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216 results for “land-use”
Land-use fluxes: data from global models and national inventories
<p>This file includes the data from Supplementary Table 1 of Grassi et al. (ESSD, submitted), for the 42 countries having a managed forest area greater than 10 Million ha. The data includes: </p> <p>(i) areas of managed forest, used in this study and based on country data</p> <p>(ii) The CO2 fluxes (2001-2020 average) from global models - i.e. bookkeeping models (BMs) and Dynamic Global Vegetation Models (DGVMs) -, and from a collection of National GHG inventories (NGHGIs) for LULUCF, forest land, deforestation, and other fluxes (organic soils, cropland, grassland etc.). </p> <p>BM values are averages of three models and DGVM values are averages of 17 models, consistent with the Global Carbon Budget 2021 (<a href="https://priv-bx-myremote.tech.ec.europa.eu/articles/14/1917/2022/,DanaInfo=.aetugDhuwm0xto76O48y,SSL+">https://essd.copernicus.org/articles/14/1917/2022/</a>). Values for NGHGIs are from <a href="https://priv-bx-myremote.tech.ec.europa.eu/preprints/essd-2022-104/,DanaInfo=.aetugDhuwm0xto76O48y,SSL+">https://essd.copernicus.org/preprints/essd-2022-104/</a> </p> <p>For further methodological details, see Grassi et al. (ESSD, submitted):</p> <p>Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, San1, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Matthew J. McGrath, Julia Nabel, Benjamin Poulter, Simone Rossi, Anthony P. Walker, Wenping Yuan, Xu YueJulia Pongratz. Mapping land-use fluxes for 2001-2020 from global models to national inventories. ESSD (submitted)</p>
Climate velocity and land-use instability
<p>Climate and land-use dataset to support "Climate and land-use risk assessment for Earth's remaining wilderness". Dataset are in Mollweide equal area projections and 24-km resolution, for baseline (1971-2005) and projected (2016-2050) periods. Temperature and precipitation velocities is based on CORDEX climate data averaged across three GCMs (including, MOHC-HadGEM2-ES, MPI-M-MPI-ESM-LR and NCC-NorESM1-M). Land-use velocity is based on land-use harmonisation dataset (LUH2 v2h and LUH2 v2f).</p>
Climate and land-use effects on dung beetle assemblages
<p>This dataset contains data from a field study conducted in 2019 and described in the paper "Dung beetle diversity is mainly affected by land use, while community specialization is driven by climate" by Englmeier et al.</p> <p>To test the effects of land use and climate on α-diversity, local community specialization (H<sub>2</sub>'), and γ-diversity of dung beetles, we used pitfall traps baited with four different dung types at 114 study sites, distributed over a spatial extent of 300 km x 300 km and 1000 m in elevation. Study sites were established in four local land-use types: forests, grasslands, arable sites, and settlements, embedded in near-natural, agricultural, or urban landscapes.</p> <p>We used negative-binomial generalized linear models for alpha-diversity, calculated community specialization on dung resources by using the standardized two-dimensional Shannon entropy (H<sub>2</sub>'), and assessed γ-diversity by Hill-numbers.</p> <p>Our results show that intensive land use i.e., agriculture and urban areas, strongly reduced dung beetle α- and γ-diversity, respectively. Dung beetle abundance and species density were strongly affected by agricultural land use on both spatial scales, whereas γ-diversity on a local scale was mainly affected by settlements and on a landscape scale equally by agricultural and urban land use. Increasing precipitation diminished dung beetle abundance, and higher temperatures reduced community specialization and γ-diversity. These results indicate that intensive land use and higher temperatures may cause a loss in dung beetle diversity and alter community networks of dung beetle assemblages. A decrease in dung beetle diversity may disturb decomposition processes at both local and regional scales and alter ecosystem functioning, which leads to drastic ecological and economic damage.</p>
Temperature and land-use rates of change for populations of fast and slow species in the LPD
