This dataset provides spatial predictions of the pooled-SDM residuals from a multiscale geographically weighted regression model (MGWR) and the resulting local R2 values for individuals in the subregion encompassing the genetic sampling locations used by Edwards et al. (2015). This region offered an opportunity to explore habitat selection across the ecotone between the Mojave and Sonoran deserts and the secondary contact zone between G. agassizii and G. morafkai, and is referred to as the focal study area. The raster layers contained here accompany the manuscript Inman et al. 2019 and were used to identify multivariate clusters and map them back to geographic space. Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. https://doi.org/10.1111/ddi.12927

LocalitiesList of 1627 localities used to generate the Maxent models in "Do ecological niche models accurately identify climatic determinants of species ranges?" Provided data include the longitude and latitude for each locality and bioclimatic variables ('bio1' to 'bio19'), as extracted from the 'WorldClim' database (for definitions see: http://www.worldclim.org/bioclim). Further information about WorldClim at Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. http://dx.doi.org/10.1002/joc.1276

the biooverlap database provides standardized measures of climatic niche overlap and geographic range overlap for over 16,000 terrestrial and marine species worldwide, spanning 20 major taxonomic groups, including amphibians, birds, mammals, reptiles, plants, marine and freshwater fish, corals, and others. it is designed to support macroecological and biogeographical research by offering a large-scale, comparative framework to study species’ environmental distinctiveness and patterns of regional co-occurrence.niche overlap is calculated using the jaccard index based on multivariate climatic data (19 worldclim bioclimatic variables), while range overlap is derived from iucn range maps. for each species pair, the database includes pairwise estimates of both niche and range overlap, alongside metadata such as taxonomic affiliation, niche centroid distances and niche breadth. pairwise assessments are available considering all species within each realm (e.g., all_biooverlap_terrestrial) but also considering only species within each taxonomic group (e.g., overlap_measures_birds).biooverlap is intended as a resource for ecologists, evolutionary biologists, conservation scientists, and data modelers working on topics such as community assembly, biodiversity change, extinction risk, niche evolution, and species coexistence.

Occurrences used to calibrate ecological niche models

This is the data repository for "The Rise of Niche Consumption": Over the last 15 years, the typical household has increasingly concentrated its spending on a few preferred products. However, this is not driven by ``superstar'' products capturing larger market shares. Instead, households increasingly purchase different products from each other. As a result, aggregate spending concentration has decreased. We develop a model of heterogeneous household demand and use it to conclude that increasing product variety drives these divergent trends. When more products are available, households select products better matched to their tastes. This delivers welfare gains from selection equal to about half a percent per year in the categories covered by our data. Our model features heterogeneous markups because producers of popular products care more about their existing customers while producers of less popular niche products care more about generating new customers. Surprisingly, our model matches the observed trends in household and aggregate concentration without any change in aggregate market power.

The NICHE-Vlaanderen project had the goal to develop an hydro-ecological prediction model, used in ecological impact assessment studies. The data in this dataset is part of the vegetation-plot data used to feed the model and contains groundwater depending terrestrial vegetation relevées in relation to groundwater levels. Vegetation plot relevés were performed near selected piezometers (WATINA database, groundwater network Flanders) between May and August in 2002, 2004 and 2005. Initially the vegetation surveys were recorded in Turboveg (Hennekens, 1998) and later on moved to INBOVEG, the INBO vegetation plot database. The dataset contains 569 vegetation relevées, recorded during the fieldwork of the NICHE-Vlaanderen project. Relevées contain species coverage data, coverage data for layers, vegetation height and the date of recording. All the vegetation relevées were classified as vegetation types. Issues related to the dataset can by submitted here: https://github.com/inbo/data-publication/tree/master/datasets/inboveg-niche-vlaanderen-events

To allow anyone to use this dataset, we have released the data to the public domain under a Creative Commons Zero waiver (http://creativecommons.org/publicdomain/zero/1.0/). We would appreciate however, if you read and follow these norms for data use (http://www.inbo.be/en/norms-for-data-use) and provide a link to the original dataset (https://doi.org/10.15468/gouexm) whenever possible. If you use these data for a scientific paper, please cite the dataset following the applicable citation norms and/or consider us for co-authorship. We are always interested to know how you have used or visualized the data, or to provide more information, so please contact us via the contact information provided in the metadata, opendata@inbo.be or https://twitter.com/LifeWatchINBO.

