EcoNicheS: enhancing ecological niche modeling, niche overlap and connectivity analysis using shiny dashboard and R Package

doi:10.21203/rs.3.rs-5096850/v1

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EcoNicheS: enhancing ecological niche modeling, niche overlap and connectivity analysis using shiny dashboard and R Package

https://doi.org/10.21203/rs.3.rs-5096850/v1

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EcoNicheS is a novel, user-friendly tool designed to facilitate ecological niche modeling and species distribution modeling, niche overlap and connectivity analysis and support biodiversity conservation efforts. This R package offers a streamlined workflow for researchers and conservation practitioners to assess habitat suitability and predict species distributions in response to environmental changes. Leveraging the power of R programming and the Shinydashboard, EcoNicheS provides an intuitive interface for data input, model parameterization, and visualization of results. By integrating species occurrence data with environmental variables, users can generate robust predictions of species distributions, aiding in the identification of priority areas for conservation and management actions. EcoNicheS incorporates advanced modeling techniques to account for uncertainty and variability in species-environment relationships, enhancing the accuracy and reliability of predictions. Through a combination of user-friendly features and sophisticated analytical capabilities, EcoNicheS empowers researchers and conservation practitioners to effectively assess and mitigate threats to biodiversity in a rapidly changing world.

Ecological Modeling

Ecological Niche Modeling

Shiny Dashboard

R Package

Habitat Suitability

Species Distribution Modeling

Landscape Connectivity

Niche Overlap

Graphical User Interface (GUI)

