EcoNeTool - Ecological Interaction Network Explorer

Functional Groups

● Benthos - Bottom-dwelling organisms

● Detritus - Organic matter

● Fish - Fish species

● Phytoplankton - Primary producers

● Zooplankton - Small drifting organisms

Ecological Interaction Network Explorer

Quick Start

Food Web Network: Explore the interactive network visualization
Topological Metrics: View structural properties of the food web
Biomass Analysis: Examine biomass-weighted metrics
Energy Fluxes: Analyze energy flow patterns

Click on the sidebar menu items to navigate!

🌊 Welcome to EcoNeTool - Marine Food Web Network Analysis Tool

Interactive Shiny Dashboard for analyzing trophic interactions, biomass distributions, and energy fluxes in marine ecosystems

📊 Analysis Features

🕸️ Network Visualization

Interactive Graphs: Force-directed and trophic-level layouts with zoom/pan
Color Coding: By functional groups (Benthos, Detritus, Fish, Phytoplankton, Zooplankton)
Node Sizing: Proportional to species biomass
Edge Weights: Interaction strength or energy flux visualization

📈 Topological Metrics

trophiclevels() - Iterative calculation of trophic positions
get_topological_indicators() - Network structure analysis (S, C, G, V, TL, Omnivory)
Species Richness: Total number of taxa in the food web
Connectance: Proportion of realized trophic links
Generality & Vulnerability: Mean prey/predator counts

⚖️ Biomass-Weighted Analysis

get_node_weighted_indicators() - Quantitative metrics accounting for biomass
Node-Weighted Connectance: Biomass-adjusted link density
Node-Weighted Generality/Vulnerability: Importance-scaled feeding patterns
Biomass Distributions: Size spectrum and functional group analysis

⚡ Energy Flux Analysis

calculate_losses() - Metabolic losses via allometric scaling
Brown et al. (2004) 'Toward a metabolic theory of ecology', Ecology
get_fluxweb_results() - Energy flux calculations using metabolic theory
Gauzens et al. (2019) 'fluxweb: An R package to easily estimate energy fluxes in food webs', Methods in Ecology and Evolution
fluxind() - Link-weighted indicators (lwC, lwG, lwV) via Shannon diversity
Bersier et al. (2002) 'Quantitative descriptors of food web matrices', Ecology
Temperature-Adjusted: T=3.5°C for Gulf of Riga conditions
Gillooly et al. (2001) 'Effects of size and temperature on metabolic rate', Science
Flux Units: kJ/day/km² with log-scale visualization
Barnes et al. (2018) 'Energy flux: The link between multitrophic biodiversity and ecosystem functioning', Trends in Ecology & Evolution

🔑 Keystone Species Identification

calculate_mti() - Mixed Trophic Impact matrix (ECOPATH method)
calculate_keystoneness() - Keystoneness index (impact/biomass ratio)
Species Classification: Keystone, Dominant, or Rare
Impact Visualization: Heatmaps showing direct & indirect effects
Reference: Libralato et al. (2006), Ecological Modelling

🎨 Visualization Functions

plotfw() - Food web plotting with trophic level layout
Interactive Networks: visNetwork integration with hover tooltips
Static Plots: Publication-quality figures with ggplot2-style
Export Options: Download plots and data tables

📊 Current Dataset

Source: Gulf of Riga food web (Frelat & Kortsch, 2020)
Location: Baltic Sea, Gulf of Riga
Period: 1979-2016 (37 years)
Taxa: 34 species across 5 functional groups
Links: 207 trophic interactions
Data Type: Temporally-averaged food web

🚀 Getting Started

Explore the Network: Navigate to Food Web Network tab to see interactive visualization
View Metrics: Check Topological Metrics for structural properties
Analyze Biomass: Visit Biomass Analysis for node-weighted indicators
Energy Flows: Examine Energy Fluxes for metabolic theory-based calculations
Find Keystones: Use Keystoneness Analysis to identify key species
Import Data: Upload your own food web via Data Import tab

