Three long-term temperature data series measured in Portugal were studied to detect and correct non-climatic homogeneity breaks and are now available for future studies of climate variability. Series of monthly minimum (Tmin) and maximum (Tmax) temperatures measured in the three Portuguese meteorological stations of Lisbon (from 1856 to 2008), Coimbra (from 1865 to 2005) and Porto (from 1888 to 2001) were studied to detect and correct non-climatic homogeneity breaks. These series together with monthly series of average temperature (Taver) and temperature range (DTR) derived from them were tested in order to detect homogeneity breaks, using, firstly, metadata, secondly, a visual analysis and, thirdly, four widely used homogeneity tests: von Neumann ratio test, Buishand test, standard normal homogeneity test and Pettitt test. The homogeneity tests were used in absolute (using temperature series themselves) and relative (using sea-surface temperature anomalies series obtained from HadISST2 close to the Portuguese coast or already corrected temperature series as reference series) modes. We considered the Tmin, Tmax and DTR series as most informative for the detection of homogeneity breaks due to the fact that Tmin and Tmax could respond differently to changes in position of a thermometer or other changes in the instrument's environment; Taver series have been used, mainly, as control. […]

The Homogenized Surface Air Temperature data consist of monthly, seasonal and annual means of homogenized daily maximum, minimum and mean surface air temperatures (degrees Celsius) for 338 locations in Canada. Homogenized climate data incorporate adjustments (derived from statistical procedures) to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The time periods of the data vary by location, with the oldest data available from the early 1880s at some stations to the most recent update in 2017. Observations at co-located sites were sometimes joined in order to create longer time series. Data availability over most of the Canadian Arctic is restricted to the mid-1940s to present. The data will continue to be updated every year.

This web site provides adjusted and homogenized climate data for many climatological stations in Canada. These data were created for use in climate research including climate change studies. They incorporate a number of adjustments applied to the original station data to address shifts due to changes in instruments and in observing procedures. Sometimes the observations from several stations were joined to generate a long time series. Users are strongly cautioned to determine the data suitability for their application. They should also be aware that ongoing research on adjustment techniques may result in future revisions of the datasets. The datasets are updated annually with the most recent data. The adjusted and homogenized data are provided for four climate elements: Surface air temperature, Precipitation, Surface pressure, and Surface wind speed. References Mekis, É. and L.A. Vincent, 2011: An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmosphere-Ocean, 49(2), 163-177. Vincent, L. A., M. M. Hartwell, and X. L. Wang, 2020: A third generation of homogenized temperature for trend analysis and monitoring changes in Canada’s climate. Atmosphere-Ocean., 58:3, 173-191, doi:10.1080/07055900.2020.1765728. Wan, H., X. L. Wang, V. R. Swail, 2010: Homogenization and trend analysis of Canadian near-surface wind speeds. Journal of Climate, 23, 1209-1225. Wan, H., X. L. Wang, V. R. Swail, 2007: A quality assurance system for Canadian hourly pressure data. J. Appl. Meteor. Climatol., 46, 1804-1817.

Temperature-driven homogenization of an ant community over 60 years in a montane ecosystem

https://doi.org/10.5061/dryad.2fqz612x4

Description of the data and file structure

GC Soil Data: This dataset is the combined soil data. This includes Browne and Gregg’s (1969) soil moisture data and the contemporary soil moisture data. Column A is the "Collection Date" (written as mm/dd/yyyy) that soil samples were taken from the field. Column B is "Timeframe." This indicates whether the data is from the historical study or the contemporary study. Column C is the "Collection Time." Time was recorded for the contemporary samples, but was not documented historically. Column D is "Transect" which references whether the collection was made from transect 1 (T1) or transect 2 (T2). Transects used were taken from Browne and Gregg (1969). Column E is "Aspect" which corresponds to whether the sample was taken from the north-facing slope, south-facing slope, or the canyon bottom. Column F is ...