<p>Human-induced environmental changes have a direct impact on species populations, with some species experiencing declines while others display population growth. Understanding why and how species populations respond differently to environmental changes is fundamental to mitigate and predict future biodiversity changes. Theoretically, species life-history strategies are key determinants shaping the response of populations to environmental impacts. Despite this, the association between species' life-histories and the response of populations to environmental changes has not been tested. In this study, we analysed the effects of recent land-cover and temperature changes on rates of population change of 1,072 populations recorded in the Living Planet Database. We selected populations with at least 5 yearly consecutive records (after imputation of missing population estimates) between 1992 and 2016, and for which we achieved high population imputation accuracy (in the cases where missing values had to be imputed). These populations were distributed across 553 different locations and included 461 terrestrial amniote vertebrate species (273 birds, 137 mammals, and 51 reptiles) with different life-history strategies. We showed that populations of fast-lived species inhabiting areas that have experienced recent expansion of cropland or bare soil present positive population trends on average, whereas slow-lived species display negative population trends. Although these findings support previous hypotheses that fast-lived species are better adapted to recover their populations after an environmental perturbation, the sensitivity analysis revealed that model outcomes are strongly influenced by the addition or exclusion of populations with extreme rates of change. Therefore, the results should be interpreted with caution. With climate and land-use changes likely to increase in the future, establishing clear links between species characteristics and responses to these threats is fundamental for designing and conducting conservation actions. The results of this study can aid in evaluating population sensitivity, assessing the likely conservation status of species with poor data coverage, and predicting future scenarios of biodiversity change.</p>
Land-use areas
<p>Data used in this study were from the Office of Agricultural Economics Thailand. Land-use areas data from Agricultural statistics of Thailand are publicly available. </p> <p><strong>File descriptions</strong>: Types of land use areas for agricultural consist of rice field area, field crops (sugarcane, cassava, peanut, common tobacco, corn, and potato), fruit trees and perennial (including rubber tree, eucalyptus tree, oil palm tree), vegetables and flowers, and other agriculture that averaged in years 2018-2020, Thailand :<br> cwt : post code<br> cwt_n : province names<br> Land : total land area each of provinces in years 2018-2020<br> LandUse : total land use area each of provinces in years 2018-2020<br> pLand : proportion of land area in years 2018-2020<br> Rice : rice field area in years 2018-2020<br> pRice : proportion of rice field area in years 2018-2020<br> Field crops : field crops area in years 2018-2020<br> pField : proportion of field crops area in years 2018-2020<br> Fruit trees / perennial : fruit trees and perennial area in years 2018-2020<br> pFruit : proportion of fruit trees and perennial area in years 2018-2020<br> Vegetables / Flowers : vegetables and flowers area in years 2018-2020<br> pVege : proportion of vegetables and flowers area in years 2018-2020<br> Other agriculture : other agriculture area in years 2018-2020<br> pOther: proportion of other agriculture area in years 2018-2020</p>
Climate regulates the effect of land-use change on the diversity of soil microbial functional groups and soil multifunctionality
<p>Although studies have explored how soil microbial diversity and soil multifunctionality respond to land-use change at local scales, they have rarely been explored at larger scales and across different climatic and soil environmental conditions.</p> <p>By sampling 40 paired sites of land-use change from natural forests to agricultural lands (including croplands and orchards) along the middle and lower Yangtze River, combined with a global meta-analysis, we investigated the effects of land-use change and climate on the alpha and beta diversity of soil bacterial and fungal functional groups (FGs) and their associated soil multifunctionality at a regional scale.</p> <p>Our results showed that land-use change strongly changed the diversity of soil bacterial and fungal FGs and decreased multifunctionality, which was supported by our meta-analysis at a global scale. Direct effects of land-use change and climate and their interaction, together with changes in soil environmental variables, were the main determinants of the land-use change-induced changes in the diversity of soil bacterial or fungal FGs. The land-use change-induced decrease in multifunctionality was mainly associated with the direct effect of forest conversion, soil fertility, and diversity of fungal FGs. Furthermore, climate also regulated the effects of land-use change on multifunctionality by affecting soil fertility and fungal FGs diversity along the Yangtze River.</p> <p><em>Synthesis and applications</em>. Taken together, our findings highlight the important effects of land-use change, climate, and their interactions on microbial diversity and multifunctionality, and suggest that effective land-use management and climate change mitigation strategies should be adopted to protect biodiversity and ecosystem function in the Yangtze River Basin.</p>
