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FIGURE 5 in Conservation assessments in climate change scenarios: spatial perspectives for present and future in two Pristidactylus (Squamata: Leiosauridae) lizards from Argentina

FIGURE 5. Area models for suitability habitat from the averaged replications output for: P. achalensis, A) Present model = 5008.55 km², B) Model for 2050 RCP 45 = 4054.00 km², C) Model for 2050 RCP 85 = 2677.83 km²; P. nigroiugulus, 2) Present model = 71957.34 km², E) Model for 2050 RCP 45 = 56162.45 km², F) Model for 2050 RCP 85 = 38501.27 km². References: Country / province names, protected areas perimeters dashed-green lines, protected areas intersected with suitable areas filled in solid green, localities in red dots, and defined accessible area (M) in the upper left box.

FIGURE 4 in Conservation assessments in climate change scenarios: spatial perspectives for present and future in two Pristidactylus (Squamata: Leiosauridae) lizards from Argentina

FIGURE 4. True skill statistic (TSS) performed on the replicates for each species. References: mod, number of model replicate; values close to 1 indicates perfect agreement, values near zero indicates a performance no better than random.

FIGURE 3. RUs histograms for P in Conservation assessments in climate change scenarios: spatial perspectives for present and future in two Pristidactylus (Squamata: Leiosauridae) lizards from Argentina

FIGURE 3. RUs histograms for P. nigroiugulus. The white columns show the distributions of available RUs, whereas grey columns show the distributions of used RUs.

FIGURE 2. RUs histograms for P in Conservation assessments in climate change scenarios: spatial perspectives for present and future in two Pristidactylus (Squamata: Leiosauridae) lizards from Argentina

FIGURE 2. RUs histograms for P. achalensis. The white columns show the distributions of available RUs, whereas grey columns show the distributions of used RUs.

opencc-by-4.0Nov 2024View details →

A companion dataset to the paper Scenarios of future climate zone changes in Europe based on EURO-CORDEX regional model ensemble by Holtanová et al., to be submitted to Regional Environmental Change

The content of the dataset is described in the metadata.txt file.

opencc-zeroNov 2021View details →

Connectivity Matrices from biophysical modelling studies for A. millepora coral larvae in the Great Barrier Reef (Australia); present day and future scenarios

These data contain connectivity matrices from biophysical modelling simulations of the dispersal of Acropora millepora coral larvae in the southern Great Barrier Reef (Australia), under present-day and future climate scenarios. The connectivity matrices represent modelled strength of larval transfer from one reef to another, and were obtained using a coupled reef-scale, high-resolution, depth-integrated finite element hydrodynamic model (SLIM) of water currents in the Great Barrier Reef, and Individual-Based particle tracking module. Biological parameters to model larval acquisition and loss of competency and mortality were based on the results of experiments detailed in the related journal article. We include connectivity matrices for 2 different water temperature scenarios representing current and future climates, for three different recent spawning seasons (2008, 2009, 2010), and also for scenarios where low-frequency currents through the domain are modulated to mimic the likely effects from future changes to large-scale circulation extracted from CMIP5 global climate models. We also include files summarising the relative changes, per reef, to certain key connectivity metrics between the 2 temperature scenarios (dispersal distance, local retention, number of incoming connections, "source index", and present day "source index" - all metrics are defined in the related journal article and Methods section of this metadata), averaged over all 3 spawning seasons modelled, as plotted in Figs 1a-e in the related journal article. Additionally, we include a separate file summarising changes to reef recovery times following a disturbance to coral cover between the 2 temperature scenarios, per reef, as modelled using the meta-population model described in the related journal article, and as shown in Fig 2 of the article.

