Running head: Global protection of abiotic conditions
Protected areas (PAs) are one of the most effective ways to conserve biodiversity and have been steadily growing in number and overall areal extent. Yet, individual PAs are still mostly small and unconnected and are often implemented without consideration of already existing PAs. This is likely to cause an over-representation of certain biophysical conditions within the PA network, which may in turn weaken their overall potential to conserve biodiversity, and could potentially exacerbate under future climate change. Here, we assess the representativeness of the world’s biophysical conditions by the current global PA network, highlighting which conditions are currently under-protected and where these conditions are located. Quantifying the protection coverage of biophysical conditions, we found that for the terrestrial realm, high temperature and low precipitation conditions as well as medium and very high elevational levels were underrepresented, while for the marine realm, different levels of salinity, sea surface temperature and particularly the deep sea were underrepresented. Overall, the protection of terrestrial conditions was evenly distributed for elevation, while for the marine realm sea surface temperature (SST) was evenly distributed. Terrestrial environments that are both cold and very dry as well as discrete SST and SSS conditions across most depths had mostly low protection. Low-protected conditions mostly occurred in the Sahara and the Arabian Peninsula for the terrestrial realm and along the Tropic of Capricorn and towards the poles for the marine realm. While biodiversity measures are of prime importance for PA planning strategies, our study adds a frequently overlooked perspective by highlighting potential biophysical gaps in the current global PA network. This may provide additional useful insights for researchers, conservation practitioners as well as policy makers in order to improve prioritization efforts for a more comprehensive PA system globally.
Keywords: temperature, precipitation, salinity, elevation, bathymetry, marine, terrestrial, abiotic conditions
Anthropogenic impacts on biodiversity often vary between land and ocean as well as across different parts of the world, thus requiring different conservation priorities for mitigation (Bowler et al. 2020). Yet, in situ conservation is recognized as a fundamental requirement to maintain biodiversity, with protected areas (PAs) being the most effective tool (Chape et al. 2005). PAs are crucial for reducing habitat loss (Geldmann et al. 2013), safeguarding biodiversity and conserving the provisioning of ecosystem services (Stolton & Dudley 2010). Subsequently, PAs have often become the last stronghold for many endangered species (Pacifici et al. 2020).
As a result, the global PA network has been steadily growing in number and areal extent over the last 50 years, which confirms the recognition by governments to conserve the planet’s natural ecosystems. The Convention on Biological Diversity (CBD) has set the target (Aichi’s Biodiversity Target 11) to protect at least 17% of terrestrial and inland water and 10% of coastal and marine areas by 2020 in order to improve the status of biodiversity (CBD 2011, http://www.cbd.int/sp/targets) and as of July 2018, around 14.9% of terrestrial areas and inland waters, 16.8% of coastal and marine areas within national jurisdiction and 1.2% of the global ocean (areas beyond national jurisdiction) were covered by PAs (UNEP-WCMC, IUCN & NGS 2018). Aichi Target 11 calls for the PA coverage to be ecologically representative (CBD 2011), recommending a focus on the coverage of ecoregions. Whilst this is likely to enhance the coverage of unique ecological communities, it is not equal to the representativeness of species or other biological properties at even lower spatial scales (Visconti et al. 2019). Over the last decade, various strategies, aiming to enhance the ecological representation of PAs, have been developed. They range from focusing on the coverage of genetic traits (Pollock et al. 2017), groups of individual species (i.e. species of conservation concern; Venter et al. 2014) or the protection of mountain biodiversity (Rodriguez-Rodriguez et al. 2011), to wider aspects including the maintenance of ecosystem functions (Harvey et al. 2017).
Accounting for future direct and indirect impacts of climate change is another aspect that is becoming increasingly important in conservation planning, and various spatial prioritization approaches incorporating potential impacts have been developed (Jones et al 2015; Maxwell et al 2020). With regard to climate change, especially the representation of the climatic space covered by the PA network will increase in its importance. Protecting a representative set of current abiotic conditions is likely to provide the required diversity of environments to support biodiversity under future climate (Ackerly et al. 2010; Anderson & Ferree 2010; Beier & Brost 2010) and the evenness of climatic representation under protection has been found to positively influence the representation of climatic conditions under climate change (Elsen et al. 2020). Overall, given that nature is often considered as a continuous gradient of biophysical conditions, rather than based on a set of discrete units (from vegetation types to realms), evaluating the representativeness of nature in PA networks using biophysical gradients has great potential. It further allows to forecast scenarios of representativeness in the face of future climate change (Elsen et al. 2020) and to assess other factors according to particular spatial, temporal, and conceptual scales (Joppa & Pfaff 2009; Baldi et al. 2017, 2019).