The data set is collected to evaluate if two parasitoids (Spathius galinae and Tetrastichus planipennisi), introduced for biocontrol of the invasive emerald ash borer (EAB), Agrilus planipennis, into North America have established niche-partitioning, co-existing populations following their sequential or simultaneous field releases to 12 hard-wood forests located in Midwest and Northeast regions of the United States. Ash trees of various sizes (large, pole-size and saplings) were debarked meter by meter in early spring of 2019 (Michigan sites) or fall of 2019 (Northeast states: Connecticut, Massachusetts and New York). Detailed data collection procedures can be found in the associated publication in Biological Control. Resources in this dataset:Resource Title: Niche partitioning and coexistence of parasitoids of the same feeding guild introduced for biological control of an invasive forest pest - Michigan data. File Name: Michigan 2019-EAB Parasitoid Niche Partition-Raw.csvResource Description: Michigan DatasetResource Software Recommended: JMP,url: https://www.JMP.com Resource Title: Niche partitioning and coexistence of parasitoids of the same feeding guild introduced for biological control of an invasive forest pest - Northeast states data. File Name: NE Dataset 2019-EAB Parasitoid Niche Partition-Raw.csvResource Description: Northeast States Data setResource Title: Niche partitioning and coexistence of parasitoids of the same feeding guild introduced for biological control of an invasive forest pest - Data Dictionary. File Name: Data Dictionary for Parasitoid niche partitioning study from Biological Control.docxResource Description: Data dictionary

Modelling responses to climate change assume zooplankton populations remain similar over time with little adaptation (niche conservatism). Oceanic barriers, genetic, phenotypic variation and species interactions in cosmopolitan species could drive niche divergence within species. We assess niche divergence among 224 globally distributed species across the seven main ocean basins. There were 357 diverged niches out of 828 ocean basin comparisons. The proportion of diverged niches varied both across and within phyla. Copepoda (156 of 223 species) were used to test for niche divergence between same species populations across different environmental gradients. Global niche divergence was found to be more likely for species in colder temperatures and near shore environments. Opposing temperature responses were found for four comparisons which may relate to the different connectivity patterns between them. This study demonstrates adaptive potential across environmental-niche gradients, which must be considered when modelling population responses to climate change. Methods

Both biological and environmental data are freely available for download. The zooplankton data are available from the 'Zoobase' dataset https://zenodo.org/records/5101349. This is a collated reseource of all observation records for the main groups of meso-zooplankton The environmental data were extracted as monthly climatologies from the World Ocean Atlas (2022). Bathymetric depth were extracted from GEBCO’s current gridded bathymetric data set (2024). Monthly climatologies of chlorophyll-a were extracted from the globcolour database (http://www.globcolour.info/). The repository contains code to each of the four main steps of the manuscript. Here we provide a detailed description to replicate the results in the manuscript. We 1) First extract the presences from the 'Zoobase' database and retain species present when n > 50 in two ocean basins, 2) Perform an ensemble model on each species for the global presence distribution in teh dataset, 3) Extract observations of a species from paired ocean basin areas to examine niche divergence, 4) Use the results of the global ensemble model and the binary niche divergence/ non-divergence classifier to perform a heriarchical generalised additive model.

CR_settlement_niche_[N]_[Yr]_[BC/AD].tif

Ecological niche models in GeoTIFF format generated with the MaxEnt software based using prehistoric settlement evidence as training data and environmental layers (elevation, mean annual precipitation, mean annual temperature, landscape water balance, soil types) as background data. Raster values represent the probability of presence of a settlement. N - chronological ordering Yr, BC/AD - calendar years BC or AD

CR_settlement_niche_combined.tif

All models combined by averaging.

