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FIGURE 3 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 3 Map of southern, central, and eastern Africa showing major geographic features (as in Figure 1a) but with biogeographical barriers elucidated by this study indicated as red dashed lines, labelled as (i) to (vii) (see Discussion), and taxa specific to different ranges indicated according to the predominant biomes (green = tropical; red = arid, turquoise = Mediterranean, blue = temperate, orange = savanna). Note that only one savanna lineage is here indicated for ease of visualization. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.
FIGURE 4 in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
FIGURE 4 Maps of south-central Africa showing the distribution of Köppen–Geiger climate zones for the present (a) and projected future (2070) (b), as well as past (last glacial maximum: left panel), present (right panel), and projected future (2070; right panel) Maxent distribution models for five species groups of bats; Rhinolophus capensis group (c–e: green = R. swinnyi; blue = R. rhodesiae; orange = R. simulator; turquoise = R. capensis; red = R. denti); R. darlingi group (f–h: blue = R. cervenyi; orange = R. darlingi; red = R. damarensis), R. ferruquinum group, in part (i–k: blue = R. acrotis), Laephotis spp (l–n: blue = L. cf. botswanae; orange = L. angolensis), Cistugo spp (o–q: blue = C. lesueuri; red = C. seabrae). Details of Maxent models given in text. Ranges of species above indicated by colors corresponding to biomes recognized in this study (Tables S1 and S2) as follows: blue or green = temperate; orange = savanna; turquoise = Mediterranean; red = arid. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
Future-ready Nordic homes: Responding to the Changing Climate with Free-running Buildings; Digital Repository of Obtained Results
<p>The figures provided here show complete results of the parametric building performance simulation for each studied location, which was a part of the Master thesis in Energy-efficient and Environmental Building Design (Faculty of Engineering, Lund University, Sweden) by Marko Ljubas.</p> <p>The simulations were performed using software IDA ICE 4.8 by EQUA.</p> <p>The weather files were generated using software Meteonorm 8.2 by Meteotest.</p>
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Fig. 4 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 4 Area projected as suitable or unsutable under current and future (2081–2100) climatic conditions (km2) for the three tick species in comparison. a Ixodes ricinus. b Dermacentor reticulatus. c D. marginatus. The corresponding maps are shown in Figs. 1–3 in the main document. Future suitable conditions refers to the area (km2) projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). Continuing suitable conditions refers to area (km2) projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable presence). Continuing unsuitable conditions refers to area (km 2) projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e. stable absence). Future unsuitable conditions refers to the area (km.2) projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction)
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8333 (average over 10 replicates using cross-validation, standard deviation = 0.001113603). Threshold to transform the logistic model output: 0.3816 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 2 Projected future changes for Dermacentor reticulatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable
Fig. 4 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 4 Area projected as suitable or unsutable under current and future (2081–2100) climatic conditions (km2) for the three tick species in comparison. a Ixodes ricinus. b Dermacentor reticulatus. c D. marginatus. The corresponding maps are shown in Figs. 1–3 in the main document. Future suitable conditions refers to the area (km2) projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). Continuing suitable conditions refers to area (km2) projected as suitable under
Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 1 Projected future changes for Ixodes ricinus until 2081–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red:
Fig. 5 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 5 Potential co-occurrence under current and future climatic conditions. a Under near current climatic conditions (1970–2000). b Under projected future climatic conditions (exemplarily for SSP 245) for the period 2041–2060. c Under projected future climatic conditions (SSP 245) for the period 2081–2100. Colors indicate areas where climatic suitability is projected for the respective species; for non-mentioned species ("none of them"), the area is climatically unsuitable according to the modelling results. The thresholds to transform the logistic model output (10% omission rate threshold) are as follows: 0.3368 for Ixodes ricinus, 0.3816 for Dermacentor reticulatus, and 0.4298 for D. marginatus. Maps were built using ESRI Arc-GIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic. (A hatch-based version of this figure is additionally provided in the Supplementary Material: Figure S11.)
Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585 in Ticks on the move-climate change-induced range shifts of three tick species in Europe: current and future habitat suitability for Ixodes ricinus in comparison with Dermacentor reticulatus and Dermacentor marginatus
Fig. 3 Projected future changes for Dermacentor marginatus until 2080–2100. a SSP 126. b SSP 245. c SSP 370. d SSP 585. In dark blue: area projected as suitable under current climatic conditions but unsuitable under future climatic conditions (i.e., potential extinction). In light blue: area projected as unsuitable under current climatic conditions as well as under future climatic conditions (i.e., stable absence). In orange: area projected as suitable under current climatic conditions as well as under future climatic conditions (i.e., stable range). In red: area projected as unsuitable under current climatic conditions but suitable under future climatic conditions (i.e., potential new range). AUC = 0.8229 (average over 10 replicates using cross-validation, standard deviation = 0.001121953). Threshold to transform the logistic model output: 0.4298 (10% omission rate threshold). Maps were built using ESRI ArcGIS (Release 10.7, www.esri.com). Projection: Europe Albers Equal Area Conic
Figure 4 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 4. Mean suitability for Arborophila crudigularis under baseline climate conditions and future climate scenarios. cccma and csiro represent two general circulation models; RCP2.6, and RCP8.5 represent two greenhouse gas emission scenarios; EN is entire suitable habitat; PR is presence records.
