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opencc-by-4.0Sep 2021View details →

Data and Code for "Climate impacts and adaptation in US dairy systems 1981-2018"

This data and code archive provides all the files that are necessary to replicate the empirical analyses that are presented in the paper "Climate impacts and adaptation in US dairy systems 1981-2018" authored by Maria Gisbert-Queral, Arne Henningsen, Bo Markussen, Meredith T. Niles, Ermias Kebreab, Angela J. Rigden, and Nathaniel D. Mueller and published in 'Nature Food' (2021, DOI: <a href="https://doi.org/10.1038/s43016-021-00372-z">10.1038/s43016-021-00372-z</a>). The empirical analyses are entirely conducted with the "R" statistical software using the add-on packages "car", "data.table", "dplyr", "ggplot2", "grid", "gridExtra", "lmtest", "lubridate", "magrittr", "nlme", "OneR", "plyr", "pracma", "quadprog", "readxl", "sandwich", "tidyr", "usfertilizer", and "usmap". The R code was written by Maria Gisbert-Queral and Arne Henningsen with assistance from Bo Markussen. Some parts of the data preparation and the analyses require substantial amounts of memory (RAM) and computational power (CPU). Running the entire analysis (all R scripts consecutively) on a laptop computer with 32 GB physical memory (RAM), 16 GB swap memory, an 8-core Intel Xeon CPU E3-1505M @ 3.00 GHz, and a GNU/Linux/Ubuntu operating system takes around 11 hours. Running some parts in parallel can speed up the computations but bears the risk that the computations terminate when two or more memory-demanding computations are executed at the same time. This data and code archive contains the following files and folders: * README Description: text file with this description * flowchart.pdf Description: a PDF file with a flow chart that illustrates how R scripts transform the raw data files to files that contain generated data sets and intermediate results and, finally, to the tables and figures that are presented in the paper. * runAll.sh Description: a (bash) shell script that runs all R scripts in this data and code archive sequentially and in a suitable order (on computers with a "bash" shell such as most computers with MacOS, GNU/Linux, or Unix operating systems) * Folder "DataRaw" Description: folder for raw data files This folder contains the following files: - DataRaw/COWS.xlsx Description: MS-Excel file with the number of cows per county Source: USDA NASS Quickstats Observations: All available counties and years from 2002 to 2012 - DataRaw/milk_state.xlsx Description: MS-Excel file with average monthly milk yields per cow Source: USDA NASS Quickstats Observations: All available states from 1981 to 2018 - DataRaw/TMAX.csv Description: CSV file with daily maximum temperatures Source: PRISM Climate Group (spatially averaged) Observations: All counties from 1981 to 2018 - DataRaw/VPD.csv Description: CSV file with daily maximum vapor pressure deficits Source: PRISM Climate Group (spatially averaged) Observations: All counties from 1981 to 2018 - DataRaw/countynamesandID.csv Description: CSV file with county names, state FIPS codes, and county FIPS codes Source: US Census Bureau Observations: All counties - DataRaw/statecentroids.csv Descriptions: CSV file with latitudes and longitudes of state centroids Source: Generated by Nathan Mueller from Matlab state shapefiles using the Matlab "centroid" function Observations: All states * Folder "DataGenerated" Description: folder for data sets that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these generated data files so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis). * Folder "Results" Description: folder for intermediate results that are generated by the R scripts in this data and code archive. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these intermediate results so that parts of the analysis can be replicated (e.g., on computers with insufficient memory to run all parts of the analysis). * Folder "Figures" Description: folder for the figures that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these figures so that people who replicate our analysis can more easily compare the figures that they get with the figures that are presented in our paper. Additionally, this folder contains CSV files with the data that are required to reproduce the figures. * Folder "Tables" Description: folder for the tables that are generated by the R scripts in this data and code archive and that are presented in our paper. In order to reproduce our entire analysis 'from scratch', the files in this folder should be deleted. We provide these tables so that people who replicate our analysis can more easily compare the tables that they get with the tables that are presented in our paper. * Folder "logFiles" Description: the shell script runAll.sh writes the output of each R script that it runs into this folder. We provide these log files so that people who replicate our analysis can more easily compare the R output that they get with the R output that we got. * PrepareCowsData.R Description: R script that imports the raw data set COWS.xlsx and prepares it for the further analyses * PrepareWeatherData.R Description: R script that imports the raw data sets TMAX.csv, VPD.csv, and countynamesandID.csv, merges these three data sets, and prepares the data for the further analyses * PrepareMilkData.R Description: R script that imports the raw data set milk_state.xlsx and prepares it for the further analyses * CalcFrequenciesTHI_Temp.R Description: R script that calculates the frequencies of days with the different THI bins and the different temperature bins in each month for each state * CalcAvgTHI.R Description: R script that calculates the average THI in each state * PreparePanelTHI.R Description: R script that creates a state-month panel/longitudinal data set with exposure to the different THI bins * PreparePanelTemp.R Description: R script that creates a state-month panel/longitudinal data set with exposure to the different temperature bins * PreparePanelFinal.R Description: R script that creates the state-month panel/longitudinal data set with all variables (e.g., THI bins, temperature bins, milk yield) that are used in our statistical analyses * EstimateTrendsTHI.R Description: R script that estimates the trends of the frequencies of the different THI bins within our sampling period for each state in our data set * EstimateModels.R Description: R script that estimates all model specifications that are used for generating results that are presented in the paper or for comparing or testing different model specifications * CalcCoefStateYear.R Description: R script that calculates the effects of each THI bin on the milk yield for all combinations of states and years based on our 'final' model specification * SearchWeightMonths.R Description: R script that estimates our 'final' model specification with different values of the weight of the temporal component relative to the weight of the spatial component in the temporally and spatially correlated error term * TestModelSpec.R Description: R script that applies Wald tests and Likelihood-Ratio tests to compare different model specifications and creates Table S10 * CreateFigure1a.R Description: R script that creates subfigure a of Figure 1 * CreateFigure1b.R Description: R script that creates subfigure b of Figure 1 * CreateFigure2a.R Description: R script that creates subfigure a of Figure 2 * CreateFigure2b.R Description: R script that creates subfigure b of Figure 2 * CreateFigure2c.R Description: R script that creates subfigure c of Figure 2 * CreateFigure3.R Description: R script that creates the subfigures of Figure 3 * CreateFigure4.R Description: R script that creates the subfigures of Figure 4 * CreateFigure5_TableS6.R Description: R script that creates the subfigures of Figure 5 and Table S6 * CreateFigureS1.R Description: R script that creates Figure S1 * CreateFigureS2.R Description: R script that creates Figure S2 * CreateTableS2_S3_S7.R Description: R script that creates Tables S2, S3, and S7 * CreateTableS4_S5.R Description: R script that creates Tables S4 and S5 * CreateTableS8.R Description: R script that creates Table S8 * CreateTableS9.R Description: R script that creates Table S9