The representation of climatic and topographic conditions across terrestrial protected areas (TPAs) has been assessed at various spatial scales, ranging from local to global (Rouget et al. 2003; Joppa & Pfaff 2009; Batllori et al. 2014; Monahan & Fisichelli 2014; Elsen et al. 2018; Wang et al. 2018). Baldi et al. (2017, 2019) additionally included the representation of human and biological factors, while Sarey et al. (2020) assessed the representation of terrestrial ecosystems. On the contrary, the representation of biophysical conditions across marine protected areas (MPAs) has been less studied (but see Devillers et al. 2015; Roberts et al. 2019) and to our knowledge so far no study has looked at the biophysical representativeness of MPAs at a global scale.
Here, we build upon this previous work by investigating and comparing the global protection of terrestrial and marine PAs with respect to various biophysical factors (temperature, precipitation and topography as well as sea surface temperature (SST), sea surface salinity (SSS) and bathymetry, respectively) at a spatial resolution of 30 arc-seconds. We compare different protection categories, as PAs with a low-protection category (e.g. IUCN categories IV to VI) have only limited restrictions on resource exploitation (Shafer, 2015). We specifically test how evenly the biophysical conditions are currently covered by PAs and under which conditions protection coverage is lower than expected considering various protection categories. In addition, we analyze which biophysical conditions are more evenly represented by terrestrial or marine PAs and assess where the protection of certain conditions is located.
Global PA data were derived from the World Database on Protected Areas (https://protectedplanet.net; IUCN & UN Environment Programme 2020) and contained polygon data for 225,098 PAs (208,796 terrestrial, 10,724 coastal, 5,578 marine, see Supporting Information Fig. S1). We excluded PAs for which only point information is available (21,250 PAs, see Supporting Information Fig. S2), as these only provide information on their overall spatial position and areal coverage, but not on their precise spatial distribution, as required for our analysis. The PA data were split into marine (coastal and marine) and terrestrial (coastal and terrestrial) PAs and then further divided into four protection categories (IUCN categories Ia, Ib and II = I-II, IUCN categories III and IV = III-IV, IUCN categories V and VI = V-VI, IUCN categories not reported, not applicable and not assigned = Non-designated, see Supporting Information Fig. S3).
The IUCN categories group PAs according to their management objective: IUCN categories Ia, Ib and II include Strict Nature Reserves, Wilderness Areas and National Parks, IUCN categories III and IV include Natural Monuments or Features and Habitat/Species Management Areas, IUCN categories V-VI include Protected Land- and Seascapes and Protected Areas with Sustainable Use of Natural Resources. The four protection categories were chosen, as IUCN categories I-IV have a biodiversity focus, while IUCN categories Ia, Ib and II additionally focus on the protection of intact ecosystems (Dudley 2008).
For each of the four protection categories, we calculated the % cover of protection for each grid cell of a raster with 30 arc-seconds resolution (Fig. 1 a). This resulted in a gridded layer of % cover of protection for each of the four protection categories (I-II, III-IV, V-VI, Non-designated) for marine and terrestrial areas respectively (see Supporting Information, Fig. S4).
In addition, we also calculated the % cover of protection for all categories together (total % protection) in order to distinguish where PAs of different designation types have overlapping extents (Deguignet et al. 2017). The total % protection was then used to adjust cells of overlapping extents (cells where the sum of the area protected of the individual protection category layers was larger than the total area protected), by adding up the area protected by each protection category, starting with the strictest protection category, until the sum of the individual areas reached the total area protected. This then resulted in a non-overlapping data set, always keeping the strictest protection category for areas with overlapping polygons. This is necessary because if overlapping PAs are not resolved, the underlying biophysical space would be counted twice when calculating the total percentage coverage.
The spatial extent of the PA layers was based on the extent of the biophysical data. The PA layers were transformed into Mollweide Equal-Area projection (ESRI:54009) and in the end covered 162,067,794 and 421,197,812 cells for terrestrial and marine areas, respectively.
Annual mean temperature (bio1) and annual precipitation (bio12) were obtained from Worldclim v2 (https://worldclim.org/; Fick & Hijmans 2017), which compiles climatic information over a 30-yr period (1970 - 2000) at a resolution of 30 arc-seconds. We chose annual mean temperature and total annual precipitation, as these two variables are among the most commonly used variables in macroecological research (Porfirio et al. 2014) and at the same time also the main determinants of the world’s terrestrial biomes (Holdridge 1947; Whittaker 1975). Nevertheless, we also provide results for temperature seasonality, temperature annual range and precipitation seasonality, which were also obtained from Worldclim v2 (see Supporting Information, Fig. S7). Worldclim v2 interpolates observations from weather stations using elevation and distance to coast, as well as maximum and minimum land surface temperature and cloud cover derived from MODIS satellite data (Fick & Hijmans 2017). While Worldclim v1 has been shown to be of limited reliability in regions with poor station density and varied topography (Soria-Auza et al. 2010), this problem has been overcome in Worldclim v2 by integrating the above-mentioned satellite variables.