CR_settlement_archeo.zip

Archaeological data used to train the MaxEnt models in ESRI SHP format with the following fields:

Site_Type: Cemetery or Settlement

Archeo_Dat: Archaeological dating (culture or period)

Source: Source dataset (AMCR or LONGWOOD)

AMCR: Archeologická mapa České republiky – Archaeological Map of the Czech Republic. Retrieved from https://digiarchiv.aiscr.cz/.

LONGWOOD: Kolář, J., Tkáč, P., Macek, M., & Szabó, P. (2016). Archaeology and Historical Ecology: the Archaeological Database of the LONGWOOD ERC Project. Archäologisches Korrespondenzblatt 46/4, 539-554.

Yrs_BP_Avg: Average dating in calendar years BP (based on the archaeological dating)

Yrs_BP_Unc: Temporal uncertainty of the dating (half of the culture or period's duration)

Loc_Accur: Spatial accuracy derived from the recorded degree of the accuracy of location (radius in meters around the center point)

aAll sample sizes reflect the total number of georeferenced specimens or observations available at time of data acquisition, including multiple specimens or observations at a given site. Sample sizes are restricted to North America.bAll band and encounter records obtained through written permission from the USGS Bird Banding Lab (http://www.pwrc.usgs.gov/bbl/homepage/datarequest.cfm).cObtained from the USGS North American Breeding Bird Survey (http://www.pwrc.usgs.gov/bbs/RawData/Choose-Method.cfm).dObtained from the Christmas Bird Count database project (http://infohost.nmt.edu/~shipman/z/cbc/homepage.html).eAll observational data from the Cornell Lab of Ornithology were obtained from the Avian Knowledge Network (http://www.avianknowledge.net/content).

Indices of niche breadth and niche overlap for categorical data are typically expressed in terms of proportions of resources use. These are unit-sum constrained data; hence, direct application of standard general linear modelling methods to such indices can lead to spurious correlations and misleading inference. To overcome these limitations, we introduce a compositional data analysis (CoDA) approach and derive compositional expressions of niche breadth, niche overlap and specialization. Compositional data analysis is specifically devoted to the analysis of vectors of proportions (i.e. compositions) and represents the appropriate framework for the study of sets of data with unit-sum constraint as those typically used in the calculation of niche indices. We show that compositional indices exhibit suitable statistical properties that make them flexible and robust, allowing downstream application of the full toolbox of multivariate analysis techniques to these estimators, a possibility not available with classical indices. In addition, we find that when characterizing niche breadth, niche overlap and specialization in terms of vectors of proportions, these concepts are naturally integrated in a coherent unifying framework. When data are categorical, we recommend the use of compositional indices for the statistical analysis of specialization metrics, niche breadth and niche overlap. We believe that the unified framework emerging from our compositional approach to niche metrics will allow a more thorough understanding of specialization at multiple levels of biological organization and provide novel insights in complex phenomena such as invasions and niche shifts.

This dataset contains location and environmental data for 30 amphipod species (Crustacea) sampled off Iceland. Species data were sampled during various deep sea expeditions i.e. the BioICE and IceAGE programs. Environmental data were downloaded from the Bio-Oracle 2.1 database (Assis et al. 2018) using the R-package sdmpredictors (Bosch 2020).

• Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E.A., De Clerck, O. Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecol Biogeogr. 2018; 27: 277– 284. https://doi.org/10.1111/geb.12693
• Bosch, S. (2020) sdmpredictors: Species Distribution Modelling Predictor Datasets. R package version 0.2.9. https://CRAN.R-project.org/package=sdmpredictors