Species distribution modeling (SDM) and ecological niche modeling (ENM) have emerged as indispensable methodologies across a wide array of ecological and biogeographical disciplines. These include biogeography, phylogeography, landscape genetics, species range dynamics, diversity pattern analysis, invasion biology, conservation planning, and climate change impact assessment (Sillero et al., 2021; Espindola et al., 2022; Sunny et al., 2022; Franklin, 2023; Rubio-Blanco et al., 2024). These models enable researchers to analyze species distributions in both spatial and temporal dimensions, offering vital insights into the ecological processes and biogeographical mechanisms shaping biodiversity patterns over time and across landscapes (Araujo et al., 2019). By integrating species occurrence data with environmental variables, the SDM/ENM can predict habitat suitability and project future species distributions under various environmental scenarios, including those driven by climate change (Vaissi & Rezaei, 2023; Martinez-Martinez et al., 2024). These approaches have become fundamental for understanding the intricate interactions between species and their environments and are increasingly utilized in conservation planning, invasive species management, climate change adaptation, and disease ecology (Alvarado-Serrano et al., 2013; Escobar, 2020; Franklin, 2023; Sunny et al., 2024). The significance of SDM/ENM lies in their ability to predict where species are likely to occur under current and future conditions, making them invaluable tools for addressing urgent environmental challenges (Sinka et al., 2020; Bates & Bertlesmeier, 2021). In the context of global climate change, these models are crucial for assessing potential shifts in species distributions as a result of changes in temperature, precipitation, and habitat availability (Préau et al., 2020; Santini et al., 2021). Understanding such shifts is essential for guiding conservation strategies aimed at preserving biodiversity (Schwartz, 2012; Hanson et al., 2020; Thuiller, 2024). Moreover, SDMs/ENMs are instrumental in prioritizing areas for protection, restoration, and sustainable management, particularly in regions most likely to support species in the future (Araujo & Guisan, 2006; Wiens et al., 2009). By facilitating the integration of environmental and species data across large spatial and temporal scales, SDMs/ENMs are uniquely suited for tackling large-scale ecological questions and generating actionable insights for resource managers and policymakers (Koerich et al. 2020; Rather 2021). SDMs/ENMs have been extensively used in historical biogeography to reconstruct past species distributions and infer the evolutionary processes that shape current biodiversity patterns (Guimarães et al. 2022; Franklin 2023). These models help identify refugia during past climatic events, such as glaciations, and examine how species respond to historical changes in climate and habitat availability, providing critical information on species diversification and potential future responses to environmental changes (Martinez-Sifuentes et al. 2020; Franklin 2023). Additionally, the SDM/ENM has been applied to explore species interactions, such as niche overlap and competition, shedding light on the ecological and evolutionary factors that drive species coexistence (Pascual-Rico et al. 2020; Mushtaq et al. 2021; Sunny et al. 2024). In conservation biology, SDMs/ENMs are crucial for designing protected areas and managing species at risk from habitat loss, invasive species, and climate change (Arenas-Castro et al. 2021; Sunny et al. 2024). These models help identify key habitats for endangered species, assess the impacts of land-use changes, and evaluate the effectiveness of current conservation strategies (Arenas-Castro et al. 2021; Sillero et al., 2021). For instance, SDMs/ENMs are frequently used to predict the spread of invasive species, facilitating proactive management to mitigate their ecological and economic impacts (Schneider et al. 2021; Ahmed et al. 2022). Moreover, these findings can inform conservationists about potential range shifts under different climate scenarios, enabling the development of climate adaptation strategies that bolster ecosystems and biodiversity resilience (Yang et al. 2022; Zhou et al. 2022; Rubio-Blanco et al., 2024). Despite their broad application, the implementation of SDM/ENM in research and conservation is often hindered by the technical complexity of these models. Traditionally, conducting SDM/ENM analyses requires proficiency in programming languages such as R or Python (Di Cola et al. 2017; Cobos et al. 2019; Kass et al. 2021; Sillero et al. 2023). Researchers often need to utilize multiple packages to perform tasks such as data cleaning, reducing sampling bias, calibrating models, validation, and visualization, each demanding extensive coding expertise (Di Cola et al. 2017; Cobos et al. 2019; Kass et al. 2021; Warren et al. 2021; Sillero et al. 2023). The steep learning curve associated with these processes has limited their use among nonspecialists, such as conservation practitioners, policymakers, and resource managers, who may lack programming experience but require robust and reliable decision-making tools. To overcome these barriers, there is a growing need for software platforms that integrate the entire SDM/ENM workflow into a single, user-friendly interface. These platforms should facilitate access to advanced modeling techniques by reducing technical complexity (Jia et al. 2022) and making SDM/ENM accessible to a broader audience (Osorio-Olvera et al. 2020; Kass et al. 2023). Additionally, intuitive graphical user interfaces (GUIs) and compatibility with GIS software enhance the usability of these tools, allowing users to seamlessly integrate species distribution modeling with spatial data analysis (Osorio‐Olvera et al. 2020; Kass et al. 2023). In response to this need, we introduce EcoNicheS 1.0: https://github.com/armandosunny/EcoNicheS. An innovative R package was designed to streamline the ecological niche modeling process by incorporating a comprehensive suite of tools for data input, preprocessing, model fitting, evaluation, and visualization. Built on the ShinyDashboard framework, EcoNicheS provides a user-friendly interface that simplifies SDM/ENM workflows, removing the need for extensive coding expertise that integrates different analyses using several algorithms that complement other graphical interface applications, such as wallace 2 and ntbox (Osorio‐Olvera et al. 2020; Kass et al. 2023). The platform adheres to best practices for developing, reporting, and evaluating species distribution models, ensuring that users can produce reliable, replicable results (Araújo et al., 2019; Zurell et al., 2020; Sillero et al., 2021a, 2021b). EcoNicheS addresses key challenges in SDM/ENM analyses (Fig. 2 and Table 1), integrating tools for data cleaning, bias reduction, and assessing multicollinearity among environmental variables. SDM/ENM using ensemble modeling facilitated by the biomod2 package. Furthermore, EcoNicheS incorporates niche overlap analysis and ecological connectivity modeling (Fig. 2 and Table 1), making it a versatile tool for tackling complex ecological questions. Its visual and spatial analysis tools allow users to explore model outputs interactively, improving the interpretability and application of results in conservation decision-making. As SDM/ENM methodologies continue to evolve, their applications have expanded beyond predicting species distributions. These areas now encompass broader research areas, including historical biogeography, biome and ecoregion classification and biodiversity pattern analysis (Araujo et al., 2019; Pérez-Consuegra et al. 2023; Luna-Aranguré et al. 2024; Manrique‐Ascencio et al. 2024). Continued development and refinement of tools such as EcoNicheS will be essential for advancing our understanding of species–environment relationships and supporting effective conservation strategies in response to global environmental changes. In this manuscript, we present the key features of the EcoNicheS and provide a comprehensive guide for its installation and use. To demonstrate its practical utility, we conducted a case study on the SDM/ENM and functional connectivity of Tapirus bairdii (the Central American tapir) in the Selva Maya. This case study illustrates how EcoNicheS can generate meaningful insights for species conservation and management. By bridging the gap between complex ecological modeling techniques and accessible tools, EcoNicheS empowers users to contribute to reproducible workflows and effective biodiversity analyses, especially for ecologists and conservation practitioners working in an era of rapid environmental change.

Information Included in Each Module

EcoNicheS, developed with Shiny Dashboard, provides an open-access platform featuring an intuitive interface for creating ecological niches and species distribution models. By streamlining the modeling process, EcoNicheS simplifies the complexity inherent in each step. The platform integrates 12 essential R libraries, enabling users to gather species records, develop ensemble models, evaluate model performance, visualize results, and optionally generate ecological connectivity models. The workflow is structured into 12 modules: Occurrence Processing, Load and Plot Maps (performed twice); Correlation Layers, Points and Pseudo-Absences; SDM/ENM using the biomod2 library; Partial ROC Analysis; Removal of Urbanization Effects; Calculation of Area and Gains/Losses; Niche Overlap Analysis via ENMTools; and Connectivity Analysis (Fig. 2, Table 1). A user manual with screenshots of each module and their results is available, along with the package code and installation instructions at https://github.com/armandosunny/EcoNicheS.

Module 1: Environmental Data

This module facilitates the download and processing of the 19 bioclimatic layers from WorldClim using the geodata (Hijmans et al. 2024) and terra (Hijmans 2024) packages for R, with options to obtain global data or country-specific layers. Moreover, monthly variable layers, including Tmin, Tmax, precipitation, wind, and vapor pressure (vapr), can be accessed. Layers are available at three spatial resolutions: 10°, 5°, 2.5°, and 0.5°. Additionally, the platform allows users to upload custom layers containing variables of interest. The downloaded layers can be clipped using an area of interest defined through an interactive map, by country, or by uploading a mask in .shp or .asc format. The clipped layers are then saved in .asc format.