Elevation data were obtained from EarthEnv (http://www.earthenv.org/topography; Amatulli et al. 2018), which are based on the global 250 m GMTED2010 digitial elevation model product and averaged into a 30 arc-seconds grid (Amatulli et al. 2018). Elevation was used as additional variable, as it is the most common non-climatic variable used in macroecological research (Porfirio et al. 2014), and is a strong explanatory variable for species richness (Kaufman & Willig 1998). The EarthEnv dataset does not include latitudes above 84° North and below -56° South, so we excluded these areas from the analysis of the terrestrial data. Worldclim and EarthEnv layers were transformed into Mollweide Equal Area projection (ESRI:54009; see Supporting Information Fig. S5).
Mean annual sea surface temperature (SST, biogeo13), mean annual sea surface salinity (SSS, biogeo08) and bathymetry were obtained from MARSPEC (http://www.marspec.org/; Sbrocco & Barber 2013). MARSPEC is the best high-resolution global marine data set currently available and is a 10-fold improvement in spatial resolution of the next-best dataset (Bio-ORACLE; Tyberghein et al. 2012). It combines different satellite and in situ observations of SST, SSS and bathymetry of the global ocean and combines them to a harmonized data set at a spatial resolution of 30 arc-seconds. Bathymetry data were derived from SRTM30_PLUS v6.0, while the climatic layers were derived from NOAA’s World Ocean Atlas (SSS) and NASA’s Ocean Color Web (SST). The climatic variables range over varying time periods (SSS = 1955 – 2006, SST = 2002 – 2010), but provide both information about inter-annual means and their variance (Sbrocco & Barber 2013). Similar to the terrestrial data, we also provide results for annual range in SSS and SST as well as annual variance in SSS and SST, which were also obtained from MARSPEC (see Supporting Information, Fig. S8). All marine layers were transformed into Mollweide Equal Area projection (ESRI:54009; see Supporting Information Fig. S6).
We divided the amplitude of each biophysical variable into percentile bins (n = 100, Fig. 1b) and divided strongly skewed variables into the respective optimal number of bins (n < 100), which were identified using the bins() function of the binr package (Izrailev 2015) in R (R Core Team, 2020). For each variable, we then combined these bins with the different layers of % protection and calculated the overall % coverage per bin that is protected, only considering PAs for which environmental data exists (Fig. 1 c, d). The expected value of protection was calculated by dividing the total area protected equally among the area covered by each bin, mimicking a uniform distribution of protection across all conditions of each variable. We then compared the % cover of protection with the expected value of protection for each bin of each variable and assessed the evenness in the distribution (by comparing it with the expected value) using a Chi-square goodness of fit test. In addition, we also assessed the % protection across each variable using equally-spaced bins (1°, 100 mm, 100 m and 1 PSU for temperature/SST, precipitation, elevation/bathymetry and SSS, respectively; see Supporting Information Fig. S11).
To look at the interaction in protection across multiple variables, we performed the same procedure for each pairwise combination of all terrestrial and all marine variables separately (see Supporting Information Fig. S9 & 10, 12 - 14). To derive a map of % environmental protection for each variable, which indicates how well the underlying environmental condition of a given location is protected globally, we then combined the binned data with the % area protected of each individual bin and each pairwise combination (Fig. 1e, see Supporting Information, Fig. S15 & S16).
The entire analysis was performed in R version 4.0 (R Core Team, 2020), but required among others the use of R packages specifically designed for handling large spatial data, such as sf (Pebesma, 2018), fasterize (Ross, 2020), exactextractr (Baston, 2020) and terra (Hijmans, 2020), as well as the use of high-performance computers. The full code of the performed analysis and to recreate the shown figures is publicly available from: https://github.com/RS-eco/globePA/.
For the terrestrial realm, mostly very high (≥ 27 °C) and low to intermediate (0.6 – 20 °C) temperature conditions were under-protected (observed lower than expected; Fig. 2 a). Low (≤ 151 mm), intermediate (270 - 571 mm) and some high (1074 – 1610 mm) annual precipitation conditions were under-protected (Fig. 2 b), as well as elevational levels between 92 and 407 m, 452 and 729 m and above 3944 m (Fig. 2 c). A Chi-square goodness of fit test between the expected and observed distribution showed that the observed distribution of temperature and precipitation significantly differed from the expected distribution (p ≤ 0.05, Fig. 2 a-c). When we only considered PAs with a strict IUCN protection category (I-II & III-IV), the % coverage of terrestrial conditions was reduced across almost all conditions, but showed a similar deviation from the then lowered expected value (Fig. 2 a-c). All of the additional terrestrial variables considered showed an under-representation towards their upper range and only temperature annual range significantly differed from the expected distribution (p ≤ 0.05, see Supporting Information Fig. S7). Looking at the protection coverage across equally-spaced bins, rare conditions usually had a higher protection coverage than common ones (see Supporting Information Fig. S11 a – c).
