DOI: 10.5061/dryad.fbg79cp7n
title: Data from: Seascape ecology of juvenile gadoid nursery areas

description: Availability of juvenile fish habitat provision can impact recruitment.
 This study focused on identifying which environmental variables
 characterise the juvenile habitats of three commercially
 important gadoid species: Atlantic cod (Gadus morhua), haddock
 (Melanogrammus aeglefinus), and whiting (Merlangius merlangus). Stereo
 baited remote underwater video surveys were conducted in the
 South Arran Marine Protected Area between 2013 and 2019 to collect
 presence/absence data on juvenile gadoids (&gt; 20 mm &lt; 120 mm)
 and demersal and epibenthic communities. Data were analysed using binomial
 generalised additive mixed models. The results revealed spatial
 segregation among species, each favouring distinct habitats. Predictive
 modelling suggests a substantial increase in presence probability from
 0.25 to 0.75 as the Inverse Simpson's Diversity Index increases,
 suggesting that biodiversity appears to be associated with species
 distribution. Boundary regions between seabed types were associated with
 variation in species distribution, underlining the importance of seascape
 heterogeneity. This study underscores the importance of conserving and
 restoring benthic and epibenthic biodiversity across spatially
 heterogeneous landscapes. Consequently, reducing benthic pressures could
 promote early survival for these species, thereby supporting broader
 ecosystem health and fisheries management goals.
description: # Data from: Seascape ecology of juvenile gadoid nursery areas [Access
 this dataset on Dryad](https://www.doi.org/10.5061/dryad.fbg79cp7n) This
 dataset contains presence/absence data and environmental variables for
 three commercially important juvenile gadoid species: Atlantic cod (*Gadus
 morhua*), haddock (*Melanogrammus aeglefinus*), and whiting (*Merlangius
 merlangus*), collected using stereo baited remote underwater video (SBRUV)
 surveys in the South Arran Marine Protected Area, Scotland, between 2013
 and 2019. The data were used to model species distribution using binomial
 generalised additive mixed models and predict nursery habitat suitability.
 Results revealed that biodiversity was the only variable consistently
 associated with presence across all three species, with each species
 showing distinct habitat preferences related to depth, seabed composition,
 and proximity to habitat patch edges. ## Description of the data and file
 structure The dataset consists of two main data files and one R Markdown
 analysis script: **gadoid.metadata.2.csv** - Primary dataset containing
 species presence/absence data and environmental variables for 488 SBRUV
 deployment sites. Each row represents one SBRUV deployment with associated
 environmental measurements and species observations. **Gadoids.rmd** - R
 Markdown file containing all statistical analyses, model fitting, and
 figure generation code used in the manuscript. ### Variable descriptions
 for gadoid.metadata.2.csv: **Species presence data:** * *G_morhua*:
 Presence (1) or absence (0) of juvenile Atlantic cod (*Gadus morhua*)
 20-120 mm * *M_aeglefinus*: Presence (1) or absence (0) of juvenile
 haddock (*Melanogrammus aeglefinus*) 20-120 mm * *M_merlangus*: Presence
 (1) or absence (0) of juvenile whiting (*Merlangius merlangus*) 20-120 mm
 **Spatial and temporal variables:** * *Latitude*: Decimal degrees (WGS84)
 * *Longitude*: Decimal degrees (WGS84) * *Year*: Survey year (2013, 2014,
 2018, 2019) * *Month*: Survey month (6-9, corresponding to June-September)
 * *MPA_Zone*: Marine Protected Area management zone (1-5), used as random
 effect in models * *Depth_m*: Water depth at deployment site (metres) *
 *Distance_to_shore_km*: Euclidean distance from deployment site to nearest
 shoreline (kilometres) **Seabed composition (proportional coverage 0-1):**
 * *Mud*: Proportional coverage of mud substrate * *Sand*: Proportional
 coverage of sand substrate * *Gravel*: Proportional coverage of gravel
 substrate (includes both dead and live maerl) * *Pebble*: Proportional
 coverage of pebble substrate * *Algae*: Proportional coverage of attached
 macroalgae of all types **Physical environment:** *