Module 2: Occurrence Processing

The first step in niche modeling is obtaining occurrence records for the target species. EcoNicheS processes these records in two ways: by accessing GBIF databases using the rgbif (Chamberlain et al. 2024) package for R or by incorporating user-provided data in a comma-delimited text file (.csv), aiming to minimize sampling bias. Bias in SDM/ENM can significantly affect the accuracy and reliability of predictions, particularly when occurrence data are derived from museum collections, heavily sampled areas, easily accessible locations, or, less commonly, systematic sampling (Araujo & Guisan, 2006; Boria et al., 2014; Sillero & Barbosa 2021b). As a result, databases often contain inaccuracies in temporal and geographic information as well as errors in taxonomic identification (Araujo & Guisan, 2006; Boria et al., 2014; Sillero & Barbosa 2021b). This presents a significant challenge in modeling, as spatial bias can lead to inflated performance metrics and potential overfitting (Boria et al., 2014). To improve the quality of the data obtained from the GBIF or provided by the user, EcoNicheS uses the CoordinateCleaner package (Zizka et al., 2020) for R. This package allows the identification and removal of temporal and spatial errors, such as records assigned to country or province centroids, urban areas, open oceans, or those containing atypical or invalid coordinates. Additionally, to reduce sampling bias and remove duplicate records, EcoNicheS can select a single occurrence per pixel of the environmental layers (e.g., one record per 1 km²). For spatial filtering of species presence records, the platform uses the spThin package (Aiello-Lammens et al. 2015) in R, which decreases the density of records in widely sampled areas, thereby reducing spatial bias in the models (Aiello-Lammens et al., 2015). During this process, users can visualize the points downloaded from the GBIF or uploaded by the user on an interactive map and observe how many records remain after the data cleaning. The resulting cleaned dataset was subsequently downloaded in .csv format and was ready for use in Module 5 for the addition of pseudoabsences.

Module 3: Load and Plot Maps

This module is designed for visualization purposes. Users can load files in .asc format for a specific area of interest, view the data with the raster package (Hijmans 2023) for R, and save the resulting map as a .pdf image. It is particularly useful for displaying variables intended for use in constructing SDM/ENMs. Additionally, this module allows zooming into the area of interest for a more detailed examination of the map with the leaflet (Cheng et al. 2024) and leaflet.extras (Gatscha et al. 2024) packages for R.

Module 4: Correlation layers

Correlations between environmental layers can significantly affect species distribution modeling, potentially leading to multicollinearity (Graham, 2003; Feng, 2019a) or model overfitting to the training data. The inclusion of correlated variables can also result in redundant or overly complex models (Dormann et al., 2013). Therefore, assessing variable collinearity is crucial for reducing the number of layers, enhancing model efficiency, and simplifying interpretation. Various strategies exist to evaluate the acceptable degree of correlation between layers. In this module, users can set a correlation threshold between 0 and 1 before performing correlation analysis. To assess the collinearity of environmental layers, EcoNicheS calculates the Pearson correlation coefficient and generates a correlation matrix using a heatmap, illustrating the degree of collinearity between layers. Additionally, collinearity issues can be identified through variance inflation factor (VIF) analysis, which quantifies the increase in variance of the regression coefficient caused by collinearity using the usdm package (Naimi et al., 2014). Layers with a VIF greater than 10 were excluded from further analysis (Naimi et al., 2014).

Module 5: Background Points and Pseudoabsences

In species distribution modeling, pseudoabsences are generated to sample the environmental space of a region (Phillips et al., 2009; Whitford, 2024). Various methods exist for generating these points, with the most prevalent being the use of numerous random samples across the study area or M (Araújo et al., 2019; Valavi et al., 2022) to ensure representation of all environmental conditions within the region of interest (Valavi et al., 2022). The EcoNicheS platform includes a module for generating pseudoabsences using the dismo (Hijmans et al. 2023) package for R, allowing users to create points that are randomly distributed within a user-defined geographical area. It is advisable to select a sufficient number of background points to provide comprehensive coverage of the accessible geographic range of the species (Merow et al., 2013; Franklin, 2023). However, users should be aware that an increased number of background samples will increase computational demands (Valavi et al., 2022). The module also produces a comma-separated values (.csv) file, which can be used for further data manipulation by other R packages or spreadsheet editors. This dataset was formatted for integration with Module 6, where it can be employed in SDM/ENM analysis using the biomod2 (Thuiller et al. 2024) package. Initially, a basic map illustrating the distribution of the generated pseudoabsences is shown, which can be downloaded as a PDF. Below this, an interactive map is available, allowing for a visual assessment of the generated database stored in your work directory. This interactive map displays both the original presence points and the added pseudoabsence points. In the .csv database, points are identified by the "response" value associated with each coordinate: a value of 1 denotes presence points, while a value of 0 corresponds to pseudoabsence points. Prior to running the analysis, the name of the generated database was modified from the default, while the .csv extension was retained to confirm proper creation and saving.

