Integrating climate adaptation and biodiversity conservation in the global ocean
Tittensor, Derek et al. (2019), Integrating climate adaptation and biodiversity conservation in the global ocean, Dryad, Dataset, https://doi.org/10.5061/dryad.44j0zpc91
The impacts of climate change and the socioecological challenges they present are ubiquitous and increasingly severe. Practical efforts to operationalize climate-responsive design and management in the global network of marine protected areas (MPAs) are required to ensure long-term effectiveness for safeguarding marine biodiversity and ecosystem services. Here, we review progress in integrating climate change adaptation into MPA design and management and provide eight recommendations to expedite this process. Climate-smart management objectives should become the default for all protected areas, and made into an explicit international policy target. Furthermore, incentives to use more dynamic management tools would increase the climate change responsiveness of the MPA network as a whole. Given ongoing negotiations on international conservation targets, now is the ideal time to proactively reform management of the global seascape for the dynamic climate-biodiversity reality.
Vulnerability of the existing global MPA network to climate change.
Data used to derive Figure 2 from Tittensor et al. (2019; Science Advances) on the time of emergence and historical variability for MPAs and the global ocean under RCP 8.5. Time of emergence refers to the year when projected mean sea surface temperature (SST) at a given location exceeds the bounds of pre-industrial conditions. Historical variability is the total thermal range, calculated from the detrended 1900 to 2018 SST time-series.
Historical surface temperature variability
Global 1 x 1° gridded monthly sea surface temperature (SST) data were extracted from the Met Office Hadley Centre Sea Surface Temperature data set between 1900 and 2018 (Rayner et al. 2003). These data are reconstructed from SST observations from the Met Office Marine Data Bank and the Comprehensive Ocean-Atmosphere Data Set (ICOADS). For each grid cell, the detrended SST series were obtained by taking the residuals from a fitted linear regression model with year as a covariate. We then calculated the range and standard deviation of the residuals to obtain proxies of SST variability that are independent of long-term trends attributable to climate change.
Future surface temperature exposure
We used the time of emergence (ToE) as an index of future climate exposure. ToE estimates were calculated as the year in which mean SST emerges from the background of natural variability and was obtained from (Henson et al. 2017) on a global 1 x 1° grid.
Temperature variability and emergence within MPAs
Information regarding the spatial distribution of all marine protected areas (MPAs) was assessed using the World Database on Protected Areas (WDPA) spatial shapefile (IUCN & UNEP-WCMC 2018). Since the resolution of the temperature and exposure observations was coarse (1°) relative to the size of many MPAs, temperature fields were related to MPAs in a two-step process. Firstly, the global 1° grid was overlaid on a spatial shapefile of MPAs to identify cells that were within the boundaries of MPA. Next, for all MPAs that did not have any overlaid cells, we identified the grid cell centroid that was geographically nearest, according to the great circle distance, to that MPA centroid. Through this process, each individual MPA was assigned at least one 1° grid cell, with the larger MPAs being assigned more.
Henson, S. A. et al.. Rapid emergence of climate change in environmental drivers of marine ecosystems. Nature Communications, 8, 1–9. (2017).
IUCN and UNEP-WCMC. Protected Planet: The World Database on Protected Areas (WDPA), 10/2018. Cambridge, UK: UNEP-WCMC and IUCN. Available at: www.protectedplanet.net. (2018).
Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research, 108 (D14), 1–37. (2003).
Tittensor, D. P. et al. Integrating climate adaptation and biodiversity conservation in the global ocean. Science Advances, in press. (2019).
Columns in the data represent:
lon - longitude midpoint of 1x1 degree grid cell
lat - latitude midpoint of 1x1 degree grid cell
wdpaid - ID of protected area (if there is one) in the World Database on Protected Areas (IUCN & UNEP-WCMC 2018)
SST_ToE - Time of emergence of mean SST value against background signal as detailed in Tittensor et al. (2019) (year)
SST_HistRng - Historical temperature range from detrended SST data as detailed in Tittensor et al. (2019) (degrees C)
MPA - Is the grid cell associated with an MPA or is it a background (global ocean) grid cell
Note that there may be multiple rows in the data set with the same lon/lat coordinates if there are multiple MPAs within the same 1x1 degree grid cell.
No data values
NA values represent no data. Observations missing both time of emergence and SST range are removed. Some observations remain which are missing either time of emergence (data missing from the source data as received) or SST range (data insufficient to calculate range). In all, 4302 of 4479 MPAs in the data set have both ToE and SST range associated.
MPA values and background ocean values
To get only obs in MPAs, subset the data to select only those rows with MPA value being 'yes'.
To get non-MPA marine observations (background grid cells), subset the data to select only those rows with MPA value being 'no'.
Canada First Research Excellence Fund Ocean Frontier Institute (OFI): Safe and Sustainable Development of the Ocean Frontier (Module G).
Global Environment Facility, Award: GEF-5810
Natural Environment Research Council core funding to British Antarctic Survey