AbstractClimate change may soon threaten much of global biodiversity, especially if species cannot adapt to changing climatic conditions quickly enough. A critical question is how quickly climatic niches change, and if this speed is sufficient to prevent extinction as climates warm. Here, we address this question in the grass family (Poaceae). Grasses are fundamental to one of Earth's most widespread biomes (grasslands), and provide roughly half of all calories consumed by humans (including wheat, rice, corn and sorghum). We estimate rates of climatic niche change in 236 species and compare these with rates of projected climate change by 2070. Our results show that projected climate change is consistently faster than rates of niche change in grasses, typically by more than 5000-fold for temperature-related variables. Although these results do not show directly what will happen under global warming, they have troubling implications for a major biome and for human food resources. Usage notesESM_guideSupplementary Figure S1Visual summary and flowchart of the methods used in this study.Appendix_S1Supplementary MethodsAppendix_S2Summary of climatic data for 170 species from Edwards & Smith [13] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Climatic data for each species for each climatic variable are summarized by the minimum, median and maximum values among localities within its distribution, as well as the mean value across all localities that was used to calculate rates. Temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S3Summary of climatic data for 62 species from Tree 1 of Spriggs et al. [2] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Climatic data for each species for each climatic variable are summarized by the minimum, median and maximum values identified within its distribution, as well as the mean value across all localities that was used to calculate rates. Temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S4Summary of climatic data for 60 species from Tree 2 of Spriggs et al. [2] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Climatic data for each species for each climatic variable are summarized by the minimum, median and maximum values identified within its distribution, as well as the mean value across all localities that was used to calculate rates. Temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S5Climatic data for all localities (including latitude and longitude) extracted from WorldClim database at 30 second resolution for 170 species from the tree of Edwards & Smith [13] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Note that temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S6Climatic data for all localities (including latitude and longitude) extracted from WorldClim database at 30 second resolution for 62 species from Tree 1 in Spriggs et al. [2] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Note that temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S7Climatic data for all localities (including latitude and longitude) extracted from WorldClim database at 30 second resolution for 60 species from Tree 2 in Spriggs et al. [2] for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12). Note that temperature values are in degrees Celsius*10 and precipitation values are in mm/year.Appendix_S8Estimated node ages (in millions of years) and rates of climatic niche change for three trees under three different models of evolution (BM = Brownian Motion; OU = Ornstein-Uhlenbeck; Lambda = estimated lambda) for each climatic variable (MAT = mean annual temperature, Bio1; TMAX = maximum temperature of the warmest month, Bio5; TMIN = minimum temperature of the coldest month, Bio6; MAP = mean annual precipitation, Bio12), along with estimated rates of future climate change under three scenarios...

Aim: Mammalian carnivores are among the best studied groups in terms of evolutionary history. However, the effects of species interactions in shaping community assemblages remain poorly understood. We hypothesize that indirect interactions via ecological trait filtering play a key role in structuring carnivoran assemblages, mediate coexistence, and thus should show high functional diversity in space and time at continental scales.

Location: Americas.

Taxon: Mammalian carnivores (Mammalia: Carnivora).

Methods: We followed a macroecological perspective via ecological networks analyses for indirect interactions, and assessed the underlying functional diversity (FD) across space and from the Last Interglacial to the present in the Americas. We analyzed the potential distributions and six ecological traits of 88 species to establish possible mechanisms that enables species to coexist and the underlying diversity patterns. We compared the empirical results with two null models, and two sen...

Funeral Service: Number of Grave and Niche: Beijing data was reported at 758.901 Unit th in 2023. This records an increase from the previous number of 709.098 Unit th for 2022. Funeral Service: Number of Grave and Niche: Beijing data is updated yearly, averaging 627.304 Unit th from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 831.574 Unit th in 2010 and a record low of 199.860 Unit th in 2000. Funeral Service: Number of Grave and Niche: Beijing data remains active status in CEIC and is reported by Ministry of Civil Affairs. The data is categorized under China Premium Database’s Other Personal Service Sector – Table CN.GZP: Number of Graves and Niches: By Region.