📝 Version 1.4.2 - Local Databases Integration + Performance & Robustness
Released: 2025-12-26
• 4 new database integrations (SeaLifeBase, freshwaterecology.info, AlgaeBase, SHARK)
• Detailed progress tracking for automated trait lookup
• Dedicated Automated Lookup tab with comprehensive database info
• Centralized version management system

Scientific Foundations: Brown et al. (2004) • Bersier et al. (2002) • Libralato et al. (2006) • Williams & Martinez (2004) • Gauzens et al. (2019)

Export Current Metaweb

Export the currently loaded metaweb to RDA format (compatible with BalticFW.Rdata structure).

File Name (without extension):

Export as RData

Format: Frelat & Kortsch (BalticFW.Rdata compatible)

Exported File Structure:

net: igraph network object with trophic interactions
info: species data frame with:
- species, functional group (fg), biomass (meanB)
- body masses, metabolic types, efficiencies
- taxon, organism type, metabolic losses

The exported file can be re-imported using 'Upload Your Data' or loaded with load('my_metaweb.Rdata') in R.

Upload Your Data

Choose File (Excel, CSV, or RData)

Browse...

Maximum file size: 10 MB

Data Validation

Data Requirements:

✓ Species names must match between network and info
✓ Network must be square (same row/column names)
✓ Biomass values must be positive
✓ Functional groups should be consistent
✓ Losses and efficiencies: 0-1 range
✓ At least 3 species recommended

After Upload:

Once your data is loaded, all analysis tabs will automatically use your uploaded data.

Reset to Default:

Refresh the page to reload the default Gulf of Riga dataset.

Import ECOPATH Native Database

ECOPATH Database File

Browse...

Add EMODnet Habitat Data (EUNIS codes, substrate, depth zones)

Enter sampling location coordinates:

Latitude (°N)

Longitude (°E)

Default: Gdansk Bay

Import ECOPATH CSV/Excel Exports

Alternative: Import Exported Files

Upload CSV/Excel exports from ECOPATH. Recommended for Windows users.

1. Basic Estimates File (.xlsx, .csv)

Browse...

2. Diet Composition Matrix (.xlsx, .csv)

Browse...

ECOPATH Export Guide

Required Files from ECOPATH

1. Basic Estimates

Export: File → Export → Basic Estimates

Required columns:

Group name - Species/group name
Biomass - Biomass (t/km²)
P/B - Production/Biomass ratio
Q/B - Consumption/Biomass ratio

2. Diet Composition

Export: File → Export → Diet Composition

Matrix format:

Rows = Prey species
Columns = Predator species
Values = Diet proportion (0-1)

Supported File Formats

1. Excel Format (.xlsx, .xls)

Excel files should contain the following sheets:

Sheet 1: Network (Adjacency Matrix)

A square matrix where rows and columns represent species:

	Species_B	Species_C
Species_A	1	0
Species_B	0	1
Species_C	0	0

Value = 1 means Species A eats Species B (row → column)

Sheet 2: Species_Info

Species attributes (one row per species):

species	fg	meanB	losses	efficiencies
Species_A	Fish	1250.5	0.12	0.85
Species_B	Zooplankton	850.2	0.08	0.75
Species_C	Phytoplankton	2100.0	0.05	0.40

Required Columns:

species: Species name (must match network row/column names)
fg: Functional group (e.g., Fish, Benthos, Phytoplankton, Zooplankton, Detritus)
meanB: Mean biomass (g/km² or your preferred unit)
losses: Metabolic losses (J/sec) for flux calculations
efficiencies: Assimilation efficiencies (0-1) for flux calculations

Optional Columns:

bodymasses: Average body mass (g)
taxon: Taxonomic classification
nbY: Number of years recorded

2. CSV Format (.csv)

Two CSV files required:

File 1: network.csv (Adjacency Matrix)

species,Species_A,Species_B,Species_C
Species_A,0,1,0
Species_B,0,0,1
Species_C,0,0,0