Module 6: SDM/ENMs via biomod2

A variety of algorithms are available for performing SDM/ENM, each using different methodologies. Ensemble models, which combine multiple algorithms, can enhance predictive accuracy by compensating for the weaknesses of individual approaches. EcoNicheS facilitates the creation, calibration, and evaluation of these ensemble models by streamlining the process through an intuitive interface. The tool employs the biomod2 platform, which supports species distribution modeling via 12 correlative modeling techniques: generalized linear models (GLM), generalized additive models (GAM), generalized boosting models (often called boosted regression trees; GBM), classification tree analysis (CTA), artificial neural networks (ANN), surface range envelope or BIOCLIM (SRE), flexible discriminant analysis (FDA), multiple adaptive regression splines (MARS), random forest (RF), and maximum entropy (MAXENT) using the MIAmaxent (Vollering et al. 2019) R package MAXNET and eXtreme Gradient Boosting (XGBOOST) (Huang et al., 2023). Users can select multiple techniques to execute analyses, depending on the specific problem and available data, as each technique has its own strengths and weaknesses. EcoNicheS also incorporates model evaluation metrics available in biomod2, offering two types of analysis: goodness-of-fit (e.g., ANOVA, AIC) and model accuracy, which includes calculating metrics such as the area under the curve (AUC), Cohen's kappa, and the true skill statistic (TSS). The TSS is defined as {1 - the maximum value of (sensitivity + specificity)}, where sensitivity and specificity are calculated based on the probability threshold that maximizes their sum. TSS values range from − 1 to + 1, with values close to + 1 indicating perfect agreement between the observations and predictions, while values of 0 or lower suggest that the model performance is no better than random (Allouche et al., 2006; Franklin, 2009). Similarly, the kappa statistic ranges from − 1 to + 1, where + 1 represents high prediction consistency and values of zero or lower indicate performance equivalent to random chance (Thuiller et al., 2009). At the end of the process, users can obtain graphical representations of each model's response curves, evaluation metrics, and results. Additionally, the module generates maps in .tiff format and deposits them in a designated folder, allowing easy access to the necessary files (Fig. 3) for the subsequent steps in the workflow (Fig. 2).

Module 7: Load and Plot Maps

This module enables the visualization of potential distribution maps generated in the EcoNicheS biomod2 module. Users can save these visualizations as images in .pdf format. It is also useful for displaying the environmental variables used in the creation of SDMs/ENMs. Additionally, this module includes a zoom function, allowing users to focus on specific areas of interest for a more detailed examination of the map.

Module 8: Partial ROC Analysis

The receiver operating characteristic (ROC) curve has long been the gold standard for evaluating model performance in species distribution modeling, primarily due to its intuitive interpretation: it indicates the probability that the model ranks a random presence higher than a random absence. However, when reliable absence data are unavailable, the probabilistic interpretation of area under the curve (AUC) values becomes problematic, necessitating a reconsideration of model comparisons in species distribution modeling (Jiménez and Soberón, 2020). Partial ROC analysis, a threshold-independent evaluation proposed by Peterson et al. (2008), addresses this issue by focusing on specific subsectors of the ROC space through the setting of an acceptable true positive error threshold (E). The pROC method evaluates the relationship between the omission error for independent points and the proportion of the area predicted to be suitable for the species, but this approach is valid only under conditions of low omission error. The AUC ratios (the partial AUC divided by random expectations) range from 0 to 2, with a value of 1 indicating random performance (Peterson, 2012; Peterson et al., 2008). This approach enhances model evaluation by avoiding artificially low AUC values, particularly in situations involving presence-only data where conventional ROC analysis and AUC interpretation can be misleading. EcoNicheS facilitates model performance assessment by calculating the partial ROC curve using the pROC (Robin et al. 2011) and ntbox (Osorio-Olvera et al., 2020) packages. This module allows users to employ a raster of continuous values and compare them with species presence points following the method proposed by Peterson et al. (2008).

Module 8: Remove Urbanization

This module allows users to exclude urbanized areas, or other features of interest, from the species distribution model. This is done by utilizing a raster layer containing urbanized areas within the region of interest using the raster package. The process is conducted using the raster package, which enables users to quantify the distribution area or ecological niche of a species while disregarding urban zones with potentially adverse environments. The module also allows users to visualize the generated map and save it in .asc format.

Module 9: Calculate the area

This module calculates the area of suitability (in km²) for the species of interest using the raster package. Users can upload the .asc file created in the previous module or use an existing file. The suitability threshold value can be adjusted according to the specific requirements of the study. The output consisted of the area of suitable habitat displayed in the main panel of the tab.

Module 10: Gains and Losses Plot

This module requires two raster files in .asc format. The first file represents the geographic data or environmental characteristics of the area of interest, while the second file contains predictions for future landscape conditions or another map of interest. The module enables users to visualize areas of change using the raster package via the raster package, allowing analysis of potential gains or losses in species distribution between two time points. Users can also download the generated maps for further use.