opencc-by-4.0Apr 2022View details →

Forest land under different scenarios of future global change

Forest loss is one of the most threats to biodiversity, forested lands drive a key role in the climate earth system that also affects species diversity and ecosystem services (Vale et al. 2021). Therefore, several initiatives had been driven to map land-use time series, mainly projections into the future (Chen et al. 2020 and reference therein). The availability of those products is valuable for earth science, ecology, conservation, and other research fields once land-use land-cover data are important predictors of species occurrence and biodiversity threat (Ruiz-Benito et al. 2020., Valet et al. 2021)  A recent land-use product called <a href="https://www.nature.com/articles/s41597-020-00669-x">GCAM-Demeter</a> presents the highest global spatial resolution (0.05 º) until now (Chen et al. 2020).  It provided current and future projections (2015-2100) under different scenarios of climate change (Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCP) ) and according to the most recent framework of Coupled Model Intercomparison Project phase 6 (CMIP6). The data in each year include grid-explicit fraction (in percent) of each of the 32 plant functional types (PFTs) that are widely used in current Earth system models. The complete dataset is available in five General Circulation Models (GCMs): gfdl, hadgem, ipsl, miroc, and noresm. Also includes the mean and standard deviation of those GCMs (Chen et al. 2020). Although the valuable contribution of the GCAM-Demeter to provide those data,  it is compressed in NetCDF format, a complex file format that needs management to become usable in several analyses, especially in ecology and biodiversity analyses (Vale et al., 2021 and reference therein). Here I managed the outputs of the mean of five GCMs (harmonized projection)  based on  the sum analysis of plant functional types considering: 1- Global Extent at 0.05-degree resolution  2- Years 2020, 2030 and 2050 3- SSPs and RCP as follow: SSP1_RCP2, SSP2_RCP45, SSP4_RCP6, SSP5_RCP85 The goals are to assess quantitatively the forest lands under different scenarios of global change and make these data available in the Tag Image File Format (TIFF)  which is a more friendly and useable format to incorporate in several spatial analyses, mainly in ecology and biodiversity studies for conservation purposes. Methods I downloaded the GCAM-Demeter NetCDF files  (the outputs of the mean of five GCMs and the first version, i.e the harmonized projection)  freely available at <a href="https://release.datahub.pnnl.gov/released_data/1190">DataHub</a> (Chean et al. 2020).  I selected, extracted, and performed the sum analysis of  plant functional types (codes PTF1 to PTF11- described in README attached) considering: 1- Global Extent at 0.05-degree resolution  2- Years 2020, 2030 and 2050 3- SSPs and RCP as follow: SSP1_RCP2, SSP2_RCP45, SSP4_RCP6, SSP5_RCP85 The data manipulation and analysis were done using ncdf4 and raster packages in the R environment (R Core Team 2020, Pierce 2019; Hijmans et al. 2020).  The outputs range from 0 to 100 and can be identified by their file name, for example: 2020_SSP5_RCP85_Forest_GCAM-Demeter_GCMsMean_Harmonized.tif . Also, outputs are provided in the Tag Image File Format (TIFF) which is a more friendly and useable format (Vale et al. 2021 and references therein). The methods and the quantitative results for forested areas are detailed better in the <a href="https://github.com/Tai-Rocha/Forest_Scenarios.github.io">GitHub repository</a> . Acknowledgments. This initiative was possible due to the high-quality data maintained and made publicly available by GCAM-Demeter authors.  Also,  the study  was developed within the scope of the Earth System Modeling Program funded by CAPES (Coordination for the Improvement  of  Higher  Education  Personnel  -  Grant  No. 88887.373031/2019-00)    References Chen, M., Vernon, C. R., Graham, N. T., Hejazi, M., Huang, M., Cheng, Y., & Calvin, K. (2020). Global land use for 2015–2100 at 0.05 resolution under diverse socioeconomic and climate scenarios. Scientific Data, 7(1), 1-11.  Hijmans, R. J. (2020). raster: Geographic Data Analysis and Modeling (R package version 3.3-13)[Computer software]. Retrieved form https://CRAN. R-project. org/package= raster. Pierce, D. (2019). ncdf4: Interface to Unidata netCDF (Version 4 or earlier) Format Data Files. R package version 1.16. Ruiz-Benito P, Vacchiano G, Lines ER, Reyer CP, Ratcliffe S, Morin X, Hartig F, Mäkelä A, Yousefpour R, Chaves JE, Palacios-Orueta A. Available and missing data to model impact of climate change on European forests. Ecological Modelling. 2020 Jan 15;416:108870. Team, R. C. (2020). R: A language and environment for statistical computing. Vale, M. M., Lima-Ribeiro, M. S., & Rocha, T. C. (2021). GLOBAL LAND-SE AND LAND-COVER DATA: HISTORICAL, CURRENT AND FUTURE SCENARIOS. Biodiversity Informatics, 16, 2021, pp. 28-38.

