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Metadata: Seahorse species predicted habitat distributions and associated environmental data layers covering the shelf seas surrounding the UK
Abstract:
This dataset contains predicted seahorse habitat distributions for two species (Hippocampus hippocampus and H. guttulatus) and the genus combined (Hippocampus hippocampus MAXENT.asc, Hippocampus guttulatus MAXENT.asc and Hippocampus sp. MAXENT.asc, respectively). Rasters of the raw predicted habitat suitability outputs from the maximum entropy (MAXENT, Phillips et al. 2006) species distribution model algorithm are provided. Additionally, the environmental data layers used for modelling the species distributions, including distance to seagrass habitat (degrees) (Distance From Seagrass.tif), distance to the coastline (DistCo.tif) bathymetry (m) (Bathy.tif), minimum winter and maximum summer SST (oC) (Kriging SST Winter Min.tif and Kriging SST Summer Max.tif, respectively) and chlorophyll a concentration (mg m-3) (Kriging Chla Winter Smooth.tif and Kriging Chla Summer Smooth.tif, respectively). All raster layers are provided in the WGS84 (EPSG::4326) spatial reference system. All raster layers are at a gridded resolution of 0.0042 x 0.0042 degrees. All layers cover the shelf seas surrounding the UK, including the English Channel, Celtic Seas, North Sea, and extend into shallow coastal and estuarine habitats. Raster layers of predicted habitat suitability are provided in ASCI format, whilst environmental predictor layers are provided in TIFF format. This work was prioritised by Natural England’s Seahorse Working Group and was funded by Natural England. This work was conducted in collaboration with The Seahorse Trust who provided the seahorse occurrence data. The information provided will support marine spatial planning to reduce the broader impact of anthropogenic activities and enable better decision-making to protect these sensitive species and their habitats.
Data holder:
Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory (CEFAS)
| Other details | ||
| Internal code | Internally assigned metadata identifier | 10543 |
| Title | The title is used to provide a brief and precise description of the dataset such as 'Date', 'Originating organisation/programme', 'Location' and 'Type of survey'. All acronyms and abbreviations should be reproduced in full. | Seahorse species predicted habitat distributions and associated environmental data layers covering the shelf seas surrounding the UK |
| File Identifier | The File Identifier is a code, preferably a GUID, that is globally unique and remains with the same metadata record even if the record is edited or transferred between portals or tools. | CEFASb4de0cbf-ecd9-411f-9046-ac2c044a4465 |
| Resource Identifier | This is the code assigned by the data owner. | CEFAS21660 |
| Resource type | The resource type will likely be a dataset but could also be a series (collection of datasets with a common specification) or a service. | dataset |
| Start date | This describes the date the resource starts. This may only be the year if month and day are not known | 2000-01-01 |
| End date | This describes the date the resource ends. This may only be the year if month and day are not known | 2024-07-24 |
| Frequency of updates | This describes the frequency with which the resource is modified or updated i.e. a monitoring programme that samples once per year has a frequency that is described as 'annually'. | notPlanned |
| Abstract | The abstract provides a clear and brief statement of the content of the resource. | This dataset contains predicted seahorse habitat distributions for two species (Hippocampus hippocampus and H. guttulatus) and the genus combined (Hippocampus hippocampus MAXENT.asc, Hippocampus guttulatus MAXENT.asc and Hippocampus sp. MAXENT.asc, respectively). Rasters of the raw predicted habitat suitability outputs from the maximum entropy (MAXENT, Phillips et al. 2006) species distribution model algorithm are provided. Additionally, the environmental data layers used for modelling the species distributions, including distance to seagrass habitat (degrees) (Distance From Seagrass.tif), distance to the coastline (DistCo.tif) bathymetry (m) (Bathy.tif), minimum winter and maximum summer SST (oC) (Kriging SST Winter Min.tif and Kriging SST Summer Max.tif, respectively) and chlorophyll a concentration (mg m-3) (Kriging