Module 11: Niche Overlap Analysis via ENMTools

Niche overlap occurs when coexisting species share portions of their ecological niche. According to ecological and evolutionary displacement models, species should exhibit less niche overlap due to competition (Schoener, 1974). Low niche overlap, indicating differential resource utilization, is crucial for the coexistence of syntopic species and promotes biodiversity (Pianka, 1974). EcoNicheS utilizes the ENMTools package (Warren & Dinnage 2024) to quantify the similarity between. It employs measures such as Schoener’s D (1968) and Hellinger’s distance-based metric ‘I’ (Warren et al., 2008). Additionally, we used a new metric, env. I, env. D, and env.cor (Warren et al. 2021), these new metrics are used in the n-dimensional space of all combinations of environmental variables instead of restricting the measures of model similarity to those sets of conditions that appear in the training region. These metrics compare habitat suitability estimates across grid cells using Maxent-generated SDMs/ENMs. The SDMs/ENMs are normalized so that the suitability scores sum to 1 within the geographic space. Additionally, ENMTools provides other similarity measures, including Spearman’s rank correlation coefficient, and enables hypothesis testing related to equivalence, similarity, and environmental barriers to species distributions using linear, blob, and ribbon rangebreak tests (Glor and Warren, 2011). Linear and blob tests are two versions of a test that allow us to assess whether the geographic regions occupied by two species are more environmentally different than expected by chance. Moreover, the ribbon test is designed to test whether the ranges of two species are divided by a region that is relatively unsuitable for one or both forms.

Module 12: Ecological Connectivity

Landscape connectivity refers to the degree to which an environment facilitates or impedes the movement of organisms between different locations (Taylor et al., 1993; Tischendorf and Fahrig, 2000). In EcoNicheS, users can generate ecological connectivity models by creating flow maps. The connectivity analysis uses habitat suitability as a resistance surface and employs conductance analysis through the gdistance package (van Etten, 2017). Species occurrence data and a resistance raster, which may contain species distribution model data or landscape elements with resistance values, are needed. The resulting flow maps integrate least-cost path analysis, such as Circuitscape-type analyses, to evaluate potential connectivity (Baecher, 2024), identifying the shortest possible paths between locations within the area of interest.

Empirical example: the distribution and connectivity of Tapirus bairdii in the Selva Maya.

We present an example of implementing EcoNicheS through a case study on the Central American tapir (Tapirus bairdii) in the Selva Maya, a concept developed by ecological actors to conserve tropical rainforests in the border region of Mexico, Guatemala, and Belize (Laako et al., 2022). We modeled the species distribution in this area, demonstrating the effectiveness of EcoNicheS in processing bioclimatic layers, recording occurrences, modeling, and evaluating the model. Additionally, we analyzed the ecological niche overlap between T. bairdii and Tayassu pecari, both of which have experienced significant population declines due to overhunting and deforestation (Reyna-Hurtado et al., 2008, Falconi-Briones et al. 2022). To construct the SDM/ENM, we gathered occurrence data from the GBIF and supplemented it with our own records. The dataset was cleaned using the "Clean My Own database" module of EcoNicheS, where we removed occurrences located within 5 km of each other. This process resulted in a total of 356 occurrence points, which aligned with the movement range of the Central American tapir in the region (Reyna-Hurtado et al. 2012; Rivero et al. 2022). Next, we assessed the correlations among the environmental layers using the "Correlation layers" module, obtaining Pearson correlation values and the variance inflation factor (VIF). We excluded environmental layers with a VIF greater than 8, and the final layers used for modeling were bio3 (isothermality), bio4 (temperature seasonality), bio6 (minimum temperature of the coldest month), bio7 (annual temperature range), bio9 (mean temperature of the driest quarter), bio15 (precipitation seasonality), and bio18 (precipitation of the warmest quarter). Subsequently, we employed the "Points and pseudoabsences" module to generate 100,000 background points, enhancing coverage of the study area (Valavi et al. 2022). The SDM/ENM was developed using the biomod2 module, model calibration was conducted using the block strategy, and evaluation metrics included kappa, TSS, and ROC, with four repetitions and a model selection threshold of 0.4 (Fig. 4A), with an ensemble model combining the Maxent, GLM, and RF algorithms (Fig. 4B, 4C, 4D). We also explored the niche overlap between Tapirus bairdii and Tayassu pecari using the "Niche Overlap Analysis" module in ENMTools (Fig. 5). Hypothesis tests for niche identity or equivalence were performed following Warren et al. (2021). These tests included three hypothesis tests—Niche Identity and background, symmetric, and asymmetric similarity tests—along with the rangebreak test (Fig. 6). The tests were conducted using Maxent with four replicates. Additionally, we assessed the functional connectivity of the Central American tapir (Fig. 7) using the "Ecological Connectivity" module. For this analysis, we utilized the previously generated species distribution model from biomod2 and processed the raster file with inverted values through the QGIS 3.22 "invert raster" tool. The connectivity model was constructed using 80% of the occurrence data for training and the remaining 20% for testing. The resulting raster was processed in QGIS to visualize the connectivity between PAs within the region for the Central American lowland tapir. Additional examples of EcoNicheS applications include several articles published during its early development stages (Sunny et al. 2023; Martinez-Martinez et al. 2024; Rubio-Blanco et al. 2024).