 *Mean_current_velocity_ms*: Mean current velocity (metres per second) *
 *TRI*: Terrain Ruggedness Index, elevation differences between grid points
 (metres) **Biodiversity:** * *Inverse_Simpsons_Diversity*: Inverse
 Simpson's diversity index calculated from epibenthic and demersal
 fish and macroinvertebrate species counts (1/D = N(N-1)/∑(n/N)) **Distance
 to habitat patch edges (metres):** * *Distance_to_sand_edge_m*: Euclidean
 distance to edge of sand patches (positive = within patch, negative =
 outside patch) * *Distance_to_mud_edge_m*: Euclidean distance to edge of
 mud patches * *Distance_to_algae_edge_m*: Euclidean distance to edge of
 algae patches * *Distance_to_gravel_edge_m*: Euclidean distance to edge of
 gravel patches **Missing data codes:** NA indicates missing or unavailable
 data for that variable at that site. **Data processing notes:** * Seabed
 composition was assessed visually from SBRUV footage and classified using
 standardised categories * Distance measurements use positive values for
 locations within habitat patches and negative values for locations outside
 patches * Focal gadoid species analysis included only juveniles &gt; 20
 &lt; 120 mm to confirm 0-group status * Survey sites deeper than 40m were
 excluded to maintain consistent spatial coverage ## Sharing/Access
 information **Data was derived from the following sources:** * SBRUV
 survey data: University of Glasgow and Marine Scotland Science
 collaborative surveys * Seabed mapping: NatureScot and Scottish
 Environmental Protection Agency drop-down video surveys * Bathymetry: 1
 arc-second resolution data * Current velocity data: Modelled mean current
 data from Sabatino et al. (2016) * Phylogenetic data: Mammalian supertree
 from VertLife database (where applicable) **Links to related datasets:** *
 Seascape mapping data and methods:
 [https://github.com/NWMilne/Seascape_mapping](https://github.com/NWMilne/Seascape_mapping) ## Code/Software The analysis was conducted entirely in R version 4.4.1. The R Markdown file (Gadoids.rmd) contains all code for: 1. **Data loading and cleaning**: Initial data processing and quality control 2. **Statistical analysis**: Binomial generalised additive mixed models (GAMMs) using the `gamm4` package 3. **Model validation**: Area under curve (AUC), percent correctly classified (PCC), and confusion matrix calculations using `PresenceAbsence` and `caret` packages 4. **Spatial prediction**: Species distribution mapping using 100m grid predictions 5. **Figure generation**: All manuscript figures using `ggplot2` **Required R packages:** * `gamm4`: Generalised additive mixed models * `PresenceAbsence`: Model performance metrics * `caret`: Classification and regression training * `ggplot2`: Graphics and visualization * `dismo`: Species distribution modelling (for MESS analysis) * `automap`: Spatial interpolation (for error mapping) **Key methodological details:** * Survey methodology: SBRUV deployments with 60-minute recording time, 3-minute settlement period * Camera setup: Paired Canon HF G25 cameras with 8° inward angle, 58 cm basal separation * Species identification: Stereo measurements for size confirmation (20-120 mm for 0-group status) * Spatial autocorrelation: Addressed using MPA zones as random effects in GAMMs * Model selection: Backward stepwise selection using AIC **External data dependencies:** Several raster and shapefile inputs used in this analysis were generated in our previous seascape mapping project. Instructions and metadata for downloading these files can be found in the [Seascape_mapping README](https://github.com/NWMilne/Seascape_mapping). These files should be placed in a `data/` subdirectory to run the full analysis. **Reproducibility notes:** To reproduce the analysis: 1. Download the CSV data file 2. Install required R packages listed above 3. Download external raster/shapefile dependencies from the Seascape_mapping repository 4. Run the R Markdown file to reproduce all analyses and figures The analysis workflow progresses through data preparation, collinearity checking, model fitting with backward stepwise selection, model validation, spatial prediction mapping, and figure generation. All code is annotated with explanatory comments throughout.