The effect of land-use modification in Sabah, Malaysia on the morphology of two beetle families: Carabidae and Chyrsomelidae
<b>Description: </b><p>Feeding morphology and body size of carabid and chrysomelid beetles</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/82"><b>Spatial scaling of beetle community diversity</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=14">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Beetle morphometrics</b> (Worksheet Data)</p><p>Dimensions: 762 rows by 18 columns</p><p>Description: Feeding morphology and body size of carabid and chrysomelid beetles</p><p>Fields: </p><ul><li><b>Date</b>: Date sample was collected in field (Field type: Date)</li><li><b>Trap_ID </b>: SAFE Project sample site (Field type: Location)</li><li><b>Traptype</b>: Component of the insect collection trap (Field type: Categorical)</li><li><b>Individual_ID</b>: Specimen reference code (Field type: ID)</li><li><b>Family</b>: Family ID (Field type: Taxa)</li><li><b>Pronotum_Length</b>: Elytra length (Field type: Numeric Trait)</li><li><b>Elytra_Length</b>: Elytra length (Field type: Numeric Trait)</li><li><b>Body_length</b>: Body length (Field type: Numeric Trait)</li><li><b>Mean_Antennae_length</b>: Antennal length (Field type: Numeric Trait)</li><li><b>Labrum_Width</b>: Labrum width (Field type: Numeric Trait)</li><li><b>Mean_Maxillary_Palp_Length</b>: Length of maxillary palp (Field type: Numeric Trait)</li><li><b>Distance_Protruding_from_Labrum</b>: distance that the mandibles protruded from the labrum (Field type: Numeric Trait)</li><li><b>No_of_Hairs_on_Upper_Lips</b>: Number of hairs on clypeus (Field type: Numeric Trait)</li><li><b>Hairs_on_Palps</b>: NA (Field type: Numeric Trait)</li><li><b>Curved_Labrum</b>: Is the labrum curved? (Field type: Categorical Trait)</li><li><b>Mandible_Width</b>: Mandible width (Field type: Numeric Trait)</li><li><b>Fringe_extending_from_Labrum</b>: Is there a fringe of hairs extending from the labrum? (Field type: Categorical Trait)</li></ul><br></li></ol><p><b>Date range: </b>2011-02-21 to 2012-07-31</p><p><b>Latitudinal extent: </b>4.6350 to 4.7716</p><p><b>Longitudinal extent: </b>116.9477 to 117.7028</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Coleoptera<br> -  -  -  - Carabidae<br> -  -  -  - Cerambycidae<br> -  -  -  - Chrysomelidae<br></div><p></p>
Partitioning Seed Dispersal Rate Amongst Vertebrates Vs Invertebrates Along a Land-Use Gradient
<b>Description: </b><p>Seed perdation and dispersal experiments</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/72"><b>Partitioning Seed Dispersal Rate Amongst Vertebrates Vs Invertebrates Along a Land-Use Gradient</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=77">here</a></p><p><b>Data worksheets: </b>There are 1 data worksheets in this dataset:</p><ol><li><p><b>Seed removal experiments</b> (Worksheet Data)</p><p>Dimensions: 904 rows by 13 columns</p><p>Description: Experimental seed removal trials. Each trial consisted of 20 pumpkin seeds being placed on a plate, with seed fates ascertained the following day.</p><p>Fields: </p><ul><li><b>Location</b>: SAFE project sample site (Field type: Location)</li><li><b>Point </b>: SAFE project sample site (Field type: ID)</li><li><b>Date</b>: Date seeds were placed in field (Field type: Date)</li><li><b>Treatment</b>: Experimental treatment (Field type: Categorical)</li><li><b>NPlacement</b>: Unknown variable (Field type: ID)</li><li><b>RemainUneat</b>: How many seeds remained on the plate and had no evidence of having been eaten? (Field type: Numeric)</li><li><b>RemovUneat</b>: How many seeds were removed from the plate and had no evidence of having been eaten? (Field type: Numeric)</li><li><b>RemainEat</b>: How many seeds remained on the plate but had evidence of having been eaten? (Field type: Numeric)</li><li><b>RemovEat</b>: How many seeds were removed from the plate and also had evidence of being eaten? (Field type: Numeric)</li><li><b>RemovUnknown</b>: How many seeds were removed from the plate and had an unknown fate? (Field type: Numeric)</li><li><b>Rain</b>: How heavily did it rain last night? 0 being no rain and 5 being torrential rain (Field type: Numeric)</li><li><b>TreatmentSuccessFail</b>: Was the treatment successful? (Field type: Categorical)</li></ul><br></li></ol><p><b>Date range: </b>2013-05-07 to 2013-07-27</p><p><b>Latitudinal extent: </b>4.6350 to 4.7523</p><p><b>Longitudinal extent: </b>116.9632 to 117.5934</p>
Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient
<b>Description: </b><p>Mosquito larval survey and thermotolerance data</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/172"><b>Investigating Temperature Tolerance in Mosquito Disease Vectors Across a Land-Use Gradient</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=82">here</a></p><p><b>Data worksheets: </b>There are 4 data worksheets in this dataset:</p><ol><li><p><b>Field microclimate data</b> (Worksheet Microclimate)</p><p>Dimensions: 6917 rows by 11 columns</p><p>Description: Data was recorded using EasyLog USB dataloggers. They were put out either hung on a tree, or suspended off the ground (in the absense of trees) and covered to avoid direct sunlight.</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample location (Field type: ID)</li><li><b>DATE</b>: Date of the measurement (Field type: Date)</li><li><b>TIME</b>: Time of the measurement (Field type: Time)</li><li><b>Temp</b>: Air temperature (Field type: Numeric)</li><li><b>RelHumid</b>: Relative humidity (Field type: Numeric)</li><li><b>DewPoint</b>: The temperature at which the air would condense and dew would form (Field type: Numeric)</li><li><b>SerialNumber</b>: Serial number of the microclimate datalogger used to collect the data (Field type: ID)</li></ul><br></li><li><p><b>Thermal tolerance</b> (Worksheet CTmax)</p><p>Dimensions: 317 rows by 6 columns</p><p>Description: Thermal tolerance experiments on mosquito larvae</p><p>Fields: </p><ul><li><b>Date</b>: The Date the CT max value was taken (Field type: Date)</li><li><b>VialNumber</b>: the vial that the individual came from, and how it is referred to in my field notebook (Field type: ID)</li><li><b>LandType</b>: Habitat type from which the individual was collected (Field type: Categorical)</li><li><b>CriticalMax</b>: The temperature in celcius that the individual became unresponsive to stimulus (Field type: Numeric)</li><li><b>GivenSpecies</b>: Identity of the individual being tested (Field type: Taxa)</li></ul><br></li><li><p><b>Site x species data</b> (Worksheet SpeciesData)</p><p>Dimensions: 192 rows by 9 columns</p><p>Description: Field observations of field mosquito communities</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample site (Field type: ID)</li><li><b>Replicate</b>: is the replicate of sampling each data collection took place. There were three weeks of sampling so the only values are 1, 2, or 3. (Field type: Replicate)</li><li><b>CollectionType</b>: Method of collection (Field type: Categorical)</li><li><b>Count</b>: Total number of individuals sampled (Field type: Abundance)</li><li><b>GivenSpecies</b>: Identity of the individual(s) (Field type: Taxa)</li></ul><br></li><li><p><b>Forest canopy measurements</b> (Worksheet Densiometer)</p><p>Dimensions: 36 rows by 12 columns</p><p>Description: Densiometer estimates of tree canopy cover</p><p>Fields: </p><ul><li><b>Block</b>: SAFE Project sampling block (Field type: Location)</li><li><b>Plot</b>: Sample site (Field type: Location)</li><li><b>Site</b>: SAFE Project sampling block (Field type: ID)</li><li><b>Point</b>: SAFE Project sample site (Field type: ID)</li><li><b>Val1</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val2</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val3</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>Val4</b>: Number of quartersquares that lack canopy cover in one of the four cardinal directions. The maximum value is 96 if there is no canopy cover. (Field type: Numeric)</li><li><b>AveVal</b>: Average densiometer reading (max = 96) (Field type: Numeric)</li><li><b>AdjustedVal</b>: Average canopy openness (Field type: Numeric)</li><li><b>CanopyCover</b>: Average canopy cover (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2017-03-20 to 2017-09-04</p><p><b>Latitudinal extent: </b>4.6314 to 4.7436</p><p><b>Longitudinal extent: </b>117.4556 to 117.6249</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Diptera<br> -  -  -  - Culicidae<br> -  -  -  -  - <i>Aedes</i><br> -  -  -  -  -  - <i>Aedes albopictus</i><br> -  -  -  -  - <i>Anopheles</i><br> -  -  -  -  -  - [Anoph]<br> -  -  -  -  - <i>Armigeres</i><br> -  -  -  -  -  - [Armigeres]<br> -  -  -  -  - <i>Culex</i><br> -  -  -  -  -  - [Culex1]<br> -  -  -  -  -  - [Culex2]<br> -  -  -  -  -  - [Culex3]<br> -  -  -  -  -  - [CulexOP]<br> -  -  -  -  - [Species7]<br> -  -  -  -  - [Species8]<br> -  -  -  -  - [Species9]<br> -  -  -  -  - <i>Uranotaenia</i><br> -  -  -  -  -  - [Urano1]<br> -  -  -  -  -  - [Urano2]<br> -  -  -  -  - <i>Zeugnomyia</i><br> -  -  -  -  -  - <i>Zeugnomyia gracilis</i><br></div><p></p>
