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opencc-zeroJun 2022View details →

Deep structure, long-distance migration and admixture in the colour polymorphic land snail Cepaea nemoralis

While snails of the genus Cepaea have historically been important in studying colour polymorphism an ongoing issue is that there is a lack of knowledge of the underlying genetics of the polymorphism, as well as an absence of genomic data to put findings in context. We therefore used phylogenomic methods to begin to investigate the post-glacial history of Cepaea nemoralis, with a long-term aim to understand the roles that selection and drift have in determining both European-wide and local patterns of colour polymorphism. By combining prior and new mitochondrial DNA data from over 1500 individuals with ddRAD genomic data from representative individuals across Europe, we show that patterns of differentiation are primarily due to multiple deeply diverged populations of snails. Minimally, there is a widespread Central European population and additional diverged groups in Northern Spain, the Pyrenees, as well as likely Italy and South Eastern Europe. The genomic analysis showed that the present-day snails in Ireland and possibly some other locations are likely descendants of admixture between snails from the Pyrenees and the Central European group, an observation that is consistent with prior inferences from mtDNA alone. The interpretation is that C. nemoralis may have arrived in Ireland via long-distance migration from the Pyrenean region, subsequently admixing with arrivals from elsewhere. This work therefore provides a baseline expectation for future studies on the genetics of the colour polymorphism, as well as providing a comparator for similar species.

NFFA-Europe|Pilot proposal "Resolving Local Coordination of Doped-Carbon Supported Soft-Landed Metal Nanoparticles For CO2 Electroreduction" (PID: 159). XPS data.

XPS data of copper oxide nanoparticles synthesized withtin the NFFA-Europe|Pilot proposal "Resolving Local Coordination of Doped-Carbon Supported Soft-Landed Metal Nanoparticles For CO2 Electroreduction” (PID: 159).

opencc-zeroDec 2021View details →

Site impacts nutrient translocation efficiency in intra- and interspecies miscanthus hybrids on marginal lands

Miscanthus, a C4 perennial rhizomatous grass, is capable of growing in varied climates and soil types in Europe, including on marginal lands. It can produce high yields with low nutrient inputs when harvested after complete senescence. Senescence induction and rate depend on complex genetic, environmental, and management interactions. To explore these interactions, we analysed four miscanthus hybrids (two novel seed-based hybrids, GRC 3 (M. sinensis × sinensis) and GRC 14 (M. sacchariflorus × sinensis); GRC 15, a novel M. sacchariflorus × sinensis clone; and GRC 9, a standard M. × giganteus clone) in Italy, Croatia, Germany and the UK. Over all trial locations and hybrids, the average aboveground biomass of the three-year-old stands in August 2020 was 15 t DM ha-1 with nutrient contents of 7.6 mg N g-1 and 14.6 mg K g-1. As expected, delaying the harvest until spring reduced overall yield and nutrient contents (12 t DM ha-1, 3.3 mg N g-1 and 5.5 mg K g-1). At lower latitudes, the late-ripening M. sacchariflorus × sinensis GRC 14 and GRC 15 combined high yields with low nutrient contents. At the most elevated latitude location (UK), the early-ripening M. sinensis × sinensis combined high biomass yields with low nutrient offtakes. The clonal M. × giganteus with intermediate flowering and senescence attained similar low nutrient contents by spring harvest at all four locations. Seasonal changes in yield and nutrient levels analysed in this study provide: 1) a first step towards recommending hybrids for specific locations and end uses in Europe; 2) crucial data for determination of harvest time and practical steps in the valorisation of biomass; and 3) key sustainability data for life cycle assessments. Identification of trade-offs resulting from genetic × environment × management interactions are critical for increasing sustainable biomass supply from miscanthus grown on marginal lands.

opencc-zeroJul 2022View details →

Data From: Evolution of woody plants to the land‐sea interface: The atypical genomic features of mangroves with atypical phenotypic adaptation

