This platform gathers the variables, R scripts and SAS procedure mentionned and used in a paper entitled "Feed efficiency of lactating Holstein cows is less reproducible when changing dietary starch and fibre concentrations than within diet over subsequent lactation stages" available on BioRXiv. These data are based on data collected during the project ANR-15-CE20-0014 and are the ones got after outlier removal and calculation as specified in the paper. The files available are: - R script to create the dataset and do the analysis indicated in the paper (Rscript_datasetcreation_analysis.R ) - dataset to be downloaded at the beginning of the R script (data_origin.tab) - description of the previous dataset (description_variables_depositpaper.tab) - SAS procedure to estimate RFI (SASscript.txt ) - R script to analyse the NIRs spectra (Script_PCA_refusals.R ) - dataset with the NIRs spectra (data_nirs_refusals_v2.tab) - dataset with RFI estimation (RFI.tab )

Zooplankton meter² counts from GSO/URI - Pump data only
The Zooplankton Meter² Database for the Georges Bank GLOBEC project was originally located in the laboratory of Ted Durbin at the Graduate School of Oceanography, University of Rhode Island. It was accessed via the U.S. GLOBEC Georges Bank data management system using SQLPlus network access to the data base management system at URI. Data were cached and are served from the local computer.

A description of the original URI database is available online and includes the design and variable definitions. A version of this document is shown http://globec.whoi.edu/globec-dir/data_doc/zoo_square_meter_URI.html\"> here.

Note: Our program's Data Acknowledgement Policy requires that any person making substantial use of a data set must communicate with the investigators who acquired the data prior to publication and anticipate that the data collectors will be co-authors of published results.

The following documentation applies to the data found locally on the WHOI GLOBEC Data Server.
The data is served as a hierarchy. The least changing variables are in higher order levels (e.g., cruise id, year, month, etc.), while variables that change the most are in the lower order levels (e.g., time of collection, net number, taxon collected, etc.) There are six levels within the data; variable names and descriptions are given in the metadata.

Most column variable names and instrument names were taken from the U.S. GLOBEC Georges Bank data thesaurus; those that were not follow the GLOBEC data protocols. The taxonomic code variable (taxon_code) is from the National Oceanographic Data Center's Taxonomic List, version 8. Taxonomic information is built into these ten-digit codes as they reflect the systematic nomenclature.

You may contact BCO-DMO for additional help.

Zooplankton Meter³ Data - MOCNESS Only
The Zooplankton Meter³ Database for the Georges Bank GLOBEC project was originally located in the laboratory of Ted Durbin at the Graduate School of Oceanography, University of Rhode Island. It was accessed via the U.S. GLOBEC Georges Bank data management system using SQLPlus network access to the data base management system at URI. The data were cached and are served from the local computer.

A description of the original URI database is available online and includes the design and variable definitions. A version of this document is shown here.

Note: Our program's Data Acknowledgement Policy requires that any person making substantial use of a data set must communicate with the investigators who acquired the data prior to publication and anticipate that the data collectors will be co-authors of published results.

The following documentation applies to the data found locally on the WHOI GLOBEC Data Server.
The data are served as a hierarchy. The least changing variables are in higher order levels (e.g., cruise id, year, month, etc.), while variables that change the most are in the lower order levels (e.g., time of collection, net number, taxon collected, etc.) There are six levels within the database; variable names and descriptions are given in the metadata.

Most column variable names and instrument names were taken from the U.S. GLOBEC Georges Bank data thesaurus; those that were not follow the GLOBEC data protocols. The taxonomic code variable (taxon_code) is from the National Oceanographic Data Center's Taxonomic List, version 8. Taxonomic information is built into these ten-digit codes as they reflect the systematic nomenclature.

You may contact BCO-DMO for additional help.

!!!WARNING~~~This dataset has a large number of flaws and is unable to properly answer many questions that people generally use it to answer, such as whether national hate crimes are changing (or at least they use the data so improperly that they get the wrong answer). A large number of people using this data (academics, advocates, reporting, US Congress) do so inappropriately and get the wrong answer to their questions as a result. Indeed, many published papers using this data should be retracted. Before using this data I highly recommend that you thoroughly read my book on UCR data, particularly the chapter on hate crimes (https://ucrbook.com/hate-crimes.html) as well as the FBI's own manual on this data. The questions you could potentially answer well are relatively narrow and generally exclude any causal relationships. ~~~WARNING!!!Version 8 release notes:Adds 2019 dataVersion 7 release notes:Changes release notes description, does not change data.Version 6 release notes:Adds 2018 dataVersion 5 release notes:Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. Version 4 release notes: Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent. Made the 'population' column which is the total population in that agency. Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), made all character values lower case, reordered columns. I also generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

