A Global Map of Human Impact on Marine Ecosystems

Benjamin S. Halpern,¹ Shaun Walbridge,^1* Kimberly A. Selkoe,^1,2* Carrie V. Kappel,¹ Fiorenza Micheli,³ Caterina D'Agrosa,⁴ John F. Bruno,⁵ Kenneth S. Casey,⁶ Colin Ebert,¹ Helen E. Fox,⁷ Rod Fujita,⁸ Dennis Heinemann,⁹ Hunter S. Lenihan,¹⁰ Elizabeth M. P. Madin,¹¹ Matthew T. Perry,¹ Elizabeth R. Selig,^6,12 Mark Spalding,¹³ Robert Steneck,¹⁴ Reg Watson¹⁵

 

The management and conservation of the world's oceans require synthesis of spatial data on the distribution and intensity of human activities and the overlap of their impacts on marine ecosystems. We developed an ecosystem-specific, multiscale spatial model to synthesize 17 global data sets of anthropogenic drivers of ecological change for 20 marine ecosystems. Our analysis indicates that no area is unaffected by human influence and that a large fraction (41%) is strongly affected by multiple drivers. However, large areas of relatively little human impact remain, particularly near the poles. The analytical process and resulting maps provide flexible tools for regional and global efforts to allocate conservation resources; to implement ecosystem-based management; and to inform marine spatial planning, education, and basic research.

¹ National Center for Ecological Analysis and Synthesis, 735 State Street, Santa Barbara, CA 93101, USA.
² Hawai'i Institute of Marine Biology, Post Office Box 1346, Kane`ohe, HI 96744, USA.
³ Hopkins Marine Station, Stanford University, Oceanview Boulevard, Pacific Grove, CA 93950–3094, USA.
⁴ Wildlife Conservation Society, 2300 Southern Boulevard, Bronx, NY 10460, USA.
⁵ Department of Marine Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599–3300, USA.
⁶ National Oceanographic Data Center, National Oceanic and Atmospheric Administration (NOAA), 1315 East-West Highway, Silver Spring, MD 20910, USA.
⁷ Conservation Science Program, World Wildlife Fund—United States, 1250 24th Street NW, Washington, DC 20037, USA.
⁸ Environmental Defense, 5655 College Avenue, Suite 304, Oakland, CA, 94618, USA.
⁹ Ocean Conservancy, 1300 19th Street, NW, Washington, DC 20006, USA.
¹⁰ Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106, USA.
¹¹ Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA 93106, USA.
¹² Curriculum in Ecology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599–3275, USA.
¹³ Conservation Strategies Division, the Nature Conservancy, 93 Centre Drive, Newmarket, CB8 8AW, UK.
¹⁴ School of Marine Sciences, University of Maine, Darling Marine Center, Walpole, ME 04353, USA.
¹⁵ Fisheries Center, 2202 Main Mall, University of British Columbia, Vancouver, V6T 1Z4, Canada.

^* These authors contributed equally to this work.

Present address: School of Life Sciences, Arizona State University, Tempe, AZ 85287–4501, USA.

To whom correspondence should be addressed. E-mail: halpern@nceas.ucsb.edu , selkoe@nceas.ucsb.edu

Humans depend on ocean ecosystems for important and valuable goods and services, but human use has also altered the oceans through direct and indirect means (1–5). Land-based activities affect the runoff of pollutants and nutrients into coastal waters (6, 7) and remove, alter, or destroy natural habitat. Ocean-based activities extract resources, add pollution, and change species composition (8). These human activities vary in their intensity of impact on the ecological condition of communities (9) and in their spatial distribution across the seascape. Understanding and quantifying, i.e., mapping, the spatial distribution of human impacts is needed for the evaluation of tradeoffs (or compatibility) between human uses of the oceans and protection of ecosystems and the services they provide (1, 2, 10). Such mapping will help improve and rationalize spatial management of human activities (11).

Determining the ecological impact of human activities on the oceans requires a method for translating human activities into ecosystem-specific impacts and spatial data for the activities and ecosystems. Past efforts to map human impacts on terrestrial ecosystems (12), coral reefs (13), and coastal regions (14–16) used either coarse categorical or ad hoc methods to translate human activities into impacts. We developed a standardized, quantitative method, on the basis of expert judgment, to estimate ecosystem-specific differences in impact of 17 anthropogenic drivers of ecological change (table S1) (9). The results provided impact weights (table S2) used to combine multiple drivers into a single comparable estimate of cumulative human impact on 20 ecosystem types (17). We focused on the current estimated impact of humans on marine ecosystems (within the last decade), as past impacts and future scenarios of human impacts are less tractable, though also important (17).

