The analysis of spatial point patterns has greatly advanced our understanding of ecological processes. However, the methods currently available for analyzing replicated spatial point patterns (RSPPs) are rarely used by ecologists. One barrier to the use of RSPP analyses is a lack of software to implement the approaches that have been developed in the statistical literature. Here, we provide a practical guide to RSPP analysis and introduce the RSPPlme4 R package that implements the approaches we discuss. The methods we outline use a linear modeling framework to link variation in the spatial structure of point patterns to discrete and continuous explanatory covariates. We describe methods for linear models and mixed-effects models of RSPPs, including approaches to estimating confidence intervals via semi-parametric bootstrapping. The syntax for model fitting is similar to that used in linear and linear mixed-effects modeling packages in R. The RSPPlme4 package also allows users to easily plot the results of model fits. We hope that this tutorial will make methods for RSPP analysis accessible to a wide range of ecologists and open new avenues for gaining insight into ecological processes from spatial data.

Paper package (PDF, data, R scriot) for: Kliegl, R., Wei, P., Dambacher, M., Yan, M., & Zhou, X. (2011). Experimental effects and individual differences in Linear Mixed Models: Estimating the relationship between spatial, object, and attraction effects in visual attention. Frontiers in Quantitative Psychology and Measurement, 1. Abstract. Linear mixed models (LMMs) provide a still underused methodological perspective on combining experimental and individual-differences research. Here we illustrate this approach with two-rectangle cueing in visual attention (Egly, Driver, & Rafal, 1994). We replicated previous experimental cue-validity effects relating to a spatial shift of attention within an object (spatial effect), to attention switch between objects (object effect), and to the attraction of attention towards the display centroid (attraction effect), taking also into account the design-inherent imbalance of valid and other trials. We simultaneously estimated variance/covariance components of subject-related random effects for these spatial, object, and attraction effects in addition to their mean RTs. The spatial effect showed a strong positive correlation with mean RT and a strong negative correlation with the attraction effect. The analysis of individual differences suggests that slow subjects engage attention more strongly at the cued location than fast subjects. We compare this joint LMM analysis of experimental effects and associated subject-related variances and correlations with two frequently used alternative statistical procedures.

The methods for compiling the occurrence data, performing species distribution modeling, and conducting GapAnalysis for these taxa are described in full in Khoury et al. (2019a, b). In short, occurrence data were drawn from biodiversity and conservation repository databases and from the authors’ own botanical explorations. Duplicates and non-wild records were removed, taxonomic names were standardized, and records were classified as H (reference) or G (ex situ sample) as per the definitions described in this paper (see Input data). Species distributions were produced with the MaxEnt algorithm (Phillips et al. 2017) using 26 climatic and topographic predictors (Jarvis et al, 2008, Fick and Hijmans 2017) at a spatial resolution of 2.5 arc-minutes, employing a subset of variables as well as number and location of pseudo-absences specific to each taxon. The occurrence data, models, and full results are available at Khoury et al. (2019c, d). A GapAnalysis R tutorial for three of the&nbs...

The shared files provide data and codes to support the paper, which is titled "Geospatial Constrained Optimization to Simulate and Predict Spatiotemporal Trends of Air Pollutants". The library files provde necessary R and Python functions to support the extrapolation and geospatial constrained optimization. Specific explanations are given in these files. Please ensure that the following libraries are installed:R packages: rgdal, sp, raster, spatialEco, SpatioTemporal (https://cran.r-project.org/web/packages/SpatioTemporal/index.html), sptemExp (https://cran.r-project.org/web/packages/sptemExp/index.html).Python packages: pandas, numpy, tensorflow and keras. Please take the following steps for the proposed method: step1_basisfuncs.R: this tutorial aims to explain how to generate temporal basis functions based on KNN and geospatial basis functions. step2_basisfuncs_extrapolation.py: this tutorial aims to explain how to use CNN to extrapolate temporal basis functions. step3_constrained_opt.R: this tutorial aims to explain how to use constrained optimization. step4_indepdendenttest_PM25.R: this is to illustrate how to use geospatial constraint optimization for location-based cross validation of PM2.5. step4_indepdendenttest_NO2x.R:this is to illustrate how to use geospatial constraint optimization for location-based cross validation of NO2 and NOx. This also illustrates how to use the other variable (e.g., NOx) to constrain the target variable (NO2).