1- Temporal fluctuations in growth rates can arise from both variation in age-specific vital rates and temporal fluctuations in age structure (i.e., the relative abundance of individuals in each age-class). However, empirical assessments of temporal fluctuations in age structure and their effects on population growth rate are rare. Most research has focused on understanding the contribution of changing vital rates to population growth rates and these analyses routinely assume that: (i) populations have stable age distributions, (ii) environmental influences on vital rates and age structure are stationary (i.e., the mean and/or variance of these processes does not change over time), and (iii) dynamics are independent of density. 2- Here we quantified fluctuations in age structure and assessed whether they were stationary for four populations of free-ranging vertebrates: moose (observed for 48 years), elk (15 years), tawny owls (15 years) and gray wolves (17 years). We also assessed the extent that fluctuations in age structure were useful for predicting annual population growth rates using models which account for density-dependence. 3- Fluctuations in age structure were of a similar magnitude to fluctuations in abundance. For three populations (moose, elk, owls), the mean and the skew of the age distribution fluctuated without stabilizing over the observed time periods. More precisely, the sample variance (interannual variance) of age structure indices increased with the length of the study period which suggests that fluctuations in age structure were non-stationary for these populations – at least over the 15-48 year periods analysed. 4- Fluctuations in age structure were associated with population growth rate for two populations. In particular, population growth varied from positive to negative for moose and from near zero to negative for elk as the average age of adults increased over its observed range. 5- Non-stationarity in age structure may represent an important mechanism by which abundance becomes non-stationary – and therefore difficult to forecast – over time scales of concern to wildlife managers. Overall, our results emphasize the need for vertebrate populations to be modelled using approaches that consider transient dynamics and density-dependence, and that do not rely on the assumption that environmental processes are stationary.

Zooplankton meter² counts from GSO/URI - Pump data only
The Zooplankton Meter² Database for the Georges Bank GLOBEC project was originally located in the laboratory of Ted Durbin at the Graduate School of Oceanography, University of Rhode Island. It was accessed via the U.S. GLOBEC Georges Bank data management system using SQLPlus network access to the data base management system at URI. Data were cached and are served from the local computer.

A description of the original URI database is available online and includes the design and variable definitions. A version of this document is shown http://globec.whoi.edu/globec-dir/data_doc/zoo_square_meter_URI.html\"> here.

Note: Our program's Data Acknowledgement Policy requires that any person making substantial use of a data set must communicate with the investigators who acquired the data prior to publication and anticipate that the data collectors will be co-authors of published results.

The following documentation applies to the data found locally on the WHOI GLOBEC Data Server.
The data is served as a hierarchy. The least changing variables are in higher order levels (e.g., cruise id, year, month, etc.), while variables that change the most are in the lower order levels (e.g., time of collection, net number, taxon collected, etc.) There are six levels within the data; variable names and descriptions are given in the metadata.

Most column variable names and instrument names were taken from the U.S. GLOBEC Georges Bank data thesaurus; those that were not follow the GLOBEC data protocols. The taxonomic code variable (taxon_code) is from the National Oceanographic Data Center's Taxonomic List, version 8. Taxonomic information is built into these ten-digit codes as they reflect the systematic nomenclature.

You may contact BCO-DMO for additional help.

Regression results after changing variable measurement methods.

Habitat stability is important for maintaining biodiversity by preventing species extinction, but this stability is being challenged by climate change. The tropical alpine ecosystem is currently one of the ecosystems most threatened by global warming, and the flora close to the permanent snow line is at high risk of extinction. The tropical alpine ecosystem, found in South and Central America, Malesia and Papuasia, Africa, and Hawaii, is of relatively young evolutionary age, and it has been exposed to changing climates since its origin, particularly during the Pleistocene. Estimating habitat loss and gain between the Last Glacial Maximum (LGM) and the present allows us to relate current biodiversity to past changes in climate and habitat stability. In order to do so, 1) we developed a unifying climate-based delimitation of tropical alpine regions across continents, and 2) we used this delimitation to assess the degree of habitat stability, i.e. the overlap of suitable areas between the ..., The dataset consists of a set of script developed for the corresponding publication using CHELSA v.1.2 (https://chelsa-climate.org/downloads/) to delimit tropical alpine regions based on bioclimatic variables. Using the scripts and setting the limits of the respective bioclimatic variables, will result in the GIS shapefiles with the delimitied region. Here, we provide the corresponding GIS shapefiles and figures for the above mentioned publication, that were the outcome of running the R scripts. The shapefiles and figures are based on the mean temperature of the coldest and warmest quarter (bioclim 10 and bioclim 11) of -3 to +10/+18°C respectively, plus a restriction to the tropics based on bioclim 3, the ratio of diurnal variation to annual variation in temperatures, ranging from 50 to 300 °C/10., , # ClimateAnalyzer

ClimateAnalyzer is a set of script written in R to delimit areas based on bioclimatic variables.