Predicted cumulative impact scores (I_C) were calculated for each 1 km² cell of ocean as follows: where D_i is the log-transformed and normalized value [scaled between 0 and 1 (17)] of an anthropogenic driver at location i, E_j is the presence or absence of ecosystem j (either 1 or 0, respectively), and µ_i,j is the impact weight for the anthropogenic driver i and ecosystem j [range 0 to 4 (table S2)], given n = 17 drivers and m = 20 ecosystems (fig. S1). We modeled the distribution of several intertidal and shallow coastal ecosystems lacking global data (17). Weighting anthropogenic drivers by their estimated ecological impact in this way resulted in a different picture of ocean condition compared with simply mapping the footprints of human activities or drivers (fig. S1). Summing across ecosystems allows cells with multiple ecosystems to have higher potential scores than areas with fewer ecosystems; sensitivity analyses showed that summing or averaging across ecosystems within cells resulted in similar global pictures of human impacts on marine ecosystems (17). The global impact of a particular driver (I_D) is and of all drivers on a particular ecosystem type (I_E) is . This method produced I_C scores ranging from 0.01 to 90.1. The I_C scores were significantly correlated with independent estimates of ecological condition in 16 mixed-ecosystem regions containing coral reefs (17, 18). The linear equation relating the two scores [R² = 0.63, P = 0.001 (fig. S5)] was then used to divide I_C scores into six categories of human impact ranging from very low impact (I_C < 1.4) to very high impact (I_C >15.5) (17).

Predicted human impact on the oceans shows strong spatial heterogeneity (Fig. 1) with a roughly bimodal distribution of per-cell I_C scores (Fig. 2), but with every square kilometer affected by some anthropogenic driver of ecological change. Over a third (41%) of the world's oceans have medium high to very high I_C scores [>8.5 (17)], with a small fraction (0.5%) but relatively large area (2.2 million km²) experiencing very high impact (I_C > 15.5). Most of the highest predicted cumulative impact is in areas of continental shelf and slope, which are subject to both land- and ocean-based anthropogenic drivers. Large areas of high predicted impact occur in the North and Norwegian seas, South and East China seas, Eastern Caribbean, North American eastern seaboard, Mediterranean, Persian Gulf, Bering Sea, and the waters around Sri Lanka (Fig. 1). Ecoregions, a classification of coastal (<200 m depth) areas based on species composition and biogeography (19), also showed variation in scores indicating differential risks to unique marine assemblages (table S3).

 

Fig. 1. Global map (A) of cumulative human impact across 20 ocean ecosystem types. (Insets) Highly impacted regions in the Eastern Caribbean (B), the North Sea (C), and the Japanese waters (D) and one of the least impacted regions, in northern Australia and the Torres Strait (E). [View Larger Version of this Image (93K GIF file)]

 

 

Fig. 2. Histogram of cumulative impact scores depicting the fraction of global area that falls within each impact category. There are no zeros; histogram bars are in bins of 0.1. Categories described in (17). (Insets) Expanded views of the tail of values. [View Larger Version of this Image (19K GIF file)]

 
 

 

The majority of very low impact areas (3.7% of the oceans) occurs in the high-latitude Arctic and Antarctic poles (Fig. 1), in areas with seasonal or permanent ice that limits human access. However, our analyses did not account for illegal, unregulated, and unreported (IUU) fishing, which may be extensive in the Southern Ocean (20), or atmospheric pollution, which may be particularly high in the Arctic (21). Furthermore, projections of future polar ice loss (22) suggest that the impact on these regions will increase substantially. In general, small human population and coastal watershed size predict light human impact (Fig. 1E) but do not ensure it, as shipping, fishing, and climate change affect even remote locations—e.g., impact scores are relatively high in the international waters of the Patagonian shelf. In some places, predicted impact scores may be higher than anticipated because many anthropogenic drivers are not readily observable. Conversely, impact scores may seem unexpectedly low in other locations because a more abundant but less-sensitive ecosystem (e.g., soft sediment) surrounds a sensitive, but rare, ecosystem (e.g., coral reefs).

Ecosystems with the highest predicted cumulative impact scores include hard and soft continental shelves and rocky reefs (Fig. 3). Coral reefs, seagrass beds, mangroves, rocky reefs and shelves, and seamounts have few to no areas remaining anywhere in the world with I_C <1.5 (Fig. 3). Indeed, our data suggest that almost half of all coral reefs experience medium high to very high impact (13, 17, 23). Shallow soft-bottom and pelagic deep-water ecosystems had the lowest scores (>50% of these ecosystems have I_C < 1.1 and 1.2, respectively), partly because of the lower vulnerability of these ecosystems to most anthropogenic drivers (table S2). Overall, these results highlight the greater cumulative impact of human activities on coastal ecosystems.

