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13 results for “climate change exposure”
Increased Radon Exposure from Thawing of Permafrost Due to Climate Change
<p>This database contains one excel data file containing all data required to produce all plot figures in the eponymous paper in Earth's Future, as well as 5 modelling output video files, the stills from which contribute to the non-plot figures in the paper.</p> <p>This database also contains high quality versions of all the display items in the eponymous paper.</p> <p>For further information please contact the author at p.w.j.glover@leeds.ac.uk</p>
Climate change exposure and vulnerability of the global protected area estate from an international perspective
<p>Aim: Protected areas are essential to conserve biodiversity and ecosystem benefits to society under increasing human pressures of the Anthropocene. Anthropogenic climate change, however, threatens the enduring effectiveness of protected areas in conserving biodiversity and providing ecosystem services, because it modifies and redistributes biodiversity with unknown consequences for ecosystem functioning within protected areas. Here we assess (1) the climate change exposure of the global terrestrial protected area estate and (2) the climate change vulnerability of national protected area estates.</p> <p>Location: Terrestrial protected areas worldwide.</p> <p>Methods: We calculated local climate change exposure as predicted climate anomalies between the present and 2070 using ten global climate models, two emission scenarios (RCP 4.5 and 8.5) and the finest spatial resolution available for global climate projections (approx. 1 km). We estimated the climate change vulnerability of national protected area estates by analysing countrywide relationships between protected areas' climate anomalies and other protected area characteristics, i.e. area, elevation, terrain ruggedness, human footprint and irreplaceability for globally threatened species.</p> <p>Results: We found predicted climate anomalies highest in protected areas of (sub-)tropical countries. The correlations between climate anomalies and protected area characteristics strongly differ between countries. Globally, protected areas showing large climate anomalies tend to be at high elevation and highly irreplaceable for threatened species, increasing climate change vulnerability. These protected areas are relatively large in area, of high topographic heterogeneity and less pressured by humans, decreasing climate change vulnerability.</p> <p>Main conclusion: This study reveals potential hotspots of climate change impact inside the terrestrial protected area estate. It thus supports and guides climate-smart conservation policy and management, particularly national to local authorities, to ensure the future effectiveness of protected areas in preserving biodiversity and ecosystem benefits under climate change.</p>
Data for: Combined threats of climate change and contaminant exposure through the lens of bioenergetics
<p>This dataset contains a detailed description of studies identified by a review examining interactive effects of climate change-sensitive environmental variables and chemical contaminant exposure.</p>
Climate change scenarios forecast increased drought exposure for terrestrial vertebrates in the contiguous United States
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Climate change exposure and vulnerability of the global protected area estate from an international perspective
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Code and data for: Exposure to climate change drives stability or collapse of desert mammal and bird communities
<p>The following code and data are used to generate the results for the publication "Exposure to climate change drives stability or collapse of desert mammal and bird communities". Climate_Data.zip contains the monthly minimum and maximum temperatures, vegetation data, and soil thickness data to generate microclimates. NicheMapR_code_and_files.zip contains the script and data necessary to generate the microhabitats. Endoscape.zip contains the data and script necessary to run the heat flux models. Resurvey_data.zip contains the jags data to run the occupancy models for small mammals and birds. The read_me file contains the descriptions of all data and script necessary to reproduce the data from the publication.</p>
Exposure of boreal aapa mires to climate change