Chla Winter Smooth.tif and Kriging Chla Summer Smooth.tif, respectively). All raster layers are provided in the WGS84 (EPSG::4326) spatial reference system. All raster layers are at a gridded resolution of 0.0042 x 0.0042 degrees. All layers cover the shelf seas surrounding the UK, including the English Channel, Celtic Seas, North Sea, and extend into shallow coastal and estuarine habitats. Raster layers of predicted habitat suitability are provided in ASCI format, whilst environmental predictor layers are provided in TIFF format. This work was prioritised by Natural England’s Seahorse Working Group and was funded by Natural England. This work was conducted in collaboration with The Seahorse Trust who provided the seahorse occurrence data. The information provided will support marine spatial planning to reduce the broader impact of anthropogenic activities and enable better decision-making to protect these sensitive species and their habitats. |
| Lineage | Lineage includes the background information, history of the sources of data, data quality statements and methods. | The outputs from this project advance the development of habitat suitability models for two sensitive species of seahorse (H. hippocampus and *H. guttulatus*) and the combined genus by predicting habitat suitability in shallow, nearshore waters (coastal and estuarine sites) that are considered important for both species. Satellite-derived gridded environmental data (sea surface temperature (SST, oC) and chlorophyll a concentration (mg m-3), MODIS-Aqua Level-3 `https://oceancolor.gsfc.nasa.gov/l3/`_[Downloaded May 2018]) were combined with Environment Agency field-collected water quality data from the Water Quality Archive [Accessed January 2023] using ordinary kriging within the "Geostatistical Analyst" tool in ArcGIS (v10.5) to produce environmental predictor layers that extend to the coastline. SST and chlorophyll summer maximums and winter minimums were calculated. Bathymetry (m) (GEBCO Compilation Group (2022) GEBCO 2022 Grid (doi:10.5285/e0f0bb80- ab44-2739-e053-6c86abc0289c) [Downloaded January 2023]) was interpolated using the natural neighbour method in ArcGIS 10.5. Seagrass (Zostera marina, Z. noltii and Ruppia spp.) polygon and point data were collated from multiple sources including Natural England’s open-access polygon data on seagrass coverage in England. Point data (from the year 2000 onwards) were converted to polygons using a buffer and merged with existing polygons to ensure all possible seagrass locations were included in the calculation. A distance from seagrass meadows layer was calculated in ArcGIS 10.5 using the “Euclidean Distance” tool. The final list of six variables was used for modelling, all with a Pearson's correlation of less than 0.7, including distance to seagrass habitat, bathymetry, minimum winter and maximum summer SST and chlorophyll concentrations which are provided. Seahorse records provided by the Seahorse Trust's National Seahorse Database, Natural England and other records from open-source databases described in Bluemel et al. 2020 were filtered to remove records that were duplicated or fell outside of the study area, or where species identification was dubious. Prior to modelling, records were reduced to one point per grid cell to reduce sampling biases. All occurrence records from the year 2000 to the present were used for model training and testing (Hippocampus genus n = 165, Hippocampus hippocampus = 144, Hippocampus guttulatus = 45) (species occurrences are not provided). Seahorse habitat distributions were modelled using maximum entropy (MAXENT, Phillips et al. 2006). The default convergence threshold of 106 and a maximum of 5,000 iterations were used. A maximum of 10,000 background samples were randomly selected from the study area. Within the model settings, random seed was selected, which means that a different random subset was used for each model run, and the number of replicates was set to 10 and the replicate run type was set to subsample. In addition, the random test percentage was set to 25%. All other MAXENT settings were default. Model performance was examined by computing the Area Under the Curve (AUC) score of the receiver operating characteristic (ROC) curve for each species, which is calculated by the MAXENT software as part of the modelling process and is considered to be an effective measure of model performance (Reiss et al. 2011). The