Dataset: Habitat scale model outputs Port Fourchon, LA during Fall 2022

Name: Habitat scale model outputs Port Fourchon, LA during Fall 2022
Published: 2025-01-08
Keywords: oceans

View Data: Data not available yet

Cite This Dataset

Spatial Extent: N:29.164671 E:-90.149744 S:29.092646 W:-90.269831

Marshes surrounding Port Fourchon, Louisiana.

Temporal Extent: 2022-09-23 - 2022-09-29

Project:

CAREER: Integrating Seascapes and Energy Flow: learning and teaching about energy, biodiversity, and ecosystem function on the frontlines of climate change (Louisiana E-scapes)

Principal Investigator:

James Nelson (University of Louisiana at Lafayette)

Student:

Herbert Leavitt (University of Louisiana at Lafayette)

Alexander Thomas (University of Louisiana at Lafayette)

Contact:

Herbert Leavitt (University of Louisiana at Lafayette)

BCO-DMO Data Manager:

Karen Soenen (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Version:

Version Date:

2025-01-08

Restricted:

Validated:

Current State:

Data not available

Drone habitat variables, Port Fourchon, 2022

Abstract:

This dataset and code form part of a broader analysis aimed at evaluating the relationship between habitat structure and species abundance across multiple spatial scales in a rapidly changing estuarine environment near Port Fourchon, Louisiana. Specifically, the code implements a Generalized Additive Modeling (GAM) approach to identify the optimal spatial scale at which habitat features—derived from satellite imagery—best explain the abundance of common estuarine species observed during the Fall 2022 drop sampling season. The data processing pipeline begins by merging species count data and environmental variables (salinity, temperature, site coordinates) with spatial habitat metrics, including percent edge habitat, mangrove edge length, and land-water ratio. These metrics are calculated at varying spatial scales, defined by buffer radii (20–600 m) and edge distances (1, 3, 5 m). The GAMs iteratively test combinations of predictors while excluding highly correlated variables to reduce multicollinearity. Models are ranked by Akaike Information Criterion (AIC), and the best models are selected based on performance across scales. The outputs include: AIC scores for all tested models across scales. Identification of the top model explaining white shrimp abundance. Evaluation of individual predictor significance and spatial autocorrelation in residuals. The results indicate that the relationship between habitat structure and estuarine species is oftne scale-dependent, with percent edge habitat and mangrove edge length emerging as significant predictors at specific scales. Outputs are saved in CSV files for model summaries and GAM diagnostics, while visualizations illustrate R² values across spatial scales, predictor significance, and observed vs. predicted species abundance. This pipeline provides a quantitative framework for identifying ecologically relevant spatial scales and assessing the effects of habitat change, such as mangrove encroachment, on species distributions. The findings contribute to a broader effort to model species-habitat relationships in coastal systems and inform management strategies in the face of climate-driven habitat change.

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Methods & Sampling

No raw data is included in this dataset. For collection methods of data used in this analysis, please refer to methods outlined in the linked datasets.

Data Processing Description

The data processing pipeline for this analysis begins with the preparation of input datasets, which include habitat data derived from satellite imagery and community composition data. The habitat data files, such as google2022_edge[edge]_buf[buffer].csv, contain spatial metrics including mangrove edge length, land-water ratios, and percent edge cover. The community composition data, PtFouSept2022count_ns.csv, provides site-specific counts for the target species (eg. Litopenaeus setiferus, abbreviated as PENSET) along with environmental variables like salinity, temperature, and geographic coordinates. These datasets are merged using a common identifier (site_date_key) to align spatial and ecological information.

Once merged, all predictor variables, such as % mangrove edge and % land-water ratio, are standardized by subtracting the mean and dividing by the standard deviation to ensure consistent scaling. A correlation matrix is computed to identify and exclude highly correlated predictor pairs (correlation > 0.8) to reduce multicollinearity in subsequent models. The core modeling process is conducted by the model_compile function, which iterates over combinations of buffer radii (100–600 m or satscale, 20-150 for smallscale) and edge distances (1, 3, 5 m) to assess species-habitat relationships. For each combination, spatial metrics are calculated, and generalized additive models (GAMs) are fitted using subsets of predictors. Knots for all GAM models were limited to k=4 to try and limit over fitting. Models with correlated variables are excluded, and the remaining models are ranked by Akaike Information Criterion (AIC). The best models within two AIC points of the top model are recorded, capturing their formula, AIC, buffer, and edge values.

The top model is then identified based on its frequency of selection across all scales and is saved for further analysis. Using this top model, the univariate_edge function evaluates individual predictor performance across scales. Residuals are examined for spatial autocorrelation using Moran’s I test, and the significance of predictors, including their interactions (e.g., mangrove edge × land-water ratio), is assessed. These results are consolidated and saved in outputs_over_scales.csv.

Visualization and diagnostics follow, with R² values plotted across buffer sizes and edge distances to identify the habitat scale with the strongest explanatory power. Key predictors, such as mangrove edge and percent edge cover, are visualized in relation to species abundance, with significance indicated by custom point shapes (determined using threshold of p<0.05). At the best scale, relationships between key variables and predicted species abundance are visualized, while residual and diagnostic plots (e.g., Cook’s distance) are used to assess model fit. Predictions from the top model are compared to observed counts to validate performance.

The final outputs of this pipeline include several CSV files and visualizations. The output_over_scales.csv file summarizes model results across scales, including R² values, p-values, and significant predictors. The species_aic.csv file captures AIC scores for all tested models, and topmodel.txt records the formula of the top-selected model. Visualizations include plots of R² across scales and relationships between predictors and species abundance, as well as diagnostic plots for residuals and influential observations. This comprehensive pipeline rigorously evaluates the influence of spatial metrics on species abundance, identifying the optimal habitat scale for common in Port Fourchon, LA.

This analysis was performed in R version 4.3.2. Relevant packages in this analysis are as follows:

mgcv_1.9-0, nlme_3.1-163, gam_1.22-4, foreach_1.5.2, spdep_1.3-5, sf_1.0-16, spData_2.3.1, lubridate_1.9.3, forcats_1.0.0, stringr_1.5.1, dplyr_1.1.4, purrr_1.0.2, readr_2.1.4, tidyr_1.3.0, tibble_3.2.1, ggplot2_3.5.1, tidyverse_2.0.0, scales_1.3.0, statmod_1.5.0, pscl_1.5.9

loaded via a namespace (and not attached): gtable_0.3.4, lattice_0.21-9, tzdb_0.4.0, vctrs_0.6.5, tools_4.3.2 generics_0.1.3, proxy_0.4-27, fansi_1.0.5, pkgconfig_2.0.3, Matrix_1.6-1.1, KernSmooth_2.23-22, lifecycle_1.0.4 compiler_4.3.2, farver_2.1.1, deldir_2.0-4, munsell_0.5.0, codetools_0.2-19, class_7.3-22, pillar_1.9.0 crayon_1.5.2, MASS_7.3-60, classInt_0.4-10, wk_0.9.1, iterators_1.0.14, boot_1.3-28.1, tidyselect_1.2.1, stringi_1.8.2 labeling_0.4.3 , grid_4.3.2, colorspace_2.1-0, cli_3.6.1, magrittr_2.0.3, utf8_1.2.4, e1071_1.7-14, withr_3.0.2, sp_2.1-3 timechange_0.2.0, hms_1.1.3, viridisLite_0.4.2, s2_1.1.6, rlang_1.1.2, Rcpp_1.0.11, glue_1.6.2, DBI_1.1.3 rstudioapi_0.15.0, R6_2.5.1, units_0.8-5

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