For the marine realm, various SST conditions were under-protected (Fig. 2 d). Particularly intermediate (32.7 – 34.9 psu) and high (≥ 36 psu) SSS conditions were under-protected (Fig. 2 e), as was most of the deep sea (-3634 - - 5999 m), in stark contrast to sites with intermediate and very shallow depth as well as depths below 6000 m (Fig. 2 f). For the marine realm, SSS and bathymetry showed a significant difference (p ≤ 0.05) in the goodness of fit between the expected and observed distribution (Fig. 2 d – f). When we only considered the strict IUCN categories I-II & III-IV, the % coverage of marine conditions also declined considerably, but here the contribution of the different IUCN categories varied strongly across conditions, at least for SST and SSS (Fig. 2 d-f). The variance and annual range in SST was under-protected towards distinct lower and upper conditions, while almost all conditions in the annual range and annual variance in SSS were under-protected. However, only annual variance in SST differed significantly from the expected distribution (p ≤ 0.05, see Supporting Information Fig. S8). Again, when considering equally-spaced bins, rare conditions usually had a higher protection coverage than more common ones (see Supporting Information, Fig. S11 d – f).
Looking at the pairwise combination of two variables in the terrestrial realm, we found that for temperature and precipitation mostly conditions at the lower temperature and upper precipitation limit had a high protection coverage, while very high temperature and very low precipitation conditions as well as low temperature and low precipitation conditions were only marginally protected (0 – 1 %). In addition, there were certain conditions with very low temperatures and low and high precipitation which were not protected at all (Fig. 3 a). For temperature and elevation, most of the lower temperature conditions across all elevational bands had a very high protection coverage (≥ 40 %), while very high temperature conditions across most elevational bands were only marginally protected (0 – 1 %, Fig. 3 b). For precipitation and elevation again the majority of the upper precipitation limits had a high protection coverage across all elevational bands (≥ 25 %), while conditions with low precipitation and very high elevation were either marginally protected or not protected at all (Fig. 3 c). Overall, the combination of temperature and elevation was best protected (largest area with high % protection), while the combination of temperature and precipitation had the largest area with a low protection (0 – 7 %; Fig 3 d).
For the marine realm, the patterns were less clear. For the combination of SST and SSS, well-protected conditions (≥ 40 %) were mostly present at high SSS conditions across almost all SST conditions, while conditions that were only marginally or not at all protected occurred across various SSS and SST conditions (Fig. 3 e). For bathymetry and SST (Fig. 3 f) and for bathymetry and SSS (Fig. 3 g), very well-protected conditions (≥ 25 %) mostly occurred at very shallow depths (0 – 46 m) across all SSS and SST conditions (Fig. 3 f, g), in depths deeper than 5755 m with very low (around 0°C) SST conditions (Fig. 3 f) and at sites with SSS of 32 – 32.5 and 35.4 – 35.7 psu across most depths (Fig. 3 g). Low-protected (0 – 1 %) and not-protected conditions occurred mostly at low SST conditions (around 10°C) across various depths (Fig. 3 f) and at very low (around 20 psu) and medium (around 34.5 psu) SSS conditions across various depths (Fig. 3 g). Similar to the terrestrial realm, the combination of SST and SSS showed the largest area with a low protection (0 – 7 %). Strikingly, for the marine realm all pairwise combinations had a considerable area with conditions that are currently not protected at all (0 %; Fig. 3 h).
Looking at the spatial patterns of the protection coverage among the different variables, we found that 79 % of the terrestrial realm had temperature conditions that were protected by 7 - 16 %. Greenland exhibited unique temperature conditions which had a higher % protection coverage of 40 – 100 %, while large parts of the Sahara and the Arabian Peninsula exhibited temperature conditions that were only protected by 2.5 – 7 % (Fig. 4 a). Around 84 % of the terrestrial realm experienced precipitation conditions that were protected by 7 – 16 %, while 9 % are protected by 16 – 25 %. The northern part of South America (mostly Colombia, Peru, Bolivia & Brazil), some tropical regions in West Africa, parts of Indonesia and some parts of China (mostly the Himalayan region) experienced precipitation conditions that were protected by 25 - 40 % (Fig. 4 b). 89.9 % of the terrestrial realm had elevational bands that were protected by 7 – 16 % and 9.9 % that were protected by 16 – 25 %. Elevational conditions that were protected by 16 – 25 % were mostly located in Greenland and China (Fig. 4 c). Looking at the spatial overlap of the different terrestrial variables, we found that unique combinations of temperature and precipitation conditions that were protected by ≤ 16 % occurred all over the world, covering about 71 % of the terrestrial realm (Fig. 3 d). Areas with a low protection (0 – 7 %) were mostly located in the western part of the USA, large parts of the Sahara and the Arabian Peninsula, as well as parts of Central Asia (Fig. 4 d). Unique combinations of temperature and elevation conditions that were protected by ≤ 16 % covered around 73 % of the terrestrial realm (Fig. 3 d). Areas where the combination of temperature and elevation conditions were protected to a high degree (≥ 25 %) mostly occurred in Greenland, parts of South America as well as parts of Russia and China, while areas that experienced conditions that were protected by ≤ 7 % were mostly located in the western part of the Sahara as well as the the Arabian Peninsula, but also across Argentina, Australia, Russia and the USA (Fig. 4 e). Sites that were protected by ≤ 16 % of unique combinations of precipitation and elevation conditions covered 75 % of the terrestrial realm (Fig. 3 d). Locations with a low protection (0 – 7 %) of this combination were found along Chile, as well as parts of the Sahara, the the Arabian Peninsula and China, while locations with a high protection (25 – 100 %) covered large parts of northern South America, parts of eastern Africa, Indonesia and New Guinea, the Himalayan Mountains, as well as parts of Greenland (Fig. 4 f).