File 2: species_info.csv

species,fg,meanB,losses,efficiencies
Species_A,Fish,1250.5,0.12,0.85
Species_B,Zooplankton,850.2,0.08,0.75
Species_C,Phytoplankton,2100.0,0.05,0.40

3. RData Format (.Rdata, .rda)

R workspace containing two objects:

net: igraph object with food web network
info: data.frame with species information (columns as above)

Example R code to create:

library(igraph)

# Create adjacency matrix
adj_matrix <- matrix(c(0,1,0, 0,0,1, 0,0,0), nrow=3, byrow=TRUE)
rownames(adj_matrix) <- colnames(adj_matrix) <- c('Species_A', 'Species_B', 'Species_C')

# Create network
net <- graph_from_adjacency_matrix(adj_matrix, mode='directed')

# Create species info
info <- data.frame(
  species = c('Species_A', 'Species_B', 'Species_C'),
  fg = factor(c('Fish', 'Zooplankton', 'Phytoplankton')),
  meanB = c(1250.5, 850.2, 2100.0),
  losses = c(0.12, 0.08, 0.05),
  efficiencies = c(0.85, 0.75, 0.40)
)

# Save
save(net, info, file='my_foodweb.Rdata')

Example Datasets

1. Simple 3-Species Chain

Perfect for testing - basic linear food chain

3 species (Phytoplankton → Zooplankton → Fish)
2 trophic links
Ideal for learning the format

Download RData Network CSV Info CSV

2. Caribbean Reef

Realistic tropical reef food web

10 species across 4 functional groups
18 trophic interactions
Multiple trophic levels

Download RData Network CSV Info CSV

3. Empty Template

Start from scratch with proper structure

3 placeholder species
Correct file format
Modify for your own data

Download RData Network CSV Info CSV

How to Use Example Files:

Download one of the example RData files above
Upload it using the file input above
Click 'Load Data' button
Explore the food web in other tabs

File Formats:

RData: Ready to upload directly to EcoNeTool
CSV files: Open in Excel to view/modify structure (2 files needed: network + info)

See the examples/README.md file for detailed format documentation.

Interactive Food Web Network

Basal Species

Top Predators

Adjacency Matrix Heatmap

Topological Indicators (Qualitative Metrics)

These metrics describe the structural properties of the food web network without considering node weights (biomass).

S: Species richness (number of taxa)
C: Connectance (proportion of realized links)
G: Generality (mean number of prey per predator)
V: Vulnerability (mean number of predators per prey)
ShortPath: Mean shortest path length
TL: Mean trophic level
Omni: Omnivory index (mean SD of prey trophic levels)

Calculated Metrics

Food Web with Biomass-Scaled Nodes

Interactive network visualization with node sizes representing biomass (logarithmic scaling). Nodes are arranged hierarchically by precise trophic level: highest trophic levels at the top, lowest at the bottom. Minor differences in trophic level are visible through vertical positioning.

Biomass Distribution by Functional Group

Box plot showing biomass distribution across functional groups.

Biomass Percentage by Functional Group

Relative contribution of each functional group to total biomass.

Node-weighted Indicators (Quantitative Metrics)

These metrics account for the relative importance of species based on their biomass.

nwC: Node-weighted connectance - weighted measure of network connectivity
nwG: Node-weighted generality - average number of prey per predator, weighted by biomass
nwV: Node-weighted vulnerability - average number of predators per prey, weighted by biomass
nwTL: Node-weighted mean trophic level - average trophic position weighted by biomass

Energy Flux Analysis

Energy fluxes are calculated using metabolic theory of ecology. Fluxes represent biomass flow between species based on allometric scaling and temperature-adjusted metabolic rates (T=3.5°C, Gulf of Riga spring conditions).

Units: kJ/day/km²

Note: Flux values span many orders of magnitude (10^-10 to 10^-1), reflecting the wide range of interaction strengths in the food web.

Flux-weighted Network

Edge widths proportional to energy flux magnitude. Hover over edges to see exact values.