Figure 3 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 3. Location map of the study area.

Data from: Local climate change velocities and evolutionary history explain multidirectional range shifts in a North American butterfly assemblage

Species are often expected to shift their distributions either poleward or upslope to evade warming climates and colonize new suitable climatic niches. However, from 18 years of fixed transect monitoring data on 88 species of butterfly in the midwestern United States, we show that butterflies are shifting their centroids in all directions, except towards the region that is warming the fastest (southeast). Butterflies shifted their centroids at a mean rate of 4.87 km yr-1. The rate of centroid shift was significantly associated with local climate change velocity (temperature by precipitation interaction), but not with mean climate change velocity throughout the species' ranges. Species tended to shift their centroids at a faster rate towards regions that are warming at slower velocities but increasing in precipitation velocity. Surprisingly, species' thermal niche breadth (range of climates butterflies experience throughout their distribution) and wingspan (often used as a metric for dispersal capability) were not correlated with the rate at which species shifted their ranges. We observed a high phylogenetic signal in the direction species shifted their centroids. However, we found no phylogenetic signal in the rate species shifted their centroids, suggesting less conserved processes determine the rate of range shift than the direction species shift their ranges. This research shows important signatures of multidirectional range shifts (latitudinal and longitudinal) and uniquely shows that local climate change velocities are more important in driving range shifts than the mean climate change velocity throughout a species' entire range.

opencc-by-4.0Apr 2011View details →

Data from: Adaptations to climate-mediated selective pressures in humans.