For the marine realm, 50 % of sites had SST conditions that were protected by 2.5 - 7 % and 47 % that were protected by 7 – 16 % (Fig. 5 a); 46 % of sites had SSS conditions that were protected by 2.5 - 7 %, while 42 % of sites were protected by 7 – 16 % (Fig. 5 b); and 61 % of sites had a depth level which was protected by 2.5 - 7 %, while 37 % were protected by 7 – 16 % (Fig. 5 c). SST conditions showing rather high levels of protection (7 – 16 %) occurred along the Tropic of Cancer and Tropic of Capricorn as well as the Arctic and Antarctic Circle (Fig. 5 a), while areas with low-protected conditions (0 – 2.5 %) were mostly located at the poles and east of New Guinea. SSS was well-protected (16 – 25 %) along the west coast of the USA and Canada, as well as the east coast of Australia, while areas with low-protected (1 – 2.5 %) SSS conditions occurred mostly in the Atlantic and the Indian Ocean and the Gulf of Oman, as well as the South Pacific Ocean (Fig. 5 b). Well-protected (7 – 16 %) bathymetry conditions were mostly located along the coasts and the ocean trenches, while very-well protected (16 – 25 %) bathymetry conditions were located along the coast of Australia, most of the South China Sea, as well as most of the Arctic Ocean. Areas that exhibited conditions that were not well-protected (2.5 – 7 %) covered the remaining marine realm and occurred in all major oceans (Fig. 5 c). All three variables only had a marginal number of sites (≤ 5 %) with a protection coverage ≥ 16 % (Fig. 5 a - c). Looking at the spatial patterns of the pairwise comparison of SST, SSS and bathymetry, we found that mostly the South Pacific Ocean exhibited unique conditions of pairwise combinations of biophysical variables that were protected by ≥ 16 %, while particularly the Tropic of Capricorn, as well as parts of the Atlantic Ocean and the North Pacific Ocean exhibited conditions that were only protected by 0 – 2.5 % (Fig. 5 d, e, f).
Our results showed that the terrestrial protected area (TPA) network provided a wide coverage of the current biophysical conditions present across the terrestrial realm, although Baldi et al. (2017) argues that current protection patterns are mostly driven by opportunistic forces rather than preferential and representative motivations. However, for the terrestrial realm both low and high temperature as well as low and medium precipitation conditions lacked protection (Fig. 2 a, b ) and well-protected biophysical conditions were usually the ones that occured less frequently (see Supporting Information, Fig. S11). We further found that low and very high elevational levels were underrepresented in protection (Fig. 2 c), which is opposing to the results of Elsen et al. (2018) who found that TPAs are biased towards high elevations. However, Elsen et al. (2018) did not consider the relative frequency of the different elevational levels, and without this consideration results would be similar (see Supporting Information, Fig. S11 c). Furthermore, the World Database on Protected Areas lacks data on Chinese PAs (Bingham et al. 2019), which might confound global results, especially with regards to elevation (You et al. 2018). Biophysical conditions of the terrestrial realm lacked protection for certain conditions across all variables and overall only elevation wass evenly represented by the global TPA network.
The patterns across the marine realm were less clear, as various sea surface salinity (SSS) and sea surface temperature (SST) conditions were under-represented, while with respect to bathymetry only the deep sea was under-represented (Fig. 2 d - f). Again, rare conditions (i.e. low SSS sites) were well-protected (see Supporting Information, Fig S11). The lack of deep-sea protection is cause for concern as the abyssal plain (2000 – 4000 m) is by surface area the largest habitat on earth (Angel 1993) and still largely unexplored with regards to biodiversity (Webb et al. 2010). Even more concerning, we found an uneven distribution in protection for SSS and bathymetry. This highlights the importance to consider biophysical conditions when implementing new MPAs in order to establish a representative global MPA network that helps safeguarding current and future biodiversity, instead of creating new MPAs in places of low economic interest and irrespective of their value for conservation (Devillers et al. 2015).
Looking at the contribution of the different protection categories, the terrestrial realm seemed to be evenly represented across all protection categories, while for the marine realm certain SST, SSS and bathymetry conditions were mostly protected by IUCN category V-VI or non-designated MPAs (Fig. 2). This is again cause for concern, given that PAs with a stricter IUCN protection category are more likely to provide an effective conservation measure (Jones et al. 2018a; Leberger et al. 2020).
