Species X reflects the focal species being modelled. The steps were carried out at five spatial resolutions. The steps were repeated using Spatial Model 2 (Step 4).Steps used for spatial distribution modelling and species richness determination, using Spatial Models 1.

Wild waterfowl (family Anatidae) are reported as secondary transmitters of HPAIV and primary reservoirs for low-pathogenic avian influenza viruses, yet spatial inputs for disease risk modeling for this group have been lacking. Using geographic information software and Monte Carlo simulations, we developed geospatial indices of waterfowl abundance at 1 km resolutions for the breeding and wintering seasons for China, the epicenter of H5N1. Two types of spatial layers were developed: cumulative waterfowl abundance (WAB), a measure of predicted abundance by species, and cumulative abundance weighted by H5N1 prevalence (WPR), whereby abundance for each species was adjusted based on species specific prevalence values. Spatial patterns of the model output differed between seasons, with higher WAB and WPR in the northern and western regions of China for the breeding season and in the southeast for the wintering season. Uncertainty measures indicated highest error in southeastern China for both WAB and WPR.

Species distribution models (SDMs) are currently the main tools to derive species niche estimates and spatially explicit predictions for species geographical distribution. However, unobserved environmental conditions and ecological processes may confound the model estimates if they have a direct impact on the species and, at the same time, they are correlated with the observed environmental covariates. This, so-called spatial confounding, is a general property of spatial models but it has not been studied in the context of SDMs before. Here we examine how the estimation accuracy of SDMs depends on the type of spatial confounding. We construct two simulation studies where we alter spatial structures of the observed and unobserved covariates and the level of dependence between them. We fit generalized linear models with and without spatial random effects applying Bayesian inference and record the bias induced to model estimates by spatial confounding. After this, we examine spatial confo...

In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg et al., 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).

It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter et al., 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (e.g., Naoki et al., 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:

In the first instance, we generated the background points and extracted the values of the variables (Code2_Extract_values_DWp_SC.R). Barbet-Massin et al. (2012) recommend the use of 10,000 background points when using regression methods (e.g., Generalized Linear Model) or distance-based models (e.g., DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (e.g., bioclimatic, topographic, demographic, habitat) in function of presence and background points (e.g., Hijmans and Elith, 2017).

Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).

After that, we ran the code for the GBM method (Gradient Boost Machine; Code3_GBM_Relative_contribution.R and Code4_Relative_contribution.R), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (e.g., Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.

Subsequently, the correlation of the variables is run by Pearson's method (Code5_Pearson_Correlation.R) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (e.g., Awan et al., 2021).

Once the above codes were run, we uploaded the same subgroups (i.e., presence and background groups with 75% training and 25% testing) (Code6_Presence&backgrounds.R) for the GLM method code (Code7_GLM_model.R). Here, we first ran the GLM models per variable to obtain the p-significance value of each variable (alpha ≤ 0.05); we selected the value one (i.e., presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (e.g., Fielding and Bell, 1997; Allouche et al., 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.

On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).

Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).

Validation set

Model

True

False

Presence

A

B

Background

C

D

We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche et al., 2006).

The last code (i.e., Code8_DOMAIN_SuitHab_model.R) is for species distribution modelling using the DOMAIN algorithm (Carpenter et al., 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.

Regarding the model evaluation and estimation, we selected the following estimators:

1) partial ROC, which evaluates the approach between the curves of positive (i.e., correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).

2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long et al., 1988).

QFLAGS by type by year (precipitation)

These data (vector and raster) were compiled for spatial modeling of salinity yield sources in the Upper Colorado River Basin (UCRB) and describe different scales of watersheds in the Upper Colorado River Basin (UCRB) for use in salinity yield modeling. Salinity yield refers to how much dissolved salts are picked up in surface waters that could be expected to be measured at the watershed outlet point annually. The vector polygons are small catchments developed originally for use in SPARROW modeling that break up the UCRB into 10,789 catchments linked together through a synthetic stream network. The catchments were used for a machine learning based salinity model and attributed with the new results in these vector GIS datasets. Although all of these feature classes include the same polygons, the attribute tables for each include differing outputs from new salinity models and a comparison with SPARROW model results from previous research. The new model presented in these datasets utilizes new predictive soil maps and a more flexible random forest function to improve on previous UCRB salinity spatial models. The raster data layers represent aspects of soils, topography, climate, and runoff characteristics that have hypothesized influences on salinity yields.