One of the core principles behind the development of EcoNicheS is to provide an accessible, user-friendly tool for ecological niche modeling, allowing users to bypass the need for building models from scratch (DeAngelis et al., 2021). The intuitive interface, built on the ShinyDashboard framework, facilitates smooth navigation through complex modeling processes. This ease of use is particularly advantageous for practitioners unfamiliar with programming languages such as R, which are commonly employed in SDM/ENM workflows. By simplifying the modeling workflow, EcoNicheS lowers the technical barriers that have previously limited the adoption of SDM/ENM tools among researchers from diverse disciplines (DeAngelis et al., 2021). EcoNicheS offers a comprehensive suite of tools for researchers and conservation professionals involved in species ecology, biodiversity conservation, and biogeography. Its user-friendly interface, combined with the ability to perform multiple analyses, makes it an invaluable resource for conservationists, ecologists, and decision makers. EcoNicheS facilitates access to advanced modeling techniques, empowering users without extensive programming experience to engage in SDM/ENMs while adhering to best practices for replicable and reliable models (Aráujo & Guisan, 2006; Feng et al., 2019; Sillero et al., 2021b). The platform integrates a wide range of analytical techniques to enhance the accuracy, reliability, and reproducibility of species distribution models (Feng et al., 2019). By utilizing key R libraries and packages, EcoNicheS streamlines tasks such as obtaining and cleaning species occurrence data, evaluating model performance, and visualizing results. The use of ensemble modeling techniques further improves predictive accuracy by combining the strengths of multiple algorithms, addressing individual weaknesses, and generating more robust predictions. EcoNicheS addresses key challenges faced in SDM/ENM applications, such as managing data bias, selecting appropriate environmental variables, and producing reliable predictions. Providing an intuitive workflow facilitates the creation of high-quality models, in line with recommendations from leading experts in the field (Sillero et al., 2021b). Sampling bias is a significant issue in most species occurrence datasets, complicating the development of accurate SDMs/ENMs (Cuervo et al., 2023). EcoNicheS tackles data quality issues through the integration of the CoordinateCleaner package, which automatically filters out erroneous records from online databases, thereby reducing the time required for manual data cleaning (Zizka et al., 2019). To further mitigate sampling bias, EcoNicheS incorporates the SpThin package, which thins species presence records, particularly in oversampled regions, while preserving the maximum amount of valuable information (Aiello-Lammens et al., 2015). Collinearity among predictor variables reduces model efficiency and increases uncertainty, leading to inflated variance, biased estimates, and challenges in interpreting variable effects (Dormann et al., 2013; De Marco & Nóbrega, 2018). EcoNicheS addresses this by incorporating a module to assess correlation between environmental layers, allowing users to avoid multicollinearity and overfitting by calculating the Pearson correlation coefficient and variance inflation factor (VIF), ensuring that each variable contributes meaningfully to the model. Species distribution models are crucial for predicting future species distributions based on current ecological niche constraints, particularly as climate change accelerates. Understanding how suitable habitats may shift in response to climate change is a key objective of SDM/ENMs (Wiens et al., 2019; Aráujo et al., 2019). EcoNicheS assists in this research by providing insights into potential distribution changes, enabling the identification of priority areas for conservation and management. This approach is essential for developing strategies to mitigate biodiversity threats in a rapidly changing world. The integration of SDMs/ENMs with climate models strengthens biogeographic hypotheses and supports decision-making by conservationists and resource managers (Wiens et al., 2009). EcoNicheS supports multiple modeling techniques through the biomod2 package, enabling users to select the most appropriate methods for accessing their data. A key feature of biomod2 is its ability to combine multiple predictions into a single-ensemble model. In EcoNicheS, this functionality allows the integration of various modeling algorithms, along with three statistical evaluation metrics: the ROC-based AUC, Cohen’s kappa, and the TSS. The integration of biomod2 tools (Thuiller et al., 2009, 2024) provides access to robust techniques for model development and validation (Sillero et al., 2023). The software's ensemble modeling capabilities significantly enhance predictive accuracy, making it an indispensable tool for biodiversity conservation and ecological hypothesis testing. Additionally, EcoNicheS incorporates the partial receiver operating characteristic (partial-ROC) method to evaluate model performance, particularly addressing issues related to low-performing algorithms and high omission rates (De Marco & Nóbrega, 2018). At the conclusion of the modeling process, models are evaluated using the partial-ROC approach (Peterson et al., 2008; Osorio-Olvera et al., 2020), providing a more nuanced assessment of model accuracy.

Comparison with Similar Tools

EcoNicheS offers a comprehensive suite of tools that include advanced modeling, niche overlap analysis, and ecological connectivity functions. In comparison to similar applications, EcoNicheS offers several unique features (Table 2). For example, while the Niche Toolbox (Osorio-Olvera et al., 2020) provides niche analysis capabilities, it lacks more advanced functions, such as pseudoabsence generation, all the niche modeling analyses included in biomod2, interactive map visualization, urbanization filters, and ecological connectivity tools. Similarly, Wallace 2 (Kass et al., 2023) and SDMapp (Hema et al., 2021) offer SDM/ENM functions but are primarily restricted to niche modeling via Maxent. ENMTML (Andrade et al., 2020) excels in terms of the variety of algorithms and ensemble modeling methods but lacks robust visual and spatial analysis tools. Overall, the EcoNicheS meets broader and different sets of ecological modeling needs for researchers.