Looking at the pairwise combination of two variables, we found that for the terrestrial realm conditions with low temperature and high precipitation at the same time (Fig 3. a), as well as low temperature and high precipitation conditions across all elevational levels had a high protection coverage (Fig. 3 b, c), while dry and hot, as well as hot conditions across all elevations had a low protection coverage (Fig. 3 a - c). This is in line with a study by Elsen et al. (2020), who found that there is a bias of protection towards rarer portions of climate space, particularly colder and wetter environments, although Elsen et al. (2020) again did not consider the relative frequency of individual conditions (see Supporting Information, Fig S12). This bias might reflect historical human settlement preferences, as TPAs are typically biased towards isolated locations with low population density and low cropland suitability (Joppa & Pfaff 2009; Baldi et al. 2017).
For the marine realm, well-protected conditions were mostly present at the upper limits of SSS across almost all SST conditions (Fig. 3e) and at very shallow depths (0 – 46 m) across all SSS and SST conditions (Fig. 3 f, g), while very deep locations across various SST conditions and low SSS conditions across most depths were not protected at all (Fig. 3 f, g). This again highlights that we are specifically lacking protection in the marine realm and, in order to create a fully representative MPA network, need to target especially these areas, irrespective of national jurisdictions (Liu et al. 2020) when establishing new PAs.
For the first time, we assessed how the representativeness of certain biophysical conditions is distributed across space. This is particularly important, as even though conditions are well represented, they might still be spatially sparse (which we account for by using equal-frequency bins) or clustered around a single location. This in turn may weaken their overall representativeness, as it would make these conditions particularly vulnerable to anthropogenic impacts and climate change, even if there are strictly protected.
Large parts of the terrestrial realm had temperature (79 %), precipitation (84 %) and elevation (90 %) conditions that were protected by 16 – 25 % (Fig. 4 a -c). Greenland, most of which is protected by a single PA, the NE Greenland National Park, exhibited unique temperature conditions, which resulted in a high protection coverage of 40 – 100 %. The northern part of South America had a high protection coverage with regard to precipitation conditions. However, this is mostly due to a large number of PAs with a low IUCN protection status (Baldi et al. 2019). Low-protected conditions in the terrestrial realm mostly occurred in parts of China (Fig. 4 d), even though this might be confounded by the fact that the Chinese government had their PA data being removed from the World Database on Protected Areas in 2018 (You et al. 2018; Bingham et al. 2019), as well as in the Sahara and the Arabian Peninsula(Fig. 4 d). The latter observation is surprising as conflicts with human population pressure or agricultural use should be relatively low in these regions. Although they harbor only low amounts of biodiversity, increasing the protection level in these areas may contribute to conserving parts of the last wilderness areas of our planet that remain largely uninfluenced by humans (Watson et al. 2018).
For the marine realm 50 % of the total areas of SST, 46 % of SSS and 42 % of bathymetry conditions were protected by 7 – 16 % (Fig. 5 a – c). Well-protected biophysical conditions mostly occurred along the coasts as well as in the Tropics, while areas with low-protected conditions were mostly located along the Cancer of Capricorn and towards the poles. But this is counteracted by the fact that anthropogenic threats to marine ecosystems concentrate especially on continental shelves (Halpern et al. 2015) and so co-occur with areas that exhibit well-protected conditions. Similar to the terrestrial realm, most marine sites had a low protection when considering pairwise combinations of the considered variables, while a small number of sites mostly situated east of Australia experienced conditions where multiple variables were protected by ≥ 16 % (Fig. 5 d, e, f). However, this result depends on the overall spatial extent considered and looking at the local representation of Australian MPAs. Roberts et al. (2019) found that Australian MPAs over-represent warm, offshore waters and under-represent temperate environments.
The lower protection coverage across the marine realm is unsurprising, given that overall about 15% of the Earth’s land surface and only 7.3% of the world’s oceans are registered with a certain status of protection (UNEP-WCMC, IUCN & NGS 2018). Given that 67% of our planet is covered by the Ocean (Reaka-Kudla 1997), the coverage of MPAs is still largely under-represented. This is also reflected in the difference of the protection coverage target (17 vs. 10%) under the Aichi Biodiversity Target 11 (CBD 2011). In addition to the uneven representation of biophysical conditions in the marine realm, this extreme mismatch in levels of protection between the terrestrial and marine realm is cause for huge concerns, as anthropogenic impacts on the world’s oceans are continuously increasing (Halpern et al. 2015).
Our study is based on temperature, precipitation and elevation for the terrestrial as well as SST, SSS and bathymetry for the marine realm (for additional variables, see Supporting Information, Fig. S7 & S8). While these variables are the most commonly used variables in macroecological research and are easily interpretable at first glance, we are aware that they do not fully represent the biophysical conditions of the world and that there are other factors, such as geology, soil and availability of resources, which influence species distribution, composition and diversity (Anderson & Ferree 2010; Lawler et al. 2015).
