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- Results and discussion
- Supporting Information
Coastal marine ecosystems rank among the most productive ecosystems on earth but are also highly threatened by the exposure to both ocean- and land-based human activities. Spatially explicit information on the distributions of land-based impacts is critical for managers to identify where the effects of land-based activities on ecosystem condition are greatest and, therefore, where they should prioritize mitigation of land-based impacts. Here, we quantify the global cumulative impact of four of the most pervasive land-based impacts on coastal ecosystems—nutrient input, organic and inorganic pollution, and the direct impact of coastal populations (e.g., coastal engineering and trampling)—and identify hotspots of land-based impact using a variety of metrics. These threat hotspots were primarily in Europe and Asia, with the top three adjacent to the Mississippi, Ganges, and Mekong rivers. We found that 95% of coastal and shelf areas (<200 m depth) and 40% of the global coastline experience little to no impact from land-based human activities, suggesting that marine conservation and resource management in these areas can focus on managing current ocean activities and preventing future spread of land-based stressors. These results provide guidance on where coordination between marine and terrestrial management is most critical and where a focus on ocean-based impacts is instead needed.
- Top of page
- Results and discussion
- Supporting Information
Coastal marine ecosystems provide valuable services to humans, including seafood, coastal protection, water filtration, and recreation (M.E.A. 2005). These ecosystems are also some of the most at-risk areas, as human activities on land and at sea can directly or indirectly impact their species and communities (Halpern et al. 2008). Given the diversity of potential impacts to coastal marine ecosystems, resource managers and conservationists must prioritize which human activities and associated impacts to mitigate. Examples of distant land-based activities driving marine ecological condition, such as the persistent anoxic dead zone at the mouth of the Mississippi River attributed to nutrient runoff from upstream farms (Rabalais et al. 2002) or algal overgrowth of coral reefs from land-based nutrient pollution (Fabricius 2005), have increased the interest in considering such land-based drivers in coastal marine conservation globally. Yet, these examples are not necessarily the norm. There are many places where rainfall is extremely low, limiting the input of land-based drivers in coastal waters, for example, the desert coastline of Namibia and the Peruvian and Chilean Atacama coastal plains. In most cases, in between these extremes, discerning where land-based input plays a dominant or minor role in the ecological condition of a coastal area is difficult but is needed for efficient allocation of limited resources (Stoms et al. 2005; Tallis et al. 2008).
Approaches for linking ocean- and land-based management and conservation for specific cases at fine scales have been described elsewhere (Bryant et al. 1998; Stoms et al. 2005; Tallis et al. 2008). Here, we provide the first integrated analysis for all coastal areas of the world with a database that assesses the cumulative impact of four key land-based drivers of ecological change with global coverage: (1) nutrient input from agriculture and urban settings, (2) organic pollutants derived from pesticides, (3) inorganic pollutants from urban runoff, and (4) the direct impact of human populations on coastal marine habitats. We then use results from these analyses to identify hotspots of cumulative effects (“threat hotspots”) using a number of different approaches: “cluster,”“source,” and “percent” threat hotspots. The three methods are designed to help address different types of management needs and questions. Cluster threat hotspots are areas with high cumulative impact from land-based drivers, often influenced by multiple watersheds or complex coastlines. Where these hotspots exist, both threat intensity and ecosystem vulnerability play a key role in producing high values. Source threat hotspots indicate where input of land-based drivers is high before it is dispersed into the ocean and translated into ecosystem impacts; these should primarily be at mouths of large, heavily populated watersheds. Source threat hotspots do not account for ecosystem vulnerability. They are useful in cases in which ecosystem distribution data are sparse and/or when management is focused on key species or other conservation targets below the ecosystem level. Finally, percent threat hotspots indicate where land-based impacts overwhelmingly drive total cumulative impacts (from 17 total land and sea drivers analyzed in (Halpern et al. 2008)). These hotspots are particularly important for efforts to prioritize where land-based threats need to be addressed regardless of the intensity of ocean-based threats. Where all three threat hotspots co-occur, land-based stressors affect the most vulnerable ecosystems, have the highest recorded input, and constitute a much larger source of impact than ocean-based drivers.