Description:

This dataset includes the database extracted from the review of scientific literature (526 articles from Web of Science and Scopus) addressing the modeling of Cultural Ecosystem Services (CES) using social media data.

This database was used as a source of analysis in the article entitled: “Spatial modeling of Cultural Ecosystem Services from social-media data: Systematic review of operability, performance and ways forward / opportunities, limitations and ways forward,” currently under review.

Table 1. Table of contents of the dataset

Folder

format

Description

Database

Database_literature_review

.csv

This dataset provides detailed information extracted from the scientific articles included in the systematic literature review on the modeling of Cultural Ecosystem Services (CES) using social media data

Validacion_CES and variables groups

.csv

This dataset was used to validate and standardize the classification of Cultural Ecosystem Services (CES) across all reviewed studies, despite differences in terminology and categorization among sources.

wos_scopus_bibliographic_data

.csv

This dataset contains the bibliographic database extracted from a systematic literature review, including 526 scientific articles sourced from Web of Science and Scopus.

Scripts

Join Scopus and WOS

.R

This script processes and merges bibliographic records exported from Scopus and Web of Science (WOS)

Analisis review

.R

This script contains all the data processing steps, statistical analyses, and visualizations conducted for the systematic review on the modeling of Cultural Ecosystem Services (CES) using social media data

The Database_literature_review.csv database includes:

Bibliographic details (authors, titles, DOI)

CES service types and classifications

Environmental variables used in models

Modeling approaches and performance metrics

Spatial coverage and study areas

Social media data sources and types

Ecosystem types analyzed

Fundamental to species conservation efforts is the development of accurate distribution models, but doing so is challenging for many stream organisms, where limited funding often necessitates the compilation of incidental observations from multiple sources, which lack an overall sampling design and may be spatially clustered. We demonstrate the application of specialized spatial-statistical-network models (SSNMs), which incorporate autocorrelation among observations and significantly outperform non-spatial models when used to develop distribution models for the Idaho giant salamander (IGS; Dicamptodon aterrimus). The study was located in the Rocky Mountains in west-central North America. We compiled a comprehensive presence-absence dataset for IGS from previous studies, natural resource agencies, museum collections, and new surveys and linked these data to geospatial habitat covariates. The dataset was modeled using a suite of candidate SSNMs and results were compared to generalized lin..., A presence-absence dataset for Idaho giant salamander that consisted of 707 unique sampling locations was collected using electrofishing and eDNA surveys. Many of the surveys were aggregated from existing sources such as previous peer-reviewed studies, grey literature reports, state and federal agency databases, and natural resource museum records. The survey locations were attached to reaches within stream networks across the species range, linked to geospatial habitat covariates, and processed using the open-source SSNbler R package into a landscape network object suitable for spatial-stream-network model analysis using the SSN2 R package. , , # Data from: Improving species distribution models for stream networks by incorporating spatial autocorrelation in multi-sourced datasets: An assessment of Idaho giant salamander status and future risk

https://doi.org/10.5061/dryad.h18931zxb

Description of the data and file structure

This dataset was used in a manuscript published in Diversity and Distributions and consists of several elements: 1) an annotated R script for running an SSNM species distribution model analysis of Idaho giant salamanders (AnnotatedRscript_IGSAnalysis_SDM_n707.R), 2) a zipfile which contains a .ssn directory of files with the observations, covariates, range-wide prediction points, and other helper files needed to conduct a spatial-stream-network model analysis (IGS_LSN3.ssn.zip), 3) the master database of presence-absence surveys as an Excel file (Isaak2025D_D_MasterDataset_IGS-Observations.xlsx), 4) a high-resolution .pdf map showing the survey locations used...,

The purpose of this project is to develop spatially discrete end-to-end models of the northern Gulf of California, linking oceanography, biogeochemistry, food web interactions, habitat, fisheries, economics, monitoring, and management into a common model framework.