Even if the current global PA network fully represents a diversity of current biophysical conditions, which is likely to support future biodiversity under global change (Ackerly et al. 2010; Anderson & Ferree 2010; Beier & Brost 2010), the representativeness of nature exceeds biophysical conditions, since there are various forces that promote conservation (Watson et al. 2014; Lawler et al. 2015). Given that the Aichi Biodiversity Target 11 calls for “ecologically representative PAs of particular importance for biodiversity and ecosystem services”, biotic measures, such as the number of threatened species (Venter et al. 2018), biotic interactions (Lawler et al. 2015), phylogenetic diversity (Rosauer et al. 2017), and the coverage of climatic niches (Hanson et al. 2020) are additional factors to be considered. However, currently neither the world’s most diverse, nor the most productive ecosystems are the most protected (Lindegren et al. 2018; Maxwell et al. 2020).
While topographic conditions (elevation, bathymetry) remain more or less constant through time, climatic conditions are subject to change drastically in the near and far future (IPCC 2013), which will also have a strong impact on the current PA network (Hoffmann et al. 2019; Holsinger et al. 2019; Elsen et al. 2020). This will affect the marine realm in particular, as here climate change was positively correlated to other drivers, such as fishing and pollution (Bowler et al. 2020).
In this study, we further only look at the total percentage of an area that is protected by a certain condition and we do not differentiate between large or small PAs, thus we do not at all consider connectivity among PAs or the respective biophysical conditions. However, increasing PA coverage and size has been found to be a good strategy for improving multi-species connectivity (Santini et al. 2015). Given that the number of PAs generally decreases with size, only 0.3% of PAs are larger than 10,000 km2 and more than 50% of all PAs are less than 1 km2 in size (UNEP-WCMC 2018), this might be a problem. Small PAs in particular are very ineffective, due to their limited coverage of many species’ habitats (Rodrigues et al. 2004). Given that connectivity among PAs will become even more important when species that are currently under protection shift their ranges in order to track changing climatic conditions (Littlefield et al. 2017), we also need to consider the size and connectivity for the establishment of future PAs.
Furthermore, PA coverage alone is not a measure of the overall effectiveness of PA performance or conservation success (Edgar et al., 2014; Klein et al. 2015). Recent reviews have concluded that the 10% target, while ambitious, is unlikely to meet all of the objectives for MPAs (O’Leary et al. 2016). Some parts of the world’s oceans are rich in biodiversity, but have no, or very little, protection (Klein et al. 2015) and currently only 13.2% of marine wilderness areas remain, of which only about 4.9% are currently protected (Jones et al. 2018b).
The results of our study highlight potential gaps in the global coverage of the biophysical conditions by the current PA network. In order to conserve global biodiversity, PAs need to represent all of the world’s ecosystems and the areas that contain exceptional or endemic species and habitats (Olson & Dinerstein 2002; Davidson & Dulvey 2017). However, the representativeness of PAs, and thus biodiversity conservation, will only be strengthened if future conservation actions are driven by preferential and representative motivations rather than opportunistic forces (Baldi et al. 2017).
Instead of focusing on achieving the Aichi Target 11 or future conservation targets, we should rather focus on the outcome of conservation measures (Visconti et al. 2019). Unfortunately, neither the areal extent or the total number, nor the spatial distribution of PAs, allow us to test if PAs achieve their conservation objectives and whether biodiversity is represented within them (Chape et al. 2005). PAs, and the habitats and species they are meant to protect, are still under severe threat, i.e. due to alteration of PAs, illegal use, and global anthropogenic changes such as pollution and global warming (Partelow et al. 2015; Hoffmann et al. 2019, Qin et al. 2019). Even PAs with the highest IUCN protection status still show accelerating rates of forest loss (Lebeger et al. 2020), highlighting the importance of PA management, as un-monitored sites will not effectively conserve the species or the ecosystem.
Given the rising challenges we face under climate change, a conservation planning approach that increases the coverage of species, ecosystems and ecological processes is crucial, as climate change might lead to new species communities and even new ecosystem types (Mawdsley et al. 2009). While biodiversity measures are of prime importance for PA planning strategies, precise knowledge on species distributions is still lacking information on many species, particularly on non-iconic ones. Our assessment of the representativeness of biophysical conditions highlights potential gaps in the current global PA network add thus adds a frequently overlooked perspective. We believe that our results provide useful insights for researchers, conservation practitioners as well as policy makers in order to improve prioritization efforts for a more comprehensive future global protected area network.
Supporting information on the global protected area network (Appendix S1), environmental data (Appendix S2), additional and extended results (Appendix S3) and results with equally-spaced bins (Appendix S4) are available online. The authors are solely responsible for the content and functionality of these materials. Queries (other than absence of the material) should be directed to the corresponding author.
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Figure 1. Illustration of the methodological approach used to identify the protection coverage of the different conditions for each biophysical variable: (a) Calculation of the % protection for each grid cell; (b) data of each biophysical variable is reclassified into equal frequency bins; (c) binned environmental and gridded % protection data is then merged; (d) merged data is summarized by calculating the total % protection of each bin; (e) summary data is then merged with the geographic locations of each bin to identify the % protection of each grid cell for a certain biophysical variable.