The gridded dataset of homogenized monthly mean values of daily maximum, minimum and mean surface air temperatures, CanGridT, was produced using a kriging-based gridding scheme, i.e., the KGNGA scheme of Abbasnezhadi and Wang (2024), to grid an updated version of the Third Generation of Homogenized Temperature dataset (see details below). More specifically, ordinary kriging was used to grid the 1961-1990 climate normal values and the anomalies, separately; the resulting gridded datasets were used to produce the gridded dataset of monthly mean values of daily maximum, minimum and mean temperatures. Here, daily mean temperature is the average of daily maximum and minimum temperatures. As detailed in Vincent et al. (2020), the Third Generation of Homogenized Temperature dataset (available at Climate data: homogenized surface air temperature data - Canada.ca) was prepared for use in climate trend analysis in Canada. Daily observations from nearby sites were often joined to create a long data series. This includes the observations taken at Reference Climate Stations and from some of the NavCan sites which are used to extend past climate observations into recent times. This dataset contains long data series of daily maximum, minimum and mean temperatures for 780 locations across Canada. The data were quality controlled. The daily minimum temperatures from 1961 to recent years were adjusted to diminish the effects of the change in observing time (climatological day definition) in 1961 at principal stations (Vincent et al. 2009). In cases of station joining, parallel daily data, when available, were used to detect and diminish non-climatic shifts (Vincent et al. 2018), as using parallel observations is the most reliable way to do data homogenization. Series of annual and seasonal mean temperatures were tested for homogeneity and homogenized when necessary. Daily temperatures were adjusted using a Quantile-Matching procedure (Wang et al. 2010) to remove non-climatic shifts if needed. The Third Generation of Homogenized Temperature dataset has been updated to the end of 2023, in which around 500 out of the 780 stations are active (reporting some data). The gridded version of the updated dataset is this CanGridT mlyV3.1 dataset, which has been used to assess warming in Canada. This dataset differs from the CanGRD data in two aspects: CanGridT contains gridded temperatures on a 10-km EASE grid, while CanGRD contains gridded anomalies of temperatures on a 50-km EASE grid. References Abbasnezhadi, K. and X. L. Wang, 2024: Comparison of gridding methods for precipitation over Canada and assessment of station/data density effects on gridding results. Atmos.-Ocean, 62:4, 320-346, https://doi.org/10.1080/07055900.2024.2394829. Vincent, L.A., M.M. Hartwell and X.L. Wang, 2020: A Third Generation of Homogenized Temperature for Trend Analysis and Monitoring Changes in Canada’s Climate. Atmosphere-Ocean. https://doi.org/10.1080/07055900.2020.1765728. Vincent, L.A., E.J. Milewska, X. L. Wang, and M. M. Hartwell, 2018. Uncertainty in homogenized daily temperatures and derived indices of extremes illustrated using parallel observations in Canada, Intl. J. Climatol., 38:2, 692-707. DOI: 10.1002/JOC.5203. Vincent, L.A., E.J. Milewska, R. Hopkinson and L. Malone, 2009: Bias in minimum temperature introduced by a redefinition of the climatological day at the Canadian synoptic stations. Journal of Applied Meteorology and Climatology, 48, 2160-2168. DOI: 10.1175/2009JAMC2191.1. Wang, X. L., H. Chen, Y. Wu, Y. Feng, and Q. Pu, 2010: New techniques for detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteor. Climatol., 49, 2416-2436. DOI: 10.1175/2010JAMC2376.1.

Adjusted and Homogenized Canadian Climate Data (AHCCD) are climate station datasets that incorporate adjustments (derived from statistical procedures) to the original historical station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. Data are provided for temperature, precipitation, pressure and wind speed. Station trend data are provided when available. Trends are calculated using the Theil-Sen method using the station's full period of available data. The availability of trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

The Homogenized Surface Wind Speed data consist of monthly, seasonal and annual means of hourly wind speed (kilometres per hour) at standard 10 metre level for 156 locations in Canada. Homogenized climate data incorporate adjustments (derived from statistical procedures) to the original station data to account for discontinuities from non-climatic factors, such as instrument changes or station relocation. The time periods of the data vary by location, with the oldest data available from 1953 at some stations to the most recent update in 2014. Data availability over most of the Canadian Arctic is restricted to 1953 to present. The data will continue to be updated every few years (as time permits).