<p>This repository contains four zipped data files which contain (i) the spatial distribution of aapa mire complexes (‘aapa mires’) and their wettest flark-dominated parts (‘wet aapa mires’) situated in the aapa mire and palsa mire zones of Finland, as selected for the study by Heikkinen et al. (in review), (ii) values for the six bioclimatic variables (growing degree days, mean January and July temperature, annual precipitation, and May and July water balance) averaged for the years 1981–2010, and developed for the studied aapa mires and wet aapa mires using a 50 x 50 m lattice system, and (iii) values for the same six bioclimatic variables developed for future climates and the two types of study mires, based on the global climate models for 2040–2069 and two Representative Concentration Pathways (RCP4.5 and RCP8.5), and (iv) values of climate velocity metrics calculated for the six bioclimatic variables and the two types of study mires. These data provide the essential data employed in conducting the analysis in the following work:</p> <p>Risto K. Heikkinen<sup>1</sup>, Kaisu Aapala<sup>1</sup>, Niko Leikola<sup>1</sup> and Juha Aalto<sup>2</sup>: Exposure of boreal aapa mires to climate change, in review.</p> <p><sup>1</sup> Biodiversity Centre, Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland</p> <p><sup>2 </sup>Finnish Meteorological Institute, Weather and climate change impact research, Helsinki, Finland</p> <p>The data files are embedded in four compressed zip files (one of them including a geodatabase folder with files) which include several ArcGIS compatible tiff-raster or shape files. The names and contents of the four zipped files are as follows: (1) mires.zip – includes shape files describing the location and spatial configuration of the aapa mires (‘Aapa_mires.shp’) and the wet aapa mires (‘Wet_aapa_mires.shp’) included in the study, and the borders of different mire zones in Finland (‘Mire_zones.shp’); (2) climate_data_aapa_mires.zip – includes 18 tiff raster files showing the values of the six bioclimatic variables in the studied aapa mires within the 50 x 50 m resolution grid. The data in this zipped file include climate data averaged for the years 1981 – 2010 and for the future time slice of 2040–2069 and two Representative Concentration Pathways (RCP4.5 and RCP8.5); (3) climate_data_wet_aapa_mires.zip – includes 18 tiff raster files showing the values of the six bioclimatic variables in the studied wet aapa mires within the 50 x 50 m resolution grid. Similarly as in (2), the data in this zipped file include climate data averaged for the years 1981 – 2010 and for the future time slice of 2040–2069 and two Representative Concentration Pathways (RCP4.5 and RCP8.5); (4) velocity_data_for_mires.zip – includes zipped geodatabase folder velocity_open_mires.gdb which, in turn, includes spatial ArcGIS surfaces for the climate change velocity metric calculated for all the six bioclimatic variables, and the two types of mires and the two RCPs.</p> <p>In the zipped files (2) and (3), first part of the names of the included files refer to one of the six bioclimatic variables as follows: GDD5 – growing degree days, PREC – annual precipitation, TEMP_Jan – mean January temperature, TEMP_July – mean July temperature, WAB_May – May water balance, WAB_July – July water balance; and the remaining part of the name indicates the time period, type of the RCP and that of the mire. </p> <p>It should be noted that these data are embargoed until the end of the SUMI project for which they were developed, i.e. 1.1.2023. The coordinate system for the data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)).</p> <p>Summarization of the key settings of the study is provided below. A detailed treatment is included in the manuscript Heikkinen et al. (in review). Once the manuscript is accepted for publication an updated link will be provided.</p> <p><strong>Study system:</strong> Aapa mires are waterlogged, peat-accumulating EU Habitats Directive priority habitats whose ecological conditions and biodiversity values may be jeopardized by climate change. Aapa mires depend on the surface water flows from the surroundings which makes them sensitive to hydrological alterations and falling water tables caused by land use (ditching for peatland drainage) as well as climate change (Gong et al. 2012, Sallinen et al. 2019). This sensitivity of aapa mires and their biodiversity to increasing temperatures and decreasing water balance and precipitation can be of particular concern as they occur in northern hemisphere, in areas where the largest climatic changes are projected to take place (AMAP 2017, Väliranta et al. 2017. Kolari et al. 2021). In the study by Heikkinen et al. (in review), we assess the climate exposure of these habitats by developing velocity metrics for both the aapa mire complexes (‘aapa mires’) and their wettest flark-dominated parts (‘wet aapa mires’) in Finland.</p> <p><strong>Aapa mire data: </strong>Occurrences of aapa mires were identified from the CORINE CLC2018 land cover data which is available in Finland as a 20 x 20 m resolution raster data, by focusing on the CORINE category 4121 (‘Peatbogs’) which includes various open mires occurring in aapa mire and palsa mire zones, as well as in raised bogs zones. We excluded open mires occurring in the raised bogs zone but included CORINE Peatbog occurrences both from the aapa mire and palsa mire zones. This opted for this decision because open mires in aapa and palsa mire zones share several matching ecological features, and because palsa mires may provide suitable habitats for aapa mire species under warming climate.