AUC represents the relationship between sensitivity and specificity and varies between 0 and 1 (Reiss et al., 2011). It is generally considered that values >0.9 indicate an excellent model fit, values between 0.7 and 0.9 represent a good fit, anything <0.7 represents a poor model fit and anything <0.5 represents nothing better than random (Reiss et al., 2011). The model for H. guttulatus had the highest AUC, which had a mean value of 0.99 ± 0.0005 across the 10 runs, followed by H. hippocampus (0.97 ± 0.0008) and the genus combined (0.97 ± 0.0006). Modelling limitations and sampling biases: Known sampling biases exist in the seahorse occurrence data used in this study and therefore the predictions should be taken with caution. Biases include higher sampling effort in the southwest and English Channel, due to high recreational SCUBA diving intensity in this area, resulting in uneven sampling distributions across the study area. Recorder biases exist due to known locations of seahorses being regularly visited and targeted by dive companies, and records are often provided by multiple diver trips of the same seahorse that has moved to a nearby locality (Garrick-Maidment pers. comm.). MAXENT is sensitive to biased data distributions, and there is a strong assumption of random sampling distribution for presences when random background data are used. Thus the underlying model assumptions are invalidated by the sampling bias, which could be restricting predictions to southerly localities where sampling biases (increased effort and recorder bias) are known to exist. To improve confidence in the habitat predictions for seahorses, further work should include testing different modelling algorithms. Variations in model predictions were observed by Bluemel et al. (2020), highlighting the need to compare outputs and identify consistent robust predictions, especially in data-limited, presence-only settings. Additionally, testing different habitat suitability thresholds that are tailored to the intended use of the predicted distributions (Freeman and Moisen 2008), testing validation approaches more suited to presence-only situations, because using AUC in isolation can be uninformative when species prevalence is low. Furthermore, sampling bias and spatial autocorrelation in the occurrence data require further work. Methods to deal with spatial autocorrelation and sampling biases could include defining a bias grid to include in the modelling process (i.e., known dive sites with higher sampling activity, thus a higher likelihood of observation), using other suitable point process models that account for these biases or assessing model performance with a block cross-validation approach (Roberts et al. 2017)). Bluemel, J.K., Lynam, C. and Ellis, J. (2020). Fish biodiversity: state and pressure indicators. Cefas Project Report for Defra, 67 pp. Freeman, E.A. and Moisen, G.G. (2008). A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecological modelling, 217(1-2): 48-58. Phillips, S.J., Anderson, R.P. and Schapire, R.E., (2006). Maximum entropy modelling of species geographic distributions. Ecological modelling, 190(3-4): 231-259. Reiss, H., Cunze, S., König, K., Neumann, H. and Kröncke, I., (2011). Species distribution modelling of marine benthos: a North Sea case study. *Marine Ecology Progress Series, 442*: 71-86. Roberts, D.R., Bahn, V., Ciuti, S., Boyce, M.S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J.J., Schröder, B., Thuiller, W., others. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography. 40: 913-929. .. _`https://oceancolor.gsfc.nasa.gov/l3/`: https://oceancolor.gsfc.nasa.gov/l3/ |
| Related keywords | ||
| Keyword | General subject area(s) associated with the resource, uses multiple controlled vocabularies | Coastal environment |
| General subject area(s) associated with the resource, uses multiple controlled vocabularies | Ecology | |
| General subject area(s) associated with the resource, uses multiple controlled vocabularies | Modelling | |
| General subject area(s) associated with the resource, uses multiple controlled vocabularies | Estuary | |
| General subject area(s) associated with the resource, uses multiple controlled vocabularies | Habitat characterisation | |
| General subject area(s) associated with the resource, uses multiple controlled vocabularies | Other physical oceanographic measurements | |