Studies of biotic homogenization have focused primarily on characterizing changes that have occurred between some past baseline and the present day. In order to understand how homogenization may change in the future, it is important to contextualize the processes driving these changes. Here, we examine empirical patterns of change in taxonomic similarity among oceanic island plant and bird assemblages. We use these empirical cases to unpack dynamic properties of biotic homogenization, thereby elucidating two important factors that have received little attention: 1) initial similarity and 2) the influence of six classes of introduction and extinction events. We use Jaccard's Index to explore the interplay among these factors in determining the changes in similarity that have occurred between human settlement and the present. Specifically, we develop general formulas for changes in similarity resulting from each of the six types of introductions and extinctions, so that the effect of each event type is formulated in terms of initial similarity and species richness. We then apply these insights to project how similarity levels would change in the future if the present patterns of introductions and extinctions continue. We show that the six event types, along with initial similarity, can show dramatically different behavior in different systems, leading to widely variable influences on similarity. Plant and bird biotas have homogenized only slightly to date, but their trajectories of change are highly divergent. Although existing patterns of colonization and extinction might not continue unchanged, if they were to do so then plant assemblages would show little additional change, whereas bird assemblages would become much more strongly homogenized. Our results suggest that moderate changes in similarity observed to date mask the potential for more dramatic changes in the future, and that the interaction among initial similarity and differential introduction and extinction regimes drives these dynamics.

Climate change is expected to increase the spatial autocorrelation of temperature, resulting in greater synchronization of climate variables worldwide. Possibly such ?homogenization of the world? leads to elevated risks of extinction and loss of biodiversity. In this study, we develop an empirical example on how increasing synchrony of global temperatures can affect population structure in migratory animals. We studied two subspecies of Bar-tailed Godwits Limosa lapponica breeding in tundra regions in Siberia: yamalensis in the west and taymyrensis further east and north. These subspecies share pre- and post-breeding stopover areas, thus being partially sympatric, but exhibiting temporal segregation. The latter is believed to facilitate reproductive isolation. Using satellite tracking data, we show that migration timing of both subspecies is correlated with the date of snowmelt in their respective breeding sites (later at the taymyrensis breeding range). Snow-cover satellite images demonstrate that the breeding ranges are on different climate trajectories and become more synchronized over time: between 1997 and 2020, the date of snowmelt advanced on average by 0.5 days/year in the taymyrensis breeding range, while it remained stable in the yamalensis breeding range. Previous findings showed how taymyrensis responded to earlier snowmelt by advancing arrival and clutch initiation. In the predicted absence of such advancements in yamalensis, we expect that the two populations will be synchronized by 2036 ? 2040. Since Bar-tailed Godwits are social migrants, this raises the possibility of population exchange and prompts the question whether the two subspecies can maintain their geographic and morphological differences and population-specific migratory routines. The proposed scenario may apply to a wide range of (social) migrants as temporal segregation is crucial for promoting and maintaining reproductive isolation in many (partially sympatric) migratory populations. Homogenization of previously isolated populations could be an important consequence of increasing synchronized environments and hence climate change.

This first version of the dataset (CanHoPmlyV1) contains homogenized time series of monthly total precipitation for 425 long-term stations in Canada. As detailed in Wang et al. (2023), it is based on the quality-controlled version 2020 of the Adjusted Daily Rainfall and Snowfall (AdjDlyRS) dataset (Wang et al. 2017; available at https://open.canada.ca/data/en/dataset/d8616c52-a812-44ad-8754-7bcc0d8de305), and on daily total precipitation data from automated gauges (including Belfort, Fisher & Porter, Nipher, Geonor, and Pluvio), with some records from neighbouring stations being joined to form long-term data series. Version 1 of ANUSPLIN surfaces of the adjusted monthly precipitation (MacDonald et al. 2021, available at https://open.canada.ca/data/en/dataset/779ea77a-0ad1-42f2-853e-833e1cbb9a13) was used to infill temporal data gaps in the 425 data series. A comprehensive semi-automatic data homogenization procedure was used to homogenize the data series. The aforementioned ANUSPLIN data and the Twentieth Century Reanalysis 20CRv3 ensemble-mean series of monthly precipitation (Slivinski et al., 2021) were used as reference in the homogeneity tests (Wang et al., 2023). The homogenized dataset provides more realistic trend estimates and shows better spatial consistency of trends than does the raw dataset (Wang et al. 2023). References: Wang, X.L, Y. Feng, V. Y. S. Cheng, H. Xu, 2023: Observed precipitation trends inferred from Canada’s homogenized monthly precipitation dataset, J. Clim., in press. DOI: 10.1175/JCLI-D-23-0193.1. Wang, X. L., H. Xu, B. Qian, Y. Feng, E. Mekis, 2017: The adjusted daily rainfall and snowfall data for Canada. Atmosphere-Ocean, 55:3, 155-168, DOI:10.1080/07055900.2017.1342163. MacDonald, H., D. W. McKenney, X. L. Wang, J. Pedlar, P. Papadopol, K. Lawrence, M. F. Hutchinson, 2021: Spatial Models of adjusted precipitation for Canada at varying time scales. J. Appl. Meteor. And Climatol., 60, 291-304. DOI: 10.1175/JAMC-D-20-0041.1. Slivinski, L. and coauthors, 2019: Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Q. J. R. Meteor. Soc., 2876-2908, https://doi.org/10.1002/qj.3598.