How plants adapt and diverge in extreme environments is a key question of plant evolution and ecology. Mangrove invasion of intertidal environments is facilitated by adaptive phenotypes such as aerial roots, salt-secreting leaf, and viviparity, and genomic mechanisms including whole genome duplication and transposable element number reduction. However, a number of mangroves lack these typical phenotypes. The question we ask is whether these phenotypically atypical mangroves also have distinct genomic features? The sibling mangrove species Lumnitzera littorea and Lumnitzera racemosa provide a model to study this question. We sequenced and assembled their genomes to chromosome level, together with a closely related species Combretum micranthum. While most mangroves have small genomes, the genomes of both Lumnitzera species are large (1443 and 1317 Mb) and carry a high proportion of repeat sequences (~75%). Moreover, Lumnitzera species have not undergone post-gamma whole-genome duplications. Their genome size increased mainly due to the expansion of repeat sequences in their ancestors. However, Lumnitzera genomes have reduced transposable elements by constraining the proliferation of new LTR-RTs. Meanwhile, the two species have more gene families contracted than expanded, and some gene families with reversed size change may underlie their differentiation in root morphology and local distribution. We identified 86 chromosomal inversions, five of which are measured between 6.5 and 12.8 megabases. A number of genes located in these inversions function in pigment biosynthesis, a process likely involved in flower color differentiation between the Lumnitzera species. We conclude that the mangroves with atypical phenotypes also have atypical genomic evolution.

Land-use fluxes: data from global models and national inventories

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:  (i) areas of managed forest, used in this study and based on country data (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.).  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>   For further methodological details, see Grassi et al. (ESSD, submitted): 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)

Land Use Trade-offs in Decarbonization of Electricity Generation in the American West

This Zenodo archive includes the code and data used to produce the publication Patankar et al. (2022): Land Use Trade-offs in Decarbonization of Electricity Generation in the American West. This analysis requires five types of scripts/modeling efforts that take in three types of inputs, as described below. The code for the GenX model is not included here as it is available at https://github.com/GenXProject/GenX with further documentation available at https://genxproject.github.io/GenX/dev/.      **Inputs**       1. Solar and wind candidate project area (CPA)       2. Renewable resource profiles       3. Real and routing cost surface data     **Scripts and Models**         1. PowerGenome - Power system data compilation software         2. GenX Power system model along with the Modeling to Generate Alternatives (MGA) algorithm         3. Transmission network building         4. Downscaling for new solar and wind capacity          5. Land use analysis presented in the paper For information on the description of the datasets, scripts and modeling efforts, see the README.md file.

opencc-by-4.0Aug 2022View details →

Supplementary Material for "Joint Inversion Based on Variation of Information - A Crustal Model of Wilkes Land, East Antarctica"

Inversion_Results_Cluster.nc - contains geographical and vertical information of clusters Inversion_Results_RealData_Run1.nc - contains inverted susceptibilities and densities after the first inversion run with high coupling Inversion_Results_RealData_Run2.nc - contains inverted susceptibilities and densities after the second continuous inversion run with low coupling Plot.ipynb - python script for visualization

opencc-zeroAug 2022View details →

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.

Climate velocity and land-use instability

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).

Supporting dataset: Towards the Optimal Representation of Sub-Grid Heterogeneity in Land Surface Models - Upper Colorado River Basin study case

Supporting files for the manuscript "Towards the Optimal Representation of Sub-Grid Heterogeneity in Land Surface Models - Upper Colorado River Basin study case." The dataset contains the ESCF derived from the original 800 HydroBlocks simulations used to train/test the random forest models (RFM) to compute the annual mean ESCFs for soil moisture content, sensible heat, latent heat, and runoff, as well as the obtained RFM to compute the resulting number of tiles for a given configuration over the study domain. Besides, the dataset includes the ESCFs for the quasi-fully distributed simulation (QFD) and the input data layers required to run HydroBlocks simulations over the study domain.

