Spatial Data
Any food web is subject to a range of stressors and environmental factors that may or may not change over time and space. Ecospace can include the ecological impacts of a wide range of stressors and environmental factors (for instance climate variables, species niches, layout of and access to Marine Protected Areas, contaminant distributions, etc.), but the magnitude and distribution of stressors and environmental factors are typically defined outside Ecospace by expert models. The spatial-temporal data framework (STDF, see chapter), available in the professional version of the software, was added to include time-varying distributions of stressors and environmental factors (typically coming from other models) into the running Ecospace model (Steenbeek et al., 2013). This has enabled a host of new applications related to the spatial-temporal impact of change on the ecosystem. For each Ecospace layer that needs to receive time-varying maps, the user must supply one or more maps that represent the distribution and magnitude of external data over time. These maps can be provided in a number of Geographic Information System (GIS) file formats; the spatial temporal data framework uses on-board GIS engines to read and interpret the maps. Each map is time-stamped to tell Ecospace when the data is relevant, and any GIS processing steps that are needed to integrate the data are selected. Last, a time series of maps is connected to the Ecospace map layers of interest. When the Ecospace model is executed, external environmental maps are automatically integrated into the Ecospace model.
There are a few special complicating considerations to using the spatial-temporal data framework (Steenbeek et al., 2021):
- GIS data processing can significantly alter the quality of data if performed carelessly. The choice of rasterization and interpolation algorithms, strategies for dealing with missing data, unit conversions, and lack of data standards or poor adherence to these standards, can affect the quality of the data that Ecospace will ingest. Automatic data processing of large volumes of GIS data is not without risk and should always follow FAIR principles;
- Different Ecospace layers have different data requirements (Table 1). This poses layer-specific challenges to GIS processing. The spatial-temporal framework contains the necessary processing tools to perform basic conversions to targeted Ecospace layers.
It is imperative that modelers first perform the GIS conversions outside Ecospace to ensure a good understanding of the data that Ecospace will be ingesting, including GIS processing artifacts that the conversion process may have created. This process can then be replicated by the STDF.
The STDF offers the means to bypass its internal GIS engine for data that has been pre-processed for a specific Ecospace spatial extent, projection and cell size (Steenbeek et al., 2021). Such data must be provided as ESRI ASCII raster maps (https://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/esri-ascii-raster-format.htm). This is the preferred method for those that wish to have full control over any GIS processing steps needed to develop the input layers. Table 1 provides an overview of the data needs for the various Ecospace layers.
| Layer | Purpose | Format | Units | Observations |
|---|---|---|---|---|
| Depth | Bathymetry, identify ecosystem dynamics cells | Floating point | meters (implied), but can be anything | Cells with values >= 0 have ecosystem dynamics |
| Excluded cells | Excludes cells from ecosystem dynamics | True/False | - | Exclude cells from ecosystem dynamics even when depth values are >= 0 |
| MPA | Prohibit specific fishing for a specific MPA | True/False | - | Blocks entire cells rom fishing, not cell fractions. MPAs can overlap (EwE 6.4+) |
| Habitat | Suitable substrate, fishing limits | Floating point | [0,1] | Cell area fraction covered by substrate |
| Environmental driver | SST, salinity, bottom dissolved O2, pH, etc | Floating point | Any | Spatial distribution and magnitude of env. conditions |
| Relative PP | Distribute primary productivity | Floating point | [0, ∞> | Weight layer for distributing PP. Applicable only to autotrophs |
| Relative contaminants | Distribute environmental contaminants concentrations | Floating point | [0, ∞> | Weight layer for distributing base environmental contaminant concentrations |
| Biomass forcing | Affect Ecospace biomasses of a functional group | Floating point | [0, ∞> | Override (absolute) or multiply (relative) biomass distributions of any functional group |
| Habitat capacity | Constrain niche model for a functional group | Floating point | [0,1] | Set capacity in HFC model. Multiplicative with niche calculations |
| Advection | Current velocities | Floating point | cm/s | u and v vectors from hydrodynamic models. Note that the Ecospace u direction is non-standard; positive currents flow South. Applied to groups identified as advected by the user |
| Migration | Move species for reasons that Ecospace cannot predict | Floating point | [0, ∞> | Weight layer to specific migration target areas (EwE v 6.4+) |
| Table 1. Ecospace layers and their data needs (reproduced with permission from Coll and Steenbeek, 2017) | ||||
Adaption
The chapter is in part adapted, with permission, from:
De Mutsert K, Marta Coll, Jeroen Steenbeek, Cameron Ainsworth, Joe Buszowski, David Chagaris, Villy Christensen, Sheila J.J. Heymans, Kristy A. Lewis, Simone Libralato, Greig Oldford, Chiara Piroddi, Giovanni Romagnoni, Natalia Serpetti, Michael Spence, Carl Walters. 2023. Advances in spatial-temporal coastal and marine ecosystem modeling using Ecopath with Ecosim and Ecospace. Treatise on Estuarine and Coastal Science, 2nd Edition. Elsevier.