Le script R nommé clapas.R permet d'utiliser l'algorithme de Clapas tel que conçu par Jean-Marc Robbez Masson dans sa thèse de 1994 : "Reconnaissance et délimitation de motifs d'organisation spatiale, application à la cartographie des pédopaysages". Il s'agit d'une suite de fonctions qui rassemblent tous les éléments de l'algorithme de Clapas. Cette suite est livrée avec un tutoriel et un jeu de données test, disponible sur demande, pour s'approprier son utilisation. The R code named clapas.R makes it possible to use the Clapas algorithm as designed by Jean-Marc Robbez Masson in his 1994 thesis: "Recognition and delimitation of spatial organization patterns, application to soil landscape mapping". This is a suite of functions that bring together all the elements of the Clapas algorithm. This suite comes with a tutorial and a test dataset, available on request, to learn how to use it.

Main article:

Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in Black American and White American patients with TNBC

There are three cohorts of IMC data: BSW-Discovery, BSW-Validation, and Roswell Park.

Raw Data Imaging Mass Cytometry (IMC):

BSW-Discovery IMC data (MCD files): TMA-1.mcd, TMA-2.mcd, TMA-4.mcd, TMA-6.mcd, TMA-7.mcd, TMA-8.mcd, TMA-9.mcd, TMA-10.mcd, where TMAs 7, 8, 10, 4 denote White American, and TMAs 1, 2, 6, 9 denote Black American.

BSW-Validation IMC data (MCD files): T1-EA1.mcd, T2-EA2.mcd, T5-EA5.mcd, T6-EA6.mcd, T7-EA7.mcd, T9-AA1.mcd, T10-AA2.mcd, T14-AA6.mcd, T16-AA8.mcd, T17-AA9.mcd, where AA denotes Black American and EA denotes White American.

Roswell Park IMC data (MCD files): B1Ca57b.mcd (all Black American), B1Ca71b.mcd (all White American)

Roswell.excluded.ROI.txt: list of ROIs being excluded and their reasons. Important as this Roswell TMA contains non-breast control tissues.

Processed Data Imaging Mass Cytometry (IMC):

roswell_deepCell.RDS: A Giotto object containing the raw expression, scaled expression, and cell centroid.

roswell.processing.R: An R script showing how the roswell_deepCell.RDS is derived

roswell.raw.expr.txt: Roswell raw cell-by-protein intensity matrix quantified by Steinbock

roswell.cell.centroid.txt: X- and Y-coordinates of cells in Roswell, quantified by Steinbock

roswell_panel.csv: Panel.csv file used for cell segmentation by Steinbock

bsw_validation_deepCell.RDS: A Giotto object containing the raw expression, scaled expression, and cell centroid.

bsw_validation_processing.R: An R script showing how the bsw_validation_deepCell.RDS is derived

bsw_validation_raw_expr.txt: BSW validation raw cell-by-protein intensity matrix quantified by Steinbock

bsw_validation_cell_centroid.txt: X- and Y-coordinates of cells in BSW validation, quantified by Steinbock

bsw_validation_panel.csv: Panel.csv file used for cell segmentation by Steinbock

bsw_discovery_cytof_test.RData: A Giotto object containing the scaled expression and cell centroid for BSW-Discovery cohort

bsw_discovery_cell_centroid.txt: X- and Y-coordinates of cells in BSW-discovery

bsw_discovery_zscored_expr.txt: Z-scored cell-by-protein intensity matrix quantified by Histocat

bsw_discovery_processing.R: processing script

Data processing information:

BSW-Discovery: Segmentation and quantification was done using Histocat.