This framework allows for thought experiments, including evaluation of alternate management strategies, identifying robust indicat...

We used Maxent to model the distribution of Cypripedium acaule in North Carolina using records from 1) publicly available databases (GBIF and iNaturalist) and 2) herbaria. We compared distribution models made with the different sets of occurrence records to evaluate how spatial and temporal biases in records affect model results.The data provided here include the original iNaturalist dataset (prior to cleaning as described in our methods) and the code for the evaluation of models based on ground-truthed populations. We cannot provide the original herbaria dataset because location records are kept in confidence due to poaching concerns.

Divergent adaptation to different environments can promote speciation, and it is thus important to consider spatial structure in models of speciation. Earlier theoretical work, however, has been limited to particularly simple types of spatial structure (linear environmental gradients and spatially discrete metapopulations), leaving unaddressed the effects of more realistic patterns of landscape heterogeneity, such as nonlinear gradients and spatially continuous patchiness. To elucidate the consequences of such complex landscapes, we adapt an established spatially explicit individual-based model of evolutionary branching. We show that branching is most probable at intermediate levels of various types of heterogeneity and that different types of heterogeneity have, to some extent, additive effects in promoting branching. In contrast to such additivity, we find a novel refugium effect in which refugia in hostile environments provide opportunities for colonization, thus increasing the proba...

We developed habitat suitability models for four invasive plant species of concern to Department of Interior land management agencies. We generally followed the modeling workflow developed in Young et al. 2020, but developed models both for two data types, where species were present and where they were abundant. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples, and constructed model ensembles using the 10 models for each species (five algorithms by two background methods) for four different thresholds. This data bundle contains the presence and abundance merged data sets to create models for medusahead rye, red brome, venanata and bur buttercup, the eight raster files associated with each species/ data type (presence or abundance), and tabular summaries by management unit (including each species/ data type combination). The spatial data are organized in a separate folder for each species, each containing four rasters. Each of the rasters represent the following, with an occurrence (occ) and abundance (abund) version: 1) 1st - one percentile threshold 2) 1st_masked - one percentile threshold with Restricted Environmental Conditions This file specifically, 2) 'mergedDataset.csv', contains the merged data set used to create the models, including location coordinates and associated environmental covariate data values. The bundle documentation files are: 1) 'AbundOccur.xml' contains FGDC project-level metadata 2) 'mergedDataset.csv', which this metadata file specifically describes, contains the merged data set used to create the models, including location and environmental data. 3) XX.tif where XX is the raster type explained above (occ or abund; masked or not). 4) managementSummaries.csv is the tabular summaries by management unit.

QFLAGS by type by year (temperature)

Abstract–Flexible spatial models that allow transitions between tail dependence classes have recently appeared in the literature. However, inference for these models is computationally prohibitive, even in moderate dimensions, due to the necessity of repeatedly evaluating the multivariate Gaussian distribution function. In this work, we attempt to achieve truly high-dimensional inference for extremes of spatial processes, while retaining the desirable flexibility in the tail dependence structure, by modifying an established class of models based on scale mixtures Gaussian processes. We show that the desired extremal dependence properties from the original models are preserved under the modification, and demonstrate that the corresponding Bayesian hierarchical model does not involve the expensive computation of the multivariate Gaussian distribution function. We fit our model to exceedances of a high threshold, and perform coverage analyses and cross-model checks to validate its ability to capture different types of tail characteristics. We use a standard adaptive Metropolis algorithm for model fitting, and further accelerate the computation via parallelization and Rcpp. Lastly, we apply the model to a dataset of a fire threat index on the Great Plains region of the United States, which is vulnerable to massively destructive wildfires. We find that the joint tail of the fire threat index exhibits a decaying dependence structure that cannot be captured by limiting extreme value models. Supplementary materials for this article are available online.