Empirical case

A case study of the Central American tapir (Tapirus bairdii) in the Selva Maya demonstrated the practical application of EcoNicheS. The workflow, which was aligned with best practices in data cleaning, thinning, modeling, and evaluation, was followed. The results from biomod2 indicated that the random forest (RF) algorithm outperformed Maxent in terms of statistical performance and accuracy (Fig. 4A, B). The final ensemble model highlighted greater environmental suitability for T. bairdii in southeastern Selva Maya, particularly between the protected areas of Belize and northern Guatemala (Fig. 4C), covering a total area of 98,282.1 km² (Fig. 4D). An analysis of the niche overlap between T. bairdii and Tayassu pecari (Fig. 5) revealed no evidence to support the rejection of the null hypothesis that the niches of both species are identical, with Schoener’s D = 0.2 and Hellinger’s I = 0.2. The rank correlation test for environmental variables showed no significant differences, consistent with the niche similarity test results (Env D = 1.0, Env I = 1.0) and the symmetric and asymmetric background tests (Fig. 5B, C). The connectivity analysis conducted using gdistance (van Etten, 2017) identified regions within the Selva Maya region with the highest connectivity potential for tapirs, particularly in Belize and northern Guatemala (Fig. 7). These regions form a crucial corridor linking PAs in both countries. Conversely, lower connectivity was observed among tapir populations in Mexico, particularly in the southern Yucatán Peninsula (Fig. 7).

EcoNicheS offers a powerful and accessible platform for conducting ecological niche modeling, incorporating various algorithms, visualizations, and functionalities. By bridging the gap between advanced analytical techniques and user-friendly interfaces, EcoNicheS empowers researchers and conservation practitioners to perform comprehensive ENM/SDM analyses. As ecological challenges continue to evolve, tools such as EcoNicheS will play an increasingly important role in guiding biodiversity conservation efforts and fostering innovation in ecological research. Furthermore, EcoNicheS will continue to be updated, adding new analyses and functionalities to meet emerging research needs.

Acknowledgments

We are grateful to the editor and two anonymous reviewers for their comments. This work was supported by the Secretary of Research and Advanced Studies (SYEA) of the Universidad Autónoma del Estado de México (grants to AS: 4732/2019CIB and 6801/2022CID), SEP (grant to AS: PRODEP 511/2022–5401) and CONACYT (grant to AS: FOINS 6828/2017). A.S: Adahy Olun Contreras-García, fuimos y estamos siendo víctimas de violencia vicaria, te extraño muchísimo, ya no se si te voy a poder ver, pero buscame y te mostrare todas las evidencias de como nos separaron.

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Table 1 Analyses performed by each module, along with corresponding citations and packages used.

Module		Analyses	Packages	References
Module 1	Environmental data	Download and Process WorldClim Bioclimatic Layers (Bios, Tmin, Tmax, Tavg, Wind, Vapr)	geodata, terra, countrycode, rgeos	Fick & Hijmans, 2017
Module 2	Occurrence Processing	Obtain and Process Presence Records of the Species of Interest. Detect and Correct Errors in Databases. Reduce Sampling Bias.	rgbif, user data, rgeos, CoordinateCleaner, countrycode, SpThin	Aiello-Lammens et al., 2015, Zizka et al., 2020
Module 3	Load and Plot Maps	Load and visualize an .asc File of the Study Area. View and Save an Image in .pdf Format.	leaflet, leaflet.extras, raster
Module 4	Correlation Layers	Calculate the Pearson Correlation Coefficient. Create a Correlation Matrix. Calculate the Variance Inflation Factor (VIF) and Exclude Layers with VIF > 10.	usdm	Naimi et al. 2014
Module 5	Points and Pseudoabsences	Generate Random Pseudo-Absence Points within the Study Area.	dismo
Module 6	biomod2	Develop SDM/ENM Models, GLM, GAM, GBM, CTA, ANN, BIOCLIM, SRE, FDA, MARS, RF, MAXENT, MAXNET, XGBOOST. Evaluate SDM/ENM Models using metrics such as ANOVA, AIC, AUC, Cohen’s Kappa, and TSS. Obtain Response Curves, Results, Metrics, and Maps in .tiff format.	biomod2, MIAmaxent	Thuiller et al., 2009
Module 7	Load and Plot Maps	View SDM/ENM Maps Generated in the biomod2 Module, with options to zoom in and download as PDF.	leaflet, leaflet.extras, raster
Module 8	Partial ROC Analysis	Evaluate SDM/ENM Model Performance Using Partial ROC Analysis.	ntbox	Peterson et al. 2018, Osorio-Olvera et al. 2020
Module 9	Remove Urbanization	Extract Urban Areas from the SDM/ENM or Other Characteristics.	raster	Hijmans, 2015
Module 10	Calculate Area	Determine the Habitat Suitability Area (km²) for the Species of Interest. Load an .asc File and Adjust the Suitability Threshold Value.	raster	Hijmans, 2015
Module 11	Gains and Losses Plot	Compare Changes in Potential Distribution Maps Across Different Time frames or SDM/ENM Models.	raster	Hijmans, 2015
Module 12	Niche Overlap Analysis via ENMTools	Compare Landscape Conditions. Quantify Similarity Between SDM/ENM Using Schoener’s D, Hellinger’s I, env D,env I and envcor, and linear, blob, and ribbon rangebreak tests. Compare Habitat Suitability Estimates. Conduct Equivalence and Similarity Tests.	ENMTools	Warren et al. 2010
Module 13	Functional Landscape Connectivity	This phrasing clearly describes the tasks of generating connectivity models and creating flow maps with specified methods.	gdistance	van Etten, 2017

Table 2. Comparisons of various analyses performed by different packages for ecological niche modeling and species distribution modeling (SDM/ENM).

