InteractIAS has many uses, but was specifically developed to inform invasive species risk assessment. InteractIAS takes interaction data from the Global Biotic Interactions (GloBI) database. It then combines these interactions with occupancy data derived from GBIF to create a species interaction network with nodes weighted by their area of occupancy. This allows users to evaluate if there are species that might be impacted directly or indirectly by an invasive species. By weighing the nodes by the occupancy, it allows users to evaluate whether the interaction is likely to have a major impact or not. Interactions between species are the most important way that invasive species impact other biodiversity. Ecosystem models are not yet sophisticated enough to interpret complex interaction networks, so we must rely on expert opinion to evaluate the potential risks of a new invasive species. Yet experts in the country where the species is invasive are not likely to have expertise in a species that may come from another continent. Therefore, we need tools to support risk assessors do their job and support their conclusions.
| Invasive in Belgium | Native to Belgium |
|---|---|
| Impatiens glandulifera (Himalayan balsam) | Fraxinus excelsior (European Ash) |
| Phytolacca americana (American pokeweed) | Vespa crabro (yellow-legged hornet) |
| Vespa velutina (yellow-legged hornet) | Capreolus capreolus (roe deer) |
| Ailanthus altissima (tree of heaven) | |
| Hydrocotyle ranunculoides (floating pennywort) | |
| Cydalima perspectalis (box tree moth) |
| Belgium | Italy | Lithuania | Slovenia |
|---|---|---|---|
| Fraxinus excelsior | Fraxinus excelsior | Fraxinus excelsior | Fraxinus excelsior |
| Under investigation |
|---|
| Alternanthera philoxeroides (alligator weed) |
| Ameiurus melas (black bullhead) |
| Ameiurus nebulosus (brown bullhead) |
| Asclepias syriaca (common milkweed) |
| Axis axis (spotted deer) |
| Boccardia proboscidea () |
| Cabomba caroliniana (Carolina fanwort) |
| Callosciurus erythraeus (Pallas's squirrel) |
| Callosciurus finlaysonii (Finlayson's squirrel) |
| Cardiospermum grandiflorum (balloon vine) |
| Castor canadensis (North American beaver) |
| Channa argus (northern snakehead) |
| Fundulus heteroclitus (mummichog) |
| Gambusia affinis (western mosquitofish) |
| Gunnera tinctoria (giant rhubarb) |
| Hemigrapsus sanguineus (Asian shore crab) |
| Humulus scandens (Japanese hop) |
| Lagarosiphon major (curly waterweed) |
| Lampropeltis getula (common kingsnake) |
| Ludwigia grandiflora (water primrose) |
| Lysichiton americanus (American skunk cabbage) |
| Morone americana (white perch) |
| Muntiacus reevesi (Reeves's muntjac) |
| Perna viridis (Asian green mussel) |
| Pistia stratiotes (water lettuce) |
| Procambarus clarkii (Louisiana crawfish) |
| Pueraria montana (Kudzu) |
| Pycnonotus cafer (red-vented bulbul) |
| Rapana venosa (veined rapa whelk) |
| Salvinia molesta () |
| Solenopsis geminata (tropical fire ant) |
| Solenopsis invicta (red imported fire ant) |
| Solenopsis richteri (black imported fire ant) |
| Threskiornis aethiopicus (African sacred ibis) |
| Wasmannia auropunctata (electric ant) |
| Xenopus laevis (African clawed frog) |
In addition to an HTML output the script generates a .dot file. This format can be opened directly by the network visualization tool Gephi (https://gephi.org/).
- The script takes a single species as input and searches for all primary interacting species in GloBI.
- Then it goes back to GloBI to get all the interacting species of those primary interacting species.
- The script then checks to see if all those species exist on GBIF and it outputs a list of missing species.
- We then use an occurrence cube of species from the country of interest. This is a data cube of taxa, 1km grid squares and years from which the 1km area of occupancy is calculated.
- Any interactions that cannot occur in the country are removed.
- Visualizations are then created to display the result in a way that allows exploration of the network.
The large volume of data required for this script mean that we have used local copies of much of the data and only use the GBIF API to consult the GBIF Taxonomic Backbone (GBIF Secretariat 2019).
