MPA Challenge: Coherence
MPA Coherence Data Evaluation (project phase II)
To evaluate the coherence of the MPA network a number of datasets is vital:
- Location of Marine Protected Areas, preferably with associated data on species and habitats.
- Distribution data of species to be evaluated, preferably with additional information on the ecology, habitat preference etc. of said species.
A matrix-based approach developed by OSPAR (2006,2008) has been used to perform a coherence analysis for the Arctic Ocean. A more technical description is available in the next section of this report/website. A geographic database with Marine Protected Areas has been collated in phase I of this project. This formed the basis of the coherence analysis. The matrix approach includes bioregion to be distinguished, for this Large Marine Ecosystem as delineated by PAME (2013) were used. Where other checkpoint projects were able to rely fairly heavily on EU-related data portals like EMODnet and Copernicus Marine Environmental Monitoring Service, with the Arctic Ocean mostly outside of the focal area of these data sources, the project team has had to make the best possible use of other – global or Arctic specific – sources of data.
The main remaining challenge was to source datasets on the species and decide which species to include.
The following data sources were identified and used:
The table below summarises species groups covered by each datasource, type of data offered, ease of use and accessibility.
|Description||AquaMaps||GBIF||Birds of the World||IUCN Red List|
|Species groups covered||Sharks and Rays; Bony Fish; Invertebrates and Sea Mammals||Sharks and Rays; Bony Fish; Invertebrates and Sea Mammals; Birds||Birds||Sharks and Rays; Bony Fish; Invertebrates and Sea Mammals; Birds|
|Species groups not covered||Birds||-||Sharks and Rays; Bony Fish; Invertebrates and Sea Mammals||-|
|Type of data offered||Map view; CSV-download (c-squares codes included, offering link to geographic display and analysis)||Map view; species dossier; CSV-download a.o. per species (includes Lon/Lat-coordinates per observation point)||Map view; species dossier; Download on request ESRI File GeoDatabase received||Map view; species dossier; Download on request Shapefile (zipped) received, with additional files|
|Resulting data in GIS||Polygons (vector grid)||Points||Polygons||Polygons|
|Ease of use||Medium||Medium||High||High|
|Data request||Free download||Free download||Required for geographic data. Granted for full dataset.||Initial single species request granted, well within stated turnaround times. Second multi-species request unanswered. Also after reminder sent.|
Outcome of the Coherence analysis
With the available data the project team has been able to complete the coherence analysis. A process that was complicated by the fact that the species data had to be sourced from different sources, requiring adaptations of the methodology to achieve comparable results nonetheless.
All included species are Arctic species, they make extensive use of the Arctic and are included in Meltofte (2013). From our list the species with the lowest scores for Replication and Adequacy/Viability are bird species that breed in the Arctic, but that are not very reliant on the Arctic Ocean and Marine Protected Areas.
The least protected Arctic marine mammals are
- the Polar Bear (Ursus maritimus), low Replication score and
- the Hooded Seal (Cystophora cristata), low proportion of the population inside MPA.
These two species have a strong association with sea ice as an important habitat. As to the following species: Ringed Seal (Pusa hispida), Narwal (Monodon Monoceros), Ivory Gull (Pagophila eburnea) and Polar Cod (Boreogadus saida). To a lesser extent also Walrus (Odobenus rosmarus) and Beluga (Delphinapterus leucas) have a requirement for sea ice as a habitat.
A single habitat has been included in our coherence analysis: sea ice (based on the min. and max. extent recorded in 2015). This habitat has the lowest Replication score within the analysis and also is poorly protected by /represented in current MPA boundaries.
The project team did not find any targets that are set (internationally or nationally) and aimed for for the protection of species and/or habitats in the Arctic Ocean. As a result this column within the matrix has been left empty.
Coherence is an important and desirable characteristic of a network of Marine Protected Areas. A coherent network offers protection to a sufficiently large proportion of species and habitats to ensure their continued survival and existence. It also means that the different elements of the MPA network are within a reasonable distance from each other so that species – when migrating or otherwise mobile, e.g. during a larval phase – stand a good chance of reaching a ‘safe haven’ (the next protected area).