This dataset contains the genotype data in PLINK binary format for the 5 populations genotyped in the Di Rienzo lab and published in  Hancock AM, Witonsky DB, Alkorta-Aranburu G, Beall CM, Gebremedhin A, Sukernik R, Utermann G, Pritchard JK, Coop G, Di Rienzo A (2011) Adaptations to climate-mediated selective pressures in humans. PLoS Genet. 7(4):e1001375

Data from: Future climatically suitable areas for bats in South Asia

Climate change majorly impacts biodiversity in diverse regions across the world, including South Asia, a megadiverse area with heterogeneous climatic and vegetation regions. However, climate impacts on bats in this region are not well‐studied, and it is unclear whether climate effects will follow patterns predicted in other regions. We address this by assessing projected near‐future changes in climatically suitable areas for 110 bat species from South Asia. We used ensemble ecological niche modelling with four algorithms (random forests, artificial neural networks, multivariate adaptive regression splines and maximum entropy) to define climatically suitable areas under current conditions (1970–2000). We then extrapolated near future (2041–2060) suitable areas under four projected scenarios (combining two global climate models and two shared socioeconomic pathways, SSP2: middle‐of‐the‐road and SSP5: fossil‐fuelled development). Projected future changes in suitable areas varied across species, with most species predicted to retain most of the current area or lose small amounts. When shifts occurred due to projected climate change, new areas were generally northward of current suitable areas. Suitability hotspots, defined as regions suitable for >30% of species, were generally predicted to become smaller and more fragmented. Overall, climate change in the near future may not lead to dramatic shifts in the distribution of bat species in South Asia, but local hotspots of biodiversity may be lost. Our results offer insight into climate change effects in less studied areas and can inform conservation planning, motivating reappraisals of conservation priorities and strategies for bats in South Asia.

Output data for: Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia

This repository contains the output data associated with the publication "Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia". It contains the FLAM modeled burned area and the GLOBIOM output, as well as the a downscaling grid. Descriptions of the results can be found in the publication (DOI will follow).

Data from: Leaf morphological traits show greater responses to changes in climate than leaf physiological traits and gas exchange variables

Adaptation to changing conditions is one of the strategies plants use to survive climate change. Here, we ask whether plants' leaf morphological and physiological traits/gas exchange variables have changed in response to recent, anthropogenic climate change. We grew seedlings from resurrected historic seeds from ex-situ seed banks and paired modern seeds in a common-garden experiment. Species pairs were collected from regions that had undergone differing levels of climate change using an emerging framework – Climate Contrast Resurrection Ecology, allowing us to hypothesise that regions with greater changes in climate (including temperature, precipitation, climate variability and climatic extremes) there would be greater trait responses in leaf morphology and physiology over time. Our found that in regions where there were greater changes in climate, there were greater changes in average leaf area, leaf margin complexity, leaf thickness and leaf intrinsic water use efficiency. Changes in leaf roundness, photosynthetic rate, stomatal density and the leaf economic strategy of our species were not correlated with changes in the climate. Our results show that leaves do have the ability to respond to changes in climate, however, there are greater inherited responses in morphological leaf traits than in physiological traits/variables, and greater responses to extreme measures of climate than gradual changes in climatic means. It is vital for accurate predictions of species' responses to impending climate change to ensure that future climate change ecology studies utilise knowledge about the difference in both leaf trait and gas exchange responses, and the climate variables that they respond to.

opencc-by-4.0May 2024View details →

Climate Change Adaptation, Social Resilience, and Perceived Values Data from Turkana, Machakos and Narok County, Kenya

This dataset provides socioeconomic and value perception interview data collected from 1,020 individuals living across three counties in Kenya: Turkana, Machakos and Narok. Socioeconomic data were collected on housing, healthcare, water sources, electricity access, experience of extreme weather events, community services and access to information. Value perception data were collected using the user-perceived value (UPV) method - a perception-based surveying approach which requires interviewees to select their most valued household items in different circumstances and explain their choice through 'why'-probing. This is done under different circumstances - here, either in daily life, or in the face of a climate shock. The data are made available with sub-county level geospatial attribution. Together, the socioeconomic and interview data can be used to better understand the views of different communities and demographic groups concerning climate change and extreme weather events across Kenya. They also provide insight as to the intrinsic, social, emotional, epistemic, functional, and indigenous values associated with everyday household items. The data can be used by policymakers to inform development planning and to identify gaps in available infrastructure. Additionally, researchers and development practitioners can use these data to design interventions which reflect the needs and values of communities in Kenya.