Our aim here is to inform several key management and conservation prioritization needs at local, regional, and global scales. Land-based threat hotspots help identify global priority areas for addressing land-based sources of stress, while also highlighting the need for local-scale management efforts to direct their attention to land-based activities. Similarly, country-level analyses help identify which countries are most in need of addressing land-based management, in turn guiding the prioritization of limited time and funds of international and federal agencies and conservation organizations in their efforts to mitigate land-based impacts to oceans. Finally, the analyses help put land-based impacts into a global context relative to the full suite of threats facing ocean ecosystems.
Results and discussion
- Top of page
- Results and discussion
- Supporting Information
We found 31 “cluster,” 30 “source,” and 28 “percent” threat hotspots globally, primarily in Europe and Asia (Figure 1; Table 1). Countries with the highest number of all three threat hotspots included Russia, China, and India. More than three-quarters of cluster threat hotspots and most source threat hotspots were in India, China, and Europe, and two-thirds of percent threat hotspots were in Russia, Vietnam, Mozambique, and Brazil. Cluster and source threat hotspots often overlapped, but percent threat hotspots rarely overlapped with either (Tables 1 and S2). Only one threat hotspot was identified by both cluster and percent threat hotspot approaches, at the Zambezi River mouth in Mozambique, and none was identified by all three methods. Cluster threat hotspots without source threat hotspots had highly vulnerable ecosystems, while source threat hotspots without cluster threat hotspots had less vulnerable ecosystems. Source threat hotspots in data-poor areas require caution because better data might identify patches of vulnerable ecosystems or species important for community or ecosystem function that were not identified in our global-scale habitat data. Percent threat hotspots represent the proportion of land-based impacts relative to total impacts and their locations diverged greatly from the other two types, as they were not necessarily tied to river mouths and could have low cumulative impact scores. In particular, high-latitude percent threat hotspots (Figure 1) had a low total cumulative impact, indicating that management in these regions should focus on land-based sources of stress. In contrast, percent threat hotspots in South America, East Africa, and southeast Asia (Figure 1) had a relatively high total cumulative impact (Halpern et al. 2008), indicating that management will need to address both land- and ocean-based stressors. In sum, our three threat hotspot types highlight that the importance of land-based impacts vary greatly depending on the extent and distribution of input sources, the vulnerability of ecosystems affected, and the extent of ocean-based stressors simultaneously affecting those ecosystems.
Figure 1. Global threat hotspots of land-based impacts on marine ecosystems. Threat hotspots were derived in three ways (see also Methods): (1) by assigning each cell the sum of all values within a 25-km-radius moving window and then selecting clusters of cells >25 km2 with summed values exceeding 60% of the global maximum (cluster threat hotspots, red), (2) by the sum of the transformed land-based input of stressors at watershed mouths before the values are plumed into the ocean (source threat hotspots, blue), and (3) by calculating the percentage of total human impact on each pixel contributed by land-based sources and then identifying clusters >25 km2 with values >75% (percent threat hotspots, green). Numbers reference the rank order of the 31 cluster threat hotspot locations (red) detailed in Table 1. Where threat hotspots overlap, the colors overlap in concentric rings. White lines on land are watershed boundaries.