Analyses	EcoNicheS	Wallace	ENMTML	SDMapp	Niche Toolbox
Platform	Shiny dashboard for SDM/ENM	Shiny dashboard for SDM/ENM	R package with Shiny interface	Shiny dashboard for SDM/ENM	Shiny for SDM/ENM
Occurrence Data Processing	GBIF API integration and user-provided CSV, spatial filtering with SpThin, data cleaning with CoordinateCleaner	GBIF data import, data cleaning through built-in methods	No direct GBIF integration; user-provided data; background point generation	User-provided data; automatic download from GBIF	GBIF data integration, custom upload, filtering, and bias correction
Environmental Data	19 bioclimatic layers from WorldClim, monthly variables (Tmin, Tmax, Precipitation), user-uploaded layers	19 bioclimatic layers from WorldClim, customizable layers	19 WorldClim variables, optional user-provided layers	19 bioclimatic variables from WorldClim or other climate datasets	WorldClim variables, user-uploaded layers, dynamic environmental layers from multiple sources
Layer Correlation Analysis	Pearson correlation matrix, Variance Inflation Factor (VIF) and Heatmap	Pearson correlation matrix, manual selection	Not available	Variance Inflation Factor (VIF) implemented	Pearson correlation matrix, user-defined thresholds for variable selection
Pseudoabsence/Background Points	Randomly generated Pseudoabsence points, export to CSV	Generates pseudo-absence/background points via Maxent or random	Automated pseudoabsence points generation	User-defined pseudo-absence/background generation	Automated generation of background points or pseudoabsences
Modeling Algorithms	Integration with biomod2, includes GLM, GAM, GBM, CTA,ANN, BIOCLIM, SRE, FDA, MARS, RF, MAXENT, MAXNET, XGBOOST.	Includes Maxent, Bioclim, Random Forest, BRT, Domain	Maxent, SVM, GLM, RF, GAM, GARP, BioClim	Maxent, Random Forest, Boosted Regression Trees (BRT)	Maxent, GARP, BIOCLIM, SVM, Random Forest
Ensemble Modeling	Available through biomod2, combines multiple algorithms	Not available	Available	Not available	Not available
Model Evaluation	ROC, AUC, Partial ROC, TSS, Kappa, Boyce Index.	ROC, AUC, TSS, Kappa	ROC, AUC, TSS, Kappa	ROC, AUC, TSS	ROC, AUC, Partial ROC, Boyce Index, TSS
Map Visualization	Interactive map visualization maps in .asc format, save in .pdf	Map viewer for distribution maps	.asc file visualization	Real-time map visualization with .asc export	Interactive map visualization and downloading in .tiff, .asc
Urbanization Filter	Module for removing urbanized areas from models	Not available	Not available	Not available	Not available
Area Calculation	Calculate suitable area in km² for species	Area calculation based on occurrence points	Area calculations based on presence	Basic area calculations	Area calculations based on habitat suitability thresholds
Gains and Losses Analysis	Comparison of present and future distribution maps	Not available	Not available	Not available	Not available
Niche Overlap Analysis	Uses ENMTools for calculating niche overlap, Schoener’s D, Hellinger’s I, env D,env I and envcor, and linear, blob, and ribbon rangebreak tests	Niche overlap Schoener’s D, Hellinger’s I with ecospat	Not available	Not available	Niche overlap and identity tests using Schoener’s D, I, and similarity metrics
Ecological Connectivity Analysis	Resistance and flow maps using gdistance	Not available	Not available	Not available	Not available
Output Formats	CSV, .tiff, .asc, .pdf	CSV, .tiff, .pdf	CSV, .tiff	CSV, .tiff	CSV, .tiff, .asc, .pdf

The authors declare no competing interests.

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EcoNicheS: enhancing ecological niche modeling, niche overlap and connectivity analysis using shiny dashboard and R Package

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Abstract

Figures

Introduction

Materials and Methods

Information Included in Each Module

Module 1: Environmental Data

Module 2: Occurrence Processing

Module 3: Load and Plot Maps

Module 4: Correlation layers

Module 5: Background Points and Pseudoabsences

Module 6: SDM/ENMs via biomod2

Module 7: Load and Plot Maps

Module 8: Partial ROC Analysis

Module 8: Remove Urbanization

Module 9: Calculate the area

Module 10: Gains and Losses Plot

Module 11: Niche Overlap Analysis via ENMTools

Module 12: Ecological Connectivity

Discussion

Comparison with Similar Tools

Empirical case

Conclusion

Declarations

Acknowledgments

References

Tables

Additional Declarations

Status:

Version 1