Species functional data and species distribution model projections for future land-use and fire management scenarios in the Transboundary Biosphere Reserve Gerês-Xurés
<p>The data includes nine functional traits and species distribution model projections for 102 species of vertebrates (amphibians, birds, and reptiles) in the Transboundary Biosphere Reserve Gerês-Xurés. The model projections are available for 2050 under six different land-use and fire management scenarios, namely two land-use scenarios of “business-as-usual” (BAU; ongoing trends of land abandonment) and “High Nature Value farmlands” (HNV), each under three fire management scenarios (low suppression - LS, current fire suppression - CS, and high fire suppression - HS). The species distribution projections for each scenario are presented as matrices of species presences/absences, obtained after reclassifying consensus predictions of species distribution models.</p>
Data from: Predicting range shifts of pikas (Mammalia, Ochotonidae) in China under scenarios incorporating land-use change, climate change, and dispersal limitations
<p><span>Two of the most important forces affecting biodiversity are land-use change (LUC) and global climate change (GCC). Previous studies have modeled their impacts on species separately and together, but few have done so for multiple species with dispersal limitations incorporated into the models.</span></p> <p><span>We integrate species distribution models plus a dispersal model to predict LUC and GCC impacts on the ranges of five species of pikas in the Qinghai-Tibet Plateau region of China. Pikas are sensitive to land-use and climate change, and have limited dispersal abilities.</span></p> <p><span>The predicted impacts of LUC and GCC on pikas vary between species as well as between LUC and GCC projections. Incorporation of dispersal limitations appreciably restricts the amount of colonized habitat. For all five species, the amount of habitat abandoned or colonized when LUC and GCC are modeled together is less than the sum of LUC and GCC modeled separately. Three of the five species experience a net increase in occupied habitat by 2080 relative to their current ranges under all modeled projections. However, relative to a "Dispersal Only" baseline scenario that assumes no environmental change but continued range expansion into suitable, unoccupied habitat, all five species suffer a net loss of occupied habitat by 2080 under some or all projections.</span></p> <p><span>Predictions of future distributions of species based solely on LUC or GCC, as well as predictions assuming additive impacts, can be misleading. Inclusion of dispersal limitations in models markedly alters predicted future distributions of species. The use of a "Dispersal Only" scenario provides a different and perhaps more accurate way to gauge net impacts to species. Future work should consider incorporating all these parameters to better predict the impacts of LUC and GCC on biodiversity.</span></p>
Data set and analytic codes supporting "The effects of land-use change on semi-aquatic bugs (Gerromorpha, Hemiptera) in rainforest streams in Sabah, Malaysia"
<p>This deposit contains data set and analytic codes** (accompanied with a meta data) supporting "The effects of land-use change on semi-aquatic bugs (Gerromorpha, Hemiptera) in rainforest streams in Sabah, Malaysia". We investigated the impacts of land-use change on semi-aquatic bug (Gerromorpha, Hemiptera) communities in Sabah, Malaysia.</p> <p>Semi-aquatic bugs were collected from streams in old-growth forest, logged forest, and oil palm with and without riparian buffer strips. A range of environmental parameters were also collected to represent environmental conditions (associated with land-use change). Environmental data were collected at catchment, riparian, and stream scales, and were used separately for the assessments of their effects on the bugs. We looked at the effects on the abundance, biomass, species richness, and community composition of semi-aquatic bugs. We also assessed the effects on the proportion of juveniles, winged individuals, and female <em>Ptilomera</em> sp. (a morphospecies with clear sexual dimorphism in this study).</p> <p>This research was funded by the Jardine Foundation, the Cambridge Trust, the Natural Environment Research Council (NERC) (studentship 1122589), Proforest, the Varley Gradwell Travelling Fellowship, the Tim Whitmore Fund, the Panton Trust, the Cambridge University Commonwealth Fund, the Hanne and Torkel Weis-Fogh Fund, and the S.T. Lee Fund.</p> <p> </p> <p>** For reproducibility of outputs of the Canonical Correspondence Analysis (CCA), do insert the following function in the R Markdown before the line of "anova.cca(CCAEnvInsect, by = 'terms', first = TRUE)":</p> <p>set.seed(42) # About set.seed: <a href="https://stackoverflow.com/questions/13605271/reasons-for-using-the-set-seed-function">r - Reasons for using the set.seed function - Stack Overflow</a></p>