</p> <p>The adjacent peatbog 20-m pixels in the aapa and palsa mire zones were merged and converted into contiguous peatland polygons. From these, polygons smaller than 10 ha in size were excluded because typically they show only limited number of ecological elements central to the representative aapa mires. These selected ≥10 ha peatland polygons formed the first study mire dataset, aapa mire complexes, or ‘aapa mires’ in short (i.e., the whole aapa mire ecosystem containing all embedded mire habitats therein). The second study mire dataset was constrained to include only the wettest parts of aapa mire complexes characterized by flarks, i.e., open water pools, referred here simply as ‘wet aapa mires’. These wet aapa mire occurrences are typically smaller than the whole aapa mire complexes and occur more sparsely in the landscape. Thus, the climatic exposure of wet aapa mires can be expected to be greater than that of aapa mire complexes. This will very likely cause elevated climate change adaptation challenges for habitat specialist species that require open water or permanently wet environments. The spatial data for the wet aapa mires were determined with the help of the topographic database developed by the National Land Survey of Finland (NLS), and the land cover class ‘Swamps classified as difficult, dangerous and impossible to cross’ therein.</p> <p><strong>Climate data: </strong>In the first phase, monthly average air temperature data for 1981–2010 were constructed at the 50 x 50 m spatial resolution across Finland, as described in Aalto et al. (2017) and Heikkinen et al. (2020, 2021). This was done by modelling the weather station data from 313 Fennoscandian stations together with variables of geographical location, local topography and water cover. Monthly precipitation data were developed by fitting kriging interpolation method to the data on 343 rain gauges, and the data on geographical location, topography and proximity to the sea. Based on the monthly temperature and precipitation data, six bioclimatic variables describing key ecological winter- and summer-time conditions for aapa mire ecosystems were calculated (cf. Parviainen and Luoto 2007, Ruuhijärvi 1988, Rydin and Jeglum 2006): (1) annual temperature sum above the base temperature of 5 °C (growing degree days, GDD5), (2) mean January temperature, (3) mean July temperature, (4) monthly climatic water balance calculated for May and (5) for July, and (6) annual precipitation sum. The two climatic water balance variables were calculated as the difference between the May - or July - total precipitation sum and the potential evapotranspiration (PET) in the corresponding month following Skov and Svenning (2004).</p> <p>In the second step, the data based on an ensemble of 23 global climate models from the Coupled Model Intercomparison Project (CMIP5) archives (Taylor et al. 2012) were employed to develop future climate surfaces averaged for the years 2040–2069 and the two Representative Concentration Pathways (RCP4.5 and RCP8.5). The monthly air temperature and precipitation data in these climate surfaces were interpolated to match the 50 × 50 m grid, then the change predicted by the GCMs was added to the 1981–2010 climate data, and finally, the values for the six bioclimatic variables were recalculated for the 50-m resolution grid across the whole Finland.</p> <p>In the third step, all the developed climate surface datasets were intersected by the spatial datasets of the two differently delimited aapa mire networks, i.e. ‘aapa mires’ and ‘wet aapa mires’. This allowed calculation of the climate change velocity metrics separately for the two types of aapa mires, namely, for both mire datasets by measuring the distance between climatically similar 50-m grid cells in the present and future climates by considering only locations with either (i) aapa mires, or (ii) wet aapa mires. Thus, matrix areas providing unsuitable habitat for aapa mire biodiversity were excluded and for both types of mires the distance from the present-day mire cell was linked to the nearest corresponding mire cell with similar future climatic conditions.</p> <p>The climate data for the years 1981 – 2010 and the future time slice of 2040–2069 and the two Representative Concentration Pathways (RCP4.5 and RCP8.5), clipped to the networks of the two types of aapa mires for all the six bioclimatic variables are included in the following two zipped files: ‘climate_data_aapa_mires.zip’ and ‘climate_data_wet_aapa_mires.zip’.