Flux Matrix Heatmap (Log-transformed)

Color intensity shows log-transformed flux values. Darker colors indicate stronger energy flows.

Link-weighted Flux Indicators

Shannon diversity indices calculated from energy flux distributions.

Flux diversity: Distribution evenness of energy flows
Effective number of fluxes: Equivalent number of equally strong flows

Keystoneness Analysis (ECOPATH Method)

Identifying Keystone Species

Keystoneness analysis identifies species with disproportionately large effects on ecosystem structure and function relative to their biomass. This analysis follows the ECOPATH methodology using Mixed Trophic Impact (MTI) calculations.

Key Concepts:

Mixed Trophic Impact (MTI): Measures direct and indirect effects of one species on all others
Overall Effect: Sum of absolute MTI values (total ecosystem impact)
Keystoneness Index: Ratio of impact to biomass (high values = keystone species)

Species Classifications:

Keystone: High impact, low biomass (KS > 1, biomass < 5% of total)
Dominant: High impact, high biomass (KS > 0, biomass ≥ 5% of total)
Rare: Low impact, low biomass

Reference: Libralato et al. (2006). Ecological Modelling, 195(3-4), 153-171.

Keystoneness Index Rankings

Species ranked by keystoneness index (highest to lowest)

Keystoneness vs Biomass Plot

Keystone species appear in upper-left (high impact, low biomass)

Mixed Trophic Impact (MTI) Heatmap

How to read:

Rows = Impacted species
Columns = Impacting species (impactor)
Red = Negative impact (impactor decreases impacted)
Blue = Positive impact (impactor increases impacted)
Values represent net effect through direct and indirect pathways

Top Keystone Species Details

Internal Data Editor

Edit Internal Datasheets

This tab allows you to directly edit the two main internal datasheets:

Species Information: Species attributes including biomass, functional groups, body masses, and metabolic parameters
Network Adjacency Matrix: The food web structure showing who eats whom

Note: Changes are applied in real-time. Use the 'Update Network' button to refresh all visualizations after editing.

Edit species attributes. Double-click cells to edit values.

Required columns: meanB, fg, bodymasses, met.types, efficiencies

Column Descriptions (click to expand)

species: Species name or identifier
meanB: Mean biomass of the species (g/km²)
fg: Functional group (Fish, Benthos, Phytoplankton, Zooplankton, Detritus)
bodymasses: Average body mass of individual organism (grams)
met.types: Metabolic type (invertebrates, ectotherm vertebrates, Other)
efficiencies: Assimilation efficiency (0-1, proportion of consumed energy assimilated)
taxon: Taxonomic classification
nbY: Number of years recorded in the dataset
losses: Metabolic losses (J/sec) calculated from body mass and temperature
org.type: Organism type classification

Edit the food web structure. Values should be 0 (no interaction) or 1 (predator eats prey).

Tip: Hover over species names (underlined) to see their role in the food web.

Rows = Predators, Columns = Prey. Value of 1 in row i, column j means species i eats species j.

Metaweb Manager

Regional Metaweb Management

MARBEFES WP3.2 Phase 2: Assemble and manage regional metawebs for ecological interaction network analysis.

A metaweb contains all documented species and trophic interactions in a region, serving as the basis for extracting local food webs.

Load: Import pre-built regional metawebs or custom CSV files
View: Visualize metaweb structure and browse species/interactions
Edit: Add/remove species and trophic links
Quality: Assess link evidence quality (1-4 scale)
Export: Download metawebs in CSV or RDS format

Pre-built Regional Metawebs

Load metawebs from published literature sources (MARBEFES guidance):

Select Region:

Note: Some metawebs require download from literature sources. See metawebs/README.md for details.

Import Custom Metaweb

Upload your own metaweb from CSV files:

Species CSV File:

Browse...

Interactions CSV File:

Browse...

Use template files in metawebs/ folder as examples.