Harmonising the land-use flux estimates of global models and national inventories for 2000-2020: background data
<p>This online repository includes all the relevant data used in the paper "Harmonizing the land-use flux estimates of global models and national inventories for 2000-2020" (Grassi et al. 2023), plus some additional methodological information, organised in the following files:</p> <p>1) "<strong>Global model</strong><strong>s</strong> <strong>land CO2 </strong><strong>data 2000-2020</strong>" (MS Excel Format), including for each country data for:</p> <p>a. Land-use CO2 fluxes from each of three Bookkeeping Models (BMs) used, and for different categories (net LULUCF, deforestation, forest, other transitions, organic soils). </p> <p>b. The ensemble mean of the ‘natural terrestrial sink’ estimated by 16 Dynamic Global Vegetation Models (DGVMs), filtered with maps of intact/non-intact forest.</p> <p>The global model data included here are consistent with those included in the Global Carbon Budget 2022 (Friedlingstein et al., 2022).</p> <p>2) “<strong>National inventories LULUCF data 2000-2020</strong>” (version Dec 2022, MS Excel Format), including a comprehensive collection of LULUCF CO2 data based on countries' submissions to the United Nations Framework Convention on Climate Change (UNFCCC). The data here represent a slight update of the dataset included in Grassi et al. (2022).</p> <p>3) “<strong>Processing steps for DGVM results”,</strong> describing the protocol used to filter the results of DGVMs with maps of intact/non-intact forest and further details on the maps (PDF Format). </p> <p>4) “<strong>Intact and non-intact forest maps</strong>”, available in two files with different resolutions (0.5 and 0.05 degrees) in NetCDF format. Grassi et al. (2023) used the 0.5 degree resolution.</p> <p>5) "<strong>IntactAndNonIntactForest_0.5deg_script.js</strong>", the Google Earth Engine Java script to produce the forest maps (.js/text format)</p> <p>For further details, please refer to:</p> <p>Grassi et al. (2023) Harmonising the land-use flux estimates of global models and national inventories for 2000-2020. Earth Syst. Sci. Data.</p> <p>Other references:</p> <p>Friedlingstein et al. (2022) Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900.</p> <p>Grassi et al (2022) Carbon fluxes from land 2000–2020: bringing clarity to countries' reporting. Earth Syst. Sci. Data, 14, 4643-4666.</p> <p> </p> <p> </p>
Data for: "Land-use intensity influences European tetrapod food-webs"
<p>These .Rdata files enable to reproduce results from our article " Signatures of land use intensity on european tetrapod food-web architectures" along with the R code provided at: https://github.com/ChrisBotella/foodwebs_vs_land_use</p> <p>- raw_data : Raw data including GBIF and iNaturalist occurrences and IUCN enveloppes used to select sites and generate species presence/absence. We provide this file for transparency and reproducibility of our methodology.</p> <p>- preprocessed_data: preprocessed data (obtained from raw_data) used to generate our article Figures along with the next file.</p> <p>- TrophicNetworksList : .Rdata containing a list of igraph objects, each igraph is a foodweb associated to a site identify by the list element name. </p> <p>- MultiRegMatrices: .Rdata containing especially the pre-computed matrix Y of cells (rows) by food web metrics (columns) and the covariate design matrix X (for the linear regressions) in order to facilitate and accelerate the reproduction of the analyses.</p>
Inconsistent responses of carabid beetles and spiders to land-use intensity and landscape complexity in Northwestern Europe