opencc-by-4.0Sep 2024View details →

Data for manuscript "Substantial Contraction of Dense Shelf Water in the Ross Sea under Future Climate Scenarios"

Relevant data shown in figures of the manuscript "Substantial Contraction of Dense Shelf Water in the Ross Sea under Future Climate Scenarios"

opencc-zeroNov 2022View details →

Potential distribution prediction of Amaranthus palmeri S. Watson in China under current and future climate scenarios

The vicious invasive alien plant <a name="_Hlk99445053"></a>Amaranthus palmeri poses a serious threat to ecological security and food security due to its strong adaptability, competitiveness, and herbicide resistance. Predicting its potential habitats under current and future climate change is critical for monitoring and early warning. In this study, we used two sets of climate data, namely, WorldClim1.4 and RCPs (the historical climate data of WorldClim version 1.4 and future climate data of RCPs), WorldClim2.1 and SSPs (the historical climate data of WorldClim version 2.1 and future climate data of SSPs), to analyze the dominant environmental variables affecting the habitat suitability and predict the potential distribution of A. palmeri to climate change in China based on the MaxEnt model. The results show that (i) Temperature has a greater impact on the distribution of A. palmeri. The relative contributions of temperature-related variables count to 70 % or more, and the annual mean temperature (bio1) reached more than 40 %. (ii) At present, the potentially suitable area is widely distributed in the central-east and parts of southwest China, and the high suitable area is focused on the North China Plain. The potential suitable area predicted by WorldClim1.4 and WorldClim2.1 both accounts for about 31% of China's total land area. (iii) Future climate change will expand the suitable habitats to high latitudes and altitudes. The overall suitable area maximum increased to 44.93% under SSPs and 38.91% under RCPs. We conclude that climate change would increase the risk of A. palmeri expanding to high latitudes and altitudes, the results have practical implications for the effective long-term management in response to the global warming of A. palmeri.

opencc-zeroJun 2023View details →

Projected changes in forest biomass to 2100 by county and species for 20 future scenarios

Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) are important drivers of forest demography. Here we apply previously-derived growth and survival responses for 94 tree species, representing >90% of the contiguous U.S. forest basal area, to project how changes in mean annual temperature, precipitation, and N and S deposition from 20 different future scenarios may affect forest composition to 2100. We find that under the low climate change scenario (RCP 4.5), reductions in aboveground tree biomass from higher temperatures are roughly offset by increases in aboveground tree biomass from reductions in N and S deposition. However, under the higher climate change scenario (RCP 8.5) the decreases from climate change overwhelm increases from reductions in N and S deposition. These broad trends underlie wide variation among species. We found that averaged across temperature scenarios, the relative abundance of 60 species was projected to decrease by more than 5%, 20 species were projected to increase by more than 5%, and reductions of N and S deposition led to a decrease for 13 species and an increase for 40 species. This suggests large shifts in the composition of U.S. forests in the future. Negative climate effects were mostly from elevated temperature and were not offset by scenarios with wetter conditions. We found that by 2100 an estimated 1 billion trees under the RCP 4.5 scenario and 20 billion trees under the RCP 8.5 scenario may be pushed outside the temperature record upon which these relationships were derived. These results may not fully capture future changes in forest composition as several other factors were not included. Overall efforts to reduce atmospheric deposition of N and S will likely be insufficient to overcome climate change impacts on forest demography across much of the United States unless we adhere to the low climate change scenario.

opencc-by-4.0Aug 2023View details →

Estuarine Hypoxia – Identifying High Risk Catchments Now and Under Future Climate Scenarios - Water Level Dataset

Historic water level data used in determining the inundation characteristics for each of the catchments within the study area. The locations of each water level gauge, a summary of the distribution of water levels and the distribution of data to each catchment is detailed in the Supporting Information accompanying the manuscript "Estuarine Hypoxia – Identifying High Risk Catchments Now and Under Future Climate Scenarios".