Figure 2 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 2. Performance of each model for predicting the suitable habitat for Arborophila crudigularis. GLM: Generalized linear model; GBM: generalized boosting model; GAM: generalized additive model; CTA: classification tree analysis; ANN: artificial neural network; FDA: flexible discriminant analysis; MARS: multiple adaptive regression splines; RF: random forest; MAXENT: maximum entropy model.
Figure 6 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 6. Changes in suitable habitat for Arborophila crudigularis under the RCP8.5 emission scenario. cccma and csiro represent two general circulation models.
Figure 5 in The potential effects of future climate change on suitable habitat for the Taiwan partridge (Arborophila crudigularis): an ensemble-based forecasting method
Figure 5. Changes in suitable habitat for Arborophila crudigularis under the RCP2.6 emission scenario. cccma and csiro represent two general circulation models.
Estimating future climate change impacts on human mortality and crop yields via air pollution: supplemental files
<p>Atmospheric chemistry model output and other gridded data sets necessary to estimate human mortality and crop yield losses associated with future climate change, as used in Murray et al. [PNAS, 2024] doi:10.1073/pnas.2400117121.</p>
Oriental Honey-Buzzards Dataset | Climate change leads to range contraction for the Oriental Honey-Buzzards: How to point out the future conservation strategies?
<p>This dataset contains raster data (.TIF) in probability and binary outputs of oriental honey-buzzards distribution within the wintering and breeding areas under changing climate.</p> <p><strong>File Size</strong>: ~184 MB (13.8 MB in compressed ZIP file)</p> <p><strong>Format File</strong>:</p> <p><em>ohb_A_B_C</em>.tif (.tfw; .XML; .dbf)</p> <p><strong>A</strong>: breeding or wintering</p> <p><strong>B</strong>: timepoint and scenario. e.g., 2050ssp5 or 2010ssp2</p> <p><strong>C</strong>: binary or probability outputs. e.g., bin or prob. <em>Note: for binary maps, value 0: non-suitable areas for OHB and value 1: suitable areas for OHB</em></p> <p>For further inquiries. Please contact: aryo_acondro@apps.ipb.ac.id</p>
Supplementary data: "Future operation of hydropower in Europe under high renewable penetration and climate change"
<p>This dataset contains modelled inflow time series described in the paper "Future operation of hydropower in Europe<br> under high renewable penetration and climate change".</p> <p>The inflow is derived from ten different combinations of five General Circulation Models and two Regional Climate Models at the beginning of the century ("Hydro_inflow_BOC_(...).csv"), 1991 - 2020, and at the end of the century ("Hydro_inflow_EOC_(...).csv"), 2071 - 2100, under three CO2-emissions scenarios (RCP2.6, RCP4.5, and RCP8.5). The ensemble mean for each emissions scenario is presented as well.</p> <p>The historical data that is not confidential is given as well. See "data_sources" for sources.</p>
Conservation of woody species in China under future climate and land-cover changes
<ol> <li>Climate and land-cover changes are major threats to biodiversity, and their impacts are expected to intensify in the future. Protected areas (PAs) are crucial for biodiversity conservation. However, their effectiveness under future climate and land-cover changes remains to be evaluated. Moreover, the impacts of climate and land-cover changes on multi-dimensions of biodiversity are rarely considered when expanding PAs.</li> <li>Using distributions of 8732 woody species in China and species distribution models, we identified species that will be threatened by future climate and land-cover changes (i.e. species with significant projected loss of suitable habitats by the 2070s) under different dispersal scenarios. We then estimated the geographical patterns in species richness (SR) and phylogenetic diversity (PD) of these threatened species, evaluated the effectiveness (i.e. the changes in SR and PD) of Chinese PAs, and identified conservation priorities for future PA expansion.</li> <li>Approximately 12-38% of woody species will be threatened under different scenarios. These species tend to be clustered in the tree of life, and their SR and PD show consistent spatial patterns, being highest at low latitudes. PAs currently protect 90% of these threatened species. However, their SR and PD of threatened species within PAs will decrease by 30-40% by the 2070s, which reduces the PA effectiveness, especially for PAs at low elevations and those with low topographic heterogeneity and high natural vegetation loss.</li> <li>The conservation priorities identified from the SR and PD of the threatened species are mainly in mountains in southern China, the Yunnan-Guizhou Plateau, and Taiwan Island. PA expansion and ecological corridors in these regions are needed to conserve these threatened species.</li> <li> <i>Synthesis and applications.</i> We present a systematic study of the impacts of future climate and land-cover changes on the conservation status of woody species and PA effectiveness in China. Our results suggest that future climate and land-cover changes will reduce PA effectiveness, and the spatial prioritization of biodiversity conservation should consider the influences of future global changes on biodiversity. These results shed new light on the conservation priorities for the post-2020 expansion of PAs in China.</li> </ol>
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.