Current Metaweb Summary

Metaweb Network Visualization

Interactive visualization of all species and trophic interactions in the metaweb:

Species List

All species in the metaweb:

Trophic Interactions

All documented feeding links:

Add Species

Species ID:

Species Name:

Functional Group:

Remove Species

Select Species:

Remove associated links

Add Trophic Link

Predator:

Prey:

Link Quality:

Source (citation/DOI):

Notes:

Remove Trophic Link

Predator:

Prey:

Edit Status

Link Quality Distribution

Quality Summary Table

Link Quality Guidelines

MARBEFES Quality Code Definitions:

1: Documented - Peer-reviewed literature for these exact species
Example: Gut content analysis published for this predator-prey pair
2: Similar species - Documented for similar species or different region
Example: Same predator, similar prey species documented elsewhere
3: Inferred - Inferred from traits or body size relationships
Example: Predator and prey body sizes suggest potential link
4: Expert opinion - Expert judgment, not yet validated
Example: Regional expert suggests link based on experience

Filter by Quality

Show links with quality:

1 - Documented

2 - Similar species

3 - Inferred

4 - Expert opinion

Export Metaweb

Download the current metaweb for sharing or backup:

Export Format:

CSV (2 files: species + interactions)

RDS (R object file)

Filename prefix:

Download Metaweb

CSV format: Creates two files (species and interactions) compatible with Excel and other tools.

RDS format: Single R object file preserving all metadata, ideal for R users.

Use Metaweb for Analysis

Convert the current metaweb to an active network for analysis in other tabs:

This will convert the metaweb to an igraph network object and make it available for topological metrics, biomass analysis, energy fluxes, and keystoneness analysis.

Trait Research - Query species traits from online databases

Species List

Species to be looked up:

Lookup Status

Progress Summary

Lookup Progress

Real-time progress of database queries.

Trait Summary

Species Found

Complete Traits

Partial Traits

Not Found

Found Traits Table

View trait data retrieved from databases. Click column headers to sort.

Download CSV

Download RDS

Download Excel

Raw Trait Details

Detailed raw data from database queries (before harmonization).

Select species to view details:

MS - Maximum Size

Code	Size Range	Examples
MS1	< 0.1 cm	Bacteria, small phytoplankton
MS2	0.1 - 0.5 cm	Copepods, rotifers
MS3	0.5 - 1 cm	Small amphipods, mysids
MS4	1 - 3 cm	Shrimps, small fish larvae
MS5	3 - 10 cm	Small fish, crabs
MS6	10 - 50 cm	Medium fish, lobsters
MS7	> 50 cm	Large fish, seals

FS - Foraging Strategy

Code	Strategy	Description
FS0	Producer	Photosynthetic organisms
FS1	Herbivore	Feeds on plants/algae
FS2	Omnivore	Mixed diet
FS3	Predator	Active hunting
FS4	Scavenger	Feeds on dead matter
FS5	Deposit feeder	Feeds on sediment
FS6	Filter feeder	Filters particles from water

MB - Mobility

Code	Mobility	Examples
MB1	Sessile	Barnacles, mussels, sponges
MB2	Limited movement	Sea anemones, some worms
MB3	Crawler	Crabs, sea stars, snails
MB4	Burrower	Lugworms, clams
MB5	Swimmer	Fish, squid, jellyfish

EP - Ecosystem Position

Code	Position	Description
EP1	Infaunal	Lives within sediment
EP2	Epifaunal	Lives on sediment surface
EP3	Demersal	Near-bottom dwelling
EP4	Pelagic	Open water column

PR - Protection

Code	Protection	Examples
PR0	None	Jellyfish, soft worms
PR1	Mucus/slime	Hagfish, some fish
PR2	Soft tissue	Sea slugs
PR3	Spines	Sea urchins, some fish
PR4	Tube	Tube worms
PR5	Soft shell	Crabs (molting)
PR6	Hard shell	Mussels, snails
PR7	Scales	Most fish
PR8	Armoured	Lobsters, sturgeon

About Trait Harmonization

Raw trait data from databases is automatically converted to these categorical codes based on the following rules:

Size (MS): Derived from maximum body length/size measurements
Foraging (FS): Inferred from trophic level, diet composition, and feeding type
Mobility (MB): Based on locomotion type and attachment status
Position (EP): Determined from habitat depth and substrate association
Protection (PR): Assessed from body covering and defensive structures

Overview

The Trait Research module allows you to query multiple online databases to retrieve functional trait data for marine and freshwater species.