Data from: Climatically robust multi-scale species distribution models to support pronghorn recovery in California

We combined two climate-based distribution models with three finer-scale suitability models to identify habitat for pronghorn recovery in California now and into the future. Location: California, United States Methods: We used a consensus approach to identify areas of suitable climate now (1980-2010) and future (2031-2060) for pronghorn in California. We compared the results of models from two separate hypotheses about their historical ecology in the state, specifically the migration hypothesis and the niche reduction hypothesis. We combined occurrences from GPS collars distributed across three populations of pronghorn in the state to create three distinct habitat models: (1) an ensemble model using Random Forests, Maxent, Classification and Regression Trees, and a Generalized Linear Model; (2) a step selection function; and (3) an expert-driven model. We evaluated consensus among both the climate models and the suitability models to prioritize areas for, and evaluate the prospects of, pronghorn recovery. Results: Climate suitability for pronghorn in the future depends heavily on model assumptions. Under the migration hypothesis, our model predicted that there will be on suitable climate in California in the future. Under the niche reduction hypothesis, by contrast, suitable climate will expand. Habitat also depended on the methods used, but areas of consensus among all three exist in large patches throughout the state. Main Conclusions: Identifying habitat for a species which has undergone extreme range collapse, and which has very fine scale habitat needs, presents novel challenges for spatial ecologists. Our multi-method, multi-hypothesis approach can allow habitat modelers to identify areas of consensus and, perhaps more importantly, critical knowledge gaps that could resolve disagreements among the models. For pronghorn, a better understanding of their upper thermal tolerances and whether historical populations migrated will be crucial to their potential recovery in California and throughout the arid Southwest.

Data from: Responses of population structure and genomic diversity to climate change and fishing pressure in a pelagic fish

The responses of marine species to environmental changes and anthropogenic pressures (e.g. fishing) interact with ecological and evolutionary processes that are not well understood. Knowledge of changes in the distribution range and genetic diversity of species and their populations into the future is essential for the conservation and sustainable management of resources. Almaco jack (Seriola rivoliana) is a pelagic fish with high importance to fisheries and aquaculture in the Pacific Ocean. In this study, we assessed contemporary genomic diversity and structure in loci that are putatively under selection (outlier loci) and determined their potential functions. Utilizing a combination of genotype-environment association, spatial distribution models, and demogenetic simulations, we modeled the effects of climate change (under three different RCP scenarios) and fishing pressure on the species' geographic distribution and genomic diversity and structure to 2050 and 2100. Our results show that most of the outlier loci identified were related to biological and metabolic processes that may be associated with temperature and salinity. Contemporary genomic structure showed three populations—two in the Eastern Pacific (Cabo San Lucas and Eastern Pacific) and one in the Central Pacific (Hawaii). Future projections suggest a loss of suitable habitat and potential range contractions for most scenarios, while fishing pressure decreased population connectivity. Our results suggest that future climate change scenarios and fishing pressure will affect the genomic structure and genotypic composition of S. rivoliana and lead to loss of genomic diversity in populations distributed in the eastern-central Pacific Ocean, which could have profound effects in fisheries that depend on this resource.

Figure 2 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 2. Schematic view of Kernel Ridge Regression (KRR) model.

Figure 1 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Figure 1. High-resolution flow chart of the Random Forest (RF) model.