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Table 1. Characteristics of threat hotspots of land-based impact on coastal marine ecosystems based on cluster methods (red spots in Figure 1), with overlapping source (by rank) and percent threat hotspots also shown for comparison. Percent hotspots are not ranked.** indicates the threat hotspot that is also a percent hotspot
|Threat hotspot rank||%||River/bay||Nearest city||Country||Size of cluster (km2)||Watershed size (km2)||No. of input watersheds|
| 1|| 1|| ||Mississippi River||New Orleans||United States||1,028 ||3,212,288||1|
| 2|| 3|| ||Ganges||Dhaka||Bangladesh||921||1,586,414||1|
| 3||14|| ||Mekong River||Saigon||Vietnam||844||807,915||2|
| 4|| 7|| ||Pearl River||Miacau (near Hong Kong)||China||802||449,392||1|
| 5||28|| ||Po River||Venice||Italy||789||89,267||2|
| 6||11|| ||Rhine and Meuse Rivers||Rotterdam||The Netherlands||731||198,378||2|
| 7||30|| ||Hai He River||Tianjin||China||721||192,873||2|
| 8|| 6|| ||Danube River||Galati||Romania||662||795,667||1|
| 9|| || ||Chao Phraya River||Bangkok||Thailand||645||212,365||5|
|10|| 5|| ||Volga||Nikolayevsk||Russia||634||2,026,992||1|
|11||10|| ||Nile River||Cairo||Egypt||606||3,002,194||1|
|12|| || ||Irrawaddy River||Yangon (Rangoon)||Myanmar||591||390,287||1|
|13|| 8|| ||Indus River||Karachi||Pakistan||552||754,261||1|
|14|| || ||Yellow River (west branch)||Cangzhou||China||538||84,438||2|
|15||21|| ||Liao River||Anshan||China||532||232,133||3|
|16|| || ||Narmada||Surat||India||510||95,981||1|
|17||27|| ||Niger River||Port Harcourt||Nigeria||430||2,163,994||1|
|18||15|| ||Godavari River||Rajahmundry||India||387||308,907||1|
|19|| ||**||Zambezi River||Tete||Mozambique||305||1,385,878||1|
|20||18|| ||Krishna River||Vijayawada||India||271||253,918||1|
|21||13|| ||Yellow River (east branch)||Binzhou||China||214||363,373||1|
|22|| || ||Vislinskiy Zaliv||Elblag||Poland||197||194,607||1|
|23|| || ||Don River||Rostov-on-Don||Russia||174||427,605||1|
|24|| || ||Ijsselmeer Lake/Wadden Sea||Kampen||The Netherlands||144||14,843||1|
|25||20|| ||Euphrates and Tigris Rivers||Al Basrah||Iraq||141||873,715||1|
|26|| || ||Imjin and Han Rivers||Seoul||South Korea||121||37,514||3|
|27|| || ||Trinity River||Galveston||United States|| 72||56,933||2|
|28|| || ||Mahi and Sabarmati Rivers||Vadodara and Ahmedabad||India|| 70||53,154||2|
|29|| || ||Han, Rong, and Lian Rivers||Shantou||China|| 49||34,399||2|
|30||23|| ||Elbe River||Bremen||Germany|| 37||43,810||1|
|31||16|| ||Luni River||Gujarat||India|| 24||402,183||1|
Large portions of coastal areas experience little to no impact from land-based stressors, as modeled here: 40% of the global coastline and nearly all (94.7%) of the world's coastal and shelf areas experience little impact (cumulative impact <1.0; 19.7% and 87.1%, respectively, have impact <0.5). Only 2.3% (20,250 km) of global coastlines but as much as 42.6% (∼3.2 million km2) of coastal and shelf areas (at depths <200 m) experience no apparent effect of land-based stressors (cumulative impact = 0). The majority of global coastlines are distant from the major sources of potential impact, so these estimates should be robust to the model's inherent assumptions about plume sizes and positions. More realistic plume models would affect the size, shape, and alongshore spread of some threat hotspots but not their general locations or global- and national-level statistics on the intensity of land-based drivers.
Our quantification of land-based impacts along coastlines may be inflated as our model assumes presence in all coastal pixels of four intertidal ecosystems (Halpern et al. 2008). Beach ecosystems are much less vulnerable than the other three (Table S1) but are globally abundant. Consequently, even more of the global coastline than what we report here may experience little to no impact from land-based stressors, and this could affect the location and number of percent hotspots. However, the result that land-based impact to coastal and shelf areas is currently concentrated in small areas is likely robust, as is the location of source or cluster hotspots.
A low land-based impact predominates worldwide primarily because most land (72%) is encompassed in only 663 large watersheds (Syvitski et al. 2005) (out of >140,000 globally) that drain to relatively small stretches of the coast. A vast portion (>99%) of global coastal area, therefore, is adjacent to small coastal watersheds with relatively small amounts of pollutant runoff. Furthermore, many locations have low human population density in adjacent terrestrial areas and/or low levels of human activities such as farming within the watersheds.