- The latest interactions.tsv.gz file can be downloaded from GloBI at https://www.globalbioticinteractions.org/data.html or there is a published snapshot of the file on Zenodo (http://doi.org/10.5281/zenodo.3950590).
- Interactions.tsv is then made into an SQLite (https://www.sqlite.org/) database using a script. https://github.com/AgentschapPlantentuinMeise/createGlobiDB. This database can then be recreated whenever it is felt necessary to use a newer version.
- We use a preconstructed Belgian occurrence cube built from GBIF observations following the methodology of Oldoni et al. (2020a). Pre-made occurrence cubes for Belgium and Italy are currently available online (Oldoni et al. 2020b). http://doi.org/10.5281/zenodo.3637911
- To create your own occurrence cube instructions are available in Oldoni et al. (2020a) and all code is available on GitHub (https://github.com/trias-project/occ-cube).
- To query the occurrence cube efficiently it is imported into an SQLite (https://www.sqlite.org/) database using a script. https://github.com/AgentschapPlantentuinMeise/occcube
e.g. Using the Anaconda Prompt run pip install pygbif
Figure 1. A flow diagram to explain the script and the sources of the data. Steps after the visualization are manual steps conducted with expert risk assessors.
Figure 2. An example of a network created by this notebook and then visualized in Gephi. The target species was the Egyptian fruit bat (Rousettus aegyptiacus (Geoffroy, 1810)) and the target country was Belgium. Egyptian fruit bat does not naturally occur in Belgium, but should they escape from a zoo it can be seen that they are unlikely to survive. Not only are their only food plants rare in Belgium, but they have a common predator, cats (Felis catus L.). In this illustration the node radius is proportional to the occupancy of that species in Belgium. The colours are modularity classes of the network. Although this is rather an extreme example, it illustrates how networks can be used to inform and evidence ecological understanding.
- Comparing networks of current ecosystems versus those under future climate scenarios
- Comparing networks with or without keystone species
- Evaluating the impact of pesticide or herbicide usage
- Understanding the biotic pressures on rare and endangered species
- The script could be adapted to size the nodes based on occupancy cooccurence.
- Users can edit the parameters of the script to omit interactions they are not interested in (e.g. visitsFlowersOf), or non-specific interactions (e.g. interactsWith).
- Pre-generated data cubes for all countries would streamline setting up of the notebook for other countries.
- The notebook only works at the species level, but can be easily adapted to create higher taxon networks. These can be a useful simplification of some complex networks.
- Running in batches to create networks for a list of species.
- Allowing the entry of more than one species to examine if and where their networks join.
- ...
GloBI is an enormous database of species interactions, but there are many missing (Cains et al. 2017). If you have additional interactions to add to GloBI you can do so editing a simple tab-delimited text file in a GitHub repository. Instructions are here https://github.com/globalbioticinteractions/template-dataset
Using this template, I have set up a repository specifically to collect interaction data for species on the List of Invasive Alien Species of Union concern (https://ec.europa.eu/environment/nature/invasivealien/list/index_en.htm). This repository can be found here... https://github.com/trias-project/eu-species-of-concern-interactions Anyone can push additions to this repository or raise issues with citations of publications that describe the interaction.
- Cains, Mariana, Altimir, Nuria, Anand, Srini, Liao, William, & Shiverick, Sean. (2017). IVMOOC 2017 - Gap Analysis of GloBI: Identifying Research and Data Sharing Opportunities for Species Interactions. Zenodo. https://doi.org/10.1101/2020.03.23.983601 GBIF Secretariat (2019). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2020-07-25.
- Oldoni, D., Groom, Q., Adriaens, T., Davis, A.J.S., Reyserhove, L., Strubbe, D., Vanderhoeven, S. & Desmet, P. (2020a). Occurrence cubes: a new paradigm for aggregating species occurrence data. BioRxiv. 2020.03.23.983601; doi: https://doi.org/10.1101/2020.03.23.983601
- Oldoni, D., Groom, Q., Adriaens, T., Davis, A.J.S., Reyserhove, L., Strubbe, D., Vanderhoeven, S. & Desmet, P. (2020b). Occurrence cubes at species level for European countries (Version 20200205) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3637911
- Poelen, J.H., Simons, J.D. & Mungall, C.J. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.