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55 results for “climate change Europe”
Tree mortality risks under climate change in Europe: assessment of silviculture practices and genetic conservation networks
<p>General context: Climate change can positively or negatively affect abiotic and biotic drivers of tree mortality. Process-based models integrating these climatic effects are only seldom used at species distribution scale.</p> <p>Objective: The main objective of this study was to investigate the multi-causal mortality risk of five major European forest tree species across their distribution range from an ecophysiological perspective, to quantify the impact of forest management practices on this risk and to identify threats on the genetic conservation network.</p> <p><br> Methods: We used the process-based ecophysiological model CASTANEA to simulate the mortality risk of \textit{Fagus sylvatica}, \textit{Quercus petraea}, \textit{Pinus sylvestris}, \textit{Pinus pinaster} and \textit{Picea abies} under current and future climate conditions, while considering local silviculture practices. The mortality risk was assessed by a composite risk index \textit{(CRIM)} integrating the risks of carbon starvation, hydraulic failure and frost damage. We took into account extreme climatic events with the \textit{CRIM$_{max}$}, computed as the maximum annual value of the \textit{CRIM}.</p> <p><br> Results: The physiological processes' contributions to \textit{CRIM} differed among species: it was mainly driven by hydraulic failure for \textit{P. sylvestris} and \textit{Q. petraea}, by frost damage for \textit{P. abies}, by carbon starvation for \textit{P. pinaster}, and by a combination of hydraulic failure and frost damage for \textit{F. sylvatica}. Under future climate, projection showed an increase of \textit{CRIM} for \textit{P. pinaster} but a decrease for \textit{P. abies}, \textit{Q. petraea} and \textit{F. sylvatica}, and little variation for \textit{P. sylvestris}. Under the harshest future climatic scenario, forest management decreased the mean \textit{CRIM} for \textit{P. sylvestris}, increased it for \textit{P. abies} and \textit{P. pinaster} and had no major impact for the two broadleaved species. By the year 2100, 38\% to 90\% of the conservation units are at extinction threat (\textit{CRIM$_{max}$}=1), depending on the species.</p> <p><br> Conclusions: Using a process-based ecophysiological model allowed us to disentangle the multiple drivers of tree mortality under current and future climate. Taking into account the positive effect of increased CO$_2$ on fertilization and water use efficiency, the average risks may increase or decrease in the future depending on species and sites. However, considering extreme climatic events, future projections are as pessimistic than those obtained with bioclimatic niche models.</p> <p> </p> <p>Abbreviation for column:</p> <p>X Longitude<br> Y Latitude<br> LAImax Leaf area index max reach<br> Nha Density per hectar<br> Vha Volume per hectar<br> NEE Net ecosystem exchange<br> NPP net primary production<br> Reco Respiration ecosystem<br> GPP Gross primary production<br> Etveg Evapotranspiration canopy<br> Etsol Evapotranspiration sol<br> TR tree transpiration<br> ETP evapotranspiration potentiel<br> BiomassOfReserves Biomass of reserve<br> rw ring width<br> dbh diameter at breast heast<br> height height<br> BBday Budburst date<br> rFD risk of frost<br> CRIM_max Maximum combined risk index of mortality reach<br> rNSC risk of carbon starvation<br> rPLC risk of embolism<br> rPLC_max Maximum risk of embolism reach<br> CRIM combined risk index of mortality<br> Climate Climatic model<br> rNSC_max maximum risk of carbon starvation reach<br> rFD_max Maximum risk of frost reach<br> Scenario_Sylvicol null means no silvulcture simulated<br> species species<br> Country Country<br> alt_watch altitude of climate simulated<br> grid_watch number of the pixel point of WATCH<br> grid_eurocordex number of the pixel point of Eurocordex<br> Pinus_sylvestris 0 abscence ; 1 presence<br> Fagus_sylvatica 0 abscence ; 1 presence<br> Quercus_petraea 0 abscence ; 1 presence<br> Picea_abies 0 abscence ; 1 presence<br> Pinus_pinaster 0 abscence ; 1 presence</p> <p> </p>
FIGURE 1 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 1. Geographic sketch showing the location of the plant-bearing sites. For locality numbers see Table 1.
