidence intervals. Significant deviations from the protected area (Sidak posthoc pairwise comparison; p<0.05) are denoted by (*).
Figure S2 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure S2. Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Gower distance measure indicating annual grass plant functional types (PFTs).
Figure 4 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure 4. Principal Component Analysis (PCA) ordination of land-use type sampling plots correlated with plant functional types (PFT's). CAF (Communal abandoned fields); CR (Communal rangelands); NRSM (Naturally restored strip mine); RASM (Recently active strip mine); UMV (Untransformed Mopaneveld).
Figure 2 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure 2. Multidimensional Scaling (NMDS) ordination of sampling plots representing herbaceous species assemblages across land-use types. Broad groupings are encircled.
Figure 1 in Effects of land-use change on herbaceous vegetation in a semi-arid Mopaneveld savanna
Figure 1. Study area and locality of sampled sites. Strip mines and untransformed Mopaneveld is located at Pompeye (top) and communal areas at Lulekani (bottom).
FIGURE 4 in Land-use changes affect the functional structure of stream fish assemblages in the Brazilian Savanna
FIGURE 4 | Structural equation model diagrams showing the effects of landscape degradation (CDI) on the functional structure of stream fish assemblages from the Araguari River basin. CDI influenced functional diversity mediated by alterations in habitat heterogeneity and stability (A. Model fit: X2 = 32.8, df = 25, p = 0.51). CDI also influenced functional identity, mediated by changes in habitat type (B. Model fit: X2 = 44.9, df = 18, p = 0.13). Arrows indicate positive (black) and negative (gray) significant direct effects (p <0.05; *p <0.10), with thickness proportional to their power (standardized path coefficients along arrows). Biodiversity metrics – FRic: Functional Richness; FDiv: Functional Divergence; FEve: Functional Evennes; FSpe: Functional Specialization; FOri: Functional Originality; CWM1-3: Functional Identity. For physical-habitat codes, calculation and ecological meaning, see Tab. 1.
FIGURE 5 in Land-use changes affect the functional structure of stream fish assemblages in the Brazilian Savanna
FIGURE 5 | Ecomorphological space showing the position of each fish species (36) from the Araguari River basin. Each plot represents two axes of a principal component analysis (PCA), where species are plotted according to their respective trait values. Codes at the ends of the arrows are the most important ecomorphological traits for each PCA axis. For trait and species codes, see Tab. 2 and Tab. S3, respectively).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.