| General subject area(s) associated with the resource, uses multiple controlled vocabularies | Species distribution | |
| General subject area(s) associated with the resource, uses multiple controlled vocabularies | Water column | |
| General subject area(s) associated with the resource, uses multiple controlled vocabularies | Marine Environmental Data and Information Network | |
| General subject area(s) associated with the resource, uses multiple controlled vocabularies | data.gov.uk | |
| Geographical coverage | ||
| North | The northern-most limit of the data resource in decimal degrees | 61.5 |
| East | The eastern-most limit of the data resource in decimal degrees | 8 |
| South | The southern-most limit of the data resource in decimal degrees | 49 |
| West | The western-most limit of the data resource in decimal degrees | -15 |
| Responsible organisations | ||
| Role | The point of contact is person or organisation with responsibility for the creation and maintenance of the metadata for the resource. | pointOfContact |
| Organisation name | Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory (CEFAS) | |
| Delivery point | Cefas Lowestoft Laboratory, Pakefield Road | |
| Postal code | NR33 0HT | |
| City | Lowestoft | |
| Administrative area | Suffolk | |
| Country | UK | |
| data.manager@cefas.co.uk | ||
| Role | The originator is the person or organisation who created, collected or produced the resource. | originator |
| Organisation name | Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory (CEFAS) | |
| Delivery point | Cefas Lowestoft Laboratory, Pakefield Road | |
| Postal code | NR33 0HT | |
| City | Lowestoft | |
| Administrative area | Suffolk | |
| Country | UK | |
| data.manager@cefas.co.uk | ||
| Role | The custodian is the person or organisation that accepts responsibility for the resource and ensures appropriate care and maintenance. If a dataset has been lodged with a Data Archive Centre for maintenance then this organisation is be entered here. | custodian |
| Organisation name | Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory (CEFAS) | |
| Delivery point | Cefas Lowestoft Laboratory, Pakefield Road | |
| Postal code | NR33 0HT | |
| City | Lowestoft | |
| Administrative area | Suffolk | |
| Country | UK | |
| data.manager@cefas.co.uk | ||
| Role | The distributor is the person or organisation that distributes the resource. | distributor |
| Organisation name | Centre for Environment, Fisheries and Aquaculture Science, Lowestoft Laboratory (CEFAS) | |
| Delivery point | Cefas Lowestoft Laboratory, Pakefield Road | |
| Postal code | NR33 0HT | |
| City | Lowestoft | |
| Administrative area | Suffolk | |
| Country | UK | |
| data.manager@cefas.co.uk | ||
| Role | The owner is the person or organisation that owns the resource. | owner |
| Organisation name | Natural England | |
| enquiries@naturalengland.org.uk | ||
| Resource locators | ||
| Locator URL | Web address (URL) that links to the resource | https://data.cefas.co.uk/view/21660 |
| Locator name | Name of the web resource | Cefas Data Portal |
| Dataset constraints | ||
| 20.1 Limitations on Public Access - Access constraints | This states `otherRestrictions` from ISO vocabulary RestrictionCode and is an INSPIRE/GEMINI requirement. | otherRestrictions |
| 20.2 Limitations on Public Access - Other constraints | noLimitations | |
| 21.1 Conditions for Access and Use - Use constraints | This states `otherRestrictions` from ISO vocabulary RestrictionCode and is an INSPIRE/GEMINI requirement. | otherRestrictions |
| 21.2 Conditions for Access and Use - Other constraints | https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ | |
| Version info | ||
| Date of publication | The publication date of the resource or if previously unpublished the date that the resource was made publicly available via the MEDIN network. | 2023-09-26 |
| Date of last revision | The most recent date that the resource was revised. | 2024-07-24 |
| Date of creation | The date that the resource was created. | 2023-04-17 |
| Harvest date | The date which this record has been (re)harvested from the provider. | 2026-04-19 |
| Metadata date | The date when the content of this metadata record was last updated. | 2024-07-24 |
| Metadata standard name | The name of the metadata standard used to create this metadata | MEDIN |
| Metadata standard version | The version of the MEDIN Discovery Metadata Standard used to create the metadata record | 3.1.1 |