Aim: Locally abundant species are usually widespread, and such pattern has been related to properties of the niches and traits of species. However, such explanations fail to account for the potential of traits to determine species niches, and often overlook statistical artifacts. Here we examine how trait distinctiveness determines species abilities to exploit either common habitats (niche position) or a range of habitats (niche breadth), and how niche position and breadth in turn affect abundance and occupancy. We also examine how statistical artifacts moderate these relations.
Location: Sixteen sites in the Neotropics.
Time period: 1993-2014.
Major taxa studied: Aquatic invertebrates from tank bromeliads.
Methods: We measured the environmental niche position and breadth of each species and calculated its trait distinctiveness, as the average trait difference with all other species at each site. Then, we used a combination of Structural Equations Models and a meta-analytic approach to test trait-niche relationships, and a null model to control for statistical artifacts.
Results: The trait distinctiveness of each species was unrelated to its niche properties, abundance, and occupancy. In contrast, niche position was the main predictor of abundance and occupancy; species that used the most common environmental conditions found across bromeliads were locally abundant and widespread. Contributions of niche breadth to such patterns were due to statistical artifacts, indicating that niche breadth effects may have been overestimated in previous studies.
Main conclusions: Our study reveals the generality of niche position in explaining one of the most common ecological patterns. The robustness of this result is underscored by the geographic extent of our study and our control of statistical artifacts. We call for a similar examination across other systems – an essential task to understanding the drivers of commonness across the tree of life.

Data from: Too much of a good thing? Supplementing current species observations with fossil data to assess climate change vulnerability via ecological niche models

Description of the data and file structure

Data folder contains:

1. Geopackages
   • Geopackage_BEYER_CHELSA: In this folder are the geopackages for the 28 species achieving AUC > 0.7 in both modern and full ENMs. In each geopackage are coordinates and filtered climatic variable values (once VIF < 5 was applied) for occurrence and background points. The geopackages are loaded in all the scripts.
2. Variables
   • CHELSA_10km: These are the variables used for present and 2080 climates. They are loaded in following scripts:
     • 1_Modern_ENM_training
     • 1b_Modern_ENMs_temporal_block
     • 2_Full_ENM_training
     • 5_RANDOM_FOREST
   • Krapp_change: These are the variables used for past climates. They are loaded in following scripts:
     • 3_NICHE_OVERLAP ...

Funeral Service: Number of Grave and Niche: Shandong data was reported at 880.788 Unit th in 2023. This records an increase from the previous number of 668.907 Unit th for 2022. Funeral Service: Number of Grave and Niche: Shandong data is updated yearly, averaging 246.116 Unit th from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 971.386 Unit th in 2020 and a record low of 30.472 Unit th in 2000. Funeral Service: Number of Grave and Niche: Shandong data remains active status in CEIC and is reported by Ministry of Civil Affairs. The data is categorized under China Premium Database’s Other Personal Service Sector – Table CN.GZP: Number of Graves and Niches: By Region.

Funeral Service: Number of Grave and Niche: Sichuan data was reported at 1,503.980 Unit th in 2023. This records an increase from the previous number of 1,441.002 Unit th for 2022. Funeral Service: Number of Grave and Niche: Sichuan data is updated yearly, averaging 900.957 Unit th from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 1,503.980 Unit th in 2023 and a record low of 212.720 Unit th in 2000. Funeral Service: Number of Grave and Niche: Sichuan data remains active status in CEIC and is reported by Ministry of Civil Affairs. The data is categorized under China Premium Database’s Other Personal Service Sector – Table CN.GZP: Number of Graves and Niches: By Region.

These data include environmental covariates used to develop species distribution models for Gopherus agassizii and Gopherus morafkai, along with PCA-reduced environmental covariates used to explore local species-environment relationships within a subregion of the ectone between the two species. We also provide the genotype association used to test the mapped clusters of multiscale geographically weighted regression coefficients against models of (i) a geographically-based taxonomic designation these two sister species, and (ii) an environmental ecoregion designation. These data support the following publication: Inman et al. 2019. Local niche differences predict genotype associations in sister taxa of desert tortoise. Diversity and Distributions. https://doi.org/10.1111/ddi.12927