</p> <p><strong>Climate change velocity metrics: </strong>The climate velocities for the six bioclimatic variables, developed separately for the two types of aapa mires and the two RCPs, were calculated with climate-analog method (see Brito-Morales et al. 2018). For these calculations, both the present-day and future climate data from the two RCP scenarios were converted from continuous values into categorical climate surfaces following Hamann et al. (2015). During these conversion processes, following categories and within-class ranges were used: GDD5, within-class range 50 °C; January and July temperatures, within-class range 0.5 °C; water balance of May and July, within-class range 2.5 mm; and annual precipitation, within-class range 25 mm.</p> <p>In the conversion process, the climate surfaces in each of the 50-m grid cells were reclassified into one of the 29 GDD5, 27 January temperature, 22 July temperature, 21 May water balance, 22 July water balance, and 19 annual precipitation categories. Using the reclassified climate surfaces, the minimum distances between mire grid cells with similar present-day and future climates for the six variables were determined with the Euclidean distance function in ArcGIS. In the final step of calculating the velocity metrics, the mire-to-mire distances were divided by the number of years between the two points in time (see Brito-Morales et al., 2018; Heikkinen et al., 2020).</p> <p>The derived velocity metrics for the six bioclimatic variables yielded six individual estimates of climate exposure for the two types of study mires, illustrating the magnitude of climate displacement that the local mire species communities are projected to experience (Hamann et al. 2015, Brito-Morales et al., 2018). In our study, for each contiguous aapa mire and wet aapa mire, the mean velocity value for the climate variables were calculated as the average of the 50-m grid cells included in it.</p> <p>The data on the 50-m resolution velocities for the six bioclimatic variables and the two types of aapa mires and the two RCPs are included in the zip file ‘velocity_data_for_mires.zip’.</p> <p><strong>References</strong></p> <p>Aalto, J., Riihimäki, H., Meineri, E., Hylander, K., Luoto, M. (2017) Revealing topoclimatic heterogeneity using meteorological station data. International Journal of Climatology 37, 544-556.</p> <p>AMAP (2017) Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017. Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway.</p> <p>Brito-Morales, I., García Molinos, J., Schoeman, D.S., Burrows, M.T., Poloczanska, E.S., Brown, C.J., Ferrier, S., Harwood, T.D., Klein, C.J., McDonald-Madden, E., Moore, P.J., Pandolfi, J.M., Watson, J.E.M., Wenger, A.S., Richardson, A.J. (2018) Climate Velocity Can Inform Conservation in a Warming World. Trends in Ecology & Evolution 33, 441-457.</p> <p>Gong, J., Wang, K., Kellomäki, S., Zhang, C., Martikainen, P.J., Shurpali, N. (2012) Modeling water table changes in boreal peatlands of Finland under changing climate conditions. Ecological Modelling 244, 65-78.</p> <p>Hamann, A., Roberts, D.R., Barber, Q.E., Carroll, C., Nielsen, S.E. (2015) Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21, 997-1004.</p> <p>Heikkinen, R.K., Kartano, L., Leikola, N., Aalto, J., Aapala, K., Kuusela, S., Virkkala, R. (2021) High-latitude EU Habitats Directive species at risk due to climate change and land use. Global Ecology and Conservation 28, e01664.</p> <p>Heikkinen, R.K., Leikola, N., Aalto, J., Aapala, K., Kuusela, S., Luoto, M., Virkkala, R. (2020) Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10.</p> <p>Kolari, T.H.M., Sallinen, A., Wolff, F., Kumpula, T., Tolonen, K., Tahvanainen, T. (2021) Ongoing Fen–Bog Transition in a Boreal Aapa Mire Inferred from Repeated Field Sampling, Aerial Images, and Landsat Data. Ecosystems.</p> <p>Parviainen, M., Luoto, M. (2007) Climate envelopes of mire complex types in fennoscandia. Geografiska Annaler: Series A, Physical Geography 89, 137-151.</p> <p>Ruuhijärvi, R., (1988) Mire vegetation. Atlas of Finland 141-143. Biogeography, nature conservation. . National Board of Survey and Geographical Society of Finland, Helsinki, pp. 2-4.</p> <p>Rydin, H., Jeglum, J. (2006) The biology of peatlands. Oxford University Press, Oxford.</p> <p>Sallinen, A., Tuominen, S., Kumpula, T., Tahvanainen, T. (2019) Undrained peatland areas disturbed by surrounding drainage: a large scale GIS analysis in Finland with a special focus on aapa mires. Mires and Peat 24, 1-22.</p> <p>Skov, F., Svenning, J.-C. (2004) Potential impact of climatic change on the distribution of forest herbs in Europe. Ecography 27, 366-380.</p> <p>Taylor, K.E., Stouffer, R.J., Meehl, G.A. (2012) An Overview of CMIP5 and the Experiment Design. Bulletin of the American meteorological Society 93, 485-498.</p> <p>Väliranta, M., Salojärvi, N., Vuorsalo, A., Juutinen, S., Korhola, A., Luoto, M., Tuittila, E.-S. (2017) Holocene fen–bog transitions, current status in Finland and future perspectives. The Holocene 27, 752-764.</p> <p> </p>
Supplemental data and code for Climate change will amplify the inequitable exposure to compound heatwave and ozone pollution