Both global- and federal-level conservation and management prioritization efforts can benefit from identifying which countries are most in need of addressing land-based sources of impact to marine ecosystems. It is at the federal level that most land-use regulations are set. Identifying the top-priority countries can help motivate those countries, and the international bodies to which they belong (e.g., the United Nations or European Union), to take action and help inform the priorities of global-scale NGOs. Not surprisingly, the countries exacting the greatest total land-based impact on coastal marine ecosystems (Figure 2A) have some of the longest coastlines and largest coastal areas in the world (but not all, e.g., Italy and India) and large human populations. Indeed, population size and coastline length are correlated with the impact of land-based stressors (multivariate linear regression: R2= 0.70, P < 0.001). With results standardized by area (i.e., average per-pixel scores), heavily populated and developed countries with relatively small coastlines top the list (Figure 2B). Countries that have high values using either approach (e.g., Italy and Turkey; see Figure 2) most urgently need to address land-based stressors.
Figure 2. The (A) sum and (B) average cumulative impact scores for coastal (<200 m depth) regions of each country grouped by continents, derived from four land-based drivers of change. The average scores account for the differences in coastal area among countries. Human drivers are color-coded: nutrient input (green), organic pollution (blue), inorganic pollution (yellow), and direct human impact (red). Countries are labeled with standard 3-letter codes. See Table S3 for region labels and country-specific values. Countries in panel A are also labeled in panel B for reference.
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Given the amount of work required to map and evaluate threat hotspots, we tested how well a variety of easily measured metrics correlate with cluster hotspot size. Although watershed size, human population size, and land-use attributes were correlated in most cases, the explanatory power was low (R2= 0.02–0.26; Table S4). Akaike information criterion model selection procedures on all possible combinations of multivariate linear regressions produced a slightly better correlation (R2= 0.46, F= 4.0, P= 0.006) based on watershed size, urban and mixed land use, and area in the International Union for Conservation of Nature (IUCN) II, III, V, VI and unprotected status (Table S4).
Our analyses likely fall short in three ways. First, no globally comprehensive data exist for a number of important land-based stressors, including past habitat destruction (e.g., dredging and filling of estuaries), point-source pollution (e.g., sewage outfall and factories), altered sediment regimes (particularly increases), and garbage from terrestrial sources. Second, we do not explicitly include connectivity among sites via dispersal and migration such that the impact to some locations may be underestimated or misaligned. Third, past habitat conversion is unaccounted for; the loss of nursery habitats such as mangroves and salt marshes augments the stress of land-based activities on remaining intact areas. New hotspots might emerge with future inclusion of these data.
The general results are not likely to differ greatly with additional data, however, since land-based impact is primarily driven by watershed processes and coastal human population size, both of which are captured well by the model. Additional stressors are likely to be spatially concordant with the four included here; their inclusion would most likely accentuate the existing threat hotspots rather than add many new threat hotspots. For example, recently published data on the global distribution and extent of dead zones arising from land-based eutrophication (Diaz & Rosenberg 2008) align closely with the locations of our threat hotspots and the nations exacting the greatest impact on coastal ecosystems from the stressors that we considered. For regions identified as low impact because there is little water flow out of the watershed (e.g., Western Australia, southwestern Africa, northern Chile, and southern Argentina), our results are most robust because no mechanism exists to transport impact of any driver to the coast. Future changes in climate or coastal human population distribution could increase (or decrease) the risk depending on how runoff is affected. Finally, global patterns of threat hotspot location and the extent of coastal and shelf area and coastline impacted by land-based stressors should be accurate, while results at local scales may be sensitive to unknown patterns of dispersal and plume dynamics.
Regardless of these issues, this first global analysis of the influences of land-based stressors on coastal marine ecosystems highlights the regions, nations, and specific locations around the world where immediate coordination of land and ocean management and conservation is crucial. This need for coordinated management of land-and ocean-based activities, and of their impacts on the suite of coastal marine ecosystems, will only increase as human populations continue to grow.