The market for Reality and Spatial Modeling Software is projected to reach $XXX million by 2033, expanding at a CAGR of XX% from 2025 to 2033. The growth of this market is primarily driven by the increasing adoption of virtual reality (VR) and augmented reality (AR) technologies in various industries, including architecture, engineering, and construction (AEC). Additionally, the growing demand for immersive experiences in entertainment and gaming is contributing to the market's growth. The market is segmented by type (cloud-based and local deployment), application (architecture, engineering, and others), and region (North America, Europe, Asia Pacific, and Rest of the World). Cloud-based solutions are gaining popularity due to their scalability, cost-effectiveness, and accessibility. The AEC sector is the largest application segment, followed by the manufacturing and healthcare industries. North America holds the largest market share, followed by Europe and Asia Pacific. The market is dominated by a few key players, including Bentley Systems, Virtuosity, Orion Spatial Solutions, and CCTTEC. Intense competition and technological advancements are expected to shape the market's growth over the forecast period.

Digital data of 3D models featuring geometry model, texture map and textual attribute to represent the geometrical shape, appearance and position of three types of ground objects i.e. Building, Infrastructure and Terrain in Max, 3ds, FBX and VRML formats.

It is widely accepted that population genetics theory is the cornerstone of evolutionary analyses. Empirical tests of the theory, however, are challenging because of the complex relationships between space, dispersal, and evolution. Critically, we lack quantitative validation of the spatial models of population genetics. Here we combine analytics, on and off-lattice simulations, and experiments with bacteria to perform quantitative tests of the theory. We study two bacterial species, the gut microbe Escherichia coli and the opportunistic pathogen Pseudomonas aeruginosa, and show that spatio-genetic patterns in colony biofilms of both species are accurately described by an extension of the one-dimensional stepping-stone model. We use one empirical measure, genetic diversity at the colony periphery, to parameterize our models and show that we can then accurately predict another key variable: the degree of short-range cell migration along an edge. Moreover, the model allows us to estimate other key parameters including effective population size (density) at the expansion frontier. While our experimental system is a simplification of natural microbial community, we argue it is a proof of principle that the spatial models of population genetics can quantitatively capture organismal evolution.

Spatial regression models have recently received a lot of attention in a variety of fields to address the spatial autocorrelation effect. One important class of spatial models is the Conditional Autoregressive (CA). Theses models have been widely used to analyze spatial data in various areas, as geography, epidemiology, disease surveillance, civilian planning, mapping of poorness signals and others. In this article, we propose the Liu-type pretest, shrinkage and positive shrinkages estimators for the large-scale effect parameter vector of the CA regression model. The set of the proposed estimators are evaluated analytically via their asymptotic bias, quadratic bias, the asymptotic quadratic risks, and numerically via their relative mean squared errors. Our results demonstrate that the proposed estimators are more efficient than Liu-type estimator. To conclude this paper, we apply the proposed estimators to the Boston housing prices data, and applied a bootstrapping technique to evaluate the estimators based on their mean squared prediction error.

1. Multiscale codependence analysis (MCA) quantifies the joint spatial distribution of a pair of variables in order to provide a spatially-explicit assessment of their relationships to one another. For the sake of simplicity, the original definition of MCA only considered a single response variable (e.g. a single species). However, that definition would limit the application of MCA when many response variables are studied jointly, for example when one wants to study the effect of the environment on the spatial organisation of a multi-species community in an explicit manner.
2. In the present paper, we generalize MCA to multiple response variables. We conducted a simulation study to assess the statistical properties (i.e. type I error rate and statistical power) of multivariate MCA (mMCA) and found that it had honest type I error rate and sufficient statistical power for practical purposes, even with modest sample sizes. We also exemplified mMCA by applying it to two ecological data set...

Based on ship-based and aerial line-transect surveys conducted in the U.S. waters of the Gulf of Mexico between 2003 and 2019, the NOAA Southeast Fisheries Science Center (SEFSC) developed spatial density models (SDMs) for cetacean and sea turtle species for the entire Gulf of Mexico. SDMs were developed using a generalized additive modeling (GAM) framework to determine the relationship between...