To assess the coherence of the MPA network the dataset constructed during phase I of the project has been used in conjunction with additional datasets on species occurrence and one major habitat for the Arctic Ocean: Sea Ice. For this assessment an evaluation framework developed by OSPAR has been used (OSPAR, 2006, 2008). It attempts to assess aspects such as: Adequacy, Viability, Replication.
The analysis has been based on 18 Arctic Bioregions, identical to the Large Marine Ecosystems as delineated for the Arctic (PAME, 2013)
The second component has been the analysis of the population, extant etc. across those 18 bioregions for a 25 species and one habitat (Sea Ice).
All of the results have been combined into table 1, with the species/habitat in rows, and, the bioregions and other characteristics to be evaluated in columns.
Table 1: MPA Coherence Analysis.
Species/habitat with blue background indicated sea ice and/or a strong association with this habitat. In the columns for the bioregions a light blue background (for mammals, fish and invertebrates) indicates year-round presence within that bioregion. Beluga has a blue outline, to indicate that it has a year-round presence in the Arctic (#1). For bird species a slightly darker blue background indicated presence within the bioregion outside the breeding season. For a few bird species bioregion have a blue outline, this indicates presence inside the bioregion outside the breeding region. However with a decreased reliance on these bioregions as the species also make use of large areas outside the Arctic during non-breeding season. For Brent Goose, the Hudson Bay has a pale yellow background indicating it is used during migration to/from the breeding grounds in the Arctic and wintering areas further south.
Numbers in the bioregion columns indicate (estimated) population sizes for that region.
Feature targets are part of the OSPAR evaluation framework, however the column remains empty, as no (international) set targets have been identified. The columns for Replication (#2) and Adequacy/Viability (#3)have been colour-coded to aid the interpretation.
#1: Beluga has been evaluated using a dataset originating from AquaMaps and within that group it is deviant. Most AquaMap suitability maps have areas where the Overall Probability reaches is maximum value of 1.0. For the Beluga map this is not the case it peaks at 0.6. Thus below the 0.8 threshold used for all other AquaMaps datasets to determine which bioregion are of prime interest to the species.
#2: Colour-coding Replication
#3: Colour-coding Adequacy/Viability
The Coherence Analysis has relied on four major sources of information – for the species:
The initial plan had been to use the distribution maps that are presented on the IUCN Red List website. Unfortunately after an initial and rewarded request for Little Auk (Alle alle) a second request did not receive any reaction. It may have been that our choice to bundle a dozen or so species into a single request did not fit within the administrative processes at IUCN. It seemed to us that not repeating the same request, with all additional text etc. for each species would be more efficient. We also did wait for a few weeks –as indicated- before an email was sent to a different address to give notice of not receiving an answer.
As an alternative the AquaMaps was found. It offers more or less full cover data on occurrence based on an ecological envelope and overall probability. The ecological envelope is extracted from a.o. data points from online collection databases such as GBIF and OBIS and includes minimum, optimal and maximum values for such habitat traits as depths, salinity, temperature etc. The resulting dataset is available for download as a csv-file and contains a reference to the c-squares system. With proper use of the c-squares information the csv-data can be coupled to the proper polygons thus yielding a geographic dataset. Below a sample map is shown for Walrus (Odobenus rosmarus).
To estimate the population within an LME (bioregion) the summed results of the Overall Probability multiplied by the area of that polygon was used.
AquaMaps (Kaschner et al., 2016) has data for a.o. Sharks and Rays; Bony Fish; Invertebrates and Sea Mammals. No data for birds.
GBIF or in full Global Biodiversity Information Facility has also been used. This source provides point data on species occurrence. It has been used for species that were not available from AquaMaps. This data source has been used for one (sea) mammal and two bird species. As an example a map for Polar Bear (Ursus maritimus) is shown.
BirdLife International also has much available data, including geographical datasets with polygons for areas used by a species during periods for 1 resident, 2 breeding, 3 non-breeding, 4 passage and 5 seasonal occurrence uncertain. For consistency the main analysis has been based on the breeding season. For most of the bird species that have been assessed a large part of the total population breeds within the Arctic (up to 100%) and it is a critical use. On our request for access to the data BirdLife International replied positively and a copy of the Birds of the World (geodatabase) was made available for the project. Please note that the geographical data is also shared with IUCN for a.o. the Red List-website.
