DOI: 10.5061/dryad.44j0zpc91
title: Integrating climate adaptation and biodiversity conservation in the global ocean

description: 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.
description: <strong>Vulnerability of the
 existing global MPA network to climate change.</strong>
 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. <i>Historical surface temperature
 variability</i> 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. <i>Future surface
 temperature exposure</i> 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. <i>Temperature variability and
 emergence within MPAs</i> 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. <em>et al.</em>. Rapid emergence of climate change in environmental drivers of marine ecosystems. <i>Nature Communications</i>, <b>8</b>, 1–9. (2017). IUCN and UNEP-WCMC. Protected Planet: The World Database on Protected Areas (WDPA), 10/2018. <i>Cambridge, UK: UNEP-WCMC and IUCN. Available at: www.protectedplanet.net</i>. (2018). Rayner, N. A. <em>et al</em>. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. <i>Journal of Geophysical Research</i>, <strong>108 </strong>(D14), 1–37. (2003). Tittensor, D. P. <em>et al. </em>Integrating climate adaptation and biodiversity conservation in the global ocean. <em>Science Advances</em>, in press. (2019).
description: <strong>Columns in the data
 represent:</strong> 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.
   <strong>No data
 values</strong> 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.  
 <strong>MPA values and background ocean
 values</strong> 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'.