A GeoNEX-based 1km hourly land surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) product

The dataset is produced by employing a well-established physical-based look-up table (LUT) approach to the GeoNEX gridded top-of-atmosphere bidirectional reflectance factor data acquired by the Advanced Himawari Imager (AHI) and Advanced Baseline Imager (ABI) sensors. It consists of DSR and PAR over both AHI and ABI coverage at an hourly temporal step with a 1km spatial resolution. Full details about the work are under review and can be cited as  Li, R., Wang, D., Wang, W., and Nemani, R.: A GeoNEX-based high spatiotemporal resolution product of land surface downward shortwave radiation and photosynthetically active radiation, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-319,  in review, 2022. Due to the file size restrictions, only some sample files are shared here. The full dataset can be accessed through the NASA Advanced Supercomputing Division GeoNEX data portal, using the following URLs: <a href="https://data.nas.nasa.gov/geonex/geonexdata/GOES16/GEONEX-L2/DSR-PAR/">https://data.nas.nasa.gov/geonex/geonexdata/GOES16/GEONEX-L2/DSR-PAR/</a> <a href="https://data.nas.nasa.gov/geonex/geonexdata/HIMAWARI8/GEONEX-L2/DSR-PAR/">https://data.nas.nasa.gov/geonex/geonexdata/HIMAWARI8/GEONEX-L2/DSR-PAR/</a>

Data from: Predicting range shifts of the giant pandas under future climate and land use scenarios

Aim: Understanding and predicting how species will respond to global environmental change (i.e., climate and land use change) is essential to efficiently inform conservation and management strategies for authorities and managers. Here, we assessed the combined effect of future climate and land use change on the potential range shifts of the giant pandas (Ailuropoda melanoleuca). Location: Sichuan Province, China. Methods: We used ensemble species distribution models (SDMs) to forecast range shifts of the giant pandas by the 2050s and 2070s under four combined climate and land use change scenarios. We also compared the differences in distributional changes of giant pandas among the five mountains in the study area. Results: Our ensemble SDMs exhibited good model performance in terms of both AUC (0.931) and TSS (0.747), and suggested that precipitation seasonality, annual mean temperature, the proportion of forest cover and total annual precipitation are the most important factors in shaping the current distribution patterns for the giant pandas. Our projections of future species distribution also suggested a range expansion under an optimistic greenhouse gas emission, while suggesting a range contraction under a pessimistic greenhouse gas emission. Moreover, we found that there is considerable variation in the projected range change patterns among the five mountains in the study area. Especially, the suitable habitat of the giant panda is predicted to increase under all scenarios in Minshan mountains, while is predicted to decrease under all scenarios in Daxiangling and Liangshan mountains, indicating the vulnerability of the giant pandas at low latitudes. Main conclusions: Our findings highlight the importance of an integrated approach that combines climate and land use change to predict the future species distribution and the need for a spatial explicit consideration of the projected range change patterns of target species for guiding conservation and management strategies.

Supplementary data for "Land tenure drives Brazil's deforestation rates across socio-environmental contexts"

File name: Supplementary Data 1 Description: Pre-processed data that contain all the information required to run the statistical analyses that produce our final results. This includes information on the land parcel identification number corresponding to the Imaflora dataset (gid), the covariates, and forest conversion to agriculture for the respective temporal scale. Note, each spatiotemporal scale and tenure regime comparison should be derived from the column “stScale”. File name: Supplementary Data 2 Description: Full regression results reporting estimated effects each spatial-temporal scale considered, compared against both an undesignated/untitled public lands and private lands counterfactual. Average Marginal Effects (AME) are reported for each specific compared tenure regime at different spatial and temporal scales (“stScale”), with recorded number of observations in matched sample (n_pop), the standard error (SE), p-value, and lower and upper confidence intervals. File name: Supplementary Data 3 Description: Rosenbaum bounds reporting upper and lower bounds for both Hodges-Lehmann estimates and p-values at each spatial-temporal scale considered. For more information on this study please see: see https://doi.org/10.31223/X5D31J and https://doi.org/10.5281/zenodo.7062285

Zarr of Sentinel-3 NDVI Long Term Statistics (1999-2019) from the Copernicus Global Land Service over Troms and Finnmark (Norway)