FIGURE 6 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 6. Representation of modern European vegetation formations for the test set of fossil assemblages as delivered by Drudges 1 and 2. Formation H - Hygrophilous thermophytic mixed deciduous broadleaved forests; Formation G - Thermophilous mixed deciduous broadleaved forests; Formation F - Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation D - Mesophytic and hygromesophytic coniferous and mixed broadleaved-coniferous forests; Formation C - Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation. More detailed information on subdivisions and units is available in Appendix 9.
FIGURE 4 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 4. Representation of East Asian and European vegetation types and formations as delivered by Drudges 1 and Drudge 2 for the IPR Similarity, Taxonomic Similarity (TS), and Results Mix. See also Appendix 8.
FIGURE 8 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 8. Mean annual temperature (MAT), warm-month mean temperature (WMMT), and cold-month mean temperature (CMMT) based on CLAMP and the Coexistence Approach (CA) for the fossil plant record (sources are Kvaček et al., 2011; Teodoridis and Kvaček, 2015; Teodoridis et al., 2009, 2012, 2015, 2017). black columns: minimum CA. light grey columns: maximum CA, narrow, dark grey columns: CLAMP result. For more comprehensive climate data see Appendix 10.
FIGURE 9 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 9. Climate parameters of the modern European vegetation Formations F, G, and H based on Bohn et al. (2004) and Traiser and Mosbrugger (2004) represented as columns spanning the minimum and maximum of the respective data. Vegetation of Formation F tends to lower temperatures (note, however, that climate data for formations F.3 – F.1 are more complex). Vegetation of Formation G tends to lower MAP. Asterisks indicate single data points (no climate interval was available). The data are listed in Appendix 11. Abbreviations: MAT = mean annual temperature; WMMT = warm-month mean temperature; CMMT = cold-month mean temperature; MAP = mean annual precipitation.
FIGURE 3 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 3. Modern vegetation types/formations delivered as proxies by Drudges 1 and 2 for the test set of fossil assemblages. Shown are the five best fitted results for the Taxonomic Similarity (TS) and the overall scores (synthesis of all similarity approaches), i.e., 25 proxies for every plant assemblage. Pastel colours represent East Asian vegetation types, bright colours European vegetation formations. For more detailed information see Appendix 4 which provides interactive colour signature (moving the cursor over the columns provides the designation of the proxies and their relevance for every fossil assemblage).
FIGURE 2 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 2. Modern vegetation types/formations delivered as proxies by Drudges 1 and 2 for the test set of fossil assemblages. Shown are the five best fitted results for the IPR Similarities based on Drudge 1 and Drudge 2 and for the Results Mix based on Drudge 1 and Drudge 2. Pastel colours represent East Asian vegetation types, bright colours European vegetation formations. For more detailed information see Appendix 4 which provides interactive colour signature (moving the cursor over the columns provides the designation of the proxies and their relevance for every fossil assemblage).
FIGURE 7 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 7 (previous page). Representation of modern European vegetation formations for the test set of fossil assemblages as delivered by Drudges 1 and 2 in more detail (see also Appendix 9). Formation H: H001, Colchic lowland to submontane mixed oak forests, in black; H002, Hyrcanian lowland-colline mixed broadleaved forests, in dark grey; H003, Hyrcanian colline to montane oak forests, in light grey. Formation G: G.1 - Subcontinental thermophilous (mixed) pedunculate oak and sessile oak forests, in black; G.2 - Sub-Mediterranean-subcontinental thermophilous bitter oak and Balkan oak and mixed forests, in dark grey; G.3 - Sub-Mediterranean and meso-supra-Mediterranean downy oak and mixed forests, in light grey; G.4 - Iberian supra- and meso-Mediterranean oak forests, in white. Formation F: F.1 - Species-poor acidophilous oak and mixed oak forests, in black; F.2 - Mixed oak-ash forests, in dark grey; F.3 - Mixed oak-hornbeam forests, in light grey; F.4 Lime-pedunculate oak forests, in white; F.5 - Beech and mixed beech forests, hatched lower left to upper right; F.6 - Oriental beech forests and hornbeam-oriental beech forests, hatched upper left to lower right; F.7 - Caucasian mixed hornbeam-oak forests, hatched vertically. Formation F, F.5 - Beech and mixed beech forests: F.5.1.1 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, lowland(-colline) types, in black; F.5.1.2 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, colline-submontane types, in dark grey; F.5.1.3 - Species-poor oligotrophic to mesotrophic beech and mixed beech forests, montane-altimontane types, in light grey; F.5.2.1 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, colline-submontane types, in white; F.5.2.2 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, colline-submontane types, hatched lower left to upper right; F.5.2.3 and 4 - Species-rich eutrophic and eu-mesotrophic beech and mixed beech forests, montane-altimontane types, hatched upper left to lower right. Formation D: D.1 - Western boreal spruce forests, in black; D.2 - Eastern boreal pine-spruce and fir-spruce forests, in dark grey; D.3 - Hemiboreal spruce and fir-spruce forests with broad-leaved trees, in light grey; D.4 - Montane to altimontane, partly submontane fir and spruce forests in the nemoral zone, in white; D.5 - Boreal and hemiboreal pine forests, hatched lower left to upper right; D.6 - Montane to altimontane (subalpine) pine forests in the nemoral zone; hatched upper left to lower right.