<p>Reconciling biodiversity conservation with agricultural production requires a better understanding of how key ecosystem service providing species respond to agricultural intensification. Carabid beetles and spiders represent two widespread guilds providing biocontrol services. Here we surveyed carabid beetles and spiders in 66 winter wheat fields in four Northwestern European countries and analyzed how the activity density and diversity of carabid beetles and spiders were related to crop yield (proxy for land-use intensity), percentage cropland (proxy for landscape complexity) and soil organic carbon content, and whether these patterns differed between dominant and non-dominant species. Less than 17% of carabid or spider species were classified as dominant, which accounted for more than 90% of individuals respectively. We found that carabids and spiders were generally related to different aspects of agricultural intensification. Carabid species richness was positively related with crop yield and evenness was negatively related to crop cover. The activity density of non-dominant carabids was positively related with soil organic carbon content. Meanwhile, spider species richness and non-dominant spider species richness and activity density were all negatively related to percentage cropland. Our results show that practices targeted to enhance one functionally important guild may not promote another key guild, which helps explain why conservation measures to enhance natural enemies generally do not ultimately enhance pest regulation. Dominant and non-dominant species of both guilds showed mostly similar responses suggesting that management practices to enhance service provisioning by a certain guild can also enhance the overall diversity of that particular guild.</p>
Data from: Multiple facets of biodiversity are threatened by mining-induced land-use change in the Brazilian Amazon
<p><strong>Aim</strong> </p> <p>Mining is increasingly pressuring areas of critical importance for biodiversity conservation, such as the Brazilian Amazon. Biodiversity data are limited in the tropics, restricting the scope for risks to be appropriately estimated before mineral licencing decisions are made. As the distributions and range sizes of other taxa differ markedly from those of vertebrates – the common proxy for analysis of risk to biodiversity from mining – whether mining threatens lesser-studied taxonomic groups differentially at a regional scale is unclear.</p> <p><strong>Location </strong></p> <p>Brazilian Amazon</p> <p><strong>Methods </strong></p> <p>We assess risks to several facets of biodiversity from industrial mining by comparing mining areas (within 70km of an active mining lease) and areas unaffected by mining, employing species richness, species endemism, phylogenetic diversity, and phylogenetic endemism metrics calculated for angiosperms, arthropods, and vertebrates.</p> <p><strong>Results </strong></p> <p>Mining areas contained higher densities of species occurrence records than the unaffected landscape, and we accounted for this sampling bias in our analyses. None of the four biodiversity metrics differed between mining and non-mining areas for vertebrates. For arthropods, species endemism was greater in mined areas. Mined areas also had greater angiosperm species richness, phylogenetic diversity, and phylogenetic endemism, although lower species endemism than unmined areas.</p> <p><strong>Main Conclusions </strong></p> <p>Unlike for vertebrates, facets of angiosperm and arthropod diversity are relatively higher in areas of mining activity, underscoring the need to consider multiple taxonomic groups and biodiversity facets when assessing risk and evaluating management options for mining threats. Particularly concerning is the proximity of mining to areas supporting deep evolutionary history, which may be impossible to recover or replace. As pressures to expand mining in the Amazon grow, impact assessments with broader taxonomic reach and metric focus will be vital to conserving biodiversity in mining regions.</p>
Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry
<p>The datasets contain country-level net and gross CO2 flux data for land use, land-use change and forestry (LULUCF) from various approaches as used in the paper "Country-level estimates of gross and net carbon fluxes from land use, land-use change and forestry" (<a href="https://doi.org/10.5194/essd-16-605-2024">Obermeier et al., 2024, <em>Earth System Science Data</em></a>).</p>
MAgPIE model runs csv for plotting: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>This .zip file contains the data used to create the figures for the paper. It includes .csv files and .nc files for maps. This version includes additional files like the mapping between countries and MAgPIE're economic regions.</p>
Land-use legacies affect flower visitation network structure after forest restoration
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Climate regulates the effect of land-use change on the diversity of soil microbial functional groups and soil multifunctionality
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