Data from: Cenozoic climate change and the evolution of North American mammalian predator ecomorphology

The trend of global cooling across the Cenozoic transformed the North American landscape from closed forest to more open grasslands, resulting in dietary adaptations in herbivores in response to shifting resources. In contrast, the material properties of the predator food source, muscle, skin, and bone, have remained constant over this transition, suggesting a corresponding lack of change in predator dietary adaptations. We investigate the North American mammal predator fossil record using a tooth shape metric and body mass, predicting that the former would exhibit stability. Instead, we found that mean molar morphology became more blade-like, with our tooth shape metric sharply increasing in the late Eocene and remaining high from the Oligocene onward. Subsequent tests in extant carnivorans reveal taxa with more blade-like teeth are prevalent in more open environments. Our results reveal an unexpected functional shift among North American predators in response to large-scale environmental changes across the Cenozoic.

opencc-by-4.0Dec 2009View details →

Fig.6 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors

Fig.6. Daily dynamics of sun-basking activity of Emys orbicularis.

opencc-by-4.0Dec 2009View details →

Fig.1 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors

Fig.1. The schema of the experimental out-door terrarium.

opencc-by-4.0Feb 2024View details →

Data from: A genome-guided strategy for climate resilience in American chestnut restoration populations

The American chestnut (Castanea dentata) is a functionally extinct tree species that was decimated by an invasive fungal pathogen in the early 20th century. An understanding of the genomic architecture of local adaptation in wild American chestnut was necessary in order to deploy locally adapted, disease-resistant American chestnut populations. Here, we characterize the genomic basis of climate adaptation in remnant wild American chestnut, develop new computational methods, and evaluate the adaptive genomic content captured within backcross breeding populations. Whole genome re-sequencing data of 356 trees from Sandercock et al. (2022) coupled with genotype-environment association methods identified 18483 climate associated loci.

opencc-by-4.0Jul 2024View details →

Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique"

Data for Tropical Cyclones flood hazards and impacts in Beira for study "Exploring coastal climate adaptation through storylines: Insights from Cyclone Idai in Beira, Mozambique" <a href="../api/records/12664900/draft/files/hmax_idai_ifs_rebuild_bc_hist_rain_surge_noadapt.tiff/content" target="_blank" rel="noopener noreferrer">hmax_idai_ifs_rebuild*</a> -> Flood maps <a href="../api/records/12664900/draft/files/spatial_idai_ifs_rebuild_bc_3c-hightide_rain_surge_retreat.gpkg/content" target="_blank" rel="noopener noreferrer">spatial_idai_ifs_rebuild*</a> -> Impacts

opencc-zeroJul 2024View details →

Data from: Evaluating the vulnerability of Tetracentron sinense habitats to climate-induced latitudinal shifts

Objective: Exploring the changing process of the geographical distribution pattern of Tetracentron sinense Oliv. and its main influencing factors since the last interglacial period can provide a scientific basis for the effective protection and management of the species. Methods: The MaxEnt model was used to construct the potential distribution areas of T. sinense in different periods such as the last interglacial (LIG), the last glacial maximum (LGM), the Mid-Holocene (MID), the current and future (2050s, 2070s). On the premise of discussing the influence of dominant environmental factors on its distribution model, the suitable area changes of T. sinense under different ecological climate situations were quantitatively analyzed. Results: (1) The AUC and TSS values predicted by the optimized model were 0.959 and 0.835, respectively, indicating a good predictive effect by the MaxEnt model; the potential suitable areas for T. sinense in the current period are mainly located in southwest China, which are wider compared to the actual habitats. (2) Jackknife testing showed that the lowest temperature in the coldest month (Bio6), elevation (Elev), seasonal variation coefficient of temperature (Bio4) and surface calcium carbonate content (T-CACO3) are the dominant environmental factors affecting the distribution of T. sinense. (3) From the last interglacial period to the current period, the total suitable area of T. sinense showed a decreasing trend; the distribution points of T. sinense populations in Mid-Holocene period may be the origin of the postglacial population, and Southwest China may be its glacial biological refuge. (4) Compared with the current period, the total suitable area ranges of T. sinense in China in the 2050s and 2070s decreased, and the centroid location of its total fitness area all migrated to the northwest, with the largest migration distance in 2070s under the SSPs 7.0 climate scenario. Conclusion: Temperature was the principal factor influencing the geographical distribution of T. sinense. With the global warming, the range of T. sinense suitable areas will show a shrinking trend, with a shift towards higher-latitude regions. Ex-situ conservation measures could be taken to preserve its germplasm resources.

opencc-by-4.0Oct 2017View details →

Figure 1 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia

Figure 1. The Black Sea coast of the Krasnodar Krai and the Republic of Abkhazia.