Fig. 6 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 6. Result of the analysis of Binomial tests (CliMond 2090 (2081–2100)): A — T. graeca; B — T. hermanni.
Fig. 3 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 3. Niche clustering (Geographic space, CliMond 1975 (1970–2000)) from: A — T. graeca (1. T. g. ibera, 2. T. nikolskii, 3. T. g. anamurensis, 4. T. g. floweri, 5. T. g. antakyensis, 6. T. g. pallasi, 7. T. g. armenica, 8. T. g. perses, buxtoni, 9. T. g. terrestris); B — T. hermanni (1. T. h. hermanni, 2. T. h. hervegovinensis, 3. T. h. boettgeri), red circles showing the approximate ranges of subspecies according to "Turtles…, 2017" World" (2017).
Fig. 2 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 2. The "Ecological envelope" — relationship bio01 "Annual mean temperature", °C & bio12 "Annual precipitation", mm (DivaGis): A — T. graeca; B — T. hermanni.
Fig. 5 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 5. Potential (probabilistic) model of T. hermanni world expansion built in the Maxent program based on the CliMond: A — 1975 (1970–2000); B — 2090 (2081–2100)) climatic data and GBIF data (2021). Areas of the highest habitat suitability (> 0.3–0.5) are colored in red and areas of the lowest (<0.2) — in blue (SAGA GIS).
Fig. 4 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 4. Potential (probabilistic) model of T. graeca expansion built in the Maxent program based on the CliMond: A — 1975 (1970–2000); B — 2090 (2081–2100)) climatic data and GBIF data (2021 a). Areas of the highest habitat suitability (> 0.3–0.5) are colored in red and areas of the lowest (<0.2) — in blue (SAGA GIS).
Fig. 4 in Changing climate-changing pathogens: Toxoplasma gondii in North-Western Europe
Fig. 4 Expected increases in T. gondii prevalence in NorthWestern Europe towards 2069 based upon the combination of climatic conditions from Figs. 2 and 3. The dotted bright green areas indicate a small increase in T. gondii prevalence as a result of climatic change, pink areas a limited increase, and red areas a substantial increase
Fig. 2 in Changing climate-changing pathogens: Toxoplasma gondii in North-Western Europe
Fig. 2 Total precipitation in North-Western Europe as calculated by the CCSR (Center for Climate System Research, University of Tokyo) and NIES (National Institute for Environmental Studies) model under a SRES A1 scenario. Presented is the total mean precipitation in period from 1970 to 1999 (a), and the projected total mean precipitation from 2010 to 2039 (b) and 2040–2069 (c). Figures obtained from www.ipcc-data.org
Data from: Climate change could fuel urinary schistosomiasis transmission in Africa and Europe
<p>This dataset contains primary, intermediate, and output data for "Climate change could fuel urinary schistosomiasis transmission in Africa and Europe". In this paper, we use mechanistic and correlative modelling to predict the distribution of schistosomiasis intermediate host snail <em>Bulinus truncatus.</em> Model projections suggest the suitable habitat for <em>B. truncatus</em> will increase by 17%, with new suitable habitat in Southern Europe and Central Africa, and a reduction in suitable habitat in the Sahel region.</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
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.