Figure 2. Percentage of area protected (see Fig. 1 d), separately for different IUCN categories, for different biophysical conditions in the terrestrial ((a) temperature, (b) precipitation and (c) elevation) and marine ((d) sea surface temperature (SST), (e) sea surface salinity (SSS) and (f) bathymetry) realm. In order to calculate the % of area protected, the different biophysical conditions were divided into 100 equal frequency bins (percentiles), unless the optimal bin size was reached earlier due to a strong skewness of the environmental data (NSST = 96, NSSS = 84). Black lines indicate the expected % of area protected given that all conditions are evenly protected. Note that the individual bins of each variable represent percentile ranges and so cover a different extent, which is also reflected in the irregular intervals of the x-axis. See Supporting Information for a similar figure based on equally-spaced bins.
Figure 3. Percentage of land area (a, b, c) and marine area (d, e, f) protected by each pairwise combination of the biophysical variables considered (temperature vs. precipitation, temperature vs. elevation and precipitation vs. elevation, sea surface temperature (SST) vs. sea surface salinity (SSS), SST vs. bathymetry, SSS vs. bathymetry). Pixels are based on the equal frequency bins shown in Fig. 2 (see Supporting Information for a similar figure based on equally-spaced bins). Bar charts (d, h) indicate the total area that is covered by certain levels of protection. See Supporting Information for a detailed figure on the available area, protected area and % area protected for each of the combinations.
Figure 4. Maps of the percentage of terrestrial realm protected by each biophysical condition ((a) temperature (Temp), (b) precipitation (Prec) and (c) elevation) and their pairwise combinations ((d) temperature and precipitation, (e) temperature and elevation, (f) precipitation and elevation). The % protected is based on the equal frequency bins shown in Figure 2 and 3, see Supporting Information for maps based on equally-spaced bins. Maps are in Mollweide projection (ESRI:54009).
Figure 5. Maps of the percentage of the marine realm protected by each biophysical condition ((a) sea surface temperature (SST), (b) sea surface salinity (SSS) and (c) bathymetry) and their pairwise combinations ((d) SST and SSS, (e) SST and bathymetry, (f) SSS and bathymetry). The % protected is based on the equal frequency bins shown in Fig. 2 and Fig. 3, see Supporting Information for maps based on equally-spaced bins. Maps are in Mollweide projection (ESRI:54009).
Figure 1. Illustration of the
methodological approach used to identify the protection coverage of the
different conditions for each biophysical variable: (a) Calculation of
the % protection for each grid cell; (b) data of each biophysical
variable is reclassified into equal frequency bins; (c) binned
environmental and gridded % protection data is then merged; (d) merged
data is summarized by calculating the total % protection of each bin;
(e) summary data is then merged with the geographic locations of each
bin to identify the % protection of each grid cell for a certain
biophysical variable.
Figure 2. Percentage of area protected
(see Fig. 1 d), separately for different IUCN categories, for different
biophysical conditions in the terrestrial ((a) temperature, (b)
precipitation and (c) elevation) and marine ((d) sea surface temperature
(SST), (e) sea surface salinity (SSS) and (f) bathymetry) realm. In
order to calculate the % of area protected, the different biophysical
conditions were divided into 100 equal frequency bins (percentiles),
unless the optimal bin size was reached earlier due to a strong skewness
of the environmental data (NSST = 96, NSSS = 84). Black lines indicate
the expected % of area protected given that all conditions are evenly
protected. Note that the individual bins of each variable represent
percentile ranges and so cover a different extent, which is also
reflected in the irregular intervals of the x-axis. See Supporting
Information for a similar figure based on equally-spaced bins.
Figure 3. Percentage of land area (a,
b, c) and marine area (d, e, f) protected by each pairwise combination
of the biophysical variables considered (temperature vs. precipitation,
temperature vs. elevation and precipitation vs. elevation, sea surface
temperature (SST) vs. sea surface salinity (SSS), SST vs. bathymetry,
SSS vs. bathymetry). Pixels are based on the equal frequency bins shown
in Fig. 2 (see Supporting Information for a similar figure based on
equally-spaced bins). Bar charts (d, h) indicate the total area that is
covered by certain levels of protection. See Supporting Information for
a detailed figure on the available area, protected area and % area
protected for each of the combinations.
Figure 4. Maps of the percentage of
terrestrial realm protected by each biophysical condition ((a)
temperature (Temp), (b) precipitation (Prec) and (c) elevation) and
their pairwise combinations ((d) temperature and precipitation, (e)
temperature and elevation, (f) precipitation and elevation). The %
protected is based on the equal frequency bins shown in Figure 2 and 3,
see Supporting Information for maps based on equally-spaced bins. Maps
are in Mollweide projection (ESRI:54009).
Figure 5. Maps of the percentage of
the marine realm protected by each biophysical condition ((a) sea
surface temperature (SST), (b) sea surface salinity (SSS) and (c)
bathymetry) and their pairwise combinations ((d) SST and SSS, (e) SST
and bathymetry, (f) SSS and bathymetry). The % protected is based on the
equal frequency bins shown in Fig. 2 and Fig. 3, see Supporting
Information for maps based on equally-spaced bins. Maps are in Mollweide
projection (ESRI:54009).
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