BSW-Validation & Roswell Park: Segmentation and quantification was done using Steinbock pipeline, choosing DeepCell as segmentation tool.

Other File list:

GSM_giotto_processed.tar.gz: Bassiouni et al Cancer Research TNBC ST datasets, Giotto normalized and processed

cytof_test.aug13.RData: An RData object for the BSW-discovery IMC dataset, used for generating Figure 1e, Figure 1f, i.e., the clustering analysis. Tutorial referring to this analysis is available at: https://qianzhulab.github.io/suppl/TNBC.scripts/table_of_content.html.

cytof_test.RData: An RData object for the BSW-discovery IMC dataset, used for generating Figure 1d, i.e., the clustering analysis. Tutorial referring to this analysis is available at: https://qianzhulab.github.io/suppl/TNBC.scripts/table_of_content.html.

Additional file 2: The hands-on R tutorial.

GAM-NICHE is a new tool developed by AZTI (Valle et al. 2023) to build Species Distribution Models (SDMs) under the ecological niche theory (Citores et al. 2020). It provides a GitHub tutorial in R language with an application to marine fish.

Species Distribution Models (SDMs) are numerical tools that combine observations of species occurrence or abundance at known locations with information on the environmental and/or spatial characteristics of those locations (Elith and Leathwick 2009). SDMs are widely used as a tool for understanding species spatial ecology and are also known as ecological niche models (ENM) or habitat suitability models.

According to ecological niche theory, species response curves are unimodal with respect to environmental gradients (Hutchinson 1957). While a variety of statistical methods have been developed for species distribution modelling, a general problem with most of these habitat modelling approaches is that the estimated response curves can display biologically implausible shapes which do not respect ecological niche theory. This is because species response curves are fit statistically with any assumption or restriction, which sometimes do not respect the ecological niche theory. To better understand species response to environmental changes, SDMs should consider theoretical background such as the ecological niche theory and pursue the unimodality of the response curves with respect to environmental gradients.

This book provides a tutorial on how to use Shape-Constrained Generalized Additive Models (SC-GAMs) to build SDMs under the ecological niche theory framework (Citores et al. 2020). SC-GAMs impose monotonicity and concavity constraints in the linear predictor of the GAMs and avoid overfitting. SC-GAM is an effective alternative to fitting nonsymmetric parametric response curves, while retaining the unimodality constraint, required by ecological niche theory, for direct variables and limiting factors.

The book is organised following the key steps in good modelling practice of SDMs (Elith and Leathwick 2009). First, presence data of a selected species are downloaded from GBIF/OBIS global public datasets and pseudo-absence data are created. Then, environmental data are downloaded from public repositories and extracted at each of the presence/pseudo-absence data points. Based on this dataset, an exploratory analysis is conducted to help deciding on the best modelling approach. The model is fitted to the dataset and the quality of the fit and the realism of the fitted response function are evaluated. After selecting a threshold to transform the continuous probability predictions into binary responses, the model is validated using a k-fold approach. Finally, the predicted maps are generated for visualization.

The code is available in AZTI’s github repository and the book is readily available. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)

To cite the book, please use:

Valle, M., Citores, L., Ibaibarriaga, L., Chust, C. (2023) GAM-NICHE: Shape-Constrained GAMs to build Species Distribution Models under the ecological niche theory. AZTI. https://doi.org/10.57762/fzpy-6w51

References Citores, L, L Ibaibarriaga, DJ Lee, MJ Brewer, M Santos, and G Chust. 2020. “Modelling Species Presence–Absence in the Ecological Niche Theory Framework Using Shape-Constrained Generalized Additive Models.” Ecological Modelling 418: 108926. https://doi.org/10.1016/j.ecolmodel.2019.108926.