Tabs

Species & Status: View your species list and monitor lookup progress in real-time
Found Traits: Browse and export the retrieved trait data table with summary statistics
Trait Reference: Quick reference guide for trait codes (MS, FS, MB, EP, PR)

Workflow

Enter species names manually or upload a file with species list
Select which databases to query
Click 'Run Trait Lookup' to start the search
Monitor progress in the 'Species & Status' tab
View results in the 'Found Traits' tab
Export data as CSV/RDS/Excel or use directly in Food Web Construction

Databases

WoRMS: World Register of Marine Species - taxonomy and classification
FishBase: Fish traits - size, trophic level, habitat, depth
SeaLifeBase: Marine invertebrates - crustaceans, mollusks, echinoderms
BIOTIC: Benthic invertebrate biological traits (local file)
freshwaterecology.info: Freshwater species traits (API)
MAREDAT: Zooplankton functional traits (local file)
PTDB: Phytoplankton traits (local file)
AlgaeBase: Algae taxonomy via WoRMS
SHARK: Swedish Ocean Archives - occurrence and depth data

Tips

Always include WoRMS for accurate taxonomic classification
Use exact scientific names (e.g., 'Gadus morhua' not 'Atlantic cod')
Results are cached for 30 days to speed up repeated queries
Click 'Use in Food Web' to transfer trait data to the Food Web Construction module

Food Web Construction

Construct trait-based food webs using interaction probability matrices. Edit traits, validate data, and visualize the resulting network.

Data Source

From Trait Research

Upload CSV File

Example Dataset

Manual Entry

Trait Codes

MS:: Size (MS1-MS7)
FS:: Foraging (FS0-FS6)
MB:: Mobility (MB1-MB5)
EP:: Position (EP1-EP4)
PR:: Protection (PR0-PR8)

Use Data from Trait Research

Load trait data that was looked up in the Trait Research module.

If no data is available, first use the Trait Research module to look up species traits.

Upload CSV File

Upload a CSV file with species trait data.

Select CSV File:

Browse...

Required Format:

species,MS,FS,MB,EP,PR
Gadus morhua,MS6,FS1,MB5,EP4,PR0
...

Download Template CSV

Example Datasets

Load a pre-built example dataset.

Select Example:

Manual Entry

Create a blank template for manual trait entry.

Number of Species:

Create template, then edit in the 'Trait Editor' tab.

Data Status

Workflow

Load or enter trait data
Edit traits in 'Trait Editor'
Validate data quality
Construct network in 'Network'

Edit and Validate Species Trait Data

Species Trait Table

Click any cell to edit. Changes are saved automatically.

Download CSV

Validation Results

Dataset Statistics

Trait Distribution

Food Web Network Construction and Visualization

Network Parameters

Interaction Probability Threshold:

Higher = fewer interactions (more restrictive)

Download Adjacency Matrix Download Network (RDS) Download Network (GraphML)

Network Properties

Food Web Network

Drag nodes to rearrange. Zoom with mouse wheel. Click nodes for details.

Interaction Probability Heatmap

Color intensity indicates interaction probability.

Interaction Probability Matrices

These matrices define the probability of interaction given trait combinations.

Consumer Size x Resource Size

Probability that a consumer of size MSi can consume a resource of size MSj.

Diagonal (0.5): Cannibalism or same-size predation
Below diagonal (0.0): Smaller cannot eat larger
Above diagonal: Probability decreases with size difference

Consumer Foraging Strategy x Resource Size

Probability that a consumer with foraging strategy FSi can access a resource of size MSj.