During the processing of the data if was noticed that – unexpectedly – the results for a number of species had multiple results (most often two) for some LME. A check was made in the GIS and the (assumed) underlying cause was identified as separate and adjoining polygons stemming from different origins. To get correct the results the affected datasets where aggregated to a single total results per LME (bioregion). To illustrate this type of data an example is shown for Ivory Gull (Pagophila eburnea).
Processing the data in GIS
The diverse datasets where brought together in GIS, where a.o. the following treatment of the data was performed.
Most datasets did not arrive with a suitable projection already defined. All datasets where projected to a polar projection: WGS 1984 North Pole LAEA Atlantic (WKID 3574). This ensured that further operations that rely on determining distance or overlap between features would work properly. It was found earlier in the project that the software apparently has difficulty to handle polar projections correctly.
The C-squares required to geographically interpret the CSV-files from AquaMaps are available for download (size 0.5 degree lon/lat), from the c-squares website. An Arctic subset was selected and then projected (WKID 3574). After this preparation a join operation was all that was needed to correctly show the species distribution on a map.
To collect the statistics as defined in the OSPAR matrix approach for each species several operations where required to:
- Determine the overlap with LMEs and estimate a population size (or similar) per bioregion (=LME).
For species that have a considerable presence outside the Arctic LMEs during the evaluated season, as was the case for several bird species, an estimate was also made of the numbers/area in use outside the Arctic LMEs. With this the total Arctic population could be scaled down from the relevant (global) number. Population sizes were for the most part taken from information presented on the ‘IUCN Red List’-website, as was the IUCN status (e.g Vulnerable or Near threatened).
- Determine the proportion of the species population that could be considered protected based on an overlap with Marine Protected Areas (Adequace/Viability). The MPA-database created during Phase I of this project has been used for this analysis. For the polygon-based datasets taken from the Bird of the World-database and the AquaMaps-distribution maps the area of overlap was used as the measure. The AquaMaps data has information on how suitable a given c-square is evaluated as and this has been used to weight the area accordingly (highly suitable squares are weight the most). For point data a search distance of 25 km. was used, so that an observation point up to that distance outside an MPA would still be considered as protected. The reasoning behind this being that there is chance that the observation position is not fully accurate, the observation is subject to coincidence and the species are mobile. Most of the species could within a fairly short period reach the relative safety offered by an MPA. N.B. The same search distance has also been applied for point data in relation to LMEs.
- Determine the number of MPA that contribute to the protection of a species (Replication).
- AquaMaps. (2017). http://www.aquamaps.org/
- BirdLife. (2017). Birds of the World, BirdLife International, http://datazone.birdlife.org/home
- c-squares (2016). http://www.cmar.csiro.au/csquares/
- GBIF. (2017). Global Biodiversity Information Facility, http://www.gbif.org/species
- IUCN. (2017) IUCN Red List, http://www.iucnredlist.org/
- Kaschner, K., K. Kesner-Reyes, C. Garilao, J. Rius-Barile, T. Rees, and R. Froese. 2016. AquaMaps: Predicted range maps for aquatic species. World wide web electronic publication, www.aquamaps.org, Version 08/2016.
- Meltofte, H. (2013). Arctic biodiversity assessment. Status and trends in Arctic biodiversity. Akureyri. Retrieved from http://www.abds.is/
- OSPAR. (2006). Guidance on developing an ecologically coherent network of OSPAR marine protected areas.
- OSPAR. (2008). A matrix approach to assessing the ecological coherence of the OSPAR MPA network
- PAME. (2013). Large Marine Ecosystems (LMEs) of the Arctic area. Akureyri. Retrieved from http://www.pame.is/images/03_Projects/EA/EA/PAME_revised_LME_map_with_explanatory_text_15_Aug_2013_-_Vefur.pdf
- Rees, Tony, 2003. "C-squares", a new spatial indexing system and its applicability to the description of oceanographic datasets. Oceanography, vol. 16(1): 11-19. Available online at: http://www.cmar.csiro.au/csquares/csq-article-Mar03-lowres.pdf.