Tex files containing data used for diagrams in the paper, plus pdf figures related to the model formulation, and svg figures from which pdf figures have been generated.

Open the .Rproj file and run 'network homogenization analysis.R' within the analysis folder to reproduce analyses and figures.

The data consist of homogenized daily maximum, minimum and mean surface air temperatures for more than 330 locations in Canada; adjusted daily rainfall, snowfall and total precipitation for more than 460 locations. The data are given for the entire period of observation. Please refer to the papers below for detailed information regarding the procedures for homogenization and adjustment. References: Mekis, É. and L.A. Vincent, 2011: An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmosphere-Ocean, 49(2), 163-177. Vincent, L. A., X. L. Wang, E. J. Milewska, H. Wan, F. Yang, and V. Swail, 2012. A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis, J. Geophys. Res., 117, D18110, doi:10.1029/2012JD017859. Wang, X.L, Y. Feng, L. A. Vincent, 2013. Observed changes in one-in-20 year extremes of Canadian surface air temperatures. Atmosphere-Ocean. Doi:10.1080/07055900.2013.818526.

Abstract Since the cost of processed cheese is high in Iran, in this research we studied the effect of homogenization at the pressures of 100, 150 and 200 bar on the quality and texture of processed cheese followed by reducing dry matter. Results showed no significant difference between the pH and dry matter of processed cheese samples at different pressures (p > 0.05). Also, different pressures of homogenization had no significant effect on overall acceptability (p > 0.05). With increasing homogenization pressure, texture improved significantly (p < 0.05). Homogenization treatment at the pressure of 200 bar had the highest influence on the improvement of the processed cheese texture.

Monthly, seasonal and annual trends of daily minimum, mean and maximum surface air temperature change (degrees Celsius) based on homogenized station data (AHCCD) are available. Trends are calculated using the Theil-Sen method using the station’s full period of available data. The availability of temperature trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.

Long-term homogenized surface wind speeds (at standard 10 m level) consist of monthly mean surface wind speeds for more than 100 locations. Series start from 1953 or later and all stations have at least 45 years of continuous observations.

The original data includes hourly surface wind speeds extracted from the National Climate Data Archive of Environment Canada.

Surface wind speeds were homogenized in two steps. First, metadata and a logarithmic wind profile were used to adjust hourly wind speeds measured from non-standard to the standard 10 m height. Then, the monthly mean wind speeds were tested for homogeneity using a technique based on regression models (Wang, 2008). Homogeneous monthly mean geostrophic wind speeds, derived from homogenized sea level pressure, were used as reference series.

The homogenization methodology involves the identification of shifts in the wind speed time series due to changes in anemometer height, site exposure, location, instrumentation and anemometer type. Monthly adjustments were derived from the regression models (Wan et al., 2009). Whenever possible, the main causes of the identified inhomogeneities were retrieved through historical evidence such as the inspection reports.