 

Fig. 3. The distribution of cumulative impact scores for each ecosystem in our analyses (means in parentheses). Individual ecosystem scores have a smaller range of values than cumulative impact scores (Fig. 2) because the latter sum all ecosystem-specific scores within a cell. Ground-truthed estimates of coral reef condition (17) were used to identify IE values at which coral reefs experience medium high to very high impact, as indicated on the coral reef histogram. Note differences in y-axis scales. [View Larger Version of this Image (31K GIF file)]

 
 

Perhaps not surprisingly, anthropogenic drivers associated with global climate change are distributed widely (Fig. 4A) and are an important component of global cumulative impact scores, particularly for offshore ecosystems. Other drivers, in particular commercial fishing, are also globally widespread but have smaller cumulative impacts because of their uneven distribution. Land-based anthropogenic drivers have relatively small spatial extents and predicted cumulative impacts (Fig. 4A), but their cumulative impact scores approach those of other more widespread drivers within coastal areas where they occur (Fig. 4B). The spatial distribution of land-based impacts is highly heterogeneous but positively spatially correlated. Therefore, management of coastal waters must contend with multiple drivers in concert. Coordination with regulating agencies for urban and agricultural runoff is warranted, although such efforts can be challenging when watersheds cross jurisdictional boundaries. Where anthropogenic drivers tend to be spatially distinct (uncorrelated), as with commercial shipping versus pelagic high-bycatch fishing, management will require independent regulation and conservation tools. Assessing positive and negative spatial correlations among drivers can help anticipate potential interactions (24) and provides guidance in adjusting spatial management accordingly.

 

Fig. 4. Total area affected (square kilometers, gray bars) and summed threat scores (rescaled units, black bars) for each anthropogenic driver (A) globally and (B) for all coastal regions <200 m in depth. Values for each bar are reported in millions. [View Larger Version of this Image (22K GIF file)]

 
 

Our approach may be used to identify regions where better management of human activities could achieve a higher return-on-investment, e.g., by reducing or eliminating anthropogenic drivers with high impact scores (fig. S2). It may also be used to assess whether or how human activities can be spatially managed to reduce their negative impacts on ecosystems. For example, fishing zones have been shifted to decrease impacts on sensitive ecosystems (25), and navigation lanes have been rerouted to protect sensitive areas of the ocean (26). Wide-ranging fish stocks and those that occur primarily in international waters present challenges in determining who must take responsibility for management. If ecosystem-specific weighting values (µ_i,j) are excluded, we can also evaluate the distribution, or footprint, of summed anthropogenic drivers of ecosystem change. This global footprint of drivers correlates with the distribution of cumulative impact scores (R² = 0.83), but ignores the important small-scale spatial patterns that emerge when accounting for ecosystem vulnerability (fig. S1) (17).

Our results represent the current best estimate of the spatial variation in anthropogenic impacts. Although these estimates are conservative and incomplete for most of the ocean, they potentially inflate human impacts on coastal areas because we used an additive model (17). Averaging impacts across ecosystems produced highly correlated results, very similar to those from the additive model (17), which suggests such inflation is limited, if it exists. Furthermore, the large extent of the ocean that our model predicts to be negatively affected by human activities will likely increase once additional drivers, their historical effects, and possible synergisms are incorporated into the model. Key activities with significant impacts on marine ecosystems but without global data include recreational fishing (27), aquaculture (28, 29), disease (30), coastal engineering (habitat alteration), and point-source pollution (31). Most of these activities primarily affect intertidal and nearshore ecosystems rather than offshore ecosystems, which suggests that our estimates for nearshore areas are particularly conservative. In addition, the spatial data for many anthropogenic drivers were derived from valid but inexact modeling approaches (17). Ecosystem data were highly variable in quality, both within and among ecosystem types, and in many cases, we may have underestimated the full extent of these ecosystems and, therefore, the cumulative impact scores. Furthermore, many changes occurred in the past with lasting negative effects, but the drivers no longer occur at a particular location, e.g., historical overfishing (4) or past coastal habitat destruction (32). Although we used a conservative, additive model, some drivers may have synergistic effects (24). Despite these limitations, this analysis provides a framework and baseline that can be built upon with future incorporation or refinement of data. It is noteworthy that the data gaps emphasize the need for research on the most basic information, such as distribution of habitat types and whether and how different anthropogenic drivers interact.

Humans depend heavily on goods and services from the oceans, and these needs will likely increase with a growing human population (10). Our approach provides a structured framework for quantifying the ecological tradeoffs associated with different human uses of marine ecosystems and for identifying locations and strategies to minimize ecological impact and maintain sustainable use. In some places, such strategies can benefit both humans and ecosystems, for example, using shellfish aquaculture both to provide food and to improve water quality. Our analytical framework can easily be applied to local- and regional-scale planning where better data are available and can be extended by incorporating other types of information, such as species distribution or diversity data (33–35) to identify hot spots with both high diversity and high cumulative human impacts that perhaps deserve conservation priority. A key next research step will be to compile regional and global databases of empirical measurements of ecosystem condition to further validate the efficacy of our approach.

References and Notes