Normalized Difference Vegetation Index (NDVI) in the form of a zarr (tarball) from Sentinel-3 NDVI Analysis Ready Cloud Optimized (ARCO dataset) - The original data is provided by the Copernicus Global Land Service. One can extract the zarr folder using a command like:   <pre><code>tar xvf c_gls_NDVI-LTS_1999-2019-Troms_Finnmark_VGT-PROBAV_V3.tar</code></pre>

High resolution ancient sedimentary DNA shows that alpine plant diversity is associated with human land use and climate change

The European Alps are highly rich in species, but their future may be threatened by ongoing changes in human land use and climate. Here, we reconstructed vegetation, temperature, human impact and livestock over the past ~12,000 years from Lake Sulsseewli, based on sedimentary ancient plant and mammal DNA, pollen, spores, chironomids, and microcharcoal. We assembled a highly-complete local DNA reference library (PhyloAlps, 3,923 plant taxa), and used this to obtain an exceptionally rich sedaDNA record of 366 plant taxa. Vegetation mainly responded to climate during the early Holocene, while human activity had an additional influence on vegetation from 6 ka onwards. Land-use shifted from episodic grazing during the Neolithic and Bronze Age to agropastoralism in the Middle Ages. Associated human deforestation allowed the coexistence of plant species typically found at different elevational belts, leading to levels of plant richness that characterise the current high diversity of this region. Our findings indicate a positive association between low-intensity agropastoral activities and precipitation with the maintenance of the unique subalpine and alpine plant diversity of the European Alps.

Data and code of Land use scenario for 'Development of common socio-economic scenarios for climate change impact assessments in Japan'

Land use scenario calculation: Executable files, source code files and data files This dataset contains program codes and input data used for reproducing land use scenarios explained in Chapter 5.2 in Yoshikawa et al. (submitted to GMDD). We found a few fatal errors in the following code. These code were fixed from version 2 (http://dx.doi.org/10.5281/zenodo.7090670). /Step3/a01_calc_land_use.py /Step3/a01_calc_land_use_std.py /Step3/a01_calc_land_use_rate.py /Step3/run03.bat

Land cover maps: Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

Land cover maps obtained with Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models for the year 2018 with Sentinel-2 acquisitions. For further details see section VII-A-2 (Results-Performance results in the Southfrance area-Qualitative results) of the article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.

Global potential invasion maps of traded birds under climate and land-cover change

Biological invasions rank among the top five threatening factors affecting biodiversity, but ongoing changes in climate and land cover might exacerbate risks. We used species distribution models for 609 traded bird species on the CITES list to examine the combined effects of projected climate change and land-cover change worldwide on the potential range expansion of bird species with commercial value as pets. The maps of potential invasion (may be inferred as the invasion risk) have been provided in the main manuscript and here, the potential invasion dataset for the current and future times is provided including the species distribution maps, all as GeoTiff files. The maps for the future time are provided for different future years and over a range of climate scenarios (SSP245, SSP370, and SSP585).

Classification Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

Classification data set (train, validation, test) from the study area based on 27 tiles on the south of the France. Data set are provided for each eco-climatic region. The size corresponds to the data set DS-A. Only one random pixel sampling is provided: seed 0. This data set was used to train Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models. For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>. The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.

Stable landings mask irreversible community reorganizations in an overexploited Mediterranean ecosystem

1. Background. Cumulative human pressures can induce non-linear discontinuous dynamics in ecosystems, known as regime shifts. Regime shifts typically imply hysteresis, a lacking or delayed system response when pressures are reverted, which can frustrate restoration efforts. 2. The goal of the study. Here we investigate whether the northern Adriatic Sea fish and macroinvertebrate community, depicted by commercial fishery landings, has undergone irreversible regime shifts over the last 40 years. 3. What was done. We use a stochastic cusp model to show that, under the interactive effect of fishing and warming, the community reorganized through discontinuous changes. 4. What was found. Part of the community has now reached a new stable state, implying that a recovery towards previous baselines might be impossible. Interestingly, total landings remained constant across decades, masking the low resilience of the community. 5. The relevance. Our study reveals the importance of carefully assessing regime shifts and resilience in marine ecosystems and advocates for their inclusion into management.