The degradation of natural habitats is causing ongoing homogenization of biological communities and declines in terrestrial insect biodiversity, particularly in agricultural landscapes. Orthoptera are focal species of nature conservation and experienced significant diversity losses over the past decades. However, the causes underlying these changes are not fully understood yet. We analysed changes in Orthoptera assemblages surveyed in 1988, 2004 and 2019 on 198 plots distributed across four major grassland types in Central Europe. We demonstrated compositional differences in Orthoptera assemblages found in wet, dry and mesic grasslands, as well as ruderal habitats decreased, indicating biotic homogenization. However, mean α-diversity of Orthoptera assemblages increased over the study period. We detected increasing numbers of species with preferences for higher temperatures in mesic and wet grasslands. By analysing the temperature, moisture and vegetation preferences of Orthoptera, we found that additive homogenization was driven by a loss of species adapted to extremely dry and nitrogen-poor habitats and a parallel spread of species preferring warmer macroclimates.

Mammals have two types of photoreceptors, rods and cones. While rods are exceptionally sensitive and mediate vision at very low illumination levels, cones operate in daylight and are responsible for the bulk of visual perception in most diurnal animals, including humans. Yet the mechanisms of phototransduction in cones is understudied, largely due to unavailability of pure cone outer segment (COS) preparations. Here we present a novel mathematical model of cone phototransduction that explicitly takes into account complex cone geometry and its multiple physical scales, faithfully reproduces features of the cone response, and is orders of magnitude more efficient than the standard 3D diffusion model. This is accomplished through the mathematical techniques of homogenization and concentrated capacity. The homogenized model is then computationally implemented by finite element method. This homogenized model permits one to analyze the effects of COS geometry on visual transduction and lends itself to performing large numbers of numerical trials, as required for parameter analysis and the stochasticity of rod and cone signal transduction. Agreement between the nonhomogenized, (i.e., standard 3D), and homogenized diffusion models is reported along with their simulation times and memory costs. Virtual expression of rod biochemistry on cone morphology is also presented for understanding some of the characteristic differences between rods and cones. These simulations evidence that 3D cone morphology and ion channel localization contribute to biphasic flash response, i.e undershoot. The 3D nonhomogenized and homogenized models are contrasted with more traditional and coarser well-stirred and 1D longitudinal diffusion models. The latter are single-scale and do not explicitly account for the multi-scale geometry of the COS, unlike the 3D homogenized model. We show that simpler models exaggerate the magnitude of the current suppression, yield accelerated time to peak, and do not predict the local concentration of cGMP at the ionic channels.

Aim: Theory suggests that increasing productivity and climate stability towards the tropics favours specialization, thus contributing to the latitudinal richness gradient. A positive relationship between species richness and specialization should therefore emerge as a fundamental biogeographical pattern. However, land‐use and climate changes disproportionally increase the local extirpation risk for specialists, potentially weakening the relationship between richness and specialization. Here, we quantify empirically the richness–specialization prediction and test how 50 years of climate and land‐use change has affected the richness–specialization relationship. Location: USA. Time period: 1966–2015. Major taxa studied: Birds. Methods: We used the North American Breeding Bird Survey to quantify bird community richness and specialization to habitat and climate. We (a) quantify temporal change in the slope of the richness–specialization relationship, using a generalized mixed model; (b) assess how this change translates spatially, using generalized additive models; and (c) attribute spatio‐temporal change in the richness–specialization relationship to land use, climate and topographic drivers. Results: We found evidence for a positive but weak richness–specialization relationship in bird communities that greatly weakened over time. Given that specialization was not the main driver of richness, this relationship did not translate spatially into a linear spatial covariation between richness and specialization. Instead, the spatial covariation in richness and specialization followed a unimodal pattern, the peak of which shifted towards less specialized communities over time. These temporal changes were associated with precipitation change, decreasing temperature stability and land use. Main conclusions: Recent climate and land‐use changes have induced two contrasting types of community responses. In human‐dominated areas, the decoupling of richness and specialization drove a general trend for biotic homogenization. In areas of low human impact experiencing increasing climate harshness, specialization increased, whereas richness decreased. Our results offer new support for specialization as a key driver of macroecological diversity patterns and show that global changes are weakening this fundamental macroecological pattern.

Monthly, seasonal and annual trends of total precipitation change (millimetres) based on adjusted station data (AHCCD) are available. Trends are calculated using the Theil-Sen method using the station’s full period of available data. The availability of precipitation trends will vary by station; if more than 5 consecutive years are missing data or more than 10% of the data within the time series is missing, a trend was not calculated.