Consumer Mobility x Resource Mobility

Probability that a consumer with mobility MBi can capture a resource with mobility MBj.

Consumer Position x Resource Size

Probability that a consumer in position EPi can access a resource of size MSj.

Resource Protection x Consumer Size

Probability that a consumer of size MSi can overcome protection PRj.

Trait Code Reference Guide

Quick Start Guide

Load trait data (from Trait Research, CSV, example, or manual entry)
Review and edit traits in the 'Trait Editor' tab
Validate data to check for issues
Go to 'Network' tab and set the probability threshold
Click 'Construct Food Web' to generate the network
Explore the network visualization and statistics

Trait-Based Food Web Construction

The method constructs food webs by calculating interaction probabilities based on five trait dimensions:

MS (Maximum Size): Determines predator-prey size relationships
FS (Foraging Strategy): Defines feeding mode and prey accessibility
MB (Mobility): Affects prey capture probability
EP (Environmental Position): Determines spatial overlap
PR (Protection): Reduces vulnerability to predation

The final interaction probability is calculated as the minimum across all five dimension-specific probabilities.

Probability Threshold

The threshold determines which interactions are included in the final network:

Low threshold (0.01-0.05): More interactions, denser network
Medium threshold (0.05-0.15): Balanced network structure
High threshold (0.15-0.30): Fewer interactions, sparser network

Adjust the threshold based on your research question and the expected connectance of the ecosystem.

Scientific References

Gravel et al. (2013). Inferring food web structure from predator-prey body size relationships. Methods in Ecology and Evolution.
Brose et al. (2006). Consumer-resource body-size relationships in natural food webs. Ecology.
Williams & Martinez (2000). Simple rules yield complex food webs. Nature.

Phase 1: Spatial Analysis (BBT Support)

Spatial Food Web Analysis

Create hexagonal grids, assign species to spatial units, extract local networks, and calculate spatial metrics following MARBEFES WP3.2 guidelines.

Define Study Area Boundary

Upload a shapefile/GeoPackage or select a BBT polygon to define your study area. The hexagonal grid will be clipped to this boundary.

Select BBT Polygon

Upload Custom Study Area

Browse...

Study Area Preview

Note: If no study area is provided, the grid will cover the entire bounding box specified in Step 1.

Configure Hexagonal Grid

Configure your spatial grid (1-3km hexagons recommended for BBT)

X Min (longitude):

Y Min (latitude):

X Max (longitude):

Y Max (latitude):

Cell Size (meters):

EMODnet Habitat Integration

Integrate EMODnet EUSeaMap habitat data with your spatial grid.

What this does:

Automatically loads regional habitat data (bbox-filtered for your area)
Clips habitat map to your study area boundary
Overlays habitat polygons with hexagonal grid cells
Calculates habitat statistics for each grid cell
Adds EUNIS codes, substrate types, and diversity metrics

Note: Habitat loads a small centered area for reliability (avoids data errors). Click 'Clip Habitat to Study Area' button below to get full coverage.

Enable Habitat Analysis

Note: Loads small test area initially. Click 'Clip Habitat' for full coverage.

Grid Cell Attributes Added:

dominant_eunis: Most common EUNIS code
dominant_habitat: Habitat description
dominant_substrate: Main substrate type
habitat_diversity: Shannon diversity of EUNIS codes
n_habitats: Number of different habitats
habitat_area_km2: Total habitat area in cell
substrate_*_pct: Percentage of each substrate

Habitat Processing Status

Upload or Generate Species Data

Upload or generate species occurrence data (CSV with lon, lat, species columns)

Upload Species Data (CSV):

Browse...

Extract Local Networks from Metaweb

Select metaweb and extract local networks for each hexagon

Select Regional Metaweb:

Calculate Food Web Metrics

Calculate food web metrics for each hexagon

Select Metrics:

Species Richness (S)

Number of Links (L)

Connectance (C)

Link Density (LD)

Mean Trophic Level

Max Trophic Level

Interactive Map

Visualize spatial metrics on interactive map (requires calculated metrics)

Color by Metric:

Map Layers: Study Area (blue), Grid (gray), Species (green), Metrics (color)

Export Results

Download Metrics (CSV) Download Full Analysis (RDS)

Download spatial metrics table or complete spatial_foodweb_data object for further analysis in R.