<p>Supplemental data and code for Climate change will amplify the inequitable exposure to compound heatwave and ozone pollution</p>
Dataset for "Assessing the exposure of forest habitat types to projected climate change – implications for Bavarian protected areas"
<p>This dataset relates to the publication C. Steinacker, C. Beierkuhnlein, A. Jaeschke (2019), "Assessing the exposure of forest habitat types to projected climate change—Implications for Bavarian protected areas", Ecology and Evolution. doi:<a href="https://doi.org/10.1002/ece3.5877"> 10.1002/ece3.5877</a>.</p> <p>The file contains:</p> <ul> <li>the R script,</li> <li>the model outputs (raster data of projected distribution of habitat types),</li> <li>the results of the range change analysis,</li> <li>the protected area shapefile with information on the elevational range inside of them and their projected environmental suitability for the corresponding habitat types.</li> </ul> <p>The products build on freely available data (e.g. distribution data from the EEA under the Habitats Directive). All data sources are cited in the related publication. Methodologically, we applied correlative species distribution models and further spatial and geostatistical analyses. We used R (e.g. biomod2-package) as well as GIS-software to conduct the analyses. More detailed descriptions of the methodology are placed in the publication.</p>
Magnitude-duration relationships of physiological sensitivity and environmental exposure improve climate change vulnerability assessments
<p class="MsoNormal"><span>Integrating thermal physiology with environmental temperature is essential to understanding distributions of species and vulnerability to climate change. Warming tolerance—the difference between an organism's maximum thermal tolerance (T<sub>max</sub>) and maximum habitat temperature (T<sub>hab</sub>)—is frequently used to integrate organismal sensitivity and environmental exposure. Traditionally, applications of warming tolerance define T<sub>max</sub> and T<sub>hab</sub> as invariable magnitudes, yet tolerance magnitude depends on exposure duration and diel temperature cycles expose organisms to a range of temperature magnitudes and durations. How traditional (<em>i.e.</em>, acute) estimates of warming tolerance compare to estimates from prolonged exposures remains poorly understood. In this study, magnitude-duration curves for tolerances of one cold-water, two cool-water, and one warm-water species of freshwater fish were compiled from the literature and compared to magnitude-duration exposures from 66 streams across the eastern United States. Warming tolerances were estimated for exposure durations spanning 0.01 to 24 hours. Current acute (0.01 hours) warming tolerances ranged from median 6.30°C for the cold-water species to 9.68°C for the warm-water species. The lowest warming tolerances corresponded to prolonged exposures lasting median 3.85 to 5.30 hours among species and were 2.51 to 4.38°C lower than acute estimates. Although acute estimates remained positive in historically occupied and unoccupied streams (6.30°C versus 2.33°C), estimates based on prolonged exposure were positive at occupied streams of the cold-water species but transitioned to negative in unoccupied streams (2.19°C versus -1.12°C). Acute warming tolerances for the cold-water species also remained positive under future climate (6.29 to 4.23°C) but approached zero at prolonged durations (2.19 to 0.09°C) and transitioned to negative for 47.2% of streams. Results demonstrate that acute measures of T<sub>max</sub> and T<sub>hab</sub> overestimate warming tolerances and therefore underestimate climate change vulnerability. Integrating magnitude-duration relationships into warming tolerance estimates can elucidate physiological mechanisms underlying species distributions and can improve accuracy of climate change vulnerability assessments.</span></p>
Chytridiomycosis and climate change: exposure to Batrachochytrium dendrobatidis and mild winter conditions do not increase mortality in juvenile agile frogs during hibernation
<p>Datasets and analyses for "Chytridiomycosis and climate change: exposure to <em>Batrachochytrium dendrobatidis</em> and mild winter conditions do not increase mortality in juvenile agile frogs during hibernation" by Kásler A., Holly D., Herczeg D., Ujszegi J. and Hettyey A., published online on 22nd January 2023 in Animal Conservation.</p> <p><a href="https://doi.org/10.1111/acv.12851">https://doi.org/10.1111/acv.12851</a></p>
Magnitude-duration relationships of physiological sensitivity and environmental exposure improve climate change vulnerability assessments
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Prenatal exposure to a climate-related disaster results in changes of the placental transcriptome and infant temperment
GEO Series GSE197697. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing.
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