Ecological Informatics

Data analysis is one of the most relevant steps of aquatic benthic environmental monitoring and research studies, and should be a fundamental consideration in both the planning (i.e., defining appropriate sampling design strategies) and implementation phases (application of appropriate standardized sampling procedures). A common objective of these studies is to identify relationships between environmental stressors and benthic bioindicator metrics. However, assessing these relationships is a complex process. Multivariate regression model adjustment coupled with forward and backward model selection routines is an appropriate complementary statistical analysis tool to test for the existence of statistically significant associations between a non-autocorrelated biological response and each variable within a group of environmental covariates included in a model. With this in mind, we developed SDesti, a user-friendly R package to analyze benthos data (number of individuals, biomass, chlorophyll concentration, or biological indices, excluding beta diversity metrics). SDesti contains four user accessible functions. AnalysisDescriptives() and Estimation() give information on the quality, homogeneity and representativeness of the data for one sampling campaign for one site. TimeLineAnalysisDescriptives() performs the descriptive analysis that usually precedes the adjustment of a regression model. TimeLineAnalysis() automatically adjusts an adequate regression model (linear, Poisson, quasipoisson, or negative binomial) and also returns the necessary measures and graphics to evaluate the quality of the adjustment and verify the model assumptions. SDesti greatly simplifies the process of data analysis and can be easily used by non-statisticians. The analytical package includes a complete manual that provides detailed information: on the data structure requirements, on the variable nomenclature rules and program operating procedures, on the data analysis (complemented with examples) and on the interpretation of the results (type ??SDesti on R console). SDesti eliminates redundancy, reduces human error and, coupled with a suitable sampling design, standard sampling and sample treatment procedures, it contributes to improve the consistency of the results in environmental studies. SDesti binary for windows users and installation instructions can be found below. Compiled for R 4.3.2 version. Refer to the program PDF manual for a detailed description of the data structures, functions, data analyses and interpretation of results. Type ??SDesti on R, or RStudio consoles and select the PDF file. Note: RStudio 2023.09.1 has a bug that delivers an error message when trying to open PDF vignettes (program manuals). Use R 4.3.2 console to open SDesti's PDF manual.

This compressed file contains the following data sets from an ensemble prediction with two different methods of selecting pseudo-absence data sets (SRE, 2 degree) and eight different methods of transforming numerical prediction into binary predictions. (1) Figure 2: Model accuracy for numerical prediction of random forests regression (RT) and classification (CT) algorithms. (2) Figure 3: Optimal threshold and model accuracy for binary predictions produced by eight threshold-selecting methods. (3) Figure 4: Spatial correspondence (as judged by the first axis of principal component analysis) among binary predictions produced by eight threshold approaches. (4) Figure 5: Spatial correspondence in binary predictions (as judged by McNemar tests) for pairwise among threshold approaches. (5) Table 1: Species range shifts predicted by classification (CT) and regression (RT) algorithms of random forests. (6) Table S1_Ecological requirements, biological characteristics and niche properties for the 52 tree species. (7) Table S2_Species range shifts estimated basing on numerical prediction of RT. (8) Species distribution maps for 52 forest trees (Raw data file, Species distribution maps). (9) Supplementary figures and tables. (10) R codes & R functions used in the study.

Training data and test data for Habitat-Net. Test data results for segmentation or canopy and understory images processed: (1) manually by two observers, (2) using a simple thresholding script in python, (3) using U-Net, and (4) using Habitat-Net.

raster map necessary to run the model

Equations of the netlogo kudzu model template