EcoBase - Online Ecopath Model Repository

Connect to EcoBase Web Service

EcoBase is an online repository of Ecopath with Ecosim (EwE) models from around the world.

Website: http://sirs.agrocampus-ouest.fr/EcoBase/

Use this interface to browse and download published Ecopath models directly into EcoNeTool.

1. Browse Available Models

2. Model Details

Parameter Type:

Hybrid (Best of Both)

Output (Balanced)

Input (Original)

Hybrid: Balanced parameters (Output) + Complete diet links (Input) - Recommended!
Output: Mass-balanced, may lack diet data for some models
Input: Original parameters with complete diet composition

Requirements

Required R Packages

EcoBase connection requires additional packages:

install.packages(c('RCurl', 'XML', 'plyr', 'dplyr'))

Internet Connection

Active internet connection required to access EcoBase web service.

Model Types

Output (Balanced): Mass-balanced parameters from EwE output
Input (Original): Original input parameters before balancing

What Gets Imported

Species/functional groups
Biomass values
P/B ratios (Production/Biomass)
Q/B ratios (Consumption/Biomass)
Ecotrophic efficiency (EE)
Diet composition (trophic links)

SHARK4R - Swedish Ocean Archives

Access Swedish Marine Environmental Data

SHARK (Svenskt HavsARKiv) is Sweden's national database for marine environmental monitoring data, maintained by SMHI (Swedish Meteorological and Hydrological Institute).

This interface provides access to:

Taxonomy: Dyntaxa (Swedish species), WoRMS, AlgaeBase
Environmental Data: Temperature, salinity, nutrients, oxygen from 1900s-present
Species Occurrence: Biological observations (phytoplankton, zooplankton, fish)
Quality Control: Validate SHARK format data files

Documentation: SHARK4R Package | SHARK Database

‌

Search Species Taxonomy

Species Name:

Data Sources:

Dyntaxa (Swedish Taxonomy)

WoRMS (World Register)

AlgaeBase (Algae Database)

Fuzzy matching

Tip: Try Swedish names like 'torsk' (cod), 'sill' (herring), or scientific names for best results.

Taxonomy Results

Query Environmental Data

Date Range:

Parameters:

Bounding Box (Optional)

Leave blank for all Swedish waters

North (Lat):

South (Lat):

East (Lon):

West (Lon):

Max Records:

Environmental Data Results

Download CSV

Query Species Occurrences

Species Name:

Date Range:

Max Records:

Note: Results will be displayed on the map and in the table. Use scientific names for best results.

Occurrence Records

Download CSV

Upload SHARK Format Data

Choose File:

Browse...

Accepted formats: CSV, TXT, TSV
Expected structure: SHARK standard format

Quality Checks:

Format validation

Data completeness

Outlier detection

Coordinate validation

Quality Control Results

Requirements & Installation

Required R Package

This module requires the SHARK4R package:

install.packages('SHARK4R')

Package Information

Version: 1.0.2+
License: MIT
Maintainer: SMHI (Swedish Meteorological and Hydrological Institute)

Documentation & Resources

Typical Use Cases

Taxonomy Validation: Verify Swedish species names using Dyntaxa
Environmental Context: Retrieve historical oceanographic data for model regions
Model Validation: Compare ECOPATH species with SHARK occurrence records
Data Quality: Validate monitoring data before analysis

Data Coverage

Temporal: 1900s - present (varies by parameter)
Spatial: Swedish waters (Baltic Sea, Skagerrak, Kattegat)
Parameters: 100+ environmental and biological variables

ECOPATH/ECOSIM Modeling

Run mass-balance models and dynamic simulations using the Rpath package (NOAA-EDAB)