The scripts have been developed to delimit tropical alpine areas based on bioclimatic variables from CHELSA (). The work has been presented in Kandziora et al (under review) "The ghost of past climate acting on present-day plant diversity: lessons from a climate-based delimitation of the tropical alpine ecosystem".

Description of the data and file structure

The uploaded GIS shapefiles and figures are based on a delimitation based on the mean temperature of the coldest and warmest quarter (bioclim 10 and bioclim 11) of -3 to +10 °C, plus a restriction to the tropics based on bioclim 3, the ratio of diurnal variation to annual variation in temperatures, ranging from 50 to 300 °C/10.

The delimitation was done for current climatic conditions as well as two reconstructions of the climate during the last glacial maximum, based on MPI-M_MPI-ESM-P (abbreviated to MPI) and...

File List Menge_Latitudinal_Abundance_Model_Code.R (MD5: 7917640d0c7cf0c449b97517fa29133d) Menge_SuccessionDynamicsModel_Script.m (MD5: e3bb898eef5b2e1d104cc90629d37120) Menge_SuccessionDynamicsModel_Pars.m (MD5: 4f3456b66d123b4a957d5a21e74951d8) Menge_SuccessionDynamicsModel_odes_ob_non.m (MD5: c32d1000d5a3c7047a5ed86d479e97ab) Menge_SuccessionDynamicsModel_odes_fac_non.m (MD5: 401ad7cdb1a9d6d6785964231f133e82) Menge_SuccessionDynamicsModel_Figures.m (MD5: e1e7346752dd3e7b8b6e9a101066a5a5) Menge_SuccessionDynamicsModel_FigB8_Script.m (MD5: 9a482ad728fb63538bde2965c79a6041) Menge_SuccessionDynamicsModel_Pars_3.m (MD5: 1ce6191d5cfc70061fd037ae6df80905) Menge_SuccessionDynamicsModel_odes_fac_ob_non.m (MD5: 7c1ba58eb3e7563bad78d2301d2a1da0) Menge_SuccessionDynamicsModel_swa.m (MD5: 2f0631669f3411753e287a264bd1ebd7) Menge_SuccessionDynamicsModel_FigB8_Figure.m (MD5: 9e4a1d2b02675b6c52f64e1e39f76bd5) Description This supplement contains files that consist of code for simulations and statistical analyses consisting of 1 .R (R) file for the "Latitudinal Abundance Model" and 10 .m (matlab) files for the "Succession Dynamics Model." The .R file, Menge_Latitudinal_Abundance_Model_Code.R, contains the statistical model code. It creates a dataframe with latitude and the 1-degree-latitude mean percent basal area occupied by N fixers (the data used in model fitting). It then creates variables for the abundance of each type in each habitat for given age distribution; these data were output from the Successional Dynamics Model and weighted by different age distributions. It then creates some functions needed for the model fitting exercise (as described in the text), and uses nls to fit the model to the data. Finally, it plots up the results. As currently set up, it creates Fig. 4 in the paper. To create Fig. B4–B7, some of the variables must be changed and the code rerun, as indicated in the code comments. The Successional Dynamics Model code is contained in the .m files. There are 5 .m files associated with running the simulation for Fig. 3. Menge_SuccessionDynamicsModel_Script.m is the main script. It calls Menge_SuccessionDynamicsModel_Pars.m to set the parameters, sets up the initial conditions for each simulation, runs the simulation(s), saves the data, and calls the script that makes the figure. Options in the file (used to create the different panels) are changing SevModNon and whichrun (as indicated in the code). The files Menge_SuccessionDynamicsModel_odes_ob_non.m and Menge_SuccessionDynamicsModel_odes_fac_non.m are the functions that describe the mathematical equations of the model, and are called in the main script with the function ode45. The file Menge_SuccessionDynamicsModel_Figures.m sets up the figure, loads the data for each panel (which must be made, first, from the main script), then fills out each panel. There are 5 .m files associated with running the simulation for Appendix B Fig. B8. The file Menge_SuccessionDynamicsModel_FigB8_Script.m is the main script. It initializes the parameter values for all three types (in the file Menge_SuccessionDynamicsModel_Pars_3.m), loops over habitat types and cost values (expressed as "psi," which is related to gamma in the paper by gamma = psi * c, with c = 120 per year), then numerically integrates the model Menge_SuccessionDynamicsModel_odes_fac_ob_non.m with the matlab function ode45, then weights the successional abundances by the FIA age distribution using the function Menge_SuccessionDynamicsModel_swa.m (also does so for the alternate age distributions, but these are not used in the figure), saves the data, then calls the figure script. The file Menge_SuccessionDynamicsModel_FigB8_Figure.m sets up the figure, loads the data, and fills out the panels. Editing of axis and panel labels directly on the pdf was done in Adobe Illustrator. Note that this run takes a long time.